codeparrot/github-jupyter · Datasets at Hugging Face

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peap/notebooks	pytexas-2013/d02t03.classes.ipynb	1	12967	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classes and Metaclasses\n", "_James Powell_ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Themes\n", "\n", "* static ignorance of the interpreter\n", " * the interpreter doesn't know what it's looking at\n", " * eg., a name followed by () could be a function, class, generator, etc.\n", "* metaclasses are a tool for enforcing constraints from a base class to a derived class\n", "* CPython as a reference implementation of Python\n", " * Guidance for how the language is supposed to work\n", " * Python is a language without a spec (unlike, say, C++)\n", " * If something that works in CPython doesn't work in PyPy, Jython, etc., the other interpreters are not necessarily broken\n", "* CPython code as a source for understanding\n", " * simple, straight-forward\n", " * there exists a natural starting point\n", "* Python as a system language with no boundary between system and app" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Order of the talk\n", "\n", "* contours of classes\n", "* class construction\n", "* what is a metaclass?\n", "* interesting things in Python 3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sys import version_info\n", "print(version_info)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "sys.version_info(major=3, minor=3, micro=2, releaselevel='final', serial=0)\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contours of Classes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from math import sqrt\n", "\n", "EUCLIDEAN, STREETS = object(), object()\n", "\n", "ARENA_TYPE = STREETS\n", "\n", "# in python3, you don't need to derive from object, since there are no old-style classes\n", "class Mob:\n", " \"\"\"\n", " Any mobile object in the game.\n", " \"\"\"\n", "# not sure why this was breaking...\n", "# def __new__(cls, args, kwargs):\n", "# # runs before instantiation\n", "# return object.__new__(cls, args, kwargs)\n", " \n", " def __init__(self, x, y):\n", " self._x, self._y = x, y\n", " \n", " def __repr__(self):\n", " return '{0.__name__}({1.x}, {1.y})'.format(type(self), self)\n", " \n", " def __str__(self):\n", " return repr(self)\n", " \n", " def move(self):\n", " pass\n", " \n", " @property\n", " def x(self):\n", " return self._x\n", " \n", " @property\n", " def y(self):\n", " return self._y\n", " \n", " @x.setter\n", " def x(self, value):\n", " if value < 0:\n", " raise ValueError(\"can't move outside of the arena\")\n", " self._x = value\n", " \n", " @y.setter\n", " def y(self, value):\n", " if value < 0:\n", " raise ValueError(\"can't move outside of the arena\")\n", " self._y = value\n", "\n", " if ARENA_TYPE == EUCLIDEAN:\n", " @staticmethod\n", " def distance(mob1, mob2):\n", " return sqrt((mob1.x + mob2.x)2 + (mob1.y + mob2.y)*2)\n", " else:\n", " @staticmethod\n", " def distance(mob1, mob2):\n", " return abs(mob1.x - mob2.x) + abs(mob1.y - mob2.y)\n", "\n", "mob1 = Mob(0, 0)\n", "mob2 = Mob(12, 3)\n", "\n", "Mob.distance(mob1, mob2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "15" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instance methods are created on the fly; this does not apply to class methods or properties." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mob3 = Mob(1, 1)\n", "assert Mob is Mob\n", "assert mob3 is mob3\n", "print(list(map(id, [mob3.x, mob3.x])))\n", "print(list(map(id, [mob3.move, mob3.move])))\n", "\n", "Mob.__dict__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[140227895093824, 140227895093824]\n", "[140227747113584, 140227326980896]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "mappingproxy({'move': <function Mob.move at 0x7f89239b4320>, '__dict__': <attribute '__dict__' of 'Mob' objects>, 'x': <property object at 0x7f89239a6998>, '__init__': <function Mob.__init__ at 0x7f89239b4170>, 'y': <property object at 0x7f89239a66d8>, '__doc__': '\\n Any mobile object in the game.\\n ', '__repr__': <function Mob.__repr__ at 0x7f89239b4200>, 'distance': <staticmethod object at 0x7f892399e1d0>, '__weakref__': <attribute '__weakref__' of 'Mob' objects>, '__str__': <function Mob.__str__ at 0x7f89239b4290>, '__module__': '__main__'})" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "James likes dis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from dis import dis\n", "\n", "def create_mob():\n", " return Mob(1,1)\n", "\n", "dis(create_mob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 4 0 LOAD_GLOBAL 0 (Mob) \n", " 3 LOAD_CONST 1 (1) \n", " 6 LOAD_CONST 1 (1) \n", " 9 CALL_FUNCTION 2 (2 positional, 0 keyword pair) \n", " 12 RETURN_VALUE \n" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to find the hooks for function calls in the CPython code, just grep for CALL_FUNCTION now.\n", "\n", "Here's where metaclasses come in... (somehow)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from dis import dis\n", "\n", "def create_monster():\n", " class Monster(Mob):\n", " def __init__(self, hp, args, *kwargs):\n", " self.hp = hp\n", " Mob.__init__(args, *kwargs)\n", "\n", "dis(create_monster)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 4 0 LOAD_BUILD_CLASS \n", " 1 LOAD_CONST 1 (<code object Monster at 0x7f895109f540, file \"<ipython-input-55-b7f99bfca70d>\", line 4>) \n", " 4 LOAD_CONST 2 ('Monster') \n", " 7 MAKE_FUNCTION 0 \n", " 10 LOAD_CONST 2 ('Monster') \n", " 13 LOAD_GLOBAL 0 (Mob) \n", " 16 CALL_FUNCTION 3 (3 positional, 0 keyword pair) \n", " 19 STORE_FAST 0 (Monster) \n", " 22 LOAD_CONST 0 (None) \n", " 25 RETURN_VALUE \n" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the CPython code, you'll see that you only use the first metaclass you find in the inheritance chain. Or is it only the metaclass of the first class (as in, the class itself)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can create your own object sytem in python...but probably don't." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What would you use metaclasses for?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Monster(Mob):\n", " def __init__(self, hp, args, *kwargs):\n", " self._hp = hp\n", " Mob.ty__init__(args, *kwargs)\n", " \n", " @property\n", " def hp(self):\n", " return self._hp\n", "\n", "class Boss(Monster):\n", " def __init__(self, prize, args, *kwargs):\n", " self.prize = prize\n", " Monster.__init__(arg, **kwargs)\n", " \n", "# you can ensure that classes you inherit from have certain attributes\n", "assert hasattr(Monster, 'hp')\n", "assert issubclass(Monster, Mob)\n", "\n", "# metaclasses allow you contrain derived classes at the parent class level\n", "# --> we don't want subclasses of Monster to be able to move outside the arena\n", "class metaclass(type):\n", " def __init__(self, name, bases, body):\n", " print(self, name, bases, body)\n", " # place contraints here...\n", " if name == 'Derived':\n", " raise ValueError('no!')\n", " return type.__init__(self, name, bases, body)\n", "\n", "class Base(metaclass=metaclass):\n", " pass\n", "\n", "class Derived(Base):\n", " pass" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "no!", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-71-fdda81cbdfa8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mclass\u001b[0m \u001b[0mDerived\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-71-fdda81cbdfa8>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, bases, body)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;31m# place contraints here...\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Derived'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbases\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: no!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "<class '__main__.Base'> Base () {'__qualname__': 'Base', '__module__': '__main__'}\n", "<class '__main__.Derived'> Derived (<class '__main__.Base'>,) {'__qualname__': 'Derived', '__module__': '__main__'}\n" ] } ], "prompt_number": 71 } ], "metadata": {} } ] }	mit
HPCC-Cloud-Computing/press	Scaling4ML/svm/svm.ipynb	1	8442	{ "cells": [ { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "file_name = 'day6_10.csv' # input file name\n", "window_size = 10\n", "k = 10 # scale -k -> k\n", "train_size = 0.9 # tỉ lệ data được dùng để train" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>8400</td>\n", " <td>10165</td>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>-40</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10165</td>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>6995</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>6995</td>\n", " <td>6593</td>\n", " <td>-25</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10\n", "0 8400 10165 9482 8041 7939 7608 8304 8485 7278 7427 -40\n", "1 10165 9482 8041 7939 7608 8304 8485 7278 7427 7556 5\n", "2 9482 8041 7939 7608 8304 8485 7278 7427 7556 7763 5\n", "3 8041 7939 7608 8304 8485 7278 7427 7556 7763 6995 7\n", "4 7939 7608 8304 8485 7278 7427 7556 7763 6995 6593 -25" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(file_name, header=None)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "# Chuẩn bị dữ liệu\n", "X = np.array(df[list(range(window_size))], dtype='float32')\n", "y = np.array(df[window_size])\n", "X_train = X[:int(train_sizelen(X))]\n", "X_test = X[int(train_sizelen(X)):]\n", "y_train = y[:int(train_sizelen(y))]\n", "y_test = y[int(train_sizelen(y)):]" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\mp\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sklearn\\model_selection\\_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n", " % (min_groups, self.n_splits)), Warning)\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=None, error_score='raise',\n", " estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False),\n", " fit_params=None, iid=True, n_jobs=1,\n", " param_grid={'decision_function_shape': ('ovo', 'ovr'), 'kernel': ('linear', 'rbf', 'sigmoid', 'poly')},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", " scoring=None, verbose=0)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "params = {\n", " 'kernel': ('linear', 'rbf', 'sigmoid', 'poly'),\n", " 'decision_function_shape': ('ovo', 'ovr')\n", "}\n", "\n", "svm = SVC()\n", "clf = GridSearchCV(svm, params)\n", "# Training\n", "clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "#sorted(clf.cv_results_.keys())" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-19, -31, 37, -19, 9, -19, 5, 37, -50, 35, 50, -50, -31,\n", " 35, 50, -50, 50, -34, -34, 35, -34, 35, -34, 35, -34, -31,\n", " -31, -34, 50, -34, -48, -34, 35, -48, -50, 35, -27, -50, 47,\n", " -42, 35, 9, -49, -48, -31, -48, 3, -48, -31, 47, 47, 47,\n", " 35, -34, -27, -50, -50, 50, -48, -48, 47, -47, -47, 47, -48,\n", " -48, -48, -50, -29, 36, 36], dtype=int64)" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.14084507042253522" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
grezesf/Research	Reservoirs/Notebooks/UBM training for Speech Data.ipynb	1	12011	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "import os\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import mixture" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['find_phone_index', 'load_phone_file']\n" ] } ], "source": [ "# import custom functions\n", "import sys\n", "# path to libraries\n", "# currently in ../scripts-lib/\n", "tool_path = os.path.abspath('../scripts-lib')\n", "\n", "if tool_path not in sys.path:\n", " sys.path.append(tool_path)\n", "import lib_phones as lph\n", "\n", "# print the loaded functions\n", "print dir(lph)[5:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "61 ['aa', 'ae', 'ah', 'ao', 'aw', 'ax', 'ax-h', 'axr', 'ay', 'b', 'bcl', 'ch', 'd', 'dcl', 'dh', 'dx', 'eh', 'el', 'em', 'en', 'eng', 'epi', 'er', 'ey', 'f', 'g', 'gcl', 'h#', 'hh', 'hv', 'ih', 'ix', 'iy', 'jh', 'k', 'kcl', 'l', 'm', 'n', 'ng', 'nx', 'ow', 'oy', 'p', 'pau', 'pcl', 'q', 'r', 's', 'sh', 't', 'tcl', 'th', 'uh', 'uw', 'ux', 'v', 'w', 'y', 'z', 'zh']\n" ] } ], "source": [ "# load phone list\n", "phone_path = os.path.abspath('../datasets/TIMIT-MFCCs/TIMIT_phone_list.txt')\n", "phone_list = lph.load_phone_file(phone_path)\n", "print len(phone_list), phone_list" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "working in path : C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT-MFCCs\\dev\n", "working on: si1027.mfcc.csv\n", "working on: si1105.mfcc.csv\n", "working on: si1657.mfcc.csv\n", "working on: si1735.mfcc.csv\n", "working on: si475.mfcc.csv\n", "working on: si648.mfcc.csv\n", "working on: sx115.mfcc.csv\n", "working on: sx127.mfcc.csv\n", "working on: sx205.mfcc.csv\n", "working on: sx217.mfcc.csv\n", "working on: sx25.mfcc.csv\n", "working on: sx295.mfcc.csv\n", "working on: sx307.mfcc.csv\n", "working on: sx37.mfcc.csv\n", "working on: sx385.mfcc.csv\n", "working on: sx397.mfcc.csv\n" ] } ], "source": [ "#load mfccs into sklearn observations, each frame is an obs\n", "\n", "train_TIMIT_dir = os.path.abspath('../datasets/TIMIT-MFCCs/dev')\n", "\n", "train_obs = []\n", "train_obs_labels = []\n", "\n", "# walk the directories\n", "for (path, dirs, files) in os.walk(train_TIMIT_dir):\n", " print \"working in path : \" + path\n", "\n", " for file in files:\n", " # skip the SA files\n", " #dev, only work on file si1573.mfcc.csv \"si1573\" in file and\n", " if \".mfcc\" in file and \"sa\" not in file:\n", " #check if corresponding .phn file exists\n", " if not os.path.exists(path + \"/\" + file[:-8] + \"phn\"):\n", " print path + \"/\" + file[:-8] + \"phn\"\n", " print \"corresponding .phn file does not exist!\"\n", " else:\n", " \n", " print \"working on: \" + file\n", "# print \"from path : \" + path\n", "\n", " # open the files\n", " mfcc_file = open(path + \"/\" + file)\n", " phn_file = open(path + \"/\" + file[:-8] + \"phn\")\n", "\n", " # extract phone times\n", " phone_times = []\n", " for phn_line in phn_file:\n", " phone_times.append(phn_line.split())\n", " # transpose for easier use\n", " phone_times = map(list, zip(*phone_times))\n", "\n", " # skip mfcc_file header\n", " next(mfcc_file)\n", "\n", " # reset frame count\n", " frame_cnt = 0\n", "\n", " # for each line of mfcc_file\n", " for mfcc_line in mfcc_file:\n", "\n", " # increment frame count\n", " frame_cnt += 1 \n", "\n", " # print \"frame line #:\", frame_cnt \n", "\n", " # frame start time in seconds\n", " start_t = mfcc_line.split(\";\")[1]\n", "\n", " # create frame (skiping first 2 values, frame_index and frame_time)\n", " frame = map( float, mfcc_line.split(\";\")[2:])\n", " # print numpy.shape(frame)\n", " # print frame\n", "\n", " # find correspond phoneme and index in the list\n", " phn_index = lph.find_phone_index(start_t, phone_times, phone_list)\n", "\n", " # add to instances\n", " train_obs.append(frame)\n", " train_obs_labels.append(phone_list[phn_index])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4266, 39)\n", "(4266,)\n" ] } ], "source": [ "print np.shape(train_obs)\n", "print np.shape(train_obs_labels)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-24.63259, -12.55975, -0.4234125, -22.09616, -11.36787, 15.35193, 0.2450585, -18.58464, -28.29842, -5.80051, 2.788154, 4.604296, -17.4054, -0.275135, 1.378781, -1.950969, 3.052481, 3.25547, 1.203516, -0.7550904, -0.3006264, 2.328182, 3.589715, 2.428725, 4.183867, -0.1700937, -0.4764511, 0.5360677, 0.24275, 0.9877825, 0.0331907, -0.5391095, 0.6034231, 1.644038, 1.840576, 0.2822124, -0.1602798, -0.7427405, -0.02820093]\n", "['h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'v', 'v', 'v', 'v', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'ix', 'ix', 'ix', 'ix', 'ix', 'f']\n" ] } ], "source": [ "print train_obs[0]\n", "print train_obs_labels[0:100]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create gmm\n", "num_components = 10\n", "num_iter=2\n", "g = mixture.GMM(n_components= num_components, n_iter=num_iter)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,\n", " n_components=10, n_init=1, n_iter=2, params='wmc', random_state=None,\n", " thresh=None, tol=0.001)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fit the GMM to the observations\n", "g.fit(train_obs) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pred = g.predict(train_obs)\n", "# print train_obs_labels\n", "# print pred" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-104.66\n", "87.85\n", "[-102. -114. -104. -102. -100.]\n" ] } ], "source": [ "print np.round(g.score(train_obs).mean(), 2)\n", "print np.round(g.score(train_obs).var(), 2)\n", "print np.round(g.score(train_obs)[0:5] )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 components 2 iterations in 1.50999999046 for 4266 observations.\n" ] } ], "source": [ "# refit, time \n", "t0 = time.time()\n", "g.fit(train_obs)\n", "t1 = time.time()\n", "print num_components, \"components\", num_iter, \"iterations in \", t1-t0, \"for\", len(train_obs_labels), \"observations.\"" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-104.65\n", "87.9\n", "[-102. -114. -104. -103. -100.]\n" ] } ], "source": [ "print np.round(g.score(train_obs).mean(), 2)\n", "print np.round(g.score(train_obs).var(), 2)\n", "print np.round(g.score(train_obs)[0:5] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Refitting does work as expected. (does not reset gmm)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saved gmm in C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT Pickled Data\\TIMIT_ubm_gmm_10.pckl\n" ] } ], "source": [ "#save gmm in pickled form\n", "import cPickle as pickle\n", "\n", "# name and location to save in\n", "pickle_name = \"TIMIT_ubm_gmm_\" + str(num_components) + \".pckl\"\n", "pickle_dir = os.path.abspath('../datasets/TIMIT Pickled Data')\n", "\n", "if not os.path.isdir(pickle_dir):\n", " os.makedirs(pickle_dir)\n", " \n", "pickle.dump( g, open( pickle_dir + os.sep + pickle_name, \"wb\") )\n", "print \"saved gmm in \", pickle_dir + '\\\\' + pickle_name" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded gmm from C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT Pickled Data\\TIMIT_ubm_gmm_10.pckl\n" ] } ], "source": [ "# reload pickled file for testing\n", "g2 = pickle.load( open(pickle_dir + os.sep + pickle_name, \"rb\") )\n", "print \"loaded gmm from \", pickle_dir + os.sep + pickle_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm	tutorial_part1/FIR Filter Design and C Headers.ipynb	1	532021	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "#%matplotlib qt\n", "from __future__ import division # use so 1/2 = 0.5, etc.\n", "import sk_dsp_comm.sigsys as ss\n", "import sk_dsp_comm.fir_design_helper as fir_d\n", "import sk_dsp_comm.coeff2header as c2h\n", "import scipy.signal as signal\n", "import imp # for module development and reload()\n", "from IPython.display import Audio, display\n", "from IPython.display import Image, SVG" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pylab.rcParams['savefig.dpi'] = 100 # default 72\n", "#pylab.rcParams['figure.figsize'] = (6.0, 4.0) # default (6,4)\n", "#%config InlineBackend.figure_formats=['png'] # default for inline viewing\n", "%config InlineBackend.figure_formats=['svg'] # SVG inline viewing\n", "#%config InlineBackend.figure_formats=['pdf'] # render pdf figs for LaTeX\n", "#%Image('fname.png',width='90%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FIR Filter Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both floating point and fixed-point FIR filters are the objective here. we will also need a means to export the filter coefficients to header files. Header export functions for `float32_t` and `int16_t` format are provided below. The next step is to actually design some filters using functions found in `scipy.signal`. To support both of these activities the Python modules `fir_design_helper.py` and `coeff2header.py` are available.\n", "\n", "Note: The MATLAB signal processing toolbox is extremely comprehensive in its support of digital filter design. The use of Python is adequate for this, but do not ignore the power available in MATLAB." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Windowed (Kaiser window) and Equal-Ripple FIR Filter Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The module `fir_design_helper.py` contains custom filter design code build on top of functions found in `scipy.signal`. Functions are available for winowed FIR design using a Kaiser window function and equal-ripple FIR design, both type have linear phase. \n", "\n", "### Example: Lowpass with $f_s = 1$ Hz\n", "For this 31 tap filter we choose the cutoff frequency to be $F_c = F_s/8$, or in normalized form $f_c = 1/8$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kaiser Win filter taps = 72.\n", "Remez filter taps = 53.\n" ] } ], "source": [ "b_k = fir_d.firwin_kaiser_lpf(1/8,1/6,50,1.0)\n", "b_r = fir_d.fir_remez_lpf(1/8,1/6,0.2,50,1.0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg 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0x1131de438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fir_d.freqz_resp_list([b_k,b_r],[[1],[1]],'dB',fs=1)\n", "ylim([-80,5])\n", "title(r'Kaiser vs Equal Ripple Lowpass')\n", "ylabel(r'Filter Gain (dB)')\n", "xlabel(r'Frequency in kHz')\n", "legend((r'Kaiser: %d taps' % len(b_k),r'Remez: %d taps' % len(b_r)),loc='best')\n", "grid();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Highpass Design" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kaiser Win filter taps = 72.\n", "Remez filter taps = 53.\n" ] } ], "source": [ "b_k_hp = fir_d.firwin_kaiser_hpf(1/8,1/6,50,1.0)\n", "b_r_hp = fir_d.fir_remez_hpf(1/8,1/6,0.2,50,1.0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD 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\"\"\"\n", " Write FIR Filter Header Files \n", " \"\"\"\n", "```\n", "## 16 Bit Signed Integer Header Export\n", "```python\n", "def FIR_fix_header(fname_out,h):\n", " \"\"\"\n", " Write FIR Fixed-Point Filter Header Files \n", " \"\"\"\n", "```\n", "These functions are available in `coeff2header.py`, which was imported as `c2h` above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write a Header File for the Bandpass Equal-Ripple" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Write a C header file\n", "c2h.FIR_header('remez_8_14_bpf_f32.h',b_r_bp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The header file, `remez_8_14_bpf_f32.h` written above takes the form:\n", "\n", "```c\n", "//define a FIR coefficient Array\n", "\n", "#include <stdint.h>\n", "\n", "#ifndef M_FIR\n", "#define M_FIR 101\n", "#endif\n", "/***********************************************************************/\n", "/ FIR Filter Coefficients /\n", "float32_t h_FIR[M_FIR] = {-0.001475936747, 0.000735580994, 0.004771062558,\n", " 0.001254178712,-0.006176846780,-0.001755945520,\n", " 0.003667323660, 0.001589634576, 0.000242520766,\n", " 0.002386316353,-0.002699251419,-0.006927087152,\n", " 0.002072374590, 0.006247819434,-0.000017122009,\n", " 0.000544273776, 0.001224920394,-0.008238424843,\n", " -0.005846603175, 0.009688130613, 0.007237935594,\n", " -0.003554185785, 0.000423864572,-0.002894644665,\n", " -0.013460012489, 0.002388684318, 0.019352295029,\n", " 0.002144732872,-0.009232278407, 0.000146728997,\n", " -0.010111394762,-0.013491956909, 0.020872121644,\n", " 0.025104278030,-0.013643042233,-0.015018451283,\n", " -0.000068299117,-0.019644863999, 0.000002861510,\n", " 0.052822261169, 0.015289946639,-0.049012297911,\n", " -0.016642744836,-0.000164469072,-0.032121234463,\n", " 0.059953731027, 0.133383985599,-0.078819553619,\n", " -0.239811117665, 0.036017541207, 0.285529343096,\n", " 0.036017541207,-0.239811117665,-0.078819553619,\n", " 0.133383985599, 0.059953731027,-0.032121234463,\n", " -0.000164469072,-0.016642744836,-0.049012297911,\n", " 0.015289946639, 0.052822261169, 0.000002861510,\n", " -0.019644863999,-0.000068299117,-0.015018451283,\n", " -0.013643042233, 0.025104278030, 0.020872121644,\n", " -0.013491956909,-0.010111394762, 0.000146728997,\n", " -0.009232278407, 0.002144732872, 0.019352295029,\n", " 0.002388684318,-0.013460012489,-0.002894644665,\n", " 0.000423864572,-0.003554185785, 0.007237935594,\n", " 0.009688130613,-0.005846603175,-0.008238424843,\n", " 0.001224920394, 0.000544273776,-0.000017122009,\n", " 0.006247819434, 0.002072374590,-0.006927087152,\n", " -0.002699251419, 0.002386316353, 0.000242520766,\n", " 0.001589634576, 0.003667323660,-0.001755945520,\n", " -0.006176846780, 0.001254178712, 0.004771062558,\n", " 0.000735580994,-0.001475936747};\n", "/**********************************************************************/\n", "```\n", "\n", " This file can be included in the main module of an ARM Cortex M4 micro controller using the [Cypress FM4](http://www.cypress.com/documentation/development-kitsboards/fm4-s6e2g-series-pioneer-kit-guide) $50 dev kit" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f_AD,Mag_AD, Phase_AD = loadtxt('BPF_8_14_101tap_48k.csv',\n", " delimiter=',',skiprows=6,unpack=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"277pt\" version=\"1.1\" viewBox=\"0 0 394 277\" width=\"394pt\" xmlns=\"http://www.w3.org/2000/svg\" 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"fir_d.freqz_resp_list([b_r_bp],[[1]],'dB',fs=48)\n", "ylim([-80,5])\n", "plot(f_AD/1e3,Mag_AD+.5)\n", "title(r'Equal Ripple Bandpass Theory vs Measured')\n", "ylabel(r'Filter Gain (dB)')\n", "xlabel(r'Frequency in kHz')\n", "legend((r'Equiripple Theory: %d taps' % len(b_r_bp),\n", " r'AD Measured (0.5dB correct)'),loc='lower right',fontsize='medium')\n", "grid();" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# FIR Design Problem\n", "Now its time to design and implement your own FIR filter using the filter design tools of `fir_design_helper.py`. The assignment here is to complete a design using a sampling rate of 48 kHz having an equiripple FIR lowpass lowpass response with 1dB cutoff frequency at 5 kHz, a passband ripple of 1dB, and stopband attenuation of 60 dB starting at 6.5 kHz. See Figure 9 for a graphical depiction of these amplitude response requirements." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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JLly4cMSIEWedddYtt9wS1rHdeOONmZn/+UE0adKkRCG+pl2gXr16aS9TgQQI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\nb9u2rZs5yYYAAgiEgwARwXC4yvQRAc8ENPGCXv369du0aVPPnj07d+6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CCCCAAAIIeCxA\nRNBjMgogED4CPXr0yJ49e+fOnf/555/ND17vvvtulixZqlatWrBgwQIPXvny5UuaNKkLk5MnT/79\n/187duxQfPHKlSsmf7ly5WbOnPnwww+bPSQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAtwJE\nBH3rSW0IhJpAw4YNtZpgp06dFi5caPVNEb5p06aZfmq8YIoUKVL9/1eyZMmuX79+7do1TQSql4J/\nN2/eNJkdEl26dBkzZkzixIkd9rOJAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACPhQgIuhDTKpC\nIDQFMmfOvGDBgqlTp2qA4L59+xw6ef/+fYX99FKk0OGQi81HH3100KBBrVq1cpGHQwgggAACCCCA\nAAIIIIAAAggggAACCCCAAAIIIOATgfg+qYVKEEAg5AUUvdu9e/c333yTJ0+emHQ2d+7c48eP1wqC\nhANjwkhZBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTcFyAi6L4VOREId4EECRK0b99ewwR//fXX\nl19+WWMH3RdR5pdeemn16tWKBXbs2DFhQgYou49HTgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nYiTAl/Ix4qMwAuEpoJUF9Ro5cqTCe1u3bt2yZcuuXbvOnz9/8cHr3r17adOmTZMmTYYMGQoXLlyi\nRImSJUsWKlQofnweQQjP+4VeI4AAAgiEjsCNGzc++ugj9Ud//Vu3bh06HaMnCCCAAAIIIIAAAnEn\n0L179woVKsTd+TkzAgggEC4CRATD5UrTTwTcFLh58+aqVatq1aoVbX5F+Ao8eDH/Z7RWZEAAAQQQ\nQCA0BG7fvr127Vr15fjx41pjODQ6RS8QQAABBBBAAAEE4lZAK9QQEYzbS8DZEUAgTASICIbJhaab\nCLgrcP369a5dux48eNDdAuRDAAEEEEAAgTATSJYsmVYFPnv27MCBA8+dOxe5906DheQ3UPiIgvuB\n+8EIcD/YKXh/4H7gfrALcD/YNUL1/aFPnz56Nj1dunT2zpJGAAEEEPCTABFBP8FSLQJBLHDmzJk7\nd+6w1F8QX0KajgACCCCAgD8F9E9C5cqVDx8+nDRpUvt5tORwlixZ9LNly5b2/Xfv3j158iT58bHu\nCu4Hy4H7gfvB/j7J/cD9wP3A/w9h+/dRl173f0REhP23gDQCCCCAgL8E7vNCAAEEbAIXLlzQ2820\nadNs+0gi4I3AM888o3tp7ty53hSmDAIIhIrApEmT9FbQtm3bUOlQuPfj33//1QVNmTJlxYoVH374\nYesbHO2xXtqjOUUPHTrkwHT06FHyiwgf68bgfuD3xf4Wwf3A/cD9wN9T/j6G+d/HePHi6Z1w8uTJ\n9ncD0ggggAACfhJgjKD17zc/EUDgvwQ0cWjp0qVz5879X3vZiFOBW7durVu3bv369Vq6SSMtrly5\n0rt37zp16rhu1I0bN2bOnLl9+/a9e/dmzJjxsccea926NQ/fuUbjKAIIIICAa4F79+5pwN+xY8dM\nNmt0S44HL32vZ/abp93Jr9GT+HA/WL8a/L5YDtwP3A/8vdAfR/4+8vdRbwW8H1rvh/xEAAEEEIgN\nAT9FGqkWAQSCVMAaI6h3n0yZMo0aNerq1atB2pFQarbGWzRo0CBFihS6LqlTp27evPkHH3wwe/Zs\nLd3kopv6uvbrr782H7PTp08fP3581ZAkSZK+ffsqvuiirE8OMUbQJ4xUgkCwCzBGMNivoEP7rTGC\nepTb6ehAjfXRxOP2Iq5H/5AfH+sTr/5d0X873A/cD9wPvH/q3YC/L7wf6heB98MwfD9kjKD9TwBp\nBBBAwH8CjBG0/sjyEwEEHAVOnz7dq1evt956q169ek2bNq1du3aqVKkcM7HtZ4FNmzb179//559/\n1nmKFSs2YMCAunXrOiza5LQJ+rOhgZ7jxo3TUQ0KHDRoUM6cOTVeUDHC119/fdiwYStXrly0aBFr\ndzvVYycCCCCAgGsB/ZXRw+xWHkY7WQ483c/9YB7DEgX3A/cD9wOj3/j7yN9HZlNwf3YE627hJwII\nIIBAbAj4L9hIzQggEIwCZoygwxtQokSJnnjiieHDh+/evTsY+xV0bdYIv4EDB1qj+hQCHD16tMOo\nCxc90pdQnTp1sq5gjx49HHJ+++231qHHH3/cr2NAGSPoIM8mAuEpwBjBELvu1hhB+z8JjO6yLjGj\nGay7gvuB+4H3B0a78n7I+6H9nx/uB+4Hd+4HKTFG0A5FGgEEEPCfAGME7R9YSCOAQJQCt2/fXv7g\npbXrcuXKpYGDGqxWrVo1d8arRVkpB6IQuHz58nPPPad5QXVci//99NNP5cuXjyKvk939+vUbP368\nDmjqV80v6pBDNavCH3/88ddff+3WrZv1Zb1DHjYRQAABBBCIViCq0Q8qaK2K5ObaSOS3qPE0txz3\njyi4H7gf3BldJCV+X/h9Mb8s3A8WBe+f5pYIlvcHrWxy7do102wSCCCAAAL+FfBfsJGaEUAgGAWi\nGiPo9J0oefLkTz/99NixY48cOeJ+Z6dPn+5+5jDMefz48UKFClngCgfu37/fI4Q9e/YkTPh/T3t8\n9dVXTsuuWrXKXFBNSeo0T8x3MkYw5obUgEAICDBGMAQuor0L9jGCUY0GU35NEqWj+krO/LlRgvxG\nEh/uB3Mz8PtivUvw/mBuCd4feH8wNwPvD7w/2G+GEL4fatWqpWvNGEGHy80mAggg4CcBxgha/2Dw\nEwEE/ksgf/78L774omYKPXDgwLp16zZv3qwxgv+V48GGHuOa9+Clrccee0yjBjV2sEKFCg7fANoL\n3rx5s0WLFs2aNbPvJG0ENDpQhn/++af2xIsXT/8Ta0SmOepOok+fPppfVDn1nF3btm2dFqlSpUrB\nggU1AayOapigTqfMTnOyEwEEEIihgP4iREREMKA8howBWFxf3+tra+uba9M8a+00/dQz6WanElE9\nrU9+Swkfy4H7gftBbywWgn5yP3A/cD/w95S/j9b7QMi/H6ZIkcK8+ZNAAAEEEPC7gJ8ijVSLAAJB\nKmCNEVSIyN7+K1euzJkz5/nnn8+aNWu070rp0qVTzE+DQs6cOWOvxErv2LFDNUTezx4JKOxqPRxn\nIb/xxhuesqxcudJcoMqVK7soroivyTllyhQXOb0+xBhBr+koiEAoCdy4cUN/DjSwLJQ6Fc59scYI\n6pmVtWvXRrVWlsKE5k+MlYhq9I+1thD58bF+p7gf+H2xv7tyP3A/cD/w95G/j2Hy97FVq1Z6x2OM\noP1NjzQCCCDgPwHGCFr/ZvMTAQT+I/DQQw9pANl/tv/nf/TElqI7VoBn69atCxYsmD9/vgYO3rt3\nz57NSiumOO3BK378+GXLln3qqadq1KihhEYcakzhyJEjIxdhjyXQpUsXzeFppRXPGzhwoKcyX375\npSlStWpVk46cKFOmjNmpRQetf8HNHhIIIICArwQ0BFkTIPuqNuoJEAH9iXc6OlBDA/VyZ+0r62l3\n8rse/YAPPtavPL8vlgP3A/cDf1/0x5e/vydPnuTvY4i9H+ofywD5F5dmIIAAAmEh4L9gIzUjgEAw\nCujZ/+LFi7vT8tOnT7/++ut6o0yVKlW0b5fJkiUrXLhw+vTprZzu1B9ueTT9qmEUlD7ueiqgb4uM\nsKpavHixixr++OMPczoN9dD0sC4ye3fICiHPnTvXu+KUQgABBBAIQAFrjGDKlCk1Q7W9edZoHj3L\nr6+ozN8XJVw/3U9+fKy7iPvH+q3hfuB+4P1T7wb8falYsSJ/H3k/DKv3wzZt2ujdjzGC9n+tSSOA\nAAL+E2CMoP1fbtIIIPC/4b0tW7a4A6GhhPqs8tFHH3366aflypWzBg5qDjGnKw5ev359165d7lQb\nnnmE1rt3b9P3Xr16ZcuWzWy6mdi4ceP58+dNZj09atKRE4UKFdLQz6tXr+qQ/sYsX768U6dOkbOx\nBwEEEEAAgcgCepTbRP4YvWT5hNjT+uaic325vvpenvuB0c9ZsmR5MDgtB/cDo9P4e2e9JfL30bd/\nH80fGhIIIIAAAn4X8F+wkZoRQCDkBbS4oN6kJk6caHp66dKlGTNmtG/fXvFCF+9fJj8JS2DEiBGG\nS+Mpz54964VM//79TSVKOF3H0V5tvnz5TP5u3brZD/kkzRhBnzBSCQIIIBBQAtYYwdSpU5tWMbrL\n+mPKaAbrluB+4H4wbw5KcD9wP3A/6K+DeYaG+4H7gfvB6f9LjBG0/2qQRgABBPwtwBhB618yfiKA\ngG8E9BXhsw9eevP67bffrIGDWnrQN7WHaC0Ko77//vumc1rSLyIiwmy6n1i5cqXJrIlA06VLZzad\nJtKkSWP2a1VIkyaBAAIIIICA+wJ6Rl4zXevlUERfgOpLH70c9pPfAsHHcuB+4H6wv0VwP3A/cD/w\n95S/j9ZvQbi9H9p/90kjgAACCPhXwN8hR+pHAIEQFog8RtBpZw8ePDh8+PDHHnvMvJ05zRa2OydN\nmmRklLDW/9M8qz/++KMihc2aNStWrFjDhg2HDBmimJ8LpTx58ph6FO1zkdM6VKNGDZNfAxOjze9p\nBsYIeipGfgQQQCDwBexjBA8dOhTVDNVOO0J+82cXHwlwP3A/2H8RuB+4H7gfzD1gT9hZTJrfF0Nk\nTOwJfILORwuaqM2sI2i/jUkjgAAC/hNgjKD5Q0kCAQT8JZAzZ85XH7ymTZum6SD0sJu/zhSc9c6c\nOdM0XJE8rc44duzYDz744Pjx42b/9u3brfhro0aNJkyYkDZtWnPIJDTXqElnyJDBpKNK2McIKgB5\n8+bNJEmSRJX5jz/+0DjCK1euRJUh8v4DBw5o5/z587XYRuSjbu7p3r27VqtyMzPZEEAAAQR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XNDakAAAQQQQAABBAJQ4O7du5s3b9akl/o3/tChQ7lz59aHer00E2aiRIl8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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 17, "metadata": { "image/png": { "width": "100%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/FIR_LPF_Design.png',width='100%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can test this filter in Lab3 using PyAudio for real-time DSP." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Design the filter here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Plot the magnitude response and phase response, and the pole-zero plot\n", "* Using the `freqz_resp_list`\n", "```Python\n", "def freqz_resp_list(b,a=np.array([1]),mode = 'dB',fs=1.0,Npts = 1024,fsize=(6,4)):\n", "\"\"\"\n", " A method for displaying a list filter frequency responses in magnitude,\n", " phase, and group delay. A plot is produced using matplotlib\n", "\n", " freqz_resp([b],[a],mode = 'dB',Npts = 1024,fsize=(6,4))\n", "\n", " b = ndarray of numerator coefficients\n", " a = ndarray of denominator coefficents\n", " mode = display mode: 'dB' magnitude, 'phase' in radians, or \n", " 'groupdelay_s' in samples and 'groupdelay_t' in sec, \n", " all versus frequency in Hz\n", " Npts = number of points to plot; default is 1024\n", " fsize = figure size; defult is (6,4) inches\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# fill in the plotting details\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-2-clause
authman/DAT210x	Module5/Module5 - Lab4.ipynb	1	16783	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DAT210x - Programming with Python for DS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module5- Lab4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "\n", "from sklearn import preprocessing\n", "from sklearn.decomposition import PCA\n", "\n", "# You might need to import more modules here..\n", "# .. your code here ..\n", "\n", "matplotlib.style.use('ggplot') # Look Pretty\n", "c = ['red', 'green', 'blue', 'orange', 'yellow', 'brown']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can experiment with these parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PLOT_TYPE_TEXT = False # If you'd like to see indices\n", "PLOT_VECTORS = True # If you'd like to see your original features in P.C.-Space" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some Convenience Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drawVectors(transformed_features, components_, columns, plt):\n", " num_columns = len(columns)\n", "\n", " # This function will project your original feature (columns)\n", " # onto your principal component feature-space, so that you can\n", " # visualize how \"important\" each one was in the\n", " # multi-dimensional scaling\n", "\n", " # Scale the principal components by the max value in\n", " # the transformed set belonging to that component\n", " xvector = components_[0] * max(transformed_features[:,0])\n", " yvector = components_[1] * max(transformed_features[:,1])\n", "\n", " ## Visualize projections\n", "\n", " # Sort each column by its length. These are your original\n", " # columns, not the principal components.\n", " important_features = { columns[i] : math.sqrt(xvector[i]2 + yvector[i]2) for i in range(num_columns) }\n", " important_features = sorted(zip(important_features.values(), important_features.keys()), reverse=True)\n", " print(\"Projected Features by importance:\\n\", important_features)\n", "\n", " ax = plt.axes()\n", "\n", " for i in range(num_columns):\n", " # Use an arrow to project each original feature as a\n", " # labeled vector on your principal component axes\n", " plt.arrow(0, 0, xvector[i], yvector[i], color='b', width=0.0005, head_width=0.02, alpha=0.75, zorder=600000)\n", " plt.text(xvector[i]1.2, yvector[i]1.2, list(columns)[i], color='b', alpha=0.75, zorder=600000)\n", " \n", " return ax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def doPCA(data, dimensions=2):\n", " model = PCA(n_components=dimensions, svd_solver='randomized', random_state=7)\n", " model.fit(data)\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def doKMeans(data, num_clusters=0):\n", " # TODO: Do the KMeans clustering here, passing in the # of clusters parameter\n", " # and fit it against your data. Then, return a tuple containing the cluster\n", " # centers and the labels.\n", " #\n", " # Hint: Just like with doPCA above, you will have to create a variable called\n", " # `model`, which will be a SKLearn K-Means model for this to work.\n", " \n", " \n", " # .. your code here ..\n", " return model.cluster_centers_, model.labels_" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Load up the dataset. It may or may not have nans in it. Make sure you catch them and destroy them, by setting them to `0`. This is valid for this dataset, since if the value is missing, you can assume no money was spent on it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As instructed, get rid of the `Channel` and `Region` columns, since you'll be investigating as if this were a single location wholesaler, rather than a national / international one. Leaving these fields in here would cause KMeans to examine and give weight to them:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Before unitizing / standardizing / normalizing your data in preparation for K-Means, it's a good idea to get a quick peek at it. You can do this using the `.describe()` method, or even by using the built-in pandas `df.plot.hist()`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having checked out your data, you may have noticed there's a pretty big gap between the top customers in each feature category and the rest. Some feature scaling algorithms won't get rid of outliers for you, so it's a good idea to handle that manually---particularly if your goal is NOT to determine the top customers. \n", "\n", "After all, you can do that with a simple Pandas `.sort_values()` and not a machine learning clustering algorithm. From a business perspective, you're probably more interested in clustering your +/- 2 standard deviation customers, rather than the top and bottom customers.\n", "\n", "Remove top 5 and bottom 5 samples for each column:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "drop = {}\n", "for col in df.columns:\n", " # Bottom 5\n", " sort = df.sort_values(by=col, ascending=True)\n", " if len(sort) > 5: sort=sort[:5]\n", " for index in sort.index: drop[index] = True # Just store the index once\n", "\n", " # Top 5\n", " sort = df.sort_values(by=col, ascending=False)\n", " if len(sort) > 5: sort=sort[:5]\n", " for index in sort.index: drop[index] = True # Just store the index once" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drop rows by index. We do this all at once in case there is a collision. This way, we don't end up dropping more rows than we have to, if there is a single row that satisfies the drop for multiple columns. Since there are 6 rows, if we end up dropping < 562 = 60 rows, that means there indeed were collisions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Dropping {0} Outliers...\".format(len(drop)))\n", "df.drop(inplace=True, labels=drop.keys(), axis=0)\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### What are you interested in?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Depending on what you're interested in, you might take a different approach to normalizing/standardizing your data.\n", " \n", "You should note that all columns left in the dataset are of the same unit. You might ask yourself, do I even need to normalize / standardize the data? The answer depends on what you're trying to accomplish. For instance, although all the units are the same (generic money unit), the price per item in your store isn't. There may be some cheap items and some expensive one. If your goal is to find out what items people tend to buy together but you didn't \"unitize\" properly before running kMeans, the contribution of the lesser priced item would be dwarfed by the more expensive item. This is an issue of scale.\n", "\n", "For a great overview on a few of the normalization methods supported in SKLearn, please check out: https://stackoverflow.com/questions/30918781/right-function-for-normalizing-input-of-sklearn-svm\n", "\n", "Suffice to say, at the end of the day, you're going to have to know what question you want answered and what data you have available in order to select the best method for your purpose. Luckily, SKLearn's interfaces are easy to switch out so in the mean time, you can experiment with all of them and see how they alter your results.\n", "\n", "5-sec summary before you dive deeper online:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say your user spend a LOT. Normalization divides each item by the average overall amount of spending. Stated differently, your new feature is = the contribution of overall spending going into that particular item: \\$spent on feature / \\$overall spent by sample." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### MinMax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What % in the overall range of $spent by all users on THIS particular feature is the current sample's feature at? When you're dealing with all the same units, this will produce a near face-value amount. Be careful though: if you have even a single outlier, it can cause all your data to get squashed up in lower percentages.\n", "\n", "Imagine your buyers usually spend \\$100 on wholesale milk, but today only spent \\$20. This is the relationship you're trying to capture with MinMax. NOTE: MinMax doesn't standardize (std. dev.); it only normalizes / unitizes your feature, in the mathematical sense. MinMax can be used as an alternative to zero mean, unit variance scaling. [(sampleFeatureValue-min) / (max-min)] * (max-min) + min Where min and max are for the overall feature values for all samples." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Back to The Assignment" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Un-comment just *ONE* of lines at a time and see how alters your results. Pay attention to the direction of the arrows, as well as their LENGTHS:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#T = preprocessing.StandardScaler().fit_transform(df)\n", "#T = preprocessing.MinMaxScaler().fit_transform(df)\n", "#T = preprocessing.MaxAbsScaler().fit_transform(df)\n", "#T = preprocessing.Normalizer().fit_transform(df)\n", "T = df # No Change" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Sometimes people perform PCA before doing KMeans, so that KMeans only operates on the most meaningful features. In our case, there are so few features that doing PCA ahead of time isn't really necessary, and you can do KMeans in feature space. But keep in mind you have the option to transform your data to bring down its dimensionality. If you take that route, then your Clusters will already be in PCA-transformed feature space, and you won't have to project them again for visualization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Do KMeans\n", "\n", "n_clusters = 3\n", "centroids, labels = doKMeans(T, n_clusters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print out your centroids. They're currently in feature-space, which is good. Print them out before you transform them into PCA space for viewing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've clustered our KMeans, let's do PCA, using it as a tool to visualize the results. Project the centroids as well as the samples into the new 2D feature space for visualization purposes:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display_pca = doPCA(T)\n", "T = display_pca.transform(T)\n", "CC = display_pca.transform(centroids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize all the samples. Give them the color of their cluster label" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "if PLOT_TYPE_TEXT:\n", " # Plot the index of the sample, so you can further investigate it in your dset\n", " for i in range(len(T)): ax.text(T[i,0], T[i,1], df.index[i], color=c[labels[i]], alpha=0.75, zorder=600000)\n", " ax.set_xlim(min(T[:,0])1.2, max(T[:,0])1.2)\n", " ax.set_ylim(min(T[:,1])1.2, max(T[:,1])1.2)\n", "else:\n", " # Plot a regular scatter plot\n", " sample_colors = [ c[labels[i]] for i in range(len(T)) ]\n", " ax.scatter(T[:, 0], T[:, 1], c=sample_colors, marker='o', alpha=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Centroids as X's, and label them" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ax.scatter(CC[:, 0], CC[:, 1], marker='x', s=169, linewidths=3, zorder=1000, c=c)\n", "for i in range(len(centroids)):\n", " ax.text(CC[i, 0], CC[i, 1], str(i), zorder=500010, fontsize=18, color=c[i])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Display feature vectors for investigation:\n", "if PLOT_VECTORS:\n", " drawVectors(T, display_pca.components_, df.columns, plt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Add the cluster label back into the dataframe and display it:\n", "df['label'] = pd.Series(labels, index=df.index)\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "58px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ajackwin/data	pew-religions/Religion-Leah.py.ipynb	37	38187	{"nbformat_minor": 0, "cells": [{"execution_count": 39, "cell_type": "code", "source": "### Leah Libresco\n### [email protected]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 40, "cell_type": "code", "source": "import numpy as np\nimport pandas as pd", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "# List of 12 U.S. religious groups stuided by Pew\nreligions = ['Buddhist', 'Catholic', 'Evangel Prot', 'Hindu', 'Hist Black Prot', 'Jehovahs Witness', 'Jewish', 'Mainline Prot', 'Mormon', 'Muslim', 'Orthodox Christian', 'Unaffiliated']", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 42, "cell_type": "code", "source": "# Create a .csv file with a function to write to it \ncsv = open(\"current.csv\", 'w')\ncsv.truncate()\n\ndef write_row(matrix):\n arr = np.asarray(matrix[0])[0]\n row = ','.join([str(a) for a in arr]) + '\\n'\n csv.write(row)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 43, "cell_type": "code", "source": "# Intitial distribution of religions in US\nfirst = np.matrix([.007, .208, .254, .007, .065, .008, .019, .147, .016, .009, .005, .228])\n\n# Normed to sum to 100%\ncurrent = first / np.sum(first)\nt0 = current\nwrite_row(current)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 44, "cell_type": "code", "source": "# Transition matrix \ntrans = np.matrix(((0.390296314, 0.027141947, 0.06791021, 0.001857564, 0, 0, 0.011166082, 0.059762879, 0, 0, 0, 0.396569533),\n (0.005370791, 0.593173325, 0.103151608, 0.000649759, 0.010486747, 0.005563864, 0.002041424, 0.053825329, 0.004760476, 0.001130529, 0.000884429, 0.199488989),\n (0.00371836, 0.023900817, 0.650773331, 0.000250102, 0.016774503, 0.003098214, 0.001865491, 0.122807467, 0.004203107, 0.000186572, 0.002123778, 0.151866648),\n (0, 0, 0.0033732, 0.804072618, 0, 0.001511151, 0, 0.01234639, 0, 0.00209748, 0, 0.17659916),\n (0.002051357, 0.016851659, 0.09549708, 0, 0.699214315, 0.010620473, 0.000338804, 0.024372871, 0.000637016, 0.009406884, 0.000116843, 0.129892558),\n (0, 0.023278276, 0.109573979, 0, 0.077957568, 0.336280578, 0, 0.074844833, 0.007624035, 0, 0, 0.35110361),\n (0.006783201, 0.004082693, 0.014329604, 0, 0, 0.000610585, 0.745731278, 0.009587587, 0, 0, 0.002512334, 0.184058682),\n (0.005770357, 0.038017215, 0.187857555, 0.000467601, 0.008144075, 0.004763516, 0.003601208, 0.451798506, 0.005753587, 0.000965543, 0.00109818, 0.25750798),\n (0.007263135, 0.01684885, 0.06319935, 0.000248467, 0.0059394, 0, 0.001649896, 0.03464334, 0.642777489, 0.002606278, 0, 0.208904711),\n (0, 0.005890381, 0.023573308, 0, 0.011510643, 0, 0.005518343, 0.014032084, 0, 0.772783807, 0, 0.15424369),\n (0.004580353, 0.042045841, 0.089264134\t, 0, 0.00527346, 0, 0, 0.061471387, 0.005979218, 0.009113978, 0.526728084, 0.243246723),\n (0.006438308, 0.044866331, 0.1928814, 0.002035375, 0.04295005, 0.010833621, 0.011541439, 0.09457963, 0.01365141, 0.005884336, 0.002892072, 0.525359211)))\n\n# Fertility array\nfert = np.matrix(((2.1, 2.3, 2.3, 2.1, 2.5, 2.1, 2, 1.9, 3.4, 2.8, 2.1, 1.7)))\n\n# Create data frame for printing later\nreligionDataFrame = pd.DataFrame()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 45, "cell_type": "code", "source": "# Run model\nfor x in range(0,100):\n\n ### beginning of conversion step\n \n # apply transition matrix to current distribution\n current = current * trans\n \n ### beginning of fertility step\n \n # divide by two for couple number\n current = current/2\n \n # adjust by fertility\n \n current = np.multiply(fert, current)\n \n # normalize to 100%\n \n current = current / np.sum(current)\n \n write_row(current)\n \n # add to data frame\n religionDataFrame = religionDataFrame.append(pd.DataFrame(current), ignore_index=True)\n\ncsv.close()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 47, "cell_type": "code", "source": "# Print data frame with results\nreligionDataFrame.columns = religions\nreligionDataFrame", "outputs": [{"execution_count": 47, "output_type": "execute_result", "data": {"text/plain": " Buddhist Catholic Evangel Prot Hindu Hist Black Prot \\\n0 0.007924 0.170263 0.309520 0.006728 0.080042 \n1 0.007981 0.140118 0.331889 0.006234 0.090308 \n2 0.007856 0.119514 0.340401 0.005752 0.097890 \n3 0.007710 0.105453 0.341544 0.005303 0.103242 \n4 0.007583 0.095775 0.338780 0.004895 0.106781 \n5 0.007482 0.088996 0.334004 0.004532 0.108869 \n6 0.007402 0.084125 0.328280 0.004213 0.109811 \n7 0.007339 0.080515 0.322217 0.003934 0.109868 \n8 0.007288 0.077748 0.316165 0.003692 0.109259 \n9 0.007247 0.075553 0.310324 0.003483 0.108167 \n10 0.007214 0.073759 0.304804 0.003302 0.106743 \n11 0.007186 0.072254 0.299659 0.003148 0.105108 \n12 0.007163 0.070967 0.294911 0.003015 0.103356 \n13 0.007144 0.069849 0.290562 0.002901 0.101562 \n14 0.007128 0.068869 0.286601 0.002804 0.099779 \n15 0.007114 0.068004 0.283011 0.002720 0.098049 \n16 0.007102 0.067237 0.279769 0.002649 0.096397 \n17 0.007093 0.066557 0.276852 0.002587 0.094844 \n18 0.007084 0.065953 0.274235 0.002535 0.093398 \n19 0.007077 0.065416 0.271894 0.002489 0.092065 \n20 0.007071 0.064939 0.269805 0.002450 0.090845 \n21 0.007066 0.064516 0.267944 0.002417 0.089735 \n22 0.007062 0.064142 0.266290 0.002387 0.088731 \n23 0.007059 0.063810 0.264823 0.002362 0.087827 \n24 0.007056 0.063517 0.263523 0.002341 0.087016 \n25 0.007053 0.063259 0.262374 0.002322 0.086291 \n26 0.007051 0.063030 0.261359 0.002305 0.085644 \n27 0.007049 0.062829 0.260463 0.002291 0.085068 \n28 0.007048 0.062652 0.259674 0.002279 0.084557 \n29 0.007047 0.062497 0.258980 0.002268 0.084104 \n.. ... ... ... ... ... \n70 0.007056 0.061429 0.254164 0.002197 0.080749 \n71 0.007056 0.061429 0.254164 0.002197 0.080746 \n72 0.007056 0.061429 0.254164 0.002197 0.080744 \n73 0.007056 0.061429 0.254164 0.002197 0.080742 \n74 0.007056 0.061429 0.254165 0.002197 0.080740 \n75 0.007057 0.061429 0.254165 0.002197 0.080738 \n76 0.007057 0.061430 0.254166 0.002197 0.080737 \n77 0.007057 0.061430 0.254167 0.002197 0.080735 \n78 0.007057 0.061430 0.254168 0.002197 0.080734 \n79 0.007057 0.061431 0.254169 0.002197 0.080733 \n80 0.007057 0.061431 0.254170 0.002197 0.080732 \n81 0.007058 0.061431 0.254171 0.002197 0.080731 \n82 0.007058 0.061432 0.254172 0.002197 0.080730 \n83 0.007058 0.061432 0.254173 0.002197 0.080729 \n84 0.007058 0.061432 0.254174 0.002197 0.080728 \n85 0.007058 0.061433 0.254175 0.002197 0.080728 \n86 0.007058 0.061433 0.254176 0.002197 0.080727 \n87 0.007058 0.061433 0.254177 0.002197 0.080727 \n88 0.007059 0.061433 0.254178 0.002197 0.080726 \n89 0.007059 0.061434 0.254179 0.002197 0.080726 \n90 0.007059 0.061434 0.254180 0.002197 0.080725 \n91 0.007059 0.061434 0.254181 0.002197 0.080725 \n92 0.007059 0.061435 0.254182 0.002197 0.080724 \n93 0.007059 0.061435 0.254183 0.002197 0.080724 \n94 0.007059 0.061435 0.254184 0.002197 0.080723 \n95 0.007059 0.061435 0.254185 0.002197 0.080723 \n96 0.007059 0.061436 0.254186 0.002197 0.080723 \n97 0.007059 0.061436 0.254186 0.002197 0.080722 \n98 0.007059 0.061436 0.254187 0.002197 0.080722 \n99 0.007059 0.061436 0.254188 0.002197 0.080722 \n\n Jehovahs Witness Jewish Mainline Prot Mormon Muslim \\\n0 0.008986 0.018492 0.128123 0.028069 0.013271 \n1 0.008980 0.017417 0.120477 0.039642 0.017382 \n2 0.008785 0.016412 0.117260 0.051127 0.021558 \n3 0.008572 0.015526 0.115117 0.062488 0.025770 \n4 0.008375 0.014768 0.113124 0.073661 0.029978 \n5 0.008197 0.014130 0.111103 0.084574 0.034138 \n6 0.008031 0.013595 0.109070 0.095151 0.038205 \n7 0.007874 0.013148 0.107080 0.105324 0.042139 \n8 0.007723 0.012773 0.105176 0.115035 0.045908 \n9 0.007578 0.012457 0.103386 0.124239 0.049485 \n10 0.007441 0.012190 0.101724 0.132901 0.052850 \n11 0.007310 0.011963 0.100194 0.141003 0.055991 \n12 0.007187 0.011770 0.098797 0.148534 0.058901 \n13 0.007072 0.011605 0.097526 0.155496 0.061578 \n14 0.006965 0.011462 0.096376 0.161900 0.064025 \n15 0.006867 0.011339 0.095339 0.167762 0.066250 \n16 0.006777 0.011232 0.094407 0.173106 0.068260 \n17 0.006695 0.011139 0.093571 0.177959 0.070069 \n18 0.006620 0.011058 0.092823 0.182351 0.071688 \n19 0.006553 0.010988 0.092155 0.186313 0.073130 \n20 0.006492 0.010925 0.091561 0.189877 0.074411 \n21 0.006437 0.010871 0.091033 0.193076 0.075542 \n22 0.006389 0.010823 0.090564 0.195942 0.076539 \n23 0.006345 0.010781 0.090148 0.198503 0.077414 \n24 0.006306 0.010744 0.089781 0.200789 0.078178 \n25 0.006272 0.010711 0.089456 0.202827 0.078845 \n26 0.006242 0.010682 0.089170 0.204642 0.079423 \n27 0.006215 0.010657 0.088917 0.206256 0.079923 \n28 0.006191 0.010635 0.088695 0.207690 0.080353 \n29 0.006170 0.010615 0.088500 0.208965 0.080723 \n.. ... ... ... ... ... \n70 0.006019 0.010457 0.087162 0.219413 0.081818 \n71 0.006019 0.010457 0.087162 0.219435 0.081798 \n72 0.006019 0.010456 0.087163 0.219457 0.081777 \n73 0.006019 0.010456 0.087163 0.219477 0.081758 \n74 0.006018 0.010456 0.087163 0.219496 0.081739 \n75 0.006018 0.010456 0.087163 0.219514 0.081721 \n76 0.006018 0.010455 0.087164 0.219531 0.081703 \n77 0.006018 0.010455 0.087164 0.219548 0.081686 \n78 0.006018 0.010455 0.087165 0.219563 0.081670 \n79 0.006018 0.010455 0.087165 0.219578 0.081654 \n80 0.006018 0.010455 0.087166 0.219592 0.081639 \n81 0.006018 0.010454 0.087166 0.219605 0.081625 \n82 0.006018 0.010454 0.087166 0.219617 0.081610 \n83 0.006018 0.010454 0.087167 0.219629 0.081597 \n84 0.006018 0.010454 0.087167 0.219641 0.081584 \n85 0.006018 0.010454 0.087168 0.219652 0.081571 \n86 0.006018 0.010454 0.087168 0.219662 0.081559 \n87 0.006018 0.010454 0.087169 0.219672 0.081548 \n88 0.006018 0.010453 0.087169 0.219682 0.081536 \n89 0.006018 0.010453 0.087169 0.219691 0.081526 \n90 0.006018 0.010453 0.087170 0.219699 0.081515 \n91 0.006018 0.010453 0.087170 0.219708 0.081505 \n92 0.006018 0.010453 0.087170 0.219716 0.081496 \n93 0.006018 0.010453 0.087171 0.219723 0.081487 \n94 0.006018 0.010453 0.087171 0.219731 0.081478 \n95 0.006018 0.010453 0.087171 0.219738 0.081469 \n96 0.006018 0.010453 0.087172 0.219744 0.081461 \n97 0.006018 0.010452 0.087172 0.219751 0.081453 \n98 0.006018 0.010452 0.087172 0.219757 0.081446 \n99 0.006018 0.010452 0.087173 0.219763 0.081438 \n\n Orthodox Christian Unaffiliated \n0 0.004466 0.224115 \n1 0.004070 0.215501 \n2 0.003812 0.209631 \n3 0.003629 0.205648 \n4 0.003485 0.202793 \n5 0.003364 0.200612 \n6 0.003258 0.198860 \n7 0.003162 0.197401 \n8 0.003076 0.196158 \n9 0.002997 0.195083 \n10 0.002925 0.194146 \n11 0.002860 0.193323 \n12 0.002800 0.192599 \n13 0.002747 0.191958 \n14 0.002699 0.191392 \n15 0.002656 0.190890 \n16 0.002617 0.190446 \n17 0.002582 0.190053 \n18 0.002551 0.189705 \n19 0.002524 0.189397 \n20 0.002499 0.189125 \n21 0.002477 0.188885 \n22 0.002458 0.188674 \n23 0.002441 0.188487 \n24 0.002425 0.188323 \n25 0.002412 0.188179 \n26 0.002400 0.188052 \n27 0.002390 0.187941 \n28 0.002380 0.187844 \n29 0.002372 0.187759 \n.. ... ... \n70 0.002315 0.187222 \n71 0.002315 0.187223 \n72 0.002315 0.187224 \n73 0.002315 0.187224 \n74 0.002315 0.187225 \n75 0.002315 0.187226 \n76 0.002315 0.187226 \n77 0.002315 0.187227 \n78 0.002315 0.187228 \n79 0.002315 0.187228 \n80 0.002315 0.187229 \n81 0.002315 0.187230 \n82 0.002315 0.187230 \n83 0.002315 0.187231 \n84 0.002315 0.187231 \n85 0.002315 0.187232 \n86 0.002315 0.187232 \n87 0.002315 0.187233 \n88 0.002315 0.187233 \n89 0.002315 0.187234 \n90 0.002315 0.187234 \n91 0.002315 0.187235 \n92 0.002315 0.187235 \n93 0.002315 0.187236 \n94 0.002315 0.187236 \n95 0.002315 0.187236 \n96 0.002315 0.187237 \n97 0.002315 0.187237 \n98 0.002315 0.187237 \n99 0.002315 0.187238 \n\n[100 rows x 12 columns]", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Buddhist</th>\n <th>Catholic</th>\n <th>Evangel Prot</th>\n <th>Hindu</th>\n <th>Hist Black Prot</th>\n <th>Jehovahs Witness</th>\n <th>Jewish</th>\n <th>Mainline Prot</th>\n <th>Mormon</th>\n <th>Muslim</th>\n <th>Orthodox Christian</th>\n <th>Unaffiliated</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 0.007924</td>\n <td> 0.170263</td>\n <td> 0.309520</td>\n <td> 0.006728</td>\n <td> 0.080042</td>\n <td> 0.008986</td>\n <td> 0.018492</td>\n <td> 0.128123</td>\n <td> 0.028069</td>\n <td> 0.013271</td>\n <td> 0.004466</td>\n <td> 0.224115</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 0.007981</td>\n <td> 0.140118</td>\n <td> 0.331889</td>\n <td> 0.006234</td>\n <td> 0.090308</td>\n <td> 0.008980</td>\n <td> 0.017417</td>\n <td> 0.120477</td>\n <td> 0.039642</td>\n <td> 0.017382</td>\n <td> 0.004070</td>\n <td> 0.215501</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.007856</td>\n <td> 0.119514</td>\n <td> 0.340401</td>\n <td> 0.005752</td>\n <td> 0.097890</td>\n <td> 0.008785</td>\n <td> 0.016412</td>\n <td> 0.117260</td>\n <td> 0.051127</td>\n <td> 0.021558</td>\n <td> 0.003812</td>\n <td> 0.209631</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0.007710</td>\n <td> 0.105453</td>\n <td> 0.341544</td>\n <td> 0.005303</td>\n <td> 0.103242</td>\n <td> 0.008572</td>\n <td> 0.015526</td>\n <td> 0.115117</td>\n <td> 0.062488</td>\n <td> 0.025770</td>\n <td> 0.003629</td>\n <td> 0.205648</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.007583</td>\n <td> 0.095775</td>\n <td> 0.338780</td>\n <td> 0.004895</td>\n <td> 0.106781</td>\n <td> 0.008375</td>\n <td> 0.014768</td>\n <td> 0.113124</td>\n <td> 0.073661</td>\n <td> 0.029978</td>\n <td> 0.003485</td>\n <td> 0.202793</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.007482</td>\n <td> 0.088996</td>\n <td> 0.334004</td>\n <td> 0.004532</td>\n <td> 0.108869</td>\n <td> 0.008197</td>\n <td> 0.014130</td>\n <td> 0.111103</td>\n <td> 0.084574</td>\n <td> 0.034138</td>\n <td> 0.003364</td>\n <td> 0.200612</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0.007402</td>\n <td> 0.084125</td>\n <td> 0.328280</td>\n <td> 0.004213</td>\n <td> 0.109811</td>\n <td> 0.008031</td>\n <td> 0.013595</td>\n <td> 0.109070</td>\n <td> 0.095151</td>\n <td> 0.038205</td>\n <td> 0.003258</td>\n <td> 0.198860</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.007339</td>\n <td> 0.080515</td>\n <td> 0.322217</td>\n <td> 0.003934</td>\n <td> 0.109868</td>\n <td> 0.007874</td>\n <td> 0.013148</td>\n <td> 0.107080</td>\n <td> 0.105324</td>\n <td> 0.042139</td>\n <td> 0.003162</td>\n <td> 0.197401</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.007288</td>\n <td> 0.077748</td>\n <td> 0.316165</td>\n <td> 0.003692</td>\n <td> 0.109259</td>\n <td> 0.007723</td>\n <td> 0.012773</td>\n <td> 0.105176</td>\n <td> 0.115035</td>\n <td> 0.045908</td>\n <td> 0.003076</td>\n <td> 0.196158</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.007247</td>\n <td> 0.075553</td>\n <td> 0.310324</td>\n <td> 0.003483</td>\n <td> 0.108167</td>\n <td> 0.007578</td>\n <td> 0.012457</td>\n <td> 0.103386</td>\n <td> 0.124239</td>\n <td> 0.049485</td>\n <td> 0.002997</td>\n <td> 0.195083</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 0.007214</td>\n <td> 0.073759</td>\n <td> 0.304804</td>\n <td> 0.003302</td>\n <td> 0.106743</td>\n <td> 0.007441</td>\n <td> 0.012190</td>\n <td> 0.101724</td>\n <td> 0.132901</td>\n <td> 0.052850</td>\n <td> 0.002925</td>\n <td> 0.194146</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 0.007186</td>\n <td> 0.072254</td>\n <td> 0.299659</td>\n <td> 0.003148</td>\n <td> 0.105108</td>\n <td> 0.007310</td>\n <td> 0.011963</td>\n <td> 0.100194</td>\n <td> 0.141003</td>\n <td> 0.055991</td>\n <td> 0.002860</td>\n <td> 0.193323</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.007163</td>\n <td> 0.070967</td>\n <td> 0.294911</td>\n <td> 0.003015</td>\n <td> 0.103356</td>\n <td> 0.007187</td>\n <td> 0.011770</td>\n <td> 0.098797</td>\n <td> 0.148534</td>\n <td> 0.058901</td>\n <td> 0.002800</td>\n <td> 0.192599</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 0.007144</td>\n <td> 0.069849</td>\n <td> 0.290562</td>\n <td> 0.002901</td>\n <td> 0.101562</td>\n <td> 0.007072</td>\n <td> 0.011605</td>\n <td> 0.097526</td>\n <td> 0.155496</td>\n <td> 0.061578</td>\n <td> 0.002747</td>\n <td> 0.191958</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 0.007128</td>\n <td> 0.068869</td>\n <td> 0.286601</td>\n <td> 0.002804</td>\n <td> 0.099779</td>\n <td> 0.006965</td>\n <td> 0.011462</td>\n <td> 0.096376</td>\n <td> 0.161900</td>\n <td> 0.064025</td>\n <td> 0.002699</td>\n <td> 0.191392</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 0.007114</td>\n <td> 0.068004</td>\n <td> 0.283011</td>\n <td> 0.002720</td>\n <td> 0.098049</td>\n <td> 0.006867</td>\n <td> 0.011339</td>\n <td> 0.095339</td>\n <td> 0.167762</td>\n <td> 0.066250</td>\n <td> 0.002656</td>\n <td> 0.190890</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 0.007102</td>\n <td> 0.067237</td>\n <td> 0.279769</td>\n <td> 0.002649</td>\n <td> 0.096397</td>\n <td> 0.006777</td>\n <td> 0.011232</td>\n <td> 0.094407</td>\n <td> 0.173106</td>\n <td> 0.068260</td>\n <td> 0.002617</td>\n <td> 0.190446</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 0.007093</td>\n <td> 0.066557</td>\n <td> 0.276852</td>\n <td> 0.002587</td>\n <td> 0.094844</td>\n <td> 0.006695</td>\n <td> 0.011139</td>\n <td> 0.093571</td>\n <td> 0.177959</td>\n <td> 0.070069</td>\n <td> 0.002582</td>\n <td> 0.190053</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 0.007084</td>\n <td> 0.065953</td>\n <td> 0.274235</td>\n <td> 0.002535</td>\n <td> 0.093398</td>\n <td> 0.006620</td>\n <td> 0.011058</td>\n <td> 0.092823</td>\n <td> 0.182351</td>\n <td> 0.071688</td>\n <td> 0.002551</td>\n <td> 0.189705</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 0.007077</td>\n <td> 0.065416</td>\n <td> 0.271894</td>\n <td> 0.002489</td>\n <td> 0.092065</td>\n <td> 0.006553</td>\n <td> 0.010988</td>\n <td> 0.092155</td>\n <td> 0.186313</td>\n <td> 0.073130</td>\n <td> 0.002524</td>\n <td> 0.189397</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 0.007071</td>\n <td> 0.064939</td>\n <td> 0.269805</td>\n <td> 0.002450</td>\n <td> 0.090845</td>\n <td> 0.006492</td>\n <td> 0.010925</td>\n <td> 0.091561</td>\n <td> 0.189877</td>\n <td> 0.074411</td>\n <td> 0.002499</td>\n <td> 0.189125</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 0.007066</td>\n <td> 0.064516</td>\n <td> 0.267944</td>\n <td> 0.002417</td>\n <td> 0.089735</td>\n <td> 0.006437</td>\n <td> 0.010871</td>\n <td> 0.091033</td>\n <td> 0.193076</td>\n <td> 0.075542</td>\n <td> 0.002477</td>\n <td> 0.188885</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 0.007062</td>\n <td> 0.064142</td>\n <td> 0.266290</td>\n <td> 0.002387</td>\n <td> 0.088731</td>\n <td> 0.006389</td>\n <td> 0.010823</td>\n <td> 0.090564</td>\n <td> 0.195942</td>\n <td> 0.076539</td>\n <td> 0.002458</td>\n <td> 0.188674</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 0.007059</td>\n <td> 0.063810</td>\n <td> 0.264823</td>\n <td> 0.002362</td>\n <td> 0.087827</td>\n <td> 0.006345</td>\n <td> 0.010781</td>\n <td> 0.090148</td>\n <td> 0.198503</td>\n <td> 0.077414</td>\n <td> 0.002441</td>\n <td> 0.188487</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 0.007056</td>\n <td> 0.063517</td>\n <td> 0.263523</td>\n <td> 0.002341</td>\n <td> 0.087016</td>\n <td> 0.006306</td>\n <td> 0.010744</td>\n <td> 0.089781</td>\n <td> 0.200789</td>\n <td> 0.078178</td>\n <td> 0.002425</td>\n <td> 0.188323</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 0.007053</td>\n <td> 0.063259</td>\n <td> 0.262374</td>\n <td> 0.002322</td>\n <td> 0.086291</td>\n <td> 0.006272</td>\n <td> 0.010711</td>\n <td> 0.089456</td>\n <td> 0.202827</td>\n <td> 0.078845</td>\n <td> 0.002412</td>\n <td> 0.188179</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 0.007051</td>\n <td> 0.063030</td>\n <td> 0.261359</td>\n <td> 0.002305</td>\n <td> 0.085644</td>\n <td> 0.006242</td>\n <td> 0.010682</td>\n <td> 0.089170</td>\n <td> 0.204642</td>\n <td> 0.079423</td>\n <td> 0.002400</td>\n <td> 0.188052</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 0.007049</td>\n <td> 0.062829</td>\n <td> 0.260463</td>\n <td> 0.002291</td>\n <td> 0.085068</td>\n <td> 0.006215</td>\n <td> 0.010657</td>\n <td> 0.088917</td>\n <td> 0.206256</td>\n <td> 0.079923</td>\n <td> 0.002390</td>\n <td> 0.187941</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 0.007048</td>\n <td> 0.062652</td>\n <td> 0.259674</td>\n <td> 0.002279</td>\n <td> 0.084557</td>\n <td> 0.006191</td>\n <td> 0.010635</td>\n <td> 0.088695</td>\n <td> 0.207690</td>\n <td> 0.080353</td>\n <td> 0.002380</td>\n <td> 0.187844</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 0.007047</td>\n <td> 0.062497</td>\n <td> 0.258980</td>\n <td> 0.002268</td>\n <td> 0.084104</td>\n <td> 0.006170</td>\n <td> 0.010615</td>\n <td> 0.088500</td>\n <td> 0.208965</td>\n <td> 0.080723</td>\n <td> 0.002372</td>\n <td> 0.187759</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080749</td>\n <td> 0.006019</td>\n <td> 0.010457</td>\n <td> 0.087162</td>\n <td> 0.219413</td>\n <td> 0.081818</td>\n <td> 0.002315</td>\n <td> 0.187222</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080746</td>\n <td> 0.006019</td>\n <td> 0.010457</td>\n <td> 0.087162</td>\n <td> 0.219435</td>\n <td> 0.081798</td>\n <td> 0.002315</td>\n <td> 0.187223</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080744</td>\n <td> 0.006019</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219457</td>\n <td> 0.081777</td>\n <td> 0.002315</td>\n <td> 0.187224</td>\n </tr>\n <tr>\n <th>73</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080742</td>\n <td> 0.006019</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219477</td>\n <td> 0.081758</td>\n <td> 0.002315</td>\n <td> 0.187224</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254165</td>\n <td> 0.002197</td>\n <td> 0.080740</td>\n <td> 0.006018</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219496</td>\n <td> 0.081739</td>\n <td> 0.002315</td>\n <td> 0.187225</td>\n </tr>\n <tr>\n <th>75</th>\n <td> 0.007057</td>\n <td> 0.061429</td>\n <td> 0.254165</td>\n <td> 0.002197</td>\n <td> 0.080738</td>\n <td> 0.006018</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219514</td>\n <td> 0.081721</td>\n <td> 0.002315</td>\n <td> 0.187226</td>\n </tr>\n <tr>\n <th>76</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254166</td>\n <td> 0.002197</td>\n <td> 0.080737</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087164</td>\n <td> 0.219531</td>\n <td> 0.081703</td>\n <td> 0.002315</td>\n <td> 0.187226</td>\n </tr>\n <tr>\n <th>77</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254167</td>\n <td> 0.002197</td>\n <td> 0.080735</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087164</td>\n <td> 0.219548</td>\n <td> 0.081686</td>\n <td> 0.002315</td>\n <td> 0.187227</td>\n </tr>\n <tr>\n <th>78</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254168</td>\n <td> 0.002197</td>\n <td> 0.080734</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087165</td>\n <td> 0.219563</td>\n <td> 0.081670</td>\n <td> 0.002315</td>\n <td> 0.187228</td>\n </tr>\n <tr>\n <th>79</th>\n <td> 0.007057</td>\n <td> 0.061431</td>\n <td> 0.254169</td>\n <td> 0.002197</td>\n <td> 0.080733</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087165</td>\n <td> 0.219578</td>\n <td> 0.081654</td>\n <td> 0.002315</td>\n <td> 0.187228</td>\n </tr>\n <tr>\n <th>80</th>\n <td> 0.007057</td>\n <td> 0.061431</td>\n <td> 0.254170</td>\n <td> 0.002197</td>\n <td> 0.080732</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087166</td>\n <td> 0.219592</td>\n <td> 0.081639</td>\n <td> 0.002315</td>\n <td> 0.187229</td>\n </tr>\n <tr>\n <th>81</th>\n <td> 0.007058</td>\n <td> 0.061431</td>\n <td> 0.254171</td>\n <td> 0.002197</td>\n <td> 0.080731</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087166</td>\n <td> 0.219605</td>\n <td> 0.081625</td>\n <td> 0.002315</td>\n <td> 0.187230</td>\n </tr>\n <tr>\n <th>82</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254172</td>\n <td> 0.002197</td>\n <td> 0.080730</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087166</td>\n <td> 0.219617</td>\n <td> 0.081610</td>\n <td> 0.002315</td>\n <td> 0.187230</td>\n </tr>\n <tr>\n <th>83</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254173</td>\n <td> 0.002197</td>\n <td> 0.080729</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087167</td>\n <td> 0.219629</td>\n <td> 0.081597</td>\n <td> 0.002315</td>\n <td> 0.187231</td>\n </tr>\n <tr>\n <th>84</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254174</td>\n <td> 0.002197</td>\n <td> 0.080728</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087167</td>\n <td> 0.219641</td>\n <td> 0.081584</td>\n <td> 0.002315</td>\n <td> 0.187231</td>\n </tr>\n <tr>\n <th>85</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254175</td>\n <td> 0.002197</td>\n <td> 0.080728</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087168</td>\n <td> 0.219652</td>\n <td> 0.081571</td>\n <td> 0.002315</td>\n <td> 0.187232</td>\n </tr>\n <tr>\n <th>86</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254176</td>\n <td> 0.002197</td>\n <td> 0.080727</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087168</td>\n <td> 0.219662</td>\n <td> 0.081559</td>\n <td> 0.002315</td>\n <td> 0.187232</td>\n </tr>\n <tr>\n <th>87</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254177</td>\n <td> 0.002197</td>\n <td> 0.080727</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087169</td>\n <td> 0.219672</td>\n <td> 0.081548</td>\n <td> 0.002315</td>\n <td> 0.187233</td>\n </tr>\n <tr>\n <th>88</th>\n <td> 0.007059</td>\n <td> 0.061433</td>\n <td> 0.254178</td>\n <td> 0.002197</td>\n <td> 0.080726</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087169</td>\n <td> 0.219682</td>\n <td> 0.081536</td>\n <td> 0.002315</td>\n <td> 0.187233</td>\n </tr>\n <tr>\n <th>89</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254179</td>\n <td> 0.002197</td>\n <td> 0.080726</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087169</td>\n <td> 0.219691</td>\n <td> 0.081526</td>\n <td> 0.002315</td>\n <td> 0.187234</td>\n </tr>\n <tr>\n <th>90</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254180</td>\n <td> 0.002197</td>\n <td> 0.080725</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219699</td>\n <td> 0.081515</td>\n <td> 0.002315</td>\n <td> 0.187234</td>\n </tr>\n <tr>\n <th>91</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254181</td>\n <td> 0.002197</td>\n <td> 0.080725</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219708</td>\n <td> 0.081505</td>\n <td> 0.002315</td>\n <td> 0.187235</td>\n </tr>\n <tr>\n <th>92</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254182</td>\n <td> 0.002197</td>\n <td> 0.080724</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219716</td>\n <td> 0.081496</td>\n <td> 0.002315</td>\n <td> 0.187235</td>\n </tr>\n <tr>\n <th>93</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254183</td>\n <td> 0.002197</td>\n <td> 0.080724</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219723</td>\n <td> 0.081487</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>94</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254184</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219731</td>\n <td> 0.081478</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>95</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254185</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219738</td>\n <td> 0.081469</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>96</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254186</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087172</td>\n <td> 0.219744</td>\n <td> 0.081461</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>97</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254186</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087172</td>\n <td> 0.219751</td>\n <td> 0.081453</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>98</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254187</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087172</td>\n <td> 0.219757</td>\n <td> 0.081446</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>99</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254188</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087173</td>\n <td> 0.219763</td>\n <td> 0.081438</td>\n <td> 0.002315</td>\n <td> 0.187238</td>\n </tr>\n </tbody>\n</table>\n<p>100 rows \u00d7 12 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}	mit
dani-lbnl/2017_ucberkeley_course	code/ZeissMicroscopyCenter2017_DaniUshizima_lecture.ipynb	1	750797	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Image exploration using Python - essentials\n", "1. Read image from web\n", "2. Querying image: matrix, sub-matrices, ROI\n", "3. Image transformations: filtering\n", "4. Immunohistochemistry example from scikit-image\n", "5. Segmentation and feature extraction \n", "6. Save information as a xls file \n", "7. Simulating 2D images - \"cells\"\n", "8. Simulate particles\n", "9. Check particle neighborhood: groups (clustering algorithms)\n", "10. Pandas and Seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read image from web and scikit-image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "\n", "from matplotlib import pyplot as plt\n", "from skimage import data, io" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x10a3752b0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QMyqKXZVLrVxa5ThcnqLaOFl0NT+B/nTl+fLMcXRCdG2SzvUoJtTSOUpjdDAT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KAAAg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\nEJUwkNCN27sbnn3hB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EQbZC1xXZnjerU7bPaOnDMxJYYhEIZAGAYu84XXb95yOp8kLaEW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\nKK5RWmE5zyznWbspQyR4G7OIESHbxG9hXmb0dG3EqDhC9ZH7c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sQrXyPVzBAHwhBoJVOX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df/1FjnK8utginSui7bhocPHvCdj77Fb33ve3zzg/c5Wq+prKFSmsrIf0bd5nGiiijk4zxz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"text/plain": [ "<matplotlib.figure.Figure at 0x107fa3b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fname = 'http://crl.berkeley.edu/files/2014/04/Holly-226x300.jpg'\n", "imageFromWeb = data.imread(fname, as_grey=False, plugin=None, flatten=None)\n", "plt.imshow(imageFromWeb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Querying image: matrix, sub-matrices, ROI" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------------------------------------------\n", "Image shape is (300, 226, 3) and type is <class 'numpy.ndarray'>\n", "Min = 0 ,Mean = 97.459906588 ,Max = 255\n", "dtype = uint8\n", "-----------------------------------------------------------------------\n" ] } ], "source": [ "print('-----------------------------------------------------------------------')\n", "print('Image shape is',imageFromWeb.shape, 'and type is',type(imageFromWeb))\n", "print('Min =',imageFromWeb.min(),\",Mean =\",imageFromWeb.mean(),',Max = ',imageFromWeb.max())\n", "print('dtype = ',imageFromWeb.dtype)\n", "print('-----------------------------------------------------------------------') " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10a591438>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AP/stj+hx/3DsJAts6cslADWBMUb4fgOA9Bvt5EeGBJ4WCKTAe7I2DRjwT0bx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\nw8nJMY5PznB8coqbJ2e4eXKKmyenOD05wUlyDpofoECCkAJo7KcMAnA0N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"text/plain": [ "<matplotlib.figure.Figure at 0x10a396550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cropping an image\n", "facecolor = imageFromWeb[50:115, 95:140]\n", "plt.imshow(facecolor)\n", "plt.title('Holly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Image transformations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from skimage.color import rgb2gray\n", "from skimage.filters import sobel\n", "from skimage.filters.rank import mean, equalize\n", "\n", "from skimage.morphology import disk\n", "from skimage import exposure\n", "from skimage.morphology import reconstruction\n", "from skimage import img_as_ubyte, img_as_float" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ushizima/anaconda/lib/python3.6/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n", " .format(dtypeobj_in, dtypeobj_out))\n" ] }, { "data": { "image/png": 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KSoSFmRZCK8BMM9OD2Y0zeI7CVGiC09PTYd+WqAwwx0HkRA4OBpiEEEImhjJA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\nB5nfCEj7PP60AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1159ff390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Turn color image into grayscale representation\n", "face = rgb2gray(facecolor)\n", "face = img_as_ubyte(face) #this generates the warning\n", "\n", "hist = np.histogram(face, bins=np.arange(0, 256))\n", "\n", "fig, ax = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", "ax[0].imshow(face, interpolation='nearest', cmap=plt.cm.gray)\n", "ax[0].axis('off')\n", " \n", "ax[1].plot(hist[1][:-1], hist[0], lw=1)\n", "ax[1].set_title('Histogram of gray values')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Smoothing\n", "smoothed = img_as_ubyte(mean(face, disk(2)))\n", "\n", "#smoothPill = ndi.median_filter(edgesPill.astype(np.uint16), 3)\n", "# Global equalization\n", "equalized = exposure.equalize_hist(face)\n", "\n", "# Extract edges\n", "edge_sobel = sobel(face)\n", "\n", "# Masking\n", "mask = face < 80\n", "facemask = face.copy()\n", "# Set to \"white\" (255) pixels where mask is True\n", "facemask[mask] = 255\n", "#facemask = img_as_uint(facemask)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZuZbySztDA5FpDbJoN3EOiREvpEmOsoQfkSnM5gL6Ooto00scw/2Z1iPaBGcy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\nKGlV3RfDfaBWyhzxUDG4SgK1QaYuhIbnfa7iQuXDNa42R60RVdpeJ85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"text/plain": [ "<matplotlib.figure.Figure at 0x1162cbda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=5, sharex=True, sharey=True,\n", " figsize=(10, 4))\n", "\n", "ax[0].imshow(face, cmap='gray')\n", "ax[0].set_title('Original')\n", "\n", "ax[1].imshow(smoothed, cmap='gray')\n", "ax[1].set_title('Smoothing')\n", "\n", "ax[2].imshow(equalized, cmap='gray')\n", "ax[2].set_title('Equalized')\n", "\n", "ax[3].imshow(edge_sobel, cmap='gray')\n", "ax[3].set_title('Sobel Edge Detection')\n", "\n", "ax[4].imshow(facemask, cmap='gray')\n", "ax[4].set_title('Masked <50')\n", "\n", "for a in ax:\n", " a.axis('off')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Immunohistochemistry example from scikit-image\n", "- More at: http://scikit-image.org/docs/dev/api/skimage.data.html#skimage.data.immunohistochemistry" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x116b06828>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/4fPxT/6QwDdou12E0JnPJvim4M++9ydcXIx5+PDLEL7CrgNa3YbTs6d0Og6H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qNoE4JUxp3c5c4Dn3i3Rsj3KHVO0VYMXItev0tCdl8YTCYz0jxjc/OCKi1x+zYPzu+R+GuC7Q53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ZKtEC1jArtHVucIVPK8Jq9SOp5JUzdUVfvA+r4LskIYb9DtNjcxSRpOTyKeH0/4nd95gyyr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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116298ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imgMicro = data.immunohistochemistry()\n", "plt.imshow(imgMicro)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Segmentation and feature extraction" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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GycBzPgSFwsAUHGxENB+qv9nMzN61/N/MbPQecb3934Ai4CXDq/OsRkooSf4o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ir4SpP2VGd3/PV6nUjYcG++pmVkky1btmzZ7t3OAtmoTEvpfmpiEG9cjY2WRWGVSN4fXMGdmZPb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"text/plain": [ "<matplotlib.figure.Figure at 0x11597ba90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.patches as mpatches\n", "\n", "from skimage import data\n", "from skimage.filters import threshold_otsu\n", "from skimage.segmentation import clear_border\n", "from skimage.measure import label, regionprops\n", "from skimage.morphology import closing, square\n", "from skimage.color import label2rgb\n", "\n", "# create a subimage for tests\n", "image = imgMicro[300:550, 200:400, 2]\n", "\n", "# apply threshold\n", "thresh = threshold_otsu(image)\n", "bw = closing(image > thresh, square(3))\n", "\n", "# remove artifacts connected to image border\n", "cleared = clear_border(bw)\n", "\n", "# label image regions\n", "label_image = label(cleared)\n", "image_label_overlay = label2rgb(label_image, image=image)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.imshow(image_label_overlay)\n", "\n", "for region in regionprops(label_image):\n", " # take regions with large enough areas\n", " if region.area >= 50:\n", " # draw rectangle around segmented coins\n", " minr, minc, maxr, maxc = region.bbox\n", " rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,\n", " fill=False, edgecolor='red', linewidth=2)\n", " ax.add_patch(rect)\n", "\n", "ax.set_axis_off()\n", "plt.tight_layout()\n", "plt.show()\n", "#plt.imshow(bw,cmap=plt.cm.gray) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Save information as a xls file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Calculate regions properties from label_image\n", "regions = regionprops(label_image) \n", "\n", "for i in range(len(regions)):\n", " all_props = {p:regions[i][p] for p in regions[i] if p not in ('image','convex_image','filled_image')}\n", " for p, v in list(all_props.items()):\n", " if isinstance(v,np.ndarray):\n", " if(len(v.shape)>1):\n", " del all_props[p]\n", "\n", " for p, v in list(all_props.items()):\n", " try:\n", " L = len(v)\n", " except:\n", " L = 1\n", " if L>1:\n", " del all_props[p]\n", " for n,entry in enumerate(v):\n", " all_props[p + str(n)] = entry\n", "\n", " k = \", \".join(all_props.keys())\n", " v = \", \".join([str(f) for f in all_props.values()]) #notice you need to convert numbers to strings\n", " if(i==0):\n", " with open('cellsProps.csv','w') as f:\n", " #f.write(k)\n", " f.writelines([k,'\\n',v,'\\n']) \n", " else:\n", " with open('cellsProps.csv','a') as f:\n", " #f.write(k)\n", " f.writelines([v,'\\n']) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Simulating 2D images - \"cells\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x117cf1c50>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116756358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test\n", "from skimage.draw import circle\n", "img = np.zeros((50, 50), dtype=np.uint8)\n", "rr, cc = circle(25, 25, 5)\n", "img[rr, cc] = 1\n", "plt.imshow(img,cmap='gray')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "\n", "import random\n", "import math\n", "from matplotlib import pyplot as plt\n", "import matplotlib.patches as mpatches\n", "from skimage import data, io\n", "from skimage.draw import circle\n", "\n", "def createMyCells(width, height, r, num_cells):\n", " \n", " image = np.zeros((width,height),dtype=np.uint8)\n", " imgx, imgy = image.shape\n", " nx = []\n", " ny = []\n", " ng = []\n", " \n", " #Creates a synthetic set of points\n", " for i in range(num_cells):\n", " nx.append(random.randrange(imgx))\n", " ny.append(random.randrange(imgy))\n", " ng.append(random.randrange(256))\n", " \n", " #Uses points as centers of circles \n", " for i in range(num_cells):\n", " rr, cc = circle(ny[i], nx[i], radius)\n", " if valid(ny[i],r,imgy) & valid(nx[i],r,imgx):\n", " image[rr, cc] = ng[i]\n", " return image\n", "\n", "def valid(v,radius,dim):\n", " if v<radius:\n", " return False\n", " else: \n", " if v>=dim-radius:\n", " return False\n", " else:\n", " return True" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x117e52550>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116572e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "width = 200\n", "height = 200\n", "radius = 5\n", "num_cells = 50\n", "image = createMyCells(width, height, radius, num_cells)\n", "plt.imshow(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Simulate particles with Scikit-learn -> sklearn" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import MeanShift, estimate_bandwidth\n", "from sklearn.datasets.samples_generator import make_blobs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11890a518>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2eB7sbIfDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInD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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1188a7a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 1000\n", "clusterSD = 10 #proportional to the pool size\n", "centers = [[50,50], [100, 100], [100, 200], [150,150], [200, 100], [200,200]]\n", "X, _ = make_blobs(n_samples=n, centers=centers, cluster_std=clusterSD)\n", "image = np.zeros(shape=(300,300), dtype=np.uint8)\n", "for i in X:\n", " x,y=i.astype(np.uint8)\n", " #print(x,',',y)\n", " image[x,y]=255\n", "plt.imshow(image,cmap=plt.cm.gray) " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of estimated clusters : 6\n" ] } ], "source": [ "myquantile=0.15 #Change this parameter (smaller numbers will produce smaller clusters and more numerous)\n", "\n", "bandwidth = estimate_bandwidth(X, quantile=myquantile, n_samples=500)\n", "\n", "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", "ms.fit(X)\n", "labels = ms.labels_\n", "cluster_centers = ms.cluster_centers_\n", "\n", "labels_unique = np.unique(labels)\n", "n_clusters_ = len(labels_unique)\n", "\n", "print(\"number of estimated clusters : %d\" % n_clusters_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. Check particle neighborhood: groups (clustering algorithms)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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shN6QQT4r190XXDHKL3ap2jN+Ql4+u1vOV4ulduu0tGzwLOb0RUWSlJV9hHPP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\nqCpGV+R0RQ22RUl6mnVzmlKq2XkfBU5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"text/plain": [ "<matplotlib.figure.Figure at 0x11890acc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from itertools import cycle \n", "\n", "plt.figure(1)\n", "plt.clf()\n", "\n", "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n", "for k, col in zip(range(n_clusters_), colors):\n", " my_members = labels == k\n", " cluster_center = cluster_centers[k]\n", " plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n", " plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=14)\n", "plt.title('Estimated number of clusters: %d' % n_clusters_)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. Pandas and seaborn\n", "### Pandas: http://pandas.pydata.org/\n", "### Seaborn: http://seaborn.pydata.org/" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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lDamoiQ/plr71VS0dPSROXFmwYAHmz58PoG/yUa/XY9++faipqcHGjRtRVFSE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\nj6HO5sT40nTcvWgCzEa9JM+iaj2F4budUP+PGoj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"text/plain": [ "<matplotlib.figure.Figure at 0x118886e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, integrate\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "df = pd.DataFrame(X, columns=[\"x\", \"y\"])\n", "# Kernel density estimation\n", "sns.jointplot(x=\"x\", y=\"y\", data=df, kind=\"kde\");" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Acknowledgements:\n", "- [A crash course on NumPy for images](http://scikit-image.org/docs/dev/user_guide/numpy_images.html)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
dsavransky/MAE4060	Notebooks/Gravitational Stabilization.ipynb	1	54271	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from miscpy.utils.sympyhelpers import \n", "init_printing()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mu,rg,I1,I2,I3,n,w1,w2,w3,w1d,w2d,w3d,t = symbols('mu,r_G,I_1,I_2,I_3,n,omega_1,omega_2,omega_3,omegadot_1,omegadot_2,omegadot_3,t')\n", "diffmap = {w1:w1d,w2:w2d,w3:w3d}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{11} & {}^\\mathcal{B}C^{\\mathcal{A}}_{12} & {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{21} & {}^\\mathcal{B}C^{\\mathcal{A}}_{22} & {}^\\mathcal{B}C^{\\mathcal{A}}_{23}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{31} & {}^\\mathcal{B}C^{\\mathcal{A}}_{32} & {}^\\mathcal{B}C^{\\mathcal{A}}_{33}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{11}__\\mathcal{B}C__{\\mathcal{A}} {}_{12}__\\mathcal{B}C__{\\mathcal{A}} {\n", "⎢ \n", "⎢{}_{21}__\\mathcal{B}C__{\\mathcal{A}} {}_{22}__\\mathcal{B}C__{\\mathcal{A}} {\n", "⎢ \n", "⎣{}_{31}__\\mathcal{B}C__{\\mathcal{A}} {}_{32}__\\mathcal{B}C__{\\mathcal{A}} {\n", "\n", "}_{13}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", " ⎥\n", "}_{23}__\\mathcal{B}C__{\\mathcal{A}}⎥\n", " ⎥\n", "}_{33}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bCa = fancyMat('{}^\\mathcal{B}C^{\\mathcal{A}}',(3,3));bCa" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAD0AAABLCAMAAAD0x38HAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nSGrPf2u0hi8d+jERf5ScPQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{12}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{22}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{32}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", "⎢ ⎥\n", "⎢{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⎥\n", "⎢ ⎥\n", "⎣{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "en_B = bCaMatrix([0,1,0]); en_B" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Ig_B = diag(I1,I2,I3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMsAAABLCAMAAADplYPZAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nGH7SIQC/UdH/VnQczT+tunYv3plIzQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(- I_{2} + I_{3}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(- I_{1} + I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(-I\n", "⎢ \n", "⎢{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁\n", "⎢ \n", "⎣{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(-I\n", "\n", "₂ + I₃)⎤\n", " ⎥\n", " - I₃) ⎥\n", " ⎥\n", "₁ + I₂)⎦" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mg_B = simplify(skew(en_B)Ig_Ben_B); Mg_B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Circular orbit" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAABLCAMAAABUWBfpAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\nMquZdlQQQOkwRIlmzd0i77ts7uXj/QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAR5JREFUSA3tltty\nhDAIhsmx2yRrdHn/d11IooVMZqeXnbFcqMMnghh/AgabWRC2dR+AQefJgmBQ2GORoZH+n+uiYXS5\n5OvWCT7p2XGrI1jDsrM7P5fwaBUHjJ3qSGww4ShcwYiOQxL6RWTFzF7TTwAqsvbINfz4WOgFhWVB\ncBycs6xfpTfBrZsAG7dvP5urqiWQ6UOdTL8KJ5Q2RUo0NUGj28L7Luqvx/dYA9Oifj2kJvwv6vlP\nue2/ohvxa6lJ2dZKCjZ0WrfPkWoSiLgYHSbAxnpZrwElckaKSQTZ1U1AgNDc9pRbrZpdhbHAaujs\nPDl4HrUJoqutLSVNsjLkWOZMG9dRbTnHrIS9RHH8o/DDJiLyhsF7bvplbRPhPbwB4fcZLT1yHgEA\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}0\\\\0\\\\n\\end{matrix}\\right]$$" ], "text/plain": [ "⎡0⎤\n", "⎢ ⎥\n", "⎢0⎥\n", "⎢ ⎥\n", "⎣n⎦" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWa_A = Matrix([0,0,n]); iWa_A" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAACYAAABLCAMAAAAriQUYAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nvh47pz4BCxkcLy3JaBcAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}\\omega_{1}\\\\\\omega_{2}\\\\\\omega_{3}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ω₁⎤\n", "⎢ ⎥\n", "⎢ω₂⎥\n", "⎢ ⎥\n", "⎣ω₃⎦" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWb_B = Matrix([w1,w2,w3]); iWb_B" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAABMCAMAAAD0mSYNAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\omega}_{1} - I_{2} \\omega_{2} \\omega_{3} + I_{3} \\omega_{2} \\omega_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{2} - I_{3}\\right)\\\\I_{1} \\omega_{1} \\omega_{3} + I_{2} \\dot{\\omega}_{2} - I_{3} \\omega_{1} \\omega_{3} - 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\- I_{1} \\omega_{1} \\omega_{2} + I_{2} \\omega_{1} \\omega_{2} + I_{3} \\dot{\\omega}_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(I_{1} - I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 \n", "⎢I₁⋅ω̇₁ - I₂⋅ω₂⋅ω₃ + I₃⋅ω₂⋅ω₃ + 3⋅n ⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎢I₁⋅ω₁⋅ω₃ + I₂⋅ω̇₂ - I₃⋅ω₁⋅ω₃ - 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎣-I₁⋅ω₁⋅ω₂ + I₂⋅ω₁⋅ω₂ + I₃⋅ω̇₃ + 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_\n", "\n", " ⎤\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₂ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₂)⎦" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1 = simplify(Ig_Bdifftotalmat(iWb_B,t,diffmap) + skew(iWb_B)Ig_BiWb_B - 3n*2Mg_B);tmp1" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAABMCAMAAAD0mSYNAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\omega}_{1} - I_{2} \\omega_{2} \\omega_{3} + I_{3} \\omega_{2} \\omega_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{2} - I_{3}\\right)\\\\I_{1} \\omega_{1} \\omega_{3} + I_{2} \\dot{\\omega}_{2} - I_{3} \\omega_{1} \\omega_{3} - 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\- I_{1} \\omega_{1} \\omega_{2} + I_{2} \\omega_{1} \\omega_{2} + I_{3} \\dot{\\omega}_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(I_{1} - I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 \n", "⎢I₁⋅ω̇₁ - I₂⋅ω₂⋅ω₃ + I₃⋅ω₂⋅ω₃ + 3⋅n ⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎢I₁⋅ω₁⋅ω₃ + I₂⋅ω̇₂ - I₃⋅ω₁⋅ω₃ - 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎣-I₁⋅ω₁⋅ω₂ + I₂⋅ω₁⋅ω₂ + I₃⋅ω̇₃ + 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_\n", "\n", " ⎤\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₂ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₂)⎦" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(tmp1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIkAAABLCAMAAABz2lREAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nQmCC\n", "text/latex": [ "$$\\left[\\begin{matrix}- n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} + \\omega_{1}\\\\- n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} + \\omega_{2}\\\\- n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} + \\omega_{3}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-n⋅{}_{13}__\\mathcal{B}C__{\\mathcal{A}} + ω₁⎤\n", "⎢ ⎥\n", "⎢-n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}} + ω₂⎥\n", "⎢ ⎥\n", "⎣-n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} + ω₃⎦" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aWb_B = iWb_B - bCaiWa_A; aWb_B" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}- {}^\\mathcal{B}C^{\\mathcal{A}}_{21} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{31} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) & - {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) & - \\omega_{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{33} + \\omega_{3} {}^\\mathcal{B}C^{\\mathcal{A}}_{23}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{11} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{31} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & \\omega_{1} {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3} {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\\\- {}^\\mathcal{B}C^{\\mathcal{A}}_{11} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{21} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & - {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & - \\omega_{1} {}^\\mathcal{B}C^{\\mathcal{A}}_{23} + \\omega_{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-{}_{21}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}}\n", "⎢ \n", "⎢{}_{11}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} \n", "⎢ \n", "⎣-{}_{11}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}}\n", "\n", " - ω₃) + {}_{31}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\math\n", " \n", "- ω₃) - {}_{31}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\mathc\n", " \n", " - ω₂) + {}_{21}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\math\n", "\n", "cal{A}} - ω₂) -{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C_\n", " \n", "al{A}} - ω₁) {}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__\n", " \n", "cal{A}} - ω₁) -{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C_\n", "\n", "_{\\mathcal{A}} - ω₃) + {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathc\n", " \n", "{\\mathcal{A}} - ω₃) - {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathca\n", " \n", "_{\\mathcal{A}} - ω₂) + {}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathc\n", "\n", "al{B}C__{\\mathcal{A}} - ω₂) -ω₂⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} + ω₃⋅{}_\n", " \n", "l{B}C__{\\mathcal{A}} - ω₁) ω₁⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} - ω₃⋅{}_{\n", " \n", "al{B}C__{\\mathcal{A}} - ω₁) -ω₁⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}} + ω₂⋅{}_\n", "\n", "{23}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", " ⎥\n", "13}__\\mathcal{B}C__{\\mathcal{A}} ⎥\n", " ⎥\n", "{13}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbCa = simplify(-skew(aWb_B)bCa); dbCa" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAABMCAMAAABwH8EjAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nTkSuQmCC\n", "text/latex": [ "$$\\left[\\begin{matrix}- {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right)\\\\- {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}}\n", "⎢ \n", "⎢{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} \n", "⎢ \n", "⎣-{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}}\n", "\n", " - ω₃) + {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\math\n", " \n", "- ω₃) - {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\mathc\n", " \n", " - ω₂) + {}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\math\n", "\n", "cal{A}} - ω₂)⎤\n", " ⎥\n", "al{A}} - ω₁) ⎥\n", " ⎥\n", "cal{A}} - ω₁)⎦" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbCa[:,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
MachineLearningStudyGroup/Smart_Review_Summarization	ipynbs/Dynamic_Aspect_Extraction_Part_B.ipynb	1	7311	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dynamic Aspect Extraction for `camera` Reviews Part B\n", "\n", "Han, Kehang ([email protected])\n", "\n", "As a follow-up demonstration, this ipynb is focused on extracting aspects from datasets called `AmazonReviews`, which has much more reviews on cameras. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "import nltk\n", "import string\n", "\n", "from srs.utilities import Product, AspectPattern" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s1: load raw data from `AmazonReviews` datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "product_name = 'B00000JFIF'\n", "reviewJsonFile = product_name + '.json'\n", "product = Product(name=product_name)\n", "product.loadReviewsFromJsonFile('../data/trainingFiles/AmazonReviews/cameras/' + reviewJsonFile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s2: define aspect patterns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "aspectPatterns = []\n", "# define an aspect pattern1\n", "pattern_name = 'adj_nn'\n", "pattern_structure =\"\"\"\n", "adj_nn:{<JJ><NN.?>}\n", "\"\"\"\n", "aspectTagIndices = [1]\n", "aspectPattern = AspectPattern(name='adj_nn', structure=pattern_structure, aspectTagIndices=aspectTagIndices)\n", "aspectPatterns.append(aspectPattern)\n", "# define an aspect pattern2\n", "pattern_name = 'nn_nn'\n", "pattern_structure =\"\"\"\n", "nn_nn:{<NN.?><NN.?>}\n", "\"\"\"\n", "aspectTagIndices = [0,1]\n", "aspectPattern = AspectPattern(name='nn_nn', structure=pattern_structure, aspectTagIndices=aspectTagIndices)\n", "aspectPatterns.append(aspectPattern)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s3: match sentence to pattern to extract aspects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pos tagging\n", "for review in product.reviews:\n", " for sentence in review.sentences:\n", " sentence.pos_tag()\n", " sentence.matchDaynamicAspectPatterns(aspectPatterns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s4: statistic analysis on aspects extracted across all reviews" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "word_dict = {}\n", "for review in product.reviews:\n", " for sentence in review.sentences:\n", " for aspect in sentence.dynamic_aspects:\n", " if aspect in word_dict:\n", " word_dict[aspect] += 1\n", " else:\n", " word_dict[aspect] = 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'camera', 47),\n", " (u'batteries', 22),\n", " (u'pictures', 18),\n", " (u'cameras', 13),\n", " (u'battery life', 12),\n", " (u'ac adapter', 11),\n", " (u'quality', 11),\n", " (u'shots', 10),\n", " (u'zoom', 9),\n", " (u'memory card', 9),\n", " (u'time', 8),\n", " (u'media', 7),\n", " (u'price', 7),\n", " (u'software', 7),\n", " (u'resolution', 7)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_sorted = sorted(word_dict.items(), key=lambda tup:-tup[1])\n", "word_sorted[:15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s5: save most frequent dynamic aspects" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "word_output = open('../data/word_list/{0}_wordlist.txt'.format(product_name), 'w')\n", "json.dump(word_sorted[:15], word_output)\n", "word_output.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s6: stemming analysis" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nltk.stem import SnowballStemmer\n", "stemmer = SnowballStemmer('english')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# collect word with same stem\n", "stemmedWord_dict = {}\n", "for word in word_dict:\n", " stemmedWord = stemmer.stem(word)\n", " if stemmedWord in stemmedWord_dict:\n", " stemmedWord_dict[stemmedWord] += word_dict[word]\n", " else:\n", " stemmedWord_dict[stemmedWord] = word_dict[word]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'camera', 61),\n", " (u'batteri', 26),\n", " (u'pictur', 23),\n", " (u'shot', 13),\n", " (u'battery lif', 12),\n", " (u'qualiti', 11),\n", " (u'ac adapt', 11),\n", " (u'memory card', 10),\n", " (u'resolut', 9),\n", " (u'zoom', 9),\n", " (u'featur', 9),\n", " (u'problem', 8),\n", " (u'time', 8),\n", " (u'card', 7),\n", " (u'softwar', 7)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# frequency ranking\n", "stemmedWord_sorted = sorted(stemmedWord_dict.items(), key=lambda tup:-tup[1])\n", "stemmedWord_sorted[:15]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# save most frequent stemmed words\n", "stemmedWord_output = open('../data/word_list/{0}_stemmedwordlist.txt'.format(product_name), 'w')\n", "json.dump(stemmedWord_sorted[:15], stemmedWord_output)\n", "stemmedWord_output.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jasonost/clinicaltrials	trial_criteria/Admin_Interaction.ipynb	1	16429	{ "metadata": { "name": "", "signature": "sha256:e0714909926f33bd9c934f43056d0ce53246aedc5faced57a65a751ac4e6b8e8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sqlalchemy\n", "from sqlalchemy import create_engine\n", "from sqlalchemy.sql import select, delete, or_\n", "import re\n", "import db_connect\n", "from db_tables import metadata, CriteriaConceptStaging, ConceptPredictors, UserHistoryCriteria, Users, CriteriaConceptLookup\n", "from db_tables import ConceptPredictorsReject, ConceptTerms, ConceptTermsReject, CriteriaText, CriteriaConcept" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_concepts(concept_id, engine):\n", " \n", " #check if the concept is new or existing\n", " new_concept = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.new_concept], distinct=True).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id))][0]\n", " \n", " concept_name = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'concept-name'))][0]\n", " concept_terms = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'term'))]\n", " \n", " \n", " if new_concept == 1:\n", " #concept is new\n", " existing_concept_terms = []\n", " return concept_name, concept_terms, existing_concept_terms\n", " \n", " \n", " else:\n", " #concept is not new, need to pull old concept data to compare\n", " existing_concept_terms = engine.execute(select([ConceptTerms.c.term]).\\\n", " where(ConceptTerms.c.concept_id ==\n", " concept_id))\n", " existing_concept_terms = [r[0] for r in existing_concept_terms]\n", " \n", " #get new terms in the staging table\n", " new_concept_terms = list(set(concept_terms).difference(set(existing_concept_terms)))\n", " \n", " return concept_name, new_concept_terms, existing_concept_terms" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "def transfer_concept(concept_id, engine, term_set, concept_name):\n", " #pull other data for concept\n", " concept_terms_reject = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'term-reject'))]\n", " concept_predictors_reject = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'predictor-reject'))]\n", " concept_predictors = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'predictor'))]\n", " #write all data into the new tables\n", " #instert data into db\n", " engine.execute(CriteriaConcept.insert(), [{\n", " 'concept_id': concept_id,\n", " 'concept_name':concept_name}])\n", " engine.execute(ConceptTerms.insert(), [{\n", " 'concept_id': concept_id,\n", " 'term':term}\n", " for term in term_set])\n", " engine.execute(ConceptTermsReject.insert(), [{\n", " 'concept_id': concept_id,\n", " 'term':term}\n", " for term in concept_terms_reject])\n", " engine.execute(ConceptPredictors.insert(), [{\n", " 'concept_id': concept_id,\n", " 'predictor':predictor}\n", " for predictor in concept_predictors])\n", " engine.execute(ConceptPredictorsReject.insert(), [{\n", " 'concept_id': concept_id,\n", " 'predictor':predictor}\n", " for predictor in concept_predictors_reject])\n", " \n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def update_user_history(concept_id, choice, term_set, engine):\n", " #keeps track of which concepts were accepted and also which terms were accepted\n", " \n", " #pull all statged data for concept\n", " all_staged_concept = engine.execute(select([CriteriaConceptStaging]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).fetchall())\n", " if choice:\n", " #at least some terms were accepted\n", " #instert data into db\n", " engine.execute(UserHistoryCriteria.insert(),\n", " [{'update_id': value[0],\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 0}\n", " if value[5] == 'term' and value[6] not in term_set\n", " else {'update_id': value[0],\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 1}\n", " for value in all_staged_concept])\n", " else:\n", " #concept was not accepted\n", " #instert data into db\n", " engine.execute(UserHistoryCriteria.insert(), [{'update_id': value[0]\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 0}\n", " for value in all_staged_concept])\n", " #delete from staging table\n", " engine.execute(delete(CriteriaConceptStaging).where(CriteriaConceptStaging.c.concept_id == concept))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def tag_criteria_sentences(concept_id, engine):\n", " tagged_sentences = []\n", " #pull the accepted terms with term_ids\n", " term_list = engine.execute(select([ConceptTerms.c.term, ConceptTerms.c.term_id]).\\\n", " where(ConceptTerms.c.concept_id == concept_id)).fetchall()\n", " \n", " criteria_text = engine.execute(select([CriteriaText.c.criteria_text,\n", " CriteriaText.c.criteria_text_id]).\\\n", " where(or_(CriteriaText.c.criteria_text.op('rlike')('[[:<:]]' + t[0] + '[[:>:]]')\n", " for t in term_list))).fetchall()\n", " \n", " #find all the sentences where a concept term occurs\n", " for sent, sent_id in criteria_text:\n", " for term, term_id in term_list:\n", " if term in sent.lower():\n", " #check if there is a negative in the sentence related to the term.\n", " #the negative term has to be within 3 words of the concept term\n", " #or at the beginning of the sentece\n", " string = sent.lower()\n", " negative_pattern_start = r\"^[not\|no\|isn't\|didn't]\"\n", " negative_beginning = re.search(negative_pattern_start, string)\n", " negative_in_sentence = re.search(\"[not\|didn't\|isn't\|no]\\W+(?:\\w+\\W+){1,2}%s\" % (term), string)\n", " if negative_beginning or negative_in_sentence:\n", " inverse = 1\n", " else:\n", " inverse = 0\n", " tagged_sentences.append((sent_id, term_id, inverse))\n", " \n", " #write tagged sentences to db\n", " engine.execute(CriteriaConceptLookup.insert(), [{'criteria_text_id': v[0],\n", " 'term_id': v[1],\n", " 'concept_id': concept_id,\n", " 'inverse': v[2]}\n", " for v in tagged_sentences])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "string = \"I am not happy happy is a test\"\n", "negative_pattern_start = r\"^[not\|no\|isn't\|didn't]\"\n", "negative_beginning = re.search(negative_pattern_start, string)\n", "negative_in_sentence = re.search(\"[not\|didn't\|isn't\|no]\\W+(?:\\w+\\W+){1,2}is\", string)\n", "if negative_beginning or negative_in_sentence:\n", " print 'inverse'\n", "#[(m.start(0), m.end(0)) for m in re.finditer(negative_pattern_start, string)]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "inverse\n" ] } ], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "print re.search(\"[not\|didn't\|isn't\|no]\\W+(?:\\w+\\W+){1,2}is\", string)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "None\n" ] } ], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "def admin_action_response(concept_id, concept_name, accepted_terms):\n", " '''This is run in response to the admins actions on the criteria review page'''\n", " #initialize the connection to the db\n", " engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", " metadata.create_all(engine)\n", " \n", "\n", " if len(accepted_terms) > 0:\n", " #at least some terms in the concept have been accepted\n", " tansfer_concept(concept_id, engine, accepted_terms, concept_name)\n", " #transfer data to user history table and delete from staging table\n", " choice = True\n", " update_user_history(concept_id, choice, accepted_terms, engine)\n", " #tag sentences with the new concept\n", " tag_criteria_sentences(concept_id, engine)\n", " else:\n", " #transfer data to user history table and delete from staging table\n", " choice = False\n", " update_user_history(concept_id, choice, accepted_terms, engine)\n", " \n", " criteria_review_page_data()\n", " " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def criteria_review_page_data(concept_id=False):\n", " '''This gets activated when an admin chooses to review staged criteria concepts\n", " when the criteria review page is loaded'''\n", " #initialize the connection to the db\n", " engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", " metadata.create_all(engine)\n", " \n", " #pull all the concept id's of staged concepts\n", " concept_ids = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.concept_id],\n", " distinct=True))]\n", " \n", " #get concept name list\n", " concept_names = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value],\n", " distinct=True).\\\n", " where(CriteriaConceptStaging.c.update_type == 'concept-name'))]\n", " \n", " \n", " if concept_id:\n", " pass\n", " else:\n", " concept_id = concept_ids[0]\n", " \n", " concept_name, concept_terms, existing_concept_terms = get_concepts(concept_id, engine)\n", " \n", " return (concept_ids, concept_names, concept_id, concept_terms,\n", " existing_concept_terms, concept_name)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "main()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", "metadata.create_all(engine)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
BBN-Q/Auspex	doc/examples/Example-Channel-Lib.ipynb	1	854914	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Q2: Save and Loading Channel Library Versions\n", "This example notebook shows how one may save and load versions of the channel library.\n", "\n", "© Raytheon BBN Technologies 2018" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving Channel Library Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize the channel library as shown in tutorial Q1:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A database item with the name q1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name BBNAPS1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name BBNAPS2 already exists. Updating parameters of this existing item instead.\n", "A database item with the name X6_1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name Holz1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name Holz2 already exists. Updating parameters of this existing item instead.\n" ] } ], "source": [ "from QGL import \n", "\n", "cl = ChannelLibrary(\":memory:\")\n", "q1 = cl.new_qubit(\"q1\")\n", "aps2_1 = cl.new_APS2(\"BBNAPS1\", address=\"192.168.5.101\") \n", "aps2_2 = cl.new_APS2(\"BBNAPS2\", address=\"192.168.5.102\")\n", "dig_1 = cl.new_X6(\"X6_1\", address=0)\n", "h1 = cl.new_source(\"Holz1\", \"HolzworthHS9000\", \"HS9004A-009-1\", power=-30)\n", "h2 = cl.new_source(\"Holz2\", \"HolzworthHS9000\", \"HS9004A-009-2\", power=-30) \n", "cl.set_control(q1, aps2_1, generator=h1)\n", "cl.set_measure(q1, aps2_2, dig_1.ch(1), generator=h2)\n", "cl.set_master(aps2_1, aps2_1.ch(\"m2\"))\n", "cl[\"q1\"].measure_chan.frequency = 0e6\n", "cl.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us save this channel library for posterity:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cl.save_as(\"NoSidebanding\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we adjust some parameters and save another version of the channel library" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cl[\"q1\"].measure_chan.frequency = 50e6\n", "cl.commit()\n", "cl.save_as(\"50MHz-Sidebanding\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maybe we forgot to change something. No worries! We can just update the parameter and create a new copy." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>id</th><th>Year</th><th>Date</th><th>Time</th><th>Name</th></tr><tr><tr><td>1</td><td>2019</td><td>Apr. 18</td><td>11:25:20 AM</td><td>working</td></tr><tr><td>2</td><td>2019</td><td>Apr. 18</td><td>11:25:37 AM</td><td>NoSidebanding</td></tr><tr><td>3</td><td>2019</td><td>Apr. 18</td><td>11:25:38 AM</td><td>50MHz-Sidebanding</td></tr><tr><td>4</td><td>2019</td><td>Apr. 18</td><td>11:25:39 AM</td><td>50MHz-Sidebanding</td></tr></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cl[\"q1\"].pulse_params['length'] = 400e-9\n", "cl.commit()\n", "cl.save_as(\"50MHz-Sidebanding\")\n", "cl.ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see the various versions of the channel library here. Note that the user is always* modifying the working version of the database: all other versions are archival, but they can be restored to the current working version as shown below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading Channel Library Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us load a previous version of the channel library, noting that the former value of our parameter is restored in the working copy. CRUCIAL POINT: do not use the old reference `q1`, which is no longer pointing to the database since the working db has been replaced with the saved version. Instead use dictionary access `cl[\"q1\"]` on the channel library to return the first qubit: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl.load(\"NoSidebanding\")\n", "cl[\"q1\"].measure_chan.frequency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's load the second oldest version of the 50MHz-sidebanding library:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2e-08, 50000000.0)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl.load(\"50MHz-Sidebanding\", -1)\n", "cl[\"q1\"].pulse_params['length'], cl[\"q1\"].measure_chan.frequency" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiled 11 sequences.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6280b8a0551141928c80fe8cc2b19c15", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(IntSlider(value=1, description='Segment', max=11, min=1), Figure(animation_duration=50, axes=[A…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# q1 = QubitFactory(\"q1\")\n", "plot_pulse_files(RabiAmp(cl[\"q1\"], np.linspace(-1, 1, 11)), time=True)" ] }, { "attachments": { "Screen%20Shot%202019-03-13%20at%2012.28.08%20PM.png": { "image/png": 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ikegwukc/INFO597-DeepLearning-GameTheory	basicGames/Basic Games.ipynb	1	303772	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Games" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iterated Prisoner's Dilemma\n", "Prisoner's Dilemma is a pretty standard game that is commonly used in game theory (for a comprehensive defintion of Prisoner's dilemma see this [link](https://en.wikipedia.org/wiki/Prisoner%27s_dilemma)). Iterated Prisoner's Dilemma is simply repeating the game and allowing the agents to choose again. Prisoner's Dilemma is implemented in a module I wrote called game_types. For an example using the PrisonersDilemma class see the pseudocode snippet below (I dicuss different types of strategies availiable in subsequent cells):\n", "```python\n", "from game_types import PrisonersDilemma\n", "...\n", "...\n", "...\n", "agent1 = Strategy() # Some Strategy for agent1\n", "agent2 = Strategy() # Some Strategy for agent2\n", "game = PrisonersDilemma(agent1, agent2) # Play Prisoners Dilemma with agent1 and agent2\n", "num_iter = 10000 # Play the game 10000 times\n", "game.play(num_iter)\n", "data = game.data # grab data from game\n", "```\n", "\n", "## Scenario - TCP User's Game\n", "\n", "In this scenario [Robert Brunner](http://www.astro.illinois.edu/people/bigdog) uploads a dataset and asks two graduate students [Edward](https://edwardjkim.github.io/) and [Will](http://publish.illinois.edu/wbiscarri/) to download and perform a variety of tasks. For the sake of simplicity in this game Edward and Will are the only ones on a special network and they recieve no interference from other people.\n", "\n", "The traffic for this network is governed by the TCP Protocol and one feature of TCP is the backoff mechanism. If the rates that Edward and Will are sending packets to this network causes congestion they (`backoff` and) each reduce the rate for a while until network congestion subsides. This is the correct implementation. A defective implementation of TCP would be one that does not backoff if the network is congested. The users in this game have 2 choices. To Cooperate (use the correct implementation of the TCP protocol) or to defect (use an incorrect implementation of the TCP protocol).\n", "\n", "The payoff matrix is below. The numbers in the box are the utility values. For this example the higher the utlity value the faster the dataset is downloaded. Will is the first player and is the first number in each box. Edward is the second player and is the the second number in each box. \n", "\n", "If Edward and Will follow the correct protocol they will both download the dataset in a reasonable amout of time (top left). If Will decides he wants the data faster and uses a defective TCP protocol while Edward still follows the correct protocol Will downloads the dataset much faster than Edward (Top Right). Vise-versa (bottom left). If they both defect they download the dataset significantly slower than if they both cooperated.\n", "![Game Theory TCP Example](http://i.imgur.com/hfWBE27.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types of Strategies\n", "\n", "The strategies of Will and Edward will depend on what they want to achieve. Edward's part may be based on Will's work so it would make sense for Will to defect and Edward cooperates on purpose. If it's a competition they may try to defect to get a head start. If both split up the work then it may be in there interest to Cooperate. There are a lot of scenarios that can unfold but in most games agents do not know the other agents intentions or strategies so for the sake of this game we assume Edward and Will know nothing about it.\n", "\n", "I've implemented the following basic strategies in a directory called strategies:\n", "\n", "### Cooperate\n", "Cooperate: With this strategy Will or Edward (the agent) always cooperates. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import cooperate as c\n", "agent = c.Cooperate()\n", "```\n", "### Defect\n", "Defect: With this strategy the agent always defects. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import defect as d\n", "agent = d.Defect()\n", "```\n", "### Chaos\n", "Chaos: An agent uses this strategu to cause chaos by cooperating or defecting at random. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import chaos as ch\n", "agent = ch.Chaos()\n", "```\n", "### Grim\n", "Grim: This strategy is the most unforgiving. The agent cooperates until the opponent defects, in which case it will defect for the remainder of the game. This is also known as a trigger strategy. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import grim as g\n", "agent = g.Grim()\n", "```\n", "### Pavlov\n", "Pavlov: This is another trigger strategy where initally the agent will cooperate until it loses then it will change it's strategy (defect). The agent will continue to change it's strategy if it loses. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import pavlov as p\n", "agent = p.Pavlov()\n", "```\n", "### Q-Learning\n", "Q-Learning: This agent uses [Q-learning](https://en.wikipedia.org/wiki/Q-learning) for it's strategy where Q Learning is a model-free reinforcement learning technique. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import machine_learning as ml\n", "agent = ml.QLearn()\n", "```\n", "### Human\n", "Human: This agent recieves the action as input from a human player. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import human as h\n", "agent = h.Human()\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from collections import Counter\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "sns.set()\n", "\n", "# Importing Games\n", "from game_types import PrisonersDilemma\n", "from game_types import Coordination\n", "\n", "# Importing Strategies\n", "from strategies import chaos as c\n", "from strategies import defect as d\n", "from strategies import machine_learning as ml\n", "from strategies import pavlov as p\n", "from strategies import grim as g\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating Games:\n", "## Chaos VS Defect" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 20385.03it/s]\n" ] }, { "data": { "image/png": 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II4/o/fff18cff5zjMR9GhF4AAAAAkOTp6aljx45p27ZtOnv2rKKjo2U2m9Wz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\nAABgtwi9AAAAAAC7RegFAAA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"text/plain": [ "<matplotlib.figure.Figure at 0x106183d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create agents and play the game for 10000 iteratations\n", "agent1 = c.Chaos()\n", "agent2 = d.Defect()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Grab Data\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Chaos Agent Vs Defect Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([1,2,5],width-.05))\n", "_ = ax.set_xticklabels(('1', '2', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('Chaos Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Defect Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this scenario defecting is the [domiant strategy](https://en.wikipedia.org/wiki/Strategic_dominance). Where the agent is better off defecting no matter what other agents do." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grim VS Pavlov" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 15135.32it/s]\n" ] }, { "data": { "image/png": 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4eLhWr16dY4Eq5I4wDAAAAABFWHBwsIKDgwu7jCKHBbQAAAAAAFaHMAwAAAAA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\nqgUAAAAAYH1yvU167ty5eX7QqVOnyt/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"text/plain": [ "<matplotlib.figure.Figure at 0x116530860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# play the game\n", "agent1 = g.Grim()\n", "agent2 = p.Pavlov()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# get data from game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Grim Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,4,5],width/2))\n", "_ = ax.set_xticklabels(('0', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('Grim Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both strategies start out cooperating, Grim never defects because pavlov never defects. Pavlov never loses a round so it doesn't change it's strategy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning VS Pavlov" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 12552.94it/s]\n" ] }, { "data": { "image/png": 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g4GCCg4Ofdxk5jm6gJSIiIiIiIrmOwrCIiIiIiIjkOgrDIiIiIiIikusoDIuI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\ndXV12bhx4xHbVq5cmccff/yYzjt79uz86Ec/et/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"text/plain": [ "<matplotlib.figure.Figure at 0x11885d048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "agent1 = ml.QLearn()\n", "agent2 = p.Pavlov()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([1,2,4,5],width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pavlov's simple rules out performs Q Learning here which is interesting." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 9596, 1: 401, 4: 2, 5: 1}) Counter({2: 9596, 5: 401, 4: 2, 1: 1})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning VS Chaos" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 15111.22it/s]\n" ] }, { "data": { "image/png": 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HH3/Izc1NJ0+e1OzZs1W5cuVcPa1YsULDhw9XUlKSYmNj1bp1a0m65bJ2dnYaOXKkMjIy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\n6uqaa14AAAAAAO5GvqF3x44dlp+zsrK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"text/plain": [ "<matplotlib.figure.Figure at 0x1165604e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = c.Chaos()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Chaos Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Chaos Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Learning significantly outperforms the Chaos Agent because the Q Learning Agent learns pretty quickly that defecting yields the highest expected utility (talked about more in appendix)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q Learning VS Q Learning" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 10919.34it/s]\n" ] }, { "data": { "image/png": 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A39k3P1lZ2QW4FyIiIiIiIvKkK9RbK9WqVYu9e/fSsGFDtm/fjp+fH3Xq1GHOnDlkZGSQ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\nvTZVVVWVHoNWpGPHjunUqVMOHjyYGTNm5NZbb63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"text/plain": [ "<matplotlib.figure.Figure at 0x118d64940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = ml.QLearn()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here both QLearning Agents tend to mirror each other. I assume this is because they have the same inital parameters which will yield the same expected utility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## QLearning Vs QLearning (Longer Game; Different Starting Parameters)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 200000/200000 [03:44<00:00, 890.01it/s] \n" ] }, { "data": { "image/png": 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9eCw/+OADhg0bhpeXFzY2NkyZMoVDhw6xfv163Nzc8PPzw8LCgnfffZfWrVs/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\n9qi6ulqjR4+W1WqV3W5XTEyMampqVFVVpZSUFO/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"text/plain": [ "<matplotlib.figure.Figure at 0x118dde160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 200000 # Play a longer game\n", "# agent 1's parameters are bit more short sighted\n", "agent1 = ml.QLearn(decay=0.4, lr=0.03, explore_period=30000, explore_random_prob=0.4, exploit_random_prob=0.2)\n", "# agent 2's parameters think more about the future\n", "agent2 = ml.QLearn(decay=0.6, lr=0.2, explore_period=40000, explore_random_prob=0.4, exploit_random_prob=0.1)\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(I haven't had the time to look through the actions of both agents but one is short sighted and the other is not, which yields the Orange QLearning agent a higher total utility score.)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 167016, 1: 20014, 5: 11536, 4: 1434}) Counter({2: 167016, 5: 20014, 1: 11536, 4: 1434})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Iterated Coordination Game\n", "## Scenario - Choosing Movies\n", "\n", "In this scenario [Vincent](https://www.linkedin.com/in/vincent-reverdy/) and [Maghav](https://www.linkedin.com/in/maghavkumar/) want to see different movies. Vincent wants to see Guardians of the Galaxy 2 and Maghav wants to see Wonder Woman. They are willing to go see the movie that don't really care for but they both don't want to go see a movie alone. They both have 2 choices to defect (see the other persons movie person), or to cooperate go and see the movie they want. \n", "The payoff matrix is below:\n", "\n", "![Game Theory Movie Example](http://i.imgur.com/t5LgITv.png)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chaos VS Defect" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 21936.61it/s]\n" ] }, { "data": { "image/png": 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FAAAAAEleXl46dOiQNm/erFOnTik+Pl4Wi0WdO3fW/Pnz1aFDB3l7e6tFixZ6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\nQAsDAAAAAOBe5Rl6f/jhB3377beFUQs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3vpGHH344H/jAB9LV1ZUXXnghTzzxRHbs2JFt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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118de7e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create agents and play the game for 10000 iteratations\n", "agent1 = c.Chaos()\n", "agent2 = d.Defect()\n", "game = Coordination(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Grab Data\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Chaos Agent Vs Defect Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,1, 2],width-.05))\n", "_ = ax.set_xticklabels(('0','1','2'))\n", "_ = ax.legend((a1[0], a2[0]), ('Chaos Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Defect Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here Defect isn't a domiant strategy. The defect agent only recieves a non 0 utility value if the chaos agent sees the movie they intended to see. A Mixed Strategy is needed." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 5022, 0: 4978}) Counter({1: 5022, 0: 4978})\n" ] } ], "source": [ "print(agent1_util_vals,agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grim VS Pavlov" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 16447.37it/s]\n" ] }, { "data": { "image/png": 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hmjp1quLj4+Xv769p06YpJCREiYmJkqRVq1apQ4cOt9WQkJCgkJAQBQQE6NCh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\nY3Pq1Kkz+lhjx47N0NDQyOOlS5dm06ZN6ezsfNl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"text/plain": [ "<matplotlib.figure.Figure at 0x119877940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# play the game\n", "agent1 = g.Grim()\n", "agent2 = p.Pavlov()\n", "game = Coordination(agent1, agent2)\n", "game.play(10000)\n", "\n", "# get data from game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Grim Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,1,2],width/2))\n", "_ = ax.set_xticklabels(('0', '1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('Grim Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grim loses in the first round and always goes to other movie, the Pavlov Agent even won a round where they both ended up at the same movie and never changed it's strategy." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 9999, 0: 1}) Counter({1: 9999, 0: 1})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning Vs Chaos" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 15487.86it/s]\n" ] }, { "data": { "image/png": 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5hw4dtGDBAgUFBcnDw0MWi0XTpk1T27Zt5enpedttxo4dq/j4eLm6umrkyJGS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\nGnqzsrIUFRUlFxcXSdLUqVM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"text/plain": [ "<matplotlib.figure.Figure at 0x119c7b710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = c.Chaos()\n", "game = Coordination(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Chaos Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('0', '1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Chaos Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is different from Prisoner's Dilema, the QLearning Agent is trying to cooperate with the chaos agent but can never predict which movie he is going to." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## QLearning Vs QLearning" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%\|██████████\| 10000/10000 [00:00<00:00, 10869.86it/s]\n" ] }, { "data": { "image/png": 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Rwtzu7Oxsbr/7aI/ixYvr5s2bOdrubQcAAAAA4M/I10cr3fu8zdTUVJUsWVIuLi5KSUl5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\npaUlSdLW1pbHH388P//5z7N9+/b87W9/O6bnffb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"text/plain": [ "<matplotlib.figure.Figure at 0x116558c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = ml.QLearn()\n", "game = Coordination(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('0','1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Still playing around with this one, but both do pretty bad here." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({0: 9243, 1: 387, 2: 370}) Counter({0: 9243, 2: 387, 1: 370})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Next Up?\n", "#### Immediately: more reading, Deep QLearning, more advanced games, N>2 player games\n", "#### Later: more reading, develop games (mechanism design) given data (Cool Paper entitled [Mechanism Design for Data Science](https://arxiv.org/abs/1404.5971)), apply other deep learning models that I learn about to games\n", "#### Much Later: more reading, apply to some financial applications" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Appendix\n", "### A Few Cool Things about Q Learning:\n", "- Q Learning a model-free reinforcement learning technique\n", "- model free meaning it doesn't a need a model of the environment to determine the next action to take. It makes pretty good choices with it's action value function. This is useful for large complex environments where the rules of the environment aren't all known.\n", "- The action value function will find expected utility of an action in a given state.\n", "- A correctly implemented Q Learning model will follow a policy (set of rules) for choosing the best action, in this case the action that gives the highest expected utility.\n", "- The expected utility for each state determined by using the following update rule\n", "![Update Rule](https://wikimedia.org/api/rest_v1/media/math/render/svg/7a2a11876f4a2bef1198beb780a769cfa5c21af3)\n", "- the discount factor determines the importance for future rewards. The closer the discount factor is 0 the more short sighted it is.\n", "\n", "\n", "### Some topics in Game Theory that were indirectly dicussed above but not formally defined:\n", "- Both games above are two player normal form games. This is where a normal form game is a finite valued player game.\n", "- The Payoff Matrix is a common way to represent normal formed games.\n", "- The agents use a utility function which maps the states of the world around around them to real numbers. In the TCP example the utility values (numbers) are the download the rates. The rates may be significantly different and have units associated with them but for each action and state the numbers will be relative to there real values. Ergo in the TCP example dicussed above if one person doesn't follow the TCP protocol and another does, the download rates will be faster than the person who has reduced the number of packets it is sending/recieving. \n", "- A Nash Equilbrium is a stable state where no agent can gain a better outcome if the strategies of other agents remain unchanged.\n", "- In the Prisoner's Dilemma Game the Nash Equilbrium for both players is to defect.\n", "- In the Coordination Game 2 Nash Equilbriums exist, both agents going to the either movie as long as it the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
rongchuhe2/workshop_data_analysis_python	example_bridge_bike_counter.ipynb	1	10927	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Unsupervised Analysis of Days of Week\n", "\n", "Treating crossing each day as features to learn about the relatinships between various days." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Data\n", "\n", "We'll start by downloading the data (available on [seattle.gov](http://www.seattle.gov/transportation/bikecounter_fremont.htm))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from urllib import request\n", "\n", "FREMONT_URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'\n", "\n", "request.urlretrieve(FREMONT_URL, 'Fremont.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# magic function to show the content of the file\n", "%more Fremont.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('Fremont.csv') # use read_csv to load the data into dataframe\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's see the type of the data\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# change the Date column to datetime data type\n", "df['Date'] = pd.to_datetime(df['Date'])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Set the index to Date\n", "df.set_index('Date', inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.apply(lambda x: sum(x.isnull()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# clear the data by delete the non-numeric\n", "df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df.apply(lambda x: sum(x.isnull()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df.resample('W').sum().plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns=['West', 'East']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.resample('w').sum().plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# To see whether there is any annual trend of the number of rides\n", "df.resample('D').sum().rolling(365).sum().plot() \n", "# each point is the sum of the number of rides in the previuos 365 days" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# The y coordinate is not from 0\n", "ax = df.resample('D').sum().rolling(365).sum().plot()\n", "ax.set_ylim(0, None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# DateimeIndex.time return numpy array of datetime.time, the time part of the Timestamps\n", "df.groupby(df.index.time).mean().plot()\n", "# plot the average of rides at each hours of the day" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Create the pivoted table to investigate the pattern in each day\n", "df['Total'] = df['West'] + df['East']\n", "pivoted = df.pivot_table(values='Total', index=df.index.time, columns=df.index.date)\n", "pivoted.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pivoted.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# delete the date with non-numeric\n", "pivoted.dropna(axis=1, inplace=True)\n", "pivoted.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pivoted.plot(legend=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# add transparent parameter alpha\n", "pivoted.plot(legend=False, alpha=0.01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Principal Component Analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get X with hours as mearsurement and date as observations\n", "X = pivoted.T.values\n", "X.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "X2 = PCA(2, svd_solver='full').fit_transform(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X2.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "plt.scatter(X2[:, 0], X2[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use cluster algorithm Gaussian mixture model\n", "from sklearn.mixture import GaussianMixture\n", "\n", "gmm = GaussianMixture(2)\n", "gmm.fit(X)\n", "labels = gmm.predict(X)\n", "labels\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# plt.scatter(X2[:, 0], X2[:, 1], c=labels, cmap='rainbow')\n", "# plt.colorbar()\n", "plt.scatter(X2[:, 0], X2[:, 1], c=labels)\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# so labels == 1 represents the weekday\n", "pivoted.T[labels == 1].T.plot(legend=False, alpha=0.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# labels == 0 represents the weekend or holiday\n", "pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing with Day of Week" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.DatetimeIndex(pivoted.columns)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The DatetimeIndex.dayof week gives the day of the week\n", "dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek\n", "dayofweek" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Then we plot the color of the weekday\n", "plt.scatter(X2[:, 0], X2[:, 1], c=dayofweek)\n", "plt.colorbar() " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# grab the day in label 0 which is not weekend\n", "dates = pd.DatetimeIndex(pivoted.columns)\n", "dates[(labels == 0) & (dayofweek < 5)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's up with Feb 6, 2017?\n", "\n", "[Snow Storm](https://www.seattletimes.com/seattle-news/weather/weather-service-predicts-3-to-6-inches-of-snow-in-seattle-area/)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
swara-salih/Portfolio	Group Project--Chicago WNV Kaggle Competition/NileVirus_Prediction-Swara .ipynb	1	3341056	null	mit
ogaway/EduEcon	Global.ipynb	1	62302	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 教育経済学：課題１\n", "##「第三期教育入学率に対する回帰分析」\n", "第三期教育の入学率をGDP, 人口, ジニ係数, 教育に対する政府支出割合によって重回帰分析を行う。\n", "\n", "出所 : \n", "OECD (2015), Gross domestic product (GDP) (indicator). doi: 10.1787/dc2f7aec-en (Accessed on 10 October 2015) \n", "UNESCO Institute for Statistics(2015), data extracted on 10 Oct 2015 09:13 UTC (GMT) from UIS/ISU \n", "World Bank, Development Research Group(2015), Data from database: Poverty and Equity Database. (Last Updated: 07/08/2015) \n", "より算出" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -- coding:utf-8 --\n", "from __future__ import print_function\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# データ読み込み\n", "# 第三期教育入学率\n", "# http://data.uis.unesco.org/\n", "data_enroll = pd.read_csv(\"tertiary.csv\", index_col='Country', dtype='O')\n", "data_enroll[data_enroll == '..'] = np.nan\n", "# GDP \n", "# https://data.oecd.org/gdp/gross-domestic-product-gdp.htm\n", "data_gdp = pd.read_csv(\"gdp.csv\", index_col='Country', dtype='O')\n", "data_gdp[data_gdp == '..'] = np.nan\n", "# 人口 \n", "# http://databank.worldbank.org/data/reports.aspx?Code=SP.POP.TOTL&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y#\n", "data_pop = pd.read_csv(\"population.csv\", index_col='Country', dtype='O')\n", "data_pop[data_pop == '..'] = np.nan\n", "# ジニ係数 \n", "# http://databank.worldbank.org/data/reports.aspx?Code=SI.POV.GINI&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y#\n", "data_gini = pd.read_csv(\"gini.csv\", index_col='Country', dtype='O')\n", "data_gini[data_gini == '..'] = np.nan\n", "# 第三期教育に対する政府支出 \n", "# https://data.oecd.org/eduresource/public-spending-on-education.htm\n", "data_public = pd.read_csv(\"public_spending.csv\", index_col='Country', dtype='O')\n", "data_public[data_public == '..'] = np.nan" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr 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<td>3.9114</td>\n", " <td>NaN</td>\n", " <td>3.74394</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>13.08853</td>\n", " <td>13.76232</td>\n", " <td>14.01383</td>\n", " <td>14.73868</td>\n", " <td>15.81263</td>\n", " <td>20.00498</td>\n", " <td>24.52325</td>\n", " <td>28.78627</td>\n", " <td>31.79338</td>\n", " <td>32.32131</td>\n", " <td>33.1062</td>\n", " <td>43.56153</td>\n", " <td>47.74272</td>\n", " <td>55.50096</td>\n", " <td>58.52989</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13.60327</td>\n", " <td>NaN</td>\n", " <td>15.24848</td>\n", " <td>16.74901</td>\n", " <td>17.744</td>\n", " <td>18.18056</td>\n", " <td>19.76258</td>\n", " <td>20.21485</td>\n", " <td>22.28536</td>\n", " <td>NaN</td>\n", " <td>28.59671</td>\n", " <td>28.75825</td>\n", " <td>30.27547</td>\n", " <td>31.46411</td>\n", " <td>33.30463</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1999 2000 2001 2002 2003 2004 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN 1.27942 1.29312 \n", "Albania 13.08853 13.76232 14.01383 14.73868 15.81263 20.00498 \n", "Algeria 13.60327 NaN 15.24848 16.74901 17.744 18.18056 \n", "American Samoa NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN \n", "\n", " 2005 2006 2007 2008 2009 2010 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN 3.9114 NaN \n", "Albania 24.52325 28.78627 31.79338 32.32131 33.1062 43.56153 \n", "Algeria 19.76258 20.21485 22.28536 NaN 28.59671 28.75825 \n", "American Samoa NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN \n", "\n", " 2011 2012 2013 2014 2015 \n", "Country \n", "Afghanistan 3.74394 NaN NaN NaN NaN \n", "Albania 47.74272 55.50096 58.52989 NaN NaN \n", "Algeria 30.27547 31.46411 33.30463 NaN NaN \n", "American Samoa NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_enroll.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>1980</th>\n", " <th>1981</th>\n", " <th>1982</th>\n", " <th>1983</th>\n", " <th>1984</th>\n", " <th>1985</th>\n", " <th>1986</th>\n", " <th>1987</th>\n", " <th>1988</th>\n", " <th>1989</th>\n", " <th>...</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " <th>2014</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>538035.5154</td>\n", " <td>601184.0944</td>\n", " <td>666237.9468</td>\n", " <td>700439.5025</td>\n", " <td>706116.0365</td>\n", " <td>780193.8888</td>\n", " <td>867545.4341</td>\n", " <td>895008.2139</td>\n", " <td>945659.3828</td>\n", " <td>927164.1469</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>155037.382</td>\n", " <td>178034.2597</td>\n", " <td>181796.1002</td>\n", " <td>196586.5927</td>\n", " <td>211230.1811</td>\n", " <td>228876.1743</td>\n", " <td>240244.2573</td>\n", " <td>261145.2252</td>\n", " <td>283619.2842</td>\n", " <td>299927.1378</td>\n", " <td>...</td>\n", " <td>718812.8644</td>\n", " <td>773857.9107</td>\n", " <td>825382.9291</td>\n", " <td>850582.9281</td>\n", " <td>897942.1402</td>\n", " <td>935668.927</td>\n", " <td>984763.1119</td>\n", " <td>999199.8604</td>\n", " <td>1040376.497</td>\n", " <td>1062956.442</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>79726.0876</td>\n", " <td>87043.3274</td>\n", " <td>94302.7153</td>\n", " <td>100940.7418</td>\n", " <td>104575.7382</td>\n", " <td>110618.6571</td>\n", " <td>115447.3111</td>\n", " <td>119999.7106</td>\n", " <td>128294.273</td>\n", " <td>138463.2414</td>\n", " <td>...</td>\n", " <td>285433.2141</td>\n", " <td>311310.3909</td>\n", " <td>325500.8904</td>\n", " <td>342442.8873</td>\n", " <td>339017.5061</td>\n", " <td>350124.3423</td>\n", " <td>369420.341</td>\n", " <td>378088.387</td>\n", " <td>382598.8984</td>\n", " <td>394485.5275</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>103557.1412</td>\n", " <td>112908.7475</td>\n", " <td>120627.0074</td>\n", " <td>125781.0802</td>\n", " <td>133456.1351</td>\n", " <td>140001.5239</td>\n", " <td>145429.1715</td>\n", " <td>152579.9061</td>\n", " <td>165380.5208</td>\n", " <td>177771.0402</td>\n", " <td>...</td>\n", " <td>346243.6361</td>\n", " <td>370161.3572</td>\n", " <td>388722.7568</td>\n", " <td>405339.5371</td>\n", " <td>406396.2064</td>\n", " <td>427441.4922</td>\n", " <td>451396.963</td>\n", " <td>459785.3992</td>\n", " <td>461907.8543</td>\n", " <td>477949.3341</td>\n", " </tr>\n", " <tr>\n", " <th>Brazil</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1988025.804</td>\n", " <td>2142004.309</td>\n", " <td>2342708.493</td>\n", " <td>2520650.653</td>\n", " <td>2538040.151</td>\n", " <td>2771866.298</td>\n", " <td>2973856.149</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 35 columns</p>\n", "</div>" ], "text/plain": [ " 1980 1981 1982 1983 1984 \\\n", "Country \n", "Argentina NaN NaN NaN NaN NaN \n", "Australia 155037.382 178034.2597 181796.1002 196586.5927 211230.1811 \n", "Austria 79726.0876 87043.3274 94302.7153 100940.7418 104575.7382 \n", "Belgium 103557.1412 112908.7475 120627.0074 125781.0802 133456.1351 \n", "Brazil NaN NaN NaN NaN NaN \n", "\n", " 1985 1986 1987 1988 1989 \\\n", "Country \n", "Argentina NaN NaN NaN NaN NaN \n", "Australia 228876.1743 240244.2573 261145.2252 283619.2842 299927.1378 \n", "Austria 110618.6571 115447.3111 119999.7106 128294.273 138463.2414 \n", "Belgium 140001.5239 145429.1715 152579.9061 165380.5208 177771.0402 \n", "Brazil NaN NaN NaN NaN NaN \n", "\n", " ... 2005 2006 2007 2008 \\\n", "Country ... \n", "Argentina ... 538035.5154 601184.0944 666237.9468 700439.5025 \n", "Australia ... 718812.8644 773857.9107 825382.9291 850582.9281 \n", "Austria ... 285433.2141 311310.3909 325500.8904 342442.8873 \n", "Belgium ... 346243.6361 370161.3572 388722.7568 405339.5371 \n", "Brazil ... 1988025.804 2142004.309 2342708.493 2520650.653 \n", "\n", " 2009 2010 2011 2012 2013 \\\n", "Country \n", "Argentina 706116.0365 780193.8888 867545.4341 895008.2139 945659.3828 \n", "Australia 897942.1402 935668.927 984763.1119 999199.8604 1040376.497 \n", "Austria 339017.5061 350124.3423 369420.341 378088.387 382598.8984 \n", "Belgium 406396.2064 427441.4922 451396.963 459785.3992 461907.8543 \n", "Brazil 2538040.151 2771866.298 2973856.149 NaN NaN \n", "\n", " 2014 \n", "Country \n", "Argentina 927164.1469 \n", "Australia 1062956.442 \n", "Austria 394485.5275 \n", "Belgium 477949.3341 \n", "Brazil NaN \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_gdp.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2000</th>\n", " <th>2001</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>19701940</td>\n", " <td>20531160</td>\n", " <td>21487079</td>\n", " <td>22507368</td>\n", " <td>23499850</td>\n", " <td>24399948</td>\n", " <td>25183615</td>\n", " <td>25877544</td>\n", " <td>26528741</td>\n", " <td>27207291</td>\n", " <td>27962207</td>\n", " <td>28809167</td>\n", " <td>29726803</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>3089027</td>\n", " <td>3060173</td>\n", " <td>3051010</td>\n", " <td>3039616</td>\n", " <td>3026939</td>\n", " <td>3011487</td>\n", " <td>2992547</td>\n", " <td>2970017</td>\n", " <td>2947314</td>\n", " <td>2927519</td>\n", " <td>2913021</td>\n", " <td>2904780</td>\n", " <td>2900489</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>31183658</td>\n", " <td>31590320</td>\n", " <td>31990387</td>\n", " <td>32394886</td>\n", " <td>32817225</td>\n", " <td>33267887</td>\n", " <td>33749328</td>\n", " <td>34261971</td>\n", " <td>34811059</td>\n", " <td>35401790</td>\n", " <td>36036159</td>\n", " <td>36717132</td>\n", " <td>37439427</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>57522</td>\n", " <td>58176</td>\n", " <td>58729</td>\n", " <td>59117</td>\n", " <td>59262</td>\n", " <td>59117</td>\n", " <td>58648</td>\n", " <td>57904</td>\n", " <td>57031</td>\n", " <td>56226</td>\n", " <td>55636</td>\n", " <td>55316</td>\n", " <td>55227</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>65399</td>\n", " <td>67770</td>\n", " <td>71046</td>\n", " <td>74783</td>\n", " <td>78337</td>\n", " <td>81223</td>\n", " <td>83373</td>\n", " <td>84878</td>\n", " <td>85616</td>\n", " <td>85474</td>\n", " <td>84419</td>\n", " <td>82326</td>\n", " <td>79316</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2000 2001 2002 2003 2004 \\\n", "Country \n", "Afghanistan 19701940 20531160 21487079 22507368 23499850 \n", "Albania 3089027 3060173 3051010 3039616 3026939 \n", "Algeria 31183658 31590320 31990387 32394886 32817225 \n", "American Samoa 57522 58176 58729 59117 59262 \n", "Andorra 65399 67770 71046 74783 78337 \n", "\n", " 2005 2006 2007 2008 2009 \\\n", "Country \n", "Afghanistan 24399948 25183615 25877544 26528741 27207291 \n", "Albania 3011487 2992547 2970017 2947314 2927519 \n", "Algeria 33267887 33749328 34261971 34811059 35401790 \n", "American Samoa 59117 58648 57904 57031 56226 \n", "Andorra 81223 83373 84878 85616 85474 \n", "\n", " 2010 2011 2012 \n", "Country \n", "Afghanistan 27962207 28809167 29726803 \n", "Albania 2913021 2904780 2900489 \n", "Algeria 36036159 36717132 37439427 \n", "American Samoa 55636 55316 55227 \n", "Andorra 84419 82326 79316 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_pop.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2000</th>\n", " <th>2001</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>32.5</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.6</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "Albania NaN NaN 32.5 NaN NaN 30.6 NaN NaN 30 NaN NaN NaN \n", "Algeria NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "American Samoa NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " 2012 \n", "Country \n", "Afghanistan NaN \n", "Albania 29 \n", "Algeria NaN \n", "American Samoa NaN \n", "Andorra NaN " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_gini.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2009</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Australia</th>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>1.4</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>1.4</td>\n", " </tr>\n", " <tr>\n", " <th>Brazil</th>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>Canada</th>\n", " <td>1.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2009\n", "Country \n", "Australia 0.7 \n", "Austria 1.4 \n", "Belgium 1.4 \n", "Brazil 0.8 \n", "Canada 1.5 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_public.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Australia', 'Austria', 'Belgium', 'Brazil', 'Canada', 'Chile', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland', 'Israel', 'Italy', 'Japan', 'Mexico', 'Netherlands', 'New Zealand', 'Norway', 'Poland', 'Portugal', 'Slovenia', 'South Africa', 'Spain', 'Sweden', 'Switzerland']\n", "31\n" ] } ], "source": [ "# 4つの指標全てにおいて調査された国を調べる\n", "country_list = []\n", "for i in np.asarray(data_enroll.index):\n", " if i in np.asarray(data_gdp.index):\n", " if i in np.asarray(data_pop.index):\n", " if i in np.asarray(data_gini.index):\n", " if i in np.asarray(data_public.index):\n", " country_list.append(i)\n", "print(country_list)\n", "print(len(country_list))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>tertiary</th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>65.841442</td>\n", " <td>13.063664</td>\n", " <td>16.572529</td>\n", " <td>32.774074</td>\n", " <td>1.166667</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>18.098664</td>\n", " <td>1.393257</td>\n", " <td>1.688527</td>\n", " <td>6.160457</td>\n", " <td>0.335123</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>16.396570</td>\n", " <td>9.471810</td>\n", " <td>12.678311</td>\n", " <td>25.600000</td>\n", " <td>0.500000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>59.257405</td>\n", " <td>12.360758</td>\n", " <td>15.661797</td>\n", " <td>27.800000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>70.519350</td>\n", " <td>12.942301</td>\n", " <td>16.168299</td>\n", " <td>32.400000</td>\n", " <td>1.200000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>77.013830</td>\n", " <td>13.995792</td>\n", " <td>17.557788</td>\n", " <td>35.050000</td>\n", " <td>1.400000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>93.721820</td>\n", " <td>15.418777</td>\n", " <td>20.917337</td>\n", " <td>50.800000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tertiary log_gdp log_pop gini public\n", "count 27.000000 27.000000 27.000000 27.000000 27.000000\n", "mean 65.841442 13.063664 16.572529 32.774074 1.166667\n", "std 18.098664 1.393257 1.688527 6.160457 0.335123\n", "min 16.396570 9.471810 12.678311 25.600000 0.500000\n", "25% 59.257405 12.360758 15.661797 27.800000 1.000000\n", "50% 70.519350 12.942301 16.168299 32.400000 1.200000\n", "75% 77.013830 13.995792 17.557788 35.050000 1.400000\n", "max 93.721820 15.418777 20.917337 50.800000 1.800000" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2000〜2014年におけるそれぞれのデータの最新をまとめる\n", "for i in reversed(range(2000, 2013)):\n", " d = {\n", " 'tertiary': data_enroll.ix[country_list][\"%s\" % i].astype(float),\n", " 'log_gdp': np.log(data_gdp.ix[country_list][\"%s\" % i], dtype=float),\n", " 'log_pop': np.log(data_pop.ix[country_list][\"%s\" % i].astype(float)),\n", " 'gini': data_gini.ix[country_list][\"%s\" % i].astype(float),\n", " 'public': data_public.ix[country_list]['2009'].astype(float),\n", " 'year': i\n", " }\n", " if i == 2012:\n", " df = pd.DataFrame(d).dropna()\n", " df_test = pd.DataFrame(d).dropna()\n", " for j in df_test.index.values:\n", " if j not in df.index.values:\n", " df.ix[j] = df_test.ix[j]\n", "print(len(df))\n", "df[['tertiary', 'log_gdp', 'log_pop', 'gini', 'public']].describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>tertiary</th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>71.143427</td>\n", " <td>12.845443</td>\n", " <td>16.192417</td>\n", " <td>31.970833</td>\n", " <td>1.195833</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>10.134027</td>\n", " <td>1.312599</td>\n", " <td>1.321017</td>\n", " <td>5.622585</td>\n", " <td>0.323673</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>55.561900</td>\n", " <td>9.471810</td>\n", " <td>12.678311</td>\n", " <td>25.600000</td>\n", " <td>0.500000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>61.977452</td>\n", " <td>12.311479</td>\n", " <td>15.528702</td>\n", " <td>27.525000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>71.034790</td>\n", " <td>12.802285</td>\n", " <td>16.139006</td>\n", " <td>31.850000</td>\n", " <td>1.200000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>77.907077</td>\n", " <td>13.692860</td>\n", " <td>16.991507</td>\n", " <td>34.000000</td>\n", " <td>1.400000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>93.721820</td>\n", " <td>15.271679</td>\n", " <td>18.668033</td>\n", " <td>50.800000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tertiary log_gdp log_pop gini public\n", "count 24.000000 24.000000 24.000000 24.000000 24.000000\n", "mean 71.143427 12.845443 16.192417 31.970833 1.195833\n", "std 10.134027 1.312599 1.321017 5.622585 0.323673\n", "min 55.561900 9.471810 12.678311 25.600000 0.500000\n", "25% 61.977452 12.311479 15.528702 27.525000 1.000000\n", "50% 71.034790 12.802285 16.139006 31.850000 1.200000\n", "75% 77.907077 13.692860 16.991507 34.000000 1.400000\n", "max 93.721820 15.271679 18.668033 50.800000 1.800000" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 外れ値を切り捨てる\n", "df = df[df['tertiary'] >= 30]\n", "print(len(df))\n", "df[['tertiary', 'log_gdp', 'log_pop', 'gini', 'public']].describe()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>log_gdp</th>\n", " <td>1.000000</td>\n", " <td>0.974037</td>\n", " <td>0.220537</td>\n", " <td>-0.298593</td>\n", " </tr>\n", " <tr>\n", " <th>log_pop</th>\n", " <td>0.974037</td>\n", " <td>1.000000</td>\n", " <td>0.337710</td>\n", " <td>-0.394632</td>\n", " </tr>\n", " <tr>\n", " <th>gini</th>\n", " <td>0.220537</td>\n", " <td>0.337710</td>\n", " <td>1.000000</td>\n", " <td>-0.451126</td>\n", " </tr>\n", " <tr>\n", " <th>public</th>\n", " <td>-0.298593</td>\n", " <td>-0.394632</td>\n", " <td>-0.451126</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " log_gdp log_pop gini public\n", "log_gdp 1.000000 0.974037 0.220537 -0.298593\n", "log_pop 0.974037 1.000000 0.337710 -0.394632\n", "gini 0.220537 0.337710 1.000000 -0.451126\n", "public -0.298593 -0.394632 -0.451126 1.000000" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 相関を求める\n", "df[['log_gdp', 'log_pop', 'gini', 'public']].corr()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.178\n", "Model: OLS Adj. R-squared: 0.141\n", "Method: Least Squares F-statistic: 4.766\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0400\n", "Time: 23:36:47 Log-Likelihood: -86.772\n", "No. Observations: 24 AIC: 177.5\n", "Df Residuals: 22 BIC: 179.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 112.9931 19.265 5.865 0.000 73.040 152.946\n", "log_gdp -3.2579 1.492 -2.183 0.040 -6.353 -0.163\n", "==============================================================================\n", "Omnibus: 1.395 Durbin-Watson: 1.855\n", "Prob(Omnibus): 0.498 Jarque-Bera (JB): 0.950\n", "Skew: 0.481 Prob(JB): 0.622\n", "Kurtosis: 2.842 Cond. No. 130.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰GDP\n", "# 説明変数設定\n", "X = df[['log_gdp']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.204\n", "Model: OLS Adj. R-squared: 0.168\n", "Method: Least Squares F-statistic: 5.633\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0268\n", "Time: 23:36:47 Log-Likelihood: -86.390\n", "No. Observations: 24 AIC: 176.8\n", "Df Residuals: 22 BIC: 179.1\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 127.2287 23.706 5.367 0.000 78.066 176.391\n", "log_pop -3.4637 1.459 -2.373 0.027 -6.490 -0.437\n", "==============================================================================\n", "Omnibus: 1.636 Durbin-Watson: 1.844\n", "Prob(Omnibus): 0.441 Jarque-Bera (JB): 0.978\n", "Skew: 0.494 Prob(JB): 0.613\n", "Kurtosis: 2.985 Cond. No. 205.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰人口\n", "# 説明変数設定\n", "X = df[['log_pop']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.073\n", "Model: OLS Adj. R-squared: 0.031\n", "Method: Least Squares F-statistic: 1.743\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.200\n", "Time: 23:36:47 Log-Likelihood: -88.210\n", "No. Observations: 24 AIC: 180.4\n", "Df Residuals: 22 BIC: 182.8\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 86.7563 12.000 7.230 0.000 61.870 111.642\n", "gini -0.4883 0.370 -1.320 0.200 -1.255 0.279\n", "==============================================================================\n", "Omnibus: 0.863 Durbin-Watson: 1.900\n", "Prob(Omnibus): 0.650 Jarque-Bera (JB): 0.773\n", "Skew: 0.152 Prob(JB): 0.679\n", "Kurtosis: 2.175 Cond. No. 191.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰ジニ係数\n", "# 説明変数設定\n", "X = df[['gini']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.095\n", "Model: OLS Adj. R-squared: 0.054\n", "Method: Least Squares F-statistic: 2.311\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.143\n", "Time: 23:36:47 Log-Likelihood: -87.927\n", "No. Observations: 24 AIC: 179.9\n", "Df Residuals: 22 BIC: 182.2\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 59.6000 7.856 7.587 0.000 43.308 75.892\n", "public 9.6530 6.350 1.520 0.143 -3.516 22.822\n", "==============================================================================\n", "Omnibus: 0.449 Durbin-Watson: 1.711\n", "Prob(Omnibus): 0.799 Jarque-Bera (JB): 0.560\n", "Skew: 0.081 Prob(JB): 0.756\n", "Kurtosis: 2.269 Cond. No. 7.86\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰政府支出\n", "# 説明変数設定\n", "X = df[['public']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.230\n", "Model: OLS Adj. R-squared: 0.068\n", "Method: Least Squares F-statistic: 1.422\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.265\n", "Time: 23:36:47 Log-Likelihood: -85.983\n", "No. Observations: 24 AIC: 182.0\n", "Df Residuals: 19 BIC: 187.9\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 116.5609 43.388 2.687 0.015 25.750 207.372\n", "log_gdp -0.5124 8.309 -0.062 0.951 -17.903 16.878\n", "log_pop -2.3396 8.698 -0.269 0.791 -20.545 15.866\n", "gini -0.1753 0.457 -0.384 0.706 -1.132 0.781\n", "public 3.8907 7.685 0.506 0.618 -12.194 19.976\n", "==============================================================================\n", "Omnibus: 0.942 Durbin-Watson: 1.795\n", "Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.659\n", "Skew: 0.393 Prob(JB): 0.719\n", "Kurtosis: 2.794 Cond. No. 857.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 重回帰分析\n", "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop', 'gini', 'public']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.220\n", "Model: OLS Adj. R-squared: 0.103\n", "Method: Least Squares F-statistic: 1.880\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.165\n", "Time: 23:36:47 Log-Likelihood: -86.144\n", "No. Observations: 24 AIC: 180.3\n", "Df Residuals: 20 BIC: 185.0\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 131.0571 31.985 4.097 0.001 64.337 197.777\n", "log_gdp 0.6721 7.823 0.086 0.932 -15.646 16.990\n", "log_pop -3.7953 8.055 -0.471 0.643 -20.598 13.007\n", "gini -0.2218 0.439 -0.505 0.619 -1.138 0.694\n", "==============================================================================\n", "Omnibus: 1.502 Durbin-Watson: 1.874\n", "Prob(Omnibus): 0.472 Jarque-Bera (JB): 0.924\n", "Skew: 0.480 Prob(JB): 0.630\n", "Kurtosis: 2.942 Cond. No. 647.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop', 'gini']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.210\n", "Model: OLS Adj. R-squared: 0.135\n", "Method: Least Squares F-statistic: 2.792\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0841\n", "Time: 23:36:47 Log-Likelihood: -86.296\n", "No. Observations: 24 AIC: 178.6\n", "Df Residuals: 21 BIC: 182.1\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 134.8078 30.554 4.412 0.000 71.267 198.349\n", "log_gdp 2.6818 6.614 0.405 0.689 -11.074 16.437\n", "log_pop -6.0592 6.572 -0.922 0.367 -19.727 7.608\n", "==============================================================================\n", "Omnibus: 1.602 Durbin-Watson: 1.837\n", "Prob(Omnibus): 0.449 Jarque-Bera (JB): 0.875\n", "Skew: 0.467 Prob(JB): 0.646\n", "Kurtosis: 3.057 Cond. No. 338.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
fggp/ctcsound	cookbook/03-threading.ipynb	1	6992	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multithreading\n", "In the preceding recipes, there was a single thread running; this is the default way to use Python, due to the GIL (Global Interpreter Lock). Then, the user has the possibility to interact with the Csound instance during the performance loop. This is illustrated in the following diagram:\n", "\n", "![Single Thread](img/03-threading-a.png)\n", "\n", "To use Csound in a more flexible way, one can use multithreading. Because of the GIL limitations, it is better to yield the multithread machinery through C libraries. When a C function is called from Python using ctypes, the GIL is released during the function call.\n", "\n", "Csound has an helper class called CsoundPerformanceThread. When there is a running Csound instance, one can start a new thread by creating a new object of type CsoundPerformanceThread with a reference to the Csound instance as argument. Then, the main Python thread will run allowing the user to interract with it, while the performance thread will run concurrently in the C world, outside of the GIL. The user can send messages to the performance thread, each message being sent with a call to a C function through ctypes, releasing the GIL during the function call. Those messages can be: _play(), pause(), togglePause(), stop(), record(), stopRecord(), scoreEvent(), inputMessage(), setScoreOffsetSeconds(), join(),_ or _flushMessageQueue()_.\n", "\n", "When a very long score is used, it is thus easy to implement a REPL (read-eval-print loop) system around Csound. This is illustrated in the following diagram:\n", "\n", "![Multithreading with CsoundPerformanceThread](img/03-threading-b.png)\n", "\n", "So let's start a Csound instance from Python, with a four hours long score:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ctcsound\n", "cs = ctcsound.Csound()\n", "\n", "csd = '''\n", "<CsoundSynthesizer>\n", "\n", "<CsOptions>\n", " -d -o dac -m0\n", "</CsOptions>\n", "\n", "<CsInstruments>\n", "sr = 48000\n", "ksmps = 100\n", "nchnls = 2\n", "0dbfs = 1\n", "\n", " instr 1\n", "idur = p3\n", "iamp = p4\n", "icps = cpspch(p5)\n", "irise = p6\n", "idec = p7\n", "ipan = p8\n", "\n", "kenv linen iamp, irise, idur, idec\n", "kenv = kenv*kenv\n", "asig poscil kenv, icps\n", "a1, a2 pan2 asig, ipan\n", " outs a1, a2\n", " endin\n", "</CsInstruments>\n", "\n", "<CsScore>\n", "f 0 14400 ; a 4 hours session should be enough\n", "</CsScore>\n", "</CsoundSynthesizer>\n", "'''\n", "cs.compileCsdText(csd)\n", "cs.start()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let's start a new thread, passing the opaque pointer of the Csound instance as argument:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pt = ctcsound.CsoundPerformanceThread(cs.csound())\n", "pt.play()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can send messages to the performance thread:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pt.scoreEvent(False, 'i', (1, 0, 1, 0.5, 8.06, 0.05, 0.3, 0.5))\n", "pt.scoreEvent(False, 'i', (1, 0.5, 1, 0.5, 9.06, 0.05, 0.3, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we're done, we stop the performance thread and reset the csound instance:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pt.stop()\n", "pt.join()\n", "cs.reset()" ] }, { "cell_type": "markdown", "metadata": { "jupyter": { "outputs_hidden": true } }, "source": [ "Note that we can still access the csound instance with other methods, like controlChannel() or setControlChannel():" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "csd = '''\n", "<CsoundSynthesizer>\n", "<CsOptions>\n", "-odac\n", "</CsOptions>\n", "<CsInstruments>\n", "sr = 44100\n", "ksmps = 64\n", "nchnls = 2\n", "0dbfs = 1\n", "seed 0\n", "\n", "instr 1\n", " iPch random 60, 72\n", " chnset iPch, \"pch\"\n", " kPch init iPch\n", " kNewPch chnget \"new_pitch\"\n", " if kNewPch > 0 then\n", " kPch = kNewPch\n", " endif\n", " aTone poscil .2, mtof(kPch)\n", " out aTone, aTone\n", "endin\n", "\n", "</CsInstruments>\n", "<CsScore>\n", "i 1 0 600\n", "</CsScore>\n", "</CsoundSynthesizer>\n", "'''\n", "cs.compileCsdText(csd)\n", "cs.start()\n", "pt = ctcsound.CsoundPerformanceThread(cs.csound())\n", "pt.play()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can ask for the values in the Csound instance ..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(69.60446804277807, c_int(0))\n" ] } ], "source": [ "print(cs.controlChannel('pch'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... or we can set our own values to the Csound instance:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cs.setControlChannel('new_pitch',73)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end, stop and reset as usual:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pt.stop()\n", "pt.join()\n", "cs.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author: François Pinot, March 2016" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }	lgpl-2.1
jessicabodosa/digit_trial	Digit.ipynb	1	575850	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# \|\| Digit \|\|\n", "___\n", "> practice really" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import scipy.special\n", "import matplotlib.pyplot\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#defining it\n", "\n", "class neuralNetwork:\n", " def __init__(self, inputnodes, hiddennodes, outputnodes, learning_rate):\n", " self.inodes = inputnodes\n", " self.hnodes = hiddennodes\n", " self.onodes = outputnodes\n", " \n", " self.lr = learning_rate\n", " \n", " self.wih = (np.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes, self.inodes)))\n", " self.who = (np.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes)))\n", " \n", " \n", " \n", " self.activation_function = lambda x:scipy.special.expit(x)\n", " \n", " pass \n", " \n", " \n", " def train(self, inputs_list, targets_list):\n", " inputs = np.array(inputs_list, ndmin=2).T\n", " targets = np.array(targets_list,ndmin=2).T\n", " \n", " hidden_inputs = np.dot(self.wih,inputs)\n", " hidden_outputs = self.activation_function(hidden_inputs)\n", " \n", " final_inputs = np.dot(self.who, hidden_outputs)\n", " final_outputs = self.activation_function(final_inputs)\n", " \n", " output_errors = targets - final_outputs\n", " hidden_errors = np.dot(self.who.T,output_errors)\n", " \n", " self.who += self.lrnp.dot((output_errorsfinal_outputs(1-final_outputs)),np.transpose(hidden_outputs))\n", " self.wih += self.lrnp.dot((hidden_errorshidden_outputs(1-hidden_outputs)),np.transpose(inputs))\n", " \n", " pass\n", " \n", " def query(self, inputs_list):\n", " inputs = np.array(inputs_list, ndmin=2).T\n", " \n", " hidden_inputs = np.dot(self.wih,inputs)\n", " hidden_outputs = self.activation_function(hidden_inputs)\n", " \n", " final_inputs = np.dot(self.who, hidden_outputs)\n", " final_outputs = self.activation_function(final_inputs)\n", " \n", " return final_outputs" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#creating the network\n", "\n", "input_nodes = 784\n", "hidden_nodes = 200\n", "output_nodes = 10\n", "learning_rate = 0.1\n", "\n", "n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_file = open(\"mnist_train.csv\",\"r\")\n", "data_list = data_file.readlines()\n", "data_file.close()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for record in data_list:\n", " all_values = record.split(',')\n", " inputs = (np.asfarray(all_values[1:])/255.0 0.99)+0.01\n", " targets = np.zeros(output_nodes)+0.01\n", " targets[int(all_values[0])] = 0.99\n", " n.train(inputs,targets)\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_data_file = open(\"mnist_test.csv\",\"r\")\n", "test_data_list = test_data_file.readlines()\n", "test_data_file.close()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'7'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_values1 = test_data_list[0].split(',')\n", "all_values1[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fe2b7ce5978>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUqKXTliPiU8DfAycAu4AHgMtKKU+M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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe2ba77c898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_array = np.asfarray(all_values1[1:]).reshape((28,28))\n", "matplotlib.pyplot.imshow(image_array, cmap=\"Greys\",interpolation=\"None\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00981296],\n", " [ 0.00291237],\n", " [ 0.01382717],\n", " [ 0.01207517],\n", " [ 0.00125286],\n", " [ 0.01440796],\n", " [ 0.0101734 ],\n", " [ 0.98560708],\n", " [ 0.00882559],\n", " [ 0.00855959]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n.query((np.asfarray(all_values1[1:])/255.0 0.99) + 0.01)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(n.query((np.asfarray(all_values[1:])/255.00.99)+0.01))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "1 correct label\n", "1 found answer\n", "0 correct label\n", "0 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "5 correct label\n", "6 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "6 correct label\n", "6 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "5 correct label\n", "5 found answer\n", "9 correct label\n", "9 found answer\n", "7 correct label\n", "7 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "6 correct label\n", "6 found answer\n", "6 correct label\n", "6 found answer\n", "5 correct label\n", "5 found answer\n", "4 correct label\n", "4 found answer\n", "0 correct label\n", "0 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "3 correct label\n", "3 found answer\n", "1 correct label\n", "1 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "7 correct label\n", "7 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "1 correct label\n", "1 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "5 correct label\n", "5 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "4 correct label\n", "4 found answer\n", "4 correct label\n", "4 found answer\n", "6 correct label\n", "6 found answer\n", "3 correct label\n", "3 found answer\n", "5 correct label\n", "5 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n", "0 correct label\n", "0 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "9 correct label\n", "9 found answer\n", "5 correct label\n", "5 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "9 correct label\n", "9 found answer\n", "3 correct label\n", "3 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "6 correct label\n", "6 found answer\n", "4 correct label\n", "4 found answer\n", "3 correct label\n", "3 found answer\n", "0 correct label\n", "0 found answer\n", "7 correct label\n", "7 found answer\n", "0 correct label\n", "0 found answer\n", "2 correct label\n", "2 found answer\n", "9 correct label\n", "9 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "3 correct label\n", "3 found answer\n", "2 correct label\n", "2 found answer\n", "9 correct label\n", "9 found answer\n", "7 correct label\n", "7 found answer\n", "7 correct label\n", "7 found answer\n", "6 correct label\n", "6 found answer\n", "2 correct label\n", "2 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "4 correct label\n", "4 found answer\n", "7 correct label\n", "7 found answer\n", "3 correct label\n", "3 found answer\n", "6 correct label\n", "6 found answer\n", "1 correct label\n", "1 found answer\n", "3 correct label\n", "3 found answer\n", "6 correct label\n", "6 found answer\n", 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label\n", "2 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "4 correct label\n", "4 found answer\n", "3 correct label\n", "3 found answer\n", "6 correct label\n", "6 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "6 correct label\n", "6 found answer\n", "5 correct label\n", "6 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n" ] } ], "source": [ "scorecard = []\n", "for record in test_data_list:\n", " all_values = record.split(',')\n", " correct_label = int(all_values[0])\n", " print(correct_label,\"correct label\")\n", " inputs = (np.asfarray(all_values[1:])/255.0 0.99)+0.01\n", " outputs = n.query(inputs)\n", " label = np.argmax(outputs)\n", " print(label,\"found answer\")\n", " if(label == correct_label):\n", " scorecard.append(1)\n", " else:\n", " scorecard.append(0)\n", " pass\n", " pass\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n" ] } ], "source": [ "print(scorecard)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "performance = 0.9609\n" ] } ], "source": [ "sum_array = np.asarray(scorecard)\n", "print(\"performance = \", sum_array.sum()/sum_array.size)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
liganega/Gongsu-DataSci	ref_materials/excs/Lab-07.ipynb	2	16909	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 연습문제\n", "\n", "아래 문제들을 해결하는 코드를 lab07.py 파일에 작성하여 제출하라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 1\n", "\n", "미국 해양대기청(NOAA)은 전세계 날씨를 실시간으로 제공한다. 한국의 경우 공항이 있는 도시의 날씨정보를 제공하며 평택도 포함된다. 평택의 현재 날씨 정보를 텍스트파일로 얻고자 하면 아래 [NOAA](http://weather.noaa.gov/pub/data/observations/metar/decoded/RKSG.TXT) 사이트를 클릭해서 파일을 다운로드받으면 된다.\n", " \n", "아니면 아래 함수를 이용하여 위 링크에 연결된 파일 내용을 확인할 수 있다.\n", "\n", " def NOAA_string():\n", " url = \"http://weather.noaa.gov/pub/data\" +\\\n", " \"/observations/metar/decoded/RKSG.TXT\"\n", " noaa_data_string = urllib.urlopen(url).read()\n", " return noaa_data_string\n", " \n", "위 코드를 사용하려면 `urllib` 모듈을 임포트해야 한다. 위 함수를 파이썬 셸에서 실행하여 리턴값을 확인해보기 바란다. \n", "\n", "이제 아래 일을 수행하는 함수 `NOAA_temperature(s)` 함수를 작성하라.\n", "\n", "* `NOAA_string()`의 리턴값을 인자로 받아서 해당 도시의 섭씨 단위 온도의 정수값을 리턴한다. \n", "* 미국은 온도를 화씨(Fahrenheit) 단위로 나타내며 우리는 섭씨(Celsius) 단위를 사용한다. \n", "\n", "주의: 위 사이트는 실시간으로 날씨 정보를 제공한다. 따라서 위 링크를 누를 때마다 온도 정보가 변한다. 예를 들어 2015년 10월 16일 0시 38분에 확인한 경우 아래 처럼 확인된 평택시 온도는 섭씨 14.2이다. 따라서 `NOAA_temperature(NOAA_string())`은 `14`를 리턴해야 한다. 하지만 다른 시각에 확인하면 다른 값이 나올 수 있음에 주의해야 한다. 어떻게 섭씨에 해당하는 숫자를 끄집어 낼 수 있는지 확인해야 한다. \n", "\n", " Pyongtaek Ab, Korea, South (RKSG) 36-56N 127-00E 16M\n", " Oct 15, 2015 - 10:58 AM EDT / 2015.10.15 1458 UTC\n", " Wind: Calm:0\n", " Visibility: 2 mile(s):0\n", " Sky conditions: partly cloudy\n", " Weather: mist\n", " Temperature: 57.6 F (14.2 C)\n", " Dew Point: 57.6 F (14.2 C)\n", " Relative Humidity: 100%\n", " Pressure (altimeter): 30.11 in. Hg (1019 hPa)\n", " ob: RKSG 151458Z 00000KT 2SM R32/2600FT BR SCT010 14/14 A3011 RMK AO2A SLP199 T01420142 \n", " cycle: 15\n", " \n", "힌트: 문자열 메소드 중에서 특정 부분 문자열(substring)의 위치, 즉 인덱스 번호를 확인해주는 메소드가 있다. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 1 견본답안 1\n", "\n", "`NOAA_string()`을 실행하여 얻어진 파일의 내용을 보면 7번째 줄에서 온도 정보를 확인할 수 있다.\n", "관건은 7번째 줄에서 `14.2`를 끄집어 내는 것이다. 그러려면 14.2를 유일하게 특징지울 수 있는 무언가를 찾아야 한다. \n", "\n", "방법 1: `split` 메소드 이용하기\n", "\n", "* 7번째 줄을 자세히 살피면 섭씨 온도 정보는 세 개의 스페이스 뒤에 위치한다. 이 정보를 활용할 수 있다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pyongtaek Ab, Korea, South (RKSG) 36-56N 127-00E 16M\n", "Oct 20, 2015 - 04:55 AM EDT / 2015.10.20 0855 UTC\n", "Wind: from the NW (310 degrees) at 6 MPH (5 KT):0\n", "Visibility: 1 1/2 mile(s):0\n", "Sky conditions: clear\n", "Weather: mist\n", "Temperature: 68 F (20 C)\n", "Dew Point: 66 F (19 C)\n", "Relative Humidity: 93%\n", "Pressure (altimeter): 30 in. Hg (1015 hPa)\n", "Pressure tendency: 0.00 inches (0.0 hPa) higher than three hours ago\n", "ob: RKSG 200855Z 31005KT 1 1/2SM BR CLR 20/19 A3000 RMK SLP162 53000\n", "cycle: 9\n", "\n", "20 C\n" ] } ], "source": [ "import urllib\n", "\n", "def NOAA_string():\n", " url = \"http://weather.noaa.gov/pub/data\" +\\\n", " \"/observations/metar/decoded/RKSG.TXT\"\n", " noaa_data_string = urllib.urlopen(url).read()\n", " return noaa_data_string\n", "\n", "print(NOAA_string())\n", "\n", "def NOAA_temperature(s):\n", " L = s.split('\\n')\n", " Line7 = L[6].split()\n", " print(str(int(Line7[-2][1:])) + \" C\")\n", "\n", "NOAA_temperature(NOAA_string())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 1 견본답안 2\n", "\n", "* 섭씨온도를 유일하게 특징지우는 문자열을 찾아야 한다.\n", "* `\" F \"`가 그런 문자열이다. (`F` 양 옆으로 스페이스가 있다.)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20 C\n" ] } ], "source": [ "def NOAA_temperature(s):\n", " d = s.find(\" F \")\n", " print(s[d+4: d+6] + \" C\")\n", " \n", "NOAA_temperature(NOAA_string())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 2\n", "\n", "텍스트 파일에 저장된 문장에서 특정 단어의 출현 횟수를 확인해주는 함수 `wc_sub(filename, s)` 함수를 작성하라. `wc`는 `Word Count`의 줄임말이다. \n", "\n", "힌트: `count` 메소드를 활용한다.\n", "\n", "예제 1: `data.txt` 파일 내용이 아래와 같을 경우\n", "\n", " One Two\n", " \n", "`wc_sub('data.txt', 'One')`는 1를 리턴한다.\n", "\n", "예제 2: `data.txt` 파일 내용이 아래와 같을 경우\n", "\n", " One Two\n", " Three Four Five\n", " \n", "`wc_sub('data.txt', 'o')`는 2를 리턴한다.\n", "\n", "`wc_sub` 함수를 이용하여 `이상한 나라의 앨리스` 원작에 'Alice'와 'alice'란 단어가 각각 몇 번 언급되는지 확인하라. 이상한 나라의 앨리스 원작은 아래 링크에서 다운 받을 수 있다.\n", "\n", " http://www.gutenberg.org/files/28885/28885-8.txt\n", " \n", "위 링크를 누르면 뜨는 화면에서 `Plain Text UTF-8` 파일을 다운로드 받으면 된다. 아마도 몇 만 단어가 사용되었을 것이다.\n", "\n", "단, `filename`에 해당하는 파일이 열리지 않을 경우 `-1`을 리턴하도록 오류처리를 해야 한다. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 2 견본답안" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The word 'Alice' occurs 402 times.\n", "The word 'alice' occurs 0 times.\n" ] } ], "source": [ "def wc_sub(filename, s):\n", " with open(filename, 'r') as f:\n", " f_content = f.read()\n", " return f_content.count(s)\n", "\n", "print(\"The word 'Alice' occurs {} times.\".format(wc_sub('Alice.txt', 'Alice')))\n", "print(\"The word 'alice' occurs {} times.\".format(wc_sub('Alice.txt', 'alice')))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 연습 3\n", "\n", "함수 `f`와 숫자들의 리스트 `xs`를 인자로 받아 `f(x)`의 값이 `0`보다 크게 되는 `x`의 값만 추출해서 리턴하는 함수 `filtering(f, xs)`를 정의하라.\n", "\n", "예제:\n", "\n", " In [1]: def f1(x):\n", " ...: return x * 3\n", "\n", " In [2]: filtering(f1, [1, -2, 2, -1, 3, 5])\n", " Out[2]: [1, 2, 3, 5]\n", "\n", " In [3]: filtering(f1, [-1, -2, -3, -4, -5])\n", " Out[3]: []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 3 견본답안" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 5]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def filtering(f, xs):\n", " L = []\n", " for x in xs:\n", " if f(x) > 0:\n", " L.append(x)\n", " return L\n", "\n", "def f1(x):\n", " return x * 3\n", "\n", "filtering(f1, [1, -2, 2, -1, 3, 5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 참조: 파이썬 내장함수 중에 `filter` 함수가 비슷한 일을 한다. 어떤 차이점이 있는지 확인해보는 것을 추천한다." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 연습 4\n", "\n", "함수 `f`와 숫자들의 리스트 `xs = [x1, ..., x_n]`를 인자로 받아 `f(xn)`들의 값의 합을 리턴하는 함수 `sum_list(f, xs)`를 정의하라. 단, `xs = []` 일 경우 `0`을 리턴한다.\n", "\n", "예제:\n", "\n", " In [4]: def f2(x):\n", " ...: return x 2\n", "\n", " In [5]: sum_list(f2, [1, -2, 2, -3,])\n", " Out[5]: 18\n", "\n", " In [6]: sum_list(f1, [-1, -2, -3, -4, -5])\n", " Out[6]: -45" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 4 견본답안" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18\n", "-45\n" ] } ], "source": [ "def sum_list(f, xs):\n", " L = 0\n", " for x in xs:\n", " L = L + f(x)\n", " return L\n", "\n", "def f2(x):\n", " return x 2\n", "\n", "print(sum_list(f2, [1, -2, 2, -3]))\n", "print(sum_list(f1, [-1, -2, -3, -4, -5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "참조: 파이썬 내장함수 중에 `sum` 함수가 비슷한 일을 한다. 어떤 차이점이 있는지 확인해보는 것을 추천한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 5\n", "\n", "밑변의 길이와 높이가 각각 `a`와 `h`인 삼각형의 면적을 리턴하는 함수 `triangle_area(a, h)`를 작성하라. 그런데 삼각형의 높이 `h`는 기본값으로 `5`를 사용해야 한다. 힌트: 키워드 인자를 사용한다.\n", "\n", "예제:\n", "\n", " In [7]: triangle_area(3)\n", " Out[7]: 7.5\n", "\n", " In [8]: triangle_area(3, 7)\n", " Out[8]: 10.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 5 견본답안" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.5\n", "10.5\n" ] } ], "source": [ "def triangle_area(a, height=5):\n", " return 1.0/2 * a * height\n", "\n", "print(triangle_area(3))\n", "print(triangle_area(3, 7))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 6\n", "\n", "함수 `f`를 입력 받으면 아래 묘사처럼 작동하는 함수를 리턴하는 함수 `fun_2_fun(f)`를 정의하라.\n", "\n", " fun_2_fun(f)(2) = (f(2)) 2\n", " fun_2_fun(f)(3) = (f(3)) 3\n", " fun_2_fun(f)(4) = (f(4)) 4\n", " ...\n", " \n", "주의: 함수를 입력받아 함수를 리턴하도록 작성해야 한다. \n", "\n", "힌트: 함수 안에서 `def` 키워드를 이용하여 새로운 함수를 정의할 수 있다. 그 함수는 지역함수가 된다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 6 견본답안 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "36\n" ] } ], "source": [ "def fun_2_fun(f):\n", " def f_exp(n):\n", " return (f(n)) n\n", " return f_exp\n", "\n", "print(f1(2))\n", "print(fun_2_fun(f1)(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 문제 핵심\n", "\n", "이 문제의 핵심은 함수를 단순히 인자로만 사용하는 것이 아니라 리턴값으로도 할용하는 것이다. 즉, 함수에 어떤 인자를 넣고 호출하였더니 어떤 함수를 리턴하는 함수를 구현해야 한다. 그리고 리턴값이 함수이므로 그 함수를 적당한 인자를 입력하여 호출할 수 있다.\n", "\n", "예를 들어 함수 `g`를 다음과 같이 정의하자." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def exp2(x):\n", " return x 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "g = fun_2_fun(exp2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그러면 `g`는 함수임을 확인할 수 있다." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "어떤 함수인가? `help` 를 이용하여 확인하면 다음과 같다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function f_exp in module __main__:\n", "\n", "f_exp(n)\n", "\n" ] } ], "source": [ "help(g)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<function __main__.f_exp>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "즉, 인자를 하나 받는 함수이며 `f_exp`를 이용해 정외되었음을 알 수 있다. 실제로 `g`는 아래와 같이 정의되어 있다. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "`g`를 정의하기 위해 `fun_2_fun(f)` 함수를 호출할 때 사용된 인자 `f` 대신에 `exp2` 함수를 삽입하였기 때문에 `g`가 아래와 같이 정의된 함수임을 알 수 있다. \n", "\n", " g(x) = fun_2_fun(exp2)\n", " = f_exp(x) # f_exp 를 정의할 때 exp2 가 사용됨에 중의\n", " = exp2(x) x\n", " = (x2) x\n", " = x ** (2x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 6 견본답안 2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "36\n" ] } ], "source": [ "def fun_2_fun(f):\n", " return lambda x: f(x) * x\n", "\n", "print(f1(2))\n", "print(fun_2_fun(f1)(2))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
landlab/landlab	notebooks/tutorials/normal_fault/normal_fault_component_tutorial.ipynb	1	20174	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"../../landlab_header.png\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to the NormalFault component\n", "\n", "This tutorial provides an introduction to the `NormalFault` component in the Landlab toolkit. This component takes the following parameters. \n", "\n", " Parameters\n", " --------\n", " grid : ModelGrid\n", " faulted_surface : str or ndarray of shape `(n_nodes, )` or list of str\n", " or ndarrays. \n", " Surface that is modified by the NormalFault component. Can be a\n", " field name or array or a list of strings or ndarrays if the fault.\n", " should uplift more than one field. Default value is \n", " `topographic__elevation`.\n", " fault_throw_rate_through_time : dict, optional\n", " Dictionary that specifies the time varying throw rate on the fault.\n", " Expected format is:\n", " ``fault_throw_rate_through_time = {'time': array, 'rate': array}``\n", " Default value is a constant rate of 0.001 (units not specified).\n", " fault_dip_angle : float, optional\n", " Dip angle of the fault in degrees. Default value is 90 degrees.\n", " fault_trace : dictionary, optional\n", " Dictionary that specifies the coordinates of two locations on the\n", " fault trace. Expected format is\n", " ``fault_trace = {'x1': float, 'y1': float, 'x2': float, 'y2': float}``\n", " where the vector from ``(x1, y1)`` to ``(x2, y2)`` defines the\n", " strike of the fault trace. The orientation of the fault dip relative\n", " to the strike follows the right hand rule.\n", " Default is for the fault to strike NE.\n", " include_boundaries : boolean, optional\n", " Flag to indicate if model grid boundaries should be uplifted. If\n", " set to ``True`` uplifted model grid boundaries will be set to the\n", " average value of their upstream nodes. Default value is False.\n", "\n", "\n", "The `NormalFault` component will divide the model domain into two regions, a 'faulted nodes' region which will experience vertical rock uplift at a rate of \n", "\n", "$t \\cdot \\sin (d)$\n", "\n", "where $t$ is the fault throw rate and $d$ is the fault dip angle. \n", "\n", "While dip angles less than 90 degrees are permitted, in its present implementation, the `NormalFault` component does not translate field information laterally. \n", "\n", "The fault orientation is specified by two coordinate pairs: (x1, y1) and (x2, y2). The strike of the fault, specified with the right-hand rule convention, is the vector from (x1, y1) to (x2, y2). Give that this component creates a normal fault, in which the footwall moves up relative to the hanging wall, this means that the nodes that are counterclockwise from the strike are the uplifted nodes. \n", "\n", "To start, let's import necessary Landlab and Python modules. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# start by importing necessary modules\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from landlab import HexModelGrid, RasterModelGrid\n", "from landlab.components import (\n", " FastscapeEroder,\n", " FlowAccumulator,\n", " NormalFault,\n", " StreamPowerEroder,\n", ")\n", "from landlab.plot import imshow_grid\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will make a default `NormalFault`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((6, 6), xy_spacing=10)\n", "\n", "grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid)\n", "\n", "plt.figure()\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")\n", "plt.plot(grid.x_of_node, grid.y_of_node, \"c.\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " This fault has a strike of NE and dips to the SE. Thus the uplifted nodes (shown in yellow) are in the NW half of the domain. \n", "\n", "The default `NormalFault` will not uplift the boundary nodes. We change this by using the keyword argument `include_boundaries`. If this is specified, the elevation of the boundary nodes is calculated as an average of the faulted nodes adjacent to the boundaries. This occurs because most Landlab erosion components do not operate on boundary nodes. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nf = NormalFault(grid, include_boundaries=True)\n", "\n", "plt.figure()\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")\n", "plt.plot(grid.x_of_node, grid.y_of_node, \"c.\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add functionality to the `NormalFault` with other keyword arguments. We can change the fault strike and dip, as well as specify a time series of fault uplift through time. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((60, 100), xy_spacing=10)\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid, fault_trace={\"x1\": 0, \"y1\": 200, \"y2\": 30, \"x2\": 600})\n", "\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By reversing the order of (x1, y1) and (x2, y2) we can reverse the location of the upthrown nodes (all else equal). " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((60, 100), xy_spacing=10)\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid, fault_trace={\"y1\": 30, \"x1\": 600, \"x2\": 0, \"y2\": 200})\n", "\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also specify complex time-rock uplift rate histories, but we'll explore that later in the tutorial. \n", "\n", "Next let's make a landscape evolution model with a normal fault. Here we'll use a HexModelGrid to highlight that we can use both raster and non-raster grids with this component. \n", "\n", "We will do a series of three numerical experiments and will want to keep a few parameters constant. Since you might want to change them, we are making it easier to change all of them together. They are defined in the next block:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# here are the parameters to change\n", "K = 0.0005 # stream power coefficient, bigger = streams erode more quickly\n", "U = 0.0001 # uplift rate in meters per year\n", "\n", "dt = 1000 # time step in years\n", "dx = 10 # space step in meters\n", "\n", "nr = 60 # number of model rows\n", "nc = 100 # number of model columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), dx, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(grid, fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500})\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += 0.0001 * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the upper left portion of the grid has been uplifted an a stream network has developed over the whole domain. \n", "\n", "How might this change when we also uplift the boundaries nodes?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(\n", " grid, fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500}, include_boundaries=True\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that when the boundary nodes are not included, the faulted region is impacted by the edge boundary conditions differently. Depending on your application, one or the other of these boundary condition options may suite your problem better. \n", "\n", "The last thing to explore is the use of the `fault_rate_through_time` parameter. This allows us to specify generic fault throw rate histories. For example, consider the following history, in which every 100,000 years there is a 10,000 year period in which the fault is active. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "time = (\n", " np.array(\n", " [\n", " 0.0,\n", " 7.99,\n", " 8.00,\n", " 8.99,\n", " 9.0,\n", " 17.99,\n", " 18.0,\n", " 18.99,\n", " 19.0,\n", " 27.99,\n", " 28.00,\n", " 28.99,\n", " 29.0,\n", " ]\n", " )\n", " * 10\n", " * dt\n", ")\n", "rate = np.array([0, 0, 0.01, 0.01, 0, 0, 0.01, 0.01, 0, 0, 0.01, 0.01, 0])\n", "\n", "plt.figure()\n", "plt.plot(time, rate)\n", "plt.plot([0, 300 * dt], [0.001, 0.001])\n", "plt.xlabel(\"Time [years]\")\n", "plt.ylabel(\"Fault Throw Rate [m/yr]\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default value for uplift rate is 0.001 (units unspecified as it will depend on the x and t units in a model, but in this example we assume time units of years and length units of meters). \n", "\n", "This will result in a total of 300 m of fault throw over the 300,000 year model time period. This amount of uplift can also be accommodated by faster fault motion that occurs over shorter periods of time. \n", "\n", "Next we plot the cumulative fault throw for the two cases. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = np.arange(0, 300 * dt, dt)\n", "rate_constant = np.interp(t, [0, 300 * dt], [0.001, 0.001])\n", "rate_variable = np.interp(t, time, rate)\n", "\n", "cumulative_rock_uplift_constant = np.cumsum(rate_constant) * dt\n", "cumulative_rock_uplift_variable = np.cumsum(rate_variable) * dt\n", "\n", "plt.figure()\n", "plt.plot(t, cumulative_rock_uplift_constant)\n", "plt.plot(t, cumulative_rock_uplift_variable)\n", "plt.xlabel(\"Time [years]\")\n", "plt.ylabel(\"Cumulative Fault Throw [m]\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A technical note: Beyond the times specified, the internal workings of the `NormalFault` will use the final value provided in the rate array. \n", "\n", "Let's see how this changes the model results. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(\n", " grid,\n", " fault_throw_rate_through_time={\"time\": time, \"rate\": rate},\n", " fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500},\n", " include_boundaries=True,\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the resulting topography is very different than in the case with continuous uplift. \n", "\n", "For our final example, we'll use `NormalFault` with a more complicated model in which we have both a soil layer and bedrock. In order to move, material must convert from bedrock to soil by weathering.\n", "\n", "First we import remaining modules and set some parameter values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from landlab.components import DepthDependentDiffuser, ExponentialWeatherer\n", "\n", "# here are the parameters to change\n", "K = 0.0005 # stream power coefficient, bigger = streams erode more quickly\n", "U = 0.0001 # uplift rate in meters per year\n", "max_soil_production_rate = (\n", " 0.001 # Maximum weathering rate for bare bedrock in meters per year\n", ")\n", "soil_production_decay_depth = 0.7 # Characteristic weathering depth in meters\n", "linear_diffusivity = 0.01 # Hillslope diffusivity and m2 per years\n", "soil_transport_decay_depth = 0.5 # Characteristic soil transport depth in meters\n", "\n", "dt = 100 # time step in years\n", "dx = 10 # space step in meters\n", "\n", "nr = 60 # number of model rows\n", "nc = 100 # number of model columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "?ExponentialWeatherer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we create the grid and run the model. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "# create a field for soil depth\n", "d = grid.add_zeros(\"soil__depth\", at=\"node\")\n", "\n", "# create a bedrock elevation field\n", "b = grid.add_zeros(\"bedrock__elevation\", at=\"node\")\n", "b[:] = z - d\n", "\n", "fr = FlowAccumulator(grid, depression_finder=\"DepressionFinderAndRouter\", routing=\"D4\")\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "ew = ExponentialWeatherer(\n", " grid,\n", " soil_production__decay_depth=soil_production_decay_depth,\n", " soil_production__maximum_rate=max_soil_production_rate,\n", ")\n", "\n", "dd = DepthDependentDiffuser(\n", " grid,\n", " linear_diffusivity=linear_diffusivity,\n", " soil_transport_decay_depth=soil_transport_decay_depth,\n", ")\n", "\n", "nf = NormalFault(\n", " grid,\n", " fault_throw_rate_through_time={\"time\": [0, 30], \"rate\": [0.001, 0.001]},\n", " fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500},\n", " include_boundaries=False,\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", "\n", " # Move normal fault\n", " nf.run_one_step(dt)\n", "\n", " # Route flow\n", " fr.run_one_step()\n", "\n", " # Erode with water\n", " fs.run_one_step(dt)\n", "\n", " # We must also now erode the bedrock where relevant. If water erosion\n", " # into bedrock has occurred, the bedrock elevation will be higher than\n", " # the actual elevation, so we simply re-set bedrock elevation to the\n", " # lower of itself or the current elevation.\n", " b = grid.at_node[\"bedrock__elevation\"]\n", " b[:] = np.minimum(b, grid.at_node[\"topographic__elevation\"])\n", "\n", " # Calculate regolith-production rate\n", " ew.calc_soil_prod_rate()\n", "\n", " # Generate and move soil around. This component will update both the\n", " # soil thickness and topographic elevation fields.\n", " dd.run_one_step(dt)\n", "\n", " # uplift the whole domain, we need to do this to both bedrock and topography\n", " z[grid.core_nodes] += U * dt\n", " b[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, \"topographic__elevation\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also examine the soil thickness and soil production rate. Here in the soil depth, we see it is highest along the ridge crests. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# and the soil depth\n", "imshow_grid(grid, \"soil__depth\", cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The soil production rate is highest where the soil depth is low, as we would expect given the exponential form. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# and the soil production rate\n", "imshow_grid(grid, \"soil_production__rate\", cmap=\"viridis\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
moonbury/pythonanywhere	kaggle/video_game_sales.ipynb	2	56909	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "sns.set(style=\"darkgrid\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Platform</th>\n", " <th>Year_of_Release</th>\n", " <th>Genre</th>\n", " <th>Publisher</th>\n", " <th>NA_Sales</th>\n", " <th>EU_Sales</th>\n", " <th>JP_Sales</th>\n", " <th>Other_Sales</th>\n", " <th>Global_Sales</th>\n", " <th>Critic_Score</th>\n", " <th>Critic_Count</th>\n", " <th>User_Score</th>\n", " <th>User_Count</th>\n", " <th>Developer</th>\n", " <th>Rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Wii Sports</td>\n", " <td>Wii</td>\n", " <td>2006.0</td>\n", " <td>Sports</td>\n", " <td>Nintendo</td>\n", " <td>41.36</td>\n", " <td>28.95</td>\n", " <td>3.77</td>\n", " <td>8.45</td>\n", " <td>82.53</td>\n", " <td>76.0</td>\n", " <td>51.0</td>\n", " <td>8</td>\n", " <td>321.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Mario Kart Wii</td>\n", " <td>Wii</td>\n", " <td>2008.0</td>\n", " <td>Racing</td>\n", " <td>Nintendo</td>\n", " <td>15.67</td>\n", " <td>12.75</td>\n", " <td>3.79</td>\n", " <td>3.28</td>\n", " <td>35.50</td>\n", " <td>82.0</td>\n", " <td>73.0</td>\n", " <td>8.3</td>\n", " <td>709.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Wii Sports Resort</td>\n", " <td>Wii</td>\n", " <td>2009.0</td>\n", " <td>Sports</td>\n", " <td>Nintendo</td>\n", " <td>15.61</td>\n", " <td>10.92</td>\n", " <td>3.28</td>\n", " <td>2.95</td>\n", " <td>32.76</td>\n", " <td>80.0</td>\n", " <td>73.0</td>\n", " <td>8</td>\n", " <td>192.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>New Super Mario Bros.</td>\n", " <td>DS</td>\n", " <td>2006.0</td>\n", " <td>Platform</td>\n", " <td>Nintendo</td>\n", " <td>11.28</td>\n", " <td>9.14</td>\n", " <td>6.50</td>\n", " <td>2.88</td>\n", " <td>29.79</td>\n", " <td>89.0</td>\n", " <td>65.0</td>\n", " <td>8.5</td>\n", " <td>431.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Wii Play</td>\n", " <td>Wii</td>\n", " <td>2006.0</td>\n", " <td>Misc</td>\n", " <td>Nintendo</td>\n", " <td>13.96</td>\n", " <td>9.18</td>\n", " <td>2.93</td>\n", " <td>2.84</td>\n", " <td>28.92</td>\n", " <td>58.0</td>\n", " <td>41.0</td>\n", " <td>6.6</td>\n", " <td>129.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Platform Year_of_Release Genre Publisher \\\n", "0 Wii Sports Wii 2006.0 Sports Nintendo \n", "1 Mario Kart Wii Wii 2008.0 Racing Nintendo \n", "2 Wii Sports Resort Wii 2009.0 Sports Nintendo \n", "3 New Super Mario Bros. DS 2006.0 Platform Nintendo \n", "4 Wii Play Wii 2006.0 Misc Nintendo \n", "\n", " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n", "0 41.36 28.95 3.77 8.45 82.53 76.0 \n", "1 15.67 12.75 3.79 3.28 35.50 82.0 \n", "2 15.61 10.92 3.28 2.95 32.76 80.0 \n", "3 11.28 9.14 6.50 2.88 29.79 89.0 \n", "4 13.96 9.18 2.93 2.84 28.92 58.0 \n", "\n", " Critic_Count User_Score User_Count Developer Rating \n", "0 51.0 8 321.0 Nintendo E \n", "1 73.0 8.3 709.0 Nintendo E \n", "2 73.0 8 192.0 Nintendo E \n", "3 65.0 8.5 431.0 Nintendo E \n", "4 41.0 6.6 129.0 Nintendo E " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('./input/Video_Games_Sales_as_at_30_Nov_2016.csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7dtlW2gAP/jbGjx+vfP3114qiFPyeoqOjlWPHjpV19QxWWDsq+v5VUYpf78jI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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa03b0c590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "ax1 = fig.add_subplot(411)\n", "\n", "idx = 0\n", "hcat1 = sorted(data.Platform.unique())\n", "for cat_val in hcat1:\n", " ax1.bar(idx,data[data['Platform']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", "\n", "ax2 = fig.add_subplot(412)\n", "idx = 0\n", "hcat2 = sorted(data.Year_of_Release.unique())\n", "for cat_val in hcat2: \n", " ax2.bar(idx,data[data['Year_of_Release']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax3 = fig.add_subplot(413)\n", "idx = 0\n", "hcat3 = sorted(data.Genre.unique())\n", "for cat_val in hcat3: \n", " ax3.bar(idx,data[data['Genre']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax4 = fig.add_subplot(414)\n", "idx = 0\n", "hcat4 = sorted(data['Rating'].dropna().unique())\n", "for cat_val in hcat4: \n", " ax4.bar(idx,data[data['Rating']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax1.set_title('Global Sales by Platform')\n", "ax2.set_title('Global Sales by Year')\n", "ax3.set_title('Global Sales by Genre')\n", "ax4.set_title('Global Sales by Rating')\n", "ax1.set_xticklabels(hcat1)\n", "ax2.set_xticklabels(hcat2)\n", "ax3.set_xticklabels(hcat3)\n", "ax4.set_xticklabels(hcat4) \n", "fig.subplots_adjust(hspace=1)\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#data['User_Score'].dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
DCPROGS/CVFIT	cvfit_DC_LOB_example_p206.ipynb	2	4974	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from pylab import\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from cvfit import fitting\n", "from cvfit.fitting import SingleFitSession\n", "from cvfit.fitting import MultipleFitSession\n", "from cvfit.fitting import simplex\n", "from cvfit import data\n", "\n", "from cvfit.errors import residuals\n", "from cvfit.errors import SSD\n", "from cvfit.errors import SSDlik" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "X\tY\ts(Y)\tweight\n", "2.5\t5.576\t0\t1\n", "5\t7.282\t0\t1\n", "10\t12.521\t0\t1\n", "20\t16.138\t0\t1\n", "40\t23.219\t0\t1\n", "\n" ] } ], "source": [ "dataset = data.XYDataSet()\n", "X = [2.5, 5.0, 10.0, 20.0, 40.0]\n", "Y = [5.576, 7.282, 12.521, 16.138, 23.219]\n", "S = None #[1.2003]5\n", "dataset.from_columns(X, Y, S)\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from cvfit.equations import Hill\n", "eq = Hill('Langmuir')\n", "#fs = SingleFitSession(dataset, eq)\n", "eq.propose_guesses(dataset)\n", "eq.guess = np.array([0.0, 31.0, 15.0, 1.0])\n", "eq.pars = eq.guess.copy()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "coeffs, Smin = simplex(SSD, eq.theta, eq, dataset.X, dataset.Y, dataset.W)\n", "eq.theta = coeffs\n", "kfit = len(np.nonzero(np.invert(eq.fixed))[0])\n", "ndf = dataset.size() - kfit\n", "var = Smin / ndf\n", "Sres = sqrt(var)\n", "Lmax = -SSDlik(eq.theta, eq, dataset)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of point fitted = 5\n", "\n", "Number of parameters estimated = 2\n", "\n", "Degrees of freedom = 3\n", "\n", "Residual error SD = 1.200 (variance = 1.441)\n", "\n", "Minimum SSD = 4.322; \n", "Max log-likelihood = -6.730\n" ] } ], "source": [ "print('Number of point fitted = {0:d}'.format(dataset.size()))\n", "print('\\nNumber of parameters estimated = {0:d}'.format(kfit))\n", "print('\\nDegrees of freedom = {0:d}'.format(ndf))\n", "print('\\nResidual error SD = {0:.3f} (variance = {1:.3f})'.format(Sres, var))\n", "print('\\nMinimum SSD = {0:.3f}; \\nMax log-likelihood = {1:.3f}'.format(Smin, Lmax))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of point fitted = 5\n", "Number of parameters estimated = 2\n", "Degrees of freedom = 3\n", "Residual error SD = 1.247 (variance = 1.555)\n", "Parameter 1: Ymin \t= 0 \t (fixed)\n", "Parameter 2: Ymax \t= 30.1201 \t Approx SD = 3.124\t CV = 10.4\n", "Parameter 3: EC50 \t= 14.1212 \t Approx SD = 3.4052\t CV = 24.1\n", "Minimum SSD = 4.665; \n", "Max log-likelihood = -6.922\n", "Correlation matrix = [!!!! PRINTOUT OF CORRELATION MATRIX NOT IMPLEMENTED YET. SORRY.\n", "\n", "WARNING: SOME PARAMETERS ARE STRONGLY CORRELATED (coeff > 0.9); try different guesses\n", "\n", "LIKELIHOOD INTERVALS\n", "5.06-unit Likelihood Intervals (equivalent SD for Gaussian- 3.18)\n", "Lmax= -6.92151; Lcrit= -11.9841\n", "Parameter 1: Ymin\t= 0\t (fixed)\n", "Parameter 2: Ymax\t= 30.1201\t LOWER = 20.2766\t UPPER = 69.3388\n", "Parameter 3: EC50\t= 14.1212\t LOWER = 5.02122\t UPPER = 67.4515\n" ] } ], "source": [ "fs.fit()\n", "fs.calculate_errors()\n", "print(fs.string_estimates())\n", "print(fs.string_liklimits())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
jakeret/abcpmc	notebooks/toy_model.ipynb	1	744751	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D gaussian toy model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "\n", "%pylab inline\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"talk\")\n", "rc('axes', labelsize=20, titlesize=20)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import triangle\n", "from clerk.stats.distances import mahalanobis\n", "import abcpmc \n", "\n", "#np.random.seed(987654321)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { 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abcpmc.TophatPrior([-5], [5])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def mean_dist(x, y):\n", " return np.abs(np.mean(x, axis=0) - np.mean(y, axis=0))\n", "\n", "dist = mean_dist" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_new_sample(theta):\n", " return np.random.normal(theta, sigma, n)\n", "postfn = create_new_sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Verification" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.4025917]\n", "0.000582605098998\n" ] } ], "source": [ "theta = prior()\n", "print theta\n", "x = postfn([mean])\n", "d = dist(x, y)\n", "print d" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ 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[]\n", " for pool in abcpmc_sampler.sample(prior, eps):\n", " print(\"T: {0}, eps: {1:>.4f}, ratio: {2:>.4f}\".format(pool.t, eps(pool.t), pool.ratio))\n", " for i, (mean, std) in enumerate(zip(abcpmc.weighted_avg_and_std(pool.thetas, pool.ws, axis=0))):\n", " print(u\" theta[{0}]: {1:>.4f} \\u00B1 {2:>.4f}\".format(i, mean,std))\n", "\n", " eps.eps = np.percentile(pool.dists, 90)\n", " if eps.eps < eps_min:\n", " eps.eps = eps_min\n", " \n", " pools.append(pool)\n", " \n", " abcpmc_sampler.close()\n", " \n", " return pools\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "T: 0, eps: 0.5000, ratio: 0.1000\n", " theta[0]: 0.9792 ± 0.2894\n", "T: 1, eps: 0.4495, ratio: 0.6137\n", " theta[0]: 1.0011 ± 0.2582\n", "T: 2, eps: 0.3917, ratio: 0.6086\n", " theta[0]: 0.9932 ± 0.2256\n", "T: 3, eps: 0.3414, ratio: 0.6001\n", " theta[0]: 0.9860 ± 0.1989\n", "T: 4, eps: 0.2992, ratio: 0.6127\n", " theta[0]: 0.9945 ± 0.1734\n", "T: 5, eps: 0.2653, ratio: 0.6077\n", " theta[0]: 0.9961 ± 0.1531\n", "T: 6, eps: 0.2335, ratio: 0.6008\n", " theta[0]: 0.9967 ± 0.1362\n", "T: 7, eps: 0.2053, ratio: 0.6335\n", " theta[0]: 0.9904 ± 0.1180\n", "T: 8, eps: 0.1806, ratio: 0.6219\n", " theta[0]: 0.9940 ± 0.1040\n", "T: 9, eps: 0.1577, ratio: 0.6099\n", " theta[0]: 0.9918 ± 0.0905\n", "T: 10, eps: 0.1387, ratio: 0.6207\n", " theta[0]: 0.9923 ± 0.0811\n", "T: 11, eps: 0.1228, ratio: 0.6177\n", " theta[0]: 0.9919 ± 0.0717\n", "T: 12, eps: 0.1081, ratio: 0.6057\n", " theta[0]: 0.9914 ± 0.0640\n", "T: 13, eps: 0.0959, ratio: 0.6107\n", " theta[0]: 0.9927 ± 0.0557\n", "T: 14, eps: 0.0835, ratio: 0.6232\n", " theta[0]: 0.9920 ± 0.0496\n", "T: 15, eps: 0.0729, ratio: 0.5928\n", " theta[0]: 0.9909 ± 0.0426\n", "T: 16, eps: 0.0624, ratio: 0.5891\n", " theta[0]: 0.9905 ± 0.0373\n", "T: 17, eps: 0.0554, ratio: 0.5872\n", " theta[0]: 0.9932 ± 0.0340\n", "T: 18, eps: 0.0498, ratio: 0.5938\n", " theta[0]: 0.9933 ± 0.0294\n", "T: 19, eps: 0.0436, ratio: 0.6011\n", " theta[0]: 0.9920 ± 0.0271\n", "T: 20, eps: 0.0385, ratio: 0.5685\n", " theta[0]: 0.9920 ± 0.0244\n", "T: 21, eps: 0.0337, ratio: 0.5599\n", " theta[0]: 0.9926 ± 0.0222\n", "T: 22, eps: 0.0299, ratio: 0.5498\n", " theta[0]: 0.9925 ± 0.0198\n", "T: 23, eps: 0.0261, ratio: 0.5549\n", " theta[0]: 0.9924 ± 0.0180\n", "T: 24, eps: 0.0232, ratio: 0.5258\n", " theta[0]: 0.9924 ± 0.0167\n", "T: 25, eps: 0.0205, ratio: 0.5056\n", " theta[0]: 0.9926 ± 0.0155\n", "T: 26, eps: 0.0181, ratio: 0.4697\n", " theta[0]: 0.9929 ± 0.0148\n", "T: 27, eps: 0.0159, ratio: 0.4240\n", " theta[0]: 0.9922 ± 0.0138\n", "T: 28, eps: 0.0141, ratio: 0.4017\n", " theta[0]: 0.9927 ± 0.0134\n", "T: 29, eps: 0.0125, ratio: 0.3784\n", " theta[0]: 0.9924 ± 0.0129\n", "T: 30, eps: 0.0112, ratio: 0.3383\n", " theta[0]: 0.9929 ± 0.0118\n", "T: 31, eps: 0.0101, ratio: 0.3437\n", " theta[0]: 0.9923 ± 0.0111\n", "T: 32, eps: 0.0100, ratio: 0.3518\n", " theta[0]: 0.9922 ± 0.0115\n", "T: 33, eps: 0.0100, ratio: 0.3458\n", " theta[0]: 0.9924 ± 0.0114\n", "T: 34, eps: 0.0100, ratio: 0.3600\n", " theta[0]: 0.9930 ± 0.0114\n", "T: 35, eps: 0.0100, ratio: 0.3515\n", " theta[0]: 0.9931 ± 0.0111\n", "T: 36, eps: 0.0100, ratio: 0.3637\n", " theta[0]: 0.9921 ± 0.0112\n", "T: 37, eps: 0.0100, ratio: 0.3531\n", " theta[0]: 0.9926 ± 0.0113\n" ] } ], "source": [ "T=38\n", "eps=0.5\n", "pools = sample(T, eps, sigma/sqrt(n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Postprocessing" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_pool(prob, samples):\n", " prob = np.array(prob).flatten()\n", " samples = np.vstack(samples)\n", " \n", " sns.distplot(prob)\n", " show()\n", " sns.distplot(samples, axlabel=r'$\\theta$')\n", " #savefig(\"1d_gauss_posterior.pdf\")\n", " show()\n", " sample_mean = np.mean(samples, axis=0)\n", " print(u\"mean: {0:>.4f} \\u00B1 {1:>.4f}\".format(float(np.mean(samples, axis=0)), float(np.std(samples, axis=0))))\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAn8AAAG4CAYAAAA0S4FqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmQm/l93/k3gAdXoxt9d7O72TyG5DwzQ86MNKOZ0eiW\n", "fEqKjmTlqJStyHa82uyWvbu1tlKbpNZxlDhRLFfsZLPrKFEsx7YiO2UrlmzJdiRLGknWMafm4nAe\n", "snn2jUYfQOPGA2D/eIBmk0OyD6Dx4Pi8qlhko9E/fvEQzefTv9NTqVQQERERke7gdbsAEREREWke\n", "hT8RERGRLqLwJyIiItJFFP5EREREuojCn4iIiEgXUfgTERER6SLGbp5kmuajwJ9YljVV/fgw8P8C\n", "bwGKwB8BH7csq1D9/CeBn6u2/3vAL1qWVW58+SIiIiKyF3fs+TNN02Oa5t8Dvgr4t33qc8A1YBJ4\n", "HfAI8MvVr/kF4D3A/cC9wJuBX2p45SIiIiKyZzsN+/5j4H8HfhXwAJimGQA2gV+1LKtgWdYy8Hng\n", "TdWv+bvAb1qWtVz93CeBnzmA2kVERERkj3Ya9v1ty7L+hWma76g9UB3afd9Nz3sf8Hz1zybwyrbP\n", "na8+JiIiIiIuu2P4syxr6U6fN03TA/xb4G7g71QfjgCZbU/LAF7TNAO1OYEiIiIi4o5dLfi4FdM0\n", 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for pool in pools])\n", "distances = np.array([pool.dists for pool in pools])\n", "\n", "plot_pool(distances[offset:], samples[offset:])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "var_vals = np.var((samples), axis=1)\n", "eps_values = np.array([pool.eps for pool in pools])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.01, 0.089999999999999997)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAApgAAAHKCAYAAACnnzuoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0FdXexvHvnPQQII0mXcqETgi9hCIgSBELCtyLFRWp\n", "Cij2jg0RFRRQVFS4UhWkqSC9R0INMFKkQ2ghJKQn5/3jYF6akHJSeT5rZUlm9uz9S7YuHvec2WPY\n", "7XZERERERJzFltcFiIiIiEjhooApIiIiIk6lgCkiIiIiTqWAKSIiIiJOpYApIiIiIk6lgCkiIiIi\n", 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"ax.set_ylabel(r\"var($\\theta$)\")\n", "ax.set_xlabel(r\"$\\epsilon$\")\n", "ax.legend(loc=\"best\")\n", "ax.invert_xaxis()\n", "ylim([-.01, None])\n", "#savefig(\"1d_gauss_variance.pdf\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x118367390>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAoQAAAHmCAYAAADqY5+DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPW9//HXmcmekJ1sLGELBxK2sIqsYrUgwnVrXVvX\n", "trd7a+tS26tee9Va21rvz97WunSxVEWrUhWkUpVFIktYAgEO+5KFhJB9X2Z+f0wSEwiQCUlOlvfz\n", "8ZhHZs6c73w/CUrefM/5fr+G2+1GRERERPovh90FiIiIiIi9FAhFRERE+jkFQhEREZF+ToFQRERE\n", "pJ9TIBQRERHp5xQIRURERPo5H7sLaGKa5nTgbcuyBp3j/ZuBx4EY4GPgbsuy8ruxRBEREZE+yfYR\n", "QtM0DdM07wL+Bfie45wJwO+BG4Fo4CTwp24rUkRERKQPsz0QAg8B3wP+BzDOcc6twDuWZW2xLKsa\n", 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= np.array([pool.ratio for pool in pools])\n", "plot(acc_ratios, label=\"ABC PMC\")\n", "plot(2 np.array(eps_values), label=\"analytic\")\n", "xticks(np.arange(len(eps_values)), [\"{0:>4.3f}\".format(eps) for eps in eps_values], rotation=70)\n", "#semilogy()\n", "ylabel(\"acceptance ratio\")\n", "xlabel(r\"$\\epsilon$\")\n", "legend(loc=\"best\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.special import erf\n", "def p_theta_eta(theta, eta):\n", " phi = lambda t: (1 + erf(t / np.sqrt(2))) / 2\n", " ybar = np.mean(y, axis=0)\n", " return 1. / (2 * eta) (phi((ybar - theta + eta) / (sigma / np.sqrt(n))) - phi((ybar - theta - eta) / (sigma / np.sqrt(n))) )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_ticks(ticks, num=7):\n", " return [float(\"{0:<.2f}\".format(tick)) for tick in np.linspace(ticks[0], ticks[-1], 7)]" ] }, { 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for i in range(36/2):\n", " subplot(5, 4, i+1)\n", " title(r\"Iteration: {0}, $\\epsilon$: {1:>4.3f}\".format(i2, eps_values[i2]), size=15)\n", " ax = sns.distplot(samples[i2], hist=False, axlabel=r'$\\theta$', hist_kws={\"rotation\":.3, \"fontsize\":30})\n", " ax.set_xlabel(r'$\\theta$', size=15)\n", " ax.locator_params(axis = 'x', nbins = nbins)\n", " ax.locator_params(axis = 'y', nbins = 5)\n", " min_val, max_val = ax.get_xlim()\n", " x_grid = np.linspace(min_val, max_val, 1000)\n", " plot(x_grid, p_theta_eta(x_grid, eps_values[i2]), \"--\")\n", " if i2>=22:\n", " xlim([0.96, 1.04])\n", " \n", " if i2<=14:\n", " ylim([None, 9])\n", " \n", " rv = norm(loc=np.mean(y), scale=1/np.sqrt(n))\n", " plot(x_grid, rv.pdf(x_grid), \":\", color=sns.xkcd_rgb[\"pale red\"])\n", " fill(x_grid, rv.pdf(x_grid), color=sns.xkcd_rgb[\"pale red\"], alpha=0.2)\n", " tick_params(axis='both', which='major', labelsize=15)\n", " #i = 37\n", " #subplot(5, 4, 20)\n", " #title(r\"Iteration: {0}, $\\epsilon$: {1:>4.3f}\".format(i, eps_values[i]))\n", " #ax = sns.distplot(samples[i], hist=False, axlabel=r'$\\theta$')\n", " #ax.locator_params(axis = 'x', nbins = nbins)\n", " #min_val, max_val = ax.get_xlim()\n", " #x_grid = np.linspace(min_val, max_val, 1000)\n", " #plot(x_grid, p_theta_eta(x_grid, eps_values[i]))\n", " \n", " ax = subplot(5, 4, i+2)\n", " plot(1, label=\"ABC PMC\")\n", " plot(1, \"--\", label=\"ABC analytic\")\n", " plot(1, \":\", label=\"Bayesian\")\n", " legend(loc=2)\n", " ax.set_axis_off()\n", " subplots_adjust(hspace = 0.40)\n", " \n", " #savefig(\"1d_gauss_posterior_evolution.pdf\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.stats import gaussian_kde" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA3MAAASNCAYAAADpdgedAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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[], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
maxentile/msm-learn	notebooks/Cython.ipynb	1	32045	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# following: http://nbviewer.ipython.org/url/jakevdp.github.com/downloads/notebooks/memview_bench.ipynb" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext cythonmagic" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import numpy.random as npr\n", "from numpy.linalg import det" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(1000,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "## 0. Python-only version\n", "\n", "def euclidean(x1,x2):\n", " return np.sqrt(sum((x1-x2)*2))\n", "\n", "def pairwise_v1(X,metric=euclidean):\n", " n,dim = X.shape\n", " D = np.zeros((n,n))\n", " \n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = metric(X[i],X[j])\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 8.13 s per loop\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# a function pointer to a metric\n", "ctypedef double (metric_ptr)(np.ndarray,np.ndarray)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean(np.ndarray[double,ndim=1,mode='c'] x1,\n", " np.ndarray[double,ndim=1,mode='c'] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmptmp\n", " return sqrt(d)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v2(np.ndarray[double, ndim=2, mode='c'] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n\n", " n = X.shape[0]\n", "\n", " cdef np.ndarray[double, ndim=2, mode='c'] D = np.empty((n,\n", " n))\n", " for i in range(n):\n", " for j in range(n):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 8.15 s per loop\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v2(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 2.26 s per loop\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "ctypedef double (metric_ptr)(double[::1], double[::1])\n", "\n", "cdef double euclidean(double[::1] x1,\n", " double[::1] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmptmp\n", " return sqrt(d)\n", "\n", "\n", "def pairwise_v3(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples\n", " n_samples = X.shape[0]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v3(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 44.7 ms per loop\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# define a function pointer to a metric\n", "ctypedef double (metric_ptr)(double, double, int)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean_distance(double* x1,\n", " double* x2,\n", " int N):\n", " cdef double tmp, d\n", " cdef np.intp_t i\n", "\n", " d = 0\n", "\n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d += tmp * tmp\n", "\n", " return sqrt(d)\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v4(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean_distance\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples, n_dim\n", " n_samples = X.shape[0]\n", " n_dim = X.shape[1]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0]\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " Dptr[i * n_samples + j] = dist_func(Xptr + i * n_dim,\n", " Xptr + j * n_dim,\n", " n_dim)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v4(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.68 ms per loop\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.spatial.distance import pdist" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pdist(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 2.5 ms per loop\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(500,100,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy.linalg import det\n", "BC = lambda X,Y: det(X.T.dot(Y))/ np.sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "BC(X[0],X[10])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "0.00097428727055857804" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "def pairwise_bc_v1(X):\n", " bc = np.zeros((len(X),len(X)))\n", " for i in range(len(X)):\n", " for j in range(len(X)):\n", " bc[i,j] = BC(X[i],X[j])\n", " return bc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 13 s per loop\n" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "ctypedef double (metric_ptr)(double[::2], double[::2])\n", "\n", "cdef double euclidean(double[::2] x1,\n", " double[::2] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmptmp\n", " return sqrt(d)\n", "\n", "\n", "def pairwise_v3(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples\n", " n_samples = X.shape[0]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def myloop(X):\n", " s=0\n", " for i in range(X.shape[0]):\n", " for j in range(X.shape[1]):\n", " for k in range(X.shape[2]):\n", " s+=X[i,j,k]\n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def myloop_c(np.ndarray[np.double_t,ndim=3] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s=0\n", " for i in range(X.shape[0]):\n", " for j in range(X.shape[1]):\n", " for k in range(X.shape[2]):\n", " s+=X[i,j,k]\n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit myloop(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 110 ms per loop\n" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit myloop_c(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 239 \u00b5s per loop\n" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def mymat_c(np.ndarray[np.double_t,ndim=3] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s = 0\n", " \n", " \n", " \n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "mymat_c(X)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "0.0" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def mymat_c(np.ndarray[np.double_t,ndim=2] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s = 0\n", " \n", " s += det(X)\n", " \n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "X[0].shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "(100, 3)" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "sq = npr.randn(100,100)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "mymat_c(sq)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ "1.5574946108189126e+76" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def binet_cauchy_c(np.ndarray[np.double_t,ndim=2] X,\n", " np.ndarray[np.double_t,ndim=2] Y):\n", " \n", " return det(X.T.dot(Y))/ sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "binet_cauchy_p = lambda X,Y: det(X.T.dot(Y))/ np.sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_p(sq,sq)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 662 \u00b5s per loop\n" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_c(sq,sq)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 616 \u00b5s per loop\n" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef double binet_cauchy_c(np.ndarray[np.double_t,ndim=2] X,\n", " np.ndarray[np.double_t,ndim=2] Y):\n", " \n", " return det(X.T.dot(Y))/ sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))\n", "\n", "def pairwise_bc_c(np.ndarray[np.double_t,ndim=3] X):\n", " n_samples = X.shape[0]\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", " cdef int i,j\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " return D\n", "\n", "# pointers\n", "def pairwise_bc_pointers(double[:,:,::1] X):\n", " cdef int n_samples,mat_size\n", " n_samples = X.shape[0]\n", " mat_size = X.shape[1]X.shape[2]\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", " cdef int i,j\n", " cdef double Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[in_samples+j] = binet_cauchy_c(Xptr+imat_size,\n", " Xptr+jmat_size)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[in_samples+j] = pairwise_bc(Xptr+imat_size,\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:34:45: undeclared name not builtin: pairwise_bc\n", "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[in_samples+j] = pairwise_bc(Xptr+imat_size,\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:34:50: Cannot convert 'double ' to Python object\n", "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[in_samples+j] = pairwise_bc(Xptr+imat_size,\n", " Xptr+jmat_size)\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:35:50: Cannot convert 'double ' to Python object\n" ] } ], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_c(X[0],X[-1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 50.6 \u00b5s per loop\n" ] } ], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 13.9 s per loop\n" ] } ], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "def pairwise_bc_p(X):\n", " n_samples = X.shape[0]\n", " D = np.empty((n_samples, n_samples))\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc_p(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 14.2 s per loop\n" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 82, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double distance(double[:,::1] X,\n", " np.intp_t i1,\n", " np.intp_t i2):\n", " cdef double tmp,d\n", " cdef np.intp_t j\n", " d = 0\n", " \n", " for j in range(X.shape[1]):\n", " tmp = X[i1,j] - X[i2,j]\n", " d+= tmptmp\n", " return sqrt(d)\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_no_slice(double[:,::1] X not None):\n", " cdef np.intp_t i,j,n,dim\n", " n=X.shape[0]\n", " dim = X.shape[1]\n", " cdef double[:,::1] D = np.empty((n,n))\n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = distance(X,i,j)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(1000,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 106 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_no_slice(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.83 ms per loop\n" ] } ], "prompt_number": 107 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pdist(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 3.25 ms per loop\n" ] } ], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# define a function pointer to a metric\n", "ctypedef double (metric_ptr)(double[:, ::1], np.intp_t, np.intp_t)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean_distance(double[:, ::1] X,\n", " np.intp_t i1, np.intp_t i2):\n", " cdef double tmp, d\n", " cdef np.intp_t j\n", "\n", " d = 0\n", "\n", " for j in range(X.shape[1]):\n", " tmp = X[i1, j] - X[i2, j]\n", " d += tmp * tmp\n", "\n", " return sqrt(d)\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v5(double[:, ::1] X not None):\n", " cdef np.intp_t i, j, n_samples, n_dim\n", " n_samples = X.shape[0]\n", " n_dim = X.shape[1]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = euclidean_distance(X, i, j)\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v5(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.77 ms per loop\n" ] } ], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef double bc(double[:,:,:] X,\n", " np.intp_t i1,\n", " np.intp_t i2):\n", " return sum(X[i1])\n", " #return det(X[i1].T.dot(X[i2]))/ sqrt(det(X[i1].T.dot(X[i1])) * det(X[i2].T.dot(X[i2])))\n", "\n", "def pairwise_bc_no_slice(double[:,:,:] X not None):\n", " cdef np.intp_t i,j,n\n", " n=X.shape[0]\n", " cdef np.ndarray D = np.zeros((n,n),dtype=np.double)\n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = bc(X,i,j)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(500,10,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "<MemoryView of 'array' at 0x10d169300>" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
texib/pixnet_hackathon_2015	doc2vec-IMDB.ipynb	1	50042	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# gensim doc2vec & IMDB sentiment dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: section on introduction & motivation\n", "\n", "TODO: prerequisites + dependencies (statsmodels, patsy, ?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fetch and prep exactly as in Mikolov's go.sh shell script. (Note this cell tests for existence of required files, so steps won't repeat once the final summary file (`aclImdb/alldata-id.txt`) is available alongside this notebook.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "rm: temp: No such file or directory\n" ] } ], "source": [ "%%bash\n", "# adapted from Mikolov's example go.sh script: \n", "if [ ! -f \"aclImdb/alldata-id.txt\" ]\n", "then\n", " if [ ! -d \"aclImdb\" ] \n", " then\n", " if [ ! -f \"aclImdb_v1.tar.gz\" ]\n", " then\n", " wget --quiet http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n", " fi\n", " tar xf aclImdb_v1.tar.gz\n", " fi\n", " \n", " #this function will convert text to lowercase and will disconnect punctuation and special symbols from words\n", " function normalize_text {\n", " awk '{print tolower($0);}' < $1 \| sed -e 's/\\./ \\. /g' -e 's/<br \\/>/ /g' -e 's/\"/ \" /g' \\\n", " -e 's/,/ , /g' -e 's/(/ ( /g' -e 's/)/ ) /g' -e 's/\\!/ \\! /g' -e 's/\\?/ \\? /g' \\\n", " -e 's/\\;/ \\; /g' -e 's/\\:/ \\: /g' > $1-norm\n", " }\n", "\n", " export LC_ALL=C\n", " for j in train/pos train/neg test/pos test/neg train/unsup; do\n", " rm temp\n", " for i in `ls aclImdb/$j`; do cat aclImdb/$j/$i >> temp; awk 'BEGIN{print;}' >> temp; done\n", " normalize_text temp\n", " mv temp-norm aclImdb/$j/norm.txt\n", " done\n", " mv aclImdb/train/pos/norm.txt aclImdb/train-pos.txt\n", " mv aclImdb/train/neg/norm.txt aclImdb/train-neg.txt\n", " mv aclImdb/test/pos/norm.txt aclImdb/test-pos.txt\n", " mv aclImdb/test/neg/norm.txt aclImdb/test-neg.txt\n", " mv aclImdb/train/unsup/norm.txt aclImdb/train-unsup.txt\n", "\n", " cat aclImdb/train-pos.txt aclImdb/train-neg.txt aclImdb/test-pos.txt aclImdb/test-neg.txt aclImdb/train-unsup.txt > aclImdb/alldata.txt\n", " awk 'BEGIN{a=0;}{print \"_\" a \" \" $0; a++;}' < aclImdb/alldata.txt > aclImdb/alldata-id.txt\n", "fi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path\n", "assert os.path.isfile(\"aclImdb/alldata-id.txt\"), \"alldata-id.txt unavailable\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is small enough to be read into memory. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[4]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[line_no//25000]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "201306 docs: 70000 train-sentiment, 70000 test-sentiment\n" ] } ], "source": [ "import gensim\n", "# from gensim.models.doc2vec import \n", "from collections import namedtuple\n", "\n", "SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment')\n", "\n", "alldocs = [] # will hold all docs in original order\n", "with open('./data/new_parsed_no_spam.txt') as alldata:\n", " for line_no, line in enumerate(alldata):\n", " tokens = line.split()\n", " words = tokens[1:]\n", " tags = [line_no] # `tags = [tokens[0]]` would also work at extra memory cost\n", " split = ['train','test','extra','extra'][line_no//70000] # 25k train, 25k test, 25k extra\n", " sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//70000] # [12.5K pos, 12.5K neg]2 then unknown\n", " alldocs.append(SentimentDocument(words, tags, split, sentiment))\n", "\n", "train_docs = [doc for doc in alldocs if doc.split == 'train']\n", "test_docs = [doc for doc in alldocs if doc.split == 'test']\n", "doc_list = alldocs[:] # for reshuffling per pass\n", "\n", "print('%d docs: %d train-sentiment, %d test-sentiment' % (len(doc_list), len(train_docs), len(test_docs)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set-up Doc2Vec Training & Evaluation Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Approximating experiment of Le & Mikolov [\"Distributed Representations of Sentences and Documents\"](http://cs.stanford.edu/~quocle/paragraph_vector.pdf), also with guidance from Mikolov's [example go.sh](https://groups.google.com/d/msg/word2vec-toolkit/Q49FIrNOQRo/J6KG8mUj45sJ):\n", "\n", "`./word2vec -train ../alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1`\n", "\n", "Parameter choices below vary:\n", "\n", "* 100-dimensional vectors, as the 400d vectors of the paper don't seem to offer much benefit on this task\n", "* similarly, frequent word subsampling seems to decrease sentiment-prediction accuracy, so it's left out\n", "* `cbow=0` means skip-gram which is equivalent to the paper's 'PV-DBOW' mode, matched in gensim with `dm=0`\n", "* added to that DBOW model are two DM models, one which averages context vectors (`dm_mean`) and one which concatenates them (`dm_concat`, resulting in a much larger, slower, more data-hungry model)\n", "* a `min_count=2` saves quite a bit of model memory, discarding only words that appear in a single doc (and are thus no more expressive than the unique-to-each doc vectors themselves)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec(dm/c,d100,n5,w5,mc2,t2)\n", "Doc2Vec(dbow,d100,n5,mc2,t2)\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t2)\n" ] } ], "source": [ "from gensim.models import Doc2Vec\n", "import gensim.models.doc2vec\n", "from collections import OrderedDict\n", "import multiprocessing\n", "\n", "cores = multiprocessing.cpu_count()\n", "assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow otherwise\"\n", "\n", "simple_models = [\n", " # PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word total window size\n", " Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores),\n", " # PV-DBOW \n", " Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores),\n", " # PV-DM w/average\n", " Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores),\n", "]\n", "\n", "# speed setup by sharing results of 1st model's vocabulary scan\n", "simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so it serves as template\n", "print(simple_models[0])\n", "for model in simple_models[1:]:\n", " model.reset_from(simple_models[0])\n", " print(model)\n", "\n", "models_by_name = OrderedDict((str(model), model) for model in simple_models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following the paper, we also evaluate models in pairs. These wrappers return the concatenation of the vectors from each model. (Only the singular models are trained.)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.test.test_doc2vec import ConcatenatedDoc2Vec\n", "models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]])\n", "models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive Evaluation Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper methods for evaluating error rate." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import statsmodels.api as sm\n", "from random import sample\n", "\n", "# for timing\n", "from contextlib import contextmanager\n", "from timeit import default_timer\n", "import time \n", "\n", "@contextmanager\n", "def elapsed_timer():\n", " start = default_timer()\n", " elapser = lambda: default_timer() - start\n", " yield lambda: elapser()\n", " end = default_timer()\n", " elapser = lambda: end-start\n", " \n", "def logistic_predictor_from_data(train_targets, train_regressors):\n", " logit = sm.Logit(train_targets, train_regressors)\n", " predictor = logit.fit(disp=0)\n", " #print(predictor.summary())\n", " return predictor\n", "\n", "def error_rate_for_model(test_model, train_set, test_set, infer=False, infer_steps=3, infer_alpha=0.1, infer_subsample=0.1):\n", " \"\"\"Report error rate on test_doc sentiments, using supplied model and train_docs\"\"\"\n", "\n", " train_targets, train_regressors = zip([(doc.sentiment, test_model.docvecs[doc.tags[0]]) for doc in train_set])\n", " train_regressors = sm.add_constant(train_regressors)\n", " predictor = logistic_predictor_from_data(train_targets, train_regressors)\n", "\n", " test_data = test_set\n", " if infer:\n", " if infer_subsample < 1.0:\n", " test_data = sample(test_data, int(infer_subsample len(test_data)))\n", " test_regressors = [test_model.infer_vector(doc.words, steps=infer_steps, alpha=infer_alpha) for doc in test_data]\n", " else:\n", " test_regressors = [test_model.docvecs[doc.tags[0]] for doc in test_docs]\n", " test_regressors = sm.add_constant(test_regressors)\n", " \n", " # predict & evaluate\n", " test_predictions = predictor.predict(test_regressors)\n", " corrects = sum(np.rint(test_predictions) == [doc.sentiment for doc in test_data])\n", " errors = len(test_predictions) - corrects\n", " error_rate = float(errors) / len(test_predictions)\n", " return (error_rate, errors, len(test_predictions), predictor)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bulk Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using explicit multiple-pass, alpha-reduction approach as sketched in [gensim doc2vec blog post](http://radimrehurek.com/2014/12/doc2vec-tutorial/) – with added shuffling of corpus on each pass.\n", "\n", "Note that vector training is occurring on all documents of the dataset, which includes all TRAIN/TEST/DEV docs.\n", "\n", "Evaluation of each model's sentiment-predictive power is repeated after each pass, as an error rate (lower is better), to see the rates-of-relative-improvement. The base numbers reuse the TRAIN and TEST vectors stored in the models for the logistic regression, while the _inferred_ results use newly-inferred TEST vectors. \n", "\n", "(On a 4-core 2.6Ghz Intel Core i7, these 20 passes training and evaluating 3 main models takes about an hour.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\n", "best_error = defaultdict(lambda :1.0) # to selectively-print only best errors achieved" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "START 2015-07-25 01:21:54.710897\n" ] }, { "ename": "PerfectSeparationError", "evalue": "Perfect separation detected, results not available", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mPerfectSeparationError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-6-ae19afedce59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0meval_duration\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0melapsed_timer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0meval_elapsed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_rate_for_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_docs\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mtrain_regressors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mpredictor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[1;31m#print(predictor.summary())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredictor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, kwargs)\u001b[0m\n\u001b[0;32m 1374\u001b[0m bnryfit = super(Logit, self).fit(start_params=start_params,\n\u001b[0;32m 1375\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m disp=disp, callback=callback, kwargs)\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mdiscretefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogitResults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbnryfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, kwargs)\u001b[0m\n\u001b[0;32m 201\u001b[0m mlefit = super(DiscreteModel, self).fit(start_params=start_params,\n\u001b[0;32m 202\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 203\u001b[1;33m disp=disp, callback=callback, kwargs)\n\u001b[0m\u001b[0;32m 204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmlefit\u001b[0m \u001b[1;31m# up to subclasses to wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, kwargs)\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[0mretall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretall\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 425\u001b[1;33m full_output=full_output)\n\u001b[0m\u001b[0;32m 426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[1;31m#NOTE: this is for fit_regularized and should be generalized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/optimizer.pyc\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[0mretall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretall\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 184\u001b[1;33m hess=hessian)\n\u001b[0m\u001b[0;32m 185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;31m# this is stupid TODO: just change this to something sane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/optimizer.pyc\u001b[0m in \u001b[0;36m_fit_newton\u001b[1;34m(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess, ridge_factor)\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 249\u001b[0m \u001b[0miterations\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[0mfval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m\u001b[0m\u001b[0mfargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this is the negative likelihood\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36m_check_perfect_pred\u001b[1;34m(self, params, args)\u001b[0m\n\u001b[0;32m 184\u001b[0m np.allclose(fittedvalues - endog, 0)):\n\u001b[0;32m 185\u001b[0m \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Perfect separation detected, results not available\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mPerfectSeparationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 188\u001b[0m def fit(self, start_params=None, method='newton', maxiter=35,\n", "\u001b[1;31mPerfectSeparationError\u001b[0m: Perfect separation detected, results not available" ] } ], "source": [ "from random import shuffle\n", "import datetime\n", "\n", "alpha, min_alpha, passes = (0.025, 0.001, 20)\n", "alpha_delta = (alpha - min_alpha) / passes\n", "\n", "print(\"START %s\" % datetime.datetime.now())\n", "\n", "for epoch in range(passes):\n", " shuffle(doc_list) # shuffling gets best results\n", " \n", " for name, train_model in models_by_name.items():\n", " # train\n", " duration = 'na'\n", " train_model.alpha, train_model.min_alpha = alpha, alpha\n", " with elapsed_timer() as elapsed:\n", " train_model.train(doc_list)\n", " duration = '%.1f' % elapsed()\n", " \n", " # evaluate\n", " eval_duration = ''\n", " with elapsed_timer() as eval_elapsed:\n", " err, err_count, test_count, predictor = error_rate_for_model(train_model, train_docs, test_docs)\n", " eval_duration = '%.1f' % eval_elapsed()\n", " best_indicator = ' '\n", " if err <= best_error[name]:\n", " best_error[name] = err\n", " best_indicator = '' \n", " print(\"%s%f : %i passes : %s %ss %ss\" % (best_indicator, err, epoch + 1, name, duration, eval_duration))\n", "\n", " if ((epoch + 1) % 5) == 0 or epoch == 0:\n", " eval_duration = ''\n", " with elapsed_timer() as eval_elapsed:\n", " infer_err, err_count, test_count, predictor = error_rate_for_model(train_model, train_docs, test_docs, infer=True)\n", " eval_duration = '%.1f' % eval_elapsed()\n", " best_indicator = ' '\n", " if infer_err < best_error[name + '_inferred']:\n", " best_error[name + '_inferred'] = infer_err\n", " best_indicator = ''\n", " print(\"%s%f : %i passes : %s %ss %ss\" % (best_indicator, infer_err, epoch + 1, name + '_inferred', duration, eval_duration))\n", "\n", " print('completed pass %i at alpha %f' % (epoch + 1, alpha))\n", " alpha -= alpha_delta\n", " \n", "print(\"END %s\" % str(datetime.datetime.now()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Achieved Sentiment-Prediction Accuracy" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.094800 dbow+dmm_inferred\n", "0.099920 dbow+dmm\n", "0.101000 Doc2Vec(dbow,d100,n5,mc2,t8)\n", "0.101320 dbow+dmc\n", "0.109600 dbow+dmc_inferred\n", "0.111600 Doc2Vec(dbow,d100,n5,mc2,t8)_inferred\n", "0.158760 Doc2Vec(dm/m,d100,n5,w10,mc2,t8)\n", "0.184000 Doc2Vec(dm/m,d100,n5,w10,mc2,t8)_inferred\n", "0.215160 Doc2Vec(dm/c,d100,n5,w5,mc2,t8)\n", "0.232400 Doc2Vec(dm/c,d100,n5,w5,mc2,t8)_inferred\n" ] } ], "source": [ "# print best error rates achieved\n", "for rate, name in sorted((rate, name) for name, rate in best_error.items()):\n", " print(\"%f %s\" % (rate, name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In my testing, unlike the paper's report, DBOW performs best. Concatenating vectors from different models only offers a small predictive improvement. The best results I've seen are still just under 10% error rate, still a ways from the paper's 7.42%.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examining Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are inferred vectors close to the precalculated ones?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "for doc 25430...\n", "Doc2Vec(dm/c,d100,n5,w5,mc2,t8):\n", " [(25430, 0.6583491563796997), (27314, 0.4142411947250366), (16479, 0.40846431255340576)]\n", "Doc2Vec(dbow,d100,n5,mc2,t8):\n", " [(25430, 0.9325973987579346), (49281, 0.5766637921333313), (79679, 0.5634804964065552)]\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t8):\n", " [(25430, 0.7970066666603088), (97818, 0.6925815343856812), (230, 0.690807580947876)]\n" ] } ], "source": [ "doc_id = np.random.randint(simple_models[0].docvecs.count) # pick random doc; re-run cell for more examples\n", "print('for doc %d...' % doc_id)\n", "for model in simple_models:\n", " inferred_docvec = model.infer_vector(alldocs[doc_id].words)\n", " print('%s:\\n %s' % (model, model.docvecs.most_similar([inferred_docvec], topn=3)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Yes, here the stored vector from 20 epochs of training is usually one of the closest to a freshly-inferred vector for the same words. Note the defaults for inference are very abbreviated – just 3 steps starting at a high alpha – and likely need tuning for other applications.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do close documents seem more related than distant ones?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TARGET (72927): «this is one of the best films of this year . for a year that was fueled by controversy and crap , it was nice to finally see a film that had a true heart to it . from the opening scene to the end , i was so moved by the love that will smith has for his son . basically , if you see this movie and walk out of it feeling nothing , there is something that is very wrong with you . loved this movie , it's the perfect movie to end the year with . the best part was after the movie , my friends and i all got up and realized that this movie had actually made the four of us tear up ! it's an amazing film and if will smith doesn't get at least an oscar nom , then the oscars will just suck . in fact will smith should actually just win an oscar for this role . ! ! ! i loved this movie ! ! ! ! everybody needs to see especially the people in this world that take everything for granted , watch this movie , it will change you !»\n", "\n", "SIMILAR/DISSIMILAR DOCS PER MODEL Doc2Vec(dm/m,d100,n5,w10,mc2,t8):\n", "\n", "MOST (2046, 0.7372332215309143): «i thought this movie would be dumb , but i really liked it . people i know hate it because spirit was the only horse that talked . well , so what ? the songs were good , and the horses didn't need to talk to seem human . i wouldn't care to own the movie , and i would love to see it again . 8/10»\n", "\n", "MEDIAN (6999, 0.4129640758037567): «okay , the recent history of star trek has not been good . the next generation faded in its last few seasons , ds9 boldly stayed where no one had stayed before , and voyager started very bad and never really lived up to its promise . so , when they announced a new star trek series , i did not have high expectations . and , the first episode , broken bow , did have some problems . but , overall it was solid trek material and a good romp . i'll get the nits out of the way first . the opening theme is dull and i don't look forward to sitting through it regularly , but that's what remotes are for . what was really bad was the completely gratuitous lotion rubbing scene that just about drove my wife out of the room . they need to cut that nonsense out . but , the plot was strong and moved along well . the characters , though still new , seem to be well rounded and not always what you would expect . the vulcans are clearly being presented very differently than before , with a slightly ominous theme . i particularly liked the linguist , who is the first star trek character to not be able to stand proud in the face of death , but rather has to deal with her phobias and fears . they seemed to stay true to trek lore , something that has been a significant problem in past series , though they have plenty of time to bring us things like shooting through shields , the instant invention of technology that can fix anything , and the inevitable plethora of time-travel stories . anyone want to start a pool on how long before the borg show up ? all in all , the series has enormous potential . they are seeing the universe with fresh eyes . we have the chance to learn how things got the way they were in the later series . how did the klingons go from just insulting to war ? how did we meet the romulans ? how did the federation form and just who put earth in charge . why is the prime directive so important ? if they address these things rather than spitting out time travel episodes , this will be an interesting series . my favorite line : zephram cochran saying \" where no man has gone before \" ( not \" no one \" )»\n", "\n", "LEAST (16617, 0.015464222989976406): «i saw this movie during a tolkien-themed interim class during my sophomore year of college . i was seated unfortunately close to the screen and my professor chose me to serve as a whipping boy- everyone else was laughing , but they weren't within constant eyesight . let's get it out of the way : the peter jackson 'lord of the rings' films do owe something to the bakshi film . in jackson's version of the fellowship of the ring , for instance , the scene in which the black riders assault the empty inn beds is almost a complete carbon copy of the scene in bakshi's film , shot by shot . you could call this plagiarism or homage , depending on your agenda . i'm sure the similarities don't stop there . i'm not going to do any research to find out what they are , because that would imply i have some mote of respect for this film . i'm sure others have outlined the similarities- look around . this movie is a complete train wreck in every sense of the metaphor , and many , many people died in the accident . i've decided to list what i can remember in a more or less chronological fashion- if i've left out anything else that offended me it's because i'm completely overwhelmed , confronted with a wealth of failure ( and , at high points , mediocrity ) . due to heavy use of rotoscoping , gandalf is no longer a gentle , wise wizard but a wildly flailing prophet of doom ( whose hat inexplicably changes color once or twice during the course of the film ) . saruman the white is sometimes referred to as 'aruman' during the film , without explanation . he wears purple and red for some mysterious reason . sam is flat out hideous . the portrayal of his friendship with frodo is strangely childlike and unsatisfying . yes , hobbits are small like children , but they are not children . merry and pippin are never introduced--they simply appear during a scene change with a one-sentence explanation . the film is filled with sloppy editing like this . frodo , sam , pippin and merry are singing merrily as they skip through along the road . one of the hobbits procures a lute at least twice as large as he is from behind his back--which was not visible before--and begins strumming in typical fantasy bard fashion as they all break into \" la-la-la \" s . awful . aragorn , apparently , is a native american dressed in an extremely stereotypical fantasy tunic ( no pants ) , complete with huge , square pilgrim belt buckle . he is arguably the worst swordsman in the entire movie--oftentimes he gets one wobbly swing in before being knocked flat on his ass . the black riders appear more like lepers than menacing instruments of evil . they limp everywhere they go at a painfully slow pace . this is disturbing to be sure , but not frightening . the scene before the black riders attempt to cross the ford of bruinen ( in which they stare at frodo , who is on the other side on horseback ) goes on forever , during which time the riders rear their horses in a vaguely threatening manner and . . . do nothing else . the scene was probably intended to illustrate frodo's hallucinatory decline as he succumbs to his wound . it turns out to be more plodding than anything else . gimli the dwarf is just as tall as legolas the elf . he's a dwarf . there is simply no excuse for that . he also looks like a bastardized david the gnome . it's a crude but accurate description . boromir appears to have pilfered elmer fudd's golden viking armor from that bugs bunny opera episode . he looks ridiculous . despite the similarity to tolkien's illustration , the balrog is howl inducing and the least-threatening villain in the entire film . it looks like someone wearing pink bedroom slippers , and it's barely taller than gandalf . \" purists \" may prefer this balrog , but i'll take jackson's version any day . the battle scenes are awkward and embarrassing . almost none of the characters display any level of competency with their armaments . i'm not asking for action-packed scenes like those in jackson's film , but they are supposed to be fighting . treebeard makes a very short appearance , and i was sorry he bothered to show up at all . watch the film , you'll see what i mean . alright , now for the good parts of the film . some of the voice acting is pretty good . it isn't that aragorn sounds bad , he just looks kind of like the jolly green giant . galadriel is somewhat interesting in this portrayal ; like tom bombadil , she seems immune to the ring's powers of temptation , and her voice actress isn't horrible either . boromir's death isn't as heart wrenching as in jackson's portrayal of the same scene , but it's still appropriately dramatic ( and more true to his death in the book , though i don't believe jackson made a mistake shooting it the way he did ) . as my professor pointed out ( between whispered threats ) , the orcs ( mainly at helm's deep , if i'm correct ) resemble the war-ravaged corpses of soldiers , a political statement that works pretty well if you realize what's being attempted . while this isn't really a positive point about the film , bakshi can't be blamed for the majority of the failures in this movie , or so i've been told--the project was on a tight budget , and late in its production he lost creative control to some of the higher-ups ( who i'm sure hadn't read the books ) . let me be clear : i respect bakshi for even attempting something of this magnitude . i simply have a hard time believing he was happy with the final product . overall , i cannot in any way recommend this blasphemous adaptation of tolkien's classic trilogy even for laughs , unless you've already read the books and have your own visualizations of the characters , places and events . i'm sure somebody , somewhere , will pick a copy of this up in confusion ; if you do , keep an open mind and glean what good you can from it .»\n", "\n" ] } ], "source": [ "import random\n", "\n", "doc_id = np.random.randint(simple_models[0].docvecs.count) # pick random doc, re-run cell for more examples\n", "model = random.choice(simple_models) # and a random model\n", "sims = model.docvecs.most_similar(doc_id, topn=model.docvecs.count) # get all* similar documents\n", "print(u'TARGET (%d): «%s»\\n' % (doc_id, ' '.join(alldocs[doc_id].words)))\n", "print(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\\n' % model)\n", "for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n", " print(u'%s %s: «%s»\\n' % (label, sims[index], ' '.join(alldocs[sims[index][0]].words)))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Somewhat, in terms of reviewer tone, movie genre, etc... the MOST cosine-similar docs usually seem more like the TARGET than the MEDIAN or LEAST.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do the word vectors show useful similarities?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "word_models = simple_models[:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "most similar words for 'comedy/drama' (38 occurences)\n" ] }, { "data": { "text/html": [ "<table><tr><th>Doc2Vec(dm/c,d100,n5,w5,mc2,t8)</th><th>Doc2Vec(dbow,d100,n5,mc2,t8)</th><th>Doc2Vec(dm/m,d100,n5,w10,mc2,t8)</th></tr><tr><td>[('comedy', 0.7255545258522034),<br>\n", "('thriller', 0.6946465969085693),<br>\n", "('drama', 0.6763534545898438),<br>\n", "('romance', 0.6251884698867798),<br>\n", "('dramedy', 0.6217159032821655),<br>\n", "('melodrama', 0.6156137585639954),<br>\n", "('adventure', 0.6091135740280151),<br>\n", "('farce', 0.6034293174743652),<br>\n", "('chiller', 0.5948368906974792),<br>\n", "('romantic-comedy', 0.5876704454421997),<br>\n", "('fantasy', 0.5863304138183594),<br>\n", "('mystery/comedy', 0.577541708946228),<br>\n", "('whodunit', 0.572147011756897),<br>\n", "('biopic', 0.5679721832275391),<br>\n", "('thriller/drama', 0.5630226731300354),<br>\n", "('sitcom', 0.5574496984481812),<br>\n", "('slash-fest', 0.5573585033416748),<br>\n", "('mystery', 0.5542301535606384),<br>\n", "('potboiler', 0.5519827604293823),<br>\n", "('mockumentary', 0.5490710139274597)]</td><td>[('1000%', 0.42290645837783813),<br>\n", "(\"gymnast's\", 0.4180164337158203),<br>\n", "('hollywoodland', 0.3898555636405945),<br>\n", "('cultures', 0.3857914209365845),<br>\n", "('hooda', 0.3851744532585144),<br>\n", "('cites', 0.38047513365745544),<br>\n", "(\"78's\", 0.3792475461959839),<br>\n", "(\"dormael's\", 0.3775535225868225),<br>\n", "('jokester', 0.3725704252719879),<br>\n", "('impelled', 0.36853262782096863),<br>\n", "('lia', 0.3684236407279968),<br>\n", "('snivelling', 0.3683513104915619),<br>\n", "('astral', 0.36715900897979736),<br>\n", "('euro-exploitation', 0.35853487253189087),<br>\n", "(\"serra's\", 0.3578598201274872),<br>\n", "('down-on-their-luck', 0.3576606214046478),<br>\n", "('rowles', 0.3567575514316559),<br>\n", "('romantica', 0.3549702763557434),<br>\n", "('bonham-carter', 0.354231059551239),<br>\n", "('1877', 0.3541453182697296)]</td><td>[('comedy-drama', 0.6274900436401367),<br>\n", "('comedy', 0.5986765623092651),<br>\n", "('thriller', 0.5765297412872314),<br>\n", "('road-movie', 0.5615973472595215),<br>\n", "('dramedy', 0.5580120086669922),<br>\n", "('time-killer', 0.5497636795043945),<br>\n", "('potboiler', 0.5456510782241821),<br>\n", "('comedy/', 0.5439876317977905),<br>\n", "('actioner', 0.5423712134361267),<br>\n", "('diversion', 0.541743278503418),<br>\n", "('romcom', 0.5402226448059082),<br>\n", "('rom-com', 0.5358527302742004),<br>\n", "('drama', 0.5320745706558228),<br>\n", "('chiller', 0.5229591727256775),<br>\n", "('romp', 0.5228806734085083),<br>\n", "('horror/comedy', 0.5219299793243408),<br>\n", "('weeper', 0.5195824503898621),<br>\n", "('mockumentary', 0.5149033069610596),<br>\n", "('camp-fest', 0.5122634768486023),<br>\n", "('mystery/comedy', 0.5020694732666016)]</td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML at 0x1535b84d0>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "from IPython.display import HTML\n", "# pick a random word with a suitable number of occurences\n", "while True:\n", " word = random.choice(word_models[0].index2word)\n", " if word_models[0].vocab[word].count > 10:\n", " break\n", "# or uncomment below line, to just pick a word from the relevant domain:\n", "#word = 'comedy/drama'\n", "similars_per_model = [str(model.most_similar(word, topn=20)).replace('), ','),<br>\\n') for model in word_models]\n", "similar_table = (\"<table><tr><th>\" +\n", " \"</th><th>\".join([str(model) for model in word_models]) + \n", " \"</th></tr><tr><td>\" +\n", " \"</td><td>\".join(similars_per_model) +\n", " \"</td></tr></table>\")\n", "print(\"most similar words for '%s' (%d occurences)\" % (word, simple_models[0].vocab[word].count))\n", "HTML(similar_table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the DBOW words look meaningless? That's because the gensim DBOW model doesn't train word vectors – they remain at their random initialized values – unless you ask with the `dbow_words=1` initialization parameter. Concurrent word-training slows DBOW mode significantly, and offers little improvement (and sometimes a little worsening) of the error rate on this IMDB sentiment-prediction task. \n", "\n", "Words from DM models tend to show meaningfully similar words when there are many examples in the training data (as with 'plot' or 'actor'). (All DM modes inherently involve word vector training concurrent with doc vector training.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are the word vectors from this dataset any good at analogies?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec(dm/c,d100,n5,w5,mc2,t8): 28.70% correct (2873 of 10012)\n", "Doc2Vec(dbow,d100,n5,mc2,t8): 0.01% correct (1 of 10012)\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t8): 27.24% correct (2727 of 10012)\n" ] } ], "source": [ "# assuming something like\n", "# https://word2vec.googlecode.com/svn/trunk/questions-words.txt \n", "# is in local directory\n", "# note: this takes many minutes\n", "for model in word_models:\n", " sections = model.accuracy('questions-words.txt')\n", " correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect'])\n", " print('%s: %0.2f%% correct (%d of %d)' % (model, float(correct*100)/(correct+incorrect), correct, correct+incorrect))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even though this is a tiny, domain-specific dataset, it shows some meager capability on the general word analogies – at least for the DM/concat and DM/mean models which actually train word vectors. (The untrained random-initialized words of the DBOW model of course fail miserably.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "This cell left intentionally erroneous. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To mix the Google dataset (if locally available) into the word tests..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.models import Word2Vec\n", "w2v_g100b = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)\n", "w2v_g100b.compact_name = 'w2v_g100b'\n", "word_models.append(w2v_g100b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get copious logging output from above steps..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "rootLogger = logging.getLogger()\n", "rootLogger.setLevel(logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To auto-reload python code while developing..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
oyamad/gth_solve_numba	loop_jitting01.ipynb	1	63015	{ "metadata": { "name": "", "signature": "sha256:e14733313b2a73e565c5741a6238ebcd7d54ced1d7266a411ce59a29890be54c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from numba import jit\n", "from gth_solve_jit import gth_solve, gth_solve_jit" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "@jit('float64[:](float64[:,:])')\n", "def gth_solve_jit2(A):\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", " # raise ValueError('matrix must be square') # Not supported\n", " raise ValueError\n", "\n", " n = A1.shape[0]\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", " # === Reduction === #\n", " for k in range(n-1):\n", " scale = np.sum(A1[k, k+1:n])\n", " if scale <= 0:\n", " # There is one (and only one) recurrent class contained in\n", " # {0, ..., k};\n", " # compute the solution associated with that recurrent class.\n", " n = k+1\n", " break\n", " for i in range(k+1, n):\n", " A1[i, k] /= scale\n", "\n", " for j in range(k+1, n):\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", " # === Backward substitution === #\n", " x[n-1] = 1\n", " for k in range(n-2, -1, -1):\n", " for i in range(k+1, n):\n", " x[k] += x[i] * A1[i, k]\n", "\n", " # === Normalization === #\n", " norm = np.sum(x)\n", " for k in range(n):\n", " x[k] /= norm\n", "\n", " return x" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2.inspect_types()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gth_solve_jit2 (array(float64, 2d, A, nonconst),)\n", "--------------------------------------------------------------------------------\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", " # label 0\n", " # A.1 = A :: pyobject\n", " # del A\n", " # $0.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.2 = getattr(attr=array, value=$0.1) :: pyobject\n", " # del $0.1\n", " # $0.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.6 = getattr(attr=float64, value=$0.5) :: pyobject\n", " # del $0.5\n", " # $0.7 = call $0.2(A.1, dtype=$0.6) :: pyobject\n", " # del A.1\n", " # del $0.6\n", " # del $0.2\n", " # A1 = $0.7 :: pyobject\n", " # del $0.7\n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", " # $0.8 = global(len: <built-in function len>) :: pyobject\n", " # $0.10 = getattr(attr=shape, value=A1) :: pyobject\n", " # $0.11 = call $0.8($0.10, ) :: pyobject\n", " # del $0.8\n", " # del $0.10\n", " # $const0.12 = const(int, 2) :: pyobject\n", " # $0.13 = $0.11 != $const0.12 :: pyobject\n", " # del $const0.12\n", " # del $0.11\n", " # branch $0.13, 71, 45\n", " # label 45\n", " # $45.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.3 = const(int, 0) :: pyobject\n", " # $45.4 = getitem(index=$const45.3, value=$45.2) :: pyobject\n", " # del $const45.3\n", " # del $45.2\n", " # $45.6 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.7 = const(int, 1) :: pyobject\n", " # $45.8 = getitem(index=$const45.7, value=$45.6) :: pyobject\n", " # del $const45.7\n", " # del $45.6\n", " # $45.9 = $45.4 != $45.8 :: pyobject\n", " # del $45.8\n", " # del $45.4\n", " # branch $45.9, 71, 80\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", " # label 71\n", " # del A1\n", " # del $45.9\n", " # del $0.13\n", " # $71.1 = global(ValueError: <type 'exceptions.ValueError'>) :: pyobject\n", " # del $71.1\n", " # raise <type 'exceptions.ValueError'>\n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", " # label 80\n", " # del $45.9\n", " # del $0.13\n", " # $80.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const80.3 = const(int, 0) :: pyobject\n", " # $80.4 = getitem(index=$const80.3, value=$80.2) :: pyobject\n", " # del $const80.3\n", " # del $80.2\n", " # n = $80.4 :: pyobject\n", " # del $80.4\n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", " # $80.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.6 = getattr(attr=zeros, value=$80.5) :: pyobject\n", " # del $80.5\n", " # $80.9 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.10 = getattr(attr=float64, value=$80.9) :: pyobject\n", " # del $80.9\n", " # $80.11 = call $80.6(n, dtype=$80.10) :: pyobject\n", " # del $80.6\n", " # del $80.10\n", " # x = $80.11 :: pyobject\n", " # del $80.11\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", " # label 134\n", " # $134.2 = iternext(value=$phi134.1) :: pyobject\n", " # $134.3 = pair_first(value=$134.2) :: pyobject\n", " # $134.4 = pair_second(value=$134.2) :: pyobject\n", " # del $134.2\n", " # $phi137.1 = $134.3 :: pyobject\n", " # del $134.3\n", " # branch $134.4, 137, 332\n", " # label 137\n", " # k = $phi137.1 :: pyobject\n", " # del $phi137.1\n", " # jump 117\n", " # label 117\n", " # $117.1 = global(range: <built-in function range>) :: pyobject\n", " # $const117.3 = const(int, 1) :: pyobject\n", " # $117.4 = n - $const117.3 :: pyobject\n", " # del $const117.3\n", " # $117.5 = call $117.1($117.4, ) :: pyobject\n", " # del $117.4\n", " # del $117.1\n", " # $117.6 = getiter(value=$117.5) :: pyobject\n", " # del $117.5\n", " # $phi134.1 = $117.6 :: pyobject\n", " # del $117.6\n", " # jump 134\n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", " # $137.2 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $137.3 = getattr(attr=sum, value=$137.2) :: pyobject\n", " # del $137.2\n", " # $k137.5 = k :: pyobject\n", " # $const137.7 = const(int, 1) :: pyobject\n", " # $137.8 = k + $const137.7 :: pyobject\n", " # del $const137.7\n", " # $137.10 = global(slice: <type 'slice'>) :: pyobject\n", " # $137.11 = call $137.10($137.8, n, ) :: pyobject\n", " # del $137.8\n", " # del $137.10\n", " # $137.12 = build_tuple(items=[Var($k137.5, <ipython-input-2-b14bcb5c0c5e> (15)), Var($137.11, <ipython-input-2-b14bcb5c0c5e> (15))]) :: pyobject\n", " # del $k137.5\n", " # del $137.11\n", " # $137.13 = getitem(index=$137.12, value=A1) :: pyobject\n", " # del $137.12\n", " # $137.14 = call $137.3($137.13, ) :: pyobject\n", " # del $137.3\n", " # del $137.13\n", " # scale = $137.14 :: pyobject\n", " # del $137.14\n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", " # $const137.16 = const(int, 0) :: pyobject\n", " # $137.17 = scale <= $const137.16 :: pyobject\n", " # del $const137.16\n", " # branch $137.17, 187, 201\n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", " # label 187\n", " # del scale\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", " # $const187.2 = const(int, 1) :: pyobject\n", " # $187.3 = k + $const187.2 :: pyobject\n", " # del $const187.2\n", " # n = $187.3 :: pyobject\n", " # del $187.3\n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", " # jump 333\n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", " # label 201\n", " # $201.1 = global(range: <built-in function range>) :: pyobject\n", " # $const201.3 = const(int, 1) :: pyobject\n", " # $201.4 = k + $const201.3 :: pyobject\n", " # del $const201.3\n", " # $201.6 = call $201.1($201.4, n, ) :: pyobject\n", " # del $201.4\n", " # del $201.1\n", " # $201.7 = getiter(value=$201.6) :: pyobject\n", " # del $201.6\n", " # $phi221.1 = $201.7 :: pyobject\n", " # del $201.7\n", " # jump 221\n", " # label 221\n", " # $221.2 = iternext(value=$phi221.1) :: pyobject\n", " # $221.3 = pair_first(value=$221.2) :: pyobject\n", " # $221.4 = pair_second(value=$221.2) :: pyobject\n", " # del $221.2\n", " # $phi224.1 = $221.3 :: pyobject\n", " # del $221.3\n", " # branch $221.4, 224, 328\n", " # label 224\n", " # i = $phi224.1 :: pyobject\n", " # del $phi224.1\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", " # $A1224.2 = A1 :: pyobject\n", " # $224.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $224.8 = getitem(index=$224.5, value=A1) :: pyobject\n", " # $224.10 = inplace_binop(rhs=scale, lhs=$224.8, fn=/?) :: pyobject\n", " # del $224.8\n", " # $A1224.2[$224.5] = $224.10 :: pyobject\n", " # del $A1224.2\n", " # del $224.5\n", " # del $224.10\n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", " # label 269\n", " # $269.2 = iternext(value=$phi269.1) :: pyobject\n", " # $269.3 = pair_first(value=$269.2) :: pyobject\n", " # $269.4 = pair_second(value=$269.2) :: pyobject\n", " # del $269.2\n", " # $phi272.1 = $269.3 :: pyobject\n", " # del $269.3\n", " # branch $269.4, 272, 324\n", " # label 272\n", " # j = $phi272.1 :: pyobject\n", " # del $phi272.1\n", " # jump 249\n", " # label 249\n", " # $249.1 = global(range: <built-in function range>) :: pyobject\n", " # $const249.3 = const(int, 1) :: pyobject\n", " # $249.4 = k + $const249.3 :: pyobject\n", " # del $const249.3\n", " # $249.6 = call $249.1($249.4, n, ) :: pyobject\n", " # del $249.4\n", " # del $249.1\n", " # $249.7 = getiter(value=$249.6) :: pyobject\n", " # del $249.6\n", " # $phi269.1 = $249.7 :: pyobject\n", " # del $249.7\n", " # jump 269\n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", " # $A1272.2 = A1 :: pyobject\n", " # $272.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # $272.8 = getitem(index=$272.5, value=A1) :: pyobject\n", " # $272.12 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $272.13 = getitem(index=$272.12, value=A1) :: pyobject\n", " # del $272.12\n", " # $272.17 = build_tuple(items=[Var(k, <ipython-input-2-b14bcb5c0c5e> (14)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # del j\n", " # $272.18 = getitem(index=$272.17, value=A1) :: pyobject\n", " # del $272.17\n", " # $272.19 = $272.13 * $272.18 :: pyobject\n", " # del $272.18\n", " # del $272.13\n", " # $272.20 = inplace_binop(rhs=$272.19, lhs=$272.8, fn=+) :: pyobject\n", " # del $272.8\n", " # del $272.19\n", " # $A1272.2[$272.5] = $272.20 :: pyobject\n", " # del $A1272.2\n", " # del $272.5\n", " # del $272.20\n", " # jump 269\n", " # label 329\n", " # jump 134\n", " # label 324\n", " # del $phi272.1\n", " # del $phi269.1\n", " # del $269.4\n", " # jump 325\n", " # label 325\n", " # jump 221\n", " # label 328\n", " # del scale\n", " # del $phi224.1\n", " # del $phi221.1\n", " # del $221.4\n", " # jump 329\n", " # label 332\n", " # del $phi137.1\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", " # jump 333\n", " # label 333\n", " # $const333.1 = const(int, 1) :: pyobject\n", " # $const333.4 = const(int, 1) :: pyobject\n", " # $333.5 = n - $const333.4 :: pyobject\n", " # del $const333.4\n", " # x[$333.5] = $const333.1 :: pyobject\n", " # del $const333.1\n", " # del $333.5\n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 350.1\n", " # $const350.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $350.1.7 = call $const350.1.1(A1, i, k, n, x, ) :: pyobject\n", " # del A1\n", " # del $const350.1.1\n", " # $350.1.10 = exhaust_iter(count=2, value=$350.1.7) :: pyobject\n", " # del $350.1.7\n", " # $350.1.8 = static_getitem(index=0, value=$350.1.10) :: pyobject\n", " # $350.1.9 = static_getitem(index=1, value=$350.1.10) :: pyobject\n", " # del $350.1.10\n", " # i = $350.1.8 :: pyobject\n", " # del i\n", " # del $350.1.8\n", " # k = $350.1.9 :: pyobject\n", " # del $350.1.9\n", " # jump 444\n", " # jump 350.1\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", " # label 444\n", " # $444.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $444.2 = getattr(attr=sum, value=$444.1) :: pyobject\n", " # del $444.1\n", " # $444.4 = call $444.2(x, ) :: pyobject\n", " # del $444.2\n", " # norm = $444.4 :: pyobject\n", " # del $444.4\n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # jump 462.1\n", " # label 462.1\n", " # $const462.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $462.1.6 = call $const462.1.1(x, k, norm, n, ) :: pyobject\n", " # del norm\n", " # del n\n", " # del $const462.1.1\n", " # $462.1.8 = exhaust_iter(count=1, value=$462.1.6) :: pyobject\n", " # del $462.1.6\n", " # $462.1.7 = static_getitem(index=0, value=$462.1.8) :: pyobject\n", " # del $462.1.8\n", " # k = $462.1.7 :: pyobject\n", " # del k\n", " # del $462.1.7\n", " # jump 498\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", " # label 498\n", " # $498.2 = cast(value=x) :: pyobject\n", " # del x\n", " # return $498.2\n", "\n", " return x\n", "\n", "# The function contains lifted loops\n", "# Loop at line 30\n", "# Has 0 overloads\n", "# Loop at line 36\n", "# Has 0 overloads\n", "\n", "================================================================================\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2([[0.4, 0.6], [0.2, 0.8]])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([ 0.25, 0.75])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2.inspect_types()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gth_solve_jit2 (array(float64, 2d, A, nonconst),)\n", "--------------------------------------------------------------------------------\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", " # label 0\n", " # A.1 = A :: pyobject\n", " # del A\n", " # $0.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.2 = getattr(attr=array, value=$0.1) :: pyobject\n", " # del $0.1\n", " # $0.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.6 = getattr(attr=float64, value=$0.5) :: pyobject\n", " # del $0.5\n", " # $0.7 = call $0.2(A.1, dtype=$0.6) :: pyobject\n", " # del A.1\n", " # del $0.6\n", " # del $0.2\n", " # A1 = $0.7 :: pyobject\n", " # del $0.7\n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", " # $0.8 = global(len: <built-in function len>) :: pyobject\n", " # $0.10 = getattr(attr=shape, value=A1) :: pyobject\n", " # $0.11 = call $0.8($0.10, ) :: pyobject\n", " # del $0.8\n", " # del $0.10\n", " # $const0.12 = const(int, 2) :: pyobject\n", " # $0.13 = $0.11 != $const0.12 :: pyobject\n", " # del $const0.12\n", " # del $0.11\n", " # branch $0.13, 71, 45\n", " # label 45\n", " # $45.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.3 = const(int, 0) :: pyobject\n", " # $45.4 = getitem(index=$const45.3, value=$45.2) :: pyobject\n", " # del $const45.3\n", " # del $45.2\n", " # $45.6 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.7 = const(int, 1) :: pyobject\n", " # $45.8 = getitem(index=$const45.7, value=$45.6) :: pyobject\n", " # del $const45.7\n", " # del $45.6\n", " # $45.9 = $45.4 != $45.8 :: pyobject\n", " # del $45.8\n", " # del $45.4\n", " # branch $45.9, 71, 80\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", " # label 71\n", " # del A1\n", " # del $45.9\n", " # del $0.13\n", " # $71.1 = global(ValueError: <type 'exceptions.ValueError'>) :: pyobject\n", " # del $71.1\n", " # raise <type 'exceptions.ValueError'>\n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", " # label 80\n", " # del $45.9\n", " # del $0.13\n", " # $80.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const80.3 = const(int, 0) :: pyobject\n", " # $80.4 = getitem(index=$const80.3, value=$80.2) :: pyobject\n", " # del $const80.3\n", " # del $80.2\n", " # n = $80.4 :: pyobject\n", " # del $80.4\n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", " # $80.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.6 = getattr(attr=zeros, value=$80.5) :: pyobject\n", " # del $80.5\n", " # $80.9 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.10 = getattr(attr=float64, value=$80.9) :: pyobject\n", " # del $80.9\n", " # $80.11 = call $80.6(n, dtype=$80.10) :: pyobject\n", " # del $80.6\n", " # del $80.10\n", " # x = $80.11 :: pyobject\n", " # del $80.11\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", " # label 134\n", " # $134.2 = iternext(value=$phi134.1) :: pyobject\n", " # $134.3 = pair_first(value=$134.2) :: pyobject\n", " # $134.4 = pair_second(value=$134.2) :: pyobject\n", " # del $134.2\n", " # $phi137.1 = $134.3 :: pyobject\n", " # del $134.3\n", " # branch $134.4, 137, 332\n", " # label 137\n", " # k = $phi137.1 :: pyobject\n", " # del $phi137.1\n", " # jump 117\n", " # label 117\n", " # $117.1 = global(range: <built-in function range>) :: pyobject\n", " # $const117.3 = const(int, 1) :: pyobject\n", " # $117.4 = n - $const117.3 :: pyobject\n", " # del $const117.3\n", " # $117.5 = call $117.1($117.4, ) :: pyobject\n", " # del $117.4\n", " # del $117.1\n", " # $117.6 = getiter(value=$117.5) :: pyobject\n", " # del $117.5\n", " # $phi134.1 = $117.6 :: pyobject\n", " # del $117.6\n", " # jump 134\n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", " # $137.2 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $137.3 = getattr(attr=sum, value=$137.2) :: pyobject\n", " # del $137.2\n", " # $k137.5 = k :: pyobject\n", " # $const137.7 = const(int, 1) :: pyobject\n", " # $137.8 = k + $const137.7 :: pyobject\n", " # del $const137.7\n", " # $137.10 = global(slice: <type 'slice'>) :: pyobject\n", " # $137.11 = call $137.10($137.8, n, ) :: pyobject\n", " # del $137.8\n", " # del $137.10\n", " # $137.12 = build_tuple(items=[Var($k137.5, <ipython-input-2-b14bcb5c0c5e> (15)), Var($137.11, <ipython-input-2-b14bcb5c0c5e> (15))]) :: pyobject\n", " # del $k137.5\n", " # del $137.11\n", " # $137.13 = getitem(index=$137.12, value=A1) :: pyobject\n", " # del $137.12\n", " # $137.14 = call $137.3($137.13, ) :: pyobject\n", " # del $137.3\n", " # del $137.13\n", " # scale = $137.14 :: pyobject\n", " # del $137.14\n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", " # $const137.16 = const(int, 0) :: pyobject\n", " # $137.17 = scale <= $const137.16 :: pyobject\n", " # del $const137.16\n", " # branch $137.17, 187, 201\n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", " # label 187\n", " # del scale\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", " # $const187.2 = const(int, 1) :: pyobject\n", " # $187.3 = k + $const187.2 :: pyobject\n", " # del $const187.2\n", " # n = $187.3 :: pyobject\n", " # del $187.3\n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", " # jump 333\n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", " # label 201\n", " # $201.1 = global(range: <built-in function range>) :: pyobject\n", " # $const201.3 = const(int, 1) :: pyobject\n", " # $201.4 = k + $const201.3 :: pyobject\n", " # del $const201.3\n", " # $201.6 = call $201.1($201.4, n, ) :: pyobject\n", " # del $201.4\n", " # del $201.1\n", " # $201.7 = getiter(value=$201.6) :: pyobject\n", " # del $201.6\n", " # $phi221.1 = $201.7 :: pyobject\n", " # del $201.7\n", " # jump 221\n", " # label 221\n", " # $221.2 = iternext(value=$phi221.1) :: pyobject\n", " # $221.3 = pair_first(value=$221.2) :: pyobject\n", " # $221.4 = pair_second(value=$221.2) :: pyobject\n", " # del $221.2\n", " # $phi224.1 = $221.3 :: pyobject\n", " # del $221.3\n", " # branch $221.4, 224, 328\n", " # label 224\n", " # i = $phi224.1 :: pyobject\n", " # del $phi224.1\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", " # $A1224.2 = A1 :: pyobject\n", " # $224.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $224.8 = getitem(index=$224.5, value=A1) :: pyobject\n", " # $224.10 = inplace_binop(rhs=scale, lhs=$224.8, fn=/?) :: pyobject\n", " # del $224.8\n", " # $A1224.2[$224.5] = $224.10 :: pyobject\n", " # del $A1224.2\n", " # del $224.5\n", " # del $224.10\n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", " # label 269\n", " # $269.2 = iternext(value=$phi269.1) :: pyobject\n", " # $269.3 = pair_first(value=$269.2) :: pyobject\n", " # $269.4 = pair_second(value=$269.2) :: pyobject\n", " # del $269.2\n", " # $phi272.1 = $269.3 :: pyobject\n", " # del $269.3\n", " # branch $269.4, 272, 324\n", " # label 272\n", " # j = $phi272.1 :: pyobject\n", " # del $phi272.1\n", " # jump 249\n", " # label 249\n", " # $249.1 = global(range: <built-in function range>) :: pyobject\n", " # $const249.3 = const(int, 1) :: pyobject\n", " # $249.4 = k + $const249.3 :: pyobject\n", " # del $const249.3\n", " # $249.6 = call $249.1($249.4, n, ) :: pyobject\n", " # del $249.4\n", " # del $249.1\n", " # $249.7 = getiter(value=$249.6) :: pyobject\n", " # del $249.6\n", " # $phi269.1 = $249.7 :: pyobject\n", " # del $249.7\n", " # jump 269\n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", " # $A1272.2 = A1 :: pyobject\n", " # $272.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # $272.8 = getitem(index=$272.5, value=A1) :: pyobject\n", " # $272.12 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $272.13 = getitem(index=$272.12, value=A1) :: pyobject\n", " # del $272.12\n", " # $272.17 = build_tuple(items=[Var(k, <ipython-input-2-b14bcb5c0c5e> (14)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # del j\n", " # $272.18 = getitem(index=$272.17, value=A1) :: pyobject\n", " # del $272.17\n", " # $272.19 = $272.13 * $272.18 :: pyobject\n", " # del $272.18\n", " # del $272.13\n", " # $272.20 = inplace_binop(rhs=$272.19, lhs=$272.8, fn=+) :: pyobject\n", " # del $272.8\n", " # del $272.19\n", " # $A1272.2[$272.5] = $272.20 :: pyobject\n", " # del $A1272.2\n", " # del $272.5\n", " # del $272.20\n", " # jump 269\n", " # label 329\n", " # jump 134\n", " # label 324\n", " # del $phi272.1\n", " # del $phi269.1\n", " # del $269.4\n", " # jump 325\n", " # label 325\n", " # jump 221\n", " # label 328\n", " # del scale\n", " # del $phi224.1\n", " # del $phi221.1\n", " # del $221.4\n", " # jump 329\n", " # label 332\n", " # del $phi137.1\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", " # jump 333\n", " # label 333\n", " # $const333.1 = const(int, 1) :: pyobject\n", " # $const333.4 = const(int, 1) :: pyobject\n", " # $333.5 = n - $const333.4 :: pyobject\n", " # del $const333.4\n", " # x[$333.5] = $const333.1 :: pyobject\n", " # del $const333.1\n", " # del $333.5\n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 350.1\n", " # $const350.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $350.1.7 = call $const350.1.1(A1, i, k, n, x, ) :: pyobject\n", " # del A1\n", " # del $const350.1.1\n", " # $350.1.10 = exhaust_iter(count=2, value=$350.1.7) :: pyobject\n", " # del $350.1.7\n", " # $350.1.8 = static_getitem(index=0, value=$350.1.10) :: pyobject\n", " # $350.1.9 = static_getitem(index=1, value=$350.1.10) :: pyobject\n", " # del $350.1.10\n", " # i = $350.1.8 :: pyobject\n", " # del i\n", " # del $350.1.8\n", " # k = $350.1.9 :: pyobject\n", " # del $350.1.9\n", " # jump 444\n", " # jump 350.1\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", " # label 444\n", " # $444.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $444.2 = getattr(attr=sum, value=$444.1) :: pyobject\n", " # del $444.1\n", " # $444.4 = call $444.2(x, ) :: pyobject\n", " # del $444.2\n", " # norm = $444.4 :: pyobject\n", " # del $444.4\n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # jump 462.1\n", " # label 462.1\n", " # $const462.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $462.1.6 = call $const462.1.1(x, k, norm, n, ) :: pyobject\n", " # del norm\n", " # del n\n", " # del $const462.1.1\n", " # $462.1.8 = exhaust_iter(count=1, value=$462.1.6) :: pyobject\n", " # del $462.1.6\n", " # $462.1.7 = static_getitem(index=0, value=$462.1.8) :: pyobject\n", " # del $462.1.8\n", " # k = $462.1.7 :: pyobject\n", " # del k\n", " # del $462.1.7\n", " # jump 498\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", " # label 498\n", " # $498.2 = cast(value=x) :: pyobject\n", " # del x\n", " # return $498.2\n", "\n", " return x\n", "\n", "# The function contains lifted loops\n", "# Loop at line 30\n", "# Has 1 overloads\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 370\n", " # $370.2 = iternext(value=$phi370.1) :: pair<int64, bool>\n", " # $370.3 = pair_first(value=$370.2) :: int64\n", " # $370.4 = pair_second(value=$370.2) :: bool\n", " # del $370.2\n", " # $phi373.1 = $370.3 :: int64\n", " # del $370.3\n", " # branch $370.4, 373, 443\n", " # label 373\n", " # k.1 = $phi373.1 :: int64\n", " # del $phi373.1\n", " # label 347\n", " # A1.1 = A1 :: array(float64, 2d, C, nonconst)\n", " # del A1\n", " # i.1 = i :: int64\n", " # del i\n", " # k.1 = k :: int64\n", " # del k\n", " # n.1 = n :: int64\n", " # del n\n", " # x.1 = x :: array(float64, 1d, C, nonconst)\n", " # del x\n", " # $347.1 = global(range: <built-in function range>) :: range\n", " # $const347.3 = const(int, 2) :: int32\n", " # $347.4 = n.1 - $const347.3 :: int64\n", " # del $const347.3\n", " # $const347.5 = const(int, -1) :: int32\n", " # $const347.6 = const(int, -1) :: int32\n", " # $347.7 = call $347.1($347.4, $const347.5, $const347.6, ) :: (int64, int64, int64) -> range_state64\n", " # del $const347.6\n", " # del $const347.5\n", " # del $347.4\n", " # del $347.1\n", " # $347.8 = getiter(value=$347.7) :: range_iter64\n", " # del $347.7\n", " # $phi370.1 = $347.8 :: range_iter64\n", " # del $347.8\n", " # jump 370\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", " # label 396\n", " # $396.2 = iternext(value=$phi396.1) :: pair<int64, bool>\n", " # $396.3 = pair_first(value=$396.2) :: int64\n", " # $396.4 = pair_second(value=$396.2) :: bool\n", " # del $396.2\n", " # $phi399.1 = $396.3 :: int64\n", " # del $396.3\n", " # branch $396.4, 399, 439\n", " # label 399\n", " # i.1 = $phi399.1 :: int64\n", " # del $phi399.1\n", " # jump 376\n", " # label 376\n", " # $376.1 = global(range: <built-in function range>) :: range\n", " # $const376.3 = const(int, 1) :: int32\n", " # $376.4 = k.1 + $const376.3 :: int64\n", " # del $const376.3\n", " # $376.6 = call $376.1($376.4, n.1, ) :: (int64, int64) -> range_state64\n", " # del $376.4\n", " # del $376.1\n", " # $376.7 = getiter(value=$376.6) :: range_iter64\n", " # del $376.6\n", " # $phi396.1 = $376.7 :: range_iter64\n", " # del $376.7\n", " # jump 396\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", " # label 443\n", " # del x.1\n", " # del n.1\n", " # del A1.1\n", " # del $phi373.1\n", " # del $phi370.1\n", " # del $370.4\n", " # jump 444\n", " # $x399.2 = x.1 :: array(float64, 1d, C, nonconst)\n", " # $k399.3 = k.1 :: int64\n", " # $399.6 = getitem(index=k.1, value=x.1) :: float64\n", " # $399.9 = getitem(index=i.1, value=x.1) :: float64\n", " # $399.13 = build_tuple(items=[Var(i.1, <ipython-input-2-b14bcb5c0c5e> (30)), Var(k.1, <ipython-input-2-b14bcb5c0c5e> (30))]) :: (int64 x 2)\n", " # $399.14 = getitem(index=$399.13, value=A1.1) :: float64\n", " # del $399.13\n", " # $399.15 = $399.9 * $399.14 :: float64\n", " # del $399.9\n", " # del $399.14\n", " # $399.16 = inplace_binop(rhs=$399.15, lhs=$399.6, fn=+) :: float64\n", " # del $399.6\n", " # del $399.15\n", " # $x399.2[$k399.3] = $399.16 :: (array(float64, 1d, C, nonconst), int64, float64) -> none\n", " # del $x399.2\n", " # del $k399.3\n", " # del $399.16\n", " # jump 396\n", " # label 440\n", " # jump 370\n", " # label 439\n", " # del $phi399.1\n", " # del $phi396.1\n", " # del $396.4\n", " # jump 440\n", " # label 444\n", " # $444.3 = build_tuple(items=[Var(i.1, <ipython-input-2-b14bcb5c0c5e> (30)), Var(k.1, <ipython-input-2-b14bcb5c0c5e> (30))]) :: (int64 x 2)\n", " # del k.1\n", " # del i.1\n", " # $444.4 = cast(value=$444.3) :: (int64 x 2)\n", " # del $444.3\n", " # return $444.4\n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", "\n", " return x\n", "\n", "\n", "# Loop at line 36\n", "# Has 1 overloads\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # label 472\n", " # $472.2 = iternext(value=$phi472.1) :: pair<int64, bool>\n", " # $472.3 = pair_first(value=$472.2) :: int64\n", " # $472.4 = pair_second(value=$472.2) :: bool\n", " # del $472.2\n", " # $phi475.1 = $472.3 :: int64\n", " # del $472.3\n", " # branch $472.4, 475, 497\n", " # label 459\n", " # x.1 = x :: array(float64, 1d, C, nonconst)\n", " # del x\n", " # k.1 = k :: int64\n", " # del k\n", " # norm.1 = norm :: float64\n", " # del norm\n", " # n.1 = n :: int64\n", " # del n\n", " # $459.1 = global(range: <built-in function range>) :: range\n", " # $459.3 = call $459.1(n.1, ) :: (int64,) -> range_state64\n", " # del n.1\n", " # del $459.1\n", " # $459.4 = getiter(value=$459.3) :: range_iter64\n", " # del $459.3\n", " # $phi472.1 = $459.4 :: range_iter64\n", " # del $459.4\n", " # jump 472\n", " # label 475\n", " # k.1 = $phi475.1 :: int64\n", " # del $phi475.1\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", " # label 497\n", " # del x.1\n", " # del norm.1\n", " # del $phi475.1\n", " # del $phi472.1\n", " # del $472.4\n", " # jump 498\n", " # label 498\n", " # $498.2 = build_tuple(items=[Var(k.1, <ipython-input-2-b14bcb5c0c5e> (36))]) :: (int64 x 1)\n", " # del k.1\n", " # $498.3 = cast(value=$498.2) :: (int64 x 1)\n", " # del $498.2\n", " # return $498.3\n", " # $x475.2 = x.1 :: array(float64, 1d, C, nonconst)\n", " # $k475.3 = k.1 :: int64\n", " # $475.6 = getitem(index=k.1, value=x.1) :: float64\n", " # $475.8 = inplace_binop(rhs=norm.1, lhs=$475.6, fn=/?) :: float64\n", " # del $475.6\n", " # $x475.2[$k475.3] = $475.8 :: (array(float64, 1d, C, nonconst), int64, float64) -> none\n", " # del $x475.2\n", " # del $k475.3\n", " # del $475.8\n", " # jump 472\n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", "\n", " return x\n", "\n", "\n", "\n", "================================================================================\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "sizes = [10, 50, 100] # [10, 50, 100, 1000]\n", "rand_matrices = []\n", "\n", "for n in sizes:\n", " Q = np.random.rand(n, n)\n", " Q /= np.sum(Q, axis=1, keepdims=True)\n", " rand_matrices.append(Q)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, Q in enumerate(rand_matrices):\n", " print 'rand_matrices[{0}] ({1} x {2})'.format(i, Q.shape[0], Q.shape[1])\n", " %timeit gth_solve(Q)\n", " %timeit gth_solve_jit(Q)\n", " %timeit gth_solve_jit2(Q)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "rand_matrices[0] (10 x 10)\n", "1000 loops, best of 3: 182 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "100000 loops, best of 3: 5.2 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 281 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rand_matrices[1] (50 x 50)\n", "1000 loops, best of 3: 1.12 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 63.1 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10 loops, best of 3: 21.1 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rand_matrices[2] (100 x 100)\n", "100 loops, best of 3: 3 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 429 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10 loops, best of 3: 164 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "import platform\n", "print platform.platform()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Darwin-13.4.0-x86_64-i386-64bit\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "print sys.version" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.7.8 (default, Jul 2 2014, 10:14:46) \n", "[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.9.0\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "import numba\n", "print numba.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.15.1\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "import llvm\n", "print llvm.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.12.7-5-gc0ae9c2\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
joshwalawender/KeckUtilities	telescopeSchedule/Site Calendar.ipynb	1	8987	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "from astropy.table import Table\n", "\n", "from telescopeSchedule import get_telsched" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def site_table(ndays=5):\n", " sched = get_telsched(from_date=None, ndays=ndays, telnr=None)\n", "\n", " site_list = ['HQ']\n", " site_list.extend( sorted(['ANU', 'CIT', 'UCB', 'UCD', 'UCLA', 'UCSD', 'UCI', 'UCR', 'Yale',\n", " 'USRA', 'NU', 'IfA', 'Stanford', 'Swinburne', 'UCSB', 'UCSC']) )\n", " site_list.append('Other')\n", "\n", " t = Table(names=['Run'] + site_list,\n", " dtype=['a40'] + ['a100']*len(site_list))\n", "\n", " for prog in sched:\n", " row = {site: '' for site in site_list}\n", " row['Run'] = f\"{prog['Date']} {prog['TelNr']} ({prog['ProjCode']})\"\n", " tonights_observers = prog['Observers'].split(',')\n", " tonights_sites = prog['Location'].split(',')\n", " for obs,s in zip(tonights_observers, tonights_sites):\n", " if s in row.keys():\n", " row[s] += f\"{obs}, \"\n", " else:\n", " row['Other'] += f\"{obs}, \"\n", " for site in site_list:\n", " if row[site] != '':\n", " nobs = len(row[site].split(',')) - 1\n", " row[site] = row[site].strip(', ')\n", " row[site] += f' ({nobs})'\n", " t.add_row(row)\n", "\n", " return t" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=16</i>\n", "<table id=\"table4830594216\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Run</th><th>HQ</th><th>ANU</th><th>CIT</th><th>IfA</th><th>NU</th><th>Stanford</th><th>Swinburne</th><th>UCB</th><th>UCD</th><th>UCI</th><th>UCLA</th><th>UCR</th><th>UCSB</th><th>UCSC</th><th>UCSD</th><th>USRA</th><th>Yale</th><th>Other</th></tr></thead>\n", "<thead><tr><th>bytes40</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th></tr></thead>\n", "<tr><td>2020-03-10 1 (H277)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Lemaux, Pelliccia (2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 1 (N028)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 2 (K300)</td><td>Alvarez (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 2 (E339)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 1 (S322)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shimakawa, Onodera (2)</td></tr>\n", "<tr><td>2020-03-11 1 (N028)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (C197)</td><td></td><td></td><td>Buzard, Camarca, Wallack (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (E350)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (E341)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-12 1 (S322)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shimakawa, Onodera (2)</td></tr>\n", "<tr><td>2020-03-12 1 (U169)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-12 2 (U149)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Theissen, Aganze, Hsu, Gerasimov (4)</td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-13 1 (C206)</td><td></td><td></td><td>Scoville, Darvish Sarvestani, Faisst (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-13 2 (H311)</td><td></td><td></td><td></td><td>Liu (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-14 1 (C206)</td><td></td><td></td><td>Scoville, Darvish Sarvestani, Faisst (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-14 2 (U010)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Martin (1)</td><td></td><td></td><td></td><td></td><td></td></tr>\n", "</table>" ], "text/plain": [ "<Table length=16>\n", " Run HQ ... Yale Other \n", " bytes40 bytes100 ... bytes100 bytes100 \n", "------------------- ------------------------ ... -------- ----------------------\n", "2020-03-10 1 (H277) ... \n", "2020-03-10 1 (N028) Topping, Runco, Pahl (3) ... \n", "2020-03-10 2 (K300) Alvarez (1) ... \n", "2020-03-10 2 (E339) Ragland (1) ... \n", "2020-03-11 1 (S322) ... Shimakawa, Onodera (2)\n", "2020-03-11 1 (N028) Topping, Runco, Pahl (3) ... \n", "2020-03-11 2 (C197) ... \n", "2020-03-11 2 (E350) Ragland (1) ... \n", "2020-03-11 2 (E341) Ragland (1) ... \n", "2020-03-12 1 (S322) ... Shimakawa, Onodera (2)\n", "2020-03-12 1 (U169) Topping, Runco, Pahl (3) ... \n", "2020-03-12 2 (U149) ... \n", "2020-03-13 1 (C206) ... \n", "2020-03-13 2 (H311) ... \n", "2020-03-14 1 (C206) ... \n", "2020-03-14 2 (U010) ... " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site_table(ndays=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-2-clause
AugurProject/Simulator.jl	test/workbench.ipynb	3	5770	{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:ffe211f4cf239e551beb097b1c14bd3a3fa754bf67ee217a2ddf4e55952a2de5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "reload(\"repl.jl\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "xdump(sim)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot;\n", "PyPlot.svg(false);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "subplot(221);\n", "colorbar(contourf(mean_repdelta[\"hierarchical\"], origin=\"upper\"));\n", "title(\"hierarchical\");\n", "ylabel(\"reporter\");\n", "\n", "subplot(222);\n", "colorbar(contourf(mean_repdelta[\"cflash\"], origin=\"upper\"));\n", "title(\"cflash\");\n", "\n", "subplot(223);\n", "contourf(mean_repdelta[\"PCA\"], origin=\"upper\");\n", "title(\"PCA\");\n", "xlabel(\"reporting round\");\n", "ylabel(\"reporter\");\n", "\n", "subplot(224);\n", "colorbar(contourf(mean_repdelta[\"k-means\"], origin=\"upper\"));\n", "title(\"k-means\");\n", "xlabel(\"reporting round\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "function buildplot(metric)\n", " plot(1:sim.TIMESTEPS, mean(track[\"PCA\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"cflash\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"fixed-variance\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"k-means\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"hierarchical\"][metric], 2))\n", "end\n", "\n", "fig = figure();\n", "\n", "subplot(221);\n", "buildplot(:MCC)\n", "grid();\n", "ylabel(\"Matthews corr. coeff.\");\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " bbox_to_anchor=(-0.15, 1.35),\n", " loc=\"upper left\",\n", " ncol=4);\n", "\n", "subplot(222);\n", "buildplot(:spearman)\n", "ax = gca();\n", "ax[:yaxis][:set_ticks_position](\"right\");\n", "ax[:set_xscale](\"linear\");\n", "ylabel(\"spearman rank corr.\");\n", "grid();\n", "\n", "subplot(223);\n", "buildplot(:beats)\n", "ax = gca();\n", "ax[:set_xscale](\"log\");\n", "xlabel(\"reporting round\");\n", "ylabel(\"beats\");\n", "grid();\n", "\n", "subplot(224);\n", "buildplot(:liars_bonus)\n", "xlabel(\"reporting round\");\n", "ylabel(\"liars' bonus\");\n", "ax = gca();\n", "ax[:yaxis][:set_ticks_position](\"right\");\n", "ax[:set_xscale](\"log\");\n", "grid();\n", "\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = figure();\n", "errorbar(timesteps, mean_rep_liars[\"PCA\"], yerr=std_rep_liars[\"PCA\"]);\n", "hold(\"on\");\n", "errorbar(timesteps, mean_rep_liars[\"cflash\"], yerr=std_rep_liars[\"cflash\"]);\n", "errorbar(timesteps, mean_rep_liars[\"fixed-variance\"], yerr=std_rep_liars[\"fixed-variance\"]);\n", "errorbar(timesteps, mean_rep_liars[\"k-means\"], yerr=std_rep_liars[\"k-means\"]);\n", "errorbar(timesteps, mean_rep_liars[\"hierarchical\"], yerr=std_rep_liars[\"hierarchical\"]);\n", "xlabel(\"time (# reporting rounds)\");\n", "ylabel(\"liars' reputation\");\n", "ax = gca();\n", "ax[:set_xscale](\"linear\");\n", "grid();\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " loc=\"center right\",\n", " bbox_to_anchor=(1.4, 0.55),\n", " ncol=1);\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = figure();\n", "metric = :MCC;\n", "timesteps = 1:sim.TIMESTEPS;\n", "errorbar(timesteps, mean(track[\"PCA\"][metric], 2), yerr=std(track[\"PCA\"][metric], 2) / sim.SQRTN);\n", "hold(\"on\");\n", "errorbar(timesteps, mean(track[\"cflash\"][metric], 2), yerr=std(track[\"cflash\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"fixed-variance\"][metric], 2), yerr=std(track[\"fixed-variance\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"k-means\"][metric], 2), yerr=std(track[\"k-means\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"hierarchical\"][metric], 2), yerr=std(track[\"hierarchical\"][metric], 2) / sim.SQRTN);\n", "grid();\n", "xlabel(\"time (# reporting rounds)\");\n", "ylabel(\"Matthews corr. coeff.\");\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " loc=\"center right\",\n", " bbox_to_anchor=(1.4, 0.55),\n", " ncol=1);\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
twosigma/beakerx	doc/python/TableAPI.ipynb	1	11263	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python API for Table Display\n", "\n", "In addition to APIs for creating and formatting BeakerX's interactive table widget, the Python runtime configures pandas to display tables with the interactive widget instead of static HTML." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from beakerx import \n", "from beakerx.object import beakerx" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6cc688d4ca4e4d6d9f0dbdf14285f73f", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c109cc21d4f948b395009abcdb65dfe2", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table.setAlignmentProviderForColumn('m3', TableDisplayAlignmentProvider.CENTER_ALIGNMENT)\n", "table.setRendererForColumn(\"y10\", TableDisplayCellRenderer.getDataBarsRenderer(False))\n", "table.setRendererForType(ColumnType.Double, TableDisplayCellRenderer.getDataBarsRenderer(True))\n", "table" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c6acb414397f494b84c0046870db07bc", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df['time'] = df['time'].str.slice(0,19).astype('datetime64[ns]')\n", "table = TableDisplay(df)\n", "table.setStringFormatForTimes(TimeUnit.DAYS)\n", "table.setStringFormatForType(ColumnType.Double, TableDisplayStringFormat.getDecimalFormat(4,6))\n", "table.setStringFormatForColumn(\"m3\", TableDisplayStringFormat.getDecimalFormat(0, 0))\n", "\n", "table" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table\n", "#freeze a column\n", "table.setColumnFrozen(\"y1\", True)\n", "#hide a column\n", "table.setColumnVisible(\"y30\", False)\n", "\n", "table.setColumnOrder([\"m3\", \"y1\", \"y5\", \"time\", \"y2\"])\n", "\n", "def config_tooltip(row, column, table):\n", " return \"The value is: \" + str(table.values[row][column])\n", "\n", "table.setToolTip(config_tooltip)\n", "\n", "table.setDataFontSize(16)\n", "table.setHeaderFontSize(18)\n", "\n", "table" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapListColorProvider = [\n", " {\"a\": 1, \"b\": 2, \"c\": 3},\n", " {\"a\": 4, \"b\": 5, \"c\": 6},\n", " {\"a\": 7, \"b\": 8, \"c\": 5}\n", "]\n", "tabledisplay = TableDisplay(mapListColorProvider)\n", "\n", "colors = [\n", " [Color.LIGHT_GRAY, Color.GRAY, Color.RED],\n", " [Color.DARK_GREEN, Color.ORANGE, Color.RED],\n", " [Color.MAGENTA, Color.BLUE, Color.BLACK]\n", "]\n", "\n", "def color_provider(row, column, table):\n", " return colors[row][column]\n", "\n", "tabledisplay.setFontColorProvider(color_provider)\n", "tabledisplay" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapListFilter = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapListFilter)\n", "\n", "def filter_row(row, model):\n", " return model[row][1] == 8\n", "\n", "display.setRowFilter(filter_row)\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m3\", TableDisplayCellHighlighter.FULL_ROW))\n", "\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display mode: Pandas default" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beakerx.pandas_display_default()\n", "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display mode: TableDisplay Widget" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beakerx.pandas_display_table()\n", "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recognized Formats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay([{'y1':4, 'm3':2, 'z2':1}, {'m3':4, 'z2':2}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay({\"x\" : 1, \"y\" : 2})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Programmable Table Actions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapList4 = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapList4)\n", "\n", "def dclick(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = sum(map(int,tabledisplay.values[row]))\n", "\n", "display.setDoubleClickAction(dclick)\n", "\n", "def negate(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = -1 int(tabledisplay.values[row][column])\n", "\n", "def incr(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = int(tabledisplay.values[row][column]) + 1\n", "\n", "display.addContextMenuItem(\"negate\", negate)\n", "display.addContextMenuItem(\"increment\", incr)\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapList4 = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapList4)\n", "\n", "#set what happens on a double click\n", "display.setDoubleClickAction(\"runDoubleClick\")\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "runDoubleClick" ] }, "outputs": [], "source": [ "print(\"runDoubleClick fired\")\n", "print(display.details)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set index to DataFrame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df.set_index(['m3'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df.index = df['time']\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Update cell" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataToUpdate = [\n", " {'a':1, 'b':2, 'c':3},\n", " {'a':4, 'b':5, 'c':6},\n", " {'a':7, 'b':8, 'c':9}\n", "]\n", "tableToUpdate = TableDisplay(dataToUpdate)\n", "\n", "tableToUpdate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tableToUpdate.values[0][0] = 99\n", "tableToUpdate.sendModel()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tableToUpdate.updateCell(2,\"c\",121)\n", "tableToUpdate.sendModel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML format\n", "\n", "HTML format allows markup and styling of the cell's content. Interactive JavaScript is not supported however." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay({\n", " 'w': '$2 \\\\sigma$',\n", " 'x': '<em style=\"color:red\">italic red</em>',\n", " 'y': '<b style=\"color:blue\">bold blue</b>',\n", " 'z': 'strings without markup work fine too',\n", " })\n", "table.setStringFormatForColumn(\"Value\", TableDisplayStringFormat.getHTMLFormat())\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Auto linking of URLs\n", "\n", "The normal string format automatically detects URLs and links them. An underline appears when the mouse hovers over such a string, and when you click it opens in a new window." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay({'Two Sigma': 'http://twosigma.com', 'BeakerX': 'http://BeakerX.com'})" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
lisitsyn/shogun	doc/ipython-notebooks/ica/ecg_sep.ipynb	4	7779	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fetal Electrocardiogram Extraction by Source Subspace Separation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### By Kevin Hughes and Andreas Ziehe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates <a href=\"http://en.wikipedia.org/wiki/Blind_signal_separation\">Blind Source Seperation</a>(BSS) on several time synchronised Electrocardiogram's (ECG's) of the baby's mother using <a href=\"http://en.wikipedia.org/wiki/Independent_component_analysis\">Independent Component Analysis</a> (ICA) in Shogun. This is used to extract the baby's ECG from it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This task has been studied before and has been published in these papers:\n", "\n", "Cardoso, J. F. (1998, May). Multidimensional independent component analysis. \n", "In Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 \n", "IEEE International Conference on (Vol. 4, pp. 1941-1944). IEEE.\n", "\n", "Dirk Callaerts, \"Signal Separation Methods based on Singular Value\n", "Decomposition and their Application to the Real-Time Extraction of the\n", "Fetal Electrocardiogram from Cutaneous Recordings\", Ph.D. Thesis,\n", "K.U.Leuven - E.E. Dept., Dec. 1989.\n", "\n", "L. De Lathauwer, B. De Moor, J. Vandewalle, \"Fetal Electrocardiogram\n", "Extraction by Source Subspace Separation\", Proc. IEEE SP / ATHOS\n", "Workshop on HOS, June 12-14, 1995, Girona, Spain, pp. 134-138.\n", "\n", "In this workbook I am going to show you how a similar result can be obtained using the ICA algorithms available in the Shogun Machine Learning Toolbox." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need some data, luckily an ECG dataset is distributed in the Shogun data repository. So the first step is to change the directory then we'll load the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# change to the shogun-data directory\n", "import os\n", "SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n", "os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# load data\n", "# Data originally from:\n", "# http://perso.telecom-paristech.fr/~cardoso/icacentral/base_single.html\n", "data = np.loadtxt('foetal_ecg.dat')\n", "\n", "# time steps\n", "time_steps = data[:,0]\n", "\n", "# abdominal signals\n", "abdominal2 = data[:,1]\n", "abdominal3 = data[:,2]\n", "abdominal4 = data[:,3]\n", "abdominal5 = data[:,4]\n", "abdominal6 = data[:,5]\n", "\n", "# thoracic signals\n", "thoracic7 = data[:,6]\n", "thoracic8 = data[:,7]\n", "thoracic9 = data[:,8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we go any further let's take a look at this data by plotting it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "# plot signals\n", "import pylab as pl\n", "\n", "# abdominal signals\n", "for i in range(1,6):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, data[:,i], 'r')\n", " pl.title('Abdominal %d' % (i))\n", " pl.grid()\n", " pl.show()\n", "\n", "# thoracic signals\n", "for i in range(6,9):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, data[:,i], 'r')\n", " pl.title('Thoracic %d' % (i))\n", " pl.grid()\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The peaks in the plot represent a heart beat but its pretty hard to interpret and I know I definitely can't see two distinc signals, lets see what we can do with ICA!\n", "\n", "In general for performing Source Separation we need at least as many mixed signals as sources we're hoping to separate and in this case we actually have a lot more (9 mixtures but there is only 2 sources, mother and baby). There are several different approaches for handling this situation, some algorithms are specifically designed to handle this case while other times the data is pre-processed with Principal Component Analysis (PCA). It is also common to simply apply the separation to all the sources and then choose some of the extracted signal manually or using some other know criteria which is what I'll be showing in this example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create our ICA data set and convert to a Shogun features type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import shogun as sg\n", "\n", "# Signal Matrix X\n", "X = (np.c_[abdominal2, abdominal3, abdominal4, abdominal5, abdominal6, thoracic7,thoracic8,thoracic9]).T\n", "\n", "# Convert to features for shogun\n", "mixed_signals = sg.features((X).astype(np.float64))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we apply the ICA algorithm to separate the sources:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Separating with SOBI\n", "sep = sg.transformer('SOBI')\n", "sep.put('tau', 1.0np.arange(0,120))\n", " \n", "sep.fit(mixed_signals)\n", "signals = sep.transform(mixed_signals)\n", "\n", "S_ = signals.get('feature_matrix')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we plot the separated signals:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Show separation results\n", "\n", "# Separated Signal i\n", "for i in range(S_.shape[0]):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, S_[i], 'r')\n", " pl.title('Separated Signal %d' % (i+1))\n", " pl.grid()\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can interpret the results! First we are going to exploit the known fact that the baby's heart rate is about twice that of the mothers.\n", "\n", "Our interpretation of the results is as follows:\n", "\n", " separated signal 1 -> baby ECG\n", "* separated signal 2 -> still a bit mixed baby +mother\n", "* separated signal 3 -> baby ECG\n", "* separated signal 4 -> slow drift due to breathing of the mother\n", "* separated signal 5 -> mainly mother ECG but still a bit mixed and noisy\n", "* separated signal 6-8 -> mothers ECG, with 8 being the best\n", "\n", "And thats the proof of concept Fetal Electrocardiogram Extraction by Source Subspace Separation using the Shogun Machine Learning Toolkit!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
robertarles/jupyter-notes	Temp_sendclicks.ipynb	1	3754	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mongo connection Collection(Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'kinesis_traffic'), 'recorded_clicks')\n", "done with [18534] lines\n", "clicks [[{'price_format_id': 2, 'campaign_type_id': 1, 'redirect_url': 'https://www.google.com/search?q=offer+link', 'click_pixel': False, 'language_raw': 'qu', 'traffic_type_id': 1, 'campaign_id': 22030, 'creative_id': 14121, 'ip_address': '52.42.0.110', 'subid_1': 'SUB_ID/', 'test': False, 'request_date_utc': '2016-10-05 21:56:26.756469', 'location_id': 107, 'offer_id': 8146, 'country': 'US', 'create_campaign_direct_channel': False, 'region': 'OR', 'domain': 'c.demo-newtrk.cakemarketing.com', 'client_id': 3, 'session_id': 'bb23b051-adf5-437b-8ca7-67f9e5fdc4bb', 'create_campaign_copy_parent': False, 'price_received': 2, 'city': 'portland', 'click_id': '08caba62-d61e-497c-b6a7-672f767117ae', 'first_touch_by_publisher': True, 'tracking_id': '81bc8891-9e8c-409e-a64a-c46deabfe7c8', 'visitor_id': 'd79af5d1-cc30-487c-9f47-e24c715209ee', 'price_owed': 1, 'offer_contract_id': 8686, 'user_agent': 'HasOffers Mobile AppTracking v1.0', 'first_touch': True, '_id': ObjectId('57f7d3f3c1518133c37786e0'), 'browser_version_minor_id': 2, 'request_date': '2016-10-05 14:56:26.756469', 'language_id': 77, 'publisher_id': 6683, 'create_campaign_copy_parent_pixel': False, 'isp_id': 35832, 'create_campaign_internal_campaign_redirect': False}]]\n" ] } ], "source": [ "import json\n", "from pymongo import MongoClient\n", "import random \n", "\n", "MONGO_CONFIG = {\"host\": \"localhost\", \"port\": 27017}\n", "user_agents = [\n", " \"Mozilla/5.0 (Windows NT 6.1, Trident/7.0, rv:11.0) like Gecko\",\n", " \"Mozilla/5.0 (iPhone, CPU iPhone OS 9_3_4 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13G35 Safari/601.1,apple_iphone_ver9\",\n", " \"HasOffers Mobile AppTracking v1.0\",\n", " \"Mozilla/5.0 (Linux, Android 5.0.1, SAMSUNG SCH-I545 4G Build/LRX22C) AppleWebKit/537.36 (KHTML, like Gecko) SamsungBrowser/2.1 Chrome/34.0.1847.76 Mobile Safari/537.36,samsung_sch_i545_ver1_suban50\"\n", "]\n", "\n", "recorded_clicks=MongoClient(**MONGO_CONFIG).kinesis_traffic.recorded_clicks\n", "print(\"mongo connection\", recorded_clicks)\n", "\n", "clicks = []\n", "with open('/Users/robert/dev/bigdata/traffic_gen/clicks.json', 'r') as clickfile:\n", " for line in clickfile.readlines():\n", " try:\n", " click = json.loads(line)\n", " click[\"user_agent\"] = random.choice(user_agents)\n", " if click[\"client_id\"] == 3:\n", " click[\"traffic_type_id\"] = 1\n", " clicks.append(click)\n", " recorded_clicks.insert_one(click)\n", " except:\n", " pass\n", " # print(\"decode skipped for [{}]\".format(line))\n", "print(\"done with [{}] lines\".format(len(clicks)))\n", "print(\"clicks [{}]\".format(clicks[:1]))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
Yangqing/caffe2	caffe2/python/tutorials/create_your_own_dataset.ipynb	1	38351	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How do I create my own dataset?\n", "\n", "So Caffe2 uses a binary DB format to store the data that we would like to train models on. A Caffe2 DB is a glorified name of a key-value storage where the keys are usually randomized so that the batches are approximately i.i.d. The values are the real stuff here: they contain the serialized strings of the specific data formats that you would like your training algorithm to ingest. So, the stored DB would look (semantically) like this:\n", "\n", "key1 value1\n", "key2 value2\n", "key3 value3\n", "...\n", "\n", "To a DB, it treats the keys and values as strings, but you probably want structured contents. One way to do this is to use a TensorProtos protocol buffer: it essentially wraps Tensors, aka multi-dimensional arrays, together with the tensor data type and shape information. Then, one can use the TensorProtosDBInput operator to load the data into an SGD training fashion.\n", "\n", "Here, we will show you one example of how to create your own dataset. To this end, we will use the UCI Iris dataset - which was a very popular classical dataset for classifying Iris flowers. It contains 4 real-valued features representing the dimensions of the flower, and classifies things into 3 types of Iris flowers. The dataset can be downloaded [here](https://archive.ics.uci.edu/ml/datasets/Iris)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\n", "WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\n" ] } ], "source": [ "# First let's import some necessities\n", "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "from __future__ import unicode_literals\n", "\n", "%matplotlib inline\n", "import urllib2 # for downloading the dataset from the web.\n", "import numpy as np\n", "from matplotlib import pyplot\n", "from StringIO import StringIO\n", "from caffe2.python import core, utils, workspace\n", "from caffe2.proto import caffe2_pb2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw data looks like this:\n", "5.1,3.5,1.4,0.2,Iris-setosa\n", "4.9,3.0,1.4,0.2,Iris-setosa\n", "4.7,3.2,1.3,0.2,Iris-setosa\n", "4.6,3.1,1.5,0.2,...\n" ] } ], "source": [ "f = urllib2.urlopen('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')\n", "raw_data = f.read()\n", "print('Raw data looks like this:')\n", "print(raw_data[:100] + '...')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# load the features to a feature matrix.\n", "features = np.loadtxt(StringIO(raw_data), dtype=np.float32, delimiter=',', usecols=(0, 1, 2, 3))\n", "# load the labels to a feature matrix\n", "label_converter = lambda s : {'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}[s]\n", "labels = np.loadtxt(StringIO(raw_data), dtype=np.int, delimiter=',', usecols=(4,), converters={4: label_converter})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we do training, one thing that is often beneficial is to separate the dataset into training and testing. In this case, let's randomly shuffle the data, use the first 100 data points to do training, and the remaining 50 to do testing. For more sophisticated approaches, you can use e.g. cross validation to separate your dataset into multiple training and testing splits. Read more about cross validation [here](http://scikit-learn.org/stable/modules/cross_validation.html)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "random_index = np.random.permutation(150)\n", "features = features[random_index]\n", "labels = labels[random_index]\n", "\n", "train_features = features[:100]\n", "train_labels = labels[:100]\n", "test_features = features[100:]\n", "test_labels = labels[100:]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11268b290>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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8ZmYdxonfzKzJlq9ZTveSbnZZuAvdS7pZvmb5qK5vp6/OaWZmI7d8zXJ6L+9l87ObAVi7aS29l/cCMPeguaOyTu/xm5k10fxr5z+X9Es2P7uZ+dfOH7V1OvGbmTXRw5serqu8EZz4zcyaaOqkqXWVN0KewdZ3k3SrpDsk3S1pYZU6n5W0Orv9QtLGsmlby6atbPQLMDNrZYtnLmbihImDyiZOmMjimYtHbZ15Du4+AxwTEU9JmgD8WNJVEXFzqUJE/J/SY0n/ABxWNv/TEXFowyI2M2sjpQO486+dz8ObHmbqpKksnrl41A7sQo7Enw2a/lT2dEJ2G2q8xncA54w8NDOzzjD3oLmjmugr5WrjlzRO0mpgA3B1RNxSo940YH/gurLi3ST1S7pZ0klDrKM3q9c/MDBQx0swM7N65Er8EbE1a66ZDBwu6cAaVecAl0XE1rKyqdkAwH8HLJH08hrrWBYRPRHR09XVVcdLMDOzetR1Vk9EbASuB46rUWUO8M2KedZn9w9k8x6242w2rPPPh76+wWV9fanczKwOec7q6ZK0V/Z4d2AWcF+Veq8E9gZ+Wla2t6Rds8f7AEcA9zQm9A4zfTqcfPL25N/Xl55Pn97cuMys5eQ5q2df4CuSxpF+KFZExBWSFgH9EVE6RfMdwCXZweCSVwGfl7Qtm/eTEeHEvzNmzIAVK1KynzcPli5Nz2fMaHZkZtZiNDhPF0NPT0/09/c3O4xiWrAAzjsPzj4bFi1qdjRmVhCSbsuOpw7LPXdbSV9f2tM/++x0X9nmb2aWgxN/qyi16a9Ykfb0S80+Tv5mVicn/laxatXgNv1Sm/+qVc2Ny8xajtv4zczagNv4zcysJid+M7MO48RvZtZhnPjNzDqME7+ZWYdx4jcz6zBO/GZmHcaJ38yswzjxm5l1GCd+M7MO48RvZtZh8ozAtZukWyXdIeluSQur1DlV0oCk1dntvWXT3iXpl9ntXY1+AR0jz9CLzRie0UNCmrWcPHv8zwDHRMQhwKHAcZLeUKXepRFxaHa7CEDSC4FzgNcDhwPnSNq7QbF3ljxDLzZjeEYPCWnWcoZN/JE8lT2dkN3yXtLzr4CrI+KJiPgtcDW1B2q3oZQPvbhgwfZr85cPvZinTjPiMrNCydXGL2mcpNXABlIiv6VKtbdJulPSZZKmZGX7AY+U1VmXlVVbR6+kfkn9AwMDdbyEDjJjRhpv97zz0n215JqnTjPiMrPCyJX4I2JrRBwKTAYOl3RgRZXLge6IOBi4BvhKVq5qi6uxjmUR0RMRPV1dXfmi7zR5hl5sxvCMHhLSrKXUdVZPRGwErqeiuSYiHo+IZ7KnXwBelz1eB0wpqzoZWL9TkXa6PEMvNmN4Rg8JadZy8pzV0yVpr+zx7sAs4L6KOvuWPZ0N3Js9/i/gWEl7Zwd1j83KrF55hl5sxvCMHhLSrOUMO/SipINJTTfjSD8UKyJikaRFQH9ErJT0CVLC3wI8AcyLiPuy+d8NfCxb3OKI+NJwQXnoRTOz+tQz9KLH3DUzawMec9fMzGpy4m8njexF6x65Zm3Lib+dNLIXrXvkmrWt8c0OwBqovBftvHnpnPqd7UXbyGWZWaF4j7/dNLIXrXvkmrUlJ/5208hetO6Ra9aWnPjbSSN70bpHrlnbcuJvJ43sReseuWZtyx24zMzagDtwmZlZTU78ZmYdxonfzKzDOPGbmXUYJ34zsw7jxG9m1mGc+M3MOkyeoRd3k3SrpDsk3S1pYZU6Z0q6R9Kdkq6VNK1s2lZJq7Pbyka/ADMzq0+eq3M+AxwTEU9JmgD8WNJVEXFzWZ2fAT0RsVnSPOB84G+zaU9HxKGNDdvMzHbWsHv8kTyVPZ2Q3aKiTl9EbM6e3gxMbmiUZmbWMLna+CWNk7Qa2ABcHRG3DFH9PcBVZc93k9Qv6WZJJw2xjt6sXv/AwECu4M3MrH65En9EbM2aayYDh0s6sFo9SacAPcCny4qnZteP+DtgiaSX11jHsojoiYierq6uul6EmZnlV9dZPRGxEbgeOK5ymqRZwHxgdkQ8UzbP+uz+gWzew3Y+XLP6nXtusyMYmaLFv3zNcrqXdLPLwl3oXtLN8jXLd6qONc+wV+eU1AU8GxEbJe0O/Aj4VERcUVbnMOAy4LiI+GVZ+d7A5oh4RtI+wE+BEyPinqHW6atzWiNJUMCL0OZWpPiXr1lO7+W9bH5283NlEydMZNkJy5h70NzcdazxGn11zn2BPkl3AqtIbfxXSFokaXZW59PAHsC3Kk7bfBXQL+kOoA/45HBJ38yKa/618wcldIDNz25m/rXz66pjzZXnrJ47I+KwiDg4Ig6MiEVZ+YKIWJk9nhURL4mIQ7Pb7Kz8JxFxUEQckt1fPLovxyw599y0pyyl56XHRWs2qaWo8T+86eFhy/PUseZyz11rS+eem5pHSk0kpcfNTpx5FTX+qZOmDluep441lxO/meW2eOZiJk6YOKhs4oSJLJ65uK461lxO/Nb2zjmn2RGMTJHin3vQXJadsIxpk6YhxLRJ03Y4aJunjjWXx9w1M2sDHnPXzMxqcuI3M+swTvyj6S1vgQsuGFx2wQWp3GwYzer9evTRI19GKXad6567RZTnssy2s2bNgrPOSo/PPDMl/bPOgs98prlxWeFV9n5du2ktvZf3Aoz6QdIbbhjZ/INi19jGbvn44O5oKyX7I4+EH/84Jf0zz2x2VFZw3Uu6Wbtp7Q7l0yZN46EzHhrVdY/0EhHNjL2T+eBukZx5Zkr6N92U7p30LYex7v169NHVewrvTLPP2o3VY6xVbmPPiX+0XXBB2tP/y79M95Vt/mZVjHXv1+uvr95T+Prr61/WtL2qx1ir3MaeE/9oKm/Tv/HGdH/WWU7+NqxW7v3ayrF3Cif+0XTNNYPb9M88Mz2/5prmxmWF18zer0cdNbL5y2Mn3HO3iHxw18ysDfjgrpmZ1eTEb2bWYYZN/JJ2k3SrpDsk3S1pYZU6u0q6VNL9km6R1F027aNZ+c8l/VVjw2+S88+Hvr7BZX19qdyGNJbXkx+Nnq/Nvh5+pVlfnYUW6rnbrK/OanZIVqGI4w/n2eN/BjgmIg4BDgWOk/SGijrvAX4bEQcAnwU+BSDp1cAc4DWkAdo/J2lco4JvmunT4eSTtyf/vr70fPr05sbVAhbusNswOkq9R9duWksQz/UeHemXbqziz2PWV2dx7YPXDiq79sFrnfwLZLQ+hyOVZ+jFiIinsqcTslvlEeETga9kjy8DZkpSVn5JRDwTEQ8C9wOHNyTyZpoxA1asSMl+wYJ0v2JFKrdC6IRxXyuT/nDlNvaK+jnM1cYvaZyk1cAG0mDrt1RU2Q94BCAitgCbgBeVl2fWZWXV1tErqV9S/8DAQH2vohlmzIB58+C889K9k35NzRg/tpE9X4s6/q0VX1HHH86V+CNia0QcCkwGDpd0YEUVVZttiPJq61gWET0R0dPV1ZUnrObq64OlS+Hss9N9ZZu/PacZ48c2sudrUce/teIr6vjDdZ3VExEbgetJ7fXl1gFTACSNByYBT5SXZyYD63cy1uIotemvWAGLFm1v9nHyL4xO6D06c/+ZdZXb2Cvq5zDPWT1dkvbKHu8OzALuq6i2EnhX9vjtwHWReoatBOZkZ/3sD7wCuLVRwTfNqlWD2/RLbf6rVjU3rhYwVuPHjlbP1yKNf3vNO6/ZIcnP3H8m17zTPcOLoqjjDw/bc1fSwaQDt+NIPxQrImKRpEVAf0SslLQb8DXgMNKe/pyIeCCbfz7wbmALcEZEXDVcUO65a2ZWn3p67vqSDWZmbcCXbDAzs5qc+K2QfMbMdt4W1mhu6rFCGunwf+3E28LycFOPmZnV5MRvheEestt5W9hoclOPFZKbN7bztrA83NRjZmY1OfFbIRWph2yzeVtYo7mpx8ysDbipx8zManLiNzPrME78VkjtfNpi3jFYT/vBaYxfNB4tFOMXjee0H5w2xpF2riKOk9tIbuO3QmrXUxhLY7CWD8c3ccLEHS7Ve9oPTmNp/9Id5p/XM4/PHf+5MYm1U+V9j4rGV+e0lteuib97STdrN63doXzapGk8dMZDzz0fv2g8W2PrDvXGaRxbFmwZzRA7Xt73qGh8cNdaUif0Vs07Bmu1pD9UuTVOUcfJbaQ8I3BNkdQn6V5Jd0s6vUqdf5K0OrvdJWmrpBdm0x6StCab5t14q6kTxrbNOwbrOI2rWq9WuTVOUcfJbaQ8e/xbgA9FxKuANwAfkPTq8goR8emIODQbkP2jwA0R8URZlRnZ9Fx/Q8zaVd4xWHtf11t1/lrl1jhFHSe3kYZN/BHxaETcnj3+HXAvsN8Qs7wD+GZjwrNO1a69VfOOwfq54z/HvJ55z+3hj9M4H9gdI0UdJ7eR6jq4K6kbuBE4MCKerDJ9IrAOOKC0xy/pQeC3QACfj4hlw63HB3fNzOpTz8Hd8XUsdA/g26QB03dI+pkTgP+uaOY5IiLWS3oxcLWk+yLixirL7wV6AaZObZ+2NDOzosl1Vo+kCaSkvzwivjNE1TlUNPNExPrsfgPwXeDwajNGxLKI6ImInq6urjxhWQtqxIHa5zo2nduaHZvavXOQFV+es3oEXAzcGxEXDFFvEnAU8P2ysudL2rP0GDgWuGukQVvrWrhwZPOXOjZtja2gdHrj0v6lLZP8S52D1m5aSxCs3bSW3st7nfxtTA3bxi/pSOAmYA2wLSv+GDAVICIuzOqdChwXEXPK5n0ZaS8fUrPSNyJi2EPjbuNvXyPtmNXqHZtatXOQFV9D2/gj4seActT7MvDlirIHgEPyBGLt69xzB+/plzponXNO/U0/W7dtrfpp3LqtNTo2dULnICs+99y1UdfIjlnjdqnRsalGedF0QucgKz4nfmsprd6xqRM6B1nxOfHbmBppx6xBHZui9To2dULnICs+X53TzKwN+OqcZmZWkxO/mVmHceK3hmnk5ZPb6VLMZkXjNn5rmEaOmtWuI3CZjRa38ZuZWU1O/DYijRwusROGXjQrAjf1WMO4qcesedzUY2ZmNTnxW8M0crjEdh160awI3NRjZtYG3NRjZmY1OfGbmXWYPEMvTpHUJ+leSXdLOr1KnaMlbZK0OrstKJt2nKSfS7pf0kca/QI6UTuf3ujxaM1GX56hF/cF9o2I27Pxc28DToqIe8rqHA2cFRFvrZh3HPAL4E3AOmAV8I7yeatxG//Q2vVUx9J4tJuf3fxc2cQJE33ZYrMcGtrGHxGPRsTt2ePfAfcC++WM5XDg/oh4ICL+CFwCnJhzXusw86+dPyjpA2x+djPzr53fpIjM2lNdbfySuoHDgFuqTP5zSXdIukrSa7Ky/YBHyuqso8aPhqReSf2S+gcGBuoJqyN0Qq9Wj0drNjZyJ35JewDfBs6IiCcrJt8OTIuIQ4B/B75Xmq3Koqo2UkTEsojoiYierq6uvGF1jEaOW1tUHo/WbGzkSvySJpCS/vKI+E7l9Ih4MiKeyh5fCUyQtA9pD39KWdXJwPoRR21tyePRmo2NPGf1CLgYuDciLqhR56VZPSQdni33cdLB3FdI2l/S84A5wMpGBd+p2rVXq8ejNRsbec7qORK4CVgDbMuKPwZMBYiICyV9EJgHbAGeBs6MiJ9k878FWAKMA74YEcPuvvmsHjOz+tRzVo8v2WBm1gZ8yQYzM6vJid8a5uijmx2BmeXhxG8Nc8MNzY7AzPJw4jcz6zBO/DYiRx9dvUexm33Mimt8swOw1nb99dsft+vF48zajff4zcw6jBO/NcxRRzU7AjPLw4nfGqa82cfMisuJ38yswzjxm5l1mPZI/OefD319g8v6+lK5jZl2GhvArJ21R+KfPh1OPnl78u/rS8+nT29uXB1m4cJmR2BmebTHefwzZsCKFSnZz5sHS5em5zNmNDsyM7PCaY89fkhJft48OO+8dO+kPyY6YSxgs3bTPom/ry/t6Z99drqvbPO3UdEJYwGbtZs8Qy9OkdQn6V5Jd0s6vUqduZLuzG4/kXRI2bSHJK2RtFrS6IyuUmrTX7ECFi3a3uzj5G9mtoM8e/xbgA9FxKuANwAfkPTqijoPAkdFxMHAecCyiukzIuLQvKPD1G3VqsFt+qU2/1WrRmV1Vl1dAGx4AAAFxklEQVS7jgVs1m7qHnpR0veB/4iIq2tM3xu4KyL2y54/BPRExG/yrsNDL5qZ1WfUhl6U1A0cBtwyRLX3AFeVPQ/gR5Juk9Q7xLJ7JfVL6h8YGKgnLDMzq0Pu0zkl7QF8GzgjIp6sUWcGKfEfWVZ8RESsl/Ri4GpJ90XEjZXzRsQysiainp4eX9zXzGyU5NrjlzSBlPSXR8R3atQ5GLgIODEiHi+VR8T67H4D8F3g8JEGbdYoy9csp3tJN7ss3IXuJd0sX7O82SGZjbo8Z/UIuBi4NyIuqFFnKvAd4H9FxC/Kyp8vac/SY+BY4K5GBG42UsvXLKf38l7WblpLEKzdtJbey3ud/K3tDXtwV9KRwE3AGmBbVvwxYCpARFwo6SLgbcDabPqWiOiR9DLSXj6kZqVvRMTi4YLywV0bC91Lulm7ae0O5dMmTeOhMx4a+4DMRqCeg7vDtvFHxI8BDVPnvcB7q5Q/AByy4xxmzffwpofrKjdrF+3Tc9esTlMnTa2r3KxdOPFbx1o8czETJ0wcVDZxwkQWzxy2NdKspTnxW8eae9Bclp2wjGmTpiHEtEnTWHbCMuYeNLfZoZmNqrp77o4FH9w1M6vPqPXcNTOz1ufEb2bWYZz4zcw6jBO/mVmHceI3M+swhTyrR9IA2y//UK99gNzX/i+YVo4dWjv+Vo4dHH8zFSX2aRHRladiIRP/SEjqH7WRvkZZK8cOrR1/K8cOjr+ZWjF2N/WYmXUYJ34zsw7Tjom/cqD3VtLKsUNrx9/KsYPjb6aWi73t2vjNzGxo7bjHb2ZmQ3DiNzPrMC2b+CWNk/QzSVdUmXaqpAFJq7PbDqODNZOkhyStyWLb4TKkSv5N0v2S7pT02mbEWUuO+I+WtKls+y9oRpzVSNpL0mWS7pN0r6Q/r5he9G0/XPyF3PaSXlkW02pJT0o6o6JOYbd9zvgLue2rGXboxQI7HbgXeEGN6ZdGxAfHMJ56zYiIWp0+3gy8Iru9Hlia3RfJUPED3BQRbx2zaPL7V+CHEfF2Sc8DJlZML/q2Hy5+KOC2j4ifA4dC2mkDfsX28bhLCrvtc8YPBdz21bTkHr+kycDxwEXNjmWUnAh8NZKbgb0k7dvsoFqdpBcAbwQuBoiIP0bExopqhd32OeNvBTOB/4mIyt75hd32FWrF3zJaMvEDS4APA9uGqPO27O/iZZKmjFFceQXwI0m3SeqtMn0/4JGy5+uysqIYLn6AP5d0h6SrJL1mLIMbwsuAAeBLWTPhRZKeX1GnyNs+T/xQzG1fbg7wzSrlRd725WrFD8Xf9kALJn5JbwU2RMRtQ1S7HOiOiIOBa4CvjElw+R0REa8l/bX9gKQ3VkxXlXmKdN7tcPHfTrpuyCHAvwPfG+sAaxgPvBZYGhGHAb8HPlJRp8jbPk/8Rd32AGTNU7OBb1WbXKWsKNseGDb+Qm/7ci2X+IEjgNmSHgIuAY6R9PXyChHxeEQ8kz39AvC6sQ1xaBGxPrvfQGonPLyiyjqg/F/KZGD92EQ3vOHij4gnI+Kp7PGVwARJ+4x5oDtaB6yLiFuy55eREmllnaJu+2HjL/C2L3kzcHtEPFZlWpG3fUnN+Ftg2z+n5RJ/RHw0IiZHRDfpL9d1EXFKeZ2KdsHZpIPAhSDp+ZL2LD0GjgXuqqi2EnhndpbDG4BNEfHoGIdaVZ74Jb1UkrLHh5M+Z4+PdayVIuLXwCOSXpkVzQTuqahW2G2fJ/6ibvsy76B2M0lht32ZmvG3wLZ/Tiuf1TOIpEVAf0SsBP5R0mxgC/AEcGozY6vwEuC72edjPPCNiPihpPcDRMSFwJXAW4D7gc3A3zcp1mryxP92YJ6kLcDTwJwoThfxfwCWZ3/ZHwD+voW2PQwff2G3vaSJwJuA95WVtcy2zxF/Ybd9JV+ywcysw7RcU4+ZmY2ME7+ZWYdx4jcz6zBO/GZmHcaJ38yswzjxm5l1GCd+M7MO8/8BlFkDyPlKrkIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112858ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's plot the first two features together with the label.\n", "# Remember, while we are plotting the testing feature distribution\n", "# here too, you might not be supposed to do so in real research,\n", "# because one should not peek into the testing data.\n", "legend = ['rx', 'b+', 'go']\n", "pyplot.title(\"Training data distribution, feature 0 and 1\")\n", "for i in range(3):\n", " pyplot.plot(train_features[train_labels==i, 0], train_features[train_labels==i, 1], legend[i])\n", "pyplot.figure()\n", "pyplot.title(\"Testing data distribution, feature 0 and 1\")\n", "for i in range(3):\n", " pyplot.plot(test_features[test_labels==i, 0], test_features[test_labels==i, 1], legend[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, as promised, let's put things into a Caffe2 DB. In this DB, what would happen is that we will use \"train_xxx\" as the key, and use a TensorProtos object to store two tensors for each data point: one as the feature and one as the label. We will use Caffe2's Python DB interface to do so." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is what the tensor proto looks like for a feature and its label:\n", "protos {\n", " dims: 4\n", " data_type: FLOAT\n", " float_data: 6.69999980927\n", " float_data: 3.0\n", " float_data: 5.19999980927\n", " float_data: 2.29999995232\n", "}\n", "protos {\n", " data_type: INT32\n", " int32_data: 2\n", "}\n", "\n", "This is the compact string that gets written into the db:\n", "\n", "\u0016\b\u0004\u0010\u0001\u001a\u0010ff�@\u0000\u0000@@ff�@33\u0013@\n", "\u0005\u0010\u0002\"\u0001\u0002\n" ] } ], "source": [ "# First, let's see how one can construct a TensorProtos protocol buffer from numpy arrays.\n", "feature_and_label = caffe2_pb2.TensorProtos()\n", "feature_and_label.protos.extend([\n", " utils.NumpyArrayToCaffe2Tensor(features[0]),\n", " utils.NumpyArrayToCaffe2Tensor(labels[0])])\n", "print('This is what the tensor proto looks like for a feature and its label:')\n", "print(str(feature_and_label))\n", "print('This is the compact string that gets written into the db:')\n", "print(feature_and_label.SerializeToString())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Now, actually write the db.\n", "\n", "def write_db(db_type, db_name, features, labels):\n", " db = core.C.create_db(db_type, db_name, core.C.Mode.write)\n", " transaction = db.new_transaction()\n", " for i in range(features.shape[0]):\n", " feature_and_label = caffe2_pb2.TensorProtos()\n", " feature_and_label.protos.extend([\n", " utils.NumpyArrayToCaffe2Tensor(features[i]),\n", " utils.NumpyArrayToCaffe2Tensor(labels[i])])\n", " transaction.put(\n", " 'train_%03d'.format(i),\n", " feature_and_label.SerializeToString())\n", " # Close the transaction, and then close the db.\n", " del transaction\n", " del db\n", "\n", "write_db(\"minidb\", \"iris_train.minidb\", train_features, train_labels)\n", "write_db(\"minidb\", \"iris_test.minidb\", test_features, test_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's create a very simple network that only consists of one single TensorProtosDBInput operator, to showcase how we load data from the DB that we created. For training, you might want to do something more complex: creating a network, train it, get the model, and run the prediction service. To this end you can look at the MNIST tutorial for details." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The net looks like this:\n", "name: \"example_reader\"\n", "op {\n", " output: \"dbreader\"\n", " name: \"\"\n", " type: \"CreateDB\"\n", " arg {\n", " name: \"db_type\"\n", " s: \"minidb\"\n", " }\n", " arg {\n", " name: \"db\"\n", " s: \"iris_train.minidb\"\n", " }\n", "}\n", "op {\n", " input: \"dbreader\"\n", " output: \"X\"\n", " output: \"Y\"\n", " name: \"\"\n", " type: \"TensorProtosDBInput\"\n", " arg {\n", " name: \"batch_size\"\n", " i: 16\n", " }\n", "}\n", "\n" ] } ], "source": [ "net_proto = core.Net(\"example_reader\")\n", "dbreader = net_proto.CreateDB([], \"dbreader\", db=\"iris_train.minidb\", db_type=\"minidb\")\n", "net_proto.TensorProtosDBInput([dbreader], [\"X\", \"Y\"], batch_size=16)\n", "\n", "print(\"The net looks like this:\")\n", "print(str(net_proto.Proto()))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "workspace.CreateNet(net_proto)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The first batch of feature is:\n", "[[5.5 2.5 4. 1.3]\n", " [5.2 4.1 1.5 0.1]\n", " [7.3 2.9 6.3 1.8]\n", " [6.5 3.2 5.1 2. ]\n", " [4.9 2.4 3.3 1. ]\n", " [5.7 2.6 3.5 1. ]\n", " [5.6 2.5 3.9 1.1]\n", " [5.2 3.5 1.5 0.2]\n", " [4.8 3. 1.4 0.3]\n", " [5.8 2.7 5.1 1.9]\n", " [7.2 3.2 6. 1.8]\n", " [6.8 2.8 4.8 1.4]\n", " [6.6 3. 4.4 1.4]\n", " [7.6 3. 6.6 2.1]\n", " [5.9 3.2 4.8 1.8]\n", " [5.5 3.5 1.3 0.2]]\n", "The first batch of label is:\n", "[1 0 2 2 1 1 1 0 0 2 2 1 1 2 1 0]\n", "The second batch of feature is:\n", "[[6. 3.4 4.5 1.6]\n", " [6.7 3.3 5.7 2.1]\n", " [5.8 2.7 4.1 1. ]\n", " [5. 3.5 1.3 0.3]\n", " [5.8 4. 1.2 0.2]\n", " [5. 3.4 1.5 0.2]\n", " [5.9 3. 4.2 1.5]\n", " [4.8 3.4 1.6 0.2]\n", " [6.7 3. 5.2 2.3]\n", " [5.5 2.3 4. 1.3]\n", " [4.8 3.1 1.6 0.2]\n", " [6.3 2.8 5.1 1.5]\n", " [5. 3.5 1.6 0.6]\n", " [5.8 2.7 3.9 1.2]\n", " [7.2 3.6 6.1 2.5]\n", " [4.3 3. 1.1 0.1]]\n", "The second batch of label is:\n", "[1 2 1 0 0 0 1 0 2 1 0 2 0 1 2 0]\n" ] } ], "source": [ "# Let's run it to get batches of features.\n", "workspace.RunNet(net_proto.Proto().name)\n", "print(\"The first batch of feature is:\")\n", "print(workspace.FetchBlob(\"X\"))\n", "print(\"The first batch of label is:\")\n", "print(workspace.FetchBlob(\"Y\"))\n", "\n", "# Let's run again.\n", "workspace.RunNet(net_proto.Proto().name)\n", "print(\"The second batch of feature is:\")\n", "print(workspace.FetchBlob(\"X\"))\n", "print(\"The second batch of label is:\")\n", "print(workspace.FetchBlob(\"Y\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
michigraber/neuralyzer	notebooks/dev/DimensionalityReduction.ipynb	1	2004103	null	mit
rsignell-usgs/notebook	IRIS/Untitled.ipynb	1	16128	{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = 'http://opendap.oceanobservatories.org/thredds/dodsC/ooi/rsignell-usgs-gov/20161123T152332-CP03ISSM-RID26-07-NUTNRB000-telemetered-nutnr_b_dcl_dark_full_instrument/deployment0001_CP03ISSM-RID26-07-NUTNRB000-telemetered-nutnr_b_dcl_dark_full_instrument.ncml'" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xarray as xr\n", "b = xr.open_dataset(url)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11ec2550>]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": 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"text/plain": [ "<matplotlib.figure.Figure at 0x8ea9e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b['nitrate_concentration'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
brucefan1983/GPUMD	examples/empirical_potentials/phonon_dispersion/Phonon Dispersion.ipynb	1	88008	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phonon Dispersion\n", "# 1. Introduction\n", "- In this example, we use harmonic lattice dynamics to calculate the phonon dispersion of diamond silicon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing Relevant Functions\n", "- The inputs/outputs for GPUMD are processed using the [Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/) and the [thermo](https://github.com/AlexGabourie/thermo) package." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pylab import \n", "from ase.lattice.cubic import Diamond\n", "from ase.build import bulk\n", "from thermo.gpumd.preproc import add_basis, repeat\n", "from thermo.gpumd.io import create_basis, create_kpoints, ase_atoms_to_gpumd\n", "from thermo.gpumd.data import load_omega2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Preparting the Inputs\n", "- The structure as specified is 64-atom diamond silicon at zero temperature and zero pressure. \n", "- Weuse the minimal Tersoff potential [[Fan 2020]](https://doi.org/10.1088/1361-648X/ab5c5f).\n", "\n", "## Generate the [xyz.in](https://gpumd.zheyongfan.org/index.php/The_xyz.in_input_file) file:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Si Unit Cell & Add Basis" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Atoms(symbols='Si2', pbc=True, cell=[[0.0, 2.717, 2.717], [2.717, 0.0, 2.717], [2.717, 2.717, 0.0]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=5.434\n", "Si_UC = bulk('Si', 'diamond', a=a)\n", "add_basis(Si_UC)\n", "Si_UC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Transform Si to Cubic Supercell" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Atoms(symbols='Si64', pbc=True, cell=[10.868, 10.868, 10.868])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create 8 atom diamond structure\n", "Si = repeat(Si_UC, [2,2,1])\n", "Si.set_cell([a, a, a])\n", "Si.wrap()\n", "\n", "# Complete full supercell\n", "Si = repeat(Si, [2,2,2])\n", "Si" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [xyz.in](https://gpumd.zheyongfan.org/index.php/The_xyz.in_input_file) File" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ase_atoms_to_gpumd(Si, M=4, cutoff=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) File\n", "- The [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file reads:\n", "```\n", "2\n", "0 28\n", "4 28\n", "0\n", "0\n", "0\n", "0\n", "1\n", "1\n", "1\n", "1\n", "...\n", "```\n", "- Here the primitive cell is chosen as the unit cell. There are only two basis atoms in the unit cell, as indicated by the number 2 in the first line.\n", "\n", "- The next two lines list the indices (0 and 4) and masses (both are 28 amu) for the two basis atoms.\n", "\n", "- The next lines map all the atoms (including the basis atoms) in the super cell to the basis atoms: atoms equivalent to atom 0 have a label 0, and atoms equivalent to atom 1 have a label 1.\n", "\n", "Note:* The [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file generated by this Jupyter notebook may look different, but the same concepts apply and the results will be the same." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "create_basis(Si)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [kpoints.in](https://gpumd.zheyongfan.org/index.php/The_kpoints.in_input_file) File\n", "- The $k$ vectors are defined in the reciprocal space with respect to the unit cell chosen in the [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file.\n", "- We use the $\\Gamma-X-K-\\Gamma-L$ path, with 400 $k$ points in total." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "linear_path, sym_points, labels = create_kpoints(Si_UC, path='GXKGL',npoints=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The <code>run.in</code> file:\n", "The <code>run.in</code> input file is given below:<br>\n", "```\n", "potential potentials/tersoff/Si_Fan_2019.txt 0\n", "compute_phonon 5.0 0.005 # in units of A\n", "```\n", "\n", "- The first line with the [potential](https://gpumd.zheyongfan.org/index.php/The_potential_keyword) keyword states that the potential to be used is specified in the file [Si_Fan_2019.txt](https://github.com/brucefan1983/GPUMD/blob/master/potentials/tersoff/Si_Fan_2019.txt).\n", "\n", "- The second line with the [compute_phonon](https://gpumd.zheyongfan.org/index.php/The_compute_phonon_keyword) keyword tells that the force constants will be calculated with a cutoff of 5.0 $\\mathring A$ (here the point is that first and second nearest neighbors need to be included) and a displacement of 0.005 $\\mathring A$ will be used in the finite-displacement method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Results and Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure Properties" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "aw = 2\n", "fs = 24\n", "font = {'size' : fs}\n", "matplotlib.rc('font', font)\n", "matplotlib.rc('axes' , linewidth=aw)\n", "\n", "def set_fig_properties(ax_list):\n", " tl = 8\n", " tw = 2\n", " tlm = 4\n", " \n", " for ax in ax_list:\n", " ax.tick_params(which='major', length=tl, width=tw)\n", " ax.tick_params(which='minor', length=tlm, width=tw)\n", " ax.tick_params(which='both', axis='both', direction='in', right=True, top=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Phonon Dispersion\n", "- The [omega2.out](https://gpumd.zheyongfan.org/index.php/The_omega2.out_output_file) output file is loaded and processed to create the following figure. The previously defined kpoints are used for the $x$-axis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "nu = load_omega2()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(10,10))\n", "set_fig_properties([gca()])\n", "vlines(sym_points, ymin=0, ymax=17)\n", "plot(linear_path, nu, color='C0',lw=3)\n", "xlim([0, max(linear_path)])\n", "gca().set_xticks(sym_points)\n", "gca().set_xticklabels([r'$\\Gamma$','X', 'K', r'$\\Gamma$', 'L'])\n", "ylim([0, 17])\n", "ylabel(r'$\\nu$ (THz)')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phonon dispersion of silicon crystal described by the mini-Tersoff potential." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The above figure shows the phonon dispersion of silicon crystal described by the mini-Tersoff potential [[Fan 2020]](https://doi.org/10.1088/1361-648X/ab5c5f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. References\n", "[Fan 2020] Zheyong Fan, Yanzhou Wang, Xiaokun Gu, Ping Qian, Yanjing Su, and Tapio Ala-Nissila, [A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties](https://doi.org/10.1088/1361-648X/ab5c5f), J. Phys.: Condens. Matter 32** 135901 (2020)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
tritemio/multispot_paper	out_notebooks/usALEX-5samples-PR-raw-out-all-ph-22d.ipynb	1	474306	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Executed: Mon Mar 27 11:34:36 2017\n", "\n", "Duration: 8 seconds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# usALEX-5samples - Template\n", "\n", "> This notebook is executed through [8-spots paper analysis](8-spots paper analysis.ipynb).\n", "> For a direct execution, uncomment the cell below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ph_sel_name = \"all-ph\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_id = \"22d\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ph_sel_name = \"all-ph\"\n", "# data_id = \"7d\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load software and filenames definitions" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.5.9).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_notebook()\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data folder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_dir = './data/singlespot/'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "data_dir = os.path.abspath(data_dir) + '/'\n", "assert os.path.exists(data_dir), \"Path '%s' does not exist.\" % data_dir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List of data files:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',\n", " '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',\n", " '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',\n", " '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',\n", " '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from glob import glob\n", "file_list = sorted(f for f in glob(data_dir + '.hdf5') if '_BKG' not in f)\n", "## Selection for POLIMI 2012-11-26 datatset\n", "labels = ['17d', '27d', '7d', '12d', '22d']\n", "files_dict = {lab: fname for lab, fname in zip(labels, file_list)}\n", "files_dict" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('22d', 'all-ph')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ph_sel_map = {'all-ph': Ph_sel('all'), 'Dex': Ph_sel(Dex='DAem'), \n", " 'DexDem': Ph_sel(Dex='Dem')}\n", "ph_sel = ph_sel_map[ph_sel_name]\n", "\n", "data_id, ph_sel_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initial loading of the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = loader.photon_hdf5(filename=files_dict[data_id])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Laser alternation selection\n", "\n", "At this point we have only the timestamps and the detector numbers:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([array([ 16030, 90752, 117066, ..., 47999910064,\n", " 47999915269, 47999979355])],\n", " [array([1, 1, 1, ..., 1, 0, 0], dtype=uint32)])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.ph_times_t, d.det_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should check if everithing is OK with an alternation histogram:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JojN7DR9uo+Y6EKqizutQfZr6mFg+Fx3jO0WVx5qJdbavqPBhmRbbyrexrbzm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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d23aef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_alternation_hist(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the plot looks good we can apply the parameters with:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Total photons (after ALEX selection): 2,103,572\n", "# D photons in D+A excitation periods: 691,365\n", "# A photons in D+A excitation periods: 1,412,207\n", "# D+A photons in D excitation period: 1,237,157\n", "# D+A photons in A excitation period: 866,415\n", "\n" ] } ], "source": [ "loader.alex_apply_period(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measurements infos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the measurement data is in the `d` variable. We can print it:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "singlespot_008_dsDNA_22d_500pM_green100u_red40u G1.000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or check the measurements duration:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "599.99974193749995" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.time_max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the background using automatic threshold:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x103bc9860>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uxKZNm5CYmIjatWvjs88+g0KhEA8QAMu1KNbej2rVqonf6Z9//tmteh09ehS1atVC1apV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103acfa20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d, timetrace_bg)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([2217.7255529583294],\n", " [591.3055432410448],\n", " [818.69564582171483],\n", " [776.94465829541809])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst search and selection" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Calculating burst periods ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Counting D and A ph and calculating FRET ... \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying background correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying leakage correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying direct excitation correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [DONE Counting D/A]\n" ] } ], "source": [ "bs_kws = dict(L=10, m=10, F=7, ph_sel=ph_sel)\n", "d.burst_search(bs_kws)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "th1 = 30\n", "ds = d.select_bursts(select_bursts.size, th1=30)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bursts = (bext.burst_data(ds, include_bg=True, include_ph_index=True)\n", " .round({'E': 6, 'S': 6, 'bg_d': 3, 'bg_a': 3, 'bg_aa': 3, 'nd': 3, 'na': 3, 'naa': 3, 'nda': 3, 'nt': 3, 'width_ms': 4}))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>E</th>\n", " <th>S</th>\n", " <th>bg_a</th>\n", " <th>bg_aa</th>\n", " <th>bg_d</th>\n", " <th>bp</th>\n", " <th>i_end</th>\n", " <th>i_start</th>\n", " <th>na</th>\n", " <th>naa</th>\n", " <th>nd</th>\n", " <th>nda</th>\n", " <th>nt</th>\n", " <th>size_raw</th>\n", " <th>t_end</th>\n", " <th>t_start</th>\n", " <th>width_ms</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.102133</td>\n", " <td>0.544892</td>\n", " <td>1.233</td>\n", " <td>1.192</td>\n", " <td>0.881</td>\n", " <td>0</td>\n", " <td>243</td>\n", " <td>173</td>\n", " <td>3.767</td>\n", " <td>30.808</td>\n", " <td>33.119</td>\n", " <td>-0.083</td>\n", " <td>67.694</td>\n", " <td>71</td>\n", " <td>0.018848</td>\n", " <td>0.017377</td>\n", " <td>1.4709</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.309349</td>\n", " <td>0.376346</td>\n", " <td>2.651</td>\n", " <td>2.563</td>\n", " <td>1.895</td>\n", " <td>0</td>\n", " <td>671</td>\n", " <td>576</td>\n", " <td>10.349</td>\n", " <td>55.437</td>\n", " <td>23.105</td>\n", " <td>-0.180</td>\n", " <td>88.890</td>\n", " <td>96</td>\n", " <td>0.075501</td>\n", " <td>0.072338</td>\n", " <td>3.1635</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.290108</td>\n", " <td>0.831108</td>\n", " <td>1.107</td>\n", " <td>1.070</td>\n", " <td>0.791</td>\n", " <td>0</td>\n", " <td>992</td>\n", " <td>949</td>\n", " <td>9.893</td>\n", " <td>6.930</td>\n", " <td>24.209</td>\n", " <td>-0.075</td>\n", " <td>41.032</td>\n", " <td>44</td>\n", " <td>0.097472</td>\n", " <td>0.096152</td>\n", " <td>1.3205</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.181006</td>\n", " <td>0.602956</td>\n", " <td>1.640</td>\n", " <td>1.585</td>\n", " <td>1.172</td>\n", " <td>0</td>\n", " <td>1970</td>\n", " <td>1890</td>\n", " <td>8.360</td>\n", " <td>30.415</td>\n", " <td>37.828</td>\n", " <td>-0.111</td>\n", " <td>76.603</td>\n", " <td>81</td>\n", " <td>0.291411</td>\n", " <td>0.289455</td>\n", " <td>1.9565</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.046636</td>\n", " <td>0.927752</td>\n", " <td>1.436</td>\n", " <td>1.388</td>\n", " <td>1.027</td>\n", " <td>0</td>\n", " <td>3166</td>\n", " <td>3127</td>\n", " <td>1.564</td>\n", " <td>2.612</td>\n", " <td>31.973</td>\n", " <td>-0.097</td>\n", " <td>36.149</td>\n", " <td>40</td>\n", " <td>0.666337</td>\n", " <td>0.664623</td>\n", " <td>1.7134</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " E S bg_a bg_aa bg_d bp i_end i_start na \\\n", "0 0.102133 0.544892 1.233 1.192 0.881 0 243 173 3.767 \n", "1 0.309349 0.376346 2.651 2.563 1.895 0 671 576 10.349 \n", "2 0.290108 0.831108 1.107 1.070 0.791 0 992 949 9.893 \n", "3 0.181006 0.602956 1.640 1.585 1.172 0 1970 1890 8.360 \n", "4 0.046636 0.927752 1.436 1.388 1.027 0 3166 3127 1.564 \n", "\n", " naa nd nda nt size_raw t_end t_start width_ms \n", "0 30.808 33.119 -0.083 67.694 71 0.018848 0.017377 1.4709 \n", "1 55.437 23.105 -0.180 88.890 96 0.075501 0.072338 3.1635 \n", "2 6.930 24.209 -0.075 41.032 44 0.097472 0.096152 1.3205 \n", "3 30.415 37.828 -0.111 76.603 81 0.291411 0.289455 1.9565 \n", "4 2.612 31.973 -0.097 36.149 40 0.666337 0.664623 1.7134 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bursts.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'results/bursts_usALEX_22d_all_F7.0_m10_size30.csv'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "burst_fname = ('results/bursts_usALEX_{sample}_{ph_sel}_F{F:.1f}_m{m}_size{th}.csv'\n", " .format(sample=data_id, th=th1, bs_kws))\n", "burst_fname" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bursts.to_csv(burst_fname)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert d.dir_ex == 0\n", "assert d.leakage == 0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n" ] }, { "data": { "image/png": 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RYfafOUNE5JWY3MlnGI1GAH4ICuqKtip/q/WuqxtRU38VRiN770TUOjG5k89R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\nTO5EREQSw+ROREQkMUzuREREEvP/AJzAOVRdPP8QAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103be96a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(d.ph_sel)\n", "dplot(d, hist_fret);" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# if data_id in ['7d', '27d']:\n", "# ds = d.select_bursts(select_bursts.size, th1=20)\n", "# else:\n", "# ds = d.select_bursts(select_bursts.size, th1=30)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_bursts_all = ds.num_bursts[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def select_and_plot_ES(fret_sel, do_sel):\n", " ds_fret= ds.select_bursts(select_bursts.ES, fret_sel)\n", " ds_do = ds.select_bursts(select_bursts.ES, do_sel)\n", " bpl.plot_ES_selection(ax, fret_sel)\n", " bpl.plot_ES_selection(ax, do_sel) \n", " return ds_fret, ds_do" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Y8HZPf/MlkU84uXuFEAjb9uMFx84in7gC/yXbBq+8ufSkAEEJoKmpifq6um4u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\nQvMg1OCkIkyqPJWKkZ0mtTKi6lKlh6j6VM5EFZEnW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"text/plain": [ "<matplotlib.figure.Figure at 0x1214e19e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1)\n", "\n", "if data_id == '7d':\n", " fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True) \n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", " \n", "elif data_id == '12d':\n", " fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '17d':\n", " fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '22d':\n", " fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) \n", "\n", "elif data_id == '27d':\n", " fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(403, 2045)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_bursts_do = ds_do.num_bursts[0]\n", "n_bursts_fret = ds_fret.num_bursts[0]\n", "\n", "n_bursts_do, n_bursts_fret" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D-only fraction: 0.164624183007\n" ] } ], "source": [ "d_only_frac = 1.n_bursts_do/(n_bursts_do + n_bursts_fret)\n", "print ('D-only fraction:', d_only_frac)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hFWj9CJU00qiw8M9DkOFEMYWoQR8XL6r8MEOLc/+E80tpjKcxfB0uY3egsT/0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\nW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"text/plain": [ "<matplotlib.figure.Figure at 0x1214df0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JMu4I7vD/amgSVw9HNbNF2oAR17LiaeE3rZQ/+CcGb9AEHOZfqzVKSZ+API1h\nCDoNhsGRCsupHOyN4H6BjggrLKMz/6DMWD5xzS6oHFpK9BYldBKUTnwXgJYSAo0vzE/E5NWdmUTH\nSqmK6+OkZgjRmU/KZxq5pWkMXWnH8J60PLpiZoufT7u/HtNdVt6NkjHLlFervLTz3S1jY9Ekrq1F\nk7h2ECTNIcm0EFmmr3DAjogqTAuIRWvVqbHgE4X2XN8UaAiUxCeGMO/wGP+7IQwQndqLT4QqIkFf\nq4tpYhCxkVY+GWo6tTSl/PpK143SIpKJyxiSVUB64ezYMIyIvIQQ6NgzUUpFWhhaR/XJMqeqQAYj\nVocss2Kt9ZF62zHr/iyTWVr58WuqmdLSTGv1mNwaJWNaejX0BBkbgy237WmiPjSJqwej2npAVodP\nzvwjDSw8VgoVajHB+ojQOtAsApNhoClJu4wQAgmYhkBjoI2AFLQC4Ws8vmlOggYD3am9KEmgmoHS\nvulRh5+gpY6UMaUDsgw+lfK/OKUyCCKtK2atRCN8RSgwORqmT6YhmRmmGT0PwzA6zY1KVazjaa1R\nSoc8hhCdz1p6nk+EpokKis5aM4wIk8rBNK3tarVjPRpAtYE37b6sAT/t/vh1WXXYFjLG0V0ybo0m\n1hg0Na6tRZO4eji60uGh0tFBxQbocBFfSolWCulJtJZ+upQIrQJzmsYIaMIrdyCEwDQESggQBoZh\nok3DN9MJE2EaPstEZBKaCJVPXFqBlqCUv16lfM3LJ4vgdEBSShP9hdqhU2qPCEsRKGnxta6YliWC\nmbUwDEzTRHgSM6irlDIir/C5hMdK+jn7Wqmfl2kaoDWu6/nHEOXvF9u1NY+utmNyjQaqOyWkDcL1\nlldr8K4X3SFjPJ+eIGNj0SSurUWTuHo4as0wk6hwhggQkpbneUhPoqSH9Dyk9FCe568jKYnQEqF9\n4tJa47ZvxjQEGAJM0x/4zRwYJiKXQ5gmaNPXuIRAoRFCR2QllJ+30P6nUhIhFVpqlNT+Z0BcSmmU\nEkitkRqU9tWf8qZNKAJCCwgrWvMK1p5C0jINA8M0MXM5zOBTmWakeUkhfNJEo6REBqQdPp9Q3TIC\nDUsDnuugdQ6tNaZpRp/Ral9C++rKrL5aO2YNzmEZ1a6rtsYUr1PWelItGbpDxiR6mozdgyZxbS2a\nxNXDUU8HCq+Ju5JH61qBViE9n6xc10W6Lq7j4LmOv4bkuSA9UB6mVpgodK4XzqZ15AwDbRqQMyGX\nw8hZiJyyiMlvAAAgAElEQVSFkhZGzkIbOcj5Wo0hNBoVkKAHykVIF+25aOmCJ31Nz1NoT6E8jZSg\npEZJkMr/85TfpXXvnWjfuAGpQeJrXIpOAouTlmGaPllZFjnLwgo+c7kcRi7nP8NwTSt4Hp6UAYFL\nXxONmfvMnE9Wtm2TUwrLsgAwg2estMYIApcmta96zFTV2jFppqtmxktqXmmfybrEv6et5SRNgGn3\nN1rGZF7bUsYss2BWvo1Dk7i2Fk3i6sGo1rninSrZsUSwjgOd61ieJ/FcF9e2cWwb17Zx7TKeYyMd\nG+35JGNqSQ4FA1uxN6yNSEtYObDyiHweZRUw83m0yiNyFlrnEKaBFhpQoFzQLkgb5dkI1wHXAddF\nux7alUhXo1yFcsHzNNIDV/qk5SpwNejW3rSteS/a1EHSSWA69PRLkJZlWeQLBax8nnyhQC6fJxdo\nYCIYvKSUeJ7nPw/X9TXRwIQqhMDM5cjlcmigXCqRV4pwaa3C+SPpZk/6O0tb245pA2oyz7QBPnlt\n1nHWZxqSdW+kjFn17iky7iiRM/6V0CSuHQRZtv1kevQ+Er7WFWoTUnq4joNj25RLHdilEnapA7dU\nQtpltGsjPIec9rCQ5Pp/iPK61eicCVYOI29hFAroQhFRaEHLIoYsovN5hGWhlIFhaDQSlAPKRntl\nhFtCu2W0XUY5Dtp2UY5E2RLP1khH47ka1wXXA0eCo/w/898+zKbVq3A1uHTuSqSE8D39DKNT27Ks\niKwKxWL0ly8WsfJ5zFwu8ij0As3TcRzc4M+LEVcul8PK5yn07k25VOrUxBDRgKoBbRjoKiaraoNy\nrXbMMm1laSq17k8jmWpEVO23t61lzKrbtpKx+QJyz0OTuHZQpA0WFQhMYmh/PSccrO1ymXJHiVJ7\nG+X2NpyOdrxSB8opYbg2OeVREJKd9lKU165GWCZG3sIsFjCKRYyWVgzPAeWitUQgQXsYORNtaMBD\naBu8EtorIZx2tN2BtkuochlVdpAlD1mWeGWFtDWOrXEccFywPbAVlBXspBSbVq3C1uDQSV4ydG8P\n1qKMXI5coGkVWlootrZSbGnBdV2KnodVKJCzrArisgPNM/z0PC96pjnLolAokO/Vi1JHBwjha3Sm\niVQ5jJSZeIWrfYB6TEzV2rGaJpFEPVpENaRpd1mD+7aWMcuUl5V/FrpTxq2D7KZ8P/hoEtcOhrjZ\nJU3bgs6XbLVSkQNCuK5ll8uUOjroaGujtHkT5bbNuB1tqHIHhlsiL11cPPooRWntaox8DrNgYbUU\nUS0taM9GSxelJQbKJy4KgAmGBuGCKoPXgXDb0c5msNvRpXYolVAdNqrD9cmrQ+GUNE5ZY9uCsgNl\nV1OSgrKCPkqxcdVqylpjA44QeFr7619CoA0TYZqYVqAhFYsUW1tpKZdxe/XCC8yABcchl8/73oVK\n+c/BtimXy9jBn+u6vvZkmlj5PMWWFvoNHky5VMIwTXIBOeakjJw04s88zVQYfu9qO6bdk2aSS6KW\nuS4r32r1jJ/L0rK6W8ZaRLctZOweNN/j2lo0iauHo1qnS6ZFLx8Hmpb/3paO1nRCU6FdKlFqb6d9\n82ZKmzbitG1CldownRJ56SDxB/Hy2jVYBYt8MY9nF8m5vZDKw9CKnO8dDwZgKBA5tKkROKBL4LWD\ntxncTWhnE9r2yVF1lPHaXbw2F7dd4nZonA6wbSiXNSUX2qWgJH3i3bhqFWX8Lm4LgQtIrVGGgTYM\njFwuWtsqtLTQ0qtX4Hjik5YnJW6xiFUoRG7xTkjgpRLlUolygrgKxSKe56G1ptTRQS6XI18oVKyF\nxV/eJmFKyjKf1duOabP+avekrTdVM4fV0ibqGbC3pYxpZr7tJWNj0TQVbi2axLUDoFpHTM5qgSjK\nRGi6Ch0SXNf117jKJUod7bS3baa0aRP25g2o9s2Ydjt55aBwfY+6de9RaLFwi3mk2wslXcB/Z0ub\nAnIGOicQpvZd5NEgHFAdaNUGcjPa24jyNqOcTahyG7JURnXYyHYPb7OH06awO6DcrinZgg4H2j3o\n8PyXpTe9t4YSghJga40nBG5AXAQOF1ahQK5QoFgs+ma/mMOFlBLXcbDyeYRp4gXEVS6VKJVKlDo6\nOolLa3K5HI7joAJyKpfL5ItFio6D9DzffT6aFCi00Rm5Q2tddS2lq+2YHLhrEUXaIJ6WT62BvCsa\nVCNkjOdVa/2pO2RMmimT+TSdM3oemsTVQMyePZt9992XE088cYtzN954I/fddx9KKaZMmcL06dO7\nlHfWYLIFYQWu2vGQTiFxRVpX2aZcKlNu76CjvQ1782ZU20YMuwPpljACjcvZsB63bOH1KiKli0ai\nTYEKtBydtzAsAywBuVygeTloVQJVQqt2UO0gN4PciPba0G4HsuzglVzcDg+3TeG0g92mKZegw4EO\nF9o8fxBpW7eeDnyNq4S/ziWF/0K0jhGXlc/jtrbiSdn5blbg7u44DlahADGNq5QgLs+TaK3IWRZe\nEC0DrX0zouNERKiUijw1zVxui7aIt0lX1qLSBvOse5MmtSSyHA2ynChqmdi2p4xZ+TVSxrR8asnY\nGDSJa2vRJK4G4I033uCiiy7i2WefZd99993i/KOPPsqyZctYvHgxUkqmTZvGvvvuy9ChQ6vmW48p\nJD6AxSPCd77T1RnmSXoerufhuIHJsFymXCpht7cj29sxS23glclp1w+3tHkznpdHShelFco0UVYB\nkS9AoYB2CmjXQngGWlpoUyEMh5BmNCUUJdBtKN2OUm1IWUJ6Np7j4tkujq2x2xXlDk25A8plKHmC\nDtcn3fa2zbQr7WtcgBvXuAzfozAXeBOGGhHBO1khYdsJ4rIdh1LMVGgHpkWAfKEACHKW5UfuSLjM\nh5pYPLhvWrzCZFt1pR2rDabJ47TfSprWlqVVpN1fjwnwX0nGpsbV89Akrgbg17/+NRMnTmTw4MGp\n5x955BHGjBlDPp8HYOzYsdxzzz01iauWbT+5eB1/ATncByvUwCKToefhuZ3rXU7Zxi7byI4SZqkD\n4ZbIK99s5rWX8KSH1AplCHTOgnwBXSyg7RaEUwbPAmmA8kNGaWwENlrYIGwQZbQooyijKKF0O1LZ\neNLFdT1cW+E4GrusKZcFpZKi5PjkhdaU2tspKU1JCGx8c6EyDCQgAo0r5/gvUutAG4Iw3qHvQZi3\nbXL5PBgGnlK+qbBc9p0zbBvHcZBBRPkwNFSh4LeV57oRYYWkpZSOBQ/uRJYm1NV2rFdDyNIuwjzi\n6dXqV+13l1V+I2WsJkc1+be1jI1H44nr9ttv5+6772bRokWsX7+ec889l7fffhuAb3zjG4wdO7bh\nZW4PNImrATjnnHMAePLJJ1PPr1q1isMOOyz6PnjwYJYtW1ZX3slOH+9Qyc5XsfFj7K/CZKgUUnq+\n2TAwHbq2jbQdlO2Qcxxc5fhBdjtKKC1RaF/bypfRLTa6bINtIxwb7RYQMkcYg1AYjm8uFI5PXEYZ\nbfifStgoHCQ2nvZwlYsrFY6rcDyB7WhsB8q2puz68pTKZWygrDRlfI1L4nsVhsSlwnew6HxJOHxe\nnudhOQ6mZfnEFWhRoVehY9t+BI3AWxAgn8/jBs4ZSdLyA/VS8bzjHoVxZGkItdoxLa+0fLOuT64x\n1TJ5Jc121eqyrWVM1rM7ZKxVrx1F43rppZdYsGABAwYMAOBHP/oRe+65JzfddBNr1qzh6KOPZtiw\nYfTr16+h5W4PNIlrGyC5OSEQDZLVUK+dP6lxRWGfCLe90pHmFa7PSOUH2g01MOm6aMfDsx2k9InL\nK3t+3EDDRJccdNFDlR207aAd/0+4NtozEVIhlAbtkxM4aOGhhYsyPJThf0rhIXHxcPG0h4PGUdIn\nLldhuwR/vjnOcV3KSuMEHoWhmVALAWHg3IC07PDBBM4pUincgLgMy0IbBlJr/3228AVkz4vWtJQK\n1riCOIZApyNG8tknJglxZ5iwvdIGxnraMTnIpv0eaiFNg6l3vSdpbku7/4MkY9p98Xp23wvIjXOH\n37x5MxdeeCFnn302t956K+CPO+3t7QC0t7f74c+6XYvcNmgS1zbArrvuypo1a6Lvq1evZtddd616\nj5SSlStXbnWZaTELi336MLhXL3b58IcjAovMa9p3oRfaj+5u9e3Hp266G0P4W4fYQuAIA2H4sQEx\nDD9WYBiEVwgCZiGkTI0CU0Nvhe6lYbDyg9wqv14FBXkF/VRo0owpikCxXz/OvP+BysC6oXxURjSo\neJ8qpgWF30ncGzycijzjmpMwDPKFAp854IDou6sU6zduZMOmTVu8v9Vd8DyPl19+uVvLqFX++/kd\nNqL87Sk/wJAhQ7op58ZpXHPmzOHb3/42vXv3jtJOO+00pkyZwiGHHMLGjRs5++yz6du3b8PK3J5o\nEtc2wIgRI7j55puZPHkySinuvfdeTjvttKr3mKYZdZi0heM0JIPqhu7vHe3tbN64kQ0bN7J2zRrW\nrV7NutWrWL96NZtXr6K89j3cDesw2zbRUi7RRzqM+dUiXp42lv4Fi/6tBXbu04veO/ehtV9fCgP6\nkx/Ql9yAvhj9+yB2bkHsZEGrhryDMjvQug0tN+DZG5ClDbibN2JvbKO8vkRpXZn2tS7t73lsXifZ\nvE6zeRNsatNsbtdssqHNhen3P8S8kUdSAspB9AwPfMcMOrcZMQ0jetcql89TaGkhVyhgFYtYLS0I\ny8K0LJRhoLTGCzRON9Cswv228oUCvXr1os9OO9Gvf3/GnXACK194gQEDBzJgwAB27tePPjvtREtr\nK4VCIQrkK+jcZDNsr6zZfj3tGL925cqV7LPPPpkaQlZash5J7aaWaS28Z+XKlQwZMqTudaitkTFe\np6Q8yfK7Q8asa7ui/W0dGkNct912G4MGDWL48OEsX748Sr/44os59thjOfPMM3n33Xc56aST2Gef\nfWqure8IaBJXN+HRRx/lscce45JLLmH48OG8+uqrTJo0Cdd1GTt2bMWaVy3UGiDipsK4JiCEQIW7\nDNOpaUQbNgZEJ5UGLdBaIJVGev6VngfSkHiO/yfLHtL2kLbrh24qu1AuI2wBZYkwNUI7kLPR2P57\nX56L8jpd1KXSfhT4IAKG1MGx1n5UeK3xNHjhBpPCr1MYn1AG5k4MA5F4Bkr53pOe64JpQvApArIS\nuVyQn8IL1qyMQCMzAvIzTdOPkBEQUhjqyQjW0+J7f4UBdo0gkkatnZDrbcf4mk38XK31oXrMeGmD\ncdI8lnV/1sD+fmWsd62pu2QMfzvbnrwaQ1z33nsv5XKZ8ePH09HRwerVq5kyZQqvvvoqs2fPBnyr\nz/Dhw/nDH/7QJK4mKjFv3rzoePjw4QwfPjz6PnPmTGbOnLlV+VYbMEIYhrFFGCL/uDKvSvLSkWlO\nqs79sWTAdtJTeIaB5+qIvLyyR65k45VsRKkEHSZmHoTpotE+UeXKIMoo6SBdD2VLlKP8P1chPYX0\nfIKUyicpqYO9uAgiwIfaC50R4WVg2lP+Q/CJJNAwDcMP52TS6VGolMILnC6M+AvDQexBI3hARhAN\nPox1WCgWfbd4IbBi0eVDk2H4jlc8xFYcWYNvve1Y7Tg5cFe7J55WzaEjSzNJOjNsDxlr3dMIGePk\nlVVm96AxxHXXXXdFxytWrGDevHnceeedTJs2jQcffJCTTz6ZtrY2/vCHP3DmmWc2pMztjSZx7QDI\nWjxO61jxNa0oLXlN4lgBSoNARPthaQ2u1Lhe4PXnKBzbw3I8craLKDuI9hK5vICcxBAmQoJ2XHTO\n9yDUyka5vreibPeQJYUsK6SNH0A+3AYs3JNLi8CNX3SSZ4ysos/YOpYSAhE/HzihmLEdkQ3DiILx\nilwuCtBLbC8vK5+nWCzS2qsXra2ttLS0IMA3CaaRl2FUuMOn7cmVbJ+utGOa40I8n2rlZJWdlX9y\nsM+6p95ytkbGamV1t4zV6rEjaFxZuPzyy7nooou46667MAyDiRMncsQRR3RrmdsKTeLq4ah3FqiC\ntZq4uTDUqLIQd3rQQQDbnBBI7d/vaoEjNY6ncFyJ40rytovZUcbI5/AsP+RTTngoZWE6Cgoe2rRR\noozSJbRno2wXVZLIdolqV6iOTvLSjkZL4TtsqM7dkHWyfkEdI+9IiHYrxgi8DE0/6K4Rbh4ZxjEM\nQkKZQdgnIyAvYfgbRobEVQiIq3efPvTq3RshhK995fPkLMu/1jACh5XY7sspDhpppr562zE5yHYl\nn2prYGn5VbuvVl0bJWNc2+mJMnafO3zjo8MfdNBBLFy4EIDdd9+dBQsWNLyMnoAmce0AqNc0Evck\nhJinXXhT6GEXedp1fioCjQuB50cdxA3c0B0tsD1F3pVYJQfDymFYJTAFIBEqj+Ga6JL23QRN239n\nS5fxwigZZT8avNemkW0a1aZQZVA2aAe027nNsa5wIRS+f6LWnTsgB7KFWpYRrFH5EeMNRC6HYVnk\nCgXyxSL5lhbygcOGaVkYluU/t4CIwh2TC8UiLa2t9Ordm959+iCEoCU0Hca0LkSw1kW6phW2T63Z\nfFY7puWVHIizNIFqGkO1tahkejVNY1vKmIZtLWNzP66ehyZx9WCEnSetAydnrGkzQ611BWnFX9AV\nwQu8GKbv6GCaYOZQhoky/HfMXGHiChFs7OhrXrbjYdouRoeBMA3QEqVcTNtEFDTCkmjTBVHG07Yf\n3sn1wzt5tkS2K2S7RrZrVEmjHIFyQ63LNxP6deyUIay/DiKxq0AuEWhcKiAscjlIkFaxVy+Kra0R\neVn5AqaVw8xV/oWbUBZbWmgNyEsI4XsQBptR+u/BmBhGMCGIPfMs54ytaccs7aHWwJ4kkWrlppWX\nvC5+f1a9ulvGZJ7dIWM1s2b3mgqb25psLZrE1YORHBRqrR9kvlsUM2mF6z7CMDCEv74jcibCzKFz\nObRpokwThMAzTFwBjjAircvyJKbtIkzh60LSIueaqILAKAgwJRiuHyFDO3jSxpMOru35fyWJV5LI\nEgF5KXRZo12Bvydl4OGoKteP4qGswrWs0LlCxf4MywLLwsznyRWLFFpbKfbuTUtrq09gIQkFuyJH\n+2xZFvlwT6+AvIRh0NraSjGmcRnhOlcVE2GILKKp1Y61NIasQb/WtdXMZ2n1zEKjZUzT2JLXbk8Z\nd5TIGf9KaBLXDoB4J8qaPSY7YuS6bWxJWoZhBJqGv+V9zrJQeQtp5RBWPnI6UJaFNASeYeAaBq4w\ncKTGdCVGyUFohZIeyjEwLYHIKYSp/WgZuCg8Pyah5+C4Ho4jcR2FW9J4ZYVXDsyFDmhXoT18U6HU\ngd7lmwE7hYppNHRumCmAnGlCQLxGLoeRz/vvcRUKFFpaaendm9ZevSi2tHSSV2AiDInLyufJB04a\nhWKx01QYe2crlzMj02SILG2r2jpKPe2YNdhmrduklZOWbzVzWrJuafc0WsZqRNgTZGyaCnsemsTV\ng5E1Y02uB4THlU4ZlZHiw8HWjO/mm89j5S2sQhFVsDGKLoYG0zR8La2lFR0E2PVMn7wcYWAqEJ6C\nsovyPKQjMAyNkdOAROOhhUIqF0+5eEriup5varRl4KWo8RyB52ikC0oKlNRoRBSiKkScGMK1Lh04\nZSiItC1tGJHJ0Ay1rkKBfLHg74zcqxctvXr5GlSgRVnhcwhILCSvfD6P43k+0eXz5Kw8OTMgLdPc\nIj5hWMdqTgL1tmO9pqlqJJf2O4rfkzQDpv3eknnXMvNtrYz1muO6S8Z6nkH3oElcW4smcfVg1DIV\nZZlpwhdrJYAmigxhxggrNIsVWlrxWsvgukilMHImVtnyPfZaWxFag9AoAVKAZwocrRHSD6rreZqc\noTFQCEOBlmgtUdpDaolU0t+FWEo/oK6ncFyNI8FxFK4HnqfxpPDd4pUOXppOhGmKxQMEorUuA/wY\nhPi+Hdo0ffKKeRiGWqVVKFAI1rGKLS3kCwXygTZlJcjLsizcjRspFouBU0au06swxVSYFmg3a/Cr\ntx3jyBpos8rK0jiq5VELafk1QsZqBLItZEy7t2kq7NloEtcOgrROmOyg8TWuMAhsFBYp0LSsxFqO\n29qKdhxMrZGmiSjlyRUchBDk+/YlpxQGGqEVWiukVrhagpZIFKanMJSHQCG0h5ZeQFwapbwoSoWn\nFJ5UOFLjKnCk9oPpSo2jwFXgBS8hB4E8fNniHpKxgLehO3zoWRi+mxWugXm60xMxPJ8k8HxAZPlC\nwde+YuSVy+XYuGkTlpXHNAO3+UBzzVrfSrbX1rZj1mCbZgpLczaI51nPQJ+mEdWDRsmYpi3Vyj+e\n5/uVsRrBKaWapsIeiCZx7QDIWuNIDgKRGzzBGpdSGEYwWAfElc/nKba04LS24to2WkoMrcmbJjKf\nh9ZWTNdFGAYtAwZSkB6WkphSgvI3avQ8F+3aeJ7rh13ytP8msfSJSykZBPGVQUgnhZQKT2lcpfGU\nT1ZO8J6YrXzXe98jXqCETzzQSVgEnoSGEBj45sHQa1KpzigZIUlKpXA8z9f0ggjwYdR3FXgnhhE0\nKkyEQVzDnJlDCIGVt6JnHNe0hF+5yuceG+DSZv31tmOaOS1N+6jm9BDPJ0zPGsirlZN2faNlzDIB\nppkgu0PGrjz3xqJJXFuLJnH1YGStF2StKUTbmlBpujINo9Plu1ik6Dh4joP0/De2TMPAzeeRLa3g\n2JiO43vUDRhAXkkszyPnuZieB3YZ5dgo00A7hs8pnuevUXka7flho7SnfLOfUkG4JpCSIA6hwFPg\naI2Lga01tvB3NnY1SCEIHfkVRKGdDCH86PWGEbnBC8N/f0tBFDXDDQIMu56H4zjYjovtONiOQ8Fx\nyLuuv1ty7FmFgXrNmMYVPr8082DoFAKdXo9ZmkZX2zF5PstEliSvZF7VtJqsAT75+0vK0R0yJokp\nq77dJWOcELNk7B40iWtr0SSuHQhZs9gwrWIjySDNNxcGZsJ8nkKhgGxp8be415qcaVKw8rgtLchy\nGRwHw/MQhkGfQYPISYnpOhiuA7YN5TLKLiE7TCTC16SkRLoSiYlSEuVbElFaoGVIKr7jgtTBS85a\n4wmBq/H35BICVyik4a/NhSZCIwiSG2papuE7ZGAYkcYTmUmFH0BXKoXtuuSDzSLzhRL5Uj7SqsLn\nkHddP/hvTGMKiT70ZswFwXXD8+Fn/GXvNFNSlimvnnYMP7ti3kpen1ZO/Np4HmkEkUUI21rGatdv\nSxm7B833uLYWTeLqwUjrYFmdK474C7GGaZLLKZTMkbcsZLEYbW9vCIFlWdj5PF5rK8pxwHURnocR\nEJfheYiAtFS5hCyVkO15pDBw8Dd3dJQf01BKjRQKJfzduLRWaOFv9KgBJQw/OrvWyJzAUxpPgGcE\nRGaYSCEiIiF4L8vUGkuIKJiGwJ+rGoaBimlCodbjBabBcKdj27YplUqVxFUsUnBdPM+rJK/gf0RW\nCdJKIiw3SyPK0gDqaceumKzS1omq3Z9FEmkkVG2gb4SMadpTT5Kx6ZzR89Akrh0ItWaEkQt52NEC\nZ4JwfUvl850DtOE7bBQKBdxiEc9xUK4LnoeQ0te4Bg9CuB7asVHlMl6pA6etHS+X813khcAGbE/5\n5CWVv2OyY6CECdLfOgSlgDDOIBVblEgNXkBmCt80CJ1mODOfx6LynS0DIlOhMgzfgzAWBDeMfB+S\nV7lcxioUKJfLFAKHDNu2ox2QZbDlShjb0X90netXoUYXH8DiWleyLbK0kHrbsRay1raqlZl1X5ge\nzyOpGcXz7Q4Z6722u2TM0mbDc03njJ6HJnH1cCTXBNLOhdAxwgrJK5wxmrkcVswkZhoGVi6HWyjg\nFYsoz0N5nr8QFRLXwF3QrotybLxSCaejBWXlcYPIGirYDsWVKlijAs/MoQwHLX0HDaTyXerDOgZx\nEXWwhUlEVsH6lRnXrITAKhajtS0Tf/3L1drXzAzfDT50fzcC8jINo9MDMdDAXNvGLRRwHAfHdXEd\nBzfQuELi8p9luNVL5/NLDlzJGXjWQJ9myqunHWuRWFp5aRpLlgaUNLnVQxzdKWMWSaXVZ3vI2CSu\nnocmce0ASHao5LmwA8a1AhEQQOhdaAbOBppg08QgIroMHRWkREsJSqGlpMMw6D1gACpw5HBLHVAo\nIHM5HCOIkA6djhHCQAoDadtIy0V7HmiFkMqPgqETM+Xg/bKQkIDoE4i89lp698YD8gFhhTsgu9rf\nXFIGbvCYQczFwOXftKwgIG7ni8tSKaSU0Z+SnRtc6lDjigoniiASJzG/WrHXDmLmp7R2ylrbqdaO\naRpDMq+s8pJ5htcm07KIpFqe3SVjVl5p9e4OGePX1fNcG4fGR4f/V0GTuHowapk7wuPwXHytBzrN\nWaZpIpS/A7AQAikNDMPEtCQ6n/dJKxy8lQKlKLW10dq3L9J1cG0HI59H5yw8w8BCkMMnGhMDA4Fh\n+uGihG1jOI5PXEohtB/fXSgdkVG0iSWdbu2h5iWD8zIw0fXu189/Jyt4odoLNDUZmAo9fI0rdNgQ\nwW7F8cC5oZegaSS8A6vMpOudZWeZmsJz9Zitku2YZorL0g6ynBPSNJ9aWs32lDEtj54kY/egqXFt\nLZrE1YOR1QmrdfAK8oqvdUFFsF3TNH3zmFSgg090tCYl2tsp9O6NdF0My/a3vTcMPMAF8kAegSv8\ncFAqn4diEdPxN47USoIn/TWpsF5xk2HMdd8vOfEZuLr36d+/k9TwiS4kr3hw3fAeDGPLfbYKBYrF\noh+DsFAgH8Ud7FwXi2biOlC4Qu0rhuT6SdpaSXxGn2Xeqqcdk/mkIU0Dq+YgkVb/Wtck690dMnaa\nadPR3TLWasemc0bPQ5O4ejCyOl9Wh0x2rvD9J+1f6G9fHxCZVgrTNNE53xMvGqy1H8pJCEG+pQVp\nWYhcDkyzU+sR4OGb6ZRpoi0LUShitfbCs22U66KV8rU8HWhdsa1I4qQQEZgQFYFzQzNnv8GDt9iL\nK5SzVCcAACAASURBVAzvpAIPxDhxhYQngpeuTdP0I4WEW5b06uVHig9jFSZ2Nw6fZxikOPlcszSL\nNBJJG3DraceuaA1Z5sUs8kmrb7KMpoyVMnXfGlfj3eFvv/127r77bhYtWsSqVau44IILeOedd1BK\nMWnSJE4++eSGl7k90CSuHox4Bw1RreOm7ccFlQNvFPXBNCOtTPsXhzf5nVgIcvkCRrB2pIVPGpGp\njs49sIx8HqulFbdcxgvMhFrKgLi0byoMNJlo+SjlUye+G4ZBv1122UIbi5w6wvW18Hzo2CFioa6C\nrUvCqO8tra209urlb1cSRn5PkJdIGazi78ZV04jqXTeq1o7VzG5p94ZIG/yTJr1kHbOuTavfB1XG\nWu24o2hcL730EgsWLGDAgAEAXHzxxQwdOpTp06fT1tbGpEmT+PSnP82BBx7Y0HK3B5rE1cORNmvM\nGlQMw9iCrOLfI+cN03eD0Er5pBRoQ5ETQnBPzsrhCcgFhKA0FALtSBv+RpSmZWEVizjlMq5tI13X\nd/IIXnAW4R+xSBPBZxp5xT+FYdB/113958CW5sQ00gqJKySvKGpIEKOxGO63FZJXuNdW4MxhmsE6\nWFiXlEGr1uCb1lZdbce0fJLlZ5nQ0sx3WUSQJkfaNd0pYxpxZNWxO2Ss1Y47glfh5s2bufDCCzn7\n7LO59dZbATj22GMZNmwYAL179+ajH/0ob7/9dsPK3J5oElcPR1ZnrTZIJLc1ydqlt4LAoDMSRaCV\nGbkcuZAALSvmdRdoNGbOjzRfLOIG3ocq0LZ0oG0RI60s4so6NgyD/oMHVxBa6npY7BOC9TECc5+R\nEmA4jBAfW/eyLCtaF4s/17i5MO5FWGugT9NEutqOyfzrXeOJl1+trGrrT9tSxrRre5qM3YPGEdec\nOXP49re/Te/evaO00aNHR8dPPvkkzz33HPPmzWtYmdsTTeLqwUh2yDjSOlOYlkVWaWbE4IR/f/x6\n4b+8rIXAtKwK8ohHWc8XCrgtLXhJt/qQtDK0rTAvqnw3DIN+gwZF5+r9C2WK9iILyCuueeUDj8Pw\n2LIsTCNGWhnrW2G9smb31TSBrrZjVno17SBev/i1aRpLWnlJGbOu+SDJWG87Nhy6i+7wGYrfbbfd\nxqBBgxg+fDjLly/f4vwDDzzApZdeyg033ED//v23oqI9D03i6sHIminWM0vMujZEVhSI5HqYYZpo\nKX2X+vB6wwhCSeXwCgU8N9hQMuZWH66ZhZ6EacQV1SVDfscw6BvY66uthW1BWp0CRMRlhHU2TX9v\nrmBrk3BnY9PM+duXBNelIW1QjiN5rpZZrBHtmHZfWjlpA3ktQkjDv6KM3bbGlV2FdKT/LLn33nsp\nl8uMHz+ejo4OVq1axVe+8hXuuOMOrr/+ehYuXMgtt9zCpz71qfdd5Z6CJnE1AI888gjXXnstruuy\n//77c9FFF5HP5yuumTdvHr///e8xDINhw4Zxzjnn1JV3VofOMnVkpWVdo5SqMINVdNDA5BaSlpD+\nDDFuflPxl3kDwopMjwnSSh6HyCKu9R0d9Nl551QzYpK8UvMJTYZxZ40Y6YZeh2aKY0YtJ5c4kjP4\ntFn8+2nHegb0NNNb8t5qJFKtHttCxmroCTJ2C7r6/nEGcd11113R8YoVK5g3bx533HEH8+fP56GH\nHuKuu+5i4MCBW1/PHogmcb1PrF27lvPPP5+7776b3XbbjYsuuoibbrqJM888M7rm4Ycf5vnnn+fe\ne+9Fa82UKVN4+OGHOfLII6vmnWXvT7smeT1U77RJU0h8fSxEWlij8Ng0TVQuh/SkH0w39EaMeSmK\n2Gd8GxCoJJnk+fB4Q7lMS+/eW5xLyyONtOJyhObP8CVkwzAQwsDMmbHvIvM5V4sAX23gzjJLZeUV\nP07TCuptx7R8s8oL0VWCbKSMW/NbbZSM9bRjt6AbA2d0dHRw0003MWjQIL75zW9GWuPJJ5/MuHHj\nuq/gbYQmcb1P/P73v2f//fdnt912A+CEE07g9NNPryAupRTlchnbtlFK4TgOhUKhrvzT1guSA0Dy\n+rSBpdogkMy3Is/YWlk4uGutMZSKonL4LvSdOpBSOgyXiOG7K0bBa4MsUyEMEQuz5JNVsaXFT0rc\nK0SCuHRnmkZEZYnwO2AYndugEJoQw/U8o3N9MP4Z1S3QSJMEXkvLyPq+rdoxjSji96XVu1q521rG\nMJ/ulLFWO3abV6Hbxetbal9y0EEHsXDhQsB3j/+goklc7xOrVq1i18BlG2Dw4MGsWrWq4pqjjjqK\n++67j2HDhiGEYOjQoZGbai2kzSwbMQDVMxvVWkfbeoTaWDSAmyYqIC6lFAZUuNTr2CBVreOnvXsW\nfRcCK0Hw8Y0y49emOaOE9Y+/g5WsTxRINyiPWN5hHkkyTxJ8WltU0wi60o5p6Eo7Jgf/tEE/bcJS\nrc6NlvH9/lbfr4z1tmPD0dU1riYibAN9+ION5NYW4K8JxXHHHXfQ3t7O73//e37/+9+jtWb+/Pl1\n5Z9mLoJsr6tk582aFdczoKQO8MIPF2UE60PhZ3icC9zKw7+cZUUeiJG7eSyeYJgWvyf8E5B6TRh7\n0IrevfLXrMK/uPu7aZpYgROGGa+rYUTHoSkxvraVtX1J1iCaHBCTz35r2zGe39a0Y1p6/DeSLGt7\nyPh+f6vdLWNaH28IZBf/mojQ1LjeJ3bddVdefvnl6Pvq1asZPHhwxTWPP/4448aNo1gsAnD88cfz\nk5/8hNNPPz0zXyklK1eu7J5K1wHP83jllVe2W/kAnpS8+te/br/ye8Iz8Lzt/jvY3uXH+9f2wL77\n7ts9GTfJaKvRJK73iUMOOYQrr7ySt956i91335277rqLESNGVFwzZMgQHnroIcaMGQP4Xoj77bdf\n1XxN02TIkCFVbf5pyFr0TlsTiKcn8fLLLzNkyJDU2WZV8x7pZru0+7PMe+BrOq+88krqoBG/L7y2\nWrlb486stWblypUMGTIEyDaF1XIMyFqLgfra8eWXX2afffbZ6nashlp5KKVYuXJlRfndIWO132q1\nNmiUjNttjatpKtxqNE2F7xMDBgzg0ksvZebMmYwePZr33nuPM844g0cffZTzzjsPgFNPPZUBAwYw\nevRoxo8fj1KKWbNm1cw73qnSBoI0c02aSaRa50zmnzTXxMkgTmLVSCdEkjySeSTJK83tPOu+ZB2r\nkWU8/1qEl3Z/2oAcf37x69Laoye0Yzw9zbzXlLF6XbsFTVPhVqOpcTUAhx9+OIcffnhF2vDhwxk+\nfDgA+XyeCy+8cKvyTutsSXt/Mj1tbSAtvxD1LLhnLZTHj+NhppIDVBoZxO+L558WWimeXk3GkHyS\nMma59sevrUfGeFpSxrSBOH68o7RjMi1ej20tY1adGiljrXbstheQm2S01WhqXD0YyU6U1amrmTqy\nzB/1zCaTnTorLTn4pJWfNUuuJmNauR80Gf8V2vH9yJhWVhq2hYwNh+riXxMRmhpXD0dap48fpw0E\ntWaiYXqW2SZtVpw1i00ru5pJplp5SRmz6tAoGbOe4baUsbvb8f3KmDzeHjKm5d1IGWu1Y495j6uJ\nCE3i6uGoZg6qhqSJJp4WzydJUvFz1Tp2Wv5ZdUjWPz6g9WQZ66lPd8uYdn2zHbetjP+/vauPrqq6\n8j8JjVhBq1QTZMbVmWlRiLCG1hlqCUoTrHwkGGlYca1Cx2JZhWVboXUJKYWRgRo7jDMyBYp0tBTt\nIF8lBKm6aEDbtKAzdtSSiZ2ZVWUcLAFpWQaQjyRv/nDd58nOPufcvHfO/XjZv7Xeeu/dc87e+5f9\n9j777nvzHv0KMGeQVmHOkI0r4dBVqoA+YegqTS5wbclF1W2qVsPYpdOZVI6cHptdOp25cuRkiB95\nRMHRKWTjyhkevSJwCbXCVdtDuuTGHadtJTpOZevk0UqbG9PZxh3XcTTJKRSONj9yHAqNYxr96ARy\njStnyBlXgkGTl62K1CU7upa2VExVpaqHJgGdjWGrW05e2Iq8UDgm3Y8mG6LgqCJOP3qBnHHlDNm4\nEo6+BHwwTl9z69WWimmDNOmzBX1YCEd+Pn3dHzmqcpLA0Slk48oZ0ipMOGwVJneMPgKoiYILfi64\ndfM4/bRC17VtqDzhqOdIX1MdhcAxDX70AmkV5gzZuBKOMAGkBqd6fUCXYNQ1tKq1Ba9a+dI5NFlQ\nncGY7npC1By59SpHzr6oOXJrXXLsD350ydEp5JszcoZsXAkGDbzgGNAzqHSVJxf86hhNBNxcbiOj\nbSpTcHO2q2tMHDm7XXLkdIfZ0F1y9O1HG8co/Jh2jt6+Hf5CHx+CLOQaV0pAA1bXMuGqRi6wg7m2\n9eocmmRoQtDppvJ0unXzC5ljf/Bjvhx1tkXF0ds/IDs+i/rBD36AxsZGXHTRRRg9ejSWL1+OgQMH\n4pFHHsEvfvELnD17FrW1tbj77rvdKo4BsnGlFLqzJIBPFFxgqnNzBVcx65KCqS0TBq456ubr1vvg\n6NuPhcCRs80kX4coP6uh4LAL+fLLL6OpqQk7d+5EcXEx7r33XjzxxBMA3v+lh+3bt+PcuXO4/fbb\nMW7cONxwww3ulMcA2bhSBtrGMwVhMF+tQLlWiSkp6pKCrvXDydVV56ZEpNPvkqNtM7CNueDo2482\njlH4MV+Oto0uCo5e4PCM61Of+hQaGxtRVFSEU6dO4Q9/+AM+8pGPYNOmTfj2t7+NoqIifPjDH8bG\njRtx+eWXu1McEzx6ReACpqDjNhn1mUOQGGiA0tc6ucFDlUHnmcDpSCpHk34T0sQx6X6kz3FzdArH\nN2cUFRVh+/btqKiowMmTJzFp0iQcPnwY//mf/4m/+Zu/QU1NDX72s59h8ODBnghFB9m4UoAggLjE\noAanOl995hKMqWI2XUOhSUDXUqFnTWpC4RJCHByp7cKxN0edvKg4qrp8cbT50dvNGd19fIRAbW0t\nXnrpJdx8881YtGgROjs78dvf/haPP/44Hn/8cWzbtg379+/3QCZayMaVEnCBGBxXX9NAVueoySRM\nq0aVyQW6qTWjs9PUgomao8km4WhuR0bFka7zwdHmR683Zzg643rjjTfw2muvZd/X1NSgra0NV199\nNaqqqlBUVIQrr7wSN998M1599VUfbCKFbFwJBhdUtBUC9O7x02AOYEtOXDJRbeAqZjqXyjYlEhtH\n9XWhcrT5keNVaByT7sc03A5/5MgRLFq0CGfOnAEAPP300xg3bhxuvfVW7Nq1CwBw5swZHDhwAKNH\nj/ZCJ0rIzRkJhlpBqoFO2zRqUqDzA9Cg1o2ZqlUumejspdDJMHGk6wuRo82POh2FxNHEw8Q/ao7O\n4fDmjPLycsycORMzZ87EwIEDcd1112Hp0qX40Ic+hIceegjTpk1DV1cXqqurUVlZ6U5xTJCNK+Gg\nQW9rjZiCzVbJ083CFNzUNlU+fc3ZRRNQHBxpchWOve2msqPmSO30wdFmVyaT8dMudHzfx5w5czBn\nzpxexx944AG3ihIAaRUmGFxw0vEgILm2DJ0XpoJU5+mqZZ2dtnW6lpFwTC5H3XMhcbT5MQ3XuPob\nZONKOExVJDdOgz7MetsxrqKmY+o62spRn03tHZONwlE4xsnRC2TjyhnSKkwwaDVrap1wlS/XDjEd\n4+Rzwa9rrXD20rm0taQeTxpHzo5C4xjGj9SGKDnS8bg+q17guFXYnyBnXCkAF6i6lgutGE1Vp3pM\n1/5RdXM2qPptbRhunYkjp6PQONr8qNpeqBzT4EcvkDOunCEblwM0NzejuroakydPRn19Pc6fP99r\nzp49ezBjxgxUVVXhvvvuw4UL4b/umQumMBWt+poGPDfPVMWaZAZraRLgbNcllzAJoxA5Jt2POtsL\niWMYP3pBdx8fgixk48oTJ06cwLJly7BhwwY8++yzGDRoENavX99jzmuvvYZVq1bh0UcfxdNPP42u\nrq7sF2CGgRqUXKtIBa0oqRxdkOqO6apnXUuI2quTEYajTn5UHHXj1F6dDNOasH7kEKUfbTK4NYX+\nWXWG8318CLKQjStPtLS0YOzYsRg2bBgAoK6uDk1NTT3m7N69G7W1tbjqqqsAAEuXLkVVVVUo+WGS\naDAvGFcDnmuRcImEa5XozoK4pGJLPvS4rn2UJI70WCFyTLofOTlRc0zTVz71F8jGlSfa29tRWlqa\nfV9SUoL29vYecw4fPoxz585h3rx5qKmpwZo1a3DZZZeF1hGmNcIlWl17JUx1rR4zJSMq01ShmpKR\nLnH45KhrE0XJ0bcf086RQ9Qc5Xb45EE2rjzBVWNFRUU93nd2dqKlpQXf/e53sWPHDrz77rtYvXq1\nVbauyqbBrqskdZUwJ4u+5tZxcqm84D1tGXHrhWPyOYaxyydHnV1x+NE55IwrZ8jt8HmitLQUra2t\n2ffHjh1DSUlJjzlXX301Ro8enf0dnOrqajz66KNGuV1dXWhra3NvcEh0dnbGqj+wQf3bxqE/CX+D\n/vw5iFs/AIwaNcrPWZecReUM2bjyRHl5OVatWoUjR45g+PDh2LZtW6/vAps0aRJWr16NuXPn4tJL\nL0Vzc7P1iy6LiopQVlbGjtEqMewFZtqiUS+cq+0W4P1fTS0rK+ulw3SxXZVr02tDd3c32traMHLk\nSG8cdX/H4LmtrY31gUuONj8GfvDF0ebH1tbWHj7wwZHOV48H+n1y5PT2hWPOkI0rZ8jGlSeGDh2K\nlStXYv78+ejs7MSIESPQ0NCAffv2Yf/+/VixYgUmTZqEo0ePoq6uDt3d3Rg1ahQWL15sla1LSrpE\nEDbouMShu76ka/HQMZokbOOqLB1H1cY4OHLzXHO0+dGEKPyoyvTFMewGEedn1Quk/ZczZONygIkT\nJ2LixIk9jlVUVKCioiL7ftasWZg1a1af5OouLnMVZnBcN5e+DqCrXql+kwyb7Zw8nRyOSyFztPmR\nkxUlR85u1xyT7kdvX7IrZ1w5QzaulIALQhqguqBWkwRNJOpxVaZOt6nSttlL18XNMUwSjJujTmZU\nHE2Iwo8mvVH50dtdheG/g0BAIBtXCkCDWpcE1HFdW6W7u7tH0jMFuS7JUB2cHp3NnP1xcuS4cGcD\ncXHU6XbF0eZHbr5rjiY/Bsd9cuzL390p5IwrZ3j0iiBf0F69GmC04uaqVlNlqa7X6aHVKZd8dG2t\nAFwlrY6ZOHJzXHIMHnFy9O1H4eiGoxd09/EhyEI2rhSBBitXhdJqMQhY3VmUroLmoKuWTcnHxMOU\nSEzVvCuOuiQcJUfffkw7R9P8KDl6geN/QP7BD36AadOmoaqqKvudqe+99x4WLlyIqVOnYtq0aThw\n4IAnMtFCWoUJBg344BgFDS51na3VwVW/Jn26gDdV4rR9Q48LRz1H03y6Nq0ck+7HNNyc8fLLL6Op\nqQk7d+5EcXEx7r33XjzxxBM4fvw4hg4dip/+9Kf43//9X8yePRt79uzB4MGD3SmPAXLGlSLoEoHa\nLuGqU3Wu+hzM01Wt3DraflF1qc9UpjrXpEs49uRos9U3R6onaj9SmUnyY95w2Cr81Kc+hcbGRhQX\nF+PUqVP4wx/+gI985CNobm5GbW0tAODaa6/F6NGj0dzc7ItRZJCNK+Gg7Q31YaowTe0TXRuFtlO4\ndWqQcwnWtJbqtHGk8wqRo82PHAqNo8mPdE4cHNPyXYVFRUXYvn07KioqcPLkSUyaNCnUd6mmEbJx\npQBqQHHVKlch0pYH91pX1auBS6tRU0LhnjmdXCXPcVTHCpWjzY+cHVFy1Ol3yTENfvQCD1+yW1tb\ni5deegk333wzFi1axH6XqldOESH9DAoYNHmpgWlrb9CWSQAa6Dad6hrdex24BEyrYNM4tcc1R1OS\ni4pj2DZVIXNMgx+94EIfHwa88cYbeO2117Lva2pq0NbWhmHDhuH48ePZ48eOHetxBpZW9NuNK5PJ\noLW1FR0dHXGbogUXhEGA0UqWq2B1Qaeu54Kf06fO4QKam6PKoS0brto2jfvgyLWcTH8PHxx9+9HG\n0eZHKitqjtwa1xxtfkzD73EdOXIEixYtwpkzZwAATz/9NMaNG4fKykps2bIFAPDWW2/hlVdewfjx\n473QiRL95q7Co0ePYuHChZg3bx7Gjx+P2bNn43e/+x0GDhyI73//+xgzZkzcJvZCEPRcEucSRl+q\nTZr0ONl0vrqO00HHuDU0kZk42vTly1HXSoqSo28/Csf8OXq9xuUI5eXlmDlzJmbOnImBAwfiuuuu\nw9KlSzFgwAAsW7Ys+8O1y5cvxxVXXOFOcUzoNxvXgw8+iNtuuw2f/vSn8cwzz+DYsWN44YUXcOTI\nESxfvhybNm2K28ReoEkcgDFwdcHIjZuSCNUVpvKn9nBr6GtaiUfNkVbWwrE3R/V9oXK0+TENt8MD\nwJw5czBnzpxexx9++GG3ihKAftMqfOONN3DXXXfh4osvxq9+9St87nOfw6BBg/AXf/EXOHHiRNzm\nacG1RbiEwFWXuqqWBip91iUsKlPXTuLGdDxsYz45qlV8XBxtflT1xcGRwgdHkx+p3jj86O2My2Gr\nsL+h32xc6q8S//rXv8Zf/dVfZd+fP38+DpNCgUsMNEi5Y9xcbg2db5Orm6MmJY4Dl2C4DZJrK/ni\naPp7RsUxzN+70DmmwY9e4OGuwv6CftMqHDJkCP7nf/4Hp06dwtGjRzFu3DgAwKuvvoqPfvSjMVvH\nQ21lmALZdEw9HqYC5QJfrZxpVUvlcVU2xycMRxMXFxzVijsujr79mC9H3ZxC4hjWj84hm1HO6Dcb\n18KFCzF79mx0dHTgvvvuw6WXXoqNGzdi3bp1WL16ddzmsaDtFKD3tQE6L5ijm2taxx3jEkDYTY+z\nib6Pk6Mu2QpH4ajC2zUu+VmTnNFvNq5PfvKTeOGFF3D27FlcdtllAIAxY8Zg69at+NjHPhavcQbo\nAtrWUrOBSy46XabkY7KDjgftGVrhmpJWWJs42DhyyZZ775NjGD+GSehp52hCEjh6gVy3yhn9ZuMC\ngOLiYhQXF2fff/KTn4zRGjto68M0h84HzEFLWyFhgzRMFR1Wvq5lExVHU6JNEkedvCg40rH+6kcv\nkFZhzojAO4J8wF0vMPXfw15/UIOUJhFVJz0W9szI1I7RtZu4tT45quNxcbT5Ucc7Ko6c7a45psmP\nTiE3Z+QM2bgSDloJBsfCtj3oMSqL6tLN5zY27sxAtwnSRKHy0nHU2eWKI20HxcHR5kcd76g4qu99\ncUy6H+V2+OShX7UK0whd20LXKuGCl5Nna+tQHVQ2N65rzeiSUF9aMz44mv6uUXG0+ZHKK0SOJj9y\nuqPmeNFFF6XiH5D7E+SMK8GgbRRTS4QLOF1i4Fo8wUNdp45x66iduuradPYUJ0dVh3DkOdLxqDlS\nxOlH55BWYc6QM66EwxbkAWjVqwtKrqUS9qxLV1n3VSadFxdHmw4VcXGkr11ztPnRBt9+1HF2ydHm\nR6+tQkFOkI0rwaCBDehbSFzwqnN0QUoThy7QaXLRJStdxaqr4JPMkY4XIsf+4EcXHL1A/o8rZ0ir\n0AGam5tRXV2NyZMno76+3vgVUl//+tfR0NAQWnYQYFxg6Y6bql1d4tHNVY+rwa9LPsExrqWjPmhb\nJ4kc1bWFytHmx2B9IXO0+dHbz5pIqzBnyMaVJ06cOIFly5Zhw4YNePbZZzFo0CCsX7+enfvEE0/g\n3/7t30LLpsFuqmBtbRQVapDa9NN53DFaBXP6aaJSx22bhCq70Dj2Bz/mw5HTxSEKjs4hG1fOkI0r\nT7S0tGDs2LEYNmwYAKCurg5NTU295v3mN7/B3r17ceedd/ZJPq0GucDjzhBoEKrHaSWtC26u/aNr\nz6g61EpXl1xNdlB9hczR5kdqb9Qc1de+OJr8qNoXlx/ldvjkQTauPNHe3t7jp7BLSkrQ3t7eY05H\nRwceeOABPPTQQz2+pT4MaJKiAa0DbcGoxzg5dCx4VoOd21BMtqhJgco0JeKkcKRz4+DI2SB+jJaj\ntAqTB9m48gT3oaab05IlSzBv3jxcc801fZZvaluoAUbXcIGua8+YEgytvGlQ69bo9FE5qn1J46iz\nyWQX1Unl9JUj9178GC1Hr7+A7Gjj2rp1K6qrq1FTU4M5c+bgrbfeyo51dHTg1ltvRXNzsw8WsUDu\nKswTpaWlaG1tzb4/duwYSkpKsu/b29vxyiuv4K233sLatWvxzjvvZCvVJUuWaOV2dXX1kBs1Ojs7\nY9WfBBs6OzvR1tYWm/7Ahrj/Bv1ZPwCUlZX5Eeyo/dfW1oZHH30UjY2NGDJkCP71X/8V3/72t/Gj\nH/0IwPuFc0dHhxtlCYFsXHmivLwcq1atwpEjRzB8+HBs27YNlZWV2fGSkhL8/Oc/z75fs2YNOjo6\nUF9fb5RbVFSEkSNHspWg2s/X9fB1r3Vj9BpBW1tbNmDpmE6WzT6OB5WnjrW1tfX6G7jkyM1Xx1pb\nW1FWVuaVo82PgQ2+OHKy1GOcD1xzNPmR4++aYxg/eoGj9t+ll16KlStXYsiQIQCA0aNHY+PGjQDe\nvyHsT/7kT3Dy5Ek3yhICaRXmiaFDh2LlypWYP38+pk6dinfeeQdf+9rXsG/fPixdujRv+Vy7yJZE\n1JaNLmGogc1dtA6jTzfW1wQgHPn5wpG/ySNOjk5xoY8PDa699lrcdNNN74u8cAH/9E//hClTpuDQ\noUN47rnn8M1vftPfdbqYIGdcDjBx4kRMnDixx7GKigpUVFT0mvvVr361T7JNgdXXQFQTCxf8XHDT\neWqCsV2voInGNp40jpyuODhyNrji2B/86IKjFzi+4eLkyZNYuHAhLr30UsydOxezZ8/GmjVr+nxD\nWBogZ1wJB61iTXPUYFWTbwBTNUsreJ3uIAnQ9ap8XdIJxrjKPA6O3HqVI2df1By5tS459gc/uuTo\nFA5vh3/zzTdRV1eHT3ziE/je976HX/ziF3j33Xfx1a9+FTU1NTh06BAaGhqwa9cuf3wihJxxs04/\nJAAAIABJREFUJRhc9UcDjqsmuQqYVp40oLlEoatCdc8cqO10jYkjZ7dLjpxuLin65Gjzo+3vkS/H\nKPzo+7Pqm2Mmk0n0t8MfP34cs2fPxle+8hXMmjULADBlyhRMmTIlO2f27Nm46667elx/TzNk40oJ\nuNaG6ThdR8+mgrm29VxyMiUwk826tbb5hcyxP/gxX44626Li6PV2eAd48skncfLkSezYsQPbt28H\nAFxyySXYvHlzdo43DjFBNq6UQneWBPCJQldp6qrPsODOCnRJwdSWCQPXHE3VN7feB0fffiwEjpxt\nJvk6RPlZDQVHXciFCxdi4cKFxjmbNm1yoywhkI0rZdC18YJj3Hy1AuV69qakqEsKqh06mKpe01qu\n2qYyORv7ytG2GdjGXHD07Ucbxyj8mC9H20YXBUcvkG/DyBkevSJwAVPQcZuM+swhSAw0QOlrndzg\nocqg80zgdCSVo0m/CWnimHQ/0ue4OTqFw5sz+hvkjCsFMLUuuEBWq0U1eLk1VJ46TvWbqmTOLlo5\nU7toBR4HxzAVdpwc1WNxcNTJi8qPNtui8KO3mzP0v34ksEA2rpRAl0zCVKG0lWJKTHSMSwS6pGWT\no5OXVI66+YXE0eZHnQ1RcdTJi/uz6gRyFpUzZONKMEzVp5oAuGAPQKthkw4umeiqUd246YxAPWZK\nKJxMHxw5mVFz9O1H4Zg/x6TfDt8f4amUELiAGpxcCyOA2mKh89U53Gv6niYXqttUfYap3gMZXAIy\nVeOFytHmR52OQuJo4mHiHxXHNHw7fH+DnHElHDTAuWRhq1QDcC0mrsKkunUyaYI1VbSms6q4OAbz\nhKOeI5UdNUdqpw+ONru8nXFJqzBnyBlXgqGrLtXxICDVhMFVsLYKlNNjq4ypnbZ1qm20UhaOyeSo\ney4kjjY/+jrjkhOu3CEbV8JhqiK5cRr0YdbbjnEVNR1T16nz6TM3XzgKx7Drbcd8cfQB2bhyh7QK\nEwxazZpaJ1zly7VDTMc4+Vzw61ornL10Lm0tqceTxpGzo9A4hvEjtSFKjnQ8rs+qDxh+qYRFsRcr\n0gk540oBuEDVtVxoxWiqOtVjuvaPqpuzQdVva8Nw60wcOR2FxtHmR9X2QuWYBj/6QHcfH4IPIGdc\nKYCuEqQJw/Ranc8FtylJ0GREZXJJgiYYm50cR86eQuOYdD9yr6PkaFv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PAAAB\ngUlEQVTZxhHGBhVr1qxBR0cH6uvrI9X/5JNPYvPmzeju7saoUaOwYsUKfPjDH45E//nz5/Hggw/i\n4MGDKC4uxujRo7F06VIMGjTIiX4V9fX1GDlyJL74xS9i3759kX0OBemCbFwCgUAgSBWkVSgQCASC\nVEE2LoFAIBCkCrJxCQQCgSBVkI1LIBAIBKmCbFwCgUAgSBVk4xIIBAJBqjAwbgMEgkLCkSNHcOut\nt+K6667L/t9ZJpPBlVdeiccffzxu8wSCgoBsXAKBYwwePBg7d+6M2wyBoGAhrUKBQCAQpApyxiUQ\nOMapU6dwxx13AEC2XTh58mR85StfidkygaAwIBuXQOAY0ioUCPxCWoUCgUAgSBVk4xIIHEO+t1og\n8AtpFQoEjnHmzJnsNS7gg+tcGzduxOWXXx6jZQJBYUB+1kQgEAgEqYK0CgUCgUCQKsjGJRAIBIJU\nQTYugUAgEKQKsnEJBAKBIFWQjUsgEAgEqYJsXAKBQCBIFWTjEggEAkGq8P8MPe79fUOhYQAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1248b4c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Donor Leakage fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Half-Sample Mode\n", "\n", "Fit peak usng the mode computed with the half-sample algorithm ([Bickel 2005](http://arxiv.org/abs/math/0505419))." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hsm_mode(s):\n", " \"\"\"\n", " Half-sample mode (HSM) estimator of `s`.\n", "\n", " `s` is a sample from a continuous distribution with a single peak.\n", " \n", " Reference:\n", " Bickel, Fruehwirth (2005). arXiv:math/0505419\n", " \"\"\"\n", " s = memoryview(np.sort(s))\n", " i1 = 0\n", " i2 = len(s)\n", "\n", " while i2 - i1 > 3:\n", " n = (i2 - i1) // 2\n", " w = [s[n-1+i+i1] - s[i+i1] for i in range(n)]\n", " i1 = w.index(min(w)) + i1\n", " i2 = i1 + n\n", "\n", " if i2 - i1 == 3:\n", " if s[i1+1] - s[i1] < s[i2] - s[i1 + 1]:\n", " i2 -= 1\n", " elif s[i1+1] - s[i1] > s[i2] - s[i1 + 1]:\n", " i1 += 1\n", " else:\n", " i1 = i2 = i1 + 1\n", "\n", " return 0.5(s[i1] + s[i2])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all: E_peak(HSM) = 8.86%\n" ] } ], "source": [ "E_pr_do_hsm = hsm_mode(ds_do.E[0])\n", "print (\"%s: E_peak(HSM) = %.2f%%\" % (ds.ph_sel, E_pr_do_hsm100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Fit\n", "\n", "Fit the histogram with a gaussian:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_fitter = bext.bursts_fitter(ds_do, weights=None)\n", "E_fitter.histogram(bins=np.arange(-0.2, 1, 0.03))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.918698</td>\n", " <td>0.0911228</td>\n", " <td>0.0517342</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 0.918698 0.0911228 0.0517342" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_fitter.fit_histogram(model=mfit.factory_gaussian())\n", "E_fitter.params" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr\n", "amplitude 0.9187 -inf inf 0.02428 True None\n", "center 0.09112 -1 2 0.001579 True None\n", "fwhm 0.1218 -inf inf 0.003718 False 2.3548200sigma\n", "height 7.084 -inf inf 0.1873 False 0.3989423amplitude/max(1.e-15, sigma)\n", "sigma 0.05173 0 inf 0.001579 True None\n" ] } ], "source": [ "res = E_fitter.fit_res[0]\n", "res.params.pretty_print()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.09112284426324946" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_pr_do_gauss = res.best_values['center']\n", "E_pr_do_gauss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## KDE maximum" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.092200000000008331" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bandwidth = 0.03\n", "E_range_do = (-0.1, 0.15)\n", "E_ax = np.r_[-0.2:0.401:0.0002]\n", "\n", "E_fitter.calc_kde(bandwidth=bandwidth)\n", "E_fitter.find_kde_max(E_ax, xmin=E_range_do[0], xmax=E_range_do[1])\n", "E_pr_do_kde = E_fitter.kde_max_pos[0]\n", "E_pr_do_kde" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Leakage summary" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gauss: 9.11%\n", " KDE: 9.22%\n", " HSM: 8.86%\n" ] }, { "data": { "image/png": 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kMvHcc8+NVmxjWlGVnZNNTvKm6YZURbS9vY38/M8wm7uZMSONpKSU4RtIdrn6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\nsoIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1214d9780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, plot_model=False)\n", "plt.axvline(E_pr_do_hsm, color='m', label='HSM')\n", "plt.axvline(E_pr_do_gauss, color='k', label='Gauss')\n", "plt.axvline(E_pr_do_kde, color='r', label='KDE')\n", "plt.xlim(0, 0.3)\n", "plt.legend()\n", "print('Gauss: %.2f%%\\n KDE: %.2f%%\\n HSM: %.2f%%' % \n", " (E_pr_do_gauss100, E_pr_do_kde100, E_pr_do_hsm100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst size distribution" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nt_th1 = 50" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x124d56518>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EpAEsLZth1ar1ant+DiokIiIiJgRERETEhICIiIjAhICIiIjAhICIiIjAhICI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\nERERVBCIiIgIKghEREQE+BNhkenh299N2wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124b3d358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist_size, which='all', add_naa=False)\n", "xlim(-0, 250)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Th_nt = np.arange(35, 120)\n", "nt_th = np.zeros(Th_nt.size)\n", "for i, th in enumerate(Th_nt):\n", " ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th)\n", " nt_th[i] = (ds_nt.nd[0] + ds_nt.na[0]).mean() - th" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x124c68710>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wUbERU2ssvOasmxWjXqEkzyAzRjFgVToCpBkgxxb/MJHsZzJKYEwgt0/jiyMe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\nCCHECiVGQgghxMr/A0i/zO0j9aCzAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124c68c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plot(Th_nt, nt_th)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "25.373585409805187" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]\n", "nt_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fret fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')\n", "E_fitter = ds_fret.E_fitter" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr\n", "amplitude 0.9743 -inf inf 0.01849 True None\n", "center 0.2799 -1 2 0.001552 True None\n", "fwhm 0.1668 -inf inf 0.003655 False 2.3548200sigma\n", "height 5.489 -inf inf 0.1042 False 0.3989423amplitude/max(1.e-15, sigma)\n", "sigma 0.07082 0 inf 0.001552 True None\n" ] } ], "source": [ "E_fitter.fit_res[0].params.pretty_print()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 27.66 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>97.4325</td>\n", " <td>27.9934</td>\n", " <td>7.08175</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 97.4325 27.9934 7.08175" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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xObZdiFmQYrvG5E0L7voFrDvtjKK1We9S8becAMDu3a8VrV0hhBDFNZo1UfQE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\nazvyBwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124b77da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(E_fitter, ax=ax[0])\n", "mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde100))\n", "display(E_fitter.params100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Weighted mean of $E$ of each burst:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28564446])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_m(weights='size')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (no weights):" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28192807])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (using burst size as weights):" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28014618])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.27660000000001367,\n", " 0.27993427838071305,\n", " 0.07081746653125176,\n", " 0.0015660071110426567,\n", " 0.001552115101731099)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_kde_w = E_fitter.kde_max_pos[0]\n", "E_gauss_w = E_fitter.params.loc[0, 'center']\n", "E_gauss_w_sig = E_fitter.params.loc[0, 'sigma']\n", "E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "E_gauss_w_fiterr = E_fitter.fit_res[0].params['center'].stderr\n", "E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err, E_gauss_w_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stoichiometry fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True)\n", "S_fitter = ds_fret.S_fitter" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 56.28 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>102.385</td>\n", " <td>56.4196</td>\n", " <td>11.286</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 102.385 56.4196 11.286" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GkpOTu2yzceNGFixYgMlkIj4+nqKiItasWRPW0EL0RknJaXQ6HSOmLiIn1UCy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\nCCGEgaQhF0IIIYQQwkD/P/I9Zki36whaAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1249cde10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(S_fitter, ax=ax[0])\n", "mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde100))\n", "display(S_fitter.params100)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.56280000000002195,\n", " 0.5641960660315466,\n", " 0.11285967014629783,\n", " 0.0024956985141657965,\n", " 0.001895906952335234)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S_kde = S_fitter.kde_max_pos[0]\n", "S_gauss = S_fitter.params.loc[0, 'center']\n", "S_gauss_sig = S_fitter.params.loc[0, 'sigma']\n", "S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "S_gauss_fiterr = S_fitter.fit_res[0].params['center'].stderr\n", "S_kde, S_gauss, S_gauss_sig, S_gauss_err, S_gauss_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Maximum likelihood fit for a Gaussian population is the mean:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.56308872263728393, 0.10287080389066304)" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S = ds_fret.S[0]\n", "S_ml_fit = (S.mean(), S.std())\n", "S_ml_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the weighted mean and weighted standard deviation we get:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.55094283590005966, 0.10118832937353994]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.)\n", "S_mean = np.dot(weights, S)/weights.sum()\n", "S_std_dev = np.sqrt(\n", " np.dot(weights, (S - S_mean)2)/weights.sum())\n", "S_wmean_fit = [S_mean, S_std_dev]\n", "S_wmean_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save data to file" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample = data_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following string contains the list of variables to be saved. When saving, the order of the variables is preserved." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "variables = ('sample n_bursts_all n_bursts_do n_bursts_fret '\n", " 'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err E_gauss_w_fiterr '\n", " 'S_kde S_gauss S_gauss_sig S_gauss_err S_gauss_fiterr '\n", " 'E_pr_do_kde E_pr_do_hsm E_pr_do_gauss nt_mean\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just a trick to format the different variables:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample,n_bursts_all,n_bursts_do,n_bursts_fret,E_kde_w,E_gauss_w,E_gauss_w_sig,E_gauss_w_err,E_gauss_w_fiterr,S_kde,S_gauss,S_gauss_sig,S_gauss_err,S_gauss_fiterr,E_pr_do_kde,E_pr_do_hsm,E_pr_do_gauss,nt_mean\n", "\n", "22d, 2552, 403, 2045, 0.276600, 0.279934, 0.070817, 0.001566, 0.001552, 0.562800, 0.564196, 0.112860, 0.002496, 0.001896, 0.092200, 0.088573, 0.091123, 25.373585\n", "\n" ] } ], "source": [ "variables_csv = variables.replace(' ', ',')\n", "fmt_float = '{%s:.6f}'\n", "fmt_int = '{%s:d}'\n", "fmt_str = '{%s}'\n", "fmt_dict = {{'sample': fmt_str}, \n", " {k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}\n", "var_dict = {name: eval(name) for name in variables.split()}\n", "var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\\n'\n", "data_str = var_fmt.format(var_dict)\n", "\n", "print(variables_csv)\n", "print(data_str)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NOTE: The file name should be the notebook name but with .csv extension\n", "with open('results/usALEX-5samples-PR-raw-%s.csv' % ph_sel_name, 'a') as f:\n", " f.seek(0, 2)\n", " if f.tell() == 0:\n", " f.write(variables_csv)\n", " f.write(data_str)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }	mit
boffi/boffi.github.io	dati_2017/hw03/01.ipynb	2	375650	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "#### Notebook stuff" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '01.css'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-7f9575a7d9b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLatex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'01.css'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '01.css'" ] } ], "source": [ "from IPython.display import display, Latex, HTML\n", "display(HTML(open('01.css').read()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Numpy and Scipy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "init_cell": true }, "outputs": [], "source": [ "import numpy as np\n", "from numpy import array, cos, diag, eye, linspace, pi\n", "from numpy import poly1d, sign, sin, sqrt, where, zeros\n", "from scipy.linalg import eigh, inv, det" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Matplotlib" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "init_cell": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-paper')\n", "plt.rcParams['figure.dpi'] = 115\n", "plt.rcParams['figure.figsize'] = (7.5, 2.5)\n", "plt.rcParams['axes.grid'] = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Miscellaneous definitions\n", "\n", "In the following `ld` and `pmat` are used to display mathematical formulas generated by the program, `rounder` ensures that a floating point number _close_ to an integer will be rounded correctly when formatted as an integer, `p` is a shorthand to calling `poly1d` that is long and requires a single argument, `vw` computes the virtual work done by moments `m` for the curvatures `c`, when the lengths of the beams are `l` and eventually\n", "`p0_p1` given an array of values `p` returns first `p[0], p[1]` then `p[1], p[2]` then..." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "init_cell": true }, "outputs": [], "source": [ "def ld(items): \n", " display(Latex('$$' + ' '.join(items) + '$$'))\n", "def pmat(mat, env='bmatrix', fmt='%+f'):\n", " opener = '\\\\begin{'+env+'}\\n '\n", " closer = '\\n\\\\end{'+env+'}'\n", " formatted = '\\\\\\\\\\n '.join('&'.join(fmt%elt for elt in row) for row in mat)\n", " return opener+formatted+closer\n", "def rounder(mat): return mat+0.01sign(mat)\n", "def p(l): return poly1d(l)\n", "def vw(emme, chi, L):\n", " return sum(((mc).integ()(l)-(mc).integ()(0)) for (m, c, l) in zip(emme, chi, L))\n", "def p0_p1(p):\n", " from itertools import tee\n", " a, b = tee(p)\n", " next(b, None)\n", " return zip(a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 DOF System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input motion\n", "\n", "We need the imposed displacement, the imposed velocity (an intermediate result) and the imposed acceleration. It is convenient to express these quantities in terms of an adimensional time coordinate $a = \\omega_0 t$,\n", "\n", "\\begin{align}\n", " u &= \\frac{4/3\\omega_0 t - \\sin(4/3\\omega_0 t)}{2\\pi}\n", " = \\frac{\\lambda_0 a- \\sin(\\lambda_0 a)}{2\\pi},\\\\\n", " \\dot{u} &= \\frac{4}{3}\\omega_0 \\frac{1-\\cos(4/3\\omega_0t)}{2\\pi}\n", " = \\lambda_0 \\omega_0 \\frac{1-\\cos(\\lambda_0 a)}{2\\pi},\\\\\n", " \\ddot{u} &= \\frac{16}{9}\\omega_0^2 \\frac{\\sin(4/3\\omega_0t)}{2\\pi}\n", " = \\lambda_0^2\\omega_0^2 \\frac{\\sin(\\lambda_0 a)}{2\\pi},\n", "\\end{align}\n", "\n", "with $\\lambda_0=4/3$.\n", "\n", "The equations above are valid in the interval \n", "\n", "$$ 0 \\le t \\le \\frac{2\\pi}{4/3 \\omega_0} \\rightarrow\n", " 0 \\le a \\le \\frac{3\\pi}2 $$\n", " \n", "(we have multiplied all terms by $\\omega_0$ and simplified the last term).\n", "Following a similar reasoning, the plotting interval is equal to $0\\le a\\le2\\pi$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l0 = 4/3\n", "# define a function to get back the time array and the 3 dependent vars\n", "def a_uA_vA_aA(t0, t1, npoints):\n", " a = linspace(t0, t1, npoints)\n", " uA = where(a<3pi/2, (l0a-sin(l0a))/2/pi, 1)\n", " vA = where(a<3pi/2, (1-cos(l0a))/2/pi, 0)\n", " aA = where(a<3pi/2, 16sin(l0a)/18/pi, 0)\n", " return a, uA, vA, aA\n", "# and use it\n", "a, uA, vA, aA = a_uA_vA_aA(0, 2pi, 501)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The plots" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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35WG2TbYr8AXR5c7mBnevMbN6oKXfLx8AfA44E3gN+AzwpJlNcPeSVhzvNuCB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\nYoCXJEmSWsQAL0mSJLWIAV6SJElqkX8B9BwWf62zKk4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a854bef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plt.plot(a/pi, uA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$u_A/\\delta$')\n", "plt.title('Imposed support motion');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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6oj+iokLfzI4ysy4Wkg2MB74DZu7bUxCpe8od/vDqfGau2EqSwfizBtC3Xfbe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\nr2I9J97/RVwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4ce8908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(a/pi, vA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$\\dot u_A/\\delta\\omega_0$')\n", "plt.title('Imposed support velocity');" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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WnwtIEREROTQqAqRGrdmRw5jxMykoKqFzs2T+cckg4mOi/Q5LwsDooztw1XFe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\nL6uxoEVEqkCjpDheuGwQiXHRbNy1l2s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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4a06518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(a/pi, aA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$\\ddot u_A/\\delta\\omega_0^2$')\n", "plt.title('Imposed support acceleration');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equation of Motion\n", "\n", "The EoM expressed in adimensional coordinates and using adimensional structural matrices is\n", "\n", "$$ m\\omega_0^2\\hat{\\boldsymbol M} \\frac{\\partial^2\\boldsymbol x}{\\partial a^2}\n", " + \\frac{EJ}{L^3}\\hat{\\boldsymbol K}\\boldsymbol x =\n", " m \\hat{\\boldsymbol M} \\boldsymbol e \\omega_0^2 \\frac{\\partial^2 u_A}{\\partial a^2}\n", "$$ \n", "\n", "using the dot notation to denote derivatives with respect to $a$, if we divide both members by $m\\omega_0^2$ we have\n", "\n", "$$ \\hat{\\boldsymbol M} \\ddot{\\boldsymbol x}\n", " + \\hat{\\boldsymbol K}\\boldsymbol x =\n", " \\hat{\\boldsymbol M} \\boldsymbol e \\ddot{u}_A.\n", "$$ \n", "\n", "We must determine the influence vector $\\boldsymbol e$ and the adimensional structural matrices\n", "\n", "### Influence vector\n", "\n", "To impose a horizontal displacement in $A$ we must remove one constraint, so that the structure has 1 DOF as a rigid system and the influence vector must be determined by a kinematic analysis." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"757.158pt\" height=\"381.639pt\" viewBox=\"0 0 757.158 381.639\" version=\"1.1\">\n", "<defs>\n", "<g>\n", "<symbol overflow=\"visible\" id=\"glyph0-0\">\n", "<path style=\"stroke:none;\" d=\"\"/>\n", "</symbol>\n", "<symbol overflow=\"visible\" id=\"glyph0-1\">\n", "<path style=\"stroke:none;\" d=\"M 9.796875 -8.421875 C 9.125 -8.296875 8.875 -7.8125 8.875 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xlink:href=\"#glyph4-1\" x=\"173.59\" y=\"142.739\"/>\n", "</g>\n", "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n", " <use xlink:href=\"#glyph4-1\" x=\"243.374028\" y=\"142.739\"/>\n", "</g>\n", "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n", " <use xlink:href=\"#glyph4-1\" x=\"291.569102\" y=\"142.739\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(HTML(open('figures/trab1_conv.svg').read()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the 3x3 flexibility using the Principle of Virtual Displacements and the 3x3 stiffness using inversion, while the mass matrix is directly assembled with the understanding that the lumped mass on $x_1$ is $2m$.\n", "\n", "The code uses a structure `m` where each of the three rows contains the \n", "computational represention (as polynomial coefficients) of the bending moments due to\n", "a unit load applied in the position of each of the three degrees of freedom,\n", "in each row six groups of polynomial coefficients, one group for each of the six\n", "intervals of definition in which the structure has been subdivided (a possible seventh interval is omitted because the bending moment is always zero for every possible unit load)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\boldsymbol F = \\frac{L^3}{12EJ}\\, \\begin{bmatrix}\n", " +92&+128&+101\\\\\n", " +128&+192&+146\\\\\n", " +101&+146&+118\n", "\\end{bmatrix}$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol K = \\frac{3 EJ}{1588L^3}\\, \\begin{bmatrix}\n", " +1340&-358&-704\\\\\n", " -358&+655&-504\\\\\n", " -704&-504&+1280\n", "\\end{bmatrix} = \\frac{EJ}{L^3}\\;\\hat{\\boldsymbol K}.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol M = m\\, \\begin{bmatrix}\n", " 2&0&0\\\\\n", " 0&1&0\\\\\n", " 0&0&1\n", "\\end{bmatrix} = m\\;\\hat{\\boldsymbol M}.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "l = [1, 2, 2, 1, 1, 1]\n", "h = 0.5 ; t = 3h\n", "m = [[p(2,0),p(h,0),p(h,1),p(h,0),p(h,h),p(1,0)],\n", " [p(2,0),p(1,0),p(0,2),p(1,0),p(1,1),p(2,0)],\n", " [p(2,0),p(h,0),p(h,1),p(h,0),p(t,h),p(2,0)]]\n", "\n", "F = array([[vw(emme, chi, l) for emme in m] for chi in m])\n", "K = inv(F)\n", "M = array(((2.0, 0.0, 0.0),\n", " (0.0, 1.0, 0.0),\n", " (0.0, 0.0, 1.0)))\n", "iM = inv(M)\n", "\n", "ld('\\\\boldsymbol F = \\\\frac{L^3}{12EJ}\\\\,', pmat(rounder(F12), fmt='%+d'))\n", "ld('\\\\boldsymbol K = \\\\frac{3 EJ}{1588L^3}\\\\,',\n", " pmat(rounder(K1588/3), fmt='%+d'),\n", " '= \\\\frac{EJ}{L^3}\\\\;\\\\hat{\\\\boldsymbol K}.')\n", "ld('\\\\boldsymbol M = m\\\\,', pmat(M, fmt='%d'),\n", " '= m\\\\;\\\\hat{\\\\boldsymbol M}.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The eigenvalues problem\n", "\n", "We solve immediately the eigenvalue problem because when we know the shortest modal period of vibration it is possible to choose the integration time step $h$ to avoid numerical unstability issues with the linear acceleration algorithm." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\boldsymbol\\Omega^2 = \\omega_0^2\\, \\begin{bmatrix}\n", " +0.024831&+0.000000&+0.000000\\\\\n", " +0.000000&+1.729964&+0.000000\\\\\n", " +0.000000&+0.000000&+3.166490\n", "\\end{bmatrix} =\\omega_0^2\\,\\boldsymbol\\Lambda^2.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol\\Omega=\\omega_0\\, \\begin{bmatrix}\n", " +0.157577&+0.000000&+0.000000\\\\\n", " +0.000000&+1.315281&+0.000000\\\\\n", " +0.000000&+0.000000&+1.779463\n", "\\end{bmatrix} =\\omega_0\\,\\boldsymbol\\Lambda.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol T_\\text{n}=\\frac{2\\pi}{\\omega_0}\\, \\begin{bmatrix}\n", " +6.346086&+0.000000&-0.000000\\\\\n", " +0.000000&+0.760294&-0.000000\\\\\n", " +0.000000&+0.000000&+0.561967\n", "\\end{bmatrix} = t_0\\,\\boldsymbol\\Theta.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\Psi= \\begin{bmatrix}\n", " -0.431570&+0.504229&-0.243928\\\\\n", " -0.623940&-0.701061&-0.345271\\\\\n", " -0.488051&+0.004508&+0.872803\n", "\\end{bmatrix} .$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wn2, Psi = eigh(K, M)\n", "wn = sqrt(wn2)\n", "li = wn\n", "Lambda2 = diag(wn2)\n", "Lambda = diag(wn)\n", "# eigenvectors are normalized → M is a unit matrix, as well as its inverse\n", "Mstar, iMstar = eye(3), eye(3)\n", "\n", "ld(r'\\boldsymbol\\Omega^2 = \\omega_0^2\\,', pmat(Lambda2),\n", " r'=\\omega_0^2\\,\\boldsymbol\\Lambda^2.')\n", "ld(r'\\boldsymbol\\Omega=\\omega_0\\,', pmat(Lambda),\n", " r'=\\omega_0\\,\\boldsymbol\\Lambda.')\n", "ld(r'\\boldsymbol T_\\text{n}=\\frac{2\\pi}{\\omega_0}\\,', pmat(inv(Lambda)),\n", " r'= t_0\\,\\boldsymbol\\Theta.')\n", "ld(r'\\Psi=', pmat(Psi), '.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical Integration\n", "\n", "The shortest period is $T_3 = 2\\pi\\,0.562/\\omega_0 \\rightarrow A_3 = 1.124 \\pi$ hence to avoid unstability of the linear acceleration algorithm we shall use a non dimensional time step $h<0.55A_3\\approx0.6\\pi$. We can anticipate that the modal response associated with mode 2 is important ($\\lambda_2\\approx\\lambda_0$) so we choose an adimensional time step $h=A_2/20=2\\pi\\,0.760/20\\approx0.08\\pi$ that is much smaller than the maximum time step for which we have a stable behaviour.\n", "\n", "### Initialization\n", "\n", "First a new, longer adimensional time vector and the corresponding support acceleration, then the efficace load vector (`peff` is an array with 2001 rows and 3 columns, each row corresponding to the force vector in a particular instant of time) " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nsppi = 200\n", "a, _, _, aA = a_uA_vA_aA(0, 16pi, nsppi16+1)\n", "peff = (- M @ e) * aA[:,None]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The constants that we need in the linear acceleration algorithm — note that we have an undamped system or, in other words, $\\boldsymbol C = \\boldsymbol 0$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = pi/nsppi\n", "K_ = K + 6M/h2\n", "F_ = inv(K_)\n", "dp_v = 6M/h\n", "dp_a = 3M" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The integration loop\n", "\n", "First we initialize the containers where to save the new results with the initial values at $a=0$, next the loop on the values of the load at times $t_i$ and $t_{i+1}$ with $i=0,\\ldots,1999$." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Xl, Vl = [zeros(3)], [zeros(3)]\n", "for p0, p1 in p0_p1(peff):\n", " x0, v0 = Xl[-1], Vl[-1]\n", " a0 = iM @ (p0 -K@x0)\n", " dp = (p1-p0) + dp_a@a0 + dp_v@v0\n", " dx = F_@dp\n", " dv = 3dx/h - 3v0 - a0h/2\n", " Xl.append(x0+dx), Vl.append(v0+dv)\n", "Xl = array(Xl) ; Vl = array(Vl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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eZh5FRUXRsWNHBg8ezOTJkxk1ahRlypShTJkyF5fx8PC4rNymMBTb5N0wjEjg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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4766668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, line in enumerate(plt.plot(a/pi, Xl), 1):\n", " line.set_label(r'$x_{%d}$'%i)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$x_i/\\delta$')\n", "plt.title('Response — numerical integration — lin.acc.')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equation of Motion\n", "\n", "Denoting with $\\boldsymbol x$ the dynamic component of the displacements, with $\\boldsymbol x_\\text{tot} = \\boldsymbol x + \\boldsymbol x_\\text{stat} = \\boldsymbol x + \\boldsymbol e \\;u_\\mathcal{A}$ the equation of motion is (the independent variable being $a=\\omega_0t$)\n", "\n", "$$ \\hat{\\boldsymbol M} \\ddot{\\boldsymbol x} + \n", " \\hat{\\boldsymbol K} \\boldsymbol x = \n", " - \\hat{\\boldsymbol M} \\boldsymbol e \\ddot u_\\mathcal{A}. $$ \n", " \n", "Using mass-normalized eigenvectors, with $\\boldsymbol x = \\delta\\boldsymbol\\Psi\\boldsymbol q$ we have\n", "\n", "$$ \\boldsymbol I \\ddot{\\boldsymbol q} + \n", " \\boldsymbol\\Lambda^2\\boldsymbol q =\n", " \\boldsymbol\\Psi^T\\hat{\\boldsymbol M} \\boldsymbol e \\frac{\\ddot u_A}{\\delta}.$$\n", "\n", "It is $$\\frac{\\ddot u_A}{\\delta} = \\frac{1}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0a)$$\n", "\n", "and $$ \\ddot q_i + \\lambda_i^2 q_i =\n", "\\frac{\\Gamma_i}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0 a),\\qquad\\text{with }\n", "\\Gamma_i = -\\boldsymbol\\psi_i^T \\hat{\\boldsymbol M} \\boldsymbol e\\text{ and }\n", "\\lambda_0 = \\frac43.$$" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = - Psi.T @ M @ e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Substituting a particular integral $\\xi_i=C_i\\sin(\\lambda_0 a)$ in the\n", "modal equation of motion we have\n", "\n", "$$(\\lambda^2_i-\\lambda^2_0)\\,C_i\\sin(\\lambda_0 a) =\n", " \\frac{\\Gamma_i}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0 a)$$\n", "\n", "and solving w/r to $C_i$ we have\n", "\n", "$$ C_i = \\frac{\\Gamma_i}{2\\pi}\\,\\frac{\\lambda_0^2}{\\lambda_i^2-\\lambda_0^2}$$" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "C = Gl02/(li2-l02)/2/pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The modal response, taking into account that we start from rest conditions, is\n", "\n", "$$ q_i = C_i\\left(\\sin(\\lambda_0 a) -\n", " \\frac{\\lambda_0}{\\lambda_i}\\,\\sin(\\lambda_i a)\\right)$$\n", "$$ \\dot q_i = \\lambda_0 C_i \\left(\n", " \\cos(\\lambda_0 a) - \\cos(\\lambda_i a) \\right).$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$q_1= -0.637609\\left(\\sin\\frac43a- 8.461449\\sin0.157577a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_2= +3.691468\\left(\\sin\\frac43a- 1.013725\\sin1.315281a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_3= -0.016167\\left(\\sin\\frac43a- 0.749290\\sin1.779463a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for n in range(3):\n", " i = n+1\n", " ld(r'q_%d=%+10f\\left(\\sin\\frac43a-%10f\\sin%1fa\\right)' % (i,C[n],l0/li[n],li[n]),\n", " r'\\qquad\\text{for }0 \\le a \\le \\frac32\\pi')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Free vibration phase, $a\\ge 3\\pi/2 = a_1$\n", "\n", "When the forced phase end, the system is in free vibrations and we can determine the constants of integration requiring that the displacements and velocities of the free vibration equal the displacements and velocities of the forced response at $t=t_0$.\n", "\n", "\\begin{align}\n", " + (\\cos\\lambda_i a_1)\\, A_i + (\\sin\\lambda_i a_1)\\, B_i &= \n", " q_i(a_1) \\\\ \n", " - (\\sin\\lambda_i a_1)\\, A_i + (\\cos\\lambda_i a_1)\\, B_i &= \n", " \\frac{\\dot q_i(a_1)}{\\lambda_i}\n", "\\end{align}\n", "\n", "Because the coefficients form an othogonal matrix,\n", "\n", "\\begin{align}\n", " A_i &= + (\\cos\\lambda_i a_1)\\, q_i(a_1)\n", " - (\\sin\\lambda_i a_1)\\, \\frac{\\dot q_i(a_1)}{\\lambda_i}\\\\\n", " B_i &= + (\\sin\\lambda_i a_1)\\, q_i(a_1) \n", " + (\\cos\\lambda_i a_1)\\, \\frac{\\dot q_i(a_1)}{\\lambda_i}.\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a1 = 3pi/2\n", "q_a1 = C(sin(l0a1)-l0sin(lia1)/li)\n", "v_a1 = Cl0(cos(l0a1)-cos(lia1))\n", "\n", "ABs = []\n", "for i in range(3):\n", " b = array((q_a1[i], v_a1[i]/li[i]))\n", " A = array(((+cos(li[i]a1), -sin(li[i]a1)), \n", " (+sin(li[i]a1), +cos(li[i]a1))))\n", " ABs.append(A@b)\n", "ABs = array(ABs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analytical expressions" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Modal responses for $a_1 \\le a$." ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{1} = +3.648\\cos 0.158a +1.420\\sin 0.158a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{2} = +0.318\\cos 1.315a -0.014\\sin 1.315a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{3} = +0.010\\cos 1.779a +0.018\\sin 1.779a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Latex(r'Modal responses for $a_1 \\le a$.'))\n", "for n in range(3):\n", " i, l, A_, B_ = n+1, li[n], ABs[n]\n", " display(Latex((r'$$q_{%d} = '+\n", " r'%+6.3f\\cos%6.3fa '+\n", " r'%+6.3f\\sin%6.3fa$$')%(i, A_, l, B_, l)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Stitching the two responses\n", "\n", "We must evaluate numerically the analytical responses" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ac = a[:,None]\n", "\n", "q = where(ac<=a1,\n", " C(sin(l0ac)-l0sin(liac)/li),\n", " ABs[:,0]cos(liac) + ABs[:,1]sin(liac))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting the Analytical Response\n", "First, we zoom around $a_1$ to verify the continuity of displacements and velocities" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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AgAPCtddeq8suu0y33nqrdu/eraGhIX3lK1/R8uXLOx0aPc0AAADzxeELerXu\nI6e1/Zgzkc/nddFFF+nmm2/WscceK0k677zzdMUVV2j58uUaGRnRypUrtXHjRl1zzTU655xz5jLs\nSUiaAQAA5olMOtXQUIl2Wrdunfr6+nTKKadUy7Zv367BwUENDQ2pUqnom9/8pq655pqOxMfwDAAA\nAHTcjh07dNhhh9WUrVmzRscff7zMTOl0Ws997nM7FB1JMwAAALrAsmXLtHHjRt1///0aGxvTNddc\no+uuu04nnHBCp0OTRNIMAACALnDSSSfpwgsv1Omnn66jjjpKW7Zs0bJly7riIkCJpBkAAABd4vLL\nL9euXbu0detWXXLJJXr44Ye7JmnmQkAAAAB0nUcffVSFQqF6Jw1Jevvb364HH3xQAwMDuu+++3TV\nVVe1LR6SZgAAAHSdDRs26JhjjlEul6uW3XTTTR2Lh+EZAAAA6DpnnHGGNm7c2OkwqkiaAQAAgAQk\nzQAAAEACkmYAAAAgQcNJs5n1mtk/m9kmM8sH04/MRXAAAABAN2jm7hkZSc9IeqOkRyS9RNJ3zGy3\nc64zPwYOAAAAzKGGk2bn3LCkj0WKNpjZ1ySdIikxaTazgyUdHCs+UpKGh4eVz+cbDanlRkZGaqbo\nPNqk+9Am3Yc26U60S/eZT21SLBbV29urcrnc6VCmValUaqazUS6XVSgUVCqVJq0bHh5uer/mnJtN\nXDKzlKT7JN3knPvEDOqvlnRxvXVXX321lixZMqt4AAAA4GUyGZ188snq6enpdChtUywW9cADD9RN\nmrdt26bzzz9fkl7knHukkf22Imn+tPxQjVc65xK7iafpaV770EMPaWhoaFbxtMLIyIjWr1+vFStW\n1NxQG51Dm3Qf2qT70CbdiXbpPvOpTcKe5r6+vk6HMq1KpaLh4WENDAwolZrdfSrGxsZUKBTqflHY\ntGlT+LPcDSfNs/pFQDO7VNJbJZ06k4RZkpxzOyXtjO1HkjQwMKDBwcHZhNRSuVyuq+IBbdKNaJPu\nQ5t0J9ql+8yHNhkbG5MkpdPpDkcyM6lUataxptNp5XK5ul8UBgYGmt5v00mzmf29pDPlE+YtTUcA\nAAAAdLmmkmYz+6ykVfIJ89bWhgQAAIA5US5J+7a195gLlkjpmaecN954o1avXq0dO3Zo1apV6uvr\n09KlS3XppZfOYZDJGk6azWyppL+WVJT0cDi0QtI9zrk3tjA2AAAAtNK+bdJnjm3vMS/4pbT4+TOq\neu211+qba5CoAAAceUlEQVSqq67Srbfeqpe85CW66KKL9KlPfUpr1qyZ4yCTNTzS2jn3mHPOnHO9\nzrnByIOEGQAAAE3J5/O66KKL9C//8i869thjlU6ndd5556lcLmv58uXasGGDXvOa1+iUU07Raaed\npkcffbSt8c3qQkAAAADsRxYs8T2/7T7mDKxbt059fX065ZRTqmXbt2/X4OCghoaG9Oyzz+q2227T\nokWLdPvtt+vjH/+4brjhhrmKehKSZgAAgPkinZnxUIl227Fjhw477LCasjVr1uj444+XmdWsy2az\nbb8jyOxuhAcAAAC0wLJly7Rx40bdf//9Ghsb0zXXXKPrrrtOJ5xwQk290dFRXXzxxfrQhz7U1vhI\nmgEAANBxJ510ki688EKdfvrpOuqoo7RlyxYtW7Ys/DESSVKpVNK5556rD3/4wzruuOPaGh9JMwAA\nALrC5Zdfrl27dmnr1q265JJL9PDDD1eTZuec3ve+9+n000/XmWee2fbYSJoBAADQdR599FEVCgUd\ne6y/Rd73vvc9fe1rX9OaNWt06qmn6oILLmhrPFwICAAAgK6zYcMGHXPMMcrlcpKkVatWaWRkpGPx\n0NMMAACArnPGGWdo48aNnQ6jiqQZAAAASEDSDAAAACQgaQYAAAASkDQDAAAACUiaAQAAgAQkzQAA\nAEACkmYAAAAgAUkzAAAAkICkGQAAAEhA0gwAAAAkyHQ6AAAAALRHqVLSsyPPtvWYh+YOVSa1/6ec\n+/8zAAAAwIw8O/Ks3nDTG9p6zDvefoeWDC6Zcf0bb7xRq1ev1o4dO7Rq1Sr19fVp6dKluvTSS+cw\nymQMzwAAAEBXuPbaa3XZZZfp1ltv1e7duzU0NKSvfOUrWr58eadDo6cZAABgvjg0d6juePsdbT/m\nTOTzeV100UW6+eabdeyxx0qSzjvvPF1xxRVavny5Nm/erHPPPVfZbFalUklXX321jj/++LkMvQZJ\nMwAAwDyRSWUaGirRTuvWrVNfX59OOeWUatn27ds1ODiooaEhlctl3XvvvUqlUrrrrrt0+eWXa82a\nNW2Lj6QZAAAAHbdjxw4ddthhNWVr1qzR8ccfLzNTJjORtu7du1cnnHBCW+NjTDMAAAA6btmyZdq4\ncaPuv/9+jY2N6ZprrtF1111Xkxw/9NBDWrFihT7wgQ9o5cqVbY2PpBkAAAAdd9JJJ+nCCy/U6aef\nrqOOOkpbtmzRsmXLai4CXL58udavX69bbrlFH/jAB9oaH0kzAAAAusLll1+uXbt2aevWrbrkkkv0\n8MMPV5PmQqFQrbdo0SLlcrm2xsaYZgAAAHSdRx99VIVCoXonjXXr1mn16tVKp9NyzunKK69sazwk\nzQAAAOg6GzZs0DHHHFPtUT7ttNN02mmndSwehmcAAACg65xxxhnauHFjp8OoImkGAAAAEpA0AwAA\nAAlImgEAAA5QqVRK5XK502G0VaVSUSrV+hSXpBkAAOAAlc1mNTo6Kudcp0Npm0KhUPPrga3C3TMA\nAAAOUGamxYsXa8eOHerv71c6ne50SHWVy2UVi0WNjY3NKsZisahsNktPMwAAABqTyWR0yCGHKJvN\ndjqUKRUKBT3wwAM1P2DSjFwup8HBwRZFVYueZgAAgAOcmamnp6fTYUypVCqpVCqpp6dHfX19nQ6n\nLnqaAQAAgAQkzQAAAEACkmYAAAAgAUkzAAAAkICkGQAAAEjQVNJsZueY2Tozy5vZ5hbHBAAAAHSV\nZnuad0r6nKT/3cJYAAAAgK7U1H2anXN3SJKZnd3acAAAAIDu0/YfNzGzgyUdHCs+UpKGh4eVz+fb\nHdIkIyMjNVN0Hm3SfWiT7kObdCfapfvQJt2nXW0yPDzc9LbmnGt+Y9/T/Cnn3Asa2Ga1pIvrrbv6\n6qu1ZMmSpuMBAAAAprJt2zadf/75kvQi59wjjWzbiZ/R/pykL8bKjpS0dsWKFRoaGupASLVGRka0\nfv16rVixQrlcrtPhQLRJN6JNug9t0p1ol+5Dm3SfdrXJpk2bmt627Umzc26n/IWEVWYmSRoYGNDg\n4GC7Q5pSLpfrqnhAm3Qj2qT70CbdiXbpPrRJ95nrNhkYGGh626aSZjNLS8oGDzOzPklyzo01HQkA\nAADQpZrtaf5TSTdElkeDqc0uHAAAAKD7NHWfZufcjc45iz9aHRwAAADQDfgZbQAAACABSTMAAACQ\ngKQZAAAASEDSDAAAACQgaQYAAAASkDQDAAAACUiaAQAAgAQkzQAAAEACkmYAAAAgAUkzAAAAkICk\nGQAAAEhA0gwAAAAkIGkGAAAAEpA0AwAAAAlImgEAAIAEJM0AAABAApJmAAAAIAFJMwAAAJCApBkA\nAABIQNIMAAAAJCBpBgAAABKQNAMAAAAJSJoBAACABCTNAAAAQAKSZgAAACABSTMAAACQgKQZAAAA\nSEDSDAAAACTIdDoAAGgb56RKaeJRHpcq5dqyqZZdtLwSWQ7XV/y8K8emlYlHzXI472rrTHq4iTpy\nE2Vyk6a94+M6YcuT6v3e7VIm7ddpYjIjFpsx8/NmE+XVstTk+UllUz3qrE+lJUtLqbAsLaUysfJ4\nWfjITDwsNTGfzk5eH32ks8E2JgCYDkkzgNlxFWl8VCoVpHJxYlp3flwqB9PqurAsnC9OMT9eWx4m\nveWiVBmXyqVgOh5JiCPlYcJ7AMtKeoEk7ehsHPslSwcJdFZKZ4JpNlIWne/xddI9kXU9teXp3mp5\ntiId/fRjyv50s9Q/KGV6I/V7pExQPyyvTvtq59NZknugg0iagf1dpeKTzvFRqTTmH+NjUilIZMOE\nNlwujc1wGpkvR5eLUmlMA6WC3jI+ptSD5U6/Au0T7eWs9mhGejxrekNj5WaR+UjPqizSsxo8anpp\ng3kpthzt8fXT8VJJTz31lJ67ZImymezEemlmyZaLdkm7SNnkXu3J08rE9pN6yuv1nkd72yO99M5F\neuSD3vp6Pfg188EZg4a61OPPvSyVypLGmt/HFHolvUyStrZgZ5m+iQQ7TKqTptn+YLk/stznH9k+\nX15vms1N1EsxmhMgaQbmQqUSJK+j0vhI/WlpLFIWPEqjseWx+tNq3TGf0HZAJB2bWe1471o6G5T1\nRnrqspF62VgvXjANT6lX58O6mdh2kd7CVDoyX+fUfLi+ui4dKYskyF3ey1fI5/XTtWu1cuVKZQcH\nOx1O+9UMm6kz3KY6HCd6NqJUe2aiUo6cpRivna+UJp/5iNare5akqHJxVHt2bteiwT6lXTl2BqY4\n8aV0Jkl/+MW43R/7TF+QbPf7aTYXJNbhfHQanR+oLevJBevi8wPBl0ige5E0Y36qlKXisE9aq9MR\naXw4mI5G5of9cnEklvgG88XhSKI7MpHQdouaXqe+Or1R9dbFerLSvf4UcqSXa3S8ogd/vkHLT/59\n5QYXT2wTPb0czjNmFO2QSklK+S9DXWQ0n9c9wZeZwam+zITj7WuGORVqk+opz/5MNQ3POtUrG508\ndZWpn0S4rXbNyWskyf/b0pPziXZPJJnuGagzP+jr9AwE9es9Bv0j0zN3MWNeIWlG93LO/yNdHJbt\nfkYLRp9UautPpIzziWpxRCrmJxLfaBJcdz6SFHeod1aS/8MQ75XJ9MV6b3KRnp2+ifKasnrT2CnY\ndO+cnVYt5/N6drOpcsSrpPnYqwm0ktnEuOlOcC641iCaTEfPeI348npnxMaHJ9aNj8TKoh0MwXxl\nvH4M5YI0WpBGW5yYp7KRJNon1H2Zfr1yz4h6x74p5Rb78t4FE/Vq5gelngXBNNgHnQDzEkkzWqNS\niSSp+eAxPLFcyEfWDdeui89H9xP0fAxIOk2Sfj3HzyPdM9GjESa1k+b7J04pRk85ZvqD3pFIQlwt\niywzNhBAtzELzib1SH2L5vZY5fHIGboRTX/WL1Ze01GSD8oif0NcnWssKuPS2G7/CGQkLZGkPQ82\n8QRsIrEOE+neQal3YZ3yhcHygonyaFk2RwK+HyFpnq/KJam4rzaZLeybYj5MgoO68eWwXrukMrFT\ncsGpumlP5dU55Revk835cbAAgLmTzkr9i/2jlZwLLn4eqf37VNg3kWgHf9uK+Z16/JGNWrrkEGUr\nhdoOn0JsOmmsufN/P4v7pH2zjNlSQQIdT64XRMqDdX0La8v6Iuuy/STfbUCGsL+oVGIf6H2xD3bS\ncmy+1PorxOvKxsaX9Q7WLmdzvmzSmLTBmuXhcemH63+iU16/SoOLDm5P7ACA/YdZMLytT8pN/3ei\nmM9rw9haPTfpolnnVNuBNMXf2sK+iQ6n+HwhLxX2+vl4T7irSGN7/GM2UplIIr1A6l00kVTXncbW\n9y2i13sGSJrnUnk88uEJP1TxD1jwYar5drtv8nbjw3MfbyoTGcsVJK3VMVzRhHdBZF3kYovomLAW\nXw3t8nmNZ37th08AANAOZsGQikFpwSz3FV6nMxb+zd878Tc+TKqr032+XjXx3hMp2zv5nvOVkjS6\n0z+aZWmfPIdJdG8wjT6qZQvrlx/gd0AhaY4rj0uju9RfeFapZzdKO8uRN+1U3ySjCXHkW+hc9+Za\nevIFCtXpgmmWB2IXNQTlmd65jRcAgPnKbOLibx3e/H7CYShhgj22JzIfJNXV6Z7YcqQ8nqO48uwT\n754FkxPt+KN/8cT8YS+VBp7T/PHabH4nzfdcKW34Rm3Pb2lMg5LeIEm/moNjZvoiFw0smEheq0lt\nZDop4V1QmyRn+jiVAgDAfBIdhjJ4WPP7KRUnJ9Z15/fUPqJl8dsUhmO99z45sxjedr10/Duafw5t\nNr+T5n1PSU/9IrleeE/ImqQ1nuwurE1ya66kjdTrsvuHAgCAeSjTI2We03xPbzjeO55Qj+4OEuvd\nE+Wjuycn32N75v5OLS02v5Pml7xZOviFsZ7cBRoppXTvj3+mV79ulQYPOpw7KgAAAERFx3svel7j\n2zvnH/uR+Z0NvvAU/4ip5PMa7dnivwGRMAMAALSW2X43xLSpX1kws4yZXWVmO8xsj5l9wcwGWh0c\nAAAA0A2a7Ub9W/lr5ZZLGpF0k6SrJL2/RXG1xecf/Lxu2XSLXHDjchecJqi4igpjBX3mts/Igm9B\n8ToudrPzeuXxbWrWRzav1otuW2+bOserKZtNvTpl9Yo6xcnJOaeLv3GxTJFvplN8SbU6K+qW1fmW\nG9arty6pXrWsTowmq9aNrp92P9Pte5r9Rsuiy/H91sQRrJ9UJ7pdZD+VSkX79u3TF9d+Uel0enIc\nNjmeuseLHjfYpqZ+nbIpy23y8aJ1U5aaul7s9UkpNaleuH00/mrZFPHUnU6xLnrMerFGyyYdX1Kx\nWNTG4kaNPz6uvr6+ydtEjlPvNYmvD9elLFUTU3y5XjzR5xPdf3ybeJ1w32Hd6Y4djz1lE68fALRa\ns0nz+yR91Dn3hCSZ2UWS7jSzDznnRqfb0MwOlhS/6/iRkjQ8PKx8vn2/LLc9v13bhrdNXaFNv/+B\nBtT5YSZ01tbdWzsdAmJueuCmTofQUVMl9vWS+Hh53S8EqpP4T1M/fhxXccrvy2vNXWuUSWcmJ//h\nl5T4F4ipvnQkHXuaL2Xx49T70jSb8nqvXTTmeLmkKWOb6jjTtXHSNmHZ2NiYtpW26RdP/UL9/f21\nr5E0bQzxmKeqZ5r8hb1m33WOUe/Y88XIyEjNdK4MDzf/uxcNJ81mtljS8yX9JFL8U0l9kl4k6ecJ\nu/hrSRfXW7F+/Xpt3ry50ZCadmjpUJ2dO7u6HL5ho/P1yqJmUhZ/0890P81sk7SfRuu1ettm1e0J\nb9H2Sfueac/8jMsSev8nncWod/aiwfpyyftp5Fj19jtVTC74r9729c6uJNWdclpn+6nmJ9Vr4Ng1\nZW7qfU9Z7pLjq/fckvZdUWXauKY8hnPTr69zrG7n5FQOfnmtHP8Ftk7a1ekAMMm6TgcwM9UkXJPz\nk2mnNn0+kzStt594PNNuN0WZ5HOj0/tO19HZo2ue6/r16xNejdnZtm2aztIEzfQ0h7+JU/3NR+fc\nqJkVJS2cwfafk/TFWNmRktauWLFCQ0NDTYTUWiMjI1q/fr1WrFihXC7X6XAg2qQb0Sbdpx1t4pxT\nRZWa4WzhcvilIJxWVKkm8JXgfq5lV65J7MPyiqvUbF9djteJJvnxutHlqeq5OvtU/f04V3/f0ect\np5rnX28fhWJBjz3+mJ7//Ocrk83Ufb2Snlv0ONGYpztuPK6ZvIY1x5kiznrtlfR61du27n4Sjpe0\nTfjl8UATvsZNbNiqAObE0ccerZVHrJTUvr8pmzZtanrbZpLmfcF0kaRtkmRm/ZJ6JO1N2tg5t1NS\nzc/NhD2xAwMDGpzuN+DbLJfLdVU8oE26EW3SfWiT7pLP57X2mbVaefxK2qVNpvsyUnEV7cvv090/\nvFt/+Id/qNxAruZL4FRfnKL7rdap8wVk0nSKMoVnhGawj7rHVu1Zpbp16x0vXi5NisUXucln4SL7\nrb7W021X78xapO6Jv3fipM/EXP/7NTDQ/H0rGk6anXO7zewJSS+X9Oug+OXyI4B/23QkAAAALRAf\nzxxXyVbUZ30azA5qsIcvMpiZpm45J+l6SR81syPM7BBJl0r6YtJFgAAAAMD+qNm7Z1wufweMnwf7\n+JakC1oVFAAAANBNmkqanXMl+SSZRBkAAAAHvGaHZwAAAADzBkkzAAAAkICkGQAAAEjQ7IWArZaV\npMcee6zTcUjyP7G4bds2bdq0aVb380Pr0CbdhzbpPrRJd6Jdug9t0n3a1SaRXDPb6LZW7yd9283M\nTpO0ttNxAAAAYF5Y6Zy7q5ENuiVpHpD0KvlfGBzvcDhS8LPeklZKerzDscCjTboPbdJ9aJPuRLt0\nH9qk+7SrTbKSlki6zzk33MiGXTE8Iwi6oWx/LoU/6y3pcefcI52MBR5t0n1ok+5Dm3Qn2qX70Cbd\np81tsrGZjbgQEAAAAEhA0gwAAAAkIGkGAAAAEpA017dT0iXBFN2BNuk+tEn3oU26E+3SfWiT7tP1\nbdIVd88AAAAAuhk9zQAAAEACkmYAAAAgAUkzAAAAkICkGQAAAEhA0gwAAAAkIGkGAAAAEpA0AwAA\nAAlImgEAAIAEJM0AAABAgnmZNJvZOWa2zszyZrY5oe7/NLMHzWyvmW01s+vMbHGbQp03GmyTd5vZ\nRjPbbWY7zewOMzuuTaHOG420SWy7NWbmzOwVcxjevNXgZ+U9ZlYO6oaPy9oU6rzR6GfFzI4xs28H\nf1d2m9l32hDmvNLg5+S7sc/IaPBv2MvbFO680WC7ZMzsU2a2Jfis/NjMVrYp1LrmZdIs/7vmn5P0\nv2dQNyvpfEnPkXS8pOdLumbuQpu3GmmTeySd6pxbLOlwSd+VdMscxjZfNdImkiQzO0P+s4K502i7\nbHTODUYeF81hbPPVjNvEzA6XdLf8v1tLJB0q6eK5DG6emnGbOOfeGP2MSLpM0q+dcz+d6yDnoUb+\n/forSe+Q9FpJi+Vzr5vN7KC5C296mU4duJOcc3dIkpmdPYO6n4gsbjezf5B03VzFNl812CaPRxZN\nUkXSkWbW65wrzFGI804jbRLUWyzp05JOl/TIHIY2rzXaLph7DbbJhZLudc79U6TsgTkJbB5r9nNi\nZibpvZL+YS7imu8abJchSXc55x4NtvlX+cR5SNKP5yzIaczXnubZWCnp550OYr4zs+PMbLekMUlX\nSbqChLnjrpR0nXNuU6cDQY0hM3vGzB4zs+vN7NBOBzTPvU7SbjO7x8x2mNn9ZvaGTgeFqj+SPwPw\nhU4HAl0naXkwnCkt6f2SNkv6ZacCmpc9zc0KTj2/T9JrOh3LfOec+4WkxWa2SNJ75D9I6BAz+yNJ\nJ8r/o4bu8Z+SjpP0qKQjJP2jpG/In+5EZxwi6VxJfyzpR/Knn79lZsfxhbMrvF/SN5xzOzodCPQ7\nSfdL+o2ksqS9ks5wzo11KiB6mmfIzP5Y0g3yDUZPc5dwzu2RP412o5kNdTqe+cjMBiRdLenPnHOl\nTseDCc65R51zjzjnKsGwpj+X9Boze16nY5vH9kn6lnPuh865cefclyX9Qn5YEzrIzA6T9FZJ/9zp\nWCDJ/105Rv4Lf6+k/yrpFjNb1qmASJpnwMzOkvTvkt7unPtBp+PBJCb/gTqq04HMUy+S9EJJt5vZ\ndjPbHpTfaWb/q4NxYTIXTK2jUcxvP9NEO4Tiy+iM/y7pd865uzsdCCT5s5dfcM5tcc6VnXO3yp81\ne12nApqXSbOZpc2sT/7OGGZmfcFyvbrvlO9hPouEee402CbvMbMXmHew/JW4I+rQhQEHqgbaZIOk\npZKWRx6SPwX92bYEO480+Fl5k5n9XjD/XPnhGfc7555sX8QHvkbaRL4X8wwze7WZpYK/McdJur1d\n8c4HDbZJeAHg+8SF/nOqwXZZL+lPzOzw4O/9KkkvlfRgu+KNm5dJs6Q/lTQq6cuSjgzmRyXJzP7W\nzDZE6l4haVDSbdH7OLY74HmgkTZ5mfx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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a49d2518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# #### Plot zooming around a1\n", "low, hi = int(0.8a1nsppi/pi), int(1.2a1*nsppi/pi)\n", "for i, line in enumerate(plt.plot(a[low:hi]/pi, q[low:hi]), 1):\n", " line.set_label('$q_{%d}$'%i)\n", "plt.title('Modal Responses, zoom on transition zone')\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "next, the modal responses over the interval $0 \\le a \\le 16\\pi$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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GSdG0riPt4grEuBxMuKN34YiPD0xfxaLtR60OSwghqrSMXB+jJixlb3ouMS47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\nbfvxegz3ntuPG8Ycp+RaRBo0JdgumT59Ovv27WPHjh3ceeedrFu3riz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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4ea47b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# #### Plot in 0 ≤ a ≤ 16 pi\n", "for i, line in enumerate(plt.plot(a/pi, q), 1):\n", " line.set_label('$q_{%d}$'%i)\n", "plt.title('Modal Responses')\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.legend(loc='best');\n", "plt.xticks()\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nodal responses" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = [email protected]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why `x = [email protected]` rather than `x = Psi@q`? Because for different reasons (mostly, ease of use with the plotting libraries) we have all the response arrays organized in the shape of `(Nsteps × 3)`. \n", "\n", "That's equivalent to say that `q` and `x`, the Pyton objects, are isomorph to $\\boldsymbol q^T$ and $\\boldsymbol x^T$ and because it is $$\\boldsymbol x^T = (\\boldsymbol\\Psi \\boldsymbol q)^T = \\boldsymbol q^T \\boldsymbol \\Psi^T,$$\n", "in Python to write `x = [email protected]` we have.\n", "\n", "That said. here are the plot of the nodal responses. Compare with the numerical solutions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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rjWIs/xLwYkHzPvroowoZW14pdR6yXFwW9yPN4qIAOObfkOWNniTLcXEFfueL\nwLQDdNv2InYri72h3fmr7j0VnSWlVD4Oh4OOHTvi7a138IqSmZnJ6tWrzwiwY2NjefjhhwGaWJa1\n09N0yzvA/jemuUgny7LOWqVURA32gg0bNtCoUfl1t5Wamsry5cvp0qUL/v7+5bbdC5WWl2e0vDyT\nu7wCsk/gM/cpHHsWAuCsG0H6gM/LvK9rp8tJfFo8calxJKQnkOZMI82ZhtNy4m3zxsfug5/Dj1Df\nUKr6VqWqX1X8HRXz3ZbF8eW17gt8FpoblGlDviW7btdSSfd8oL9Hz2h5eaa8yutUDbavr2+ZbaM8\nuFwuUlJSCAgIwFbAAGEul4vatWvzxx9/0LhxYwAWLlzI//3f/7Fjxw4yMzNp3rw5ixYtKrAs0tPT\nycjIOONCZNeuXbRt2xZKGGCXWy8iIvIqpg129+IE1wCWZSUCifnSASAgIIBKlcq/dsrf379Ctnuh\n0vLyjJZX8dlcmQRv+QqfZeMhI8lMvPIRHH1eppK9dE9tLstF9NFo1sevZ3PCZrYkbmFf0j5clme3\nLKv4VqF+UH0aBDegQXADGgY3pFmVZlTzq5ZzbitLpXp8XfUI7JgF+1fgN/9peHjZRdd1n/4ePaPl\n5ZmyLq/09HSAPMOLX8hsNluB+7Jy5UqqVq1Ks2bNALAsizvuuIPx48dz5513kp2dzcaNGwkICCgw\nXbvdjr+//xnBd2HLF1e5BNgi8hZwEya4Plge21RKnSdOHDAPHu5fBfGbIeWoGbLcyw98g83AKJXr\nm76cQxtBaGMIqQt2rzPTcmXD4b/x3jCTPn9Px8d53EwPqAb9/g2XDyi1bGdkZ7AydiWL9i9i8f7F\nHE07WuByNrFRxbcKAV4B+Dn8cIiDTFcmmdmZnMw8ybGMYzmBeGJ6IonpiayLz/twYIhPCE0rNz39\nqtKUxiGN8bH7lNr+lDqbDW6cCB9HQOJuWPwG9Blb0blSSl2gxo8fz6effkp0dDR2u50lS5YwcOBA\nvv/+e1q3bl3oelFRUQwYcPrcLyK0adOGN954gyVLltC/f3/69etXHruQR5kH2CLyHhCJCa4PnW15\npdRF4tB6WPQ67JgLFNAULSMJkg/D0e1nzhM7VK4HwbXB4WtGZEyJh4TdkHmSUzfyLLsPcsVw6D4G\n/PO3JvPciYwT/HHgDxbtX8TSg0tJdeZ9CKlBcANahrakeWhzmlVuRq3AWoT5h+FlK+BiwM3pcpKY\nnkh8ajxr3/I1AAAgAElEQVT7kvax58Qe9ibtZc+JPew5sYcsVxbHM46zKm4Vq+JW5axnFzv1gurR\ntHJTmlVplhN8V/evXi613cVSrSlc8xQsehWWfwBt7zDTlFLnp2wnnIwtv+0FhkMx7yiOGDGCiRMn\nMnXqVNq1a8egQYOYPn06ERERnDx5stD1oqKimDJlSs7nxYsX07p1a2bPnk1iYiJDhgxhzpw5TJxY\nvl2LlmmALSL1gJFAJrA91z+FPy3Luq4st62UqiAZyTDvBVjz+elplapDo55m2O3AGuDwMyMRpibA\nsb3mlbgbEnZBdgZY2eZz4u4CN+EKqc9237bUGfA8AeFNzim7B5MPsihmEYv2L2Lt4bVkW6efuLeL\nnSuqX0GPOj3oXqc7tQNre5y+w+YgzD+MMP8wWlZtmWdeliuLfSf2se3YNrYf225eiduJT4sn28pm\n94nd7D6xmzl75+SsE+QdlBNsnwq8G4c0xtdRQe0sI0bCX19D4i6Y8zTc+YP2Oa7U+epkLExoefbl\nSsvj0RBSp1iLent7M27cOJ5++mlcLhcffPABffv2ZePGjTzwwAN4eXnh5eXFZ599RsOGDQHYtm0b\nSUlJdOrUCYC4uDiGDRvG5s2bcTgchIWF8eijj3LPPfdcXAG2ZVn7AD3TKnWpSNwNXw2BhB3mc43W\n0P0ZaHJt8WoxXC5IOmiCtYSdcPKwCbjBNAMJrg21OpBqD2HbwoXUDvS8JyHLstiSuIVF+xexKGYR\n245tyzPfz+HHVbWuokedHlxT+xqCfYI93kZxedm8aFy5MY0rN6bF0e78dPwQRxKOsD/2ENmOg9h8\n4rD7xmLzicPmcxixOUnKTGLN4TWsObwmJx272GlepTntqrejfVh72ldvTxXfc6/RLxaHjxmG/r+3\nwK6FsG02XHZ9+WxbKXVRadu2LbGxsdx5550MGTIEgGrVqjFz5kxq167NvHnzeOWVV/jiiy8AU3vd\nv3//nLt606dPp02bNnnaticlJREcXHbn8cLoUOlKqdKxfzX8dwikJYLdB3q/CJ0fNm11i8tmM7Ud\nIXWgYffCl/OwD/wsVxZr4taYoHr/IuJS4vLMD/UNpXud7vSs25PO4Z3Lre2zZVks2XmUjxbvYtmu\nhFxz/CCzMdmpjcnKmZaNzfsoNt84vP3jqFntGC6vQxxx13ZHJ0QTnRDNtM3TAGhVtRXd63Sne53u\nhDvKuEvTptdCk76w4zf47Rlzt8Lrwu65QKmLUmC4qVUuz+0VU0xMDJGRkYwYMYLJkyfz1ltvERYW\nRlhYWE4TES8vrzwPOkZFRfHss8/mfN66dSshISF50p05cyYDBw48xx3xnAbYSqlzd2gDTB9k2lUH\nVIPbZkDtDhWapeTMZJYcXMLC/QtZcmAJJ7PytuFrENyAnnV60qNuD1pVbYVNynpg27w2HTjBCz9F\nsz7meM60msG+XN8qnCsbhtKsRiBVK/ngsiyOJmewJTaJxduOMDs6jhNxWeyMg0AfBw/1rEHrRifZ\ncGQ96+PXs/HIRjKyM9h0dBObjm5i4vqJNAxqSNPMprRNb1t2vRZEvm5qsI/theUT4ZrRZbMdpVTJ\n2R3FbrJRnuLj4+nTpw+jRo1i1KhR7Nq1i7Fjx/Lhhx/mLJOWlsaLL77IRx99lLNOdHQ0vXr1ylmm\nadOmjB8/nu3bt1OnTh3efPNNoqOjc2q8y5MG2Eqpc3N0J0wbaILroNowfBZUaVAhWUlMT2RBzAIW\n7FvAyriVOF2nBw4QhLZhbelRpwc96vSgfnD9CsnjibQs3vltG9NX7uPUMARdG4XycPdGRDSqis12\nZqu6AB8H9UIDiGwZzgv9L+eb1fuZuHAnCSmZvD37AFc2rMJ7Q+/n0Xa+ZGVnsT5+PYsPLGbx/sXs\nP7mf3Um72c1u5v06jz71+jC8xXBaVG1RujsW2gi6PAJLJ8Cf480Dj0E1S3cbSqmLTlJSEpGRkQwe\nPJhRo0YBMG7cODp16sRjjz1Go0aNcDqdDB8+nCeffJJWrVoBMGvWLHr37p2ne72RI0eyc+dOIiIi\ncDgcdOvWjaVLl1bIwIQaYCulSi49CWbcZpqFBITBsKhyD64T0hJYELOAuXvnsvrw6jx9U/vYfegS\n3oUedU176qp+Vcs1b7lZlsWP6w/y2q9bOJqcCcDl4UG8PKAFHeoXv720v7eD4RENGNi+Nu/8to1p\nK/axYnci1733Jx/c1o6ujavSKbwTncI7MbrDaLYmbuW7Ld8xa/csUq1U5uydw5y9c+hQvQMPtnmQ\nK8OvLL2dvOZJ88Bj8mGYPxYGfVJ6aSulLkpBQUGsW5e369LWrVvn9OPtdDoZOXIk1157LTfddFPO\nMlFRUdx888151vP19WXSpElMmjSp7DN+FhpgK6VKxuWCHx8y3ezZfUyzkKqNy2XTmdmZbMrcxM9L\nfmZ1fN6g2mb5YU9viaS0JFhacjQ1iE2pgUhyOhGN0gkLKv+2wdsPn+T5/0Wzao8ZNyvQx8G/rm3K\nXVfWw2EvWdOUYD8vXrmpJT0uq8YT3/xFYkomwyav4vVBrbilg7kFLCI0D23OqLajuPzo5dAUZu6a\nyZbELeZByblr6BzemcfaPUaraq3OfUd9AqHXixD1T9g4AzrdX+FNhZRSF7bffvuN//3vfxw8eJBv\nvvmGtm3bMmHCBCIiIujfv39FZ69QGmArpUpm1STY9ot5f8N4qH1FmW9yf9J+/rv1v/y06yeSMpPA\n3U21le2L82QLspJakZ3SmFOntgSy2BWfwPLdpx8gbFsnhH6twhnYvhZVK5Xtw4zJGU4mLtjB50v2\n4HSZ9iAD2tbkueubl1qg3/Oy6vz62NXc++UaNscmMfq7jew/lsao3k3y9JftEAe96vZiUPNBrI5b\nzScbP2FV3CpWxq7k9tjb6VOvD6M7jCa80jneSm1zG6z+1PSDPmcM3DtPu+1TSpVYZGQkhw4dIjAw\nMM8Djk899VQF5ursNMBWSnnuyHaY/6J53+4uaHdnmW5uQ/wGpvw9hQUxC7BODVpj2cg62ZysEx3I\nTm5CsJ8fvZpUpXmNQGoE++FlF5LSncQeTyP6UBIbYo6RlO5kw/7jbNh/nLd+20rv5tW5tWMdrm5S\nDXsBbZ9LyrIsfvrrEK/9uoXDSaabwYbVAnh1QEu6Ni79ZirhwX5881AXHv3vOhZtO8L7C3aQnO7k\n/25ofsagNCJCp/BOdKzRkeWxy3l/3fv8nfA38/bN488Df3J/6/sZ3mI43nbvQrZ2Fjab6bZvcl84\nsBo2fQuth5TCXiql1IVDA2yllGeys+DHB8CZboY5j3y9zDYVfTSaCesmsDJ2Zc60IEd1Eg5dQcbx\n9ljZlejaKJR7b25At6bVimxukZXtYvXeRH6LjiPqr0McT81idnQcs6PjqBXix5AOdRjSsTbhwX7n\nlOdlu44yfu521uw7BoCfl50RPRtz/9UN8XaUXU8llXwcfDqsA8/8sIlv1x5g8tI9pDuzeXVAwYNK\niAhda3alS3gX5uydwztr3iE+NZ6J6ycStTOK5zo/R9daXUuWmbpXQsubIfp7mPciXNYPvAPOYe+U\nUurCogG2UsozK/5jbv8jMPBj0+62lO09sZf317/PvH3zcqY1r9yCtKNXs2lLXcBGmK/Fy0NaEdmm\nbrHS9LLb6NqoKl0bVeWZ65szd/NhZq6OYenOBA4eT+Pd+dt5b8F2ujcL49aOdejRLKzYAXGm08W8\nzYeZsnxvTjtrgH6tw3nu+ubUDDm3oL24HHYbb97cGh8vG9NXxPDflTFkZLl4IbJhoeuICNc1uI5u\ntbsxaeMkpmyeQszJGB6c/yA3NrqR0R1GE+IbUuj6heo9Frb+CicPwdL3oMezZ19HKaUuEhpgK6WK\n78RBWPymed/5IahXwhrOQqQ50/h046d88fcXOV3sXR56OUMbP8j7s4Q9R02j66FX1OQKewxXNSrZ\naIW+XnZubFOTG9vUJCYhlZlrYvh2zQHiT2awcGs8C7fGE+Btp2vjqkQ0CuXymsE0rBZAsJ8XdhFS\ns7LZn5jKltgkluw8yuJtR0hMycxJv3ODKvyrT1M6Nww990LxkM0mvDKgJb4OO58t2cP36w6QlpFJ\n77NcB/l7+fP4FY9zU+ObeGXFK6yKW8VPu35iycElPNPpGfrW73tGc5MihdQxw6j//qYJsNvddV72\nv6uUUmVBA2ylVPHNfQ6yUqBS9VKvkfzjwB+8tvI1DiYfBKBOYB0eb/84YbaO3DtlDQkpmfg4bIwf\n0pZuDQNZsCCmVLZbN9Sf0X0vY1TvpizcGs+M1ftZvC2elMxs5m0+zLzNh4uVjt0m9GlenWFd69G1\nUcV1BwimVvq5fs3x9bLzwaKd/Pp3PIdCbfTo4TrruvWD6/PZtZ/x484feWf1OySmJzL6j9H8svsX\nnrvyOWoE1Ch+RiIeg3XTTC32/Bdh8ORz2CullLpwaICtlCqeXYvg7x/N+2vHgW9QqSSb7kznrdVv\n8e32bwHwtnlzX+v7uKflPWw9lMadn6/kZLqT0ABvPru7A+3qVibZw6HSi8Nht3Ftixpc26IGiSmZ\n/LnjCIu2xrNh/3H2JabmDAqTW2V/L9rVrUyPZtXo26JGhXQBWBgR4cm+zfCy23h3/nY2JNh48oct\nfHRXx7M2fRERBjUZxFW1ruK1la+xIGYBiw8sZk3UGkZdMYrBTQcXb+RL7wDoMxZ+uN+0x+70gGmf\nrZRSFzkNsJVSZ+fKNl2uAdS/GloNLpVkd5/YzZO/P8mOYzsA6BLeheevfJ66QXXZeOB4TnAdHuzL\n1/dfSf2q5fOgXJUAbwa0rcWAtrUASMlwcuh4GifSssh2WQT4OKge5EvVSt6eNZuoAI/1bkK2M5P3\nF+9lwbaj/POrtXx4R3t8HPazrhvmH8aEHhOYt28e41aMIyE9gVdWvMIvu3/hxa4v0jC48LbdOVrd\nYrp0PLAaZj8N9y8yPY0opdRFTM9ySqmz2/QtHNkKCFz3Zqn0axy1M4qhs4ay49gOHOLgyQ5P8nGf\nj6kbVJetcUnc+dnp4HrGA+UXXBckwMdBk+qBdKhfhc4NQ2lZK5hqgT7nfXB9ygNX1ePGutkAzN8S\nz0PT1pKelV3s9fvU60PUTVEMbDwQgHXx6xj802A++usjsrKzil5ZBCLd7fZjN5iRHpVS6iKnAbZS\nqmjZWbDY3RVfq8FQvcU5JZealcqzfz7L80ufJ82ZRq1KtZhy3RTubnE3NrFx8Hgad09eRVK6kxpB\npua6Xqh28XauetWyeLpPIwAWbTvC/VPXeBRkB/sE83LEy3x27WfUCaxDliuL/2z4D0NmDWFD/Iai\nV659BbQeat4vGAsZJ0u6G0opdUHQAFspVbT10+DYXhA7dH/mnJLamriVW2fdys+7fwZMzeg3/b+h\ndbXWAJxIzWL45FUcTsogyNfB1Hs7VWjN9cXmrs61eXmAuUD6c8dR7vlyNSfTz1IDnU/n8M78cOMP\n3NvyXuxiZ+fxnQybPYxxK8aRnFlE2/jeL4KXPyQfhj/Hn8tuKKXUeU8DbKVU4bLS4fe3zft2d0Bo\noxIlY1kWM7fO5I5f7mBv0l68bd481/k5/t3t3wR5m4cl07OyuX/qGnbEJ+PtsPHZ3R1pWr30+9i+\n1A3rUp/XBrYCYNmuBG7+aBn7E1M9SsPX4cvjVzzOjBtm0CK0BRYWM7bN4Kaom1gUs6jglYJqwlX/\nMu+Xf2gu2pRS6hy5XC6qV6/Ozp07c6YtXLiQLl26EBoaSmBgIJ07dyY9Pb1c86UBtlKqcGsmmy7W\n7N5wzVMlSiIpM4knfn+CV1e+SqYrk/pB9fmq31cMvWxoThvmbJfFv77ZwKq9iYjAhFvb0qlByfq4\nVmd3e+e6TLi1Ld52G9sPJ3PTh0tZszfx7Cvmc1mVy5h+/XRGdxiNn8OPw6mHGbloJI8vepy4lLgz\nV+g6AoLrQnYG/DqaArtmUUopD6xYsYKqVavSuHFjwFToDB06lEceeYSEhASOHz/Oxx9/jK9v+fby\npL2IKKUKlpEMf/7bvO9wT4kGCdl0ZBOj/xid07d1/4b9ef7K5/H38s9ZxrIsXpm1mV83mYDshRsu\n5/pW4eeef1Wkm9rVonZlPx6YtpaElExunbSCUb2b8HD3xthtxX9402FzMKzFMHrV68UrK15h6cGl\nLIhZwPJDyxnZfiRDmw3FbnP3WOLlB5Gvw8w7YMdc2PBfc2dEKVUunC4nR1KPlNv2qvlXw2ErXqg5\nfvx4Pv30U6Kjo7Hb7SxZsoSBAwfy/fff07p160LXi4qKYsCAATmfRYQ2bdrwxhtvsGTJEvr370+/\nfv3OeV88pQG2UqpgKz+G1KOm3ezVT3i0qstyMW3zNCasnYDTcuLn8OO5zs8xoPGAM5b9fMkevly2\nF4AHr2nIPyIalEbuVTF0qF+F//0zggenr2VLbBLvzN3O79uPMG5gK4+b59SqVIuPen3Eb/t+481V\nb3I07ShvrHqDn3f9zAtdXuDy0MvNgs1vgJaDIfo70/Vjw+4QXKvU900pdaYjqUe49vtry217c2+e\nS3il4lWYjBgxgokTJzJ16lTatWvHoEGDmD59OhEREZw8WfiD0VFRUUyZMiXn8+LFi2ndujWzZ88m\nMTGRIUOGMGfOHCZOnHjO++MJbSKilDpT2nFY9r553/lBqBRW7FWPpR9jxIIRvLPmHZyWkyaVmzCj\n34wCg+tfNsby6i9bAOjfpiZPR15WKtlXxVc31J8f/9mVe9wXNqv3HuO69/5k7M9/E5/kWZtFESGy\nfiRRN0Vxa7NbEYS/E/7mtl9u463Vb5Ga5W7rff3bEBAGGUnw80htKqKUwtvbm3HjxvHCCy/Qr18/\nPvjgA/r27cvevXu59tpr6dGjBxEREWzcuDFnnW3btpGUlESnTp0AiIuLY9iwYYwdOxaHw0FYWBiP\nPvooU6dOLff90RpspdSZlk2E9BPgEwRdRxZ7tdVxqxnzxxji0+IBuKXpLTzV8Sl8HWe2fVuzN5FR\n35ju3To3qMI7t7TG5kHTBFV6fL3svND/cno1D+OFqGh2HUnhi6V7+WpFDAPa1mRg+1p0bhBa7KYj\nQd5BPH/l8/Rv1J+Xl7/M9mPbmbZ5GvP2zeOZTs/Qs25P6D8BZtwOO+eb4y2i+MeZUqpkqvlXY+7N\nc8t1e55o27YtsbGx3HnnnQwZMgSA2rVrM2fOHIKDg/n999957bXXmDFjBmBqr/v375/zPM/06dNp\n06YNlSpVykkzKSmJ4ODgUtqj4tMAWymVV/IRWPGRed/1UfA/+8OG2a5sJm2axMd/fYzLclHJqxIv\ndn2RyPqRBS6/+0gy901dQ6bTReOwSky6q0OxRhZUZSuicVVmP3YNU5fv5ePfd3M0OYNv1x7g27UH\nCA3w5op6lWlTJ4Q6VfypGeyLv7cDb4cgIqRnZZOelU1apou0rGwynS4gjLvrTWCZ/w/Mi51GXEoc\njy16jJ51evJM52eoccVwWPslzH8JaneEel0qdP+Vutg5bI5iN9kobzExMURGRjJixAgmT57MW2+9\nRVhYGA6HA5t79NekpCTatGmTs05UVBTPPvtszuetW7cSEhKSJ92ZM2cycODA8tmJXDTAVkrlteRd\nyEoBvypw5cNnXTwuJY4xf45h7eG1ALQIbcHb3d6mTmDBD0UeOZnB8C9Wczw1i6qVfPhieEeC/b1K\ndRdUyXk7bNx3dUPuvLIeP64/yHdrD7B23zESUjKZu/kwczcfLkGqjRGvx/CtEYWj0jYW7l/IH/uX\ncUuDf/BUjZY44qLhu3/A/QtNd35KqUtKfHw8ffr0YdSoUYwaNYpdu3YxduxYPvzwQwA2bdrE6NGj\nOXDgAD/88EPOOtHR0fTq1SsnnaZNmzJ+/Hi2b99OnTp1ePPNN4mOjuaLL74o933SAFspdVrSIVj9\nmXl/1SjwKfpBt/n75vPishdJykwC4O7L7+ax9o/hZS84YD6RmsWwyauISUzFz8vOF8M7UqeKf4HL\nqorl62Xntk51ua1TXQ4eT+PP7UfYsP84fx9KIvZEGkeTMwtcz9tuw8fLho/DBpzqhtHF8bQqpO0f\njiNwEz41fsbpOMnXez7iR3sNPvULpu3JWPjqFvjHbPANKr8dVUpVqKSkJCIjIxk8eDCjRo0CYNy4\ncXTq1InHHnuMRo0a0apVK5YuXcpff/3FQw89xKpVq5g1axa9e/fO0/3eyJEj2blzJxERETgcDrp1\n68bSpUsJDy//WnsNsJVSp/3xtumjuFIN6HR/oYulOdN4e/XbfLv9WwCq+FbhtateI6JWRKHrpGQ4\nGf7lKrbEJuFlFz66sz2tapd/uzjluVohfgztVJehnermTMt0ushwZuPMtsi2LHy97Pg6bDjsBT87\n78x2cSQ5g61xnVi1N5KomM9I8vqTdO847qoewl1Jdh4/HI3XjDuQ22eCt154KXUpCAoKYt26dXmm\ntW7dOmdgmNTU0wNhBQcH4+9vzg1RUVHcfPPNedbz9fVl0qRJTJo0qYxzfXYaYCuljMQ9sM79pPU1\nT5o+iwuw/dh2nvr9KXad2AVARM0IXr3qVar6VS006bRMM0rj+pjj2ATeG9qO7s2K3zOJOv94O2x4\nO4rfEZXDbiM82I/wYD96NAvjaT5kzo6VvLriZU64YpgWXIk1vt68dWAZNacMwvuub7QmWynF0qVL\nefHFF/H29gZMf9kAERER9O/fvyKzVqQyD7BFZCjwKNAGOGpZVv2y3qZSqgR+fxNcTgipC+3vPmP2\nqb6t31v3HlmuLBw2B4+3f5y7Lr8LmxQeaJ1Mz+LeL9ewyj1S4Js3t9aBZBQAkU0606vR/5iw+hOm\nbf2ULT7eDKlVg+cTNtLjk0gC755hjkel1CWrZ8+edOzYkcDAQOz20w/DP/VUyUYXLi/l0Q92IvA+\n8EI5bEspVRJHtsHGmeZ9tzHg8M4zOy4ljgfmPsA7a94hy5VFvaB6TL9+One3uLvI4PpYSiZ3fLYy\nZwj0cQNbcksHz0eEVBcvL5sXozuPYMp1X1LZO4w0m43nqoXyjj2Wkx9eBVt/qegsKqWUx8q8Btuy\nrLkAIjLY03VFpAqQv4+wugApKSkkJyefewaL6VQboNxtgVThtLw8U9Hl5TtvLA7LhatKI1Ib9YNc\nv625++fyzvp3OJllRtIa1HAQI1qNwM/hV+RvcNfRFB6d+Tcxx9KwC7w24DL6tQgtld9tRZfXheZC\nKK8m/k2Y0XcaL60cx/L4P/ghsBI7vTJ499u7qNygD5lXP4MV2rhc8nIhlNf5RMvLM+VVXpmZmfj4\n+JCdnV2m2ylrLpcrz9/Slp2dTUZGBk6nM8/0lJSUc0pXrHIaQcsdYL/jSRMREXkJeLGgeR999FGF\nPBWq1MUmJHU33ba9BMDq+o9wqHJnANJcafyU9hObsjYBUEkqMdB/IM28mp01zehjwtQdNjKyBS+b\nxd1NXLSqoqP1qbOzLIvF6UtYkP4bCFRzOnk3/iitM7KIDW7P/tCriQ9shcumXTsqVRSHw0HHjh1z\n2i6rgmVmZrJ69eozAuzY2FgefvhhgCaWZe30NN3zPcAurAZ7wYYNG2jUqFEp5rBoqampLF++nC5d\nuuQ8waoKp+XlmYosL99vb8Ox7w+yq7ci7c5fQWysjl/NK2te4UjaEQCuqXkNY9qPobJP5SLTSsvK\nZvyC3Xy95hAA1QN9mDikBZeHF93dn6f0+PLMhVheS2OXM2bZ8zhJxWFZvH4kgcgUU+NnefmTXbMD\n2bU64qrWHFdoE6yQ+mArnZuyF2J5VSQtL8+UV3k5nU5sNhsBAQFlto3y4HK5SElJISAgIGfAmdKU\nkpKCy+XC4ch7/ti1axdt27aFEgbY53UvIpZlJWLacOc4NRxmQEBAnqEwy4u/v3+FbPdCpeXlmXIv\nr92LYd8fANj7vIS3vy8T1k1g2uZpJj8Of8Z0GsNNjW/K+e0VxLIs5m0+zLhft7AvwQRBnRtU4YPb\n21Mt0KfMsq/Hl2cupPLq26QPTao14bafHiSVQ4wOq8qOjBBGxP6NZKXi2PcHDvexC4DdG6o2hfA2\n5lXrCqjZDmwlHyH0Qiqv84GWl2fKurwsyyIhIYHAwMAiz98XCpvNluchx9JgWRaZmZmEhoaeUUbn\nemFyXgfYSqkyZFmw4GXzvv7VxFRrwpO/3smWxC0AtAtrx7irxhU6IiOAy2Xx+44jfLx4Fyv3mGth\nb7uN0X2bce9VDbDZLvyTuqo4DUPqM3vwTG745j5OyjYm+RxnR4f7ebdeJ+wxK+HgGvOAbmYyZGfC\n4Wjz2vCVScA/FJpeBx3+AbU7VOzOKFXORISQkBASEhLw8/Mr9eC0vGRnZ5OZmUl6enqp7kN2djZp\naWmEhISUyQVIeXTTZwe83C8REV8Ay7LSy3rbSqkibJ0FB83w5rNbRjL2l1tJyUrBLnb+2faf3Nvy\nXuwF1P5ZlsX2w8n8uimWn/86xO6jpx8E6d08jGeub06jalqLpUpHFf8Qfr11KpFfPUKK1xoWHf2V\nB22pfNj3HXzsPuZC8cQBOLIVDv8NsX9B7AZI3A2pCbBhunnV7gR9XoZ6XSp6l5QqNw6Hg9DQULKy\nssrsIcGylpGRwerVq0u9SY2Xlxf+/v5lVrtfHjXYdwG5B4FPc//Vqi2lKoozE+a/RLoIbzRszfd/\nfwJAdf/qvN3tbdqFtSPbZRF3Ip2YxFTzSkhhc2wS62KOk5iSd5jsa5pW46FuDenaqPDBZpQqqRA/\nf6KG/Id+08eQEbCQlfGLeXjeCD7o9R7+Xv4QUse8mvQ5vdKJA7BttqnNPrQeDqyCLyKh9a1w/dvg\nq6OIqkuDiFzQDzo6nU6cTife3t55hkU/35VHN31fAl+W9XaUUh5YNYnY43sYGV6dra5jAIR7tad2\n2j946dtUDp9YwJHkDLJdhT8EXT/Un+tbhXNTu1o0rV66DzEqlV/1ID+mDXyFm7+qhL3aT6w+vIIH\n5ubLJNEAACAASURBVD3If3p/SJB3ASM+BteGTvdDx/sgZgXMe8EE2Rtnms9DpkLNtuW/I0qpS4K2\nwVbqEhMfu5/dS97m6Vo1SLTbsSwbGfHXsT3xKrZTcL+f4cG+1K3iT8NqlWhfN4Qr6lWmQdWAi+LB\nGXXhaB4exOu9/8mTs73wCf+Bv45s4L7f7uPjPh9TxTd/h1NuIqZZyL1zYd0UmD0Gju+DL66HodOh\nUc/y3Qml1CVBA2ylLhFr9yXy4aJdhB55ivlhgThFwOlP2sE7qO7VgiZNA6kf6k/1YF9qBJlX9WBf\naoX44et1YT4coy4+A9rWYuOBIUz5yxvfWjPZkriF4XOG82mfT6keUL3wFUXgiuFQ50r47xATZH91\nCwyZBpddX275V0pdGjTAVuoit/tIMi/9vJk/th/m/9m77/AoqraBw7/Zlk3vPfQWCDV0EATpCKJU\nAelNFMUCInYU8VUURF56lyKggkjvSJEOIbRAIEBISEjv2Wyb74/hQ3mpAZJNwrmvi2snu1OeGTa7\nT86c85wA3xVk+iQBEn6yG0PqTKdD7xBcHcSkHULxMb5DMKdjWnI8xg6HoGVcSb/CgC0DmNd23gOr\n3gDgEwxDtsOybnDzNPw6EPqtgbLPFUrsgiA8G55+xW5BEIoEi1Vm+s5I2v+4j72RN3AIXE6mxxkA\n2pq1rOu9mVdDa4vkWih2NGoVP75aG2drDXKiB6GS7YjNimXg5oFEpUU9fAfOvtB/nVI325IHv/SG\npMiCD1wQhGeGSLAFoQRKzMyj/8LD/LD9IkY5C/fyi1C7nAVgUHomk9vPw8FOlNITiq8AN3smd6+F\nJacCmVeHoJMcSchNYOCWgZxLPvfwHTh6Qr+14BwAeRmw6jXIyyr4wAVBeCaIBFsQSpiI+Aw6T9/P\ngUvJSJo0AqstwKyLQpJlPkxO4b3gfqgC69o6TEF4Ym2q+TKwSVmshtKkXxmGi9ad1LxUhmwdwsmE\nkw/fgWsQ9FqmzAKZGAF/jlLqaguCIDwhkWALQgly5EoKPWYfJD7DgLNTBqVDFpNhiUUnww8JSfRV\ne0OL8bYOUxCemvEdgwkJcMGc64cl5nV87H3JMmUxYvsIDt44+PAdBNWFDt8qy2fXKmX8BEEQnpBI\nsAWhhDh4OZl+Cw6TaTDj456Nd6WFpBjjsUfNnPibtDGYoNt80D29mbAEwdbsNGqm966Dg05NfIor\nQbljKOVcilxzLm/ufJNd0bsevpO6g6Day8rypg+QMmILNmhBEEo8kWALQglwMjqVoUuOkme2UsYn\nB6dyc0k0xOOo0jHnRiz1DHnwwicQVM/WoQrCU1fe24mvX6kOwF/nLXT0/IqKbhUxWU28t+c9NkZt\nfPAOJAlenAKOPpCXjt22saKriCAIT0Qk2IJQzF2Iz2TAwiNkGy34e2WiCpxNYu5NnNT2zLkRT508\nI1RsDU1G2zpUQSgwr9QJonvdIAB+2pbABzWnEeIZgkW2MH7feH69+OuDd+DoCZ2nAaC5+hcBaUcK\nOmRBEEowkWALQjGWlJXHkCVHyTCY8fZIRxc0hyRDAs4aR+YlpFIrNwu8g6H7QlCJX3ehZPuySwgV\nvB0xWqyM/+0y01rMJtQnFBmZLw9+yZKzSx68g+COULUzANVjV4BRVBURBOHxiG9cQSim8swWXl96\nnJjUXBwdk9CXmktKXhIuWifmJaZSPSMBHLyg90rQu9o6XEEocA46Df/tE4qdRsXV5BwmbbjCrNaz\naBrQFIDvj33P1ONTscrW+++k3TfIGnvsTano/p5aSJELglDSiARbEIqpT/84w7FrqajtbuJSfj5p\nxmTctE4siE8kJC0e7N2VyTQ8ytk6VEEoNFX9XfisczUA1oXdYENYMj+98BNtyrQBYOGZhYzfNx6j\nxXjvHbiVwtj4HQC0J+ZD8uVCiVsQhJJFJNiCUAytC4tl9bEYVHZxeFRcQJY5DXe1PfOjrxKckai0\nXPdfB37VbR2qIBS6Pg1K82INfwA++/MMV5PymNx8Mr2DewOw6comXt/xOhnGjHtub6o3nGydD5LV\nDDu/LLS4BUEoOUSCLQjFTExqDp/8cQaV3Q1cyy/AYM3AAzULrkVRxZCj9LkethP8a9k6VEGwCUmS\n+KZbDUp52GMwWRm57DjZRivjG4xnTL0xAByNP8qAzQOIy4q7ewdqHecCuivL5/6AmOOFGL0gCCWB\nSLAFoRixWGXeW3WKbK7hXGYuZrLwMltYFHOdSiYT1OoNQ7aDe1lbhyoINuWi1zKjTyg6jYrLidm8\nszIMqwwDQgYwuflktCotl9Iu0XdTX84mn71r+xtuDbD43vojdftnomyfIAj5IhJsQShGlm/dj1fC\nPLxK/xer2oC32czC+JuUdwxUpnx+ZTboXWwdpiAUCTWD3PjmlRoA7IpIYMr2CwC0L9eeuW3m4qxz\nJjE3kYGbB7Ll6pY7N5ZUGJ//WFm+th8itxVm6IIgFHMiwRaEosxshCt7YdsnGH6sR62wroQFHSZX\nLeNjNrMoW0O5F76CUUdvlxcTBOEf3eoGMfQ5ZaDvjN2XWRemzNJYz68eyzsup4xLGQwWA2P/GsuM\nsBl3VBixlG4KFZXBkez+WrRiC4LwyESCLQhFTW4ahK+GXwfCd+VhSWf4ezpnc6MZ7udDplqFn6Rj\nUd3xlBkVBo3fAI2draMWhCLrww7BNKvkBcCYX0+xLzIRgHKu5VjecTmN/BsBMPvUbMb8NYZcc+4/\nG7f8SHmMOwUX/6eVWxAE4T5Egi0IRYRbThR2m9+BH6rAmmFwdi0YMwGJ9a6VGernT45KhY/en8Vd\n/6R07f6gUts6bEEo8jRqFTP6hlLV3wWTRWbE0uOcup4GgKudK7Naz6JPcB8Atl/bzog9I0izKq8T\nGAqV2yvLe74RrdiCIDwSkWALgq0lnEe/dhDPX/gC7dlfwWwAraPS5ePlWfy3xUzGu5kxq6y4af1Z\n3ulnAp0CbR21IBQrLnotSwbXp7SHAzlGCwMXHeFMbDoAGpWG8Q3H82mjT9FIGiLTI5mVOYvw5HBl\n4+fHKY+iFVsQhEckEmxBsBWLCf76DmY3Q3NZGUBl8a0BL/0XxkZCr2X8Ye/NnKvfIanM2Mn+/NZl\nKX6OfjYOXBCKJx9nPUuHNMDb2Y7UHBO95x3i+LWU26/3rNKTuW3n4qJzIVvOZtTeUay/vF60YguC\nkG8iwRYEW8iMh0UdlIFTVhNWjwocLjea3Nc2Q2g/0DmyKWoznx0cC5IFjH4se3ERvo6+to5cEIq1\nMp6OrB7RmABXPZkGM/0WHGHHuZu3X6/vV5+FLRfio/LBZDXx0f6PmHZiGtbmY5UV4k7Bhc02il4Q\nhOJCJNiCUNjiwmHeCxBzFCQVNH2HnP7biHerC5IEwPLzyxm3bxyyZMFi8OerBjMI9hbdQgThaSjn\n5cjq1xtTxlPpLjJs6TGm74zEalVapgOdAhnuPJzGvo0BmH96Pu9FLiOnUltlB3u/E63YgiA8kEiw\nBaEw3TgJSzpBRizYuULfX6HNBNDoAZBlmeknp/OfI/8BZMzZ5ejk/RUv16ps27gFoYQJcndgzcgm\nNCjngSzDD9sv0m/hYWLTlAoieknP5KaTea3qawDsjN7JAH0O8Wq18nt8aYctwxcEoYgTCbYgFJa4\nU/BzFzCkg3MADN0OFVvfftkqW/n25LfMDZ8LgCmzGn65bzGhUz1bRSwIJZqnkx3LhjSkX6MyABy4\nlEy7qXtZcug6ZiuoJTXjGozjs8afoZE0RGRF07t0aU7rdPDXt6IVWxCE+xIJtiAUhvRYWN7zVnLt\nDwM3gHeV2y8bzAZW5qxk3ZV1ABhT62OJe43przbAXidK8QlCQdFpVHz1cnUWDqyHj7MdWXlmJu+I\n4uswNSuP3yA7z0yPyj2Y02YOLjoXkrAwyN+HzalnIWqPrcMXBKGIKvAEW5IkjSRJUyVJSpYkKV2S\npCWSJDkW9HEFocgwZsMvr0JWPOjdoP+f4Fnh9ssJOQm8sfcNzpnOAZCX1JK8+K6MaVuN6oGutopa\nEJ4pLwT7su3d5gxqWhaNSiIlT2Li5kgaTdrJuN/CyUovw6K2SynrUpY8lYoPfLxYsvdTW4ctCEIR\nVRgt2B8BbYHaQHmgDDC1EI4rCLYny7D+HYgPB5UGev4M3v/0pz6ffJ7eG3tzPvU8Eiqk5K4YE9vR\npIIXw5qVt2HggvDscXPQ8XnnENaPrE8TXyv2WhWZeWZWHbvO4MXH6DTlAur40ZSRlC4l36vSmbzz\nnTumVxcEQQDQFMIxhgLjZVm+DiBJ0sfADkmSRsuynPugDSVJ8gA8/ufp0gDZ2dlkZWUVRLz3NHBF\nU0xYWbriMzSyCi1q3CQXfPVBNKzYiYYh7VCpxa38/5eTk3PH47NKc/ZX9KdXA2BoOQGzT1249b7d\ne2Mvnx/5HIPFgKPGEV18T6ITq+DuoGVip0rk5GTbMvQiTby/8kdcr/zxtJPpVd7Kl91qsScqkx0R\nSRy9lobRYiXsWh5Iw2gZ9DnHnCz8HLOTDYtH8KLvKGoFuhPi74ybg9bWp1CoxPsrf8T1yh9bXa/s\n7Cf7DpbkAhykIUmSG5AKVJVlOeLWc/ZADlBLluXwh2z/BfD5vV6bNWsW/v7+TzfgB5iY8jEGlXTf\n1/1NMjXzKhLq0x1HnXOhxSUUXY6GeFpc+BSNNY8bbvU5WnYUSBKyLLM/bz/bDNuQkXFXuVMhsz+7\no5X38/BgCyHuYvCUIBQlOWaIypS4kiFxJVMiMOc01XznsNxV+bw3Z1UmN6YvyHZ42slUcpUJ9VQe\nH/DVIQhCERUXF8fIkSMBKsmyfCm/2xd0gl0KiAYCZFmO+9fzeUArWZb3P2T7+7Vg7wwLC6NChQr3\n2KpgTFszirTMVHR6DRZMGK0Gkq2pxKpzuKn9p6eNl1mmu3tXBrT9oNBiK4pycnI4ePAgjRs3xsHB\nwdbhFD7Ziv0vr6C+cQyrcwA5A7aD3g2T1cR3J79jw9UNANTyrEWf0h/x1i9XsMjwah1fPnkx2MbB\nF33P/Psrn8T1yp9HuV5mixX1ss6sMkYxzcMNAEtuaXKiB4NVf3s9H2cdfesH0jM0AGd9Ydw0Lnzi\n/ZU/4nrlj62u1+XLl6lduzY8ZoJd0L/tmbceXYE4uN2CrQMyHraxLMspQMq/n5NuTcTh6OiIk5PT\n04z1gUZ3/S87d+6kVatWdxzXarGwP2w9a05M54AmniSNitmZazm/8gjf9fsDBzv9A/Za8jk4OBTq\n/1ORcWQe3DgGgKrrXJy8gkjPS+f9Pe9zNP4oAC9VeInXQ8bRdeYRLDIEOsh80K7ys3m9HtMz+/56\nTOJ65c9Dr1fbTxm6vBueFgtfeHuBfTQ1662kveen7InI5siVFBIyjUzddYX5B67zdqtKDGxaFq26\nZBbwEu+v/BHXK38K+3o5Oj5ZPY4C/S2XZTkNuA6E/uvpUMAARBbksQuLSq2med2X+XHYTuY3nklt\ng/I3y1/aWAYtaUFaTuH1ExeKiPRY2DFBWQ4dAOWacSX9Cn029rmdXI8OHc2nDSfw9i+nSczMw0Wv\nYXAVCzpNyfziFYQSqWIrCAjllaxsvlUHoJbUXMmMYGf6BGYPqMKBD19gRPPyONlpyMwz8/Wm83SY\nto/wmDRbRy4IQgErjG/z+cB4SZKCJEnyBCYCyx42wLE4qhX8PIsGH+JlcxAA5+yyGbGsLZm5Je5U\nhQfZ/AEYM8HJF9p8yaG4Q/Td1JfozGj0aj1TWkxhSPUhfL7uHCej01BJMPmVqng92zc7BKH4kSR4\nXukO2P7SQX6oOQqNSsPF1IsM2jIInS6L8R2rcmDcCwxqWha1SuJSQhZdZ/7NnL8uU5BdNAVBsK3C\nSLAnATuBcOAKSov2O4VwXJvQaO34avAmBsgVAThnl8lbK3pgtYoyTs+Ey7sgQulfTYfvWB29jde3\nv06mMRMfex8Wt19MmzJt+GnnJVYduw7AmHZVaFrhf4caCIJQLFRuD341AGgVsZtpLaehU+mISo9i\n4JaBxGfH4+qgVcr/jXqOyr5OmK0y32yO4L3Vp8gzW2x8AoIgFIQCT7BlWTbLsvyOLMsesiy7yLLc\nX5blkl1/TJIYM2ANvU2+ABzXXOPT3z60cVBCgbOYYevHymK5ZnybHcFXh77CIluo6lGVFS+uIMQr\nhJVHopm64yIAPesFMfL5whusKwjCUyZJ0PzWoPaIDTTXeDCj9Qz0aj3RmdEM2jKIuCxljH+1ABf+\nHPUcvRuUAmDtyVj6zT9Ceq7JVtELglBARIfPgiJJfNhvHc1zlUu8KWcTG8IfWDRFKO5OLIGEc2Sp\n1Lzl5c6y88sBaF26NYvbL8bX0Zc1J2L4aO1pAF4I9mHSKzVuD9wVBKGYCu4EPtWU5b2TaeTfiFmt\nZ2GvsScmK4ZBWwcRmxULgF6rZtIrNfjkxapIEhy5mkL/BYdFki0IJYxIsAuQys6RSZ1/prTJjFmS\n+Onwu6TliP7YJVJuGuz+mni1mn7lKrMvKQyAYTWG8UOLH3DQOrDqaDTv/3oKqwyhpd34b586aEpo\nNQFBeKaoVNB8rLJ8bh0kRFDPrx5z2szBQeNAbFYsg7YM4nqm0i1MkiSGNivP9N51UKskTsWk03/B\nYTIMIskWhJJCfLsXMNfAWoz1fxmVLBOnM/D+72NtHZJQEPZPJdKUQd9Afy5Zs9GqtEx6bhJvh74N\nssTU7RcZ9/tpZBkalPPg5yENcdCVzJq4gvBMqtYFvCoDMuz7HoA6PnWY02YOjlpH4rLjGLx1MNEZ\n0bc36VQzgJ9e/SfJHrnsOEazGK8jCCWBSLALQYsOX/Nqrg6AE+a/2Bl5zsYRCU9VViLHwhYywN+X\nBLUKZ60zc9rMoXOFzqTnmHhzxQmm7VSqUjar5MXiQfVxshPJtSCUKCr1P63YZ36HJGVeito+tZnb\nZi5OWifis+MZtHUQV9Ov3t7sxZr+/NCjFgAHLiXz4ZpwUV1EEEoAkWAXBpWKUW1+wN9sxqyCH3a/\nh9UqPkBLim07P2C4tyuZahU+9t4s7rCY+n71+etiIu1+3MvmM/EADGhchoUD64uWa0EoqUK6gkcF\nkK2w74fbT9f0rsm8tvNw1jmTkJPA4K2DiUqPuv36y3UC+aB9FQDWnIjlp535njROEIQiRiTYhcS5\nYkveUJcH4LpdLN/tWWvjiISn4ZewuYxJPYpJkqigc2P5iyuwGPwYvPgoAxYeIT7DgKNOzXfdazKh\nS/USO4ObIAiAWgPN3leWw1dCwvnbL1X3qs78tvNx0bmQmJvI4C2DuZx2+fbrI5+vQN+GpQGYuuMi\nuyJuFmrogiA8XeLbvhB16TSNBrkGADZdmkqu0WzjiIQnMTd8LpNOTUeWJEKNFvqW/4n3Vlylw7R9\n7IpIAKBJBU+2vNOcnvVK2ThaQRAKRc1e4FVFacW+Vbbz/1XzrMaCdgtws3Mj2ZDM4K2DiUxVuo9J\nksQXL4XQoKxSE/+dlWFcTSrZFW0FoSQTCXYhkrwq8rqrMmt8ql0an29fbuOIhPwyW6xEJWbx7rZv\nmH5yOgAtsnOoeP15xq69wd+XkwGo7OvE/P71WD60IaU8HGwZsiAIhUmtgbYTleXLOyFy+x0vB3sE\nM7/tfNzt3EkxpDBk6xAupFwAQKtW8d++dfB1sSPDYOb1ZcfJNYqJaAShOBIJdiGr32YiLbOVUn37\nbswmOUuU7SuKrFaZqMQsNobHMWXbBd5Yfpx2U/dS7bMtdFg2hh1xKwDokJXN+JtmlhvboNeqaBfi\ny9IhDdj6TnNaV/MVNa4F4VlUqQ1UaKUsb/0YLHeW36viUYUF7RbgofcgNS+VoduGEpESAYCPs56Z\nfUPRqiUi4jP5coMYFC8IxZEYbVXYvCox0q0ue4xnydJl8cn2Zcx6ZZitoxKAlGwjm8/E8deFRA5e\nTiYz73+78Fix8/sDnfsRAF7KzOHLpGT2l3+fn+o14blKXmIAoyAIyuyO7b6GWXsg6QIcnQ+NRt6x\nSiX3Sixst5AhW4eQbEhmyNYhzGs7j2qe1ahbxoNx7YOZuPE8vxyJ5vnKXrSv7m+bcxEE4bGIFmwb\nqNr8A9pm5wBwLHEZadlGG0f0bDt1PY1RK07QaNJOPl57hm3nbt5Orp3tNNQv606v+oHUr7/tdnL9\nql0pvkpKQu3sz/N9xtI2xE8k14Ig/MOnKtQbrCzv/ArSrt+1SgW3CixsvxBve28yjBkM3TaUM0ln\nABjctBzPV/YGYNzvp7mRJu52CkJxIhJsWwiqy0BdEAAGuxQm7l5j44CeTVGJWQxdcowuMw6wITwO\no8WKi15Dl9oB/NCjFnvHtiT8i7asGFYfk+fPRGTtAWBQxW58dPGI8svz3HugtbflaQiCUFS1+hSc\n/cGUDRvfh3vUty7vWp6F7RbiY+9DpjGTYduGEZ4Yjkol8X2PWng52ZGea+KdVWFYRHlX4Vkjy+h2\nfYZP+ilbR5JvIsG2keqN3qbprWnTd8cuIeuu7ghCQTFbrMzac5n20/ax47xSCqtGoCs/9qrNkY9b\nM+3VOnSrG0RpTwfyLHmM3j2a7deUgUpv1HqDd5NTkawmcAmE0P62PBVBEIoyvSt0VGZ1JHKrMgHN\nPZR1Lcui9ovwdfAly5TF8O3DCUsIw9vZjh96KpPQHLmSwozdoj628AyRZdjyIVdO/0yla9NRJZ5/\n+DZFiEiwbaVaF4YYtQAY9TFM3rPFxgE9G5Ky8ui34AjfbonAaLYS5G7P7Nfq8ueoprxcJxC9Vn17\n3RxTDqN2jWJf7D4A3qv7HiPLdEQKu1X9pdl7oNXb4jQEQSguqnaCqi8pyxvfv2dXEYDSLqVZ1H4R\n/o7+ZJuyGb59OPti9vF8ZW+GPlcOgGk7Izl+LbWwIhcE29o/hcOnFjEgwJfhgWXJditj64jyRSTY\ntqLWUq96X2oZ8gD4I2o5BpMox1SQzsSm03n6fg5GKaX0BjUty7Z3m9O+ut9d1T6yjFmM3DGSw3GH\nARjfYDyDqg+Cfd+D1QSupaBOv0I/B0EQiqEXfwBHbzCkwe9DwXLvO5alnEuxqP0iAp0CyTXn8tau\nt/j14q+MbV+Fav4uWKwy76w6SabBdM/tBaHEOP0bWw59z0g/H7JVKhI0diTnpdg6qnwRCbYNSaH9\n6JeeCYDV/iyLDp+0cUQl1+GoZHrPPURcugEnOw1z+tXl884h9xyYmJ6XzvDtwzmRcAIJiQlNJtCn\nah9IuQJhSnk+mr0PGrtCPgtBEIolJx94ZY6yfP0Q7P76vqsGOgWyrOMyQjxDsMgWvjz4JbNOTefH\nV2uh16q4npLL5+vOFlLggmADscdZvuN9PvD2xCRJlHcpxwjnEQQ5Bdk6snwRCbYtuZehlW99/Mxm\nkGQWnV6GVQxieep2X0ig/8IjZOaZCXSz5483m9IuxO+e66YaUhm2bRink06jklR8/dzXdK3UVXlx\n7/dgNYNbaajdtxDPQBCEYq9iK3juXWV5/xQ4tfK+q3rZe7Gw3UJalGoBwIIzC5gX8RUfdqwAwJqT\nsawLiy3oiAWh0Mk5qUzdMJD/eLggSxJ1vWsxse5P6GUXW4eWbyLBtjFNaH9ezVBasXPtDrD57DUb\nR1SyHLyczIilx8kzWynv7civrzemoo/TPddNyk1i8NbBnE85j0bSMLn5ZDpX6Ky8mHwZTv2iLDcf\nCxpdIZ2BIAglRstP/pmAZt0ouLL3vqs6aB34scWP9AnuA8CWq1vYnPwpzasqd90++eMMMak5BR6y\nIBQWk8XIJ793YeGtoU2tfRvyQZ0fGb7sEj9HqopdFR2RYNtacCe6mTTYWa2gNjDt8CpbR1RinI5J\nZ9jPxzCarVTyceLXEY0JcLt3Sb2b2TcZtGUQl9IuoVVpmdpyKm3Ltv1nhb2TQbaAe1mo1btwTkAQ\nhJJFrYEei8EnRBnLseJVuLLv/qur1IxvOJ4PG3yIWlJzLvkcUbqJeHpdJ9Ng5t1VYZgt1sKLXxAK\nSI4ph7f+6MafVmUQby/PUDqU+pKes48Tl5HHxQyJK8nF6w9KkWDbmlaPW0h3Ot2aeCbGsp1T18Uo\n8ScVnZzDgEVHyLrVLWTpkIZ4Ot27z/TV9Kv039yfqxlXsVPbMf2F6bdvzQKQeBHCb/3h0/wDUGsL\n/gQEQSiZ9C7Qd7Xyx7opG5b3gIvbHrhJ36p9mdtmLu527qQb0zB7z0Lrvp+jV1OYuedy4cQtCAUk\nxZDCkE39OJB1FYA3daWRpHGMWHaSrDwzfi52vB1ioaK3o20DzSeRYBcFNXrQ59ZgR7X+JpP3brRx\nQMWbwWTh9WXHSck24umoY+mQBvi53ruc3pmkM/Tf3J8b2Tdw1Doyq/UsmgY2vXOlPd+AbAXPilCz\nVyGcgSAIJZprEAzarHymmHNhRU/YNwWs92+NbuDfgJWdVlLVoypWrOj9NqAPWMm0XWdE6T6h2Lqe\neZ3+m/pzJu0ialnmkzQTG2NGMm/fFWQZGpT1YNWQUIKKV24NiAS7aCjVgMqO/jTINQBwPHWD6Fv3\nBD5bd4ZzcRmoVRKz+9WlvPe9+1z/feNvBm8dTGpeKh56Dxa2W0h9v/p3rnTzLJy9NdNmi/HKLV5B\nEIQn5RKgJNlBDQAZdk6AJZ2V8R73EeAUwM8dfqZzeWVsiNb1FPoyP/HW7xtE6T6h2DmffJ5+m/px\nLfMaequVaTcT2XazD6eTJbRqiTFtK7N8WEM8HYvnmCeRYBcFkgTVu9P71mBHtdM5/rv3qI2DKp5W\nHY1m9bEYAMZ3CKZ+WY97rrcpahNv7nyTXHMuQU5BLO2wlGqe1e5ecfck5dG7KoR0LaiwBUF4Fjn5\nwMANUG+w8vO1/TCzEWwaCxk37rmJXqPn6+e+5rPGn6FV6VDZJZHhPoXBv/+EfI+p2AWhKJBlmRyj\nmZsZBiLiM/jpwCb6bhxAsiEZF4uVefEJpGTVZae1Lq2Cfdj4djNGvVAJrbr4pqmiOa6oqNGDwgcP\nVwAAIABJREFUFvun4Gs2c1OjYcOV3xmf2wRXe9Hf91GdiU3n01v1YTvW8GPIrdnP/k2WZWafms3M\nUzMBqOJehdltZuNl73X3Dm+EQcQGZbnleFAV3190QRCKKI0ddJqqzPa4/m1Ii4Yjc+HYQqjUDmq9\nChVeALt/7sRJkkSPyj2o6VWT4VtGk2KKJcK8mJ5rL7Oo03c46e59104QCkJ2npmw62lciM8kOiWH\n2LRcMnJNZBrMZOWZyTQoy+ZbVUA0LqfQB6xGkiy4mDQsvRmNh9GO34I/YG2zWtQp7W7jM3o6RIJd\nVPhWQ+MTQq+M6/zk4YbsfJglhyJ5u+U9WlWFu6TlGHl92XGMt8rxfdut5l2zMxrMBj498ClbrirT\n0jfyb8SUFlNw1jnfe6f/33rtVxOCOxdk+IIgPOsqtIQ3j8LxxUqd7KybcGGj8k+lhTKNlUS73PPg\nXwtUaqp4VGFj99/osvI9EuSDRGTuo8va7kxvPeXed+QE4SlJzMxjY/gN1ofHEXY97ZFL6Gnd96P3\nUxqunMxe/B53Cj+Lhdx2k5jYpEUBRlz4RIJdlNToTtfdXzHL3RWTJofFYX8wolkV7DRqW0dWpFmt\nMu+tPkVMai72WjWzX6uLs/7Olv/EnETe3vU2Z5LPANCrSi/GNRiHVnWfOwRX90PkVmW55cei9VoQ\nhIKn1UOj16HeILi4BU4ug8u7lZJ+V/b+Uzdb7wZln4PyLXAq34J1Pf5Lh0VTSLVfRYIhltc2vca4\n+uPoWaXnXQ0NgvAkriRlM3vPZdacjMFk+SepVklQwduJMp6OBLnb4+6gw1mvuf3PyU7NptgFrL+m\nJNf1fOsxLfY6LhYL+NbAvtFQW51SgREJdlFSvSueOyfQPiuH9c6OGOz3svZEX15tUMbWkRVpM/dc\nYldEAgD/6VaDyr53tkgfjT/K2L/GkmxIRiWpGFd/nDL1+f1YrbD1Y2W5dBOo3K6gQhcEQbibxg6q\ndVH+5WUqtbIv7YCoPZByGQxpSve1W13YnJwD2FThRXqe6801ry1gl8jEwxM5dvMYXzT5AkdtMSzB\nIBQp2XlmftoZyYL9V2539XBz0NKxhj/tQvyoW8YdJ7t7p5Qmq4nPD3zO+mvrAWhTpg3fuNbF7tAb\nygodvwNVyWtIFAl2UeJeFvxq0jslgvXOjqjtY5lxcDc96w1ApRKtEPeyLzKRH7ZfBGBA4zJ0qR14\n+zWrbGXhmYVMPzkdq2zFWefMd82/47nA5x680zO/QVyYstx2ojIIVRAEwRbsnCG4o/IPID0Gov6C\nK38pj1nxkHkD57B5bAa2xtbiM8+K5LheYsvVLUSkRPD9899TxaOKTU9DKL6OXk1h9C8nuZGuVDor\n7eHAyBYV6Boa+NA77FnGLN7b8x4H4w4C8GqVV/mwzmjUMxooK9ToAWWaFGj8tlKg970lSVogSVKE\nJEkWSZK+KMhjlRjVXqKG0UiISfkLMVHaxc5brbPCnW6k5TJ6ZRiyDLVLufHxi//0OUzPS+etXW8x\n7cQ0rLKVEM8QVnda/fDk2pQLO79Ulqt3h6C6BXgGgiAI+eQaBHX6Qte58H4EvHkEWk8AjwoAtJNO\ncShlF50SPJFkDVczrtJ3U1/WRq4VVUaEfLFaZWbsvsSrcw9xI92AXqtibLsqbH+vOb0blH5ocn0z\n+yYDtwy8nVy/VectPmr4EepjCyEjFtR20OrzwjgVmyjojqVhwCjgrwI+TslRtQsAfdJSANA4hzPj\nr5O2jKhIMpqtvLH8BCnZRjwcdczsG4pOo7yd98bs5ZV1r7A3Rumv2KtKL37u8DNBzkEP3/GhmZB+\n/dYv/mcFeQqCIAhPRpLAuwo89w68dRz6rAa/GkjAN9knmR2bhrPVlTxLHp/9/RmfHPiEHJOYY0F4\nuDyzhdGrwpi89QIWq0yNQFe2jG7Omy0rPtK4sMjUSPpu6suF1AtoJA2TnpvE8JrDkQxpsO8HZaWG\nw8GtVAGfie0UaBcRWZanA0iS9M7jbC9Jkgfwv4WMSwNkZ2eTlZX1ZAHmQ05Ozh2PBcY+AHvPyrRL\nuci33n5kqEycy9rOvvM1qFPKtWCP/RQV9PWatCWSsOtpqCT47uVgXDQWbqbeZFr4NNZfVfp5OWgc\nGBc6jral2mLMNWLE+MB9SukxOPw1GQkwhg7BqPWEQnqPFdr7q4QQ1yt/xPXKn2J7vQKaQt9NaMJX\noNo1gSamFDZfS2OQXyiR9gn8eflPTiecZlKjSZR1KfvUDltsr5eNFPXrlZ5rYvSvZzkWnQ5Ar7oB\njGtTAZ1GfqS861jCMcYfGk+WKQtHjSPfNP6G+j71ycrKQrd3MjpDGrKdC9l1RjzSd6ytrld2dvYT\nbS8Vxi0jSZI2AMdkWf4in9t9Adzz/sGsWbPw9/d/8uCKoCpxawiO/4PJXgH87KzBanKhXPJYhgWL\nvsAAxxIlll5S/oJ+sZSFtkEyl02XWZOzhnRZ+UAorynPKw6v4K569HqaDaKm4p9+klytB7uqfoNZ\nbV8g8QuCIBQ0vTGZqhf/S2nTZWRgjFNddninYMWCHXZ0c+hGNZ0o5SfcKccMM86piclW8o2Xy1ho\n4S8/0lAkWZY5ZDzE5tzNWLHiIrnQ36k/fmo/APTGFFqfG4taNnHOvweRfkW7/G1cXBwjR44EqCTL\n8qX8bv9YCbYkSYuBAQ9YpZ8sy8v+tf7jJtj3a8HeGRYWRoUKFfKzuyeSk5PDwYMHady4MQ4ODgV6\nLFXiORyWtCFOraZd6SBkZHJj+rK690Aq+xSPCQQK6npFJmTTZ9EJck1Wnq/kwXddKzD77Cx+u/wb\nAHq1nlE1RvFK+VdQSY/eA0p9aSv2fyizqeW+NA9L5Y5PLeZHUZjvr5JAXK/8Edcrf0rM9TIbSF/z\nLoHRfwIwVd2IFaUNGFC6IA6uOpghVYfk67PyXkrM9SokRfV6peeaGLYinHNxWWhUEv95OZj21Xwe\naVujxch3J79j47WNAFR2q8zkxpPxcfhne7utY9GeXoHVyY+cIftB+2iNWLa6XpcvX6Z27drwmAn2\n43YRGQWMecDrmY+53zvIspwCtz4Jbvn/mp6Ojo44ORV+sung4FDwx3WsDx7l8U+J4gX7QHbmxqB1\nP8icA62Z069ewR77KXua1ys9x8Q7vx8l12SllIc9Q9toGLx7ENGZ0QDU8anDxKYTKe1SOn87zk2D\nXZ8qy5XaYV+nh80qhxTK+6sEEdcrf8T1yp/if72ccBr0M5nrxuAcNp93LYfQR7Vkpr872F9m4fmF\nRGVGManZpPtPuJUPxf96Fa6idL0yDSZGrgrjXFwWapXEf/vUoX31R+slcDP7Ju/tf4/wpHAAOpbr\nyBdNvsBe868EOvECnFkJgKrleJzcvfMdY2FfL0fHJytv+VgJtizLWUDhdYB+1kiSMm3ugR/pnZLE\nTnvQOEax/dIpzsRWonpg8emL/bRYrTLvrDrJteQc9DoLzRoc5s3dvyAjo1PpeDv0bV6r+hrqx6ml\nuWmsMqJZ56TU4xRl+QRBKCkkCecu32PV61Ed+i8jpd2kXu/JEi8/dB4H2BOzh/aru/N68FfU8a+C\nt7Md6ltlYQ0mC+m5JtJzTWTkmsgwmDGYLBhMFvJMVnQaFU56DS5amYRcbtdHFooXo9nKyGUnCI9J\nR62SmN770ZPrv67/xScHPiEtLw2VpOLd0HcZEDLg7gmOdn4JshU8K0Ht1wrgLIqeAh3kKEmSDqVS\niQrQSJKkByyyLJsK8rglQjUlwW4Qf5HyNZoQlaW0Yk/dXpMFA+vbOrpC9+OOi+y+kIhKH0Ng8J+s\nv6a0Wod4hvD1c19Twe0xuwud+R1Or1aW23+j1CIXBEEoSSQJVbuJkJME4Sv5SLuatMwxrMntid5/\nDRnc4NvwN8jd0gtL1uP2y9Yw9dwBGpf3pFVVXzrX8r9rRl2h6JFlmQ9/D2f/pSQAJnevSccaD0+u\njRYjU49PZdl5pTewu5073zT7hqaBTe9e+fqR25Mi0eozUD8bU7AUdJm+bUAu0AH4+NbyvAI+ZskQ\nEAougUhAb71SxkbreoKdF69xMjrVtrEVsj9P3eCnXRfQeW3HqdxMEvKi0ag0jKo9imUdlz1+cp0S\nBRveVZYrd4A6/Z5e0IIgCEWJJMFL06FccwC+1cxhdbt2tHKbgMbqjqTOw6HUz+i8dgDWOzZVScqs\nfaU9HAj2c6ZWKTcalPOgVik3yns7Yq9VUokco4WdEQl8tPY09b/ewfg1p4lNyy3sMxXyYfLWC6w5\nGQvA2HZV6Br68HK2F1Mv8tqm124n1w38GvDbS7/dO7mWZdh+q1ZFYD2oWrQHNj5NBV2mr0VB7r9E\nkyTljXh4Np1vRPKjoyPZpmy0rseZtCmA1SMa330LpgQ6FJXM2LXbcSj7C2r7WGSgsntlvn7ua4I9\ngh9/x3lZ8EsfMKSDky+89JPoGiIIQsmm0UH3xTCnOVJGDKGH3iJ06E6SrU15/6/3OX7zOHbeO2ga\nksu7tT7HXe+Kq4MWJ53mgbMJZ2Rm8vvmXTiWrsGhaxlsORtPjtHCL0ei+f14DP0bl+HdNpVxvM9U\n2oJtLD10jZl7LgPwWqPSvNHiwY1VJouJ+afnM/f0XMxWM2pJzRu132BI9SH3754ZuQ2i/1aW20x4\npr5nC7oFW3gSwZ0AcIw9QZdSrQDQuR/i6NVk1ofH2TKyQnExPoNha39EW+ZH1PaxqCQVQ2sM5ZcX\nf3my5NpqgbUjIPE8qLTQcyk4PdpIaUEQhGLN0RN6/qx89iVGwNaP8LT3ZF7befQJ7gPAsYQDjD80\nFIMUi4te+8DkGkAlSXjYQcfqPkzpVZsjH7fmq5er4++qx2ixMn//FdpO3cu+yMTCOEPhEew4d5PP\n150BoE01Xya8VP2BjXZnk87Sa2MvZp6aidlqppxrORa3X8zwmsPvn1xbLbBjgrJcqS2UfchMyiWM\nSLCLstKNwcETgF6yMsJbZZeE2vES32w6T67RYsvoCtSR6Cv0WDcEvP5AUpnxcwhgcfvFjA4djU6t\ne/wdyzKsH/1Pf7COk6F0w6cTtCAIQnEQVBfaTlSWTyyBC1vQqrSMbzieSc9NQq/WE50ZTd9Nfdly\nZUu+d+9kp6FfozLsGduCce2DsdOoiE3Lpd+CI/ywTZkZULCdM7HpvL3yJFYZ6pR246dX69we2Pq/\nknKT+Pzvz+m9sTeRqZGoJTVDawzl186/Utun9oMPFL4aEs4CUomeEv1+RIJdlKk1UEWpx1w+aj+N\n/RsDoPc4SFy6gR93XLRldAVmafifDNnxKlZ9BAAtAzvxx8trqONT58l2bLXApjFwcqnyc7P3od6g\nJ4xWEAShGGowHMo9ryz/+RZkK4PcOlfozNKOSwl0CiTXnMvYvWP5/uj3mK3mfB/CTqNmZIsKbHu3\nOfXLKpN+Td91if4LD5OW8+CZdYWCEZeey5AlR8kxWijlYc+8/vWw193dAm20GFl0ZhGd1nZiTeQa\nZGSquFdh+YvLGR06Gju13YMPZM6D3ZOU5Zo9wa96AZxN0SYS7KKu6kvK47W/6V1O6TKicopApUtg\n3r4oTpSgAY+ZxkyGbx7Ddyc/BnUOWBwZU+sbfmr9DY7aJ6tHiTEHfhsER+crP9cfCi98+uRBC4Ig\nFEcqFbw8E+xcITtBaXy4JdgjmFWdVtE0QBm0tuTcEkZsH0FybvJjHaqMpyMrhjViRPPyABy4lEz3\n2QeJSS2aU4WXVFl5ZgYvPsbNjDxc9BoWDayPl9OdibIsy+yK3sXL615myvEpZJuycbdz57PGn7Gq\n0ypCPEMe7WBHF0B6NKh10PLjAjibok8k2EVd+edB5wyyheaZaZR1KQvI+JTaj1WGMb+eIjsv/y0L\nRc3R+KO0//VlDiZsBUCVW5U5LX9hQO1OT77zhPMw7wU4t075uek70PH7Z2qwhSAIwl1cg5Ta/wBn\n18LFbf+8ZOfKjFYzGFZjGABH4o/Q7c9uHIg98FiH0qpVjO9YlZl9Q9FpVFxKyOKVmX9zJjb9iU9D\neDizxcpbK05wPi4DjUpidr+6VPS5c3KhyNRIhm8fzujdo7meeR2NpKF/tf5s6LqBHpV7PPo8E4Z0\n2DtZWa43BNzLPOWzKR5Egl3UaeygclsA1BEbGVpjKAC5dsfR6ZOJSsxm3O/hPM6U94/CZLGSlJVH\nYmYeqdlGrE+575zRYmTi35MZvHUIGeYEZKsWt+xX2dRrMU3KlXuynRvSYftnMLvZPwMaO37/zI1k\nFgRBuK+avaB8S2V54/tgzL79klql5u3Qt5nWchrOOmeSDcm8vuN1vjv6HUbL43Xx6FjDnxVDG+Lm\noCUxM48+8w5x6nra0zgT4T5kWearDefYfUEZZPpN1xo0qeB1+/U0QxoTD02k+/ruHIo7BEDzoOas\n6bKGsfXH4qJzyd8B/54OuSlK42DzB036XbKJmjnFQdXOyoQol3fR8ZXZzHYKIiYrhnq1w/j7UCs2\nhMdR1d+FN1tWfOJDnYlNZ9u5m5y4lsrFm5kkZuXx79xdrZLwctJRzsuRYD8Xqvg5U81fedRr8zeL\n4vEb53lv9wekmK8CYMktRTO3t/ihTxucHreckyzDjRMQ/iucXAbGTOV5jwrQfQEEPGE/bkEQhJJE\nkqDTFJjZWLmlv+c/0ParO1Z5ofQLrPFcw4f7PuT4zeMsPbeUI3FHmNRsEpXdK+f7kPXKevDb6014\nbf5h4jMMvDb/MIsHN6BuGfendVbCv8zcc5klB68BMKplRXrUU+bWMFlNrL6wmplhM8kwZgBQ3rU8\nH9T/4N41rR9FZjwcnKEsN3kLHL0evH4JJhLs4qBiG1DbgSUP7eXdDK0xlC8OfsH5jN10Cu3AhhNG\nJm+9gINOzaCm+W/1zc4zs/7UDZYfjub0Q27XWawyNzPyuJmRx6GolNvPq1USFb2dCAlwoVqAC+Xd\ntWSZwPqv7NxothKdks3J6FSWnV/OZfNqJJUZWVahyWjDxOffpkut0g8P2GqBjBuQdg1Sr935mHwJ\nsv9VCkrnDM3eg8ZvKncDBEEQhDt5lIfnP1Cmsz4449agtBp3rOLn6MeCtgtYeGYhM8JmcCH1Ar3W\n92JQ9UH0rdA334es6OPEqhGN6D33EDfSDfRfcJhFgxrQoJzH0zorAVh++BqTt14A4OXaAbzXRvmD\n6O/Yv/n26LdEpUcB4Kxz5s3ab9KzSk+0qieYgXPnl2DKAUcf5Xv3GSYS7OLAzgkqtoILm+D8el7q\nOpc54XOIy47D0Xcnrat2Y8f5BCasP0d8uoGx7aqgUT+898+5GxmsOHKNP07eIOtf/bgr+zrRpIIX\nNYNcCXSzx9NJh0qSMFlkkrPyiEs3EJmQxYX4DCLiM4lLN2Cxyly4mcmFm5m3Z4UCDZ8e34uTnQaL\nVSbHZAF1KvqAX9E4RiGpAJMXnf3f5+Pe7e9utZZlSIpUitQnXoCki8rsi2nXwWp68Mn5VIPafSC0\nP+hd83e9BUEQnjWN31Lu/CWeh41jYPCWu7rSqVVqhtUcRkP/hnxy4BOupF9h3ul5bL2yldZya1rR\nKl+HLOPpyKoRjek97xAxqbkMWHiERYPq06i859M8s2fWhvAbfPKHUuu6ZRVvJveoRWx2DJOPTmb3\n9d0AqCQVPSv35M3ab+Kmd3uyA8Yeh7DlynKrz5Tc5RkmEuziIriTkmBHbkNrtTCqzig+3v8xm65u\n5Of2fQEfdpxPYM7eKPZfSuKjjlVpUsHzrsLxaTlG1ofH8fvxGML+1e9Nr1XRqWYAfRqWpk4ptwcU\nnHe+65mUbCPnbmRw9kY6Z289RiVmIwNWGTIMZkBG43ocve96JHUeAHXdOzKl9Sd4OPzPPm+eU7p3\nnF0LmTcecFEkcAkA97LgVkYZSOFWBko1AM/HnD5dEAThWaTRwYs/wOKOcP2Q0i2xRvd7rlrTuya/\ndf6N+afnM+/0PKKzolnIQi4dvMTYBmMp61r2kQ9bysOBVSMa02feIa4l5zBo0VEWDKx3Rx9hIf82\nhsfxzsowZBnqlXHnh17VmHXqvyw5uwSjVek/39CvIeMajKOSe6UnP6Asw5bxyrJ/Laid/7saJY1I\nsIuLKh1AUoMxC6L20KlyJ5aeW0pESgTTw6Yy97X5/LD9IrP/uszZGxn0nX+YIHd76pZxx91BR4bB\nROTNLM7eSOff4xQr+TjRt2FpXgkNwtX+8W4LeTjqeK6SF89V+ucDMSElnd+37CG4Vl2STZmsif6R\n06nKdKne9t5MaDKBZkHN7txR3CnYNVGZWvXfnPzAvyZ4VVZuZbqXVf65BoluH4IgCE9L2aYQ8orS\nuLHtU+V7R3fvEqk6tY43ar9Bu7Lt+OLAF4QlhbH3xl7+Xvc3vYJ7MbTGULzsHy1JDnSzZ+VwpbvI\n1eQcBi8+ysIB9WlSsegn2RarTHquidQcI7Iso9eq8XS0u2dt6cKy5kQMY349hVWGagHOvPpCMj02\ndiEhJwGAAMcAxtQfQ+vSrR84e2O+nPkdrh9Wltv/RykD+YwTCXZx4eChTDN65S84vx5VlfaMqTeG\noduGcjT+KLuu72Bc+7a0D/Fj0qbzHL6SQkxqLjGpuXftyslOw4s1/OleL4h6Zdyf3i/Yv8PVqfFz\nsBJn3cuM8zNuD6DoULYDHzf6GFe7f3XbyMuCHV/A0Xn/POdZCeq8pnzAe1UWVT8EQRAKQ5uv4MIW\n5e7hvinQ6sHzBVRwq8DM5jOZvmU6+6R9xGTHsPz8cn67+BvdKnVjUPVB+Dn6PfSw/q72rByudBe5\nkpTNoMVHWTCg/h0NN0WBwWThwKUkdl9I4HRMOhHxmeSZrXet5+tiR2VfZxqW86BheU9CS7vfd7bE\np0WWZWb/FcV3WyOQZahWNgP3UiuZcCgMAL1az5AaQxgYMhC9Rv/0DmzIgG2fKMshr0CZJk9v38WY\nSLCLk6qdlQT7wiawmGno35AXSr3Aruu7+ObINzQKaEStUm6sGtGYyJuZ7I1M4nxcBpkGE856LUHu\n9tQv60HdMu75rviRX1cyrrAgawHXTigjl93s3Pio4Ud0KNfhzhXjTsHq/pB6VfnZtwa88AlUbieS\nakEQhMLmVgqeexf2TFLKrdV5DTwePHhekiRCdCGMbDmSDTEbmH96PimGFFZErGD1xdV0Lt+ZvlX7\nUsWjygP34+eqV1qy5x0iKjGbIUuOMq9/PZpX9n6aZ/hYwmPS+PngNTaGx5Frsjx0/f8vBrAvUpkh\n09NRR9sQPzrW8KOG71NMbm8xmCx8tPY0a07Egjqb0uX3Eqvdx/UkJflvV7Yd79d9H38n/6d+bHZO\ngMw40Doqf6AJgEiwi5fgTspsW7kpysC/cs0Z33A8h+MPk5SbxJRjU/iiyRcAVPJ1ppLv3f2lC1qa\nIY054XNYGbESs6wMnOxSoQvv13sfd/3/lGA69yesHaGMONbolZkVG42ERy1mLwiCIDx9Td9WxsGk\nRystk68uf6TNtCot/ar1o3vl7qyJXMPCMwtJyElg7aW1rL20llCfUHoH96ZVmVb3rVTh66Jn5TAl\nyb6cmM3Qn48xr389nrdRkn3wcjKTt0ZwIvqfMUtqlaS0TJfzJCTAhdKeDrg5aFFJEgaThZsZBq4k\n5RAek8aRKylExGeSnG3klyPR/HIkGk9HLSHOKrxiMmhSxfGJ7yKfiU3nnVVhXErIQOt2FGf/7aSS\nBTJUdq/Mhw0+pL5f/Se9FPcWfViZtRGUxjG3UgVznGJIJNjFiYs/BNWHmKNwfj2Ua46fox+jQ0cz\n6fAkfo/8nWaBzWhVJn8juZ8Gg9nALxG/MC98Hpkmpfa0p8qTCU0n8Hz55+/e4PhiWD9aWXYvB71/\nAZ+qhRewIAiCcG9ae2g3Ubm7GLEBLu+GCi0feXN7jT19q/alR+UerL+8nuURy4lMjeREwglOJJzA\n296blyu+TNdKXQlyDrprex8XPb8Mb0TfeYeJTMhi2M/HmPNaXVoG+zzNs3ygiPgMvt0ccXtyFoCQ\nABf6NSpDh+r+uDrcf8xSkLsDdct40L2ucm5x6blsORPP5tPxHL2WQnK2ib3ZKvYuPklpjwu8VCuA\nLrUD8t0olpJtZNqOiyw/HI3V7iqO5f5EpY/FhFJ27606b9Gjcg80qgJK9cx5t77HZQgIhYYjCuY4\nxZRIsIubqp1vJdgboP23oFLRq0ov9lzfw983/ubTA59SxaPKPT+0CkKmMZNVF1ax9NxSUgxKXWwX\nnQsDgwficc2Duj51797o2CLY8I6yXKYp9Fqm9DEXBEEQioaqL0HZZnB1H2weByMPgDp/A+F1ah3d\nKneja6WuHL95nBURK9gVvYvE3ETmnZ7H/NPzaRzQmG6VutGyVEu0/9q/j7OeFcMa0Xf+IS7eVJLs\nr16uTu8GjzBXwhO4kZbLlO0X+f3E/7V35/FRVecfxz9nsi9sCVnYQQxEFIgICKK4gBsuKLhQd+uu\nRa1trba2an9V61Kt1ioudam7GAS0aBFQQUBAkEVFQtjCEsISiGQj2/n9cSYQBEImTGYmyff9euWV\nuXfu3PvMITPzcOac52zYs8jawK4J/O6snvWes9SuVQzXDunGtUO6kVtQQuaCtbw7dxUbigw5+cU8\n+3k2z36ezVHtWnJ+3/acfUwqXRJjD3itisoqFq7bwcTFG/nw243stjuJSvmUiNaLADAYRqWN4vZ+\nt5MQ3cCfq9MedGUdTRic/4y+ff4ZJdiNTfq5bvnvXZtg07fQ8Tg8xsPDJz7MxR9dzNaSrYydMZbX\nznpt34mEfrZ652rGZ41nUvakPT3WkZ5IxqSP4cY+NxJWHsb0nOn7P3Dp+L3Jdbeh8Iv3IDK2weIU\nEZF6MAbOfgzGnQjbVsD8l2DwrfU8laF/an/6p/Znc9FmJqycwISVE8grzmPOpjnM2TSHhOgERnYf\nyai0UXvK/CW1iOLtGwZxzavz+W7jT9w7YRlrtxVx91npfp8wWFBSzvNfrOLV2Wv2TFr9ynr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a48a83c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, line in enumerate(plt.plot(a/pi, x), 1):\n", " line.set_label('$x_{%d}/\\delta$'%i)\n", "plt.title('Normalized Nodal Displacements — analytical solution')\n", "plt.xlabel(r'$\\omega_0 t / \\pi$')\n", "plt.legend(loc='best')\n", "plt.show();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
eds-uga/csci1360-fa16	assignments/A2/A2_header.ipynb	1	1622	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "a1_intro", "locked": true, "solution": false } }, "source": [ "# Assignment 2\n", "CSCI 1360: Foundations for Informatics and Analytics\n", "\n", "## Important Dates\n", "\n", " - Released: 2016-09-01 at 12am [EDT](http://www.timeanddate.com/worldclock/usa/athens)\n", " - Deadline: 2016-09-08 at 11:59:59pm [EDT](http://www.timeanddate.com/worldclock/usa/athens)\n", "\n", "## Grading Breakdown\n", "\n", " - Q1: 10pts\n", " - Q2: 15pts\n", " - Q3: 15pts\n", " - Q4: 15pts\n", " - Q5: 15pts\n", " - Q6: 15pts\n", " - Q7: 15pts\n", " \n", "Total: 100pts\n", " \n", "## Overview\n", "\n", "This assignment will familiarize you with the built-in Python data structures, looping, conditionals, and exceptions.\n", "\n", "Remember in your code to delete (or comment out) any lines that say `raise NotImplementedError()` and replace it with your own code." ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
wasit7/tutorials	django/django_generic_view/generic/.ipynb_checkpoints/edit_widget-checkpoint.ipynb	1	2906	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from django.forms import modelform_factory" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m=modelform_factory(Car,exclude=([]), widgets={\"BigIntegerField\": Textarea()} )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'Meta',\n", " '__class__',\n", " '__delattr__',\n", " '__dict__',\n", " '__doc__',\n", " '__format__',\n", " '__getattribute__',\n", " '__getitem__',\n", " '__hash__',\n", " '__html__',\n", " '__init__',\n", " '__iter__',\n", " '__module__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", " '__unicode__',\n", " '__weakref__',\n", " '_clean_fields',\n", " '_clean_form',\n", " '_get_validation_exclusions',\n", " '_html_output',\n", " '_meta',\n", " '_post_clean',\n", " '_save_m2m',\n", " '_update_errors',\n", " 'add_error',\n", " 'add_initial_prefix',\n", " 'add_prefix',\n", " 'as_p',\n", " 'as_table',\n", " 'as_ul',\n", " 'base_fields',\n", " 'changed_data',\n", " 'clean',\n", " u'declared_fields',\n", " 'errors',\n", " 'field_order',\n", " 'full_clean',\n", " 'has_changed',\n", " 'has_error',\n", " 'hidden_fields',\n", " 'is_multipart',\n", " 'is_valid',\n", " 'media',\n", " 'non_field_errors',\n", " 'order_fields',\n", " 'prefix',\n", " 'save',\n", " 'use_required_attribute',\n", " 'validate_unique',\n", " 'visible_fields']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(m)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ipa-lth/jupyter_nb	optimization/2010-Dissertation-Dilyana_Yankulova-picos.ipynb	1	31773	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "* 29 Aug. 15: Picos 1.1.1 Released\n", " Minor release with following changes:\n", "\n", " Initial support for the SDPA solver (with the option solver='sdpa', picos works as a wrapper around the SDPA executable based on the write_to_file() function; thanks to Petter Wittek )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from __future__ import division" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import picos as pic\n", "import cvxopt as cvx\n", "import control as con\n", "\n", "from numpy import linalg as LA\n", "from scipy.special import comb as nchoosek # n Choose k (n ueber k)\n", "import optim_tools as optim_tools#own file with helper" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "###########################\n", "# Hydraulischer Aktor #\n", "###########################\n", "\n", "A0 = np.matrix([[0, 1, 0],\n", " [-10, -1.167, 25],\n", " [0, 0, -0.8]])\n", "print \"Eigenvalues: {}\".format(LA.eigvals(A0))\n", "#a = -A[-1,:].T ### !!!!!\n", "#print a\n", "b0 = np.matrix([[0],[0],[2.4]])\n", "c0 = np.matrix([1, 0, 0])\n", "d0 = np.matrix([0])\n", "u_max = 10.5\n", "n = 3\n", "\n", "X00 = [np.matrix([-20.0, -10.0, -10.0]).T,\n", " np.matrix([-20.0, -10.0, 10.0]).T,\n", " np.matrix([-20.0, 10.0, -10.0]).T,\n", " np.matrix([-20.0, 10.0, 10.0]).T,\n", " np.matrix([20.0, -10.0, -10.0]).T,\n", " np.matrix([20.0, -10.0, 10.0]).T,\n", " np.matrix([20.0, 10.0, -10.0]).T,\n", " np.matrix([20.0, 10.0, 10.0]).T,\n", " ]\n", "\n", "#print \"A:\\n\", A\n", "#print \"a:\\n\", a\n", "#print \"b:\\n\", b\n", "#print \"c:\\n\", c\n", "\n", "# Convert to Normalform\n", "(A1, b1, c1, d1), T1, Q1 = optim_tools.get_Steuerungsnormalform(A0, b0, c0.T, d0)\n", "a1 = -A1[-1][:].T #!!!!\n", "print \"T1:\\n\", T1\n", "\n", "# Convert to Normalform\n", "ss, T2 = con.canonical_form(con.ss(A0, b0, c0, d0), form='reachable')\n", "\n", "assert np.allclose(T1X00[1],\n", " optim_tools.reverse_x_order(T2X00[1])),\\\n", "\"own Steuerungsnormalform Transformation not equal python control version\"\n", "#print \"x_r1:\\n\", T1X00[1]\n", "#print \"x_r2(backwards):\\n\", optim_tools.reverse_x_order(T2X00[1])\n", "\n", "A = optim_tools.reverse_x_order(np.matrix(ss.A))\n", "a = -A[-1][:].T #!!!!\n", "\n", "b = optim_tools.reverse_x_order(np.matrix(ss.B))\n", "c = optim_tools.reverse_x_order(np.matrix(ss.C))\n", "d = optim_tools.reverse_x_order(np.matrix(ss.D)) # == 0!\n", "\n", "print \"A:\\n\", A\n", "assert np.allclose(A, A1)\n", "print \"a:\\n\", a\n", "assert np.allclose(a, a1)\n", "print \"b:\\n\", b\n", "assert np.allclose(b, b1)\n", "print \"c:\\n\", c\n", "assert np.allclose(c, c1.T)\n", "\n", "X0 = [T1.dot(x0) for x0 in X00]\n", "#print \"X0:\\n\"\n", "#for x0 in X0:\n", "# print x0\n", "#print \"A1:\\n\", A1\n", "#print \"a1:\\n\", a1\n", "#print \"b1:\\n\", b1\n", "#print \"c1:\\n\", c1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pmin = 0.1" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Testsystem aufsetzen\n", "import control as con\n", "\n", "# pt2 System\n", "K = 1\n", "d = 0.5\n", "T = 10\n", "delay = 0\n", "\n", "a0 = 1\n", "a1 = (2 * d * T) #16\n", "a2 = (T*2) #100\n", "b0 = K\n", "\n", "# Polynom\n", "tf_1 = con.matlab.tf(K, [a2, a1, a0])\n", "#print tf_1\n", "\n", "# Zustandsraum\n", "#ss_1a = con.matlab.tf2ss(tf_1)\n", "#print ss_1a\n", "\n", "# Füge Zeitversatz zu\n", "d_num, d_den = con.pade(delay, 1)\n", "tf_delay = con.tf(d_num, d_den)\n", "ss_delay = con.series(tf_delay, tf_1)\n", "\n", "ss_1a = con.matlab.tf2ss(ss_delay)\n", "\n", "############################################################\n", "############################################################\n", "############################################################\n", "############################################################\n", "\n", "# Sammle Systemteile\n", "A0 = ss_1a.A\n", "b0 = ss_1a.B\n", "C0 = ss_1a.C\n", "d0 = ss_1a.D\n", "\n", "# Max output\n", "u_max = 0.1\n", "\n", "# Einzugsgebiet (convex)\n", "X0 = [np.matrix([-1, -0.1]).T,\n", " np.matrix([-1, 0.1]).T,\n", " np.matrix([1, -0.1]).T,\n", " np.matrix([1, 0.1]).T]\n", "#print len(X0)\n", "\n", "# Transformation in Regelungsnormalform\n", "ss, T = con.canonical_form(con.ss(A0, b0, C0, d0), form='reachable')\n", "\n", "\n", "A = optim_tools.reverse_x_order(np.matrix(ss.A))\n", "a = -A[-1][:].T #!!!!\n", "\n", "b = optim_tools.reverse_x_order(np.matrix(ss.B))\n", "C = optim_tools.reverse_x_order(np.matrix(ss.C))\n", "d = optim_tools.reverse_x_order(np.matrix(ss.D)) # == 0!\n", "print \"A:\\n\", A\n", "print \"a:\\n\", a\n", "print \"b:\\n\", b\n", "#print C\n", "\n", "n = b.size" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# !!!!!\n", "# This Section is disabled \n", "# !!!!!\n", "\n", "##############################\n", "# Boris Paper UBoot #\n", "##############################\n", "A = np.matrix([[0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., -0.005]])\n", "\n", "############### TODO ###################\n", "############### CHECK ##################\n", "#last line of Regelungsnormalform (characteristical polynom coefficients) in a colunm vector\n", "a = -np.matrix(A[np.array(A).shape[1]-1])[np.newaxis].T \n", "\n", "b = np.matrix([[0], [0], [1.]])\n", "\n", "d = 0\n", "c = np.matrix([[1], [0], [0]]).T\n", "\n", "print np.matrix(con.matlab.pole(con.matlab.ss(A, b, c, d))).T\n", "\n", "u_max = 2.5e-5\n", "\n", "X0 = [np.matrix([-10, -0.05, -0.0046]).T,\n", " np.matrix([-10, -0.05, 0.0046]).T,\n", " np.matrix([-10, 0.05, -0.0046]).T,\n", " np.matrix([-10, 0.05, 0.0046]).T,\n", " np.matrix([ 10, -0.05, -0.0046]).T,\n", " np.matrix([ 10, -0.05, 0.0046]).T,\n", " np.matrix([ 10, 0.05, -0.0046]).T,\n", " np.matrix([ 10, 0.05, 0.0046]).T]\n", "\n", "x_max = [10, 0.05, 0.0046]\n", "\n", "#simulation time\n", "T = 1.0/200.0\n", "\n", "#Introduced for fun\n", "u_max_sys = u_max\n", "\n", "#a_hat = np.matrix([[4.4469e-8], [2.3073e-5], [4.9148e-3]])\n", "#a_hat_star = np.matrix([[1.073e-7], [4.919e-5], [10.4078e-3]])\n", "\n", "#R1 = np.matrix([[1.6021e-5, 3.26098e-3, 0.4031],\n", "# [3.2698e-3, 1.5666, 163.46],\n", "# [0.4031, 163.46, 40.713]])\n", "\n", "##### Entwurf parameter #####\n", "beta = 2 # beta >=1 !\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import picos;picos.tools.available_solvers()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "######################################################\n", "# Helper for Constraint variant (4.65) -> Q_sum #\n", "######################################################\n", "\n", "# TODO: Was ist m? Kann man das berechnen oder wird das festgelegt m<=2n-1, meistens n+1?\n", "\n", "from scipy.special import comb as nchoosek # n Choose k (n ueber k)\n", "\n", "def get_a_list(a, Q, z, m):\n", " n = len(a) # dim of a, z, Q(nxn)\n", " m = m # Is this to choose or somehow given?\n", " l = m+1 # length of the array with all a_i in it i:{0,m}\n", " H = lambda k: optim_tools._H(k, n) # Convinence function to fix n and make specialized H(k)\n", " N = optim_tools._N(n)\n", "\n", " a_list = [np.zeros((n, n))] l\n", " for i in range(0, l): # m eingeschlossen\n", " if i <= (m-1)/2.0: # 0 <= i <= (m-1)/2\n", " for k in range(1, i+1):\n", " a_list[i] += a.T * H(n+k-i)QNH(n-k+1)a - z.TNH(n-i)a\n", " elif i <= m: # (m-1)/2 < i <=m\n", " for k in range(1, 2n-i+1):\n", " a_list[i] += a.T * H(k)QNH(2n-i-k+1)a\n", " else:\n", " # this branch is currently not possible in this program\n", " print \"i={} < m={}\".format(i, m)\n", " a_list[i] = 0\n", " \n", " return a_list\n", "\n", "\n", "def trans_a_list(a_list, eps):\n", " l = len(a_list)\n", " m = l-1 #biggest index in a_list\n", " a1_list = [np.zeros(a_list[0].shape)] l\n", " for j in range(0, l): #for each in a_list\n", " for i in range(j, l): # for each coefficient including m\n", " a1_list[j] += nchoosek(i, i-j) * ((1.0+eps)/(1.0-eps))*(i-j) ((1.0-eps)/(2.0))*i a_list[i]\n", " return a1_list\n", "\n", "def calc_a_Sum(a_list):\n", " l = len(a_list) # number of matrizen in a_list\n", " m = l-1 # Index of Q_m\n", " n = a_list[0].size[0] # shape of each matrix, first element\n", " \n", " if m is 0:\n", " a_sum = cvxpy.bmat([[2a_list[0], np.zeros(n)], \n", " [np.zeros(n), np.zeros(n)]])\n", " elif m is 1:\n", " a_sum = cvxpy.bmat([[2a_list[0], a_list[1]],\n", " [a_list[1], np.zeros(n)]])\n", " else: # e.g. m is 2 or more\n", " a_sum = cvxpy.bmat([[2a_list[0], a_list[1]],\n", " [a_list[1], 2a_list[2]]])\n", "\n", " for i1 in range(3, l, 2):\n", " S_new_col = cvxpy.vstack(np.zeros((((i1+1)/2-1)n, n)), a_list[i1])\n", "\n", " if i1 is m:\n", " S_new_row = cvxpy.hstack(np.zeros((n, ((i1+1)/2-1)n)), a_list[i1], np.zeros((n,n)))\n", " else:\n", " S_new_row = cvxpy.hstack(np.zeros((n, ((i1+1)/2-1)n)), a_list[i1], 2a_list[i1+1])\n", "\n", " a_sum = cvxpy.bmat([[a_sum, S_new_col],\n", " [S_new_row]])\n", "\n", " a_sum = -0.5a_sum\n", " \n", " return a_sum\n", "\n", "def calc_lmi_cond(a_sum, n):\n", " k = a_sum.size[1] / n # This dimension is from Boris. Dilyana does not specifiy the dimension though\n", "\n", " J = np.hstack([np.zeros((n(k-1), n)), np.eye(n(k-1))])\n", " C = np.hstack([np.eye(n(k-1)), np.zeros((n(k-1), n))])\n", " return np.vstack([C, J]), n(k-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define and solve optimations problem" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective variant (4.66) -> max(det(Q)) #\n", "# Volumenmaximierung -> geomean(Q) #\n", "###########################################\n", "# Problem\n", "prob_451 = pic.Problem()\n", "\n", "# Variables\n", "QQ = prob_451.add_variable('Q', (n, n), vtype='symmetric')\n", "zz = prob_451.add_variable('z', n)\n", "\n", "\n", "# Constants\n", "XX0 = pic.new_param('X0', X0)\n", "N = cvx.matrix(optim_tools._N(n))\n", "\n", "# Constraints\n", "#(4.59)\n", "prob_451.add_constraint(QQ >> 0)\n", "\n", "#(4.60)\n", "prob_451.add_constraint(QQ(A.T+ab.T) + (A+ba.T)QQ - zzb.T - bzz.T << 0)\n", "\n", "#(4.61)\n", "prob_451.add_constraint(QQN+NQQ << 0) \n", "\n", " #(4.62)\n", "prob_451.add_list_of_constraints([((1 & XX0[i].T) //\n", " (XX0[i] & QQ )) >> 0\n", " for i in range(0, len(X0))])\n", "\n", "#(4.63)\n", "prob_451.add_constraint(((u_max*2 - a.TQQa + 2a.Tzz & zz.T) //\n", " (zz & QQ )) >> 0) \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Constraint variant (4.64) #\n", "###########################################\n", "\n", "m = n\n", "\n", "## BEWARE OF SECOND RANGE ####\n", "prob_451.add_list_of_constraints(\n", " [pic.sum(\n", " [nchoosek(i, k) a.T * optim_tools._P(0, i-k, n) * QQ * N * optim_tools._P(0, k, n) * a - zz.T * N * optim_tools._P(n, i, n) * a\n", " for k in range(0,i)], 'k', '[0,1,..,i]')>=0\n", " for i in range(1, m+1)])" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "###########################################\n", "# Constraint variant (4.65) -> Q_sum #\n", "###########################################\n", "\n", "m = n\n", "\n", "# Preparation for constraint (4.65)\n", "a_list = get_a_list(a, Q, z, m) # Matrizes of Polynom coefficients: S(p)=S_0 + S_1p + ... S_np^n\n", "a1_list = trans_a_list(a_list, pmin) # Intervaltransformation p:[0,1] -> p1:[-1,1] (S1 -> S^tilde)\n", "a1_sum = calc_a_Sum(a1_list) # S^tilde_sum Matrix (30)\n", "CJ, l = calc_lmi_cond(a1_sum ,n) # \"Selection matrizes\" of (31), l=dimension of P and G\n", "\n", "# Further variables of optimization\n", "S = cvxpy.Semidef(l) #symmetrical\n", "G = cvxpy.Variable(l,l) #skew\n", "\n", "# Constraints on new variables\n", "constraint_G = G + G.T == 0 # skew symmetry\n", "constraint_S = S == S.T # symmetry\n", "\n", "# Actual constraint\n", "constraint_465 = a1_sum << CJ.T * cvxpy.bmat([[-S, G],\n", " [G.T, S]]) * CJ" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective disabled because problem is not feasible with the objective anymore :-/\n", "###########################################\n", "\n", "#ss = prob_451.add_variable('s', 1)\n", "#prob_451.add_constraint(pic.geomean(QQ) > ss) \n", "#prob_451.add_constraint(pic.detrootn(QQ) > ss) \n", "\n", "#prob_451.set_objective('max', ss)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print \"Objective: {}\\n\".format(prob_451.objective)\n", "prob_451.solve(verbose=1, solver='cvxopt', solve_via_dual = None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Output\n", "print \"Status:\", prob_451.status\n", "print prob_451\n", "\n", "Q_451 = QQ.value\n", "R1_451 = LA.inv(QQ.value)\n", "a_hat_451 = R1_451.dot(zz.value)\n", "\n", "print \"Q:\\n\", Q_451\n", "print \"R1:\\n\", R1_451\n", "print \"z:\\n\", zz.value\n", "print \"a_hat:\\n\", a_hat_451\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "print \"Checking constraints (no output is ok!)\"\n", "eig_459 = np.linalg.eigvals(QQ.value)\n", "if np.any(eig_459 <= 0): print(\"Q not pos definit: {}\".format(eig_459))\n", "\n", "eig_460 = np.linalg.eigvals((QQ(A.T+ab.T)+(A+ba.T)QQ-zzb.T-bzz.T).value)\n", "if np.any(eig_460 >= 0): print(\"4.60 not neg definit: {}\".format(eig_460))\n", "\n", "eig_461 = np.linalg.eigvals((QQN+NQQ).value)\n", "if np.any(eig_461 >= 0): print(\"4.61 not neg definit: {}\".format(eig_461))\n", " \n", "eig_463 = np.linalg.eigvals(((u_max*2-a.TQQa+2a.Tzz & zz.T) // (zz & QQ)).value)\n", "if np.any(eig_463 <= 0): print(\"4.63 not pos definit: {}\".format(eig_463))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective variant (4.70) #\n", "# Max. Abklingrate #\n", "###########################################\n", "# Problem\n", "prob_452 = pic.Problem()\n", "\n", "# Variables\n", "QQ = prob_452.add_variable('Q', (n, n), vtype='symmetric')\n", "zz = prob_452.add_variable('z', n)\n", "\n", "\n", "# Constants\n", "XX0 = pic.new_param('X0', X0) #We also point out that new_param() converts lists into vectors and lists of lists into matrices (given in row major order)\n", "N = cvx.matrix(optim_tools._N(n))\n", "\n", "# Bisection parameter\n", "beta = pic.new_param('beta', 0) # sign='positive'\n", "\n", "\n", "# Constraints\n", "#(4.59)\n", "prob_452.add_constraint(QQ >> 0)\n", "\n", "#(4.60)\n", "prob_452.add_constraint(QQ(A.T+ab.T) + (A+ba.T)QQ - zzb.T - bzz.T << 0)\n", "\n", "#(4.61)\n", "prob_452.add_constraint(QQN+NQQ << 0) \n", "\n", " #(4.62)\n", "prob_452.add_list_of_constraints([((1 & XX0[i].T) //\n", " (XX0[i] & QQ )) >> 0\n", " for i in range(0, len(X0))])\n", "\n", "#(4.63)\n", "prob_452.add_constraint(((u_max2 - a.TQQa + 2a.Tzz & zz.T) //\n", " (zz & QQ )) >> 0) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#(4.71)\n", "constraint_471 = prob_452.add_constraint(QQ(A.T + ab.T) + (A + ba.T)QQ - zzb.T - bzz.T + 2betaQQ << 0, ret=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Constraint variant (4.64) #\n", "###########################################\n", "\n", "m = n\n", "\n", "## BEWARE OF SECOND RANGE ####\n", "prob_452.add_list_of_constraints(\n", " [pic.sum(\n", " [nchoosek(i, k) a.T * optim_tools._P(0, i-k, n) * QQ * N * optim_tools._P(0, k, n) * a - zz.T * N * optim_tools._P(n, i, n) * a\n", " for k in range(0,i)], 'k', '[0,1,..,i]')>=0\n", " for i in range(1, m+1)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def bisect_max(l, u, func_solve_with_param, prob,\n", " bisection_tol=1e-3, bisect_verbose=False):\n", "\n", " #if (u is not None):\n", " # # cross check bound\n", " # if (u < l): raise ValueError(\"upperBound({}) < lowerBound({})\".format(u, l))\n", " #\n", " #elif (u is None) and (l is not None):\n", " # # First iteration to find upper bound\n", " # u = l + 1.0\n", " # if bisect_verbose: print \"processing upper bound: {}\".format(u)\n", " # \n", " # parameter.value = u\n", " # problem = accurate_solve(problem, solver, *kwargs_solver)\n", " # uStatus = problem.status\n", " # \n", " # while 'optimal' in uStatus:\n", " # #if u >= l: # shift upper bound if found feasible, this condition is always true\n", " # #l = u\n", " # u = 2.0u\n", " # if bisect_verbose:\n", " # print \"processing upper bound: {}\".format(u)\n", " # parameter.value = u\n", " # problem = accurate_solve(problem, solver, *kwargs_solver)\n", " # uStatus = problem.status\n", " # print 'found bounds: [{}-{}]'.format(l, u)\n", " #else:\n", " # raise ValueError(\"Not implemented\")\n", " \n", " # check validity solution of l is optimal, solution of u is infeasible\n", " val_opt = l\n", " problem_opt, lStatus = func_solve_with_param(prob, l) # Set lower bound and solve, shall return (problem, status[True/False])\n", "\n", " problem, uStatus = func_solve_with_param(prob, u) # Set upper bound and solve, shall return (problem, status[True/False])\n", "\n", " if lStatus is False and uStatus is True:\n", " raise ValueError(\"UpperBound({})={}, LowerBound({})={}\".format(u, uStatus, l, lStatus))\n", "\n", " while u - l >= bisection_tol:\n", " val = (l + u) / 2.0 # Solve for parameter in the middle of upper and lower bound\n", " \n", " ## solve the feasibility problem\n", " problem, status = func_solve_with_param(problem_opt, val)\n", "\n", " if bisect_verbose:\n", " print \"Range: {}-{}; parameter {} -> {}\".format(l, u, val, problem.status)\n", "\n", " if status is False:\n", " u = val\n", " else:\n", " l = val\n", " val_opt = val\n", " problem_opt = problem #Store last (optimal) solved problem\n", "\n", " # Solve it a last time with the last optimal value, to set the status of problem (since it is not a copy but a ref)\n", " problem_opt, _ = func_solve_with_param(problem_opt, val_opt)\n", " return problem_opt, val_opt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def picos_solve_with_param(problem, val):\n", " global constraint_471\n", " #beta = pic.new_param('beta', val) # sign='positive'\n", "\n", " constraint_471.delete()\n", " constraint_471 = problem.add_constraint(QQ(A.T + ab.T) + (A + ba.T)QQ - zzb.T - bzz.T + 2valQQ << 0, ret=True)\n", " \n", " problem.solve(verbose=0, solver='cvxopt', solve_via_dual = None)\n", " return problem, problem.status == 'optimal'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "problem_opt, val_opt = bisect_max(0, 2, picos_solve_with_param, prob_452)\n", "print \"Objective variant (4.5.2) -> Bisection(Max. Abklingrate)\"\n", "print \"Bisection Param:\", val_opt\n", "\n", "# Output\n", "print \"Status:\", problem_opt.status\n", "#print problem_opt\n", "\n", "Q_452 = QQ.value\n", "R1_452 = LA.inv(QQ.value)\n", "a_hat_452 = R1_452.dot(zz.value)\n", "\n", "print \"Q:\\n\", Q_452\n", "print \"R1:\\n\", R1_452\n", "print \"z:\\n\", zz.value\n", "print \"a_hat:\\n\", a_hat_452\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper functions to run simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T = np.arange(0, 5, 1e-2) \n", "\n", "#s: input, e.g., step function with amplitude of 0.2\n", "s = np.zeros(len(T));\n", "\n", "p_init = pmin\n", "\n", "# Initial state\n", "x0 = np.matrix([[20.],[10.],[10.]])\n", "\n", "p_t = np.zeros(len(T))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Skalierte Version! aus Yankulova\n", "\n", "# k(v) (4.5)\n", "def get_k(v, a, a_hat):\n", " try:\n", " v = v.squeeze() # This is weird, needed for minimize for some reason\n", " except:\n", " pass\n", " D_inv = optim_tools._D_inv(v, len(a))\n", " #print D_inv\n", " k = D_inv.dot(a_hat) - a\n", " return k\n", "\n", "# Fixing a and a_hat for convinience\n", "# func_k = lambda v: get_k(v, a, a_hat)\n", "\n", "# G(v) (4.4)\n", "def get_g(v, x, R1, u_max, a, a_hat):\n", " try:\n", " v = v.squeeze() # This is weird, needed for minimize for some reason\n", " except:\n", " pass\n", " D_inv = optim_tools._D_inv(v, len(x))\n", " R = D_inv.dot(R1).dot(D_inv) # R(v) = D^⁻1 R1 * D^-1\n", " \n", " k = get_k(v, a, a_hat) # k(v)\n", " e = (u_max*(-2)) (k.T.dot(LA.inv(R)).dot(k))\n", " #assert e < 1.0\n", " g = e(x.T.dot(R).dot(x)) - 1.0\n", " #assert g <= 0, \"g = {} > 0\".format(g)\n", " # Update 2016: As of python 3.5, there is a new matrix_multiply symbol, @:\n", " # g = x' @ D^-1 @ R1 @ D^-1 @ x - 1.0\n", " return g" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "last_p = p_init\n", "\n", "def contr_func(y, s, x, u_max, a, a_hat, R1, T1=None):\n", " global last_p, pmin\n", " \n", " # Transformation in Regelungsnormalform\n", " if T1 is None:\n", " T1 = np.eye(len(x))\n", "\n", " x_R = T1x\n", " \n", " func_g = lambda p: np.absolute(get_g(p, x_R, R1, u_max, a, a_hat))\n", " res = minimize(func_g, last_p, method='Nelder-Mead') # find 0 -fzero\n", " \n", " # Saturate if too small\n", " if res.x < pmin:\n", " p = pmin\n", " elif res.x > 1.0:\n", " p = 1.0\n", " print \"WARNING: p=({})>1! -> p is saturated to 1.0\".format(res.x)\n", " else:\n", " p = res.x\n", "\n", " p_t.append(p)\n", " p2_t.append(res.x)\n", "\n", " last_p = p\n", " \n", " ## Calc K according to p\n", " K = get_k(p, a, a_hat)\n", "\n", " # Calc u\n", " u = s-K.T.dot(x_R)\n", " \n", " # Saturate u\n", " u = optim_tools.sat(u, u_max)\n", " #print \"u\", u\n", " \n", " return u" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulation of WSVC Control" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a_hat_x = np.matrix([[14.345160], [14.837268], [3.598276]])\n", "\n", "R1_x = np.matrix([[1.677977, 0.543373, 0.140299],\n", " [0.543373, 0.32290, 0.062725],\n", " [0.140298, 0.06272, 0.026148]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p_t = []\n", "p2_t = []\n", "\n", "y, u, u_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_451, R1_451, T1), s, T, umax=u_max, x0=x0)\n", "y1, u1, u1_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_x, R1_x, T1), s, T, umax=u_max, x0=x0)\n", "y2, u2, u2_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_452, R1_452, T1), s, T, umax=u_max, x0=x0)\n", "\n", "#y1x, u1x, u1x_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_x, R1_x, T1), s, T, umax=u_max, x0=x0)\n", "#y1, u1, u1_sat = optim_tools.simulate(A1, b1, c1.T, d1, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat), s, T, umax=u_max, x0=T1x0)\n", "\n", "#y2, u2, u2_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-get_k(1, a, a_hat_451).T.dot(T1x), s, T, umax=u_max, x0=x0)\n", "#y3, u3, u3_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-np.matrix([-0.0833, 0.0828, 0.76068]).dot(x), s, T, umax= u_max, x0=x0)\n", "y4, u4, u4_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-np.matrix([13.25659, 7.097648, 4.0502]).dot(x), s, T, umax= u_max, x0=x0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (10, 6)\n", "import matplotlib.pyplot as plt\n", "\n", "#plt.figure()\n", "line0, = plt.plot(T[:], np.array(y[0,:].T), 'b', label='WSVC 451')\n", "line1, = plt.plot(T[:], np.array(y1[0,:].T), 'y-', label='WSVC x')\n", "line2, = plt.plot(T[:], np.array(y2[0,:].T), 'r-', label='WSVC 452')\n", "#line2, = plt.plot(T[:], np.array(y2[0,:].T), 'r-', label='linear(v=1.0)')\n", "#line3, = plt.plot(T[:], np.array(y3[0,:].T), 'g-', label='linear')\n", "line4, = plt.plot(T[:], np.array(y4[0,:].T), 'g-', label='y4 lin, apx evo')\n", "\n", "\n", "\n", "#first_legend = plt.legend(handles=[line1], loc=1)\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.xlabel('T')\n", "plt.ylabel('y')\n", "plt.title('Closed Loop Step Response')\n", "plt.show()\n", "\n", "#plt.figure()\n", "#line0, = plt.plot(T, u_sat, 'b', label='u')\n", "\n", "#line0, = plt.plot(T, u1x_sat, 'b', label='WSVC x')\n", "\n", "line0, = plt.plot(T, u, 'b.-', label='WSVC 451')\n", "line1, = plt.plot(T, u1_sat, 'y-', label='WSVC x')\n", "line2, = plt.plot(T, u2_sat, 'r-', label='WSVC 452')\n", "#line2b, = plt.plot(T, u2, 'r.-', label='u fixed')\n", "#line3, = plt.plot(T, u3_sat, 'g', label='u, linear')\n", "#line3b, = plt.plot(T, u2, 'g.-', label='u lin')\n", "line4, = plt.plot(T, u4_sat, 'g-', label='u lin, apx evo')\n", "\n", "\n", "#>first_legend = plt.legend(handles=[line1, line2, line1b, line2b], loc=1)\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.xlabel('T')\n", "plt.ylabel('u')\n", "plt.title('Output values')\n", "plt.show()\n", "\n", "line6, = plt.plot(p_t, 'r', label='p')\n", "line7, = plt.plot(p2_t, 'r.', label='p')\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
MTgeophysics/mtpy	examples/workshop/Workshop Exercises Core.ipynb	1	12112	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This workbook contains some examples for reading, analysing and plotting processed MT data. It covers most of the steps available in MTPy. For more details on specific input parameters and other functionality, we recommend looking at the mtpy documentation, which can be found at: https://mtpy2.readthedocs.io/en/develop/.\n", "\n", "This workbook is structured according to some of the key modules in MTPy: Core, Analysis, Imaging, and Modeling." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start with, you will need to make sure MTPy is installed and is working correctly. Please see the installation guide (https://github.com/MTgeophysics/mtpy/wiki/MTPy-installation-guide-for-Windows-10-and-Ubuntu-18.04) for details.\n", "\n", "Before you begin these examples, we suggest you make a temporary folder (e.g. C:/tmp) to save all example outputs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Useful tricks and tips" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This workbook exists as a Jupyter notebook and a pdf. If you are running the Jupyter notebook, you can run each of the cells, modifying the inputs to suit your requirements. Most of these examples have been written to be self contained.\n", "\n", "In Jupyter, you can add the following line to the top of any cell and it will write the contents of that cell to a python script: %%writefile example.py\n", "\n", "You can also select multiple cells and copy them to a new Jupyter notebook.\n", "\n", "Many of the examples below make use of the matplotlib colour maps. Please see https://matplotlib.org/examples/color/colormaps_reference.html for colour map options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Core" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These first few examples cover some of the basic functions and tools that can be used to look at data contained in an edi file, plot it, and make changes (e.g. sample onto different frequencies)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read an edi file into an MT object" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "\n", "# Define the path to your edi file\n", "edi_file = \"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mt_obj contains all the data from the edi file, e.g. impedance, tipper, frequency as well as station information (lat/long). To look at any of these parameters you can type, for example:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-19.01 136.01\n" ] } ], "source": [ "# To see the latitude and longitude\n", "print(mt_obj.lat, mt_obj.lon)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "606300.4060199939 7897760.860594714 95.0\n" ] } ], "source": [ "# To see the easting, northing, and elevation\n", "print(mt_obj.east, mt_obj.north, mt_obj.elev)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many other parameters you can look at in the mt_obj. Just type mt_obj.[TAB] to see what is available.\n", "In the MT object are the Z and Tipper objects (mt_obj.Z; mt_obj.Tipper). These contain all information related to, respectively, the impedance tensor and the tipper." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.256500e+04 9.751601e+03 7.876300e+03 6.188500e+03 5.250801e+03\n", " 4.265799e+03 3.515799e+03 8.437800e+02 6.562798e+02 4.922399e+02\n", " 3.867599e+02 3.164400e+02 2.578400e+02 2.109600e+02 1.728900e+02\n", " 1.367200e+02 1.015600e+02 7.421900e+01 5.761700e+01 4.882800e+01\n", " 4.101600e+01 3.222700e+01 2.636700e+01 2.148400e+01 1.757800e+01\n", " 1.440400e+01 1.147500e+01 8.593800e+00 6.591801e+00 5.371100e+00\n", " 4.394500e+00 3.601100e+00 2.868700e+00 2.304700e+00 1.914100e+00\n", " 1.601600e+00 1.328100e+00 1.074200e+00 8.789100e-01 6.835900e-01\n", " 5.078100e-01 3.710900e-01 2.880900e-01 2.050800e-01 1.318400e-01\n", " 8.789098e-02 6.835900e-02 5.127000e-02 4.028299e-02 3.295900e-02\n", " 2.685500e-02 2.197300e-02 1.709000e-02 1.281700e-02 1.007100e-02\n", " 8.239700e-03 6.713900e-03 5.493201e-03 4.272499e-03 2.822900e-03\n", " 2.059900e-03 1.678500e-03 1.373300e-03 1.068100e-03 7.629400e-04]\n" ] } ], "source": [ "# for example, to see the frequency values represented in the impedance tensor:\n", "print(mt_obj.Z.freq)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[ 26.58566 -4.302123j 482.4492 +604.7747j ]\n", " [-410.0502 -800.4257j 8.994784 +44.07396j ]]\n", "\n", " [[ 12.43271 +7.519158j 434.8246 +514.6176j ]\n", " [-372.7205 -666.402j 17.64062 +36.09528j ]]\n", "\n", " [[ 7.652151 +6.28703j 398.3996 +460.0998j ]\n", " [-349.9875 -580.3959j 21.57495 +33.98854j ]]\n", "\n", " [[ 3.59474 +1.225811j 362.5121 +413.2823j ]\n", " [-328.0029 -501.5329j 25.02421 +33.02813j ]]]\n" ] } ], "source": [ "# or to see the impedance tensor (first 4 elements)\n", "print(mt_obj.Z.z[:4])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[1.15448560e-02 9.52661629e+00]\n", " [1.28742136e+01 3.22072438e-02]]\n", "\n", " [[4.32975088e-03 9.30931663e+00]\n", " [1.19572611e+01 3.31035019e-02]]\n", "\n", " [[2.49056438e-03 9.40578869e+00]\n", " [1.16641228e+01 4.11538240e-02]]\n", "\n", " [[4.66179794e-04 9.76706091e+00]\n", " [1.16060807e+01 5.54922342e-02]]]\n", "[[[ -9.19198953 51.41945343]\n", " [-117.1256163 78.46525668]]\n", "\n", " [[ 31.16505948 49.80396162]\n", " [-119.21840697 63.95414249]]\n", "\n", " [[ 39.40662667 49.11076951]\n", " [-121.09061209 57.59379146]]\n", "\n", " [[ 18.82944281 48.74426019]\n", " [-123.18471678 52.85011776]]]\n" ] } ], "source": [ "# or the resistivity or phase (first 4 values)\n", "print(mt_obj.Z.resistivity[:4])\n", "print(mt_obj.Z.phase[:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the MT object, you can explore the object by typing mt_obj.Z.[TAB] to see the available attributes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot an edi file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we plot MT data from an edi file." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Figure size 960x720 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "import os\n", "\n", "# Define the path to your edi file and save path\n", "edi_file = \"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "savepath = r\"C:/tmp\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)\n", "\n", "# To plot the edi file we read in in Part 1 & save to file:\n", "pt_obj = mt_obj.plot_mt_response(plot_num=1, # 1 = yx and xy; 2 = all 4 components\n", " # 3 = off diagonal + determinant\n", " plot_tipper = 'yri',\n", " plot_pt = 'y' # plot phase tensor 'y' or 'n'\n", " )\n", "#pt_obj.save_plot(os.path.join(savepath,\"Synth00.png\"), fig_dpi=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make some change to the data and save to a new file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example demonstrates how to resample the data onto new frequency values and write to a new edi file. In the example below, you can either choose every second frequency or resample onto five periods per decade. \n", "To do this we need to make a new Z object, and save it to a file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\mtpywin\\mtpy\\mtpy\\utils\\calculator.py:371: RuntimeWarning: invalid value encountered in double_scalars\n", " z_rel_err = error/z_amp\n" ] }, { "data": { "text/plain": [ "'C:\\\\tmp\\\\Synth00_5ppd.edi'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "import os\n", "\n", "# Define the path to your edi file and save path\n", "edi_file = r\"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "savepath = r\"C:/tmp\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)\n", "\n", "# First, define a frequency array:\n", "# Every second frequency:\n", "new_freq_list = mt_obj.Z.freq[::2] \n", "\n", "# OR 5 periods per decade from 10^-4 to 10^3 seconds\n", "from mtpy.utils.calculator import get_period_list\n", "new_freq_list = 1./get_period_list(1e-4,1e3,5)\n", "\n", "# Create new Z and Tipper objects containing interpolated data\n", "new_Z_obj, new_Tipper_obj = mt_obj.interpolate(new_freq_list)\n", "\n", "# Write a new edi file using the new data\n", "mt_obj.write_mt_file(\n", " save_dir=savepath, \n", " fn_basename='Synth00_5ppd', \n", " file_type='edi',\n", " new_Z_obj=new_Z_obj, # provide a z object to update the data\n", " new_Tipper_obj=new_Tipper_obj, # provide a tipper object\n", " longitude_format='LONG', # write longitudes as 'LONG' not ‘LON’\n", " latlon_format='dd'# write as decimal degrees (any other input\n", " # will write as degrees:minutes:seconds\n", " )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
alexandrejaguar/strata-sv-2015-tutorial	resources/Exploring Graphs.ipynb	5	59378	{ "metadata": { "name": "", "signature": "sha256:61f24f38b76cb12d46c178eadc5d85d61bd0fc27f959236ea756ef8e1adc2693" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Explore Random Graphs Using NetworkX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interact" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "import networkx as nx" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# wrap a few graph generation functions so they have the same signature\n", "\n", "def random_lobster(n, m, k, p):\n", " return nx.random_lobster(n, p, p / m)\n", "\n", "def powerlaw_cluster(n, m, k, p):\n", " return nx.powerlaw_cluster_graph(n, m, p)\n", "\n", "def erdos_renyi(n, m, k, p):\n", " return nx.erdos_renyi_graph(n, p)\n", "\n", "def newman_watts_strogatz(n, m, k, p):\n", " return nx.newman_watts_strogatz_graph(n, k, p)\n", "\n", "def plot_random_graph(n, m, k, p, generator):\n", " g = generator(n, m, k, p)\n", " nx.draw(g)\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n", " generator={'lobster': random_lobster,\n", " 'power law': powerlaw_cluster,\n", " 'Newman-Watts-Strogatz': newman_watts_strogatz,\n", " u'Erd\u0151s-R\u00e9nyi': erdos_renyi,\n", " });" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cnK6urvznn3+KdE8//fQTPTw8Cn1d1mktnp6e7NKlC2vWrElVVVVqamoWaVrL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UcfkZqayo033uhQ/dIBWFnY2RuKZT/n5eXh6uqK1WrFZrNhs9mq/Ny0aVN8fX3Jycnh7MGDUFRU\n7Z/PWaCJzVbt+uJ8GsFKvZWZmcmgQYMICgpiwYIF1Vrn1BjD/v37WbFiBe+88w5+fn7k5+fj9tVX\nfFam7FourEO6Bcjn8oAFKALaeHjwwSefXBZwxhjS09O56667iIiI4IsvviAtLQ2r1XrJqNTf39/h\nMCotODiYfv36ERERUe027PFbAJYXXqdPn2bhwoX89NNPTJkyBWNMtQLSxcXF7vBz9LPVaqVBgwbV\nOvctW7YQFRzMx+VMlXkG2Av05sIIKAGYChwA2pUq96rFwmcjRxL3739Xqw/ifApYqddOnDjBkCFD\n6NSpE0uXLsXNrfoXdYqKiti0aROPhoez4PRpKpqVeA7wA+UHLMBzrq6cCAtj+tNPX7y8u2/fPvbv\n34+npydnzpxh7NixDB06lK5du9KyZctq97mskydPcuutt5KZmYmXl1eVI0BHR31lP/8WgKUDzN3d\nnfT0dKxWKz169MDT07PaQVjdAHS24uJibm3VivdOnKBrmX0ngMHAN4ArcAcwH+hXqkwJ0MFmI3bL\nFgICAmqlz+I4BazUe+fOnePBBx/Ezc2NhIQErFZrtds6duwYHdq04WhhIRVNx/4scISKA/YHLoxU\nWrVpwx//+MeLo1J/f39atGhBUFAQoaGhjBo1qsIRYHUvi2ZnZ5Obm4u7uzt5eXlYLJYaG/nZE4BH\njhwhMDCQPn368Oqrr+Li4lLt38W17sXoaNJfeIF/FRQ4XPcDYObNN3MgI6NaV16kdihgRbgw+pw4\ncSKHDh1i/fr1NG/evFrtHDhwgLDevfns558rLFPVCBbA3WJhRHAwhYWFl4Xg0aNHKSoqori4GKBG\nA2/s2LHMmTOH++67D5vNVqsjwK+//prAwECmTZvGU089dd0HR05ODp1uuYWE3Fz6OFDvDNDDamXm\nG28QFh7upN5JTdBDTiJAgwYNiIuLIzIykp49e7JlyxZuuukmh9spKCigYRXBYM832kaurvTr148b\nbrjhsiB8//33OXDgAHFxcTUagAcOHCA3N5fg4GBcXV1rrF17pKamEhQUxKJFixg3blytHvtqadGi\nBQnr1hEyZAiJ+fn0sKPOaeB+q5XeY8YwPizM2V2UK6SAFfmVi4sLL7/8Mq1atSIgIIBNmzY5/CSt\nt7c3p38dWVakqnFZMXCuuJiJEyeWG3T33HMPSUlJNT66jI2NJSwsrNbDNSkpicmTJ7Ny5UoGDRpU\nq8e+2vr06UP8++8TFBTE1MJCpp4/T+tyyhUA/wZesNkYEhbGotdfv+5H+NeF2n3tVqRuiI+PNzfc\ncINJSUlxqF5BQYG5oUkT8205kwecB5MPJhLMODAFv24rW24jmLs7dKjwGD/88EONr6pTWFhofHx8\nan1Kwbfeesv4+vrW+HqzdU16erp5ZNIk4+3hYUbYbOYNMPFg/hfMkw0amBaNGplB995b4QQkcm3S\nPViRCmzdupWxY8fyr3/9i+HDh9tdb9bMmRTGxPCPX365ZPtc4PkyZecCUWW2DW3cmAdjYgiv4P6a\nMYbGjRvz448/4unpaXe/KvPee+8RExPDjh07aqS9qhhjmDdvHvHx8WzevJl27dpVXakeyM3NZfWq\nVaSlpnLm5Ek8bDZuateOcRMm6GdUBylgRSrxySefMHz4cObPn8+kSZPsqpOZmUnXDh04XFhIYweP\nlwH8qXFjso4fx8PDo8JynTt3JjY2Fn9/fwePUL6hQ4cyatQoxo8fXyPtVeb8+fNMmzaNtLQ0Nm7c\nWKOvGYlcS67fZ+BFasDdd9/Nzp07WbBgAS+88AL2fB9t27YtQQ8+yFgPDyq/G3upXCDYauVvzzxT\nabgCtGvX7pIZna5EdnY2qampjBw5skbaq0xeXh4jR47k8OHDJCcnK1zluqaAFanCbbfdxu7du/nP\nf/7Do48+evH1mMq8sXw5+V26EOLhQb4dx8gBBtps3D1yJE9GRlZZvuyUiVdi5cqVBAUFYXPytHsn\nT56kf//+eHp6sn79epo0aeLU44lcbQpYETv4+vqyc+dOvvzyS0JDQymoYnIAd3d31m/fjvugQfze\nZuMVFxdOllPueyAS6OTuzp8jIngzLs6up0Pbt2/PoUOHqnUupRljiI2NZcKECVfcVmUOHz5Mjx49\n6NmzJytWrMDd3d2pxxO5FihgRezUtGlTNm3aBEBgYCBnzpyptHzDhg1ZlZjI6m3bODhiBLc2bMhQ\nT08etloJs9no7+lJZw8PPu/Xj5a33Ub0okV2z1xUUyPYvXv3Yozh3nvtXfPHcQcPHiQgIICIiAhe\neuml63p2JpHS9JCTiIOKi4t57LHH2LVrF5s2bcLX19euejk5OaSkpHD69GlcXFzw8fGhT58+Fyfr\nnzt3boWr6JSVnZ3NH/7wB44fP34lp8KUKVO45ZZbiLTjsnR17Nixg5CQEGJiYhg1apRTjiFyrVLA\nilSDMYbo6GiWL1/Oli1baN++/RW1t2HDBmbNmsWBAwfsmujBGEOTJk04cuQITZs2rdYxz507R+vW\nrfnyyy+vaPWdiqxZs4ZHHnmEhIQE+vRxZDJAkeuDrtWIVIPFYuHZZ5/lmWeeoVevXnz66adX1N6Q\nIUNo3LgxCQkJdh+/Xbt2V3QfNjExke7duzslXGNiYnjiiSf44IMPFK5SbylgRa7ApEmTWLJkCYGB\ngWzdurXa7VgsFqKjo4mKiqLIzkW4r/RVHWc83GSMITIykjfeeINdu3bRuXPnGm1fpC5RwIpcofvv\nv5+1a9cybtw4Vq1aVe12+vbtS9u2bYmLi7Or/JU86PTf//6XgwcPOjRDVVWKiooIDw9n586d7N69\nm7Zt29ZY2yJ1kQJWpAb06NGD7du3M2vWLP7xj39Uu53o6Gief/75Kl8DgisL2BUrVjBmzBgaNqxo\n1VrH5ObmMmzYME6dOsW2bduqvdyfyPVEAStSQzp16sSuXbtYtmwZTz/9NCUlJQ630a1bN/z9/Vmy\nZEmVZav7LmxJSQlxcXE1dnn4+PHj9OnTh5tuuom1a9de0YL1ItcTBaxIDfLz8yMlJYWUlBTCw8Pt\nvp9a2vz583nxxRfJzc2ttFx178EmJyfj5eVFly5dHK5bVkZGBgEBAQwePJilS5fi5qYVMEV+o4AV\nqWHNmzdn27ZtnDp1ivvvv59z5845VP+uu+6ib9++LF68uNJyrVq1oqCggJ9++smh9mvq4aZ9+/bR\ns2dPZs6cybx587Q+qUgZeg9WxEmKioqYMmUKX331FRs3bsTHx8fuuunp6XTv3p309HS8vb0rLNel\nSxeWLl3K3XffbVe7Z86coU2bNhw6dMih/pS1detWHnroIZYuXWr35Bgi9Y1GsCJO0qBBA5YvX06/\nfv0ICAggMzPT7rrt27dnxIgRLFq0qMpyjtyHTUhIoH///lcUrvHx8YwfP561a9cqXEUqoYAVcSKL\nxcKCBQv461//So8ePTh48KDddefMmcOSJUs4evRohWUcvQ+7fPnyal8eNsawaNEiZs+ezfbt2wkI\nCKhWOyL1hQJWpBZMnz6dv//97/Tv35+dO3faVcfPz4/x48ezcOHCCss48qrO119/TVZWFoMGDbKr\nfGklJSXMmDGDt99+m927d9OxY0eH2xCpbxSwIrUkJCSE1atXExwcTGJiol11Zs2aRXx8PFlZWeXu\nd+QScWxsLOPGjXP4Sd/CwkJGjx7N/v37SUlJoXXr1g7VF6mv9JCTSC1LS0tj6NChREVFERERUWX5\n2bNnc+zYMZYtW3bZvqNHj/L73/+eEydOVNpGUVERfn5+JCcn06FDB7v7eubMGUaMGEGzZs2Ij4+n\nUaNGdtcVqe80ghWpZf7+/qSkpPDKK68wd+5cqvqOO3PmTJKSkvjuu+8u29eyZUsKCws5ffp0pW1s\n3ryZm2++2aFwzc7OplevXnTs2JGEhASFq4iDFLAiV8Gtt97K7t27Wb9+PRERERQXF1dY1tvbmxkz\nZvDcc89dts9isdh1H9bRd1+//fZbAgICCA0NJSYmxq4l9ETkUgpYkaukZcuW7Nixg4yMDIKDgyud\nf3j69OkkJyfz2WefXbavqvuwOTk5bN++nZCQELv6tWfPHnr37s1zzz3HrFmzNIGESDUpYEWuoiZN\nmrBx40bc3d0ZOHBghbMyNW7cmFmzZjFnzpzL9lX1qs6qVasYNmwYnp6eVfZnw4YNDB8+nNjYWMLD\nw+0+DxG5nAJW5Cpr2LAhq1evpkuXLvTq1Yvs7Oxyy02dOpUDBw6wd+/eS7ZXdonYGMPy5ct5+OGH\nq+zHsmXLmDx5Mhs3biQwMNDxExGRSyhgRa4BLi4uvPbaa4wZM4aAgAC+/fbby8o0atSIqKgoZs+e\nfcn2ygI2LS2Ns2fP0rt37wqPbYxh/vz5LFy4kI8++oh77rnnyk5GRAAFrMg1w2KxEBkZSVRUFL17\n9+bjjz++rExYWBhZWVls27bt4rbK7sH+dqnXxaX8P/Xi4mKmTZvG2rVr2b17N+3bt6+ZkxERvQcr\nci3asGEDEyZM4O23377scu0777zD4sWL2bNnDxaLBWMMTZs2JTMzk2bNml0sV1BQQOvWrfn0009p\n27btZcfIz89nzJgx5ObmkpiYSJMmTZx9WiL1ikawItegoUOHsm7duoshW1pISAh5eXls2LABqPhV\nnXXr1tG5c+dyw/XUqVMMGDAAq9XKxo0bFa4iTqCAFblGde/eneTkZObMmcOiRYsuTkjh4uLC/Pnz\nefbZZykpKQHg5ptvZs+ePaSnp5OTk4MxhtjY2HIfbsrKyqJHjx786U9/YuXKlbi7u9fqeYnUF7pE\nLHKN++GHH7jvvvsYOHAgr7zyCi4uLhhj6NatGwMHDuSbTz9l4wcf0NTVFVvDhpwqKqK5lxc/njlD\nekYGN95448W2vvjiCwIDA5kxYwZPPPHEVTwrkeufAlakDjh9+jTDhg2jTZs2xMbG8vnnnxM8eDCW\nnBxmGsNDwG8XeQ2QCrzq5sY2NzcemzGD56KjSUlJITg4mNdee43Ro0dfvZMRqScUsCJ1RH5+PqGh\noRw5coTDX3/NG3l5BAOVzbP0IzDSZqPRnXfy+aFDvPvuu/Tr16+WeixSvylgReqQ/fv38+d77mHd\n+fNU/GbrpfKBXkDHkBBWvPuuE3snIqXpISeROmTWI4/wYplw/QWYCLQFPIEuwOZS+z2ALcDmdevK\nncBCRJxDAStSRxw6dIi0tDTKrolzHvADPgJ+Bl4ARgGHS5VpBjxcVMSSxYtrpa8iokvEInXGzEcf\nxfWtt3ipqKjKsp2BucCIUtsyga5WK1nHj2Oz2ZzSRxH5fxrBitQRGxITGWNHuB4DvgM6ldneFujk\n5kZqamrNd05ELqOAFakjTv78M75VlCkCxgLhwG3l7Pc1hpMnT9Z010SkHApYketECTAOaAT88yr3\nRUQUsCJ1RnNPT36sYJ/hwpPEOcB7gGsF5X60WGjevLkzuiciZShgReqIoUFBrG7QoNx9fwG+AdYB\nDSuonwl8ef48AQEBTumfiFxKAStSR0Q89hixrq4UlNl+GFgKfAa04sKUiU2Ad8qUe8vNjfFhYVit\nVud3VkT0mo5IXXJfQAAPpKYS4WC9U8AdHh58tH8/t99+uzO6JiJlaAQrUocsWrKEKJuNnQ7UyQdG\n2GyMmzhR4SpSixSwInXInXfeybvr1vGg1coaLjzcVJkfgb5WK20CA3lZsziJ1CoFrEgd07dvX7ak\npDDXz4+7GjfmTeBsqf0G2AWMsVrp2KgR9z3+OCvWrMHFRX/uIrVJ92BF6ihjDDt27OB/Xn6ZTdu2\n4dWgAQ1dXDhVVMQNzZszbeZMwiZMwMvL62p3VaReUsCKXAfy8vI4efIkhYWFeHl50bx5cyyWylaK\nFRFnU8CKiIg4gW7KiIiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBApY\nERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIsRum1wAAACiSURB\nVCLiBApYERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBApY\nERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBApYERERJ1DA\nioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBP8HyD8bEel+oXYAAAAA\nSUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x1076c6e10>" ] } ], "prompt_number": 16 } ], "metadata": {} } ] }	bsd-3-clause
ramch101/juliasets	juliasets3.ipynb	1	3627672	null	mit
ledeprogram/algorithms	class1/homework/najmabadi_shannon_1_4.ipynb	1	4803	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The writer of this code wants to count the mean and median article length for recent articles on gay marriage in the New York Times. This code has several issues, including errors. When they checked their custom functions against the numpy functions, they noticed some discrepancies. Fix the code so it executes properly, retrieves the articles, and outputs the correct result from the custom functions, compared to the numpy functions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests # a better package than urllib2" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_mean(input_list):\n", " list_sum = 0\n", " list_count = 0\n", " for el in input_list:\n", " intel = int(el)\n", " list_sum += intel\n", " list_count += 1\n", " return list_sum / list_count" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_median(input_list):\n", " sorted_list = sorted(input_list)\n", " list_length = len(sorted_list)\n", " half_length = int(list_length/2)\n", " \n", " return float(sorted_list[half_length])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "api_key = \"ffaf60d7d82258e112dd4fb2b5e4e2d6:3:72421680\"" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = \"http://api.nytimes.com/svc/search/v2/articlesearch.json?q=gay+marriage&api-key=%s\" % api_key" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = requests.get(url)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wc_list = []\n", "for article in r.json()['response']['docs']:\n", " wc_list.append(article['word_count'])" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[25, 576, 920, 868, 684, 1101, 367, 588, 96]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wc_list = [int(i) for i in wc_list if i != None]\n", "wc_list" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "580.5555555555555" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "580.55555555555554" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "588.0" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_median(wc_list)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "588.0" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(wc_list)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
qubvel/sent_analysis	MVideo/data preprocessing - additional data.ipynb	1	22797	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Анализ дополнительных данных\n", "Отзывы об интернет провайдерах, спарсенные с сайта: http://www.moskvaonline.ru/rating" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews = pd.read_csv('data/internet_reviews (1).csv')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>comment</th>\n", " <th>rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>Пользуюсь уже более 2лет все в...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>Подключил только интернет за 5...</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>Подключил себе скорость 100 мб...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>Подключились недавно, скорость...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>Сегодня пришел домой и обнаруж...</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 comment rating\n", "0 0 Пользуюсь уже более 2лет все в... 1\n", "1 1 Подключил только интернет за 5... 2\n", "2 2 Подключил себе скорость 100 мб... 5\n", "3 3 Подключились недавно, скорость... 3\n", "4 4 Сегодня пришел домой и обнаруж... 1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(21713, 3)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(21680, 3)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = reviews[~reviews.comment.duplicated()]\n", "reviews.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 12876\n", "5 6536\n", "3 2200\n", "2 46\n", "0 14\n", "4 8\n", "Name: rating, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.rating.value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1d6010bf7f0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZcXgEYGaWKRcAM7NMuQCYmWXKBcDMLFMuAGZmmXIBMDPLlC8DtY6ovTx0+5qpHz6K25eH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\nZpn6ZxAxPBTm+ICsAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1d67e556be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reviews[reviews.comment.apply(len) < 3000].comment.apply(len).hist(bins=50)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# reviews = reviews[reviews.comment.apply(len) < 500]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1d600482d68>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1d60057b748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reviews[reviews.comment.apply(lambda s: len(s.split()) < 200)].comment.apply(lambda s: len(s.split())).hist(bins=30)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
davidgutierrez/HeartRatePatterns	Jupyter/MimicII/0g FullWords.ipynb	1	68311	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import psycopg2\n", "import gc\n", "from psycopg2.extensions import register_adapter, AsIs\n", "from time import time\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def obtainMaxRecords(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = '''SELECT replace(split_part(record, '/',3),'s',''),max(record)\n", " FROM rstq\n", " WHERE cast(replace(split_part(record, '/',3),'s','') as integer)\n", " NOT IN (select subject_id from subjectrecord)\n", " AND centroid IS NOT NULL\n", " GROUP BY split_part(record, '/',3)'''\n", " cur.execute(select_stament)\n", " subject = []\n", " for row in cur :\n", " subject.append({\"subject_id\":int(row[0]),\"record\":row[1]})\n", " conn.close()\n", " return subject" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def insert(words,table,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " insert_statement = 'INSERT into '+table+' (%s) values %s'\n", " columns = words.keys()\n", " values = [words[column] for column in columns]\n", "# print(cur.mogrify(insert_statement, (AsIs(','.join(columns)), tuple(values))))\n", " cur.execute(insert_statement, (AsIs(','.join(columns)), tuple(values)))\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def fillsubjectRecord() :\n", " for subject in obtainMaxRecords() :\n", " insert(subject,\"subjectrecord\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def obtainSubjects(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = '''SELECT subject_id,record FROM subjectrecord'''\n", " cur.execute(select_stament)\n", " subject = []\n", " for row in cur :\n", " subject.append({\"subject_id\":int(row[0]),\"record\":row[1]})\n", " cur.close()\n", " conn.close()\n", " return subject" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def patientIsAlive(patient,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT dod \"\n", " \" FROM patients WHERE subject_id = \"+str(patient)+\" LIMIT 1\"\n", " )\n", " cur.execute(select_stament)\n", " select = []\n", " for row in cur :\n", " select.append(1 if row[0] is not None else 0 )\n", " cur.close()\n", " conn.close()\n", " return select" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def obtainWord(subject,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT centroid \"\n", " \" FROM rstq WHERE record='\"+str(subject)+\"' ORDER BY r_s\"\n", " )\n", " cur.execute(select_stament)\n", " centroids = \"\"\n", " for row in cur :\n", " centroid = row[0]\n", " if centroid is not None :\n", " centroids= centroids+centroid\n", " if(len(centroids)<3600): centroids = None\n", " conn.close()\n", " return centroids" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def deleteWord(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = \"DELETE FROM subjectwords\"\n", " cur.execute(select_stament)\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def insertSubjectWords(words,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " insert_statement=('INSERT INTO subjectwords(word,subject_id,isalive)'\n", " ' SELECT unnest( %(word)s ) ,'\n", " ' unnest( %(subject_id)s) ,'\n", " ' unnest( %(isalive)s)')\n", " word=[r['word'] for r in words]\n", " subject_id=[r['subject_id'] for r in words]\n", " isalive=[r['isalive'] for r in words]\n", "# print(cur.mogrify(insert_statement,locals()))\n", " cur.execute(insert_statement,locals())\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def createListOfWords() :\n", " subjects = obtainSubjects()\n", " lenSubjects = len(subjects)\n", " deleteWord()\n", " i,j=0,0\n", " words = []\n", " for subject in subjects :\n", " subject_id = subject['subject_id']\n", " print(subject_id)\n", " isAlive = patientIsAlive(subject_id)\n", " if isAlive != [] :\n", " j=j+1\n", " word = obtainWord(subject['record'])\n", " if word is not None:\n", " words.append({'subject_id':subject_id,'word':word,'isalive':isAlive[0]})\n", " insertSubjectWords(words)\n", " print()\n", " print(\"In a list of \"+str(lenSubjects)+\" we know the status of \"+str(j)+\" patients\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def existMatrix(word,subject,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT 1 \"\n", " \" FROM matrix WHERE subject_id='\"+str(subject)+\"' AND word='\"+str(word)+\"'\"\n", " )\n", " cur.execute(select_stament)\n", " exist = False\n", " for row in cur :\n", " exist = True\n", " cur.close()\n", " conn.close()\n", " return exist" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "def printGroups(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT count(1),LENGTH(word) FROM subjectwords GROUP BY LENGTH(word) ORDER BY LENGTH(word)\"\n", " )\n", " cur.execute(select_stament)\n", " words = []\n", " maximun,minimun = 0,1000000000\n", " for row in cur :\n", " words.append({\"subjects\":row[0],\"wordSize\":row[1]})\n", " maximun = maximun if maximun>row[1] else row[1]\n", " minimun = minimun if minimun<row[1] else row[1]\n", " cur.close()\n", " conn.close()\n", " print(\"maximun\",maximun,\"minimun\",minimun)\n", " minimun-=1\n", " division = (maximun-minimun)/8\n", " means_men = defaultdict(int) #'0':0,'1':0,'2':0,'3':0,'4':0,'5':0,'6':0,'7':0,'8':0,\n", " for r in words :\n", " for x in range(0, 11):\n", " floor = (divisionx)+minimun+1\n", " top = division(x+1)+minimun\n", " if(r['wordSize']>=floor and r['wordSize']<top):\n", " floor = str(int(floor)).zfill(5)\n", " top = str(int(top)).zfill(5)\n", " means_men[floor+\"-\"+top] += r['subjects']\n", " columns = sorted(means_men.keys())\n", " means_men = [means_men[column] for column in sorted(means_men)]\n", " index = np.arange(len(means_men))\n", " bar_width = 0.35\n", " fig, ax = plt.subplots() \n", " for i, v in enumerate(means_men):\n", " ax.text(v-10,i-0.1, str(v), color='white', fontweight='bold')\n", " plt.barh(index,means_men,label='Pacientes')\n", " plt.ylabel('Número de latidos')\n", " plt.xlabel('Pacientes')\n", " plt.title('Pacientes por Número de latidos')\n", " plt.yticks(index + bar_width / 2, (columns))\n", " plt.legend()\n", "# plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def printWords(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT count(1),LENGTH(word) FROM subjectwords GROUP BY LENGTH(word) ORDER BY LENGTH(word)\"\n", " )\n", " cur.execute(select_stament)\n", " x,y = [],[]\n", " for row in cur :\n", " x.append(row[0])\n", " y.append(row[1])\n", " cur.close()\n", " conn.close()\n", " bar_width = 0.35\n", " plt.plot(x, y, 'ro')\n", " plt.ylabel('Número de latidos')\n", " plt.xlabel('Pacientes')\n", " plt.title('Pacientes por Número de latidos')\n", " plt.legend()\n", "# plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fillsubjectRecord done in 96.242s.\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t0 = time()\n", "fillsubjectRecord()\n", "print(\"fillsubjectRecord done in %0.3fs.\" % (time() - t0))\n", "gc.collect()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5223\n", "15079\n", "20968\n", "20269\n", "11679\n", "23922\n", "20766\n", "19372\n", "3498\n", "14897\n", "4018\n", "5114\n", "6692\n", "10534\n", 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"9951\n", "7234\n", "41902\n", "6673\n", "16961\n", "17798\n", "7683\n", "8870\n", "1029\n", "6075\n", "7567\n", "17412\n", "9667\n", "12319\n", "7470\n", "462\n", "23034\n", "8466\n", "16511\n", "18403\n", "9783\n", "7328\n", "42574\n", "2467\n", "7153\n", "8207\n", "8670\n", "14909\n", "8674\n", "7522\n", "7289\n", "7265\n", "682\n", "408\n", "9005\n", "377\n", "6917\n", "6981\n", "10247\n", "7224\n", "7138\n", "8996\n", "12712\n", "17522\n", "18740\n", "7125\n", "11757\n", "571\n", "7472\n", "9043\n", "42694\n", "20\n", "8432\n", "9685\n", "7977\n", "8871\n", "8009\n", "317\n", "11877\n", "16161\n", "12679\n", "9036\n", "2228\n", "8461\n", "10030\n", "11988\n", "6636\n", "618\n", "17262\n", "1459\n", "9176\n", "16504\n", "7217\n", "17018\n", "12663\n", "5933\n", "11951\n", "279\n", "17457\n", "14651\n", "16447\n", "7009\n", "8406\n", "9871\n", "18837\n", "8936\n", "8121\n", "7782\n", "12704\n", "1569\n", "14448\n", "8984\n", "8122\n", "9949\n", "12215\n", "17366\n", "7303\n", "14777\n", "16881\n", "4859\n", "17717\n", "8964\n", "8198\n", "7360\n", "16909\n", "2514\n", "952\n", "12708\n", "6708\n", "7528\n", "9708\n", "6983\n", "9494\n", "11372\n", "7048\n", "8989\n", "16336\n", "91428\n", "11852\n", "21575\n", "8231\n", "8573\n", "9847\n", "42310\n", "7452\n", "9253\n", "8451\n", "15333\n", "43459\n", "42404\n", "1414\n", "1650\n", "12251\n", "8318\n", "21517\n", "8568\n", "9705\n", "6945\n", "16280\n", "7798\n", "7371\n", "16607\n", "9021\n", "368\n", "\n", "In a list of 1098 we know the status of 1074 patients\n", "createListOfWords done in 2461.363s.\n" ] } ], "source": [ "t0 = time()\n", "createListOfWords()\n", "print(\"createListOfWords done in %0.3fs.\" % (time() - t0))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maximun 29296 minimun 3636\n" ] }, { "data": { "image/png": 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EdABeldQ4j/rViBJKI2A58Jqkc8zseUlnAA9KSgOGE527y8tjwAgz+yK8HwA8LGks0bm9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\ntwNISjezoulnOuecczsolTu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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe0ed32f438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "printGroups()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f889530b128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "printWords()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
itdxer/neupy	notebooks/growing-neural-gas/Growing Neural Gas animated.ipynb	1	437992	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-03-30T19:42:39.360507Z", "start_time": "2018-03-30T21:42:39.055830+02:00" }, "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-03-30T19:42:39.981925Z", "start_time": "2018-03-30T21:42:39.362204+02:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10e2eca58>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_moons\n", "data, _ = make_moons(10000, noise=0.06, random_state=0)\n", "plt.scatter(data.T)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-03-31T18:30:33.870792Z", "start_time": "2018-03-31T20:21:06.204721+02:00" }, "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<video width=\"432\" height=\"288\" controls autoplay loop>\n", " <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAM9TG1kYXQAAAKuBgX//6rcRem9\n", "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTQ4IHIyNjY4IGZkMmMzMjQgLSBILjI2NC9NUEVHLTQg\n", "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE2IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n", "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n", 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DxuJEccvbTgzWO5Iko9tNGW8lJCbHWkFfPWKs0WHdulX8zwWfZddXnEwjq6Ec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O6BWIWSQFFFWrT1EbqaW4+tPMJ5AoIt6+5FCEFz3pIoTVpEhMaqa3/Of3z8FaStkRnvs2DPfbEl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\nGO9pNnM6rkAKRZJkhFox2kmIhaRjYdNIm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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tqdm import tqdm_notebook as tqdm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.animation as animation\n", "from IPython.display import HTML\n", "from neupy import algorithms, utils\n", " \n", "utils.reproducible()\n", "\n", "gng = algorithms.GrowingNeuralGas(\n", " n_inputs=2,\n", " n_start_nodes=2,\n", "\n", " shuffle_data=True,\n", " verbose=False,\n", " \n", " step=0.1,\n", " neighbour_step=0.001,\n", " \n", " max_edge_age=50,\n", " max_nodes=100,\n", " \n", " n_iter_before_neuron_added=100,\n", " after_split_error_decay_rate=0.5,\n", " error_decay_rate=0.995,\n", " min_distance_for_update=0.2,\n", ")\n", "\n", "fig = plt.figure()\n", "plt.scatter(data.T, alpha=0.02)\n", "plt.xticks([], [])\n", "plt.yticks([], [])\n", "\n", "def animate(i):\n", " for line in animate.prev_lines:\n", " line.remove()\n", " \n", " # Training will slow down overtime and we increase number\n", " # of data samples for training\n", " n = int(0.5 * gng.n_iter_before_neuron_added * (1 + i // 100))\n", " \n", " sampled_data_ids = np.random.choice(len(data), n)\n", " sampled_data = data[sampled_data_ids, :]\n", " gng.train(sampled_data, epochs=1)\n", " \n", " lines = []\n", " for node_1, node_2 in gng.graph.edges:\n", " weights = np.concatenate([node_1.weight, node_2.weight])\n", " line, = plt.plot(weights.T, color='black')\n", "\n", " plt.setp(line, linewidth=1, color='black')\n", " \n", " lines.append(line)\n", " lines.append(plt.scatter(weights.T, color='black', s=10))\n", " \n", " animate.prev_lines = lines\n", " return lines\n", "\n", "animate.prev_lines = []\n", "anim = animation.FuncAnimation(fig, animate, tqdm(np.arange(220)), interval=30, blit=True)\n", "HTML(anim.to_html5_video())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "widgets": { "state": { "8a9fe811f3c4492c84849e2e1f5d4c3b": { "views": [ { "cell_index": 2 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
bhermanmit/openmc	docs/source/examples/mg-mode-part-i.ipynb	1	53410	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark model using the `openmc.MGXSLibrary` class to create the supporting data library on the fly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate MGXS Library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "import numpy as np\n", "\n", "import openmc\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now create the multi-group library using data directly from Appendix A of the [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark documentation. All of the data below will be created at 294K, consistent with the benchmark.\n", "\n", "This notebook will first begin by setting the group structure and building the groupwise data for UO2. As you can see, the cross sections are input in the order of increasing groups (or decreasing energy).\n", "\n", "Note: The C5G7 benchmark uses transport-corrected cross sections. So the total cross section we input here will technically be the transport cross section." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a 7-group structure with arbitrary boundaries (the specific boundaries are unimportant)\n", "groups = openmc.mgxs.EnergyGroups(np.logspace(-5, 7, 8))\n", "\n", "uo2_xsdata = openmc.XSdata('uo2', groups)\n", "uo2_xsdata.order = 0\n", "\n", "# When setting the data let the object know you are setting the data for a temperature of 294K.\n", "uo2_xsdata.set_total([1.77949E-1, 3.29805E-1, 4.80388E-1, 5.54367E-1,\n", " 3.11801E-1, 3.95168E-1, 5.64406E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_absorption([8.0248E-03, 3.7174E-3, 2.6769E-2, 9.6236E-2,\n", " 3.0020E-02, 1.1126E-1, 2.8278E-1], temperature=294.)\n", "uo2_xsdata.set_fission([7.21206E-3, 8.19301E-4, 6.45320E-3, 1.85648E-2,\n", " 1.78084E-2, 8.30348E-2, 2.16004E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_nu_fission([2.005998E-2, 2.027303E-3, 1.570599E-2, 4.518301E-2,\n", " 4.334208E-2, 2.020901E-1, 5.257105E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_chi([5.87910E-1, 4.11760E-1, 3.39060E-4, 1.17610E-7,\n", " 0.00000E-0, 0.00000E-0, 0.00000E-0], temperature=294.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now add the scattering matrix data. \n", "\n", "Note: Most users familiar with deterministic transport libraries are already familiar with the idea of entering one scattering matrix for every order (i.e. scattering order as the outer dimension). However, the shape of OpenMC's scattering matrix entry is instead [Incoming groups, Outgoing Groups, Scattering Order] to best enable other scattering representations. We will follow the more familiar approach in this notebook, and then use numpy's `numpy.rollaxis` function to change the ordering to what we need (scattering order on the inner dimension)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The scattering matrix is ordered with incoming groups as rows and outgoing groups as columns\n", "# (i.e., below the diagonal is up-scattering).\n", "scatter_matrix = \\\n", " [[[1.27537E-1, 4.23780E-2, 9.43740E-6, 5.51630E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 3.24456E-1, 1.63140E-3, 3.14270E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 4.50940E-1, 2.67920E-3, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 4.52565E-1, 5.56640E-3, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 1.25250E-4, 2.71401E-1, 1.02550E-2, 1.00210E-8],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 1.29680E-3, 2.65802E-1, 1.68090E-2],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 8.54580E-3, 2.73080E-1]]]\n", "scatter_matrix = np.array(scatter_matrix)\n", "scatter_matrix = np.rollaxis(scatter_matrix, 0, 3)\n", "uo2_xsdata.set_scatter_matrix(scatter_matrix, temperature=294.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the UO2 data has been created, we can move on to the remaining materials using the same process.\n", "\n", "However, we will actually skip repeating the above for now. Our simulation will instead use the `c5g7.h5` file that has already been created using exactly the same logic as above, but for the remaining materials in the benchmark problem.\n", "\n", "For now we will show how you would use the `uo2_xsdata` information to create an `openmc.MGXSLibrary` object and write to disk." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Initialize the library\n", "mg_cross_sections_file = openmc.MGXSLibrary(groups)\n", "\n", "# Add the UO2 data to it\n", "mg_cross_sections_file.add_xsdata(uo2_xsdata)\n", "\n", "# And write to disk\n", "mg_cross_sections_file.export_to_hdf5('mgxs.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate 2-D C5G7 Problem Input Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the actual 2-D model, we will first begin by creating the `materials.xml` file.\n", "\n", "First we need to define materials that will be used in the problem. In other notebooks, either `openmc.Nuclide`s or `openmc.Element`s were created at the equivalent stage. We can do that in multi-group mode as well. However, multi-group cross-sections are sometimes provided as macroscopic cross-sections; the C5G7 benchmark data are macroscopic. In this case, we can instead use `openmc.Macroscopic` objects to in-place of `openmc.Nuclide` or `openmc.Element` objects.\n", "\n", "`openmc.Macroscopic`, unlike `openmc.Nuclide` and `openmc.Element` objects, do not need to be provided enough information to calculate number densities, as no number densities are needed.\n", "\n", "When assigning `openmc.Macroscopic` objects to `openmc.Material` objects, the density can still be scaled by setting the density to a value that is not 1.0. This would be useful, for example, when slightly perturbing the density of water due to a small change in temperature (while of course ignoring any resultant spectral shift). The density of a macroscopic dataset is set to 1.0 in the `openmc.Material` object by default when an `openmc.Macroscopic` dataset is used; so we will show its use the first time and then afterwards it will not be required.\n", "\n", "Aside from these differences, the following code is very similar to similar code in other OpenMC example Notebooks." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# For every cross section data set in the library, assign an openmc.Macroscopic object to a material\n", "materials = {}\n", "for xs in ['uo2', 'mox43', 'mox7', 'mox87', 'fiss_chamber', 'guide_tube', 'water']:\n", " materials[xs] = openmc.Material(name=xs)\n", " materials[xs].set_density('macro', 1.)\n", " materials[xs].add_macroscopic(xs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can go ahead and produce a `materials.xml` file for use by OpenMC" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate a Materials collection, register all Materials, and export to XML\n", "materials_file = openmc.Materials(materials.values())\n", "\n", "# Set the location of the cross sections file to our pre-written set\n", "materials_file.cross_sections = 'c5g7.h5'\n", "\n", "materials_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our next step will be to create the geometry information needed for our assembly and to write that to the `geometry.xml` file.\n", "\n", "We will begin by defining the surfaces, cells, and universes needed for each of the individual fuel pins, guide tubes, and fission chambers." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create the surface used for each pin\n", "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", "\n", "# Create the cells which will be used to represent each pin type.\n", "cells = {}\n", "universes = {}\n", "for material in materials.values():\n", " # Create the cell for the material inside the cladding\n", " cells[material.name] = openmc.Cell(name=material.name)\n", " # Assign the half-spaces to the cell\n", " cells[material.name].region = -pin_surf\n", " # Register the material with this cell\n", " cells[material.name].fill = material\n", " \n", " # Repeat the above for the material outside the cladding (i.e., the moderator)\n", " cell_name = material.name + '_moderator'\n", " cells[cell_name] = openmc.Cell(name=cell_name)\n", " cells[cell_name].region = +pin_surf\n", " cells[cell_name].fill = materials['water']\n", " \n", " # Finally add the two cells we just made to a Universe object\n", " universes[material.name] = openmc.Universe(name=material.name)\n", " universes[material.name].add_cells([cells[material.name], cells[cell_name]])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The next step is to take our universes (representing the different pin types) and lay them out in a lattice to represent the assembly types" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lattices = {}\n", "\n", "# Instantiate the UO2 Lattice\n", "lattices['UO2 Assembly'] = openmc.RectLattice(name='UO2 Assembly')\n", "lattices['UO2 Assembly'].dimension = [17, 17]\n", "lattices['UO2 Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['UO2 Assembly'].pitch = [1.26, 1.26]\n", "u = universes['uo2']\n", "g = universes['guide_tube']\n", "f = universes['fiss_chamber']\n", "lattices['UO2 Assembly'].universes = \\\n", " [[u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n", " [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, f, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n", " [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u]]\n", " \n", "# Create a containing cell and universe\n", "cells['UO2 Assembly'] = openmc.Cell(name='UO2 Assembly')\n", "cells['UO2 Assembly'].fill = lattices['UO2 Assembly']\n", "universes['UO2 Assembly'] = openmc.Universe(name='UO2 Assembly')\n", "universes['UO2 Assembly'].add_cell(cells['UO2 Assembly'])\n", "\n", "# Instantiate the MOX Lattice\n", "lattices['MOX Assembly'] = openmc.RectLattice(name='MOX Assembly')\n", "lattices['MOX Assembly'].dimension = [17, 17]\n", "lattices['MOX Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['MOX Assembly'].pitch = [1.26, 1.26]\n", "m = universes['mox43']\n", "n = universes['mox7']\n", "o = universes['mox87']\n", "g = universes['guide_tube']\n", "f = universes['fiss_chamber']\n", "lattices['MOX Assembly'].universes = \\\n", " [[m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m],\n", " [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n", " [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n", " [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n", " [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n", " [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, g, o, o, g, o, o, f, o, o, g, o, o, g, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n", " [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n", " [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n", " [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n", " [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n", " [m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m]]\n", " \n", "# Create a containing cell and universe\n", "cells['MOX Assembly'] = openmc.Cell(name='MOX Assembly')\n", "cells['MOX Assembly'].fill = lattices['MOX Assembly']\n", "universes['MOX Assembly'] = openmc.Universe(name='MOX Assembly')\n", "universes['MOX Assembly'].add_cell(cells['MOX Assembly'])\n", " \n", "# Instantiate the reflector Lattice\n", "lattices['Reflector Assembly'] = openmc.RectLattice(name='Reflector Assembly')\n", "lattices['Reflector Assembly'].dimension = [1,1]\n", "lattices['Reflector Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['Reflector Assembly'].pitch = [21.42, 21.42]\n", "lattices['Reflector Assembly'].universes = [[universes['water']]]\n", "\n", "# Create a containing cell and universe\n", "cells['Reflector Assembly'] = openmc.Cell(name='Reflector Assembly')\n", "cells['Reflector Assembly'].fill = lattices['Reflector Assembly']\n", "universes['Reflector Assembly'] = openmc.Universe(name='Reflector Assembly')\n", "universes['Reflector Assembly'].add_cell(cells['Reflector Assembly'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's now create the core layout in a 3x3 lattice where each lattice position is one of the assemblies we just defined.\n", "\n", "After that we can create the final cell to contain the entire core." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lattices['Core'] = openmc.RectLattice(name='3x3 core lattice')\n", "lattices['Core'].dimension= [3, 3]\n", "lattices['Core'].lower_left = [-32.13, -32.13]\n", "lattices['Core'].pitch = [21.42, 21.42]\n", "r = universes['Reflector Assembly']\n", "u = universes['UO2 Assembly']\n", "m = universes['MOX Assembly']\n", "lattices['Core'].universes = [[u, m, r],\n", " [m, u, r],\n", " [r, r, r]]\n", "\n", "# Create boundary planes to surround the geometry\n", "min_x = openmc.XPlane(x0=-32.13, boundary_type='reflective')\n", "max_x = openmc.XPlane(x0=+32.13, boundary_type='vacuum')\n", "min_y = openmc.YPlane(y0=-32.13, boundary_type='vacuum')\n", "max_y = openmc.YPlane(y0=+32.13, boundary_type='reflective')\n", "\n", "# Create root Cell\n", "root_cell = openmc.Cell(name='root cell')\n", "root_cell.fill = lattices['Core']\n", "\n", "# Add boundary planes\n", "root_cell.region = +min_x & -max_x & +min_y & -max_y\n", "\n", "# Create root Universe\n", "root_universe = openmc.Universe(name='root universe', universe_id=0)\n", "root_universe.add_cell(root_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we commit to the geometry, we should view it using the Python API's plotting capability" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fefbdc521d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "root_universe.plot(center=(0., 0., 0.), width=(3 * 21.42, 3 * 21.42), pixels=(500, 500),\n", " color_by='material')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, it looks pretty good, let's go ahead and write the file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", "geometry = openmc.Geometry(root_universe)\n", "\n", "# Export to \"geometry.xml\"\n", "geometry.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now create the tally file information. The tallies will be set up to give us the pin powers in this notebook. We will do this with a mesh filter, with one mesh cell per pin." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tallies_file = openmc.Tallies()\n", "\n", "# Instantiate a tally Mesh\n", "mesh = openmc.Mesh()\n", "mesh.type = 'regular'\n", "mesh.dimension = [17 * 2, 17 * 2]\n", "mesh.lower_left = [-32.13, -10.71]\n", "mesh.upper_right = [+10.71, +32.13]\n", "\n", "# Instantiate tally Filter\n", "mesh_filter = openmc.MeshFilter(mesh)\n", "\n", "# Instantiate the Tally\n", "tally = openmc.Tally(name='mesh tally')\n", "tally.filters = [mesh_filter]\n", "tally.scores = ['fission']\n", "\n", "# Add tally to collection\n", "tallies_file.append(tally)\n", "\n", "# Export all tallies to a \"tallies.xml\" file\n", "tallies_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the geometry and materials finished, we now just need to define simulation parameters for the `settings.xml` file. Note the use of the `energy_mode` attribute of our `settings_file` object. This is used to tell OpenMC that we intend to run in multi-group mode instead of the default continuous-energy mode. If we didn't specify this but our cross sections file was not a continuous-energy data set, then OpenMC would complain.\n", "\n", "This will be a relatively coarse calculation with only 500,000 active histories. A benchmark-fidelity run would of course require many more!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# OpenMC simulation parameters\n", "batches = 150\n", "inactive = 50\n", "particles = 5000\n", "\n", "# Instantiate a Settings object\n", "settings_file = openmc.Settings()\n", "settings_file.batches = batches\n", "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "\n", "# Tell OpenMC this is a multi-group problem\n", "settings_file.energy_mode = 'multi-group'\n", "\n", "# Set the verbosity to 6 so we dont see output for every batch\n", "settings_file.verbosity = 6\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-32.13, -10.71, -1e50, 10.71, 32.13, 1e50]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Tell OpenMC we want to run in eigenvalue mode\n", "settings_file.run_mode = 'eigenvalue'\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's go ahead and execute the simulation! You'll notice that the output for multi-group mode is exactly the same as for continuous-energy. The differences are all under the hood." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%%%%%%\n", " ##################### %%%%%%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%\n", " ################# %%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%\n", " ############ %%%%%%%%%%%%%%%\n", " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", " \| The OpenMC Monte Carlo Code\n", " Copyright \| 2011-2017 Massachusetts Institute of Technology\n", " License \| http://openmc.readthedocs.io/en/latest/license.html\n", " Version \| 0.8.0\n", " Git SHA1 \| 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", " Date/Time \| 2017-03-09 08:18:02\n", " OpenMP Threads \| 8\n", "\n", " Reading settings XML file...\n", " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections HDF5 file...\n", " Reading tallies XML file...\n", " Loading cross section data...\n", " Loading uo2 data...\n", " Loading mox43 data...\n", " Loading mox7 data...\n", " Loading mox87 data...\n", " Loading fiss_chamber data...\n", " Loading guide_tube data...\n", " Loading water data...\n", " Building neighboring cells lists for each surface...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Creating state point statepoint.150.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 1.3630E-01 seconds\n", " Reading cross sections = 3.0827E-02 seconds\n", " Total time in simulation = 8.9648E+00 seconds\n", " Time in transport only = 8.2752E+00 seconds\n", " Time in inactive batches = 2.4798E+00 seconds\n", " Time in active batches = 6.4849E+00 seconds\n", " Time synchronizing fission bank = 1.4553E-02 seconds\n", " Sampling source sites = 1.0318E-02 seconds\n", " SEND/RECV source sites = 4.0840E-03 seconds\n", " Time accumulating tallies = 4.2427E-04 seconds\n", " Total time for finalization = 1.7081E-02 seconds\n", " Total time elapsed = 9.1340E+00 seconds\n", " Calculation Rate (inactive) = 1.00813E+05 neutrons/second\n", " Calculation Rate (active) = 77101.8 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", " k-effective (Collision) = 1.18880 +/- 0.00179\n", " k-effective (Track-length) = 1.18853 +/- 0.00244\n", " k-effective (Absorption) = 1.18601 +/- 0.00111\n", " Combined k-effective = 1.18628 +/- 0.00111\n", " Leakage Fraction = 0.00175 +/- 0.00006\n", "\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run OpenMC\n", "openmc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results Visualization\n", "\n", "Now that we have run the simulation, let's look at the fission rate and flux tallies that we tallied." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fefe4188d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load the last statepoint file and keff value\n", "sp = openmc.StatePoint('statepoint.' + str(batches) + '.h5')\n", "\n", "# Get the OpenMC pin power tally data\n", "mesh_tally = sp.get_tally(name='mesh tally')\n", "fission_rates = mesh_tally.get_values(scores=['fission'])\n", "\n", "# Reshape array to 2D for plotting\n", "fission_rates.shape = mesh.dimension\n", "\n", "# Normalize to the average pin power\n", "fission_rates /= np.mean(fission_rates)\n", "\n", "# Force zeros to be NaNs so their values are not included when matplotlib calculates\n", "# the color scale\n", "fission_rates[fission_rates == 0.] = np.nan\n", "\n", "# Plot the pin powers and the fluxes\n", "plt.figure()\n", "plt.imshow(fission_rates, interpolation='none', cmap='jet', origin='lower')\n", "plt.colorbar()\n", "plt.title('Pin Powers')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There we have it! We have just successfully run the C5G7 benchmark model!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
atlas-outreach-data-tools/notebooks	september_2018_v-2.0/ATLAS_OpenData_06-cpp_simple_cut_and_count_analysis_example.ipynb	1	22978	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><img src=\"../images/opendata-top-transblack.png\" style=\"width:30%\"></CENTER>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><h1>Simple ATLAS OpenData HEP analysis C++ notebook example</h1></CENTER>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "//%jsroot on" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TChain dataset = new TChain(\"mini\");" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "//dataset->Add(\"mc_105986.ZZ.root\");\n", "//dataset->Add(\"mc_147770.Zee.root\");\n", "\n", "//This input is readed directly from the Internet. If you are ofline, you can use the line above\n", "dataset->Add(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\");" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "const int vs = 5;\n", "\n", "Int_t lepton_n = -1,\n", " lepton_charge[vs], \n", " lepton_type[vs];\n", "\n", "Float_t lepton_pt[vs],\n", " lepton_eta[vs],\n", " lepton_phi[vs];" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset->SetBranchAddress(\"lep_n\", &lepton_n);\n", "dataset->SetBranchAddress(\"lep_charge\", &lepton_charge);\n", "dataset->SetBranchAddress(\"lep_type\", &lepton_type);\n", "dataset->SetBranchAddress(\"lep_pt\", &lepton_pt);\n", "dataset->SetBranchAddress(\"lep_eta\", &lepton_eta);\n", "dataset->SetBranchAddress(\"lep_phi\", &lepton_phi);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TH1F h_lep_pt_leptons = new TH1F(\"h_lep_pt_leptons\",\"Lepton pt in GeV\",20,0,200);\n", "h_lep_pt_leptons->SetFillColor(kRed);" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "int nentries, nbytes, i;\n", "nentries = (Int_t)dataset->GetEntries();" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total # events = 7500000. Events to run = 750000 corresponding to 10% of total events!\r\n" ] } ], "source": [ "// IMPORTANT: fraction events we want to run\n", "fraction_events = 0.1;\n", "events_to_run = nentriesfraction_events;\n", "\n", "std::cout << \"Total # events = \" << nentries\n", " << \". Events to run = \" << events_to_run\n", " << \" corresponding to \" << fraction_events100\n", " << \"% of total events!\" << std::endl;" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for (i=0; i<events_to_run; i++)\n", "{\n", " nbytes = dataset->GetEntry(i);\n", " \n", " if(lepton_n>1) // Number of leptons in the events has to be at least 2\n", " {\n", " if(lepton_type[0] == lepton_type[1]) //Leptons of the same family, i.e. 2 electrons or 2 muons (those are the two most energetic leptons)\n", " {\n", " if(lepton_charge[0] != lepton_charge[1]) // The two selected leptons must have opposite charge\n", " {\n", " //PT\n", " float lepton_pt_inGeV = lepton_pt[0]/1000.; // The default value in the root file is in MeV, so, we divide by 1000 to get it in GeV\n", " h_lep_pt_leptons->Fill(lepton_pt_inGeV);\n", " }\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ALEEBAMgSFACALEEBAMgSFACALEEBAMgSFACALEEBAMj64+wC9leW5eL94zge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\nocjPW/z3338dNgkAb/IxQaG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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TCanvas *cz = new TCanvas(\"cz\",\"cz\",10,10,700,700);\n", "h_lep_pt_leptons->Draw();\n", "cz->Draw();" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Your <b>first</b> histogram is done!" ] } ], "metadata": { "kernelspec": { "display_name": "ROOT Prompt", "language": "c++", "name": "root" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
dandtaylor/MetroShare	bikeshare_cleaning.ipynb	1	57775	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Get the bikeshare data ready for analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### import 2015-Q4 through 2016-Q3 bike data" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes5 = pd.read_csv('2015-Q4-Trips-History-Data.csv')\n", "bikes6 = pd.read_csv('2016-Q1-Trips-History-Data.csv')\n", "bikes7 = pd.read_csv('2016-Q2-Trips-History-Data.csv')\n", "bikes8 = pd.read_csv('2016-Q3-Trips-History-Data-1.csv')\n", "bikes9 = pd.read_csv('2016-Q3-Trips-History-Data-2.csv')\n", "bikes7.rename(columns={'Account type':'Member Type'}, inplace=True)\n", "bikes5.rename(columns={'Bike #':'Bike number', 'Member type':'Member Type'}, inplace=True)\n", "\n", "bikes = pd.concat([bikes5, bikes6, bikes7, bikes8,\n", " bikes9], ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Duration (ms)</th>\n", " <th>Start date</th>\n", " <th>End date</th>\n", " <th>Start station number</th>\n", " <th>Start station</th>\n", " <th>End station number</th>\n", " <th>End station</th>\n", " <th>Bike number</th>\n", " <th>Member Type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>166050</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:04</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>31105</td>\n", " <td>14th & Harvard St NW</td>\n", " <td>W21109</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>379172</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31314</td>\n", " <td>34th & Water St NW</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W20603</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>696038</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W01233</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>219423</td>\n", " <td>10/1/2015 0:02</td>\n", " <td>10/1/2015 0:06</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31121</td>\n", " <td>Calvert St & Woodley Pl NW</td>\n", " <td>W00218</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>253230</td>\n", " <td>10/1/2015 0:03</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>W21612</td>\n", " <td>Registered</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Duration (ms) Start date End date Start station number \\\n", "0 166050 10/1/2015 0:01 10/1/2015 0:04 31602 \n", "1 379172 10/1/2015 0:01 10/1/2015 0:07 31314 \n", "2 696038 10/1/2015 0:01 10/1/2015 0:13 31214 \n", "3 219423 10/1/2015 0:02 10/1/2015 0:06 31104 \n", "4 253230 10/1/2015 0:03 10/1/2015 0:07 31102 \n", "\n", " Start station End station number \\\n", "0 Park Rd & Holmead Pl NW 31105 \n", "1 34th & Water St NW 31237 \n", "2 17th & Corcoran St NW 31214 \n", "3 Adams Mill & Columbia Rd NW 31121 \n", "4 11th & Kenyon St NW 31102 \n", "\n", " End station Bike number Member Type \n", "0 14th & Harvard St NW W21109 Registered \n", "1 25th St & Pennsylvania Ave NW W20603 Registered \n", "2 17th & Corcoran St NW W01233 Registered \n", "3 Calvert St & Woodley Pl NW W00218 Registered \n", "4 11th & Kenyon St NW W21612 Registered " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bikes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create DatetimeIndex from `Start date`" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes['Start date'] = pd.to_datetime(bikes['Start date'], format='%m/%d/%Y %H:%M')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes = bikes.set_index('Start date')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.tseries.index.DatetimeIndex" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(bikes.index)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Duration (ms)</th>\n", " <th>End date</th>\n", " <th>Start station number</th>\n", " <th>Start station</th>\n", " <th>End station number</th>\n", " <th>End station</th>\n", " <th>Bike number</th>\n", " <th>Member Type</th>\n", " </tr>\n", " <tr>\n", " <th>Start date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>166050</td>\n", " <td>10/1/2015 0:04</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>31105</td>\n", " <td>14th & Harvard St NW</td>\n", " <td>W21109</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>379172</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31314</td>\n", " <td>34th & Water St NW</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W20603</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>696038</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W01233</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:02:00</th>\n", " <td>219423</td>\n", " <td>10/1/2015 0:06</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31121</td>\n", " <td>Calvert St & Woodley Pl NW</td>\n", " <td>W00218</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>253230</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>W21612</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>655251</td>\n", " <td>10/1/2015 0:14</td>\n", " <td>31242</td>\n", " <td>18th St & Pennsylvania Ave NW</td>\n", " <td>31114</td>\n", " <td>18th St & Wyoming Ave NW</td>\n", " <td>W22093</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:06:00</th>\n", " <td>309212</td>\n", " <td>10/1/2015 0:11</td>\n", " <td>31280</td>\n", " <td>11th & S St NW</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>776195</td>\n", " <td>10/1/2015 0:20</td>\n", " <td>31226</td>\n", " <td>34th St & Wisconsin Ave NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W20820</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>151604</td>\n", " <td>10/1/2015 0:10</td>\n", " <td>31017</td>\n", " <td>Wilson Blvd & N Uhle St</td>\n", " <td>31029</td>\n", " <td>N Veitch & 20th St N</td>\n", " <td>W00244</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>809509</td>\n", " <td>10/1/2015 0:21</td>\n", " <td>31301</td>\n", " <td>Ward Circle / American University</td>\n", " <td>31113</td>\n", " <td>Columbia Rd & Belmont St NW</td>\n", " <td>W20352</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:08:00</th>\n", " <td>842712</td>\n", " <td>10/1/2015 0:22</td>\n", " <td>31276</td>\n", " <td>15th & L St NW</td>\n", " <td>31118</td>\n", " <td>3rd & Elm St NW</td>\n", " <td>W21327</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>171124</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31213</td>\n", " <td>17th & K St NW</td>\n", " <td>31267</td>\n", " <td>17th St & Massachusetts Ave NW</td>\n", " <td>W20487</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>241872</td>\n", " <td>10/1/2015 0:15</td>\n", " <td>31203</td>\n", " <td>14th & Rhode Island Ave NW</td>\n", " <td>31101</td>\n", " <td>14th & V St NW</td>\n", " <td>W22183</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>294746</td>\n", " <td>10/1/2015 0:16</td>\n", " <td>31212</td>\n", " <td>21st & M St NW</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>W20769</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>590515</td>\n", " <td>10/1/2015 0:21</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>31312</td>\n", " <td>Wisconsin Ave & O St NW</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:14:00</th>\n", " <td>1217591</td>\n", " <td>10/1/2015 0:34</td>\n", " <td>31620</td>\n", " <td>5th & F St NW</td>\n", " <td>31615</td>\n", " <td>6th & H St NE</td>\n", " <td>W20668</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:14:00</th>\n", " <td>957676</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>31103</td>\n", " <td>16th & Harvard St NW</td>\n", " <td>W01031</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>1644646</td>\n", " <td>10/1/2015 0:42</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31617</td>\n", " <td>Bladensburg Rd & Benning Rd NE</td>\n", " <td>W21001</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>580130</td>\n", " <td>10/1/2015 0:25</td>\n", " <td>31610</td>\n", " <td>Eastern Market / 7th & North Carolina Ave SE</td>\n", " <td>31256</td>\n", " <td>10th & E St NW</td>\n", " <td>W00379</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>222180</td>\n", " <td>10/1/2015 0:19</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>W20592</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>267251</td>\n", " <td>10/1/2015 0:08</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>W21206</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>867784</td>\n", " <td>10/1/2015 0:29</td>\n", " <td>31600</td>\n", " <td>5th & K St NW</td>\n", " <td>31605</td>\n", " <td>3rd & D St SE</td>\n", " <td>W20970</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:17:00</th>\n", " <td>737845</td>\n", " <td>10/1/2015 0:29</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31509</td>\n", " <td>New Jersey Ave & R St NW</td>\n", " <td>W21281</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:19:00</th>\n", " <td>286638</td>\n", " <td>10/1/2015 0:24</td>\n", " <td>31017</td>\n", " <td>Wilson Blvd & N Uhle St</td>\n", " <td>31015</td>\n", " <td>Rosslyn Metro / Wilson Blvd & Ft Myer Dr</td>\n", " <td>W20617</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:19:00</th>\n", " <td>5628236</td>\n", " <td>10/1/2015 1:53</td>\n", " <td>31063</td>\n", " <td>S George Mason Dr & Four Mile Run</td>\n", " <td>31062</td>\n", " <td>S George Mason Dr & 13th St S</td>\n", " <td>W20439</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:20:00</th>\n", " <td>102044</td>\n", " <td>10/1/2015 0:22</td>\n", " <td>31049</td>\n", " <td>Utah St & 11th St N</td>\n", " <td>31037</td>\n", " <td>Ballston Metro / N Stuart & 9th St N</td>\n", " <td>W00900</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>504552</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31277</td>\n", " <td>17th & G St NW</td>\n", " <td>31239</td>\n", " <td>17th & Rhode Island Ave NW</td>\n", " <td>W21239</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>526336</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31277</td>\n", " <td>17th & G St NW</td>\n", " <td>31239</td>\n", " <td>17th & Rhode Island Ave NW</td>\n", " <td>W21241</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>867396</td>\n", " <td>10/1/2015 0:36</td>\n", " <td>31312</td>\n", " <td>Wisconsin Ave & O St NW</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:23:00</th>\n", " <td>278050</td>\n", " <td>10/1/2015 0:27</td>\n", " <td>31638</td>\n", " <td>1st & H St NW</td>\n", " <td>31627</td>\n", " <td>M St & Delaware Ave NE</td>\n", " <td>W21823</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:21:00</th>\n", " <td>933505</td>\n", " <td>9/1/2016 0:37</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>31509</td>\n", " <td>New Jersey Ave & R St NW</td>\n", " <td>W00906</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:20:00</th>\n", " <td>1574827</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W22628</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>8871227</td>\n", " <td>9/1/2016 2:47</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>W01127</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>1648284</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W00957</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>679655</td>\n", " <td>9/1/2016 0:30</td>\n", " <td>31264</td>\n", " <td>6th St & Indiana Ave NW</td>\n", " <td>31628</td>\n", " <td>1st & K St SE</td>\n", " <td>W00721</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>1633724</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W21699</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>8835027</td>\n", " <td>9/1/2016 2:46</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>W00996</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>191644</td>\n", " <td>9/1/2016 0:23</td>\n", " <td>31285</td>\n", " <td>22nd & P ST NW</td>\n", " <td>31234</td>\n", " <td>20th & O St NW / Dupont South</td>\n", " <td>W22575</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:18:00</th>\n", " <td>173292</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31110</td>\n", " <td>20th St & Florida Ave NW</td>\n", " <td>31234</td>\n", " <td>20th & O St NW / Dupont South</td>\n", " <td>W20893</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:18:00</th>\n", " <td>256249</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31109</td>\n", " <td>7th & T St NW</td>\n", " <td>31223</td>\n", " <td>Convention Center / 7th & M St NW</td>\n", " <td>W20689</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:17:00</th>\n", " <td>453588</td>\n", " <td>9/1/2016 0:25</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>31275</td>\n", " <td>New Hampshire Ave & 24th St NW</td>\n", " <td>W21256</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:15:00</th>\n", " <td>195777</td>\n", " <td>9/1/2016 0:18</td>\n", " <td>31257</td>\n", " <td>22nd & I St NW / Foggy Bottom</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W22398</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:15:00</th>\n", " <td>430642</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31267</td>\n", " <td>17th St & Massachusetts Ave NW</td>\n", " <td>31223</td>\n", " <td>Convention Center / 7th & M St NW</td>\n", " <td>W21480</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>1309689</td>\n", " <td>9/1/2016 0:35</td>\n", " <td>31240</td>\n", " <td>Ohio Dr & West Basin Dr SW / MLK & FDR Memorials</td>\n", " <td>31252</td>\n", " <td>21st St & Pennsylvania Ave NW</td>\n", " <td>W22661</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>513823</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31504</td>\n", " <td>10th & Monroe St NE</td>\n", " <td>31510</td>\n", " <td>18th St & Rhode Island Ave NE</td>\n", " <td>W20265</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>1038905</td>\n", " <td>9/1/2016 0:31</td>\n", " <td>31279</td>\n", " <td>19th & G St NW</td>\n", " <td>31605</td>\n", " <td>3rd & D St SE</td>\n", " <td>W20524</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:12:00</th>\n", " <td>499112</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31111</td>\n", " <td>10th & U St NW</td>\n", " <td>31400</td>\n", " <td>Georgia & New Hampshire Ave NW</td>\n", " <td>W22865</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>1019381</td>\n", " <td>9/1/2016 0:26</td>\n", " <td>31290</td>\n", " <td>17th St & Independence Ave SW</td>\n", " <td>31281</td>\n", " <td>8th & O St NW</td>\n", " <td>W22637</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>680488</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31248</td>\n", " <td>Smithsonian / Jefferson Dr & 12th St SW</td>\n", " <td>W21299</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>674944</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31248</td>\n", " <td>Smithsonian / Jefferson Dr & 12th St SW</td>\n", " <td>W21011</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:08:00</th>\n", " <td>371207</td>\n", " <td>9/1/2016 0:14</td>\n", " <td>31628</td>\n", " <td>1st & K St SE</td>\n", " <td>31613</td>\n", " <td>Eastern Market Metro / Pennsylvania Ave & 7th ...</td>\n", " <td>W20941</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>1091877</td>\n", " <td>9/1/2016 0:23</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>31103</td>\n", " <td>16th & Harvard St NW</td>\n", " <td>W00785</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>259558</td>\n", " <td>9/1/2016 0:09</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>W21529</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>254785</td>\n", " <td>9/1/2016 0:09</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>W22528</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>878992</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31403</td>\n", " <td>5th & Kennedy St NW</td>\n", " <td>W22689</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>283200</td>\n", " <td>9/1/2016 0:10</td>\n", " <td>31600</td>\n", " <td>5th & K St NW</td>\n", " <td>31264</td>\n", " <td>6th St & Indiana Ave NW</td>\n", " <td>W01320</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>867363</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31280</td>\n", " <td>11th & S St NW</td>\n", " <td>31113</td>\n", " <td>Columbia Rd & Belmont St NW</td>\n", " <td>W22933</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:03:00</th>\n", " <td>4798819</td>\n", " <td>9/1/2016 1:23</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31240</td>\n", " <td>Ohio Dr & West Basin Dr SW / MLK & FDR Memorials</td>\n", " <td>W22953</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:02:00</th>\n", " <td>763129</td>\n", " <td>9/1/2016 0:15</td>\n", " <td>31641</td>\n", " <td>2nd St & Massachusetts Ave NE</td>\n", " <td>31276</td>\n", " <td>15th & L St NW</td>\n", " <td>W22911</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:00:00</th>\n", " <td>590832</td>\n", " <td>9/1/2016 0:10</td>\n", " <td>31200</td>\n", " <td>Massachusetts Ave & Dupont Circle NW</td>\n", " <td>31245</td>\n", " <td>7th & R St NW / Shaw Library</td>\n", " <td>W22867</td>\n", " <td>Registered</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3268722 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " Duration (ms) End date Start station number \\\n", "Start date \n", "2015-10-01 00:01:00 166050 10/1/2015 0:04 31602 \n", "2015-10-01 00:01:00 379172 10/1/2015 0:07 31314 \n", "2015-10-01 00:01:00 696038 10/1/2015 0:13 31214 \n", "2015-10-01 00:02:00 219423 10/1/2015 0:06 31104 \n", "2015-10-01 00:03:00 253230 10/1/2015 0:07 31102 \n", "2015-10-01 00:03:00 655251 10/1/2015 0:14 31242 \n", "2015-10-01 00:06:00 309212 10/1/2015 0:11 31280 \n", "2015-10-01 00:07:00 776195 10/1/2015 0:20 31226 \n", "2015-10-01 00:07:00 151604 10/1/2015 0:10 31017 \n", "2015-10-01 00:07:00 809509 10/1/2015 0:21 31301 \n", "2015-10-01 00:08:00 842712 10/1/2015 0:22 31276 \n", "2015-10-01 00:11:00 171124 10/1/2015 0:13 31213 \n", "2015-10-01 00:11:00 241872 10/1/2015 0:15 31203 \n", "2015-10-01 00:11:00 294746 10/1/2015 0:16 31212 \n", "2015-10-01 00:11:00 590515 10/1/2015 0:21 31278 \n", "2015-10-01 00:14:00 1217591 10/1/2015 0:34 31620 \n", "2015-10-01 00:14:00 957676 10/1/2015 0:30 31228 \n", "2015-10-01 00:15:00 1644646 10/1/2015 0:42 31258 \n", "2015-10-01 00:15:00 580130 10/1/2015 0:25 31610 \n", "2015-10-01 00:15:00 222180 10/1/2015 0:19 31270 \n", "2015-10-01 00:03:00 267251 10/1/2015 0:08 31306 \n", "2015-10-01 00:15:00 867784 10/1/2015 0:29 31600 \n", "2015-10-01 00:17:00 737845 10/1/2015 0:29 31104 \n", "2015-10-01 00:19:00 286638 10/1/2015 0:24 31017 \n", "2015-10-01 00:19:00 5628236 10/1/2015 1:53 31063 \n", "2015-10-01 00:20:00 102044 10/1/2015 0:22 31049 \n", "2015-10-01 00:21:00 504552 10/1/2015 0:30 31277 \n", "2015-10-01 00:21:00 526336 10/1/2015 0:30 31277 \n", "2015-10-01 00:21:00 867396 10/1/2015 0:36 31312 \n", "2015-10-01 00:23:00 278050 10/1/2015 0:27 31638 \n", "... ... ... ... \n", "2016-09-01 00:21:00 933505 9/1/2016 0:37 31237 \n", "2016-09-01 00:20:00 1574827 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 8871227 9/1/2016 2:47 31270 \n", "2016-09-01 00:19:00 1648284 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 679655 9/1/2016 0:30 31264 \n", "2016-09-01 00:19:00 1633724 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 8835027 9/1/2016 2:46 31270 \n", "2016-09-01 00:19:00 191644 9/1/2016 0:23 31285 \n", "2016-09-01 00:18:00 173292 9/1/2016 0:21 31110 \n", "2016-09-01 00:18:00 256249 9/1/2016 0:22 31109 \n", "2016-09-01 00:17:00 453588 9/1/2016 0:25 31278 \n", "2016-09-01 00:15:00 195777 9/1/2016 0:18 31257 \n", "2016-09-01 00:15:00 430642 9/1/2016 0:22 31267 \n", "2016-09-01 00:13:00 1309689 9/1/2016 0:35 31240 \n", "2016-09-01 00:13:00 513823 9/1/2016 0:22 31504 \n", "2016-09-01 00:13:00 1038905 9/1/2016 0:31 31279 \n", "2016-09-01 00:12:00 499112 9/1/2016 0:20 31111 \n", "2016-09-01 00:09:00 1019381 9/1/2016 0:26 31290 \n", "2016-09-01 00:09:00 680488 9/1/2016 0:21 31258 \n", "2016-09-01 00:09:00 674944 9/1/2016 0:21 31258 \n", "2016-09-01 00:08:00 371207 9/1/2016 0:14 31628 \n", "2016-09-01 00:05:00 1091877 9/1/2016 0:23 31228 \n", "2016-09-01 00:05:00 259558 9/1/2016 0:09 31102 \n", "2016-09-01 00:05:00 254785 9/1/2016 0:09 31102 \n", "2016-09-01 00:05:00 878992 9/1/2016 0:20 31102 \n", "2016-09-01 00:05:00 283200 9/1/2016 0:10 31600 \n", "2016-09-01 00:05:00 867363 9/1/2016 0:20 31280 \n", "2016-09-01 00:03:00 4798819 9/1/2016 1:23 31258 \n", "2016-09-01 00:02:00 763129 9/1/2016 0:15 31641 \n", "2016-09-01 00:00:00 590832 9/1/2016 0:10 31200 \n", "\n", " Start station \\\n", "Start date \n", "2015-10-01 00:01:00 Park Rd & Holmead Pl NW \n", "2015-10-01 00:01:00 34th & Water St NW \n", "2015-10-01 00:01:00 17th & Corcoran St NW \n", "2015-10-01 00:02:00 Adams Mill & Columbia Rd NW \n", "2015-10-01 00:03:00 11th & Kenyon St NW \n", "2015-10-01 00:03:00 18th St & Pennsylvania Ave NW \n", "2015-10-01 00:06:00 11th & S St NW \n", "2015-10-01 00:07:00 34th St & Wisconsin Ave NW \n", "2015-10-01 00:07:00 Wilson Blvd & N Uhle St \n", "2015-10-01 00:07:00 Ward Circle / American University \n", "2015-10-01 00:08:00 15th & L St NW \n", "2015-10-01 00:11:00 17th & K St NW \n", "2015-10-01 00:11:00 14th & Rhode Island Ave NW \n", "2015-10-01 00:11:00 21st & M St NW \n", "2015-10-01 00:11:00 18th & R St NW \n", "2015-10-01 00:14:00 5th & F St NW \n", "2015-10-01 00:14:00 8th & H St NW \n", "2015-10-01 00:15:00 Lincoln Memorial \n", "2015-10-01 00:15:00 Eastern Market / 7th & North Carolina Ave SE \n", "2015-10-01 00:15:00 8th & D St NW \n", "2015-10-01 00:03:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:15:00 5th & K St NW \n", "2015-10-01 00:17:00 Adams Mill & Columbia Rd NW \n", "2015-10-01 00:19:00 Wilson Blvd & N Uhle St \n", "2015-10-01 00:19:00 S George Mason Dr & Four Mile Run \n", "2015-10-01 00:20:00 Utah St & 11th St N \n", "2015-10-01 00:21:00 17th & G St NW \n", "2015-10-01 00:21:00 17th & G St NW \n", "2015-10-01 00:21:00 Wisconsin Ave & O St NW \n", "2015-10-01 00:23:00 1st & H St NW \n", "... ... \n", "2016-09-01 00:21:00 25th St & Pennsylvania Ave NW \n", "2016-09-01 00:20:00 Lincoln Memorial \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 Lincoln Memorial \n", "2016-09-01 00:19:00 6th St & Indiana Ave NW \n", "2016-09-01 00:19:00 Lincoln Memorial \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 22nd & P ST NW \n", "2016-09-01 00:18:00 20th St & Florida Ave NW \n", "2016-09-01 00:18:00 7th & T St NW \n", "2016-09-01 00:17:00 18th & R St NW \n", "2016-09-01 00:15:00 22nd & I St NW / Foggy Bottom \n", "2016-09-01 00:15:00 17th St & Massachusetts Ave NW \n", "2016-09-01 00:13:00 Ohio Dr & West Basin Dr SW / MLK & FDR Memorials \n", "2016-09-01 00:13:00 10th & Monroe St NE \n", "2016-09-01 00:13:00 19th & G St NW \n", "2016-09-01 00:12:00 10th & U St NW \n", "2016-09-01 00:09:00 17th St & Independence Ave SW \n", "2016-09-01 00:09:00 Lincoln Memorial \n", "2016-09-01 00:09:00 Lincoln Memorial \n", "2016-09-01 00:08:00 1st & K St SE \n", "2016-09-01 00:05:00 8th & H St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 5th & K St NW \n", "2016-09-01 00:05:00 11th & S St NW \n", "2016-09-01 00:03:00 Lincoln Memorial \n", "2016-09-01 00:02:00 2nd St & Massachusetts Ave NE \n", "2016-09-01 00:00:00 Massachusetts Ave & Dupont Circle NW \n", "\n", " End station number \\\n", "Start date \n", "2015-10-01 00:01:00 31105 \n", "2015-10-01 00:01:00 31237 \n", "2015-10-01 00:01:00 31214 \n", "2015-10-01 00:02:00 31121 \n", "2015-10-01 00:03:00 31102 \n", "2015-10-01 00:03:00 31114 \n", "2015-10-01 00:06:00 31278 \n", "2015-10-01 00:07:00 31214 \n", "2015-10-01 00:07:00 31029 \n", "2015-10-01 00:07:00 31113 \n", "2015-10-01 00:08:00 31118 \n", "2015-10-01 00:11:00 31267 \n", "2015-10-01 00:11:00 31101 \n", "2015-10-01 00:11:00 31278 \n", "2015-10-01 00:11:00 31312 \n", "2015-10-01 00:14:00 31615 \n", "2015-10-01 00:14:00 31103 \n", "2015-10-01 00:15:00 31617 \n", "2015-10-01 00:15:00 31256 \n", "2015-10-01 00:15:00 31228 \n", "2015-10-01 00:03:00 31306 \n", "2015-10-01 00:15:00 31605 \n", "2015-10-01 00:17:00 31509 \n", "2015-10-01 00:19:00 31015 \n", "2015-10-01 00:19:00 31062 \n", "2015-10-01 00:20:00 31037 \n", "2015-10-01 00:21:00 31239 \n", "2015-10-01 00:21:00 31239 \n", "2015-10-01 00:21:00 31306 \n", "2015-10-01 00:23:00 31627 \n", "... ... \n", "2016-09-01 00:21:00 31509 \n", "2016-09-01 00:20:00 31288 \n", "2016-09-01 00:19:00 31270 \n", "2016-09-01 00:19:00 31288 \n", "2016-09-01 00:19:00 31628 \n", "2016-09-01 00:19:00 31288 \n", "2016-09-01 00:19:00 31270 \n", "2016-09-01 00:19:00 31234 \n", "2016-09-01 00:18:00 31234 \n", "2016-09-01 00:18:00 31223 \n", "2016-09-01 00:17:00 31275 \n", "2016-09-01 00:15:00 31237 \n", "2016-09-01 00:15:00 31223 \n", "2016-09-01 00:13:00 31252 \n", "2016-09-01 00:13:00 31510 \n", "2016-09-01 00:13:00 31605 \n", "2016-09-01 00:12:00 31400 \n", "2016-09-01 00:09:00 31281 \n", "2016-09-01 00:09:00 31248 \n", "2016-09-01 00:09:00 31248 \n", "2016-09-01 00:08:00 31613 \n", "2016-09-01 00:05:00 31103 \n", "2016-09-01 00:05:00 31602 \n", "2016-09-01 00:05:00 31602 \n", "2016-09-01 00:05:00 31403 \n", "2016-09-01 00:05:00 31264 \n", "2016-09-01 00:05:00 31113 \n", "2016-09-01 00:03:00 31240 \n", "2016-09-01 00:02:00 31276 \n", "2016-09-01 00:00:00 31245 \n", "\n", " End station \\\n", "Start date \n", "2015-10-01 00:01:00 14th & Harvard St NW \n", "2015-10-01 00:01:00 25th St & Pennsylvania Ave NW \n", "2015-10-01 00:01:00 17th & Corcoran St NW \n", "2015-10-01 00:02:00 Calvert St & Woodley Pl NW \n", "2015-10-01 00:03:00 11th & Kenyon St NW \n", "2015-10-01 00:03:00 18th St & Wyoming Ave NW \n", "2015-10-01 00:06:00 18th & R St NW \n", "2015-10-01 00:07:00 17th & Corcoran St NW \n", "2015-10-01 00:07:00 N Veitch & 20th St N \n", "2015-10-01 00:07:00 Columbia Rd & Belmont St NW \n", "2015-10-01 00:08:00 3rd & Elm St NW \n", "2015-10-01 00:11:00 17th St & Massachusetts Ave NW \n", "2015-10-01 00:11:00 14th & V St NW \n", "2015-10-01 00:11:00 18th & R St NW \n", "2015-10-01 00:11:00 Wisconsin Ave & O St NW \n", "2015-10-01 00:14:00 6th & H St NE \n", "2015-10-01 00:14:00 16th & Harvard St NW \n", "2015-10-01 00:15:00 Bladensburg Rd & Benning Rd NE \n", "2015-10-01 00:15:00 10th & E St NW \n", "2015-10-01 00:15:00 8th & H St NW \n", "2015-10-01 00:03:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:15:00 3rd & D St SE \n", "2015-10-01 00:17:00 New Jersey Ave & R St NW \n", "2015-10-01 00:19:00 Rosslyn Metro / Wilson Blvd & Ft Myer Dr \n", "2015-10-01 00:19:00 S George Mason Dr & 13th St S \n", "2015-10-01 00:20:00 Ballston Metro / N Stuart & 9th St N \n", "2015-10-01 00:21:00 17th & Rhode Island Ave NW \n", "2015-10-01 00:21:00 17th & Rhode Island Ave NW \n", "2015-10-01 00:21:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:23:00 M St & Delaware Ave NE \n", "... ... \n", "2016-09-01 00:21:00 New Jersey Ave & R St NW \n", "2016-09-01 00:20:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 1st & K St SE \n", "2016-09-01 00:19:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 20th & O St NW / Dupont South \n", "2016-09-01 00:18:00 20th & O St NW / Dupont South \n", "2016-09-01 00:18:00 Convention Center / 7th & M St NW \n", "2016-09-01 00:17:00 New Hampshire Ave & 24th St NW \n", "2016-09-01 00:15:00 25th St & Pennsylvania Ave NW \n", "2016-09-01 00:15:00 Convention Center / 7th & M St NW \n", "2016-09-01 00:13:00 21st St & Pennsylvania Ave NW \n", "2016-09-01 00:13:00 18th St & Rhode Island Ave NE \n", "2016-09-01 00:13:00 3rd & D St SE \n", "2016-09-01 00:12:00 Georgia & New Hampshire Ave NW \n", "2016-09-01 00:09:00 8th & O St NW \n", "2016-09-01 00:09:00 Smithsonian / Jefferson Dr & 12th St SW \n", "2016-09-01 00:09:00 Smithsonian / Jefferson Dr & 12th St SW \n", "2016-09-01 00:08:00 Eastern Market Metro / Pennsylvania Ave & 7th ... \n", "2016-09-01 00:05:00 16th & Harvard St NW \n", "2016-09-01 00:05:00 Park Rd & Holmead Pl NW \n", "2016-09-01 00:05:00 Park Rd & Holmead Pl NW \n", "2016-09-01 00:05:00 5th & Kennedy St NW \n", "2016-09-01 00:05:00 6th St & Indiana Ave NW \n", "2016-09-01 00:05:00 Columbia Rd & Belmont St NW \n", "2016-09-01 00:03:00 Ohio Dr & West Basin Dr SW / MLK & FDR Memorials \n", "2016-09-01 00:02:00 15th & L St NW \n", "2016-09-01 00:00:00 7th & R St NW / Shaw Library \n", "\n", " Bike number Member Type \n", "Start date \n", "2015-10-01 00:01:00 W21109 Registered \n", "2015-10-01 00:01:00 W20603 Registered \n", "2015-10-01 00:01:00 W01233 Registered \n", "2015-10-01 00:02:00 W00218 Registered \n", "2015-10-01 00:03:00 W21612 Registered \n", "2015-10-01 00:03:00 W22093 Registered \n", "2015-10-01 00:06:00 W00231 Registered \n", "2015-10-01 00:07:00 W20820 Registered \n", "2015-10-01 00:07:00 W00244 Registered \n", "2015-10-01 00:07:00 W20352 Registered \n", "2015-10-01 00:08:00 W21327 Registered \n", "2015-10-01 00:11:00 W20487 Registered \n", "2015-10-01 00:11:00 W22183 Registered \n", "2015-10-01 00:11:00 W20769 Registered \n", "2015-10-01 00:11:00 W00231 Registered \n", "2015-10-01 00:14:00 W20668 Casual \n", "2015-10-01 00:14:00 W01031 Registered \n", "2015-10-01 00:15:00 W21001 Registered \n", "2015-10-01 00:15:00 W00379 Registered \n", "2015-10-01 00:15:00 W20592 Registered \n", "2015-10-01 00:03:00 W21206 Registered \n", "2015-10-01 00:15:00 W20970 Registered \n", "2015-10-01 00:17:00 W21281 Registered \n", "2015-10-01 00:19:00 W20617 Registered \n", "2015-10-01 00:19:00 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W22637 Registered \n", "2016-09-01 00:09:00 W21299 Casual \n", "2016-09-01 00:09:00 W21011 Casual \n", "2016-09-01 00:08:00 W20941 Registered \n", "2016-09-01 00:05:00 W00785 Registered \n", "2016-09-01 00:05:00 W21529 Registered \n", "2016-09-01 00:05:00 W22528 Registered \n", "2016-09-01 00:05:00 W22689 Registered \n", "2016-09-01 00:05:00 W01320 Registered \n", "2016-09-01 00:05:00 W22933 Registered \n", "2016-09-01 00:03:00 W22953 Registered \n", "2016-09-01 00:02:00 W22911 Registered \n", "2016-09-01 00:00:00 W22867 Registered \n", "\n", "[3268722 rows x 8 columns]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bikes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "save this DataFrame with pickle" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pickle.dump( bikes, open( \"bikeshare_rides_all.p\", \"wb\" ) )" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3268722" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(bikes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mne-tools/mne-tools.github.io	0.18/_downloads/012b7ba30b03ebda4c3419b2e4f5161a/plot_ssp_projs_sensitivity_map.ipynb	1	2421	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Sensitivity map of SSP projections\n\n\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nfrom mne import read_forward_solution, read_proj, sensitivity_map\n\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nsubjects_dir = data_path + '/subjects'\nfname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\necg_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'\n\nfwd = read_forward_solution(fname)\n\nprojs = read_proj(ecg_fname)\n# take only one projection per channel type\nprojs = projs[::2]\n\n# Compute sensitivity map\nssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show sensitivity map\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(ssp_ecg_map.data.ravel())\nplt.show()\n\nargs = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,\n hemi='rh', subjects_dir=subjects_dir)\nssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
AmericasWater/awash	docs/Tutorial 1 - Running the model.ipynb	1	265715	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Scope of this tutorial\n", "\n", "Welcome to the first America's Water Model tutorial! In this tutorial, you are going to learn how to (0) install the model, (1) run the model in simulation and optimization mode with different configurations, (2) look at the results, and (3) make requests of and get updates from the modeling team." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Some terminology\n", "\n", "We are going to use a bunch of terms in this tutorial, so it's important to clear up the confusion.\n", "\n", "* The model: A computer program that can provide information on how water is or could be supplied and consumed in the US.\n", "* Julia: The programming language used for the model. <img style=\"float: right; position: relative; top: -20px; width: 150px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Julia_prog_language.svg/2000px-Julia_prog_language.svg.png\"> From the [Julia website](http://julialang.org/):\n", "\n", " Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.\n", " \n", "\n", "* Jupyter Notebook: A program to make documents, like this one, that combine Julia programming and text.\n", "* Git: A program that saves all previous versions of the model, and lets the modeling team collaborate. All of these previous versions are stored on the website [Github](github.com/AmericasWater/operational-problem).\n", "* Terminal: A text interface to your computer, needed to use Git and Jupyter Notebook.\n", "* Mimi: The foundational code used by the model to provide its structure, developed by [David Anthoff](http://www.david-anthoff.com/).\n", "* Simulation: A way to run the model, where inputs (or parameters) are used to determine the value of outputs (or variables).\n", "* Optimization: Another way to run the model, where constraints on outputs (or variables) are used to determine inputs (or parameters)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Installing the model\n", "\n", "If you are an advanced user, you can follow the more succinct [installation instructions](install.md) and then skip to \"What you just got\" below.\n", "\n", "## Setting up your environment\n", "\n", "You will need some basic software to use the America's Water Model.\n", "\n", "* Julia (command line version) from http://julialang.org/downloads/\n", "\n", " Check your installation by running Julia and writing some arithmetic.\n", " \n", " \n", "* Git from https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\n", "\n", " If you're on Windows, also download PowerShell (https://msdn.microsoft.com/en-us/powershell/)\n", "\n", " Check that it's installed by opening a terminal/PowerShell and typing `git --version` and see a version number.\n", " \n", " \n", "* Jupyter Notebook from http://jupyter.readthedocs.io/en/latest/install.html\n", "\n", " Check that it's installed by opening a terminal/PowerShell and typing `jupyter notebook` and see a browser window pop up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it's time to download the model. First, decide where you want to put the model, and open a terminal to this location.\n", "\n", "## Aside: How to navigate to the model in the terminal\n", "\n", "1. Opening a terminal. Doing this is different by OS:\n", " - On Mac OS: Open the Terminal program.\n", " - On Windows: Open the PowerShell program.\n", " - On Linux: Open up a shell window.\n", "2. To change directory, type `cd <directory>`. `<directory>` can be any of the following:\n", " - The name of a subdirectory.\n", " - Two periods (`..`) to go to the parent directory.\n", " - A single period (`.`) to not go anywhere.\n", " - Any combination of the above, separated by slashes (`/`).\n", " - A tilde (`~`) to go back to your home directory.\n", " - A slash (`/`) to go to the root of the server.\n", " - Either of the previous two, combined with any of the first four, using slashes.\n", " - Use the tab key to auto-complete the name you have started to type.\n", " - If there are spaces in your directory or filename, consider the life choices that led to this moment.\n", "3. The model will be created in a new folder, when you download it. From then on, for anything from this tutorial, you will want to go into the `docs` subdirectory of that folder.\n", "\n", "## Downloading the model\n", "\n", "In the directory where you want the model folder, paste the following command:\n", "\n", "```\n", "git clone https://github.com/AmericasWater/awash.git```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What you just got\n", "\n", "The model is organized into different folders. Here is what is in there:\n", "* `configs/`: Configuration files, that determine the basic operation of the model.\n", "* `data/`: Input data files. There is also a `data/cache/` folder, with saved preparation files. You can delete this to force the model to reinitialize.\n", "* `docs/`: Documentation, including tutorials like this one.\n", "* `prepare/`: Some source data files and the scripts needed to prepare them for use in the model.\n", "* `results/`: The standard location for output results, along with code for post-processing.\n", "* `src/`: The main source code for the model. All the magic happens here.\n", "* `test/`: Automatic testing scripts, to make sure changes don't break how the model should work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Opening the model\n", "\n", "For the purposes of this tutorial, the main interface of the model is Jupyter Notebooks, and the existing tutorials in the `docs/` subdirectory. To start the model, do the following:\n", "\n", "1. Open a terminal to the `docs/` subdirectory of the model.\n", "2. Type/paste the following command: `jupyter notebook`.\n", "3. In the browser window that opens, find the item `start.ipynb` and click on it.\n", "4. Click the \"<span style=\"font-family: FontAwesome\" class=\"fa-step-forward fe\"></span>\" button, which will run the line below.\n", "\n", "If this is the first time you're running the model, it is going to install some needed libraries and data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0;1;34;94m┌───────────────\u001b[0;34m────────────────\u001b[0;37m────────────────\u001b[0;1;30;90m───┐\u001b[0m\n", "\u001b[0;34m│\u001b[0m \u001b[0;34m▄▄\u001b[0m \u001b[0;34m▄▄\u001b[0m \u001b[0;37m▄▄\u001b[0m \u001b[0;37m▄▄\u001b[0m \u001b[0;1;30;90m▄▄▄▄\u001b[0m \u001b[0;1;30;90m▄▄\u001b[0m \u001b[0;1;34;94m▄▄\u001b[0m \u001b[0;1;34;94m│\u001b[0m\n", "\u001b[0;34m│\u001b[0m \u001b[0;34m████\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;1;30;90m████\u001b[0m \u001b[0;1;30;90m▄█▀▀▀▀█\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m│\u001b[0m\n", "\u001b[0;37m│\u001b[0m \u001b[0;37m████\u001b[0m \u001b[0;37m▀█▄\u001b[0m \u001b[0;37m█\u001b[0;1;30;90m█\u001b[0m \u001b[0;1;30;90m▄█▀\u001b[0m \u001b[0;1;30;90m████\u001b[0m \u001b[0;1;34;94m██▄\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;34m██\u001b[0m \u001b[0;34m│\u001b[0m\n", "\u001b[0;37m│\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m█\u001b[0;1;30;90m█\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m█\u001b[0;1;34;94m█\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m▀████▄\u001b[0m \u001b[0;34m████████\u001b[0m \u001b[0;34m│\u001b[0m\n", "\u001b[0;1;30;90m│\u001b[0m \u001b[0;1;30;90m██████\u001b[0m \u001b[0;1;30;90m███▀\u001b[0;1;34;94m▀███\u001b[0m \u001b[0;1;34;94m██████\u001b[0m \u001b[0;34m▀██\u001b[0m \u001b[0;34m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n", "\u001b[0;1;30;90m│\u001b[0m \u001b[0;1;30;90m▄██\u001b[0m \u001b[0;1;30;90m█\u001b[0;1;34;94m█▄\u001b[0m \u001b[0;1;34;94m███\u001b[0m \u001b[0;1;34;94m███\u001b[0m \u001b[0;1;34;94m▄█\u001b[0;34m█\u001b[0m \u001b[0;34m██▄\u001b[0m \u001b[0;34m█▄▄▄▄▄█▀\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n", "\u001b[0;1;34;94m│\u001b[0m \u001b[0;1;34;94m▀▀\u001b[0m \u001b[0;1;34;94m▀▀\u001b[0m \u001b[0;1;34;94m▀▀▀\u001b[0m \u001b[0;34m▀▀▀\u001b[0m \u001b[0;34m▀▀\u001b[0m \u001b[0;34m▀▀\u001b[0m \u001b[0;37m▀▀▀▀▀\u001b[0m \u001b[0;37m▀▀\u001b[0m \u001b[0;1;30;90m▀▀\u001b[0m \u001b[0;1;30;90m│\u001b[0m\n", "\u001b[0;34m└───────\u001b[0;37m────────────────\u001b[0;1;30;90m────────────────\u001b[0;1;34;94m───────────┘\u001b[0m\n", "\n", "Welcome to AWASH, the America's Water Model, version 0.7.\n", "\n" ] } ], "source": [ "include(\"../src/nui.jl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuration parameters\n", "\n", "The configuration file determines the general bounds of the model. The current configuration parameters are:\n", "\n", "* `netset`: The top-level decision of the world we are deciding.\n", "\n", " options: `usa` (full-country), `three` (3-counties), `dummy` (5-counties)\n", " \n", " \n", "* `filterstate`: Only include counties within this state (give as 2 digit FIPS).\n", "\n", " e.g., \"`10`\" for Delaware (3 counties), \"`08`\" for Colorado (64 counties)\n", " \n", " \n", "* `startmonth` and `endmonth`: First and last month of the simulation.\n", "\n", " e.g., 10/2000 - 9/2010\n", " \n", " \n", "* `startweather`: The weather entry to match to `startmonth`.\n", "\n", " e.g., 1 to use the first month in `VIC_WB`for the first month in the simulation.\n", " \n", " \n", "* `timestep`: The number of months for each timestep of the model.\n", "\n", " The larger this number, the faster the model will run. Currently agriculture is calculated separately for each timestep.\n", " \n", "# Standard configuration sets\n", "\n", "There are three configuration sets to know about:\n", "\n", "* `complete.yml`: A monthly run from 1949 - 2010. This is basically impossible on a laptop.\n", "* `standard.yml`: A 6-month timestep run from 2000 - 2010. This is about the maximum practical for a laptop.\n", "* `standard-1year.yml`: A 6-month timestep run for 10/2009 - 9/2010 (two timesteps). This is good for testing.\n", "* `single.yml`: A 12-month timestep run for 10/2009 - 9/2010 (one timestep). We will use this one here.\n", "\n", "Here is what the `single.yml` configuration looks like:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Only include counties within this state (give as 2 digit FIPS)\n", "# \"10\" for Delaware (3 counties), \"08\" for Colorado (64 counties)\n", "filterstate: null\n", "\n", "# Current options: usa (full-country), dummy (5-counties)\n", "netset: usa\n", "\n", "# First and last month of the simulation\n", "startmonth: 10/2009\n", "endmonth: 9/2010\n", "\n", "# First entry in VIC_WB to include\n", "startweather: 720\n", "\n", "# Months per time step\n", "timestep: 12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running a simulation\n", "\n", "There are three steps for running a simulation:\n", "1. Select a configuration: Create or select a file in `configs/`.\n", "2. Prepare the model: Call `prepsimulate(\"<config filename>\");`.\n", "3. Run the prepared model: Call `runmodel();`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading from saved region network...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: imported binding for edges overwritten in module Main\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading from saved water network...\n", "Loading from saved region network...\n", "Creating model...\n" ] } ], "source": [ "prepsimulate(\"single.yml\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This loaded up the model geography of the model, put together the sectors, and prepared all of the inputs. Now we can run the model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "runmodel();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Looking at the results of the model\n", "\n", "There are lots of ways to see the results of the model, but they all require that you know which variables you want to look at. Start by identifying the component that you are interested, as a box in this image:\n", "\n", "![Operational Problem Model](https://raw.githubusercontent.com/AmericasWater/operational-problem/master/docs/ModelDiagram.png)\n", "\n", "Let's consider the Agriculture component. Next, we need to know what variables are available within that component, which we do with the `getvariables` function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>name</th><th>dims</th></tr><tr><th>1</th><td>rainfedareas</td><td>(3109,9,1)</td></tr><tr><th>2</th><td>deficit_coeff</td><td>(3109,9)</td></tr><tr><th>3</th><td>precipitation</td><td>(3109,1)</td></tr><tr><th>4</th><td>water_demand</td><td>(9,)</td></tr><tr><th>5</th><td>logirrigatedyield</td><td>(3109,9,1)</td></tr><tr><th>6</th><td>irrigatedareas</td><td>(3109,9,1)</td></tr><tr><th>7</th><td>allagarea</td><td>(3109,1)</td></tr><tr><th>8</th><td>totalirrigation</td><td>(3109,1)</td></tr><tr><th>9</th><td>production</td><td>(3109,9,1)</td></tr><tr><th>10</th><td>cultivationcost</td><td>(3109,9,1)</td></tr><tr><th>11</th><td>water_deficit</td><td>(3109,9,1)</td></tr><tr><th>12</th><td>lograinfedyield</td><td>(3109,9,1)</td></tr><tr><th>13</th><td>totalareas</td><td>(3109,9,1)</td></tr></table>" ], "text/plain": [ "13×2 DataFrames.DataFrame\n", "│ Row │ name │ dims │\n", "├─────┼───────────────────┼──────────────┤\n", "│ 1 │ rainfedareas │ \"(3109,9,1)\" │\n", "│ 2 │ deficit_coeff │ \"(3109,9)\" │\n", "│ 3 │ precipitation │ \"(3109,1)\" │\n", "│ 4 │ water_demand │ \"(9,)\" │\n", "│ 5 │ logirrigatedyield │ \"(3109,9,1)\" │\n", "│ 6 │ irrigatedareas │ \"(3109,9,1)\" │\n", "│ 7 │ allagarea │ \"(3109,1)\" │\n", "│ 8 │ totalirrigation │ \"(3109,1)\" │\n", "│ 9 │ production │ \"(3109,9,1)\" │\n", "│ 10 │ cultivationcost │ \"(3109,9,1)\" │\n", "│ 11 │ water_deficit │ \"(3109,9,1)\" │\n", "│ 12 │ lograinfedyield │ \"(3109,9,1)\" │\n", "│ 13 │ totalareas │ \"(3109,9,1)\" │" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getvariables(:Agriculture)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `:` is important for both the component name and the variable names below.\n", "\n", "Then to see the results, call `getdata(<component>, <variable>)`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "3109x9x1 Array{Float64,3}:\n", "[:, :, 1] =\n", " 0.0 0.0 … 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 21570.7 … 0.0 33936.2 \n", " 0.0 37424.6 0.0 0.0 \n", " 0.0 0.0 0.0 32082.3 \n", " 0.0 32438.7 0.0 8336.67 \n", " 0.0 0.0 0.0 3.35729e5 \n", " 0.0 0.0 … 0.0 0.0 \n", " 0.0 9.73389e-23 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " ⋮ ⋱ \n", " 54033.4 42940.9 0.0 0.0 \n", " 1.10014e5 15292.0 0.0 0.0 \n", " 30610.1 5819.68 0.0 0.0 \n", " 78542.1 16184.3 … 0.0 0.0 \n", " 40925.4 30863.0 0.0 0.0 \n", " 78652.4 28882.8 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 42416.2 26897.1 0.0 0.0 \n", " 5701.34 12085.1 … 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 26212.3 3313.67 31500.0 0.0 \n", " 10993.2 1075.5 1.55547e-13 1.28253e-15" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getdata(:Agriculture, :production)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save the data to a file, call `savedata(<filename>, <component>, <variable>)`. The `savedata` function is best used on 2-D data (or less) and since production is of dimension COUNTY x CROP x TIME, we would like to drop one. We can do this with a fourth argument, which has the syntax for a Matlab index." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "savedata(\"../results/agprod.csv\", :Agriculture, :production, (:, :, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can plot it. This only works so far for results that describe nation-wide values, but it's a start." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: RCall.jl Warning: package ‘ggplot2’ was built under R version 3.2.3\n", "Need help? Try the ggplot2 mailing list:\n", "http://groups.google.com/group/ggplot2.\n", "WARNING: RCall.jl \n", "-----------------------------------------------------------\n", "PBS Mapping 2.69.76 -- Copyright (C) 2003-2016 Fisheries and Oceans Canada\n", "\n", "PBS Mapping comes with ABSOLUTELY NO WARRANTY;\n", "for details see the file COPYING.\n", "This is free software, and you are welcome to redistribute\n", "it under certain conditions, as outlined in the above file.\n", "\n", "A complete user guide 'PBSmapping-UG.pdf' is located at \n", "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/PBSmapping/doc/PBSmapping-UG.pdf\n", "\n", "Packaged on 2015-04-23\n", "Pacific Biological Station, Nanaimo\n", "\n", "All available PBS packages can be found at\n", "http://code.google.com/p/pbs-software/\n", "\n", "To see demos, type '.PBSfigs()'.\n", "-----------------------------------------------------------\n", "\n", "\n", "WARNING: RCall.jl Loading required package: maptools\n", "Loading required package: sp\n", "Warning: package ‘sp’ was built under R version 3.2.5\n", "Checking rgeos availability: TRUE\n", "Loading required package: foreign\n" ] }, { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.VecSxp}\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapdata(:Agriculture, :production, (:, 5, 1))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using RCall" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.62122279 0.64831833 -0.48935885 0.92764611 1.02611765 1.59837396\n", " [7] -0.96725780 -0.49321665 -1.10164360 -1.33944063 1.31890750 0.25991008\n", " [13] -0.36737477 0.08100751 0.01820744 0.57968764 -0.83115791 -0.30559390\n", " [19] 0.82804637 0.87722282 -0.36368721 0.29606504 -0.01717716 0.18193819\n", " [25] 0.98784232 -0.04729326 -0.76462815 -0.23869830 0.87341128 -0.56540542\n", " [31] 0.24752053 -0.93169866 -0.01312905 1.17846349 0.26131608 1.81919341\n", " [37] 1.72917614 0.14759056 0.88830378 -1.09978466 0.01376463 -0.23974337\n", " [43] 0.20255462 -1.12002824 -0.17669488 0.34420994 -0.46455188 1.14323986\n", " [49] 0.18923975 -0.17559747 -0.38363851 -1.08267915 1.09002894 0.94087581\n", " [55] -0.43349359 -0.44734339 3.37063110 1.36829580 -0.44395899 -1.16338533\n", " [61] -0.97981821 0.20694984 -0.06456482 0.47541359 0.52327356 1.63704958\n", " [67] 0.32423036 0.49562667 -0.33220034 0.54913155 -0.37544318 0.26960028\n", " [73] 0.55244578 0.89318511 -0.02618433 -1.09897372 0.95858630 0.87128071\n", " [79] -0.45338845 -0.44877374 1.78908111 -0.36408830 -0.88978697 -0.99465919\n", " [85] 0.33044888 -0.37553791 -1.42606459 -0.65207303 0.44036986 0.75407073\n", " [91] -0.75762170 -0.52891009 0.30297497 -2.11217177 -0.73390772 1.09533811\n", " [97] 1.34026477 0.26010075 0.97643020 0.18347344\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"x = rnorm(100)\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.VecSxp}\n", "$breaks\n", " [1] -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5\n", "\n", "$counts\n", " [1] 1 0 8 13 25 22 17 8 5 0 0 1\n", "\n", "$density\n", " [1] 0.02 0.00 0.16 0.26 0.50 0.44 0.34 0.16 0.10 0.00 0.00 0.02\n", "\n", "$mids\n", " [1] -2.25 -1.75 -1.25 -0.75 -0.25 0.25 0.75 1.25 1.75 2.25 2.75 3.25\n", "\n", "$xname\n", "[1] \"x\"\n", "\n", "$equidist\n", "[1] TRUE\n", "\n", "attr(,\"class\")\n", "[1] \"histogram\"\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"hist(x)\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myjuliavar = [1, 2, 3]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myjuliavar" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.NilSxp}\n", "NULL\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"plot($myjuliavar)\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n", " [6] -0.625395697 0.788866194 0.503396481 0.806131966 -0.685962168\n", " [11] 1.409655399 -0.011001650 1.221350838 0.687594625 0.660120389\n", " [16] 0.323121302 1.121587552 -0.125611715 1.772632044 -1.576097599\n", " [21] -0.660744500 -0.429240447 -0.490280026 0.468150307 1.198544526\n", " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809 1.008367742\n", " [31] 1.042998949 -0.182158343 -0.198155519 0.064799689 1.055615930\n", " [36] 1.396022389 0.363787500 -0.265379634 -0.485115714 0.513091917\n", " [41] -0.340089246 -0.725946945 0.465066983 0.925172640 -0.499757044\n", " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050 1.549266436\n", " [51] -1.311331940 1.201508605 -0.271954598 2.534004825 -1.623282056\n", " [56] 0.001984485 -0.509082941 0.023470704 -0.710153768 -1.048219819\n", " [61] 0.950574020 0.223231617 -1.496654999 -0.660755363 -0.865973022\n", " [66] -1.125167386 -0.679917414 -1.014248154 1.170601061 1.133173229\n", " [71] 0.217915251 -0.863843341 0.858204953 -0.204352963 -0.521392645\n", " [76] 1.510171510 0.598071927 0.638948425 1.397193548 -0.639814179\n", " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727 1.295730250\n", " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995 0.781161860\n", " [91] -0.734598252 0.817520424 -1.157498298 0.815970762 -0.476418750\n", " [96] 0.162498461 -1.002618330 0.975990869 -0.457368921 0.461530679\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = R\"rnorm(100)\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n", " [6] -0.625395697 0.788866194 0.503396481 0.806131966 -0.685962168\n", " [11] 1.409655399 -0.011001650 1.221350838 0.687594625 0.660120389\n", " [16] 0.323121302 1.121587552 -0.125611715 1.772632044 -1.576097599\n", " [21] -0.660744500 -0.429240447 -0.490280026 0.468150307 1.198544526\n", " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809 1.008367742\n", " [31] 1.042998949 -0.182158343 -0.198155519 0.064799689 1.055615930\n", " [36] 1.396022389 0.363787500 -0.265379634 -0.485115714 0.513091917\n", " [41] -0.340089246 -0.725946945 0.465066983 0.925172640 -0.499757044\n", " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050 1.549266436\n", " [51] -1.311331940 1.201508605 -0.271954598 2.534004825 -1.623282056\n", " [56] 0.001984485 -0.509082941 0.023470704 -0.710153768 -1.048219819\n", " [61] 0.950574020 0.223231617 -1.496654999 -0.660755363 -0.865973022\n", " [66] -1.125167386 -0.679917414 -1.014248154 1.170601061 1.133173229\n", " [71] 0.217915251 -0.863843341 0.858204953 -0.204352963 -0.521392645\n", " [76] 1.510171510 0.598071927 0.638948425 1.397193548 -0.639814179\n", " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727 1.295730250\n", " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995 0.781161860\n", " [91] -0.734598252 0.817520424 -1.157498298 0.815970762 -0.476418750\n", " [96] 0.162498461 -1.002618330 0.975990869 -0.457368921 0.461530679\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Nothing to be done\n", "INFO: METADATA is out-of-date — you may not have the latest version of YAML\n", "INFO: Use `Pkg.update()` to get the latest versions of your packages\n" ] } ], "source": [ "Pkg.add(\"YAML\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>FIPS</th><th>County</th><th>ST</th><th>Neighboring</th><th>UrbanPop_2010</th><th>RuralPop_2010</th><th>MedianHouseholdInc_2008_12</th><th>Elevation_ft</th><th>TotalArea_sqmi</th><th>LandArea_sqmi</th><th>WaterArea_sqmi</th></tr><tr><th>1</th><td>1001</td><td>Autauga</td><td>AL</td><td>-1677436972259693513</td><td>31650</td><td>22921</td><td>53773</td><td>290.0</td><td>604.39</td><td>594.44</td><td>9.95</td></tr><tr><th>2</th><td>1003</td><td>Baldwin</td><td>AL</td><td>-7503806633868418079</td><td>105205</td><td>77060</td><td>50706</td><td>155.0</td><td>2027.31</td><td>1589.78</td><td>437.53</td></tr><tr><th>3</th><td>1005</td><td>Barbour</td><td>AL</td><td>-9162367971339582421</td><td>8844</td><td>18613</td><td>31889</td><td>220.0</td><td>904.52</td><td>884.88</td><td>19.64</td></tr><tr><th>4</th><td>1007</td><td>Bibb</td><td>AL</td><td>-3263215093836123959</td><td>7252</td><td>15663</td><td>36824</td><td>224.0</td><td>626.17</td><td>622.58</td><td>3.59</td></tr><tr><th>5</th><td>1009</td><td>Blount</td><td>AL</td><td>281814405218882823</td><td>5760</td><td>51562</td><td>45192</td><td>870.0</td><td>650.63</td><td>644.78</td><td>5.85</td></tr><tr><th>6</th><td>1011</td><td>Bullock</td><td>AL</td><td>-7528364842213094795</td><td>5307</td><td>5607</td><td>34500</td><td>455.0</td><td>625.14</td><td>622.8</td><td>2.34</td></tr><tr><th>7</th><td>1013</td><td>Butler</td><td>AL</td><td>7263807205192509739</td><td>6026</td><td>14921</td><td>30752</td><td>445.0</td><td>777.88</td><td>776.83</td><td>1.05</td></tr><tr><th>8</th><td>1015</td><td>Calhoun</td><td>AL</td><td>4089652745186409211</td><td>78617</td><td>39955</td><td>40093</td><td>565.0</td><td>612.29</td><td>605.87</td><td>6.42</td></tr><tr><th>9</th><td>1017</td><td>Chambers</td><td>AL</td><td>1546726499117356153</td><td>17399</td><td>16816</td><td>32181</td><td>745.0</td><td>603.11</td><td>596.53</td><td>6.58</td></tr><tr><th>10</th><td>1019</td><td>Cherokee</td><td>AL</td><td>3606844344126915727</td><td>3707</td><td>22282</td><td>36241</td><td>589.0</td><td>599.98</td><td>553.7</td><td>46.28</td></tr><tr><th>11</th><td>1021</td><td>Chilton</td><td>AL</td><td>2336210146209538225</td><td>5785</td><td>37858</td><td>40834</td><td>580.0</td><td>700.8</td><td>692.85</td><td>7.95</td></tr><tr><th>12</th><td>1023</td><td>Choctaw</td><td>AL</td><td>-6248320840704236021</td><td>0</td><td>13859</td><td>35123</td><td>350.0</td><td>920.86</td><td>913.5</td><td>7.36</td></tr><tr><th>13</th><td>1025</td><td>Clarke</td><td>AL</td><td>-2480994685536455671</td><td>6205</td><td>19628</td><td>30954</td><td>405.0</td><td>1252.57</td><td>1238.46</td><td>14.11</td></tr><tr><th>14</th><td>1027</td><td>Clay</td><td>AL</td><td>165275177167130605</td><td>0</td><td>13932</td><td>34556</td><td>745.0</td><td>606.0</td><td>603.96</td><td>2.04</td></tr><tr><th>15</th><td>1029</td><td>Cleburne</td><td>AL</td><td>-6945485711834679467</td><td>0</td><td>14972</td><td>37244</td><td>1175.0</td><td>561.01</td><td>560.1</td><td>0.91</td></tr><tr><th>16</th><td>1031</td><td>Coffee</td><td>AL</td><td>6132202507106331951</td><td>26375</td><td>23573</td><td>44626</td><td>195.0</td><td>680.49</td><td>678.97</td><td>1.52</td></tr><tr><th>17</th><td>1033</td><td>Colbert</td><td>AL</td><td>1077010790105928141</td><td>30537</td><td>23891</td><td>40158</td><td>536.0</td><td>622.13</td><td>592.62</td><td>29.51</td></tr><tr><th>18</th><td>1035</td><td>Conecuh</td><td>AL</td><td>1099010130103901053</td><td>2520</td><td>10708</td><td>27064</td><td>216.0</td><td>852.72</td><td>850.16</td><td>2.57</td></tr><tr><th>19</th><td>1037</td><td>Coosa</td><td>AL</td><td>6308886048509289181</td><td>0</td><td>11539</td><td>37425</td><td>660.0</td><td>666.35</td><td>650.93</td><td>15.42</td></tr><tr><th>20</th><td>1039</td><td>Covington</td><td>AL</td><td>-7456065813052386533</td><td>11461</td><td>26304</td><td>35321</td><td>260.0</td><td>1043.79</td><td>1030.46</td><td>13.33</td></tr><tr><th>21</th><td>1041</td><td>Crenshaw</td><td>AL</td><td>4065287195876681327</td><td>0</td><td>13906</td><td>37309</td><td>594.0</td><td>610.92</td><td>608.84</td><td>2.08</td></tr><tr><th>22</th><td>1043</td><td>Cullman</td><td>AL</td><td>6438249654864425607</td><td>21517</td><td>58889</td><td>39244</td><td>802.0</td><td>755.02</td><td>734.84</td><td>20.18</td></tr><tr><th>23</th><td>1045</td><td>Dale</td><td>AL</td><td>3596880842466902597</td><td>24679</td><td>25572</td><td>45247</td><td>470.0</td><td>562.7</td><td>561.15</td><td>1.55</td></tr><tr><th>24</th><td>1047</td><td>Dallas</td><td>AL</td><td>3774141952490839331</td><td>23821</td><td>19999</td><td>26178</td><td>147.0</td><td>993.8</td><td>978.69</td><td>15.11</td></tr><tr><th>25</th><td>1049</td><td>DeKalb</td><td>AL</td><td>-701081407554769413</td><td>7018</td><td>64091</td><td>36853</td><td>1040.0</td><td>778.69</td><td>777.09</td><td>1.59</td></tr><tr><th>26</th><td>1051</td><td>Elmore</td><td>AL</td><td>4377449246493266349</td><td>36330</td><td>42973</td><td>55514</td><td>290.0</td><td>657.04</td><td>618.48</td><td>38.56</td></tr><tr><th>27</th><td>1053</td><td>Escambia</td><td>AL</td><td>3957980972351392731</td><td>13982</td><td>24337</td><td>31075</td><td>85.0</td><td>953.14</td><td>945.08</td><td>8.06</td></tr><tr><th>28</th><td>1055</td><td>Etowah</td><td>AL</td><td>1525514402121635003</td><td>65286</td><td>39144</td><td>37660</td><td>565.0</td><td>548.63</td><td>534.99</td><td>13.64</td></tr><tr><th>29</th><td>1057</td><td>Fayette</td><td>AL</td><td>-6531450309154316941</td><td>3408</td><td>13833</td><td>33090</td><td>365.0</td><td>629.36</td><td>627.66</td><td>1.7</td></tr><tr><th>30</th><td>1059</td><td>Franklin</td><td>AL</td><td>7764312890763704205</td><td>9395</td><td>22309</td><td>37235</td><td>840.0</td><td>646.52</td><td>633.82</td><td>12.7</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>" ], "text/plain": [ "3229×11 DataFrames.DataFrame\n", "│ Row │ FIPS │ County │ ST │ Neighboring │ UrbanPop_2010 │\n", "├──────┼───────┼─────────────────┼──────┼──────────────────────┼───────────────┤\n", "│ 1 │ 1001 │ \"Autauga\" │ \"AL\" │ -1677436972259693513 │ 31650 │\n", "│ 2 │ 1003 │ \"Baldwin\" │ \"AL\" │ -7503806633868418079 │ 105205 │\n", "│ 3 │ 1005 │ \"Barbour\" │ \"AL\" │ -9162367971339582421 │ 8844 │\n", "│ 4 │ 1007 │ \"Bibb\" │ \"AL\" │ -3263215093836123959 │ 7252 │\n", "│ 5 │ 1009 │ \"Blount\" │ \"AL\" │ 281814405218882823 │ 5760 │\n", "│ 6 │ 1011 │ \"Bullock\" │ \"AL\" │ -7528364842213094795 │ 5307 │\n", "│ 7 │ 1013 │ \"Butler\" │ \"AL\" │ 7263807205192509739 │ 6026 │\n", "│ 8 │ 1015 │ \"Calhoun\" │ \"AL\" │ 4089652745186409211 │ 78617 │\n", "│ 9 │ 1017 │ \"Chambers\" │ \"AL\" │ 1546726499117356153 │ 17399 │\n", "│ 10 │ 1019 │ \"Cherokee\" │ \"AL\" │ 3606844344126915727 │ 3707 │\n", "│ 11 │ 1021 │ \"Chilton\" │ \"AL\" │ 2336210146209538225 │ 5785 │\n", "⋮\n", "│ 3218 │ 72137 │ \"Toa Baja\" │ \"PR\" │ NA │ 89609 │\n", "│ 3219 │ 72139 │ \"Trujillo Alto\" │ \"PR\" │ NA │ 74842 │\n", "│ 3220 │ 72141 │ \"Utuado\" │ \"PR\" │ NA │ 17593 │\n", "│ 3221 │ 72143 │ \"Vega Alta\" │ \"PR\" │ NA │ 39251 │\n", "│ 3222 │ 72145 │ \"Vega Baja\" │ \"PR\" │ NA │ 57170 │\n", "│ 3223 │ 72147 │ \"Vieques\" │ \"PR\" │ NA │ 8230 │\n", "│ 3224 │ 72149 │ \"Villalba\" │ \"PR\" │ NA │ 22564 │\n", "│ 3225 │ 72151 │ \"Yabucoa\" │ \"PR\" │ NA │ 32192 │\n", "│ 3226 │ 72153 │ \"Yauco\" │ \"PR\" │ NA │ 34303 │\n", "│ 3227 │ 78010 │ \"St. Croix\" │ \"VI\" │ NA │ 46601 │\n", "│ 3228 │ 78020 │ \"St. John\" │ \"VI\" │ NA │ 3090 │\n", "│ 3229 │ 78030 │ \"St. Thomas\" │ \"VI\" │ NA │ 50916 │\n", "\n", "│ Row │ RuralPop_2010 │ MedianHouseholdInc_2008_12 │ Elevation_ft │\n", "├──────┼───────────────┼────────────────────────────┼──────────────┤\n", "│ 1 │ 22921 │ 53773 │ 290.0 │\n", "│ 2 │ 77060 │ 50706 │ 155.0 │\n", "│ 3 │ 18613 │ 31889 │ 220.0 │\n", "│ 4 │ 15663 │ 36824 │ 224.0 │\n", "│ 5 │ 51562 │ 45192 │ 870.0 │\n", "│ 6 │ 5607 │ 34500 │ 455.0 │\n", "│ 7 │ 14921 │ 30752 │ 445.0 │\n", "│ 8 │ 39955 │ 40093 │ 565.0 │\n", "│ 9 │ 16816 │ 32181 │ 745.0 │\n", "│ 10 │ 22282 │ 36241 │ 589.0 │\n", "│ 11 │ 37858 │ 40834 │ 580.0 │\n", "⋮\n", "│ 3218 │ 0 │ 23572 │ NA │\n", "│ 3219 │ 0 │ 31354 │ NA │\n", "│ 3220 │ 15556 │ 14752 │ NA │\n", "│ 3221 │ 700 │ 17616 │ NA │\n", "│ 3222 │ 2492 │ 16462 │ NA │\n", "│ 3223 │ 1071 │ 16598 │ NA │\n", "│ 3224 │ 3509 │ 16014 │ NA │\n", "│ 3225 │ 5749 │ 17926 │ NA │\n", "│ 3226 │ 7740 │ 14728 │ NA │\n", "│ 3227 │ 4000 │ NA │ NA │\n", "│ 3228 │ 1080 │ NA │ NA │\n", "│ 3229 │ 718 │ NA │ NA │\n", "\n", "│ Row │ TotalArea_sqmi │ LandArea_sqmi │ WaterArea_sqmi │\n", "├──────┼────────────────┼───────────────┼────────────────┤\n", "│ 1 │ 604.39 │ 594.44 │ 9.95 │\n", "│ 2 │ 2027.31 │ 1589.78 │ 437.53 │\n", "│ 3 │ 904.52 │ 884.88 │ 19.64 │\n", "│ 4 │ 626.17 │ 622.58 │ 3.59 │\n", "│ 5 │ 650.63 │ 644.78 │ 5.85 │\n", "│ 6 │ 625.14 │ 622.8 │ 2.34 │\n", "│ 7 │ 777.88 │ 776.83 │ 1.05 │\n", "│ 8 │ 612.29 │ 605.87 │ 6.42 │\n", "│ 9 │ 603.11 │ 596.53 │ 6.58 │\n", "│ 10 │ 599.98 │ 553.7 │ 46.28 │\n", "│ 11 │ 700.8 │ 692.85 │ 7.95 │\n", "⋮\n", "│ 3218 │ 41.81 │ 23.24 │ 18.57 │\n", "│ 3219 │ 21.38 │ 20.76 │ 0.62 │\n", "│ 3220 │ 115.07 │ 113.53 │ 1.54 │\n", "│ 3221 │ 37.5 │ 27.73 │ 9.77 │\n", "│ 3222 │ 68.18 │ 45.86 │ 22.32 │\n", "│ 3223 │ 263.99 │ 50.77 │ 213.22 │\n", "│ 3224 │ 37.04 │ 35.64 │ 1.4 │\n", "│ 3225 │ 83.24 │ 55.21 │ 28.03 │\n", "│ 3226 │ 68.82 │ 68.19 │ 0.63 │\n", "│ 3227 │ 332.66 │ 83.32 │ 249.34 │\n", "│ 3228 │ 91.76 │ 19.69 │ 72.07 │\n", "│ 3229 │ 308.51 │ 31.31 │ 277.2 │" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ " countyinfo = readtable(\"../data/county-info.csv\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3229-element DataArrays.DataArray{Float64,1}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "areas = countyinfo[:, :LandArea_sqmi]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: replacing module _mimi_implementation_Brewery\n" ] }, { "data": { "text/plain": [ "initbrewery (generic function with 1 method)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The Brewery Component\n", "# This is how we make beer\n", "\n", "using Mimi\n", "\n", "@defcomp Brewery begin\n", " region = Index()\n", "\n", " production = Parameter(index=[region, time], unit=\"liter\")\n", "\n", " hopsdemand = Variable(index=[region, time], unit=\"MT\")\n", " waterdemand = Variable(index=[region, time], unit=\"1000 m^3\")\n", "end\n", "\n", "function run_timestep(c::Brewery, tt::Int64)\n", " v, p, d = getvpd(c)\n", "\n", " v.hopsdemand[:, tt] = .07 * p.production[:, tt]\n", " v.waterdemand[:, tt] = 2 * p.production[:, tt] / 1e6\n", "end\n", "\n", "function initbrewery(m::Model)\n", " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n", "\n", " brewery[:production] = repeat(production, outer=[1, numsteps])\n", "end\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = Model()\n", "setindex(m, :time, [1]) # Single period\n", "setindex(m, :region, length(dropna(readtable(\"../data/county-info.csv\")[:LandArea_sqmi])))\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n ...\nwhile loading In[31], in expression starting on line 1", "output_type": "error", "traceback": [ "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n ...\nwhile loading In[31], in expression starting on line 1", "", " in call at essentials.jl:57", " in initbrewery at In[18]:27" ] } ], "source": [ "initbrewery(m)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3229-element DataArrays.DataArray{Float64,1}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ " brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "initbrewer (generic function with 1 method)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function initbrewer(m::Model)\n", " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n", "\n", " brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n", "end\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "run(m)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 41.6108\n", " 111.285 \n", " 61.9416\n", " 43.5806\n", " 45.1346\n", " 43.596 \n", " 54.3781\n", " 42.4109\n", " 41.7571\n", " 38.759 \n", " 48.4995\n", " 63.945 \n", " 86.6922\n", " ⋮ \n", " 1.6268\n", " 1.4532\n", " 7.9471\n", " 1.9411\n", " 3.2102\n", " 3.5539\n", " 2.4948\n", " 3.8647\n", " 4.7733\n", " 5.8324\n", " 1.3783\n", " 2.1917" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:Brewery, :hopsdemand]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 0.00118888\n", " 0.00317956\n", " 0.00176976\n", " 0.00124516\n", " 0.00128956\n", " 0.0012456 \n", " 0.00155366\n", " 0.00121174\n", " 0.00119306\n", " 0.0011074 \n", " 0.0013857 \n", " 0.001827 \n", " 0.00247692\n", " ⋮ \n", " 4.648e-5 \n", " 4.152e-5 \n", " 0.00022706\n", " 5.546e-5 \n", " 9.172e-5 \n", " 0.00010154\n", " 7.128e-5 \n", " 0.00011042\n", " 0.00013638\n", " 0.00016664\n", " 3.938e-5 \n", " 6.262e-5 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:Brewery, :waterdemand]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.0", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
fastats/fastats	fastats/maths/_notebooks/sum_squared_dev.ipynb	2	38868	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sum of squared deviations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TL;DR Comparison of approaches" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import jit\n", "import numpy as np\n", "import pandas as pd\n", "from numpy import sum, power, mean\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "import matplotlib\n", "matplotlib.rcParams['figure.figsize'] = (16, 8)\n", "\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit(nopython=True)\n", "def sum_sq_dev(x):\n", " return sum(power(x - mean(x), 2))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit(nopython=True)\n", "def sum_sq_dev_experimental(x):\n", " population_mean = mean(x)\n", " total = 0.0\n", "\n", " for value in x:\n", " total += power((value - population_mean), 2)\n", "\n", " return total" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.random.randn(1000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.testing.assert_almost_equal(sum_sq_dev(x), sum_sq_dev_experimental(x)) # Basic sanity test" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 12.79 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 463 ns per loop\n", "The slowest run took 10.28 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 346 ns per loop\n", "The slowest run took 5.93 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 466 ns per loop\n", "The slowest run took 11.84 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 334 ns per loop\n", "The slowest run took 4.91 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 724 ns per loop\n", "The slowest run took 7.88 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 551 ns per loop\n", "The slowest run took 8.12 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "100000 loops, best of 3: 3.16 µs per loop\n", "100000 loops, best of 3: 2.93 µs per loop\n", "10000 loops, best of 3: 27.6 µs per loop\n", "10000 loops, best of 3: 26 µs per loop\n", "1000 loops, best of 3: 291 µs per loop\n", "1000 loops, best of 3: 257 µs per loop\n", "100 loops, best of 3: 5.69 ms per loop\n", "100 loops, best of 3: 2.62 ms per loop\n" ] } ], "source": [ "results = []\n", "\n", "for exponent in range(7):\n", " population_size = 10*exponent\n", " x = np.random.randn(population_size)\n", " timings_v1 = %timeit -o sum_sq_dev(x)\n", " timings_v2 = %timeit -o sum_sq_dev_experimental(x)\n", " np.testing.assert_almost_equal(sum_sq_dev(x), sum_sq_dev_experimental(x))\n", " results.append((population_size, timings_v1.best, timings_v2.best))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sum_sq_dev</th>\n", " <th>sum_sq_dev_experimental</th>\n", " </tr>\n", " <tr>\n", " <th>population_size</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.000463</td>\n", " <td>0.000346</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0.000466</td>\n", " <td>0.000334</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>0.000724</td>\n", " <td>0.000551</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>0.003164</td>\n", " <td>0.002926</td>\n", " </tr>\n", " <tr>\n", " <th>10000</th>\n", " <td>0.027611</td>\n", " <td>0.025958</td>\n", " </tr>\n", " <tr>\n", " <th>100000</th>\n", " <td>0.291489</td>\n", " <td>0.257195</td>\n", " </tr>\n", " <tr>\n", " <th>1000000</th>\n", " <td>5.694846</td>\n", " <td>2.621306</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sum_sq_dev sum_sq_dev_experimental\n", "population_size \n", "1 0.000463 0.000346\n", "10 0.000466 0.000334\n", "100 0.000724 0.000551\n", "1000 0.003164 0.002926\n", "10000 0.027611 0.025958\n", "100000 0.291489 0.257195\n", "1000000 5.694846 2.621306" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x21f33ac0470>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x21f37305400>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lF5XSLiuDT57fP+koLZrFWJIkSZLaoJ17avjDwrV8/Jy+dOvg0K3DsRhLkiRJ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\ngKFc3/sW4B8AUkr/CkyNiElZ2cMppZ0ppQ2Uku7pB9nHs8B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"text/plain": [ "<matplotlib.figure.Figure at 0x21f37378358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(np.array(results), columns=['population_size', 'sum_sq_dev', 'sum_sq_dev_experimental'])\n", "df.population_size = df.population_size.astype(int)\n", "df = df.set_index('population_size')\n", "df.apply(lambda x: x 1000)\n", "\n", "df.plot(logx=True);\n", "plt.ylabel('best time (ms)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Stanford-BIS/syde556	SYDE 556 Lecture 2 Representation.ipynb	1	658037	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## SYDE 556/750: Simulating Neurobiological Systems\n", "\n", "Accompanying Readings: Chapter 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NEF Principle 1 - Representation\n", "\n", "- Activity of neurons change over time\n", "\n", "<img src=files/lecture2/spikes.jpg width=800px>\n", "\n", "- This probably means something\n", "- Sometimes it seems pretty clear what it means" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/KE952yueVLA?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10185c990>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('KE952yueVLA', width=720, height=400, loop=1, autoplay=0)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/lfNVv0A8QvI?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10189b2d0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('lfNVv0A8QvI', width=720, height=400, loop=1, autoplay=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Some sort of mapping between neural activity and a state in the world\n", " - my location\n", " - head tilt\n", " - image\n", " - remembered location\n", " \n", "- Intuitively, we call this \"representation\"\n", " - In neuroscience, people talk about the 'neural code'\n", " - To formalize this notion, the NEF uses information theory (or coding theory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representation formalism\n", "\n", "- Value being represented: $x$\n", "- Neural activity: $a$\n", "- Neuron index: $i$\n", "\n", "### Encoding and decoding\n", "\n", "- Have to define both to define a code\n", "- Lossless code (e.g. Morse Code):\n", " - encoding: $a = f(x)$\n", " - decodng: $x = f^{-1}(a)$\n", "- Lossy code:\n", " - encoding: $a = f(x)$\n", " - decoding: $\\hat{x} = g(a) \\approx x$\n", "\n", "## Distributed representation\n", "\n", "- Not just one neuron per $x$ value (or per $x$)\n", " - Many different $a$ values for a single $x$\n", "- Encoding: $a_i = f_i(x)$\n", "- Decoding: $\\hat{x} = g(a_0, a_1, a_2, a_3, ...)$\n", "\n", "## Example: binary representation\n", "\n", "Encoding (nonlinear):\n", "$$\n", "a_i = \\begin{cases}\n", " 1 &\\mbox{if } x \\mod {2^{i}} > 2^{i-1} \\\\ \n", " 0 &\\mbox{otherwise} \n", " \\end{cases}\n", "$$\n", "\n", "Decoding (linear):\n", "$$\n", "\\hat{x} = \\sum_i a_i 2^{i-1}\n", "$$\n", "\n", "--------------------\n", "\n", "Suppose: $x = 13$\n", "\n", "Encoding: \n", "$a_1 = 1$, $a_2 = 0$, $a_3 = 1$, $a_4 = 1$\n", "\n", "Decoding:\n", "$\\hat{x} = 11+02+14+18 = 13$\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear decoding\n", "\n", "- Write decoder as $\\hat{x} = \\sum_ia_i d_i$\n", "\n", "- Linear decoding is nice and simple\n", " - Works fine with non-linear encoding (!)\n", " \n", "- The NEF uses linear decoding, but what about the encoding?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neuron encoding\n", "\n", "$a_i = f_i(x)$ \n", "\n", "- What do we know about neurons?\n", "\n", "<img src=files/lecture1/NeuronStructure.jpg>\n", "\n", "- Firing rate goes up as total input current goes up\n", " - $a_i = G_i(J)$\n", " \n", "- What is $G_i$?\n", " - depends on how detailed a neuron model we want.\n", " \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/hxdPdKbqm_I?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10189b210>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('hxdPdKbqm_I', width=720, height=400, loop=1, autoplay=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rectified Linear Neuron" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1019bbad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Rectified linear neuron\n", "%pylab inline\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.RectifiedLinear()\n", "\n", "J = numpy.linspace(-1,1,100)\n", "\n", "plot(J, n.rates(J, gain=10, bias=-5))\n", "xlabel('J (current)')\n", "ylabel('$a$ (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Leaky integrate-and-fire neuron\n", "\n", "$ a = {1 \\over {\\tau_{ref}-\\tau_{RC}ln(1-{1 \\over J})}}$" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1040585d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "\n", "# Leaky integrate and fire\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate(tau_rc=0.02, tau_ref=0.002) #n is a Nengo LIF neuron, these are defaults\n", "\n", "J = numpy.linspace(-1,10,100)\n", "\n", "plot(J, n.rates(J, gain=1, bias=-3)) \n", "xlabel('J (current)')\n", "ylabel('$a$ (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Response functions\n", "- These are called \"response functions\"\n", " - How much neural firing changes with change in current\n", " - Similar for many classes of cells (e.g. pyramidal cells - most of cortex)\n", " - This is the $G_i$ function in the NEF: it can be pretty much anything\n", " \n", "## Tuning Curves\n", "- Neurons seem to be sensitive to particular values of $x$\n", " - How are neurons 'tuned' to a representation? or...\n", " \n", "- What's the mapping between $x$ and $a$?\n", " - Recall 'place cells', and 'edge detectors'\n", "\n", "- Sometimes they are fairly straight forward:\n", "\n", "<img src=files/lecture2/tuning_curve_auditory.gif>\n", "\n", "- But not often:\n", "\n", "<img src=files/lecture2/tuning_curve.jpg>\n", "\n", "<img src=files/lecture2/orientation_tuning.png>\n", "\n", "- Is there a general form?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tuning curves (cont.)\n", "- The NEF suggests that there is...\n", " - Something generic and simple\n", " - That covers all the above cases (and more)\n", "- Let's start with the simpler case:\n", "\n", "<img src=files/lecture2/tuning_curve_auditory.gif>\n", "\n", "- Note that the experimenters are graphing $a$, as a function of $x$\n", " - $x$ is much easier to measure than $J$\n", " - So, there are two mappings of interest:\n", " 1. $x$->$J$\n", " 2. $J$->$a$ (response function)\n", " - Together these give the tuning curve\n", " \n", "- $x$ is the volume of the sound in this case\n", "\n", "- Any ideas?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1041727d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate() #n is a Nengo LIF neuron\n", "\n", "x = numpy.linspace(-100,0,100)\n", "\n", "plot(x, n.rates(x, gain=1, bias=50), 'b') # x1+50\n", "plot(x, n.rates(x, gain=0.1, bias=10), 'r') # x0.1+10\n", "plot(x, n.rates(x, gain=0.5, bias=5), 'g') # x0.05+5\n", "plot(x, n.rates(x, gain=0.1, bias=4), 'c') #x0.1+4))\n", "\n", "xlabel('x')\n", "ylabel('a');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For mapping #1, the NEF uses a linear map:\n", "$ J = \\alpha x + J^{bias} $\n", "\n", "- But what about type (c) in this graph?\n", "\n", "<img src=files/lecture2/tuning_curve.jpg>\n", "\n", "- Easy enough:\n", "\n", "$ J = - \\alpha x + J^{bias} $\n", "\n", "- But what about type(b)? Or these ones?\n", "\n", "<img src=files/lecture2/orientation_tuning.png>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- There's usually some $x$ which gives a maximum firing rate\n", " - ...and thus a maximum $J$\n", "- Firing rate (and $J$) decrease as you get farther from the preferred $x$ value\n", " - So something like $J = \\alpha [sim(x, x_{pref})] + J^{bias}$\n", "- What sort of similarity measure? \n", "- Let's think about $x$ for a moment\n", " - $x$ can be anything... scalar, vector, etc.\n", " - Does thinking of it as a vector help?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Encoding Equation (i.e. Tuning Curves)\n", "\n", "- Here is the general form we use for everything (it has both 'mappings' in it)\n", "- $a_i = G_i[\\alpha_i x \\cdot e_i + J_i^{bias}] $\n", " - $\\alpha$ is a gain term (constrained to always be positive)\n", " - $J^{bias}$ is a constant bias term\n", " - $e$ is the encoder, or the preferred direction vector\n", " - $G$ is the neuron model\n", " - $i$ indexes the neuron\n", "- To simplify life, we always assume $e$ is of unit length\n", " - Otherwise we could combine $\\alpha$ and $e$\n", "- In the 1D case, $e$ is either +1 or -1\n", "- In higher dimensions, what happens?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UQvyZZVnfGjpGDYAmjmPDonuzXMjwRNhvDm13cSDE04jbZSuIhXtYiLdx4Fvw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"text/plain": [ "<matplotlib.figure.Figure at 0x1047ba0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate()\n", "\n", "e = numpy.array([1.0, 1.0]) \n", "e = e/numpy.linalg.norm(e)\n", "\n", "a = numpy.linspace(-1,1,50)\n", "b = numpy.linspace(-1,1,50)\n", "\n", "X,Y = numpy.meshgrid(a, b)\n", "\n", "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", "fig = figure()\n", "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", "p = ax.plot_surface(X, Y, n.rates((Xe[0]+Ye[1]), gain=1, bias=1.5), \n", " linewidth=0, cstride=1, rstride=1, cmap=pylab.cm.jet)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- But that's not how people normally plot it\n", "- It might not make sense to sample every possible x\n", "- Instead they might do some subset\n", " - For example, what if we just plot the points around the unit circle?" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104e60490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104e60b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import nengo\n", "import numpy\n", "\n", "n = nengo.neurons.LIFRate()\n", "\n", "theta = numpy.linspace(0, 2numpy.pi, 100)\n", "x = numpy.array([numpy.cos(theta), numpy.sin(theta)])\n", "plot(x[0],x[1])\n", "axis('equal')\n", "\n", "e = numpy.array([1.0, 1.0])\n", "e = e/numpy.linalg.norm(e)\n", "\n", "plot([0,e[0]], [0,e[1]],'r')\n", "\n", "gain = 1\n", "bias = 2.5\n", "\n", "figure()\n", "plot(theta, n.rates(numpy.dot(x.T, e), gain=gain, bias=bias))\n", "plot([numpy.arctan2(e[1],e[0])],0,'rv')\n", "xlabel('angle')\n", "ylabel('firing rate')\n", "xlim(0, 2numpy.pi);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- That starts looking a lot more like the real data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notation\n", "\n", "- Encoding\n", " - $a_i = G_i[\\alpha_i x \\cdot e_i + J^{bias}_i]$\n", " \n", "- Decoding\n", " - $\\hat{x} = \\sum_i a_i d_i$\n", " \n", "- The textbook uses $\\phi$ for $d$ and $\\tilde \\phi$ for $e$\n", " - We're switching to $d$ (for decoder) and $e$ (for encoder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoder\n", "\n", "- But where do we get $d_i$ from?\n", " - $\\hat{x}=\\sum a_i d_i$\n", " \n", "- Find the optimal $d_i$\n", " - How?\n", " - Math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving for $d$\n", "\n", "- Minimize the average error over all $x$, i.e.,\n", "\n", "$ E = \\frac{1}{2}\\int_{-1}^1 (x-\\hat{x})^2 \\; dx $\n", "\n", "- Substitute for $\\hat{x}$:\n", "\n", "$ \n", "\\begin{align}\n", "E = \\frac{1}{2}\\int_{-1}^1 \\left(x-\\sum_i^N a_i d_i \\right)^2 \\; dx \n", "\\end{align}\n", "$\n", "\n", "- Take the derivative with respect to $d_i$:\n", "\n", "$\n", "\\begin{align}\n", "{{\\partial E} \\over {\\partial d_i}} &= {1 \\over 2} \\int_{-1}^1 2 \\left[ x-\\sum_j a_j d_j \\right] (-a_i) \\; dx \\\\\n", "{{\\partial E} \\over {\\partial d_i}} &= - \\int_{-1}^1 a_i x \\; dx + \\int_{-1}^1 \\sum_j a_j d_j a_i \\; dx \n", "\\end{align}\n", "$\n", "\n", "- At the minimum (i.e. smallest error), $ {{\\partial E} \\over {\\partial d_i}} = 0$\n", "\n", "$\n", "\\begin{align}\n", "\\int_{-1}^1 a_i x \\; dx &= \\int_{-1}^1 \\sum_j(a_j d_j a_i) \\; dx \\\\\n", "\\int_{-1}^1 a_i x \\; dx &= \\sum_j \\left(\\int_{-1}^1 a_i a_j \\; dx\\right)d_j \n", "\\end{align}\n", "$\n", "\n", "- That's a system of $N$ equations and $N$ unknowns\n", "- In fact, we can rewrite this in matrix form\n", " \n", "$ \\Upsilon = \\Gamma d $\n", "\n", "where\n", "\n", "$ \n", "\\begin{align}\n", "\\Upsilon_i &= {1 \\over 2} \\int_{-1}^1 a_i x \\;dx\\\\\n", "\\Gamma_{ij} &= {1 \\over 2} \\int_{-1}^1 a_i a_j \\;dx \n", "\\end{align}\n", "$\n", "\n", "- Do we have to do the integral over all $x$?\n", " - Approximate the integral by sampling over $x$\n", " - $S$ is the number of $x$ values to use ($S$ for samples) \n", "\n", "$ \n", "\\begin{align}\n", "\\sum_x a_i x / S &= \\sum_j \\left(\\sum_x a_i a_j /S \\right)d_j \\\\\n", "\\Upsilon &= \\Gamma d \n", "\\end{align}\n", "$\n", "\n", "where\n", "\n", "$\n", "\\begin{align}\n", "\\Upsilon_i &= \\sum_x a_i x / S \\\\\n", "\\Gamma_{ij} &= \\sum_x a_i a_j / S \n", "\\end{align}\n", "$\n", "\n", "- Notice that if $A$ is the matrix of activities (the firing rate for each neuron for each $x$ value), then $\\Gamma=A^T A / S$ and $\\Upsilon=A^T x / S$\n", "\n", "So given \n", "\n", "$ \\Upsilon = \\Gamma d $\n", "\n", "then\n", "\n", "$ d = \\Gamma^{-1} \\Upsilon $\n", "\n", "or, equivalently\n", "\n", "$ d_i = \\sum_j \\Gamma^{-1}_{ij} \\Upsilon_j $\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.0244915143696\n" ] }, { "data": { "image/png": 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BTgPuSHLcKcATBFH3QpEd0T7VJ2JtPPdQwgC1g+2B5sBbqU85NolWEG2BoejT/hJUFdUA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\nmCHRBqIz2lOjpPEGIjNqqYEAMPeglb6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"text/plain": [ "<matplotlib.figure.Figure at 0x100cea7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1044c9e90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104e594d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 10\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates=Uniform(100,200)) #Defaults to LIF neurons, \n", " #with random gains and biases for\n", " #neurons between 100-200hz over -1,1\n", "\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqL2(reg=0)) #reg=0 means ignore noise\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "xhat = numpy.dot(A, d)\n", "\n", "pyplot.plot(x, A)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "figure()\n", "plot(x, xhat-x)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}-x$')\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)*2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What happens to the error with more neurons?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Noise\n", "\n", "- Neurons aren't perfect\n", " - Axonal jitter\n", " - Neurotransmitter vesicle release failure (~80%)\n", " - Amount of neurotransmitter per vesicle\n", " - Thermal noise\n", " - Ion channel noise (# of channels open and closed)\n", " - Network effects\n", " - More information: http://icwww.epfl.ch/~gerstner/SPNM/node33.html\n", "- How do we include this noise as well?\n", " - Make the neuron model more complicated\n", " - Simple approach: add gaussian random noise to $a_i$\n", " - Set noise standard deviation $\\sigma$ to 20% of maximum firing rate\n", " - Each $a_i$ value for each $x$ value gets a different noise value added to it\n", "- What effect does this have on decoding? " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.365644156802\n" ] }, { "data": { "image/png": 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kW76G8ATilG2ed7oVRdTPE2s2Fsin1C/1fRWSoYz528rqvhoxdasQU3UHLmWV\nx7GoGzDAdGKHc6KPR7RMAB7hwU457GupgvdrH8DDoZhgDdPNkT68LItfAat1nffC71Ilmvu0ZkAx\nSVgKElXwOUWyQIYi8kAhYmFMsjqVpW7jZHXrenkibuG8g4iRQMzZ03npTDcMBhkG957r8/iSIh3J\nMToJqiMwo7w85Vuapo589FGr94KT+PiQ6AikoRbIcGClrUBqfRhGJjCGTuX/AUqhz0DcCWQhMM4M\nz40MfWQlxiY3n4nTRLcPrDyGSPLgTiCO9bGA3LLkkJ4khuFDrCq3qhMx4VwTiBMyCDis7JJNNsGs\n6erbaMXpMT5qNES/dLRAzEghG4GozEqSOsdT/SjShxj8JKzOZDiRHehQzwLRDiA1pOrMRwWJSqNL\nciHdMj6qs6zCQSpvBiJGYpWwbkDq/Fio19rWzBLuiJDUZOoaaGlBFkbqVsorOtTLUak3Y1SixXbF\nfZYYDq8gBRadnInnEhOBXxvGefm+nO8wZ8fqWmRSsQEYEx9fXUD993IOMM5sJe2GcAQSKZkwGgf6\nNcCH6Ppp2bbDYNwkLF0/jLxTgyPsU3DR2GR2Hc7AuTOokw9kGCw9TuB6Y7ZAKpJYTbAF0hE4ga5X\nOGwfM873F0Lh0IM8ZH09/BxUJrdUwIjHeJyuOIfIdUfMQitmehJgxFPzT+DCZ4ehA/M/6MlvVIMI\nBAiRsUp6Mqg8A6pas77P1KjyREKtn1glrB7AGGU6FnWdU8g9S7Nt0xE4rutUIQQyx7auTsZK3QZl\nneo9LyuAjmZ0loU+wA4NYn5AzcZTHwC3xfrZJkYect9Hn+sT+bLhwIH8vps2jWyH+DiuBe2XZu0q\nh4mddhJ5pnTnvSmNs/OBRHagiw9wpvn3SojrQfgk2+jCef3E06ZjKq1aLkXqb4XCyQcyDFYoAjkr\nsRJILlKl294V0S5fTQT8tp+YcT4SiF2myUIysEGY3s7QnXExwx7zMe1B2u+Gz1dSwmzgDPUH9/HA\nQpvj6WJgrganF+Qxr0U1M4HpnU8RtzOd0ijO241AhoIUSjxwGX+vSqco+SBPAddGsc9QAok1Cqsr\ncv9usS0LlbHMJEKVgszQ7BFhdQSSvI+kM7m0tO/c9AsFO9sb5v+w4x/AdxSOMfHnCnmIRdVULYub\nKdSl7733/adOnuxwDBhshZeacPNNzsS9KVsmUGVWvXZCEZIDFurHxCSf8A50KU2iEwjjXwEVnWkM\nAoHeJLYxSSWDAAAgAElEQVRVtGj1Is4TJKf7MQwK0oDNZjWNTJzHvHoEYmD4gJxOhSwAOmMYllVn\nJ5D5NEMCmUGgNtKdSHVba/nNSNRPF2SmsdxpB6VdWTuZOa0JDuUN/XLs8pWUb++4biF+nrrhRsbe\nsp5K5efVPkcp/bBn8MDpAicCWUXAAvm/5CIyU7bzBlJw8TJT7gmHUAKJtZxJV+DPwG22ATmUQCwn\n4XhghdmcxsJKoD2oLonHaH2mM20cZJxQGetsCWQhktE/5iz20djIA/4GXGOX/zy4QaWAegZ4fvLk\nV+aOGfPhyyHPFbhL0KYfxDEJN5x8henb2ImzFZKN5HQ4zdQtjETKgFgS+kooaQNJblFYIARyYUQ/\nSEL6KHxJUFMzDbEIQscjiRatU0pUPFQMhNMZiDSXBRwJbiRVBycLJAM49ftP9BJk8mONhY3mQIdz\nTyCvISZWL2SgvAvJpJyMmKMXmf+DmHFvmr9nIeVHHCWsnd9jf1d25SdSMcBc5OQ8rovAAnrSYUMc\n3x/0MpB5pBWDEmp5Cbg//zjM7iaykWGgGQb5hsFthsFzhsEThoHF7K4S1ty5cVcD3876kFW+ajZo\nQgTrENksHEIIRKtEupeFHscNXZEkPR+B0iouFgiXUOf/qDueFRkzxVdDp4oOlFJfQpsNTDTDneEs\nCcS0CP8JfLuh+2gC5CITmSQCrQIiwjAYYhiMbG6+E8OgpWGw2jDqvnMbVHskz6EFMLBbt/UazoO2\nW3j9ZuQZ6OewLgKBAO4ylilfaeEk8ZB3QDsi7o8/OFynCV0/iLzPA1y3AUjqcAG15ac4erQceAcp\nbRKAhPaWEXi3h8DGQ8i9q8A9B8QKtAm1kuyO9XUE/CCNFsIL555AvoHMDBKRC3sROIbIST2RL9Qe\n9vYI4rvojQxcjjgxjHSFtmskyweaM5kgC0QJY7fEymad8oMH+fbYdDSeA24w+w7/P+Du9mW0LujG\neMPgVeQLXIZIGZuQL3u5YdAXZwIpiour4vDhnBeA6+LL6EpAz3yHyDKWUwRYVI50JdfXFnlR/wvc\naq5ys0BC/R8WLBkrt6JD/fa2mtQa20wgnPdsLRCQ6LtrVLCv5pzAMGiJnMchYuj1YgYnvIMEBhww\nDF4yDG40jGbRgXE04jge6rBuLFJL7i7Tp2GV2AiFi4SlKdyjscL5Pyy4OdKjcaCHTqLiodgH9+QB\nKOjsUi0hgoylHoW2o1DKsmReIfA+2mH3g/wQ3l1CZAd6DnDAoW2C3SqxmktBM7NAmgp5CVTPuYSP\nawgwrv1hvRBYqPkBP7+m7zvX8dETfvw8a5UJ0GBPZRsWqSQSd8eTX6uYi1hEmbrO9brOX4E7kDIo\nCz59n8uAtnbt3jC01gMHLkx+990fvDpRZxUyM7Kik94FrjLrb7mhjkBUoFlOtI70fKBAg1qEQG42\nj1XPAlm3btwZc59OxRw/TubMeAWtK9qzHeemPbOQ6KkM5BjhNOOI0GSwnkew7+ZcIRfYZ/axeBeJ\n0okGU4Ajuk5PpB/EckSS3WsYzDcM/scwIvZUOV8xHvF/OZX070dwYrBbiGm4/Cw3P0i4HBALESwQ\nFxhGOiLz2Dt7ZkByKaRY2ea/QlpKh2I+rvlLKhmt5mckJPckLskK2PkUmdyFWi1Whn5uW4qvnMJj\nbTk7B7q1vd0CabRKvNB8CSQf+ORrzKxGHujQQXNCrcwaHkdp1/PcimpW3/3v0J3s+AFLS3uyt1xx\naNJCFuo6280OgQDoOkrXeQG4qCaFn9ckQuFV4uQ2Z6Cvtm17cOO0af9zErMEgyYOfTT50vcQvpFL\nZ2C/khd1pwrMhKNxpHdFSk6jyaysALHsQpv5ZL/99v1ZwDzQHJIlteM92L71DC2LiatvgZiw/CAD\ngXWhGbHhYBg8YBg85rDqfJGx7C/op0C+2a8+Eu5D6pyh6xToOk/rOpcj391jSCvf8+H6GoILkT4u\nTkmk/ZBadBgGyUjIamgdKTDL6rjIewYwFFSotRaNhOVggSiNyBbIRcAidL3ctiwL4g4RkH8HIHWx\nQrEAGG/mWISiPx02HUDlx1Ha8pi5rBa5f6FWiEWq93+fZxalUnlJquS7QQNCeAmVsKSkiWeBRIE8\nYGF/NqS0pXgw9SWs8ZfezkXAGJ5e9zdKs1eaBRCDcHgS7dY+wbPIzGWE28F0nfXAiJpkKvZfxwJT\n0vodkLx48denIo70vtQvIR1GxlItgNY38OYh4E/IjK8X0eeC1BGIif8i0R/1LJD16y/ohbN8BcAE\nFqzdQ14NuBLICkSHvZIo5SvTn/Qb4Cc4yxVzgA4q+lpDTYW6F9HsqfIBktTqCsOgGzLovBG6Ttc5\no+vMBH4I/PLsWrtGB8PAbxhOeUeqM6inQPWvv851XwmIs3kqMM4hqKCOQJBBz+pCGARdpxzx5znk\n/GhnELK2lStSrREyijR7drJAugBlZm4WyPM2M2QbBx8g2VC+Cxh6A2/4cCMQXS+6hNmVVzPdyac5\nhB4zd5GQd4zP2vWyLf8vYmHbx+D9lKf1AL55P08c2QpxPwv4FhtCIHvN8zuCTFzzCN+6OmY0VwLJ\n16CkmHZ7pzBrIjYCWZZNbmkiXT7pQiowmSP9bweecdnPACTx6XMi9PfQdUoTTrK1zRpeRx7+W4Ab\ny8tbLQeG1aI5Eci7iNbv9D10Bore5KarkF7k7yIEEq2EFUogbwBXtFvEcWwEUlvryz5xouNIwhDI\nTbyxdwP9pVGNA4GYMtlsZEYdkUDMQedhJNFxFNAtVM4xQ4RfJLis/FnBMIg3DH4TYyRV7ptv/jQR\n1N/N/6cTWcb6PvCiOUg6QtfVH0+fTtvGF2OFjCfI0lVpoH6PaOOTEcs0WgwFduk6G5Ge4rZBUcWZ\n/1tWgtvgZiGcjBXqB+kFbA2bRS44AsSDsvsj7fJVFvAvJFxXfGwyM78URwKp2AMU92TbRKRoYT3C\nMwxS/4fH23Zl168dzmcI3T6uoWO7vWxO7WcVKEVkvuMEKxD7ONJHB2a0oXjgFqj9nvh8oWESln35\nWiRopxRdd6v8HTOaK4HkGAa+46QvHM1nAwnU22/10hBmrMuguNbH5fhVHjJbcSsp0h/5olcQxgKx\noEFxr7+wBHEkTtJ1jpjHVqdIG0aI+a3J/yUu+85JoHIf0pvjAYR8LAskJgnLPNZhYHG/hxkFtDcH\n0+SdOwemKqUdM1vxOmIMS7UC8vnnP3+vcPaBgMhYyUQgEHPw/iNSskbX9Topr5fD5i8DN9oadZ0t\nrkLi3TvG8Jm8DRsuiCMQFfQxMNwwnBt7mbLNnUh12XAY/txzjy0AftGUVogpEQ0DhuflbUgA9X0k\neKQz4gh/Cud77warejVI1Wq7jNUFOAKaVfLGzf9hIVyh05nAFFvIejT+D0wnfGhzKUu+8iEBGs8j\nhGL1Ku+OPGOhEzyrkOKKNpz4GkJ4ThLWbfFUl7fhxEgMI9TJPoTMtW1IzSgnUc3BqnIhsIJbfEAm\n81qmsevUgFb0P/ojGOSDox0DSZVnI2GBvJfWNTQamiuBnAQykyl7ayTLO/KScQpx7n5yyU6SRhby\nF/xUAN8D/glavdhqwyAdmaHsQR6+Ifgjtl0tBtrpOlt1nZ2ySFPAyhriBuPcBe0dnGe0OQ/xh0TE\n4vgIeel703AJC+AVXxW3EOjSmLVkyVWloIXOvILgQ+VWkbDm7bd/PADIddGtZyOSxEaHdUAdefwZ\nme1dZBIsCEnXk1E0ienfhcySGwP3IdEq0fbOBsgrLOwGJmnrOmeQdsdXuGx/M7Bc1+vdextUSyDt\n/ffvaY+Een83hvOJFT2U4lhlZVJiZWXyJiQLegpo3wRtH/JcNZRAFhPsSLfLV3BWFoi2G3mfrDyq\naPwfFkJlLMsC+QnQCpGXlxDINRL5StdDfXdWhOKK9hwdjXzvQQRiPtM/8KF+Ppg1FQRV11ZxxJ/p\nT3JxdxLSExlT/DxwGyjLB/kqEohTBqxlaYsH2LNb68zGbmlQlCAqRrcXBtMFIbjQuoHgnESYhkS2\nHrMtXmtep0cgUWAPkNeV3Z/0Y6N2VcHJAchgtOzqLVQl1GKYmde3IpnPThgAbNB1lBnWewAZwMPB\nKZSXOKpXplCai/ML8A5wXWjmdQcOd/0xf+0H/Nx0Slsv+kEiWCDmvroQKNNiYQYwJukwhxAZq9Py\n5Zf5CCNfmchNosIoL0+ZjDzE9XqKaHLt2VaQQMgZaQkJZXnAE0i44yRdx55NbO/fEgq3kMeYYBj0\nQQa4t4i+dzZA3tGjnRMJJu1w4bz3YjrPw8CapY5ALKIHTMulKTBi586BJ9evvyDh9tt//xowGbS6\nfhRLGNOpJ1udci7qwZw4jMPdAnEikHBlgCI1e7NHY8VCIDZHuvIBQ2FcFTL7vxWZRFhKATj7PyBA\nICszONQTce6nh0jOE5D37R8plNa04+g3zYKMAL3oMeskGmvwxXdg8IlNwEsErBCr/lUaqGzidp/m\ntsrqTfD5ozDtlEQiLuh2nOtwaCRlnodTy9pcYI9OECGuQ6Rwj0CiQAHiBynfQu9jk5lzPTC25A/8\nVhMNfzWSg/KpOQtzwgCCwxHDOtJNOBLI5Xy49xRpNZpzK8pVyGwh6CV+hAen7CV3mxbQbrcB3dM5\ndpjIFkgGcFojuHquJuXSZ2TPIA7oVFycmbdjx+BWSBhiOORewOL3gbGlpa1X4lKryNx/Pfh81Rck\nJZ3ZiEgGF+t60MwIwhPIm8DlSvTns8G9SGTXBqK0QAyDeCCrpKRNKtKrwpKaPgAmmTki9u1HIt+/\na46SiSzzPAbqutqAhPjeE+2FxIgRq1ZNio+Lq5k5ZcqLcfZEOgVtR/PZE29xfYfv8Fw0sl4fpG6a\nFaa9CWhnGHUTmr40mgUCBPtBehN9gU67BdILSo7C4meBHyFjA8BnwGgefTQRCcEN7UIKNgLJY0+7\nElLWIu9Uum2b+4C/6zq1PtSyCSxYDPzMXDeEAa+WgfYB8k4eRCLwbgdlTSKOIImCV1GRdgyttqwy\njgkIwQHMyShlMs5EnAUc16jna3Mi7q1AJY0YwgvRE0gfJK79UiLPws8H7MGsgruRfht7sF3HT2lK\nFeOApRqqBnF0ujnPoT6BROMHcSSQB5hasZF+PqfyDKZ1ERSNpaD9DUwb8ii/eN62XSlQvJohLZDy\nIuHkNCf5ysIrHT8hA+g0Z85t4zp23HsYtFNuOzKtmZwxfLYFWP7yyw9tQZzfUaN//8XfLC1t2+o3\nv5l2h67j1A/BlUA0ecEWcRZ1qAyDVGTm+Rz19fFw6AQcVirOIuyOAKb1tJL60tp9wDO6HrF3TGZb\ninchL3lfxAr5eSghxQIFbZRzVdjhK1dOTq+piZ9H/UCQn2jwVgH5Jx/kkXDvgoXxBKwPzOiqJQRk\nrH4Ey7SRfCCuFbVNLAZ6SrQYeUTu42HBHso7Ar6hkOZVr9u2OQwcYffum4AdZqRSKLKAAwrtTFd2\nacNZUYPIvx0ADIPOiGP6P+b2y2/lvzuBuzGMjlA7hK7z2tF21KdAmYQIawfN7e1Sl4ZYJX/yKfbH\n1zLKPF+AOdkljNDUWTnQQderkO/mC7NAuiD1f3YgDsE7kFIjzyFy0F+J0PP7HKIA89xWMszoyTar\n/MR4JGZ7JBISGE77tyKwLDTYAhnNZx020bcG53ayIBFW9nDeX73LNade45bQWl9b89jbHbFk5Dh+\neuCvm61YCEcgnyScILn1Gvpv3Dh2aJcuGze4bGehHVBuEtis6dPvaw1MiCWjuqYm/hKfr7p64cLr\n+7hsshPIMgxSXNbHJGMpGBAiCd4GzNd19iGz02h9ILnAnmt5O289/av+xd325LZ3sZGaYdAecdK/\nEGmnPmqyttPjoq/x4W5guK6zFhkwvhfleTnhDmCOss2ODYN4pRi0YcPY9KysXR8gzn8NxPpArLI/\n3M0Ln6Rz/AIlDdvCwe7/sGDKWHURWJvMY8dRvwVDKMI50TFL98xFQp73mP9HA3OSoDSYejMsTTP3\nEYqlFBffhPM4kIA8+4eBPofIKNlGr0HIhMbyg3wXeNVs+QywrC3H+yIlmv6HzsvH4auqoN/vThDc\n0fQx4E5QluU2zjzW9KEHOF4RT5kWKCG/tUYj/uJdTtKwq0ToRiy/RWSxRkM4ApkKvI9YHxMQyedm\n8+/eiHnplAB2PqDOAvkvt37QnqNpSmaPE5Bifd8HnnMLCTRfMisCy8JqoD/+sNnDjgTiQ/UpInsX\n7j3SFwNZCropGfxv/wWPxlPf3LT8IHZH+ihgNP4giceVQDSoPt2VJZkfM3rHjsE9Bgz4NJR8QmGP\n/phZWdlycm2tZiCDZURMn94upbCwe+ekpLK3cWkAZuZXbMG9ztQMYJSKIvpMSen6lZgOUvO7rEvq\nQyZE3aKsT5VXXR2/7yI+STtMx2M38/rDCl4xz+M94EpT5gIJN34vxLfjiCv4YFBbjqfcy9NxBCYl\nvwX+11ZbLVb0Q6zZ39qXVVQkHz1zpvXWW2/dVYT4p6wouvuB6RrsOkqHjXfzwgzgSeXSU9y8j04E\nshgZAM3Kz5pVuTobKNb1sGX99wOdI4RVz0TkvWj9H4B2PC6uqvzPf05/1ud7ZDJ8/yGo1wwNYAmH\nD4/EmUAyELKoBgYW024XYsEdQaIYE5HmZ3Z/13JgRAolU1F8h37zB1KTOBNffBZBBKIVIZOi/zUX\n/Bz4M2g1V25D7WgbIAUN1NIcDn97pWPUXzRZ6AHo+nR0/QuzQG5EnKtO1R+rkJt+Y2OeTCOiANMC\nKab9xk+5kDJaXAP0nsLMHcjM8cUwn88BTgcNBlLsbCfhi6Y5EgjQt4hse2XeIJg5D+8h0Vi/K6XV\ns4fITDT3Z8cW6ueCDJRdBNX8D2eBUDyK91ovi+t58kS7lClTXlwU5nog+GHcDKi5c29bTJTf/aZN\no75dWxtXWVaW+l/CW3DhZKwzCIlEmiGDhAhXIRMfEKszDomgQdcpRQIBwmnvFvKKirodHsei2if5\n4bJebP0tMmteP1HnKmrYgyTTxSHWQyTnOQCX8dGgQ3TccyGf5mPKSrrOOmQ271QqIxr0QyyKm1Tg\nGR2+bt2FR5DB/OeIDDvctFLuA/5gbrf1Ha5rjUQnvWErjGlHHjIr3xGy/HOgb2bm7iHE5v/AnLlX\nQ1hrdhaiFsTUoOzCC9868sILJ++65ZbauI8/fuxKw3Co2zVgwEZ27WoH9Sx4CO6FPqCCpBXI+2tJ\nWNcBm3Q9QGxmVOGx97mqJafiZ3JleRLJJ6YhE47QROWpwF2gdOS9+A/AxN2kfJob/N6/35PKcXvp\n6nCO0YbwNhmimYXtov5D/UETnEtjYg8SbqqBVraEscU+ah8CPv+IKbcAH5gd99wQ6v+wEEnGqkcg\nppTSdwP95+FugYD4QX4I6Jfx0TvAfofKoXYLxJqND0QGePsLEpZA9t3MgtO1reJv7fp8VUrKyUhO\nNRuBaAqY++STf1XAhWaoc1gUFXX7ZmrqsbXUJWM6lumG8I50kBlb2EZTSqyOMUiosGXN3Ac8bS9B\nQ/R+kLyiVX3Le7DdN49Jm/eTk66JVj0RuH7MzWS2X8D3gMuQ2XakZkUAjGJZ1+l8/aVUSjpkU9gP\nlDVg/xb4WaxWiPmM9UOsaz9iSWiLF1/5Nb//rQGI5Dkak0AQ6+M9LfCMWM/VU4jVO9XhMBcCn4bc\nRyujfM3o0R9eQmz+DwsRHOnaASTQxCkE3hVjxz6WtmtXcu2bbxZuTUiomgPMMAw+NAxbU7A//Smb\no0dr0XUn6dTeC31AJgfnAoOribMI5AfI/QrFcmAkTyd+To+hPgZMXYsjgWhWkdMZwJOglQH0P0zW\njN7Bk/Z3+5CScZqBDvlQkbPQmxjREEgV8sK8SGBmEs7xdc5hzmzKMbNGFzFuXRKVObVoC5GZYiSH\nYTgCCZeR7mSBZAKVnzNyPuEJ5BNkpvW7xYxrh7N27JQLMghJjoqaQFQChdNbXuG7o+WLiUQufBhq\nDn9SWpp+AaKlRirpkbF3b58+x45lTTedh6W4SCREJpBPgE7KJWfBHEQfRwrerQD6GgbZiKP7PyGb\nR+sHyWs/39d+Oz1OlZBWiHnPNZlp62WZPNnzCa7PeY0/EqX1oSCtF1vbPMUPZmvw8Q1MO4ppMeg6\nGxAf3X3R7MuGLKDaTBZ9HmjzDN/729SpL12VlbVzD1IKZyCwIuE4owm2PsCMWtKE3O8Gvq7qBy04\nyVcWFnXtum4EsYXwWojkSAexdt+KYl8AGAYdyss3dKyqupjKyrTlus7fkGTBD4A3DIM5hsFIkpIu\nJj19F86dJu0EMrALBZ8BRZvp4yvLog+iUrzv8LllwCiq3xtP0baTtB15L84WCEirir2Y45GCtmkV\ntJ6fb/MF+ok7mEqmpthJfQnYKQckAZHrG61cSThEQyBnEOlgMzLDyWvSM2o87MGUsRYxbkkprc48\nxQ9KEWKJpPu7EUikSKxTQAtFkJ+kL3Lv9gIJtiSiIGgSYjcCGQDcKmbuA9KzKTwGZOKnI9J3YQYm\ngSj5vz1hHqDJk8vbTjvxDd+gY5s1U9IJhyACGcrt40FNrKhoMY3IMtZNK1ZMLikrS11g/v85Ln4Q\nhEBc5UFT5nsdd2f6dUgm/CvIbLUvolG/ruv1wqejtUByc3Ydy9lIvyJCKgBooNY8ydQtD3AwYx59\ncah75YJJnzG6egMDCoAPb+KNGoInJZYVEkul3rr8Cw3FHfx701XM+N5TUy+sLirqkYZI0ZkzZ969\npdM7jFTwvpmkaV3NKcRKydFE3rsZeE4Fv+s2AlFxoF6RpkcALMrP39SFGCUsE5FCeQFtp1kfK1rc\nvnBh8tGamslJmAUUdZ1yXecZ5Ht/VwkhXYJMhJyal2UBRUomhK2Qd2DlUsa0Kc9gNPCs6bsLhVgg\n2StG8fnOmYh60xdHAtEKQesHmhXWPvp0Iuuq4oIINQs46hOXQV1tMHPC5HSPOwMHdHSnc2t0xJIH\n8hjwEHIh0ejH5xoFmC9ADfEbJjFv8U/4y2jgmQhNZaB+BJaFdUB3/M7hlmZI7jGCrRCzBpZkpOPc\nQ8H6/HZzoHTqA2LVnNp2OR/6kNnwACTDdL15XskIae419+OI6uqke1Iv2n28VZGyonHCwU4g2lbe\n/U4ip8q/853V+4EL7CU9VEj/jjNnUu44eDA/BZEgILwEuA9oaUYzueEV4NbQpEuTsKcCPzOvu0BB\nh7jT3IOzZRCpd7blNM7NKy3KW8XQbTjUINN11PFh/KNFEWUT9frvhIGRaGAEnWsNvikfcnk8Yi18\nNJRVGa0oHWXb5yZkcJ8Y7vxCYBKISgdmvswdHVLSjy2c+PHmkvLyVsnIILN5yZ/u6tHpPRL23sJ/\nHfZh+dfQJEfiz8BrCuINg47IQGaVqemIEPlIgAMH8j/r2nVd6uDBxjbb/qKVsCJEYsUG83v71oYN\nFZjRxUGyoq5TCTxTiy+lI4cSOHjwfQIJhXZkIz6QAcB6891euLZV3z74yENyipywSin6JuYsy2Jd\n7t+RpNPLcbZAQjE2TpktaOs6E9a9f3MIDhtvC9Ro1AuLdyt50iSIhkDsBcLmIiz4ZNOcTqOizgIB\nNixn1KBa4nRkEHKFWW20B06aq5Q/2YRzvL2FUBnLXkRxJeFlLAuOBGJi6yTmtUIGs0HAOvO8tiIP\nu1MGug0qCbhrytX/2lrahROIkzkc7A9kx9Oc9qWxumTfvt5jkOfhagAlxHBISbYrhkGvLVtGdqmt\n9a0xmF9hYHQkDIGY2voGnDvRWViNJF2FSg73Als0M0RRg5rqVA6lbuWgKQuFwq3pkB3tqaUiV+3N\nnc2la3CpQaYSeDiujBdxdvC/ga1MjQJNoX1tDpOPgVajwZFyWmy/mLmh30FQiHAU6LeTroWIfLIJ\nmLLzd+XzMmfTph8bdpiTl/UP8siPTvVl7+7vONYCCy1p8jgy8x6DRFktseW3WFb0pQC33LK7zfHj\nmVV/+ctFlqM3jka1QGLCiBMnaFFeXtkK+hcjE6wg6DqqkE6F41i0CaU+QyzA0L48loQ1kIAa8U6b\nsTtGJB2gRNc57HRwXaestCruQHdfe1j0y6WIVFhDlASSUsl8c3srsMB6/xYh4emtzeXn3IEO4Qlk\nGDJbLjJ/Wz/tkBDe8x0FBEzw7UjkyRu2Qm9u6AXs1XXcKlZGkrFCCcRegqFRCGQQa9sgg5m9+98q\n5PsJ6/9ApJ513buv3VHakwJcssohqF3mAYCWtOwvv+ckIv0T3kSKHfqQSUUcgeinW+fN+8Ym8C01\nz2sRcu8G26SPUIT1g5izQKssvXWO6cCDBCVmwam+tMz8qC4ZKxQ7gTxbCK4T8lK3cegEbSo3MGAn\nLlWQdR2libR2s8M+RhFscfauIS5uI/3qvtskKt6+iE9yzfpYFqYDX4+hFW6/p7m3LdLT/iegVZ/q\nR4/lffpsepG72gN0Yde28SycvO9GpuHsxwsiENPaXWBeQ6j/IxuZ+V5qHX/37v4HEaLph7S0jcUH\n0pgE8q2ZM1kELIeETpZzOhQrGB6vY5xBJLv91JdPs5CxbwDmO2YY2pGxV7+lOJoULuyYtYdT1MCE\nDidBq0XXdyHVjsPmWykhsBGI9We/J7mIolCO5ApZ76sbUfTmPLFAHjd//ozVfCl42fkOmwWiVSEO\nNKeoCQ0ZgKx7Ycv/UGmgQhOQIkZi1eBrZ4s2CrZAsj8fjT+ibBSWQDpRmIEMZg0hEMnAV+w92U9b\nRxgCQZybByw5LIWUofnkU82MNsBYw7jxY2BsaZe6KL1pQH9TRrh18eKrqpGHvh/QzWB+GfJyuFkZ\nkRzpIAXobrBFpDwEvKvZ9HfDoH9JL5I7fhKSfOXHh59n9QXUIqSYH+Y4eW2XU76C4acR8jgFJIYM\n9BY+A1KV7dwNjAxkELJf62U76L5a4aubjbagYsbVvFeDLQxb19mGDGwRK0BbEVgzuEoh0pyFEQ/5\nfiM/1BAAACAASURBVLe7GzuTFUx5jW/0msvFJ08MZTZREIiJZYi1F0ogWYgDua9ZNr3fnj191iME\n0h/of+gQNQ6+JydE40SPCmYm/w3TpnEKWAyacw7KP29M+ehYu/xebLUSAu2FFS0ESVjmsktr2tQe\njq+qTQ2VUe1Yfry2w/CM0kB1B12fj65Hqk4wENhjSlL1CMT82y5jOTnQByJ+v5cjHKvREI5AJiKD\ni47EfushP+c76pIJBdq1oDlViu2MmJnWzNn+wIwEfh8SehoxEusZvn8b8LCS2XsigXjyPVzys3SO\ndb3d/eNKIzyBbGnF6TxQ7aiN601gZhMFgShL4prBdW93OvqPlwqAPOVcohpC9FSFGjCe8eoo29JA\nbXv44Tf6aZV8cnIAf0BCkK2quqOV0ipPnuzQFyGQ3siz1pXwBByRQDSR57YCl5pJl3cBvwnZ7L7a\nBGb6auqV3clGktImE9mRntdmNUnzmaiAg6YM5GiFmLP1Nwm2QoYgM1g7gUz5kMt3EXgeANakcarm\na3w4JWS30fQdAXl+y3fQoy1m4ISZ0d9l6dqvZUzn648DfxvB51f+gkeTT59OWwUMMXNX7HAikM+U\nxmjk+7P7ErIRC38hMrvuu2HDWAMhkO4AS5ZERR7QuBbI9cDSEycYRHBr2hBoP9+++bX4eFU9wJSs\nlxLsB0kA2n5Xkgb7E3jHbq5MSvh7NfFxf+F+Z1+dn9z1J2uTe+XsjbU45gUEgnvsvdGjIhADIxWZ\nwP1ER48h6fLs0FyLKYKZTBhF8yCrV7DV2MVOIL0Qx7B90NiMOLla44ziWnzdkXLffYDNWl2LV02R\nsa6W411dHekENE63F3CbD9XTR2UJpRlF+Otm2euAvrUSJutmgXwf+IfB/MEcb3tn7ZHcDORFm+Cy\nfRCBVFLZK5/83emko1G4GpiU/2/iD1xBqSYDjEUAt61fP26mWH7aPuQ+WmXUIxHIgCi+M0vGegR4\nQjP1ZcMg2TB4CpiSsosnqZ/Znm/+vo3Ioby5KTvpMI9JLQno1+F6sbyO9J23zn0I8DaQY2AkK9Of\n8Dd+ZFV2BkSW20m3lZOY50Qg0fhB+iH3zR52OkQpNlRVtej3LV74F7DJh3p/K73LrrjiZIp5PaFk\nsQfoAMqeg7JLxZHSoogNIRnl1rE+QmSsfitWXLIA0FJTGZyWxo5ly6Ju2nUcSApTxiYW3H3qFP9B\nJlKfKScL0zDi8CV+l+rT8fvK2Ie8/6EWSCZw+DkZpIs1OGE+k5Paty969xRp5QuYcL3LOVy+d/0l\n5cktSlsahmNSsRvGEiA9q0ov1O9t3saMjqsjEDNQ43lggY4e1sfb2GjOBGJFJ0Sq2TQQiYixylLb\nI7B6hfwGP9WIxuvmyziaQFUWMGgfnUdhd8b76UDyiURKssP1QTetD+dIMU1KMhzPT9xYzrHuAaLw\nc1pT7FaaG4GoVODmRGr+iZRVn4v4agzcI36CCKSc8i455OzOIae8He+WpVByRed3GF3ajTQzempD\nbTz9gRufeuqJfYi0AzKDXYgM2Mtxd6QfQbTeSJLGtAoSry0n6SJEUsUwJM8BaDf26+zpaHAEyFYE\nRczlI7O4rx2pYC9hLJCkg/SIO0PLDfRvSaCvQrhukKuQiYL1XAw2r3Uncv0TgJWFdG4LHMTPTPyS\nXXyIjHdHsjy0TtgKIMUwIhYvtUJ47a1KR5w82X4zcBq0YiTc+h6s3tiBhEIbtBrzXOvuiQbqTA6H\nOxr1cpKsLO3ZCIH0rKlJ2AwsTkxkwIQJrNi0iTaEkXksmMEToW2WY4Zh0B3oe/vtFALbTSLfquo7\nx2+idFs8ULn4KIWIRLcV8aVZ3611ffbJZA/E0twBHKogybkVdU381bWbbmihaSpcyLoTxhKwQBwl\nLNPStawQuwXyPWSy+uMYjtcoCEcgT9p+OiGFFa3//9b0p3Z2MB/MAiIXfByI9AS50Kza2pFAjHwv\nnPuAuM6iq4g/lkxZOjDvAFmTCI7mEjkjrjILlNvAUF++MozWGMZLGIb1fW3tm/SZj4ODg3I9ck+y\nscqH0pytl1sBYzafjkVmw48jEpuBuyRpJ5BWVVS1ySe/MIOMQ+14tW018UOKyzs8ho9ZiNyy99hI\n0rVqdmzfPrQ7sNTAiEekpg+Rl3AN0BuUm4kfjYxVPI0byh7m16vmG5QbBvcjEViPXnAl9ySeZDwy\nMOwgeKbdBRk8P31qBx0JY4G0Xk/vkuTkLbXEHbHVTHO1QEwr0+5MH/IthvddQ2vL53MZMmPPJGPd\nKaS69RQAH7UvDmJtq4f5VV03O/P5jUbGciSQtWsnHMMc/DSo0CR6LQyBALZQXgvHh5HQYUG9nuaW\nBbIDGdSOmTWwFpWXk3P11ZTU1FBN9FWPG0PGugt45dQpRiEDcQ4iHwf8jYYRD/g5se408PbSYmqQ\n56QWmexYVohTBNYk4BNdR7XhxM54qseGWGv8f+reO0yq+uz/f52Z2d5778vuAkuVjpQDiA1R0ajR\nx0JM9LHEaExRn2gmap4UE03URJMYe69gowgcEOmwwLKw7AK7C9t7LzOzM+f3x+ecmTOzs8vmeb75\nfv3d18UF7J4zc+rnfZf3/b6xEoqkLuLMRSdMJnUf4wQQVZx7KB6ZGHE9hL5dMN6SRl4AoqBcgDYe\nWub/3Kja8dpYAHII8aAdRDBc9P8f0v78u60G8cAfRnhyIB6GrxDph82cP7rwqYP4tamIpqLgjRtZ\nDpQb6IqFiCY933B/tBeQA8w2J9NkB9YH4JiKtwhcMVBL0tF6RvcW/NU/HkWMSdXP92Rh4OFg6uZ5\nTShbVk19Q4S/mRyqBNwVhf0lRD/PjxGLYRzi+qaq/j1rI4AURhHVZsHSlkzy6RAa5xdTZsvjzDE0\nNpYEroYrGIg6xjeIl3EPYtFuRNzLApCGEAvVaFTocRTS1eibeTPi9+af5iHE9q4H5soybwT0uVlP\ns/A0FOqWjaihvHmgk7mMscBFnCL1rCunDG/65fnm0b8LXL+HtyKBtCrC5K9JGMIDIBuAFOb9KQZB\nTFgBcBkbu44xpbuAytt9Pm88dN7JTSSdQtCn27Sfzdq06RYYyfwpRXjVo9XxdKUDABSFoM5ZJIdV\nuQdg6aYtsJJOve4BqKzkkMNBSHY2sYmJHEUw9cZj/6tCulbPuRWhhLwQkQrSU0DG+t7NDA804hpM\nAF4/3U88HtAwDpjSAcTNwEIAyFaAYGwNBVRW4T2zHWAZPen1DMYfRO9IH5/NB3Z7Ut3uGkgGIwdJ\nfYWI+sJ3874N8e7dLSOPV+r+/6iNBSCvIiQyXhvl3/9uUxGplRl4kPwhxAUsQNzMh87zGTWMHYEE\nIRa4cuCbigpW4WFghSAWi02MBJBRIxAFOTKFRhvwVRLNSTYCfSOQbcSdHgBu0BgsvuYNIIpSCNyG\nWLx0/n5Fvqk8lLq5Xh7HFRX0VsT7Hbs7Hwj5kD3TgKMysoLwauI1htVO/KexjABSlERSN9CeRXJp\nNz3ZwEd9RCxFLIyzFYW8rhlERj6d1IFYiErwDAIy1hxGTWNhGG+roMxTUPy9hDMjI9tOhIT05lVW\nzjwBLDKMkJ2FeOn9AUgO4pn4bNBJcZuNFPc8ckUJQFGWiH8SHllO4KaBS/UGQt3GnEevMcG60vj4\nJuA4SFnVhA2EcXo2YiBWKZBCztZMBJtsqT4m+SCzSrM4e4XPR34NTFAU/4urrrP2AM90AI0gqZo+\nWfL+/ZcmMVJNQY9ADgNT/dCYfQvps3qKqDA5me5JBakWhOPRrG3TjpYmfPBBBlJTUc1mpsfHs4N/\nDUD+NxHISqBeljmOp5bgDSCKEgj8knNvvol4Hg8OOikcdhGvKCTgXUg3UniPaXRqGY8UeusCdp9g\npBLD5VSu6sTj9M4ZRz0PvNNX4KmBjKBCSyLKbFDhnJ2El4EvZOQPxvEd/xYbC0BeZmwa4VzGVrT9\nP2G+F381HvB6jfN7Z+eLQCYi0lU24JuGBubgnfOsQixCvumm00A01pHspf3MiU+kRe0kuimKblMc\n7cYc/GRAweyIRaQn/E2hy8BbB+uPCM2c02gAUh5PQ8FwnZmuHC/VVLkajiUS6Wd2+11x2N6woP4E\nj4R0GxCnFeC801hW5oQ/TDwCQHQwK8okcwDouI7ypHp6TQfJfg0x23wAASKvBTdSsavuotnAMS3a\nKEIsTHVAjIISzvglTe7wd43MZsecpUs/yAwL69l5552HSn0kJWYhCopTte8dGYEI4sG6DU30gFvl\ndCHwGYoSENxIXvgppOf5oe8ch/OOEwbejaDy+gHMpUBKLaGuWA5NAzZKwpFMJrK+CBHZ1qPVTLay\n/LNCKqaqhndSlnEgUn+jaY5lAr3v8t0IPOmrC4DDTmeAXlw3WjmQJ8uqHc8wK6P5Asii4Si24029\nTkSkrPRrro37VcP6+siNjqYdyMvOZj3imRpPnfV/CyC3I9arXARZ4xwjI5DbgXJq35HwNOadO97D\nCcRath8RFQcCqQni/chCXJPpQIthEmPrcrY2Aiu10dhoneOrKPleOHBY23YQ/Kro+povgOgU4GL8\n93R8Vc3tKiJq+6mf3/9fs7Fu7jMIamYlgvP9d0St4DPtZ3ehFTD/TaYiCr0HEdxmEBGB7vkYBQVH\nsxrGjkCMOc6dtbXk4s3AqkCASJphnClYcTFKGus4k1Oj6A6IpruoieT2fsJXaPtIiAdCARIx2f8E\n3AOqW/NIhaUSLs+irSiXAgXUBz9LbUgwLgEgv7kQy8SBVhdIXucfbSOtPpJOvBYBNQFY9VdK8oDX\n9VBXRtbZW6EYAUQc59upvVwnDsldTynKJ384nFNSBgcuTyLZAXO7gYmgRiNC6YXhp1l3jCnT8UxU\nKwJOysguBFjnMzYT6wQwUUtLTEFEoF6WnFxzRU5OWWNzc/ZbaGkgg81CCC/WIxyDidp5WRAvnA6I\nb25qJhBPGmsiIkqYn7SZZbYE+s6RFcPICOR8z9x74VTNOUZIHUAHgSHRHIl1EKkA0eAawuScjUhx\nbNGP/1Ou/LKTGLOf8x0rjeW3/uF0mg8hoj0fNQXJhnieJ+K/IbZC7Oemrev9H8Z0jJHtBZ4hUkuA\n/OBgTgH2e+7hCIJ8MNb4A93+x3Im1yt3TlHFNXwXT/pKRQDIEJCAooQg+oUeRYDBkHbclV+30Y6o\ng/QinLQZQOptgspbKQkx2WV4D2Jqi6Y7ArHo60PGpqDioGl6Dp60l9DFGsNUod9WjKbZBaClrOoQ\nwOIGEAUlUkG5aD+vBtfxnWTgOhl5rHkr/3YbC0COIaacTUH0SWxFpI+eRCy8t3Ge7sr/pS1E3MxL\nEeqhvswlFbxyg0azAta1a1m5d++Y0hjuRrwJEzjS1UXoe++5X0QNQCQHAojyffb125FeS0Z2MEMh\nwGQbQeV4eNvJiHRRLTDIY0FnEaG0MQx+P5ihPKBWC7mfAR7gP+YFUxIzi+bgCQDvFZMUax8yRdPp\nK8yY2xrGCby7n9fGYtuRiO1SxLwHo7UhCumlQLwqFod5QN7EVtHRasjLTiygwJTBu9cG0fJcHvnm\nYBqLEUCxGFEg/jh5Iy8fZ3KahEsHkEI8sxx0DaoTCFAeUcPSlJSb6Q3PR3jIRQqKl7CgzRY6MyKi\n82+4F2Cx4Gld6Yna9xzUzi1bFanKNKBVk30B2N5lx7y73c2+m4hY8C4Jq2HBQAa1iHvWjMfOG4FI\ncHqIRDWGz+KBwWAGk6M5QjmP1AMpxJ1qQ3jJddrxL9d2PfkZV9BJtC+7ZxMwbxTpfB1AjIv67BMn\n5tUC9aMIEI7FxOpCiKemKgrLEc+Rgj4/XJie3kFLZxUgoqmLgXynk/0IJQcXAsjHk8Y6XwTiK2Mu\nTFGiVKQD+5nTI6OAB0BAAEgpIgL5T+AAsqwpIRCI8PJNBzoIMJybTudNuULUG70K6IZv1qcSvo/n\n/b2c7sx9YDoLkl6HHE8dZBZQJjFC+aI2oy1j0U/W/yRDQXlRQSlFXPfHBsjqchG0VEau8f0wFaar\now9m87WlaGul9udftvGElzbEA/Qe4oLtY+QQ93+H6Vz5VoQXNgfvHHQK+NejQbsgr7zCf82bN6Y4\nnxtA/v53EoqKGP7b39xy43oEAv6brPymYewEFSJewnkJtO4AFoMagP6yC++iBeHJCoBAlVRIUiHB\nTmASAmTuQRR8vwTS6AqAzsDJAHYLU9qDgtomcSLD5+tz6yLZhxtAVBOodz7CyVTgCRm502f7diDO\nIFshI3ok6tJ7ycXj/ZiB/AnkhoZSO9eM7alUUjuiiV6IeLGWyzKDssw1zx76Zc1+5gT8Jy/qRT29\nBgJuDSppGJEnHo0KXUZ1znLtOlVhaMb79a9XLezvjwwKD+96HqQqoB9P0f0C4LBW19EXixoEaGVj\n1Aiz4pwQwR6lRSzgkd0U3/dnDgCXBDcypT+H44yU4dYikFFnmqCgBDZzUcAMNswADsoo2UMkdHQw\nNx1IIWebDdinPQdfA3OFCKbk3M7SkyqSF+tKlulHLOKXqRCoevcr+ItAZn344f0ODM6dCnNUz7XW\nC+mjEUEqFi78ZCHa/BVZpoOREYj+buZq1+czNAA5fJgv8BSXt+EByLGsLryS7DEWvjfxP5L29qtY\nN6QgHwQOEBi4DG8AKWmLjExFzHB5TEvtTkGsHc8CuQ1DZCDqd2Y8dZDUyWKbY5oq8kJgu+F7WxHO\nyTqEAxMBrOHI2jrEc63bfsS1v0xlVHVl3/QVAHduvjPi2ZefjZt1ZlY+IvX4fSBWRl4kI/9cRj7s\nu49mdyDSdeOx7WhrpaLwt3Hu42Xf1j6QUHCPaA1DFMmOITydW7Wf34q4gWNZG6JJKXKU3xtZFlNy\nc6lTVXekcz4A2Yt4+Q2LiRoLBEmobcCiBNoOIsLieRgktxELYyICHMIR0VVxO3EE4HCibA9FyKs8\ngCyrQCpdgWCX9HzqNJtJqszntFsUT3tAk48moeCJQFZEMKzOpDMc/D4gegQCoAxLLEd4VM8k9msM\nEGHZQHM4AclmhvZK0JtKarUT5wxEZOr2Mn+FNVdCdfyVe2IVlHjA/CqvtmifYSykj1UHOYbTfCHi\nnh82nA/d3fEPx8Q0n33ooQ36fGx3GgixIOqpAH2B1AvpegHdbXNi+KCki0lYMU05xtSr13HxtMNq\nfnADWd3T2MNIANGl78dqepvUyuKaSRyfEUr//hVsSegn5wji/ieTvcOEWJDBSg9C7G8hwGZWbgtl\nIMePMoBO570RbwKL3iWdBtQrCklA+M6daxLwLqDfiEfs8RjCcToCTFYU7+mDgYGDFffdd99/A3+R\nZXfa5hhCsSAK72hHf6aPADGQVzg8zClZRncetiOebf8RhMda8p8nWjXx36P8fhJizouHNqsollzO\nPBhPm+0hfnctNTV/xGzO48svCzVyQQZQ8qtbb50PbEeWjyGabFu1434eSHWq5PQP06x9x27teGOi\nxbalCOCs1IBUN22olNQJfMOk978PZLDz4SC8AeQgME0dOa/HaCMAREFZdcXBK4rv+sFd3PDjG34g\nI/9ZRt4vI9vFqVOgKPgSLnRLY/SZO35N6+EaITo5Hvu2AkgSIvd6BPGyfY6g7f4WkRKqRCxavx3r\nQzQu/WiF9EREakP33Kbk53MYWKR5mHrxF4QH7V1It1KPCDuNN6sQqJCEZ1+E8By+QgBgMSMARHIh\nmvp+DEw5R6YjnrYBRKrpDWRZ99xT6QoYRiJNy+VPirPb9uZSFSFmM4B2jnU9wRwCZmDFZEL97nXU\nRkrwoIzsbzSxUfhRGbJwqXbMuxL6ScADIBOBkyrmKDMDnwBkk106wEAe4oVJB1WvDczP53Stdr5F\nwMnXeG0lcKSV1ho8NQdRB7EyCSsP+BxXGaEDen3qMFpdQFGIaWtLW9bbG7PRsO1XjASQaPQZ9p5Z\n69n4qBSvSWNTuAUzIekXZdQSocLZX/yxYdhllgI6Z3II8RwaZ1mPKmdisOlDpB2oIbvvUZ6QVrLZ\nUssMBbFopZByKBIPLR0EAK8AGCBs/37mdKD1hxjsc+AiZzB3ot0vrdg+EQGQ+qI+GzioqibfcQRx\neBh8pcBUjfhwGh/K9K9/vXpCd3e8GTyLuSRSbiXa56dgHmrEyn/gBhDJBfZtcFE83gSQVsQ1H1NA\ndKnMhLAaVFSWqx7H0fD1ZCHWgnsNP7/6UjY4Axh+W5ZxsXZtLSbTAUJCnoj6/PO/2s1m+4nMzIE3\nVq4swpOemY5YO7qx0oxIuzbu6+AMwsmrQkTbzSZPfdRN3/U5Lx3k3yd36x3AuziDpmMAEFmmzzxA\ntSSctBGUcQ3oFuCpF6KgzABe+ceKfzzXFNME/gfL/QzvgWBGS2d8hXujPYmoIf3L9q8AiN8ZGP8m\nq0bc7OmIB/w32s87EC9bAWJR9tXC92ejAYjeZarn+KfMmcM2YCp8kQPYDINe/EUg4J0bBk+6ph0x\nIKoKT+OPkRWjRyAgpuUt3M/sBUeZVhYd1iohWDePGz43FYeplEBXHKIW0xA57CwtpsyB50EWGlhW\n2oAuurIKzKjXLKH1tIy8YZRrY4xAjg+biFlazRdAQ0I/0RgovMFYqkA1W+j7CKCQwoN27OEghSFS\nMUu1bednUHsEA4AgUhpRP+Nn6Yyk8l4OPI3Vq1BcRnRXJuL+lOApLH/vwIGVbb29cV8btlUQoB+I\nDiCRkaekjIwViHvfyygRSJCZupVJqAGq7e7CCvok+E1e+1G1c7JFUi2cw/8kuTGpvNqxHnmXG3q/\nz0tyCo3q89y5H5iMZTCN6LPxGAum3hHUwfe4PghPYRYQHfrmfio6ZlMIRKlikcsGOrSmUT2FpQOo\nQRAUEACig14tEAJqIj5pLEXhmsmTdxc99NCXZ7QahtH0NFYqV99SBLxBdNVM3E7R/qOwepCRc2jG\nk8b6XutiGh1RlCEkgIymS/vcDzyIe+aM+sAlbDThGeS1kP7+TcAsi9NZvObxxwML6+re+NGHH9oM\njth0hHSK7m2vB0w7WkXKGbEW7E4TABGIuKbL8K5/gFh3wlQIIKhrPblbCmiaug4hiOmVWgo/5U7f\n+mtaLQT6JQ0kFJR0RJblrvVz1u8EmrF6lwu0iHENkKvNafG1NCB3LLFHn8+bgYhuffXkxmXjAZAF\nCC9H98anA3/9n3zZ/yOrwT8Ty6hkC1AcG8sh4BhsWo3nfMENICNy38buVfAGkArNc9sFrsmokjGF\n1YwbQKR+4KW/cM/i7Sw50zQ9OBJ4DFk2gmMqTulrwoZDsYfN1I775ETKVTwLg1FEsYRDP7gpDltf\nBoNGb93X3BGIZCVqay6mtz+iG2hK6SN4wOL2foouJjrQQr/dgq0JwIKlIo20QcRiZVwk5mdybive\nAHIJ8FINNZcCAQpKrHasoQxFzkDQwf+GlUz39Y7pDCet7iTC85yq3HZLAHDPyZNzLHgtwFI7UDmL\nAxcD0RMiImrp64vHZntI2y4YTwRSYzx5WcYlJ1DltHdelFltV4FjqaZPj/ZNcZp+8Yuv+hC9D77y\n/+eLQGYAh1/meyFxtE/bzYKOdyiwAQmhmdsnYQ9v0VJXuu0FCjSF5tPruCrAhbRShWDjhyZ9xUDd\nGs4hACMGzxApCUh9mPIB+sLk7u64o4hF5LRhdwOASCp+6iCKQgHw4s6da+7q6EjxlwIRyrwBfWkU\nrb8BaCTp2DTcTK+fN8KiEK3eZ7QxC+mqSG/d0nQZpR2zOQp8x2cTXbKjHEEouB9FmVtAZWYY/U70\ndKAuRijLnQ3XXPPUbRs39lYnJ/OLN9/UdchArF0SHgDZCCQd7SYajyO4Z6HILJRuF+q++igCt2k1\nw3YgnodjCjHbB/nb4SVAt/Y8ui2mxJ3u89e0ugjhfKHR2z8DnpeRP0TcI39TLi/Vjn8bPgoS2rWM\nRTiv52ML6sO3ngMelWV866PjsvEAyJ8QC4De5XqE0cX3vo02WgTiBhCtgDYRscDvhCoZLwCRdOaM\n703Zw+gRiNaBLg0Rd6qE4SAXVrckgTECIYquv3zGFUkf516cbE4YMAH/9PmeVDoCjxPrcNGbIiMe\noIocqgODGNK7hL0BpH7uqsW0duENkr5mjEC+czyBspQ+5mLFkdWF+vp0NzNk4mqiskE1PmSVE5hg\nQQCFVgdRw4DCFWxZB0wGtWgHO7oQXuR/AZf106/pLUkqcFAL+19FUMLfwUoAsmKiMQWev9ekFf7b\nmHLs9vb25G6HIygUr3GsAHxlwnUDcOh0UdFUYmOhu3vqdnE9UoH8AOfIFBZAagjHo0Ii+89G7I0C\nyhMcFUkdWZHq9Ja4x/Co8Bpt1AhEQTEB0w4Qc7yBtDgVacNGLqmyYU4EKmJS9hYxEOfd3GfFjij8\nyiC5Gkg72EzSGcTcFkCMKc54nyndU8hQTW7Q1x2SWGBopdT4HKo0/4UXnuoFKjX2oG7GFBb4AIgm\ng/4h8NhvfvPGZ0CKH6mZvcBcae4zebgCSnCZPiLuVCbu53x3ArR3MHLY19eIWlcw/u1y4HTPJMpr\nbqMNWK5615gyCaYTK5GIcb/30d7+s7W8ckKC97U0dQACCPcABA0Pp1+7c+dQTG/v9wKcThOedNx0\nBPge1a59N1DS7WAqkKkoRAMvPyTuxzHEAn9IIzL4mlYH4T9om7gJ1fQgPtEHQOw+evqzsOE/AlkM\nfK2gmBEppEMIpQiRIrf6Vau4EdGE6lV71EwnFp1ifHWQGxE0Yt/1Ztw23hSWbzPL/5V5u/8LMxYh\na/AfgRgL6HmIRqFeYCecnYZ3BAL+01iHgSI8I251ADmKSK1oP11XQUd+P4CCcu3EuokShhe6i5jA\nZWwb6DuXdOGtJZ86VVn2fXlTqQmtJGTYhEm6ACiVoGeIYPsCduu1GQ+AOEKO0DBz0ioao/A/2103\nYw3k5poY/gnIKliS+pGeWIyE8NgmziN8motg43PQkEeeOYigCxALWQTasKpL2NwI9Ek4i1/nkccO\nZgAAIABJREFU9TSEp9cC7PmSLwdwv0yuAwT25WjX7A8Ib/9XwCQaUnuI7tbP7TDpdXevX3/3VyCV\nGLSpdNvSTNIi4CAhIfPJyYELL6y/R9zXGSrU5XWQgv988qlZMRmuzdM2qUuRhyUnubWFUW1pdayV\nUJv9bD9WBJIDdP+MaWFAvQn1yr9w7yEE4ByXko8mYIvc52c/I5334AvcVYl3A+XVIY0cxsy5jtnY\n8QaQ1FCGe0humokjwLJv8w1pjKTXxwGJhrSGXkgvRTzTL2n/flFjyFXjQ1uXoM4JzqyJz0QwHPxT\nmqc1knpwyEBZzYeKI3iGTOnWox2Pv7GxIBhD/+Tjq08PxQXerIqCsjGNlUUBkxBpm9NERHzFRx9e\nNof9eXg89BmIZ1/vWcoBkuJ6e9dp3/+dfalciVgsCxBOsG7vAJFNQxxDAF3rDHG9Rqt/6NbaE0gy\ncB2S6wlEPXUEgEScIrBjDiYVJvhJK+l9Ns8gAPYuGXm01gQ0MtDFCJVnf6lBPZV5hvPUQTTdv98D\n9xmkm/5lGw+AnENjiSDygj/BW9/p22jGC+svArHgiTjAW3VzF1SlQa+vtsxIABH5yTLgAi0HnwWc\nkeCfErzo3m7K2z3UzQ9XUKTf8btXM/ZlXIK3R1icvqiklmETS84dbAJ3Kke3VOzmeuymfqKcE9CA\nr5mktukc0amPHgB5fUuAKbLOnM5AFGOOt9UiECvZwKSdmfwD8SIs6A7C1hBJIhBvBnMMCcNOQtwS\nKzKymkZabQABF2heugI8jOYFOgk6rmJKrab6AkSXOsC7m9gUhx7OZ+w9ictiRvRnuBB9R7e8tuS1\nG2iLr0FvQpt1oJbwvrx33/1pH971A912tZCYUk7RCRyO6cTGdnDzzUEn4JIBmNQbyLkZTXRhxR+R\noHJ5aET4kewS08k4lttjOGePsZeWJwWSxUCYn+3HqoHMwDON76zkGWWaDJT1pZQGMxyi+NnPXUgn\n3lb62xdWrFQh30Br/QGiiXdd4+VEnL2RBY4IFp78KcX33/vDx4tyj6Qdve928wevhA3OUbuXY3Aa\nNAmSCAT1Xu+70Qvpui7ZVOBOzZuHUWp+B1IsgwtKiu38vv04pTc6SDlkXHgmwNHNjAQQGCWNpYoF\nb2EFD3zFcz+00hYf3LqE0xjTWKFMIhHPM/PUUz2mdR+bujpUEx5NPmP/B4hopFoStYoW4PHJrbwY\nNcRxhHNpjGA/BaTtrTTiiZ5059Jf/UO31s8KkYEqXt96BMG02u67keQi82Q0Na4AVAzPjSqekZDt\nbLsYsV5dOwrRxWhXAds1RlgZEKEoXmtbOgJAqjh/BPJfCHHIMeamnN/GAyB3IXoSdHSbof3/22zG\n4fM1QDaoL4KqNyvlI7jsOi3TCCDtkOaC+32vzUgmljC9DpIHnPM7BS2pNJbmqdIXBKzZwY4wc6u5\nAAOAOCWp+JN7pkVT1PPRdI6cwQtARI4baMTp6iAkOAQNFFpJqMumJk/zbDwAUrtgWWLqLpvD7Dil\ndYD7sznP8mwSwtu6CXj/9HPYES/BrR0h9CDu+cQM6OlnwjEDqQCAPPLKbNgKEN+/Vbs+ewC6mdzg\noq1LRV2EIBIArDvL2axOOkVfx4qf99E6UcKqrVtWWoBbPpj/wV313QGV6Ayh/3wxly0rmh2O4On4\nFfKUhuayz3k7/wxkcLCA4OBjZGXFYLEcfhnaz0Vhn1XvNw1BDxHVWQEdITktudVVsaztLaA5nrZD\nXywIDJxIT56C4jv3ZawIZAbCC83GU29pApJKE8/VD0Q2SVgGR3D+EYtVDFYy+eXxEEeRPe5kZuYG\n4A5VPKvFCCrve91TiKu7invMQ2S2zyd0sGzaxFCbqf2RJ4Zsf/+8KTCaffPxjkBiEQup8bjLENME\nzQjQX+2TphmhyouVjE+LXClyaYqo3xy5LZzos2EaKxAgH/75OaKT3ZeGPFod5Fbgg0ZWW0Gq453v\ndp66j9nACncaK5QLiMQGTEBRwiksvHbGNPXUH/9IswHwfAGkEE8GoBUoK0uk/p/rSQLKsBo8biu1\nQPu2FmKAeRpBYdKpe6lHgJaRMWe0ttooViBm0wDSHSBt991owELh407yOlJw4V0HWTRI8j6QfgWs\nkpHHQwjS01c6w9T3uqYhouwzjAEgisIEhFPy83F855g2HgApQBx4IgK9b8L/QvptspV4wsVmVSUy\nKKj/B3jokb4FdAOAqEGw1AxvZft85mhMLL0OYuy49jaJYnrS9jSx6+eDDGLpsaShegDkg6VLl/eE\nhjp5oeS6VBpP4x2BxAIDIA0g9XQzMF3VvHXaiK9Koz5N28YlQadoHmTNbEtdXX1sfesY1+jHG9l4\nq4oaD9yMZwymAnynJ4gWBHAVzYbQbqZU4y0rTRJJx8yYTQjPSvfU9gB0MK+vVPyoDM88ja5wwvdv\nZ7ugdWZ9k0hHvh1juG1l2/Jjy5t/krBtskulWFGIJKf6Qt66KQpUY5+H21RIWcEW5x7mT6O/P4Oo\nqBPAMVas2PIMhFTGET6jCRdWErDSiJXfYxWd3X/hHmdcaB0zqqdvjhpiUccchgJxVHX0hp5zRDib\nGak1dL4IxBdAmoHkF+Z+GZ/bWAB/LxmZ/hX3cyuwnMk9cxgyub67+o9hiKbO/wTekMAmy5xceDV/\nW3gd75oc1DnWKH8Z2L4iY399R+nAAA2zmPVyCU+mw6AvA6sNLxkWqRfhQOXLMpsMQpS6+XvWHz8l\n5e2d7ygTgDEYX4AjtE3bLlhck4ozCAfkIp99dyPeOXc/lkZD/t5ZbjyEeC+/w8ZLkh0xJNmjKEdn\noqnkYeZLBJCulXApP73fEbF3LxMROX8JA4BozlQi8LH2Va1AwtU3UL20htSbj+DvndhW088EYK7L\nItLZ9dcwG/hGlrH72Z6+ALqcElPwX+h2m91MYXU0al0W4cOhXo2Si2q5rgfYKiOPlSUAQOvxmYco\ntLuPG+9sizGFNVYE8gzwO4O21//YxgMg/uaI+/vZt8lUtBdAlnENDYW1JyWdAw/VdAQDC4/Xlgez\nW8Hmm7P1kro2mIhAJKex49pjVo2v3zpxfRv7pwcSeK57sFsFIrGKBqu/rV49e9LZsy8hyy5EytAY\nlnoat9QWG7aJaBRMmkkqT6cuAe8C+nygbUl7bG9ZZtlo0YcELB1kcF4ffQmINIc+/EkBIvoCxZCf\nJJgzHYJ6KezDB0BMmCpTSe1FAHADIp+7CtToTmaYDrInDE/6CgALltd3sztdE3Esoj+pGh9JmB9u\n+GFop3mw781zpAH3IKlbOlrSXZJYgPxNW7xgAbuPgrSCrq44EhJKgMM8+KCjHdSdcaQXtBOKmBmx\nB5HKqcDKT7YMz08zSw7phvaiTcUtRB+dSSRwljPhLQeXDrcDdykoRjnzsSIQvQ8gG8+wnyYguSLl\n1MTJdUWqn4hGN5HGkljF2bD3jyYWXeZCOojoQH7JsF07Ip9+HHhxH+FH7HwxG/hxNk8/PUyHyUK0\nsZAdp+1jYP4BnjqIP/MGECtTgMt2HnpjQz6nwzVpmMkMB5Vpn5Gjne/wFEp3Pc6j9/rk+wcRnrxR\njmixijRUzfcfAH4kI1fhtAzSlPxK3TVYgO9gxcwQsfTxPJgmoKo/+j4vbUhKwuZy8TLwMHy8Bswq\nHsCeiVjXdOZUK5DYGMGkB1dy+LkNzPfTa/KyQyU15BSDXcERzzWQUsvY9Q825pNa0E4TVr+ABMCM\nOygKGSakKZxXqiPpHshkpfH8W1maiBDLHI9dB3ym9e/othVYZlD8PW8NRFG4HBEJ/Wmc3zumjQUg\n8xG86wREo9uD2h/refb7Nthm8NyslpZM26RJezWpDlXCUEDXOjqj8BTNC2HhccSEQuNLcAah3+TV\nuYt4cSQSyy5wf4aVJKzuRSYT6P6vdtvJw+wNsOP8S4Pa0Bc4HNgPxKd88MH0kxkZEctLSp7Wtj+H\ndwTiAZChxhACMjq146eanMMZ1EbgDSDXAh/mtuSGHsw7ONpiVYjIie/YJ+0LjByIfA/PzIHTQL3N\nIhhMkTA/BvaCKRa8unEBTk1ggi4SeSsCiJYBNbuYfulezoQGE7DZuEMbbe+d4ITpHd6ZBBQxEFeC\noSNdQYm3uCzBLkm95qM6pLJuHkPiuRJizsVgrxllUuOsRexUQI2nMzyEKVP2AEewWKYVwNtfxJOS\n1Ees2cWdwG+xcgeCSXihq+7Tv1KXrk5JeDq3L5Duz5zkAWc5GzbUNaM/RxUMsccM36VFIN6UbgUl\nCeGJ6w5AjfarJiBZDW2fkdRQPACjarNtQTJfjKr2kNf3Kyb1BGwT2QmT5F1zbAeyuphmBkyV1CeA\n7TBw8F3mJ9/IAzYJ9c94+rZ0ANHlc3TTNbH8mS9t/TfAb5q754a3Ed++iZXLgXwC+/Zon5EPnFbB\n8g0XrvkRf56L6Bw3mm+65fuNXHoOpEo80UIZP32qrPEKclSJi4uqWcYgsIEdxF+oog53f5d3JiK8\n/t+CdBNc/DpcUo6nn+t6YEjyyC21dgSTAuS+NoOgjhD2AE/pB6FCsNNK8tcvY5r6IAk7gi/Mmsu+\nOM4DIEoO06Y2j+gR8rIgJ0/0BtLpNHHwcChtqkVQplVIdGFOdhA9Gx8Hawxzp68MVo2g7OqOrV4D\naUT0C3kpJmg9JH8C7h8tsvpXbSwgCEQgtVn7O1z704NYpL7NZhw+T03NpNClS98vxTOXeypQqigs\nRlDYVmvS2QCFMOkIaHlXt0kOBFh4iyqKhXcPafum44lAfoanEbAYKIvmq6v6sLvgl2dbaCFqIGoY\nSFIl6ZdrN2xoe+KVV3QGyegAMlgfRVhCu3b8vM2Nh2PoDEAASpWWvrrWjOvDqIGotNLM0pxRrs8S\nYDtm1m8zb5NWHVrlfog18cTnT8eyE0jrg9wowfqIxScCASonMjECpKkIcsXPQLoOyLNQZenCTjYl\nH4H6K1D1c+ovprj9EIfWAkXMSqknyn6nJsyHdi5ljicddZelcOiDOgaBnXuJ6y+i17cnQ7dZFpwH\nkbr2EbpaIivrNFoHexr8riqOwN5ABgvaGER0wIOVcqxclT58aelwQzKOyq7fflrI5zvbiAPO0W+O\nINY++OgTfAJ8R0HRngVpAPHS+srjzACOaCyabLxSWK4kQluLohqmNTEagFipxhxqpvPgHixqJdF2\ne0ds6FIVnKq3om2bk8DUFi5a8AmfWEHNhcv+oP2uOILFVZlkNol7AQiatr8IZAwAkdrRaetWlmjH\n/AKQcpasMztY8nfgSQIHDmEAEOCJMPr7JnHCocJa1VuTyZ1uUSFaRVpdzQ/mAvfKCgEoyvObVjJh\nR176rY3Rce/259Bx5TEeIIgewEnmjUgdez6VhCf+njifCW9BaChcYmTLXY73QLbWHdluWaJJH0/k\nPxHaVLergjpea4JbXphN45VPcvxHIU9H1ZE+saEhN4HRKPBWEk/FMiGvg5H1Ts82uTGDrIy0cQw4\nszOKQUsPqZq21oXtzD8J0hkZeUwQAlAUchEpqS3Gn2t1kK140lhpQL3Wp1LNyCjkfuCkLPsDrdH1\n3caysQBkByLamI+gVup/ngZ8GUrfNtuK4FgHghpcVTUlZtq0r4cBBc5cAiRs2EAEgvt+oyx7Fcr0\nh20nIxWA/RfSVWkvSccy8ABIEZ502WTgeBll14YyswZuLu6jLyyxJzGQiKKijsjIi+5ev95YWPUH\nIPVYMTPYkEBcdCfai19HRvMZ8nAhXYyIQGYDvVv4uk9Spf6u8K4ArCOmyaEd23ZupveI84h0ydZL\nvLwRCX577+VsiekmrROCE0ShMA6fCERG7sghx24mfinCw90FoLC94xS74uZAwx/4+dOImoG7oXE+\n84+eCzh3BZDK5WmxZA6EkDR0s/Zrdz0qPpBn97TjkHfAcSJDZtPhG/3pOW9RG4na3YS00oUs2xAp\nyYJ1itIeEIOrLJCgm46x3RBpAZBbH5eWvG+oM8HGkPVKlvY7UeUd5IGURIhz264LmY945n9v2M1Q\nB1F/AuoDuOsfqjYfQ6cMS0NE1wzhsphcnbmnGT0CgZjZw5x9oxdZdoXZBw/Zi23mT1ldijelt91B\ndKKDyPXP8uwtkDcEq3T66JRaQvf/gl80IsQHs/mfRSAAFZiGC4HfAb/QVIzT3uWG9IvZNLCRr18z\nfEb+rwRF9kYzru/Wk177Wx66G/i16umqP4BIdcWrSDd2MmvIQfTjskI/okEwI8DBZxG9TL6LF64/\nc3Vg+oLexOWEhrWgKLMITjZf1f1kIIL4orEn39cW1IvtIGphiOjPmEpuPZpMLmJBbfzJHup7mPhT\nBxHPIByBuRJc/M4UPjvYHZDV0pKRmJl5cv8XX3z/rJ9ufN2uHzajWFQ3Bd6fPbKyiq8DXZwGzpQk\nEBfSiCTZWQEsbmSVDaGFNx77LqLnxR9LayuwTHsPxFohzCuNpfX6PIjf+SGqhFjv/mUbTypqAMHR\n/xKRH1cYndr2bbE2BMjNA+b09MTVBgcPpALb4dgVQUGcDg7mC+AuWfZGdTwA8g0jAcSdG1ZQwhUU\nMWuibm4l6XvNhi7UIiAPK6nA5Nym3JYSShLayXwLMhe5cEmx3bGhQSFzbpEPHy5Pb2srMXxHHWLE\nrK5xpUcgeQz3dpOgDuN+8SVnFbk2kyguV6Glr4CpEpKvDIhuEgJAdpDNFWmmtMG3eXslI61t/lEi\nkqB3pYg6/UUgZJFd4aI3EwaeMqSXUvayV5qF4+DlfBmG0DDK0WeqXMRF27tiu7Jwcg5z0AKubGjC\nIekpDzeAPH+GdxwqTiC3laCMWSPl60F4XRJQR/K7EgMLAFVCloeA0ww1TneEQskAwXft88lXK4op\no86RUbDjeEj97RyOCyVhQSytqNwEJBNjX49oon0amKSg6FLrbln3H/PH/zjCtEejKP1+Ou+nvMv1\nt1hwtKnG7GfW1320TOluJrSC0QBEUdKImx9MT1kuwOrdu/lo8eLKH/HnKS6kG/Vu6hM8lB9Ip+Vu\nnn8LApZCeCAeyfniE0R+kU32LESq4g9AXHlm5uD+wsIBvAHkDJAI6mhCoydZ/tANiHrHO9oiM3cP\n81sW8Y0lCFf1x7//eACImhrCjJ8L6u11kqg5VD7CbwIQkjyvqkICxYF4p5Y6iPp5A1d0XrmOjYja\n2yFgzTKF12cepq+NhKRTsyN2LDvbGhhWND0R2EP7vm/mRDtWAO952FfTMsDVChN0qfvLEVpXxn6l\n1pNxpIJbuJIS/mraxaeh21F+I3lSv+929odFFxUdYM2a51yKcv0IZ8VgNwU4eRtGUfq2kgtcddth\nKhFZi9reIBJdFvpCa7kZWNzF1AzGUf/Q6hs3MTJ95d4EWDqYTDwidaez6XwL6bcCe2TZL9FnDviV\nRTmvjQdA3kIgei4iIqnBPxf/22Z6Gmtxf3/UAYQ3poSFHZm7aBG5wGOyzEd+9jNGIBf6/M5YXLwX\n2KKghLDu1UESy0xYCcJKMGLR34RIFU2+ZPclOSc5iYuk58A0C0yNqp2OuK4Q+akXX+zGwNuXROqs\nHdyRgw4gU3EOnSTSEYwY4mQBqCG7B8BGYBWike8jPItwCSOF7AqQJAebv/gJiStWz3LNOn2SkyO1\niqyoheU4gyzuYrDuyXpZP4mdwcS4IFHvqaGd9ilHOWq5SjgdxVr6rxqtgTCMsJOZSZm9dJhsQDYX\ntj2IRAqo8zEy4qwaVbEz52onUkAag5EKiq/XNws4KIFKwNF0LMMOPCmfI/RVyWEOhlrB3mwfMbY3\n88YdnzBsDm1oXUFEZiit8+OoQTXdhOQcIsG+CZgji9GvPwCeU1CiMTCaFrGzcDMrg/rJCY9lv9PC\n8A9mcTAW6FPhhAoXkL3DxbmFzlNE6Kq8/mwVUcWbQF10y1UE3bx5c/HWedPtZ8l+4zAzuoAbFJTg\nfnIfVTG5dtLyKCx6GqRWkJws/aWENFy8nUQFGHiZl9cDM1+HKXfff//sS3//+/tcXouE5ETIkBRj\nJRArhVhZhZUfY+VF7stfzJy/rgV+prHEfgVEVFD4XQkSA+gKiBmIuSzcxvG3h5ldCX+SPLIilUCB\n9v+1wHpV3Pttqwm62UVg+lNr5/+hJ4qvgaeQ5Z8gy05EZFGoyJj+0dm+oSpWVR/cs2sbMMFy5k9v\nTYxkCmKkhG6TwPQJnqzAKkT+3yuFdSqOGER9TpcwmYlw0BYbtttPYL+UM3l710UXvTGvqSk7E9SR\nNUSrGA1w7QnWAXGq/zX0EeCv0TaSgBqsDAO1/Waqg9q4bMCcVOAiOILxraNTEdHdHn+/lGUagcaW\nZSzDE32AoRdEU9r4McKh8Ge3En/yfzQWdzwAEodggdgRaa21jH/W8f9L0wvpi/r6or4CshRF6pkz\n50+hEH1Alr2YLZqp8YgHqwXxcsWCVwrImMJajfCq7qa9MJu+5E6Et5+PWCy/Qnj6RaYK07JIIo/A\nk63AQYgb6AkwDcc1NPdOqa7OZWTnsDGNlYYOIMP9pZjRFU8nANSQ3ayCK4uzcYjGNV2mohQhI/FL\nrHRjpRorJVxTtIVli1OxNX6X9Gv6l6vLjzbQMBsfyW0VggKbCWyJ4oTGmPJXRGcX8bnJJLVDv3th\n3Mzmy+KJb58pvEtd7bUcfTogVKYmpFqotaQA+wlyreP6Wgh2PoJHdDIIeIajDKCaVoF0UBILwHSf\nQ/BQewcGskmorcUjTngYe/t8FSyzXPSqPmA6u7x89rIzbwfUR8pPAQU5YXRFBtCGM6iXvK+6keU+\nBHtIlpG/RjSd/R53BKImFVAe+CHXDNiJiCjlD2uv5aMX9zL/fYRkxvvAXaTtD+bskoC9xJYC4QqK\nv+FQqwhO/hCoyunkh/NOnDjXGxY2gcndv3uCR+MGCPkR8FYUx2qbsElACHy5G33RKFx/B7cvDMUq\ndQA7c8iZC/z4UbiwISIiuCMiYsaHS5f61MRcpdw5/WmEd/4lor8rCyjj5JVv8mLJN1j5CtQ7EEVc\nqZfImiGS6yMp6wbXFevfIe5wJOY1eEmxu2X7JaEk/Atg4+VQdT2WlRVxC6pev8XyJHAjsvwPfSdt\nSmYdkL+5meKNYQxeG8UMZLnmvpzBoB4Hkix7EQomafck6znuiUBoQ9kxAEhnMG2VcYQiyEA6gMxA\nOIgr3J9kpZ/OnOHBCeuGQ0P76lVV+hr/TZE3Au/dUcIgwtv3HopmJQfR8PcnvNl4ZxqCaQyvYOjs\nrEmdIG2UkcfTAX4T8I6h58WfbRsOE3PhDT8zRiCrEe/uN747ghqE2XYdd0++YxzHMsLGAyB6frwJ\ngfAzwe90tG+b7QYmQvv8pqaczxEP0IbyctPQli2PjaZ9r08hVBGFqF14p7EqgMIN7EhCPLxXAz+L\nwTaFnowKRL1Il4HfDqwwuUwd5bbyrF5639Q+4yhSurM1IjgmrLNNRXiyvtpORgDRI5Bp2Nv3aefh\nzl9XUFjbS0R7M8lXAR9qxy68eCtfIMgQ2WTedBPzP+6mOTYWc8tnHH/sCcILwrIs+XVRRHXj7Y0B\nLC2x4OjKoAWtk1mfR+AxdXoFEXEFpHRjKPSWU74om+wS7TrkquIYjABy2hxjjqBBDaesrAxZHuTi\npnUmJ8vrCe6RkcO063cFu0knrPkCcB3CIO1uMA+A9PQkkd6+Dy8A6ZrdH0jXpSqhWRAeZpAv/++X\nXrqnlUVqY/3dHwL2qVFYqvsx0ThzBzP+qQPqRkQaC+Ah4LJCekKApCSa5jzCKVMjr/f3EFAvIw+j\nFdAlQV192W7iKmJPR3FuYZgTUyPCMfGOQhQlFBGtbsJl2va25Zr/Cu8b+jtwhucPp2/kkj84iZgY\nxdHUdF7Y/ilIefAoBGk5b1ViIO56Ug8GIArXX2v3c30qOOu3bMkOsdn+8Yvbb09CUTyOwrLHVMJa\nC4BYrORh5VKs/Agrz7P5j+/RXpQF6ipE9HEb0ArScDfFQ8lsKklj3aVFLUTduYC+03ixerTBYcIk\n4YC++gEhf7iE/uAbfndtILAEWfbHcipzSs6pwMUvdVBb0E9WXyZhK5KYvaUFE17zd5iE6Pw/m0P1\njQiA0AeyAbDgdsLjBpEklcnAUc0ZmolgYhkibzWJ2vlqWfCx6CEnr4H0KWLh9ZjVnU7Smge9ZN11\newR4ASsdeLPxzhxLxBZ7AHv7EnsE8KVIC6o3+bkGACgKJkT9Y7T0lW5bJZV5eEv1GGsgDwJ/GAWE\nrmDa6w2YXMf9/O68Nh4AeRKBsg8imDYvwYj5Dd9Gs0HcMfi8s7p6agtQ63JR1tTUJ8H1o+nEGIdI\nwYg0ltQGODsJ/C6wWZsKtvEWzq6kN8W3obAUSEzuTO7fwx7Vjv1T7UPOkJ0b2+ka6HExFKPCSWmk\nBLYGIKoZz0yKqdha9wEBRDrK0QBkA5eeuIwvX0bkoD/Uxr/mo1M/lygSS5SbyPn+eixRO/hmdzeb\nyx/koYZnkEzHT0wiYjazaxBgaLQrj0o4yHFrL41IXwE/s2N6tZhJoRgW5lOcKiikcKNGpTyLWEzc\nACIjD5xNOOsKip/Wz9/+JuRCIoffTC1uGnyaIAlRcBUvcDOFOMLMTH2zER8AMRTQDwFmuroiWGr7\nQNwzNQg4irMvyWWSdgZB+TAMTtDYSSrkzjtRPu+U5bZ+GbldVanMDSPjVB9hKNZS8jfGaunIjcCl\nKIokI3cD91xE8+UWXGmJPHTHp6hYOGE+Tbiee87WzhkJzu1PoyGwP6ofW1QYIrI9js8MDsRCdghZ\n7sw7MaPmVEZr9HK27gX2SC4WrGd/3EmulmL5c/NJhlf1g/20eywwTmAXAUOzaJ76AvCkMlnRhQDV\nF6C9/4sv0m0HDnyS1dSkhg8MiMl+VsKZ/8cr+PyFeqxevQW6VSGGMr2C8KhtaJMIO5gTHsv+Cdm8\nYn4obcL2gYwRxWbj4DDdnmgKnR+y22Th5OcfXmaQWPe1Y/Wx9cuB7lOdmDszGeiZzKPvh+v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7kOjyqKadSOUa/FOgXpIoIyIV8Dt2FjpgooGhtrHMGoDIAAtGzO5WzEdV+ILWz8cVgEooCxoDXJ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\n8GC5pDIZBeSbyG7gbaTlxt3W/buQIqRdSBuLz3LqFsMPEAFZT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"text/plain": [ "<matplotlib.figure.Figure at 0x1019bba10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1040aefd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Have to run previous python cell first\n", "A_noisy = A + numpy.random.normal(scale=0.2numpy.max(A), size=A.shape)\n", "\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "pyplot.plot(x, A_noisy)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)*2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What if we just increase the number of neurons? Will it help?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Taking noise into account\n", "\n", "- Include noise while solving for decoders\n", "\n", " - Introduce noise term $\\eta$\n", "\n", "$ \n", "\\begin{align}\n", "\\hat{x} &= \\sum_i(a_i+\\eta)d_i \\\\\n", "E &= {1 \\over 2} \\int_{-1}^1 (x-\\hat{x})^2 \\;dx d\\eta\\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i(a_i+\\eta)d_i\\right)^2 \\;dx d\\eta\\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i - \\sum \\eta d_i \\right)^2 \\;dx d\\eta\n", "\\end{align}\n", "$\n", "- Assume noise is gaussian, independent, mean zero, and has the same variance for each neuron\n", " - $\\eta = \\mathcal{N}(0, \\sigma)$\n", " - All the noise cross-terms disappear (independent)\n", "\n", "$ \n", "\\begin{align}\n", "E &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sum_{i,j} d_i d_j <\\eta_i \\eta_j>_\\eta \\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sum_{i} d_i d_i <\\eta_i \\eta_i>_\\eta \n", "\\end{align}\n", "$\n", "\n", "- Since the average of $\\eta_i \\eta_i$ noise is its variance (since the mean is zero), $\\sigma^2$, we get\n", " \n", "$ \n", "\\begin{align}\n", "E = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$\n", "\n", "- The practical result is that, when computing the decoder, we get\n", "\n", "$ \n", "\\begin{align}\n", "\\Gamma_{ij} = \\sum_x a_i a_j / S + \\sigma^2 \\delta_{ij}\n", "\\end{align}\n", "$\n", "\n", "\n", "- Where $\\delta_{ij}$ is the Kronecker delta: http://en.wikipedia.org/wiki/Kronecker_delta\n", " \n", "- To simplfy computing this using matrices, this can be written as $\\Gamma=A^T A /S + \\sigma^2 I$\n", "\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.0608650480064\n" ] }, { "data": { "image/png": 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C8NkCuyrpISAKip+C0jOQ/1PoaYGUoxUT/qJQIU0VKbPHi2TEpKaOvk0WPBlY0jDEZKfn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UwBLsIrIK5VeYbubldEVUhq2MUu5T6XQ2EnErVLlLhu7HtwtEQVQ6/L6Fro5N2yKqqBly+OqYNt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UTByDjt7ovYJzO5/0GB/Pwzsc1lLFqn/5nTR7OpbViJWNWNNX2tJi1mZuV6RURYWf9EHyM1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"text/plain": [ "<matplotlib.figure.Figure at 0x104166fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10435a290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 100\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates=Uniform(100,200)) #Defaults to LIF neurons, \n", " #with random gains and biases for\n", " #neurons between 100-200hz over -1,1\n", "\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqNoise(noise=0.2)) #Add noise ###NEW\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "A_noisy = A + numpy.random.normal(scale=0.2numpy.max(A), size=A.shape)\n", "\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "pyplot.plot(x, A_noisy)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)*2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Number of neurons\n", "\n", "- What happens to the error with more neurons?\n", " - Note that the error has two parts:\n", " \n", "$ \n", "\\begin{align}\n", "E = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$ \n", "\n", "- Error due to static distortion (i.e. the error introduced by the decoders themselves)\n", " - This is present regardless of noise\n", " \n", "$ \n", "\\begin{align}\n", "E_{distortion} = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 dx \n", "\\end{align}\n", "$ \n", "\n", "\n", "- Error due to noise\n", " \n", "$\n", "\\begin{align}\n", "E_{noise} = \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$ \n", " \n", "- What do these look like as number of neurons $N$ increases? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"files/lecture2/repn_noise.png\">\n", "- Noise error is proportional to $1/N$\n", "- Distortion error is proportional to $1/N^2$\n", "- Remember this error $E$ is defined as\n", "\n", "$ E = {1 \\over 2} \\int_{-1}^1 (x-\\hat{x})^2 dx $\n", "\n", "- So that's actually a squared error term\n", "\n", "- Also, as number of neurons is greater than 100 or so, the error is dominated by the noise term ($1/N$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "\n", "- Methodology for building models with the Neural Engineering Framework (outlined in Chapter 1)\n", " 1. System Description: Describe the system of interest in terms of the neural data, architecture, computations, representations, etc. (e.g. response functions, tuning curves, etc.)\n", " 2. Design Specification: Add additional performance constraints (e.g. bandwidth, noise, SNR, dynamic range, stability, etc.)\n", " 3. Implement the model: Employ the NEF principles given the System Description and Design Specification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Horizontal Eye Control (1D)\n", "\n", "From http://www.nature.com/nrn/journal/v3/n12/full/nrn986.html\n", "\n", "<img src=\"files/lecture2/horizontal_eye.jpg\">\n", "\n", "There are also neurons whose response goes the other way. All of the neurons are directly connected to the muscle controlling the horizontal direction of the eye, and that's the only thing that muscle does, so we're pretty sure this is what's being repreesnted.\n", "\n", "- System Description\n", "\n", " - We've only done the first NEF principle, so that's all we'll worry about\n", " - What is being represented?\n", " - $x$ is the horizontal position\n", " - Tuning curves: extremely linear (high $\\tau_{RC}$, low $\\tau_{ref}$)\n", " - some have $e=1$, some have $e=-1$\n", " - these are often called \"on\" and \"off\" neurons, respectively\n", " - Firing rates of up to 300Hz\n", " \n", "- Design Specification\n", " - Range of values for $x$: -60 degrees to +60 degrees\n", " - Normal levels of noise: $\\sigma$ is 20% of maximum firing rate\n", " - the book goes a bit higher, with $\\sigma^2=0.1$, meaning that $\\sigma = \\sqrt{0.1} \\approx 0.32$ times the maximum firing rate\n", "\n", "- Implementation\n", " - Examine the tuning curves\n", " - Then use principle 1" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MSpaNHmmQytbG4GP7CU0YUpnREfBgCG+NiQnA4KRNk4g6vEKZmgDMHGCe/rjw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\n/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eCZhno/1EYztGsJKelBeAUIx+IKZBAeq8LcpJ7mOgete3rjWTRA3IbaMz7r/vsd6ATv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\nDCmo8N5SVKKgT0aNgyA9UF96U4MJlOM9Euhph+voBjj9m5cei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"text/plain": [ "<matplotlib.figure.Figure at 0x1043ce6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#%pylab inline\n", "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 40\n", "tau_rc = .2\n", "tau_ref = .001\n", "lif_model = nengo.LIFRate(tau_rc=tau_rc, tau_ref=tau_ref)\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates = Uniform(250,300),\n", " neuron_type = lif_model)\n", "sim = nengo.Simulator(model)\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "plot(x, A)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- How good is the representation?" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE with 40 neurons is 0.0809303\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10435a290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Have to run previous code cell first\n", "noise = 0.2\n", "\n", "with model:\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqNoise(noise=0.2)) #Add noise ###NEW\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "x, A = tuning_curves(neurons, sim)\n", "A_noisy = A + numpy.random.normal(scale=noisenumpy.max(A), size=A.shape)\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "print 'RMSE with %d neurons is %g'%(N, np.sqrt(np.average((x-xhat)*2)))\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Possible questions\n", " - How many neurons do we need for a particular level of accuracy?\n", " - What happens with different firing rates?\n", " - What happens with different distributions of x-intercepts?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: Arm Movements (2D)\n", "\n", "- [Georgopoulos et al., 1982. \"On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.\"](http://www.ncbi.nlm.nih.gov/pubmed/7143039)\n", "\n", "<img src=\"files/lecture2/armmovement1.jpg\">\n", "\n", "<img src=\"files/lecture2/armmovement2.png\">\n", "\n", "<img src=\"files/lecture2/armtuningcurve.png\">\n", "\n", "- System Description\n", "\n", " - What is being represented?\n", " - $x$ is the hand position\n", " - Note that this is different* from what Georgopoulos talks about in this initial paper\n", " - Initial paper only looks at those 8 positions, so it only talks about direction of movement (angle but not magnitude)\n", " - More recent work in the same area shows the cells do respond to both (Fu et al, 1993; Messier and Kalaska, 2000)\n", " - Bell-shaped tuning curves\n", " - Encoders: randomly distributed around the unit circle\n", " - Firing rates of up to 60Hz\n", " \n", "- Design Specification\n", " - Range of values for $x$: Anywhere within a unit circle (or perhaps some other radius)\n", " - Normal levels of noise: $\\sigma$ is 20% of maximum firing rate\n", " - the book goes a bit higher, with $\\sigma^2=0.1$, meaning that $\\sigma = \\sqrt{0.1} \\approx 0.32$ times the maximum \n", "- Implementation\n", " - Examine the tuning curves\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1046095d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate() \n", "\n", "theta = numpy.linspace(-numpy.pi, numpy.pi, 100)\n", "x = numpy.array([numpy.sin(theta), numpy.cos(theta)])\n", "\n", "e = numpy.array([1.0, 0])\n", "\n", "plot(theta*180/numpy.pi, n.rates(numpy.dot(x.T, e), bias=1, gain=0.2)) #bias 1->1.5\n", "xlabel('angle')\n", "ylabel('firing rate')\n", "xlim(-180, 180)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Does it match empirical data?\n", " - When tuning curves are plotted just considering $\\theta$, they are fit by $a_i=b_0+b_1cos(\\theta-\\theta_e)$ \n", " - Where $\\theta_e$ is the angle for the encoder $e_i$ and $b_0$ and $b_1$ are constants\n", " \n", "- Interestingly, Georgopoulos suggests doing linear decoding:\n", " - $\\hat{x}=\\sum_i a_i e_i$\n", " - This gives a somewhat decent estimate of the direction of movement (but a terrible estimate of magnitude)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Higher-dimensional Tuning\n", "- Note that there can be different ways of organizing the representation of a higher dimensional space\n", "\n", "<img src=\"files/lecture2/semicircular_canal.png\">\n", "\n", "- Here, the neurons respond to angular velocity. This is a 3D vector. \n", "- But, instead of randomly distributing encoders around the 3D space, they are aligned with a major axis\n", " - encoders are chosen from [1,0,0], [-1,0,0], [0,1,0], [0,-1,0], [0,0,1], [0,0,-1]\n", "- This can affect on the representation\n", "\n", "<img src=\"files/lecture2/aligned_encoders.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Administrative Notes\n", "\n", "- Assignment 1 has been posted \n", " - [HTML](http://nbviewer.ipython.org/github/celiasmith/syde556/blob/master/Assignment%201.ipynb)\n", "- Due: January 25th at midnight\n", "- Total marks: 20 (20% of final grade)\n", "- Late penalty: 1 mark per day\n", "- It is recommended that you use a language with a matrix library and graphing capabilities. Two main suggestions are Python and MATLAB.\n", "\n", "- Tutoring Services\n", " - If you would like more personalized help for the assignments in this course, two of the PhD students in the lab (Xuan Choo and Travis DeWolf) offer tutoring services. They can be contacted at [[email protected]](mailto:[email protected]) and they charge \\$20 per half hour (or \\$15 per person per half hour for groups)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
CompPhysics/MachineLearning	doc/src/LectureNotes/chapter4.ipynb	2	198219	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression and more Advanced Regression Analysis\n", "\n", "\n", "## Why Linear Regression (aka Ordinary Least Squares and family)\n", "\n", "Fitting a continuous function with linear parameterization in terms of the parameters $\\boldsymbol{\\beta}$.\n", "* Method of choice for fitting a continuous function!\n", "\n", "* Gives an excellent introduction to central Machine Learning features with understandable pedagogical links to other methods like Neural Networks, Support Vector Machines etc\n", "\n", "* Analytical expression for the fitting parameters $\\boldsymbol{\\beta}$\n", "\n", "* Analytical expressions for statistical propertiers like mean values, variances, confidence intervals and more\n", "\n", "* Analytical relation with probabilistic interpretations \n", "\n", "* Easy to introduce basic concepts like bias-variance tradeoff, cross-validation, resampling and regularization techniques and many other ML topics\n", "\n", "* Easy to code! And links well with classification problems and logistic regression and neural networks\n", "\n", "* Allows for easy hands-on understanding of gradient descent methods\n", "\n", "* and many more features\n", "\n", "For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n", "Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n", "\n", "\n", "## Regression analysis, overarching aims\n", "\n", "Regression modeling deals with the description of the sampling distribution of a given random variable $y$ and how it varies as function of another variable or a set of such variables $\\boldsymbol{x} =[x_0, x_1,\\dots, x_{n-1}]^T$. \n", "The first variable is called the dependent, the outcome or the response variable while the set of variables $\\boldsymbol{x}$ is called the independent variable, or the predictor variable or the explanatory variable. \n", "\n", "A regression model aims at finding a likelihood function $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$, that is the conditional distribution for $\\boldsymbol{y}$ with a given $\\boldsymbol{x}$. The estimation of $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$ is made using a data set with \n", "* $n$ cases $i = 0, 1, 2, \\dots, n-1$ \n", "\n", "* Response (target, dependent or outcome) variable $y_i$ with $i = 0, 1, 2, \\dots, n-1$ \n", "\n", "* $p$ so-called explanatory (independent or predictor) variables $\\boldsymbol{x}_i=[x_{i0}, x_{i1}, \\dots, x_{ip-1}]$ with $i = 0, 1, 2, \\dots, n-1$ and explanatory variables running from $0$ to $p-1$. See below for more explicit examples. \n", "\n", " The goal of the regression analysis is to extract/exploit relationship between $\\boldsymbol{y}$ and $\\boldsymbol{x}$ in or to infer causal dependencies, approximations to the likelihood functions, functional relationships and to make predictions, making fits and many other things.\n", "\n", "\n", "\n", "## Regression analysis, overarching aims II\n", "\n", "\n", "Consider an experiment in which $p$ characteristics of $n$ samples are\n", "measured. The data from this experiment, for various explanatory variables $p$ are normally represented by a matrix \n", "$\\mathbf{X}$.\n", "\n", "The matrix $\\mathbf{X}$ is called the design\n", "matrix. Additional information of the samples is available in the\n", "form of $\\boldsymbol{y}$ (also as above). The variable $\\boldsymbol{y}$ is\n", "generally referred to as the response variable. The aim of\n", "regression analysis is to explain $\\boldsymbol{y}$ in terms of\n", "$\\boldsymbol{X}$ through a functional relationship like $y_i =\n", "f(\\mathbf{X}_{i,\\ast})$. When no prior knowledge on the form of\n", "$f(\\cdot)$ is available, it is common to assume a linear relationship\n", "between $\\boldsymbol{X}$ and $\\boldsymbol{y}$. This assumption gives rise to\n", "the linear regression model where $\\boldsymbol{\\beta} = [\\beta_0, \\ldots,\n", "\\beta_{p-1}]^{T}$ are the regression parameters. \n", "\n", "Linear regression gives us a set of analytical equations for the parameters $\\beta_j$.\n", "\n", "\n", "\n", "\n", "\n", "## Examples\n", "In order to understand the relation among the predictors $p$, the set of data $n$ and the target (outcome, output etc) $\\boldsymbol{y}$,\n", "consider the model we discussed for describing nuclear binding energies. \n", "\n", "There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model.\n", "Assuming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "BE(A) = a_0+a_1A+a_2A^{2/3}+a_3A^{-1/3}+a_4A^{-1},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have five predictors, that is the intercept, the $A$ dependent term, the $A^{2/3}$ term and the $A^{-1/3}$ and $A^{-1}$ terms.\n", "This gives $p=0,1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a \n", "$p\\times n$ matrix $\\boldsymbol{X}$.\n", "\n", "Here the predictors are based on a model we have made. A popular data set which is widely encountered in ML applications is the\n", "so-called [credit card default data from Taiwan](https://www.sciencedirect.com/science/article/pii/S0957417407006719?via%3Dihub). The data set contains data on $n=30000$ credit card holders with predictors like gender, marital status, age, profession, education, etc. In total there are $24$ such predictors or attributes leading to a design matrix of dimensionality $24 \\times 30000$. This is however a classification problem and we will come back to it when we discuss Logistic Regression.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "## General linear models\n", "Before we proceed let us study a case from linear algebra where we aim at fitting a set of data $\\boldsymbol{y}=[y_0,y_1,\\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\\boldsymbol{x}=[x_0,x_1,\\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. \n", "\n", "Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of $y$ which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree $n-1$ with $n$ points, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y=y(x) \\rightarrow y(x_i)=\\tilde{y}_i+\\epsilon_i=\\sum_{j=0}^{n-1} \\beta_j x_i^j+\\epsilon_i,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\epsilon_i$ is the error in our approximation.\n", "\n", "\n", "\n", "\n", "## Rewriting the fitting procedure as a linear algebra problem\n", "For every set of values $y_i,x_i$ we have thus the corresponding set of equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "y_0&=\\beta_0+\\beta_1x_0^1+\\beta_2x_0^2+\\dots+\\beta_{n-1}x_0^{n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0+\\beta_1x_1^1+\\beta_2x_1^2+\\dots+\\beta_{n-1}x_1^{n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0+\\beta_1x_2^1+\\beta_2x_2^2+\\dots+\\beta_{n-1}x_2^{n-1}+\\epsilon_2\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0+\\beta_1x_{n-1}^1+\\beta_2x_{n-1}^2+\\dots+\\beta_{n-1}x_{n-1}^{n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rewriting the fitting procedure as a linear algebra problem, more details\n", "Defining the vectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = [y_0,y_1, y_2,\\dots, y_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = [\\beta_0,\\beta_1, \\beta_2,\\dots, \\beta_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\epsilon} = [\\epsilon_0,\\epsilon_1, \\epsilon_2,\\dots, \\epsilon_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the design matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\n", "\\begin{bmatrix} \n", "1& x_{0}^1 &x_{0}^2& \\dots & \\dots &x_{0}^{n-1}\\\\\n", "1& x_{1}^1 &x_{1}^2& \\dots & \\dots &x_{1}^{n-1}\\\\\n", "1& x_{2}^1 &x_{2}^2& \\dots & \\dots &x_{2}^{n-1}\\\\ \n", "\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n", "1& x_{n-1}^1 &x_{n-1}^2& \\dots & \\dots &x_{n-1}^{n-1}\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can rewrite our equations as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above design matrix is called a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix).\n", "\n", "\n", "\n", "\n", "## Generalizing the fitting procedure as a linear algebra problem\n", "\n", "We are obviously not limited to the above polynomial expansions. We\n", "could replace the various powers of $x$ with elements of Fourier\n", "series or instead of $x_i^j$ we could have $\\cos{(j x_i)}$ or $\\sin{(j\n", "x_i)}$, or time series or other orthogonal functions. For every set\n", "of values $y_i,x_i$ we can then generalize the equations to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_2\\\\\n", "\\dots & \\dots \\\\\n", "y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_i\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we have $p=n$ here. The matrix is symmetric. This is generally not the case!\n", "\n", "\n", "\n", "\n", "## Generalizing the fitting procedure as a linear algebra problem\n", "We redefine in turn the matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\n", "\\begin{bmatrix} \n", "x_{00}& x_{01} &x_{02}& \\dots & \\dots &x_{0,n-1}\\\\\n", "x_{10}& x_{11} &x_{12}& \\dots & \\dots &x_{1,n-1}\\\\\n", "x_{20}& x_{21} &x_{22}& \\dots & \\dots &x_{2,n-1}\\\\ \n", "\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n", "x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \\dots & \\dots &x_{n-1,n-1}\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and without loss of generality we rewrite again our equations as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The left-hand side of this equation is kwown. Our error vector $\\boldsymbol{\\epsilon}$ and the parameter vector $\\boldsymbol{\\beta}$ are our unknow quantities. How can we obtain the optimal set of $\\beta_i$ values?\n", "\n", "\n", "\n", "\n", "## Optimizing our parameters\n", "We have defined the matrix $\\boldsymbol{X}$ via the equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_1\\\\\n", "\\dots & \\dots \\\\\n", "y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_1\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we noted above, we stayed with a system with the design matrix \n", " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$, that is we have $p=n$. For reasons to come later (algorithmic arguments) we will hereafter define \n", "our matrix as $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors refering to the column numbers and the entries $n$ being the row elements.\n", "\n", "\n", "\n", "\n", "## Our model for the nuclear binding energies\n", "\n", "In our [introductory notes](https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html) we looked at the so-called [liquid drop model](https://en.wikipedia.org/wiki/Semi-empirical_mass_formula). Let us remind ourselves about what we did by looking at the code.\n", "\n", "We restate the parts of the code we are most interested in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Common imports\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from IPython.display import display\n", "import os\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"MassEval2016.dat\"),'r')\n", "\n", "\n", "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", " names=('N', 'Z', 'A', 'Element', 'Ebinding'),\n", " widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),\n", " header=39,\n", " index_col=False)\n", "\n", "# Extrapolated values are indicated by '#' in place of the decimal place, so\n", "# the Ebinding column won't be numeric. Coerce to float and drop these entries.\n", "Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')\n", "Masses = Masses.dropna()\n", "# Convert from keV to MeV.\n", "Masses['Ebinding'] /= 1000\n", "\n", "# Group the DataFrame by nucleon number, A.\n", "Masses = Masses.groupby('A')\n", "# Find the rows of the grouped DataFrame with the maximum binding energy.\n", "Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])\n", "A = Masses['A']\n", "Z = Masses['Z']\n", "N = Masses['N']\n", "Element = Masses['Element']\n", "Energies = Masses['Ebinding']\n", "\n", "# Now we set up the design matrix X\n", "X = np.zeros((len(A),5))\n", "X[:,0] = 1\n", "X[:,1] = A\n", "X[:,2] = A(2.0/3.0)\n", "X[:,3] = A(-1.0/3.0)\n", "X[:,4] = A(-1.0)\n", "# Then nice printout using pandas\n", "DesignMatrix = pd.DataFrame(X)\n", "DesignMatrix.index = A\n", "DesignMatrix.columns = ['1', 'A', 'A^(2/3)', 'A^(-1/3)', '1/A']\n", "display(DesignMatrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With $\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p\\times 1}$, it means that we will hereafter write our equations for the approximation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "throughout these lectures. \n", "\n", "\n", "## Optimizing our parameters, more details\n", "With the above we use the design matrix to define the approximation $\\boldsymbol{\\tilde{y}}$ via the unknown quantity $\\boldsymbol{\\beta}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and in order to find the optimal parameters $\\beta_i$ instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values $y_i$ (which represent hopefully the exact values) and the parameterized values $\\tilde{y}_i$, namely" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or using the matrix $\\boldsymbol{X}$ and in a more compact matrix-vector notation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is one possible way to define the so-called cost function.\n", "\n", "\n", "\n", "It is also common to define\n", "the function $C$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{2n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "since when taking the first derivative with respect to the unknown parameters $\\beta$, the factor of $2$ cancels out.\n", "\n", "\n", "\n", "\n", "## Interpretations and optimizing our parameters\n", "\n", "The function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "can be linked to the variance of the quantity $y_i$ if we interpret the latter as the mean value. \n", "When linking (see the discussion below) with the maximum likelihood approach below, we will indeed interpret $y_i$ as a mean value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y_{i}=\\langle y_i \\rangle = \\beta_0x_{i,0}+\\beta_1x_{i,1}+\\beta_2x_{i,2}+\\dots+\\beta_{n-1}x_{i,n-1}+\\epsilon_i,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\langle y_i \\rangle$ is the mean value. Keep in mind also that\n", "till now we have treated $y_i$ as the exact value. Normally, the\n", "response (dependent or outcome) variable $y_i$ the outcome of a\n", "numerical experiment or another type of experiment and is thus only an\n", "approximation to the true value. It is then always accompanied by an\n", "error estimate, often limited to a statistical error estimate given by\n", "the standard deviation discussed earlier. In the discussion here we\n", "will treat $y_i$ as our exact value for the response variable.\n", "\n", "In order to find the parameters $\\beta_i$ we will then minimize the spread of $C(\\boldsymbol{\\beta})$, that is we are going to solve the problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practical terms it means we will require" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)^2\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_{ij}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or in a matrix-vector form as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretations and optimizing our parameters\n", "We can rewrite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and if the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ is invertible we have the solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} =\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note also that since our design matrix is defined as $\\boldsymbol{X}\\in\n", "{\\mathbb{R}}^{n\\times p}$, the product $\\boldsymbol{X}^T\\boldsymbol{X} \\in\n", "{\\mathbb{R}}^{p\\times p}$. In the above case we have that $p \\ll n$,\n", "in our case $p=5$ meaning that we end up with inverting a small\n", "$5\\times 5$ matrix. This is a rather common situation, in many cases we end up with low-dimensional\n", "matrices to invert. The methods discussed here and for many other\n", "supervised learning algorithms like classification with logistic\n", "regression or support vector machines, exhibit dimensionalities which\n", "allow for the usage of direct linear algebra methods such as LU decomposition or Singular Value Decomposition (SVD) for finding the inverse of the matrix\n", "$\\boldsymbol{X}^T\\boldsymbol{X}$.\n", "\n", "\n", "\n", "Small question: Do you think the example we have at hand here (the nuclear binding energies) can lead to problems in inverting the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$? What kind of problems can we expect?\n", "\n", "\n", "\n", "## Some useful matrix and vector expressions\n", "\n", "The following matrix and vector relation will be useful here and for the rest of the course. Vectors are always written as boldfaced lower case letters and \n", "matrices as upper case boldfaced letters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "6\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "7\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "8\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\log{\\vert\\boldsymbol{A}\\vert}}{\\partial \\boldsymbol{A}} = (\\boldsymbol{A}^{-1})^T.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretations and optimizing our parameters\n", "The residuals $\\boldsymbol{\\epsilon}$ are in turn given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\epsilon} = \\boldsymbol{y}-\\boldsymbol{\\tilde{y}} = \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{\\epsilon}=\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "meaning that the solution for $\\boldsymbol{\\beta}$ is the one which minimizes the residuals. Later we will link this with the maximum likelihood approach.\n", "\n", "\n", "\n", "\n", "Let us now return to our nuclear binding energies and simply code the above equations. \n", "\n", "## Own code for Ordinary Least Squares\n", "\n", "It is rather straightforward to implement the matrix inversion and obtain the parameters $\\boldsymbol{\\beta}$. After having defined the matrix $\\boldsymbol{X}$ we simply need to \n", "write" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# matrix inversion to find beta\n", "beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)\n", "# and then make the prediction\n", "ytilde = X @ beta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can use the least squares functionality in Numpy as" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n", "ytildenp = np.dot(fit,X.T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally we plot our fit with and compare with data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Masses['Eapprox'] = ytilde\n", "# Generate a plot comparing the experimental with the fitted values values.\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(r'$A = N + Z$')\n", "ax.set_ylabel(r'$E_\\mathrm{bind}\\,/\\mathrm{MeV}$')\n", "ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,\n", " label='Ame2016')\n", "ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',\n", " label='Fit')\n", "ax.legend()\n", "save_fig(\"Masses2016OLS\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding error analysis and training set up\n", "\n", "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of Scikit-Learn in the introductory slides.\n", "Since we are not using Scikit-Learn here we can define our own $R2$ function as" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def R2(y_data, y_model):\n", " return 1 - np.sum((y_data - y_model) 2) / np.sum((y_data - np.mean(y_data)) 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and we would be using it as" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(R2(Energies,ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily add our MSE score as" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", " return np.sum((y_data-y_model)2)/n\n", "\n", "print(MSE(Energies,ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and finally the relative error as" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def RelativeError(y_data,y_model):\n", " return abs((y_data-y_model)/y_data)\n", "print(RelativeError(Energies, ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "Normally, the response (dependent or outcome) variable $y_i$ is the\n", "outcome of a numerical experiment or another type of experiment and is\n", "thus only an approximation to the true value. It is then always\n", "accompanied by an error estimate, often limited to a statistical error\n", "estimate given by the standard deviation discussed earlier. In the\n", "discussion here we will treat $y_i$ as our exact value for the\n", "response variable.\n", "\n", "Introducing the standard deviation $\\sigma_i$ for each measurement\n", "$y_i$, we define now the $\\chi^2$ function (omitting the $1/n$ term)\n", "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\chi^2(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\frac{\\left(y_i-\\tilde{y}_i\\right)^2}{\\sigma_i^2}=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\frac{1}{\\boldsymbol{\\Sigma^2}}\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the matrix $\\boldsymbol{\\Sigma}$ is a diagonal matrix with $\\sigma_i$ as matrix elements.\n", "\n", "\n", "\n", "## The $\\chi^2$ function\n", "\n", "In order to find the parameters $\\beta_i$ we will then minimize the spread of $\\chi^2(\\boldsymbol{\\beta})$ by requiring" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)^2\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}\\frac{x_{ij}}{\\sigma_i}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or in a matrix-vector form as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have defined the matrix $\\boldsymbol{A} =\\boldsymbol{X}/\\boldsymbol{\\Sigma}$ with matrix elements $a_{ij} = x_{ij}/\\sigma_i$ and the vector $\\boldsymbol{b}$ with elements $b_i = y_i/\\sigma_i$.\n", "\n", "\n", "\n", "## The $\\chi^2$ function\n", "\n", "We can rewrite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{A}^T\\boldsymbol{b} = \\boldsymbol{A}^T\\boldsymbol{A}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and if the matrix $\\boldsymbol{A}^T\\boldsymbol{A}$ is invertible we have the solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} =\\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1}\\boldsymbol{A}^T\\boldsymbol{b}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "If we then introduce the matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{H} = \\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have then the following expression for the parameters $\\beta_j$ (the matrix elements of $\\boldsymbol{H}$ are $h_{ij}$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_j = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}\\frac{y_i}{\\sigma_i}\\frac{x_{ik}}{\\sigma_i} = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}b_ia_{ik}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We state without proof the expression for the uncertainty in the parameters $\\beta_j$ as (we leave this as an exercise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sigma^2(\\beta_j) = \\sum_{i=0}^{n-1}\\sigma_i^2\\left( \\frac{\\partial \\beta_j}{\\partial y_i}\\right)^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "resulting in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sigma^2(\\beta_j) = \\left(\\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}a_{ik}\\right)\\left(\\sum_{l=0}^{p-1}h_{jl}\\sum_{m=0}^{n-1}a_{ml}\\right) = h_{jj}!\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "The first step here is to approximate the function $y$ with a first-order polynomial, that is we write" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y=y(x) \\rightarrow y(x_i) \\approx \\beta_0+\\beta_1 x_i.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By computing the derivatives of $\\chi^2$ with respect to $\\beta_0$ and $\\beta_1$ show that these are given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -2\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_i\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "For a linear fit (a first-order polynomial) we don't need to invert a matrix!! \n", "Defining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma = \\sum_{i=0}^{n-1}\\frac{1}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_x = \\sum_{i=0}^{n-1}\\frac{x_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_y = \\sum_{i=0}^{n-1}\\left(\\frac{y_i}{\\sigma_i^2}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_{xx} = \\sum_{i=0}^{n-1}\\frac{x_ix_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_{xy} = \\sum_{i=0}^{n-1}\\frac{y_ix_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_0 = \\frac{\\gamma_{xx}\\gamma_y-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_1 = \\frac{\\gamma_{xy}\\gamma-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This approach (different linear and non-linear regression) suffers\n", "often from both being underdetermined and overdetermined in the\n", "unknown coefficients $\\beta_i$. A better approach is to use the\n", "Singular Value Decomposition (SVD) method discussed below. Or using\n", "Lasso and Ridge regression. See below.\n", "\n", "\n", "\n", "\n", "## Fitting an Equation of State for Dense Nuclear Matter\n", "\n", "Before we continue, let us introduce yet another example. We are going to fit the\n", "nuclear equation of state using results from many-body calculations.\n", "The equation of state we have made available here, as function of\n", "density, has been derived using modern nucleon-nucleon potentials with\n", "[the addition of three-body\n", "forces](https://www.sciencedirect.com/science/article/pii/S0370157399001106). This\n", "time the file is presented as a standard csv file.\n", "\n", "The beginning of the Python code here is similar to what you have seen\n", "before, with the same initializations and declarations. We use also\n", "pandas again, rather extensively in order to organize our data.\n", "\n", "The difference now is that we use Scikit-Learn's regression tools\n", "instead of our own matrix inversion implementation. Furthermore, we\n", "sneak in Ridge regression (to be discussed below) which includes a\n", "hyperparameter $\\lambda$, also to be explained below.\n", "\n", "## The code" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n", "import sklearn.linear_model as skl\n", "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "X = np.zeros((len(Density),4))\n", "X[:,3] = Density(4.0/3.0)\n", "X[:,2] = Density\n", "X[:,1] = Density(2.0/3.0)\n", "X[:,0] = 1\n", "\n", "# We use now Scikit-Learn's linear regressor and ridge regressor\n", "# OLS part\n", "clf = skl.LinearRegression().fit(X, Energies)\n", "ytilde = clf.predict(X)\n", "EoS['Eols'] = ytilde\n", "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, ytilde))\n", "# Explained variance score: 1 is perfect prediction \n", "print('Variance score: %.2f' % r2_score(Energies, ytilde))\n", "# Mean absolute error \n", "print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))\n", "print(clf.coef_, clf.intercept_)\n", "\n", "# The Ridge regression with a hyperparameter lambda = 0.1\n", "_lambda = 0.1\n", "clf_ridge = skl.Ridge(alpha=_lambda).fit(X, Energies)\n", "yridge = clf_ridge.predict(X)\n", "EoS['Eridge'] = yridge\n", "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, yridge))\n", "# Explained variance score: 1 is perfect prediction \n", "print('Variance score: %.2f' % r2_score(Energies, yridge))\n", "# Mean absolute error \n", "print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))\n", "print(clf_ridge.coef_, clf_ridge.intercept_)\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(r'$\\rho[\\mathrm{fm}^{-3}]$')\n", "ax.set_ylabel(r'Energy per particle')\n", "ax.plot(EoS['Density'], EoS['Energy'], alpha=0.7, lw=2,\n", " label='Theoretical data')\n", "ax.plot(EoS['Density'], EoS['Eols'], alpha=0.7, lw=2, c='m',\n", " label='OLS')\n", "ax.plot(EoS['Density'], EoS['Eridge'], alpha=0.7, lw=2, c='g',\n", " label='Ridge $\\lambda = 0.1$')\n", "ax.legend()\n", "save_fig(\"EoSfitting\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above simple polynomial in density $\\rho$ gives an excellent fit\n", "to the data. \n", "\n", "We note also that there is a small deviation between the\n", "standard OLS and the Ridge regression at higher densities. We discuss this in more detail\n", "below.\n", "\n", "\n", "## Splitting our Data in Training and Test data\n", "\n", "It is normal in essentially all Machine Learning studies to split the\n", "data in a training set and a test set (sometimes also an additional\n", "validation set). Scikit-Learn has an own function for this. There\n", "is no explicit recipe for how much data should be included as training\n", "data and say test data. An accepted rule of thumb is to use\n", "approximately $2/3$ to $4/5$ of the data as training data. We will\n", "postpone a discussion of this splitting to the end of these notes and\n", "our discussion of the so-called bias-variance tradeoff. Here we\n", "limit ourselves to repeat the above equation of state fitting example\n", "but now splitting the data into a training set and a test set." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "def R2(y_data, y_model):\n", " return 1 - np.sum((y_data - y_model) 2) / np.sum((y_data - np.mean(y_data)) 2)\n", "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", " return np.sum((y_data-y_model)2)/n\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organized into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "X = np.zeros((len(Density),5))\n", "X[:,0] = 1\n", "X[:,1] = Density(2.0/3.0)\n", "X[:,2] = Density\n", "X[:,3] = Density(4.0/3.0)\n", "X[:,4] = Density(5.0/3.0)\n", "# We split the data in test and training data\n", "X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", "# matrix inversion to find beta\n", "beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)\n", "# and then make the prediction\n", "ytilde = X_train @ beta\n", "print(\"Training R2\")\n", "print(R2(y_train,ytilde))\n", "print(\"Training MSE\")\n", "print(MSE(y_train,ytilde))\n", "ypredict = X_test @ beta\n", "print(\"Test R2\")\n", "print(R2(y_test,ypredict))\n", "print(\"Test MSE\")\n", "print(MSE(y_test,ypredict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- !split -->\n", "## The Boston housing data example\n", "\n", "The Boston housing \n", "data set was originally a part of UCI Machine Learning Repository\n", "and has been removed now. The data set is now included in Scikit-Learn's \n", "library. There are 506 samples and 13 feature (predictor) variables\n", "in this data set. The objective is to predict the value of prices of\n", "the house using the features (predictors) listed here.\n", "\n", "The features/predictors are\n", "1. CRIM: Per capita crime rate by town\n", "\n", "2. ZN: Proportion of residential land zoned for lots over 25000 square feet\n", "\n", "3. INDUS: Proportion of non-retail business acres per town\n", "\n", "4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", "\n", "5. NOX: Nitric oxide concentration (parts per 10 million)\n", "\n", "6. RM: Average number of rooms per dwelling\n", "\n", "7. AGE: Proportion of owner-occupied units built prior to 1940\n", "\n", "8. DIS: Weighted distances to five Boston employment centers\n", "\n", "9. RAD: Index of accessibility to radial highways\n", "\n", "10. TAX: Full-value property tax rate per USD10000\n", "\n", "11. B: $1000(Bk - 0.63)^2$, where $Bk$ is the proportion of [people of African American descent] by town\n", "\n", "12. LSTAT: Percentage of lower status of the population\n", "\n", "13. MEDV: Median value of owner-occupied homes in USD 1000s\n", "\n", "## Housing data, the code\n", "We start by importing the libraries" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt \n", "\n", "import pandas as pd \n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and load the Boston Housing DataSet from Scikit-Learn" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", "boston_dataset = load_boston()\n", "\n", "# boston_dataset is a dictionary\n", "# let's check what it contains\n", "boston_dataset.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we invoke Pandas" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n", "boston.head()\n", "boston['MEDV'] = boston_dataset.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and preprocess the data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# check for missing values in all the columns\n", "boston.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then visualize the data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# set the size of the figure\n", "sns.set(rc={'figure.figsize':(11.7,8.27)})\n", "\n", "# plot a histogram showing the distribution of the target values\n", "sns.distplot(boston['MEDV'], bins=30)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is now useful to look at the correlation matrix" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# compute the pair wise correlation for all columns \n", "correlation_matrix = boston.corr().round(2)\n", "# use the heatmap function from seaborn to plot the correlation matrix\n", "# annot = True to print the values inside the square\n", "sns.heatmap(data=correlation_matrix, annot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don't include this in our features together to avoid multi-colinearity" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", "\n", "features = ['LSTAT', 'RM']\n", "target = boston['MEDV']\n", "\n", "for i, col in enumerate(features):\n", " plt.subplot(1, len(features) , i+1)\n", " x = boston[col]\n", " y = target\n", " plt.scatter(x, y, marker='o')\n", " plt.title(col)\n", " plt.xlabel(col)\n", " plt.ylabel('MEDV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we start training our model" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n", "Y = boston['MEDV']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We split the data into training and test sets" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# splits the training and test data set in 80% : 20%\n", "# assign random_state to any value.This ensures consistency.\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n", "print(X_train.shape)\n", "print(X_test.shape)\n", "print(Y_train.shape)\n", "print(Y_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we use the linear regression functionality from Scikit-Learn" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "\n", "lin_model = LinearRegression()\n", "lin_model.fit(X_train, Y_train)\n", "\n", "# model evaluation for training set\n", "\n", "y_train_predict = lin_model.predict(X_train)\n", "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n", "r2 = r2_score(Y_train, y_train_predict)\n", "\n", "print(\"The model performance for training set\")\n", "print(\"--------------------------------------\")\n", "print('RMSE is {}'.format(rmse))\n", "print('R2 score is {}'.format(r2))\n", "print(\"\\n\")\n", "\n", "# model evaluation for testing set\n", "\n", "y_test_predict = lin_model.predict(X_test)\n", "# root mean square error of the model\n", "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n", "\n", "# r-squared score of the model\n", "r2 = r2_score(Y_test, y_test_predict)\n", "\n", "print(\"The model performance for testing set\")\n", "print(\"--------------------------------------\")\n", "print('RMSE is {}'.format(rmse))\n", "print('R2 score is {}'.format(r2))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plotting the y_test vs y_pred\n", "# ideally should have been a straight line\n", "plt.scatter(Y_test, y_test_predict)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reducing the number of degrees of freedom, overarching view\n", "\n", "Many Machine Learning problems involve thousands or even millions of\n", "features for each training instance. Not only does this make training\n", "extremely slow, it can also make it much harder to find a good\n", "solution, as we will see. This problem is often referred to as the\n", "curse of dimensionality. Fortunately, in real-world problems, it is\n", "often possible to reduce the number of features considerably, turning\n", "an intractable problem into a tractable one.\n", "\n", "Later we will discuss some of the most popular dimensionality reduction\n", "techniques: the principal component analysis (PCA), Kernel PCA, and\n", "Locally Linear Embedding (LLE). \n", "\n", "\n", "Principal component analysis and its various variants deal with the\n", "problem of fitting a low-dimensional [affine\n", "subspace](https://en.wikipedia.org/wiki/Affine_space) to a set of of\n", "data points in a high-dimensional space. With its family of methods it\n", "is one of the most used tools in data modeling, compression and\n", "visualization.\n", "\n", "\n", "\n", "\n", "## Preprocessing our data\n", "\n", "Before we proceed however, we will discuss how to preprocess our\n", "data. Till now and in connection with our previous examples we have\n", "not met so many cases where we are too sensitive to the scaling of our\n", "data. Normally the data may need a rescaling and/or may be sensitive\n", "to extreme values. Scaling the data renders our inputs much more\n", "suitable for the algorithms we want to employ.\n", "\n", "Scikit-Learn has several functions which allow us to rescale the\n", "data, normally resulting in much better results in terms of various\n", "accuracy scores. The StandardScaler function in Scikit-Learn\n", "ensures that for each feature/predictor we study the mean value is\n", "zero and the variance is one (every column in the design/feature\n", "matrix). This scaling has the drawback that it does not ensure that\n", "we have a particular maximum or minimum in our data set. Another\n", "function included in Scikit-Learn is the MinMaxScaler which\n", "ensures that all features are exactly between $0$ and $1$. The\n", "\n", "## More preprocessing\n", "\n", "\n", "The Normalizer scales each data\n", "point such that the feature vector has a euclidean length of one. In other words, it\n", "projects a data point on the circle (or sphere in the case of higher dimensions) with a\n", "radius of 1. This means every data point is scaled by a different number (by the\n", "inverse of it’s length).\n", "This normalization is often used when only the direction (or angle) of the data matters,\n", "not the length of the feature vector.\n", "\n", "The RobustScaler works similarly to the StandardScaler in that it\n", "ensures statistical properties for each feature that guarantee that\n", "they are on the same scale. However, the RobustScaler uses the median\n", "and quartiles, instead of mean and variance. This makes the\n", "RobustScaler ignore data points that are very different from the rest\n", "(like measurement errors). These odd data points are also called\n", "outliers, and might often lead to trouble for other scaling\n", "techniques.\n", "\n", "\n", "\n", "## Simple preprocessing examples, Franke function and regression" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import sklearn.linear_model as skl\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "\n", "def FrankeFunction(x,y):\n", "\tterm1 = 0.75np.exp(-(0.25(9x-2)2) - 0.25((9y-2)2))\n", "\tterm2 = 0.75np.exp(-((9x+1)2)/49.0 - 0.1(9y+1))\n", "\tterm3 = 0.5np.exp(-(9x-7)2/4.0 - 0.25((9y-3)2))\n", "\tterm4 = -0.2np.exp(-(9x-4)2 - (9y-7)*2)\n", "\treturn term1 + term2 + term3 + term4\n", "\n", "\n", "def create_X(x, y, n ):\n", "\tif len(x.shape) > 1:\n", "\t\tx = np.ravel(x)\n", "\t\ty = np.ravel(y)\n", "\n", "\tN = len(x)\n", "\tl = int((n+1)(n+2)/2)\t\t# Number of elements in beta\n", "\tX = np.ones((N,l))\n", "\n", "\tfor i in range(1,n+1):\n", "\t\tq = int((i)(i+1)/2)\n", "\t\tfor k in range(i+1):\n", "\t\t\tX[:,q+k] = (x(i-k))(yk)\n", "\n", "\treturn X\n", "\n", "\n", "# Making meshgrid of datapoints and compute Franke's function\n", "n = 5\n", "N = 1000\n", "x = np.sort(np.random.uniform(0, 1, N))\n", "y = np.sort(np.random.uniform(0, 1, N))\n", "z = FrankeFunction(x, y)\n", "X = create_X(x, y, n=n) \n", "# split in training and test data\n", "X_train, X_test, y_train, y_test = train_test_split(X,z,test_size=0.2)\n", "\n", "\n", "clf = skl.LinearRegression().fit(X_train, y_train)\n", "\n", "# The mean squared error and R2 score\n", "print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test), y_test)))\n", "print(\"R2 score before scaling {:.2f}\".format(clf.score(X_test,y_test)))\n", "\n", "scaler = StandardScaler()\n", "scaler.fit(X_train)\n", "X_train_scaled = scaler.transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "\n", "print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n", "print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n", "\n", "print(\"Feature min values after scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n", "print(\"Feature max values after scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n", "\n", "clf = skl.LinearRegression().fit(X_train_scaled, y_train)\n", "\n", "\n", "print(\"MSE after scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test_scaled), y_test)))\n", "print(\"R2 score for scaled data: {:.2f}\".format(clf.score(X_test_scaled,y_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The singular value decomposition\n", "\n", "\n", "The examples we have looked at so far are cases where we normally can\n", "invert the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$. Using a polynomial expansion as we\n", "did both for the masses and the fitting of the equation of state,\n", "leads to row vectors of the design matrix which are essentially\n", "orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. \n", "\n", "\n", "\n", "This may\n", "however not the be case in general and a standard matrix inversion\n", "algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.\n", "\n", "There is however a way to partially circumvent this problem and also gain some insights about the ordinary least squares approach, and later shrinkage methods like Ridge and Lasso regressions. \n", "\n", "This is given by the Singular Value Decomposition** algorithm, perhaps\n", "the most powerful linear algebra algorithm. Let us look at a\n", "different example where we may have problems with the standard matrix\n", "inversion algorithm. Thereafter we dive into the math of the SVD.\n", "\n", "\n", "\n", "\n", "\n", "## Linear Regression Problems\n", "\n", "One of the typical problems we encounter with linear regression, in particular \n", "when the matrix $\\boldsymbol{X}$ (our so-called design matrix) is high-dimensional, \n", "are problems with near singular or singular matrices. The column vectors of $\\boldsymbol{X}$ \n", "may be linearly dependent, normally referred to as super-collinearity. \n", "This means that the matrix may be rank deficient and it is basically impossible to \n", "to model the data using linear regression. As an example, consider the matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "\\mathbf{X} & = \\left[\n", "\\begin{array}{rrr}\n", "1 & -1 & 2\n", "\\\\\n", "1 & 0 & 1\n", "\\\\\n", "1 & 2 & -1\n", "\\\\\n", "1 & 1 & 0\n", "\\end{array} \\right]\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The columns of $\\boldsymbol{X}$ are linearly dependent. We see this easily since the \n", "the first column is the row-wise sum of the other two columns. The rank (more correct,\n", "the column rank) of a matrix is the dimension of the space spanned by the\n", "column vectors. Hence, the rank of $\\mathbf{X}$ is equal to the number\n", "of linearly independent columns. In this particular case the matrix has rank 2.\n", "\n", "Super-collinearity of an $(n \\times p)$-dimensional design matrix $\\mathbf{X}$ implies\n", "that the inverse of the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ (the matrix we need to invert to solve the linear regression equations) is non-invertible. If we have a square matrix that does not have an inverse, we say this matrix singular. The example here demonstrates this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "\\boldsymbol{X} & = \\left[\n", "\\begin{array}{rr}\n", "1 & -1\n", "\\\\\n", "1 & -1\n", "\\end{array} \\right].\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see easily that $\\mbox{det}(\\boldsymbol{X}) = x_{11} x_{22} - x_{12} x_{21} = 1 \\times (-1) - 1 \\times (-1) = 0$. Hence, $\\mathbf{X}$ is singular and its inverse is undefined.\n", "This is equivalent to saying that the matrix $\\boldsymbol{X}$ has at least an eigenvalue which is zero.\n", "\n", "\n", "## Fixing the singularity\n", "\n", "If our design matrix $\\boldsymbol{X}$ which enters the linear regression problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto1\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\\boldsymbol{\\beta} = (\\boldsymbol{X}^{T} \\boldsymbol{X})^{-1} \\boldsymbol{X}^{T} \\boldsymbol{y},\n", "\\label{_auto1} \\tag{1}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "has linearly dependent column vectors, we will not be able to compute the inverse\n", "of $\\boldsymbol{X}^T\\boldsymbol{X}$ and we cannot find the parameters (estimators) $\\beta_i$. \n", "The estimators are only well-defined if $(\\boldsymbol{X}^{T}\\boldsymbol{X})^{-1}$ exits. \n", "This is more likely to happen when the matrix $\\boldsymbol{X}$ is high-dimensional. In this case it is likely to encounter a situation where \n", "the regression parameters $\\beta_i$ cannot be estimated.\n", "\n", "A cheap ad hoc approach is simply to add a small diagonal component to the matrix to invert, that is we change" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^{T} \\boldsymbol{X} \\rightarrow \\boldsymbol{X}^{T} \\boldsymbol{X}+\\lambda \\boldsymbol{I},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\boldsymbol{I}$ is the identity matrix. When we discuss Ridge regression this is actually what we end up evaluating. The parameter $\\lambda$ is called a hyperparameter. More about this later. \n", "\n", "\n", "\n", "## Basic math of the SVD\n", "\n", "\n", "From standard linear algebra we know that a square matrix $\\boldsymbol{X}$ can be diagonalized if and only it is \n", "a so-called [normal matrix](https://en.wikipedia.org/wiki/Normal_matrix), that is if $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$\n", "we have $\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ or if $\\boldsymbol{X}\\in {\\mathbb{C}}^{n\\times n}$ we have $\\boldsymbol{X}\\boldsymbol{X}^{\\dagger}=\\boldsymbol{X}^{\\dagger}\\boldsymbol{X}$.\n", "The matrix has then a set of eigenpairs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\lambda_1,\\boldsymbol{u}_1),\\dots, (\\lambda_n,\\boldsymbol{u}_n),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the eigenvalues are given by the diagonal matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\Sigma}=\\mathrm{Diag}(\\lambda_1, \\dots,\\lambda_n).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix $\\boldsymbol{X}$ can be written in terms of an orthogonal/unitary transformation $\\boldsymbol{U}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ or $\\boldsymbol{U}\\boldsymbol{U}^{\\dagger}=\\boldsymbol{I}$.\n", "\n", "Not all square matrices are diagonalizable. A matrix like the one discussed above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\begin{bmatrix} \n", "1& -1 \\\\\n", "1& -1\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "is not diagonalizable, it is a so-called [defective matrix](https://en.wikipedia.org/wiki/Defective_matrix). It is easy to see that the condition\n", "$\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ is not fulfilled. \n", "\n", "\n", "## The SVD, a Fantastic Algorithm\n", "\n", "\n", "However, and this is the strength of the SVD algorithm, any general\n", "matrix $\\boldsymbol{X}$ can be decomposed in terms of a diagonal matrix and\n", "two orthogonal/unitary matrices. The [Singular Value Decompostion\n", "(SVD) theorem](https://en.wikipedia.org/wiki/Singular_value_decomposition)\n", "states that a general $m\\times n$ matrix $\\boldsymbol{X}$ can be written in\n", "terms of a diagonal matrix $\\boldsymbol{\\Sigma}$ of dimensionality $m\\times n$\n", "and two orthognal matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$, where the first has\n", "dimensionality $m \\times m$ and the last dimensionality $n\\times n$.\n", "We have then" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, the above defective matrix can be decomposed as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& 1 \\\\ 1& -1\\\\ \\end{bmatrix} \\begin{bmatrix} 2& 0 \\\\ 0& 0\\\\ \\end{bmatrix} \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& -1 \\\\ 1& 1\\\\ \\end{bmatrix}=\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with eigenvalues $\\sigma_1=2$ and $\\sigma_2=0$. \n", "The SVD exits always! \n", "\n", "The SVD\n", "decomposition (singular values) gives eigenvalues \n", "$\\sigma_i\\geq\\sigma_{i+1}$ for all $i$ and for dimensions larger than $i=p$, the\n", "eigenvalues (singular values) are zero.\n", "\n", "In the general case, where our design matrix $\\boldsymbol{X}$ has dimension\n", "$n\\times p$, the matrix is thus decomposed into an $n\\times n$\n", "orthogonal matrix $\\boldsymbol{U}$, a $p\\times p$ orthogonal matrix $\\boldsymbol{V}$\n", "and a diagonal matrix $\\boldsymbol{\\Sigma}$ with $r=\\mathrm{min}(n,p)$\n", "singular values $\\sigma_i\\geq 0$ on the main diagonal and zeros filling\n", "the rest of the matrix. There are at most $p$ singular values\n", "assuming that $n > p$. In our regression examples for the nuclear\n", "masses and the equation of state this is indeed the case, while for\n", "the Ising model we have $p > n$. These are often cases that lead to\n", "near singular or singular matrices.\n", "\n", "The columns of $\\boldsymbol{U}$ are called the left singular vectors while the columns of $\\boldsymbol{V}$ are the right singular vectors.\n", "\n", "## Economy-size SVD\n", "\n", "If we assume that $n > p$, then our matrix $\\boldsymbol{U}$ has dimension $n\n", "\\times n$. The last $n-p$ columns of $\\boldsymbol{U}$ become however\n", "irrelevant in our calculations since they are multiplied with the\n", "zeros in $\\boldsymbol{\\Sigma}$.\n", "\n", "The economy-size decomposition removes extra rows or columns of zeros\n", "from the diagonal matrix of singular values, $\\boldsymbol{\\Sigma}$, along with the columns\n", "in either $\\boldsymbol{U}$ or $\\boldsymbol{V}$ that multiply those zeros in the expression. \n", "Removing these zeros and columns can improve execution time\n", "and reduce storage requirements without compromising the accuracy of\n", "the decomposition.\n", "\n", "If $n > p$, we keep only the first $p$ columns of $\\boldsymbol{U}$ and $\\boldsymbol{\\Sigma}$ has dimension $p\\times p$. \n", "If $p > n$, then only the first $n$ columns of $\\boldsymbol{V}$ are computed and $\\boldsymbol{\\Sigma}$ has dimension $n\\times n$.\n", "The $n=p$ case is obvious, we retain the full SVD. \n", "In general the economy-size SVD leads to less FLOPS and still conserving the desired accuracy.\n", "\n", "## Codes for the SVD" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "# SVD inversion\n", "def SVDinv(A):\n", " ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).\n", " SVD is numerically more stable than the inversion algorithms provided by\n", " numpy and scipy.linalg at the cost of being slower.\n", " '''\n", " U, s, VT = np.linalg.svd(A)\n", "# print('test U')\n", "# print( (np.transpose(U) @ U - U @np.transpose(U)))\n", "# print('test VT')\n", "# print( (np.transpose(VT) @ VT - VT @np.transpose(VT)))\n", " print(U)\n", " print(s)\n", " print(VT)\n", "\n", " D = np.zeros((len(U),len(VT)))\n", " for i in range(0,len(VT)):\n", " D[i,i]=s[i]\n", " UT = np.transpose(U); V = np.transpose(VT); invD = np.linalg.inv(D)\n", " return np.matmul(V,np.matmul(invD,UT))\n", "\n", "\n", "X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ])\n", "print(X)\n", "A = np.transpose(X) @ X\n", "print(A)\n", "# Brute force inversion of super-collinear matrix\n", "#B = np.linalg.inv(A)\n", "#print(B)\n", "C = SVDinv(A)\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix $\\boldsymbol{X}$ has columns that are linearly dependent. The first\n", "column is the row-wise sum of the other two columns. The rank of a\n", "matrix (the column rank) is the dimension of space spanned by the\n", "column vectors. The rank of the matrix is the number of linearly\n", "independent columns, in this case just $2$. We see this from the\n", "singular values when running the above code. Running the standard\n", "inversion algorithm for matrix inversion with $\\boldsymbol{X}^T\\boldsymbol{X}$ results\n", "in the program terminating due to a singular matrix.\n", "\n", "\n", "\n", "## Mathematical Properties\n", "\n", "There are several interesting mathematical properties which will be\n", "relevant when we are going to discuss the differences between say\n", "ordinary least squares (OLS) and Ridge regression.\n", "\n", "We have from OLS that the parameters of the linear approximation are given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix to invert can be rewritten in terms of our SVD decomposition as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{U}^T\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the orthogonality properties of $\\boldsymbol{U}$ we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{\\Sigma}\\boldsymbol{V}^T = \\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{D}$ being a diagonal matrix with values along the diagonal given by the singular values squared. \n", "\n", "This means that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}^T\\boldsymbol{X})\\boldsymbol{V} = \\boldsymbol{V}\\boldsymbol{D},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the eigenvectors of $(\\boldsymbol{X}^T\\boldsymbol{X})$ are given by the columns of the right singular matrix of $\\boldsymbol{X}$ and the eigenvalues are the squared singular values. It is easy to show (show this) that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is, the eigenvectors of $(\\boldsymbol{X}\\boldsymbol{X})^T$ are the columns of the left singular matrix and the eigenvalues are the same. \n", "\n", "Going back to our OLS equation we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will come back to this expression when we discuss Ridge regression. \n", "\n", "\n", "$$ \\tilde{y}^{OLS}=\\boldsymbol{X}\\hat{\\beta}^{OLS}=\\sum_{j=1}^p \\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y}$$ and for Ridge we have \n", "\n", "$$ \\tilde{y}^{Ridge}=\\boldsymbol{X}\\hat{\\beta}^{Ridge}=\\sum_{j=1}^p \\boldsymbol{u}_j\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{u}_j^T\\boldsymbol{y}$$ . \n", "\n", "It is indeed the economy-sized SVD, note the summation runs up tp $$p$$ only and not $$n$$. \n", "\n", "Here we have that $$\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T$$, with $$\\Sigma$$ being an $$ n\\times p$$ matrix and $$\\boldsymbol{V}$$ being a $$ p\\times p$$ matrix. We also have assumed here that $$ n > p$$. \n", "\n", "\n", "## Ridge and LASSO Regression\n", "\n", "Let us remind ourselves about the expression for the standard Mean Squared Error (MSE) which we used to define our cost function and the equations for the ordinary least squares (OLS) method, that is \n", "our optimization problem is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or we can state it as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have used the definition of a norm-2 vector, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert\\vert \\boldsymbol{x}\\vert\\vert_2 = \\sqrt{\\sum_i x_i^2}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By minimizing the above equation with respect to the parameters\n", "$\\boldsymbol{\\beta}$ we could then obtain an analytical expression for the\n", "parameters $\\boldsymbol{\\beta}$. We can add a regularization parameter $\\lambda$ by\n", "defining a new cost function to be optimized, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which leads to the Ridge regression minimization problem where we\n", "require that $\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\\le t$, where $t$ is\n", "a finite number larger than zero. By defining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have a new optimization equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which leads to Lasso regression. Lasso stands for least absolute shrinkage and selection operator. \n", "\n", "Here we have defined the norm-1 as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert\\vert \\boldsymbol{x}\\vert\\vert_1 = \\sum_i \\vert x_i\\vert.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More on Ridge Regression\n", "\n", "Using the matrix-vector expression for Ridge regression," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})^T(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\right\\}+\\lambda\\boldsymbol{\\beta}^T\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "by taking the derivatives with respect to $\\boldsymbol{\\beta}$ we obtain then\n", "a slightly modified matrix inversion problem which for finite values\n", "of $\\lambda$ does not suffer from singularity problems. We obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{X}^T\\boldsymbol{X}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{I}$ being a $p\\times p$ identity matrix with the constraint that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sum_{i=0}^{p-1} \\beta_i^2 \\leq t,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $t$ a finite positive number. \n", "\n", "We see that Ridge regression is nothing but the standard\n", "OLS with a modified diagonal term added to $\\boldsymbol{X}^T\\boldsymbol{X}$. The\n", "consequences, in particular for our discussion of the bias-variance tradeoff \n", "are rather interesting.\n", "\n", "Furthermore, if we use the result above in terms of the SVD decomposition (our analysis was done for the OLS method), we had" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can analyse the OLS solutions in terms of the eigenvectors (the columns) of the right singular value matrix $\\boldsymbol{U}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ridge regression this becomes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T+\\lambda\\boldsymbol{I} \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\sum_{j=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the vectors $\\boldsymbol{u}_j$ being the columns of $\\boldsymbol{U}$. \n", "\n", "## Interpreting the Ridge results\n", "\n", "Since $\\lambda \\geq 0$, it means that compared to OLS, we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda} \\leq 1.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ridge regression finds the coordinates of $\\boldsymbol{y}$ with respect to the\n", "orthonormal basis $\\boldsymbol{U}$, it then shrinks the coordinates by\n", "$\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}$. Recall that the SVD has\n", "eigenvalues ordered in a descending way, that is $\\sigma_i \\geq\n", "\\sigma_{i+1}$.\n", "\n", "For small eigenvalues $\\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.\n", "Actually, calculating the variance of $\\boldsymbol{X}\\boldsymbol{v}_j$ shows that this quantity is equal to $\\sigma_j^2/n$.\n", "With a parameter $\\lambda$ we can thus shrink the role of specific parameters. \n", "\n", "\n", "## More interpretations\n", "\n", "For the sake of simplicity, let us assume that the design matrix is orthonormal, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X}=(\\boldsymbol{X}^T\\boldsymbol{X})^{-1} =\\boldsymbol{I}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case the standard OLS results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{OLS}} = \\boldsymbol{X}^T\\boldsymbol{y}=\\sum_{i=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{I}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\left(1+\\lambda\\right)^{-1}\\boldsymbol{\\beta}^{\\mathrm{OLS}},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the Ridge estimator scales the OLS estimator by the inverse of a factor $1+\\lambda$, and\n", "the Ridge estimator converges to zero when the hyperparameter goes to\n", "infinity.\n", "\n", "We will come back to more interpreations after we have gone through some of the statistical analysis part. \n", "\n", "For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n", "Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n", "\n", "\n", "<!-- !split -->\n", "## A better understanding of regularization\n", "\n", "The parameter $\\lambda$ that we have introduced in the Ridge (and\n", "Lasso as well) regression is often called a regularization parameter\n", "or shrinkage parameter. It is common to call it a hyperparameter. What does it mean mathemtically?\n", "\n", "Here we will first look at how to analyze the difference between the\n", "standard OLS equations and the Ridge expressions in terms of a linear\n", "algebra analysis using the SVD algorithm. Thereafter, we will link\n", "(see the material on the bias-variance tradeoff below) these\n", "observation to the statisical analysis of the results. In particular\n", "we consider how the variance of the parameters $\\boldsymbol{\\beta}$ is\n", "affected by changing the parameter $\\lambda$.\n", "\n", "## Decomposing the OLS and Ridge expressions\n", "\n", "We have our design matrix\n", " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. With the SVD we decompose it as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U\\Sigma V^T},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{U}\\in {\\mathbb{R}}^{n\\times n}$, $\\boldsymbol{\\Sigma}\\in {\\mathbb{R}}^{n\\times p}$\n", "and $\\boldsymbol{V}\\in {\\mathbb{R}}^{p\\times p}$.\n", "\n", "The matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are unitary/orthonormal matrices, that is in case the matrices are real we have $\\boldsymbol{U}^T\\boldsymbol{U}=\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ and $\\boldsymbol{V}^T\\boldsymbol{V}=\\boldsymbol{V}\\boldsymbol{V}^T=\\boldsymbol{I}$.\n", "\n", "\n", "\n", "## Introducing the Covariance and Correlation functions\n", "\n", "Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about\n", "the definition of the covariance and the correlation function. These are quantities \n", "\n", "Suppose we have defined two vectors\n", "$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{y}] \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where for example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this definition and recalling that the variance is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{var}[\\boldsymbol{x}]=\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can rewrite the covariance matrix as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] & \\mathrm{var}[\\boldsymbol{y}] \\\\\n", " \\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The covariance takes values between zero and infinity and may thus\n", "lead to problems with loss of numerical precision for particularly\n", "large values. It is common to scale the covariance matrix by\n", "introducing instead the correlation matrix defined via the so-called\n", "correlation function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]=\\frac{\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}]}{\\sqrt{\\mathrm{var}[\\boldsymbol{x}] \\mathrm{var}[\\boldsymbol{y}]}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The correlation function is then given by values $\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]\n", "\\in [-1,1]$. This avoids eventual problems with too large values. We\n", "can then define the correlation matrix for the two vectors $\\boldsymbol{x}$\n", "and $\\boldsymbol{y}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{K}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} 1 & \\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{corr}[\\boldsymbol{y},\\boldsymbol{x}] & 1 \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example this is the function we constructed using pandas.\n", "\n", "## Correlation Function and Design/Feature Matrix\n", "\n", "In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression\n", "we defined the design/feature matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n", "x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n", "x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n", "\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n", "x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n", "x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ refering to the column numbers and the\n", "entries $n$ being the row elements.\n", "We can rewrite the design/feature matrix in terms of its column vectors as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} \\boldsymbol{x}_0 & \\boldsymbol{x}_1 & \\boldsymbol{x}_2 & \\dots & \\dots & \\boldsymbol{x}_{p-1}\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with a given vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{x}_i^T = \\begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \\dots & \\dots x_{n-1,i}\\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these definitions, we can now rewrite our $2\\times 2$\n", "correaltion/covariance matrix in terms of a moe general design/feature\n", "matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. This leads to a $p\\times p$\n", "covariance matrix for the vectors $\\boldsymbol{x}_i$ with $i=0,1,\\dots,p-1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}] = \\begin{bmatrix}\n", "\\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & \\mathrm{var}[\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & \\mathrm{var}[\\boldsymbol{x}_{p-1}]\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the correlation matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{K}[\\boldsymbol{x}] = \\begin{bmatrix}\n", "1 & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & 1 & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & 1 & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & 1\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Covariance Matrix Examples\n", "\n", "\n", "The Numpy function np.cov calculates the covariance elements using\n", "the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have\n", "the exact mean values. The following simple function uses the\n", "np.vstack function which takes each vector of dimension $1\\times n$\n", "and produces a $2\\times n$ matrix $\\boldsymbol{W}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 \\\\\n", " x_1 & y_1 \\\\\n", " x_2 & y_2\\\\\n", " \\dots & \\dots \\\\\n", " x_{n-2} & y_{n-2}\\\\\n", " x_{n-1} & y_{n-1} & \n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which in turn is converted into into the $2\\times 2$ covariance matrix\n", "$\\boldsymbol{C}$ via the Numpy function np.cov(). We note that we can also calculate\n", "the mean value of each set of samples $\\boldsymbol{x}$ etc using the Numpy\n", "function np.mean(x). We can also extract the eigenvalues of the\n", "covariance matrix through the np.linalg.eig() function." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Importing various packages\n", "import numpy as np\n", "n = 100\n", "x = np.random.normal(size=n)\n", "print(np.mean(x))\n", "y = 4+3x+np.random.normal(size=n)\n", "print(np.mean(y))\n", "W = np.vstack((x, y))\n", "C = np.cov(W)\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Matrix\n", "\n", "The previous example can be converted into the correlation matrix by\n", "simply scaling the matrix elements with the variances. We should also\n", "subtract the mean values for each column. This leads to the following\n", "code which sets up the correlations matrix for the previous example in\n", "a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\\times 2$ correlation matrix (since we have only two vectors)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "n = 100\n", "# define two vectors \n", "x = np.random.random(size=n)\n", "y = 4+3x+np.random.normal(size=n)\n", "#scaling the x and y vectors \n", "x = x - np.mean(x)\n", "y = y - np.mean(y)\n", "variance_x = np.sum(x@x)/n\n", "variance_y = np.sum(y@y)/n\n", "print(variance_x)\n", "print(variance_y)\n", "cov_xy = np.sum(x@y)/n\n", "cov_xx = np.sum(x@x)/n\n", "cov_yy = np.sum(y@y)/n\n", "C = np.zeros((2,2))\n", "C[0,0]= cov_xx/variance_x\n", "C[1,1]= cov_yy/variance_y\n", "C[0,1]= cov_xy/np.sqrt(variance_yvariance_x)\n", "C[1,0]= C[0,1]\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the matrix elements along the diagonal are one as they\n", "should be and that the matrix is symmetric. Furthermore, diagonalizing\n", "this matrix we easily see that it is a positive definite matrix.\n", "\n", "The above procedure with numpy* can be made more compact if we use pandas.\n", "\n", "## Correlation Matrix with Pandas\n", "\n", "We whow here how we can set up the correlation matrix using pandas, as done in this simple code" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "n = 10\n", "x = np.random.normal(size=n)\n", "x = x - np.mean(x)\n", "y = 4+3x+np.random.normal(size=n)\n", "y = y - np.mean(y)\n", "X = (np.vstack((x, y))).T\n", "print(X)\n", "Xpd = pd.DataFrame(X)\n", "print(Xpd)\n", "correlation_matrix = Xpd.corr()\n", "print(correlation_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We expand this model to the Franke function discussed above.\n", "\n", "## Correlation Matrix with Pandas and the Franke function" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "def FrankeFunction(x,y):\n", "\tterm1 = 0.75np.exp(-(0.25(9x-2)*2) - 0.25((9y-2)2))\n", "\tterm2 = 0.75np.exp(-((9x+1)2)/49.0 - 0.1(9y+1))\n", "\tterm3 = 0.5np.exp(-(9x-7)2/4.0 - 0.25((9y-3)2))\n", "\tterm4 = -0.2np.exp(-(9x-4)2 - (9y-7)*2)\n", "\treturn term1 + term2 + term3 + term4\n", "\n", "\n", "def create_X(x, y, n ):\n", "\tif len(x.shape) > 1:\n", "\t\tx = np.ravel(x)\n", "\t\ty = np.ravel(y)\n", "\n", "\tN = len(x)\n", "\tl = int((n+1)(n+2)/2)\t\t# Number of elements in beta\n", "\tX = np.ones((N,l))\n", "\n", "\tfor i in range(1,n+1):\n", "\t\tq = int((i)(i+1)/2)\n", "\t\tfor k in range(i+1):\n", "\t\t\tX[:,q+k] = (x(i-k))(yk)\n", "\n", "\treturn X\n", "\n", "\n", "# Making meshgrid of datapoints and compute Franke's function\n", "n = 4\n", "N = 100\n", "x = np.sort(np.random.uniform(0, 1, N))\n", "y = np.sort(np.random.uniform(0, 1, N))\n", "z = FrankeFunction(x, y)\n", "X = create_X(x, y, n=n) \n", "\n", "Xpd = pd.DataFrame(X)\n", "# subtract the mean values and set up the covariance matrix\n", "Xpd = Xpd - Xpd.mean()\n", "covariance_matrix = Xpd.cov()\n", "print(covariance_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note here that the covariance is zero for the first rows and\n", "columns since all matrix elements in the design matrix were set to one\n", "(we are fitting the function in terms of a polynomial of degree $n$).\n", "\n", "This means that the variance for these elements will be zero and will\n", "cause problems when we set up the correlation matrix. We can simply\n", "drop these elements and construct a correlation\n", "matrix without these elements. \n", "\n", "\n", "## Rewriting the Covariance and/or Correlation Matrix\n", "\n", "We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}= \\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}].\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see this let us simply look at a design matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{2\\times 2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{00} & x_{01}\\\\\n", "x_{10} & x_{11}\\\\\n", "\\end{bmatrix}=\\begin{bmatrix}\n", "\\boldsymbol{x}_{0} & \\boldsymbol{x}_{1}\\\\\n", "\\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we then compute the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n", "x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is just" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n", " \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n", "\n", "It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$.\n", "\n", "\n", "## Linking with SVD\n", "\n", "See lecture september 11. More text to be added here soon.\n", "\n", "\n", "\n", "\n", "## Where are we going?\n", "\n", "Before we proceed, we need to rethink what we have been doing. In our\n", "eager to fit the data, we have omitted several important elements in\n", "our regression analysis. In what follows we will\n", "1. look at statistical properties, including a discussion of mean values, variance and the so-called bias-variance tradeoff\n", "\n", "2. introduce resampling techniques like cross-validation, bootstrapping and jackknife and more\n", "\n", "This will allow us to link the standard linear algebra methods we have discussed above to a statistical interpretation of the methods. \n", "\n", "\n", "\n", "\n", "\n", "## Resampling methods\n", "Resampling methods are an indispensable tool in modern\n", "statistics. They involve repeatedly drawing samples from a training\n", "set and refitting a model of interest on each sample in order to\n", "obtain additional information about the fitted model. For example, in\n", "order to estimate the variability of a linear regression fit, we can\n", "repeatedly draw different samples from the training data, fit a linear\n", "regression to each new sample, and then examine the extent to which\n", "the resulting fits differ. Such an approach may allow us to obtain\n", "information that would not be available from fitting the model only\n", "once using the original training sample.\n", "\n", "Two resampling methods are often used in Machine Learning analyses,\n", "1. The bootstrap method\n", "\n", "2. and Cross-Validation\n", "\n", "In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular\n", "cross-validation and the bootstrap method.\n", "\n", "\n", "\n", "\n", "## Resampling approaches can be computationally expensive\n", "\n", "Resampling approaches can be computationally expensive, because they\n", "involve fitting the same statistical method multiple times using\n", "different subsets of the training data. However, due to recent\n", "advances in computing power, the computational requirements of\n", "resampling methods generally are not prohibitive. In this chapter, we\n", "discuss two of the most commonly used resampling methods,\n", "cross-validation and the bootstrap. Both methods are important tools\n", "in the practical application of many statistical learning\n", "procedures. For example, cross-validation can be used to estimate the\n", "test error associated with a given statistical learning method in\n", "order to evaluate its performance, or to select the appropriate level\n", "of flexibility. The process of evaluating a model’s performance is\n", "known as model assessment, whereas the process of selecting the proper\n", "level of flexibility for a model is known as model selection. The\n", "bootstrap is widely used.\n", "\n", "\n", "\n", "## Why resampling methods ?\n", "Statistical analysis.*\n", "\n", "\n", " Our simulations can be treated as computer experiments. This is particularly the case for Monte Carlo methods\n", "\n", "* The results can be analysed with the same statistical tools as we would use analysing experimental data.\n", "\n", "* As in all experiments, we are looking for expectation values and an estimate of how accurate they are, i.e., possible sources for errors.\n", "\n", " \n", "\n", "## Statistical analysis\n", "\n", "* As in other experiments, many numerical experiments have two classes of errors:\n", "\n", " * Statistical errors\n", "\n", " * Systematical errors\n", "\n", "\n", "* Statistical errors can be estimated using standard tools from statistics\n", "\n", "* Systematical errors are method specific and must be treated differently from case to case.\n", "\n", " \n", "\n", "\n", "\n", "\n", "<!-- !split -->\n", "## Linking the regression analysis with a statistical interpretation\n", "\n", "\n", "The\n", "advantage of doing linear regression is that we actually end up with\n", "analytical expressions for several statistical quantities. \n", "Standard least squares and Ridge regression allow us to\n", "derive quantities like the variance and other expectation values in a\n", "rather straightforward way.\n", "\n", "\n", "It is assumed that $\\varepsilon_i\n", "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n", "independent, i.e.:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align} \n", "\\mbox{Cov}(\\varepsilon_{i_1},\n", "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n", "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2. \\end{array} \\right.\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The randomness of $\\varepsilon_i$ implies that\n", "$\\mathbf{y}_i$ is also a random variable. In particular,\n", "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n", "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n", "non-random scalar. To specify the parameters of the distribution of\n", "$\\mathbf{y}_i$ we need to calculate its first two moments. \n", "\n", "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n", "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n", "row number $i$ and perform a sum over all values $p$.\n", "\n", "\n", "## Assumptions made\n", "\n", "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n", "that there exists a function $f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n", "which describe our data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We approximate this function with our model from the solution of the linear regression equations, that is our\n", "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Expectation value and variance\n", "\n", "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align} \n", "\\mathbb{E}(y_i) & =\n", "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n", "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while\n", "its variance is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n", "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n", "[\\mathbb{E}(y_i)]^2 \\\\ & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n", "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n", "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n", "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n", "\\ast} \\, \\beta)^2 \\\\ & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n", "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n", "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n", "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n", "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2. \n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n", "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD). \n", "\n", "## Expectation value and variance for $\\boldsymbol{\\beta}$\n", "\n", "With the OLS expressions for the parameters $\\boldsymbol{\\beta}$ we can evaluate the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}(\\boldsymbol{\\beta}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that the estimator of the regression parameters is unbiased.\n", "\n", "We can also calculate the variance\n", "\n", "The variance of $\\boldsymbol{\\beta}$ is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", "\\mbox{Var}(\\boldsymbol{\\beta}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n", "\\\\\n", "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n", "\\\\\n", "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "\\\\\n", "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n", "% \\\\\n", "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n", "\\\\\n", "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n", "\\end{eqnarray}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have used that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n", "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n", "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n", "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n", "variance of the estimate of the $j$-th regression coefficient:\n", "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n", "[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n", "construct a confidence interval for the estimates.\n", "\n", "\n", "In a similar way, we can obtain analytical expressions for say the\n", "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n", "when we employ Ridge regression, allowing us again to define a confidence interval. \n", "\n", "It is rather straightforward to show that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see clearly that \n", "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n", "\n", "We can also compute the variance as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1} \\mathbf{X}^{T} \\mathbf{X} \\{ [ \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n", "\n", "With this, we can compute the difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference is non-negative definite since each component of the\n", "matrix product is non-negative definite. \n", "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below. \n", "\n", "\n", "## Resampling methods\n", "\n", "With all these analytical equations for both the OLS and Ridge\n", "regression, we will now outline how to assess a given model. This will\n", "lead us to a discussion of the so-called bias-variance tradeoff (see\n", "below) and so-called resampling methods.\n", "\n", "One of the quantities we have discussed as a way to measure errors is\n", "the mean-squared error (MSE), mainly used for fitting of continuous\n", "functions. Another choice is the absolute error.\n", "\n", "In the discussions below we will focus on the MSE and in particular since we will split the data into test and training data,\n", "we discuss the\n", "1. prediction error or simply the test error $\\mathrm{Err_{Test}}$, where we have a fixed training set and the test error is the MSE arising from the data reserved for testing. We discuss also the \n", "\n", "2. training error $\\mathrm{Err_{Train}}$, which is the average loss over the training data.\n", "\n", "As our model becomes more and more complex, more of the training data tends to used. The training may thence adapt to more complicated structures in the data. This may lead to a decrease in the bias (see below for code example) and a slight increase of the variance for the test error.\n", "For a certain level of complexity the test error will reach minimum, before starting to increase again. The\n", "training error reaches a saturation.\n", "\n", "\n", "\n", "\n", "## Resampling methods: Jackknife and Bootstrap\n", "\n", "Two famous\n", "resampling methods are the independent bootstrap and the jackknife. \n", "\n", "The jackknife is a special case of the independent bootstrap. Still, the jackknife was made\n", "popular prior to the independent bootstrap. And as the popularity of\n", "the independent bootstrap soared, new variants, such as the dependent bootstrap.\n", "\n", "The Jackknife and independent bootstrap work for\n", "independent, identically distributed random variables.\n", "If these conditions are not\n", "satisfied, the methods will fail. Yet, it should be said that if the data are\n", "independent, identically distributed, and we only want to estimate the\n", "variance of $\\overline{X}$ (which often is the case), then there is no\n", "need for bootstrapping. \n", "\n", "## Resampling methods: Jackknife\n", "\n", "The Jackknife works by making many replicas of the estimator $\\widehat{\\theta}$. \n", "The jackknife is a resampling method where we systematically leave out one observation from the vector of observed values $\\boldsymbol{x} = (x_1,x_2,\\cdots,X_n)$. \n", "Let $\\boldsymbol{x}_i$ denote the vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{x}_i = (x_1,x_2,\\cdots,x_{i-1},x_{i+1},\\cdots,x_n),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which equals the vector $\\boldsymbol{x}$ with the exception that observation\n", "number $i$ is left out. Using this notation, define\n", "$\\widehat{\\theta}_i$ to be the estimator\n", "$\\widehat{\\theta}$ computed using $\\vec{X}_i$. \n", "\n", "\n", "## Jackknife code example" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import \n", "from numpy.random import randint, randn\n", "from time import time\n", "\n", "def jackknife(data, stat):\n", " n = len(data);t = zeros(n); inds = arange(n); t0 = time()\n", " ## 'jackknifing' by leaving out an observation for each i \n", " for i in range(n):\n", " t[i] = stat(delete(data,i) )\n", "\n", " # analysis \n", " print(\"Runtime: %g sec\" % (time()-t0)); print(\"Jackknife Statistics :\")\n", " print(\"original bias std. error\")\n", " print(\"%8g %14g %15g\" % (stat(data),(n-1)mean(t)/n, (nvar(t)).5))\n", "\n", " return t\n", "\n", "\n", "# Returns mean of data samples \n", "def stat(data):\n", " return mean(data)\n", "\n", "\n", "mu, sigma = 100, 15\n", "datapoints = 10000\n", "x = mu + sigmarandom.randn(datapoints)\n", "# jackknife returns the data sample \n", "t = jackknife(x, stat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling methods: Bootstrap\n", "Bootstrapping is a nonparametric approach to statistical inference\n", "that substitutes computation for more traditional distributional\n", "assumptions and asymptotic results. Bootstrapping offers a number of\n", "advantages: \n", "1. The bootstrap is quite general, although there are some cases in which it fails. \n", "\n", "2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small. \n", "\n", "3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically. \n", "\n", "4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).\n", "\n", "\n", "\n", "\n", "## Resampling methods: Bootstrap background\n", "\n", "Since $\\widehat{\\theta} = \\widehat{\\theta}(\\boldsymbol{X})$ is a function of random variables,\n", "$\\widehat{\\theta}$ itself must be a random variable. Thus it has\n", "a pdf, call this function $p(\\boldsymbol{t})$. The aim of the bootstrap is to\n", "estimate $p(\\boldsymbol{t})$ by the relative frequency of\n", "$\\widehat{\\theta}$. You can think of this as using a histogram\n", "in the place of $p(\\boldsymbol{t})$. If the relative frequency closely\n", "resembles $p(\\vec{t})$, then using numerics, it is straight forward to\n", "estimate all the interesting parameters of $p(\\boldsymbol{t})$ using point\n", "estimators. \n", "\n", "\n", "## Resampling methods: More Bootstrap background\n", "\n", "In the case that $\\widehat{\\theta}$ has\n", "more than one component, and the components are independent, we use the\n", "same estimator on each component separately. If the probability\n", "density function of $X_i$, $p(x)$, had been known, then it would have\n", "been straight forward to do this by: \n", "1. Drawing lots of numbers from $p(x)$, suppose we call one such set of numbers $(X_1^, X_2^, \\cdots, X_n^)$. \n", "\n", "2. Then using these numbers, we could compute a replica of $\\widehat{\\theta}$ called $\\widehat{\\theta}^$. \n", "\n", "By repeated use of (1) and (2), many\n", "estimates of $\\widehat{\\theta}$ could have been obtained. The\n", "idea is to use the relative frequency of $\\widehat{\\theta}^$\n", "(think of a histogram) as an estimate of $p(\\boldsymbol{t})$.\n", "\n", "## Resampling methods: Bootstrap approach\n", "\n", "But\n", "unless there is enough information available about the process that\n", "generated $X_1,X_2,\\cdots,X_n$, $p(x)$ is in general\n", "unknown. Therefore, [Efron in 1979](https://projecteuclid.org/euclid.aos/1176344552) asked the\n", "question: What if we replace $p(x)$ by the relative frequency\n", "of the observation $X_i$; if we draw observations in accordance with\n", "the relative frequency of the observations, will we obtain the same\n", "result in some asymptotic sense? The answer is yes.\n", "\n", "\n", "Instead of generating the histogram for the relative\n", "frequency of the observation $X_i$, just draw the values\n", "$(X_1^,X_2^,\\cdots,X_n^)$ with replacement from the vector\n", "$\\boldsymbol{X}$. \n", "\n", "## Resampling methods: Bootstrap steps\n", "\n", "The independent bootstrap works like this: \n", "\n", "1. Draw with replacement $n$ numbers for the observed variables $\\boldsymbol{x} = (x_1,x_2,\\cdots,x_n)$. \n", "\n", "2. Define a vector $\\boldsymbol{x}^$ containing the values which were drawn from $\\boldsymbol{x}$. \n", "\n", "3. Using the vector $\\boldsymbol{x}^$ compute $\\widehat{\\theta}^$ by evaluating $\\widehat \\theta$ under the observations $\\boldsymbol{x}^$. \n", "\n", "4. Repeat this process $k$ times. \n", "\n", "When you are done, you can draw a histogram of the relative frequency\n", "of $\\widehat \\theta^$. This is your estimate of the probability\n", "distribution $p(t)$. Using this probability distribution you can\n", "estimate any statistics thereof. In principle you never draw the\n", "histogram of the relative frequency of $\\widehat{\\theta}^$. Instead\n", "you use the estimators corresponding to the statistic of interest. For\n", "example, if you are interested in estimating the variance of $\\widehat\n", "\\theta$, apply the etsimator $\\widehat \\sigma^2$ to the values\n", "$\\widehat \\theta ^$.\n", "\n", "\n", "## Code example for the Bootstrap method\n", "\n", "The following code starts with a Gaussian distribution with mean value\n", "$\\mu =100$ and variance $\\sigma=15$. We use this to generate the data\n", "used in the bootstrap analysis. The bootstrap analysis returns a data\n", "set after a given number of bootstrap operations (as many as we have\n", "data points). This data set consists of estimated mean values for each\n", "bootstrap operation. The histogram generated by the bootstrap method\n", "shows that the distribution for these mean values is also a Gaussian,\n", "centered around the mean value $\\mu=100$ but with standard deviation\n", "$\\sigma/\\sqrt{n}$, where $n$ is the number of bootstrap samples (in\n", "this case the same as the number of original data points). The value\n", "of the standard deviation is what we expect from the central limit\n", "theorem." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import \n", "from numpy.random import randint, randn\n", "from time import time\n", "import matplotlib.mlab as mlab\n", "import matplotlib.pyplot as plt\n", "\n", "# Returns mean of bootstrap samples \n", "def stat(data):\n", " return mean(data)\n", "\n", "# Bootstrap algorithm\n", "def bootstrap(data, statistic, R):\n", " t = zeros(R); n = len(data); inds = arange(n); t0 = time()\n", " # non-parametric bootstrap \n", " for i in range(R):\n", " t[i] = statistic(data[randint(0,n,n)])\n", "\n", " # analysis \n", " print(\"Runtime: %g sec\" % (time()-t0)); print(\"Bootstrap Statistics :\")\n", " print(\"original bias std. error\")\n", " print(\"%8g %8g %14g %15g\" % (statistic(data), std(data),mean(t),std(t)))\n", " return t\n", "\n", "\n", "mu, sigma = 100, 15\n", "datapoints = 10000\n", "x = mu + sigmarandom.randn(datapoints)\n", "# bootstrap returns the data sample \n", "t = bootstrap(x, stat, datapoints)\n", "# the histogram of the bootstrapped data \n", "n, binsboot, patches = plt.hist(t, 50, normed=1, facecolor='red', alpha=0.75)\n", "\n", "# add a 'best fit' line \n", "y = mlab.normpdf( binsboot, mean(t), std(t))\n", "lt = plt.plot(binsboot, y, 'r--', linewidth=1)\n", "plt.xlabel('Smarts')\n", "plt.ylabel('Probability')\n", "plt.axis([99.5, 100.6, 0, 3.0])\n", "plt.grid(True)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- !split -->\n", "## Various steps in cross-validation\n", "\n", "When the repetitive splitting of the data set is done randomly,\n", "samples may accidently end up in a fast majority of the splits in\n", "either training or test set. Such samples may have an unbalanced\n", "influence on either model building or prediction evaluation. To avoid\n", "this $k$-fold cross-validation structures the data splitting. The\n", "samples are divided into $k$ more or less equally sized exhaustive and\n", "mutually exclusive subsets. In turn (at each split) one of these\n", "subsets plays the role of the test set while the union of the\n", "remaining subsets constitutes the training set. Such a splitting\n", "warrants a balanced representation of each sample in both training and\n", "test set over the splits. Still the division into the $k$ subsets\n", "involves a degree of randomness. This may be fully excluded when\n", "choosing $k=n$. This particular case is referred to as leave-one-out\n", "cross-validation (LOOCV). \n", "\n", "<!-- !split -->\n", "## How to set up the cross-validation for Ridge and/or Lasso\n", "\n", " Define a range of interest for the penalty parameter.\n", "\n", "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n", "\n", "* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n", "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n", "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $\|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)\|$, the relative error, the error squared or the R2 score function.\n", "\n", "* Repeat the first three steps such that each sample plays the role of the test set once.\n", "\n", "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align}\n", "\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation in brief\n", "\n", "For the various values of $k$\n", "\n", "1. shuffle the dataset randomly.\n", "\n", "2. Split the dataset into $k$ groups.\n", "\n", "3. For each unique group:\n", "\n", "a. Decide which group to use as set for test data\n", "\n", "b. Take the remaining groups as a training data set\n", "\n", "c. Fit a model on the training set and evaluate it on the test set\n", "\n", "d. Retain the evaluation score and discard the model\n", "\n", "\n", "5. Summarize the model using the sample of model evaluation scores\n", "\n", "## Code Example for Cross-validation and $k$-fold Cross-validation\n", "\n", "The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import KFold\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# A seed just to ensure that the random numbers are the same for every run.\n", "# Useful for eventual debugging.\n", "np.random.seed(3155)\n", "\n", "# Generate the data.\n", "nsamples = 100\n", "x = np.random.randn(nsamples)\n", "y = 3x2 + np.random.randn(nsamples)\n", "\n", "## Cross-validation on Ridge regression using KFold only\n", "\n", "# Decide degree on polynomial to fit\n", "poly = PolynomialFeatures(degree = 6)\n", "\n", "# Decide which values of lambda to use\n", "nlambdas = 500\n", "lambdas = np.logspace(-3, 5, nlambdas)\n", "\n", "# Initialize a KFold instance\n", "k = 5\n", "kfold = KFold(n_splits = k)\n", "\n", "# Perform the cross-validation to estimate MSE\n", "scores_KFold = np.zeros((nlambdas, k))\n", "\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", " j = 0\n", " for train_inds, test_inds in kfold.split(x):\n", " xtrain = x[train_inds]\n", " ytrain = y[train_inds]\n", "\n", " xtest = x[test_inds]\n", " ytest = y[test_inds]\n", "\n", " Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n", " ridge.fit(Xtrain, ytrain[:, np.newaxis])\n", "\n", " Xtest = poly.fit_transform(xtest[:, np.newaxis])\n", " ypred = ridge.predict(Xtest)\n", "\n", " scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])2)/np.size(ypred)\n", "\n", " j += 1\n", " i += 1\n", "\n", "\n", "estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n", "\n", "## Cross-validation using cross_val_score from sklearn along with KFold\n", "\n", "# kfold is an instance initialized above as:\n", "# kfold = KFold(n_splits = k)\n", "\n", "estimated_mse_sklearn = np.zeros(nlambdas)\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", "\n", " X = poly.fit_transform(x[:, np.newaxis])\n", " estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n", "\n", " # cross_val_score return an array containing the estimated negative mse for every fold.\n", " # we have to the the mean of every array in order to get an estimate of the mse of the model\n", " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n", "\n", " i += 1\n", "\n", "## Plot and compare the slightly different ways to perform cross-validation\n", "\n", "plt.figure()\n", "\n", "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n", "plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n", "\n", "plt.xlabel('log10(lambda)')\n", "plt.ylabel('mse')\n", "\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The bias-variance tradeoff\n", "\n", "\n", "We will discuss the bias-variance tradeoff in the context of\n", "continuous predictions such as regression. However, many of the\n", "intuitions and ideas discussed here also carry over to classification\n", "tasks. Consider a dataset $\\mathcal{L}$ consisting of the data\n", "$\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=0\\ldots n-1\\}$. \n", "\n", "Let us assume that the true data is generated from a noisy model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y}=f(\\boldsymbol{x}) + \\boldsymbol{\\epsilon}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\epsilon$ is normally distributed with mean zero and standard deviation $\\sigma^2$.\n", "\n", "In our derivation of the ordinary least squares method we defined then\n", "an approximation to the function $f$ in terms of the parameters\n", "$\\boldsymbol{\\beta}$ and the design matrix $\\boldsymbol{X}$ which embody our model,\n", "that is $\\boldsymbol{\\tilde{y}}=\\boldsymbol{X}\\boldsymbol{\\beta}$. \n", "\n", "Thereafter we found the parameters $\\boldsymbol{\\beta}$ by optimizing the means squared error via the so-called cost function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta}) =\\frac{1}{n}\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2=\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right].\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can rewrite this as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\frac{1}{n}\\sum_i(f_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\frac{1}{n}\\sum_i(\\tilde{y}_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\sigma^2.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The three terms represent the square of the bias of the learning\n", "method, which can be thought of as the error caused by the simplifying\n", "assumptions built into the method. The second term represents the\n", "variance of the chosen model and finally the last terms is variance of\n", "the error $\\boldsymbol{\\epsilon}$.\n", "\n", "To derive this equation, we need to recall that the variance of $\\boldsymbol{y}$ and $\\boldsymbol{\\epsilon}$ are both equal to $\\sigma^2$. The mean value of $\\boldsymbol{\\epsilon}$ is by definition equal to zero. Furthermore, the function $f$ is not a stochastics variable, idem for $\\boldsymbol{\\tilde{y}}$.\n", "We use a more compact notation in terms of the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}})^2\\right],\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and adding and subtracting $\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]$ we get" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}}+\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right],\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which, using the abovementioned expectation values can be rewritten as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{y}-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right]+\\mathrm{Var}\\left[\\boldsymbol{\\tilde{y}}\\right]+\\sigma^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the rewriting in terms of the so-called bias, the variance of the model $\\boldsymbol{\\tilde{y}}$ and the variance of $\\boldsymbol{\\epsilon}$.\n", "\n", "\n", "\n", "\n", "\n", "## Example code for Bias-Variance tradeoff" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.utils import resample\n", "\n", "np.random.seed(2018)\n", "\n", "n = 500\n", "n_boostraps = 100\n", "degree = 18 # A quite high value, just to show.\n", "noise = 0.1\n", "\n", "# Make data set.\n", "x = np.linspace(-1, 3, n).reshape(-1, 1)\n", "y = np.exp(-x2) + 1.5 np.exp(-(x-2)2) + np.random.normal(0, 0.1, x.shape)\n", "\n", "# Hold out some test data that is never used in training.\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", "# Combine x transformation and model into one operation.\n", "# Not neccesary, but convenient.\n", "model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", "\n", "# The following (m x n_bootstraps) matrix holds the column vectors y_pred\n", "# for each bootstrap iteration.\n", "y_pred = np.empty((y_test.shape[0], n_boostraps))\n", "for i in range(n_boostraps):\n", " x_, y_ = resample(x_train, y_train)\n", "\n", " # Evaluate the new model on the same test data each time.\n", " y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n", "\n", "# Note: Expectations and variances taken w.r.t. different training\n", "# data sets, hence the axis=1. Subsequent means are taken across the test data\n", "# set in order to obtain a total value, but before this we have error/bias/variance\n", "# calculated per data point in the test set.\n", "# Note 2: The use of keepdims=True is important in the calculation of bias as this \n", "# maintains the column vector form. Dropping this yields very unexpected results.\n", "error = np.mean( np.mean((y_test - y_pred)2, axis=1, keepdims=True) )\n", "bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))2 )\n", "variance = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n", "print('Error:', error)\n", "print('Bias^2:', bias)\n", "print('Var:', variance)\n", "print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance))\n", "\n", "plt.plot(x[::5, :], y[::5, :], label='f(x)')\n", "plt.scatter(x_test, y_test, label='Data points')\n", "plt.scatter(x_test, np.mean(y_pred, axis=1), label='Pred')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding what happens" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.utils import resample\n", "\n", "np.random.seed(2018)\n", "\n", "n = 40\n", "n_boostraps = 100\n", "maxdegree = 14\n", "\n", "\n", "# Make data set.\n", "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", "y = np.exp(-x2) + 1.5 * np.exp(-(x-2)2)+ np.random.normal(0, 0.1, x.shape)\n", "error = np.zeros(maxdegree)\n", "bias = np.zeros(maxdegree)\n", "variance = np.zeros(maxdegree)\n", "polydegree = np.zeros(maxdegree)\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", "for degree in range(maxdegree):\n", " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", " y_pred = np.empty((y_test.shape[0], n_boostraps))\n", " for i in range(n_boostraps):\n", " x_, y_ = resample(x_train, y_train)\n", " y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n", "\n", " polydegree[degree] = degree\n", " error[degree] = np.mean( np.mean((y_test - y_pred)2, axis=1, keepdims=True) )\n", " bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))2 )\n", " variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n", " print('Polynomial degree:', degree)\n", " print('Error:', error[degree])\n", " print('Bias^2:', bias[degree])\n", " print('Var:', variance[degree])\n", " print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n", "\n", "plt.plot(polydegree, error, label='Error')\n", "plt.plot(polydegree, bias, label='bias')\n", "plt.plot(polydegree, variance, label='Variance')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- !split -->\n", "## Summing up\n", "\n", "\n", "\n", "\n", "The bias-variance tradeoff summarizes the fundamental tension in\n", "machine learning, particularly supervised learning, between the\n", "complexity of a model and the amount of training data needed to train\n", "it. Since data is often limited, in practice it is often useful to\n", "use a less-complex model with higher bias, that is a model whose asymptotic\n", "performance is worse than another model because it is easier to\n", "train and less sensitive to sampling noise arising from having a\n", "finite-sized training dataset (smaller variance). \n", "\n", "\n", "\n", "The above equations tell us that in\n", "order to minimize the expected test error, we need to select a\n", "statistical learning method that simultaneously achieves low variance\n", "and low bias. Note that variance is inherently a nonnegative quantity,\n", "and squared bias is also nonnegative. Hence, we see that the expected\n", "test MSE can never lie below $Var(\\epsilon)$, the irreducible error.\n", "\n", "\n", "What do we mean by the variance and bias of a statistical learning\n", "method? The variance refers to the amount by which our model would change if we\n", "estimated it using a different training data set. Since the training\n", "data are used to fit the statistical learning method, different\n", "training data sets will result in a different estimate. But ideally the\n", "estimate for our model should not vary too much between training\n", "sets. However, if a method has high variance then small changes in\n", "the training data can result in large changes in the model. In general, more\n", "flexible statistical methods have higher variance.\n", "\n", "\n", "You may also find this recent [article](https://www.pnas.org/content/116/32/15849) of interest.\n", "\n", "## Another Example from Scikit-Learn's Repository" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "============================\n", "Underfitting vs. Overfitting\n", "============================\n", "\n", "This example demonstrates the problems of underfitting and overfitting and\n", "how we can use linear regression with polynomial features to approximate\n", "nonlinear functions. The plot shows the function that we want to approximate,\n", "which is a part of the cosine function. In addition, the samples from the\n", "real function and the approximations of different models are displayed. The\n", "models have polynomial features of different degrees. We can see that a\n", "linear function (polynomial with degree 1) is not sufficient to fit the\n", "training samples. This is called underfitting. A polynomial of degree 4\n", "approximates the true function almost perfectly. However, for higher degrees\n", "the model will overfit the training data, i.e. it learns the noise of the\n", "training data.\n", "We evaluate quantitatively overfitting / underfitting** by using\n", "cross-validation. We calculate the mean squared error (MSE) on the validation\n", "set, the higher, the less likely the model generalizes correctly from the\n", "training data.\n", "\"\"\"\n", "\n", "print(__doc__)\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import cross_val_score\n", "\n", "\n", "def true_fun(X):\n", " return np.cos(1.5 * np.pi * X)\n", "\n", "np.random.seed(0)\n", "\n", "n_samples = 30\n", "degrees = [1, 4, 15]\n", "\n", "X = np.sort(np.random.rand(n_samples))\n", "y = true_fun(X) + np.random.randn(n_samples) * 0.1\n", "\n", "plt.figure(figsize=(14, 5))\n", "for i in range(len(degrees)):\n", " ax = plt.subplot(1, len(degrees), i + 1)\n", " plt.setp(ax, xticks=(), yticks=())\n", "\n", " polynomial_features = PolynomialFeatures(degree=degrees[i],\n", " include_bias=False)\n", " linear_regression = LinearRegression()\n", " pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n", " (\"linear_regression\", linear_regression)])\n", " pipeline.fit(X[:, np.newaxis], y)\n", "\n", " # Evaluate the models using crossvalidation\n", " scores = cross_val_score(pipeline, X[:, np.newaxis], y,\n", " scoring=\"neg_mean_squared_error\", cv=10)\n", "\n", " X_test = np.linspace(0, 1, 100)\n", " plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n", " plt.plot(X_test, true_fun(X_test), label=\"True function\")\n", " plt.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n", " plt.xlabel(\"x\")\n", " plt.ylabel(\"y\")\n", " plt.xlim((0, 1))\n", " plt.ylim((-2, 2))\n", " plt.legend(loc=\"best\")\n", " plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(\n", " degrees[i], -scores.mean(), scores.std()))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More examples on bootstrap and cross-validation and errors" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.utils import resample\n", "from sklearn.metrics import mean_squared_error\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "\n", "Maxpolydegree = 30\n", "X = np.zeros((len(Density),Maxpolydegree))\n", "X[:,0] = 1.0\n", "testerror = np.zeros(Maxpolydegree)\n", "trainingerror = np.zeros(Maxpolydegree)\n", "polynomial = np.zeros(Maxpolydegree)\n", "\n", "trials = 100\n", "for polydegree in range(1, Maxpolydegree):\n", " polynomial[polydegree] = polydegree\n", " for degree in range(polydegree):\n", " X[:,degree] = Density(degree/3.0)\n", "\n", "# loop over trials in order to estimate the expectation value of the MSE\n", " testerror[polydegree] = 0.0\n", " trainingerror[polydegree] = 0.0\n", " for samples in range(trials):\n", " x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n", " ypred = model.predict(x_train)\n", " ytilde = model.predict(x_test)\n", " testerror[polydegree] += mean_squared_error(y_test, ytilde)\n", " trainingerror[polydegree] += mean_squared_error(y_train, ypred) \n", "\n", " testerror[polydegree] /= trials\n", " trainingerror[polydegree] /= trials\n", " print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n", " print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n", " print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n", "\n", "plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", "plt.plot(polynomial, np.log10(testerror), label='Test Error')\n", "plt.xlabel('Polynomial degree')\n", "plt.ylabel('log10[MSE]')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- !split -->\n", "## The same example but now with cross-validation" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "\n", "Maxpolydegree = 30\n", "X = np.zeros((len(Density),Maxpolydegree))\n", "X[:,0] = 1.0\n", "estimated_mse_sklearn = np.zeros(Maxpolydegree)\n", "polynomial = np.zeros(Maxpolydegree)\n", "k =5\n", "kfold = KFold(n_splits = k)\n", "\n", "for polydegree in range(1, Maxpolydegree):\n", " polynomial[polydegree] = polydegree\n", " for degree in range(polydegree):\n", " X[:,degree] = Density(degree/3.0)\n", " OLS = LinearRegression()\n", "# loop over trials in order to estimate the expectation value of the MSE\n", " estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n", "#[:, np.newaxis]\n", " estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)\n", "\n", "plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n", "plt.xlabel('Polynomial degree')\n", "plt.ylabel('log10[MSE]')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation with Ridge" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import KFold\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# A seed just to ensure that the random numbers are the same for every run.\n", "np.random.seed(3155)\n", "# Generate the data.\n", "n = 100\n", "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", "y = np.exp(-x*2) + 1.5 np.exp(-(x-2)*2)+ np.random.normal(0, 0.1, x.shape)\n", "# Decide degree on polynomial to fit\n", "poly = PolynomialFeatures(degree = 10)\n", "\n", "# Decide which values of lambda to use\n", "nlambdas = 500\n", "lambdas = np.logspace(-3, 5, nlambdas)\n", "# Initialize a KFold instance\n", "k = 5\n", "kfold = KFold(n_splits = k)\n", "estimated_mse_sklearn = np.zeros(nlambdas)\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n", " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n", " i += 1\n", "plt.figure()\n", "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n", "plt.xlabel('log10(lambda)')\n", "plt.ylabel('MSE')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Ising model\n", "\n", "The one-dimensional Ising model with nearest neighbor interaction, no\n", "external field and a constant coupling constant $J$ is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto2\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = -J \\sum_{k}^L s_k s_{k + 1},\n", "\\label{_auto2} \\tag{2}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins\n", "in the system is determined by $L$. For the one-dimensional system\n", "there is no phase transition.\n", "\n", "We will look at a system of $L = 40$ spins with a coupling constant of\n", "$J = 1$. To get enough training data we will generate 10000 states\n", "with their respective energies." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import seaborn as sns\n", "import scipy.linalg as scl\n", "from sklearn.model_selection import train_test_split\n", "import tqdm\n", "sns.set(color_codes=True)\n", "cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n", "\n", "L = 40\n", "n = int(1e4)\n", "\n", "spins = np.random.choice([-1, 1], size=(n, L))\n", "J = 1.0\n", "\n", "energies = np.zeros(n)\n", "\n", "for i in range(n):\n", " energies[i] = - J np.dot(spins[i], np.roll(spins[i], 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use ordinary least squares\n", "regression to predict the energy for the nearest neighbor\n", "one-dimensional Ising model on a ring, i.e., the endpoints wrap\n", "around. We will use linear regression to fit a value for\n", "the coupling constant to achieve this.\n", "\n", "## Reformulating the problem to suit regression\n", "\n", "A more general form for the one-dimensional Ising model is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto3\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n", "\\label{_auto3} \\tag{3}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we allow for interactions beyond the nearest neighbors and a state dependent\n", "coupling constant. This latter expression can be formulated as\n", "a matrix-product" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto4\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{H} = \\boldsymbol{X} J,\n", "\\label{_auto4} \\tag{4}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $X_{jk} = s_j s_k$ and $J$ is a matrix which consists of the\n", "elements $-J_{jk}$. This form of writing the energy fits perfectly\n", "with the form utilized in linear regression, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto5\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon},\n", "\\label{_auto5} \\tag{5}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We split the data in training and test data as discussed in the previous example" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.zeros((n, L 2))\n", "for i in range(n):\n", " X[i] = np.outer(spins[i], spins[i]).ravel()\n", "y = energies\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear regression\n", "\n", "In the ordinary least squares method we choose the cost function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto6\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " C(\\boldsymbol{X}, \\boldsymbol{\\beta})= \\frac{1}{n}\\left\\{(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})\\right\\}.\n", "\\label{_auto6} \\tag{6}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then find the extremal point of $C$ by taking the derivative with respect to $\\boldsymbol{\\beta}$ as discussed above.\n", "This yields the expression for $\\boldsymbol{\\beta}$ to be" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = \\frac{\\boldsymbol{X}^T \\boldsymbol{y}}{\\boldsymbol{X}^T \\boldsymbol{X}},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which immediately imposes some requirements on $\\boldsymbol{X}$ as there must exist\n", "an inverse of $\\boldsymbol{X}^T \\boldsymbol{X}$. If the expression we are modeling contains an\n", "intercept, i.e., a constant term, we must make sure that the\n", "first column of $\\boldsymbol{X}$ consists of $1$. We do this here" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_own = np.concatenate(\n", " (np.ones(len(X_train))[:, np.newaxis], X_train),\n", " axis=1\n", ")\n", "X_test_own = np.concatenate(\n", " (np.ones(len(X_test))[:, np.newaxis], X_test),\n", " axis=1\n", ")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", " return scl.inv(x.T @ x) @ (x.T @ y)\n", "beta = ols_inv(X_train_own, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Singular Value decomposition\n", "\n", "Doing the inversion directly turns out to be a bad idea since the matrix\n", "$\\boldsymbol{X}^T\\boldsymbol{X}$ is singular. An alternative approach is to use the singular\n", "value decomposition. Using the definition of the Moore-Penrose\n", "pseudoinverse we can write the equation for $\\boldsymbol{\\beta}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = \\boldsymbol{X}^{+}\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the pseudoinverse of $\\boldsymbol{X}$ is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^{+} = \\frac{\\boldsymbol{X}^T}{\\boldsymbol{X}^T\\boldsymbol{X}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using singular value decomposition we can decompose the matrix $\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma} \\boldsymbol{V}^T$,\n", "where $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are orthogonal(unitary) matrices and $\\boldsymbol{\\Sigma}$ contains the singular values (more details below).\n", "where $X^{+} = V\\Sigma^{+} U^T$. This reduces the equation for\n", "$\\omega$ to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto7\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{\\beta} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^{+} \\boldsymbol{U}^T \\boldsymbol{y}.\n", "\\label{_auto7} \\tag{7}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that solving this equation by actually doing the pseudoinverse\n", "(which is what we will do) is not a good idea as this operation scales\n", "as $\\mathcal{O}(n^3)$, where $n$ is the number of elements in a\n", "general matrix. Instead, doing $QR$-factorization and solving the\n", "linear system as an equation would reduce this down to\n", "$\\mathcal{O}(n^2)$ operations." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", " u, s, v = scl.svd(x)\n", " return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta = ols_svd(X_train_own,y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When extracting the $J$-matrix we need to make sure that we remove the intercept, as is done here" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "J = beta[1:].reshape(L, L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A way of looking at the coefficients in $J$ is to plot the matrices as images." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J, cmap_args)\n", "plt.title(\"OLS\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is interesting to note that OLS\n", "considers both $J_{j, j + 1} = -0.5$ and $J_{j, j - 1} = -0.5$ as\n", "valid matrix elements for $J$.\n", "In our discussion below on hyperparameters and Ridge and Lasso regression we will see that\n", "this problem can be removed, partly and only with Lasso regression. \n", "\n", "In this case our matrix inversion was actually possible. The obvious question now is what is the mathematics behind the SVD?\n", "\n", "\n", "\n", "\n", "\n", "## The one-dimensional Ising model\n", "\n", "Let us bring back the Ising model again, but now with an additional\n", "focus on Ridge and Lasso regression as well. We repeat some of the\n", "basic parts of the Ising model and the setup of the training and test\n", "data. The one-dimensional Ising model with nearest neighbor\n", "interaction, no external field and a constant coupling constant $J$ is\n", "given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto8\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = -J \\sum_{k}^L s_k s_{k + 1},\n", "\\label{_auto8} \\tag{8}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins in the system is determined by $L$. For the one-dimensional system there is no phase transition.\n", "\n", "We will look at a system of $L = 40$ spins with a coupling constant of $J = 1$. To get enough training data we will generate 10000 states with their respective energies." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import seaborn as sns\n", "import scipy.linalg as scl\n", "from sklearn.model_selection import train_test_split\n", "import sklearn.linear_model as skl\n", "import tqdm\n", "sns.set(color_codes=True)\n", "cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n", "\n", "L = 40\n", "n = int(1e4)\n", "\n", "spins = np.random.choice([-1, 1], size=(n, L))\n", "J = 1.0\n", "\n", "energies = np.zeros(n)\n", "\n", "for i in range(n):\n", " energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more general form for the one-dimensional Ising model is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto9\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n", "\\label{_auto9} \\tag{9}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we allow for interactions beyond the nearest neighbors and a more\n", "adaptive coupling matrix. This latter expression can be formulated as\n", "a matrix-product on the form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto10\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = X J,\n", "\\label{_auto10} \\tag{10}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $X_{jk} = s_j s_k$ and $J$ is the matrix consisting of the\n", "elements $-J_{jk}$. This form of writing the energy fits perfectly\n", "with the form utilized in linear regression, viz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto11\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}.\n", "\\label{_auto11} \\tag{11}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We organize the data as we did above" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.zeros((n, L 2))\n", "for i in range(n):\n", " X[i] = np.outer(spins[i], spins[i]).ravel()\n", "y = energies\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.96)\n", "\n", "X_train_own = np.concatenate(\n", " (np.ones(len(X_train))[:, np.newaxis], X_train),\n", " axis=1\n", ")\n", "\n", "X_test_own = np.concatenate(\n", " (np.ones(len(X_test))[:, np.newaxis], X_test),\n", " axis=1\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will do all fitting with Scikit-Learn," ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf = skl.LinearRegression().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When extracting the $J$-matrix we make sure to remove the intercept" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "J_sk = clf.coef_.reshape(L, L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we plot the results" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_sk, cmap_args)\n", "plt.title(\"LinearRegression from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results perfectly with our previous discussion where we used our own code.\n", "\n", "## Ridge regression\n", "\n", "Having explored the ordinary least squares we move on to ridge\n", "regression. In ridge regression we include a regularizer. This\n", "involves a new cost function which leads to a new estimate for the\n", "weights $\\boldsymbol{\\beta}$. This results in a penalized regression problem. The\n", "cost function is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "3\n", "6\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_lambda = 0.1\n", "clf_ridge = skl.Ridge(alpha=_lambda).fit(X_train, y_train)\n", "J_ridge_sk = clf_ridge.coef_.reshape(L, L)\n", "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_ridge_sk, cmap_args)\n", "plt.title(\"Ridge from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LASSO regression\n", "\n", "In the Least Absolute Shrinkage and Selection Operator (LASSO)-method we get a third cost function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto13\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " C(\\boldsymbol{X}, \\boldsymbol{\\beta}; \\lambda) = (\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y}) + \\lambda \\sqrt{\\boldsymbol{\\beta}^T\\boldsymbol{\\beta}}.\n", "\\label{_auto13} \\tag{13}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finding the extremal point of this cost function is not so straight-forward as in least squares and ridge. We will therefore rely solely on the function ``Lasso`` from Scikit-Learn." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n", "J_lasso_sk = clf_lasso.coef_.reshape(L, L)\n", "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_lasso_sk, cmap_args)\n", "plt.title(\"Lasso from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is quite striking how LASSO breaks the symmetry of the coupling\n", "constant as opposed to ridge and OLS. We get a sparse solution with\n", "$J_{j, j + 1} = -1$.\n", "\n", "\n", "\n", "## Performance as function of the regularization parameter\n", "\n", "We see how the different models perform for a different set of values for $\\lambda$." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lambdas = np.logspace(-4, 5, 10)\n", "\n", "train_errors = {\n", " \"ols_sk\": np.zeros(lambdas.size),\n", " \"ridge_sk\": np.zeros(lambdas.size),\n", " \"lasso_sk\": np.zeros(lambdas.size)\n", "}\n", "\n", "test_errors = {\n", " \"ols_sk\": np.zeros(lambdas.size),\n", " \"ridge_sk\": np.zeros(lambdas.size),\n", " \"lasso_sk\": np.zeros(lambdas.size)\n", "}\n", "\n", "plot_counter = 1\n", "\n", "fig = plt.figure(figsize=(32, 54))\n", "\n", "for i, _lambda in enumerate(tqdm.tqdm(lambdas)):\n", " for key, method in zip(\n", " [\"ols_sk\", \"ridge_sk\", \"lasso_sk\"],\n", " [skl.LinearRegression(), skl.Ridge(alpha=_lambda), skl.Lasso(alpha=_lambda)]\n", " ):\n", " method = method.fit(X_train, y_train)\n", "\n", " train_errors[key][i] = method.score(X_train, y_train)\n", " test_errors[key][i] = method.score(X_test, y_test)\n", "\n", " omega = method.coef_.reshape(L, L)\n", "\n", " plt.subplot(10, 5, plot_counter)\n", " plt.imshow(omega, **cmap_args)\n", " plt.title(r\"%s, $\\lambda = %.4f$\" % (key, _lambda))\n", " plot_counter += 1\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that LASSO reaches a good solution for low\n", "values of $\\lambda$, but will \"wither\" when we increase $\\lambda$ too\n", "much. Ridge is more stable over a larger range of values for\n", "$\\lambda$, but eventually also fades away.\n", "\n", "## Finding the optimal value of $\\lambda$\n", "\n", "To determine which value of $\\lambda$ is best we plot the accuracy of\n", "the models when predicting the training and the testing set. We expect\n", "the accuracy of the training set to be quite good, but if the accuracy\n", "of the testing set is much lower this tells us that we might be\n", "subject to an overfit model. The ideal scenario is an accuracy on the\n", "testing set that is close to the accuracy of the training set." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "\n", "colors = {\n", " \"ols_sk\": \"r\",\n", " \"ridge_sk\": \"y\",\n", " \"lasso_sk\": \"c\"\n", "}\n", "\n", "for key in train_errors:\n", " plt.semilogx(\n", " lambdas,\n", " train_errors[key],\n", " colors[key],\n", " label=\"Train {0}\".format(key),\n", " linewidth=4.0\n", " )\n", "\n", "for key in test_errors:\n", " plt.semilogx(\n", " lambdas,\n", " test_errors[key],\n", " colors[key] + \"--\",\n", " label=\"Test {0}\".format(key),\n", " linewidth=4.0\n", " )\n", "plt.legend(loc=\"best\", fontsize=18)\n", "plt.xlabel(r\"$\\lambda$\", fontsize=18)\n", "plt.ylabel(r\"$R^2$\", fontsize=18)\n", "plt.tick_params(labelsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure we can see that LASSO with $\\lambda = 10^{-2}$\n", "achieves a very good accuracy on the test set. This by far surpasses the\n", "other models for all values of $\\lambda$." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }	cc0-1.0
davidrglass/antidengue	src/fasta_to_patient_sorted.ipynb	1	9737	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from Bio import SeqIO \n", "import numpy as np\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import os\n", "import sys\n", "from subprocess import call" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_igblast(filename):\n", " '''Takes a file from antidengue/data/fasta/ and runs it through igblast\n", " \n", " Parameters:\n", " filename - the name of the fasta\n", " '''\n", " if filename.endswith('.fasta'):\n", " outfile = filename[:-6] + '_out'\n", " else:\n", " outfile = filename + '_out'\n", " \n", " fasta_path = '/Users/davidglass/antidengue/data/fasta/'\n", " ncbi_path = '/Users/davidglass/Documents/resources/ncbi-igblast-1.4.0/'\n", " blast = '/Users/davidglass/Documents/resources/ncbi-igblast-1.4.0/bin/igblastn'\n", " igblast_dump = '/Users/davidglass/antidengue/data/ig_parse_dump/' + outfile[:-4]\n", "\n", " call(['cp', fasta_path + filename, ncbi_path])\n", " os.chdir(ncbi_path)\n", " call([blast, '-out', outfile, '-query', ncbi_path+filename, '-num_alignments_V=1', '-num_alignments_D=1',\n", " '-num_alignments_J=1', '-evalue=1e-20', '-germline_db_V', ncbi_path+'database/IGHV_imgt.fasta', \n", " '-germline_db_D', ncbi_path+'database/IGHD_imgt.fasta', '-germline_db_J',\n", " ncbi_path+'database/IGHJ_imgt.fasta', '-domain_system', 'imgt', '-auxiliary_data',\n", " ncbi_path+'optional_file/human_gl.aux'])\n", " call(['mkdir', igblast_dump])\n", " call(['mv', filename, igblast_dump])\n", " call(['mv', outfile, igblast_dump])\n", " \n", " return outfile" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_sample_info_from_csv(csv):\n", " '''Puts the info from the sample_info csv into a pandas dataframe and returns it\n", " \n", " Parameters:\n", " csv - the csv file\n", " '''\n", " \n", " sample_info = pd.DataFrame().from_csv(csv)\n", " \n", " return sample_info" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_parse(fasta, blast_out, sample_info):\n", " '''Runs parse_igblast.py in the igblast_dump folder and sends the parsedfile to the by_patient directory\n", " \n", " Parameters:\n", " fasta - the fasta file\n", " blast_out - the output of igblast\n", " dataframe - the pandas dataframe containing patient info\n", " '''\n", " accession_num = blast_out[:-4]\n", " igblast_dump = '/Users/davidglass/antidengue/data/ig_parse_dump/' + accession_num\n", " destination_directory = '/Users/davidglass/antidengue/data/by_sample'\n", " patient_id = \"\"\n", " time_point = \"\"\n", " \n", " call(['cp', '/Users/davidglass/antidengue/src/parse_igblast.py', igblast_dump])\n", " os.chdir(igblast_dump)\n", " call(['python', 'parse_igblast.py', fasta, blast_out])\n", " \n", " # find the ID from the sample_info DataFrame\n", " patient_id = str(sample_info.loc[accession_num][0])\n", " time_point = str(sample_info.loc[accession_num][4])\n", " new_filename = patient_id + \"_\" + time_point + \"_\" + accession_num\n", " \n", " call(['mv', 'parsed_igblast.txt', new_filename])\n", " call(['mv', new_filename, destination_directory])\n", " call(['rm', fasta])\n", " call(['rm', 'parse_igblast.py'])\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def pipeline(filename, sample_info):\n", " '''Runs a fasta through igblast and then parses the output and properly sorts files\n", " \n", " Parameters:\n", " filename - the name of the fasta\n", " sample_info - the pandas dataframe containing patient info\n", " '''\n", "\n", " blast_out = run_igblast(filename)\n", " run_parse(filename, blast_out, sample_info)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample_info = get_sample_info_from_csv('/Users/davidglass/antidengue/data/sample_info.csv')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Runs the pipeline on all the files\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/fasta/')\n", " \n", "for file in dirs:\n", " if file.endswith('.fasta'):\n", " pipeline(file, sample_info)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Renames the files with proper labels\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "os.chdir('/Users/davidglass/antidengue/data/by_sample/')\n", " \n", "for file in dirs:\n", " name = file\n", " first = name.find('_')\n", " sec = name.find('_', first + 1)\n", " name = \"patient_\" + name[:first] + \"_time\" + name[first:sec] + \"_sample\" + name[sec:]\n", " call(['mv', file, name])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Replaces header with amended header.\n", "\n", "header = \"###sequence_id\tabundance\tV-gene\tD-gene\tJ-gene\tV_E-value\tD_E-value\t\" + \\\n", " \"J_E-value\tFR3_seq\tCDR3_seq\tFR4_seq\tconst_seq\tlen_FR3\tlen_CDR3\tlen_FR4\t\" + \\\n", " \"len_const\tsequence_boundary_indices:FR1_CDR1_FR2_CDR2_FR3_CDR3_FR4\tlen_boundary\t\" + \\\n", " \"stop_codon_present\tproductive_sequence\tAA_seq_whole_read\tmutation_positions\t\" + \\\n", " \"germline_bases\tderived_bases\tmutation_density\tV_germline_identity\tleader_seq\t\" + \\\n", " \"reads_per_molecule\tprimer_isotype\tsequence_reversed\tseq\tquality###\\n\"\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "lines = []\n", "for file in dirs:\n", " if (file.startswith('patient')):\n", " with open (file, 'r') as f:\n", " lines = f.readlines()\n", " lines[0] = header\n", " with open (file, 'w') as f:\n", " f.writelines(lines)\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_seq_and_quality(filename):\n", " '''Takes a filename from a parsed_ig file, finds the corresponding fastq, and appends the\n", " input file with sequence and quality information for each sequence.\n", " \n", " Parameters:\n", " filename - the name of the parsed_igfile\n", " '''\n", " fastq_directory = '/Users/davidglass/antidengue/data/fastq/'\n", " by_samples_directory = '/Users/davidglass/antidengue/data/by_sample/'\n", " \n", " last_underscore = filename.rfind('_')\n", " fastq_file = filename[last_underscore + 1:] + '.fastq'\n", " fastq_lines = []\n", " parsed_lines = []\n", " \n", " with open (by_samples_directory + filename, 'r') as fi:\n", " parsed_lines = fi.readlines()\n", " with open (fastq_directory + fastq_file, 'r') as fi:\n", " fastq_lines = fi.readlines()\n", " for i in range(len(parsed_lines)):\n", " if i != 0:\n", " parsed_lines[i] = parsed_lines[i].strip('\\n') + \"\\t\" + \\\n", " fastq_lines[(i4)-3].strip('\\n') + \"\\t\" + fastq_lines[(i4)-1]\n", " with open (by_samples_directory + filename, 'w') as fo:\n", " for line in parsed_lines:\n", " fo.write(line)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_seq_and_quality('patient_148_time_ACUTE_sample_SRR2150126')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "for file in dirs:\n", " if (file.startswith('patient')):\n", " write_seq_and_quality(file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
LeeYiFang/Carkinos	src/Untitled5.ipynb	1	139686	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pathlib import Path\n", "import pandas as pd\n", "import numpy as np\n", "root=Path('../').resolve()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "WindowsPath('C:/Users/user/Documents/GitHub/Carkinos')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "root" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "raw_path=root.joinpath('src','PCA_TEST.quantile_normalized.tsv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sample_raw_data_df = pd.read_table(raw_path.as_posix())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, 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<td>9.047948</td>\n", " <td>8.616065</td>\n", " <td>9.203023</td>\n", " <td>8.572035</td>\n", " <td>8.506107</td>\n", " <td>8.229169</td>\n", " <td>8.695021</td>\n", " <td>8.915824</td>\n", " <td>8.233599</td>\n", " <td>...</td>\n", " <td>8.776563</td>\n", " <td>9.155915</td>\n", " <td>9.336024</td>\n", " <td>8.824805</td>\n", " <td>8.772813</td>\n", " <td>8.802262</td>\n", " <td>8.678147</td>\n", " <td>8.625144</td>\n", " <td>8.695912</td>\n", " <td>8.629976</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>117_at</td>\n", " <td>7.612412</td>\n", " <td>8.043859</td>\n", " <td>7.603927</td>\n", " <td>7.714699</td>\n", " <td>7.742822</td>\n", " <td>7.551629</td>\n", " <td>7.504556</td>\n", " <td>7.720968</td>\n", " <td>7.730913</td>\n", " <td>...</td>\n", " <td>7.729079</td>\n", " <td>11.647400</td>\n", " <td>11.342093</td>\n", " <td>7.850625</td>\n", " <td>8.028290</td>\n", " <td>8.037324</td>\n", " <td>7.655276</td>\n", " <td>7.888281</td>\n", " <td>7.691653</td>\n", " <td>7.624577</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>121_at</td>\n", " <td>8.218106</td>\n", " <td>8.235331</td>\n", " <td>8.400500</td>\n", " <td>8.405479</td>\n", " <td>9.297814</td>\n", " <td>8.178474</td>\n", " <td>8.067798</td>\n", " <td>8.176426</td>\n", " <td>11.259210</td>\n", " <td>...</td>\n", " <td>11.123192</td>\n", " <td>8.063912</td>\n", " <td>8.138332</td>\n", " <td>8.213297</td>\n", " <td>8.035581</td>\n", " <td>8.203285</td>\n", " <td>8.021791</td>\n", " <td>8.373332</td>\n", " <td>8.081797</td>\n", " <td>8.261245</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1255_g_at</td>\n", " <td>7.392744</td>\n", " <td>7.395735</td>\n", " <td>7.453283</td>\n", " <td>7.493720</td>\n", " <td>7.474639</td>\n", " <td>7.436698</td>\n", " <td>7.388679</td>\n", " <td>7.508520</td>\n", " <td>7.432427</td>\n", " <td>...</td>\n", " <td>7.445189</td>\n", " <td>9.069289</td>\n", " <td>8.621514</td>\n", " <td>7.446887</td>\n", " <td>7.512241</td>\n", " <td>7.456834</td>\n", " <td>7.453321</td>\n", " <td>7.509484</td>\n", " <td>7.315354</td>\n", " <td>7.513548</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1294_at</td>\n", " <td>9.003722</td>\n", " <td>8.375226</td>\n", " <td>7.975448</td>\n", " <td>7.762306</td>\n", " <td>7.763070</td>\n", " <td>7.981557</td>\n", " <td>8.389011</td>\n", " <td>8.376507</td>\n", " <td>7.903314</td>\n", " <td>...</td>\n", " <td>8.412039</td>\n", " <td>7.796222</td>\n", " <td>7.722225</td>\n", " <td>7.956599</td>\n", " <td>8.201499</td>\n", " <td>8.151006</td>\n", " <td>8.215746</td>\n", " <td>9.143522</td>\n", " <td>8.090617</td>\n", " <td>7.830512</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1316_at</td>\n", " <td>7.909244</td>\n", " <td>7.878632</td>\n", " <td>7.903908</td>\n", " <td>7.741367</td>\n", " <td>8.015132</td>\n", " <td>7.772626</td>\n", " <td>7.807954</td>\n", " <td>7.809478</td>\n", " <td>7.864759</td>\n", " <td>...</td>\n", " <td>8.065336</td>\n", " <td>7.948937</td>\n", " <td>8.050267</td>\n", " <td>7.966506</td>\n", " <td>8.097026</td>\n", " <td>7.900293</td>\n", " <td>7.847609</td>\n", " <td>8.001678</td>\n", " <td>8.084945</td>\n", " <td>7.888374</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1320_at</td>\n", " <td>7.651478</td>\n", " <td>7.708905</td>\n", " <td>7.602916</td>\n", " <td>7.705086</td>\n", " <td>7.712262</td>\n", " <td>7.723882</td>\n", " <td>7.739506</td>\n", " <td>7.641239</td>\n", " <td>7.621617</td>\n", " <td>...</td>\n", " <td>7.761721</td>\n", " <td>7.783990</td>\n", " <td>7.627924</td>\n", " <td>7.675847</td>\n", " <td>7.875980</td>\n", " <td>7.544660</td>\n", " <td>7.588426</td>\n", " <td>7.574985</td>\n", " <td>7.583279</td>\n", " <td>7.611891</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1405_i_at</td>\n", " <td>7.834398</td>\n", " <td>7.697773</td>\n", " <td>7.633690</td>\n", " <td>7.601535</td>\n", " <td>7.726134</td>\n", " <td>7.614437</td>\n", " <td>8.776613</td>\n", " <td>8.449633</td>\n", " <td>7.636824</td>\n", " <td>...</td>\n", " <td>7.801481</td>\n", " <td>7.580300</td>\n", " <td>7.648297</td>\n", " <td>7.794772</td>\n", " <td>8.369012</td>\n", " <td>7.921049</td>\n", " <td>7.615833</td>\n", " <td>7.820004</td>\n", " <td>7.807366</td>\n", " <td>7.596185</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1431_at</td>\n", " <td>7.728028</td>\n", " <td>7.771812</td>\n", " <td>7.403523</td>\n", " <td>7.667112</td>\n", " <td>7.520539</td>\n", " <td>7.713980</td>\n", " <td>7.536457</td>\n", " <td>7.460594</td>\n", " <td>7.545543</td>\n", " <td>...</td>\n", " <td>7.672223</td>\n", " <td>7.619240</td>\n", " <td>7.575923</td>\n", " <td>7.509735</td>\n", " <td>7.758326</td>\n", " <td>7.674408</td>\n", " <td>7.513123</td>\n", " <td>7.458537</td>\n", " <td>7.479716</td>\n", " <td>7.611531</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1438_at</td>\n", " <td>7.657973</td>\n", " <td>7.839882</td>\n", " <td>7.600658</td>\n", " <td>7.751388</td>\n", " <td>8.271726</td>\n", " <td>7.653040</td>\n", " <td>7.639229</td>\n", " <td>7.651966</td>\n", " <td>7.630050</td>\n", " <td>...</td>\n", " <td>7.703543</td>\n", " <td>7.879641</td>\n", " <td>7.665347</td>\n", " <td>8.257526</td>\n", " <td>7.835643</td>\n", " <td>7.662081</td>\n", " <td>7.729477</td>\n", " <td>7.615180</td>\n", " <td>7.752624</td>\n", " <td>8.094068</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1487_at</td>\n", " <td>8.206241</td>\n", " <td>7.972470</td>\n", " <td>8.424972</td>\n", " <td>8.281678</td>\n", " <td>8.992934</td>\n", " <td>8.402891</td>\n", " <td>8.278146</td>\n", " <td>8.351512</td>\n", " <td>8.908842</td>\n", " <td>...</td>\n", " <td>8.344273</td>\n", " <td>8.172797</td>\n", " <td>7.982788</td>\n", " <td>7.879871</td>\n", " <td>8.472595</td>\n", " <td>8.059953</td>\n", " <td>9.227652</td>\n", " <td>8.028662</td>\n", " <td>8.095783</td>\n", " <td>8.198551</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1494_f_at</td>\n", " <td>7.858453</td>\n", " <td>7.830963</td>\n", " <td>7.711877</td>\n", " <td>7.809028</td>\n", " <td>7.815555</td>\n", " <td>7.735942</td>\n", " <td>7.664429</td>\n", " <td>7.825576</td>\n", " <td>7.818208</td>\n", " <td>...</td>\n", " <td>7.844457</td>\n", " <td>7.801330</td>\n", " <td>7.819480</td>\n", " <td>7.692542</td>\n", " <td>7.922304</td>\n", " <td>7.911433</td>\n", " <td>7.801025</td>\n", " <td>7.791079</td>\n", " <td>7.718452</td>\n", " <td>7.767680</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1598_g_at</td>\n", " <td>8.087957</td>\n", " <td>7.959245</td>\n", " <td>8.664610</td>\n", " <td>7.890980</td>\n", " <td>8.003252</td>\n", " <td>8.358760</td>\n", " <td>9.062693</td>\n", " <td>9.483724</td>\n", " <td>8.192223</td>\n", " <td>...</td>\n", " <td>10.170631</td>\n", " <td>9.618505</td>\n", " <td>9.566406</td>\n", " <td>8.119021</td>\n", " <td>7.823470</td>\n", " <td>8.055860</td>\n", " <td>8.487177</td>\n", " <td>9.481194</td>\n", " <td>8.711049</td>\n", " <td>8.056754</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>160020_at</td>\n", " <td>8.028948</td>\n", " <td>8.007019</td>\n", " <td>8.531796</td>\n", " <td>8.034021</td>\n", " <td>7.950786</td>\n", " <td>8.030606</td>\n", " <td>7.891479</td>\n", " <td>8.148135</td>\n", " <td>8.369209</td>\n", " <td>...</td>\n", " <td>7.966837</td>\n", " <td>8.077760</td>\n", " <td>8.133854</td>\n", " <td>8.968220</td>\n", " <td>7.858555</td>\n", " <td>8.259718</td>\n", " <td>8.591962</td>\n", " <td>9.294565</td>\n", " <td>8.483516</td>\n", " <td>8.049743</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1729_at</td>\n", " <td>8.096292</td>\n", " <td>8.180304</td>\n", " <td>8.388173</td>\n", " <td>8.260304</td>\n", " <td>8.831569</td>\n", " <td>8.349998</td>\n", " <td>8.593277</td>\n", " <td>8.616233</td>\n", " <td>9.019362</td>\n", " <td>...</td>\n", " <td>8.835704</td>\n", " <td>8.007914</td>\n", " <td>8.141308</td>\n", " <td>8.310294</td>\n", " <td>8.290844</td>\n", " <td>8.510494</td>\n", " <td>8.542961</td>\n", " <td>8.556058</td>\n", " <td>8.174816</td>\n", " <td>8.341683</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>177_at</td>\n", " <td>7.514178</td>\n", " <td>7.606598</td>\n", " <td>7.693805</td>\n", " <td>7.581838</td>\n", " <td>7.708548</td>\n", " <td>7.708384</td>\n", " <td>7.597449</td>\n", " <td>7.693610</td>\n", " <td>7.620039</td>\n", " <td>...</td>\n", " <td>7.656180</td>\n", " <td>7.591175</td>\n", " <td>7.567320</td>\n", " <td>7.698346</td>\n", " <td>7.841765</td>\n", " <td>7.630390</td>\n", " <td>7.571643</td>\n", " <td>7.708900</td>\n", " <td>7.706586</td>\n", " <td>7.618176</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1773_at</td>\n", " <td>7.677061</td>\n", " <td>7.844826</td>\n", " <td>7.771058</td>\n", " <td>7.596594</td>\n", " <td>7.590436</td>\n", " <td>7.767117</td>\n", " <td>7.812929</td>\n", " <td>7.795710</td>\n", " <td>7.775275</td>\n", " <td>...</td>\n", " <td>7.741154</td>\n", " <td>7.675076</td>\n", " <td>7.812365</td>\n", " <td>7.961307</td>\n", " <td>7.810738</td>\n", " <td>7.865056</td>\n", " <td>7.612419</td>\n", " <td>7.775011</td>\n", " <td>7.651543</td>\n", " <td>7.819192</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>179_at</td>\n", " <td>8.243753</td>\n", " <td>8.264494</td>\n", " <td>8.455341</td>\n", " <td>8.109603</td>\n", " <td>8.178008</td>\n", " <td>8.221881</td>\n", " <td>8.216369</td>\n", " <td>8.128763</td>\n", " <td>8.127175</td>\n", " <td>...</td>\n", " <td>8.079943</td>\n", " <td>8.025528</td>\n", " <td>8.051105</td>\n", " <td>8.290394</td>\n", " <td>7.986633</td>\n", " <td>8.172114</td>\n", " <td>8.114658</td>\n", " <td>8.182000</td>\n", " <td>8.141716</td>\n", " <td>8.375676</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1861_at</td>\n", " <td>7.947907</td>\n", " <td>8.087725</td>\n", " <td>8.533923</td>\n", " <td>8.077407</td>\n", " <td>8.486724</td>\n", " <td>8.214426</td>\n", " <td>8.467074</td>\n", " <td>8.454226</td>\n", " <td>8.380016</td>\n", " <td>...</td>\n", " <td>8.391886</td>\n", " <td>8.459854</td>\n", " <td>8.388810</td>\n", " <td>8.171772</td>\n", " <td>8.105932</td>\n", " <td>8.126414</td>\n", " <td>8.459284</td>\n", " <td>8.558124</td>\n", " <td>8.431172</td>\n", " <td>8.444880</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>200000_s_at</td>\n", " <td>10.943962</td>\n", " <td>10.554185</td>\n", " <td>10.010714</td>\n", " <td>10.351770</td>\n", " <td>10.582500</td>\n", " <td>10.918780</td>\n", " <td>10.779839</td>\n", " <td>11.012558</td>\n", " <td>10.666718</td>\n", " <td>...</td>\n", " <td>10.323892</td>\n", " <td>11.362956</td>\n", " <td>11.045315</td>\n", " <td>10.389930</td>\n", " <td>10.730466</td>\n", " <td>10.670736</td>\n", " <td>10.434048</td>\n", " <td>10.010854</td>\n", " <td>11.172518</td>\n", " <td>9.934250</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>200001_at</td>\n", " <td>10.147493</td>\n", " <td>10.029987</td>\n", " <td>9.968916</td>\n", " <td>10.557932</td>\n", " <td>11.251398</td>\n", " <td>10.456606</td>\n", " <td>11.179078</td>\n", " <td>11.440406</td>\n", " <td>11.391664</td>\n", " <td>...</td>\n", " <td>11.327322</td>\n", " <td>9.867762</td>\n", " <td>9.967675</td>\n", " <td>11.068072</td>\n", " <td>10.325408</td>\n", " <td>10.568185</td>\n", " <td>11.879458</td>\n", " <td>11.553767</td>\n", " <td>11.167581</td>\n", " <td>11.182587</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>200002_at</td>\n", " <td>12.598934</td>\n", " <td>12.884603</td>\n", " <td>12.851004</td>\n", " <td>12.440221</td>\n", " <td>12.163264</td>\n", " <td>12.102061</td>\n", " <td>11.971443</td>\n", " <td>11.775090</td>\n", " <td>11.991532</td>\n", " <td>...</td>\n", " <td>12.225448</td>\n", " <td>12.452044</td>\n", " <td>12.234126</td>\n", " <td>12.269134</td>\n", " <td>12.278390</td>\n", " <td>11.997784</td>\n", " <td>12.361425</td>\n", " <td>11.893040</td>\n", " <td>12.207024</td>\n", " <td>12.139081</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>200003_s_at</td>\n", " <td>13.962958</td>\n", " <td>13.866020</td>\n", " <td>13.939309</td>\n", " <td>13.421075</td>\n", " <td>13.495911</td>\n", " <td>13.614290</td>\n", " <td>13.357725</td>\n", " <td>13.273866</td>\n", " <td>13.261886</td>\n", " <td>...</td>\n", " <td>13.665884</td>\n", " <td>13.199544</td>\n", " <td>13.201468</td>\n", " <td>13.438959</td>\n", " <td>13.736109</td>\n", " <td>13.757036</td>\n", " <td>13.518426</td>\n", " <td>13.648311</td>\n", " <td>13.487201</td>\n", " <td>13.645801</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>200004_at</td>\n", " <td>11.638454</td>\n", " <td>11.515864</td>\n", " <td>12.092778</td>\n", " <td>11.388483</td>\n", " <td>11.110027</td>\n", " <td>11.023252</td>\n", " <td>12.139457</td>\n", " <td>11.722688</td>\n", " <td>10.971977</td>\n", " <td>...</td>\n", " <td>10.811857</td>\n", " <td>11.674871</td>\n", " <td>11.621797</td>\n", " <td>12.281479</td>\n", " <td>11.026658</td>\n", " <td>11.344845</td>\n", " <td>11.860932</td>\n", " <td>11.245582</td>\n", " <td>11.229407</td>\n", " <td>12.435254</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>200005_at</td>\n", " <td>11.789568</td>\n", " <td>11.780158</td>\n", " <td>11.265116</td>\n", " <td>10.932197</td>\n", " <td>11.208736</td>\n", " <td>11.723449</td>\n", " <td>11.019480</td>\n", " <td>10.959582</td>\n", " <td>11.198749</td>\n", " <td>...</td>\n", " <td>10.613178</td>\n", " <td>11.048126</td>\n", " <td>10.848619</td>\n", " <td>10.751546</td>\n", " <td>10.975890</td>\n", " <td>10.848473</td>\n", " <td>10.825055</td>\n", " <td>10.997519</td>\n", " <td>10.427742</td>\n", " <td>11.391999</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>200006_at</td>\n", " <td>11.829138</td>\n", " <td>11.970993</td>\n", " <td>12.057994</td>\n", " <td>12.046755</td>\n", " <td>12.081040</td>\n", " <td>12.305364</td>\n", " <td>11.943838</td>\n", " <td>11.873483</td>\n", " <td>11.524197</td>\n", " <td>...</td>\n", " <td>11.690109</td>\n", " <td>11.795945</td>\n", " <td>11.744640</td>\n", " <td>12.418210</td>\n", " <td>11.275798</td>\n", " <td>11.125740</td>\n", " <td>11.738490</td>\n", " <td>12.302239</td>\n", " <td>12.460312</td>\n", " <td>11.976653</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>200007_at</td>\n", " <td>11.819296</td>\n", " <td>11.439260</td>\n", " <td>11.757965</td>\n", " <td>11.494313</td>\n", " <td>11.563859</td>\n", " <td>11.501570</td>\n", " <td>12.002326</td>\n", " <td>11.871924</td>\n", " <td>11.536442</td>\n", " <td>...</td>\n", " <td>11.674007</td>\n", " <td>11.710358</td>\n", " <td>11.783797</td>\n", " <td>12.263241</td>\n", " <td>12.122391</td>\n", " <td>12.432593</td>\n", " <td>11.948162</td>\n", " <td>12.127104</td>\n", " <td>12.066296</td>\n", " <td>12.259765</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>200008_s_at</td>\n", " <td>10.676458</td>\n", " <td>10.690479</td>\n", " <td>9.862556</td>\n", " <td>8.386338</td>\n", " <td>8.236503</td>\n", " <td>9.655560</td>\n", " <td>9.618241</td>\n", " <td>8.874053</td>\n", " <td>7.748188</td>\n", " <td>...</td>\n", " <td>7.779491</td>\n", " <td>8.238553</td>\n", " <td>8.077361</td>\n", " <td>9.726048</td>\n", " <td>8.215377</td>\n", " <td>8.102943</td>\n", " <td>8.383057</td>\n", " <td>7.727232</td>\n", " <td>7.818090</td>\n", " <td>10.854761</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>200009_at</td>\n", " <td>12.241562</td>\n", " <td>12.123915</td>\n", " <td>11.613592</td>\n", " <td>11.436513</td>\n", " <td>12.077685</td>\n", " <td>11.656556</td>\n", " <td>11.656313</td>\n", " <td>11.804873</td>\n", " <td>11.717407</td>\n", " <td>...</td>\n", " <td>11.635394</td>\n", " <td>11.511827</td>\n", " <td>11.450963</td>\n", " <td>11.001656</td>\n", " <td>11.963714</td>\n", " <td>11.745106</td>\n", " <td>11.711815</td>\n", " <td>10.430322</td>\n", " <td>10.139406</td>\n", " <td>12.053892</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>22247</th>\n", " <td>AFFX-PheX-3_at</td>\n", " <td>7.587780</td>\n", " <td>7.739597</td>\n", " <td>7.637001</td>\n", " <td>7.572844</td>\n", " <td>7.566232</td>\n", " <td>7.626215</td>\n", " <td>7.597273</td>\n", " <td>7.609240</td>\n", " <td>7.693653</td>\n", " <td>...</td>\n", " <td>7.636286</td>\n", " <td>7.581094</td>\n", " <td>7.677399</td>\n", " <td>7.564076</td>\n", " <td>7.684905</td>\n", " <td>7.730319</td>\n", " <td>7.591921</td>\n", " <td>7.646766</td>\n", " <td>7.617262</td>\n", " <td>7.655340</td>\n", " </tr>\n", " <tr>\n", " <th>22248</th>\n", " <td>AFFX-PheX-5_at</td>\n", " <td>7.615105</td>\n", " <td>7.584478</td>\n", " <td>7.455959</td>\n", " <td>7.645467</td>\n", " <td>7.560949</td>\n", " <td>7.619625</td>\n", " <td>7.598112</td>\n", " <td>7.582925</td>\n", " <td>7.627934</td>\n", " <td>...</td>\n", " <td>7.525731</td>\n", " <td>7.579176</td>\n", " <td>7.570382</td>\n", " <td>7.418292</td>\n", " <td>7.732098</td>\n", " <td>7.588400</td>\n", " <td>7.546730</td>\n", " <td>7.637900</td>\n", " <td>7.641483</td>\n", " <td>7.602141</td>\n", " </tr>\n", " <tr>\n", " <th>22249</th>\n", " <td>AFFX-PheX-M_at</td>\n", " <td>7.500879</td>\n", " <td>7.558350</td>\n", " <td>7.423706</td>\n", " <td>7.425247</td>\n", " <td>7.467854</td>\n", " <td>7.400606</td>\n", " <td>7.364192</td>\n", " <td>7.400171</td>\n", " <td>7.531035</td>\n", " <td>...</td>\n", " <td>7.510141</td>\n", " <td>7.385669</td>\n", " <td>7.557117</td>\n", " <td>7.408859</td>\n", " <td>7.686879</td>\n", " <td>7.642765</td>\n", " <td>7.437861</td>\n", " <td>7.544820</td>\n", " <td>7.509462</td>\n", " <td>7.375522</td>\n", " </tr>\n", " <tr>\n", " <th>22250</th>\n", " <td>AFFX-r2-Bs-dap-3_at</td>\n", " <td>7.415058</td>\n", " <td>7.538497</td>\n", " <td>7.444206</td>\n", " <td>7.537508</td>\n", " <td>7.526306</td>\n", " <td>7.475735</td>\n", " <td>7.457193</td>\n", " <td>7.489438</td>\n", " <td>7.510553</td>\n", " <td>...</td>\n", " <td>7.641837</td>\n", " <td>7.473934</td>\n", " <td>7.497987</td>\n", " <td>7.439749</td>\n", " <td>7.762372</td>\n", " <td>7.470218</td>\n", " <td>7.395558</td>\n", " <td>7.626874</td>\n", " <td>7.440026</td>\n", " <td>7.339268</td>\n", " </tr>\n", " <tr>\n", " <th>22251</th>\n", " <td>AFFX-r2-Bs-dap-5_at</td>\n", " <td>7.510356</td>\n", " <td>7.599167</td>\n", " <td>7.576703</td>\n", " <td>7.430549</td>\n", " <td>7.595861</td>\n", " <td>7.407454</td>\n", " <td>7.450202</td>\n", " <td>7.493626</td>\n", " <td>7.640243</td>\n", " <td>...</td>\n", " <td>7.489692</td>\n", " <td>7.520011</td>\n", " <td>7.573883</td>\n", " <td>7.419995</td>\n", " <td>7.758236</td>\n", " <td>7.611355</td>\n", " <td>7.547459</td>\n", " <td>7.573749</td>\n", " <td>7.517581</td>\n", " <td>7.443672</td>\n", " </tr>\n", " <tr>\n", " <th>22252</th>\n", " <td>AFFX-r2-Bs-dap-M_at</td>\n", " <td>7.428785</td>\n", " <td>7.492951</td>\n", " <td>7.480116</td>\n", " <td>7.301982</td>\n", " <td>7.369849</td>\n", " <td>7.324410</td>\n", " <td>7.369699</td>\n", " <td>7.345805</td>\n", " <td>7.429821</td>\n", " <td>...</td>\n", " <td>7.307410</td>\n", " <td>7.394087</td>\n", " <td>7.483675</td>\n", " <td>7.442209</td>\n", " <td>7.438441</td>\n", " <td>7.523250</td>\n", " <td>7.365466</td>\n", " <td>7.517269</td>\n", " <td>7.419273</td>\n", " <td>7.410981</td>\n", " </tr>\n", " <tr>\n", " <th>22253</th>\n", " <td>AFFX-r2-Bs-lys-3_at</td>\n", " <td>7.541610</td>\n", " <td>7.804846</td>\n", " <td>7.405789</td>\n", " <td>7.458813</td>\n", " <td>7.653536</td>\n", " <td>7.562071</td>\n", " <td>7.451183</td>\n", " <td>7.573437</td>\n", " <td>7.857587</td>\n", " <td>...</td>\n", " <td>7.581681</td>\n", " <td>7.649899</td>\n", " <td>7.749900</td>\n", " <td>7.500011</td>\n", " <td>7.735787</td>\n", " <td>7.835627</td>\n", " <td>7.567267</td>\n", " <td>7.681973</td>\n", " <td>7.696307</td>\n", " <td>7.495174</td>\n", " </tr>\n", " <tr>\n", " <th>22254</th>\n", " <td>AFFX-r2-Bs-lys-5_at</td>\n", " <td>7.424384</td>\n", " <td>7.714397</td>\n", " <td>7.318250</td>\n", " <td>7.494257</td>\n", " <td>7.518036</td>\n", " <td>7.493577</td>\n", " <td>7.360679</td>\n", " <td>7.566592</td>\n", " <td>7.668429</td>\n", " <td>...</td>\n", " <td>7.515631</td>\n", " <td>7.450158</td>\n", " <td>7.508255</td>\n", " <td>7.357867</td>\n", " <td>7.726456</td>\n", " <td>7.789662</td>\n", " <td>7.445381</td>\n", " <td>7.663948</td>\n", " <td>7.512095</td>\n", " <td>7.388519</td>\n", " </tr>\n", " <tr>\n", " <th>22255</th>\n", " <td>AFFX-r2-Bs-lys-M_at</td>\n", " <td>7.598653</td>\n", " <td>7.533525</td>\n", " <td>7.550970</td>\n", " <td>7.556028</td>\n", " <td>7.639852</td>\n", " <td>7.378454</td>\n", " <td>7.558760</td>\n", " <td>7.513993</td>\n", " <td>7.536908</td>\n", " <td>...</td>\n", " <td>7.759471</td>\n", " <td>7.566438</td>\n", " <td>7.647331</td>\n", " <td>7.538462</td>\n", " <td>8.024200</td>\n", " <td>7.592782</td>\n", " <td>7.435744</td>\n", " <td>7.452174</td>\n", " <td>7.626060</td>\n", " <td>7.519610</td>\n", " </tr>\n", " <tr>\n", " <th>22256</th>\n", " <td>AFFX-r2-Bs-phe-3_at</td>\n", " <td>7.588818</td>\n", " <td>7.700379</td>\n", " <td>7.608957</td>\n", " <td>7.617782</td>\n", " <td>7.668815</td>\n", " <td>7.544640</td>\n", " <td>7.420475</td>\n", " <td>7.465998</td>\n", " <td>7.587814</td>\n", " <td>...</td>\n", " <td>7.579117</td>\n", " <td>7.570955</td>\n", " <td>7.700903</td>\n", " <td>7.527279</td>\n", " <td>7.805261</td>\n", " <td>7.738460</td>\n", " <td>7.566220</td>\n", " <td>7.714249</td>\n", " <td>7.558661</td>\n", " <td>7.564291</td>\n", " </tr>\n", " <tr>\n", " <th>22257</th>\n", " <td>AFFX-r2-Bs-phe-5_at</td>\n", " <td>7.626531</td>\n", " <td>7.575690</td>\n", " <td>7.474051</td>\n", " <td>7.700783</td>\n", " <td>7.609364</td>\n", " <td>7.330687</td>\n", " <td>7.302846</td>\n", " <td>7.577040</td>\n", " <td>7.630244</td>\n", " <td>...</td>\n", " <td>7.705097</td>\n", " <td>7.578413</td>\n", " <td>7.756379</td>\n", " <td>7.508484</td>\n", " <td>7.899927</td>\n", " <td>7.602541</td>\n", " <td>7.482776</td>\n", " <td>7.755496</td>\n", " <td>7.608196</td>\n", " <td>7.514778</td>\n", " </tr>\n", " <tr>\n", " <th>22258</th>\n", " <td>AFFX-r2-Bs-phe-M_at</td>\n", " <td>7.491488</td>\n", " <td>7.581517</td>\n", " <td>7.428292</td>\n", " <td>7.495536</td>\n", " <td>7.631848</td>\n", " <td>7.480835</td>\n", " <td>7.453392</td>\n", " <td>7.509254</td>\n", " <td>7.501456</td>\n", " <td>...</td>\n", " <td>7.597136</td>\n", " <td>7.554519</td>\n", " <td>7.478137</td>\n", " <td>7.444631</td>\n", " <td>7.700423</td>\n", " <td>7.555826</td>\n", " <td>7.438705</td>\n", " <td>7.460913</td>\n", " <td>7.608836</td>\n", " <td>7.501917</td>\n", " </tr>\n", " <tr>\n", " <th>22259</th>\n", " <td>AFFX-r2-Bs-thr-3_s_at</td>\n", " <td>7.576096</td>\n", " <td>7.616859</td>\n", " <td>7.621567</td>\n", " <td>7.633570</td>\n", " <td>7.607185</td>\n", " <td>7.675595</td>\n", " <td>7.595108</td>\n", " <td>7.778257</td>\n", " <td>7.824489</td>\n", " <td>...</td>\n", " <td>7.665649</td>\n", " <td>7.608371</td>\n", " <td>7.698390</td>\n", " <td>7.623490</td>\n", " <td>7.792992</td>\n", " <td>7.790727</td>\n", " <td>7.587139</td>\n", " <td>7.693699</td>\n", " <td>7.539833</td>\n", " <td>7.562464</td>\n", " </tr>\n", " <tr>\n", " <th>22260</th>\n", " <td>AFFX-r2-Bs-thr-5_s_at</td>\n", " <td>7.728724</td>\n", " <td>7.708178</td>\n", " <td>7.608102</td>\n", " <td>7.591521</td>\n", " <td>7.683940</td>\n", " <td>7.517711</td>\n", " <td>7.579549</td>\n", " <td>7.641952</td>\n", " <td>7.686149</td>\n", " <td>...</td>\n", " <td>7.659568</td>\n", " <td>7.594628</td>\n", " <td>7.740366</td>\n", " <td>7.635615</td>\n", " <td>7.985159</td>\n", " <td>7.910271</td>\n", " <td>7.584600</td>\n", " <td>7.754253</td>\n", " <td>7.666207</td>\n", " <td>7.649350</td>\n", " </tr>\n", " <tr>\n", " <th>22261</th>\n", " <td>AFFX-r2-Bs-thr-M_s_at</td>\n", " <td>7.586706</td>\n", " <td>7.730064</td>\n", " <td>7.519281</td>\n", " <td>7.657503</td>\n", " <td>7.557744</td>\n", " <td>7.527900</td>\n", " <td>7.502069</td>\n", " <td>7.569939</td>\n", " <td>7.534255</td>\n", " <td>...</td>\n", " <td>7.508732</td>\n", " <td>7.479604</td>\n", " <td>7.536840</td>\n", " <td>7.606430</td>\n", " <td>7.901393</td>\n", " <td>7.671241</td>\n", " <td>7.452707</td>\n", " <td>7.548825</td>\n", " <td>7.389860</td>\n", " <td>7.605472</td>\n", " </tr>\n", " <tr>\n", " <th>22262</th>\n", " <td>AFFX-r2-Ec-bioB-3_at</td>\n", " <td>10.184430</td>\n", " <td>9.529309</td>\n", " <td>8.619244</td>\n", " <td>10.147914</td>\n", " <td>8.838657</td>\n", " <td>8.555512</td>\n", " <td>8.452804</td>\n", " <td>8.597299</td>\n", " <td>8.768882</td>\n", " <td>...</td>\n", " <td>9.190201</td>\n", " <td>8.810104</td>\n", " <td>8.790299</td>\n", " <td>9.860041</td>\n", " <td>9.168162</td>\n", " <td>9.268455</td>\n", " <td>8.586246</td>\n", " <td>9.029980</td>\n", " <td>8.885536</td>\n", " <td>10.091491</td>\n", " </tr>\n", " <tr>\n", " <th>22263</th>\n", " <td>AFFX-r2-Ec-bioB-5_at</td>\n", " <td>9.998544</td>\n", " <td>9.456845</td>\n", " <td>8.516449</td>\n", " <td>9.808780</td>\n", " <td>8.703090</td>\n", " <td>8.361782</td>\n", " <td>8.357288</td>\n", " <td>8.425705</td>\n", " <td>8.778259</td>\n", " <td>...</td>\n", " <td>9.107011</td>\n", " <td>8.799799</td>\n", " <td>8.807281</td>\n", " <td>9.658986</td>\n", " <td>9.130400</td>\n", " <td>9.104295</td>\n", " <td>8.460702</td>\n", " <td>8.724103</td>\n", " <td>8.768013</td>\n", " <td>9.928022</td>\n", " </tr>\n", " <tr>\n", " <th>22264</th>\n", " <td>AFFX-r2-Ec-bioB-M_at</td>\n", " <td>9.886580</td>\n", " <td>9.316752</td>\n", " <td>8.414869</td>\n", " <td>9.810127</td>\n", " <td>8.594883</td>\n", " <td>8.288756</td>\n", " <td>8.271467</td>\n", " <td>8.283263</td>\n", " <td>8.720208</td>\n", " <td>...</td>\n", " <td>8.906124</td>\n", " <td>8.707507</td>\n", " <td>8.462430</td>\n", " <td>9.729706</td>\n", " <td>8.889075</td>\n", " <td>9.032003</td>\n", " <td>8.428403</td>\n", " <td>8.866616</td>\n", " <td>8.680978</td>\n", " <td>10.030972</td>\n", " </tr>\n", " <tr>\n", " <th>22265</th>\n", " <td>AFFX-r2-Ec-bioC-3_at</td>\n", " <td>9.496804</td>\n", " <td>10.373924</td>\n", " <td>9.122523</td>\n", " <td>9.469340</td>\n", " <td>9.079933</td>\n", " <td>8.593055</td>\n", " <td>8.660188</td>\n", " <td>8.902277</td>\n", " <td>9.101185</td>\n", " <td>...</td>\n", " <td>9.508558</td>\n", " <td>9.213596</td>\n", " <td>9.361792</td>\n", " <td>9.247763</td>\n", " <td>9.475856</td>\n", " <td>9.678426</td>\n", " <td>8.899431</td>\n", " <td>9.424195</td>\n", " <td>9.112191</td>\n", " <td>9.572530</td>\n", " </tr>\n", " <tr>\n", " <th>22266</th>\n", " <td>AFFX-r2-Ec-bioC-5_at</td>\n", " <td>9.791266</td>\n", " <td>10.589859</td>\n", " <td>9.408320</td>\n", " <td>9.824942</td>\n", " <td>9.469546</td>\n", " <td>8.960363</td>\n", " <td>9.020998</td>\n", " <td>9.076730</td>\n", " <td>9.407147</td>\n", " <td>...</td>\n", " <td>9.819895</td>\n", " <td>9.604219</td>\n", " <td>9.799778</td>\n", " <td>9.653474</td>\n", " <td>9.793151</td>\n", " <td>10.135800</td>\n", " <td>9.191924</td>\n", " <td>9.603816</td>\n", " <td>9.369659</td>\n", " <td>10.024467</td>\n", " </tr>\n", " <tr>\n", " <th>22267</th>\n", " <td>AFFX-r2-Ec-bioD-3_at</td>\n", " <td>12.953441</td>\n", " <td>12.664495</td>\n", " <td>10.985948</td>\n", " <td>12.976145</td>\n", " <td>11.643625</td>\n", " <td>11.059735</td>\n", " <td>11.240215</td>\n", " <td>10.678759</td>\n", " <td>11.696586</td>\n", " <td>...</td>\n", " <td>12.190513</td>\n", " <td>12.099303</td>\n", " <td>11.631617</td>\n", " <td>13.011969</td>\n", " <td>12.132080</td>\n", " <td>11.715813</td>\n", " <td>11.564594</td>\n", " <td>11.437572</td>\n", " <td>11.593778</td>\n", " <td>13.032810</td>\n", " </tr>\n", " <tr>\n", " <th>22268</th>\n", " <td>AFFX-r2-Ec-bioD-5_at</td>\n", " <td>12.661458</td>\n", " <td>12.620283</td>\n", " <td>10.741545</td>\n", " <td>12.682194</td>\n", " <td>11.454273</td>\n", " <td>10.980560</td>\n", " <td>10.843183</td>\n", " <td>10.394221</td>\n", " <td>11.313661</td>\n", " <td>...</td>\n", " <td>11.888910</td>\n", " <td>12.019156</td>\n", " <td>11.254002</td>\n", " <td>12.499611</td>\n", " <td>11.828679</td>\n", " <td>11.341177</td>\n", " <td>11.334232</td>\n", " <td>11.093700</td>\n", " <td>11.469527</td>\n", " <td>12.721535</td>\n", " </tr>\n", " <tr>\n", " <th>22269</th>\n", " <td>AFFX-r2-P1-cre-3_at</td>\n", " <td>14.346964</td>\n", " <td>14.126229</td>\n", " <td>13.314945</td>\n", " <td>14.450152</td>\n", " <td>13.692042</td>\n", " <td>13.304899</td>\n", " <td>13.486599</td>\n", " <td>12.848590</td>\n", " <td>13.609245</td>\n", " <td>...</td>\n", " <td>13.908696</td>\n", " <td>13.938217</td>\n", " <td>13.516265</td>\n", " <td>14.690593</td>\n", " <td>13.816831</td>\n", " <td>13.493863</td>\n", " <td>13.490274</td>\n", " <td>13.326792</td>\n", " <td>13.702848</td>\n", " <td>14.523304</td>\n", " </tr>\n", " <tr>\n", " <th>22270</th>\n", " <td>AFFX-r2-P1-cre-5_at</td>\n", " <td>14.085980</td>\n", " <td>13.808825</td>\n", " <td>13.040669</td>\n", " <td>14.181299</td>\n", " <td>13.383539</td>\n", " <td>12.818351</td>\n", " <td>12.867753</td>\n", " <td>12.499426</td>\n", " <td>13.264427</td>\n", " <td>...</td>\n", " <td>13.662149</td>\n", " <td>13.547378</td>\n", " <td>13.314796</td>\n", " <td>14.407109</td>\n", " <td>13.565362</td>\n", " <td>13.300146</td>\n", " <td>12.954981</td>\n", " <td>13.003925</td>\n", " <td>13.351522</td>\n", " <td>14.308021</td>\n", " </tr>\n", " <tr>\n", " <th>22271</th>\n", " <td>AFFX-ThrX-3_at</td>\n", " <td>7.642534</td>\n", " <td>7.676568</td>\n", " <td>7.645716</td>\n", " <td>7.607399</td>\n", " <td>7.644112</td>\n", " <td>7.667333</td>\n", " <td>7.561026</td>\n", " <td>7.579219</td>\n", " <td>7.733833</td>\n", " <td>...</td>\n", " <td>7.692174</td>\n", " <td>7.644974</td>\n", " <td>7.750016</td>\n", " <td>7.645419</td>\n", " <td>7.866301</td>\n", " <td>7.810828</td>\n", " <td>7.567872</td>\n", " <td>7.703038</td>\n", " <td>7.648424</td>\n", " <td>7.679152</td>\n", " </tr>\n", " <tr>\n", " <th>22272</th>\n", " <td>AFFX-ThrX-5_at</td>\n", " <td>7.631033</td>\n", " <td>7.542400</td>\n", " <td>7.455327</td>\n", " <td>7.540580</td>\n", " <td>7.566635</td>\n", " <td>7.543538</td>\n", " <td>7.455010</td>\n", " <td>7.436875</td>\n", " <td>7.456654</td>\n", " <td>...</td>\n", " <td>7.611665</td>\n", " <td>7.523678</td>\n", " <td>7.454592</td>\n", " <td>7.464096</td>\n", " <td>7.792424</td>\n", " <td>7.527578</td>\n", " <td>7.494294</td>\n", " <td>7.534673</td>\n", " <td>7.519013</td>\n", " <td>7.507123</td>\n", " </tr>\n", " <tr>\n", " <th>22273</th>\n", " <td>AFFX-ThrX-M_at</td>\n", " <td>7.501928</td>\n", " <td>7.610702</td>\n", " <td>7.495555</td>\n", " <td>7.614399</td>\n", " <td>7.472724</td>\n", " <td>7.444107</td>\n", " <td>7.438042</td>\n", " <td>7.424471</td>\n", " <td>7.599155</td>\n", " <td>...</td>\n", " <td>7.550857</td>\n", " <td>7.478097</td>\n", " <td>7.575058</td>\n", " <td>7.438450</td>\n", " <td>7.684441</td>\n", " <td>7.549105</td>\n", " <td>7.424649</td>\n", " <td>7.584892</td>\n", " <td>7.525257</td>\n", " <td>7.490484</td>\n", " </tr>\n", " <tr>\n", " <th>22274</th>\n", " <td>AFFX-TrpnX-3_at</td>\n", " <td>7.513615</td>\n", " <td>7.633015</td>\n", " <td>7.515081</td>\n", " <td>7.546858</td>\n", " <td>7.547228</td>\n", " <td>7.525382</td>\n", " <td>7.396986</td>\n", " <td>7.511896</td>\n", " <td>7.630086</td>\n", " <td>...</td>\n", " <td>7.598619</td>\n", " <td>7.590259</td>\n", " <td>7.718625</td>\n", " <td>7.468810</td>\n", " <td>7.863286</td>\n", " <td>7.591056</td>\n", " <td>7.523585</td>\n", " <td>7.703311</td>\n", " <td>7.504957</td>\n", " <td>7.483636</td>\n", " </tr>\n", " <tr>\n", " <th>22275</th>\n", " <td>AFFX-TrpnX-5_at</td>\n", " <td>7.567607</td>\n", " <td>7.427258</td>\n", " <td>7.534118</td>\n", " <td>7.453376</td>\n", " <td>7.448552</td>\n", " <td>7.526502</td>\n", " <td>7.432864</td>\n", " <td>7.508867</td>\n", " <td>7.492580</td>\n", " <td>...</td>\n", " <td>7.532869</td>\n", " <td>7.484638</td>\n", " <td>7.444738</td>\n", " <td>7.487153</td>\n", " <td>7.535606</td>\n", " <td>7.609732</td>\n", " <td>7.415789</td>\n", " <td>7.611814</td>\n", " <td>7.523121</td>\n", " <td>7.502138</td>\n", " </tr>\n", " <tr>\n", " <th>22276</th>\n", " <td>AFFX-TrpnX-M_at</td>\n", " <td>7.561439</td>\n", " <td>7.654442</td>\n", " <td>7.551581</td>\n", " <td>7.494555</td>\n", " <td>7.597676</td>\n", " <td>7.508101</td>\n", " <td>7.503649</td>\n", " <td>7.559667</td>\n", " <td>7.629655</td>\n", " <td>...</td>\n", " <td>7.596008</td>\n", " <td>7.595698</td>\n", " <td>7.743632</td>\n", " <td>7.567720</td>\n", " <td>8.060336</td>\n", " <td>7.605666</td>\n", " <td>7.532778</td>\n", " <td>7.542190</td>\n", " <td>7.611075</td>\n", " <td>7.470764</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>22277 rows × 799 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 GSM1687570_5500024035100021608461.G01.CEL.gz \\\n", "0 1007_s_at 9.525619 \n", "1 1053_at 9.047948 \n", "2 117_at 7.612412 \n", "3 121_at 8.218106 \n", "4 1255_g_at 7.392744 \n", "5 1294_at 9.003722 \n", "6 1316_at 7.909244 \n", "7 1320_at 7.651478 \n", "8 1405_i_at 7.834398 \n", "9 1431_at 7.728028 \n", "10 1438_at 7.657973 \n", "11 1487_at 8.206241 \n", "12 1494_f_at 7.858453 \n", "13 1598_g_at 8.087957 \n", "14 160020_at 8.028948 \n", "15 1729_at 8.096292 \n", "16 177_at 7.514178 \n", "17 1773_at 7.677061 \n", "18 179_at 8.243753 \n", "19 1861_at 7.947907 \n", "20 200000_s_at 10.943962 \n", "21 200001_at 10.147493 \n", "22 200002_at 12.598934 \n", "23 200003_s_at 13.962958 \n", 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crendl/ilearnpython	jupyter/Untitled.ipynb	2	88400	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab notebook\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rain = (20, 35, 30, 35, 27, 12, 21, 16, 16, 20, 11, 12)\n", "\n", "temp = (25, 32, 34, 20, 25, 14, 13, 14, 15, 16, 17, 18)\n", "\n", "i = np.arange(len(rain))\n", "fig, rr = plt.subplots()\n", "\n", "#bar_width = 1\n", "\n", "opacity = 0.4\n", "#error_config = {'ecolor': '0.3'}\n", "\n", "\n", "rr.bar(i, rain, alpha=opacity, color='b',\n", " label='rain', align='center')\n", "\n", "tt=plt.twinx()\n", "tt.plot(temp, linestyle='-', linewidth=1.0, label='temp')\n", "\n", "plt.xlabel('months')\n", "rr.set_ylabel('(mm)')\n", "#plt.ylabel('(mm)')\n", "tt.set_ylabel('(C)')\n", "plt.title('rain and temp')\n", "plt.xticks(i,('jan', 'feb', 'mar', 'apr', 'mai', 'jun', 'jul', 'aug', 'sept', 'oct', 'nov', 'dez'))\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "\n", "rr.set_ylim([0,40])\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
selimnairb/2014-02-25-swctest	lessons/swc-setdict/setdict-aggregate-instructor.ipynb	1	13527	{ "metadata": { "name": "setdict-aggregate-instructor" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Sets and Dictionaries in Python: Aggregation (Instructor Version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objectives\n", "\n", "* Recognize problems that can be solved by aggregating values.\n", "* Use dictionaries to aggregate values.\n", "* Explain why actual data values should be used as initializers rather than \"impossible\" values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lesson\n", "\n", "To see how useful dictionaries can be, let's switch tracks and do some\n", "birdwatching. We'll start by asking how early in the day we saw each\n", "kind of bird? Our data consists of the date and time of the observation,\n", "the bird's name, and an optional comment:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!cat some_birds.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2010-07-03 05:38 loon\r\n", "2010-07-03 06:02 goose\r\n", "2010-07-03 06:07 loon\r\n", "2010-07-04 05:09 ostrich # hallucinating?\r\n", "2010-07-04 05:29 loon" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rephrasing our problem, we want the minimum of all the times associated\n", "with each bird name. If our data was stored in memory like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " loon = ['05:38', '06:07', '05:20', ...]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the solution would simply be `min(loon)`, and similarly for the other\n", "birds. However, we have to work with the data we have, so let's start by\n", "reading our data file and creating a list of tuples, each of which\n", "contains a date, time, and bird name as strings:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def read_observations(filename):\n", " '''Read data, returning [(date, time, bird)...].'''\n", "\n", " reader = open(filename, 'r')\n", " result = []\n", "\n", " for line in reader:\n", " fields = line.split('#')[0].strip().split()\n", " assert len(fields) == 3, 'Bad line \"%s\"' % line\n", " result.append(fields)\n", "\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function follows the pattern we've seen many times before. We set\n", "up by opening the input file and creating an empty list that we'll\n", "append records to. We then process each line of the file in turn.\n", "Splitting the line on the `'#'` character and taking the first part of\n", "the result gets rid of any comment that might be present; stripping off\n", "whitespace and then splitting breaks the remainder into fields.\n", "\n", "To prevent trouble later on, we check that there actually are three\n", "fields before going on. (An industrial-strength version of this function\n", "would also check that the date and time were properly formatted, but\n", "we'll skip that for now.) Once we've done our check, we append the\n", "triple containing the date, time, and bird name to the list we're going\n", "to return." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the function that turns that list of tuples into a dictionary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def earliest_observation(data):\n", " '''How early did we see each bird?'''\n", "\n", " result = {}\n", " for (date, time, bird) in data:\n", " if bird not in result:\n", " result[bird] = time\n", " else:\n", " result[bird] = min(result[bird], time)\n", "\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, the pattern should by now be familiar. We start by creating\n", "an empty dictionary, then use a loop to inspect each tuple in turn. The\n", "loop explodes the tuple into separate variables for the date, time and\n", "bird. If the bird's name is not already a key in our dictionary, this\n", "must be the first time we've seen it, so we store the time we saw it in\n", "the dictionary. If the bird's name is already there, on the other hand,\n", "we keep the minimum of the stored time and the new time. This is almost\n", "exactly the same as our earlier counting example, but instead of either\n", "storing 1 or adding 1 to the count so far, we're either storing the time\n", "or taking the minimum of it and the least time so far.\n", "\n", "Let's test what we've written so far:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "entries = read_observations('some_birds.txt')\n", "result = earliest_observation(entries)\n", "print result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'loon': '05:29', 'goose': '06:02', 'ostrich': '05:09'}\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, what if we want to find out which birds were seen on particular\n", "days? Once again, we are [aggregating](glossary.html#aggregation)\n", "values, i.e., combining many separate values to create one new one.\n", "However, since we probably saw more than one kind of bird each day, that\n", "\"new value\" needs to be a collection of some kind. We're only interested\n", "in which birds we saw, so the right kind of collection is a set. Here's\n", "our function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def birds_by_date(data):\n", " '''Which birds were seen on each day?'''\n", "\n", " result = {}\n", " for (date, time, bird) in data:\n", " if date not in result:\n", " result[date] = {bird}\n", " else:\n", " result[date].add(bird)\n", "\n", " return result\n", "\n", "print birds_by_date(entries)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'2010-07-04': set(['loon', 'ostrich']), '2010-07-03': set(['loon', 'goose'])}\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we start by creating an empty dictionary, and then process each\n", "tuple in turn. Since we're recording birds by date, the keys in our\n", "dictionary are dates rather than bird names. If the current date isn't\n", "already a key in the dictionary, we create a set containing only this\n", "bird, and store it in the dictionary with the date as the key.\n", "Otherwise, we add this bird to the set associated with the date. (As\n", "always, we don't need to check whether the bird is already in that set,\n", "since the set will automatically eliminate any duplication.)\n", "\n", "Let's watch this function in action for the first few records from our\n", "data:\n", "\n", "<table>\n", " <tr>\n", " <th>Input</th>\n", " <th>Dictionary</th>\n", " </tr>\n", " <tr>\n", " <td><em>start</em></td>\n", " <td><code>{}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  05:38  loon</code></td>\n", " <td><code>{'2010-07-03' : {'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  06:02  goose</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  06:07  loon</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-04  05:09  ostrich</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}, '2010-07-04' : {'ostrich'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-04  05:29  loon</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}, '2010-07-04' : {'ostrich', 'loon'}}</code></td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our last example, we'll figure out which bird we saw least\n", "frequently\u2014or rather, which birds, since two or more may be tied for\n", "the low score. Forgetting that values may not be unique is a common\n", "mistake in data crunching, and often a hard one to track down.\n", "\n", "Our first strategy is simple: figure out how many times we've seen each\n", "bird, then find the minimum of those counts and get the set of birds\n", "we've seen that many times. The function below implements this fairly\n", "directly:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def least_common_birds(data):\n", " '''Which bird or birds have been seen least frequently?'''\n", " \n", " counts = count_by_bird(data) # need to write this\n", " least = min(counts.values())\n", " result = set()\n", " for bird in counts:\n", " if counts[bird] == least:\n", " result.add(bird)\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`least_common_birds` depends on a function `count_by_bird`, but this is\n", "yet another example of using a dictionary to aggregate values (in this\n", "case, to sum the number of birds we have seen). Just for variety's sake,\n", "we'll use a slightly different strategy that we've used before: whenever\n", "we see a new kind of bird, we'll set its count to zero, and then always\n", "add one to the stored count:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def count_by_bird(data):\n", " '''How many times was each bird seen?'''\n", " result = {}\n", " for (date, time, bird) in data:\n", " if bird not in result:\n", " result[bird] = 0\n", " result[bird] += 1\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll test our function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print least_common_birds(entries)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set(['goose', 'ostrich'])\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This does the job, but is somewhat inefficient: we do one pass through\n", "all the data while counting birds, then another pass through all the\n", "birds to find those that we've seen the least number of times. We can\n", "actually do the whole job with a single pass through the data, but as\n", "we'll see in the challenges, the resulting code is significantly more\n", "complex than what we have written so far. Unless we're sure that the\n", "second pass is really a performance bottleneck, we should stick with\n", "this simple implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Points\n", "\n", "* Use dictionaries to count things.\n", "* Initialize values from actual data instead of trying to guess what values could \"never\" occur." ] } ], "metadata": {} } ] }	bsd-2-clause
slundberg/shap	notebooks/tabular_examples/tree_based_models/Census income classification with LightGBM.ipynb	1	2193199	null	mit
eoinmurray/icarus	notebooks/Fidelity verus Fine structure splitting.ipynb	1	110619	{ "metadata": { "name": "Fidelity verus Fine structure splitting" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<script type=\"text/javascript\">\n", " on = \"View input\";\n", " off = \"Hide input\"\n", " function onoff(){\n", " currentvalue = document.getElementById('onoff').value;\n", " if(currentvalue == off){\n", " document.getElementById(\"onoff\").value=on;\n", " $('div.input').hide();\n", " }else{\n", " document.getElementById(\"onoff\").value=off;\n", " $('div.input').show();\n", " }\n", "}\n", "</script>\n", "<input type=\"button\" class=\"ui-button ui-widget ui-state-default ui-corner-all ui-button-text-only\" value=\"Hide input\" id=\"onoff\" onclick=\"onoff();\">\n", "\n", "_(You should click this button, you dont need to see the code)._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fidelity versus Fine Structure splitting." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import sys\n", "sys.path.append('../')\n", "from mpl_toolkits.axes_grid1 import Grid\n", "from Icarus.Classes.QuantumDot import QuantumDot\n", "from constants import Constants\n", "from Experiments.utils.plot_fidelity_curves import " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "constants = Constants()\n", "qd = QuantumDot(constants.xtau, constants.xxtau, constants.ptau, constants.FSS, constants.crosstau)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## No decoherence." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 0.\n", "qd.bg_emission_rate = 0.\n", "fss = np.linspace(-10, 10, 500)1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_no_dec = np.loadtxt('../out/fidelity_vs_fss/xtau1/fss_v_fidelity.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_no_dec[:,0], numerical_data_no_dec[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('\|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.text.Text at 0x106018550>" ] }, { "output_type": 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AuKMKEZkkBkQiIgDh5x2Q3drL+Bdu3x64fh1I4mxmIjIdDIhERBkZmJz9GeKu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\n" } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoherence time of 2.5 ns, coherence of 71%." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 2.5\n", "fss = np.linspace(-10, 10, 500)1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_with_dec = np.loadtxt('../out/fidelity_vs_fss/cross_dephasing2.5ns/fss_v_fidelity.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_with_dec[:,0], numerical_data_with_dec[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('\|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "<matplotlib.text.Text at 0x10643d6d0>" ] }, { "output_type": "display_data", "png": 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qtRoJCQnGLt/gGBCJiIjoualUqufaX6vV6qmSgvn8888xZswYhIWFwdraGosWLcq2TYcO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\nfS1gmQAAAABJRU5ErkJggg==\n" } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoherence time of 2.5 ns, background emission of 0.05." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 2.5\n", "qd.bg_emission_rate = 0.05\n", "fss = np.linspace(-10, 10, 500)1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_dec_and_bg = np.loadtxt('../out/2013-08-07/fid_v_fss/fid_v_fss.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_dec_and_bg[:,0]/1e-6, numerical_data_dec_and_bg[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('\|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 6, "text": [ "<matplotlib.text.Text at 0x106383c10>" ] }, { "output_type": "display_data", "png": 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REZEMczgcGXp+fHx8JlWSOi+//DLPP/88u3fvpkSJEkyZMiXRMe3atWPjxo1s3LiRX3/9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\nEEIIIUz8fwfPJ7wWf8KCAAAAAElFTkSuQmCC\n" } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
hbwzhsh/pyDataScienceToolkits_Base	Scikit-learn/.ipynb_checkpoints/(1)getting_started_with_iris-checkpoint.ipynb	8	51025	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##从iris数据集入门scikit-learn\n", "iris数据集是由三种鸢尾花，各50组数据构成的数据集。每个样本包含4个特征，分别为萼片(sepals)的长和宽、花瓣(petals)的长和宽。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](Image/iris_petal_sepal.jpg)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#from IPython.diasplay import Image\n", "#Image(filename=\"Image/iris_petal_sepal.jpg\", width=400, height=432)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "##1. 载入iris数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "你还可以通过python的csv模块，或者NumPy的loadtxt函数，或者Pandas的read_csv()函数读取从[UCI Iris dataset](http://archive.ics.uci.edu/ml/datasets/Iris)下载的csv文件。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sklearn.datasets.base.Bunch" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "type(iris)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", "['setosa' 'versicolor' 'virginica']\n" ] } ], "source": [ "print iris.feature_names\n", "print iris.target_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在scikit-learn中对数据有如下要求：\n", "- 特征和标签要用分开的对象存储\n", "- 特征和标签要是数字\n", "- 特征和标签都要使用numpy的array来存储" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'numpy.ndarray'>\n", "<type 'numpy.ndarray'>\n" ] } ], "source": [ "print type(iris.data)\n", "print type(iris.target)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n", "(150,)\n" ] } ], "source": [ "print iris.data.shape\n", "print iris.target.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# store features matrix in \"X\"\n", "X = iris.data\n", "\n", "# store response vector in \"y\"\n", "y = iris.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###回顾iris数据集" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###绘制iris的2d图" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x554f850>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fjs/jITooRy8+PafxYepUGMjNmFUhXi/eqVPzt+v1rim8I3EPAz7nWIQMBAwD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\nOE5zgPeAPyNjZi5D/mlyVftYlRKTmwM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"text/plain": [ "<matplotlib.figure.Figure at 0x5541b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "X_sepal = X[:, :2]\n", "plt.scatter(X_sepal[:, 0], X_sepal[:, 1], c=y, cmap=plt.cm.gnuplot)\n", "plt.xlabel('Sepal length')\n", "plt.ylabel('Sepal width')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x5705e90>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DNAYdPgvBJ3Vee0zQzoD6Z8xDck6YxohDMuBw2jBjWh/TIxE+3n13mubMoSkW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\nTGOp3hzUWCvsLsA92Omvc7H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"text/plain": [ "<matplotlib.figure.Figure at 0x5556d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_petal = X[:, 2:4]\n", "plt.scatter(X_petal[:, 0], X_petal[:, 1], c=y, cmap=plt.cm.gnuplot)\n", "plt.xlabel('Petal length')\n", "plt.ylabel('Petal width')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##2. 使用K近邻进行分类" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "KNN分类的基本步骤：\n", "1. 选择K的值\n", "2. 在训练数据集中搜索K个距离最近的观测值\n", "3. 使用最多的那个标签作为未知数据的预测" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面给出KNN的演示图例，\n", "分别是训练数据、K=1时的KNN分类图、K=5时的KNN分类图\n", "![Example training data](Image/Data3classes.png)\n", "![KNN classification map(K=1)](Image/Map1NN.png)\n", "![KNN classification map(K=5)](Image/Map5NN.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###scikit-learn进行模型匹配的4个一般步骤" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第一步：载入你要使用的模型类" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第二步：实例化分类器" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# looking for the one nearest neighbor\n", "knn = KNeighborsClassifier(n_neighbors=1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')\n" ] } ], "source": [ "print knn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第三步：用数据来拟合模型（进行模型的训练）" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第四步：对新的观测值进行预测" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.predict([3, 5, 4, 2])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 1])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]\n", "knn.predict(X_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###使用不同的K值" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn5 = KNeighborsClassifier(n_neighbors=5)\n", "\n", "knn5.fit(X, y)\n", "\n", "knn5.predict(X_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###依照同样的流程，使用不同的分类模型" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 0])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import the class\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression()\n", "\n", "# fit the model with data\n", "logreg.fit(X, y)\n", "\n", "# predict the response for new observations\n", "logreg.predict(X_new)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
elivre/arfe	e2014/020-rede2014_converte_receitas.ipynb	1	34728	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 020-rede2014_converte_receitas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ano_eleicao = '2014'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dbschema = f'rede{ano_eleicao}'\n", "dbschema_tse = f'tse{ano_eleicao}'\n", "table_receitas = f'{dbschema}.receitas_{ano_eleicao}'\n", "table_receitas_do = f'{dbschema}.receitas_do_{ano_eleicao}'\n", "table_receitas_candidatos = f'{dbschema_tse}.receitas_candidatos_{ano_eleicao}'\n", "table_receitas_partidos = f'{dbschema_tse}.receitas_partidos_{ano_eleicao}'\n", "table_receitas_comites = f'{dbschema_tse}.receitas_comites_{ano_eleicao}'\n", "table_candidaturas = f\"{dbschema}.candidaturas_{ano_eleicao}\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append('../')\n", "import mod_tse as mtse\n", "home = os.environ[\"HOME\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "mtse.execute_query(f'CREATE SCHEMA IF NOT EXISTS {dbschema};')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CRIA TABELA RECEITAS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "query_create_table_receitas = f\"\"\"\n", "DROP TABLE IF EXISTS {table_receitas} CASCADE;\n", "CREATE TABLE {table_receitas} (\n", " tabela_id varchar,\n", "\n", " eleicao_ano varchar,\n", " eleicao_turno varchar,\n", " prestador_contas_sq varchar,\n", " prestador_contas_cnpj varchar,\n", "\n", " --RECEITA\n", " receita_fonte_cd varchar,\n", " receita_fonte_ds varchar,\n", " receita_origem_cd varchar,\n", " receita_origem_sg varchar,\n", " receita_origem_ds varchar,\n", " receita_valor numeric(18,2),\n", "\n", " --RECEPTOR\n", " receptor_id varchar,\n", " receptor_tipo_cd varchar,\n", " receptor_tipo_ds varchar,\n", " receptor_candidatura_id varchar,\n", " \n", " receptor_esfera_partidaria_cd varchar,\n", " receptor_esfera_partidaria_ds varchar,\n", " receptor_uf varchar,\n", " receptor_ue varchar,\n", " receptor_ue_nome varchar,\n", " receptor_cnpj varchar,\n", " receptor_nome varchar,\n", " receptor_sq varchar,\n", " receptor_cargo_ds varchar,\n", " receptor_candidato_nr varchar,\n", " receptor_candidato_cpf varchar,\n", " receptor_vice_candidato_cpf varchar,\n", " receptor_partido_nr varchar,\n", " receptor_partido_sg varchar,\n", "\n", " --DOADOR\n", " doador_id varchar,\n", " doador_tipo_cd varchar,\n", " doador_tipo_ds varchar,\n", " doador_candidatura_id varchar,\n", " \n", " doador_cnae_cd varchar,\n", " doador_cnae_ds varchar,\n", " doador_cpf_cnpj varchar,\n", " doador_nome varchar,\n", " doador_nome_rfb varchar,\n", " doador_originario_tipo varchar,\n", " doador_esfera_partidaria_cd varchar, \n", " doador_esfera_partidaria_ds varchar,\n", " doador_uf varchar,\n", " doador_ue varchar,\n", " doador_ue_nome varchar,\n", " doador_candidato_nr varchar,\n", " doador_candidato_cargo_ds varchar,\n", " doador_partido_nr varchar,\n", " doador_partido_sg varchar \n", ");\n", "\n", "CREATE INDEX ON {table_receitas} (tabela_id);\n", "CREATE INDEX ON {table_receitas} (receita_origem_sg);\n", "CREATE INDEX ON {table_receitas} (receita_origem_cd);\n", "CREATE INDEX ON {table_receitas} (receita_origem_ds);\n", "\n", "\n", "CREATE INDEX ON {table_receitas} (receptor_id);\n", "CREATE INDEX ON {table_receitas} (receptor_candidatura_id);\n", "CREATE INDEX ON {table_receitas} (receptor_tipo_cd);\n", "CREATE INDEX ON {table_receitas} (receptor_tipo_ds);\n", "CREATE INDEX ON {table_receitas} (receptor_cnpj);\n", "CREATE INDEX ON {table_receitas} (receptor_candidato_cpf);\n", "CREATE INDEX ON {table_receitas} (receptor_uf); \n", "CREATE INDEX ON {table_receitas} (receptor_candidato_nr); \n", "CREATE INDEX ON {table_receitas} (receptor_sq);\n", "\n", "\n", "CREATE INDEX ON {table_receitas} (doador_id);\n", "CREATE INDEX ON {table_receitas} (doador_candidatura_id);\n", "CREATE INDEX ON {table_receitas} (doador_tipo_cd);\n", "CREATE INDEX ON {table_receitas} (doador_tipo_ds);\n", "CREATE INDEX ON {table_receitas} (doador_cpf_cnpj);\n", "CREATE INDEX ON {table_receitas} (doador_nome);\n", "CREATE INDEX ON {table_receitas} (doador_nome_rfb);\n", "\n", "\n", "DROP TABLE IF EXISTS {table_receitas_do} CASCADE;\n", "CREATE TABLE {table_receitas_do} (\n", "LIKE {table_receitas}\n", ");\n", "\n", "\n", "\"\"\"\n", "\n", "mtse.execute_query(query_create_table_receitas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS DE CANDIDATOS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_candidatos = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'RC' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor,\n", " \n", " --RECEPTOR \n", " get_candidato_id(cpf_do_candidato) as receptor_id,\n", " 'CD' as receptor_tipo_cd,\n", " 'Candidato' as receptor_tipo_ds,\n", " get_candidatura_id(uf,numero_candidato) as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " nome_candidato as receptor_nome,\n", " sequencial_candidato as receptor_sq,\n", " cargo as receptor_cargo_ds,\n", " numero_candidato as receptor_candidato_nr,\n", " cpf_do_candidato as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatra_id, \n", " \n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_candidatos}\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_candidatos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS DE CANDIDATOS DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_candidatos_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select \n", " 'RCDO' as tabela_id,\n", " \n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor,\n", " \n", " --RECEPTOR \n", " get_candidato_id(cpf_do_candidato) as receptor_id,\n", " 'CD' as receptor_tipo_cd,\n", " 'Candidato' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " nome_candidato as receptor_nome,\n", " sequencial_candidato as receptor_sq,\n", " cargo as receptor_cargo_ds,\n", " numero_candidato as receptor_candidato_nr,\n", " cpf_do_candidato as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candidatura_id, \n", " \n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_candidatos} \n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_candidatos_doador_originario)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS PARTIDOS" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_partidos = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'ROP' as tabela_id, \n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_diretorio as receptor_nome,\n", " sequencial_diretorio as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatura_id, \n", "\n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg \n", "from {table_receitas_partidos}\n", "--where \n", "-- cpf_cnpj_do_doador_originario in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_partidos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS PARTIDOS DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_partidos_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select\n", " 'ROPDO' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_diretorio as receptor_nome,\n", " sequencial_diretorio as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candidatura_id, \n", "\n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_partidos} as rpdo\n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_partidos_doador_originario)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS COMITES" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_comites = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'ROP' as tabela_id, \n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_comite as receptor_nome,\n", " sequencial_comite as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatura_id,\n", " \n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg \n", "from {table_receitas_comites}\n", "--where \n", "-- cpf_cnpj_do_doador_originario in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_comites)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS COMITES DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_comites_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select\n", " 'ROPDO' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_comite as receptor_nome,\n", " sequencial_comite as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candiatura_id,\n", " \n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_comites} as rpdo\n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_comites_doador_originario)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-10-20 22:44:03.586346\n" ] } ], "source": [ "import datetime\n", "print(datetime.datetime.now())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
Ttl/scikit-rf	doc/source/examples/metrology/One Port Tiered Calibration.ipynb	3	7960	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# One Port Tiered Calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A one-port network analyzer can be used to measure a two-port device, provided that the device is reciprocal. This is accomplished by performing two calibrations, which is why its called a tiered calibration. \n", "\n", "First, the VNA is calibrated at the test-port like normal. This is called the first tier. Next, the device is connected to the test-port, and a calibration is performed at the far end of the device, the second tier. A diagram is shown below," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import SVG\n", "SVG('oneport_tiered_calibration/images/boxDiagram.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook will demonstrate how to use [skrf](www.scikit-rf.org) to do a two-tiered one-port calibration. We'll use data that was taken to characterize a waveguide-to-CPW probe. So, for this specific example the diagram above looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "SVG('oneport_tiered_calibration/images/probe.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some Data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data available is the folders `'tier1/'` and `'tier2/'`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(if you dont have the git repo for these examples, the data for this notebook can be found [here](https://github.com/scikit-rf/examples/tree/master/oneport_tiered_calibration))\n", "\n", "In each folder you will find the two sub-folders, called `'ideals/' ` and `'measured/'`. These contain touchstone files of the calibration standards ideal and measured responses, respectively. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/tier1/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first tier is at waveguide interface, and consisted of the following set of standards\n", "\n", "* short \n", "* delay short\n", "* load\n", "* radiating open (literally an open waveguide)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/tier1/measured/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating Calibrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tier 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First defining the calibration for Tier 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skrf.calibration import OnePort\n", "import skrf as rf \n", "%matplotlib inline\n", "from pylab import * \n", "rf.stylely()\n", "\n", "\n", "tier1_ideals = rf.read_all_networks('oneport_tiered_calibration/tier1/ideals/')\n", "tier1_measured = rf.read_all_networks('oneport_tiered_calibration/tier1/measured/')\n", " \n", "\n", "tier1 = OnePort(measured = tier1_measured,\n", " ideals = tier1_ideals,\n", " name = 'tier1',\n", " sloppy_input=True)\n", "tier1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we saved corresponding ideal and measured standards with identical names, the Calibration will automatically align our standards upon initialization. (More info on creating Calibration objects this can be found in [the docs](http://scikit-rf.readthedocs.org/en/latest/tutorials/calibration.html).)\n", "\n", "Similarly for the second tier 2," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tier 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier2_ideals = rf.read_all_networks('oneport_tiered_calibration/tier2/ideals/')\n", "tier2_measured = rf.read_all_networks('oneport_tiered_calibration/tier2/measured/')\n", " \n", "\n", "tier2 = OnePort(measured = tier2_measured,\n", " ideals = tier2_ideals,\n", " name = 'tier2',\n", " sloppy_input=True)\n", "tier2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each one-port Calibration contains a two-port error network, that is determined from the calculated error coefficients. The error network for tier1 models the VNA, while the error network for tier2 represents the VNA and the DUT. These can be visualized through the parameter `'error_ntwk'`.\n", "\n", "\n", "For tier 1, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier1.error_ntwk.plot_s_db()\n", "title('Tier 1 Error Network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly for tier 2, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier2.error_ntwk.plot_s_db()\n", "title('Tier 2 Error Network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## De-embedding the DUT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As previously stated, the error network for tier1 models the VNA, and the error network for tier2 represents the VNA+DUT. So to deterine the DUT's response, we cascade the inverse S-parameters of the VNA with the VNA+DUT. \n", "\n", "$$ DUT = VNA^{-1}\\cdot (VNA \\cdot DUT)$$\n", "\n", "In skrf, this is done as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dut = tier1.error_ntwk.inv ** tier2.error_ntwk\n", "dut.name = 'probe'\n", "dut.plot_s_db()\n", "title('Probe S-parameters')\n", "ylim(-60,10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to save this to disk, for future use, \n", "\n", " dut.write_touchstone()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls probe*" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
fabriziocosta/GraphLearn_examples	notebooks/Evaluation/EvaluateProbabilityOfSamples.ipynb	1	16876	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from eden.util import configure_logging\n", "import logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from itertools import tee, chain, islice\n", "import numpy as np\n", "import random\n", "from time import time\n", "import datetime\n", "from graphlearn.graphlearn import GraphLearnSampler\n", "from eden.util import fit,predict\n", "from eden.graph import Vectorizer\n", "# get data\n", "from eden.converter.graph.gspan import gspan_to_eden\n", "from itertools import islice\n", "def get_graphs(dataset_fname, size=100):\n", " return islice(gspan_to_eden(dataset_fname),size)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fit_sample(graphs, random_state=42):\n", " graphs, graphs_ = tee(graphs)\n", " sampler=GraphLearnSampler(radius_list=[0,1],thickness_list=[2,3],\n", " min_cip_count=2, min_interface_count=2,\n", " vectorizer=Vectorizer(5), random_state=random_state)\n", " \n", " sampler.fit(graphs, nu=0.25, n_jobs=-1)\n", "\n", " logger.info('graph grammar stats:')\n", " dataset_size, interface_counts, core_counts, cip_counts = sampler.grammar().size()\n", " logger.info('#instances:%d #interfaces: %d #cores: %d #core-interface-pairs: %d' % (dataset_size, interface_counts, core_counts, cip_counts))\n", "\n", " graphs = sampler.sample(graphs_,\n", " n_steps=5, n_samples=4,\n", " target_orig_cip=False,\n", " probabilistic_core_choice=False,\n", " score_core_choice= False,\n", " max_core_size_diff=3,\n", " burnin=1,\n", " omit_seed=True,\n", " max_cycle_size=6,\n", " improving_threshold=0.25,\n", " accept_static_penalty=0,\n", " n_jobs=-1,\n", " select_cip_max_tries=200,\n", " keep_duplicates=True,\n", " generator_mode=True)\n", " return graphs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fit_and_evaluate(original, sampled, local_estimator):\n", " outputs = []\n", " for desc,train in [('original',original),\n", " ('sample',sampled)]:\n", " train,train_ = tee(train)\n", " size=sum(1 for x in train_)\n", " logger.info( \"-\"80)\n", " logger.info( 'working on %s'%(desc))\n", " logger.info( 'training set sizes: #: %d'%(size))\n", " if size == 0:\n", " logger.info( 'WARNING: empty dataset')\n", " outputs.append(0)\n", " else:\n", " start=time()\n", " predictions = predict(train, \n", " estimator=local_estimator, \n", " vectorizer=Vectorizer(4), \n", " mode='predict_proba',\n", " n_jobs=-1)\n", " avg_score=np.mean(predictions[:,1])\n", " logger.info( 'avg score: %.5f' % avg_score)\n", " outputs.append(avg_score)\n", " logger.info( 'elapsed: %.1f sec'%(time()-start))\n", " return outputs" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evaluate(data_fname, size=None, percentages=None, n_repetitions=None, train_test_split=None):\n", " # initializing \n", " graphs = get_graphs(data_fname, size=size)\n", "\n", " # train/test split\n", " from eden.util import random_bipartition_iter\n", " train_global,test_global = random_bipartition_iter(graphs,train_test_split)\n", "\n", " original_repetitions = []\n", " sample_repetitions = []\n", "\n", " for percentage in percentages:\n", " originals = []\n", " originals_samples = []\n", " samples = []\n", " for repetition in range(n_repetitions):\n", " random_state = int(313379percentage+repetition) \n", " random.seed(random_state)\n", " \n", "\n", " train_global,train_global_ = tee(train_global)\n", " test_global,test_global_ = tee(test_global)\n", "\n", " from sklearn.linear_model import SGDClassifier\n", " estimator = SGDClassifier(average=True, class_weight='auto', shuffle=True, loss='log', n_jobs=-1)\n", " local_estimator = fit(test_global_, \n", " iterable_neg=None,\n", " vectorizer=Vectorizer(4),\n", " estimator=estimator, n_jobs=-1, n_iter_search=1)\n", " \n", " # use shuffled list to create test and sample set\n", " train,train_reminder = random_bipartition_iter(train_global_,percentage)\n", " train,train_ = tee(train)\n", " sampled = fit_sample(train_, random_state=random_state)\n", "\n", " #evaluate the predictive performance on held out test set\n", " start=time()\n", " logger.info( \"=\"80)\n", " logger.info( 'repetition: %d/%d'%(repetition+1, n_repetitions))\n", " logger.info( \"training percentage:\"+str(percentage))\n", " perf_orig, perf_samp = fit_and_evaluate(train, sampled, local_estimator)\n", " logger.info( 'Time elapsed: %.1f sec'%((time()-start)))\n", " originals.append(perf_orig)\n", " samples.append(perf_samp)\n", "\n", " original_repetitions.append(originals)\n", " sample_repetitions.append(samples)\n", " \n", " return original_repetitions, sample_repetitions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot(dataset, percentages, original_repetitions, sample_repetitions):\n", " gc={'color':'g'}\n", " rc={'color':'r'}\n", " bc={'color':'b'}\n", " ws = 0.02\n", " o = np.mean(original_repetitions, axis=1)\n", " s = np.mean(sample_repetitions, axis=1)\n", " plt.figure(figsize=(18,8))\n", " plt.grid()\n", "\n", " plt.boxplot(original_repetitions, positions=percentages, widths=ws, capprops=rc, medianprops=rc, boxprops=rc, whiskerprops=rc, flierprops=rc)\n", " plt.plot(percentages,o, color='r', marker='o', markeredgewidth=1, markersize=7, markeredgecolor='r', markerfacecolor='w', label='original')\n", "\n", " plt.boxplot(sample_repetitions, positions=percentages, widths=ws, capprops=bc, medianprops=bc, boxprops=bc, whiskerprops=bc, flierprops=bc)\n", " plt.plot(percentages,s, color='b', marker='o', markeredgewidth=1, markersize=7, markeredgecolor='b', markerfacecolor='w', label='sample')\n", "\n", " plt.xlim(percentages[0]-.05,percentages[-1]+.05)\n", " plt.title(dataset+'\\n',fontsize=17)\n", " plt.legend(loc='upper right',fontsize=16)\n", " plt.ylabel('Likelihood',fontsize=16)\n", " plt.xlabel('Dataset size (fraction)',fontsize=16)\n", " plt.savefig('%s_plot_probability_of_samples.pdf' % dataset)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def save_results(result_fname,percentages, original_repetitions,sample_repetitions):\n", " with open(result_fname,'w') as f:\n", " f.write('dataset sizes list:\\n')\n", " for perc in percentages:\n", " f.write('%s '% perc)\n", " f.write('\\n')\n", " f.write('AUC scores:\\n')\n", " for repetitions in original_repetitions,sample_repetitions:\n", " f.write('%s\\n' % len(repetitions))\n", " for repetition in repetitions:\n", " for auc in repetition:\n", " f.write('%s ' % auc)\n", " f.write('\\n')\n", " \n", "def load_results(result_fname):\n", " with open(result_fname) as f:\n", " comment = next(f)\n", " line = next(f)\n", " percentages = [float(x) for x in line.split()]\n", " comment = next(f)\n", "\n", " original_repetitions = []\n", " size = int(next(f))\n", " for i in range(size):\n", " line = next(f)\n", " repetition = [float(x) for x in line.split()]\n", " original_repetitions.append(repetition)\n", "\n", " sample_repetitions = []\n", " size = int(next(f))\n", " for i in range(size):\n", " line = next(f)\n", " repetition = [float(x) for x in line.split()]\n", " sample_repetitions.append(repetition)\n", " \n", " return percentages, original_repetitions,sample_repetitions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#setup\n", "dataset_names = !cat NCI60/names\n", "random.shuffle(dataset_names)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "******************************************************************************\n", "Working with dataset: HCT_15_t\n", "Working with dataset: HCT_15_t\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 1/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99622\n", "elapsed: 0.7 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 112\n", "avg score: 0.99699\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.4 sec\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 2/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99586\n", "elapsed: 0.6 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 110\n", "avg score: 0.99692\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.0 sec\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 3/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99576\n", "elapsed: 0.6 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 112\n", "avg score: 0.99688\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.2 sec\n", "graph grammar stats:\n", "#instances:112 #interfaces: 157 #cores: 43 #core-interface-pairs: 400\n", "================================================================================\n", "repetition: 1/3\n", "training percentage:0.4\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 112\n", "avg score: 0.98841\n", "elapsed: 1.1 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 212\n", "avg score: 0.99597\n", "elapsed: 2.0 sec\n", "Time elapsed: 14.7 sec\n", "graph grammar stats:\n", "#instances:112 #interfaces: 157 #cores: 43 #core-interface-pairs: 400\n", "================================================================================\n", "repetition: 2/3\n", "training percentage:0.4\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 112\n", "avg score: 0.98852\n", "elapsed: 1.0 sec\n" ] } ], "source": [ "%%time\n", "for dataset in dataset_names:\n", " #logging\n", " logger = logging.getLogger()\n", " if True:\n", " logger_fname = '%s_probability_of_samples.log'%dataset\n", " else:\n", " logger_fname = None\n", " configure_logging(logger,verbosity=1, filename=logger_fname)\n", " \n", " #main \n", " start=time()\n", " print(''*80)\n", " print( 'Working with dataset: %s' % dataset )\n", " logger.info( 'Working with dataset: %s' % dataset )\n", " dataset_fname = 'NCI60/' + dataset + '_orig_pos.gspan'\n", " #pos_dataset_fname = 'bursi.pos.gspan'\n", " \n", " percentages=[.2,.4,.6,.8,.95]\n", "\n", " original_repetitions,\\\n", " sample_repetitions = evaluate(dataset_fname,\n", " size=400,\n", " percentages=percentages,\n", " n_repetitions=3,\n", " train_test_split=0.7)\n", " plot(dataset, percentages, original_repetitions, sample_repetitions)\n", " \n", " #save and display results\n", " result_fname='%s_probability_of_samples.data'%dataset\n", " save_results(result_fname,percentages, original_repetitions,sample_repetitions) \n", " percentages_l, original_repetitions_l,sample_repetitions_l = load_results(result_fname)\n", " plot(dataset, percentages_l, original_repetitions_l, sample_repetitions_l)\n", " \n", " print('Time elapsed: %s'%(datetime.timedelta(seconds=(time() - start))))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#display\n", "for dataset in dataset_names:\n", " result_fname='%s_predictive_performance_of_samples.data'%dataset\n", " percentages_l, original_repetitions_l,sample_repetitions_l = load_results(result_fname)\n", " plot(dataset, percentages_l, original_repetitions_l, sample_repetitions_l)\n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
zzsza/TIL	python/pyecharts.ipynb	1	157838	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## pyecharts\n", "- [HTML](http://htmlpreview.github.io/?https://github.com/zzsza/TIL/blob/master/python/pyecharts.html) 에서 확인하면 이쁜 그래프도 보입니다!!!\n", "- Baidu에서 데이터 시각화를 위해 만든 오픈소스인 Echarts의 파이썬 버전\n", "- 다양한 그래프 제공\n", "- [공식 문서](http://pyecharts.org/#/en-us/)\n", "- Dynamic\n", "- 단, 옵션의 단추가 중국어\n", "- 그래프를 그릴 때, echarts와 echartql을 로컬에서 찾으려고 함\n", " - 따라서 nbconvert를 사용해 HTML으로 저장한 후, 쉘에서 수정\n", " \n", " ```\n", " sed -i \"\" \"s\|/nbextensions/echarts/echarts-gl.min\|https://cdn.jsdelivr.net/npm/[email protected]/dist/echarts-gl.min\|g; s\|/nbextensions/echarts/echarts.min\|https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min\|g\" 파일이름.ipynb\n", " ```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pyecharts\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "attr = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", "v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]\n", "v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]\n", "bar = pyecharts.Bar(\"Bar chart\", \"precipitation and evaporation one year\")\n", "bar.add(\"precipitation\", attr, v1, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.add(\"evaporation\", attr, v2, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.render()\n", "bar.height = 500\n", "bar.width = 800" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"4c6948eec4b548ae8750f0d9ecd60f0d\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_4c6948eec4b548ae8750f0d9ecd60f0d = echarts.init(document.getElementById('4c6948eec4b548ae8750f0d9ecd60f0d'), 'light', {renderer: 'canvas'});\n", "\n", "var option_4c6948eec4b548ae8750f0d9ecd60f0d = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar chart\",\n", " \"subtext\": \"precipitation and evaporation one year\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7954956,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"precipitation\",\n", " \"data\": [\n", " 2.0,\n", " 4.9,\n", " 7.0,\n", " 23.2,\n", " 25.6,\n", " 76.7,\n", " 135.6,\n", " 162.2,\n", " 32.6,\n", " 20.0,\n", " 6.4,\n", " 3.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 7954956\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"evaporation\",\n", " \"data\": [\n", " 2.6,\n", " 5.9,\n", " 9.0,\n", " 26.4,\n", " 28.7,\n", " 70.7,\n", " 175.6,\n", " 182.2,\n", " 48.7,\n", " 18.8,\n", " 6.0,\n", " 2.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 7954956\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"precipitation\",\n", " \"evaporation\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"Jan\",\n", " \"Feb\",\n", " \"Mar\",\n", " \"Apr\",\n", " \"May\",\n", " \"Jun\",\n", " \"Jul\",\n", " \"Aug\",\n", " \"Sep\",\n", " \"Oct\",\n", " \"Nov\",\n", " \"Dec\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_4c6948eec4b548ae8750f0d9ecd60f0d.setOption(option_4c6948eec4b548ae8750f0d9ecd60f0d);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c4c2e8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"f01f0ae4fa2746a99fe3ccb43af4a333\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_f01f0ae4fa2746a99fe3ccb43af4a333 = echarts.init(document.getElementById('f01f0ae4fa2746a99fe3ccb43af4a333'), 'dark', {renderer: 'canvas'});\n", "\n", "var option_f01f0ae4fa2746a99fe3ccb43af4a333 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar chart\",\n", " \"subtext\": \"precipitation and evaporation one year\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3700519,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"precipitation\",\n", " \"data\": [\n", " 2.0,\n", " 4.9,\n", " 7.0,\n", " 23.2,\n", " 25.6,\n", " 76.7,\n", " 135.6,\n", " 162.2,\n", " 32.6,\n", " 20.0,\n", " 6.4,\n", " 3.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 3700519\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"evaporation\",\n", " \"data\": [\n", " 2.6,\n", " 5.9,\n", " 9.0,\n", " 26.4,\n", " 28.7,\n", " 70.7,\n", " 175.6,\n", " 182.2,\n", " 48.7,\n", " 18.8,\n", " 6.0,\n", " 2.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 3700519\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"precipitation\",\n", " \"evaporation\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"Jan\",\n", " \"Feb\",\n", " \"Mar\",\n", " \"Apr\",\n", " \"May\",\n", " \"Jun\",\n", " \"Jul\",\n", " \"Aug\",\n", " \"Sep\",\n", " \"Oct\",\n", " \"Nov\",\n", " \"Dec\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": []\n", "};\n", "myChart_f01f0ae4fa2746a99fe3ccb43af4a333.setOption(option_f01f0ae4fa2746a99fe3ccb43af4a333);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c99630>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "attr = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", "v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]\n", "v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]\n", "bar = pyecharts.Bar(\"Bar chart\", \"precipitation and evaporation one year\")\n", "bar.use_theme(\"dark\")\n", "bar.add(\"precipitation\", attr, v1, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.add(\"evaporation\", attr, v2, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.height = 500\n", "bar.width = 800\n", "bar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In pandas" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"eee57829df8d461f9d1cc8ebc388a707\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_eee57829df8d461f9d1cc8ebc388a707 = echarts.init(document.getElementById('eee57829df8d461f9d1cc8ebc388a707'), 'light', {renderer: 'canvas'});\n", "\n", "var option_eee57829df8d461f9d1cc8ebc388a707 = {\n", " \"title\": [\n", " {\n", " \"text\": \"bar chart2\",\n", " \"subtext\": \"Profit and loss situation\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7257727,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"profit\",\n", " \"data\": [\n", " 0.1263776233106191,\n", " 0.8478258644254499,\n", " -0.7564831723889021,\n", " 0.5878566074181374,\n", " 0.841223198034568,\n", " 0.9710967562559695\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7257727\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"loss\",\n", " \"data\": [\n", " 0.8531044872139661,\n", " 0.20747839963171455,\n", " 0.7117874354985663,\n", " 0.5497325372182054,\n", " 0.07878415027622787,\n", " 0.7974613893080286\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7257727\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"profit\",\n", " \"loss\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"2018-08-31\",\n", " \"2018-09-30\",\n", " \"2018-10-31\",\n", " \"2018-11-30\",\n", " \"2018-12-31\",\n", " \"2019-01-31\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_eee57829df8d461f9d1cc8ebc388a707.setOption(option_eee57829df8d461f9d1cc8ebc388a707);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c99f28>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "title = \"bar chart2\"\n", "index = pd.date_range(\"8/24/2018\", periods=6, freq=\"M\")\n", "df1 = pd.DataFrame(np.random.randn(6), index=index)\n", "df2 = pd.DataFrame(np.random.rand(6), index=index)\n", "\n", "dfvalue1 = [i[0] for i in df1.values]\n", "dfvalue2 = [i[0] for i in df2.values]\n", "_index = [i for i in df1.index.format()]\n", "\n", "bar = pyecharts.Bar(title, \"Profit and loss situation\")\n", "bar.add(\"profit\", _index, dfvalue1)\n", "bar.add(\"loss\", _index, dfvalue2)\n", "bar.height = 500\n", "bar.width = 800\n", "bar" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"461b39c851d14ee99387f227aafdbd9b\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_461b39c851d14ee99387f227aafdbd9b = echarts.init(document.getElementById('461b39c851d14ee99387f227aafdbd9b'), 'light', {renderer: 'canvas'});\n", "\n", "var option_461b39c851d14ee99387f227aafdbd9b = {\n", " \"title\": [\n", " {\n", " \"text\": \"Line Bar\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3925989,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"bar\",\n", " \"data\": [\n", " 10,\n", " 20,\n", " 30,\n", " 40,\n", " 50,\n", " 60\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 3925989\n", " },\n", " {\n", " \"type\": \"line\",\n", " \"name\": \"line\",\n", " \"symbol\": \"emptyCircle\",\n", " \"symbolSize\": 4,\n", " \"smooth\": false,\n", " \"step\": false,\n", " \"showSymbol\": true,\n", " \"data\": [\n", " [\n", " \"A\",\n", " 38\n", " ],\n", " [\n", " \"B\",\n", " 28\n", " ],\n", " [\n", " \"C\",\n", " 58\n", " ],\n", " [\n", " \"D\",\n", " 48\n", " ],\n", " [\n", " \"E\",\n", " 78\n", " ],\n", " [\n", " \"F\",\n", " 68\n", " ]\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"lineStyle\": {\n", " \"normal\": {\n", " \"width\": 1,\n", " \"opacity\": 1,\n", " \"curveness\": 0,\n", " \"type\": \"solid\"\n", " }\n", " },\n", " \"areaStyle\": {\n", " \"opacity\": 0\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 3925989,\n", " \"xAxisIndex\": 0,\n", " \"yAxisIndex\": 0\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"bar\",\n", " \"line\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_461b39c851d14ee99387f227aafdbd9b.setOption(option_461b39c851d14ee99387f227aafdbd9b);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.custom.overlap.Overlap at 0x110c99a20>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Bar, Line, Overlap\n", "\n", "attr = ['A','B','C','D','E','F']\n", "v1 = [10, 20, 30, 40, 50, 60]\n", "v2 = [38, 28, 58, 48, 78, 68]\n", "bar = Bar(\"Line Bar\")\n", "bar.add(\"bar\", attr, v1)\n", "line = Line()\n", "line.add(\"line\", attr, v2)\n", "\n", "overlap = Overlap()\n", "overlap.add(bar)\n", "overlap.add(line)\n", "\n", "overlap" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"1610b0c60e594bb3aba3d9ac043d6b19\" style=\"width:600px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_1610b0c60e594bb3aba3d9ac043d6b19 = echarts.init(document.getElementById('1610b0c60e594bb3aba3d9ac043d6b19'), 'light', {renderer: 'canvas'});\n", "\n", "var option_1610b0c60e594bb3aba3d9ac043d6b19 = {\n", " \"title\": [\n", " {\n", " \"text\": \"pie chart\",\n", " \"left\": \"center\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 4535433,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"pie\",\n", " \"name\": \"A\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 10\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 20\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 30\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 40\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 50\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 60\n", " }\n", " ],\n", " \"radius\": [\n", " \"30%\",\n", " \"75%\"\n", " ],\n", " \"center\": [\n", " \"25%\",\n", " \"50%\"\n", " ],\n", " \"roseType\": \"radius\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"outside\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " }\n", " },\n", " \"seriesId\": 4535433\n", " },\n", " {\n", " \"type\": \"pie\",\n", " \"name\": \"B\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 38\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 28\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 58\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 48\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 78\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 68\n", " }\n", " ],\n", " \"radius\": [\n", " \"30%\",\n", " \"75%\"\n", " ],\n", " \"center\": [\n", " \"75%\",\n", " \"50%\"\n", " ],\n", " \"roseType\": \"area\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"outside\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " }\n", " },\n", " \"seriesId\": 4535433\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": false,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#444693\",\n", " \"#905a3d\",\n", " \"#61a0a8\",\n", " \"#749f83\",\n", " \"#b2d235\",\n", " \"#1d953f\",\n", " \"#c23531\",\n", " \"#f47920\",\n", " \"#6d8346\",\n", " \"#f05b72\",\n", " \"#6950a1\",\n", " \"#ac6767\",\n", " \"#ca8622\",\n", " \"#ef5b9c\",\n", " \"#c4ccd3\",\n", " \"#d48265\",\n", " \"#f6f5ec\",\n", " \"#546570\",\n", " \"#2a5caa\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#726930\",\n", " \"#2f4554\",\n", " \"#918597\",\n", " \"#fab27b\"\n", " ]\n", "};\n", "myChart_1610b0c60e594bb3aba3d9ac043d6b19.setOption(option_1610b0c60e594bb3aba3d9ac043d6b19);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.pie.Pie at 0x10cf6c4a8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Pie\n", "\n", "attr = ['A','B','C','D','E','F']\n", "v1 = [10, 20, 30, 40, 50, 60]\n", "v2 = [38, 28, 58, 48, 78, 68]\n", "pie = Pie(\"pie chart\", title_pos=\"center\", width=600)\n", "pie.add(\"A\", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius')\n", "pie.add(\"B\", attr, v2, center=[75, 50], is_randome=True, radius=[30, 75], rosetype='area', is_legend_show=False,\n", " is_label_show=True)\n", "pie" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 가로 그래프" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"ac7aa1a3028c490db76818513967b325\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_ac7aa1a3028c490db76818513967b325 = echarts.init(document.getElementById('ac7aa1a3028c490db76818513967b325'), 'light', {renderer: 'canvas'});\n", "\n", "var option_ac7aa1a3028c490db76818513967b325 = {\n", " \"title\": [\n", " {\n", " \"text\": \"\\uac00\\ub85c \\uadf8\\ub798\\ud504\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7986890,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"A\",\n", " \"data\": [\n", " 10,\n", " 20,\n", " 30,\n", " 40,\n", " 50,\n", " 60\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7986890\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"B\",\n", " \"data\": [\n", " 38,\n", " 28,\n", " 58,\n", " 48,\n", " 78,\n", " 68\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7986890\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"A\",\n", " \"B\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ]\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_ac7aa1a3028c490db76818513967b325.setOption(option_ac7aa1a3028c490db76818513967b325);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c89cc0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar(\"가로 그래프\")\n", "bar.add(\"A\", attr, v1)\n", "bar.add(\"B\", attr, v2, is_convert=True)\n", "bar.width=800\n", "bar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 슬라이더" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"86daa120791743a4b00ca98406bf4054\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_86daa120791743a4b00ca98406bf4054 = echarts.init(document.getElementById('86daa120791743a4b00ca98406bf4054'), 'light', {renderer: 'canvas'});\n", "\n", "var option_86daa120791743a4b00ca98406bf4054 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar - datazoom - slider \",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 8740746,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"data\": [\n", " 20,\n", " 20,\n", " 22,\n", " 7,\n", " 16,\n", " 30,\n", " 25,\n", " 24,\n", " 2,\n", " 18,\n", " 15,\n", " 23,\n", " 26,\n", " 22,\n", " 27,\n", " 17,\n", " 11,\n", " 19,\n", " 20,\n", " 15,\n", " 14,\n", " 30,\n", " 27,\n", " 22,\n", " 16,\n", " 19,\n", " 15,\n", " 4,\n", " 10,\n", " 27\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 8740746\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"0th\",\n", " \"1th\",\n", " \"2th\",\n", " \"3th\",\n", " \"4th\",\n", " \"5th\",\n", " \"6th\",\n", " \"7th\",\n", " \"8th\",\n", " \"9th\",\n", " \"10th\",\n", " \"11th\",\n", " \"12th\",\n", " \"13th\",\n", " \"14th\",\n", " \"15th\",\n", " \"16th\",\n", " \"17th\",\n", " \"18th\",\n", " \"19th\",\n", " \"20th\",\n", " \"21th\",\n", " \"22th\",\n", " \"23th\",\n", " \"24th\",\n", " \"25th\",\n", " \"26th\",\n", " \"27th\",\n", " \"28th\",\n", " \"29th\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ],\n", " \"dataZoom\": [\n", " {\n", " \"show\": true,\n", " \"type\": \"slider\",\n", " \"start\": 50,\n", " \"end\": 100,\n", " \"orient\": \"horizontal\"\n", " }\n", " ]\n", "};\n", "myChart_86daa120791743a4b00ca98406bf4054.setOption(option_86daa120791743a4b00ca98406bf4054);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c89978>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "\n", "attr = [\"{}th\".format(i) for i in range(30)]\n", "v1 = [random.randint(1, 30) for _ in range(30)]\n", "bar = Bar(\"Bar - datazoom - slider \")\n", "bar.add(\"\", attr, v1, is_label_show=True, is_datazoom_show=True)\n", "# bar.render()\n", "bar" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"154e53f451da4d009a405f450aa79202\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_154e53f451da4d009a405f450aa79202 = echarts.init(document.getElementById('154e53f451da4d009a405f450aa79202'), 'light', {renderer: 'canvas'});\n", "\n", "var option_154e53f451da4d009a405f450aa79202 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar - datazoom - xaxis/yaxis\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"series_id\": 2716897,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"data\": [\n", " 15,\n", " 28,\n", " 27,\n", " 25,\n", " 6,\n", " 10,\n", " 15,\n", " 22,\n", " 8,\n", " 23,\n", " 21,\n", " 20,\n", " 14,\n", " 21,\n", " 30,\n", " 20,\n", " 14,\n", " 3,\n", " 4,\n", " 7,\n", " 23,\n", " 22,\n", " 8,\n", " 19,\n", " 2,\n", " 28,\n", " 23,\n", " 26,\n", " 28,\n", " 8\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 2716897\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"0th\",\n", " \"1th\",\n", " \"2th\",\n", " \"3th\",\n", " \"4th\",\n", " \"5th\",\n", " \"6th\",\n", " \"7th\",\n", " \"8th\",\n", " \"9th\",\n", 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\"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ],\n", " \"visualMap\": {\n", " \"type\": \"continuous\",\n", " \"min\": 0,\n", " \"max\": 20,\n", " \"text\": [\n", " \"high\",\n", " \"low\"\n", " ],\n", " \"textStyle\": {},\n", " \"inRange\": {\n", " \"color\": [\n", " \"#313695\",\n", " \"#4575b4\",\n", " \"#74add1\",\n", " \"#abd9e9\",\n", " \"#e0f3f8\",\n", " \"#ffffbf\",\n", " \"#fee090\",\n", " \"#fdae61\",\n", " \"#f46d43\",\n", " \"#d73027\",\n", " \"#a50026\"\n", " ]\n", " },\n", " \"calculable\": true,\n", " \"splitNumber\": 5,\n", " \"orient\": \"vertical\",\n", " \"left\": \"left\",\n", " \"top\": \"bottom\",\n", " \"showLabel\": true\n", " }\n", "};\n", "myChart_e242e555d23f4588b6b5f71d4a11c3e1.setOption(option_e242e555d23f4588b6b5f71d4a11c3e1);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar3D.Bar3D at 0x10cf8ddd8>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Bar3D\n", "\n", "bar3d = Bar3D(\"3D Graph\", width=1200, height=600)\n", "x_axis = [\n", " \"12a\", \"1a\", \"2a\", \"3a\", \"4a\", \"5a\", \"6a\", \"7a\", \"8a\", \"9a\", \"10a\", \"11a\",\n", " \"12p\", \"1p\", \"2p\", \"3p\", \"4p\", \"5p\", \"6p\", \"7p\", \"8p\", \"9p\", \"10p\", \"11p\"\n", " ]\n", "y_axis = [\n", " \"Saturday\", \"Friday\", \"Thursday\", \"Wednesday\", \"Tuesday\", \"Monday\", \"Sunday\"\n", " ]\n", "data = [\n", " [0, 0, 5], [0, 1, 1], [0, 2, 0], [0, 3, 0], [0, 4, 0], [0, 5, 0],\n", " [0, 6, 0], [0, 7, 0], [0, 8, 0], [0, 9, 0], [0, 10, 0], [0, 11, 2],\n", " [0, 12, 4], [0, 13, 1], [0, 14, 1], [0, 15, 3], [0, 16, 4], [0, 17, 6],\n", " [0, 18, 4], [0, 19, 4], [0, 20, 3], [0, 21, 3], [0, 22, 2], [0, 23, 5],\n", " [1, 0, 7], [1, 1, 0], [1, 2, 0], [1, 3, 0], [1, 4, 0], [1, 5, 0],\n", " [1, 6, 0], [1, 7, 0], [1, 8, 0], [1, 9, 0], [1, 10, 5], [1, 11, 2],\n", " [1, 12, 2], [1, 13, 6], [1, 14, 9], [1, 15, 11], [1, 16, 6], [1, 17, 7],\n", " [1, 18, 8], [1, 19, 12], [1, 20, 5], [1, 21, 5], [1, 22, 7], [1, 23, 2],\n", " [2, 0, 1], [2, 1, 1], [2, 2, 0], [2, 3, 0], [2, 4, 0], [2, 5, 0],\n", " [2, 6, 0], [2, 7, 0], [2, 8, 0], [2, 9, 0], [2, 10, 3], [2, 11, 2],\n", " [2, 12, 1], [2, 13, 9], [2, 14, 8], [2, 15, 10], [2, 16, 6], [2, 17, 5],\n", " [2, 18, 5], [2, 19, 5], [2, 20, 7], [2, 21, 4], [2, 22, 2], [2, 23, 4],\n", " [3, 0, 7], [3, 1, 3], [3, 2, 0], [3, 3, 0], [3, 4, 0], [3, 5, 0],\n", " [3, 6, 0], [3, 7, 0], [3, 8, 1], [3, 9, 0], [3, 10, 5], [3, 11, 4],\n", " [3, 12, 7], [3, 13, 14], [3, 14, 13], [3, 15, 12], [3, 16, 9], [3, 17, 5],\n", " [3, 18, 5], [3, 19, 10], [3, 20, 6], [3, 21, 4], [3, 22, 4], [3, 23, 1],\n", " [4, 0, 1], [4, 1, 3], [4, 2, 0], [4, 3, 0], [4, 4, 0], [4, 5, 1],\n", " [4, 6, 0], [4, 7, 0], [4, 8, 0], [4, 9, 2], [4, 10, 4], [4, 11, 4],\n", " [4, 12, 2], [4, 13, 4], [4, 14, 4], [4, 15, 14], [4, 16, 12], [4, 17, 1],\n", " [4, 18, 8], [4, 19, 5], [4, 20, 3], [4, 21, 7], [4, 22, 3], [4, 23, 0],\n", " [5, 0, 2], [5, 1, 1], [5, 2, 0], [5, 3, 3], [5, 4, 0], [5, 5, 0],\n", " [5, 6, 0], [5, 7, 0], [5, 8, 2], [5, 9, 0], [5, 10, 4], [5, 11, 1],\n", " [5, 12, 5], [5, 13, 10], [5, 14, 5], [5, 15, 7], [5, 16, 11], [5, 17, 6],\n", " [5, 18, 0], [5, 19, 5], [5, 20, 3], [5, 21, 4], [5, 22, 2], [5, 23, 0],\n", " [6, 0, 1], [6, 1, 0], [6, 2, 0], [6, 3, 0], [6, 4, 0], [6, 5, 0],\n", " [6, 6, 0], [6, 7, 0], [6, 8, 0], [6, 9, 0], [6, 10, 1], [6, 11, 0],\n", " [6, 12, 2], [6, 13, 1], [6, 14, 3], [6, 15, 4], [6, 16, 0], [6, 17, 0],\n", " [6, 18, 0], [6, 19, 0], [6, 20, 1], [6, 21, 2], [6, 22, 2], [6, 23, 6]\n", " ]\n", "range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',\n", " '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']\n", "bar3d.add(\n", " \"\",\n", " x_axis,\n", " y_axis,\n", " [[d[1], d[0], d[2]] for d in data],\n", " is_visualmap=True,\n", " visual_range=[0, 20],\n", " visual_range_color=range_color,\n", " grid3d_width=200,\n", " grid3d_depth=80,\n", ")\n", "bar3d.width=700\n", "bar3d.height=500\n", "\n", "bar3d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boxplot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"922d391f322e43a1b0addf5a632c1b92\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_922d391f322e43a1b0addf5a632c1b92 = echarts.init(document.getElementById('922d391f322e43a1b0addf5a632c1b92'), 'light', {renderer: 'canvas'});\n", "\n", "var option_922d391f322e43a1b0addf5a632c1b92 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Box plot\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 2083032,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"boxplot\",\n", " \"name\": \"boxplot\",\n", " \"data\": [\n", " [\n", " 650,\n", " 850.0,\n", " 940.0,\n", " 980.0,\n", " 1070\n", " ],\n", " [\n", " 760,\n", " 800.0,\n", " 845.0,\n", " 895.0,\n", " 960\n", " ],\n", " [\n", " 620,\n", " 840.0,\n", " 855.0,\n", " 880.0,\n", " 970\n", " ],\n", " [\n", " 720,\n", " 762.5,\n", " 815.0,\n", " 875.0,\n", " 920\n", " ],\n", " [\n", " 740,\n", " 802.5,\n", " 810.0,\n", " 870.0,\n", " 950\n", " ]\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 2083032\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"boxplot\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"expr1\",\n", " \"expr2\",\n", " \"expr3\",\n", " \"expr4\",\n", " \"expr5\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_922d391f322e43a1b0addf5a632c1b92.setOption(option_922d391f322e43a1b0addf5a632c1b92);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.boxplot.Boxplot at 0x111688588>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Boxplot\n", "\n", "boxplot = Boxplot(\"Box plot\")\n", "x_axis = ['expr1', 'expr2', 'expr3', 'expr4', 'expr5']\n", "y_axis = [\n", " [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880,\n", " 1000, 980, 930, 650, 760, 810, 1000, 1000, 960, 960],\n", " [960, 940, 960, 940, 880, 800, 850, 880, 900, 840,\n", " 830, 790, 810, 880, 880, 830, 800, 790, 760, 800],\n", " [880, 880, 880, 860, 720, 720, 620, 860, 970, 950,\n", " 880, 910, 850, 870, 840, 840, 850, 840, 840, 840],\n", " [890, 810, 810, 820, 800, 770, 760, 740, 750, 760,\n", " 910, 920, 890, 860, 880, 720, 840, 850, 850, 780],\n", " [890, 840, 780, 810, 760, 810, 790, 810, 820, 850,\n", " 870, 870, 810, 740, 810, 940, 950, 800, 810, 870]\n", "]\n", "_yaxis = boxplot.prepare_data(y_axis) \n", "boxplot.add(\"boxplot\", x_axis, _yaxis)\n", "boxplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 퍼널" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"2547fda17d11453b90bee930d3fe169c\" style=\"width:700px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_2547fda17d11453b90bee930d3fe169c = echarts.init(document.getElementById('2547fda17d11453b90bee930d3fe169c'), 'light', {renderer: 'canvas'});\n", "\n", "var option_2547fda17d11453b90bee930d3fe169c = {\n", " \"title\": [\n", " {\n", " \"text\": \"\\ud37c\\ub110 \\uadf8\\ub798\\ud504\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3591842,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"funnel\",\n", " \"name\": \"\\ud37c\\ub110\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 20\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 40\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 60\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 80\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 100\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 120\n", " }\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"inside\",\n", " \"textStyle\": {\n", " \"color\": \"#fff\",\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"E\",\n", " \"B\",\n", " \"F\",\n", " \"C\",\n", " \"A\",\n", " \"D\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_2547fda17d11453b90bee930d3fe169c.setOption(option_2547fda17d11453b90bee930d3fe169c);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.funnel.Funnel at 0x110c89b38>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Funnel\n", "\n", "attr = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]\n", "value = [20, 40, 60, 80, 100, 120]\n", "funnel = Funnel(\"퍼널 그래프\")\n", "funnel.add(\n", " \"퍼널\",\n", " attr,\n", " value,\n", " is_label_show=True,\n", " label_pos=\"inside\",\n", " label_text_color=\"#fff\",\n", ")\n", "funnel.width=700\n", "funnel.height=500\n", "funnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gauge" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"50e7288ced72497f84ed17005f045e18\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_50e7288ced72497f84ed17005f045e18 = echarts.init(document.getElementById('50e7288ced72497f84ed17005f045e18'), 'light', {renderer: 'canvas'});\n", "\n", "var option_50e7288ced72497f84ed17005f045e18 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Gauge Graph\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 6762080,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove\|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"formatter\": \"{a} <br/>{b} : 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\"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_50e7288ced72497f84ed17005f045e18.setOption(option_50e7288ced72497f84ed17005f045e18);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.gauge.Gauge at 0x10cf8d9e8>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Gauge\n", "\n", "gauge = Gauge(\"Gauge Graph\")\n", "gauge.add(\"이용률\", \"가운데\", 66.66)\n", "gauge" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
colour-science/colour-ipython	notebooks/adaptation/cmccat2000.ipynb	1	796	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# !!! D . R . A . F . T !!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CMCCAT2000 Chromatic Adaptation Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliography" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
nick-youngblut/SIPSim	ipynb/bac_genome/fullCyc/trimDataset/rep4_DBL-comm_bw_ALL_FINAL.ipynb	1	1461974	null	mit
hetaodie/hetaodie.github.io	assets/media/uda-ml/deep/azjc/卷积神经网络的例子/dog/dog_app_zh.ipynb	1	546585	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 卷积神经网络（Convolutional Neural Network, CNN）\n", "\n", "## 项目：实现一个狗品种识别算法App\n", "\n", "在这个notebook文件中，有些模板代码已经提供给你，但你还需要实现更多的功能来完成这个项目。除非有明确要求，你无须修改任何已给出的代码。以'(练习)'开始的标题表示接下来的代码部分中有你需要实现的功能。这些部分都配有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示。\n", "\n", "除了实现代码外，你还需要回答一些与项目及代码相关的问题。每个需要回答的问题都会以 '问题 X' 标记。请仔细阅读每个问题，并且在问题后的 '回答' 部分写出完整的答案。我们将根据你对问题的回答和撰写代码实现的功能来对你提交的项目进行评分。\n", "\n", ">提示：Code 和 Markdown 区域可通过 Shift + Enter 快捷键运行。此外，Markdown可以通过双击进入编辑模式。\n", "\n", "项目中显示为_选做_的部分可以帮助你的项目脱颖而出，而不是仅仅达到通过的最低要求。如果你决定追求更高的挑战，请在此 notebook 中完成_选做_部分的代码。\n", "\n", "---\n", "\n", "### 让我们开始吧\n", "在这个notebook中，你将迈出第一步，来开发可以作为移动端或 Web应用程序一部分的算法。在这个项目的最后，你的程序将能够把用户提供的任何一个图像作为输入。如果可以从图像中检测到一只狗，它会输出对狗品种的预测。如果图像中是一个人脸，它会预测一个与其最相似的狗的种类。下面这张图展示了完成项目后可能的输出结果。（……实际上我们希望每个学生的输出结果不相同！）\n", "\n", "![Sample Dog Output](images/sample_dog_output.png)\n", "\n", "在现实世界中，你需要拼凑一系列的模型来完成不同的任务；举个例子，用来预测狗种类的算法会与预测人类的算法不同。在做项目的过程中，你可能会遇到不少失败的预测，因为并不存在完美的算法和模型。你最终提交的不完美的解决方案也一定会给你带来一个有趣的学习经验！\n", "\n", "### 项目内容\n", "\n", "我们将这个notebook分为不同的步骤，你可以使用下面的链接来浏览此notebook。\n", "\n", "* [Step 0](#step0): 导入数据集\n", "* [Step 1](#step1): 检测人脸\n", "* [Step 2](#step2): 检测狗狗\n", "* [Step 3](#step3): 从头创建一个CNN来分类狗品种\n", "* [Step 4](#step4): 使用一个CNN来区分狗的品种(使用迁移学习)\n", "* [Step 5](#step5): 建立一个CNN来分类狗的品种（使用迁移学习）\n", "* [Step 6](#step6): 完成你的算法\n", "* [Step 7](#step7): 测试你的算法\n", "\n", "在该项目中包含了如下的问题：\n", "\n", "* [问题 1](#question1)\n", "* [问题 2](#question2)\n", "* [问题 3](#question3)\n", "* [问题 4](#question4)\n", "* [问题 5](#question5)\n", "* [问题 6](#question6)\n", "* [问题 7](#question7)\n", "* [问题 8](#question8)\n", "* [问题 9](#question9)\n", "* [问题 10](#question10)\n", "* [问题 11](#question11)\n", "\n", "\n", "---\n", "<a id='step0'></a>\n", "## 步骤 0: 导入数据集\n", "\n", "### 导入狗数据集\n", "在下方的代码单元（cell）中，我们导入了一个狗图像的数据集。我们使用 scikit-learn 库中的 `load_files` 函数来获取一些变量：\n", "- `train_files`, `valid_files`, `test_files` - 包含图像的文件路径的numpy数组\n", "- `train_targets`, `valid_targets`, `test_targets` - 包含独热编码分类标签的numpy数组\n", "- `dog_names` - 由字符串构成的与标签相对应的狗的种类" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 133 total dog categories.\n", "There are 8351 total dog images.\n", "\n", "There are 6680 training dog images.\n", "There are 835 validation dog images.\n", "There are 836 test dog images.\n" ] } ], "source": [ "from sklearn.datasets import load_files \n", "from keras.utils import np_utils\n", "import numpy as np\n", "from glob import glob\n", "\n", "# define function to load train, test, and validation datasets\n", "def load_dataset(path):\n", " data = load_files(path)\n", " dog_files = np.array(data['filenames'])\n", " dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n", " return dog_files, dog_targets\n", "\n", "# load train, test, and validation datasets\n", "train_files, train_targets = load_dataset('/data/dog_images/train')\n", "valid_files, valid_targets = load_dataset('/data/dog_images/valid')\n", "test_files, test_targets = load_dataset('/data/dog_images/test')\n", "\n", "# load list of dog names\n", "dog_names = [item[20:-1] for item in sorted(glob(\"/data/dog_images/train//\"))]\n", "\n", "# print statistics about the dataset\n", "print('There are %d total dog categories.' % len(dog_names))\n", "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n", "print('There are %d training dog images.' % len(train_files))\n", "print('There are %d validation dog images.' % len(valid_files))\n", "print('There are %d test dog images.'% len(test_files))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 导入人脸数据集\n", "\n", "在下方的代码单元中，我们导入人脸图像数据集，文件所在路径存储在名为 `human_files` 的 numpy 数组。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 13233 total human images.\n" ] } ], "source": [ "import random\n", "random.seed(8675309)\n", "\n", "# 加载打乱后的人脸数据集的文件名\n", "human_files = np.array(glob(\"/data/lfw//\"))\n", "random.shuffle(human_files)\n", "\n", "# 打印数据集的数据量\n", "print('There are %d total human images.' % len(human_files))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step1'></a>\n", "## 步骤1：检测人脸\n", " \n", "我们将使用 OpenCV 中的 [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) 来检测图像中的人脸。OpenCV 提供了很多预训练的人脸检测模型，它们以XML文件保存在 [github](https://github.com/opencv/opencv/tree/master/data/haarcascades)。我们已经下载了其中一个检测模型，并且把它存储在 `haarcascades` 的目录中。\n", "\n", "在如下代码单元中，我们将演示如何使用这个检测模型在样本图像中找到人脸。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of faces detected: 1\n" ] }, { "data": { 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa0c1014b38>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import cv2 \n", "import matplotlib.pyplot as plt \n", "%matplotlib inline \n", "\n", "# 提取预训练的人脸检测模型\n", "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n", "\n", "# 加载彩色（通道顺序为BGR）图像\n", "img = cv2.imread(human_files[3])\n", "\n", "# 将BGR图像进行灰度处理\n", "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", "# 在图像中找出脸\n", "faces = face_cascade.detectMultiScale(gray)\n", "\n", "# 打印图像中检测到的脸的个数\n", "print('Number of faces detected:', len(faces))\n", "\n", "# 获取每一个所检测到的脸的识别框\n", "for (x,y,w,h) in faces:\n", " # 在人脸图像中绘制出识别框\n", " cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n", " \n", "# 将BGR图像转变为RGB图像以打印\n", "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", "\n", "# 展示含有识别框的图像\n", "plt.imshow(cv_rgb)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在使用任何一个检测模型之前，将图像转换为灰度图是常用过程。`detectMultiScale` 函数使用储存在 `face_cascade` 中的的数据，对输入的灰度图像进行分类。\n", "\n", "在上方的代码中，`faces` 以 numpy 数组的形式，保存了识别到的面部信息。它其中每一行表示一个被检测到的脸，该数据包括如下四个信息：前两个元素 `x`、`y` 代表识别框左上角的 x 和 y 坐标（参照上图，注意 y 坐标的方向和我们默认的方向不同）；后两个元素代表识别框在 x 和 y 轴两个方向延伸的长度 `w` 和 `d`。 \n", "\n", "### 写一个人脸识别器\n", "\n", "我们可以将这个程序封装为一个函数。该函数的输入为人脸图像的路径，当图像中包含人脸时，该函数返回 `True`，反之返回 `False`。该函数定义如下所示。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# 如果img_path路径表示的图像检测到了脸，返回\"True\" \n", "def face_detector(img_path):\n", " img = cv2.imread(img_path)\n", " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", " faces = face_cascade.detectMultiScale(gray)\n", " return len(faces) > 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【练习】* 评估人脸检测模型" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n", "<a id='question1'></a>\n", "### __问题 1:__ \n", "\n", "在下方的代码块中，使用 `face_detector` 函数，计算：\n", "\n", "- `human_files` 的前100张图像中，能够检测到人脸的图像占比多少？\n", "- `dog_files` 的前100张图像中，能够检测到人脸的图像占比多少？\n", "\n", "理想情况下，人图像中检测到人脸的概率应当为100%，而狗图像中检测到人脸的概率应该为0%。你会发现我们的算法并非完美，但结果仍然是可以接受的。我们从每个数据集中提取前100个图像的文件路径，并将它们存储在`human_files_short`和`dog_files_short`中。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percentage of detecting human faces in human files is: 1.0\n", "The percentage of detecting human faces in dog files is: 0.11\n" ] } ], "source": [ "human_files_short = human_files[:100]\n", "dog_files_short = train_files[:100]\n", "## 请不要修改上方代码\n", "\n", "## TODO: 基于human_files_short和dog_files_short\n", "## 中的图像测试face_detector的表现\n", "human_files_short_detect = 0\n", "dog_files_short_detect = 0\n", "\n", "for i in range(100):\n", " if (face_detector(human_files_short[i])):\n", " human_files_short_detect += 1\n", " if (face_detector(dog_files_short[i])):\n", " dog_files_short_detect += 1\n", "\n", "print(\"The percentage of detecting human faces in human files is:\", human_files_short_detect/human_files_short.size)\n", "print(\"The percentage of detecting human faces in dog files is:\", dog_files_short_detect/dog_files_short.size)\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='question2'></a>\n", "\n", "### __问题 2:__ \n", "\n", "就算法而言，该算法成功与否的关键在于，用户能否提供含有清晰面部特征的人脸图像。\n", "那么你认为，这样的要求在实际使用中对用户合理吗？如果你觉得不合理，你能否想到一个方法，即使图像中并没有清晰的面部特征，也能够检测到人脸？\n", "\n", "__回答:__\n", "\n", "不太合理，因为图片的来源不同，不能保证所有的图片的脸部都是清晰的。如果脸部特征不太清晰，应对图片进行前期的预处理。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='Selection1'></a>\n", "### 选做：\n", "\n", "我们建议在你的算法中使用opencv的人脸检测模型去检测人类图像，不过你可以自由地探索其他的方法，尤其是尝试使用深度学习来解决它:)。请用下方的代码单元来设计和测试你的面部监测算法。如果你决定完成这个_选做_任务，你需要报告算法在每一个数据集上的表现。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "## (选做) TODO: 报告另一个面部检测算法在LFW数据集上的表现\n", "### 你可以随意使用所需的代码单元数" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step2'></a>\n", "\n", "## 步骤 2: 检测狗狗\n", "\n", "在这个部分中，我们使用预训练的 [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) 模型去检测图像中的狗。下方的第一行代码就是下载了 ResNet-50 模型的网络结构参数，以及基于 [ImageNet](http://www.image-net.org/) 数据集的预训练权重。\n", "\n", "ImageNet 这目前一个非常流行的数据集，常被用来测试图像分类等计算机视觉任务相关的算法。它包含超过一千万个 URL，每一个都链接到 [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中所对应的一个物体的图像。任给输入一个图像，该 ResNet-50 模型会返回一个对图像中物体的预测结果。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n", "102858752/102853048 [==============================] - 1s 0us/step\n" ] } ], "source": [ "from keras.applications.resnet50 import ResNet50\n", "\n", "# 定义ResNet50模型\n", "ResNet50_model = ResNet50(weights='imagenet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 数据预处理\n", "\n", "- 在使用 TensorFlow 作为后端的时候，在 Keras 中，CNN 的输入是一个4维数组（也被称作4维张量），它的各维度尺寸为 `(nb_samples, rows, columns, channels)`。其中 `nb_samples` 表示图像（或者样本）的总数，`rows`, `columns`, 和 `channels` 分别表示图像的行数、列数和通道数。\n", "\n", "\n", "- 下方的 `path_to_tensor` 函数实现如下将彩色图像的字符串型的文件路径作为输入，返回一个4维张量，作为 Keras CNN 输入。因为我们的输入图像是彩色图像，因此它们具有三个通道（ `channels` 为 `3`）。\n", " 1. 该函数首先读取一张图像，然后将其缩放为 224×224 的图像。\n", " 2. 随后，该图像被调整为具有4个维度的张量。\n", " 3. 对于任一输入图像，最后返回的张量的维度是：`(1, 224, 224, 3)`。\n", "\n", "\n", "- `paths_to_tensor` 函数将图像路径的字符串组成的 numpy 数组作为输入，并返回一个4维张量，各维度尺寸为 `(nb_samples, 224, 224, 3)`。在这里，`nb_samples`是提供的图像路径的数据中的样本数量或图像数量。你也可以将 `nb_samples` 理解为数据集中3维张量的个数（每个3维张量表示一个不同的图像。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from keras.preprocessing import image \n", "from tqdm import tqdm\n", "\n", "def path_to_tensor(img_path):\n", " # 用PIL加载RGB图像为PIL.Image.Image类型\n", " img = image.load_img(img_path, target_size=(224, 224))\n", " # 将PIL.Image.Image类型转化为格式为(224, 224, 3)的3维张量\n", " x = image.img_to_array(img)\n", " # 将3维张量转化为格式为(1, 224, 224, 3)的4维张量并返回\n", " return np.expand_dims(x, axis=0)\n", "\n", "def paths_to_tensor(img_paths):\n", " list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n", " return np.vstack(list_of_tensors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 基于 ResNet-50 架构进行预测\n", "\n", "对于通过上述步骤得到的四维张量，在把它们输入到 ResNet-50 网络、或 Keras 中其他类似的预训练模型之前，还需要进行一些额外的处理：\n", "1. 首先，这些图像的通道顺序为 RGB，我们需要重排他们的通道顺序为 BGR。\n", "2. 其次，预训练模型的输入都进行了额外的归一化过程。因此我们在这里也要对这些张量进行归一化，即对所有图像所有像素都减去像素均值 `[103.939, 116.779, 123.68]`（以 RGB 模式表示，根据所有的 ImageNet 图像算出）。\n", "\n", "导入的 `preprocess_input` 函数实现了这些功能。如果你对此很感兴趣，可以在 [这里](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py) 查看 `preprocess_input`的代码。\n", "\n", "\n", "在实现了图像处理的部分之后，我们就可以使用模型来进行预测。这一步通过 `predict` 方法来实现，它返回一个向量，向量的第 i 个元素表示该图像属于第 i 个 ImageNet 类别的概率。这通过如下的 `ResNet50_predict_labels` 函数实现。\n", "\n", "通过对预测出的向量取用 argmax 函数（找到有最大概率值的下标序号），我们可以得到一个整数，即模型预测到的物体的类别。进而根据这个 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)，我们能够知道这具体是哪个品种的狗狗。\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from keras.applications.resnet50 import preprocess_input, decode_predictions\n", "def ResNet50_predict_labels(img_path):\n", " # 返回img_path路径的图像的预测向量\n", " img = preprocess_input(path_to_tensor(img_path))\n", " return np.argmax(ResNet50_model.predict(img))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 完成狗检测模型\n", "\n", "\n", "在研究该 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 的时候，你会注意到，狗类别对应的序号为151-268。因此，在检查预训练模型判断图像是否包含狗的时候，我们只需要检查如上的 `ResNet50_predict_labels` 函数是否返回一个介于151和268之间（包含区间端点）的值。\n", "\n", "我们通过这些想法来完成下方的 `dog_detector` 函数，如果从图像中检测到狗就返回 `True`，否则返回 `False`。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def dog_detector(img_path):\n", " prediction = ResNet50_predict_labels(img_path)\n", " return ((prediction <= 268) & (prediction >= 151)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【作业】评估狗狗检测模型\n", "\n", "---\n", "\n", "<a id='question3'></a>\n", "### __问题 3:__ \n", "\n", "在下方的代码块中，使用 `dog_detector` 函数，计算：\n", "\n", "- `human_files_short`中图像检测到狗狗的百分比？\n", "- `dog_files_short`中图像检测到狗狗的百分比？" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percentage of detecting dogs in human files is: 0.0\n", "The percentage of detecting dogs in dog files is: 1.0\n" ] } ], "source": [ "### TODO: 测试dog_detector函数在human_files_short和dog_files_short的表现\n", "human_files_short_detect = 0\n", "dog_files_short_detect = 0\n", "\n", "for i in range(100):\n", " if (dog_detector(human_files_short[i])):\n", " human_files_short_detect += 1\n", " if (dog_detector(dog_files_short[i])):\n", " dog_files_short_detect += 1\n", "\n", "print(\"The percentage of detecting dogs in human files is:\", human_files_short_detect/human_files_short.size)\n", "print(\"The percentage of detecting dogs in dog files is:\", dog_files_short_detect/dog_files_short.size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='step3'></a>\n", "\n", "## 步骤 3: 从头开始创建一个CNN来分类狗品种\n", "\n", "\n", "现在我们已经实现了一个函数，能够在图像中识别人类及狗狗。但我们需要更进一步的方法，来对狗的类别进行识别。在这一步中，你需要实现一个卷积神经网络来对狗的品种进行分类。你需要__从头实现__你的卷积神经网络（在这一阶段，你还不能使用迁移学习），并且你需要达到超过1%的测试集准确率。在本项目的步骤五种，你还有机会使用迁移学习来实现一个准确率大大提高的模型。\n", "\n", "在添加卷积层的时候，注意不要加上太多的（可训练的）层。更多的参数意味着更长的训练时间，也就是说你更可能需要一个 GPU 来加速训练过程。万幸的是，Keras 提供了能够轻松预测每次迭代（epoch）花费时间所需的函数。你可以据此推断你算法所需的训练时间。\n", "\n", "值得注意的是，对狗的图像进行分类是一项极具挑战性的任务。因为即便是一个正常人，也很难区分布列塔尼犬和威尔士史宾格犬。\n", "\n", "\n", "布列塔尼犬（Brittany） \| 威尔士史宾格犬（Welsh Springer Spaniel）\n", "- \| - \n", "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> \| <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n", "\n", "不难发现其他的狗品种会有很小的类间差别（比如金毛寻回犬和美国水猎犬）。\n", "\n", "\n", "金毛寻回犬（Curly-Coated Retriever） \| 美国水猎犬（American Water Spaniel）\n", "- \| -\n", "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> \| <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n", "\n", "同样，拉布拉多犬（labradors）有黄色、棕色和黑色这三种。那么你设计的基于视觉的算法将不得不克服这种较高的类间差别，以达到能够将这些不同颜色的同类狗分到同一个品种中。\n", "\n", "黄色拉布拉多犬（Yellow Labrador） \| 棕色拉布拉多犬（Chocolate Labrador） \| 黑色拉布拉多犬（Black Labrador）\n", "- \| -\n", "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> \| <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> \| <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n", "\n", "我们也提到了随机分类将得到一个非常低的结果：不考虑品种略有失衡的影响，随机猜测到正确品种的概率是1/133，相对应的准确率是低于1%的。\n", "\n", "请记住，在深度学习领域，实践远远高于理论。大量尝试不同的框架吧，相信你的直觉！当然，玩得开心！\n", "\n", "\n", "### 数据预处理\n", "\n", "\n", "通过对每张图像的像素值除以255，我们对图像实现了归一化处理。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 6680/6680 [01:25<00:00, 78.01it/s] \n", "100%\|██████████\| 835/835 [00:09<00:00, 86.10it/s] \n", "100%\|██████████\| 836/836 [00:09<00:00, 86.80it/s] \n" ] } ], "source": [ "from PIL import ImageFile \n", "ImageFile.LOAD_TRUNCATED_IMAGES = True \n", "\n", "# Keras中的数据预处理过程\n", "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n", "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n", "test_tensors = paths_to_tensor(test_files).astype('float32')/255" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【练习】模型架构\n", "\n", "\n", "创建一个卷积神经网络来对狗品种进行分类。在你代码块的最后，执行 `model.summary()` 来输出你模型的总结信息。\n", " \n", "我们已经帮你导入了一些所需的 Python 库，如有需要你可以自行导入。如果你在过程中遇到了困难，如下是给你的一点小提示——该模型能够在5个 epoch 内取得超过1%的测试准确率，并且能在CPU上很快地训练。\n", "\n", "![Sample CNN](images/sample_cnn.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='question4'></a> \n", "\n", "### __问题 4:__ \n", "\n", "在下方的代码块中尝试使用 Keras 搭建卷积网络的架构，并回答相关的问题。\n", "\n", "1. 你可以尝试自己搭建一个卷积网络的模型，那么你需要回答你搭建卷积网络的具体步骤（用了哪些层）以及为什么这样搭建。\n", "2. 你也可以根据上图提示的步骤搭建卷积网络，那么请说明为何如上的架构能够在该问题上取得很好的表现。\n", "\n", "__回答:__ \n", "我选择根据上图提示搭建卷积神经网络。首先，搭建三层卷积层可以检测更高级的特征，以达到狗狗品种分类的目的。同时，两个卷积层之间的池化层有效降低了数据的复杂度，使得训练效率得到有效提升" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_9 (Conv2D) (None, 223, 223, 16) 208 \n", "_________________________________________________________________\n", "max_pooling2d_10 (MaxPooling (None, 111, 111, 16) 0 \n", "_________________________________________________________________\n", "dropout_8 (Dropout) (None, 111, 111, 16) 0 \n", "_________________________________________________________________\n", "conv2d_10 (Conv2D) (None, 110, 110, 32) 2080 \n", "_________________________________________________________________\n", "max_pooling2d_11 (MaxPooling (None, 55, 55, 32) 0 \n", "_________________________________________________________________\n", "dropout_9 (Dropout) (None, 55, 55, 32) 0 \n", "_________________________________________________________________\n", "conv2d_11 (Conv2D) (None, 54, 54, 64) 8256 \n", "_________________________________________________________________\n", "max_pooling2d_12 (MaxPooling (None, 27, 27, 64) 0 \n", "_________________________________________________________________\n", "dropout_10 (Dropout) (None, 27, 27, 64) 0 \n", "_________________________________________________________________\n", "global_average_pooling2d_3 ( (None, 64) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 133) 8645 \n", "=================================================================\n", "Total params: 19,189\n", "Trainable params: 19,189\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n", "from keras.layers import Dropout, Flatten, Dense\n", "from keras.models import Sequential\n", "\n", "model = Sequential()\n", "\n", "### TODO: 定义你的网络架构\n", "model.add(Conv2D(filters=16, kernel_size=2, input_shape=(224, 224, 3), activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "model.add(Dropout(0.2))\n", "model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "\n", "model.add(Dropout(0.2))\n", "model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "model.add(Dropout(0.2))\n", "\n", "model.add(GlobalAveragePooling2D())\n", "model.add(Dense(133, activation='softmax'))\n", " \n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "## 编译模型\n", "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 【练习】训练模型\n", "\n", "\n", "---\n", "\n", "<a id='question5'></a> \n", "\n", "### __问题 5:__ \n", "\n", "在下方代码单元训练模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n", "\n", "可选题：你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)，来优化模型的表现。\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8817 - acc: 0.0105Epoch 00001: val_loss improved from inf to 4.87374, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 22s 3ms/step - loss: 4.8816 - acc: 0.0106 - val_loss: 4.8737 - val_acc: 0.0120\n", "Epoch 2/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8539 - acc: 0.0120Epoch 00002: val_loss improved from 4.87374 to 4.84636, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.8536 - acc: 0.0120 - val_loss: 4.8464 - val_acc: 0.0168\n", "Epoch 3/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8110 - acc: 0.0195Epoch 00003: val_loss improved from 4.84636 to 4.80888, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 22s 3ms/step - loss: 4.8109 - acc: 0.0195 - val_loss: 4.8089 - val_acc: 0.0168\n", "Epoch 4/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.7768 - acc: 0.0201Epoch 00004: val_loss improved from 4.80888 to 4.77889, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.7767 - acc: 0.0202 - val_loss: 4.7789 - val_acc: 0.0251\n", "Epoch 5/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.7451 - acc: 0.0243Epoch 00005: val_loss improved from 4.77889 to 4.75447, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.7449 - acc: 0.0243 - val_loss: 4.7545 - val_acc: 0.0240\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fa0ac6f8390>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from keras.callbacks import ModelCheckpoint \n", "\n", "### TODO: 设置训练模型的epochs的数量\n", "\n", "epochs = 5\n", "\n", "### 不要修改下方代码\n", "\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "model.fit(train_tensors, train_targets, \n", " validation_data=(valid_tensors, valid_targets),\n", " epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "## 加载具有最好验证loss的模型\n", "\n", "model.load_weights('saved_models/weights.best.from_scratch.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 测试模型\n", "\n", "在狗图像的测试数据集上试用你的模型。确保测试准确率大于1%。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 2.6316%\n" ] } ], "source": [ "# 获取测试数据集中每一个图像所预测的狗品种的index\n", "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step4'></a>\n", "## 步骤 4: 使用一个CNN来区分狗的品种\n", "\n", "\n", "使用迁移学习（Transfer Learning）的方法，能帮助我们在不损失准确率的情况下大大减少训练时间。在以下步骤中，你可以尝试使用迁移学习来训练你自己的CNN。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 得到从图像中提取的特征向量（Bottleneck Features）" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')\n", "train_VGG16 = bottleneck_features['train']\n", "valid_VGG16 = bottleneck_features['valid']\n", "test_VGG16 = bottleneck_features['test']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 模型架构\n", "\n", "该模型使用预训练的 VGG-16 模型作为固定的图像特征提取器，其中 VGG-16 最后一层卷积层的输出被直接输入到我们的模型。我们只需要添加一个全局平均池化层以及一个全连接层，其中全连接层使用 softmax 激活函数，对每一个狗的种类都包含一个节点。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "global_average_pooling2d_4 ( (None, 512) 0 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 133) 68229 \n", "=================================================================\n", "Total params: 68,229\n", "Trainable params: 68,229\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "VGG16_model = Sequential()\n", "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n", "VGG16_model.add(Dense(133, activation='softmax'))\n", "\n", "VGG16_model.summary()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "## 编译模型\n", "\n", "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 11.8988 - acc: 0.1303Epoch 00001: val_loss improved from inf to 10.57918, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 308us/step - loss: 11.8949 - acc: 0.1307 - val_loss: 10.5792 - val_acc: 0.2132\n", "Epoch 2/20\n", "6500/6680 [============================>.] - ETA: 0s - loss: 9.6765 - acc: 0.2878Epoch 00002: val_loss improved from 10.57918 to 9.51589, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 253us/step - loss: 9.6727 - acc: 0.2880 - val_loss: 9.5159 - val_acc: 0.2862\n", "Epoch 3/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 8.9693 - acc: 0.3641Epoch 00003: val_loss improved from 9.51589 to 9.13973, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 248us/step - loss: 8.9632 - acc: 0.3645 - val_loss: 9.1397 - val_acc: 0.3341\n", "Epoch 4/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 8.7415 - acc: 0.4006Epoch 00004: val_loss improved from 9.13973 to 9.10053, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 255us/step - loss: 8.7140 - acc: 0.4024 - val_loss: 9.1005 - val_acc: 0.3485\n", "Epoch 5/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 8.4844 - acc: 0.4242Epoch 00005: val_loss improved from 9.10053 to 8.91824, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 250us/step - loss: 8.4887 - acc: 0.4241 - val_loss: 8.9182 - val_acc: 0.3593\n", "Epoch 6/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 8.2685 - acc: 0.4466Epoch 00006: val_loss improved from 8.91824 to 8.75737, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 255us/step - loss: 8.2920 - acc: 0.4454 - val_loss: 8.7574 - val_acc: 0.3701\n", "Epoch 7/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 8.1670 - acc: 0.4601Epoch 00007: val_loss improved from 8.75737 to 8.58505, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 250us/step - loss: 8.1624 - acc: 0.4605 - val_loss: 8.5851 - val_acc: 0.3904\n", "Epoch 8/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 7.8733 - acc: 0.4834Epoch 00008: val_loss improved from 8.58505 to 8.57290, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 253us/step - loss: 7.9312 - acc: 0.4799 - val_loss: 8.5729 - val_acc: 0.3892\n", "Epoch 9/20\n", "6660/6680 [============================>.] - ETA: 0s - loss: 7.8558 - acc: 0.4898Epoch 00009: val_loss improved from 8.57290 to 8.37072, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.8589 - acc: 0.4897 - val_loss: 8.3707 - val_acc: 0.3952\n", "Epoch 10/20\n", "6640/6680 [============================>.] - ETA: 0s - loss: 7.7289 - acc: 0.5017Epoch 00010: val_loss improved from 8.37072 to 8.36277, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.7267 - acc: 0.5016 - val_loss: 8.3628 - val_acc: 0.4084\n", "Epoch 11/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.6539 - acc: 0.5122Epoch 00011: val_loss did not improve\n", "6680/6680 [==============================] - 2s 250us/step - loss: 7.6913 - acc: 0.5097 - val_loss: 8.3630 - val_acc: 0.4048\n", "Epoch 12/20\n", "6480/6680 [============================>.] - ETA: 0s - loss: 7.6779 - acc: 0.5133Epoch 00012: val_loss improved from 8.36277 to 8.26086, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 259us/step - loss: 7.6780 - acc: 0.5133 - val_loss: 8.2609 - val_acc: 0.4240\n", "Epoch 13/20\n", "6660/6680 [============================>.] - ETA: 0s - loss: 7.6599 - acc: 0.5171Epoch 00013: val_loss did not improve\n", "6680/6680 [==============================] - 2s 255us/step - loss: 7.6636 - acc: 0.5169 - val_loss: 8.3361 - val_acc: 0.4204\n", "Epoch 14/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 7.6194 - acc: 0.5199Epoch 00014: val_loss improved from 8.26086 to 8.22236, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 251us/step - loss: 7.6497 - acc: 0.5181 - val_loss: 8.2224 - val_acc: 0.4180\n", "Epoch 15/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.5786 - acc: 0.5213Epoch 00015: val_loss improved from 8.22236 to 8.12911, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 254us/step - loss: 7.5671 - acc: 0.5222 - val_loss: 8.1291 - val_acc: 0.4263\n", "Epoch 16/20\n", "6500/6680 [============================>.] - ETA: 0s - loss: 7.4843 - acc: 0.5260Epoch 00016: val_loss did not improve\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.4813 - acc: 0.5256 - val_loss: 8.1297 - val_acc: 0.4251\n", "Epoch 17/20\n", "6580/6680 [============================>.] - ETA: 0s - loss: 7.3097 - acc: 0.5305Epoch 00017: val_loss improved from 8.12911 to 8.00258, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 259us/step - loss: 7.2970 - acc: 0.5314 - val_loss: 8.0026 - val_acc: 0.4311\n", "Epoch 18/20\n", "6480/6680 [============================>.] - ETA: 0s - loss: 7.1743 - acc: 0.5424Epoch 00018: val_loss improved from 8.00258 to 7.90895, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 256us/step - loss: 7.1896 - acc: 0.5416 - val_loss: 7.9089 - val_acc: 0.4251\n", "Epoch 19/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.0790 - acc: 0.5492Epoch 00019: val_loss did not improve\n", "6680/6680 [==============================] - 2s 249us/step - loss: 7.0858 - acc: 0.5488 - val_loss: 7.9667 - val_acc: 0.4335\n", "Epoch 20/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 7.0128 - acc: 0.5554Epoch 00020: val_loss improved from 7.90895 to 7.80676, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 249us/step - loss: 7.0278 - acc: 0.5543 - val_loss: 7.8068 - val_acc: 0.4335\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fa08fc43d68>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## 训练模型\n", "\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "VGG16_model.fit(train_VGG16, train_targets, \n", " validation_data=(valid_VGG16, valid_targets),\n", " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "## 加载具有最好验证loss的模型\n", "\n", "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 测试模型\n", "现在，我们可以测试此CNN在狗图像测试数据集中识别品种的效果如何。我们在下方打印出测试准确率。" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 43.4211%\n" ] } ], "source": [ "# 获取测试数据集中每一个图像所预测的狗品种的index\n", "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 使用模型预测狗的品种" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from extract_bottleneck_features import \n", "\n", "def VGG16_predict_breed(img_path):\n", " # 提取bottleneck特征\n", " bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n", " # 获取预测向量\n", " predicted_vector = VGG16_model.predict(bottleneck_feature)\n", " # 返回此模型预测的狗的品种\n", " return dog_names[np.argmax(predicted_vector)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step5'></a>\n", "## 步骤 5: 建立一个CNN来分类狗的品种（使用迁移学习）\n", "\n", "现在你将使用迁移学习来建立一个CNN，从而可以从图像中识别狗的品种。你的 CNN 在测试集上的准确率必须至少达到60%。\n", "\n", "在步骤4中，我们使用了迁移学习来创建一个使用基于 VGG-16 提取的特征向量来搭建一个 CNN。在本部分内容中，你必须使用另一个预训练模型来搭建一个 CNN。为了让这个任务更易实现，我们已经预先对目前 keras 中可用的几种网络进行了预训练：\n", "\n", "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n", "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n", "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n", "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n", "\n", "这些文件被命名为为：\n", "\n", " Dog{network}Data.npz\n", "\n", "其中 `{network}` 可以是 `VGG19`、`Resnet50`、`InceptionV3` 或 `Xception` 中的一个。选择上方网络架构中的一个，他们已经保存在目录 `/data/bottleneck_features/` 中。\n", "\n", "\n", "### 【练习】获取模型的特征向量\n", "\n", "在下方代码块中，通过运行下方代码提取训练、测试与验证集相对应的bottleneck特征。\n", "\n", " bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')\n", " train_{network} = bottleneck_features['train']\n", " valid_{network} = bottleneck_features['valid']\n", " test_{network} = bottleneck_features['test']" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "### TODO: 从另一个预训练的CNN获取bottleneck特征\n", "bottleneck_features = np.load('/data/bottleneck_features/DogXceptionData.npz')\n", "train_Xception = bottleneck_features['train']\n", "valid_Xception = bottleneck_features['valid']\n", "test_Xception = bottleneck_features['test']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【练习】模型架构\n", "\n", "建立一个CNN来分类狗品种。在你的代码单元块的最后，通过运行如下代码输出网络的结构：\n", " \n", " <your model's name>.summary()\n", " \n", "---\n", "\n", "<a id='question6'></a> \n", "\n", "### __问题 6:__ \n", "\n", "\n", "在下方的代码块中尝试使用 Keras 搭建最终的网络架构，并回答你实现最终 CNN 架构的步骤与每一步的作用，并描述你在迁移学习过程中，使用该网络架构的原因。\n", "\n", "\n", "__回答:__ \n", "\n", "Xception_model = Sequential()\n", "这一步是调用Xception的预训练模型\n", "\n", "Xception_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))\n", "这一步添加一个全局平均池化层避免过拟合\n", "\n", "Xception_model.add(Dropout(0.2))\n", "这一步是添加Dropout层避免过拟合\n", "\n", "Xception_model.add(Dense(133, activation='softmax'))\n", "这一步添加133个节点的全连接层，使用softmax激活函数输出每个狗狗品种的概率\n", "\n", "使用该网络架构的原因是由于Xception具有如下优点：\n", "1.相比传统的卷积神经网络如VGG复杂度降低，需要的参数数量下降。\n", "2.可以做到更深，不会出现梯度消失的问题。\n", "3.优化简单，分类准确度加深由于使用更深的网络。\n", "4.Xception在众多图像识别领域中拔得头筹。\n", "因此，选取Xception网络可以比之前的VGG网络取得更好的预测效果。\n", "\n", "- 为什么这一架构会在这一分类任务中成功？\n", "\n", " 这四个架构都是经过反复多次实验确定的，非常有效果的架构。以Inception net为例，inception net是多层特征提取器，通过分别多次同时提取特征，然后叠加，就可以学到不同层次的特征，所以效果非常好。\n", "\n", "- 为什么早期（第三步）的尝试不成功？\n", "\n", " 第三步中，第一，使用的网络在架构上，非常浅，学到的特征非常少，其次学习库非常小，上面四个网络是在Imagenet上经过大量训练在不同种类的训练集上得来的，这是这个小库无法比拟的。\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "global_average_pooling2d_5 ( (None, 2048) 0 \n", "_________________________________________________________________\n", "dropout_11 (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 133) 272517 \n", "=================================================================\n", "Total params: 272,517\n", "Trainable params: 272,517\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "### TODO: 定义你的框架\n", "# 调用Xception的预训练模型\n", "Xception_model = Sequential()\n", "\n", "#加一个全局平均池化层避免过拟合\n", "Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))\n", "\n", "#添加Dropout层避免过拟合\n", "Xception_model.add(Dropout(0.2))\n", "\n", "#添加133个节点的全连接层，使用softmax激活函数输出每个狗狗品种的概率\n", "Xception_model.add(Dense(133, activation='softmax'))\n", "\n", "Xception_model.summary()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "### TODO: 编译模型\n", "Xception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】训练模型\n", "\n", "<a id='question7'></a> \n", "\n", "### __问题 7:__ \n", "\n", "在下方代码单元中训练你的模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n", "\n", "当然，你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) 以优化模型的表现，不过这不是必须的步骤。\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 1.1255 - acc: 0.7239Epoch 00001: val_loss improved from inf to 0.52573, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 475us/step - loss: 1.1172 - acc: 0.7256 - val_loss: 0.5257 - val_acc: 0.8335\n", "Epoch 2/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 0.4270 - acc: 0.8647Epoch 00002: val_loss improved from 0.52573 to 0.49305, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.4260 - acc: 0.8647 - val_loss: 0.4931 - val_acc: 0.8347\n", "Epoch 3/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.3540 - acc: 0.8870Epoch 00003: val_loss improved from 0.49305 to 0.48966, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.3586 - acc: 0.8864 - val_loss: 0.4897 - val_acc: 0.8431\n", "Epoch 4/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 0.3193 - acc: 0.9047Epoch 00004: val_loss improved from 0.48966 to 0.47889, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.3186 - acc: 0.9046 - val_loss: 0.4789 - val_acc: 0.8587\n", "Epoch 5/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2913 - acc: 0.9126Epoch 00005: val_loss did not improve\n", "6680/6680 [==============================] - 3s 409us/step - loss: 0.2898 - acc: 0.9129 - val_loss: 0.5032 - val_acc: 0.8503\n", "Epoch 6/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9162Epoch 00006: val_loss did not improve\n", "6680/6680 [==============================] - 3s 409us/step - loss: 0.2683 - acc: 0.9165 - val_loss: 0.4957 - val_acc: 0.8467\n", "Epoch 7/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2508 - acc: 0.9262Epoch 00007: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2489 - acc: 0.9268 - val_loss: 0.5211 - val_acc: 0.8539\n", "Epoch 8/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2384 - acc: 0.9277Epoch 00008: val_loss did not improve\n", "6680/6680 [==============================] - 3s 408us/step - loss: 0.2373 - acc: 0.9278 - val_loss: 0.5091 - val_acc: 0.8599\n", "Epoch 9/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2300 - acc: 0.9330Epoch 00009: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.2286 - acc: 0.9334 - val_loss: 0.5349 - val_acc: 0.8575\n", "Epoch 10/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2114 - acc: 0.9398Epoch 00010: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2109 - acc: 0.9398 - val_loss: 0.5300 - val_acc: 0.8575\n", "Epoch 11/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2026 - acc: 0.9383Epoch 00011: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2020 - acc: 0.9385 - val_loss: 0.5516 - val_acc: 0.8659\n", "Epoch 12/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1893 - acc: 0.9433Epoch 00012: val_loss did not improve\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.1884 - acc: 0.9434 - val_loss: 0.5614 - val_acc: 0.8587\n", "Epoch 13/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1849 - acc: 0.9450Epoch 00013: val_loss did not improve\n", "6680/6680 [==============================] - 3s 407us/step - loss: 0.1841 - acc: 0.9452 - val_loss: 0.5743 - val_acc: 0.8575\n", "Epoch 14/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1761 - acc: 0.9489Epoch 00014: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1770 - acc: 0.9487 - val_loss: 0.5765 - val_acc: 0.8551\n", "Epoch 15/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1657 - acc: 0.9514Epoch 00015: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1688 - acc: 0.9509 - val_loss: 0.6255 - val_acc: 0.8515\n", "Epoch 16/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1554 - acc: 0.9539Epoch 00016: val_loss did not improve\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.1581 - acc: 0.9536 - val_loss: 0.6189 - val_acc: 0.8695\n", "Epoch 17/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1474 - acc: 0.9582Epoch 00017: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1476 - acc: 0.9581 - val_loss: 0.6452 - val_acc: 0.8575\n", "Epoch 18/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1457 - acc: 0.9568Epoch 00018: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1461 - acc: 0.9569 - val_loss: 0.6144 - val_acc: 0.8599\n", "Epoch 19/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1396 - acc: 0.9606Epoch 00019: val_loss did not improve\n", "6680/6680 [==============================] - 3s 414us/step - loss: 0.1408 - acc: 0.9603 - val_loss: 0.6590 - val_acc: 0.8563\n", "Epoch 20/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1361 - acc: 0.9605Epoch 00020: val_loss did not improve\n", "6680/6680 [==============================] - 3s 415us/step - loss: 0.1349 - acc: 0.9608 - val_loss: 0.6394 - val_acc: 0.8683\n" ] } ], "source": [ "### TODO: 训练模型\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception1.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "history = Xception_model.fit(train_Xception, train_targets, \n", " validation_data=(valid_Xception, valid_targets),\n", " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "### TODO: 加载具有最佳验证loss的模型权重\n", "Xception_model.load_weights('saved_models/weights.best.Xception1.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】测试模型\n", "\n", "<a id='question8'></a> \n", "\n", "### __问题 8:__ \n", "\n", "在狗图像的测试数据集上试用你的模型。确保测试准确率大于60%。" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 84.4498%\n" ] } ], "source": [ "### TODO: 在测试集上计算分类准确率\n", "Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100np.sum(np.array(Xception_predictions)==np.argmax(test_targets, axis=1))/len(Xception_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】使用模型测试狗的品种\n", "\n", "\n", "实现一个函数，它的输入为图像路径，功能为预测对应图像的类别，输出为你模型预测出的狗类别（`Affenpinscher`, `Afghan_hound` 等）。\n", "\n", "与步骤5中的模拟函数类似，你的函数应当包含如下三个步骤：\n", "\n", "1. 根据选定的模型载入图像特征（bottleneck features）\n", "2. 将图像特征输输入到你的模型中，并返回预测向量。注意，在该向量上使用 argmax 函数可以返回狗种类的序号。\n", "3. 使用在步骤0中定义的 `dog_names` 数组来返回对应的狗种类名称。\n", "\n", "提取图像特征过程中使用到的函数可以在 `extract_bottleneck_features.py` 中找到。同时，他们应已在之前的代码块中被导入。根据你选定的 CNN 网络，你可以使用 `extract_{network}` 函数来获得对应的图像特征，其中 `{network}` 代表 `VGG19`, `Resnet50`, `InceptionV3`, 或 `Xception` 中的一个。\n", " \n", "---\n", "\n", "<a id='question9'></a> \n", "\n", "### __问题 9:__" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "### TODO: 写一个函数，该函数将图像的路径作为输入\n", "### 然后返回此模型所预测的狗的品种\n", "def Xception_predict_breed(img_path):\n", " # extract bottleneck features\n", " bottleneck_feature = extract_Xception(path_to_tensor(img_path))\n", " # obtain predicted vector\n", " predicted_vector = Xception_model.predict(bottleneck_feature)\n", " # return dog breed that is predicted by the model\n", " return dog_names[np.argmax(predicted_vector)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='step6'></a>\n", "## 步骤 6: 完成你的算法\n", "\n", "\n", "\n", "实现一个算法，它的输入为图像的路径，它能够区分图像是否包含一个人、狗或两者都不包含，然后：\n", "\n", "- 如果从图像中检测到一只__狗__，返回被预测的品种。\n", "- 如果从图像中检测到__人__，返回最相像的狗品种。\n", "- 如果两者都不能在图像中检测到，输出错误提示。\n", "\n", "我们非常欢迎你来自己编写检测图像中人类与狗的函数，你可以随意地使用上方完成的 `face_detector` 和 `dog_detector` 函数。你__需要__在步骤5使用你的CNN来预测狗品种。\n", "\n", "下面提供了算法的示例输出，但你可以自由地设计自己的模型！\n", "\n", "![Sample Human Output](images/sample_human_output.png)\n", "\n", "\n", "\n", "\n", "<a id='question10'></a> \n", "\n", "### __问题 10:__\n", "\n", "在下方代码块中完成你的代码。\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "### TODO: 设计你的算法\n", "### 自由地使用所需的代码单元数吧\n", "from IPython.core.display import Image, display\n", "\n", "def dog_breed_algorithm(img_path):\n", " if dog_detector(img_path) == 1:\n", " print(\"hello, dog!\")\n", " display(Image(img_path,width=200,height=200))\n", " print(\"Your predicted breed is ... \")\n", " return print(Xception_predict_breed(img_path))\n", " elif face_detector(img_path) == 1:\n", " print(\"hello, human!\")\n", " display(Image(img_path,width=200,height=200))\n", " print(\"You look like a ... \")\n", " return print(Xception_predict_breed(img_path))\n", " else:\n", " display(Image(img_path,width=200,height=200))\n", " return print(\"Could not identify a human or dog in the chosen image. Please try again.\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step7'></a>\n", "## 步骤 7: 测试你的算法\n", "\n", "在这个部分中，你将尝试一下你的新算法！算法认为__你__看起来像什么类型的狗？如果你有一只狗，它可以准确地预测你的狗的品种吗？如果你有一只猫，它会将你的猫误判为一只狗吗？\n", "\n", "上传方式：点击左上角的Jupyter回到上级菜单，你可以看到Jupyter Notebook的右上方会有Upload按钮。\n", "\n", "<a id='question11'></a> \n", "\n", "### __问题 11:__\n", "\n", "在下方编写代码，用至少6张现实中的图片来测试你的算法。你可以使用任意照片，不过请至少使用两张人类图片（要征得当事人同意哦）和两张狗的图片。\n", "同时请回答如下问题：\n", "\n", "1. 输出结果比你预想的要好吗 :) ？或者更糟 :( ？\n", "2. 提出至少三点改进你的模型的想法。\n", "\n", "\n", "1.结果比我预想的好。该算法可以准确识别出图片中是否含有狗或者人\n", "2. 1）对训练集进行数据增强以优化模型的表现\n", " 2）优化神经网络结构\n", " 3）增大数据集数据" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "filename = images/1.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "83689472/83683744 [==============================] - 1s 0us/step\n", "in/045.Cardigan_welsh_corgi\n", "\n", "\n", "filename = images/2.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "in/005.Alaskan_malamute\n", "\n", "\n", "filename = images/3.jpg\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Could not identify a human or dog in the chosen image. Please try again.\n", "\n", "\n", "filename = images/4.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "in/006.American_eskimo_dog\n", "\n", "\n", "filename = images/5.jpg\n", "hello, human!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "You look like a ... \n", "in/056.Dachshund\n", "\n", "\n", "filename = images/6.jpg\n", "hello, human!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "You look like a ... \n", "in/050.Chinese_shar-pei\n", "\n", "\n" ] } ], "source": [ "## TODO: 在你的电脑上，在步骤6中，至少在6张图片上运行你的算法。\n", "## 自由地使用所需的代码单元数吧\n", "for i in range(1, 7):\n", " filename = 'images/' + str(i) + '.jpg'\n", " print('filename = ' + filename)\n", " dog_breed_algorithm(filename)\n", " print('\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注意: 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出File -> Download as -> HTML (.html)把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ES-DOC/esdoc-jupyterhub	notebooks/uhh/cmip6/models/sandbox-1/seaice.ipynb	1	99801	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: UHH \n", "Source ID: SANDBOX-1 \n", "Topic: Seaice \n", "Sub-Topics: Dynamics, Thermodynamics, Radiative Processes. \n", "Properties: 80 (63 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:41" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-1', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --> Model](#1.-Key-Properties--->-Model) \n", "[2. Key Properties --> Variables](#2.-Key-Properties--->-Variables) \n", "[3. Key Properties --> Seawater Properties](#3.-Key-Properties--->-Seawater-Properties) \n", "[4. Key Properties --> Resolution](#4.-Key-Properties--->-Resolution) \n", "[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n", "[6. Key Properties --> Key Parameter Values](#6.-Key-Properties--->-Key-Parameter-Values) \n", "[7. Key Properties --> Assumptions](#7.-Key-Properties--->-Assumptions) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid --> Discretisation --> Horizontal](#9.-Grid--->-Discretisation--->-Horizontal) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Seaice Categories](#11.-Grid--->-Seaice-Categories) \n", "[12. Grid --> Snow On Seaice](#12.-Grid--->-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --> Energy](#14.-Thermodynamics--->-Energy) \n", "[15. Thermodynamics --> Mass](#15.-Thermodynamics--->-Mass) \n", "[16. Thermodynamics --> Salt](#16.-Thermodynamics--->-Salt) \n", "[17. Thermodynamics --> Salt --> Mass Transport](#17.-Thermodynamics--->-Salt--->-Mass-Transport) \n", "[18. Thermodynamics --> Salt --> Thermodynamics](#18.-Thermodynamics--->-Salt--->-Thermodynamics) \n", "[19. Thermodynamics --> Ice Thickness Distribution](#19.-Thermodynamics--->-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --> Ice Floe Size Distribution](#20.-Thermodynamics--->-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --> Melt Ponds](#21.-Thermodynamics--->-Melt-Ponds) \n", "[22. Thermodynamics --> Snow Processes](#22.-Thermodynamics--->-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --> Model \n", "Name of seaice model used." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Variables \n", "List of prognostic variable in the sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the sea ice component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Seawater Properties \n", "Properties of seawater relevant to sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant seawater freezing point, specify this value." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Resolution \n", "Resolution of the sea ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Tuning Applied \n", "Tuning applied to sea ice model component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which simulations had tuning applied, e.g. all, not historical, only pi-control? " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any observed metrics used in tuning model/parameters" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Which variables were changed during the tuning process?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Key Parameter Values \n", "Values of key parameters" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### What values were specificed for the following parameters if used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "Is Required:* FALSE    Type: STRING    Cardinality: 0.N\n", "### If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Assumptions \n", "Assumptions made in the sea ice model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### General overview description of any key* assumptions made in this model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.N\n", "### Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### List any key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the sea ice component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.1\n", "### Provide a general description of conservation methodology." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in sea ice by the numerical schemes." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does conservation involved flux correction?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any variables which are conserved by more* than the numerical scheme alone." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --> Discretisation --> Horizontal \n", "Sea ice discretisation in the horizontal" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Grid on which sea ice is horizontal discretised?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the type of sea ice grid?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the advection scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model thermodynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model dynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional horizontal discretisation details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Sea ice vertical properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using multi-layers specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional vertical grid details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Seaice Categories \n", "What method is used to represent sea ice categories ?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Set to true if the sea ice model has multiple sea ice categories." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using sea ice categories specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### If using sea ice categories specify each of the category limits." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the sea ice thickness distribution scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --> Snow On Seaice \n", "Snow on sea ice details" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow on ice represented in this model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels of snow on ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how the snow fraction on sea ice is determined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional details related to snow on ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "Sea Ice Dynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of horizontal advection of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice transport in thickness space (i.e. in thickness categories)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Which method of sea ice strength formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which processes can redistribute sea ice (including thickness)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Rheology, what is the ice deformation formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --> Energy \n", "Processes related to energy in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the energy formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of thermal conductivity is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of heat diffusion?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method by which basal ocean heat flux is handled?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which the heat content of precipitation is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --> Mass \n", "Processes related to mass in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which new sea ice is formed in open water." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs the vertical growth and melt of sea ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice lateral melting?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs sea ice surface sublimation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of frazil ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --> Salt \n", "Processes related to salt in sea ice thermodynamics." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does sea ice salinity impact the thermal properties of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --> Salt --> Mass Transport \n", "Mass transport of salt" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the mass transport of salt calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --> Salt --> Thermodynamics \n", "Salt thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the thermodynamic calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --> Ice Thickness Distribution \n", "Ice thickness distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice thickness distribution represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --> Ice Floe Size Distribution \n", "Ice floe-size distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice floe-size represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Please provide further details on any parameterisation of floe-size." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --> Melt Ponds \n", "Characteristics of melt ponds." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are melt ponds included in the sea ice model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What method of melt pond formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What do melt ponds have an impact on?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --> Snow Processes \n", "Thermodynamic processes in snow on sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has a snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has snow ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow ice formation scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What is the impact of ridging on snow cover?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the heat diffusion through snow methodology in sea ice thermodynamics?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "Sea Ice Radiative Processes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method used to handle surface albedo." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method by which solar radiation through sea ice is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
caganze/wisps	notebooks/manjavacas data.ipynb	1	2151175	null	mit
MartinThoma/LaTeX-examples	presentations/causality-presentation/pynb/Interventions.ipynb	1	15400	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import bernoulli\n", "import matplotlib.pyplot as plt; plt.rcdefaults()\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sympy import latex" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def generate_data(n):\n", " \"\"\"\n", " Generate n datapoints for 3 variables A, H, B\n", " with the model\n", " A -> H -> B\n", " \"\"\"\n", "\n", " data = []\n", " for i in range(n):\n", " N_A = bernoulli.rvs(1./20)\n", " N_H = bernoulli.rvs(1./3)\n", " N_B = bernoulli.rvs(1./2)\n", " A = N_A\n", " H = (A + N_H) % 2\n", " B = (H + N_B) % 2\n", " data.append((A, H, B))\n", " return data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc41c826cd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "p(H=0\|A=0) = 0.67\n", "p(H=0\|A=1) = 0.32\n", "p(H=0\|B=0) = 0.65\n", "p(H=0\|B=1) = 0.65\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "\n", "data = generate_data(100000)\n", "\n", "# Example data\n", "people = ('(0,0,0)', '(0,0,1)', '(0,1,0)', '(0,1,1)', '(1,0,0)', '(1,0,1)', '(1,1,0)', '(1,1,1)')\n", "get_bin = lambda x, n: x >= 0 and str(bin(x))[2:].zfill(n) or \"-\" + str(bin(x))[3:].zfill(n)\n", "y_pos = np.arange(len(people))\n", "# ('(0,0,0)', '(0,0,1)', '(0,1,0)', '(0,1,1)', '(1,0,0)', '(1,0,1)', '(1,1,0)', '(1,1,1)')\n", "count = [sum([1 for d in data if map(int, list(get_bin(i, 3))) == list(d)]) for i in range(8)]\n", "\n", "error = np.random.rand(len(people))\n", "\n", "plt.barh(y_pos, count, xerr=error, align='center', alpha=0.4)\n", "plt.yticks(y_pos, people)\n", "plt.xlabel('Anzahl fur (A, H, B)')\n", "plt.title('Ergebnisse')\n", "\n", "plt.show()\n", "\n", "from sympy.interactive import printing\n", "printing.init_printing(use_latex=True)\n", "p_1 = (count[0] + count[1]) # h=0 and A=0\n", "p_2 = (count[4] + count[5]) # h=0 and A=1\n", "p_3 = (count[2] + count[3]) # h=1 and A=0\n", "p_4 = (count[6] + count[7]) # h=1 and A=1\n", "print(latex(\"p(H=0\|A=0) = %0.2f\" % (p_1/float(p_1+p_3))))\n", "print(latex(\"p(H=0\|A=1) = %0.2f\" % (p_2/float(p_2+p_4))))\n", "\n", "p_1 = (count[0] + count[4]) # h=0 and B=0\n", "p_2 = (count[1] + count[5]) # h=0 and B=1\n", "p_3 = (count[2] + count[6]) # h=1 and B=0\n", "p_4 = (count[3] + count[7]) # h=1 and B=1\n", "print(latex(\"p(H=0\|B=0) = %0.2f\" % (p_1/float(p_1+p_3))))\n", "print(latex(\"p(H=0\|B=1) = %0.2f\" % (p_2/float(p_2+p_4))))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
hanhanwu/Hanhan_Data_Science_Practice	sequencial_analysis/after_2020_practice/ts_1DCNN.ipynb	1	119915	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Forecast with 1D CNN\n", "\n", "* We can combine the speed and lightness of convnets with the order-sensitivity of RNNs is to use a 1D convnet as a preprocessing step before a RNN. \n", " * This is especially beneficial when dealing with sequences that are so long that they couldn't realistically be processed with RNNs, e.g. sequences with thousands of steps. The convnet will turn the long input sequence into much shorter (downsampled) sequences of higher-level features. This sequence of extracted features then becomes the input to the RNN part of the network.\n", "* 1D CNN means the kernel slides only need to move along 1 dimension\n", " * 2D means wdidth and height\n", " * 3D means wdidth, height and depth" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "from sklearn.preprocessing import MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(43824, 13)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>No</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>hour</th>\n", " <th>pm2.5</th>\n", " <th>DEWP</th>\n", " <th>TEMP</th>\n", " <th>PRES</th>\n", " <th>cbwd</th>\n", " <th>Iws</th>\n", " <th>Is</th>\n", " <th>Ir</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1021.0</td>\n", " <td>NW</td>\n", " <td>1.79</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-12.0</td>\n", " <td>1020.0</td>\n", " <td>NW</td>\n", " <td>4.92</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>6.71</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-14.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>9.84</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>NaN</td>\n", " <td>-20</td>\n", " <td>-12.0</td>\n", " <td>1018.0</td>\n", " <td>NW</td>\n", " <td>12.97</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " No year month day hour pm2.5 DEWP TEMP PRES cbwd Iws Is Ir\n", "0 1 2010 1 1 0 NaN -21 -11.0 1021.0 NW 1.79 0 0\n", "1 2 2010 1 1 1 NaN -21 -12.0 1020.0 NW 4.92 0 0\n", "2 3 2010 1 1 2 NaN -21 -11.0 1019.0 NW 6.71 0 0\n", "3 4 2010 1 1 3 NaN -21 -14.0 1019.0 NW 9.84 0 0\n", "4 5 2010 1 1 4 NaN -20 -12.0 1018.0 NW 12.97 0 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/pm25.csv')\n", "\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train: (33096, 15)\n", "Shape of test: (8661, 15)\n" ] } ], "source": [ "df.dropna(subset=['pm2.5'], axis=0, inplace=True)\n", "df.reset_index(drop=True, inplace=True)\n", "\n", "df['datetime'] = df[['year', 'month', 'day', 'hour']].apply(\n", " lambda row: datetime.datetime(year=row['year'], \n", " month=row['month'], day=row['day'],hour=row['hour']), axis=1)\n", "df.sort_values('datetime', ascending=True, inplace=True)\n", "\n", "scaler = MinMaxScaler(feature_range=(0, 1))\n", "df['scaled_pm2.5'] = scaler.fit_transform(np.array(df['pm2.5']).reshape(-1, 1))\n", "\n", "split_date = datetime.datetime(year=2014, month=1, day=1, hour=0) \n", "df_train = df.loc[df['datetime']<split_date]\n", "df_val = df.loc[df['datetime']>=split_date]\n", "df_val.reset_index(drop=True, inplace=True)\n", "print('Shape of train:', df_train.shape)\n", "print('Shape of test:', df_val.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def makeXy(ts, nb_timesteps):\n", " \"\"\"\n", " Input: \n", " ts: original time series\n", " nb_timesteps: number of time steps in the regressors\n", " Output: \n", " X: 2-D array of regressors\n", " y: 1-D array of target \n", " \"\"\"\n", " X = []\n", " y = []\n", " for i in range(nb_timesteps, ts.shape[0]):\n", " X.append(list(ts.loc[i-nb_timesteps:i-1]))\n", " y.append(ts.loc[i])\n", " \n", " X, y = np.array(X), np.array(y)\n", " return X, y" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train arrays: (33089, 7) (33089,)\n", "Shape of validation arrays: (8654, 7) (8654,)\n" ] } ], "source": [ "X_train, y_train = makeXy(df_train['scaled_pm2.5'], 7)\n", "X_val, y_val = makeXy(df_val['scaled_pm2.5'], 7)\n", "\n", "print('Shape of train arrays:', X_train.shape, y_train.shape)\n", "print('Shape of validation arrays:', X_val.shape, y_val.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of arrays after reshaping: (33089, 7, 1) (8654, 7, 1)\n" ] } ], "source": [ "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n", "X_val = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n", "print('Shape of arrays after reshaping:', X_train.shape, X_val.shape)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.layers import LSTM, Conv1D\n", "from tensorflow.keras.layers import Dense, Dropout, Input, ZeroPadding1D, AveragePooling1D, Flatten\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "\n", "from sklearn.metrics import mean_absolute_error\n", "\n", "tf.random.set_seed(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1D CNN Only\n", "\n", "* ZeroPadding1D layer is added next to add zeros at the begining and end of each series. \n", " * Zeropadding ensure that the downstream convolution layer does not reduce the dimension of the output sequences.\n", " * Pooling layer, added after the convolution layer is used to downsampling the input.\n", "* In Con1D\n", " * param 1 determines the number of features in the output\n", " * param 2 indicates length of the 1D convolution window, moving window size\n", " * param 3 is strides of the moving window, representing the number of places to shift the convolution window\n", " * `use_bias` as True, adds a bias value during computation of an output feature\n", " * 1D convolution can be thought of as generating local AR models over rolling window of three time units.\n", "* AveragePooling1D is added next to downsample the input by taking average over pool size of three with stride of one timesteps. \n", " * The average pooling in this case can be thought of as taking moving averages over a rolling window of three time units.\n", "* The Flatten layer reshapes the input to `(number of samples, number of timestepsnumber of features per timestep)`, which is then fed to the output layer" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_11\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "zero_padding1d_7 (ZeroPaddin (None, 9, 1) 0 \n", "_________________________________________________________________\n", "conv1d_18 (Conv1D) (None, 7, 64) 256 \n", "_________________________________________________________________\n", "conv1d_19 (Conv1D) (None, 5, 32) 6176 \n", "_________________________________________________________________\n", "average_pooling1d_7 (Average (None, 3, 32) 0 \n", "_________________________________________________________________\n", "flatten_4 (Flatten) (None, 96) 0 \n", "_________________________________________________________________\n", "dense_12 (Dense) (None, 32) 3104 \n", "_________________________________________________________________\n", "dense_13 (Dense) (None, 16) 528 \n", "_________________________________________________________________\n", "dropout_4 (Dropout) (None, 16) 0 \n", "_________________________________________________________________\n", "dense_14 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 10,081\n", "Trainable params: 10,081\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(ZeroPadding1D(padding=1,\n", " input_shape=(X_train.shape[1:])))\n", "model.add(Conv1D(64, 3, strides=1, use_bias=True))\n", "model.add(Conv1D(32, 3, strides=1, use_bias=True))\n", "model.add(AveragePooling1D(pool_size=3, strides=1))\n", "model.add(Flatten())\n", "model.add(Dense(32))\n", "model.add(Dense(16))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(1, activation='tanh'))\n", "\n", "model.compile(optimizer=Adam(amsgrad=True), loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "1655/1655 [==============================] - 16s 9ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0129 - val_mae: 0.0129\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 2/50\n", "1655/1655 [==============================] - 16s 10ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 3/50\n", "1655/1655 [==============================] - 19s 11ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0124 - val_mae: 0.0124ss: 0.0145 - m - ETA: 0s - loss: 0.0145 - mae: \n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 4/50\n", "1655/1655 [==============================] - 22s 13ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 5/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 6/50\n", "1655/1655 [==============================] - 19s 11ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 7/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 8/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 9/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 10/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 11/50\n", "1655/1655 [==============================] - 27s 16ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0124 - val_mae: 0.0124 mae\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 12/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 13/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 14/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 15/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 16/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 17/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 18/50\n", "1655/1655 [==============================] - 29s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 19/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 20/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 21/50\n", "1655/1655 [==============================] - 29s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 22/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 23/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0131 - val_mae: 0.0131\n", "Epoch 24/50\n", "1655/1655 [==============================] - 18s 11ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0131 - val_mae: 0.0131 1s - loss: 0.0143 - m - ETA: 0s - loss: 0.\n", "Epoch 25/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 26/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 27/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 28/50\n", "1655/1655 [==============================] - 18s 11ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 29/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0133 - val_mae: 0.0133\n", "Epoch 30/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 31/50\n", "1655/1655 [==============================] - 24s 14ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0135 - val_mae: 0.0135014 - ETA: 0s - loss: 0.0\n", "Epoch 32/50\n", "1655/1655 [==============================] - 24s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 33/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0129 - val_mae: 0.01290142 - \n", "Epoch 34/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 35/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0123 - val_mae: 0.0123\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 36/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 37/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 38/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 39/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 40/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125ae: 0.01\n", "Epoch 41/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0129 - val_mae: 0.01290142 - mae: 0.0\n", "Epoch 42/50\n", "1655/1655 [==============================] - 22s 13ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0124 - val_mae: 0.0124loss: 0.0\n", "Epoch 43/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0126 - val_mae: 0.0126s: 0.0141 - ETA: 0s - loss: 0.0141 - mae: 0.014\n", "Epoch 44/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0135 - val_mae: 0.0135.0142 - mae - ETA: 2s - lo - ETA: 1s - loss:\n", "Epoch 45/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 46/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 47/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0124 - val_mae: 0.01240s - loss: 0.0141 - mae: 0.\n", "Epoch 48/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0125 - val_mae: 0.0125: 0.0140 - ETA: 3s - loss: 0.014\n", "Epoch 49/50\n", "1655/1655 [==============================] - 27s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0123 - val_mae: 0.0123\n", "Epoch 50/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0125 - val_mae: 0.0125\n" ] } ], "source": [ "save_weights_at = '1dcnn_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=20, epochs=50,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 12.2419\n", "MAE for the scaled validation set: 0.0123\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load the best model\n", "best_model = load_model('1dcnn_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))\n", "\n", "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add LSTM after 1D CNN" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_13\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "zero_padding1d_9 (ZeroPaddin (None, 9, 1) 0 \n", "_________________________________________________________________\n", "conv1d_22 (Conv1D) (None, 7, 64) 256 \n", "_________________________________________________________________\n", "conv1d_23 (Conv1D) (None, 5, 32) 6176 \n", "_________________________________________________________________\n", "average_pooling1d_9 (Average (None, 3, 32) 0 \n", "_________________________________________________________________\n", "lstm (LSTM) (None, 32) 8320 \n", "_________________________________________________________________\n", "dense_15 (Dense) (None, 1) 33 \n", "=================================================================\n", "Total params: 14,785\n", "Trainable params: 14,785\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(ZeroPadding1D(padding=1,\n", " input_shape=(X_train.shape[1:])))\n", "model.add(Conv1D(64, 3, strides=1, use_bias=True))\n", "model.add(Conv1D(32, 3, strides=1, use_bias=True))\n", "model.add(AveragePooling1D(pool_size=3, strides=1))\n", "model.add(LSTM(32, dropout=0.1, recurrent_dropout=0.5, activation='tanh'))\n", "model.add(Dense(1, activation='tanh'))\n", "\n", "model.compile(optimizer=Adam(amsgrad=True), loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "2069/2069 [==============================] - 24s 9ms/step - loss: 0.0218 - mae: 0.0218 - val_loss: 0.0140 - val_mae: 0.0140\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 2/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0164 - mae: 0.0164 - val_loss: 0.0141 - val_mae: 0.0141\n", "Epoch 3/20\n", "2069/2069 [==============================] - 26s 13ms/step - loss: 0.0158 - mae: 0.0158 - val_loss: 0.0133 - val_mae: 0.0133 0.0158 \n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 4/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0155 - mae: 0.0155 - val_loss: 0.0145 - val_mae: 0.0145\n", "Epoch 5/20\n", "2069/2069 [==============================] - 28s 14ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0143 - val_mae: 0.0143\n", "Epoch 6/20\n", "2069/2069 [==============================] - 28s 13ms/step - loss: 0.0155 - mae: 0.0155 - val_loss: 0.0140 - val_mae: 0.0140\n", "Epoch 7/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0139 - val_mae: 0.0139\n", "Epoch 8/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0150 - mae: 0.0150 - val_loss: 0.0128 - val_mae: 0.0128\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 9/20\n", "2069/2069 [==============================] - 24s 12ms/step - loss: 0.0149 - mae: 0.0149 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 10/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0152 - mae: 0.0152 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 11/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 12/20\n", "2069/2069 [==============================] - 28s 13ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 13/20\n", "2069/2069 [==============================] - 28s 14ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 14/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 15/20\n", "2069/2069 [==============================] - 30s 14ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 16/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 17/20\n", "2069/2069 [==============================] - 30s 15ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 18/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0133 - val_mae: 0.0133s - ETA: 0s - loss: 0.0147 - mae: 0\n", "Epoch 19/20\n", "2069/2069 [==============================] - 26s 12ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 20/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0126 - val_mae: 0.0126\n" ] } ], "source": [ "save_weights_at = '1dcnn_lstm_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=16, epochs=20,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 12.4811\n", "MAE for the scaled validation set: 0.0126\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load the best model\n", "best_model = load_model('1dcnn_lstm_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))\n", "\n", "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note\n", "\n", " 1D CNN only is faster when there is same amount of epoches and batch_size\n", " * In this case, both experiments got same performance whe epoch and batch_size were the same\n", "* Both experiments appear to be a little bit underfitting" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
zklgame/CatEyeNets	test/two_layer_net.ipynb	1	628786	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing a Neural Network\n", "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "os.chdir(os.getcwd() + '/..')\n", "\n", "# Run some setup code for this notebook\n", "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from utils.data_utils import load_CIFAR10\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# Some more magic so that the notebook will reload external python modules;\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from classifiers.neural_net import TwoLayerNet\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / np.maximum(1e-8, np.abs(x) + np.abs(y)))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a small net and toy data to check implementations.\n", "# set random seed for repeatable experiments.\n", "input_size = 4\n", "hidden_size = 10\n", "num_classes = 3\n", "num_inputs = 5\n", "\n", "def init_toy_model():\n", " np.random.seed(0)\n", " return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n", "\n", "def init_toy_data():\n", " np.random.seed(1)\n", " X = 10 * np.random.randn(num_inputs, input_size)\n", " y = np.array([0, 1, 2, 2, 1])\n", " return X, y\n", "\n", "net = init_toy_model()\n", "X, y = init_toy_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute scores" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scores: \n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "correct scores:\n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "Difference between your scores and correct scores:\n", "3.68027204961e-08\n" ] } ], "source": [ "scores = net.loss(X)\n", "print('scores: ')\n", "print(scores)\n", "print\n", "\n", "print('correct scores:')\n", "correct_scores = np.asarray([\n", " [-0.81233741, -1.27654624, -0.70335995],\n", " [-0.17129677, -1.18803311, -0.47310444],\n", " [-0.51590475, -1.01354314, -0.8504215 ],\n", " [-0.15419291, -0.48629638, -0.52901952],\n", " [-0.00618733, -0.12435261, -0.15226949]])\n", "print(correct_scores)\n", "print\n", "\n", "# The difference should be very small, get < 1e-7\n", "print('Difference between your scores and correct scores:')\n", "print(np.sum(np.abs(scores - correct_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute loss" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference between your loss and correct loss:\n", "1.79412040779e-13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "classifiers/neural_net.py:74: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if y == None:\n" ] } ], "source": [ "loss, _ = net.loss(X, y, reg=0.05)\n", "corrent_loss = 1.30378789133\n", "\n", "# should be very small, get < 1e-12\n", "print('Difference between your loss and correct loss:')\n", "print(np.sum(np.abs(loss - corrent_loss)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Backward pass\n", "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b2 max relative error: 4.447687e-11\n", "b1 max relative error: 1.555470e-09\n", "W1 max relative error: 3.669858e-09\n", "W2 max relative error: 3.440708e-09\n" ] } ], "source": [ "from utils.gradient_check import eval_numerical_gradient\n", "\n", "loss, grads = net.loss(X, y, reg=0.05)\n", "\n", "# these should all be less than 1e-8 or so\n", "for param_name in grads:\n", " f = lambda W: net.loss(X, y, reg=0.05)[0]\n", " param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n", " print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Train the network\n", "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.2." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Final training loss: ', 0.017149607938731846)\n" ] }, { "data": { "image/png": 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s7c2xqB8AAMyLQNYgW3ty2ndiUu4edykAAKDJEMgaZFtvuyZLFR0bK8ZdCgAA\naDKRBjIzu8rMHjSzPWb2zkX2e6aZlc3s16OsJ04zZ1oybQkAAE4XWSAzs6Sk90vaJWmHpFea2Y4F\n9vtzSV+LqpZm8HgvMgIZAAA4VZQjZJdJ2uPue929KOlGSVfPs99vSfonSUcjrCV2W3oIZAAAYH5R\nBrLNkvbNub8/3DbLzDZLeqmkD0ZYR1NoTyfVX8jQrR8AADxB3Iv63yvpHe5eXWwnM7vGzHab2e7B\nwcEGlVZ/23rbWUMGAACeIMpAdkDS1jn3t4Tb5top6UYze0TSr0v6gJm95PQXcvcb3H2nu+9ct25d\nVPVGbmtvjssnAQCAJ4gykN0l6XwzO9vM0pJeIemmuTu4+9nuvt3dt0v6nKS3uPsXIqwpVlt7cjo0\nPKVSZdEBQQAAsMZEFsjcvSzpbZJukXS/pM+6+31mdq2ZXRvV+zazbb05VaquQ0NTcZcCAACaSCrK\nF3f3myXdfNq26xfY9/VR1tIMtvS2Swp6kW3ry8VcDQAAaBZxL+pfU7bS+gIAAMyDQNZAA11ZpRLG\nwn4AAHAKAlkDpZIJbepu176T9CIDAACPI5A12NbedqYsAQDAKQhkDbatN0cgAwAApyCQNdiWnpyO\njxc1Pl2OuxQAANAkCGQNtrU3ONNyP+vIAABAiEDWYNvCQMaZlgAAYAaBrMG29oTNYQlkAAAgRCBr\nsN58Wrl0UvtOEsgAAECAQNZgZsaZlgAA4BQEshhs6clp3wkW9QMAgACBLAZbe9v12IkJuXvcpQAA\ngCZAIIvBtt6cJksVHR8vxl0KAABoAgSyGGztCVpfsI4MAABIBLJYbOujFxkAAHgcgSwGW8JeZHTr\nBwAAEoEsFrl0Sv2FNFOWAABAEoEsNlt7c0xZAgAASQSy2GztydGtHwAASCKQxWZ7f14HTk5qqlSJ\nuxQAABAzAllMLtzYoapLe46OxV0KAACIGYEsJk/a0CFJevDwaMyVAACAuBHIYrK9L6d0KqEHjxDI\nAABY6whkMUklEzpvXYERMgAAQCCL0wUbOwhkAACAQBanCzZ26PDIlIYnSnGXAgAAYkQgi9EFMwv7\nWUcGAMCaRiCL0QUbCWQAAIBAFquBrqw6sik9eHgk7lIAAECMCGQxMjNdsKFDDx2mOSwAAGsZgSxm\nT9rYoQcOj8jd4y4FAADEhEAWsws3dmhkqqwjI9NxlwIAAGJCIIvZkzjTEgCANY9AFrPZ1hcs7AcA\nYM0ikMVtMR8ZAAAaKElEQVSsJ5/W+o6MHmRhPwAAaxaBrAlcsLFDDx5hhAwAgLWKQNYELtjQoZ8c\nGVOlypmWAACsRQSyJnDBxg5Nl6t69Ph43KUAAIAYEMiawMwllB7iTEsAANYkAlkTOH99h8ykBw4T\nyAAAWIsIZE2gPZ3UWb05RsgAAFijCGRN4oKNHYyQAQCwRhHImsQFGzr0yLFxTZUqcZcCAAAajEDW\nJC7Y2KmqSz8dpEEsAABrDYGsSVywsSBJepBpSwAA1hwCWZM4qy+vdDLBRcYBAFiDCGRNoi2Z0Lnr\nC4yQAQCwBhHImsgFGwp6iEAGAMCaQyBrIhcNdOrg8JQODE3GXQoAAGggAlkTedElA0qY9Kk7H427\nFAAA0EAEsiaytTenF1y0QZ/+7j76kQEAsIYQyJrM667YrhPjRX353kNxlwIAABqEQNZkfva8Pp27\nLq+P3/FI3KUAAIAGIZA1GTPT6561XT/YP6x79g3FXQ4AAGiASAOZmV1lZg+a2R4ze+c8j7/azO41\nsx+a2e1m9tQo62kVv/r0LSpkUvr72x+JuxQAANAAkQUyM0tKer+kXZJ2SHqlme04bbeHJT3H3S+R\n9G5JN0RVTyspZFL6tadv1pfvPaTB0em4ywEAABGLcoTsMkl73H2vuxcl3Sjp6rk7uPvt7n4yvPsd\nSVsirKelvPaK7SpWqvrMXY/FXQoAAIhYlIFss6R9c+7vD7ct5I2SvhJhPS3lvPUF/dz5/frEdx5T\nqVKNuxwAABChpljUb2bPUxDI3rHA49eY2W4z2z04ONjY4mL0m1ds1+GRKf3rj4/EXQoAAIhQlIHs\ngKStc+5vCbedwsyeIunDkq529+PzvZC73+DuO91957p16yIpthk9/8L12tLTzuJ+AABWuSgD2V2S\nzjezs80sLekVkm6au4OZbZP0eUmvdfeHIqylJSUTptdefpbufPiE9hzlouMAAKxWkQUydy9Lepuk\nWyTdL+mz7n6fmV1rZteGu/2xpD5JHzCze8xsd1T1tKqXPC1YdnfzDw/HXAkAAIiKuXvcNSzLzp07\nfffutZXbfu2Dt2uiWNFXrvu5uEsBAADLYGZ3u/vOpfZrikX9WNyuizfq/kMjevT4eNylAACACBDI\nWsCVT94oSfrqj5i2BABgNSKQtYCtvTldvLlTXyGQAQCwKhHIWsSuiwd0z74hHRqejLsUAABQZwSy\nFjEzbXkLo2QAAKw6BLIWcd76gs5fX2DaEgCAVYhA1kJ2XbxRdz1yQsfGpuMuBQAA1BGBrIVcefFG\nVV1c2xIAgFWGQNZCdgx0altvjvYXAACsMgSyFmJm2nXxRt3+02ManizFXQ4AAKgTAlmLufLijSpV\nXLfez7QlAACrBYGsxVy6pVsbO7NMWwIAsIoQyFpMImG66uKN+uZDgxqfLsddDgAAqAMCWQvadfFG\nTZer+vC3Ho67FAAAUAcEshZ02dm9eunTNuu9tz6krz/AWjIAAFodgawFmZn+10sv0Y6BTl336Xu0\nd3As7pIAAMAZIJC1qPZ0Uh967TOUSpre/A93a4z1ZAAAtCwCWQvb0pPT+1/1dO09Nq7f/ew9qlY9\n7pIAAMAKEMha3LPO69cf7LpQt9x3RB/4xp64ywEAACtAIFsF3vjss/WSSzfpL//1IX17z7G4ywEA\nAMtEIFsFzEx/9qtP0bbenN79pR8zdQkAQIshkK0S7emkfveFF+iBw6P64g8OxF0OAABYBgLZKvLi\nSwb05E2d+suvPaTpciXucgAAQI0IZKtIImF6+1UXav/JSX3qzsfiLgcAANSIQLbK/Pz5/brinD79\n7df30JsMAIAWQSBbZcxM79h1oY6PF/V/btsbdzkAAKAGBLJV6NKt3dp18UZ9+Ft7dWxsOu5yAADA\nEghkq9TvXXmBpspV/e3XaRYLAECzI5CtUueuK+hlO7fok3c+qn0nJuIuBwAALIJAtopd94InKZkw\nveWT39PJ8WLc5QAAgAUQyFaxjV1ZfeDVT9eDR0b18hvu0NGRqbhLAgAA8yCQrXLPv3CDPvaGZ+rA\nyUn9xofuYPoSAIAmRCBbA551br8+8aaf0dBESb9x/R3ac3Qs7pIAAMAcBLI14mnbenTjNZerXHW9\n7EN36Fs/GeQi5AAANAkC2Rpy0UCn/vHaK9TeltRrP/JdXf5nt+pPbrpP3334BOEMAIAYmXtr/SLe\nuXOn7969O+4yWtr4dFm3PnBUN997SP/+4FFNl6va0JnRSy7drNc9a7s2dbfHXSIAAKuCmd3t7juX\n3I9AtraNTZd16/1H9OV7D+nWB47KJP3KUzfp//n5c3TRQGfc5QEA0NIIZFi2/Scn9JH/eFifuWuf\nJooV/dz5/Xrr887T5ef0xV0aAAAtiUCGFRueKOkTdz6qj93+iAZHp/XKy7bpv/3SRSpkUnGXBgBA\nS6k1kLGoH0/QlWvTW593nr719ufpzT9/jm686zFd+de36fafHou7NAAAViUCGRaUbUvqD150kT53\n7RVKpxJ61f+5U+/64o80USzHXRoAAKsKgQxLesZZvbr5t39Ob/jZ7fr7Ox7VVe/9lr5232G12nQ3\nAADNikCGmrSnk3rXLz9ZN15zudKphK75h7v1mo/cqfsPjcRdGgAALY9F/Vi2UqWqT935mP763x7S\nyGRJr7hsm/7rLz5J6VRCg6PTs7fhyZJ2bOrUUzZ3KZUk+wMA1h7OskTkhiaKet+tP9E/3PGoyot0\n+s+nk3rm2b264pw+XXFuny7e1KVEwhpYKQAA8SCQoWH2HB3TTfccUEe2Tes6MrO39rakfrB/SHf8\n9Lju2HtcewfHJUlbetr1a0/fol9/xhZt7c3FXD0AANEhkKHpHBmZ0rd+ckxf+P4Bffunx+QuXX5O\nr379GVt1yeYuFbIpFTLBLckIGgBgFSCQoakdGJrUP39vvz539349cnziCY/n0kmlUwmZJDMLP0o9\nubQu3twV3DZ1asemTnVk2xpePwAAtSCQoSW4u36wf1gHhyY1NlXWyFRJY9NljU6VVapU5S65XO5S\n1aWjI1P60cFhHRmZnn2N7X05XTTQqQs3durCgQ5dtLFTm7qzOjFe1NHwBIOjo1Mam67owo0dumRL\nlzoJcQCABqg1kHEtHMTKzHTp1m5durV7Wc87Ojql+w6O6L4Dw7rv4IgeODyqr953WLX+fXHOurye\nuqVbOwY6VXHX6FRJo1NBEJwolnXJ5i698Mkbdf76gsyYPgUARIsRMqwa49NlPXRkVA8cHtXh4Sn1\nF9Ja15HV+s6M1hUyak8n9eODI7p3/5B+sH9YP9g3pKOjwUhbMmHqyKbUkU2pLZmYPQHhrL6cXrhj\ng37hog3KtCX12IkJ7Qtvj52YULFcVaYtoUwqqWz4MZ9JqjefUV8+rd58OvhYSKs3l1ZPPq02WoAA\nwJrBlCVQg6GJotKphNrbkqeMhB0ZmdK/3X9EX7vviO746XEVK9VTntdfyGhrb7ty6aSmSlVNlyua\nLlU1Va5obKqsocnSgqN1HdnUbFDb0pPTlp52be0NPq7vyKpYrmq8WNZksTL70aUnrKdLmMksCJMJ\nMyVMSiUSSqeCW1syoUz4MZU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"text/plain": [ "<matplotlib.figure.Figure at 0x10e936550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net = init_toy_model()\n", "stats = net.train(X, y, X, y, learning_rate=1e-1, reg=5e-6, num_iters=100, verbose=False)\n", "print('Final training loss: ', stats['loss_history'][-1])\n", "\n", "# plot the loss history\n", "plt.plot(stats['loss_history'])\n", "plt.xlabel('iteration')\n", "plt.ylabel('training loss')\n", "plt.title('Training Loss history')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load the data\n", "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((49000, 3072), (1000, 3072), (1000, 3072))\n" ] } ], "source": [ "# Load the raw CIFAR-10 data\n", "cifar10_dir = 'datasets/cifar-10-batches-py'\n", "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", "# Split the data\n", "num_training = 49000\n", "num_validation = 1000\n", "num_test = 1000\n", "\n", "mask = range(num_training, num_training+num_validation)\n", "X_val = X_train[mask]\n", "y_val = y_train[mask]\n", "\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "mask = xrange(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]\n", "\n", "# Preprocessing: reshape the image data into rows\n", "X_train = X_train.reshape(X_train.shape[0], -1)\n", "X_val = X_val.reshape(X_val.shape[0], -1)\n", "X_test = X_test.reshape(X_test.shape[0], -1)\n", "\n", "# Normalize the data: subtract the mean rows\n", "mean_image = np.mean(X_train, axis=0)\n", "X_train -= mean_image\n", "X_val -= mean_image\n", "X_test -= mean_image\n", "\n", "print(X_train.shape, X_val.shape, X_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a network\n", "To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 1 / 1000: loss 2.302957\n", "iteration 101 / 1000: loss 2.302440\n", "iteration 201 / 1000: loss 2.298435\n", "iteration 301 / 1000: loss 2.262676\n", "iteration 401 / 1000: loss 2.200829\n", "iteration 501 / 1000: loss 2.114582\n", "iteration 601 / 1000: loss 2.080862\n", "iteration 701 / 1000: loss 2.099089\n", "iteration 801 / 1000: loss 1.999965\n", "iteration 901 / 1000: loss 1.961439\n", "('Validation accuracy: ', 0.28599999999999998)\n" ] } ], "source": [ "input_size = 32 * 32 * 3\n", "hidden_size = 50\n", "num_classes = 10\n", "net = TwoLayerNet(input_size, hidden_size, num_classes)\n", "\n", "# Train the network\n", "stats = net.train(X_train, y_train, X_val, y_val,\n", " learning_rate=1e-4, learning_rate_decay=0.95,\n", " reg=0.25, num_iters=1000, batch_size=200, verbose=True)\n", "\n", "# Predict on the validation set\n", "val_acc = (net.predict(X_val) == y_val).mean()\n", "print('Validation accuracy: ', val_acc)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Debug the training\n", "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n", "\n", "### One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n", "\n", "### Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rHWFW8dDteLaFB5lYuq0a9721whSJQljWR9tnlNbjxoSJnTVN7XL/fOGe6Stw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\nI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"text/plain": [ "<matplotlib.figure.Figure at 0x117e3d150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the loss function and train / validation accuracies\n", "plt.subplot(2, 1, 1)\n", "plt.plot(stats['loss_history'])\n", "plt.title('Loss history')\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Loss')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(stats['train_acc_history'], label='train')\n", "plt.plot(stats['val_acc_history'], label='val')\n", "plt.title('Classification accuracy history')\n", "plt.legend()\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Classification accuracy')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PTeMs6/+duJ/8psk6ipqXajyEJyn4/mqfiz2LQlwqzhyBZaWbRZu+X41N7MpbyzZGYlvb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Y83UDNh3cgvNKTHIBHF61OtNpzxfFe5AOBPpp34ww3qdVA/hsQBx0A//L8DADy1V3FbIPCIXoqX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i2cCKBzl8I5opqAfUBMWSr2wdjgJTpbFGGQgX3YPk883Ycb8HlFBUPIgh7q2Cj6eHHX5rT/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\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FRJEQfW9XfIx8J2VSaAZEkln0BAkTkytdjSLiZ3lFZbD9CNlgT7YAAObOBHxyRoKB9r+G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\nhKrfx9oWI8/G+50j3iM+Jk7di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"text/plain": [ "<matplotlib.figure.Figure at 0x11a0fb190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils.vis_utils import visualize_grid\n", "\n", "# Visualize the weights of the network\n", "def show_net_weights(net):\n", " W1 = net.params['W1']\n", " W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n", " plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n", " plt.gca().axis('off')\n", " plt.show()\n", " \n", "show_net_weights(net)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Tune your hyperparameters\n", "\n", "What's wrong?. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n", "\n", "Tuning. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n", "\n", "Approximate results. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n", "\n", "Experiment: You goal in this exercise is to get as good of a result on CIFAR-10 as you can, with a fully-connected Neural Network. For every 1% above 52% on the Test set we will award you with one extra bonus point. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 1 / 5000: loss 2.409961\n", "iteration 101 / 5000: loss 2.158140\n", "iteration 201 / 5000: loss 2.009294\n", "iteration 301 / 5000: loss 1.979639\n", "iteration 401 / 5000: loss 1.942002\n", "iteration 501 / 5000: loss 1.827258\n", "iteration 601 / 5000: loss 1.835251\n", "iteration 701 / 5000: loss 1.704696\n", "iteration 801 / 5000: loss 1.769029\n", "iteration 901 / 5000: loss 1.675095\n", "iteration 1001 / 5000: loss 1.689062\n", "iteration 1101 / 5000: loss 1.786561\n", "iteration 1201 / 5000: loss 1.822278\n", "iteration 1301 / 5000: loss 1.721067\n", "iteration 1401 / 5000: loss 1.730675\n", "iteration 1501 / 5000: loss 1.686564\n", "iteration 1601 / 5000: loss 1.544546\n", "iteration 1701 / 5000: loss 1.619521\n", "iteration 1801 / 5000: loss 1.678234\n", "iteration 1901 / 5000: loss 1.666344\n", "iteration 2001 / 5000: loss 1.637763\n", "iteration 2101 / 5000: loss 1.687455\n", "iteration 2201 / 5000: loss 1.479651\n", "iteration 2301 / 5000: loss 1.572565\n", "iteration 2401 / 5000: loss 1.578272\n", "iteration 2501 / 5000: loss 1.612838\n", "iteration 2601 / 5000: loss 1.767758\n", "iteration 2701 / 5000: loss 1.624772\n", "iteration 2801 / 5000: loss 1.643853\n", "iteration 2901 / 5000: loss 1.544022\n", "iteration 3001 / 5000: loss 1.699063\n", "iteration 3101 / 5000: loss 1.603541\n", "iteration 3201 / 5000: loss 1.684917\n", "iteration 3301 / 5000: loss 1.572763\n", "iteration 3401 / 5000: loss 1.582653\n", "iteration 3501 / 5000: loss 1.643857\n", "iteration 3601 / 5000: loss 1.476511\n", "iteration 3701 / 5000: loss 1.603652\n", "iteration 3801 / 5000: loss 1.518479\n", "iteration 3901 / 5000: loss 1.536813\n", "iteration 4001 / 5000: loss 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1.570371\n", "iteration 4201 / 5000: loss 1.623433\n", "iteration 4301 / 5000: loss 1.589593\n", "iteration 4401 / 5000: loss 1.700008\n", "iteration 4501 / 5000: loss 1.610730\n", "iteration 4601 / 5000: loss 1.647724\n", "iteration 4701 / 5000: loss 1.635629\n", "iteration 4801 / 5000: loss 1.684117\n", "iteration 4901 / 5000: loss 1.735978\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n", "\n", "\n", "best val_acc: 0.511000\n", "\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 7.000000e-01, train_acc: 0.500429, val_acc: 0.485000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 8.000000e-01, train_acc: 0.500510, val_acc: 0.482000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 9.000000e-01, train_acc: 0.494612, val_acc: 0.486000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 1.000000e+00, train_acc: 0.494184, val_acc: 0.487000\n", "\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 7.000000e-01, train_acc: 0.533082, val_acc: 0.495000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 8.000000e-01, train_acc: 0.529490, val_acc: 0.499000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 9.000000e-01, train_acc: 0.523714, val_acc: 0.489000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 1.000000e+00, train_acc: 0.514755, val_acc: 0.487000\n", "\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 7.000000e-01, train_acc: 0.530490, val_acc: 0.506000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 8.000000e-01, train_acc: 0.527694, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 9.000000e-01, train_acc: 0.517122, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 1.000000e+00, train_acc: 0.520612, val_acc: 0.472000\n", "\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 7.000000e-01, train_acc: 0.518082, val_acc: 0.511000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 8.000000e-01, train_acc: 0.516796, val_acc: 0.491000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 9.000000e-01, train_acc: 0.505898, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n" ] } ], "source": [ "input_size = 32 * 32 * 3\n", "num_classes = 10\n", "\n", "hidden_layer_size = [50]\n", "learning_rates = [3e-4, 9e-4, 1e-3, 3e-3]\n", "regularization_strengths = [7e-1, 8e-1, 9e-1, 1]\n", "\n", "results = {}\n", "\n", "best_model = None\n", "best_val = -1\n", "\n", "for hidden_size in hidden_layer_size:\n", " for lr in learning_rates:\n", " for reg in regularization_strengths:\n", " model = TwoLayerNet(input_size, hidden_size, num_classes, std=1e-3)\n", " stats = model.train(X_train, y_train, X_val, y_val,\n", " learning_rate=lr, learning_rate_decay=0.95,\n", " reg=reg, num_iters=5000, batch_size=200, verbose=True)\n", " \n", " train_acc = (model.predict(X_train) == y_train).mean()\n", " val_acc = (model.predict(X_val) == y_val).mean()\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))\n", " \n", " results[(hidden_size, lr, reg)] = (train_acc, val_acc)\n", " if val_acc > best_val:\n", " best_val = val_acc\n", " best_model = model\n", " print\n", "print\n", " \n", "print('best val_acc: %f' % (best_val))\n", " \n", "old_lr = -1\n", "for hidden_size, lr, reg in sorted(results):\n", " if old_lr != lr:\n", " old_lr = lr\n", " print\n", " \n", " train_acc, val_acc = results[(hidden_size, lr, reg)]\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hidden_layer_size: 50, lr: 3.000000e-04, reg: 7.000000e-01, train_acc: 0.500429, val_acc: 0.485000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 8.000000e-01, train_acc: 0.500510, val_acc: 0.482000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 9.000000e-01, train_acc: 0.494612, val_acc: 0.486000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 1.000000e+00, train_acc: 0.494184, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 7.000000e-01, train_acc: 0.533082, val_acc: 0.495000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 8.000000e-01, train_acc: 0.529490, val_acc: 0.499000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 9.000000e-01, train_acc: 0.523714, val_acc: 0.489000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 1.000000e+00, train_acc: 0.514755, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 7.000000e-01, train_acc: 0.530490, val_acc: 0.506000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 8.000000e-01, train_acc: 0.527694, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 9.000000e-01, train_acc: 0.517122, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 1.000000e+00, train_acc: 0.520612, val_acc: 0.472000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 7.000000e-01, train_acc: 0.518082, val_acc: 0.511000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 8.000000e-01, train_acc: 0.516796, val_acc: 0.491000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 9.000000e-01, train_acc: 0.505898, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n" ] } ], "source": [ "for hidden_size, lr, reg in sorted(results):\n", " train_acc, val_acc = results[(hidden_size, lr, reg)]\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3aiO5xybo0DMEfdxFKN/K70duCJNkOIUyQlqXQ2VNbxMtUSey3qUZYGTLfp+P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8ds6zRJx2R2fUgS5DFJUP4SHQkpRZZiRnjYYT9zWxwwiyS6y/oS2wN7IvUKHdVZLEJj89UM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\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hocvk70/CEKD+MpQSkyu52MfsJ0dpAseTf47zAg3dekSngHs3KlrQHM5rg2D9/AK2keFP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\ngzeTzDTtbGy38lkOfYg8k01raz2NZJY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"text/plain": [ "<matplotlib.figure.Figure at 0x11e31cbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize the weights of the best network\n", "show_net_weights(best_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run on the test set\n", "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%.\n", "\n", "We will give you extra bonus point for every 1% of accuracy above 52%." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Test accuracy: ', 0.504)\n" ] } ], "source": [ "test_acc = (best_model.predict(X_test) == y_test).mean()\n", "print('Test accuracy: ', test_acc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
sueiras/training	tensorflow_old/02-template_class/template_class.ipynb	1	8425	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Standar usage of TensoFlow with model class\n", "\n", "Tipically use 3 files:\n", " - data_utils.py: With the data access and batch generator functions\n", " - model.py: With the class model. A constructor with the graph definition and method to manage model needs\n", " - train.py: With parameters. Access to the data, instance the model and train it. Optionaly add a parameter to train or inference.\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## data_utils.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#! /usr/bin/env python\n", "\n", "import tensorflow as tf\n", "\n", "# Access to the data\n", "def get_data(data_dir='/tmp/MNIST_data'):\n", " from tensorflow.examples.tutorials.mnist import input_data\n", " return input_data.read_data_sets(data_dir, one_hot=True)\n", "\n", "\n", "#Batch generator\n", "def batch_generator(mnist, batch_size=256, type='train'):\n", " if type=='train':\n", " return mnist.train.next_batch(batch_size)\n", " else:\n", " return mnist.test.next_batch(batch_size)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## model_mnist_cnn.py" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#! /usr/bin/env python\n", "\n", "import tensorflow as tf\n", "\n", "class mnistCNN(object):\n", " \"\"\"\n", " A NN for mnist classification.\n", " \"\"\"\n", " def __init__(self, dense=500):\n", " \n", " # Placeholders for input, output and dropout\n", " self.input_x = tf.placeholder(tf.float32, [None, 784], name=\"input_x\")\n", " self.input_y = tf.placeholder(tf.float32, [None, 10], name=\"input_y\")\n", " \n", " # First layer\n", " self.dense_1 = self.dense_layer(self.input_x, input_dim=784, output_dim=dense)\n", "\n", " # Final layer\n", " self.dense_2 = self.dense_layer(self.dense_1, input_dim=dense, output_dim=10)\n", "\n", " self.predictions = tf.argmax(self.dense_2, 1, name=\"predictions\")\n", " \n", " # Loss function\n", " self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(self.dense_2, self.input_y))\n", " \n", " # Accuracy\n", " correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))\n", " self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n", " \n", "\n", " def dense_layer(self, x, input_dim=10, output_dim=10, name='dense'):\n", " '''\n", " Dense layer function\n", " Inputs:\n", " x: Input tensor\n", " input_dim: Dimmension of the input tensor.\n", " output_dim: dimmension of the output tensor\n", " name: Layer name\n", " '''\n", " W = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1), name='W_'+name)\n", " b = tf.Variable(tf.constant(0.1, shape=[output_dim]), name='b_'+name)\n", " dense_output = tf.nn.relu(tf.matmul(x, W) + b)\n", " return dense_output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## train.py" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Parameters:\n", "BATCH_SIZE=256\n", "DATA_DIRECTORY=/tmp/MNIST_data\n", "DENSE_SIZE=500\n", "LEARNING_RATE=0.001\n", "LOG_DEVICE_PLACEMENT=False\n", "NUM_EPOCHS=20\n", "\n", "Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz\n", "0 0.91909\n", "1 0.881748\n", "2 0.777025\n", "3 0.937195\n", "4 0.783779\n", "5 0.353136\n", "6 0.256104\n", "7 0.28514\n", "8 0.277642\n", "9 0.225344\n", "10 0.301154\n", "11 0.249453\n", "12 0.324219\n", "13 0.202852\n", "14 0.244397\n", "15 0.211011\n", "16 0.199964\n", "17 0.246017\n", "18 0.289519\n", "19 0.280718\n" ] } ], "source": [ "#! /usr/bin/env python\n", "\n", "from __future__ import print_function\n", "\n", "import tensorflow as tf\n", "\n", "#from data_utils import get_data, batch_generator\n", "#from model_mnist_cnn import mnistCNN\n", "\n", "\n", "# Parameters\n", "# ==================================================\n", "\n", "# Data loading params\n", "tf.flags.DEFINE_string(\"data_directory\", '/tmp/MNIST_data', \"Data dir (default /tmp/MNIST_data)\")\n", "\n", "# Model Hyperparameters\n", "tf.flags.DEFINE_integer(\"dense_size\", 500, \"dense_size (default 500)\")\n", "\n", "# Training parameters\n", "tf.flags.DEFINE_float(\"learning_rate\", 0.001, \"learning rate (default: 0.001)\")\n", "tf.flags.DEFINE_integer(\"batch_size\", 256, \"Batch Size (default: 256)\")\n", "tf.flags.DEFINE_integer(\"num_epochs\", 20, \"Number of training epochs (default: 20)\")\n", "\n", "# Misc Parameters\n", "tf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\n", "\n", "FLAGS = tf.flags.FLAGS\n", "FLAGS._parse_flags()\n", "print(\"\\nParameters:\")\n", "for attr, value in sorted(FLAGS.__flags.items()):\n", " print(\"{}={}\".format(attr.upper(), value))\n", "print(\"\")\n", "\n", "\n", "# Data Preparation\n", "# ==================================================\n", "\n", "#Access to the data\n", "mnist_data = get_data(data_dir= FLAGS.data_directory)\n", "\n", "\n", "# Training\n", "# ==================================================\n", "\n", "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333, allow_growth = True)\n", "with tf.Graph().as_default():\n", " session_conf = tf.ConfigProto(\n", " gpu_options=gpu_options,\n", " log_device_placement=FLAGS.log_device_placement)\n", " sess = tf.Session(config=session_conf)\n", " with sess.as_default():\n", " \n", " # Create model\n", " cnn = mnistCNN(dense=FLAGS.dense_size)\n", " \n", " # Trainer\n", " train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(cnn.loss)\n", "\n", " # Saver\n", " saver = tf.train.Saver(max_to_keep=1)\n", "\n", " # Initialize all variables\n", " sess.run(tf.global_variables_initializer())\n", "\n", " # Train proccess\n", " for epoch in range(FLAGS.num_epochs):\n", " for n_batch in range(int(55000/FLAGS.batch_size)):\n", " batch = batch_generator(mnist_data, batch_size=FLAGS.batch_size, type='train')\n", " _, ce = sess.run([train_op, cnn.loss], feed_dict={cnn.input_x: batch[0], cnn.input_y: batch[1]})\n", "\n", " print(epoch, ce)\n", " model_file = saver.save(sess, '/tmp/mnist_model')\n", " print('Model saved in', model_file)\n", "\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
dariox2/CADL	session-1/.ipynb_checkpoints/session-1-checkpoint.ipynb	1	32768	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Session 1 - Introduction to Tensorflow\n", "<p class=\"lead\">\n", "Assignment: Creating a Dataset/Computing with Tensorflow\n", "</p>\n", "\n", "<p class=\"lead\">\n", "Parag K. Mital<br />\n", "<a href=\"https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\">Creative Applications of Deep Learning w/ Tensorflow</a><br />\n", "<a href=\"https://www.kadenze.com/partners/kadenze-academy\">Kadenze Academy</a><br />\n", "<a href=\"https://twitter.com/hashtag/CADL\">#CADL</a>\n", "</p>\n", "\n", "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Goals\n", "\n", "* Learn how to normalize a dataset by calculating the mean/std. deviation\n", "* Learn how to use convolution\n", "* Explore what representations exist in your dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Outline\n", "\n", "<!-- MarkdownTOC autolink=true autoanchor=true bracket=round -->\n", "\n", "- [Assignment Synopsis](#assignment-synopsis)\n", "- [Part One - Create a Small Dataset](#part-one---create-a-small-dataset)\n", " - [Instructions](#instructions)\n", " - [Code](#code)\n", "- [Part Two - Compute the Mean](#part-two---compute-the-mean)\n", " - [Instructions](#instructions-1)\n", " - [Code](#code-1)\n", "- [Part Three - Compute the Standard Deviation](#part-three---compute-the-standard-deviation)\n", " - [Instructions](#instructions-2)\n", " - [Code](#code-2)\n", "- [Part Four - Normalize the Dataset](#part-four---normalize-the-dataset)\n", " - [Instructions](#instructions-3)\n", " - [Code](#code-3)\n", "- [Part Five - Convolve the Dataset](#part-five---convolve-the-dataset)\n", " - [Instructions](#instructions-4)\n", " - [Code](#code-4)\n", "- [Part Six - Sort the Dataset](#part-six---sort-the-dataset)\n", " - [Instructions](#instructions-5)\n", " - [Code](#code-5)\n", "- [Assignment Submission](#assignment-submission)\n", "\n", "<!-- /MarkdownTOC -->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Notebook</h1>\n", "\n", "Everything you will need to do will be inside of this notebook, and I've marked which cells you will need to edit by saying <b><font color='red'>\"TODO! COMPLETE THIS SECTION!\"</font></b>. For you to work with this notebook, you'll either download the zip file from the resources section on Kadenze or clone the github repo (whichever you are more comfortable with), and then run notebook inside the same directory as wherever this file is located using the command line \"jupyter notebook\" or \"ipython notebook\" (using Terminal on Unix/Linux/OSX, or Command Line/Shell/Powershell on Windows). If you are unfamiliar with jupyter notebook, please look at [Installation Preliminaries](https://github.com/pkmital/CADL/blob/master/README.md#installation-preliminaries) and [Session 0](https://github.com/pkmital/CADL/blob/master/session-0/session-0.ipynb) before starting!\n", "\n", "Once you have launched notebook, this will launch a web browser with the contents of the zip files listed. Click the file \"session-1.ipynb\" and this document will open in an interactive notebook, allowing you to \"run\" the cells, computing them using python, and edit the text inside the cells." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"assignment-synopsis\"></a>\n", "# Assignment Synopsis\n", "\n", "This first homework assignment will guide you through working with a small dataset of images. For Part 1, you'll need to find 100 images and use the function I've provided to create a montage of your images, saving it to the file \"dataset.png\" (template code provided below). You can load an existing dataset of images, find your own images, or perhaps create your own images using a creative process such as painting, photography, or something along those lines. Each image will be reshaped to 100 x 100 pixels. There needs to be at least 100 images. For Parts 2 and 3, you'll then calculate the mean and deviation of it using a tensorflow session. In Part 4, you'll normalize your dataset using the mean and deviation. Then in Part 5, you will convolve your normalized dataset. For Part 6, you'll need to sort the entire convolved dataset. Finally, the last part will package everything for you in a zip file which you can upload to Kadenze to get assessed (only if you are a Kadenze Premium member, $10 p/m, free for the first month). Remember to complete the additional excercises online, including the Gallery participation and the Forum post. If you have any questions, be sure to enroll in the course and ask your peers in the \\#CADL community or me on the forums!\n", "\n", "https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n", "\n", "The following assignment breakdown gives more detailed instructions and includes template code for you to fill out. Good luck!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-one---create-a-small-dataset\"></a>\n", "# Part One - Create a Small Dataset\n", "\n", "<a name=\"instructions\"></a>\n", "## Instructions\n", "\n", "Use Python, Numpy, and Matplotlib to load a dataset of 100 images and create a montage of the dataset as a 10 x 10 image using the function below. You'll need to make sure you call the function using a 4-d array of `N x H x W x C` dimensions, meaning every image will need to be the same size! You can load an existing dataset of images, find your own images, or perhaps create your own images using a creative process such as painting, photography, or something along those lines.\n", "\n", "When you are creating your dataset, I want you to think about what representations might exist in the limited amount of data that you are organizing. It is only 100 images after all, not a whole lot for a computer to reason about and learn something meaningful. So <b>think about creating a dataset of images that could possibly reveal something fundamental about what is contained in the images</b>. Try to think about creating a set of images that represents something. For instance, this might be images of yourself over time. Or it might be every picture you've ever taken of your cat. Or perhaps the view from your room at different times of the day. Consider making the changes within each image as significant as possible. As \"representative\" of the thing you want to capture as possible. Hopefully by the end of this lesson, you'll understand a little better the difference between what a computer thinks is significant and what you yourself thought was significant.\n", "\n", "The code below will show you how to resize and/or crop your images so that they are 100 pixels x 100 pixels in height and width. Once you have 100 images loaded, we'll use a `montage` function to draw and save your dataset to the file <b>dataset.png</b>.\n", "\n", "<a name=\"code\"></a>\n", "## Code\n", "\n", "This next section will just make sure you have the right version of python and the libraries that we'll be using. Don't change the code here but make sure you \"run\" it (use \"shift+enter\")!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First check the Python version\n", "import sys\n", "if sys.version_info < (3,4):\n", " print('You are running an older version of Python!\\n\\n' \\\n", " 'You should consider updating to Python 3.4.0 or ' \\\n", " 'higher as the libraries built for this course ' \\\n", " 'have only been tested in Python 3.4 and higher.\\n')\n", " print('Try installing the Python 3.5 version of anaconda '\n", " 'and then restart `jupyter notebook`:\\n' \\\n", " 'https://www.continuum.io/downloads\\n\\n')\n", "\n", "# Now get necessary libraries\n", "try:\n", " import os\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", " from skimage.transform import resize\n", "except ImportError:\n", " print('You are missing some packages! ' \\\n", " 'We will try installing them before continuing!')\n", " !pip install \"numpy>=1.11.0\" \"matplotlib>=1.5.1\" \"scikit-image>=0.11.3\" \"scikit-learn>=0.17\"\n", " import os\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", " from skimage.transform import resize\n", " print('Done!')\n", "\n", "# Import Tensorflow\n", "try:\n", " import tensorflow as tf\n", "except ImportError:\n", " print(\"You do not have tensorflow installed!\")\n", " print(\"Follow the instructions on the following link\")\n", " print(\"to install tensorflow before continuing:\")\n", " print(\"\")\n", " print(\"https://github.com/pkmital/CADL#installation-preliminaries\")\n", "\n", "# This cell includes the provided libraries from the zip file\n", "try:\n", " from libs import utils\n", "except ImportError:\n", " print(\"Make sure you have started notebook in the same directory\" +\n", " \" as the provided zip file which includes the 'libs' folder\" +\n", " \" and the file 'utils.py' inside of it. You will NOT be able\"\n", " \" to complete this assignment unless you restart jupyter\"\n", " \" notebook inside the directory created by extracting\"\n", " \" the zip file or cloning the github repo.\")\n", "\n", "# We'll tell matplotlib to inline any drawn figures like so:\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style> .rendered_html code { \n", " padding: 2px 4px;\n", " color: #c7254e;\n", " background-color: #f9f2f4;\n", " border-radius: 4px;\n", "} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Bit of formatting because inline code is not styled very good by default:\n", "from IPython.core.display import HTML\n", "HTML(\"\"\"<style> .rendered_html code { \n", " padding: 2px 4px;\n", " color: #c7254e;\n", " background-color: #f9f2f4;\n", " border-radius: 4px;\n", "} </style>\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Places your images in a folder such as `dirname = '/Users/Someone/Desktop/ImagesFromTheInternet'`. We'll then use the `os` package to load them and crop/resize them to a standard size of 100 x 100 pixels.\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# You need to find 100 images from the web/create them yourself\n", "# or find a dataset that interests you (e.g. I used celeb faces\n", "# in the course lecture...)\n", "# then store them all in a single directory.\n", "# With all the images in a single directory, you can then\n", "# perform the following steps to create a 4-d array of:\n", "# N x H x W x C dimensions as 100 x 100 x 100 x 3.\n", "\n", "dirname = ...\n", "\n", "# Load every image file in the provided directory\n", "filenames = [os.path.join(dirname, fname)\n", " for fname in os.listdir(dirname)]\n", "\n", "# Make sure we have exactly 100 image files!\n", "filenames = filenames[:100]\n", "assert(len(filenames) == 100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read every filename as an RGB image\n", "imgs = [plt.imread(fname)[..., :3] for fname in filenames]\n", "\n", "# Crop every image to a square\n", "imgs = [utils.imcrop_tosquare(img_i) for img_i in imgs]\n", "\n", "# Then resize the square image to 100 x 100 pixels\n", "imgs = [resize(img_i, (100, 100)) for img_i in imgs]\n", "\n", "# Finally make our list of 3-D images a 4-D array with the first dimension the number of images:\n", "imgs = np.array(imgs).astype(np.float32)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Plot the resulting dataset:\n", "# Make sure you \"run\" this cell after you create your `imgs` variable as a 4-D array!\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(imgs, saveto='dataset.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-two---compute-the-mean\"></a>\n", "# Part Two - Compute the Mean\n", "\n", "<a name=\"instructions-1\"></a>\n", "## Instructions\n", "\n", "First use Tensorflow to define a session. Then use Tensorflow to create an operation which takes your 4-d array and calculates the mean color image (100 x 100 x 3) using the function `tf.reduce_mean`. Have a look at the documentation for this function to see how it works in order to get the mean of every pixel and get an image of (100 x 100 x 3) as a result. You'll then calculate the mean image by running the operation you create with your session (e.g. <code>sess.run(...)</code>). Finally, plot the mean image, save it, and then include this image in your zip file as <b>mean.png</b>.\n", "\n", "<a name=\"code-1\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First create a tensorflow session\n", "sess = ...\n", "\n", "# Now create an operation that will calculate the mean of your images\n", "mean_img_op = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# And then run that operation using your session\n", "mean_img = sess.run(mean_img_op)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting mean image:\n", "# Make sure the mean image is the right size!\n", "assert(mean_img.shape == (100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(mean_img)\n", "plt.imsave(arr=mean_img, fname='mean.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have seen the mean image of your dataset, how does it relate to your own expectations of the dataset? Did you expect something different? Was there something more \"regular\" or \"predictable\" about your dataset that the mean image did or did not reveal? If your mean image looks a lot like something recognizable, it's a good sign that there is a lot of predictability in your dataset. If your mean image looks like nothing at all, a gray blob where not much seems to stand out, then it's pretty likely that there isn't very much in common between your images. Neither is a bad scenario. Though, it is more likely that having some predictability in your mean image, e.g. something recognizable, that there are representations worth exploring with deeper networks capable of representing them. However, we're only using 100 images so it's a very small dataset to begin with.\n", "\n", "<a name=\"part-three---compute-the-standard-deviation\"></a>\n", "# Part Three - Compute the Standard Deviation\n", "\n", "<a name=\"instructions-2\"></a>\n", "## Instructions\n", "\n", "Now use tensorflow to calculate the standard deviation and upload the standard deviation image averaged across color channels as a \"jet\" heatmap of the 100 images. This will be a little more involved as there is no operation in tensorflow to do this for you. However, you can do this by calculating the mean image of your dataset as a 4-D array. To do this, you could write e.g. `mean_img_4d = tf.reduce_mean(imgs, reduction_indices=0, keep_dims=True)` to give you a `1 x H x W x C` dimension array calculated on the `N x H x W x C` images variable. The reduction_indices parameter is saying to calculate the mean over the 0th dimension, meaning for every possible `H`, `W`, `C`, or for every pixel, you will have a mean composed over the `N` possible values it could have had, or what that pixel was for every possible image. This way, you can write `images - mean_img_4d` to give you a `N x H x W x C` dimension variable, with every image in your images array having been subtracted by the `mean_img_4d`. If you calculate the square root of the sum of the squared differences of this resulting operation, you have your standard deviation!\n", "\n", "In summary, you'll need to write something like: `subtraction = imgs - tf.reduce_mean(imgs, reduction_indices=0, keep_dims=True)`, then reduce this operation using `tf.sqrt(tf.reduce_sum(subtraction * subtraction, reduction_indices=0))` to get your standard deviation then include this image in your zip file as <b>std.png</b>\n", "\n", "<a name=\"code-2\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a tensorflow operation to give you the standard deviation\n", "\n", "# First compute the difference of every image with a\n", "# 4 dimensional mean image shaped 1 x H x W x C\n", "mean_img_4d = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "subtraction = imgs - mean_img_4d\n", "\n", "# Now compute the standard deviation by calculating the\n", "# square root of the sum of squared differences\n", "std_img_op = tf.sqrt(tf.reduce_sum(subtraction * subtraction, reduction_indices=0))\n", "\n", "# Now calculate the standard deviation using your session\n", "std_img = sess.run(std_img_op)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting standard deviation image:\n", "# Make sure the std image is the right size!\n", "assert(std_img.shape == (100, 100) or std_img.shape == (100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "std_img_show = std_img / np.max(std_img)\n", "plt.imshow(std_img_show)\n", "plt.imsave(arr=std_img_show, fname='std.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have plotted your dataset's standard deviation per pixel, what does it reveal about your dataset? Like with the mean image, you should consider what is predictable and not predictable about this image.\n", "\n", "<a name=\"part-four---normalize-the-dataset\"></a>\n", "# Part Four - Normalize the Dataset\n", "\n", "<a name=\"instructions-3\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to normalize your dataset using the mean and standard deviation. \n", "\n", "<a name=\"code-3\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs_op = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs = sess.run(norm_imgs_op)\n", "print(np.min(norm_imgs), np.max(norm_imgs))\n", "print(imgs.dtype)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting normalized dataset montage:\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(norm_imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(norm_imgs, 'normalized.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We apply another type of normalization to 0-1 just for the purposes of plotting the image. If we didn't do this, the range of our values would be somewhere between -1 and 1, and matplotlib would not be able to interpret the entire range of values. By rescaling our -1 to 1 valued images to 0-1, we can visualize it better." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs_show = (norm_imgs - np.min(norm_imgs)) / (np.max(norm_imgs) - np.min(norm_imgs))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(norm_imgs_show, 'normalized.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-five---convolve-the-dataset\"></a>\n", "# Part Five - Convolve the Dataset\n", "\n", "<a name=\"instructions-4\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to convolve your dataset with one of the kernels we created during the lesson, and then in the next part, we'll take the sum of the convolved output to use for sorting. You should use the function `utils.gabor` to create an edge detector. You can also explore with the `utils.gauss2d` kernel. What you must figure out is how to reshape your kernel to be 4-dimensional: K_H, K_W, C_I, and C_O, corresponding to the kernel's height and width (e.g. 16), the number of input channels (RGB = 3 input channels), and the number of output channels, (1).\n", "\n", "<a name=\"code-4\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First build 3 kernels for each input color channel\n", "ksize = ...\n", "kernel = np.concatenate([utils.gabor(ksize)[:, :, np.newaxis] for i in range(3)], axis=2)\n", " \n", "# Now make the kernels into the shape: [ksize, ksize, 3, 1]:\n", "kernel_4d = ...\n", "assert(kernel_4d.shape == (ksize, ksize, 3, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll Perform the convolution with the 4d tensor in `kernel_4d`. This is a `ksize` x `ksize` x 3 x 1 tensor, where each input color channel corresponds to one filter with 1 output. Each filter looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(5, 5))\n", "plt.imshow(kernel_4d[:, :, 0, 0], cmap='gray')\n", "plt.imsave(arr=kernel_4d[:, :, 0, 0], fname='kernel.png', cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform the convolution with the 4d tensors:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "convolved = utils.convolve(..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "convolved_show = (convolved - np.min(convolved)) / (np.max(convolved) - np.min(convolved))\n", "print(convolved_show.shape)\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(convolved_show[..., 0], 'convolved.png'), cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we've just done is build a \"hand-crafted\" feature detector: the Gabor Kernel. This kernel is built to respond to particular orientation: horizontal edges, and a particular scale. It also responds equally to R, G, and B color channels, as that is how we have told the convolve operation to work: use the same kernel for every input color channel. When we work with deep networks, we'll see how we can learn the convolution kernels for every color channel, and learn many more of them, in the order of 100s per color channel. That is really where the power of deep networks will start to become obvious. For now, we've seen just how difficult it is to get at any higher order features of the dataset. We've really only picked out some edges!\n", "\n", "<a name=\"part-six---sort-the-dataset\"></a>\n", "# Part Six - Sort the Dataset\n", "\n", "<a name=\"instructions-5\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to organize your dataset. We'll try sorting based on the mean value of each convolved image's output to use for sorting. To do this, we could calculate either the sum value (`tf.reduce_sum`) or the mean value (`tf.reduce_mean`) of each image in your dataset and then use those values, e.g. stored inside a variable `values` to sort your images using something like `tf.nn.top_k` and `sorted_imgs = np.array([imgs[idx_i] for idx_i in idxs])` prior to creating the montage image, `m = montage(sorted_imgs, \"sorted.png\")` and then include this image in your zip file as <b>sorted.png</b>\n", "\n", "<a name=\"code-5\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a set of operations using tensorflow which could\n", "# provide you for instance the sum or mean value of every\n", "# image in your dataset:\n", "\n", "# First flatten our convolved images so instead of many 3d images,\n", "# we have many 1d vectors.\n", "# This should convert our 4d representation of N x H x W x C to a\n", "# 2d representation of N x (HWC)\n", "flattened = tf.reshape(convolved...\n", "assert(flattened.get_shape().as_list() == [100, 10000])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Now calculate some statistics about each of our images\n", "values = tf.reduce_sum(flattened, reduction_indices=1)\n", "\n", "# Then create another operation which sorts those values\n", "# and then calculate the result:\n", "idxs_op = tf.nn.top_k(values, k=100)[1]\n", "idxs = sess.run(idxs_op)\n", "\n", "# Then finally use the sorted indices to sort your images:\n", "sorted_imgs = np.array([imgs[idx_i] for idx_i in idxs])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting sorted dataset montage:\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(sorted_imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(sorted_imgs, 'sorted.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What does your sorting reveal? Could you imagine the same sorting over many more images reveal the thing your dataset sought to represent? It is likely that the representations that you wanted to find hidden within \"higher layers\", i.e., \"deeper features\" of the image, and that these \"low level\" features, edges essentially, are not very good at describing the really interesting aspects of your dataset. In later sessions, we'll see how we can combine the outputs of many more convolution kernels that have been assembled in a way that accentuate something very particular about each image, and build a sorting that is much more intelligent than this one!\n", "\n", "<a name=\"assignment-submission\"></a>\n", "# Assignment Submission\n", "\n", "Now that you've completed all 6 parts, we'll create a zip file of the current directory using the code below. This code will make sure you have included this completed ipython notebook and the following files named exactly as:\n", "\n", "<pre>\n", " session-1/\n", " session-1.ipynb\n", " dataset.png\n", " mean.png\n", " std.png\n", " normalized.png\n", " kernel.png\n", " convolved.png\n", " sorted.png\n", " libs/\n", " utils.py\n", "</pre>\n", "\n", "You'll then submit this zip file for your first assignment on Kadenze for \"Assignment 1: Datasets/Computing with Tensorflow\"! If you have any questions, remember to reach out on the forums and connect with your peers or with me.\n", "\n", "<b>To get assessed, you'll need to be a premium student which is free for a month!</b> If you aren't already enrolled as a student, register now at http://www.kadenze.com/ and join the #CADL community to see what your peers are doing! https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n", "\n", "Then remember to complete the remaining parts of Assignemnt 1 on Kadenze!:\n", "* Comment on 1 student's open-ended arrangement (Part 6) in the course gallery titled \"Creating a Dataset/ Computing with Tensorflow\". Think about what images they've used in their dataset and how the arrangement reflects what could be represented by that data.\n", "* Finally make a forum post in the forum for this assignment \"Creating a Dataset/ Computing with Tensorflow\".\n", " - Including a link to an artist making use of machine learning to organize data or finding representations within large datasets\n", " - Tell a little about their work (min 20 words).\n", " - Comment on at least 2 other student's forum posts (min 20 words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure your notebook is named \"session-1\" or else replace it with the correct name in the list of files below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "utils.build_submission('session-1.zip',\n", " ('dataset.png',\n", " 'mean.png',\n", " 'std.png',\n", " 'normalized.png',\n", " 'kernel.png',\n", " 'convolved.png',\n", " 'sorted.png',\n", " 'session-1.ipynb'))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
power-system-simulation-toolbox/psst	docs/notebooks/validation/Validation13-5Bus.ipynb	2	31982	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Validation 13 - 5 Bus" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.case import read_matpower" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "case = read_matpower('./../../psst/cases/case5.m')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>GEN_BUS</th>\n", " <th>PG</th>\n", " <th>QG</th>\n", " <th>QMAX</th>\n", " <th>QMIN</th>\n", " <th>VG</th>\n", " <th>MBASE</th>\n", " <th>GEN_STATUS</th>\n", " <th>PMAX</th>\n", " <th>PMIN</th>\n", " <th>PC1</th>\n", " <th>PC2</th>\n", " <th>QC1MIN</th>\n", " <th>QC1MAX</th>\n", " <th>QC2MIN</th>\n", " <th>QC2MAX</th>\n", " <th>RAMP_AGC</th>\n", " <th>RAMP_10</th>\n", " <th>RAMP_30</th>\n", " <th>RAMP_Q</th>\n", " <th>APF</th>\n", " <th>STARTUP_RAMP</th>\n", " <th>SHUTDOWN_RAMP</th>\n", " <th>MINIMUM_UP_TIME</th>\n", " <th>MINIMUM_DOWN_TIME</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GenCo0</th>\n", " <td>Bus1</td>\n", " <td>40.00</td>\n", " <td>0</td>\n", " <td>30.0</td>\n", " <td>-30.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo1</th>\n", " <td>Bus1</td>\n", " <td>170.00</td>\n", " <td>0</td>\n", " <td>127.5</td>\n", " <td>-127.5</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>170</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo2</th>\n", " <td>Bus3</td>\n", " <td>323.49</td>\n", " <td>0</td>\n", " <td>390.0</td>\n", " <td>-390.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>520</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo3</th>\n", " <td>Bus4</td>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>150.0</td>\n", " <td>-150.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>200</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo4</th>\n", " <td>Bus5</td>\n", " <td>466.51</td>\n", " <td>0</td>\n", " <td>450.0</td>\n", " <td>-450.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GEN_BUS PG QG QMAX QMIN VG MBASE GEN_STATUS PMAX PMIN \\\n", "GenCo0 Bus1 40.00 0 30.0 -30.0 1 100 1 40 0 \n", "GenCo1 Bus1 170.00 0 127.5 -127.5 1 100 1 170 0 \n", "GenCo2 Bus3 323.49 0 390.0 -390.0 1 100 1 520 0 \n", "GenCo3 Bus4 0.00 0 150.0 -150.0 1 100 1 200 0 \n", "GenCo4 Bus5 466.51 0 450.0 -450.0 1 100 1 600 0 \n", "\n", " PC1 PC2 QC1MIN QC1MAX QC2MIN QC2MAX RAMP_AGC RAMP_10 RAMP_30 \\\n", "GenCo0 0 0 0 0 0 0 0 40 0 \n", "GenCo1 0 0 0 0 0 0 0 170 0 \n", "GenCo2 0 0 0 0 0 0 0 520 0 \n", "GenCo3 0 0 0 0 0 0 0 200 0 \n", "GenCo4 0 0 0 0 0 0 0 600 0 \n", "\n", " RAMP_Q APF STARTUP_RAMP SHUTDOWN_RAMP MINIMUM_UP_TIME \\\n", "GenCo0 0 0 40 40 0 \n", "GenCo1 0 0 170 170 0 \n", "GenCo2 0 0 520 520 0 \n", "GenCo3 0 0 200 200 0 \n", "GenCo4 0 0 600 600 0 \n", "\n", " MINIMUM_DOWN_TIME \n", "GenCo0 0 \n", "GenCo1 0 \n", "GenCo2 0 \n", "GenCo3 0 \n", "GenCo4 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.gen" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>MODEL</th>\n", " <th>STARTUP</th>\n", " <th>SHUTDOWN</th>\n", " <th>NCOST</th>\n", " <th>COST_2</th>\n", " <th>COST_1</th>\n", " <th>COST_0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GenCo0</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.005</td>\n", " <td>14</td>\n", " <td>56.90</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo1</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.006</td>\n", " <td>15</td>\n", " <td>0.11</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo2</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.010</td>\n", " <td>25</td>\n", " <td>2267.53</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo3</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.012</td>\n", " <td>30</td>\n", " <td>5.19</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo4</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.007</td>\n", " <td>10</td>\n", " <td>1391.16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " MODEL STARTUP SHUTDOWN NCOST COST_2 COST_1 COST_0\n", "GenCo0 2 0 0 3 0.005 14 56.90\n", "GenCo1 2 0 0 3 0.006 15 0.11\n", "GenCo2 2 0 0 3 0.010 25 2267.53\n", "GenCo3 2 0 0 3 0.012 30 5.19\n", "GenCo4 2 0 0 3 0.007 10 1391.16" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.gencost" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>F_BUS</th>\n", " <th>T_BUS</th>\n", " <th>BR_R</th>\n", " <th>BR_X</th>\n", " <th>BR_B</th>\n", " <th>RATE_A</th>\n", " <th>RATE_B</th>\n", " <th>RATE_C</th>\n", " <th>TAP</th>\n", " <th>SHIFT</th>\n", " <th>BR_STATUS</th>\n", " <th>ANGMIN</th>\n", " <th>ANGMAX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Bus1</td>\n", " <td>Bus2</td>\n", " <td>0.00281</td>\n", " <td>0.0281</td>\n", " <td>0.00712</td>\n", " <td>400</td>\n", " <td>400</td>\n", " <td>400</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bus1</td>\n", " <td>Bus4</td>\n", " <td>0.00304</td>\n", " <td>0.0304</td>\n", " <td>0.00658</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Bus1</td>\n", " <td>Bus5</td>\n", " <td>0.00064</td>\n", " <td>0.0064</td>\n", " <td>0.03126</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Bus2</td>\n", " <td>Bus3</td>\n", " <td>0.00108</td>\n", " <td>0.0108</td>\n", " <td>0.01852</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Bus3</td>\n", " <td>Bus4</td>\n", " <td>0.00297</td>\n", " <td>0.0297</td>\n", " <td>0.00674</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Bus4</td>\n", " <td>Bus5</td>\n", " <td>0.00297</td>\n", " <td>0.0297</td>\n", " <td>0.00674</td>\n", " <td>240</td>\n", " <td>240</td>\n", " <td>240</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " F_BUS T_BUS BR_R BR_X BR_B RATE_A RATE_B RATE_C TAP SHIFT \\\n", "0 Bus1 Bus2 0.00281 0.0281 0.00712 400 400 400 0 0 \n", "1 Bus1 Bus4 0.00304 0.0304 0.00658 0 0 0 0 0 \n", "2 Bus1 Bus5 0.00064 0.0064 0.03126 0 0 0 0 0 \n", "3 Bus2 Bus3 0.00108 0.0108 0.01852 0 0 0 0 0 \n", "4 Bus3 Bus4 0.00297 0.0297 0.00674 0 0 0 0 0 \n", "5 Bus4 Bus5 0.00297 0.0297 0.00674 240 240 240 0 0 \n", "\n", " BR_STATUS ANGMIN ANGMAX \n", "0 1 -360 360 \n", "1 1 -360 360 \n", "2 1 -360 360 \n", "3 1 -360 360 \n", "4 1 -360 360 \n", "5 1 -360 360 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.branch" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.case.utils import solve_pf\n", "\n", "results, _ = solve_pf(case)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " <th>16</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.00281</td>\n", " <td>0.0281</td>\n", " <td>0.00712</td>\n", " <td>400.0</td>\n", " <td>400.0</td>\n", " <td>400.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>249.773373</td>\n", " <td>21.599095</td>\n", " <td>-248.006760</td>\n", " <td>-4.637368</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>4.0</td>\n", " <td>0.00304</td>\n", " <td>0.0304</td>\n", " <td>0.00658</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>186.500142</td>\n", " <td>-13.612148</td>\n", " <td>-185.437396</td>\n", " <td>23.581606</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>0.00064</td>\n", " <td>0.0064</td>\n", " <td>0.03126</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>-226.273515</td>\n", " <td>22.738212</td>\n", " <td>226.604972</td>\n", " <td>-22.549636</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>0.00108</td>\n", " <td>0.0108</td>\n", " <td>0.01852</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>-51.993240</td>\n", " <td>-93.972632</td>\n", " <td>52.118657</td>\n", " <td>93.394588</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>0.00297</td>\n", " <td>0.0297</td>\n", " <td>0.00674</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>-28.628657</td>\n", " <td>2.650132</td>\n", " <td>28.653264</td>\n", " <td>-3.078060</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " 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-226.273515 22.738212 226.604972 -22.549636 \n", "3 -360.0 360.0 -51.993240 -93.972632 52.118657 93.394588 \n", "4 -360.0 360.0 -28.628657 2.650132 28.653264 -3.078060 \n", "5 -360.0 360.0 -238.188688 32.149384 239.905028 -15.659987 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(results['branch'])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " 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11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(results['bus'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.model import build_model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "model = build_model(case)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FICO Xpress-Optimizer 64-bit v29.01.10 (Hyper capacity)\n", "(c) Copyright Fair Isaac Corporation 1983-2016. All rights reserved\n", "Using Xpress-Optimizer [/Users/dkrishna/opt/xpressmp/lib/libxprs.dylib]\n", "\n", "Reading Problem /tmpgRr1VC.pyomo\n", "Problem Statistics\n", " 106 ( 0 spare) rows\n", " 87 ( 0 spare) structural columns\n", " 239 ( 0 spare) non-zero elements\n", "Global Statistics\n", " 5 entities 0 sets 0 set members\n", "Minimizing MILP /tmpgRr1VC.pyomo\n", "Original problem has:\n", " 106 rows 87 cols 239 elements 5 globals\n", "Presolved problem has:\n", " 16 rows 31 cols 61 elements 5 globals\n", "Will try to keep branch and bound tree memory usage below 5.5Gb\n", "Starting concurrent solve with dual\n", "\n", " Concurrent-Solve, 0s\n", " Dual \n", " objective dual inf\n", " D 20013.800 .0000000\n", "------- optimal --------\n", "Concurrent statistics:\n", " Dual: 13 simplex iterations, 0.00s\n", "Optimal solution found\n", "\n", " Its Obj Value S Ninf Nneg Sum Dual Inf Time\n", " 13 20013.80036 D 0 0 .000000 0\n", "Dual solved problem\n", " 13 simplex iterations in 0s\n", "\n", "Final objective : 2.001380036213346e+04\n", " Max primal violation (abs / rel) : 2.274e-13 / 2.274e-13\n", " Max dual violation (abs / rel) : 0.0 / 0.0\n", " Max complementarity viol. (abs / rel) : 0.0 / 0.0\n", "All values within tolerances\n", "\n", "Starting root cutting & heuristics\n", "\n", " Its Type BestSoln BestBound Sols Add Del Gap GInf Time\n", "k 22255.36973 20013.80036 1 10.07% 0 0\n", " * Search completed * Time: 0 Nodes: 1\n", "Number of integer feasible solutions found is 1\n", "Best integer solution found is 22255.36973\n", "Best bound is 22255.36973\n", "Uncrunching matrix\n" ] } ], "source": [ "model.solve(solver='xpress', verbose=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "s1 = (model.results.angles / 2 / pd.np.pi * 360).T[0]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case.gen['PG'] = model.results.power_generated.T[0]\n", "\n", "from psst.case.utils import solve_pf\n", "\n", "results, _ = solve_pf(case)\n", "\n", "s2 = pd.DataFrame(results['bus'])[8]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np = pd.np" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rms(x):\n", " return np.sqrt(x.dot(x)/x.size)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.052620045089577835" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rms(s1.values - s2.values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
arcyfelix/Courses	17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/04-Visualization-Matplotlib-Pandas/04b-Pandas Visualization/03 - Advanced Matplotlib Concepts.ipynb	2	854839	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced Matplotlib Concepts Lecture\n", "\n", "In this lecture we cover some more advanced topics which you won't usually use as often. You can always reference the documentation for more resources!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Logarithmic scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact only one application of a more general transformation system in Matplotlib. Each of the axes' scales are set seperately using `set_xscale` and `set_yscale` methods which accept one parameter (with the value \"log\" in this case):" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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nHT2Z0GICBXJZX0hm8ep2PSLyFlAmWYF8V6Cjqtb3PM8PHABqqup2EYkBxqvq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\npETLYz1ObWmWsmABvPEGzJxpiZYxvpa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"text/plain": [ "<matplotlib.figure.Figure at 0x10b54ab10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", " \n", "axes[0].plot(x, x2, x, np.exp(x))\n", "axes[0].set_title(\"Normal scale\")\n", "\n", "axes[1].plot(x, x2, x, np.exp(x))\n", "axes[1].set_yscale(\"log\")\n", "axes[1].set_title(\"Logarithmic scale (y)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Placement of ticks and custom tick labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explicitly determine where we want the axis ticks with `set_xticks` and `set_yticks`, which both take a list of values for where on the axis the ticks are to be placed. We can also use the `set_xticklabels` and `set_yticklabels` methods to provide a list of custom text labels for each tick location:" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "image/png": 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LHuRk4Uk6N+/M7JGzSe6a7HRpDYpCnIiIiNSY/dn7mfCfCSzdtRSAsb3G8szwZ2ge09zh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\nEPT/AT0NOs5Aq2cMAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b3de690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", "ax.plot(x, x2, x, x3, lw=2)\n", "\n", "ax.set_xticks([1, 2, 3, 4, 5])\n", "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$'], fontsize=18)\n", "\n", "yticks = [0, 50, 100, 150]\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18); # use LaTeX formatted labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a number of more advanced methods for controlling major and minor tick placement in matplotlib figures, such as automatic placement according to different policies. See http://matplotlib.org/api/ticker_api.html for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scientific notation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With large numbers on axes, it is often better use scientific notation:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10b7a0f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x2, x, np.exp(x))\n", "ax.set_title(\"scientific notation\")\n", "\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "from matplotlib import ticker\n", "formatter = ticker.ScalarFormatter(useMathText=True)\n", "formatter.set_scientific(True) \n", "formatter.set_powerlimits((-1,1)) \n", "ax.yaxis.set_major_formatter(formatter) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis number and axis label spacing" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106d4c710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# distance between x and y axis and the numbers on the axes\n", "matplotlib.rcParams['xtick.major.pad'] = 5\n", "matplotlib.rcParams['ytick.major.pad'] = 5\n", "\n", "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x2, x, np.exp(x))\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "ax.set_title(\"label and axis spacing\")\n", "\n", "# padding between axis label and axis numbers\n", "ax.xaxis.labelpad = 5\n", "ax.yaxis.labelpad = 5\n", "\n", "ax.set_xlabel(\"x\")\n", "ax.set_ylabel(\"y\");" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# restore defaults\n", "matplotlib.rcParams['xtick.major.pad'] = 3\n", "matplotlib.rcParams['ytick.major.pad'] = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Axis position adjustments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, when saving figures the labels are sometimes clipped, and it can be necessary to adjust the positions of axes a little bit. This can be done using `subplots_adjust`:" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "image/png": 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sh0v0u3Z5narHHFP/lJ5e/dq7t+5/I7FBhUAOUekqOVB6gD0H9zSY3INH8IWl\nhXRL6eYl8lrJveZr99TudEnpQitr1ewYKyq8h44c7mi99lRWVp3AG5rS06FrV10zL/EnJguBmXUC\n5gDnADuBO51zz9VaJ2YKQXllOQUlBRSUFlBQUkB+SX7V+4LSwOeay0vzD7v+gbIDJCcm0ym50yHJ\nvHu77nWSe3pqOp3bdm5ScnfOu/HYvn3etH9/9fv65tVM/Hv3eqNZjya5H3OMd4Sva+RFDi9WC0Ew\n6U8GTgJeB8Y653JrrBPWQuCco7yynJKKEorLiykuL6ak3HtfUlHCgdIDDSfyBhJ7SUUJqW1SaZ/U\nnrQ2aaQlpZHWJs37HHhf53NSGptWbGLcaeMOWT+1TepRNclUVnoP/G4okTeU4Pfvh+RkL6HXnjp0\nqDuvU6fqxN+lS+guk8zOziYrKys0O4tC8f77QX8DiMGH15tZCvATYIhz7iCwxMxeBiYCd9Zcd9V3\nq6oSce3kXHN+ffOOanmNRF9cXgxA28S2JCUmkZyYTHJiMkkJ3vu2rdtWJ/JAom6f1J70dun079y/\n3kQeXD+ldUqDl0WWl3sP/CgqqvFaBK++PY1hXUezraju8uDR+uESeUGB11HaUBLv08d7AHh9ib19\ne2gdAQ/bivckEO+/H/Q3aKyoKATAQKDMOZdXY94q4PTaK056aVJVIk5OTK5K0IfMq/E+LSmt7vJ6\nknpSYhJJCckkkkyiJdE68GoukYoKr427vJyq9xUVUFpaT7LeW/15axF8WXt5Pa/1zauo8K5cSUk5\n9HXXLu8Sx9rzU1KgXTtvRGp9SbxjR6+5JZ4f9i0Sr6KlEKQC+bXm5QN1Bth3X7CqKhEXV8CBwyTp\nwyXvhuY75yXK+qbExLrz2rSpm5DrS96dOkHPnvUn7/pe27b19l3fCcO0ad4kInK0oqKPwMxGAB85\n51JrzLsDOM05d1GNeZH/Y0REwiSm+giA9UCimWXWaB4aDnxRc6Wj/dEiIlItKs4IAMzsWcAB1+Fd\nNfQq8P2aVw2JiEjjNX8kUPjcDKQAO4CngRtVBEREmi9qzghERKRlRNMZwWGZWSczW2BmhWa2ycx+\n6ndM4WRmN5vZUjMrNrM5fsfjBzNrY2azzOwrM9tvZjlm9kO/4wo3M3vKzLaZ2T4zW2dmU/yOyQ9m\nNsDMDprZk37HEm5mlh347flmVmBmR2w5iYlCADwCFAPdgJ8BfzWzwf6GFFZbgT8As/0OxEeJwGbg\nB865DsDUTOOzAAADVElEQVR/AC+Y2bH+hhV2fwT6Oec6AhcC95jZiT7H5IeHgc/9DsInDrjJOdfe\nOZfmnDtiLoz6QlBj1PHvnHMHnXNLgOCo47jgnHvJOfcKsMfvWPzinCtyzv3eOfdN4PPrwCZgpL+R\nhZdzbq1zrjjw0fCSQqaPIYWdmV0J7AXe9TsWHzXqCsqoLwQcftTxUJ/ikQhgZunAAGpdYhwPzOz/\nm9kBIBf4Fljoc0hhY2btgf8HTKWRyTDG/NHMdpjZYjOrcweG2mKhEBz1qGOJD2aWiHdl2ePOufV+\nxxNuzrmb8f5fjAP+DpT4G1FY/R6Y6Zz71u9AfPQrIAPoBcwEXjWzfg1tEAuFoBBoX2teB6DAh1jE\nZ+bdqe9pvOR3q8/h+MZ5Pgb6AP/qdzzhELgDwdnAX/yOxU/OuaXOuQPOuTLn3JPAEuBHDW0TLSOL\nG3JUo44lbswGugI/cs5V+B1MBEgkfvoITgf6ApsDBwSpQIKZDXHOnexvaL5yHKGZLOrPCJxzRXin\nv783sxQzGwf8GHjK38jCx8wSzCwZSMAriklmFnf3ETWzR4FBwIXOuVK/4wk3M+tmZleYWTsza2Vm\n5wJXAu/4HVuYPIZX9EbgHQw+CrwGjPczqHAysw5mNj6YA8zsKuAHwJsNbRf1hSAg3kcd/w4oAn4N\nXBV4f5evEYVZ4DLR6/GSwPbA9dP5cTamxOE1A32DdwXZvcBtgSuoYp5zrtg5tyM44TUbFzvn4ulq\nutbAPXi5cCdebrzIOfdlQxtpZLGISJyLlTMCERFpIhUCEZE4p0IgIhLnVAhEROKcCoGISJxTIRAR\niXMqBCIicU6FQEQkzqkQiIjEORUCEZE4p0IgEgJmlmFmuwO3QsbMegYeDHKa37GJHIkKgUgIOOc2\n4j0Q5GkzawvMBeY65z70NzKRI9NN50RCyMxewns6VCUwyjlX5nNIIkekMwKR0JqF97zsh1QEJFro\njEAkRMysHbAKeA84DxjmnNvnb1QiR6ZCIBIiZjYbaOucm2BmjwEdnXNX+B2XyJGoaUgkBMzsQrxH\nIt4UmDUVODHOnpAmUUpnBCIicU5nBCIicU6FQEQkzqkQiIjEORUCEZE4p0IgIhLnVAhEROKcCoGI\nSJxTIRARiXP/B7XhPC7uO4akAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b7b4950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x2, x, np.exp(x))\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "ax.set_title(\"title\")\n", "ax.set_xlabel(\"x\")\n", "ax.set_ylabel(\"y\")\n", "\n", "fig.subplots_adjust(left=0.15, right=.9, bottom=0.1, top=0.9);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the `grid` method in the axis object, we can turn on and off grid lines. We can also customize the appearance of the grid lines using the same keyword arguments as the `plot` function:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "image/png": 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VI/VLiOrHrd088M0D5OTnMCx2GL1b9bYsFlnrUAhRitaa3p/25se9PxIXG8f0QeZ/YiZr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\nf7cL+fv8n8z/hK+2fcUKvYJ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Ltgv2F/8xbne73eTk55CVn05Wfjon3emc0sZPrkonNyid\n/OB08kPS0aHpUCP9zA1SSRoOBW+AkmOStILUc4s1ULVPtSFKt6VBUBsaRNYxikx9iGpTvOiULERR\nUbLIpxB+UqEaNm3peqM25Z2uUXkF9Ssv36hPRfUq//RP8Zqli9Uvd2Ft8/hxuzV57nyy8jLIyjPq\nV7Zn/VLp5BXUL3dIOoSlQ+hZ5k7xpFtyPGgplDWw+2RUsQYqLLMtdfONj/wahjclul6QUZ8uLL92\nFf7UrGn/j+uEMIuvX64jgJKfrKcBkSUPvH9dN/PPrjAanqoOxM4LQ+VGEpwXSXB+JCHuSMJ0JGFE\nUlNFUjMoklrBkdQKiWRP8jwuD3qYOjUiiQqvQ1R4JNG169K2YQsa169ZVHjq1LH/t1QSEhKsDsEU\nTskDnJVLgKhQDRu+9jLfnD2Ispseb7iDULkRBOUZNSzEHUmo26hfNVQdwlUk4QX1K8G9me48T2SN\nSOrUjKRuzUiiwiOJqd+cFg2ji5qmqCj7j80Uwi58+q1DpVQs8LPWOsJj3+NAT631II998pVDIaoh\nu3/rsCI1TOqXENWTXb51uAMIUUq18bj03hnY5nmQ3YutEKLaOmsNk/olhDgTn8+jpZT6AtDACIxv\n7CwCrnDqtw6FEM4iNUwIURX+GCX0MFALSARmACOlQAkhAojUMCFEpfm80dJan9BaD9ZaR2itY7TW\nswpvU0rVU0rNU0plKKX2KKX+4ut4fEEp9bBSar1SKlspNc3qeKpCKRWmlPpIKZWglEpVSm1USvWz\nOq7KUEr9Ryl1WCmVopT6XSk13OqYqkop1U4pdVIp9ZnVsVSWUspVkEOaUipdKWXrpqW8GuaU+gXO\nqWFOql/gvBpWXeuX1d9785wI8G5gslLqQmtDqpSDwEvAx1YHYoIQYB9wlda6LvAcMFsp1cLasCrl\nVaCV1joKGAi8rJTqYnFMVfUu8IvVQVSRBh7SWtfRWkdqrQPx/zw4p36Bc2qYk+oXOK+GVcv6ZVmj\n5TER4LNa65Na61VA4USAAUVrPV9rvRBItjqWqtJaZ2mtJ2it9xdsfwvsAS6xNjLvaa1/01oXTiKk\nMP6DtLEwpCpRSg0BTgArrI7FBAE9gNxJ9QucU8OcVL/AWTWsOtcvK69olTcRYAeL4hFlUEo1BtpR\n4puigUIp9Z5SKhPYDhwCFlscUqUopeoALwJjCfAmpcCrSqlEpdRPSqlSExgHAKlfASDQ6xc4o4ZV\n9/plZaNV4clMhTWUUiEYg38/0VrvsDqeytBaP4zxb60HMBeKrQQSSCYAU7XWh6wOxARPAa2B5sBU\nYJFSqpW1IXlN6pfNOaF+gWNqWLWuX1Y2WhlAnRL76gLpFsQiSlBKKYwidQp41OJwqkQbVgPnAqOs\njsdbBZNmXgu8ZXUsZtBar9daZ2qtc7XWnwGrgP5Wx+UlqV825qT6BYFdw6R++X7C0jOp0GSmwjIf\nAw2A/lprLxZ7tLUQAnN8w9VAS2BfwQtIBBCslGqvte5qbWim0ATexwlSv+zNifULArOGVfv6ZdkV\nLa11FsZl0AlKqVpKqR7ATcB/rIqpspRSwUqpmhirKoYopWoopaq6wqJllFIfABcAA7XWOVbHUxlK\nqYZKqTuVUrWVUkFKqeuBIcD3VsdWCVMwimssxov5B8A3wHVWBlUZSqm6SqnrCv+PKKWGAlcBS6yO\nzRtOql/grBrmhPoFjqph1b5+WT29g1MmAnwWyAL+Dgwt+H2cpRFVUsHXoB/A+E9xtGCekLQAnCNI\nY1xi34/xTap/AY8VfAspoGits7XWiYU/GB9bZWutA/EbYqHAyxj/549h1IBBWutdlkZVOU6pX+CQ\nGuag+gUOqWFSv/ywBI8QQgghRHVl9RUtIYQQQgjHkkZLCCGEEMJHpNESQgghhPARabSEEEIIIXxE\nGi0hhBBCCB+RRksIIYQQwkek0RJCCCGE8BFptIQQQgghfEQaLSGEEEIIH/l/2yHKsIcQa58AAAAA\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1084dc3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n", "\n", "# default grid appearance\n", "axes[0].plot(x, x2, x, x3, lw=2)\n", "axes[0].grid(True)\n", "\n", "# custom grid appearance\n", "axes[1].plot(x, x2, x, x3, lw=2)\n", "axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis spines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also change the properties of axis spines:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "image/png": 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C\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b7d5c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6,2))\n", "\n", "ax.spines['bottom'].set_color('blue')\n", "ax.spines['top'].set_color('blue')\n", "\n", "ax.spines['left'].set_color('red')\n", "ax.spines['left'].set_linewidth(2)\n", "\n", "# turn off axis spine to the right\n", "ax.spines['right'].set_color(\"none\")\n", "ax.yaxis.tick_left() # only ticks on the left side" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Twin axes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it is useful to have dual x or y axes in a figure; for example, when plotting curves with different units together. Matplotlib supports this with the `twinx` and `twiny` functions:" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "image/png": 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Jb9Iky/iysqw85quvhizwDR8Ozz5rg3rjDdhtt6BH5OBrIGGbXmIJfpcAr6vy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\n1RMTMLCDsIB3BfBXIAeYCIw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"text/plain": [ "<matplotlib.figure.Figure at 0x109b88690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "\n", "ax1.plot(x, x2, lw=2, color=\"blue\")\n", "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n", "for label in ax1.get_yticklabels():\n", " label.set_color(\"blue\")\n", " \n", "ax2 = ax1.twinx()\n", "ax2.plot(x, x3, lw=2, color=\"red\")\n", "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n", "for label in ax2.get_yticklabels():\n", " label.set_color(\"red\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axes where x and y is zero" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1071d7110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0)) # set position of x spine to x=0\n", "\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0)) # set position of y spine to y=0\n", "\n", "xx = np.linspace(-0.75, 1., 100)\n", "ax.plot(xx, xx3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other 2D plot styles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the regular `plot` method, there are a number of other functions for generating different kind of plots. See the matplotlib plot gallery for a complete list of available plot types: http://matplotlib.org/gallery.html. Some of the more useful ones are show below:" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = np.array([0,1,2,3,4,5])" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "image/png": 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uYDtwFDCY4NuRr5nZwwSB9FeAJ4HvEMzafKxDnZYuYfnG5ThOj7zOTaSI7Gpm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\nnP/bmdyd3Qd2s+fgHgA+OOyDnH3q2Yw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"text/plain": [ "<matplotlib.figure.Figure at 0x10a72ed90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n", "\n", "axes[0].scatter(xx, xx + 0.25np.random.randn(len(xx)))\n", "axes[0].set_title(\"scatter\")\n", "\n", "axes[1].step(n, n2, lw=2)\n", "axes[1].set_title(\"step\")\n", "\n", "axes[2].bar(n, n2, align=\"center\", width=0.5, alpha=0.5)\n", "axes[2].set_title(\"bar\")\n", "\n", "axes[3].fill_between(x, x2, x3, color=\"green\", alpha=0.5);\n", "axes[3].set_title(\"fill_between\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text annotation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Annotating text in matplotlib figures can be done using the `text` function. It supports LaTeX formatting just like axis label texts and titles:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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5AbhHa937lgEoNQD4P611i2xekwFuQgiRR7YOcLvlHYPWuuMtAvAB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\nQgghMpHEIIQQIhNJDEIIITL5f/mSKrvCXrVbAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109b0aa50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(xx, xx2, xx, xx3)\n", "\n", "ax.text(0.15, 0.2, r\"$y=x^2$\", fontsize=20, color=\"blue\")\n", "ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=20, color=\"green\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figures with multiple subplots and insets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Axes can be added to a matplotlib Figure canvas manually using `fig.add_axes` or using a sub-figure layout manager such as `subplots`, `subplot2grid`, or `gridspec`:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### subplots" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1070b7e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2, 3)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### subplot2grid" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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fwP8jTZZZAfwwIq5vsk3nS4+0my+FKjVIuhsI4HJS0qwgHUgak+YI4EngbGA1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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a08ce10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n", "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n", "ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n", "ax4 = plt.subplot2grid((3,3), (2,0))\n", "ax5 = plt.subplot2grid((3,3), (2,1))\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### gridspec" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.gridspec as gridspec" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a341c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "gs = gridspec.GridSpec(2, 3, height_ratios=[2,1], width_ratios=[1,2,1])\n", "for g in gs:\n", " ax = fig.add_subplot(g)\n", " \n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### add_axes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manually adding axes with `add_axes` is useful for adding insets to figures:" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "image/png": 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Nfn7AL7+I50faTkYJ6QmY7z8fO5Q78G2/b+Hp6qmzD3+ZYbp9W7QWrF5dsmR0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\nju3K7YhPi8eoNqMwus1ouNR00e6BWYX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"text/plain": [ "<matplotlib.figure.Figure at 0x10a0a39d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(xx, xx2, xx, xx3)\n", "fig.tight_layout()\n", "\n", "# inset\n", "inset_ax = fig.add_axes([0.2, 0.55, 0.35, 0.35]) # X, Y, width, height\n", "\n", "inset_ax.plot(xx, xx2, xx, xx3)\n", "inset_ax.set_title('zoom near origin')\n", "\n", "# set axis range\n", "inset_ax.set_xlim(-.2, .2)\n", "inset_ax.set_ylim(-.005, .01)\n", "\n", "# set axis tick locations\n", "inset_ax.set_yticks([0, 0.005, 0.01])\n", "inset_ax.set_xticks([-0.1,0,.1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Colormap and contour figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Colormaps and contour figures are useful for plotting functions of two variables. In most of these functions we will use a colormap to encode one dimension of the data. There are a number of predefined colormaps. It is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see: http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "alpha = 0.7\n", "phi_ext = 2 np.pi * 0.5\n", "\n", "def flux_qubit_potential(phi_m, phi_p):\n", " return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2phi_p)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "phi_m = np.linspace(0, 2np.pi, 100)\n", "phi_p = np.linspace(0, 2np.pi, 100)\n", "X,Y = np.meshgrid(phi_p, phi_m)\n", "Z = flux_qubit_potential(X, Y).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### pcolor" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "image/png": 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cDZ3wbUDePCya8OUxySN4OKqkm8/iWDvEyUMzI87IyDgSyIxYwSO7o0h4zm6s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\num5ZRQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107305f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "p = ax.pcolor(X/(2np.pi), Y/(2np.pi), Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", "cb = fig.colorbar(p, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### imshow" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "image/png": 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VzgefYYc9st+GqYl5gG3NIakD0AYkQUBobnoFoPkru/5Eo1xSY8LSKMFtafZ5ZYY0gTQG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\nNqdMbaINDmhK3MG64bMMkFxkw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hJALoKRmKNclCNFTJPlDT0607A0GvwasInxkzoEYC7s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\ntUUWWeS6kv8HUacFWVNPEqAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a083b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "im = ax.imshow(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n", "im.set_interpolation('bilinear')\n", "\n", "cb = fig.colorbar(im, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### contour" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "image/png": 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Zg4DDNVSgSjbT3q/ItDWL++lgGKCrCF0FbajWk1oCtWjWf3L7zMHF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"text/plain": [ "<matplotlib.figure.Figure at 0x10b038c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "cnt = ax.contour(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use 3D graphics in matplotlib, we first need to create an instance of the `Axes3D` class. 3D axes can be added to a matplotlib figure canvas in exactly the same way as 2D axes; or, more conveniently, by passing a `projection='3d'` keyword argument to the `add_axes` or `add_subplot` methods." ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d.axes3d import Axes3D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Surface plots" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "image/png": 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/8Ad/gC984QsDJ23bT+bOlU4B/qyALMueSHo6nW7Y0JvPW/jN33wNd965F9u2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\neOOXr/cTAACLF6dx8GAO73znfBQKBvb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P4aGHHmppDRtkG01yv/R19bkL8advv9D9agGG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\nBkqQZf7zElyXRsLbDz74gnjQPth58oXV8RShqwGtFIy++10KTePHa0c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8hsmGEjyolvJ9VZJEUGUWTIajJCoJh60EGXFRxLmgIla2L5zg3BdjQmTuDU06Po7Ps5V98dh0av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D8/P/j5+aF79+4A2tfvoLmmZ5cuXUJERASSk5NF27mh8oVmQbLnVnlA65SJzZkzByqVCuvW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\nIYQQQgi/KDtMOhXKEBBCCCGEEGLB/g9AJazGUaVqWAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a0bd650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(14,6))\n", "\n", "# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot\n", "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", "\n", "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n", "\n", "# surface_plot with color grading and color bar\n", "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0, antialiased=False)\n", "cb = fig.colorbar(p, shrink=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Wire-frame plot" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "image/png": 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XKhgGDBvmYvfu9K/Len+J7kFNDJK2h31dLlekUjT2Gq3FJp1+xsPNiIZCcO+9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24//yzO99o13ASkM2ck6tSxAiLLnDHDRkaGxIABBj/9pNCihTBaDg11kZtEnxWgRQuDI0dEj7hh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\nQvpNAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109e0e890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", "\n", "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Coutour plots with projections" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "image/png": 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tZs8eN88/v4uGhoaE7+HesbXguS7qvvcAT8y4mU/fVYbFPDq2QbyAxg/mfv81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\nP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C7kchlarYFkMrn1mwdzXwZDLQ0NrTvyT319B3jvvZdoa+tBpdKSyaSw26d55JHH8fu9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DaOt+j+3Dn8sQLMg4r7OtnH3qOdpffJPYiQZcNZW4ZlRgK8hDdtoRSZ1kNIba7ife2EzsZAPW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\nURWN9ARjUAmFywOvfW5g5hU+osOPf8fTx/u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"text/plain": [ "<matplotlib.figure.Figure at 0x109e08610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "ax = fig.add_subplot(1,1,1, projection='3d')\n", "\n", "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n", "cset = ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n", "cset = ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n", "cset = ax.contour(X, Y, Z, zdir='y', offset=3np.pi, cmap=matplotlib.cm.coolwarm)\n", "\n", "ax.set_xlim3d(-np.pi, 2np.pi);\n", "ax.set_ylim3d(0, 3np.pi);\n", "ax.set_zlim3d(-np.pi, 2np.pi);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " http://www.matplotlib.org - The project web page for matplotlib.\n", "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n", "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n", "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n", "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
anthonybu/api_sdk	DEMO.ipynb	1	41839	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SDK for the Brandwatch API: Demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this notebook is to demonstrate the capabilities of the Python Software Development Kit for Brandwatch's API. The SDK was designed to address many of the challenges involved in building complex applications which interact with RESTful API's in general and Brandwatch's API in particular:\n", "<ul>\n", "<li>The SDK's object hierarchy roughly mirrors the API's resource hierarchy, making the code intuitive for those familiar with the Brandwatch platform</li>\n", "<li>All required parameters are enforced, and most optional parameters are supported and documented</li>\n", "<li>Typical Brandwatch workflows are supported behind the scenes; for instance, one can validate, upload, and backfill a query with a single function call</li>\n", "<li>The SDK is designed to support simple and readable code: sensible defaults are chosen for rarely used parameters and all resource IDs are handled behind the scenes\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the user's perspective, the basic structure of the SDK is as follows. One first creates an instance of the class `BWProject`; this class handles authentication (via a user name and password or API key) and keeps track of project-level data such as the project's ID. (Behind the scenes, the user-level operations are handled by the class `BWUser` from which `BWProject` is inherited.) One passes `BWProject` instance as an argument in the constructor for a series of classes which manage the various Brandwatch resources: queries, groups, tags, categories, etc. These resource classes manage all resource-level operations: for example a single `BWQueries` instance handles all HTTP requests associated with queries in its attached project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically, you'd import only the classes you plan on using, but for this demo all classes are listed except for superclasses which you do not use explicitly)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from bwapi.bwproject import BWProject, BWUser\n", "from bwapi.bwresources import BWQueries, BWGroups, BWAuthorLists, BWSiteLists, BWLocationLists, BWTags, BWCategories, BWRules, BWMentions, BWSignals\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SDK uses the Python logging module to tell you what it's doing; if desired you can control what sort of output you see by uncommenting one of the lines below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "logger = logging.getLogger(\"bwapi\")\n", "\n", "#(Default) All logging messages enabled\n", "#logger.setLevel(logging.DEBUG)\n", "\n", "#Does not report URL's of API requests, but all other messages enabled\n", "#logger.setLevel(logging.INFO)\n", "\n", "#Report only errors and warnings\n", "#logger.setLevel(logging.WARN)\n", "\n", "#Report only errors\n", "#logger.setLevel(logging.ERROR)\n", "\n", "#Disable logging\n", "#logger.setLevel(logging.CRITICAL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you use the API for the first time you have to authenticate with Brandwatch. This will get you an access token. The access token is stored in a credentials file (`tokens.txt` in this example). Once you've authenticated your access token will be read from that file so you won't need to enter your password again.\n", "\n", "You can authenticate from command line using the provided console script `bwapi-authenticate`:\n", "\n", "```\n", "$ bwapi-authenticate\n", "Please enter your Brandwatch credentials below\n", "Username: example@example\n", "Password:\n", "Authenticating user: example@example\n", "Writing access token for user: example@example\n", "Writing access token for user: example@example\n", "Success! Access token: 00000000-0000-0000-0000-000000000000\n", "```\n", "\n", "Alternatively, you can authenticate directly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BWUser(username=\"[email protected]\", password=\"YOUR_PASSWORD\", token_path=\"tokens.txt\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you have authenticated you can load your project:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "YOUR_ACCOUNT = your_account\n", "YOUR_PROJECT = your_project\n", "\n", "project = BWProject(username=YOUR_ACCOUNT, project=YOUR_PROJECT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we really begin, please note that you can get documentation for any class or function by viewing the help documentation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(BWProject)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Queries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create some objects which can manipulate queries and groups in our project:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries = BWQueries(project)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check what queries already exist in the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also upload queries directly via the API by handing the \"name\", \"searchTerms\" and \"backfillDate\" to the upload funcion. If you don't pass a backfillDate, then the query will not backfill.\n", "\n", "The BWQueries class inserts default values for the \"languages\", \"type\", \"industry\", and \"samplePercent\" parameters, but we can override the defaults by including them as keyword arguments if we want. \n", "\n", "Upload accepts two boolean keyword arguments - \"create_only\" and \"modify_only\" (both defaulting to False) - which specifies what API verbs the function is allowed to use; for instance, if we set \"create_only\" to True then the function will post a new query if it can and otherwise it will do nothing. Note: this is true of all upload functions in this package." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload(name = \"Brandwatch Engagement\", \n", " includedTerms = \"at_mentions:Brandwatch\",\n", " backfill_date = \"2015-09-01\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're uploading many queries at a time, you can upload in batches. This saves API calls and allows you to just pass in a list rather than iterating over the upload function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload_all([\n", " {\"name\":\"Pets\", \n", " \"includedTerms\":\"dogs OR cats\", \n", " \"backfill_date\":\"2016-01-01T05:00:00\"}, \n", " \n", " {\"name\":\"ice cream cake\", \n", " \"includedTerms\":\"(\\\"ice cream\\\" OR icecream) AND (cake)\"},\n", " \n", " {\"name\": \"Test1\",\n", " \"includedTerms\": \"akdnvaoifg;anf\"},\n", " \n", " {\"name\": \"Test2\",\n", " \"includedTerms\": \"anvoapihajkvn\"},\n", " \n", " {\"name\": \"Test3\",\n", " \"includedTerms\": \"nviuphabaveh\"},\n", "\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Channels will be shown as queries and can be deleted as queries, but must be uploaded differently. You must be authenticated in the app to upload channels.\n", "\n", "In order to upload a channel you must pass in the name of the channel, the handle you'd like to track and the type of channel. As with keyword queries, we can upload channels individually or in batches.\n", "\n", "Note: Currently we can only support uploading Twitter channels through the API." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload_channel(name = \"Brandwatch\", \n", " handle = \"brandwatch\", \n", " channel_type = \"twitter\")\n", "\n", "queries.upload_all_channel([{\"name\": \"BWReact\",\n", " \"handle\": \"BW_React\",\n", " \"channel_type\": \"twitter\"},\n", " {\"name\": \"Brandwatch Careers\",\n", " \"handle\": \"BrandwatchJobs\",\n", " \"channel_type\": \"twitter\"}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can delete queries one at a time, or in batches." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.delete(name = \"Brandwatch Engagement\")\n", "queries.delete_all([\"Pets\", \"Test3\", \"Brandwatch\", \"BWReact\", \"Brandwatch Careers\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that a lot of the things that were true for queries are also true for groups. Many of the functions are nearly identical with any adaptations necessary handled behind the scenes for ease of use.\n", "\n", "Again (as with queries), we need to create an object with which we can manipulate groups within the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups = BWGroups(project)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And can check for exisiting groups in the same way as before." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's check which queries are in each group in the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for group in groups.names:\n", " print(group)\n", " print(groups.get_group_queries(group))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily create a group with any preexisting queries.\n", "\n", "(Recall that upload accepts two boolean keyword arguments - \"create_only\" and \"modify_only\" (both defaulting to False) - which specifies what API verbs the function is allowed to use; for instance, if we set \"create_only\" to True then the function will post a new query if it can and otherwise it will do nothing.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.upload(name = \"group 1\", queries = [\"Test1\", \"Test2\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or upload new queries and create a group with them, all in one call" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.upload_queries_as_group(group_name = \"group 2\", \n", " query_data_list = [{\"name\": \"Test3\",\n", " \"includedTerms\": \"adcioahnanva\"},\n", " \n", " {\"name\": \"Test4\",\n", " \"includedTerms\": \"ioanvauhekanv;\"}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can either delete just the group, or delete the group and the queries at the same time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.delete(\"group 1\")\n", "print()\n", "groups.deep_delete(\"group 2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Mentions (From a Query or a Group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can download mentions from a Query or from a Group (the code does not yet support Channels)\n", "\n", "There is a function get_mentions() in the classes BWQueries and in BWGroups. They are used the same way.\n", "\n", "Be careful with time zones, as they affect the date range and alter the results. If you're using the same date range for all your operations, I reccomend setting some variables at the start with dates and time zones. \n", "\n", "Here, today is set to the current day, and start is set to 30 days ago. Each number is offset by one to make it accurate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "today = (datetime.date.today() + datetime.timedelta(days=1)).isoformat() + \"T05:00:00\"\n", "start = (datetime.date.today() - datetime.timedelta(days=29)).isoformat() + \"T05:00:00\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use get_mentions(), the minimum parameters needed are name (query name in this case, or group name if downloading mentions from a group), startDate, and endDate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\",\n", " startDate = start, \n", " endDate = today)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are over a hundred filters you can use to only download the mentions that qualify. see the full list in the file filters.py\n", "\n", "Here, different filters are used, which take different data types. filters.py details which data type is used with each filter. Some filters, like sentiment and xprofession below, have a limited number of settings to choose from.\n", "\n", "You can filter many things by inclusion or exclusion. The x in xprofession stands for exclusion, for example." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today, \n", " sentiment = \"positive\", \n", " twitterVerified = False, \n", " impactMin = 50, \n", " xprofession = [\"Politician\", \"Legal\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To filter by tags, pass in a list of strings where each string is a tag name.\n", "\n", "You can filter by categories in two differnt ways: on a subcategory level or a parent category level. To filter on a subcategory level, use the category keyword and pass in a dictionary, where each the keys are the parent categories and the values are lists of the subcategories. To filter on a parent category level, use the parentCategory keyword and pass in a list of parent category names.\n", "\n", "Note: In the following call the parentCategory filter is redundant, but executed for illustrative purposes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today,\n", " parentCategory = [\"Colors\", \"Days\"],\n", " category = {\"Colors\": [\"Blue\", \"Yellow\"], \n", " \"Days\": [\"Monday\"]}, \n", " tag = [\"Tastes Good\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWCategories object by passing in your project as a parameter, which loads all of the categories in your project.\n", "\n", "Print out ids to see which categories are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories = BWCategories(project)\n", "\n", "categories.ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upload categories individually with upload(), or in bulk with upload_all(). If you are uploading many categories, it is more efficient to use upload_all().\n", "\n", "For upload(), pass in name and children. name is the string which represents the parent category, and children is a list of dictionaries where each dictionary is a child category- its key is \"name\" and its value is the name of the child category.\n", "\n", "By default, a category will allow multiple subcategories to be applies, so the keyword argument \"multiple\" is set to True. You can manually set it to False by passing in multipe=False as another parameter when uploading a category.\n", "\n", "For upload_all(), pass in a list of dictionaries, where each dictionary corrosponds to one category, and contains the parameters described above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's upload a category and then check what's in the category." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.upload(name = \"Droids\", \n", " children = [\"r2d2\", \"c3po\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's upload a few categories and then check what parent categories are in the system" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "categories.upload_all([{\"name\":\"month\", \n", " \"children\":[\"January\",\"February\"]}, \n", " {\"name\":\"Time of Day\", \n", " \"children\":[\"morning\", \"evening\"]}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add children/subcategories, call upload() and pass in the parent category name and a list of the new subcategories to add. \n", "\n", "If you'd like to instead overwrite the existing subcategories with new subcategories, call upload() and pass in the parameter overwrite_children = True." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.upload(name = \"Droids\", children = [\"bb8\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To rename a category, call rename(), with parameters name and new_name." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.rename(name = \"month\", new_name = \"Months\")\n", "categories.ids[\"Months\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can delete categories either individually with delete(), or in bulk with delete_all(). \n", "\n", "You also have the option to delete the entire parent category or just some of the subcategories. \n", "\n", "To delete ALL CATEGORIES in a project, call clear_all_in_project with no parameters. Be careful with this one, and do not use unless you want to delete all categories in the current project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's delete just some subcategories." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.delete({\"name\": \"Months\", \"children\":[\"February\"]})\n", "categories.delete_all([{\"name\": \"Droids\", \"children\": [\"bb8\", \"c3po\"]}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.delete(\"Droids\")\n", "categories.delete_all([\"Months\", \"Time of Day\"])\n", "\n", "categories.ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tags" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWTags object by passing in your project as a parameter, which loads all of the tags in your project.\n", "\n", "Print out ids to see which tags are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags = BWTags(project)\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to upload tags: individually and in bulk. When uploading many tags, it is more efficient to use upload_all. \n", "\n", "In upload, pass in the name of the tag.\n", "\n", "In upload_all, pass in a list of dictionaries, where each dictionary contains \"name\" as the key and the tag name as the its value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.upload(name = \"yellow\")\n", "tags.upload_all([{\"name\":\"green\"}, \n", " {\"name\":\"blue\"}, \n", " {\"name\":\"purple\"}])\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the name of a tag, but mantain its id, upload it with keyword arguments name and new_name. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.upload(name = \"yellow\", new_name = \"yellow-orange blend\")\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with categories, there are three ways of deleting tags. \n", "\n", "Delete one tag by calling delete and passing in a string, the name of the tag to delete\n", "\n", "Delete multiple tags by calling delete_all and passing in a list of strings, where each string is a name of a tag to delete\n", "\n", "To delete ALL TAGS in a project, call clear_all_in_project with no parameters. Be careful with this one, and do not use unless you want to delete all tags in the current project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.delete(\"purple\")\n", "tags.delete_all([\"blue\", \"green\", \"yellow-orange blend\"])\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Brandwatch Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: to avoid ambiguity between the python data type \"list\" and a Brandwatch author list, site list, or location list, the latter is referred to in this demo as a \"Brandwatch List.\"\n", "\n", "BWAuthorLists, BWSiteLists, BWLocationLists work almost identically.\n", "\n", "First, instantiate your the object which contains the Brandwatch Lists in your project, with your project as a the parameter. This will load the data from your project so you can see what's there, upload more Brandwatch Lists, edit existing Brandwatch Lists, and delete Brandwatch Lists from your project\n", "\n", "Printing out ids will show you the Brandwatch Lists (by name and ID) that are currently in your project." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists = BWAuthorLists(project)\n", "authorlists.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To upload a Brandwatch List, pass in a name as a string and the contents of your Brandwatch List as a list of strings. The keyword \"authors\" is used for BWAuthorLists, shown below. The keyword \"domains\"is used for BWSiteLists. The keyword \"locations\" is used for BWLocationLists.\n", "\n", "To see the contents of a Brandwatch List, call get_list with the name as the parameter\n", "\n", "Uploading is done with either a POST call, for new Brandwatch Lists, or a PUT call, for existing Brandwatch Lists, where the ID of the Brandwatch Lists is mantained, so if you upload and then upload a list with the same name and different contents, the first upload will create a new Brandwatch List, and the second upload will modify the existing list and keep its ID. Similarly, you can change the name of an existing Brandwatch List by passing in both \"name\" and \"new_name\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.upload(name = \"Writers\", \n", " authors = [\"Edward Albee\", \"Tenessee Williams\", \"Anna Deavere Smith\"])\n", "\n", "authorlists.get(\"Writers\")[\"authors\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.upload(name = \"Writers\", \n", " new_name = \"Playwrights\", \n", " authors = [\"Edward Albee\", \"Tenessee Williams\", \"Anna Deavere Smith\", \"Susan Glaspell\"])\n", "\n", "authorlists.get(\"Playwrights\")[\"authors\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add items to a Brandwatch List without reentering all of the existing items, call add_items " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.add_items(name = \"Playwrights\", \n", " items = [\"Eugene O'Neill\"])\n", "\n", "authorlists.get(\"Playwrights\")[\"authors\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To delete a Brandwatch List, pass in its name. Note the ids before the Brandwatch List is deleted, compared to after it is deleted. The BWLists object is updated to reflect the Brandwatch Lists in the project after each upload and each delete" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.names" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.delete(\"Playwrights\")\n", "\n", "authorlists.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only difference between how you use BWAuthorlists compared to how you use BWSiteLists and BWLocationLists is the parameter which is passed in. \n", "\n", "BWAuthorlists:\n", "\n", "authors = [\"edward albee\", \"tenessee williams\", \"Anna Deavere Smith\"]\n", "\n", "BWSiteLists:\n", "\n", "domains = [\"github.com\", \"stackoverflow.com\", \"docs.python.org\"]\n", "\n", "BWLocationLists:\n", "\n", "locations = [{\"id\": \"mai4\", \"name\": \"Maine\", \"type\": \"state\", \"fullName\": \"Maine, United States, North America\"}, \n", "{\"id\": \"verf\", \"name\": \"Vermont\", \"type\": \"state\", \"fullName\": \"Vermont, United States, North America\"}, \n", "{\"id\": \"rho4\", \"name\": \"Rhode Island\", \"type\": \"state\", \"fullName\": \"Rhode Island, United States, North America\"} ]\n", "\n", "Requires dictionary of location data instead of a string\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rules" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWRules object by passing in your project as a parameter, which loads all of the rules in your project.\n", "\n", "Print out names and IDs to see which rules are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules = BWRules(project)\n", "rules.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every rule must have a name, an action, and filters.\n", "\n", "The first step to creating a rule through the API is to prepare filters by calling filters(). \n", "\n", "If your desired rules applies to a query (or queries), include queryName as a filter and pass in a list of the queries you want to apply it to.\n", "\n", "There are over a hundred filters you can use to only download the mentions that qualify. See the full list in the file filters.py. Here, different filters are used, which take different data types. filters.py details which data type is used with each filter. Some filters, like sentiment and xprofession below, have a limited number of settings to choose from. You can filter many things by inclusion or exclusion. The x in xprofession stands for exclusion, for example.\n", "\n", "If you include search terms, be sure to use nested quotes - passing in \"cat food\" will result in a search that says cat food (i.e. cat AND food)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters = rules.filters(queryName = \"ice cream cake\", \n", " sentiment = \"positive\", \n", " twitterVerified = False, \n", " impactMin = 50, \n", " xprofession = [\"Politician\", \"Legal\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters = rules.filters(queryName = [\"Australian Animals\", \"ice cream cake\"], \n", " search = '\"cat food\" OR \"dog food\"')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second step is to prepare the rule action by calling rule_action().\n", "\n", "For this function, you must pass in the action and setting. Below I've used examples of adding categories and tags, but you can also set sentiment or workflow (as in the front end).\n", "\n", "If you pass in a category or tag that does not yet exist, it will be automatically uploaded for you." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "action = rules.rule_action(action = \"addTag\", \n", " setting = [\"animal food\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last step is to upload!\n", "\n", "Pass in the name, filters, and action. Scope is optional - it will default to query if queryName is in the filters and otherwise be set to project. Backfill is also optional - it will default to False.\n", "\n", "The upload() function will automatically check the validity of your search string and give a helpful error message if errors are found." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.upload(name = \"rule\", \n", " scope = \"query\", \n", " filter = filters, \n", " ruleAction = action,\n", " backfill = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also upload rules in bulk. Below we prepare a bunch of filters and actions at once." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters1 = rules.filters(search = \"caknvfoga;vnaei\")\n", "filters2 = rules.filters(queryName = [\"Australian Animals\"], search = \"(bloop NEAR/10 blorp)\")\n", "filters3 = rules.filters(queryName = [\"Australian Animals\", \"ice cream cake\"], search = '\"hello world\"')\n", "\n", "action1 = rules.rule_action(action = \"addCategories\", setting = {\"Example\": [\"One\"]})\n", "action2 = rules.rule_action(action = \"addTag\", setting = [\"My Example\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When uploading in bulk, it is helpful (but not necessary) to use the rules() function before uploading in order to keep the dictionaries organized." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rule1 = rules.rule(name = \"rule1\", \n", " filter = filters1, \n", " action = action1, \n", " scope = \"project\")\n", "\n", "rule2 = rules.rule(name = \"rule2\", \n", " filter = filters2, \n", " action = action2)\n", "\n", "rule3 = rules.rule(name = \"rule3\", \n", " filter = filters3, \n", " action = action1,\n", " backfill = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.upload_all([rule1, rule2, rule3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with other resources, we can delete, delete_all or clear_all_in_project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.delete(name = \"rule\")\n", "rules.delete_all(names = [\"rule1\", \"rule2\", \"rule3\"])\n", "\n", "rules.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Signals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWSignals object by passing in your project as a parameter, which loads all of the signals in your project.\n", "\n", "Print out ids to see which signals are currently in your project." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals = BWSignals(project)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can upload signals individually or in batch.\n", "\n", "You must pass at least a name, queries (list of queries you'd like the signal to apply to) and subscribers. For each subscriber, you have to pass both an emailAddress and notificationThreshold. The notificationThreshold will be a number 1, 2 or 3 - where 1 means send all notifications and 3 means send only high priority signals.\n", "\n", "Optionally, you can also pass in categories or tags to filter by. As before, you can filter by an entire category with the keyword parentCategory or just a subcategory (or list of subcategories) with the keyword category. An example of how to pass in each filter is shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.upload(name= \"New Test\",\n", " queries= [\"ice cream cake\"],\n", " parentCategory = [\"Colors\"],\n", " subscribers= [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 1}])\n", "\n", "signals.upload_all([{\"name\": \"Signal Me\",\n", " \"queries\": [\"ice cream cake\"],\n", " \"category\": {\"Colors\": [\"Blue\", \"Yellow\"]},\n", " \"subscribers\": [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 3}]},\n", " {\"name\": \"Signal Test\",\n", " \"queries\": [\"ice cream cake\"],\n", " \"tag\": [\"Tastes Good\"],\n", " \"subscribers\": [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 2}]}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Signals can be deleted individually or in bulk." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.delete(\"New Test\")\n", "signals.delete_all([\"Signal Me\", \"Signal Test\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Patching Mentions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To patch the metadata on mentions, whether those mentions come from queries or from groups, you must first instantiate a BWMentions object and pass in your project as a parameter. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions = BWMentions(project)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today,\n", " parentCategory = [\"Colors\", \"Days\"],\n", " category = {\"Colors\": [\"Blue\", \"Yellow\"], \n", " \"Days\": [\"Monday\"]}, \n", " tag = [\"Tastes Good\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "if you don't want to upload your tags and categories ahead of time, you don't have to! BWMentions will do that for you, but if there are a lot of differnet tags/categories, it's definitely more efficient to upload them in bulk ahead of time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example, i'm arbitrarily patching a few of the mentions, rather than all of them" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[0:10], action = \"addTag\", setting = [\"cold\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[5:12], action = \"starred\", setting = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[6:8], action = \"addCategories\", setting = {\"color\":[\"green\", \"blue\"]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
dsacademybr/PythonFundamentos	Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb	1	3693	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.8.8\n" ] } ], "source": [ "# Versão da Linguagem Python\n", "from platform import python_version\n", "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Herança" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Criando a classe Animal - Super-classe\n", "class Animal():\n", " \n", " def __init__(self):\n", " print(\"Animal criado\")\n", "\n", " def Identif(self):\n", " print(\"Animal\")\n", "\n", " def comer(self):\n", " print(\"Comendo\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Criando a classe Cachorro - Sub-classe\n", "class Cachorro(Animal):\n", " \n", " def __init__(self):\n", " Animal.__init__(self)\n", " print(\"Objeto Cachorro criado\")\n", "\n", " def Identif(self):\n", " print(\"Cachorro\")\n", "\n", " def latir(self):\n", " print(\"Au Au!\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Animal criado\n", "Objeto Cachorro criado\n" ] } ], "source": [ "# Criando um objeto (Instanciando a classe)\n", "rex = Cachorro()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cachorro\n" ] } ], "source": [ "# Executando o método da classe Cachorro (sub-classe)\n", "rex.Identif()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Comendo\n" ] } ], "source": [ "# Executando o método da classe Animal (super-classe)\n", "rex.comer()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Au Au!\n" ] } ], "source": [ "# Executando o método da classe Cachorro (sub-classe)\n", "rex.latir()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obrigado\n", "\n", "### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
johntanz/ROP	.ipynb_checkpoints/ROP Data Analysis v1-checkpoint.ipynb	1	467433	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ROP Data Analysis 0.1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Call ROPtimes dict, and clean data up" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Import Modules and Identify Low Signals\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pylab import \n", "import pandas as pd\n", "from datetime import datetime, timedelta\n", "from pandas import Series, DataFrame, concat\n", "import scipy\n", "import scipy.stats\n", "import scipy.fftpack\n", "\n", "#Define any string with 'C' as NaN. 'C' is an indicator for the Masimo as a low signal reading\n", "def readD(val):\n", " if 'C' in val:\n", " return np.nan\n", " return val\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import ROPtimes as rt\n", "\n", "Baby = 'ROP015' #change depending on which baby you're doing!\n", "BabyDict = getattr(rt, Baby) " ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "\"\\nParse_dates tells the read_csv function to combine the date and time column\\nInto one timestamp column and parse it as a timestamp.\\nPandas is smart enough to know how to parse a date in various formats\\n\\nIndex_col sets the timestamp column to be the index.\\n\\nUsecols tells the read_csv function to select only the subset of the columns.\\nNa_values is used to turn 0 into NaN\\n\\nConverters: readD is the dict that means any string with 'C' with be NaN (for PI)\\n\"" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfNIRSpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/NIRS/Clean/' + Baby + 'NIRS.csv',\n", " parse_dates={'timestamp': ['Date',' Time']},\n", " index_col='timestamp',\n", " usecols=['Date', ' Time', ' Ch2 %StO2'],\n", " na_values=['0'])\n", "\n", "dfPOpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/Pulse Ox/' + Baby + 'PO.csv',\n", " parse_dates={'timestamp': ['Date','Time']},\n", " index_col='timestamp',\n", " usecols=['Date', 'Time', 'SpO2', 'PR'],\n", " na_values=['0'])\n", "\n", "dfPIpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/Pulse Ox/' + Baby + 'PO.csv',\n", " parse_dates={'timestamp': ['Date','Time']},\n", " index_col='timestamp',\n", " usecols=['Date', 'Time', 'PI', 'Exceptions'],\n", " na_values=['0'],\n", " converters={'Exceptions': readD}\n", " ) #make a PI df as well to clean it better\n", "\n", "dfNIRSpre = dfNIRSpre0.rename(columns={' Ch2 %StO2': 'StO2'}) #rename NIRS column\n", "\n", "#Clean the PI dataframe to get rid of rows that have NaN\n", "dfPIpre = dfPIpre0[dfPIpre0.loc[:, ['PI', 'Exceptions']].apply(pd.notnull).all(1)]\n", "\n", "dfPOpre = dfPIpre.combine_first(dfPOpre0)\n", "#combine PI dataframe with the rest of the Pulse Ox dataframe after cleaning\n", "\n", "'''\n", "Parse_dates tells the read_csv function to combine the date and time column\n", "Into one timestamp column and parse it as a timestamp.\n", "Pandas is smart enough to know how to parse a date in various formats\n", "\n", "Index_col sets the timestamp column to be the index.\n", "\n", "Usecols tells the read_csv function to select only the subset of the columns.\n", "Na_values is used to turn 0 into NaN\n", "\n", "Converters: readD is the dict that means any string with 'C' with be NaN (for PI)\n", "'''" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Both NIRS and PO indices are even.\n" ] } ], "source": [ "# Phone almost always equals Philips monitor\n", "\n", "# NIRS date/time is 2 mins and 10 seconds slower than phone. Have to correct for it.\n", "# Pulse ox date/time is 1 mins and 32 seconds faster than phone. Have to correct for it.\n", "\n", "#This code is to make the combined dataframe come in even seconds.\n", "#The corrected is 1 second, + for NIRS and - for PO.\n", "\n", "TCcorrect = timedelta(seconds=1)\n", "\n", "ncorr = dfNIRSpre.index+TCcorrect\n", "pcorr = dfPOpre.index-TCcorrect\n", "\n", "\n", "if dfNIRSpre.index[:1].second % 2 == 0:\n", " if dfPOpre.index[:1].second % 2 == 0:\n", " print '\\nBoth NIRS and PO indices are even.'\n", " elif dfPOpre.index[:1].second % 2 != 0:\n", " print '\\nNIRS even, PO odd. PO index corrected.'\n", " dfPOpre = dfPOpre.set_index(pcorr)\n", " else:\n", " raise NameError('Indices are messed up')\n", "elif dfNIRSpre.index[:1].second % 2 != 0:\n", " if dfPOpre.index[:1].second % 2 == 0:\n", " print '\\nNIRS odd, PO even. NIRS index corrected.'\n", " dfNIRSpre = dfNIRSpre.set_index(ncorr)\n", " elif dfPOpre.index[:1].second % 2 != 0:\n", " print '\\nBoth NIRS and PO indices are odd. Both corrected'\n", " dfNIRSpre = dfNIRSpre.set_index(ncorr)\n", " dfPOpre = dfPOpre.set_index(pcorr)\n", "else:\n", " raise NameError('Indices are messed up')\n", "\n", "TCnirs = timedelta(minutes=2, seconds=10)\n", "TCpo = timedelta(minutes=1, seconds=32)\n", "# NIRS is slower than correct time, need to add TCnirs to catch it up\n", "# PO is faster than correct time, need to subtract TCpo to slow it down\n", "\n", "dfNIRS = dfNIRSpre.set_index([dfNIRSpre.index+TCnirs]) #NIRS Time Correction\n", "dfPO = dfPOpre.set_index([dfPOpre.index-TCpo]) #PO Time Correction" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Both machines on\n" ] } ], "source": [ "#for babies that only had one machine\n", "\n", "dffakePO = pd.DataFrame({'SpO2':0, 'PR':0, 'PI':0}, index=dfNIRS.index)\n", "dffakeNIRS = pd.DataFrame({'StO2':0}, index=dfPO.index)\n", "\n", "if len(dfNIRS) > 5:\n", " if len(dfPO) > 5:\n", " df = dfNIRS.combine_first(dfPO) #Combine two DataFrame objects and default to non-null values in frame\n", " print 'Both machines on'\n", " elif len(dfPO) < 5:\n", " df = dfNIRS.combine_first(dffakePO)\n", " print 'Only NIRS on'\n", "elif len(dfNIRS) < 5:\n", " df = dffakeNIRS.combine_first(dfPO)\n", " print 'Only Masimo on'\n", "else:\n", " raise NameError('Check your files')" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total (h) Baseline recording = 15.748345932\n", "Total (h) After Exam recording = 25.46390926\n" ] } ], "source": [ "a = -(df.index[0]-BabyDict['ExamStart']).total_seconds()0.000277778\n", "print 'Total (h) Baseline recording = %s' % a\n", "b = ((df.index[-1])-BabyDict['ExamEnd']).total_seconds()0.000277778\n", "print 'Total (h) After Exam recording = %s' % b" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#Convert to Relative Time and Time Windows\n", "def reltimecalc(ROPNumber, df):\n", " ROPreltime = {\n", " 'BaselineStart' : (df.index[0].to_datetime()-ROPNumber['ExamStart']).total_seconds(), #should be negative\n", " 'BaselineEnd': (ROPNumber['EyeDrop1'] - ROPNumber['ExamStart']).total_seconds(), #redundant, just for labeling sake\n", " 'EyeDrop1' : (ROPNumber['EyeDrop1'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'EyeDrop2' : (ROPNumber['EyeDrop2'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'EyeDrop3' : (ROPNumber['EyeDrop3'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'ExamStart' : (ROPNumber['ExamStart'] - ROPNumber['ExamStart']).total_seconds(), \n", " 'ExamEnd' : (ROPNumber['ExamEnd'] - ROPNumber['ExamStart']).total_seconds(), #positive number\n", " 'DataEnd' : (df.index[-1].to_datetime()-ROPNumber['ExamStart']).total_seconds()\n", " }\n", " \n", " TimeWindows = {\n", " 'tw0' : -10800.0, # three hours before 0\n", " 'tw1' : 10800.0, # three hours after 0\n", " 'tw2' : 21600.0, # six hoours\n", " 'tw3' : 32400.0, # nine hours\n", " 'tw4' : 43200.0, # 12 hours\n", " 'tw5' : 54000.0, # 15 hours\n", " 'tw6' : 64800.0, # 18 hours\n", " 'tw7' : 75600.0, # 21 hours\n", " 'tw8' : 86400.0 #24 hours\n", " }\n", " \n", " return ROPreltime, TimeWindows\n" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "tw = timedelta(hours=3)\n", "twend = timedelta(hours=24)\n", "df = df[(BabyDict['ExamStart']-tw):(BabyDict['ExamStart']+twend)]" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "Data, tw = reltimecalc(BabyDict, df)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df = df.set_index(np.linspace(tw['tw0'],tw['tw8'],len(df)))" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df = df[df.PI.between(0.02, 20)]\n", "df = df.drop('Exceptions', 1) # Drop exceptions now that you don't need it anymore\n", "#filter PI values to only allow =< 0.02 and >= 20\n", "\n", "dfen = df.dropna(how='any') #df for no NaN" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "psa = dfen['SpO2'].values\n", "prs = dfen['StO2'].values\n", "\n", "FTOE = pd.Series(((psa-prs)/psa)100)\n", "\n", "dfen = dfen.assign(FTOE=FTOE.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analyis Start" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Linear Measures:\n", "\n", "Avgs, Std Dev, and Coefficient of Variation\n", " - Avg, std dev, and CoV for every time window\n", " - Avg every 10 sec for 4 mins of ROP exam, std dev, CoV.\n", " - O2 DeSat Counts" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = df[tw['tw0']:0]\n", "b = df[0:tw['tw1']]\n", "c = df[tw['tw1']:tw['tw2']]\n", "d = df[tw['tw2']:tw['tw3']]\n", "e = df[tw['tw3']:tw['tw4']]\n", "f = df[tw['tw4']:tw['tw5']]\n", "g = df[tw['tw5']:tw['tw6']]\n", "h = df[tw['tw6']:tw['tw7']]\n", "i = df[tw['tw7']:tw['tw8']]\n", "\n", "dflst = [a, b, c, d, e, f, g, h, i]\n", "\n", "tlst = [tw['tw0'], 0, tw['tw1'], tw['tw2'], tw['tw3'], tw['tw4'], tw['tw5'], \n", " tw['tw6'], tw['tw7'], tw['tw8']]\n", "\n", "#dfen for no na values\n", "\n", "ea = dfen[tw['tw0']:0]\n", "eb = dfen[0:tw['tw1']]\n", "ec = dfen[tw['tw1']:tw['tw2']]\n", "ed = dfen[tw['tw2']:tw['tw3']]\n", "ee = dfen[tw['tw3']:tw['tw4']]\n", "ef = dfen[tw['tw4']:tw['tw5']]\n", "eg = dfen[tw['tw5']:tw['tw6']]\n", "eh = dfen[tw['tw6']:tw['tw7']]\n", "ei = dfen[tw['tw7']:tw['tw8']]\n", "\n", "dfenlst = [ea, eb, ec, ed, ee, ef, eg, eh, ei] #basically same dataframe but all NaNs dropped\n", "#some functions will return NaN if there's even one NaN present so this is why this is necessary\n", "\n", "#entropy functions replace original data, so need to make a copy\n", "dfenlstcopy1 = [] #use this for entropy measures\n", "dfenlstcopy2 = [] #use this for cross entropy meaures\n", "dfenlstcopy3 = [] #use this for baseline cross entropy measures\n", "for i in dfenlst:\n", " cpy = i.copy(deep=True)\n", " dfenlstcopy1.append(cpy)\n", " dfenlstcopy2.append(cpy)\n", " dfenlstcopy3.append(cpy)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#AVERAGE DURING ROP EXAM FOR FIRST FOUR MINUTES\n", "\n", "\n", "#tensec needs df['y']\n", "\n", "def tensec(a, b): #a = baseline values, b = next time epoch\n", "\n", " blavg = np.nanmean(a.values)\n", " blstd = np.nanstd(a.values)\n", " blcv = scipy.stats.variation(a.values, nan_policy='omit')\n", " \n", " r = np.arange(0, 250, 10)\n", " \n", " avg = [blavg]\n", " stdev = [blstd]\n", " cv = [blcv]\n", " \n", " ind = list(np.arange(0, 250, 10))\n", " ind.insert(0, 'Baseline')\n", " \n", " for i in r:\n", " x = b[i:(i+10)].mean()\n", " y = b[i:(i+10)].std()\n", " z = scipy.stats.variation((b[i:(i+10)]).values, nan_policy='omit')\n", " \n", " avg.append(x)\n", " stdev.append(y)\n", " cv.append(z)\n", " \n", " dftensec = pd.DataFrame({\n", " 'a_mean': avg,\n", " 'b_std dev' : stdev,\n", " 'c_CoV' : cv,\n", " }, index = ind)\n", " \n", " return dftensec" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIten = tensec(a['PI'], b['PI'])\n", "PIten.columns = ['PI mean', 'PI std dev', 'PI CV']\n", "\n", "PRten = tensec(a['PR'], b['PR'])\n", "PRten.columns = ['PR mean', 'PR std dev', 'PR CV']\n", "\n", "SpO2ten = tensec(a['SpO2'], b['SpO2'])\n", "SpO2ten.columns = ['SpO2 mean', 'SpO2 std dev', 'PR CV']\n", "\n", "StO2ten = tensec(a['StO2'], b['StO2'])\n", "StO2ten.columns = [\"StO2 mean\", \"StO2 std dev\", \"StO2 CV\"]\n", "\n", "FTOEten = tensec(ea['FTOE'], eb['FTOE']) #e means dropped nan\n", "FTOEten.columns = ['FTOE mean', 'FTOE std dev', 'FTOE CV']" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def linearmeasures(avglst): \n", " \n", " #mean\n", " \n", " PImean = []\n", " PRmean = []\n", " SpO2mean = []\n", " StO2mean = []\n", " FTOEmean = []\n", " \n", " #std dev\n", " \n", " PIstd = []\n", " PRstd = []\n", " SpO2std = []\n", " StO2std = []\n", " FTOEstd = []\n", "\n", "\n", " #coefficient of varience\n", " \n", " PIcv = []\n", " PRcv = []\n", " SpO2cv = []\n", " StO2cv = []\n", " FTOEcv = []\n", " \n", " \n", " for i in avglst:\n", "\n", " ax = np.nanmean(i['PI'].values) #mean\n", " PImean.append(ax)\n", " az = np.nanstd(i['PI'].values) #std dev\n", " PIstd.append(az)\n", " av = scipy.stats.variation(i['PI'].values, nan_policy='omit') #CoV\n", " PIcv.append(av)\n", " \n", " bx = np.nanmean(i['PR'].values) #mean\n", " PRmean.append(bx)\n", " bz = np.nanstd(i['PR'].values) #std dev\n", " PRstd.append(bz)\n", " bv = scipy.stats.variation(i['PR'].values, nan_policy='omit') #CoV\n", " PRcv.append(bv)\n", " \n", " cx = np.nanmean(i['SpO2'].values) #mean\n", " SpO2mean.append(cx)\n", " cz = np.nanstd(i['SpO2'].values) #std dev\n", " SpO2std.append(cz)\n", " cv = scipy.stats.variation(i['SpO2'].values, nan_policy='omit') #CoV\n", " SpO2cv.append(cv)\n", " \n", " dx = np.nanmean(i['StO2'].values) #mean\n", " StO2mean.append(dx)\n", " dz = np.nanstd(i['StO2'].values) #std dev\n", " StO2std.append(dz)\n", " dv = scipy.stats.variation(i['StO2'].values, nan_policy='omit') #CoV\n", " StO2cv.append(dv)\n", " \n", " ex = np.nanmean(i['FTOE'].values) #mean\n", " FTOEmean.append(ex)\n", " ez = np.nanstd(i['FTOE'].values) #std dev\n", " FTOEstd.append(ez)\n", " ev = scipy.stats.variation(i['FTOE'].values, nan_policy='omit') #CoV\n", " FTOEcv.append(ev)\n", " \n", " \n", " PIlin = pd.DataFrame({\n", " 'a_PI mean': PImean,\n", " 'b_PI std dev' : PIstd,\n", " 'c_PI CoV' : PIcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " PRlin = pd.DataFrame({\n", " 'a_PR mean': PRmean,\n", " 'b_PR std dev' : PRstd,\n", " 'c_PR CoV' : PRcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " SpO2lin = pd.DataFrame({\n", " 'a_SpO2 mean': SpO2mean,\n", " 'b_SpO2 std dev' : SpO2std,\n", " 'c_SpO2 CoV' : SpO2cv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " StO2lin = pd.DataFrame({\n", " 'a_StO2 mean': StO2mean,\n", " 'b_StO2 std dev' : StO2std,\n", " 'c_StO2 CoV' : StO2cv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " FTOElin = pd.DataFrame({\n", " 'a_FTOE mean': FTOEmean,\n", " 'b_FTOE std dev' : FTOEstd,\n", " 'c_FTOE CoV' : FTOEcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " dfCoV = pd.DataFrame({\n", " 'PI CoV' : PIcv,\n", " 'PR CoV' : PRcv,\n", " 'SpO2 CoV' : SpO2cv,\n", " 'StO2 CoV' : StO2cv,\n", " 'FTOE CoV' : FTOEcv\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " \n", " return PIlin, PRlin, SpO2lin, StO2lin, FTOElin, dfCoV" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIlin, PRlin, SpO2lin, StO2lin, FTOElin, dfCoV = linearmeasures(dfenlst) #USE dfenlst" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_PI mean</th>\n", " <th>b_PI std dev</th>\n", " <th>c_PI CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>1.400518</td>\n", " <td>0.607776</td>\n", " <td>0.433965</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>1.470180</td>\n", " <td>0.408768</td>\n", " <td>0.278039</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>1.328343</td>\n", " <td>0.666908</td>\n", " <td>0.502060</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>1.516548</td>\n", " <td>0.507242</td>\n", " <td>0.334471</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>1.880443</td>\n", " <td>0.617186</td>\n", " <td>0.328213</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>2.044417</td>\n", " <td>0.810196</td>\n", " <td>0.396297</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>1.722643</td>\n", " <td>0.592473</td>\n", " <td>0.343933</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>1.780041</td>\n", " <td>0.529919</td>\n", " <td>0.297700</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>1.238639</td>\n", " <td>0.605162</td>\n", " <td>0.488570</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_PI mean b_PI std dev c_PI CoV\n", "-3:0 1.400518 0.607776 0.433965\n", "0:3 1.470180 0.408768 0.278039\n", "3:6 1.328343 0.666908 0.502060\n", "6:9 1.516548 0.507242 0.334471\n", "9:12 1.880443 0.617186 0.328213\n", "12:15 2.044417 0.810196 0.396297\n", "15:18 1.722643 0.592473 0.343933\n", "18:21 1.780041 0.529919 0.297700\n", "21:24 1.238639 0.605162 0.488570" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_PR mean</th>\n", " <th>b_PR std dev</th>\n", " <th>c_PR CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>147.459532</td>\n", " <td>15.811006</td>\n", " <td>0.107223</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>148.137215</td>\n", " <td>17.468538</td>\n", " <td>0.117921</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>146.487619</td>\n", " <td>15.656282</td>\n", " <td>0.106878</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>154.990661</td>\n", " <td>15.570040</td>\n", " <td>0.100458</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>150.472088</td>\n", " <td>14.765096</td>\n", " <td>0.098125</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>164.673462</td>\n", " <td>16.413125</td>\n", " <td>0.099671</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>151.246552</td>\n", " <td>14.890699</td>\n", " <td>0.098453</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>156.654985</td>\n", " <td>14.957723</td>\n", " <td>0.095482</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>153.593688</td>\n", " <td>17.143984</td>\n", " <td>0.111619</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_PR mean b_PR std dev c_PR CoV\n", "-3:0 147.459532 15.811006 0.107223\n", "0:3 148.137215 17.468538 0.117921\n", "3:6 146.487619 15.656282 0.106878\n", "6:9 154.990661 15.570040 0.100458\n", "9:12 150.472088 14.765096 0.098125\n", "12:15 164.673462 16.413125 0.099671\n", "15:18 151.246552 14.890699 0.098453\n", "18:21 156.654985 14.957723 0.095482\n", "21:24 153.593688 17.143984 0.111619" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_SpO2 mean</th>\n", " <th>b_SpO2 std dev</th>\n", " <th>c_SpO2 CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>98.768316</td>\n", " <td>1.969275</td>\n", " <td>0.019938</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>97.741483</td>\n", " <td>2.303449</td>\n", " <td>0.023567</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>98.665524</td>\n", " <td>1.748614</td>\n", " <td>0.017723</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>98.020172</td>\n", " <td>1.972344</td>\n", " <td>0.020122</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>98.063082</td>\n", " <td>1.734714</td>\n", " <td>0.017690</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>98.151674</td>\n", " <td>2.144752</td>\n", " <td>0.021851</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>97.956578</td>\n", " <td>2.278130</td>\n", " <td>0.023257</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>97.970015</td>\n", " <td>1.944758</td>\n", " <td>0.019851</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>98.817949</td>\n", " <td>1.833986</td>\n", " <td>0.018559</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_SpO2 mean b_SpO2 std dev c_SpO2 CoV\n", "-3:0 98.768316 1.969275 0.019938\n", "0:3 97.741483 2.303449 0.023567\n", "3:6 98.665524 1.748614 0.017723\n", "6:9 98.020172 1.972344 0.020122\n", "9:12 98.063082 1.734714 0.017690\n", "12:15 98.151674 2.144752 0.021851\n", "15:18 97.956578 2.278130 0.023257\n", "18:21 97.970015 1.944758 0.019851\n", "21:24 98.817949 1.833986 0.018559" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_StO2 mean</th>\n", " <th>b_StO2 std dev</th>\n", " <th>c_StO2 CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>78.951285</td>\n", " <td>4.364783</td>\n", " <td>0.055285</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>79.807937</td>\n", " <td>4.488576</td>\n", " <td>0.056242</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>78.421524</td>\n", " <td>3.891876</td>\n", " <td>0.049628</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>78.064625</td>\n", " <td>3.017061</td>\n", " <td>0.038648</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>79.340901</td>\n", " <td>3.176529</td>\n", " <td>0.040036</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>78.302226</td>\n", " <td>3.366731</td>\n", " <td>0.042997</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>81.875885</td>\n", " <td>3.955571</td>\n", " <td>0.048312</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>77.831709</td>\n", " <td>3.228982</td>\n", " <td>0.041487</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>81.162525</td>\n", " <td>5.011192</td>\n", " <td>0.061743</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_StO2 mean b_StO2 std dev c_StO2 CoV\n", "-3:0 78.951285 4.364783 0.055285\n", "0:3 79.807937 4.488576 0.056242\n", "3:6 78.421524 3.891876 0.049628\n", "6:9 78.064625 3.017061 0.038648\n", "9:12 79.340901 3.176529 0.040036\n", "12:15 78.302226 3.366731 0.042997\n", "15:18 81.875885 3.955571 0.048312\n", "18:21 77.831709 3.228982 0.041487\n", "21:24 81.162525 5.011192 0.061743" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_FTOE mean</th>\n", " <th>b_FTOE std dev</th>\n", " <th>c_FTOE CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>20.027616</td>\n", " <td>4.778722</td>\n", " <td>0.238607</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>18.295492</td>\n", " <td>5.087889</td>\n", " <td>0.278095</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>20.489832</td>\n", " <td>4.247597</td>\n", " <td>0.207303</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>20.323137</td>\n", " <td>3.546801</td>\n", " <td>0.174520</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>19.063551</td>\n", " <td>3.603959</td>\n", " <td>0.189050</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>20.174427</td>\n", " <td>4.068943</td>\n", " <td>0.201688</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>16.356134</td>\n", " <td>4.786585</td>\n", " <td>0.292648</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>20.521799</td>\n", " <td>3.698035</td>\n", " <td>0.180200</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>17.821530</td>\n", " <td>5.556991</td>\n", " <td>0.311813</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_FTOE mean b_FTOE std dev c_FTOE CoV\n", "-3:0 20.027616 4.778722 0.238607\n", "0:3 18.295492 5.087889 0.278095\n", "3:6 20.489832 4.247597 0.207303\n", "6:9 20.323137 3.546801 0.174520\n", "9:12 19.063551 3.603959 0.189050\n", "12:15 20.174427 4.068943 0.201688\n", "15:18 16.356134 4.786585 0.292648\n", "18:21 20.521799 3.698035 0.180200\n", "21:24 17.821530 5.556991 0.311813" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display\n", "display(PIlin)\n", "display(PRlin)\n", "display(SpO2lin)\n", "display(StO2lin)\n", "display(FTOElin)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4b/748WOaNm0aXbt2jV68eEH29va0Y8cOkslktHPnTrK3t6cXL17ktZGWlkbW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\noVyqUEHshNWpk/g9w4gI8Q170yZg9Gi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"text/plain": [ "<matplotlib.figure.Figure at 0x103c09c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfCoV.plot() #CV graph\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Pearson Correlation Coefficients of 3 Hour Epochs with graph to see trend" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ar = dfen[tw['tw0']:0].corr()\n", "#excludes NaN values\n", "#pearson correlation coefficeint\n", "\n", "#pearson, kendall or spearman?\n", "br = dfen[0:tw['tw1']].corr()\n", "cr = dfen[tw['tw1']:tw['tw2']].corr()\n", "dr = dfen[tw['tw2']:tw['tw3']].corr()\n", "er = dfen[tw['tw3']:tw['tw4']].corr()\n", "fr = dfen[tw['tw4']:tw['tw5']].corr()\n", "gr = dfen[tw['tw5']:tw['tw6']].corr()\n", "hr = dfen[tw['tw6']:tw['tw7']].corr()\n", "ir = dfen[tw['tw7']:tw['tw8']].corr()\n", "\n", "dfcorrlst = [ar, br, cr, dr, er, fr, gr, hr, ir]" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 0 1 2 3 4\n", "# PI, PR, SpO2, StO2, FTOE\n", "# PI aa ab ac ad ae\n", "# PR ba bb bc bd be\n", "# SpO2 ca cb cc cd ce\n", "# StO2 da db dc dd de\n", "# FTOE ea eb ec ed ee\n", "\n", "def corrlst(dflst):\n", " \n", " a = [] #ab\n", " b = [] #ac\n", " c = [] #ad\n", " d = [] #ae\n", " e = [] #bc\n", " f = [] #bd\n", " g = [] #be\n", " h = [] #cd\n", " i = [] #ce\n", " j = [] #de\n", " \n", " for x in dflst:\n", "\n", " ab = x['PI'][1] #ab\n", " a.append(ab)\n", " ac = x['PI'][2] #ac \n", " b.append(ac)\n", " ad = x['PI'][3] #ad\n", " c.append(ad)\n", " ae = x['PI'][4] #ae\n", " d.append(ae)\n", " bc = x['PR'][2] #bc\n", " e.append(bc)\n", " bd = x['PR'][3] #bd\n", " f.append(bd)\n", " be = x['PR'][4] #be\n", " g.append(be)\n", " cd = x['SpO2'][3] #cd\n", " h.append(cd)\n", " ce = x['SpO2'][4] #ce\n", " i.append(ce)\n", " de = x['StO2'][4] #de\n", " j.append(de)\n", " \n", " rgraph = pd.DataFrame({\n", " 'PI:PR': a,\n", " 'PI:SpO2' : b,\n", " 'PI:StO2' : c,\n", " 'PI:FTOE' : d,\n", " 'PR:SpO2' : e,\n", " 'PR:StO2' : f,\n", " 'PR:FTOE' : g,\n", " 'SpO2:StO2' : h,\n", " 'SpO2:FTOE' : i,\n", " 'StO2:FTOE': j,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return rgraph" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rgraph = corrlst(dfcorrlst)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIg = rgraph.ix[:,0:4]\n", "PRg = rgraph[['PI:PR', 'PR:FTOE', 'PR:SpO2', 'PR:StO2']]\n", "SpO2g = rgraph[['PI:SpO2', 'PR:SpO2', 'SpO2:StO2', 'SpO2:FTOE']]\n", "StO2g = rgraph[['PI:StO2', 'PR:StO2', 'SpO2:StO2', 'StO2:FTOE']]\n", "FTOEg = rgraph[['PI:FTOE', 'PR:FTOE', 'SpO2:FTOE', 'StO2:FTOE']]" ] }, { "cell_type": "code", "execution_count": 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TAFdXlotXIim6vEkZE/So0wOHQoseBuN8gIgNwc6fD5TXbiwSlWjfni3pGTFC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"text/plain": [ "<matplotlib.figure.Figure at 0x1186cdf10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PIg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/abvety/w/ff+GDas4H4/LXFdm+uZNbf3O5fvwBgzur6sz2FhYTAUuWcb7du3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\nMMOJQose43jb5sD8csYMhvs7q4WP2NzQwOVbt7Jj5ky/zev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"text/plain": [ "<matplotlib.figure.Figure at 0x113ef4610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PRg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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29oaTkxPq168PAMjIyICnpyecnJywceNGtV5buSgok2tyg5Z/M2SMMW0DHS5p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\nNVGPHhXQKU0d8fFEa9YQNWxI1LixuBJ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"text/plain": [ "<matplotlib.figure.Figure at 0x103b39610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SpO2g.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zcyBa3bpb5XDm97+0jDTptaCXTN402a7x5eWoCbtp06bi7u4unp6eUr9+fRk7\ndqwkJCRIcHCwzJs3z+r7pk+fLs2bNxdPT09p3LixjBo1Kvs1a++dMWOGtG7dWmrVqiVNmzaVF154\nQVJSrizM8vbbb8u1114rnp6e0qJFC3n77bdt+2GLqbCE7RAznRVH67qtWXn3Su7/9n4OnD9g73Dy\nSUoyZ1i67jpzLeQjR2DcOKzO+73s0DLe3PEm6+9dj39t/4oN1sE90P4B3uv3Hn0X9WXfuX32Dkc5\noMaNzXusz5yBt9+GqzJnjnh+0/O4OrvySq9X7BtgFWEYBmvWrCEuLo5ff/2Vffv28dprrxU6/fTC\nhQv58ssv2bJlC3Fxcezbt4/evXsXep4nnniCzz77jMWLFxMfH8/atWvZvHkzI0eOzFVu0aJFxMTE\nsHbtWubOncuyZcts8jltxlomt9dGGb8RLju4TBq928gmg49swWIR+fprs/9r+PArfWCF2fL3FvGf\n4S/7IyrxyCYH8N1f34n/DH/Zfnq7vUNRVcCSP5ZI81nNJepylL1DyQcHrWE3a9ZMNudYzm7SpEky\nePBg6dWrl9Ua9uOPPy5PP/10ga+9+OKL4uzsnF1rf+KJJ+TYsWPi7Ows+/bty1U2LCxMatasabWl\nZ+LEiTJx4sTSfbAysPa7lKpUw84you0IJt48kUFfDSIupZwmjS6mn3+G7t3NWbAWLYJvvoHmzQt/\nz4HzB7h7+d0sG7GM9vXbV0ygVdQdbe5g8V2LGbp0KFtObrF3OMqBHbxwkMfXPs7Ku1dSx72OvcOp\nksLCwvjhhx+44YYbsr5sZPP19WXnzp0AdO7cmS+++IKZM2fyyy+/YLFYssu99tpr9OjRg7lz5xIX\nF8fs2bPZvHkzjRs35sYbb8x1zEaNGtG5c2c2btxYYDzbtm2jbdu2Nv6UZVPkfdiO6D9d/8PJmJMM\nXzacNaPXFGtlp5IIDQ3Nvi+yIOHh8MILZpPa9Olw//3Wm75z+jv6bwZ9NYgPB31IcDPrx7dVnJVF\necbZt2Vflo9YzohvRrBg6AIGth5Y6mPp9Sw5ESEqKYqjUUeztyNRRzhx6QRJx5Nocn0TfNx88Knp\nY/7Msfm6++bb5+7iXuGr9a3esJqnjzzNe/3eq5Jfom11OfPk2GIbOnQoLi4ueHt7M3jwYCZPnsxP\nP/2Uq0x0dHT24zFjxuDk5MTnn3/Oyy+/jJubG5MmTeLZZ58t8PgXL14kMLDgxZECAwO5WMBaq1On\nTkVEGDtkCB1+AAAgAElEQVR2bOk+VDmpkgnbMAxmD5jN0CVDmbBmAp/e/mmF/CO/fBneeQdmzYJ/\n/cvsp/b0LN57LyReoN/ifkzuPpnh1wwv30CrmZ7NerLqnlUMWTKE/w76L3ddfZe9Q6pyLqdd5ljU\nsSuJ+VJmcr54BItYaOPXhqC6QbSp24a7295NS9+W7N2xl5Y3tCQmOYbo5GhikmOISY4hPD48+3He\nLd2Sni+Jl2QracK3iIXp26YzsOtA7m13bzleQfspbaK1le+++45evXqV6D333HMP99xzDxkZGXz7\n7beMHj2aDh060KdPn3xl/fz8iIiIKPA4ERERtGjRIte+uXPnsnjxYrZv306NGrat7JVVlUzYAC5O\nLiwZvoRbPr+F17e9zou3vGizY+etvYjAkiXw3HPQpYu5BGbmRG3FkpCawKCvBjGq7SibrqFbWWpZ\nRamIODs36sy6MesY+NVAktOTGX3d6BIfo7pfz3RLOqdjTnMk6kiuGvPRqKP8c/kfWvq2JKhuEEF1\ng+jZtCcP3/AwQXWD8KvlV2CSvHHEjQWcpXAp6SnEpsRaTegxyTGcjTtb4oTv65a/Nu/j5sPmk5up\n0aIGM/vOtMUlVAXI2/xdEs7OzgwbNox27dpx8OBB+vTpk+9v7dZbb+Wxxx5j37593HTTTdn7w8LC\n2L17N1OnTs3eN3/+fGbMmMG2bdus1srtqcombAAPVw9Wj15Nl3ldaObTjDHtxtj8HHv2wNNPQ0oK\nfPmluc5uSaRmpDJs2TDaB7TXkaflrENgBzbdt4m+i/tyOe0yD97woL1DqnREhMjEyCvN1xePZNeW\nT0afpL5HfbO2XCeIq/2uZkibIQTVDaKJdxOcnYrR71NGNV1qUs+lHvVq1yvV+wtL+NFJZi3/TOwZ\nYlLMfQYGy0Yss3m3miq9hQsX4u/vzy233ELt2rVZt24df/75J507dwYgICCAv//+O7t869ateeSR\nRxgzZgxffPEFHTt25PDhw4wbN46+fftm1+6//PJLXnzxRUJDQ2natKldPluRrI1Gs9dGOYxq/CPy\nD/Gf4S+hJ0NtcrytW7dKWJjIvfeKNGgg8vnnxVs0IK8MS4aMWTFG7vj6DknLKMFSQSWI0xFUdJxH\nLx6VJu81kdm7Z5fofVXpesYmx8q+8H3y1e9fybSt02T0itFy48c3iufrnuI3w0+6zusqId+GyBvb\n3pAVf66QPyL/sPk6z1XpetobDjpKvHnz5rlGiWfJO0rcw8NDtm837/ZYuXKldOvWTerUqSPe3t7S\nrl07+eKLL7LL7tq1S4KCgqROnTry5JNPZu+fMWOGtGrVSmrVqiVNmjSR559/Ptd92M2bNxdXV1fx\n9PQUDw8P8fT0lAkTJpTHxy6Utd+lSDEW/6gKrq13LV8P+5qRy0cS+kAoV/tfXaL3i0BUFJw7Z25f\nfgk//AATJpj91B4epYvr2Y3PcjLmJBvv24iLU7X4VVQKreu25seQH+n9RW8up13mue7P2TukcpGa\nkcrf0X/na74+GnWU2JRYWtdpnd2E3a9lPybePJHWdVvrKGhVYXLWhHPasiX3XR3xORatv/POO7nz\nzjutHrNz584cOXIk3/5JkyYxadKkEsdSmRhi7xEHeRiGIeUV08IDC3n5x5fZNX4XAR4BiEB8vJmE\nw8OvJOSsLWtfRISZlBs0MLdWrWDSpJL1U+c1c+dMPj/wOdvGbtP/IO0kPC6c2xbdxshrRjIteFqF\njz62pfMJ59kVtotdZ3dx6J9DHI06SlhsGI28GmU3YWcl56C6QTT0aoiTUeXu6qy2DMNARPL9AZfn\n/6eqfFj7XUIVTthJSWaizZuI16VM43SNH6j3w1bOh9UGoGHDK8m4QYP8zwMDwd29zCFlW/TbIl7a\n+hLbx26nsXdj2x1YldiFxAv0WdSHvi36MqPPDIdI2umWdH6P/J1dYbvYeXYnu8J2EZMcQ+dGnenS\nqAvX17+eoLpBtPBtgauzq73DVRVAE3bVUaUSdloanD+fvzact5acmFhwEg4MFBYlhGCpEcs3w1fg\n61PygTJluc913fF1PPDtA2x9YCvX+F9TqmMUV2W6H7cw9o7zUtIl+i3ux80NbmbOwDlWa572ivPi\n5YvsPrubnWE72XV2F/vO7aOJdxO6NOpC18Zd6dKoC2382mTHbe/rWVwap+1owq46CkvYlbLjdM2a\ngpPwuXNw6RL4++evCffokTs516ljbUIAg7szPqX/4v5M2/UM7w94v8I+18/hP3Pf/+7ju1HflXuy\nVsVXx70Om+7bxKCvBvHgqgf59PZPK2TEc0EyLBn8+c+f2cl519ldnE84z80Nb6Zro6481+05OjXs\nhK+7r13iU0rZT6WsYQ8YILlqxzmTc716xZs1rCgxyTF0m9+Nh294mCc7P1n2AxbhaNRRbvn8Fj65\n/RPuaHNHuZ9PlVxiaiJ3LLmDerXr8cXQLyrkVp6Y5Bj2nN2TnaD3hO8hoHZAds25S+MutPVva7cv\nEMoxaA276qhSTeK2dDrmNF3nd2XugLncebX1UYdlFREfQdf5XXmpx0uMv2F8uZ1HlV1SWhLDvxlO\nDacaLB2+lJouNW12bItYOBp11EzOmf3PZ2LPcFODm7Kbtzs36oxfLT+bnVNVD5qwqw5N2IXYd24f\nA74cwOp7VtOpUadivackfVqxybH0XNCTEdeMsOlsa8XhCH1vUPniTM1IZfSK0SSkJrDy7pXUqlEL\nKHmc8Snx/Bz+c3bT9q6wXfi4+dClcRe6NupKl8ZdaBfQzua39FW262mNxmk7mrCrDofrw65INzW4\nifl3zGfo0qHsGLeDFr4tin5TMSWnJzN06VC6N+nO5B6TbXZcVb5cnV1ZMnwJId+GMOirQawatQrP\nmoVPCi8inIg+kX1r1c6wnRy/dJz29dvTtXFXHuzwIPPumEd9j/oV9CmUUlVNta9hZ/ng5w+Y8/Mc\ndo7faZP7ojMsGYxaMQqAJcOWaB+kA8qwZPCv1f/i4D8HWTtmLT5uPtmvXU67zL5z+3LdWlXTpeaV\nvudGXegQ2EFvq1IVQmvYVUdhNWydOSHTYzc/xuCgwdy59E5S0lPKdCwRYeLaiURdjmLxnYs1WTso\nZydnPrn9E25ucDO3LryVL3//kolrJ9Lx0474v+3Psxuf5XzCee697l5+feRXwp4OY+nwpTzV+Sk6\nNeqkyVqpIjRr1oxatWrh5eVFYGAg48aNIzExkeDgYNzd3fHy8sLf358hQ4YQHh5u9TixsbGMHz+e\nwMBAvL29ueqqq5gxY0b2605OTvlmMouNjWXChAkEBgbi4eHB9ddfz4IFC7JfT01N5cEHH6RZs2Z4\ne3tzww03sG7dOptfg5LQhJ3DjD4z8K/lz9jvxmIRi9VyoaGhhR5n+rbp7Ajbwf/u/p9NBy2VVFFx\nVhaVOU7DMJjVfxbDrxnOR8s/orFXY2b1m8XFSRfZ/eBu3uv/HiPajqCRVyN7h5qtMl/PnDROZRgG\na9asIS4ujl9//ZV9+/bx2muv4eTkxAcffEBcXBwnTpwgOTmZZ555xupxnn76aRITEzly5AixsbGs\nWrWKVq1a5TpPTmlpafTu3ZuwsDD27NlDbGwsM2bM4Pnnn2fWrFkApKen06RJE7Zt20ZsbCyvvvoq\nI0eO5MyZM+VzMYqh2vdh5+RkOLHozkXc+sWtTNkyhem9p5f4GJ/9+hnz989nx7gdeLt5l0OUqqIZ\nhsHkHpPpmtGV4G7B9g5HqSolq8k+MDCQ/v37c/DgwVyve3l5MXToUD744AOrx9i7dy/Tp0/Hy8sL\ngKCgIIKCggDo2bMnIkK7du1wcnJi3rx5xMfHc/bsWbZv346bmxsA/fr1Y/bs2YwfP54HH3wQDw8P\n/u///i/7HIMGDaJ58+b88ssvNGnSxKbXoLi0hp2Hew13Vo1axdJDS/ns188KLGNtxOiqI6uYsnUK\n6+5dR6Cn/ddSrewjW7NonLalcdqWo8Tp6MLCwvjhhx+44YYbcq2RHRUVxcqVK+nU6cpdPDt27KBO\nnStjjTp37szkyZNZsGABx48fz3XcH3/8EYA//viDuLg4RowYwcaNGxkwYEB2ss4ybNgwkpOT2bVr\nV774IiMjOXbsGG3btrXJ5y0NHXRmRdZEJwuHLqRfq35Flt9xZgd3Lr2TNaPX0LFhxwqIUCmlTGUZ\ndGa8bJv582Vqyf/fbt68OVFRUbi4uODt7c3gwYOZOXMm/fv3Z+/evdSoUYPY2Fg6derEli1bcLey\nqENKSgrvvfceK1as4Pfff6dp06bMnj2b/v37A2Yf9vHjx2nRwrwLqE+fPnTs2JHXX38937ECAwN5\n9913ueeee7L3paenM2DAAFq3bs2HH35Y4s9ZEoUNOrP7+td5NyrR+q3bTm8Tvxl+ciDiQK79edfH\nPRh5UOq9XU/WH19fgdEVzRHW8RXROG1N47QtR4gTB10Pu1mzZrJly5Z8+4ODg7PXwz548KAEBgbK\nihUrinXM+Ph4mTx5snh4eEh0dLSIiBiGISdOnMguM2rUKAkJCcn33vT0dHFxcZENGzZk77NYLHL3\n3XfLoEGDJD09vUSfrzSs/S5FRJvEC9O9SXfmDpjL4K8HczbubIFlwmLDGPDlAN7t+y59W/at4AiV\nUsqxSREtAG3btuWVV17hueeeK7IsgIeHB5MnTyYxMZGTJ08WWOa2225j7dq1JCUl5dq/fPly3Nzc\n6Ny5c/a+8ePHc/HiRVauXImzLebFLgtrmdxeG5XwG+Gb296Udv9tJ7HJsbn2R12OkqvnXi0zd8y0\nU2RKKeXYNezNmzfn25+zhi0ikpqaKg0bNpSlS5cWeJxXX31V9u7dK6mpqZKcnCyvvfaa1KlTRxIT\nE0VEJDAwUDZu3JhdPiUlRW688UYZNGiQnDp1StLS0mTdunUSEBAg77zzTna5Rx55RLp06ZJ9nIpg\n7XcpWsMunme7PUuXRl0Y+c1I0jLSAHPijNu/vp2BrQfy767/tnOESinleKytP593f40aNZg4cSJv\nvfUWANu3b88eEZ5VfuzYsfj7+9OwYUM2b97MmjVrqFXLnFZ42rRp3H///dSpU4fly5fj6urKpk2b\naNy4MZ06dcLb25v//Oc/vPHGG9m3j505c4ZPPvmEAwcOEBAQgKenJ15eXnz99dflcSmKRQedFVO6\nJZ07vr6Dhp4NGeUxilmRs/Bx82Hh0IVW10+2N0eYAxk0TlvTOG3LEeLUmc6qjnKb6cwwDF/DMDYY\nhnHEMIz1hmFYvfHYMAwnwzB+NQxjVVnOaS8uTi4sHb6UfRH7eOj7h0jLSGP+HfMrbbJWSilVtZSp\nhm0YxltAlIjMMAzjOcBXRJ63UvZp4EbAS0SsLghd2b8Rnos/x/SfpvNWn7fwcPWwdzhKKaU17Cqk\n3JbXNAzjL6CniEQahlEfCBWRqwoo1wj4HJgOPOPICVsppSobTdhVR3ku/lFPRCIBROQ8UM9KufeA\nSUCV+MtxlLmFNU7b0jhtS+NUqmSKnEvcMIyNQEDOXZiJ96UCiudLyIZhDAIiReSAYRjBme8vVEhI\nCM2aNQPAx8eH9u3bZw/6yPrHY8/nBw4cqFTxOPpzvZ56PSvz88p4PbMenzp1ClV9lLVJ/DAQnKNJ\nfKuIXJ2nzOvAvUA64A54AitF5H4rx9QmHKWUKgFtEq86yrNJfBUQkvn4AeC7vAVEZLKINBGRFsAo\nYIu1ZK2UUkqpgpU1Yb8F9DEM4wjQG3gTwDCMQMMwVpc1uMoqZ7NUZaZx2pbGaVsap1IlU6b1sEXk\nEnBbAfsjgMEF7P8R+LEs51RKKaWqI531oxSyBoBUdhqnbWmctqVxKjCnGe3WrRs+Pj74+fnRo0cP\nfvnll2K9d/Xq1XTq1AkPDw/8/f257777CA8Pz379hx9+oEePHvj6+tKgQQMefvhhEhMTrR7vzz//\npF+/ftStW5c6derQsWNH1q1bB5jrajdu3LjA9wwZMgQfHx+8vb3p3bt3rvW0jx07xtChQ6lXrx5+\nfn4MGDCAo0ePFvfy5KIJWymllF3Ex8dz++238+STTxIdHU14eDhTp06lZs2aRb53+fLljBkzhmee\neYaoqCgOHTqEq6sr3bt3JzY2FoC4uDimTJlCREQEhw8f5uzZs0yaNMnqMW+//Xb69etHZGQkFy5c\nYPbs2dlzlotIvjnOT5w4Qffu3bn++us5deoU586dY+jQofTt25c9e/YAEBMTw5AhQzh69CiRkZF0\n7NiRIUOGlO6CWVsVxF4blXx1GRHHWB9XROO0NY3TtjRO28FBV+vat2+f+Pr6FvjaggULpFu3bvL4\n44+Lt7e3XH311blW9mratKnMnJl7pUSLxSLXXnutTJ06tcBjrly5Utq1a1fgaxcvXhQnJyeJjY3N\n91piYqK4u7uLs7OzeHh4iKenp0RERMi9994rgwYNyld+woQJ0rNnzwLPc+nSJTEMQy5dulTg69Z+\nl6KrdSmllLKXoKAgnJ2dCQkJYd26dcTExOR6fc+ePbRu3ZqoqCimTZvGXXfdRUxMDEeOHCEsLIzh\nw4fnKm8YBsOGDWPjxo0Fnu/HH3+kbdu22c/feust7rjDnHizbt26tGrVijFjxvDdd99x4cKF7HK1\natVi7dq1NGjQgPj4eOLi4qhfvz6bNm1ixIgR+c4zcuRIduzYQUpKSoExBAYG4uvrW/wLlcVaJrfX\nRiX/RqiUUpUNZalhg222Uvrrr79k7Nix0rhxY3FxcZEhQ4ZIZGSkLFiwQBo2bJirbKdOnWTx4sWy\nfft2cXJykpSUlHzH++ijjyQoKCjf/g0bNkidOnXk+PHjVmMJDw+XJ554Qlq1aiXOzs7Ss2fP7PKh\noaHSuHHjXOVdXFxk/fr1BX4mJycnOXfuXK79YWFhha7rLaI1bKWUUtbYKmWXUps2bZg/fz5nzpzh\n0KFDhIeH89RTTwHQsGHDXGWbNGnCuXPn8PPzQ0SIiIjId7yIiAj8/Pxy7du9ezdjxoxhxYoVtGzZ\n0mosDRo0YPbs2Rw7dozTp09Tq1Yt7r/f+rQhfn5+VmNwcnLKVYv+559/6NevH48//jgjR460eszC\naMIuBUe5L1PjtC2N07Y0TpVXUFAQISEhHDp0CCDXiG+AM2fO0KBBA9q0aUOjRo345ptvcr0uIqxY\nsYLbbrtyt/H+/fsZOnQoCxYsKNGI/4YNG/LYY49x8OBBgHwDzgBuu+22fDEALF26lC5duuDm5gaY\nA8/69evH0KFDef75Ahe0LBZN2EoppeziyJEjvPvuu9mJOSwsjK+//prOnTsDEBkZyZw5c0hPT+eb\nb77hr7/+YuDAgQDMnDmT1157jSVLlpCSksL58+cZP3488fHx2TX0gwcPMmDAAObMmZP9PmtiYmKY\nNm0aJ06cQES4ePEi8+fPp0uXLgAEBAQQFRVFXFxc9numTp3Kzp07mTJlCtHR0SQkJDBnzhwWL17M\njBkzAHMkfN++fenevTvTp08v2wWz1lZurw3tw1ZKqRLBQUeJh4eHy8iRI6Vhw4bi4eEhjRo1kgkT\nJkh8fLwsWLBAunfvLk888YR4e3tLmzZtZNOmTbnev2rVKunYsaN4eHhI3bp1ZfTo0XL27Nns18eO\nHSvOzs7i6ekpHh4e4uHhIddee23266+//roMHDhQRMyR4A888IA0b95cPD09JTAwUEaPHp2rH3r8\n+PFSt25d8fX1lYiICBEROXTokAwePFi8vLzE09NTevXqJTt37sx+z8KFC8XJySn7/FmjzMPCwgq8\nJtZ+lyJStsU/yoNOVq+UUiVTFRf/WLhwIfPmzeOnn36ydygVqjwX/6iWHKVPS+O0LY3TtjROpUpG\nE7ZSSinlALRJXCmlHFxVbBKvrrRJXCmllHJwmrBLwVH6tDRO29I4bUvjVKpkNGErpZRSDkD7sJVS\nysFpH3bVoX3YSimllIPThF0KjtKnpXHalsZpWxqnUiWjCVsppZTdbN++nW7duuHj44Ofnx89evTg\nl19+YeHChfTo0SNf+Z07d9K7d2+8vLzw9fVlyJAhHD58OPv1PXv20LdvX+rWrUtAQAB3330358+f\nt3r+sWPHUrNmTby8vPD09MTLy4tly5ZlP/by8sLZ2ZlatWpl7/v6668B+PPPPxkyZAg+Pj54e3vT\nu3dvdu3alX3s06dP4+TklH2crPcXtGBIsVibs9ReG5V87lullKpscNC5xOPi4sTHx0eWLl0qFotF\nkpOTZePGjfLHH3/IggULpEePHrnK79y5Uzw8PGTOnDmSkJAg0dHR8tJLL4mvr6+cPHlSRETWrl0r\ny5cvl/j4eElKSpJx48ZJ//79rcYQEhIiU6ZMKTTO5s2by5YtW3LtO378uPj6+sqUKVMkOjpaEhIS\nZPbs2eLh4SG7d+8WEZFTp06Jk5OTWCyWYl8Ta79L0bnElVLK8TnqoLNffvmFPn36cOnSpVz7//rr\nLzp06EB6ejpubm7UqFGDS5cu0aNHD9q3b8+cOXNylR84cCD16tVjwYIF+c6xf/9+goODiY2NLTCG\nsWPH0rhxY1555RWrcTZv3px58+Zx6623Zu+77777iI6OZvXq1bnKPvroo/z555+EhoZy+vRpWrRo\nQVpaGk5OxWvQ1kFnNuYofVoap21pnLalcaqgoCCcnZ0JCQlh3bp1xMTEAHDVVVfx0Ucf0aVLF+Lj\n47l06RJJSUns2rWL4cOH5zvOyJEj2bhxY4Hn+PHHH2nbtm3286+//pr27duXOfZNmzYxYsSIAmPZ\nsWMHKSkp2fts9aXJxSZHUUop5ZAMG30hkeDgEr/H09OT7du389Zbb/Hwww8TERHBoEGD+OSTT/KV\nvXTpEhaLhcDAwHyvBQYGcvHixXz7f//9d1599VW+//777H333HMP99xzT65yb7/9NnPnzkVEqFGj\nBhcuXCgy9osXL1qNxWKxZLcaiAj+/v7Zjw3DYNeuXbRp06bIc+SlCbsUgkvxh2kPGqdtaZy2pXFW\nDqVJtLbUpk0b5s+fD8DRo0cZM2YMTz31FP369ctVztfXFycnJyIiIggKCsr1WkREBH5+frn2HT9+\nnIEDBzJnzhy6du1aaAyTJk0qtEm8IH5+fkREROTbHxERgZOTE76+vkRGRmIYBlFRURhGga3cJaJN\n4koppSqFoKAgQkJCOHToUL4EV6tWLbp06VLgCOtly5bRu3fv7OenT5+mT58+TJ06ldGjR5dLrLfd\ndluBsSxdupQuXbrg5uaWvc9WTeKasEvBUfq0NE7b0jhtS+NUR44c4d133yU8PByAsLAwvv76a7p0\n6UJAQABnz54lLS0tu/ybb77JwoULmTt3LgkJCURHR/PSSy+xe/dupk6dCkB4eDi9e/fmiSee4KGH\nHiq32KdOncrOnTuZMmUK0dHRJCQkMGfOHBYvXsyMGTOyy8mVEftlpglbKaWUXXh6erJnzx46deqE\np6cnXbt2pV27dsycOZNbb72Vtm3bUr9+ferVqwdAt27dWL9+PStWrCAwMJDmzZvz22+/sX37dlq2\nbAnAvHnzOHnyJNOmTct173OWr776iuuuuy77eXGaqgsq06pVK7Zv386BAwdo1qwZDRo04H//+x8b\nNmygc+fOud7r6+ubK5ZZs2aV6nqV6bYuwzB8gaVAU+AUMFJE8o2dNwzDG/gMuBawAONEZI+VY1bq\n2xCUUqqycdTbulR+5Xlb1/PAJhFpA2wBXrBS7n3gBxG5GrgeOGylnFJKKaUKUNaEPQRYmPl4ITA0\nbwHDMLyAHiLyOYCIpItIXBnPa1eO0qelcdqWxmlbGqdSJVPWhF1PRCIBROQ8UK+AMs2Bi4ZhfG4Y\nxq+GYXxiGIZ7Gc+rlFJKVStF3odtGMZGICDnLkCAlwooXlBniQtwA/CYiOwzDGMWZlP6VGvnDAkJ\noVmzZgD4+PjQvn377Hshs77t2vt5lsoST0HPg4ODK1U8hT3PUlni0etZ/s/1epYtntDQUE6dOoWq\nPso66OwwECwikYZh1Ae2ZvZT5ywTAOwSkRaZz7sDz4nI7VaOqYMklFKqBHTQWdVRnoPOVgEhmY8f\nAL7LWyCzyTzMMIysqWl6A3+W8bx2lfdbd2WlcdqWxmlbGqdSJVPWqUnfApYZhjEOOA2MBDAMIxD4\nVEQGZ5abCHxpGEYN4G9gbBnPq5RSqghubm6Rma2cykG4ublFWntNl9dUSikHV1gzqqo6dKYzpZRS\nygFowi4FR+nT0jhtS+O0LY1TqZLRhK2UUko5AO3DVkopB6d92NWD1rCVUkopB6AJuxQcpU9L47Qt\njdO2NE6lSkYTtlJKKeUAtA9bKaUcnPZhVw9aw1ZKKaUcgCbsUnCUPi2N07Y0TtvSOJUqGU3YSiml\nlAPQPmyllHJw2oddPWgNWymllHIAmrBLwVH6tDRO29I4bUvjVKpkNGErpZRSDkD7sJVSysFpH3b1\noDVspZRSygFowi4FR+nT0jhtS+O0LY1TqZLRhK2UUko5AO3DVkopB6d92NWD1rCVUkopB6AJuxQc\npU9L47QtjdO2NE6lSkYTtlJKKeUAtA9bKaUcnPZhVw9aw1ZKKaUcgCbsUnCUPi2N07Y0TtvSOJUq\nGU3YSimllAPQPmyllHJw2oddPZSphm0Yhq9hGBsMwzhiGMZ6wzC8rZR7wTCMQ4Zh/G4YxpeGYbiW\n5bxKKaVUdVPWJvHngU0i0gbYAryQt4BhGE2Bh4AOItIOcAFGlfG8duUofVoap21pnLalcSpVMmVN\n2EOAhZmPFwJDCygTB6QCtQ3DcAFqAefKeF6llFKqWilTH7ZhGJdEpI615zn2PwS8C1wGNojIfYUc\nU/uwlVKqBLQPu3pwKaqAYRgbgYCcuwABXiqgeL5MaxhGC+BpoCkQCyw3DGO0iHxVqoiVUkqpaqjI\nhC0ifay9ZhhGpGEYASISaRhGfeBCAcVuAnaIyKXM96wEugJWE3ZISAjNmjUDwMfHh/bt2xMcHAxc\n6eXbh9sAABA7SURBVE+y5/MDBw7w1FNPVZp4rD3P2fdWGeKx9lyvp17PyhCPteeV8XpmPT516hSq\n+ihrk/hbwCURecswjOcAXxF5Pk+Z64HFQEcgBfgc2CsiH1g5ZqVvEg8NDc3+B1SZaZy2pXHalsZp\nO9okXj2UNWHXAZYBjYHTwEgRiTEMIxD4VEQGZ5abBIQAGcB+4EERSbNyzEqfsJVSqjLRhF096MQp\nSinl4DRhVw86NWkp5OxHqsw0TtvSOG1L41SqZDRhK6WUUg5Am8SVUsrBaZN49aA1bKWUUsoBaMIu\nBUfp09I4bUvjtC2NU6mS0YStlFJKOQDtw1ZKKQenfdjVg9awlVJKKQegCbsUHKVPS+O0LY3TtjRO\npUpGE7ayq4T0dCJTU8nQbhCllCqU9mGrCpWYkcHO2Fi2xsSwNSaGPxIS8HZxISotjaZubrR0d6eV\nuzst3d1pmfm8uZsbbs7O9g5dqUpL+7CrB03YqlwlZWSwMy6OrdHRhMbEcCAhgfYeHvTy9aWXjw+d\nvbyo5exMUkYGJ5OTOZ6UxImsLTmZE0lJnElOxt/VlZZubleSeY6k7lOjhr0/pt0kZWRwPjWViNRU\nzmduGSK4OjlR0zCo6eRETScnXHM8zvc883HWe1ydnHAy9P9+R6IJu3rQhF0KjrDcHtgnzuSMDHbH\nxWXXoH+Nj6edhwfBPj708vGhq7c3tfPUlouKM91iISwlJVcSz9qOJyVR08kpdyLPrJm3dHcn0NUV\nw0bJp6Kup4gQnZ5ORGoqESkpuRJyRM7HKSkkWSzUd3Ul0NWV+plb5N69+HfsSIrFQorFQqpI9uMU\nEVJzPC6oTKoINQyjyCTvmifhF+eLQc73/LlzJ9d07Wp+5qzPnvdn5v8FUsRrRb3X2utFvdfVyQmX\n335jbP/+Nvs7Kg+asKsHF3sHoBxbisXCnswEHRoTw964ONrWrk0vHx9ebNqUbl5eeLiU7c/MxcmJ\n5u7uNHd357Y8r4kIF9LSciXxzTExfBwRwYmkJBIzMmiRJ4lnJfWmbm7UcKq4YRxpFguRhSTfnInZ\n3cmJwJo1sxNx1s8OHh4E1qyZvc/XxSVfIgmNiCC4TZtSxykipBWR5FPzJPzCvhjEZ2RwMS0t33su\nxMRw5OJFsqLP+hzZz/P+NIxCXyvqvXn3F+e9iRYL60+e5KVdu+jj60vfOnW4zdeXAFfXkl1UpWxA\na9iqRFItFvbGx7M1OpqtMTH8HB/PVbVq0cvHh2AfH7p7e+NVxgRtS3Hp6fydp2ae1ewekZpKo5o1\nC6yZt3R3z9cSUBARISEjw2ryzZmcY9LT8a9RI1cSzpl8c9aS3bXPvlI5kZTExkuX2BAdzZboaJq7\nu9M3M4F38/Ky+xgLrWFXD5qw80izWIhOTze3tDQu5XgcnZ7OpfR0kjIyaFCzJk3d3GhasyZN3Nxo\nVLMmrhVYW6soaRYL++LjCc1s4t4VF0drd3d6ZTZxd/f2dtg+5FSLhVN5E3nm85PJyfi4uORK4i6G\nkbt5OiWFiNRUAKvJN/tnzZr41aiBcyVuVlXFk26x8HN8PBsyE/jBxES6eXtnJ/BratWq8OZzTdjV\nQ5VM2OkWCzFZiTZzu5SZcPMm3+gc+y+lpZEigo+LC76ZW50aNbIf+7q44FujBmG7d+Nx442cTknh\ndHIyZ5KTiUhNxb9GDZpkNrVmJfKcjyu65lmaPtd0i4VfExLMPujoaHbGxdHCzY1evr4E+/hwi7c3\nvjZO0JVxTIBFhHMpKZzIMRDu1K5ddOrZM3cidnXFsxK1KEDlvJ4FqSpxxqSlsSUmhg2XLrE+OppU\niyVX83m9Cmg+14RdPVSu/2lysIgQW1CyLUbyTczIwDtHgvV1caFOjscBrq5cVatW7mRcowZ1XFzw\ncHYu8ttx6IkTBLdokWtfusXCudRUziQnZyfy3xMS+D4qitPJyZxOTsbVyanARN7UzY0mNWsS4Opa\n4aNzM0TYn6MGvT02lqZubgT7+PBIgwZ8ec011HXQGnRZOBkGjdzcaOTmRk8fHwBCz5whuFEjO0em\nKhufGjW4y9+fu/z9ERFOJCWxITqaZRcu8OjRo7TI2Xzu7U3NKtgSpypGpaxh+2zbRnx6Oh7OzvmS\nat7kW1At2MvFpdLdliIiXEpPz66R56ydZz2OS0+n8f+3d7YxdlRlHP/9l27XrsDe3RZabKUF2vKW\nCpQWiAVaeanVD4AIiRANQiIkiprwZg0xaCS+8AHfMUEBXxKECAQQRAqhUAy2UNqlLZRS0gItbwK7\nF7SUZdk+fphzl/HuvXfndmfm3mWfXzLJmTNn7vOfc2bnmfOcM2eD854ec+Sl9LS2thH/sQ+Ysa7U\ngy4WebRYZGpb2+AY9MJCgX18Qo3jpEJ/Wfj86R0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"text/plain": [ "<matplotlib.figure.Figure at 0x118692250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "StO2g.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LlxoJ4eRJo9k8Ph7queBKM3f//SxRG3Hm5hp9zB99BP/7n5G0HVWVOG31c198\nDr7+2ogjOBheesm4X4GZv9qStL2HW7Y5T+4xmUd6PuLqMDxWp6Yt+PXR9fxr3r9ofkVj8grzyCvM\nIys/k7ScvNL1qiz5Rfl2n8styEUp5XTi37BuA/kt8nmo20NMnzydhvUauvoUOszX16hh33670W0x\nY4bRLOrug9bqsqQkGDvWaEX69VdoWIO/Vvb6uZVSKAXDhsFtt8EXXxh96Q0awNSpUNPfrYKDg0/k\n5OTIb58HCgoKOpmdnV1u0n63rGm7W0zCvsKiQoe+BNhamoU3Y2DbgXWuG+S334xBa4sWGYPW/v53\nGbRWG4qK4O23jRrtK6/AfffV7lgDW/3clgoL4ZNPjC91LVsazebXOHYDv3Ls1bTl/6nnsvuZutsH\nKr9koq4pO2jtiSfg2mtl0FpN+PNP41rp8+eNJvFLLnFNHPb6uS3l5xuXm730knF3wH//u/qD4yRp\n1z32PtO6VbWpJQkJCa4OoUokTnNVN85GjeDFF+HQIRg0CO6910jeX3xh1LrMVtfPpz2LFxuzCV5z\nDaxfb17Crk6cgX6B/O/W/zGp+yT6vN+Hb/d/W66Mv7/RCvDbb3DrrUYT+rBhxgxtQtgjSVuIWhIS\nAhMmwL59xvXBr70G7dvDzJnG5C2ietLTjdr1008bgwGnTKndiU3sKennXjxyMfctvY///PQfbNV6\nAwNh4kTYv9/o477xRmMOhD17aj9m4f6keVwIFyoZtPbTT8agtcmTZdCaIzZsMGYfGzDA+BIU6p5X\nBlbaz20pK8vok3/1VbjpJuNLSLvyretWpHm87pHmcSHc0DXXwJIlRvI5c8a4QcyDDxq1cWFffj48\n9xyMGGEM9pszx30TNkBseCxr49cSERRBr/d6cSD1gN2y9eoZLTEHDhgtMb16Gc3ohw7VXrzCfUnS\nrgZv7TOsKRKn8c959mxITjamSr3uOhg61KiBO1pRquvnMznZ+LKzYwckJhrnqSaZdT5L+rkn95hs\nt5/bUni4MTHL/v0QEwNXX200o6ekmBKOW2jVqhUhISGEh4cTExPDvffeS1ZWFv369eP999+3+Zq1\na9fi6+tLeHg4YWFhhIeHM3ToUCZMmFC6HhgYSEBAAOHh4YSHh3NL8QX6eXl5PPPMM7Rs2ZJ69erR\noUMHZsyYYbX/uLg4goODS19bsn93YUrSVkoNUkrtU0r9ppR6yk6ZN5VS+5VSiUqprmYcV4i6xtag\ntV69am7K7DlrAAAgAElEQVTQmifR2vhi06ePcV6WLYMm5a5idW9KKR7q9hBLRi7h/m/ut9vPbSky\n0rimOznZaE24/HLjWu8TJ2op6BqklGL58uWkp6fzyy+/sG3bNqZOnVrppEqxsbGkp6eTkZFBeno6\nX3/9NbNnzy5df/bZZxk1ahTp6emkp6ezfPlyAO644w7WrFnDypUrycjI4KOPPmLOnDk8+uijVjHN\nmjWr9LUl+3cXTidtpZQP8DZwE9AZGK2U6limzGCgrdb6EuCvwDvOHteVPGG2KZA4zVabcVoOWnv6\n6YuD1t5+u/JBa3XxfJ48aYywnjvXGBk+YULtXTJXE+ezT4s+/Hz/zyxNXsqdn99JZl5mpa+Jjobp\n040Balobl4k99ZTRreLJSr60xMTEMGjQIHbv3l0jx/nhhx9YvXo1S5YsoVOnTvj4+NCjRw8+/vhj\nZs6cye+//14uJndkxhjLHsB+rfVhAKXUImAoYNkrNxT4EEBrvUUpVV8p1VhrfdLWDt1p6koh3Edv\nHn74CR5+uC/G9963gVMujqk23Aq8C8wFXqJTp3wXx2MiX9hy8xYWr10Mi4BUR14cy/Tp/2T69JE1\nFFztOnr0KCtWrGDEiBGsW7fO6rnIyEiWL1/ONU7MQrN69Wp69uxJ05J7Ghfr0aMHzZo144cffqBN\nRfdodRNmNI/HAkct1o8Vb6uoTIqNMkKICm0CRgB9gGiM78XvAnV1mrV6GO/vdeAO4HmgDiVsgELg\nG+Bn4F6gklHi1lKAiYBztytTypyluoYNG0ZUVBTXXXcd/fr149lnny1XJi0tzSphp6SkEBUVRWRk\nJFFRUXzxxReVHufMmTPExMTYfC4mJoYzFk0WDz/8sNX+p0yZUo13VjPc4GpGIYRj9mP8s54C3Ay8\nDPgC/wXWuzAuM/UAPgbeAx4Dsl0bTk3bBpwEegHBwC5HXnzIqUO7uiX466+/pl+/fg69JjY2liNH\njjj0mujoaA4csD1q//jx40RHR5euv/XWW9x7770O7b+2mFHTTgFaWKw3K95WtkzzSsoIIRxyGpgP\njAG+Bd4HNgMvAfcDAzFq4cGuCrAafIEXgKXAM8B06nzCLnEUWAn0BEYCAa4Np7bUVv/xgAED2LJl\nCyllht9v2bKFY8eO0b9//1qJw1lmJO2tQDulVEulVAAwCuMvztJS4C8ASqlewDl7/dlCCEdlY/Rx\ndwSmAgVAb+AfGG2vqRjVuK3AF8BrwKPA7cBVQIPaD9mmthgtBX2AK4HFrg3HFTKADzA+0vuBKNeG\nU5f079+f/v37M2LECPbs2UNRURGbN29m3LhxTJw40SP6s8GE5nGtdaFSajKwCuNLwFyt9V6l1F+N\np/UcrfUKpdTNSqkDQBYwvpJ9OhtWjZL7FZtL4jRX2TiLiuDUqSAOH27EkSPdOHwYjhzB6mduLrRo\nYSwtW5b/GRtrzJVdE3FqbYwKf+YZ47rkyZPBx+dPcw/mBFd87lpr3t3+Li+0eoH5w+Zb3Z/bFk8d\nvGsv7rLbw8LCWLlyJX369HHqeIsXL2bKlCkMGjSIs2fPEhsbywMPPMCTTz5pVW7y5Mk89thjgPFZ\ndOzYka1btzp1bLPINKbV4Kn/vN2VxGmu6sSZkVE+kZf8PHzYuOSqcWPbCb3kcViY43F27hzHAw8Y\n16UvWACdOzu2j9rgys99w5ENjPxiJJO6Tyq9P7ctMo1p3SO35hRCVFt+vjETl73EfuSIceMLewm9\nZUtj4hgfiw65FSvg/vuNucNfesl4vSivZN7y2PBY5g2dR1hg+W9HkrTrHknaQogaozWcPWs/oR8+\nbNyNq3lzI4kHB8OuXcb9pK+/3tXRu7/cglwmr5jMxmMb+equr7ikgfV9RyVp1z2StE1Ul5tJXUHi\nNJe7xnnhAhw9aiTwEycgKiqBIUPiXB1WpdzlfJb2c68p388tSbvusfeZynXaQohaERICHToYC4CH\n3NfEbZTMW355o8ur1M8t6iapaQshhIcp288dHhQuNe06Ru6nLYQQdUTJ/bmjgqLoNbeXq8MRtUiS\ndjXU9fsV1zaJ01wSp7ncNc5Av0Dm3DqHh3s87OpQRC2SpC2EEB6qpJ9beA/p0xZCCA8no8frHunT\nFkII4VZatWpFSEgI4eHhxMTEcO+995KVlUVcXBzBwcGEh4fTsGFDhg4dWu5GH5b+9a9/ERAQQHh4\nOGFhYYSHhzNjxgwuu+wywsPDCQ8Px8/Pj+Dg4NLnX3nlFcC4zefYsWOJjo4mLCyMXr16sXz5cqv9\n+/j4lL7Ocv+uIEm7Gty1j6ssidNcEqe5JE6hlGL58uWkp6fzyy+/sG3bNqZOnYqPjw8zZ84kPT2d\ngwcPkpOTw+OPP17hvkaNGkV6ejoZGRmkp6fzxBNPsHv3btLT00lPT6dv377MmjWr9Pmnn36atLQ0\nrr32WoKCgti7dy9nzpzhscce4+6772bJkiVWce7cubPc/l1BkrYQQgiXKWm+j4mJYdCgQezevdvq\n+fDwcIYNG0ZSUpJpxyrx2muvERYWxnvvvUfDhg0JDAxk1KhR/POf/7T6kqC1dpsbWUnSrgZ3mB2p\nKiROc0mc5pI4haWjR4+yYsUKrrrqKqsEefbsWZYsWULPnj1Lt23YsIGoKOfvW7p69WpGjBhRbvvI\nkSM5cuQI+/fvd/oYZpMZ0YQQwoupf5kzo5qeUr2a6LBhw/Dz86N+/foMGTKEZ599lnXr1vHII4/w\n97//nfPnz9OzZ0/efvvt0tf06dOH1NRUq/18+umnLFu2DK01Sin27NlDkyZNKjz2mTNniImJKbe9\nZNuZM2e45BJjnverrroKHx+f0v1/+umnDBw4sFrv2RmStKvBXeYirozEaS6J01wSp3uobrI1y9df\nf02/fv3KbX/zzTe59957SUpKYuDAgXz77bcMHz7c7n7uuusuPvzwQ4eOHR0dzfHjx8ttL9nWsGHD\n0m07duygdevWDu2/JkjzuBBCCJeprK+4c+fOvPTSSzz11FOm9ysPGDDAasBZiU8//ZQWLVrQrl27\nKsdZWyRpV4OnfOuWOM0lcZpL4hRVdc8995Cdnc3nn39u6n7/9re/cf78ee677z5OnjxJbm4uCxcu\n5OWXX3bZJV2VkaQthBDCJezdoazsdn9/fx555BGmTZsGwPr16wkPD3f6WFFRUaxfv57s7GwuvfRS\noqOjef311/n444+54447rF57xRVXWF2nXdklaDVFZkSrBk/p45I4zSVxmkviNI/MiFb31MiMaEqp\nSKXUKqVUslLqO6VUfRtlmimlflRKJSmldimlHnHmmEIIIYS3cqqmrZSaBpzVWk9XSj0FRGqtny5T\npgnQRGudqJQKBbYDQ7XW++zsU74ZCiGEA6SmXffU1NzjQ4H5xY/nA8PKFtBan9BaJxY/zgT2ArFO\nHlcIIYTwOs4m7UZa65NgJGegUUWFlVKtgK7AFieP61KeMhexxGkuidNcEqcQjqt0chWl1PdAY8tN\ngAaes1HcbjtMcdP4F8CjxTVuu+Lj42nVqhUAERERdO3atXQgSMkfkCvXExMT3SoeT1+X8ynn053X\n3fF8ljw+dOgQwrs426e9F4jTWp8s7rteo7XuZKOcH7AM+FZr/UYl+5Q+GCGEcID0adc9NdWnvRSI\nL358D/C1nXLvA3sqS9hCCCGEsM/ZpD0NGKiUSgb6A68AKKVilFLLih/3AcYANyildiilflFKDXLy\nuC5l2UTlziROc0mc5pI4hXCcUzcM0VqnAgNsbD8ODCl+vAHwdeY4QgghhJBpTKulZFCIu5M4zSVx\nmkviFGBMSdqnTx8iIiKIjo6mb9++bN++vUqvXbZsGT179iQ0NJSGDRsybtw4UlJSSp9fsWIFffv2\nJTIykqZNm/Lggw+SlZVld39xcXEEBwdbTVe6bt260sdhYWH4+PgQGhpaum3Dhg0AbNy4kf79+xMe\nHk5kZCRDhw5l7969pfteu3Ytvr6+hIeHW+1/yxbHLqaSpC2EEMIlMjIyuPXWW3n00UdJS0sjJSWF\nKVOmEBgYWOlrv/jiC8aMGcPjjz/O2bNnSUpKIiAggGuvvZbz588DkJ6ezvPPP8/x48fZu3cvx44d\n48knn7S7T6UUs2bNIj09nYyMDNLT07nuuutKH2dkZKCUYteuXaXb+vTpw6ZNm7jpppu4/fbbOX78\nOH/88QddunShT58+ViP8Y2NjSU9Pt9p/z549HTtpWmu3WoyQ3NuaNWtcHUKVSJzmkjjNJXGap/j/\npsf9P922bZuOjIy0+dy8efN0nz599OTJk3X9+vV1p06d9A8//FD6fMuWLfWMGTOsXlNUVKQvu+wy\nPWXKFJv7XLJkie7SpYvdeOLi4vTcuXMrjFkppQ8ePGi1rW/fvnry5Mnlyg4ePFjfc889WmutExIS\ndPPmzSvctyV7n6nUtIUQQrhE+/bt8fX1JT4+npUrV3Lu3Dmr57ds2cIll1zC2bNnefHFFxk+fDjn\nzp0jOTmZo0ePWt2JC4ya8ogRI/j+++9tHm/t2rV07ty5dH3atGncdtttTr2H7OxsNm7cWC4WgJEj\nR9qNpdpsZXJXLrj5N0MhhHA3OFPTBnOWatq3b58eP368bt68ufbz89NDhw7VJ0+e1PPmzdOxsbFW\nZXv27Kk//vhjvX79eu3j46Nzc3PL7e+dd97R7du3L7d91apVOioqSh84cMBuLHFxcbpevXo6MjJS\nR0RE6KuvvrpcmbI17WPHjmmllE5OTi5XduXKlTogIEBrbdS0fXx8dGRkZOn+IyMj9YULF2zGYu8z\nlZq2EEJ4M7PSdjV16NCB999/nyNHjpCUlERKSgqPPfYYYPQBW2rRogV//vkn0dHRaK05fvx4uf0d\nP36c6Ohoq22bN29mzJgxLF68mLZt21YYz5tvvklqaippaWls27at0vgjIyPx8fGpUiyxsbGkpqaW\n7j81NZXg4OBKj2FJknY1eMp1mxKnuSROc0mcoqz27dsTHx9PUlISgNVIcIAjR47QtGlTOnToQLNm\nzfj888+tntdas3jxYgYMuHgl8o4dOxg2bBjz5s2rkSsBQkJC6N27d7lYAD777DOrWMwgSVsIIYRL\nJCcn89prr5Um56NHj7Jw4UJ69eoFwMmTJ3nrrbcoKCjg888/Z9++fdx8880AzJgxg6lTp7Jo0SJy\nc3M5ceIE9913HxkZGaU19d27dzN48GDeeuut0tfVhFdeeYX58+fz9ttvk5mZSVpaGs899xybN29m\nypQppeW0Ey0SVjtxpwXp0xZCCIfgoaPHU1JS9MiRI3VsbKwODQ3VzZo10xMmTNAZGRl63rx5+tpr\nr9UPP/ywrl+/vu7QoYNevXq11euXLl2qu3fvrkNDQ3WDBg303XffrY8dO1b6/Pjx47Wvr68OCwvT\noaGhOjQ0VF922WWlz//nP//RN998c+l6v379Kh097uPjU270uNZab9iwQcfFxenQ0FBdv359PWTI\nEL1nz57S5xMSEkpjKYknLCxML1myxOZx7H2mTt0wpCbIBPdCCOGYunjDkPnz5zN37lzWrVvn6lBc\noqZuGOKVPKWPS+I0l8RpLolTCMdJ0hZCCCE8hDSPCyGEh6uLzePeTprHhRBCCA8nSbsaPKWPS+I0\nl8RpLolTCMdJ0hZCCCE8hPRpCyGEh5M+7bpH+rSFEEIIDydJuxo8pY9L4jSXxGkuiVMIxzmVtJVS\nkUqpVUqpZKXUd0qp+hWU9VFK/aKUWurMMYUQQtQd69evp0+fPkRERBAdHU3fvn3Zvn078+fPp2/f\nvuXKb9y4kf79+xMeHk5kZCRDhw5l7969pc9v2bKFG2+8kQYNGtC4cWPuuusuTpw4Yff448ePJzAw\nkPDwcMLCwggPD+ezzz4rfRweHo6vry8hISGl2xYuXAjAnj17GDp0KBEREdSvX5/+/fuzadOm0n0f\nPnwYHx+f0v2UvN7WzUWqzNbcplVdgGnAP4ofPwW8UkHZvwEfA0sr2WeF874KIYSwhofOPZ6enq4j\nIiL0p59+qouKinROTo7+/vvv9a5du/S8efN03759rcpv3LhRh4aG6rfeektnZmbqtLQ0/dxzz+nI\nyEj9xx9/aK21/vbbb/UXX3yhMzIydHZ2tr733nv1oEGD7MYQHx+vn3/++QrjbN26tf7xxx+tth04\ncEBHRkbq559/XqelpenMzEz95ptv6tDQUL1582attdaHDh3SPj4+uqioyOFzY+8zdWogmlJqH3C9\n1vqkUqoJkKC17mijXDPgA+D/AY9rrW+rYJ/amZiEEMLbeOpAtO3btzNw4EBSU1Ottu/bt48rr7yS\ngoICgoKC8Pf3JzU1lb59+9K1a1feeustq/I333wzjRo1Yt68eeWOsWPHDuLi4jh//rzNGMaPH0/z\n5s156aWX7MbZunVr5s6dyw033FC6bdy4caSlpbFs2TKrshMnTmTPnj0kJCRw+PBh2rRpQ35+Pj4+\njjVs19RAtEZa65MAWusTQCM75f4PeBJw398eB3hKH5fEaS6J01wSp2jfvj2+vr7Ex8ezcuVKzp07\nB0DHjh1555136N27NxkZGaSmppKdnc2mTZu44447yu1n5MiRfP/99zaPsXbtWjp37ly6vnDhQrp2\n7ep07KtXr+bOO++0GcuGDRvIzc0t3WbmFye/ygoopb4HGltuwki+z9koXi4ypdQtwEmtdaJSKq74\n9RWKj4+nVatWAERERNC1a9fSm5eX/AG5cj0xMdGt4vH0dTmfcj7ded0dz2fJ40OHDuEsZdKXEl0c\noyPCwsJYv34906ZN48EHH+T48ePccsstzJkzp1zZ1NRUioqKiImJKfdcTEwMZ86cKbd9586d/Pvf\n/+abb74p3TZ69GhGjx5tVe6///0vb7/9Nlpr/P39OXXqVKWxnzlzxm4sRUVFpa0HWmsaNmxY+lgp\nxaZNm+jQoUOlx7DJVpt5VRdgL9C4+HETYK+NMv8BjgC/A8eBTODDCvbpcNu/EEJ4Mzy0T7us5ORk\n3a1bNz169OhyfdpZWVna19dXJyQklHvdBx98oJs2bWq1bf/+/To2NlYvWLCgwmNWpU+7VatW+ocf\nfrDa1qRJEz1v3rxyZdesWaP9/Px0dnZ2jfRpO9s8vhSIL358D/C1jS8Fz2qtW2it2wCjgB+11n9x\n8rhCCCHqmPbt2xMfH09SUhJKWTfKhoSE0Lt3b5sjrz/77DP69+9fun748GEGDhzIlClTuPvuu2sk\n1gEDBtiM5dNPP6V3794EBQWVbtMmNo87m7SnAQOVUslAf+AVAKVUjFJqWYWv9GAJJjUn1TSJ01wS\np7kkTpGcnMxrr71GSkoKAEePHmXhwoX07t2bxo0bc+zYMfLz80vLv/LKK8yfP5+3336bzMxM0tLS\neO6559i8eTNTpkwBICUlhf79+/Pwww/zwAMP1FjsU6ZMYePGjTz//POkpaWRmZnJW2+9xccff8z0\n6dNLy+mLrR6mcCppa61TtdYDtNYdtNY3aq3PFW8/rrUeYqP8Wl3ByHEhhBDeIywsjC1bttCzZ0/C\nwsK45ppr6NKlCzNmzOCGG26gc+fONGnShEaNjDHOffr04bvvvmPx4sXExMTQunVrfv31V9avX0/b\ntm0BmDt3Ln/88Qcvvvii1bXRJT755BMuv/zy0vWyNXpbbJVp164d69evJzExkVatWtG0aVO+/PJL\nVq1aRa9evaxeGxkZaRXL66+/Xu1zJnOPCyGEh/PUS76EfTL3uBBCCOHhJGlXg6f0cUmc5pI4zSVx\nCuE4SdpCCCGEh5A+bSGE8HDSp133SJ+2EEII4eEkaVeDp/RxSZzmkjjNJXEK4bhK5x4XQgjhmYKC\ngk4qpRpXXlK4m6CgoJO2tkufthBCeDh7/Z+i7pHmcSGEEMJDSNKuBk/p45I4zSVxmkviFMJxkrSF\nEEIIDyF92kII4eGkT9t7SE1bCCGE8BCStKvBU/q4JE5zSZzmkjiFcJwkbSGEEMJDSJ+2EEJ4OOnT\n9h5S0xZCCCE8hCTtavCUPi6J01wSp7kkTiEc51TSVkpFKqVWKaWSlVLfKaXq2ylXXyn1uVJqr1Iq\nSSnV05njCiGEEN7IqT5tpdQ04KzWerpS6ikgUmv9tI1y84C1WusPlFJ+QIjWOt3OPqVPWwghHCB9\n2t7D2aS9D7hea31SKdUESNBadyxTJhzYobVuW8V9StIWQggHSNL2Hs72aTfSWp8E0FqfABrZKNMa\nOKOU+kAp9YtSao5SKtjJ47qUp/RxSZzmkjjNJXEK4bhK76etlPoesLwfqwI08JyN4raqyH7AVcAk\nrfU2pdTrwNPAFHvHjI+Pp1WrVgBERETQtWtX4uLigIt/QK5cT0xMdKt4PH1dzqecT3ded8fzWfL4\n0KFDCO/ibPP4XiDOonl8jda6U5kyjYFNWus2xevXAk9prW+1s09pHhdCCAdI87j3cLZ5fCkQX/z4\nHuDrsgWKm8+PKqXaF2/qD+xx8rhCCCG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"text/plain": [ "<matplotlib.figure.Figure at 0x11342a510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "FTOEg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plots comparing baseline and 3H after ROP Exam" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def savitzky_golay(y, window_size, order, deriv=0, rate=1):\n", " r\"\"\"Smooth (and optionally differentiate) data with a Savitzky-Golay filter.\n", " The Savitzky-Golay filter removes high frequency noise from data.\n", " It has the advantage of preserving the original shape and\n", " features of the signal better than other types of filtering\n", " approaches, such as moving averages techniques.\n", " \n", " Parameters\n", " ----------\n", " y : array_like, shape (N,)\n", " the values of the time history of the signal.\n", " window_size : int\n", " the length of the window. Must be an odd integer number.\n", " order : int\n", " the order of the polynomial used in the filtering.\n", " Must be less then `window_size` - 1.\n", " deriv: int\n", " the order of the derivative to compute (default = 0 means only smoothing)\n", " \n", " Returns\n", " -------\n", " ys : ndarray, shape (N)\n", " the smoothed signal (or it's n-th derivative).\n", " \n", " Notes\n", " -----\n", " The Savitzky-Golay is a type of low-pass filter, particularly\n", " suited for smoothing noisy data. The main idea behind this\n", " approach is to make for each point a least-square fit with a\n", " polynomial of high order over a odd-sized window centered at\n", " the point.\n", " \n", " Examples\n", " --------\n", " t = np.linspace(-4, 4, 500)\n", " y = np.exp( -t2 ) + np.random.normal(0, 0.05, t.shape)\n", " ysg = savitzky_golay(y, window_size=31, order=4)\n", " import matplotlib.pyplot as plt\n", " plt.plot(t, y, label='Noisy signal')\n", " plt.plot(t, np.exp(-t2), 'k', lw=1.5, label='Original signal')\n", " plt.plot(t, ysg, 'r', label='Filtered signal')\n", " plt.legend()\n", " plt.show()\n", " \n", " References\n", " ----------\n", " .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of\n", " Data by Simplified Least Squares Procedures. Analytical\n", " Chemistry, 1964, 36 (8), pp 1627-1639.\n", " .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing\n", " W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery\n", " Cambridge University Press ISBN-13: 9780521880688\n", " \"\"\"\n", " import numpy as np\n", " from math import factorial\n", "\n", " try:\n", " window_size = np.abs(np.int(window_size))\n", " order = np.abs(np.int(order))\n", " except ValueError, msg:\n", " raise ValueError(\"window_size and order have to be of type int\")\n", " if window_size % 2 != 1 or window_size < 1:\n", " raise TypeError(\"window_size size must be a positive odd number\")\n", " if window_size < order + 2:\n", " raise TypeError(\"window_size is too small for the polynomials order\")\n", " order_range = range(order+1)\n", " half_window = (window_size -1) // 2\n", " # precompute coefficients\n", " b = np.mat([[k*i for i in order_range] for k in range(-half_window, half_window+1)])\n", " m = np.linalg.pinv(b).A[deriv] rate*deriv factorial(deriv)\n", " # pad the signal at the extremes with\n", " # values taken from the signal itself\n", " firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )\n", " lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])\n", " y = np.concatenate((firstvals, y, lastvals))\n", " return np.convolve( m[::-1], y, mode='valid')" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ypsg = savitzky_golay(dfen['SpO2'].values, 31, order=4, deriv=0, rate=1)\n", "yrsg = savitzky_golay(dfen['PR'].values, 31, order=4, deriv=0, rate=1)\n", "ytsg = savitzky_golay(dfen['StO2'].values, 31, order=4, deriv=0, rate=1)\n", "yi = dfen['PI'].values #don't filter PI values\n", "yf = dfen['FTOE'].values #don't filter FTOE\n", "\n", "dfgraph = pd.DataFrame({'SpO2':ypsg, 'PR':yrsg, 'StO2':ytsg, 'PI':yi, 'FTOE':yf}, index=dfen.index)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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Kq1bJ+ZsCvMcecv2906bn50e2oDMyRMjWrBH3y2OPiUCNGSOi75eAOCPDX7gA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\n6o47bPfLd77j/7hJk1gkhIYG9gXrRT0BXvFedFFwlJacHL29tsUjqyQ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4vBgwgL8H+W7k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"text/plain": [ "<matplotlib.figure.Figure at 0x11ac6d990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['StO2'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline StO2');\n", "dfgraph['StO2'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam StO2');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VqhXd99/fPwZYD4JDfADz94gjuAKGE1BAYuXZCaGgRu7ui6RB1avHj/9FWB5y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\nYNy1a+R1nT+/tCp+pVXrb745eDP27QtW2LIqj5vx4ysLVKf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"text/plain": [ "<matplotlib.figure.Figure at 0x11204bf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['SpO2'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline SpO2')\n", "dfgraph['SpO2'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam SpO2');\n", "\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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L6apVfkE8/HDJb6etJbWkKiokEub884P7mDFDPq+7TvajyRgzhZkn7YNyBUpf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"text/plain": [ "<matplotlib.figure.Figure at 0x102320d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['PR'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PR');\n", "dfgraph['PR'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam PR');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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77jmvK6XhrLOYn33W3X/hhcF5vvCCe86vv8p5qVxLzTXM+g03yPbtt7v7vv8+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\nsyduPsbddpPl/vu7xw44QJbmBTadnc8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lj65bVz72L7+UvppjjvGma9Mm+GO2vXH8ZTzu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\nyEt1xx3B5h+TrkOH5LA0/mvZlV9QH5R5aXpZs4X4TQdBlXT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"text/plain": [ "<matplotlib.figure.Figure at 0x118477d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['PI'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PI');\n", "dfgraph['PI'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('After Exam PI');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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I+p0AhoWcy4D07dx6a/TssOYBi1Kg27Yxb9gQfMwVUE8+KffPyZEXy9igr7tO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\nf/+NIX4TaiyYudg/AKMBPO7bNxzAnZH1OwEMCzm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"text/plain": [ "<matplotlib.figure.Figure at 0x11af6a0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['FTOE'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PI');\n", "dfgraph['FTOE'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('After Exam PI');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Information Theory\n", " - Fuzzy Entropy\n", " - Cross Fuzzy Entropy" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from entropy import \n", "\n", "#def fuzzyen(x, dim, r, n, scale=True):\n", "# return entropy(x, dim, r, n=n, scale=scale, remove_baseline=True)\n", "\n", "def FuzzyEnLst(dfenlstcopy1):\n", " \n", " PI_FE = []\n", " PR_FE = []\n", " SpO2_FE = []\n", " StO2_FE = []\n", " FTOE_FE = []\n", " \n", " for i in dfenlstcopy1:\n", " a = fuzzyen(i['PI'].values, 2, .2np.nanstd(i['PI'].values), n=1)\n", " b = fuzzyen(i['PR'].values, 2, .2np.nanstd(i['PR'].values), n=1)\n", " c = fuzzyen(i['SpO2'].values, 2, .2np.nanstd(i['SpO2'].values), n=1)\n", " d = fuzzyen(i['StO2'].values, 2, .2np.nanstd(i['StO2'].values), n=1)\n", " e = fuzzyen(i['FTOE'].values, 2, .2np.nanstd(i['FTOE'].values), n=1)\n", "\n", " PI_FE.append(a)\n", " PR_FE.append(b)\n", " SpO2_FE.append(c)\n", " StO2_FE.append(d)\n", " FTOE_FE.append(e)\n", " \n", " FuzzyEnDf = pd.DataFrame({\n", " 'PI FuzzyEn': PI_FE,\n", " 'PR FuzzyEn' : PR_FE,\n", " 'SpO2 FuzzyEn' : SpO2_FE,\n", " 'StO2 FuzzyEn' : StO2_FE,\n", " 'FTOE FuzzyEn' : FTOE_FE\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return FuzzyEnDf" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf = FuzzyEnLst(dfenlstcopy1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['PI FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['PR FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['SpO2 FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['StO2 FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['FTOE FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#def cross_fuzzyen(x1, x2, m, r, n, scale=True):\n", "# return entropy([x1, x2], dim, r, n, scale=scale, remove_baseline=True)\n", "\n", "def CrossFuzzyLst(dfenlstcopy2):\n", " \n", "#Cross Fuzzy Entropy between variables\n", "\n", " a = [] #ab\n", " b = [] #ac\n", " c = [] #ad\n", " d = [] #ae\n", " e = [] #bc\n", " f = [] #bd\n", " g = [] #be\n", " h = [] #cd\n", " i = [] #ce\n", " j = [] #de\n", " \n", " rlst = [a, b, c, d, e, f, g, h, i, j]\n", " \n", " # 0 1 2 3 4\n", " # PI, PR, SpO2, StO2, FTOE\n", " # PI aa ab ac ad ae\n", " # PR ba bb bc bd be\n", " # SpO2 ca cb cc cd ce\n", " # StO2 da db dc dd de\n", " # FTOE ea eb ec ed ee\n", " \n", " for x in dfenlstcopy2:\n", "\n", " ab = cross_fuzzyen(x['PI'].values, x['PR'].values, 2, .2, n=1) #ab\n", " a.append(ab)\n", " ac = cross_fuzzyen(x['PI'].values, x['SpO2'].values, 2, .2, n=1) #ac\n", " b.append(ac)\n", " ad = cross_fuzzyen(x['PI'].values, x['StO2'].values, 2, .2, n=1) #ad\n", " c.append(ad)\n", " ae = cross_fuzzyen(x['PI'].values, x['FTOE'].values, 2, .2, n=1) #ae\n", " d.append(ae)\n", " bc = cross_fuzzyen(x['PR'].values, x['SpO2'].values, 2, .2, n=1) #bc\n", " e.append(bc)\n", " bd = cross_fuzzyen(x['PR'].values, x['StO2'].values, 2, .2, n=1) #bd\n", " f.append(bd)\n", " be = cross_fuzzyen(x['PR'].values, x['FTOE'].values, 2, .2, n=1) #be\n", " g.append(be)\n", " cd = cross_fuzzyen(x['SpO2'].values, x['StO2'].values, 2, .2, n=1) #cd\n", " h.append(cd)\n", " ce = cross_fuzzyen(x['SpO2'].values, x['FTOE'].values, 2, .2, n=1) #ce\n", " i.append(ce)\n", " de = cross_fuzzyen(x['StO2'].values, x['FTOE'].values, 2, .2, n=1) #de\n", " j.append(de)\n", " \n", " XFuzzyEnDf = pd.DataFrame({\n", " 'xf PI:PR': a,\n", " 'xf PI:SpO2' : b,\n", " 'xf PI:StO2' : c,\n", " 'xf PI:FTOE' : d,\n", " 'xf PR:SpO2' : e,\n", " 'xf PR:StO2' : f,\n", " 'xf PR:FTOE' : g,\n", " 'xf SpO2:StO2' : h,\n", " 'xf SpO2:FTOE' : i,\n", " 'xf StO2:FTOE': j,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return XFuzzyEnDf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xengraph = CrossFuzzyLst(dfenlstcopy2) #error up to 4 decimal places" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIxg = xengraph.ix[:,0:4]\n", "PRxg = xengraph[['xf PI:PR', 'xf PR:FTOE', 'xf PR:SpO2', 'xf PR:StO2']]\n", "SpO2xg = xengraph[['xf PI:SpO2', 'xf PR:SpO2', 'xf SpO2:StO2', 'xf SpO2:FTOE']]\n", "StO2xg = xengraph[['xf PI:StO2', 'xf PR:StO2', 'xf SpO2:StO2', 'xf StO2:FTOE']]\n", "FTOExg = xengraph[['xf PI:FTOE', 'xf PR:FTOE', 'xf SpO2:FTOE', 'xf StO2:FTOE']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(PIxg, PRxg, SpO2xg, StO2xg, FTOExg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIxg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PRxg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "SpO2xg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "StO2xg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FTOExg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def OrderDetn(x1, x2):\n", " \n", " x1\n", " \n", " #Takes the length of two arrays and\n", " #arranges them from least to greatest\n", " #return (smaller x, bigger x)\n", " if len(x1) <= len(x2):\n", " return x1, x2\n", " else:\n", " return x2, x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#def cross_fuzzyen(x1, x2, m, r, n, scale=True):\n", "# return entropy([x1, x2], dim, r, n, scale=scale, remove_baseline=True)\n", "\n", "def CrossFuzzyLst(dfenlstcopy3):\n", " \n", " #cross entropy of baseline across different time epochs\n", " #comparison across the same variable\n", " \n", " #len(x1) <= len(x2) for it to work\n", "\n", " PIxbl = []\n", " PRxbl = []\n", " SpO2xbl = []\n", " StO2xbl = []\n", " FTOExbl = []\n", " \n", " for i in np.arange(0, 9): #9 time epochs\n", " \n", " a1, a2 = OrderDetn(dfenlstcopy3[0]['PI'].values, dfenlstcopy3[i]['PI'].values)\n", " b1, b2 = OrderDetn(dfenlstcopy3[0]['PR'].values, dfenlstcopy3[i]['PR'].values)\n", " c1, c2 = OrderDetn(dfenlstcopy3[0]['SpO2'].values, dfenlstcopy3[i]['SpO2'].values)\n", " d1, d2 = OrderDetn(dfenlstcopy3[0]['StO2'].values, dfenlstcopy3[i]['StO2'].values)\n", " e1, e2 = OrderDetn(dfenlstcopy3[0]['FTOE'].values, dfenlstcopy3[i]['FTOE'].values)\n", " \n", " a = cross_fuzzyen(a1, a2, 2, .2, n=1)\n", " b = cross_fuzzyen(b1, b2, 2, .2, n=1)\n", " c = cross_fuzzyen(c1, c2, 2, .2, n=1) \n", " d = cross_fuzzyen(d1, d2, 2, .2, n=1)\n", " e = cross_fuzzyen(e1, e2, 2, .2, n=1)\n", " \n", " PIxbl.append(a)\n", " PRxbl.append(b)\n", " SpO2xbl.append(c)\n", " StO2xbl.append(d)\n", " FTOExbl.append(e)\n", "\n", " XblFuzzyEnDf = pd.DataFrame({\n", " 'BL X FuzzyEn PI': PIxbl,\n", " 'BL X FuzzyEn PR' : PRxbl,\n", " 'BL X FuzzyEn SpO2' : SpO2xbl,\n", " 'BL X FuzzyEn StO2' : StO2xbl,\n", " 'BL X FuzzyEn FTOE' : FTOExbl,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return XblFuzzyEnDf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph = CrossFuzzyLst(dfenlstcopy3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph.plot(grid=True)\n", "plt.title('Cross Fuzzy Entropy', color='black')\n", "plt.tight_layout()\n", "plt.axvline(1, color='y', linestyle=':', lw='4') #vertical line at ROP Exam time\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chaos Theory\n", " -Lempel-Ziv complexity\n", " -Lyapunov largest exponent\n", " -Hurst exponent" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Scripts for Chaos Theory based variables\n", "# Import from other scientists\n", "#\n", "\n", "# #1\n", "from analysis import \n", " #Source: https://github.com/thelahunginjeet/pydynet/blob/master/analysis.py\n", " #def lz_complexity(s):\n", " #Aboy 2006: turn into two bit data based off threshold (threshold = median)\n", " #turn data into a string of 0's and 1's\n", "\n", "# #2\n", "def LLEcal(data):\n", " #Source: http://systems-sciences.uni-graz.at/etextbook/sw2/lyapunov.html\n", " result = []\n", " lambdas = []\n", " \n", " # loop through data\n", " for x in data:\n", " #take the log of the absolute of the data\n", " result.append(np.log(abs(x))) #np.log = ln\n", " # take average\n", " lambdas.append(mean(result))\n", " \n", " return max(lambdas)\n", "\n", "\n", "# #4\n", "def hurst_mod(data):\n", " #Source: http://pyeeg.sourceforge.net/index.html?highlight=hurst#pyeeg.hurst\n", " #modified lstsq line to to start from third iterable\n", " X = data\n", " N = len(X)\n", "\n", " T = array([float(i) for i in xrange(1,N+1)])\n", " Y = cumsum(X)\n", " Ave_T = Y/T\n", "\n", " S_T = zeros((N))\n", " R_T = zeros((N))\n", " for i in xrange(N):\n", " S_T[i] = std(X[:i+1])\n", " X_T = Y - T Ave_T[i]\n", " R_T[i] = max(X_T[:i + 1]) - min(X_T[:i + 1])\n", "\n", " R_S = R_T / S_T\n", " R_S = log(R_S)\n", " n = log(T).reshape(N, 1)\n", " \n", " #remove NaNs from both R_S and n\n", " rrr = np.ndarray.flatten(n)\n", "\n", " dfxxx = pd.DataFrame({\n", " 'n': rrr,\n", " 'R_S' : R_S,\n", " })\n", " \n", " dfxxx = dfxxx.dropna(how='any')\n", " \n", " N = len(dfxxx)\n", " \n", " n = dfxxx['n'].values.reshape(N, 1)\n", " R_S = dfxxx['R_S'].values\n", "\n", " H = lstsq(n[1:], R_S[1:])[0]\n", " return H[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ChaosVars(dflst, col): #or dfenlst if it doesn't work. \n", " #make sure you put in a['PI'] etc.\n", " #tau = 1 + n #embedding lag\n", " # n = m from fnn function, embedding dimension\n", " fs = 0.5 #sampling frequency, 1/2\n", " \n", " \n", " LZlst = []\n", " LLElst = []\n", " Hlst = []\n", " \n", " for i in dflst:\n", " \n", " data = i[col].values\n", " \n", " datalz = []\n", " \n", " thresh = np.nanmedian(data)\n", " for i in data:\n", " if i < thresh:\n", " datalz.append(0)\n", " else:\n", " datalz.append(1)\n", " \n", " strlz = ''.join(str(x) for x in datalz)\n", " \n", " a = ((1.0*lz_complexity(strlz))/random_lz_complexity(len(strlz),p=0.5)) #normalize\n", " LZlst.append(a)\n", " \n", " b = LLEcal(data)\n", " LLElst.append(b)\n", " \n", " try:\n", " d = hurst_mod(data)\n", " Hlst.append(d)\n", " except ValueError:\n", " Hlst.append(np.nan)\n", " \n", " dfchaos = pd.DataFrame({\n", " col + ' LZ': LZlst,\n", " col + ' LLE' : LLElst,\n", " col + ' H' : Hlst,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return dfchaos" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfcPI = ChaosVars(dfenlst, 'PI')\n", "dfcPR = ChaosVars(dfenlst, 'PR')\n", "dfcSpO2 = ChaosVars(dfenlst, 'SpO2')\n", "dfcStO2 = ChaosVars(dfenlst, 'StO2')\n", "dfcFTOE = ChaosVars(dfenlst, 'FTOE')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(dfcPI, dfcPR, dfcSpO2, dfcStO2, dfcFTOE)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
DistrictDataLabs/yellowbrick	examples/examples.ipynb	1	1497985	null	apache-2.0
ML4DS/ML4all	U_lab1.Clustering/Lab_ShapeSegmentation_draft/LabSessionClustering.ipynb	1	347350	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab Session: Clustering algorithms for Image Segmentation\n", "\n", "Author: Jesús Cid Sueiro\n", "Jan. 2017" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.misc import imread" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Introduction\n", "\n", "In this notebook we explore an application of clustering algorithms to shape segmentation from binary images. We will carry out some exploratory work with a small set of images provided with this notebook. Most of them are not binary images, so we must do some preliminary work to extract he binary shape images and apply the clustering algorithms to them. We will have the opportunity to test the differences between $k$-means and spectral clustering in this problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1. Load Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several images are provided with this notebook:\n", "\n", "* BinarySeeds.png\n", "* birds.jpg\n", "* blood_frog_1.jpg\n", "* cKyDP.jpg\n", "* Matricula.jpg\n", "* Matricula2.jpg\n", "* Seeds.png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select image `birds.jpg` from file and plot it in grayscale" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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fx+VysXHjRiorKymVSni9XpxOJxs2bMBgMBAOhwkGg/o7hemYmpriySef5JVX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\nVcfvicStVCqFvr4+uazcbrcQtnb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GmiDUUEONVUNNEGqoocaqoSYINdRQY9VQE4Qaaqix\naqgJQg011Fg1/gvLlHywlhiLRgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11814a8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "name = \"birds.jpg\"\n", "name = \"Seeds.jpg\"\n", "\n", "birds = imread(\"Images/\" + name)\n", "birdsG = np.sum(birds, axis=2)\n", "\n", "plt.imshow(birdsG, cmap=plt.get_cmap('gray'))\n", "plt.grid(False)\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Thresholding\n", "\n", "Select an intensity threshold by manual inspection of the image histogram" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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yACSpowwArRp3DUlnFgNA0rK4q+fMZwBoTfSvDNwykEaTAaChmh0MOjP477YxGQAaCYOs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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11814a550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(birdsG.ravel(), bins=256) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the binary image after thresholding." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQgAAAD8CAYAAACLgjpEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10febbfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if name == \"birds.jpg\":\n", " th = 256\n", "elif name == \"Seeds.jpg\":\n", " th = 650\n", "\n", "birdsBN = birdsG > th\n", "\n", "# If there are more white than black pixels, reverse the image\n", "if np.sum(birdsBN) > float(np.prod(birdsBN.shape)/2):\n", " birdsBN = 1-birdsBN\n", "plt.imshow(birdsBN, cmap=plt.get_cmap('gray'))\n", "plt.grid(False)\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Dataset generation\n", "\n", "Extract pixel coordinates dataset from image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(h, w) = birdsBN.shape\n", "bW = birdsBN * range(w)\n", "bH = birdsBN * np.array(range(h))[:,np.newaxis]\n", "pSet = [t for t in zip(bW.ravel(), bH.ravel()) if t!=(0,0)]\n", "X = np.array(pSet)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 17 0]\n", " [ 18 0]\n", " [ 19 0]\n", " ..., \n", " [214 253]\n", " [215 253]\n", " [216 253]]\n" ] } ], "source": [ "print X" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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UF6U4UtkiqRPDshymry6nLvXj0wnnzFa/IA8ALAfcump30O3t58xW1UGeljZHl586ZW+h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"text/plain": [ "<matplotlib.figure.Figure at 0x11ac70290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X[:, 0], X[:, 1], s=5);\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. k-means clustering algorithm\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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pJ5rWYLp/GRkzyMqaidORbVzZIiq97eduYQI/sQ9z/zA4g1/0NycYjTpuzk5F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"text/plain": [ "<matplotlib.figure.Figure at 0x11b099510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.cluster import KMeans\n", "\n", "est = KMeans(50) # 4 clusters\n", "est.fit(X)\n", "y_kmeans = est.predict(X)\n", "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=5, cmap='rainbow',\n", " linewidth=0.0)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Spectral clustering algorithm\n", "\n", "#### 5.1. Affinity matrix\n", "\n", "Compute and visualize the affinity matrix" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4206, 2)\n" ] } ], "source": [ "from sklearn.metrics.pairwise import rbf_kernel\n", "\n", "gamma = 5\n", "sf = 4\n", "Xsub = X[0::sf]\n", "print Xsub.shape\n", "gamma = 0.001\n", "K = rbf_kernel(Xsub, Xsub, gamma=gamma)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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5Ang4vTLw3Si5ae7qwcBibpaHuDsUJ9eHVJtcMgPZJc0c4M+oPKopZQn5QT3O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\nugIFCqz2KARdgQIFVnsUgq5AgQKrPf4/E++LvuzEvWQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b9f0f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(K, cmap='hot')\n", "plt.colorbar()\n", "plt.title('RBF Affinity Matrix for gamma = ' + str(gamma))\n", "plt.grid('off')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import SpectralClustering\n", "\n", "spc = SpectralClustering(n_clusters=50, gamma=gamma, affinity='rbf')\n", "y_kmeans = spc.fit_predict(Xsub)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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hQkVZ6d/n5PEN/RmMlJIQIQx0bBy6iWFiYGHTRQ8fMm4bs3MPTMgCWnBQ7jbbBv5IAsLk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\n/JRXwT2T9IYLlO3dC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"text/plain": [ "<matplotlib.figure.Figure at 0x13a5d8bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Xsub[:,0], Xsub[:,1], c=y_kmeans, s=5, cmap='rainbow', linewidth=0.0)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 0 0 ..., 253 253 253]\n" ] } ], "source": [ "print X[:,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ogaway/Econometrics	Dummy.ipynb	1	54300	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#構造変化、理論の妥当性のテスト\n", "『Rによる計量経済学』第9章「構造変化、理論の妥当性のテスト」をPythonで実行する。 \n", "テキスト付属データセット(「k0901.csv」等)については出版社サイトよりダウンロードしてください。 \n", "また、以下の説明は本書の一部を要約したものですので、より詳しい説明は本書を参照してください。 \n", "\n", "##ダミー変数(Dummy Variable)\n", "###例題9.1「定数項ダミー」\n", "以下のようにモデルを設定して回帰分析を行う。 \n", "$Y_{i} = \\alpha + \\beta X_{i} + \\gamma D_{i} + u_{i}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -- coding:utf-8 --\n", "from __future__ import print_function\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>i</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " i X Y D\n", "0 1 1 2 0\n", "1 2 2 5 1\n", "2 3 3 7 1\n", "3 4 4 3 0\n", "4 5 5 7 1\n", "5 6 6 5 0\n", "6 7 7 9 1\n", "7 8 8 5 0\n", "8 9 9 7 0\n", "9 10 10 10 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データ読み込み\n", "data = pd.read_csv('example/k0901.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>const</th>\n", " <th>X</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " const X D\n", "0 1 1 0\n", "1 1 2 1\n", "2 1 3 1\n", "3 1 4 0\n", "4 1 5 1\n", "5 1 6 0\n", "6 1 7 1\n", "7 1 8 0\n", "8 1 9 0\n", "9 1 10 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 説明変数設定\n", "X = data[['X', 'D']]\n", "X = sm.add_constant(X)\n", "X" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2\n", "1 5\n", "2 7\n", "3 3\n", "4 7\n", "5 5\n", "6 9\n", "7 5\n", "8 7\n", "9 10\n", "Name: Y, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 被説明変数設定\n", "Y = data['Y']\n", "Y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.948\n", "Model: OLS Adj. R-squared: 0.933\n", "Method: Least Squares F-statistic: 64.02\n", "Date: Sun, 19 Jul 2015 Prob (F-statistic): 3.17e-05\n", "Time: 04:03:42 Log-Likelihood: -8.0050\n", "No. Observations: 10 AIC: 22.01\n", "Df Residuals: 7 BIC: 22.92\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 1.1650 0.491 2.374 0.049 0.005 2.325\n", "X 0.5777 0.071 8.143 0.000 0.410 0.745\n", "D 3.3155 0.408 8.136 0.000 2.352 4.279\n", "==============================================================================\n", "Omnibus: 3.054 Durbin-Watson: 3.236\n", "Prob(Omnibus): 0.217 Jarque-Bera (JB): 0.973\n", "Skew: 0.000 Prob(JB): 0.615\n", "Kurtosis: 1.472 Cond. No. 16.9\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ダミー別データ\n", "data_d0 = data[data[\"D\"] == 0]\n", "data_d1 = data[data[\"D\"] == 1]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1060c7550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# グラフ生成\n", "plt.plot(data[\"X\"], data[\"Y\"], 'o', label=\"data\")\n", "plt.plot(data_d0.X, results.fittedvalues[data_d0.index], label=\"D=0\")\n", "plt.plot(data_d1.X, results.fittedvalues[data_d1.index], label=\"D=1\")\n", "plt.xlim(min(data[\"X\"])-1, max(data[\"X\"])+1)\n", "plt.ylim(min(data[\"Y\"])-1, max(data[\"Y\"])+1)\n", "plt.title('9-1: Dummy Variable')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "###例題9-2 「係数ダミー」\n", "以下のようにモデルを設定して回帰分析を行う。 \n", "$Y_{i} = \\alpha + \\beta X_{i} + \\gamma D_{i} + \\delta D_{i} X_{i} + u_{i}$ " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>i</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>D</th>\n", " <th>DX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>5.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>7.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>7.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7.5</td>\n", " <td>1</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>7.2</td>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>8.4</td>\n", " <td>1</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " i X Y D DX\n", "0 1 1 1.0 0 0\n", "1 2 2 1.5 0 0\n", "2 3 3 5.0 0 0\n", "3 4 4 7.0 0 0\n", "4 5 5 7.5 0 0\n", "5 6 6 7.0 1 6\n", "6 7 7 7.5 1 7\n", "7 8 8 7.2 1 8\n", "8 9 9 7.0 1 9\n", "9 10 10 8.4 1 10" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データ読み込み\n", "data = pd.read_csv('example/k0902.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>const</th>\n", " <th>X</th>\n", " <th>D</th>\n", " <th>DX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " const X D DX\n", "0 1 1 0 0\n", "1 1 2 0 0\n", "2 1 3 0 0\n", "3 1 4 0 0\n", "4 1 5 0 0\n", "5 1 6 1 6\n", "6 1 7 1 7\n", "7 1 8 1 8\n", "8 1 9 1 9\n", "9 1 10 1 10" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 説明変数設定\n", "X = data[['X', 'D', 'DX']]\n", "X = sm.add_constant(X)\n", "X" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1.0\n", "1 1.5\n", "2 5.0\n", "3 7.0\n", "4 7.5\n", "5 7.0\n", "6 7.5\n", "7 7.2\n", "8 7.0\n", "9 8.4\n", "Name: Y, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 被説明変数設定\n", "Y = data['Y']\n", "Y" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.946\n", "Model: OLS Adj. R-squared: 0.918\n", "Method: Least Squares F-statistic: 34.73\n", "Date: Sun, 19 Jul 2015 Prob (F-statistic): 0.000346\n", "Time: 04:03:43 Log-Likelihood: -8.6672\n", "No. Observations: 10 AIC: 25.33\n", "Df Residuals: 6 BIC: 26.54\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const -1.1500 0.779 -1.475 0.191 -3.057 0.757\n", "X 1.8500 0.235 7.872 0.000 1.275 2.425\n", "D 6.7300 2.062 3.263 0.017 1.684 11.776\n", "DX -1.6200 0.332 -4.874 0.003 -2.433 -0.807\n", "==============================================================================\n", "Omnibus: 1.187 Durbin-Watson: 2.627\n", "Prob(Omnibus): 0.552 Jarque-Bera (JB): 0.835\n", "Skew: -0.432 Prob(JB): 0.659\n", "Kurtosis: 1.878 Cond. No. 75.1\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ダミー別データ\n", "data_d0 = data[data[\"D\"] == 0]\n", "data_d1 = data[data[\"D\"] == 1]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109489890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# グラフ生成\n", "plt.plot(data[\"X\"], data[\"Y\"], 'o', label=\"data\")\n", "plt.plot(data_d0.X, results.fittedvalues[data_d0.index], label=\"D=0\")\n", "plt.plot(data_d1.X, results.fittedvalues[data_d1.index], label=\"D=1\")\n", "plt.xlim(min(data[\"X\"])-1, max(data[\"X\"])+1)\n", "plt.ylim(min(data[\"Y\"])-1, max(data[\"Y\"])+1)\n", "plt.title('9-2: Dummy Variable')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###例題9-3 「t検定による構造変化のテスト」\n", "例題9-2において$\\gamma = 0$に関するP値は0.017であり、$\\delta = 0$に関するP値は0.003であることから、標準的な有意水準を設定すれば、いずれのダミー変数も有意であるといえる。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###例題9-4 「F検定による構造変化のテスト」" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ダミー変数を加えない時のOLSモデル作成\n", "X = data[['X']]\n", "X = sm.add_constant(X)\n", "model2 = sm.OLS(Y,X)\n", "results2 = model2.fit()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 8 19.316242 0 NaN NaN NaN\n", "1 6 3.314000 2 16.002242 14.486037 0.00505\n" ] } ], "source": [ "# anova(Analysis of Variance)\n", "print(sm.stats.anova_lm(results2, results))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "F値は14.486、それに対応するP値は0.005より $\\gamma$ , $\\delta$ のうち少なくとも1つは0ではないと分かる。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
SatoshiNakamotoGeoscripting/SatoshiNakamotoGeoscripting	Lecture 11/.ipynb_checkpoints/Satoshi Nakamoto Lecture 11 Jupyter Notebook-checkpoint.ipynb	1	15229	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Team: Satoshi Nakamoto <br>\n", "Names: Alex Levering & Hèctor Muro <br>\n", "Lesson 10 Exercise solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import standard libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import mean\n", "import os\n", "from os import makedirs,chdir\n", "from os.path import exists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import non-standard libraries (install as needed)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from osgeo import ogr,osr\n", "import folium\n", "import simplekml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional directory creation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "if not exists('./data'):\n", " makedirs('./data')\n", "\n", "chdir(\"./data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is the ESRI Shapefile driver available?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ESRI Shapefile driver IS available.\n", "\n" ] } ], "source": [ "driverName = \"ESRI Shapefile\"\n", "drv = ogr.GetDriverByName( driverName )\n", "if drv is None:\n", " print \"%s driver not available.\\n\" % driverName\n", "else:\n", " print \"%s driver IS available.\\n\" % driverName" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function which will create a shapefile from the points input and export it as kml if the option is set to True." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def shpFromPoints(filename, layername, points, save_kml = True):\n", " spatialReference = osr.SpatialReference()\n", " spatialReference.ImportFromProj4('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')\n", " ds = drv.CreateDataSource(filename)\n", " layer=ds.CreateLayer(layername, spatialReference, ogr.wkbPoint)\n", " layerDefinition = layer.GetLayerDefn()\n", " \n", " point = ogr.Geometry(ogr.wkbPoint)\n", " feature = ogr.Feature(layerDefinition)\n", " \n", " kml = simplekml.Kml()\n", " for i, value in enumerate(points):\n", " point.SetPoint(0,value[0], value[1])\n", " feature.SetGeometry(point)\n", " layer.CreateFeature(feature)\n", " kml.newpoint(name=str(i), coords = [(value[0],value[1])])\n", " ds.Destroy() \n", " if save_kml == True:\n", " kml.save(\"my_points2.kml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the file and layer name as well as the points to be mapped. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filename = \"wageningenpoints.shp\"\n", "layername = \"wagpoints\"\n", "pts = [(5.665777,51.987398),\n", " (5.663133,51.978434)]\n", "shpFromPoints(filename, layername, pts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function to create a nice map with the points using folium library." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mapFromPoints(pts, outname, zoom_level, save = True):\n", " mean_long = mean([pt[1] for pt in pts])\n", " mean_lat = mean([pt[0] for pt in pts])\n", " point_map = folium.Map(location=[mean_long, mean_lat], zoom_start = zoom_level)\n", " for pt in pts:\n", " folium.Marker([pt[1], pt[0]],\\\n", " popup = folium.Popup(folium.element.IFrame(\n", " html='''\n", " <b>Latitude:</b> {lat}<br>\n", " <b>Longitude:</b> {lon}<br>\n", " '''.format(lat = pt[1], lon = pt[0]),\\\n", " width=150, height=100),\\\n", " max_width=150)).add_to(point_map)\n", " if save == True:\n", " point_map.save(\"{}.html\".format(outname))\n", " return point_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call the function specifying the list of points, the output map name and its zoom level. If not False, the map is saved as an html" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;base64,
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wilomaku/IA369Z	dev/Autoencoderxclass.ipynb	2	620811	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Corpus callosum's shape signature for segmentation error detection in large datasets\n", "\n", "## Abstract\n", "\n", "Corpus Callosum (CC) is a subcortical, white matter structure with great importance in clinical and research studies because its shape and volume are correlated with subject's characteristics and neurodegenerative diseases. CC segmentation is a important step for any medical, clinical or research posterior study. Currently, magnetic resonance imaging (MRI) is the main tool for evaluating brain because it offers the better soft tissue contrast. Particullary, segmentation in MRI difussion modality has great importante given information associated to brain microstruture and fiber composition.\n", "\n", "In this work a method for detection of erroneous segmentations in large datasets is proposed based-on shape signature. Shape signature is obtained from segmentation, calculating curvature along contour using a spline formulation. A mean correct signature is used as reference for compare new segmentations through root mean square error. This method was applied to 152 subject dataset for three different segmentation methods in diffusion: Watershed, ROQS and pixel-based presenting high accuracy in error detection. This method do not require per-segmentation reference and it can be applied to any MRI modality and other image aplications." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 2.7.13\n", "Numpy version: 1.12.1\n", "Scipy version: 0.19.0\n", "Matplotlib version: 2.0.2\n" ] } ], "source": [ "## Functions\n", "\n", "import sys,os\n", "import copy\n", "path = os.path.abspath('../dev/')\n", "if path not in sys.path:\n", " sys.path.append(path)\n", "\n", "import bib_mri as FW\n", "import numpy as np\n", "import scipy as scipy\n", "import scipy.misc as misc \n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from numpy import genfromtxt\n", "import platform\n", "import torch\n", "from torch.autograd import Variable\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "%matplotlib inline\n", "\n", "def sign_extract(seg, resols): #Function for shape signature extraction\n", " splines = FW.get_spline(seg,smoothness)\n", "\n", " sign_vect = np.array([]).reshape(0,points) #Initializing temporal signature vector\n", " for resol in resols:\n", " sign_vect = np.vstack((sign_vect, FW.get_profile(splines, n_samples=points, radius=resol)))\n", " \n", " return sign_vect\n", "\n", "def sign_fit(sig_ref, sig_fit): #Function for signature fitting\n", " dif_curv = []\n", " for shift in range(points):\n", " dif_curv.append(np.abs(np.sum((sig_ref - np.roll(sig_fit[0],shift))*2)))\n", " return np.apply_along_axis(np.roll, 1, sig_fit, np.argmin(dif_curv))\n", "\n", "print \"Python version: \", platform.python_version()\n", "print \"Numpy version: \", np.version.version\n", "print \"Scipy version: \", scipy.__version__\n", "print \"Matplotlib version: \", mpl.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "The Corpus Callosum (CC) is the largest white matter structure in the central nervous system that connects both brain hemispheres and allows the communication between them. The CC has great importance in research studies due to the correlation between shape and volume with some subject's characteristics, such as: gender, age, numeric and mathematical skills and handedness. In addition, some neurodegenerative diseases like Alzheimer, autism, schizophrenia and dyslexia could cause CC shape deformation.\n", "\n", "CC segmentation is a necessary step for morphological and physiological features extraction in order to analyze the structure in image-based clinical and research applications. Magnetic Resonance Imaging (MRI) is the most suitable image technique for CC segmentation due to its ability to provide contrast between brain tissues however CC segmentation is challenging because of the shape and intensity variability between subjects, volume partial effect in diffusion MRI, fornex proximity and narrow areas in CC. Among the known MRI modalities, Diffusion-MRI arouses special interest to study the CC, despite its low resolution and high complexity, since it provides useful information related to the organization of brain tissues and the magnetic field does not interfere with the diffusion process itself.\n", "\n", "Some CC segmentation approaches using Diffusion-MRI were found in the literature. Niogi et al. proposed a method based on thresholding, Freitas et al. e Rittner et al. proposed region methods based on Watershed transform, Nazem-Zadeh et al. implemented based on level surfaces, Kong et al. presented an clustering algorithm for segmentation, Herrera et al. segmented CC directly in diffusion weighted imaging (DWI) using a model based on pixel classification and Garcia et al. proposed a hybrid segmentation method based on active geodesic regions and level surfaces.\n", "\n", "With the growing of data and the proliferation of automatic algorithms, segmentation over large databases is affordable. Therefore, error automatic detection is important in order to facilitate and speed up filter on CC segmentation databases. presented proposals for content-based image retrieval (CBIR) using shape signature of the planar object representation.\n", "\n", "In this work, a method for automatic detection of segmentation error in large datasets is proposed based on CC shape signature. Signature offers shape characterization of the CC and therefore it is expected that a \"typical correct signature\" represents well any correct segmentation. Signature is extracted measuring curvature along segmentation contour. The method was implemented in three main stages: mean correct signature generation, signature configuration and method testing. The first one takes 20 corrects segmentations and generates one correct signature of reference (typical correct signature), per-resolution, using mean values in each point. The second stage stage takes 10 correct segmentations and 10 erroneous segmentations and adjusts the optimal resolution and threshold, based on mean correct signature, that lets detection of erroneous segmentations. The third stage labels a new segmentation as correct and erroneous comparing with the mean signature using optimal resolution and threshold." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"../figures/workflow.png\">\n", "\n", "The comparison between signatures is done using root mean square error (RMSE). True label for each segmentation was done visually. Correct segmentation corresponds to segmentations with at least 50% of agreement with the structure. It is expected that RMSE for correct segmentations is lower than RMSE associated to erroneous segmentation when compared with a typical correct segmentation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mask List [ 0 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18\n", " 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36\n", " 37 38 39 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55\n", " 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73\n", " 74 75 76 77 78 79 80 81 82 83 84 85 86 88 89 90 91 92\n", " 93 94 95 96 97 99 100 101 102 103 104 105 106 107 108 109 110 111\n", " 112 113 114 115 116 117 118 119 120 122 123 124 125 126 127 128 129 130\n", " 131 132 133 134 135 137 138 140 141 142 143 144 145 146 147 148 149 150\n", " 151 152 153]\n", "Label List [0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0\n", " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0\n", " 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1\n", " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", "Correct List [ 0 2 3 4 5 6 8 9 10 13 14 15 16 17 18 19 21 22\n", " 23 24 25 26 27 29 30 31 33 36 37 38 39 41 42 43 44 45\n", " 46 47 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 65\n", " 66 67 68 69 71 73 74 75 76 77 80 82 83 84 85 86 88 89\n", " 90 91 92 93 94 95 97 99 100 102 104 105 106 107 108 109 110 111\n", " 112 113 116 117 118 119 120 122 123 124 125 126 127 128 129 130 132 133\n", " 134 135 141 142 143 144 145 146 147 148 149 150 151 152 153]\n", "Erroneous List [ 1 7 11 20 28 32 34 35 48 64 70 72 78 79 81 96 101 103\n", " 114 115 131 137 138 140]\n" ] } ], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_Watershed/watershed_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]\n", "print \"Mask List\", list_masks\n", "print \"Label List\", list_labels\n", "print \"Correct List\", ind_ex_cor\n", "print \"Erroneous List\", ind_ex_err" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb0cc2bf550>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e379c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask_correct = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex_cor[10]))\n", "mask_error = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex_err[10]))\n", "\n", "plt.figure()\n", "plt.axis('off')\n", "plt.imshow(mask_correct,'gray',interpolation='none')\n", "plt.title(\"Correct segmentation example\")\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.axis('off')\n", "plt.imshow(mask_error,'gray',interpolation='none')\n", "plt.title(\"Erroneous segmentation example\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Shape signature for comparison\n", "\n", "Signature is a shape descriptor that measures the rate of variation along the segmentation contour. As shown in figure, the curvature $k$ in the pivot point $p$, with coordinates ($x_p$,$y_p$), is calculated using the next equation. This curvature depict the angle between the segments $\\overline{(x_{p-ls},y_{p-ls})(x_p,y_p)}$ and $\\overline{(x_p,y_p)(x_{p+ls},y_{p+ls})}$. These segments are located to a distance $ls>0$, starting in a pivot point and finishing in anterior and posterior points, respectively.\n", "The signature is obtained calculating the curvature along all segmentation contour.\n", "\n", "\\begin{equation} \\label{eq:per1}\n", "k(x_p,y_p) = \\arctan\\left(\\frac{y_{p+ls}-y_p}{x_{p+ls}-x_p}\\right)-\\arctan\\left(\\frac{y_p-y_{p-ls}}{x_p-x_{p-ls}}\\right)\n", "\\end{equation}\n", "\n", "<img src=\"../figures/curvature.png\">\n", "\n", "Signature construction is performed from segmentation contour of the CC. From contour, spline is obtained. Spline purpose is twofold: to get a smooth representation of the contour and to facilitate calculation of\n", "the curvature using its parametric representation. The signature is obtained measuring curvature along spline. $ls$ is the parametric distance between pivot point and both posterior and anterior points and it determines signature resolution. By simplicity, $ls$ is measured in percentage of reconstructed spline points.\n", "\n", "In order to achieve quantitative comparison between two signatures root mean square error (RMSE) is introduced. RMSE measures distance, point to point, between signatures $a$ and $b$ along all points $p$ of signatures.\n", "\n", "\\begin{equation} \\label{eq:per4}\n", "RMSE = \\sqrt{\\frac{1}{P}\\sum_{p=1}^{P}(k_{ap}-k_{bp})^2}\n", "\\end{equation}\n", "\n", "Frequently, signatures of different segmentations are not fitted along the 'x' axis because of the initial point on the spline calculation starts in different relative positions. This makes impossible to compare directly two signatures and therefore, a prior fitting process must be accomplished. The fitting process is done shifting one of the signature while the other is kept fixed. For each shift, RMSE between the two signatures is measured. The point giving the minor error is the fitting point. Fitting was done at resolution $ls = 0.35$. This resolution represents globally the CC's shape and eases their fitting.\n", "\n", "After fitting, RMSE between signatures can be measured in order to achieve final quantitative comparison.\n", "\n", "## Signature for segmentation error detection\n", "\n", "For segmentation error detection, a typical correct signature is obtained calculating mean over a group of signatures from correct segmentations. Because of this signature could be used in any resolution, $ls$ must be chosen for achieve segmentation error detection. The optimal resolution must be able to return the greatest RMSE difference between correct and erroneous segmentation when compared with a typical correct signature.\n", "\n", "In the optimal resolution, a threshold must be chosen for separate erroneous and correct segmentations. This threshold stays between RMSE associated to correct ($RMSE_E$) and erroneous ($RMSE_C$) signatures and it is given by the next equation where N (in percentage) represents proximity to correct or erroneous RMSE. If RMSE calculated over a group of signatures, mean value is applied.\n", "\n", "\\begin{equation} \\label{eq:eq3}\n", "th = N(\\overline{RMSE_E}-\\overline{RMSE_C})+\\overline{RMSE_C}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments and results\n", "\n", "In this work, comparison of signatures through RMSE is used for segmentation error detection in large datasets. For this, it will be calculated a mean correct signature based on 20 correct segmentation signatures. This mean correct signature represents a tipycal correct segmentation. For a new segmentation, signature is extracted and compared with mean signature.\n", "\n", "For experiments, DWI from 152 subjects at the University of Campinas, were acquired on a Philips scanner Achieva 3T in the axial plane with a $1$x$1mm$ spatial resolution and $2mm$ slice thickness, along $32$ directions ($b-value=1000s/mm^2$, $TR=8.5s$, and $TE=61ms$). All data used in this experiment was acquired through a project approved by the research ethics committee from the School of Medicine at UNICAMP. From each acquired DWI volume, only the midsaggital slice was used.\n", "\n", "Three segmentation methods were implemented to obtained binary masks over a 152 subject dataset: Watershed, ROQS and pixel-based. 40 Watershed segmentations were chosen as follows: 20 correct segmentations for mean correct signature generation and 10 correct and 10 erroneous segmentations for signature configuration stage. Watershed was chosen to generate and adjust the mean signature because of its higher error rate and its variability in the erroneous segmentation shape. These characteristics allow improve generalization. The method was tested on the remaining Watershed segmentations (108 masks) and two additional segmentations methods: ROQS (152 masks) and pixel-based (152 masks)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean correct signature generation\n", "\n", "In this work, segmentations based on Watershed method were used for implementation of the first and second stages. From the Watershed dataset, 20 correct segmentations were chosen. Spline for each one was obtained from segmentation contour. The contour was obtained using mathematical morphology, applying xor logical operation, pixel-wise, between original segmentation and the eroded version of itself by an structuring element b:\n", "\n", "\\begin{equation} \\label{eq:per2}\n", "G_E = XOR(S,S \\ominus b)\n", "\\end{equation}\n", "\n", "From contour, it is calculated spline. The implementation, is a B-spline (Boor's basic spline). This formulation has two parameters: degree, representing polynomial degrees of the spline, and smoothness, being the trade off between proximity and smoothness in the fitness of the spline. Degree was fixed in 5 allowing adequate representation of the contour. Smoothness was fixed in 700. This value is based on the mean quantity of pixels of the contour that are passed for spline calculation. The curvature was measured over 500 points over the spline to generate the signature along 20 segmentations. Signatures were fitted to make possible comparison (Fig. signatures). Fitting resolution was fixed in 0.35. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to get a representative correct signature, mean signature per-resolution is generated using 20 correct signatures. The mean is calculated in each point." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Signature configuration\n", "\n", "Because of the mean signature was extracted for all the resolutions, it is necessary to find resolution in that diference between RMSE for correct signature and RMSE for erroneous signature is maximum. So, 20 news segmentations were used to find this optimal resolution, being divided as 10 correct segmentations and 10 erroneous segmentations. For each segmentation, it was extracted signature for all resolutions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (123, 50, 500)\n", "Erroneous segmentations' vector: (24, 50, 500)\n" ] } ], "source": [ "smoothness = 700 #Smoothness\n", "degree = 5 #Spline degree\n", "fit_res = 0.35\n", "resols = np.arange(0.01,0.5,0.01) #Signature resolutions\n", "resols = np.insert(resols,0,fit_res) #Insert resolution for signature fitting\n", "points = 500 #Points of Spline reconstruction\n", "\n", "prof_vec = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " #Loading correct mask\n", " mask_pn = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(mask))\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec[ind] = refer_temp\n", " if mask > 0: #Fitting curves using the first one as basis\n", " prof_ref = prof_vec[0]\n", " prof_vec[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(123,)\n", "(123,)\n" ] } ], "source": [ "print(ind_rel_cor.shape)\n", "print(ind_ex_cor.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_ex = 15\n", "#for ind_ex, ind_rel in zip(ind_ex_cor, ind_rel_cor):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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RhzMscYWiffTPKPoScbjl0PqKCRMmyE3xQTWF4vMSqKyk5tLLMBYXk//sM2gm\nE1JKtq9cy65Vm9B3p5Hoy8IkoDEkcUUk+VY/uSZJ2JFAVaqVFl8NpsadmGpLCXe347I7CFmsGIxO\nWikgWTcamzQydYIDf/7tuKlnT/cEzm6EpPYoj1Sl8EzWHEa0VfCjaDVRQzYYLazFxZjG1ezMzePf\nlvPokQaSiBDItOHuimANSq5JtfJBrp6tKRbsoQjfXdKDxfvpfkpg+WmwKjeZ6yr8TLFYWNLp4a0D\nLqwiwEhdAz/5zhVM7BWa2fiHDTwywsrj9jD1Z45WM2Z+SRBCbJZSTviscsrCV3ypCLtc1N9wI8Ji\nIfehB9FMJsLhCG8+vJDGMhtm3VAiUke1jBKVoNcijLMayTDaeVwGaOjyMqQ7QBgnLjmV5KzhFKcF\nKIlEqEKwL9BFatiLLryciGU0azdFGN1t4UxfI3NCb34kx09zjUxp28let5MdMgxUAZAaCPNc1mls\nMk8kg26GRJpotDnpatLQ0Lhcv5TrO99le+4PMUXH8+8NH5JsGoQ0OmnQd7DFVMkKy05CIsrlzZmc\ntWYW7sk9PFriYH+ji98MsXBus50fR0NsC+ewdnf1JxS+ZtXjDESJ2KAnEiVBTaDWr1AKX/GlIdLV\nRd13v0eooYG8J5/AkJlJOBjhtXvfpqXewTCzRkmSiYpQAx9qu9FjIjmkpyYS4RFnC7nJOxmR4sFq\n0kiUXgIhE+aIhfRgGlp3PnNbbEwNufl7NJMV4ek4ojDTF2F/5U1sN1/GekMLe2UKUz2bGNBWC1Li\ndOawNb8VV7CSoExmf8+lNJtyyDOvZFzuerLdAab5KykIG/mp/3qeZA7brVNYkzqUm8r82L1j2GLb\nSbvWSZFvJPM9UxgTzeKvaQv43dD3+OGBzVyy4UbSewIsHZbE6qiPC0eWcZULnqrN4PG19Vw9cxTO\n+BKHmtVAwsEZM0NhpfD7GUrhK05pon4/njVr8KxZS/fixUQ6O8l96EGs48cTCoZ59Q/v0NbgYIQz\nQkGSlde6N9JubMfqTcPpTmNz+gZK899leoKe2qSv8TSziAg9A0LV/J/1MQZoZXRFavFYNuCUXga7\nAvwlLKkyDefpwE2cpUunUNMB+ZREg6zpeANHT5D9GX7WD23Dba0BQLgH4m24nKjJxtdT/83scSs/\n6kNt9QzeNl2M3ZmNrSXA5s4Ehpd1YK9+h2+W7EDfMY26zlFEhCDFVMptwWzu2/8TdloqeDdxLdG0\njUwuPZPmAn1eAAAgAElEQVSR9a2Ujvbw7/Qx+NNN6ArDePd7+Pa/1vDSjdMx6DR0DiMOVzdgwBWK\nkG857GFVfElRPnzFKYt75Uoabv0ZEZcLYbFgmzSJ1BtvxDJyBN1tPt58+F26muyMSpQQ0Vhp2E5Q\n38kwTwPTHW/zq4wM/PoA56U6+KPzTlrI4Nx1H5Db0sRrp8+jJSmFG1Y/xtTkdwkPkhx8CTcSgux2\nH9kNejzhYWw3W1jX4sNRrRHRoGFwFC03gtncQ7LVTXnTmXxYM5dEfQ93FP+VIdp+aM3C2FPA2lwj\nd2VMwIsRc0TDGAwTrogQDhjJZydtjhABWxsGSz1SCwEQDTmw+HP5dufpXOAeQxTJSp2HHpcBi4Az\nk5dRnr6Nn6TfREd6Olqbn28eiHD3D6fR/V4tK7c28J1JVl4aXciMY1xIXXFyc6w+fKXwFackvh07\nqL3yKoxFRaT/9KfYJk0Eg4H6Mhe7VzZQta0ZjTBFA7cQHbCKtoCkq9pAXmMzJQMquTklj69FetAN\nSeUe7sQddfCVbQvQUgQH7EU0h1PxWSzUJeVw7ofvMHzvapIzuylI6qBE30l20IMmISxALyEqYXsw\nhfqxGtbU2DXV5E7j8V1XYe2RfC/hVWZFt2EJRYkC79itPOFMYJ/xM+akj+qw+xIodqcxJDIEv3E1\nW4w6am3dCJ0fQzCFC/3f4BsNg2gySja7QpijIean/527AiPYmTOOPUOL0dW4edDkZF5xKtuWVHHJ\n6TYeGZbPRRlJR29fcUrwhSt8IcQA4Bkgg1iAwKNSygeFEMnAy0ABUANcJqV0xevcBlwDRIAfSimX\nHK0NpfAVx4IMhaj6ynyiAT+Fr76KLjGRtno3y58vo7m6G4MlSkLmWpKLl4Gjgbcq5rGhbRQhacDp\n2Mrs8BKmDgywN7WEB8O34JUWrln3b2ZGt1Mk9pNGO5IIVUYDLs2IWRjJ8UdIi3YjBHhDBrZ7U2lJ\nsmGd3IM1ECJrn5n87kYQ0J5iYG84l/r2PCaJMgq1JiIIOnUlNAfmcmvuJmotBzCSxkAtnx/UraUo\n2kK7zcBa+yDK6mdB1IgpmIANH1GzGxEJY+6IYg1PQ9NsdOj8vJ67gkDGe2giRJF3JPc0XEuXEda2\n+TGEBUHrTh7VF5E/Qsf2nFwS1rSw4LTByGX1zJlp556SHL6dm/bZB1xx0nMionTCwM1Syi1CCAew\nWQixFPgm8L6U8g9CiJ8DPwd+JoQYBlwODAeygfeEEIOklOoVP8V/ReerrxGsrib3kb+jS0ykpbab\nNx/chk6vMWE+dOt/iE4KRMDB7z68lb2p+ZgHSeymDhJ1A6lzXMh2fyqr5XQKPAd4YtdvmBzdTrem\n8ZI5h6X2HKqsEO41m4JV6iiKDGeQaxIJndm48gdwIDWVkCuEw7qHIaPfYWzAz8CaINYWHVNlNV6t\nkUYyWB49n+zgeawVQf5e+DgevZuE1tlUjfsG49Yup67WgymplrHRvYzq2kGts55dNWfidlkwtPQQ\nMIWoHTQAX7qdoHcFJVV+BvYYGVzn5G+DbqCl4B32ObZzW8bD3N10LeMyNZY2h8jyjOUqLUpS5Wb2\nZGTgG+zkh8v38vdgbI58Nb1C/+OYFb6UshFojH/vEULsAXKA+cCMeLGngeXAz+LpL0kpA0C1EGIf\nMAlY+0UJr+h/SClxPfcs5mHDsM+YQcAXZuEjOzFa9My+1kJZ9XVoXhuaFuaJ9d+jrLiYSIEdR6AF\nuzHKPjmYdZyOSRfmW/ve4LbGR/GKCD9zjGFpchcRXRiLP53p7cVM9g9FSI315jK2Jjaw01jNjvR3\n8JRcgs8xiTRPAC0iabeN5VVtIlm04+x2UeO3MFjXwjRdPROiSUyWZdxv3MOigoWEo2aml05m3cR5\nZLW3Mby1iuaSNAKmKIGopFA2UNDVQUbWG5SfYafUmE115QDSgluwaBJ/ZjJ16dkkNJsoqVzIH1YG\nuM3zfZoHDqAy/X1+aPwTf6q/kbm5Hpb09GBuL0I0jWFMdRUbBg2hytTNvwJ+7DKBTvW2bb/jc0Xp\nCCEKgLHAeiAjfjMAaCLm8oHYzWBdr2r18TSF4nPjXb+eQMW+2NuzQrDm1X14uwJc8OOBVO6/AlxZ\nkFDHhjW3sj4tl0iBnTnBxVxleIKeqmm07ryQHt0mJjleYZRuEw8kZPJ6goGAvp0Rrnwu7jmHKd6R\ntOs7Wa/fjGF/AyX+HrKTsmjNLGZr6m5a5AuktSzmtFV5fGvjVnYWD+GtmbPZpuXj6rQQHmDFlZxG\naZuX1rZWnjTpKc95HYc/k6vWJbA9v5DGtAz+troFW7ue1rY2IpqNSm04e7UR5GVVMc24mzG7uslM\n81MxtIWg8eDjxn5gG12daWxOn0RRTyM31bzIX6qvoC4wgJacF/hOwb1cXHcd0+0hlgfDJLu9zCnV\nsbUwhL3AwKs7vDhb/bRnKgu/v3HcCl8IYQcWADdJKbt7v6knpZRCiOMaBRZCXAdcB5CXl/cZpRX9\nnY7nnkOXmEjCuefQ0eChdFUDo2Zm09R1M6K2CJmzlpqt3+KdQAKBYXYGRXcyputN3mqYTo/eRHH2\nQ5j0XSw2u7jZkktAg0xfCtc2fpVpntHUGZu4L/MpCmurCTfYSDUFmJI2mjWaE9G+nTO846hNzmJ7\nynaWzdhJ28gxFPQU0dVswE+UAqMbg1VPRVImdekpONofxeTbhD00ijlbTezKLeGNM+cwedsahnZk\nY06fi2PbP7A2V3IgyUF1mpNdHjst7uGc6awlBxcpLhcbU/O4oe4WksJubo0+jW2GizFjFlFRPpkW\ncQbndmxl+4FU9rm/iqfkHV4qeIhXDlzBhQmluPUObO1FTN5fy+qBhWTt9eHe7aKhQA3Y9jeOK0pH\nCGEA3gaWSCnvj6eVAzOklI1CiCxguZRycHzAFinl7+PllgB3SimP6NJRg7aKoxE6cIB9Z88h5Zpr\nSL/5J7z72C6qd7Yz5er36NxVSTh3PS0103lm6+l0zsqmzVBLUts/8fFpS9YWgtwmGGaeyzVt5+MV\nAZ5LXcgW1nLxbgvdHgcpCRZGpn6NDBl7aA0R5h+Olwj2+LDoc9mYtYUm+wGEZyDulvOY2FnAdL8e\nXdRPnbOUD0rewmPsxKTNoiHrSqLxuM4Je/dwzZYaLHojxdbhmHU2ekIuOgKNhEIefFonpR1bSCbK\nWd69JI/qxGEI0W4y8Q9tLoFMyXjDDpBR7Fk+DpQVUNU4FXR6zJ3t+LwNLBvdhsvmItRwKecJyHYl\n4w8P5uHzUyjcX09DmYa1wMHu753xP/0PFSeGExGlI4j56DuklDf1Sv8T0N5r0DZZSnmrEGI48AIx\nv3028D5QcrRBW6XwFUej5b77aH/iSYrfW4pHn8jzd6xj8GkhhP4htKRagu50XllxEf5hqazObsHR\n8Rip0szPWxqJegZg1MEgrZyGymQ2RM7gzOzLSMZGZXAfH3oXonncWII6DJpEl1OCf3w53d0tFHen\nkB4YTJbvLJIjqdQbm3nNuof33QV02fdhTlsMei9ZniSS29x0W4LUpQtSuiU/eDPCkDpYO9TOf867\niIunXI3uqSosdgOX/HwCIhSm4Z3tBPd3YemREIrN6BmOBqno3orb4qXg8uF4PriXyeFydBFJXZaF\n98xjGFd9PuU5b5NWVEEooKelajD7G0YQNhiRgR7WDdhKg7UN04Gr+KoIYq+bwJunS8rTUhi9opGd\nYT2vXj+NcXnK0j/VOREK/3RgJbATODjV3u3E/PivEFtLrZZYWGZHvM4vgG8Ti/C5SUq56GhtKIWv\nOBJRv599Z87AOmkSuX99iFX/qWDHB3VMzCvDO/RFOgJ6lpWdB5EsXpsCiS1/ZHRQ8njDfl70zaPY\n1MrpYjNVVSlss/yYCYmj8Ec8bGpbzAF/FR2OAPlGN8Uilby8XEpYigxFiIh09ulyeDR/HGsd47mt\nNIK73cKdeNGJMKeFJddpVtYlrec1xxa8lg4SdX5yRBoycgZ1/k460hO5ZOgcLgkNYNWzZcio5JKf\nTSAxw/qpfoY7unC/swV/dYCgx4JA0BSqo65rF8m2DvLt2ykw7CEQ1lNnuAACF/PosKXMy1mIlKBr\n0ShfOJqWrAKCNjMfZK6m29jF5JobGBz10BEdzVOznYws28v+egdFGXbeuvH0j6ZQVpyaqBevFF8q\nXP/+N02/+jV5Tz+NrnA4z961kSSdn9SJj/OYeQqrrNMoqe9i10An6Y0/JTPUyRN1nfy++5vMtpZx\nofYe5a58gtqvSDJlUuMpZX1oCw1JPXxQvIvLPV1ca7+AxKsfxLuxhc63KiEi0adaMA9NJjotgb9U\nvMr7tck0b7ZQhIa9aB/faslhRE8K/zGGMbYJdFGICOi0aWDSSDIbSNXp8LT7CXjCJGZYOe/6UYdV\n9ofirm9l4/0vka0VYtMn4CUAQHnLPxiZtJECRydRaaPMPZRFOdmMGLUenYigaxOUvVyI12InlJbE\nSyNKCUkjt1b9gO4uK4/NTsBvkoxb3c3aKNx90QiunJx/ov9CxQlEKXzFlwYZjVJ1/gUIo5Hcfz7H\n5j9vYUt3iPQhpdw+fBQ9+tj0AIZwlJLOu2jzVvDIgS6eaL2G0xz7uVq8RmdoIJ7IffhkhC1sJNvy\nIZX2eh5I15jp8fGHIT/COvNGvNtb6XipHPPgJJznF2JI+1gx13V4+crDH2CUbh6J2EkPOokSZcXI\nNewtNCH1o0hry8bWHsXRHcYShWhYomkCW5KJ3CFJFI1JR2c4tuUSASLhMJ3NTYRawviWHsDQESUi\nI7T692MQVRTa12DXbcAv9byiTSF5ajUWvZ+QS0/pK8XktXezZ3wSrwyrQd85lrvqLuGdFCevT01g\n2ua9uCNp9ESjLL9lBiY1kdopi5oeWfGlwf3hhwSrqsj+0710L66hzh+mxxHgTyPH4xdmhu+pY1K9\njcS8B3jOUsNlXg8LW77KsIRuLhcLCctM3JG7WCM8DDG8ziDbQl5OsLDIbmOEN4W7LvgH1gFDCDV7\ncC2owJifQMr/DUPoPlbMLk+Qa5/ZRDhq4N/Xn0+u0Yu7tA5jtoMr8m5B007MpaTT60nJyYUckKPz\n8e1opfv9/aQ15xIRWXSFZ+GJ1GKL/IL/s6zkhfen0HNGJ+lJbeSc1sT+1ZnMWFbPtvQBVKRt4SlP\nDuc2zOQ9X4jK4jTmbYnyctDPyxvr+L+pBSekD4qTh2M3NRTHTTQqWb2vjUdXVLKxpqOvxTklkVLS\n/o9/os/KwnbaWbhK23GForw53YxfmBm5fC+XboaBvs0scVSRRgRrzVkYExO4XLyPmSivRa/nD6mv\n8cbA27imeD3fzE5lqcXJ9O4LeOKbS3AOGELUF6b9uT0Ik46UK4d8QtmXNXVz6T/XUtXm4ZErx1Oc\nbsecmE7qtPEkFAw6Ycr+UIQmsI5JJ+Mn40k8uwijMCOjEcIyH1fPr/E0mrjSuo7OZbkccGeRPKyb\nhIIeNg/M5JLFXej8yZRnrKA9cQ9n7ArQ7EyiyeZnbLqDJ1fXcDI97StODMrCP06klNTuamfLklqE\nJjBN7OaV1tVsqUgi4LeTaA8yJDOBPEcBK8s91LR/vFTRT+cM4sazSvpQ+lMP97Jl+LZvJ/O3v2Hv\ngq0YJSQVWqhyWMkpbeKSehOICGsnP0VrQGNO8zh6zAP4amQvOboa/mobzKPpj6KXkuF+ydSWERjc\nY7lwxFymXz0anU5DhqO0P1dKuMNP2jUj0CWYAOjwBLln4R4WbKknwWzgmW9PYkphSh8fERBC4Dw7\nH9uEDELNXlwL9gKDqB/8MM5dP+O7tvd54MO5OM52k3tWC80fRGgkkelb9SybUs0rqZu5oaaAdT1G\n1g1P5vJtIZ52e1hX1cHUor7vn+LEoXz4x0FHg4dV/6mgrrQDa7KeDr+LbRETKy0RzCY/SU43Hd0G\nAn4HAPaERvJzG/EbtuJtnkdj0wBeuHYy04pS+7gnpwYyGKTq4oshFKbr9jtJXRQgJGH+GVa6NLjt\nP/Xo0Ng08R42WDwMiSQxpeYCzgpGGGn6I+9aLfwpxck1nZKc9pm8YHEwVGZysXcimhDo062YixPx\nV3YSbvaSdOkgbONjMfdb97u49pnNdPmCfOu0gVw/o4hE62fMbNlHhJo8NP9lC+gFy6cEOWfN1dg1\nD8+GxzNwThVSgvYfC7X703n69ATasyuZsP9S0oIzeHamg2G1Plpqepg5JJ2Hvj62r7uj+Bwcqw9f\nuXSOgRfW1jL6V4u55Y+rqd/XSepYPa/nPMZTKRWstEQYEtTxh/QiFl15Kbt+cBFvXZDH7Wd1MTu/\nhjRrHeOzB2HOfA1haOe6F95nf9eBvu7SKUHbY48R3FeJvOF6gkuaEAiesArarAbO2+xCh561Rf9g\nr91Dgib4Ss3lnNWTRqF4gGadxnqLlUcqz2Wn+ypuKtnAqOREvn/1d8i+bRKJ84vQrHo8m5oRQpDy\njWEfKfuaNg/XPL0Ji1HjzRtP5/Zzh560yh7AkGnDNjkTwpIZqwwsy3mYqNRxqbadru1OhIDwRQHG\nH2jgkvU9iKCTjRnLKPG0Mq2sg90FVpz5Dt4tbcKvJlT7UtOvLPxoNMqePXtoaWkhGo1itVqx2Wwf\nbXq9nnA4TCQSIRKJYLM7eHF1E39ZXYU+Cn4NrFHIDWs06SXdmmSaX8+Z0kgkJMnU7abAtIWeaApl\nvhl0GH2ApDAnj5EzcnmqdRmvrEzGkv4uF0+yM79oPuMzxqPTVHTEoQQqK6m+8CJsZ89mVd4opjcV\nccBezvcGD6fLbuTHrzWyIeU1QsO2Uxr186P2c5hYNwEzv6TI2cK71kT0rb/mleS9ONN6mNs2jZnX\nXYI+2XzUdpu6/Fz+6Fq6fCEWfH8ahWn2/1GP/ztkKELTX7YQdYeQgQjb0/Yxr/PHtAasbJ/mQJ8Q\npmrNILIXuXhtQibrx1Vib5/G9yov48mpQcpystCXdfHsWUOZOTi9r7ujOE5UlM5hWL58OStWrEBE\noxjDYcKaRkR/+EPQFTWzIlRIu7SRi49zAlE6Ek28RwP10QRyAxGu9LsYZNLT2ZPOZPuLDLO+T5Xe\nwNtOO4tti+nRxV5mMUYslCwbz/SOrzAm2cCu9tksrniQNyvfJM2SxtyCuVw76lqSzcmfkEFKSe+5\nivoLUkoa77gDzWqlcf58hix2E9F7WS4NNCWbmVbmZatxPbKomt3RAHOimUytP40D3ns5O6uFPQYj\n/pabaUscwJ8mzcK9tI7kK4ccVdlXtbp5ZVM9L27YTyQqefrbE08ZZQ8gDDqSLxlE6z93YBzgYHRd\nMa9m/5xL2n/PmJWSXedayJxQR8XqMZS0tFHRPoiOlDVs9Qziqm3ZPBtNoHyIk3/sa1QK/wsg5Pez\ne8UH7Hx/CW11NdiTUxkz9zzGnzsfTdd3Bl6/UPiBYBsNB8qpeeklzttfh+PAAaKhEG7HALwlk3AN\nymOhuRSXoQm/uQudN5fytgsAwWyayTU0EkoKIXQ9XOraQ0JjlFBUj0Ai7B3MTGkizebiFudg3nUG\n0CGY5fEw1udnR0cRZQk6SjNXU5mygfF75rFLPwVz+TVcXdJEdVI1L5W/xOKaxTw480FGpY0C4MnV\n1TywdC/XTi/kB7P610Bv1xtv4Nu0mbTf3Mm61WWcJYfRlL+A9b5ZAOSU7qJi5GoaDa0M1mtct+Mm\ntrc9xoUFe2jTNFztl9GujeHq706g9aGtWEakYB358UIf0ajklU11LC1txheK0NoToKLFjU4TzBqS\nzq3zBlOc7uir7n9uTAOd2KZm4VnXiHGAg6kNp7M04/+YI55BbIqwazzsmpvJuGUtzN2h8fKUbN7L\neZELgrfwo9IGbrHmscJpoMkfJNN88rqwTmZ6OtrYsvBNdn6whIDHQ0ZhMePOnU/TzjJWPPcEuxYs\nYtqwS0gtLiCUpKGzGrA7HZjyEtDiC82fSL4ULh13ezcrnllITlEBmRlJHNiynubtm9D5OogM8bJ/\ngI7cF70UVlbhszmpmjKJzWnptIsA+qgBpy+NDPdAdFE97SleluhCtAWNfNUbIitkZV3OC+S1+2h2\nRGhMqKMzIYQZSUG3mUHlGZi7o7Ql+inNc+O3aRT2jGZS8xQuTvwNVq2TV9tOZ3cEVoxooD0hQHbF\neCqDFxIWegpp51tfKeH5pt/iC/t4/tzn6ex28JWHVxGN/zWLfjSdoVn9Y+1RGQyyb9Zs9NlZ9Pzi\nF/BiPU69YK1+IbdP+ia5zW7c7j9icBzAogvzp/03ULVnO2dkv0Oy2c2B0FgWuX7JNfdNx/PaPvzl\nLjJuHo8+MWbdN3T6uOU/21m9r53CVBvJNiNJNiPj85O4eGwO6QlHd/mc7EQDEZof2Aw6gQxFQQj2\n2x5nSsdLtCYb2FScyvIXz8AZraeyaBTr8peTELbxVO3P2Ggw8dMzErgk2cnDYwv7uiunFK6mBja+\nuYDdy99HyihDJ8xg5Jy5ZA8fSs/S/XR/sJ9GYw3rq94gGA4SsScSstmJGkwYdCYStSSGFA5lxnXz\nP1f7/epN2+0Ll/LBOw+wP20gW7yjqNVyMYcDXFWxlK+WL/+o3B8vOJ89+fV47bvhkFmctaggO5BC\ntHsU+3wlnNmdxmBTK2W2RppMpTQl1xDWhUnwJZLTU0RYi1KTVEpEhJi4fx6Dai3oPNuAEBLwmiR+\nzclYQyPp+i5aAyNpCYTwRnrwmMLUZVhwBb7CXoOFnv9n773j46rPvO3rlOl9RtKMumSrWe4dV2wD\ntimmh5IAAVI2ZZNN2zy7eVO2EdKzSSA9kEIJEMAJHUwzNraxLRfZkmWr9xnNjKbXU94/xALZZ58N\nhBgZ4+sfW0ejc+77fGa+8zv37y6yg6uWuNlR+D/UOetI9P0d4/E8D39iJZv+czuXLKjg1svnvb2b\n+y4h8eSTjHzms1T/7Kds7ehj3cBMwjMf5hejK3hwbSO1+55FL7kLUS7yRWUe5pdbcMh/YI1/gBge\nHgj+iGXvm09ztYPwr47gOK+GF0sMPHZ4lIFIhp6JFLIo8rUtrVy9tPq0DJllu6JE7jyKfW0lmf1B\nQKDbeh9rkneQcMj81LGO/I40oqOSh/zNFGvuZk5iBt8c/QxXzzUx4jfRtX4+FulMTsf/hq7rjJ3o\n4sCTj9D18kuIssTSlkXU5+egFUvQ0dGsGlJGYnJGgf7sfyJYx1FVhYkTLuLDUyHDCstMjJKZqDnJ\nR27/8V9ly3sqhn/wxPP8zP0hisI4zpkP4zGNY8w08kv5eozmHOcc28VXrlrOYOXToJrRwmu5LjVA\nPFXGoWINonuMqhmv0G2LETI/j1V4jr3A3lfPb1BNNE4sYO5IC+syOWocOSxmP2PRzXyr/CH21D2O\n4vXww0gHNl2hVyvnhUKA7rhGV8rDYNGGyzCC6PLjk+Ziyo1gHYyQM96Hq9LHUGYu9++by+zqf2Lv\n4B6KsQQ/unYh1V4r61vKeKYjxC2XTpXon+4knnoKqbSEdFMTzqf7AJgIjbNjbj2+aISc724kUeET\nbqh5YT3d8d+wsWWQnCbzYPIzyA4Ps1dVEL7tILLPzA/SKe54pp8qj4WWgIO1TaVcf1Yt1d6/3Mvm\n3Yq5yYNppovM/iC+m+YQvbuTGfErecozwnnJp/hU8TkeKpnNWDBKo8dGW/BCjgQe4b7sU3xy5EK+\nUCHw+GiUK6rPpA//T+i6TueOF3hl6wNEhgeRTGbEBcuQ7U34h8u42zjBEXMPTlWiNe2mxaQhmX6E\ntaEPMQa6APamBOZdFkoGP0a0ZAY/sfdQoSdOuu2nheCfUCIEqn9MxBLCraoEMkaO2I5R6XmAX9jm\ncc85J1BNbZjDc1jXDi+5F/Fz8/kgg0nKUcwvpKNzCwAObYyLor8jaymiSBKelERj1sxSd456z72Y\nvX2vXbcM+F7Yyz1FA3d6olxrLucDEz4253v5iKWNY+ZW+n1XEBLNzND2sFJ+kNvKL0HWq1nYO4d9\noZ3M6ouRaP0jXnmAzqEL0DgLh6+djbPPBWBjq5/HDo9xeCTOgmr3dNzedwxdVUm/vAvHhg10Huti\nFn7S3g62hy8k6NYp6f8Ouljk5pIc84cu4MDQU5xXM4SOwCOmJnKheaw4v5rs7jGUiSzHzqvkjmc6\n+eCKWr62ZfZ74gsTXi3M2lxP6PaD5DqjlH18PsHbDtI0+Qn2zz3Agq4wV3mPcn92Fn5tgOLkWkqt\nXTxQ+gR39JyNsWDhj8dDZwT/jeRTEBtAtZfz7N130/7sUzjdXiqKMq1HOrH0Q/eKRVwnj7Ckajer\nS49glvJkFTNJWxADCnufWUNmwkLB4aJpSZCGs15ie9OjfPfgh0DRWWM7cdLdOD0EXxNJGCc5t6+O\ny18ZIbvAyq+bk7T7D2IWDlLIl+I9toVLuo/QMBHhwvSPOeqrJ2Yz07FgFoeblpGJibiTeT4cNbLI\ncgOSaEEUzTjcAiVl+3Ebfoaim3iucDYhv0iZbRhfKoWUkrhyLEtrn8zXW+3cWhnhocw8lmXynJM/\nzBLhX3lOu4ACGxjJn8+1g22YxS5U2UCj0MoLSjcrjgq0OHqZsP6UzgYrMVs3j/U1cXnj5axumPrQ\n7ewOn/aCnz9+HC0ex7ZyBcMHe2hQmxiU2tk++zrcoZ8gSMNcJLmYZVAZPWSmxDFMnT1Gj+wmbbwF\nSRZpnuMj9tNDSC0evn5wkDqflS9dOOs9I/b/hbHagWVeCckXhhBkEUEUMBYk9Mwa9s3dzoKOKFdU\ndBIcq8DjWgrj55Jt+jFb3c8xL3IFu73592yW2J+hafDiN2DHf4KaR0BgdsaB11GKY89xBpwVdK48\nj8qSag7aH+aWhpfRfVn0tAHBrCLIU6Hj6Kib0uQQk7oLi+Eox+IFtqkuutIGDPXfB0OSQ5nASXfn\ntBD8JakEH5bB7+1EvjZLwi2zLuEgjc48g4bxOQfddUYOr1rNmsRvadXHWav3kJVMjJgOEx15ikmD\nnXUqFCIAACAASURBVHZvE7tqGzgSNrBoPEiDNkRAegkvx+mhmj/J51Cz4BVK3SGGctXsEZuoM/ZR\nk02jj3loPbYKo/IKhxri/K4kxu8ow6ZpXJp8mlWxP1Kp5QBQNBlJUnHWWtkY/gj7u16mCxOeJBQs\nIQ41i9y++7tcUncxPruJWeVOdpwI88n1DdN8p08uuY4OABLOcizRcQD2DzcxOPNF7PG9tMaaWN96\nBOvwQg5H2rm2foi0bqSj6TsUdko0LCkl9+IwuqbzXK2F3mNp7rhxyXu2C6T7kgaUSI7EU/1ILiOm\nJg+u8bMYqX6RPt96Wsa28b6y3TxdPJ9epZKZsQZecL/IpsmL2VdupGM4wexq13S7MX1oGvzpU3Dw\nLrL+s9lxOIpDjDHbHKSyqgeqYAZhDBwAYE0R6IRJl8yhJieJYS+9hWpKikPEalKc2JCmKzfOSHFq\nb0TUJFShiM/sosxWweJ88KS7dFoIfrUQ5rx4z9QPr/YoK0HGLzfjXH6M8PIsl8S3Mjvej47Adt9C\njlhaKFMnWJI/wOz8EOa4xgWRHa+fVAAkKCLxFGt41hpg8ZxnUEwWvqLcQq+lZeplusZ6cRsfnPFL\nLvI8wsQRDzPbHCSLdiJukUJ5D/dVOPij3cXGiQrMqVkUJDvr2ME8vY+ykjtYIN/CzOdvY1eDg9n9\ndob9GuPeOF96+Eq+8b6HWd3g4zcvD5AtqFiMp6945Y52IHkrOPrcCWpFG1nLGE/WBLDFb8WW97PW\naEaUFEY7zdTaBwhYUuwMfIj6wEoG8100N7jIPtqLbUM1v2rrZ3aF8z2dUy7ZDJR9cgFKJIvsNqMV\nVHLfjqHlPARdBexKLbWhAVaKe+gSLsEfWUab+x7c8V5gLs8cC723BX/n9+HgXXSazmfbiynsFQIz\nkxJtwhX8dsUMPha9i1IxhGIQyJolkpIB6YSJpbExyrfB9pjE3qZRhsoU1JgRQReoVwxsyYlUx91k\nMpVIDpkKPcG6gb20OZpOukunheA/Km7iucaNuEdyVKTCtNDDCr2NS5SdsBMgQlGQuCuwhR/UXI+i\nm1k62c6F8cdAKXDQYOSQxYjBITLfptOXnseoOoNCVmDOo/sJty5j/dIneN54DvdwLYpspHRiB/aB\nUuTyIs9VbmQkXslnjd+kek0Q1gRJJT1kByVSgwEWHkxz9zkyDwWGKcYaWRQuQZfWYtLTLBFO4HE8\niHbOv7DopVt4od7MxldcPLIanhS6ue7g11nV8BF+8VIf+wairGks/Uu3411L7tgxzAsuZ0yLMEtt\nYY92jB7vXgw5ibPHF1M3exdy2kf/QIgrqkeJSg5W3HwL93+znZIqO8bDE6hOI68EjPQ+l+YH1yx4\nz4ckBFF4rae/ZBDxbJ6B7+hZRGqf4on2c7nJMshN8pPcWbwEPVmOqAuMCG3I6hzaYulptn4a6duO\n/tx/cFhrYfuJKA1XDGL0FSkC8AzvB+IeSAg2EnkH7cH5NHWeYHCsko4aE/fPzjNsyGIryixIW7k4\nO8GmXBjbG7MiDUAOklkznfJM+mKVJ92t00LwN+ZCfM13JTMNec7uSDBpKfLVUhMpSzctmRPkJZk+\ncxOmkIdrOo6iDotsXTaDdt/fs2KgnaWZTpq1Is6DE5iCo1Rc3UlnZYA7hI9hbLyaJrGDAeEWYoKX\n1nCemoHHafc8Qrq0QNnYCjwRF11zZvGvof/gpl23wWIHkr1IQ8sJ8rMtPJl/H4XJcpyJrSTczzOR\nb8eTOJen5E1UFoNUG14gaTkX75r/j1Xtd7Ldm+GinR62Lc7xh8Hfct28OgyShx3d4dNa8Av9/ZjX\nXk9B6ELSTGwtkTDk2yjNLMYqq5jdo+SOzMJlyFBnjxFb9gWCQwUiIylWnlNFcX8Q92UN/GJnH5Vu\nCxfOLZ9ul045bMvK4ZVFiOITSKVZjmtu5sYnWSB2My568CfqOWg/RFVapVfV0VQN8b2WnpkYJXvX\n+0nnzbzY76bpogEku8b20TnMLRnEZUgwmKhGjc1kZqyJqnAjo5yg01zLsUVddHg0agoCPxqfYG02\nS0x0ssc1l+87LmFXppn+mA/VYKDFHWHeZD+iIOBWFKoLmb9s29vktBD8rDHM9f1Bft5YyYfW2ACQ\nlCJ1wWaO00ntRISynISzaCED4ILzu/Zj1oxU5dzMla7CkcmTkSMMeu8k/KyXxcv2UjNzkMelixhS\nGiiPiGw+nqRhrIhPWsmWsYU8FniMfaUvY9JfwTt4GWM1F/KtFV+hpKcPqyhRLPcxVBLAaMoTCIwy\n5P8S7siDjPsfwcAj2FObuctwFZ9Tf4mkf4+0fBvlSz7NlvBRXs7tY9NejVCfi27h35kT+C4vd0em\n9T6fTNRkEi0vExFUykQjcVXlSPleUKysmKiiLtAPgs7ACYXF/lE0BNwrPsSBR8aQjSJlw0lEn5lj\npUb2DUzytS2tyO81oXoTCKJA/dVriey5g9aqcfbvqma2fZJrjU/zL9qHWZZs4KDzGeYmM7zithEZ\nSVNa8+6rOv6rUYvEf3kZlmKa54eWs8gySD5QJDhQyqrKDrSCTPeBFaxIXIlX97LP1MZL8iFQFNps\n7fR7+jDGmugfv5IvmwqkmgKc0x3mqrt+whXJx7mCxwGYLAtwZMGFWOwNLNH9OHQLIT100t07LQS/\npaoVqf9BnkwcocM+k/39JaRdVj40tp3flYg8X5ugenAL38m7GTKMIykKPs1PCQ4EUSCnFNFMRp6J\nPkquWEbRZKVqTxbHfoUPmI8QFU2kBBsFQ4qYfZxsqIeypJUbc/O4anItd3tepNPzKGq4g6Tv7xif\nP1UkJSpJSoI7uPywlQUNzxKum+An7s+QMNoZ4l5k7XnquZD7OZ/rc4+SEz5NJHklHuc6zi2Zza78\nCxSDuzi+dSbVS5/mkYGNTKYLeGynX9l7oX8AyV3DmDhJQDTxuG0MTWnDW5hPQcrT4E6QzzhJTcCM\nuQkKleswWAL07O+mptaBHszguqaZr+3ow201cPXS6ul26ZTF42+gXalivmcvudzZhCuPsTHRxj8h\n44470Kp0HNkTJCoXMzgYf88IvpYpojzwBVyJY2yNLGRWWz+R/1AQkhIVnhAkBEq+rdNU14JU52In\nD9MluDBkYzzoTVH091GIriQfXo1WVcJorYefHcwyK1PD0Orr+ZnpDqrMi1lQOJcWrZQaYFxP0i2M\nMqL1oOhjLOKKk+rjaSH4wUANt3Y3cG+8nTbrTLZVL+ZCc5p/kVczEFEodKX4dtGFA5H6XY+DkMGy\n7HOvxXcNWoHMnp/gKzVRMxbEH5ukd85yoitupJjQsSmjWOUOjOUDRFxpxqjGlKxmR8cwruMv8zHz\nTIbd/8FxwwS66zATfpEJxkindhDypHm4qcCx0FmcH2zkKwv+nR84PsWQfjF9/IkjrjwPOD/Hkr1H\nqRSCVBt/yomBbcj5q1lRs44jc8aQDg9SMpxAB57rOMwVS/9iQd27jsLgAKKzipAYp14p5+mKQwio\ntE4GyNjHyXu7SB+vZkYghkdJwbIb6T0SIZ9RKE8VMASsjAYsPHNfkL9f34DVeFq8tU8aq2fWkYzs\nxd0YoUd1s0IPsVToIl+YWkyIxX5gMYfHkyyeVkvfGXRVI3HbD3Fnfs1kYRPOjkmK5+ooLh3jgEqh\nQiD5UAPK0iYqTHN4PLeVlMeDZXCEuxvN6IFXqA6WE4iE2Lu8hpTNwS/2ZZEyw9zr7eDa6EY+xzcR\ncxLdqNwnRJljuB+v2sN+w2ZAYiJ/8gfJv+lPhSAIdwAXASFd1+e8euxfgI8AE6++7Eu6rj/+6u/+\nGfgQoAKf1nX9qb+h3X/GJeetZ6zwYx6r+gjHC60ktqW4Jzf1xp1X5uKfd7+Ma8bFqOEXKPvMNdg2\nric+dJRE9iDJ/GG0bAqTrLDsUBipqRT7NR9kwXw34ci9hMPPIQgy5eWXUVvz71it9X927WNDUcIf\n/RAt7Y/Dlm8THg+wNCoyxzIPQdiMFtF43niQ75a+Qp/lSS7bfh2fmvMHtgVa2JOfjS12PzNDIt+u\nuZk7ur7C94WruKRqO6N7t1Jp8XA9l/G92m9T1x3B3JDnkVceZ1OrC7vt9GqoVhwdRXJVMSGmyac9\njNsOANWYshILXSK6pDDeDXMqC2h5M2LjRrru6MFslvBmijivbuZrzxzHapD44Mq66XbnlGdG1WYe\nGd+Np26YE7vLWG4OsUFq41Hzeuw5Dyn6AehMZKfX0HeI7PZdONPfZlKrIaF+hJrqPQxs+g1yGAq1\nEDrURHROGWvDc3k89AQpfznCZIT7Z1jQK56ncdjBBQcq+cZNH0eVZW7bn8GbSvGp6t/TIEzQES9j\nVG3ifjTclk6unHcfdmMQ9biTzkk3EdXFxlldJ93Pt7IM+jVwG/Db/3b8+7quf+eNBwRBaAWuAWYD\nFcA2QRCadF0/KdMVtgV7uNWzhfKCyiyzidkNUbzBEOcFnqHa1Yc/+UWKQoTYJ3oJqvtJ7flndL0A\niNjtzYh2M+mL4+Q3BYFx4Ah0gcnop7bmI1RX34jJ9D+n97VUe1F++3P6rriSRXtvZez679K+I0Ta\nM0jdjF2c6LQz0dXJ2okWHivZxO7A44h7rmV19S4unrOXbxUtpPgDxqGr2OlcwA3pp1hX/DbXz9vG\nBS89gPvsr9DgXYo2cIhWU5a2YDMHDn6ElWc9iSS9uxt9vRFlbJyCswkHWXYY46D14VHX0O86yvsc\nMoWChVTIwBzvMMKsi8gVDfS3h6m3SJhnuDhqhiePjvO585oosZum251THo/7LFIGDzWGIcaLrcTK\nDJwTa+OX6iVUpByM2ceQNZ3eYgFd0xFO48I1PZPE8OLH0JC4vzfAYmEn0uosmDUUQAlayRocLPIk\n2XXoEKkKP3KuQLuQpFC9n8qEh6H6T/PllQ34Egrf35uhMavxMUFnZPCjpIQse3UJVchyY/PvqfaN\ncjhzBf2RueyJ6pgNEotX5SmrW3/SfX3Tgq/r+nZBEOre5MsvAX6v63oe6BMEoRtYBux6yxa+CTZV\nzufTw19nce73yAWFkvQV+Ca2wETzlO2iRnLDsxTVKAbZRXX1DXjcZ+F2L0GWX49PalqebHaEQiGE\nyeTHYqlFEP7yxp9cWkrlf36fgRs+SP3un2O64B/Y9zgUbXPxrbsNzbIW6Xg3s40NtBev40p3P7Ge\n8zEMqVy34XF+qMGE/WkeU9azSj3Ip13PcEv8Mo4tbuMrI/u5mHXc4T1I+fAB2rzraR8zUjP8G2pr\n/+5k3M5poTg+TtTZists537f1EpnxoSTCXcXRV+Q4lCAspo8DjUHcy5n4EgETdWpQMeysZYvbz1M\nwGnmw2vq/8KVzgAgSWbqHBWoiSPYmrOMFyy0CmPIeh5vWuSAb4KaVI4xu0Q8nMVddvr2HlLu+iSy\nPszD2U2kigmk0T8Qvl5DToBmgchYI/Vz9jG+9VLGSw1IShFt4BBHN45iy/rpmvnv5I1mlh0L8tke\nEw2SyOfFOBNignnkOaxOTVL72pLv0Zm7jp/tnFo81vnMbJnvZdmSCv6+b4QS4eS3svhbBDo/JQjC\nDcA+4PO6rk8ClcDuN7xm+NVj/xeCIHwU+ChATU3NX2WALJn47KyP0/24hhx24xhbhnVhGaYGN1q6\niLnZQ7X/7L94HlE0YbPNwGZ7661hrYsWUfaFzxP6xjdpWLwIw2Xr2fUwoH2RspW30nrJCi5kFpf9\nPsXvCjP5hC3BaPoSZt/TxTlX9bONMOXJYR73rOED8UfYI81lm30RSyd2soXFOAMNODq6MZau52h8\nMwuP/5GA8RpM5adHYUw2GCPqyWJVPQw7d6GJbsRJhbO9JWiGfkLdRlpKRtELNoSGc+n7SScmASqX\n+Lmzd4Jj40l+ccOSM7H7t8CSqk3ce3Sc1poOBo74aSXBGqmdYakSTejHnxqhy1lPeCh12gq+uv8B\nDKN/ZFi7kO7BOPWRBJkrcgiijuaE/MEAMzwHid9ZTUdpFs3ooKntOb5xaQ6h6Ga49qs4ixIz+n/F\n9X3n0ChbuUOMcpQcn5O3MUw1Z5l0VmdmMnr0q2zNpLh2WQ2fPbeRMqeZUL7Ihr1dtNjMfG1mxUn3\n9+3mrf0EmAEsAMaA777VE+i6/nNd15four6ktPSvyzHXMkUSvx3F07kZr7gB0W7Ac3kDtsV+HGur\nMPhtf9V53yreD34Qx3nnEfrOd2gpCbPm6iai/TVMHvge8ckuhqI38+GKo5xA41HZTkHOc9zxd5z/\nuIGArNHm2M3jpkXIusJ5gZeZJ/Vxe+1CiokR1mYXI1FkliHJ3tEW/C9+kokfHCZ7JPyO+HayUeJF\nwmISJWEhK3ZiEJvoLRljti2HoBgZG5OYnRtBaNlCsSAy2BklYJEYWOTjh8+eYMv8Cs5r9U+3G+8q\n/P5NJCQ/BkOBpCtAThY5S+wkrUxtHlpzA8SsIqHR5DRbenLQs5MIj32evNbI9/UMki5QUR0lN19H\nEMB8QqAhPo7wCxvtchlBVy17sw6+e3ERFYms6bNYNAOW8a9x8chZrJBKaCPGbzSRr0oPMiTUUll1\nHFE3cljQWJ0RecTl499WzaDMaUYtqnx8+zFSuSLf7FUwqSe/Vf3bEnxd14O6rqu6rmvAL5gK2wCM\nAG/Mi6t69dhJQYnmUJMF3BfUUxxJYV9diWB451sQCIJA+ddvwVBVychnP0frPAsbbmhhos9C4vDt\nzGr+IZvOifBhU47nBYU/mKFocKGMnMv5EyIpYNK2lXv8F3BF5GnkWisp0cq+XIgavR7F5yAwvI9Q\nWue4MrXiGv79Pjp3vPCO+/q3RMvlMGhGQmKGTiGKoGfxZkopODuxegYRgnWUVuaxKgX0lgs4cedR\nihqUr6vkH7a243ea+Y9L50y3G+86RNHEusBsgoVyzGWjJFwyy8RO4tlyZFVEVQbRRIETkZNfEDQd\nFP/wLUQtzt3GWXj7c5RoKfLvy6NrAlIInG0m0s+aOFBWwWRlE08Xm+it3UlezqON30S6soqKiZ/x\nqdjnuUKtpUfL8AVEPm0+QL+5HtmU4Ve91/LTrJeX6y2kttThUCH4o4NkOiL865Md7JRVvjgpUXlo\nktjD3Sfd57cl+IIgvLGU8TLgyKv//xNwjSAIJkEQ6oFG4JW3c63/DWOVg8AXl5LvjSGYJexnTV+F\npeRwUPWDH6DG44x84Qu0LPez/roWhjpjHHqkhpaWW/nCzRt4EBv/5A1jMsUZqDmPNfcbWYnAYU1n\nq7NIVjLzudhvCZhj/NzrQ9dUGi0t1KZ6kXSdp4sR2iLPYFFsvPTTOwj29Uybz28XZXwcrF6iBhft\nJb0AuMNOmk0WNFOC9ICX5tIwumQgOdzM4PEYiHB7OMpoLMcPr12Ay3Lyx8OdjqxquJ5D+lJs9jhh\ns4tqIYyq69iLDjIMAHAsNjnNVv7t0TNR5J5fE2cZDxi6MBdlquaF0M0gFHTKfi3gSCQ5ZillMlDJ\nXqkBSp9FtneTH7+EVHkrZ4XD3H3sZs6JGNlezHGzqHAJGTyWAtmihW3ZhUgOO7+7eRn3fHQFLauq\n8f/DIgx+K+HfddCTynG1buLjV87FddEMrItOft+nNy34giDcy9Sma7MgCMOCIHwI+JYgCO2CIBwG\n1gOfBdB1/ShwP9ABPAl88mRl6PwX+e4Y2SMR7CsrEM3TG8c1t7QQ+OpXyezaTfj222ldVcHqqxrp\nPTjBC/d0YapxU7+qmoWJOupax9BEI6PeC2k8UmS2rNFXPMp3qi5jdeYAzf4wJ2Q3E6kR5mvzMOkF\nZqhZDttMmNa+BECNs5WDTz06rT6/HSaHR8g5fXiKbkZdPaiSj2ShyAqTCTSJvmEjrblRlNJlJJ6b\nYFiE5/3w3PEJ/mVLK4trvX/5Imf4HzEYnMyqPJdcwULMOfWlOV/qxVD0MSGPAtBrjqCp2nSa+TdH\n2X4XIhkekMqY0+fAohdgeR4E8N4pI1gMdI4EGClxESxpodcSwlCynfLkKlrs56DV2PiH4yIZ7Ri3\nZXJ8yVDgnEoXl5sVerMiQYODScnHvR9bwZqm10PVRavMt1a5OOySuPVQjn+V7GipIralAUwNJ7/9\n+ZsWfF3Xr9V1vVzXdYOu61W6rv9K1/XrdV2fq+v6PF3XL9Z1fewNr79F1/WZuq4367r+xMkxf4pc\nd4zI7zqQ/VYc606NCkv3FZfjuuJywj/+Cant25m/oZolF9TRuXOMPX/qxXXBDMwtXlr65mJ0hBip\nWM2aIzbUqAkN2O7MEDJ4+XL4lxikAtsMRazGUhz2cmqyo/QURDxzzyLn6qfeO4++g/s5lcZVvhWG\n+odIOh1YU2YSxl4UYyMpZzu1jhjmaCOaO4Y7nyUxvpQ/OuDHcoZ92Sxf2NjE9Svqptv8dz0fmHMB\n7bGzyPnyKKLAUrETJVtKSs7izGcpGsoYGTn5rXvfSYTD91PQ6rjP2Utg0kJ94xjIkO91YG4XSegG\nOv2lCGVNvIQPS/mDWLQANxy/nBNNNuZHJzGpD/G7TDkPWgssC8h8JqfzgtSJaCywLdnAP25q/rPJ\najlV4+YjfdwdT3DQLWKqdpB6op+xW/Yw+tWXid5z7KT7fVo0GzHVu3BuqqPsEwsQT6H2wYGvfAVT\nczOj//hFiqOjLNtST+uqcvY/MUDbMwN4P9CCdWEZK4QAuiASFM7DoaYozcjkMof4Vu2NNKqDvN/+\nMr+3uNHVIvXmmbjSJ9CBSS4hWfYKNsWBnBKZHBudbpf/KsLDw8QsNuJKGk1IIQt1BNw9yJY4+kg9\ns8rG0YGbci18O5lABH565Xz+fsPpVXw2XRhEAYN4MXnFTMIps1I8Sio7lTHiTw2TsBv51WO3T7OV\nfzv0UDdypp1erZVZPRZETcO0NIOug/9uFdGjcFD0kzR52GpbQDrwNJKUY8nYTeRrbMSMIrnsQ3TH\nmnjIKlJrSvMFq5VHUy+h6BodpnrcDhvvW1L1Z9f90olhnosm+XJHjk94PJR9Yj6lH52Le8sMnJvr\nsM4/+Y0RTwvBFyQB57pqRNOpI/YAotlM1Q/+E11VGf6Hz6Dncpz9/mYal5Sxe2svD33vAMLqSrwe\nGx4pwWjFaq5rF0mJGilN4UWPlT5zBZ/M/4EMGoPZMWaa5lCan3qQah+1oTSNoco5NlbeRPKXPeT7\n49Ps9VsnMzpGVLQTsg8CYFDKaXAWAIiNlTC7MEqb1kjY7ONmp4fPO3xs/m8fpjO8PW5YsYrh4dlM\nOg00iGMUlKl9MGOum6BdIjxuJJVPTbOVfxuU7fcB8CgatUErTaZxdCdk4y6sYwpHfX62+Vfz68pr\nGLcNYHB0UCat5ZJgOU9XGTEqSa4/ZuSXcguaUORT9XaeGnkewaxS3rSX3RNV3Liy7s8G7+ycTHLP\nWJSbYwKXhTRcF9QjCAKmGW7sqypxrqvGMufk5+GfFoJ/KmOsq6PiW98kd+QII//4jwjonHfzbDbc\nMItYMMOTvzqK/dwaZlt8KLKVfOhsXFIBSRUIpJ7i32Z+nDKifNr4KC8YRIxGF5XGMrxqlj3Hxyiv\nv4zhed9jIN2BkNNJPD0w3S6/ZQxjYRKqhbC9Bx0ZXXWw2CBhjtcTFFNU5mI8ry3mtx9cQslwnvp5\nZ2at/q0prfYRjiwj5jAiorO4MAG6SFHpZ8IqUjG5gCf2/XG6zfyboPe8SF6rJjEWA1nBsWJqeLj5\nsExRFHmqdAl7PUtokCYo9T9MTVGkaWwhlVaJnaUSJdEu2pObGTBo3Fw+zuH+vfjNXtaeM8TR9GJE\ngT9r3qfrOl8+MUKNKHHz3gTOjbVIjulpgHhG8N8BHBs24P/Sl0hte5bgrd8AAWatLOecG1uJjqbp\njRfx1zkwKgkmHMv5YCiFQREIZYK0eeo4YG/mA+JTPG4CTclTY2/BV0zQF8kQCFxM0T/MUX0b46ZB\n8n1x1GRhul1+06i6TslkBrVgJ2ztQTFWYxVGKbFmsEZnIdmnvsB85eegTUyV+dedEfyTwvyyRgYK\n9ejAOexHz/tQlKmnSYPVxfP7t0+vgW+DfD7EiRNfZ/TEg8iZQ4wW/bhSBhaK4xSbp/a+ytpS9FS6\nedm7nBolRZnvWZKGPJp1HWuCJbSVGlBEkfUdteyw5qgVgojJJE7ByrU3XUkm8xJtE4tYWuf9s/Ye\n2yIJOtM5Pnw8j6PMhv2sk19g9f/ijOC/Q3ivvw7vjTcyedddxB98EIC6uT6qW73se6If68Z6qmSN\npLOO6q65aLJCRoQ1hV/ztYa/x0WKjxn+SHd2mBpLM658kGhRYiKSxe+/GNkRZSR+HHQoBt89k4p6\nMzkwViJoEiF7GMVYj8d4DEHUEaNVNGjDDOklXPq+C+g9GMbqNOKvc0632aclrcvm0js2h4xVYrmp\nA6XgJ0sMgJxbQo8GyObfPe+tN9J57J8YHPoV6SceQRRyHItpxLwZHBUpEKCQM2Po1tgemEvS4GSB\nOUqvq4umgkAuPUaL4ObpMjAVFOTJNCFR5nxvjpSa5px5q8mIBxhNuhmIWTh/zp8PI799MES5LrCx\nN4v70pkI0vT1JToj+O8gZV/8R6xLlxL85rcoBkMIgsCqKxrIZxS6umMsuXoJaCpD2dVcmkojapCd\n7CJpM/K0dwVXSs/xtDmLUbZRg4CGyK5DXVRVvh+TM0toYiqHXYnmptnTN89AcIKQt4mEKURRVlCM\nddQIQdAF4ikLC9VeugyNOD1WBo9GqJtXclo38ppOqlvqyKVKmbSZqJXDWNNWFDkGWpGYS6Q2Opfn\n77ttus18y6TTvUQiLzKj7P9QOjFVdd8XN1BVOklmjo6uChjHZUI2Gx2uZqxqgYzjIClDjqhpBgtC\nDbhlkZdLZZpGVYbtITwkKaYnqRZKmHPRcsIT2zgYXg7AxtmvC/6hZIbd8TRXn8jhXOTHVDe9rVDO\nCP47iCCKlP/7v6EXCkx8b6oLha/STu0cH+0vDONZXYlZCZG1NnP2hANRFThSELmJH3Fr/YcxN8CW\ncgAAIABJREFUUmCR9Sm0YobZhqkd/QM9Izgcs3H4ykgXEiAKqJF3j+DH9u0nai0naTgEgGKsZ744\niTlRR584ik3IoVfOZfj4JMW8Sv38M+Gck4UkSVR5/QzpfsyqxrzUBAg6tpEexhwqroKX544ffNel\n/0YndwDg6FmOiS5impUJg8i6dASlEgRZxzWYpbfEQ6+1jrlinn5nNy4VwtoAm5NLOeoUyBgNtEz0\nslsLsNiWQtOLbDjrbESjRCz2CgfDS5lf7abCbXnt2ncOh7FocGlIxXV+3TTdgdc5I/jvMMa6OjzX\nXkv80ccoDA0BsODcarLJIsf3BKlY4KNg9jAyuoYmNUdGhLwSYpZ5Hw+XnctF0ssMpTqZa55qNNc7\nHkNRFPzVZ6Gjo9v1d9UKX9qxm4zgIC53gi6hyJXMcIxhmWzGpp+gqEu0nnsFfQcnkE0SVS2e6Tb5\ntGb+iqV0p2cBsM7UB4AU6ue4HfwGyBkaSLz08nSa+JaZnNyN2VRD4VAGo3GA0ZSNnDdFzva6/Bm6\nNfb5Z1EUjXgs3QQtIVTJg61ookHz8aRPAaBCHURFwlGM0CJUU7uulVx+nJFYlhMRN5vfsLqPFhUe\nDk6yeaRAxepqJPv0T6o7I/jTgPemmxBEkcgvfwVAZbOHkmo7B7cNsvq6NQhqkXRmFh+M5kCH9oSB\n83Ivc6//fKzkGSvZR0A0IekaqbyDkZER/NUrAchLkyix/HS695YwDMRBEBl3jSOJfmxqGoNUxDrZ\nzBz9GEfEWgLli+g7HKa21Ys8DT2S3ks0z55Ff2YGqgDL7ZMImgDCMONFCbe5QGVkHi/d+evpNvMt\nkUp14SmuhkwESQ0xkbVTYcuQrJIQYlPhwfCYk0FrNWZdJW0/AQKk9BgfCJ+PSRTY5zFREcuwM+XC\nJWSpQWD9sjWIJol4/ABtoamxppvfEL+/dzRCXte5Jgr2VdO3UftGzgj+NGDwl+G69BLiW7eixuMI\ngsCCc2uYHM8QGcliKo6jm8rxjVdgV3Ta0xJ2U5D5ph3scC1kpWUXaiGJT9coalb6+vooKZ8NQFaN\noGWK0+zhm0MvFtFUH+gKQyU5dLkGjxIBHYSkmRZhmH5bDWM9aTLxAjMWnvzClPc6VqsV2WAmbHAQ\nIIsvI2OwDiAMZOhzTVKh+PhNZQXFxLujg6aq5slmB7FOtmIQp55YQjkbC8mQrwFFMyBlICg5GDZX\nMkPIM2ofwqjLgM6CxAIigka/10xtcpSjahVzTTm2aEvxra0DIB5vY19wMc0BO/UlU3sEqq7z64EQ\ni6IKS1bVnDIFoWcEf5rwvP/96Pk8sYcfBqBhSRl2j4mD24awVRopmL10ZteyJp8hLEFEV1kmHuR5\n8zIqlDBd1iNUCEYyusbo6Cg291SoI1uIoGWU6XTtTZM+fJiEuwm1eJicUSNvmkEZE8iJSkLCVJm5\nXtPMsV1jGM0S9QvOCP47QVV1FYPFOhxJhXqjimweRgpnedaSo94kMCfVzO0/e2a6zXxTZLP9gIYh\nVobJOlXYN160UpePgxEwg3lMp9frJ2Z04zIOMmGeoIBKVTZAneZhm6uIJgnkIll0BK4vVuFbXPVa\niObYSDfdsTouXfB6MeCzkQRDqsrVE/o70hTtzXJG8KcJ86xZWBYuJHbv79E1DUkSmbu+ipGuSWau\nmQuAmq/i2qGpnPr2tIQ54yIkO0mJFnLe/QQEibgoExyaQJJlTDYTOSWJnlXQtVN/Y23k+Z0kbBWk\n5KkN24y1iYBxHHusFUXrJasbqW5ZRk9biIalfgynyCrpdGfF0qUM6xXIGsxxpdFEBcEUoltxolgm\nqCr6uG9cZCJ56ocO05mpzDUhasFo6iehmEgbZQ44p6bhSeYCuSET/bapQind1oMuAILO5sn1CILA\nK14QNRUxksIva7SqIvbVU/OcVDXPthNOBHQuW/j6jKc7+oKU5jQuaixDkE4dmT11LHkP4nn/tRQG\nBkjvmpr8OHt1BQaTRGxMwJQZA7zkU/V4ihqH40bSthCVvhPssixgQ3oHFrlIHBlh0kMikcBst5NX\npjZs9dypv8of39eHLkiEnEOIukDBVEOpGMYabaGKHg5Tjz48E6WgMWvF9LW8fq8xs6WVfm0qfXB+\ncUrUDd4hIhkTR6QhWk1m1hYkvvXo0ek0802Rz40iaDJaREVWewjlrIgGL7pdAQUQIRaxMWyuxI5K\n2jqEoINRkdmcXEqoqHLEbyEQj9KtBFgrWjHPcGMonWqKNhk/zM6RxSyrFQm4pmZMD+UKvJDKcNlI\nEffyU+t9e0bwpxHHpk1IXi+T994LgMlqoHV1BSf2hjBZs+TMAY7qy9icTjEggGbIM8d6gN5CDU41\njeTsQBOgXPMxNjaGxeF5TfBP9bCOmkySiBsRNJVBfwaH6gLBgI8JbLFqZjLEgLmUoy+mqWh0E5hx\neoxyfDcgiCIJsZSiZmTWYBGroGOxdqPmBU4IKnZjhiZB45m2MQYip3YhVi4/hilXA5qCmOsnkrNT\nNDlxyROQndqwDWacDFlqKBfjhC1j6AJsjq/Gpps5IkeIOG1URkPkMbKmIGJb/vrG7Na2LiI5Hzes\nbHjt2MPjUQAuM1pPicycN3JG8KcR0WjEfeWVpJ57nuLYVAn7ok21SAYRu8sDgkhea2TNKOgCdGYk\nUgUzx5y15AQjrabDr57IwNjQKBaHm3xxSujVU3zjNrNvHzF3I45kHyOePAZtKovBmyqS13oQBZ2o\n3EomXmDpRWcGk7/T+KrqCOkB3NEizWYVwdCFjkaf5qVXmqDVaGNBQeTuPYPTber/Sj43jrXYhCyM\nIaISLVjAYEQvURARoACDSgVp2YLTNEjKkEdWJd4fvoCwotHmm5oDYIpEcIsw32TEMnuqFkTTdH6z\nV6bKEeb8ua93bn1oJMq8SZWmllNvz+mM4E8znquvAl1n8v77AbA6jSw4p5rRmAtDIYmYkjBFAjgU\njUMxEwYhjckb57hYz7npVwCdJBLJYzHMdgf5wpTga9lTe4Wf2NNG0lmLpraTMWiI0lT8syLuJCkd\npqhLRIMrKW9wUdl08gdDnOHPmb9kOUNiKWazyhxVRRVyiCWjDAgOOsQgVZKRBaS4b88QueJJnW30\ntsjlx7AUapCFYQAieStVqSBqKQi6jhaUGTJNbbbK1ql4/6rkQlyana6cRkeZHYNS5Fi8gpWCEcec\nUgR5Sja3H59gKO7g6vlBxFervztTWY4VCmweL2Ke7ZsGj/93zgj+NGOorMS+bh2xB/6AXpjaoF1w\nXg1muwlrIYxgKONFeQUbMhm6VHBYc8y276ZfraamME6jMMI4Go4xE2a7nWzu3RHSGTk0gi5IBL1T\nxWeCJYBZz1IVacFBF0f1WuwpJ0svmmoje4Z3liWzW+nVTIiSzpqOIgYBDN79JIs2kmKOtJBnhsGI\nM6Xy1NHx6Tb3/0k+N4YhW47BMDVSO6y5qEoNgwiaSSMTMTNirsJJgYx1AHS4eeJSJoU0KTnIQIkD\n32SclGZhrSphfUPjvt/u6sJpTHDRvNezcx4MTiLpOhcYzUi2U2/s5hnBPwXwvP9a1HCY5LZtAJgs\nMovPr0VXdRSjgzGpibOCCnkRevMiihJlv2WqGnKD4QDtJnBjxWixkc1lAVAzp+7gaTWZJBg3ImgK\nw6UJJB1UewU+wrgizdQKvXSqzZTXW6lqPlNZOx1YDAYG9amQRFWnyiKrglHaD2KeMc3BMSLUCS7m\nKkWeaD81BV/XNQrFMHLajWwcIaGYyBmdWOUY6KCbBKIxO8PWSvxSnKAlQnnRR5ni5VhKImeLEbfZ\nMUVTOESB5Wbza2MIR2JZXjgeZ3Xlbko8CwHQdJ2HxydZHlapqD81x26eEfxTANuqVRiqq5m8597X\njs09uwqhYqp9Qumkgms8gEHTORQzIosakz4zI/g5V2rjuBEsuoBS0FH0qaeEQvrUHTydbWsj5m7E\nmRxgpCSLv2AkZXbiUSeRlXGMgsJQfg7LtzSdWd1PI6mKZWiaAZtBYZVBQaCAwd3GiGanRx7FK0nM\nkPK8cCxEpnDqPVEqSgJdVxFTNgR9kGjOgma2IHoKSKGp1/QlasiLRkoM42QlhTWJRUyIMUJFiUO+\nKdGORgysE404m72vpVg+cmgUTRdYX3MQu70JgFfiaUYKRc4fK2JqPDXDkGcE/xRAEEU811xDZt8+\ncsePAyAZRJZfMx9zNkIAKw8aN7I8l+NITsJvz+OxHqabOhbpx9HUOAVRQJlUUHUFHZViOjHNXv2/\nie3eR9JRgzN+ghFHGlfORUxy4coVSIntAOTNVVTPOjVXSe8VKuYsIKNVYvYUmTGhYzd6MZa8xLhm\nImWYUky34MRW0Hmha2Karf2/KRQioAsICRmjMkC0YMWMhhrQMIQEdB16s1MJAbKtC4CzUvMZERJI\nxiTHSx3IxSKFJJynSJgbX3/afLYzSL1rgoZALYIwVR/yUHASiw7r4jrGSsc77/Cb4E0LviAIdwiC\nEBIE4cgbjnkFQXhGEIQTr/7recPv/lkQhG5BELoEQdj0tzb8dMN1+WUIRuNrKZoADYvLsAgZcrYq\n9otNrJgsEJN0JjQBWTpCh9CAhM7awgF2lsgor65aVCmPmj110+VGDo2iCxIpe5C0pOMUHCQFF6UZ\nI7J0lONaJWet0s+s7qeZOS2tTIoVmDwKhnaJJU4HojRJ1tlDUbMwoWVpEUyUqjrbOk+9IeeFYhQ5\n70ZSo8jkiOYtBMKTKH4dXYBizMCwsQa3kCHo6EXWJcoKPkbibpzO4wyXuDBHs/iNIvORML+aPJDI\nFdk/MMlsXxsu16Kpa2kaj4RinB3V8NS5p7Xn/f/GW1nh/xrY/N+O/RPwrK7rjcCzr/6MIAitwDXA\n7Ff/5sfCf30NnuF/RPZ4cF5wAYk//gk1NTU7VBAFqpfWoUlGNkRjlA75ATicMOATdY66KojrNjYI\nB3jeJ1Gam3pDqkIONXdqVkFqhQKhV+P3/WVT+wxux1T/kcaoh3Khi8NaE4uWNU2nmWcA5nqchKVS\nJIOGpx3ONg6jCNUYfS8S0c30S31USSJZUWN718Qp1za5WIhgyJQii1OJAZGiHf94GKEgUPTrJCI2\nhq3llIsJwqYETdlauuUgimJhTFBJma3koyrnWWyY/DYk59QUq339UTQdZnm7cLmm4vcvRP9/9t47\nTK6zvvv+3KdM79v7rna1u9Jq1ZstWbJs2dgYG7AxNaEEQp7EyZv3DSFXIE/KkwcICaGEEEjoLYbY\n2GCDsXHvKpassuq7q+11dnqfM+fc7x8jHCCEhFjSrqT5XJeunTkazf07R+d8975/96+kiJVMXjOW\nx9G1NN058GsIvpTyWSD6C4dfD3zj3OtvAG/4mePflVIWpJQjwBCw+VXaetkTfPvbsLJZEvff/8qx\nDe/cirBKLC+aPMkuegtFBlI6y3wGOfcQx2UnO5UjHHCYqHp5GWkpBlZhacbhpx57nLh/Ob7kKNON\nKTQpcfvL9cPXxSK4RI6wy0cgsG6RLa0Q1DVmbeWSA161QMhIoXl3oNgXOOuaZc5+Ek0Itlh2Iuki\nZ+aWVpPzohFFz1eji7LgLxDCpyawnRZYNXB6rouS0KjVIpSESV+2i7QBipbj5WA5TFhEilyXkji6\n/92ds/dsFF216PSP4/OV79P752IEEVy1YL6ysbsUebU+/Dop5cy517NA3bnXTcDEz3xu8tyx/4AQ\n4v1CiANCiAPh8NLzA15MnKtX41y/nug3vokslTfBXF47XhnHdFTzlGMj27MFJhVJQZFI51GGaSck\n0vTFT/ByVXkGYmIiC9ZinsovRRaLTH/yH0h5W/CnhpkPLFBf0DDOZSP68uWaOmqHgqq6FtPUCudY\nqFmLlAqOoIE+Kljh0zCLtcyFDrGglVdoW6WKAJ46tbTcOsViBK0QRBMT5E2NpOZDqc6jzZXdLadT\nXQgk+cARpJB05ZvJpmtwhYaZqK9GLZToQNJtiV8Q/AjdoTChQDe67iNdMvnJQoIb0wK7z45W4/zP\nTFp0ztumrSyv537tNZ2U8otSyo1Syo01NUsvM+1iU/Vb78GYmiL12L9XI2xc5iXtaWZHJEJX2IEU\ncDyj0avlOWlvwpSC62P7OBEox/2aogTFpedDXPjSl1jIe5FCRSHFpCOPv+QipgZBSnxygBGrjr7u\nyn2wVNBbOzFkK/aQxH5QpVecIl+4AdMeYdRmkJJZWhUVBbj/5anFNvfnMIwIerEWRZ0gWnBitwTS\nJ5Ge8t8PW8upFhnGPeWEK9WwYxU9qNoME6EgRItcH/AjNBX7uT7KybzBsakEXb4jBAPlloaPLCTI\nWZIbTmdwrAgt6b2nVyv4c0KIBoBzP89tGzIFtPzM55rPHavwX+DZtQtbWxuRr37tFZ9o721rAVib\nTjAaW09NqcSRuI0V3hLTnghHZSfXZ1/iRFDDZQtQkiYYSysAy0wkiH71a8RrViIsg+mgJKUK6nSN\nBWrwFwo0KCc4bq2krW7tYptb4Rz1tbWUZBuOgMT5ssIK8xgF+xasYpDT/mHiYpigqtBbFAzOp5dU\n1m2xGEEv1qAzzkLBTXUyhchBoc0iEvczq9XQosYpqQathQYMw4FQDAZULzm7A2Uhz1VZiX2ZH3Gu\n8c6LQxEsCT3BUwSDW4FyslWTqrJ6oYRjiUeWvVpVeBB417nX7wIe+JnjbxVC2IUQHcByYP+rHOuK\nQKgqoXe/i/zAAPmBcohiw4p6NDOPXdh5SNnFjmyeIQk2h4nhOcVh2cMqRgjbY/jtTZSsEsLQkXLp\nuHUSDzyAlckQc3fgT44y2VHOE+j0FZi2mrl6fhivyJCmh1Bo+yJbW+GndNR6Maw2dEcWm2FRlw5T\n7UlSjOwk7ohx2LcHuyLYURJI4MEjS2deZxSj6DkNXSSJFF3URBPoU4JiFxwaL3eoCtnnKIkSfdlO\nipkQzupBTtUtA8ATLdAdK/1cOOaTp+bw2Ew6A6P4/RuZKRR5JpriloyCqis4li1d/z38emGZ3wH2\nAD1CiEkhxHuBjwM3CCEGgd3n3iOlPA7cA5wAHgHuklIunV/9SxzfLbcgdJ3ED38EgKIIavxFkr42\nfCU3q3MKRQUG8wrdjhjDoryY2jnzAsWqTgyrhFKyUyotnVj8xA8eQHT2kPK24EuNEKmaRLcky0Nx\nZqlny+xBAOq0dpzO1kW2tsJPaa5yY8g2APRgCfugYK33CEZyNUIqPOcrC3yXooGEew5MLqa5P0fR\niGDPJYByhE5VKoYaF1hVkoFoH06KRP2HKSkmy3Mt5PI+bJ45RqvqUBIFNgc8qIhXwjEtS/LkqTCr\na89SFehH133cMxPDAm4+miq7c/SltbL+RX6dKJ23SSkbpJS6lLJZSvkVKWVESnm9lHK5lHK3lDL6\nM5//qJSyU0rZI6V8+MKYf3mi+nx4du0i+eMfv7J527mtg6I9wDULEyjxZhyWxeGonX6XwbyrwKSs\n5sbw88xWN2MYBRTTQbH4i0FVi0NhcJD8iROk2zeBUBAyy7wzSb2hUtJ0MoqH7uxRpqw6mhz2Je0D\nvdJQ7RqFc/EWot6B63mV7c7nkMKNyHcx6IpTkEWqFZ3WkmBgMrHIFv879jO9uErlQJB5GcKh5Ci2\nWVgIhorLaFITTLrKv6Cq8+WuVPOKyrw/gAgXuFrRUX02tNpyAMHAVIKFdIG+4B5qam/CkpLvzEbY\noui0xAw8Vy+NvrW/iqX96+gKxnfr6zAjETJ79gKwbGc5Lr3OMHg4s4OrcnlOGgo+X4mid4T9Vh87\nCoeZ9tooFAsIqVHMLw3BTzz0EKgqYSOAsAySbpURm0WN0JmT9egjCdZzgqTVC3plIbjUyDjcWNKB\nXmPHfkbQYZ5FdUty2Q3kVYPD7gMEVMH6gkqhZHFqZvFXllJaeMc3oYtxLOnEo/eiZiW5jZLxVDN5\nHDQpKYpqgZDhw56pxREa4UVvF1II1LkcfZES9uXBVyYgT5yaRxGSVVUnqK15DS/G04zmitw2UkBv\ncGNr8y3yWf/XVAR/ieLZsQPF5SL16KMAeENO3CJDwVHFiLmSTQWLpCqZNgQt7lkmRRMOYeBJHaRY\nKCddGen4Yp7CK6SfehrXhg2ECz78yRHmOuLkFUG72+TJsZ1sHDpMQGTwWH0Ie+WWXGoUgzkM2YrL\nbyIQ6GFBm2eKQmwFQirs9e3FrwqazrXV/M7+xa+RX4gv4Ei1oolxilYz/RkvSkFQ7LUYmCoXHvTb\n5lFR6Mt1kc57cVaf5lR9M85kDnvWpLVg/Vw45pOn5lgemqMh1IHT2cLdM1G8QrDzTAbP1Y2XxMq0\n8nQtURS7HffOHaSefBJplme9zT1+Ev5lbA+P01gszyYGYjb6vEmiSogkLtamXsSkHJ65FOrpGNPT\nFE6fRt+8nZSjAW9qjHT1CABBh509g2vZ6R5ASkFJrEXYKwnZSw1RZWJYbXj0CEbIh/2kYIt/H8Kw\nYaldHPBMoQpBrVBwWfDc4MJim0x+uuzK0ZVxokUvnoljWE6J5ZecWOghIHLMeE9RVEqsynYRNyWT\ndi9hXwB9KkeLw4YixCtJVLOJPMemkqwK7aeh8U7iRomHwnFuSQlcDg3X2ksjlLgi+EsY7+7dmJEI\nucOHAejevQKp6PSlIhwsrqenUORkWqc6UCTtnWWPYzXXiZc5Yi/X7C7lFl/w0888A0DU1gBCQcoi\ncWcUpwWHpneCEOy2HSVZakeRIRT70qshfqXjrtMpyTZsSgpP0xocLws6vKMAZJXVLOg55rUoQVWj\nq6gyFslgWotbZqEYTqCQRBVxZmIZSrNHSN5cwlA0zubaaVCSTLnLG849qS4sW5JHQutRTZPCTJ61\nQsXW4n2lpv1Tp8sR5+vqRmiofyP3zcUoWJLXDaRwbap/JWxzqVMR/CWMZ+dOhK6TeqxcJ7+xJ4Qi\nSyiqm8cTW9iSLzKpWxQk1FSd5Zijh2qRZM5V9t2XcotfEz/99DPora1MDyVRLIOcW2fMXqTOUjgw\nt4GaQJJV2TPM5ppRLQeqY2n1AK0A9g4HRVmOBNPqQtjGFZpc5QR7f7i82fnZ9pMEVYUeQ8GU8NLo\n4u4flSI5VDEGgDqdRqoK2e2SqXQDJXSkXyfh0KguVaPlazjRl+JEfRNdY/MIQ7Il8/PlFB47Pk6V\nI8qm5degqm7unonQJzR6kxbudbWLdZq/NhXBX8KoHg+uq7aSevxxpJRoukpVlSAe7GbFTJouU8cU\ncHzOwbJgmBHaMVDpUw+zgIWZyy2q/bJYJPPSS3i2b2MuouBLjFBoSjNo06jSXGQMN9e4TgEwki63\ng1OdjsU0ucIvwRYIknaVZ7Dz4gCmQ+COFanSIzjCDgQ+XvAP81KrjTqr7Md+6OjMr/rKC44ZNVCV\nsuC7pg1yHSbY4dlkOcfjyIouhMwwEVjHW26p5p7l/TTEFwieKVeZ7UPFfk7w84bJC8NRVtecoK3t\nvRxN5ziezvPGBQs15ECru3TKgFQEf4nj3b0bY3KSwulyve7lOzrJuuq4OnyWmUI/TsviWMJOyJvD\nS4IXvWvYrRzkBUqY59odLha5gQFkNou2fitJrRp3eoJ07RksIXBS9nleWzpESqliMlu+FTXnpfPw\nXCnY9BCxpgEs6cGuzpKuUXGcFrT6J0mZbprSfly5E3y2z0HeJbBbsO9sZFFtLiVAU0YxTZViTiN7\nrcmQuZwnUteDCmsmv4aQRd4208sNx7O89uQRbj28l2HpJKAqhFw2bM3lYoTPnT5LoaSyqzuIw9HI\n3dMRHIrg+mMpnL1Lu5TCL1IR/CWO9/rrQYhX3Drta8v16fyW4FvRW9mcLzCiWUgLOvxH+UnNdrqU\naabFOGZucUskZ/bsBSGIOppBKJiYRFwzKFKSSXdjVwrsTB1kng1YlDemNefSbBxxJaPrQUqOOIZs\npdqRJ+zxYDsraPFNk8FJKFeDJZNgzbN3lYfGkmAsusjuxLSKnSEKUZWpoJdMn8oX1P8HPZmnRqZJ\naAN4TBvvnOhkx+gY7bMT2At+UgJ6UcvhmOcak//w4IvY1AK3brmTrGlx/1yMm3Qn3ryFY+XSLqXw\ni1QEf4mjVVXh3LD+lX63gToXDs0gFuylI5ykt6ASt5uMTbloqBvkOW+5XGuVcmDxBX/vHhwrVjC+\nZxjFMig5nQw7CtRbCguZBjbYB/GZGRK5lTid5Zr4utu/qDZX+I/YbCFK9jiG1ULQlmJSC6JNC5q9\nU0gU9Fy5ifeW2RO83GqnRdUplCwmY4sj+tKUaHkVuxgjH9OJtNl51n4ts6IBkSxRpS6Q1tNsT/WT\nNlVU3ySWUiKqB0DAFlN5xX9fKCzwwojCuoYkQV8HD4XjpEyLN8ybCLuKvf3Sul8rgn8J4N29m8Lp\n0xQnJhBC0LGhkViwh9dPHCKaKBcaOzLnxOXIUVB1hrUWNipHmEwvnj/cymbJHTmK66qtzEwW8SeG\nkY0pjtltdDgk85lartGOU0IlnKnD4SiXlLV5qhbN5gq/HEWxI10FSrIVh5IHxcL0W7Tp5SzVuNGA\nZmn4M8exFEG8peyW+/6hxSmzYGWK6GIORTFIZ/0obQUe5Waa0yOYUgXfAAi4KXYd0RKkCzaQgsNa\n2c3Yj/aK4D979G4i+RA3rekH4O6ZCB1OG/3HEjh6ggjt0pLQS8vaKxTv7hsAXnHrrLimBalohEyV\ng/ktNBklRjQoGSqN6hRPBzazRTnJmbQX01ycRijZgwfBMFDWbiUpgjgyU+RrjlNUBN2+AtGCn2vk\nUYYdq4gbEey2cvib6rAvir0VfjXCo1KU5RILVbYMRrVC7UyCoC3GnOUnVAgxZjtDX7TEWHP5//CZ\n04sTj1+IF9AKLwMwq7Yz1N/DpGijO3kcKLEQPERVyU53vp1IySRJHlFyMZwrZ7CsaPCiem0YRpyH\njw4BcNPqlYxkC+yJZ3iT041MGzhXXHqTk4rgXwLYmpuwr1jxilunbpkfTRaIB3voTc64TeFGAAAg\nAElEQVSxNmcx688TGQ3SbB/lJw1XYRcljEyMZPLlRbE5s2cv6DpRe3m5b0iLsKfcE6cKD1WkWFUa\nISbWU1Qj6OfimCuJV0sTmyNAzlme9VbZsmRsOs4x6A4NESt5qMnWMm2Ls30+zlxQw6OrnJlLLYqt\n4/NptOLLWCaMa3UcrNuMU2bRIxnsVc9S0LPcFusHCSnnLCU9R/rc/bccBW9fOY9lfPzr7Jlew7pm\nBw1+J9+ZiaAAt86aoICjJ/grrFiaVAT/EsF34w3kDh2iMDyMogiaOtxEQn1cNziIPdNIXpOcGXNS\no8yxL7CagrTRap1hIfLkotib3bsX15o1jO0bQS3lkA4fQ44C1RaksnVcoxwFQMmvo0AKm1p+4BSn\ntij2VvjV6HqIokdSkg6q7FkSWS9KUdITGiKPDUe+HEzQlCrPiL01LpL5EoncxV9hTs1GsKkjFFNO\n5vwFjimr2WDtZzirYqt5HLfh5rrobhKmJKeU9xlG3OXVyzY0HH3VlEopXjjxKDOZeu7Y2IVhSe6Z\njXFdlQ//yRi2Nj+K69JLEqwI/iVC4K1vRTidzH/yU0gp6btlBabmQCt6McwOAMaEhbeQpqDYGRT9\nbFaOMjZ7+KLbWorFyJ88ieuqrUyfzRCID6I2pTjgsNPjtJiIdLBTPUpYCyJyTeSzGXSlIvhLGbut\nhqJrmpJsJeTIE8v7MEOSnuAwANl8G0hYUM/SlDDJ1pX3j546Of+rvvaCkBsZxu6Jkc/Vc2JTGynh\nJ5B7gnj9w2iGn2DBTchsIVySpChimTqn0uVSJRu9bvR6F/PzD/P85Ao0BW7pb+De2SizRYN3+nwY\ns1mcS7zRyX9GRfAvEbRgkJrf/33STz5J+NOfoc6eQrGKJHxdhObttBcNojVZmC5nqp5xrmaZMsvo\nSBzTvLgJWNl9+0FK5KotZKQbPTdLquYQRUXQ5y0yHW/kGuUoJ5wbKQooZDPYFBtStxBq5ZZcitjt\n9eTto5hWK9X2HEkclBokNbkIPjXJBLV4DA8nnUOsDxdYqNKRCjx8/OInYFVN7UfVLbJmJ+Mt7ej5\nU+yLjmLmm1g+dTPX5JtQhGDWylNwhFnQM6QLFjqwvr8OIQRjM0/z/NTV3LCyHo9T5zNjc6zxOtky\nUl4ROFdVX/TzOh9Unq5LiNC73on/9tuJfPGLjN12C4HoGRaqV7P6aIT2nJMpX4HisI4iTQ42lZeo\n2oydWHzfRbUzs2cPittN2CzPgvJCMO6bQJXQabdwFk1CIk1OrifhKICU6MKBcF46CSxXGnZ7HUX3\nHIZswa3m0WQBkRcoMwqrqk4SNrzU5Go45RxlQ7yAqQqE38ah8dhFtTORy1KTOw5AWO1iWq+nKvZl\nHJZObvy3cOoxNqa2kLckUS0OAsbt5ZDgDaj4VldjGHEeOGqSLTn4Xzs7+eeJecbzRT7QXk/ucBhb\nmw8tdGlmhFcE/xJCqCqNH/sobXffTcPHPsbat2/GsHkR+HEVaymoMF/KEbDinKjzMmXVsSwTIRp5\n7qLamdm7B9fmzYy/NI5eTCFcIQ47S7RKkEUnfYxiIahKr0bpKUd06MKJ6qrU0Vmq2O11FF1zlGS5\nG1mVLYM1p6MUYE3DMUpSRcs3kVcM2nOzCEsS8DuJJ4skshfPj//8vn0E5TilvML32tuJZoYwjTn8\ns5uwSYWiY5KuXB9zhiQrcliWwmyxF4Bb7E5srT4mpr/Po2Pb2djqwBa084mRWW6p8bOzqFKay14y\nlTF/GRXBvwRxrV9H4PY30n3rZlSzQNLfQe1U2Y8f9UbxFrJEVR/DrGIjgyyEL57gFyenMMbGcW29\niumxHIH4GWiMMmzXWektEY83crV1nGPKMrx5Fy2dZd+pLl2obudFs7PCr4fdXk/JEcEQ5airKnuW\nbNyJEjDoqzqNIk3SuXIv2AnbKE3xEmbIhpBwYOziFVI7tP8xPI45cnEPB3rAlbiPOpubaGQb1UqG\nJhnAJlSmDIucc5Zpe5iFjAMncNP6JhAWD760h2g+xHt3ruT3T47h01T+truF7L4ZUAXO/kvTnQMV\nwb+kUXWF5uo84eq19JwQtBct4oEozkyJBWqIu2pwiQKps/Pk89MXxabs3j0AFJZvIG/ZEfl55usO\nALDSYxCP1rGOQY6q64lQxHau1bENX2WGv4RxOOpBSEq+ACY2QrYs8YIfsxUcIs8ybYzpYge6qXPK\nOcKKaJ5oSCevwWNHD1wUG81SiXh8H3ZXhlyuiZT1LMLKctOcRkwNUSPS3JS6irRpMUcaUy3wUqk8\nUXoDOv4NdSwsPMHDwytp8MEhu+R4Os8ne1sIpEtkDs7h3lCH6rl079OK4F/i9N+xAVNzoJegMetj\n0lPAmckQpYpSU5SC1NDmHMRiey6KPZkX96DV1DA5W36fUm2c8U3jMQWNuiSf96IJi6S5jjFKZBPl\nrlya5UZxVSJ0liqaFkDTfJRCMQxaqHLmiRg+EGBFNPqqTpI2nQTyNRxzDrE2kcNUBS6vnf2n5ikU\nLny0zuTJY6y0lzdV97fUksyeQnGtZexIP1IIavQovYV2BgsWz2k2vlnYQCLXg03AXVUB9CYPzw38\ngNOx5dywoYPPTczzlvoQN4Z8xL4/iBAC7/WtF/w8LiTnRfCFEKNCiAEhxGEhxIFzx0JCiMeEEIPn\nfl56WQqXAC3rW7CZaeKB5bROtVFQBFp+ECkUCg1THLJ66EjHiMZevOC2SMsis3cv7quvYvTAFN7U\nOGowyEsuwQpVks766JBTJKWL2mwX0XqFZGQGASiGA8V56cU1XykIIXC5Osj7RjDNVqrsOXKWgj4D\nSgrWtZbzKkSuiWl7mL50HGFJ3EEH2VQtA8f/BCkvbFOUF/c9ztZ8FrMk2L9hEClLtOQ6OevuRMPk\nBqOWSctgvCixOSepd51CFZKbpYZvfT2p9HEeOOHHpkqedUtqbTp/3dlI7L5BCoNx/K9bhua/tDPB\nz+cMf5eUcq2UcuO5938KPCGlXA48ce59hfOMogg62jUiVatYdcSBkBLFKpdSjihVnFZ6WcYsmZnn\nLvgDlx8YwIxG0TZvJxxV8MVOE287Q14RrA0WiEWb2JY/wrPWapylDJ1rgsTDo3jcToQUqMFL+2G6\n3HE5O0i7j1KyWvCpOWxmDmVaRQRNmoMzBEvJV/z4BTVGQ8KgFLIhEQyMDzM59a0Lat8LE8/SroSJ\nBXVeztkpaY34RyVn3Z00KQnq8XI8ZWGqOZbbJyi6BzGl4DXYcK2rZeD0F9gzs5nergCnDIOPdDVS\nun+I7ME5fLtb8WxpuKD2XwwupEvn9cA3zr3+BvCGCzjWFc2qOzYgFQ2XYaMx56aolRs/zJUaiHjK\n1fxsU/NksyMX1I7UU0+BqhKt6gMEBTPLaNUAugk9bpNYtpk6M8ZT5losK8vq7laSkSl87nJJZC14\naYa6XSm4XB0kHS9R0tsBqLLlyKedEJIIAb3KaRZyvSDhhHOYFfE04ZBOUZUcjb2eoaGPX7B7MBVZ\nQNPSuPQMk9U6E0WTonMdtpSfnLDRq8T5RvDH5KVCzjXFpP8UVmIHLYrCxq4qstoZHhzIUzRtHK1y\ncGOVj61PzpI7HMb3mnZ8u9suiN0Xm/Ml+BJ4XAhxUAjx/nPH6qSUP826mAXqftk/FEK8XwhxQAhx\nIBwOnydzrizqempxWXFm67fQNR5kzpYGWWQi04SjaZRpGUKfsxO7wG6d9FNP41q/nrGjYXQjTcER\n5GVPks6SgiIFQSOLhWDQWseMJairqyMTjeJ3net2VZnhL2l8vv5ypcymn0bq5IgVy7kW0oDOqiFM\n04mrGOSA6yQbUllMVeDxOjg00gsojIx+7oLYNn7iKFc5yuXAX1yowxJQdKxiXO3CTYFM4CB9469F\nYqEEzrLHPUusGORmS8ezpYFTZ/6WpyZ20ljnJO/V+MMRg/yRBXw3tePb1XJBbF4Mzpfgb5dSrgVu\nBu4SQuz42b+UZV/CL/UnSCm/KKXcKKXcWFNz6ca3LiZCCDrW+En6Olg1lMNQwJM+yWyhidbqMzxr\nrqY9EyMaef6C2WBMTVE4fRrXzl2Mn04Sipwg2l4iqiv0u0tE09WsLZ5mQHbiNp0s+B0IIclECwTc\nTSAqM/yljs+3DhBkO7NY0k67R2UiXw+Aloa+xjPl1/kGRpxTXLvgLCfVBe3ksgrBmt9gdvZBstnR\n827b3pPPsl6dp6ALhtNekAJ1rp6zCNZoU+glOzbDh3TPkuj8MfaZW9GB13lcJKpe5OnBLPPZKpIt\nHq5SbdTtmcezsxnftZeP2MN5Enwp5dS5n/PA94HNwJwQogHg3M+LX1TjCmLjW7cgLJOgsRkhwZ05\nyqytGada4JjSjZsC1ugzyHNhkOebxA9/CEBq+dUUTRV3cpippkMICWtq85xJrWdN+jRPldYQNNM0\ndNuYn96LZSgE9XbUgP2Sqy1+paHrPryelYTdP6QkOmlw5TAyEpEQaLqkui5KXT5MMd+KoRhkRYTm\nZJF0lR0h4fnZm1EUnfHxL593207ODdGaXSBp2pj1J0DWwpkCDUoSl2eAzngfitRpWXcvz8VbieZ6\neD02mra6ODP0UR6buJVqv52FkM4bDiexL/Pjf037ebdzsXnVT5gQwi2E8P70NXAjcAx4EHjXuY+9\nC3jg1Y5V4T/HU+XBlz1DvHoH7XMCtXiaiLcKI1rDvM9LWjqomoyTSp0472NLyyJ+3/24tm5ldKyE\nWsojhMZJ/zhNOQ2PCva8joLkcWsdwVKaDf1tjJ/+CQIFdyaEY3kliOtSoKHhDpKZIxSCLXjVKTxF\ni2LMjaEoKJqkRUwQS5czV5/y7GdDIs90lYYLwXdemKa29jZmZn+AYSTPm025VJJ1vjnsJYv0kIPT\nHgUj2YzDLtihDyGFhVIIoTpjTDjijI/+Du2azm8rdiaCn+NYuI7BaB2+rgA1Jbg2KQm9teeVFoeX\nE+djSlUHPC+EOALsBx6SUj4CfBy4QQgxCOw+977CBURpKFLS3CybD1KSExiKZHKmjcba0zxkbqU2\nbBCff/q8j5t96QDGxAS+N97O8IE5qiMDjLb4GHNAn24RNYJszRxlUqnnmOzAI1Xa29uZPruXbv9G\nREng6L00qw9eaTQ03IHb3c2U/ziKKLLKV0ch6cSylz22nZ6zlIq1qKaNl9wn2R6xUdIEfq+D+bRF\nTLkTy8oxM3vfebPpwP2P0afNYAGPpPso6CVKso2OpiRh1zgLthT+XCOh7sfYN7iDzYbGP0kX1tXP\nEo4/zYOj76XWZ+dEUOUNY0Vq7+xB9V2e+0mvWvCllGellGvO/emTUn703PGIlPJ6KeVyKeVuKeXF\ny6++QvHeuB6bmSdoXo0UFnpxiKPZRrpDQ9xr7sCOQenIg+d93MT996F4vaQ6NlMsQvXCEQaXvwwS\nNtVl2Z/exbb4YfaWthGwSug+D5IRQuNXsTa0C8fKEI5LtNzslYamedi44V5qt30IgGVehWJagApq\nUdJXfQpQcOarWbDFWBU711c5ZMNtwYPHNPz+9UxOfgsprVdtTyme58Wh/SzLzLKguPj82q0A5DtX\nUV1MUXSP0RnZgNDy1GVe4D0zFh9uqkUNDDDt+jrH0+/jdFjH3uHFbcH7mqtx9ly+92LFaXoZsXzV\nChoTZ/AaVyMs0PMnGfE3Uy8KhO1VnLWaCJwYwTLPX3NzM5Ui+ZNH8b3uFgYPR1HMIpgFTlXP0JDR\nCLklVckSKhbfNLbSVYzR0e9h7syjLDNuYEGbpuodKxHi8ls+X65omodQ59uxVDdB2xyx2XIAnloU\n1NdFqCosYGbbSOkp9thfoDNVYq7GhkcKHjw0RaD6N8nlxohGX30QwfwDg/iD8/iyBo+nNlJ0hxFS\nUHK3YXGWaUWybGE93ro9LDutMVS6Dq8xwczaf6GgrOMrh9bRUO1isM7Gny4IWm/seNU2LWUqgn8Z\nsby+hqrYIeymm8ZEAEfuGJFAC+ZUCxuaXuLL5k2EzDALL33zvI2Z/PHDyHwez623c3rvNLXhQxxv\ng3GHoB/By+Z63jT/GENiOUdlE91Gku6+BnL7MkgsCn0g1IrYX3IoCjRtwq4coy7fD1GFklRRa0xa\nilPEU/0g4DnPETZELSarNZoslbwpeXGqD03zMzN7/6syoTCR5GO5Ufq0QQC+rN1GQD+NTi0IG9Ps\nZ9P4bViKyTUHf8BT+ge5uk9ncuUnKCp2/mXgd8mXLEZ7PeyOWrz7tb2XfT+Gy/vsrjBUIUi7i2h6\ngfpMP6oxQsLp53RUY2PDAe43t5O2PJSevPu8jRm//z7s3d2MJANIS9Aw8yIj/VMgYX1NjplYH125\nCZ4u3IxTmjSrdlyuAVzTq5nNjbJs25bzZkuFi4uy4kZ0ZYJWbxWZOReWzQQBK5VBioV2dFMjoadp\nTcxR1AQenwMXksdOLlBXdwvh8GOUSulfa0zTzDE49HFeOnAHf3n0+2QK4ywvnWVKhJguBSl5Igi1\nhWC+SFWqldZ4H6vkDznS9h6qO9tJNn6MuJ7lK2c+wpGpHJlVATYZCl/Y3oMeuPzDgiuCf5lRaqmn\nXitQl+oDTFL6DIdtJRrVAq2uMHdbu6gvHCZ28tVH6xQGB8kfOUrgjtt5+ZExbIU4RSPC0ZoUDUmd\naPVyXj/zHFElwD9ZG+nOzRJsD7Iw/COcZg1Je4zqlssjg/GKpPN6AFpc88Rmg3Bun3OL4zig4Ek1\nEnaEmc89jWZJsg12nKZg7/AC/tBtWFae+fAj/+3hpDQZOHYX4+Nf4ki4mnuVLjbkX6YumeF+4xo6\n1BMUVQNh68WfT7Fj4k789jFSWgfO/l1Ud/4jp2SB/7P/r9k3bmCsCnCrw8ndu/rw1nsuwAVaelQE\n/zKjpquTjmKaxnQHiqWhFwZI2HYgwz28rvshvlJ6DRaC6A/+7lWPFb/vftB1CuuvIxMv0jT1LCdX\na8zYFNYLwVBiLbtiL3GweDMxNPqKC9T0pygeLIt88w3rKr77S5maHqSrAZdyCGdsJQCiIKjqiFBT\nWKAQX05eyzOtz7E2ZjDUqNNSUjEsGJhvweFoIhx+7L893JETn+DFwUlM68/4gvl+1s4Ms736WQTw\noLmNQHACgLinm4aonZJi55sburj/tl0otR/hwYSNj770RyxYOr41VXy6upovvK4fzxUws/8pFcG/\nzOjoXY43E0N1FWlMdmHPHSHuaWco46Kv9jhBovzI2kJz7mFmjo39j8eRxSKJBx/Eu2sXex8Lg5Q0\nzu7leH8cvSSoaa3hzrFniSo+HjFuxi4tVisOMrP34Z5bR1ZJ0bN7x389UIWlixDQeyN25RCNnjVk\nZh1IAwrLJStTo0TymxBSkNNyhBKDTHhVltl0NCRPnQ5TVbWTWOxFLKv4S7/eMJIYRhwpJZ956Lu8\n5e5u/u7AH/L+x+uYPx7hOuvHtCZiDFNPynSRd86hWzopZyveOHgabPxeOsY7kn/Gl8+u5tsn34zX\n6+DPA35eWNnJm2/qQdHUi3zRFpeK4F9muDo6kJkIPiVGW6wPtTTLrF8ykzBRCz7ev/yH3F28FruS\n58w9f/c/rqCZevJJzGgU5223Mz0Yxxc/TcxTZF/IYnncTrbQzfb4IR4xXs8TQmd5bpoV7jbCj9ZR\nY2+lalsXQqncfpc6Yu3bUUWOleoEYnIFeCSogo3aACUZIJioZsIzgZE5CIDe4MRhSfYOhakK7cQ0\ns8Tj/7FByuzcD3n+hS088cwW3vbZj/OZ57z01cb5/JtWoXd6WW+dYF31HkJJg3uMXTTb0kTsERqN\nDhAq7bkim2q/h9n6Uf7q+J3sn93A+7DzsCPIu96xBu/qK7OMS+WJu8ywtbZipudoUl2sTHcDkFbO\nslDqRp9bj79tjP7wAZ4z+1klHuSr3/mfdSOK33MPemMjh6eqQULP8A/Yt6NAXhH0Vgd49/BPGNab\nmC7cRBxJT26CoFLNTU3vRSgC/7ZLu5FEhXO0bEHWrkTVv09/4r3IogYlWN5bLtHtnGsjp+Xw5tLU\nZgsMNui0lDRGojmkbQNC6ESiz/7cV5pmjsHBj4DeyxdPfoy9M6v57a1Z7v3932B4LkO208Vv9n6H\n5pk8Bgr3mDtpkXMkbGmCdAJQX/0k0WU/4idnf4Nwto7P+kP84Wt7afiDdeg1rot+mZYKFcG/zFAc\nDoSepckM0WFT8eWbsGf3MV3bwuiCDxSLhu4IQ/NOqkSSsePf4itPD/9aYxQnJsi8uAfP7W9i8KV5\n7LkwzuwEj3RCU8xGr+mhpTDLp/O/yaSwYZMWNaljHLG+B9Ulgm/qRruC/KaXNUIgNr0XP5PM2R6j\nZuR20EDvz9KZnmChcDWaqTLpmSSQPMOBKo3dSnl3d2C6SMC/4T/E48die0hkU3x83/s5OuPkU29e\nw5+94U6KBWg4Eefh0/dT45qnMVzgadGPiY7QLCzFQtG6UE3JlpWbcLU8whOznbx1axuv+9DVeHc0\nI/Qry4Xzi1QE/zLEVu3ALe2YWpiV82vRi0MMhhwMpb04Yl209M8TSeiMFOu4S32Iv3nkGP/yzH9f\n9OPfuw8UhdHq7ZglSfvIAzy1QxKxKWx1O7lj4lke9W7Bb6znOVFiWW4ShwbBWwdp+sBO3Bt+aaXs\nCpcq634TWbWcZuWb2MY3oM2XZ9nbmvexoDfSPFnNpHuStrmzFFWBbHKgSJMDx04SCG4hnT5FqZR6\n5esSySN879QdrAhr3Lu2g1ubQ1hZg7NfOMzq/BTzXffRNaihWpJP5d9CixonYo8BMOPupDpp8mKm\nl/d/+yyNAQcfvKlnUS7LUqQi+JchtvZmrNQUGTxcn1oPQFI7TcLhITLVh+bMYfVLDs9VUacs8Afy\nef7m4VP8677/ehPXymSIf/e72K+9gSMvRNCLSXyJw9y93kZDUucdiQgp1c3/Tr6blSZEkLSlT1G7\ncZaunrsQonLLXXZodsQtn6TJNMh4PkLHwAexsjY2rduPx8wSS9wISNLKYbz5LHuabOws2XjqyDAB\n/wZAEk8cfOXrhiaHuW16F38sHdS8vMDcpw4y/ZF9uBYKHFj7bexpg4bIPIdczZyU7bSRIRJ4me5c\nG2d9QfyJEp98Zog1LQG++/6r8DkqrTN/SuXpuwyxr+ilNHWQFcUa+nUXNellONNPcbS6lUNRH3q6\nga51owzkG5jIB3if7d/YUMrzFz84xjNnfnUTmtg992ImEkz0v5mSYdE58jX+5bU+MnbJOx0Flqen\n+Xj9e1hbqOaQqqBJk2X5aVo3+qirfe1FugIVLjrLdmLu+BM2GiM45Ndp2PcnuNQ8b+z8EZN6H95w\nO2e9w7TNH2Vvlcpr7XDWFsOdAiFUEuc2bqWUyL1r2IyOubuFhj/bgv+WZcz0+fnrbYM02Y+y+mQS\nUxV8cuFO7BiELIMxd5K12TWkHQrrQ25e/vMb+Pb7ttAUcC7yhVlaVAT/MsS1YQPG+B6qFTfzaoTr\n5rehmguMVofJKjrTo+vwO4rYrjvDMzPtuNUEfy7uoclS+L1vHWRwLvVLv9fKZol+9auUNlzDsSNZ\nvPkT7G1OcqA7Q78UvHl6hh9Xbed7k1t5nyV4ShZozY5R3xOhq+suhLiy/aeXO+quDzPaup1a249o\nli9SO3Q9O5c/zw3pF5lOvA0pFczcjzAVwVSDjyY1zT9/48N4PX2vROrEnj3OqmgvD2p5Wq9vQ/Xa\ncGxr5P+tL/Ku9D+w6WACr2HwlLOZ/WItrUqcmO9lhASNctb2tZ3VhNy2xbwUS5aK4F+G2Do6UJyg\nGPvJqg7en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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e2cfa90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Vbeq/ThAYx4/HvX8/0tfTS1rn9vB6XSvLU+JIMOhD7r8Ln8+N3bEX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NAAAg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\nrMhYwaCwQfjr/FvdryVMg8IwVHQBSVBavInengb6BPnxzItvcTI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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c4b00d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoencoder" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(model,train_loader,loss_fn,optimizer,epochs=100,patience=5,criteria_stop=\"loss\"):\n", " hist_train_loss = hist_val_loss = hist_train_acc = hist_val_acc = np.array([])\n", " best_epoch = patience_count = 0\n", "\n", " print(\"Training starts along %i epoch\"%epochs)\n", " for e in range(epochs):\n", " correct_train = correct_val = total_train = total_val = 0\n", " cont_i = loss_t_e = loss_v_e = 0\n", " for data_train in train_loader:\n", " var_inputs = Variable(data_train)\n", "\n", " predict, encode = model(var_inputs)\n", " loss = loss_fn(predict, var_inputs.view(-1, 500))\n", " loss_t_e += loss.data[0]\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " cont_i += 1\n", "\n", " #Stacking historical\n", " hist_train_loss = np.hstack((hist_train_loss, loss_t_e/(cont_i*1.0)))\n", " print('Epoch: ', e, 'train loss: ', hist_train_loss[-1])\n", "\n", " if(e == epochs-1):\n", " best_epoch = e\n", " best_model = copy.deepcopy(model)\n", " print(\"Training stopped\")\n", " patience_count += 1\n", "\n", " return(best_model, hist_train_loss, hist_val_loss)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class autoencoder(nn.Module):\n", " def __init__(self):\n", " super(autoencoder, self).__init__()\n", " self.fc1 = nn.Linear(500, 200)\n", " self.fc21 = nn.Linear(200, 2)\n", " self.fc3 = nn.Linear(2, 200)\n", " self.fc4 = nn.Linear(200, 500)\n", " self.relu = nn.ReLU()\n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def encode(self, x):\n", " h1 = self.relu(self.fc1(x))\n", " return self.fc21(h1)\n", "\n", " def decode(self, z):\n", " h3 = self.relu(self.fc3(z))\n", " return self.sigmoid(self.fc4(h3))\n", "\n", " def forward(self, x):\n", " z = self.encode(x.view(-1, 500))\n", " return self.decode(z), z\n", " \n", "class decoder(nn.Module):\n", " def __init__(self):\n", " super(decoder, self).__init__()\n", " self.fc3 = nn.Linear(2, 200)\n", " self.fc4 = nn.Linear(200, 500)\n", " self.relu = nn.ReLU()\n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def decode(self, z):\n", " h3 = self.relu(self.fc3(z))\n", " return self.sigmoid(self.fc4(h3))\n", "\n", " def forward(self, x):\n", " return self.decode(x.view(-1, 2))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "autoencoder (\n", " (fc1): Linear (500 -> 200)\n", " (fc21): Linear (200 -> 2)\n", " (fc3): Linear (2 -> 200)\n", " (fc4): Linear (200 -> 500)\n", " (relu): ReLU ()\n", " (sigmoid): Sigmoid ()\n", ")\n" ] } ], "source": [ "net = autoencoder()\n", "print(net)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "314.804579605\n", "torch.Size([147, 500])\n" ] } ], "source": [ "res_chs = res_ex\n", "trainloader = prof_vec[:,res_chs,:]\n", "val_norm = np.amax(trainloader).astype(float)\n", "print val_norm\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training starts along 20 epoch\n", "('Epoch: ', 0, 'train loss: ', 0.0095215536444923105)\n", "('Epoch: ', 1, 'train loss: ', 0.0050852037164411147)\n", "('Epoch: ', 2, 'train loss: ', 0.00376094494262064)\n", "('Epoch: ', 3, 'train loss: ', 0.0035085569192864455)\n", "('Epoch: ', 4, 'train loss: ', 0.0036567833340333989)\n", "('Epoch: ', 5, 'train loss: ', 0.003376171098575376)\n", "('Epoch: ', 6, 'train loss: ', 0.0031902957710613286)\n", "('Epoch: ', 7, 'train loss: ', 0.0030486840006561165)\n", "('Epoch: ', 8, 'train loss: ', 0.0030420855841746704)\n", "('Epoch: ', 9, 'train loss: ', 0.0028730589579862107)\n", "('Epoch: ', 10, 'train loss: ', 0.0027359282127882474)\n", "('Epoch: ', 11, 'train loss: ', 0.0026264062204174889)\n", "('Epoch: ', 12, 'train loss: ', 0.0025942252269196861)\n", "('Epoch: ', 13, 'train loss: ', 0.00255845210631378)\n", "('Epoch: ', 14, 'train loss: ', 0.0027449630140675371)\n", "('Epoch: ', 15, 'train loss: ', 0.0026573979062761567)\n", "('Epoch: ', 16, 'train loss: ', 0.0025321919434200214)\n", "('Epoch: ', 17, 'train loss: ', 0.0023269568295550664)\n", "('Epoch: ', 18, 'train loss: ', 0.00239070575228151)\n", "('Epoch: ', 19, 'train loss: ', 0.0024925716195396482)\n", "Training stopped\n" ] } ], "source": [ "loss_fn = torch.nn.MSELoss()\n", "optimizer = torch.optim.Adam(net.parameters())\n", "epochs = 20\n", "patience = 5\n", "max_batch = 64\n", "criteria = \"loss\"\n", "\n", "best_model, loss, loss_test = train(net, trainloader, loss_fn, optimizer, epochs = epochs, \n", " patience = patience, criteria_stop = criteria)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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3uHuXmc1Ochkipy0SEUlHyW6RPG9m3yEIkq1mVgSMNqjUKqDe3Q+6ezfwNEEAJFoHPOmB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\nERGRlChIREQkJQoSERFJiYJERERS8v8BgQWtuespTfIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb080cb3a10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Loss')\n", "plt.xlabel('epochs')\n", "plt.ylabel('loss')\n", "plt.plot(loss, label='Train')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((147, 500), (147, 2), (147,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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5P5FM0NjaQG5V1wYf2xJGU2sjxe4in/3+xzn8pDmDiSoSin6Ly8zuAjatsut0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\nRCRWVFwiIhIrKi4REYkVFZeIiMTK/weVXPd5PW/EWAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb07975da10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing in new datasets\n", "## ROQS test" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_ROQS/roqs_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (136, 50, 500)\n", "Erroneous segmentations' vector: (16, 50, 500)\n" ] } ], "source": [ "prof_vec_roqs = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " mask_pn = np.load('../../dataset/Seg_ROQS/mask_roqs_{}.npy'.format(mask)) #Loading mask\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec_roqs[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting using Watershed as basis\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec_roqs[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec_roqs[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "#for ind_ex, ind_rel in zip(ind_ex_err, ind_rel_err):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec_roqs[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_ROQS/mask_roqs_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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wFMn/1JJ7w0SsP31uALee4nhQAr3ilBF/uUY9nk8aUZm12OcW0mPSsuSJPahU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\nCljoGfo8Z8p+Sl2vn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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07968c210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zN521GzeCq4Q2LTdjDCrBXBNG/u7eSHsAUTU1mGtyMWv86ZJXilutRu7ewN7d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hqF9++aRvUo5Jpcgx7MWWhGmKJSIItOW1VpBexBjZvDDX26mGbKMjHSOl6f60g1zTs72lSWDf0s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\n/htwfY/y5YqidCiKcgYopKttLiuKolQoi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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e2df9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec_roqs[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec_roqs[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([152, 500])\n" ] } ], "source": [ "trainloader = prof_vec_roqs[:,res_chs,:]\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((152, 500), (152, 2), (152,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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bmWMHjhf5ulablfN6nlus0AOY/r/Z+L159/B0p7k5tOff/88un7mau9o+zpwv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\ny07NitxUmXQsmVtbPkTKidTMXrE2h5V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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e3d4450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pixel-based test" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_pixel/pixel_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (131, 50, 500)\n", "Erroneous segmentations' vector: (21, 50, 500)\n" ] } ], "source": [ "prof_vec_pixe = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " mask_pn = np.load('../../dataset/Seg_pixel/mask_pixe_{}.npy'.format(mask)) #Loading mask\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec_pixe[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting using Watershed as basis\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec_pixe[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec_pixe[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "#for ind_ex, ind_rel in zip(ind_ex_cor, ind_rel_cor):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec_pixe[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_pixel/mask_pixe_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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cXljfRarzx3MQXM5GSrwNfDAomeZwIyF+PS7HHBpZhdllZFzjOMKNHcSoLCQ3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\nvuc6o9DPUXeVLwDg3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c37cad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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BfsG+dCgrYmbOVu4bPp4kqSEw0Q8Av8g4qq/6gIaf7sK16BE63buO8rkVFNWb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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c604ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec_pixe[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec_pixe[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([152, 500])\n" ] } ], "source": [ "trainloader = prof_vec_pixe[:,res_chs,:]\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((152, 500), (152, 2), (152,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb079740fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RMSE over the 10 correct segmentations was compared with RMSE over the 10 erroneous segmentations. As expected, RMSE for correct segmentations was greater than RMSE for erroneous segmentations along all the resolutions. In general, this is true, but optimal resolution guarantee the maximum difference between both of RMSE results: correct and erroneous.\n", "\n", "So, to find optimal resolution, difference between correct and erroneous RMSE was calculated over all resolutions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The greatest difference resulted at resolution 0.1. In this resolution, threshold for separate erroneous and correct segmentations is established as 30% of the distance between the mean RMSE of the correct masks and the mean RMSE of the erroneous masks.\n", "\n", "# Method testing\n", "\n", "Finally, method test was performed in the 152 subject dataset: Watershed dataset with 112 segmentations, ROQS dataset with 152 segmentations and pixel-based dataset with 152 segmentations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Discussion and conclusion\n", "\n", "In this work, a method for segmentation error detection in large datasets was proposed based-on shape signature. RMSE was used for comparison between signatures. Signature can be extracted en various resolutions but optimal resolution (ls=0.1) was chosen in order to get maximum separation between correct RMSE and erroneous RMSE. In this optimal resolution, threshold was fixed at 27.95 allowing separation of the two segmentation classes.The method achieved 95% of accuracy on the test Watershed segmentations, and 95% and 94% on new datasets: ROQS and pixel-based, respectively.\n", "\n", "40 Watershed segmentations on dataset were used to generation and configuration mean correct signature because of the greater number of erroneous segmentations and major variability on the error shape. Because the signature holds the CC shape, the method can be extended to new datasets segmented with any method. Accuracy and generalization can be improve varying the segmentations used to generate and adjust the mean correct signature." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
CrowdTruth/CrowdTruth-core	tutorial/notebooks/Multiple Choice Task - Person Type Annotation in Video.ipynb	1	112567	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CrowdTruth for Multiple Choice Tasks: Person Type Annotation in Video\n", "\n", "In this tutorial, we will apply CrowdTruth metrics to a multiple choice crowdsourcing task for Person Type Annotation from video fragments. The workers were asked to watch a video of about 3-5 seconds and then pick from a multiple choice list which are the types of person that appear in the video fragment. The task was executed on [FigureEight](https://www.figure-eight.com/). For more crowdsourcing annotation task examples, click [here](https://raw.githubusercontent.com/CrowdTruth-core/tutorial/getting_started.md).\n", "\n", "To replicate this experiment, the code used to design and implement this crowdsourcing annotation template is available here: [template](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.html), [css](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.css), [javascript](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.js). \n", "\n", "This is a screenshot of the task as it appeared to workers:\n", "![Task Template](../img/person-video-multiple-choice.png)\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sample dataset for this task is available in [this file](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/data/person-video-multiple-choice.csv), containing raw output from the crowd on FigureEight. Download the file and place it in a folder named `data` that has the same root as this notebook. Now you can check your data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_unit_id</th>\n", " <th>_created_at</th>\n", " <th>_id</th>\n", " <th>_started_at</th>\n", " <th>_tainted</th>\n", " <th>_channel</th>\n", " <th>_trust</th>\n", " <th>_worker_id</th>\n", " <th>_country</th>\n", " <th>_region</th>\n", " <th>...</th>\n", " <th>description</th>\n", " <th>descriptiontags</th>\n", " <th>hiddeninput_gold</th>\n", " <th>imagelocation</th>\n", " <th>imagetags</th>\n", " <th>keyframeid_gold</th>\n", " <th>selected_answer_gold</th>\n", " <th>subtitles</th>\n", " <th>subtitletags</th>\n", " <th>videolocation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 18:40:05</td>\n", " <td>3990340198</td>\n", " <td>8/20/2018 18:39:51</td>\n", " <td>False</td>\n", " <td>clixsense</td>\n", " <td>1.0</td>\n", " <td>40712302</td>\n", " <td>GBR</td>\n", " <td>J8</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:07:38</td>\n", " <td>3990381441</td>\n", " <td>8/20/2018 19:07:29</td>\n", " <td>False</td>\n", " <td>clixsense</td>\n", " <td>1.0</td>\n", " <td>40925305</td>\n", " <td>CAN</td>\n", " <td>QC</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:19:02</td>\n", " <td>3990407780</td>\n", " <td>8/20/2018 19:18:52</td>\n", " <td>False</td>\n", " <td>imerit_india</td>\n", " <td>1.0</td>\n", " <td>44399792</td>\n", " <td>USA</td>\n", " <td>LA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:20:32</td>\n", " <td>3990410322</td>\n", " <td>8/20/2018 19:20:14</td>\n", " <td>False</td>\n", " <td>elite</td>\n", " <td>1.0</td>\n", " <td>44185847</td>\n", " <td>USA</td>\n", " <td>FL</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:27:03</td>\n", " <td>3990420566</td>\n", " <td>8/20/2018 19:26:19</td>\n", " <td>False</td>\n", " <td>imerit_india</td>\n", " <td>1.0</td>\n", " <td>42395899</td>\n", " <td>USA</td>\n", " <td>LA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 26 columns</p>\n", "</div>" ], "text/plain": [ " _unit_id _created_at _id _started_at _tainted \\\n", "0 1856509899 8/20/2018 18:40:05 3990340198 8/20/2018 18:39:51 False \n", "1 1856509899 8/20/2018 19:07:38 3990381441 8/20/2018 19:07:29 False \n", "2 1856509899 8/20/2018 19:19:02 3990407780 8/20/2018 19:18:52 False \n", "3 1856509899 8/20/2018 19:20:32 3990410322 8/20/2018 19:20:14 False \n", "4 1856509899 8/20/2018 19:27:03 3990420566 8/20/2018 19:26:19 False \n", "\n", " _channel _trust _worker_id _country _region ... description \\\n", "0 clixsense 1.0 40712302 GBR J8 ... NaN \n", "1 clixsense 1.0 40925305 CAN QC ... NaN \n", "2 imerit_india 1.0 44399792 USA LA ... NaN \n", "3 elite 1.0 44185847 USA FL ... NaN \n", "4 imerit_india 1.0 42395899 USA LA ... NaN \n", "\n", " descriptiontags hiddeninput_gold \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " imagelocation \\\n", "0 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1 https://joran.org/ct/entity.admin.unit.2649/85... \n", "2 https://joran.org/ct/entity.admin.unit.2649/85... \n", "3 https://joran.org/ct/entity.admin.unit.2649/85... \n", "4 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", " imagetags keyframeid_gold \\\n", "0 industry__c0_###_grinder__c1_###_production__c... NaN \n", "1 industry__c0_###_grinder__c1_###_production__c... NaN \n", "2 industry__c0_###_grinder__c1_###_production__c... NaN \n", "3 industry__c0_###_grinder__c1_###_production__c... NaN \n", "4 industry__c0_###_grinder__c1_###_production__c... NaN \n", "\n", " selected_answer_gold subtitles \\\n", "0 NaN Italian astronaut samantha cristoforetti uploa... \n", "1 NaN Italian astronaut samantha cristoforetti uploa... \n", "2 NaN Italian astronaut samantha cristoforetti uploa... \n", "3 NaN Italian astronaut samantha cristoforetti uploa... \n", "4 NaN Italian astronaut samantha cristoforetti uploa... \n", "\n", " subtitletags \\\n", "0 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "1 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "2 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "3 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "4 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "\n", " videolocation \n", "0 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1 https://joran.org/ct/entity.admin.unit.2649/85... \n", "2 https://joran.org/ct/entity.admin.unit.2649/85... \n", "3 https://joran.org/ct/entity.admin.unit.2649/85... \n", "4 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "test_data = pd.read_csv(\"../data/person-video-multiple-choice.csv\")\n", "test_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Declaring a pre-processing configuration\n", "\n", "The pre-processing configuration defines how to interpret the raw crowdsourcing input. To do this, we need to define a configuration class. First, we import the default CrowdTruth configuration class:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import crowdtruth\n", "from crowdtruth.configuration import DefaultConfig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our test class inherits the default configuration `DefaultConfig`, while also declaring some additional attributes that are specific to the Person Type Annotation in Video task:\n", "\n", "* `inputColumns`: list of input columns from the .csv file with the input data\n", "* `outputColumns`: list of output columns from the .csv file with the answers from the workers\n", "* `annotation_separator`: string that separates between the crowd annotations in `outputColumns`\n", "* `open_ended_task`: boolean variable defining whether the task is open-ended (i.e. the possible crowd annotations are not known beforehand, like in the case of free text input); in the task that we are processing, workers pick the answers from a pre-defined list, therefore the task is not open ended, and this variable is set to `False`\n", "* `annotation_vector`: list of possible crowd answers, mandatory to declare when `open_ended_task` is `False`; for our task, this is the list of relations\n", "* `processJudgments`: method that defines processing of the raw crowd data; for this task, we process the crowd answers to correspond to the values in `annotation_vector`\n", "\n", "The complete configuration class is declared below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class TestConfig(DefaultConfig):\n", " inputColumns = [\"videolocation\", \"subtitles\", \"imagetags\", \"subtitletags\"]\n", " outputColumns = [\"selected_answer\"]\n", " \n", " # processing of a closed task\n", " open_ended_task = False\n", " annotation_vector = [\"archeologist\", \"architect\", \"artist\", \"astronaut\", \"athlete\", \"businessperson\",\"celebrity\", \n", " \"chef\", \"criminal\", \"engineer\", \"farmer\", \"fictionalcharacter\", \"journalist\", \"judge\", \n", " \"lawyer\", \"militaryperson\", \"model\", \"monarch\", \"philosopher\", \"politician\", \"presenter\", \n", " \"producer\", \"psychologist\", \"scientist\", \"sportsmanager\", \"writer\", \"none\", \"other\"]\n", " \n", " def processJudgments(self, judgments):\n", " # pre-process output to match the values in annotation_vector\n", " for col in self.outputColumns:\n", " # transform to lowercase\n", " judgments[col] = judgments[col].apply(lambda x: str(x).lower())\n", " # remove square brackets from annotations\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace('[',''))\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace(']',''))\n", " # remove the quotes around the annotations\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace('\"',''))\n", " return judgments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pre-processing the input data\n", "\n", "After declaring the configuration of our input file, we are ready to pre-process the crowd data:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>output.selected_answer</th>\n", " <th>output.selected_answer.count</th>\n", " <th>output.selected_answer.unique</th>\n", " <th>unit</th>\n", " <th>worker</th>\n", " <th>started</th>\n", " <th>submitted</th>\n", " <th>duration</th>\n", " <th>job</th>\n", " </tr>\n", " <tr>\n", " <th>judgment</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3990340198</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>40712302</td>\n", " <td>2018-08-20 18:39:51</td>\n", " <td>2018-08-20 18:40:05</td>\n", " <td>14</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990381441</th>\n", " <td>{'astronaut': 1, 'scientist': 1, 'archeologist...</td>\n", " <td>2</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>40925305</td>\n", " <td>2018-08-20 19:07:29</td>\n", " <td>2018-08-20 19:07:38</td>\n", " <td>9</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990407780</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>44399792</td>\n", " <td>2018-08-20 19:18:52</td>\n", " <td>2018-08-20 19:19:02</td>\n", " <td>10</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990410322</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>44185847</td>\n", " <td>2018-08-20 19:20:14</td>\n", " <td>2018-08-20 19:20:32</td>\n", " <td>18</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990420566</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>42395899</td>\n", " <td>2018-08-20 19:26:19</td>\n", " <td>2018-08-20 19:27:03</td>\n", " <td>44</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " output.selected_answer \\\n", "judgment \n", "3990340198 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990381441 {'astronaut': 1, 'scientist': 1, 'archeologist... \n", "3990407780 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990410322 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990420566 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "\n", " output.selected_answer.count output.selected_answer.unique \\\n", "judgment \n", "3990340198 1 28 \n", "3990381441 2 28 \n", "3990407780 1 28 \n", "3990410322 1 28 \n", "3990420566 1 28 \n", "\n", " unit worker started submitted \\\n", "judgment \n", "3990340198 1856509899 40712302 2018-08-20 18:39:51 2018-08-20 18:40:05 \n", "3990381441 1856509899 40925305 2018-08-20 19:07:29 2018-08-20 19:07:38 \n", "3990407780 1856509899 44399792 2018-08-20 19:18:52 2018-08-20 19:19:02 \n", "3990410322 1856509899 44185847 2018-08-20 19:20:14 2018-08-20 19:20:32 \n", "3990420566 1856509899 42395899 2018-08-20 19:26:19 2018-08-20 19:27:03 \n", "\n", " duration job \n", "judgment \n", "3990340198 14 ../data/person-video-multiple-choice \n", "3990381441 9 ../data/person-video-multiple-choice \n", "3990407780 10 ../data/person-video-multiple-choice \n", "3990410322 18 ../data/person-video-multiple-choice \n", "3990420566 44 ../data/person-video-multiple-choice " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data, config = crowdtruth.load(\n", " file = \"../data/person-video-multiple-choice.csv\",\n", " config = TestConfig()\n", ")\n", "\n", "data['judgments'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing the CrowdTruth metrics\n", "\n", "The pre-processed data can then be used to calculate the CrowdTruth metrics:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "results = crowdtruth.run(data, config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Video fragment quality\n", "\n", "The video fragments metrics are stored in `results[\"units\"]`. The uqs column in `results[\"units\"]` contains the video fragment quality scores, capturing the overall workers agreement over each video fragment." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>duration</th>\n", " <th>input.imagetags</th>\n", " <th>input.subtitles</th>\n", " <th>input.subtitletags</th>\n", " <th>input.videolocation</th>\n", " <th>job</th>\n", " <th>output.selected_answer</th>\n", " <th>output.selected_answer.annotations</th>\n", " <th>output.selected_answer.unique_annotations</th>\n", " <th>worker</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " <th>uqs_initial</th>\n", " <th>unit_annotation_score_initial</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509899</th>\n", " <td>24.95</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'astronaut': 20, 'scientist': 3, 'other': 1, ...</td>\n", " <td>25</td>\n", " <td>4</td>\n", " <td>20</td>\n", " <td>0.919461</td>\n", " <td>{'astronaut': 1.0, 'scientist': 0.169109758893...</td>\n", " <td>0.865963</td>\n", " <td>{'astronaut': 1.0, 'scientist': 0.15, 'other':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509900</th>\n", " <td>30.00</td>\n", " <td>man__c0_###_soccer__c1_###_portrait__c2_###_pe...</td>\n", " <td>this phenomena is it's massive the</td>\n", " <td>phenomena__0_###_massive__1_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'celebrity': 2, 'journalist': 2, 'presenter':...</td>\n", " <td>29</td>\n", " <td>11</td>\n", " <td>20</td>\n", " <td>0.290902</td>\n", " <td>{'celebrity': 0.007285683902434039, 'journalis...</td>\n", " <td>0.239176</td>\n", " <td>{'celebrity': 0.1, 'journalist': 0.1, 'present...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509901</th>\n", " <td>32.15</td>\n", " <td>people__c0_###_man__c1_###_adult__c2_###_portr...</td>\n", " <td>around could the lights be coming from</td>\n", " <td>lights__0_###_coming__1_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'artist': 1, 'celebrity': 1, 'producer': 1, '...</td>\n", " <td>25</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>0.295147</td>\n", " <td>{'artist': 0.006139167492493724, 'celebrity': ...</td>\n", " <td>0.216495</td>\n", " <td>{'artist': 0.05, 'celebrity': 0.05, 'producer'...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509902</th>\n", " <td>46.20</td>\n", " <td>water__c0_###_no person__c1_###_ocean__c2_###_...</td>\n", " <td>when investigators map the coordinates onto lo...</td>\n", " <td>investigators__0_###_map__1_###_coordinates__2...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'scientist': 5, 'none': 11, 'presenter': 2, '...</td>\n", " <td>24</td>\n", " <td>8</td>\n", " <td>20</td>\n", " <td>0.552869</td>\n", " <td>{'scientist': 0.15804046047341153, 'none': 0.7...</td>\n", " <td>0.334078</td>\n", " <td>{'scientist': 0.25, 'none': 0.55, 'presenter':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509903</th>\n", " <td>35.85</td>\n", " <td>sky__c0_###_no person__c1_###_power__c2_###_el...</td>\n", " <td>the bright lights are part of a</td>\n", " <td>bright lights__0_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'none': 15, 'other': 2, 'fictionalcharacter':...</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>20</td>\n", " <td>0.963842</td>\n", " <td>{'none': 0.9830831242894289, 'other': 0.008259...</td>\n", " <td>0.557895</td>\n", " <td>{'none': 0.75, 'other': 0.1, 'fictionalcharact...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration input.imagetags \\\n", "unit \n", "1856509899 24.95 industry__c0_###_grinder__c1_###_production__c... \n", "1856509900 30.00 man__c0_###_soccer__c1_###_portrait__c2_###_pe... \n", "1856509901 32.15 people__c0_###_man__c1_###_adult__c2_###_portr... \n", "1856509902 46.20 water__c0_###_no person__c1_###_ocean__c2_###_... \n", "1856509903 35.85 sky__c0_###_no person__c1_###_power__c2_###_el... \n", "\n", " input.subtitles \\\n", "unit \n", "1856509899 Italian astronaut samantha cristoforetti uploa... \n", "1856509900 this phenomena is it's massive the \n", "1856509901 around could the lights be coming from \n", "1856509902 when investigators map the coordinates onto lo... \n", "1856509903 the bright lights are part of a \n", "\n", " input.subtitletags \\\n", "unit \n", "1856509899 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "1856509900 phenomena__0_###_massive__1_###_ \n", "1856509901 lights__0_###_coming__1_###_ \n", "1856509902 investigators__0_###_map__1_###_coordinates__2... \n", "1856509903 bright lights__0_###_ \n", "\n", " input.videolocation \\\n", "unit \n", "1856509899 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509900 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509901 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509902 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509903 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", " job \\\n", "unit \n", "1856509899 ../data/person-video-multiple-choice \n", "1856509900 ../data/person-video-multiple-choice \n", "1856509901 ../data/person-video-multiple-choice \n", "1856509902 ../data/person-video-multiple-choice \n", "1856509903 ../data/person-video-multiple-choice \n", "\n", " output.selected_answer \\\n", "unit \n", "1856509899 {'astronaut': 20, 'scientist': 3, 'other': 1, ... \n", "1856509900 {'celebrity': 2, 'journalist': 2, 'presenter':... \n", "1856509901 {'artist': 1, 'celebrity': 1, 'producer': 1, '... \n", "1856509902 {'scientist': 5, 'none': 11, 'presenter': 2, '... \n", "1856509903 {'none': 15, 'other': 2, 'fictionalcharacter':... \n", "\n", " output.selected_answer.annotations \\\n", "unit \n", "1856509899 25 \n", "1856509900 29 \n", "1856509901 25 \n", "1856509902 24 \n", "1856509903 21 \n", "\n", " output.selected_answer.unique_annotations worker uqs \\\n", "unit \n", "1856509899 4 20 0.919461 \n", "1856509900 11 20 0.290902 \n", "1856509901 9 20 0.295147 \n", "1856509902 8 20 0.552869 \n", "1856509903 6 20 0.963842 \n", "\n", " unit_annotation_score uqs_initial \\\n", "unit \n", "1856509899 {'astronaut': 1.0, 'scientist': 0.169109758893... 0.865963 \n", "1856509900 {'celebrity': 0.007285683902434039, 'journalis... 0.239176 \n", "1856509901 {'artist': 0.006139167492493724, 'celebrity': ... 0.216495 \n", "1856509902 {'scientist': 0.15804046047341153, 'none': 0.7... 0.334078 \n", "1856509903 {'none': 0.9830831242894289, 'other': 0.008259... 0.557895 \n", "\n", " unit_annotation_score_initial \n", "unit \n", "1856509899 {'astronaut': 1.0, 'scientist': 0.15, 'other':... \n", "1856509900 {'celebrity': 0.1, 'journalist': 0.1, 'present... \n", "1856509901 {'artist': 0.05, 'celebrity': 0.05, 'producer'... \n", "1856509902 {'scientist': 0.25, 'none': 0.55, 'presenter':... \n", "1856509903 {'none': 0.75, 'other': 0.1, 'fictionalcharact... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of video fragment quality scores\n", "\n", "The histogram below shows video fragment quality scores are nicely distributed, with both low and high quality video fragments." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Video Fragments')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.hist(results[\"units\"][\"uqs\"])\n", "plt.xlabel(\"Video Fragment Quality Score\")\n", "plt.ylabel(\"Video Fragments\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `unit_annotation_score` column in `results[\"units\"]` contains the video fragment-annotation scores, capturing the likelihood that an annotation is expressed in a video fragment. For each video fragment, we store a dictionary mapping each annotation to its video fragment-annotation score." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "unit\n", "1856509899 {'astronaut': 1.0, 'scientist': 0.169109758893...\n", "1856509900 {'celebrity': 0.007285683902434039, 'journalis...\n", "1856509901 {'artist': 0.006139167492493724, 'celebrity': ...\n", "1856509902 {'scientist': 0.15804046047341153, 'none': 0.7...\n", "1856509903 {'none': 0.9830831242894289, 'other': 0.008259...\n", "Name: unit_annotation_score, dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"][\"unit_annotation_score\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ambiguous video fragments\n", "\n", "A low unit quality score can be used to identify ambiguous video fragments. First, we sort the unit quality metrics stored in `results[\"units\"]` based on the quality score (`uqs`), in ascending order. Thus, the most clear video fragments are found at the tail of the new structure:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>input.videolocation</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509905</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>0.146278</td>\n", " <td>{'other': 0.28844109454744976, 'presenter': 0....</td>\n", " </tr>\n", " <tr>\n", " <th>1856509931</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.194667</td>\n", " <td>{'engineer': 0.15692508334014804, 'journalist'...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509938</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.207788</td>\n", " <td>{'businessperson': 0.06949004651596365, 'prese...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509921</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2848/88...</td>\n", " <td>0.220392</td>\n", " <td>{'monarch': 0.42098378954483356, 'other': 0.27...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509944</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2784/11...</td>\n", " <td>0.242865</td>\n", " <td>{'politician': 0.006580944290157723, 'none': 0...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " input.videolocation uqs \\\n", "unit \n", "1856509905 https://joran.org/ct/entity.admin.unit.2649/85... 0.146278 \n", "1856509931 https://joran.org/ct/entity.admin.unit.2833/90... 0.194667 \n", "1856509938 https://joran.org/ct/entity.admin.unit.2833/90... 0.207788 \n", "1856509921 https://joran.org/ct/entity.admin.unit.2848/88... 0.220392 \n", "1856509944 https://joran.org/ct/entity.admin.unit.2784/11... 0.242865 \n", "\n", " unit_annotation_score \n", "unit \n", "1856509905 {'other': 0.28844109454744976, 'presenter': 0.... \n", "1856509931 {'engineer': 0.15692508334014804, 'journalist'... \n", "1856509938 {'businessperson': 0.06949004651596365, 'prese... \n", "1856509921 {'monarch': 0.42098378954483356, 'other': 0.27... \n", "1856509944 {'politician': 0.006580944290157723, 'none': 0... " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].sort_values(by=[\"uqs\"])[[\"input.videolocation\", \"uqs\", \"unit_annotation_score\"]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we show an example video fragment with low quality score, where workers couldn't agree on what annotation best describes the person in the video. The role of the person in the video is not directly specified, so the workers made assumptions based on the topic of discussion." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uqs 0.146278\n", "Name: 1856509905, dtype: float64\n", "\n", "\n", "Person types picked for the video below:\n", "other : 0.28844109454744976\n", "presenter : 0.144930232611664\n", "scientist : 0.22919915879815284\n", "journalist : 0.14534325083723273\n", "artist : 0.005696670688424697\n", "businessperson : 0.10163406871362905\n", "politician : 0.03781678033568675\n", "none : 0.21943896131257373\n", "lawyer : 0.020785177196552786\n", "philosopher : 0.0056418079486984535\n", "architect : 3.3953714172294374e-05\n", "astronaut : 3.3953714172294374e-05\n", "athlete : 3.3953714172294374e-05\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8576.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "print(results[\"units\"].sort_values(by=[\"uqs\"])[[\"uqs\"]].iloc[0])\n", "print(\"\\n\")\n", "\n", "print(\"Person types picked for the video below:\")\n", "for k, v in results[\"units\"].sort_values(by=[\"uqs\"])[[\"unit_annotation_score\"]].iloc[0][\"unit_annotation_score\"].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = list(results[\"units\"].sort_values(by=[\"uqs\"])[[\"input.videolocation\"]].iloc[0])\n", "HTML(\"<video width='320' height='240' controls><source src=\" + vid_url[0] + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unambiguous video fragments\n", "\n", "Similarly, a high unit quality score represents lack of ambiguity of the video fragment." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>input.videolocation</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509933</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'militaryperson':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509930</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'model': 0.000696...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509908</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'philosopher': 0....</td>\n", " </tr>\n", " <tr>\n", " <th>1856509909</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2643/86...</td>\n", " <td>0.998512</td>\n", " <td>{'none': 0.9992954829530591, 'fictionalcharact...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509937</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998456</td>\n", " <td>{'none': 0.9992692794931864, 'journalist': 0.0...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " input.videolocation uqs \\\n", "unit \n", "1856509933 https://joran.org/ct/entity.admin.unit.2833/90... 0.998529 \n", "1856509930 https://joran.org/ct/entity.admin.unit.2833/90... 0.998529 \n", "1856509908 https://joran.org/ct/entity.admin.unit.2649/85... 0.998529 \n", "1856509909 https://joran.org/ct/entity.admin.unit.2643/86... 0.998512 \n", "1856509937 https://joran.org/ct/entity.admin.unit.2833/90... 0.998456 \n", "\n", " unit_annotation_score \n", "unit \n", "1856509933 {'none': 0.9993036353660087, 'militaryperson':... \n", "1856509930 {'none': 0.9993036353660087, 'model': 0.000696... \n", "1856509908 {'none': 0.9993036353660087, 'philosopher': 0.... \n", "1856509909 {'none': 0.9992954829530591, 'fictionalcharact... \n", "1856509937 {'none': 0.9992692794931864, 'journalist': 0.0... " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"input.videolocation\", \"uqs\", \"unit_annotation_score\"]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we show an example unambiguous video fragment - no person appears in the video, so most workers picked the `none` option in the crowd task." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uqs 0.998529\n", "Name: 1856509933, dtype: float64\n", "\n", "\n", "Person types picked for the video below:\n", "none : 0.9993036353660087\n", "militaryperson : 0.0006963646339910918\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2833/9012.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"uqs\"]].iloc[0])\n", "print(\"\\n\")\n", "\n", "print(\"Person types picked for the video below:\")\n", "for k, v in results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"unit_annotation_score\"]].iloc[0][\"unit_annotation_score\"].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = list(results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"input.videolocation\"]].iloc[0])\n", "HTML(\"<video width='320' height='240' controls><source src=\" + vid_url[0] + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Worker Quality Scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The worker metrics are stored in `results[\"workers\"]`. The `wqs` columns in `results[\"workers\"]` contains the worker quality scores, capturing the overall agreement between one worker and all the other workers." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3587109</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>12.840000</td>\n", " <td>0.470469</td>\n", " <td>0.603399</td>\n", " <td>0.779698</td>\n", " <td>0.215188</td>\n", " <td>0.371369</td>\n", " <td>0.579445</td>\n", " </tr>\n", " <tr>\n", " <th>4316379</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>24.040000</td>\n", " <td>0.511478</td>\n", " <td>0.668219</td>\n", " <td>0.765435</td>\n", " <td>0.280311</td>\n", " <td>0.436881</td>\n", " <td>0.641618</td>\n", " </tr>\n", " <tr>\n", " <th>6330997</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>38.714286</td>\n", " <td>0.468025</td>\n", " <td>0.615484</td>\n", " <td>0.760417</td>\n", " <td>0.320348</td>\n", " <td>0.457103</td>\n", " <td>0.700822</td>\n", " </tr>\n", " <tr>\n", " <th>6339764</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>80.500000</td>\n", " <td>0.019778</td>\n", " <td>0.122949</td>\n", " <td>0.160866</td>\n", " <td>0.028307</td>\n", " <td>0.122603</td>\n", " <td>0.230880</td>\n", " </tr>\n", " <tr>\n", " <th>6367365</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>29.280000</td>\n", " <td>0.172523</td>\n", " <td>0.385336</td>\n", " <td>0.447722</td>\n", " <td>0.122195</td>\n", " <td>0.275177</td>\n", " <td>0.444062</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "3587109 25 25 1 12.840000 0.470469 0.603399 0.779698 \n", "4316379 25 25 1 24.040000 0.511478 0.668219 0.765435 \n", "6330997 7 7 1 38.714286 0.468025 0.615484 0.760417 \n", "6339764 10 10 1 80.500000 0.019778 0.122949 0.160866 \n", "6367365 25 25 1 29.280000 0.172523 0.385336 0.447722 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "3587109 0.215188 0.371369 0.579445 \n", "4316379 0.280311 0.436881 0.641618 \n", "6330997 0.320348 0.457103 0.700822 \n", "6339764 0.028307 0.122603 0.230880 \n", "6367365 0.122195 0.275177 0.444062 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of worker quality scores\n", "\n", "The histogram below shows the worker quality scores are distributed across a wide spectrum, from low to high quality workers." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Workers')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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A13VbN7jy9HnHvI71B4s/OQt1zTn2oww7WDzJOXsW3aDVnjNW155ztn8FuHhWapt3/DkMN1g8yXl74pztfwd8dUbqOhT4WL+9G91lkZ+ehdr64/YGbqS/72oW6qK7THtUv/1zdGMEi65v6n+ooX7oukdf739xHds/9i66T7LQJeangOuBC4GnzEhdB9B9IvoBXQ/l6hk6Z18Avgtc3v+cMiN1vQ+4uq/p7Ef7ZTx0bfOOHSwIJjxvf9aftyv687b3jNQVuktq1wBXAqtn5Zz1++8EjhuqpgnP2T7Auf3f5eXAIZvSnncWS1LjlsoYgSRpkQwCSWqcQSBJjTMIJKlxBoEkNc4g0GD6mRzfOGf/9CQnzNn/70netBHvtzLJVZuxvp2T/FU/4+M3k3w8ya6b8H4fXTejbJIT1s2SmuSti3ivY/uZOb/Wz4T5rxdblzSfQaAhnQscCD+ZjmE34Olznj8QOG+SN+rvDl+0R3j9h4Abquqp1U1fcT3djX6brKp+p6qu6Xc3KgiSPAc4DNi/uukhDmb9uWg22qaePy0tBoGGdB7wnH776cBVwPeS7JrkMXR3SF6azp8nuapfd+BwgCQvSPKVJKfQ3Xz0E0me0s/NfkCSrfvXX9R/gv69CV7/VLopF/50zsPvAp6R5Gn9az835/j3Jzmq3/7jvq2rkqzd0Ky2Sc5JsirJccB2/af6jyd517xe0n9J8oZ5L38icFf100NU1V1VdWt//AFJzktyRZILk+yYZNskH+nP3WVJXtgfe1SSU5J8ETirf+yP5pynP3nUvz0tWX4q0GCq6tZ0C/CsoPv0fz7drIrPoZsN9sqqeiDJy4FnAs+g6zVclOTL/dvsD+xbVd9Kv7hQkqfRzTl0VFVdkWQNcF9VHdAHzLlJzpj/+nnl7QNcXnMmFauqh5JcRhdQj7Ygyfur6l19LX9N9+n9s49wDo5J8gdV9cz++JXAp4H39r2k1XTTEM91BvDHSb5Od7f3SVX1pSTbACcBh1fVRUl2Av4ZeEPXVP18kr2BM5LsNefPv19V3ZPkELo5fZ5Nd3fvKUmeX1VfRk0xCDS08+hC4EC6aQV277fvo7t0BPA84MT+l/J3k3yJbiqO+4EL5/0SXw78HfDv51x6OQTYLw+v+LYz3S+8Bzbw+s3hhUneAmwPPI5uGocNBsF8VXVjkruTPAt4AnBZVd0975jvJ/kF4JfoFgs6Kd2qVZcAt1XVRf1x9wMkeR7wF/1j/5jk23RTPQOcWVX39NuH9D+X9fs70J0ng6AxBoGGtm6c4OfpLg19h27a5vuBj0zw+h/M278PuIkuPNYFQYCjq+r0uQcmecEGXr/ONcAzk2xVVT/uj9+KrldyKbCC9S+lbtsfsy3wP+nmFPpOkneue24jnEC3etjPAB/e0AF9KJ5DN7PplcCr6YJgY8398wf4s6r64CLeR0uIYwQa2nl0l07uqaqH+k+nu9BdHlo3UPwV4PD+Wv9y4Pl0EwVuyAN0M2n+ZpJf6x87HXhtkp8CSLJXksc+WlFVdT3dJ+O5axu8DTirqm4Cvg3sk27t612Ag/pj1v3SvyvJDsAk607/aF1tvc/QzcB5QF/7evoxirnTMj+zr+c64IlJDuiP27EfBP4K8Ov9Y3vRhdh1G6jjdOC3+7pJsnuSx09Qv5YYewQa2pV01/3/dt5jO1TVXf3+Z+iC4Qq6JfneUlW399e7/4Wq+kGSw4Azk3yf7hP2SvqBZ+BOJluz4LeBv0i38tlOwEV001zTf9r/JF0v5lv0l1Oq6t4kx/eP396/ZiFrga8lubSqfr0fFzkbuLc2vPDJDn1duwAP0n2baU3/usP757ajGx84mK6H8pd9z+FBurGTH84fw66qM5L8HHB+/9z3gSOBoZaK1Ixw9lFpA/oB6FOB11fVaVNuayu6y0+vrKpvTLMtaUPsEUgbUFXXAU+ddjvpbjL7HPAZQ0BjsUcgSY1zsFiSGmcQSFLjDAJJapxBIEmNMwgkqXEGgSQ17v8Darg2JLJlxRUAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(results[\"workers\"][\"wqs\"])\n", "plt.xlabel(\"Worker Quality Score\")\n", "plt.ylabel(\"Workers\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Low quality workers\n", "\n", "Low worker quality scores can be used to identify spam workers, or workers that have misunderstood the annotation task." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>44656777</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16.0</td>\n", " <td>0.000267</td>\n", " <td>0.014584</td>\n", " <td>0.018305</td>\n", " <td>0.015075</td>\n", " <td>0.073468</td>\n", " <td>0.205196</td>\n", " </tr>\n", " <tr>\n", " <th>44606916</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>70.6</td>\n", " <td>0.000573</td>\n", " <td>0.022518</td>\n", " <td>0.025460</td>\n", " <td>0.006691</td>\n", " <td>0.057175</td>\n", " <td>0.117028</td>\n", " </tr>\n", " <tr>\n", " <th>31508822</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>573.0</td>\n", " <td>0.001310</td>\n", " <td>0.038485</td>\n", " <td>0.034045</td>\n", " <td>0.021185</td>\n", " <td>0.102832</td>\n", " <td>0.206015</td>\n", " </tr>\n", " <tr>\n", " <th>15965551</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>30.0</td>\n", " <td>0.008752</td>\n", " <td>0.088362</td>\n", " <td>0.099049</td>\n", " <td>0.010984</td>\n", " <td>0.091147</td>\n", " <td>0.120512</td>\n", " </tr>\n", " <tr>\n", " <th>6339764</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>80.5</td>\n", " <td>0.019778</td>\n", " <td>0.122949</td>\n", " <td>0.160866</td>\n", " <td>0.028307</td>\n", " <td>0.122603</td>\n", " <td>0.230880</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "44656777 1 1 1 16.0 0.000267 0.014584 0.018305 \n", "44606916 5 5 1 70.6 0.000573 0.022518 0.025460 \n", "31508822 3 3 1 573.0 0.001310 0.038485 0.034045 \n", "15965551 25 25 1 30.0 0.008752 0.088362 0.099049 \n", "6339764 10 10 1 80.5 0.019778 0.122949 0.160866 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "44656777 0.015075 0.073468 0.205196 \n", "44606916 0.006691 0.057175 0.117028 \n", "31508822 0.021185 0.102832 0.206015 \n", "15965551 0.010984 0.091147 0.120512 \n", "6339764 0.028307 0.122603 0.230880 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].sort_values(by=[\"wqs\"]).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from low quality worker `44606916` (with the second lowest quality score) for video fragment `1856509900`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF LOW QUALITY WORKER 44606916 FOR VIDEO 1856509900:\n", "celebrity : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509900\n", "other : 9\n", "scientist : 6\n", "none : 3\n", "celebrity : 2\n", "journalist : 2\n", "presenter : 2\n", "archeologist : 1\n", "fictionalcharacter : 1\n", "producer : 1\n", "psychologist : 1\n", "writer : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8561.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import operator\n", "\n", "work_id = results[\"workers\"].sort_values(by=[\"wqs\"]).index[1]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[0]]\n", "\n", "print(\"JUDGMENTS OF LOW QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[0]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[0])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[0]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[0]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from the same low quality worker (`44606916`) for a second video fragment (`1856509903`):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF LOW QUALITY WORKER 44606916 FOR VIDEO 1856509903:\n", "archeologist : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509900\n", "none : 15\n", "other : 2\n", "fictionalcharacter : 1\n", "writer : 1\n", "archeologist : 1\n", "scientist : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8570.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[1]]\n", "\n", "print(\"JUDGMENTS OF LOW QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[1]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[0])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[1]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[1]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## High quality workers\n", "\n", "High worker quality scores can be used to identify reliable workers." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6432269</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>23.36</td>\n", " <td>0.789141</td>\n", " <td>0.836564</td>\n", " <td>0.943312</td>\n", " <td>0.579480</td>\n", " <td>0.671050</td>\n", " <td>0.863542</td>\n", " </tr>\n", " <tr>\n", " <th>15176395</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>17.24</td>\n", " <td>0.784259</td>\n", " <td>0.832825</td>\n", " <td>0.941685</td>\n", " <td>0.571864</td>\n", " <td>0.664663</td>\n", " <td>0.860381</td>\n", " </tr>\n", " <tr>\n", " <th>39021485</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>17.24</td>\n", " <td>0.781853</td>\n", " <td>0.831344</td>\n", " <td>0.940468</td>\n", " <td>0.573010</td>\n", " <td>0.666604</td>\n", " <td>0.859595</td>\n", " </tr>\n", " <tr>\n", " <th>40712302</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>16.56</td>\n", " <td>0.755567</td>\n", " <td>0.816770</td>\n", " <td>0.925067</td>\n", " <td>0.537336</td>\n", " <td>0.640505</td>\n", " <td>0.838926</td>\n", " </tr>\n", " <tr>\n", " <th>43620110</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>194.50</td>\n", " <td>0.752887</td>\n", " <td>0.772451</td>\n", " <td>0.974672</td>\n", " <td>0.637002</td>\n", " <td>0.674885</td>\n", " <td>0.943867</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "6432269 25 25 1 23.36 0.789141 0.836564 0.943312 \n", "15176395 25 25 1 17.24 0.784259 0.832825 0.941685 \n", "39021485 25 25 1 17.24 0.781853 0.831344 0.940468 \n", "40712302 25 25 1 16.56 0.755567 0.816770 0.925067 \n", "43620110 2 2 1 194.50 0.752887 0.772451 0.974672 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "6432269 0.579480 0.671050 0.863542 \n", "15176395 0.571864 0.664663 0.860381 \n", "39021485 0.573010 0.666604 0.859595 \n", "40712302 0.537336 0.640505 0.838926 \n", "43620110 0.637002 0.674885 0.943867 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from worker `6432269` (with the highest worker quality score) for video fragment `1856509904`:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF HIGH QUALITY WORKER 6432269 FOR VIDEO 1856509904:\n", "scientist : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509908\n", "scientist : 10\n", "other : 6\n", "presenter : 3\n", "farmer : 2\n", "businessperson : 2\n", "journalist : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8574.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_id = results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).index[0]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[0]]\n", "\n", "print(\"JUDGMENTS OF HIGH QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[0]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[1])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[0]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[0]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from worker `6432269` (with the highest worker quality score) for video fragment `1856509908`:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF HIGH QUALITY WORKER 6432269 FOR VIDEO 1856509908:\n", "none : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509908\n", "none : 19\n", "philosopher : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8583.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_id = results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).index[0]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[1]]\n", "\n", "print(\"JUDGMENTS OF HIGH QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[1]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[1])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[1]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[1]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Worker Quality vs. # Annotations\n", "\n", "As we can see from the plot below, there is no clear correlation between worker quality and number of annotations collected from the worker." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '# Annotations')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(results[\"workers\"][\"wqs\"], results[\"workers\"][\"judgment\"])\n", "plt.xlabel(\"WQS\")\n", "plt.ylabel(\"# Annotations\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Annotation Quality Scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The annotation metrics are stored in `results[\"annotations\"]`. The `aqs` column contains the annotation quality scores, capturing the overall worker agreement over one annotation.\n", "\n", "There is a slight correlation between the number of annotations (column `output.selected_answer`) and the annotation quality score - annotations that have not been picked often (e.g. `engineer`, `farmer`) tend to have lower quality scores - this is because these annotations are less present in the corpus, therefore the likelihood that they are picked is lower, and when they do get picked it is more likely it was a mistake by the worker. However, it is not a set rule, and there exist annotations that are picked less often (e.g. `astronaut`) that can have high quality scores." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>output.selected_answer</th>\n", " <th>aqs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>astronaut</th>\n", " <td>21</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>none</th>\n", " <td>379</td>\n", " <td>0.858</td>\n", " </tr>\n", " <tr>\n", " <th>militaryperson</th>\n", " <td>20</td>\n", " <td>0.846</td>\n", " </tr>\n", " <tr>\n", " <th>celebrity</th>\n", " <td>157</td>\n", " <td>0.746</td>\n", " </tr>\n", " <tr>\n", " <th>model</th>\n", " <td>29</td>\n", " <td>0.457</td>\n", " </tr>\n", " <tr>\n", " <th>other</th>\n", " <td>176</td>\n", " <td>0.450</td>\n", " </tr>\n", " <tr>\n", " <th>presenter</th>\n", " <td>84</td>\n", " <td>0.430</td>\n", " </tr>\n", " <tr>\n", " <th>scientist</th>\n", " <td>65</td>\n", " <td>0.407</td>\n", " </tr>\n", " <tr>\n", " <th>monarch</th>\n", " <td>11</td>\n", " <td>0.319</td>\n", " </tr>\n", " <tr>\n", " <th>fictionalcharacter</th>\n", " <td>89</td>\n", " <td>0.306</td>\n", " </tr>\n", " <tr>\n", " <th>athlete</th>\n", " <td>15</td>\n", " <td>0.280</td>\n", " </tr>\n", " <tr>\n", " <th>artist</th>\n", " <td>59</td>\n", " <td>0.251</td>\n", " </tr>\n", " <tr>\n", " <th>criminal</th>\n", " <td>8</td>\n", " <td>0.127</td>\n", " </tr>\n", " <tr>\n", " <th>journalist</th>\n", " <td>31</td>\n", " <td>0.076</td>\n", " </tr>\n", " <tr>\n", " <th>businessperson</th>\n", " <td>29</td>\n", " <td>0.062</td>\n", " </tr>\n", " <tr>\n", " <th>writer</th>\n", " <td>22</td>\n", " <td>0.057</td>\n", " </tr>\n", " <tr>\n", " <th>politician</th>\n", " <td>12</td>\n", " <td>0.056</td>\n", " </tr>\n", " <tr>\n", " <th>engineer</th>\n", " <td>8</td>\n", " <td>0.036</td>\n", " </tr>\n", " <tr>\n", " <th>farmer</th>\n", " <td>4</td>\n", " <td>0.001</td>\n", " </tr>\n", " <tr>\n", " <th>producer</th>\n", " <td>13</td>\n", " <td>0.001</td>\n", " </tr>\n", " <tr>\n", " <th>philosopher</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>psychologist</th>\n", " <td>3</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>sportsmanager</th>\n", " <td>0</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>archeologist</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>architect</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>judge</th>\n", " <td>2</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>chef</th>\n", " <td>1</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>lawyer</th>\n", " <td>2</td>\n", " <td>0.000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " output.selected_answer aqs\n", "astronaut 21 1.000\n", "none 379 0.858\n", "militaryperson 20 0.846\n", "celebrity 157 0.746\n", "model 29 0.457\n", "other 176 0.450\n", "presenter 84 0.430\n", "scientist 65 0.407\n", "monarch 11 0.319\n", "fictionalcharacter 89 0.306\n", "athlete 15 0.280\n", "artist 59 0.251\n", "criminal 8 0.127\n", "journalist 31 0.076\n", "businessperson 29 0.062\n", "writer 22 0.057\n", "politician 12 0.056\n", "engineer 8 0.036\n", "farmer 4 0.001\n", "producer 13 0.001\n", "philosopher 8 0.000\n", "psychologist 3 0.000\n", "sportsmanager 0 0.000\n", "archeologist 8 0.000\n", "architect 8 0.000\n", "judge 2 0.000\n", "chef 1 0.000\n", "lawyer 2 0.000" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"annotations\"][\"output.selected_answer\"] = 0\n", "\n", "for idx in results[\"judgments\"].index:\n", " for k,v in results[\"judgments\"][\"output.selected_answer\"][idx].items():\n", " if v > 0:\n", " results[\"annotations\"].loc[k, \"output.selected_answer\"] += 1\n", " \n", "\n", "results[\"annotations\"] = results[\"annotations\"].sort_values(by=[\"aqs\"], ascending=False)\n", "results[\"annotations\"].round(3)[[\"output.selected_answer\", \"aqs\"]]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "rows = []\n", "header = [\"unit\", \"videolocation\", \"subtitles\", \"imagetags\", \"subtitletags\", \"uqs\", \"uqs_initial\"]\n", "annotation_vector = [\"archeologist\", \"architect\", \"artist\", \"astronaut\", \"athlete\", \"businessperson\",\"celebrity\", \n", " \"chef\", \"criminal\", \"engineer\", \"farmer\", \"fictionalcharacter\", \"journalist\", \"judge\", \n", " \"lawyer\", \"militaryperson\", \"model\", \"monarch\", \"philosopher\", \"politician\", \"presenter\", \n", " \"producer\", \"psychologist\", \"scientist\", \"sportsmanager\", \"writer\", \"none\", \"other\"]\n", "header.extend(annotation_vector)\n", "annotation_vector_in = [\"archeologist_initial_initial\", \"architect_initial\", \"artist_initial\", \"astronaut_initial\", \n", " \"athlete_initial\", \"businessperson_initial\",\"celebrity_initial\", \"chef_initial\", \n", " \"criminal_initial\", \"engineer_initial\", \"farmer_initial\", \"fictionalcharacter_initial\", \n", " \"journalist_initial\", \"judge_initial\", \"lawyer_initial\", \"militaryperson_initial\", \n", " \"model_initial\", \"monarch_initial\", \"philosopher_initial\", \"politician_initial\", \n", " \"presenter_initial\", \"producer_initial\", \"psychologist_initial\", \"scientist_initial\", \n", " \"sportsmanager_initial\", \"writer_initial\", \"none_initial\", \"other_initial\"]\n", "header.extend(annotation_vector_in)\n", "units = results[\"units\"].reset_index()\n", "for i in range(len(units.index)):\n", " row = [units[\"unit\"].iloc[i], units[\"input.videolocation\"].iloc[i], units[\"input.subtitles\"].iloc[i], \\\n", " units[\"input.imagetags\"].iloc[i], units[\"input.subtitletags\"].iloc[i], units[\"uqs\"].iloc[i], \n", " units[\"uqs_initial\"].iloc[i]]\n", " for item in annotation_vector:\n", " row.append(units[\"unit_annotation_score\"].iloc[i][item])\n", " for item in annotation_vector_in:\n", " row.append(units[\"unit_annotation_score_initial\"].iloc[i][item])\n", " rows.append(row)\n", "rows = pd.DataFrame(rows, columns=header)\n", "rows.to_csv(\"../data/results/multchoice-people-video-units.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "results[\"workers\"].to_csv(\"../data/results/multchoice-people-video-workers.csv\", index=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "results[\"annotations\"].to_csv(\"../data/results/multchoice-people-video-annotations.csv\", index=True)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
NuclearTalent/NuclearStructure	doc/pub/mbpt/ipynb/mbpt.ipynb	1	24951	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Slides for PHY981 -->\n", "<!-- dom:TITLE: Many-body perturbation theory and effective operators for the nuclear shell model -->\n", "# Many-body perturbation theory and effective operators for the nuclear shell model\n", "<!-- dom:AUTHOR: Morten Hjorth-Jensen at [National Superconducting Cyclotron Laboratory](http://www.nscl.msu.edu/) and [Department of Physics and Astronomy](https://www.pa.msu.edu/), [Michigan State University](http://www.msu.edu/), East Lansing, MI 48824, USA -->\n", "<!-- Author: --> \n", "Morten Hjorth-Jensen, [National Superconducting Cyclotron Laboratory](http://www.nscl.msu.edu/) and [Department of Physics and Astronomy](https://www.pa.msu.edu/), [Michigan State University](http://www.msu.edu/), East Lansing, MI 48824, USA\n", "\n", "Date: Jul 19, 2017\n", "\n", "Copyright 2013-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", "## Many-body perturbation theory\n", "\n", "We assume here that we are only interested in the ground state of the system and \n", "expand the exact wave function in term of a series of Slater determinants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle = \\vert \\Phi_0\\rangle + \\sum_{m=1}^{\\infty}C_m\\vert \\Phi_m\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have assumed that the true ground state is dominated by the \n", "solution of the unperturbed problem, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{H}_0\\vert \\Phi_0\\rangle= W_0\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The state $\\vert \\Psi_0\\rangle$ is not normalized, rather we have used an intermediate \n", "normalization $\\langle \\Phi_0 \\vert \\Psi_0\\rangle=1$ since we have $\\langle \\Phi_0\\vert \\Phi_0\\rangle=1$. \n", "\n", "\n", "## Many-body perturbation theory\n", "The Schroedinger equation is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{H}\\vert \\Psi_0\\rangle = E\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and multiplying the latter from the left with $\\langle \\Phi_0\\vert $ gives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\hat{H}\\vert \\Psi_0\\rangle = E\\langle \\Phi_0\\vert \\Psi_0\\rangle=E,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and subtracting from this equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Psi_0\\vert \\hat{H}_0\\vert \\Phi_0\\rangle= W_0\\langle \\Psi_0\\vert \\Phi_0\\rangle=W_0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and using the fact that the both operators $\\hat{H}$ and $\\hat{H}_0$ are hermitian \n", "results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=E-W_0=\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is an exact result. We call this quantity the correlation energy.\n", "\n", "\n", "## Many-body perturbation theory\n", "This equation forms the starting point for all perturbative derivations. However,\n", "as it stands it represents nothing but a mere formal rewriting of Schroedinger's equation and is not of much practical use. The exact wave function $\\vert \\Psi_0\\rangle$ is unknown. In order to obtain a perturbative expansion, we need to expand the exact wave function in terms of the interaction $\\hat{H}_I$. \n", "\n", "Here we have assumed that our model space defined by the operator $\\hat{P}$ is one-dimensional, meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{P}= \\vert \\Phi_0\\rangle \\langle \\Phi_0\\vert ,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}=\\sum_{m=1}^{\\infty}\\vert \\Phi_m\\rangle \\langle \\Phi_m\\vert .\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "We can thus rewrite the exact wave function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle= (\\hat{P}+\\hat{Q})\\vert \\Psi_0\\rangle=\\vert \\Phi_0\\rangle+\\hat{Q}\\vert \\Psi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Going back to the Schr\\\"odinger equation, we can rewrite it as, adding and a subtracting a term $\\omega \\vert \\Psi_0\\rangle$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\left(\\omega-\\hat{H}_0\\right)\\vert \\Psi_0\\rangle=\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\omega$ is an energy variable to be specified later. \n", "\n", "## Many-body perturbation theory\n", "We assume also that the resolvent of $\\left(\\omega-\\hat{H}_0\\right)$ exits, that is\n", "it has an inverse which defined the unperturbed Green's function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\left(\\omega-\\hat{H}_0\\right)^{-1}=\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can rewrite Schroedinger's equation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\frac{1}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and multiplying from the left with $\\hat{Q}$ results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\vert \\Psi_0\\rangle=\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is possible since we have defined the operator $\\hat{Q}$ in terms of the eigenfunctions of $\\hat{H}$.\n", "\n", "\n", "\n", "## Many-body perturbation theory\n", "These operators commute meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}\\hat{Q}=\\hat{Q}\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}=\\frac{\\hat{Q}}{\\left(\\omega-\\hat{H}_0\\right)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these definitions we can in turn define the wave function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\vert \\Phi_0\\rangle+\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This equation is again nothing but a formal rewrite of Schr\\\"odinger's equation\n", "and does not represent a practical calculational scheme. \n", "It is a non-linear equation in two unknown quantities, the energy $E$ and the exact\n", "wave function $\\vert \\Psi_0\\rangle$. We can however start with a guess for $\\vert \\Psi_0\\rangle$ on the right hand side of the last equation.\n", "\n", "\n", "## Many-body perturbation theory\n", " The most common choice is to start with the function which is expected to exhibit the largest overlap with the wave function we are searching after, namely $\\vert \\Phi_0\\rangle$. This can again be inserted in the solution for $\\vert \\Psi_0\\rangle$ in an iterative fashion and if we continue along these lines we end up with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\sum_{i=0}^{\\infty}\\left\\{\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\right\\}^i\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for the wave function and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\sum_{i=0}^{\\infty}\\langle \\Phi_0\\vert \\hat{H}_I\\left\\{\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\right\\}^i\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is now a perturbative expansion of the exact energy in terms of the interaction\n", "$\\hat{H}_I$ and the unperturbed wave function $\\vert \\Psi_0\\rangle$.\n", "\n", "\n", "## Many-body perturbation theory\n", "In our equations for $\\vert \\Psi_0\\rangle$ and $\\Delta E$ in terms of the unperturbed\n", "solutions $\\vert \\Phi_i\\rangle$ we have still an undetermined parameter $\\omega$\n", "and a dependecy on the exact energy $E$. Not much has been gained thus from a practical computational point of view. \n", "\n", "\n", "## Many-body perturbation theory\n", "In Brilluoin-Wigner perturbation theory it is customary to set $\\omega=E$. This results in the following perturbative expansion for the energy $\\Delta E$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "7\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "9\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This expression depends however on the exact energy $E$ and is again not very convenient from a practical point of view. It can obviously be solved iteratively, by starting with a guess for $E$ and then solve till some kind of self-consistency criterion has been reached. \n", "\n", "Actually, the above expression is nothing but a rewrite again of the full Schr\\\"odinger equation. \n", "\n", "## Many-body perturbation theory\n", "Defining $e=E-\\hat{H}_0$ and recalling that $\\hat{H}_0$ commutes with \n", "$\\hat{Q}$ by construction and that $\\hat{Q}$ is an idempotent operator\n", "$\\hat{Q}^2=\\hat{Q}$. \n", "Using this equation in the above expansion for $\\Delta E$ we can write the denominator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "1\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\left[\\frac{1}{\\hat{e}}+\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\n", "\\frac{1}{\\hat{e}}+\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\n", "\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\\frac{1}{\\hat{e}}+\\dots\\right]\\hat{Q}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "Inserted in the expression for $\\Delta E$ leads to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\n", "\\langle \\Phi_0\\vert \\hat{H}_I+\\hat{H}_I\\hat{Q}\\frac{1}{E-\\hat{H}_0-\\hat{Q}\\hat{H}_I\\hat{Q}}\\hat{Q}\\hat{H}_I\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In RS perturbation theory we set $\\omega = W_0$ and obtain the following expression for the energy difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "4\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)+\n", "\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "Recalling that $\\hat{Q}$ commutes with $\\hat{H_0}$ and since $\\Delta E$ is a constant we obtain that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\Delta E\\vert \\Phi_0\\rangle = \\hat{Q}\\Delta E\\vert \\hat{Q}\\Phi_0\\rangle = 0.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inserting this results in the expression for the energy results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "We can now this expression in terms of a perturbative expression in terms\n", "of $\\hat{H}_I$ where we iterate the last expression in terms of $\\Delta E$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\sum_{i=1}^{\\infty}\\Delta E^{(i)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get the following expression for $\\Delta E^{(i)}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(1)}=\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is just the contribution to first order in perturbation theory," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is the contribution to second order.\n", "\n", "\n", "## Many-body perturbation theory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(3)}=\\langle \\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\Phi_0\\rangle-\n", "\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Phi_0\\rangle\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "being the third-order contribution. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "\n", "In the shell-model lectures we showed that we could rewrite the exact state function for say the ground state, as a linear expansion in terms of all possible Slater determinants. That is, we \n", "define the ansatz for the ground state as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Phi_0\\rangle = \\left(\\prod_{i\\le F}\\hat{a}_{i}^{\\dagger}\\right)\|0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the index $i$ defines different single-particle states up to the Fermi level. We have assumed that we have $N$ fermions. \n", "A given one-particle-one-hole ($1p1h$) state can be written as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Phi_i^a\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_i\|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while a $2p2h$ state can be written as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Phi_{ij}^{ab}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i\|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and a general $ApAh$ state as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Phi_{ijk\\dots}^{abc\\dots}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_{c}^{\\dagger}\\dots\\hat{a}_k\\hat{a}_j\\hat{a}_i\|\\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "We use letters $ijkl\\dots$ for states below the Fermi level and $abcd\\dots$ for states above the Fermi level. A general single-particle state is given by letters $pqrs\\dots$.\n", "\n", "We can then expand our exact state function for the ground state \n", "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Psi_0\\rangle=C_0\|\\Phi_0\\rangle+\\sum_{ai}C_i^a\|\\Phi_i^a\\rangle+\\sum_{abij}C_{ij}^{ab}\|\\Phi_{ij}^{ab}\\rangle+\\dots\n", "=(C_0+\\hat{C})\|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have introduced the so-called correlation operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{C}=\\sum_{ai}C_i^a\\hat{a}_{a}^{\\dagger}\\hat{a}_i +\\sum_{abij}C_{ij}^{ab}\\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i+\\dots\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the normalization of $\\Psi_0$ is at our disposal and since $C_0$ is by hypothesis non-zero, we may arbitrarily set $C_0=1$ with \n", "corresponding proportional changes in all other coefficients. Using this so-called intermediate normalization we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Psi_0 \| \\Phi_0 \\rangle = \\langle \\Phi_0 \| \\Phi_0 \\rangle = 1,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "resulting in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\|\\Psi_0\\rangle=(1+\\hat{C})\|\\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "In a shell-model calculation, the unknown coefficients in $\\hat{C}$ are the \n", "eigenvectors which result from the diagonalization of the Hamiltonian matrix.\n", "\n", "How can we use perturbation theory to determine the same coefficients? Let us study the contributions to second order in the interaction, namely" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The intermediate states given by $\\hat{Q}$ can at most be of a $2p-2h$ nature if we have a two-body Hamiltonian. This means that second order in the perturbation theory can have $1p-1h$ and $2p-2h$ at most as intermediate states. When we diagonalize, these contributions are included to infinite order. This means that higher-orders in perturbation theory bring in more complicated correlations. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "If we limit the attention to a Hartree-Fock basis, then we have that\n", "$\\langle\\Phi_0\\vert \\hat{H}_I \\vert 2p-2h\\rangle$ is the only contribution and the contribution to the energy reduces to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\frac{1}{4}\\sum_{abij}\\langle ij\\vert \\hat{v}\\vert ab\\rangle \\frac{\\langle ab\\vert \\hat{v}\\vert ij\\rangle}{\\epsilon_i+\\epsilon_j-\\epsilon_a-\\epsilon_b}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "If we compare this to the correlation energy obtained from full configuration interaction theory with a Hartree-Fock basis, we found that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "E-E_0 =\\Delta E=\n", "\\sum_{abij}\\langle ij \| \\hat{v}\| ab \\rangle C_{ij}^{ab},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the energy $E_0$ is the reference energy and $\\Delta E$ defines the so-called correlation energy.\n", "\n", "We see that if we set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C_{ij}^{ab} =\\frac{1}{4}\\frac{\\langle ab \\vert \\hat{v} \\vert ij \\rangle}{\\epsilon_i+\\epsilon_j-\\epsilon_a-\\epsilon_b},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have a perfect agreement between FCI and MBPT. However, FCI includes such $2p-2h$ correlations to infinite order. In order to make a meaningful comparison we would at least need to sum such correlations to infinite order in perturbation theory. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "Summing up, we can see that\n", "* MBPT introduces order-by-order specific correlations and we make comparisons with exact calculations like FCI\n", "\n", "* At every order, we can calculate all contributions since they are well-known and either tabulated or calculated on the fly.\n", "\n", "* MBPT is a non-variational theory and there is no guarantee that higher orders will improve the convergence. \n", "\n", "* However, since FCI calculations are limited by the size of the Hamiltonian matrices to diagonalize (today's most efficient codes can attach dimensionalities of ten billion basis states, MBPT can function as an approximative method which gives a straightforward (but tedious) calculation recipe. \n", "\n", "* MBPT has been widely used to compute effective interactions for the nuclear shell-model.\n", "\n", "* But there are better methods which sum to infinite order important correlations. Coupled cluster theory is one of these methods. \n", "\n", "Codes for computing effective Hamiltonians for the shell model as well as many other codes can be obtained from the [CENS website](https://github.com/ManyBodyPhysics/CENS)." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
erikdrysdale/erikdrysdale.github.io	_rmd/extra_BCa/bca_python.ipynb	1	406413	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing the BCa Bootstrapping in Python\n", "\n", "The [bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) is a powerful tool for carrying out inference on statistics whose distribution is unknown. The non-parametric version of the bootstrap obtains variation around the point estimate of a statistic by randomly resampling the data with replacement and recalculating the bootstrap-statistic based on these resamples. This simulated distribution can be used to obtain (close-to) valid frequentist inference measures like p-values and confidence intervals (CIs). This post will show how to implement the bias-corrected and accelerated ([BCa](https://www.tandfonline.com/doi/abs/10.1080/01621459.1987.10478410)) bootstrap to calculate either one and two-sided CIs. A good discussion of bootstrapped p-values can be found [here](http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1127.pdf). Nathaniel Helwig's excellent class [notes](http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf) are used or paraphrased throughout this post.\n", "\n", "If you are want to use an existing `python` package, [`arch`](https://arch.readthedocs.io/en/latest) is an excellent choice (I have made use of it in other [posts](http://www.erikdrysdale.com/threshold_and_power)). There are two advantages for implementing a custom procedure. First, `arch` is limited to a number of sampling strategies, and I recently found myself needing to implement a stratified bootstrap, which I could not do with the package. Second, the `conf_int` method only allows inference for a specific level (i.e. type-I error rate) for every sampling run. If you are carrying out statistical simulations this is a huge waste since computing the 90% or 95% CI can be done from the same bootstrap simulation. Lastly this blueprint can modified for other uses.\n", "\n", "The motivating example for this post will be to estimate the threshold for determining the positive predictive value ([PPV](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values)) of a classifier. This statistic is a good target for the BCa bootstrap because its distribution is skewed, biased, and non-normal. Anyone developing machine learning tools for the real world will understand the importance of [this task](http://www.erikdrysdale.com/threshold_and_power/), since researchers want to ensure their algorithms obtain at least some level of performance in a generalized setting. For simplicity the scores of the algorithm will come from a normal distribution to allow for an easy comparison to the \"ground truth\". \n", "\n", "# (1) Background\n", "\n", "To begin I'll establish the notation that will be used through the rest of the post. Define the statistic of interest as $\\theta = s(X)$, where $X_i \\sim F$ from IID draws. The statistic $s$ can be anything like the mean, median, skew, PPV, etc. Of course, if we knew the cdf $F$, one could simply sample from it and estimate $\\theta$ by appealing to the law of large numbers. However researchers almost never know the distribution the data comes from, and if they do, the exact parameters of the distribution are unknown. Instead we can use an empirical draw of the data: $\\hat{X} = (X_1, \\dots, X_n)$ to approximate the actual CDF: $\\hat{F}(\\hat{X} \\leq x) \\approx F(X \\leq x)$. From the [Glivenko-Cantelli theorem](https://en.wikipedia.org/wiki/Glivenko%E2%80%93Cantelli_theorem) we know that as $n \\to \\infty$, the empirical CDF (ECDF) approaches the true CDF.\n", "\n", "Using the ECDF from a given draw of data as an estimate of $F$, means using the CDF of a discrete random variable. This non-parametric approximation naturally creates some estimation error, but for a reasonably sized $n$ it will be small. When bootstrapping is referred to \"sampling with replacement\", it is actually referring to sampling from the ECDF. Since this is a discrete distribution it means that some values can be sampled more than once. Draws for the ECDF, or bootstrapped samples, will be denoted as follows:\n", "\n", "$$\n", "\\begin{align}\n", "\\hat\\theta &= s(X) \\\\\n", "X_1^, \\dots, X_n^* &\\sim \\hat{F} \\\\\n", "\\hat\\theta^* &= s(X^)\n", "\\end{align}\n", "$$\n", "\n", "The PPV is the ratio of the true positive rate (TPR) to the predicted positive rate (which includes the false positive rate (FPR)), weighted by the prevalence of the binary outcome: $\\mu_y = E[y==1]$. In this post we will assume that the scores of the classifier come from a normal distribution: $x\|y=i \\sim N(\\mu_i,\\sigma_i^2)$, for $i=[1,2]$. The distribution for the ground-truth PPV will be smooth and monotonically increasing over $t$ since $\\mu_y$ is fixed, as defined by \\eqref{eq:PPV_true}. However, its empirical estimate will necessarily by discontinuous, and not even necessarily monotonic! This is because the count of the number of TPs and FPs is used rather than the theoretical CDF and prevalence. Note that $\\Phi$ refers to the standard normal CDF.\n", "\n", "$$\n", "\\begin{align}\n", "\\text{PPV}_\\Phi(t) &= \\frac{\\Phi_1(t)\\cdot\\mu_y}{\\Phi_1\\cdot\\mu_y + \\Phi_0\\cdot(1-\\mu_y)} \\label{eq:PPV_true} \\\\\n", "\\Phi_1(t) &= \\Phi([\\mu_1 - t]/\\sigma_1), \\hspace{3mm} \\Phi_0(t) = \\Phi([\\mu_0 - t]/\\sigma_0) \\\\\n", "\\hat{\\text{PPV}}(t) &= \\frac{NTP(t)}{NTP(t) + NFP(t)} \\\\\n", "NTP(t) &= \\sum_{i: y_i =1} y_i \\geq t, \\hspace{3mm} NFP(t) = \\sum_{i: y_i =0} y_i \\geq t\n", "\\end{align}\n", "$$\n", "\n", "The codeblock below loads in the necessary modules and shows that the theoretical PPV aligns with the average empirical PPV across different thresholds ($t$)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 450x350 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766715492109)>\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 450x350 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766715658605)>\n" ] } ], "source": [ "import os\n", "from time import time\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.utils import resample\n", "from numpy.random import randn\n", "from scipy.stats import norm, t\n", "import plotnine\n", "from plotnine import \n", "from scipy.optimize import minimize_scalar\n", "from scipy import stats\n", "# !pip install git+https://github.com/retostauffer/python-colorspace \n", "from colorspace.colorlib import HCL\n", "\n", "def gg_color_hue(n): \n", " hues = np.linspace(15, 375, num=n + 1)[:n]\n", " hcl = []\n", " for h in hues:\n", " hcl.append(HCL(H=h, L=65, C=100).colors()[0])\n", " return hcl\n", "\n", "def dgp(n0, n1, mu):\n", " \"\"\"\n", " FUNCTION TO CREATE BINARY RISK SCORES\n", " \"\"\"\n", " y = np.append(np.repeat(0, n0), np.repeat(1, n1))\n", " score = np.append(randn(n0), randn(n1) + mu)\n", " return y, score\n", "\n", "def PPV_theory(thresh, mu0, mu1, prev):\n", " \"\"\"\n", " FUNCTION TO RETURN ASYMPTOTIC PPV FOR NORMALLY DISTRIBUTED SCORES\n", " \"\"\"\n", " tpr = norm.cdf(mu1 - thresh)\n", " fpr = norm.cdf(mu0 - thresh)\n", " ppv = tprprev / (tprprev + fpr(1-prev))\n", " return ppv\n", "\n", "def PPV_fun(y, score, thresh):\n", " \"\"\"\n", " FUNCTION TO CALCULATE EMPIRICAL PPV VALUE FOR A GIVEN THRESHOLD\n", " \"\"\"\n", " ntp = np.sum(score[y == 1] >= thresh)\n", " nfp = np.sum(score[y == 0] >= thresh)\n", " if ntp == nfp == 0:\n", " return np.NaN\n", " else:\n", " ppv = ntp / (ntp + nfp)\n", " return ppv\n", "\n", "thresh_seq = np.round(np.arange(-3,3.1,0.1),2)\n", "theory_seq = np.array([PPV_theory(tt, mu0=0, mu1=2, prev=0.1) for tt in thresh_seq])\n", "\n", "mu0 = 0\n", "mu1 = 2\n", "n0, n1 = 90, 10\n", "prev = n1 / (n1 + n0)\n", "\n", "np.random.seed(1234)\n", "nsim = 5000\n", "\n", "mat = np.zeros([nsim, len(thresh_seq)])\n", "for ii in range(nsim):\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " mat[ii] = [PPV_fun(y, score, tt) for tt in thresh_seq]\n", " \n", "df_sim = pd.DataFrame({'thresh':thresh_seq, 'theory':theory_seq, 'emp':np.nanmean(mat,0)}).melt('thresh',None,'tt')\n", "\n", "plotnine.options.figure_size = (4.5, 3.5)\n", "gg_theory = (ggplot(df_sim, aes(x='thresh', y='value', color='tt')) + theme_bw() + \n", " geom_point() + labs(x='Threshold', y='PPV') + \n", " ggtitle('Figure 1: Simulated vs Theoretical PPV') + \n", " scale_color_discrete(name='Type',labels=['Empirical','Theory']))\n", "print(gg_theory)\n", "\n", "np.random.seed(102)\n", "y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", "thresh_seq = np.sort(np.unique(score))\n", "df_ex = pd.DataFrame({'ppv':[PPV_fun(y, score, tt) for tt in thresh_seq],'thresh':thresh_seq})\n", "yy, xx = tuple(df_ex.query('ppv<0.4').tail(1).values.flatten())\n", "yend, xend = tuple(df_ex.query('ppv>0.4').head(1).values.flatten())\n", "xstar = (0.4-yy)/((yend - yy)/(xend - xx))+xx\n", "\n", "plotnine.options.figure_size = (4.5,3.5)\n", "gg_ppv_ex = (ggplot(df_ex.query('thresh>0.5 & thresh<1.5 & ppv>0.25 & ppv<0.5 & ppv!=0.4'),aes(x='thresh',y='ppv')) + \n", " theme_bw() + geom_step() + \n", " labs(x='Threshold', y='PPV') + \n", " ggtitle('Figure 2: Empirical PPV interpolation') + \n", " geom_segment(x=xx,xend=xend,y=yy,yend=yend,color='red') + \n", " geom_hline(yintercept=0.4,color='blue',linetype='--') + \n", " geom_vline(xintercept=xstar,color='darkgreen',linetype='--'))\n", "print(gg_ppv_ex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Though the asymptotic PPV curve is smooth (see Figure 1), the empirical PPV has is non-smooth (as Figure 2 shows). Our statistic of interest will be the value of the threshold that is needed to meet a targeted PPV. For the PPV curve, this amounts to the following:\n", "\n", "$$\n", "\\begin{align}\n", "t^(p) &= \\Big\\{\\inf_t : PPV(t)=p \\Big\\} \\label{eq:tstar}\n", "\\end{align}\n", "$$\n", "\n", "Note that we want \\eqref{eq:tstar} to find the infimum of thresholds since if there are two distinct thresholds that obtain the same PPV, the smaller one will have a higher senitivity. For the empirical curve, the threshold will need to be interpolated when the targeted value cannot be found. For example, if the targeted PPV is 0.5, but the closest values we can find are PPVs of 0.49 and 0.54, for threshold values of 1 and 2, respectively, then a linear interpolation will estimate a threshold of 1.2. In the case where the targeted PPV is larger than the largest empirical PPV, an interpolation will also be used. However it is not advised to pick PPVs that are generally beyond the range of the classifier because these estimates will be noisy.\n", "\n", "The `thresh_ppv` function below also has a built-it [Jackknife](https://en.wikipedia.org/wiki/Jackknife_resampling) estimate of the statistic, since these will be required later. The Jackknife calculates the statistic by leaving one sample value, so there are $n$ estimates of it. Because the empirical PPV is discrete, deleting an observation whose value is below a given threshold will not affect the PPV and hence the choice of threshold. Deleting a positive label observation whose score is at or above the threshold will necessarily lower the PPV, and so we can interpolate the new slope/intercept position to obtain the original PPV. Similarly, deleting a negative observation will make whatever the the next smallest threshold is to the current one the optimal choice.[[^1]] It is exceedingly quick therefore to approximate the leave-one-observation out statistic for the PPV. \n", "\n", "Note the `draw_samp` function, which draws data with replacement, can make use of the `strafify` argument. Normally one would want to make use of the variation in the label prevalence, however, since this post is inspired by a validation trial for a machine learning algorithm, it is likely that the label balance is already known with a high degree of confidence. Lastly, because there can be discontinuities in the data, the `thresh_ppv` function will always try to find the infimum of values, because we know on a statistical level the relationship should be monotonic (for reasonable assumptions about the distribution of $\\Phi_0$ and $\\Phi_1$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def draw_samp(args, strata=None):\n", " \"\"\"\n", " FUNCTION DRAWS DATA WITH REPLACEMENT (WITH STRATIFICATION IF DESIRED)\n", " \"\"\"\n", " args = list(args)\n", " if strata is not None:\n", " out = resample(args, stratify=strata)\n", " else:\n", " out = resample(args)\n", " if len(args) == 1:\n", " out = [out]\n", " return out\n", " \n", "def thresh_interp(df, target):\n", " \"\"\"\n", " LINEARLY INTERPOLATES PPV TO FIND THRESHOLD\n", " \"\"\"\n", " idx = df.ppv.isnull()\n", " if idx.sum() > 0:\n", " df = df[~idx]\n", " df = df.assign(err=lambda x: x.ppv - target).assign(err1 = lambda x: x.err.shift(1))\n", " if df.ppv.max() < target:\n", " df = df.query('ppv == ppv.max()').sort_values('thresh1').head(1)\n", " else:\n", " df = df[((np.sign(df.err1)==-1) & (np.sign(df.err)==1)) \| ((np.sign(df.err1)==-1) & (np.sign(df.err)==0))]\n", " df = df.sort_values('thresh1').head(1)\n", " thresh0, thresh1 = df.thresh1.values[0], df.thresh.values[0]\n", " ppv0, ppv1 = df.ppv1.values[0], df.ppv.values[0]\n", " slope = (ppv1 - ppv0) / (thresh1 - thresh0)\n", " tt = thresh1 - (ppv1 - target)/slope\n", " return tt\n", "\n", "def thresh_PPV(args, kwargs):\n", " \"\"\"\n", " FUNCTION TO FIND THE THRESHOLD THAT CORRESPONDS TO A TARGET PPV\n", " First two args should be y and score. target (PPV) should be in kwargs\n", " Other optional kwargs include: jackknife (whether to use jackknife), ret_df (whether to return data_frame)\n", " \"\"\"\n", " # --- assign --- #\n", " jackknife = False\n", " ret_df = False\n", " if 'jackknife' in kwargs:\n", " jackknife = kwargs['jackknife']\n", " if 'ret_df' in kwargs:\n", " ret_df = kwargs['ret_df']\n", " assert 'target' in kwargs\n", " target = kwargs['target']\n", " assert len(args) == 2\n", " y, score = args[0], args[1]\n", " assert len(y) == len(score)\n", " assert np.all((y==0) \| (y==1))\n", " # --- calculate --- #\n", " s0, s1 = score[y == 0], score[y == 1]\n", " u_scores = np.sort(score) # Useful for step function\n", " store = np.zeros([len(u_scores),2],int)\n", " for ii, tt in enumerate(u_scores):\n", " store[ii] = [np.sum(s0 >= tt), np.sum(s1 >= tt)]\n", " dat = pd.DataFrame(store,columns=['n0','n1']).assign(thresh=u_scores,tot=store.sum(1))\n", " dat = dat.assign(thresh1=lambda x: x.thresh.shift(1), ppv=lambda x: x.n1/(x.tot))\n", " dat = dat.assign(ppv1=lambda x: x.ppv.shift(1), tot1=lambda x: x.tot.shift(1)).iloc[1:]\n", " if ret_df:\n", " return dat\n", " tstar = thresh_interp(dat, target)\n", " # Do a fast interpolation with the Jackknife\n", " # Remember: all s[1]<t and s[0]<t do not impact calculation (i.e. False negatives and True Negatives)\n", " if jackknife:\n", " tmp = dat.query('thresh>=@tstar & thresh1<@tstar')\n", " n0, n1, tot0, tot1 = tmp.n0.values[0], tmp.n1.values[0], tmp.tot1.values[0], tmp.tot.values[0]\n", " thresh0, thresh1 = tmp.thresh1.values[0], tmp.thresh.values[0]\n", " ppv0, ppv1 = tmp.ppv1.values[0], tmp.ppv.values[0]\n", " holder = []\n", " holder.append(np.repeat(tstar,len(score) - tot1)) # Removing all false/true negatives\n", " # Slope for removing TP\n", " ppv1_new, ppv0_new = (n1-1)/tot1, (n1-1)/tot0\n", " slope_new = (ppv1_new - ppv0_new) / (thresh1 - thresh0)\n", " assert ppv1_new < ppv1 # Has to decrease\n", " holder.append(np.repeat(thresh1 + (ppv1 - ppv1_new)/slope_new, n1))\n", " # Note that becasue n1/(tot0-1) = n1/tot1, implies thresh0 will be be the new choice\n", " holder.append(np.repeat(thresh0, n0))\n", " tstar = np.concatenate(holder)\n", " tstar = tstar[np.abs(tstar)!=np.Inf]\n", " return tstar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation below will show the distribution of thresholds that are found for PPV targets of 25%, 50% and 75% when the scores and labels are generated from the true data generating process (DGP). An example of the distribution generated by the bootstrap will also be made. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 900x350 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766757080797)>\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 800x350 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766756878801)>\n" ] } ], "source": [ "# Visualize a threshold/PPV curve\n", "np.random.seed(1)\n", "target_ppv = 0.5\n", "nsim = 100\n", "holder = []\n", "for ii in range(nsim):\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " # Get threshold for different PPV scores\n", " t25 = thresh_PPV(y, score, target=target_ppv/2)\n", " t50 = thresh_PPV(y, score, target=target_ppv1)\n", " t75 = thresh_PPV(y, score, target=target_ppv1.5)\n", " tmp = thresh_PPV(y, score, target=target_ppv, ret_df=True)\n", " tmp = tmp[['thresh','ppv']].assign(sim=ii,t25=t25,t50=t50,t75=t75)\n", " holder.append(tmp)\n", "dat = pd.concat(holder).reset_index(None,True)\n", "dat2 = dat.groupby('sim')[['t25','t50','t75']].min().reset_index().melt('sim',None,'target')\n", "dat2 = dat2.assign(target=lambda x: x.target.str.replace('t','').astype(float)/100)\n", "\n", "# Repeat visualization for bootstrap\n", "holder_bs = []\n", "for ii in range(nsim):\n", " ytil, stil = draw_samp(y, score, strata=y)\n", " # Get threshold for different PPV scores\n", " t25 = thresh_PPV(ytil, stil, target=target_ppv/2)\n", " t50 = thresh_PPV(ytil, stil, target=target_ppv1)\n", " t75 = thresh_PPV(ytil, stil, target=target_ppv1.5)\n", " tmp = thresh_PPV(ytil, stil, target=target_ppv, ret_df=True)\n", " tmp = tmp[['thresh','ppv']].assign(sim=ii,t25=t25,t50=t50,t75=t75)\n", " holder_bs.append(tmp)\n", "dat_bs = pd.concat(holder_bs).reset_index(None,True)\n", "dat2_bs = dat_bs.groupby('sim')[['t25','t50','t75']].min().reset_index().melt('sim',None,'target')\n", "dat2_bs = dat2_bs.assign(target=lambda x: x.target.str.replace('t','').astype(float)/100)\n", "dat_both = pd.concat([dat.assign(tt='DGP'), dat_bs.assign(tt='Example bootstrap')])\n", "dat2_both = pd.concat([dat2.assign(tt='DGP'), dat2_bs.assign(tt='Example bootstrap')])\n", "\n", "plotnine.options.figure_size = (9,3.5)\n", "gg_ex = (ggplot(dat_both, aes(x='thresh',y='ppv',group='sim')) + theme_bw() + \n", " geom_line(alpha=0.1) + facet_wrap('~tt') + \n", " geom_point(aes(x='value',y='target',color='target.astype(str)'),\n", " size=1,alpha=0.25,data=dat2_both, inherit_aes=False) + \n", " ggtitle('Figure 3: Empirical distribution of PPV tradeoff') + \n", " guides(color=False) + labs(y='PPV',x='Threshold'))\n", "print(gg_ex)\n", "\n", "target_seq = [0.25, 0.5, 0.75]\n", "thresh_seq = [minimize_scalar(fun=lambda x: (PPV_theory(x, 0, 2, 0.1)-pp)*2).x for pp in target_seq]\n", "tmp = pd.DataFrame({'thresh':thresh_seq, 'target':target_seq})\n", "tmp2 = dat2_both.groupby(['tt','target']).value.mean().reset_index().query('tt==\"DGP\"')\n", "title = 'Figure 4: Empirical distribution of PPV tradeoff\\nBlack vertical line is ground truth'\n", "plotnine.options.figure_size = (8,3.5)\n", "gg_bs = (ggplot(dat2_both,aes(x='value',fill='tt')) + theme_bw() + guides(color=False) + \n", " facet_wrap('~target',labeller=label_both,nrow=1) + geom_density(alpha=0.5) + \n", " labs(x='Threshold',y='Density') + scale_fill_discrete(name=' ') + ggtitle(title) + \n", " geom_vline(aes(xintercept='thresh'),data=tmp) + \n", " geom_vline(aes(xintercept='value',color='tt'),data=tmp2))\n", "print(gg_bs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 3 shows the range of thresholds that would be chosen by the `thresh_PPV` function for PPV different targets, along with the different realizations of the empirical PPV curves. The second figure takes the last draw of the data in the simulation and shows the variation in the threshold statistics generated in that bootstrapping instance. Unsurprisingly the latter has more variation, since the empirical PPV for this data is noisier. Figure 4 compares the distribution of a bootstrap instance with those of the point estimates from the simulation. Note these distributions are not statistically comparable, since the DGP is the true variation and the bootstrap distribution only comes from one example. The inference procedures needed to obtain statistically meaningful insights from the bootstrap distribution are be described below.\n", "\n", "## (2) Bootstrapping approaches\n", "\n", "The statistical quality of a bootstrap CI is determined by its coverage. For example, a two-sided confidence interval of level $1-\\alpha$ has the \"right\" coverage when:\n", "\n", "$$\n", "P(\\theta_l \\leq \\theta \\leq \\theta_u) = c = 1-\\alpha\n", "$$\n", "\n", "If $c > 1-\\alpha$, then the CI is said to be conservative because it under-rejects the true null. Though it is better to be conservative than to over-reject the null, it is still problematic because the test will have less power than expected. In the case of a one-sided CI, proper coverage will entail:\n", "\n", "$$\n", "\\begin{align}\n", "P(\\theta_l \\leq \\theta) &= 1-\\alpha \\\\\n", "P(\\theta_u \\geq \\theta) &= 1-\\alpha,\n", "\\end{align}\n", "$$\n", "\n", "Depending on whether it is an upper or lower bound. One-sided CIs are used when the direction of statistic is known. Using the running example of the PPV, if the researcher wants to establish that the PPV as at least* some amount, say 50%, then it would be desirable to find a upper-bound of the threshold, $t_u$ such that: $P(t_u \\geq t^{}(p)) = 1-\\alpha$. If $t_u > t^{}(p)$ from \\eqref{eq:tstar}, then $PPV_\\Phi(t_u) > p$ from \\eqref{eq:PPV_true}. \n", "\n", "There are several reasons why the bootstrap distribution of statistics will not provide the right coverage. First, the ECDF is an approximation of the CDF which introduces sampling error. Second, the bootstrap statistic may be biased. When the average of the bootstrapped statistics will differ in expectation to the original statistic $E[\\bar\\theta^* - \\hat\\theta] \\neq 0$, the bootstrap is said to be biased. This phenomenon can arise for any statistic with [finite-sample bias](https://en.wikipedia.org/wiki/Bias_of_an_estimator). Since sampling with replacement is equivalent to generating a statistic with fewer observations, finite sampling bias will be seen indirectly through this mechanism.[[^2]] Third, the distribution of a (bootstrapped) statistic may be [skewed](https://en.wikipedia.org/wiki/Skewness), which will cause symmetric CIs to be erroneous. \n", "\n", "### Approach #1: CI with BS-standard error\n", "\n", "If the statistic of interest is normally distributed, then confidence intervals can be obtain using the well-known formula,\n", "\n", "$$\n", "\\begin{align}\n", "[\\hat\\theta_l, \\hat\\theta_u ] &= \\hat{\\theta} \\pm t_{\\alpha/2} \\cdot \\hat{\\sigma}_\\theta\n", "\\end{align}\n", "$$\n", "\n", "The variance of the statistic $\\sigma$ is simply estimated from the bootstrapped samples. Note that a student-t distribution with $n-1$ degrees of freedom is used for the quantile since the value of the standard error is estimated rather than known. The drawbacks with this approach are first that it assumes the statistic has a normal distribution, and second that it is symmetric. Many statistics (such as the PPV threshold) will neither be normal nor symmetric.\n", "\n", "### Approach #2: Quantile Bootstrap\n", "\n", "If we rank order the realizations of the bootstrap statistics: $\\theta^_{(1)} \\leq \\theta^_{(2)} \\leq \\dots \\leq \\theta^_{(n)}$, then the quantile bootstrap simply returns the empirical quantiles for the $\\alpha/2$ and $1-\\alpha/2$ percentiles as the confidence intervals.\n", "\n", "$$\n", "\\begin{align}\n", "[\\hat\\theta_l, \\hat\\theta_u ] = [ \\hat\\theta^_{(n\\cdot\\alpha/2)}, \\hat\\theta^_{(n\\cdot(1-\\alpha/2))} ]\n", "\\end{align}\n", "$$\n", "\n", "The advantage of the quantile bootstrap is that is easy to calculate, intuitive, and can handle skewed data (i.e. the CIs are not necessarily symmetric about the mean/median). However if the quantile method will be unable to account for a biased statistic.\n", "\n", "### Approach #3: Bias corrected and accelerated (BCa) Bootstrap\n", "\n", "The BCa procedure largely remedies the problems of bias and skewness and will obtain coverage rates that are very close to their nominal level. \n", "\n", "$$\n", "\\begin{align}\n", "[\\hat\\theta_l, \\hat\\theta_u ] &= [ \\hat\\theta^_{\\alpha_1}, \\hat\\theta^_{\\alpha_2} ] \\\\\n", "\\alpha_1 &= \\Phi\\Bigg(\\hat{z}_0 + \\frac{\\hat{z}_0+z_{\\alpha}}{1-\\hat{a}(\\hat{z}_0+z_{\\alpha})}\\Bigg) \\\\\n", "\\alpha_2 &= \\Phi\\Bigg(\\hat{z}_0 + \\frac{\\hat{z}_0+z_{1-\\alpha}}{1-\\hat{a}(\\hat{z}_0+z_{1-\\alpha})}\\Bigg)\n", "\\end{align}\n", "$$\n", "\n", "There are two terms which need to be estimated when using the BCa: 1) the acceleration parameter $\\hat{a}$, and 2) the bias-correction factor $\\hat{z}_0$. The other terms are deterministic: ($\\Phi$ and $z_\\alpha=\\Phi^{-1}(\n", "\\alpha)$). Notice if the acceleration and bias correction factors are zero, then $l=\\alpha_1=\\alpha$ and $u=\\alpha_2=1-\\alpha$, which is equivalent to the quantile bootstrap.[[^3]] \n", "\n", "$$\n", "\\begin{align}\n", "\\hat{z}_0 &= \\Phi^{-1}\\Bigg(\\frac{1}{B}\\sum_i \\hat{\\theta}^_i < \\hat\\theta \\Bigg) \\\\\n", "\\hat{a} &= \\frac{\\sum_i (\\bar{\\theta}^{-i} - \\hat{\\theta}^{-i} )^3 }{6 \\cdot \\big[\\sum_i (\\bar{\\theta}^{-i} - \\hat{\\theta}^{-i} )^2 \\big]^{3/2}}\n", "\\end{align}\n", "$$\n", "\n", "Also note that the bias correction term is a count in the median bias (rather than the magnitude of the bias). The acceleration factor is basically Pearson's skewness coefficient divided by 1/6th (see the original Efron paper for a discussion of this constant).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (3) Simulation of PPV CIs\n", "\n", "In this section I will define the `bootstrap` class which will carry the three bootstrap-CI approaches discussed above. A simulation will then be run using the DGP discussed above with $\\mu_0=0$, $\\mu_1=2$, and $\\sigma_0=\\sigma_1=1$. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "BOOTSTRAPPING SUPPORT FUNCTIONS\n", "\"\"\"\n", " \n", "class bootstrap():\n", " def __init__(self, nboot, func):\n", " self.nboot = nboot\n", " self.stat = func\n", " \n", " def fit(self, args, kwargs):\n", " strata=None\n", " if 'strata' in kwargs:\n", " strata = kwargs['strata']\n", " # Get the baseline stat\n", " self.theta = self.stat(args, *kwargs)\n", " self.store_theta = np.zeros(self.nboot)\n", " self.jn = self.stat(args, *kwargs, jackknife=True)\n", " self.n = len(y)\n", " for ii in range(self.nboot): # Fit bootstrap\n", " args_til = draw_samp(args, strata=strata)\n", " self.store_theta[ii] = self.stat(args_til, kwargs)\n", " self.se = self.store_theta.std()\n", " \n", " def get_ci(self, alpha=0.05, symmetric=True):\n", " self.di_ci = {'quantile':[], 'se':[], 'bca':[]}\n", " self.di_ci['quantile'] = self.ci_quantile(alpha, symmetric)\n", " self.di_ci['se'] = self.ci_se(alpha, symmetric)\n", " self.di_ci['bca'] = self.ci_bca(alpha, symmetric)\n", "\n", " def ci_quantile(self, alpha, symmetric):\n", " if symmetric:\n", " return np.quantile(self.store_theta, [alpha/2,1-alpha/2])\n", " else:\n", " return np.quantile(self.store_theta, alpha)\n", " \n", " def ci_se(self, alpha, symmetric):\n", " if symmetric:\n", " qq = t(df=self.n).ppf(1-alpha/2)\n", " return np.array([self.theta - self.seqq, self.theta + self.seqq])\n", " else:\n", " qq = t(df=self.n).ppf(1-alpha)\n", " return self.theta - qqself.se\n", " \n", " def ci_bca(self, alpha, symmetric):\n", " if symmetric:\n", " ql, qu = norm.ppf(alpha/2), norm.ppf(1-alpha/2)\n", " else:\n", " ql, qu = norm.ppf(alpha), norm.ppf(1-alpha)\n", " # Acceleration factor\n", " num = np.sum((self.jn.mean() - self.jn)*3)\n", " den = 6np.sum((self.jn.mean() - self.jn)2)1.5\n", " ahat = num / den\n", " # Bias correction factor\n", " zhat = norm.ppf(np.mean(self.store_theta < self.theta))\n", " a1 = norm.cdf(zhat + (zhat + ql)/(1-ahat(zhat+ql)))\n", " a2 = norm.cdf(zhat + (zhat + qu)/(1-ahat(zhat+qu)))\n", " #print('Accel: %0.3f, bz: %0.3f, a1: %0.3f, a2: %0.3f' % (ahat, zhat, a1, a2))\n", " if symmetric:\n", " return np.quantile(self.store_theta, [a1, a2])\n", " else:\n", " return np.quantile(self.store_theta, a1)\n", " \n", "np.random.seed(1234)\n", "alpha_seq = np.linspace(0.05,0.4, 8)\n", "#nsim, nboot = 2000, 2000\n", "nsim, nboot = 2, 2000\n", "n0, n1, mu1 = 90, 10, 2\n", "\n", "stime = time()\n", "store = []\n", "for ii in range(nsim):\n", " print('Simulation %i of %i' % (ii+1,nsim))\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " sim = bootstrap(nboot=nboot, func=thresh_PPV)\n", " sim.fit(y, score, target=target_ppv)\n", " holder = []\n", " for alpha in alpha_seq:\n", " sim.get_ci(alpha=alpha,symmetric=True)\n", " holder.append(pd.DataFrame.from_dict(sim.di_ci).assign(tt=['lb','ub']).assign(alpha=alpha))\n", " holder = pd.concat(holder).assign(sim=ii)\n", " store.append(holder)\n", " nleft, rate = nsim-(ii+1), (ii+1)/(time() - stime)\n", " print('ETA: %0.1f minutes' % (nleft/rate/60))\n", "res = pd.concat(store).melt(['alpha','sim','tt'],None,'method')\n", "#res.to_csv('res.csv',index=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/edrysdale/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 550x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8766715698169)>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = pd.read_csv('res.csv')\n", "# Get ground truth threshold for 50% PPV\n", "thresh_ppv50 = minimize_scalar(fun=lambda x: (PPV_theory(x, mu0, mu1, prev)-target_ppv)*2).x\n", "res = res.assign(ppv=lambda x: PPV_theory(x.value, mu0, mu1, prev))\n", "res = res[res.value.notnull()].reset_index(None,True)\n", "cn_gg = ['sim','alpha','method']\n", "res2 = res.pivot_table('ppv',cn_gg,'tt').assign(lb=lambda x: x.lb > target_ppv, ub=lambda x: x.ub < target_ppv)\n", "res2 = res2.reset_index().melt(cn_gg).rename(columns={'value':'viol'})\n", "assert res.shape[0] == res2.shape[0]\n", "res = res.merge(res2)\n", "del res2\n", "res_agg = res.groupby(['method','alpha','tt']).viol.apply(lambda x: pd.Series({'mu':x.mean(),'n':x.shape[0]})).reset_index()\n", "res_agg = res_agg.pivot_table('viol',['method','alpha','tt'],'level_3').reset_index().assign(n=lambda x: x.n.astype(int))\n", "res_agg = res_agg.assign(ci_u=lambda x: x.mu+norm.ppf(0.975)np.sqrt(x.mu(1-x.mu)/x.n),\n", " ci_l=lambda x: x.mu-norm.ppf(0.975)np.sqrt(x.mu(1-x.mu)/x.n),\n", " alpha2=lambda x: x.alpha/2)\n", "di_method = {'bca':'BCa', 'quantile':'Quantile', 'se':'SE', 'arch':'ARCH-BCa'}\n", "di_tt = {'lb':'lower bound', 'ub':'upper bound'}\n", "res_agg = res_agg.assign(gg = lambda x: x.method.map(di_method)+', ('+x.tt.map(di_tt)+')')\n", "\n", "colz = list(np.repeat(gg_color_hue(3),2))\n", "shapez = list(np.tile(['+','x'],3))\n", "\n", "plotnine.options.figure_size = (5.5,4)\n", "xx = list(np.arange(0,0.25,0.05))\n", "gg_inf = (ggplot(res_agg,aes(x='alpha2',y='mu',color='gg',shape='gg')) + theme_bw() + geom_point(size=3) + \n", " scale_shape_manual(name='Method',values=shapez) + \n", " scale_color_manual(name='Method',values=colz) + \n", " geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", " labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", " scale_x_continuous(limits=[0,0.25],breaks=xx) + \n", " scale_y_continuous(limits=[0,0.25],breaks=xx) + \n", " ggtitle('Figure 5: CI coverage for PPV threshold'))\n", "gg_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 5 shows that only the BCa approach gets anywhere near the right coverage for the upper bound, and the other approaches are much too conservative. Interestingly even the BCa approach has a lower bound interval which is conservative. This likely stems from the obvious finite-sample bias that comes from the empirical PPV curve." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# res_agg2 = res_agg.groupby(['method','alpha']).mu.sum().reset_index()\n", "# res_agg2 = res_agg2.assign(gg = lambda x: x.method.map(di_method))\n", "# plotnine.options.figure_size = (5.5,4)\n", "# gg_inf = (ggplot(res_agg2,aes(x='alpha',y='mu',color='gg')) + theme_bw() + \n", "# scale_color_discrete(name='Method') + geom_point(size=3) + \n", "# geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", "# labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", "# scale_x_continuous(limits=[0,0.25],breaks=xx) + \n", "# scale_y_continuous(limits=[0,0.25],breaks=xx) + \n", "# ggtitle('Simulation'))\n", "# gg_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (4) Compare to ARCH\n", "\n", "To show that our functions developed in the post will obtain (nearly) identical results to using the `arch` package's BCa approach, we will turn off the stratified sampling and calculate the variance of a standard normal distribution. To comply with the `bootstrap` class, the `var_calc` function will have a built-in Jackknife estimator, which is can calculated efficiently since the determintic formula for the sample variance can be vectorized." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from arch.bootstrap import IIDBootstrap\n", "\n", "def vfun(x):\n", " return x.var(ddof=1)\n", "\n", "def var_calc(args, *kwargs):\n", " \"\"\"\n", " args should be an IID draw\n", " \"\"\"\n", " jackknife = False\n", " if 'jackknife' in kwargs:\n", " jackknife = kwargs['jackknife']\n", " # Calculate stat\n", " x = args[0]\n", " n = len(x)\n", " vv = x.var(ddof=1)\n", " if jackknife: # Built in jackknife\n", " mu = x.mean()\n", " s2 = np.sum(x*2)\n", " mu = (mu - x/n)(n/(n-1))\n", " s2 = s2 - x*2\n", " vv = (s2 - (n-1)mu**2)/(n-2)\n", " return vv\n", "\n", "np.random.seed(1234)\n", "# nsim, nboot, n = 2000, 2000, 100\n", "nsim, nboot, n = 2, 2000, 100\n", "t1err = 0.1\n", "alpha_seq = np.arange(0.05, 0.25, 0.05)\n", "store = []\n", "stime = time()\n", "for ii in range(nsim):\n", " x = stats.norm(0,1).rvs(n)\n", " sim = bootstrap(nboot=nboot, func=var_calc)\n", " sim.fit(x)\n", " holder = []\n", " for alpha in alpha_seq:\n", " sim.get_ci(alpha=alpha,symmetric=True)\n", " holder.append(pd.DataFrame.from_dict(sim.di_ci).assign(tt=['lb','ub']).assign(alpha=alpha))\n", " holder = pd.concat(holder).assign(sim=ii)\n", " # Calculate with ARCH\n", " bs = IIDBootstrap(x)\n", " mat = np.zeros([len(alpha_seq), 2])\n", " for jj, alpha in enumerate(alpha_seq):\n", " ci_bca = bs.conf_int(func=vfun, reps=nboot, method='bca',size=1-alpha, tail='two').flatten()\n", " mat[jj] = ci_bca\n", " holder2 = pd.DataFrame(mat,columns=['lb','ub']).assign(alpha=alpha_seq,sim=ii)\n", " holder2 = holder2.melt(['alpha','sim'],None,'tt').rename(columns={'value':'arch'})\n", " holder3 = holder2.merge(holder,on=['alpha','sim','tt'])\n", " store.append(holder3)\n", " nleft, rate = nsim-(ii+1), (ii+1)/(time() - stime)\n", " if (ii+1) % 100 == 0:\n", " print('Simulation %i of %i' % (ii+1,nsim))\n", " print('ETA: %0.1f minutes' % (nleft/rate/60))\n", "res_all = pd.concat(store).reset_index(None,True)\n", "res_all = res_all.melt(['sim','alpha','tt'],None,'method').assign(viol=lambda x: np.where(x.tt=='lb',x.value>1,x.value<1))\n", "#res_all.to_csv('res_all.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 550x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8766715455761)>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_all = pd.read_csv('res_all.csv')\n", "res_all_agg = res_all.groupby(['method','alpha','tt']).viol.apply(lambda x: pd.Series({'mu':x.mean(),'n':x.shape[0]})).reset_index()\n", "res_all_agg = res_all_agg.pivot_table('viol',['method','alpha','tt'],'level_3').reset_index().assign(n=lambda x: x.n.astype(int))\n", "res_all_agg = res_all_agg.assign(gg = lambda x: x.method.map(di_method)+', ('+x.tt.map(di_tt)+')',\n", " alpha2 = lambda x: x.alpha / 2)\n", "colz = list(np.repeat(['black']+gg_color_hue(3),2))\n", "shapez = list(np.tile(['+','x'],4))\n", "\n", "plotnine.options.figure_size = (5.5,4)\n", "yy = list(np.arange(0,0.1751,0.025))\n", "xx = list(np.arange(0,0.1251,0.025))\n", "gg_comp = (ggplot(res_all_agg,aes(x='alpha2',y='mu',color='gg',shape='gg')) + theme_bw() + geom_point(size=3) + \n", " scale_shape_manual(name='Method',values=shapez) + \n", " scale_color_manual(name='Method',values=colz) + \n", " geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", " labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", " scale_x_continuous(limits=[min(xx),max(xx)],breaks=xx) + \n", " scale_y_continuous(limits=[min(yy),max(yy)],breaks=yy) + \n", " ggtitle('Figure 6: Comparison to arch package'))\n", "gg_comp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 6 shows that the BCa approach yields (virtually) identical estimate to the one obtainable from the `arch` package. Since the estimate of the variance is skewed, it also shows the superiority of the BCa method over the simpler bootstrapping approaches since ther coverage for both lower and upper bounds is close to the expected level.\n", "\n", "Most researchers that use the bootstrap to carry out inference on a statistic should probably be using the BCa approach. When the Jackknife estimator can be implemented effeciently, there is almost no additional computational overhead for using the BCa and it obtains an empirical coverage much closer to the expected level. Beyond sample means, most statistics show some form of skew or bias meaning the symmetric standard-error or quantile approach will be suboptimal. The BCa not only helps to remedy these problems, it gives researcher estimates that are much more efficient." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
dnc1994/Kaggle-Playground	misc/santander/Explore.ipynb	1	173384	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "import pandas as pd\n", "import zipfile\n", "import sklearn\n", "from sklearn import cross_validation\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = zipfile.ZipFile('train.csv.zip')\n", "df = pd.read_csv(z.open('train.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Target Exploration" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xab67940>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x3f764e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.TARGET.value_counts().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0411987070619\n" ] } ], "source": [ "num_positive = len(df[df['TARGET'] == 1])\n", "num_negative = len(df) - num_positive\n", "print num_positive / float(num_negative)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reverse Feature Engineering" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drop_constants(df):\n", " constant_features = []\n", " for col in df.columns:\n", " if df[col].std() == 0:\n", " constant_features.append(col)\n", " ndf = df.drop(constant_features, axis=1)\n", " print len(constant_features), 'features dropped because they are constant.'\n", " return (constant_features, ndf)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drop_lin_coms(df):\n", " with open('rfe_from_forum.txt', 'r') as f:\n", " lines = f.readlines()\n", " lines = [line.strip().split(' ')[0] for line in lines]\n", " lin_com_features = lines\n", " ndf = df.drop(lin_com_features, axis=1)\n", " print len(lin_com_features), 'features dropped because they are linear combinations.'\n", " return (lin_com_features, ndf)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def drop_high_corrs(df):\n", " high_corr_features = []\n", " safe = []\n", " for x in df.columns.values:\n", " for y in safe:\n", " if (abs(np.corrcoef(df[y], df[x])[0,1]) > 0.999):\n", " high_corr_features.append(x)\n", " break\n", " safe.append(x)\n", " ndf = df.drop(high_corr_features, axis=1)\n", " print len(high_corr_features), 'features dropped because they are in linear relation with other features.'\n", " return (high_corr_features, ndf)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34 features dropped because they are constant.\n", "40 features dropped because they are linear combinations.\n", "56 features dropped because they are in linear relation with other features.\n" ] } ], "source": [ "df = pd.read_csv(z.open('train.csv'))\n", "constant_features, ndf = drop_constants(df)\n", "lin_com_features, ndf = drop_lin_coms(ndf)\n", "high_corr_features, ndf = drop_high_corrs(ndf)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dropped_features = constant_features + lin_com_features + high_corr_features\n", "with open('dropped_features.txt', 'w') as f:\n", " f.writelines([s+'\\n' for s in dropped_features])\n", "import cPickle as pickle\n", "with open('dropped_features.dump', 'w') as f:\n", " pickle.dump(dropped_features, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downsampling Majority Class" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# not necessary if we use boosting" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_positive = df[df['TARGET'] == 1]\n", "df_negative = df[df['TARGET'] == 0].sample(frac=num_positive / float(num_negative))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ndf = pd.concat([df_positive, df_negative])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Importance" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x172de2e8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gpZVWbuH+zMzaj7OjrWJ2mb+NlrV2cP1Fc/3FKnP9zo4eIM6ZNjOzVnATrsE502Zm1gqd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\nJbttH/o/N3poRJwsaWyTWmu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"text/plain": [ "<matplotlib.figure.Figure at 0x162d5710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = df.drop(['ID', 'TARGET'], axis=1)\n", "y = df['TARGET']\n", "rfc = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=16, oob_score=True)\n", "rfc.fit(x, y)\n", "feat_imp = pd.Series(rfc.feature_importances_, index=x.columns)\n", "feat_imp.sort_values(inplace=True, ascending=False)\n", "feat_imp.head(20).plot(kind='barh', title='Feature importance')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Auc: 0.829 (+/- 0.013)\n" ] }, { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=16, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=True, random_state=None, verbose=0, warm_start=False)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cv = cross_validation.StratifiedKFold(y, n_folds=4, shuffle=True)\n", "scores = cross_validation.cross_val_score(rfc, x, y, cv=cv, scoring='roc_auc')\n", "print(\"Auc: %0.3f (+/- %0.3f)\" % (scores.mean(), scores.std()))\n", "rfc.fit(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Starter Submission" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import datetime" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def export_predictions(model, method, num=1):\n", " test = pandas.read_csv(zipfile.ZipFile('test.csv.zip').open('test.csv'))\n", " id_test = test['ID']\n", " x_test = test.drop(['ID'], axis=1)\n", " y_pred = model.predict_proba(x_test)\n", " sub = pandas.DataFrame({'ID': id_test, 'TARGET': y_pred[:,1]})\n", " filename = 'submission_{date}#{num}_{method}.csv'.format(date=datetime.date.today().isoformat(), num=num, method=method)\n", " sub.to_csv(filename, index=False)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": true }, "outputs": [], "source": [ "export_predictions(rfc, 'rfc-undersampling-tuned', 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Visualization" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def filter_feature_by_importance_percentage(imp, per):\n", " assert per <= 1.0 and per >= 0\n", " imp_sorted = imp.sort_values(ascending=False)\n", " total_per = 0\n", " for (i, v) in enumerate(imp_sorted):\n", " total_per += v\n", " if total_per >= per:\n", " return imp[:i+1]\n", " return imp" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "369\n", "110\n" ] } ], "source": [ "print len(feat_imp)\n", "filtered_feat_imp = filter_feature_by_importance_percentage(feat_imp, 0.95)\n", "filtered_feat_imp_list = list(filtered_feat_imp.index.values)\n", "print len(filtered_feat_imp)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x15de6b70>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Xc+9n3tsUfmN22+9ntNPX2ByukhfT4bbb7t+HX5CSZIuHwZpSuRe+NSinDS4\nuA0aEEPfZPVrNjdtoK97UW5ZvVyzez//WJKrVhjPc+/nHjMnTZKkJtzuVCJItbDfOXftbG5aq7ll\n5qRJkrQ8BmlKxJUW3hyYu3Y2N63V3DJz0iRJWh63O5XIn95L7sWPNM1Ji2d6iWcGKJ7ZRxBA9MQI\nZ0cm2bWtr+m9bRUlSdLyGKQpkbvx5JI5aaWZgaROWgyMTRX5yCePc9edr2l6b1tFSZK0PG53KtFK\nTlq9OmkLa6NJkqRLZ5CmRFxp4Zo6ddIKpQq/dc83mJgutGFWkiRtTgZpuqjJewOVCpSe20XxzL66\n5x88fZ4j959q08QkSdp8zElTollbqCBOJblojVhWQ5Kk1eOTNCWatYWKS5nGJ2dZVkOSpNVjkKZE\n/nyaSqUarM3/qlSgMt1Ddt+XyNzwIHQtzj17yQu3WlZDkqRV5HanErkrSg1LcKQGR6sH/WPMtYSa\n7+zIDP092fZPUpKkTcInaUo0y0mruc4yHJIktZ1BmhLNctJqrqtThqOvZ+mcNUmS1Dq3O5XIj0Ju\ncPETtTgGSl0EXTFxKUPxyRsB6EpVq3YM9OV439uav/kpSZKWp+UgLQzDAaAYRdFMG+ejdZQbpHFb\nqGy5+n1Xnsy1pyk+up8tfbkl20FJkqSVabjdGYbh78/+eU0Yhl8EngCeCcPwM2EYXr1WE9TaWW5O\nmnlokiS1T7MnaS+b/fPjwJEoin4XIAzDnwb+GPgHbZ6b1lgctxaozeWkdWe7Fp2bmCpw5Ogphken\nGRrs4dDBPUu+9bmSz0iStNG18uLAC+cCNIAoiv4Y2Nm+KalTVYrpBa2hFr9pcOToKY6dPMeZs+Mc\nO3mupVZRK/mMJEkbXbMg7YVhGP4iMBKG4RsBwjAMwjD8x8DYmsxOa2qpp2hxvrdaH61cfco1U1jc\nkX1ha6hWWkWt5DOSJG10zYK024EicA740dmxXwZ+EfjZNs9L62CpEhwLS2/UK7uxsDVUK62iVvIZ\nSZI2uoY5aVEUfQH4woLh/xxF0X9q75S0XpqV4KiMbaX45I1kbjhOkJsizvcQP/cyDt93oiaH7NDB\nPZTKFaInRoGYYqnMxHShaY7ZXDup+Tlp0mZnrqakZdVJi6KoxXKnuhw1LcHRM0Pm2tOkd5ytDvaP\nMclxjp2s1ke74803V4d7sqS7UkzlSwAcP/0cR+4/lZyvp78n2/S8tBnN5WoCnDk7DuC/J9IKhWH4\nB8D1wF7I09nIAAAgAElEQVTgGeAC1Rcj/wZ4EvjnURTdM3vtPwM+CDxOtRzoNuD/jKLoT2fP/yzw\nTqBAdUfyt6Mo+lSdz8Wzf8crgVcDu4Ep4FngniiKPrHUvBsGaWEYfrDZB6Mo+tBSN9flpVlOWpAu\nLmoHNXe8VE6ZOWbS8vnvkbR6oih6FyTB2u9EUfSV2eNDwB8CPwPcM+8jvxtF0YdnrxkE/j/gT8Mw\n/EfADwOvjaKoOFtD9jNhGN6/8HPz/MXsfT4InIyi6FOtzrtZTloaeD/QRTUiXPilDaZZTlpcyhDn\na3PF5nLUlsopM8dMWj7/PZLaYmH8cgj4LaAnDMMXNrhuJ9UnYADvBt4bRVERIIqi8SiKfiCKogsN\n7t/s715Ss5y0D4ZheBUwWScq1AaUH4HctgZP1FIlSBUoPb+TIDtTfdPzzD5SAXzz0WE+ds9DvOvH\nX0J/T9YcM2kV+O+R1F6zhflzURQ9Hobhn1J9KXJuF/HnwjD8EeA64NvAT8+OXxtF0ZOzn38X1SBv\nEPi38z53kIvbnT8bRdF3VjrHpXLS/hXwppXeXJeX3Lb6OWkAQaZMatsIped2UXj4lmS8AswU45rc\nM3PMpEvnv0dS2x0CdoRh+D+AbmB3GIb/fvbc70ZR9OEwDG8BPkE1zwzgu2EYXh1F0dNRFP0B8Aez\nn+mb/7nVmmDTYrZRFI0Bz6/WX6bO1kq3gYV5afOZMyNJuoy8DbgtiqIfi6Lo9cBXgR+bf0EURV8C\n/gz4zdmh/wv4SBiGWYAwDHuBl3OxuvuqpoO18nbnh4FPr+Zfqs7USluohbXS5jNnRpLU4WKAMAxf\nCXw3iqLn5p37b1S3PO9d8JlfB74ehuGrZt/i7AGOhmEYA/1UXzi4F3g78O4F251/FUXRb8z/u5cj\niJeoYBqG4V8B54EvA8mjktn2UOspHh4eX+cpbCzvuvf95AbqB2pxOUVlbAfFx16adByYL9MV8P6f\nehlXbu21tlMDQ0MD+Du7+lzX9nBd28N1bY+hoYEN+UJjK0/SnqMaEX7/vLGYapN1bSC5gcY5aXEQ\nUzz9ioafLZZjPvEX3+LGq7da20mSpFWwZJAWRdE7F47NPurTBtO0Tlpq6ae0k9NFaztJkrRKlgzS\nZgu3fZDqvmtAtW5aD9W6IdpAmuWkxTFkbvwaQTZPnO+heOamRduepXKFCxOFmrELE4WGbaFseyNJ\nUmNN3+6c9WHgPcAjVJPi/hBouVquNoZUCtLbh+nqHyO941kyux9edE0lhpGJPJmui5HeyESeI/ef\nqnvPubY3Z86Oc+zkuYbXSZK0GbUSpI1EUfT3VFsibI2i6P+g2oNKG0wrJTiSa5uU4ggW3KjRlqdb\no5IkNdZKkDYdhuEeqk/SXjtbG2Rre6el9bDEi7611zYpxdHXnak5blSaw7Y3kiQ11srbnf8G+I/A\nTwG/BPxz4L+2c1JaH/lxGpfgKHZRmRwkyBSTllBzerJdZDIptvZm2bWjj9tvu557P/fYku1sbHsj\nSdrIwjAMqHYs+F5ghmW2iWolSLuL6osC/wp4CzARRdHICuaqDtesBEeQLVMZz1B4+MCicze/aMei\nMhutlN2w7Y0kqZO88b1/uYtq/v0F4A//+q43lS/xlm+m2h/0ljAMXwV8dHasJUtud0ZRdGD2hhmq\nnQfuDcPwZ1Y4WXWwpXLSGuWhmUsmSbrcvfG9f/lC4G+pdhj4PeDP3vjev2wlLayZHwD+BiCKoi8D\nr1zOh1v6y6MoOk01+vs1YIDqtqc2mKVy0hrloZlLJknaAO4A5m/vvAXYf4n33EL1qdycUhiGLQd+\nrdRJewvwT4FXAf8d+IXZhqPaYBrlpMUxVMb7a/LQ5tx4VT8Hb7mSX/qb32a8OAqFXm7gNfzcj+3v\n6Jpnq1mjzXpvjbk2ki4jCx9VVIBL3e4co/pwa04qiqJKqx9uJSft7cAR4G1RFBWXOTldRhrlpAUB\n0D9Zt2fnc+NFfu/rn2I8+zhkgb4LRM99kSP393Z0vtlcjTa49PZVq3mvjca1kXQZ+RjwI8DLqAZo\nfwp84xLv+QDwE8A9YRh+P/DN5Xy4lbZQ/2iFE9NlZiVtoSani5Tisdprc1MMP9/ZeWqrWaPNem+N\nuTaSLhd/fdebzr7xvX/5euCtwPPAPX9915uWUZyqrnuBN4Rh+MDs8aJWm8208iRNm0TTtlCV+ie6\nc130BFsY4+ILv3G+l8H+zt7SGhrsSZ7szB13wr02moW/B53+eyFpc/vru940Cvzuat0viqKYaq7b\nihikqTWl+sPXXTnA2255G//hf/4Rpa6JpIZa8KJltC9YB6tZo816b40t7D6x8FiS1JhBmhJNtzvT\n9U+OTxW5css2dl54Tc3TpJHx/GpPb1WtZo026701tvD3oNN/LySpk1xq/Q9tIM1KcMSlTN3xua09\nWzypHn8vJGnlfJKmRMMSHBWozHSTe9nfAVAZ3078+M28dPdVHDq4h4mpAsVSmd5cFxAQvnCw6ZZf\nu8syWPahc7gVLEkrZ5CmRMMSHF2Q2nqxFl9q+zlK8cOku66hvyfL4ftOcPz0c8n5dFeqaVDU7rIM\nln3oHG4FS9LKud2pxHJyuoPcVFJOYbllFtpdlsGyD5KkjcAgTYml2kLVXJvvXXE+WrvzlMyDkiR1\nkjAMXxWG4d8v93NudyrRrC3U3FBc7qIyvoPimX18vXyO//Kp40zPFJJrM10BP/yqa5r+Pe3OUzIP\nSpK0Ej/553fsotpp6QLwh5966+FLbQtFGIbvAw4BE8v9rEGaEk3bQs2Ki2mKp18OVBuaffM7z9dc\nWyzHfOIvvsVdd76m4d/T7jwl86AkScv1k39+xwuBT3OxyfrBn/zzO976qbcebrnXZgOngduptthc\nFrc7lWglJy1IL92+dXLaFq+SpMvOHVwM0ADeAuy/1JtGUXQvDUvCN2eQpkQrOWmN6qXN19ez9DWS\nJHWYhf8vWKG6abRu3O5UomlO2uxYZaYbugpQrpbY6M6m6EoFTM5c/D0eG8/zr37ri2zpz3Dltj7r\nlKnjWVtPEvAx4EeAl1EN0P4U+MYq3n/ZffF8kqbEXE5aENR+zR9Lb71AZvfDyWdmCpWaAA2q/9kx\nOlngiWcnOXbyHEfuP7XGP4m0PHO19c6cHfd3VtqkPvXWw2eB1wP/AvgnwD/71FsPL6PuwZKWfS+f\npCnRap20IDe1rPtap0ydztp6kgA+9dbDo8DvrvZ9oyh6HLhluZ8zSFNi/rZm0+sKuWXddzl1yppt\nO7klpXYZGuxJulPMHUvSejNI0wpcjORSKag0eDm5J9vFzS/asaw6Zc1aOtnuSe1ibT1JnWjdgrQw\nDHcCXwV+iGoa091UE/VORFF053rNazNrebszO5N8n06lKDSI0q7c3rvsIKrZtpNbUmoXa+tJ6kTr\n8uJAGIZp4HeAueSmjwIfiKLoNiAVhuGb1mNem12rbaHifG/yfV9343IbK9kyatbSyXZPkqTNZL2e\npP06cBj4Zap7Zy+PougLs+c+A7wB+Mt1mtum1awEB1T/QcXFLMUnb0zO5TKwbSDHQG+aHVt6KJbK\nnHpihEIZvnryHO/5zc9z3Qu2MD5VvLiNFNMwt6zZtpNbUpKkzWTNg7QwDN8BnIui6G/DMPzA7PD8\nJ3rjwNa1npdaawsV5Apkrj1N8dFqEeazI3kAbrx6K3e8+WYO33eCwmxFjhgYmy4lraPmJ2Y3yi1r\ntu3klpQkaTNZjydp7wQqYRi+Afhe4I+BoXnnB4DRVm40NDSw+rPbxC6lBMfoZIGhoQFGJwt1PlF7\nXaPPbgab5edca65re7iu7eG6qlVrHqTN5p0BEIbhZ6kWjftIGIa3RlH0eeBHgc+2cq/h4fGlL1LL\nWi7BMS8nbc5gX5bh4XEG+5qXxKh3fu6zG93Q0MCm+DnXmuvaHq5re7iu7bFRA99OKcHxr4HfC8Mw\nAzwC3LPO89mUmuakxRDMboWmBs5DdgIK/ck1X43O8Z7f/BwDfVm29GYYm6o2WU8F0N+bYWtvll07\n+mryyMwtkySpsXUN0qIoev28w9eu1zxU1UpOGkCQLZF7yTHyD70uGYtjGJsuMzZdLYuxbSDHyHie\nSgxjk0XCa7fV5JOZWyZJUnP27lSi1Zw0gCBdbHp+crr2vDXNJElaHoM0JVqtkwYQlxrXRwMolmoL\n3FrTTJKk5emUnDRdJuIY4kKG/CMHml9Hdctza1/WvDNJklbAIE2JVrY7K5NbKDx8S0v329qX5YPv\naB7MSZKk+tzuVKKV7c565TcacYtTkqSV80maEo1KcADV/UsgtWWYzI1fpfjY90A5y0BPmm1bcqTS\nRZ7u/gpBboo438P20Zfx7adG+Re//r/o687wvrfvZ9e2vjX9eTrdxFShYXssSZJ8kqbEXAmOIKjz\nlap+pTJl0tvPk9n9MADj0yWu3NbHd3u+QnrHWbr6x0jveJbnB48zOlGgUKowMpHnI588vs4/Xec5\ncvQUx06e48zZcY6dPMeR+0+t95QkSR3EIE2JZZXgmNcaanh0mjg71fA8LC7JocVlSSxTIkmazyBN\niWWV4JiXmzY02ENQ6G14HqCvp3nJjs1oYc6eOXySpPnMSVOiWU5aXLk4HhczFJ+8EYCuAL7x7XMU\n2Ec6ZjYnrZctIy9nrCumXI7p6grozaU5fN+JRXlXnZyXNVGY5M9P3cv56efZ0bOdf7LndvqzjfPq\nlvuzzJUlsT2WJKkegzQlGrWFAoiDi707g1yRzLWnKT66n3IM5TJAluKj+5Prn6NazHauPdTT5yd5\n+vwkUNsSai4vC+DM2fFF59fTn5+6l6+f+wYAT4w/RQD8zM0/1fD65f4s/T3ZjvlZJUmdx+1OJZrl\npC3q37kg56yRpdpDdXJe1vnp55seL9TJP4sk6fJjkKZEs5y0hedarZfW112bi7ZUHlYn5WXt6Nle\nc3zFguOFOvlnkSRdftzuVKJpnbQSlCa3E2RKxPleimf21b1HVwDluHqPrgDGJ/N0BQHZTIq9121b\nlHfVyXlZ/2TP7QRUn6Bd0bOdt+65ven1nfyzSJIuPwZpSjTLSQuyQDxJ/qHXNb1HefaJWxxDKXn6\nFjNdKJPuSi1KpO/kvKz+bF/THLRF13fwzyJJuvy43anEUnXSgvSl1TozR0uSpNb5JE2JOF4qUIvJ\n3PAgxTM3QXn5ZTKGBns6uuSGJEmdxCBNLQu6YtI7ngWCmnIbUA3uujNdZDIpCoUyQSogk4JCqUIq\n1UX4wkEOHdzDkfs7t+SGJEmdxCBNiVbbQtUrv3HdlQN88B0HlvysZSokSWqNOWlKtNoWql75jVbL\nTVimQpKk1vgkTYmmbaFioNxFZXzHovIbXSk4fvo87/34A/z8W27i6FeeaphztpIyFRs1j22j/lyS\npNVhkKZE0xIcAZAqU4lTi14aKFegXKkwMpHnw3/yIMXZOhz1cs5WUqaik1tHXYqN+nNJklaH251K\ntJKTtlQ7qLkAbc5q5Jxt1Dy2jfpzSZJWh0GaEq3kpC3VDirTVRvprUbO2UbNY9uoP5ckaXW43alE\n07ZQMcTFDMVnriNzw3GC3BRxvofimZtIVbLEwEBvhl/4xy/l0w88zqknR4GAUqnCxHQBYlacf7VR\n2y1t1J9LkrQ6DNKUaJaTRgBBrkhuz3FSuXx1rH+M+TXTwmu3ccMLBsmkn2IqXwbgwdPnSd9/CmDF\n+Vcbtd3SRv25JEmrwyBNiZZy0ha0hpqfozaXU9VKrpX5V5IkNWeQpsTSbaEgLmUIuvLJcZDJQ1cB\nylkuTBb40N3HuDBRqPnMXK7V3BM0ILm2HaUnNkJpi43wM0iSLo1BmlpWyefIP3KA3EuOJVueqVye\nnhc9QvfZ72NkPM/IeHV820COrX3ZRblWw6PTXJgsJNe2o/TERihtsRF+BknSpTFIU2LJp2jFHBT6\nq3/mLj5NS/fOsLUvmwRoAFv7sovaRM0FGR+6+1jNtau99bkRSltshJ9BknRpLMGhxFIlOObKb8T5\n2lIRvcGWZZWTaHfpiY1Q2mIj/AySpEvjkzQlGpXgiGOojGer7aC6ChBUqBRnf3UmtjN29nqO7zxK\ndl+1LEfl8ZuSNlHve/t+dm3rq7lfu0tPbITSFhvhZ5AkXZogbrWrdueJh4fHl75KLbvj797fsARH\npRyQ/9pBMjccJ73jbDJeem4XwKKxubIc2wZy3HXna9o36cvI0NAA/s6uPte1PVzX9nBd22NoaKCF\n+gSXH5+kKdEsJy1IVYP5hW2h6rWJmj82OV1cdF6SJC3NnDQlmj1UjSvVCG5hPlqc7607NqevJ7N6\nE5QkaRPxSZoSTXPSJrvofsX9EMRUyhDP9BHn+yAoE2RnqORzxMUMcb6f4pl9BEG1TdT73rZ/0d+z\nVA0wa4RJkmSQpnkatYUKAkhtKV08BirpEvFMqjYXbWJbkosWU20TtfClAVi6Bpg1wiRJcrtT87TS\nFiq5Nl1cMj+tUW2vpWqAWSNMkiSDNM2znBd941KmaS4aNK7ttVQNMGuESZLkdqfmaZSTxoLgLS5m\nyJ/aT+bqx5J6aZXxbQRP7COYd/k3Tg/zW/d8g3f++N6anLKlaoBZI0ySJIM0zdMoJ415QdtcDbTM\nDcdJbz+XjFfiLgrF2uT+fCnmwdPnSd9/qianrL8n2zTHbKnzkiRtBm53KtFKTtpc3lkr9dLmmFMm\nSdLy+SRNiTheOlALstNkbvzaoqAsyUfrKpDZ/TBBrtoiqnjmJrYN5JLrLL8hSVJrDNK0LKlskdT2\n4eS4UkxTGbui2tcTyOx++GJZjv4xICCOX5Bcb/kNSZJaY5CmxHJKcMyJ871JbTSovw06+nwhObb8\nhiRJrTEnTYnllOBIPrOg7Ea9shzzS2hYfkOSpNb4JE2JhiU45otna6QVckkLqPmKT76YVP8oQbpI\nXMpQeupGpl9Q5JuPDvPxvzhBsVyNBLNdkE6nKZUqTEwXkryzZuU3zFdbOddu9bmmktrNIE2JhiU4\n5gsgyBYpje+o2eack7n2NKlcvnppV570Nac58Wg/3zozUlNurVCGQrm0qERHs/Ib5qutnGu3+lxT\nSe3mdqcSy2oL1aDkRqPSHM12UlvNOzNfbeVcu9XnmkpqN4M0JZbVFmpBLtrF8fqtoprFf63mnZmv\ntnKu3epzTSW1m9udSiyVkxbHQDlNZWIQgjLZfV9KaqFRrubiFM/cBASzddJ6k5y1hfFfby5FqQx9\n3Rluv+16YOkcn3r5auYFtcZWW6vPNZXUbkG8klf6OkM8PDy+3nPYUO74u/c3zUmr5HPkH3pdtSXU\nXC00LraKWqkDe3dyx5tv5vB9J5Icn/njzazkM+tlaGgAf2dXn+vaHq5re7iu7TE0NLCCIlKdz+1O\nJZbsNpAuVv9cRkuoVszl8qwkx8e8IEnSRuV2pxJLtYWKqUBXoZp31j92cbxBflqrzo1Mcfi+E2zr\nz3GGi/+F2SjHZ/4W54WJQs25S80Lcvt0dbSyjq61JDVnkKaWpbqqbZ8a5Z0tV1cQUI5jpvJljp08\nx/4bd3Bg784lc3zmlz4A2DaQY2tfdlXygiyrsDpaWUfXWpKaM0hTopUSHEFuCsrZFeWgZdMpCqVK\nctzVFVAuXcyJHJ0o8MF3HFjyPgu3NLf2ZVv6XCvcPl0drayjay1JzZmTpkQr75BcytZmX3em9rin\n9rgTSnFYVmF1tLKOrrUkNeeTNCVaaQvVte0sqe/9n+RPv5Tcjd9K2j/lHzkAhf6Gn3vx1QM8+/wU\nQQCpIGDf7m380ze8mHs/9xjDo9MM9meZKZS48zf/Dq75Jrn+PDcM7aLyxEs5/1yJiZkSA71prtzW\nx+23XU+pXCF6YpRiscRXT57jXb/2WTJdATdcNcBMMa7JcVqY+3T7rddz7+cfq5sLtdZlFc4+N8lH\n/uw4k9NF+rozvO/t+9m1ra+tf2e7TUwVKJUr9ObSQMyeawfrruNy13ojrpW0HswHvXxYgkOJpUpw\nzFep1LaQmivP0UhAba20bQM57rrzNcnxXCmNVsp7HNi7E6AmL62eRqU9tg3kGBnPL7qu3eq9ev/e\n336gZi4L1+Vy1K6yKI3WypIG7eG6tkcnrOvlVLqoVRu1BIdP0pRYVluoBdfOledoZOF/CkxO114/\nl4/USnmP5baRWnh9o797PSycy8Ljy1G7cs024lpJ68F80MuHOWlKLKst1IJr41Km/oWzFsZ/jfLR\nGrWVWnhtK/lLc9csvHaluXDtsFSe3uWoXblmG3GtpPVgPujlwydpSrSSk0YMcTFTPyetgf7uLn7u\nzfv4w0+fquYT9WR439tqtzAPHdxDqVzh5FPfC6l5OWljL+X8UG1O2vzcpSe+e4FzF/LEUDcnbe7e\ncDH36fbbrk9y4da7nc/73r6fj3zyeMN1uRy1K69vI66VtB5saXb5MCdNiXf92mdX/NmNkNPQbp2Q\ni7IRua7t4bq2h+vaHhs1J83tTq0Kcxo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"text/plain": [ "<matplotlib.figure.Figure at 0x11841128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='TARGET', y='var15', data=df)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xf73cf60>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb47b898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# How to scale this?\n", "sns.boxplot(x='TARGET', y='var38', data=df)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def top_k_feature_pairwise_plot(df, imp, target):\n", " df = df[imp + [target]]\n", " sns.pairplot(data = df, hue=target, vars=imp, size=10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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emxpLkjz59JkkyRf/7MWcPDGlZBT+C5yans/Hn3h659r3EgCvRUuzFwAAAPBG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t5IPctvzvX4kWPZu3Pggea62p97NR+KbPQWNb3ygAgAAABYfUJ0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\niL604QmB2656h9aaT9rH0FHJ1fLqU9/7bHfasRv0qQ1PKZw4J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"text/plain": [ "<matplotlib.figure.Figure at 0xb47b240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top_k_feature_pairwise_plot(df, filtered_feat_imp_list[:2], 'TARGET')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
staeiou/wiki-stat-notebooks	botvbot/exploratory-botplots-samples.ipynb	1	81471	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bot-vs-bot reverts\n", "## Getting and processing data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-03-10 21:44:24-- https://quarry.wmflabs.org/run/161195/output/0/tsv?download=true\n", "Resolving quarry.wmflabs.org (quarry.wmflabs.org)... 10.68.21.68\n", "Connecting to quarry.wmflabs.org (quarry.wmflabs.org)\|10.68.21.68\|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [text/csv]\n", "Saving to: ‘enwiki-botvbot-pagetitles.tsv’\n", "\n", "enwiki-botvbot-page [ <=> ] 36.78M 3.61MB/s in 11s \n", "\n", "2017-03-10 21:44:35 (3.41 MB/s) - ‘enwiki-botvbot-pagetitles.tsv’ saved [38564767]\n", "\n" ] } ], "source": [ "# bot-vs-bot revert table: https://quarry.wmflabs.org/query/17237\n", "\n", "!wget https://quarry.wmflabs.org/run/161195/output/0/tsv?download=true -O enwiki-botvbot-pagetitles.tsv" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting ftfy\n", " Downloading ftfy-5.0.1.tar.gz\n", "Requirement already satisfied: html5lib in /srv/paws/lib/python3.4/site-packages (from ftfy)\n", "Requirement already satisfied: wcwidth in /srv/paws/lib/python3.4/site-packages (from ftfy)\n", "Requirement already satisfied: six in /srv/paws/lib/python3.4/site-packages (from html5lib->ftfy)\n", "Installing collected packages: ftfy\n", " Running setup.py install for ftfy ... \u001b[?25l-\b \b\\\b \bdone\n", "\u001b[?25hSuccessfully installed ftfy-5.0.1\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "!pip install ftfy\n", "import ftfy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "158606" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"enwiki-botvbot-pagetitles.tsv\", sep=\"\\t\", encoding='utf-8')\n", "len(df)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['rev_id', 'rev_timestamp', 'rev_user', 'rev_user_text', 'rev_page',\n", " 'rev_sha1', 'rev_minor_edit', 'rev_deleted', 'rev_parent_id',\n", " 'archived', 'reverting_id', 'reverting_timestamp', 'reverting_user',\n", " 'reverting_user_text', 'reverting_page', 'reverting_sha1',\n", " 'reverting_minor_edit', 'reverting_deleted', 'reverting_parent_id',\n", " 'reverting_archived', 'rev_revert_offset', 'revisions_reverted',\n", " 'reverted_to_rev_id', 'page_namespace', 'page_title'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rev_id</th>\n", " <th>rev_timestamp</th>\n", " <th>rev_user</th>\n", " <th>rev_user_text</th>\n", " <th>rev_page</th>\n", " <th>rev_sha1</th>\n", " <th>rev_minor_edit</th>\n", " <th>rev_deleted</th>\n", " <th>rev_parent_id</th>\n", " <th>archived</th>\n", " <th>...</th>\n", " <th>reverting_sha1</th>\n", " <th>reverting_minor_edit</th>\n", " <th>reverting_deleted</th>\n", " <th>reverting_parent_id</th>\n", " <th>reverting_archived</th>\n", " <th>rev_revert_offset</th>\n", " <th>revisions_reverted</th>\n", " <th>reverted_to_rev_id</th>\n", " <th>page_namespace</th>\n", " <th>page_title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>56161718</td>\n", " <td>20060531172522</td>\n", " <td>91310</td>\n", " <td>CanisRufus</td>\n", " <td>584516</td>\n", " <td>1lyohbi8ymubjfzdb4w4ssfiflfpgat</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>54170358</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>b3lf3olmh1hw99f68tjh187jl7dzi1j</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>56161718</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>54170358</td>\n", " <td>0</td>\n", " <td>Sharon_Pratt_Kelly</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>56161633</td>\n", " <td>20060531172452</td>\n", " <td>91310</td>\n", " <td>CanisRufus</td>\n", " <td>793703</td>\n", " <td>bxfv9z3d7uypsla28qfioc5qs4l963w</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>54169562</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>1nlk1pfhntoa3m0inrdpotrqb5ajxif</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>56161633</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>54169562</td>\n", " <td>0</td>\n", " <td>Anthony_A._Williams</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>103117636</td>\n", " <td>20070125105128</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>5202035</td>\n", " <td>6ruzx0gegy5xy0mw5yxsdnh1fir7mwd</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>102641383</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>c6uu9wkg34ko0nidqakdktfkkvcds3k</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104315087</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>102641383</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Core_topics_article...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>105582355</td>\n", " <td>20070204154028</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>6744678</td>\n", " <td>p14vq3lzxeu50upo1ax9h3ya2hrz1hv</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104618440</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>qaiydx752b8av3qp2zbnfpqspttpw91</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>105582355</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>104618440</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Medicine_articles_b...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>105627219</td>\n", " <td>20070204194135</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>8468041</td>\n", " <td>rebsad1ch2hvs85nhf0nwm2r8ukboil</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104982162</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>3lda2892o6z3p29r8xipbqh780h2rph</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>105627219</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>104982162</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Soft_drinks_article...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 25 columns</p>\n", "</div>" ], "text/plain": [ " rev_id rev_timestamp rev_user rev_user_text rev_page \\\n", "0 56161718 20060531172522 91310 CanisRufus 584516 \n", "1 56161633 20060531172452 91310 CanisRufus 793703 \n", "2 103117636 20070125105128 234358 Mathbot 5202035 \n", "3 105582355 20070204154028 234358 Mathbot 6744678 \n", "4 105627219 20070204194135 234358 Mathbot 8468041 \n", "\n", " rev_sha1 rev_minor_edit rev_deleted \\\n", "0 1lyohbi8ymubjfzdb4w4ssfiflfpgat 1 0 \n", "1 bxfv9z3d7uypsla28qfioc5qs4l963w 1 0 \n", "2 6ruzx0gegy5xy0mw5yxsdnh1fir7mwd 0 0 \n", "3 p14vq3lzxeu50upo1ax9h3ya2hrz1hv 0 0 \n", "4 rebsad1ch2hvs85nhf0nwm2r8ukboil 0 0 \n", "\n", " rev_parent_id archived ... \\\n", "0 54170358 0 ... \n", "1 54169562 0 ... \n", "2 102641383 0 ... \n", "3 104618440 0 ... \n", "4 104982162 0 ... \n", "\n", " reverting_sha1 reverting_minor_edit reverting_deleted \\\n", "0 b3lf3olmh1hw99f68tjh187jl7dzi1j 1 0 \n", "1 1nlk1pfhntoa3m0inrdpotrqb5ajxif 1 0 \n", "2 c6uu9wkg34ko0nidqakdktfkkvcds3k 0 0 \n", "3 qaiydx752b8av3qp2zbnfpqspttpw91 0 0 \n", "4 3lda2892o6z3p29r8xipbqh780h2rph 0 0 \n", "\n", " reverting_parent_id reverting_archived rev_revert_offset \\\n", "0 56161718 0 1 \n", "1 56161633 0 1 \n", "2 104315087 0 1 \n", "3 105582355 0 1 \n", "4 105627219 0 1 \n", "\n", " revisions_reverted reverted_to_rev_id page_namespace \\\n", "0 1 54170358 0 \n", "1 1 54169562 0 \n", "2 3 102641383 4 \n", "3 1 104618440 4 \n", "4 1 104982162 4 \n", "\n", " page_title \n", "0 Sharon_Pratt_Kelly \n", "1 Anthony_A._Williams \n", "2 Version_1.0_Editorial_Team/Core_topics_article... \n", "3 Version_1.0_Editorial_Team/Medicine_articles_b... \n", "4 Version_1.0_Editorial_Team/Soft_drinks_article... \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:5]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['reverting_timestamp_dt'] = pd.to_datetime(df['reverting_timestamp'], format=\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df.set_index('reverting_timestamp_dt')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#df = df[df['page_namespace'] == 0].copy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'Series' object has no attribute 'find'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-21-427b3df18e94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usertext_pair'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mftfy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reverting_user_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" reverting \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mftfy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rev_user_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usertext_pair'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/paws/lib/python3.4/site-packages/ftfy/__init__.py\u001b[0m in \u001b[0;36mfix_text\u001b[0;34m(text, fix_entities, remove_terminal_escapes, fix_encoding, fix_latin_ligatures, fix_character_width, uncurl_quotes, fix_line_breaks, fix_surrogates, remove_control_chars, remove_bom, normalization, max_decode_length)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mtextbreak\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 156\u001b[0m \u001b[0mfix_encoding_this_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix_encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtextbreak\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/paws/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 2742\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2743\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2744\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2746\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'find'" ] } ], "source": [ "df['usertext_pair'] = df['reverting_user_text'] + \" reverting \" + df['rev_user_text']\n", "df['usertext_pair'].value_counts().head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Cydebot reverting CanisRufus', 'Cydebot reverting CanisRufus',\n", " 'WP 1.0 bot reverting Mathbot', ...,\n", " 'Citation bot reverting Citation bot 2',\n", " 'AvicBot reverting EmausBot', 'DumbBOT reverting Lowercase sigmabot'], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['usertext_pair'].values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gp = df.groupby(['usertext_pair'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 83295\n", "4 31227\n", "14 27417\n", "1 10354\n", "3 3365\n", "2 1350\n", "10 890\n", "5 253\n", "11 204\n", "6 177\n", "109 28\n", "100 27\n", "7 5\n", "108 4\n", "101 3\n", "118 3\n", "12 2\n", "15 2\n", "Name: page_namespace, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['page_namespace'].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def botpair_ns_table(botpair_df):\n", " tablestr = \"Reverts by namespace<br>\\n\"\n", " for x, y in botpair_df.page_namespace.value_counts().iteritems():\n", " namespaces_dict={0: \"Article:\", \n", " 1: \"Talk:\",\n", " 2:\"User:\",\n", " 3: \"User_talk:\",\n", " 4:\"Wikipedia:\",\n", " 5: \"Wikipedia_talk:\",\n", " 6:\"File:\",\n", " 7:\"File_talk:\",\n", " 8:\"MediaWiki:\",\n", " 9:\"MediaWiki_talk:\",\n", " 10:\"Template:\", \n", " 11:\"Template_talk:\",\n", " 12:\"Help:\",\n", " 13:\"Help_talk:\",\n", " 14:\"Category:\", \n", " 15:\"Category_talk:\",\n", " 100:\"Portal:\",\n", " 101:\"Portal_talk:\",\n", " 108:\"Book:\",\n", " 109:\"Book_talk:\",\n", " 118:\"Draft:\"}\n", " \n", " tablestr = tablestr + namespaces_dict[x] + \" \" + str(y) + \"<br>\\n\"\n", " return tablestr\n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "namespaces_dict={0: \"\", \n", " 1: \"Talk:\",\n", " 2:\"User:\",\n", " 3: \"User_talk:\",\n", " 4:\"Wikipedia:\",\n", " 5: \"Wikipedia_talk:\",\n", " 6:\"File:\",\n", " 7:\"File_talk:\",\n", " 8:\"MediaWiki:\",\n", " 9:\"MediaWiki_talk:\",\n", " 10:\"Template:\", \n", " 11:\"Template_talk:\",\n", " 12:\"Help:\",\n", " 13:\"Help_talk:\",\n", " 14:\"Category:\", \n", " 15:\"Category_talk:\",\n", " 100:\"Portal:\",\n", " 101:\"Portal_talk:\",\n", " 108:\"Book:\",\n", " 109:\"Book_talk:\",\n", " 118:\"Draft:\"}" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"botvbot.html\", \"w\") as f:\n", " f.write(\"<h1>Bot vs bot reverts on enwiki</h1>\")\n", " f.write(\"<p>For bot-bot pairs with more than 20 reverts</p>\")\n", " f.write(\"<p>Jupyter notebook for this code <a href=\\\"http://paws-public.wmflabs.org/paws-public/User:Staeiou/wiki-stat-notebooks/botvbot/exploratory-botplots-samples.ipynb#\\\"> here</a></p>\")\n", " for botpair in df['usertext_pair'].unique():\n", " botpair_df = df[df['usertext_pair'] == botpair]\n", " total_reverts = len(botpair_df)\n", " if total_reverts > 20:\n", " \n", " first_revert = df[df['usertext_pair'] == botpair].index.min()\n", " last_revert = df[df['usertext_pair'] == botpair].index.max()\n", " sample = df[df['usertext_pair'] == botpair].sample(2) \n", " count = 0\n", " f.write(\"<h2>\" + ftfy.fix_text(botpair) + \"</h2>\\n\")\n", " f.write(\"<p>Total reverts: \" + str(total_reverts) + \"</p>\\n\")\n", " f.write(botpair_ns_table(botpair_df))\n", " f.write(\"<p>First revert: \" + str(first_revert) + \"</p>\\n\")\n", " f.write(\"<p>Last revert: \" + str(last_revert) + \"</p>\\n\")\n", " f.write(\"<p>Random examples: \")\n", " \n", " for revid in df[df['usertext_pair'] == botpair].sample(20).iterrows():\n", " page_title = namespaces_dict[revid[1].page_namespace] + revid[1].page_title\n", " \n", " f.write(\"<a href=\\\"https://en.wikipedia.org/w/index.php?title=\" + page_title + \"&diff=prev&oldid=\" + str(revid[1].reverting_id) + \"\\\">\" + str(count) + \"</a> \")\n", " count = count + 1" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-49-69398291b023>, line 30)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-49-69398291b023>\"\u001b[0;36m, line \u001b[0;32m30\u001b[0m\n\u001b[0;31m \"ex0\":ex{0},\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "output_df = pd.DataFrame(columns=[\"reverting_bot\", \"reverted_bot\", \"total_reverts\", \"ns0_reverts\", \"ex0\", \"ex1\", \"ex2\", \"ex3\", \"ex4\", \"ex5\", \"ex6\", \"ex7\", \"ex8\", \"ex9\", \"ex10\"])\n", "\n", "with open(\"botvbot.tsv\", \"w\") as f:\n", " for botpair in df['usertext_pair'].unique():\n", " botpair_df = df[df['usertext_pair'] == botpair]\n", " total_reverts = len(botpair_df)\n", " ns0_reverts = len(botpair_df[botpair_df['page_namespace'] == 0])\n", " if total_reverts > 20:\n", " sample = df[df['usertext_pair'] == botpair].sample(1) \n", " \n", " reverting_user_text_link = \"=HYPERLINK(\\\"http://enwp.org/User:\" + sample.reverting_user_text[0] + \"\\\",\\\"\" + sample.reverting_user_text[0] + \"\\\")\"\n", " rev_user_text_link = \"=HYPERLINK(\\\"http://enwp.org/User:\" + sample.rev_user_text[0] + \"\\\",\\\"\" + sample.rev_user_text[0] + \"\\\")\"\n", "\n", " first_revert = df[df['usertext_pair'] == botpair].index.min()\n", " last_revert = df[df['usertext_pair'] == botpair].index.max()\n", "\n", " ex = {}\n", " count = 0\n", "\n", " for revid in df[df['usertext_pair'] == botpair].sample(10).iterrows():\n", " page_title = namespaces_dict[revid[1].page_namespace] + revid[1].page_title\n", " ex[count] = \"=HYPERLINK(\\\"https://en.wikipedia.org/w/index.php?title=\" + page_title + \"&diff=prev&oldid=\" + str(revid[1].reverting_id) + \"\\\",\\\"diff\\\")\"\n", " \n", " count = count + 1\n", " \n", " output_df = output_df.append({\"reverting_bot\":reverting_user_text_link,\n", " \"reverted_bot\":rev_user_text_link,\n", " \"total_reverts\": total_reverts,\n", " \"ns0_reverts\":ns0_reverts,\n", " \"ex0\":ex{0},\n", " \"ex1\":ex{1},\n", " \"ex2\":ex{2},\n", " \"ex3\":ex{3},\n", " \"ex4\":ex{4},\n", " \"ex5\":ex{5},\n", " \"ex6\":ex{6},\n", " \"ex7\":ex{7},\n", " \"ex8\":ex{8},\n", " \"ex9\":ex{9}},\n", " ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>reverting_bot</th>\n", " <th>reverted_bot</th>\n", " <th>total_reverts</th>\n", " <th>ns0_reverts</th>\n", " <th>examples</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td><a href=\"http://enwp.org/User:FrescoBot\">Fresc...</td>\n", " <td><a href=\"http://enwp.org/User:Mathbot\">Mathbot...</td>\n", " <td>58.0</td>\n", " <td>58.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>1825.0</td>\n", " <td>154.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>34.0</td>\n", " <td>34.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>294.0</td>\n", " <td>92.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>172.0</td>\n", " <td>7.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>2056.0</td>\n", " <td>1981.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td><a href=\"http://enwp.org/User:Cydebot\">Cydebot...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>964.0</td>\n", " <td>882.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td><a href=\"http://enwp.org/User:BOTijo\">BOTijo</a></td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>31.0</td>\n", " <td>31.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>402.0</td>\n", " <td>396.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>5163.0</td>\n", " <td>4689.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td><a href=\"http://enwp.org/User:Thehelpfulbot\">T...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>55.0</td>\n", " <td>18.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>878.0</td>\n", " <td>836.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td><a href=\"http://enwp.org/User:AvicBot\">AvicBot...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>1789.0</td>\n", " <td>1528.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td>24.0</td>\n", " <td>8.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>1354.0</td>\n", " <td>1232.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td><a href=\"http://enwp.org/User:BOTijo\">BOTijo</a></td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>21.0</td>\n", " <td>21.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>34.0</td>\n", " <td>33.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>1076.0</td>\n", " <td>999.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td><a href=\"http://enwp.org/User:Thehelpfulbot\">T...</td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>31.0</td>\n", " <td>26.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>223.0</td>\n", " <td>211.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td><a href=\"http://enwp.org/User:AvicBot\">AvicBot...</td>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td>528.0</td>\n", " <td>463.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:SuggestBot\">Sugg...</td>\n", " <td>296.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:SuggestBot\">Sugg...</td>\n", " <td>38.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:MiszaBot\">MiszaB...</td>\n", " <td>68.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td><a href=\"http://enwp.org/User:Cydebot\">Cydebot...</td>\n", " <td>1174.0</td>\n", " <td>969.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td><a href=\"http://enwp.org/User:Cydebot\">Cydebot...</td>\n", " <td>86.0</td>\n", " <td>45.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:JAnDbot\">JAnDbot...</td>\n", " <td>165.0</td>\n", " <td>59.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:JAnDbot\">JAnDbot...</td>\n", " <td>35.0</td>\n", " <td>32.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:JAnDbot\">JAnDbot...</td>\n", " <td>63.0</td>\n", " <td>30.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:JAnDbot\">JAnDbot...</td>\n", " <td>24.0</td>\n", " <td>5.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>142</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:DPL bot\">DPL bot...</td>\n", " <td>160.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>143</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:DPL bot\">DPL bot...</td>\n", " <td>159.0</td>\n", " <td>159.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:DPL bot\">DPL bot...</td>\n", " <td>57.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>145</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:BattyBot\">BattyB...</td>\n", " <td>22.0</td>\n", " <td>22.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>146</th>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>53.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>825.0</td>\n", " <td>467.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>148</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>207.0</td>\n", " <td>42.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>149</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>69.0</td>\n", " <td>2.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>150</th>\n", " <td><a href=\"http://enwp.org/User:Hazard-Bot\">Haza...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot I\">Cybe...</td>\n", " <td>71.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>151</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot II\">Cyb...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td><a href=\"http://enwp.org/User:Amalthea (bot)\">...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot II\">Cyb...</td>\n", " <td>779.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td><a href=\"http://enwp.org/User:Hazard-Bot\">Haza...</td>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td>28.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>154</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>692.0</td>\n", " <td>195.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>155</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>141.0</td>\n", " <td>7.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>156</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>83.0</td>\n", " <td>2.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>157</th>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>248.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>158</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>5260.0</td>\n", " <td>3417.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>159</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>31.0</td>\n", " <td>30.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>160</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>1761.0</td>\n", " <td>595.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>161</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>452.0</td>\n", " <td>195.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>162</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:Theo's Little Bo...</td>\n", " <td>136.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:Theo's Little Bo...</td>\n", " <td>41.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>164</th>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td><a href=\"http://enwp.org/User:VoxelBot\">VoxelB...</td>\n", " <td>111.0</td>\n", " <td>111.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>165</th>\n", " <td><a href=\"http://enwp.org/User:AvicBot\">AvicBot...</td>\n", " <td><a href=\"http://enwp.org/User:VoxelBot\">VoxelB...</td>\n", " <td>27.0</td>\n", " <td>27.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>166</th>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td>28.0</td>\n", " <td>10.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>167</th>\n", " <td><a href=\"http://enwp.org/User:Cydebot\">Cydebot...</td>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td>318.0</td>\n", " <td>317.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>168</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:BracketBot\">Brac...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>169</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:BracketBot\">Brac...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>170</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:MediaWiki messag...</td>\n", " <td>312.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>171</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:MediaWiki messag...</td>\n", " <td>458.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>172 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " reverting_bot \\\n", "0 <a href=\"http://enwp.org/User:FrescoBot\">Fresc... \n", "1 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "2 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "3 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "4 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "5 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... \n", "6 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... \n", "7 <a href=\"http://enwp.org/User:BOTijo\">BOTijo</a> \n", "8 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "9 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "10 <a href=\"http://enwp.org/User:Thehelpfulbot\">T... \n", "11 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "12 <a href=\"http://enwp.org/User:AvicBot\">AvicBot... \n", "13 <a href=\"http://enwp.org/User:ArmbrustBot\">Arm... \n", "14 <a href=\"http://enwp.org/User:RussBot\">RussBot... \n", "15 <a href=\"http://enwp.org/User:BOTijo\">BOTijo</a> \n", "16 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "17 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "18 <a href=\"http://enwp.org/User:Thehelpfulbot\">T... \n", "19 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "20 <a href=\"http://enwp.org/User:AvicBot\">AvicBot... \n", "21 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "22 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "23 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "24 <a href=\"http://enwp.org/User:RussBot\">RussBot... \n", "25 <a href=\"http://enwp.org/User:ArmbrustBot\">Arm... \n", "26 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "27 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "28 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "29 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", ".. ... \n", "142 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "143 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "144 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "145 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "146 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> \n", "147 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "148 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "149 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "150 <a href=\"http://enwp.org/User:Hazard-Bot\">Haza... \n", "151 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "152 <a href=\"http://enwp.org/User:Amalthea (bot)\">... \n", "153 <a href=\"http://enwp.org/User:Hazard-Bot\">Haza... \n", "154 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "155 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "156 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "157 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> \n", "158 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "159 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "160 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "161 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "162 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "163 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "164 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... \n", "165 <a href=\"http://enwp.org/User:AvicBot\">AvicBot... \n", "166 <a href=\"http://enwp.org/User:RussBot\">RussBot... \n", "167 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... \n", "168 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "169 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "170 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "171 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "\n", " reverted_bot total_reverts \\\n", "0 <a href=\"http://enwp.org/User:Mathbot\">Mathbot... 58.0 \n", "1 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 1825.0 \n", "2 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 34.0 \n", "3 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 294.0 \n", "4 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 172.0 \n", "5 <a href=\"http://enwp.org/User:RussBot\">RussBot... 2056.0 \n", "6 <a href=\"http://enwp.org/User:RussBot\">RussBot... 964.0 \n", "7 <a href=\"http://enwp.org/User:RussBot\">RussBot... 31.0 \n", "8 <a href=\"http://enwp.org/User:RussBot\">RussBot... 402.0 \n", "9 <a href=\"http://enwp.org/User:RussBot\">RussBot... 5163.0 \n", "10 <a href=\"http://enwp.org/User:RussBot\">RussBot... 55.0 \n", "11 <a href=\"http://enwp.org/User:RussBot\">RussBot... 878.0 \n", "12 <a href=\"http://enwp.org/User:RussBot\">RussBot... 1789.0 \n", "13 <a href=\"http://enwp.org/User:RussBot\">RussBot... 24.0 \n", "14 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 1354.0 \n", "15 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 21.0 \n", "16 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 34.0 \n", "17 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 1076.0 \n", "18 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 31.0 \n", "19 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 223.0 \n", "20 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 528.0 \n", "21 <a href=\"http://enwp.org/User:SuggestBot\">Sugg... 296.0 \n", "22 <a href=\"http://enwp.org/User:SuggestBot\">Sugg... 38.0 \n", "23 <a href=\"http://enwp.org/User:MiszaBot\">MiszaB... 68.0 \n", "24 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... 1174.0 \n", "25 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... 86.0 \n", "26 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 165.0 \n", "27 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 35.0 \n", "28 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 63.0 \n", "29 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 24.0 \n", ".. ... ... \n", "142 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 160.0 \n", "143 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 159.0 \n", "144 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 57.0 \n", "145 <a href=\"http://enwp.org/User:BattyBot\">BattyB... 22.0 \n", "146 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 53.0 \n", "147 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 825.0 \n", "148 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 207.0 \n", "149 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 69.0 \n", "150 <a href=\"http://enwp.org/User:Cyberbot I\">Cybe... 71.0 \n", "151 <a href=\"http://enwp.org/User:Cyberbot II\">Cyb... 31.0 \n", "152 <a href=\"http://enwp.org/User:Cyberbot II\">Cyb... 779.0 \n", "153 <a href=\"http://enwp.org/User:Lowercase sigmab... 28.0 \n", "154 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 692.0 \n", "155 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 141.0 \n", "156 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 83.0 \n", "157 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 248.0 \n", "158 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 5260.0 \n", "159 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 31.0 \n", "160 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 1761.0 \n", "161 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 452.0 \n", "162 <a href=\"http://enwp.org/User:Theo's Little Bo... 136.0 \n", "163 <a href=\"http://enwp.org/User:Theo's Little Bo... 41.0 \n", "164 <a href=\"http://enwp.org/User:VoxelBot\">VoxelB... 111.0 \n", "165 <a href=\"http://enwp.org/User:VoxelBot\">VoxelB... 27.0 \n", "166 <a href=\"http://enwp.org/User:ArmbrustBot\">Arm... 28.0 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"Installing collected packages: jdcal, et-xmlfile, openpyxl\n", " Running setup.py install for jdcal ... \u001b[?25l-\b \bdone\n", "\u001b[?25h Running setup.py install for et-xmlfile ... \u001b[?25l-\b \bdone\n", "\u001b[?25h Running setup.py install for openpyxl ... \u001b[?25l-\b \b\\\b \b\|\b \b/\b \b-\b \bdone\n", "\u001b[?25hSuccessfully installed et-xmlfile-1.0.1 jdcal-1.3 openpyxl-2.4.5\n" ] } ], "source": [ "!pip install openpyxl\n", "output_df.to_csv(\"revert_by_botpair.tsv\", sep=\"\\t\")\n", "output_df.to_excel(\"revert_by_botpair.xlsx\")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\treverting_bot\treverted_bot\ttotal_reverts\tns0_reverts\texamples\r\n", "0\t\"<a href=\"\"http://enwp.org/User:FrescoBot\"\">FrescoBot</a>\"\t\"<a href=\"\"http://enwp.org/User:Mathbot\"\">Mathbot</a>\"\t58.0\t58.0\t\"<a 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href=\"\"https://en.wikipedia.org/w/index.php?title=Newport_High_Street_station&diff=prev&oldid=257683000\"\">8</a> <a href=\"\"https://en.wikipedia.org/w/index.php?title=Hiderabad&diff=prev&oldid=255826376\"\">9</a> \"\r\n" ] } ], "source": [ "!head revert_by_botpair.tsv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
samgoodgame/sf_crime	iterations/misc/Cha_Goodgame_Kao_Moore_W207_Final_Project_updated_08_19_2230.ipynb	1	74063	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kaggle San Francisco Crime Classification\n", "## Berkeley MIDS W207 Final Project: Sam Goodgame, Sarah Cha, Kalvin Kao, Bryan Moore\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Environment and Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Bryan/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/Users/Bryan/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "# Import relevant libraries:\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn import preprocessing\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.naive_bayes import BernoulliNB\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import log_loss\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import svm\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "# Import Meta-estimators\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "# Import Calibration tools\n", "from sklearn.calibration import CalibratedClassifierCV\n", "\n", "# Set random seed and format print output:\n", "np.random.seed(0)\n", "np.set_printoptions(precision=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### DDL to construct table for SQL transformations:\n", "\n", "```sql\n", "CREATE TABLE kaggle_sf_crime (\n", "dates TIMESTAMP, \n", "category VARCHAR,\n", "descript VARCHAR,\n", "dayofweek VARCHAR,\n", "pd_district VARCHAR,\n", "resolution VARCHAR,\n", "addr VARCHAR,\n", "X FLOAT,\n", "Y FLOAT);\n", "```\n", "#### Getting training data into a locally hosted PostgreSQL database:\n", "```sql\n", "\\copy kaggle_sf_crime FROM '/Users/Goodgame/Desktop/MIDS/207/final/sf_crime_train.csv' DELIMITER ',' CSV HEADER;\n", "```\n", "\n", "#### SQL Query used for transformations:\n", "\n", "```sql\n", "SELECT\n", " category,\n", " date_part('hour', dates) AS hour_of_day,\n", " CASE\n", " WHEN dayofweek = 'Monday' then 1\n", " WHEN dayofweek = 'Tuesday' THEN 2\n", " WHEN dayofweek = 'Wednesday' THEN 3\n", " WHEN dayofweek = 'Thursday' THEN 4\n", " WHEN dayofweek = 'Friday' THEN 5\n", " WHEN dayofweek = 'Saturday' THEN 6\n", " WHEN dayofweek = 'Sunday' THEN 7\n", " END AS dayofweek_numeric,\n", " X,\n", " Y,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS bayview_binary,\n", " CASE\n", " WHEN pd_district = 'INGLESIDE' THEN 1\n", " ELSE 0\n", " END AS ingleside_binary,\n", " CASE\n", " WHEN pd_district = 'NORTHERN' THEN 1\n", " ELSE 0\n", " END AS northern_binary,\n", " CASE\n", " WHEN pd_district = 'CENTRAL' THEN 1\n", " ELSE 0\n", " END AS central_binary,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS pd_bayview_binary,\n", " CASE\n", " WHEN pd_district = 'MISSION' THEN 1\n", " ELSE 0\n", " END AS mission_binary,\n", " CASE\n", " WHEN pd_district = 'SOUTHERN' THEN 1\n", " ELSE 0\n", " END AS southern_binary,\n", " CASE\n", " WHEN pd_district = 'TENDERLOIN' THEN 1\n", " ELSE 0\n", " END AS tenderloin_binary,\n", " CASE\n", " WHEN pd_district = 'PARK' THEN 1\n", " ELSE 0\n", " END AS park_binary,\n", " CASE\n", " WHEN pd_district = 'RICHMOND' THEN 1\n", " ELSE 0\n", " END AS richmond_binary,\n", " CASE\n", " WHEN pd_district = 'TARAVAL' THEN 1\n", " ELSE 0\n", " END AS taraval_binary\n", "FROM kaggle_sf_crime;\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading the data, version 2, with weather features to improve performance: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)\n", "\n", "We seek to add features to our models that will improve performance with respect to out desired performance metric. There is evidence that there is a correlation between weather patterns and crime, with some experts even arguing for a causal relationship between weather and crime [1]. More specifically, a 2013 paper published in Science showed that higher temperatures and extreme rainfall led to large increases in conflict. In the setting of strong evidence that weather influences crime, we see it as a candidate for additional features to improve the performance of our classifiers. Weather data was gathered from (insert source). Certain features from this data set were incorporated into the original crime data set in order to add features that were hypothesizzed to improve performance. These features included (insert what we eventually include)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#data_path = \"./data/train_transformed.csv\"\n", "\n", "#df = pd.read_csv(data_path, header=0)\n", "#x_data = df.drop('category', 1)\n", "#y = df.category.as_matrix()\n", "\n", "########## Adding the date back into the data\n", "#import csv\n", "#import time\n", "#import calendar\n", "#data_path = \"./data/train.csv\"\n", "#dataCSV = open(data_path, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allData = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#df2 = pd.DataFrame(allData)\n", "#df2.columns = csvFields\n", "#dates = df2['Dates']\n", "#dates = dates.apply(time.strptime, args=(\"%Y-%m-%d %H:%M:%S\",))\n", "#dates = dates.apply(calendar.timegm)\n", "#print(dates.head())\n", "\n", "#x_data['secondsFromEpoch'] = dates\n", "#colnames = x_data.columns.tolist()\n", "#colnames = colnames[-1:] + colnames[:-1]\n", "#x_data = x_data[colnames]\n", "##########\n", "\n", "########## Adding the weather data into the original crime data\n", "#weatherData1 = \"./data/1027175.csv\"\n", "#weatherData2 = \"./data/1027176.csv\"\n", "#dataCSV = open(weatherData1, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allWeatherData1 = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#dataCSV = open(weatherData2, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allWeatherData2 = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#weatherDF1 = pd.DataFrame(allWeatherData1)\n", "#weatherDF1.columns = csvFields\n", "#dates1 = weatherDF1['DATE']\n", "#sunrise1 = weatherDF1['DAILYSunrise']\n", "#sunset1 = weatherDF1['DAILYSunset']\n", "\n", "#weatherDF2 = pd.DataFrame(allWeatherData2)\n", "#weatherDF2.columns = csvFields\n", "#dates2 = weatherDF2['DATE']\n", "#sunrise2 = weatherDF2['DAILYSunrise']\n", "#sunset2 = weatherDF2['DAILYSunset']\n", "\n", "#functions for processing the sunrise and sunset times of each day\n", "#def get_hour_and_minute(milTime):\n", " # hour = int(milTime[:-2])\n", " # minute = int(milTime[-2:])\n", " # return [hour, minute]\n", "\n", "#def get_date_only(date):\n", "# return time.struct_time(tuple([date[0], date[1], date[2], 0, 0, 0, date[6], date[7], date[8]]))\n", "\n", "#def structure_sun_time(timeSeries, dateSeries):\n", "# sunTimes = timeSeries.copy()\n", "# for index in range(len(dateSeries)):\n", "# sunTimes[index] = time.struct_time(tuple([dateSeries[index][0], dateSeries[index][1], dateSeries[index][2], timeSeries[index][0], timeSeries[index][1], dateSeries[index][5], dateSeries[index][6], dateSeries[index][7], dateSeries[index][8]]))\n", "# return sunTimes\n", "\n", "#dates1 = dates1.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "#sunrise1 = sunrise1.apply(get_hour_and_minute)\n", "#sunrise1 = structure_sun_time(sunrise1, dates1)\n", "#sunrise1 = sunrise1.apply(calendar.timegm)\n", "#sunset1 = sunset1.apply(get_hour_and_minute)\n", "#sunset1 = structure_sun_time(sunset1, dates1)\n", "#sunset1 = sunset1.apply(calendar.timegm)\n", "#dates1 = dates1.apply(calendar.timegm)\n", "\n", "#dates2 = dates2.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "#sunrise2 = sunrise2.apply(get_hour_and_minute)\n", "#sunrise2 = structure_sun_time(sunrise2, dates2)\n", "#sunrise2 = sunrise2.apply(calendar.timegm)\n", "#sunset2 = sunset2.apply(get_hour_and_minute)\n", "#sunset2 = structure_sun_time(sunset2, dates2)\n", "#sunset2 = sunset2.apply(calendar.timegm)\n", "#dates2 = dates2.apply(calendar.timegm)\n", "\n", "#weatherDF1['DATE'] = dates1\n", "#weatherDF1['DAILYSunrise'] = sunrise1\n", "#weatherDF1['DAILYSunset'] = sunset1\n", "#weatherDF2['DATE'] = dates2\n", "#weatherDF2['DAILYSunrise'] = sunrise2\n", "#weatherDF2['DAILYSunset'] = sunset2\n", "\n", "#weatherDF = pd.concat([weatherDF1,weatherDF2[32:]],ignore_index=True)\n", "\n", "# Starting off with some of the easier features to work with-- more to come here . . . still in beta\n", "#weatherMetrics = weatherDF[['DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed', \\\n", "# 'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY', 'DAILYSunrise', 'DAILYSunset']]\n", "#weatherMetrics = weatherMetrics.convert_objects(convert_numeric=True)\n", "#weatherDates = weatherMetrics['DATE']\n", "#'DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed',\n", "#'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY'\n", "#timeWindow = 10800 #3 hours\n", "#hourlyDryBulbTemp = []\n", "#hourlyRelativeHumidity = []\n", "#hourlyWindSpeed = []\n", "#hourlySeaLevelPressure = []\n", "#hourlyVisibility = []\n", "#dailySunrise = []\n", "#dailySunset = []\n", "#daylight = []\n", "#test = 0\n", "#for timePoint in dates:#dates is the epoch time from the kaggle data\n", "# relevantWeather = weatherMetrics[(weatherDates <= timePoint) & (weatherDates > timePoint - timeWindow)]\n", "# hourlyDryBulbTemp.append(relevantWeather['HOURLYDRYBULBTEMPF'].mean())\n", "# hourlyRelativeHumidity.append(relevantWeather['HOURLYRelativeHumidity'].mean())\n", "# hourlyWindSpeed.append(relevantWeather['HOURLYWindSpeed'].mean())\n", "# hourlySeaLevelPressure.append(relevantWeather['HOURLYSeaLevelPressure'].mean())\n", "# hourlyVisibility.append(relevantWeather['HOURLYVISIBILITY'].mean())\n", "# dailySunrise.append(relevantWeather['DAILYSunrise'].iloc[-1])\n", "# dailySunset.append(relevantWeather['DAILYSunset'].iloc[-1])\n", "# daylight.append(1.0((timePoint >= relevantWeather['DAILYSunrise'].iloc[-1]) and (timePoint < relevantWeather['DAILYSunset'].iloc[-1])))\n", " #if timePoint < relevantWeather['DAILYSunset'][-1]:\n", " #daylight.append(1)\n", " #else:\n", " #daylight.append(0)\n", " \n", "# if test%100000 == 0:\n", "# print(relevantWeather)\n", "# test += 1\n", "\n", "#hourlyDryBulbTemp = pd.Series.from_array(np.array(hourlyDryBulbTemp))\n", "#hourlyRelativeHumidity = pd.Series.from_array(np.array(hourlyRelativeHumidity))\n", "#hourlyWindSpeed = pd.Series.from_array(np.array(hourlyWindSpeed))\n", "#hourlySeaLevelPressure = pd.Series.from_array(np.array(hourlySeaLevelPressure))\n", "#hourlyVisibility = pd.Series.from_array(np.array(hourlyVisibility))\n", "#dailySunrise = pd.Series.from_array(np.array(dailySunrise))\n", "#dailySunset = pd.Series.from_array(np.array(dailySunset))\n", "#daylight = pd.Series.from_array(np.array(daylight))\n", "\n", "#x_data['HOURLYDRYBULBTEMPF'] = hourlyDryBulbTemp\n", "#x_data['HOURLYRelativeHumidity'] = hourlyRelativeHumidity\n", "#x_data['HOURLYWindSpeed'] = hourlyWindSpeed\n", "#x_data['HOURLYSeaLevelPressure'] = hourlySeaLevelPressure\n", "#x_data['HOURLYVISIBILITY'] = hourlyVisibility\n", "#x_data['DAILYSunrise'] = dailySunrise\n", "#x_data['DAILYSunset'] = dailySunset\n", "#x_data['Daylight'] = daylight\n", "\n", "#x_data.to_csv(path_or_buf=\"C:/MIDS/W207 final project/x_data.csv\")\n", "##########\n", "\n", "# Impute missing values with mean values:\n", "#x_complete = x_data.fillna(x_data.mean())\n", "#X_raw = x_complete.as_matrix()\n", "\n", "# Scale the data between 0 and 1:\n", "#X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "# Shuffle data to remove any underlying pattern that may exist:\n", "#shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "#X, y = X[shuffle], y[shuffle]\n", "\n", "# Separate training, dev, and test data:\n", "#test_data, test_labels = X[800000:], y[800000:]\n", "#dev_data, dev_labels = X[700000:800000], y[700000:800000]\n", "#train_data, train_labels = X[:700000], y[:700000]\n", "\n", "#mini_train_data, mini_train_labels = X[:75000], y[:75000]\n", "#mini_dev_data, mini_dev_labels = X[75000:100000], y[75000:100000]\n", "#labels_set = set(mini_dev_labels)\n", "#print(labels_set)\n", "#print(len(labels_set))\n", "#print(train_data[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local, individual load of updated data set (with weather data integrated) into training, development, and test subsets.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37 37 37 37\n" ] } ], "source": [ "data_path = \"/Users/Bryan/Desktop/UC_Berkeley_MIDS_files/Courses/W207_Intro_To_Machine_Learning/Final_Project/x_data_3.csv\"\n", "df = pd.read_csv(data_path, header=0)\n", "x_data = df.drop('category', 1)\n", "y = df.category.as_matrix()\n", "\n", "# Impute missing values with mean values:\n", "x_complete = x_data.fillna(x_data.mean())\n", "X_raw = x_complete.as_matrix()\n", "\n", "# Scale the data between 0 and 1:\n", "X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "# Shuffle data to remove any underlying pattern that may exist. Must re-run random seed step each time:\n", "np.random.seed(0)\n", "shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "X, y = X[shuffle], y[shuffle]\n", "\n", "# Due to difficulties with log loss and set(y_pred) needing to match set(labels), we will remove the extremely rare\n", "# crimes from the data for quality issues.\n", "X_minus_trea = X[np.where(y != 'TREA')]\n", "y_minus_trea = y[np.where(y != 'TREA')]\n", "X_final = X_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "y_final = y_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "\n", "# Separate training, dev, and test data:\n", "test_data, test_labels = X_final[800000:], y_final[800000:]\n", "dev_data, dev_labels = X_final[700000:800000], y_final[700000:800000]\n", "train_data, train_labels = X_final[100000:700000], y_final[100000:700000]\n", "calibrate_data, calibrate_labels = X_final[:100000], y_final[:100000]\n", "\n", "# Create mini versions of the above sets\n", "mini_train_data, mini_train_labels = X_final[:20000], y_final[:20000]\n", "mini_calibrate_data, mini_calibrate_labels = X_final[19000:28000], y_final[19000:28000]\n", "mini_dev_data, mini_dev_labels = X_final[49000:60000], y_final[49000:60000]\n", "\n", "# Create list of the crime type labels. This will act as the \"labels\" parameter for the log loss functions that follow\n", "crime_labels = list(set(y_final))\n", "crime_labels_mini_train = list(set(mini_train_labels))\n", "crime_labels_mini_dev = list(set(mini_dev_labels))\n", "crime_labels_mini_calibrate = list(set(mini_calibrate_labels))\n", "print(len(crime_labels), len(crime_labels_mini_train), len(crime_labels_mini_dev),len(crime_labels_mini_calibrate))\n", "\n", "#print(len(train_data),len(train_labels))\n", "#print(len(dev_data),len(dev_labels))\n", "#print(len(mini_train_data),len(mini_train_labels))\n", "#print(len(mini_dev_data),len(mini_dev_labels))\n", "#print(len(test_data),len(test_labels))\n", "#print(len(mini_calibrate_data),len(mini_calibrate_labels))\n", "#print(len(calibrate_data),len(calibrate_labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sarah's School data that we may still get to work as features: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Read in zip code data\n", "#data_path_zip = \"./data/2016_zips.csv\"\n", "#zips = pd.read_csv(data_path_zip, header=0, sep ='\\t', usecols = [0,5,6], names = [\"GEOID\", \"INTPTLAT\", \"INTPTLONG\"], dtype ={'GEOID': int, 'INTPTLAT': float, 'INTPTLONG': float})\n", "#sf_zips = zips[(zips['GEOID'] > 94000) & (zips['GEOID'] < 94189)]\n", "\n", "### Mapping longitude/latitude to zipcodes\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)2+(long1-long2)2)\n", "# return abs(lat1-lat2)+abs(long1-long2)\n", "#def find_zipcode(lat, long): \n", "# distances = sf_zips.apply(lambda row: dist(lat, long, row[\"INTPTLAT\"], row[\"INTPTLONG\"]), axis=1)\n", "# return sf_zips.loc[distances.idxmin(), \"GEOID\"]\n", "#x_data['zipcode'] = 0\n", "#for i in range(0, 1):\n", "# x_data['zipcode'][i] = x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "#x_data['zipcode']= x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "\n", "\n", "### Read in school data\n", "#data_path_schools = \"./data/pubschls.csv\"\n", "#schools = pd.read_csv(data_path_schools,header=0, sep ='\\t', usecols = [\"CDSCode\",\"StatusType\", \"School\", \"EILCode\", \"EILName\", \"Zip\", \"Latitude\", \"Longitude\"], dtype ={'CDSCode': str, 'StatusType': str, 'School': str, 'EILCode': str,'EILName': str,'Zip': str, 'Latitude': float, 'Longitude': float})\n", "#schools = schools[(schools[\"StatusType\"] == 'Active')]\n", "\n", "### Find the closest school\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)2+(long1-long2)2)\n", "\n", "#def find_closest_school(lat, long): \n", "# distances = schools.apply(lambda row: dist(lat, long, row[\"Latitude\"], row[\"Longitude\"]), axis=1)\n", "# return min(distances)\n", "#x_data['closest_school'] = x_data_sub.apply(lambda row: find_closest_school(row['y'], row['x']), axis=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formatting to meet Kaggle submission standards: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The Kaggle submission format requires listing the ID of each example.\n", "# This is to remember the order of the IDs after shuffling\n", "#allIDs = np.array(list(df.axes[0]))\n", "#allIDs = allIDs[shuffle]\n", "\n", "#testIDs = allIDs[800000:]\n", "#devIDs = allIDs[700000:800000]\n", "#trainIDs = allIDs[:700000]\n", "\n", "# Extract the column names for the required submission format\n", "#sampleSubmission_path = \"./data/sampleSubmission.csv\"\n", "#sampleDF = pd.read_csv(sampleSubmission_path)\n", "#allColumns = list(sampleDF.columns)\n", "#featureColumns = allColumns[1:]\n", "\n", "# Extracting the test data for a baseline submission\n", "#real_test_path = \"./data/test_transformed.csv\"\n", "#testDF = pd.read_csv(real_test_path, header=0)\n", "#real_test_data = testDF\n", "\n", "#test_complete = real_test_data.fillna(real_test_data.mean())\n", "#Test_raw = test_complete.as_matrix()\n", "\n", "#TestData = MinMaxScaler().fit_transform(Test_raw)\n", "\n", "# Here we remember the ID of each test data point, in case we ever decide to shuffle the test data for some reason\n", "#testIDs = list(testDF.axes[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate baseline prediction probabilities from MNB classifier and store in a .csv file (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate a baseline MNB classifier and make it return prediction probabilities for the actual test data\n", "#def MNB():\n", "# mnb = MultinomialNB(alpha = 0.0000001)\n", "# mnb.fit(train_data, train_labels)\n", "# print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", "# return mnb.predict_proba(dev_data)\n", "#MNB()\n", "\n", "#baselinePredictionProbabilities = MNB()\n", "\n", "# Place the resulting prediction probabilities in a .csv file in the required format\n", "# First, turn the prediction probabilties into a data frame\n", "#resultDF = pd.DataFrame(baselinePredictionProbabilities,columns=featureColumns)\n", "# Add the IDs as a final column\n", "#resultDF.loc[:,'Id'] = pd.Series(testIDs,index=resultDF.index)\n", "# Make the 'Id' column the first column\n", "#colnames = resultDF.columns.tolist()\n", "#colnames = colnames[-1:] + colnames[:-1]\n", "#resultDF = resultDF[colnames]\n", "# Output to a .csv file\n", "# resultDF.to_csv('result.csv',index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: the code above will shuffle data differently every time it's run, so model accuracies will vary accordingly." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.016 0.985 0.826 0.667 0.055 0.002 0. 0. 0. 1. 0.\n", " 0. 0. 0. 0. 0. 0. 0.514 0.405 0.375 0.661 1.\n", " 0.985 0.985 0. ]]\n", "['LARCENY/THEFT']\n" ] } ], "source": [ "## Data sub-setting quality check-point\n", "print(train_data[:1])\n", "print(train_labels[:1])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "MultinomialNB accuracy on dev data: 0.22347\n" ] } ], "source": [ "# Modeling quality check-point with MNB--fast model\n", "\n", "def MNB():\n", " mnb = MultinomialNB(alpha = 0.0000001)\n", " mnb.fit(train_data, train_labels)\n", " print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", " \n", "MNB()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining Performance Criteria\n", "\n", "As determined by the Kaggle submission guidelines, the performance criteria metric for the San Francisco Crime Classification competition is Multi-class Logarithmic Loss (also known as cross-entropy). There are various other performance metrics that are appropriate for different domains: accuracy, F-score, Lift, ROC Area, average precision, precision/recall break-even point, and squared error.\n", "\n", "(Describe each performance metric and a domain in which it is preferred. Give Pros/Cons if able)\n", "\n", "- Multi-class Log Loss:\n", "\n", "- Accuracy:\n", "\n", "- F-score:\n", "\n", "- Lift:\n", "\n", "- ROC Area:\n", "\n", "- Average precision\n", "\n", "- Precision/Recall break-even point:\n", "\n", "- Squared-error:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Prototyping\n", "We will start our classifier and feature engineering process by looking at the performance of various classifiers with default parameter settings in predicting labels on the mini_dev_data:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", " weights='uniform') Multi-class Log Loss: 21.0240643644 \n", "\n", "\n", "BernoulliNB(alpha=1, binarize=0.5, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.6947927812 \n", "\n", "\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.60974496429 \n", "\n", "\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 2.59547592791 \n", "\n", "\n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False) Multi-class Log Loss: 2.60265495281 \n", "\n", "\n", "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 15.5020995603 \n", "\n", "\n", "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best') Multi-class Log Loss: 29.8634820265 \n", "\n", "\n", "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False) Multi-class Log Loss: 2.62373605675 \n", "\n", "\n" ] } ], "source": [ "def model_prototype(train_data, train_labels, eval_data, eval_labels):\n", " knn = KNeighborsClassifier(n_neighbors=5).fit(train_data, train_labels)\n", " bnb = BernoulliNB(alpha=1, binarize = 0.5).fit(train_data, train_labels)\n", " mnb = MultinomialNB().fit(train_data, train_labels)\n", " log_reg = LogisticRegression().fit(train_data, train_labels)\n", " neural_net = MLPClassifier().fit(train_data, train_labels)\n", " random_forest = RandomForestClassifier().fit(train_data, train_labels)\n", " decision_tree = DecisionTreeClassifier().fit(train_data, train_labels)\n", " support_vm_step_one = svm.SVC(probability = True)\n", " support_vm = support_vm_step_one.fit(train_data, train_labels)\n", " \n", " models = [knn, bnb, mnb, log_reg, neural_net, random_forest, decision_tree, support_vm]\n", " for model in models:\n", " eval_prediction_probabilities = model.predict_proba(eval_data)\n", " eval_predictions = model.predict(eval_data)\n", " print(model, \"Multi-class Log Loss:\", log_loss(y_true = eval_labels, y_pred = eval_prediction_probabilities, labels = crime_labels_mini_dev), \"\\n\\n\")\n", "\n", "model_prototype(mini_train_data, mini_train_labels, mini_dev_data, mini_dev_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Features, Hyperparameter Tuning, and Model Calibration To Improve Prediction For Each Classifier\n", "\n", "Here we seek to optimize the performance of our classifiers in a three-step, dynamnic engineering process. \n", "\n", "##### 1) Feature addition\n", "\n", "We previously added components from the weather data into the original SF crime data as new features. We will not repeat work done in our initial submission, where our training dataset did not include these features. For comparision with respoect to how the added features improved our performance with respect to log loss, please refer back to our initial submission.\n", "\n", "We can have Kalvin expand on exactly what he did here.\n", "\n", "##### 2) Hyperparameter tuning\n", "\n", "Each classifier has parameters that we can engineer to further optimize performance, as opposed to using the default parameter values as we did above in the model prototyping cell. This will be specific to each classifier as detailed below.\n", "\n", "##### 3) Model calibration\n", "\n", "We can calibrate the models via Platt Scaling or Isotonic Regression to attempt to improve their performance.\n", "\n", "- Platt Scaling: ((brief explanation of how it works))\n", "\n", "- Isotonic Regression: ((brief explanation of how it works))\n", "\n", "For each classifier, we can use CalibratedClassifierCV to perform probability calibration with isotonic regression or sigmoid (Platt Scaling). The parameters within CalibratedClassifierCV that we can adjust are the method ('sigmoid' or 'isotonic') and cv (cross-validation generator). As we will already be training our models before calibration, we will only use cv = 'prefit'. Thus, in practice the cross-validation generator will not be a modifiable parameter for us.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### K-Nearest Neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning:\n", "\n", "For the KNN classifier, we can seek to optimize the following classifier parameters: n-neighbors, weights, and the power parameter ('p')." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For KNN the best log loss with hyperparameter tuning is 2.62923629844 with k = 2001 w = uniform p = 1\n", "Computation time for this step is 351.40 seconds\n" ] } ], "source": [ "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_log_loss = []\n", "\n", "def k_neighbors_tuned(k,w,p):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\", p, \"is:\", working_log_loss)\n", "\n", "k_value_tuning = [i for i in range(1,5002,500)]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " k_neighbors_tuned(this_k, this_w, this_p)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "Here we will calibrate the KNN classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively.\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For KNN the best log loss with hyperparameter tuning and calibration is 2.71372469963 with k = 1 w = uniform p = 1 m = sigmoid\n", "Computation time for this step is 49.62 seconds\n" ] } ], "source": [ "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def knn_calibrated(k,w,p,m):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " ccv = CalibratedClassifierCV(tuned_KNN, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\",p,\",\",m,\"is:\", working_log_loss)\n", "\n", "#k_value_tuning = [i for i in range(1,5002,500)]\n", "k_value_tuning = [1]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " for this_m in methods:\n", " knn_calibrated(this_k, this_w, this_p, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss], 'm =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Comments on results for Hyperparameter tuning and Calibration for KNN:\n", "\n", "We see that the best log loss we achieve for KNN is with _ neighbors, _ weights, and _ power parameter.\n", "\n", "When we add-in calibration, we see that the the best log loss we achieve for KNN is with _ neighbors, _ weights, _ power parameter, and _ calibration method.\n", "\n", "(Further explanation here?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multinomial, Bernoulli, and Gaussian Naive Bayes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Bernoulli Naive Bayes\n", "\n", "For the Bernoulli Naive Bayes classifier, we seek to optimize the alpha parameter (Laplace smoothing parameter) and the binarize parameter (threshold for binarizing of the sample features). For the binarize parameter, we will create arbitrary thresholds over which our features, which are not binary/boolean features, will be binarized." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For BNB the best log loss with hyperparameter tuning is 2.6247750866 with alpha = 1.2 binarization threshold = 1e-20\n", "Computation time for this step is 186.46 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_bs = []\n", "list_for_log_loss = []\n", "\n", "def BNB_tuned(a,b):\n", " bnb_tuned = BernoulliNB(alpha = a, binarize = b).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = bnb_tuned.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_bs.append(this_b)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a,b =\", a,\",\",b,\"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "binarize_thresholds_tuning = [1e-20, 1e-19, 1e-18, 1e-17, 1e-16, 1e-15, 1e-14, 1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 0.999, 0.9999]\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_b in binarize_thresholds_tuning:\n", " BNB_tuned(this_a, this_b)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For BNB the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'binarization threshold =', list_for_bs[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: BernoulliNB\n", "\n", "Here we will calibrate the BNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For BNB the best log loss with hyperparameter tuning and calibration is 2.61370308039 with alpha = 1.0 binarization threshold = 0.5 method = sigmoid\n", "Computation time for this step is 1066.40 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_bs = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def BNB_calibrated(a,b,m):\n", " bnb_tuned = BernoulliNB(alpha = a, binarize = b).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = bnb_tuned.predict_proba(mini_dev_data)\n", " ccv = CalibratedClassifierCV(bnb_tuned, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_bs.append(this_b)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a,b,m =\", a,\",\", b,\",\", m, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "binarize_thresholds_tuning = [1e-20, 1e-19, 1e-18, 1e-17, 1e-16, 1e-15, 1e-14, 1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 0.999, 0.9999]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_b in binarize_thresholds_tuning:\n", " for this_m in methods:\n", " BNB_calibrated(this_a, this_b, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For BNB the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'binarization threshold =', list_for_bs[index_best_logloss], 'method = ', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Multinomial Naive Bayes\n", "\n", "For the Multinomial Naive Bayes classifer, we seek to optimize the alpha parameter (Laplace smoothing parameter)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For MNB the best log loss with hyperparameter tuning is 2.60930490132 with alpha = 1.8\n", "Computation time for this step is 5.96 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_log_loss = []\n", "\n", "def MNB_tuned(a):\n", " mnb_tuned = MultinomialNB(alpha = a).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities =mnb_tuned.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a =\", a, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " MNB_tuned(this_a)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For MNB the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: MultinomialNB\n", "\n", "Here we will calibrate the MNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For MNB the best log loss with hyperparameter tuning and calibration is 2.6145055659 with alpha = 2.0 and method = sigmoid\n", "Computation time for this step is 34.13 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def MNB_calibrated(a,m):\n", " mnb_tuned = MultinomialNB(alpha = a).fit(mini_train_data, mini_train_labels)\n", " ccv = CalibratedClassifierCV(mnb_tuned, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with MNB and a =\", a, \"and m =\", m, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_m in methods:\n", " MNB_calibrated(this_a, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For MNB the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'and method =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Tuning: Gaussian Naive Bayes\n", "\n", "For the Gaussian Naive Bayes classifier there are no inherent parameters within the classifier function to optimize, but we will look at our log loss before and after adding noise to the data that is hypothesized to give it a more normal (Gaussian) distribution, which is required by the GNB classifier." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with pre-tuned GNB is: 34.1076504549\n", "Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution is: 31.8040829494\n" ] } ], "source": [ "def GNB_pre_tune():\n", " gnb_pre_tuned = GaussianNB().fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities =gnb_pre_tuned.predict_proba(mini_dev_data)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " print(\"Multi-class Log Loss with pre-tuned GNB is:\", working_log_loss)\n", "\n", "GNB_pre_tune()\n", " \n", "def GNB_post_tune():\n", " # Gaussian Naive Bayes requires the data to have a relative normal distribution. Sometimes\n", " # adding noise can improve performance by making the data more normal:\n", " mini_train_data_noise = np.random.rand(mini_train_data.shape[0],mini_train_data.shape[1])\n", " modified_mini_train_data = np.multiply(mini_train_data,mini_train_data_noise) \n", " gnb_with_noise = GaussianNB().fit(modified_mini_train_data,mini_train_labels)\n", " dev_prediction_probabilities =gnb_with_noise.predict_proba(mini_dev_data)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " print(\"Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution is:\", working_log_loss)\n", " \n", "GNB_post_tune()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: GaussianNB\n", "\n", "Here we will calibrate the GNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For GNB the best log loss with tuning and calibration is 2.67904020299 with method = sigmoid\n", "Computation time for this step is 1.36 seconds\n" ] } ], "source": [ "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def GNB_calibrated(m):\n", " # Gaussian Naive Bayes requires the data to have a relative normal distribution. Sometimes\n", " # adding noise can improve performance by making the data more normal:\n", " mini_train_data_noise = np.random.rand(mini_train_data.shape[0],mini_train_data.shape[1])\n", " modified_mini_train_data = np.multiply(mini_train_data,mini_train_data_noise) \n", " gnb_with_noise = GaussianNB().fit(modified_mini_train_data,mini_train_labels)\n", " ccv = CalibratedClassifierCV(gnb_with_noise, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution and after calibration is:\", working_log_loss, 'with calibration method =', m)\n", " \n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_m in methods:\n", " GNB_calibrated(this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For GNB the best log loss with tuning and calibration is',list_for_log_loss[index_best_logloss], 'with method =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logistic Regression\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Logistic Regression classifier, we can seek to optimize the following classifier parameters: penalty (l1 or l2), C (inverse of regularization strength), solver ('newton-cg', 'lbfgs', 'liblinear', or 'sag')\n", "\n", "###### Model calibration:\n", "\n", "See above\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decision Tree (Bryan)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Decision Tree classifier, we can seek to optimize the following classifier parameters: min_samples_leaf (the minimum number of samples required to be at a leaf node), max_depth\n", "\n", "From readings, setting min_samples_leaf to approximately 1% of the data points can stop the tree from inappropriately classifying outliers, which can help to improve accuracy (unsure if significantly improves MCLL).\n", "\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support Vector Machines (Kalvin)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the SVM classifier, we can seek to optimize the following classifier parameters: C (penalty parameter C of the error term), kernel ('linear', 'poly', 'rbf', sigmoid', or 'precomputed')\n", "\n", "See source [2] for parameter optimization in SVM\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural Nets (Sarah)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Neural Networks MLP classifier, we can seek to optimize the following classifier parameters: hidden_layer_sizes, activation ('identity', 'logistic', 'tanh', 'relu'), solver ('lbfgs','sgd', adam'), alpha, learning_rate ('constant', 'invscaling','adaptive')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'theano'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-99-746a0d71593d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m### All the work from Sarah's notebook:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msandbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrng_mrg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMRG_RandomStreams\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mRandomStreams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'theano'" ] } ], "source": [ "### All the work from Sarah's notebook:\n", "\n", "import theano\n", "from theano import tensor as T\n", "from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n", "print (theano.config.device) # We're using CPUs (for now)\n", "print (theano.config.floatX )# Should be 64 bit for CPUs\n", "\n", "np.random.seed(0)\n", "\n", "from IPython.display import display, clear_output" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features = 25\n", "Train set = 700000\n", "Test set = 78049\n", "['DRUG/NARCOTIC', 'RUNAWAY', 'DRUNKENNESS', 'LOITERING', 'STOLEN PROPERTY', 'MISSING PERSON', 'ARSON', 'FRAUD', 'SEX OFFENSES NON FORCIBLE', 'NON-CRIMINAL', 'WEAPON LAWS', 'RECOVERED VEHICLE', 'ASSAULT', 'TRESPASS', 'GAMBLING', 'SUSPICIOUS OCC', 'TREA', 'BAD CHECKS', 'VANDALISM', 'FAMILY OFFENSES', 'DRIVING UNDER THE INFLUENCE', 'WARRANTS', 'PROSTITUTION', 'SEX OFFENSES FORCIBLE', 'DISORDERLY CONDUCT', 'LIQUOR LAWS', 'ROBBERY', 'FORGERY/COUNTERFEITING', 'OTHER OFFENSES', 'EXTORTION', 'VEHICLE THEFT', 'SUICIDE', 'PORNOGRAPHY/OBSCENE MAT', 'LARCENY/THEFT', 'BRIBERY', 'EMBEZZLEMENT', 'SECONDARY CODES', 'KIDNAPPING', 'BURGLARY']\n" ] } ], "source": [ "numFeatures = train_data[1].size\n", "numTrainExamples = train_data.shape[0]\n", "numTestExamples = test_data.shape[0]\n", "print ('Features = %d' %(numFeatures))\n", "print ('Train set = %d' %(numTrainExamples))\n", "print ('Test set = %d' %(numTestExamples))\n", "\n", "class_labels = list(set(train_labels))\n", "print(class_labels)\n", "numClasses = len(class_labels)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classes = 39\n", "\n", " [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 1.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]] \n", "\n", "['BURGLARY' 'LARCENY/THEFT' 'OTHER OFFENSES' 'OTHER OFFENSES'\n", " 'SUSPICIOUS OCC' 'VANDALISM' 'DRUG/NARCOTIC' 'MISSING PERSON'\n", " 'LARCENY/THEFT' 'OTHER OFFENSES'] \n", "\n" ] } ], "source": [ "### Binarize the class labels\n", "\n", "def binarizeY(data):\n", " binarized_data = np.zeros((data.size,39))\n", " for j in range(0,data.size):\n", " feature = data[j]\n", " i = class_labels.index(feature)\n", " binarized_data[j,i]=1\n", " return binarized_data\n", "\n", "train_labels_b = binarizeY(train_labels)\n", "test_labels_b = binarizeY(test_labels)\n", "numClasses = train_labels_b[1].size\n", "\n", "print ('Classes = %d' %(numClasses))\n", "print ('\\n', train_labels_b[:5, :], '\\n')\n", "print (train_labels[:10], '\\n')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'theano' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-94-8c49129ca7e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnumHiddenNodeslayer2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mw_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumFeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumHiddenNodeslayer1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mw_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumHiddenNodeslayer1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumHiddenNodeslayer2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mw_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumHiddenNodeslayer2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumClasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'theano' is not defined" ] } ], "source": [ "###1) Parameters\n", "numFeatures = train_data.shape[1]\n", "\n", "numHiddenNodeslayer1 = 50\n", "numHiddenNodeslayer2 = 30\n", "\n", "w_1 = theano.shared(np.asarray((np.random.randn((numFeatures, numHiddenNodeslayer1))0.01)))\n", "w_2 = theano.shared(np.asarray((np.random.randn((numHiddenNodeslayer1, numHiddenNodeslayer2))0.01)))\n", "w_3 = theano.shared(np.asarray((np.random.randn((numHiddenNodeslayer2, numClasses))0.01)))\n", "params = [w_1, w_2, w_3]\n", "\n", "\n", "###2) Model\n", "X = T.matrix()\n", "Y = T.matrix()\n", "\n", "srng = RandomStreams()\n", "def dropout(X, p=0.):\n", " if p > 0:\n", " X = srng.binomial(X.shape, p=1 - p)\n", " X /= 1 - p\n", " return X\n", "\n", "def model(X, w_1, w_2, w_3, p_1, p_2, p_3):\n", " return T.nnet.softmax(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(X, p_1), w_1)),p_2), w_2)),p_3),w_3))\n", "y_hat_train = model(X, w_1, w_2, w_3, 0.2, 0.5,0.5)\n", "y_hat_predict = model(X, w_1, w_2, w_3, 0., 0., 0.)\n", "\n", "### (3) Cost function\n", "cost = T.mean(T.sqr(y_hat - Y))\n", "cost = T.mean(T.nnet.categorical_crossentropy(y_hat_train, Y))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'cost' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-ef1dde28bb64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mupdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_input_downcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'cost' is not defined" ] } ], "source": [ "### (4) Objective (and solver)\n", "\n", "alpha = 0.01\n", "def backprop(cost, w):\n", " grads = T.grad(cost=cost, wrt=w)\n", " updates = []\n", " for wi, grad in zip(w, grads):\n", " updates.append([wi, wi - grad * alpha])\n", " return updates\n", "\n", "update = backprop(cost, params)\n", "train = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)\n", "y_pred = T.argmax(y_hat_predict, axis=1)\n", "predict = theano.function(inputs=[X], outputs=y_pred, allow_input_downcast=True)\n", "\n", "miniBatchSize = 10 \n", "\n", "def gradientDescent(epochs):\n", " for i in range(epochs):\n", " for start, end in zip(range(0, len(train_data), miniBatchSize), range(miniBatchSize, len(train_data), miniBatchSize)):\n", " cc = train(train_data[start:end], train_labels_b[start:end])\n", " clear_output(wait=True)\n", " print ('%d) accuracy = %.4f' %(i+1, np.mean(np.argmax(test_labels_b, axis=1) == predict(test_data))) )\n", "\n", "gradientDescent(50)\n", "\n", "### How to decide what # to use for epochs? epochs in this case are how many rounds?\n", "### plot costs for each of the 50 iterations and see how much it decline.. if its still very decreasing, you should\n", "### do more iterations; otherwise if its looking like its flattening, you can stop" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Forest (Sam, possibly in AWS)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Random Forest classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_features, max_depth, min_samples_leaf, bootstrap (whether or not bootstrap samples are used when building trees), oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Meta-estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AdaBoost Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "There are no major changes that we seek to make in the AdaBoostClassifier with respect to default parameter values.\n", "\n", "###### Adaboosting each classifier:\n", "\n", "We will run the AdaBoostClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bagging Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Bagging meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_samples, max_features, bootstrap (whether or not bootstrap samples are used when building trees), bootstrap_features (whether features are drawn with replacement), and oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Bagging each classifier:\n", "\n", "We will run the BaggingClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gradient Boosting Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Gradient Boosting meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_depth, min_samples_leaf, and max_features\n", "\n", "###### Gradient Boosting each classifier:\n", "\n", "We will run the GradientBoostingClassifier with loss = 'deviance' (as loss = 'exponential' uses the AdaBoost algorithm) on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final evaluation on test data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Here we will likely use Pipeline and GridSearchCV in order to find the overall classifier with optimized Multi-class Log Loss.\n", "# This will be the last step after all attempts at feature addition, hyperparameter tuning, and calibration are completed\n", "# and the corresponding performance metrics are gathered.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "1) Hsiang, Solomon M. and Burke, Marshall and Miguel, Edward. \"Quantifying the Influence of Climate on Human Conflict\". Science, Vol 341, Issue 6151, 2013 \n", "\n", "2) Huang, Cheng-Lung. Wang, Chieh-Jen. \"A GA-based feature selection and parameters optimization for support vector machines\". Expert Systems with Applications, Vol 31, 2006, p 231-240\n", "\n", "3) More to come \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
albertxavier001/graduation-project	pytorch/New Gradient Network NO DSN 1.ipynb	1	27417	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, glob, platform, datetime, random\n", "from collections import OrderedDict\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.utils.data as data_utils\n", "import torch.nn.parallel\n", "import torch.backends.cudnn as cudnn\n", "import torch.optim as optim\n", "from torch.autograd import Variable\n", "from torch import functional as F\n", "# import torchvision.datasets as datasets\n", "import torchvision.models as models\n", "import torchvision.transforms as transforms\n", "\n", "import cv2\n", "from PIL import Image\n", "from tensorboardX import SummaryWriter\n", "\n", "import numpy as np\n", "from numpy.linalg import inv as denseinv\n", "from scipy import sparse\n", "from scipy.sparse import lil_matrix, csr_matrix\n", "from scipy.sparse.linalg import spsolve\n", "from scipy.sparse.linalg import inv as spinv\n", "import scipy.misc\n", "\n", "from myimagefolder import MyImageFolder\n", "from mymodel import GradientNet\n", "from myargs import Args\n", "from myutils import MyUtils" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Configurations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myutils = MyUtils()\n", "\n", "args = Args()\n", "args.arch = \"densenet121\"\n", "args.epoches = 500\n", "args.epoches_unary_threshold = 0\n", "args.image_h = 256\n", "args.image_w = 256\n", "args.img_extentions = [\"png\"]\n", "args.training_thresholds = [250,200,150,50,0,300]\n", "args.base_lr = 1\n", "args.lr = args.base_lr\n", "args.snapshot_interval = 5000\n", "args.debug = True\n", "\n", "\n", "# growth_rate = (4(2(args.gpu_num)))\n", "transition_scale=2\n", "pretrained_scale=4\n", "growth_rate = 32\n", "\n", "#######\n", "# args.test_scene = ['alley_2', 'bamboo_2', 'bandage_2', 'cave_4', 'market_5', 'mountain_1', 'shaman_3', 'sleeping_2', 'temple_3']\n", "args.test_scene = 'alley_1'\n", "gradient=False\n", "args.gpu_num = 1\n", "#######\n", "\n", "writer_comment = '{}_rgb_no_dsn'.format(args.test_scene)\n", "if gradient == True:\n", " writer_comment = '{}_gd_no_dsn'.format(args.test_scene)\n", "\n", "offset = 0.\n", "if gradient == True: offset = 0.5\n", "\n", "args.display_interval = 50\n", "args.display_curindex = 0\n", "\n", "system_ = platform.system()\n", "system_dist, system_version, _ = platform.dist()\n", "if system_ == \"Darwin\": \n", " args.train_dir = '/Volumes/Transcend/dataset/sintel2'\n", " args.pretrained = False\n", "elif platform.dist() == ('debian', 'jessie/sid', ''):\n", " args.train_dir = '/home/lwp/workspace/sintel2'\n", " args.pretrained = True\n", "elif platform.dist() == ('debian', 'stretch/sid', ''):\n", " args.train_dir = '/home/cad/lwp/workspace/dataset/sintel2'\n", " args.pretrained = True\n", "\n", "if platform.system() == 'Linux': use_gpu = True\n", "else: use_gpu = False\n", "if use_gpu:\n", " torch.cuda.set_device(args.gpu_num)\n", " \n", "\n", "print(platform.dist())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# My DataLoader" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "train_dataset = MyImageFolder(args.train_dir, 'train',\n", " transforms.Compose(\n", " [transforms.ToTensor()]\n", " ), random_crop=True, \n", " img_extentions=args.img_extentions, test_scene=args.test_scene, image_h=args.image_h, image_w=args.image_w)\n", "test_dataset = MyImageFolder(args.train_dir, 'test', \n", " transforms.Compose(\n", " [transforms.CenterCrop((args.image_h, args.image_w)),\n", " transforms.ToTensor()]\n", " ), random_crop=False,\n", " img_extentions=args.img_extentions, test_scene=args.test_scene, image_h=args.image_h, image_w=args.image_w)\n", "\n", "train_loader = data_utils.DataLoader(train_dataset,1,True,num_workers=1)\n", "test_loader = data_utils.DataLoader(test_dataset,1,True,num_workers=1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Load Pretrained Model\n", "\n", "[Defination](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py)\n", " DenseNet-121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)\n", " * First Convolution: 32M -> 16M -> 8M\n", " * every transition: 8M -> 4M -> 2M (downsample 1/2, except the last block)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "densenet = models.__dict__[args.arch](pretrained=args.pretrained)\n", "\n", "for param in densenet.parameters():\n", " param.requires_grad = False\n", "\n", "if use_gpu: densenet.cuda()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ss = 6\n", "s0 = ss5\n", "s0 = 0\n", "\n", "args.display_curindex = 0\n", "args.base_lr = 0.05\n", "args.display_interval = 20\n", "args.momentum = 0.9\n", "args.epoches = 240\n", "args.training_thresholds = [0,0,0,0,0,s0]\n", "args.training_merge_thresholds = [s0+ss33,s0+ss23, s0+ss13, s0, -1, s0+ss43]\n", "args.power = 0.5\n", "\n", "\n", "\n", "# pretrained = PreTrainedModel(densenet)\n", "# if use_gpu: \n", "# pretrained.cuda()\n", "\n", "\n", "net = GradientNet(densenet=densenet, growth_rate=growth_rate, \n", " transition_scale=transition_scale, pretrained_scale=pretrained_scale,\n", " gradient=gradient)\n", "if use_gpu:\n", " net.cuda()\n", "\n", "if use_gpu: \n", " mse_losses = [nn.MSELoss().cuda()] 6\n", " test_losses = [nn.MSELoss().cuda()] * 6\n", " mse_merge_losses = [nn.MSELoss().cuda()] * 6\n", " test_merge_losses = [nn.MSELoss().cuda()] * 6\n", "else:\n", " mse_losses = [nn.MSELoss()] * 6\n", " mse_merge_losses = [nn.MSELoss()] * 6\n", " test_losses = [nn.MSELoss()] * 6\n", " test_merge_losses = [nn.MSELoss()] * 6 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test_model(epoch, go_through_merge=False, phase='train'):\n", " if phase == 'train': net.train()\n", " else: net.eval()\n", " \n", " test_losses_trainphase = [0] * len(args.training_thresholds)\n", " test_cnts_trainphase = [0.00001] * len(args.training_thresholds) \n", " test_merge_losses_trainphase = [0] * len(args.training_thresholds)\n", " test_merge_cnts_trainphase = [0.00001] * len(args.training_thresholds)\n", " \n", " for ind, data in enumerate(test_loader, 0):\n", " input_img, gt_albedo, gt_shading, test_scene, img_path = data\n", " input_img = Variable(input_img)\n", " gt_albedo = Variable(gt_albedo)\n", " gt_shading = Variable(gt_shading)\n", " if use_gpu:\n", " input_img = input_img.cuda(args.gpu_num)\n", " \n", "# pretrained.train(); ft_pretreained = pretrained(input_img)\n", " ft_test, merged_RGB = net(input_img, go_through_merge=go_through_merge)\n", " \n", " for i,v in enumerate(ft_test):\n", " if epoch < args.training_thresholds[i]: continue\n", " if i == 5: s = 1\n", " else: s = (2*(i+1))\n", " gt0 = gt_albedo.cpu().data.numpy()\n", " n,c,h,w = gt0.shape\n", " gt, display = myutils.processGt(gt0, scale_factor=s, gd=gradient, return_image=True)\n", " gt_mg, display_mg = myutils.processGt(gt0, scale_factor=s//2, gd=gradient, return_image=True)\n", " \n", " if use_gpu: \n", " gt = gt.cuda()\n", " gt_mg = gt_mg.cuda()\n", " \n", " if i != 5: \n", " loss = mse_losses[i](ft_test[i], gt)\n", " test_losses_trainphase[i] += loss.data.cpu().numpy()[0]\n", " test_cnts_trainphase[i] += 1\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " if i==5: gt2=gt\n", " else: gt2=gt_mg\n", "# print(i)\n", "# print('merge size', merged_RGB[i].size())\n", "# print('gt2 size', gt2.size())\n", " loss = mse_merge_losses[i](merged_RGB[i], gt2)\n", " test_merge_losses_trainphase[i] += loss.data.cpu().numpy()[0]\n", " test_merge_cnts_trainphase[i] += 1\n", " \n", "\n", " \n", " if ind == 0: \n", " if i != 5:\n", " v = v[0].cpu().data.numpy()\n", " v = v.transpose(1,2,0)\n", " v = v[:,:,0:3]\n", " cv2.imwrite('snapshot{}/test-phase_{}-{}-{}.png'.format(args.gpu_num, phase, epoch, i), (v[:,:,::-1]+offset)255)\n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " v = merged_RGB[i][0].cpu().data.numpy()\n", " v = v.transpose(1,2,0)\n", " v = v[:,:,0:3]\n", " cv2.imwrite('snapshot{}/test-mg-phase_{}-{}-{}.png'.format(args.gpu_num, phase, epoch, i), (v[:,:,::-1]+offset)255)\n", " \n", " run_losses = test_losses_trainphase\n", " run_cnts = test_cnts_trainphase\n", " writer.add_scalars('16M loss', {'test 16M phase {}'.format(phase): np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'test 8M phase {}'.format(phase): np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'test 4M phase {}'.format(phase): np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'test 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'test 1M phase {}'.format(phase): np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'test merged phase {}'.format(phase): np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch)\n", " \n", " run_losses = test_merge_losses_trainphase\n", " run_cnts = test_merge_cnts_trainphase\n", " writer.add_scalars('16M loss', {'mg test 16M phase {}'.format(phase): np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'mg test 8M phase {}'.format(phase): np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'mg test 4M phase {}'.format(phase): np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'mg test 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'mg test 1M phase {}'.format(phase): np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'mg test merged phase {}'.format(phase): np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# training loop\n", "\n", "writer = SummaryWriter(comment='-{}'.format(writer_comment))\n", "\n", "parameters = filter(lambda p: p.requires_grad, net.parameters())\n", "optimizer = optim.SGD(parameters, lr=args.base_lr, momentum=args.momentum)\n", "\n", "def adjust_learning_rate(optimizer, epoch, beg, end, reset_lr=None, base_lr=args.base_lr):\n", " \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n", " for param_group in optimizer.param_groups:\n", "# print('para gp', param_group)\n", " if reset_lr != None:\n", " param_group['lr'] = reset_lr\n", " continue\n", " param_group['lr'] = base_lr (float(end-epoch)/(end-beg)) ** (args.power)\n", " if param_group['lr'] < 1.0e-8: param_group['lr'] = 1.0e-8\n", " \n", "\n", "for epoch in range(args.epoches):\n", "# epoch = 234\n", " net.train()\n", " print('epoch: {} [{}]'.format(epoch, datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n", "\n", " if epoch < args.training_thresholds[-1]: \n", " adjust_learning_rate(optimizer, epoch, beg=0, end=s0-1)\n", " elif epoch < args.training_merge_thresholds[-1]:\n", " adjust_learning_rate(optimizer, (epoch-s0)%(ss), beg=0, end=ss-1, base_lr=args.base_lr)\n", " else:\n", " adjust_learning_rate(optimizer, epoch, beg=args.training_merge_thresholds[-1], end=args.epoches-1, base_lr=args.base_lr) \n", " \n", " \n", "# if epoch < args.training_thresholds[-1]: go_through_merge = False\n", "# elif epoch >= args.training_merge_thresholds[5]: go_through_merge = '32M'\n", "# elif epoch >= args.training_merge_thresholds[0]: go_through_merge = '16M'\n", "# elif epoch >= args.training_merge_thresholds[1]: go_through_merge = '08M'\n", "# elif epoch >= args.training_merge_thresholds[2]: go_through_merge = '04M'\n", "# elif epoch >= args.training_merge_thresholds[3]: go_through_merge = '02M'\n", " go_through_merge = '32M'\n", " \n", " run_losses = [0] * len(args.training_thresholds)\n", " run_cnts = [0.00001] * len(args.training_thresholds)\n", " run_merge_losses = [0] * len(args.training_thresholds)\n", " run_merge_cnts = [0.00001] * len(args.training_thresholds)\n", " if (epoch in args.training_thresholds) == True: \n", " adjust_learning_rate(optimizer, epoch, reset_lr=args.base_lr, beg=-1, end=-1)\n", " if (epoch in args.training_merge_thresholds) == True:\n", " adjust_learning_rate(optimizer, epoch, reset_lr=args.base_lr, beg=-1, end=-1)\n", " \n", " writer.add_scalar('learning rate', optimizer.param_groups[0]['lr'], global_step=epoch)\n", " for ind, data in enumerate(train_loader, 0):\n", "# if ind == 1 : break\n", " \"\"\"prepare training data\"\"\"\n", " input_img, gt_albedo, gt_shading, test_scene, img_path = data\n", " im = input_img[0,:,:,:].numpy(); im = im.transpose(1,2,0); im = im[:,:,::-1]255\n", " input_img, gt_albedo, gt_shading = Variable(input_img), Variable(gt_albedo), Variable(gt_shading)\n", " if use_gpu: input_img, gt_albedo, gt_shading = input_img.cuda(), gt_albedo.cuda(), gt_shading.cuda()\n", "\n", " if args.display_curindex % args.display_interval == 0: cv2.imwrite('snapshot{}/input.png'.format(args.gpu_num), im)\n", "\n", " optimizer.zero_grad()\n", " \n", " \n", " ft_predict, merged_RGB = net(input_img, go_through_merge=go_through_merge)\n", " for i, threshold in enumerate(args.training_thresholds):\n", " if epoch >= threshold:\n", "# if epoch >= 0:\n", " \"\"\"prepare resized gt\"\"\"\n", " if i == 5: s = 1\n", " else: s = (2(i+1))\n", " gt0 = gt_albedo.cpu().data.numpy()\n", " n,c,h,w = gt0.shape\n", " gt, display = myutils.processGt(gt0, scale_factor=s, gd=gradient, return_image=True)\n", " gt_mg, display_mg = myutils.processGt(gt0, scale_factor=s//2, gd=gradient, return_image=True)\n", " if use_gpu: \n", " gt = gt.cuda()\n", " gt_mg = gt_mg.cuda()\n", " if args.display_curindex % args.display_interval == 0:\n", " display = display[:,:,0:3]\n", " cv2.imwrite('snapshot{}/gt-{}-{}.png'.format(args.gpu_num, epoch, i), display[:,:,::-1]255) \n", " \n", " \"\"\"compute loss\"\"\"\n", " if i != 5: \n", " loss = mse_losses[i](ft_predict[i], gt)\n", " run_losses[i] += loss.data.cpu().numpy()[0]\n", "# loss.backward(retain_graph=True)\n", " run_cnts[i] += 1\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", "# print(epoch, go_through_merge, i)\n", " \n", "# print (merged_RGB[i].cpu().data.numpy().max(), merged_RGB[i].cpu().data.numpy().min())\n", " if i!=5: continue\n", " if i==5: gt2=gt\n", " else: gt2=gt_mg\n", "# print(i)\n", "# print('merge size', merged_RGB[i].size())\n", "# print('gt2 size', gt2.size())\n", " loss = mse_merge_losses[i](merged_RGB[i], gt2)\n", " run_merge_losses[i] += loss.data.cpu().numpy()[0]\n", " loss.backward(retain_graph=True)\n", " run_merge_cnts[i] += 1\n", " \n", " \"\"\"save training image\"\"\"\n", " if args.display_curindex % args.display_interval == 0:\n", " \n", " if i != 5:\n", " im = (ft_predict[i].cpu().data.numpy()[0].transpose((1,2,0))+offset) * 255\n", " im = im[:,:,0:3]\n", " \n", " cv2.imwrite('snapshot{}/train-{}-{}.png'.format(args.gpu_num, epoch, i), im[:,:,::-1])\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " im = (merged_RGB[i].cpu().data.numpy()[0].transpose((1,2,0))+offset) * 255\n", " im = im[:,:,0:3]\n", " cv2.imwrite('snapshot{}/train-mg-{}-{}.png'.format(args.gpu_num, epoch, i), im[:,:,::-1])\n", " optimizer.step()\n", " args.display_curindex += 1\n", "\n", " \"\"\" every epoch \"\"\"\n", "# loss_output = 'ind: ' + str(args.display_curindex)\n", " loss_output = ''\n", " \n", " \n", " \n", " for i,v in enumerate(run_losses):\n", " if i == len(run_losses)-1: \n", " loss_output += ' merged: %6f' % (run_losses[i] / run_cnts[i])\n", " continue\n", " loss_output += ' %2dM: %6f' % ((2(4-i)), (run_losses[i] / run_cnts[i]))\n", " print(loss_output)\n", " loss_output = ''\n", " for i,v in enumerate(run_merge_losses):\n", " if i == len(run_merge_losses)-1: \n", " loss_output += 'mg merged: %6f' % (run_merge_losses[i] / run_merge_cnts[i])\n", " continue\n", " loss_output += ' mg %2dM: %6f' % ((2(4-i)), (run_merge_losses[i] / run_merge_cnts[i]))\n", " print(loss_output)\n", " \n", " \"\"\"save at every epoch\"\"\"\n", " if (epoch+1) % 10 == 0:\n", " torch.save({\n", " 'epoch': epoch,\n", " 'args' : args,\n", " 'state_dict': net.state_dict(),\n", " 'optimizer': optimizer.state_dict()\n", " }, 'snapshot{}/snapshot-{}.pth.tar'.format(args.gpu_num, epoch))\n", " \n", " # test \n", " if (epoch+1) % 5 == 0:\n", " test_model(epoch, phase='train', go_through_merge=go_through_merge)\n", " test_model(epoch, phase='test', go_through_merge=go_through_merge)\n", "\n", " writer.add_scalars('16M loss', {'train 16M ': np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'train 8M ': np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'train 4M ': np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'train 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'train 1M ': np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'train merged ': np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch) \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Visualize Graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from graphviz import Digraph\n", "import torch\n", "from torch.autograd import Variable\n", "\n", "\n", "def make_dot(var, params=None):\n", " \"\"\" Produces Graphviz representation of PyTorch autograd graph\n", " Blue nodes are the Variables that require grad, orange are Tensors\n", " saved for backward in torch.autograd.Function\n", " Args:\n", " var: output Variable\n", " params: dict of (name, Variable) to add names to node that\n", " require grad (TODO: make optional)\n", " \"\"\"\n", " if params is not None:\n", " assert isinstance(params.values()[0], Variable)\n", " param_map = {id(v): k for k, v in params.items()}\n", "\n", " node_attr = dict(style='filled',\n", " shape='box',\n", " align='left',\n", " fontsize='12',\n", " ranksep='0.1',\n", " height='0.2')\n", " dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"10240,10240\"), format='svg')\n", " seen = set()\n", "\n", " def size_to_str(size):\n", " return '('+(', ').join(['%d' % v for v in size])+')'\n", "\n", " def add_nodes(var):\n", " if var not in seen:\n", " if torch.is_tensor(var):\n", " dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')\n", " elif hasattr(var, 'variable'):\n", " u = var.variable\n", " name = param_map[id(u)] if params is not None else ''\n", " node_name = '%s\\n %s' % (name, size_to_str(u.size()))\n", " dot.node(str(id(var)), node_name, fillcolor='lightblue')\n", " else:\n", " dot.node(str(id(var)), str(type(var).__name__))\n", " seen.add(var)\n", " if hasattr(var, 'next_functions'):\n", " for u in var.next_functions:\n", " if u[0] is not None:\n", " dot.edge(str(id(u[0])), str(id(var)))\n", " add_nodes(u[0])\n", " if hasattr(var, 'saved_tensors'):\n", " for t in var.saved_tensors:\n", " dot.edge(str(id(t)), str(id(var)))\n", " add_nodes(t)\n", " add_nodes(var.grad_fn)\n", " return dot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# x = Variable(torch.zeros(1,3,256,256))\n", "# y = net(x.cuda())\n", "# g = make_dot(y[-1])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# g.render('net-transition_scale_{}'.format(transition_scale)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
LSSTC-DSFP/LSSTC-DSFP-Sessions	Sessions/Session13/Day2/02-Fast-GPs.ipynb	1	11479	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fast GP implementations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib import rcParams\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"figure.figsize\"] = 12, 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking GP codes\n", "Implemented the right way, GPs can be super fast! Let's compare the time it takes to evaluate our GP likelihood and the time it takes to evaluate the likelihood computed with the snazzy ``george`` and ``celerite`` packages. We'll learn how to use both along the way. Let's create a large, fake dataset for these tests:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "np.random.seed(0)\n", "t = np.linspace(0, 10, 10000)\n", "y = np.random.randn(10000)\n", "sigma = np.ones(10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Our GP" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def ExpSquaredCovariance(t, A=1.0, l=1.0, tprime=None):\n", " \"\"\"\n", " Return the ``N x M`` exponential squared\n", " covariance matrix.\n", " \n", " \"\"\"\n", " if tprime is None:\n", " tprime = t\n", " TPrime, T = np.meshgrid(tprime, t)\n", " return A ** 2 * np.exp(-0.5 * (T - TPrime) 2 / l 2)\n", "\n", "\n", "def ln_gp_likelihood(t, y, sigma=0, A=1.0, l=1.0):\n", " \"\"\"\n", " Return the log of the GP likelihood for a datatset y(t)\n", " with uncertainties sigma, modeled with a Squared Exponential\n", " Kernel with amplitude A and lengthscale l.\n", " \n", " \"\"\"\n", " # The covariance and its determinant\n", " npts = len(t)\n", " K = ExpSquaredCovariance(t, A=A, l=l) + sigma ** 2 * np.eye(npts)\n", " \n", " # The log marginal likelihood\n", " log_like = -0.5 * np.dot(y.T, np.linalg.solve(K, y))\n", " log_like -= 0.5 * np.linalg.slogdet(K)[1]\n", " log_like -= 0.5 * npts * np.log(2 * np.pi)\n", " \n", " return log_like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time to evaluate the GP likelihood:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "ln_gp_likelihood(t, y, sigma)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### george" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's time how long it takes to do the same operation using the ``george`` package (``pip install george``).\n", "\n", "The kernel we'll use is\n", "\n", "```python\n", "kernel = amp ** 2 * george.kernels.ExpSquaredKernel(tau 2)\n", "```\n", "\n", "where ``amp = 1`` and ``tau = 1`` in this case.\n", "\n", "To instantiate a GP using ``george``, simply run\n", "\n", "```python\n", "gp = george.GP(kernel)\n", "```\n", "\n", "The ``george`` package pre-computes a lot of matrices that are re-used in different operations, so before anything else, we'll ask it to compute the GP model for our timeseries:\n", "\n", "```python\n", "gp.compute(t, sigma)\n", "```\n", "\n", "Note that we've only given it the time array and the uncertainties, so as long as those remain the same, you don't have to re-compute anything. This will save you a lot of time in the long run!\n", "\n", "Finally, the log likelihood is given by ``gp.log_likelihood(y)``.\n", "\n", "How do the speeds compare? Did you get the same value of the likelihood?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import george" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "kernel = george.kernels.ExpSquaredKernel(1.0)\n", "gp = george.GP(kernel)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "print(gp.log_likelihood(y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``george`` also offers a fancy GP solver called the HODLR solver, which makes some approximations that dramatically speed up the matrix algebra. Let's instantiate the GP object again by passing the keyword ``solver=george.HODLRSolver`` and re-compute the log likelihood. How long did that take? Did we get the same value for the log likelihood?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp = george.GP(kernel, solver=george.HODLRSolver)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp.log_likelihood(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### celerite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``george`` package is super useful for GP modeling, and I recommend you read over the [docs and examples](https://george.readthedocs.io/en/latest/). It implements several different [kernels](https://george.readthedocs.io/en/latest/user/kernels/) that come in handy in different situations, and it has support for multi-dimensional GPs. But if all you care about are GPs in one dimension (in this case, we're only doing GPs in the time domain, so we're good), then ``celerite`` is what it's all about:\n", "\n", "```bash\n", "pip install celerite\n", "```\n", "\n", "Check out the [docs](https://celerite.readthedocs.io/en/stable/) here, as well as several tutorials. There is also a [paper](https://arxiv.org/abs/1703.09710) that discusses the math behind ``celerite``. The basic idea is that for certain families of kernels, there exist extremely efficient methods of factorizing the covariance matrices. Whereas GP fitting typically scales with the number of datapoints $N$ as $N^3$, ``celerite`` is able to do everything in order $N$ (!!!) This is a huge advantage, especially for datasets with tens or hundreds of thousands of data points. Using ``george`` or any homebuilt GP model for datasets larger than about ``10,000`` points is simply intractable, but with ``celerite`` you can do it in a breeze.\n", "\n", "Next we repeat the timing tests, but this time using ``celerite``. Note that the Exponential Squared Kernel is not available in ``celerite``, because it doesn't have the special form needed to make its factorization fast. Instead, we'll use the ``Matern 3/2`` kernel, which is qualitatively similar and can be approximated quite well in terms of the ``celerite`` basis functions:\n", "\n", "```python\n", "kernel = celerite.terms.Matern32Term(np.log(1), np.log(1))\n", "```\n", "\n", "Note that ``celerite`` accepts the log of the amplitude and the log of the timescale. Other than this, we can compute the likelihood using the same syntax as ``george``.\n", "\n", "How much faster did it run? Is the value of the likelihood different from what you found above? Why?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import celerite\n", "from celerite import terms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "kernel = terms.Matern32Term(np.log(1), np.log(1))\n", "gp = celerite.GP(kernel)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp.log_likelihood(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"background-color: #D6EAF8; border-left: 15px solid #2E86C1;\">\n", " <h1 style=\"line-height:2.5em; margin-left:1em;\">Exercise (the one and only)</h1>\n", "</div>\n", "\n", "Let's use what we've learned about GPs in a real application: fitting an exoplanet transit model in the presence of correlated noise.\n", "\n", "Here is a (fictitious) light curve for a star with a transiting planet: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "t, y, yerr = np.loadtxt(\"data/sample_transit.txt\", unpack=True)\n", "plt.errorbar(t, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "plt.xlabel(\"time\")\n", "plt.ylabel(\"relative flux\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a transit visible to the eye at $t = 0$, which (say) is when you'd expect the planet to transit if its orbit were perfectly periodic. However, a recent paper claims that the planet shows transit timing variations, which are indicative of a second, perturbing planet in the system, and that a transit at $t = 0$ can be ruled out at 3 $\\sigma$. Your task is to verify this claim.*\n", "\n", "Assume you have no prior information on the planet other than the transit occurs in the observation window, the depth of the transit is somewhere in the range $(0, 1)$, and the transit duration is somewhere between $0.1$ and $1$ day. You don't know the exact process generating the noise, but you are certain that there's correlated noise in the dataset, so you'll have to pick a reasonable kernel and estimate its hyperparameters.\n", "\n", "\n", "Fit the transit with a simple inverted Gaussian with three free parameters:\n", "\n", "```python\n", "def transit_shape(depth, t0, dur):\n", " return -depth np.exp(-0.5 * (t - t0) ** 2 / (0.2 * dur) ** 2)\n", "```\n", "\n", "HINT: I borrowed heavily from [this tutorial](https://celerite.readthedocs.io/en/stable/tutorials/modeling/) in the celerite documentation, so you might want to take a look at it..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
thehackerwithin/berkeley	code_examples/SQL/SQL_Tutorial-0.ipynb	1	65862	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro to SQL - The Hacker Within (2018-02-21)\n", "This tutorial will show you the basics of SQL:\n", "* creating a table\n", "* inserting rows\n", "* querying with conditions\n", "* grouping and ordering\n", "* joining tables with a common field\n", "* aggregating rows using COUNT, MAX, etc.\n", "* alternates to SQL like pandas and Django\n", "* indexing frequently queried columns for performance\n", "* alter existing table to add, drop, or rename a column\n", "* drop a table\n", "* vacuum a database\n", "\n", "## SQLite\n", "We are going to use [Python](https://docs.python.org/3/library/sqlite3.html) because it comes with the popular [SQLite](https://sqlite.org/index.html) (_aka_ [`etilqs`](https://www.google.com/search?q=etilqs)) relational database builtin. \n", "\n", "## Requirements\n", "The following examples assume you have Python 3 installed on your computer. Any distribution will do. If you have [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://conda.io/miniconda.html), you can create a new conda environment with the requirements for this tutorial from a terminal. For example with miniconda on Mac OS X, you might use the following to activate the root conda environment and create a new one called \"py36-thw-sql\".\n", "\n", " ~$ source ~/miniconda/bin/activate\n", " (root) ~$ conda create -n py36-thw-sql python=3.6.3 psycopg2 django pandas\n", "\n", "Or on Windows with Anaconda, you might use the following in a CMD terminal.\n", "\n", " ~> %LOCALAPPDATA%\\Continuum\\anaconda3\\Scripts\\activate\n", " (root) ~> conda create -n py36-thw-sql python=3.6.3 psycopg2 django pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python DB-API 2.0\n", "[PEP 249](https://www.python.org/dev/peps/pep-0249/) specifies a `connection` to a database, and a `cursor` from the connection to execute SQL." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# imports\n", "import io # we'll need this way later\n", "import os\n", "import sqlite3 # this is the module that binds to SQLite\n", "import numpy as np # never know when you might need NumPy, oh, right, always!\n", "import pandas as pd # you'll see why we can use this later\n", "\n", "DBFILE = 'sqlite3.db' # this will be our database\n", "BASEDIR = %pwd # os.path.abspath(os.path.dirname(__file__))\n", "DBPATH = os.path.join(BASEDIR, DBFILE)\n", "\n", "# we may need to delete the existing file first\n", "if os.path.exists(DBPATH):\n", " os.remove(DBPATH)\n", "\n", "# open a connection to the database for this tutorial\n", "conn = sqlite3.connect(DBPATH)\n", "\n", "# get a cursor to the database\n", "cur = conn.cursor()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notes\n", "If a file with the same name doesn't already exist, then this creates a new database otherwise it connects to the existing database contained in the file. You can also use ':memory:' to create an \"in-memory\" database that has no file, but then you can't connect to that from another process.\n", "\n", "We'll have to close the connection and cursor later. Next time we could use a [`with` context to automatically close the connection](https://docs.python.org/3.6/library/sqlite3.html#using-the-connection-as-a-context-manager).\n", "\n", " with sqlite3.connect('sqlite3.db') as conn:\n", " cur = conn.execute('SQL QUERY ...') # e.g.: 'SELECT * FROM table_name;'\n", " output = cur.fetchall() # get the results\n", "\n", "Closing the connection automatically closes the cursor. [Other bindings may offer similar context managers.](http://initd.org/psycopg/docs/usage.html#with-statement) to commit and close changes or rollback changes and raise an exception." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating tables\n", "A relational database or SQL database is a tabular structure consisting of rows of data with columns of fields. The [data definition language or DDL](https://en.wikipedia.org/wiki/Data_definition_language) used to create the table is the same language used to query it, called [SQL or Structured Query Language](https://en.wikipedia.org/wiki/SQL).\n", "\n", "Although [the basic SQL commands](https://sqlite.org/lang.html) are nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/sql.html), the data types may be different. [SQLite only has 5 datatypes](https://sqlite.org/datatype3.html): `NULL`, `INTEGER`, `REAL`, `TEXT`, `BLOB`. For [boolean, use integer zero for false, and one for true](https://sqlite.org/datatype3.html#boolean_datatype). For [dates and times use text and ISO8601](https://sqlite.org/datatype3.html#date_and_time_datatype), _e.g._: `\"2018-02-21T17:05-0800\"`. By comparison, [PostgreSQL has too many to list here](https://www.postgresql.org/docs/10/static/datatype.html) including booleans, date, time, arrays, JSON, etc.\n", "\n", "## `CREATE`\n", "The [basic SQL command to create a table](https://sqlite.org/lang_createtable.html) is\n", "\n", " CREATE TABLE <table_name> (<field_name> <TYPE> <CONSTRAINTS>, <field_name> <TYPE> <CONSTRAINTS>, <CONSTRAINTS>, ...);\n", "\n", "Some languages enforce the semicolon, some don't. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-basics.html).\n", "\n", "### Constraints, Defaults, and Options\n", "Constraints are optional and set conditions, limitations, or options for columns and the table. The most common constraints are: `PRIMARY KEY`, `UNIQUE`, `NOT NULL`, `DEFAULT`, `FOREIGN KEY`, `REFERENCES`, etc. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-constraints.html).\n", "\n", "#### `PRIMARY KEY`\n", "The most important of these is `PRIMARY KEY` which is equivalent to `UNIQUE NOT NULL`. A [primary key](https://www.postgresql.org/docs/10/static/ddl-constraints.html#DDL-CONSTRAINTS-PRIMARY-KEYS) is a unique references that identifies each record in the table. Although it is not required, every table should have a primary key. Only one primary key is allowed, and it can be constructed from multiple columns, `PRIMARY KEY (<field_A>, <field_B)`, to create a unique together, non-null identifier. [In SQLite, if missing then a integer primary key named, `rowid`, is created by default](https://sqlite.org/lang_createtable.html#rowid). Also in SQLite, any integer primary key is automatically incremented, so [the _AUTOINCREMENT_ command is usually not needed](https://sqlite.org/autoinc.html). [In PostgreSQL the `SERIAL` command is used](https://www.postgresql.org/docs/10/static/datatype-numeric.html#DATATYPE-SERIAL) to create a corresponding [sequence](https://www.postgresql.org/docs/10/static/functions-sequence.html) for the primary key.\n", "\n", "## Practice\n", "The other constraints and options are also important, but we'll discover those as we learn. Let's create some simple databases with fictitious data to practice. Imagine you are testing several different materials with different properties $\\alpha$ and $\\beta$ under different stresses like different temperatures and light intensity and changing thickness. How would you organize this data? Take a moment to design a schema or structure for your data. The schema consists of the column names and data types and the column and table constraints." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# we can use Python triple quoted strings to span multiple lines, but use single quotes, since SQL only uses double quotes\n", "\n", "# first create a materials table\n", "cur.execute('''CREATE TABLE materials (\n", " material_id TEXT PRIMARY KEY,\n", " long_name TEXT UNIQUE NOT NULL,\n", " alpha REAL NOT NULL,\n", " beta REAL NOT NULL,\n", " material_type TEXT NOT NULL\n", ")''')\n", "conn.commit()\n", "# if you don't commit the changes, they won't be written to the file, and won't be visible to other connections\n", "\n", "# then create an experiments table\n", "cur.execute('''CREATE TABLE experiments (\n", " experiment_id INTEGER PRIMARY KEY,\n", " temperature REAL DEFAULT 298.15,\n", " irradiance REAL DEFAULT 1000.0,\n", " uv_filter INTEGER DEFAULT 0,\n", " material_id NOT NULL REFERENCES materials ON UPDATE CASCADE ON DELETE CASCADE,\n", " thickness REAL DEFAULT 0.005,\n", " UNIQUE (temperature, irradiance, uv_filter, material_id)\n", ")''')\n", "conn.commit()\n", "\n", "# and finally create a trials table\n", "cur.execute('''CREATE TABLE trials (\n", " trial_id INTEGER PRIMARY KEY,\n", " experiment_id NOT NULL REFERENCES experiments ON UPDATE CASCADE ON DELETE CASCADE,\n", " results BLOB NOT NULL,\n", " duration REAL NOT NULL,\n", " avg_temperature REAL NOT NULL,\n", " std_temperature REAL NOT NULL,\n", " avg_irradiance REAL NOT NULL,\n", " std_irradiance REAL NOT NULL,\n", " init_visible_transmittance REAL NOT NULL,\n", " final_visible_transmittance REAL NOT NULL,\n", " notes TEXT\n", ")''')\n", "conn.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `FOREIGN KEY`\n", "A [foreign key constraint](https://sqlite.org/foreignkeys.html) creates a relationship between two tables. The `FOREIGN KEY` is implied when the `REFERENCES` column constraint is applied. In the experiments table above, the column constraint on `material_id` is the same as adding this table constriant:\n", "\n", " FOREIGN KEY (material_id) REFERENCES materials (material_id)\n", "\n", "Specifying the referenced column in the table constraint isn't necessary, and if omitted defaults to the primary key of the referenced table. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-constraints.html#DDL-CONSTRAINTS-FK).\n", "\n", "You can use the same name for the foreign key and it's related field, but it may make joining tables more difficult because you will need to use the table name to avoid an ambigous column name. _E.G._: you can use `trials.experiment_id` and `experiments.experiment_id` to differentiate between them. You can also use `AS` to create a temporary name like `trials.experiment_id AS experiment`. Or you could just use different names for the foreign key and it's related field like `FOREIGN KEY (material) REFERENCES materials (material_id)` and then there's no ambiguity. Your call.\n", "\n", "### `DELETE` and `UPDATE`\n", "What happens if the reference of a foreign key is deleted or updated? That's up to you: in SQLite the default is to do nothing, but typically you want the action to cascade. Add the desired [`ON DELETE` or `ON UPDATE` action](https://sqlite.org/foreignkeys.html#fk_actions) to the constraint.\n", "\n", "\n", "### Bonus Questions\n", "1. What is the difference between a column constraint and a table constraint?\n", "2. What [other table constraint](https://www.postgresql.org/docs/9.1/static/ddl-constraints.html#AEN2496) is in the experiments table?\n", "3. What other constraints or defaults are applied in the tables?\n", "4. What part of the materials table schema is fragile and can be improved?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `INSERT`\n", "The [basic SQL command to put data into a table is](https://sqlite.org/lang_insert.html)\n", "\n", " INSERT INTO <table_name> (<field_name>, <field_name>, ...) VALUES (<value>, <value>, ...)\n", "\n", "[Other relational databases use the same SQL syntax.](https://www.postgresql.org/docs/current/static/dml-insert.html)\n", "\n", "Let's add some pretend data to the database" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add a EVA as a material\n", "cur.execute('INSERT INTO materials VALUES (\"EVA\", \"ethylene vinyl acetate\", 0.123, 4.56, \"polymer\")')\n", "conn.commit() # you must commit for it to become permanent\n", "cur.rowcount # tells you how many rows written, sometimes, it's quirky" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Placeholders\n", "You can use placeholders to loop over insert statements to add multiple records.\n", "\n", ">WARNING: Never use string formatters to in lieu of placeholders or you may be subject to [a SQL injection attack](https://xkcd.com/327/).\n", "\n", "SQLite uses `?` but [other relational databases may use `%s`](http://initd.org/psycopg/docs/usage.html#passing-parameters-to-sql-queries) or another placeholder.\n", "\n", "Also, in `sqlite3` [`executemany`](https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executemany) is a convenient shortcut, [but it may not be convenient for all database bindings](http://initd.org/psycopg/docs/extras.html#fast-exec)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rowcount = 2\n" ] } ], "source": [ "# add some more fake materials\n", "fake_materials = [\n", " ('PMMC', 'poly methyl methacrylate', 0.789, 10.11, 'polymer'),\n", " ('KBr', 'potassium bromide', 1.213, 14.15, 'crystal')\n", "]\n", "for mat in fake_materials:\n", " # must have same number of place holders as values\n", " cur.execute('INSERT INTO materials VALUES (?, ?, ?, ?, ?)', mat) # use place holders\n", "conn.commit() # you can commit all of the changes at the end of the loop\n", "\n", "# use the executemany shortcut\n", "fake_materials = [\n", " ('SiO2', 'silicon dioxide', 1.617, 18.19, 'crystal'),\n", " ('CaF2', 'calcium flouride', 2.0, 21.22, 'crystal')\n", "]\n", "cur.executemany('INSERT INTO materials VALUES (?, ?, ?, ?, ?)', fake_materials)\n", "conn.commit()\n", "print('rowcount = %d' % cur.rowcount) # with executemany, cur.rowcount shows total number of rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `DELETE` and `UPDATE`\n", "Oops I made a mistake. How do I fix it? The opposite of `INSERT` is [`DELETE`](https://sqlite.org/lang_delete.html). But don't throw the baby out with the bathwater, you can also [`UPDATE`] a record. [Other relational databases use the same SQL syntax to manipulate data](https://www.postgresql.org/docs/10/static/dml.html)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cur.execute('DELETE FROM materials WHERE material_id = \"SiO2\"')\n", "cur.execute('UPDATE materials SET alpha=1.23E-4, beta=8.910E+11 WHERE material_id = \"CaF2\"')\n", "conn.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Queries\n", "The way you [select](https://sqlite.org/lang_select.html) data is by executing queries. [The language is the same for all relational databases.](https://www.postgresql.org/docs/10/static/queries.html) The star `` means select all columns, or you can give the columns explicitly." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('EVA', 'ethylene vinyl acetate', 0.123, 4.56, 'polymer'),\n", " ('PMMC', 'poly methyl methacrylate', 0.789, 10.11, 'polymer'),\n", " ('KBr', 'potassium bromide', 1.213, 14.15, 'crystal'),\n", " ('CaF2', 'calcium flouride', 0.000123, 891000000000.0, 'crystal')]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cur.execute('SELECT FROM materials')\n", "cur.fetchall() # fetch all the results of the query" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditions\n", "You can limit a query using [`WHERE`](https://www.postgresql.org/docs/10/static/queries-table-expressions.html#QUERIES-FROM) and [`LIMIT`](https://www.postgresql.org/docs/10/static/queries-limit.html). You can combine `WHERE` with a conditional expression, `IN` to check a set, or `LIKE` to compare with strings. Use `AND` and `OR` to combine conditions.\n", "\n", "### Python DB-API Cursor Methods\n", "The Python DB-API cursor can be used as an iterator or you can call it's [fetch methods](https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.fetchone)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EVA is ethylene vinyl acetate\n", "PMMC is poly methyl methacrylate\n" ] } ], "source": [ "# limit the query using WHERE and LIMIT\n", "cur.execute('SELECT material_id, long_name FROM materials WHERE alpha < 1 LIMIT 2')\n", "for c in cur: print('{} is {}'.format(c)) # user the cursor as an iterator" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('EVA', (0.123, 4.56)), ('PMMC', (0.789, 10.11))]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "materials_list = (\"EVA\", \"PMMC\")\n", "cur.execute('SELECT alpha, beta FROM materials WHERE material_id IN (?, ?)', materials_list)\n", "[(mat, cur.fetchone()) for mat in materials_list] # use the cursor fetchone() method to get next item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregates\n", "Your query can aggregate results like `AVG`, `SUM`, `COUNT`, `MAX`, `MIN`, etc." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4,)\n" ] } ], "source": [ "cur.execute('SELECT COUNT() FROM materials')\n", "print(cur.fetchone())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `GROUP BY`\n", "You can group queries by a column or a condition such as an expression, `IN`, or `LIKE`, if your selection is an aggregate." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('crystal', 2, 0.6065615000000001, 891000000000.0),\n", " ('polymer', 2, 0.456, 10.11)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cur.execute('SELECT material_type, COUNT(), AVG(alpha), MAX(beta) FROM materials GROUP BY material_type')\n", "cur.fetchmany(2) # use fetchmany() with size parameter, just for fun" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More Practice\n", "Add a fictitious experiment schedule and doctor up some data!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# use defaults, let primary key auto-increment, just supply material ID\n", "cur.execute('INSERT INTO experiments (material_id) VALUES (\"EVA\")') # use defaults, \n", "conn.commit()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sqlite3.IntegrityError: %s NOT NULL constraint failed: experiments.material_id\n" ] } ], "source": [ "# set up a test matrix for EVA\n", "temp = range(300, 400, 25)\n", "irrad = range(400, 800, 100)\n", "try:\n", " for T in temp:\n", " for E in irrad:\n", " cur.execute('INSERT INTO experiments (temperature, irradiance) VALUES (?, ?)', (T, E))\n", "except sqlite3.IntegrityError as exc:\n", " print('sqlite3.IntegrityError: %s', exc)\n", "\n", "# Oops! We forgot to specify the material, there is not default, and it is constrained as NOT NULL!\n", "conn.rollback() # undo any changes\n", "try:\n", " for T in temp:\n", " for E in irrad:\n", " cur.execute('INSERT INTO experiments (temperature, irradiance, material_id) VALUES (?, ?, \"EVA\")', (T, E))\n", "except sqlite3.IntegrityError as exc:\n", " print(exc)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1, 298.15, 1000.0, 0, 'EVA', 0.005),\n", " (2, 300.0, 400.0, 0, 'EVA', 0.005),\n", " (3, 300.0, 500.0, 0, 'EVA', 0.005),\n", " (4, 300.0, 600.0, 0, 'EVA', 0.005),\n", " (5, 300.0, 700.0, 0, 'EVA', 0.005),\n", " (6, 325.0, 400.0, 0, 'EVA', 0.005),\n", " (7, 325.0, 500.0, 0, 'EVA', 0.005),\n", " (8, 325.0, 600.0, 0, 'EVA', 0.005),\n", " (9, 325.0, 700.0, 0, 'EVA', 0.005),\n", " (10, 350.0, 400.0, 0, 'EVA', 0.005),\n", " (11, 350.0, 500.0, 0, 'EVA', 0.005),\n", " (12, 350.0, 600.0, 0, 'EVA', 0.005),\n", " (13, 350.0, 700.0, 0, 'EVA', 0.005),\n", " (14, 375.0, 400.0, 0, 'EVA', 0.005),\n", " (15, 375.0, 500.0, 0, 'EVA', 0.005),\n", " (16, 375.0, 600.0, 0, 'EVA', 0.005),\n", " (17, 375.0, 700.0, 0, 'EVA', 0.005)]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this list is hard to read\n", "list(cur.execute('SELECT FROM experiments'))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>298.15</td>\n", " <td>1000.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>300.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>300.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>300.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>300.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>325.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>325.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>325.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>325.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>350.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>350.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>350.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>350.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>375.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>375.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>375.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>375.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "1 298.15 1000.0 0 EVA 0.005\n", "2 300.00 400.0 0 EVA 0.005\n", "3 300.00 500.0 0 EVA 0.005\n", "4 300.00 600.0 0 EVA 0.005\n", "5 300.00 700.0 0 EVA 0.005\n", "6 325.00 400.0 0 EVA 0.005\n", "7 325.00 500.0 0 EVA 0.005\n", "8 325.00 600.0 0 EVA 0.005\n", "9 325.00 700.0 0 EVA 0.005\n", "10 350.00 400.0 0 EVA 0.005\n", "11 350.00 500.0 0 EVA 0.005\n", "12 350.00 600.0 0 EVA 0.005\n", "13 350.00 700.0 0 EVA 0.005\n", "14 375.00 400.0 0 EVA 0.005\n", "15 375.00 500.0 0 EVA 0.005\n", "16 375.00 600.0 0 EVA 0.005\n", "17 375.00 700.0 0 EVA 0.005" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# not only is Pandas much nicer, it also executes queries!\n", "pd.read_sql('SELECT * FROM experiments', conn, index_col='experiment_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `ORDER BY`\n", "Does what it says; order the query results by a column. Default is ascending, but use `ASC` or `DESC` to change the order." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>300.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>325.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>350.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>375.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "5 300.0 700.0 0 EVA 0.005\n", "9 325.0 700.0 0 EVA 0.005\n", "13 350.0 700.0 0 EVA 0.005\n", "17 375.0 700.0 0 EVA 0.005" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Python's SQLite let's you use either '==' or '=', but I think SQL only allows '=', okay?\n", "pd.read_sql('SELECT * FROM experiments WHERE irradiance = 700 ORDER BY temperature', conn, index_col='experiment_id')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>17</th>\n", " <td>375.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>375.0</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>375.0</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>375.0</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "17 375.0 700.0 0 EVA 0.005\n", "16 375.0 600.0 0 EVA 0.005\n", "15 375.0 500.0 0 EVA 0.005\n", "14 375.0 400.0 0 EVA 0.005" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# descending order\n", "pd.read_sql('SELECT * FROM experiments WHERE temperature = 375 ORDER BY irradiance DESC', conn, index_col='experiment_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Dr. Data](http://www.startrek.com/database_article/data)\n", "![Dr. Data](http://www.startrek.com/uploads/assets/db_articles/26da32597d9bd37fde9da22660aa524f24fd725c.jpg)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Dr. Data\n", "start_time, end_time = '2018-02-21T17:00-0800', '2018-02-21T18:30-0800'\n", "timestamps = pd.DatetimeIndex(start=start_time, end=end_time, freq='T')\n", "# use http://poquitopicante.blogspot.com/2016/11/panda-pop.html to help you recall what offset alias to use\n", "size = len(timestamps)\n", "data = {\n", " 'temperature': np.random.randn(size) + 298.15,\n", " 'irradiance': np.random.randn(size) + 1000,\n", " 'visible_transmittance': np.logspace(np.log10(0.9), np.log10(0.8), size) + np.random.randn(size) / 100\n", "}\n", "results = pd.DataFrame(data, index=timestamps)\n", "duration = (results.index[-1] - results.index[0]).value # [ns]\n", "avg_temperature = results.temperature.mean() # [K]\n", "std_temperature = results.temperature.std() # [K]\n", "avg_irradiance = results.irradiance.mean() # [W/m^2]\n", "std_irradiance = results.irradiance.std() # [W/m^2]\n", "init_visible_transmittance = results.visible_transmittance[start_time]\n", "final_visible_transmittance = results.visible_transmittance[end_time]\n", "values = (1, results.to_csv(), duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance, final_visible_transmittance,\n", " 'this is doctored data')\n", "cur.execute('''INSERT INTO trials (\n", " experiment_id, results, duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance,\n", " final_visible_transmittance, notes\n", ") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', values)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>irradiance</th>\n", " <th>temperature</th>\n", " <th>visible_transmittance</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2018-02-21 17:00:00-08:00</th>\n", " <td>998.959655</td>\n", " <td>298.918741</td>\n", " <td>0.893836</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:01:00-08:00</th>\n", " <td>1001.346465</td>\n", " <td>298.553568</td>\n", " <td>0.888956</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:02:00-08:00</th>\n", " <td>1000.222570</td>\n", " <td>296.760235</td>\n", " <td>0.916820</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:03:00-08:00</th>\n", " <td>998.697424</td>\n", " <td>297.998634</td>\n", " <td>0.895405</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:04:00-08:00</th>\n", " <td>999.766542</td>\n", " <td>298.686229</td>\n", " <td>0.903194</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:05:00-08:00</th>\n", " <td>998.521437</td>\n", " <td>298.325881</td>\n", " <td>0.892011</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:06:00-08:00</th>\n", " <td>1000.991891</td>\n", " <td>297.175668</td>\n", " <td>0.896553</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:07:00-08:00</th>\n", " <td>1000.054543</td>\n", " <td>297.946657</td>\n", " <td>0.878752</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:08:00-08:00</th>\n", " <td>1002.449126</td>\n", " <td>298.814267</td>\n", " <td>0.887048</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:09:00-08:00</th>\n", " <td>998.311166</td>\n", " <td>296.710237</td>\n", " <td>0.883333</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:10:00-08:00</th>\n", " <td>1001.106463</td>\n", " <td>297.809162</td>\n", " <td>0.887446</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:11:00-08:00</th>\n", " <td>998.999252</td>\n", " <td>298.670059</td>\n", " <td>0.892166</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:12:00-08:00</th>\n", " <td>999.143503</td>\n", " <td>297.965134</td>\n", " <td>0.881345</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:13:00-08:00</th>\n", " <td>998.718983</td>\n", " <td>299.050471</td>\n", " <td>0.899170</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:14:00-08:00</th>\n", " <td>999.689110</td>\n", " <td>298.396573</td>\n", " <td>0.888901</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:15:00-08:00</th>\n", " <td>998.906026</td>\n", " <td>298.081031</td>\n", " <td>0.883191</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:16:00-08:00</th>\n", " <td>1001.441733</td>\n", " <td>300.027797</td>\n", " <td>0.889522</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:17:00-08:00</th>\n", " <td>999.874945</td>\n", " <td>298.704170</td>\n", " <td>0.880713</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:18:00-08:00</th>\n", " <td>999.763816</td>\n", " <td>299.457996</td>\n", " <td>0.866446</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:19:00-08:00</th>\n", " <td>999.328643</td>\n", " <td>297.217506</td>\n", " <td>0.869135</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:20:00-08:00</th>\n", " <td>999.746232</td>\n", " <td>297.342810</td>\n", " <td>0.900115</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:21:00-08:00</th>\n", " <td>1000.737338</td>\n", " <td>297.912401</td>\n", " <td>0.882068</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:22:00-08:00</th>\n", " <td>1000.274663</td>\n", " <td>297.772786</td>\n", " <td>0.878944</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:23:00-08:00</th>\n", " <td>1000.158990</td>\n", " <td>297.938545</td>\n", " <td>0.888503</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:24:00-08:00</th>\n", " <td>1000.922571</td>\n", " <td>298.359145</td>\n", " <td>0.877483</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:25:00-08:00</th>\n", " <td>1001.872280</td>\n", " <td>298.087752</td>\n", " <td>0.873660</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:26:00-08:00</th>\n", " <td>1000.656051</td>\n", " <td>297.877323</td>\n", " <td>0.866980</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:27:00-08:00</th>\n", " <td>1000.853954</td>\n", " <td>296.797606</td>\n", " <td>0.856736</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:28:00-08:00</th>\n", " <td>999.942882</td>\n", " <td>299.841713</td>\n", " <td>0.856718</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:29:00-08:00</th>\n", " <td>998.647786</td>\n", " <td>298.861976</td>\n", " <td>0.882361</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:01:00-08:00</th>\n", " <td>1000.933114</td>\n", " <td>297.728089</td>\n", " <td>0.836169</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:02:00-08:00</th>\n", " <td>999.142461</td>\n", " <td>298.791704</td>\n", " <td>0.842719</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:03:00-08:00</th>\n", " <td>998.136343</td>\n", " <td>298.775994</td>\n", " <td>0.813755</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:04:00-08:00</th>\n", " <td>1001.349441</td>\n", " <td>297.735862</td>\n", " <td>0.834132</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:05:00-08:00</th>\n", " <td>1000.251661</td>\n", " <td>299.644198</td>\n", " <td>0.817461</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:06:00-08:00</th>\n", " <td>1000.057671</td>\n", " <td>298.579453</td>\n", " <td>0.808162</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:07:00-08:00</th>\n", " <td>1000.744842</td>\n", " <td>300.871544</td>\n", " <td>0.819319</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:08:00-08:00</th>\n", " <td>1000.056866</td>\n", " <td>296.959531</td>\n", " <td>0.823236</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:09:00-08:00</th>\n", " <td>999.557475</td>\n", " <td>298.471216</td>\n", " <td>0.806808</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:10:00-08:00</th>\n", " <td>999.519186</td>\n", " <td>298.048904</td>\n", " <td>0.822102</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:11:00-08:00</th>\n", " <td>1000.148397</td>\n", " <td>298.552681</td>\n", " <td>0.806129</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:12:00-08:00</th>\n", " <td>999.258875</td>\n", " <td>293.823340</td>\n", " <td>0.827836</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:13:00-08:00</th>\n", " <td>1001.182033</td>\n", " <td>299.571102</td>\n", " <td>0.832395</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:14:00-08:00</th>\n", " <td>997.366496</td>\n", " <td>297.647049</td>\n", " <td>0.817866</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:15:00-08:00</th>\n", " <td>1000.163225</td>\n", " <td>296.983521</td>\n", " <td>0.818295</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:16:00-08:00</th>\n", " <td>1000.236309</td>\n", " <td>297.914401</td>\n", " <td>0.808314</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:17:00-08:00</th>\n", " <td>999.754813</td>\n", " <td>298.566226</td>\n", " <td>0.822418</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:18:00-08:00</th>\n", " <td>999.857364</td>\n", " <td>299.251087</td>\n", " <td>0.802571</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:19:00-08:00</th>\n", " <td>1000.809811</td>\n", " <td>296.530036</td>\n", " <td>0.810362</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:20:00-08:00</th>\n", " <td>999.982595</td>\n", " <td>296.600307</td>\n", " <td>0.831929</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:21:00-08:00</th>\n", " <td>998.040597</td>\n", " <td>298.726982</td>\n", " <td>0.809474</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:22:00-08:00</th>\n", " <td>1000.357286</td>\n", " <td>296.702854</td>\n", " <td>0.792977</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:23:00-08:00</th>\n", " <td>999.828010</td>\n", " <td>296.561526</td>\n", " <td>0.792677</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:24:00-08:00</th>\n", " <td>998.066797</td>\n", " <td>298.541645</td>\n", " <td>0.795909</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:25:00-08:00</th>\n", " <td>999.942954</td>\n", " <td>300.276747</td>\n", " <td>0.805300</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:26:00-08:00</th>\n", " <td>998.832891</td>\n", " <td>297.323483</td>\n", " <td>0.791858</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:27:00-08:00</th>\n", " <td>998.118673</td>\n", " <td>297.802544</td>\n", " <td>0.794021</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:28:00-08:00</th>\n", " <td>1000.149277</td>\n", " <td>299.374961</td>\n", " <td>0.811664</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:29:00-08:00</th>\n", " <td>998.827775</td>\n", " <td>298.726997</td>\n", " <td>0.796634</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:30:00-08:00</th>\n", " <td>1000.273102</td>\n", " <td>299.743819</td>\n", " <td>0.808884</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>91 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " irradiance temperature visible_transmittance\n", "2018-02-21 17:00:00-08:00 998.959655 298.918741 0.893836\n", "2018-02-21 17:01:00-08:00 1001.346465 298.553568 0.888956\n", "2018-02-21 17:02:00-08:00 1000.222570 296.760235 0.916820\n", "2018-02-21 17:03:00-08:00 998.697424 297.998634 0.895405\n", "2018-02-21 17:04:00-08:00 999.766542 298.686229 0.903194\n", "2018-02-21 17:05:00-08:00 998.521437 298.325881 0.892011\n", "2018-02-21 17:06:00-08:00 1000.991891 297.175668 0.896553\n", "2018-02-21 17:07:00-08:00 1000.054543 297.946657 0.878752\n", "2018-02-21 17:08:00-08:00 1002.449126 298.814267 0.887048\n", "2018-02-21 17:09:00-08:00 998.311166 296.710237 0.883333\n", "2018-02-21 17:10:00-08:00 1001.106463 297.809162 0.887446\n", "2018-02-21 17:11:00-08:00 998.999252 298.670059 0.892166\n", "2018-02-21 17:12:00-08:00 999.143503 297.965134 0.881345\n", "2018-02-21 17:13:00-08:00 998.718983 299.050471 0.899170\n", "2018-02-21 17:14:00-08:00 999.689110 298.396573 0.888901\n", "2018-02-21 17:15:00-08:00 998.906026 298.081031 0.883191\n", "2018-02-21 17:16:00-08:00 1001.441733 300.027797 0.889522\n", "2018-02-21 17:17:00-08:00 999.874945 298.704170 0.880713\n", "2018-02-21 17:18:00-08:00 999.763816 299.457996 0.866446\n", "2018-02-21 17:19:00-08:00 999.328643 297.217506 0.869135\n", "2018-02-21 17:20:00-08:00 999.746232 297.342810 0.900115\n", "2018-02-21 17:21:00-08:00 1000.737338 297.912401 0.882068\n", "2018-02-21 17:22:00-08:00 1000.274663 297.772786 0.878944\n", "2018-02-21 17:23:00-08:00 1000.158990 297.938545 0.888503\n", "2018-02-21 17:24:00-08:00 1000.922571 298.359145 0.877483\n", "2018-02-21 17:25:00-08:00 1001.872280 298.087752 0.873660\n", "2018-02-21 17:26:00-08:00 1000.656051 297.877323 0.866980\n", "2018-02-21 17:27:00-08:00 1000.853954 296.797606 0.856736\n", "2018-02-21 17:28:00-08:00 999.942882 299.841713 0.856718\n", "2018-02-21 17:29:00-08:00 998.647786 298.861976 0.882361\n", "... ... ... ...\n", "2018-02-21 18:01:00-08:00 1000.933114 297.728089 0.836169\n", "2018-02-21 18:02:00-08:00 999.142461 298.791704 0.842719\n", "2018-02-21 18:03:00-08:00 998.136343 298.775994 0.813755\n", "2018-02-21 18:04:00-08:00 1001.349441 297.735862 0.834132\n", "2018-02-21 18:05:00-08:00 1000.251661 299.644198 0.817461\n", "2018-02-21 18:06:00-08:00 1000.057671 298.579453 0.808162\n", "2018-02-21 18:07:00-08:00 1000.744842 300.871544 0.819319\n", "2018-02-21 18:08:00-08:00 1000.056866 296.959531 0.823236\n", "2018-02-21 18:09:00-08:00 999.557475 298.471216 0.806808\n", "2018-02-21 18:10:00-08:00 999.519186 298.048904 0.822102\n", "2018-02-21 18:11:00-08:00 1000.148397 298.552681 0.806129\n", "2018-02-21 18:12:00-08:00 999.258875 293.823340 0.827836\n", "2018-02-21 18:13:00-08:00 1001.182033 299.571102 0.832395\n", "2018-02-21 18:14:00-08:00 997.366496 297.647049 0.817866\n", "2018-02-21 18:15:00-08:00 1000.163225 296.983521 0.818295\n", "2018-02-21 18:16:00-08:00 1000.236309 297.914401 0.808314\n", "2018-02-21 18:17:00-08:00 999.754813 298.566226 0.822418\n", "2018-02-21 18:18:00-08:00 999.857364 299.251087 0.802571\n", "2018-02-21 18:19:00-08:00 1000.809811 296.530036 0.810362\n", "2018-02-21 18:20:00-08:00 999.982595 296.600307 0.831929\n", "2018-02-21 18:21:00-08:00 998.040597 298.726982 0.809474\n", "2018-02-21 18:22:00-08:00 1000.357286 296.702854 0.792977\n", "2018-02-21 18:23:00-08:00 999.828010 296.561526 0.792677\n", "2018-02-21 18:24:00-08:00 998.066797 298.541645 0.795909\n", "2018-02-21 18:25:00-08:00 999.942954 300.276747 0.805300\n", "2018-02-21 18:26:00-08:00 998.832891 297.323483 0.791858\n", "2018-02-21 18:27:00-08:00 998.118673 297.802544 0.794021\n", "2018-02-21 18:28:00-08:00 1000.149277 299.374961 0.811664\n", "2018-02-21 18:29:00-08:00 998.827775 298.726997 0.796634\n", "2018-02-21 18:30:00-08:00 1000.273102 299.743819 0.808884\n", "\n", "[91 rows x 3 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check the blob, is it really there?\n", "cur.execute('SELECT results FROM trials WHERE trial_id = 1')\n", "trial1_results = cur.fetchone()\n", "pd.read_csv(io.StringIO(trial1_results[0]), index_col=0)\n", "# yay! it works!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `JOIN`\n", "The foreign keys relate tables, but how do we use this relation? By joining the tables." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# add the results for experiment 17: T=375[K], E=700[W/m^2]\n", "experiment_id, temperature, irradiance = list(cur.execute(\n", " 'SELECT experiment_id, temperature, irradiance FROM experiments WHERE (temperature = 375 AND irradiance = 700)'\n", "))[0]\n", "start_time, end_time = '2018-02-28T17:00-0800', '2018-02-28T18:30-0800'\n", "timestamps = pd.DatetimeIndex(start=start_time, end=end_time, freq='T')\n", "# use http://poquitopicante.blogspot.com/2016/11/panda-pop.html to help you recall what offset alias to use\n", "size = len(timestamps)\n", "data = {\n", " 'temperature': np.random.randn(size) + temperature,\n", " 'irradiance': np.random.randn(size) + irradiance,\n", " 'visible_transmittance': np.logspace(np.log10(0.9), np.log10(0.7), size) + np.random.randn(size) / 100\n", "}\n", "results = pd.DataFrame(data, index=timestamps)\n", "duration = (results.index[-1] - results.index[0]).value # [ns]\n", "avg_temperature = results.temperature.mean() # [K]\n", "std_temperature = results.temperature.std() # [K]\n", "avg_irradiance = results.irradiance.mean() # [W/m^2]\n", "std_irradiance = results.irradiance.std() # [W/m^2]\n", "init_visible_transmittance = results.visible_transmittance[start_time]\n", "final_visible_transmittance = results.visible_transmittance[end_time]\n", "values = (experiment_id, results.to_csv(), duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance, final_visible_transmittance,\n", " 'this is doctored data')\n", "cur.execute('''INSERT INTO trials (\n", " experiment_id, results, duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance,\n", " final_visible_transmittance, notes\n", ") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', values)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>experiment</th>\n", " <th>init_visible_transmittance</th>\n", " <th>final_visible_transmittance</th>\n", " <th>material</th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>alpha</th>\n", " <th>beta</th>\n", " <th>material_type</th>\n", " </tr>\n", " <tr>\n", " <th>trial_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0.893836</td>\n", " <td>0.808884</td>\n", " <td>EVA</td>\n", " <td>298.15</td>\n", " <td>1000.0</td>\n", " <td>0.123</td>\n", " <td>4.56</td>\n", " <td>polymer</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>17</td>\n", " <td>0.917532</td>\n", " <td>0.690272</td>\n", " <td>EVA</td>\n", " <td>375.00</td>\n", " <td>700.0</td>\n", " <td>0.123</td>\n", " <td>4.56</td>\n", " <td>polymer</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " experiment init_visible_transmittance final_visible_transmittance \\\n", "trial_id \n", "1 1 0.893836 0.808884 \n", "2 17 0.917532 0.690272 \n", "\n", " material temperature irradiance alpha beta material_type \n", "trial_id \n", "1 EVA 298.15 1000.0 0.123 4.56 polymer \n", "2 EVA 375.00 700.0 0.123 4.56 polymer " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql('''\n", "SELECT\n", " trial_id, trials.experiment_id AS experiment, init_visible_transmittance, final_visible_transmittance,\n", " experiments.material_id AS material, temperature, irradiance, alpha, beta, material_type\n", "FROM trials\n", "JOIN experiments ON experiments.experiment_id = experiment\n", "JOIN materials ON materials.material_id = material\n", "''', conn, index_col='trial_id')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "cur.close()\n", "conn.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Epilogue\n", "Unfortunately that's all we have time for, but we covered a lot, even if we didn't get this far. If there's anything I hope you learned from this it's that:\n", "\n", "1. You can read the SQLite and PostgreSQL manuals or use [StackOverflow](https://stackoverflow.com/questions/tagged/SQL) to teach yourself SQL. It's not rocket science. I was a mechanical engineer, and I learned it.\n", "2. The basics of making a table, inserting data, and conducting queries.\n", "3. How to interact programmatically with a database.\n", "\n", "But there's still so much to learn! Hopefully you will continue on your own and try some of these intereting topics.\n", "1. Use what you've learned on a more advanced database management system like PostgreSQL, MySQL, or MS SQL Server.\n", "2. Use an Object Relational Mapper (ORM) like Django or SQLAlchemy to simplify your workflow, by creating and manipulating data in native Python.\n", "3. Explore a NoSQL database like Cassandra or MongoDB and see if the flexible structure and large scale cluster computing allow you to explore big data with tools like [Spark](http://spark.apache.org/) and [Hadoop](http://hadoop.apache.org/).\n", "\n", "Did you learn about everything you expected today? What did wish we had covered? Leave your comments or improve this tutorial by sending a PR to [The Hacker Within, Berkeley](https://github.com/thehackerwithin/berkeley).\n", "\n", "Thanks!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
vzg100/Post-Translational-Modification-Prediction	old/Phosphorylation Sequence Tests -MLP -dbptm+ELM -EnzymeBenchmarks-VectorAvr..ipynb	1	128979	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Template for test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from pred import Predictor\n", "from pred import sequence_vector\n", "from pred import chemical_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\n", "\n", "Included is N Phosphorylation however no benchmarks are available, yet. \n", "\n", "\n", "Training data is from phospho.elm and benchmarks are from dbptm. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 11, 12, 2, 10, 20, 11, 20, 20, 12, 10, 0, 0, -0.018181818181818136, 44.10909090909091, 0.0]\n", "Finished working with Data\n", "Training Data Points: 200363\n", "Test Data Points: 50091\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.013149454778704297\n", "Specificity : 0.996563320814002\n", "Accuracy: 0.8129204847178135\n", "ROC 0.504856387796\n", "Matthews Correlation Coeff: 0.0523747492729\n", "TP 123 FP 140 TN 40597 FN 9231\n", "\n", "\n", "\n", "None\n", "Cross Validation: [ 0.81553941 0.81579525 0.81242139 0.81593132 0.81531244]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.0625\n", "Specificity : 0.9971330275229358\n", "Accuracy: 0.9640486725663717\n", "ROC 0.529816513761\n", "Matthews Correlation Coeff: 0.156571693721\n", "TP 4 FP 5 TN 1739 FN 60\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 8, 19, 20, 10, 3, 8, 8, 10, 12, 10, 0, 0, -0.6636363636363637, 100.02727272727273, 0.0]\n", "Finished working with Data\n", "Training Data Points: 200363\n", "Test Data Points: 50091\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.02822405557967868\n", "Specificity : 0.9931749798184887\n", "Accuracy: 0.8157153979756843\n", "ROC 0.510699517699\n", "Matthews Correlation Coeff: 0.0803519307738\n", "TP 260 FP 279 TN" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"S\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"S\")\n", " del x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y Phosphorylation " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 3, 17, 20, 11, 15, 15, 10, 12, 2, 8, 12, 1, 0.13076923076923075, 21.20769230769231, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.064\n", "Specificity : 1.0\n", "Accuracy: 0.9536633663366336\n", "ROC 0.532\n", "TP 8 FP 0 TN 2400 FN 117\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95566112 0.95683168 0.95524752 0.95324881 0.95324881]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 20, 7, 18, 3, 19, 15, 12, 2, 20, 10, 13, 16, -0.30769230769230776, 18.723076923076924, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19360\n", "Test Data Points: 4840\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.945583926329008\n", "Specificity : 0.9951040391676866\n", "Accuracy: 0.9706611570247934\n", "ROC 0.970343982748\n", "TP 2259 FP 12 TN 2439 FN 130\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85953315 0.98347449 0.975 0.97396156 0.98140112]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 8, 19, 4, 10, 2, 15, 3, 2, 9, 8, 10, 17, -1.2000000000000002, 121.27692307692308, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19222\n", "Test Data Points: 4806\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9496157130657558\n", "Specificity : 0.9951298701298701\n", "Accuracy: 0.9729504785684561\n", "ROC 0.972372791598\n", "TP 2224 FP 12 TN 2452 FN 118\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89679567 0.98918019 0.9852268 0.98314256 0.98772112]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 8, 3, 6, 7, 2, 15, 10, 18, 2, 16, 1, 9, -0.7538461538461539, 32.21538461538462, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4661016949152542\n", "Specificity : 0.7142857142857143\n", "Accuracy: 0.5907172995780591\n", "ROC 0.5901937046\n", "TP 55 FP 34 TN 85 FN 63\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63445378 0.56779661 0.6059322 0.62288136 0.58898305]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 17, 3, 16, 3, 11, 15, 2, 3, 3, 8, 9, 12, 0.0076923076923075965, 82.15384615384616, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9236\n", "Test Data Points: 2310\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 0.9990888382687927\n", "Accuracy: 0.9528138528138528\n", "ROC 0.53432702783\n", "TP 8 FP 2 TN 2193 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94761905 0.95106107 0.95019489 0.95106107 0.95149415]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [16, 19, 5, 7, 5, 10, 15, 10, 9, 13, 18, 5, 8, -0.8538461538461539, 9.938461538461542, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5847457627118644\n", "Specificity : 0.7899159663865546\n", "Accuracy: 0.6877637130801688\n", "ROC 0.687330864549\n", "TP 69 FP 25 TN 94 FN 49\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.68487395 0.70762712 0.63135593 0.57627119 0.66525424]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 7, 7, 3, 4, 2, 15, 1, 1, 7, 3, 7, 15, -0.6230769230769231, -10.969230769230771, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.03418803418803419\n", "Specificity : 0.9991694352159468\n", "Accuracy: 0.9544554455445544\n", "ROC 0.516678734702\n", "TP 4 FP 2 TN 2406 FN 113\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95486936 0.95722772 0.95366337 0.95641838 0.9544374 ]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 395 FN 1\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 1, 11, 11, 11, 3, 15, 18, 20, 18, 16, 19, 5, -1.369230769230769, 14.969230769230771, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07462686567164178\n", "Specificity : 0.999163529903806\n", "Accuracy: 0.95009900990099\n", "ROC 0.536895197788\n", "TP 10 FP 2 TN 2389 FN 124\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95447348 0.95524752 0.95485149 0.9544374 0.95522979]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 396 FN 1\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 20, 15, 1, 19, 12, 15, 7, 2, 17, 18, 12, 16, -0.9615384615384616, -0.13846153846153825, 0.15384615384615385]\n", "Balancing Data\n", "Balanced 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"\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.87493696 0.97907211 0.98108926 0.9820888 0.98410696]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 18 TN 379 FN 1\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 18, 10, 12, 9, 19, 15, 7, 19, 3, 16, 9, 3, -1.176923076923077, 17.51538461538462, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19232\n", "Test Data Points: 4809\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.944136460554371\n", "Specificity : 0.9833603896103896\n", "Accuracy: 0.9642337284258682\n", "ROC 0.963748425082\n", "TP 2214 FP 41 TN 2423 FN 131\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89873155 0.98378041 0.9841963 0.98252548 0.98814229]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 392 FN 1\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 19, 10, 3, 17, 13, 15, 15, 13, 7, 18, 15, 12, -0.5076923076923078, 56.63846153846155, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5\n", "Specificity : 0.7394957983193278\n", "Accuracy: 0.620253164556962\n", "ROC 0.61974789916\n", "TP 59 FP 31 TN 88 FN 59\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56302521 0.53813559 0.5720339 0.59322034 0.55932203]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6523929471032746\n", "Accuracy: 0.6532663316582915\n", "ROC 0.826196473552\n", "TP 1 FP 138 TN 259 FN 0\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 12, 20, 1, 9, 10, 15, 1, 1, 13, 15, 12, 11, 0.09230769230769229, 16.430769230769233, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5254237288135594\n", "Specificity : 0.7478991596638656\n", "Accuracy: 0.6371308016877637\n", "ROC 0.636661444239\n", "TP 62 FP 30 TN 89 FN 56\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56722689 0.55508475 0.50847458 0.66949153 0.53813559]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6372795969773299\n", "Accuracy: 0.6381909547738693\n", "ROC 0.818639798489\n", "TP 1 FP 144 TN 253 FN 0\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 15, 10, 19, 15, 3, 15, 5, 2, 10, 10, 18, 7, -0.38461538461538464, 42.261538461538464, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9229\n", "Test Data Points: 2308\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06086956521739131\n", "Specificity : 0.9990880072959416\n", "Accuracy: 0.9523396880415944\n", "ROC 0.529978786257\n", "TP 7 FP 2 TN 2191 FN 108\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94932871 0.95188557 0.94971825 0.9514521 0.95188557]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 397 FN 1\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 18, 1, 4, 14, 8, 15, 15, 19, 2, 8, 14, 8, -1.0076923076923077, 2.1769230769230776, 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Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5423728813559322\n", "Specificity : 0.7226890756302521\n", "Accuracy: 0.6329113924050633\n", "ROC 0.632530978493\n", "TP 64 FP 33 TN 86 FN 54\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.67226891 0.67372881 0.63983051 0.57627119 0.57627119]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.4282115869017632\n", "Accuracy: 0.4296482412060301\n", "ROC 0.714105793451\n", "TP 1 FP 227 TN 170 FN 0\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 8, 19, 3, 5, 10, 15, 8, 2, 3, 15, 10, 15, -0.3538461538461538, 38.861538461538466, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5508474576271186\n", "Specificity : 0.7563025210084033\n", "Accuracy: 0.6540084388185654\n", "ROC 0.653574989318\n", "TP 65 FP 29 TN 90 FN 53\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63445378 0.65677966 0.69067797 0.55508475 0.65254237]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.4332493702770781\n", "Accuracy: 0.43467336683417085\n", "ROC 0.716624685139\n", "TP 1 FP 225 TN 172 FN 0\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 1, 7, 13, 3, 5, 15, 18, 12, 12, 12, 9, 18, 0.3153846153846155, 24.500769230769233, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.049586776859504134\n", "Specificity : 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [14, 8, 18, 18, 7, 2, 15, 14, 15, 18, 1, 4, 14, -0.7461538461538463, 10.084615384615386, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9388682362660058\n", "Specificity : 0.996694214876033\n", "Accuracy: 0.9677752530468912\n", "ROC 0.967781225571\n", "TP 2273 FP 8 TN 2412 FN 148\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85398596 0.97892997 0.97624458 0.97747934 0.97809917]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 19, 12, 8, 19, 3, 15, 9, 2, 1, 9, 3, 7, -1.0000000000000002, 20.730769230769234, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19228\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9483126868859462\n", "Specificity : 0.993109039319011\n", "Accuracy: 0.971297836938436\n", "ROC 0.970710863102\n", "TP 2220 FP 17 TN 2450 FN 121\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89455075 0.98772879 0.98689684 0.98585102 0.98730753]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [14, 3, 10, 3, 17, 10, 15, 10, 2, 20, 13, 8, 2, 0.44615384615384623, 72.66153846153847, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4745762711864407\n", "Specificity : 0.5546218487394958\n", "Accuracy: 0.5147679324894515\n", "ROC 0.514599059963\n", "TP 56 FP 53 TN 66 FN 62\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56722689 0.53389831 0.58898305 0.59322034 0.54237288]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 3, 10, 13, 18, 3, 15, 9, 3, 13, 7, 17, 18, 0.24615384615384622, 31.46923076923077, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05309734513274336\n", "Specificity : 0.9975124378109452\n", "Accuracy: 0.9552475247524752\n", "ROC 0.525304891472\n", "TP 6 FP 6 TN 2406 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95407759 0.95485149 0.95405941 0.9544374 0.9544374 ]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 5, 20, 10, 2, 17, 15, 13, 17, 3, 17, 5, 8, -0.5846153846153846, 18.24615384615385, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9439368770764119\n", "Specificity : 0.9934237566789971\n", "Accuracy: 0.9688080975005164\n", "ROC 0.968680316878\n", "TP 2273 FP 16 TN 2417 FN 135\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85997522 0.97149349 0.97376575 0.97334711 0.97995868]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 2, 3, 17, 20, 11, 15, 15, 10, 12, 2, 8, 12, -0.10769230769230778, 27.738461538461543, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19229\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9401964972234088\n", "Specificity : 0.9927036886907175\n", "Accuracy: 0.9671381031613977\n", "ROC 0.966450092957\n", "TP 2201 FP 18 TN 2449 FN 140\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88831115 0.98710483 0.99043261 0.98439775 0.98626717]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 13, 6, 12, 1, 18, 15, 10, 20, 20, 12, 7, 6, 0.3076923076923077, -12.576923076923077, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4745762711864407\n", "Specificity : 0.680672268907563\n", "Accuracy: 0.5780590717299579\n", "ROC 0.577624270047\n", "TP 56 FP 38 TN 81 FN 62\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55882353 0.56779661 0.5720339 0.58474576 0.55084746]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 2, 4, 1, 7, 2, 15, 16, 11, 1, 14, 5, 20, -0.4000000000000001, 9.976923076923077, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9230\n", "Test Data Points: 2308\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 0.9981760145918832\n", "Accuracy: 0.9519064124783362\n", "ROC 0.533870615992\n", "TP 8 FP 4 TN 2189 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9497618 0.95147314 0.95101864 0.9479844 0.95188557]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 11, 15, 18, 15, 10, 15, 13, 5, 7, 15, 13, 13, 0.2384615384615385, 12.484615384615388, 0.38461538461538464]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.576271186440678\n", "Specificity : 0.7310924369747899\n", "Accuracy: 0.6540084388185654\n", "ROC 0.653681811708\n", "TP 68 FP 32 TN 87 FN 50\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.68907563 0.73305085 0.63559322 0.5720339 0.69067797]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 17, 12, 3, 17, 8, 15, 5, 19, 13, 18, 18, 10, -0.46153846153846156, 57.36923076923077, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07258064516129033\n", "Specificity : 0.9975010412328197\n", "Accuracy: 0.952079207920792\n", "ROC 0.535040843197\n", "TP 9 FP 6 TN 2395 FN 115\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95368171 0.95683168 0.95405941 0.95562599 0.95562599]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9964788732394366\n", "Accuracy: 0.9964850615114236\n", "ROC 0.99823943662\n", "TP 1 FP 2 TN 566 FN 0\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 2, 3, 5, 20, 3, 15, 19, 19, 8, 12, 20, 5, 0.19230769230769224, 24.607692307692307, 0.23076923076923078]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05504587155963303\n", "Specificity : 0.9979304635761589\n", "Accuracy: 0.9572277227722772\n", "ROC 0.526488167568\n", "TP 6 FP 5 TN 2411 FN 103\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95407759 0.95485149 0.95445545 0.95364501 0.95602219]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9947183098591549\n", "Accuracy: 0.9947275922671354\n", "ROC 0.99735915493\n", "TP 1 FP 3 TN 565 FN 0\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 13, 8, 15, 3, 12, 15, 8, 4, 3, 7, 1, 3, 0.32307692307692304, -21.961538461538467, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9454545454545454\n", "Specificity : 0.9962825278810409\n", "Accuracy: 0.970873786407767\n", "ROC 0.970868536668\n", "TP 2288 FP 9 TN 2412 FN 132\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85543164 0.97665772 0.98223508 0.97789256 0.97520661]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9964788732394366\n", "Accuracy: 0.9964850615114236\n", "ROC 0.99823943662\n", "TP 1 FP 2 TN 566 FN 0\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [16, 10, 17, 19, 11, 16, 15, 15, 19, 19, 3, 17, 10, -2.1461538461538465, 134.16923076923078, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19360\n", "Test Data Points: 4840\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9466221851542952\n", "Specificity : 0.9946764946764947\n", "Accuracy: 0.9708677685950413\n", "ROC 0.970649339915\n", "TP 2270 FP 13 TN 2429 FN 128\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.86304483 0.98058252 0.98243802 0.97210167 0.97458153]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 1, 15, 20, 3, 7, 15, 12, 2, 5, 18, 1, 20, 0.37692307692307686, -28.453846153846158, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19228\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9414780008543358\n", "Specificity : 0.9971625456019457\n", "Accuracy: 0.9700499168053245\n", "ROC 0.969320273228\n", "TP 2204 FP 7 TN 2460 FN 137\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88997504 0.98668885 0.98960067 0.98314607 0.98564295]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9894366197183099\n", "Accuracy: 0.9894551845342706\n", "ROC 0.994718309859\n", "TP 1 FP 6 TN 562 FN 0\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 3, 13, 5, 10, 13, 15, 9, 10, 8, 7, 13, 12, 0.6846153846153845, 20.0, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19237\n", "Test Data Points: 4810\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9404001702852277\n", "Specificity : 0.9922795611540024\n", "Accuracy: 0.9669438669438669\n", "ROC 0.96633986572\n", "TP 2209 FP 19 TN 2442 FN 140\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89189189 0.98877339 0.98336798 0.98939488 0.98564892]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9894366197183099\n", "Accuracy: 0.9894551845342706\n", "ROC 0.994718309859\n", "TP 1 FP 6 TN 562 FN 0\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 17, 13, 11, 19, 1, 15, 3, 9, 7, 3, 8, 13, -0.08461538461538466, 13.230769230769234, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5084745762711864\n", "Specificity : 0.6470588235294118\n", "Accuracy: 0.5780590717299579\n", "ROC 0.5777666999\n", "TP 60 FP 42 TN 77 FN 58\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5 0.58050847 0.55508475 0.59745763 0.58474576]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6919014084507042\n", "Accuracy: 0.6924428822495606\n", "ROC 0.845950704225\n", "TP 1 FP 175 TN 393 FN 0\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 13, 3, 16, 12, 3, 15, 20, 19, 14, 13, 17, 8, 0.16153846153846152, 32.330769230769235, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4661016949152542\n", "Specificity : 0.6638655462184874\n", "Accuracy: 0.5654008438818565\n", "ROC 0.564983620567\n", "TP 55 FP 40 TN 79 FN 63\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.57563025 0.54237288 0.57627119 0.61440678 0.56355932]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6619718309859155\n", "Accuracy: 0.6625659050966608\n", "ROC 0.830985915493\n", "TP 1 FP 192 TN 376 FN 0\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 9, 1, 4, 2, 15, 3, 9, 10, 7, 17, 5, -0.12307692307692299, 58.61538461538462, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9240\n", "Test Data Points: 2311\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05217391304347826\n", "Specificity : 0.9977231329690346\n", "Accuracy: 0.9506707053223713\n", "ROC 0.524948523006\n", "TP 6 FP 5 TN 2191 FN 109\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95023799 0.94935065 0.95194805 0.95021645 0.95238095]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 4, 13, 9, 10, 18, 15, 16, 10, 10, 18, 11, 15, -1.3384615384615386, 79.93846153846155, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9225\n", "Test Data Points: 2307\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 1.0\n", "Accuracy: 0.9536194191590811\n", "ROC 0.534782608696\n", "TP 8 FP 0 TN 2192 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94974003 0.95143105 0.94969644 0.95056375 0.95143105]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 13, 1, 1, 9, 12, 15, 11, 11, 20, 12, 7, 19, 0.015384615384615228, 29.215384615384615, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5508474576271186\n", "Specificity : 0.7058823529411765\n", "Accuracy: 0.6286919831223629\n", "ROC 0.628364905284\n", "TP 65 FP 35 TN 84 FN 53\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.69747899 0.72881356 0.6440678 0.58898305 0.61864407]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.43661971830985913\n", "Accuracy: 0.437609841827768\n", "ROC 0.718309859155\n", "TP 1 FP 320 TN 248 FN 0\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 20, 15, 19, 11, 15, 3, 19, 12, 2, 3, 9, -0.46923076923076945, 15.884615384615385, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5423728813559322\n", "Specificity : 0.6722689075630253\n", "Accuracy: 0.6075949367088608\n", "ROC 0.607320894459\n", "TP 64 FP 39 TN 80 FN 54\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.69747899 0.62288136 0.66949153 0.58050847 0.62288136]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.44366197183098594\n", "Accuracy: 0.4446397188049209\n", "ROC 0.721830985915\n", "TP 1 FP 316 TN 252 FN 0\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " try:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"bagging\")\n", " y.benchmark(j, \"Y\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"bagging\")\n", " x.benchmark(j, \"Y\")\n", " del x\n", " except:\n", " print(\"Benchmark not relevant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "T Phosphorylation " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 20, 13, 7, 7, 3, 20, 3, 8, 10, 2, 0, 0, -0.28181818181818186, 55.518181818181816, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0029411764705882353\n", "Specificity : 0.9981510243325198\n", "Accuracy: 0.8144864604064893\n", "ROC 0.500546100402\n", "TP 9 FP 25 TN 13496 FN 3051\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81859848 0.80014474 0.80838359 0.81785283 0.80874548]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 3 TN 1150 FN 43\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 16, 10, 3, 2, 12, 20, 1, 20, 3, 3, 3, 8, 1.1307692307692307, 14.715384615384618, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.00398406374501992\n", "Specificity : 0.9977153806470631\n", "Accuracy: 0.8172004101079549\n", "ROC 0.500849722196\n", "TP 12 FP 31 TN 13538 FN 3000\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81811603 0.81775419 0.81586248 0.81212304 0.81863691]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 4 TN 1149 FN 43\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 2, 9, 7, 20, 12, 20, 2, 9, 18, 19, 16, 12, -0.6692307692307692, 2.7, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109784\n", "Test Data Points: 27446\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6471138620987477\n", "Specificity : 0.8769923258559622\n", "Accuracy: 0.7606208554980689\n", "ROC 0.762053093977\n", "TP 8991 FP 1667 TN 11885 FN 4903\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.54118847 0.76317995 0.79483349 0.79872472 0.80572053]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.32558139534883723\n", "Specificity : 0.8881179531656548\n", "Accuracy: 0.8678929765886287\n", "ROC 0.606849674257\n", "TP 14 FP 129 TN 1024 FN 29\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 16, 3, 8, 9, 20, 10, 11, 3, 17, 19, 2, -0.45384615384615395, 91.20769230769231, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109789\n", "Test Data Points: 27448\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6709561466570813\n", "Specificity : 0.826931599940907\n", "Accuracy: 0.747886913436316\n", "ROC 0.748943873299\n", "TP 9333 FP 2343 TN 11195 FN 4577\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51103906 0.77969251 0.79644406 0.78839217 0.79356578]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.46511627906976744\n", "Specificity : 0.8438855160450998\n", "Accuracy: 0.830267558528428\n", "ROC 0.654500897557\n", "TP 20 FP 180 TN 973 FN 23\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 14, 1, 9, 12, 20, 18, 15, 13, 9, 9, 7, -1.0307692307692309, 44.215384615384615, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75724\n", "Test Data Points: 18931\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8786101744612227\n", "Specificity : 0.5337492909812819\n", "Accuracy: 0.7822618984734034\n", "ROC 0.706179732721\n", "TP 11986 FP 2466 TN 2823 FN 1656\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.72866047 0.79463342 0.80154244 0.79809826 0.80036978]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.9069767441860465\n", "Specificity : 0.3217692974848222\n", "Accuracy: 0.342809364548495\n", "ROC 0.614373020835\n", "TP 39 FP 782 TN 371 FN 4\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 2, 2, 5, 1, 1, 20, 18, 12, 9, 11, 10, 12, 0.23846153846153836, 30.261538461538464, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102802\n", "Test Data Points: 25701\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9276861200032832\n", "Specificity : 0.3412487054297973\n", "Accuracy: 0.6192366055795494\n", "ROC 0.634467412717\n", "TP 11302 FP 8905 TN 4613 FN 881\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60968018 0.69440878 0.69081712 0.68105058 0.6711284 ]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.9302325581395349\n", "Specificity : 0.2419774501300954\n", "Accuracy: 0.26672240802675584\n", "ROC 0.586105004135\n", "TP 40 FP 874 TN 279 FN 3\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 10, 20, 11, 12, 12, 20, 10, 1, 3, 20, 15, 13, 1.0384615384615385, 17.51538461538462, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5782894736842106\n", "Specificity : 0.64\n", "Accuracy: 0.6088113050706567\n", "ROC 0.609144736842\n", "TP 1758 FP 1071 TN 1904 FN 1282\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5880984 0.60222739 0.60907882 0.5857998 0.60409046]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.627906976744186\n", "Specificity : 0.5758889852558543\n", "Accuracy: 0.5777591973244147\n", "ROC 0.601897981\n", "TP 27 FP 489 TN 664 FN 16\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 13, 2, 16, 3, 3, 20, 10, 11, 10, 15, 7, 12, 0.1769230769230769, 78.58461538461539, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7690789473684211\n", "Specificity : 0.4319327731092437\n", "Accuracy: 0.6023275145469659\n", "ROC 0.600505860239\n", "TP 2338 FP 1690 TN 1285 FN 702\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.58926197 0.59258644 0.59727303 0.58779514 0.59411373]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.8837209302325582\n", "Specificity : 0.3434518647007806\n", "Accuracy: 0.362876254180602\n", "ROC 0.613586397467\n", "TP 38 FP 757 TN 396 FN 5\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 19, 8, 8, 3, 17, 20, 2, 10, 3, 11, 2, 13, -0.4538461538461536, 83.30769230769232, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47212\n", "Test Data Points: 11803\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05589638718473074\n", "Specificity : 0.9875972488442891\n", "Accuracy: 0.7559942387528594\n", "ROC 0.521746818015\n", "TP 164 FP 110 TN 8759 FN 2770\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75516774 0.75482887 0.74870796 0.74775462 0.73284189]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.06976744186046512\n", "Specificity : 0.9774501300954033\n", "Accuracy: 0.9448160535117057\n", "ROC 0.523608785978\n", "TP 3 FP 26 TN 1127 FN 40\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [6, 7, 12, 16, 12, 5, 20, 12, 12, 8, 3, 20, 14, 1.0, 2.7, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47218\n", "Test Data Points: 11805\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.1826235093696763\n", "Specificity : 0.9489289740698985\n", "Accuracy: 0.7584074544684456\n", "ROC 0.56577624172\n", "TP 536 FP 453 TN 8417 FN 2399\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75554803 0.7519695 0.75652321 0.74559471 0.74923755]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.16279069767441862\n", "Specificity : 0.9184735472679966\n", "Accuracy: 0.8913043478260869\n", "ROC 0.540632122471\n", "TP 7 FP 94 TN 1059 FN 36\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 9, 5, 3, 19, 1, 20, 17, 7, 20, 18, 18, 13, -1.353846153846154, 24.938461538461542, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5631578947368421\n", "Specificity : 0.7636974789915967\n", "Accuracy: 0.6623441396508728\n", "ROC 0.663427686864\n", "TP 1712 FP 703 TN 2272 FN 1328\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63796543 0.69381649 0.66544729 0.65696708 0.61805786]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.4418604651162791\n", "Specificity : 0.4787510841283608\n", "Accuracy: 0.4774247491638796\n", "ROC 0.460305774622\n", "TP 19 FP 601 TN 552 FN 24\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 10, 11, 8, 8, 1, 20, 17, 2, 2, 10, 13, 9, -0.9230769230769229, 114.74615384615386, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6194078947368421\n", "Specificity : 0.706890756302521\n", "Accuracy: 0.6626766417290108\n", "ROC 0.66314932552\n", "TP 1883 FP 872 TN 2103 FN 1157\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63048537 0.67536569 0.64466245 0.63917526 0.61972065]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.46511627906976744\n", "Specificity : 0.40763226366001737\n", "Accuracy: 0.4096989966555184\n", "ROC 0.436374271365\n", "TP 20 FP 683 TN 470 FN 23\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 2, 7, 15, 8, 8, 20, 19, 12, 18, 18, 10, 11, -1.7923076923076924, 57.846153846153854, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0032658393207054214\n", "Specificity : 0.9988164805089134\n", "Accuracy: 0.8149689403534166\n", "ROC 0.501041159915\n", "TP 10 FP 16 TN 13503 FN 3052\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81829695 0.81112049 0.8181544 0.81845597 0.8181544 ]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 702 FN 15\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 11, 20, 3, 3, 7, 20, 5, 9, 2, 3, 7, 5, 0.07692307692307689, 5.661538461538462, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06833333333333333\n", "Specificity : 0.9693689713570429\n", "Accuracy: 0.8063446113020928\n", "ROC 0.518851152345\n", "TP 205 FP 416 TN 13165 FN 2795\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81823664 0.80937161 0.81851628 0.81121834 0.79764777]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.9559659090909091\n", "Accuracy: 0.9401947148817803\n", "ROC 0.577982954545\n", "TP 3 FP 31 TN 673 FN 12\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 9, 9, 9, 9, 3, 20, 3, 20, 13, 3, 17, 8, -0.23076923076923084, 162.70769230769227, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109770\n", "Test Data Points: 27443\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.565138546351371\n", "Specificity : 0.9324888335652046\n", "Accuracy: 0.7479502969791932\n", "ROC 0.748813689958\n", "TP 7791 FP 922 TN 12735 FN 5995\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55290774 0.772984 0.78387144 0.79848408 0.78245026]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9147727272727273\n", "Accuracy: 0.8970792767732962\n", "ROC 0.49071969697\n", "TP 1 FP 60 TN 644 FN 14\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 12, 20, 12, 20, 12, 20, 18, 18, 20, 9, 7, 0, -0.5166666666666665, 0.4416666666666706, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109747\n", "Test Data Points: 27437\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6750180505415162\n", "Specificity : 0.8369029219106499\n", "Accuracy: 0.7551846047308379\n", "ROC 0.755960486226\n", "TP 9349 FP 2216 TN 11371 FN 4501\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51067862 0.76660106 0.80419886 0.80383438 0.76964572]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.4666666666666667\n", "Specificity : 0.8224431818181818\n", "Accuracy: 0.8150208623087621\n", "ROC 0.644554924242\n", "TP 7 FP 125 TN 579 FN 8\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 7, 8, 1, 17, 17, 20, 12, 19, 12, 19, 1, 16, -1.3000000000000003, 36.792307692307695, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75727\n", "Test Data Points: 18932\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9187613843351549\n", "Specificity : 0.4351834069521798\n", "Accuracy: 0.7857595605324319\n", "ROC 0.676972395644\n", "TP 12610 FP 2941 TN 2266 FN 1115\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74420324 0.7911583 0.80185938 0.79773916 0.79879563]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.8666666666666667\n", "Specificity : 0.27698863636363635\n", "Accuracy: 0.28929068150208626\n", "ROC 0.571827651515\n", "TP 13 FP 509 TN 195 FN 2\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 8, 8, 13, 3, 3, 20, 3, 8, 16, 3, 11, 3, 0.8692307692307693, 103.83076923076923, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102990\n", "Test Data Points: 25748\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7671746393936925\n", "Specificity : 0.5981301476589745\n", "Accuracy: 0.6786934907565636\n", "ROC 0.682652393526\n", "TP 9414 FP 5416 TN 8061 FN 2857\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60278069 0.68164518 0.69076009 0.69433332 0.69565386]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.5355113636363636\n", "Accuracy: 0.5396383866481224\n", "ROC 0.634422348485\n", "TP 11 FP 327 TN 377 FN 4\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 13, 15, 17, 9, 17, 20, 7, 14, 9, 7, 9, 10, -2.0769230769230766, 4.50769230769231, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6532894736842105\n", "Specificity : 0.5626890756302521\n", "Accuracy: 0.6084788029925187\n", "ROC 0.607989274657\n", "TP 1986 FP 1301 TN 1674 FN 1054\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.57480053 0.59275266 0.59760559 0.60093116 0.53092784]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.48011363636363635\n", "Accuracy: 0.4853963838664812\n", "ROC 0.606723484848\n", "TP 11 FP 366 TN 338 FN 4\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 9, 9, 2, 3, 3, 20, 2, 8, 8, 9, 17, 19, -0.8615384615384616, 63.938461538461546, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.44144736842105264\n", "Specificity : 0.7431932773109243\n", "Accuracy: 0.5906899418121363\n", "ROC 0.592320322866\n", "TP 1342 FP 764 TN 2211 FN 1698\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60787899 0.59524601 0.56451613 0.6005986 0.60192883]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.6789772727272727\n", "Accuracy: 0.6801112656467315\n", "ROC 0.70615530303\n", "TP 11 FP 226 TN 478 FN 4\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 9, 3, 2, 7, 7, 20, 7, 5, 18, 3, 14, 17, -0.4923076923076923, 9.230769230769232, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47275\n", "Test Data Points: 11819\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.027551020408163266\n", "Specificity : 0.9939182340353644\n", "Accuracy: 0.753532447753617\n", "ROC 0.510734627222\n", "TP 81 FP 54 TN 8825 FN 2859\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.7535533 0.74678511 0.75156541 0.72702657 0.75156541]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9758522727272727\n", "Accuracy: 0.9568845618915159\n", "ROC 0.521259469697\n", "TP 1 FP 17 TN 687 FN 14\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 7, 17, 18, 12, 3, 20, 1, 3, 18, 3, 7, 15, -0.5692307692307693, 30.738461538461536, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47229\n", "Test Data Points: 11808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07101399795151929\n", "Specificity : 0.9841198333145624\n", "Accuracy: 0.7576219512195121\n", "ROC 0.527566915633\n", "TP 208 FP 141 TN 8738 FN 2721\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74678184 0.75643631 0.75455238 0.75107987 0.74930126]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.9446022727272727\n", "Accuracy: 0.9290681502086231\n", "ROC 0.572301136364\n", "TP 3 FP 39 TN 665 FN 12\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 7, 17, 13, 11, 15, 20, 11, 1, 9, 13, 11, 7, -1.1384615384615384, 32.361538461538466, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7115131578947368\n", "Specificity : 0.6110924369747899\n", "Accuracy: 0.6618453865336659\n", "ROC 0.661302797435\n", "TP 2163 FP 1157 TN 1818 FN 877\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64311835 0.67985372 0.66195544 0.64166944 0.63801131]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.6\n", "Specificity : 0.3068181818181818\n", "Accuracy: 0.3129346314325452\n", "ROC 0.453409090909\n", "TP 9 FP 488 TN 216 FN 6\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 2, 17, 1, 1, 18, 20, 11, 11, 2, 3, 1, 19, -0.9769230769230768, 28.761538461538464, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7325657894736842\n", "Specificity : 0.6151260504201681\n", "Accuracy: 0.6744804655029094\n", "ROC 0.673845919947\n", "TP 2227 FP 1145 TN 1830 FN 813\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64178856 0.69863697 0.65829731 0.65862986 0.62520785]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.6\n", "Specificity : 0.2911931818181818\n", "Accuracy: 0.29763560500695413\n", "ROC 0.445596590909\n", "TP 9 FP 499 TN 205 FN 6\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 3, 16, 1, 15, 1, 20, 8, 7, 10, 10, 3, 7, -0.9230769230769232, -15.853846153846154, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Failed\n", "TP 0 FP 0 TN 13588 FN 2993\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81298999 0.81455795 0.8185766 0.81845597 0.81791315]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 8, 10, 5, 20, 3, 20, 10, 1, 10, 2, 14, 1, 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"Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.668160462594868\n", "Specificity : 0.8574472775369241\n", "Accuracy: 0.7620244862264975\n", "ROC 0.762803870066\n", "TP 9244 FP 1940 TN 11669 FN 4591\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51608672 0.78152669 0.79991984 0.7948475 0.79331706]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.07692307692307693\n", "Specificity : 0.8430379746835444\n", "Accuracy: 0.7957244655581948\n", "ROC 0.459980525803\n", "TP 2 FP 62 TN 333 FN 24\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 17, 2, 11, 11, 8, 20, 16, 3, 11, 10, 1, 2, -1.0769230769230769, 76.7769230769231, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109776\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test 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0.9620512820512821\n", "Specificity : 0.3363705391040243\n", "Accuracy: 0.7878211227402474\n", "ROC 0.649210910578\n", "TP 13132 FP 3496 TN 1772 FN 518\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.71260175 0.79025267 0.79605646 0.79901676 0.79991542]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.9615384615384616\n", "Specificity : 0.15443037974683543\n", "Accuracy: 0.2042755344418052\n", "ROC 0.557984420643\n", "TP 25 FP 334 TN 61 FN 1\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 20, 13, 11, 19, 19, 20, 11, 4, 14, 17, 0, 0, -1.0090909090909088, -20.9, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102995\n", "Test Data Points: 25749\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.738437653211309\n", "Specificity : 0.6266005477018726\n", "Accuracy: 0.6797545535748961\n", "ROC 0.682519100457\n", "TP 9037 FP 5045 TN 8466 FN 3201\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61984466 0.66897087 0.70428771 0.68339288 0.69356843]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.38461538461538464\n", "Specificity : 0.529113924050633\n", "Accuracy: 0.5201900237529691\n", "ROC 0.456864654333\n", "TP 10 FP 186 TN 209 FN 16\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 7, 19, 2, 8, 18, 20, 15, 2, 4, 3, 20, 20, -0.36153846153846153, 14.384615384615385, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5572368421052631\n", "Specificity : 0.6363025210084033\n", "Accuracy: 0.5963424771404822\n", "ROC 0.596769681557\n", "TP 1694 FP 1082 TN 1893 FN 1346\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59906915 0.58510638 0.58995677 0.59461257 0.60192883]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.5\n", "Specificity : 0.5848101265822785\n", "Accuracy: 0.5795724465558195\n", "ROC 0.542405063291\n", "TP 13 FP 164 TN 231 FN 13\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 7, 10, 16, 17, 17, 20, 10, 3, 11, 14, 13, 11, -0.8923076923076922, 60.0923076923077, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6480263157894737\n", "Specificity : 0.5653781512605042\n", "Accuracy: 0.6071487946799667\n", "ROC 0.606702233525\n", "TP 1970 FP 1293 TN 1682 FN 1070\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60388963 0.58427527 0.60691719 0.58629864 0.60093116]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.46153846153846156\n", "Specificity : 0.4936708860759494\n", "Accuracy: 0.4916864608076009\n", "ROC 0.477604673807\n", "TP 12 FP 200 TN 195 FN 14\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 12, 13, 10, 18, 11, 20, 8, 19, 19, 20, 2, 7, -0.5692307692307692, 4.469230769230773, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47210\n", "Test Data Points: 11803\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.18336741649625085\n", "Specificity : 0.9498252339609877\n", "Accuracy: 0.7592984834364145\n", "ROC 0.566596325229\n", "TP 538 FP 445 TN 8424 FN 2396\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74610302 0.7439634 0.75385528 0.71869175 0.7514828 ]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.11538461538461539\n", "Specificity : 0.8860759493670886\n", "Accuracy: 0.838479809976247\n", "ROC 0.500730282376\n", "TP 3 FP 45 TN 350 FN 23\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 7, 18, 11, 9, 10, 20, 12, 11, 13, 2, 11, 9, -0.4615384615384616, 75.9846153846154, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47257\n", "Test Data Points: 11815\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.11862678450033991\n", "Specificity : 0.9706976219993237\n", "Accuracy: 0.7585272958104105\n", "ROC 0.54466220325\n", "TP 349 FP 260 TN 8613 FN 2593\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75133305 0.75150233 0.75715253 0.74860335 0.75198917]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.07692307692307693\n", "Specificity : 0.9189873417721519\n", "Accuracy: 0.8669833729216152\n", "ROC 0.497955209348\n", "TP 2 FP 32 TN 363 FN 24\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 10, 19, 2, 14, 10, 20, 3, 11, 7, 10, 10, 17, -0.7769230769230769, 102.29230769230772, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6529605263157895\n", "Specificity : 0.6635294117647059\n", "Accuracy: 0.6581878636741479\n", "ROC 0.65824496904\n", "TP 1985 FP 1001 TN 1974 FN 1055\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61602394 0.68101729 0.67442634 0.65580313 0.61938809]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.4230769230769231\n", "Specificity : 0.3924050632911392\n", "Accuracy: 0.39429928741092635\n", "ROC 0.407740993184\n", "TP 11 FP 240 TN 155 FN 15\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 1, 3, 12, 11, 10, 20, 17, 17, 9, 9, 17, 1, -1.6076923076923075, 94.93846153846154, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5305921052631579\n", "Specificity : 0.7852100840336135\n", "Accuracy: 0.6565253532834581\n", "ROC 0.657901094648\n", "TP 1613 FP 639 TN 2336 FN 1427\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64976729 0.6200133 0.67924842 0.65147988 0.63518457]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.2692307692307692\n", "Specificity : 0.5139240506329114\n", "Accuracy: 0.498812351543943\n", "ROC 0.391577409932\n", "TP 7 FP 192 TN 203 FN 19\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [4, 1, 7, 13, 3, 7, 20, 16, 11, 7, 2, 12, 8, -0.37692307692307697, 2.2384615384615394, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.08374875373878365\n", "Specificity : 0.9685381668140289\n", "Accuracy: 0.8079729811229721\n", "ROC 0.526143460276\n", "TP 252 FP 427 TN 13145 FN 2757\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81775419 0.81811603 0.81785283 0.81803378 0.8185766 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.20689655172413793\n", "Specificity : 0.9776297001427892\n", "Accuracy: 0.9671361502347418\n", "ROC 0.592263125933\n", "TP 6 FP 47 TN 2054 FN 23\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 8, 12, 12, 20, 1, 20, 8, 18, 10, 9, 1, 8, -0.9615384615384616, -7.146153846153848, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0029605263157894738\n", "Specificity : 0.9989661029466066\n", "Accuracy: 0.8163560702008322\n", "ROC 0.500963314631\n", "TP 9 FP 14 TN 13527 FN 3031\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.78904837 0.81715113 0.81785283 0.81863691 0.81845597]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.06896551724137931\n", "Specificity : 0.9990480723465016\n", "Accuracy: 0.9863849765258216\n", "ROC 0.534006794794\n", "TP 2 FP 2 TN 2099 FN 27\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 10, 2, 3, 19, 20, 13, 20, 17, 19, 17, 12, 0.16153846153846171, -8.130769230769232, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109772\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6610860699720971\n", "Specificity : 0.8575035271404173\n", "Accuracy: 0.7574697565952485\n", "ROC 0.759294798556\n", "TP 9240 FP 1919 TN 11548 FN 4737\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55983093 0.78636496 0.78570127 0.79492038 0.79957729]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.41379310344827586\n", "Specificity : 0.886244645406949\n", "Accuracy: 0.8798122065727699\n", "ROC 0.650018874428\n", "TP 12 FP 239 TN 1862 FN 17\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 3, 10, 18, 14, 17, 20, 10, 14, 11, 5, 8, 11, -0.7153846153846154, 70.00769230769231, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109776\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.639202705619918\n", "Specificity : 0.8693437661474865\n", "Accuracy: 0.7528057134528494\n", "ROC 0.754273235884\n", "TP 8883 FP 1770 TN 11777 FN 5014\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5269448 0.77693569 0.78811398 0.77764822 0.80224465]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.8900523560209425\n", "Accuracy: 0.8845070422535212\n", "ROC 0.686405488355\n", "TP 14 FP 231 TN 1870 FN 15\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 17, 12, 2, 3, 19, 20, 3, 3, 7, 20, 12, 13, 0.9538461538461538, -8.861538461538462, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75672\n", "Test Data Points: 18919\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9660905229236854\n", "Specificity : 0.34112060778727443\n", "Accuracy: 0.7921666050002643\n", "ROC 0.653605565355\n", "TP 13191 FP 3469 TN 1796 FN 463\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.734394 0.79380517 0.79955598 0.79818163 0.79362478]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.896551724137931\n", "Specificity : 0.2213231794383627\n", "Accuracy: 0.23051643192488264\n", "ROC 0.558937451788\n", "TP 26 FP 1636 TN 465 FN 3\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 6, 20, 10, 16, 17, 20, 11, 7, 2, 20, 10, 12, -0.5846153846153845, -2.3307692307692305, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102901\n", "Test Data Points: 25726\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6959967320261438\n", "Specificity : 0.6523060952098473\n", "Accuracy: 0.6730933685765373\n", "ROC 0.674151413618\n", "TP 8519 FP 4689 TN 8797 FN 3721\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60180362 0.69785431 0.68894072 0.68691934 0.69372206]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.3448275862068966\n", "Specificity : 0.6506425511661114\n", "Accuracy: 0.6464788732394366\n", "ROC 0.497735068687\n", "TP 10 FP 734 TN 1367 FN 19\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 10, 20, 7, 11, 1, 20, 11, 1, 18, 10, 11, 10, -1.3230769230769233, 38.98461538461538, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4832236842105263\n", "Specificity : 0.7132773109243697\n", "Accuracy: 0.5970074812967581\n", "ROC 0.598250497567\n", "TP 1469 FP 853 TN 2122 FN 1571\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59823803 0.5802859 0.59062188 0.60143 0.60276023]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.7339362208472157\n", "Accuracy: 0.7305164319248826\n", "ROC 0.608347420768\n", "TP 14 FP 559 TN 1542 FN 15\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 3, 15, 18, 12, 2, 20, 2, 18, 3, 17, 11, 8, 0.0461538461538464, 53.67692307692308, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.3105263157894737\n", "Specificity : 0.8077310924369748\n", "Accuracy: 0.5564422277639235\n", "ROC 0.559128704113\n", "TP 944 FP 572 TN 2403 FN 2096\n", "\n", "\n", "\n", "None\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cross: Validation: [ 0.59674202 0.59242021 0.61207183 0.58696375 0.60242767]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.41379310344827586\n", "Specificity : 0.8181818181818182\n", "Accuracy: 0.8126760563380282\n", "ROC 0.615987460815\n", "TP 12 FP 382 TN 1719 FN 17\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 2, 2, 2, 2, 17, 20, 8, 2, 11, 0, 0, 0, -0.2, 20.720000000000002, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47229\n", "Test Data Points: 11808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.3496073745305565\n", "Specificity : 0.8558396215790066\n", "Accuracy: 0.7302676151761518\n", "ROC 0.602723498055\n", "TP 1024 FP 1280 TN 7599 FN 1905\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74864499 0.75482724 0.75074109 0.74455831 0.74701448]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.8524512137077582\n", "Accuracy: 0.8474178403755869\n", "ROC 0.667604917199\n", "TP 14 FP 310 TN 1791 FN 15\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 3, 7, 12, 4, 8, 20, 19, 9, 10, 18, 7, 9, -1.6, 16.86923076923077, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47285\n", "Test Data Points: 11822\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.20950764006791173\n", "Specificity : 0.9359017686155232\n", "Accuracy: 0.7549484012857385\n", "ROC 0.572704704342\n", "TP 617 FP 569 TN 8308 FN 2328\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75097276 0.75241076 0.75188224 0.75137467 0.7508671 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.3448275862068966\n", "Specificity : 0.9300333174678724\n", "Accuracy: 0.9220657276995305\n", "ROC 0.637430451837\n", "TP 10 FP 147 TN 1954 FN 19\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 20, 20, 10, 1, 20, 1, 16, 18, 8, 20, 17, 0, -1.6583333333333332, 8.466666666666667, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7223684210526315\n", "Specificity : 0.5858823529411765\n", "Accuracy: 0.6548628428927681\n", "ROC 0.654125386997\n", "TP 2196 FP 1232 TN 1743 FN 844\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61884973 0.68417553 0.65314267 0.64183572 0.6232125 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.7931034482758621\n", "Specificity : 0.3683960019038553\n", "Accuracy: 0.3741784037558685\n", "ROC 0.58074972509\n", "TP 23 FP 1327 TN 774 FN 6\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 7, 13, 12, 12, 15, 20, 2, 14, 18, 18, 1, 10, 0.20769230769230784, -0.4538461538461551, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6243421052631579\n", "Specificity : 0.7075630252100841\n", "Accuracy: 0.6655029093931837\n", "ROC 0.665952565237\n", "TP 1898 FP 870 TN 2105 FN 1142\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.62965426 0.67869016 0.64715663 0.66711008 0.61240439]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.6896551724137931\n", "Specificity : 0.44407425035697284\n", "Accuracy: 0.44741784037558685\n", "ROC 0.566864711385\n", "TP 20 FP 1168 TN 933 FN 9\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 9, 18, 7, 2, 20, 1, 8, 1, 17, 9, 18, -2.3, 20.915384615384614, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.13507340946166393\n", "Specificity : 0.9426605504587156\n", "Accuracy: 0.7933779627284241\n", "ROC 0.53886697996\n", "TP 414 FP 775 TN 12741 FN 2651\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81805572 0.81624653 0.81863691 0.81869723 0.8185766 ]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.10714285714285714\n", "Specificity : 0.9498069498069498\n", "Accuracy: 0.9276315789473685\n", "ROC 0.528474903475\n", "TP 3 FP 52 TN 984 FN 25\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 17, 10, 20, 10, 10, 20, 10, 10, 1, 10, 18, 10, -1.2384615384615385, 72.66153846153847, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.002711864406779661\n", "Specificity : 0.9988995671630841\n", "Accuracy: 0.8216633496170316\n", "ROC 0.500805715785\n", "TP 8 FP 15 TN 13616 FN 2942\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.8121457 0.81847787 0.81103739 0.81694813 0.81302774]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1036 FN 28\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 20, 11, 2, 12, 7, 20, 11, 20, 7, 2, 10, 7, -1.0384615384615385, 21.830769230769235, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109775\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6144630484988453\n", "Specificity : 0.8990285546070061\n", "Accuracy: 0.7553563620463489\n", "ROC 0.756745801553\n", "TP 8514 FP 1372 TN 12216 FN 5342\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.52745491 0.78367644 0.80523266 0.79328062 0.83081296]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.25\n", "Specificity : 0.917953667953668\n", "Accuracy: 0.900375939849624\n", "ROC 0.583976833977\n", "TP 7 FP 85 TN 951 FN 21\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 9, 5, 13, 3, 18, 20, 11, 20, 13, 20, 2, 3, 0.5384615384615384, 32.330769230769235, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109760\n", "Test Data Points: 27440\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6762538456034914\n", "Specificity : 0.8212879744484884\n", "Accuracy: 0.7474125364431486\n", "ROC 0.748770910026\n", "TP 9452 FP 2406 TN 11057 FN 4525\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.52858861 0.7794541 0.79438776 0.78198914 0.80884872]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.35714285714285715\n", "Specificity : 0.86003861003861\n", "Accuracy: 0.8468045112781954\n", "ROC 0.608590733591\n", "TP 10 FP 145 TN 891 FN 18\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 20, 1, 9, 9, 3, 20, 10, 2, 13, 7, 12, 2, -0.0999999999999999, 8.915384615384614, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102932\n", "Test Data Points: 25733\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5642664045219956\n", "Specificity : 0.7691113411208044\n", "Accuracy: 0.6719387556833638\n", "ROC 0.666688872821\n", "TP 6888 FP 3123 TN 10403 FN 5319\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59928499 0.68578534 0.68950375 0.6805534 0.68634385]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.2857142857142857\n", "Specificity : 0.7799227799227799\n", "Accuracy: 0.7669172932330827\n", "ROC 0.532818532819\n", "TP 8 FP 228 TN 808 FN 20\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [4, 9, 17, 17, 3, 17, 20, 6, 3, 9, 17, 9, 3, -1.2923076923076924, 66.03846153846155, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102978\n", "Test Data Points: 25745\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7093996062992126\n", "Specificity : 0.6549841363535749\n", "Accuracy: 0.6807535443775491\n", "ROC 0.682191871326\n", "TP 8649 FP 4676 TN 8877 FN 3543\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59826769 0.69893183 0.67363269 0.69037446 0.69371504]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.4642857142857143\n", "Specificity : 0.6573359073359073\n", "Accuracy: 0.6522556390977443\n", "ROC 0.560810810811\n", "TP 13 FP 355 TN 681 FN 15\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 3, 3, 10, 12, 17, 20, 2, 18, 10, 3, 9, 11, -0.11538461538461542, 24.04615384615385, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done training\n", "Test Results\n", "Sensitivity: 0.6638157894736842\n", "Specificity : 0.5277310924369748\n", "Accuracy: 0.5965087281795511\n", "ROC 0.595773440955\n", "TP 2018 FP 1405 TN 1570 FN 1022\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59142287 0.58859707 0.59527769 0.6084137 0.56767542]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.6428571428571429\n", "Specificity : 0.553088803088803\n", "Accuracy: 0.5554511278195489\n", "ROC 0.597972972973\n", "TP 18 FP 463 TN 573 FN 10\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 7, 19, 7, 17, 2, 20, 17, 1, 7, 7, 9, 7, -2.9769230769230766, 14.715384615384613, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.20296052631578948\n", "Specificity : 0.894453781512605\n", "Accuracy: 0.5449709060681629\n", "ROC 0.548707153914\n", "TP 617 FP 314 TN 2661 FN 2423\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59790559 0.58793218 0.58546724 0.59843698 0.59860326]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.14285714285714285\n", "Specificity : 0.8996138996138996\n", "Accuracy: 0.8796992481203008\n", "ROC 0.521235521236\n", "TP 4 FP 104 TN 932 FN 24\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 12, 5, 18, 3, 1, 20, 4, 19, 3, 0, 0, 0, 0.7699999999999998, 0.5099999999999998, 0.1]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47224\n", "Test Data Points: 11807\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.11885245901639344\n", "Specificity : 0.9724068025678567\n", "Accuracy: 0.7607351571101889\n", "ROC 0.545629630792\n", "TP 348 FP 245 TN 8634 FN 2580\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75209621 0.75192682 0.74606132 0.74733187 0.75459551]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.07142857142857142\n", "Specificity : 0.9594594594594594\n", "Accuracy: 0.9360902255639098\n", "ROC 0.515444015444\n", "TP 2 FP 42 TN 994 FN 26\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 3, 8, 19, 13, 2, 20, 3, 2, 19, 8, 17, 17, -0.8846153846153846, 86.65384615384616, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47241\n", "Test Data Points: 11811\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0126193724420191\n", "Specificity : 0.9979727446784548\n", "Accuracy: 0.7533655067310134\n", "ROC 0.50529605856\n", "TP 37 FP 18 TN 8861 FN 2895\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75175684 0.74024215 0.75969517 0.75173582 0.75004234]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 1031 FN 28\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 9, 3, 11, 8, 7, 20, 12, 19, 14, 20, 20, 6, -0.9230769230769229, 40.815384615384616, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6983552631578948\n", "Specificity : 0.6285714285714286\n", "Accuracy: 0.6638403990024938\n", "ROC 0.663463345865\n", "TP 2123 FP 1105 TN 1870 FN 917\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63663564 0.68833112 0.66029265 0.63219155 0.631859 ]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.6785714285714286\n", "Specificity : 0.36003861003861004\n", "Accuracy: 0.3684210526315789\n", "ROC 0.519305019305\n", "TP 19 FP 663 TN 373 FN 9\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 3, 18, 7, 9, 13, 20, 10, 12, 1, 10, 10, 7, -0.2307692307692308, 50.54615384615384, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5976973684210526\n", "Specificity : 0.7347899159663865\n", "Accuracy: 0.6655029093931837\n", "ROC 0.666243642194\n", "TP 1817 FP 789 TN 2186 FN 1223\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63181516 0.6775266 0.67342867 0.64516129 0.63119388]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.5714285714285714\n", "Specificity : 0.47586872586872586\n", "Accuracy: 0.4783834586466165\n", "ROC 0.523648648649\n", "TP 16 FP 543 TN 493 FN 12\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"T\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"T\")\n", " del x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ledeprogram/algorithms	class2/donow/schuetz_rebecca_2_donow.ipynb	1	1321	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import pi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sphere_vol(r):\n", " r = abs(r)\n", " return 4/3 * pi * r ** 3 " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4188.790204786391" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sphere_vol(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
crocha700/dp_spectra	altimeter/plot_spectra_altika.ipynb	1	214978	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n", "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n", "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n" ] } ], "source": [ "import numpy as np\n", "from numpy import pi\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import seawater as sw\n", "\n", "#import seaborn as sns\n", "#sns.set(style=\"darkgrid\")\n", "#sns.set(style=\"whitegrid\")\n", "\n", "from pyspec import spectrum\n", "from plots_aux import " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams.update({'font.size': 12\n", " , 'legend.markerscale': 1., 'axes.titlesize': 12, 'axes.labelsize' : 12,\n", " 'legend.fontsize' : 10,'legend.handlelength': 3})\n", "\n", "plt.rc('xtick', labelsize=12) \n", "plt.rc('ytick', labelsize=12)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "color2 = '#6495ed'\n", "color1 = '#ff6347'\n", "color5 = '#8470ff'\n", "color3 = '#3cb371'\n", "color4 = '#ffd700'\n", "color6 = '#ba55d3'\n", "lw1=1\n", "aph=.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load SSH and geostrophic vel. spectra calculated by Sarah Gille\n", "\n", "column 1: wavenumber (in cycles per km)\n", "\n", "column 2: descending track spectrum\n", "\n", "column 3: ascending track spectrum\n", "\n", "column 4: descending track lower error bar\n", "\n", "column 5: descending track upper error bar\n", "\n", "column 6: ascending track lower error bar\n", "\n", "column 7: ascending track upper error bar" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "spec = np.loadtxt('spec/spec_altika_dp.dat')\n", "spec_vel = np.loadtxt('spec/plot_vdata1.dat')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = spec[:,0]\n", "kv = spec_vel[:,0]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## -2 and -3 slopes in the loglog space\n", "ks = np.array([1.e-3,1])\n", "Es2 = .1e-3(ks*(-3/2.))\n", "Es4 = 1.e-9(ks*(-4))\n", "Es5 = 1.e-11(ks*(-5))\n", "rd1 = 22.64 # [km]\n", "Enoise = np.ones(2)2.1.e-4\n", "\n", "def add_second_axis(ax1):\n", " \"\"\" Add a x-axis at the top of the spectra figures \"\"\"\n", " ax2 = ax1.twiny() \n", " ax2.set_xscale('log')\n", " ax2.set_xlim(ax1.axis()[0], ax1.axis()[1])\n", " kp = 1./np.array([500.,200.,100.,40.,20.,10.,5.])\n", " lp=np.array([500,200,100,40,20,10,5])\n", " ax2.set_xticks(kp)\n", " ax2.set_xticklabels(lp)\n", " plt.xlabel('Wavelength [km]')\n", " \n", "f0 = sw.f(59)\n", "A = (9.81/f0)2/(1.e6)\n", "slab1=np.load('../adcp/outputs/adcp_spec_slab1.npz')\n", "\n", "win = np.hanning(k.size)\n", "fac = (k.size/(win2).sum())" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sBNRUY6ytJriuBmoriC/OQ6or0NdUYDUGUBkaysQ+0bzS9ySjTpSzef8QYkIq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\nWkXyjkez1LWI+ADvAg85vsAmNWmWem5PdKrRqDqWPHgAecDQqjVWIrI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"text/plain": [ "<matplotlib.figure.Figure at 0x115b20b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dk = 5.649717514124294\n", "\n", "lw = 1.\n", "\n", "Essh = fac(slab1['Eu']+slab1['Ev'])/(A((2pislab1['k'])2))/2.\n", "Esshu = fac(slab1['Euu']+slab1['Evu'])/(A((2pislab1['k'])2))/2.\n", "Esshl = fac(slab1['Eul']+slab1['Evl'])/(A((2pislab1['k'])2))/2.\n", "\n", "fig = plt.figure(figsize=(8.27/2-.25,11.69/3-.25))\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.fill_between(k,facspec[:,3]/dk,facspec[:,4]/dk, color=color1, alpha=0.25)\n", "ax1.fill_between(k,facspec[:,5]/dk,facspec[:,6]/dk, color=color2, alpha=0.25)\n", "ax1.fill_between(slab1['k'],Esshl,Esshu, color=color3, alpha=0.25)\n", "\n", "ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(k,facspec[:,1]/dk,color=color1,linewidth=lw,label=\"Descending\")\n", "ax1.loglog(k,facspec[:,2]/dk,color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "ax1.loglog(slab1['k'],Essh,color=color3,linewidth=lw,label=\"from ADCP\")\n", "\n", "\n", "ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "ax1.loglog(ks,Es2/4.,'-', color='0.5',linewidth=1.)\n", "ax1.loglog(ks,Es5/5.,'-', color='0.5',linewidth=1.)\n", "\n", "plt.text(0.0011, 3.1/10/2.,u'-3/2',fontsize=12)\n", "plt.text(0.005675/3.25, 16.51,u'-5',fontsize=12)\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'SSH variance density [m$^{2}$/ cpkm]')\n", " \n", "#plt_spec_error(sn=158)\n", " \n", "#lg = plt.legend(loc=(.35,.65), numpoints=1,ncol=1)\n", "lg = plt.legend(loc=3, numpoints=1,ncol=1)\n", "#lg.set_title(r\"SSH variance spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "plt.axis((1./1.e3,1./4.,1./1.e4,5.e1))\n", "\n", "plt.text(1./15, 18., \"AltiKa\", size=8, rotation=0.,\n", " ha=\"center\", va=\"center\",\n", " bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nJSXx6KOPsmHDBp588knGjx9Pv3798Pv9mBnr1q0jKyuLwYMHs3r16irjOPXU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ikmBeuRqMXu9iSvvjPI/kH3Ce+70B/lY+dnYnm8LMx3UCQrtgghxLxypncSRWs9emSZxfcd3zeb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\nSyR9ErgW2Eb6ewHTImJnuVGZg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"text/plain": [ "<matplotlib.figure.Figure at 0x1154d86d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12.,10.))\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.fill_between(kv,spec_vel[:,3],spec_vel[:,4], color=color1, alpha=0.25)\n", "ax1.fill_between(kv,spec_vel[:,5],spec_vel[:,6], color=color2, alpha=0.25)\n", "\n", "ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(kv,spec_vel[:,1],color=color1,linewidth=lw,label=\"Descending\")\n", "ax1.loglog(kv,spec_vel[:,2],color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "#ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "#ax1.loglog(ks,Es2,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es5,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es4,'--', color='k',linewidth=2.,alpha=.7)\n", "\n", "#plt.text(0.0011, 3.1,u'k$^{-3/2}$',fontsize=25)\n", "#plt.text(0.0047, 5.51,u'k$^{-5}$',fontsize=25)\n", "#plt.text(0.002, 5.51,u'k$^{-4}$',fontsize=25)\n", "\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'SSH variance spectral density [m$^{2}$/ cpkm]')\n", " \n", "lg = plt.legend(loc=(.5,.75), numpoints=1,ncol=2)\n", "lg.set_title(r\"SSH variance spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "#plt.axis((1./1.e3,1./4.,1./1.e3,1.e1))\n", "\n", "plt.text(1./15, 5., \"AltiKa\", size=25, rotation=0.,\n", " ha=\"center\", va=\"center\",\n", " bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc_vel',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps_vel',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc_vel.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f0 = sw.f(59)\n", "A = (9.81/f0)2/(1.e6)\n", "slab1=np.load('../adcp/outputs/adcp_spec_slab1.npz')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tIHTGyc4d0otP+QC6cE7uc0aOl046Szr2NKl3n9LWBwAA0ApCZ1yteVN65l/Ss49Km95q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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112c8bc10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(11.,12.))\n", "ax1 = fig.add_subplot(111)\n", "ax1.loglog(k,(spec[:,1]),color=color1,linewidth=lw,label=\"Descending\")\n", "\n", "#plt.axis((1./1.e3,1.,1./1.e4,1.e0))\n", "\n", "add_second_axis(ax1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Mvet16tShQ4cOWGt59tlnOXXqlEdjDA0NpWvXrlhrefXVV7PtmVu3bh2fffYZ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\n7Xp+K8KtFI0p0FYTLux2iZ6Nd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"text/plain": [ "<matplotlib.figure.Figure at 0x114a70850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "fig = plt.figure(figsize=(12.,10.))\n", "ax1 = fig.add_subplot(111)\n", "\n", "#ax1.fill_between(kv,spec_vel[:,3],spec_vel[:,4], color=color1, alpha=0.25)\n", "#ax1.fill_between(kv,spec_vel[:,5],spec_vel[:,6], color=color2, alpha=0.25)\n", "\n", "#ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(kv,spec_vel[:,1],color=color1,linewidth=lw,label=\"Geostrophic vel.\")\n", "#ax1.loglog(kv,spec_vel[:,2],color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "#ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "#ax1.loglog(ks,Es2,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es5,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es4,'--', color='k',linewidth=2.,alpha=.7)\n", "\n", "#plt.text(0.0011, 3.1,u'k$^{-3/2}$',fontsize=25)\n", "#plt.text(0.0047, 5.51,u'k$^{-5}$',fontsize=25)\n", "#plt.text(0.002, 5.51,u'k$^{-4}$',fontsize=25)\n", "\n", "ax1.loglog(k,A((2pik)*2)(spec[:,1]),color=color2,linewidth=lw,label=\"from SSH spectrum\")\n", "ax1.loglog(slab1['k'],slab1['Eu'],color=color3,linewidth=lw,label=\"ADCP\")\n", "\n", "\n", "\n", "\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'KE psectrum [m$^{2}$ s$^{-2}$/ cpkm]')\n", " \n", "lg = plt.legend(loc=2, numpoints=1,ncol=2)\n", "lg.set_title(r\"Across-track KE spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "#plt.axis((1./1.e3,1./4.,1./1.e3,1.e1))\n", "\n", "#plt.text(1./15, 5., \"AltiKa\", size=25, rotation=0.,\n", "# ha=\"center\", va=\"center\",\n", "# bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc_vel',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps_vel',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc_vel.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Eul',\n", " 'Evl',\n", " 'Es2',\n", " 'k',\n", " 'kK1',\n", " 'Kpsi',\n", " 'ks',\n", " 'Eu',\n", " 'Ev1',\n", " 'Euu',\n", " 'Es3',\n", " 'Ew1',\n", " 'Ev',\n", " 'Kphi',\n", " 'Evu',\n", " 'Enoise']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
MarjorieH/camera_model	python/notebooks/g2i_collinear.ipynb	3	3300	{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import sin, cos, tan, pi\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ground point, should produce image point 400 samples and 500 lines\n", "X = 1116890\n", "Y = -1604470\n", "Z = 1459570\n", "\n", "# Hardcoded values from json\n", "# some set to 0 for simplicity\n", "omega = 2.256130940792258\n", "phi = 0.09433201631102328\n", "kappa = -0.9630375478615623\n", "focal_length = 549.1178195372703\n", "pixel_pitch = 0\n", "principal_point_x = 0\n", "principal_point_y = 0\n", "spacecraft_x = 1728357.7031238307\n", "spacecraft_y = -2088409.0061042644\n", "spacecraft_z = 2082873.9280557402" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rotation_from_opk(o, p, k):\n", " om = np.empty((3,3))\n", " om[:,0] = [1,0,0]\n", " om[:,1] = [0, cos(o), -sin(o)]\n", " om[:,2] = [0, sin(o), cos(o)]\n", " \n", " pm = np.empty((3,3))\n", " pm[:,0] = [cos(p), 0, sin(p)]\n", " pm[:,1] = [0,1,0]\n", " pm[:,2] = [-sin(p), 0, cos(p)]\n", " \n", " km = np.empty((3,3))\n", " km[:,0] = [cos(k), -sin(k), 0]\n", " km[:,1] = [sin(k), cos(k), 0]\n", " km[:,2] = [0,0,1]\n", " \n", " return km.dot(pm).dot(om)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample: 4337.42884664\n", "Line: -13809.8886083\n" ] } ], "source": [ "rm = np.empty((3, 3))\n", "rm[:3, :3] = rotation_from_opk(omega, phi, kappa)\n", "\n", "# Collinearity equations\n", "samp = -focal_length * ((rm[0, 0] * (X - spacecraft_x) + rm[0, 1] * (Y - spacecraft_y) + rm[0, 2] * (Z - spacecraft_z))/(rm[2, 0] * (X - spacecraft_x) + rm[2, 1] * (Y - spacecraft_y) + rm[2, 2] * (Z - spacecraft_z))) + principal_point_x\n", "line = -focal_length * ((rm[1, 0] * (X - spacecraft_x) + rm[1, 1] * (Y - spacecraft_y) + rm[1, 2] * (Z - spacecraft_z))/(rm[2, 0] * (X - spacecraft_x) + rm[2, 1] * (Y - spacecraft_y) + rm[2, 2] * (Z - spacecraft_z))) + principal_point_y\n", "\n", "# Image point\n", "print(\"Sample: \", samp)\n", "print(\"Line: \", line)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	unlicense
briennakh/BIOF509	Wk08/Wk08_Numpy_model_package_survey_inclass_exercises.ipynb	1	128890	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Week 8 - Implementing a model in numpy and a survey of machine learning packages for python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This week we will be looking in detail at how to implement a supervised regression model using the base scientific computing packages available with python.\n", "\n", "We will also be looking at the different packages available for python that implement many of the algorithms we might want to use." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression with numpy\n", "\n", "Why implement algorithms from scratch when dedicated packages already exist? \n", "\n", "The packages available are very powerful and a real time saver but they can obscure some issues we might encounter if we don't know to look for them. By starting with just numpy these problems will be more obvious. We can address them here and then when we move on we will know what to look for and will be less likely to miss them.\n", "\n", "The dedicated machine learning packages implement the different algorithms but we are still responsible for getting our data in a suitable format." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048ddecc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 20\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", "\n", "\n", "plt.plot(x, y, 'b.')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a very simple dataset. There is only one input value for each record and then there is the output value. Our goal is to determine the output value or dependent variable, shown on the y-axis, from the input or independent variable, shown on the x-axis.\n", "\n", "Our approach should scale to handle multiple input, or independent, variables. The independent variables can be stored in a vector, a 1-dimensional array:\n", "\n", "$$X^T = (X_{1}, X_{2}, X_{3})$$\n", "\n", "As we have multiple records these can be stacked in a 2-dimensional array. Each record becomes one row in the array. Our `x` variable is already set up in this way.\n", "\n", "In linear regression we can compute the value of the dependent variable using the following formula:\n", "\n", "$$f(X) = \\beta_{0} + \\sum_{j=1}^p X_j\\beta_j$$\n", "\n", "The $\\beta_{0}$ term is the intercept, and represents the value of the dependent variable when the independent variable is zero.\n", "\n", "Calculating a solution is easier if we don't treat the intercept as special. Instead of having an intercept co-efficient that is handled separately we can instead add a variable to each of our records with a value of one." ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.44929165],\n", " [ 1. , 0.30446841],\n", " [ 1. , 0.83918912],\n", " [ 1. , 0.23774183],\n", " [ 1. , 0.50238946],\n", " [ 1. , 0.9425836 ],\n", " [ 1. , 0.6339977 ],\n", " [ 1. , 0.86728941],\n", " [ 1. , 0.94020969],\n", " [ 1. , 0.75076486],\n", " [ 1. , 0.69957506],\n", " [ 1. , 0.96796557],\n", " [ 1. , 0.99440079],\n", " [ 1. , 0.45182168],\n", " [ 1. , 0.07086978],\n", " [ 1. , 0.29279403],\n", " [ 1. , 0.15235471],\n", " [ 1. , 0.41748637],\n", " [ 1. , 0.13128933],\n", " [ 1. , 0.6041178 ]])" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intercept_x = np.hstack((np.ones((n,1)), x))\n", "intercept_x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Numpy contains the linalg module](http://docs.scipy.org/doc/numpy/reference/routines.linalg.html) with many common functions for performing linear algebra. Using this module finding a solution is quite simple." ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([[ 3.8217988 ],\n", " [ 6.15768399]]),\n", " array([ 8.50971729]),\n", " 2,\n", " array([ 5.17434252, 1.146272 ]))" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.lstsq(intercept_x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values returned are:\n", "\n", "* The least-squares solution\n", "* The sum of squared residuals\n", "* The rank of the independent variables\n", "* The singular values of the independent variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "1. Calculate the predictions our model would make\n", "2. Calculate the sum of squared residuals from our predictions. Does this match the value returned by lstsq?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coeff, residuals, rank, sing_vals = np.linalg.lstsq(intercept_x,y)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((20, 2), (1, 2))" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intercept_x.shape, coeff.T.shape" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6.5883948 , 5.69661904, 8.98926023, 5.28573784, 6.91535432,\n", " 9.62593075, 7.72575628, 9.16229289, 9.61131296, 8.44477158,\n", " 8.12956095, 9.78222488, 9.94500463, 6.60397395, 4.2581925 ,\n", " 5.62473192, 4.75995094, 6.39254797, 4.630237 , 7.54176534])" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(intercept_x * coeff.T, axis=1)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predictions = np.sum(intercept_x * coeff.T, axis=1)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTs6oVjv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"text/plain": [ "<matplotlib.figure.Figure at 0x2004a156ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'bo')\n", "plt.plot(x, predictions, 'ko')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20,)" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions.shape" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(8.5097172851712308, array([ 8.50971729]))" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum((predictions.reshape((20,1)) - y) ** 2), residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Least squares refers to the cost function for this algorithm. The objective is to minimize the residual sum of squares. The difference between the actual and predicted values is calculated, it is squared and then summed over all records. The function is as follows:\n", "\n", "$$RSS(\\beta) = \\sum_{i=1}^{N}(y_i - x_i^T\\beta)^2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matrix arithmetic\n", "\n", "Within lstsq all the calculations are performed using matrix arithmetic rather than the more familiar element-wise arithmetic numpy arrays generally perform. Numpy does have a matrix type but matrix arithmetic can also be performed on standard arrays using dedicated methods.\n", "\n", "![Matrix multiplication](https://upload.wikimedia.org/wikipedia/commons/e/eb/Matrix_multiplication_diagram_2.svg)\n", "_Source: Wikimedia Commons (User:Bilou)_\n", "\n", "In matrix multiplication the resulting value in any position is the sum of multiplying each value in a row in the first matrix by the corresponding value in a column in the second matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residual sum of squares can be calculated with the following formula:\n", "\n", "$$RSS(\\beta) = (y - X\\beta)^T(y-X\\beta)$$\n", "\n", "The value of our co-efficients can be calculated with:\n", "\n", "$$\\hat\\beta = (X^TX)^{-1}X^Ty$$\n", "\n", "Unfortunately, the result is not as visually appealing as in languages that use matrix arithmetic by default." ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 3.8217988 ]\n", " [ 6.15768399]] \n", " [[ 3.8217988 ]\n", " [ 6.15768399]]\n" ] } ], "source": [ "our_coeff = np.dot(np.dot(np.linalg.inv(np.dot(intercept_x.T, intercept_x)), intercept_x.T), y)\n", "\n", "print(coeff, '\\n', our_coeff)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 6.5883948 , 5.69661904, 8.98926023, 5.28573784, 6.91535432,\n", " 9.62593075, 7.72575628, 9.16229289, 9.61131296, 8.44477158,\n", " 8.12956095, 9.78222488, 9.94500463, 6.60397395, 4.2581925 ,\n", " 5.62473192, 4.75995094, 6.39254797, 4.630237 , 7.54176534]),\n", " array([[ 6.5883948 ],\n", " [ 5.69661904],\n", " [ 8.98926023],\n", " [ 5.28573784],\n", " [ 6.91535432],\n", " [ 9.62593075],\n", " [ 7.72575628],\n", " [ 9.16229289],\n", " [ 9.61131296],\n", " [ 8.44477158],\n", " [ 8.12956095],\n", " [ 9.78222488],\n", " [ 9.94500463],\n", " [ 6.60397395],\n", " [ 4.2581925 ],\n", " [ 5.62473192],\n", " [ 4.75995094],\n", " [ 6.39254797],\n", " [ 4.630237 ],\n", " [ 7.54176534]]))" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "our_predictions = np.dot(intercept_x, our_coeff)\n", "\n", "predictions, our_predictions" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048f5be10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'ko', label='True values')\n", "plt.plot(x, our_predictions, 'ro', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(12).reshape((3,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "1. Plot the residuals. The x axis will be the independent variable (x) and the y axis the residual between our prediction and the true value.\n", "2. Plot the predictions generated for our model over the entire range of 0-1. One approach is to use the np.linspace method to create equally spaced values over a specified range." ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048f62400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y - our_predictions, 'ko')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 2) (2, 1)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048d69d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'ko', label='True values')\n", "\n", "all_x = np.linspace(0, 1, 1000).reshape((1000,1))\n", "intercept_all_x = np.hstack((np.ones((1000,1)), all_x))\n", "\n", "print(intercept_all_x.shape, our_coeff.shape)\n", "\n", "#all_x_predictions = np.dot(intercept_all_x, our_coeff)\n", "all_x_predictions = np.sum(intercept_all_x * our_coeff.T, axis=1)\n", "\n", "plt.plot(all_x, all_x_predictions, 'r-', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types of independent variable\n", "\n", "The independent variables can be many different types.\n", "\n", "* Quantitative inputs\n", "* Categorical inputs coded using dummy values\n", "* Interactions between multiple inputs\n", "* Tranformations of other inputs, e.g. logs, raised to different powers, etc.\n", "\n", "It is important to note that a _linear_ model is only _linear_ with respect to its inputs. Those input variables can take any form.\n", "\n", "One approach we can take to improve the predictions from our model would be to add in the square, cube, etc of our existing variable." ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2.48528927e+05]\n", " [ -1.71103499e+07]\n", " [ 4.65315707e+08]\n", " [ -6.91480377e+09]\n", " [ 6.44385305e+10]\n", " [ -4.06133878e+11]\n", " [ 1.81054068e+12]\n", " [ -5.85359860e+12]\n", " [ 1.38391083e+13]\n", " [ -2.36324769e+13]\n", " [ 2.76241766e+13]\n", " [ -1.78960448e+13]\n", " [ -3.36195922e+12]\n", " [ 2.22108078e+13]\n", " [ -2.64916311e+13]\n", " [ 1.80609411e+13]\n", " [ -7.62315594e+12]\n", " [ 1.86509989e+12]\n", " [ -2.03646141e+11]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048da9e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_expanded = np.hstack((x*i for i in range(1,20)))\n", "\n", "b, residuals, rank, s = np.linalg.lstsq(x_expanded, y)\n", "print(b)\n", "\n", "plt.plot(x, y, 'ko', label='True values')\n", "plt.plot(x, np.dot(x_expanded, b), 'ro', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a tradeoff with model complexity. As we add more complexity to our model we can fit our training data increasingly well but eventually will lose our ability to generalize to new data.\n", "\n", "Very simple models __underfit__ the data and have high __bias__.\n", "\n", "Very complex models __overfit__ the data and have high __variance__.\n", "\n", "The goal is to detect true sources of variation in the data and ignore variation that is just noise.\n", "\n", "How do we know if we have a good model? A common approach is to break up our data into a training set, a validation set, and a test set. \n", "\n", " We train models with different parameters on the training set.\n", "* We evaluate each model on the validation set, and choose the best\n", "* We then measure the performance of our best model on the test set.\n", "\n", "__What would our best model look like?__ Because we are using dummy data here we can easily make more." ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048d64048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 20\n", "p = 12\n", "training = []\n", "val = []\n", "for i in range(1, p):\n", " np.random.seed(0)\n", " x = np.random.random((n,1))\n", " y = 5 + 6 * x 2 + np.random.normal(0,0.5, size=(n,1))\n", " x = np.hstack((xj for j in np.arange(i)))\n", " our_coeff = np.dot(\n", " np.dot(\n", " np.linalg.inv(\n", " np.dot(\n", " x.T, x\n", " )\n", " ), x.T\n", " ), y\n", " )\n", " our_predictions = np.dot(x, our_coeff)\n", " our_training_rss = np.sum((y - our_predictions) ** 2)\n", " training.append(our_training_rss)\n", " \n", " val_x = np.random.random((n,1))\n", " val_y = 5 + 6 * val_x 2 + np.random.normal(0,0.5, size=(n,1))\n", " val_x = np.hstack((val_xj for j in np.arange(i)))\n", " our_val_pred = np.dot(val_x, our_coeff)\n", " our_val_rss = np.sum((val_y - our_val_pred) ** 2)\n", " val.append(our_val_rss)\n", " #print(i, our_training_rss, our_val_rss)\n", "\n", "plt.plot(range(1, p), training, 'ko-', label='training')\n", "plt.plot(range(1, p), val, 'ro-', label='validation')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient descent\n", "\n", "One limitation of our current implementation is that it is resource intensive. For very large datasets an alternative is needed. Gradient descent is often preferred, and particularly stochastic gradient descent for very large datasets.\n", "\n", "Gradient descent is an iterative process, repetitively calculating the error and changing the coefficients slightly to reduce that error. It does this by calculating a gradient and then descending to a minimum in small steps.\n", "\n", "Stochastic gradient descent calculates the gradient on a small batch of the data, updates the coefficients, loads the next chunk of the data and repeats the process.\n", "\n", "We will just look at a basic gradient descent model." ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lstsq [[ 4.07959732]\n", " [ 5.69608619]]\n", "[[ 4.08212025]\n", " [ 5.69136537]] [10215.151832573791, 151.49738175162241, 120.99886225509709, 104.87099032951339, 96.334352755009746, 91.815828439379132, 89.424130090614341, 88.158181167489815, 87.488102235174722, 87.13342301602863, 86.945687896436965, 86.846317893274772, 86.793720395534692, 86.765880034455108, 86.751143863756042, 86.743343866972538, 86.739215253707584, 86.737029939025462, 86.735873230960678, 86.735260974264634]\n" ] } ], "source": [ "np.random.seed(0)\n", "n = 200\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", "intercept_x = np.hstack((np.ones((n,1)), x))\n", "coeff, residuals, rank, sing_vals = np.linalg.lstsq(intercept_x,y)\n", "print('lstsq', coeff)\n", "\n", "\n", "\n", "def gradient_descent(x, y, rounds = 1000, alpha=0.01):\n", " theta = np.zeros((x.shape[1], 1))\n", " costs = []\n", " for i in range(rounds):\n", " prediction = np.dot(x, theta)\n", " error = prediction - y\n", " gradient = np.dot(x.T, error / y.shape[0])\n", " theta -= gradient * alpha\n", " costs.append(np.sum(error ** 2))\n", " return (theta, costs) \n", "theta, costs = gradient_descent(intercept_x, y, rounds=10000)\n", "print(theta, costs[::500])" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lstsq [[ 5.47554054e+00]\n", " [ -2.39855397e+01]\n", " [ -2.16608732e+03]\n", " [ 1.83443739e+05]\n", " [ -5.27245313e+06]\n", " [ 8.26516683e+07]\n", " [ -8.23118154e+08]\n", " [ 5.62639497e+09]\n", " [ -2.76438408e+10]\n", " [ 1.00543636e+11]\n", " [ -2.75843668e+11]\n", " [ 5.77141231e+11]\n", " [ -9.24891464e+11]\n", " [ 1.13282985e+12]\n", " [ -1.05057010e+12]\n", " [ 7.23919989e+11]\n", " [ -3.58600661e+11]\n", " [ 1.20492881e+11]\n", " [ -2.45402172e+10]\n", " [ 2.28151842e+09]]\n", "[[ 4.79516406]\n", " [ 2.15892204]\n", " [ 1.59022686]\n", " [ 1.12552832]\n", " [ 0.76583482]\n", " [ 0.50446167]\n", " [ 0.31909476]\n", " [ 0.18845052]\n", " [ 0.09618612]\n", " [ 0.0306512 ]\n", " [-0.01621254]\n", " [-0.04991523]\n", " [-0.07421911]\n", " [-0.09170582]\n", " [-0.10416419]\n", " [-0.11285053]\n", " [-0.11866168]\n", " [-0.12224985]\n", " [-0.1240988 ]\n", " [-0.12457462]] [10215.151832573791, 54.942826326885879, 49.019636887639798, 48.508526705309478, 48.258178349345279, 48.117210951931447, 48.030663705920169, 47.971678161563304, 47.927021249546534, 47.890143189067288, 47.857775390826845, 47.828262392661784, 47.800743812843862, 47.77475299941122, 47.750019966197222, 47.726374493746306, 47.70369835417678, 47.681901638557513, 47.660910919846714, 47.640663236621648]\n" ] }, { "data": { "image/png": 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uqDjIuS4f6INRhe/7pCRWLlxo+pCJiYnxew2u5tBSXbFtIKIuCGGioKCAHTt2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\n5+bmhq0miL+CTY0Vc/Lld09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"text/plain": [ "<matplotlib.figure.Figure at 0x2004a13f668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(0)\n", "n = 200\n", "\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x 2 + np.random.normal(0,0.5, size=(n,1))\n", "x = np.hstack((xj for j in np.arange(20)))\n", "\n", "coeff, residuals, rank, sing_vals = np.linalg.lstsq(x,y)\n", "print('lstsq', coeff)\n", "\n", "theta, costs = gradient_descent(x, y, rounds=10000)\n", "print(theta, costs[::500])\n", "\n", "plt.plot(x[:,1], y, 'ko')\n", "plt.plot(x[:,1], np.dot(x, coeff), 'co')\n", "plt.plot(x[:,1], np.dot(x, theta), 'ro')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Machine learning packages available in the python ecosystem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Overview in the python wiki](https://wiki.python.org/moin/PythonForArtificialIntelligence)\n", "\n", "General\n", "* [scikit-learn](http://scikit-learn.org/stable/)\n", "* [milk](https://pythonhosted.org/milk/)\n", "* [Orange](http://orange.biolab.si/)\n", "* [Shogun](http://www.shogun-toolbox.org/)\n", "* [GraphLab Create (dato)](https://dato.com/learn/userguide/)\n", "\n", "There is a collection of field specific packages including some with machine learning components on the [scipy website](http://www.scipy.org/topical-software.html#science-basic-tools). Other packages can often be found searching the [python package index](https://pypi.python.org/pypi).\n", "\n", "\n", "Deep learning is receiving a lot of attention recently and a number of different packages have been developed.\n", "* [Theano](http://www.deeplearning.net/software/theano/)\n", "* [pylearn2](http://deeplearning.net/software/pylearn2/)\n", "* [keras](http://keras.io/)\n", "* [Blocks](http://blocks.readthedocs.org/en/latest/)\n", "* [Lasagne](http://lasagne.readthedocs.org/en/latest/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scikit-learn\n", "\n", "Scikit-learn is now widely used. It includes modules for:\n", "* Classification\n", "* Regression\n", "* Clustering\n", "* Dimensionality reduction\n", "* Model selection\n", "* Preprocessing\n", "\n", "There are modules for training online models, enabling very large datasets to be analyzed.\n", "\n", "There is also a semi-supervised module for situations when you have a large dataset, but only have labels for part of the dataset.\n", "\n", "## Milk\n", "\n", "Milk works very well with mahotas, a package for image processing. With the recent improvements in scikit-image milk is now less attractive, although still a strong option\n", "\n", "## Orange and Shogun\n", "\n", "These are both large packages but for whatever reason do not receive the attention that scikit-learn does.\n", "\n", "## Dato\n", "\n", "Dato is a relative newcomer and has been receiving a lot of attention lately. Time will tell whether it can compete with scikit-learn." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignments\n", "\n", "This week we will continue working on our project ideas. As you develop the outline some points you may want to consider:\n", "\n", "For projects developing the object oriented programming component of the course:\n", "\n", "* What will your classes be?\n", "* What will each class have as attributes and methods?\n", "* How will your classes interact?\n", "\n", "For projects developing GUIs or web applications:\n", "\n", "* What will your screens/pages be?\n", "* What components will each page need?\n", "* How will you store any data needed/produced?\n", "\n", "For projects developing machine learning models:\n", "\n", "* What will be your data?\n", "* How is your data structured?\n", "* How much data do you have?\n", "* Is your data labeled?\n", "* What type of machine learning task is it?\n", "* How good would the performance need to be for the model to be useful?\n", "\n", "You do not need to answer all these questions. Each answer does not need to be complete. Your final project will likely be different to your initial idea.\n", "\n", "The goal of the project description is to document your project as you currently envision it and to encourage planning for the earliest stage.\n", "\n", "__Your project descriptions should be sent to me by our class next week.__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
andrewzwicky/puzzles	advent_of_code/2021/12.ipynb	1	6520	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import requests\n", "from session import SESSION\n", "from common import print_problem, get_problem_input, submit_answer, neighbors\n", "from bs4 import BeautifulSoup\n", "from IPython.core.display import HTML\n", "from collections import Counter\n", "import re\n", "from collections import defaultdict\n", "from copy import copy, deepcopy\n", "import itertools\n", "from pprint import pprint\n", "from functools import lru_cache\n", "import numpy as np\n", "import colorama" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "DAY = 12" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw Data:\n", "'bm-XY\\nol-JS\\nbm-im\\nRD-ol\\nbm-QI\\nJS-ja\\nim-gq\\nend-im\\nja-ol\\nJS-gq\\nbm-AF\\nRD-start\\nRD-j'\n", "Split Data:\n", "['bm-XY',\n", " 'ol-JS',\n", " 'bm-im',\n", " 'RD-ol',\n", " 'bm-QI',\n", " 'JS-ja',\n", " 'im-gq',\n", " 'end-im',\n", " 'ja-ol',\n", " 'JS-gq']\n" ] } ], "source": [ "raw_data, data = get_problem_input(DAY)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "data = [\n", "\"fs-end\",\n", "\"he-DX\",\n", "\"fs-he\",\n", "\"start-DX\",\n", "\"pj-DX\",\n", "\"end-zg\",\n", "\"zg-sl\",\n", "\"zg-pj\",\n", "\"pj-he\",\n", "\"RW-he\",\n", "\"fs-DX\",\n", "\"pj-RW\",\n", "\"zg-RW\",\n", "\"start-pj\",\n", "\"he-WI\",\n", "\"zg-he\",\n", "\"pj-fs\",\n", "\"start-RW\",\n", "]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "dd = defaultdict(set)\n", "for x,y in [d.split(\"-\") for d in data]:\n", " dd[x].add(y)\n", " dd[y].add(x)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "def recurse_paths(this_dd, current, visited, found_paths):\n", " visited = deepcopy(visited)\n", " visited.append(current)\n", " \n", " if current == 'end':\n", " return found_paths + 1\n", " \n", " for n in this_dd[current]:\n", " if (n != n.lower()) or (n not in visited):\n", " found_paths = (recurse_paths(this_dd, n, visited, found_paths))\n", "\n", " return found_paths" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "first = recurse_paths(dd, \"start\", [], 0)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3887\n" ] } ], "source": [ "print(first)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3887\n", "(\"You don't seem to be solving the right level. Did you already complete it? \"\n", " '[Return to Day 12]')\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submit_answer(DAY, 1, first)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "def twice_recurse(this_dd, current, visited, found_paths, doubled):\n", " visited = deepcopy(visited)\n", " visited.append(current)\n", " \n", " if current == 'end':\n", " p = \",\".join(visited)\n", " return found_paths + 1\n", " \n", " for n in this_dd[current]:\n", " if n == \"start\":\n", " continue\n", " if (n != n.lower()):\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, doubled)\n", "\n", " else:\n", " #lowercase\n", " if n not in visited:\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, doubled)\n", " else:\n", " #already found:\n", " if doubled is None:\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, n)\n", "\n", " return found_paths" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "second = twice_recurse(dd, \"start\", [], 0, None)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "104834\n" ] } ], "source": [ "print(second)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "104834\n", "(\"That's the right answer! You are one gold star closer to finding the sleigh \"\n", " 'keys.You have completed Day 12! You can [Shareon\\n'\n", " ' Twitter\\n'\n", " 'Mastodon] this victory or [Return to Your Advent Calendar].')\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submit_answer(DAY, 2, second)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit

peap/notebooks

pytexas-2013/d02t03.classes.ipynb

1

12967

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classes and Metaclasses\n", "_James Powell_ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Themes\n", "\n", "* static ignorance of the interpreter\n", " * the interpreter doesn't know what it's looking at\n", " * eg., a name followed by () could be a function, class, generator, etc.\n", "* metaclasses are a tool for enforcing constraints from a base class to a derived class\n", "* CPython as a reference implementation of Python\n", " * Guidance for how the language is supposed to work\n", " * Python is a language without a spec (unlike, say, C++)\n", " * If something that works in CPython doesn't work in PyPy, Jython, etc., the other interpreters are not necessarily broken\n", "* CPython code as a source for understanding\n", " * simple, straight-forward\n", " * there exists a natural starting point\n", "* Python as a system language with no boundary between system and app" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Order of the talk\n", "\n", "* contours of classes\n", "* class construction\n", "* what is a metaclass?\n", "* interesting things in Python 3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sys import version_info\n", "print(version_info)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "sys.version_info(major=3, minor=3, micro=2, releaselevel='final', serial=0)\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contours of Classes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from math import sqrt\n", "\n", "EUCLIDEAN, STREETS = object(), object()\n", "\n", "ARENA_TYPE = STREETS\n", "\n", "# in python3, you don't need to derive from object, since there are no old-style classes\n", "class Mob:\n", " \"\"\"\n", " Any mobile object in the game.\n", " \"\"\"\n", "# not sure why this was breaking...\n", "# def __new__(cls, *args, **kwargs):\n", "# # runs before instantiation\n", "# return object.__new__(cls, *args, **kwargs)\n", " \n", " def __init__(self, x, y):\n", " self._x, self._y = x, y\n", " \n", " def __repr__(self):\n", " return '{0.__name__}({1.x}, {1.y})'.format(type(self), self)\n", " \n", " def __str__(self):\n", " return repr(self)\n", " \n", " def move(self):\n", " pass\n", " \n", " @property\n", " def x(self):\n", " return self._x\n", " \n", " @property\n", " def y(self):\n", " return self._y\n", " \n", " @x.setter\n", " def x(self, value):\n", " if value < 0:\n", " raise ValueError(\"can't move outside of the arena\")\n", " self._x = value\n", " \n", " @y.setter\n", " def y(self, value):\n", " if value < 0:\n", " raise ValueError(\"can't move outside of the arena\")\n", " self._y = value\n", "\n", " if ARENA_TYPE == EUCLIDEAN:\n", " @staticmethod\n", " def distance(mob1, mob2):\n", " return sqrt((mob1.x + mob2.x)**2 + (mob1.y + mob2.y)**2)\n", " else:\n", " @staticmethod\n", " def distance(mob1, mob2):\n", " return abs(mob1.x - mob2.x) + abs(mob1.y - mob2.y)\n", "\n", "mob1 = Mob(0, 0)\n", "mob2 = Mob(12, 3)\n", "\n", "Mob.distance(mob1, mob2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "15" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instance methods are created on the fly; this does not apply to class methods or properties." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mob3 = Mob(1, 1)\n", "assert Mob is Mob\n", "assert mob3 is mob3\n", "print(list(map(id, [mob3.x, mob3.x])))\n", "print(list(map(id, [mob3.move, mob3.move])))\n", "\n", "Mob.__dict__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[140227895093824, 140227895093824]\n", "[140227747113584, 140227326980896]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "mappingproxy({'move': <function Mob.move at 0x7f89239b4320>, '__dict__': <attribute '__dict__' of 'Mob' objects>, 'x': <property object at 0x7f89239a6998>, '__init__': <function Mob.__init__ at 0x7f89239b4170>, 'y': <property object at 0x7f89239a66d8>, '__doc__': '\\n Any mobile object in the game.\\n ', '__repr__': <function Mob.__repr__ at 0x7f89239b4200>, 'distance': <staticmethod object at 0x7f892399e1d0>, '__weakref__': <attribute '__weakref__' of 'Mob' objects>, '__str__': <function Mob.__str__ at 0x7f89239b4290>, '__module__': '__main__'})" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "James likes dis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from dis import dis\n", "\n", "def create_mob():\n", " return Mob(1,1)\n", "\n", "dis(create_mob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 4 0 LOAD_GLOBAL 0 (Mob) \n", " 3 LOAD_CONST 1 (1) \n", " 6 LOAD_CONST 1 (1) \n", " 9 CALL_FUNCTION 2 (2 positional, 0 keyword pair) \n", " 12 RETURN_VALUE \n" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to find the hooks for function calls in the CPython code, just grep for CALL_FUNCTION now.\n", "\n", "Here's where metaclasses come in... (somehow)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from dis import dis\n", "\n", "def create_monster():\n", " class Monster(Mob):\n", " def __init__(self, hp, *args, **kwargs):\n", " self.hp = hp\n", " Mob.__init__(*args, **kwargs)\n", "\n", "dis(create_monster)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 4 0 LOAD_BUILD_CLASS \n", " 1 LOAD_CONST 1 (<code object Monster at 0x7f895109f540, file \"<ipython-input-55-b7f99bfca70d>\", line 4>) \n", " 4 LOAD_CONST 2 ('Monster') \n", " 7 MAKE_FUNCTION 0 \n", " 10 LOAD_CONST 2 ('Monster') \n", " 13 LOAD_GLOBAL 0 (Mob) \n", " 16 CALL_FUNCTION 3 (3 positional, 0 keyword pair) \n", " 19 STORE_FAST 0 (Monster) \n", " 22 LOAD_CONST 0 (None) \n", " 25 RETURN_VALUE \n" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the CPython code, you'll see that you only use the first metaclass you find in the inheritance chain. Or is it only the metaclass of the first class (as in, the class itself)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can create your own object sytem in python...but probably don't." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What would you use metaclasses for?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Monster(Mob):\n", " def __init__(self, hp, *args, **kwargs):\n", " self._hp = hp\n", " Mob.ty__init__(*args, **kwargs)\n", " \n", " @property\n", " def hp(self):\n", " return self._hp\n", "\n", "class Boss(Monster):\n", " def __init__(self, prize, *args, **kwargs):\n", " self.prize = prize\n", " Monster.__init__(*arg, **kwargs)\n", " \n", "# you can ensure that classes you inherit from have certain attributes\n", "assert hasattr(Monster, 'hp')\n", "assert issubclass(Monster, Mob)\n", "\n", "# metaclasses allow you contrain derived classes at the parent class level\n", "# --> we don't want subclasses of Monster to be able to move outside the arena\n", "class metaclass(type):\n", " def __init__(self, name, bases, body):\n", " print(self, name, bases, body)\n", " # place contraints here...\n", " if name == 'Derived':\n", " raise ValueError('no!')\n", " return type.__init__(self, name, bases, body)\n", "\n", "class Base(metaclass=metaclass):\n", " pass\n", "\n", "class Derived(Base):\n", " pass" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "no!", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-71-fdda81cbdfa8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mclass\u001b[0m \u001b[0mDerived\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-71-fdda81cbdfa8>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, bases, body)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;31m# place contraints here...\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Derived'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbases\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: no!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "<class '__main__.Base'> Base () {'__qualname__': 'Base', '__module__': '__main__'}\n", "<class '__main__.Derived'> Derived (<class '__main__.Base'>,) {'__qualname__': 'Derived', '__module__': '__main__'}\n" ] } ], "prompt_number": 71 } ], "metadata": {} } ] }

mit

HPCC-Cloud-Computing/press

Scaling4ML/svm/svm.ipynb

1

8442

{ "cells": [ { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "file_name = 'day6_10.csv' # input file name\n", "window_size = 10\n", "k = 10 # scale -k -> k\n", "train_size = 0.9 # tỉ lệ data được dùng để train" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>8400</td>\n", " <td>10165</td>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>-40</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10165</td>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>9482</td>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>8041</td>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>6995</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7939</td>\n", " <td>7608</td>\n", " <td>8304</td>\n", " <td>8485</td>\n", " <td>7278</td>\n", " <td>7427</td>\n", " <td>7556</td>\n", " <td>7763</td>\n", " <td>6995</td>\n", " <td>6593</td>\n", " <td>-25</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10\n", "0 8400 10165 9482 8041 7939 7608 8304 8485 7278 7427 -40\n", "1 10165 9482 8041 7939 7608 8304 8485 7278 7427 7556 5\n", "2 9482 8041 7939 7608 8304 8485 7278 7427 7556 7763 5\n", "3 8041 7939 7608 8304 8485 7278 7427 7556 7763 6995 7\n", "4 7939 7608 8304 8485 7278 7427 7556 7763 6995 6593 -25" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(file_name, header=None)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "# Chuẩn bị dữ liệu\n", "X = np.array(df[list(range(window_size))], dtype='float32')\n", "y = np.array(df[window_size])\n", "X_train = X[:int(train_size*len(X))]\n", "X_test = X[int(train_size*len(X)):]\n", "y_train = y[:int(train_size*len(y))]\n", "y_test = y[int(train_size*len(y)):]" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\mp\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sklearn\\model_selection\\_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n", " % (min_groups, self.n_splits)), Warning)\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=None, error_score='raise',\n", " estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False),\n", " fit_params=None, iid=True, n_jobs=1,\n", " param_grid={'decision_function_shape': ('ovo', 'ovr'), 'kernel': ('linear', 'rbf', 'sigmoid', 'poly')},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", " scoring=None, verbose=0)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "params = {\n", " 'kernel': ('linear', 'rbf', 'sigmoid', 'poly'),\n", " 'decision_function_shape': ('ovo', 'ovr')\n", "}\n", "\n", "svm = SVC()\n", "clf = GridSearchCV(svm, params)\n", "# Training\n", "clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "#sorted(clf.cv_results_.keys())" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-19, -31, 37, -19, 9, -19, 5, 37, -50, 35, 50, -50, -31,\n", " 35, 50, -50, 50, -34, -34, 35, -34, 35, -34, 35, -34, -31,\n", " -31, -34, 50, -34, -48, -34, 35, -48, -50, 35, -27, -50, 47,\n", " -42, 35, 9, -49, -48, -31, -48, 3, -48, -31, 47, 47, 47,\n", " 35, -34, -27, -50, -50, 50, -48, -48, 47, -47, -47, 47, -48,\n", " -48, -48, -50, -29, 36, 36], dtype=int64)" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.14084507042253522" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

grezesf/Research

Reservoirs/Notebooks/UBM training for Speech Data.ipynb

1

12011

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "import os\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import mixture" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['find_phone_index', 'load_phone_file']\n" ] } ], "source": [ "# import custom functions\n", "import sys\n", "# path to libraries\n", "# currently in ../scripts-lib/\n", "tool_path = os.path.abspath('../scripts-lib')\n", "\n", "if tool_path not in sys.path:\n", " sys.path.append(tool_path)\n", "import lib_phones as lph\n", "\n", "# print the loaded functions\n", "print dir(lph)[5:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "61 ['aa', 'ae', 'ah', 'ao', 'aw', 'ax', 'ax-h', 'axr', 'ay', 'b', 'bcl', 'ch', 'd', 'dcl', 'dh', 'dx', 'eh', 'el', 'em', 'en', 'eng', 'epi', 'er', 'ey', 'f', 'g', 'gcl', 'h#', 'hh', 'hv', 'ih', 'ix', 'iy', 'jh', 'k', 'kcl', 'l', 'm', 'n', 'ng', 'nx', 'ow', 'oy', 'p', 'pau', 'pcl', 'q', 'r', 's', 'sh', 't', 'tcl', 'th', 'uh', 'uw', 'ux', 'v', 'w', 'y', 'z', 'zh']\n" ] } ], "source": [ "# load phone list\n", "phone_path = os.path.abspath('../datasets/TIMIT-MFCCs/TIMIT_phone_list.txt')\n", "phone_list = lph.load_phone_file(phone_path)\n", "print len(phone_list), phone_list" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "working in path : C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT-MFCCs\\dev\n", "working on: si1027.mfcc.csv\n", "working on: si1105.mfcc.csv\n", "working on: si1657.mfcc.csv\n", "working on: si1735.mfcc.csv\n", "working on: si475.mfcc.csv\n", "working on: si648.mfcc.csv\n", "working on: sx115.mfcc.csv\n", "working on: sx127.mfcc.csv\n", "working on: sx205.mfcc.csv\n", "working on: sx217.mfcc.csv\n", "working on: sx25.mfcc.csv\n", "working on: sx295.mfcc.csv\n", "working on: sx307.mfcc.csv\n", "working on: sx37.mfcc.csv\n", "working on: sx385.mfcc.csv\n", "working on: sx397.mfcc.csv\n" ] } ], "source": [ "#load mfccs into sklearn observations, each frame is an obs\n", "\n", "train_TIMIT_dir = os.path.abspath('../datasets/TIMIT-MFCCs/dev')\n", "\n", "train_obs = []\n", "train_obs_labels = []\n", "\n", "# walk the directories\n", "for (path, dirs, files) in os.walk(train_TIMIT_dir):\n", " print \"working in path : \" + path\n", "\n", " for file in files:\n", " # skip the SA files\n", " #dev, only work on file si1573.mfcc.csv \"si1573\" in file and\n", " if \".mfcc\" in file and \"sa\" not in file:\n", " #check if corresponding .phn file exists\n", " if not os.path.exists(path + \"/\" + file[:-8] + \"phn\"):\n", " print path + \"/\" + file[:-8] + \"phn\"\n", " print \"corresponding .phn file does not exist!\"\n", " else:\n", " \n", " print \"working on: \" + file\n", "# print \"from path : \" + path\n", "\n", " # open the files\n", " mfcc_file = open(path + \"/\" + file)\n", " phn_file = open(path + \"/\" + file[:-8] + \"phn\")\n", "\n", " # extract phone times\n", " phone_times = []\n", " for phn_line in phn_file:\n", " phone_times.append(phn_line.split())\n", " # transpose for easier use\n", " phone_times = map(list, zip(*phone_times))\n", "\n", " # skip mfcc_file header\n", " next(mfcc_file)\n", "\n", " # reset frame count\n", " frame_cnt = 0\n", "\n", " # for each line of mfcc_file\n", " for mfcc_line in mfcc_file:\n", "\n", " # increment frame count\n", " frame_cnt += 1 \n", "\n", " # print \"frame line #:\", frame_cnt \n", "\n", " # frame start time in seconds\n", " start_t = mfcc_line.split(\";\")[1]\n", "\n", " # create frame (skiping first 2 values, frame_index and frame_time)\n", " frame = map( float, mfcc_line.split(\";\")[2:])\n", " # print numpy.shape(frame)\n", " # print frame\n", "\n", " # find correspond phoneme and index in the list\n", " phn_index = lph.find_phone_index(start_t, phone_times, phone_list)\n", "\n", " # add to instances\n", " train_obs.append(frame)\n", " train_obs_labels.append(phone_list[phn_index])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4266, 39)\n", "(4266,)\n" ] } ], "source": [ "print np.shape(train_obs)\n", "print np.shape(train_obs_labels)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-24.63259, -12.55975, -0.4234125, -22.09616, -11.36787, 15.35193, 0.2450585, -18.58464, -28.29842, -5.80051, 2.788154, 4.604296, -17.4054, -0.275135, 1.378781, -1.950969, 3.052481, 3.25547, 1.203516, -0.7550904, -0.3006264, 2.328182, 3.589715, 2.428725, 4.183867, -0.1700937, -0.4764511, 0.5360677, 0.24275, 0.9877825, 0.0331907, -0.5391095, 0.6034231, 1.644038, 1.840576, 0.2822124, -0.1602798, -0.7427405, -0.02820093]\n", "['h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'h#', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'iy', 'v', 'v', 'v', 'v', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'ih', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'eh', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'q', 'ix', 'ix', 'ix', 'ix', 'ix', 'f']\n" ] } ], "source": [ "print train_obs[0]\n", "print train_obs_labels[0:100]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#create gmm\n", "num_components = 10\n", "num_iter=2\n", "g = mixture.GMM(n_components= num_components, n_iter=num_iter)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,\n", " n_components=10, n_init=1, n_iter=2, params='wmc', random_state=None,\n", " thresh=None, tol=0.001)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fit the GMM to the observations\n", "g.fit(train_obs) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pred = g.predict(train_obs)\n", "# print train_obs_labels\n", "# print pred" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-104.66\n", "87.85\n", "[-102. -114. -104. -102. -100.]\n" ] } ], "source": [ "print np.round(g.score(train_obs).mean(), 2)\n", "print np.round(g.score(train_obs).var(), 2)\n", "print np.round(g.score(train_obs)[0:5] )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 components 2 iterations in 1.50999999046 for 4266 observations.\n" ] } ], "source": [ "# refit, time \n", "t0 = time.time()\n", "g.fit(train_obs)\n", "t1 = time.time()\n", "print num_components, \"components\", num_iter, \"iterations in \", t1-t0, \"for\", len(train_obs_labels), \"observations.\"" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-104.65\n", "87.9\n", "[-102. -114. -104. -103. -100.]\n" ] } ], "source": [ "print np.round(g.score(train_obs).mean(), 2)\n", "print np.round(g.score(train_obs).var(), 2)\n", "print np.round(g.score(train_obs)[0:5] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Refitting does work as expected. (does not reset gmm)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saved gmm in C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT Pickled Data\\TIMIT_ubm_gmm_10.pckl\n" ] } ], "source": [ "#save gmm in pickled form\n", "import cPickle as pickle\n", "\n", "# name and location to save in\n", "pickle_name = \"TIMIT_ubm_gmm_\" + str(num_components) + \".pckl\"\n", "pickle_dir = os.path.abspath('../datasets/TIMIT Pickled Data')\n", "\n", "if not os.path.isdir(pickle_dir):\n", " os.makedirs(pickle_dir)\n", " \n", "pickle.dump( g, open( pickle_dir + os.sep + pickle_name, \"wb\") )\n", "print \"saved gmm in \", pickle_dir + '\\\\' + pickle_name" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded gmm from C:\\Users\\FG\\Desktop\\PhD\\Research\\Reservoirs\\datasets\\TIMIT Pickled Data\\TIMIT_ubm_gmm_10.pckl\n" ] } ], "source": [ "# reload pickled file for testing\n", "g2 = pickle.load( open(pickle_dir + os.sep + pickle_name, \"rb\") )\n", "print \"loaded gmm from \", pickle_dir + os.sep + pickle_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm

tutorial_part1/FIR Filter Design and C Headers.ipynb

1

532021

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "#%matplotlib qt\n", "from __future__ import division # use so 1/2 = 0.5, etc.\n", "import sk_dsp_comm.sigsys as ss\n", "import sk_dsp_comm.fir_design_helper as fir_d\n", "import sk_dsp_comm.coeff2header as c2h\n", "import scipy.signal as signal\n", "import imp # for module development and reload()\n", "from IPython.display import Audio, display\n", "from IPython.display import Image, SVG" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pylab.rcParams['savefig.dpi'] = 100 # default 72\n", "#pylab.rcParams['figure.figsize'] = (6.0, 4.0) # default (6,4)\n", "#%config InlineBackend.figure_formats=['png'] # default for inline viewing\n", "%config InlineBackend.figure_formats=['svg'] # SVG inline viewing\n", "#%config InlineBackend.figure_formats=['pdf'] # render pdf figs for LaTeX\n", "#%Image('fname.png',width='90%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FIR Filter Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both floating point and fixed-point FIR filters are the objective here. we will also need a means to export the filter coefficients to header files. Header export functions for `float32_t` and `int16_t` format are provided below. The next step is to actually design some filters using functions found in `scipy.signal`. To support both of these activities the Python modules `fir_design_helper.py` and `coeff2header.py` are available.\n", "\n", "**Note**: The MATLAB signal processing toolbox is extremely comprehensive in its support of digital filter design. The use of Python is adequate for this, but do not ignore the power available in MATLAB." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Windowed (Kaiser window) and Equal-Ripple FIR Filter Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The module `fir_design_helper.py` contains custom filter design code build on top of functions found in `scipy.signal`. Functions are available for winowed FIR design using a Kaiser window function and equal-ripple FIR design, both type have linear phase. \n", "\n", "### Example: Lowpass with $f_s = 1$ Hz\n", "For this 31 tap filter we choose the cutoff frequency to be $F_c = F_s/8$, or in normalized form $f_c = 1/8$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kaiser Win filter taps = 72.\n", "Remez filter taps = 53.\n" ] } ], "source": [ "b_k = fir_d.firwin_kaiser_lpf(1/8,1/6,50,1.0)\n", "b_r = fir_d.fir_remez_lpf(1/8,1/6,0.2,50,1.0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "\n", "<svg height=\"277pt\" version=\"1.1\" viewBox=\"0 0 394 277\" 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len(b_r)),loc='best')\n", "grid();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Highpass Design" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kaiser Win filter taps = 72.\n", "Remez filter taps = 53.\n" ] } ], "source": [ "b_k_hp = fir_d.firwin_kaiser_hpf(1/8,1/6,50,1.0)\n", "b_r_hp = fir_d.fir_remez_hpf(1/8,1/6,0.2,50,1.0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "\n", "<svg height=\"277pt\" version=\"1.1\" viewBox=\"0 0 394 277\" width=\"394pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", 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{}, "output_type": "display_data" } ], "source": [ "fir_d.freqz_resp_list([b_k_bp,b_r_bp],[[1],[1]],'dB',fs=48)\n", "ylim([-80,5])\n", "title(r'Kaiser vs Equal Ripple Bandpass')\n", "ylabel(r'Filter Gain (dB)')\n", "xlabel(r'Frequency in kHz')\n", "legend((r'Kaiser: %d taps' % len(b_k_bp),\n", " r'Remez: %d taps' % len(b_r_bp)),\n", " loc='lower right')\n", "grid();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exporting Coefficients to Header Files\n", "Once a filter design is complete it can be exported as a C header file using `FIR_header()` for floating-point design and `FIR_fix_header()` for 16-bit fixed-point designs.\n", "## Float Header Export\n", "```python\n", "def FIR_header(fname_out,h):\n", " \"\"\"\n", " Write FIR Filter Header Files \n", " \"\"\"\n", "```\n", "## 16 Bit Signed Integer Header Export\n", "```python\n", "def FIR_fix_header(fname_out,h):\n", " \"\"\"\n", " Write FIR Fixed-Point Filter Header Files \n", " \"\"\"\n", "```\n", "These functions are available in `coeff2header.py`, which was imported as `c2h` above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write a Header File for the Bandpass Equal-Ripple" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Write a C header file\n", "c2h.FIR_header('remez_8_14_bpf_f32.h',b_r_bp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The header file, `remez_8_14_bpf_f32.h` written above takes the form:\n", "\n", "```c\n", "//define a FIR coefficient Array\n", "\n", "#include <stdint.h>\n", "\n", "#ifndef M_FIR\n", "#define M_FIR 101\n", "#endif\n", "/************************************************************************/\n", "/* FIR Filter Coefficients */\n", "float32_t h_FIR[M_FIR] = {-0.001475936747, 0.000735580994, 0.004771062558,\n", " 0.001254178712,-0.006176846780,-0.001755945520,\n", " 0.003667323660, 0.001589634576, 0.000242520766,\n", " 0.002386316353,-0.002699251419,-0.006927087152,\n", " 0.002072374590, 0.006247819434,-0.000017122009,\n", " 0.000544273776, 0.001224920394,-0.008238424843,\n", " -0.005846603175, 0.009688130613, 0.007237935594,\n", " -0.003554185785, 0.000423864572,-0.002894644665,\n", " -0.013460012489, 0.002388684318, 0.019352295029,\n", " 0.002144732872,-0.009232278407, 0.000146728997,\n", " -0.010111394762,-0.013491956909, 0.020872121644,\n", " 0.025104278030,-0.013643042233,-0.015018451283,\n", " -0.000068299117,-0.019644863999, 0.000002861510,\n", " 0.052822261169, 0.015289946639,-0.049012297911,\n", " -0.016642744836,-0.000164469072,-0.032121234463,\n", " 0.059953731027, 0.133383985599,-0.078819553619,\n", " -0.239811117665, 0.036017541207, 0.285529343096,\n", " 0.036017541207,-0.239811117665,-0.078819553619,\n", " 0.133383985599, 0.059953731027,-0.032121234463,\n", " -0.000164469072,-0.016642744836,-0.049012297911,\n", " 0.015289946639, 0.052822261169, 0.000002861510,\n", " -0.019644863999,-0.000068299117,-0.015018451283,\n", " -0.013643042233, 0.025104278030, 0.020872121644,\n", " -0.013491956909,-0.010111394762, 0.000146728997,\n", " -0.009232278407, 0.002144732872, 0.019352295029,\n", " 0.002388684318,-0.013460012489,-0.002894644665,\n", " 0.000423864572,-0.003554185785, 0.007237935594,\n", " 0.009688130613,-0.005846603175,-0.008238424843,\n", " 0.001224920394, 0.000544273776,-0.000017122009,\n", " 0.006247819434, 0.002072374590,-0.006927087152,\n", " -0.002699251419, 0.002386316353, 0.000242520766,\n", " 0.001589634576, 0.003667323660,-0.001755945520,\n", " -0.006176846780, 0.001254178712, 0.004771062558,\n", " 0.000735580994,-0.001475936747};\n", "/************************************************************************/\n", "```\n", "\n", "* This file can be included in the main module of an ARM Cortex M4 micro controller using the [Cypress FM4](http://www.cypress.com/documentation/development-kitsboards/fm4-s6e2g-series-pioneer-kit-guide) $50 dev kit" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f_AD,Mag_AD, Phase_AD = loadtxt('BPF_8_14_101tap_48k.csv',\n", " delimiter=',',skiprows=6,unpack=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "\n", "<svg height=\"277pt\" version=\"1.1\" viewBox=\"0 0 394 277\" width=\"394pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", "*{stroke-linecap:butt;stroke-linejoin:round;}\n", " </style>\n", " </defs>\n", " <g id=\"figure_1\">\n", " <g id=\"patch_1\">\n", " <path d=\"M 0 277.314375 \n", "L 394.808949 277.314375 \n", "L 394.808949 0 \n", "L 0 0 \n", "z\n", "\" style=\"fill:none;\"/>\n", " 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"text/plain": [ "<matplotlib.figure.Figure at 0x115c34198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fir_d.freqz_resp_list([b_r_bp],[[1]],'dB',fs=48)\n", "ylim([-80,5])\n", "plot(f_AD/1e3,Mag_AD+.5)\n", "title(r'Equal Ripple Bandpass Theory vs Measured')\n", "ylabel(r'Filter Gain (dB)')\n", "xlabel(r'Frequency in kHz')\n", "legend((r'Equiripple Theory: %d taps' % len(b_r_bp),\n", " r'AD Measured (0.5dB correct)'),loc='lower right',fontsize='medium')\n", "grid();" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# FIR Design Problem\n", "Now its time to design and implement your own FIR filter using the filter design tools of `fir_design_helper.py`. The assignment here is to complete a design using a sampling rate of 48 kHz having an equiripple FIR lowpass lowpass response with 1dB cutoff frequency at 5 kHz, a passband ripple of 1dB, and stopband attenuation of 60 dB starting at 6.5 kHz. See Figure 9 for a graphical depiction of these amplitude response requirements." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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JLly4cMSIEWedddYtt9wS1rHdeOONmZn/+UE0adKkRCG+pl2gXr16aS9TgQQI\nECBQbgU2bNgQtqd2Tm25nQA6ToAAAQIECBAgQIAAAQIRFbBraEQHTrMJlBWBd955Z+bMmVlZWSEu\n2KdPn0SzGjdufMQRR4S1gAceeOCPP/44ffr0cH/UqFFhE9HUdmdkZPTv33/58uV33XXX22+/nfpI\nOo0CYRPRNJamKAIECBAgQIAAAQIECBAgQIAAAQIECBCInIA1gpEbMg0mULYE3njjjdCgsOYvGQ5M\ntm+33XZ79tlnK1SoEO4ceuihJ598cvJRauL666/ffvvtZ8+enXpTOo0CFStWTGNpiiJAgAABAgQI\nECBAgAABAgQIECBAgACByAmICEZuyDSYQNkS+OKLL8JhQv369dtis7p27brvvvuGR7169dpihnBz\nxx13PO6441asWJFXBveLKBBWcBaxBK8TIECAAAECBAgQIECAAAECBAgQIECAQKQFRAQjPXwaT6D0\nBRYvXtyxY8dGjRrl1ZROnTqFR0ceeWReGcL9Fi1arFy5Mp8MHhVFIOzOWpTXvUuAAAECBAgQIECA\nAAECBAgQIECAAAECURcQEYz6CGo/gVIWWL16dbNmzfJpRN26dTMzM8PJgvnkadq0qYhgPj4eESBA\ngAABAgQIECBAgAABAgQIECBAgACBogiICBZFz7sECPzmp59+yj8iWLNmzdq1a4egYD5YYd/RDRs2\n5JPBIwIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDQAvn9jr7QhXqRAIHyI7Bx48atdrZq1ar551mw\nYEH+GTwlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECi2QVeg3vUiAAIGEwFdffZUPxZo1a9avX59P\nhvBIRDB/n7Lw9IUXXnjxxRfnzp1b8MZ8/PHHIfMNN9wwdOjQgr+VK+eECRMqVqyY66aPBAgQIECA\nAAECBAgQIECAAAECBAgQILBNAiKC28QlMwECWxDIPyK4aNGiH3/8cQuvpdwSEUzBKKPJhQsXzpw5\nc+rUqdvavk8++WRbX0nNf+SRR+a/5WxqZmkCBMqgwLffflsGW6VJBAgQIECAAAECBAgQIECAAIHy\nJiAiWN5GXH8JpF9g2rRpRx99dF7lhoBQWCZ41FFH5RPX+de//pXX6+6XEYHjjz9+zz33XLduXcHb\nE1YHvv322wXPv8WckydP3uJ9NwkQIECAAAECBAgQIECAAAECBAgQIECg4AIiggW3kpMAgS0LrFy5\nMmztuOVn/7378ssv/zfpv5EUaLjp2qamb75Z6I477hjChPXq1atdu3aFChWSpS1dunTgwIHh65Il\nS5I3Q0L+hAafhIP5EMX58Pzzz4vrJwbOVwIECBAgQIAAAQIECBAgQIBA6QqICJauv9oJECBQLgRC\n/K9+/fq77LLL73//+0aNGiX7vGHDhrCjYOXKlZcvX54aDpQ/QcQn4WA+RHc+hGXiIoLJn3gSBAgQ\nIECAAAECBAgQIECAAIFSFBARLEV8VROIj0CI6IR4T61atVIXfm21ezk5OWF9YQgIha9bzVzeMoSt\nVoNP/r3OysqqVKlS/nnKyNMwPZ544okQEQyJ1CaF0e/Zs2c4ijLXSWPyJ5T4JBzMh3jMh9TvfWkC\nBAgQIECAAAECBAgQIECAAIESFhARLGFw1RGIocA555xz1113bb/99oXr2/r164cMGXLNNdcU7vW4\nvlWzZs21a9fm37szzjjjkUceyT9PWXjaoEGDEAsM1+arA0MsMFxff/11sp3J1WDyh3AgH/Mh8a2R\nWC0a3fmQ/AaXIECAAAECBAgQIECAAAECBAgQKC0BEcHSklcvgZgIZGZmFiUcGBTCQre+ffs++OCD\nMRHRjc0ERo0a1aZNG6sDrYYMU8Nqv8T3R3lb/bnZTwU3CBAgQIAAAQIECBAgQIAAAQIESlpARLCk\nxdVHIGYC4ff7hV4dmEqx++67p36UjpNAWCOYujow0bWw5iksDUxdHZi4H9aEhczy8zEfUn8IRH0+\npPZFmgABAgQIECBAgAABAgQIECBAoFQERARLhV2lBOIjcMUVVxSxM8uWLRs5cmTRyyliM8ra66ec\nckqIAeTfqoMOOij/DGXkaZMmTVJbEnY+7Ny5c/iaejOR3uLRifInofgECvMhWvPBMbHJ8ZIgQIAA\nAQIECBAgQIAAAQIECJSugIhg6fqrnUDkBbp06VLEPtSpU2fo0KEzZ84sYjkxe/3hhx+OWY9Cd0KM\nM2yWGNYF5gp2Js6KC19zdVn+BAifhIP5EMX58NNPP+X6vvaRAAECBAgQIECAAAECBAgQIECgVARE\nBEuFXaUEYiKQK65T8F6FpU7h3fXr1//www/PPvvsokWLHnvssfPPP7/gJcgZRYH8z07bfKdQ+ROj\nnDh7j4/5EMX58OGHH0bxh5U2EyBAgAABAgQIECBAgAABAgTiJyAiGL8x1SMCJSQQonoVK1bc4jaG\nhWjBfffdJyJYCLeovJJY3RX2ewxX6tmBidVvu2y6UiNe8idGlk/CwXyI7nywRjAqP6W1kwABAgQI\nECBAgAABAgQIEIi9gIhg7IdYBwkUl0BGRsYRRxzx8ssvp6WCTz755M033+zUqVNaSlNIqkCIpiQ/\nbty4MZkuyUT+q7tCQDAsg0ttj/wJjcTqQD7mQzzmQ+r3uDQBAgQIECBAgAABAgQIECBAgEAJC4gI\nljC46gjESuDCCy9MV0QwuLzyyisigsUxP3755ZdksWvXrk2mSyzxzTffZGdnWx0YwntWQ1rtl/i+\nK2+rP1u1atWgQYMS+5mjIgIECBAgQIAAAQIECBAgQIAAgc0FRAQ3N3GHAIGCChx77LFhp8fGjRv/\n/e9/b9GiRdWqVbOysjIzM8P706dPP+CAA2rVqnXxxRcfffTR++67b+J+rqLXrVt3+OGHd+nS5cYb\nb8z1yMd0CaxevTpZ1KpVq5LpEkucddZZISgYlnml1mj1W0LD6reEg/kQ7/nQpk2bAw88MPUngDQB\nAgQIECBAgAABAgQIECBAgEAJC4gIljC46gjESiAsc+nTp0/lypX32WefXB274IILdt5551dffbV1\n69a5HqV+DCcRPvXUU+GXxQcddFDYgzT1kXS6BGbMmJEsavbs2eFYr2rVqiXvlEBi4cKFixYtSlaU\n1+qokCGxjrCAqwnlT5DyTE4t8ydQlLX5EPaXDsfNdu/e/bjjjkuOlAQBAgQIECBAgAABAgQIECBA\ngEDJC4gIlry5GgnESuDcc899+umnc3Xpxx9/fO+99yZMmJB/ODDxVr169Tp37nzaaaeFoFFYYpir\nKB+LIhD26vzHP/5x3333JQsJd0488cQ777xzt912C7+pT94v1sTixYtTy89rNVjIE2ZCYldJ+Tc/\nO5BPYlaYP8nvjkh8v4S14+GvEJJtliBAgAABAgQIECBAgAABAgQIECgtAb98Ly159RKIiUD47fxl\nl12WqzOzZs0Kq9DCXqC57uf1MRSydOnSTz75ZPO1hnm94n7+An/5y1/uuOOO1BMEk/lDpDZcYRPX\ndu3affTRR8n7xZfYuHFjovC8Vi8looDha1jjldoM+RMafMyfsD9z8lsjWvMhnF0qIpgcOwkCBAgQ\nIECAAAECBAgQIECAQCkKiAiWIr6qCcRWoGXLluvXrw+/ti5gD6dMmRJyvvPOOyKCBRTbarabNl1b\nzVbCGfJa3ZU4S+/rr7/O1R75EyB8Eg7mQxTnQzhl9oknnsj1re0jAQIECBAgQIAAAQIECBAgQIBA\nyQuICJa8uRoJxF+gdu3aLVq0GDNmTK9evbba2yeffPKzzz4L2cLmclvNLENEBfJf7ReWBoYrNSIo\nf2KgE6vB+JgP0Z0P2223XUR/amk2AQIECBAgQIAAAQIECBAgQCBmAiKCMRtQ3SFQVgQ6dep04YUX\nhsPq9t1333zaNHny5DPPPDOR4cADD8wnp0eRFsh/dVeIeIVlcKkdlD+hkVgdyMd8iMd8SP0elyZA\ngAABAgQIECBAgAABAgQIEChhgYycnJwSrlJ1BAiUB4FvvvmmY8eOIZ5xxhln3HjjjU2aNMnV608/\n/TTcD+sIEz+Fwkaj4fTBjIyMXNl8jK7A8ccfP3bs2ND+ihUrVqlSpVWrVpUrV052J4x7OGAsnHQY\nxj0kkvdDolKlSiGn/HwSs8J8CA7R/X6ZM2fOkiVLRo4cefbZZ6d+m0tHWuCFF1447rjjevTokfgh\nH+m+aDwBAgQIECBAgAABAgQIECg/AiKC5Wes9ZRASQt88sknYaXgypUrQ5wvLPFptukKEaCw4Gn+\n/Pnhd8TJBoX9Qt9+++299947eUciBgLJiGAM+qILBAgURUBEsCh6ZfBdEcEyOCiaRIAAAQIECBAg\nQIAAAQIEtipg19CtEslAgEAhBfbcc8/nn3/+rLPOWrBgwaJN11tvvbV5WWEB2fDhw4UDN5eJx51B\ngwYdcsgh8eiLXhAgsK0CYb14tWrVOnTosK0vyl+WBcL/uPfZZ5/wtz5luZHaRoAAAQIECBAgQIAA\nAQIECOQSEBHMBeIjAQLpFOjatWvYE/Lvf//7Lbfckp2dvXnRRx111N/+9rfWrVtv/sideAiEsySd\nEBmPodQLAgQIJARq1apVt25dEUHzgQABAgQIECBAgAABAgQIREvArqHRGi+tJRBVgeXLl7/55puf\nbbq+++67cGpgCBS1b99erCiqI1qAdid2DQ2nTIWzpgqQXRYCBAgQIECAAAECBAgQIECAAAECBAgQ\nKC4BawSLS1a5BAikCuywww7HbLpSb0oTIECAAAECBAgQIECAAAECBAgQIECAAAECJSCQWQJ1qIIA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgdISEBEsLXn1EiBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECgJARHBklBWBwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHSEhARLC159RIgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAoCQERwZJQVgcBAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngACB0hIQESwtefUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAkBEcGSUFYHAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAgdISyCqtitVLgEARBb766qsZM2YUsZAy+3qlSpWaNm266667ltkW\nahgBAgQIECBAgAABAgQIECBAgAABAgQIEIiKgIhgVEZKOwnkFhg/fvzFF1+c+25cPnfu3Llr1679\n+vWLS4f0gwABAgQIECBAgAABAgQIECBAgAABAgQIlJqAiGCp0auYQBEFKlasWL169SIWUmZfr1y5\nclgmWGabp2EECBAgQIAAAQIECBAgQIAAAQIECBAgQCBCAiKCERosTSXwK4HzNl2/uuUDAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQGAzgczN7rhBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB8\nBEQE4zOWekKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgcwERwc1N3CFAgAABAgQIEEiPwNdf\nfz1u3LiPPvooPcUphQABAgQIECBAgAABAgQIECBAoFACIoKFYvMSAQIECBAgQIBAAQQmT57co0eP\nIUOGFCCvLAQIECBAgAABAgQIECBAgAABAsUlICJYXLLKJUCAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIFAWBLLKQiO0gQCB6Ark5OR8+eWXn3766ffff//jpivcqbnpqlWrVps2bVq3bl2hQoXodlDL\nCRAgQIAAAQIECBAgQIAAAQIECBAgQIBA1AVEBKM+gtpPoHQEZs2a9dRTT73yyivhaKgVK1bk04hq\n1artvffenTp1Ovnkk/fZZ598cnpEgAABAgQIECBAgAABAgQIECBAgAABAgQIFIeAiGBxqCqTQGwF\n1qxZM2LEiFGjRoVAYAE7+dNPP7296br99ttbtWrVq1evSy65pHbt2gV8XTYCBAgQIECAAAECBAgQ\nIECAAAECBAgQIECgiALOESwioNcJlBeBENgbMmRI8+bNL7/88oKHA3PphJWF/fv3b9q0ab9+/cIO\no7me+kiAAAECBAgQIECAAAECBAgQIECAAAECBAgUh4CIYHGoKpNA3ATCMYF77bXX1VdfvXjx4qL3\nbeXKlQMHDmzWrNkjjzxS9NKUQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOQvYNfQ/H08JUDgN088\n8cQ555wT1gimWoRtP1tuulq0aBFiezVq1AjnBW636apateovv/wSwn7hWrVqVXZ2dlga+MWma8GC\nBRs3bkyUE9YI9u7de+zYscOHD99xxx1TC5cmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE0iggIphG\nTEURiJvA+vXr+/bte9dddyU6FqKAhx9++BGbrsaNGxeit+EYwrfeemv8pmv27NmhhDFjxrzzzjv/\n/Oc/99xzz0IU6BUCBAgQIECAAAECBAgQIECAAAECBAgQIEBgqwJ2Dd0qkQwEyqnAkiVLunXrlggH\nHnLIISFot3Tp0ieffDKsFyxcODA4huWDIaYYygyrBmfOnHnZZZdVrlz522+/7dy589tvv11OoXWb\nAAECBAhESmDdunVhA4CwDUCkWq2xBAgQIECAAAECBAgQIECgvAuICJb3GaD/BLYoELb0POCAA6ZM\nmdKpU6fXX3996tSpRx55ZGZmOn9itGrV6u67754zZ06fPn3CbxVD9PHNN9/cYmPcJECAAAECBMqO\nwIQJE+rUqdOrV6+y0yQtIUCAAAECBAgQIECAAAECBLYqkM7f72+1MhkIEIiKwMUXXzx//vyrr746\nBAUPPfTQ4mt2o0aNhg0b9tprr1WpUuWkk04K6wWLry4lEyBAgAABAgQIECBAgAABAgQIECBAgACB\n8ikgIlg+x12vCeQn8PTTT4fj/R577LE77rijQoUK+WVN07MQdAynCW6//fYhKBj2IktTqYohQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIE/iMgImgeECDwK4HFixdfdNFFo0ePPvXUU3/1oJg/7LrrrmHX\n0C+//DJxcmEx16Z4AgQIECBAgAABAgQIECBAgAABAgQIECBQjgREBMvRYOsqgYIInHPOOWHL0BNP\nPLEgmdObZ6eddnr44Ydvu+227Ozs9JasNAIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ4FRATL8+jr\nO4HcAs8880zNmjX79++f+0FJfT7yyCPPO++8Bx54oKQqVA8BAgQIECBAgAABAgQIECBAgAABAgQI\nEIi/QFb8u6iHBAgUWCAsDSyV1YGpDRw0aFDqR2kCBAgQIECAAAECBAgQIECAAAECBAgQIECgiALW\nCBYR0OsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEyrSAiGCZHh6NI0CAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIFBEARHBIgJ6nQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECZFhARLNPD\no3EECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEiiggIlhEQK8TIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQKNMCWWW6dRpHgEA6BObOnfvee+/N33TNmzcv/DcnJ6dp06bN/nvtsccebdq0SUdV\nyiBAgAABAgQIECBAgAABAgQIECBAgAABAgTKnICIYJkbEg0ikC6BDRs2jB8//r777ps4cWIIAeYq\ndtasWal3DjrooEsvvfQPf/hDxYoVU+9LEyBAgAABAgQIECBAgAABAgQIECBAgAABAlEXEBGM+ghq\nP4EtCKxcufKee+4ZNmzYggULtvB4S7fe3nTVr1//gk3XTjvttKVc7hEgQIAAAQIECBAgQIAAAQIE\nCBAgQIAAAQLRExARjN6YaTGB/AW++OKLE044IXzNla1KlSr16tULob7wtWbNmsuWLVu06fr++++T\nKwi//fbb/v3733vvvU8//fShhx6aqwQfCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSgKiAhGcdS0\nmUCeAs8991zv3r3DGsFq1ap17tz5iCOO6NChQyIQWKNGjS2+tnbt2hAIDMHBcNzgyy+/PGHChCVL\nlhx22GF33XXXJZdcssVX3CRAgAABAgQIECBAgAABAgQIECBAgAABAgQiJCAiGKHB0lQC+Qls3Ljx\nf/7nf2677baWLVuGYN5pp51WuXLl/F7477NKlSrtsuk68MADe/XqFcp5/fXXBwwYEI4VnD59+v33\n31/Acv5bnv8SIECAAAECBAgQIECAAAECBAgQIECAAAECZUsgs2w1R2sIECiswKBBg+68886hQ4eG\n/ULPOeecQofxMjMzu3TpMnny5Fc3XWeccUZhW+Q9AgQIECBAgAABAgQIECBAgAABAgQIECBAoEwI\niAiWiWHQCAJFFPjoo4/CYr5JkyaFhX0VKlQoYmmJ18PGodOmTfvyyy/DTqRpKVAhBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAQKkIiAiWCrtKCaRT4JdffrnwwgvHjRvXqVOndJb7m9/Ur19/ypQpf/vb\n37Kzs9NbstIIECBAgAABAgQIECBAgAABAgQIECBAgACBEhMQESwxahURKC6B/v37/+lPf9prr72K\no4Lq1auHzUj//Oc/F0fhyiRAgAABAgQIECBAgAABAgQIECBAgAABAgRKQCCrBOpQBQECxSpw3nnn\ntWjRoviq6NChQ506dXJycjIyMoqvFiUTIECAAAECBAgQIECAAAECBAgQIECAAAECxSQgIlhMsIol\nUHICxRoOTHSjefPmJdcfNREgQIBAjASqVKkSNrUO21DHqE+6QoAAAQIECBAgQIAAAQIECBCInoCI\nYPTGTIsJECBAgAABAlERaN++fe/evXfdddeoNFg7CRAgQIAAAQIECBAgQIAAAQKxFBARjOWw6hSB\nwgjYF7Qwat4hQIAAgXwFWm668s3iIQECBAgQIECAAAECBAgQIECAQLELiAgWO7EKCERCYMOGDW3b\ntg27uu2zzz5HHXVUt27dItFsjSRAgAABAgQIECBAgAABAgQIECBAgAABAgS2KpC51RwyECBQHgQq\nVKjwu9/9bt26dQceeGDyYMLwccqUKWPHjl2+fHl5QNBHAgQIECBAgAABAgQIECBAgAABAgQIECAQ\nSwFrBGM5rDpFoDAC06ZNGz9+fK1atRIvP/vssxdeeOHSpUvDx0aNGr344ot77bVXYcr1DgECBAgQ\nIECAAAECBAgQIECAAAECBAgQIFCqAtYIliq/ygmkSeDBBx/8y1/+snjx4kKXt3Hjxp133jkZDuzf\nv/+JJ54YwoFNmzYdPHjwMccc06dPn7CzaKHL9yIBAgQIECBAgAABAgQIECBAgAABAgQIECBQWgLW\nCJaWvHoJpE1g1KhR5513XiguOzv7vvvuK1y5mZmZc+bMmTFjRr169cLSwLBAMJRz2mmn3XvvvTVq\n1AjpSy+9dPbs2bvttlvhyvcWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUFoCIoKlJa9eAmkTGDNm\nTMeOHd99990mTZoUpdALLrigXbt2GRkZOTk5IQoYgou9evVKFrjffvstW7Ys+VGCAAECBAgQIECA\nAAECBAgQIECAAAECBAgQiIqAiGBURko7CeQpEDYLDeHAVatWVa9ePc9MBXhw0UUXhY1Dx44d27Bh\nw3PPPbd58+apLz3xxBNPPvlk6h1pAgQIECBAgAABAgQIECBAgAABAgQIECBAIBICIoKRGCaNJJCf\nwOrVqxcuXNi4ceP8MhXs2e83Xal5w/mCX3311aOPPhqqSGwfmvpUmgABAgQIEChvApUqVTr44IPT\n8g+P8kanvwQIECBAgAABAgQIECBAoBQFMkuxblUTIJAWgZNOOun+++9PS1GbF/L444+HEwSHDRs2\nb968+fPnb57BHQIECBAgQKBcCYQdBXbddddWrVqVq17rLAECBAgQIECAAAECBAgQiLqANYJRH0Ht\nJ/Cbiy++uG3bttWqVbv++uszM38V5g8r/AJQrpvbRBaOEkycJrh06dKwJmCb3pWZAAECBAgQiJ/A\n3nvvPWrUqPj1S48IECBAgAABAgQIECBAgEC8BX4VPIh3V/WOQFwF6tatO3LkyP79+3fo0GHy5Mmp\n3Vy0aFG9evW+//771JuFS4dadthhh8K96y0CBAgQIECAAAECBAgQIECAAAECBAgQIECgFAVEBEsR\nX9UE0ibQvXv30aNHf/zxx127dt19990HDhz4+eefJ0pftmzZunXr0laTgggQIECAAAECBAgQIECA\nAAECBAgQIECAAIGoCYgIRm3EtJdAHgJhb8+JEyc2btz4iy++6NevX5s2bRKhwZBdRDAPM7cJECBA\ngAABAgQIECBAgAABAgQIECBAgEC5EBARLBfDrJPlRCAsEJwxY0Y4TTCcKRi6HEKDw4cPD4lWrVqF\nRwMGDHjjjTfWrl1bTjR0kwABAgQIECBAgAABAgQIECBAgAABAgQIEEgIiAiaCQRiJVCjRo2bb755\n9uzZ559/foUKFRJ9+/nnn8P5guGgwUMPPbRmzZqHHXbYTTfd9Oabb1o7GKux1xkCBAgQIECAAAEC\nBAgQIECAAAECBAgQIJCHgIhgHjBuE4iyQIMGDR544IEQ86tevXroR5MmTZK9WbNmzaRJk2644YZD\nDjmkTp06J5xwwogRIxYuXJjMIEGAAAECBAgQIECAAAECBAgQIECAAAECBAjETEBEMGYDqjsE/k+g\nY8eOgwYNCp+nTZu2YMGChx9++MwzzwwHDSZzrFy58vnnn+/Tp08IGbZr1y5kXrx4cfKpBAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIBAPARHBeIyjXhDYskDnzp0TD0IgMIQDQ1AwhAbDnqLDhg3r2bNn\nvXr1kq+FAwivvfbakO24444LW4wm70sQIECAAAECBAgQIECAAAECBAgQIECAAAECURcQEYz6CGo/\ngfwE6tat2759+8zMX32nt2zZMqwLfPzxx8OKwBAIHDp0aNg7tFatWqGg9evXv/DCC127dg135s6d\nm1/RnhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIREcjIycmJSFM1kwCBYhTYuHHjhx9++Nprr736\n6qtvvPHGzz//XLly5b59+9544425AorF2AhFx0vg+OOPH7vp6tGjR7x6pjcECBAgQIAAAQIECBAg\nQIAAAQIECBCImMCvVg5FrO2aS4BA+gRC2G/fffcNIcCJEydmZ2eHlYLdu3e/7bbbzj777BAsTF89\nSiJAgAABAgQIECBAgAABAgQIECBAgAABAgRKWkBEsKTF1Ueg7AtUrVo1hAOffvrpmTNnLlq0KBxA\nWPbbrIUECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAXgIignnJuE+AwG+aNWs2bty4cNxg+IqDAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQiKiAiGBEB06zCZSQQDhN8Mknn7zjjjtKqD7VECBAgAABAgQI\nECBAgAABAgQIECBAgAABAukWEBFMt6jyCJS4wIIFC5YvX15M1YZdQ6tXrx7ighs2bCimKhRLgAAB\nAjEWmD179rBhwyZPnhzjPuoaAQIECBAgQIAAAQIECBAgQKDsC4gIlv0x0kICWxH48MMP27RpM3bs\n2K3k2/bH2dnZzZs3b9u27ddff52Tk7PtBXiDAAECBMq7wDvvvHPBBReMGjWqvEPoPwECBAgQIECA\nAAECBAgQIECgVAVEBEuVX+UE0iHQqVOngw466Pjjj+/Zs+fSpUvTUeT/L6NKlSqVKlWaM2dOly5d\nsrKy0liyoggQIECAAAECBAgQIECAAAECBAgQIECAAIESExARLDFqFREoLoE6deo8/fTTzz333NSp\nU3fffffHHnssXTVVq1btjTfeePjhh4cMGZKuMpVDgAABAgQIECBAgAABAgQIECBAgAABAgQIlLCA\niGAJg6uOQHEJnHDCCZ999lmPHj1OO+20Y445ZuHChWmpaa+99jrzzDPDOYJpKU0hBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAQMkLiAiWvLkaCRSXQM2aNR966KGJEyeG0GA4/O+BBx5w+F9xWSuXAAEC\nBAgQIECAAAECBAgQIECAAAECBAhER0BEMDpjpaUECibQrVu3GTNmnHXWWRdffHE4/2/27NkFe08u\nAgQIECBAgAABAgQIECBAgAABAgQIECBAIJ4CIoLxHFe9KucC22233d133x2OAFyyZEnY9nPw4MEb\nNmwo5ya6T4AAAQIECBAgQIAAAQIECBAgQIAAAQIEyq2AiGC5HXodj7/AQQcd9OGHH1511VXXX399\nx44dP/nkk/j3WQ8JECBAgAABAgQIECBAgAABAgQIECBAgACBzQREBDcjcYNAjAQqV6580003vf/+\n+xs3btxvv/369eu3du3aGPVPVwgQIECAAAECBAgQIECAAAECBAgQIECAAIGtC4gIbt1IDgJRFwgb\nh06bNm3gwIFh+9D27du/8847Ue+R9hMgQIAAAQKlJbBy5cpwYvGCBQtKqwHqJUCAAAECBAgQIECA\nAAECBAohICJYCDSvEIieQFZW1jXXXPPxxx/XqVPn4IMPvvLKK3/66afodUOLCRAgQIAAgdIWmDx5\ncrt27S699NLSboj6CRAgQIAAAQIECBAgQIAAgW0QEBHcBixZCURdoFWrVq+//vo999zz4IMPht/l\nvfbaa1HvkfYTIEDwV80RAABAAElEQVSAAAECBAgQIECAAAECBAgQIECAAAECWxUQEdwqkQwEYiWQ\nkZFx0UUX/fvf/w7RwcMPP/y8885bvnx5rHqoMwQIECBAgAABAgQIECBAgAABAgQIECBAgMCvBUQE\nf+3hE4HyIdCkSZMJEyY88sgjzz33XJs2bcaOHVs++q2XBAgQIECAAAECBAgQIECAAAECBAgQIECg\nPAqICJbHUdfn+AmEXUD/8pe/LF68eJu6dsYZZ3z22WfhWMHjjz++Z8+eS5Ys2abXZSZAgAABAgQI\nECBAgAABAgQIECBAgAABAgQiISAiGIlh0kgC+QmMGjUqbP558803DxgwIL98W3pWr169p556KqwU\nnDp1algs+Oijj24pl3sECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAhAVEBCM8eJpOICEwZsyYjh07\nZmZmhr1AC2dywgknhMWCPXr0OP3004899tjCFeItAgQIECBAgAABAgQIECBAgAABAgQIECBAoGwK\nZJXNZmkVAQIFFwibhb777rurVq2qXr16wd/KlbNmzZoPPfTQKaeccuGFF+Z65CMBAgQIECBAgAAB\nAgQIECBAgAABAgQIECAQaQFrBCM9fBpP4D8Cq1evXrhwYVHCgUnHbt26ffLJJ8mPEgQIECBAgAAB\nAgQIECBAgAABAgQIECBAgEAMBEQEYzCIulDeBU466aT7778/XQrVqlVLV1HKIUCAAAECBAgQIECA\nAAECBAgQIECAAAECBMqCgF1Dy8IoaAOBIglcfPHFbdu2DZG866+/PpwmmFrWxo0bw8dcN1MzSBMg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQKxF/hV8CD2vdVBArEUqFu37siRI/v379+hQ4fJkyen9nHR\nokX16tX7/vvvU29KEyBAgAABAgQIECBAgAABAgQIECBAgAABAuVKQESwXA23zsZWoHv37qNHj/74\n44+7du26++67Dxw48PPPP0/0dtmyZevWrYttz3WMAAECBAgQIECAAAECBAgQIECAAAECBAgQ2JqA\niODWhDwnEBGBXr16TZw4sXHjxl988UW/fv3atGmTCA2G5osIRmQMNZMAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBSLgIhgsbAqlECpCIQFgjNmzAinCYYzBUMDQmhw+PDhIdGqVavwaMCAAW+88cbatWtL\npW0qJUCAAAECBAgQIECAAAECBAgQIECAAAECBEpLQESwtOTVS6BYBGrUqHHzzTfPnj37/PPPr1Ch\nQqKOn3/+OZwvGA4aPPTQQ2vWrHnYYYfddNNNb775prWDxTIGCiVAgAABAgQIECBAgAABAgQIECBA\ngAABAmVMQESwjA2I5hBIh0CDBg0eeOCBEPOrXr16KK9JkybJUtesWTNp0qQbbrjhkEMOqVOnzgkn\nnDBixIiFCxcmM0gQIECAAAECBAgQIECAAAECBAgQIECAAAECMRMQEYzZgOoOgf8T6Nix46BBg8Ln\nadOmLViw4OGHHz7zzDPDQYPJHCtXrnz++ef79OkTQobt2rULmRcvXpx8KkGAAAECBAgQIECAAAEC\nBAgQIECAAAECBAjEQ0BEMB7jqBcEtizQuXPnxIMQCAzhwBAUDKHBsKfosGHDevbsWa9eveRr4QDC\na6+9NmQ77rjjwhajyfsSBAgQIECAAAECBAgQIECAAAECBAgQIECAQNQFRASjPoLaTyA/gbp167Zv\n3z4z81ff6S1btgzrAh9//PGwIjAEAocOHRr2Dq1Vq1YoaP369S+88ELXrl3Dnblz5+ZXtGcECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIBARAR+FSeISJs1kwCBggrstNNO06dPD1/zeqFt27aXXnrpc889\nt2zZsvfffz9sHNqtW7cqVaqE3UTbtGnTr1+/jRs35vWu+wQIECBAgAABAgQIECBAgAABAgQIECBA\ngEAkBEQEIzFMGkmg2AXCOsJ99923b9++EydOzM7ODisFu3fvftttt5199tmCgsWurwICBAgQIECA\nAAECBAgQIECAAAECBAgQIFCcAiKCxamrbALRFKhatWoIBz799NMzZ85ctGhROIAwmv3QagIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQOA/AiKC5gEBAnkKNGvWbNy4ceG4wfA1z0weECBAgACBvAUqV668\n3377hXNt887iCQECBAgQIECAAAECBAgQIECAQLELZBV7DSogQCDKAuE3uU8++eQJJ5wQVg1GuR/a\nToAAAQKlI9CpU6ftt9++YcOGpVO9WgkQIECAAAECBAgQIECAAAECBDYJWCNoIhCIvMDtt9/+888/\nF1M3wq6hFStW/Oabb9avX19MVSiWAAECBGIs0KBBg9/97nd77rlnjPuoawQIECBAgAABAgQIECBA\ngACBsi8gIlj2x0gLCWxFYJ999rnmmmu2kqlQj7Ozs5s3bx4Wdvzyyy+FKsBLBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAQOkLiAiW/hhoAYEiChx++OFr1qwZPXp0EcvZ/PUqVark5OSsXLmyR48eWVk2\nGd5cyB0CBAgQIECAAAECBAgQIECAAAECBAgQIBABAb/ij8AgaSKBrQoMHjx4jz32mDt37o033rjV\nzAXMENYFXnLJJTvuuOMNN9xw1llnFfAt2QgQIECAAAECBAgQIECAAAECBAgQIECAAIGyJmCNYFkb\nEe0hUBiBHXbYIawRHDJkyCGHHDJ16tTCFJHyTlgXOG7cuAMOOOCpTdcFF1xQuXLllOeSBAgQIECA\nAAECBAgQIECAAAECBAgQIECAQJQERASjNFraSiAfgS5durzzzjvfffdd586dQ/qBBx5YuHBhPvm3\n+GjmzJlDhw7de++9wzahq1evDgUefPDBW8zpJgECBAgQIECAAAECBAgQIECAAAECBAgQIBAVAbuG\nRmWktJPA1gXatGnz7rvvnnrqqRMmTJgyZUp4oW3bti1btmzYsGGDTVciUbdu3RUrVoTYYeJasmRJ\nIjF9+vSvvvoqUc2xxx776KOPhqWHW69VDgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBsC4gIlu3x\n0ToC2yhQs2bNF1988a9//eudd94ZFvn9e9O1TWWEDUKvv/76cHZgRkbGNr0oMwECBAgQIECAAAEC\nBAgQIECAAAECBAgQIFA2BewaWjbHRasIFF4gMzMzRAS/+eabcKxgixYtCl5Q06ZNb7311rDXaL9+\n/YQDC+4mJwECBAgQKFcClSpVCluU77LLLuWq1zpLgAABAgQIECBAgAABAgSiLiAiGPUR1H4CWxYI\nG35eeeWVs2bNCksGu3fv3qRJkwoVKmyeNUT+6tevH/YIHTdu3JdffnnttdeGPUU3z+YOAQIECBAg\nQCAhEP6EKBwzvP/++wMhQIAAAQIECBAgQIAAAQIEIiRg19AIDZamEthmgbBe8JhNV3hz3bp1CxYs\nmLfp2rhxY/h1XrjCH/hXqVJlm8v1AgECBAgQIFBeBXbbbbebb765vPZevwkQIECAAAECBAgQIECA\nQFQFRASjOnLaTWBbBSpWrBg2Ed2mfUS3tQr5CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTIoYNfQ\nMjgomkSAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgbQIigmmjVBABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgACBMihg19AyOCiaRCBtAuHQwFWrVoX9QsORP3kVmp2dXbt27byeuk+AAAECBAgQ\nIECAAAECBAgQIECAAAECBAhEXcAawaiPoPYTyE/grLPO2nPPPbt27ZpPptGjRzdp0uSoo46aOHFi\nPtk8IkCAAAECBAgQIECAAAECBAgQIECAAAECBCIqICIY0YHTbAJpE7jiiivef//9t99++8gjj7zo\noovSVq6CCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbIhICJYNsZBKwiUqsBOO+3UvXv30IT777/f\nSsFSHQqVEyBAgAABAgQIECBAgAABAgQIECBAgACB9AuICKbfVIkEIiewbt26Tz/9NNHst956K3Lt\n12ACBAgQIECAAAECBAgQIECAAAECBAgQIEAgH4GsfJ55RIBAPAS+//77Y489tm7dujtuulITa9eu\nnTNnzt13352MCLZp0yYevdYLAgQIECBAgAABAgQIECBAgAABAgQIECBAICEgImgmEIi/QAj7jR8/\nviD97NGjx4knnliQnPIQIECAAAECBAgQIECAAAECBAgQIECAAAECUREQEYzKSGkngcIL1KlTZ9So\nUd9suhZvupYuXZqdnb1y5cr169fXrl07LB1s3rz5aaed1q1bt8xMmwkXntqbBAgQIECAAAECBAgQ\nIECAAAECBAgQIECgDAqICJbBQdEkAmkWqFSpUvfu3dNcqOIIECBAgAABAgQIECBAgAABAgQIECBA\ngACBiAhYDBSRgdJMAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoUSEBEsFJuXCBAgQIAAAQIE\nCBAgQIAAAQIECBAgQIAAAQIECEREQEQwIgOlmQQIECBAgACBCAp8/PHH/fv3HzNmTATbrskECBAg\nQIAAAQIECBAgQIAAgfgIOEcwPmOpJwTyEvj222+rVq1au3btWrVqha916tTZedNVv379Zs2atWjR\nYpdddqlQoUJer7tPgAABAgQKLfDJJ58MGDDg9NNPP+GEEwpdiBcJECBAgAABAgQIECBAgAABAgSK\nKCAiWERArxMo0wKZmf9ZB1y9evVzzz136aZryZIlM2fODF9T2x3ihR06dDjttNN69+5dsWLF1EfS\nBAgQIECAAAECBAgQIECAAAECBAgQIECAQNQFRASjPoLaTyA/gYYNG4bHIS44ZMiQjIyMZNbFixdP\nnz79gw8+ePDBBxcsWLBmzZqpm65bbrnl/fffD4sIkzklCBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\ngagLOEcw6iOo/QTyEzjwwAPD4xUrVsyaNSs1X9g09Oijj77hhhvmzJkzbNiwsGto4un8+fMfeeSR\n1JzSBAgQIECAAAECBAgQIECAAAECBAgQIECAQNQFRASjPoLaTyA/gbBZaPv27UOORx99dIv5wh6h\nffr0CfHCcMhTlSpVGjRo0K1bty3mdJMAAQIECBAgQIAAAQIECBAgQIAAAQIECBCIqICIYEQHTrMJ\nFEigUqVKb731VogLhu1A77jjjpycnC2+FrKF9YJhKWHYQbRdu3ZbzOMmAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgEFEBEcGIDpxmEyioQNWqVUeMGPHZZ599/fXX999/fz6vhfWCFSpUyCeDRwQIECBA\ngAABAgQIECBAgAABAgQIECBAgEAUBbKi2GhtJkBgWwVat279t7/9bVvfkp8AAQIECBAgQIAAAQIE\nCBAgQIAAAQIECBCIgYA1gjEYRF0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkKeANYJ50nhA\ngAABAgQIECBAgEAugaVLl3700Uc77bTTXnvtleuRjwQIECBAgAABAlEXWL9+/bhx4z744INvvvmm\ncePGxx577P777x/1Tmk/AQIECCQErBE0EwgQIECAAAECBAgQKKjAv/71ryOOOKJfv34FfUE+AgQI\nECBAgACBKAjk5OTcc889u+yyy8CBA7Oysvbee+/HHnvsgAMOuOKKK0qm+VdffXW3bt1CPHKr1W3Y\nsGHatGkPPvhgaNvxxx8fXnz44YdDFHPdunVbfVcGAgQIlGcBawTL8+jrO4H/CKxZs6Zq1aosCBAg\nQIAAAQIECBAgQIAAAQIEyqfAt99+e/rpp7/22mt33HHHVVddlZGRERxeeeWVuXPn3n333SFQd8wx\nxxSrzHPPPTdkyJA99tjjyCOPzL+iDz/88Lzzzgvxv82z7b777iNGjDj44IM3f+QOAQIECAQBawRN\nAwLlXeDiiy/u3r37U0899fPPP5d3C/0nQIAAAQIECBAgQIAAAQIECJQzgRAO7Ny5cwgH3nrrrWG9\nXSIc+MMPP7z88ssJiXnz5hUrSagr/HqqQoUKI0eOrFSpUj51XXfddWEX0y2GA8Nbn3/++SGHHBKK\nWr16dT6FeESAAIFyK2CNYLkdeh2Pp8CKFSumTp36/vvvh93eGzVqFPZ2OPzww8NWD/n0dtiwYT17\n9jz55JN32GGHE088MWy5kE9mjwgQIECAAAECBAgQIECAAAECBGIjkJ2dfdhhh82ePfuoo4669tpr\nk/2qUaNG06ZN58yZU7du3T/+8Y/J+8WRCGHIxYsXh6/5n1l4//3333bbbfk3IOx9et999y1fvvzR\nRx/NP6enBAgQKIcC+cUJyiGHLhOItMCkSZPCDg+LFi1K7UX4d9uf//znyy+/PK+/sapYsWL459SY\nMWPCv5bCn2KJCKbqSRMgQIAAAQIECBAgQIAAAQIEYixw/vnnh6V14a/Jw6adqd0MK/ZmzJgRjuvr\n2LFjXr9TSs1f6PTEiRNHjRrVokWLcH5hPoXMmjXrT3/6U8hQrVq1sArw6KOPbt26dZUqVcJ6wbfe\nemvw4MGp6wLDCYghwHnaaaflU6BHBAgQKIcCdg0th4Ouy/EUeOCBB8JywFzhwNDVpUuX9u3bN+zD\n/uKLL+bV81133bVZs2Z5PXWfAAECBAgQIECAAAECBAgQIEAgfgL/+Mc/nnnmmdCvc845JxzCl6uD\nlStXPvTQQ4s1HLhq1aoQkgz7lIa/UK9atWquBqR+7N27908//RQCgV9++eXtt9/+29/+tn79+rVq\n1Qq/Devfv//06dP33Xff1PwXXXRR2EAr9Y40AQIECIgImgME4iAQznkOWyuEjRESnQn/Hgr/Qgp/\nORVOWm7fvn1YBRg2fwiHBfbp02ft2rVb7HCdOnW2eN9NAgQIECBAgAABAgQIECBAgACB+AmsWbMm\n/BF5ol8XXHBBqXTw+uuvnz9/fvj9VYjw5dOAcJDhv/71r27duj333HM777zz5jlbtWr1+uuv77LL\nLslHK1euDCcjJj9KECBAgEAQEBE0DQjEQeCyyy4LfyeV6En4q66wyfv48ePDhgnDhw8PfyQV/g30\nxBNPNG/efMSIEV26dPnuu+8273Ox/sHX5tW5Q4AAAQIECBAgQIAAAQIECBAgUIoCd999d2IVXZs2\nbfbee++Sb0nY7fPee+9t1KhR+BVW/rW/8sorO+20UzjyJixbzCvndtttF0pLffree++lfpQmQIAA\nARFBc4BA5AU2bNiQ/KOn8CddYZuF2rVrp/Yq/Gvp5JNPDpvChx3hv/jii/322+/DDz9MzRDSmZl+\nGuQi8ZEAAQIECBAgQIAAAQIECBAgEE+B7Ozs2267LdG3P/7xjyXfyV9++eXcc8/duHHj/fffX6NG\njfwbEM4aDJtjhZhf/tmOOeaYgw46KJnn/fffT6YlCBAgQCAIiAGYBgQiLxD2T//5559DNxo0aJDr\nFOjUvoVVgFdeeWU4h7lt27adO3eePHly6lNpAgQIECBAgAABAgQIECBAgACBciIQwoHLly9PdPbE\nE08s+V4PGDAg/Nn6qaeeeuyxx2619pYtW4ZzAbeaLWQIp+cks4WgYzItQYAAAQJBQETQNCAQeYGF\nCxcm+nDwwQfnfwhzyBbOC3zppZcuv/zycNDg888/H/nO6wABAgQIECBAgAABAgQIECBAgMC2CPz7\n3/8eOnRo4o1wAl/42/FteTsNecPmVbfffnvdunXDzqUFKS7EL6tXr16QnKl9ady4cUFekYcAAQLl\nR0BEsPyMtZ7GVmDXXXdN9C2cFFiQToYNQgcOHHjfffedcsopjzzySEFekYcAAQIECBAgQIAAAQIE\nCBAgQCAGAtOnTz/uuOOS6+eOP/74Eu7U+vXrzznnnPA1RCV33HHH9NaeWmDr1q3TW7jSCBAgEHWB\nrKh3QPsJEGjSpEnYb33FihXz5s0ruMZZZ51Vr169sFP8ypUrL7nkkoK/KCcBAgQIECBAgAABAgQI\nECBAgEC0BAYNGvTDDz/MmTNn3Lhxa9euTTZ+8eLFt956a/jYtGnT8LfjyfvFlxg8eHBYI9ijR4+e\nPXumvZZvv/02WWap7IaarF2CAAECZVBARLAMDoomEdhmgfPPPz/8c2rKlCmrV6/e6jHLydLDxqHh\nX4Hdu3dP/l1Y8pEEAQIECBAgQIAAAQIECBAgQIBAPAS+/vrra6+9dot9GT16dOJ+3759SyAiGM4O\n/Otf/7rDDjuEzau22J4i3pw5c2aihBYtWuy///5FLM3rBAgQiJmAiGDMBlR3yqnAjTfe+Oyzz86d\nOzdswh7+XVVwhS5duowfPz6c4ZyTk1Pwt+QkQIAAAQIECBAgQIAAAQIECBCIikA4se/LL79MtDas\nouvUqVMifdhhhw0fPjyRDnmKuzsbN24899xzwx+m33PPPQ0bNiyO6l566aVEsXlFQIujUmUSIEAg\nKgLOEYzKSGkngfwEqlWrFgJ77du3HzBgwF133ZVf1s2ede7c+cUXXwz/JtvsiRsECBAgQIAAAQIE\nCBAgQIAAAQKRF6hcuXLz/15fffVVsj9HHnnkf28333777ZP3iykR1gW+9dZbXbt2DXHB4qjio48+\nmj9/fih53333Pfvss4ujCmUSIEAg0gIigpEePo0n8H8Cu+2227Rp0/r16xciggcddFDYQfT/nm0t\nFYKCzzzzTMWKFbeW0XMCBAgQIECAAAECBAgQIECAAIEIC0yaNCnZ+rB3VDJd3IkQibzuuuvCH7WP\nGDEiIyOjOKq78847E8XefffdmZl+710cxsokQCDaAn4yRnv8tJ5AqkAI6YUtQxcsWPDQQw+tX78+\n9dFW0+FMwf/93//1r6WtQslAgAABAgQI/D/27jtOiqLd+/Ah57xIEkFykpwkIxIERILkIElAEUFR\nwAAGkKASBBUVQQFBEBCQLEgGAQEBQQQk5yBBcn5/hz5vPf3Mzs7OzM7sTvjOH/up7q6qrrq6d3Zn\n7q4qBBBAAAEEEEAAAQSCV8BEBLWYn6abirWOdOnS5cqVK4MGDdKoRH+cVKMDp02bpppbtWpVsWJF\nf5yCOhFAAIFgF2AdwWC/grQfAScCGi+ol5MDLnc1b948Nv8RdNkWDiKAAAIIIIAAAggggAACCCCA\nAAII+FhAA/UOHjxoVVq5cuUECRL4+ARRVPftt9/+/PPP5cqV69mzZxRZYrr7lVde0fPxOXPm1ADB\nmNZFeQQQQCBEBRgjGKIXlm4h4JVAvnz5vCpHIQQQQAABBBBAAAEEEEAAAQQQQACBQBcwAwTV0GrV\nqsVOc0+dOvXqq68mTpx4/Pjxfpqeavbs2XPmzNGUpPoZERERO/3iLAgggEDQCRARDLpLRoMRQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAGPBeIkIti9e/cLFy689dZbhQsX9rjFbhQ4c+aMTqGMGolYrFgx\nN0qQBQEEEAhTASKCYXrh6XZICmzbtm3t2rUh2TU6hQACCCCAAAIIIIAAAggggAACCCAQQwETEUyb\nNm3srB0za9asH3/8sWjRom+88UYMG++0+N27d1u0aHHy5Mk333yzadOmTvOwEwEEEEDAEiAiyJ2A\nQIgIjBs3Tv/JaQr4d955J0S6RDcQQAABBBBAAAEEEEAAAQQQQAABBHwksGfPnhMnTliV6RskP03g\naW/s+fPnNXpPqxVqvtBEiRLZD/kqrUDgihUrmjRpMnDgQF/VST0IIIBAqAoQEQzVK0u/wk5g4cKF\nVp9NQpuTJ0/WuspLly69efNm2InQYQQQQACBABDQYiH58+fXA8gB0BaagAACCCCAAAIIIIBAWAso\ncmb6X716dZP2X0LLB54+fVo/S5cu7Y+zTJgw4cMPP3ziiSemTJkSCwFOf3SBOhFAAIHYFEgYmyfj\nXAgg4D+B9u3bL168+M6dO507dzZn0Tewox68UqRIUaNGjbp16z711FOPPPKIyUACAQQQQAABvwrU\nqlWrQIEC6dOn9+tZqBwBBBBAAAEEEEAAAQSiFTBThipntWrVos0fwwz6nmrixIl58+Z97733YliV\n0+ILFizo2rWrYo1z5sxJkiSJ0zzsRAABBBCwCxARtGuQRiCIBZ555pljx45pLGDWrFlNNzRLu5W+\nevXqTw9e2ixSpIhCg3pVrFgxYULeBIwWCQQQQAAB3wuke/Dyfb3UiAACCCCAAAIIIIAAAp4I3L9/\n34wRzJQpU/HixT0p7XHeK1euKFwXL148LXOTLFkyj8tHV2Djxo3NmjXLkyfPokWLUqVKFV12jiOA\nAAII/K8As4ZyHyAQOgIZMmSwhwPVsRw5cqRJk0ZhPz2QZfq5c+dOzaigZ8EiIiK05PI333xz6tQp\nc5QEAggggAACCCCAAAIIIIAAAggggECICfzxxx/nzp2zOlW7dm3F6vzawX79+h05ckRBwapVq/r8\nRNu2bdOT7hkzZtRCOfp2y+f1UyECCCAQqgJEBEP1ytIvBP5PQMMEZ86cuXfv3uPHj2t2da20nDp1\nauvYpUuXdKhjx46KI5YqVap///4bNmzQI2PYIYAAAggggAACCCCAAAIIIIAAAgiEkoB9ylCtKePX\nrq1du/bzzz/Pnj27Hkn3+Ym2b9/+5JNPasHyZcuWPfzww67r13df8+fPd52HowgggED4CBARDJ9r\nTU/DVEARwbJly6rzCvt16NBBIUA9Eab/Anv37l2wYEELRVHArVu3Dho06PHHH8+XL5/+XTtz5kyY\netFtBBBAAAEEEEAAAQQQQAABBBBAIOQEzJShCRIk0Grf/uvfjRs3OnXqpO+avvjiC5/P56mRjgoH\naoCjwoGaMtR1L27fvt24cWNNK+o6G0cRQACB8BEgIhg+15qehqlAly5d9NiUvfOJEiWqXr36xx9/\n/Oeffx44cGDMmDF6NCxp0qRWnr///rtv3756xqpNmzYXLlywFySNAAIIIIAAAggggAACCCCAAAII\nIBB0Anfv3l21apXV7HLlyqVPn95/XXjvvfc0VZW+VtLEnr49i0YH1qhRQ3UqHFi4cGHXla9cubJe\nvXp6Jr5du3auc3IUAQQQCB+BhOHTVXqKQHgKaIygi44/+uijLz14Xbt2TQ+LLViwYOHChYcPH9ZT\nVFOmTFm/fr3GFJYsWdJFDRxCAAEEEEAAAQQQQAABBBBAAAEEEAhkAU0NpfkzrRb6dcpQnUjPoD/0\n0EOjRo3yLcivv/6qEOPFixf1TZeCjk4rV+Dz1q1bly9f1ldbx44dU568efMqAuo0MzsRQACBMBQg\nIhiGF50uI+BEIHny5HpySi8d27Vr17x58yZPnqxBhBUrVlyyZEmVKlWclGEXAggggAACCCCAAAII\nIIAAAggggEDAC/zyyy+mjU2bNjVp3ybu3LnTsWNH/dR8VBkyZPBh5UuXLm3UqNHVq1dV544HLzcr\nb9u2rZs5yYYAAgiEgwARwXC4yvQRAc8ENPGCXv369du0aVPPnj07d+6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CCCCAAAIIeCxA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XNDakAAAQQQQAABBAJQ4O7du5s3b9akl/o3/tChQ7lz59aHer00E2aiRIl8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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 17, "metadata": { "image/png": { "width": "100%" } }, "output_type": "execute_result" } ], "source": [ "Image('images/FIR_LPF_Design.png',width='100%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can test this filter in Lab3 using PyAudio for real-time DSP." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Design the filter here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Plot the magnitude response and phase response, and the pole-zero plot\n", "* Using the `freqz_resp_list`\n", "```Python\n", "def freqz_resp_list(b,a=np.array([1]),mode = 'dB',fs=1.0,Npts = 1024,fsize=(6,4)):\n", "\"\"\"\n", " A method for displaying a list filter frequency responses in magnitude,\n", " phase, and group delay. A plot is produced using matplotlib\n", "\n", " freqz_resp([b],[a],mode = 'dB',Npts = 1024,fsize=(6,4))\n", "\n", " b = ndarray of numerator coefficients\n", " a = ndarray of denominator coefficents\n", " mode = display mode: 'dB' magnitude, 'phase' in radians, or \n", " 'groupdelay_s' in samples and 'groupdelay_t' in sec, \n", " all versus frequency in Hz\n", " Npts = number of points to plot; default is 1024\n", " fsize = figure size; defult is (6,4) inches\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# fill in the plotting details\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-2-clause

authman/DAT210x

Module5/Module5 - Lab4.ipynb

1

16783

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DAT210x - Programming with Python for DS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module5- Lab4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "\n", "from sklearn import preprocessing\n", "from sklearn.decomposition import PCA\n", "\n", "# You might need to import more modules here..\n", "# .. your code here ..\n", "\n", "matplotlib.style.use('ggplot') # Look Pretty\n", "c = ['red', 'green', 'blue', 'orange', 'yellow', 'brown']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can experiment with these parameters:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PLOT_TYPE_TEXT = False # If you'd like to see indices\n", "PLOT_VECTORS = True # If you'd like to see your original features in P.C.-Space" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some Convenience Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drawVectors(transformed_features, components_, columns, plt):\n", " num_columns = len(columns)\n", "\n", " # This function will project your *original* feature (columns)\n", " # onto your principal component feature-space, so that you can\n", " # visualize how \"important\" each one was in the\n", " # multi-dimensional scaling\n", "\n", " # Scale the principal components by the max value in\n", " # the transformed set belonging to that component\n", " xvector = components_[0] * max(transformed_features[:,0])\n", " yvector = components_[1] * max(transformed_features[:,1])\n", "\n", " ## Visualize projections\n", "\n", " # Sort each column by its length. These are your *original*\n", " # columns, not the principal components.\n", " important_features = { columns[i] : math.sqrt(xvector[i]**2 + yvector[i]**2) for i in range(num_columns) }\n", " important_features = sorted(zip(important_features.values(), important_features.keys()), reverse=True)\n", " print(\"Projected Features by importance:\\n\", important_features)\n", "\n", " ax = plt.axes()\n", "\n", " for i in range(num_columns):\n", " # Use an arrow to project each original feature as a\n", " # labeled vector on your principal component axes\n", " plt.arrow(0, 0, xvector[i], yvector[i], color='b', width=0.0005, head_width=0.02, alpha=0.75, zorder=600000)\n", " plt.text(xvector[i]*1.2, yvector[i]*1.2, list(columns)[i], color='b', alpha=0.75, zorder=600000)\n", " \n", " return ax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def doPCA(data, dimensions=2):\n", " model = PCA(n_components=dimensions, svd_solver='randomized', random_state=7)\n", " model.fit(data)\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def doKMeans(data, num_clusters=0):\n", " # TODO: Do the KMeans clustering here, passing in the # of clusters parameter\n", " # and fit it against your data. Then, return a tuple containing the cluster\n", " # centers and the labels.\n", " #\n", " # Hint: Just like with doPCA above, you will have to create a variable called\n", " # `model`, which will be a SKLearn K-Means model for this to work.\n", " \n", " \n", " # .. your code here ..\n", " return model.cluster_centers_, model.labels_" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Load up the dataset. It may or may not have nans in it. Make sure you catch them and destroy them, by setting them to `0`. This is valid for this dataset, since if the value is missing, you can assume no money was spent on it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As instructed, get rid of the `Channel` and `Region` columns, since you'll be investigating as if this were a single location wholesaler, rather than a national / international one. Leaving these fields in here would cause KMeans to examine and give weight to them:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Before unitizing / standardizing / normalizing your data in preparation for K-Means, it's a good idea to get a quick peek at it. You can do this using the `.describe()` method, or even by using the built-in pandas `df.plot.hist()`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having checked out your data, you may have noticed there's a pretty big gap between the top customers in each feature category and the rest. Some feature scaling algorithms won't get rid of outliers for you, so it's a good idea to handle that manually---particularly if your goal is NOT to determine the top customers. \n", "\n", "After all, you can do that with a simple Pandas `.sort_values()` and not a machine learning clustering algorithm. From a business perspective, you're probably more interested in clustering your +/- 2 standard deviation customers, rather than the top and bottom customers.\n", "\n", "Remove top 5 and bottom 5 samples for each column:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "drop = {}\n", "for col in df.columns:\n", " # Bottom 5\n", " sort = df.sort_values(by=col, ascending=True)\n", " if len(sort) > 5: sort=sort[:5]\n", " for index in sort.index: drop[index] = True # Just store the index once\n", "\n", " # Top 5\n", " sort = df.sort_values(by=col, ascending=False)\n", " if len(sort) > 5: sort=sort[:5]\n", " for index in sort.index: drop[index] = True # Just store the index once" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Drop rows by index. We do this all at once in case there is a collision. This way, we don't end up dropping more rows than we have to, if there is a single row that satisfies the drop for multiple columns. Since there are 6 rows, if we end up dropping < 5*6*2 = 60 rows, that means there indeed were collisions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Dropping {0} Outliers...\".format(len(drop)))\n", "df.drop(inplace=True, labels=drop.keys(), axis=0)\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### What are you interested in?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Depending on what you're interested in, you might take a different approach to normalizing/standardizing your data.\n", " \n", "You should note that all columns left in the dataset are of the same unit. You might ask yourself, do I even need to normalize / standardize the data? The answer depends on what you're trying to accomplish. For instance, although all the units are the same (generic money unit), the price per item in your store isn't. There may be some cheap items and some expensive one. If your goal is to find out what items people tend to buy together but you didn't \"unitize\" properly before running kMeans, the contribution of the lesser priced item would be dwarfed by the more expensive item. This is an issue of scale.\n", "\n", "For a great overview on a few of the normalization methods supported in SKLearn, please check out: https://stackoverflow.com/questions/30918781/right-function-for-normalizing-input-of-sklearn-svm\n", "\n", "Suffice to say, at the end of the day, you're going to have to know what question you want answered and what data you have available in order to select the best method for your purpose. Luckily, SKLearn's interfaces are easy to switch out so in the mean time, you can experiment with all of them and see how they alter your results.\n", "\n", "5-sec summary before you dive deeper online:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say your user spend a LOT. Normalization divides each item by the average overall amount of spending. Stated differently, your new feature is = the contribution of overall spending going into that particular item: \\$spent on feature / \\$overall spent by sample." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### MinMax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What % in the overall range of $spent by all users on THIS particular feature is the current sample's feature at? When you're dealing with all the same units, this will produce a near face-value amount. Be careful though: if you have even a single outlier, it can cause all your data to get squashed up in lower percentages.\n", "\n", "Imagine your buyers usually spend \\$100 on wholesale milk, but today only spent \\$20. This is the relationship you're trying to capture with MinMax. NOTE: MinMax doesn't standardize (std. dev.); it only normalizes / unitizes your feature, in the mathematical sense. MinMax can be used as an alternative to zero mean, unit variance scaling. [(sampleFeatureValue-min) / (max-min)] * (max-min) + min Where min and max are for the overall feature values for all samples." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Back to The Assignment" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Un-comment just ***ONE*** of lines at a time and see how alters your results. Pay attention to the direction of the arrows, as well as their LENGTHS:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#T = preprocessing.StandardScaler().fit_transform(df)\n", "#T = preprocessing.MinMaxScaler().fit_transform(df)\n", "#T = preprocessing.MaxAbsScaler().fit_transform(df)\n", "#T = preprocessing.Normalizer().fit_transform(df)\n", "T = df # No Change" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Sometimes people perform PCA before doing KMeans, so that KMeans only operates on the most meaningful features. In our case, there are so few features that doing PCA ahead of time isn't really necessary, and you can do KMeans in feature space. But keep in mind you have the option to transform your data to bring down its dimensionality. If you take that route, then your Clusters will already be in PCA-transformed feature space, and you won't have to project them again for visualization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Do KMeans\n", "\n", "n_clusters = 3\n", "centroids, labels = doKMeans(T, n_clusters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print out your centroids. They're currently in feature-space, which is good. Print them out before you transform them into PCA space for viewing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# .. your code here .." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've clustered our KMeans, let's do PCA, using it as a tool to visualize the results. Project the centroids as well as the samples into the new 2D feature space for visualization purposes:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display_pca = doPCA(T)\n", "T = display_pca.transform(T)\n", "CC = display_pca.transform(centroids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize all the samples. Give them the color of their cluster label" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "if PLOT_TYPE_TEXT:\n", " # Plot the index of the sample, so you can further investigate it in your dset\n", " for i in range(len(T)): ax.text(T[i,0], T[i,1], df.index[i], color=c[labels[i]], alpha=0.75, zorder=600000)\n", " ax.set_xlim(min(T[:,0])*1.2, max(T[:,0])*1.2)\n", " ax.set_ylim(min(T[:,1])*1.2, max(T[:,1])*1.2)\n", "else:\n", " # Plot a regular scatter plot\n", " sample_colors = [ c[labels[i]] for i in range(len(T)) ]\n", " ax.scatter(T[:, 0], T[:, 1], c=sample_colors, marker='o', alpha=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Centroids as X's, and label them" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ax.scatter(CC[:, 0], CC[:, 1], marker='x', s=169, linewidths=3, zorder=1000, c=c)\n", "for i in range(len(centroids)):\n", " ax.text(CC[i, 0], CC[i, 1], str(i), zorder=500010, fontsize=18, color=c[i])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Display feature vectors for investigation:\n", "if PLOT_VECTORS:\n", " drawVectors(T, display_pca.components_, df.columns, plt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Add the cluster label back into the dataframe and display it:\n", "df['label'] = pd.Series(labels, index=df.index)\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "58px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ajackwin/data

pew-religions/Religion-Leah.py.ipynb

37

38187

{"nbformat_minor": 0, "cells": [{"execution_count": 39, "cell_type": "code", "source": "### Leah Libresco\n### [email protected]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 40, "cell_type": "code", "source": "import numpy as np\nimport pandas as pd", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "# List of 12 U.S. religious groups stuided by Pew\nreligions = ['Buddhist', 'Catholic', 'Evangel Prot', 'Hindu', 'Hist Black Prot', 'Jehovahs Witness', 'Jewish', 'Mainline Prot', 'Mormon', 'Muslim', 'Orthodox Christian', 'Unaffiliated']", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 42, "cell_type": "code", "source": "# Create a .csv file with a function to write to it \ncsv = open(\"current.csv\", 'w')\ncsv.truncate()\n\ndef write_row(matrix):\n arr = np.asarray(matrix[0])[0]\n row = ','.join([str(a) for a in arr]) + '\\n'\n csv.write(row)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 43, "cell_type": "code", "source": "# Intitial distribution of religions in US\nfirst = np.matrix([.007, .208, .254, .007, .065, .008, .019, .147, .016, .009, .005, .228])\n\n# Normed to sum to 100%\ncurrent = first / np.sum(first)\nt0 = current\nwrite_row(current)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 44, "cell_type": "code", "source": "# Transition matrix \ntrans = np.matrix(((0.390296314, 0.027141947, 0.06791021, 0.001857564, 0, 0, 0.011166082, 0.059762879, 0, 0, 0, 0.396569533),\n (0.005370791, 0.593173325, 0.103151608, 0.000649759, 0.010486747, 0.005563864, 0.002041424, 0.053825329, 0.004760476, 0.001130529, 0.000884429, 0.199488989),\n (0.00371836, 0.023900817, 0.650773331, 0.000250102, 0.016774503, 0.003098214, 0.001865491, 0.122807467, 0.004203107, 0.000186572, 0.002123778, 0.151866648),\n (0, 0, 0.0033732, 0.804072618, 0, 0.001511151, 0, 0.01234639, 0, 0.00209748, 0, 0.17659916),\n (0.002051357, 0.016851659, 0.09549708, 0, 0.699214315, 0.010620473, 0.000338804, 0.024372871, 0.000637016, 0.009406884, 0.000116843, 0.129892558),\n (0, 0.023278276, 0.109573979, 0, 0.077957568, 0.336280578, 0, 0.074844833, 0.007624035, 0, 0, 0.35110361),\n (0.006783201, 0.004082693, 0.014329604, 0, 0, 0.000610585, 0.745731278, 0.009587587, 0, 0, 0.002512334, 0.184058682),\n (0.005770357, 0.038017215, 0.187857555, 0.000467601, 0.008144075, 0.004763516, 0.003601208, 0.451798506, 0.005753587, 0.000965543, 0.00109818, 0.25750798),\n (0.007263135, 0.01684885, 0.06319935, 0.000248467, 0.0059394, 0, 0.001649896, 0.03464334, 0.642777489, 0.002606278, 0, 0.208904711),\n (0, 0.005890381, 0.023573308, 0, 0.011510643, 0, 0.005518343, 0.014032084, 0, 0.772783807, 0, 0.15424369),\n (0.004580353, 0.042045841, 0.089264134\t, 0, 0.00527346, 0, 0, 0.061471387, 0.005979218, 0.009113978, 0.526728084, 0.243246723),\n (0.006438308, 0.044866331, 0.1928814, 0.002035375, 0.04295005, 0.010833621, 0.011541439, 0.09457963, 0.01365141, 0.005884336, 0.002892072, 0.525359211)))\n\n# Fertility array\nfert = np.matrix(((2.1, 2.3, 2.3, 2.1, 2.5, 2.1, 2, 1.9, 3.4, 2.8, 2.1, 1.7)))\n\n# Create data frame for printing later\nreligionDataFrame = pd.DataFrame()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 45, "cell_type": "code", "source": "# Run model\nfor x in range(0,100):\n\n ### beginning of conversion step\n \n # apply transition matrix to current distribution\n current = current * trans\n \n ### beginning of fertility step\n \n # divide by two for couple number\n current = current/2\n \n # adjust by fertility\n \n current = np.multiply(fert, current)\n \n # normalize to 100%\n \n current = current / np.sum(current)\n \n write_row(current)\n \n # add to data frame\n religionDataFrame = religionDataFrame.append(pd.DataFrame(current), ignore_index=True)\n\ncsv.close()", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 47, "cell_type": "code", "source": "# Print data frame with results\nreligionDataFrame.columns = religions\nreligionDataFrame", "outputs": [{"execution_count": 47, "output_type": "execute_result", "data": {"text/plain": " Buddhist Catholic Evangel Prot Hindu Hist Black Prot \\\n0 0.007924 0.170263 0.309520 0.006728 0.080042 \n1 0.007981 0.140118 0.331889 0.006234 0.090308 \n2 0.007856 0.119514 0.340401 0.005752 0.097890 \n3 0.007710 0.105453 0.341544 0.005303 0.103242 \n4 0.007583 0.095775 0.338780 0.004895 0.106781 \n5 0.007482 0.088996 0.334004 0.004532 0.108869 \n6 0.007402 0.084125 0.328280 0.004213 0.109811 \n7 0.007339 0.080515 0.322217 0.003934 0.109868 \n8 0.007288 0.077748 0.316165 0.003692 0.109259 \n9 0.007247 0.075553 0.310324 0.003483 0.108167 \n10 0.007214 0.073759 0.304804 0.003302 0.106743 \n11 0.007186 0.072254 0.299659 0.003148 0.105108 \n12 0.007163 0.070967 0.294911 0.003015 0.103356 \n13 0.007144 0.069849 0.290562 0.002901 0.101562 \n14 0.007128 0.068869 0.286601 0.002804 0.099779 \n15 0.007114 0.068004 0.283011 0.002720 0.098049 \n16 0.007102 0.067237 0.279769 0.002649 0.096397 \n17 0.007093 0.066557 0.276852 0.002587 0.094844 \n18 0.007084 0.065953 0.274235 0.002535 0.093398 \n19 0.007077 0.065416 0.271894 0.002489 0.092065 \n20 0.007071 0.064939 0.269805 0.002450 0.090845 \n21 0.007066 0.064516 0.267944 0.002417 0.089735 \n22 0.007062 0.064142 0.266290 0.002387 0.088731 \n23 0.007059 0.063810 0.264823 0.002362 0.087827 \n24 0.007056 0.063517 0.263523 0.002341 0.087016 \n25 0.007053 0.063259 0.262374 0.002322 0.086291 \n26 0.007051 0.063030 0.261359 0.002305 0.085644 \n27 0.007049 0.062829 0.260463 0.002291 0.085068 \n28 0.007048 0.062652 0.259674 0.002279 0.084557 \n29 0.007047 0.062497 0.258980 0.002268 0.084104 \n.. ... ... ... ... ... \n70 0.007056 0.061429 0.254164 0.002197 0.080749 \n71 0.007056 0.061429 0.254164 0.002197 0.080746 \n72 0.007056 0.061429 0.254164 0.002197 0.080744 \n73 0.007056 0.061429 0.254164 0.002197 0.080742 \n74 0.007056 0.061429 0.254165 0.002197 0.080740 \n75 0.007057 0.061429 0.254165 0.002197 0.080738 \n76 0.007057 0.061430 0.254166 0.002197 0.080737 \n77 0.007057 0.061430 0.254167 0.002197 0.080735 \n78 0.007057 0.061430 0.254168 0.002197 0.080734 \n79 0.007057 0.061431 0.254169 0.002197 0.080733 \n80 0.007057 0.061431 0.254170 0.002197 0.080732 \n81 0.007058 0.061431 0.254171 0.002197 0.080731 \n82 0.007058 0.061432 0.254172 0.002197 0.080730 \n83 0.007058 0.061432 0.254173 0.002197 0.080729 \n84 0.007058 0.061432 0.254174 0.002197 0.080728 \n85 0.007058 0.061433 0.254175 0.002197 0.080728 \n86 0.007058 0.061433 0.254176 0.002197 0.080727 \n87 0.007058 0.061433 0.254177 0.002197 0.080727 \n88 0.007059 0.061433 0.254178 0.002197 0.080726 \n89 0.007059 0.061434 0.254179 0.002197 0.080726 \n90 0.007059 0.061434 0.254180 0.002197 0.080725 \n91 0.007059 0.061434 0.254181 0.002197 0.080725 \n92 0.007059 0.061435 0.254182 0.002197 0.080724 \n93 0.007059 0.061435 0.254183 0.002197 0.080724 \n94 0.007059 0.061435 0.254184 0.002197 0.080723 \n95 0.007059 0.061435 0.254185 0.002197 0.080723 \n96 0.007059 0.061436 0.254186 0.002197 0.080723 \n97 0.007059 0.061436 0.254186 0.002197 0.080722 \n98 0.007059 0.061436 0.254187 0.002197 0.080722 \n99 0.007059 0.061436 0.254188 0.002197 0.080722 \n\n Jehovahs Witness Jewish Mainline Prot Mormon Muslim \\\n0 0.008986 0.018492 0.128123 0.028069 0.013271 \n1 0.008980 0.017417 0.120477 0.039642 0.017382 \n2 0.008785 0.016412 0.117260 0.051127 0.021558 \n3 0.008572 0.015526 0.115117 0.062488 0.025770 \n4 0.008375 0.014768 0.113124 0.073661 0.029978 \n5 0.008197 0.014130 0.111103 0.084574 0.034138 \n6 0.008031 0.013595 0.109070 0.095151 0.038205 \n7 0.007874 0.013148 0.107080 0.105324 0.042139 \n8 0.007723 0.012773 0.105176 0.115035 0.045908 \n9 0.007578 0.012457 0.103386 0.124239 0.049485 \n10 0.007441 0.012190 0.101724 0.132901 0.052850 \n11 0.007310 0.011963 0.100194 0.141003 0.055991 \n12 0.007187 0.011770 0.098797 0.148534 0.058901 \n13 0.007072 0.011605 0.097526 0.155496 0.061578 \n14 0.006965 0.011462 0.096376 0.161900 0.064025 \n15 0.006867 0.011339 0.095339 0.167762 0.066250 \n16 0.006777 0.011232 0.094407 0.173106 0.068260 \n17 0.006695 0.011139 0.093571 0.177959 0.070069 \n18 0.006620 0.011058 0.092823 0.182351 0.071688 \n19 0.006553 0.010988 0.092155 0.186313 0.073130 \n20 0.006492 0.010925 0.091561 0.189877 0.074411 \n21 0.006437 0.010871 0.091033 0.193076 0.075542 \n22 0.006389 0.010823 0.090564 0.195942 0.076539 \n23 0.006345 0.010781 0.090148 0.198503 0.077414 \n24 0.006306 0.010744 0.089781 0.200789 0.078178 \n25 0.006272 0.010711 0.089456 0.202827 0.078845 \n26 0.006242 0.010682 0.089170 0.204642 0.079423 \n27 0.006215 0.010657 0.088917 0.206256 0.079923 \n28 0.006191 0.010635 0.088695 0.207690 0.080353 \n29 0.006170 0.010615 0.088500 0.208965 0.080723 \n.. ... ... ... ... ... \n70 0.006019 0.010457 0.087162 0.219413 0.081818 \n71 0.006019 0.010457 0.087162 0.219435 0.081798 \n72 0.006019 0.010456 0.087163 0.219457 0.081777 \n73 0.006019 0.010456 0.087163 0.219477 0.081758 \n74 0.006018 0.010456 0.087163 0.219496 0.081739 \n75 0.006018 0.010456 0.087163 0.219514 0.081721 \n76 0.006018 0.010455 0.087164 0.219531 0.081703 \n77 0.006018 0.010455 0.087164 0.219548 0.081686 \n78 0.006018 0.010455 0.087165 0.219563 0.081670 \n79 0.006018 0.010455 0.087165 0.219578 0.081654 \n80 0.006018 0.010455 0.087166 0.219592 0.081639 \n81 0.006018 0.010454 0.087166 0.219605 0.081625 \n82 0.006018 0.010454 0.087166 0.219617 0.081610 \n83 0.006018 0.010454 0.087167 0.219629 0.081597 \n84 0.006018 0.010454 0.087167 0.219641 0.081584 \n85 0.006018 0.010454 0.087168 0.219652 0.081571 \n86 0.006018 0.010454 0.087168 0.219662 0.081559 \n87 0.006018 0.010454 0.087169 0.219672 0.081548 \n88 0.006018 0.010453 0.087169 0.219682 0.081536 \n89 0.006018 0.010453 0.087169 0.219691 0.081526 \n90 0.006018 0.010453 0.087170 0.219699 0.081515 \n91 0.006018 0.010453 0.087170 0.219708 0.081505 \n92 0.006018 0.010453 0.087170 0.219716 0.081496 \n93 0.006018 0.010453 0.087171 0.219723 0.081487 \n94 0.006018 0.010453 0.087171 0.219731 0.081478 \n95 0.006018 0.010453 0.087171 0.219738 0.081469 \n96 0.006018 0.010453 0.087172 0.219744 0.081461 \n97 0.006018 0.010452 0.087172 0.219751 0.081453 \n98 0.006018 0.010452 0.087172 0.219757 0.081446 \n99 0.006018 0.010452 0.087173 0.219763 0.081438 \n\n Orthodox Christian Unaffiliated \n0 0.004466 0.224115 \n1 0.004070 0.215501 \n2 0.003812 0.209631 \n3 0.003629 0.205648 \n4 0.003485 0.202793 \n5 0.003364 0.200612 \n6 0.003258 0.198860 \n7 0.003162 0.197401 \n8 0.003076 0.196158 \n9 0.002997 0.195083 \n10 0.002925 0.194146 \n11 0.002860 0.193323 \n12 0.002800 0.192599 \n13 0.002747 0.191958 \n14 0.002699 0.191392 \n15 0.002656 0.190890 \n16 0.002617 0.190446 \n17 0.002582 0.190053 \n18 0.002551 0.189705 \n19 0.002524 0.189397 \n20 0.002499 0.189125 \n21 0.002477 0.188885 \n22 0.002458 0.188674 \n23 0.002441 0.188487 \n24 0.002425 0.188323 \n25 0.002412 0.188179 \n26 0.002400 0.188052 \n27 0.002390 0.187941 \n28 0.002380 0.187844 \n29 0.002372 0.187759 \n.. ... ... \n70 0.002315 0.187222 \n71 0.002315 0.187223 \n72 0.002315 0.187224 \n73 0.002315 0.187224 \n74 0.002315 0.187225 \n75 0.002315 0.187226 \n76 0.002315 0.187226 \n77 0.002315 0.187227 \n78 0.002315 0.187228 \n79 0.002315 0.187228 \n80 0.002315 0.187229 \n81 0.002315 0.187230 \n82 0.002315 0.187230 \n83 0.002315 0.187231 \n84 0.002315 0.187231 \n85 0.002315 0.187232 \n86 0.002315 0.187232 \n87 0.002315 0.187233 \n88 0.002315 0.187233 \n89 0.002315 0.187234 \n90 0.002315 0.187234 \n91 0.002315 0.187235 \n92 0.002315 0.187235 \n93 0.002315 0.187236 \n94 0.002315 0.187236 \n95 0.002315 0.187236 \n96 0.002315 0.187237 \n97 0.002315 0.187237 \n98 0.002315 0.187237 \n99 0.002315 0.187238 \n\n[100 rows x 12 columns]", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Buddhist</th>\n <th>Catholic</th>\n <th>Evangel Prot</th>\n <th>Hindu</th>\n <th>Hist Black Prot</th>\n <th>Jehovahs Witness</th>\n <th>Jewish</th>\n <th>Mainline Prot</th>\n <th>Mormon</th>\n <th>Muslim</th>\n <th>Orthodox Christian</th>\n <th>Unaffiliated</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 0.007924</td>\n <td> 0.170263</td>\n <td> 0.309520</td>\n <td> 0.006728</td>\n <td> 0.080042</td>\n <td> 0.008986</td>\n <td> 0.018492</td>\n <td> 0.128123</td>\n <td> 0.028069</td>\n <td> 0.013271</td>\n <td> 0.004466</td>\n <td> 0.224115</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 0.007981</td>\n <td> 0.140118</td>\n <td> 0.331889</td>\n <td> 0.006234</td>\n <td> 0.090308</td>\n <td> 0.008980</td>\n <td> 0.017417</td>\n <td> 0.120477</td>\n <td> 0.039642</td>\n <td> 0.017382</td>\n <td> 0.004070</td>\n <td> 0.215501</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 0.007856</td>\n <td> 0.119514</td>\n <td> 0.340401</td>\n <td> 0.005752</td>\n <td> 0.097890</td>\n <td> 0.008785</td>\n <td> 0.016412</td>\n <td> 0.117260</td>\n <td> 0.051127</td>\n <td> 0.021558</td>\n <td> 0.003812</td>\n <td> 0.209631</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0.007710</td>\n <td> 0.105453</td>\n <td> 0.341544</td>\n <td> 0.005303</td>\n <td> 0.103242</td>\n <td> 0.008572</td>\n <td> 0.015526</td>\n <td> 0.115117</td>\n <td> 0.062488</td>\n <td> 0.025770</td>\n <td> 0.003629</td>\n <td> 0.205648</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 0.007583</td>\n <td> 0.095775</td>\n <td> 0.338780</td>\n <td> 0.004895</td>\n <td> 0.106781</td>\n <td> 0.008375</td>\n <td> 0.014768</td>\n <td> 0.113124</td>\n <td> 0.073661</td>\n <td> 0.029978</td>\n <td> 0.003485</td>\n <td> 0.202793</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 0.007482</td>\n <td> 0.088996</td>\n <td> 0.334004</td>\n <td> 0.004532</td>\n <td> 0.108869</td>\n <td> 0.008197</td>\n <td> 0.014130</td>\n <td> 0.111103</td>\n <td> 0.084574</td>\n <td> 0.034138</td>\n <td> 0.003364</td>\n <td> 0.200612</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 0.007402</td>\n <td> 0.084125</td>\n <td> 0.328280</td>\n <td> 0.004213</td>\n <td> 0.109811</td>\n <td> 0.008031</td>\n <td> 0.013595</td>\n <td> 0.109070</td>\n <td> 0.095151</td>\n <td> 0.038205</td>\n <td> 0.003258</td>\n <td> 0.198860</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 0.007339</td>\n <td> 0.080515</td>\n <td> 0.322217</td>\n <td> 0.003934</td>\n <td> 0.109868</td>\n <td> 0.007874</td>\n <td> 0.013148</td>\n <td> 0.107080</td>\n <td> 0.105324</td>\n <td> 0.042139</td>\n <td> 0.003162</td>\n <td> 0.197401</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.007288</td>\n <td> 0.077748</td>\n <td> 0.316165</td>\n <td> 0.003692</td>\n <td> 0.109259</td>\n <td> 0.007723</td>\n <td> 0.012773</td>\n <td> 0.105176</td>\n <td> 0.115035</td>\n <td> 0.045908</td>\n <td> 0.003076</td>\n <td> 0.196158</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 0.007247</td>\n <td> 0.075553</td>\n <td> 0.310324</td>\n <td> 0.003483</td>\n <td> 0.108167</td>\n <td> 0.007578</td>\n <td> 0.012457</td>\n <td> 0.103386</td>\n <td> 0.124239</td>\n <td> 0.049485</td>\n <td> 0.002997</td>\n <td> 0.195083</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 0.007214</td>\n <td> 0.073759</td>\n <td> 0.304804</td>\n <td> 0.003302</td>\n <td> 0.106743</td>\n <td> 0.007441</td>\n <td> 0.012190</td>\n <td> 0.101724</td>\n <td> 0.132901</td>\n <td> 0.052850</td>\n <td> 0.002925</td>\n <td> 0.194146</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 0.007186</td>\n <td> 0.072254</td>\n <td> 0.299659</td>\n <td> 0.003148</td>\n <td> 0.105108</td>\n <td> 0.007310</td>\n <td> 0.011963</td>\n <td> 0.100194</td>\n <td> 0.141003</td>\n <td> 0.055991</td>\n <td> 0.002860</td>\n <td> 0.193323</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 0.007163</td>\n <td> 0.070967</td>\n <td> 0.294911</td>\n <td> 0.003015</td>\n <td> 0.103356</td>\n <td> 0.007187</td>\n <td> 0.011770</td>\n <td> 0.098797</td>\n <td> 0.148534</td>\n <td> 0.058901</td>\n <td> 0.002800</td>\n <td> 0.192599</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 0.007144</td>\n <td> 0.069849</td>\n <td> 0.290562</td>\n <td> 0.002901</td>\n <td> 0.101562</td>\n <td> 0.007072</td>\n <td> 0.011605</td>\n <td> 0.097526</td>\n <td> 0.155496</td>\n <td> 0.061578</td>\n <td> 0.002747</td>\n <td> 0.191958</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 0.007128</td>\n <td> 0.068869</td>\n <td> 0.286601</td>\n <td> 0.002804</td>\n <td> 0.099779</td>\n <td> 0.006965</td>\n <td> 0.011462</td>\n <td> 0.096376</td>\n <td> 0.161900</td>\n <td> 0.064025</td>\n <td> 0.002699</td>\n <td> 0.191392</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 0.007114</td>\n <td> 0.068004</td>\n <td> 0.283011</td>\n <td> 0.002720</td>\n <td> 0.098049</td>\n <td> 0.006867</td>\n <td> 0.011339</td>\n <td> 0.095339</td>\n <td> 0.167762</td>\n <td> 0.066250</td>\n <td> 0.002656</td>\n <td> 0.190890</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 0.007102</td>\n <td> 0.067237</td>\n <td> 0.279769</td>\n <td> 0.002649</td>\n <td> 0.096397</td>\n <td> 0.006777</td>\n <td> 0.011232</td>\n <td> 0.094407</td>\n <td> 0.173106</td>\n <td> 0.068260</td>\n <td> 0.002617</td>\n <td> 0.190446</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 0.007093</td>\n <td> 0.066557</td>\n <td> 0.276852</td>\n <td> 0.002587</td>\n <td> 0.094844</td>\n <td> 0.006695</td>\n <td> 0.011139</td>\n <td> 0.093571</td>\n <td> 0.177959</td>\n <td> 0.070069</td>\n <td> 0.002582</td>\n <td> 0.190053</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 0.007084</td>\n <td> 0.065953</td>\n <td> 0.274235</td>\n <td> 0.002535</td>\n <td> 0.093398</td>\n <td> 0.006620</td>\n <td> 0.011058</td>\n <td> 0.092823</td>\n <td> 0.182351</td>\n <td> 0.071688</td>\n <td> 0.002551</td>\n <td> 0.189705</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 0.007077</td>\n <td> 0.065416</td>\n <td> 0.271894</td>\n <td> 0.002489</td>\n <td> 0.092065</td>\n <td> 0.006553</td>\n <td> 0.010988</td>\n <td> 0.092155</td>\n <td> 0.186313</td>\n <td> 0.073130</td>\n <td> 0.002524</td>\n <td> 0.189397</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 0.007071</td>\n <td> 0.064939</td>\n <td> 0.269805</td>\n <td> 0.002450</td>\n <td> 0.090845</td>\n <td> 0.006492</td>\n <td> 0.010925</td>\n <td> 0.091561</td>\n <td> 0.189877</td>\n <td> 0.074411</td>\n <td> 0.002499</td>\n <td> 0.189125</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 0.007066</td>\n <td> 0.064516</td>\n <td> 0.267944</td>\n <td> 0.002417</td>\n <td> 0.089735</td>\n <td> 0.006437</td>\n <td> 0.010871</td>\n <td> 0.091033</td>\n <td> 0.193076</td>\n <td> 0.075542</td>\n <td> 0.002477</td>\n <td> 0.188885</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 0.007062</td>\n <td> 0.064142</td>\n <td> 0.266290</td>\n <td> 0.002387</td>\n <td> 0.088731</td>\n <td> 0.006389</td>\n <td> 0.010823</td>\n <td> 0.090564</td>\n <td> 0.195942</td>\n <td> 0.076539</td>\n <td> 0.002458</td>\n <td> 0.188674</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 0.007059</td>\n <td> 0.063810</td>\n <td> 0.264823</td>\n <td> 0.002362</td>\n <td> 0.087827</td>\n <td> 0.006345</td>\n <td> 0.010781</td>\n <td> 0.090148</td>\n <td> 0.198503</td>\n <td> 0.077414</td>\n <td> 0.002441</td>\n <td> 0.188487</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 0.007056</td>\n <td> 0.063517</td>\n <td> 0.263523</td>\n <td> 0.002341</td>\n <td> 0.087016</td>\n <td> 0.006306</td>\n <td> 0.010744</td>\n <td> 0.089781</td>\n <td> 0.200789</td>\n <td> 0.078178</td>\n <td> 0.002425</td>\n <td> 0.188323</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 0.007053</td>\n <td> 0.063259</td>\n <td> 0.262374</td>\n <td> 0.002322</td>\n <td> 0.086291</td>\n <td> 0.006272</td>\n <td> 0.010711</td>\n <td> 0.089456</td>\n <td> 0.202827</td>\n <td> 0.078845</td>\n <td> 0.002412</td>\n <td> 0.188179</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 0.007051</td>\n <td> 0.063030</td>\n <td> 0.261359</td>\n <td> 0.002305</td>\n <td> 0.085644</td>\n <td> 0.006242</td>\n <td> 0.010682</td>\n <td> 0.089170</td>\n <td> 0.204642</td>\n <td> 0.079423</td>\n <td> 0.002400</td>\n <td> 0.188052</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 0.007049</td>\n <td> 0.062829</td>\n <td> 0.260463</td>\n <td> 0.002291</td>\n <td> 0.085068</td>\n <td> 0.006215</td>\n <td> 0.010657</td>\n <td> 0.088917</td>\n <td> 0.206256</td>\n <td> 0.079923</td>\n <td> 0.002390</td>\n <td> 0.187941</td>\n </tr>\n <tr>\n <th>28</th>\n <td> 0.007048</td>\n <td> 0.062652</td>\n <td> 0.259674</td>\n <td> 0.002279</td>\n <td> 0.084557</td>\n <td> 0.006191</td>\n <td> 0.010635</td>\n <td> 0.088695</td>\n <td> 0.207690</td>\n <td> 0.080353</td>\n <td> 0.002380</td>\n <td> 0.187844</td>\n </tr>\n <tr>\n <th>29</th>\n <td> 0.007047</td>\n <td> 0.062497</td>\n <td> 0.258980</td>\n <td> 0.002268</td>\n <td> 0.084104</td>\n <td> 0.006170</td>\n <td> 0.010615</td>\n <td> 0.088500</td>\n <td> 0.208965</td>\n <td> 0.080723</td>\n <td> 0.002372</td>\n <td> 0.187759</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>70</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080749</td>\n <td> 0.006019</td>\n <td> 0.010457</td>\n <td> 0.087162</td>\n <td> 0.219413</td>\n <td> 0.081818</td>\n <td> 0.002315</td>\n <td> 0.187222</td>\n </tr>\n <tr>\n <th>71</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080746</td>\n <td> 0.006019</td>\n <td> 0.010457</td>\n <td> 0.087162</td>\n <td> 0.219435</td>\n <td> 0.081798</td>\n <td> 0.002315</td>\n <td> 0.187223</td>\n </tr>\n <tr>\n <th>72</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080744</td>\n <td> 0.006019</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219457</td>\n <td> 0.081777</td>\n <td> 0.002315</td>\n <td> 0.187224</td>\n </tr>\n <tr>\n <th>73</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254164</td>\n <td> 0.002197</td>\n <td> 0.080742</td>\n <td> 0.006019</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219477</td>\n <td> 0.081758</td>\n <td> 0.002315</td>\n <td> 0.187224</td>\n </tr>\n <tr>\n <th>74</th>\n <td> 0.007056</td>\n <td> 0.061429</td>\n <td> 0.254165</td>\n <td> 0.002197</td>\n <td> 0.080740</td>\n <td> 0.006018</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219496</td>\n <td> 0.081739</td>\n <td> 0.002315</td>\n <td> 0.187225</td>\n </tr>\n <tr>\n <th>75</th>\n <td> 0.007057</td>\n <td> 0.061429</td>\n <td> 0.254165</td>\n <td> 0.002197</td>\n <td> 0.080738</td>\n <td> 0.006018</td>\n <td> 0.010456</td>\n <td> 0.087163</td>\n <td> 0.219514</td>\n <td> 0.081721</td>\n <td> 0.002315</td>\n <td> 0.187226</td>\n </tr>\n <tr>\n <th>76</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254166</td>\n <td> 0.002197</td>\n <td> 0.080737</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087164</td>\n <td> 0.219531</td>\n <td> 0.081703</td>\n <td> 0.002315</td>\n <td> 0.187226</td>\n </tr>\n <tr>\n <th>77</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254167</td>\n <td> 0.002197</td>\n <td> 0.080735</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087164</td>\n <td> 0.219548</td>\n <td> 0.081686</td>\n <td> 0.002315</td>\n <td> 0.187227</td>\n </tr>\n <tr>\n <th>78</th>\n <td> 0.007057</td>\n <td> 0.061430</td>\n <td> 0.254168</td>\n <td> 0.002197</td>\n <td> 0.080734</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087165</td>\n <td> 0.219563</td>\n <td> 0.081670</td>\n <td> 0.002315</td>\n <td> 0.187228</td>\n </tr>\n <tr>\n <th>79</th>\n <td> 0.007057</td>\n <td> 0.061431</td>\n <td> 0.254169</td>\n <td> 0.002197</td>\n <td> 0.080733</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087165</td>\n <td> 0.219578</td>\n <td> 0.081654</td>\n <td> 0.002315</td>\n <td> 0.187228</td>\n </tr>\n <tr>\n <th>80</th>\n <td> 0.007057</td>\n <td> 0.061431</td>\n <td> 0.254170</td>\n <td> 0.002197</td>\n <td> 0.080732</td>\n <td> 0.006018</td>\n <td> 0.010455</td>\n <td> 0.087166</td>\n <td> 0.219592</td>\n <td> 0.081639</td>\n <td> 0.002315</td>\n <td> 0.187229</td>\n </tr>\n <tr>\n <th>81</th>\n <td> 0.007058</td>\n <td> 0.061431</td>\n <td> 0.254171</td>\n <td> 0.002197</td>\n <td> 0.080731</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087166</td>\n <td> 0.219605</td>\n <td> 0.081625</td>\n <td> 0.002315</td>\n <td> 0.187230</td>\n </tr>\n <tr>\n <th>82</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254172</td>\n <td> 0.002197</td>\n <td> 0.080730</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087166</td>\n <td> 0.219617</td>\n <td> 0.081610</td>\n <td> 0.002315</td>\n <td> 0.187230</td>\n </tr>\n <tr>\n <th>83</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254173</td>\n <td> 0.002197</td>\n <td> 0.080729</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087167</td>\n <td> 0.219629</td>\n <td> 0.081597</td>\n <td> 0.002315</td>\n <td> 0.187231</td>\n </tr>\n <tr>\n <th>84</th>\n <td> 0.007058</td>\n <td> 0.061432</td>\n <td> 0.254174</td>\n <td> 0.002197</td>\n <td> 0.080728</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087167</td>\n <td> 0.219641</td>\n <td> 0.081584</td>\n <td> 0.002315</td>\n <td> 0.187231</td>\n </tr>\n <tr>\n <th>85</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254175</td>\n <td> 0.002197</td>\n <td> 0.080728</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087168</td>\n <td> 0.219652</td>\n <td> 0.081571</td>\n <td> 0.002315</td>\n <td> 0.187232</td>\n </tr>\n <tr>\n <th>86</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254176</td>\n <td> 0.002197</td>\n <td> 0.080727</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087168</td>\n <td> 0.219662</td>\n <td> 0.081559</td>\n <td> 0.002315</td>\n <td> 0.187232</td>\n </tr>\n <tr>\n <th>87</th>\n <td> 0.007058</td>\n <td> 0.061433</td>\n <td> 0.254177</td>\n <td> 0.002197</td>\n <td> 0.080727</td>\n <td> 0.006018</td>\n <td> 0.010454</td>\n <td> 0.087169</td>\n <td> 0.219672</td>\n <td> 0.081548</td>\n <td> 0.002315</td>\n <td> 0.187233</td>\n </tr>\n <tr>\n <th>88</th>\n <td> 0.007059</td>\n <td> 0.061433</td>\n <td> 0.254178</td>\n <td> 0.002197</td>\n <td> 0.080726</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087169</td>\n <td> 0.219682</td>\n <td> 0.081536</td>\n <td> 0.002315</td>\n <td> 0.187233</td>\n </tr>\n <tr>\n <th>89</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254179</td>\n <td> 0.002197</td>\n <td> 0.080726</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087169</td>\n <td> 0.219691</td>\n <td> 0.081526</td>\n <td> 0.002315</td>\n <td> 0.187234</td>\n </tr>\n <tr>\n <th>90</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254180</td>\n <td> 0.002197</td>\n <td> 0.080725</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219699</td>\n <td> 0.081515</td>\n <td> 0.002315</td>\n <td> 0.187234</td>\n </tr>\n <tr>\n <th>91</th>\n <td> 0.007059</td>\n <td> 0.061434</td>\n <td> 0.254181</td>\n <td> 0.002197</td>\n <td> 0.080725</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219708</td>\n <td> 0.081505</td>\n <td> 0.002315</td>\n <td> 0.187235</td>\n </tr>\n <tr>\n <th>92</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254182</td>\n <td> 0.002197</td>\n <td> 0.080724</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087170</td>\n <td> 0.219716</td>\n <td> 0.081496</td>\n <td> 0.002315</td>\n <td> 0.187235</td>\n </tr>\n <tr>\n <th>93</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254183</td>\n <td> 0.002197</td>\n <td> 0.080724</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219723</td>\n <td> 0.081487</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>94</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254184</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219731</td>\n <td> 0.081478</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>95</th>\n <td> 0.007059</td>\n <td> 0.061435</td>\n <td> 0.254185</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087171</td>\n <td> 0.219738</td>\n <td> 0.081469</td>\n <td> 0.002315</td>\n <td> 0.187236</td>\n </tr>\n <tr>\n <th>96</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254186</td>\n <td> 0.002197</td>\n <td> 0.080723</td>\n <td> 0.006018</td>\n <td> 0.010453</td>\n <td> 0.087172</td>\n <td> 0.219744</td>\n <td> 0.081461</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>97</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254186</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087172</td>\n <td> 0.219751</td>\n <td> 0.081453</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>98</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254187</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087172</td>\n <td> 0.219757</td>\n <td> 0.081446</td>\n <td> 0.002315</td>\n <td> 0.187237</td>\n </tr>\n <tr>\n <th>99</th>\n <td> 0.007059</td>\n <td> 0.061436</td>\n <td> 0.254188</td>\n <td> 0.002197</td>\n <td> 0.080722</td>\n <td> 0.006018</td>\n <td> 0.010452</td>\n <td> 0.087173</td>\n <td> 0.219763</td>\n <td> 0.081438</td>\n <td> 0.002315</td>\n <td> 0.187238</td>\n </tr>\n </tbody>\n</table>\n<p>100 rows \u00d7 12 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}

mit

dani-lbnl/2017_ucberkeley_course

code/ZeissMicroscopyCenter2017_DaniUshizima_lecture.ipynb

1

750797

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Image exploration using Python - essentials\n", "1. Read image from web\n", "2. Querying image: matrix, sub-matrices, ROI\n", "3. Image transformations: filtering\n", "4. Immunohistochemistry example from scikit-image\n", "5. Segmentation and feature extraction \n", "6. Save information as a xls file \n", "7. Simulating 2D images - \"cells\"\n", "8. Simulate particles\n", "9. Check particle neighborhood: groups (clustering algorithms)\n", "10. Pandas and Seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read image from web and scikit-image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "\n", "from matplotlib import pyplot as plt\n", "from skimage import data, io" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x10a3752b0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QMyqKXZVLrVxa5ThcnqLaOFl0NT+B/nTl+fLMcXRCdG2SzvUoJtTSOUpjdDAT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KAAAg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\nEJUwkNCN27sbnn3hB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EQbZC1xXZnjerU7bPaOnDMxJYYhEIZAGAYu84XXb95yOp8kLaEW\nTvfvsQreH7/C3WGPNpaUMookN5MSr9MwBlCFUlVfxOUCtdZw2E3sxj1uf8dlvnCZZ+Y5ssSFmhKj\n1uz2imEcsCZQLsIQ1IcbJm0wWfoMtrsaddfg5CzmyNwaNSdiTKxrpilpRKdcuCwrD6cTBc1u2rEf\nB6ZpwgSH2wX2dwfcECh1JbYslowwcVpPzGWFBj6eGbTisLvrE7LCuJsY9oHYFrCNaT+xv5kEpkhD\nO9C264Uaki7aRaI5VnJqqKLwdpAQQzfh3IDtSJPtCFZr65W3um6y6yoVqiiKyzW40PvOn66F9XER\n7xSSJDKXE/MSIUSqcyx1FYCakQqGqyROSa+mNlmMihAsTR8hl9r7QykT198fns4nSGfjI35ztfMR\n8Hc/9RyvlLr5LdXOR/1nv+3j//iVb2F7h3xj6vzQiyf8yFc+6uM+sP1GNFsWVm/Aaq17jng/cvVF\nZ1mFbyOSdispmxW8EXhRa0XCx5LEoxSjOs+Gq6+lNShZ9XJzayp3AWeV8XbtBEOnG7UolLJoLV6x\npkQZLExbcyW55ZyBxocMKINWBdURAttCAWCd66pkOWrImd12JKXogtYkPicJDoyImNdfOSkpJayz\n3ITA7TgyzCPH0wkfPIfDgePpJcs6s8aJT16/YRgGpuBJdaWtmbHnkENXiVuZKiptSaVQYpZRaymc\nl8RQwDvLYA7oaUecZ+JlwRrhHQU7YL3ncZ5ZW8b4gPEG7TVrE/Gb7gdmpWXPaFXel3VeJFs9F5Sx\n4qWrlfOy8P7xgVgNdtA8e3qL9ZamBdbWNMSW8EG8RXo0WGd4ap5xyQuvX74hqcyTw4ANcoz2fhA0\nRrBoA+PeM+684JxqwlgwVovq1xg0Vm7gjDSEU6MVmYSG4BnCjhB2WONQSia2OQpPSDRRFoPq11np\n3Op6FQPK4g+uBy2KlktG3Tl1RGuD1hS5VGpKuGJYm2Z/I9VoqWJBEfJmx9wuEd1kYHOcL9wf7699\n1u9899fFDf8ZHv+/LjqttV9TSn0C/Cng7/WL7wb448iECuBnkYnpnwL+2/6cHwN+APg73+///4kf\n/ToH76Rp5Zw0eLVE1Sqjr1qDlDNGCx+2mT7m7mJCbeR4tSU7rimTYiHlKs8lg5aQN6WhFihFFhmU\nUPKU0miDNChT6X87Ak/q6Z2fZv3kDhZTgLet5xk5gvPSrOwgro2oB1uqpry26nXr1fXbd7qNGd2a\nEANluqW6H0sTk9g+xtH3I5yCqZHmFWsjKUmlNQyO/X5Pyol5vnA6SYSxUmJKffLkjhBGXr95Kw1U\nA28fHnj6/Lk44JVMwc7nM85YhmEihIHSVmIW3ZIxGjavVatkU7mPK+W0QszYAiZVPBpKY54jeVlF\nwjA43DTQBkO0DT0okquE2rpmSxZZAbxVasldkZ2v3y+tEUvivJyY44XmB5ophNFjgwWruXl2i5sC\nRWXGnSymkcj+8IThEPiKUxye3KCqxtuBkhPLsqKiRnvQw4FxdDjf00BQktFlNcE7XIfNmyrN2tqr\nh5YVFN0LWotSoseRPmJH8HaNmupevVqlmt0Ux61Xw8ZoESKmclXcb9HCa1pQCi7ni0QRW0Pjgwk6\nJxEMbvaKUgql/46q+xq3tsBh3PP0yTNyEXX33e0tJS38nZ/9md9xnfjd6HR2wI9yndXww0qpPwK8\na619jIzD/wOl1DeQkflfBX4D+O/l5myPSqm/Dvw1pdR74Aj8Z8Df/n6Tq+1Ra4UsuUNWy8JjdW/A\n6q3kFKqecwZtZbRcSqX1sboIfSraKEhShufSGF2gGStn6e4PaqZA4zddBHJU694j2nXaJA3Y0oVT\nlWsIYPcTiVkTSah0oYuq8vXotk2yNmFf+9QOVmvp52jR8Si2hrPuNg+pnkKQo5e1jtYWSvmQAAkQ\nQugxMpplWSXpwgtn2FjLy1cvef36Ne8f3hOGAecsT58+obb37A8Txhj2hz3nNfL24UF8QdrSSJ9a\ndEZijBhrGbyhVGEXGaPQWJaSJYl0EC2Jsoq0JskcN+KVy7mRTMEMHrcfyU5TvCI7BbaCLQz9iNu6\nmTLGSFoX4roQ00Lt4/yiJPmhtMxaVrKKuBDQAXTQ2FEmT1/+gS+xu9tznB9JdZWUhbySVGQaAs+G\nZ4Rd4Pj+JLB4a9E1czqdqDox3joObsBZiwuaIXgsGlMgGIvVBl27SDVDS0BRqGavR5qaIJmKItGc\n6qxlxO5jjFzv/QhUa7/WOnpVDMsiSs0tXxcq+gZWa2WwjjivlNRzx2hY51FJSJjzZZbqW8MWfpBy\npmlDjFkM0Ft/yMj9ZJ0A9S9x/UxryO+m0vkJ4H8Btm7nf9K//18Cf7m1kfYfmQAAIABJREFU9h8r\npSbgv0DEgf8r8Gc/pdEB+HcRGdt/jYgDfwr4N3+nF3bW4rRmLelDnk9HJOqtjyKGERm7KiVjvCaM\nmKK63V9mQnjnqaUyzzPeO27unqCNIzcpYUMYUNtf2RATomZD+aOV7SpNKTFb7x0Zaz984Krh+0Sq\n1Uqaz9ejzKezsbqwmm38bozCe2nCllpQvTEoEy/xncWYiDnjnGeaxj5RkElHyRVrHPtp04BUnJNc\n7d1uIsaIc57LPEsFqDXPnz/j5vbAq9cveffuLVornj17hvcSm/vu3Tt+/fgx79+/Z/WGh/v3fPnF\nC5yRSUesSycGzoTdjlKl0tr6RiUL/EwrgbhrrbHOym7qZYOISlGUJB5oo2je0YJGeUuzBuONNC+r\nvP9amesRVj6L0nscokfRVirfSsFPFjcYYomcHl+ztjP7A3z1a1/DDprH8z1mB34yXM4R7RvPXjzh\nxYtnjMOAVYa75Y7Ht0cux5nlsuB9Y7efqCoT00opmSHsGEeHd46gHTUWPAGLlf7NnEhLIUaxKbhh\nQutCikU2j6b6dDNizAfiowjXJQa41SyIi5KZ57PoyVo/rgODl+liXFchJ9LpBE0u3xgjWIuxHf9B\n6UMKuR4l4XXtqmN6zLGYkhWINUJr4U7LLSeaqM/w+N3odH4avr8KqLX2V4C/8n1+vgL/dv/6zA+t\nxLjWNrdyFwrW0uFdVuBWtQeHlZKJtfa4XS9NzlYpNUER/rE1hlxrj1u1oIxAvHtwXa2SSlBrpaqK\nbRapcgSNsWWpb3oFaQT3VUptJa+kMFhjODfh1JS44nE4Z7vCuco0om0ZQ6CN6pyaeq2clBatkATx\nVXISz5fRQrDbCtCtKtK663DokPK+Sz15cot3gdN55jLPlFqwyvLk7o7DzZ4vf1kGkdY5ahOV93y5\ncDme+M53P+F0TDy/23NZVmxwTJ39sgX0aR+ujGKFRAS3LhcoOaFywVfIVRTMdavGlGBgle7wrf5/\n6FaFxVwUwVi8sozDdE142P7u3G0qSoPzovDNNWEHj62N/W3gxZdueD+vPHt+w/7pjv3THcNtAFN4\n+/4lwz6Q8sx0E/jCV54TgsTaWGvY3+y5ubnh3cv3vHr5GmtEKGesZrqRo+h+t8NZjbcGrxzNgKkG\nXTR5zVzOC+tZjrchDOzDjtGPWNOIMYkOpqTeaxP/21ZZt5ppJXdxp2jIJASgN3Sr0AS7lUrwLH0Q\nYpTG+0BrSeQmpdC6ncg7Q8nr9ZgvSa9yTB9CuIY1OmtZ54VaG7vdTgL+SsYHj5ndZ7qPP1fTK20N\nPoTrLtkQwn2R7Flsk6asMv1C7md6CaD3FDQlR1oSYVPt+ITz6czlchGAuQusqZBzZZ476jFFGSdb\nda1gWmus68qyLNcz8zRNsnAhK3/O0rSb55ngRQyWc/mU8CqR8torna0/odBN9CWbh8paf/2ZNBYV\na4y9Wa16qd1FkVqxLMtVq0Qt2H7er1XO98MYMEa0KS54xmWUCZQSguDejdze7FBadX+V4nJYOvsm\ncXd3yxwzL57c4odAXGdG7wWbEE88PDxgQiDs9nIU1RrvDMrCqug3ToPc0E0qm9rH6k1xhdTThW9K\nCbLVqIYvMGIYtMMaw7ouIqOgkuLKslyEW9Q/h9yngClGmrPcPr3lB9RX+JH9nv3Njru7SSZtRkL5\nHs+PVDNSbWV/u8d6qFoAWfenEzWDyppcC4e7HaYJXtYHw7gL7A8T3nmpGppBN4dqClXF7pLXzHJa\nOB7PIj6dGm5I0GTkL9eXTPoyPY/qU4znkoQQWHPCOc1uGoXgmEWWkUpCORmz1CoQ/tJjmmrtHq1S\nJGzReznio/jBr3+Ndy9f8vb9e4oqhGEgDEHsI/BB3mHF4DuvC8fTkdgHEilFSbL9DI/P1aJjjMM4\nqTRAYNityvGjdVKdtuJ9ss6CFe2Bs1bmlUgYnK2V1qNwa63cPzzw5t1b3LRjmPYo03U9HRNRmoTW\nX8tcpX5TjErO+SoK3KqjbcG5XC5SFQ2esZ+pQ5APZ54v1CrCLWs1pSTWKDe36ekD3pvr/y39HgdK\nhH+bFCClLI3AWoSNawWTsVVdcsFJTyWEQOuxgymVbmEIgGJdF6HfVXDOYKy59oO8NYzD1/nyl77I\nGjPVBgajef3xt/nuN35FFuwx4K0jlcz5ckF5wY5aY4SSiEyzDIaqFqqVaONSSj/Bigwi9+Y6VRZR\ng4TtuQ6qss4JFK10yweNmBPny4W4RowG21MuG2C9x48Dq1F8/aPn/Pgf/6Nkq5nzzJLu+w5uJBLG\nJ7CV8+WB128SbmgoqzHWsqyJlhoW198jx34aGf2Ac/3zCjINlHQMQ62atjZ0lo1hOSfOp5XLWcbL\nbghS3VQ5nre2VaqVNYq+Sng5irQulJTEoMwW2SNftbZO+6toXfqi0yO1a4VSUUpIC8qI/kZbGcCU\nmpl2E/nmlofTiXmepdlfMzFFIVbSmOPKJt/YNl3rDMMwYIxmXf8AQryMlfGrsvKmNyO84Uq5jpeD\nlYZv03Q9gbmaPYE+4TJXgaBSkvt8f3/Ps4++yIBoa1qX4+cso9lmPpWK2L+U0ozjh91YzuHb2FCy\ntwSQDtM4st/v2A2SAR3j2hvN4sdRqrEsUGpGtYa15qqzgA9M6NpAG+nvOG/7OFSxroLcgMowDHKc\ns4I1WJaFdS1d/5NQWvQyOYuidZPAb793Sis5Q/AOaukLnmEaJarleFo4Jqn4jqcj3jmcFuuEs5qa\nCvN8xgwB5TzGelFLG4dpCq81ZQhdVNkZSKLVvy6S2wQQkH/rdHdsO4z3VGfJy3p9vsDpJenAOket\nimVeWGLC7xxKWR4eHzi+f8fXDzu+8iNfJ+lIsTs5Hjc4n47s7waMa6R8h/KamGe8D0y7nUQyXxI1\nyzUy+gltFYlIaxKGqEtBN4k5omhKhDJXypqIc+bx/SMPj4+kkgmjqIG3zxilxDSrFDGfOzWgEpyD\nviA3BINiO1p3uVxYl/lqo1G96rUaqWSdw5bUo3MMwzhSW+LMTM1Jfl8cr16/wjc4HHa8u7/ndD5J\nhlpHnxhjuLk5YJQiRoG7OS8b75Iip9Ox6+V+58fnatHJrZH6CFB3q4C2hkbFeI8fPLb3FdYkzebB\nB4L1GGWF+Idoa2rlaltQKC6Xi+h7elWRS2ReLtcGXa0ZHxzBDV1MJ7zdwXlag2VZOV9OtFYl42oI\nTMPQF4v+/2JAGVKWUeSWwni5JATVAd4FStnUyjNKzZ+aahmMk9AzkCap3yZ41oppU8tidSUOIixn\nbQSsFWNkGDzGjDgnQKhS+ji2W0xabwg2muxkYejIDyQmZvDUwfLtb3yDVy9fsVNyIcUYMdpjrWGO\nkfPlTLMOZRwhjAze4CeHHkZizcRarqhW3dRVc7PF5G7vXWmF0iRNwwWLsrJp5CYu8vN8kddqnRuj\nJRIl58o07bHDSGqKFCt//5d+ib/7y7/Mn/5zf4Y/9I/+MEyS9KAaPB0OaH1gWc/k6tC2ocOBWAu5\nrjRg2o8YHHkttFRl/N4qoXqUbrIYKIfC0Yomx0paKstp5fRw5u3bdxKtvBvxwyB+rRCkb6Wlr5h6\nDnwIjhACd7d3GAUP9+84H5Pc3ErTiph61yhaLdX1OKVKP9F19IvZgHClsC4LNUZKTYLw1eIZPB2P\nTM5LgGRKwvHekBU5Qal45zjsp+tmEELoGreE95b5dP5M9/HnatGZ48ro5I0simsXvalGGALaOeHf\n1Hwt0XVOUk5qjarqOrpu3S8E0vM5n88cj0dubp/gnb++ptK6m/FkJGuUUO9AdhLvh74rz70vIroR\nESIaSs0syyIlaymM49Qbz4qcI+syk5LsHGEQGFKMM+uy9FF6ufaMhmHAD14C0JQ0rxUKlNyMYcOz\n9sWslCQLsxH41DaCd85ej16SXCGLsTWaGBUxNnEdL4vgQDdwl5FFal0Xfv4ffpNf/LmfY2yN/Ytn\nWKVRxl4D7JTqvSV9gk4JHKxnGEa8kyqAIouONEE1BgGDiRhls39AzhKMpw04H2RCtSwsc+/FnU+k\nFLFOFuMYBRMriZRWfGDGoXVgmeEXf+lj3pz+Jn/68o/z4//kl7jdH6g5y3GjVbRpDF4mXUtOTLsB\npQ00Q02GNGc5vhuNRgD4zsgC4azHKo+pjpZE1Hc+LsRLZFkSpYIfBg53tzx59oTD3Q0G1+UP8vvX\nJhOrw+HA4XDg6ZM7FI1aEut8oWXpMYnhE1qpfZMC3YWYVQlsS1JSpNl9nmfO5zMldlNt4Bq1FHPG\ndsGsC4FUBOmyDUq8d0z7UZrHapsjSaLqpkFrvx/iwN/rR8yJJUWJ2xWgTdfnNDFYdk1LUxDGsWsY\nCpd5RtUF6haPWvuESPQLVmuWZeE73/kOw7jnbvO5tK0Bmym58m6JKO67qfHAzc0ttVbRh3RH83bs\nkulRIhf52bpGUtomFqEfb+LVJIlqEuZmdQ8L7EzfpvuIvaC1YDFUd5tvmV/WeoIf8F53hbRM7jZP\njrGqo0oHvHe9hyRHr1IEUg9cK7RlsdCqKJu7d18pAUa9ef2Kv/cPfomf+fn/i49/9Vf5Yz/+45Lz\ntMx4JGfLWIt3jrkUCdqbFy52ZrAer03vrzUcSj6NRu+/NKr6EIbYWumaFJlYag0eTcqJukTO5zOX\ny3asqihliDkRl4VWQBvp4zkfKMayLonLJbGsjW/92vf4yZ/8KV7FH+Sf+af+aZ7d3uCcJi4r+73Y\nF87rqTfwG+s6Y4zH2hE/BEwQAqHqBiqNwSrx5aWSaEVTlsblvHI+L7QEWhmmYSJMgWfPX3D7dM+4\nmyB2fVmqrB1hu2004zhK/7A1Qk8iLYDV4r6X6e0HXdhmj2hdxLrxuL33xN57rIi6WcWIr5XBOejH\n89ok190qg/VeRKarVNvOOdYYybXgjEy4jHOSeqGUqLg/w+NztehoJdaEXCSXWjdh1IgnyaCTcHpp\nHUatNEsWjm5KCY1iGgYpz6sIxpx11G5ge/f6Fc5KRTDt9qhWcVoRwsi6rqTeVFvXmdP5yNt3r659\nF2stzgvZr9ZILrImbpMkpUSaTk3EpbBWWfSmccL6AE16Q8452gAxrsS8XiXupRUhGOYGsVynYD44\nlHbELH0r7zo5sE+CEgWVZWHTRqGNpTnTm4OJQqEaYc3kDmYK4y21KRqGhiXnxv3DiV/+lW/yC7/w\n9/nk5VvenxyVHc8Ot+woaCtZa0UHstKY1tAt02IkHivnUrClYGvFTRMakeBvC7tWorTWWoBmSimq\nEmRDzoWqxem8UjguM/eXM/PxPZf5DA3cMECR9zvFiHMGF8APlaZWWsk4pRi1w0ZYE7z+xsr//F/9\nOqdv/p/8s3/+T/L1H32O8Q8QVoqZSeWMGwxrPNG6+bZWRVNOOoRao6vDqQFVNTVrdHGo5iEZyiUR\n59Jpewo9WG4ON+wPE/v9AY0lXgSZCxsJYQXVGKcgR5blLHnhtfF4XpmzIVcrE87WiLpw1IaHVljo\nEdPWyKZWVm5vn5JOR6zSPNnfoq3hodzjO5bC18o+F5QKLA2WElHN4mrFrDJJs8rgURy0ZdrvSLpv\n7NaRLguxFfAK5s92H3+uFp3e9bymGdRWpCRtPRxedf9Tv3hFyKn6ccfirME4KwCi7opNOaFqoWlD\nipHH4yPv3r2l1iZNPu9x1gmHdgiy6CxLNynOIoQLgf1+zzTtrsF2tVaWee29Iol2hf56/fTgvZej\nTUdMtKbQ2kr0jI5oJc1TbQS4VGvnLtM+sGutvRr8NLqznBs0Edg5mWOA6mZWBaVoGkaMmDmSS0I3\n0xu+g7zHuXFZEt/75CXf+tZv8K1v/Qbf/LVvc/9wZBz3pFioVaZhRotkQRVpnhclYkS1meSLmAFP\n+oQDTBbOjOqeMJCDbgNK/x0b7Tr2b8jksGoJDzydzzw8PJDmi6jC1TbBax1y3/tf1og+SIsY0VnJ\nhBcNi+i0Hu9n/ref/gVO8wP/wr/yz/GVH7xlmTNmBG0tKUaZwI2BkhUlxa6nquRYYQ1SrSqHx2Ob\nxvZEVqOsGDZVITUxYg7jyDTt0dqQYrqKPa3V1wmec6LqRimOxzMlX0QmMa9c5qVD9kEhCJJcC0VB\n7ErzrXpuTXAcp9OJuCaUKhAVpdSrz89omQrSsSrFNOpS0bXJ1NcgiSNFKqqSM83JUbqUxnyeSbrR\nmvCGPsvjc7XoKPUhWsZai8IKiV58Cn0iJefNBjLZsRYXpD+gO84C6B9KT1posijUKurkd+/eoYzl\n7u5J5+5qhkEahbULE5XWXbH5YfQuX1LqppiuFZb0g1JPblCigejnf9dfN3eGSalJWEFdV+G1xTlL\nUgIV00piZW2/aFCQi4z2lyzxI+Nu6IuthZzkZkQmXzRFM17MrLUQs+J0TszLmXW9Z02Jh8cTx+OF\nh+OZ3/jOJ7x8+Zb7hzOXy8q022PsRDq9Q7fGMAwoVpnc2IZItrfbh+vkMKZILWdImbzM7Mad8GpU\nb871Ptt2NKCrxm0/DqaSiefI+XLh/uGey/FIW1dZ7GjX9AuUYgj+WoEabShaozFdHuDlSNSPpyU2\nZjXzD37+mxj7P/IX/8U/w5e+/oQ0n9F2olZJbsgx9d6TQSsxmhpTAYdKFShyzG/i/bJV462TYUKu\nlPN8/ftyzpJ9VsVL5YwjroWmZGoZwo5x2lNyw+jImhdpcdFAy4SztorSlVRWck3XY9V2fYYQGMaR\nmMT9H6Msysu6XDfore82zzPBgR9GarWscQFaXwitaNRqQdAtnoqA5RQa5wdCcEy7G+6Px890H3+u\nFp3aGTDWOZQTS4TtEm7VMRAVAZ7XVqXisZbQsRY5yXm/5K0xuqEsujai2xOOxyN+nJimPcZEQLLB\nt9zy2pXBPgTZkZuAj87LhbWLpdZ1JafcNRRyTNBaM+0GjLa9ByMWhTjPzMtCo7KmBasFAtWotGqg\naihVANxKpjatQdGiIcpdGNaU6EnUIjofpURhvUXwbHE3w27Hu/cnvv3xx3z729/iey9f8fh45DJf\nWKI0PHOFlCoPjyfmWbxpzg3szUTKMjadtGIcB2zO0CRaWBWEY0R34aO65qmQ6spcC6RIWlas6VVa\nh9rXrXmsNs6PxiXPqhRrXDmez8zzRdJDc7nK4muvcEqT3Xlz7IuWqmc3aKkafc8rV43OF9bkpfLw\nJvGzP/NrPDz8d/z5f/5P8qN/+Gu4YaBlmfD0ZF2g9N5HwdqeGV8HSGCrRleoUZzfTluCC7RRKseY\nRY2ecyaX+KFvE0S2oY3h5nYg+BGjA8pWpukAykmFrDWhdAplk81EX0WkMrUspVw1ZDmlDtySDdJY\nI5n2n/L5bZ7BajJKFbQwOaWv40yvvkWWsiFUaOLbqq1RUqOowul43vr+/zd37xJrW5alZ31jPtZa\ne+9zzn3FI1+VlJyFS0JQSICQLGFhhBCmQ4cOLWRogmjQR9BENEAIyU0L0UUGJDdsYwmMhZAlS1jI\nFoWLciorszIy40bEfZ1z9t5rrfmiMcZce9+ozIygoGQiV+rqRp5z7j57rzXnmGP84x///5XXNyro\n5FzJpTJELZViCHgaJQtNlGUpOPVRQs9a32UWLRspKHvSOWE3TmrRK2IuhR2kdEzHI8fT0TIjgbs7\npnEk56ge2Ou6OSkKoi6WtdpAXGVZFtZl3XRjlS08kEvj8TSTSuP2tjFOk5ZPNbMsesIob0OzBb+4\n7YR0ZjciNn2u2saNJSVSrurJFWBe1YokxKjM7FpZ5szj4wPH04nX737Kz372KT/+8Y/59OVL7h8e\n1IxvmfU1a0PEgwTWXMhJQdon4y0+jJzPC+ty5ulOiYWhnajeKTFRTHGxODXRw3ynrPTNJTPXTF4X\nnD0f6U4arSG2yFUeUz/7mpWk1jtjrTV1jnBCqY1cE62JkRA7xmYKjnLJkH1QHWfpU/pKO6RUZak/\nvsr87t/9KfP8N/hX/tV/kW999wMOtwP7w8Qwiio/RlUKaC0pFjgLfgFXTU+p6fybimIFfHQmZdFo\n84lsAH+pyeRYKi0re33cTbQm5FSYRTWsnY/EqKS/2jRzH2W0RoIQYiMOeqjGGCmlbKXTknoGhMqW\nDHELNDlnkz7VAebaylbCO2nE6BUvbJVxN+CGgcNe2+XH85kCDHjykpWnFQbmX8egc+GQmR6OdTxy\nLcopET01xetpe5nf0Ra1HyJ+1a5NKoXYGmOMOBGSOVVSdSblPM8cTyecOKZRCX3jbmKgUaWx5JX1\nlGhFtXv81aLuc1hHOTKflm0qV1vcjpzUZ90HleNI60oqiWS2Jt2NstZKPc2kpFYmMUQlR4rTlnYp\nOo2dHbk5gkSaRE5zprKQy4nz+czDw5FXb17zxasvuH93z+Nx5fPPX/H5F59xPp/JOTMvC+u66Gnm\nI85Xqm3mPhwbh6gZx/07yrqwe/YE71U3R6onNNFSt1bVCW6YvUnbOlIYJ0ixN6EWzTJb60Gni5Vd\nNI+6PGzFZs/0LukgYtEg6byWTsOg3l6dOoDIRgLtOr9dG1qao1aHMNJaxjthfVz4h3/vUx7e/s98\n+zsf8a3vfMD+MDFOjmlyPH164OmzA7d3O+7u9rgyMLSJIQwECYSmvvNRVBdHBfcX1jExp5lcIATH\nFA9qUCeQVi33vXPM55mcMuNuxzTuFHczfSWd1HcMUYOOqjYq36yP5oAGn2mawHkrrRLTNLHb7RRr\nNDpGKcWm9NUyO6VEbgXQln20ruo4jpTzyjwv+KgZs4uRIQ5I1q5czpemwFdd36igs0VllPJfs9oC\nl6rlg8oiOLWLQXkK2zwPqpmsvkAmeG4L2VnNTx80xASy1oU0TdsDmZcFH4MuXO+26ebivNquwtbm\nPOyVwfq6vGE5K+W9diN6wGGgaH3gfD4xLwr9K1FMbGpXyGshV8/Omalg0k3up4lYIZ3PHI8PvHl3\nz/3DUeVXTaRpSWnjH727v+f+4YHz6URt8PjwyPF4NKZ2Q8OE2sW2koy57AmDembv9wfGyZPzmVJm\naskcDnsE1YZu3uGaQ4rq50i1IGNzZuI0eHUspUeO1gP9xhhX00+pbIvYm3uBDsZerKW7d3YfUvRX\nJ7fbJrMduD6+4rYsp78n1xy1VTX0qwXvd+Ql8ck//IJXL8/88PdeMo4RHyCExjAKw+j46MNn/OAH\nv8mf/v4P+P7HO+LtiEigNT1YkKjd1hDwteJXzTwrChpP40iIWvqdm2Y10hrrciYlPbQYJtWMQs0N\nxyEyjp4QoNZAKo55znT1gT5fpeVbtQDuWdeVEMM2FtEZ9YhjGCa8V35SLap1hAV7HR5ViF9b6U61\ndsyuaRwHBkWaDTP9NQSS+83SyF8QKQQjynUpx+qMV9ZPOdvAgGnaKuuypETKylWI1nHCghRbCqrj\nFcuy8vDwQEZlKXuK3x0oaKYrW41qPuqw3DhOhBhIq3bLWqtqxmcdKKHpxLAtEucdzQeKZRfNgG/n\nBgg7qgusVa1tfBKOpxOfffY5L19+wavXb3j9+jXv7u/JVU+qbip3PD5yPJ1Y5pmUEyE4ljSTDIB0\nXnDBIUUsIKsAlYbfhPeRcRBq0dfKJeGB/TQBzebALAsVNrtZrp7B9rdxfjTQ2BSdMgLZDDW2mHQB\n6JtoV7KaSWE17V/ntc3fXTScc+bw6cCcQ8VrJoi9Pw2A5sdNwnsNYK2amZx1Ieb7hfk+qSe7YR3i\nVa3vx7tX/OSH7/iD3/icf+K3/zQ/+M0/xYsnz7gd9+yGiRaF2BzRcMVhGjlwYEjqMkEr1FSsw1fI\nti5CDMTBOq1mmBqCNjKcm5h2Ee8quXjmubEux42Tta6qHjPPC+K7EmXd7n+X/FA+mWofd9zLi4JW\nkqEUnYlbl0yNwrwktQaaqkperDOjm+jmbT4IQ/R8PTWdb1jQGUzRDC5pO3A51cROUyP96Smnp5qC\nlOBiJNpwXCmFNWfEazByFmzsIKQUDTrn+Ux708hS7fU0S/Lm4YzVzN13/HjSuZm0ant8mMZtMwWn\nesiHmz1xHJiXRdN/56kI81oYxok4TCYWX0niuZ+NH3Q6M2fNvO7v73n79i3v3j1wf6+tUR0SrFqi\nOMfj46PNJc0sy4z3OsKR0qV7Uop5KJWsUonGqqapCV4ribSe9D6WpHM9OIYYLXA0xAtUdbxsVSeT\nvZU0OGcBlO25qNazllQ2QsgWudRwXLEsY5xXA8+rmPVyq3bf3GaBE2LcBmNVLjWo6ZxlTdWoFbWp\n1EmqyqnSZ9M/dy9tUaaB6EBlyfr5/DDiBJZj44ufnTje/5iXnz/wD37vp3zw9Dnf/vAjvvetb/PR\nBx/wwfPG7c1BX8MHI9uJkkbTopIpLWt3q9WroV7BuUptGVd1Q8ewQ5wjBgeSKUWs3W7qg02tp0ut\n2+fsgHIcB47nk65Rc491PmxYIcBhf8s5JZMcVQpHyYaBZnViXdeFllfWdWEYI00qOS/kOTHcDmrV\n/TWub1TQ2e+VMZtTohn41Wt18X7baOKsDWtpZ8pZbTRsUYtzeBuyU7ZvpiJE0dld7/rXE+uyMISR\nEpSfo3KnbcNCvE3x6pxTn6U6EXzYmKDTpLe5GjnuxYcvePrsGcfzmfvHI6UKPk6qcSKB3DxpqRzP\nC4/HmXXVlvvpdObxPLNWtRk5Ph6ZF8WMlrWSqqNJoIpZ5JTCmhIpp21aOA7quyXoACaoTosgpmds\nEmdWhjizPM6dcW3dpmBDo74HCqdyluI8SLHxjD7W4ChcMp4+Hd16qtPBesPsOhbUWttkSigX3o54\nw2jE8C/ncOH9QVxxKnPSrYSdve/WcSYLjjSzmpar4IcgrqlmdtasRHTh4DG8CGg18HhuzJ++44tX\nZ/bjp7x48gkfPvsDPnr+nO9+52O+8+2PefbsCTeHkcFGeFyIhFaY61lxQyy4eQcrGnydvq86aMas\n771qo6NmI6smcxMRDTihsc6LepFbp6nBBhoHH3l4fNzY9or3rHbbvVjCAAAgAElEQVRvArVk5dtI\n3BoCKXchNqciZa3hAsTB412luKIKnN1a9mtc36ygsxu5OVyEm3oN29vfch10UMkL1f09s0l4lqq+\nT0YQTKsNV9qJ64eI81c1clHMYmelxLJoiXI2yQoVS4duI5PSquVebIxxtPbtYCVgRXLiybOnHG5u\neZwXUoGkKg6kJpwez8zpkWUtzMvKeU4sa1apyFxYSyGjGdk5NZak6vz4gI/DlunVpHyObfHZzJXz\ngpOB7igBGLFLrBWqoHyfD1OLZiUddp908cLgA1McVCKhOkrlUk55h1SPa5dZnM7ZYetUaeC5RBq2\n8qrVShPLZEWb7tK7YP0FnTMJEGXfdkKo8846fLJRLOheZFauaZxrxiSKFlAUb2smoO+8I3iv2jNN\nxd4EFbnfXr80ivdQI+ckrGlmngtv3z7yyc9e8qM//EOeP73jyd0NL1484eMPn/PhB8948fwpvjnO\na+bt/ZFaErtpYrff4YJqObdVM7zc1NI6+KBuKHmllGTC+nUrW32IkNSCSMplRMZ7Ry4XPMuHsJVa\noNSOcVRXVhGnzhO1INKopbGUVaV/rZOY84ofJ2LoEqjJbKVle45fdX2jgk7wnt004HYT2dw5V2sL\nbpbD6MNqTSeVSyqkVXWQGwsCjDGyn0brKBVD8q0koIHoIp1Gfd3gdR6o83jmeeZ86oHMMwyRaVD9\nmmwaya1dpDBqVZas856G8HCaOS6Ftw9nUnOclsr98ZElVR6OM8fzzLxkcnd9TKqP7Jwjt6ZqiKKo\nkOA1xc3JZqg0O6k2B9XT51IyISh71sugXtat69ZY1winvJ5WdRQBFZ5q1VOLMwKfYl8qpzpsvlmp\nqLmcsqn74Kkq27cLSHMJTK0vUtkwHNgqZqRpl1DDkmwaMmCJFSC1qiqiN3Df7rHb3DAAs5KuaOmb\nU8/qzO+r9CC4AsVkb5sxqXWgl6YeZ040AHehtmVZlJUcrDzWQTacFEpdOM0zLz//nCF67u72PL27\n5cMXT/mN732HD549Q0rhvGiZ31yh+kQRxz4GpiHioie3zHlROV2lHKzUmmlOhdWd600HHQHqHldd\nz2ncaddqiBMvX758DxcFNp6OIJqdl7yZC4Ae2imrkNgwDODVKbmUArEpLWSIHES2Gb6v3Md/vO3/\nj+aqJRO9sNsd6B5UtVYtl/xlQrfUQi1tI0EFH1lFiVnReVroBmLWzao6/1JrhU71Hga8dUVojXVZ\nKK5RWmE5zyznWbspQyR4G7OIESHbxG9hXmb0dG3EqDhC9ZH7c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sQrXyPVzBAHwhBoJVOX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df/1FjnK8utginSui7bhocPHvCdj77Fb33ve3zzg/c5Wq+prKFSmsrIf0bd5nGiiijk4zxz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"text/plain": [ "<matplotlib.figure.Figure at 0x107fa3b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fname = 'http://crl.berkeley.edu/files/2014/04/Holly-226x300.jpg'\n", "imageFromWeb = data.imread(fname, as_grey=False, plugin=None, flatten=None)\n", "plt.imshow(imageFromWeb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Querying image: matrix, sub-matrices, ROI" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------------------------------------------\n", "Image shape is (300, 226, 3) and type is <class 'numpy.ndarray'>\n", "Min = 0 ,Mean = 97.459906588 ,Max = 255\n", "dtype = uint8\n", "-----------------------------------------------------------------------\n" ] } ], "source": [ "print('-----------------------------------------------------------------------')\n", "print('Image shape is',imageFromWeb.shape, 'and type is',type(imageFromWeb))\n", "print('Min =',imageFromWeb.min(),\",Mean =\",imageFromWeb.mean(),',Max = ',imageFromWeb.max())\n", "print('dtype = ',imageFromWeb.dtype)\n", "print('-----------------------------------------------------------------------') " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10a591438>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AP/stj+hx/3DsJAts6cslADWBMUb4fgOA9Bvt5EeGBJ4WCKTAe7I2DRjwT0bx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\nw8nJMY5PznB8coqbJ2e4eXKKmyenOD05wUlyDpofoECCkAJo7KcMAnA0N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"text/plain": [ "<matplotlib.figure.Figure at 0x10a396550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cropping an image\n", "facecolor = imageFromWeb[50:115, 95:140]\n", "plt.imshow(facecolor)\n", "plt.title('Holly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Image transformations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from skimage.color import rgb2gray\n", "from skimage.filters import sobel\n", "from skimage.filters.rank import mean, equalize\n", "\n", "from skimage.morphology import disk\n", "from skimage import exposure\n", "from skimage.morphology import reconstruction\n", "from skimage import img_as_ubyte, img_as_float" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ushizima/anaconda/lib/python3.6/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n", " .format(dtypeobj_in, dtypeobj_out))\n" ] }, { "data": { "image/png": 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KSoSFmRZCK8BMM9OD2Y0zeI7CVGiC09PTYd+WqAwwx0HkRA4OBpiEEEImhjJA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\nB5nfCEj7PP60AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1159ff390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Turn color image into grayscale representation\n", "face = rgb2gray(facecolor)\n", "face = img_as_ubyte(face) #this generates the warning\n", "\n", "hist = np.histogram(face, bins=np.arange(0, 256))\n", "\n", "fig, ax = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", "ax[0].imshow(face, interpolation='nearest', cmap=plt.cm.gray)\n", "ax[0].axis('off')\n", " \n", "ax[1].plot(hist[1][:-1], hist[0], lw=1)\n", "ax[1].set_title('Histogram of gray values')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Smoothing\n", "smoothed = img_as_ubyte(mean(face, disk(2)))\n", "\n", "#smoothPill = ndi.median_filter(edgesPill.astype(np.uint16), 3)\n", "# Global equalization\n", "equalized = exposure.equalize_hist(face)\n", "\n", "# Extract edges\n", "edge_sobel = sobel(face)\n", "\n", "# Masking\n", "mask = face < 80\n", "facemask = face.copy()\n", "# Set to \"white\" (255) pixels where mask is True\n", "facemask[mask] = 255\n", "#facemask = img_as_uint(facemask)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZuZbySztDA5FpDbJoN3EOiREvpEmOsoQfkSnM5gL6Ooto00scw/2Z1iPaBGcy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\nKGlV3RfDfaBWyhzxUDG4SgK1QaYuhIbnfa7iQuXDNa42R60RVdpeJ85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"text/plain": [ "<matplotlib.figure.Figure at 0x1162cbda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=5, sharex=True, sharey=True,\n", " figsize=(10, 4))\n", "\n", "ax[0].imshow(face, cmap='gray')\n", "ax[0].set_title('Original')\n", "\n", "ax[1].imshow(smoothed, cmap='gray')\n", "ax[1].set_title('Smoothing')\n", "\n", "ax[2].imshow(equalized, cmap='gray')\n", "ax[2].set_title('Equalized')\n", "\n", "ax[3].imshow(edge_sobel, cmap='gray')\n", "ax[3].set_title('Sobel Edge Detection')\n", "\n", "ax[4].imshow(facemask, cmap='gray')\n", "ax[4].set_title('Masked <50')\n", "\n", "for a in ax:\n", " a.axis('off')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Immunohistochemistry example from scikit-image\n", "- More at: http://scikit-image.org/docs/dev/api/skimage.data.html#skimage.data.immunohistochemistry" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x116b06828>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/4fPxT/6QwDdou12E0JnPJvim4M++9ydcXIx5+PDLEL7CrgNa3YbTs6d0Og6H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qNoE4JUxp3c5c4Dn3i3Rsj3KHVO0VYMXItev0tCdl8YTCYz0jxjc/OCKi1x+zYPzu+R+GuC7Q53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ZKtEC1jArtHVucIVPK8Jq9SOp5JUzdUVfvA+r4LskIYb9DtNjcxSRpOTyKeH0/4nd95gyyr\niKMFhqHjuB1W0RrNMPnW+29DI1ivl4yHXfKsBEWQpYI4yUAs8RyL0/WCkRSQ5QmyApatoWntJmO5\nydnqb1NVGbrdGoLcPdhjPB6DqrGzPWQ1uSQvStJwQ6/noxkG3vAO39p/gu25LGYTOh2LPF4ghMTe\n7i5plEBTYdgGeZridwd0+2OWmw1vPHjI8cuj1tdANViGCeWmokbHGx6gGw5ZuODZhz8lK2H3/ps8\nfu/3KKI5alOhKw7IMULO27Wf6ZIUDYZpIkvg2Bqma4JUs1pGKJrM1niH8/NLXNckyyJ0XcN1Hco8\nI0kSJEnCdd2WIVoUrcJRtO5Mr0lOr6/fHh2A31hVtoK29mzUdd1GDiY3lOzyV2pNRZGwLINatN2B\n51sIUSMrrUGLJEtAxdeEGL4hheHm0nWdoihuDCu0VvdeVzcCloKiaCPJhBCohoGi6uiGgqKsURWT\nOCqoRcNgaxvXdamaGlXV0DXlhtKrICsqWZZj2T5pGmPpBjt37iE1AiG3wONyvaLJCwy7g6Lq7Ow/\nYDq7JIlC8jTFssDWVdZlwXh7iGzqvP3+36NpKnRdo8hTgq2W3n1xekJeJXzrd3+fNF4ThSH/z5/9\n3xzcP6Q/2ube9j9murji7OgZqgRJFJMlCULL8L2Aq6s5VschSlLWs5igF9A0Cbqu4HoeSZoSZznd\n3hAhNxydniIahaIyiWIJoUjIsk6eJSiKxs7ODutww3p5xXC8z+7OgM1qhSTpxHGEjIQkJM6vp3ie\nS1GWKLLE/v4umioRbdZ0fBdRyji2QZrnRGHFImzodLa49+AB12cn+LZEulwxW2x4kdVkRcn51QxZ\nNTi49xAv6HMVyQjDo5QMoqQgTyL2twLuP3jIehUiNzVpvCZQe3huh/39AzZJgpCgqhoso2WfaoZH\nESV0tx8gap1aadA9QbSao8uCbtdkOXnOL6KQg7ffpywkJFT6PZt+YDGbLhjs3uP86or7BzsYhkHH\nt4mzmDSNUWRQFA3DMJBliT/43u/f8D40ft2fSAiB67pkWYbv+3gdn806uqWT//b1mzyF3wxnbj+3\nnYMkQxKnFEWB3wnIioZaNLdKT1WV0XUVqEBqFcdJUt5uO0zTJuffK3D+W9c3ozBI3KKvbYhG3To3\nvTa0aMQNF9xEVDWSgKqo8b0erquxWq2osyWeY2Nq2ximyWw6xdRUXp4+BQSWZaOZLpqpockaVVGh\niIayyIji9Ca6TaM3HmJ2BlxcT5GsIfPFAlVY2P42ttejyVNOjl6ymC1RHRdhOJjukEL2qBsdz9Mp\nUplatigrnd7eIyxTY3L6isZQCbwt/ot/GnBxcYGu6/ybf/Ov6fsWyWZOniVg1UyvLzHEAMuxePrF\nV/ze3/99BgMFTTY4OTlDlmWGwz5lXdPtBpyfz4lv7Mz7owGmYbM4XvDl82cMxzbz+ZSDrQ5l2bAJ\nV5Rljev6LJdzDg4OcSyd1XqBqkgEvsfnXxxzfNFSofd2+vQHPopoKPIcSarZRCG67uF6JkVVIuke\nR5MVbz3cQVa6XF3+gh//+Z/hWRbbd+7hj8fUW7t89OHH3HvjAzaNRF1oZGVKMbtCURRsU0cVEpvV\nmh/96MdYuoouKwzHA9ZRQdAbs7u9i3x5zfOj53hP3uXJO+/yv/7v/xtvvfseh4+ekEQxiqbQH+2R\nhjMOHpmMRwM21+fk02ui2Rn/7l99gm35vPvkXS4XgvPrMzqDbbpbb6H3HAxvjNcfICkSeRGj6QqO\n44Os89VXXzEeb2NZBienr3BUH11XkWnXw62moaVDx3HIbDbDdfzfIDa1hq/NrQPT6+t1t/Drq8xW\nip2RJgmyDFVVEEVtgZfEa1Wy2m5GlIYsz/F9H13Xmc+WpGlGmq4pqpK6+ruorvy16zVim+c3mnSp\nQb5pp2RZAkmiqWvqqr0RVV20IhYhWC3mXJwetW6/uk6n40OekcURhbZmsQkZjkfYtk2WZQz7I1zP\nQ5Zqmqbi+nqOYroIzaY3uENRC1A7NFXFKoqIwyXr6Tl7O2PGgyHX81kbQLqasUhXWE6H6eyCcLli\na2cbSdORkdnZ2WYdZxiaQs/v0fG7FIpNXRZUoqKsMgzH5/7hPRrdIYsT9IFA1A2SJLNcrzEci07f\nRZ40mKbNq1cnjLZHlEWNpCp8+IvPkWSdJ+8+ZmtbI0oTDEeirBIsy2j366ZHEiaYrothWIy2+lxf\nXtDxu3Q7AWkSoSkC2zapSsHLo2MC32A49HBdm7CpyLL8VhBkaAqGbdEo8OL0jJNpRDUTBKMHfNtz\nGPomm7QhkX2qvGH/wWMKWaA6OrNozerijM1izmA0RDNVDKlBNhVUVWXvzh18y+HZq5dsbTvcvXNA\nXQlM3UCqS1aLKacnF4z6AyanJxzee0RTZqw3BZqZossSUVxQNwZhpVBLKr2eRRxvaPKQP//Tf829\nN++Q1DVOsEXeGNhBgN0ZYdg+m80Gy7Zb0ZXpcnp2QdPQdgqWjCyDaepIosUEbLtNIy/KjOVySRD4\nREl8q/H5dcbjbzs4vS4Qt3+7wRWaqqauaxRFuqVGJ0mCbvoYRhsdqOs6RZmRFzmmYaPIGleXc8Iw\nxDAsdrbvEKcJn3zy6dc6h9+IwiDxK7mpqirkeXHjmKNSVhlVVVMWBbJoUBUZRZWpqwpN05CEQJUV\nFtM5q+szPv/4l3Q8D12Vef7FBkNVkCRQFJVaatjMBX3ngM+//Iz14JpOt0/QH5BLLblK13WyWsGw\nPeaLkOlygadrlEJF0VzKWmI2X9GUFYHfIasaLN/H6jg0soIsG+iEOFqNpguWqxWf/PyY8XibwWhE\nHKfEZYM32MFzrJa9uLqiG3h4vYB10vpW6mmOZggc22MdJ9zpd5GkgsN7dzENh4O9A6bTKxoJen4f\nv9NntYz5+KNnVE0b1WbasL83xLWNdu6UVYQkIRqJOEox7ZI8zwk3G4LAR5EhLTIsy6Lb7bJaLVmv\nVlR5n7ipkZv2e0jjDK/fxwt8luEFhu3S7Q04max46+AJ8+OSrdGIeHXK+WLCpkwo8gbbCag1A81x\noW548fRTLFmwmBzzxttv4XccvvysZaT2+kNc2yP68jmSZqEZNsvlkjyLURWJp599Tq+/xZN33ubF\nixdcT045fGNMVufUuUJjmdidPuFGxuxuoZkGWpUyfPcdJufXBLbDeDzgwTvvE0k2RaVgeQGqYZGX\nJWVd47smq01EmGakaYZtW6iqTBRFrSJ1s0HXTKqqFTtpWut3kd+8uZfr1W0noCgKeZ7f4g2vC8Wv\nFw5ZlqlFc6s2Fk0bFCPLrSZHUlTCTULP6NzYwQkkWbSGyUWJFVjIssp0Omc03ELTdMqyYXJxTb/3\n9dKuvxHrSsFrezeFqshQadDklvWmyRpyU1NnG9LNjHxzTRIvKJsUtRYk0xUjL0CkG+QqQpEk4iTH\n8F32Hh2g+BalAigypm6iVIKf/+ivOdge8vjRIeOtHsfPv2T28gVSHrGaX1IUKZPlihwTtzPi6fOX\nvDqbc3y5oRAeFxcrPvzFp/z0x39FvLnC1SrKeE62XqKjszW8y6izh1GrbPs2h9sBs5MveP7hj5l8\n9Sm6suHq8hXXiyXd7Yf4vX1Wy4QPP/qYRNIY7z3AMjtUQsXuDZhNF+RZQlWkrOczpBzyKKXj+WwP\nB8TRjDv7I7rdDnECJy9mZJuC/+QPvsP7T+6hSzWWZZCXBbpukUQ5gd+lylIGgx5lmbdvIs3EMn1s\nU+HJoy3+/gdvcX1xShImlGlGTY2qStSiQpVhvZnieEAZM+z0USuVUjZJtAGZd4jk32EwGjE0cvZH\nOp2OhixgdT0jnS+phcSDt9/lzv1HSHVFstxwfXqFN9jjPIG56uN4W8yWMVlVIiioy5wkkXl1tsF0\nPdBNhsMhLz/9iD/9P/4XtCoi2VxhexbrZM0qSehv77Nz901Ud8TLq4jadPF2xvR27nB6uSCtZLb2\nWrBUU8w2ZMfvkOY1qmYRRimOZSEjgRDkaUHgd1vHJU3GMDXiJCQvWhyg9S2tcB3/djyQJAnfMQlX\nc0xFINUVTV0jAWVRoCoKmqqiIKE0MkoDNK0WSFZ1UHWKWma2DNH0lmxWlFnrPq1o+F6PumyYz+f4\nXkAYpSAZZCVIaofL668XOPPNKAyiZccBJDdZEXVTESYRZZmiahLUFVKTo6oyNA1lXuC4OmW54ez0\nRVuJTQfDcdi7e4jXGTGdZ+hmgBeM2blziNsZoRoOXnfIsxcv+eu//CF5nDAYDHACn8VsydXpKVKa\n4BsKqlSwXF2zf2ePg/v3efjmOzx+7wO27j7B7h5SKD6vJjM+/+o5w46HrUgUWYpuusRlyXyz4sNP\nP+LTp5+yu7+F6ahcX51xcfQMs45plhf0tYyOXuIaEqqh0yCjGTaKopBlKY5rtUKYKkfXTIqy4XK2\nIK8bZFVDSK1JR5ZlbG1t4bgWWZaQZSnDgYtpKchKSw5TVRVFVUGWqKvqJo7ORVX1W3pvlmVomoIs\nVfR7HlvjEV9++SVhHKPrGnGakmYVaVIiSyqapmIZGp5jo+sqWZFj2h6rMGe8u4+maUwvTzh79Zw0\nmuM5Kp6l8dGHP+U73/ldrE6PZZxTNBKmZzNdL9AslziXyWqLRVJSSwqzxRzN0OkPx+zt7QENmmlR\nNjKW22U43sVWVZ4//RhHU9lcXyOXJYYk4Qcdvjo+JlcU/K0Rwc4O9qDP3fv3efOtJ+xtH6Choikq\nqipTFSVllrcp303T8kmKAkluvTWzLGvp4TXkWXl7+F8bxBqGcZMHod52BE1dIkkSQeCzWCzwfBfL\nsm6l1UK0vAlN07BtG9dxbr4L43ZbV1XVLRW+xRfa7kUIcesXuVyuSfOcsqyJwpijV2dMp1Oa+u8g\n+ChJ7RqyaerbNitNEwQlEpDEbZXM84oiT2kkkyoTTOIjomTK5eQVBgLb6pJXNW7QZ7XKkJQeQpIx\nLIkSGcVUsTyfKMlx3QFNXTC7uubhk3eYLTb0+wPOXr4kXkU8eO8DijCiKVccvvHtlnYrq0goeN09\ndu6FnB9/wnJ+zKvjCRoqvfEuZSMzW8/Z2dmjq40xfJU4XrLJU1TT4nt/9EdMjp8RTS+p8wy3nHN9\neYZl27z7/u9gDg7QdB3NMMiRyeIcx9ZxLJu8qrDcgCQWlGlNo6SoWk2aZni2yzoq6HZcJCoMowGR\nU5VJ6y5UVci0HZnt+BwdveKud4gKOI7VtqtVzmDYYz5bYjsm61XI/fuHrTW9olAULYhVVyrLRcT+\n3V3CZE5Du760HZPVJmZrOGDy6imq2mpWXqgqnu3g2iqSIdGIlk5sug6NqjHYPkAqQ65XS97/3W/j\ndAMOgkOSQkP1BnRHAYohY3k+i6sZQpTE6yV10+D4A64vJkQlDMYjonRDOD3F9sdkTYPjB3z51XNM\nv0cpC+7cfUSZZiwmR5xeXGM5AYarosslVG2rn6YxeVlgIVOWNUXR7vo6nQ6a0o5pr7USnU6HKAxv\nn1tN026oyzbSb/kvNE3D0dERcRghqxqD0Ta6rhLHMZIk43VclvMVZ4s5n37yCf/wH32fsqlBQJrE\nIFQUpT2yr4vFa2v6sqipGoEiaxiaSSUgKyuyrKBpKkaj/tc6k8oPfvCD/+iD/R97/cmf/MkP/qt/\n9k/bOHFNJUszJBkQFZaucXV5Tp6s0GUoi5ReMGpbnSYkiqeYpkbQGXByNuWTzz/lwZtvo5tdamFj\nO512vSUaFNVA0UyQlfbtl6ToqgqKxvjgEM9yubd3gGMa/Oznf8VqeYGpy3z8i4+IwzWdjsfLk0uc\nYEAudGTD4v69Bzy4d5ezl19wdXmGaVrce/iIPK9YrzfIqo6quTh2j707D5kvUwzDYzTsI9U5J6++\nwjBdnN4Qs7dDlIPt9jA7W6zXEVVTYbsmklyjKCrLZcbJ+ZQoSXA9neGwQ1XkLOYrBqMe4/GQu3e3\nGY08TKPBNNrUI02zKEuBEBqFECBpiKZGU/Wbji3Ftk3Oz8+pmwLXdfC8Vno9Go2RJEjTGE2zWC5j\nyqztWIJuQJTEVCXIso4ky/S6Ll3PRpQbBoEPRUxdpeRJTFM3FOmGJ4/uIlQwvR5be48om5rjk1d0\nej4lNsHwAZLeZ+/+Iboq8ZMf/iXf/uB3mW5ShCTYGfeYzkPG99+j1DpcXi7wHJ07B7v8zQ//knfe\neYM8Tyjygv3Dxzx4+B4oXYTcJ8tkgqCHrTYoUgVqQSPX+N0BsmayWs7Z2dmmqto8SFVR8H2bOI5o\nRIPnecxms5sQF4mzs1MePHjAcrlsC8KNUOp1sWgJTpClKev1iv29PV4dv2K5WnF2dsq9e4ccHx+z\nXMxvFcVvvvEGF5ML6qYhSYo2WGcdEYYRW1sjoEE3VGzbJo5jkqxkuQpphCDJaubzJVGctWOIgNEo\n4F/+z//D5Ac/+MH/9B9yJr8RowRIt4o+IQS2bWOZJqIWN/FaCtStHLouSsLlNXWVslrOURQFx3WJ\nspx1FGHZAaByfjkhK2Pm6xlhEtLv99F0lSSLkDSdYLjF3QcPuZzOKLKMMivJ6wbFsqjlhju72ySL\nK5rNjJ2By/r6Fc8/+Ql1MiPwNbqBTpxsOLs4Z70JefPxOzx8+JDzo2ecfvUJSramitfkYYQqJHrd\nEatlTFGA7vWIG43dR++g+D0+/PIFn3x5xJfPTzEtjwaFNMkoyxLD0CmSkI5jY6ltOynLMnEck8YR\nos7peCa2o5FEa7J0TRwv0VUJmgYZhSIriaOML5+94vJ6SRhV1EImyfKbotFyRtpxZARSTSNKiqJl\ny1lWS/m1bRvbNvF9G2Rx6+5tmgZpGmKZKoYCmiTh+z5+MECgoDkulu+ytTfCdVQeHO6iUmEaKkkS\nEWcpfneE2+njeiNAoaoKUCAXGo1Q2d+7x1cvr9ikgovrNUXe+lCczyKwB2j9A6Zhidcd8A//yT/h\nJx/+8kY+L1NEEV98+gllHJOsYzyrgygbHMtgvZmzXs/RLYUoDhFViSxLFFkGTX17f14TiV5bub8W\nNgVBcDtevJZI/5pg+XIAACAASURBVLaC8jU3oc0UbYHE4XCIbbed2sXFBZ7noigKy+WCLMtI03aj\nsVm3kXSy3GarCCEwjPb7yLLsVkxXlDWSojFfhjieB5JGVTVcXl5jWQadwP5aJ/IbUhi43fGWZd0G\n1mYllm5hmw6BF6ApOpbRCqwEFaKKGQxGyFKb3vzy1RGmbfKdD75LnlXIFHiegm7WDPsBy+WC2XSC\nJNfEWYhQZPxuFzfocnJ8xvJyjuO6mP0u2BaGpfOf/uEf0uQJep3QNRqUfMnl0Uf86f/5L/ns5/8v\n84vnrBYTyrIkzktMw+bR/buEkxc8/dm/o2dW7A1MHLViOb/CdS3uHOzhBR3cbp9K0Xnjve9w58Ej\nptM5rmkRJTlCVkjT9tDWZUW342NoEpokUZclMmAZKuvVgsX0grKI2BoH2I5yA9ClFGXSxrkLiSAY\nkCY1TaXx8cdf8eLlKVkuQKhMpzMc28OyHMoyR9e1m06hIi9SkBqiKGoNdLIcx7HY2R23uI/UoGry\nbdGoqxxTk9BU8F0PVdPBtAnG+0wX63Z+1g3qImIyOWY5vcTUZJqmwvM6WEaAaAxkSUdRQVIyBBpx\nlJNnFZLqoVhDCslka3sX23ZxghG9nYe88e0/JJNs5mHO/ccf8Phb3+dimvOzn37MyYvP6NoVyfIF\nWjOjya4hXxKFS4IgIOhsgTAIOl2SNGY0aP0nJRpsy8Cx2/Xsa35CWZbUdWueIsvqrYntr68ef/25\n/nX6s3ODHSwWrUeCpiu3xrKClrDUmuS0pCpZVlitVnz51UuurqYt/f/XipPneRiGQVUVxHHcAp95\ni0UIqS1CvV4P+WsmS3xDRon/8Qf/3X/7zymKAtPQKMoSSW4Vl3VVUlcNvmOhqSqTk2PSZMn55Qly\nJVgt14yGOyxXCwxdZ3trjx//9Cfs39llMnmFSoWtqQy7AXvbQ5arNYPRmIPDuyhyG/qaRBt+8ZOf\nEgzaBKisSMjiNsWqSFO+9e1vITUFmgKPH9+j42rsbQUc7g3I1nOms3OWizmWaaDLYFChyxV5tCRc\ntVoL27WoqoS6TpmcHBGuZmi6goyErmr88fe/z/V0jmzYSLqF7faQFRXPdTAsiTxN0VWdMCqI0hTb\n1LF16HsGYbRmf/8ASZGQZUGRt25IEi3IWGQVimpSCZm8hDSvW5+hpsJQZaqqwPUcqrJgs1lhGQbS\nzX2QsciLhtV6geu5pFmGojYE3R6q0nZ0uqqjKWo7ashgaRKmDnkR0+l0yIuKe3f3uL6a0OQ5Jy+e\nkUYRaZrT6fYIs4JGSDS1wHP91vlXqimLiEaxUWpBuF6wd/8eut0nSxpsJUFWZM4XFcHoAWEqsbM3\n5GryCqE5WMHb7N1/m717j7maTvjpj/8CS4lRqgVNNieNL3FsG8vt0ulvIcsumuG0Ij7VaoE/3SAr\nCnzPp6qKG/q5z/X1FNNsBVKTyeS2o8qzjG6vR11VVDdhSbcmr0jkRYHjepyenBFnGePxGNOwWCyW\nFEXBg/sPsW2bNInwPRdFVdF0C98fsFhEeH6Ppq6AgtFoyCZeM+j3OD0/ZzjcJooLyqIdmREKSZbS\n6QQMhx4d3+Bf/Iv//j94lPhGgI8Iga5KqLJ241YjE2cJXb9DU4MiS4h0w2q1YjAa8eLocxaLGY8f\nPMK0dLI0QpMUBoMRqyRi7/4h17MZB3t3ef7ZL7FEw7jjsdkkdLsdwrBFcmvROuP4vssbb97n8vgZ\ngaNhqwpOv0cSR9idLopu4w+30ZKIcBMjNxXzi1cYusXO0KPbNdANkzTOmM1W6EIj2TQU5QrTKmmq\nHJEsQanJ0hVd0yFNK7QypW4qks2C67mDkNr5teO56KpEU9Vt1oPvUeYtVTwrExqRYzsBtqHRoGJZ\nTiveCQIMo3W1StIU1/dQFZU0SmlkGdVQUVRBU5Roik4WJ2hdh6Iob0NOHMclTwuSdE3ZxERJg6K6\nWJZDnEYYusNms+bgoEuaqG26lapTIbDNlgkp5LoVF9VVGwOvu5RNyM7+IddHL+n4fSQht5F9yxDJ\nDlhFa1TbIGtqsjIlWybUDeRGw+5gB/Pxm2RNRlPZ9Pp3iaMlQ0dQXF8gpSGW4dHIAWgWl1fnWIHP\n6M4DVkJn6/E/QHcHzM8+5/rsCx7cvUOjytx79Aan5xdsVlNsv0RXBtRyQFMrFJVCkxd4jsVms0LT\nDMoyRr3BZBRFYBgK5+dzLGsLy2qLiSzL5EWB67qtsYqqtvoHSUKSVTRd5933v81qs0TULb+h2+1R\n1zXn5xdUVYl+w/7NswohaSRxhqQYjLd3OdgbUhSrtiC5BmEco+kmSZIRxzGKrLOcr1reTp6zfzjE\nsQ2ar0d8/GaMEkIIkiglDMOWzaVqmIZNXtZkeU6eFUiqium4XM2XDLcOePf977IM25sSxStkKrJo\njdI03Blu0fc6PPv8M7bHAacnX/H5pz+mzNp8hzyLMBSQmwZFBtO0GfUH7O3tcfzymMn5JbPZijAs\nkRWDZRiS5Bmm5WCaFoHfJfC7GIaJ1+kwHG0TxQ1HJ1OE4nK5SLhYbLhargiGfQaDHpommJw94+Tl\nJ9TlAk1KWFwfY6o5jiW4vnzO3cNdHj16iO/7qFqbiG3qOooioagSMhVbPZ/drT6WrdHtdlE1A9Ow\nSdOYpi5xLZMgCFCVFltohEZeaYhGQVd0PMdGVQR1ntxEq7c6kqps0GSttSfPS2zLxTRcNuuY2XRN\nJWC9irEsB0VRqaqS5XJOEATtQ5ynqDK342AUp1SN1Cr7FBVZMSjqmqA/Zv/gDr3eANEUyCIjjxck\n6zmmoSCoibJWNLYKI9SmpEgiZrMFimZhu10azeHV2YKjkwm+pTM5/QLXbvB9n/H2AeFqjSjXGFqN\noqo0qs3+g/e4+/A7DPce8NXJOa7f5ep6QtXUGI6D4QYklaBuVNabjCSuEEJBVlt7+DxtI+lep0Wp\nqv4bHcHrUfg11fk3NQ8tFvN65Xh6dsZyubzNqvR9/3ZlPx63RKSyrkjTlKpqiJIYw2jXlkkS4fsu\nRdla3c9na8ajXa6mC+qKmwJWUlUleZ5imhpZliBJytc6k9+MjgHaHbBuIqNQ0+C7LkVd0MgGBQ1p\nUaNYHo+efAchKWRJBGVMmWwo0xTPgjSeohkmSqMy7ijc/b13efniS3a2RwSey4//+i/wgg6L5Yar\nk5eslxuqqqDb7ZLmGUFvxN2DQ45enbHahLhOh90Dmd09g62tLeqipN/tE8cxlxdT8kZidjQF1SCp\ndd777j9GCIl7TxREk/Dy2accTa/pdvv85Kc/Z2d7wO7uNi8+/5hNFOJYNpcXCtvjLUb9gGg9xXVd\nlklN4HtEWU5RFGyWG4okpOt3MOSKcdfg/HJObcsIBKZqMOh2aJqKMq0xdZ3d3V3msxUff/KcxTxh\nPOziOAb9boClC6DB9hyQalzPYbGYcWdnlxevXtAP+kSbELPjUFQZ0+kC29PJ04o8a+gGQySpBd4k\nSUWWa0xDb0VDUUZRltRCkBUS1DW62ioNvd4Qu69yfX6M1fEwNpdslufYwQDbCciWlyiGTcfvEic5\n/eEehgR9T+HqNOboaIbVtfG8Plv3vkU5e87q+oJ4OmX3YESUC2y7j2c6pNNnLH2LrDDwhvcxzC59\n1cL0LHbuPyJeTNgsV1heQH/rDmHTwfT3ePl8yeRySXfUZbDdZ7OM8N0Oq/W8NU8xjJsnVibPa3w/\n+I3OoCzLX0Ud3ACQrz/XdU2WZcxmM7a2R0RRdKsm7na7hGHIZhOiaAbhaoNhdUiyEl2zmC3O6A97\nIJUkac6dO7skWcblZEEcFYSbDF1zWS0jTFMnzWIO7u5Q1UmbZF78HcyVkCQJQ1PI8nZlJgm51S8U\nKVEU0hq1S8iaTpyX1FWNYwWsNms6jo/tevz5X/1b8mTDYDzgjQcPybMCQ+3w1qMnhGGI57p4gYdl\naCRhThjG2PcekpcFRSPY2t3h4mpK1QgevfmYn/78Iyynw8nxBcvllMePH1MVbY7Aer1msZjx4PG7\nTBcxRSURxoKiMRBSG6lX1QpbB+9h+zMMtebtd3TizZQiV9jdu89u0xAnIS9ePENTbTZhQn+3XYO5\ng22QGhaza+bzKY/u32GxXuIbOlLdriB7HZOmTLBdl7oqsA2T6/kMzw9aZFyWb0hSLXJdFQkPH+wj\naom3Ht/n9PQEv+MSRSEP7h+i6ypR2vo9doM+YRjSNNDrjsjSdn9eV4KmhsViRb/fQVJUaMB2TMIw\npCxSyiwjzipqIWFpLkVdo6hAI3jx4oh3336HYGuXLMux56fUhYqpQZSs6XYHnE3mdLfu4XVGVAJW\nk1d0XZVex2HetMlkgoJuf8zF1UvWixmrLGZx+QLZ2ibL8pYWfP4VVqdHqfWQ65TlfIkmaaCY5EXI\ncHuXH/7Vv+W//Gf/DarhodQeutEliqeESUWnVlgt1uiyoDaqWz9Ix/E4O7sg6AzI84IibwiC4HaL\n8LoYvO4Q6psUKlVVOT8/p6oqtre32YQr9nZ2GY1GHB0d4TgO/X6f1WqFaRpEaUbX7FJVJfUNxb0o\nM2RFv/1egyDg2VfHt//3tV9nWaUMhz2Goy6SJCgLwfVs9bXO5DeiMDRNzWazaNc18wLLslAaGVkU\nmJpACImyqpGE1LZrVQ3I2K6P52hsTBuv0+Hb779HUSe8ujjFtf12hXm9wrcD0k5F1ZSIouWib42H\n6JpJ2TR43QFJUfHBB3d5cXSKoVs8fucJV1dTGgHHx+c0jcz2aIwkWtPavb09yrJtK93+gHoDRa1S\nCkjWIYaq0fGHNOuc+XJGv7fPeHufKouJlnNc3yHo7zAY3+Hy8hIhCwy7Q9m0yPR6s2a1nhNGKxZz\nHb9jk5cZruMTJzFdv2VHqqpKHGWUZbsx0FSZPM1aRqSusLvTpRd02N0ZEXQdXFsnChfs7Y84Pb9A\nNWSurs6RZZn1es329nYr59X0m2wLlbwqidMc03GZLxccHm6360nLoWlqoihFlhWSJMLzfbIqZr1K\nKXSBLECEIeF8hirJzFcJaZxhen1Mt4Opyiw3a4SQKZKY4aBH1VQYpoZIKzRZwtQaNBU2lwv8Xp9h\nT0HP2jdtv+uxPllw+eqIwyc7KIZBo5lYisL6+py7T3Yp8wWakDBkWBcplqEhaSrbdx6gGj6yaqEr\nOpOrOXFW0aBguz6GbqArramqbZuUZc50OqXXHVDekIdeq4AVRbklN70+qL8dJNvpdHj69ClBELC3\nv8NoNLrhQzi3uZUtwSpFllSSuCDJWiC+bEoUXUWS5XZrlxdsNhu2t7eJ4g2WZSJLDYvFgqDrcHhv\nn81mQ13XPH/+FNR/fxLWb1/fiMIgRI2uN7Ru0AWbTUy4mdMxdYq6wjDddh9u2cgy6JqB1NRURcGy\nSJjONzx597utws32uYo/5tXklNViwqODbYSaUkrw6vSSKFzjmzpZEqLbDmGa4nVH7O7fo9FNDu/d\nYx2FVEqDpNZMREW/O6IoE/7mRz9DUSoODnd5eO+Q66sTTHvA5OIEf/QWadUgFI20KnACn+vlHFlT\nuPfWG1yePOPibMKw16ExdaZhiK7njMYD3tzZbq2+3QGaoZOmKYvFCt/1eOP+Xa4vj5BlFUXXKMqE\nIg2pwhmG7aDIGlGcc1YWmLaF49U0okFVDRQFDvcD6qZEkjKicEW4ETi+S4nAdDQ0TaeqC8JVyKA3\nZLFYUBbtqLE1GpLmJWVd4fsuebJmsVhweG8PVf1VFHy7m8/QVBldUcjSitm89cK0zRrH1Rh2e0TU\nZLmE398l3czxBnuks2PMImezDFHUlCwtKJuCo+MzLNNj0DOZTE9QhM3hzh6UMyYvnmMKm7LJKKKQ\ngePz4f/3N/ijQ0Z7ByjdDlfPBYvJBMd/hup02/Qy28PXKtarKb57wMGb79PdOSRJS7JsyXRTE8Y1\nDYK8LFBUHc2QKcuUqqoIgoCmkdFUg9UyJk1yVFWjruv2ZaYoxGlCx2sxg0ZUCCHdGhqPx2OyLMN1\nXbq9DicnJyyXC7a3d0jTlKLIURSVKIqxHJvFPEFRDEzLQUigmxplDSDhOB5CanBdE2QZWWno9X22\nd3tIUs1kco7vB1xfLYnTioa/g7kSstRKTAFE06AqGr1OD1HE1EWNamvEeYkkV5hW6xBUVyV1XVCX\nCbalk26i1pCi0fH6d+mP75HECyZHn9IxG3QhePfJO1xPLxGixrUNdF3nqxfPmU0v2r1yGbP/vT9k\nEq0o0wTX1Ai6Lj1/yGQyYe/uPXq+y2YzZb7YUFc5k8kLTG+IJEJUxcUJBhRVQ1GA5XaJwikXsxlu\n0EFWBXVZ0BuOKKscSUDX90jSiKyu8TwHN+gghMJ0OueNNx5ydTVhPNxBM8wbspdKt28Tra+RaRB1\njmebyGpNspmjym1iuGaoRElCt9OhEhBGKZqqtrL1skZXNGQh4VouWdLa5q83EapqYNgqYRxT1DKq\nJhj0FNJkju8GtLN1gaYqgMFsNruxMivo+C66ZtAJHNTrBYahIIkKTTWx3AFdr0uJznS1QVVNOr0B\nm+kxFxcXjLd2WW5yJMXFsg3ueH6bI5mGpPEaQ20omwvyek1RLOj6A1xHY7MWjIZ9alXnq6efYAd9\nDnYOeKZZROsVtibI4xmWYaHWkOU5/f4QTTfo9HfIKwkkhXCzQcJFtyQcxUaWakxTR5ErwmiDEBKm\n7TC7nqOpdat6VMCUDTRFpUhS/G6AIgtURSIvBJKktM5jUjtexNGGju+2YbjLDaISuG5rcqsgI0mt\nmVBRChRNJisLfNOnEXXLwJQ1JNEgpBpFMSjrVpbdlCVbgx6gIKsSp6cXJFlJGBVMzpekuYzf+XoE\np29EYWjqmiLN0HUTTTeQaJ12BTqOpVMWDYbuoCjajXV2gbgh4BhSwenJCwY9n/VmRlLo6IaPpJm4\nTsCB4VBtJsiiDerwAw9FV3j69Cmj/oB33nobRW6I4pxPPv+M/2tyxvf/0X9Gx3E4OjpCkWqqJqZs\nKgzHZe/uA8p0zGeffkQQmMSbObZjcXH8CU5nh7qI8awBmumxyRIsLyDP1uieiWN7yHVFuDnHMByk\numGzmIOoyBsohQSqjiQ0er0elm2gKn0Go71WX29rxHFK13PJiw2qDJcXV0RRTL/fpxESi+uSTqdL\nIzJk4Pr6GlnS0G0PXjPzBGiSga4aVGXLbpQkjbKAphZczqdcLTbElcLBVp/f++ANVus5itJhtQ5J\nkgRT11sm5LANNnn9xgzDmPF4wFcvjqmrHNfVUCQFZIEsQ76eokqtJVlSNLhelzcePGS1aQlr67ih\nakryLAYqinCOIgnyPEbTQCli8s0SyXUpS5moaFDjiMHIYZZXvHpxxOxqToLKaGuX9XzG1rCHkAvy\nrGC4vU+FgqabmLZFLUDTbPyezXxdkWeX9EZDXLvNHfH8Luv1muFghCy0NmujLMnzBCEqTFPFNQ1m\nUYilG5SVhiRXNEJBFkobbitVJGHEeGeLL7/8kvVywXC0xyJZ0RsFTCZn7Iy3MXSLLG3QTQPH9zCW\nOZLSYGg6hmpgKjp5HmJo7eajKuoWrK8bNL3lpeRZwWg0Ii0El5MlaSFT5OA5/tc6k9+IwiDJEqZp\n3hphKqpClCZ4uoEiC0zdompkkFtWXVkUINqK3uQ5dw7uEcdLiqqhrnKQUpBA1y3cToDTt1lNXrFZ\nX6LqCkLW+d73vsf08op1HJOGIY8e3Ec8fhNdN/nhX/4FkqLx+PFjhASz1Zq337rP8ckVl5MT1ssp\ne7tjFtMNq1nNcnrE3t0dXp08oz/e4sGjt2mKLsWqwnA8ep0h8+W8TbHS4GB7vxXhSILJs1eUyYr5\nesP9tz9AriVUTcd3XJIkRKZis1kgJA3dNFqmp9Sq6RpREXR79DotMl4LmYoW+KKsSZIEMAmjlOGW\nQ5iEyIpE17NBqqjKHMcNkCXQdZkobAG2Zz/6iJPziF/+8pR//l//MUghkqiR5BLPNQnXG/p3D2ia\ndqa1LOOGUn0TOJxFvPnmPc6OT1ARKJpKkmU4HRczD9FkwXK9+v/bO5cYya77vP/Ofd+6devd1dVd\n3dPz5kMkPZEoWq9YimErshLEWXphwIsg2mSRIItEhoEA2SVZBFllYSgJAjiOHCAxQju2EUmRosiS\nLFEUKZGcGc57unu6q7vedd+vk8WtGY1IWSKhEacJ1AcU6tbpW1Vfz3T969xz/t/3Yeg6erWBoasY\n9RzPz6hpKbHvEc8GkGVoRUbNbTCbeizmpYOzzKHRXOdomrFzoQt5gCThoy98iCCvU2/2eJ0cPfPY\n6FbRshnjZQZmUbexnDbDg10a3S2qdpVMMVFziVQitvoNXNfCtgWev8CtlrHyZYp0jqIKwoUPQrLe\nW2N/7ybNWum5+JWvfYXOWp0PfOADpeI1iGG5denUXN5443K5BtBoMF9MkUophOp0OtRqNW7euUu9\n0aLRaOAFAb2NLr4XoahQc200TaXIy92NfKnWvN9IFccpR4MR1Vqd5loTs6Jx4/q9Mm/UkJjm+1Jd\nKVBUQZJlGJYFSEzbIstSkjglTDMURUfRDaQokFmMKgsMTSeKFZrdLpPrI2zT5PDObabzGAyHKInR\nZIKeR1hKhiw8ms0mRpaxt7dXtjJ7IYODQ6Ik5dKlS8wnU9yqg6qq3Lh6BRQ4e/EiUiasd2rs3dnj\naLDPneszfvmjnyaXNrWmw+DoLs2ay9G9OyiywF3bpGKv401iqnaVtU6fTreH50/IFElCQRwEZFnB\nbDrGMU3UIib2Pex2g3a7TS4DKo5GnimESQKajm7qCAoSL2P37i1qlTqtdhPpSwzFZDT1qekGbrWO\nbljs7R4znAT4icRxK1imTkFOnmel27bjltNky6Cz1iRNcs6du8C9wzc5Phjw6qs3eOHD58jzUlvQ\nuXCe+XTGdDam2WwCZVAQFChCYBgKi8WcZqPN0NRRtaX9mGEQ5ymaoSHyHAWBVCQ5gkJoYBhsNhsc\n7O9hSR0tdRgc7rPe22AwOEZVNCqGyjyOqNhVbHeNeDKm3egxGe6zv7dP70KKYhT4UUj/9EUmh7eI\nkgTpL1jrNDE0qDVscgFOruHYVrlqn6ZUqnWUyZi6a9NsOFSrVVr1sm+h7tbK6IJS8U+jUWMwOF6G\n2LoYjomYKVy8+CSv/vB7NJtNms1tKo6FLBKiJGY+nXHr1h36W1vMZguyXFJ1HBb+nH6/z60bN9F0\nfamD8VCkwFAVPFmgCdANBU0pUC0LVdHKLmGrwmI+wjRtojDFDxLMSsFsGjA4GpcO6opAtVTqtfdh\nEpUEFM3EsR0QKnkuEbkkI0fopUGGppuomiCJE0QhSaIIVTXRDAfIGA5H6Aqc3eqQ54IoBbNis3vr\nJtPhiJnv0emUBrCa6pCmYFtVNrfOcvrsU1y5coX/9Ad/xK/+yic5e+YCe7dv0qg4jKcjxodHpHlG\nKgT1usn6+i8xPR7xxrU36J8+T5hGOOvb1GsdnA2JW23y5tXvs7NTwVRtDu5cw2mugaWCUpCjYzh1\nwiRnc/s0TUswGQ843r1Nt/808/ExuVCWfhMpEpWKW0a8iUIv1xVqDdqdLppi4DgOh4f3aLTW2Nza\nJpcqummRodDqrqNoIbuHQxZeTKNZZa1dY3B8TK+3gSzE8o+shu+HKKrC9laP5z9U4VvffJlvfftl\n+tst+hsViixlPBqgqnqZAJZWcBwT3VCZL6bEcUy72cGxdJQio1G1UNQC1Sin1boBaiGYhwGmqZML\nl9D3WHgerWadLPTpNqt4Q5/CUnFtg3v7exR5ys7ODldu7FIoBrlwmMU6em2NztYFrIrL8WSMimRj\nrcY8SolVBbu3zs03XiI83mVjzUGkIac0i1TL6J1+gjgB07UxNR27UuGppy/i+4vSlEWBPEs4vLdP\nu9kpBXiBR9kTWGCaOpqm4EcRQZRzMJiiKDqd9gZxklEUCcPhlGarThCGHBwegVB5/fXLPPnk07TX\n2niLGaZiMxhM8f2C8xcvMDw6psghy1JMPcXQwDQF3U6dIk9ASJaNlGhaqVHJMojiHN20GI0mpPmE\nyWSCW61TcW1abZe6++56Gd/R2UKI20KIHwohXhFCvLQcawkhviSEuLa8bz50/u8KIa4LIa4KIf72\nz3x9FIRioKo2eQaO26BAp+q2cSp1TKtaZktoVnndV29iVetESU6cSlqdLv31Hq6hMTrcK92eZgMm\ng10sVfLcUxfZ6HXprK2RpilRFKEiEKrG3AvJ9QobOxeIC40///LXeOmVH7J9aod6vUEUxATzBZqi\nst5eI8syNKVsv3YrBndvXkbTVBTFYuYX2LUNdo8Sqq1TRCmMR8eIwqdd1xGZhyaz0txWKDRanTJk\nhzKW7WhwQOhP8BZTVKU0V8nSgrKLQ0VRVDTDBLVsprEqNvVGCy8IWOutMZqMybIMXTdQhI5lVksP\nAVPDtl1U3WIxDzk8HmLoFoIyD1HXNIIgIE1jsjxCKAm2nfPCR5+j020TRCHJ0n+wUrGIooBer0eS\nJA+6+RqNBrZtl9u3bgVkiqELDF1dCt9y8iwiTwJkkaCbBppdA83GMAxuvfkaliEQRcrRcEAuIRUq\no/GE8WTG4OiIdqeD6dTY3LmA1WhTazbJhUKcSRS99L3Y373Mwd3XsQoPXXp0GlUODw+5fWsXWehI\nzaJSL9eAdKtKoWgUUpCmSZn0jYqulaY1FOVugqYrqMs4NMMwCIOgjDFIEvJcEkYK9w5mDEc+7c4m\nYRhzfHTAvYM9bty6wSLwubu3y3gy48bN29w7GOD7IZpeCsYGh2MazS7zWUgUZziOQ5aW3pqqAkJI\nLFNHUgoMFaGRpimTSZnqVeSS6XRKpWIhhViu+ZisdZt0e3V0LSMKZ4++MCzxt6SUl6SUzy8ffx74\nipTyAvCV5WOEEE8DvwV8APgM8O/Fz+jHFKK0807THM2ooiomllklyxXiJAehIVDIk5w0KfD8CN2s\n4McJZqXCdRtriQAAE3tJREFUcDhEFRK3YuNWdEaHuzQqGrapkMcBh/v7hF5pbfXMM8+wsbGOppd5\njQgVz4/JFZMPf+Rv0l7f5JUfvM7X//Kb2E6F9fUeR4fHpcw1CGg1mrTbbQzDoNuogx8yvHsHXYBb\nr5IrBevb27TXz1Ktd9m7t8fBwTVC7x4y8VCylMhbEPoBRSGp1RrUWy0QCrt3bjEeDqjVbYo0XToK\nacuiaSCUH4WSZFlGnpeNM3FaLv6V8ecGCEEuBbkst4DbnSaqWio2NdUkjsotYD8Myl4IXX+g9Iui\niEbdJC0m7B+8yfMfvkSr1XrgQagssxNKF6FSExHHEUHgY1kW8/mcyWSEQka1Yjy4zKBICOZjgmCB\nZmjouonQTTTbZTGfsn/7Gtcu/4DD/btMF3MUq0L/zAXcRhPbqbDR36TiVvHjFMOpk0oVhGDuLciK\ngvF4yODeLe7dvIqrJ4TjWxzevUoeeZw9c569/SPSXCPIFJx2F6veRbMcslyQ52U7sj/3UVUdTdGZ\nLoVNjmOXBSDLQBTl4ywuU9hlRtVpcDxc8OabuxS5jm3VcJzq0iuhdHN6+eXvMZ/PGQ6HXHjiaWpu\nkzTPabfXQNFLYVuQsX8wJIrK/ghFUUjTBF3XUJTyEk1VBaqiP6S8VJYFPUWoCmmRYtoGO2dO8+yz\nz+JULRo1E6GkmNZ7dynxm8Cnlsf/Gfga8M+X41+UUsbALSHEdeAF4Ft/3QtJBBkCgUARlJ2K9RpJ\nFFIEIDRBnpQBGrquIzWNOC3orG9wtH+H3Bvx+ivfQ8lDNE2hv32Ke8cDPM+j01pjeDxDU+FPXnyR\nXnebT37ik9RbVQzXJcwdkrhNXgi653ZQnS36pwZki3t84Qt/yM52h0vPPcXVK1dodHr0Nvoc+Ae4\nrsNat83meo8oz/jua2/yS+tNhJ1yNL5Do95lNJjz9JNPoIiE2eAe3W6HSiVFMUykyKjXNaTRYLR7\nRH9rk3Vg/+51Gp1tKu0OaZagSgPTrIBioOQFkR+iawWTyQTD1IiLjMb6KRCSzpaDNw9RNIlmWuia\nA5pCGC7YObXO/sEBtZqLKlOGx8d0Wk2SOEZVFNxqlelsQcWu4s0XnN/u84NXL/PKD1/lhY98iM56\nh8TbR03AcV3iKCql2EmOrhvM53Nct4axbO5ZLBbomoFlWbx+5TLdTp1qcx0Z18gRaKpNFsU4doGu\n11nr9lAtnSBJOf/sB9HtClEa8vzHPobMM1QhiIc+uhagKpIiiaBQqTgGR3tHnD9/HtdMqNccFpMj\nlDwlSyM0ReeZp87jmHB4cEQPnfWNc8xjCOYhuqVxPJyS55Ka62BVXI6Gx9SbDWaLKe7yd0WU+Y9B\nEFB1ati2zfFwwOmdZ/izL/wRcZxz7txZpCLJcolT0ZnOYyazOfVqC9dWaLc7bG9vY2gaSRowGc6Z\n+hmvvnbAdHKNS5cuUnNjWt11TN1g4ZfFdrHwUA2Jrmq4NZfFbIpbs4njCBSdSTAjTCS5SLEskzha\n0GnarK218b0pFdtkNnl3nY/vdMYggS8LIb4nhPjccmxdSnmwPD4E7tvQ9oHdh567txz7MQghPieE\neEkI8dJ4MsG0LUzbQjXKra0g8EjzDM1QUTUNw9AeGF2YpkmuFGiaQbu9xsZGn83NTc6dO8fW6TN4\nUcx45lNxm/hRilF1sGoNPvLLH8OpdPjfX/oWQZiS5AleEGIYDRSjwcQHLzEozDqnzj7NpRc+wdyP\nSPKM559/HssymM0mZDLDciwKReCutVjb6PHMM09y+8Y1Um+MiKeMj29g24LFbMKbV69z5Y3LfOVL\nf8Z8tE+zKjCViHBxxMKbEcYR+7t7BPM5daeKbpkUaCiaharopUfjMjAkz3PIi2XwqYFuWCh6BUVz\nMCwHxdQxLZ00jZFZThbG5FGCY+tsbnTQRU7VXa7bKJSmH4XAW0QICd5iQaPWROZw4ew5xqMJg8GI\nwM/orK2TFeVuh5Tlop3runiLgJrb4OjoCN9fEMRBaZCSJMRZiiIVdN1GaA6LCHJpoKkGFcPC0E36\nW+dZ2ziL5TZRzRqF4lKoDprVwG20sasNvDin0+ujaDrDo3tsdlo06y5ZGpAXEYXMyPOMNIpptZs0\nm00atTqmqTOfTTAti43+Juubp5lM5oRBRL3ZYDpfMA8idNvBcmqYponnedi2jWVZOI6DH0SlWYui\nMxwOMYyy4M2m5S5Jq9kkTUOmsyPyvJzpBWGMtwhoN9t84KlnePrJp9nZPs3xYECWR2iKSlEIFvOY\n41FIWpgMR3Msu5y9qZpJloLvhcRJRprkIFQKylZrVVWRQpDkBcPxnOPJHFUxydMcQxMkacDg4B6O\nUy6kNxqtd1UY3umM4RNSyn0hRBf4khDiysM/lFJK8XAkzzuAlPL3gd8HuHTpOQngLyvk/R5zKG3f\nszSFZW4fhUQrNDSKZe6CIM0FquWyd3Cbrf4mvjeiWq2jqBZxIen2emUjCGPWt1QycchXv/b/ePLZ\ni6xvPIEiAwytnB5rpoHb6JKmHr1T52m1mly+/F2q7iFPPvsUSQGj8Zy5F2OZJrO5T3/rFJZl0Wq1\nONi/h2W7JFlRbqvV6jQbNQ73bnP7zpSXX34Jp1IavlacOmkScuPqNSqKJMunnHrWZjodU1HqmLZV\nLnXlKUWWIFQDQxfEcYBhWGXCVFpQMcotMU1oZaaGopRhprZNkUtkDmEQlNvCTpU09VB1jayANC4v\nISp2FdOsMp/PGQwG6IaNohZsb3WZjI9I4z6+L9E0HU01HpiYBkFAEARsb+8wHB2BaZUmO7ZNkvvk\nsUA3baRQqFZrFElKHPtkeYyugWkpJKpFa22LLAuwnYIsL8NvdVNANMWpuPhRRnt9nc989jc4npYG\nwR3HIfJjmq5BOJvQqOok3ojcdHEqLrVaFxQVu+IyHE/JC4XOWo9JIilUhfksIIxzfD/EdXM8z6NZ\ns3Fdl+l0imNbTKdTVFUtk82yDN/36ff7jMdjqtUqC29Gf6vLwaHLtetXSJNNDB10Q1CpuFTsKrpm\nMh4dEYYheVGuDbQaTbKkYDxalDsfDQNJ6ew0Ph6Sm+WXQJqWZrNCKS368mxpM79sbgr8+IHiMwxj\nTENZBt+EVCwTmReYponh6H/dR/En4h0VBinl/vL+SAjxx5SXBgMhxIaU8kAIsQEcLU/fB7YfevrW\ncuynvT5h6OM45YKOrqukabyUioqyZ3+5H2waBlkaQhqTZ6XtltANuqfOEMcxtlWhv9FnPFmwCFM0\nu87QL4jjjGqtjS0qPNXtMzqu89r3X+Fof8i5J8Ho7HBqq8fU0Rgc3kO1HXIEQRHx5NMf52h4kzfe\neI2tMzs88+yz6Fabv/r2d1h4QxZRzsZGn+1+H3sy5OqV63Q3NnEdi4pZ4fDePltbW9RcC7uic+vq\nZQzD4PBwyMULT7K1ucHezZvMBnMuej6KGxDGEaquIVUFFRAiI0tK3z+yFMNSKfICKQSaFMiiIC9y\novkckYaMR0c0nr2Eig5Fhu8nKKpKrd2k0uqSRjGJhAKVqu1Sa9SIQp+DgwN6m328hc+pU31qjRZX\nr14j9Kbk1SZRnNBulr6GaZoyHA6p1WqMRsdomoZpVPCDCW6tiaompFnG+kZvuUU3wTE04izB0HJG\nkyOSwCfNYsyKgYxyhCoIA4FuOkSZh2PWSJKAbv8MqqET+wn97U1Mu0Ich+SahlcpUKIMQ0DF0Wi4\nNqmiEgsoCgXDqrP5xA6WXSXVbJq1Fl4Co3nCdBySxGUTUa3qlJ4frTp5UaapLxYL1tfX8f3y36bV\naiGlZDKZ0Ov10AyFZkPnc//wtxmNRty9c4s8BR2VM09cIIkz7ty5w+3btzl3/gy6Xn7ApVDww5i9\n/WOklMwmh3z4gy/gWg6pG3N8NMY0quRZjpQCRSkTrAxTI4sKpCwwdIswHGHoDkiPMIjQFBvbrpCk\nZdzidCZxqjaDweG7Kgw/81JCCOEIIdz7x8CngdeAF4HfWZ72O8D/XB6/CPyWEMIUQpwBLgDf+VmF\nQdd1wjBcKtEMVFV/MHY/tKOsiv6DzD6hKmiGhaLpVJwabrPJ3b1diiyl2+1imgZzb8F0MipDVqKC\neRQRF5IwzfnQBz+CN53zza/9KfvXv8/R7lXIFnQ7dTRN4+h4hFVr4qUFnY1tTMvlcP8eu3fvkqYp\nm6fOsHP2CTIUbt+6w+HhIZrQOL21yd7uHXRVwbHL3IM8zahWXVRVp93poRsV1rsbqIqCoQgsW6PI\nY+bjY9Y6HWzbRlAKZjSRI9OY2J8hsghbV0nDBUm4oFW1yGKPPPVR8hzyhCyOyOOAg90b+N4ERSkI\n/Hm53ZskBGFCmsN8HhGnBWmac+vOTfI8p9frsZj7ZEWBhqTdrPHpX/sktqUjBEsbsYwsy0iWhiT3\nm9Ns2y7j60yT8XiMULQyTk2o2JVquYiqa5i6zmKxYDGbksYxQTIjimeoWspwdMDCmzCdHVEUGZpl\nU6m6ZHKZ9JzF5EVMngVYao43G9Jbb3P61A5Vt0Gl0cZpdKk2N6m3t+j2z1FtbaFVWihOC6PaoEAj\nDHImYw8/iLHtygNfivv2728NhxkOh9h2aeU/HA6XruYFYeiz3m0wHB3S3+zy5MULPPfcM6UkXSoE\nQczu7i4bGxtYlkG9Xse2bZAKd/cOKaRBrV7l3Lk+vfU6+/v76GoZgBuGMYcHQxbzsPywqmUSlqot\nvyxVHUXRQCr4fkwYxnieh++VLe5pmjOZzBgdD3GXYcOPrDBQrh18Qwjx6vID/r+klH8B/Cvg14UQ\n14BfWz5GSvk68N+AN4C/AP6RvJ/O+VMKg6JomKZNmub4vk8cxw+sse9PnRRFKY1CFB1Ft5CUXYIo\n5eq9ZRhYpsF4NCAIpvQ32nTbNUwdNC2nSIsyu9LSWFvbJk4UPv7RjzI5vs3htVe4/cZLXP3Bt7lz\n4w2iYM7mxgZOvYaz3qPb32Gzfw7bcJmMpvz5n7zI6dOn2Tlzno1en2rdZTwccXxwD1NV6DQafOPr\n/5dv/uU3iMKALC81+6qiExc2tfoGtXqLLE6wLINq1UbXFQ4P9pdJRyqKXvoJZnFEGvuE3hytSAnn\nIww1RytiZsNDxscHZIlPGM7I4ghVSkxDo0hDpuNDNK0gDhcsfB+BShhLZvOYOAWJgm6ZxFnKeDzE\nNE1275TBuACz0RFZvCD0p4TRgna7+UA56DjuMk4wXl4XGyRp6VGw8D003UTTTbJcIlSNopCkSUGW\nls+tmJWy+1EVTOYTDgf7OBWDc2d2MIzSCCVNMyyzgqYpyDzhcG+XxWSEoQAyRUjIc4lVrXPqwrNs\nnn6Kevc0qt3Erq9j1TrU1/rYtS7Veo84EwhFR9UtkkwAGrpmYhv2g/e8nxMRhiFCCHz/R5LqcnY0\nwjAMNM0oY/0MFV0DWWSkWUzFMrAtg8lkwv7eAUEQsN5bo1530fVSEZtkGVkOirCQQLNdo99vE3k+\nQpYqy9nUJ0lKJeV9BWcc3/+ivO9hYj8whykKSJJs6ejl4fs+tm1jmjae572rwiDeKg19HBBCHAM+\nMHzcXN4BOqx4Pmq8X7i+X3jCT+a6I6VceydPPhGFAUAI8dJDPRInFiuejx7vF67vF57w83M9EZ6P\nK6ywwsnCqjCssMIKb8NJKgzvyO/+BGDF89Hj/cL1/cITfk6uJ2aNYYUVVjg5OEkzhhVWWOGE4LEX\nBiHEZ5by7OtCiM+fAD7/UQhxJIR47aGxRyYxf4Q8t4UQXxVCvCGEeF0I8Y9PIlchhCWE+I4Q4tUl\nz395Enk+9N6qEOL7Qog/PeE8f6FWCKVW/jHdABW4AZwFDOBV4OnHzOlXgA8Crz009m+Azy+PPw/8\n6+Xx00vOJnBm+buo7xHPDeCDy2MXeHPJ50RxBQRQXR7rwF8BHzlpPB/i+0+BPwT+9KT+3y/f/zbQ\necvYI+P6uGcMLwDXpZQ3pZQJ8EVK2fZjg5Ty68D4LcO/SSktZ3n/9x8a/6KUMpZS3gLuS8zfC54H\nUsqXl8cL4DKlivVEcZUl7rfd6cubPGk8AYQQW8DfAb7w0PCJ4/lT8Mi4Pu7C8I4k2icAP5fE/BcN\nIcRp4G9QfhufOK7L6fkrlEK7L0kpTyRP4N8B/wx+LDP+JPKEX4AVwsM4EZ6P7ydI+e4l5r9ICCGq\nwH8H/omUci7Ej2KNTwpXWWplLgkhGsAfCyGeecvPHztPIcTfBY6klN8TQnzqJ51zEng+hEduhfAw\nHveM4V1LtB8TBktpOT+vxPxRQgihUxaF/yKl/B8nmSuAlHIKfJXS8u+k8fw48PeEELcpL2l/VQjx\nByeQJ/DjVgjAj1khPAquj7swfBe4IIQ4I4QwKL0iX3zMnH4SHpnE/FFBlFOD/wBcllL+25PKVQix\ntpwpIISwgV8Hrpw0nlLK35VSbkkpT1P+Hf4fKeVvnzSe8N5YIbwnK6g/Y3X1s5Qr6jeA3zsBfP4r\ncACklNdi/wBoUxreXgO+DLQeOv/3ltyvAr/xHvL8BOV15g+AV5a3z540rsBzwPeXPF8D/sVy/ETx\nfAvnT/GjXYkTx5NyF+/V5e31+5+bR8l11fm4wgorvA2P+1JihRVWOIFYFYYVVljhbVgVhhVWWOFt\nWBWGFVZY4W1YFYYVVljhbVgVhhVWWOFtWBWGFVZY4W1YFYYVVljhbfj/f9aTKENYRIAAAAAASUVO\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116298ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imgMicro = data.immunohistochemistry()\n", "plt.imshow(imgMicro)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Segmentation and feature extraction" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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GycBzPgSFwsAUHGxENB+qv9nMzN61/N/MbPQecb3934Ai4CXDq/OsRkooSf4o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ir4SpP2VGd3/PV6nUjYcG++pmVkky1btmzZ7t3OAtmoTEvpfmpiEG9cjY2WRWGVSN4fXMGdmZPb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"text/plain": [ "<matplotlib.figure.Figure at 0x11597ba90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.patches as mpatches\n", "\n", "from skimage import data\n", "from skimage.filters import threshold_otsu\n", "from skimage.segmentation import clear_border\n", "from skimage.measure import label, regionprops\n", "from skimage.morphology import closing, square\n", "from skimage.color import label2rgb\n", "\n", "# create a subimage for tests\n", "image = imgMicro[300:550, 200:400, 2]\n", "\n", "# apply threshold\n", "thresh = threshold_otsu(image)\n", "bw = closing(image > thresh, square(3))\n", "\n", "# remove artifacts connected to image border\n", "cleared = clear_border(bw)\n", "\n", "# label image regions\n", "label_image = label(cleared)\n", "image_label_overlay = label2rgb(label_image, image=image)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.imshow(image_label_overlay)\n", "\n", "for region in regionprops(label_image):\n", " # take regions with large enough areas\n", " if region.area >= 50:\n", " # draw rectangle around segmented coins\n", " minr, minc, maxr, maxc = region.bbox\n", " rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,\n", " fill=False, edgecolor='red', linewidth=2)\n", " ax.add_patch(rect)\n", "\n", "ax.set_axis_off()\n", "plt.tight_layout()\n", "plt.show()\n", "#plt.imshow(bw,cmap=plt.cm.gray) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Save information as a xls file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Calculate regions properties from label_image\n", "regions = regionprops(label_image) \n", "\n", "for i in range(len(regions)):\n", " all_props = {p:regions[i][p] for p in regions[i] if p not in ('image','convex_image','filled_image')}\n", " for p, v in list(all_props.items()):\n", " if isinstance(v,np.ndarray):\n", " if(len(v.shape)>1):\n", " del all_props[p]\n", "\n", " for p, v in list(all_props.items()):\n", " try:\n", " L = len(v)\n", " except:\n", " L = 1\n", " if L>1:\n", " del all_props[p]\n", " for n,entry in enumerate(v):\n", " all_props[p + str(n)] = entry\n", "\n", " k = \", \".join(all_props.keys())\n", " v = \", \".join([str(f) for f in all_props.values()]) #notice you need to convert numbers to strings\n", " if(i==0):\n", " with open('cellsProps.csv','w') as f:\n", " #f.write(k)\n", " f.writelines([k,'\\n',v,'\\n']) \n", " else:\n", " with open('cellsProps.csv','a') as f:\n", " #f.write(k)\n", " f.writelines([v,'\\n']) \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Simulating 2D images - \"cells\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x117cf1c50>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116756358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test\n", "from skimage.draw import circle\n", "img = np.zeros((50, 50), dtype=np.uint8)\n", "rr, cc = circle(25, 25, 5)\n", "img[rr, cc] = 1\n", "plt.imshow(img,cmap='gray')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "\n", "import random\n", "import math\n", "from matplotlib import pyplot as plt\n", "import matplotlib.patches as mpatches\n", "from skimage import data, io\n", "from skimage.draw import circle\n", "\n", "def createMyCells(width, height, r, num_cells):\n", " \n", " image = np.zeros((width,height),dtype=np.uint8)\n", " imgx, imgy = image.shape\n", " nx = []\n", " ny = []\n", " ng = []\n", " \n", " #Creates a synthetic set of points\n", " for i in range(num_cells):\n", " nx.append(random.randrange(imgx))\n", " ny.append(random.randrange(imgy))\n", " ng.append(random.randrange(256))\n", " \n", " #Uses points as centers of circles \n", " for i in range(num_cells):\n", " rr, cc = circle(ny[i], nx[i], radius)\n", " if valid(ny[i],r,imgy) & valid(nx[i],r,imgx):\n", " image[rr, cc] = ng[i]\n", " return image\n", "\n", "def valid(v,radius,dim):\n", " if v<radius:\n", " return False\n", " else: \n", " if v>=dim-radius:\n", " return False\n", " else:\n", " return True" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x117e52550>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116572e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "width = 200\n", "height = 200\n", "radius = 5\n", "num_cells = 50\n", "image = createMyCells(width, height, radius, num_cells)\n", "plt.imshow(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Simulate particles with Scikit-learn -> sklearn" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import MeanShift, estimate_bandwidth\n", "from sklearn.datasets.samples_generator import make_blobs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11890a518>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2eB7sbIfDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInDwsySOCzMLInD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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1188a7a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 1000\n", "clusterSD = 10 #proportional to the pool size\n", "centers = [[50,50], [100, 100], [100, 200], [150,150], [200, 100], [200,200]]\n", "X, _ = make_blobs(n_samples=n, centers=centers, cluster_std=clusterSD)\n", "image = np.zeros(shape=(300,300), dtype=np.uint8)\n", "for i in X:\n", " x,y=i.astype(np.uint8)\n", " #print(x,',',y)\n", " image[x,y]=255\n", "plt.imshow(image,cmap=plt.cm.gray) " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of estimated clusters : 6\n" ] } ], "source": [ "myquantile=0.15 #Change this parameter (smaller numbers will produce smaller clusters and more numerous)\n", "\n", "bandwidth = estimate_bandwidth(X, quantile=myquantile, n_samples=500)\n", "\n", "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", "ms.fit(X)\n", "labels = ms.labels_\n", "cluster_centers = ms.cluster_centers_\n", "\n", "labels_unique = np.unique(labels)\n", "n_clusters_ = len(labels_unique)\n", "\n", "print(\"number of estimated clusters : %d\" % n_clusters_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. Check particle neighborhood: groups (clustering algorithms)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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shN6QQT4r190XXDHKL3ap2jN+Ql4+u1vOV4ulduu0tGzwLOb0RUWSlJV9hHPP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\nqCpGV+R0RQ22RUl6mnVzmlKq2XkfBU5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"text/plain": [ "<matplotlib.figure.Figure at 0x11890acc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from itertools import cycle \n", "\n", "plt.figure(1)\n", "plt.clf()\n", "\n", "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n", "for k, col in zip(range(n_clusters_), colors):\n", " my_members = labels == k\n", " cluster_center = cluster_centers[k]\n", " plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n", " plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=14)\n", "plt.title('Estimated number of clusters: %d' % n_clusters_)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. Pandas and seaborn\n", "### Pandas: http://pandas.pydata.org/\n", "### Seaborn: http://seaborn.pydata.org/" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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lDamoiQ/plr71VS0dPSROXFmwYAHmz58PoG/yUa/XY9++faipqcHGjRtRVFSE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\nj6HO5sT40nTcvWgCzEa9JM+iaj2F4budUP+PGoj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"text/plain": [ "<matplotlib.figure.Figure at 0x118886e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, integrate\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "df = pd.DataFrame(X, columns=[\"x\", \"y\"])\n", "# Kernel density estimation\n", "sns.jointplot(x=\"x\", y=\"y\", data=df, kind=\"kde\");" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Acknowledgements:\n", "- [A crash course on NumPy for images](http://scikit-image.org/docs/dev/user_guide/numpy_images.html)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

dsavransky/MAE4060

Notebooks/Gravitational Stabilization.ipynb

1

54271

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from miscpy.utils.sympyhelpers import *\n", "init_printing()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mu,rg,I1,I2,I3,n,w1,w2,w3,w1d,w2d,w3d,t = symbols('mu,r_G,I_1,I_2,I_3,n,omega_1,omega_2,omega_3,omegadot_1,omegadot_2,omegadot_3,t')\n", "diffmap = {w1:w1d,w2:w2d,w3:w3d}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{11} & {}^\\mathcal{B}C^{\\mathcal{A}}_{12} & {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{21} & {}^\\mathcal{B}C^{\\mathcal{A}}_{22} & {}^\\mathcal{B}C^{\\mathcal{A}}_{23}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{31} & {}^\\mathcal{B}C^{\\mathcal{A}}_{32} & {}^\\mathcal{B}C^{\\mathcal{A}}_{33}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{11}__\\mathcal{B}C__{\\mathcal{A}} {}_{12}__\\mathcal{B}C__{\\mathcal{A}} {\n", "⎢ \n", "⎢{}_{21}__\\mathcal{B}C__{\\mathcal{A}} {}_{22}__\\mathcal{B}C__{\\mathcal{A}} {\n", "⎢ \n", "⎣{}_{31}__\\mathcal{B}C__{\\mathcal{A}} {}_{32}__\\mathcal{B}C__{\\mathcal{A}} {\n", "\n", "}_{13}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", " ⎥\n", "}_{23}__\\mathcal{B}C__{\\mathcal{A}}⎥\n", " ⎥\n", "}_{33}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bCa = fancyMat('{}^\\mathcal{B}C^{\\mathcal{A}}',(3,3));bCa" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAD0AAABLCAMAAAD0x38HAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nSGrPf2u0hi8d+jERf5ScPQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{12}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{22}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{32}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", "⎢ ⎥\n", "⎢{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⎥\n", "⎢ ⎥\n", "⎣{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "en_B = bCa*Matrix([0,1,0]); en_B" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Ig_B = diag(I1,I2,I3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMsAAABLCAMAAADplYPZAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\nMquZdlQQQOkwRCK7Zs3die9sjGUlPgAAAAlwSFlzAAAOxAAADsQBlSsOGwAAB+NJREFUaAXtW+ma\n3CgMxGey7e62vX7/d12ukoQA40m82U6+9Y8RhkKoBPiocZvu8EdvzPJ8mak/3p2pH9sQ2y6ACVt3\nd0/LGigY0x3DaI/JmMcx9Z2ZV4RbGGjaLWd/tMGMLTi6terlCPSH44J5WI7VFU/mZR77Z4yiCRZY\ndHndysA6ey3s8ZVwmY7RzONG7fP47Pv+ZZYNPUYzvGO5CRbY0GVBGuAtWpvR4z3QqKo1PdXYnrul\nXLbdDMfxRPO4+zxOQ7DW6WMx4xqdt8ASG7q84TgNz54dSFDWklek2Jl7plyebqM81t07mJ/rHDwN\nRwxitnvK9fBHA5xgfYdXdRs+Dpr46DyaaUzP7ZnGbgRJuYSN8wihv49IxTwCOWOe677vqG6AE6yP\naK1Oy3Y8sph9RYGLxs4IzmeZ9n4XqoMZDjsJ4ehiQkdHbonjNsAJ1rtZsDijV2HemGtR54sFLhn2\njTwk89L3bgN2fr/YqxQ5nl3t2g2h0x6YnYAzrPc0VpdYfbsUuKTbxXqmm5jkMveP97Ha26U7NntJ\nk8d8oGLbXcsZWGODn+hYOo3lR3C9Dc8Byzq25FyAHYc+Yl/Y/ZJLMspKN51Y7SandmhwEbvSmtVu\nNj+WuxX0iCtCci4BO9iED/EKP2EBVbnQFrdeXQKQNNgkngwcs5aA97BE5/dKR7wphy3gLg0drphm\ne7tj3b3Bo4YdNGAHe91zobujw2arclmBMGayzwJIGmxwg78KjKyl4MgFfYQ9fIZXyxUXFjTm8xKw\nrh3z0uZC9xTbyXZE0mAxWLAKjKyl4Ooai1vAuZpwwY/+My6Mnff45PVorrGFbl+bW2JIGmwcKxoF\ndrUuaym4uvfDFvCu1vSCYzIuhB3fgLb3vk1RfIChSwuSBhuJOJOBKWsMpmun6OeLvEIzSMaFsbTG\n3CrwR3W/2B0/2AfLeE8JY8ZM6OS5RgXmrDGY1oJ3hj9L/z6OPoSTP+SkXCTW7a1wYaSn+hMuGA0W\nSYNFfdnGnSnBbvucHQ/L6IHXjwBMuVBnP+tLWDj8OHGdC5IGS47LhZC1BJyc5L269zRNT1rRHlC8\nUdkWl5Zx99jas2U+ANUgabDUkBc4awpMLxN5H1uz+/fcYlNW2W3jFt5cqs/8WR+qQNJgqaFUQNY0\nuPYuVvJxtU7k5+oaQ9JgT4dC1jLwEp71Tjt/rbH+jvw1P5+GvjovnxZ3KZ4/n8sFGY9fgS6A5V2m\nlNF76srz0pbxDOt4bTBj7wm64qXMpSnjWRXtv9X85PUL1MpcmjKeETpeEyywYVjcZ7Ruh6BKVmOF\nxgd4mUtLxrtN88uECMRVsClW3O+BLXNpyHgm0fEa4ATrh+XnMq3bIaqS1Vh+DgO6zKUh492m+Wnd\nDlGVrMayxgd0kUtDxjOJjtcAJ1g/Kj+kRyECsZzbusaHfkUuJzLevZpfugUQU9lm2OymVeJyJuMZ\nreOdgTU2BMnv/RAiOqWKlcicaHyAl7igzVst45na65FDa3ARy3pMECK6oYeSkoycnpxofAA2uWgZ\nz8QkJjIevGnwdqL52T7YAqQ82rqCFOi9n2h8GN1x+fb9L5zmVggfTvNDElMZD90UuKX5QbeTXOBK\nW2ALGh+gf393ImYqGKDNWyXjWfnIL4hUxkMPBW5oftgu0SWcFC1hDalVma7TXGOZjBe5FARTJ/NA\nq/ICoYvqRPMj3e7CvBCW1SrS+EC+ySWT8XhglvHgLQNzFhlM11JakeySHOkCYXmNkcYHbJuLlvF4\nYJbx4O0Lmp/U7dglO5IliS1ofIBe4AJotDQw5VcB0tMrmh+5TLvmZ6xWWUrZhfzHufATYj6mqLmi\n+U3071PRsViEWmUbrz5bFv3EyphEJeOVenAWFVhoWrbfMqxHD9G+5EfUQa1y9yFRHYo/MC8+iVrG\nyxy7CmRRg/EuVux0sTLNh+/0VS5IYibjlUJAFjPwz2t+19+RS4F9ft1X5+WTGf25XC7Idh+o8WGt\npPPSlu0+UeMrc/l8jQ9xw8rrWTovTdnuHo0PgcBqHQ/1JauxQvNLufwyjU9HmQkTGiDOU6y4/6dc\nGrLdbRqfiMwXtY5H7YX/I2ssP5elXH6VxkexxoLW8ai9wEVjWfNLuDRku/s0Poo1FiBi6Pr8GxIW\nPAj7/3d9IRX/2nd9kKso41Yoyd4FoPnxN4D03p+sMeEkk+3u1PjkOK5c1/xyLgErJS16K61y0bId\nknZN44tZS8Ct7/ogVwWm59/1SUmr/S2cED6+rPEha7AhutZ3fSmX0Cefl/wbwDYXJdthIJmQMJz7\nq8AAwQYg68jc0ZdIx6PVQoCMC2FtQPEbQNL8qmssk+3CQOmnehhTgQGCDTDW99EtWtLxLnAhrH0D\nj9eF9t7PZDseCAkRIWXf9VHWGFzVoGg58xBwnc0LYfndgzS/6rxksh0PhIRgQGfVd32cNQbTWpD9\njNTxeAhAUi4Sy5LWD3zXRwNV84sAnAUI1tW57XN60BCESrlQtf3+j74BZM3vZF5ET1fEQJwQBZCn\nAMH6tuREolHONb/iP6MsXKhUtWdLOC3ZyIUTUgLFOoBgY3VB0xJOIFeJqmqRVarqM3+1r20ISRMJ\nqYMBggXyDo0PvmBFfq6uMSSNEwJnBQsQLEF+XuMjV7FQf0fWyN/rPMwLfiv6e8UuoqXfis7uh5bj\nGH7SIQC/UdH/VnQczT+tunYv3plIzQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\left[\\begin{matrix}{}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(- I_{2} + I_{3}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(- I_{1} + I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(-I\n", "⎢ \n", "⎢{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁\n", "⎢ \n", "⎣{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(-I\n", "\n", "₂ + I₃)⎤\n", " ⎥\n", " - I₃) ⎥\n", " ⎥\n", "₁ + I₂)⎦" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mg_B = simplify(skew(en_B)*Ig_B*en_B); Mg_B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Circular orbit" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAABLCAMAAABUWBfpAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\nMquZdlQQQOkwRIlmzd0i77ts7uXj/QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAR5JREFUSA3tltty\nhDAIhsmx2yRrdHn/d11IooVMZqeXnbFcqMMnghh/AgabWRC2dR+AQefJgmBQ2GORoZH+n+uiYXS5\n5OvWCT7p2XGrI1jDsrM7P5fwaBUHjJ3qSGww4ShcwYiOQxL6RWTFzF7TTwAqsvbINfz4WOgFhWVB\ncBycs6xfpTfBrZsAG7dvP5urqiWQ6UOdTL8KJ5Q2RUo0NUGj28L7Luqvx/dYA9Oifj2kJvwv6vlP\nue2/ohvxa6lJ2dZKCjZ0WrfPkWoSiLgYHSbAxnpZrwElckaKSQTZ1U1AgNDc9pRbrZpdhbHAaujs\nPDl4HrUJoqutLSVNsjLkWOZMG9dRbTnHrIS9RHH8o/DDJiLyhsF7bvplbRPhPbwB4fcZLT1yHgEA\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}0\\\\0\\\\n\\end{matrix}\\right]$$" ], "text/plain": [ "⎡0⎤\n", "⎢ ⎥\n", "⎢0⎥\n", "⎢ ⎥\n", "⎣n⎦" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWa_A = Matrix([0,0,n]); iWa_A" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAACYAAABLCAMAAAAriQUYAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\nMquZdlQQQOkwRLsi72aJzd1sJj67BAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAZ9JREFUSA3tltt2\nhCAMRbnaqYpo+f9/bRIuTRxw2nlqu8YHlsD2hIAHUDrRY1T3OXKvUjpZB4/vUmrDPpMQ033iq3X7\nb9hSclvD/JUlvolMt9o5JQd9em+swGxrjl5pa45W59hMYiSxrUD4PoaBQATLiMUAI4CWdr3CFlha\n+A1AZ8eYI7UYog0+mRg2pEbYtKRdKxcCDXKIkQQrBikwgl59aC183lojvaz2SKaEl4slOV4bq3Hq\npSZmAyp/Z96esXO0i51KyiJTYecItjHV9xyTdj4A04ksluft7fZO2tLOB+wBayq7yscNv8l779nO\n4K1UBseDnu2s1NHzwtnOKracuNrZzlujrpZ+hv1mLicGV5N/j96990snBYkFOqt6qyA5XhsH5dRV\nCpx7qfHZgPdfMCFP2dlZ89jOFs43W+V5ptLOFv5x7KWHY9LO2N1Vu7PzFOodi6ud7ez26maxCnd2\n7gc92xm3mnL340FzUqWkca0pn/oiqMAU7m8uPLSzji6asgteqAnt4dgE9TO1b12PJ7wAO1fveDJc\nvh47pz4BCxkcLy3JaBcAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}\\omega_{1}\\\\\\omega_{2}\\\\\\omega_{3}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ω₁⎤\n", "⎢ ⎥\n", "⎢ω₂⎥\n", "⎢ ⎥\n", "⎣ω₃⎦" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWb_B = Matrix([w1,w2,w3]); iWb_B" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAABMCAMAAAD0mSYNAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\omega}_{1} - I_{2} \\omega_{2} \\omega_{3} + I_{3} \\omega_{2} \\omega_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{2} - I_{3}\\right)\\\\I_{1} \\omega_{1} \\omega_{3} + I_{2} \\dot{\\omega}_{2} - I_{3} \\omega_{1} \\omega_{3} - 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\- I_{1} \\omega_{1} \\omega_{2} + I_{2} \\omega_{1} \\omega_{2} + I_{3} \\dot{\\omega}_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(I_{1} - I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 \n", "⎢I₁⋅ω̇₁ - I₂⋅ω₂⋅ω₃ + I₃⋅ω₂⋅ω₃ + 3⋅n ⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎢I₁⋅ω₁⋅ω₃ + I₂⋅ω̇₂ - I₃⋅ω₁⋅ω₃ - 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎣-I₁⋅ω₁⋅ω₂ + I₂⋅ω₁⋅ω₂ + I₃⋅ω̇₃ + 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_\n", "\n", " ⎤\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₂ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₂)⎦" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp1 = simplify(Ig_B*difftotalmat(iWb_B,t,diffmap) + skew(iWb_B)*Ig_B*iWb_B - 3*n**2*Mg_B);tmp1" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd4AAABMCAMAAAD0mSYNAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\omega}_{1} - I_{2} \\omega_{2} \\omega_{3} + I_{3} \\omega_{2} \\omega_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{2} - I_{3}\\right)\\\\I_{1} \\omega_{1} \\omega_{3} + I_{2} \\dot{\\omega}_{2} - I_{3} \\omega_{1} \\omega_{3} - 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(I_{1} - I_{3}\\right)\\\\- I_{1} \\omega_{1} \\omega_{2} + I_{2} \\omega_{1} \\omega_{2} + I_{3} \\dot{\\omega}_{3} + 3 n^{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{12} {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(I_{1} - I_{2}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 \n", "⎢I₁⋅ω̇₁ - I₂⋅ω₂⋅ω₃ + I₃⋅ω₂⋅ω₃ + 3⋅n ⋅{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎢I₁⋅ω₁⋅ω₃ + I₂⋅ω̇₂ - I₃⋅ω₁⋅ω₃ - 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_{\n", "⎢ \n", "⎢ 2 \n", "⎣-I₁⋅ω₁⋅ω₂ + I₂⋅ω₁⋅ω₂ + I₃⋅ω̇₃ + 3⋅n ⋅{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅{}_\n", "\n", " ⎤\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₂ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "32}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₃) ⎥\n", " ⎥\n", " ⎥\n", "{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(I₁ - I₂)⎦" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(tmp1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIkAAABLCAMAAABz2lREAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nQmCC\n", "text/latex": [ "$$\\left[\\begin{matrix}- n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} + \\omega_{1}\\\\- n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} + \\omega_{2}\\\\- n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} + \\omega_{3}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-n⋅{}_{13}__\\mathcal{B}C__{\\mathcal{A}} + ω₁⎤\n", "⎢ ⎥\n", "⎢-n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}} + ω₂⎥\n", "⎢ ⎥\n", "⎣-n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} + ω₃⎦" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aWb_B = iWb_B - bCa*iWa_A; aWb_B" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}- {}^\\mathcal{B}C^{\\mathcal{A}}_{21} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{31} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) & - {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) & - \\omega_{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{33} + \\omega_{3} {}^\\mathcal{B}C^{\\mathcal{A}}_{23}\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{11} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{31} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & \\omega_{1} {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3} {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\\\- {}^\\mathcal{B}C^{\\mathcal{A}}_{11} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{21} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & - {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right) & - \\omega_{1} {}^\\mathcal{B}C^{\\mathcal{A}}_{23} + \\omega_{2} {}^\\mathcal{B}C^{\\mathcal{A}}_{13}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-{}_{21}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}}\n", "⎢ \n", "⎢{}_{11}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} \n", "⎢ \n", "⎣-{}_{11}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}}\n", "\n", " - ω₃) + {}_{31}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\math\n", " \n", "- ω₃) - {}_{31}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\mathc\n", " \n", " - ω₂) + {}_{21}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\math\n", "\n", "cal{A}} - ω₂) -{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C_\n", " \n", "al{A}} - ω₁) {}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__\n", " \n", "cal{A}} - ω₁) -{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C_\n", "\n", "_{\\mathcal{A}} - ω₃) + {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathc\n", " \n", "{\\mathcal{A}} - ω₃) - {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathca\n", " \n", "_{\\mathcal{A}} - ω₂) + {}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathc\n", "\n", "al{B}C__{\\mathcal{A}} - ω₂) -ω₂⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} + ω₃⋅{}_\n", " \n", "l{B}C__{\\mathcal{A}} - ω₁) ω₁⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} - ω₃⋅{}_{\n", " \n", "al{B}C__{\\mathcal{A}} - ω₁) -ω₁⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}} + ω₂⋅{}_\n", "\n", "{23}__\\mathcal{B}C__{\\mathcal{A}}⎤\n", " ⎥\n", "13}__\\mathcal{B}C__{\\mathcal{A}} ⎥\n", " ⎥\n", "{13}__\\mathcal{B}C__{\\mathcal{A}}⎦" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbCa = simplify(-skew(aWb_B)*bCa); dbCa" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAABMCAMAAABwH8EjAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nTkSuQmCC\n", "text/latex": [ "$$\\left[\\begin{matrix}- {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right)\\\\{}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{33} - \\omega_{3}\\right) - {}^\\mathcal{B}C^{\\mathcal{A}}_{32} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right)\\\\- {}^\\mathcal{B}C^{\\mathcal{A}}_{12} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{23} - \\omega_{2}\\right) + {}^\\mathcal{B}C^{\\mathcal{A}}_{22} \\left(n {}^\\mathcal{B}C^{\\mathcal{A}}_{13} - \\omega_{1}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡-{}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}}\n", "⎢ \n", "⎢{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{33}__\\mathcal{B}C__{\\mathcal{A}} \n", "⎢ \n", "⎣-{}_{12}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\mathcal{A}}\n", "\n", " - ω₃) + {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{23}__\\mathcal{B}C__{\\math\n", " \n", "- ω₃) - {}_{32}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\mathc\n", " \n", " - ω₂) + {}_{22}__\\mathcal{B}C__{\\mathcal{A}}⋅(n⋅{}_{13}__\\mathcal{B}C__{\\math\n", "\n", "cal{A}} - ω₂)⎤\n", " ⎥\n", "al{A}} - ω₁) ⎥\n", " ⎥\n", "cal{A}} - ω₁)⎦" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbCa[:,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

MachineLearningStudyGroup/Smart_Review_Summarization

ipynbs/Dynamic_Aspect_Extraction_Part_B.ipynb

1

7311

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dynamic Aspect Extraction for `camera` Reviews Part B\n", "\n", "Han, Kehang ([email protected])\n", "\n", "As a follow-up demonstration, this ipynb is focused on extracting aspects from datasets called `AmazonReviews`, which has much more reviews on cameras. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "import nltk\n", "import string\n", "\n", "from srs.utilities import Product, AspectPattern" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s1: load raw data from `AmazonReviews` datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "product_name = 'B00000JFIF'\n", "reviewJsonFile = product_name + '.json'\n", "product = Product(name=product_name)\n", "product.loadReviewsFromJsonFile('../data/trainingFiles/AmazonReviews/cameras/' + reviewJsonFile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s2: define aspect patterns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "aspectPatterns = []\n", "# define an aspect pattern1\n", "pattern_name = 'adj_nn'\n", "pattern_structure =\"\"\"\n", "adj_nn:{<JJ><NN.?>}\n", "\"\"\"\n", "aspectTagIndices = [1]\n", "aspectPattern = AspectPattern(name='adj_nn', structure=pattern_structure, aspectTagIndices=aspectTagIndices)\n", "aspectPatterns.append(aspectPattern)\n", "# define an aspect pattern2\n", "pattern_name = 'nn_nn'\n", "pattern_structure =\"\"\"\n", "nn_nn:{<NN.?><NN.?>}\n", "\"\"\"\n", "aspectTagIndices = [0,1]\n", "aspectPattern = AspectPattern(name='nn_nn', structure=pattern_structure, aspectTagIndices=aspectTagIndices)\n", "aspectPatterns.append(aspectPattern)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s3: match sentence to pattern to extract aspects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# pos tagging\n", "for review in product.reviews:\n", " for sentence in review.sentences:\n", " sentence.pos_tag()\n", " sentence.matchDaynamicAspectPatterns(aspectPatterns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s4: statistic analysis on aspects extracted across all reviews" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "word_dict = {}\n", "for review in product.reviews:\n", " for sentence in review.sentences:\n", " for aspect in sentence.dynamic_aspects:\n", " if aspect in word_dict:\n", " word_dict[aspect] += 1\n", " else:\n", " word_dict[aspect] = 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'camera', 47),\n", " (u'batteries', 22),\n", " (u'pictures', 18),\n", " (u'cameras', 13),\n", " (u'battery life', 12),\n", " (u'ac adapter', 11),\n", " (u'quality', 11),\n", " (u'shots', 10),\n", " (u'zoom', 9),\n", " (u'memory card', 9),\n", " (u'time', 8),\n", " (u'media', 7),\n", " (u'price', 7),\n", " (u'software', 7),\n", " (u'resolution', 7)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_sorted = sorted(word_dict.items(), key=lambda tup:-tup[1])\n", "word_sorted[:15]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s5: save most frequent dynamic aspects" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "word_output = open('../data/word_list/{0}_wordlist.txt'.format(product_name), 'w')\n", "json.dump(word_sorted[:15], word_output)\n", "word_output.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## s6: stemming analysis" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nltk.stem import SnowballStemmer\n", "stemmer = SnowballStemmer('english')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# collect word with same stem\n", "stemmedWord_dict = {}\n", "for word in word_dict:\n", " stemmedWord = stemmer.stem(word)\n", " if stemmedWord in stemmedWord_dict:\n", " stemmedWord_dict[stemmedWord] += word_dict[word]\n", " else:\n", " stemmedWord_dict[stemmedWord] = word_dict[word]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'camera', 61),\n", " (u'batteri', 26),\n", " (u'pictur', 23),\n", " (u'shot', 13),\n", " (u'battery lif', 12),\n", " (u'qualiti', 11),\n", " (u'ac adapt', 11),\n", " (u'memory card', 10),\n", " (u'resolut', 9),\n", " (u'zoom', 9),\n", " (u'featur', 9),\n", " (u'problem', 8),\n", " (u'time', 8),\n", " (u'card', 7),\n", " (u'softwar', 7)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# frequency ranking\n", "stemmedWord_sorted = sorted(stemmedWord_dict.items(), key=lambda tup:-tup[1])\n", "stemmedWord_sorted[:15]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# save most frequent stemmed words\n", "stemmedWord_output = open('../data/word_list/{0}_stemmedwordlist.txt'.format(product_name), 'w')\n", "json.dump(stemmedWord_sorted[:15], stemmedWord_output)\n", "stemmedWord_output.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jasonost/clinicaltrials

trial_criteria/Admin_Interaction.ipynb

1

16429

{ "metadata": { "name": "", "signature": "sha256:e0714909926f33bd9c934f43056d0ce53246aedc5faced57a65a751ac4e6b8e8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sqlalchemy\n", "from sqlalchemy import create_engine\n", "from sqlalchemy.sql import select, delete, or_\n", "import re\n", "import db_connect\n", "from db_tables import metadata, CriteriaConceptStaging, ConceptPredictors, UserHistoryCriteria, Users, CriteriaConceptLookup\n", "from db_tables import ConceptPredictorsReject, ConceptTerms, ConceptTermsReject, CriteriaText, CriteriaConcept" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_concepts(concept_id, engine):\n", " \n", " #check if the concept is new or existing\n", " new_concept = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.new_concept], distinct=True).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id))][0]\n", " \n", " concept_name = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'concept-name'))][0]\n", " concept_terms = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'term'))]\n", " \n", " \n", " if new_concept == 1:\n", " #concept is new\n", " existing_concept_terms = []\n", " return concept_name, concept_terms, existing_concept_terms\n", " \n", " \n", " else:\n", " #concept is not new, need to pull old concept data to compare\n", " existing_concept_terms = engine.execute(select([ConceptTerms.c.term]).\\\n", " where(ConceptTerms.c.concept_id ==\n", " concept_id))\n", " existing_concept_terms = [r[0] for r in existing_concept_terms]\n", " \n", " #get new terms in the staging table\n", " new_concept_terms = list(set(concept_terms).difference(set(existing_concept_terms)))\n", " \n", " return concept_name, new_concept_terms, existing_concept_terms" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "def transfer_concept(concept_id, engine, term_set, concept_name):\n", " #pull other data for concept\n", " concept_terms_reject = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'term-reject'))]\n", " concept_predictors_reject = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'predictor-reject'))]\n", " concept_predictors = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).\\\n", " where(CriteriaConceptStaging.c.update_type ==\n", " 'predictor'))]\n", " #write all data into the new tables\n", " #instert data into db\n", " engine.execute(CriteriaConcept.insert(), [{\n", " 'concept_id': concept_id,\n", " 'concept_name':concept_name}])\n", " engine.execute(ConceptTerms.insert(), [{\n", " 'concept_id': concept_id,\n", " 'term':term}\n", " for term in term_set])\n", " engine.execute(ConceptTermsReject.insert(), [{\n", " 'concept_id': concept_id,\n", " 'term':term}\n", " for term in concept_terms_reject])\n", " engine.execute(ConceptPredictors.insert(), [{\n", " 'concept_id': concept_id,\n", " 'predictor':predictor}\n", " for predictor in concept_predictors])\n", " engine.execute(ConceptPredictorsReject.insert(), [{\n", " 'concept_id': concept_id,\n", " 'predictor':predictor}\n", " for predictor in concept_predictors_reject])\n", " \n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def update_user_history(concept_id, choice, term_set, engine):\n", " #keeps track of which concepts were accepted and also which terms were accepted\n", " \n", " #pull all statged data for concept\n", " all_staged_concept = engine.execute(select([CriteriaConceptStaging]).\\\n", " where(CriteriaConceptStaging.c.concept_id ==\n", " concept_id).fetchall())\n", " if choice:\n", " #at least some terms were accepted\n", " #instert data into db\n", " engine.execute(UserHistoryCriteria.insert(),\n", " [{'update_id': value[0],\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 0}\n", " if value[5] == 'term' and value[6] not in term_set\n", " else {'update_id': value[0],\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 1}\n", " for value in all_staged_concept])\n", " else:\n", " #concept was not accepted\n", " #instert data into db\n", " engine.execute(UserHistoryCriteria.insert(), [{'update_id': value[0]\n", " 'user_id': value[1],\n", " 'update_time': value[2],\n", " 'concept_id': value[3],\n", " 'new_concept': value[4],\n", " 'update_type': value[5],\n", " 'value':value[6],\n", " 'accepted': 0}\n", " for value in all_staged_concept])\n", " #delete from staging table\n", " engine.execute(delete(CriteriaConceptStaging).where(CriteriaConceptStaging.c.concept_id == concept))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def tag_criteria_sentences(concept_id, engine):\n", " tagged_sentences = []\n", " #pull the accepted terms with term_ids\n", " term_list = engine.execute(select([ConceptTerms.c.term, ConceptTerms.c.term_id]).\\\n", " where(ConceptTerms.c.concept_id == concept_id)).fetchall()\n", " \n", " criteria_text = engine.execute(select([CriteriaText.c.criteria_text,\n", " CriteriaText.c.criteria_text_id]).\\\n", " where(or_(CriteriaText.c.criteria_text.op('rlike')('[[:<:]]' + t[0] + '[[:>:]]')\n", " for t in term_list))).fetchall()\n", " \n", " #find all the sentences where a concept term occurs\n", " for sent, sent_id in criteria_text:\n", " for term, term_id in term_list:\n", " if term in sent.lower():\n", " #check if there is a negative in the sentence related to the term.\n", " #the negative term has to be within 3 words of the concept term\n", " #or at the beginning of the sentece\n", " string = sent.lower()\n", " negative_pattern_start = r\"^[not|no|isn't|didn't]\"\n", " negative_beginning = re.search(negative_pattern_start, string)\n", " negative_in_sentence = re.search(\"[not|didn't|isn't|no]\\W+(?:\\w+\\W+){1,2}%s\" % (term), string)\n", " if negative_beginning or negative_in_sentence:\n", " inverse = 1\n", " else:\n", " inverse = 0\n", " tagged_sentences.append((sent_id, term_id, inverse))\n", " \n", " #write tagged sentences to db\n", " engine.execute(CriteriaConceptLookup.insert(), [{'criteria_text_id': v[0],\n", " 'term_id': v[1],\n", " 'concept_id': concept_id,\n", " 'inverse': v[2]}\n", " for v in tagged_sentences])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "string = \"I am not happy happy is a test\"\n", "negative_pattern_start = r\"^[not|no|isn't|didn't]\"\n", "negative_beginning = re.search(negative_pattern_start, string)\n", "negative_in_sentence = re.search(\"[not|didn't|isn't|no]\\W+(?:\\w+\\W+){1,2}is\", string)\n", "if negative_beginning or negative_in_sentence:\n", " print 'inverse'\n", "#[(m.start(0), m.end(0)) for m in re.finditer(negative_pattern_start, string)]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "inverse\n" ] } ], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "print re.search(\"[not|didn't|isn't|no]\\W+(?:\\w+\\W+){1,2}is\", string)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "None\n" ] } ], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "def admin_action_response(concept_id, concept_name, accepted_terms):\n", " '''This is run in response to the admins actions on the criteria review page'''\n", " #initialize the connection to the db\n", " engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", " metadata.create_all(engine)\n", " \n", "\n", " if len(accepted_terms) > 0:\n", " #at least some terms in the concept have been accepted\n", " tansfer_concept(concept_id, engine, accepted_terms, concept_name)\n", " #transfer data to user history table and delete from staging table\n", " choice = True\n", " update_user_history(concept_id, choice, accepted_terms, engine)\n", " #tag sentences with the new concept\n", " tag_criteria_sentences(concept_id, engine)\n", " else:\n", " #transfer data to user history table and delete from staging table\n", " choice = False\n", " update_user_history(concept_id, choice, accepted_terms, engine)\n", " \n", " criteria_review_page_data()\n", " " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def criteria_review_page_data(concept_id=False):\n", " '''This gets activated when an admin chooses to review staged criteria concepts\n", " when the criteria review page is loaded'''\n", " #initialize the connection to the db\n", " engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", " metadata.create_all(engine)\n", " \n", " #pull all the concept id's of staged concepts\n", " concept_ids = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.concept_id],\n", " distinct=True))]\n", " \n", " #get concept name list\n", " concept_names = [r[0] for r in engine.execute(select([CriteriaConceptStaging.c.value],\n", " distinct=True).\\\n", " where(CriteriaConceptStaging.c.update_type == 'concept-name'))]\n", " \n", " \n", " if concept_id:\n", " pass\n", " else:\n", " concept_id = concept_ids[0]\n", " \n", " concept_name, concept_terms, existing_concept_terms = get_concepts(concept_id, engine)\n", " \n", " return (concept_ids, concept_names, concept_id, concept_terms,\n", " existing_concept_terms, concept_name)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "main()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "engine = create_engine('mysql+pymysql://' + db_connect.conn)\n", "metadata.create_all(engine)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

BBN-Q/Auspex

doc/examples/Example-Channel-Lib.ipynb

1

854914

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Q2: Save and Loading Channel Library Versions\n", "This example notebook shows how one may save and load versions of the channel library.\n", "\n", "© Raytheon BBN Technologies 2018" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving Channel Library Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize the channel library as shown in tutorial *Q1*:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A database item with the name q1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name BBNAPS1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name BBNAPS2 already exists. Updating parameters of this existing item instead.\n", "A database item with the name X6_1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name Holz1 already exists. Updating parameters of this existing item instead.\n", "A database item with the name Holz2 already exists. Updating parameters of this existing item instead.\n" ] } ], "source": [ "from QGL import *\n", "\n", "cl = ChannelLibrary(\":memory:\")\n", "q1 = cl.new_qubit(\"q1\")\n", "aps2_1 = cl.new_APS2(\"BBNAPS1\", address=\"192.168.5.101\") \n", "aps2_2 = cl.new_APS2(\"BBNAPS2\", address=\"192.168.5.102\")\n", "dig_1 = cl.new_X6(\"X6_1\", address=0)\n", "h1 = cl.new_source(\"Holz1\", \"HolzworthHS9000\", \"HS9004A-009-1\", power=-30)\n", "h2 = cl.new_source(\"Holz2\", \"HolzworthHS9000\", \"HS9004A-009-2\", power=-30) \n", "cl.set_control(q1, aps2_1, generator=h1)\n", "cl.set_measure(q1, aps2_2, dig_1.ch(1), generator=h2)\n", "cl.set_master(aps2_1, aps2_1.ch(\"m2\"))\n", "cl[\"q1\"].measure_chan.frequency = 0e6\n", "cl.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us save this channel library for posterity:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cl.save_as(\"NoSidebanding\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we adjust some parameters and save another version of the channel library" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cl[\"q1\"].measure_chan.frequency = 50e6\n", "cl.commit()\n", "cl.save_as(\"50MHz-Sidebanding\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maybe we forgot to change something. No worries! We can just update the parameter and create a new copy." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>id</th><th>Year</th><th>Date</th><th>Time</th><th>Name</th></tr><tr><tr><td>1</td><td>2019</td><td>Apr. 18</td><td>11:25:20 AM</td><td>working</td></tr><tr><td>2</td><td>2019</td><td>Apr. 18</td><td>11:25:37 AM</td><td>NoSidebanding</td></tr><tr><td>3</td><td>2019</td><td>Apr. 18</td><td>11:25:38 AM</td><td>50MHz-Sidebanding</td></tr><tr><td>4</td><td>2019</td><td>Apr. 18</td><td>11:25:39 AM</td><td>50MHz-Sidebanding</td></tr></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cl[\"q1\"].pulse_params['length'] = 400e-9\n", "cl.commit()\n", "cl.save_as(\"50MHz-Sidebanding\")\n", "cl.ls()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see the various versions of the channel library here. Note that the user is *always* modifying the working version of the database: all other versions are archival, but they can be *restored* to the current working version as shown below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading Channel Library Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us load a previous version of the channel library, noting that the former value of our parameter is restored in the working copy. **CRUCIAL POINT**: do not use the old reference `q1`, which is no longer pointing to the database since the working db has been replaced with the saved version. Instead use dictionary access `cl[\"q1\"]` on the channel library to return the first qubit: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl.load(\"NoSidebanding\")\n", "cl[\"q1\"].measure_chan.frequency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's load the *second oldest* version of the 50MHz-sidebanding library:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2e-08, 50000000.0)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl.load(\"50MHz-Sidebanding\", -1)\n", "cl[\"q1\"].pulse_params['length'], cl[\"q1\"].measure_chan.frequency" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compiled 11 sequences.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6280b8a0551141928c80fe8cc2b19c15", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(IntSlider(value=1, description='Segment', max=11, min=1), Figure(animation_duration=50, axes=[A…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# q1 = QubitFactory(\"q1\")\n", "plot_pulse_files(RabiAmp(cl[\"q1\"], np.linspace(-1, 1, 11)), time=True)" ] }, { "attachments": { "Screen%20Shot%202019-03-13%20at%2012.28.08%20PM.png": { "image/png": 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CCCCAAALBAlEDNW0Zuuqqq+RXv/qV1KhRQ4444ggTpM2aNcs9xubNm2X48OGmte3AAw8UvVz4448/mvWLFi2Szp07y+mnny716tWTCy64QAYNGmS2ffjhh802emmxZ8+e8te//lV0f3397W9/C7m8qcfU4+h2mp958+a55491YteuXe45tGVQ87xly5aQ3Z9++mnp3r27yauut+UI2SjKzKmnniqbNm2SN954I+JW2hq4fPly+frrr6VLly4Rt4lloQaCvXv3NvlUl/vuuy9kt7Vr18pvf/tbd/2dd94Z4qlB94033uh633DDDcZ32rRp5jh33HGHDB06NOSY+hl44IEH3GWVeWp9/vGPf5Tbb7/dnEfNb7rpJgl+EMbKlSvNZ0PXab3rPnrc4KStmlrv+vk58cQTZcGCBcGrTVAczSJkY2YQQAABBBDINAF9hFSktHXrVn20VOD555+PtNpddtRRRwWcS6MB53JeYNSoUQGnRStwzjnnmPVOq5E5hnP5NHDxxRebaefLOvC73/3ObOcEDIGnnnrKLNfjOIFS4JJLLjHzo0ePds/hBD6B+++/P6D7ap4+//xzd134hJ7/X//6V/jiwIgRI8w5H330UZNXzfMpp5zibvfxxx+bY59xxhmBZ599NjB48GAz7wQa7jaxTmzbts3sG82ub9++gcsuuyzWQ7rbOffomWOrp9O6GHCCLDP/4osvmm2cy9KBTp06BXr06BF45ZVXAk6wbdb/73//c4/hBMJm2S233BKYMGFCoH///mb+ww8/NNv84Q9/CPTq1cvdXidatmwZcAIpd1llnhdddJE55kknnRR47rnnAldffbWZd4JMcwwnCDbH7NatW0Dz7gSBbp7sSXQ/re9bb7018Nprr5l8ar1t377dbFKZhT0O7wgggAACCGSqgLZwRExOq4z5khw/fnzE9brQuRRqtnEujbrbOPeImWXfffddwAZq+oX65ZdfmuXLli0L2KDIuZ/LDdQ2btzoHuPYY4/dJ1DQlU5LmjlGvIGanlO/8DUAs2ncuHFm2apVq8wiDR7btWsX0OBRkwY8GvT5LVDT+tCyqKlNziVXE8jovJZR12uZbXJatkzZ7LyWSwMpm+bOnWv2iTVQi8VTj68Bozpqcu53NAH9X/7yFzNvgzCn1dXM6w9dpwGmTRocauBuU3FxscmnDUors7D78Y4AAggggECmCuQ5X+oRU/Xqe6+KlpeXR1yvC+1N+8ccc4y7zcCBA820Xp5s1aqVu9weTxcET9sNioqK7KS5PJrMG+3nzJljjq0dDZyWGTNtL3vqvWTNmzc3ZdHLsnqJV1NBQYHUqlXLTPvph96P5wSU4rTIybBhw+SEE04QpwVM6tSpY7JpL0tff/31brYXL15s7pnTS55Oa5Q4LX5i60k30rLGk2Lx1OPtv//+7rGrVasmTuBlLg3rOic4NPPa6cImveStL016CVkvjU6fPl3OPPNMu4l510vqmiqzMBvxAwEEEEAAgQwWqDBQq1+/vilW+JAc2qvRaYUygYK9n8gGN7qD9sDUVJWeofYYTvQr+gVf1eRcxjWH6NixowQHhIceeqgJJnSlDqWRn59f1VOlfH/t3ao9UZ3Lqub+LA3WtFeudvg4+OCDRe8ZdFrMTDBnM6OB3fHHH2/uDysrKzOLKwvONKirKMXiGWnf4M+Jfnai5UGDSU3aOUPzb5PeW+dcljWzlVnYfXhHAAEEEEAgUwUqDNQ0aHHuXZK33npLnMtPbvluvvlm0xoydepU0cBHk7awaJCgSVtANAW3lJgFlfzQoMwmPbZz2SwpQZoe0+bTue/MBCz2PBq02OBMb8rX8/o9aUDmXHo0nSEuvPBCcS4Zi3Ofl7nR37lH0HRS0CBHWyQbNWrkFkcDr7y8PGncuLFZpvV02mmnmelge12gAdCaNWvMOv2hw7TYoFznY/HU7aKl9u3bm1a+H374Qfbbbz+z6QcffGDG69MhYTRA06Sfq+BexsF1VpmFOQA/EEAAAQQQyGCBqL0+9ZKacy+XaADg3B9kWnH08qEN3AYMGGBaO3ReL2XNnDnT9MrUAM8GbrHa/PnPfxa9RKc9L3WcsfPOO8/dVXv6ffbZZ24QqJf3dN5eAtO86by+NC1dutRM216M2nJ2+OGHi3PflEyZMsUECP/9739DLsXp+XT4EacjgsmH9nwMb010M+ThhF4OdDoSGCft3WkHItZWNU1nnXWWaVE7++yzTQCtl6e1B+uQIUPcXDs39ouWTwcGVlunU4G7TicOOeQQU3bnPjJzDP0cBFvE4hlywAgzv/nNb0w+nU4JxluDZK0fe1lVW9+0l6jm84knnjC9ZZ170kydaT1pqswi+LTaI1d7l2pgS0IAAQQQQCBjBCq7uc4JaLSpy33ddttt5sZwu5/e1O4EQe56556pgL1B37lEZ5YvXLgw4LTgmGlneAq3E4LeHG57fWrvQHse7TVqe/bpeZzLe+46u42+n3/++SYbI0eOjLheewjapHnSXp52f12nHR9s0pvdtRekXa89IXWbRDoTOIPamuPYm97tOYLf1ezyyy8PXhTTtHPPoLnp3uZT37WnqnPPnbv/F198YW7Kt9voDframcMm5/JoiEV4r089h3ausPtrfWjvTPWxqTJP7ZWqPXmDk/Z0dYZXcRdppxDtcBB8Hs2bTdoz1PZqtds4994FnNY9s0ksFvZY2otVj7FixQq7iHcEEEAAAQR8L1BNc+h8gUVN2qFAWyL0UlrwfUbBO+mI+9pJoHbt2sGLK53W1rNzzz3X3NOml9cSOUalJwnaQO+d044EegkwUqcGXa/jpzVo0CBoL/9N6uVIfbSX3ktYWFgYMYN6CVQveeqlzEhJ60zvAdTLnHqp2un1KUcffbS7qe6v9R2tTivzdA8WZULvq9P7EivqvKGfvw0bNphLpPZSdfDhYrHQexD15fd6DS4X0wgggAACCFR4j1owjX5Z20trwcuDp4Nv0g9eHs90Mo5R2fk0IIhWFl1vOzNUdiwv12uQqZfyoiXtVBAtVeZd2f567Mo8o53frqsseNLPX7SyxmKhwWxFAa3NB+8IIIAAAgj4TSDqPWrpyKx+SevN8JFat9Jxfs6xN9jSOojWcoYTAggggAACCKRfIKZLn+nPFmdEAAEEEEAAAQQQ8LxFjSpAAAEEEEAAAQQQiCxAoBbZhaUIIIAAAggggIDnAgRqnlcBGUAAAQQQQAABBCILEKhFdmEpAggggAACCCDguQCBmudVQAYQQAABBBBAAIHIAgRqkV1YigACCCCAAAIIeC5AoOZ5FZABBBBAAAEEEEAgsgCBWmQXliKAAAIIIIAAAp4LEKh5XgVkAAEEEEAAAQQQiCxAoBbZhaUIIIAAAggggIDnAgRqnlcBGUAAAQQQQAABBCILEKhFdmEpAggggAACCCDguQCBmudVQAYQQAABBBBAAIHIAgRqkV1YigACCCCAAAIIeC5AoOZ5FZABBBBAAAEEEEAgsgCBWmQXliKAAAIIIIAAAp4LEKh5XgVkAAEEEEAAAQQQiCxAoBbZhaUIIIAAAggggIDnAgRqnlcBGUAAAQQQQAABBCILEKhFdmEpAggggAACCCDguUCe5zkgAyECJSUl8u2338qaNWvkhx9+MOv2228/OeCAA6R79+5SVFQUsj0zCCCAAAIIIJC9AtUCTsre4mVWyb788ktZtGiRCchat24tGqBp0oBt+fLlJoDr2LGjHHbYYZlVMHKLAAIIIIAAAgkJEKglxJb8nV5//XUTmGkQVlBQEPEEu3btEg3mNHA75ZRTIm7DQgQQQAABBBDIHgHfBmrTpk2TCRMmyPr16+XAAw+Uc845R9q0aZM98kEl0eBLg7CjjjoqaGnFk5MnTzaXQGlZq9iINekRmDFjhjRo0EDat2+fnhNyFgQQQCDHBHzZmeChhx6Sfv36yYIFC6RatWry8MMPS9u2bWXq1KkZUT3/+te/5Pe//31MedV70vRyZzxBlwZ0uo/uS0LAK4ENGzbIqaeeKm+99ZZXWeC8CCCAQNYL+DJQu+222+Rvf/ubvPDCC6JB25IlS6Rly5by73//OyMqZPPmzaYzQCyZ1Y4D2kmgosudkY6h2+o+ui8JgXQLrFq1Ss4880xp166drFy5Mt2n53wIIIBATgn4rtfn7t27Zd26dbJ9+3a3IvLz803QpjfUa9Jt7rzzThk1apRs2bJFhg0bJvfdd5/UqVNHysrK5C9/+Ys888wz5nLiNddcI88++6y8+OKLsnPnTrn88svNl8xdd91l1t9+++1mHz2eHut3v/ud3H///VK9enX55ptv5A9/+IO8//77cvjhh8v/+3//T04++WSZPXu2Oc5vf/tbEzxqIHnJJZfIgw8+KH/6059kzJgxsm3bNpkzZ06lwZT27oz1kqcL4kxoZ4MPPvjA5C14OdOZKXDsscdmTMZr1aolxx9/vHndeuutGZNvMooAAghkooDvArW8vDy54YYbRC8faoD061//Wo444gj3pcj//Oc/5YknnpCnn35a9EvjyiuvlAsvvNAEcxqkPf744zJ27Fhp2LChXH311Saw0iBtx44d8umnn0rjxo1l4sSJ5pLNpZdeKv3795ennnrKBIhnnXWWaADWuXNns1wvYWrgZvPy9ddfu8fRoTJefvllc4n27LPPliFDhsjFF18s2uKg99bF8iWmHQNs7854PkC6jwaWen8QCYF0Cuhn76KLLjKnfPTRR9N5as6FAAII5JyALy99auvWJ598IkcffbRob8iBAweaccSef/55U0F33323CcC0c0HTpk3lpptuMi1mGhxpgKcv/Y+/b9++8sADD+xTqdoS17NnTzn//PPNur/+9a8yYMAA09JWt25d2bhxo7z00ktSv35984WkLXWDBw82LV8a0Nmkl2UPPfRQ0eCuV69esmzZMunWrZu5sbpJkyZyzDHH2E15RwABBBBAAAEE4hbwXYuathKtWLHCtGZpS5cmvRR63XXXibZaafCllxX1kqS+gpN2PtDUqVMnd7HeRxOetEVNk15S1aSXEcPT0qVLzf032okhONl9dVmrVq3cVRrM6SXZeJO2+iXSqqb7aCBJQgABBBBAAIHsFfBdoKa9GTUY0/u+bJCkrVN//OMfzeVMvVFfk1661PvFNOn9bDqcR+/evc287quXSzXZ4M3MxPFDOy9oK5kOP2CT3ryvN/Jri5sm7ZFa1dSsWTMzmG28lz/1fr0WLVrE1Vu0qnllfwQQQAABBBBIr4DvLn0efPDBpjfZeeedZ27mLy8vl+LiYrn33ntFL0vq+hEjRoherly8eLFs2rRJrr/+etGeooWFheZS5S233CIzZ86UuXPnmkukiZBqEPjVV1+ZDgt6b5sGggcddJC5/6yy4+l9dprnWHrEae9NzaeOoxZr0m11H92XhAACCCCAAALZK+C7QK1mzZryxhtvmMuSPXr0EA16mjdvbnpPfvjhh6ZF64477jD3genjlLQlSoMWveFfW7h0CA/toamtYfqunRE06XG0J6cm2xJm3+1yXaedEzTpJdNx48aZgFAva+pxNAD85S9/uc9xzA5BP/TetFmzZpmBeoMWR5zUDgkdOnQwTxyIuEGEhR9//LHZh+d+RsBhEQIIIIAAAlkk4NsnE6ixtpZpy5QGY/pQ8vD0448/miE3gns+jh8/3lwy7dq1qwnItCXsF7/4hekhWa9evfBDxDSv98jpvWnBAV1MO8ax0WuvvSaNGjUylzIrGlNNW9I0SNN79HSgURICCCCAAAIIZLeArwO1ROhvvPFGM4aaDo2hl011nDTt0RncWzOR46ZjH32UlF7O1Uua2lHB3remHQe+//5706qorW/xPMUgHfnmHAgggAACCCCQGoGsC9S056WOsfbRRx9JjRo1TFCjYz7ZHp6pYUzeUfWxUNppQVsStUVRk/YM1U4HGsBxuTN51hwJAQQQQAABvwtkXaDmd3DyhwACCCCAAAIIxCrgu84EsWac7RBAAAEEEEAAgWwXIFDL9hqmfAgggAACCCCQsQIEahlbdWQcAQQQQAABBLJdgEAt22uY8iGAAAIIIIBAxgoQqGVs1ZFxBBBAAAEEEMh2AQK1bK9hyocAAggggAACGStAoJaxVUfGEUAAAQQQQCDbBQjUsr2GKR8CCCCAAAIIZKwAgVrGVh0ZRwABBBBAAIFsFyBQy/YapnwIIIAAAgggkLECBGoZW3VkHAEEEEAAAQSyXYBALdtrmPIhgAACCCCAQMYKEKhlbNWRcQQQQAABBBDIdgECtWyvYcqHAAIIIIAAAhkrQKCWsVVHxhFAAAEEEEAg2wUI1LK9hikfAggggAACCGSsAIFaxlYdGUcAAQQQQACBbBcgUMv2GqZ8CCCAAAIIIJCxAgRqGVt1ZBwBBBBAAAEEsl2AQC3ba5jyIYAAAggggEDGChCoZWzVkXEEEEAAAQQQyHYBArVsr2HKhwACCCCAAAIZK0CglrFVR8YRQAABBBBAINsFCNSyvYYpHwIIIIAAAghkrACBWsZWHRlHAAEEEEAAgWwXIFDL9hqmfAgggAACCCCQsQIEahlbdWQcAQQQQAABBLJdgEAt22uY8iGAAAIIIIBAxgoQqGVs1ZFxBBBAAAEEEMh2AQK1bK9hyocAAggggAACGStAoJaxVUfGEUAAAQQQQCDbBQjUsr2GKR8CCCCAAAIIZKwAgVrGVh0ZRwABBBBAAIFsFyBQy/YapnwIIIAAAgggkLECBGoZW3VkHAEEEEAAAQSyXYBALdtrmPIhgAACCCCAQMYKEKhlbNWRcQQQQAABBBDIdgECtWyvYcqHAAIIIIAAAhkrQKCWsVVHxhFAAAEEEEAg2wUI1LK9hikfAggggAACCGSsAIFaxlYdGUcAAQQQQACBbBcgUMv2GqZ8CCCAAAIIIJCxAgRqGVt1ZBwBBBBAAAEEsl2AQC3ba5jyIZAjAjt27JAFCxbI9u3bc6TEFBMBBHJBgEAtF2qZMiIQg8Do0aOlWrVqMmzYsH22fuyxx8y6evXqye7du0PWL1261KzTfcvKykLWpWvmvvvukzp16kiXLl3k008/TddpYz7Ptm3bXCN1Cn41bdpUTj/9dJk3b557vKefftps89xzz7nLYpnYs2ePrFixQn744Qd380jL3JVMIICA7wUI1HxfRWQQgfQI9O/f35zorbfekkAgEHJSXaZJA46pU6eGrPvyyy/N/MCBAyU/Pz9kXTpmNK9/+ctfzKn++te/SteuXdNx2rjOocGSTRdddJHY1xlnnCFFRUXyyiuvSLdu3WTx4sVmM+tfXl5ud4vpfdWqVdKqVSu5+uqr3e0jLXNXMoEAAr4XyPN9DskgAgikRUADhSZNmsi6detMwNCxY0dz3l27dslLL73k5uG9996TI444wp23LVjHH3+8uyydEz/++KMJII866ii57bbb0nnquM/VsmVLefTRR0P206BMA6sHHnhA7r//ftHWQRICCCBgBWhRsxK8I5DjAno57qSTTjIKX3zxhathW9BuuOEGs2zixInuOp14//33zfyAAQPM+5o1a+Tee++VE088UexlPQ0+NADUpK1zQ4YMkf/+979mPviHLtN19py67vHHH5dBgwbJgQceKL/+9a/loYceEtvSpK15559/vjnErFmz5Le//a3Mnj3bzGsA9+c//1l69+4teslW3++8807RwNMmzbueT8urx+3evbu8+uqr8uabb5rlH3/8sdx1113Sr18/s/8///lP2blzpzz//POiganmaejQofL111/bQ8b9ru5aPk3Blz/DD1RZebQs1uLdd981+X/hhRdClp1yyimycuVK99DRbHWjinzcAzCBAAKpF3D+myMhgAACRmDs2LF6zTNwySWXuCLO5USz7KOPPgr85je/MdNr16416zds2GDm69atG3DuTzOvww8/3F122mmnBZxWJDPfo0cPs49zT5uZd1rvAs79bu55dH89jp5/y5YtZvmFF15o5nWZc2k2oPvotHPJMOAEXIEpU6YE+vbta5bpvrqNE7wFnKAm0KtXL7PcaSkMOPfdufn45S9/GXACPXP8p556ymzTrl07867Hdu4LCzz88MPuMXWZLYNO2+Pq+Wx+dNrm2S1Q0MTmzZvN8fQ4kdJVV11l1mt5Ndl8aX1oilYeJ0A25Zk0aVJI3tTCCcRClh155JGBZcuWBZxLsYGKbNVKbTU9+eSTJl/WxwkqjY9ZyQ8EEEiLgN6LQkIAAQSMwPLly90vZktiAxOnJSngXLYz6zWY0fTGG2+Yef1y1/T222+beQ0CdHtNGoDZL3o9viYNljTo+eSTT8y8/nBab8wyp1XILLPH0sDvu+++M8s0YDnhhBPMduPHjzfLnF6eZn7w4MFmXn/873//c49lgzLNj93X6RxhtrUBkeZFA1LnRnyzPDhQc1rbzDINVHU7fd14440BPa8GPPaYziVgs12kHzZQ08Duq6++cl9Oy13Aaal0jzthwgSzu82XDdRseS644AI3yCwtLTXn1uDJluf77783x3JaFt1sRFrmtGqa7X7xi1+4tk6vWfd41tYGauE+7sGZQACBlAtw6dP5C0RCAIG9AnojuhNUyZIlS0QvYRYXF4sTWIjTMiYFBQVy7LHHmg2dIMq8O4GWedeOBJr0vjYnuBC9qV+316Q9Qdu3b2+m7dAZ5513npkfN26cedcf9j643/3ud2bZyJEjzfu1115r8qQzhYWF5nKmTtvtdTo82X2dliqpXn3vnznNz0033WQ2tZ0j7H6af72/Te8hC06///3v5bDDDjOLbGcLnbnlllukdu3apmemXjrVtHDhQvMe7Yde/nUCX/ell5r/9a9/mV2uueYac2k30v62PFdeeaVbnpo1a4oTMJqOH7Y+Iu0bvsz5VpFRo0aZvOs5tb411apVS26++WZzvJdffjlkN73MG8knZCNmEEAgJQJ0JkgJKwdFIHMFTj75ZHNTu97/VVJSYgpi76Fq27at+WLXXop6U/yHH35o1jstZOZd12sA88gjj4jeM7Zo0SL3njGzwU8/NPBzWodMUHf33XebIT90SAqnxUmOOeYYs9W3335r3nXYEKeFyd3daUky09qbMVLSHpZz5841q5zLrSGbHHzwwWZ+zpw5IcudS7oh83amU6dOdtIESM4lThPQaMBokwZMmsKHLbHrg991fw3ygpPex9ezZ0+xeQtep9OJlCf8GOHzaqsBWyy2eg+d3odHQgABbwQI1Lxx56wI+FbgV7/6lQnUPvvsM1m9erXJp21J0xm9IV07C+h67fGprVAdOnQw2+nN8Np7VJMGXdoKdeqpp4q2vOmN6TbVr19fzjnnHHn22Wdl+vTp4tzfZXpuagtPjRo1zGbamqdJOw7Y4MwscH5oC1jnzp3tbMi7bbXTICsvL/RPnLbuaQpfrmOwRUoaWIUn21IYvlwDn8qSlltbCONJiZSnsuNXZqvj0QWninyCt2EaAQRSIxD6Vyw15+CoCCCQQQI6zIUmHYZDW8T00pi9PKbLNUjSQO3WW2/VWbfHok7fc889+mbW6eVPbY3RpL0xw9P//d//mUDttddek/Xr15vVwdtpsDBt2jT5z3/+Y1qc7P7acqVPIGjQoIFdFPKuwZW+9FLkxo0bpVGjRu56vYyryQ494q7w8UQqyqO2GiDHa+tjJrKGQNYK7L15I2uLR8EQQCBegf3220+cnpTm3jQd4FZb0IKTDeRsC9lxxx3nrtbhLDRpwGWDNG29cW6Sd7exE7qfBiF6CVWHu9DWt+AAyt4b5nRYsLuYd31KwkEHHSRjxowJWR484/RuNLM2j3advTctOM92nZ/fI5VHW/D03jR11lbQ4BTpMmzwsops1VQvwUazDT4P0wggkHoBArXUG3MGBDJOwOnJ6OY5fCBbHUnfdh7QjY4++mh3W6cXoZnWAVx1PDK9/0zHINOAT5OOwaaXOTXpUwycYUDM+Gq63ukpapbbH/bGf73BXccv07HN9Ib2Sy+91AR4w4cPt5vu825b+/RxWDqQrDN0hbnx3uk9aS7J2s4M++zo0wW2PGeeeaZbHvUJL48G2ZrUSsekc3qbSvgy9bedEHScuWDbESNGVGrrUyKyhUD2Cjj/lZEQQACBEIHJkyfrDVfm5XQoCFmnM84lTrPOjo1mN3AGUw3oMBl2X313gqKAHs9pPTPLncuZdvPAjBkz3G03bdrkLrcTOjSGc9nV3UaPp+OiBQ+FocNk6HKn5c/uZt6dDg/uOW1+dKgRHcfNJjtunHOvnF1k3p1eluaYzmXZkOU6vEb4WGg6NIYeX4cuqSht3brVbBO+b0XbR8pXpPI4g/iGlEeHC7FjsjktbQGn04AZQiR8mZ73888/38fWGfA3xFaHCdHjhPtUlG+WI4BA8gWq6SGdPzIkBBBAIGkCOrSHDvFxyCGHmF6gemDtQao9NXWojvCb+aOd2N6Tpg8bb968ubk0Zy+rRttP1+lo/trBwRmY1zwDVDs+xLpvZcf2Yn2s5VF//dPerFkzN5uRllXF1j0wEwggkFIBArWU8nJwBBBAAAEEEEAgcQHuUUvcjj0RQAABBBBAAIGUChCopZSXgyOAAAIIIIAAAokLEKglbseeCCCAAAIIIIBASgUI1FLKy8ERQAABBBBAAIHEBQjUErdjTwQQQAABBBBAIKUCBGop5eXgCCCAAAIIIIBA4gIEaonbsScCCCCAAAIIIJBSAQK1lPJycAQQQAABBBBAIHEBArXE7dgTAQQQQAABBBBIqUBeSo+eoQfnqVoZWnFkGwEEEEAAgRQJePX4OQK1sAotKyuT/Px8KS0tDVuTubOFhYVZU55sKot+oiiPv3+vqB//1g9149+60ZxlY/2Ul5dLjRo10g7Ppc+0k3NCBBBAAAEEEEAgNgECtdic2AoBBBBAAAEEEEi7AIFa2sk5IQIIIIAAAgggEJsAgVpsTmyFAAIIIIAAAgikXSCnOhNob85XXnlFiouLzQ2BHTt2lIEDB6YdnRMigAACCCCAAAKxCORUoPbmm2/KsmXLpHXr1qJB29dffy15eXkyYMCAWKzYBgEEEEAAAQQQSKtATl36/O6772T//feXM844Q4YNGyZFRUUyf/78tIJzMgQQQAABBBBAIFaBnArU9uzZY8ZIszjaqpZN46XZcvGOAAIIIIAAAtkhkFOXPlu2bGkufb777ruiA9tu375dwkca1sFuNelgfdmUsqk82VQWPmv+/S17Z8y3smjaWv9mkJwhgEDaBI6/qLu069U4becLPlFOBWrHHXecPPPMM+beNA3Q9As//HFRPJkg+OPhv2mts2xqBaU8/vuM2RzpKOQkBBBAwGuBnArUli5dKieddJK0adPGtKQ98sgjpjOB15XA+RFAwL8CA87t5Nl/0slWyaZ/DLKpLFrPlCfZn/bkHk/rx6t/3nLqHrWZM2fKa6+9Zmpv2rRpsmXLFunevXtya5OjIYAAAggggAACSRLIqUCtf//+hu2ee+6RDz/8UJo2bSr9+vVLEiWHQQABBBBAAAEEkiuQU5c+O3XqJPqyw3TUq1cvuZocDQEEEEAAAQQQSKJATgVq1q19+/Z2kncEEEAAAQQQQMC3Ajl16dO3tUDGEEAAAQQQQACBCAIEahFQWIQAAggggAACCPhBgEDND7VAHhBAAAEEEEAAgQgCBGoRUFiEAAIIIIAAAgj4QYBAzQ+1QB4QQAABBBBAAIEIAgRqEVBYhAACCCCAAAII+EGAQM0PtUAeEEAAAQQQQACBCAIEahFQWIQAAggggAACCPhBgEDND7VAHhBAAAEEEEAAgQgCBGoRUFiEAAIIIIAAAgj4QYBAzQ+1QB4QQAABBBBAAIEIAgRqEVBYhAACCCCAAAII+EGAQM0PtUAeEEAAAQQQQACBCAIEahFQWIQAAggggAACCPhBgEDND7VAHhBAAAEEEEAAgQgCBGoRUFiEAAIIIIAAAgj4QYBAzQ+1QB4QQAABBBBAAIEIAgRqEVBYhAACCCCAAAII+EGAQM0PtUAeEEAAAQQQQACBCAIEahFQWIQAAggggAACCPhBgEDND7VAHhBAAAEEEEAAgQgCBGoRUFiEAAIIIIAAAgj4QYBAzQ+1QB4QQAABBBBAAIEIAnkRlmX1oilTpsiCBQtkz5490q5dOzn++OOlenXi1ayudAqHAAIIIIBAhgrkVKD2+eefy4wZM0yApvU1Z84cU20nnnhihlYf2UYAAQQQQACBbBbIqaakefPmSUFBgQwdOtS8atasKStWrMjm+qVsCCCAAAIIIJDBAjnVotalSxf57LPPZOzYsRIIBGTnzp1y8MEHZ3D1kXUEEEAAAQQQyGaBnArUmjdvLtWqVZO1a9e6ddqwYUN3Wify8/PlnTHfyqJpP28TsgEzCCCQUwL6N6GwsDBrykxZ/FuV2VQ3qpxt5fHqk5NTgdqkSZOkRo0acsEFF5iA7YknnpBPPvlEevbs6fqXlZW500wggAAC+jehtLQ0KyD0i5Oy+LMqs6luVDgby+PVJyenAjX9A6WtavXr1zfeLVu2lCVLluxjf/xF3eXo37bfZ3mmLsimX5hsKot+niiPv3+rsq1+/K1N7hBAIJJATgVqegljzZo1smzZMqlbt66sXLlSGjRoEMmFZQgggAACCCCAgOcCORWoHXfccfLuu+/Kyy+/bOC1B+jRRx/teSWQAQQQQAABBBBAIJJATgVq3bp1E31pZ4Ldu3dLixYtIpmwDAEEEEAAAQQQ8IVATgVqVrxp06Z2kncEEEAAAQQQQMC3Ajk14K1va4GMIYAAAggggAACEQQI1CKgsAgBBBBAAAEEEPCDAIGaH2qBPCCAAAIIIIAAAhEECNQioLAIAQQQQAABBBDwgwCBmh9qgTwggAACCCCAAAIRBAjUIqCwCAEEEEAAAQQQ8IMAgZofaoE8IIAAAggggAACEQQI1CKgsAgBBBBAAAEEEPCDAIGaH2qBPCCAAAIIIIAAAhEECNQioLAIAQQQQAABBBDwgwCBmh9qgTwggAACCCCAAAIRBAjUIqCwCAEEEEAAAQQQ8IMAgZofaoE8IIAAAggggAACEQQI1CKgsAgBBBBAAAEEEPCDAIGaH2qBPCCAAAIIIIAAAhEECNQioLAIAQQQQAABBBDwgwCBmh9qgTwggAACCCCAAAIRBAjUIqCwCAEEEEAAAQQQ8IMAgZofaoE8IIAAAggggAACEQQI1CKgsAgBBBBAAAEEEPCDAIGaH2qBPCCAAAIIIIAAAhEECNQioLAIAQQQQAABBBDwgwCBmh9qgTwggAACCCCAAAIRBPIiLMvaRY8//rjs3Llzn/KdffbZUr9+/X2WswABBBBAAAEEEPBSIKcCtQYNGoQEauvWrZOysjLJz8/3sg44NwIIIIAAAgggEFEgpwK10047zUVYtWqVPPfcczJo0CCpXbu2u5wJBBBAAAEEEEDALwI5e4/aq6++Kg0bNpRu3br5pS7IBwIIIIAAAgggECKQUy1qtuRffvmluQQ6bNgwu8h9t5dBCwsL3WXZMJFN5cmmsuhni/L4+zcs3fWz/v4HZMODD7ootfv2ldZjn3LnqzqR1vIEArL83PNkx7RpbrYbX3GF7H/Vle58VSbSWhbNaHh5qlWT1k88IbUP61eVYrj7elGeeV2DGisyvDw7vvhSlp9/vuspTnm6zpv783yGTlULOClD855wth966CHJy8uTSy65ZJ9j2HvWSktL91mXqQv0lz9bypNNZdHPE+Xx929VOutn08MjZfOoUVFB2s6aGXV9ZSvTVh7na6V4+MVSOmNGhVkq7NNHmj36SIXrK1uRtrJoRpzyLD20V9QsNbj0Uml42Yio20Rbmc7ylE6dJsURvv/c/DkBTrPRo6Wwbx93UbwT6SxPpb87Tnnazvwq3iKEbK/lKS8vlxo1aoQsT8dMzl36XLt2rezYsUMOPvjgdPhyDgQQQCAhgcLevRPaz687ZVt5/OqcaL6yqX6yqSxanzl36XPWrFnmc3zYYYcl+nlmPwQQQCBlApFaZpYecmjKzpfSA0domam0NSelGariwSO0zFTamlPFU6Zy931aNW1L6FdVa31KZZ6jHXuf350YWkKjHc8v63IuUFuxYoUUFBRI9eo515jol88c+UAAgSCBktfHy+7i4qiXCe3mm0fuvTTaYMSldpHv3m0etUzR0u7Vq0W3zWvWTIpOGRJtU8/WaR5Lxk+I6fx6mTdTylPmlCtqcgKcbRMmmM+ktk5V5RJo1PNUcWXptOkmj9EusZtTOOWxn8uiIYMlr3nzKp45vbvnXKA2fPjw9ApzNgQQQCCKwLbx42MK0vQQm0aOFL1B2reBmvOFaPJoy+vktaKkQZBuq606vg3UVq4KLU9FhXGWl053ggbnlS3lKXE+l5q0lcqvgdqPjndl93WaQjg/7O+OBp4EalaFdwQQQACBmAWKBu/9T7+WE7iEJ/2y1BTrl1L4/l7M2zzntQhtvchr2cJ8+ZvWKqfVJhOSafUbErnVz9ZXJpYnP7xlyQms6zrl1GDTBJ5ROoP4qd5Mq5+TZ1sXbt70n5qffnc08Ny9Zo27KpMmcq5FLZMqh7wigEDuCJgvyAp62dnehBkTqDlfkDbP4TWorRm6Tu9VK8mUQK1FiwrLo61N+sqW8tjWTb33rtJLiuGV69G8BpYVfd7McqelVwPPTA3UuFHLow8Wp0UAAQQQQAABBCoToEWtMiHWI4AAAikQKJ2+d4yx8m3b4j663bdm505SrW7duPdPxQ4mT07LRbypfOtWp7Vjr0VhH38MSRJw6mTngoWya/GieIsjtjzV6xZJQefOce+fih30suzu1cUJlcd0dHHqJ695M9/c27VrwQLZs63EdMJJhZffjkmg5rcaIT8IIJATAsWJdmxygiGzr3N5saqDkiYN2uYpgQOWLVzolqeqg5ImcPqIu+ycNz/6gLAR99q70JZHL8dVZUDfKKeIe9U2p2dxopfN9d4ufem9XhVdXow7Q1XZwfmsbbzr7sQuy+rn9OKLzdl9U54YLAjUYkBiEwQQQCBVAnZwzur1Km8Zs9vunD9fAs7A3X5MJo9OEFlZ0vLa8vj1XqhqdepIzS5dpKBTx8qKI7Y82qJWtij+lrhKT5CEDbRThN4jGEt5tKOB1o9pjatkqJUkZC2hQ+R37Cg16tWTfTpFJHQ0/+5EoObfuiFnCCCQAwLNxjwacynNtqb1ynk8kx8HJdVWvhjLU+AEQLY8lT2eKWagJG9Ys2vXmFvFbHn8PKBvkdOjM9ZWMe1UoC/fDujrfNYaX399bEOHBH0ufVueKJ9dOhNEwWEVAggggAACCCDgpQCBmpf6nBsBBBBAAAEEEIgiQKAWBYdVCCCAAAIIIICAlwIEal7qc24EEEAAAQQQQCCKAIFaFBxWIYAAAskWWHrIoaKvKqefhhrQG/H14dReJL1x3pTHyUOVk1MeeyyvyqM3mmseii+5pMrF0ZHwbXmqfLBEDqCfj4uGmzwkOjRH8Gn1GMZm+N7hLYLXpWXalkc/70l4tJUtj9a53xOBmt9riPwhgAACCCCAQM4KEKjlbNVTcAQQ8FKg7ayZoq+4009DDejgsHYcsriPkeQddHBXUx4nT3Enpzx2X7+URwdD1TwlMmBtYb++bnnitkjFDvp5eeQRk6dYh+YIzobuYyxGjw5e7N20LY9+/it4Nm60zNny2Ie1R9vWL+sI1PxSE+QDAQQQQAABBBAIEyBQCwNhFgEEEEAAAQQQ8IsAgZpfaoJ8IIAAAggggAACYQIEamEgzCKAAAIIIIAAAn4RIFDzS02QDwQQQAABBBBAIEyAh7KHgTCLAAIIJFtAxwUrvjhF40/9NJ6a5ll7siXSsy/e8qb0wda2PNq7z+lpmEjPPr+VR8cfE+3dmkiv2HgLo37OWGfuWGPOeZOZ7Phw6SyPjhXopiSXZ3dxsZROnyHV6xZJQefO7mn8NEGLmp9qg7wggAACCCCAQNoESsaPdwLb4bLx7n+n7ZzxnogWtXjF2B4BBBBIUEDHCWs25tEE9w7bTVucfjpWSlu4wk4bPJvUFjxbHtsi9FUCY7IFZy6B6WSXR8cfE6c8IS1CCeQroV3UM4ktknZ8OC/Lk8wWyfzmzc04hOVbt0rZokUJEadrJ1rU0iXNeRBAAAEEEEDAFwJFpwwx/+g0/tOffJGfaJkgUIumwzoEEEAAAQQQQMBDgZy79Dlz5kyZ7jwsd8eOHVJUVCRDhw6Vhg0belgFnBoBBBBAAAEEEIgskFMtaitXrpTJkyfLnj17pFOnTrJlyxaZMGFCZBmWIoAAAggggAACHgvkVIva+++/LzVr1pRLnS7smlq1aiWrVq3yuAo4PQIIIIAAAgggEFkgpwK1kpISqVWrljz00ENSVlZmArWBAwdGlmEpAggggAACCCDgsUBOBWo7d+4096bVr19f6tSpI999953osrPOOsuthvz8fFn+u3Nlx7Rp7jImEEAgdwXqn3aaFLRoIbX79pXah/WLC2L9/Q/IhgcfdPepXr26FBYWuvPJmsjL2/unfPOoUaKv2v36Seunnox4+KqcP7w8et6qHC9iBp3hLNRJh4GwgwQ3vuIK2f+qK/fZvErndo6//NzzQv7Wp6o8JuM6TMdPA9+2fuKJiJ+lqpZnXtduPxs5w3MUFBSkpH7Cy9N13tyfzxs0VZXy7PjiS1l+/vlBR5Pkl8U5+h7HSFPwQL62PGvvuENK5y8w65vedKPkO7dMeZFyKlBTYA3ELv5phPCXXnpJVqxYEeKuLW0kBBBAwApsGTfOTOoYW9V79rCLY3rfvXt3yHZ6f2xpaWnIsmTMxHoe/eKsyvnDz6PzVTlexLI7AY06BadI56lqWTQQjOU8wflIaNo5T3jatWuXVA/7HCSjPLGcJ3ybuOcjlCfSZ6Cq5VGj8BTpPOHbxDu/z3mc8q35733mMNvemyy7V682T5XYs3VbvIdO2vY5FahpkKYvm7TXp/6iBpyKqeb892FT67FPJf+Pjz24B+9V/YXxIMsVnjKbyqKFpDwVVrXnK0peHy+Bdeuk5IsvzON4qvKomaQOpBpBRh8bpa/SqdOk+JJLImyR3EUpLY8d+NbJcloG8k3ywLD7SDvHtwPfmkc7pXogXz1fKh9VFVSedAzkW9injzR79JF9WJO1INJAvptGjgw5fKPrrpXCrl1k35A7ZLOUzeRUoNbcGYl4yZIl8pXzi9KsWTNZ5IxG3KhRo5AgLWXSHBgBBDJKQAfE1EDatObMmCH6qBl9JfXpAhklQmYRyC0B/YdEU+0BA6R6vXpSXl7uCUBOBWqDBg2SMWPGiPb+1KStaMccc4wn8JwUAQQyQyCTHjWTGaLkEoEMEHDiA22l9kPKqUBN/zu+wrkpVcdT+/HHH6V9+/Z7b1r1Q02QBwQQ8KWAtqzpq3TadPfmdl9mlEwhgEDiAvaSbuJHSNmeORWoWcWWLVvaSd4RQAABBBBAAAHfCuTUkwl8WwtkDAEEEEAAAQQQiCCQky1qERxYhAACCCRNQLv0715dLNpT1ItUvnWrMy7UDKlet0gKOneuchZ2LVgge7aVeFaeqvS4jVR4tdHhOcq3eTDkgnPeXYsWmiEf8po3kzynk1tVUsApw84FzvGc43qVjKdz8pqdO0m1unWrlA1bnl2LF1XpONm0M4FaNtUmZUEAAV8IbHOG9tCBZ71KZQsXSvHw4ZKUoQ2cAGDjXXebIUq8Ko/b4zYZQzU45VEbNzn3JqU7qaemZAxzsnPe/LQMyVKhkfV0HJuNHi2FfftUuGksKzwvTyyZTPM2BGppBud0CCCQOwJ5zjBA2mKiPUfTkarXq2uGD9EWtTJn+KFkp/yOHaWGM0xBusqT6h63OtSKJnVLeXICmQKnxUmTaXFNcmtrNedpOzW7dDEtdSkvy08nsH4758+XwI4dST2tLU9Bp45JPW4mHoxALRNrjTwjgIBnAnq5LNbLikVDhqS1i3+B80XdbMyjqRn41gk0Gl9/fZVbTOKpOLfHbSoG8tUWIMcqnamR46cpFQP51uzaNaUDw+7jZP1Mi9rFUprkgXzTXp59CuifBXQm8E9dkBMEEMgAAXtZ0V6+yoAsk0UEEMhgAQK1DK48so4AAukTsJcV9fIfCQEEEEiXAIFauqQ5DwIIZLSA9p7US2V6+Y+EAAIIpEuAQC1d0pwHAQQQQAABBBCIU4DOBHGCsTkCCCBQkYDX441VlC+WI4BA5goQqGVu3ZFzBBDwmYDX442Fc5ROny5LDznUDNnQduZX4aujzzu9+ZYe2uvnbZxefl4nWx4dH67t02Pjy054eeLbOyVb61h7+tLx1Jr98Q9xnaM0FT1h48pB2Mba+/Pii83CRMqTip6wYTnM2FkCtYytOjKOAAJ+FUj3eGN+dSBfCCBQdQECtaobcgQEEEAgRCDd442FnNyZKezXV9rOmmkeKxTSKha+YSzzTkta3K1xsRw3jm1seZLSiuSD8jS8bIQZXy8ZrUhJefpEHHWxz6Z2PDVnRTLKk4ynNeyTxwxfQGeCDK9Aso8AAggggAAC2StAoJa9dUvJEEAAAQQQQCDDBQjUMrwCyT4CCCCAAAIIZK8A96hlb91SMgQQSJOAHZZDnwNKQgABBJIpQKCWTE2OhQACOSOwc8ECKR6+dzgCcYYmKJ0xw79l16Epfhqmo9no0dEfrO7DYSzCYXWYjnldusY07EhSOiCEZyDJ83aYjlg6BiTjhv0kZ3+fw9nyVNoxQIf0cH6HfP27s0/p0r+AQC395pwRAQSyQCBQUiIaMGgq7N3bvNthOfS5oCQEEEAgGQIEaslQ5BgIIJAzAjW7OM/8fOQRU147wKctvNfDcth8uO86FMVPw3SYlouv4hj01gfDWLjl+GnCDtOhLZjxDjsSS2tV+PlSPW+H6Uik1a/S1qpUZz7C8W154m710yE+KmvpjXC+XFlEoJYrNU05EUAgKQLV6tbd59Lh7uLipBybgyCAAALhAvT6DBdhHgEEEIhTYPfq1XHuweYIIIBAbAK0qMXmxFYIIIDAPgJ6+Sk45bVoHjzrv2nnkuGGu++WGvXqSaM/XScFnTu7edw1f75s/Pc97nxGTPx0M7ropbNHRodkueT18bJtwgQp37o1ZLmfZ3Y6dWA6qEQpTyb9U1Di+GtHgYJOHaXR9deH0G+86y7ZtXCRaKccUnSBnArUysrKZMyYMSEi7du3l4EDB4YsYwYBBBCIRUDvycm0VLZwoZQ5gcCeraFDiei87RyRSWUyeXbKE57KnFbOTCuP20ElS8qjQWXEwNIJsHctWPhzb88I5Q2vz1yez6lAbdmyZVLi9NRq06aNW+f77befO80EAgggkJUCzhdho+v/JHk/lsrqf/xDyhYvrrCY2nNVO0X4Ojnlaf3kk7Jr504pvuSSqFktGjxY6g4ZIn7uiVuza5e4y+Pn1tu6pwyR+kccISVzvpGNd/87av2Ylt1OnUU76ZAiC+RUoLbaie7z8vLk9NNPj6zBUgQQQCBLBfQyZ2HNmuayZ5m2aCxaaMYhq163SPZsK5FdixeZkteoX3+fzhJ+JKl9WD+p/uOPbtZKp+8dx86Wx3bwyGve3Pfl0Q4qtbKoPMa8XTsTSGsF6eVnrR9bN9pr1wwO7QTcBU6QVti3j1uPTOwrkFOB2vr166W8vFz++9//Ggl72bOwsHBfGZYggAACWSyw8a67Tel0DLiMH3DU3Ks2nPL49POql9uLhw834w2GfNacQI1UuUBOBWpbnag+4PxCt3Mi/W3Oo14WODcxavB24YUXulL5+flmOtuCt2wqTzaVRT9slMf99fPlRCbUz0OzHpKHv37Y9evbtK88duJj7nzwRO3u3aR69epStmqVmPu4fnqiQvWiIins2tV5dfH0MxmQgFz41oUyfe3ewYQ175f1vEwuP+Ty4GKYaa2b2n37munSefNkjw5C/FN58p2WtPwWLaR269ael6fHkz3cvFeTajLmhDHS94C9+XZXOBPB5dkxbdrecv1UnppOi6h2AvFjeWafNzu4GO50YeNGpn60RU07Ddi60Q1svek2mfA75hbKg4lqTuAS8OC8npxS70/TVOT8QdL0+OOPy8aNG+W6664z8/pDOxxosFZaWuouy/QJ/SXIlvJkU1n0c0V5/P3b5ef60YBmxPsj5Kv1FQ9i23v/3jLy2JEucnB5wgcl9XpA2HjLE1wWvZQW/igirweE1fL0e6Gfax9p4uLuF8slB+29xy68PCED+jotT14PCDt93XS5bMplkYphlmkA+vCAh6V3k71P6Qguzz4D+jrlaTuz4s9thSfxcIWWR6/I1ahRI+25yKkWtU8++UTaOB0JunTpYqDrOvcFbNq0Ke3onBABBBBIhUCv/XtFDdyCz2lHkQ9e5rfpmMujgcyYR/2W/X3yE095zBMl9jlCZi5wnyiRmdn3PNc5Faitcpr6586dK62dpvBlTg/QFStWSJMmTTyvBDKAAAIIJCoQ3pKhx6ms9SPRc6Vjv2wsz9Qzp4bQjZ4zWh75du9jyEJWZMBMeCutbQmduX5mBuT+5yyG10F4uX7e0vspzwM1vUds5MiR8sEHH8iBBx4ow50bDmfOnCmXXXaZ6aGZTKLjjjtOXn31VXnwwQfNYbUJ85e//GUyT8GxEEAAgZQLTFg6QdbsWCPFO4qjnmv1jtVuQKCX2fyabNASa3kOqH2AnNH1DF8WZ/X21TJp2aSY8qaXrbXs2tLWv1X/mPZJ90Yz1s0wrbRarmhJA7aJyyaabf1cHi1HLL870cqa7nWeBmrbt2+XE088URYtWiQdnbF79Ab/3bt3y9VXXy1ff/21PPpocpuytSXtj3/8o3z//ffmZtqWLVum25vzIYAAAlUSMF+IS/d+IeqBtAWqolS8vVi05UC38WugpuXRPNoUS3m09cPPgVpweWy5Ir1rEKQvrRs/B2o2kI5UhuBlE53PpSY/l0fzGFyemC9HBxc0zdOeBmpvv/22CdLmOb11Zs2aJbfddptp4XrxxRdl2LBhcuONN0qHDh2STtKqVaukH5MDIoAAAukWOLnNydK8TnNpVqdZyKl1mQ3Mgr+UQjby4YzNc0Xliae1yuviaavf4LaDI2bD3nBvW6sibuSzhRrQaL7D60YD61+3/bVZl0nl0fpxf3fW+ww7LDueBmp6v1hvZ06GFRsAADaRSURBVAyfFk4Xag3UbDr55JPNpA6fkYpAzZ6HdwQQQCBTBfQLUgMB+6UfXA79AtLehNpalSmBmpbH9oAMLotO2/LovXexXlYMP0a651vUaVFhebTO9KUtb9F67aY7z9HOp/mtqH5sQJpJ5dE8a3ky4TNVPVrFpHqdBmF6b5re2B+c3n33XTOrlypJCCCAAAIIIIBAKgW2lm01QbMfA2dPW9ROOeUU6dGjh7Rt21b6OoMWzp8/X4Y4z2SbMGGCnHTSSdK9e/dU1gvHRgABBDJKQL9EtJVsW1noA9VjKYTuW1BQIK1rt5a6+XVj2SWl22gZFm1eZMoT74n0S1UHxN21a5e5ET/e/VOxvS3P4i0VP0e1ovNqJwotT0GgQDo16FTRZmldXpWb7m159svbz7SGpjXjCZ5MP4uXvn+p065bTcJ76SZ4yKTt5mmgVqtWLXn//fflH85Dgl9//XXTmeDbb7+Vm2++WW666Sap5oyNQ0IAAQQQ2CugXyQ26RdKrEmDO/slFDwoaaz7p2K7BZsWRB1ANdo59Uv1grcu8NWXalXKoze468tPQ0SE33QfrT7C19ny6D2HFV0uDd8nlfP6+a9ocOi6BXXdYN+PrWnq4mmgphlo1KiR/Oc//zEvnSchgAACCEQX0Bu7NemXTGXJbquBxI7dOyrbPO3r6+TVkc4NO5ugq7KTZ8KXqi1PhwYdKiuOuTFf62frrq2SSEtcpSdIwgYhN91XcjztaKDlsa1xlWzui9WdG3SWUceOMi27lT1JwqsMpz1QW716tZx11lkxlXfSpEmiTw8gIYAAAgjsFdCWNP1iiSXZbW2Lgh8HJe3SsEvIY66ilSsTvlTjKY/e0K4vPw9QbG+6j1Yvdp0tj3Yq8GMnFv198EuLsjWL5T3tnQn0eVlHHnmkefXv39/09vz444/N87N69uwpW7ZsEZ3X53HaB6THUhC2QQABBBBAAAEEsk0g7S1q++23n9xxxx3G8dNPP5U777xT9L60bt26ubZPPPGEXHDBBfLjjz+ah1a7K5hAAAEEEEAAAQRySCDtLWrBtlOnTpV27dqFBGm6XnuDatJHSZEQQAABBBBAAIFcFfA0UNNney5ZskTeeeedEP/Ro/c+ToQHpoewMIMAAggggAACOSaQ9kufwb6DBg2Sww8/XE444QTTstanTx/57LPPZOXKlXLOOecwjlowFtMIIJCzAn1f6JuUsptOBVNGmB6WXt1Uncwb57U8aqM3iXs19lUyb5yfsX6Gp+WxnU6SNUyFdijQl1fDjmh5gnty6uckE5OnLWq1a9eWyZMny/333y+dO3c2A95qhwJ9GPuTTz7JOGqZ+IkizwgggAACCCCQNAFPW9S0FBqsXXnlleaVtFJxIAQQQCALBaadOS2hUmlLgg7pUbOwppz/xvnih2E6qtLKouX55rxv5MfSH0NaTBLCSdJOVRnctU+TPr4qj/pWpcVVB7m9us/V8sn3nyQ8qHGSqsUcRsvjVYtrMsrhaaD20UcfyahRFY8HpPeq1alTJxnl5BgIIIAAAggggEDGCXgaqJWXl5shOKxaSUmJfP3117Ju3Trz7E8eIWVleEcAAQQQQACBXBTwNFAbMGCA6Cs83XXXXeYeNb0sSkIAAQQQQAABBNIhoB0Q7FMV9HK2H5KnnQkqArjoootk7ty5pnNBRduwHAEEEEAAAQQQSLaA6ck755FkHzbh43naorZ7927ZtWtXSOZ1/sEHHwxZxgwCCCCAAAIIIJAqAe1wYFvQbItaqs4V73E9DdSee+45OffccyPmefDgwdKxY8eI61iIAAIIZLvAjHUzZIQz5lkqkhkv66djV6W3Yjx5S+Z4Y+Hn1fLY8dSq0lsx/LgVzRu/90dIssYbCz9PcHnS0VsxvDwatCQz2fHhqtLTN578aHmCx0+LdV/trar7EqgFienD2Z9//vmgJSLVq1eXDh06yEEHHWQe1B6ykhkEEEAAAQQQQCCHBDxtUVu6dKnMmTNHbr/99hByvfz5m9/8RnR4jgMOOCBkHTMIIIBALgn02r+XGQMtGWXWlhIdT01TKlu4ouU1mS14Wh4dW862CKV7fDg9fzJb8ILLk0iLUDT3WNYluzw6PpzWTzKfRhFLOew2Wp50tEja86Xq3ZNA7YsvvpAJEyaYoTimTZsme/bsCSnfihUrzPotW7YQqIXIMIMAAggggAACuSTgSaC2bNkyee2112TDhg1mzDSdDk55eXlyww03mMdKBS9nGgEEEEAAAQQQyCUBTwK1s846S/Q1fvx489Jne6Y7TZo0STRgvOKKK9J9as6HAAIIIIAAAgjEJJD2QK20tFSmTJki3bt3N08fyM/PlzfffDNiZgcOHCjaupbstHjxYpk3bx4PfU82LMdDAAEEEEAgCwTMfY8/9Yy+5tBrpMcBPTwrVfKjoEqKsnHjRhk0aJB5xmfDhg1l2LBhFe7xww8/iG6TzFRWViYTJ06UWrVqiQaNJAQQQAABBBBAIFxAh8jRDgnbdm0LX5XW+bQHas2aNTP3pRUVFZmhOPS5nhWlBg0aVLQq4eU6HEhBQYG0bdvWPP0g4QOxIwIIZLXAhKUTZMOuDfLl6i9F/2DblMxemPaYvCOAgD8ENDAbOWCk6Ul8z1f3yOIti2XisonSZr82ckAtb0ahSHugpg9a18uZtjUr2qXNZD+U/csvv5S1a9eaQXZnzPj5D2/wx0MvxV7w1gUyfe304MVMI4AAAkZABznVwVX7NO0jj5/4eNJVHpr1kDz89cPucXVsycLCQne+qhP2WPZvrw7uqa++TfvKYyc+VtXDh+yvl48ufOvCkL+nel6bh5CNE5ixx9HzqFPw5arLel4mlx9yeQJHrXiX8PLol7r+42/zUfGesa2xx9HzaNJ3O5DvmBPGSN8D+sZ2oBi30uP3ePLnS3qpKo8aabID3+p5Zp83O8Zcxr7Z1DVT5aK3LwrZwZqGLKxkpn+r/sZ+zNwxIltEJi2dJEM7Dc2dQG316tXSsmXLSpj2rk7mpc9AICCffPKJ1K9f3zxDVFvydNlHH30khx9+uPll07PqpVESAgggYAVObnOyNK/T3MwGj1iuwwrZfzjttsl410frBadknke/tGyeU3kem38NBMKHX9Lz2jzY7RJ5Dy5LKs8TnLdI59FxP1NRnuDz6nSyzhN8XC1PeErWeYLrR48ZnpJhFn7MZJ9nUOtBcmjjQ81pmhft/RsQfs50zKe9Ra1Ro0byxhtvxFS2unXrxrRdLBvpHwttodu2bZtMnz7d/eOh0127dpX999/fPYz+l5yKD5F7gjRPBP/CpPnUST9dNpVFcShP0j8iST1geP3oI2ZS+Win4Mwnc2DY4OPaaS2LvtIxGKm2oCRzYFhbBvuux0/nQL7pKE86B/LV8qRyYFg78K0GhukYyDdZj6oa3Haw/YiZv9Xl5eXufDon0h6o6R++k046KaSMOkzGN998IzrAbadOnczjo2rXrh2yTVVnatSoIddcc417mHfeececM3iZu5IJBBBAAAEEEEDABwJpD9TCy/y3v/1NbrnllpDFTZo0Mc8AHTBgQMhyZhBAAAEEEEAAgVwSqO5lYXU8Mw3S9KXTeqP/uHHjzBMJdEDc8HsbkpnX448/Xq699tpkHpJjIYAAAggggAACSRXwtEXtmWeeMYPe3nrrrW6hTj31VHPPWJcuXWTq1KnmRn93JRMIIICATwSKdxSb3pKaHb2fjIQAAgikQsDTFrVu3bqZ+9LCC9a4cWOzKPgG//BtmEcAAQS8FCjeXiyj54w2Ly/zwbkRQCC7BTxtUTvyyCNl8+bNMnToULnooovkwAMPlBUrVsjf//530SBu+/btMnv2bNNb8+CDD87umqB0CCCQEQLN6jRzW9CCh+uoauZXb18ta3asEW2p8yJtLdsqOkZcUX6RdGrQqcpZWLh5oWwr22ZeVT5YAgdQRy3PAbUPcIdXSeAw7i56LO21qGVKd9LzLtqyyHwXJqM8WoZFmxeZ8qS7LPZ86qmpY4OOUje/aiM82N8dHZw2G5OngZqOa6bjmel9afoKTz179nQX6ZhnJAQQQMBrAR1TTYe10JTMQG3i0olJPV68TvrFfen7l0oyhjbQwEJHdbdfxjr8Q7qTeuorGcOcaHnUxiYvyqOempJRngWbFshlUy6zxUn7u/VM1jAnXv/upBrQ00BNb+jXYTkqS8l+QkFl52M9Aggg4JWAbTHRlrt0pLoFdUUfi7V111bzuJxkn7ND/Q5Sr6Ce6HnSkdRNy2NbWZJ9Tj22pnSURwMZbXHSlIry1MmrI50bdnbOkr5A2vppsLhj9w5TtmT9sL87HRp0SNYhfXEcTwM1fUqAvnT8tEjP/Gzfvr15LIgvpMgEAgggkAYBHWTTttil4XTSuUFnM1hsKga+1QDgul7XSe8mvdNRFHMO9dOX3j+YzBZPPbiWxw6sm64CqZ+mVJSnS8MuMvLYkekqiuunLWoj3h8hM9fPTOq50/27k9TMRzmYp4Hapk2b5JRTTpGPP/44YhaT+QipiCdgIQIIIIAAAggg4GMBTwO1l19+2QRpDzzwgHkaQbhTMh8hFX5s5hFAAAEEEEAAAb8LeBqobdiwQXr06CFXXHGF353IHwIIIIAAAgggkHYBT8dRGzhwoBl+Y+LEiVJWVpb2wnNCBBBAIBkC2rtRX14M3ZCM/HMMBBDwr4CnLWo6/MZRRx0lgwcPFr3M2axZaC8nfTKBdjYgIYAAAn4WsEM3jBwwMq03zvvZhLwhgEByBDwN1N555x1zj5oGagcddJAZzC+4WAUFBcGzTCOAAAK+EggeamD77u0J5U2DPDveWEIHSPJOM9bPkL4v9DU99KaeOTWuo2tvvn4v9HP3SeewD+5Jwya056e+EhkfLhU9YcOyF/esLU8i46n5rTym9+eUEeaz9vCAh+P+JycVPWHjrpA07OBpoKZPHWjXrp28/vrr+wRpaSg7p0AAAQSqJGCHavBbsFWlQrEzAgj4SsDTQG3AgAFy8803y5o1a/a57OkrJTKDAAIIpFjA68umfZr0kWlnTnPaxEJbxRIptrakxdsal8h5ou2jY9HpKxmtSIm0xkXLWyLrbHmS0YrkdXnseHTJGk8tkdbFROrAq308DdRq1qwpTZo0kUMOOcQ871Mfwh78FIIbb7xRatWq5ZUN50UAAQQQQAABBDwV8DRQ0wewN27c2AB89NFH+0Bce+21BGr7qLAAAQQQQAABBHJFwNNAbciQIaKvSGnBggVSu3btSKtYhgACCCCAAAII5ISAp+OohQvr8z4feugh6devn3Tp0kW2bdsWvgnzCCCAgG8FFm1ZxHhqvq0dMoZAZgp42qKmZDt27JBJkybJ2LFjZcKECUaxU6dOcvvtt0udOnUyU5VcI4BATgrc89U9ptyxdAzQITD8nPRG71iH6UhGB4RUW8Qz7EgyOiCkujx2mI5YOgYkowNCKstjOhU4w3RoqqxjgO2A4KchbVJpo8f2JFArLy+XTz75RJ555hl55JFHQsr4+eefy+GHHx6yjBkEEEDAzwIdG3Q02VuwaYEkOp6an8tH3hBAwDuBtAdqGzdulG7duole5tSnEYwYMUKGDRsmDRo0kF69eplLnt5xcGYEEEAgfoHrel1ndkpkPDUdEsNPSYdOSHSYDj8MyxFuWZVhR2JprQo/X6rn7TAdibT6VdZaleq8hx/fDtOhy+Nt9dN9ExkkNzwPmTCf9kCttLTUBGmKc91118m5554rbdq0kW+++SYTvMgjAggggAACCCCQNoG0dyZo3ry56OVNbUm75ZZbpG3btqID3z777LNpKzQnQgABBBBAAAEEMkEg7S1qOqCt3oOmr3vuuUfGjx8vjz32mNx5553Ga/jw4XLeeefJ8ccfLzogLgkBBBDINIF7Zt4j9QrqycltTpbBbQeHZH/ETzdNhyz08Uzwjd7XHHqNdGrQyc3tgs0L5N6Z97rzmTBhy2MvnQXnecLSCTJp2STZumtr8GJfT2sd6GeqQ/0OYi/B2wz/+6t/y+Iti2X19tV2ke/fJy6baHpORyvPws0LfV+OZGYw7YFacOZ1nLSzzjrLvL7//nvTueDRRx+VV155xdy/tnLlSqlXr17wLlWaDgQC8vLLL5tHVuXn50vr1q3lpJNOqtIx2RkBBBAIF1i0eZFZZB/aHrx+xroZwbMZMa151sBm267QIZN0PpPLE45fvL0448pTUlayN8+B0NJoQKqfw0zrHal1oC8n+yEpvDz6ecyV5GmgFozcqlUruemmm0QfG/XZZ5/JU089FfI4qeBtE53WIG358uWil1/10VTffvutGQLk6KOPTvSQ7IcAAgi4Atf2utYEM7Zlxl0RYUKH8PBz0i9CzaN+QeqwI99t+a7C7EZq/ahwY49WBJfnsimXRc2FbQmtW1A36nZeruzcsLOpH21d+s/M/0TNim0JbVanWdTtvFz567a/lt5Neks85enU8OfWXS/znupz+yZQswXVS6P9+/c3L7ssWe/68PcWLVrI2WefbQ553333yaJFi4RALVnCHAeB3BawlwVtK1PxjmLTolGUXyTa8hGc9EvJ70nzqIGaXsY1LRrOgL76N1qvTui7XlbTVL+gvvmSzZTy2Hza1iZbP1pfmprXae778tTNr2vyqPWiaWvZVrf1TMuzrWybeWmAqp9Lv3/e1Fxf4eWxdaPLtUyZUh5TKUn64btALUnliniYyy+/XKpXry7z58+XefPmSVlZmbRv3z7itixEAAEEqiowcelE0ZdeArVBQVWP6eX+dkBfL/OQrHPrF78Op6IpG+pHL3NGKo8GNpmYbHnC6yZTy1OVOsipQK1GjRrG6v333zdPRNCZhg0bhvjpvWvyynAp/OalkOWZPlOY6QUIyn82lUWLRXmCKteHk4W/eVTk4DPiylmrBq2kT9M+sqpklbnfxgZp2jrQZb8u5liFhd7UfCLn7dq4q/kn15bHYuiltBZFLaTzfp0lkePa4yT6nsg5NUDTutE0/4f5pqXT1o8tj9ZfIsdOtBx2v0TO2biosVue6Wunm0PZ8uilQW0NbVTUKOPKox06Fm5aGPIPjq03r8pj6ynd79WcJuywW/bSnQVvzldcXCwvvviiOfnvf/97NxPaypY/3rl/IcsCNbeATCCAQFwCZUMelvJuQ+Pax24cPointg6MOnaUXZ32dw0EdCzLRFN4ebwcQLWqZdGAbcT7I0ICgUwvT78X+rlVqy1PXg4IW9X6CR/QV8sz9cypbvnSPaHl0acq2QafdJ4/pwK1e++9V/r27StHHnmkMbadC6699lrX3ARqTqtaVf6YuQfzyURVf2F8UgyTjWwqixaI8vjp0xWaF/2HrcbcV6UqgVroEb2fy6bPWzaVRT8ZlMf7349oOdD68SpQy6lLn9rTc+bMmdKsWTPJy8sTHf6jUaNG0eqGdQgggAACCCCAgGcCORWoaWvalClTZNy4cQZcA7djjjnGM3xOjAACCCCAAAIIRBPIqUCtd+/e0r17d1myZIlpVQvvSBANinUIIIAAAggggEC6BXIqUFNcvc7crVu3dDtzPgQQQAABBBBAIG6BtD+UPe4csgMCCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAgRqOVrxFBsBBBBAAAEE/C9AoOb/OiKHCCCAAAIIIJCjAnm5Vu433nhDli5dKoFAQNq1ayfHHHOM1KlTJ9cYKC8CCCCAAAIIZIBATrWovffeezJ37lypWbOmtGrVSubNmycvvvhiBlQTWUQAAQQQQACBXBTIqRY1bUmrV6+eDB8+3NS1Bmnff/99LtY7ZUYAAQQQQACBDBDIqUDtF7/4hdStW9etlg0bNkitWrXceSYQQAABBBBAAAE/CVRz7tUK+ClD6cjLpk2b5IUXXpCSkhLp37+/aABHQgABBEIEXnFa3r95KWQRMwggkIsC1UR+84iUdxsqNWrUSDtATrWoqe7MmTNl8uTJBnvQoEHSrVu3EPSysjLJz8+X0tLSkOWZPFNYWJg15cmmsuhnivL49zcrv7xc0v8n2b8e5AwBBLwRyKkWtW+++Ubefvttad68uZx55pkRI2MCNW8+iLGelcAmVilvtqN+vHGP9azZVD/ZVBatP8oT66fYm+20fsr1nzda1FJbAZ9++qnk5eXJIYccIgsWLDAnU/TOnTun9sQcHQEEEEAAAQQQSEAgpy59bt++3YyfpmOpBScCtWANphFAAAEEEEDALwI5Fahde+21fnEnHwgggAACCCCAQKUCOTXgbaUabIAAAggggAACCPhIgEDNR5VBVhBAAAEEEEAAgWABArVgDaYRQAABBBBAAAEfCRCo+agyyAoCCCCAAAIIIBAsQKAWrME0AggggAACCCDgIwECNR9VBllBAAEEEEAAAQSCBQj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apache-2.0

ikegwukc/INFO597-DeepLearning-GameTheory

basicGames/Basic Games.ipynb

1

303772

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Games" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Iterated Prisoner's Dilemma\n", "Prisoner's Dilemma is a pretty standard game that is commonly used in game theory (for a comprehensive defintion of Prisoner's dilemma see this [link](https://en.wikipedia.org/wiki/Prisoner%27s_dilemma)). Iterated Prisoner's Dilemma is simply repeating the game and allowing the agents to choose again. Prisoner's Dilemma is implemented in a module I wrote called game_types. For an example using the PrisonersDilemma class see the pseudocode snippet below (I dicuss different types of strategies availiable in subsequent cells):\n", "```python\n", "from game_types import PrisonersDilemma\n", "...\n", "...\n", "...\n", "agent1 = Strategy() # Some Strategy for agent1\n", "agent2 = Strategy() # Some Strategy for agent2\n", "game = PrisonersDilemma(agent1, agent2) # Play Prisoners Dilemma with agent1 and agent2\n", "num_iter = 10000 # Play the game 10000 times\n", "game.play(num_iter)\n", "data = game.data # grab data from game\n", "```\n", "\n", "## Scenario - TCP User's Game\n", "\n", "In this scenario [Robert Brunner](http://www.astro.illinois.edu/people/bigdog) uploads a dataset and asks two graduate students [Edward](https://edwardjkim.github.io/) and [Will](http://publish.illinois.edu/wbiscarri/) to download and perform a variety of tasks. For the sake of simplicity in this game Edward and Will are the only ones on a special network and they recieve no interference from other people.\n", "\n", "The traffic for this network is governed by the TCP Protocol and one feature of TCP is the backoff mechanism. If the rates that Edward and Will are sending packets to this network causes congestion they (`backoff` and) each reduce the rate for a while until network congestion subsides. This is the correct implementation. A defective implementation of TCP would be one that does not backoff if the network is congested. The users in this game have 2 choices. To Cooperate (use the correct implementation of the TCP protocol) or to defect (use an incorrect implementation of the TCP protocol).\n", "\n", "The payoff matrix is below. The numbers in the box are the utility values. For this example the higher the utlity value the faster the dataset is downloaded. Will is the first player and is the first number in each box. Edward is the second player and is the the second number in each box. \n", "\n", "If Edward and Will follow the correct protocol they will both download the dataset in a reasonable amout of time (top left). If Will decides he wants the data faster and uses a defective TCP protocol while Edward still follows the correct protocol Will downloads the dataset much faster than Edward (Top Right). Vise-versa (bottom left). If they both defect they download the dataset significantly slower than if they both cooperated.\n", "![Game Theory TCP Example](http://i.imgur.com/hfWBE27.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types of Strategies\n", "\n", "The strategies of Will and Edward will depend on what they want to achieve. Edward's part may be based on Will's work so it would make sense for Will to defect and Edward cooperates on purpose. If it's a competition they may try to defect to get a head start. If both split up the work then it may be in there interest to Cooperate. There are a lot of scenarios that can unfold but in most games agents do not know the other agents intentions or strategies so for the sake of this game we assume Edward and Will know nothing about it.\n", "\n", "I've implemented the following basic strategies in a directory called strategies:\n", "\n", "### Cooperate\n", "Cooperate: With this strategy Will or Edward (the agent) always cooperates. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import cooperate as c\n", "agent = c.Cooperate()\n", "```\n", "### Defect\n", "Defect: With this strategy the agent always defects. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import defect as d\n", "agent = d.Defect()\n", "```\n", "### Chaos\n", "Chaos: An agent uses this strategu to cause chaos by cooperating or defecting at random. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import chaos as ch\n", "agent = ch.Chaos()\n", "```\n", "### Grim\n", "Grim: This strategy is the most unforgiving. The agent cooperates until the opponent defects, in which case it will defect for the remainder of the game. This is also known as a trigger strategy. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import grim as g\n", "agent = g.Grim()\n", "```\n", "### Pavlov\n", "Pavlov: This is another trigger strategy where initally the agent will cooperate until it loses then it will change it's strategy (defect). The agent will continue to change it's strategy if it loses. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import pavlov as p\n", "agent = p.Pavlov()\n", "```\n", "### Q-Learning\n", "Q-Learning: This agent uses [Q-learning](https://en.wikipedia.org/wiki/Q-learning) for it's strategy where Q Learning is a model-free reinforcement learning technique. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import machine_learning as ml\n", "agent = ml.QLearn()\n", "```\n", "### Human\n", "Human: This agent recieves the action as input from a human player. To create an agent that uses this strategy you can do the following in Python:\n", "```python\n", "from strategies import human as h\n", "agent = h.Human()\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from collections import Counter\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "sns.set()\n", "\n", "# Importing Games\n", "from game_types import PrisonersDilemma\n", "from game_types import Coordination\n", "\n", "# Importing Strategies\n", "from strategies import chaos as c\n", "from strategies import defect as d\n", "from strategies import machine_learning as ml\n", "from strategies import pavlov as p\n", "from strategies import grim as g\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating Games:\n", "## Chaos VS Defect" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 20385.03it/s]\n" ] }, { "data": { "image/png": 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II4/o/fff18cff5zjMR9GhF4AAAAAkOTp6aljx45p27ZtOnv2rKKjo2U2m9Wz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\nAABgtwi9AAAAAAC7RegFAAA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"text/plain": [ "<matplotlib.figure.Figure at 0x106183d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create agents and play the game for 10000 iteratations\n", "agent1 = c.Chaos()\n", "agent2 = d.Defect()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Grab Data\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Chaos Agent Vs Defect Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([1,2,5],width-.05))\n", "_ = ax.set_xticklabels(('1', '2', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('Chaos Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Defect Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this scenario defecting is the [domiant strategy](https://en.wikipedia.org/wiki/Strategic_dominance). Where the agent is better off defecting no matter what other agents do." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grim VS Pavlov" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 15135.32it/s]\n" ] }, { "data": { "image/png": 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4eLhWr16dY4Eq5I4wDAAAAABFWHBwsIKDgwu7jCKHBbQAAAAAAFaHMAwAAAAA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\nqgUAAAAAYH1yvU167ty5eX7QqVOnyt/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"text/plain": [ "<matplotlib.figure.Figure at 0x116530860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# play the game\n", "agent1 = g.Grim()\n", "agent2 = p.Pavlov()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# get data from game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Grim Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,4,5],width/2))\n", "_ = ax.set_xticklabels(('0', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('Grim Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both strategies start out cooperating, Grim never defects because pavlov never defects. Pavlov never loses a round so it doesn't change it's strategy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning VS Pavlov" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 12552.94it/s]\n" ] }, { "data": { "image/png": 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g4GCCg4Ofdxk5jm6gJSIiIiIiIrmOwrCIiIiIiIjkOgrDIiIiIiIikusoDIuI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\ndXV12bhx4xHbVq5cmccff/yYzjt79uz86Ec/et/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"text/plain": [ "<matplotlib.figure.Figure at 0x11885d048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "agent1 = ml.QLearn()\n", "agent2 = p.Pavlov()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([1,2,4,5],width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pavlov's simple rules out performs Q Learning here which is interesting." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 9596, 1: 401, 4: 2, 5: 1}) Counter({2: 9596, 5: 401, 4: 2, 1: 1})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning VS Chaos" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 15111.22it/s]\n" ] }, { "data": { "image/png": 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HH3/Izc1NJ0+e1OzZs1W5cuVcPa1YsULDhw9XUlKSYmNj1bp1a0m65bJ2dnYaOXKkMjIy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\n6uqaa14AAAAAAO5GvqF3x44dlp+zsrK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"text/plain": [ "<matplotlib.figure.Figure at 0x1165604e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = c.Chaos()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Chaos Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Chaos Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q Learning significantly outperforms the Chaos Agent because the Q Learning Agent learns pretty quickly that defecting yields the highest expected utility (talked about more in appendix)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q Learning VS Q Learning" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 10919.34it/s]\n" ] }, { "data": { "image/png": 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A39k3P1lZ2QW4FyIiIiIiIvKkK9RbK9WqVYu9e/fSsGFDtm/fjp+fH3Xq1GHOnDlkZGSQ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\nvTZVVVWVHoNWpGPHjunUqVMOHjyYGTNm5NZbb63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"text/plain": [ "<matplotlib.figure.Figure at 0x118d64940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = ml.QLearn()\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here both QLearning Agents tend to mirror each other. I assume this is because they have the same inital parameters which will yield the same expected utility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## QLearning Vs QLearning (Longer Game; Different Starting Parameters)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 200000/200000 [03:44<00:00, 890.01it/s] \n" ] }, { "data": { "image/png": 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9eCw/+OADhg0bhpeXFzY2NkyZMoVDhw6xfv163Nzc8PPzw8LCgnfffZfWrVs/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\n9qi6ulqjR4+W1WqV3W5XTEyMampqVFVVpZSUFO/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"text/plain": [ "<matplotlib.figure.Figure at 0x118dde160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 200000 # Play a longer game\n", "# agent 1's parameters are bit more short sighted\n", "agent1 = ml.QLearn(decay=0.4, lr=0.03, explore_period=30000, explore_random_prob=0.4, exploit_random_prob=0.2)\n", "# agent 2's parameters think more about the future\n", "agent2 = ml.QLearn(decay=0.6, lr=0.2, explore_period=40000, explore_random_prob=0.4, exploit_random_prob=0.1)\n", "game = PrisonersDilemma(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('1', '2', '4', '5'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(I haven't had the time to look through the actions of both agents but one is short sighted and the other is not, which yields the Orange QLearning agent a higher total utility score.)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 167016, 1: 20014, 5: 11536, 4: 1434}) Counter({2: 167016, 5: 20014, 1: 11536, 4: 1434})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Iterated Coordination Game\n", "## Scenario - Choosing Movies\n", "\n", "In this scenario [Vincent](https://www.linkedin.com/in/vincent-reverdy/) and [Maghav](https://www.linkedin.com/in/maghavkumar/) want to see different movies. Vincent wants to see Guardians of the Galaxy 2 and Maghav wants to see Wonder Woman. They are willing to go see the movie that don't really care for but they both don't want to go see a movie alone. They both have 2 choices to defect (see the other persons movie person), or to cooperate go and see the movie they want. \n", "The payoff matrix is below:\n", "\n", "![Game Theory Movie Example](http://i.imgur.com/t5LgITv.png)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chaos VS Defect" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 21936.61it/s]\n" ] }, { "data": { "image/png": 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FAAAAAEleXl46dOiQNm/erFOnTik+Pl4Wi0WdO3fW/Pnz1aFDB3l7e6tFixZ6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\nQAsDAAAAAOBe5Rl6f/jhB3377beFUQs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3vpGHH344H/jAB9LV1ZUXXnghTzzxRHbs2JFt\n27bl4MGDp3xVGgDgTNTXegAAknHjXvtnkK8VlKfj31dTW1tb88Mf/jDf+ta3XrXNkiVLcuutt2b1\n6tXZtWtX5s6dm8bGxiRJV1dXHnvssfz4xz/O/v37849//OOUjvvUU0/l0KFDueWWW1KpVDI8PJw3\nv/nNufDCCzNhwoQsX7481157be644440NDSc1WsEAHg9ohdgDGhubk5/f/+ItcOHD6e5ufmsvu6c\nOXPyt7/9LT/72c9y8cUXZ+rUqa/a5qKLLsrb3/72/Pa3v80jjzySW2+9tfrc8uXLM3fu3Lzvfe/L\n3Llz8+lPf3rEvnV1dalUKtXHQ0NDedOb3pSXX345V111VXp6epKcuII8MDCQcePGZfv27dmzZ096\ne3tz0003ZevWrZk2bdpZvU4AgJNxezPAGDB58uRMmzYtP/3pT6tr27dvz9VXX50kGT9+fF5++eVT\n+lrjx4/P0NBQ9fHixYuzfv36tLa2nnSfpUuX5vvf/34OHjyY97///UmSo0eP5uDBg1m5cmXmzZuX\nX/ziFxkeHh6xX3Nzc44dO5YjR45kcHAwTzzxRJJk1qxZ2bdvX5599tkkSXd3d+6///784Q9/yIoV\nKzJnzpysWbMm73znO/PMM8+c0usCADgTrvQCjBFdXV25995709PTk6GhobzrXe/KPffckySZPn16\njh07lrVr12bp0qXVfV7r9uerr746mzZtypQpU/KhD30oN9xwQ775zW/muuuuO+mxr7/++nzhC1/I\nxz/+8eralClTcuONN2bRokVpamrKFVdckZdeeikvvfRSdZvGxsZ84hOfyNKlS3PRRRdl1qxZSZK3\nvvWt+eIXv5g77rgjw8PDufDCC9PV1ZUpU6Zk9uzZWbRoUSZOnJjLLrss8+bNO+vvHQDAydRVXnlf\nGgBFqVQq+c53vpNnn322+nt+AQDOJ670AhTs9ttvz6FDh/L1r3+91qMAANSEK70AAAAUywdZAQAA\nUCzRCwAAQLFELwAAAMUSvQAAABRL9AIAAFAs0QsAAECx/ge5fYWEmb3uIwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118de7e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create agents and play the game for 10000 iteratations\n", "agent1 = c.Chaos()\n", "agent2 = d.Defect()\n", "game = Coordination(agent1, agent2)\n", "game.play(10000)\n", "\n", "# Grab Data\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Chaos Agent Vs Defect Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,1, 2],width-.05))\n", "_ = ax.set_xticklabels(('0','1','2'))\n", "_ = ax.legend((a1[0], a2[0]), ('Chaos Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Defect Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here Defect isn't a domiant strategy. The defect agent only recieves a non 0 utility value if the chaos agent sees the movie they intended to see. A Mixed Strategy is needed." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 5022, 0: 4978}) Counter({1: 5022, 0: 4978})\n" ] } ], "source": [ "print(agent1_util_vals,agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grim VS Pavlov" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 16447.37it/s]\n" ] }, { "data": { "image/png": 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hmjp1quLj4+Xv769p06YpJCREiYmJkqRVq1apQ4cOt9WQkJCgkJAQBQQE6NCh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\nY3Pq1Kkz+lhjx47N0NDQyOOlS5dm06ZN6ezsfNl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"text/plain": [ "<matplotlib.figure.Figure at 0x119877940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# play the game\n", "agent1 = g.Grim()\n", "agent2 = p.Pavlov()\n", "game = Coordination(agent1, agent2)\n", "game.play(10000)\n", "\n", "# get data from game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('Grim Agent Vs Pavlov Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add([0,1,2],width/2))\n", "_ = ax.set_xticklabels(('0', '1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('Grim Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Pavlov Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grim loses in the first round and always goes to other movie, the Pavlov Agent even won a round where they both ended up at the same movie and never changed it's strategy." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({2: 9999, 0: 1}) Counter({1: 9999, 0: 1})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-Learning Vs Chaos" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 15487.86it/s]\n" ] }, { "data": { "image/png": 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5hw4dtGDBAgUFBcnDw0MWi0XTpk1T27Zt5enpedttxo4dq/j4eLm6umrkyJGS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\nGnqzsrIUFRUlFxcXSdLUqVM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"text/plain": [ "<matplotlib.figure.Figure at 0x119c7b710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = c.Chaos()\n", "game = Coordination(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs Chaos Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('0', '1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'Chaos Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is different from Prisoner's Dilema, the QLearning Agent is trying to cooperate with the chaos agent but can never predict which movie he is going to." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## QLearning Vs QLearning" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Playing Game: 100%|██████████| 10000/10000 [00:00<00:00, 10869.86it/s]\n" ] }, { "data": { "image/png": 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Rwtzu7Oxsbr/7aI/ixYvr5s2bOdrubQcAAAAA4M/I10cr3fu8zdTUVJUsWVIuLi5KSUl5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\npaUlSdLW1pbHH388P//5z7N9+/b87W9/O6bnffb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"text/plain": [ "<matplotlib.figure.Figure at 0x116558c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Play the Game\n", "N = 10000\n", "agent1 = ml.QLearn()\n", "agent2 = ml.QLearn()\n", "game = Coordination(agent1, agent2)\n", "game.play(N)\n", "\n", "# Get Data from Game\n", "agent1_util_vals = Counter(game.data['A'])\n", "agent2_util_vals = Counter(game.data['B'])\n", "a1_total_score = sum(game.data['A'])\n", "a2_total_score = sum(game.data['B'])\n", "\n", "\n", "# Plot the results\n", "x1, y1, x2, y2 = [], [], [], []\n", "\n", "for i, j in zip(agent1_util_vals, agent2_util_vals):\n", " x1.append(i)\n", " y1.append(agent1_util_vals[i])\n", " x2.append(j)\n", " y2.append(agent2_util_vals[j])\n", "\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "width = 0.35\n", "a1 = ax.bar(x1, y1, width, color='#8A9CEF')\n", "a2 = ax.bar(np.asarray(x2)+width, y2, width, color='orange')\n", "\n", "_ = ax.set_title('QLearning Agent Vs QLearning Agent')\n", "_ = ax.set_ylabel('Number of Games')\n", "_ = ax.set_xlabel('Utility Values')\n", "ax.set_xticks(np.add(x2,width/2))\n", "_ = ax.set_xticklabels(('0','1', '2'))\n", "_ = ax.legend((a1[0], a2[0]), ('QLearning Agent\\nTotal Utility Score: {}'.format(str(a1_total_score)),\n", " 'QLearning Agent\\nTotal Utility Score: {}'.format(str(a2_total_score))), loc=1, bbox_to_anchor=(1.35, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Still playing around with this one, but both do pretty bad here." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({0: 9243, 1: 387, 2: 370}) Counter({0: 9243, 2: 387, 1: 370})\n" ] } ], "source": [ "print(agent1_util_vals, agent2_util_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Next Up?\n", "#### Immediately: more reading, Deep QLearning, more advanced games, N>2 player games\n", "#### Later: more reading, develop games (mechanism design) given data (Cool Paper entitled [Mechanism Design for Data Science](https://arxiv.org/abs/1404.5971)), apply other deep learning models that I learn about to games\n", "#### Much Later: more reading, apply to some financial applications" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Appendix\n", "### A Few Cool Things about Q Learning:\n", "- Q Learning a model-free reinforcement learning technique\n", "- model free meaning it doesn't a need a model of the environment to determine the next action to take. It makes pretty good choices with it's action value function. This is useful for large complex environments where the rules of the environment aren't all known.\n", "- The action value function will find expected utility of an action in a given state.\n", "- A correctly implemented Q Learning model will follow a policy (set of rules) for choosing the best action, in this case the action that gives the highest expected utility.\n", "- The expected utility for each state determined by using the following update rule\n", "![Update Rule](https://wikimedia.org/api/rest_v1/media/math/render/svg/7a2a11876f4a2bef1198beb780a769cfa5c21af3)\n", "- the discount factor determines the importance for future rewards. The closer the discount factor is 0 the more short sighted it is.\n", "\n", "\n", "### Some topics in Game Theory that were indirectly dicussed above but not formally defined:\n", "- Both games above are two player normal form games. This is where a normal form game is a finite valued player game.\n", "- The Payoff Matrix is a common way to represent normal formed games.\n", "- The agents use a utility function which maps the states of the world around around them to real numbers. In the TCP example the utility values (numbers) are the download the rates. The rates may be significantly different and have units associated with them but for each action and state the numbers will be relative to there real values. Ergo in the TCP example dicussed above if one person doesn't follow the TCP protocol and another does, the download rates will be faster than the person who has reduced the number of packets it is sending/recieving. \n", "- A Nash Equilbrium is a stable state where no agent can gain a better outcome if the strategies of other agents remain unchanged.\n", "- In the Prisoner's Dilemma Game the Nash Equilbrium for both players is to defect.\n", "- In the Coordination Game 2 Nash Equilbriums exist, both agents going to the either movie as long as it the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

rongchuhe2/workshop_data_analysis_python

example_bridge_bike_counter.ipynb

1

10927

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Unsupervised Analysis of Days of Week\n", "\n", "Treating crossing each day as features to learn about the relatinships between various days." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Data\n", "\n", "We'll start by downloading the data (available on [seattle.gov](http://www.seattle.gov/transportation/bikecounter_fremont.htm))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from urllib import request\n", "\n", "FREMONT_URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'\n", "\n", "request.urlretrieve(FREMONT_URL, 'Fremont.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# magic function to show the content of the file\n", "%more Fremont.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('Fremont.csv') # use read_csv to load the data into dataframe\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's see the type of the data\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# change the Date column to datetime data type\n", "df['Date'] = pd.to_datetime(df['Date'])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Set the index to Date\n", "df.set_index('Date', inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.apply(lambda x: sum(x.isnull()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# clear the data by delete the non-numeric\n", "df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df.apply(lambda x: sum(x.isnull()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df.resample('W').sum().plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.columns=['West', 'East']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "df.resample('w').sum().plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# To see whether there is any annual trend of the number of rides\n", "df.resample('D').sum().rolling(365).sum().plot() \n", "# each point is the sum of the number of rides in the previuos 365 days" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# The y coordinate is not from 0\n", "ax = df.resample('D').sum().rolling(365).sum().plot()\n", "ax.set_ylim(0, None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# DateimeIndex.time return numpy array of datetime.time, the time part of the Timestamps\n", "df.groupby(df.index.time).mean().plot()\n", "# plot the average of rides at each hours of the day" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Create the pivoted table to investigate the pattern in each day\n", "df['Total'] = df['West'] + df['East']\n", "pivoted = df.pivot_table(values='Total', index=df.index.time, columns=df.index.date)\n", "pivoted.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pivoted.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# delete the date with non-numeric\n", "pivoted.dropna(axis=1, inplace=True)\n", "pivoted.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pivoted.plot(legend=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# add transparent parameter alpha\n", "pivoted.plot(legend=False, alpha=0.01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Principal Component Analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get X with hours as mearsurement and date as observations\n", "X = pivoted.T.values\n", "X.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "X2 = PCA(2, svd_solver='full').fit_transform(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X2.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "plt.scatter(X2[:, 0], X2[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use cluster algorithm Gaussian mixture model\n", "from sklearn.mixture import GaussianMixture\n", "\n", "gmm = GaussianMixture(2)\n", "gmm.fit(X)\n", "labels = gmm.predict(X)\n", "labels\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# plt.scatter(X2[:, 0], X2[:, 1], c=labels, cmap='rainbow')\n", "# plt.colorbar()\n", "plt.scatter(X2[:, 0], X2[:, 1], c=labels)\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# so labels == 1 represents the weekday\n", "pivoted.T[labels == 1].T.plot(legend=False, alpha=0.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# labels == 0 represents the weekend or holiday\n", "pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing with Day of Week" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.DatetimeIndex(pivoted.columns)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The DatetimeIndex.dayof week gives the day of the week\n", "dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek\n", "dayofweek" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Then we plot the color of the weekday\n", "plt.scatter(X2[:, 0], X2[:, 1], c=dayofweek)\n", "plt.colorbar() " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# grab the day in label 0 which is not weekend\n", "dates = pd.DatetimeIndex(pivoted.columns)\n", "dates[(labels == 0) & (dayofweek < 5)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's up with Feb 6, 2017?\n", "\n", "[Snow Storm](https://www.seattletimes.com/seattle-news/weather/weather-service-predicts-3-to-6-inches-of-snow-in-seattle-area/)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

swara-salih/Portfolio

Group Project--Chicago WNV Kaggle Competition/NileVirus_Prediction-Swara .ipynb

1

3341056

null

mit

ogaway/EduEcon

Global.ipynb

1

62302

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 教育経済学：課題１\n", "##「第三期教育入学率に対する回帰分析」\n", "第三期教育の入学率をGDP, 人口, ジニ係数, 教育に対する政府支出割合によって重回帰分析を行う。\n", "\n", "出所 : \n", "OECD (2015), Gross domestic product (GDP) (indicator). doi: 10.1787/dc2f7aec-en (Accessed on 10 October 2015) \n", "UNESCO Institute for Statistics(2015), data extracted on 10 Oct 2015 09:13 UTC (GMT) from UIS/ISU \n", "World Bank, Development Research Group(2015), Data from database: Poverty and Equity Database. (Last Updated: 07/08/2015) \n", "より算出" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding:utf-8 -*-\n", "from __future__ import print_function\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# データ読み込み\n", "# 第三期教育入学率\n", "# http://data.uis.unesco.org/\n", "data_enroll = pd.read_csv(\"tertiary.csv\", index_col='Country', dtype='O')\n", "data_enroll[data_enroll == '..'] = np.nan\n", "# GDP \n", "# https://data.oecd.org/gdp/gross-domestic-product-gdp.htm\n", "data_gdp = pd.read_csv(\"gdp.csv\", index_col='Country', dtype='O')\n", "data_gdp[data_gdp == '..'] = np.nan\n", "# 人口 \n", "# http://databank.worldbank.org/data/reports.aspx?Code=SP.POP.TOTL&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y#\n", "data_pop = pd.read_csv(\"population.csv\", index_col='Country', dtype='O')\n", "data_pop[data_pop == '..'] = np.nan\n", "# ジニ係数 \n", "# http://databank.worldbank.org/data/reports.aspx?Code=SI.POV.GINI&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y#\n", "data_gini = pd.read_csv(\"gini.csv\", index_col='Country', dtype='O')\n", "data_gini[data_gini == '..'] = np.nan\n", "# 第三期教育に対する政府支出 \n", "# https://data.oecd.org/eduresource/public-spending-on-education.htm\n", "data_public = pd.read_csv(\"public_spending.csv\", index_col='Country', dtype='O')\n", "data_public[data_public == '..'] = np.nan" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr 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<td>3.9114</td>\n", " <td>NaN</td>\n", " <td>3.74394</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>13.08853</td>\n", " <td>13.76232</td>\n", " <td>14.01383</td>\n", " <td>14.73868</td>\n", " <td>15.81263</td>\n", " <td>20.00498</td>\n", " <td>24.52325</td>\n", " <td>28.78627</td>\n", " <td>31.79338</td>\n", " <td>32.32131</td>\n", " <td>33.1062</td>\n", " <td>43.56153</td>\n", " <td>47.74272</td>\n", " <td>55.50096</td>\n", " <td>58.52989</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13.60327</td>\n", " <td>NaN</td>\n", " <td>15.24848</td>\n", " <td>16.74901</td>\n", " <td>17.744</td>\n", " <td>18.18056</td>\n", " <td>19.76258</td>\n", " <td>20.21485</td>\n", " <td>22.28536</td>\n", " <td>NaN</td>\n", " <td>28.59671</td>\n", " <td>28.75825</td>\n", " <td>30.27547</td>\n", " <td>31.46411</td>\n", " <td>33.30463</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1999 2000 2001 2002 2003 2004 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN 1.27942 1.29312 \n", "Albania 13.08853 13.76232 14.01383 14.73868 15.81263 20.00498 \n", "Algeria 13.60327 NaN 15.24848 16.74901 17.744 18.18056 \n", "American Samoa NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN \n", "\n", " 2005 2006 2007 2008 2009 2010 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN 3.9114 NaN \n", "Albania 24.52325 28.78627 31.79338 32.32131 33.1062 43.56153 \n", "Algeria 19.76258 20.21485 22.28536 NaN 28.59671 28.75825 \n", "American Samoa NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN \n", "\n", " 2011 2012 2013 2014 2015 \n", "Country \n", "Afghanistan 3.74394 NaN NaN NaN NaN \n", "Albania 47.74272 55.50096 58.52989 NaN NaN \n", "Algeria 30.27547 31.46411 33.30463 NaN NaN \n", "American Samoa NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_enroll.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>1980</th>\n", " <th>1981</th>\n", " <th>1982</th>\n", " <th>1983</th>\n", " <th>1984</th>\n", " <th>1985</th>\n", " <th>1986</th>\n", " <th>1987</th>\n", " <th>1988</th>\n", " <th>1989</th>\n", " <th>...</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " <th>2014</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>538035.5154</td>\n", " <td>601184.0944</td>\n", " <td>666237.9468</td>\n", " <td>700439.5025</td>\n", " <td>706116.0365</td>\n", " <td>780193.8888</td>\n", " <td>867545.4341</td>\n", " <td>895008.2139</td>\n", " <td>945659.3828</td>\n", " <td>927164.1469</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>155037.382</td>\n", " <td>178034.2597</td>\n", " <td>181796.1002</td>\n", " <td>196586.5927</td>\n", " <td>211230.1811</td>\n", " <td>228876.1743</td>\n", " <td>240244.2573</td>\n", " <td>261145.2252</td>\n", " <td>283619.2842</td>\n", " <td>299927.1378</td>\n", " <td>...</td>\n", " <td>718812.8644</td>\n", " <td>773857.9107</td>\n", " <td>825382.9291</td>\n", " <td>850582.9281</td>\n", " <td>897942.1402</td>\n", " <td>935668.927</td>\n", " <td>984763.1119</td>\n", " <td>999199.8604</td>\n", " <td>1040376.497</td>\n", " <td>1062956.442</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>79726.0876</td>\n", " <td>87043.3274</td>\n", " <td>94302.7153</td>\n", " <td>100940.7418</td>\n", " <td>104575.7382</td>\n", " <td>110618.6571</td>\n", " <td>115447.3111</td>\n", " <td>119999.7106</td>\n", " <td>128294.273</td>\n", " <td>138463.2414</td>\n", " <td>...</td>\n", " <td>285433.2141</td>\n", " <td>311310.3909</td>\n", " <td>325500.8904</td>\n", " <td>342442.8873</td>\n", " <td>339017.5061</td>\n", " <td>350124.3423</td>\n", " <td>369420.341</td>\n", " <td>378088.387</td>\n", " <td>382598.8984</td>\n", " <td>394485.5275</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>103557.1412</td>\n", " <td>112908.7475</td>\n", " <td>120627.0074</td>\n", " <td>125781.0802</td>\n", " <td>133456.1351</td>\n", " <td>140001.5239</td>\n", " <td>145429.1715</td>\n", " <td>152579.9061</td>\n", " <td>165380.5208</td>\n", " <td>177771.0402</td>\n", " <td>...</td>\n", " <td>346243.6361</td>\n", " <td>370161.3572</td>\n", " <td>388722.7568</td>\n", " <td>405339.5371</td>\n", " <td>406396.2064</td>\n", " <td>427441.4922</td>\n", " <td>451396.963</td>\n", " <td>459785.3992</td>\n", " <td>461907.8543</td>\n", " <td>477949.3341</td>\n", " </tr>\n", " <tr>\n", " <th>Brazil</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1988025.804</td>\n", " <td>2142004.309</td>\n", " <td>2342708.493</td>\n", " <td>2520650.653</td>\n", " <td>2538040.151</td>\n", " <td>2771866.298</td>\n", " <td>2973856.149</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 35 columns</p>\n", "</div>" ], "text/plain": [ " 1980 1981 1982 1983 1984 \\\n", "Country \n", "Argentina NaN NaN NaN NaN NaN \n", "Australia 155037.382 178034.2597 181796.1002 196586.5927 211230.1811 \n", "Austria 79726.0876 87043.3274 94302.7153 100940.7418 104575.7382 \n", "Belgium 103557.1412 112908.7475 120627.0074 125781.0802 133456.1351 \n", "Brazil NaN NaN NaN NaN NaN \n", "\n", " 1985 1986 1987 1988 1989 \\\n", "Country \n", "Argentina NaN NaN NaN NaN NaN \n", "Australia 228876.1743 240244.2573 261145.2252 283619.2842 299927.1378 \n", "Austria 110618.6571 115447.3111 119999.7106 128294.273 138463.2414 \n", "Belgium 140001.5239 145429.1715 152579.9061 165380.5208 177771.0402 \n", "Brazil NaN NaN NaN NaN NaN \n", "\n", " ... 2005 2006 2007 2008 \\\n", "Country ... \n", "Argentina ... 538035.5154 601184.0944 666237.9468 700439.5025 \n", "Australia ... 718812.8644 773857.9107 825382.9291 850582.9281 \n", "Austria ... 285433.2141 311310.3909 325500.8904 342442.8873 \n", "Belgium ... 346243.6361 370161.3572 388722.7568 405339.5371 \n", "Brazil ... 1988025.804 2142004.309 2342708.493 2520650.653 \n", "\n", " 2009 2010 2011 2012 2013 \\\n", "Country \n", "Argentina 706116.0365 780193.8888 867545.4341 895008.2139 945659.3828 \n", "Australia 897942.1402 935668.927 984763.1119 999199.8604 1040376.497 \n", "Austria 339017.5061 350124.3423 369420.341 378088.387 382598.8984 \n", "Belgium 406396.2064 427441.4922 451396.963 459785.3992 461907.8543 \n", "Brazil 2538040.151 2771866.298 2973856.149 NaN NaN \n", "\n", " 2014 \n", "Country \n", "Argentina 927164.1469 \n", "Australia 1062956.442 \n", "Austria 394485.5275 \n", "Belgium 477949.3341 \n", "Brazil NaN \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_gdp.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2000</th>\n", " <th>2001</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>19701940</td>\n", " <td>20531160</td>\n", " <td>21487079</td>\n", " <td>22507368</td>\n", " <td>23499850</td>\n", " <td>24399948</td>\n", " <td>25183615</td>\n", " <td>25877544</td>\n", " <td>26528741</td>\n", " <td>27207291</td>\n", " <td>27962207</td>\n", " <td>28809167</td>\n", " <td>29726803</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>3089027</td>\n", " <td>3060173</td>\n", " <td>3051010</td>\n", " <td>3039616</td>\n", " <td>3026939</td>\n", " <td>3011487</td>\n", " <td>2992547</td>\n", " <td>2970017</td>\n", " <td>2947314</td>\n", " <td>2927519</td>\n", " <td>2913021</td>\n", " <td>2904780</td>\n", " <td>2900489</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>31183658</td>\n", " <td>31590320</td>\n", " <td>31990387</td>\n", " <td>32394886</td>\n", " <td>32817225</td>\n", " <td>33267887</td>\n", " <td>33749328</td>\n", " <td>34261971</td>\n", " <td>34811059</td>\n", " <td>35401790</td>\n", " <td>36036159</td>\n", " <td>36717132</td>\n", " <td>37439427</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>57522</td>\n", " <td>58176</td>\n", " <td>58729</td>\n", " <td>59117</td>\n", " <td>59262</td>\n", " <td>59117</td>\n", " <td>58648</td>\n", " <td>57904</td>\n", " <td>57031</td>\n", " <td>56226</td>\n", " <td>55636</td>\n", " <td>55316</td>\n", " <td>55227</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>65399</td>\n", " <td>67770</td>\n", " <td>71046</td>\n", " <td>74783</td>\n", " <td>78337</td>\n", " <td>81223</td>\n", " <td>83373</td>\n", " <td>84878</td>\n", " <td>85616</td>\n", " <td>85474</td>\n", " <td>84419</td>\n", " <td>82326</td>\n", " <td>79316</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2000 2001 2002 2003 2004 \\\n", "Country \n", "Afghanistan 19701940 20531160 21487079 22507368 23499850 \n", "Albania 3089027 3060173 3051010 3039616 3026939 \n", "Algeria 31183658 31590320 31990387 32394886 32817225 \n", "American Samoa 57522 58176 58729 59117 59262 \n", "Andorra 65399 67770 71046 74783 78337 \n", "\n", " 2005 2006 2007 2008 2009 \\\n", "Country \n", "Afghanistan 24399948 25183615 25877544 26528741 27207291 \n", "Albania 3011487 2992547 2970017 2947314 2927519 \n", "Algeria 33267887 33749328 34261971 34811059 35401790 \n", "American Samoa 59117 58648 57904 57031 56226 \n", "Andorra 81223 83373 84878 85616 85474 \n", "\n", " 2010 2011 2012 \n", "Country \n", "Afghanistan 27962207 28809167 29726803 \n", "Albania 2913021 2904780 2900489 \n", "Algeria 36036159 36717132 37439427 \n", "American Samoa 55636 55316 55227 \n", "Andorra 84419 82326 79316 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_pop.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2000</th>\n", " <th>2001</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>32.5</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.6</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>American Samoa</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 \\\n", "Country \n", "Afghanistan NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "Albania NaN NaN 32.5 NaN NaN 30.6 NaN NaN 30 NaN NaN NaN \n", "Algeria NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "American Samoa NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "Andorra NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " 2012 \n", "Country \n", "Afghanistan NaN \n", "Albania 29 \n", "Algeria NaN \n", "American Samoa NaN \n", "Andorra NaN " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_gini.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>2009</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Australia</th>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>1.4</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>1.4</td>\n", " </tr>\n", " <tr>\n", " <th>Brazil</th>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>Canada</th>\n", " <td>1.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2009\n", "Country \n", "Australia 0.7 \n", "Austria 1.4 \n", "Belgium 1.4 \n", "Brazil 0.8 \n", "Canada 1.5 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_public.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Australia', 'Austria', 'Belgium', 'Brazil', 'Canada', 'Chile', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland', 'Israel', 'Italy', 'Japan', 'Mexico', 'Netherlands', 'New Zealand', 'Norway', 'Poland', 'Portugal', 'Slovenia', 'South Africa', 'Spain', 'Sweden', 'Switzerland']\n", "31\n" ] } ], "source": [ "# 4つの指標全てにおいて調査された国を調べる\n", "country_list = []\n", "for i in np.asarray(data_enroll.index):\n", " if i in np.asarray(data_gdp.index):\n", " if i in np.asarray(data_pop.index):\n", " if i in np.asarray(data_gini.index):\n", " if i in np.asarray(data_public.index):\n", " country_list.append(i)\n", "print(country_list)\n", "print(len(country_list))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>tertiary</th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " <td>27.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>65.841442</td>\n", " <td>13.063664</td>\n", " <td>16.572529</td>\n", " <td>32.774074</td>\n", " <td>1.166667</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>18.098664</td>\n", " <td>1.393257</td>\n", " <td>1.688527</td>\n", " <td>6.160457</td>\n", " <td>0.335123</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>16.396570</td>\n", " <td>9.471810</td>\n", " <td>12.678311</td>\n", " <td>25.600000</td>\n", " <td>0.500000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>59.257405</td>\n", " <td>12.360758</td>\n", " <td>15.661797</td>\n", " <td>27.800000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>70.519350</td>\n", " <td>12.942301</td>\n", " <td>16.168299</td>\n", " <td>32.400000</td>\n", " <td>1.200000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>77.013830</td>\n", " <td>13.995792</td>\n", " <td>17.557788</td>\n", " <td>35.050000</td>\n", " <td>1.400000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>93.721820</td>\n", " <td>15.418777</td>\n", " <td>20.917337</td>\n", " <td>50.800000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tertiary log_gdp log_pop gini public\n", "count 27.000000 27.000000 27.000000 27.000000 27.000000\n", "mean 65.841442 13.063664 16.572529 32.774074 1.166667\n", "std 18.098664 1.393257 1.688527 6.160457 0.335123\n", "min 16.396570 9.471810 12.678311 25.600000 0.500000\n", "25% 59.257405 12.360758 15.661797 27.800000 1.000000\n", "50% 70.519350 12.942301 16.168299 32.400000 1.200000\n", "75% 77.013830 13.995792 17.557788 35.050000 1.400000\n", "max 93.721820 15.418777 20.917337 50.800000 1.800000" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2000〜2014年におけるそれぞれのデータの最新をまとめる\n", "for i in reversed(range(2000, 2013)):\n", " d = {\n", " 'tertiary': data_enroll.ix[country_list][\"%s\" % i].astype(float),\n", " 'log_gdp': np.log(data_gdp.ix[country_list][\"%s\" % i], dtype=float),\n", " 'log_pop': np.log(data_pop.ix[country_list][\"%s\" % i].astype(float)),\n", " 'gini': data_gini.ix[country_list][\"%s\" % i].astype(float),\n", " 'public': data_public.ix[country_list]['2009'].astype(float),\n", " 'year': i\n", " }\n", " if i == 2012:\n", " df = pd.DataFrame(d).dropna()\n", " df_test = pd.DataFrame(d).dropna()\n", " for j in df_test.index.values:\n", " if j not in df.index.values:\n", " df.ix[j] = df_test.ix[j]\n", "print(len(df))\n", "df[['tertiary', 'log_gdp', 'log_pop', 'gini', 'public']].describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>tertiary</th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " <td>24.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>71.143427</td>\n", " <td>12.845443</td>\n", " <td>16.192417</td>\n", " <td>31.970833</td>\n", " <td>1.195833</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>10.134027</td>\n", " <td>1.312599</td>\n", " <td>1.321017</td>\n", " <td>5.622585</td>\n", " <td>0.323673</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>55.561900</td>\n", " <td>9.471810</td>\n", " <td>12.678311</td>\n", " <td>25.600000</td>\n", " <td>0.500000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>61.977452</td>\n", " <td>12.311479</td>\n", " <td>15.528702</td>\n", " <td>27.525000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>71.034790</td>\n", " <td>12.802285</td>\n", " <td>16.139006</td>\n", " <td>31.850000</td>\n", " <td>1.200000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>77.907077</td>\n", " <td>13.692860</td>\n", " <td>16.991507</td>\n", " <td>34.000000</td>\n", " <td>1.400000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>93.721820</td>\n", " <td>15.271679</td>\n", " <td>18.668033</td>\n", " <td>50.800000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tertiary log_gdp log_pop gini public\n", "count 24.000000 24.000000 24.000000 24.000000 24.000000\n", "mean 71.143427 12.845443 16.192417 31.970833 1.195833\n", "std 10.134027 1.312599 1.321017 5.622585 0.323673\n", "min 55.561900 9.471810 12.678311 25.600000 0.500000\n", "25% 61.977452 12.311479 15.528702 27.525000 1.000000\n", "50% 71.034790 12.802285 16.139006 31.850000 1.200000\n", "75% 77.907077 13.692860 16.991507 34.000000 1.400000\n", "max 93.721820 15.271679 18.668033 50.800000 1.800000" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 外れ値を切り捨てる\n", "df = df[df['tertiary'] >= 30]\n", "print(len(df))\n", "df[['tertiary', 'log_gdp', 'log_pop', 'gini', 'public']].describe()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>log_gdp</th>\n", " <th>log_pop</th>\n", " <th>gini</th>\n", " <th>public</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>log_gdp</th>\n", " <td>1.000000</td>\n", " <td>0.974037</td>\n", " <td>0.220537</td>\n", " <td>-0.298593</td>\n", " </tr>\n", " <tr>\n", " <th>log_pop</th>\n", " <td>0.974037</td>\n", " <td>1.000000</td>\n", " <td>0.337710</td>\n", " <td>-0.394632</td>\n", " </tr>\n", " <tr>\n", " <th>gini</th>\n", " <td>0.220537</td>\n", " <td>0.337710</td>\n", " <td>1.000000</td>\n", " <td>-0.451126</td>\n", " </tr>\n", " <tr>\n", " <th>public</th>\n", " <td>-0.298593</td>\n", " <td>-0.394632</td>\n", " <td>-0.451126</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " log_gdp log_pop gini public\n", "log_gdp 1.000000 0.974037 0.220537 -0.298593\n", "log_pop 0.974037 1.000000 0.337710 -0.394632\n", "gini 0.220537 0.337710 1.000000 -0.451126\n", "public -0.298593 -0.394632 -0.451126 1.000000" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 相関を求める\n", "df[['log_gdp', 'log_pop', 'gini', 'public']].corr()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.178\n", "Model: OLS Adj. R-squared: 0.141\n", "Method: Least Squares F-statistic: 4.766\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0400\n", "Time: 23:36:47 Log-Likelihood: -86.772\n", "No. Observations: 24 AIC: 177.5\n", "Df Residuals: 22 BIC: 179.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 112.9931 19.265 5.865 0.000 73.040 152.946\n", "log_gdp -3.2579 1.492 -2.183 0.040 -6.353 -0.163\n", "==============================================================================\n", "Omnibus: 1.395 Durbin-Watson: 1.855\n", "Prob(Omnibus): 0.498 Jarque-Bera (JB): 0.950\n", "Skew: 0.481 Prob(JB): 0.622\n", "Kurtosis: 2.842 Cond. No. 130.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰GDP\n", "# 説明変数設定\n", "X = df[['log_gdp']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.204\n", "Model: OLS Adj. R-squared: 0.168\n", "Method: Least Squares F-statistic: 5.633\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0268\n", "Time: 23:36:47 Log-Likelihood: -86.390\n", "No. Observations: 24 AIC: 176.8\n", "Df Residuals: 22 BIC: 179.1\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 127.2287 23.706 5.367 0.000 78.066 176.391\n", "log_pop -3.4637 1.459 -2.373 0.027 -6.490 -0.437\n", "==============================================================================\n", "Omnibus: 1.636 Durbin-Watson: 1.844\n", "Prob(Omnibus): 0.441 Jarque-Bera (JB): 0.978\n", "Skew: 0.494 Prob(JB): 0.613\n", "Kurtosis: 2.985 Cond. No. 205.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰人口\n", "# 説明変数設定\n", "X = df[['log_pop']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.073\n", "Model: OLS Adj. R-squared: 0.031\n", "Method: Least Squares F-statistic: 1.743\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.200\n", "Time: 23:36:47 Log-Likelihood: -88.210\n", "No. Observations: 24 AIC: 180.4\n", "Df Residuals: 22 BIC: 182.8\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 86.7563 12.000 7.230 0.000 61.870 111.642\n", "gini -0.4883 0.370 -1.320 0.200 -1.255 0.279\n", "==============================================================================\n", "Omnibus: 0.863 Durbin-Watson: 1.900\n", "Prob(Omnibus): 0.650 Jarque-Bera (JB): 0.773\n", "Skew: 0.152 Prob(JB): 0.679\n", "Kurtosis: 2.175 Cond. No. 191.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰ジニ係数\n", "# 説明変数設定\n", "X = df[['gini']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.095\n", "Model: OLS Adj. R-squared: 0.054\n", "Method: Least Squares F-statistic: 2.311\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.143\n", "Time: 23:36:47 Log-Likelihood: -87.927\n", "No. Observations: 24 AIC: 179.9\n", "Df Residuals: 22 BIC: 182.2\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 59.6000 7.856 7.587 0.000 43.308 75.892\n", "public 9.6530 6.350 1.520 0.143 -3.516 22.822\n", "==============================================================================\n", "Omnibus: 0.449 Durbin-Watson: 1.711\n", "Prob(Omnibus): 0.799 Jarque-Bera (JB): 0.560\n", "Skew: 0.081 Prob(JB): 0.756\n", "Kurtosis: 2.269 Cond. No. 7.86\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 単回帰政府支出\n", "# 説明変数設定\n", "X = df[['public']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.230\n", "Model: OLS Adj. R-squared: 0.068\n", "Method: Least Squares F-statistic: 1.422\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.265\n", "Time: 23:36:47 Log-Likelihood: -85.983\n", "No. Observations: 24 AIC: 182.0\n", "Df Residuals: 19 BIC: 187.9\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 116.5609 43.388 2.687 0.015 25.750 207.372\n", "log_gdp -0.5124 8.309 -0.062 0.951 -17.903 16.878\n", "log_pop -2.3396 8.698 -0.269 0.791 -20.545 15.866\n", "gini -0.1753 0.457 -0.384 0.706 -1.132 0.781\n", "public 3.8907 7.685 0.506 0.618 -12.194 19.976\n", "==============================================================================\n", "Omnibus: 0.942 Durbin-Watson: 1.795\n", "Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.659\n", "Skew: 0.393 Prob(JB): 0.719\n", "Kurtosis: 2.794 Cond. No. 857.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 重回帰分析\n", "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop', 'gini', 'public']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.220\n", "Model: OLS Adj. R-squared: 0.103\n", "Method: Least Squares F-statistic: 1.880\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.165\n", "Time: 23:36:47 Log-Likelihood: -86.144\n", "No. Observations: 24 AIC: 180.3\n", "Df Residuals: 20 BIC: 185.0\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 131.0571 31.985 4.097 0.001 64.337 197.777\n", "log_gdp 0.6721 7.823 0.086 0.932 -15.646 16.990\n", "log_pop -3.7953 8.055 -0.471 0.643 -20.598 13.007\n", "gini -0.2218 0.439 -0.505 0.619 -1.138 0.694\n", "==============================================================================\n", "Omnibus: 1.502 Durbin-Watson: 1.874\n", "Prob(Omnibus): 0.472 Jarque-Bera (JB): 0.924\n", "Skew: 0.480 Prob(JB): 0.630\n", "Kurtosis: 2.942 Cond. No. 647.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop', 'gini']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: tertiary R-squared: 0.210\n", "Model: OLS Adj. R-squared: 0.135\n", "Method: Least Squares F-statistic: 2.792\n", "Date: Sun, 25 Oct 2015 Prob (F-statistic): 0.0841\n", "Time: 23:36:47 Log-Likelihood: -86.296\n", "No. Observations: 24 AIC: 178.6\n", "Df Residuals: 21 BIC: 182.1\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 134.8078 30.554 4.412 0.000 71.267 198.349\n", "log_gdp 2.6818 6.614 0.405 0.689 -11.074 16.437\n", "log_pop -6.0592 6.572 -0.922 0.367 -19.727 7.608\n", "==============================================================================\n", "Omnibus: 1.602 Durbin-Watson: 1.837\n", "Prob(Omnibus): 0.449 Jarque-Bera (JB): 0.875\n", "Skew: 0.467 Prob(JB): 0.646\n", "Kurtosis: 3.057 Cond. No. 338.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# 説明変数設定\n", "X = df[['log_gdp', 'log_pop']]\n", "X = sm.add_constant(X)\n", "X.head()\n", "# 被説明変数設定\n", "Y = df['tertiary']\n", "Y.head()\n", "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

fggp/ctcsound

cookbook/03-threading.ipynb

1

6992

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multithreading\n", "In the preceding recipes, there was a single thread running; this is the default way to use Python, due to the GIL (Global Interpreter Lock). Then, the user has the possibility to interact with the Csound instance during the performance loop. This is illustrated in the following diagram:\n", "\n", "![Single Thread](img/03-threading-a.png)\n", "\n", "To use Csound in a more flexible way, one can use multithreading. Because of the GIL limitations, it is better to yield the multithread machinery through C libraries. When a C function is called from Python using ctypes, the GIL is released during the function call.\n", "\n", "Csound has an helper class called CsoundPerformanceThread. When there is a running Csound instance, one can start a new thread by creating a new object of type CsoundPerformanceThread with a reference to the Csound instance as argument. Then, the main Python thread will run allowing the user to interract with it, while the performance thread will run concurrently in the C world, outside of the GIL. The user can send messages to the performance thread, each message being sent with a call to a C function through ctypes, releasing the GIL during the function call. Those messages can be: _play(), pause(), togglePause(), stop(), record(), stopRecord(), scoreEvent(), inputMessage(), setScoreOffsetSeconds(), join(),_ or _flushMessageQueue()_.\n", "\n", "When a very long score is used, it is thus easy to implement a REPL (read-eval-print loop) system around Csound. This is illustrated in the following diagram:\n", "\n", "![Multithreading with CsoundPerformanceThread](img/03-threading-b.png)\n", "\n", "So let's start a Csound instance from Python, with a four hours long score:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ctcsound\n", "cs = ctcsound.Csound()\n", "\n", "csd = '''\n", "<CsoundSynthesizer>\n", "\n", "<CsOptions>\n", " -d -o dac -m0\n", "</CsOptions>\n", "\n", "<CsInstruments>\n", "sr = 48000\n", "ksmps = 100\n", "nchnls = 2\n", "0dbfs = 1\n", "\n", " instr 1\n", "idur = p3\n", "iamp = p4\n", "icps = cpspch(p5)\n", "irise = p6\n", "idec = p7\n", "ipan = p8\n", "\n", "kenv linen iamp, irise, idur, idec\n", "kenv = kenv*kenv\n", "asig poscil kenv, icps\n", "a1, a2 pan2 asig, ipan\n", " outs a1, a2\n", " endin\n", "</CsInstruments>\n", "\n", "<CsScore>\n", "f 0 14400 ; a 4 hours session should be enough\n", "</CsScore>\n", "</CsoundSynthesizer>\n", "'''\n", "cs.compileCsdText(csd)\n", "cs.start()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let's start a new thread, passing the opaque pointer of the Csound instance as argument:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pt = ctcsound.CsoundPerformanceThread(cs.csound())\n", "pt.play()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can send messages to the performance thread:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pt.scoreEvent(False, 'i', (1, 0, 1, 0.5, 8.06, 0.05, 0.3, 0.5))\n", "pt.scoreEvent(False, 'i', (1, 0.5, 1, 0.5, 9.06, 0.05, 0.3, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we're done, we stop the performance thread and reset the csound instance:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "pt.stop()\n", "pt.join()\n", "cs.reset()" ] }, { "cell_type": "markdown", "metadata": { "jupyter": { "outputs_hidden": true } }, "source": [ "Note that we can still access the csound instance with other methods, like controlChannel() or setControlChannel():" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "csd = '''\n", "<CsoundSynthesizer>\n", "<CsOptions>\n", "-odac\n", "</CsOptions>\n", "<CsInstruments>\n", "sr = 44100\n", "ksmps = 64\n", "nchnls = 2\n", "0dbfs = 1\n", "seed 0\n", "\n", "instr 1\n", " iPch random 60, 72\n", " chnset iPch, \"pch\"\n", " kPch init iPch\n", " kNewPch chnget \"new_pitch\"\n", " if kNewPch > 0 then\n", " kPch = kNewPch\n", " endif\n", " aTone poscil .2, mtof(kPch)\n", " out aTone, aTone\n", "endin\n", "\n", "</CsInstruments>\n", "<CsScore>\n", "i 1 0 600\n", "</CsScore>\n", "</CsoundSynthesizer>\n", "'''\n", "cs.compileCsdText(csd)\n", "cs.start()\n", "pt = ctcsound.CsoundPerformanceThread(cs.csound())\n", "pt.play()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can ask for the values in the Csound instance ..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(69.60446804277807, c_int(0))\n" ] } ], "source": [ "print(cs.controlChannel('pch'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... or we can set our own values to the Csound instance:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cs.setControlChannel('new_pitch',73)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end, stop and reset as usual:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pt.stop()\n", "pt.join()\n", "cs.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author: François Pinot, March 2016" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }

lgpl-2.1

jessicabodosa/digit_trial

Digit.ipynb

1

575850

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# || Digit ||\n", "___\n", "> practice really" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import scipy.special\n", "import matplotlib.pyplot\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#defining it\n", "\n", "class neuralNetwork:\n", " def __init__(self, inputnodes, hiddennodes, outputnodes, learning_rate):\n", " self.inodes = inputnodes\n", " self.hnodes = hiddennodes\n", " self.onodes = outputnodes\n", " \n", " self.lr = learning_rate\n", " \n", " self.wih = (np.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes, self.inodes)))\n", " self.who = (np.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes)))\n", " \n", " \n", " \n", " self.activation_function = lambda x:scipy.special.expit(x)\n", " \n", " pass \n", " \n", " \n", " def train(self, inputs_list, targets_list):\n", " inputs = np.array(inputs_list, ndmin=2).T\n", " targets = np.array(targets_list,ndmin=2).T\n", " \n", " hidden_inputs = np.dot(self.wih,inputs)\n", " hidden_outputs = self.activation_function(hidden_inputs)\n", " \n", " final_inputs = np.dot(self.who, hidden_outputs)\n", " final_outputs = self.activation_function(final_inputs)\n", " \n", " output_errors = targets - final_outputs\n", " hidden_errors = np.dot(self.who.T,output_errors)\n", " \n", " self.who += self.lr*np.dot((output_errors*final_outputs*(1-final_outputs)),np.transpose(hidden_outputs))\n", " self.wih += self.lr*np.dot((hidden_errors*hidden_outputs*(1-hidden_outputs)),np.transpose(inputs))\n", " \n", " pass\n", " \n", " def query(self, inputs_list):\n", " inputs = np.array(inputs_list, ndmin=2).T\n", " \n", " hidden_inputs = np.dot(self.wih,inputs)\n", " hidden_outputs = self.activation_function(hidden_inputs)\n", " \n", " final_inputs = np.dot(self.who, hidden_outputs)\n", " final_outputs = self.activation_function(final_inputs)\n", " \n", " return final_outputs" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#creating the network\n", "\n", "input_nodes = 784\n", "hidden_nodes = 200\n", "output_nodes = 10\n", "learning_rate = 0.1\n", "\n", "n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_file = open(\"mnist_train.csv\",\"r\")\n", "data_list = data_file.readlines()\n", "data_file.close()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for record in data_list:\n", " all_values = record.split(',')\n", " inputs = (np.asfarray(all_values[1:])/255.0 *0.99)+0.01\n", " targets = np.zeros(output_nodes)+0.01\n", " targets[int(all_values[0])] = 0.99\n", " n.train(inputs,targets)\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_data_file = open(\"mnist_test.csv\",\"r\")\n", "test_data_list = test_data_file.readlines()\n", "test_data_file.close()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'7'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_values1 = test_data_list[0].split(',')\n", "all_values1[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fe2b7ce5978>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EhrAkJTKEJSmRISxJiQxhSUpkCEtSIkNYkhIZwpKUqKXTliPiU8DfAycAu4AHgMtKKU+M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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe2ba77c898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_array = np.asfarray(all_values1[1:]).reshape((28,28))\n", "matplotlib.pyplot.imshow(image_array, cmap=\"Greys\",interpolation=\"None\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00981296],\n", " [ 0.00291237],\n", " [ 0.01382717],\n", " [ 0.01207517],\n", " [ 0.00125286],\n", " [ 0.01440796],\n", " [ 0.0101734 ],\n", " [ 0.98560708],\n", " [ 0.00882559],\n", " [ 0.00855959]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n.query((np.asfarray(all_values1[1:])/255.0 * 0.99) + 0.01)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(n.query((np.asfarray(all_values[1:])/255.0*0.99)+0.01))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "1 correct label\n", "1 found answer\n", "0 correct label\n", "0 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "5 correct label\n", "6 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "6 correct label\n", "6 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "5 correct label\n", "5 found answer\n", "9 correct label\n", "9 found answer\n", "7 correct label\n", "7 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "6 correct label\n", "6 found answer\n", "6 correct label\n", "6 found answer\n", "5 correct label\n", "5 found answer\n", "4 correct label\n", "4 found answer\n", "0 correct label\n", "0 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "3 correct label\n", "3 found answer\n", "1 correct label\n", "1 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "7 correct label\n", "7 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "1 correct label\n", "1 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "5 correct label\n", "5 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "4 correct label\n", "4 found answer\n", "4 correct label\n", "4 found answer\n", "6 correct label\n", "6 found answer\n", "3 correct label\n", "3 found answer\n", "5 correct label\n", "5 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n", "0 correct label\n", "0 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "9 correct label\n", "9 found answer\n", "5 correct label\n", "5 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "9 correct label\n", "9 found answer\n", "3 correct label\n", "3 found answer\n", "7 correct label\n", "7 found answer\n", "4 correct label\n", "4 found answer\n", "6 correct label\n", "6 found answer\n", "4 correct label\n", "4 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"2 correct label\n", "2 found answer\n", "8 correct label\n", "8 found answer\n", "1 correct label\n", "1 found answer\n", "5 correct label\n", "5 found answer\n", "1 correct label\n", "1 found answer\n", "0 correct label\n", "0 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "3 correct label\n", "3 found answer\n", "5 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "0 correct label\n", "0 found answer\n", "6 correct label\n", "6 found answer\n", "8 correct label\n", "8 found answer\n", "6 correct label\n", "0 found answer\n", "3 correct label\n", "3 found answer\n", "9 correct label\n", "9 found answer\n", "9 correct label\n", "9 found answer\n", "8 correct label\n", "8 found answer\n", "2 correct label\n", "2 found answer\n", "7 correct label\n", "7 found answer\n", "7 correct label\n", "7 found answer\n", "1 correct label\n", "1 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct 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"4 correct label\n", "4 found answer\n", "0 correct label\n", "0 found answer\n", "6 correct label\n", "6 found answer\n", "1 correct label\n", "1 found answer\n", "0 correct label\n", "0 found answer\n", "0 correct label\n", "0 found answer\n", "6 correct label\n", "6 found answer\n", "2 correct label\n", "2 found answer\n", "1 correct label\n", "1 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "4 correct label\n", "4 found answer\n", "6 correct label\n", "6 found answer\n", "0 correct label\n", "0 found answer\n", "7 correct label\n", "7 found answer\n", "0 correct label\n", "0 found answer\n", "3 correct label\n", "3 found answer\n", "6 correct label\n", "6 found answer\n", "8 correct label\n", "8 found answer\n", "7 correct label\n", "7 found answer\n", "1 correct label\n", "1 found answer\n", "5 correct label\n", "5 found answer\n", "2 correct label\n", "2 found answer\n", "4 correct label\n", "4 found answer\n", "9 correct label\n", "9 found answer\n", "4 correct label\n", "4 found answer\n", "3 correct label\n", "3 found answer\n", "6 correct label\n", "6 found answer\n", "4 correct label\n", "4 found answer\n", "1 correct label\n", "1 found answer\n", "7 correct label\n", "7 found answer\n", "2 correct label\n", "2 found answer\n", "6 correct label\n", "6 found answer\n", "5 correct label\n", "6 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n", "7 correct label\n", "7 found answer\n", "8 correct label\n", "8 found answer\n", "9 correct label\n", "9 found answer\n", "0 correct label\n", "0 found answer\n", "1 correct label\n", "1 found answer\n", "2 correct label\n", "2 found answer\n", "3 correct label\n", "3 found answer\n", "4 correct label\n", "4 found answer\n", "5 correct label\n", "5 found answer\n", "6 correct label\n", "6 found answer\n" ] } ], "source": [ "scorecard = []\n", "for record in test_data_list:\n", " all_values = record.split(',')\n", " correct_label = int(all_values[0])\n", " print(correct_label,\"correct label\")\n", " inputs = (np.asfarray(all_values[1:])/255.0 *0.99)+0.01\n", " outputs = n.query(inputs)\n", " label = np.argmax(outputs)\n", " print(label,\"found answer\")\n", " if(label == correct_label):\n", " scorecard.append(1)\n", " else:\n", " scorecard.append(0)\n", " pass\n", " pass\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n" ] } ], "source": [ "print(scorecard)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "performance = 0.9609\n" ] } ], "source": [ "sum_array = np.asarray(scorecard)\n", "print(\"performance = \", sum_array.sum()/sum_array.size)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

liganega/Gongsu-DataSci

ref_materials/excs/Lab-07.ipynb

2

16909

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 연습문제\n", "\n", "아래 문제들을 해결하는 코드를 lab07.py 파일에 작성하여 제출하라." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 1\n", "\n", "미국 해양대기청(NOAA)은 전세계 날씨를 실시간으로 제공한다. 한국의 경우 공항이 있는 도시의 날씨정보를 제공하며 평택도 포함된다. 평택의 현재 날씨 정보를 텍스트파일로 얻고자 하면 아래 [NOAA](http://weather.noaa.gov/pub/data/observations/metar/decoded/RKSG.TXT) 사이트를 클릭해서 파일을 다운로드받으면 된다.\n", " \n", "아니면 아래 함수를 이용하여 위 링크에 연결된 파일 내용을 확인할 수 있다.\n", "\n", " def NOAA_string():\n", " url = \"http://weather.noaa.gov/pub/data\" +\\\n", " \"/observations/metar/decoded/RKSG.TXT\"\n", " noaa_data_string = urllib.urlopen(url).read()\n", " return noaa_data_string\n", " \n", "위 코드를 사용하려면 `urllib` 모듈을 임포트해야 한다. 위 함수를 파이썬 셸에서 실행하여 리턴값을 확인해보기 바란다. \n", "\n", "이제 아래 일을 수행하는 함수 `NOAA_temperature(s)` 함수를 작성하라.\n", "\n", "* `NOAA_string()`의 리턴값을 인자로 받아서 해당 도시의 섭씨 단위 온도의 정수값을 리턴한다. \n", "* 미국은 온도를 화씨(Fahrenheit) 단위로 나타내며 우리는 섭씨(Celsius) 단위를 사용한다. \n", "\n", "주의: 위 사이트는 실시간으로 날씨 정보를 제공한다. 따라서 위 링크를 누를 때마다 온도 정보가 변한다. 예를 들어 2015년 10월 16일 0시 38분에 확인한 경우 아래 처럼 확인된 평택시 온도는 섭씨 14.2이다. 따라서 `NOAA_temperature(NOAA_string())`은 `14`를 리턴해야 한다. 하지만 다른 시각에 확인하면 다른 값이 나올 수 있음에 주의해야 한다. 어떻게 섭씨에 해당하는 숫자를 끄집어 낼 수 있는지 확인해야 한다. \n", "\n", " Pyongtaek Ab, Korea, South (RKSG) 36-56N 127-00E 16M\n", " Oct 15, 2015 - 10:58 AM EDT / 2015.10.15 1458 UTC\n", " Wind: Calm:0\n", " Visibility: 2 mile(s):0\n", " Sky conditions: partly cloudy\n", " Weather: mist\n", " Temperature: 57.6 F (14.2 C)\n", " Dew Point: 57.6 F (14.2 C)\n", " Relative Humidity: 100%\n", " Pressure (altimeter): 30.11 in. Hg (1019 hPa)\n", " ob: RKSG 151458Z 00000KT 2SM R32/2600FT BR SCT010 14/14 A3011 RMK AO2A SLP199 T01420142 \n", " cycle: 15\n", " \n", "힌트: 문자열 메소드 중에서 특정 부분 문자열(substring)의 위치, 즉 인덱스 번호를 확인해주는 메소드가 있다. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 1 견본답안 1\n", "\n", "`NOAA_string()`을 실행하여 얻어진 파일의 내용을 보면 7번째 줄에서 온도 정보를 확인할 수 있다.\n", "관건은 7번째 줄에서 `14.2`를 끄집어 내는 것이다. 그러려면 14.2를 유일하게 특징지울 수 있는 무언가를 찾아야 한다. \n", "\n", "방법 1: `split` 메소드 이용하기\n", "\n", "* 7번째 줄을 자세히 살피면 섭씨 온도 정보는 세 개의 스페이스 뒤에 위치한다. 이 정보를 활용할 수 있다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pyongtaek Ab, Korea, South (RKSG) 36-56N 127-00E 16M\n", "Oct 20, 2015 - 04:55 AM EDT / 2015.10.20 0855 UTC\n", "Wind: from the NW (310 degrees) at 6 MPH (5 KT):0\n", "Visibility: 1 1/2 mile(s):0\n", "Sky conditions: clear\n", "Weather: mist\n", "Temperature: 68 F (20 C)\n", "Dew Point: 66 F (19 C)\n", "Relative Humidity: 93%\n", "Pressure (altimeter): 30 in. Hg (1015 hPa)\n", "Pressure tendency: 0.00 inches (0.0 hPa) higher than three hours ago\n", "ob: RKSG 200855Z 31005KT 1 1/2SM BR CLR 20/19 A3000 RMK SLP162 53000\n", "cycle: 9\n", "\n", "20 C\n" ] } ], "source": [ "import urllib\n", "\n", "def NOAA_string():\n", " url = \"http://weather.noaa.gov/pub/data\" +\\\n", " \"/observations/metar/decoded/RKSG.TXT\"\n", " noaa_data_string = urllib.urlopen(url).read()\n", " return noaa_data_string\n", "\n", "print(NOAA_string())\n", "\n", "def NOAA_temperature(s):\n", " L = s.split('\\n')\n", " Line7 = L[6].split()\n", " print(str(int(Line7[-2][1:])) + \" C\")\n", "\n", "NOAA_temperature(NOAA_string())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 1 견본답안 2\n", "\n", "* 섭씨온도를 유일하게 특징지우는 문자열을 찾아야 한다.\n", "* `\" F \"`가 그런 문자열이다. (`F` 양 옆으로 스페이스가 있다.)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20 C\n" ] } ], "source": [ "def NOAA_temperature(s):\n", " d = s.find(\" F \")\n", " print(s[d+4: d+6] + \" C\")\n", " \n", "NOAA_temperature(NOAA_string())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 2\n", "\n", "텍스트 파일에 저장된 문장에서 특정 단어의 출현 횟수를 확인해주는 함수 `wc_sub(filename, s)` 함수를 작성하라. `wc`는 `Word Count`의 줄임말이다. \n", "\n", "힌트: `count` 메소드를 활용한다.\n", "\n", "예제 1: `data.txt` 파일 내용이 아래와 같을 경우\n", "\n", " One Two\n", " \n", "`wc_sub('data.txt', 'One')`는 1를 리턴한다.\n", "\n", "예제 2: `data.txt` 파일 내용이 아래와 같을 경우\n", "\n", " One Two\n", " Three Four Five\n", " \n", "`wc_sub('data.txt', 'o')`는 2를 리턴한다.\n", "\n", "`wc_sub` 함수를 이용하여 `이상한 나라의 앨리스` 원작에 'Alice'와 'alice'란 단어가 각각 몇 번 언급되는지 확인하라. 이상한 나라의 앨리스 원작은 아래 링크에서 다운 받을 수 있다.\n", "\n", " http://www.gutenberg.org/files/28885/28885-8.txt\n", " \n", "위 링크를 누르면 뜨는 화면에서 `Plain Text UTF-8` 파일을 다운로드 받으면 된다. 아마도 몇 만 단어가 사용되었을 것이다.\n", "\n", "단, `filename`에 해당하는 파일이 열리지 않을 경우 `-1`을 리턴하도록 오류처리를 해야 한다. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 2 견본답안" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The word 'Alice' occurs 402 times.\n", "The word 'alice' occurs 0 times.\n" ] } ], "source": [ "def wc_sub(filename, s):\n", " with open(filename, 'r') as f:\n", " f_content = f.read()\n", " return f_content.count(s)\n", "\n", "print(\"The word 'Alice' occurs {} times.\".format(wc_sub('Alice.txt', 'Alice')))\n", "print(\"The word 'alice' occurs {} times.\".format(wc_sub('Alice.txt', 'alice')))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 연습 3\n", "\n", "함수 `f`와 숫자들의 리스트 `xs`를 인자로 받아 `f(x)`의 값이 `0`보다 크게 되는 `x`의 값만 추출해서 리턴하는 함수 `filtering(f, xs)`를 정의하라.\n", "\n", "예제:\n", "\n", " In [1]: def f1(x):\n", " ...: return x * 3\n", "\n", " In [2]: filtering(f1, [1, -2, 2, -1, 3, 5])\n", " Out[2]: [1, 2, 3, 5]\n", "\n", " In [3]: filtering(f1, [-1, -2, -3, -4, -5])\n", " Out[3]: []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 3 견본답안" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 5]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def filtering(f, xs):\n", " L = []\n", " for x in xs:\n", " if f(x) > 0:\n", " L.append(x)\n", " return L\n", "\n", "def f1(x):\n", " return x * 3\n", "\n", "filtering(f1, [1, -2, 2, -1, 3, 5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 참조: 파이썬 내장함수 중에 `filter` 함수가 비슷한 일을 한다. 어떤 차이점이 있는지 확인해보는 것을 추천한다." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 연습 4\n", "\n", "함수 `f`와 숫자들의 리스트 `xs = [x1, ..., x_n]`를 인자로 받아 `f(xn)`들의 값의 합을 리턴하는 함수 `sum_list(f, xs)`를 정의하라. 단, `xs = []` 일 경우 `0`을 리턴한다.\n", "\n", "예제:\n", "\n", " In [4]: def f2(x):\n", " ...: return x ** 2\n", "\n", " In [5]: sum_list(f2, [1, -2, 2, -3,])\n", " Out[5]: 18\n", "\n", " In [6]: sum_list(f1, [-1, -2, -3, -4, -5])\n", " Out[6]: -45" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 4 견본답안" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18\n", "-45\n" ] } ], "source": [ "def sum_list(f, xs):\n", " L = 0\n", " for x in xs:\n", " L = L + f(x)\n", " return L\n", "\n", "def f2(x):\n", " return x ** 2\n", "\n", "print(sum_list(f2, [1, -2, 2, -3]))\n", "print(sum_list(f1, [-1, -2, -3, -4, -5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "참조: 파이썬 내장함수 중에 `sum` 함수가 비슷한 일을 한다. 어떤 차이점이 있는지 확인해보는 것을 추천한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 5\n", "\n", "밑변의 길이와 높이가 각각 `a`와 `h`인 삼각형의 면적을 리턴하는 함수 `triangle_area(a, h)`를 작성하라. 그런데 삼각형의 높이 `h`는 기본값으로 `5`를 사용해야 한다. 힌트: 키워드 인자를 사용한다.\n", "\n", "예제:\n", "\n", " In [7]: triangle_area(3)\n", " Out[7]: 7.5\n", "\n", " In [8]: triangle_area(3, 7)\n", " Out[8]: 10.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 5 견본답안" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.5\n", "10.5\n" ] } ], "source": [ "def triangle_area(a, height=5):\n", " return 1.0/2 * a * height\n", "\n", "print(triangle_area(3))\n", "print(triangle_area(3, 7))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 연습 6\n", "\n", "함수 `f`를 입력 받으면 아래 묘사처럼 작동하는 함수를 리턴하는 함수 `fun_2_fun(f)`를 정의하라.\n", "\n", " fun_2_fun(f)(2) = (f(2)) ** 2\n", " fun_2_fun(f)(3) = (f(3)) ** 3\n", " fun_2_fun(f)(4) = (f(4)) ** 4\n", " ...\n", " \n", "주의: 함수를 입력받아 함수를 리턴하도록 작성해야 한다. \n", "\n", "힌트: 함수 안에서 `def` 키워드를 이용하여 새로운 함수를 정의할 수 있다. 그 함수는 지역함수가 된다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 6 견본답안 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "36\n" ] } ], "source": [ "def fun_2_fun(f):\n", " def f_exp(n):\n", " return (f(n)) ** n\n", " return f_exp\n", "\n", "print(f1(2))\n", "print(fun_2_fun(f1)(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 문제 핵심\n", "\n", "이 문제의 핵심은 함수를 단순히 인자로만 사용하는 것이 아니라 리턴값으로도 할용하는 것이다. 즉, 함수에 어떤 인자를 넣고 호출하였더니 어떤 함수를 리턴하는 함수를 구현해야 한다. 그리고 리턴값이 함수이므로 그 함수를 적당한 인자를 입력하여 호출할 수 있다.\n", "\n", "예를 들어 함수 `g`를 다음과 같이 정의하자." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def exp2(x):\n", " return x ** 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "g = fun_2_fun(exp2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그러면 `g`는 함수임을 확인할 수 있다." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "어떤 함수인가? `help` 를 이용하여 확인하면 다음과 같다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function f_exp in module __main__:\n", "\n", "f_exp(n)\n", "\n" ] } ], "source": [ "help(g)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<function __main__.f_exp>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "즉, 인자를 하나 받는 함수이며 `f_exp`를 이용해 정외되었음을 알 수 있다. 실제로 `g`는 아래와 같이 정의되어 있다. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "`g`를 정의하기 위해 `fun_2_fun(f)` 함수를 호출할 때 사용된 인자 `f` 대신에 `exp2` 함수를 삽입하였기 때문에 `g`가 아래와 같이 정의된 함수임을 알 수 있다. \n", "\n", " g(x) = fun_2_fun(exp2)\n", " = f_exp(x) # f_exp 를 정의할 때 exp2 가 사용됨에 중의\n", " = exp2(x) ** x\n", " = (x**2) ** x\n", " = x ** (2*x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 연습 6 견본답안 2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "36\n" ] } ], "source": [ "def fun_2_fun(f):\n", " return lambda x: f(x) ** x\n", "\n", "print(f1(2))\n", "print(fun_2_fun(f1)(2))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

landlab/landlab

notebooks/tutorials/normal_fault/normal_fault_component_tutorial.ipynb

1

20174

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"../../landlab_header.png\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to the NormalFault component\n", "\n", "This tutorial provides an introduction to the `NormalFault` component in the Landlab toolkit. This component takes the following parameters. \n", "\n", " Parameters\n", " --------\n", " grid : ModelGrid\n", " faulted_surface : str or ndarray of shape `(n_nodes, )` or list of str\n", " or ndarrays. \n", " Surface that is modified by the NormalFault component. Can be a\n", " field name or array or a list of strings or ndarrays if the fault.\n", " should uplift more than one field. Default value is \n", " `topographic__elevation`.\n", " fault_throw_rate_through_time : dict, optional\n", " Dictionary that specifies the time varying throw rate on the fault.\n", " Expected format is:\n", " ``fault_throw_rate_through_time = {'time': array, 'rate': array}``\n", " Default value is a constant rate of 0.001 (units not specified).\n", " fault_dip_angle : float, optional\n", " Dip angle of the fault in degrees. Default value is 90 degrees.\n", " fault_trace : dictionary, optional\n", " Dictionary that specifies the coordinates of two locations on the\n", " fault trace. Expected format is\n", " ``fault_trace = {'x1': float, 'y1': float, 'x2': float, 'y2': float}``\n", " where the vector from ``(x1, y1)`` to ``(x2, y2)`` defines the\n", " strike of the fault trace. The orientation of the fault dip relative\n", " to the strike follows the right hand rule.\n", " Default is for the fault to strike NE.\n", " include_boundaries : boolean, optional\n", " Flag to indicate if model grid boundaries should be uplifted. If\n", " set to ``True`` uplifted model grid boundaries will be set to the\n", " average value of their upstream nodes. Default value is False.\n", "\n", "\n", "The `NormalFault` component will divide the model domain into two regions, a 'faulted nodes' region which will experience vertical rock uplift at a rate of \n", "\n", "$t \\cdot \\sin (d)$\n", "\n", "where $t$ is the fault throw rate and $d$ is the fault dip angle. \n", "\n", "While dip angles less than 90 degrees are permitted, in its present implementation, the `NormalFault` component does not translate field information laterally. \n", "\n", "The fault orientation is specified by two coordinate pairs: (x1, y1) and (x2, y2). The strike of the fault, specified with the right-hand rule convention, is the vector from (x1, y1) to (x2, y2). Give that this component creates a normal fault, in which the footwall moves up relative to the hanging wall, this means that the nodes that are counterclockwise from the strike are the uplifted nodes. \n", "\n", "To start, let's import necessary Landlab and Python modules. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# start by importing necessary modules\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from landlab import HexModelGrid, RasterModelGrid\n", "from landlab.components import (\n", " FastscapeEroder,\n", " FlowAccumulator,\n", " NormalFault,\n", " StreamPowerEroder,\n", ")\n", "from landlab.plot import imshow_grid\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will make a default `NormalFault`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((6, 6), xy_spacing=10)\n", "\n", "grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid)\n", "\n", "plt.figure()\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")\n", "plt.plot(grid.x_of_node, grid.y_of_node, \"c.\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " This fault has a strike of NE and dips to the SE. Thus the uplifted nodes (shown in yellow) are in the NW half of the domain. \n", "\n", "The default `NormalFault` will not uplift the boundary nodes. We change this by using the keyword argument `include_boundaries`. If this is specified, the elevation of the boundary nodes is calculated as an average of the faulted nodes adjacent to the boundaries. This occurs because most Landlab erosion components do not operate on boundary nodes. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nf = NormalFault(grid, include_boundaries=True)\n", "\n", "plt.figure()\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")\n", "plt.plot(grid.x_of_node, grid.y_of_node, \"c.\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add functionality to the `NormalFault` with other keyword arguments. We can change the fault strike and dip, as well as specify a time series of fault uplift through time. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((60, 100), xy_spacing=10)\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid, fault_trace={\"x1\": 0, \"y1\": 200, \"y2\": 30, \"x2\": 600})\n", "\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By reversing the order of (x1, y1) and (x2, y2) we can reverse the location of the upthrown nodes (all else equal). " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grid = RasterModelGrid((60, 100), xy_spacing=10)\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "\n", "nf = NormalFault(grid, fault_trace={\"y1\": 30, \"x1\": 600, \"x2\": 0, \"y2\": 200})\n", "\n", "imshow_grid(grid, nf.faulted_nodes.astype(int), cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also specify complex time-rock uplift rate histories, but we'll explore that later in the tutorial. \n", "\n", "Next let's make a landscape evolution model with a normal fault. Here we'll use a HexModelGrid to highlight that we can use both raster and non-raster grids with this component. \n", "\n", "We will do a series of three numerical experiments and will want to keep a few parameters constant. Since you might want to change them, we are making it easier to change all of them together. They are defined in the next block:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# here are the parameters to change\n", "K = 0.0005 # stream power coefficient, bigger = streams erode more quickly\n", "U = 0.0001 # uplift rate in meters per year\n", "\n", "dt = 1000 # time step in years\n", "dx = 10 # space step in meters\n", "\n", "nr = 60 # number of model rows\n", "nc = 100 # number of model columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), dx, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(grid, fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500})\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += 0.0001 * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the upper left portion of the grid has been uplifted an a stream network has developed over the whole domain. \n", "\n", "How might this change when we also uplift the boundaries nodes?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(\n", " grid, fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500}, include_boundaries=True\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that when the boundary nodes are not included, the faulted region is impacted by the edge boundary conditions differently. Depending on your application, one or the other of these boundary condition options may suite your problem better. \n", "\n", "The last thing to explore is the use of the `fault_rate_through_time` parameter. This allows us to specify generic fault throw rate histories. For example, consider the following history, in which every 100,000 years there is a 10,000 year period in which the fault is active. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "time = (\n", " np.array(\n", " [\n", " 0.0,\n", " 7.99,\n", " 8.00,\n", " 8.99,\n", " 9.0,\n", " 17.99,\n", " 18.0,\n", " 18.99,\n", " 19.0,\n", " 27.99,\n", " 28.00,\n", " 28.99,\n", " 29.0,\n", " ]\n", " )\n", " * 10\n", " * dt\n", ")\n", "rate = np.array([0, 0, 0.01, 0.01, 0, 0, 0.01, 0.01, 0, 0, 0.01, 0.01, 0])\n", "\n", "plt.figure()\n", "plt.plot(time, rate)\n", "plt.plot([0, 300 * dt], [0.001, 0.001])\n", "plt.xlabel(\"Time [years]\")\n", "plt.ylabel(\"Fault Throw Rate [m/yr]\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default value for uplift rate is 0.001 (units unspecified as it will depend on the x and t units in a model, but in this example we assume time units of years and length units of meters). \n", "\n", "This will result in a total of 300 m of fault throw over the 300,000 year model time period. This amount of uplift can also be accommodated by faster fault motion that occurs over shorter periods of time. \n", "\n", "Next we plot the cumulative fault throw for the two cases. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = np.arange(0, 300 * dt, dt)\n", "rate_constant = np.interp(t, [0, 300 * dt], [0.001, 0.001])\n", "rate_variable = np.interp(t, time, rate)\n", "\n", "cumulative_rock_uplift_constant = np.cumsum(rate_constant) * dt\n", "cumulative_rock_uplift_variable = np.cumsum(rate_variable) * dt\n", "\n", "plt.figure()\n", "plt.plot(t, cumulative_rock_uplift_constant)\n", "plt.plot(t, cumulative_rock_uplift_variable)\n", "plt.xlabel(\"Time [years]\")\n", "plt.ylabel(\"Cumulative Fault Throw [m]\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A technical note: Beyond the times specified, the internal workings of the `NormalFault` will use the final value provided in the rate array. \n", "\n", "Let's see how this changes the model results. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "fr = FlowAccumulator(grid)\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "nf = NormalFault(\n", " grid,\n", " fault_throw_rate_through_time={\"time\": time, \"rate\": rate},\n", " fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500},\n", " include_boundaries=True,\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", " nf.run_one_step(dt)\n", " fr.run_one_step()\n", " fs.run_one_step(dt)\n", " z[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the resulting topography is very different than in the case with continuous uplift. \n", "\n", "For our final example, we'll use `NormalFault` with a more complicated model in which we have both a soil layer and bedrock. In order to move, material must convert from bedrock to soil by weathering.\n", "\n", "First we import remaining modules and set some parameter values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from landlab.components import DepthDependentDiffuser, ExponentialWeatherer\n", "\n", "# here are the parameters to change\n", "K = 0.0005 # stream power coefficient, bigger = streams erode more quickly\n", "U = 0.0001 # uplift rate in meters per year\n", "max_soil_production_rate = (\n", " 0.001 # Maximum weathering rate for bare bedrock in meters per year\n", ")\n", "soil_production_decay_depth = 0.7 # Characteristic weathering depth in meters\n", "linear_diffusivity = 0.01 # Hillslope diffusivity and m2 per years\n", "soil_transport_decay_depth = 0.5 # Characteristic soil transport depth in meters\n", "\n", "dt = 100 # time step in years\n", "dx = 10 # space step in meters\n", "\n", "nr = 60 # number of model rows\n", "nc = 100 # number of model columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "?ExponentialWeatherer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we create the grid and run the model. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# instantiate the grid\n", "grid = HexModelGrid((nr, nc), 10, node_layout=\"rect\")\n", "\n", "# add a topographic__elevation field with noise\n", "z = grid.add_zeros(\"topographic__elevation\", at=\"node\")\n", "z[grid.core_nodes] += 100.0 + np.random.randn(grid.core_nodes.size)\n", "\n", "# create a field for soil depth\n", "d = grid.add_zeros(\"soil__depth\", at=\"node\")\n", "\n", "# create a bedrock elevation field\n", "b = grid.add_zeros(\"bedrock__elevation\", at=\"node\")\n", "b[:] = z - d\n", "\n", "fr = FlowAccumulator(grid, depression_finder=\"DepressionFinderAndRouter\", routing=\"D4\")\n", "fs = FastscapeEroder(grid, K_sp=K)\n", "ew = ExponentialWeatherer(\n", " grid,\n", " soil_production__decay_depth=soil_production_decay_depth,\n", " soil_production__maximum_rate=max_soil_production_rate,\n", ")\n", "\n", "dd = DepthDependentDiffuser(\n", " grid,\n", " linear_diffusivity=linear_diffusivity,\n", " soil_transport_decay_depth=soil_transport_decay_depth,\n", ")\n", "\n", "nf = NormalFault(\n", " grid,\n", " fault_throw_rate_through_time={\"time\": [0, 30], \"rate\": [0.001, 0.001]},\n", " fault_trace={\"x1\": 0, \"x2\": 800, \"y1\": 0, \"y2\": 500},\n", " include_boundaries=False,\n", ")\n", "\n", "# Run this model for 300 100-year timesteps (30,000 years).\n", "for i in range(300):\n", "\n", " # Move normal fault\n", " nf.run_one_step(dt)\n", "\n", " # Route flow\n", " fr.run_one_step()\n", "\n", " # Erode with water\n", " fs.run_one_step(dt)\n", "\n", " # We must also now erode the bedrock where relevant. If water erosion\n", " # into bedrock has occurred, the bedrock elevation will be higher than\n", " # the actual elevation, so we simply re-set bedrock elevation to the\n", " # lower of itself or the current elevation.\n", " b = grid.at_node[\"bedrock__elevation\"]\n", " b[:] = np.minimum(b, grid.at_node[\"topographic__elevation\"])\n", "\n", " # Calculate regolith-production rate\n", " ew.calc_soil_prod_rate()\n", "\n", " # Generate and move soil around. This component will update both the\n", " # soil thickness and topographic elevation fields.\n", " dd.run_one_step(dt)\n", "\n", " # uplift the whole domain, we need to do this to both bedrock and topography\n", " z[grid.core_nodes] += U * dt\n", " b[grid.core_nodes] += U * dt\n", "\n", "# plot the final topography\n", "imshow_grid(grid, \"topographic__elevation\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also examine the soil thickness and soil production rate. Here in the soil depth, we see it is highest along the ridge crests. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# and the soil depth\n", "imshow_grid(grid, \"soil__depth\", cmap=\"viridis\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The soil production rate is highest where the soil depth is low, as we would expect given the exponential form. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# and the soil production rate\n", "imshow_grid(grid, \"soil_production__rate\", cmap=\"viridis\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

moonbury/pythonanywhere

kaggle/video_game_sales.ipynb

2

56909

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "sns.set(style=\"darkgrid\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Platform</th>\n", " <th>Year_of_Release</th>\n", " <th>Genre</th>\n", " <th>Publisher</th>\n", " <th>NA_Sales</th>\n", " <th>EU_Sales</th>\n", " <th>JP_Sales</th>\n", " <th>Other_Sales</th>\n", " <th>Global_Sales</th>\n", " <th>Critic_Score</th>\n", " <th>Critic_Count</th>\n", " <th>User_Score</th>\n", " <th>User_Count</th>\n", " <th>Developer</th>\n", " <th>Rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Wii Sports</td>\n", " <td>Wii</td>\n", " <td>2006.0</td>\n", " <td>Sports</td>\n", " <td>Nintendo</td>\n", " <td>41.36</td>\n", " <td>28.95</td>\n", " <td>3.77</td>\n", " <td>8.45</td>\n", " <td>82.53</td>\n", " <td>76.0</td>\n", " <td>51.0</td>\n", " <td>8</td>\n", " <td>321.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Mario Kart Wii</td>\n", " <td>Wii</td>\n", " <td>2008.0</td>\n", " <td>Racing</td>\n", " <td>Nintendo</td>\n", " <td>15.67</td>\n", " <td>12.75</td>\n", " <td>3.79</td>\n", " <td>3.28</td>\n", " <td>35.50</td>\n", " <td>82.0</td>\n", " <td>73.0</td>\n", " <td>8.3</td>\n", " <td>709.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Wii Sports Resort</td>\n", " <td>Wii</td>\n", " <td>2009.0</td>\n", " <td>Sports</td>\n", " <td>Nintendo</td>\n", " <td>15.61</td>\n", " <td>10.92</td>\n", " <td>3.28</td>\n", " <td>2.95</td>\n", " <td>32.76</td>\n", " <td>80.0</td>\n", " <td>73.0</td>\n", " <td>8</td>\n", " <td>192.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>New Super Mario Bros.</td>\n", " <td>DS</td>\n", " <td>2006.0</td>\n", " <td>Platform</td>\n", " <td>Nintendo</td>\n", " <td>11.28</td>\n", " <td>9.14</td>\n", " <td>6.50</td>\n", " <td>2.88</td>\n", " <td>29.79</td>\n", " <td>89.0</td>\n", " <td>65.0</td>\n", " <td>8.5</td>\n", " <td>431.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Wii Play</td>\n", " <td>Wii</td>\n", " <td>2006.0</td>\n", " <td>Misc</td>\n", " <td>Nintendo</td>\n", " <td>13.96</td>\n", " <td>9.18</td>\n", " <td>2.93</td>\n", " <td>2.84</td>\n", " <td>28.92</td>\n", " <td>58.0</td>\n", " <td>41.0</td>\n", " <td>6.6</td>\n", " <td>129.0</td>\n", " <td>Nintendo</td>\n", " <td>E</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Platform Year_of_Release Genre Publisher \\\n", "0 Wii Sports Wii 2006.0 Sports Nintendo \n", "1 Mario Kart Wii Wii 2008.0 Racing Nintendo \n", "2 Wii Sports Resort Wii 2009.0 Sports Nintendo \n", "3 New Super Mario Bros. DS 2006.0 Platform Nintendo \n", "4 Wii Play Wii 2006.0 Misc Nintendo \n", "\n", " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n", "0 41.36 28.95 3.77 8.45 82.53 76.0 \n", "1 15.67 12.75 3.79 3.28 35.50 82.0 \n", "2 15.61 10.92 3.28 2.95 32.76 80.0 \n", "3 11.28 9.14 6.50 2.88 29.79 89.0 \n", "4 13.96 9.18 2.93 2.84 28.92 58.0 \n", "\n", " Critic_Count User_Score User_Count Developer Rating \n", "0 51.0 8 321.0 Nintendo E \n", "1 73.0 8.3 709.0 Nintendo E \n", "2 73.0 8 192.0 Nintendo E \n", "3 65.0 8.5 431.0 Nintendo E \n", "4 41.0 6.6 129.0 Nintendo E " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('./input/Video_Games_Sales_as_at_30_Nov_2016.csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7dtlW2gAP/jbGjx+vfP3114qiFPyeoqOjlWPHjpV19QxWWDsq+v5VUYpf78jI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\nZrTqctMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa03b0c590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "ax1 = fig.add_subplot(411)\n", "\n", "idx = 0\n", "hcat1 = sorted(data.Platform.unique())\n", "for cat_val in hcat1:\n", " ax1.bar(idx,data[data['Platform']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", "\n", "ax2 = fig.add_subplot(412)\n", "idx = 0\n", "hcat2 = sorted(data.Year_of_Release.unique())\n", "for cat_val in hcat2: \n", " ax2.bar(idx,data[data['Year_of_Release']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax3 = fig.add_subplot(413)\n", "idx = 0\n", "hcat3 = sorted(data.Genre.unique())\n", "for cat_val in hcat3: \n", " ax3.bar(idx,data[data['Genre']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax4 = fig.add_subplot(414)\n", "idx = 0\n", "hcat4 = sorted(data['Rating'].dropna().unique())\n", "for cat_val in hcat4: \n", " ax4.bar(idx,data[data['Rating']==cat_val].Global_Sales.sum(), color=np.random.rand(3,1))\n", " idx = idx + 1\n", " \n", "ax1.set_title('Global Sales by Platform')\n", "ax2.set_title('Global Sales by Year')\n", "ax3.set_title('Global Sales by Genre')\n", "ax4.set_title('Global Sales by Rating')\n", "ax1.set_xticklabels(hcat1)\n", "ax2.set_xticklabels(hcat2)\n", "ax3.set_xticklabels(hcat3)\n", "ax4.set_xticklabels(hcat4) \n", "fig.subplots_adjust(hspace=1)\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#data['User_Score'].dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

DCPROGS/CVFIT

cvfit_DC_LOB_example_p206.ipynb

2

4974

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from pylab import*\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from cvfit import fitting\n", "from cvfit.fitting import SingleFitSession\n", "from cvfit.fitting import MultipleFitSession\n", "from cvfit.fitting import simplex\n", "from cvfit import data\n", "\n", "from cvfit.errors import residuals\n", "from cvfit.errors import SSD\n", "from cvfit.errors import SSDlik" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "X\tY\ts(Y)\tweight\n", "2.5\t5.576\t0\t1\n", "5\t7.282\t0\t1\n", "10\t12.521\t0\t1\n", "20\t16.138\t0\t1\n", "40\t23.219\t0\t1\n", "\n" ] } ], "source": [ "dataset = data.XYDataSet()\n", "X = [2.5, 5.0, 10.0, 20.0, 40.0]\n", "Y = [5.576, 7.282, 12.521, 16.138, 23.219]\n", "S = None #[1.2003]*5\n", "dataset.from_columns(X, Y, S)\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from cvfit.equations import Hill\n", "eq = Hill('Langmuir')\n", "#fs = SingleFitSession(dataset, eq)\n", "eq.propose_guesses(dataset)\n", "eq.guess = np.array([0.0, 31.0, 15.0, 1.0])\n", "eq.pars = eq.guess.copy()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "coeffs, Smin = simplex(SSD, eq.theta, eq, dataset.X, dataset.Y, dataset.W)\n", "eq.theta = coeffs\n", "kfit = len(np.nonzero(np.invert(eq.fixed))[0])\n", "ndf = dataset.size() - kfit\n", "var = Smin / ndf\n", "Sres = sqrt(var)\n", "Lmax = -SSDlik(eq.theta, eq, dataset)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of point fitted = 5\n", "\n", "Number of parameters estimated = 2\n", "\n", "Degrees of freedom = 3\n", "\n", "Residual error SD = 1.200 (variance = 1.441)\n", "\n", "Minimum SSD = 4.322; \n", "Max log-likelihood = -6.730\n" ] } ], "source": [ "print('Number of point fitted = {0:d}'.format(dataset.size()))\n", "print('\\nNumber of parameters estimated = {0:d}'.format(kfit))\n", "print('\\nDegrees of freedom = {0:d}'.format(ndf))\n", "print('\\nResidual error SD = {0:.3f} (variance = {1:.3f})'.format(Sres, var))\n", "print('\\nMinimum SSD = {0:.3f}; \\nMax log-likelihood = {1:.3f}'.format(Smin, Lmax))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of point fitted = 5\n", "Number of parameters estimated = 2\n", "Degrees of freedom = 3\n", "Residual error SD = 1.247 (variance = 1.555)\n", "Parameter 1: Ymin \t= 0 \t (fixed)\n", "Parameter 2: Ymax \t= 30.1201 \t Approx SD = 3.124\t CV = 10.4\n", "Parameter 3: EC50 \t= 14.1212 \t Approx SD = 3.4052\t CV = 24.1\n", "Minimum SSD = 4.665; \n", "Max log-likelihood = -6.922\n", "Correlation matrix = [!!!! PRINTOUT OF CORRELATION MATRIX NOT IMPLEMENTED YET. SORRY.\n", "\n", "WARNING: SOME PARAMETERS ARE STRONGLY CORRELATED (coeff > 0.9); try different guesses\n", "\n", "LIKELIHOOD INTERVALS\n", "5.06-unit Likelihood Intervals (equivalent SD for Gaussian- 3.18)\n", "Lmax= -6.92151; Lcrit= -11.9841\n", "Parameter 1: Ymin\t= 0\t (fixed)\n", "Parameter 2: Ymax\t= 30.1201\t LOWER = 20.2766\t UPPER = 69.3388\n", "Parameter 3: EC50\t= 14.1212\t LOWER = 5.02122\t UPPER = 67.4515\n" ] } ], "source": [ "fs.fit()\n", "fs.calculate_errors()\n", "print(fs.string_estimates())\n", "print(fs.string_liklimits())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

jakeret/abcpmc

notebooks/toy_model.ipynb

1

744751

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D gaussian toy model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "\n", "%pylab inline\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"talk\")\n", "rc('axes', labelsize=20, titlesize=20)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import triangle\n", "from clerk.stats.distances import mahalanobis\n", "import abcpmc \n", "\n", "#np.random.seed(987654321)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mean = 1\n", "sigma = 1\n", "n = 10000\n", "y = np.random.normal(mean, sigma, n)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1175fccd0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAncAAAG4CAYAAAAnnMGeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl0XOd95vlvbagCCvsOcCchvRRJLZRkWbG8hF4U2Umr\n", "M46TjNNxnHROMvaxTmc6ne6TXtwz6e7EczJJpo/TbUU+cduJrU4nnU4cyVEk2ZasfRcpcX1JcAVB\n", "bAWgAFQBVaht/rgABYIgqrDeWp7POTgi7r117w98VYWH973v+3pyuRwiIiIiUh68bhcgIiIiIutH\n", "4U5ERESkjCjciYiIiJQRhTsRERGRMqJwJyIiIlJGFO5EREREyog/3wHGmIPAI8A+4AzwBWvta8sc\n", "/zHgaaDOWjs9t+23gN8DkgsOfcBa+9IaahcRERGRRZYNd8aYEPA48B+BPwV+CXjMGLPbWhtf4vgm\n", "4L8tcao7gN+21v7R2ksWERERkRvJ1y17CMhYax+x1mastd8EhoBP3eD4h4G/ADyLth8E3llTpSIi\n", 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"collapsed": false }, "outputs": [], "source": [ "def mean_dist(x, y):\n", " return np.abs(np.mean(x, axis=0) - np.mean(y, axis=0))\n", "\n", "dist = mean_dist" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_new_sample(theta):\n", " return np.random.normal(theta, sigma, n)\n", "postfn = create_new_sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Verification" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.4025917]\n", "0.000582605098998\n" ] } ], "source": [ "theta = prior()\n", "print theta\n", "x = postfn([mean])\n", "d = dist(x, y)\n", "print d" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11780e8d0>" ] }, "execution_count": 10, "metadata": {}, 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{1:>.4f}, ratio: {2:>.4f}\".format(pool.t, eps(pool.t), pool.ratio))\n", " for i, (mean, std) in enumerate(zip(*abcpmc.weighted_avg_and_std(pool.thetas, pool.ws, axis=0))):\n", " print(u\" theta[{0}]: {1:>.4f} \\u00B1 {2:>.4f}\".format(i, mean,std))\n", "\n", " eps.eps = np.percentile(pool.dists, 90)\n", " if eps.eps < eps_min:\n", " eps.eps = eps_min\n", " \n", " pools.append(pool)\n", " \n", " abcpmc_sampler.close()\n", " \n", " return pools\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "T: 0, eps: 0.5000, ratio: 0.1000\n", " theta[0]: 0.9792 ± 0.2894\n", "T: 1, eps: 0.4495, ratio: 0.6137\n", " theta[0]: 1.0011 ± 0.2582\n", "T: 2, eps: 0.3917, ratio: 0.6086\n", " theta[0]: 0.9932 ± 0.2256\n", "T: 3, eps: 0.3414, ratio: 0.6001\n", " theta[0]: 0.9860 ± 0.1989\n", "T: 4, eps: 0.2992, ratio: 0.6127\n", " theta[0]: 0.9945 ± 0.1734\n", "T: 5, eps: 0.2653, ratio: 0.6077\n", " theta[0]: 0.9961 ± 0.1531\n", "T: 6, eps: 0.2335, ratio: 0.6008\n", " theta[0]: 0.9967 ± 0.1362\n", "T: 7, eps: 0.2053, ratio: 0.6335\n", " theta[0]: 0.9904 ± 0.1180\n", "T: 8, eps: 0.1806, ratio: 0.6219\n", " theta[0]: 0.9940 ± 0.1040\n", "T: 9, eps: 0.1577, ratio: 0.6099\n", " theta[0]: 0.9918 ± 0.0905\n", "T: 10, eps: 0.1387, ratio: 0.6207\n", " theta[0]: 0.9923 ± 0.0811\n", "T: 11, eps: 0.1228, ratio: 0.6177\n", " theta[0]: 0.9919 ± 0.0717\n", "T: 12, eps: 0.1081, ratio: 0.6057\n", " theta[0]: 0.9914 ± 0.0640\n", "T: 13, eps: 0.0959, ratio: 0.6107\n", " theta[0]: 0.9927 ± 0.0557\n", "T: 14, eps: 0.0835, ratio: 0.6232\n", " theta[0]: 0.9920 ± 0.0496\n", "T: 15, eps: 0.0729, ratio: 0.5928\n", " theta[0]: 0.9909 ± 0.0426\n", "T: 16, eps: 0.0624, ratio: 0.5891\n", " theta[0]: 0.9905 ± 0.0373\n", "T: 17, eps: 0.0554, ratio: 0.5872\n", " theta[0]: 0.9932 ± 0.0340\n", "T: 18, eps: 0.0498, ratio: 0.5938\n", " theta[0]: 0.9933 ± 0.0294\n", "T: 19, eps: 0.0436, ratio: 0.6011\n", " theta[0]: 0.9920 ± 0.0271\n", "T: 20, eps: 0.0385, ratio: 0.5685\n", " theta[0]: 0.9920 ± 0.0244\n", "T: 21, eps: 0.0337, ratio: 0.5599\n", " theta[0]: 0.9926 ± 0.0222\n", "T: 22, eps: 0.0299, ratio: 0.5498\n", " theta[0]: 0.9925 ± 0.0198\n", "T: 23, eps: 0.0261, ratio: 0.5549\n", " theta[0]: 0.9924 ± 0.0180\n", "T: 24, eps: 0.0232, ratio: 0.5258\n", " theta[0]: 0.9924 ± 0.0167\n", "T: 25, eps: 0.0205, ratio: 0.5056\n", " theta[0]: 0.9926 ± 0.0155\n", "T: 26, eps: 0.0181, ratio: 0.4697\n", " theta[0]: 0.9929 ± 0.0148\n", "T: 27, eps: 0.0159, ratio: 0.4240\n", " theta[0]: 0.9922 ± 0.0138\n", "T: 28, eps: 0.0141, ratio: 0.4017\n", " theta[0]: 0.9927 ± 0.0134\n", "T: 29, eps: 0.0125, ratio: 0.3784\n", " theta[0]: 0.9924 ± 0.0129\n", "T: 30, eps: 0.0112, ratio: 0.3383\n", " theta[0]: 0.9929 ± 0.0118\n", "T: 31, eps: 0.0101, ratio: 0.3437\n", " theta[0]: 0.9923 ± 0.0111\n", "T: 32, eps: 0.0100, ratio: 0.3518\n", " theta[0]: 0.9922 ± 0.0115\n", "T: 33, eps: 0.0100, ratio: 0.3458\n", " theta[0]: 0.9924 ± 0.0114\n", "T: 34, eps: 0.0100, ratio: 0.3600\n", " theta[0]: 0.9930 ± 0.0114\n", "T: 35, eps: 0.0100, ratio: 0.3515\n", " theta[0]: 0.9931 ± 0.0111\n", "T: 36, eps: 0.0100, ratio: 0.3637\n", " theta[0]: 0.9921 ± 0.0112\n", "T: 37, eps: 0.0100, ratio: 0.3531\n", " theta[0]: 0.9926 ± 0.0113\n" ] } ], "source": [ "T=38\n", "eps=0.5\n", "pools = sample(T, eps, sigma/sqrt(n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Postprocessing" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_pool(prob, samples):\n", " prob = np.array(prob).flatten()\n", " samples = np.vstack(samples)\n", " \n", " sns.distplot(prob)\n", " show()\n", " sns.distplot(samples, axlabel=r'$\\theta$')\n", " #savefig(\"1d_gauss_posterior.pdf\")\n", " show()\n", " sample_mean = np.mean(samples, axis=0)\n", " print(u\"mean: {0:>.4f} \\u00B1 {1:>.4f}\".format(float(np.mean(samples, axis=0)), float(np.std(samples, axis=0))))\n" ] }, 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"\n", "plot_pool(distances[offset:], samples[offset:])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "var_vals = np.var((samples), axis=1)\n", "eps_values = np.array([pool.eps for pool in pools])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.01, 0.089999999999999997)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAApgAAAHKCAYAAACnnzuoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0FdXexvHvnPQQII0mXcqETgi9hCIgSBELCtyLFRWp\n", "Cij2jg0RFRRQVFS4UhWkqSC9R0INMFKkQ2ghJKQn5/3jYF6akHJSeT5rZUlm9uz9S7YuHvec2WPY\n", "7XZERERERJzFltcFiIiIiEjhooApIiIiIk6lgCkiIiIiTqWAKSIiIiJOpYApIiIiIk6lgCkiIiIi\n", "TuWa1wXkN5s3b9a+TSIiIiJASEiIkZXrFDCvo1atWnh6euZ1GZJNCQkJREREaD4LEc1p4aL5LFw0\n", 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"ax.legend(loc=\"best\")\n", "ax.invert_xaxis()\n", "ylim([-.01, None])\n", "#savefig(\"1d_gauss_variance.pdf\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x118367390>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAoQAAAHmCAYAAADqY5+DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPW9//HXmcmekJ1sLGELBxK2sIqsYrUgwnVrXVvX\n", "trd7a+tS26tee9Va21rvz97WunSxVEWrUhWkUpVFIktYAgEO+5KFhJB9X2Z+f0wSEwiQCUlOlvfz\n", "8ZhHZs6c73w/CUrefM/5fr+G2+1GRERERPovh90FiIiIiIi9FAhFRERE+jkFQhEREZF+ToFQRERE\n", "pJ9TIBQRERHp5xQIRURERPo5H7sLaGKa5nTgbcuyBp3j/ZuBx4EY4GPgbsuy8ruxRBEREZE+yfYR\n", "QtM0DdM07wL+Bfie45wJwO+BG4Fo4CTwp24rUkRERKQPsz0QAg8B3wP+BzDOcc6twDuWZW2xLKsa\n", "eABYaJrmwG6qUURERKTP6gmB8CXLsiYBW89zjgnsaXphWVYhUNh4XEREREQugu2B0LKsk+04LRio\n", 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PMC\")\n", "plot(2 * np.array(eps_values), label=\"analytic\")\n", "xticks(np.arange(len(eps_values)), [\"{0:>4.3f}\".format(eps) for eps in eps_values], rotation=70)\n", "#semilogy()\n", "ylabel(\"acceptance ratio\")\n", "xlabel(r\"$\\epsilon$\")\n", "legend(loc=\"best\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.special import erf\n", "def p_theta_eta(theta, eta):\n", " phi = lambda t: (1 + erf(t / np.sqrt(2))) / 2\n", " ybar = np.mean(y, axis=0)\n", " return 1. / (2 * eta) *(phi((ybar - theta + eta) / (sigma / np.sqrt(n))) - phi((ybar - theta - eta) / (sigma / np.sqrt(n))) )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_ticks(ticks, num=7):\n", " return [float(\"{0:<.2f}\".format(tick)) for tick in np.linspace(ticks[0], ticks[-1], 7)]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import norm" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA30AAASqCAYAAADtFgKgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8FHX+x/HX7G52s7vpjZAEQv8qglhP7IiIZz31ztOz\n", "+xMbKiIqdsWCvZ56et7peXZPPRv27qnYO8hYIIUSSgIEElJ3fn/MZkkgJAuZ3dnZ/TwfD4w7Ozvz\n", "2STvnUz5zFczDAMhhBBCCCGEEMnJZXcBQgghhBBCCCFiR3b6hBBCCCGEECKJyU6fEEIIIYQQQiQx\n", "2ekTQgghhBBCiCQmO31CCCGEEEIIkcRkp08IIYQQQgghkpjs9AkhhBBCCCFEEvPYXYCdlFIPA6W6\n", "ru8XfjwSGKTr+qsxXm+X9SilQsBxuq4/Ecv1dlq/G7gOOBHIBF4HztJ1fVkirjv8/fqxm6f20HX9\n", "k96Waef7FSmds37AzcB+gB/4DDhf1/U5cVj3luSsDLgDGI95QPB1YJqu60vCz2dhvp9DgHRgVvj5\n", "2vDztr1fkbo526CWscBHwHhd1z+Mw/q2JGc95iSKnPX4vIi9VM6aUmoSMB0oA+YCF+q6/l4c1huL\n", "rPW4zdtgWXH9bImVVD/TZ4T/dXgR2CkO691wPcXAc3FYb4cZwAnA8cBemOGN1/q3ZN2jgRWY36fO\n", 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$\\epsilon$: {1:>4.3f}\".format(i*2, eps_values[i*2]), size=15)\n", " ax = sns.distplot(samples[i*2], hist=False, axlabel=r'$\\theta$', hist_kws={\"rotation\":.3, \"fontsize\":30})\n", " ax.set_xlabel(r'$\\theta$', size=15)\n", " ax.locator_params(axis = 'x', nbins = nbins)\n", " ax.locator_params(axis = 'y', nbins = 5)\n", " min_val, max_val = ax.get_xlim()\n", " x_grid = np.linspace(min_val, max_val, 1000)\n", " plot(x_grid, p_theta_eta(x_grid, eps_values[i*2]), \"--\")\n", " if i*2>=22:\n", " xlim([0.96, 1.04])\n", " \n", " if i*2<=14:\n", " ylim([None, 9])\n", " \n", " rv = norm(loc=np.mean(y), scale=1/np.sqrt(n))\n", " plot(x_grid, rv.pdf(x_grid), \":\", color=sns.xkcd_rgb[\"pale red\"])\n", " fill(x_grid, rv.pdf(x_grid), color=sns.xkcd_rgb[\"pale red\"], alpha=0.2)\n", " tick_params(axis='both', which='major', labelsize=15)\n", " #i = 37\n", " #subplot(5, 4, 20)\n", " #title(r\"Iteration: {0}, $\\epsilon$: {1:>4.3f}\".format(i, eps_values[i]))\n", " #ax = sns.distplot(samples[i], hist=False, axlabel=r'$\\theta$')\n", " #ax.locator_params(axis = 'x', nbins = nbins)\n", " #min_val, max_val = ax.get_xlim()\n", " #x_grid = np.linspace(min_val, max_val, 1000)\n", " #plot(x_grid, p_theta_eta(x_grid, eps_values[i]))\n", " \n", " ax = subplot(5, 4, i+2)\n", " plot(1, label=\"ABC PMC\")\n", " plot(1, \"--\", label=\"ABC analytic\")\n", " plot(1, \":\", label=\"Bayesian\")\n", " legend(loc=2)\n", " ax.set_axis_off()\n", " subplots_adjust(hspace = 0.40)\n", " \n", " #savefig(\"1d_gauss_posterior_evolution.pdf\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.stats import gaussian_kde" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA3MAAASNCAYAAADpdgedAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FNf1sN8tWvUuJCQhECC49N4NGPfeE3fHNXbsxD+n\n", 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"language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

maxentile/msm-learn

notebooks/Cython.ipynb

1

32045

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# following: http://nbviewer.ipython.org/url/jakevdp.github.com/downloads/notebooks/memview_bench.ipynb" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext cythonmagic" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import numpy.random as npr\n", "from numpy.linalg import det" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(1000,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "## 0. Python-only version\n", "\n", "def euclidean(x1,x2):\n", " return np.sqrt(sum((x1-x2)**2))\n", "\n", "def pairwise_v1(X,metric=euclidean):\n", " n,dim = X.shape\n", " D = np.zeros((n,n))\n", " \n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = metric(X[i],X[j])\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 8.13 s per loop\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# a function pointer to a metric\n", "ctypedef double (*metric_ptr)(np.ndarray,np.ndarray)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean(np.ndarray[double,ndim=1,mode='c'] x1,\n", " np.ndarray[double,ndim=1,mode='c'] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmp*tmp\n", " return sqrt(d)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v2(np.ndarray[double, ndim=2, mode='c'] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n\n", " n = X.shape[0]\n", "\n", " cdef np.ndarray[double, ndim=2, mode='c'] D = np.empty((n,\n", " n))\n", " for i in range(n):\n", " for j in range(n):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 8.15 s per loop\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v2(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 2.26 s per loop\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "ctypedef double (*metric_ptr)(double[::1], double[::1])\n", "\n", "cdef double euclidean(double[::1] x1,\n", " double[::1] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmp*tmp\n", " return sqrt(d)\n", "\n", "\n", "def pairwise_v3(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples\n", " n_samples = X.shape[0]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v3(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 44.7 ms per loop\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# define a function pointer to a metric\n", "ctypedef double (*metric_ptr)(double*, double*, int)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean_distance(double* x1,\n", " double* x2,\n", " int N):\n", " cdef double tmp, d\n", " cdef np.intp_t i\n", "\n", " d = 0\n", "\n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d += tmp * tmp\n", "\n", " return sqrt(d)\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v4(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean_distance\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples, n_dim\n", " n_samples = X.shape[0]\n", " n_dim = X.shape[1]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0]\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " Dptr[i * n_samples + j] = dist_func(Xptr + i * n_dim,\n", " Xptr + j * n_dim,\n", " n_dim)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v4(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.68 ms per loop\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.spatial.distance import pdist" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pdist(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 2.5 ms per loop\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(500,100,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy.linalg import det\n", "BC = lambda X,Y: det(X.T.dot(Y))/ np.sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "BC(X[0],X[10])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "0.00097428727055857804" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "def pairwise_bc_v1(X):\n", " bc = np.zeros((len(X),len(X)))\n", " for i in range(len(X)):\n", " for j in range(len(X)):\n", " bc[i,j] = BC(X[i],X[j])\n", " return bc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc_v1(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 13 s per loop\n" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "ctypedef double (*metric_ptr)(double[::2], double[::2])\n", "\n", "cdef double euclidean(double[::2] x1,\n", " double[::2] x2):\n", " cdef double tmp,d\n", " cdef np.intp_t i,N\n", " \n", " d = 0\n", " N = x1.shape[0]\n", " \n", " for i in range(N):\n", " tmp = x1[i] - x2[i]\n", " d+=tmp*tmp\n", " return sqrt(d)\n", "\n", "\n", "def pairwise_v3(double[:, ::1] X not None,\n", " metric = 'euclidean'):\n", " cdef metric_ptr dist_func\n", " if metric == 'euclidean':\n", " dist_func = &euclidean\n", " else:\n", " raise ValueError(\"unrecognized metric\")\n", "\n", " cdef np.intp_t i, j, n_samples\n", " n_samples = X.shape[0]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = dist_func(X[i], X[j])\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def myloop(X):\n", " s=0\n", " for i in range(X.shape[0]):\n", " for j in range(X.shape[1]):\n", " for k in range(X.shape[2]):\n", " s+=X[i,j,k]\n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def myloop_c(np.ndarray[np.double_t,ndim=3] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s=0\n", " for i in range(X.shape[0]):\n", " for j in range(X.shape[1]):\n", " for k in range(X.shape[2]):\n", " s+=X[i,j,k]\n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit myloop(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10 loops, best of 3: 110 ms per loop\n" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit myloop_c(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 239 \u00b5s per loop\n" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def mymat_c(np.ndarray[np.double_t,ndim=3] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s = 0\n", " \n", " \n", " \n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "mymat_c(X)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "0.0" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def mymat_c(np.ndarray[np.double_t,ndim=2] X):\n", " cdef int i,j,k\n", " cdef double s\n", " s = 0\n", " \n", " s += det(X)\n", " \n", " return s" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "X[0].shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "(100, 3)" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "sq = npr.randn(100,100)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "mymat_c(sq)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ "1.5574946108189126e+76" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def binet_cauchy_c(np.ndarray[np.double_t,ndim=2] X,\n", " np.ndarray[np.double_t,ndim=2] Y):\n", " \n", " return det(X.T.dot(Y))/ sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "binet_cauchy_p = lambda X,Y: det(X.T.dot(Y))/ np.sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_p(sq,sq)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 662 \u00b5s per loop\n" ] } ], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_c(sq,sq)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 616 \u00b5s per loop\n" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef double binet_cauchy_c(np.ndarray[np.double_t,ndim=2] X,\n", " np.ndarray[np.double_t,ndim=2] Y):\n", " \n", " return det(X.T.dot(Y))/ sqrt(det(X.T.dot(X)) * det(Y.T.dot(Y)))\n", "\n", "def pairwise_bc_c(np.ndarray[np.double_t,ndim=3] X):\n", " n_samples = X.shape[0]\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", " cdef int i,j\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " return D\n", "\n", "# pointers\n", "def pairwise_bc_pointers(double[:,:,::1] X):\n", " cdef int n_samples,mat_size\n", " n_samples = X.shape[0]\n", " mat_size = X.shape[1]*X.shape[2]\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", " cdef int i,j\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[i*n_samples+j] = binet_cauchy_c(Xptr+i*mat_size,\n", " Xptr+j*mat_size)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[i*n_samples+j] = pairwise_bc(Xptr+i*mat_size,\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:34:45: undeclared name not builtin: pairwise_bc\n", "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double* Dptr = &D[0, 0]\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[i*n_samples+j] = pairwise_bc(Xptr+i*mat_size,\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:34:50: Cannot convert 'double *' to Python object\n", "\n", "Error compiling Cython file:\n", "------------------------------------------------------------\n", "...\n", " cdef double* Xptr = &X[0, 0, 0]\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " #D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " Dptr[i*n_samples+j] = pairwise_bc(Xptr+i*mat_size,\n", " Xptr+j*mat_size)\n", " ^\n", "------------------------------------------------------------\n", "\n", "/Users/joshuafass/.ipython/cython/_cython_magic_57aa37c15d80b7bc1ad7aebcebadc62b.pyx:35:50: Cannot convert 'double *' to Python object\n" ] } ], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit binet_cauchy_c(X[0],X[-1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 50.6 \u00b5s per loop\n" ] } ], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 13.9 s per loop\n" ] } ], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "def pairwise_bc_p(X):\n", " n_samples = X.shape[0]\n", " D = np.empty((n_samples, n_samples))\n", " for i in xrange(n_samples):\n", " for j in xrange(n_samples):\n", " D[i,j] = binet_cauchy_c(X[i,:,:],X[j,:,:])\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_bc_p(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 14.2 s per loop\n" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 82, "text": [ "(500, 100, 3)" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double distance(double[:,::1] X,\n", " np.intp_t i1,\n", " np.intp_t i2):\n", " cdef double tmp,d\n", " cdef np.intp_t j\n", " d = 0\n", " \n", " for j in range(X.shape[1]):\n", " tmp = X[i1,j] - X[i2,j]\n", " d+= tmp*tmp\n", " return sqrt(d)\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_no_slice(double[:,::1] X not None):\n", " cdef np.intp_t i,j,n,dim\n", " n=X.shape[0]\n", " dim = X.shape[1]\n", " cdef double[:,::1] D = np.empty((n,n))\n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = distance(X,i,j)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(1000,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 106 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_no_slice(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.83 ms per loop\n" ] } ], "prompt_number": 107 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pdist(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 3.25 ms per loop\n" ] } ], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "\n", "import numpy as np\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "# define a function pointer to a metric\n", "ctypedef double (*metric_ptr)(double[:, ::1], np.intp_t, np.intp_t)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef double euclidean_distance(double[:, ::1] X,\n", " np.intp_t i1, np.intp_t i2):\n", " cdef double tmp, d\n", " cdef np.intp_t j\n", "\n", " d = 0\n", "\n", " for j in range(X.shape[1]):\n", " tmp = X[i1, j] - X[i2, j]\n", " d += tmp * tmp\n", "\n", " return sqrt(d)\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def pairwise_v5(double[:, ::1] X not None):\n", " cdef np.intp_t i, j, n_samples, n_dim\n", " n_samples = X.shape[0]\n", " n_dim = X.shape[1]\n", "\n", " cdef double[:, ::1] D = np.empty((n_samples, n_samples))\n", "\n", " for i in range(n_samples):\n", " for j in range(n_samples):\n", " D[i, j] = euclidean_distance(X, i, j)\n", "\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit pairwise_v5(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 loops, best of 3: 4.77 ms per loop\n" ] } ], "prompt_number": 104 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%%cython\n", "import numpy as np\n", "from numpy.linalg import det\n", "\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef double bc(double[:,:,:] X,\n", " np.intp_t i1,\n", " np.intp_t i2):\n", " return sum(X[i1])\n", " #return det(X[i1].T.dot(X[i2]))/ sqrt(det(X[i1].T.dot(X[i1])) * det(X[i2].T.dot(X[i2])))\n", "\n", "def pairwise_bc_no_slice(double[:,:,:] X not None):\n", " cdef np.intp_t i,j,n\n", " n=X.shape[0]\n", " cdef np.ndarray D = np.zeros((n,n),dtype=np.double)\n", " for i in range(n):\n", " for j in range(n):\n", " D[i,j] = bc(X,i,j)\n", " return D" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "X = npr.randn(500,10,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "<MemoryView of 'array' at 0x10d169300>" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

texib/pixnet_hackathon_2015

doc2vec-IMDB.ipynb

1

50042

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# gensim doc2vec & IMDB sentiment dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: section on introduction & motivation\n", "\n", "TODO: prerequisites + dependencies (statsmodels, patsy, ?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fetch and prep exactly as in Mikolov's go.sh shell script. (Note this cell tests for existence of required files, so steps won't repeat once the final summary file (`aclImdb/alldata-id.txt`) is available alongside this notebook.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "rm: temp: No such file or directory\n" ] } ], "source": [ "%%bash\n", "# adapted from Mikolov's example go.sh script: \n", "if [ ! -f \"aclImdb/alldata-id.txt\" ]\n", "then\n", " if [ ! -d \"aclImdb\" ] \n", " then\n", " if [ ! -f \"aclImdb_v1.tar.gz\" ]\n", " then\n", " wget --quiet http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n", " fi\n", " tar xf aclImdb_v1.tar.gz\n", " fi\n", " \n", " #this function will convert text to lowercase and will disconnect punctuation and special symbols from words\n", " function normalize_text {\n", " awk '{print tolower($0);}' < $1 | sed -e 's/\\./ \\. /g' -e 's/<br \\/>/ /g' -e 's/\"/ \" /g' \\\n", " -e 's/,/ , /g' -e 's/(/ ( /g' -e 's/)/ ) /g' -e 's/\\!/ \\! /g' -e 's/\\?/ \\? /g' \\\n", " -e 's/\\;/ \\; /g' -e 's/\\:/ \\: /g' > $1-norm\n", " }\n", "\n", " export LC_ALL=C\n", " for j in train/pos train/neg test/pos test/neg train/unsup; do\n", " rm temp\n", " for i in `ls aclImdb/$j`; do cat aclImdb/$j/$i >> temp; awk 'BEGIN{print;}' >> temp; done\n", " normalize_text temp\n", " mv temp-norm aclImdb/$j/norm.txt\n", " done\n", " mv aclImdb/train/pos/norm.txt aclImdb/train-pos.txt\n", " mv aclImdb/train/neg/norm.txt aclImdb/train-neg.txt\n", " mv aclImdb/test/pos/norm.txt aclImdb/test-pos.txt\n", " mv aclImdb/test/neg/norm.txt aclImdb/test-neg.txt\n", " mv aclImdb/train/unsup/norm.txt aclImdb/train-unsup.txt\n", "\n", " cat aclImdb/train-pos.txt aclImdb/train-neg.txt aclImdb/test-pos.txt aclImdb/test-neg.txt aclImdb/train-unsup.txt > aclImdb/alldata.txt\n", " awk 'BEGIN{a=0;}{print \"_*\" a \" \" $0; a++;}' < aclImdb/alldata.txt > aclImdb/alldata-id.txt\n", "fi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path\n", "assert os.path.isfile(\"aclImdb/alldata-id.txt\"), \"alldata-id.txt unavailable\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is small enough to be read into memory. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[4]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[line_no//25000]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "201306 docs: 70000 train-sentiment, 70000 test-sentiment\n" ] } ], "source": [ "import gensim\n", "# from gensim.models.doc2vec import \n", "from collections import namedtuple\n", "\n", "SentimentDocument = namedtuple('SentimentDocument', 'words tags split sentiment')\n", "\n", "alldocs = [] # will hold all docs in original order\n", "with open('./data/new_parsed_no_spam.txt') as alldata:\n", " for line_no, line in enumerate(alldata):\n", " tokens = line.split()\n", " words = tokens[1:]\n", " tags = [line_no] # `tags = [tokens[0]]` would also work at extra memory cost\n", " split = ['train','test','extra','extra'][line_no//70000] # 25k train, 25k test, 25k extra\n", " sentiment = [1.0, 0.0, 1.0, 0.0, None, None, None, None][line_no//70000] # [12.5K pos, 12.5K neg]*2 then unknown\n", " alldocs.append(SentimentDocument(words, tags, split, sentiment))\n", "\n", "train_docs = [doc for doc in alldocs if doc.split == 'train']\n", "test_docs = [doc for doc in alldocs if doc.split == 'test']\n", "doc_list = alldocs[:] # for reshuffling per pass\n", "\n", "print('%d docs: %d train-sentiment, %d test-sentiment' % (len(doc_list), len(train_docs), len(test_docs)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set-up Doc2Vec Training & Evaluation Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Approximating experiment of Le & Mikolov [\"Distributed Representations of Sentences and Documents\"](http://cs.stanford.edu/~quocle/paragraph_vector.pdf), also with guidance from Mikolov's [example go.sh](https://groups.google.com/d/msg/word2vec-toolkit/Q49FIrNOQRo/J6KG8mUj45sJ):\n", "\n", "`./word2vec -train ../alldata-id.txt -output vectors.txt -cbow 0 -size 100 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 40 -binary 0 -iter 20 -min-count 1 -sentence-vectors 1`\n", "\n", "Parameter choices below vary:\n", "\n", "* 100-dimensional vectors, as the 400d vectors of the paper don't seem to offer much benefit on this task\n", "* similarly, frequent word subsampling seems to decrease sentiment-prediction accuracy, so it's left out\n", "* `cbow=0` means skip-gram which is equivalent to the paper's 'PV-DBOW' mode, matched in gensim with `dm=0`\n", "* added to that DBOW model are two DM models, one which averages context vectors (`dm_mean`) and one which concatenates them (`dm_concat`, resulting in a much larger, slower, more data-hungry model)\n", "* a `min_count=2` saves quite a bit of model memory, discarding only words that appear in a single doc (and are thus no more expressive than the unique-to-each doc vectors themselves)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec(dm/c,d100,n5,w5,mc2,t2)\n", "Doc2Vec(dbow,d100,n5,mc2,t2)\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t2)\n" ] } ], "source": [ "from gensim.models import Doc2Vec\n", "import gensim.models.doc2vec\n", "from collections import OrderedDict\n", "import multiprocessing\n", "\n", "cores = multiprocessing.cpu_count()\n", "assert gensim.models.doc2vec.FAST_VERSION > -1, \"this will be painfully slow otherwise\"\n", "\n", "simple_models = [\n", " # PV-DM w/concatenation - window=5 (both sides) approximates paper's 10-word total window size\n", " Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=cores),\n", " # PV-DBOW \n", " Doc2Vec(dm=0, size=100, negative=5, hs=0, min_count=2, workers=cores),\n", " # PV-DM w/average\n", " Doc2Vec(dm=1, dm_mean=1, size=100, window=10, negative=5, hs=0, min_count=2, workers=cores),\n", "]\n", "\n", "# speed setup by sharing results of 1st model's vocabulary scan\n", "simple_models[0].build_vocab(alldocs) # PV-DM/concat requires one special NULL word so it serves as template\n", "print(simple_models[0])\n", "for model in simple_models[1:]:\n", " model.reset_from(simple_models[0])\n", " print(model)\n", "\n", "models_by_name = OrderedDict((str(model), model) for model in simple_models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following the paper, we also evaluate models in pairs. These wrappers return the concatenation of the vectors from each model. (Only the singular models are trained.)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.test.test_doc2vec import ConcatenatedDoc2Vec\n", "models_by_name['dbow+dmm'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[2]])\n", "models_by_name['dbow+dmc'] = ConcatenatedDoc2Vec([simple_models[1], simple_models[0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive Evaluation Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper methods for evaluating error rate." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import statsmodels.api as sm\n", "from random import sample\n", "\n", "# for timing\n", "from contextlib import contextmanager\n", "from timeit import default_timer\n", "import time \n", "\n", "@contextmanager\n", "def elapsed_timer():\n", " start = default_timer()\n", " elapser = lambda: default_timer() - start\n", " yield lambda: elapser()\n", " end = default_timer()\n", " elapser = lambda: end-start\n", " \n", "def logistic_predictor_from_data(train_targets, train_regressors):\n", " logit = sm.Logit(train_targets, train_regressors)\n", " predictor = logit.fit(disp=0)\n", " #print(predictor.summary())\n", " return predictor\n", "\n", "def error_rate_for_model(test_model, train_set, test_set, infer=False, infer_steps=3, infer_alpha=0.1, infer_subsample=0.1):\n", " \"\"\"Report error rate on test_doc sentiments, using supplied model and train_docs\"\"\"\n", "\n", " train_targets, train_regressors = zip(*[(doc.sentiment, test_model.docvecs[doc.tags[0]]) for doc in train_set])\n", " train_regressors = sm.add_constant(train_regressors)\n", " predictor = logistic_predictor_from_data(train_targets, train_regressors)\n", "\n", " test_data = test_set\n", " if infer:\n", " if infer_subsample < 1.0:\n", " test_data = sample(test_data, int(infer_subsample * len(test_data)))\n", " test_regressors = [test_model.infer_vector(doc.words, steps=infer_steps, alpha=infer_alpha) for doc in test_data]\n", " else:\n", " test_regressors = [test_model.docvecs[doc.tags[0]] for doc in test_docs]\n", " test_regressors = sm.add_constant(test_regressors)\n", " \n", " # predict & evaluate\n", " test_predictions = predictor.predict(test_regressors)\n", " corrects = sum(np.rint(test_predictions) == [doc.sentiment for doc in test_data])\n", " errors = len(test_predictions) - corrects\n", " error_rate = float(errors) / len(test_predictions)\n", " return (error_rate, errors, len(test_predictions), predictor)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bulk Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using explicit multiple-pass, alpha-reduction approach as sketched in [gensim doc2vec blog post](http://radimrehurek.com/2014/12/doc2vec-tutorial/) – with added shuffling of corpus on each pass.\n", "\n", "Note that vector training is occurring on *all* documents of the dataset, which includes all TRAIN/TEST/DEV docs.\n", "\n", "Evaluation of each model's sentiment-predictive power is repeated after each pass, as an error rate (lower is better), to see the rates-of-relative-improvement. The base numbers reuse the TRAIN and TEST vectors stored in the models for the logistic regression, while the _inferred_ results use newly-inferred TEST vectors. \n", "\n", "(On a 4-core 2.6Ghz Intel Core i7, these 20 passes training and evaluating 3 main models takes about an hour.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\n", "best_error = defaultdict(lambda :1.0) # to selectively-print only best errors achieved" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "START 2015-07-25 01:21:54.710897\n" ] }, { "ename": "PerfectSeparationError", "evalue": "Perfect separation detected, results not available", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mPerfectSeparationError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-6-ae19afedce59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0meval_duration\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0melapsed_timer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0meval_elapsed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_rate_for_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_docs\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mtrain_regressors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mpredictor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[1;31m#print(predictor.summary())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpredictor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m 1374\u001b[0m bnryfit = super(Logit, self).fit(start_params=start_params,\n\u001b[0;32m 1375\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mdiscretefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogitResults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbnryfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m 201\u001b[0m mlefit = super(DiscreteModel, self).fit(start_params=start_params,\n\u001b[0;32m 202\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 203\u001b[1;33m disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m 204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmlefit\u001b[0m \u001b[1;31m# up to subclasses to wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[0mretall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretall\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 425\u001b[1;33m full_output=full_output)\n\u001b[0m\u001b[0;32m 426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 427\u001b[0m \u001b[1;31m#NOTE: this is for fit_regularized and should be generalized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/optimizer.pyc\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)\u001b[0m\n\u001b[0;32m 182\u001b[0m \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[0mretall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mretall\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 184\u001b[1;33m hess=hessian)\n\u001b[0m\u001b[0;32m 185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;31m# this is stupid TODO: just change this to something sane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/base/optimizer.pyc\u001b[0m in \u001b[0;36m_fit_newton\u001b[1;34m(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess, ridge_factor)\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 247\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 249\u001b[0m \u001b[0miterations\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[0mfval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mfargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this is the negative likelihood\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/statsmodels-0.6.1-py2.7-linux-x86_64.egg/statsmodels/discrete/discrete_model.pyc\u001b[0m in \u001b[0;36m_check_perfect_pred\u001b[1;34m(self, params, *args)\u001b[0m\n\u001b[0;32m 184\u001b[0m np.allclose(fittedvalues - endog, 0)):\n\u001b[0;32m 185\u001b[0m \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Perfect separation detected, results not available\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mPerfectSeparationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 188\u001b[0m def fit(self, start_params=None, method='newton', maxiter=35,\n", "\u001b[1;31mPerfectSeparationError\u001b[0m: Perfect separation detected, results not available" ] } ], "source": [ "from random import shuffle\n", "import datetime\n", "\n", "alpha, min_alpha, passes = (0.025, 0.001, 20)\n", "alpha_delta = (alpha - min_alpha) / passes\n", "\n", "print(\"START %s\" % datetime.datetime.now())\n", "\n", "for epoch in range(passes):\n", " shuffle(doc_list) # shuffling gets best results\n", " \n", " for name, train_model in models_by_name.items():\n", " # train\n", " duration = 'na'\n", " train_model.alpha, train_model.min_alpha = alpha, alpha\n", " with elapsed_timer() as elapsed:\n", " train_model.train(doc_list)\n", " duration = '%.1f' % elapsed()\n", " \n", " # evaluate\n", " eval_duration = ''\n", " with elapsed_timer() as eval_elapsed:\n", " err, err_count, test_count, predictor = error_rate_for_model(train_model, train_docs, test_docs)\n", " eval_duration = '%.1f' % eval_elapsed()\n", " best_indicator = ' '\n", " if err <= best_error[name]:\n", " best_error[name] = err\n", " best_indicator = '*' \n", " print(\"%s%f : %i passes : %s %ss %ss\" % (best_indicator, err, epoch + 1, name, duration, eval_duration))\n", "\n", " if ((epoch + 1) % 5) == 0 or epoch == 0:\n", " eval_duration = ''\n", " with elapsed_timer() as eval_elapsed:\n", " infer_err, err_count, test_count, predictor = error_rate_for_model(train_model, train_docs, test_docs, infer=True)\n", " eval_duration = '%.1f' % eval_elapsed()\n", " best_indicator = ' '\n", " if infer_err < best_error[name + '_inferred']:\n", " best_error[name + '_inferred'] = infer_err\n", " best_indicator = '*'\n", " print(\"%s%f : %i passes : %s %ss %ss\" % (best_indicator, infer_err, epoch + 1, name + '_inferred', duration, eval_duration))\n", "\n", " print('completed pass %i at alpha %f' % (epoch + 1, alpha))\n", " alpha -= alpha_delta\n", " \n", "print(\"END %s\" % str(datetime.datetime.now()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Achieved Sentiment-Prediction Accuracy" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.094800 dbow+dmm_inferred\n", "0.099920 dbow+dmm\n", "0.101000 Doc2Vec(dbow,d100,n5,mc2,t8)\n", "0.101320 dbow+dmc\n", "0.109600 dbow+dmc_inferred\n", "0.111600 Doc2Vec(dbow,d100,n5,mc2,t8)_inferred\n", "0.158760 Doc2Vec(dm/m,d100,n5,w10,mc2,t8)\n", "0.184000 Doc2Vec(dm/m,d100,n5,w10,mc2,t8)_inferred\n", "0.215160 Doc2Vec(dm/c,d100,n5,w5,mc2,t8)\n", "0.232400 Doc2Vec(dm/c,d100,n5,w5,mc2,t8)_inferred\n" ] } ], "source": [ "# print best error rates achieved\n", "for rate, name in sorted((rate, name) for name, rate in best_error.items()):\n", " print(\"%f %s\" % (rate, name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In my testing, unlike the paper's report, DBOW performs best. Concatenating vectors from different models only offers a small predictive improvement. The best results I've seen are still just under 10% error rate, still a ways from the paper's 7.42%.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examining Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are inferred vectors close to the precalculated ones?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "for doc 25430...\n", "Doc2Vec(dm/c,d100,n5,w5,mc2,t8):\n", " [(25430, 0.6583491563796997), (27314, 0.4142411947250366), (16479, 0.40846431255340576)]\n", "Doc2Vec(dbow,d100,n5,mc2,t8):\n", " [(25430, 0.9325973987579346), (49281, 0.5766637921333313), (79679, 0.5634804964065552)]\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t8):\n", " [(25430, 0.7970066666603088), (97818, 0.6925815343856812), (230, 0.690807580947876)]\n" ] } ], "source": [ "doc_id = np.random.randint(simple_models[0].docvecs.count) # pick random doc; re-run cell for more examples\n", "print('for doc %d...' % doc_id)\n", "for model in simple_models:\n", " inferred_docvec = model.infer_vector(alldocs[doc_id].words)\n", " print('%s:\\n %s' % (model, model.docvecs.most_similar([inferred_docvec], topn=3)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Yes, here the stored vector from 20 epochs of training is usually one of the closest to a freshly-inferred vector for the same words. Note the defaults for inference are very abbreviated – just 3 steps starting at a high alpha – and likely need tuning for other applications.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do close documents seem more related than distant ones?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TARGET (72927): «this is one of the best films of this year . for a year that was fueled by controversy and crap , it was nice to finally see a film that had a true heart to it . from the opening scene to the end , i was so moved by the love that will smith has for his son . basically , if you see this movie and walk out of it feeling nothing , there is something that is very wrong with you . loved this movie , it's the perfect movie to end the year with . the best part was after the movie , my friends and i all got up and realized that this movie had actually made the four of us tear up ! it's an amazing film and if will smith doesn't get at least an oscar nom , then the oscars will just suck . in fact will smith should actually just win an oscar for this role . ! ! ! i loved this movie ! ! ! ! everybody needs to see especially the people in this world that take everything for granted , watch this movie , it will change you !»\n", "\n", "SIMILAR/DISSIMILAR DOCS PER MODEL Doc2Vec(dm/m,d100,n5,w10,mc2,t8):\n", "\n", "MOST (2046, 0.7372332215309143): «i thought this movie would be dumb , but i really liked it . people i know hate it because spirit was the only horse that talked . well , so what ? the songs were good , and the horses didn't need to talk to seem human . i wouldn't care to own the movie , and i would love to see it again . 8/10»\n", "\n", "MEDIAN (6999, 0.4129640758037567): «okay , the recent history of star trek has not been good . the next generation faded in its last few seasons , ds9 boldly stayed where no one had stayed before , and voyager started very bad and never really lived up to its promise . so , when they announced a new star trek series , i did not have high expectations . and , the first episode , broken bow , did have some problems . but , overall it was solid trek material and a good romp . i'll get the nits out of the way first . the opening theme is dull and i don't look forward to sitting through it regularly , but that's what remotes are for . what was really bad was the completely gratuitous lotion rubbing scene that just about drove my wife out of the room . they need to cut that nonsense out . but , the plot was strong and moved along well . the characters , though still new , seem to be well rounded and not always what you would expect . the vulcans are clearly being presented very differently than before , with a slightly ominous theme . i particularly liked the linguist , who is the first star trek character to not be able to stand proud in the face of death , but rather has to deal with her phobias and fears . they seemed to stay true to trek lore , something that has been a significant problem in past series , though they have plenty of time to bring us things like shooting through shields , the instant invention of technology that can fix anything , and the inevitable plethora of time-travel stories . anyone want to start a pool on how long before the borg show up ? all in all , the series has enormous potential . they are seeing the universe with fresh eyes . we have the chance to learn how things got the way they were in the later series . how did the klingons go from just insulting to war ? how did we meet the romulans ? how did the federation form and just who put earth in charge . why is the prime directive so important ? if they address these things rather than spitting out time travel episodes , this will be an interesting series . my favorite line : zephram cochran saying \" where no man has gone before \" ( not \" no one \" )»\n", "\n", "LEAST (16617, 0.015464222989976406): «i saw this movie during a tolkien-themed interim class during my sophomore year of college . i was seated unfortunately close to the screen and my professor chose me to serve as a whipping boy- everyone else was laughing , but they weren't within constant eyesight . let's get it out of the way : the peter jackson 'lord of the rings' films do owe something to the bakshi film . in jackson's version of the fellowship of the ring , for instance , the scene in which the black riders assault the empty inn beds is almost a complete carbon copy of the scene in bakshi's film , shot by shot . you could call this plagiarism or homage , depending on your agenda . i'm sure the similarities don't stop there . i'm not going to do any research to find out what they are , because that would imply i have some mote of respect for this film . i'm sure others have outlined the similarities- look around . this movie is a complete train wreck in every sense of the metaphor , and many , many people died in the accident . i've decided to list what i can remember in a more or less chronological fashion- if i've left out anything else that offended me it's because i'm completely overwhelmed , confronted with a wealth of failure ( and , at high points , mediocrity ) . *due to heavy use of rotoscoping , gandalf is no longer a gentle , wise wizard but a wildly flailing prophet of doom ( whose hat inexplicably changes color once or twice during the course of the film ) . *saruman the white is sometimes referred to as 'aruman' during the film , without explanation . he wears purple and red for some mysterious reason . *sam is flat out hideous . the portrayal of his friendship with frodo is strangely childlike and unsatisfying . yes , hobbits are small like children , but they are not children . *merry and pippin are never introduced--they simply appear during a scene change with a one-sentence explanation . the film is filled with sloppy editing like this . *frodo , sam , pippin and merry are singing merrily as they skip through along the road . one of the hobbits procures a lute at least twice as large as he is from behind his back--which was not visible before--and begins strumming in typical fantasy bard fashion as they all break into \" la-la-la \" s . awful . *aragorn , apparently , is a native american dressed in an extremely stereotypical fantasy tunic ( no pants ) , complete with huge , square pilgrim belt buckle . he is arguably the worst swordsman in the entire movie--oftentimes he gets one wobbly swing in before being knocked flat on his ass . *the black riders appear more like lepers than menacing instruments of evil . they limp everywhere they go at a painfully slow pace . this is disturbing to be sure , but not frightening . *the scene before the black riders attempt to cross the ford of bruinen ( in which they stare at frodo , who is on the other side on horseback ) goes on forever , during which time the riders rear their horses in a vaguely threatening manner and . . . do nothing else . the scene was probably intended to illustrate frodo's hallucinatory decline as he succumbs to his wound . it turns out to be more plodding than anything else . *gimli the dwarf is just as tall as legolas the elf . he's a dwarf . there is simply no excuse for that . he also looks like a bastardized david the gnome . it's a crude but accurate description . *boromir appears to have pilfered elmer fudd's golden viking armor from that bugs bunny opera episode . he looks ridiculous . *despite the similarity to tolkien's illustration , the balrog is howl inducing and the least-threatening villain in the entire film . it looks like someone wearing pink bedroom slippers , and it's barely taller than gandalf . \" purists \" may prefer this balrog , but i'll take jackson's version any day . *the battle scenes are awkward and embarrassing . almost none of the characters display any level of competency with their armaments . i'm not asking for action-packed scenes like those in jackson's film , but they are supposed to be fighting . *treebeard makes a very short appearance , and i was sorry he bothered to show up at all . watch the film , you'll see what i mean . alright , now for the good parts of the film . *some of the voice acting is pretty good . it isn't that aragorn sounds bad , he just looks kind of like the jolly green giant . *galadriel is somewhat interesting in this portrayal ; like tom bombadil , she seems immune to the ring's powers of temptation , and her voice actress isn't horrible either . *boromir's death isn't as heart wrenching as in jackson's portrayal of the same scene , but it's still appropriately dramatic ( and more true to his death in the book , though i don't believe jackson made a mistake shooting it the way he did ) . *as my professor pointed out ( between whispered threats ) , the orcs ( mainly at helm's deep , if i'm correct ) resemble the war-ravaged corpses of soldiers , a political statement that works pretty well if you realize what's being attempted . *while this isn't really a positive point about the film , bakshi can't be blamed for the majority of the failures in this movie , or so i've been told--the project was on a tight budget , and late in its production he lost creative control to some of the higher-ups ( who i'm sure hadn't read the books ) . let me be clear : i respect bakshi for even attempting something of this magnitude . i simply have a hard time believing he was happy with the final product . overall , i cannot in any way recommend this blasphemous adaptation of tolkien's classic trilogy even for laughs , unless you've already read the books and have your own visualizations of the characters , places and events . i'm sure somebody , somewhere , will pick a copy of this up in confusion ; if you do , keep an open mind and glean what good you can from it .»\n", "\n" ] } ], "source": [ "import random\n", "\n", "doc_id = np.random.randint(simple_models[0].docvecs.count) # pick random doc, re-run cell for more examples\n", "model = random.choice(simple_models) # and a random model\n", "sims = model.docvecs.most_similar(doc_id, topn=model.docvecs.count) # get *all* similar documents\n", "print(u'TARGET (%d): «%s»\\n' % (doc_id, ' '.join(alldocs[doc_id].words)))\n", "print(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\\n' % model)\n", "for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:\n", " print(u'%s %s: «%s»\\n' % (label, sims[index], ' '.join(alldocs[sims[index][0]].words)))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Somewhat, in terms of reviewer tone, movie genre, etc... the MOST cosine-similar docs usually seem more like the TARGET than the MEDIAN or LEAST.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Do the word vectors show useful similarities?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "word_models = simple_models[:]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "most similar words for 'comedy/drama' (38 occurences)\n" ] }, { "data": { "text/html": [ "<table><tr><th>Doc2Vec(dm/c,d100,n5,w5,mc2,t8)</th><th>Doc2Vec(dbow,d100,n5,mc2,t8)</th><th>Doc2Vec(dm/m,d100,n5,w10,mc2,t8)</th></tr><tr><td>[('comedy', 0.7255545258522034),<br>\n", "('thriller', 0.6946465969085693),<br>\n", "('drama', 0.6763534545898438),<br>\n", "('romance', 0.6251884698867798),<br>\n", "('dramedy', 0.6217159032821655),<br>\n", "('melodrama', 0.6156137585639954),<br>\n", "('adventure', 0.6091135740280151),<br>\n", "('farce', 0.6034293174743652),<br>\n", "('chiller', 0.5948368906974792),<br>\n", "('romantic-comedy', 0.5876704454421997),<br>\n", "('fantasy', 0.5863304138183594),<br>\n", "('mystery/comedy', 0.577541708946228),<br>\n", "('whodunit', 0.572147011756897),<br>\n", "('biopic', 0.5679721832275391),<br>\n", "('thriller/drama', 0.5630226731300354),<br>\n", "('sitcom', 0.5574496984481812),<br>\n", "('slash-fest', 0.5573585033416748),<br>\n", "('mystery', 0.5542301535606384),<br>\n", "('potboiler', 0.5519827604293823),<br>\n", "('mockumentary', 0.5490710139274597)]</td><td>[('1000%', 0.42290645837783813),<br>\n", "(\"gymnast's\", 0.4180164337158203),<br>\n", "('hollywoodland', 0.3898555636405945),<br>\n", "('cultures', 0.3857914209365845),<br>\n", "('hooda', 0.3851744532585144),<br>\n", "('cites', 0.38047513365745544),<br>\n", "(\"78's\", 0.3792475461959839),<br>\n", "(\"dormael's\", 0.3775535225868225),<br>\n", "('jokester', 0.3725704252719879),<br>\n", "('impelled', 0.36853262782096863),<br>\n", "('lia', 0.3684236407279968),<br>\n", "('snivelling', 0.3683513104915619),<br>\n", "('astral', 0.36715900897979736),<br>\n", "('euro-exploitation', 0.35853487253189087),<br>\n", "(\"serra's\", 0.3578598201274872),<br>\n", "('down-on-their-luck', 0.3576606214046478),<br>\n", "('rowles', 0.3567575514316559),<br>\n", "('romantica', 0.3549702763557434),<br>\n", "('bonham-carter', 0.354231059551239),<br>\n", "('1877', 0.3541453182697296)]</td><td>[('comedy-drama', 0.6274900436401367),<br>\n", "('comedy', 0.5986765623092651),<br>\n", "('thriller', 0.5765297412872314),<br>\n", "('road-movie', 0.5615973472595215),<br>\n", "('dramedy', 0.5580120086669922),<br>\n", "('time-killer', 0.5497636795043945),<br>\n", "('potboiler', 0.5456510782241821),<br>\n", "('comedy/', 0.5439876317977905),<br>\n", "('actioner', 0.5423712134361267),<br>\n", "('diversion', 0.541743278503418),<br>\n", "('romcom', 0.5402226448059082),<br>\n", "('rom-com', 0.5358527302742004),<br>\n", "('drama', 0.5320745706558228),<br>\n", "('chiller', 0.5229591727256775),<br>\n", "('romp', 0.5228806734085083),<br>\n", "('horror/comedy', 0.5219299793243408),<br>\n", "('weeper', 0.5195824503898621),<br>\n", "('mockumentary', 0.5149033069610596),<br>\n", "('camp-fest', 0.5122634768486023),<br>\n", "('mystery/comedy', 0.5020694732666016)]</td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML at 0x1535b84d0>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "from IPython.display import HTML\n", "# pick a random word with a suitable number of occurences\n", "while True:\n", " word = random.choice(word_models[0].index2word)\n", " if word_models[0].vocab[word].count > 10:\n", " break\n", "# or uncomment below line, to just pick a word from the relevant domain:\n", "#word = 'comedy/drama'\n", "similars_per_model = [str(model.most_similar(word, topn=20)).replace('), ','),<br>\\n') for model in word_models]\n", "similar_table = (\"<table><tr><th>\" +\n", " \"</th><th>\".join([str(model) for model in word_models]) + \n", " \"</th></tr><tr><td>\" +\n", " \"</td><td>\".join(similars_per_model) +\n", " \"</td></tr></table>\")\n", "print(\"most similar words for '%s' (%d occurences)\" % (word, simple_models[0].vocab[word].count))\n", "HTML(similar_table)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the DBOW words look meaningless? That's because the gensim DBOW model doesn't train word vectors – they remain at their random initialized values – unless you ask with the `dbow_words=1` initialization parameter. Concurrent word-training slows DBOW mode significantly, and offers little improvement (and sometimes a little worsening) of the error rate on this IMDB sentiment-prediction task. \n", "\n", "Words from DM models tend to show meaningfully similar words when there are many examples in the training data (as with 'plot' or 'actor'). (All DM modes inherently involve word vector training concurrent with doc vector training.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Are the word vectors from this dataset any good at analogies?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doc2Vec(dm/c,d100,n5,w5,mc2,t8): 28.70% correct (2873 of 10012)\n", "Doc2Vec(dbow,d100,n5,mc2,t8): 0.01% correct (1 of 10012)\n", "Doc2Vec(dm/m,d100,n5,w10,mc2,t8): 27.24% correct (2727 of 10012)\n" ] } ], "source": [ "# assuming something like\n", "# https://word2vec.googlecode.com/svn/trunk/questions-words.txt \n", "# is in local directory\n", "# note: this takes many minutes\n", "for model in word_models:\n", " sections = model.accuracy('questions-words.txt')\n", " correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect'])\n", " print('%s: %0.2f%% correct (%d of %d)' % (model, float(correct*100)/(correct+incorrect), correct, correct+incorrect))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even though this is a tiny, domain-specific dataset, it shows some meager capability on the general word analogies – at least for the DM/concat and DM/mean models which actually train word vectors. (The untrained random-initialized words of the DBOW model of course fail miserably.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "This cell left intentionally erroneous. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To mix the Google dataset (if locally available) into the word tests..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim.models import Word2Vec\n", "w2v_g100b = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)\n", "w2v_g100b.compact_name = 'w2v_g100b'\n", "word_models.append(w2v_g100b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get copious logging output from above steps..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "rootLogger = logging.getLogger()\n", "rootLogger.setLevel(logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To auto-reload python code while developing..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

oyamad/gth_solve_numba

loop_jitting01.ipynb

1

63015

{ "metadata": { "name": "", "signature": "sha256:e14733313b2a73e565c5741a6238ebcd7d54ced1d7266a411ce59a29890be54c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from numba import jit\n", "from gth_solve_jit import gth_solve, gth_solve_jit" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "@jit('float64[:](float64[:,:])')\n", "def gth_solve_jit2(A):\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", " # raise ValueError('matrix must be square') # Not supported\n", " raise ValueError\n", "\n", " n = A1.shape[0]\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", " # === Reduction === #\n", " for k in range(n-1):\n", " scale = np.sum(A1[k, k+1:n])\n", " if scale <= 0:\n", " # There is one (and only one) recurrent class contained in\n", " # {0, ..., k};\n", " # compute the solution associated with that recurrent class.\n", " n = k+1\n", " break\n", " for i in range(k+1, n):\n", " A1[i, k] /= scale\n", "\n", " for j in range(k+1, n):\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", " # === Backward substitution === #\n", " x[n-1] = 1\n", " for k in range(n-2, -1, -1):\n", " for i in range(k+1, n):\n", " x[k] += x[i] * A1[i, k]\n", "\n", " # === Normalization === #\n", " norm = np.sum(x)\n", " for k in range(n):\n", " x[k] /= norm\n", "\n", " return x" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2.inspect_types()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gth_solve_jit2 (array(float64, 2d, A, nonconst),)\n", "--------------------------------------------------------------------------------\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", " # label 0\n", " # A.1 = A :: pyobject\n", " # del A\n", " # $0.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.2 = getattr(attr=array, value=$0.1) :: pyobject\n", " # del $0.1\n", " # $0.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.6 = getattr(attr=float64, value=$0.5) :: pyobject\n", " # del $0.5\n", " # $0.7 = call $0.2(A.1, dtype=$0.6) :: pyobject\n", " # del A.1\n", " # del $0.6\n", " # del $0.2\n", " # A1 = $0.7 :: pyobject\n", " # del $0.7\n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", " # $0.8 = global(len: <built-in function len>) :: pyobject\n", " # $0.10 = getattr(attr=shape, value=A1) :: pyobject\n", " # $0.11 = call $0.8($0.10, ) :: pyobject\n", " # del $0.8\n", " # del $0.10\n", " # $const0.12 = const(int, 2) :: pyobject\n", " # $0.13 = $0.11 != $const0.12 :: pyobject\n", " # del $const0.12\n", " # del $0.11\n", " # branch $0.13, 71, 45\n", " # label 45\n", " # $45.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.3 = const(int, 0) :: pyobject\n", " # $45.4 = getitem(index=$const45.3, value=$45.2) :: pyobject\n", " # del $const45.3\n", " # del $45.2\n", " # $45.6 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.7 = const(int, 1) :: pyobject\n", " # $45.8 = getitem(index=$const45.7, value=$45.6) :: pyobject\n", " # del $const45.7\n", " # del $45.6\n", " # $45.9 = $45.4 != $45.8 :: pyobject\n", " # del $45.8\n", " # del $45.4\n", " # branch $45.9, 71, 80\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", " # label 71\n", " # del A1\n", " # del $45.9\n", " # del $0.13\n", " # $71.1 = global(ValueError: <type 'exceptions.ValueError'>) :: pyobject\n", " # del $71.1\n", " # raise <type 'exceptions.ValueError'>\n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", " # label 80\n", " # del $45.9\n", " # del $0.13\n", " # $80.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const80.3 = const(int, 0) :: pyobject\n", " # $80.4 = getitem(index=$const80.3, value=$80.2) :: pyobject\n", " # del $const80.3\n", " # del $80.2\n", " # n = $80.4 :: pyobject\n", " # del $80.4\n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", " # $80.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.6 = getattr(attr=zeros, value=$80.5) :: pyobject\n", " # del $80.5\n", " # $80.9 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.10 = getattr(attr=float64, value=$80.9) :: pyobject\n", " # del $80.9\n", " # $80.11 = call $80.6(n, dtype=$80.10) :: pyobject\n", " # del $80.6\n", " # del $80.10\n", " # x = $80.11 :: pyobject\n", " # del $80.11\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", " # label 134\n", " # $134.2 = iternext(value=$phi134.1) :: pyobject\n", " # $134.3 = pair_first(value=$134.2) :: pyobject\n", " # $134.4 = pair_second(value=$134.2) :: pyobject\n", " # del $134.2\n", " # $phi137.1 = $134.3 :: pyobject\n", " # del $134.3\n", " # branch $134.4, 137, 332\n", " # label 137\n", " # k = $phi137.1 :: pyobject\n", " # del $phi137.1\n", " # jump 117\n", " # label 117\n", " # $117.1 = global(range: <built-in function range>) :: pyobject\n", " # $const117.3 = const(int, 1) :: pyobject\n", " # $117.4 = n - $const117.3 :: pyobject\n", " # del $const117.3\n", " # $117.5 = call $117.1($117.4, ) :: pyobject\n", " # del $117.4\n", " # del $117.1\n", " # $117.6 = getiter(value=$117.5) :: pyobject\n", " # del $117.5\n", " # $phi134.1 = $117.6 :: pyobject\n", " # del $117.6\n", " # jump 134\n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", " # $137.2 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $137.3 = getattr(attr=sum, value=$137.2) :: pyobject\n", " # del $137.2\n", " # $k137.5 = k :: pyobject\n", " # $const137.7 = const(int, 1) :: pyobject\n", " # $137.8 = k + $const137.7 :: pyobject\n", " # del $const137.7\n", " # $137.10 = global(slice: <type 'slice'>) :: pyobject\n", " # $137.11 = call $137.10($137.8, n, ) :: pyobject\n", " # del $137.8\n", " # del $137.10\n", " # $137.12 = build_tuple(items=[Var($k137.5, <ipython-input-2-b14bcb5c0c5e> (15)), Var($137.11, <ipython-input-2-b14bcb5c0c5e> (15))]) :: pyobject\n", " # del $k137.5\n", " # del $137.11\n", " # $137.13 = getitem(index=$137.12, value=A1) :: pyobject\n", " # del $137.12\n", " # $137.14 = call $137.3($137.13, ) :: pyobject\n", " # del $137.3\n", " # del $137.13\n", " # scale = $137.14 :: pyobject\n", " # del $137.14\n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", " # $const137.16 = const(int, 0) :: pyobject\n", " # $137.17 = scale <= $const137.16 :: pyobject\n", " # del $const137.16\n", " # branch $137.17, 187, 201\n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", " # label 187\n", " # del scale\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", " # $const187.2 = const(int, 1) :: pyobject\n", " # $187.3 = k + $const187.2 :: pyobject\n", " # del $const187.2\n", " # n = $187.3 :: pyobject\n", " # del $187.3\n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", " # jump 333\n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", " # label 201\n", " # $201.1 = global(range: <built-in function range>) :: pyobject\n", " # $const201.3 = const(int, 1) :: pyobject\n", " # $201.4 = k + $const201.3 :: pyobject\n", " # del $const201.3\n", " # $201.6 = call $201.1($201.4, n, ) :: pyobject\n", " # del $201.4\n", " # del $201.1\n", " # $201.7 = getiter(value=$201.6) :: pyobject\n", " # del $201.6\n", " # $phi221.1 = $201.7 :: pyobject\n", " # del $201.7\n", " # jump 221\n", " # label 221\n", " # $221.2 = iternext(value=$phi221.1) :: pyobject\n", " # $221.3 = pair_first(value=$221.2) :: pyobject\n", " # $221.4 = pair_second(value=$221.2) :: pyobject\n", " # del $221.2\n", " # $phi224.1 = $221.3 :: pyobject\n", " # del $221.3\n", " # branch $221.4, 224, 328\n", " # label 224\n", " # i = $phi224.1 :: pyobject\n", " # del $phi224.1\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", " # $A1224.2 = A1 :: pyobject\n", " # $224.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $224.8 = getitem(index=$224.5, value=A1) :: pyobject\n", " # $224.10 = inplace_binop(rhs=scale, lhs=$224.8, fn=/?) :: pyobject\n", " # del $224.8\n", " # $A1224.2[$224.5] = $224.10 :: pyobject\n", " # del $A1224.2\n", " # del $224.5\n", " # del $224.10\n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", " # label 269\n", " # $269.2 = iternext(value=$phi269.1) :: pyobject\n", " # $269.3 = pair_first(value=$269.2) :: pyobject\n", " # $269.4 = pair_second(value=$269.2) :: pyobject\n", " # del $269.2\n", " # $phi272.1 = $269.3 :: pyobject\n", " # del $269.3\n", " # branch $269.4, 272, 324\n", " # label 272\n", " # j = $phi272.1 :: pyobject\n", " # del $phi272.1\n", " # jump 249\n", " # label 249\n", " # $249.1 = global(range: <built-in function range>) :: pyobject\n", " # $const249.3 = const(int, 1) :: pyobject\n", " # $249.4 = k + $const249.3 :: pyobject\n", " # del $const249.3\n", " # $249.6 = call $249.1($249.4, n, ) :: pyobject\n", " # del $249.4\n", " # del $249.1\n", " # $249.7 = getiter(value=$249.6) :: pyobject\n", " # del $249.6\n", " # $phi269.1 = $249.7 :: pyobject\n", " # del $249.7\n", " # jump 269\n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", " # $A1272.2 = A1 :: pyobject\n", " # $272.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # $272.8 = getitem(index=$272.5, value=A1) :: pyobject\n", " # $272.12 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $272.13 = getitem(index=$272.12, value=A1) :: pyobject\n", " # del $272.12\n", " # $272.17 = build_tuple(items=[Var(k, <ipython-input-2-b14bcb5c0c5e> (14)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # del j\n", " # $272.18 = getitem(index=$272.17, value=A1) :: pyobject\n", " # del $272.17\n", " # $272.19 = $272.13 * $272.18 :: pyobject\n", " # del $272.18\n", " # del $272.13\n", " # $272.20 = inplace_binop(rhs=$272.19, lhs=$272.8, fn=+) :: pyobject\n", " # del $272.8\n", " # del $272.19\n", " # $A1272.2[$272.5] = $272.20 :: pyobject\n", " # del $A1272.2\n", " # del $272.5\n", " # del $272.20\n", " # jump 269\n", " # label 329\n", " # jump 134\n", " # label 324\n", " # del $phi272.1\n", " # del $phi269.1\n", " # del $269.4\n", " # jump 325\n", " # label 325\n", " # jump 221\n", " # label 328\n", " # del scale\n", " # del $phi224.1\n", " # del $phi221.1\n", " # del $221.4\n", " # jump 329\n", " # label 332\n", " # del $phi137.1\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", " # jump 333\n", " # label 333\n", " # $const333.1 = const(int, 1) :: pyobject\n", " # $const333.4 = const(int, 1) :: pyobject\n", " # $333.5 = n - $const333.4 :: pyobject\n", " # del $const333.4\n", " # x[$333.5] = $const333.1 :: pyobject\n", " # del $const333.1\n", " # del $333.5\n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 350.1\n", " # $const350.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $350.1.7 = call $const350.1.1(A1, i, k, n, x, ) :: pyobject\n", " # del A1\n", " # del $const350.1.1\n", " # $350.1.10 = exhaust_iter(count=2, value=$350.1.7) :: pyobject\n", " # del $350.1.7\n", " # $350.1.8 = static_getitem(index=0, value=$350.1.10) :: pyobject\n", " # $350.1.9 = static_getitem(index=1, value=$350.1.10) :: pyobject\n", " # del $350.1.10\n", " # i = $350.1.8 :: pyobject\n", " # del i\n", " # del $350.1.8\n", " # k = $350.1.9 :: pyobject\n", " # del $350.1.9\n", " # jump 444\n", " # jump 350.1\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", " # label 444\n", " # $444.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $444.2 = getattr(attr=sum, value=$444.1) :: pyobject\n", " # del $444.1\n", " # $444.4 = call $444.2(x, ) :: pyobject\n", " # del $444.2\n", " # norm = $444.4 :: pyobject\n", " # del $444.4\n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # jump 462.1\n", " # label 462.1\n", " # $const462.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $462.1.6 = call $const462.1.1(x, k, norm, n, ) :: pyobject\n", " # del norm\n", " # del n\n", " # del $const462.1.1\n", " # $462.1.8 = exhaust_iter(count=1, value=$462.1.6) :: pyobject\n", " # del $462.1.6\n", " # $462.1.7 = static_getitem(index=0, value=$462.1.8) :: pyobject\n", " # del $462.1.8\n", " # k = $462.1.7 :: pyobject\n", " # del k\n", " # del $462.1.7\n", " # jump 498\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", " # label 498\n", " # $498.2 = cast(value=x) :: pyobject\n", " # del x\n", " # return $498.2\n", "\n", " return x\n", "\n", "# The function contains lifted loops\n", "# Loop at line 30\n", "# Has 0 overloads\n", "# Loop at line 36\n", "# Has 0 overloads\n", "\n", "================================================================================\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2([[0.4, 0.6], [0.2, 0.8]])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([ 0.25, 0.75])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "gth_solve_jit2.inspect_types()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gth_solve_jit2 (array(float64, 2d, A, nonconst),)\n", "--------------------------------------------------------------------------------\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", " # label 0\n", " # A.1 = A :: pyobject\n", " # del A\n", " # $0.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.2 = getattr(attr=array, value=$0.1) :: pyobject\n", " # del $0.1\n", " # $0.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $0.6 = getattr(attr=float64, value=$0.5) :: pyobject\n", " # del $0.5\n", " # $0.7 = call $0.2(A.1, dtype=$0.6) :: pyobject\n", " # del A.1\n", " # del $0.6\n", " # del $0.2\n", " # A1 = $0.7 :: pyobject\n", " # del $0.7\n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", " # $0.8 = global(len: <built-in function len>) :: pyobject\n", " # $0.10 = getattr(attr=shape, value=A1) :: pyobject\n", " # $0.11 = call $0.8($0.10, ) :: pyobject\n", " # del $0.8\n", " # del $0.10\n", " # $const0.12 = const(int, 2) :: pyobject\n", " # $0.13 = $0.11 != $const0.12 :: pyobject\n", " # del $const0.12\n", " # del $0.11\n", " # branch $0.13, 71, 45\n", " # label 45\n", " # $45.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.3 = const(int, 0) :: pyobject\n", " # $45.4 = getitem(index=$const45.3, value=$45.2) :: pyobject\n", " # del $const45.3\n", " # del $45.2\n", " # $45.6 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const45.7 = const(int, 1) :: pyobject\n", " # $45.8 = getitem(index=$const45.7, value=$45.6) :: pyobject\n", " # del $const45.7\n", " # del $45.6\n", " # $45.9 = $45.4 != $45.8 :: pyobject\n", " # del $45.8\n", " # del $45.4\n", " # branch $45.9, 71, 80\n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", " # label 71\n", " # del A1\n", " # del $45.9\n", " # del $0.13\n", " # $71.1 = global(ValueError: <type 'exceptions.ValueError'>) :: pyobject\n", " # del $71.1\n", " # raise <type 'exceptions.ValueError'>\n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", " # label 80\n", " # del $45.9\n", " # del $0.13\n", " # $80.2 = getattr(attr=shape, value=A1) :: pyobject\n", " # $const80.3 = const(int, 0) :: pyobject\n", " # $80.4 = getitem(index=$const80.3, value=$80.2) :: pyobject\n", " # del $const80.3\n", " # del $80.2\n", " # n = $80.4 :: pyobject\n", " # del $80.4\n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", " # $80.5 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.6 = getattr(attr=zeros, value=$80.5) :: pyobject\n", " # del $80.5\n", " # $80.9 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $80.10 = getattr(attr=float64, value=$80.9) :: pyobject\n", " # del $80.9\n", " # $80.11 = call $80.6(n, dtype=$80.10) :: pyobject\n", " # del $80.6\n", " # del $80.10\n", " # x = $80.11 :: pyobject\n", " # del $80.11\n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", " # label 134\n", " # $134.2 = iternext(value=$phi134.1) :: pyobject\n", " # $134.3 = pair_first(value=$134.2) :: pyobject\n", " # $134.4 = pair_second(value=$134.2) :: pyobject\n", " # del $134.2\n", " # $phi137.1 = $134.3 :: pyobject\n", " # del $134.3\n", " # branch $134.4, 137, 332\n", " # label 137\n", " # k = $phi137.1 :: pyobject\n", " # del $phi137.1\n", " # jump 117\n", " # label 117\n", " # $117.1 = global(range: <built-in function range>) :: pyobject\n", " # $const117.3 = const(int, 1) :: pyobject\n", " # $117.4 = n - $const117.3 :: pyobject\n", " # del $const117.3\n", " # $117.5 = call $117.1($117.4, ) :: pyobject\n", " # del $117.4\n", " # del $117.1\n", " # $117.6 = getiter(value=$117.5) :: pyobject\n", " # del $117.5\n", " # $phi134.1 = $117.6 :: pyobject\n", " # del $117.6\n", " # jump 134\n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", " # $137.2 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $137.3 = getattr(attr=sum, value=$137.2) :: pyobject\n", " # del $137.2\n", " # $k137.5 = k :: pyobject\n", " # $const137.7 = const(int, 1) :: pyobject\n", " # $137.8 = k + $const137.7 :: pyobject\n", " # del $const137.7\n", " # $137.10 = global(slice: <type 'slice'>) :: pyobject\n", " # $137.11 = call $137.10($137.8, n, ) :: pyobject\n", " # del $137.8\n", " # del $137.10\n", " # $137.12 = build_tuple(items=[Var($k137.5, <ipython-input-2-b14bcb5c0c5e> (15)), Var($137.11, <ipython-input-2-b14bcb5c0c5e> (15))]) :: pyobject\n", " # del $k137.5\n", " # del $137.11\n", " # $137.13 = getitem(index=$137.12, value=A1) :: pyobject\n", " # del $137.12\n", " # $137.14 = call $137.3($137.13, ) :: pyobject\n", " # del $137.3\n", " # del $137.13\n", " # scale = $137.14 :: pyobject\n", " # del $137.14\n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", " # $const137.16 = const(int, 0) :: pyobject\n", " # $137.17 = scale <= $const137.16 :: pyobject\n", " # del $const137.16\n", " # branch $137.17, 187, 201\n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", " # label 187\n", " # del scale\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", " # $const187.2 = const(int, 1) :: pyobject\n", " # $187.3 = k + $const187.2 :: pyobject\n", " # del $const187.2\n", " # n = $187.3 :: pyobject\n", " # del $187.3\n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", " # jump 333\n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", " # label 201\n", " # $201.1 = global(range: <built-in function range>) :: pyobject\n", " # $const201.3 = const(int, 1) :: pyobject\n", " # $201.4 = k + $const201.3 :: pyobject\n", " # del $const201.3\n", " # $201.6 = call $201.1($201.4, n, ) :: pyobject\n", " # del $201.4\n", " # del $201.1\n", " # $201.7 = getiter(value=$201.6) :: pyobject\n", " # del $201.6\n", " # $phi221.1 = $201.7 :: pyobject\n", " # del $201.7\n", " # jump 221\n", " # label 221\n", " # $221.2 = iternext(value=$phi221.1) :: pyobject\n", " # $221.3 = pair_first(value=$221.2) :: pyobject\n", " # $221.4 = pair_second(value=$221.2) :: pyobject\n", " # del $221.2\n", " # $phi224.1 = $221.3 :: pyobject\n", " # del $221.3\n", " # branch $221.4, 224, 328\n", " # label 224\n", " # i = $phi224.1 :: pyobject\n", " # del $phi224.1\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", " # $A1224.2 = A1 :: pyobject\n", " # $224.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $224.8 = getitem(index=$224.5, value=A1) :: pyobject\n", " # $224.10 = inplace_binop(rhs=scale, lhs=$224.8, fn=/?) :: pyobject\n", " # del $224.8\n", " # $A1224.2[$224.5] = $224.10 :: pyobject\n", " # del $A1224.2\n", " # del $224.5\n", " # del $224.10\n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", " # label 269\n", " # $269.2 = iternext(value=$phi269.1) :: pyobject\n", " # $269.3 = pair_first(value=$269.2) :: pyobject\n", " # $269.4 = pair_second(value=$269.2) :: pyobject\n", " # del $269.2\n", " # $phi272.1 = $269.3 :: pyobject\n", " # del $269.3\n", " # branch $269.4, 272, 324\n", " # label 272\n", " # j = $phi272.1 :: pyobject\n", " # del $phi272.1\n", " # jump 249\n", " # label 249\n", " # $249.1 = global(range: <built-in function range>) :: pyobject\n", " # $const249.3 = const(int, 1) :: pyobject\n", " # $249.4 = k + $const249.3 :: pyobject\n", " # del $const249.3\n", " # $249.6 = call $249.1($249.4, n, ) :: pyobject\n", " # del $249.4\n", " # del $249.1\n", " # $249.7 = getiter(value=$249.6) :: pyobject\n", " # del $249.6\n", " # $phi269.1 = $249.7 :: pyobject\n", " # del $249.7\n", " # jump 269\n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", " # $A1272.2 = A1 :: pyobject\n", " # $272.5 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # $272.8 = getitem(index=$272.5, value=A1) :: pyobject\n", " # $272.12 = build_tuple(items=[Var(i, <ipython-input-2-b14bcb5c0c5e> (22)), Var(k, <ipython-input-2-b14bcb5c0c5e> (14))]) :: pyobject\n", " # $272.13 = getitem(index=$272.12, value=A1) :: pyobject\n", " # del $272.12\n", " # $272.17 = build_tuple(items=[Var(k, <ipython-input-2-b14bcb5c0c5e> (14)), Var(j, <ipython-input-2-b14bcb5c0c5e> (25))]) :: pyobject\n", " # del j\n", " # $272.18 = getitem(index=$272.17, value=A1) :: pyobject\n", " # del $272.17\n", " # $272.19 = $272.13 * $272.18 :: pyobject\n", " # del $272.18\n", " # del $272.13\n", " # $272.20 = inplace_binop(rhs=$272.19, lhs=$272.8, fn=+) :: pyobject\n", " # del $272.8\n", " # del $272.19\n", " # $A1272.2[$272.5] = $272.20 :: pyobject\n", " # del $A1272.2\n", " # del $272.5\n", " # del $272.20\n", " # jump 269\n", " # label 329\n", " # jump 134\n", " # label 324\n", " # del $phi272.1\n", " # del $phi269.1\n", " # del $269.4\n", " # jump 325\n", " # label 325\n", " # jump 221\n", " # label 328\n", " # del scale\n", " # del $phi224.1\n", " # del $phi221.1\n", " # del $221.4\n", " # jump 329\n", " # label 332\n", " # del $phi137.1\n", " # del $phi134.1\n", " # del $137.17\n", " # del $134.4\n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", " # jump 333\n", " # label 333\n", " # $const333.1 = const(int, 1) :: pyobject\n", " # $const333.4 = const(int, 1) :: pyobject\n", " # $333.5 = n - $const333.4 :: pyobject\n", " # del $const333.4\n", " # x[$333.5] = $const333.1 :: pyobject\n", " # del $const333.1\n", " # del $333.5\n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 350.1\n", " # $const350.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $350.1.7 = call $const350.1.1(A1, i, k, n, x, ) :: pyobject\n", " # del A1\n", " # del $const350.1.1\n", " # $350.1.10 = exhaust_iter(count=2, value=$350.1.7) :: pyobject\n", " # del $350.1.7\n", " # $350.1.8 = static_getitem(index=0, value=$350.1.10) :: pyobject\n", " # $350.1.9 = static_getitem(index=1, value=$350.1.10) :: pyobject\n", " # del $350.1.10\n", " # i = $350.1.8 :: pyobject\n", " # del i\n", " # del $350.1.8\n", " # k = $350.1.9 :: pyobject\n", " # del $350.1.9\n", " # jump 444\n", " # jump 350.1\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", " # label 444\n", " # $444.1 = global(np: <module 'numpy' from '/usr/local/lib/python2.7/site-packages/numpy/__init__.pyc'>) :: pyobject\n", " # $444.2 = getattr(attr=sum, value=$444.1) :: pyobject\n", " # del $444.1\n", " # $444.4 = call $444.2(x, ) :: pyobject\n", " # del $444.2\n", " # norm = $444.4 :: pyobject\n", " # del $444.4\n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # jump 462.1\n", " # label 462.1\n", " # $const462.1.1 = const(LiftedLoop, LiftedLoop(<function gth_solve_jit2 at 0x108e181b8>)) :: pyobject\n", " # $462.1.6 = call $const462.1.1(x, k, norm, n, ) :: pyobject\n", " # del norm\n", " # del n\n", " # del $const462.1.1\n", " # $462.1.8 = exhaust_iter(count=1, value=$462.1.6) :: pyobject\n", " # del $462.1.6\n", " # $462.1.7 = static_getitem(index=0, value=$462.1.8) :: pyobject\n", " # del $462.1.8\n", " # k = $462.1.7 :: pyobject\n", " # del k\n", " # del $462.1.7\n", " # jump 498\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", " # label 498\n", " # $498.2 = cast(value=x) :: pyobject\n", " # del x\n", " # return $498.2\n", "\n", " return x\n", "\n", "# The function contains lifted loops\n", "# Loop at line 30\n", "# Has 1 overloads\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", " # label 370\n", " # $370.2 = iternext(value=$phi370.1) :: pair<int64, bool>\n", " # $370.3 = pair_first(value=$370.2) :: int64\n", " # $370.4 = pair_second(value=$370.2) :: bool\n", " # del $370.2\n", " # $phi373.1 = $370.3 :: int64\n", " # del $370.3\n", " # branch $370.4, 373, 443\n", " # label 373\n", " # k.1 = $phi373.1 :: int64\n", " # del $phi373.1\n", " # label 347\n", " # A1.1 = A1 :: array(float64, 2d, C, nonconst)\n", " # del A1\n", " # i.1 = i :: int64\n", " # del i\n", " # k.1 = k :: int64\n", " # del k\n", " # n.1 = n :: int64\n", " # del n\n", " # x.1 = x :: array(float64, 1d, C, nonconst)\n", " # del x\n", " # $347.1 = global(range: <built-in function range>) :: range\n", " # $const347.3 = const(int, 2) :: int32\n", " # $347.4 = n.1 - $const347.3 :: int64\n", " # del $const347.3\n", " # $const347.5 = const(int, -1) :: int32\n", " # $const347.6 = const(int, -1) :: int32\n", " # $347.7 = call $347.1($347.4, $const347.5, $const347.6, ) :: (int64, int64, int64) -> range_state64\n", " # del $const347.6\n", " # del $const347.5\n", " # del $347.4\n", " # del $347.1\n", " # $347.8 = getiter(value=$347.7) :: range_iter64\n", " # del $347.7\n", " # $phi370.1 = $347.8 :: range_iter64\n", " # del $347.8\n", " # jump 370\n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", " # label 396\n", " # $396.2 = iternext(value=$phi396.1) :: pair<int64, bool>\n", " # $396.3 = pair_first(value=$396.2) :: int64\n", " # $396.4 = pair_second(value=$396.2) :: bool\n", " # del $396.2\n", " # $phi399.1 = $396.3 :: int64\n", " # del $396.3\n", " # branch $396.4, 399, 439\n", " # label 399\n", " # i.1 = $phi399.1 :: int64\n", " # del $phi399.1\n", " # jump 376\n", " # label 376\n", " # $376.1 = global(range: <built-in function range>) :: range\n", " # $const376.3 = const(int, 1) :: int32\n", " # $376.4 = k.1 + $const376.3 :: int64\n", " # del $const376.3\n", " # $376.6 = call $376.1($376.4, n.1, ) :: (int64, int64) -> range_state64\n", " # del $376.4\n", " # del $376.1\n", " # $376.7 = getiter(value=$376.6) :: range_iter64\n", " # del $376.6\n", " # $phi396.1 = $376.7 :: range_iter64\n", " # del $376.7\n", " # jump 396\n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", " # label 443\n", " # del x.1\n", " # del n.1\n", " # del A1.1\n", " # del $phi373.1\n", " # del $phi370.1\n", " # del $370.4\n", " # jump 444\n", " # $x399.2 = x.1 :: array(float64, 1d, C, nonconst)\n", " # $k399.3 = k.1 :: int64\n", " # $399.6 = getitem(index=k.1, value=x.1) :: float64\n", " # $399.9 = getitem(index=i.1, value=x.1) :: float64\n", " # $399.13 = build_tuple(items=[Var(i.1, <ipython-input-2-b14bcb5c0c5e> (30)), Var(k.1, <ipython-input-2-b14bcb5c0c5e> (30))]) :: (int64 x 2)\n", " # $399.14 = getitem(index=$399.13, value=A1.1) :: float64\n", " # del $399.13\n", " # $399.15 = $399.9 * $399.14 :: float64\n", " # del $399.9\n", " # del $399.14\n", " # $399.16 = inplace_binop(rhs=$399.15, lhs=$399.6, fn=+) :: float64\n", " # del $399.6\n", " # del $399.15\n", " # $x399.2[$k399.3] = $399.16 :: (array(float64, 1d, C, nonconst), int64, float64) -> none\n", " # del $x399.2\n", " # del $k399.3\n", " # del $399.16\n", " # jump 396\n", " # label 440\n", " # jump 370\n", " # label 439\n", " # del $phi399.1\n", " # del $phi396.1\n", " # del $396.4\n", " # jump 440\n", " # label 444\n", " # $444.3 = build_tuple(items=[Var(i.1, <ipython-input-2-b14bcb5c0c5e> (30)), Var(k.1, <ipython-input-2-b14bcb5c0c5e> (30))]) :: (int64 x 2)\n", " # del k.1\n", " # del i.1\n", " # $444.4 = cast(value=$444.3) :: (int64 x 2)\n", " # del $444.3\n", " # return $444.4\n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", "\n", " return x\n", "\n", "\n", "# Loop at line 36\n", "# Has 1 overloads\n", "# File: <ipython-input-2-b14bcb5c0c5e>\n", "# --- LINE 1 --- \n", "\n", "@jit('float64[:](float64[:,:])')\n", "\n", "# --- LINE 2 --- \n", "\n", "def gth_solve_jit2(A):\n", "\n", " # --- LINE 3 --- \n", "\n", " A1 = np.array(A, dtype=np.float64)\n", "\n", "# --- LINE 4 --- \n", "\n", "\n", "\n", " # --- LINE 5 --- \n", "\n", " if len(A1.shape) != 2 or A1.shape[0] != A1.shape[1]:\n", "\n", " # --- LINE 6 --- \n", "\n", " # raise ValueError('matrix must be square') # Not supported\n", "\n", " # --- LINE 7 --- \n", "\n", " raise ValueError\n", "\n", "# --- LINE 8 --- \n", "\n", "\n", "\n", " # --- LINE 9 --- \n", "\n", " n = A1.shape[0]\n", "\n", "# --- LINE 10 --- \n", "\n", "\n", "\n", " # --- LINE 11 --- \n", "\n", " x = np.zeros(n, dtype=np.float64)\n", "\n", "# --- LINE 12 --- \n", "\n", "\n", "\n", " # --- LINE 13 --- \n", "\n", " # === Reduction === #\n", "\n", " # --- LINE 14 --- \n", "\n", " for k in range(n-1):\n", "\n", " # --- LINE 15 --- \n", "\n", " scale = np.sum(A1[k, k+1:n])\n", "\n", " # --- LINE 16 --- \n", "\n", " if scale <= 0:\n", "\n", " # --- LINE 17 --- \n", "\n", " # There is one (and only one) recurrent class contained in\n", "\n", " # --- LINE 18 --- \n", "\n", " # {0, ..., k};\n", "\n", " # --- LINE 19 --- \n", "\n", " # compute the solution associated with that recurrent class.\n", "\n", " # --- LINE 20 --- \n", "\n", " n = k+1\n", "\n", " # --- LINE 21 --- \n", "\n", " break\n", "\n", " # --- LINE 22 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 23 --- \n", "\n", " A1[i, k] /= scale\n", "\n", "# --- LINE 24 --- \n", "\n", "\n", "\n", " # --- LINE 25 --- \n", "\n", " for j in range(k+1, n):\n", "\n", " # --- LINE 26 --- \n", "\n", " A1[i, j] += A1[i, k] * A1[k, j]\n", "\n", "# --- LINE 27 --- \n", "\n", "\n", "\n", " # --- LINE 28 --- \n", "\n", " # === Backward substitution === #\n", "\n", " # --- LINE 29 --- \n", "\n", " x[n-1] = 1\n", "\n", " # --- LINE 30 --- \n", "\n", " for k in range(n-2, -1, -1):\n", "\n", " # --- LINE 31 --- \n", "\n", " for i in range(k+1, n):\n", "\n", " # --- LINE 32 --- \n", "\n", " x[k] += x[i] * A1[i, k]\n", "\n", "# --- LINE 33 --- \n", "\n", "\n", "\n", " # --- LINE 34 --- \n", "\n", " # === Normalization === #\n", "\n", " # --- LINE 35 --- \n", "\n", " norm = np.sum(x)\n", "\n", " # --- LINE 36 --- \n", " # label 472\n", " # $472.2 = iternext(value=$phi472.1) :: pair<int64, bool>\n", " # $472.3 = pair_first(value=$472.2) :: int64\n", " # $472.4 = pair_second(value=$472.2) :: bool\n", " # del $472.2\n", " # $phi475.1 = $472.3 :: int64\n", " # del $472.3\n", " # branch $472.4, 475, 497\n", " # label 459\n", " # x.1 = x :: array(float64, 1d, C, nonconst)\n", " # del x\n", " # k.1 = k :: int64\n", " # del k\n", " # norm.1 = norm :: float64\n", " # del norm\n", " # n.1 = n :: int64\n", " # del n\n", " # $459.1 = global(range: <built-in function range>) :: range\n", " # $459.3 = call $459.1(n.1, ) :: (int64,) -> range_state64\n", " # del n.1\n", " # del $459.1\n", " # $459.4 = getiter(value=$459.3) :: range_iter64\n", " # del $459.3\n", " # $phi472.1 = $459.4 :: range_iter64\n", " # del $459.4\n", " # jump 472\n", " # label 475\n", " # k.1 = $phi475.1 :: int64\n", " # del $phi475.1\n", "\n", " for k in range(n):\n", "\n", " # --- LINE 37 --- \n", " # label 497\n", " # del x.1\n", " # del norm.1\n", " # del $phi475.1\n", " # del $phi472.1\n", " # del $472.4\n", " # jump 498\n", " # label 498\n", " # $498.2 = build_tuple(items=[Var(k.1, <ipython-input-2-b14bcb5c0c5e> (36))]) :: (int64 x 1)\n", " # del k.1\n", " # $498.3 = cast(value=$498.2) :: (int64 x 1)\n", " # del $498.2\n", " # return $498.3\n", " # $x475.2 = x.1 :: array(float64, 1d, C, nonconst)\n", " # $k475.3 = k.1 :: int64\n", " # $475.6 = getitem(index=k.1, value=x.1) :: float64\n", " # $475.8 = inplace_binop(rhs=norm.1, lhs=$475.6, fn=/?) :: float64\n", " # del $475.6\n", " # $x475.2[$k475.3] = $475.8 :: (array(float64, 1d, C, nonconst), int64, float64) -> none\n", " # del $x475.2\n", " # del $k475.3\n", " # del $475.8\n", " # jump 472\n", "\n", " x[k] /= norm\n", "\n", "# --- LINE 38 --- \n", "\n", "\n", "\n", " # --- LINE 39 --- \n", "\n", " return x\n", "\n", "\n", "\n", "================================================================================\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "sizes = [10, 50, 100] # [10, 50, 100, 1000]\n", "rand_matrices = []\n", "\n", "for n in sizes:\n", " Q = np.random.rand(n, n)\n", " Q /= np.sum(Q, axis=1, keepdims=True)\n", " rand_matrices.append(Q)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, Q in enumerate(rand_matrices):\n", " print 'rand_matrices[{0}] ({1} x {2})'.format(i, Q.shape[0], Q.shape[1])\n", " %timeit gth_solve(Q)\n", " %timeit gth_solve_jit(Q)\n", " %timeit gth_solve_jit2(Q)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "rand_matrices[0] (10 x 10)\n", "1000 loops, best of 3: 182 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "100000 loops, best of 3: 5.2 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 281 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rand_matrices[1] (50 x 50)\n", "1000 loops, best of 3: 1.12 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 63.1 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10 loops, best of 3: 21.1 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "rand_matrices[2] (100 x 100)\n", "100 loops, best of 3: 3 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1000 loops, best of 3: 429 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10 loops, best of 3: 164 ms per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "import platform\n", "print platform.platform()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Darwin-13.4.0-x86_64-i386-64bit\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "print sys.version" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.7.8 (default, Jul 2 2014, 10:14:46) \n", "[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.9.0\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "import numba\n", "print numba.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.15.1\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "import llvm\n", "print llvm.__version__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.12.7-5-gc0ae9c2\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

joshwalawender/KeckUtilities

telescopeSchedule/Site Calendar.ipynb

1

8987

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "from astropy.table import Table\n", "\n", "from telescopeSchedule import get_telsched" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def site_table(ndays=5):\n", " sched = get_telsched(from_date=None, ndays=ndays, telnr=None)\n", "\n", " site_list = ['HQ']\n", " site_list.extend( sorted(['ANU', 'CIT', 'UCB', 'UCD', 'UCLA', 'UCSD', 'UCI', 'UCR', 'Yale',\n", " 'USRA', 'NU', 'IfA', 'Stanford', 'Swinburne', 'UCSB', 'UCSC']) )\n", " site_list.append('Other')\n", "\n", " t = Table(names=['Run'] + site_list,\n", " dtype=['a40'] + ['a100']*len(site_list))\n", "\n", " for prog in sched:\n", " row = {site: '' for site in site_list}\n", " row['Run'] = f\"{prog['Date']} {prog['TelNr']} ({prog['ProjCode']})\"\n", " tonights_observers = prog['Observers'].split(',')\n", " tonights_sites = prog['Location'].split(',')\n", " for obs,s in zip(tonights_observers, tonights_sites):\n", " if s in row.keys():\n", " row[s] += f\"{obs}, \"\n", " else:\n", " row['Other'] += f\"{obs}, \"\n", " for site in site_list:\n", " if row[site] != '':\n", " nobs = len(row[site].split(',')) - 1\n", " row[site] = row[site].strip(', ')\n", " row[site] += f' ({nobs})'\n", " t.add_row(row)\n", "\n", " return t" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=16</i>\n", "<table id=\"table4830594216\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Run</th><th>HQ</th><th>ANU</th><th>CIT</th><th>IfA</th><th>NU</th><th>Stanford</th><th>Swinburne</th><th>UCB</th><th>UCD</th><th>UCI</th><th>UCLA</th><th>UCR</th><th>UCSB</th><th>UCSC</th><th>UCSD</th><th>USRA</th><th>Yale</th><th>Other</th></tr></thead>\n", "<thead><tr><th>bytes40</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th><th>bytes100</th></tr></thead>\n", "<tr><td>2020-03-10 1 (H277)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Lemaux, Pelliccia (2)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 1 (N028)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 2 (K300)</td><td>Alvarez (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-10 2 (E339)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 1 (S322)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shimakawa, Onodera (2)</td></tr>\n", "<tr><td>2020-03-11 1 (N028)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (C197)</td><td></td><td></td><td>Buzard, Camarca, Wallack (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (E350)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-11 2 (E341)</td><td>Ragland (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-12 1 (S322)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shimakawa, Onodera (2)</td></tr>\n", "<tr><td>2020-03-12 1 (U169)</td><td>Topping, Runco, Pahl (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Shapley (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-12 2 (U149)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Theissen, Aganze, Hsu, Gerasimov (4)</td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-13 1 (C206)</td><td></td><td></td><td>Scoville, Darvish Sarvestani, Faisst (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-13 2 (H311)</td><td></td><td></td><td></td><td>Liu (1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-14 1 (C206)</td><td></td><td></td><td>Scoville, Darvish Sarvestani, Faisst (3)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>\n", "<tr><td>2020-03-14 2 (U010)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Martin (1)</td><td></td><td></td><td></td><td></td><td></td></tr>\n", "</table>" ], "text/plain": [ "<Table length=16>\n", " Run HQ ... Yale Other \n", " bytes40 bytes100 ... bytes100 bytes100 \n", "------------------- ------------------------ ... -------- ----------------------\n", "2020-03-10 1 (H277) ... \n", "2020-03-10 1 (N028) Topping, Runco, Pahl (3) ... \n", "2020-03-10 2 (K300) Alvarez (1) ... \n", "2020-03-10 2 (E339) Ragland (1) ... \n", "2020-03-11 1 (S322) ... Shimakawa, Onodera (2)\n", "2020-03-11 1 (N028) Topping, Runco, Pahl (3) ... \n", "2020-03-11 2 (C197) ... \n", "2020-03-11 2 (E350) Ragland (1) ... \n", "2020-03-11 2 (E341) Ragland (1) ... \n", "2020-03-12 1 (S322) ... Shimakawa, Onodera (2)\n", "2020-03-12 1 (U169) Topping, Runco, Pahl (3) ... \n", "2020-03-12 2 (U149) ... \n", "2020-03-13 1 (C206) ... \n", "2020-03-13 2 (H311) ... \n", "2020-03-14 1 (C206) ... \n", "2020-03-14 2 (U010) ... " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site_table(ndays=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }

bsd-2-clause

AugurProject/Simulator.jl

test/workbench.ipynb

3

5770

{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:ffe211f4cf239e551beb097b1c14bd3a3fa754bf67ee217a2ddf4e55952a2de5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "reload(\"repl.jl\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "xdump(sim)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot;\n", "PyPlot.svg(false);" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "subplot(221);\n", "colorbar(contourf(mean_repdelta[\"hierarchical\"], origin=\"upper\"));\n", "title(\"hierarchical\");\n", "ylabel(\"reporter\");\n", "\n", "subplot(222);\n", "colorbar(contourf(mean_repdelta[\"cflash\"], origin=\"upper\"));\n", "title(\"cflash\");\n", "\n", "subplot(223);\n", "contourf(mean_repdelta[\"PCA\"], origin=\"upper\");\n", "title(\"PCA\");\n", "xlabel(\"reporting round\");\n", "ylabel(\"reporter\");\n", "\n", "subplot(224);\n", "colorbar(contourf(mean_repdelta[\"k-means\"], origin=\"upper\"));\n", "title(\"k-means\");\n", "xlabel(\"reporting round\");" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "function buildplot(metric)\n", " plot(1:sim.TIMESTEPS, mean(track[\"PCA\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"cflash\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"fixed-variance\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"k-means\"][metric], 2),\n", " 1:sim.TIMESTEPS, mean(track[\"hierarchical\"][metric], 2))\n", "end\n", "\n", "fig = figure();\n", "\n", "subplot(221);\n", "buildplot(:MCC)\n", "grid();\n", "ylabel(\"Matthews corr. coeff.\");\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " bbox_to_anchor=(-0.15, 1.35),\n", " loc=\"upper left\",\n", " ncol=4);\n", "\n", "subplot(222);\n", "buildplot(:spearman)\n", "ax = gca();\n", "ax[:yaxis][:set_ticks_position](\"right\");\n", "ax[:set_xscale](\"linear\");\n", "ylabel(\"spearman rank corr.\");\n", "grid();\n", "\n", "subplot(223);\n", "buildplot(:beats)\n", "ax = gca();\n", "ax[:set_xscale](\"log\");\n", "xlabel(\"reporting round\");\n", "ylabel(\"beats\");\n", "grid();\n", "\n", "subplot(224);\n", "buildplot(:liars_bonus)\n", "xlabel(\"reporting round\");\n", "ylabel(\"liars' bonus\");\n", "ax = gca();\n", "ax[:yaxis][:set_ticks_position](\"right\");\n", "ax[:set_xscale](\"log\");\n", "grid();\n", "\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = figure();\n", "errorbar(timesteps, mean_rep_liars[\"PCA\"], yerr=std_rep_liars[\"PCA\"]);\n", "hold(\"on\");\n", "errorbar(timesteps, mean_rep_liars[\"cflash\"], yerr=std_rep_liars[\"cflash\"]);\n", "errorbar(timesteps, mean_rep_liars[\"fixed-variance\"], yerr=std_rep_liars[\"fixed-variance\"]);\n", "errorbar(timesteps, mean_rep_liars[\"k-means\"], yerr=std_rep_liars[\"k-means\"]);\n", "errorbar(timesteps, mean_rep_liars[\"hierarchical\"], yerr=std_rep_liars[\"hierarchical\"]);\n", "xlabel(\"time (# reporting rounds)\");\n", "ylabel(\"liars' reputation\");\n", "ax = gca();\n", "ax[:set_xscale](\"linear\");\n", "grid();\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " loc=\"center right\",\n", " bbox_to_anchor=(1.4, 0.55),\n", " ncol=1);\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = figure();\n", "metric = :MCC;\n", "timesteps = 1:sim.TIMESTEPS;\n", "errorbar(timesteps, mean(track[\"PCA\"][metric], 2), yerr=std(track[\"PCA\"][metric], 2) / sim.SQRTN);\n", "hold(\"on\");\n", "errorbar(timesteps, mean(track[\"cflash\"][metric], 2), yerr=std(track[\"cflash\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"fixed-variance\"][metric], 2), yerr=std(track[\"fixed-variance\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"k-means\"][metric], 2), yerr=std(track[\"k-means\"][metric], 2) / sim.SQRTN);\n", "errorbar(timesteps, mean(track[\"hierarchical\"][metric], 2), yerr=std(track[\"hierarchical\"][metric], 2) / sim.SQRTN);\n", "grid();\n", "xlabel(\"time (# reporting rounds)\");\n", "ylabel(\"Matthews corr. coeff.\");\n", "legend([\"PCA\", \"cflash\", \"fixed-variance\", \"k-means\", \"hierarchical\"],\n", " loc=\"center right\",\n", " bbox_to_anchor=(1.4, 0.55),\n", " ncol=1);\n", "fig[:canvas][:draw]();" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

twosigma/beakerx

doc/python/TableAPI.ipynb

1

11263

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python API for Table Display\n", "\n", "In addition to APIs for creating and formatting BeakerX's interactive table widget, the Python runtime configures pandas to display tables with the interactive widget instead of static HTML." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from beakerx import *\n", "from beakerx.object import beakerx" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6cc688d4ca4e4d6d9f0dbdf14285f73f", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c109cc21d4f948b395009abcdb65dfe2", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table.setAlignmentProviderForColumn('m3', TableDisplayAlignmentProvider.CENTER_ALIGNMENT)\n", "table.setRendererForColumn(\"y10\", TableDisplayCellRenderer.getDataBarsRenderer(False))\n", "table.setRendererForType(ColumnType.Double, TableDisplayCellRenderer.getDataBarsRenderer(True))\n", "table" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c6acb414397f494b84c0046870db07bc", "version_major": 2, "version_minor": 0 } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df['time'] = df['time'].str.slice(0,19).astype('datetime64[ns]')\n", "table = TableDisplay(df)\n", "table.setStringFormatForTimes(TimeUnit.DAYS)\n", "table.setStringFormatForType(ColumnType.Double, TableDisplayStringFormat.getDecimalFormat(4,6))\n", "table.setStringFormatForColumn(\"m3\", TableDisplayStringFormat.getDecimalFormat(0, 0))\n", "\n", "table" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table\n", "#freeze a column\n", "table.setColumnFrozen(\"y1\", True)\n", "#hide a column\n", "table.setColumnVisible(\"y30\", False)\n", "\n", "table.setColumnOrder([\"m3\", \"y1\", \"y5\", \"time\", \"y2\"])\n", "\n", "def config_tooltip(row, column, table):\n", " return \"The value is: \" + str(table.values[row][column])\n", "\n", "table.setToolTip(config_tooltip)\n", "\n", "table.setDataFontSize(16)\n", "table.setHeaderFontSize(18)\n", "\n", "table" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapListColorProvider = [\n", " {\"a\": 1, \"b\": 2, \"c\": 3},\n", " {\"a\": 4, \"b\": 5, \"c\": 6},\n", " {\"a\": 7, \"b\": 8, \"c\": 5}\n", "]\n", "tabledisplay = TableDisplay(mapListColorProvider)\n", "\n", "colors = [\n", " [Color.LIGHT_GRAY, Color.GRAY, Color.RED],\n", " [Color.DARK_GREEN, Color.ORANGE, Color.RED],\n", " [Color.MAGENTA, Color.BLUE, Color.BLACK]\n", "]\n", "\n", "def color_provider(row, column, table):\n", " return colors[row][column]\n", "\n", "tabledisplay.setFontColorProvider(color_provider)\n", "tabledisplay" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapListFilter = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapListFilter)\n", "\n", "def filter_row(row, model):\n", " return model[row][1] == 8\n", "\n", "display.setRowFilter(filter_row)\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay(pd.read_csv('../resources/data/interest-rates.csv'))\n", "table.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m3\", TableDisplayCellHighlighter.FULL_ROW))\n", "\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display mode: Pandas default" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beakerx.pandas_display_default()\n", "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display mode: TableDisplay Widget" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beakerx.pandas_display_table()\n", "pd.read_csv('../resources/data/interest-rates.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recognized Formats" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay([{'y1':4, 'm3':2, 'z2':1}, {'m3':4, 'z2':2}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay({\"x\" : 1, \"y\" : 2})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Programmable Table Actions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapList4 = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapList4)\n", "\n", "def dclick(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = sum(map(int,tabledisplay.values[row]))\n", "\n", "display.setDoubleClickAction(dclick)\n", "\n", "def negate(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = -1 * int(tabledisplay.values[row][column])\n", "\n", "def incr(row, column, tabledisplay):\n", " tabledisplay.values[row][column] = int(tabledisplay.values[row][column]) + 1\n", "\n", "display.addContextMenuItem(\"negate\", negate)\n", "display.addContextMenuItem(\"increment\", incr)\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mapList4 = [\n", " {\"a\":1, \"b\":2, \"c\":3},\n", " {\"a\":4, \"b\":5, \"c\":6},\n", " {\"a\":7, \"b\":8, \"c\":5}\n", "]\n", "display = TableDisplay(mapList4)\n", "\n", "#set what happens on a double click\n", "display.setDoubleClickAction(\"runDoubleClick\")\n", "\n", "display" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "runDoubleClick" ] }, "outputs": [], "source": [ "print(\"runDoubleClick fired\")\n", "print(display.details)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set index to DataFrame" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df.set_index(['m3'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "df = pd.read_csv('../resources/data/interest-rates.csv')\n", "df.index = df['time']\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Update cell" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataToUpdate = [\n", " {'a':1, 'b':2, 'c':3},\n", " {'a':4, 'b':5, 'c':6},\n", " {'a':7, 'b':8, 'c':9}\n", "]\n", "tableToUpdate = TableDisplay(dataToUpdate)\n", "\n", "tableToUpdate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tableToUpdate.values[0][0] = 99\n", "tableToUpdate.sendModel()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tableToUpdate.updateCell(2,\"c\",121)\n", "tableToUpdate.sendModel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML format\n", "\n", "HTML format allows markup and styling of the cell's content. Interactive JavaScript is not supported however." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "table = TableDisplay({\n", " 'w': '$2 \\\\sigma$',\n", " 'x': '<em style=\"color:red\">italic red</em>',\n", " 'y': '<b style=\"color:blue\">bold blue</b>',\n", " 'z': 'strings without markup work fine too',\n", " })\n", "table.setStringFormatForColumn(\"Value\", TableDisplayStringFormat.getHTMLFormat())\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Auto linking of URLs\n", "\n", "The normal string format automatically detects URLs and links them. An underline appears when the mouse hovers over such a string, and when you click it opens in a new window." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TableDisplay({'Two Sigma': 'http://twosigma.com', 'BeakerX': 'http://BeakerX.com'})" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

lisitsyn/shogun

doc/ipython-notebooks/ica/ecg_sep.ipynb

4

7779

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fetal Electrocardiogram Extraction by Source Subspace Separation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### By Kevin Hughes and Andreas Ziehe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates <a href=\"http://en.wikipedia.org/wiki/Blind_signal_separation\">Blind Source Seperation</a>(BSS) on several time synchronised Electrocardiogram's (ECG's) of the baby's mother using <a href=\"http://en.wikipedia.org/wiki/Independent_component_analysis\">Independent Component Analysis</a> (ICA) in Shogun. This is used to extract the baby's ECG from it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This task has been studied before and has been published in these papers:\n", "\n", "Cardoso, J. F. (1998, May). Multidimensional independent component analysis. \n", "In Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 \n", "IEEE International Conference on (Vol. 4, pp. 1941-1944). IEEE.\n", "\n", "Dirk Callaerts, \"Signal Separation Methods based on Singular Value\n", "Decomposition and their Application to the Real-Time Extraction of the\n", "Fetal Electrocardiogram from Cutaneous Recordings\", Ph.D. Thesis,\n", "K.U.Leuven - E.E. Dept., Dec. 1989.\n", "\n", "L. De Lathauwer, B. De Moor, J. Vandewalle, \"Fetal Electrocardiogram\n", "Extraction by Source Subspace Separation\", Proc. IEEE SP / ATHOS\n", "Workshop on HOS, June 12-14, 1995, Girona, Spain, pp. 134-138.\n", "\n", "In this workbook I am going to show you how a similar result can be obtained using the ICA algorithms available in the Shogun Machine Learning Toolbox." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need some data, luckily an ECG dataset is distributed in the Shogun data repository. So the first step is to change the directory then we'll load the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# change to the shogun-data directory\n", "import os\n", "SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n", "os.chdir(os.path.join(SHOGUN_DATA_DIR, 'ica'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# load data\n", "# Data originally from:\n", "# http://perso.telecom-paristech.fr/~cardoso/icacentral/base_single.html\n", "data = np.loadtxt('foetal_ecg.dat')\n", "\n", "# time steps\n", "time_steps = data[:,0]\n", "\n", "# abdominal signals\n", "abdominal2 = data[:,1]\n", "abdominal3 = data[:,2]\n", "abdominal4 = data[:,3]\n", "abdominal5 = data[:,4]\n", "abdominal6 = data[:,5]\n", "\n", "# thoracic signals\n", "thoracic7 = data[:,6]\n", "thoracic8 = data[:,7]\n", "thoracic9 = data[:,8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we go any further let's take a look at this data by plotting it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "# plot signals\n", "import pylab as pl\n", "\n", "# abdominal signals\n", "for i in range(1,6):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, data[:,i], 'r')\n", " pl.title('Abdominal %d' % (i))\n", " pl.grid()\n", " pl.show()\n", "\n", "# thoracic signals\n", "for i in range(6,9):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, data[:,i], 'r')\n", " pl.title('Thoracic %d' % (i))\n", " pl.grid()\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The peaks in the plot represent a heart beat but its pretty hard to interpret and I know I definitely can't see two distinc signals, lets see what we can do with ICA!\n", "\n", "In general for performing Source Separation we need at least as many mixed signals as sources we're hoping to separate and in this case we actually have a lot more (9 mixtures but there is only 2 sources, mother and baby). There are several different approaches for handling this situation, some algorithms are specifically designed to handle this case while other times the data is pre-processed with Principal Component Analysis (PCA). It is also common to simply apply the separation to all the sources and then choose some of the extracted signal manually or using some other know criteria which is what I'll be showing in this example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create our ICA data set and convert to a Shogun features type:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import shogun as sg\n", "\n", "# Signal Matrix X\n", "X = (np.c_[abdominal2, abdominal3, abdominal4, abdominal5, abdominal6, thoracic7,thoracic8,thoracic9]).T\n", "\n", "# Convert to features for shogun\n", "mixed_signals = sg.features((X).astype(np.float64))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we apply the ICA algorithm to separate the sources:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Separating with SOBI\n", "sep = sg.transformer('SOBI')\n", "sep.put('tau', 1.0*np.arange(0,120))\n", " \n", "sep.fit(mixed_signals)\n", "signals = sep.transform(mixed_signals)\n", "\n", "S_ = signals.get('feature_matrix')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we plot the separated signals:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Show separation results\n", "\n", "# Separated Signal i\n", "for i in range(S_.shape[0]):\n", " pl.figure(figsize=(14,3))\n", " pl.plot(time_steps, S_[i], 'r')\n", " pl.title('Separated Signal %d' % (i+1))\n", " pl.grid()\n", " pl.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can interpret the results! First we are going to exploit the known fact that the baby's heart rate is about twice that of the mothers.\n", "\n", "Our interpretation of the results is as follows:\n", "\n", "* separated signal 1 -> baby ECG\n", "* separated signal 2 -> still a bit mixed baby +mother\n", "* separated signal 3 -> baby ECG\n", "* separated signal 4 -> slow drift due to breathing of the mother\n", "* separated signal 5 -> mainly mother ECG but still a bit mixed and noisy\n", "* separated signal 6-8 -> mothers ECG, with 8 being the best\n", "\n", "And thats the proof of concept Fetal Electrocardiogram Extraction by Source Subspace Separation using the Shogun Machine Learning Toolkit!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

robertarles/jupyter-notes

Temp_sendclicks.ipynb

1

3754

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mongo connection Collection(Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'kinesis_traffic'), 'recorded_clicks')\n", "done with [18534] lines\n", "clicks [[{'price_format_id': 2, 'campaign_type_id': 1, 'redirect_url': 'https://www.google.com/search?q=offer+link', 'click_pixel': False, 'language_raw': 'qu', 'traffic_type_id': 1, 'campaign_id': 22030, 'creative_id': 14121, 'ip_address': '52.42.0.110', 'subid_1': 'SUB_ID/', 'test': False, 'request_date_utc': '2016-10-05 21:56:26.756469', 'location_id': 107, 'offer_id': 8146, 'country': 'US', 'create_campaign_direct_channel': False, 'region': 'OR', 'domain': 'c.demo-newtrk.cakemarketing.com', 'client_id': 3, 'session_id': 'bb23b051-adf5-437b-8ca7-67f9e5fdc4bb', 'create_campaign_copy_parent': False, 'price_received': 2, 'city': 'portland', 'click_id': '08caba62-d61e-497c-b6a7-672f767117ae', 'first_touch_by_publisher': True, 'tracking_id': '81bc8891-9e8c-409e-a64a-c46deabfe7c8', 'visitor_id': 'd79af5d1-cc30-487c-9f47-e24c715209ee', 'price_owed': 1, 'offer_contract_id': 8686, 'user_agent': 'HasOffers Mobile AppTracking v1.0', 'first_touch': True, '_id': ObjectId('57f7d3f3c1518133c37786e0'), 'browser_version_minor_id': 2, 'request_date': '2016-10-05 14:56:26.756469', 'language_id': 77, 'publisher_id': 6683, 'create_campaign_copy_parent_pixel': False, 'isp_id': 35832, 'create_campaign_internal_campaign_redirect': False}]]\n" ] } ], "source": [ "import json\n", "from pymongo import MongoClient\n", "import random \n", "\n", "MONGO_CONFIG = {\"host\": \"localhost\", \"port\": 27017}\n", "user_agents = [\n", " \"Mozilla/5.0 (Windows NT 6.1, Trident/7.0, rv:11.0) like Gecko\",\n", " \"Mozilla/5.0 (iPhone, CPU iPhone OS 9_3_4 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13G35 Safari/601.1,apple_iphone_ver9\",\n", " \"HasOffers Mobile AppTracking v1.0\",\n", " \"Mozilla/5.0 (Linux, Android 5.0.1, SAMSUNG SCH-I545 4G Build/LRX22C) AppleWebKit/537.36 (KHTML, like Gecko) SamsungBrowser/2.1 Chrome/34.0.1847.76 Mobile Safari/537.36,samsung_sch_i545_ver1_suban50\"\n", "]\n", "\n", "recorded_clicks=MongoClient(**MONGO_CONFIG).kinesis_traffic.recorded_clicks\n", "print(\"mongo connection\", recorded_clicks)\n", "\n", "clicks = []\n", "with open('/Users/robert/dev/bigdata/traffic_gen/clicks.json', 'r') as clickfile:\n", " for line in clickfile.readlines():\n", " try:\n", " click = json.loads(line)\n", " click[\"user_agent\"] = random.choice(user_agents)\n", " if click[\"client_id\"] == 3:\n", " click[\"traffic_type_id\"] = 1\n", " clicks.append(click)\n", " recorded_clicks.insert_one(click)\n", " except:\n", " pass\n", " # print(\"decode skipped for [{}]\".format(line))\n", "print(\"done with [{}] lines\".format(len(clicks)))\n", "print(\"clicks [{}]\".format(clicks[:1]))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

Yangqing/caffe2

caffe2/python/tutorials/create_your_own_dataset.ipynb

1

38351

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How do I create my own dataset?\n", "\n", "So Caffe2 uses a binary DB format to store the data that we would like to train models on. A Caffe2 DB is a glorified name of a key-value storage where the keys are usually randomized so that the batches are approximately i.i.d. The values are the real stuff here: they contain the serialized strings of the specific data formats that you would like your training algorithm to ingest. So, the stored DB would look (semantically) like this:\n", "\n", "key1 value1\n", "key2 value2\n", "key3 value3\n", "...\n", "\n", "To a DB, it treats the keys and values as strings, but you probably want structured contents. One way to do this is to use a TensorProtos protocol buffer: it essentially wraps Tensors, aka multi-dimensional arrays, together with the tensor data type and shape information. Then, one can use the TensorProtosDBInput operator to load the data into an SGD training fashion.\n", "\n", "Here, we will show you one example of how to create your own dataset. To this end, we will use the UCI Iris dataset - which was a very popular classical dataset for classifying Iris flowers. It contains 4 real-valued features representing the dimensions of the flower, and classifies things into 3 types of Iris flowers. The dataset can be downloaded [here](https://archive.ics.uci.edu/ml/datasets/Iris)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.\n", "WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu\n" ] } ], "source": [ "# First let's import some necessities\n", "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "from __future__ import unicode_literals\n", "\n", "%matplotlib inline\n", "import urllib2 # for downloading the dataset from the web.\n", "import numpy as np\n", "from matplotlib import pyplot\n", "from StringIO import StringIO\n", "from caffe2.python import core, utils, workspace\n", "from caffe2.proto import caffe2_pb2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw data looks like this:\n", "5.1,3.5,1.4,0.2,Iris-setosa\n", "4.9,3.0,1.4,0.2,Iris-setosa\n", "4.7,3.2,1.3,0.2,Iris-setosa\n", "4.6,3.1,1.5,0.2,...\n" ] } ], "source": [ "f = urllib2.urlopen('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')\n", "raw_data = f.read()\n", "print('Raw data looks like this:')\n", "print(raw_data[:100] + '...')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# load the features to a feature matrix.\n", "features = np.loadtxt(StringIO(raw_data), dtype=np.float32, delimiter=',', usecols=(0, 1, 2, 3))\n", "# load the labels to a feature matrix\n", "label_converter = lambda s : {'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2}[s]\n", "labels = np.loadtxt(StringIO(raw_data), dtype=np.int, delimiter=',', usecols=(4,), converters={4: label_converter})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we do training, one thing that is often beneficial is to separate the dataset into training and testing. In this case, let's randomly shuffle the data, use the first 100 data points to do training, and the remaining 50 to do testing. For more sophisticated approaches, you can use e.g. cross validation to separate your dataset into multiple training and testing splits. Read more about cross validation [here](http://scikit-learn.org/stable/modules/cross_validation.html)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "random_index = np.random.permutation(150)\n", "features = features[random_index]\n", "labels = labels[random_index]\n", "\n", "train_features = features[:100]\n", "train_labels = labels[:100]\n", "test_features = features[100:]\n", "test_labels = labels[100:]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11268b290>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112858ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's plot the first two features together with the label.\n", "# Remember, while we are plotting the testing feature distribution\n", "# here too, you might not be supposed to do so in real research,\n", "# because one should not peek into the testing data.\n", "legend = ['rx', 'b+', 'go']\n", "pyplot.title(\"Training data distribution, feature 0 and 1\")\n", "for i in range(3):\n", " pyplot.plot(train_features[train_labels==i, 0], train_features[train_labels==i, 1], legend[i])\n", "pyplot.figure()\n", "pyplot.title(\"Testing data distribution, feature 0 and 1\")\n", "for i in range(3):\n", " pyplot.plot(test_features[test_labels==i, 0], test_features[test_labels==i, 1], legend[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, as promised, let's put things into a Caffe2 DB. In this DB, what would happen is that we will use \"train_xxx\" as the key, and use a TensorProtos object to store two tensors for each data point: one as the feature and one as the label. We will use Caffe2's Python DB interface to do so." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is what the tensor proto looks like for a feature and its label:\n", "protos {\n", " dims: 4\n", " data_type: FLOAT\n", " float_data: 6.69999980927\n", " float_data: 3.0\n", " float_data: 5.19999980927\n", " float_data: 2.29999995232\n", "}\n", "protos {\n", " data_type: INT32\n", " int32_data: 2\n", "}\n", "\n", "This is the compact string that gets written into the db:\n", "\n", "\u0016\b\u0004\u0010\u0001\u001a\u0010ff�@\u0000\u0000@@ff�@33\u0013@\n", "\u0005\u0010\u0002\"\u0001\u0002\n" ] } ], "source": [ "# First, let's see how one can construct a TensorProtos protocol buffer from numpy arrays.\n", "feature_and_label = caffe2_pb2.TensorProtos()\n", "feature_and_label.protos.extend([\n", " utils.NumpyArrayToCaffe2Tensor(features[0]),\n", " utils.NumpyArrayToCaffe2Tensor(labels[0])])\n", "print('This is what the tensor proto looks like for a feature and its label:')\n", "print(str(feature_and_label))\n", "print('This is the compact string that gets written into the db:')\n", "print(feature_and_label.SerializeToString())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Now, actually write the db.\n", "\n", "def write_db(db_type, db_name, features, labels):\n", " db = core.C.create_db(db_type, db_name, core.C.Mode.write)\n", " transaction = db.new_transaction()\n", " for i in range(features.shape[0]):\n", " feature_and_label = caffe2_pb2.TensorProtos()\n", " feature_and_label.protos.extend([\n", " utils.NumpyArrayToCaffe2Tensor(features[i]),\n", " utils.NumpyArrayToCaffe2Tensor(labels[i])])\n", " transaction.put(\n", " 'train_%03d'.format(i),\n", " feature_and_label.SerializeToString())\n", " # Close the transaction, and then close the db.\n", " del transaction\n", " del db\n", "\n", "write_db(\"minidb\", \"iris_train.minidb\", train_features, train_labels)\n", "write_db(\"minidb\", \"iris_test.minidb\", test_features, test_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's create a very simple network that only consists of one single TensorProtosDBInput operator, to showcase how we load data from the DB that we created. For training, you might want to do something more complex: creating a network, train it, get the model, and run the prediction service. To this end you can look at the MNIST tutorial for details." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The net looks like this:\n", "name: \"example_reader\"\n", "op {\n", " output: \"dbreader\"\n", " name: \"\"\n", " type: \"CreateDB\"\n", " arg {\n", " name: \"db_type\"\n", " s: \"minidb\"\n", " }\n", " arg {\n", " name: \"db\"\n", " s: \"iris_train.minidb\"\n", " }\n", "}\n", "op {\n", " input: \"dbreader\"\n", " output: \"X\"\n", " output: \"Y\"\n", " name: \"\"\n", " type: \"TensorProtosDBInput\"\n", " arg {\n", " name: \"batch_size\"\n", " i: 16\n", " }\n", "}\n", "\n" ] } ], "source": [ "net_proto = core.Net(\"example_reader\")\n", "dbreader = net_proto.CreateDB([], \"dbreader\", db=\"iris_train.minidb\", db_type=\"minidb\")\n", "net_proto.TensorProtosDBInput([dbreader], [\"X\", \"Y\"], batch_size=16)\n", "\n", "print(\"The net looks like this:\")\n", "print(str(net_proto.Proto()))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "workspace.CreateNet(net_proto)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The first batch of feature is:\n", "[[5.5 2.5 4. 1.3]\n", " [5.2 4.1 1.5 0.1]\n", " [7.3 2.9 6.3 1.8]\n", " [6.5 3.2 5.1 2. ]\n", " [4.9 2.4 3.3 1. ]\n", " [5.7 2.6 3.5 1. ]\n", " [5.6 2.5 3.9 1.1]\n", " [5.2 3.5 1.5 0.2]\n", " [4.8 3. 1.4 0.3]\n", " [5.8 2.7 5.1 1.9]\n", " [7.2 3.2 6. 1.8]\n", " [6.8 2.8 4.8 1.4]\n", " [6.6 3. 4.4 1.4]\n", " [7.6 3. 6.6 2.1]\n", " [5.9 3.2 4.8 1.8]\n", " [5.5 3.5 1.3 0.2]]\n", "The first batch of label is:\n", "[1 0 2 2 1 1 1 0 0 2 2 1 1 2 1 0]\n", "The second batch of feature is:\n", "[[6. 3.4 4.5 1.6]\n", " [6.7 3.3 5.7 2.1]\n", " [5.8 2.7 4.1 1. ]\n", " [5. 3.5 1.3 0.3]\n", " [5.8 4. 1.2 0.2]\n", " [5. 3.4 1.5 0.2]\n", " [5.9 3. 4.2 1.5]\n", " [4.8 3.4 1.6 0.2]\n", " [6.7 3. 5.2 2.3]\n", " [5.5 2.3 4. 1.3]\n", " [4.8 3.1 1.6 0.2]\n", " [6.3 2.8 5.1 1.5]\n", " [5. 3.5 1.6 0.6]\n", " [5.8 2.7 3.9 1.2]\n", " [7.2 3.6 6.1 2.5]\n", " [4.3 3. 1.1 0.1]]\n", "The second batch of label is:\n", "[1 2 1 0 0 0 1 0 2 1 0 2 0 1 2 0]\n" ] } ], "source": [ "# Let's run it to get batches of features.\n", "workspace.RunNet(net_proto.Proto().name)\n", "print(\"The first batch of feature is:\")\n", "print(workspace.FetchBlob(\"X\"))\n", "print(\"The first batch of label is:\")\n", "print(workspace.FetchBlob(\"Y\"))\n", "\n", "# Let's run again.\n", "workspace.RunNet(net_proto.Proto().name)\n", "print(\"The second batch of feature is:\")\n", "print(workspace.FetchBlob(\"X\"))\n", "print(\"The second batch of label is:\")\n", "print(workspace.FetchBlob(\"Y\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

michigraber/neuralyzer

notebooks/dev/DimensionalityReduction.ipynb

1

2004103

null

mit

rsignell-usgs/notebook

IRIS/Untitled.ipynb

1

16128

{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = 'http://opendap.oceanobservatories.org/thredds/dodsC/ooi/rsignell-usgs-gov/20161123T152332-CP03ISSM-RID26-07-NUTNRB000-telemetered-nutnr_b_dcl_dark_full_instrument/deployment0001_CP03ISSM-RID26-07-NUTNRB000-telemetered-nutnr_b_dcl_dark_full_instrument.ncml'" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xarray as xr\n", "b = xr.open_dataset(url)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11ec2550>]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x8ea9e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b['nitrate_concentration'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

brucefan1983/GPUMD

examples/empirical_potentials/phonon_dispersion/Phonon Dispersion.ipynb

1

88008

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phonon Dispersion\n", "# 1. Introduction\n", "- In this example, we use harmonic lattice dynamics to calculate the phonon dispersion of diamond silicon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing Relevant Functions\n", "- The inputs/outputs for GPUMD are processed using the [Atomic Simulation Environment (ASE)](https://wiki.fysik.dtu.dk/ase/) and the [thermo](https://github.com/AlexGabourie/thermo) package." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pylab import *\n", "from ase.lattice.cubic import Diamond\n", "from ase.build import bulk\n", "from thermo.gpumd.preproc import add_basis, repeat\n", "from thermo.gpumd.io import create_basis, create_kpoints, ase_atoms_to_gpumd\n", "from thermo.gpumd.data import load_omega2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Preparting the Inputs\n", "- The structure as specified is 64-atom diamond silicon at zero temperature and zero pressure. \n", "- Weuse the minimal Tersoff potential [[Fan 2020]](https://doi.org/10.1088/1361-648X/ab5c5f).\n", "\n", "## Generate the [xyz.in](https://gpumd.zheyongfan.org/index.php/The_xyz.in_input_file) file:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Si Unit Cell & Add Basis" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Atoms(symbols='Si2', pbc=True, cell=[[0.0, 2.717, 2.717], [2.717, 0.0, 2.717], [2.717, 2.717, 0.0]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=5.434\n", "Si_UC = bulk('Si', 'diamond', a=a)\n", "add_basis(Si_UC)\n", "Si_UC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Transform Si to Cubic Supercell" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Atoms(symbols='Si64', pbc=True, cell=[10.868, 10.868, 10.868])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create 8 atom diamond structure\n", "Si = repeat(Si_UC, [2,2,1])\n", "Si.set_cell([a, a, a])\n", "Si.wrap()\n", "\n", "# Complete full supercell\n", "Si = repeat(Si, [2,2,2])\n", "Si" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [xyz.in](https://gpumd.zheyongfan.org/index.php/The_xyz.in_input_file) File" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ase_atoms_to_gpumd(Si, M=4, cutoff=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) File\n", "- The [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file reads:\n", "```\n", "2\n", "0 28\n", "4 28\n", "0\n", "0\n", "0\n", "0\n", "1\n", "1\n", "1\n", "1\n", "...\n", "```\n", "- Here the primitive cell is chosen as the unit cell. There are only two basis atoms in the unit cell, as indicated by the number 2 in the first line.\n", "\n", "- The next two lines list the indices (0 and 4) and masses (both are 28 amu) for the two basis atoms.\n", "\n", "- The next lines map all the atoms (including the basis atoms) in the super cell to the basis atoms: atoms equivalent to atom 0 have a label 0, and atoms equivalent to atom 1 have a label 1.\n", "\n", "**Note:** The [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file generated by this Jupyter notebook may look different, but the same concepts apply and the results will be the same." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "create_basis(Si)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write [kpoints.in](https://gpumd.zheyongfan.org/index.php/The_kpoints.in_input_file) File\n", "- The $k$ vectors are defined in the reciprocal space with respect to the unit cell chosen in the [basis.in](https://gpumd.zheyongfan.org/index.php/The_basis.in_input_file) file.\n", "- We use the $\\Gamma-X-K-\\Gamma-L$ path, with 400 $k$ points in total." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "linear_path, sym_points, labels = create_kpoints(Si_UC, path='GXKGL',npoints=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The <code>run.in</code> file:\n", "The <code>run.in</code> input file is given below:<br>\n", "```\n", "potential potentials/tersoff/Si_Fan_2019.txt 0\n", "compute_phonon 5.0 0.005 # in units of A\n", "```\n", "\n", "- The first line with the [potential](https://gpumd.zheyongfan.org/index.php/The_potential_keyword) keyword states that the potential to be used is specified in the file [Si_Fan_2019.txt](https://github.com/brucefan1983/GPUMD/blob/master/potentials/tersoff/Si_Fan_2019.txt).\n", "\n", "- The second line with the [compute_phonon](https://gpumd.zheyongfan.org/index.php/The_compute_phonon_keyword) keyword tells that the force constants will be calculated with a cutoff of 5.0 $\\mathring A$ (here the point is that first and second nearest neighbors need to be included) and a displacement of 0.005 $\\mathring A$ will be used in the finite-displacement method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Results and Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure Properties" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "aw = 2\n", "fs = 24\n", "font = {'size' : fs}\n", "matplotlib.rc('font', **font)\n", "matplotlib.rc('axes' , linewidth=aw)\n", "\n", "def set_fig_properties(ax_list):\n", " tl = 8\n", " tw = 2\n", " tlm = 4\n", " \n", " for ax in ax_list:\n", " ax.tick_params(which='major', length=tl, width=tw)\n", " ax.tick_params(which='minor', length=tlm, width=tw)\n", " ax.tick_params(which='both', axis='both', direction='in', right=True, top=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Phonon Dispersion\n", "- The [omega2.out](https://gpumd.zheyongfan.org/index.php/The_omega2.out_output_file) output file is loaded and processed to create the following figure. The previously defined kpoints are used for the $x$-axis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "nu = load_omega2()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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MBvs7eMzEMxuKWJtb+6X7LpqVgNGg49P8OhoGmeMBEKjTcN6MeO67aJr0Fo4xbz1xi1Pjife1o9vG4do2yho7KGvs7Ll0UNfmDABHm07Fm0SHBDAvNYJzZ8Rz6ZxEjKeYbN9qd/CnDUf4w6dFA7I0TIoz8tDqmSzL9r1pM95+TrHYHLy1t5JnNhRT3jQwPU+MMYC7z5nMtYvSCBijUnbtZisfHqxhfUEthypbqWk1D3txSpBeS0ZMMIszorhkTiILM6J8pvdQgkE38PVgsFd+dSvPfFrMB4eqByQEDdBquPG0dC6cncBznx9lY2H9kL/eM6KD+fqKbK5ZKL2FY8HbT9xidNz9vpqtdme1jIpmcquctXJLGztGXe5yPCkKhAToCNRpUBQwWx20dw9dhWlOSjirZiWwamYCWbHGMWtHcV0b3//3fg4cM7luC9Jruee8yXxtWeaYBSPjzVfOKTa7g7f3VfHMhmJKjpvSlB4dzI8umMrFsxPdEnzlV7fy7v5Kth5pori+nbZhVgFTFOd8x2nxoSzNjubiOYmkR4eMefvGggSDbuAvwWCvwzVtPLH2MOvzB/YUhgfp+cH5U7h2YQpv7K3ir58dobTxy4lVwdlbeMmcRO6/ZIZHEqv6C185cYuRGev31Wp3sLO0iS3FjWwvaWR/hQmLfWQ9fQE6Z53YGGMAkSEBGAN1BAdoCQ7ovdYSqNOi0ShoFWfqFOe/+67736bVgOa42zSKgkbDgMcG6jQYA3UE6bVUm8xsKKjjo9waCmraBm2nosCi9CgumJXABTPjSYkc29EIVVV5fWcFD7yTO+BHb056JE9ePZeMGO/8ch8uXzun2B0q/9tzjN99XEiVaWAZvMWZUTx46UxmJIW5tQ1dFjsf59ewPq+WfRUmqlu6sA6z91CrKESG6MmKNTI/NYKVU2NZnBHl8frLEgy6gb8Fg712ljbx2IcF7C5rHnD7tIRQfnHZTJZkRVPR3MljH+SzPr9u0N5CBZibGsH9l8wgJz1ynFruP3ztxO2vVFWl2+ag02Kn0+Kc1+ZwzY/rmy+n1SjotRr0Wud1gE6DXqshJFA7IFXGWLyvpk4rGwvrWJ9fx8bDdSftvdBqFLJiQpgUZyQtOpiM6BDSo4JJCDcQExpIaKBu3Ie4VFXlYKWJDw/VsPZQzZA1f3UahaXZ0ayalcB5M+KJCzW4pT1dFjs/f+sQb+w55rotQKvh++dP4Y7lWWg1vjEEeCK+ek4xW+38c2sZz2woxtTVl0ZGo8ANS9L5wflTxrXjoaS+nXf2V7GpuIGi2nZauoaf2gac1VjiwwxkxYQwOyWc0zKjWZAeOW49zhIMuoG/BoPg/D+ty6vlkQ/yKTuuF3D1vCTuv2QGMcZAHA4H/9l9jD9vHLq3MD40kDvPzOKW0zNkCHmYfPXE7e3sDpWG9m6qTWZqTF1Um8zOhROdVpo7LDR1WGjptNLcaaGj20aX1T7iVavHC9BqMBp0GAN11FSUoLGZOXf5acQYA4kNDXT2yoUGEGs0EBfm3NYcF3yYrXY+ya/jzb2VfFZYh/UERcqzY0NYnBnFnJQIZiSGMTUhFIMXJNu1O1R2lzXz0aEa1ubWDFm6LECn4czJsayalcC541C67Wh9O998Zc+AHslpCaE8de08piW4t+dpPPn6OcXUZeUPnxTx4pbSAfM4I4P1/PziGVyxINkj8/YsNjsbD9ezLq+G/RUmjjV3jaqyTaBOQ1RIAInhBtKjgpkcH8rMpDDmp0USNoYpiyQYdAN/DgZ7ma12nt9UwjOfFg84wCOC9dx/3AewrLGDX7yby2eFDYNOxA3UabhodiL3XzyDKKMMIZ+Ir5+4PanLYqesqYPShg5KGjopbeigtLGDiqZOakdQwcBT9Fpn7rPkiCASwgyYrQ42FzfQNsT8ueSIIM6aFsuy7BgWZUSNatWsu1jtDrYdbeTDQzWsy62lYYgKIMEBWs6a5qzdu3Jq3CmtHB2J9Xm1fO/1fQPmJl65IIVffWUWQQGeD6DHkr+cU4rr2njwnTw2FTcMuH355Bge/sps0qI9v5ixs9vGZ4X1fFFUz8HKVsqbOmntso4oAXp/GgUMei2hBh3hQXqiggOIDTOQFG4gJTKI1KhgkiKCiA81EBZ04p5+CQbdYCIEg72qWrp45IN83jtQPeD25ZNjeOTy2aRG9X0ALTYHv/+kiJe3lQ3o1u+l4Jz8ff8lM1iYEeXupvskfzlxu5PF5qC4rp2CmlYKatrIr26lqLadmlbzyXcegQCthqCeuXMGvRZN77y5nrlwWo2CQ1Wx2h1Y7SoWmwOr3UG3zUFHt21ENcOHa25KOOfNiOec6fFMSwj1qpWMZqudL4oa+OhQDevzawc9B4BzLvK50+NZNSuB5ZNjxrX3UlVVnt9UwsMf5LsW1wToNDx02UyuWZTqVX/PseJP5xRVVVmbW8sv38sb0MNs0Gv4/nlTuPX0THQenpt3PFVVKahu47OiOvaUtVDS0EFNq5n2btuYL/BS6Ju3q9UoKAo95ys4+ItVve2RYHCsTKRgsNdnhfX89H8HB3wAg/RafnbxdG5Ykvalk+i63BqeWHuYokFqUwLEhwXyzZWTuOk0KX3Xnz+duMdCl8XOoSoT+ytaOFRpoqCmjeK69lEFWlEhASSEGUgMN5AQbiA2NJCokAAigwOICgkgIlhPZHAARoOOYL32lL5UeucctplttHfbuH7NrTh0Bu598GEa2izUt3fT0NZNfXs39W3d1LaaaR6ivFZGdDBfmZ/MV+Yle91ihvZuGxsP1/HhoRo2FtQNmcMtxhjA+TMTuHBWAqdlRQ9Z7cOdbHYHD76by8vbyl23pUYF8ecbcpiVHD7u7Rkv/nhO6ei28duPC3lhc8mA6RwL0iJ48qvzyPSyz8lQKpo62VRUz4FjJo7UO4PE5k4Lnd127GMcX5T9+hJAgsExNRGDQXB+AJ9cV8gLW0oG/KI5a2osv75qzqCTvCuaO3nonVw2HK4f9AvcoNewel4yP794+rDLEPkzfzxxD5fdoXKkvp195S3sO9bCvvIWDte2DXt4V6tRSIkMIiM6hMyYEDKig8mICSE9OoTEcINH588N533ttNioajFT1dJFXVs3qqoyOT6UuSneVQmhpdPC+vw6PjpUw+dF9ViGSDuVHBHEBTMTWDUrgZz0SI8uxmgzW/nWq3v5vLDedVtOeiR/uynHJ6qInAp/Pqfsr2jhJ28cGDDvM0iv5acXT+fGQTopfEmXxcbBqlbyq1qdvYkmM/XtZpo7rLSarX1J4R3qsIahJRh0g4kaDPbaW97Mj/97YECvX2SwnkevmM2qWYmD7mOxOXj6kyL+OdQQsgKLMqJ48LIZzEj031/pJ+PPJ+7jWe0ODlWa2FHSxPaSJnaWNg07v1dqVBDTEsKYnhDKtMQwpsSHkhYV7LW54Hz9fa02dbEut5a1uTVsL2kaMkDPjAlh1SxnD+BsLynn1dDezc3P7yCvutV126Vzk3jiqjlescDG3Xz92DsZq93BXz87wlPrByYKP3NKLE9cNYf4MPesRPcmqqrS3m2jvs050tDQbqHLYqPb7sBic2CzO7hzxaTex0owOFYmejAIzvlBj390mL9vLhlw+3WLU3ng0pknPMmuPVTD42sLOFI/eFqJ1MggvnfuZK7MSR3TNvsCfz5xm6129le0sKOkiR2lTewua/5SbdLBTIozMjclgrmp4cxIDGNKQihhPtaL7Ivva3FdO2tza1iXW8P+fomYjzctIZQLZyWyalYCU+KNXhEA9qpq6eLG57dztN+55u5zJnPPuZO9qp3u5IvH3mgcqjTx/X/vo7C2r5MiKiSA3351LiunxnmwZd5BFpC4gQSDfTYXN/DD/+ynul9y0GkJofzxhgVkn6Q6QEl9Ow+8m8umooZB03iEBGq5ZmEqP75gKoaA8Vll6Gn+dOJ2OFTyqlvZVNzApqIGdpQ2DTmk2CvGGMi81Ajmp0UwLzWC2SnhPhf4DcYX3ldVVTlwzMTaXGcKmKF+rAHMT4twDgHPTPC6eYy9Shs6uOG57a55zhoFHrtiDl9dNLF+ZPrCsTdWzFY7v/24kGe/ODpgKtPXz8zihxdM9chcVW8hwaAbSDA4kKnLys/ePDhgxXFwgJZHLp/NV+Ynn3T/TouN36w9zOu7Kujo/nJPkVaBMybH8tDqmV5b6mes+PqJu8Zk5ouier4oamBzcQONQ9S37pUcEUROeiQzk8OYEhdKWJCOTosdm13F5lCxOxzYHWBzOIPIgJ7EzgE6jevfxkBn2oWwIL3XDvt56/vabbOzo6SJ9Xm1rMurHfCjrr/eJNDnz0zg/BnxXj/0VlDTyk3P76C+zZnSRq9V+P2187lo9uDTWPyZtx577rT1SCPf/dde6tr6UhrNT4vgD9fNH/MKNr5CgkE3kGDwy1RV5dUd5fzi3bwBvT/XL0njwUtnDnsu1393VfD7T4qoaB48MW12rJGfrJrK+TMTxqTd3sbXTtydFhvbjzbxeVE9m4oahlw93ssYqCMk0BmwdXbbabeMbXqFAJ2GMIOeiGA9scZAEsKdCZ3jQw3EhxlICA8kLtS5ong8U1B40/ta12ZmY0E9nxTUsqmoYcgVwEF6LSumxHLBrHjOnhpPeLBv9NAW1LRy3d+2uVZnG/Qa/nJjzoQdKvSmY288NbZ384P/7Gfj4b5FQxHBev5w3XyWT471YMs8Q4JBN5BgcGi5VSa+/ereAYXGF6ZH8qcbF4yopFRelYkH381jZ2nToMFCeJCeNUvTufvsyei8dNHAaHj7iVtVVQpr29l4uI6Nh+vZVdZ0wqoY3kqrUUiOCCI9Opi0qGDXdVqUcyXyWCce9uT76nA4S8B9WlDHhsN1HDjB/L+IYD3nTIvngpnxLJ8c63MJmIvr2rn2b1tpaHf2SIcG6nj+a4tYnDlx85p6+znFnRwOlWe/OMoTaw+7FpdoFPjRBdO4a0XWhJk3ChIMuoUEgyfW3m3j3jcODBg2Tggz8NebcpibGjGi5zJ1WnjkwwLe3leJ2frl+WY6jcLKqbHcd9H0k85R9AXeeOJuNVvZXNTAxsJ6NhTUDRh6GQvBAVqMgTpCDTqMBj3GQC16rQadRhmQQFUFrDYHFrszubPF5ry0ddto7bJi6rKOSWCqKM7h60lxRibFGp3XPZfRlkgb7/e1qcPC5uIGPi+sZ8Ph+iErgACkRQVz9rQ4zp8Rz+LMKK9L2jtcpQ0dfPWvW13HZ2igjpdvXzLic46/8cZzynjbXdbMN1/ZTW1r3+fgotkJPH7V3HGreuNpEgy6gQSDJ6eqzl9kj31Y4FocEqDT8Ojls7kyJ2XEz+dwOPjntnL+tLF4wAe6v6RwA2uWZXDb6Zk+21voDSduVXUu/Piop5RYUV3bqOr0ajUKqZFBpEWHkBYV5BqqjQ0LJC7UOVwbGawfs+BDVVXMVgemLmeN4bqeZM61JjO1bWZqW7upazVTZTK75pKNVIwxgOzjAsTsWCOJ4YYT9jK4+301W+3sKm3mi+J6Nhc3kFvVOuTwu06jsDAjkrOnxXH2tHiyY0N8vofkWHMn1/x1m2uxSHCAln/etpic9InbI9jLG84p3qCuzcy3XtnDztJm121T40N5bs3CAZW0/JUEg24gweDwfV5Yz3de2zsgt+B3zp7E98+bMuovoF2lTfzyvbwh0130Tna/78JpzEjyrZyFnjpxmzqtfHiomrf3VbKvwjSiguvBAVqmxIcyLSGUqQmhZMUayYh21sz01tV7XRY7Fc2dlDV2UtbYQXlTJ+VNznrG5U2dIw5+gwO0ZMcayY4NcQWI2XFGMqJDCNBpxvx9tTtU8qtb2VzcwKbiBnaUNNF9gpXaUSEBrJway9nT4lg+OZbwIN+Y/zccdW1mrvrzVsqbOgFnLfQXb1nM0uxoD7fMO0gw2Mdic/Dw+3n8Y2uZ67YYYwB/u3khC9IiPdgy95Ng0A0kGByZssYO7nxpN4dr+7LEr56XxONXzSFQN/o5SQ3tZh55v4CPcmuGzFcXHRLAxXMSufucScQYvXsFJIzfidvhUNlytJF/7yxn29GmYQ/9BgVoyUmLYF5qJLNTwpmWEEpqZDAaD1aXGGvdNjulDZ0U16G/qx8AACAASURBVLU7L/XO66P17ScMuAaj1SikRQVTU7gfvbmJH33jFlKigkmNDCI5InjYc/K6bXYOHHMm6N5R4szR2N49dIJurUZhXmoEZ0yKYcXUWOamRHi0Aoi7tHfbuOavW8mtciaUDtBqeG7NQs6cMvEWCAxFgsEv++/uY/z0fwex2HuyFOg0/ObquVw2N8nDLXMfCQbdQILBkWvvtvHtV/cMWNm1OCOKv96UQ2TI6OZh9ffBgWr+sKGIguq2IUvzpEQGcVVOCneemUWwl+YtdNeJW1VVdpY28cbuY2w92sSx5pP3fik4/2anZUWzNDuauakRZEaH+FXgNxJ2h0plcxfF9W2uQPFIfQfFde2DVtUZjhhjAMmRwSRHGIg1BhJjDCQ2NJDIkABUVeVQZSs7SpvYV9Fy0hyNWbEhLJ8UwxmTY1mSFeUX+RlPxGp3cOuLO/miqAFwBsB/uTGH82bEe7hl3kWCwcHtLG3izpd2DagJfs+5U7j7nEk+P21iMBIMuoEEg6Njszt44J1cXtneVyg+MyaEl25dPGZzNlo6LTy5rpC391XSOkRpMwVIiw7molmJ3LY8w6t6DMfqxK2qKnvKm/nvrmNsPdpIRXPXsGr8JoQFsmxSDJfOSWJRZtSEmVx9KlRVpbHDwpGe4PBIfW+g2E5lS9eYps7pLyHMwOLMKM6YFMPpk2NIjghyzwt5IVVV+cF/9vO/PZWu2x67YjbXLk7zYKu8kwSDQytr7ODWF3cOSLB+3eJUfrl6ls8upBqKBINuIMHg6KmqynNflPDIh/muL8n4sEBeunUJUxNCx/S1th9t5A+fFrG95MTpT2JCAjhzSiy3L8/0+BzD0Z64LTY7n+TX8eGhavaUt1BtMg8r+IsK1rMgPZLL5yezcmocIRL8jakui52Shg5u/s6PsRkiueDy6zjW0smx5i4qm7sG1FI9mcyYEBZnRLEoM4olmVGkRAb5ZQ/GcDyxtoA/bjji2v7uOZO557wpHmyR95Jg8MRMXVa+9coeNhU3uG47d3o8f7huvs+lVjoRCQbdQILBU/fhwWq++/o+19BXeJCev39tETnpYz+J1+Fw8N7Bap7/ooSDlaYTDo/qtQpZMUZWTo3h2kVpZI5zuprhnLhVVaWop27s1iONFNS00tQxvGHKiGA9s5PDuWhWIpfOTcTo50OJ3mKw99XuUKltNXOsuYtqUxcN7ZaeIvPdtHRaUFVIjQpmUUYUizIjR5Sn05/9a0c59/7voGv7moWpPHbl7AkbGJ+MBIMnZ7U7+MkbBwb0NOekR/L8moWjTiflbSQYdAMJBsfGliMN3PnSbtdE+CC9lr/clMMKN07+ttkcvLH3GP/eVcHBY62uCcRD0WkUEsINzEoK48wpcayYGkNyhPvSEBx/4jZ1Wdh+1Llo4MAxE0cb2mnqsAx7tWt4kJ7ZyWGsmpXIV+YlSfDnIfKFPDZ2lDRxw3PbXD39Z02N5dmbF/rdkN5YkmNveFRV5dcfHeYvn/X1OE+KM/LybUtICPf9H2ISDLqBBINj5+AxE2te2EFTTw1bvVbh6Wvnc+E41RDdUtzAC5tL2F7SNOQcw+NpFAg16EgICyIxwkBKRBCZMSFMSQglKSKIuNBAQgK0aDRDf0HZbA7q2s3UmLqpa+umpL6dksZO3ln/BfaAEIwxSbSabcMa6u2lKBBnDGR2SjgXzEzgotmJMuzrJeQL+dRVNHWy+o+bXeeK6Ylh/PeupXKMn4QceyPz900l/PL9PNc0prSoYF69Y4nP1zSWYNANJBgcW0fq27n5+R2uhLFajcLvr53HJXPGd5l/S6eF13dWsC6vhvzqtiHT1QzXYJ+4sTxiggO0pEQGMT8tkkvnJLI0O8Yv04f4A/lCPjUd3Tau/PMWCmqc6amiQwJ4+9un+/wX9HiQY2/k3t1fxff/vc/VA50UbuDVO04jIybEwy0bPQkG3UCCwbFXberihue2c7RnVZdGgd9dM4/V85I91iZTp4WPcmvYeLieQ5Umatu6T5rewx20GoUwg46UyCBmJ0ewcmosyyfHEOSl6XHEl8kX8ug5HCp3vbybdXm1gHP04LU7TmNhhlQXGQ459kbnk/xavvHyHtdUorjQQF65fQmT48d2oeN4kWDQDSQYdI+6VjPXP7ed4rp2wBkQ/ubquVyxYOTl69zFbLGxs6yZ7UebyK9upcZkpqnTQqvZitnqwOFQR9T7p1GcwZ5BryXMoKe+4ghaSztrLl9FToZz1WiYH1WLmKjkC3n0nlpfyFPri1zbj185h68uSvVgi3yLHHuj90VRPXe8tAuz1RkQRoUE8PJtS5iRFObhlo2cBINuIMGg+9S3dXPjc9td1UoUBZ64ai5XjaKesSe1m23Ut5nptjnQahQ0GgWtoqDVOC/RIQEE6r+ctkBO3P5J3tfR+aywnq+9sMM1f+vW0zP5v0tneLZRPkaOvVOz/Wgjt764k46eaUPhQXpeunUxc1MjPNyykTmVYFCWZ4lxFxsayKt3LGFaT85BVYUf/3c/7x2o8nDLRsZo0JEZa2RaYhiT40PJjjWSERNCapSzZu9ggaAQok9lSxff+9deVyC4NCuan140zbONEhPOkqxo/nn7EkINzmk5pi4rNz6/nUOVJg+3bPxIMCg8ItoYyGt3nMb0RGdXvEOF7/1rH+t75gwJIfybxebgW6/scZUKiwsN5Onr5ksKGeERC9Iiee2O04gMdk7ZaTPbuOn57RzuWdDk7+RTJzwmMiSAf962mOxY5+otm0Plm6/uYVNRw0n2FEL4ukc+yGdfRQvgnFP7zPULiA0N9HCrxEQ2KzmcV24/jfCeOdzNnVZueG6ba467P5NgUHhUjDGQV24/jbSeusUWm4M7XtrFztImD7dMCOEu7+6v4sUtpa7te1dNY3GmrBwWnjcjKYyXbl1MaE9uy4Z2Czc8t42yxo6T7OnbJBgUHpcQbuCV25eQ1JMBvstq59YXd1JQ0+rhlgkhxlp5Yyf39Ss1t2pmArcvz/Rgi4QYaG5qBC/euojgnrrFta3dXP/sdo41d3q4Ze7j9cGgoiihiqJcpijKLxVF+VBRlAZFUdSey4hnGiuK8lS//Te6ocliFFKjgnn59iXEGJ3DRG1mG2v+vsOvP3xCTDRWu4O7/7XXVZ4yPTqYx6+eIzWHhdfJSY/i+TWLCNQ5w6TKli6uf3Y7ta1mD7fMPbw+GATOAd4Gfg6sAqJH+0SKouQA3x6jdokxlhVr5B+3LsLY0z1f29rNzX/fQXNPaSohhG97an2ha56gTuMsSxkmtbSFl1qaHc2zNy8koGdRU3lTJ2v+vgNTl9XDLRt7vhAMAtQBHwC/AO4czRMoiqIB/oqzWtjusWuaGEszk8L52805rg/f0foObv3HTjotw6spLITwTluPNPKnjUdc2z84f6rP5XETE8+ZU2L5840L0PWUAS2oaetJUn1qpUy9jS8Eg++qqhqvqurFqqo+CHw8yuf5DpAD/AE4NFaNE2NvWXYMv7tmHr0jR3vLW/j2q3ux2ce/TJwQ4tQ1d1i45/V9rnyCy7Kj+fqZWZ5tlBDDdM70eH595RzX9o6SJu5+bS92h/8UpfD6YFBV1VMOvxVFSQF+CVQBD5xyo4TbXTwnkQcu6atC8GlBHb96P9+DLRJCjIaqqtz7vwPU9My1igzW87tr5qHRyDxB4TuuzEkZkBB9XV4tP3/roN9UKfP6YHCMPA2EAt9XVXViZJD0A187PZNvrMx2bb+4pZQXN5d4sEVCiJF6Y08la3P7ksk/cdVc4sMMHmyREKNz55nZ3NmvR/u1HRX87uNCD7Zo7Ph9MKgoyqXA5cB6VVVf93R7xMj86PypXDw70bX90Ht5bCio82CLhBDDVdnSxS/eyXVt37AkjXNnxHuwRUKcmntXTeOK+cmu7ac/LealraUea89Y8etgUFGUEOAZwAJ8a5TPMeRFuJ9Go/DkV+cyr2eiuUOFb7+6h/xqyUEohDdzOFR+/N/9tPVLI/Ozi6d7uFVCnBqNRuHXV81h5dRY120PvpM7bqVU3RWT+HUwCDwEpAFPqKrqH325E5BBr+XZmxeSHBEEQIfFzm0v7qSuzT/zPQnhD/65rYzNxY0AaBT47VfnEhyg83CrhDh1eq2GP92wYEAnxXde28vBYyYPt2z0/DYYVBRlHvBdoBR4eLTPo6rqkBcxfmJDA3nhlkWuEkFVJjPffHkPFpusMBbC2xytb+fRD/sWfN15ZjY56VJuTviP4AAdz61ZSEqks5Oiy2rntn/spKqly62v666YxC+DwZ6cgn8DtMDdqqq6990R42JKfCjP3LCA3kWIu8qa+cW7uSfeSQgxruwOlR/8Zz9mq/OH2rSEUO45b7KHWyXE2IsxBvLiLYsIMzg7Keraurn1xZ20mX0vKbVfBoPAGmARsA7YoCiKsf8F6B2r0Pa7Xeux1ophWzEllnsv7Fve/8r2cl7dXu7BFgkh+nthcwl7y51VRvRa55zfQJ2cXoV/mhQXyl9uyhmQlNoX8+L6azCY3nN9PtA2yOWGnvvP6Hfb8nFuoxilO5ZncdncJNf2A+8cYldpkwdbJIQAKGvs4DfrDru2v3P2ZGYmhXuwRUK437LsGB7rl5T6s8J6/u+dXJ+aTuavwaDwY4qi8Osr5zAjMQwAq13lrpf3UGOSBSVCeIqqqtz7xsEBw8P984QK4c+uyknh7rMnubZf3V7O85t8Jy+uXwaDqqo+qKqqMtQF+EfPQz/rd/tGDzZZjFBQgJa/3ZxDVEgAAA3t3Xzr1T1YfaxrXgh/8frOCrYe7Vs9/MRVc9Fr/fIrRohB3XPeFFbP6xu1euSDfD4rrPdgi4bPJz6piqLE9F6AyH53RfS/r2fhiJggUiKD+eP1C9D2zNXYXdbMrz8s8HCrhJh4akxmHu5XLvKOM7OYnSLDw2JiURSFx6+aw4K0gXlxj9S3e7hlJ+crwVN9v8uefrdvPe6+tPFvmvCkpdnR/OiCqa7t5zaV8NGhag+2SIiJRVVVfv7WIVdy6YzoYO45d4qHWyWEZwTqtPzlphwSw50lF9vMNm7/xy5Mnd69wthXgkEhhnTn8izOnd5X4upH/zlAaUOHB1skxMTx/sFq1uf3VV947Mo5GPSyelhMXHGhBp69eSEGvTPEKmno4Nuv7fHqFcY+EQyeaP7fcZfSYT7f13oev9K9LRfjQaNRePLquaRGOZN/tnXb+MYrezBb7R5umRD+rbnDwgNvD6w9fFpWtAdbJIR3mJUczpNXz3Ntf1HUwMMf5J9gD8/yiWBQiJMJD9bzp+tzCOiZsJ5f3SoJqYVws1+9n09jhwWAxHDDgBygQkx0F89J5O5z+hKuv7C5lNd3emdeXAkGhd+YnRLOA5fNcG2/tqOC9w/I/EEh3GHb0Ube2HPMtf3w5bMINeg92CIhvM/3zpnMhbMSXNs/f+sQO70wL64Eg8KvXL84jUv7JaS+938HONbc6cEWCeF/LDYH9791yLV90ewEzp4Wf4I9hJiYNBpnFZ7p/fLifv2fu6lo8q7vJQkGhV9RFIWHL5/lKh7eZrbx3X/t8+qJu0L4muc3lVBU50yXERKg5f8umenhFgnhvYIDdDx7cw7RPXlxmzos3PHSLjp6VuB7AwkGhd8JM+h5+rr5A/IPPv1JkYdbJYR/ONbcOeDzdM95U0joSaMhhBhcSmQwf70pB722r4bx9/+9D4fDO0rWSTAo/NKCtEi+f15frrNnNhSzrac6ghBi9H7xbh5dPSv1pyWE8rVlGZ5tkBA+YmFGFA9/ZbZre21uLU95SUeFBIPCb921Iptl2c40Fw4V7nl9H6Yu7078KYQ3W59Xy8d5fTkFH758FjopOSfEsH11USq3nJ7h2n76kyKvWOgon2Lht7Qahd9dM4/IYOcKx2qTmQfePnSSvYQQg+my2Hngnb50TdcuSiUnPcqDLRLCN/3souksnxzj2v7Bf/ZxqNLkwRZJMCj8XHyYgUevmOPafmtflVf8ChPC1/zh0yIqW7oAiAzW85NVklNQiNHQaTU8c90CMmNCADBbHdz50i7q27o91iYJBoXfWzUrgSsXpLi2f/bWQepazR5skRC+pbiujWe/OOravu/C6UT2rIwUQoxceLCeZ29eSGigDoAqk5m7Xt5Nt80zlbMkGBQTwgOXzSA5wplupqXTyo/fOICqescqLiG8maqq/PytQ1jtzs9LTnokV+WknGQvIcTJTIoz8vT18+lJfMHusmZ+/uYhj3w3STAoJoQwg57fXD0XpedDt/FwPa9s986yQEJ4k7f3VbHtqLNiglaj8KuvzELT++0lhDglZ02N474Lp7u2/7P7GH/fXDru7ZBgUEwYS7Ojue30TNf2w+/nU97oXVnghfAmpi4rv3o/z7V9y7IMVyUFIcTYuH15JlcsSHZtP/x+HhsP141rGyQYFBPKDy+YypR4IwBdVjs/keFiIYb0m7WHaWi3AJAQZuB7/XJ3CiHGhqIoPHL5bOanRQDOVGjfemXPuK4wlmBQTCgGvZYnrprrmqOx9Wgjr+2o8GyjhPBCB4618PL2Mtf2A5fOwNgz2V0IMbYMei1/vSmHpJ5qPh0WO197YQdljR3j8voSDIoJZ25qBHecmeXafuSDfKpNXR5skRDexe5Q+dmbh+jtNF8xJZZVsxI82ygh/FxcqIF/3LqY8CBnbtyGdgtr/r6Dhnb3p5yRYFBMSPecO8WV46m928ZP/3dQhouF6PHq9jIO9gxRBeg0PLR6Jooii0aEcLfJ8aE8t2YhgTpneFba2MltL+6ko9vm1teVYFBMSAa9lsevmuNaXbzhcD1v7av0bKOE8AJ1bWYeX3vYtf2tlZNIjw7xYIuEmFgWZUTx9HV9KWf2HzNx5z930WVxXw5CCQbFhLUoI4qbT0t3bf/i3TyPZoAXwhs8+kEBbWZnL0RmTAhfX5F1kj2EEGPtgpkJPLR6lmt7c3Ejt7y4w209hBIMigntx6umDUhG/cA7UrtYTFxbjjTw5t6+HvKHVs/EoNd6sEVCTFw3npbOjy6Y6tredrSJNX/fQavZOuavJcGgmNBCAnU8duVs1/YHB2v46JDULhYTj8Xm4P63+n4MXTInkeWTYz3YIiHEt86axH0X9tUB31XWzJV/2kJF09jmyJVgUEx4yyfH8tWFfeW1/u/tXNrc8MtLCG/27BdHOVLvTGNhDNRx/yUzPNwiIQTA11dk83/9Po9Fde1c/PQXfHhw7DouJBgUAvjZxTOIDQ0EoK6tmyfXFXq4RUKMn4qmTv7waZFr+/vnTSE+zODBFgkh+rv1jEx++9W5BGidYVur2cY3XtnD7f/YRUFN6yk/vwSDQgDhQfoBPSEvbS0d1+zvQniKqqr84t1czFYHADMSw7h5afpJ9hJCjLcrFqTw2p2nuea5A6zPr2XVU1+w+plNp/TcEgwK0ePSOYksnxwDOMsB/ezNg9gdkntQ+Le1ubWsz++rg/qry2eh08pXgxDeKCc9kg/uXj5gahM408+cCvnEC9FDURQeWj2LgJ5kn/uPmXi1XzkuIfxNe7eNX7yb69q+fkkaC9IiPdgiIcTJhAfrefyqubzz7dO5eHYiWs2pJ4SXYFCIfjJjQvjmymzX9uNrD1PXZvZgi4Rwn999XEi1yXl8xxgD+MkF006yhxDCW8xJieCPNyxgz8/P4883LDil55JgUIjj3LUi21Wqrs1s4+H38z3cIiHG3qFKEy9sLnFt//ziGYQH6z3YIiHEaIQH67lwduIpPYcEg0Icx6DX8st+md/f3lfFpqIGD7ZIiLFld6j87K1D9E6JPX1SNKvnJXm2UUIIj5FgUIhBnDE5hsvm9n053v/2IcxW99WFFGI8vbqjnP0VLQAEaDX8cvUsFOXU5x0JIXyTBINCDOHnl0wnNFAHQElDB89vKjnJHkJ4v7o2M49/VODa/sbKbLJijR5skRDC0yQYFGIIcaEGftivLuQfNxRTY5LFJMK3/eq9fNrMzmL3mTEhfKPfgikhxMQkwaAQJ3DDkjSmJYQC0Gmx89iHsphE+K7PCut5Z3+Va/uXq2dh0Gs92CIhhDeQYFCIE9BpNfzfpX2VSd7aV8XusiYPtkiI0WkzW7nvjQOu7dXzkjijJ8m6EGJik2BQiJNYlh3DhbMSXNsPvpOHQyqTCB/z648KqOqZ5hAZrB9Q+F4IMbFJMCjEMPz0oumuyiQHK038d/cxD7dIiOHbeqSRl7eVu7YfvGwm0cZAD7ZICOFNJBgUYhhSo4L5+plZru3H1xbQarZ6sEVCDE+nxcZP+g0Pnzs9fkDaJCGEkGBQiGH6xspsEsMNADS0W/jDJ0UebpEQJ/fkukLKmzoBCDXoePhyySkohBhIgkEhhik4QMe9F/bVbn1hcylH6ts92CIhTmx3WTN/71dy7v5LZhAfZvBgi4QQ3kjn6QYI4Usum5vEy9vK2FnajM2h8sv38njxlsWjfr6WTgsFNW2UNXZQ0dRFVUsXLV1W2sxWWrtstJqt2B0qWo2CRlHQaMCg0xIZHEBEsJ6okACiQgJIiQwmLSqY1KggkiKC0Gvld95E195t457X96H2rHVaPjmGq3NSPNsoIYRXkmBQiBFQFIUHLp3Jpc9sQlVh4+F6Pi+s58wpsSfd12Z3kFfdiilhIeawFJY++gnVbkhirdUoZEQHMy0xjOkJoUxNCGN2cjgJ4dIjNJH84p3cvuHhQB2PXjFbhoeFEIOSYFCIEZqVHM7VOSn8e5dzRfEjH+Rz+qQYtJovf9G2d9vYeLiO9Xm1bDhcj6nLChlnAdDlpmomdofKkfoOjtR38P6BatftieEGFqRFsiA9kgVpEcxMCnetkBb+5cOD1fyn34r3X10+i5TIYA+2SAjhzSQYFGIUfnD+VN7dX02X1U5BTRv/3V3BNYvSAGcw9kVRPW/urWRtbg1mq2PI5wnQaZgSbyQ71khqZDApkUFEGwMJM+gINegJNegI0GmwO1TsDhVVhU6rjeYOK82dFpo7LdS1dlPR3ElFUycVTV3UtA4eZFabzLx/sJr3D1a7XjsnLZIzJsdw+qQYZieHDxrQCt9SberivjcPurZXz0ti9bxkD7ZICOHtJBgUYhTiwwx8fUUWT613rih+Yu1hlmVH8/7BGv65tYzKlq4h9guk/eheDK0VvPr0w2THhqAb4/l9nRYbRbXtFNS0kl/dRl51KwePmeiy2gc8zmJzsPVoI1uPNvLE2sOEGXQszY7mjEnO4DAzJkSGFX2MxebgW6/soaXTmfYoOSKIh1bP8nCrhBDeToJBIUbpzjOzeG1HObWt3TS0W1j++MZBHzc1PpQLZydw7vR4ZiaFcdZZv3Le3lPzeKwFB+iYmxrB3NQI1202u4OCmjb2ljezp7yFPeXNlDV2Dtiv1WxjbW4ta3NrAUiNCuKcafGcPS2OJVlRBOqkhq23e/TDfPaUtwDOuaO/u2Ye4UF6D7dKCOHtvD4YVBQlFDgLWAQs7LmO7rl7uqqqBUPsZwAuBi4EFgNZgB6oBbYCf1ZVdaNbGy/8WnCAjhVTYl1zB/vTahTOmhrLPedOYUZSmMd72HRaDbOSw5mVHM5NS5231baa2VzcwKbiBjYXN1Db2j1gn4qmLl7cUsqLW0oJDtByxqQYzpkex1lT44iT9CRe570DVbywudS1/ZNVU1mcGeW5BgkhfIbXB4PAOcCbo9jvXeDcftvdgBVI7bl8VVGU36uq+r1Tb6KYiMxWO58X1g96n92hsj6/jmqTmfsvmcFpWdGDPs6T4sMMXLEghSsWpKCqzkUnvcHh1iONtHfbXI/ttNhZl1fLujxnr+Hs5HDOnhbHudPjmZXs+WB3osutMvGT//ZVGTl/Rjx3LM86wR5CCNHHF4JBgDpgF7ATqAT+Nox99EAR8Czwbm8PoqIo2cCjwNXAdxVFKVRV9U9uabXway9uKaWmpzctxhjAG99YxsvbyvjntjLXopHcqlau/ds2Lp6dyH0XTfPaFZ2KojApzsikOCNrlmVgsTnYWdrEJ/l1fFpQS+lxQ8oHK00crDTx+0+KiA8L5Oxp8Zw3I45l2TEY9DKcPJ6qTV3c+uJOOizOOaHp0cE8cfVcCdCFEMOmqL0ZSb2UoihaVVXt/bYzgN6U+icaJl4GbO+/b7/7FGA9cDZQoqrql35CK4qiAnj730d4RkunhTMf30Cr2dl79svVM7lpaQYA9W3dPPvFUV7aWjpgJXGgTsNdK7J585FvolFtbNy4cfwbPkpH69v5tKCOT/Lr2FnahM0x+OfCoNdwxqRYzp0ex9nT44gLnTjDyStXrgQY1/e1zWzl6r9spaCmDXCWm3vjG8uYEu+e+ajCO3ni2BPep/cHoKqqI/4l6PU9g4MFc8Pcb8sJ7lMVRXkJZzCYqShKlKqqTaNto5h4/rih2BUIZsaEcO3iNNd9saGB/PSi6dxyegaPfVjA2/uqAOi2Ofj9J0Xo5qwh5shHHmn3aGXFGsmKNXL78ixMXVY+L6zn04I6Nhyuc61cBTBbHazPr2V9vnM4eW5KOOdMj+fc6fFMTwyV3qox1GWxc9fLu12BoE6j8JcbcyQQFEKMmNcHg27U2O/fMq4lhq2qpYt/bC1zbf/ogqmDln9LDA/i99fO56bT0nnw3VwOVbYCYAuKombmdTz4Ti4/umAqIYG+9TEMD9Jz6dwkLp2bhM3uYE95C5/k1/Jxfi1H6zsGPHb/MRP7j5n47ceFJIUbOGd6POdMj2NpdrSsTj4FnRYbt/9jF1uO9J3GHr1iNqdPivFgq4QQvsrrh4mPN9xh4mE8zxPAD3GuLk5Uj/tDyDCxGMp9/zvAazsqAJibGsFb31x20h4vu0PltR3lPPZhwYCFGSmRQTxx1VyWZnvfApPRKGno4JOensGdpc3YhxhODg7QsnxyDOdMd6auiTEGjnNLx954DdV1Wmzc+uJOth3tG8z4+6xz1QAAIABJREFU0QVT+dZZk9z6usJ7yTCxgFMbJp6QwaCiKMlAAWAEfq2q6r2DPOakfxhf+9uJU1fS0MG5v/3MFeS8cvuSEfXGVLV0cc5PnqMrsm+aqqLAN1dm871zpwzaw+irTJ1WNhbWsT6/jo2H62gz2wZ9nKLAzKQwTp8UwxmTYliUEeWTi1DG4wu5oqmTr/9zN3nVra7bJBAUEgxOHMOZauOXcwbHmqIoOuAVnIFgOc6VxUIMy1PrC12B4NKs6BEPyyVFBBF3+A06YmZgnf0VTF1WVBX+uOEIm4sbefra+aRFe+eK45EKD9azel4yq+clY7X3rU7+JH/g6mRVhUOVrRyqbOWvnx0lQKdhYXqkKzicmRQ25lVafNHm4ga+/eoemvvN0bz3wmnctSLbg60SQviDCdczqCjKn4G7AAtwtqqqm4d4nAwTiwHyq1u56Okv6D0k3vjGMnLSI0f8PL2/4l9/5yN+8O/9bCpucN1nDNTx8OWz/LqWbG9Ow0/ya/kkv47d5UMPJ4NzSHleagQL0yPJyYhifloEYQbvq6rhrt6ZLoudpz4p5NnPj9L7Z9JrFR5aPYvr+i1cEhOX9AwK8PPVxGNJUZRHcAaCduCGoQJBIQbz5LpCVyB47vS4UQWC/cWHGXjp1sU8+8VRnlh7GJtDpb3bxnf/tY+dpU3cf8kMv1xk0T+n4ddXZNNqtrL9aJMr4XVxXfuAx3da7Gw50uhaLKEoMDnOyKykcGYkhTGz59rfyq512+y8vbeK360vpNpkdt0eFxrIn2/MOeXjTwghek2YYFBRlJ8B9wEqcIeqqv/1cJOED9lT3uxKlwLw/fOmjsnzajQKX1+RzdLsaO5+ba9r+PTlbeUcqmzlTzcsICkiaExey1uFGfScNyOe82bEA1BjMrPliDMw3Hakkap+gRA4h5ULa9sprG3nf3srXbenRgUxJS6UzJgQMmJCyOq5TggzoNH4Rkobs9XOvooW1ufV8vb+KurbBpYIXJYdzVPXzJNygEKIMTUhgkFFUe4BftWz+V1VVV/wZHuE73ly3WHXvy+dm8SMpLAxff45KRG8d/dyfvLfA7x/sBqAfRUtXPKHTTx97XzOmDxxUoYkhPeVyQPnoptdZc3sLm1iV1kz+dWtDDaqXNHURUVT15duD9RpiA8zEBcaSFxYIHGhBuLCAokOCSAkUEdIoA5jv4tOq6BVFBRFQatR0CigoGCxO7DYHVhtDqx2B9091xabA7PNQWfkJBwaHf/ZVYHZ5qDbaqe759psc2C22um2OjDbBl47VBWr3UFjh4VjzV2DDpnHGAP44flTuWZRquRqFEKMOb+fM6goyjeA3nJz96qq+uthvo7MGRSAc+L+Dc9tB0CrUfj4njPJijWO+vlONL9HVVWe31TCox8WuIICjQI/vWg6t52RKYEA0NFtI6+6ldxKE7lVreRWtVJU14bV7n+f1fiwQNYsy+DmpRkYfSwfpRg/MmdQgMwZHJKiKGuAP/ZsPjTcQFCIXqqq8sTavl7BqxaknFIgeDKKonD78v9n777DoyyzBg7/nknvIQkJpBF6771XxQYqiG3tva66rnVd111d3c+y6roudrGLKAKCHaT3EnoPIZVU0ttk5vn+mMnMBCmBzGSSybmvK1fyTntP0Myc9ynndKJffDj3fr6VvNJqzBqeW7KXAzmlPHtZH49cR3g2gvy8GZoUwdCkCNttNbVmDuWWkZJfRmp+OSn55RzJLyc1v7ze7tvmTinoGBnE8E6RTO0dw+guUR5VbkgI0Ty1iGRQKeU4R+a4ajr8hPsKtdZm63NmAu8DCnhJa/0310cqPM3SvbkkpxcB4Otl4I9TujbJeYd1jGDJ/WO469MtbE2znP+rzRkcyS9n9nWDPaJIszP5ehvoFRt60un70iojuaXV5JRUkVdaTW5JNbmlVRRVGCmrrqWsupZy23cTtWYzZg1ms8asNSazRmOZbvbxsnz5Wn/29VL4eBnw9/Fiy8b1KF3LBedNxt/HCz9vw0m/+/sY8PO2f1cKvA0GwgN9SGgTSIBv6072hRBNr0VMEzekALRVR611qvU5KUBH6+05p3yGxYwTexnLNLHQWnPJG6vZnWUp8HvTqCSemd670a97NlM61bUmnpi/k/lb7Rsl4sIDeO/GIfRs79x1i6JxZKpOuIv8vyegcdPEnjz/4Pi7xZzhy7fJoxPN3tK9ubZE0M/bwD0Tm764r5+3F6/M6s+TF/WgbrlgZlElM2ev5efdx5o8HiGEEJ6nRSSDWmvVwK9Uh+ckncXzlrvvtxPNkdaa15cetB1fN6ID0SHuKeehlOKOcZ1574Yhtk0EFTUm7vx0C++vPnKGZwshhBCn1yKSQSGa2rJ9uezMLAYso4J3ju90hme43uSeMcy/ZxSJEZZ2dVrDs4v38Myi3aft4CGEEEKcjiSDQpzgxFHBa4cnum1U8ETdYkL49p5RDEoMt902Z20qd326hcoakxsjE0II0VJJMijECZbvz2NHhn1U8O7xTb9W8HQig/34/PYRXNinne22X/bkcPU7637XsUIIIYQ4E0kGhXCgteY1h1HBa4YlNsvWX/4+Xrx57SDuGGefvt6eUczl/1vDodxSN0YmhBCipZFkUAgHKw7ksb2urqC3gbsnNK9RQUcGg+LJi3ry7KW9qWu9m3G8khn/W8v6lAL3BieEEKJJVBlN/LircdUlWkTRaSGawolrBa8ZmkBMMxwVPNH1I5OIDQ/gvs+3UWk0UVJVyw3vb+SlWf24dECcu8MTQgjhAuXVtcxefpg5a1Mpq65t1GvJyKAQVqsO5rMtzd5t5O4JXdwcUcNN7hnDV3eOpG2IpTNJjcnMA18m899lB6VwuhBCeJitacc5/9WV/Pe3Q41OBEGSQSGA348KXj0sgXZhzX9U0FHf+DC+vWcUXaPtvZNf/vkAj3+zE6PJ7MbIhBBCOMuCbZlc9fY6Mosqbbd1jApq1GtKMigEsO5wAVuOHgfqRgWb71rB04lvE8jXd49iZKdI221zN6dzy5xNlFYZ3RiZEEKIxvpiYxoPfZWM0WSZ8Qn19+blWf1Z+qfxjXpdSQaFAP63/LDt51lD4mkfFuDGaBonLMCHj24ZxoxB9vWCqw7mM+utdWQXV57mmUIIIZqrJTuyefLbndSt/OkeE8KSP47lisHxGAxn3Y64HkkGRau3Pb2I1YfyAfAyKO5qZnUFz4Wvt4FXZvXngcldbbftO1bKZW+uYXdWsRsjE0IIcbaS04t4aG6yLRHsFx/G3DtHkGDtSNVYkgyKVu9/yw/Zfp7Wr73T/rjcTSnFQ+d14+VZ/fG2XjXmlFRz5VvrWL4/183RCSGEaIjj5TXc+9lWaqxrvzu1DWLOzcMID/R12jkkGRSt2sGcUn7anWM7bkk7iBvqisHxfHTLMEL8LZWkymtM3PrRZr7YmObmyIQQQpyO2ax56Ktk22aREH9v5tw0jIgg5yWCIMmgaOVmr7CvFZzSM4bu7ULcGI3rjO4SxTd3jyIu3LIW0mTWPDF/Jy/+uA+zWUrPCCFEczR7xWGW78+zHb8yqz+Jkc6fvZJkULRa6YUVLEzOsh3fM7HlrxU8nW4xIXx7zyj6xIXabvvf8sM8MDeZKqPJjZEJIYQ40a7MYv79ywHb8R3jOnF+73aneca5k2RQtFrvrkrBZB0VG9kpkkGJbdwcketFh/oz946RTOoRbbvtu+1ZXPXOenJKqtwYmRBCiDpGk5lHvt5h+4wa3KENj0zt7rLzSTIoWqW80mrmbkq3HXv6qKCjID9v3rl+MNeP6GC7bXt6EdPeWE2ytS+zEEII93lr+WH2ZpcA4O9jqQ7h4+W6lE2SQdEqfbDmCNW1lp1ZfePCGNMlys0RNS1vLwP/uLQ3f5/eGy/rTuPc0mqufHsd87dmuDk6IYRovQ7mlPLGMnuVi4fP605SIzuMnIkkg6LVKaky8um6o7bjeyZ0RqnGFexsiZRS3DgqiY9vGUZ4oA8ANbVm/vTVdp7/fq9tekIIIUTTMJs1j8/faSsj0z8hnFvGdHT5eSUZFK3OJ+uOUmpt7N25bRBTXbQgt6UY3SWKhfeOrtfT+J2VKdz04UYKy2vcGJkQQrQuC5Izba1RfbwUL13RzzZ740qSDIpWpcpo4sM1qbbju8Z3bnQbH0/QITKI+feMYkpP+8aSVQfzufg/q9hytNCNkXkWrTVrD+dz72dbGfrPXxn87C/c9ckWKmpq3R2aEMLNyqpreeGHfbbj28Z2oltM05Q7k2RQtCoLtmWSX1YNQLtQfy4dEHeGZ7QeIf4+vHP9EO6baC+8nV1cxVVvr+e9VSloLdPG56qkysicNUc479WVXPvuBpbszCavtJqC8hp+3H2Mx77ZKf++QrRybyw7SF6p5fMpJtSv3nuxq3k32ZmEcDOzWfPuqhTb8S1jkvD1lushRwaD4s9TuzOoQzgPzd1OcaWRWrPmuSV72XikkH/N7Of0yveebE9WCZ+sP8rC5Ewqak5dy/G77Vn0jw/jtrGdmjA6IURzcTivjA9WH7EdP3FhT4L8mi5Fk09C0Wr8tj+Xw3nlAAT7eXP1sEQ3R9R8TeoRw5I/jqF/Qrjttp/35DD1tZX8Jn2NT6u61sSCbZnMnL2Wi/6zii82ptVLBIP9vLlhZAe+u28M0/rH2m5//vu9rD2U746QhRBu9q8f9mE02WsKXjog9gzPcC4ZGRStxjsr7aOC1wxLINTfx43RNH/xbQKZd+dInv9+L3PWpgKW+ow3f7iJ60Yk8uRFPQn0lbeQOka/MMqi+zHyhWUn3XjTPSaE60d24LKBcQRbr/hfmdWfzOMVbE0rwqzhvi+2sei+0cS3cX67KSFE87Q5tZBf9uTYjp+Z1rvJK1zIyKBoFbanF7HhiGUjhLdBcfNo12/V9wS+3gaemd6bD24aQlSwn+32T9encfF/VrP2cOseyaquNbF4RxbXv7+BzAG3Uxw3ol4i6OOlmN4/lnl3jeTHB8dy3YgOtkQQLP++s68bTNsQy79tYXkNd326RdoDCtFKaK35vx/tm0YuHRBL3/iwJo9DLutFq+C4VvCSfu2JDQ9wYzQtz6QeMfz0YDhPfruTn3ZbrmCP5Jdz7bsbmDEojicv6lkvWfR0B3JKmbspnflbMzheYbTc6HAlHxvmzx9GdODKIQm2RO9UYkL9mf2HQVz9znpqzZpdmSU8+e1OXpnVv1XWvxSiNVm2L5dNqfZSMg+f57qWc6cjyaDweOmFFXy/M9t2fPs4WaR/LiKD/XjrusF8szWTZxbtpsxaq3H+1kyW7s3l8Qt7cNWQBI8t1VNeXcuSHdl8uSmNrWknadunNf7Fqfzn/iuY1CMa77NoHTUkKYK/TevFXxfuBiz/pv3jw7lxVJKTohdCNDcms+bFH/fbjq8dlkhipHuWiEgyKDzeB2uOUNdMY0yXKHrHNv0QvKdQSnHF4HjGdo3iH4v3sGSHJckurjTyxPydfLr+KE9c2JMxXT2jvZ/JrFl3uIBvt2Xy465syk+yIzguPIBZQ+L5+sWH8a4p5fze953Tua4b0YEdGcXM22JpB/js4j30bB/KsI4RjfodhBDN04JtmezPKQUg0NeL+yZ1dVsskgwKj1ZcYWTupnTbsYwKOkdMqD9vXjuIWYNzeXrhbtIKKwDYnVXCde9vYGzXKB6c0o3BHdq4OdKzp7Vmd1YJC7Zlsmh7FrnWul+OfLwU5/dqx5VDExjTJQovg2LBc6WNOq9Simcv68P+nFJ2ZBRTa9bc89kWFt8/lnZh/o16bSFE81JlNPHvXw7Yjm8b2+mMS0pcSZJB4dE+23jUVtaje0wI4zxkxKq5mNA9mp8fiuR/vx3inVUpVBkt/TRXHcxn1cF8RnWO5N6JXRjVObJZr3/TWrM9o5ifdh/jp93HSLGWIDpR57ZBXD00kRmD4oh0wRpJfx8v3rpuMNPeWE1BeQ35ZZYNJXPvHIGft5fTzyeEcI/PNqSRWVQJQGSQL7ePde+mRkkGhceqrq3feu72cZ2adULSUvn7ePGn87tz7fAOvPbrAb7anG6bll97uIC1hwvoGh3M9SM7cPnAOEKaSUkfo8nMxiOF/LT7GD/vzuFYSdVJHxcV7Me0/u25fGAcfePCXP7/UGx4AP+9dhDXvb8Bk1mTnF7EM4t288KMfi49rxCiaVTWmJi9/JDt+L5JXdz+vijJoPBYi5Kz6rX2md6/aYt4tjbtwvz518x+3Da2E7OXH2ZBciYma1Z4MLeMpxfu5p9L9jKlZwyXDohlXLe2+Ps03WiX1ppDuWWsOpjPmkP5rE8pOOkaQLCs35naux2XDYxjdOfIs9oM4gwjO0fy5EU9eXbxHgC+2JhO37hwrh0uhdKFaOk+23CU/DJLCarYMP9m8XctyaDwSFrreqOCN46S1nNNpUt0MK9c2Z8Hp3Tl3VUpfLMlw5Z0VdeaWbIzmyU7s/H3MTCyUyQTukczNCmCbjHBTk26SqqM7MooZlt6EdvSjpOcXmR7Az6Z8EAfJveIYWrvmCZPVE/mltFJ7MgoYmFyFgB/W7SLHu1DGJTY8tZhCiEsqowm3lphL3V298QuzWIJiCSDwiNtOFLInuwSAPx9DFwrreeaXEJEIP+4tA+PTO3Ot9sy+WJjOnut/00Aqoxmftufx2/78wDLaFyf2DC6xATTKSqIDpFBRAX70jbEjxB/H/y8Dfh5GzBryxRvldFEflkN+WXV5JdVk1dazZH8cg7llnE4r4yckt9v/DhRfJsApvSM4fzeMQxLimjyEcDTUUrxrxn9OJBTxt7sEowmzd2fbuG7+8cQHSIbSoRoiT7fkEZ+meW9qV2oP1cOiXdzRBaSDAqP9OEae8PvGYPiCQ/0dWM0rVuIvw83jEzihpFJHMgpZWFyJj/sPEZKfv1NGhU1JjamFrIxtdBlsYT6ezOqcxRjukYxtmsUiRGBzXodaYCvF+9cP5hp/11NUYWRnJJq7v98G5/dNrxZJa5CiDOzjAoeth3fPaFzsxgVBEkGhQdKL6yo1+fxZinc22x0iwnhkak9eGRqD9IKKlh+IJd1hwvYllZ0yg0c58rXy0Dn6GAGJoYzMCGcgYlt6BQV1OKKYidEBPKfqwdy44cb0doy6v3Sz/t54sKe7g5NCHEW5m5Kt5Wqig7x46qhCW6OyE6SQeFxPlqbatvNOrZrFF1jQtwbkDipxMhA24ghQFZRJfuPlXI4r4zDeeVkF1fapn8rqk1U15qpMZlRypLo+XobiAjyJSrYj6hgXyKD/UiMCKRz22C6RAeT0CbAY0bPxnVry5+mdOMVa12yt1ekMDChDRf0aefmyIQQDVFda2L2cvuo4F3jO7t9XbIjSQaFRymvrmXuZnuR6VtGu7d2k2i42PAAYsMDmNgj+pSPMZt1ixvZc5Z7J3Zha9px2xrLR+Ztp3u7EDpGBbk5MiHEmXy1Kd02+xEV7NcsdhA78ozLZiGsvtmaQWmVpWdup6ggxndr6+aIhDO11kQQLL/7q1cNIL5NAACl1bXc/ekWKk9RHkcI0TwYTeYTRgU7NatRQZBkUHgQs7l+OZmbRie16uRBeJ7wQF9m/2GwrUzSvmOl/OXbnWit3RyZEOJUFiVnkVVsGRWMDPLlD8M7uDmi35NkUHiMFQfyOGLdoRri783MQc1jy74QztQ3Pox/TO9tO56/LZPPN6a5MSIhxKmYzZq3V9pHBW8enUSAb/MaFQRJBoUH+cChnMzVQxMI8pMlscIzXTU0gVmD7Rc7f1+0h+3pRW6MSAhxMsv25XIgpwyAIF8vrh+R5N6ATkGSQeERDuaUsupgPgAGhW2HqhCeSCnFs5f1oWf7UABqTGbu+Wwrx8tP3WFFCNH0HOsKXjMskbDA5tGb/USSDAqP8OHaVNvP5/WKISEi0H3BCNEE/H28eOu6QYT4W0bAM4sqeWBusq0ftBDCvTanFrL56HEAfLwUt45tvtUtmn0yqJQKUUpNV0o9q5T6QSmVr5TS1q8eDXi+QSl1h1JqnVKqSClVqpTappR6RCklbSk8QHGFkflbM2zHUk5GtBYdIoP495UDbMcrD+TxxrKDboxICFHHcVTwsgFxtA8LcGM0p9fsk0FgMrAQeAq4AIhs6BOVUj7Ad8DbwAggAPACBgAvAquVUsHODlg0rXlb0qkymgHo2T6UYR0j3ByREE3nvF4x3DOhs+349aUHWXso340RCSEO5JTy695c2/Gd4zu5MZozawnJIEAu8D3wd+COs3jec8BFQBVwExAIBAHTgEJgKJZEUbRQZrPm0/VHbcc3jOzQrHvNCuEKfzqvGyM6WS6CtIYH5iaTZ217JYRoeo6jguf3iqFLdPPuhNUSksHvtNYxWuuLtdbPAL805ElKqXbAA9bDx7TWH2mtTdpiMXCL9b5rlFL9nB+2aAqrDuWTWlABWMrJXDog1s0RCdH0vL0MvH71QCKDLCtf8kqreWhuMmZZPyhEk8surmRRcpbt+C6Hkfvmqtkng1rrcy2vPxPwA4qBd07yuguBA4ACrj3nAIVbfbIu1fbzrMEJBPpKORnROsWE+vPqVQOoGxhffSif/y0/5N6ghGiFPlp7lFrrhdiwpAgGJbZxc0Rn1uyTwUaYaP2+UmtddYrH/Gz9PqkJ4hFOll5YwdJ99jUZ149sflXdhWhK47q1rbd+8N+/HGBDSoEbIxKidSmvruXzDfalS7c14x3Ejjw5Gexl/b77NI/ZY/3eU8lCsxbnsw1p1HXhGts1io5RQe4NSIhm4KEp3RiaZBmJMGv445fbKCiT9YNCNIVvtmZQUlULQIfIQCb3jHFzRA3jyclge+v3rNM8pu6+YOvX7yilTvkl3KfKaGLuJnsLLikyLYSFt5eB/1wzkDbW4rY5JdU8PG+7rB8UwsXMZs0Hq+2dsG4Z3REvg3NzBVflJJ6cDNYNE1We5jEVDj9LiZkWZMmObI5XGAGICw9gUo9oN0ckRPPRPiyAV67sbztevj+Pd1aluDEiITzf0n25tg2Nof7eXOHQMrK58+Rk0Cm01qf8Eu7zsUM5mT+MSHT61ZcQLd2kHjHcMc5e2+yln/az5WihGyMSwrO953DBdc3wRIL8nL+h0VU5iScng+XW76cr+e3Ys6zMhbEIJ9qeXsT29CIAfL0MXDUkwc0RCdE8PTK1OwMTwwEwmTV//CKZkiqjm6MSwvPsyixmwxHLxZa3QXHTqCT3BnSWPDkZrFsPeLrCc3X3lWmtS10cj3CSj9fZRwUv6deeyGA/N0YjRPPl42XgjWsGEurQv/jpBbvcHJUQnud9h7WCF/Vt36xbz52MJyeDdTuFe5/mMXU7jve6OBbhJIXlNXy3w74nSMrJCHF68W0CeWGGva7+guQsFiZnujEiITxLTkkV3223fy61lHIyjjw5GfzN+n2sUsr/FI85z/p9aRPEI5xg3uZ0amotfYj7xoUxICHczREJ0fxd3K89MwfZF7M/9e0u0gsrTvMMIURDfbYhzVZkemhSG/rFt7zPJU9OBucD1UA4cNuJdyqlpgHdAQ180bShiXNhNms+32gvJ3O99CEWosGemd6LxAjLMunS6lr+9FUyJik3I0Sj1NSa+XyD/XPpplEtb1QQnJAMKqWClFKdlFLDlFLjlVJ9rH2BnUYpFVX3BTj2dQl3vE8pZft9tNbHgNethy8qpa5XSnlZX+8i4EPrfV9orXc4M17hGmsPF3DUoQ/xtH7Sh1iIhgrx9+HVqwbYdt5vSj3ObGlXJ0Sj/LArm3xrUfd2of6c37tlFJk+0Vkng0opL6XUpUqpN5VSO7D0/j0IrAOWAduBTKVUoVJqsVLqUaVUUiPjzHP42upw+7oT7ks84XlPAd9j2VH8MVCulCoHlgCRwCbgrkbGJprI5xvtG0dmDoonwNfLjdEI0fIM7tCGP07qajt+7deDJFt35gshzt6ctam2n/8wPBEfr5Y54drgqJVSHZRSLwGZWKZg7wb6WF9DneQrHLgIeAE4pJT6RSl1pXPDPz2ttRGYhiXhW49l2lgDycBjwBjZRdwy5JZW8fPuHNvxtcNPzPuFEA1x78TODO5gmWCpNWse/HIb5dW1bo5KiJZnR0YR29LsZc6uHtZyP5fOmAwqpdoqpf4D7AceBqKBHcD/gFuAwUAHIBTwBWKAHsAFwF+B77DU/JsMfKGU2mFdr9dgWmvVwK/UkzzXrLV+W2s9UmsdprUO1loP1Fq/qLWuOZs4hPvM25xRb4Fut5gQN0ckRMvk7WXg1SsHEGwtiJtaUMG/ftjn5qiEaHkcy5xd3K89bUNabpmzhpTHTsHS2u0I8BGWNXYHT/P4uinbA8DPANbdvBcD1wLTgQVKqUe01v9uROyilTCbNV84bByRUUEhGicxMpC/T+/Nw/O2A/DJ+qNc2Kcdo7pEuTkyIVqGgrJqFjmUk7mhhZc5a8g0cQZwE9BNa/2PMySCJ6W1rtJaf6O1ngn0xLJ5w/dsX0e0TisP5pFx3NJiOjzQhwv7tHdzREK0fDMGxTGlp32x+yNf76BMpouFaJC5DmXO+seHMTCxzRme0bw1JBnspbX+WGttcsYJtdaHtNa3Af/njNcTns9x2/7MQfH4+8jGESEaSynF8zP6EB7oA1i6kzz/vdTfF+JMak1mPnWYIr5hZJL7gnGSMyaDurHdj5v4dYVnySmpYum+XNvxNS14ga4QzU10iD9/n25v0vT5hjRWHcxzY0RCNH+/7s0lq7gKgMggXy7u1/Jnq85pD7RS6mnr1+gGPv5PSqmnz+VconWbuyndVhh3eMcIukQHuzkiITzL9P6xTHWojfbY1zsorTK6MSIhmreP16Xafr56WIJHzFada0GcZ4C/AUuVUnc04PGPWB8vRIOZzJovZeOIEC6llOK5y/rSxjpdnFVcxT+XyHSxECdzIKeUtYcLAPAyKP4wvGVvHKnT2OqIvsBsawHqlp+QALyRAAAgAElEQVQai2ZlxQH7UHxEkC8X9HFqYxshhFXbED/+cWkf2/GXm9JZcUCmi4U4keOo4Pm9YogND3BbLM7UmGQwB0vh6VosRZ1/VUpFOiUqIai/ceSKwfH4ecv1hhCuckm/9lzU137B9fg3srtYCEdl1bV8uzXTdnx9Cy8n46hRI4Na67eBKUABMA7YqJTqc/pnCXFm2cWVLJONI0I0GaUUz17ah4ggS9Wv7OIqXv5pv5ujEqL5WJScRXmNpbBKl+hgRnbynPGvRjfR01qvAoZg6UncEVirlLq8sa8rWrd5mzOw7hthVOdIOkYFuTcgIVqByGA//jatl+34o3Wp0rtYCEBrzWcb7OVkrh2WiFLKjRE5l1M6Kmut04DRwDwgGJinlJINI+KcmM2arzan245bcr9HIVqa6f1jGdetLQBawxPzd2I0md0clRDutSOjmN1ZJQD4eRuYOSjezRE5l1OSQQCtdaXW+irgKetNTyulvlZKBTrrHKJ1WJdSYOs4Ehbgw/m9Ys7wDCGEsyil+OdlffD3sXw87M0u4f3VR9wclRDu5biG/eJ+7Qmz7r73FE5LButorZ8HLgNKgcuBdYAkhKLB5m6yjwpePjDOI2o4CdGSJEQE8tCUbrbj1349QFpBhRsjEsJ9SqqM9foQe0o5GUdOTwYBtNaLgZHAYaAvEOKK8wjPU1xh5Mfdx2zHVw5JcGM0QrRet47pSK/2oQBUGc38ZcFOpHGUaI0WbMuk0mjZONKjXQiDEsPdHJHznWsyuBJYe7oHaK33AkOBn8/xHKIVWpCcaWv+3TcujF6xoW6OSIjWydvLwAsz+mKwrpFfdTCf73ceO/2ThPAwWms+W2+fIv7DcM/aOFLnnJJBrfUErfUVDXhcsdb6Aq21QWstc33itLTWfOkwRXzlUBkVFMKd+ieEc8PIJNvxc0v2UC61B0UrsjXtOPtzSgEI8PHi0oFxbo7INVwyTSzEudiVWcLebPturen9Y90ckRDiofO6ERVsrz345m+H3ByREE3nM4eNI9P7xxLq71kbR+pIMiiajbmb7X90F/VtT1iAZ/7RCdGShAX48PiFPW3H765KISWvzI0RCdE0iipqWLwj23b8hxGeW+bMuyEPUkotc8K5tNZ6shNeR3igKqOJhcn23VpXyRSxEM3GjIFxfL7hKFvTijCaNM98t4ePbh7qkWunhKjzzVb7GvY+caH0i/e8jSN1GpQMAhMADZzsL79ue9mZ3hVkG5o4pR92ZVNaZVmLlBQZyPCOEW6OSAhRx2BQ/OPSPkz772q0hpUH8vh5Tw5Te7c785OFaIFO7DjiieVkHDU0GfyYUydzVwF+wEdOiUi0So61BWcNSZARByGamT5xYfxheCKfWndW/uO7PYzr2pYAX9kbKDzPxiOFpOSVAxDs5+3xa9gblAxqrW861X1KqQuAaK31zc4KSrQuRwvKWZ9SCIBBwRWDPavNjxCe4s/nd2fJjmyOVxjJLKrkvVUp3D+5q7vDEsLpHAcoLh0QS5BfQ8fOWibZQCLczrEP8cTu0cSE+rsxGiHEqYQH+vLI1B6249krDpNbUuXGiIRwvuJKI9/vsm8cuXqo524cqSPJoHCrWpOZeZszbMdSW1CI5u2qoQn0aGdpKlVRY+Lfvxxwc0RCONei7VlUGS0bR3q2D6VPnOc3P5BkULjVigN55JZWAxAV7MekHtFujkgIcTpeBsWTF9lLzczdnG6rDyqEJ/jKYYr46qGtYw27JIPCrRzXZcwcFIePl/wvKUSdKqOJXZnF/Lgrm4XJmSzdm8PBnFLMZvcWZxjXrS3ju7UFQGv455K90rdYeITdWcXszCwGwNfbwGUDPLPjyIk8e0WkaNbySqtZti/XdjxriEwRC1FTa+bnPcf4eksG6w4XUG2tc+YoPNCHSd2juWpoAsM6Rrhl5OIvF/dk1cE8zBpWH8pn+f48JsrIvmjhHEcFL+jdjrDA1tH8QJJB4TYLkzOptY5wDOnQhi7RwW6OSAj30VqzIDmTV34+QMbxytM+tqjCyPxtmczflsmwpAgev6gHgxLbNFGkFt1iQrh6WCKfW9t1/fP7vYztGoW3jO6LFqrKaGKBQ/ODq1vRGnZJBoVbaK35eot948isIVJORjQfpVVGDuaWkXG8kszjleSUVFFSaaS40khFjQmT1mit8TIoAn29CfD1oiBpCl7GMuZuSiMpMohuMSG0CfJt0PnSCyt49OsdrEsp+N19HSID6dw2mAAfL4orjew7VkJ+WY3t/o2phcycvZYbRybx+IU98Pdpurp/D03pxqLkLMqqazmUW8aXm9K5boRnF+cVnuun3ccorjQCkBARwIhOkW6OqOk4ox1dRAMeA9KOTjjYnVXCvmOlAPj7GLiob3s3RyRaqyqjiS1Hj7P16HH2ZJewJ7uEowUVZ/9C7QYC8Ng3O203RQX70jU6hD5xoQzrGMmwpIjfTTutOZTPvZ9vpajCaLutTaAPN4xM4orB8SREBNZ7vNaanZnFfL4hja+3ZFBr1mgNc9amsvFIIW9dN5jEyPrPcZW2IX7cPaEzL/20H4D/LD3IzEHxUohatEiOa9ivHJyAweD5G0fqOKMdneNjTkdWFwubb7baRwWn9m5HiH/rWJch3E9rzZ7sElYeyGfNoXw2pRaedF2eM+SX1ZBfVsC6lALeXXUEpaB7TAgjOkUyuksUm1ILeX/1EUzW5RIGBbeN7cT9k7qc8m9CKUW/+HD6xYdzz4Qu/HXhLlYcyANgT3YJl/9vDe/fNJQBCU3TR/XWMR35eF0qOSXV5JZW8/G6VO4c37lJzi2Es6QVVLD2sGVk3qDgilY2W+WMdnRCnBWjycwih3UZ0nFEuJrWmn3HSlm8I4slO7JJPcPIn7dB0bltMB0iA4lrE0BsWADhgT6EBfgQ5OeNQSm8DIpak5mKGhPlNbX8/V+vUOsTzMSLL+dwXhmHcststcrsccC+Y6XsO1bKnLWp9e6LDvFj9nWDGdyh4Wv/EiMDmXPzUD7dkMaz3+2hxmSmoLyGa95Zzye3DmNIkut7fPv7eHH/pK48tWAXYClEfc3wRELlAk+0IPO22EcFx3drS/uwADdG0/Qa3Y5OiLO1fH8eBeWWNU/tQv0Z1TnKzREJT1VYXsPXW9KZuymdw9Y+oyfTuW0QozpH0Tc+jF7tQ+kaE4yf99lNdb56bCsAr1/9DwBMZk3m8Ur2HSth89HjbDhSyK7MYtsooKP+CeG8c/3gc+q+o5Ti+hEd6NU+hFs/2kxRhZFKo4mbP9zEZ7cPp1+860cIrxySwDsrU0grrKCowsh7K1P40/ndXX5eIZzBZNb1mh9c1Yo2jtSRDSSiyX3jsHHk8kFxeLWidRnC9bTWbDxSyOcb0/hh5zFqTL+fAg7282ZSj2jGdWvL6C6RLhkF8DIoEiMDSYwM5Pze7QAor65l89HjrNifx+6sYrSGkZ0juXtC50Zv/BjcIYKv7xrF1e+sI7+shtLqWm74YCNf3jGCHu1c20HB19vAQ+d15aG52wF4b/URbhiVRFSwn0vPK4QzrDyQxzFrW8WoYF8m9Yhxc0RN72w2kBRorWe5OB7h4Y6X17B0X47teOYgmSIWzlFrMrNkZzZvr0hhz0k6YgT6ejG5ZwyX9GvP+G5tm3TXbZ0gP2/GOxRsdrYu0cF8cutwrn5nPcWVRooqjNzw/kYW3z+GaBf3/J7eP463lqewP6eUihoTs5cf5q+X9HLpOYVwBseNIzMGxePr3frKIzX0N54AjHZhHKKVWLQ9C6PJMk3WPyFcaguKRqusMfHR2lQmvLycB75M/l0i2D8hnBdn9mPzU1N445qBTO3dzi2JYFPp2T6Uj28ZRrCf5Vo/t7SaOz/dQnWtyaXn9TIoHj6/m+34k/VHySo6fb1EIdyt8IQBiitb2caROq0v/RVu5biLWDaOiMaoMpr4YPURxr64jL8t2l2vULO/j4Frhyey+P4xLLx3NFcOTSDQt/WsiumfEM5b1w2mbgXGtrQinl6w2+Ut487rFWPbxVxTa+Y/Sw+69HxCNNai5EzbAMXAxHC6RIe4OSL3kGRQNJmDOaXsyLD2fPQyMK2f1BYUZ89oMvPFxjQmvrycfyzeU68Ac5tAHx6c0pW1j0/m+cv70icuzI2RuteYrlE8eVFP2/Hczel8au0W4ipKKR6dat848vWWDDKOn0PNRiGayDdbM20/t+ZlS63nUlm43dcOo4JTekUTHtiw7gxCAJjNmkXbs3jt1wO/Kw3TPsyfu8Z3ZtaQ+FY1Angmt47pyO6sEr7dZvnAe3bxHoYlRdC9netGP0Z1iWJYxwg2Himk1qyZvfww/7y8r8vOJ8S52n+slJ2Z1gEKbwPT+sW6OSL3kZFB0SRMZs2CbXIFJs7N+pQCpv13NQ/OTa6XCEYF+/L0Jb347c8TuHFUkiSCJ1BK8cKMvvRsb9lNXFNr5oEvt7l8/eADk7vafp63OYPsYlk7KJofx2VL5/WK+V13oNbkbN45w5RSHzTiXFprfWsjni9asNWH8skpqQYgKtiPcS7aTSk8y9GCcl74fh8/7j5W7/awAB/uHN+JmyQBPCN/Hy9ev3oA095YTXWtmX3HSnn5p/385WLX7fQd1TmSwR3asOXocWpMZt5ekcIz03u77HxCnK1ak9k2Yg5wRSsfoDibd1F/4MZzPI/C0sFEksFWyrG24GUDYvHxkkFpcWolVUb+u+wQc9ak1qsT6O9j4PaxnbhtbCfCAlrvVfzZ6hYTwhMX9uCZ7/YA8O6qI0zsHs2oLq4p+K6U4v5JXbjpw00AfL4xjXsmdHZ5eRshGmrVwXzySi0DFG1D/BjbtXU3PzibZNAIrHNVIMJzlVQZ+clhZGem7CIWp2A2a77ZmsG/fthn61JT5/KBcTwytTux4a2rTZSz3Dgqid/259n6GD82fwc/PzieAF/XlNkZ360t/ePD2J5RTE2tmbdXpkjdQdFsOK5hv3xgHN6tfIDibJLBQq31RJdFIjzWkh3ZVNdaRnd6tQ+1rV8SwtG+YyX8dcEuNqUer3f7oMRwnp7W21ayRJwbpRQvzerHlFdWUFJVS3phJa/+eqDejmNnn++Pk7ty60ebAfhsw1HuGt+ZtiHSlUS4V3GFkV92S/MDRx6fCiulDEqpm5VSvyql8pRSRqVUkVJqg1LqL0qp1llUqAk5ThHLqKA4UVl1Lc8t3sPF/1ldLxGMDfPnP9cM5Ju7R0ki6CTRIf485bBW8L1VKeyy7qZ0hUk9oukda7n4qzKaeW9VisvOJURDfbcjy7b8pG9cmEt317cUHp0MKqUCgV+AD4DJQBRQDoQCw4DngJ1KqU5uC9LDpeaXs/mo5QPe26C4dEDr3bov6tNas3hHFpNfWc57q49gMlsKv3obFHdP6MyvD49nev9YlJLe1c40a0g8IztFAmDW8Ng3O6g9Sf9mZ6gbHazzyfqjFFcYXXIuIRrKcRfxzEFxboyk+fDoZBD4KzAJy+aVJ4BwrXU4ls0w1wBFQAfgPbdF6OEWJNt3a03o3lYa1wsAUvLKuP79jdz3+TbbLnOAEZ0i+OGBsTx2QQ/ZJewiSimen9HX1n91d1YJH6076rLzndczhu4xlpGXihoTn25w3bmEOJPDeWVsSysCwMdLMX2AJIPg+cngtdbvH2qt/6W1LgbQWtdorb8EHrLeP1Ep1cYtEXowrevXFrx8oEwRt3aVNSZe+Xk/F7y2itWH8m23RwX78frVA/ji9hF0jZEpG1frGBVUrxbga78eIL+s+jTPOHcGg+LO8fbJlw/XHKHK6No6h0KciuOypUk9ookIkuYH4PnJYIz1+7ZT3L/F4edAF8fS6iSnF9kKBIf4eTO5Z7SbIxLutGxfDue9uoI3lh2yrdcxKLhpVBLL/jyeSwfEyZRwE7ptbEc6RgUBUFpVy8s/7XfZuab1j6V9mKWsTH5ZDfMdWoAJ0VRMZl2/tuDgBDdG07w0KBnUWhu01i1xsVeq9fvAU9w/2Po9R2st705OtjA5y/bzhX3b4e/jmhIWonnLLq7krk+2cMuczWQct3eiGJAQzqL7xvDM9N6E+kvNwKbm5+3F0w6lXuZuTmdHRpFLzuXjZeDWMR1tx++uSrGtERWiqaw9nE92cRUAkUG+TOguzQ/qnDEZdMXmCusO30Rnv+5JvGv9frNS6nGlVJj1/L5KqauAV7GsJ/xzE8TSqhhNZr7bbk8GL5N1Ga1Orcmye3TKKyvqdRAJC/DhhRl9mX/3KPrEhbkxQjGxRzSTe1hG7LWGvy3ajdlFSdrVwxIJ8besAz2SX84ve46d4RlCOJfjFPGlA+Kk+YGDhvxL7FNKfaSU6t7YkymlfJRSdwAHgZsa+3oN8BrwJpYOKC8ARUqpIqAS+BLYB0zXWn96qhdQSp3yS5za6oP5tqLB7UL9GW7dvShahy1HjzPtv2t4bsleymvs68OuGBzPsofHc82wRAwG+RtqDv56SS98rR+K29KKWLwz2yXnCfbz5voRHWzHb61IQWsZHRRNo7TKWO+idObgljlA4aqcpCHJ4EbgemC3Uuo3pdSdSqkGf7Iri4lKqbeBLGA20BbYfk4RnwWttQl4EHgYqLXeHIb99w6xxiKczHFdxqUDYvGSD/5Woaiihifm72Tm7LXszS6x3d41Opi5d4zg5Vn9iZQd5c1KUlQQN49Jsh2/+OM+qmtds8HjplFJtsQzOb3odwXGhXCV73dmU2W0rFXu0S6E3rEyK+HojLUbtNZjlFLTgeeB8cA44E2l1EEsGzB2APnAcaAGCAfaAB2BIVjW6wVhGZ0zYhmpe1Zrnef03+YESql2wEIsNQU/Av4NHAbaA1cATwMfKKW6aa2fONlryJXr2SurruVnhymgywa2zCsw0XBaa+ZvzeT57/fWayPn72PggcnduHVMR1spE9H83DuxC19tSud4hZGM45V8su4ot411fvnV6FB/ZgyK48tN6QC8veIwwzpGOP08Qpzom62OG0dabmWL0+UkjRkdbFAhL631IqXUd8AFwG3AJUB369c1p3lqXWQpWAo/f6i1ds0cxMl9jCURfF9rfZvD7YeAfymlMq2PeVQp9anWencTxuaxft59zHYF1j0mRNrPebhDuaX85dtdbDhSWO/2yT2ieWZ6bxIiZKN+cxfq78P9k7ryj8V7AHhj2SFmDU4gLND5G3tuH9eJuZvT0RqW7svlUG4pXaKlnJBwnYzjFWy0vj95GRSXyhr232nwpbq2+EFrPRPLyNosLGvy1mHZtVuGZeQvB9gNzAf+BAzTWnfRWj/flImgUqoXcJ718NWTPUZr/QlQgOXfYVoThebxHKeIZVTQc5VWGXnh+71c+PqqeolgbJg/b18/mPduHCKJYAty3YgOJFr/exVXGvnf8kMuOU/ntsFM7hFjO/5orRShFq7lWNliTJco6Y99EudU4l9rXQh8Y/1qrhy7rx85zeNSgEggyaXRtBK5pVWscSgmLO3nPI/ZrJm/LZP/+3EfeaX2QsVeBsWtYzrywOSuBPlJ95CWxtfbwKMXdOe+zy1lWT9cm8pNo5NoHxbg9HPdMjqJX/fmAJbWYH+e2p2wACkvJJzv980PZIDiZDx5EY9js83TlbGp295W6sJYWo3vtmdTV5liRKcIYsOd/0Ei3Gd7ehEzZq/lz/O210sEB3dow+L7x/DkRT0lEWzBLu7bnv7xloX1NbVm3vzNNaODIztH1mtRN29zukvOI8Se7BIO5pYBEODjxXm9Ys7wjNbJk5NBx93Kt5/sAUqpaUBdW4wNLo+oFXC8ApPagp4jr7SaR+Zt59I315Ccbi9MHBPqx2tXDeDru0bK2lAPoJTiT+fbq4jN3ZROxvEKl5znptFJtuM5a1OlCLVwCcfPpKm9Y+Ri9RQ8NhnUWqcAP1sPH1RKvaCUigZQSgUrpW4C5ljvTwUWNXWMnuZQbik7M4sB8PUycGHf9m6OSDRWldHE/5YfYtLLy5nnULDV18vAPRM6s+zhCVw2UNrIeZJxXaMY0sHSqt1o0ryx1DWjg5cNiCPcukEl43glS63TxkI4i8msWeTY/ECmiE/JY5NBq5uAvVh+z8eBHKVUCZYp4Q+BCCwbXmZorWtO9SKiYRZss//RTe4ZLWuAWjCTWfPV5nQmvrycF3/cT2l1re2+KT1j+PmhcTx6QQ+5yvZAltHBbrbjr7dmkJpf7vTzBPh6cfVQ+wqeD9ekOv0conVbn1JATollOUtUsC9jukS5OaLmy6OTQevu5cFYCk+vBAqBQKAE2Ao8C/TVWm9zW5AeQmvNgmTHQtNyBdYSaa35bV8uF72+ike/3mHr4wnQqW0Qc24eyns3DiEpKsiNUQpXG9U5ipHWrkEms+Y/Sw+65DzXj+xgK0i/LqWAfcdKzvAMIRrOsbLFJf1i8Zb2c6fk8f8yWutKrfXrWuvxWutIrbW31jpMaz1Ya/10UxS/bg22HD1OxvFKAEL9vZnYQxq7tDQbUgq45t313DxnE/tz7PupooL9eO6yPvz04DgmdI8+zSsIT/Kww+jgguRMDlkX4TtTXHgAU3vbF/TPkdFB4SRVRhM/7rI3P5BdxKfn8cmgaBqOV2AX94vFz9vLjdGIs7EhpYBr3lnPVe+sZ32KvV5goK8XD07pyopHJnDdiA7S1L2VGZIUwbhulos6s4bXfj3gkvPcPLqj7edvt2VyvFxW7IjG+3VvDmXW5S0do4LoFy/t505H3t1Fo9XUmlni0NxersBaBsckcF1Kge12L4PiuhGJrHhkIg9O6SbrAluxP51nHx1cvCObAznOr8A1pEMbesdadqJX15qZt0XKzIjGO7GyhWxyOz1JBkWjrTiQR1GFEbBM+9TtRBTNj8ms+Wn3MWa9tfakSeCswfEse3g8z13WV6r0CwYkhDOlp31pwOzlh51+DqUUN45Msh1/sTEds5SZEY1QWF7D8v32FWDS/ODMzumSXynlDZwPjAE6Wl9nH7BQa73ZeeGJluA7h6370wfEYjDIFVhzU1FTy9dbMvhg9RFSC+rXjfMyKGYMjOO+SV3oECkbQ0R9903qyq97cwFYtD2Lh6Z0IzHSuW0GL+nfnmeX7KG0qpYj+eWsSylgtOz8FOdoyc5saq0XFAMTw2XDWwOc6/xPGlC36rfuk18DTyqllgP3aq33NTI20QJU1NTyyx57fbDp/eUKrDlJL6zgi41pfLYhjeJKY737vA2KyyUJFGcwICGcMV2iWH0oH5NZ89bKwzx/eV+nniPQ15uZg+KZszYVgM82HJVkUJwzaT939s41GWwHbAPWAumAHzASmAJMBLYopW7XWn/ulChFs/Xr3lwqjSYAukQH06NdiJsjEjW1ZpbuzeHzjWmsPpSPPmHGLdTfm2uHd+CmUUm0C/N3T5CiRblnYmdWW3uOf705gz9O6ur0/3euHZ5oSwZ/3p1DbmkV0SHy/6c4O2kFFWw5ehywzHpcLM0PGuRck8ExWuu1J96olEoE3gQuBj5RSsVorV9tTICieVuU7DBF3D9WFum6idaa7RnFLErOYtH2TPLLfr8jMzEikFvHdOSKwfGyKUSclZGdIhmUGM7WtCJqTGbeXZXCXy/p5dRzdIsJYVhSBBtTC6k1a+ZtzuDeiV2ceg7h+RY61Lsd360tkcGy9rkhzukT4WSJoPX2NGCaUupO4HXgZaVUsNb62UbEKJqp4gojKw7k2o5lirhpaa3Zd6yU73dms2h7FkcLft9DVikY27Ut1w5L5LxeMVQZTeSXVbPvWCkFZdUUltdQUF5DUUUNlUYTlTVmqowmqowmlLIs7vdSCoMBgny9CQvwISzAh/BAH6KC/UiICCQxMpBQf+k248mUUtw3qQu3zLEsCf98Qxr3TuxCRJCvU8/zhxGJbEwttJ3jrvGdbUWphTgTrTXfOiSD0n6u4VwyPKC1flsptRP4FnhGKWXQWv/dFecS7vPT7mMYTZY5yH7xYbJItwlU15rYkFLI0r05/Lo3l8yiypM+LiLIl0GJ4cSHB3C80shbKw7z5Lc7KXRRDbfwQB86RAbROzaUfnFh9IkLo1tMCL7eUrDAU0zsHk3P9qHszS6h0mjiwzVHePj87k49xwV92tEm0IfjFUYyiypZeSCPiT2k0LlomJ2ZxaTkWVonBvl6cV7PmDM8Q9Rx2VyR1nqtUmo8sAx4WilVrLV+zVXnE03PsQH4tH4yKugKRpOZHRnFrE8pYH1KAZtTj9vWaJ7IoCy7uUzaUlqhbgdoUyiqMFJUUcT29CLqFgr7ehsYnNiGMV2jGNe1Lb1jQ2WneQumlOLeiZ2573NL9845a1O5fVwnp44K+3l7MWtIAu+sTAEsG0kkGRQNtWCb/TNpap92BPhK84OGcloyqJQKAOJO+IrFssGkHfASIMmgh8gtrWLtYcuCcqUspSFE4xhNZo4WlLMjo5hNqYVsSyvicF6ZbfT1TBpSms3Xy0DbED+ign2JDPYjIsiXyGBf2gT6EuTrhb+PFwG+XrYOMmatMZs1tWZNeXUtxZVGiiuNFFUaySmuIq2wgrTCCqprzb87V02tmXUpBaxLKeCln/bTJtCHid2jubhfe8Z2bSujhi3QhX3a06ntAVLyyimtqrVN5TrTNcMSbcngsn25ZBVVEhse4NRzCM9jMmsW77Ang5cNkCnis3GudQafxZLoOSZ+Z+r1Iu/8HuSHncdsycfQpAjah8mb9ZmYTGayiiupCE3CGBjJU9/uJP14JRnHK8grraa0qhZnlNoNC/ChQ2QgiRGBdIgMpENEEInW43ah/k4fnTObNXll1RzIKWVnZjG7MovZkVFs61Vd53iFkfnbMpm/LZNQf2+m9m7H9AGxjO4cJSOGLYSXQXHXuM48+s0OwNJL+JbRHZ2a2HeMCrKVsjFrmLspnYccOqEIcTIbjhSQW1oNQFSwL6M6R7o5opblXEcG/4KlruCp3sFrgOhFXugAACAASURBVGwgE8iyfs88xWNFC1RvirgVbRw5kFPK6oP5bM8oIrWgnLIqy2aLGpMZs1lbRtK0ZURNa02tSWM0W0bXbIler1kAfLoh7ZxiUArah/rbErwOkUH1Er+wwKbdzGEwKGJC/YkJ9Wds17a223NKqlh9MJ9VB/NYfSi/3g7nkqpa5m3JYN6WDBIiArh6aCKzhsRLKZEW4NKBsbz0837ySqs5VlLFd9uzmDk43qnnuHZ4or2UzZYMHpjcVS4YxGl9t93eEvWivu3xll7qZ+Vck8HtQAb2RM8x4cvSWuc7JzzRHGUcr1/H6aI+7dwckesUVdQwe/khftufx5H88gZP2TaWr5eB6FA/OrcNonPbEBIiAqyjfUHEtwnA36f5r4WJCfVn5uB4Zg6Ox2zW7MoqZsnObBZvz6638SW9sJKXftrPq78c4LxeMdwypiNDOrSRMkXNlJ+3FzeNSuKln/YD8O6qFGYMcm7v18k9o+ttJFl7uIAxXaUItTi5mlozP+yyJ4OtaYDCWc61tMxAZwciWo7FO+x/dGO6RHlcHSez2cyctUf5aF3qScu1OOEEKLOR6IgwIoJ8iAsPoFNUED3bh9I1JoSENoGEBnh7VDJkMCj6xYfTLz6cxy/oQXJ6EQuTs/h2W6atM0qtWfPDrmP8sOsYAxLCuWNcJ6b2bielRZqh64Z34M3fDlFRY2LfsVJWHsxnfLe2Z35iA/l5e3HZwDg+XJMKwFeb0yUZFKe05lA+RRWW95HYMH8GJ7Zxc0Qtj1SeFWftxELTnqKqppbnf9jHV5vTqTL+fkNEnQAfL6KCfUmICCQy2JdQfx+C/SybLry9DPh4KXy8DHgbFCH+PrQL87d8hfoR5OfDhAkTAFi+fHnT/GLNjFKKgYltGJjYhscv7MH3O7P5YmMam1KP2x6TnF7EPZ9tJSEigDvHdWbWkHjbphbhfmGBPlw1NMGWrL2z8rBTk0GAWYPtr//j7mMUVxibfAmEaBkcly1d0j9WlhScA0kGxVk5lFvGnuwSwFI65PzeLb+Ok9ls5v9+3Mf7q1Ntzc0deRsUAxLCmdonhisHJxAW6NxCu62Zv48XMwbFM2NQPPuPlfLB6iN8uy2TGpMlGU8vrOSpBbt487dD3D2hM1cOSWgRU+StwS2jO/LxuqOYzJo1hwrYlVlMn7gz7SNsuF6xofSNC2NnZjE1tWYWbc/k+pFJTnt94RmqjCZ+3n3MduxJAxRNSVZYirPieAU2qXs0IS2888SCbZn0+/svvL3yyO8SwS7Rwbx4RT8OPHcBX989itvHdpZE0IW6twvh/67ox+rHJ3L/pC6EO4wCZRdX8fTC3Yx/6TfmrDlC1SlqLYqmkxARyEUOfV/fXZXi9HNcOcS+MeWrzRlOf33R8i3bl0t5jeX9oGOUpfC9OHuSDIoG01qz2CEZnD6g5V6BFVXUcOmbq3lwbjJl1bX17hveMYJfHxrHr38az5VDEjAY5M+kKUWH+PPw+d1Z+/gknrq4J1EOa1JzSqp55rs9jHtRksLm4I6xnWw/L96RfcqOOOdqev84W9manZnF7Mkqcerri5bvuxMqW3jSWuumJJ9yosF2Z5WQkm9v9TOphXYG+HxDGkOe+5Xt6cX1bu/dPpTfHh7P3DtH0iUmxE3RiTqBvt7cNrYTqx6dyF8v6UXbEHtSmFtqSQonvrycT9cfpeYkRa+F6/WND2NkJ0s9N5NZ8+HqI059/bBAHy7oba9WMG9LulNfX7RspVVGlu6zd1qaLs0Pzpkkg6LBHKeIz+/drsWt3aqtNXPThxt58tud9aaEQ/29+eTWYSx5YCwd2wa7MUJxMgG+Xtw6piOrHp3IM9N6ERNqTwqzi6t4asEuJr68nLmb0jCaJClsareP62j7ee7mdMpPGGlvrCuHJNh+XrAtk+paGQ0WFr/sybFdCPZsH0qXaLmIP1eSDIoGMZt1veH4lrZI90heGcNeWMry/Xn1br98YBzJT59Xr1iyaJ78fby4aXRHVjwykacv6VVv+jizqJLHvtnJlH+v4JstGdRKUthkJnSLplNUEAClVbV8s9W5a/tGdY4kztqO7niFkaVN2HNbNG/1mx/IqGBjSDIoGmRL2nGyi6sACA/0aVE1v5buzeG8V1dSWG7vgBHo68XXd43k1asGyJrAFsbfx4tbrCOFT17Ug4gg+6aeowUVPDxvO+e/tpKFyZmYGtKwWTSKwaC4cVSS7XjOmlTMTvx3NxhUvQ4n8zbLVLGAwvIaVh+097eY1q9lDVA0N/IpKBpkiUOh6Qv7tMOnhbT6+e+yg9z60eZ608L948PY/NQUhiRFuDEy0VgBvl7cMa4zqx6dyCNTuxMWYN99nJJXzgNfJnPh6yv5fme2U5MT8XszB8cT4mepVJaSX86Kg3lneMbZmeWQDK48mE+etQetaL1+2JVte18fmBhOQkSgmyNq2VrGJ7pwK7NZ8/1OezJ4SQu5Anvk6+28/POBerfdOa4jC+8bQ6CvlNj0FEF+3tw7sQurH5vIQ1O6EeJv/297IKeMez7bysVvrObn3cfQWpJCVwj28+bKofa1fXXFop0lISKQoUmWrhIms2bxjqwzPEN4upa8bKk5kmRQnNHmo8fJtV6JRwb5Mrxj8x9Ru3XOJuY51CUzKHj96gE8cVEvN0YlXCnE34cHpnRl9aOTuH9SF4J87Ruc9maXcMcnW5j+3zX8sDNbpo9d4MaRSdRV9Vh5II9DuWVOff3LBsbZfv52W6ZTX1u0LMeKq9hwpBAApeDivrJesLEkGRRntMThKvyCPu3wbsZTxGazmZmz19QrN+DvbWDx/WO4dEDcaZ4pPEVYoA8Pn9+dVY9N4q7xnQlw2PW+M7OYuz/bysSXl/PxulQqapy787U1S4wMZEpPe0eiOWudW2bmkr6x+Frfe3ZkFDs92RQtx5Kd2dQN8o/oGEl0qL97A/IAzfdTXTQLJrPm+132Vj8X92u+V2Bms5nL/7eWLUeLbLeF+Huz9M/j6RXrvDZZomWICPLl8Qt7sOqxidw2piN+3va3u7TCCp5euJtR/1rGC9/v5Yi1fqZonJtHJ9l+/mZLJsUVRqe9dligT73apgtkdLDVWuQhzQ+aE0kGxWltSi20LdaOCvZleMdIN0d0cmazmRmz17E9w15IOirYl1WPTiQuXBYWt2ZRwX48dUkvVj02kfsmdqm30aSowsjbK1OY+PJyrnlnPYu2Z0kdu0YY2SmSHu0std4qjSbmbk5z6uufOFUsG4Nan7SCCranWy74vQ2qXlFyce4kGRSnVX8XcXu8DM2z1c9V76wnOd0+Ihgd4sfyP08kXHoJC6voEH/+PLU7656YxN+n9yYhIqDe/etSCvjjF9sY8uyvPDQ3mV/25Ei7u/9n77zDojrT93+/U2CAoTcRUQQpYkOxx67RxDRjEtPbpm/6JtndfONukk3bJL9N2ZLd9F6MaZpoNPbesCtFiggivbdhGOb8/jjjKUgZmDP9+VyXV+aVc868BpjznKfcdz9hjMmyg5/uPqOo5uPc1EghmC+tb0PmmTrFrk24Bz9L2pZmJUciNIA+45WAgkGiRzrNHH6VlIgXu2iT7v2fZ+JAkXhTiND7YvMTc6DX0cQwcSH+PhrcPj0eW5+ci4/umIgFI6MhfcZpajfhx8OluOezTGS8sAEPfnkIX+0rxpmaFsWmkc1mDhWNBpQ3GDxuwvmq9FiE+osB28bsCsWu7atRy1pVfjysrMA14fr8IklQkNC0ctDdkuiR/adrUd18vkTsi8kuOEX83OqTWHdSvNmEB/hg65MUCBJ9o1YxzEuNxrzUaJQ1tGFl5lmsPFiCkto24ZgWYyfWHC/DGou0UlSgL8YOCcbo2GCMiNIjPjwAcaH+CPLr/ueN4zi0GDtRXNOKwupm5FU041BxHY6U1KPJwA+vJEQE4O0bxmPMEM/oa9Vp1bhpylD8Z0sBAOCjXUW4ZLRyN+2l42Px1T6+/PzLsTI8e8Uot7PGJAZGYVUzsssaAQA+GpVsYImwDbpjEj2y5riYjl88ZpDLlYjf316AT3YXCesAXzU2PD6bAkGi38QE++GR+Ul4eN4InDzXiLXHy7D2eBmKalplx1U2tWNjdiU2drFE06gYzBm/h6qzA7Nf3wKO43vmGlo7YOyjTFpY3YLr3t2N168dhys8RC/t1qnxeHdbIUxmDvtP1yLrXCPSBgcpcu2MYaGIC/NDSW0bmgwmbMmpxKUuWrUglEWqdzs7ORKBOm0vRxP9gcrERLeYOs1YJ50idrEP21VHSvHS2hxhrVUzrH1kJsL01D9CDBzGGEbHBuOPl6Riy5NzsP6xWVh+2UjMS42S6RZ2xWTmYNYGwKQLwZmaVhTXtqKqqb3XQDBI8tBi6DDj4a8P4x+/5XrEUMSgYB0uGS029n++94xi12aM4WqJTNQPNFXsNaw57rr3JHeHUihEt/AlYt7LNyrQ16Ws2w4X1+HxFUeEtYoB39w7DcPCA5y4K8LTYIwhZVAgUgYF4u6ZCeg0cyisasbx0gacPNeIMzUtOFPTitL6NrQaex400WlVGBzsh+ERARgeEYDRscHIGBaKIaF+OF3dgrs/zUShRdrmX5vzkVvehLduSHd7l5zbpsUL/V0/HS7F04tTEaRQJmfJ+Fj8c3M+AGBrbiUaWjsQ7E9ZIk+ma4l4/sioPs4g+oN7f9oQduMXSTp+8RjXmSKubTbixvf3Qpo8+c/NE5AxLNR5myK8ArWKISk6EEnRgVg6Qf61dlMn5l96JTi1Fl9+8SUAwFerQqi/T6/9bAmRevz44EV4+OvD2H6K9/P9LasCN763Fx/eMQkRel+7/XvszaT4UKQOCkROeRPaOjrx/cGzuPOi4YpcOyFSj7FDgnHsbAM6OjmszyrHsolxfZ9IuC1UIrYvVCYmLuCCErGLCE2bzWZc8e+dMHSIpbdnLkvFpQo2pxPEQPDVqKHpaIHWUI/4iADERwQgJtjPqsGGYD8tPrp9Iu6eIQZKR8824Jr/7kaRG4thM8Zwy9RhwvrzvWcUnZy+QuKRLvWpJTwT6RTx5S5yT/IkKBgkLmDf6VrUtvAl4uggX2QMdY2s2+8+zURpvTjpefnYGNwzM9GJOyIIZdCoVVh+eRpeWDJakLk5U9OKa/67W6af6W4sGR8LvS9fgCqsasGeghrFri19SN1dUCMoHxCeR0FVM3LKmwCcLxHTFLHSUDBIXMAvXYSmVS5QIn5r4ylsza0S1omRevzzhnQn7ogglOfWqcPw31syBOu8mhYjbnxvL7bmVvZxpmui99XgmgnisMdne5QbJBkc4odJ8fyDaldNVMKzWCu5J81JjhQeMAjloGCQkMGXiF0rHb8jrwpvbcwT1npfDVY9eBFUKvrxJTyPRaMG4at7piDEMhDR1tGJez7LlP1euhPSUvGG7AqUNbT1cnT/kErxUKnYc1kj6Rd0lbYlT4PupoSMPYU1qLOYyw8K0mGCk0vEDa1G3P1pprBWM+CH308nLUHCo8kYFobvH5iO2BDeMq+jk8ODXx12S8eNpOhATEvgPc07zRy+3qecX/Glo2OEsvqBolpFA03CNaASsWOgYJCQIfUiXjzG+SXia/+3B+0mcWDkH8vSkRwd6MQdEYRjSIzUY+X905AQwUsmdZo5/OHbo/hyn3KlVkdx6zQxO/j1gRIYTcr4FUcG+mJ6YgQAgOPkn1+EZ0AlYsdAwSAh0NFpxrqTrjNF/Nzqk8irbBbWV4yLwZLxsb2cQRCexeAQP6y4bxpSB/EPQBwHPPPjCXywo9DJO+sfF6dFIzqIl8mpamrH+pPK9fdJ/Wl/pmDQ46ASsWOgYJAQ2F1Qg3pLiTgmWIfxcSFO28vOvCqZ1VxMsA5vX08DI4T3ERnoi2/unYpxEu/iF9dk46Odp524q/6hVatw4+ShwlpJR5JFowZBq+YrGEdL6lHcxUKQcF/yK8USsS+ViO0KBYOEwFoXKRE3tBpx92din6BGxfDdA9NoYITwWkL8ffDF3VOE6VkA+NsvWfhsT5HT9tRfbpw8FBrLZ8r+07XItdzkbSXE3wezkiKF9c/HaJDEU5AKTc9JoRKxPfGauytjLIUx9i/GWC5jrIUx1sAYy2aMfcQYm+3s/TkbVyoR3/D+Xpmw9OvXjkNsiL/T9kMQrkCgTouP75yMiRK3nb+uOokvFMyy2ZPoIB0WjZL6FRcpdm2aKvZM1spKxIN7OZKwFa8IBhljjwA4BuAhAMkAzAB8AKQCuBPArc7bnWuwK78aDW18iTg2xM9pJeJ3tuYju0zMGCweMwhXT6A+QYIAeFmlj++chAlDxd/P5T+dwDf7lZvQtSdSmZmfDp9DS7tJkesuSIsWtBlzypuQL+k1JtyTC0rEqeRFbE88PhhkjN0H4G3wPsyvAhjGcVwgx3F+AGIA3AZgtxO36BLIp4gHgTHHl4hLalrx/9bnCuuIAB/8+8bxDt8HQbgygTotPvndZIyTPLA9/eNxrHaDjNjUhDAkRvLT0c3tJsWyeHpfDeamiMGCu2oyEiLSrODclCgEUInYrnh0MMgYiwfwhmV5P8dxf+Y4TniE5jiunOO4zzmO+8gZ+3MVOjrN+C2rQlgvHuOcEvEN7++F2WJdygB8cfcU6hMkiG4I0mnx2e8mY6xlqITjgD+sOIItOa7tVMIYkw2SfKVgRvPSMWIJeu1xciNxd2QJCpoitjuefqd9FIA/gH0cx73v7M24KnsLa4QS8eBgHdKdUCJ+dvVJme/w/bMTkRoT5PB9EIS7EOynxad3TkZSlB4AYDJzeODLg9h/utbJO+udayYMgY+lpHvsbANOlDYoct15qVHCdbPKGnGmpkWR6xKOJ7+yCbkVVCJ2JJ4eDN5k+e/XTt2FiyP19Fw02vEl4mNn6/GpREYmPtwff7o01aF7IAh3JDTAB5/fNQVDQnmnEkOHGXd9ckCxAMsehAb4YPFoMYunVHYwUKeVTRWTV7H7suaY+L2jErFj8NhgkDGWCOD848RhxthUxtjPjLEaxlgbYyyHMfY6Y8yrHzk6zRx+k0wRXzrasel4s9mM2z/aL6zVKoav75nq0D0QhDszKFiHL+6aggg9L+rc1G7C7R/td+nM2E1TxEGSVYdL0azQIMmlkiDz1+PUN+iurCWhaYfjscEggCTJ6zkAdgK4HIAWAAcgBcCTAI4wxkb1dBHGWI9/PIHMolpUNxsBABF6X2QMc6wX8Z9/OC54IQPAXy9PQ4zFj5UgCOuIjwjA53dNRpDFs7umxYg7Pj6A2hajk3fWPZPiQzHCUt5uMXZi9RFlBkkWjIwWBajPNuBsHQlQuxtdS8TzqEQsw14xiScHg9LGt2cBnAIwleO4IAB6AIsBVIKfKP6eMeaVeWhZiXhUNNQOFJrOKmvAt5lnhfWowUG4fXq8w96fIDyJkTFB+OiOSULf3OnqFtzzWSYMHZ1O3tmFdB0k+VqhUnGwvxYXjYgQ1uuoVOx2SEvE81KpROwoPDkYlP7bOABXcxy3DwA4jjNzHPcrgN9Zvp4CYGl3F+E4rsc/7o7ZzMk+LB09RXznRweE1xoVw6e/m+TQ9ycIT2NifBjevj4d55MEB8/U4fEVR2A2u97n1TUTYoXA9XhpA46fVabPcbGk1WUtlYrdDun3zFnKFq6MvWISTw4Gpaqj6ziOy+16AMdxa8BnDAFgvkN25UIcOVuP8kYDACDUX4spw8Mc9t4vrslCRVO7sP7TJSmI0Osc9v4E4alcOiYGyy9LE9a/nijHy2uznbij7gnx98Hlkpv9V/uVcVK5OE2scBwqrkdZQ1sfZxCuQkFVM5WInYQnB4PSJpQLAsFuvhZnx724JNKs4MVp0dCoHfPjcKamBR/uPC2sEyMDcM+sRIe8N0F4A3fNGI47L4oX1h/sPI2Pd53u+QQnceMUsVS86sg5NBk6ejnaOkIDfDA9MVxYU6nYfZB+r2YnR1KJ2IF4cjCYBd52zlpcr45iRziOw68SlX5HThHf+uE+nM9oqxjwye8mO+y9CcJbWH5ZGhaNihbWf/sly+UCo4nDQgWdxFZjJ1YpNEgi/Tz7lQSo3Qbpz6dURJywPx4bDHIc1wpgj2WZ0suh579WZNcNuRgnzzWipJYvnwT6ajB9RHgfZyjD+9sLUFwrlm0emDMCcaH+DnlvgvAm1CqGt28Yj/EWH2OOAx5fcQQnz7mOBiFjDDdJsoNf7StWpB974ahonJ+FO3CmFpWWdhjCdSmpbcVxiz6mVs0wLzW6jzMIJfHYYNDCZ5b/XsIYuyAgZIxdBiDZslzrsF25ANKsIG/yrrb7ezYZOvCaxHs4JliHpxb1FqcTBGELOq0aH9w2EcPC+Qeuto5O3PvZQVQ3t/dxpuNYOn4IfCXOIccUGCSJ0PtisqUHmuOADdkVfZxBOJv1Er3bi0ZEINhP68TdeB+eHgx+BL5crAbwA2NsMgAwxlSMsUsAfGg5bi+8KBjkS8TiL94lox2Tjr//84Po6BSf+t+/baJD3pcgvJlwvS8+uG0i9Jb+q9L6NjzwxUEYTf3porEfwf5ambDwV/uUkZm5ZJT4ufbbSQoGXR3pPelSB92TCBGPDgY5jjMBuAJACYA0APsYY40AmgD8CiAafLB4LecJWjFWklfZjMIq3p3A30eN2cmRfZxhO7vzq7GroEZYXzp6EEbHBtv9fQmCAJKiA/GvG8cLkjMHiurwl59OuIxE1s2SUvHqo+fQqMAgyYI0scy4u6BakeEUwj5UNhpw8EwdAL6P/OI0CgYdjUcHgwDAcVwhgDEAXgIf+GnAD4scAvA0gMkcx5U6b4eOR9pQPTclCjqtfUvEZrMZD351SFj7adV4+4bxdn1PgiDkzE2Nwp8vET2/V2SW4BOJJ7gzmTA0FCnRgQD4UrYSgyRDQv0xanAQAKCjk8O2U1U2X5OwD9IS8ZTh4QgL8HHibrwTjw8GAYDjuAaO45ZzHDeK4zh/juP0HMdlcBz3d47jXNfA005I+wUdUSJ+aW2OzHLuxSWjBLFZgiAcx72zErB0fKywfuGXLOzIc36Q1HWQ5GuFSsUL06hU7A78SlPETofuyF7G6eoW5JTzop4+GhXm2lnUs6LRINM3S4kOxDUZXifpSBAuAWMMLy8dg/Q4fsLYzAEPfnkIRdXOfyZeMj5WNkhyotT2QZKFEmmdLTmVLtMnSYjUthix73StsF40ioJBZ0DBoJchzQrOTo4Umsrtxb2fZeK8ExZjwAd30NAIQTgTnVaN927NwKAg3vGn0WDC/V8cRJvRuR7GwX5aXCZxJPnmgO3ZwdRBgRgS6gcAaGo3YW9hTR9nEI5mQ1Y5Oi03iYxhoYgOIicqZ0DBoJexzoETW9tyK3FUIhNx0+ShpClIEC5AVJAO792WAR+L61BOeROe+fG40wdKlk0SqwarDp+zOUBljMlLxVkkQO1q0BSxa0DBoBdxtq5V0PDSqhnmj7SvqOfj3x4VXgf4qvHCVaPs+n4EQVjP2CEheF7yO/nD4VJ8oVCv3kCZMjwM8RZNxKZ2E9YeL+vjjL6Rloo3ZlXCbHaNCWoCaDR0YFd+tbCmErHzoGDQi5BmBacn2lfU880NuahtMQrrl5aMgUpFP24E4UrcMCkO12UMEdZ/+/kkDhfXOW0/jDFcP0kcJFlxoMTma04cFopQf/6zrrzRILhcEM5nc3aloD07OjYIcWFUOXIWdHf2IhxVIm4ydOA/WwqEdWJkAJZIJhgJgnANGGN4YclomQTL7788hBonOpRckxELtcVLbn9RLQqrmm26nkatklmbUanYdZD2sEv9pAnHQ8Ggl1DRaMBByxO/WsWw0I7p+Ie/PgyTpBTz7i0ZdnsvgiBsQ6dV4783ZyBIxw+TlTUY8Og3R4SmfkcTFajDfInKwYpM27OD0lIxScy4Bq1Gk0z70VFOWET3UDDoJaw/WY7zveFThofZTdQz61wDtuaKv+CLRkVjhEVMliAI12RouD/euiFdWO/Mr8YbG3J7OcO+XC8ZJPn+4Fl0dNomCTMrKRI6LX+7y6tsxmkXkNLxdrbmVsHQwX9fk6P1SIzUO3lH3g0Fg16C1HXEniViqdOIj1qFN69P7+VogiBchXmp0Xhk3ghh/Z8tBU5z7ZidHInoIF8AQHWzEZuyK226np+PGjOTRNvNDVQqdjrStqVLqETsdCgY9AJ4UU9eX4sx+01srT1WhtPVrcL68YuT4O9jXx1DgiCU49EFyZiZFCGs/7DiCCobDQ7fh0atwnUScfoVCmgOLkyjUrGr0G7qxOYcMcAnSRnnQ8GgF7Axq0IQfs4YGoooO4h6ms1m/N9Px4V1WIAPHpgzopczCIJwNdQqhjevT0dkIJ+Vq2kx4rEVzukfXDZRDAa3napCWUObTdebPzIalrkUHCyuQ1WT84ZkvJ2dedVobjcBAOLD/ZE6iFqJnA0Fg16A1ATcXlnBf28pQL3Ef/jvS8fY5X0IgrAvEXpfvHV9OpglcNpdUIN3tuQ7fB9Dw/1x0YhwALxt3neZZ226XliADybGhwEAOA7YlE3ZQWchFZpeNHoQ2PkfNsJpUDDo4TS3m7DDzqKeBqMJ/94s3ixGROrtOq1MEIR9uWhEBB6eK2b239x4Cvsl/rGOQpodXJFZYrNgtKxUnEXBoDPo6DRjg+T/PUnKuAYUDHo423KrBHP21EGBGBquvKjn0z+egFEy7ffPm2hohCDcnUfmJ2GyJZNm5oBHvj4sE5J3BItGDRLE8c/WtWF3gW3ewlJrup351Wg1mmy6HtF/9hXWoqGNryINDtZh3JBgJ++IACgY9HjsXSKubDTgpyOlwvqixHCkxdAvN0G4Oxq1Cm/fmC5z73hq5VGH+hfrtGpcLRGs/8bGQZKh4f5IiuIlTIwmM3bn2xZcEv1HKjRNJWLXgYJBD8ZoMmOLZGLLHsHgQ18fFvQLVQz4140TFH8PgiCcQ0yw8IHxAAAAIABJREFUH/6xbJyw3pRTiY92FTl0D1LNwd9OVqDOxuzkvJGioPWmHNska4j+0WnmsP4klYhdEQoGPZjdBdVoskxsxYX5YWSMshNbx87Wy/qIrpsYhzC9fcSsCYJwDvNSo3H3jOHC+tV1Ocgpb3TY+4+MCcK4uBAAgLHTjB8Pl/ZxRu/Ml1jTbcmpdGim09s5VFyHaovVYYTeFxnDQp28I+I8FAx6MNInsEVpyqfjH/n6sPBap1HhhatGK3p9giBcgz9ekir4FxtNZjz2zREYOjod9v43TJJqDpbYFMBNGBoi9CGWNxqQVea4wNbbWS+ZIr44LVrwoCacDwWDHkqnmZNNbC1SWNRz/YlyFNVIBaaT4aOhHyeC8ER8NCq8fUM6fC2/4znlTXh9vePs6i4fGwM/rRoAkFvRhCMl9QO+lkatwpwU0Y1ks43uJoR1cBwnm+BeJPGLJvqP2WxGVlkDfjxUiv9tLcCLa7Jsuh7ZQ3goh2XpeB9MGKpsOn75qhPC67AAH9w3O1HR6xME4VqMiArE8stG4i+rTgIAPtx5GnNSImU2b/YiUKfF5WNjsPIgrzW44kAJxtvwmTYvNQqrjpwDwPcNPjw/SZF9Ej2TU96E4lo+gRDoq8H0xIg+ziDOU91swHeZpdhTWIO8iiZUNbejo1PZ9gYKBj0U6RSx0un4r/YVy9T7n7syTbFrEwThutwydRi25FYJVmJPrjyKdY/OQmiA/XuFb5gcJwSDq4+ew18uT0OA78BuYbOTI6FWMXSaORw9W4+qpnbBdYWwD1ILwDmpUVRJ6gWz2Yy1x8vxxb4zOFHaKLi12BP6bnggHCef2FJSANpsNuPvv2YL69gQP1w5LraXMwiC8BQYY3j1mrEItwR/FY3tePqH4w4ZwpgwNBQjLLIwrcZOrDle1scZPRPi74MMS2aR44CtuVQqtjdymTMqEXfH1txKLPnPLiQtX4eHvj6MvYW1fQaCvhoVgnQaRNn4MEOZQQ9Emo7X+2owPTFcsWv/d1shGg3iD+er15DtHEF4E5GBvnjt2rG469NMAMC6k+VYefCszC3EHjDGsGziELy8NgcAb09ny3vOGxmF/UW8GsLmnEpcZ+f9ezMlta3CoI6PWoXZyfZvLXAXmg0mvLQmC6uPnUNLe89DWT4aFYaG+WPU4CBcNCIC0xLDERcqN5Fgywe+DwoGPRDpE9iclEj4atSKXNdkMstt56L0mOGAfiGCIFyL+SOjccvUofhiLy8C/fzqk5g6PNwuDkdSloyPxavrctFp5rC/qBaFVc1IiNQP6FrzU6Pw91/5wHJHXjWMJjOVLu2EdHDkohHhCNRpnbgb16CkrhX/98Nx7MqvRk8ui7Ehfrg4LRo3TRmK5GhlpeG6Qj/5HohMUkbBEvEr63LQJpGTeEMiRksQhHfxzOI0JEQGAABajJ146rujNnsH90VUoA5zU0TR6O8sPYQDYUSUHnFhfgB4D/cDRY73XvYW7O2E5U5UNhpw64f7MPPVLdiRd2EgGBXoiwfnjsDx5xZi15/n4bkrR9k9EAQoGPQ4SmpbkS1Jx0slFGzBYDThsz1FwnpcXDDGDglR5NoEQbgffj5qvLksXRhO23e6Fp/sLrL7+y6bOER4/f2hs+gcYADKGJMJUG8iiRm7UNPcjkxLoM0Yn1X2Rhpajbj3s0xMeWUTduRVy76mYsDMpAj89vgs7H9mAZ5alOLw7CkFgx6G9AlMyXT8Mz+dkI2y//OG8YpclyAI92VcXAh+P0eUlXp1XQ4Kqprt+p5zU6MQoRcHWLbnVQ34WvNSpdZ0FeRGYgc2ZVcK2a+Jw0K9cmr77U2nkPHiRvyWVQHpj5hGxXD9pDgce3YRPr9rikMygD1BwaCH8ZsdSsQNrUaZBdSMEeEYFh6gyLUJgnBvHp6XhJExvDtJu8mMJ1cehanTbLf306pVuHq8qGCwMrNkwNeakhAGfx++p/pMTSsKq1ts3h8hR5qgWJjmXSXiw8V1mPLyRry5IQ8mSQZbzYCl42Nx7LmFePWasdDrnD++QcGgB1Hd3I4DZ/h0vIoBC9KUScc/ufKo8GTHGPDGsnRFrksQhPvjo1HhH9eNg1bNl4sPF9fjvR2Fdn1P6eTvhqwK1LYYB3QdX40aM5NE8WNyI1GWlnYTduSLJVFv6Rc0mcy459MDuPqd3ahobJd9bVFaNI48uxBvXJ8Ofx/nB4HnoWDQg9goSUFPHBaGCL3t6fjqZgM2Sj4gLxk1CFFBOpuvSxCE55A2OAiPSlw83tqQh5xy+3n+JkcHIj2O71nu6OTwk6Ry0V+6looJ5dh2qgpGE58lTh0UaPdpc1cgs6gWE17cgA1dHiyGhfnjt8dn4d3bJrrkNDUFgx6ELB2vkKjnUyuP4XxyW82A164dq8h1CYLwLO6fnYhxQ4IBAMZOM5749ig67FgulmoMfptZMuB+P+l08oGiOjS0ddi8N4LnN9k9ybOzgmazGU9/fwzX/m+PTIvXR6PC81eOwrY/znVqT2BfUDDoITQZOrArv0ZYK5GOr2w0YGuu2Jx9xbhYl3yiIQjC+WjUKvxj2ThBq+/kuUaZLqnSXD4uBjot/1455U04eW5gmcioIB3GWoLYTjOH7acGPpBCiBhNZmzKEbNjnuw6Ulrfiote3YKvD8j7VyfFh+LIXy/G7dPjnbOxfkDBoIewNbcKRstTeFpMEOLCbE/HP/ndUUlWkOHFJaNtviZBEJ7LiKhAPLUwRVj/Z0u+IHWlNEE6LS4dHSOsv7VhkERaKpY+ABMDZ29hDZosGbLYED+kWYaMPI31J8ox+7WtKGswCH+nUTG8fPUYrLx/ukv1BfYGBYMegtKinmX1bdhxSmz8vSp9sEtMPBEE4dr8bsZwZAzjfX9NZg5/+v6Y3aaLr5NoDq46cg6Gjp7tvHpjjqRUvO1Uld3Fs72B37Lk9yTGmBN3Yx/+8tMJ3PfFQdmkcGKkHrv/PA83TRnqxJ31HwoGPYB2U6fsaXbRaNvT8U9Js4IqhhevpqwgQRB9o1YxvHrNGPio+dvLsbMN+GjXabu819Th4RgSyruINLR1YEPWwAZAxsQGI9Sfb4Gpbm4XfHSJgWE2c11kzjyrRNxqNOHSt7bj871nZH9/+/R4bHpitlsOWVIw6AHszq9Bczufjh8W7o8UG5tUS+tbsVPSf7h0QqzbpLoJgnA+I6IC8egCcbr4H7+dQpEdNPxUKobrMuSDJANBrWKYlSy6NW2jvkGbOHq2HpVNvKRKWIAPJsaHOXlHylFa34rpf9+M7PIm4e+0aob3bs3A81eOcuLObIOCQQ9A6XT8k98eE15rVAwvuPEPOEEQzuHeWQlCn1i7yYw/fX/MLuXXazJicf4jb2d+NUrr2wZ0ndmSYHBrLukN2sJ6SVZwwcgowbLQ3cksqsXc17ehvlWcOI8O9MX2p+a6/bQ0BYNujtnMYUOW+MG10Eah6ZK6VuwpFLOC12UMgY6yggRB9BOtWoXXrh0r8y7++kCx4u8zJNQfM0bwwtEcB/xw8OyAriPNDB4qrieJmQHCcZxcUsZDXEe+yyzBde/uEQY1AWBqQhj2PD0PMSF+TtyZMlAw6OYcLqlHdTOfjg8P8MH4oaE2Xe+Jb48KrzUqhmevSLPpegRBeC+jY4Nx76wEYf3K2hyUNQwsc9cb12aIgyQrD54dUAYyQu8rk5jZJXHOIKynoKpZsPXz91FjhsThxV15a+MpPPndMZmv8M1ThuKbe6dBpfKMMMoz/hVejLRher6N6fgzNS3Yf7pWWN8wKY6yggRB2MSj85OQEMF7mTe3m/DMjycGLBDdE4tGDUKQRe2guLYV+ySfY/2BSsW2Iy0Rz06OhE6rduJubOfZ1Sfx1sY8Yc0APHtFGl66eozzNmUHKBh0czZI+gUvtjEd/8fvxF5BrZrhr1dQryBBELah06rx6rVjhb6+zTmVWH30nOLvcVV6rLBeOcBBkjkp8iESpYNWb+A3hWXOnMlj3xzBp7uLhLVaxfDJnZNw50XDnbcpO0HBoBtTWNWMgio+Ha/TqoS+mYFQVt8me5q+fmKc4CRAEARhC5Piw3Db1GHC+rnVJ4X2FqWQ2tOtPVGGJkP/e/7GDQlBsB8vMVPR2I4cycQo0TdlDW04erYBAN9mNFci5u1u3PHxfvx0RPS89lGr8OPvp2N2ivv+m3qD7vZujLREPDMpEn4+A0/H//F7+QTx8stG2rQ3giAIKU9dkopYS6N9XWsH/vZzlqLXHx0bhNRBvKyWocOMX46V9fsaGrVK1uNGEjP9Q3pPmpYYLgTW7sYN7+2Raff6adVY/9hMjB0S4sRd2RcKBt0Y6S/exTZMEVc3G7AzT2yWvnp8LPUKEgShKHpfDV5eKvZZrT56TlEfYMYYrptou+bgHOobHDDSe5KtyhbO4qb392JvoVglC9RpsPmJ2RgeqXfiruwPBYNuSnVzOw4W1wEAVAyYb0M6/unvj8s8iJ+/inoFCYJQntnJkViSPlhYL//pxIAt5LpjSfpgaNV8c+Lh4nrkV/a/zCsdIsksqhtQudkbaTR0YK9ElmyBGwaDt3ywF7sLxH9DqL8WO/441yOkY/rC64JBxpieMVbCGOMsf+5w9p4GwubsSmHMPWNYKML1vgO6TkOrEZtyxKffxWMHkdsIQRB2Y/nlaUL5sLi2Ff/anNfHGdYTrvfF/FQxCFmZ2X/NwaggnSCWbTJzsuCA6JmtuVXo6ORvSmNigxET7F4B1G0f7ZM5b4X4a7H1yTkI8fdx4q4ch9cFgwBeBDCkz6NcnN8UKhEvX3UC5yW5VAx48SryICYIwn5E6H3x9KWpwvrdbYU4VaHcoMaySeLH+/eHStEhEQm2FulUsbR3jOgZpdqWnMFdnxzA9lNiq1SIHx8IBntJIAh4WTDIGJsA4CEA+5y9F1toM3ZiZ774ATVQSZlmgwlrj4kyAPNTo7zqh58gCOewbGIcJsXzAvkmM4f/++G4YlZ1s5IiERXIV0qqm9sHFMxJS8XbSWKmT4wms6y/csFI9wkGn1p5VFYdC/bTYPMT3pMRPI/XBIOMMRWAdy3LB5y5F1vZkVcFQwf/tDsiSo/hFkHX/vL8zyfRafmQYwBeucazRDQJgnBNVCqGl68eI/T3ZZ6pw4oBDnx0RaNW4RqpI8kArjthWCgCffl2mdL6NuRXNiuyN09l/+laNBlMAIDYED+MjAl08o6s45W12VgpsS8M9NVg0xOzEab3rkAQ8KJgEMDDACYC+C/HcYedvRlbUCIdbzCa8ONhUUNpZnIEIvQ6m/dGEARhDUnRgbhvVqKwfmVtNqqalNEevE4SDG7Oqez3dbVdJGaoVNw7cvODaDA2cCcsR/H+9gK8u71QWOu0Kqx7fKbX3ge9IhhkjMUCeAFABYDlTt6OTXSaOWyWpLQHGgy+tDYHJklZ5tWlY23eG0EQRH94aN4IDAv3BwA0Gkx4cY0y2oMJkXpMHCaWoX+SPPhai7RUTHqDPcNxHDZm235PciTfHyzBS2tzhLVGxbD6oRmIDfF34q6ci1cEgwD+BSAQwJMcxzU4ezO2cKi4DjUtRgBAZKAv0gcggmkymbHigFg6mTI8zCtG5wmCcC10WjVeXCIOra06cg478pQJvJZ10Rzsb9/fbMkQyf7TtWg1mhTZl6eRVdaI0vo2AECQToPJw8OcvKPe2Z1fjSdXiiYLKgZ8dc8UJEe7R2nbXnh8MMgYuwLA1QC2chz3xQDO7/GPM5CWiBeMjIJK1f99vP5bDoySCbvXrqWsIEEQzmFmkn20BxePjYG/xZUpr7JZsEmzlphgP6RYAgRjp1lm10mISO9Jc1OjoFW7blhxuqoZt3+8X9DVZQD+d0sGJg8Pd+a2+oW9YhLX/a4pAGMsAMC/AXQAeNDJ27EZjuNs7hc0m834dM8ZYZ0eF4Jh4QMbQCEIglACqfbgmZpW/Htzvs3X1PtqsHhMjLCWVkOsZaakb3CHRHqEEJEnKFy3RNzQasQV/94laCECwItXj8bCUQNT4/A0PDoYBPA3AEMBvMlx3ICaUTiO6/GPoymoasbp6hYAgL+PGtMTI/o440L+uTlfmEQGKCtIEITz6ao9+N72QhRW2T7BKy0V/3L0HNqM/cs4zpT0DSpVvvYkztW34eS5RgCAVs1k+oyuhMlkxqX/3InmdrHUf9eM4bh5yjAn7mpg2Csm8dhgkDGWDuBRACXgg0K3Ryo0PSspEjqtut/X+HDnaeF1Wkyg1/dJEAThGiybGIcJQ/keaGOnGc+uPmnzDW5SfCjiLQMqTe0mrD9Z3scZcibHh8FHw98m8yqbcc7SG0fwbMwW70lTE8IRqNM6cTc9s+y9PbLv3bzUSPzl8jQn7sj18NhgEMDbANQAngHALDZ0wh/Jcb6Wv3P5MSJbS8Rf7SsWtKAA4KWrSVeQIAjXQKVi+NtVo3G+DXpHXjXWnehf8NYVxhiu6zJI0h/8fNSYHC8OROzMo1KxFHdwHfm/H47hUHG9sE6O1uOD2yY6cUeuiScHg+fzv58BaOrmz3n+Z1kro2lgJyqbDDhSwv9Aq1UM81Kj+n2NNzeeEl4PC/PH+KGhiu2PIAjCVkbHBuPWqWLp7m+/ZKGl3bYp3qUTYoUAc3dBDUpqW/t1vrRvcDuVigUaDR3YWyh6+bpiv+C3mSX4ar/4ABAe4IPVD14ElcqTQ5+BQf9H3IRN2ZU4XzGZOCwUoQH9U0jfmFUuE179y+UjldweQRCEIvxhYQoiLA4QZQ0G/MvGYZKYYD/MTBJ72b6TOE5Yg/TcnfnV6FTINs/d2ZZbJQxjjI4NwmAXkyc7UdqAP38vSsj4qFX45eEZ0PlonLgr18Vjg0GO4+I5jmM9/ZEceqfl7+KdtVdrsDUd/8KabOF1hN4XCwboZ0wQBGFPgv20ePpS8WH1gx2FyK9s6uWMvpEOknx38Gy/fJBTBwUiQs97Hde3duDkObeWqlUMV54ibmg1Ytm7e3D+28wAfHLnJNLT7QWPDQY9iZZ2E3bmi70qC/sZyB0ursOZGrE08oeLkxTbG0EQhNIsnRCLSfGig8hfV9k2TLIgLQoh/vxwQ2l9G/ZIypt9oVIxucQM9Q2io9OMLbmu6TpiNptxxb93olUyOf7nS1MxfUT/1Te8CQoG3YAdeVUwmng5mJToQAwN79+sy/KfTgivA301uMkNx+kJgvAeGOOHSdSWZr/dBTX45VjZgK/nq1FjSXqssF7Zz0ESWd8gWdNh/+laYRgxNsQPaTFBTt6RyL2fH0RxrTg5fMmoaNw3O7GXMwiAgkG34DcbSsRnaloEHSgAuGtmvFLbIgiCsBsjY4Jw+7R4Yf3imiyZTlx/uTZjiPD61xPlaGjrsPrcGZKs0qHiOpv24Ql0bVtyliNXV/6zJV/mkzw8wh/v3DzBiTtyH7w2GJT0D37i7L30hqnTjM05A0/HP/3DceG1j0aFR+ZRiZggCPfgsYuTEBnI9+tVNLbjbYkiQn8ZHRssZLDaTWb8fPSc1edGBemQOojXZO3o5LCvH2VmT6OrE5ar9AsePFOH/7c+V1gH+Kqx+qEZNDlsJfR/ycXJPFOH+lb+CTY6yBdjYoOtPre22Yg9BeKH1rKMIfSLQRCE2xCk0+KZxeIwyUe7ipBbPvBhkusmitnBlf2cKp4lcyPx3r7B7LImlFoEnAN1GkxJCOvjDPvTbDDh1g/3CZ7DKgasvH+ay4pguyIUGbg4XZ/AVCrr0/HLVx0XfjnUjOGZy0hOhiAI9+Kq9MGYMpwPODrNHJ5dfWLAwyRL0mPho+Zve0dL6nGqwvrAkvQGeaT3pLkpUdCqnR9GXPe/3bKBkb9dOQppMdYnTggKBl2arun4/pSIW40mrD8hnrtwVDT8SF+JIAg3gzGGF5aIwyR7C2vx6wCdSUIDfLAgTRTs788gyaT4MPharOkKq1pwtq5/4tWewoZs8f/9AheYIn529UlkS7LFC0ZG4RZJrylhHRQMujCnKppRbFHL1/tqMC0x3OpzX16TjU7L0zMD8NISsp4jCMI9SY4OxG3TRBWEl9Zko02SCeoPUnu6Hw+XoqPTbNV5Oq0ak4d7tzXdufo2nCjlBxK1aoY5KZF9nGFfNmVX4NPdRcI6OtAX792a4bwNuTEUDLowUhPw2cmR8NWorTrPbDbL+mGmJoQjTN8/xxKCIAhX4rEFyQizOC+V1rfh3e0FA7rOrKRIRAfxQynVzUZskQzoWXPuebyxb3CT5J40NSEcQU7syatsNOCBLw4Ja42K4YcHp1Nf/ACh/2sujKxfMM16L+J/bclHu0l82n1lKWUFCYJwb4L9tHhyYYqw/u/WggGVatUqhmsmiIMk32ZaP0gyM1nsG/RGa7rfXGSK2Gw2Y+l/d8Moyeq+dX06YkP6p8FLiFAw6KJUNbXj6Nl6APyH19wU64PBj3aeFl6nxQQhPiJA8f0RBEE4musnxWHUYFEe5pW1OQO6jlRzcEtuJSqbDFadlxIdKEjdNLR14Hip91jTNRo6sFciqePMfsE/fX8cZ+tEYenrJg7B5eMGO20/ngAFgy7KlpxKnB+YyxgWihB/68q8q46UoqFNFER9/spR9tgeQRCEw1GrGJ6TfKatOV4mk8+yloRIvWB312nm8NPhUqvOY4x5rRvJttwqdHTyN6VRg4MQ6ySf3805lbI2qOER/nj92nFO2YsnQcGgi7IhW5qOtz4r+LpEdHNwsA6ThjtfA4ogCEIpJsWH4UpJFuj5n0/CZOUQiJTrMsRBkpWZZ62Wq5H2De7K956+wY3Zzi8RN7Qa8cAXB4W1Vs2w8v5pTtmLp0HBoAti6OiUTapZ+4t3tKROljp/YmGy4nsjCIJwNk8vToWflh+oyylvwtf7i/t9jcVjY+Dvw18jr7IZR0rqrTpv+ghR1eFQcR1ajZ5vTdfRaZYN2vTXCUspbnx/r6wf/s1l6YjQ65yyF0+DgkEXZE9BDdo6eNmEhIgAJETqrTrvr6tOCq8DdRpcI3nyJQiC8BRigv3w4NxEYf2PDadQ32rs1zX0vhpcNiZGWFvrSBIVqENKtGhNt/90bb/e1x05UFSLRgMf9A4O1gl9m47kjd9ykVUm6gkuHjOI+gQVhIJBF0RaIp5vZYm4rL4NR8+Kzcy3k+gmQRAezN0zExAXxvet1bd24I0N/fctlmoO/nzknNXahReNEPsGvaFUvClbzArOHxkNxqx3wlKCE6UN+NfmfGEdoffBv28c79A9eDoUDLoYHMdhs+QXz9oS8V9XnRBea9UMj81PUnxvBEEQroJOq8byy9KE9Rd7zyC7rLFf15gUH4r4cF6OpKndhPUnrXM2mZEklop35vd/gMXd2CwpEc/rRw+7EhhNZtz8wV6Z7/CK+6aRnqDC0P9NF+PkuUaUN/IyB8F+WmQMC+3zHIPRhM054lTb4jEx0GjoW0sQhGezMC0aMyxZOjPHD5P0x7eYMSbLDn5rpT3d5OHh0Fjs8bLLGlHT3N6PXbsXhVXNOF3dAgDw06oxLcF6JywluOezTJlCxtOLU5FoZesUYT0UMbgYUqHpealR0FhhAv7KulyZ9RzJyRAE4Q0wxvDsFWk2+RYvnRALy+nYXVCDktq+haz1vhqkx4UI690DkLdxF6Ql4hlJEdBprXPCUoLVR0uxTSLfMyk+FPfMTOzlDGKgUDDoYmzK6V+/oNlsxrcHxKfZycPDrNYkJAiCcHeSuvEtNnRY71scE+yHmRK5mO+sHCTxlr5B2T0p1XEl4oZWI5789piw9vdR4/PfTXbY+3sbFAy6EOUNBsEEXKNimJXctwn4J7vPCJPHAPDCktF22x9BEIQr0tW3+IMdhf06f5mkVPzdwbMwW2EzJw0Gd3poMNjQ1oEDRXXCep4Dg8GbP9gns5v7780ToPPROOz9vQ0KBl0I6ROYtSbg72wVJ6wSI/VItkgeEARBeAvBflr84WJRV/WdrQVWW8wBvPd7iD//eVta34Y9hX2XfdPjQgSdwrN1bSiu6b9Psquz7VSV4L88dkgwooIco+n3/o4CnDgnDgNdMS4Gs/thyUr0HwoGXYiNWf0rEW/NrUR1s6it9cxlI+2yL4IgCFfnhklxSI7mBwtajZ34x3rrpWZ8NWosSY8V1tYMkvhoVJgicXjyxOzg5mx5D7sjKK1vxd8lntOh/lq8fX26Q97bm6Fg0EVoNZqwS9KEbI2kzAu/ZAuvI/Q+Dk3hEwRBuBIatUomNfPtwRKcPNfQyxlyrs0YIrxed6IcDW0dfZ7jyX2Dpk4ztkqGNxxlQXfje3thsUAGA/Dl3VNIRsYB0P9hF2FnXjWMFpudlOhAxIX593p8fkUTCqqahfXv54yw6/4IgiBcnVnJkZiTwvdacxzw4i/ZVkvNjI4NRloM76zRbjLj56Pn+jxHGgzuLqi2qtfQXThUXI/6Vj4gjg7ydYjryItrslBcK1qq3jVzONIGB9v9fQkKBl2Gjf10HVkuEZn206pxx/RhvRxNEAThHSy/bKQgNbOnsEYm19UX100Us4PW2NOlRAciQs8PrtS1diCrn6LXroy0h31eapTdXUeyyhrw4Y7Twjou1E+W6SXsCwWDLoDZzMlEo+f3kY6vbzViX6Hoh7lsUhyl0QmCIACMiArEzVOGCuuX12YLVZe+WJIeCx+LtuvRknqcqmjq9XiVimF6omeWiqVOWPNT7VsiNpvNuOOjA4LLiJoBX9071a7vScihCMIFOHq2HtUWBfsIvY9MzLQ7nludJfmlYXj6khQ775AgCMJ9eGxBMgJ1vAxJUU0rPttTZNV5oQE+WJAmVmZWWjFIMsMDJWaKa1qRV8m3IflqVLJyuD14/pdsVDaJLi5PLUpFXGjvrVKEslAw6AJIS8RzU6KEEkd3mExmrDku9rLMTY0k7SWCIAgJYQE+eFTiz/7PTXmoazH2coaI1J7ux8Ol6OjsPas4fYRoz3agqBbtJuun5A6qAAAgAElEQVQFr10VaYl4emI4/Hzs5zqSU9aIz3YXCevESD3un0MuI46GgkEXQGr301eJ+J1tBejoFJuUX7iKRKYJgiC6ctu0eMSH89mlRoMJb2/Ks+q8WUmRGGTR06tuNmJLTmWvxw8J9Rfex9BhxqEz9Tbs2jXYnGP9PclW7vh4v6w8/MVd5DLiDCgYdDIlta3IKef7Unw0KsxM6j0d//EuscF2VEwQYkL87Lo/giAId8RHo8LTi0Xt1c/3nkF+ZXMvZ/CoVQxLJ0g1B/seJPEkiZkmQwf2SkS37SlZ9sIvWShvFMvDf1iYQvc0J0HBoJPZlC1Pxwf49lzy3ZRdgbpWUfvqL5fTpBVBEERPLEyLxtQEXhi608zh5bXZfZzBIy0Vb8mt7NPNxJP6BnfmVQvVp7SYIAy2U3CWX9GEj3aKyY2EiAA8OJck0pwFBYNOZlM/0vF//1VUZY8K9MXUxPBejiYIgvBuGGNYflkazquibM6pxI68qt5PAjA8IgCT4kMB8EHkT4dLez1+WmK48B7HztZbJVjtqsjvSfbLCt72kVgeVjHg87upPOxMKBh0Il3T8Qt6+cU7U9MiTHcBwP2zqcGWIAiiL0bHBuPaCaJ+4Iu/ZMPUx1AIIM8Orsw826t4dYi/D0ZbxJHNHLDPCm9jV6TTzMl6JO1VIn5pTRbONYjZ1kcXJCE2hKaHnQkFg05k+ykxHT9qcBBigntOx/911UnhtU6jIpFpgiAIK3lqUQr8LROxuRVNVvUBXjYmRjgnr7IZR0p6HwzxhL7Bo2frUWOZuo7Q+2DckN5lzgZCQVUzPpCUh+PD/fHo/GTF34foHxQMOhFpv2Bvvo+tRpOstHHV+FgSmSYIgrCSqCAdHpBUU97YcAot7aZezwnw1eCyMTHCui9HkoskEjPu2jcoFZqemxIFVS8yZwPltg/343ySlS8PT1H8PYj+QxGFkzB1mrElV/zF6y0YfG1dLs5bXjLG2y0RBEEQ1nP3zASJZEw73tte2Oc50lLxz0fOoc3Ys4bgpPgw+Gj4W2pBVQvKG3ofOnFF+muL2l/e+C0XpfWi9/DD80aQuLSLQMGgkzhUXC9MBkcH+WJ0bM8m4N9KVPCnDA9DoE5r9/0RBEF4En4+avxhoViOfG97ISobew/YJsWHChqCTe0mrD9Z3uOxOq0aE4eFCmt3KxWX1reJMmdqFWYkRSp6/YpGA/6zJV9YDw31w+MXk3uWq0DBoJPYJHsCi+7RBPzr/cVolTyNPnfFKLvvjSAIwhO5ZsIQpA4KBAC0dXTizY2nej2eMSbLDn7bhz2dO/cNSoWmpySEQd+LzNlAuOPj/Tjvl8AAfPw7mh52JSgYdBIbZf2CPafj/7VZfJIaFu6P1JieM4gEQRBEz6hVTCZEveJACU5VNPV6ztIJsTjfOre7oAYlta09HjtdIve1u6Cm1wlkV0OWoFB4ivjbzBJkl4n/n2+aMhSJkXpF34OwDQoGncDp6hYUVLUAAHRaFaYndu86crSkDuck/RVPLKSJK4IgCFuYnRwpOD2ZObl+a3fEBPthpqRk+l0vgyRjYoOFjFp5owFFNT0Hjq5Eq9GE3QWiHI6SFnStRhP+8tMJYR3qr8ULV1GFy9WgYNAJSJ/AZiZFQqft3gT8uZ+zhNdBOg2uHBfb7XEEQRCE9Tx96UiZEPXugt5LusskpeLvDp6F2dx9xk+jVmHK8DBh3dd1XYVd+TUwmnjtxeRoPeLClBvquO/zg2g3ibqO/7slg9QwXBD6jjgBa0rEtc1GHCkWda1umUq6ggRBEEqQNjgIS8eLQtQvr83uMcADgAVpUQjx5wf3SuvbsKcXUelpXUrF7sDmHPGeNC9Vuazgjrwq7MgTA+J5qZGYkkDOWa4IBYMOpqG1AweK6oT13B56M57/5aRg1aNRMTy2gErEBEEQSvHkomT4WqRgTpQ2YvXRcz0e66tRY0m6WJnpbZBEGgzudYO+QbOZw6ZsqcyZMv2CZrMZD311SFjrtCq8c9MERa5NKA8Fgw5m66lKdFqeQNPjQhAVqLvgGJPJjLXHy4T1/NQoQb+KIAiCsJ2YYD/cPXO4sH59fS4MHT3rCF6bIWYS150o79F/eOSgICGLWNNixKmK5m6PcxVOnmtEZVM7ACDEX4vxQ0P7OMM6/vzDcTS0icLef186FjofZSeUCeWgCMPBWPME9r/tBYJNHQA8dyU12xIEQSjN/bMTER7gA4Av/366u6jHY0fHBiPNoubQbjLj5x4yiSoVw7QEaanYtfsGN0lKxHNToqBWwHXkVEUTVkos/8bEBmHJeOp5d2UoGHQgpk4ztkpcR3qa2PpoV5HwOi0mEDEhPXsWEwRBEAMjUKfFowuShPW/t+SjzuLN2x3LJorZwd7s6bpKzLgy0gSFUq4jd358QGhzUqsYPr5zkiLXJewHBYMO5OCZOjQa+LT54GCdIH4qZWtuJWolH0bLL0tz2P4IgiC8jRsnD0VCRAAAoMlgkmm7duWq9Fj4qPnb5tGS+h41CqV9g/sKa4TWIFejotGA46UNAPje9JkKuI68szVfZjn3+IIkROgvbIciXAsKBh2IVOF93siobl1HpJpXEXpfTB/RvQYhQRAEYTtatQp/vCRVWH++twhnalq6PTY0wAcXp4kVnZU9DJIkRuoRGegLAGg0mJB1rlHBHSvHFsk9aVJ8GIL9bLM6bWg14o3fRFeXIaF+eGheUi9nEK6CRweDjLGhjLHHGGM/M8aKGWPtjLEmxthRxtjfGWMxjtzPJskv3vxuxvfLJN6QAHCPpLmZIAiCsA+LRkULvsIdnRxeW5/b47HXSkrFPx4uRUen+YJjGGNdSsWu2Te4UeES8QNfHoJJkgV977YMm69JOAaPDQYZY3EAigC8CeByAHEADAD8AIwF8CcAJxljcx2xnzM1Lciv5KfKdFqVrIxwnud/Pim89lGrKBgkCIJwAIwx/N9lok3dmmNlOFxc1+2xs5IiMSiIL3tWNxtl2TUp0mCwN11CZ2Ho6JT5J9vqOrI7v1rWH3nZmEFIiwm26ZqE4/DYYBDAeVuPNQCuAxDGcVwwAH8AiwGcBhAK4CfG2CB7b0ZaIr4oMeIC1xGTySx7Sls0OppU2gmCIBzEhKGhuGyMWCx6eW12txqBahXD0glSzcHuB0mkNqP7T9d2m0F0JnsKa9BmkdJJiAjAcEvf5EAwm814UKIp6KdV483rx9u8R8JxeHK0UQdgPMdxl3Mc9x3HcXUAwHGckeO4X8EHhAYAQQDus/dmuvYLduWdbQWy9PpfaHCEIAjCofzxkhRo1Xwv94GiOmzIquj2uOsk9nRbcitR2WS44Ji4MH/EWpQgWo2dOHa2/oJjnInUFtXWEvErv+agrlXUXXxxySjSxnUzPPa7xXFcA8dxR3v5eg6AvZalXRsbmttN2FdYK6zndeM68olE32rU4CBEBdH0FUEQhCMZFh4gs/58fX0uTN1k9IZHBGBSPN9j2Gnm8NPh0m6vJ+sbzHedUjHHcdgsqUTZYkFX0WjAhztPC+vkaD2uyYjr5QzCFfHYYNBKzv92qns9ykZ25lXDaPlASYsJQkywXDdwR16VTE7mmcUjQRAEQTieh+aOgN6Xd8rIq2zGD4e6D/Sk2cGVmWe7LSlPH+GafYM55U0418BnMwN1GkyMH7jryN2fHsD5ohZjwId3kKagO+K1wSBjTAPgIsvyhD3fS2oC3l06/pW12cLr8AAfkpMhCIJwEuF6X9w3K0FYv7HhVLc2dZeNiYG/D59HyKtsxpGSC8vA0xLEz/LMM3W92t05EmmJeE5KFLTqgYUCa4+V4XipKJtz0+ShiAv1t3l/hOPx2mAQwIMABgEwA/i0p4MYYz3+sQazmcPmnCph3bVEXNloQFaZKCdz50Xx/fk3EARBEApz18zhiNDzOoHljQZZG895Anw1soGT7hxJBgXrBEFro8mMQz1MKDsauczZwPoFTSYznvpe7MQK0mnwwlVknWpvbI1JesIrg0HG2FgAr1iW/+Y4Lste73W8tAHVzbwJeHiAD8YNCZF9/W+/iG+tVTPcPyvRXlshCIIgrMDfRyOzqXtnSz4aJAMS55GWin8+cg5txgszf1IZsT0uYE1X3dwuZDFVDJidPDDXkad/PI6WdvHf++b16aSA4cZ43XfOIjT9E3i9wYPg9QZ7hOO4Hv9Yg/QJbE5KFFQSE3Cz2Yz1J8uF9YKR0dDQBBZBEITTuWFSHOLD+ZJno8GEd7ZdaFM3KT5UOKap3YR1J8suOEYqMeMKweCWnEqcv31NHBaG0ACffl/jdFUzvpNkQicMDbFZp5CwDltjkp7wqsiDMRYG4DcAwwHkAbiM47gLNQEUpLd+wfe2n0ZHp/gN/OvlJCdDEAThCmjVKjy1SLSp+3hXEc5JPHcBvmTXdZCkK1MTwoTXR0rq0dJussNuracvmTNruOvTTJy/c6kZwwe30dCIu+M1wSBjLBjAegCjARQDWMBxXPciUgpR0WjACUtzLW8CLh8M+UAyjj9yUCBiQuRTxgRBEITzWDxmEMYN4V00jCYz3tp46oJjlk6IxfmCz+6CGpTUtsq+Hq73ReqgQACAyczhQFFt10s4DKPJjO2nxB72BQMIBr/aV4zCatG7+YE5iQjT9z+7SLgWXhEMMsYCAKwFMBFAOfhAsNje7yu1KZqSEIZAnWgCvregRuglBIA/X5oKgiAIwnVgjOFPl4ifzd8dPIu8iibZMTHBfpiZFCk7pivTXMSabt/pGrRY+hqHhvkjMVLfr/ONJjP+9otomxqh98WTi1IU3SPhHDw+GGSM+QH4GcB08LqCCziOy3PEe0v7BbuKer60VhwcCfXXYnaK7SbhBEEQhLJMHxGBWZYhCzMHvLY+94JjlklKxd8dPAuzWd6/5Sp9g5tkQtNR/Z5A/eN3x2DoEEW437mZLOc8BY8OBhljPgB+ADAXQD2AhRzHnez9LGUwdHRiZ57EBFwyvl/dbJBpM902bRgIgiAI1+RPl4jZrw1ZFcjsUupdkBaFEH++8lNa34bdXQK+ycPDhFLyidKGbieT7Q3Hcdgk6WFf0M+Bj9NVzVh1RBTgnpoQhsnDw3s5g3AnPDYYZIypAXwF4BIATQAu5TjuUO9nKcdeqQl4ZADiJSbgL/6SI7zWqBgenpt0wfkEQRCEazBqcDCWpA8W1n//NUc2vemrUWNJeqywXpFZIjs/2E+L0bF876GZ48u1jia/shkltfwATICPGpOHh/Vxhpz7vjgoDo2oGP53i11dXAkH47HBIHh3kWssr7UAfmKMlffw54DSb765B1FPs9mMtSdE+YG5KZEkJ0MQBOHiPLEwBVo1n97LPFOHjZKSKwBcP0ksFa8/WY76VqPs687uG5S2Lc1KjoRPP+47q46U4lRFs7C+d2YCQvxpaMST8OQoRPpv0wGI7uXPwFQ3e4DjOFlvxlxJMPjxriIYTWLPxV+vJMV2giAIVycuzB83TxFbel5bl4NOSW/gyJggjJVMHv90WO5p7Oy+wc2Se1J/NAHNZjOe+fG4sA711+KpRcmK7o1wPh4bDHIct5XjOGbln3gl3/tURTNKLXpUgb4aTIoX0/Hvbi8UXidH68nHkSAIwk14eN4I6H01AHg/4u8PySeHpYMk3xwokZWSJ8WHQmNpHMwpb0KNRE3C3tS1GJF5hu9zZAyYk2J9/uMvq06imZxGPB76jtoBaZPurJRIwQQ8s6gWlU3iB8AfaSSfIAjCbQjX++LeWQnC+s0Np2DoEAOlK9MHQ6flP+9zyptwvLRB+Jq/jwbpcaId6d5Cx+kNbjtVhfNJzPS4EMF3uS/K6tvw9X5RhW1cXDDmkPKFR0LBoB3Y0kO/4ItrsoXXwX5aLEgb5NB9EQRBELZx14zhQjBV1mDAp7uLhK8F6bRYPDpGWK84IB8kmS7pG9xdUA1HIe0X7M8U8X1fHBSCSBUD3rt1otJbI1wECgYVpq7FiINn6gCcT8fzwWB9qxFHLebgAHDzlKFO2R9BEAQxcAJ8NXh0/ghh/Z8t+TKpmGWSQZLVR86hzShmDqdJ+ga7ys/Yi45OM7bmyvUFrWFTdgWOnRUzmzdPGYboIJ3i+yNcAwoGFUaajp8wNBRhFhPwl9Zky8byH1tADbgEQRDuyA2ThyI+nO/3bjSY8M62fOFrU4aHCV9rajfhV4l6xPihIfC1TPGerm5BeYPB7nvNLKpDk4H3Qx4crBOs8XrDbDbjiW+PCutAXw2evzLNbnsknA8Fgwojdx3hn8DMZjN+PnpO+PvZyRH9GusnCIIgXAetWiWzYftkVxHKGvihQcaYLDsoLRXrtGpMGBoqrB2hN7hZ0sM+f2S0Va4jr67LQX2bmO18ZekYGhrxcOi7qyAdnWZsy5WO7/PB4Bf7imGQyMk8ewXJyRAEQbgzi0fHCFIy7SYz3t4oupxeO2EI1JbJ4X2na3G6ukX42tQEid6gA0rFMgu6kX2XiGubjfhgR5GwTh0UiMvHDe75BMIjoGBQQQ6eqUOjJB2fEs2n4/+3tUA4JjEyAMPCA7o9nyAIgnAPVCqGPy5KFdYrD55FYRUvzBwVpMNciXzLtxJHEqn49F47i08XVjWj0BKI+mnVmJbQt33c/V8eRKdFEocBeJ+GRrwCCgYVROo6Mm8kbwKeVdaAc5K+kMepV5AgCMIjmJEUgYtG8AFWp5nDPzacEr4m1Rz8/uBZmDr56tC4uGChb7CoplUoL9sD6T1pRlIEdFp1r8cfPFOH/adFyZsl42MRF05auN4ABYMKsilb0puRyo/vv/iLKCej91VTup0gCMKDeEqSHVxzrAwnLNqCc1OjBAmayqZ2bM2tAsD7GGcMk/QN2lFvUFoinm/FFPEjXx8WXuu0Krx+zVi77ItwPSgYVIgzNS0oqOLT8TqtCtMSw2EwmmRlgKUThjhrewRBEIQdSI8LwaJRonbfa+tzAfBDJtdmiJ/5K6SlYgf0DTa0deBAkRho9iUp8+W+M4JzFgA8felIaGjQ0Wug77RCyNLxI/h0/D82nBJkZhiDrL+EIAiC8AyeXJgCy7wItp+qEpIAyyaKweDmnEpUNvEtQ1OlfYN2mijekVcFk+UGNHZIMKJ60Qg0mcyyKlZMsA63T4+3y74I14SCQYWQ9QtaSsRSSYGJw0Kh12kcvi+CIAjCviRFB+Lq8WLg99q6HHAch4RIPSZbvOk7zRy+P1gKgA/OztvWnalpxbl65fsGZVPEfWQF/7r6BNoktnpvXZ+u+H4I14aCQQVobpeXg+elRmFTdoUwWQwA/7d4pDO2RhAEQTiAxxYkwcfiQ3+ouF4IxqSagyszS8BxHHw1akwcFib8vdJTxZ1mDlukMmepPVvQVTYa8I0kcTFhaAimWDF1THgWFAwqwM68KnR08un4tJggDArW4XVL3wgARAX6YrxEaJQgCILwLOLC/HGTxGb09fW56DRzWDxmEPS+fFWosLoFB4p4u9KpCfYLBg8X16HeYpEXHeSL0bFBPR770FeHZP7D/70lQ9G9EO4BBYMKIJvYGhmFikYDcsqbhL+7a8ZwZ2yLIAiCcCAPzRsBfx9eviW3ogmrj5bC30eDKyQqEufbh6Ti03sVnije2KVE3JPryMEzddhvCU4B4JqMIeQ/7KVQMGgj5i7p+HmpUXhxTZaw1qoZ7plJwSBBEISnE6H3lT38v7HhFIwmM26QlIrXHi9Dk6EDY4eEwM+i+1dc2yqb5LUVqQXdvF5KxA93kZJ5ZckYxfZAuBcUDNrIsdIGVDcbAQDhAT4YMzgI60+Iv4gLRkaTpyNBEISXcM+sBIT4awEAJbVtWHGgGGOHBCN1EO9I1dbRiZ+PlsFHo8LEeLF9aK9CEjMlta04VcE7ofhqVJgxIqLb477cd0Y2uEJSMt4NfedtZLNEaHpuahQ+31cMY6foQ7z8chocIQiC8BaCdFr8fk6isP7n5ny0dXTKHEnOaw7KS8XKBINS84PpieHw87nQdYSkZIiuUDBoI5tz5Qrv720vFNYjovSIDSErH4IgCG/itmnxGGTpvatqasfHu4pw9fhYYdr4aEk9csobZcHgHqWCQZktavcl4r+skkvJvH0DScl4OxQM2kB5gwEnShsB8L2BkXoflEl8iJ9YSD7EBEEQ3oZOq8Yj85OE9bvbCqBiDBdLnEpWHCjB2CHBQt/g2bo2lNS22vS+ze0mmb1ddxZ0lY0GmRtKxrAQTB5OUjLeDgWDNiAdHJkyPBz/T2JSHuirwaWjY5yxLYIgCMLJXDdxCOLD+cpQo8GE/20vwPWSUvGPh0th5jhZ3+C+07ZNFe/MqxLalEbGBGFwiN8FxzzYRUrmnZtJSoagYNAmpJIyM0aEY7/kF/maDPIhJgiC8Fa0ahX+sDBFWH+86zSSo/WItQRo9a0d2JBVgWmJyvkUy2TOuskKZhbVCjqHAHAtSckQFigYHCCGjk7syq8W1gXVLTIf4iclHwIEQRCE93H5mBikxfCCz4YOM/6zpQDXSfyKVxwoUWyIhIO8WjV/5IXB4CPfHBFe67QqvExSMoQFCgYHyJ7CGqEBNyEyAOtPlAtfm0Q+xARBEF6PSsXw1CViYuDr/cWYnhiO8xrQO/OrEeavFYSqS+sH3jdo1McIMmcReh+MGxIi+/oXe4pISoboEfpJGCCbJen4xEi9zId4+eVpztgSQRAE4WLMSY7E5Hjees5k5vDVvmJB+4/jgB8Pn8OkeNGabqBTxa0hCcLruSlRUKlE1xGTyYyX1uYIa5KSIf5/e/caHFddxnH8+yRpmt7SSem9tA2lFGhruUiLUpQCtqjcZRAUUZkiKioqFwfE6gvGC4ijgo46jpcXXl6ggiiOWlMaBqFoEWEsUAqhhV5I03sbmqQkjy/Oye7JZjfZTXf3tGd/n5nMnst/M0/bTc+Tc/7/58mkZHAI3J1VkeX7L2zbm9qeVD+cBRm/kYmISGUyM74UuTv4x2e38q4T0oWgf/f0Zs487vD7FB9sSNc2zHxErFIyMhglg0PwUuuBVOug0cNr2Lw7XU7merWeExGRiDMax6UWdLgH3UYawi4lW/YcTJWXAXiqZRfuXtD3f6t2DF2jgrI1tdVVnH3ChNS5zFIyZ8xsUCkZ6UfJ4BA0Rfo+1kfmBg6rNpYvVjIoIiJ93XrBiam5gqvWt3FWZBXxvzbtYlSfeYOF9SmOPiI+c9Y4Rg9PX5f6l5I5fYh/AkkyJYNDEJ0v2LqvM7W9dK76EIuISH8nT6nnklOmpvY3RRaKND2/nVNnpKcXFfqouM8j4khJmWylZCaqlIxkocylQLvau/jPa+kfru7I7fwVF2rhiIiIZHfz0jnUhAs7/rdlH7PGjwKgq7uHupr0o+JCksGDXd10jJ2R2j8/0oLupt8+k9oeMaxapWQkJyWDBWp+aXvqlvuw6vRqrTmTRjMlS7V3ERERgJnHjOKqhekuJNFFHRu2H0htP9myM+95g/98eQdeFcw/nDNpNNPHBV1PfvXkRrZG2qPe8f6TVEpGctIno0DRCu+HutM/rCoyLSIig7np/BOoGxZcerft7aC2Oth+bdebqYUk2/Z28Fqe9QabIpUtzjspuCuYrZTMR9/ZWIzwJaGUDBbgUHcPzS+19Ts+pq6GZfMmxxCRiIgcTSbV1/Hxs9ILDWsjd+t6VxhDfo+KgzJn6QWNvSVl7swoJXP/h047rJgl+ZQMFmDtxt3sjxSX7nWl+hCLiEiePn3O8YwJK1Ec6ExfU3o7iEB+fYrXbd2XWsRYdeggp89oYPu+Dh7IKCVzRqSotUg2SgYLEO372KvKgpIBIiIi+Rg7chifOie9Arg6XFTS1d2TOrYmj3qD0WlLI/a0UF1l3PhrlZKRwikZLEDTC639ji1sHMfIWvUhFhGR/F23uJHxo4cD0N2TTvqqw2KEb+zrYNPOgecNRmvejtzzCms37mLtpnS1iyvPmK5SMpIXJYN52rijnVfa2vsdX6E+xCIiUqCRtTV87rzZ/Y5Hy5UN1Kd4+74OntsctkLt6aZuz0Y+l1FK5uuXzi9ewJJoSgbzFO1F3GvK2DrmTxsbQzQiInK0u3rRdKYNUJJsoEUk0WlLdfs30z7+ZLZFSsncqVIyUgB9UvKULRm8/my1nhMRkaEZXlPNF95zQs7zawaoN/iPyHzBut0t7J6xJLU/dWwdH1EpGSmAksE87O84xFOv9v0Nrba6iusWN8YTkIiIJMLlp03j+Amjsp5r3dfJqzv6T0/qONTN4xt2pPY7x0zGq9Nlae5TKRkpkJLBPDy+YUefAtMAy+apD7GIiByemuoqbhmgacGall1Zju1M1RGcMW4kB8el37+wUaVkpHDKZvLQlOURsRaOiIhIMbx33mTmT6vPei7bvMFoSZmOQ91gwaW8yuCHH1YpGSmcksFB9PQ4qzPqC544aQyTtFxfRESKoKrKcrY0zexTHHQdSV+Ttu/vTG2rlIwMlZLBQTy3ZW+fqvAAtyybE1M0IiKSROfMmcCiLI932/Z30hKZN7i+dT9b9hwEwCLjrLtLpWRkyJQMDmJVRqHpevUhFhGRIjOznN2soo+Ko4+IozPZGzatVikZGbKK+OSY2WQz+76ZvWJmHWbWamZ/MrPzB3vvyuf7JoMfXDi9ZHFKZWhubqa5uTnuMKTI9O8qh2vRceNYcuKEfseji0iydcJ6a+92nnngvpLGJsmW+GTQzBYA/wNuAmYBncB44CJgpZndPtD7X3hjf/p7ATcv1SNiEREpjWxzB594eQfuzs4DnTzz+p5+59sevqccoUmCJToZNLMRwMPAMcAzwHx3Hws0AN8hyO++YWbL8vl+75h1jPoQi4hIycyfNpYL3zalz7Gd7V280tbOo+vbyKxBvbCxga6tL5YxQkmiRCeDwCeBmcAB4GJ3Xwfg7vvc/VbgIYKE8Jv5fLMVF51cqjhFREQA+OLSOX0WhwCsaWAMteEAAAWSSURBVNnBqhf7PiJWKRkplqQng9eEr79x9y1Zzn87fD3dzHJX/QQmjK5l7lT1IRYRkdKaPXE0l5wytc+xPz+3jUdfbOtz7CqVkpEiSWwyaGZjgLeHu3/LMWwNsDfcHnAxyY3nzi5SZCIiIgO77b1970+sadmV6joCUFdTxV0qJSNFkthkEDiZdBmmddkGuHsPsD7czdlSpMrgY++cWdzoREREcji2YSQXzJ2U8/yKi+eqlIwUjXnmbNSEMLNLCeYEAtS7+/4c4x4ELgP+4O5XRI4n8y9GREREEsvdM6ecDirJv1aMimwfHGDcm+Hr6BLGIiIiInJEUp2UHIaSWYuIiIgcbZJ8Z7A9sj1igHEjw9cDJYxFRERE5IiU5GRwa2R7as5R6XPbShiLiIiIyBEpycngi6T7eM/LNsDMqoDe9fvPlyMoERERkSNJYpPBcPXw2nB3aY5hZwK9laSbzOyXZuZ5fi0p9Z9Bjm5mtsDMusLPy/JBxt4bjms1s2PKFaMMXeT/i9UDjJloZs+G43aZ2cIyhigJpuuVQH7/D+Ujsclg6Dfh6zVmNiXL+VvD16fdfX3k+CGgdZCvrpJELInh7s8Bd4e79+b4DGJmi4AvhLufdfed5YhPSiv8914NLADagHPd/d+xBiVJpOuVHLakryb+CcFFdibwZzO71t2fD7uTrAA+EI77csb7nnD3JeULUxLsLoLP2Vzgh6Q/cwCYWS3wc6AaeMjdHyh7hFJ0ZjYdWAXMJpiPfL67vxBvVJJQul7JYUv0nUF3PwhcCuwETgfWmdleYA9wG8Gcwjvc/e/xRSlJ5u5dwHKgB7jczK7MGPIVgjmte4AbyxyelICZHQc8RpAIvg6co0RQRI5kiU4GAdz9WWA+cB/QAgwnSA4fAZa6+7diDE8qgLuvAe4Pd39gZuMAzOwU4Pbw+M3urhXtRzkzm0OQCDYCrwLvdvcNsQYlIjKIxCeDAO7+hrt/3t2Pd/c6d5/o7he5e1PcsUnFuJMgOZgIfM/MaggeDw8DVrr7L+IMTg6fmc0DmoFjgZcIEsGNsQYlIpKHikgGReLm7u3AJ8Lda4HfE0xdaAduiCsuKY7wLu9qYDKwjuDR8OZYgxIRyZOSQZEyCe9E/yzcvSR8vUN3j456jcCjwHjgv8ASd38j1ohERAqgZFCkvH4U2d5AsMJYjm4zgYZw+zPuviPOYERECqVkUKRMzMyAeyOHZgOLYwpHiqcF6E0AHzCz2XEGIyJSKCWDIuVzA7AE6CSYX2bAT81seIwxyeF7HXgPsJug13mTmc2MNyQRkfwpGRQpAzM7Frgn3L0LuJogeTgR+GpccUlxhCWslgF7gRnAKjObFm9UIiL5UTIoUh4/BuqBZ4G73b0VuCU8d5uZLYgtMikKd18LvA84AMwiuEM4Kd6oREQGp2RQpMTM7BrgQqAbWO7ubwGEtQWbCGoN/tTM9PN4lHP3J4GLgDcJ7vo2mdn4eKMSERmYLj4iJWRmE4DvhbvfcfenM4bcQJA4LAI+X87YpDTcvRm4jGBu6DxgpZk1DPwuEZH4KBkUKa37CerPbQC+lnnS3Vsix+8ys8ayRSYl4+4rgSuALuBU4K9mVh9vVCKSYMPMbPwgX8NyvVnJoEiJmNklwFWAA9e7e0eOod8FngZGEcwtlARw90cIFgq9RXDn9y9mNireqEQkoc4C2gb5ylnKTMmgSAmY2VjSBaZ/4u6P5Rrr7t3AcoKk4QIzu7YMIUoZuPuDwEcI5osuBv5kZiPijUpEpC9z97hjEBEREZGY6M6giIiISAVTMigiIiJSwZQMioiIiFQwJYMiIiIiFUzJoIiIiEgFUzIoIiIiUsGUDIqIiIhUMCWDIiIiIhVMyaCIiIhIBVMyKCIiIlLBlAyKiIiIVLD/A1yhXdtx+vACAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 720x720 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(10,10))\n", "set_fig_properties([gca()])\n", "vlines(sym_points, ymin=0, ymax=17)\n", "plot(linear_path, nu, color='C0',lw=3)\n", "xlim([0, max(linear_path)])\n", "gca().set_xticks(sym_points)\n", "gca().set_xticklabels([r'$\\Gamma$','X', 'K', r'$\\Gamma$', 'L'])\n", "ylim([0, 17])\n", "ylabel(r'$\\nu$ (THz)')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phonon dispersion of silicon crystal described by the mini-Tersoff potential." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The above figure shows the phonon dispersion of silicon crystal described by the mini-Tersoff potential [[Fan 2020]](https://doi.org/10.1088/1361-648X/ab5c5f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. References\n", "[Fan 2020] Zheyong Fan, Yanzhou Wang, Xiaokun Gu, Ping Qian, Yanjing Su, and Tapio Ala-Nissila, [A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties](https://doi.org/10.1088/1361-648X/ab5c5f), J. Phys.: Condens. Matter **32** 135901 (2020)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

tritemio/multispot_paper

out_notebooks/usALEX-5samples-PR-raw-out-all-ph-22d.ipynb

1

474306

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Executed:** Mon Mar 27 11:34:36 2017\n", "\n", "**Duration:** 8 seconds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# usALEX-5samples - Template\n", "\n", "> *This notebook is executed through [8-spots paper analysis](8-spots paper analysis.ipynb).*\n", "> *For a direct execution, uncomment the cell below.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ph_sel_name = \"all-ph\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_id = \"22d\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ph_sel_name = \"all-ph\"\n", "# data_id = \"7d\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load software and filenames definitions" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.5.9).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import *" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_notebook()\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data folder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_dir = './data/singlespot/'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "data_dir = os.path.abspath(data_dir) + '/'\n", "assert os.path.exists(data_dir), \"Path '%s' does not exist.\" % data_dir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List of data files:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',\n", " '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',\n", " '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',\n", " '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',\n", " '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from glob import glob\n", "file_list = sorted(f for f in glob(data_dir + '*.hdf5') if '_BKG' not in f)\n", "## Selection for POLIMI 2012-11-26 datatset\n", "labels = ['17d', '27d', '7d', '12d', '22d']\n", "files_dict = {lab: fname for lab, fname in zip(labels, file_list)}\n", "files_dict" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('22d', 'all-ph')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ph_sel_map = {'all-ph': Ph_sel('all'), 'Dex': Ph_sel(Dex='DAem'), \n", " 'DexDem': Ph_sel(Dex='Dem')}\n", "ph_sel = ph_sel_map[ph_sel_name]\n", "\n", "data_id, ph_sel_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initial loading of the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = loader.photon_hdf5(filename=files_dict[data_id])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Laser alternation selection\n", "\n", "At this point we have only the timestamps and the detector numbers:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([array([ 16030, 90752, 117066, ..., 47999910064,\n", " 47999915269, 47999979355])],\n", " [array([1, 1, 1, ..., 1, 0, 0], dtype=uint32)])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.ph_times_t, d.det_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should check if everithing is OK with an alternation histogram:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JojN7DR9uo+Y6EKqizutQfZr6mFg+Fx3jO0WVx5qJdbavqPBhmRbbyrexrbzm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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d23aef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_alternation_hist(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the plot looks good we can apply the parameters with:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Total photons (after ALEX selection): 2,103,572\n", "# D photons in D+A excitation periods: 691,365\n", "# A photons in D+A excitation periods: 1,412,207\n", "# D+A photons in D excitation period: 1,237,157\n", "# D+A photons in A excitation period: 866,415\n", "\n" ] } ], "source": [ "loader.alex_apply_period(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measurements infos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the measurement data is in the `d` variable. We can print it:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "singlespot_008_dsDNA_22d_500pM_green100u_red40u G1.000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or check the **measurements duration**:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "599.99974193749995" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.time_max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the background using automatic threshold:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x103bc9860>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uxKZNm5CYmIjatWvjs88+g0KhEA8QAMu1KNbej2rVqonf6Z9//tmteh09ehS1atVC1apV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103acfa20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d, timetrace_bg)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([2217.7255529583294],\n", " [591.3055432410448],\n", " [818.69564582171483],\n", " [776.94465829541809])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst search and selection" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Calculating burst periods ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Counting D and A ph and calculating FRET ... \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying background correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying leakage correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying direct excitation correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [DONE Counting D/A]\n" ] } ], "source": [ "bs_kws = dict(L=10, m=10, F=7, ph_sel=ph_sel)\n", "d.burst_search(**bs_kws)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "th1 = 30\n", "ds = d.select_bursts(select_bursts.size, th1=30)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bursts = (bext.burst_data(ds, include_bg=True, include_ph_index=True)\n", " .round({'E': 6, 'S': 6, 'bg_d': 3, 'bg_a': 3, 'bg_aa': 3, 'nd': 3, 'na': 3, 'naa': 3, 'nda': 3, 'nt': 3, 'width_ms': 4}))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>E</th>\n", " <th>S</th>\n", " <th>bg_a</th>\n", " <th>bg_aa</th>\n", " <th>bg_d</th>\n", " <th>bp</th>\n", " <th>i_end</th>\n", " <th>i_start</th>\n", " <th>na</th>\n", " <th>naa</th>\n", " <th>nd</th>\n", " <th>nda</th>\n", " <th>nt</th>\n", " <th>size_raw</th>\n", " <th>t_end</th>\n", " <th>t_start</th>\n", " <th>width_ms</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.102133</td>\n", " <td>0.544892</td>\n", " <td>1.233</td>\n", " <td>1.192</td>\n", " <td>0.881</td>\n", " <td>0</td>\n", " <td>243</td>\n", " <td>173</td>\n", " <td>3.767</td>\n", " <td>30.808</td>\n", " <td>33.119</td>\n", " <td>-0.083</td>\n", " <td>67.694</td>\n", " <td>71</td>\n", " <td>0.018848</td>\n", " <td>0.017377</td>\n", " <td>1.4709</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.309349</td>\n", " <td>0.376346</td>\n", " <td>2.651</td>\n", " <td>2.563</td>\n", " <td>1.895</td>\n", " <td>0</td>\n", " <td>671</td>\n", " <td>576</td>\n", " <td>10.349</td>\n", " <td>55.437</td>\n", " <td>23.105</td>\n", " <td>-0.180</td>\n", " <td>88.890</td>\n", " <td>96</td>\n", " <td>0.075501</td>\n", " <td>0.072338</td>\n", " <td>3.1635</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.290108</td>\n", " <td>0.831108</td>\n", " <td>1.107</td>\n", " <td>1.070</td>\n", " <td>0.791</td>\n", " <td>0</td>\n", " <td>992</td>\n", " <td>949</td>\n", " <td>9.893</td>\n", " <td>6.930</td>\n", " <td>24.209</td>\n", " <td>-0.075</td>\n", " <td>41.032</td>\n", " <td>44</td>\n", " <td>0.097472</td>\n", " <td>0.096152</td>\n", " <td>1.3205</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.181006</td>\n", " <td>0.602956</td>\n", " <td>1.640</td>\n", " <td>1.585</td>\n", " <td>1.172</td>\n", " <td>0</td>\n", " <td>1970</td>\n", " <td>1890</td>\n", " <td>8.360</td>\n", " <td>30.415</td>\n", " <td>37.828</td>\n", " <td>-0.111</td>\n", " <td>76.603</td>\n", " <td>81</td>\n", " <td>0.291411</td>\n", " <td>0.289455</td>\n", " <td>1.9565</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.046636</td>\n", " <td>0.927752</td>\n", " <td>1.436</td>\n", " <td>1.388</td>\n", " <td>1.027</td>\n", " <td>0</td>\n", " <td>3166</td>\n", " <td>3127</td>\n", " <td>1.564</td>\n", " <td>2.612</td>\n", " <td>31.973</td>\n", " <td>-0.097</td>\n", " <td>36.149</td>\n", " <td>40</td>\n", " <td>0.666337</td>\n", " <td>0.664623</td>\n", " <td>1.7134</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " E S bg_a bg_aa bg_d bp i_end i_start na \\\n", "0 0.102133 0.544892 1.233 1.192 0.881 0 243 173 3.767 \n", "1 0.309349 0.376346 2.651 2.563 1.895 0 671 576 10.349 \n", "2 0.290108 0.831108 1.107 1.070 0.791 0 992 949 9.893 \n", "3 0.181006 0.602956 1.640 1.585 1.172 0 1970 1890 8.360 \n", "4 0.046636 0.927752 1.436 1.388 1.027 0 3166 3127 1.564 \n", "\n", " naa nd nda nt size_raw t_end t_start width_ms \n", "0 30.808 33.119 -0.083 67.694 71 0.018848 0.017377 1.4709 \n", "1 55.437 23.105 -0.180 88.890 96 0.075501 0.072338 3.1635 \n", "2 6.930 24.209 -0.075 41.032 44 0.097472 0.096152 1.3205 \n", "3 30.415 37.828 -0.111 76.603 81 0.291411 0.289455 1.9565 \n", "4 2.612 31.973 -0.097 36.149 40 0.666337 0.664623 1.7134 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bursts.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'results/bursts_usALEX_22d_all_F7.0_m10_size30.csv'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "burst_fname = ('results/bursts_usALEX_{sample}_{ph_sel}_F{F:.1f}_m{m}_size{th}.csv'\n", " .format(sample=data_id, th=th1, **bs_kws))\n", "burst_fname" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bursts.to_csv(burst_fname)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert d.dir_ex == 0\n", "assert d.leakage == 0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n" ] }, { "data": { "image/png": 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RYfafOUNE5JWY3MlnGI1GAH4ICuqKtip/q/WuqxtRU38VRiN770TUOjG5k89R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\nTO5EREQSw+ROREQkMUzuREREEvP/AJzAOVRdPP8QAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103be96a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(d.ph_sel)\n", "dplot(d, hist_fret);" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# if data_id in ['7d', '27d']:\n", "# ds = d.select_bursts(select_bursts.size, th1=20)\n", "# else:\n", "# ds = d.select_bursts(select_bursts.size, th1=30)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_bursts_all = ds.num_bursts[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def select_and_plot_ES(fret_sel, do_sel):\n", " ds_fret= ds.select_bursts(select_bursts.ES, **fret_sel)\n", " ds_do = ds.select_bursts(select_bursts.ES, **do_sel)\n", " bpl.plot_ES_selection(ax, **fret_sel)\n", " bpl.plot_ES_selection(ax, **do_sel) \n", " return ds_fret, ds_do" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Y8HZPf/MlkU84uXuFEAjb9uMFx84in7gC/yXbBq+8ufSkAEEJoKmpifq6um4u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\nQvMg1OCkIkyqPJWKkZ0mtTKi6lKlh6j6VM5EFZEnW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"text/plain": [ "<matplotlib.figure.Figure at 0x1214e19e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1)\n", "\n", "if data_id == '7d':\n", " fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True) \n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", " \n", "elif data_id == '12d':\n", " fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '17d':\n", " fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '22d':\n", " fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) \n", "\n", "elif data_id == '27d':\n", " fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(403, 2045)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_bursts_do = ds_do.num_bursts[0]\n", "n_bursts_fret = ds_fret.num_bursts[0]\n", "\n", "n_bursts_do, n_bursts_fret" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D-only fraction: 0.164624183007\n" ] } ], "source": [ "d_only_frac = 1.*n_bursts_do/(n_bursts_do + n_bursts_fret)\n", "print ('D-only fraction:', d_only_frac)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hFWj9CJU00qiw8M9DkOFEMYWoQR8XL6r8MEOLc/+E80tpjKcxfB0uY3egsT/0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\nW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"text/plain": [ "<matplotlib.figure.Figure at 0x1214df0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JMu4I7vD/amgSVw9HNbNF2oAR17LiaeE3rZQ/+CcGb9AEHOZfqzVKSZ+API1h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\nKYCuEqQJw/Ranc8FtylJ0GREZXJJgiYYm50cR86eQuOYdD9yr6PkaFv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PAAAB\ngUlEQVTZxhHGBhVr1qxBR0cH6uvrI9X/5JNPYvPmzeju7saoUaOwYsUKfPjDH45E//nz5/Hggw/i\n4MGDKC4uxujRo7F06VIMGjTIiX4V9fX1GDlyJL74xS9i3759kX0OBemCbFwCgUAgSBWkVSgQCASC\nVEE2LoFAIBCkCrJxCQQCgSBVkI1LIBAIBKmCbFwCgUAgSBVk4xIIBAJBqjAwbgMEgkLCkSNHcOut\nt+K6667L/t9ZJpPBlVdeiccffzxu8wSCgoBsXAKBYwwePBg7d+6M2wyBoGAhrUKBQCAQpApyxiUQ\nOMapU6dwxx13AEC2XTh58mR85StfidkygaAwIBuXQOAY0ioUCPxCWoUCgUAgSBVk4xIIHEO+t1og\n8AtpFQoEjnHmzJnsNS7gg+tcGzduxOWXXx6jZQJBYUB+1kQgEAgEqYK0CgUCgUCQKsjGJRAIBIJU\nQTYugUAgEKQKsnEJBAKBIFWQjUsgEAgEqYJsXAKBQCBIFWTjEggEAkGq8P8MPe79fUOhYQAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1248b4c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Donor Leakage fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Half-Sample Mode\n", "\n", "Fit peak usng the mode computed with the half-sample algorithm ([Bickel 2005](http://arxiv.org/abs/math/0505419))." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hsm_mode(s):\n", " \"\"\"\n", " Half-sample mode (HSM) estimator of `s`.\n", "\n", " `s` is a sample from a continuous distribution with a single peak.\n", " \n", " Reference:\n", " Bickel, Fruehwirth (2005). arXiv:math/0505419\n", " \"\"\"\n", " s = memoryview(np.sort(s))\n", " i1 = 0\n", " i2 = len(s)\n", "\n", " while i2 - i1 > 3:\n", " n = (i2 - i1) // 2\n", " w = [s[n-1+i+i1] - s[i+i1] for i in range(n)]\n", " i1 = w.index(min(w)) + i1\n", " i2 = i1 + n\n", "\n", " if i2 - i1 == 3:\n", " if s[i1+1] - s[i1] < s[i2] - s[i1 + 1]:\n", " i2 -= 1\n", " elif s[i1+1] - s[i1] > s[i2] - s[i1 + 1]:\n", " i1 += 1\n", " else:\n", " i1 = i2 = i1 + 1\n", "\n", " return 0.5*(s[i1] + s[i2])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all: E_peak(HSM) = 8.86%\n" ] } ], "source": [ "E_pr_do_hsm = hsm_mode(ds_do.E[0])\n", "print (\"%s: E_peak(HSM) = %.2f%%\" % (ds.ph_sel, E_pr_do_hsm*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Fit\n", "\n", "Fit the histogram with a gaussian:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_fitter = bext.bursts_fitter(ds_do, weights=None)\n", "E_fitter.histogram(bins=np.arange(-0.2, 1, 0.03))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.918698</td>\n", " <td>0.0911228</td>\n", " <td>0.0517342</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 0.918698 0.0911228 0.0517342" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_fitter.fit_histogram(model=mfit.factory_gaussian())\n", "E_fitter.params" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr\n", "amplitude 0.9187 -inf inf 0.02428 True None\n", "center 0.09112 -1 2 0.001579 True None\n", "fwhm 0.1218 -inf inf 0.003718 False 2.3548200*sigma\n", "height 7.084 -inf inf 0.1873 False 0.3989423*amplitude/max(1.e-15, sigma)\n", "sigma 0.05173 0 inf 0.001579 True None\n" ] } ], "source": [ "res = E_fitter.fit_res[0]\n", "res.params.pretty_print()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.09112284426324946" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_pr_do_gauss = res.best_values['center']\n", "E_pr_do_gauss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## KDE maximum" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.092200000000008331" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bandwidth = 0.03\n", "E_range_do = (-0.1, 0.15)\n", "E_ax = np.r_[-0.2:0.401:0.0002]\n", "\n", "E_fitter.calc_kde(bandwidth=bandwidth)\n", "E_fitter.find_kde_max(E_ax, xmin=E_range_do[0], xmax=E_range_do[1])\n", "E_pr_do_kde = E_fitter.kde_max_pos[0]\n", "E_pr_do_kde" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Leakage summary" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gauss: 9.11%\n", " KDE: 9.22%\n", " HSM: 8.86%\n" ] }, { "data": { "image/png": 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kMvHcc8+NVmxjWlGVnZNNTvKm6YZURbS9vY38/M8wm7uZMSONpKSU4RtIdrn6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\nsoIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1214d9780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, plot_model=False)\n", "plt.axvline(E_pr_do_hsm, color='m', label='HSM')\n", "plt.axvline(E_pr_do_gauss, color='k', label='Gauss')\n", "plt.axvline(E_pr_do_kde, color='r', label='KDE')\n", "plt.xlim(0, 0.3)\n", "plt.legend()\n", "print('Gauss: %.2f%%\\n KDE: %.2f%%\\n HSM: %.2f%%' % \n", " (E_pr_do_gauss*100, E_pr_do_kde*100, E_pr_do_hsm*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst size distribution" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nt_th1 = 50" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x124d56518>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EpAEsLZth1ar1ant+DiokIiIiJgRERETEhICIiIjAhICIiIjAhICIiIjAhICI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\nERERVBCIiIgIKghEREQE+BNhkenh299N2wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124b3d358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist_size, which='all', add_naa=False)\n", "xlim(-0, 250)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Th_nt = np.arange(35, 120)\n", "nt_th = np.zeros(Th_nt.size)\n", "for i, th in enumerate(Th_nt):\n", " ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th)\n", " nt_th[i] = (ds_nt.nd[0] + ds_nt.na[0]).mean() - th" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x124c68710>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wUbERU2ssvOasmxWjXqEkzyAzRjFgVToCpBkgxxb/MJHsZzJKYEwgt0/jiyMe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\nCCHECiVGQgghxMr/A0i/zO0j9aCzAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124c68c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plot(Th_nt, nt_th)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "25.373585409805187" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]\n", "nt_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fret fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')\n", "E_fitter = ds_fret.E_fitter" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr\n", "amplitude 0.9743 -inf inf 0.01849 True None\n", "center 0.2799 -1 2 0.001552 True None\n", "fwhm 0.1668 -inf inf 0.003655 False 2.3548200*sigma\n", "height 5.489 -inf inf 0.1042 False 0.3989423*amplitude/max(1.e-15, sigma)\n", "sigma 0.07082 0 inf 0.001552 True None\n" ] } ], "source": [ "E_fitter.fit_res[0].params.pretty_print()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 27.66 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>97.4325</td>\n", " <td>27.9934</td>\n", " <td>7.08175</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 97.4325 27.9934 7.08175" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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xObZdiFmQYrvG5E0L7voFrDvtjKK1We9S8becAMDu3a8VrV0hhBDFNZo1UfQE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\nazvyBwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124b77da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(E_fitter, ax=ax[0])\n", "mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde*100))\n", "display(E_fitter.params*100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Weighted mean of $E$ of each burst:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28564446])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_m(weights='size')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (no weights):" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28192807])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (using burst size as weights):" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.28014618])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.27660000000001367,\n", " 0.27993427838071305,\n", " 0.07081746653125176,\n", " 0.0015660071110426567,\n", " 0.001552115101731099)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_kde_w = E_fitter.kde_max_pos[0]\n", "E_gauss_w = E_fitter.params.loc[0, 'center']\n", "E_gauss_w_sig = E_fitter.params.loc[0, 'sigma']\n", "E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "E_gauss_w_fiterr = E_fitter.fit_res[0].params['center'].stderr\n", "E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err, E_gauss_w_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stoichiometry fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True)\n", "S_fitter = ds_fret.S_fitter" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 56.28 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>102.385</td>\n", " <td>56.4196</td>\n", " <td>11.286</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 102.385 56.4196 11.286" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GkpOTu2yzceNGFixYgMlkIj4+nqKiItasWRPW0EL0RknJaXQ6HSOmLiIn1UCy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\nCCGEgaQhF0IIIYQQwkD/P/I9Zki36whaAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1249cde10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(S_fitter, ax=ax[0])\n", "mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde*100))\n", "display(S_fitter.params*100)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.56280000000002195,\n", " 0.5641960660315466,\n", " 0.11285967014629783,\n", " 0.0024956985141657965,\n", " 0.001895906952335234)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S_kde = S_fitter.kde_max_pos[0]\n", "S_gauss = S_fitter.params.loc[0, 'center']\n", "S_gauss_sig = S_fitter.params.loc[0, 'sigma']\n", "S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "S_gauss_fiterr = S_fitter.fit_res[0].params['center'].stderr\n", "S_kde, S_gauss, S_gauss_sig, S_gauss_err, S_gauss_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Maximum likelihood fit for a Gaussian population is the mean:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.56308872263728393, 0.10287080389066304)" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S = ds_fret.S[0]\n", "S_ml_fit = (S.mean(), S.std())\n", "S_ml_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the weighted mean and weighted standard deviation we get:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.55094283590005966, 0.10118832937353994]" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.)\n", "S_mean = np.dot(weights, S)/weights.sum()\n", "S_std_dev = np.sqrt(\n", " np.dot(weights, (S - S_mean)**2)/weights.sum())\n", "S_wmean_fit = [S_mean, S_std_dev]\n", "S_wmean_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save data to file" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample = data_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following string contains the list of variables to be saved. When saving, the order of the variables is preserved." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "variables = ('sample n_bursts_all n_bursts_do n_bursts_fret '\n", " 'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err E_gauss_w_fiterr '\n", " 'S_kde S_gauss S_gauss_sig S_gauss_err S_gauss_fiterr '\n", " 'E_pr_do_kde E_pr_do_hsm E_pr_do_gauss nt_mean\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just a trick to format the different variables:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample,n_bursts_all,n_bursts_do,n_bursts_fret,E_kde_w,E_gauss_w,E_gauss_w_sig,E_gauss_w_err,E_gauss_w_fiterr,S_kde,S_gauss,S_gauss_sig,S_gauss_err,S_gauss_fiterr,E_pr_do_kde,E_pr_do_hsm,E_pr_do_gauss,nt_mean\n", "\n", "22d, 2552, 403, 2045, 0.276600, 0.279934, 0.070817, 0.001566, 0.001552, 0.562800, 0.564196, 0.112860, 0.002496, 0.001896, 0.092200, 0.088573, 0.091123, 25.373585\n", "\n" ] } ], "source": [ "variables_csv = variables.replace(' ', ',')\n", "fmt_float = '{%s:.6f}'\n", "fmt_int = '{%s:d}'\n", "fmt_str = '{%s}'\n", "fmt_dict = {**{'sample': fmt_str}, \n", " **{k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}\n", "var_dict = {name: eval(name) for name in variables.split()}\n", "var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\\n'\n", "data_str = var_fmt.format(**var_dict)\n", "\n", "print(variables_csv)\n", "print(data_str)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NOTE: The file name should be the notebook name but with .csv extension\n", "with open('results/usALEX-5samples-PR-raw-%s.csv' % ph_sel_name, 'a') as f:\n", " f.seek(0, 2)\n", " if f.tell() == 0:\n", " f.write(variables_csv)\n", " f.write(data_str)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }

mit

boffi/boffi.github.io

dati_2017/hw03/01.ipynb

2

375650

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization\n", "#### Notebook stuff" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '01.css'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-7f9575a7d9b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLatex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'01.css'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '01.css'" ] } ], "source": [ "from IPython.display import display, Latex, HTML\n", "display(HTML(open('01.css').read()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Numpy and Scipy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "init_cell": true }, "outputs": [], "source": [ "import numpy as np\n", "from numpy import array, cos, diag, eye, linspace, pi\n", "from numpy import poly1d, sign, sin, sqrt, where, zeros\n", "from scipy.linalg import eigh, inv, det" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Matplotlib" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "init_cell": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-paper')\n", "plt.rcParams['figure.dpi'] = 115\n", "plt.rcParams['figure.figsize'] = (7.5, 2.5)\n", "plt.rcParams['axes.grid'] = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Miscellaneous definitions\n", "\n", "In the following `ld` and `pmat` are used to display mathematical formulas generated by the program, `rounder` ensures that a floating point number _close_ to an integer will be rounded correctly when formatted as an integer, `p` is a shorthand to calling `poly1d` that is long and requires a single argument, `vw` computes the virtual work done by moments `m` for the curvatures `c`, when the lengths of the beams are `l` and eventually\n", "`p0_p1` given an array of values `p` returns first `p[0], p[1]` then `p[1], p[2]` then..." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "init_cell": true }, "outputs": [], "source": [ "def ld(*items): \n", " display(Latex('$$' + ' '.join(items) + '$$'))\n", "def pmat(mat, env='bmatrix', fmt='%+f'):\n", " opener = '\\\\begin{'+env+'}\\n '\n", " closer = '\\n\\\\end{'+env+'}'\n", " formatted = '\\\\\\\\\\n '.join('&'.join(fmt%elt for elt in row) for row in mat)\n", " return opener+formatted+closer\n", "def rounder(mat): return mat+0.01*sign(mat)\n", "def p(*l): return poly1d(l)\n", "def vw(emme, chi, L):\n", " return sum(((m*c).integ()(l)-(m*c).integ()(0)) for (m, c, l) in zip(emme, chi, L))\n", "def p0_p1(p):\n", " from itertools import tee\n", " a, b = tee(p)\n", " next(b, None)\n", " return zip(a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 DOF System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input motion\n", "\n", "We need the imposed displacement, the imposed velocity (an intermediate result) and the imposed acceleration. It is convenient to express these quantities in terms of an adimensional time coordinate $a = \\omega_0 t$,\n", "\n", "\\begin{align}\n", " u &= \\frac{4/3\\omega_0 t - \\sin(4/3\\omega_0 t)}{2\\pi}\n", " = \\frac{\\lambda_0 a- \\sin(\\lambda_0 a)}{2\\pi},\\\\\n", " \\dot{u} &= \\frac{4}{3}\\omega_0 \\frac{1-\\cos(4/3\\omega_0t)}{2\\pi}\n", " = \\lambda_0 \\omega_0 \\frac{1-\\cos(\\lambda_0 a)}{2\\pi},\\\\\n", " \\ddot{u} &= \\frac{16}{9}\\omega_0^2 \\frac{\\sin(4/3\\omega_0t)}{2\\pi}\n", " = \\lambda_0^2\\omega_0^2 \\frac{\\sin(\\lambda_0 a)}{2\\pi},\n", "\\end{align}\n", "\n", "with $\\lambda_0=4/3$.\n", "\n", "The equations above are valid in the interval \n", "\n", "$$ 0 \\le t \\le \\frac{2\\pi}{4/3 \\omega_0} \\rightarrow\n", " 0 \\le a \\le \\frac{3\\pi}2 $$\n", " \n", "(we have multiplied all terms by $\\omega_0$ and simplified the last term).\n", "Following a similar reasoning, the plotting interval is equal to $0\\le a\\le2\\pi$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l0 = 4/3\n", "# define a function to get back the time array and the 3 dependent vars\n", "def a_uA_vA_aA(t0, t1, npoints):\n", " a = linspace(t0, t1, npoints)\n", " uA = where(a<3*pi/2, (l0*a-sin(l0*a))/2/pi, 1)\n", " vA = where(a<3*pi/2, (1-cos(l0*a))/2/pi, 0)\n", " aA = where(a<3*pi/2, 16*sin(l0*a)/18/pi, 0)\n", " return a, uA, vA, aA\n", "# and use it\n", "a, uA, vA, aA = a_uA_vA_aA(0, 2*pi, 501)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The plots" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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35WG2TbYr8AXR5c7mBnevMbN6oKXfLx8AfA44E3gN+AzwpJlNcPeSVhzvNuCB\nuLZhwIzi4mKKiorCxt8u1dXVzJ49m+LiYnJzczv12KlI+Wo95Sqcjs7Xkk2V3PTwQjZV1ZGRZlx3\nxig+PanrXCDQ9ysc5Ssc5Ssc5SucROarpKQ1pWrLkq2Ar4guC4FS2HUFPgso30P/1939pej7v5nZ\nd4DTgT+20H837l4GlMW2NU/dlpeXR35+fhs+Qvvl5uYm7NipSPlqPeUqnI7I14tLN/PNB+dRVd9E\nQY8Mpn/+CI4b1beDIkwu+n6Fo3yFo3yFo3yFk4h85eXtcY6WfUqqAt7dd5jZWmAysCTaPBmoBZa1\nsMm7QPyTTpJnTJCIdGv3v76a6/+5kIjDkF45/PVLRzJ6QMG+NxQREdmLpCrgo+4Crjazl4Ea4Ebg\ngRZuYAW4E3jWzI4BXgfOByYAz3ZWsCIi8Zoizi//s5i7Zq0E4PADe3HXF6bQr6BHgiMTEZGuIBkL\n+JsIZoaZTxDfk8AVAGZ2DXCxu48DcPfXzGwqcB8wEFgKnOPuKxIRuIhIdX0j33l4Hs+/twmAj48b\nyLQLJ5KTFf/LQhERkbZJugLe3RsJCvYrWlh3E0GBH9v2IPBg50QnIrJnm8tr+fK9c1iwPrgP//+d\nMJIffvxQ0jTVjIiIdKCkK+BFRFLRko3lXPbXt9iws5b0NONn54zj4o8MT3RYIiLSBYUu4M3sJ8Bs\nd3/ezHoDVwGDgUXAw+6+roNjFBFJai+9v4VvPDiXyrpG8ntk8IeLJ3Pi6H6JDktERLqotDZs8zVg\nY/TP/wDOBg4GrgVWmNmHhr6IiHRVf3tjDZfd8xaVdY0MLszm0anFKt5FRGS/assQmj7AVjMrIrgS\nfx2AmWUAXwKmmdkqd3+yA+MUEUkqkYhz87NL+NPLwT3zE4YUcvcXp9C/Z3aCIxMRka6uLQV8GUER\nfyxwR3Nj9ObTP5tZOvADgtljRES6nJr6Jr73yDyeWRj8MvLUsQP4/UUTyc3SbUUiIrL/teV/mxeA\nacBwYA6wPm79DODmdsYlIpKUtlTUcfl9c3h37Q4ALj9uBFd/YgzpmmlGREQ6SVvGwF8JlBPMuX6M\nmV1kZpkx688BtnREcCIiyWTZpgrO/cOrvLt2B2kGPz93PD/+5FgV7yIi0qlCX4F39y0ETzzFzNKA\nW4E7zWwZkAeMAq7uyCBFRBJt1rKtTH3wbSpqG8nLSuf2iyfz0UP6JzosERHphto1YNPdI8AVZnY3\ncB5wAPBkOmU1AAAgAElEQVRTd3+oI4ITEUkGf39rDdc+sZDGiDOoMJu7v3gkYwf3THRYIiLSTbVl\nHvhbgSeAV6MFPO6+AFjQwbGJiCRUxIOZZqbPLAFg3OCe/OXSIxmgmWZERCSB2nIFPgd4GMgys6cJ\nZpt5zt1rOjQyEZEEqm+C7z++mOcWB7f0nDKmP7+/aBJ5PTTTjIiIJFbom1jdfaq7DwHOJJiB5kaC\neeH/ZWaXmZmeYCIiKW1bVT1/eC99V/H+pWMP4k+XTFHxLiIiSaEts9AA4O5vuvu17j4eOBx4CbgU\nWGdms8zsKjMb0kFxioh0iuWbK7j4r++wqtJIM7jhrLFcf9Y4zTQjIiJJo80FfCx3X+7uv3X3E4Ch\nwF+A44DPdsT+RUQ6w2vLt3LeH19j3Y5astKc2y4Yz6XHjkh0WCIiIrvp8N8HR6eZ/Avwl+g0kyIi\nSe+ROWu55vEFNEac/gVZfHFENSeNOiDRYYmIiHxIhxTwZmbAwcC4mNd4YCSQ3xHHEBHZHyIR59bn\n3+f2F5cDMGZQT/7vM2NZ9NasBEcmIiLSsnYX8GY2DxgCrAKWAIuBjwEnACvau38Rkf2ltqGJ7z86\nn6fe3QDARw/px/99bjI01LIowbGJiIjsSUdcgX8HqAeud/dnAMzsK+7+fgfsW0Rkv9hWWcdX73+b\nt1dvB+ALxcP5ySfHkpGeRmVDgoMTERHZi3YX8O7+JTMbDfzczK4GfgJ4uyMTEdlPSrZUctk9b7F6\nWzVmcN2ZY/nSsQcRjAYUERFJbh0yBj56tf1CM5tIMC/8QDM7xt1f64j9i4h0lNdXbOP/3f82O2sa\nyMlM5/cXTeS0cQMTHZaIiEirtbmAN7PR8cNk3H0e8EkzOwa40czc3U9ub5AiIh3h8bnr+OFj82lo\ncvoX9ODuLx7JhKGFiQ5LREQklPZcgV9iZlXAQmAe8G50OT965f1jZnZKB8QoItIu7s6059/ntv8F\nM80cOrCAv1x6JIN75SQ4MhERkfDaU8AfCEyKviYC1xA8xCliZiXufoi7v9ABMYqItFn8TDMnju7H\n7Z+bREF2ZoIjExERaZs2F/Duvh5YD/y7uc3MTgDuBB5qf2giIu2zrbKOr9w3h7lrdgBwydHDuf6s\nYKYZERGRVNWhT2J195fN7PPAdzpyvyIiYS3fXMGX7nmLtWU1mmlGRES6lPbcxJrh7o3x7e4+J3ol\nXkQkIV5dvpWvPfA2FbWN5Galc9tFkzhl7IBEhyUiItIh2nMFvsrMFrH7DazLgClAbgfEJiIS2sNv\nruHHTy6kMeIM7JnNXV+cwvghmmlGRES6jvYU8GcS3Lw6EfgqMBpII3iI0zXtD01EpPUiEefm55bw\np5dWADBucE/u/uKRDCzMTnBkIiIiHas9N7G+AOyaZcbMsoEiYKu7b+qA2EREWqWmvonv/n0ezy7a\nCMApYwbw+4smktejQ2/zERERSQodNhWDu9e6+6L2Fu9mlmFm08xsm5ntNLN7zSyvFds9bGZuZlPa\nc3wRSS2by2u58M7Zu4r3y48bwZ8uOULFu4iIdFnJOJfaNcBpBENzRgLDgWl728DMzgH67v/QRCSZ\nLNlYzrl/eJX563aSnmb8/Nzx/PiTY0lP00wzIiLSdSXjJarLgavdfS2AmV0LvGBm33H3mvjOZtYL\n+C1wOrA8zIHMrA/QJ655GEBVVRWVlZVtCL/tqqurd1vK3ilfrdcVc/XK8m1c9fhiquqbyMtK59ZP\nj+XYoj4d8u+2K+Zrf1K+wlG+wlG+wlG+wklkvqqqqtq8rbl7B4bSPtFifDswxt2XRNtygGrgcHef\n38I2fwGWuvvNZubAke4+p5XHuwG4vqV106dPZ9CgQW37ICKy37jDKxuNx1el4Ri9s5yvjmlisOa+\nEhGRFFJaWsrUqVMBRrl7qIvQyXYFviC63Nnc4O41ZlYP9IzvbGanApMIZsFpi9uAB+LahgEziouL\nKSoqauNu26a6uprZs2dTXFxMbq6qkX1Rvlqvq+SqoSnCTc8u57FVpQCMH1zA7ReMp29+Vocep6vk\nq7MoX+EoX+EoX+EoX+EkMl8lJSVt3jbZCviK6LIQKIVdV+CzgPLYjtEbW6cDF7X0QKnWcPcyoCxu\nvwDk5eWRn5/flt22W25ubsKOnYqUr9ZL5Vxtr6pn6t/f5vUVwT/Zsw4fzC3nH0Z2Zvp+O2Yq5ysR\nlK9wlK9wlK9wlK9wEpGvvLx9ztGyR0l1E6u77wDWApNjmicDtQQPiYo1ChgBPGtmW81sa7T9BTO7\nbr8HKyKdZtmmCs75w6u7ivcrTx3NbRdN3K/Fu4iISLJKtivwAHcBV5vZy0ANcCPwQAs3sC4imKEm\n1lrgc8Cs/R6liHSKF5du5tt/e4eKukZyMtO59YLDOWOC7k8REZHuKxkL+JsIZoaZTxDfk8AVAGZ2\nDXCxu49z9wZgXeyG0eEvm919t+E2IpJ63J27Z63kpv8sJuIwqDCbP39hCuOHFCY6NBERkYRKugI+\nOp79iugrft1NBAX+nrbV5M8iXUB9Y4TrnlzI3+esBWDigb248wtH0L8gO8GRiYiIJF7SFfAi0r1t\nq6xj6gNzeXNVMN793ImD+dWn9+/NqiIiIqlEBbyIJI2lGyv48r1vsW57DWbw/dMPYeqJRbtmhxIR\nEREV8CKSJJ5/bxPf/fs8Kusayc1K53cXTuS0cQMTHZaIiEjSUQEvIgkViTi/n7GM388IZood0iuH\nP39hCmMHf+jZbSIiIoIKeBFJoIraBr7793d5YfEmAD4yog9/uHgyffN7JDgyERGR5KUCXkQSomRL\nJV+9bw4lW6oAuPSYg7j2zDFkpifV8+VERESSjgp4Eel0MxZv4oqH51FR10hWRho3nTeB848Ymuiw\nREREUoIKeBHpNJGI84cXl3PrC+/jDgN7ZvOnS47g8AN7JTo0ERGRlKECXkQ6RWVdI1c98i7PLtoI\nwJEH9eaPFx9BvwKNdxcREQlDBbyI7HertlbxlfvmsGxzJQCXHD2c6z45lqwMjXcXEREJSwW8iOxX\nz7+3ie89Mo+K2kay0tP4+bnjuPDIYYkOS0REJGWpgBeR/aKxKcJvn3+f6TNLAOhf0IM7LjmCycN6\nJzgyERGR1KYCXkQ63JaKOr790DvMXrENgOKRB3DbZydpvLuIiEgHUAEvIh1qzqoyvv7gXDZX1AEw\n9aQirjx1NBma311ERKRDqIAXkQ7h7tw9ayW/emYJjRGnIDuDWy+YyKljByQ6NBERkS5FBbyItFtl\nXSM/ePRd/rMgmCJyzKCe3PH5yQw/IC/BkYmIiHQ9KuBFpF2Wbqxg6oNvs2JLFQCfOWIoPz93PNmZ\n6QmOTEREpGtSAS8ibeLuPPTmWn761CLqGiNkZaTx83M0RaSIiMj+pgJeREIrr23g6scX8PT8UgCG\nH5DLHz43mfFDChMcmYiISNenAl5EQnl37Q6+9dA7rCmrBuCciYO58dzxFGRnJjgyERGR7kEFvIi0\nSiQSzDJz87PBLDPZmWn87OzxfGbKUMws0eGJiIh0GyrgRWSfyqrqufKReby4dAsAowfkc/vnJjN6\nQEGCIxMREel+VMCLyF69vmIb33n4HTaVBw9m+uxRw/jJJ8eSk6VZZkRERBJBBbyItKi+McLvXnif\n6S+V4A75PTL45acmcNbhgxMdmoiISLemAl5EPmT55kqu+Ps7LFxfDsDhQwu57bOT9GAmERGRJKAC\nXkR2cXceeH01v/jPYmobIqQZfPOjB/Otk0eRmZ6W6PBEREQEFfAiErW5opYfPDqfmdEbVQ/sk8Pv\nLpzIEcP7JDgyERERiaUCXkT476KN/OjxBZRV1QPwmSOG8pOzxmpudxERkSSkAl6kG9tZ3cDP/v0e\nj81dB0Cv3ExuOm8Cn5gwKMGRiYiIyJ6ogBfppv63ZBNXP75g1/SQx4/qyy3nH87AwuwERyYiIiJ7\nk3R3pZlZhplNM7NtZrbTzO41sxanvjCzK83sHTMrN7MNZvZnM+vV2TGLpJKdNQ1c9Y93ueyeOWwq\nryMvK51fnDee+y47SsW7iIhICki6Ah64BjgNmAiMBIYD0/bQNxOYCvQFDgMOBO7ohBhFUtLLy7dx\n+rSXefTtYMjMsQcfwHPfPYGLPzIcM0twdCIiItIayTiE5nLgandfC2Bm1wIvmNl33L0mtqO7/yrm\n7VYz+z/gz609kJn1AeKn2BgGUFVVRWVlZVvib7Pq6urdlrJ3ylfrbSor52/L03hj9kIAcrPSuerk\nkXxm8iDMIp3+XU92+m6Fo3yFo3yFo3yFo3yFk8h8VVVVtXlbc/cODKV9osNftgNj3H1JtC0HqAYO\nd/f5+9j+VmCsu3+8lce7Abi+pXXTp09n0CDdyCepzR3mlRmPr0yjvCG4wj66MMJFIyMcoNEyIiIi\nCVNaWsrUqVMBRrn78jDbJtsV+ILocmdzg7vXmFk90HNvG5rZOQRX748LcbzbgAfi2oYBM4qLiykq\nKgqxq/arrq5m9uzZFBcXk5ub26nHTkXK195t2FHLjc8u4+XlZQD0SHeuOOkgPn+0hsvsi75b4Shf\n4Shf4Shf4Shf4SQyXyUlJW3eNtkK+IroshAohV1X4LOA8j1tZGZnAn8FztnXVfpY7l4GlMXtC4C8\nvDzy8/PDxN5hcnNzE3bsVKR87a6xKcI9r63i1uffp7q+CYCTD+nLCXkb+XTxQcpVCPpuhaN8haN8\nhaN8haN8hZOIfOXltThHS6skVQHv7jvMbC0wGVgSbZ4M1ALLWtrGzM4D7gY+7e4vdkqgIklq4fqd\n/Ojx+SxcH/y8O7BnNj89ZxzHDs9nxoyNCY5OREREOkJSFfBRdwFXm9nLQA1wI/BA/A2sAGZ2AXAn\nwZX3lzo3TJHkUV7bwO+eX8Y9r60k4mAGXyw+iCtPG01BdqZuUhUREelCkrGAv4lgZpj5BPE9CVwB\nYGbXABe7+7ho35uBfODp2DG97q7fGUm3EIk4j81dx83PLmFrZT0Ahw4s4JefmsCkYb0THJ2IiIjs\nD0lXwLt7I0HBfkUL624iKPCb34/oxNBEksr8dTu4/l+LeGfNDgDystL5zimj+NKxI8hMT8ZHPIiI\niEhHSLoCXkT2bltlHbc8t5S/z1lL8yywn5o0hB+dcSj9e2puSBERka5OBbxIimhoivDg66u59fn3\nKa9tBGDc4J789OxxTDko/nlkIiIi0lWpgBdJcu7Of9/bxM3PLGHF1uCpbb1yM/n+6Ydw0ZHDSE/T\nnO4iIiLdiQp4kSQ2b+0Obnp6MW+uCh5XkJ5mfO6oYXzv1NH0zstKcHQiIiKSCCrgRZLQ2rJqfv3c\nUp56d8OutlPGDOBHZxzKwf01yZKIiEh3pgJeJIlsLq/lDy8u56E311LfFAHgsKGFXPOJMRw98oAE\nRyciIiLJQAW8SBIoq6rnTy+VcO/sVdQ2BIX7kF45/ODjh3DWYYNJ0zh3ERERiVIBL5JA5bUN3PXK\nSv4yayWVdcHMMn3ze/DNjxZx0VHDyM5MT3CEIiIikmxUwIskwI7qeu55bRV/fXUVO2saACjMyeRr\nJxbxxWOGk5ulf5oiIiLSMlUJIp1oS0Udd89ayf2zV1FV3wRAfo8MLj9+BJcdN4Ke2ZmJDVBERESS\nngp4kU5QurOGP720gofeXENdYzDGvWd2BpcecxBfOnaEpoQUERGRVlMBL7IfLdqwk7tnreSpdzfQ\n0OQAHJCXxZePH8ElRw+nQFfcRUREJCQV8CIdLBJxZr6/mbteWclrJdt2tQ/smc1XTxjJZ48aRk6W\nbk4VERGRtlEBL9JBqusbeXzuev7y6kpWbKna1X7owAIuP34kZx0+iB4ZKtxFRESkfVTAi7TT+5sq\nePD11Tw+dz0V0akgAU46pB9fOX4kxxQdgJnmcRcREZGOoQJepA3qGyM8u2gjD7y+mjdXlu1q75GR\nxnmThvDl40YwakBBAiMUERGRrkoFvEgISzaW89jb63jinfVsrazf1T6yXx4Xf2Q4508eSmGubkwV\nERGR/UcFvMg+lFXV869563l07joWri/f1Z6RZpw+biAXHz2M4pEaJiMiIiKdQwW8SAtq6puYuXQz\nT85bz/+WbN41BSRAUb88Pn3EUM6fPJT+PbMTGKWIiIh0RyrgRaJqG5p46f0t/Ht+KTMWb6I6+qRU\nCB66dPbEwXx68lAmHthLV9tFREQkYVTAS7dWVdfIK8u28OzCjbyweDOVMbPIZKYbJ47ux3mThnLy\nmP5kZ2oKSBEREUk8FfDS7WzYUcOMxZt4YfFmZpdso74psmtdRppx/Ki+nHnYYE4dO4DCHN2QKiIi\nIslFBbx0eXWNTby9ejuzlm1l5tItvFdavtv6rPQ0iosO4MwJgzht3AB65WYlKFIRERGRfVMBL12O\nu7NkYwWzlm1l1vKtvLmyjJqGpt369MnL4mOH9ueUMf05blQ/8nvon4KIiIikBlUtkvIamiLMXbOd\nOavKeGtVsNxe3bBbHzMYP7iQ40b15eRD+zNpWG/S03QjqoiIiKQeFfCSUtydddtrWLh+J3NWbuGl\nRWn88K1XqW2MfKjv0N45HD+qL8ce3JdjivrSJ09DY0RERCT1qYCXpBWJOOt31LBoQzkL1u9gwfpy\nFqzbEXd1PQ0IiveR/fI4cngfphzUm6NG9GFYn1xN9ygiIiJdjgp4SbjmQv39TRUs21zJsk2VLN8c\n/Dl2LvZYeVnpjBmYT37Dds49dgLHHTqIvvk9OjlyERERkc6nAl46RW1DE2vLqlkT84p9X9vw4SEw\nzXIy0xk/pCfjhxRy2NBCJgwpZETffGqqq5gxYwYnH9qXfBXvIiIi0k0kXQFvZhnALcAXCOJ7Evi6\nu1ftof9lwE+A/sCbwFfcfVknhdutuTsVdY1sr6pne3UDWyvq2FRRy6adtWwqj/65vI5N5bWUVdXv\nc389MtI4uH8+o/rnM2pAAQf3z2f0gAKG9cnVDaciIiIiUUlXwAPXAKcBE4Fq4DFgGvDV+I5mdiLw\ne+BM4C3gp8C/zGy8u7c89qKbcnfqmyI0NDn1jREamiLUN0aojy5rGpqormuisq6R6vpGquoaqapv\nCpZ1TVTWNbC9uoEd1fW7ljuqG2iMeKg4cjLTGdYnlwP75DKsTy7D+uRwYJ9cDu6fz9DeKtRFRERE\n9iUZC/jLgavdfS2AmV0LvGBm33H3mri+XwYedveXo31/AnwNOB6Yua8DmVkfoE9c8zCAqqoqKisr\n2/M5Qrn/jXU8Ma+Uyqp0/m/Zm5ilEXHHAfegAG/+c8SDojniHqzjw+sdaIr4roI9bKHdFrlZ6Qwo\nyKJffg/6FWQxoOCDZf+CLIb2yuGAvMw93Fjq1FS3+EuWPaqurt5tKXumXIWjfIWjfIWjfIWjfIWj\nfIWTyHxVVYWre2KZ+/4v7FrLzHoB24Ex7r4k2pZDcCX+cHefH9d/HnCHu98R0/YG8KC739aK490A\nXN/SuunTpzNo0KC2fpTQ/rk6jf9tSOu047Uk3Zwe6ZCdDj3SoEc69Ej36BJyMyAvw8nLgLxMgmX0\nfW4GZKUnNHwRERGRlFFaWsrUqVMBRrn78jDbJtsV+ILocmdzg7vXmFk90HMP/XfGte3YQ9+W3AY8\nENc2DJhRXFxMUVFRK3fTfr3X7mTSmjJWrVzJyJEj6JGVhZlhBA8hiv1zWvTPxPw5vo9hpKcZWRlG\nZlpasExPIys9jcxdbWlkphtZ6WlkZ6aRmZ7YHyDCqq6uZvbs2RQXF5Obm5vocJKachWO8hWO8hWO\n8hWO8hWO8hVOIvNVUlLS5m2TrYCviC4LgVLYdQU+CyjfQ//CuLZee+j7Ie5eBpTFtjUP78jLyyM/\nP7+1cbfbCWPymXxgITNqV3DyMSM69dipLjc3V/lqJeUqHOUrHOUrHOUrHOUrHOUrnETkKy8vr83b\nJtUlV3ffAawFJsc0TwZqgZZmlpkf29fMsoEx0XYRERERkS4nqQr4qLuAq81sqJkdANwIPNDCDawA\ndwMXmdnx0eL9Z8B64JXOC1dEREREpPMkYwF/EzCD4Cr6SoIr8lcAmNk1ZraouaO7vwR8l2Acexnw\nEeBsTSEpIiIiIl1Vso2Bx90bCQr2K1pYdxNBgR/bdjfBlXgRERERkS4vGa/Ai4iIiIjIHqiAFxER\nERFJIUk3hCYJZAKsXr260w9cVVVFaWkpJSUl7ZpaqLtQvlpPuQpH+QpH+QpH+QpH+QpH+QonkfmK\nqTUzw26bVE9iTQZm9jGCm2hFRERERPa3k939f2E2UAEfx8zyCGazKQUaOvnwwwh+eDgZWNPJx05F\nylfrKVfhKF/hKF/hKF/hKF/hKF/hJDJfmcAg4A13rwqzoYbQxIkmMNRPQR2l+SmwwBp3X56IGFKJ\n8tV6ylU4ylc4ylc4ylc4ylc4ylc4SZCvxW3ZSDexioiIiIikEBXwIiIiIiIpRAW8iIiIiEgKUQGf\nXMqAn0aXsm/KV+spV+EoX+EoX+EoX+EoX+EoX+GkZL40C42IiIiISArRFXgRERERkRSiAl5ERERE\nJIWogBcRERERSSEq4EVEREREUogKeBERERGRFKICXkREREQkhaiAFxERERFJISrgRURERERSiAp4\nEREREZEUogJ+PzKzDDObZmbbzGynmd1rZnl76X+Zma0ys2ozm2lmo+LWf9LMlkTXv21mR+3/T9F5\nwuTLzK40s3fMrNzMNpjZn82sV8z6k8zMzawy5nV/532a/S9kvi41s6a4fPwiro++Xx/0fSYuVzXR\n79Pk6Pru8P26yMxejX62Va3o393PX63Ol85fofPVrc9fIXOlc5dZDzO708xKop+vxMx+tI9tUu/8\n5e567acX8BNgEXAgcAAwE7hzD31PBCqAE4Ac4NfAYiA9uv5goBo4H+gBfBvYAhQm+nMmKF8/Ao4G\nsoC+wLPAwzHrTwIqE/2ZkihflwIL97Ivfb/2vu2PgcXd7Pt1GnAh8D1g1T766vwVLl86f4XLV7c+\nf4XJVQvbdsdzVx5wIzCa4EL1OGA18LU99E/J81fCE92VX8Aa4OKY98cCNUBOC33vA/4c8z4bKAdO\nir7/GfB83DbLgEsT/TkTka8Wtj0T2BDzvjucpMJ8v/b1H6C+X3vezoCVwPdi2rr89yvms57figKr\n25+/wuSrhW263fkrTL50/mp9ruL6d+tzV1wubgH+tod1KXn+0hCa/ST669ADgbdjmucSfDFGtbDJ\nYbF93b2W4CfAw1paH7O/w+gC2pCveCcD8+PacsxsffT1DzMb0THRJl4b81VkZpvNbLWZ3WVm/WLW\n6fu1Z6cCg4B749q77PerDbr1+asDdKvzVxt12/NXO+jcBZhZGsEPLvH/xpql5PlLBfz+UxBd7mxu\ncPcaoB7ouYf+O+PadsT03df6VBc2X7uY2TnA5cAPYpqXABOBYdFlOfCcmWV3YMyJFDZfLwMTgIHA\n8cAA4PG4/en71bKvAo+7+7aYtq7+/Qqru5+/2qybnr/C6u7nr7bSuStwC8Gwmtv3sD4lz18q4Pef\niuiysLnBzHIIxjyW76F/YVxbr5i++1qf6sLmq7nPmcBfgXPcfddP1+6+0d0XuHuTu28B/h8wBDhi\nfwSfAKHy5e4r3H25u0fcfQ1BPo4zsyEx+9P3K46Z9QfOBu6Mbe8G36+wuvv5q0268fkrFJ2/wtO5\nK2BmNxLk4VR3r9xDt5Q8f6mA30/cfQewFpgc0zwZqCUYOxVvfmzf6E/DY/jgVz67rY/Z355+JZRS\n2pAvzOw84H7g0+7+4r4O0bxZO0NNCm3JV/wuosvmfOj71bIvASvdfea+DhFddonvVxt06/NXW3Tn\n81cH6Fbnrzbq9ucuM/s1cAHBWPb1e+mamuevRN9Y0JVf/7+d+wmxsgrjOP59cKhQAoOiNCHSrBah\n46IsolSIoMHKoqRaBe4CIcQW/Vm0i3KhualWQZs2lUVEFIhECpVRmKkIEpRZFmWL/IMy+bQ4x3jn\nOnd0ZPD2zvv9wGXu+55zX95zeO+Z333vOZfyqxe7gXmUX73YRmOhRE/dZZRPc3dR5uX2WwX9MOWu\n4f9iFfQA+2s15SusZX3KVwDzKYPSbOA1ymKemYNu54D6awSYW59fQ/n6+ctGudfX2fWDEu7Xd/T6\nmlHHoscpv+BwGXBZn7qOX5PrL8evyfVXp8evyfRVrd/psau281Vg/5nr5hx1Wzl+DbyTp/MDGAI2\nAUfqxfEWMKuWPQfs6am/pr45jwOfAQt7ylfWC/IEZQHF0kG3cVD9VQecUeBo89EoX0f51ZFjwGHg\nXeDGQbdxgP21Afi1XluHKF/bz/H6mvD9uAI4CVw5zrG6cH09Sbk7N+YxQX91ffw67/5y/Jp0f3V6\n/LqA92LXx67rah+d7HmPfTxBn7Vu/Ip6YpIkSZJawDnwkiRJUosY4CVJkqQWMcBLkiRJLWKAlyRJ\nklrEAC9JkiS1iAFekiRJahEDvCRJktQiBnhJkiSpRQzwkiRJUosY4CVJUyYiNkTEJ+Psfz0iNg3i\nnCRpujHAS5Km0m3AV80dERHAA8D7AzkjSZpmDPCSJCJiY0R8HRFn/V+o+ye8ex4Rl0TEKeBu4IWI\nyIjYW4tvBS4Ftte6e2r5eI8Xp7ZlkjT9GOAlqeMi4iZgLfBMZp4ep8o+YMk5DjMK3FGfLwXmAHfW\n7VXAR5k5Wrcfqn9Har25wHFgDfDyhbRBkrrEAC9JWg/sysxtfcqPUII2ETESEfsj4kBErD1ToQb/\nOcDfwM7MPJyZf9XiBxk7feZqIIHPM/MwMAuYCWzPzBNT2TBJmo4M8JLUYXXKzCPAO419G5vhHLgc\nOBYRQ8Bm4B5gEfBURMxr1FtC+SCQjWPdAMwHmgtbFwM/ZObRuj1MuQN/YMoaJknTmAFekrrtemA2\nsLuxbzUlUJ+xGNhLWaC6LzMPZuZx4D3g/ka9YeDbnuOvArZm5rHGvkXAdz2v+77P9B1JUg8DvCR1\n2wPkAUwAAAFlSURBVBX171GAiFhOmZN+qm4vpATsLXX/ocZrfwaubWwvZmwwh7Onz0AJ8Lsa28M9\n25KkCRjgJanbfgJOA09ExDBlisyHwMqIWAS8SQnlW87jWEPAzRExNyJmR8RVwO31eMB/U3ZuYWzQ\nXwD8OBWNkaQuMMBLUodl5u/As8CjwKfAG5RFrUuAL4A/gZHM/Af4hbF33Ocx9o7888BjlDvzL1Gm\n1+zMzN8adRZQFq02A/xuYF1E3Dd1LZOk6Ssaa40kSeqrLmLdDywH/gC+Ae7NzIN96n8A7MjMVy7a\nSUpSBwwN+gQkSe2QmaMR8TSwFZgBbO4X3qsdwNsX5eQkqUO8Ay9JkiS1iHPgJUmSpBYxwEuSJEkt\nYoCXJEmSWsQAL0mSJLWIAV6SJElqEQO8JEmS1CIGeEmSJKlFDPCSJElSixjgJUmSpBYxwEuSJEkt\nYoCXJEmSWsQAL0mSJLWIAV6SJElqkX8B9BwWf62zKk4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a854bef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plt.plot(a/pi, uA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$u_A/\\delta$')\n", "plt.title('Imposed support motion');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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6oj+iokLfzI4ysy4Wkg2MB74DZu7bUxCpe8od/vDqfGau2EqSwfizBtC3Xfbe\ndxSJ8usju3LGkPYA/PWdBbw597uAIxIRqV/ivdi/ndBNtXOApYSu9F8JYGbXm9nciLZ3AVnA65Fz\n6UdsHwB8AOQRGr+/H3Ccu+sWdJEob61M4s1vNgDwx+E9+cmBrQKOSOoqM+OWU3rvesruDa/OZ+n2\nvewkIiI1Jq6LfXff4e5Xunszd2/s7ue5e3542+3u3iuibWd3T4maRz8rYvu97t7B3TPdvbW7n+bu\nC4I4L5F49sqcdby9OvRPw3lDOzL6kE7BBiR1XmpyEhPOHkTXlpmUlDmPzE9m1Zb/+YJWRET2gbgu\n9kWkdk1bsombXgv9DXx412bcOLynHpolNaJJRiqPjT6Iphmp5O0wfj35a3ILS4MOS0Qk4anYFxEA\nlm7M51cTZ7Cj3MnJcO4+tQcpyfonQmpOh+YZjPt5L1LMWbKxgF8/PYPSsvKgwxIRSWj6TS4i5BaU\ncsHjX7C1oJQWWWlcdGAZWQ3ieWZeqasGtG/CWd1CBf6nizZx4ytfE68PdxQRSQQq9kXquR1l5Vz6\nzEyWbswnPTWJ+0b1olmDoKOSRDaohXPpjzsB8MznK3nqs+XBBiQiksBU7IvUc3e9NZ+PF24E4K8/\n70efNnt8QLVIjfjVYR04sV8bAP7072+YsnhjwBGJiCQmFfsi9dhLM1fx8MdLAbjkqK4M79sm4Iik\nvjAz/nJaX3q1aUxZuXPJ0zNZuVkzIYuI1DQV+yL11Jcrt3LtS18BcPSB+3H1Tw8IOCKpbxqmJfPQ\neYNpkZXGloJSfvnkdPKLdwQdlohIQlGxL1IPfbetiIuemk7JjnK6tsxk7Bn9SUrSFJtS+9pmN2TC\nOYNITTbmr9vOVc/NprxcN+yKiNQUFfsi9UzxjjIunjiD9duKaZSewsPnDaZxemrQYUk9NqRTM/58\ncm8A3p67nnH/WRhwRCIiiUPFvkg94u7c+K+5zFyxlSSD+84cQJeWWXvfUWQfO/OgDpw3tCMAf3tv\nIW99vTbgiEREEoOKfZF65Mmpy5k8fSUAvz/+QI48YL+AIxL53h+H92Rol+YAXPXclyxcvz3giERE\n6j4V+yL1xNTFm/jza98AcHL/Nlx0RJeAIxL5odTkJO4/eyBtsxtSUFLGrybOYHtRadBhiYjUaSr2\nReqBtbmFXDppJmXlTu+2jbnrtL6Y6YZciT/NMtN48JxBpKUksWRDPtc8P0dP2BURqQYV+yIJrmRH\nOb9+eibWAXA6AAAgAElEQVSb8ktompHKg+cMIj01OeiwRHarT7sm3Bq+Yfetuet48MMlAUckIlJ3\nqdgXSXC3vv4Ns1ZsxQz+fsYA2jXNCDokkb0aNaQ9Zx7UAYC7357Pp4v0hF0RkapQsS+SwF6etYon\npy4H4Oqf7s8R+7cMOCKRyrv5pJ70a59NucNlz8xi9dbCoEMSEalzVOyLJKh5a7dxXfgJucf02I9f\nH9kt4IhEYtMgJZkJZw+keWYam/NLGDNxBkWlZUGHJSJSp6jYF0lAuYWlXDxxBkWl5XRsnsE9o/SE\nXKmb2mQ35L4zB5BkMGdVLje/OjfokERE6hQV+yIJprzcufq52SzfVEB6ahITzh5Ek4Z6Qq7UXYd0\na8Hvjz8QgGe/WMmzn68IOCIRkbpDxb5Igpnw4WLem/cdALeP6EPPNo0Djkik+i46ogsn9GkNwI2v\nzOWrVbkBRyQiUjeo2BdJIB8v3MBf3/kWgHN/1JFTB7YLOCKRmmFm/GVkP7q2zKSkrJxfT5pBbqEe\nuCUisjdxXeybWYqZjTWzTWaWa2ZPmFnmbtpebWazzGybma0xs4fNLDuqzXAzm29mBWY2w8wOqp0z\nEdn31m8r4spnZ+MOAzpk88fhPYMOSaRGZTVIYcI5g2iYmszKzYVc8/yXeuCWiMhexHWxD1wPHAv0\nB7oAHYGxu2mbCowBWgB9gfbAgzs3mlk34DngD0BT4AngdTNrsq+CF6ktZeXOFc/OYlN+CdkZqdx/\n1kDSUuL9f2+R2O3fqhG3jQg9cOudb9bzz0+WBhyRiEh8Swk6gL24ELjO3VcCmNkNwHtmdoW7/2DC\nZXe/M2Jxo5ndBzwcse484FN3fyG8PM7MLgNGAI/vLRAzawY0i1rdASA/P5+8vLzKn1U1FRQU/OCn\n7Fl9yNf9Hy7jsyWbAbjtxANonFJW5c9kfchXTVK+YlMT+Tp2/2xO69+aF2ev484353Ngywb0b5eY\n1230+YqN8hUb5Ss2QeYrPz+/yvtavH4FGh6CswXo4e7zw+saAgVAP3efs5f97wV6uvvx4eV/AfPd\n/dqINpOB1e5+VSXiuRm4qaJtEyZMICcnp1LnJVLTFuQaD3yThGMclVPOKZ3Kgw5JZJ8rKYOxXyez\npsDITnOu6VtGliadEpEEtXbtWsaMGQPQ3d0XxbJvPF/ZbxT+uWvKBXcvNLMSYI/Ti5jZyYS+FTgs\nqr/o6Ru27q2vCOOAiVHrOgDvDx06lK5du1aym+orKChg6tSpDB06lIyMjFo7bl2VyPnalF/CLQ/P\nwCmhT5tG3Ht+f1KTqzd8J5HztS8oX7GpyXz1GFTA6f+cydaSMt7c0pIHzuhDkiXW8yT0+YqN8hUb\n5Ss2QeZr8eLFVd43nov97eGfTYC1sOvKfhqwbXc7mdkw4DHg5Kir/9vDfUXKBlZXJhh33wxsjjoW\nAJmZmWRlZVWmmxqVkZERyHHrqkTLV3m5M2byXDbmldAoPYUHzhlM0yY1949PouVrX1O+YlMT+eqd\nlcVfRvbjkkkz+WTxFp6avp5LjkrMJ0Xr8xUb5Ss2yldsgshXZmaF89NUStzewefuW4GVwMCI1QOB\nImBhRfuY2QjgKeA0d/9v1OY5UX3t7G+Pw4FE4tWEDxfz8cKNANw9si/tm+mqjNQ/w/rmMPqQTgDc\n8863TF28KdiARETiTI0X+2bWxsx+ZWZ/Dr9+ZWZtq9jdI8B1ZtbOzJoDtwITo2/ODR93FKEr+iMq\nKPQBngQOM7NTzSzNzC4ndGX/5SrGJhKYL5Zt5t53FwBw/tCOHN9b94xI/XXdCQfSr10Tyh0uf3YW\n320vCjokEZG4UaPFvpldCrxHaNrLNeFXe+Dd8LZY3Q68T+jq+1JCV/qvDB/rejObG9H2LiCL0HSa\neTtfOzeGb2YYBdxBaOz+aGC4u+sxjFKnbMkv4fJnZlFW7vRq05jrTugRdEgigWqQksz4swbSOD2F\nDduLueKZ2ZSVx+fkEyIita2mr+xfDgxy9z+4+4Ph1x+AQcAVsXbm7jvc/Up3b+bujd39PHfPD2+7\n3d17RbTt7O4p7p4V+Yrq7zV3P8DdG7r7QHefVs3zFalV7s5vn/+StblFZKaFCpz01OSgwxIJXPtm\nGdwzqj8AU5ds4u/vLQg4IhGR+FDTxX45oQdWRWsW3iYi1fDPT5by/vzvALj91D50blH1G3ZEEs1P\ne7biV0d0AeC+/y7i00UbA45IRCR4NV3sXwV8aGavmNkD4derwAfhbSJSRbNXbuWut+YDcOZB7Tm5\nf1VvhRFJXL897gAGdsjGHa6cPJsN24uDDklEJFA1Wuy7+xvAgcCdhMbav09ojPyB7v56TR5LpD7J\nLSzl0kkzKS1zDmjViBuH99r7TiL1UGpyEuPOHLBr/P5Vz82mXOP3RaQeq/HZeNy9zN2nuvuL4ddU\ndy+r6eOI1BfuzrUvzmHVlkIapiYz/qwBNEzTOH2R3WnXNIO7f94PgI8XbuTBj6r+MBoRkbqu2sW+\nmd1rZv81s/lmNsPMnjKzEWYJ9hhDkYBM/Gw5b369DoA/n9yL7q0a7WUPETmuV2vOH9oRgHveWcD0\nZZv3soeISGKqiSv7hxOaEvMtYDbQA3ge+MLMWtVA/yL11tw1udzy2jwATh3QlpGD2gUckUjdcd0J\nPejVpjFl5c7lz8xia0FJ0CGJiNS6ahf77j4kPCXmle7+f+4+mFDBXwRMqHaEIvVUXvEOLp00i5Ky\ncrq0zOSWU3qjL8xEKi89NTQ9bWZaMmtyi7jmhTm4a/y+iNQvNT5mH8DdFwJjgGP3Rf8iic7dueHl\nr1i6MZ+0lCTuP2sgmQ1Sgg5LpM7p3CKT20b0AeDdb9bz+JRlwQYkIlLLarR6MLNLgAKgBDgRWF2T\n/YvUF89NX8krs9cAcNOJPemR0zjgiETqrlMGtGXK4o08N30Vd7wxnyGdmtG7bZOgwxIRqRU1fWW/\nK3AL8BSQDgyr4f5FEt6367Zz06tzARjWN4ezDuoQcEQidd/NJ/Wi235ZlJSVc+mkmWwvKg06JBGR\nWlHT8+xf5e7tgJ8AnYFjarJ/kURXULKDSyfNpKi0nA7NMrjj1D4apy9SAzLSUrj/rIE0SEli2aYC\nbnj5a43fF5F6oSam3vzczI6IXOfuHwDnATdXt3+R+uTmV+ey8Ls8UpON8WcNoHF6atAhiSSMA1o3\n4uaTQg+ke/XLNTw3fWXAEYmI7Hs1cWX/U+B9M/vCzH5vZieEi/8xgCoVkUp6edYqnpu+CoDrT+hB\n33bZAUckknjOGNKe4X1zALjp1bksWL894IhERPatmph68zdAb0Jz7P8eeA34ALgQuKO6/YvUB4s3\n5HHDy18D8NOerRh9SKdgAxJJUGbGHaf2oWPzDIpKy7nk6ZkUlugh7yKSuGpkzL67f+vuvwSaA92A\nIUCOu/+1JvoXSWRFpWVc8vRMCkrKaJvdkLtH9tU4fZF9qFF6KvedOYDUZGPhd3n86d9zgw5JRGSf\nqekbdN3dl7j7DHffUJN9iySqW1//hvnrtpOcZIw7cwDZGWlBhySS8Pq2y+ban/UA4NkvVvLKbM0U\nLSKJqcrFvpndaGY/Df93UzO7zcweM7Pfmlm7mgtRJHG9PmctEz9bAcA1xx3AoI5NA45IpP644NBO\nHNOjFQDXv/QVyzbmBxyRiEjNq86V/YuBdeH/fh44idAQnhuAJWZ2ZTVjE0loyzflc+2LcwA48oCW\nXHR4l4AjEqlfzIy7R/Ylp0k6+SVlXPrMTIp3aPy+iCSW6hT7zYCNZtYVmOrufdz9cKAlcAlwq5md\nUhNBiiSa4h1lXDppFtuLd9CqcQPu+Xk/kpI0Tl+ktjXNTGPcmQNITjK+Xr2NO96YH3RIIiI1qjrF\n/mZCBf/RwIM7V7r7Dnd/GPgt8LvqhSeSmO5681u+Wp1LksHfzxhA86wGQYckUm8N6dSMq366PwCP\nT1nG23PX7WUPEZG6ozrF/nvAWOBqoFUF298HelWjf5GE9O4363n006UAXHnM/vyoS/OAIxKRMT/u\nyuHdWwDwuxfmsHprYcARiYjUjOoU+1cD24BvgUPM7Awzi3yI1slAtWbkMbMUMxtrZpvMLNfMnjCz\nzN207Wdmb5nZBjNzM+sUtf3I8Pq8iNdT1YlPJFartxby2+e/BOCQrs255KhuAUckIgBJSca9o/rT\nIqsBuYWlXP7MLErLyoMOS0Sk2qpc7Lv7Bncf6e4nAQ8APwI2mdkMM5sP3AU8VM34rgeOBfoDXYCO\nhL5NqEgJ8AJwxh76y3f3rIjXudWMT6TSSsvKuWzSTHILS2mRlcbfzuhPssbpi8SNlo0a8LfT+2MG\nM5Zv4d53FwQdkohItaVUdUczuxd4GfjU3cuBK83sn8AIQg/X+pO7P1PN+C4ErnP3leFj3gC8Z2ZX\nuPsPvmN193nAPDNrUc1jVsjMmhG6RyFSB4D8/Hzy8vL2xWErVFBQ8IOfsmfxkq+x/1nCzBVbMeCO\nkw4kw3bU6uemsuIlX3WF8hWbeM9X/5x0fnlIBx76dAUTPlhM/5wMDu0a/U9/7Yn3fMUb5Ss2ylds\ngsxXfn7VpwY2d6/ajmYTCE23mQa8Tqjwfye6CK9yYGbZwBagh7vPD69rCBQA/dx9zm72a0Fo+FBn\nd18Wsf5IQvcR7LzzagrwO3dfWsl4bgZuqmjbhAkTyMnJqUw3Uk/N22I8OD8ZgGPbljOsg4YHiMSr\nMofxc5NZst3ISnF+16+MJnrWnYgEaO3atYwZMwagu7svimXfKhf7uzowO4jQ+PyTgc6ECup/Af+u\nzlN0zaw9sAJo4+5rI9YXA0e7+ye72W93xX5rQtOCfkPoCv2dwOFAX3cvqkQ8u7uy//7s2bPp2rVr\nDGdXPQUFBUydOpWhQ4eSkZFRa8etq4LO13fbiznt4RlsKShlYPvGPHpuf1LiePhO0Pmqa5Sv2NSV\nfK3bVsxpD08nt3AHB3XM5uGz+wYy7K6u5CteKF+xUb5iE2S+Fi9eTP/+/aEKxX6Vh/Hs5O6fA58D\nN5hZN0JF/2hggpl9Qajwf8bdY30W+fbwzybAWth1ZT+N0I3Bsca5ju+v6m8ws18BucAg4NNK7L+Z\n0HSju5iF/uHPzMwkKysr1pCqLSMjI5Dj1lVB5Kus3Lnu6a/YUlBK04xU7j9nMNmNG9ZqDFWlz1ds\nlK/YxHu+umVlcc/P+3Phk9P5fPlWnvhiHZcf3T2weOI9X/FG+YqN8hWbIPKVmVnh/DSVUp3ZeP6H\nuy9y93vc/QigLfAocBhwZhX62gqsBAZGrB4IFAELayLc8M/4vcQqdd649xcybWnob8R7RvUjp0nd\nKPRFBI7p2Yr/O6wzAH97bwGfLdkUcEQiIrGr0WI/krtvdPdH3f0Ud/+rmVXlWI8A15lZOzNrDtwK\nTKzovgALSQd2Pp2ogZml7zyumR1lZl3C7bKB8cB3wMwqnaDIXkxZtJFx/wn9XXrREV34yYEVPY5C\nROLZ748/kL7tmlDucMWzs9iUVxx0SCIiManxYj9cTHc3s1PM7AYzm2Rmc6jC0BvgdkL3AMwBlhK6\n0n9l+DjXm9nciLYdgUJgVXh5fnj5iPDyAOADIC+8bT/gOHfXLehS4zZsL+aKybNxh/7ts/ntsQcE\nHZKIVEFaShLjzxxIowYprN9WzNXPf0l5efXudRMRqU01Wuyb2WxCV8snAacRGirzE2AkkB1rf+6+\nw92vdPdm7t7Y3c9z9/zwttvdvVdE22XubhW8Pghvv9fdO7h7pru3dvfT3F2TKEuNKy93rnpuNhu2\nF9MoPYX7zhxAWso++xJNRPaxDs0zuPO0vgB88O0GHvlkScARiYhUXk1XILMIXYG/0d3PdffbgUJ3\nX+DuO2r4WCJxacKHi/l44UYA7h7Zl/bNNMOBSF03rG8OZx/cAYC/vPUts1ZsCTgiEZHKqekbdH8B\nnAOMNrOPwnPb6/tOqTemLdnEPe98C8D5QztyfG89f0EkUfxxeE8ObN2IHeXOpZNmkVtQGnRIIiJ7\nVeNjC8JX8U8HLgd+C7Q2s0Nq+jgi8WZjXjGXPzuLcoc+bZtw/bAeQYckIjUoPTWZ8WcNpGFqMqu3\nFvL7F+dQ3WfViIjsa/tyNp7Z7j4cOAa41cze31fHEglaebnzm8mzWb+tmEYNUrj/rIE0SEkOOiwR\nqWHd9svillN6A/DW3HVM/Gx5wBGJiOxZtYt9M9t/T9vdfYq7/wS4o7rHEolXkeP0/zKyLx2aa5y+\nSKIaOagdpw5sC8Atr81j7prcgCMSEdm9mriyP9/MtpvZVDObYGYXm9mPzOwH1Y67v1cDxxKJO5Hj\n9Ecf0omf9dE4fZFEd8vJvenSMpOSsnIumzSLvGLNQSEi8akmiv32hJ6Q+wahueuvB6YA28zs2xro\nXyRuRY7T79uuCdedcGDQIYlILchskML4MweSlpLEko35/OHlrzR+X0TiUszFvpkNjHwarruvdvfX\n3P2W8Nz1HYAjgUWE5tsXSUg/GKefHvrFr3H6IvVHzzaNuXF4TwD+NXsNz89YtZc9RERqX1Wu7E8H\nWuypgbt/RGgKzm5VCUqkLoieT1/j9EXqn7MP7sAJfVoDcOMrXzN/XVUeFi8isu9UpdjvDGzYuWBm\nKRU1cvfpwBFVjEskrkWP09d8+iL1k5lx52l96dAsg6LScn49cabG74tIXIm52Hf35f7DgYn5ZjbT\nzB41syvM7Mdm1sbMTgJ0qVMSjsbpi0ikxumpPHD29+P3Nf++iMSTmrhBdxihsflpwEXAe8BK4CXg\n7hroXyRuaJy+iFSkd9sm/OmkXgC8PmctT07V/PsiEh8qHIITi/CUmrum1TSzdKArsNHd11e3f5F4\n8sAHizROX0QqdMaQ9nyxbDMvzVzNra9/Q992TRjQoWnQYYlIPVfjT9B19yJ3n6tCXxLNp4s2cu+7\nCwCN0xeR/2Vm3HpKb/ZvlUVpmXPppFlsyS8JOiwRqedqvNgXSURrcwu5/JnQOP3+7bM1Tl9EKpSR\nlsKEcwaRmZbM6q2F/Oa52ZSXa/y+iARHxb7IXpTsKOeSp2eyKb+EphmhG/E0Tl9EdqdryyzuPK0v\nAB98u4EJHy4OOCIRqc9U7Ivsxe1vzGPmiq2YwbgzB9Amu2HQIYlInDuxXxvOH9oRgHve+ZYpizcG\nHJGI1Fcq9kX24NUv1/D4lGUAXHXM/hzevWWwAYlInXH9sB70a59NucPlz8xi/baioEMSkXpIxb7I\nbixcv51rX5wDwFEHtOSSo/RAaBGpvAYpydx/1gCaNExlY14Jlz0zix1l5UGHJSL1jIp9kQrkFe/g\n4okzKCgpo13Thow9vT9JSRZ0WCJSx7RrmsHfTu8PwOdLN3PXW/MDjkhE6hsV+yJR3J3fvziHxRvy\nSUtJYsLZg8jOSAs6LBGpo446cD8u+0nom8GHP17Kq1+uCTgiEalP4rrYN7MUMxtrZpvMLNfMnjCz\nzN207Wdmb5nZBjNzM+tUQZvhZjbfzArMbIaZHbSvz0Hqnsc+Xcbrc9YC8OeTetGnXZOAIxKRuu7K\nY/bnx/uH7vn5/QtzmLd2W8ARiUh9EdfFPnA9cCzQH+gCdATG7qZtCfACcEZFG82sG/Ac8AegKfAE\n8LqZqZKTXaYt2cTtb8wD4OeD2nH6kPYBRyQiiSA5yfj7Gf3p0CyDwtIyfvXUDHILSoMOS0TqgZSg\nA9iLC4Hr3H0lgJndALxnZle4e2FkQ3efB8wzsxa76es84FN3fyG8PM7MLgNGAI/vLRAzawY0i1rd\nASA/P5+8vLxKnlL1FRQU/OCn7Fll87U2t4gxE2eyo9w5sFUWvz+mE/n5+bURYlzR5ys2ylds6nO+\nUoB7T+vBOY/NYsXmAi59ejrjT+9N8h7uB6rP+aoK5Ss2yldsgsxXdeoRc4/PJ/uZWTawBejh7vPD\n6xoCBUA/d5+zm/1aABuAzu6+LGL9v4D57n5txLrJwGp3v6oS8dwM3FTRtgkTJpCTk1PJM5N4VFIG\n4+YmszLfyExxru5TRvP0oKMSkUQ0Y6Px5MLQg/mObVvOsA6aoUdE9mzt2rWMGTMGoLu7L4pl33i+\nst8o/DN35wp3LzSzEqBxFfvLjVq3NYa+xgETo9Z1AN4fOnQoXbt2rUJIVVNQUMDUqVMZOnQoGRkZ\ntXbcumpv+XJ3rn/1W1bmryfZYNzp/Ti4c9MAIo0P+nzFRvmKjfIFRwO8u4gnp63mndVJDD+kDz85\noOIvpZWv2ChfsVG+YhNkvhYvrvqTuOO52N8e/tkEWAu7ruynAVW5s2l7uK9I2cDqyuzs7puBzZHr\nzEJfvWZmZpKVlVWFkKonIyMjkOPWVbvL16OfLOXfX60H4IZhPTm6j8bpgz5fsVK+YlPf83XjSX1Z\nsKGQz5Zs5vpXv+WVS1vQteXu81Hf8xUr5Ss2yldsgshXZmaF89NUStzeoOvuW4GVwMCI1QOBImBh\nFbqcE9XXzv4qHA4k9cOURRu5LXxD7qkD2/KLQzsFG5CI1AspyUmMP2sgOU3SySvewa+emkFe8Y6g\nwxKRBBS3xX7YI8B1ZtbOzJoDtwITo2/OBbCQdKBBeFUDM0s3s53n+CRwmJmdamZpZnY5oSv7L9fC\neUgcWrm5gEsmzaSs3OnTtgm3j+iz69saEZF9rUVWAyacM4i05CQWfZfHbybPprw8Pu+jE5G6K96L\n/duB9wldfV9K6Er/lQBmdr2ZzY1o2xEoBFaFl+eHl48ACN/MMAq4g9DY/dHAcHePHscv9UBhSWjq\nuy0FpTTPTOMf5w4iPTU56LBEpJ7p3z6bW0/pDcC736zn3ncXBByRiCSauC723X2Hu1/p7s3cvbG7\nn+fu+eFtt7t7r4i2y9zdKnh9ENHmNXc/wN0buvtAd58WwGlJwHY+IfebtdtISTIeOHsgbbIbBh2W\niNRTo4a03zWEcPx/F/HK7ErdSiYiUilxXeyL7AsTPly863H1N57Yk4O7NA84IhGp7244oQeHdw/N\nyPO7F+bw5cqtAUckIolCxb7UK299vY6/vPUtAKMGt+PcH3UMOCIRkfANu2cOpEuLTIp3lPPLJ6ez\nfltR0GGJSAJQsS/1xrx12/nN5NkAHNS5GbeeohtyRSR+NMlI5ZHzB9M4PYXvthdz0ZPTKSotCzos\nEanjVOxLvZBbApdO/prC0jI6Ns/gwXMGkZaij7+IxJcuLbMYf9ZAkgy+XJXLja8tIE4fdC8idYSq\nHUl4haVlPDw/mfXbS2iUnsI/zx9Cs8y0oMMSEanQEfu35I/DewLwxtzveHe1voEUkapTsS8Jrbzc\nueHV+azMN5INHjh7IN3201MCRSS+jT6kE2ceFHqa9+srk3nj6+8CjkhE6ioV+5LQxr63gHfmbQTg\nuuO6cXj3lgFHJCKyd2bGn07qzY86ZQNww7/n88WyzQFHJSJ1kYp9SVgvzVzFff9ZBMARrcs5Y3Db\ngCMSEam8tJQk7h3Zi9YNndIy55dPTmfJhrygwxKROkbFviSkTxZu5HcvzAHg0C5NOaVTecARiYjE\nrnF6Cr/qUUaLrDS2FpTyi8e/YFNecdBhiUgdomJfEs7cNblcPHEGO8qdnjmNuee0niTr/jYRqaOa\nNYDxo3rTMDWZ5ZsK+KWm5BSRGKjYl4SyaksBv3jsC/KKd9A2uyGP/WIIWQ1Sgg5LRKRaerdpxH1n\nDiDJYOaKrVz13GzKyzUnp4jsnYp9SRi5BaWMfuwLvtteTJOGqTxxwRBaNU4POiwRkRpxTM9W3Lhz\nSs6v1nHHm/MCjkhE6gIV+5IQikrL+OWT01n0XR5pKUk8cv5guu3XKOiwRERq1OhDO3PBoZ0BePjj\npTz00eKAIxKReKdiX+q88nLn6ue+5PNlmzGDv53enyGdmgUdlojIPnHDsB4M75sDwO1vzOf56SsD\njkhE4pmKfanT3J0/v/YNr3+1FoA/DuvJCX1yAo5KRGTfSU4y7h3Vn8O7twDg2pe+4t1v1gcclYjE\nKxX7UqeNfXcBj09ZBsAvD+/MBYd1DjYgEZFakJaSxIPnDKJf+2zKyp1LJs1k2pJNQYclInFIxb7U\nWY98vIRx4YdmjRzUjut+1iPgiEREak9mgxQeGz2Eri0zKdlRzoVPTOebNduCDktE4oyKfamTnvti\nJbe+HpqJ4vherbnz1D4kJWkyfRGpX5r9f3t3Hl9Vde99/PNLQkIGCJOMMg+KKCIiiApqna5otVXr\ndZ5wKLaOt72PQx87vDrcR5+WyqMVFeqAVmxFe21tL1WcUJkUFSpjwhQgjGHKREjye/44J/RwTEh2\nSHJOzvm+X6/zOpy11t5n7d9rn8Vv76y9d3Y6MyaOoWduW/btr+SG3y9k/c6SWHdLROKIkn1pdf62\ntJAHXg89HXfc4C48fvUI0lK1K4tIcurZIZMXJ46hY1YbdhTv59ppC9i8uyzW3RKROKEMSVqV91du\n456Zn1PtcFKfDky97mQy0lJj3S0RkZga1DWH524eTXZ6Kht3lXHNs/PZurc81t0SkTigZF9ajQ9W\nbef2GZ9xoMo5tns7nr9pNNl6Oq6ICAAjenfg9zedQmabVNbtLOWaZ+ezfd/+WHdLRGIsrpN9M0sz\ns8lmttPM9pjZC2aWfZj2t5jZOjMrNbP3zWxwRN1ZZuZmVhzxmtEyWyJH6qPVO7j9xU+pqKxmcNcc\nXrp1DLlZbWLdLRGRuDJmQGem3ziKjLQU8reXcO20+ewsVsIvksziOtkHHgLOB0YAA4C+wOTaGprZ\nmcDjwA1AZ2Ah8KaZRc7xKHH3nIjX9c3ae2kSn+TtYOILi9hfWc3Ao7L5w22n0iUnI9bdEhGJS6cN\n6sIzN4wiPTWFVVuLuW76QnaXVsS6WyISI/E+B+JW4EF3LwAws4eBd8zsHnePvvpoIjDT3T8Mt30E\n+C4wDnj/SDtiZp2A6Mey9gEoKSmhuLj4SL+iwUpLSw95T2QL1+3mzplL2V9ZTb9OmUy75gQy7QDF\nxT1biYIAABgDSURBVAcavI5kildTULyCUbyCUbyCaWy8Tu6ZyW+uOI57//QVywv3cvUz83jmmuF0\nTPC/iGr/CkbxCiaW8Sopafxdtszdm7ArTcfMOgC7gKHuviJclgmUAie6+5Ko9l8AU919akTZAuBl\nd59iZmcBc4At4epPgP9097UN7M9PgB/XVvfUU0/Ro4ee2trUVu4xpq1IoaLaOKqtc9ewKnLTY90r\nEZHW48udxvOrU6h2o0emc+dxVbTXOCrS6hQWFjJp0iSAwe6eF2TZeD6z3y78vqemwN3LzKwCaF9H\n+z1RZbsj2q4gNB1oGaEz9P8FzDaz4e7ekFsWTAFeiirrA8wZO3YsAwcObMAqmkZpaSnz5s1j7Nix\nZGVltdj3tqT3V+1g2sJlVFQ7fTpm8tz1J9KtfeOm7iRDvJqS4hWM4hWM4hXMkcbrHGDkqh3cN2sZ\nhWUwfW07pl13It0bOZ7GO+1fwShewcQyXvn5+Y1eNp6T/X3h91ygEA6e2U8HantE4L5w20gdatq6\n+xb+dVZ/u5ndQejg4GTg4/o64+5FQFFkmVnoIU7Z2dnk5OTUu0FNLSsrKybf29ze/HIz97+2jMpq\nP3gxbrf2bY94vYkar+aieAWjeAWjeAVzJPG6eGQO7XOyuX3Gp6wrKuPml77kD7eeSu9OiZvcaf8K\nRvEKJhbxys6u8/409YrbC3TdfTdQAIyMKB4JlAOra1lkSWRbM2sLDA2X1/oVNU2PuLPSZGYu3MA9\nMz+nsto5oVcur94xtkkSfRGRZDZ+yFE8f/NostJTKSgq48qn57Fme8tdayYisRO3yX7YNOBBMzva\nzDoDPwdequXiXIDpwFVmNi6c6P8M2ATMBTCzs81sgIV0AJ4AtgGLW2RLpF7T5q7hgdeX4g6n9OvI\ny7eNoVO2JpeKiDSFUwd0ZsbEMbRrm0bhnnKumDqPLwp2x7pbItLM4j3Z/yWhi2qXAGsJnem/F8DM\nHjKzr2oauvsHwH2E5tUXAWOAS9y9KtzkJEJ35SkmNH+/K3CBu+sS9BirrnZ+9bfl/Pyt5QCMG9yF\nF24ZTfu2iX3XCBGRlnZy3468ctupdM5Op6ikgqufmc97K7fFulsi0oziOtl390p3v9fdO7l7e3e/\nwd1LwnW/dPdhUe2nu3tfd89y9zPdfXVE3W/cvY+7Z7t7d3e/3N1XtfQ2yaH2V1Zxz6tf8PSHawC4\n8PjuTLtxFFnp8Xw5iYhI63V8r1xmTTqNvp2zKDtQxa0vfMqfPi2IdbdEpJnEdbIviW1P6QGun76Q\nv3y5GYCbT+/HE9eMJCMttZ4lRUTkSPTrks2sSadxQq9cqqqdH762hCffyyNeb8ctIo2nZF9iYuOu\nUi6f+gkL14ZucPSji4by428OIzVF10uLiLSELjkZzLz9VMYN7gLAY7NX8tAbS6morI5xz0SkKSnZ\nlxb32foivv27T8jbVkx6WgpPXjOSW8cNiHW3RESSTnZGGtNvPIXLTuoFwCsLC7hu+gKKSipi3DMR\naSpK9qVFzVy4gauemc/2ffvJzWzDSxPHcNFwPX1YRCRW0tNS+PWVJ/LDC44BYOHaIi554iNWbKnt\nkTYi0too2ZcWcaCqmv/953/ywOtLOVDlHNOtHW9+/3RG9+8U666JiCQ9M+N7Zw/i6etPJis9lY27\nyrj8d5/w9rKtse6aiBwhJfvS7HYU7+faaQuYMX89AP82rDuv33kafTs3/mlwIiLS9C4Y1p1Zk06j\nV4dMSiqquH3Gp/z6HyupqtaFuyKtlZJ9aVaL1hVx8ZSPDl6Ie/95Q/jdtSPJztCtNUVE4tHQHu35\n7++fzuh+nXCH//duHtdOm8+2veWx7pqINIKSfWkW1dXOk+/lcdUz89myt5ycjDSevWEUd58zmBTd\ncUdEJK51ycng5dvGcMf40M0T5q8pYsKUuXyctyPGPRORoJTsS5Pbtq+cG59byGOzQ3/6PaFXLm/d\nfQbnHdct1l0TEZEGapOawoMThjLthlHkZrZhR3EF101fwG/eXkVllW7PKdJaKNmXJvXWkkIumPwh\nc1eHzv7cfHo/Xps0VvPzRURaqXOP68bf7hnHSX064A5T5qzmiqnzyN9eHOuuiUgDKNmXJrG7tIK7\nX/mc7/1hMbtKD9Axqw1PX38yP/7mMD0RV0SklevVIZNXbx/LHeMHYAZfFOzmoilzee7jtVTr4l2R\nuKZkX46Iu/O3pYWcP/lD3vxyMwDnDu3K7PvGc8Gw7jHunYiINJX0tNC0npm3ncrRHTMpP1DNT/+y\njKuenU/eNp3lF4lXSval0QqKSpn4wqfc+fJitu3bT05GGo9eMZxnbxhF13ZtY909ERFpBmMGdOZ/\n7h3P1aN7A6GHcE14fC6T315F+YGqGPdORKIp2ZfAyg9U8bv38zh/8oe8u2IbAOcO7cY/7hvPlaN6\nY6a77YiIJLKcjDR+ddlwXpo4hr6ds6ioqubxOauZ8Phc3lu5LdbdE5EIutm5NJi789bSQv7r7yvY\nuKsMgO7t2/LTS4dpyo6ISBI6Y3AXZt87nifezePpD/NZs6OEm59bxPghR/HwhKEc071drLsokvSU\n7EuDzF+zk8dmr+Sz9bsASE9N4abT+3H3OYPJ0QOyRESSVts2qfzggmO4dERPfvbXZcxdvYMPV23n\no9Xb+fdT+nDXNwbRs0NmrLspkrSUpclhzV+zk9++s4r5a4oOlk04oTv/69+O1e00RUTkoMHd2vHi\nLaN5f9V2fvHWcvK2FfPKwg3M+mwj/35KbyadNVBJv0gMKNmXr6mqduYs38r0j9ayYO2/kvzR/Tvx\nwwuO4ZR+nWLYOxERiVdmxtnHdGXcoC7MXFTAE+/msWVvOTPmr+fVRQVcNrIXt5zRnyHdNL1HpKUo\n2ZeD9pQdYNZnG3n+k3VsKCo9WD66XyfuPW8wYwd01sW3IiJSr7TUFK47tS/fGXU0f1xUwJPv5bNl\nbzkzFxUwc1EBZwzqws2n9+OsY7qSmqL/V0Sak5L9JFdZVc3cvB3M+mwj/1i2lYrK0CPQzeCcY7sy\n8YwBnDqgk5J8EREJLCMtlevH9uM7o3rz+uJN/P7jteRtK+ajvB18lLeDbu0z+NZJvbhi5NEM1tl+\nkWahZD8JlR+o4pP8Hby9bBvvLN/K9n37D9Zlp6fynVG9ufG0fvTvojn5IiJy5Nq2SeWaMX24enRv\n5q7ewe8/XssHq7azde9+nv5gDU9/sIZhPdtz3nHdOHdoN4b1bK+TTCJNJK6TfTNLAx4DbiDU1z8D\nd7p7SR3tbwEeAboCC4Hb3H11RP3FwP8F+gDLgUnuvrBZNyIOVFRWs3TTbhau3cWidUXMy99JWcSD\nT8zg9IFduPzkXlwwrDtZ6XG9W4iISCtlZowfchTjhxzFpt1lvLF4I7MWb2LtjhK+2ryXrzbv5bfv\nrKZHbltOG9iF0f07ckq/TvTvkq3kX6SR4j2rewg4HxgBlAKzgMnA7dENzexM4HHgImAR8FPgTTM7\n3t2rzGwQ8EdCBw5/Ae4A3jKzQe6+pyU2pjlVVzu7SivYsrecLXvKyd9ezKqtxazauo+VW/axPzw9\np0ZqijG6XyfOPa4bFx7fXXdIEBGRFtWrQybf/8Zgvnf2ID4v2M3sr7bwzrKt5G8voXBPObMWb2TW\n4o0AdMpO59ju7RjSLfTq1zmLbrlt6da+rW7/LFKPeP+F3Ao86O4FAGb2MPCOmd3j7mVRbScCM939\nw3DbR4DvAuOA9wkl+R+7+2vh9lPM7C7g28Dz9XXEzDoB0beh6QNQUlJCcXFx8K1rpEdnr2TOV6n8\nesUCKqqcispq9pZXUlnth12uf+csTu6Tyyl9czljYCdyM9uEa6patP8trbS09JB3OTzFKxjFKxjF\nK5hkideQTm0YMq43d43rzbqdpczNK+LTDXv4vGAPRaUHKCqp4JP8nXySv/Nry2alp5KVnkpGagrp\nqVBRnsqTeYtISUmJwZa0LtXV1ZSUKF4NVROv3P7bGDWgZb+7pKTWSS0NYu6HTxBjxcw6ALuAoe6+\nIlyWSegM/4nuviSq/RfAVHefGlG2AHjZ3aeY2Z+BFe7+QET9q8Amd7+/Af35CfDj2uqeeuopevTo\nEXQTG+2FVSks3ln3j7KNOV0yoUem0yPL6ZEF/ds5OW3qXERERCTuuMO2cthQbBSWGoWlsKXM2L0f\nqtG0HomN7x9XxeDcls2fCwsLmTRpEsBgd88Lsmw8n9mvuSz/4BQbdy8zswqgfR3to6fj7I5oW199\nfaYAL0WV9QHmjB07loEDBzZwNUfOu2+m/+crOGbwQNpltaVtWgrZGal0a5fBUe0yaJeRqrmNEUpL\nS5k3bx5jx44lKysr1t2Je4pXMIpXMIpXMIpX7aqqnaKSCrYVV7CjuIKyA1XsP1DN3tJyVuevoX+/\n/rRJ1xmu+hyoOMDadWsVrwaqidcFp4+kf7cOLfrd+fn5jV42npP9feH3XKAQDp7ZTwf21tE+N6qs\nQ0Tbuuo3NaQz7l4EFEWW1STU2dnZ5OTkNGQ1TeLcYT2xLcs5Z0zfFv3e1i4rK0vxCkDxCkbxCkbx\nCkbx+rrc9tA/qqy4uJg5pfmcc0Z/xasBiouLmbN/jeLVQDXx6t+tQ4vHKzu78XdIjNsJWu6+GygA\nRkYUjwTKgdW1LLIksq2ZtQWGhsu/Vh+xviWIiIiIiCSguE32w6YBD5rZ0WbWGfg58FItF+cCTAeu\nMrNx4UT/Z4TO2s8N178InGFml5lZupndTejM/hvNvxkiIiIiIi0v3pP9XwJzCJ19X0voTP+9AGb2\nkJl9VdPQ3T8A7iM0r74IGANc4u5V4fo84ErgV4Tm7t8EXJwIt90UEREREalNPM/Zx90rCSX399ZS\n90tCBwORZdMJneGva31/Bf7axN0UEREREYlL8X5mX0REREREGknJvoiIiIhIgorraTytQBuA9evX\nt+iXlpSUUFhYSH5+/hHdiilZKF7BKF7BKF7BKF7BKF7BKF7BKF7BxDJeEblm4AcixO0TdFsDM/sG\noQuIRURERESa2znu/m6QBZTsHwEzyyZ0159C4EALfnUfQgcZ5wAbWvB7WyvFKxjFKxjFKxjFKxjF\nKxjFKxjFK5hYxqsN0ANY4O4lQRbUNJ4jEA52oKOrplDz5F5gQ/iWonIYilcwilcwilcwilcwilcw\nilcwilcwcRCv5Y1ZSBfoioiIiIgkKCX7IiIiIiIJSsm+iIiIiEiCUrLfOhUBPw2/S/0Ur2AUr2AU\nr2AUr2AUr2AUr2AUr2BaZbx0Nx4RERERkQSlM/siIiIiIglKyb6IiIiISIJSsi8iIiIikqCU7IuI\niIiIJCgl+yIiIiIiCUrJvoiIiIhIglKyLyIiIiKSoJTsi4iIiIgkKCX7IiIiIiIJSsl+nDCzNDOb\nbGY7zWyPmb1gZtmHaX+Lma0zs1Ize9/MBkfVX2xmK8L1n5nZ6ObfipYTJF5m9h9m9rmZ7TWzzWb2\nrJl1iKg/y8zczIojXjNabmuaX8B43WRmVVHx+EVUG+1f/2r796hYlYX3p5Hh+oTev8zsKjP7OLxd\n6xrQPtnHrgbHS2NX4Hhp7AoWr6QeuwDMLMPMnjGz/PD25ZvZA/Us0+rGMCX78eMh4HxgBDAA6AtM\nrq2hmZ0JPA7cAHQGFgJvmllquH4Q8EfgR0BH4AXgLTPLbeZtaEkNjhfQBpgEdAGGA72BqVFtStw9\nJ+J1ffN0O2aCxAtgeVQ8Hq6p0P51KHe/MDJWwC+AFe6+OKJZIu9fRcAU4JH6GmrsAgLEC41dECxe\noLGrwfHS2AVAGrANuBBoD1wCTDKz79bWuNWOYe6uVxy8gA3AtRGfTwfKgMxa2r4IPBvxuS2wFzgr\n/PlnwNtRy6wGbor1dsYiXrUsexGwOeLzWUBxrLcpXuIF3AT88zDr0v5V93IGrAXujyhL+P0rvJ1X\nAOvqaZP0Y1eQeNWyTNKNXUHipbErWLyi2ift2FVLLB4D/lBHXascw3RmPw6E/yzbG/gsongxoZ1o\ncC2LDI9s6+7lwPJw+dfqI9Y3nATQiHhFOwdYElWWaWabwq8/mVn/pult7DUyXgPNbJuZrTezaWZ2\nVESd9q+6nQf0IHQ2J1LC7l8BJfXY1QSSauxqpKQdu46Qxi7AzFIIHeRE/85qtMoxTMl+fGgXft9T\nU+DuZUAFoT8r1dZ+T1TZ7oi29dW3dkHjdZCZXQrcCvxnRPEKQtM1+oTf9wKzzaxtE/Y5loLG60Pg\nBKA7MA7oBrwetT7tX7W7HXjd3XdGlCX6/hVEso9djZakY1dQyT52HQmNXSGPAdnAE3XUt8oxTMl+\nfNgXfj84p8vMMoF0Qj+u2tpHz//qENG2vvrWLmi8atpcBDwHXOruB4/a3X2Luy919yp33w7cAfQC\nTm6OzsdAoHi5+xp3z3P3anffQCgeZ5hZr4j1af+KYmZdCc33fCayPAn2ryCSfexqlCQeuwLR2NU4\nGrtCzOznhOJwnrsX19GsVY5hSvbjgLvvBgqAkRHFI4FyQnO9oi2JbBs+yh7Kv/7sdEh9xPrq+rNU\nq9KIeGFm3wZmAJe7+3v1fUXNYkfY1bjQmHhFryL8XhMP7V+1uxlY6+7v1/cV4feE2L8CSuqxqzGS\neexqAkk1dh2BpB+7zOxR4EpCc+83HaZp6xzDYn0hhF4HL+B4BFgKHE3oCu/3iLgIJKrtmYSOEscR\nmkf8KKE5Y6nh+kFAKXAZobORdwPbgdxYb2eM4nUloT+jnVlH/dmE7rhihI7AnyJ0oVJWrLczRvGa\nAPQM/7s7oT+DL4io1/719fZG6EDgB8m2fwGp4XHoamB9+N9t62irsStYvDR2BYuXxq4A8Qq3T9qx\nK2I7HwdW1uw79bRtlWNYzIOs18EdKA34LaHbZu0ldMV3drjuIeCrqPYTwz/kUuADYHBU/cXhnbeM\n0MUhY2K9jbGKV3hwqgSKI18R9fcTuvtKCbAFmAUMifU2xjBejwGF4X1rE6HpAz20fx3293g2sB/o\nUsu6Enr/InQHFI9+HSZWyT52NTheGrsCx0tjV/DfY9KOXeFt7BuO0f6o39nfDxOzVjeGWbhjIiIi\nIiKSYDRnX0REREQkQSnZFxERERFJUEr2RUREREQSlJJ9EREREZEEpWRfRERERCRBKdkXEREREUlQ\nSvZFRERERBKUkn0RERERkQSlZF9EREREJEEp2RcRkZgws8fMbHYt5VPN7Lex6JOISKJRsi8iIrEy\nGlgYWWBmBlwC/DkmPRIRSTBK9kVEJBAzm2xmn5rZ1/4PCZcf9qy8maWbWQUwHviRmbmZLQtXnwJk\nAB+F234Vrq/t9ZOm3TIRkcSjZF9ERBrMzI4B7gJ+6O7VtTRZDpxUz2oqgbHhf48BegCnhz9/C3jL\n3SvDn78dfp8QbtcTKAUmAv+nMdsgIpJMlOyLiEgQPwC+dPf36qgvIpSUY2YTzGylmeWZ2V01DcIH\nCT2AfcAid9/i7rvC1Zdy6BSeboADc919C5ANZAEfuXtZU26YiEgiUrIvIiINEp62cwXwWkTZ5MhE\nHmgHlJhZGjAFOBcYDtxpZkdHtDuJ0EGDR6xrEDAAiLxo90RgjbsXhz+PIHRmP6/JNkxEJIEp2RcR\nkYbqD3QAlkaUXUko+a5xIrCM0MW3y929wN1LgdeBb0a0GwF8HrX+bwFz3L0komw4sCRquX/WMYVI\nRESiKNkXEZGG6hh+LwYws7MIzaGvCH8eTCgZfyNcvili2Y1Ar4jPJ3JoEg9fn8IDoWT/y4jPI6I+\ni4jIYSjZFxGRhtoAVAPXmNkIQtN0/gJcbGbDgecIJfBvNGBdacCxZtbTzDqY2VHAqeH1AQenDR3P\noQcFA4H1TbExIiLJQMm+iIg0iLtvAx4EvgP8A3ia0AW7JwHzgZ3ABHevAjZz6Jn8ozn0TP/DwFWE\nzvj/itAUn0XuvjWizUBCF+RGJvtLgfvN7MKm2zIRkcRlEddGiYiINInwBborgbOAHcBi4Hx3L6ij\n/X8DH7v7oy3WSRGRJJAW6w6IiEjicfdKM7sXmAOkAlPqSvTDPgZeaZHOiYgkEZ3ZFxERERFJUJqz\nLyIiIiKSoJTsi4iIiIgkKCX7IiIiIiIJSsm+iIiIiEiCUrIvIiIiIpKglOyLiIiIiCQoJfsiIiIi\nIglKyb6IiIiISIJSsi8iIiIikqCU7IuIiIiIJCgl+yIiIiIiCUrJvoiIiIhIglKyLyIiIiKSoP4/\nr2I9J97/RVwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4ce8908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(a/pi, vA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$\\dot u_A/\\delta\\omega_0$')\n", "plt.title('Imposed support velocity');" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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WnwtIEREROTQqAqRGrdmRw5jxMykoKqFzs2T+cckg4mOi/Q5LwsDooztw1XFe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\nL6uxoEVEqkCjpDheuGwQiXHRbNy1l2s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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4a06518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(a/pi, aA)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$\\ddot u_A/\\delta\\omega_0^2$')\n", "plt.title('Imposed support acceleration');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equation of Motion\n", "\n", "The EoM expressed in adimensional coordinates and using adimensional structural matrices is\n", "\n", "$$ m\\omega_0^2\\hat{\\boldsymbol M} \\frac{\\partial^2\\boldsymbol x}{\\partial a^2}\n", " + \\frac{EJ}{L^3}\\hat{\\boldsymbol K}\\boldsymbol x =\n", " m \\hat{\\boldsymbol M} \\boldsymbol e \\omega_0^2 \\frac{\\partial^2 u_A}{\\partial a^2}\n", "$$ \n", "\n", "using the dot notation to denote derivatives with respect to $a$, if we divide both members by $m\\omega_0^2$ we have\n", "\n", "$$ \\hat{\\boldsymbol M} \\ddot{\\boldsymbol x}\n", " + \\hat{\\boldsymbol K}\\boldsymbol x =\n", " \\hat{\\boldsymbol M} \\boldsymbol e \\ddot{u}_A.\n", "$$ \n", "\n", "We must determine the influence vector $\\boldsymbol e$ and the adimensional structural matrices\n", "\n", "### Influence vector\n", "\n", "To impose a horizontal displacement in $A$ we must remove one constraint, so that the structure has 1 DOF as a rigid system and the influence vector must be determined by a kinematic analysis." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"757.158pt\" height=\"381.639pt\" viewBox=\"0 0 757.158 381.639\" version=\"1.1\">\n", "<defs>\n", "<g>\n", "<symbol overflow=\"visible\" id=\"glyph0-0\">\n", "<path style=\"stroke:none;\" d=\"\"/>\n", "</symbol>\n", "<symbol overflow=\"visible\" id=\"glyph0-1\">\n", "<path style=\"stroke:none;\" d=\"M 9.796875 -8.421875 C 9.125 -8.296875 8.875 -7.8125 8.875 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xlink:href=\"#glyph4-1\" x=\"173.59\" y=\"142.739\"/>\n", "</g>\n", "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n", " <use xlink:href=\"#glyph4-1\" x=\"243.374028\" y=\"142.739\"/>\n", "</g>\n", "<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n", " <use xlink:href=\"#glyph4-1\" x=\"291.569102\" y=\"142.739\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(HTML(open('figures/trab1_conv.svg').read()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the 3x3 flexibility using the Principle of Virtual Displacements and the 3x3 stiffness using inversion, while the mass matrix is directly assembled with the understanding that the lumped mass on $x_1$ is $2m$.\n", "\n", "The code uses a structure `m` where each of the three rows contains the \n", "computational represention (as polynomial coefficients) of the bending moments due to\n", "a unit load applied in the position of each of the three degrees of freedom,\n", "in each row six groups of polynomial coefficients, one group for each of the six\n", "intervals of definition in which the structure has been subdivided (a possible seventh interval is omitted because the bending moment is always zero for every possible unit load)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\boldsymbol F = \\frac{L^3}{12EJ}\\, \\begin{bmatrix}\n", " +92&+128&+101\\\\\n", " +128&+192&+146\\\\\n", " +101&+146&+118\n", "\\end{bmatrix}$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol K = \\frac{3 EJ}{1588L^3}\\, \\begin{bmatrix}\n", " +1340&-358&-704\\\\\n", " -358&+655&-504\\\\\n", " -704&-504&+1280\n", "\\end{bmatrix} = \\frac{EJ}{L^3}\\;\\hat{\\boldsymbol K}.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol M = m\\, \\begin{bmatrix}\n", " 2&0&0\\\\\n", " 0&1&0\\\\\n", " 0&0&1\n", "\\end{bmatrix} = m\\;\\hat{\\boldsymbol M}.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "l = [1, 2, 2, 1, 1, 1]\n", "h = 0.5 ; t = 3*h\n", "m = [[p(2,0),p(h,0),p(h,1),p(h,0),p(h,h),p(1,0)],\n", " [p(2,0),p(1,0),p(0,2),p(1,0),p(1,1),p(2,0)],\n", " [p(2,0),p(h,0),p(h,1),p(h,0),p(t,h),p(2,0)]]\n", "\n", "F = array([[vw(emme, chi, l) for emme in m] for chi in m])\n", "K = inv(F)\n", "M = array(((2.0, 0.0, 0.0),\n", " (0.0, 1.0, 0.0),\n", " (0.0, 0.0, 1.0)))\n", "iM = inv(M)\n", "\n", "ld('\\\\boldsymbol F = \\\\frac{L^3}{12EJ}\\\\,', pmat(rounder(F*12), fmt='%+d'))\n", "ld('\\\\boldsymbol K = \\\\frac{3 EJ}{1588L^3}\\\\,',\n", " pmat(rounder(K*1588/3), fmt='%+d'),\n", " '= \\\\frac{EJ}{L^3}\\\\;\\\\hat{\\\\boldsymbol K}.')\n", "ld('\\\\boldsymbol M = m\\\\,', pmat(M, fmt='%d'),\n", " '= m\\\\;\\\\hat{\\\\boldsymbol M}.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The eigenvalues problem\n", "\n", "We solve immediately the eigenvalue problem because when we know the shortest modal period of vibration it is possible to choose the integration time step $h$ to avoid numerical unstability issues with the linear acceleration algorithm." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\boldsymbol\\Omega^2 = \\omega_0^2\\, \\begin{bmatrix}\n", " +0.024831&+0.000000&+0.000000\\\\\n", " +0.000000&+1.729964&+0.000000\\\\\n", " +0.000000&+0.000000&+3.166490\n", "\\end{bmatrix} =\\omega_0^2\\,\\boldsymbol\\Lambda^2.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol\\Omega=\\omega_0\\, \\begin{bmatrix}\n", " +0.157577&+0.000000&+0.000000\\\\\n", " +0.000000&+1.315281&+0.000000\\\\\n", " +0.000000&+0.000000&+1.779463\n", "\\end{bmatrix} =\\omega_0\\,\\boldsymbol\\Lambda.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\boldsymbol T_\\text{n}=\\frac{2\\pi}{\\omega_0}\\, \\begin{bmatrix}\n", " +6.346086&+0.000000&-0.000000\\\\\n", " +0.000000&+0.760294&-0.000000\\\\\n", " +0.000000&+0.000000&+0.561967\n", "\\end{bmatrix} = t_0\\,\\boldsymbol\\Theta.$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$\\Psi= \\begin{bmatrix}\n", " -0.431570&+0.504229&-0.243928\\\\\n", " -0.623940&-0.701061&-0.345271\\\\\n", " -0.488051&+0.004508&+0.872803\n", "\\end{bmatrix} .$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wn2, Psi = eigh(K, M)\n", "wn = sqrt(wn2)\n", "li = wn\n", "Lambda2 = diag(wn2)\n", "Lambda = diag(wn)\n", "# eigenvectors are normalized → M* is a unit matrix, as well as its inverse\n", "Mstar, iMstar = eye(3), eye(3)\n", "\n", "ld(r'\\boldsymbol\\Omega^2 = \\omega_0^2\\,', pmat(Lambda2),\n", " r'=\\omega_0^2\\,\\boldsymbol\\Lambda^2.')\n", "ld(r'\\boldsymbol\\Omega=\\omega_0\\,', pmat(Lambda),\n", " r'=\\omega_0\\,\\boldsymbol\\Lambda.')\n", "ld(r'\\boldsymbol T_\\text{n}=\\frac{2\\pi}{\\omega_0}\\,', pmat(inv(Lambda)),\n", " r'= t_0\\,\\boldsymbol\\Theta.')\n", "ld(r'\\Psi=', pmat(Psi), '.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical Integration\n", "\n", "The shortest period is $T_3 = 2\\pi\\,0.562/\\omega_0 \\rightarrow A_3 = 1.124 \\pi$ hence to avoid unstability of the linear acceleration algorithm we shall use a non dimensional time step $h<0.55A_3\\approx0.6\\pi$. We can anticipate that the modal response associated with mode 2 is important ($\\lambda_2\\approx\\lambda_0$) so we choose an adimensional time step $h=A_2/20=2\\pi\\,0.760/20\\approx0.08\\pi$ that is much smaller than the maximum time step for which we have a stable behaviour.\n", "\n", "### Initialization\n", "\n", "First a new, longer adimensional time vector and the corresponding support acceleration, then the efficace load vector (`peff` is an array with 2001 rows and 3 columns, each row corresponding to the force vector in a particular instant of time) " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nsppi = 200\n", "a, _, _, aA = a_uA_vA_aA(0, 16*pi, nsppi*16+1)\n", "peff = (- M @ e) * aA[:,None]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The constants that we need in the linear acceleration algorithm — note that we have an undamped system or, in other words, $\\boldsymbol C = \\boldsymbol 0$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = pi/nsppi\n", "K_ = K + 6*M/h**2\n", "F_ = inv(K_)\n", "dp_v = 6*M/h\n", "dp_a = 3*M" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The integration loop\n", "\n", "First we initialize the containers where to save the new results with the initial values at $a=0$, next the loop on the values of the load at times $t_i$ and $t_{i+1}$ with $i=0,\\ldots,1999$." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Xl, Vl = [zeros(3)], [zeros(3)]\n", "for p0, p1 in p0_p1(peff):\n", " x0, v0 = Xl[-1], Vl[-1]\n", " a0 = iM @ (p0 -K@x0)\n", " dp = (p1-p0) + dp_a@a0 + dp_v@v0\n", " dx = F_@dp\n", " dv = 3*dx/h - 3*v0 - a0*h/2\n", " Xl.append(x0+dx), Vl.append(v0+dv)\n", "Xl = array(Xl) ; Vl = array(Vl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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eZh5FRUXRsWNHBg8ezOTJkxk1ahRlypShTJkyF5fx8PC4rNymMBTb5N0wjEjg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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4766668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, line in enumerate(plt.plot(a/pi, Xl), 1):\n", " line.set_label(r'$x_{%d}$'%i)\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.ylabel(r'$x_i/\\delta$')\n", "plt.title('Response — numerical integration — lin.acc.')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equation of Motion\n", "\n", "Denoting with $\\boldsymbol x$ the dynamic component of the displacements, with $\\boldsymbol x_\\text{tot} = \\boldsymbol x + \\boldsymbol x_\\text{stat} = \\boldsymbol x + \\boldsymbol e \\;u_\\mathcal{A}$ the equation of motion is (the independent variable being $a=\\omega_0t$)\n", "\n", "$$ \\hat{\\boldsymbol M} \\ddot{\\boldsymbol x} + \n", " \\hat{\\boldsymbol K} \\boldsymbol x = \n", " - \\hat{\\boldsymbol M} \\boldsymbol e \\ddot u_\\mathcal{A}. $$ \n", " \n", "Using mass-normalized eigenvectors, with $\\boldsymbol x = \\delta\\boldsymbol\\Psi\\boldsymbol q$ we have\n", "\n", "$$ \\boldsymbol I \\ddot{\\boldsymbol q} + \n", " \\boldsymbol\\Lambda^2\\boldsymbol q =\n", " \\boldsymbol\\Psi^T\\hat{\\boldsymbol M} \\boldsymbol e \\frac{\\ddot u_A}{\\delta}.$$\n", "\n", "It is $$\\frac{\\ddot u_A}{\\delta} = \\frac{1}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0a)$$\n", "\n", "and $$ \\ddot q_i + \\lambda_i^2 q_i =\n", "\\frac{\\Gamma_i}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0 a),\\qquad\\text{with }\n", "\\Gamma_i = -\\boldsymbol\\psi_i^T \\hat{\\boldsymbol M} \\boldsymbol e\\text{ and }\n", "\\lambda_0 = \\frac43.$$" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = - Psi.T @ M @ e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Substituting a particular integral $\\xi_i=C_i\\sin(\\lambda_0 a)$ in the\n", "modal equation of motion we have\n", "\n", "$$(\\lambda^2_i-\\lambda^2_0)\\,C_i\\sin(\\lambda_0 a) =\n", " \\frac{\\Gamma_i}{2\\pi}\\,\\lambda_0^2\\,\\sin(\\lambda_0 a)$$\n", "\n", "and solving w/r to $C_i$ we have\n", "\n", "$$ C_i = \\frac{\\Gamma_i}{2\\pi}\\,\\frac{\\lambda_0^2}{\\lambda_i^2-\\lambda_0^2}$$" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "C = G*l0**2/(li**2-l0**2)/2/pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The modal response, taking into account that we start from rest conditions, is\n", "\n", "$$ q_i = C_i\\left(\\sin(\\lambda_0 a) -\n", " \\frac{\\lambda_0}{\\lambda_i}\\,\\sin(\\lambda_i a)\\right)$$\n", "$$ \\dot q_i = \\lambda_0 C_i \\left(\n", " \\cos(\\lambda_0 a) - \\cos(\\lambda_i a) \\right).$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$q_1= -0.637609\\left(\\sin\\frac43a- 8.461449\\sin0.157577a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_2= +3.691468\\left(\\sin\\frac43a- 1.013725\\sin1.315281a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_3= -0.016167\\left(\\sin\\frac43a- 0.749290\\sin1.779463a\\right) \\qquad\\text{for }0 \\le a \\le \\frac32\\pi$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for n in range(3):\n", " i = n+1\n", " ld(r'q_%d=%+10f\\left(\\sin\\frac43a-%10f\\sin%1fa\\right)' % (i,C[n],l0/li[n],li[n]),\n", " r'\\qquad\\text{for }0 \\le a \\le \\frac32\\pi')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Free vibration phase, $a\\ge 3\\pi/2 = a_1$\n", "\n", "When the forced phase end, the system is in free vibrations and we can determine the constants of integration requiring that the displacements and velocities of the free vibration equal the displacements and velocities of the forced response at $t=t_0$.\n", "\n", "\\begin{align}\n", " + (\\cos\\lambda_i a_1)\\, A_i + (\\sin\\lambda_i a_1)\\, B_i &= \n", " q_i(a_1) \\\\ \n", " - (\\sin\\lambda_i a_1)\\, A_i + (\\cos\\lambda_i a_1)\\, B_i &= \n", " \\frac{\\dot q_i(a_1)}{\\lambda_i}\n", "\\end{align}\n", "\n", "Because the coefficients form an othogonal matrix,\n", "\n", "\\begin{align}\n", " A_i &= + (\\cos\\lambda_i a_1)\\, q_i(a_1)\n", " - (\\sin\\lambda_i a_1)\\, \\frac{\\dot q_i(a_1)}{\\lambda_i}\\\\\n", " B_i &= + (\\sin\\lambda_i a_1)\\, q_i(a_1) \n", " + (\\cos\\lambda_i a_1)\\, \\frac{\\dot q_i(a_1)}{\\lambda_i}.\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a1 = 3*pi/2\n", "q_a1 = C*(sin(l0*a1)-l0*sin(li*a1)/li)\n", "v_a1 = C*l0*(cos(l0*a1)-cos(li*a1))\n", "\n", "ABs = []\n", "for i in range(3):\n", " b = array((q_a1[i], v_a1[i]/li[i]))\n", " A = array(((+cos(li[i]*a1), -sin(li[i]*a1)), \n", " (+sin(li[i]*a1), +cos(li[i]*a1))))\n", " ABs.append(A@b)\n", "ABs = array(ABs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analytical expressions" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "Modal responses for $a_1 \\le a$." ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{1} = +3.648\\cos 0.158a +1.420\\sin 0.158a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{2} = +0.318\\cos 1.315a -0.014\\sin 1.315a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$$q_{3} = +0.010\\cos 1.779a +0.018\\sin 1.779a$$" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Latex(r'Modal responses for $a_1 \\le a$.'))\n", "for n in range(3):\n", " i, l, A_, B_ = n+1, li[n], *ABs[n]\n", " display(Latex((r'$$q_{%d} = '+\n", " r'%+6.3f\\cos%6.3fa '+\n", " r'%+6.3f\\sin%6.3fa$$')%(i, A_, l, B_, l)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Stitching the two responses\n", "\n", "We must evaluate numerically the analytical responses" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ac = a[:,None]\n", "\n", "q = where(ac<=a1,\n", " C*(sin(l0*ac)-l0*sin(li*ac)/li),\n", " ABs[:,0]*cos(li*ac) + ABs[:,1]*sin(li*ac))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting the Analytical Response\n", "First, we zoom around $a_1$ to verify the continuity of displacements and velocities" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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AgAPCtddeq8suu0y33nqrdu/eraGhIX3lK1/R8uXLOx0aPc0AAADzxeELerXu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\nzDZE6l4haVDSbdH7OLY74HmgkTZ5mfx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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a49d2518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# #### Plot zooming around a1\n", "low, hi = int(0.8*a1*nsppi/pi), int(1.2*a1*nsppi/pi)\n", "for i, line in enumerate(plt.plot(a[low:hi]/pi, q[low:hi]), 1):\n", " line.set_label('$q_{%d}$'%i)\n", "plt.title('Modal Responses, zoom on transition zone')\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "next, the modal responses over the interval $0 \\le a \\le 16\\pi$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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GSdG0riPt4grEuBxMuKN34YiPD0xfxaLtR60OSwghqrSMXB+jJixlb3ouMS47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\nbfvxegz3ntuPG8Ycp+RaRBo0JdgumT59Ovv27WPHjh3ceeedrFu3riz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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a4ea47b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# #### Plot in 0 ≤ a ≤ 16 pi\n", "for i, line in enumerate(plt.plot(a/pi, q), 1):\n", " line.set_label('$q_{%d}$'%i)\n", "plt.title('Modal Responses')\n", "plt.xlabel(r'$\\omega_0 t/\\pi$')\n", "plt.legend(loc='best');\n", "plt.xticks()\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nodal responses" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = [email protected]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why `x = [email protected]` rather than `x = Psi@q`? Because for different reasons (mostly, ease of use with the plotting libraries) we have all the response arrays organized in the shape of `(Nsteps × 3)`. \n", "\n", "That's equivalent to say that `q` and `x`, the Pyton objects, are isomorph to $\\boldsymbol q^T$ and $\\boldsymbol x^T$ and because it is $$\\boldsymbol x^T = (\\boldsymbol\\Psi \\boldsymbol q)^T = \\boldsymbol q^T \\boldsymbol \\Psi^T,$$\n", "in Python to write `x = [email protected]` we have.\n", "\n", "That said. here are the plot of the nodal responses. Compare with the numerical solutions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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rjWIs/xLwYkHzPvroowoZW14pdR6yXFwW9yPN4qIAOObfkOWNniTLcXEFfueL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4bPGGO2ut12Q/CiCnmP4b6qX4h7Pc4FzrfWVgY1qtA1EtdrvQEIA5YB51prS4MaVWi4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\npmNiEeOvAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd1a48a83c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i, line in enumerate(plt.plot(a/pi, x), 1):\n", " line.set_label('$x_{%d}/\\delta$'%i)\n", "plt.title('Normalized Nodal Displacements — analytical solution')\n", "plt.xlabel(r'$\\omega_0 t / \\pi$')\n", "plt.legend(loc='best')\n", "plt.show();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

eds-uga/csci1360-fa16

assignments/A2/A2_header.ipynb

1

1622

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "nbgrader": { "grade": false, "grade_id": "a1_intro", "locked": true, "solution": false } }, "source": [ "# Assignment 2\n", "CSCI 1360: Foundations for Informatics and Analytics\n", "\n", "## Important Dates\n", "\n", " - Released: 2016-09-01 at 12am [EDT](http://www.timeanddate.com/worldclock/usa/athens)\n", " - Deadline: 2016-09-08 at 11:59:59pm [EDT](http://www.timeanddate.com/worldclock/usa/athens)\n", "\n", "## Grading Breakdown\n", "\n", " - Q1: 10pts\n", " - Q2: 15pts\n", " - Q3: 15pts\n", " - Q4: 15pts\n", " - Q5: 15pts\n", " - Q6: 15pts\n", " - Q7: 15pts\n", " \n", "Total: 100pts\n", " \n", "## Overview\n", "\n", "This assignment will familiarize you with the built-in Python data structures, looping, conditionals, and exceptions.\n", "\n", "Remember in your code to delete (or comment out) any lines that say `raise NotImplementedError()` and replace it with your own code." ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

wasit7/tutorials

django/django_generic_view/generic/.ipynb_checkpoints/edit_widget-checkpoint.ipynb

1

2906

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from django.forms import modelform_factory" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m=modelform_factory(Car,exclude=([]), widgets={\"BigIntegerField\": Textarea()} )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[u'Meta',\n", " '__class__',\n", " '__delattr__',\n", " '__dict__',\n", " '__doc__',\n", " '__format__',\n", " '__getattribute__',\n", " '__getitem__',\n", " '__hash__',\n", " '__html__',\n", " '__init__',\n", " '__iter__',\n", " '__module__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", " '__unicode__',\n", " '__weakref__',\n", " '_clean_fields',\n", " '_clean_form',\n", " '_get_validation_exclusions',\n", " '_html_output',\n", " '_meta',\n", " '_post_clean',\n", " '_save_m2m',\n", " '_update_errors',\n", " 'add_error',\n", " 'add_initial_prefix',\n", " 'add_prefix',\n", " 'as_p',\n", " 'as_table',\n", " 'as_ul',\n", " 'base_fields',\n", " 'changed_data',\n", " 'clean',\n", " u'declared_fields',\n", " 'errors',\n", " 'field_order',\n", " 'full_clean',\n", " 'has_changed',\n", " 'has_error',\n", " 'hidden_fields',\n", " 'is_multipart',\n", " 'is_valid',\n", " 'media',\n", " 'non_field_errors',\n", " 'order_fields',\n", " 'prefix',\n", " 'save',\n", " 'use_required_attribute',\n", " 'validate_unique',\n", " 'visible_fields']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(m)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Django Shell-Plus", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

ipa-lth/jupyter_nb

optimization/2010-Dissertation-Dilyana_Yankulova-picos.ipynb

1

31773

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "* 29 Aug. 15: Picos 1.1.1 Released\n", " Minor release with following changes:\n", "\n", " **Initial support for the SDPA solver (with the option solver='sdpa', picos works as a wrapper around the SDPA executable based on the write_to_file() function; thanks to Petter Wittek )**\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from __future__ import division" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import picos as pic\n", "import cvxopt as cvx\n", "import control as con\n", "\n", "from numpy import linalg as LA\n", "from scipy.special import comb as nchoosek # n Choose k (n ueber k)\n", "import optim_tools as optim_tools#own file with helper" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "###########################\n", "# Hydraulischer Aktor #\n", "###########################\n", "\n", "A0 = np.matrix([[0, 1, 0],\n", " [-10, -1.167, 25],\n", " [0, 0, -0.8]])\n", "print \"Eigenvalues: {}\".format(LA.eigvals(A0))\n", "#a = -A[-1,:].T ### !!!!!\n", "#print a\n", "b0 = np.matrix([[0],[0],[2.4]])\n", "c0 = np.matrix([1, 0, 0])\n", "d0 = np.matrix([0])\n", "u_max = 10.5\n", "n = 3\n", "\n", "X00 = [np.matrix([-20.0, -10.0, -10.0]).T,\n", " np.matrix([-20.0, -10.0, 10.0]).T,\n", " np.matrix([-20.0, 10.0, -10.0]).T,\n", " np.matrix([-20.0, 10.0, 10.0]).T,\n", " np.matrix([20.0, -10.0, -10.0]).T,\n", " np.matrix([20.0, -10.0, 10.0]).T,\n", " np.matrix([20.0, 10.0, -10.0]).T,\n", " np.matrix([20.0, 10.0, 10.0]).T,\n", " ]\n", "\n", "#print \"A:\\n\", A\n", "#print \"a:\\n\", a\n", "#print \"b:\\n\", b\n", "#print \"c:\\n\", c\n", "\n", "# Convert to Normalform\n", "(A1, b1, c1, d1), T1, Q1 = optim_tools.get_Steuerungsnormalform(A0, b0, c0.T, d0)\n", "a1 = -A1[-1][:].T #!!!!\n", "print \"T1:\\n\", T1\n", "\n", "# Convert to Normalform\n", "ss, T2 = con.canonical_form(con.ss(A0, b0, c0, d0), form='reachable')\n", "\n", "assert np.allclose(T1*X00[1],\n", " optim_tools.reverse_x_order(T2*X00[1])),\\\n", "\"own Steuerungsnormalform Transformation not equal python control version\"\n", "#print \"x_r1:\\n\", T1*X00[1]\n", "#print \"x_r2(backwards):\\n\", optim_tools.reverse_x_order(T2*X00[1])\n", "\n", "A = optim_tools.reverse_x_order(np.matrix(ss.A))\n", "a = -A[-1][:].T #!!!!\n", "\n", "b = optim_tools.reverse_x_order(np.matrix(ss.B))\n", "c = optim_tools.reverse_x_order(np.matrix(ss.C))\n", "d = optim_tools.reverse_x_order(np.matrix(ss.D)) # == 0!\n", "\n", "print \"A:\\n\", A\n", "assert np.allclose(A, A1)\n", "print \"a:\\n\", a\n", "assert np.allclose(a, a1)\n", "print \"b:\\n\", b\n", "assert np.allclose(b, b1)\n", "print \"c:\\n\", c\n", "assert np.allclose(c, c1.T)\n", "\n", "X0 = [T1.dot(x0) for x0 in X00]\n", "#print \"X0:\\n\"\n", "#for x0 in X0:\n", "# print x0\n", "#print \"A1:\\n\", A1\n", "#print \"a1:\\n\", a1\n", "#print \"b1:\\n\", b1\n", "#print \"c1:\\n\", c1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pmin = 0.1" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Testsystem aufsetzen\n", "import control as con\n", "\n", "# pt2 System\n", "K = 1\n", "d = 0.5\n", "T = 10\n", "delay = 0\n", "\n", "a0 = 1\n", "a1 = (2 * d * T) #16\n", "a2 = (T**2) #100\n", "b0 = K\n", "\n", "# Polynom\n", "tf_1 = con.matlab.tf(K, [a2, a1, a0])\n", "#print tf_1\n", "\n", "# Zustandsraum\n", "#ss_1a = con.matlab.tf2ss(tf_1)\n", "#print ss_1a\n", "\n", "# Füge Zeitversatz zu\n", "d_num, d_den = con.pade(delay, 1)\n", "tf_delay = con.tf(d_num, d_den)\n", "ss_delay = con.series(tf_delay, tf_1)\n", "\n", "ss_1a = con.matlab.tf2ss(ss_delay)\n", "\n", "############################################################\n", "############################################################\n", "############################################################\n", "############################################################\n", "\n", "# Sammle Systemteile\n", "A0 = ss_1a.A\n", "b0 = ss_1a.B\n", "C0 = ss_1a.C\n", "d0 = ss_1a.D\n", "\n", "# Max output\n", "u_max = 0.1\n", "\n", "# Einzugsgebiet (convex)\n", "X0 = [np.matrix([-1, -0.1]).T,\n", " np.matrix([-1, 0.1]).T,\n", " np.matrix([1, -0.1]).T,\n", " np.matrix([1, 0.1]).T]\n", "#print len(X0)\n", "\n", "# Transformation in Regelungsnormalform\n", "ss, T = con.canonical_form(con.ss(A0, b0, C0, d0), form='reachable')\n", "\n", "\n", "A = optim_tools.reverse_x_order(np.matrix(ss.A))\n", "a = -A[-1][:].T #!!!!\n", "\n", "b = optim_tools.reverse_x_order(np.matrix(ss.B))\n", "C = optim_tools.reverse_x_order(np.matrix(ss.C))\n", "d = optim_tools.reverse_x_order(np.matrix(ss.D)) # == 0!\n", "print \"A:\\n\", A\n", "print \"a:\\n\", a\n", "print \"b:\\n\", b\n", "#print C\n", "\n", "n = b.size" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# !!!!!\n", "# This Section is disabled \n", "# !!!!!\n", "\n", "##############################\n", "# Boris Paper UBoot #\n", "##############################\n", "A = np.matrix([[0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., -0.005]])\n", "\n", "############### TODO ###################\n", "############### CHECK ##################\n", "#last line of Regelungsnormalform (characteristical polynom coefficients) in a colunm vector\n", "a = -np.matrix(A[np.array(A).shape[1]-1])[np.newaxis].T \n", "\n", "b = np.matrix([[0], [0], [1.]])\n", "\n", "d = 0\n", "c = np.matrix([[1], [0], [0]]).T\n", "\n", "print np.matrix(con.matlab.pole(con.matlab.ss(A, b, c, d))).T\n", "\n", "u_max = 2.5e-5\n", "\n", "X0 = [np.matrix([-10, -0.05, -0.0046]).T,\n", " np.matrix([-10, -0.05, 0.0046]).T,\n", " np.matrix([-10, 0.05, -0.0046]).T,\n", " np.matrix([-10, 0.05, 0.0046]).T,\n", " np.matrix([ 10, -0.05, -0.0046]).T,\n", " np.matrix([ 10, -0.05, 0.0046]).T,\n", " np.matrix([ 10, 0.05, -0.0046]).T,\n", " np.matrix([ 10, 0.05, 0.0046]).T]\n", "\n", "x_max = [10, 0.05, 0.0046]\n", "\n", "#simulation time\n", "T = 1.0/200.0\n", "\n", "#Introduced for fun\n", "u_max_sys = u_max\n", "\n", "#a_hat = np.matrix([[4.4469e-8], [2.3073e-5], [4.9148e-3]])\n", "#a_hat_star = np.matrix([[1.073e-7], [4.919e-5], [10.4078e-3]])\n", "\n", "#R1 = np.matrix([[1.6021e-5, 3.26098e-3, 0.4031],\n", "# [3.2698e-3, 1.5666, 163.46],\n", "# [0.4031, 163.46, 40.713]])\n", "\n", "##### Entwurf parameter #####\n", "beta = 2 # beta >=1 !\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import picos;picos.tools.available_solvers()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "######################################################\n", "# Helper for Constraint variant (4.65) -> Q_sum #\n", "######################################################\n", "\n", "# TODO: Was ist m? Kann man das berechnen oder wird das festgelegt m<=2n-1, meistens n+1?\n", "\n", "from scipy.special import comb as nchoosek # n Choose k (n ueber k)\n", "\n", "def get_a_list(a, Q, z, m):\n", " n = len(a) # dim of a, z, Q(nxn)\n", " m = m # Is this to choose or somehow given?\n", " l = m+1 # length of the array with all a_i in it i:{0,m}\n", " H = lambda k: optim_tools._H(k, n) # Convinence function to fix n and make specialized H(k)\n", " N = optim_tools._N(n)\n", "\n", " a_list = [np.zeros((n, n))] * l\n", " for i in range(0, l): # m eingeschlossen\n", " if i <= (m-1)/2.0: # 0 <= i <= (m-1)/2\n", " for k in range(1, i+1):\n", " a_list[i] += a.T * H(n+k-i)*Q*N*H(n-k+1)*a - z.T*N*H(n-i)*a\n", " elif i <= m: # (m-1)/2 < i <=m\n", " for k in range(1, 2*n-i+1):\n", " a_list[i] += a.T * H(k)*Q*N*H(2*n-i-k+1)*a\n", " else:\n", " # this branch is currently not possible in this program\n", " print \"i={} < m={}\".format(i, m)\n", " a_list[i] = 0\n", " \n", " return a_list\n", "\n", "\n", "def trans_a_list(a_list, eps):\n", " l = len(a_list)\n", " m = l-1 #biggest index in a_list\n", " a1_list = [np.zeros(a_list[0].shape)] * l\n", " for j in range(0, l): #for each in a_list\n", " for i in range(j, l): # for each coefficient including m\n", " a1_list[j] += nchoosek(i, i-j) * ((1.0+eps)/(1.0-eps))**(i-j) * ((1.0-eps)/(2.0))**i * a_list[i]\n", " return a1_list\n", "\n", "def calc_a_Sum(a_list):\n", " l = len(a_list) # number of matrizen in a_list\n", " m = l-1 # Index of Q_m\n", " n = a_list[0].size[0] # shape of each matrix, first element\n", " \n", " if m is 0:\n", " a_sum = cvxpy.bmat([[2*a_list[0], np.zeros(n)], \n", " [np.zeros(n), np.zeros(n)]])\n", " elif m is 1:\n", " a_sum = cvxpy.bmat([[2*a_list[0], a_list[1]],\n", " [a_list[1], np.zeros(n)]])\n", " else: # e.g. m is 2 or more\n", " a_sum = cvxpy.bmat([[2*a_list[0], a_list[1]],\n", " [a_list[1], 2*a_list[2]]])\n", "\n", " for i1 in range(3, l, 2):\n", " S_new_col = cvxpy.vstack(np.zeros((((i1+1)/2-1)*n, n)), a_list[i1])\n", "\n", " if i1 is m:\n", " S_new_row = cvxpy.hstack(np.zeros((n, ((i1+1)/2-1)*n)), a_list[i1], np.zeros((n,n)))\n", " else:\n", " S_new_row = cvxpy.hstack(np.zeros((n, ((i1+1)/2-1)*n)), a_list[i1], 2*a_list[i1+1])\n", "\n", " a_sum = cvxpy.bmat([[a_sum, S_new_col],\n", " [S_new_row]])\n", "\n", " a_sum = -0.5*a_sum\n", " \n", " return a_sum\n", "\n", "def calc_lmi_cond(a_sum, n):\n", " k = a_sum.size[1] / n # This dimension is from Boris. Dilyana does not specifiy the dimension though\n", "\n", " J = np.hstack([np.zeros((n*(k-1), n)), np.eye(n*(k-1))])\n", " C = np.hstack([np.eye(n*(k-1)), np.zeros((n*(k-1), n))])\n", " return np.vstack([C, J]), n*(k-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define and solve optimations problem" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective variant (4.66) -> max(det(Q)) #\n", "# Volumenmaximierung -> geomean(Q) #\n", "###########################################\n", "# Problem\n", "prob_451 = pic.Problem()\n", "\n", "# Variables\n", "QQ = prob_451.add_variable('Q', (n, n), vtype='symmetric')\n", "zz = prob_451.add_variable('z', n)\n", "\n", "\n", "# Constants\n", "XX0 = pic.new_param('X0', X0)\n", "N = cvx.matrix(optim_tools._N(n))\n", "\n", "# Constraints\n", "#(4.59)\n", "prob_451.add_constraint(QQ >> 0)\n", "\n", "#(4.60)\n", "prob_451.add_constraint(QQ*(A.T+a*b.T) + (A+b*a.T)*QQ - zz*b.T - b*zz.T << 0)\n", "\n", "#(4.61)\n", "prob_451.add_constraint(QQ*N+N*QQ << 0) \n", "\n", " #(4.62)\n", "prob_451.add_list_of_constraints([((1 & XX0[i].T) //\n", " (XX0[i] & QQ )) >> 0\n", " for i in range(0, len(X0))])\n", "\n", "#(4.63)\n", "prob_451.add_constraint(((u_max**2 - a.T*QQ*a + 2*a.T*zz & zz.T) //\n", " (zz & QQ )) >> 0) \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Constraint variant (4.64) #\n", "###########################################\n", "\n", "m = n\n", "\n", "## BEWARE OF SECOND RANGE ####\n", "prob_451.add_list_of_constraints(\n", " [pic.sum(\n", " [nchoosek(i, k) * a.T * optim_tools._P(0, i-k, n) * QQ * N * optim_tools._P(0, k, n) * a - zz.T * N * optim_tools._P(n, i, n) * a\n", " for k in range(0,i)], 'k', '[0,1,..,i]')>=0\n", " for i in range(1, m+1)])" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "###########################################\n", "# Constraint variant (4.65) -> Q_sum #\n", "###########################################\n", "\n", "m = n\n", "\n", "# Preparation for constraint (4.65)\n", "a_list = get_a_list(a, Q, z, m) # Matrizes of Polynom coefficients: S(p)=S_0 + S_1*p + ... S_n*p^n\n", "a1_list = trans_a_list(a_list, pmin) # Intervaltransformation p:[0,1] -> p1:[-1,1] (S1 -> S^tilde)\n", "a1_sum = calc_a_Sum(a1_list) # S^tilde_sum Matrix (30)\n", "CJ, l = calc_lmi_cond(a1_sum ,n) # \"Selection matrizes\" of (31), l=dimension of P and G\n", "\n", "# Further variables of optimization\n", "S = cvxpy.Semidef(l) #symmetrical\n", "G = cvxpy.Variable(l,l) #skew\n", "\n", "# Constraints on new variables\n", "constraint_G = G + G.T == 0 # skew symmetry\n", "constraint_S = S == S.T # symmetry\n", "\n", "# Actual constraint\n", "constraint_465 = a1_sum << CJ.T * cvxpy.bmat([[-S, G],\n", " [G.T, S]]) * CJ" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective disabled because problem is not feasible with the objective anymore :-/\n", "###########################################\n", "\n", "#ss = prob_451.add_variable('s', 1)\n", "#prob_451.add_constraint(pic.geomean(QQ) > ss) \n", "#prob_451.add_constraint(pic.detrootn(QQ) > ss) \n", "\n", "#prob_451.set_objective('max', ss)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print \"Objective: {}\\n\".format(prob_451.objective)\n", "prob_451.solve(verbose=1, solver='cvxopt', solve_via_dual = None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Output\n", "print \"Status:\", prob_451.status\n", "print prob_451\n", "\n", "Q_451 = QQ.value\n", "R1_451 = LA.inv(QQ.value)\n", "a_hat_451 = R1_451.dot(zz.value)\n", "\n", "print \"Q:\\n\", Q_451\n", "print \"R1:\\n\", R1_451\n", "print \"z:\\n\", zz.value\n", "print \"a_hat:\\n\", a_hat_451\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "print \"Checking constraints (no output is ok!)\"\n", "eig_459 = np.linalg.eigvals(QQ.value)\n", "if np.any(eig_459 <= 0): print(\"Q not pos definit: {}\".format(eig_459))\n", "\n", "eig_460 = np.linalg.eigvals((QQ*(A.T+a*b.T)+(A+b*a.T)*QQ-zz*b.T-b*zz.T).value)\n", "if np.any(eig_460 >= 0): print(\"4.60 not neg definit: {}\".format(eig_460))\n", "\n", "eig_461 = np.linalg.eigvals((QQ*N+N*QQ).value)\n", "if np.any(eig_461 >= 0): print(\"4.61 not neg definit: {}\".format(eig_461))\n", " \n", "eig_463 = np.linalg.eigvals(((u_max**2-a.T*QQ*a+2*a.T*zz & zz.T) // (zz & QQ)).value)\n", "if np.any(eig_463 <= 0): print(\"4.63 not pos definit: {}\".format(eig_463))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Objective variant (4.70) #\n", "# Max. Abklingrate #\n", "###########################################\n", "# Problem\n", "prob_452 = pic.Problem()\n", "\n", "# Variables\n", "QQ = prob_452.add_variable('Q', (n, n), vtype='symmetric')\n", "zz = prob_452.add_variable('z', n)\n", "\n", "\n", "# Constants\n", "XX0 = pic.new_param('X0', X0) #We also point out that new_param() converts lists into vectors and lists of lists into matrices (given in row major order)\n", "N = cvx.matrix(optim_tools._N(n))\n", "\n", "# Bisection parameter\n", "beta = pic.new_param('beta', 0) # sign='positive'\n", "\n", "\n", "# Constraints\n", "#(4.59)\n", "prob_452.add_constraint(QQ >> 0)\n", "\n", "#(4.60)\n", "prob_452.add_constraint(QQ*(A.T+a*b.T) + (A+b*a.T)*QQ - zz*b.T - b*zz.T << 0)\n", "\n", "#(4.61)\n", "prob_452.add_constraint(QQ*N+N*QQ << 0) \n", "\n", " #(4.62)\n", "prob_452.add_list_of_constraints([((1 & XX0[i].T) //\n", " (XX0[i] & QQ )) >> 0\n", " for i in range(0, len(X0))])\n", "\n", "#(4.63)\n", "prob_452.add_constraint(((u_max**2 - a.T*QQ*a + 2*a.T*zz & zz.T) //\n", " (zz & QQ )) >> 0) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#(4.71)\n", "constraint_471 = prob_452.add_constraint(QQ*(A.T + a*b.T) + (A + b*a.T)*QQ - zz*b.T - b*zz.T + 2*beta*QQ << 0, ret=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "###########################################\n", "# Constraint variant (4.64) #\n", "###########################################\n", "\n", "m = n\n", "\n", "## BEWARE OF SECOND RANGE ####\n", "prob_452.add_list_of_constraints(\n", " [pic.sum(\n", " [nchoosek(i, k) * a.T * optim_tools._P(0, i-k, n) * QQ * N * optim_tools._P(0, k, n) * a - zz.T * N * optim_tools._P(n, i, n) * a\n", " for k in range(0,i)], 'k', '[0,1,..,i]')>=0\n", " for i in range(1, m+1)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def bisect_max(l, u, func_solve_with_param, prob,\n", " bisection_tol=1e-3, bisect_verbose=False):\n", "\n", " #if (u is not None):\n", " # # cross check bound\n", " # if (u < l): raise ValueError(\"upperBound({}) < lowerBound({})\".format(u, l))\n", " #\n", " #elif (u is None) and (l is not None):\n", " # # First iteration to find upper bound\n", " # u = l + 1.0\n", " # if bisect_verbose: print \"processing upper bound: {}\".format(u)\n", " # \n", " # parameter.value = u\n", " # problem = accurate_solve(problem, solver, **kwargs_solver)\n", " # uStatus = problem.status\n", " # \n", " # while 'optimal' in uStatus:\n", " # #if u >= l: # shift upper bound if found feasible, this condition is always true\n", " # #l = u\n", " # u = 2.0*u\n", " # if bisect_verbose:\n", " # print \"processing upper bound: {}\".format(u)\n", " # parameter.value = u\n", " # problem = accurate_solve(problem, solver, **kwargs_solver)\n", " # uStatus = problem.status\n", " # print 'found bounds: [{}-{}]'.format(l, u)\n", " #else:\n", " # raise ValueError(\"Not implemented\")\n", " \n", " # check validity solution of l is optimal, solution of u is infeasible\n", " val_opt = l\n", " problem_opt, lStatus = func_solve_with_param(prob, l) # Set lower bound and solve, shall return (problem, status[True/False])\n", "\n", " problem, uStatus = func_solve_with_param(prob, u) # Set upper bound and solve, shall return (problem, status[True/False])\n", "\n", " if lStatus is False and uStatus is True:\n", " raise ValueError(\"UpperBound({})={}, LowerBound({})={}\".format(u, uStatus, l, lStatus))\n", "\n", " while u - l >= bisection_tol:\n", " val = (l + u) / 2.0 # Solve for parameter in the middle of upper and lower bound\n", " \n", " ## solve the feasibility problem\n", " problem, status = func_solve_with_param(problem_opt, val)\n", "\n", " if bisect_verbose:\n", " print \"Range: {}-{}; parameter {} -> {}\".format(l, u, val, problem.status)\n", "\n", " if status is False:\n", " u = val\n", " else:\n", " l = val\n", " val_opt = val\n", " problem_opt = problem #Store last (optimal) solved problem\n", "\n", " # Solve it a last time with the last optimal value, to set the status of problem (since it is not a copy but a ref)\n", " problem_opt, _ = func_solve_with_param(problem_opt, val_opt)\n", " return problem_opt, val_opt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def picos_solve_with_param(problem, val):\n", " global constraint_471\n", " #beta = pic.new_param('beta', val) # sign='positive'\n", "\n", " constraint_471.delete()\n", " constraint_471 = problem.add_constraint(QQ*(A.T + a*b.T) + (A + b*a.T)*QQ - zz*b.T - b*zz.T + 2*val*QQ << 0, ret=True)\n", " \n", " problem.solve(verbose=0, solver='cvxopt', solve_via_dual = None)\n", " return problem, problem.status == 'optimal'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "\n", "problem_opt, val_opt = bisect_max(0, 2, picos_solve_with_param, prob_452)\n", "print \"Objective variant (4.5.2) -> Bisection(Max. Abklingrate)\"\n", "print \"Bisection Param:\", val_opt\n", "\n", "# Output\n", "print \"Status:\", problem_opt.status\n", "#print problem_opt\n", "\n", "Q_452 = QQ.value\n", "R1_452 = LA.inv(QQ.value)\n", "a_hat_452 = R1_452.dot(zz.value)\n", "\n", "print \"Q:\\n\", Q_452\n", "print \"R1:\\n\", R1_452\n", "print \"z:\\n\", zz.value\n", "print \"a_hat:\\n\", a_hat_452\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper functions to run simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T = np.arange(0, 5, 1e-2) \n", "\n", "#s: input, e.g., step function with amplitude of 0.2\n", "s = np.zeros(len(T));\n", "\n", "p_init = pmin\n", "\n", "# Initial state\n", "x0 = np.matrix([[20.],[10.],[10.]])\n", "\n", "p_t = np.zeros(len(T))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Skalierte Version! aus Yankulova\n", "\n", "# k(v) (4.5)\n", "def get_k(v, a, a_hat):\n", " try:\n", " v = v.squeeze() # This is weird, needed for minimize for some reason\n", " except:\n", " pass\n", " D_inv = optim_tools._D_inv(v, len(a))\n", " #print D_inv\n", " k = D_inv.dot(a_hat) - a\n", " return k\n", "\n", "# Fixing a and a_hat for convinience\n", "# func_k = lambda v: get_k(v, a, a_hat)\n", "\n", "# G(v) (4.4)\n", "def get_g(v, x, R1, u_max, a, a_hat):\n", " try:\n", " v = v.squeeze() # This is weird, needed for minimize for some reason\n", " except:\n", " pass\n", " D_inv = optim_tools._D_inv(v, len(x))\n", " R = D_inv.dot(R1).dot(D_inv) # R(v) = D^⁻1 * R1 * D^-1\n", " \n", " k = get_k(v, a, a_hat) # k(v)\n", " e = (u_max**(-2)) * (k.T.dot(LA.inv(R)).dot(k))\n", " #assert e < 1.0\n", " g = e*(x.T.dot(R).dot(x)) - 1.0\n", " #assert g <= 0, \"g = {} > 0\".format(g)\n", " # Update 2016: As of python 3.5, there is a new matrix_multiply symbol, @:\n", " # g = x' @ D^-1 @ R1 @ D^-1 @ x - 1.0\n", " return g" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "last_p = p_init\n", "\n", "def contr_func(y, s, x, u_max, a, a_hat, R1, T1=None):\n", " global last_p, pmin\n", " \n", " # Transformation in Regelungsnormalform\n", " if T1 is None:\n", " T1 = np.eye(len(x))\n", "\n", " x_R = T1*x\n", " \n", " func_g = lambda p: np.absolute(get_g(p, x_R, R1, u_max, a, a_hat))\n", " res = minimize(func_g, last_p, method='Nelder-Mead') # find 0 -fzero\n", " \n", " # Saturate if too small\n", " if res.x < pmin:\n", " p = pmin\n", " elif res.x > 1.0:\n", " p = 1.0\n", " print \"WARNING: p=({})>1! -> p is saturated to 1.0\".format(res.x)\n", " else:\n", " p = res.x\n", "\n", " p_t.append(p)\n", " p2_t.append(res.x)\n", "\n", " last_p = p\n", " \n", " ## Calc K according to p\n", " K = get_k(p, a, a_hat)\n", "\n", " # Calc u\n", " u = s-K.T.dot(x_R)\n", " \n", " # Saturate u\n", " u = optim_tools.sat(u, u_max)\n", " #print \"u\", u\n", " \n", " return u" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulation of WSVC Control" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a_hat_x = np.matrix([[14.345160], [14.837268], [3.598276]])\n", "\n", "R1_x = np.matrix([[1.677977, 0.543373, 0.140299],\n", " [0.543373, 0.32290, 0.062725],\n", " [0.140298, 0.06272, 0.026148]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p_t = []\n", "p2_t = []\n", "\n", "y, u, u_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_451, R1_451, T1), s, T, umax=u_max, x0=x0)\n", "y1, u1, u1_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_x, R1_x, T1), s, T, umax=u_max, x0=x0)\n", "y2, u2, u2_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_452, R1_452, T1), s, T, umax=u_max, x0=x0)\n", "\n", "#y1x, u1x, u1x_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat_x, R1_x, T1), s, T, umax=u_max, x0=x0)\n", "#y1, u1, u1_sat = optim_tools.simulate(A1, b1, c1.T, d1, lambda y, s, x: contr_func(y, s, x, u_max, a, a_hat), s, T, umax=u_max, x0=T1*x0)\n", "\n", "#y2, u2, u2_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-get_k(1, a, a_hat_451).T.dot(T1*x), s, T, umax=u_max, x0=x0)\n", "#y3, u3, u3_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-np.matrix([-0.0833, 0.0828, 0.76068]).dot(x), s, T, umax= u_max, x0=x0)\n", "y4, u4, u4_sat = optim_tools.simulate(A0, b0, c0, d0, lambda y, s, x: s-np.matrix([13.25659, 7.097648, 4.0502]).dot(x), s, T, umax= u_max, x0=x0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (10, 6)\n", "import matplotlib.pyplot as plt\n", "\n", "#plt.figure()\n", "line0, = plt.plot(T[:], np.array(y[0,:].T), 'b', label='WSVC 451')\n", "line1, = plt.plot(T[:], np.array(y1[0,:].T), 'y*-', label='WSVC x')\n", "line2, = plt.plot(T[:], np.array(y2[0,:].T), 'r-', label='WSVC 452')\n", "#line2, = plt.plot(T[:], np.array(y2[0,:].T), 'r-', label='linear(v=1.0)')\n", "#line3, = plt.plot(T[:], np.array(y3[0,:].T), 'g-', label='linear')\n", "line4, = plt.plot(T[:], np.array(y4[0,:].T), 'g-', label='y4 lin, apx evo')\n", "\n", "\n", "\n", "#first_legend = plt.legend(handles=[line1], loc=1)\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.xlabel('T')\n", "plt.ylabel('y')\n", "plt.title('Closed Loop Step Response')\n", "plt.show()\n", "\n", "#plt.figure()\n", "#line0, = plt.plot(T, u_sat, 'b', label='u')\n", "\n", "#line0, = plt.plot(T, u1x_sat, 'b', label='WSVC x')\n", "\n", "line0, = plt.plot(T, u, 'b.-', label='WSVC 451')\n", "line1, = plt.plot(T, u1_sat, 'y*-', label='WSVC x')\n", "line2, = plt.plot(T, u2_sat, 'r-', label='WSVC 452')\n", "#line2b, = plt.plot(T, u2, 'r.-', label='u fixed')\n", "#line3, = plt.plot(T, u3_sat, 'g', label='u, linear')\n", "#line3b, = plt.plot(T, u2, 'g.-', label='u lin')\n", "line4, = plt.plot(T, u4_sat, 'g-', label='u lin, apx evo')\n", "\n", "\n", "#>first_legend = plt.legend(handles=[line1, line2, line1b, line2b], loc=1)\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.xlabel('T')\n", "plt.ylabel('u')\n", "plt.title('Output values')\n", "plt.show()\n", "\n", "line6, = plt.plot(p_t, 'r', label='p')\n", "line7, = plt.plot(p2_t, 'r.', label='p')\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

MTgeophysics/mtpy

examples/workshop/Workshop Exercises Core.ipynb

1

12112

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This workbook contains some examples for reading, analysing and plotting processed MT data. It covers most of the steps available in MTPy. For more details on specific input parameters and other functionality, we recommend looking at the mtpy documentation, which can be found at: https://mtpy2.readthedocs.io/en/develop/.\n", "\n", "This workbook is structured according to some of the key modules in MTPy: Core, Analysis, Imaging, and Modeling." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start with, you will need to make sure MTPy is installed and is working correctly. Please see the installation guide (https://github.com/MTgeophysics/mtpy/wiki/MTPy-installation-guide-for-Windows-10-and-Ubuntu-18.04) for details.\n", "\n", "Before you begin these examples, we suggest you make a temporary folder (e.g. C:/tmp) to save all example outputs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Useful tricks and tips" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This workbook exists as a Jupyter notebook and a pdf. If you are running the Jupyter notebook, you can run each of the cells, modifying the inputs to suit your requirements. Most of these examples have been written to be self contained.\n", "\n", "In Jupyter, you can add the following line to the top of any cell and it will write the contents of that cell to a python script: %%writefile example.py\n", "\n", "You can also select multiple cells and copy them to a new Jupyter notebook.\n", "\n", "Many of the examples below make use of the matplotlib colour maps. Please see https://matplotlib.org/examples/color/colormaps_reference.html for colour map options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Core" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These first few examples cover some of the basic functions and tools that can be used to look at data contained in an edi file, plot it, and make changes (e.g. sample onto different frequencies)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read an edi file into an MT object" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "\n", "# Define the path to your edi file\n", "edi_file = \"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mt_obj contains all the data from the edi file, e.g. impedance, tipper, frequency as well as station information (lat/long). To look at any of these parameters you can type, for example:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-19.01 136.01\n" ] } ], "source": [ "# To see the latitude and longitude\n", "print(mt_obj.lat, mt_obj.lon)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "606300.4060199939 7897760.860594714 95.0\n" ] } ], "source": [ "# To see the easting, northing, and elevation\n", "print(mt_obj.east, mt_obj.north, mt_obj.elev)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many other parameters you can look at in the mt_obj. Just type mt_obj.[TAB] to see what is available.\n", "In the MT object are the Z and Tipper objects (mt_obj.Z; mt_obj.Tipper). These contain all information related to, respectively, the impedance tensor and the tipper." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.256500e+04 9.751601e+03 7.876300e+03 6.188500e+03 5.250801e+03\n", " 4.265799e+03 3.515799e+03 8.437800e+02 6.562798e+02 4.922399e+02\n", " 3.867599e+02 3.164400e+02 2.578400e+02 2.109600e+02 1.728900e+02\n", " 1.367200e+02 1.015600e+02 7.421900e+01 5.761700e+01 4.882800e+01\n", " 4.101600e+01 3.222700e+01 2.636700e+01 2.148400e+01 1.757800e+01\n", " 1.440400e+01 1.147500e+01 8.593800e+00 6.591801e+00 5.371100e+00\n", " 4.394500e+00 3.601100e+00 2.868700e+00 2.304700e+00 1.914100e+00\n", " 1.601600e+00 1.328100e+00 1.074200e+00 8.789100e-01 6.835900e-01\n", " 5.078100e-01 3.710900e-01 2.880900e-01 2.050800e-01 1.318400e-01\n", " 8.789098e-02 6.835900e-02 5.127000e-02 4.028299e-02 3.295900e-02\n", " 2.685500e-02 2.197300e-02 1.709000e-02 1.281700e-02 1.007100e-02\n", " 8.239700e-03 6.713900e-03 5.493201e-03 4.272499e-03 2.822900e-03\n", " 2.059900e-03 1.678500e-03 1.373300e-03 1.068100e-03 7.629400e-04]\n" ] } ], "source": [ "# for example, to see the frequency values represented in the impedance tensor:\n", "print(mt_obj.Z.freq)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[ 26.58566 -4.302123j 482.4492 +604.7747j ]\n", " [-410.0502 -800.4257j 8.994784 +44.07396j ]]\n", "\n", " [[ 12.43271 +7.519158j 434.8246 +514.6176j ]\n", " [-372.7205 -666.402j 17.64062 +36.09528j ]]\n", "\n", " [[ 7.652151 +6.28703j 398.3996 +460.0998j ]\n", " [-349.9875 -580.3959j 21.57495 +33.98854j ]]\n", "\n", " [[ 3.59474 +1.225811j 362.5121 +413.2823j ]\n", " [-328.0029 -501.5329j 25.02421 +33.02813j ]]]\n" ] } ], "source": [ "# or to see the impedance tensor (first 4 elements)\n", "print(mt_obj.Z.z[:4])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[1.15448560e-02 9.52661629e+00]\n", " [1.28742136e+01 3.22072438e-02]]\n", "\n", " [[4.32975088e-03 9.30931663e+00]\n", " [1.19572611e+01 3.31035019e-02]]\n", "\n", " [[2.49056438e-03 9.40578869e+00]\n", " [1.16641228e+01 4.11538240e-02]]\n", "\n", " [[4.66179794e-04 9.76706091e+00]\n", " [1.16060807e+01 5.54922342e-02]]]\n", "[[[ -9.19198953 51.41945343]\n", " [-117.1256163 78.46525668]]\n", "\n", " [[ 31.16505948 49.80396162]\n", " [-119.21840697 63.95414249]]\n", "\n", " [[ 39.40662667 49.11076951]\n", " [-121.09061209 57.59379146]]\n", "\n", " [[ 18.82944281 48.74426019]\n", " [-123.18471678 52.85011776]]]\n" ] } ], "source": [ "# or the resistivity or phase (first 4 values)\n", "print(mt_obj.Z.resistivity[:4])\n", "print(mt_obj.Z.phase[:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with the MT object, you can explore the object by typing mt_obj.Z.[TAB] to see the available attributes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot an edi file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we plot MT data from an edi file." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Figure size 960x720 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "import os\n", "\n", "# Define the path to your edi file and save path\n", "edi_file = \"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "savepath = r\"C:/tmp\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)\n", "\n", "# To plot the edi file we read in in Part 1 & save to file:\n", "pt_obj = mt_obj.plot_mt_response(plot_num=1, # 1 = yx and xy; 2 = all 4 components\n", " # 3 = off diagonal + determinant\n", " plot_tipper = 'yri',\n", " plot_pt = 'y' # plot phase tensor 'y' or 'n'\n", " )\n", "#pt_obj.save_plot(os.path.join(savepath,\"Synth00.png\"), fig_dpi=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make some change to the data and save to a new file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example demonstrates how to resample the data onto new frequency values and write to a new edi file. In the example below, you can either choose every second frequency or resample onto five periods per decade. \n", "To do this we need to make a new Z object, and save it to a file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\mtpywin\\mtpy\\mtpy\\utils\\calculator.py:371: RuntimeWarning: invalid value encountered in double_scalars\n", " z_rel_err = error/z_amp\n" ] }, { "data": { "text/plain": [ "'C:\\\\tmp\\\\Synth00_5ppd.edi'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import required modules\n", "from mtpy.core.mt import MT\n", "import os\n", "\n", "# Define the path to your edi file and save path\n", "edi_file = r\"C:/mtpywin/mtpy/examples/data/edi_files_2/Synth00.edi\"\n", "savepath = r\"C:/tmp\"\n", "\n", "# Create an MT object\n", "mt_obj = MT(edi_file)\n", "\n", "# First, define a frequency array:\n", "# Every second frequency:\n", "new_freq_list = mt_obj.Z.freq[::2] \n", "\n", "# OR 5 periods per decade from 10^-4 to 10^3 seconds\n", "from mtpy.utils.calculator import get_period_list\n", "new_freq_list = 1./get_period_list(1e-4,1e3,5)\n", "\n", "# Create new Z and Tipper objects containing interpolated data\n", "new_Z_obj, new_Tipper_obj = mt_obj.interpolate(new_freq_list)\n", "\n", "# Write a new edi file using the new data\n", "mt_obj.write_mt_file(\n", " save_dir=savepath, \n", " fn_basename='Synth00_5ppd', \n", " file_type='edi',\n", " new_Z_obj=new_Z_obj, # provide a z object to update the data\n", " new_Tipper_obj=new_Tipper_obj, # provide a tipper object\n", " longitude_format='LONG', # write longitudes as 'LONG' not ‘LON’\n", " latlon_format='dd'# write as decimal degrees (any other input\n", " # will write as degrees:minutes:seconds\n", " )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

alexandrejaguar/strata-sv-2015-tutorial

resources/Exploring Graphs.ipynb

5

59378

{ "metadata": { "name": "", "signature": "sha256:61f24f38b76cb12d46c178eadc5d85d61bd0fc27f959236ea756ef8e1adc2693" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Explore Random Graphs Using NetworkX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interact" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "import networkx as nx" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# wrap a few graph generation functions so they have the same signature\n", "\n", "def random_lobster(n, m, k, p):\n", " return nx.random_lobster(n, p, p / m)\n", "\n", "def powerlaw_cluster(n, m, k, p):\n", " return nx.powerlaw_cluster_graph(n, m, p)\n", "\n", "def erdos_renyi(n, m, k, p):\n", " return nx.erdos_renyi_graph(n, p)\n", "\n", "def newman_watts_strogatz(n, m, k, p):\n", " return nx.newman_watts_strogatz_graph(n, k, p)\n", "\n", "def plot_random_graph(n, m, k, p, generator):\n", " g = generator(n, m, k, p)\n", " nx.draw(g)\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n", " generator={'lobster': random_lobster,\n", " 'power law': powerlaw_cluster,\n", " 'Newman-Watts-Strogatz': newman_watts_strogatz,\n", " u'Erd\u0151s-R\u00e9nyi': erdos_renyi,\n", " });" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cnK6urvznn3+KdE8//fQTPTw8Cn1d1mktnp6e7NKlC2vWrElVVVVqamoWaVrL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UcfkZqayo033uhQ/dIBWFnY2RuKZT/n5eXh6uqK1WrFZrNhs9mq/Ny0aVN8fX3Jycnh7MGDUFRU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\nSUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x1076c6e10>" ] } ], "prompt_number": 16 } ], "metadata": {} } ] }

bsd-3-clause

ramch101/juliasets

juliasets3.ipynb

1

3627672

null

mit

ledeprogram/algorithms

class1/homework/najmabadi_shannon_1_4.ipynb

1

4803

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The writer of this code wants to count the mean and median article length for recent articles on gay marriage in the New York Times. This code has several issues, including errors. When they checked their custom functions against the numpy functions, they noticed some discrepancies. Fix the code so it executes properly, retrieves the articles, and outputs the correct result from the custom functions, compared to the numpy functions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests # a better package than urllib2" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_mean(input_list):\n", " list_sum = 0\n", " list_count = 0\n", " for el in input_list:\n", " intel = int(el)\n", " list_sum += intel\n", " list_count += 1\n", " return list_sum / list_count" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def my_median(input_list):\n", " sorted_list = sorted(input_list)\n", " list_length = len(sorted_list)\n", " half_length = int(list_length/2)\n", " \n", " return float(sorted_list[half_length])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "api_key = \"ffaf60d7d82258e112dd4fb2b5e4e2d6:3:72421680\"" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "url = \"http://api.nytimes.com/svc/search/v2/articlesearch.json?q=gay+marriage&api-key=%s\" % api_key" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = requests.get(url)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wc_list = []\n", "for article in r.json()['response']['docs']:\n", " wc_list.append(article['word_count'])" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[25, 576, 920, 868, 684, 1101, 367, 588, 96]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wc_list = [int(i) for i in wc_list if i != None]\n", "wc_list" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "580.5555555555555" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "580.55555555555554" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(wc_list)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "588.0" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_median(wc_list)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "588.0" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(wc_list)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

qubvel/sent_analysis

MVideo/data preprocessing - additional data.ipynb

1

22797

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Анализ дополнительных данных\n", "Отзывы об интернет провайдерах, спарсенные с сайта: http://www.moskvaonline.ru/rating" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews = pd.read_csv('data/internet_reviews (1).csv')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unnamed: 0</th>\n", " <th>comment</th>\n", " <th>rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>Пользуюсь уже более 2лет все в...</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>Подключил только интернет за 5...</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>Подключил себе скорость 100 мб...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>Подключились недавно, скорость...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>Сегодня пришел домой и обнаруж...</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 comment rating\n", "0 0 Пользуюсь уже более 2лет все в... 1\n", "1 1 Подключил только интернет за 5... 2\n", "2 2 Подключил себе скорость 100 мб... 5\n", "3 3 Подключились недавно, скорость... 3\n", "4 4 Сегодня пришел домой и обнаруж... 1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(21713, 3)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(21680, 3)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews = reviews[~reviews.comment.duplicated()]\n", "reviews.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 12876\n", "5 6536\n", "3 2200\n", "2 46\n", "0 14\n", "4 8\n", "Name: rating, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews.rating.value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1d6010bf7f0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZcXgEYGaWKRcAM7NMuQCYmWXKBcDMLFMuAGZmmXIBMDPLlC8DtY6ovTx0+5qpHz6K25eH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\nZpn6ZxAxPBTm+ICsAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1d67e556be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reviews[reviews.comment.apply(len) < 3000].comment.apply(len).hist(bins=50)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# reviews = reviews[reviews.comment.apply(len) < 500]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1d600482d68>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1d60057b748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reviews[reviews.comment.apply(lambda s: len(s.split()) < 200)].comment.apply(lambda s: len(s.split())).hist(bins=30)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

davidgutierrez/HeartRatePatterns

Jupyter/MimicII/0g FullWords.ipynb

1

68311

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import psycopg2\n", "import gc\n", "from psycopg2.extensions import register_adapter, AsIs\n", "from time import time\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def obtainMaxRecords(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = '''SELECT replace(split_part(record, '/',3),'s',''),max(record)\n", " FROM rstq\n", " WHERE cast(replace(split_part(record, '/',3),'s','') as integer)\n", " NOT IN (select subject_id from subjectrecord)\n", " AND centroid IS NOT NULL\n", " GROUP BY split_part(record, '/',3)'''\n", " cur.execute(select_stament)\n", " subject = []\n", " for row in cur :\n", " subject.append({\"subject_id\":int(row[0]),\"record\":row[1]})\n", " conn.close()\n", " return subject" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def insert(words,table,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " insert_statement = 'INSERT into '+table+' (%s) values %s'\n", " columns = words.keys()\n", " values = [words[column] for column in columns]\n", "# print(cur.mogrify(insert_statement, (AsIs(','.join(columns)), tuple(values))))\n", " cur.execute(insert_statement, (AsIs(','.join(columns)), tuple(values)))\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def fillsubjectRecord() :\n", " for subject in obtainMaxRecords() :\n", " insert(subject,\"subjectrecord\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def obtainSubjects(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = '''SELECT subject_id,record FROM subjectrecord'''\n", " cur.execute(select_stament)\n", " subject = []\n", " for row in cur :\n", " subject.append({\"subject_id\":int(row[0]),\"record\":row[1]})\n", " cur.close()\n", " conn.close()\n", " return subject" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def patientIsAlive(patient,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT dod \"\n", " \" FROM patients WHERE subject_id = \"+str(patient)+\" LIMIT 1\"\n", " )\n", " cur.execute(select_stament)\n", " select = []\n", " for row in cur :\n", " select.append(1 if row[0] is not None else 0 )\n", " cur.close()\n", " conn.close()\n", " return select" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def obtainWord(subject,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT centroid \"\n", " \" FROM rstq WHERE record='\"+str(subject)+\"' ORDER BY r_s\"\n", " )\n", " cur.execute(select_stament)\n", " centroids = \"\"\n", " for row in cur :\n", " centroid = row[0]\n", " if centroid is not None :\n", " centroids= centroids+centroid\n", " if(len(centroids)<3600): centroids = None\n", " conn.close()\n", " return centroids" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def deleteWord(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = \"DELETE FROM subjectwords\"\n", " cur.execute(select_stament)\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def insertSubjectWords(words,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " insert_statement=('INSERT INTO subjectwords(word,subject_id,isalive)'\n", " ' SELECT unnest( %(word)s ) ,'\n", " ' unnest( %(subject_id)s) ,'\n", " ' unnest( %(isalive)s)')\n", " word=[r['word'] for r in words]\n", " subject_id=[r['subject_id'] for r in words]\n", " isalive=[r['isalive'] for r in words]\n", "# print(cur.mogrify(insert_statement,locals()))\n", " cur.execute(insert_statement,locals())\n", " conn.commit()\n", " cur.close()\n", " conn.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def createListOfWords() :\n", " subjects = obtainSubjects()\n", " lenSubjects = len(subjects)\n", " deleteWord()\n", " i,j=0,0\n", " words = []\n", " for subject in subjects :\n", " subject_id = subject['subject_id']\n", " print(subject_id)\n", " isAlive = patientIsAlive(subject_id)\n", " if isAlive != [] :\n", " j=j+1\n", " word = obtainWord(subject['record'])\n", " if word is not None:\n", " words.append({'subject_id':subject_id,'word':word,'isalive':isAlive[0]})\n", " insertSubjectWords(words)\n", " print()\n", " print(\"In a list of \"+str(lenSubjects)+\" we know the status of \"+str(j)+\" patients\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def existMatrix(word,subject,dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT 1 \"\n", " \" FROM matrix WHERE subject_id='\"+str(subject)+\"' AND word='\"+str(word)+\"'\"\n", " )\n", " cur.execute(select_stament)\n", " exist = False\n", " for row in cur :\n", " exist = True\n", " cur.close()\n", " conn.close()\n", " return exist" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "def printGroups(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT count(1),LENGTH(word) FROM subjectwords GROUP BY LENGTH(word) ORDER BY LENGTH(word)\"\n", " )\n", " cur.execute(select_stament)\n", " words = []\n", " maximun,minimun = 0,1000000000\n", " for row in cur :\n", " words.append({\"subjects\":row[0],\"wordSize\":row[1]})\n", " maximun = maximun if maximun>row[1] else row[1]\n", " minimun = minimun if minimun<row[1] else row[1]\n", " cur.close()\n", " conn.close()\n", " print(\"maximun\",maximun,\"minimun\",minimun)\n", " minimun-=1\n", " division = (maximun-minimun)/8\n", " means_men = defaultdict(int) #'0':0,'1':0,'2':0,'3':0,'4':0,'5':0,'6':0,'7':0,'8':0,\n", " for r in words :\n", " for x in range(0, 11):\n", " floor = (division*x)+minimun+1\n", " top = division*(x+1)+minimun\n", " if(r['wordSize']>=floor and r['wordSize']<top):\n", " floor = str(int(floor)).zfill(5)\n", " top = str(int(top)).zfill(5)\n", " means_men[floor+\"-\"+top] += r['subjects']\n", " columns = sorted(means_men.keys())\n", " means_men = [means_men[column] for column in sorted(means_men)]\n", " index = np.arange(len(means_men))\n", " bar_width = 0.35\n", " fig, ax = plt.subplots() \n", " for i, v in enumerate(means_men):\n", " ax.text(v-10,i-0.1, str(v), color='white', fontweight='bold')\n", " plt.barh(index,means_men,label='Pacientes')\n", " plt.ylabel('Número de latidos')\n", " plt.xlabel('Pacientes')\n", " plt.title('Pacientes por Número de latidos')\n", " plt.yticks(index + bar_width / 2, (columns))\n", " plt.legend()\n", "# plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def printWords(dbname=\"mimic\") :\n", " conn = psycopg2.connect(\"dbname=\"+dbname)\n", " cur = conn.cursor()\n", " select_stament = (\"SELECT count(1),LENGTH(word) FROM subjectwords GROUP BY LENGTH(word) ORDER BY LENGTH(word)\"\n", " )\n", " cur.execute(select_stament)\n", " x,y = [],[]\n", " for row in cur :\n", " x.append(row[0])\n", " y.append(row[1])\n", " cur.close()\n", " conn.close()\n", " bar_width = 0.35\n", " plt.plot(x, y, 'ro')\n", " plt.ylabel('Número de latidos')\n", " plt.xlabel('Pacientes')\n", " plt.title('Pacientes por Número de latidos')\n", " plt.legend()\n", "# plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fillsubjectRecord done in 96.242s.\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t0 = time()\n", "fillsubjectRecord()\n", "print(\"fillsubjectRecord done in %0.3fs.\" % (time() - t0))\n", "gc.collect()\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5223\n", "15079\n", "20968\n", "20269\n", "11679\n", "23922\n", "20766\n", "19372\n", "3498\n", "14897\n", "4018\n", "5114\n", "6692\n", "10534\n", "11318\n", "3745\n", 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"7234\n", "41902\n", "6673\n", "16961\n", "17798\n", "7683\n", "8870\n", "1029\n", "6075\n", "7567\n", "17412\n", "9667\n", "12319\n", "7470\n", "462\n", "23034\n", "8466\n", "16511\n", "18403\n", "9783\n", "7328\n", "42574\n", "2467\n", "7153\n", "8207\n", "8670\n", "14909\n", "8674\n", "7522\n", "7289\n", "7265\n", "682\n", "408\n", "9005\n", "377\n", "6917\n", "6981\n", "10247\n", "7224\n", "7138\n", "8996\n", "12712\n", "17522\n", "18740\n", "7125\n", "11757\n", "571\n", "7472\n", "9043\n", "42694\n", "20\n", "8432\n", "9685\n", "7977\n", "8871\n", "8009\n", "317\n", "11877\n", "16161\n", "12679\n", "9036\n", "2228\n", "8461\n", "10030\n", "11988\n", "6636\n", "618\n", "17262\n", "1459\n", "9176\n", "16504\n", "7217\n", "17018\n", "12663\n", "5933\n", "11951\n", "279\n", "17457\n", "14651\n", "16447\n", "7009\n", "8406\n", "9871\n", "18837\n", "8936\n", "8121\n", "7782\n", "12704\n", "1569\n", "14448\n", "8984\n", "8122\n", "9949\n", "12215\n", "17366\n", "7303\n", "14777\n", "16881\n", "4859\n", "17717\n", "8964\n", "8198\n", "7360\n", "16909\n", "2514\n", "952\n", "12708\n", "6708\n", "7528\n", "9708\n", "6983\n", "9494\n", "11372\n", "7048\n", "8989\n", "16336\n", "91428\n", "11852\n", "21575\n", "8231\n", "8573\n", "9847\n", "42310\n", "7452\n", "9253\n", "8451\n", "15333\n", "43459\n", "42404\n", "1414\n", "1650\n", "12251\n", "8318\n", "21517\n", "8568\n", "9705\n", "6945\n", "16280\n", "7798\n", "7371\n", "16607\n", "9021\n", "368\n", "\n", "In a list of 1098 we know the status of 1074 patients\n", "createListOfWords done in 2461.363s.\n" ] } ], "source": [ "t0 = time()\n", "createListOfWords()\n", "print(\"createListOfWords done in %0.3fs.\" % (time() - t0))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maximun 29296 minimun 3636\n" ] }, { "data": { "image/png": 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EdABeldQ4j/rViBJKI2A58Jqkc8zseUlnAA9KSgOGE527y8tjwAgz+yK8HwA8LGks0bm9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\ntwNISjezoulnOuecczsolTu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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe0ed32f438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "printGroups()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labelled objects found. \"\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f889530b128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "printWords()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

itdxer/neupy

notebooks/growing-neural-gas/Growing Neural Gas animated.ipynb

1

437992

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-03-30T19:42:39.360507Z", "start_time": "2018-03-30T21:42:39.055830+02:00" }, "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-03-30T19:42:39.981925Z", "start_time": "2018-03-30T21:42:39.362204+02:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10e2eca58>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_moons\n", "data, _ = make_moons(10000, noise=0.06, random_state=0)\n", "plt.scatter(*data.T)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-03-31T18:30:33.870792Z", "start_time": "2018-03-31T20:21:06.204721+02:00" }, "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<video width=\"432\" height=\"288\" controls autoplay loop>\n", " <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAM9TG1kYXQAAAKuBgX//6rcRem9\n", "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTQ4IHIyNjY4IGZkMmMzMjQgLSBILjI2NC9NUEVHLTQg\n", "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE2IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n", "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n", 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DxuJEccvbTgzWO5Iko9tNGW8lJCbHWkFfPWKs0WHdulX8zwWfZddXnEwjq6Ec\nQEBtcDoT69bRP28eNnWYAOYsWMBBJ53Flae/ic5YISrrVyzj8287lLhap90YZ2D6LGbM2YL+GXOY\nMXcBOz5zX8bWrmJsZPXU6xFSctmZ7+P5Lzmcuc8/kL4wIHWOOf21orVYQFaLOOGDp3PGh/6Tna/8\nH9AVtIB1q1eyZPvt6A8LI/sw0Ditsb4oGQuVoB7F5BVf3OLnhm5mWN9MiDXUqiHDlYi0HpNllrUb\nc6TWdFOH9I6x9Ws2O/fd1gSfe+fhVGr9RLU6lVofOq6w4cEVtEbXA2AnNvKHb53B4SecTicrFkwb\nHeh0U3SY4kzRUZiHIaMjqymk8iGiWh+jjQ62BitHa7SijHotpq8SkqQ5gdLgO7R0QD1UvVFTslgc\nFYXlqEJMifHUeS5rmUv+RTylBXlTg3korC6lhCy3JMaQG4ezjnaW0UkMI40ujU5OZgyB0mzo5DTa\nGbUoJLUG4yztVoevn3YcI8vvIu4bpjJ7J5wFn4OxUJ02j5GVq5g1ex61oRhjLFoF9IWG7sTmJWom\nTXnzpy5i3sIt0EqTmSKSd8JSD2L2O+D5fOh1L2Vk1QPMXbCQz37zCq775f9w9Q8v46JzP8aBLzmU\ngw89kv7ZuyCEpxKECAWvPOI/+M3PruT8L3yOo48/ie74CAODQ/TXqxgPLnf0xZIwUiTGkRsQSIJA\n0CcDkjTDa01VKuZpTe4BIQi0oKoUic4Za2qaaU5/rEAGLNp+J+65/aap17bVDrvynk9+hYlWi8bG\n9TRaDfJ2m4vPPGWzc7DqrltpphlJ5jAelCxau0MJSQ4jOdTFeq445xSe/vI3cv+Nv2bD/XcxvMUi\ngiDk2su+zGFvOZlONyPtZkzLHYEUyDBASsdYxyCVIc0k1TgmDlTPwQ+qUhSDWL2c+owAf7WWuaTk\nn+UpJcgPT08Y25u4MflzDyDo5EXrczPJGWl2GW2k5M6QJjnWOJqdjDzvEASSLM/QRIRSYCWc96G3\nMnLvXQAkzY3c+I2TeMZrziKMod4H9enzaI6sxOq96AsCbCWmk+SsuuN6hNh8pOeC7XZiyU6LkQ5k\nKKhLjdfFMSoBeeY4+5IfM1CrECsBXnDYEUdy9NFHseLeZfzwvy/jpLccxRZbbslrjnkdL3vFK6hW\n6ljrOPPsc9h/77045NBX0BgfY8uFW1ELFF4qnCumXYdaFX+CCgJBN83oZjlKSSpakFtPLVbgBUoW\nnhTWObRUbDu3n9XjKUmSkaSWkz9zAZ844XWsuOdOtlm8E5+64DLanQwVBvT39eOVoqIlN/zy2dz6\n218+9J5Yw+qVK4n655JY6K/0Zm7uAAAgAElEQVRApMFZQENz/QT/72vvY8fnvpCtn3sI2+99CL/4\n4sns9qKjmLbFNvz8Sx/mqovP5ah3n4pyjmaSYschikPm1GNS56lh6SLorxbVFLrn1ucD8Mhen0jh\nez2ZQ9aqLFAqefR5ynyqpuqKKdITRcG/JUlz0l6NKt6TG4NzjrFOyshEh/FOTrubMjaR0kpyUpOT\n55Z2nhbjlBysGx3njpt/zw++fAbremI8SWvtPUhViH03hcr0LWiNrSYQgmbqUNZz848v4ycXnsux\nZ5zPgsW7oHTAgsW78KGvXEqsA6JQ0RdFzJlWpa8SMquvwuyBGsP9FYaqMVUtCZSgXgnoizTOOrbb\nbjtOPf1j3PCnO3n3Se/l5//zU56+4xLe/fa3cfP1NzB3zjxO/uCH+egpJ3Hv8mVsuXArgjAgjjT9\n9Yi+SmFjqVSvW00VC2DVKGCgHjF3qI/5w30smDHEzMEafZWYabUq9SjEGEusA7YYjJk/rcasoQp9\ntSpnXfQdfnrj3ZzztW8TRyFDAzWiSoVqvcpQLcZYxxs+eA67Pnt/4lofWz/jOTz3iLfw/z57MhtX\n3YH0EAVFM0piYGK0xe8v/CDzd3k2u73wCIQqapvDgdmMrFlLUK3yond8hJX3LOWycz/KukaTDZ2E\nZtfgrWO01cWmBqEUkRIYY5H0fJRFIcqhKmYiit5nSFAu6JX863jKRMgPT094z9TsukhJPBSz6Kyn\nm+WMNBI6nZRmZhlPDeONNrnNcF6gtUYieHDFvSz97S+463e/IKrW2fHZB9A3Yz7N9aumnlfgue+a\ni9lmn8OQQY3p8+bx56W/ol6pkiRdLv3yx5kYeZBTv3QZs+bO5ulP+wbd3BBpzbRahLEeYwyVQFCJ\nQuJKSE0ppJTU4gzrRK9My9MXR2RZRhRq4lBTiTT9ccTLDzmEg174QtatXcf3vnMZ7z7+rUgpefXR\nxyCAL57zKTqtFt1Om/+64EKU0GilyIwjDjTGOXLjCJQgDALIc4zzRVMFnkoY9ObcQW410+oRWisy\nKQm1Z1pftXC+M0Wtr5aSShgw2uqinCXQmsFaQBwGOBzHfvgc2kmX3EArzRmcOZufnvcJdn3ZW6jt\nuS8DFU0ja/D7r5/K0Ja7sPigo8msQUroq8C0ObMxjbVYC0bVOOCtH+HnX/oQl593Di9/4zvQ/TUy\n4xhtWYZrCtPyDMQRzdwybCyBLga1AoRaFwZG5QJeyb+Bp0zZ26bz7wDSzBRCYz2hEgghi/xkbrl/\nQ4ORRpfRTspYM2X9eJtGp8UPPn86K5bexPDMOeggZGz9GrZ75n7sts+LiGfMJ/eOP377PJbf/FvS\n1jiDW2zD0w4/mbuvvoT1d1/PkgMO52nP3oeL3v96EAKlA7bffW+OPfljzJ8znUAJ2klOlhvqUcBg\nvYLFo5wnjjQD9YhaqMAJurlBCoGSklaa4aWgGgSkWc5ANUbJ4uIzXK8gpaCbGqJAkeYW6yy//93v\n+ObFF/Kdyy7FGDN1Xg552cv52sWXoJXAO0/Yc0nzvUWsVpqB9wRBMUE6703nUKJoKW+leeF6V5hC\nkJvC49kJicWRGY8WoIRg9UQXJQRhqOiPA5zzeOdZMdoiQBDHmg3NjFXrR1l291KuOPeDBFGF9vgG\npNLM2/nZ7HLY2+mvCoSFjoFqCA/ceDX33Xo9z3j1e8lzqITgkha/Ou+DLNrpabz49ScgvSQKNZVI\nEuuIIJTM7IuphRqEYsZgzPz+PqqV4BGrcErj+5J/hLLs7WFsNv/OeVLrkKKwY9RKYq3HuqKkrZ3m\ngKedWjJTmM5/93MfYcXN1wEw8sByZm+9hCM/dhEyLKLVNHekiWX5TddywDs+wez582kmxQLU844+\ngfF1K7jjp9/g4h9fNGXk46yhtXEdg8P9aOnpiwKGqwGp8UhfTF4OlaIaBVQCxUA1QggY72ZoCYHW\nxcBPq0FApKBWiwl6Iuqc690ZSLRkquFBCMGznv0c9nzms7jqR1cyPv7QYuK111xDJSpKxJQWWO97\nk7QLKaoEQa+TrVgEdd7inKMSBng8tTCgmaRoAQiJEx4vJX2hwqLJlSExFu8g0oo4kggv8L3ys1aS\nM1yNQHichUhJpg/0EyzZmd8tWMR9t/xh6lhN0mBmn0AIRdsW6YZGB2w8m4n1awmKrmcM4HWdQ9/9\nCX7wmVP40dc+x4GvOQGtHRsalrlDklDWSI0j0pK5QxEVrekag0gFQSD/ogpnsia59LkoeTR50gvy\npgZBxW1tz7zAObwsvljWeppJSjczpKYQ6kaSY7OMdpKR25wH77xps/1uXL2SibZCpVCJHULBuntv\nJ+obYvrs+USxJlSWSqVCVQeYGYt5+i5n8JGj9t9sP6uW38X0Wox0jkAp4kBQixTVnqhKJQmlINAa\nKRVxqDBeYJ0lNx4lBf21kNQ4BIIoDDDO4fFEupjmrGRx653kxW29lroQFyfZ93n7c8Xl3586nn32\n26+IgHum7tJ5jLXg6e2rWPhyPaGvRQECQSXSU57QOtfF7wiBljBcjwvntGIkNFleDCWtRYqxjsEL\nSyVSReQsFdP7JM4rNjQ7GG/QwlOtBqxddudm5+7B228ga47jo0GkKGw+rYWB6bNpj64lyQonuGoE\neQZj3Sp7vv50rvvqB7jjty+j2xxnzqLFfOAL38R6h3MCpYpGkEpUmBF1ckfkQ7x3GO+LGYkWlJJl\nTXLJo86TWpA3NQjyvdtL631vuocH68nzImLLckPmiseMczQ6Fm8dxloaG0bw1m627+EFO9HsFDaS\nSQIygJU3X8M2e+xDvaoIhaDSV0OKgHolZGLtWi78zGnElRpJpzW1n0WLdyKWgjAKqceKOIqQwoGD\nKNA4UYy9F6Io+VJKUgkUxgm0KsxwtNIIn+E9PWEp8uW4wpJzchFKWVncbjuPc0X32SWXfZujXvlK\nrvn11ez3vP255JJLi2i4F/AJUXSuKVG0kSOKCgPvi6naRdqk2FjJYtpzNVTkVpBbh5GKgbhIldCb\ndReGCo2mGijiOCNJc4xzxEFAfzXCe0lmM5Ss9lqpHZ3MsPMee/HHq382de6GZszistOOY6tn7MP2\nzzuc/ukziAOozRzEZCmdsQ5xX5Uso2guySHW/QipaI8X9d4P3nM7Hz/+NZz8mYuQQzWS3PHgWAch\nIFS6uIsKXbHYh6AWaZxxBNojpShrkkseVZ7UgpybIhLLjJsSFuE9KYWbWmptEQUlBtGbXpzljk7u\nkMJjEIw3mnzn7FPY7YWvZOSB5ay751aCqMLg3AXEURFshzF0mzkrbvkdL/vAuWjAO0GkQvqqAX/8\n2fe48htf5iXHHMf+L/kPzjrxjdx79x1sv+MunHfZ5URKUolDtCxW+6XSdDOLkIKKVlNR6eSCZKAV\nWEGge8+vZSGYpsgrR1HA5AzmyUUpoMgLU4hISq/axHu+demlUxM0nPMEqhjc6lxRb6t7/9dKYpyf\nSv14X1hXhjoAHhr6ild4BFGgqPWc1GRP5K2D1BiUEFRCTS3Q+P7C5Ef1Su1ya+nkkkAZtHDkDlqJ\n4bSzv8xp7zmW22/8A1vv/Aze/IGzuW/lKn7zw2/wk7PeyVZPfw67Pv9w4qHZ1IZm0R5fS+oXISTU\nq4726j+z/M7rGV25bLPPyerlS3lw/ThhoAgVmNyjBEyrBYRhgAo0SktcbugknnocTEXFZU1yyaPJ\nk1KQJ+0VG0le2C16ULJo9c1tERHn1mNMYSqfGkuaGta3k57QQCPLaDa6XPWVTzA8bwGL9vsPtvIC\n66DbWM8vz3kn2z/rIGT/TOp1aNx3c2FtOTQTrxSD1YhOcz3f+vhHAcdpX7yU2ow5zJnZz5cuuZxu\nYhnqD4i1xguo9TwVALKsED8pCt9lvCfWcqpGeVL4JlMJgkKUAx328rpFzlyKYgFqEiVlr+mhEBHn\ninHNkzW1k+LySFUF+dRtut9ErAtx3zR/KqUgCjWB3txruBJoinaLIpVSHJkgCuTUxWDKIB4YDgLS\nwNBOciaaXZLcYp3j5E9+AWs9XnlGGynTZw5zwKuO5RkHvoLbf30VPzzzPczfaU864+u57ivvojZt\nNtMW7MC6u28gqA4wfdEe9M3ckubIAw+dFx3Q7YzSSvswGx0zhms0kgzjYVqfIg4tTgkCrUh6sxOd\np6xJLnnUedII8sPNxr33BEr0ytlcEZnlhrFOglaSVpITSImQnkY7o5MbWmlOu23xOLLE8JNLv0Rr\nfIzD3/dJEidotDxJCiqawcJnvZibfnwRe73mP6lKwQM3XMMOe+3PrMF+avWQ237xY35w4Rc45Kg3\ncuDhxxAEQXFsuUeFkkBbciOZ1hegpSRUktQUEzy8AG8dmYAqAUGgpupiJ6PTyYh/Mh2R5hYlBQ/P\nZG56Oz0p5NYV+eZi6KeYipT/lrhMLopuKtbee/5acDi5XaA3f2+89yg1OSW6OPaHN+wEvTJEKQT1\nOChSJYBFYXNLC0M10Jg+SX8lYKSa0BeFzDriLTznJa/iS/95DDYr5vC1N6xGessBx5+N0bNAg1av\n4/fnn8To/XcxNG8hi3ffhws/+DZef8qnWbzb0+h0DcI7okCTG8NYCyqxIdYaYx3jnS5REFCPNJEK\nsMKR27LqouSf50khyJt6UvheyJhZjxKC3BZNH600QwJZ7sEZksySekPmBNY7MufYMNqklRdR5x9+\nfiW3XfcLjjr1CwQyJtSCluxSCSE1sO0+h/Hzs9/KyPK7qWy3gPtvu54DX/sOZNLg4nM/SWt8lE+e\n9y3mLtqWJLNEUiJDEFpgnaevGpNZixaSeljcAnscTkgCUdhD4hxdYwhUiJCCONBTOeDJVMLkl3/T\nKpJJHul2+pGE8pH293A2ja7/0Y61TZ/zb713kxcGYz25c2R5Ti0uov4wCIgCQSfJcRNdkIK+AKwO\nmC0kSSWk2c5oBIpuY/MW9NbYBobnzKKbAgI6XTjg7WdxzXmnsNuBRzBryW5M32IhX/3IuzjsrSez\n63MOIIoUcVuSW09mu/RHklkDtV5kH1GrFGmZZpZTQxMEqqy6KPmneVII8qZNH4WPscRbQ8vaYqGJ\nngub93hvaSVFtJZZS6ubFqbwrYRVY02cF1x0yusZW7OSafO3wodVDB6NwKRQCcBLkGGFxQe+hj/9\n+Ku49kuZt+0OrLjjFr7+rS/y3INfwZvfcxL1KMY56I8C0sxMtSMjCo+EWqiJQkkQKLSTBHhsr03X\nOxA9xzHw/2t32P9FMP+RhodNo+u/R8D/Xh7esCOEAFHkcB2i8MgIFHXvSa1HScnc4RogSNOcscSQ\nZzkDUchAHDIWRyzYbgfuXXrrQ8euFJ31a4inz8EBNVm0Xkf1ISbGRpnhYGjxnhx60hn88POnsfzP\nt7PfEW/GOhiyljDUNLoOLzrMqMUEsqgDz4zHOeikKfU4ItJqKt9eVl2U/F94UjSGbNr0MVl61c5y\nMmMJtGKilbCx2aGbOeJQ4qxnPMkZaaTkSYLWmmaSs3a8xXknHcXEyEPDOqNaH9vssS+V/kGsHqJ/\n2hBB3zCDw4MQ9vP99x+JszkqCBmeOYcj33kq2+60K3Nm9BFJRT1UzBqoMtY1pHlGIIuFo1q1wmA1\nQCvJYBwV1R/GonsevL7nqaBkcWs/OQfub/G3BrE+Xnl4w87kY1B4LKe9CR7GedI0o5vmvYuTxAvB\naKvLhmaK9eCNoZPlNBPHGccfxcpldzF30fZsvftz+cOPLuH5r38v83Z5Gsp5NjYdf/zuBfRNH2CX\n5x9OLYKKDhgd3cjPv/oJ+geHefUJpzN3zjRkr/FlIA7YckYffZWIxDiSrHCMMyZnsF6hGgVUw6Aw\n9Y+eFLFOyaPEU6oxZPJ2vSgv9rTTnNwYpJZsbCVsGG/TSXPancKlTElIrcfmOan1tPKUpN3mqq+c\nsZkYA2TdNkOz5zIxOsbExjt58PYxss44SWOMpDE6tZ3NM7z37LjzM1ABuNRSG4roCwOUFMysBzQT\n8F5QiRVDtQgpBbq3KBbIorqgcH+TqF5JlexNPv67zsMTsMX3kVItorcQqYTEO4dUxcUpoXf3g0TI\nYoG1ohRDtRApBBtaECFQgeET53+bbpbT7jrGWy0WbbuEb5/7YZ718tey5TOfj1fQP30I09qIMzBh\noatzXNTPS9/1cX77nfP44gfezNtP/xwzZ81haCDEClgzkdDNLdY6GknOYKSJIt3r/Cwiey2Dx+hs\nljzReVIIspKStNdsMBltJbljrNGh3TGEgaJfa5rtlIlGB9dbEPNC0ElSlt78e6788qfYbve9mbP1\nDqxZ/tBA0plbbc/TDzqcdicjNdBoQl8f1GPFV449eLPj2LBmJWEsCZUiigL6Qk010igpqMURYaiJ\ntMJBb3SQpFqY76K1pC+OSHKD7/18sopBySfvKv4jpVqkKF6vk45aFJDmObn1RFpSCwMS6xDWkjuH\n8xYpFNVYMxPopDljLU8tEkwfqJJlhhXrPcEuu/P6077AJZ96H2tWLGfXl76J2sAQa1YvI/WQtAs3\nvoqGoBLxvGPewZ9+cSWfPuFoXv3eM3ja05/JtMEKjXbCmvEm1aBYbA20gtyjpEUYQ6gk9Th6TM9p\nyROXJ4UgSyl6ZV6QZpZWluPxpKklyXOCsILEEYQhsfWMdzKEFHTaHX7w1bO464ZrOfgNJ7Jotz1x\nznPB+9/IxgfvZ9qCxRz+gXORztEXh8yMQkZUi9RCtzmKVAq3ScPI9Dlb4q0niCWOwr/Xi6IbsL8a\nkaQSqQRaqqkqg0lTHgCtJVUZPOHSDv8XNk2v0Lu78b33cDI94zOHpSgJrLiigK+T5cRCIkON7WZ4\nCrMlax2JEBgvUIEgNTA63kJ5RRQqdKaYOXchrzn9y1z+2Q/xm/NPY+u9XkxzbBQpwErIUhiIC/P9\nKAx4xkGHs9U2i/nmGSczcvRx3HL1j1m57E4WbrcDp37pW0S6aJvPnMN0MwIlECJhRr3yWJ7akicw\nT4ocMhR5R+c8I8023a7B4xmZaNNKDXEYEGhF7jwbx9usbrRZdtvNXPqZ05i19WL2PuJYomoVaz3G\nQ25zvnniqzn0tAuZPWOAwVqdVpISKoWQsGFsA1d88iR2fu5B3PLrqxhdu4oZc7fkfV/8bwZqEdW4\nMAaa2R8x1FelGihqUYjwHqVUr/PuoYW3p5qd46aVFX/rPGy63WRzTyc1aCUAgXOOdpIVVqrGYJwg\nyTMS45jopGxsJCTWESHZ0O6Qp45Gt0MzTfnNZV/h5l9eiTP5/2fvvMPtKqv8/3nb3vuU29KABBIh\nAUITFKRIURTBOhbELo6oWEYdFNuIP3Gsg6OijDpjGXsbRAfFBohiowginRBaIEAg7ZbTdnnL74/3\nnEsSYdQZSW5Cvs+TJ89z7zn73LPLete71nd9v4wt2pPjTjmDTFiMgqJXkBlBgkDoQG/N/Xz5w2/H\nbSDCtGSfA/jwF75DkmisC+TWMZwoGplml7kjzB9qUMvMI+q6bsdD4xFVQwYgBDplSSe3CAmV9eTO\n0cmj+lgsF0jef+prWXbVpRACz3nj6ez22COoXEVeFvSne6knGTvuugeTd97IrFmHUlQWowTtoktN\nKs4/673s/bgjefoJJ3HCK16HJ6CNodlMkD4wnBh2mtXAAFpAIzEkOnKGgb85U2Frw4MyKx5EE2JD\nZkdfJZ6aUQg5WNAkRkp6VRy9Hko1UoKRlkxpMqVY3ymZ6jNp6jUNog5Kc98dy/G2AmD8zpv5zlue\ngzIpSiuk0iilMUky/f+GwRjgjuU3sqZd0i1bJEgamaGhMgiCdrdkwiikis3YR9r13Y7/PbadgAwU\nNkpLDpL+epJgU4ELjnav5PR3vZEbLv/V9Ouv+92F7HvoMbRLTwiQpopmkmG0YeelB7D2jhvY9YDH\nsabVZqhmyKTk3E+exqIlS3n5m07F2cCskQapjk7GI2ls5mg1GHmOjalBMJ7mDG9ljbe/NSI1ceMg\n9VCaEING5aDWHELoj2/HKcPExN95HxBSxolADwiP0RIIzGnWqdcS2t2SsqioG8V9d9y88ecozUvP\n/B7BRZGiWbUa8+fOoixKfPB85p2vYeUt10+/fuHiveh2eqyfajEyMkTmFJioFdLtFfQyg697nN/6\nGq3bseWw7XSLhED1GQndvGKik2OkZLTxgFj7tVf8bqO33PKH33LVJefT7nWZ6EKvdHTKnNx2mbdk\nL+6++VoKB0ZCJjU//dyHGJk1i78/9X3MnzXE/ovmsXBWnR2HGyRSUVYeJaBpNCCoJYZGsn3buikG\nzIoN8ec0IaSMzh1K9q8zUVNZyag0N1rPSFRc/IZqmqFa3JU0jWakoalrjZYSlcRm3Pzdlm50/Fk7\nL6GsoN2FiUnIfcVUp0u3crR6FS9576dQxiCkRpuERz/xuEjZ0zqWw1xgvF0wlVtauaVdluSVo1fG\nwaTtAkTb8ZdgmwnIsq/PW3mJkhLr6XORowX83KEMvUmmMneX3bjsh9/i6+86iWt+eg6d9ZPUk4TK\nwuxd92T9vXeRhor5Y2Nc+OWP46zj1A98jF3mDjFWS2nWNEIoHIHhukaomOlJrainimaaUMtM3y7K\nb38o+1BS9h1b4vkY1JD/HJtEyqgnUUs19SyhlsZgONDPqCWGTCukkIgAjTRl9mhG5QJCCUbrCQvm\nDtFIE/7p019n4dL9AJi98648910fQycwNgxzRiUEWNfq0O51qVyF85601uC0z/+AN3z86/ziv77E\nLX/8LYk2dLsVvbIizy3tXkW3tHRyS68sovTrwL18+/Xfjj+DbaZkIYhyjyo4KgGegHOBoVrU/r34\ngvPYdckejMyZx1WXXcIue+/PwSe+A1/AfXffwvKLf8x/f/Bk9jzocJYe+TRGFuyOlILPv/l4hkZm\nMXfBLrzljM8xb2wU7yq61jEsUoZqMqp+ec9wLaWRaqSUpFKSJXqD7Hi7bu4AD8fU3yCDdl5SFwZv\nFEGUGJUhhGCo8ngh6JU2umhby6n/+hU+ddrrOeDJzyFREgQo4aPfYddSSxU1Y+hUluAqyrzHlLMM\nz1nAC079MN8+4x089y0fYNEe+yFlhu0VMeiqlLXtgrFGRiOlr69SUVlHLdXbLHNmO/7v2OoC8kNN\noznv8dbTrhxTPQsi4By0S0vlLJ8586P847s/xB6PeRzX3rOW2+9ZSzeHLIMFu+3O/MWnULZeyf1X\n/4oLvvAv9FqTVEUOwNT4Wkbm7Ei9WafTy9GpYXYjo55pvI8yk4hAonSkswnZr31uMOywXTd3Izwc\nQyyBQGLUdLMwM4ZOWcWhEm2xfRH/HYcDq1uBZi1hZNYc8s4kxmTUlUBJRStvEwQYo1nTzUmI49O2\nKpGmTrdow5z5POu1/8S5n3ovjZFZrL33LnbadQ/e+9lvofuynIX1fa40GBndVwa7pe16Fw8/tsbJ\n1a2qZPFgztFF5ejlFeOdgl7l8M5D8Ex1LD1b0OkW/OR7Z9Mcm8Oc3ffm/vUtulNdHDAybBht1MmM\nJEsls2eP8Jinv5BTz/oOrqo2+ux7VywH61FKsfOsBqM1Q7cMKBVItGCkllFP4+BHTLb+uhrpdvzf\nsaFmNERedyMxZEkc1GnUE2YPpSRaEXwgSyXz5s2FvI1RkRHjgyVTKbOaCQKB8p5aUsNXJSbNmNts\nIkRC0yj2OOhwkqzO6rtuw9uKe265gX895RXYssJVnk6vYKKTA6Hv3+gobTQ9qKx76C+yHf9nPLjL\n/MwvG25VAfnBnKOt9xTW4oMnBE+rFz3ehhsJPkgm213O+c/P8syXv5571nS4eyIqhdWNwlYVpYuU\nKVd5lE6QwRGUZsfd9tjosxftvhf1umHeaI3hLCHRmkxDolU/0wvTDhpSSKSQf3WNdDv+b3iwZqFS\ngrFGjXnNOrVEk1cxS549lDIyVGf+Tjshqx4LZ49SSxOG0pQsUdRMggDmjAyRmYTW5BQ6yRjvdmnl\nXbRK6PS6TKy5b6PPu+PmGyiKCiGiS826Vk67V9Eu7ANlGSH6UqszOzhszXjQWOEc3aKa0U3Wrapk\nsSldyvVHpfMSrA9UNhCUY+1USVH16HYLLvnpd5m38yJ23edAplodyl6FcyBVoNOGTlGRGVABwDKU\njaBlYMGixUytW02v3WLXPfbmg1/4FrNrGVpIunlFYhSZ0ljrqZm4sgnxgBjQ4O97JPONNzc2HcN2\nzmOdj9cjRNlSJQLDNU0907Rzy7x5c7lj+TLmjBjqtQTrStq9hHa3i0gMEFjf67J2vINJM8aLHtJD\nt8hZf+9KNhWEnr/bUsrgKF38/ERKqqJA19KNmspKsr2n8DBiw1jxwIBRPzgzc8tGW1VA3lSIZtq1\nAY8k0HOWoqDv0Rb42Ftfwcrl17PDosWMT3VBgFeSXlUSgsKoaGiaaQneM7s+zC5zR/jDpb/hlmuu\n4H1fOJcFO80jSxSVjayNIZuQGhWdPghoKUm1ftABgC39sG2NNbT/CzZsFlrrp22nhBD0qshPz4yh\nU5S0c4tzlpGx2UxNrKNZa7DjmEQITbuXc+dqTWktuRMUVUldlZishrTQqGlWrVrFeWe9l0Nf9Eau\nOOdz2LKgMTLGSf/8GSovSY1ABBnV+6SmrhWBsMFUouqbxT6yrtHmwoaxYpAtD34+k81pt6o9tEBQ\nVo68tBSlpawqOnmJ94HSQbdXYV1BCJ6PvPFlrFweifz333kbn3rXK1m5doJ2p0Onm1NUFYFoEW+M\nZGxkiNRoZHB897Mf5oTXvYM5O8wlSIVSGq0coq8uphEEH6JymzEoFS/6TMJfWkPzPlCUlqluwXi7\nx1SnoCjtjN3S/TkMqHFa9YdGpv0B4wMaxZsEmQgYZdhxh3m0xtdHp29jSBUIFSVSs8QwWteMNYcZ\nSg1pVqPZSMm7Pc4/6/+x22HPZLfDnkJjbA4v/qczcVXF+nVrKauCmlF4GUiMJEsEUkKvtJSV62t3\nBAhbZ51za8CG1MrB6adCXBgAACAASURBVNywbDjQTp9pmNEBeeCNV1SOorRUrp/xAN3KUlhHXjom\nuwWdssT5QKsXxedX3nLDRsdac8ctTLYdlY+BOFjQGmbVFXObQ9SylAD84Gv/zi5LlnLoMU9lrK4Z\nrikSGUilodbQ1DKNSRRaC0brKVqrGXlxH2w8OZqMPrBweB8duHPrsCFE4fwQyKutnze7YYPP90X/\nAfonhTRNaKSKhbvM556VKzjpucfy8Y98iPFehXWerJ6hhEBIGGqkGFuR1uq4suIHn3g3iw88nAOO\nfR4GqLodhndYwJIDj+Dai39EqjWlDfQqR6tT0OpZxjsVIUR9bvraHN6HP3uNtuMvx4bxwvlomiuI\n3pFFWVFUUS+7KC3O+RnZZJ+xAXnTDM/2CfbWeQrnCCGgpcLjaJclk+2cvLIIPLfccD1ykwaarg2x\ndrwFJeQWeiXUtaCe1hgZqtMwCeP33sHPf/BfnPzWdzNaNyQyPjwjWcJYwzCnVmOonjFSz8iSBKXE\nA15zM+zibso4gD/NCuJW2ff/foHsb5cDAR/8nwSGDW/4mdwYgY0bfPHaCIIQJEqSGYVzjgCc/dUv\n0e10WHHLzZz95c/ylU9/HCUDrvJ0y4KiZ3GVZXJqCm0Szv3kexhbsJB9n/EyFFGhLu+2CUZz2NNf\nwB8uPJeqzBnvlbQ7Ba3SQfAoQgzSpYW+U7jbRAcaZm7mNtPxUDtCa6PrfKeoKCtHaR1TecFkr8TO\nwMbqjK0hb5rhOU/kcfqAG0gxVhXjvZJWN658XkhWrriDL37gFJ5+8jv46Rc/hneOsfm7MrxgCRef\n+VrGj30e+x/9LLLhGniogifvVqSZ5vufO4MXn/xmRuftGKe8agqhNDhLFTSNeoqWAh8CikAIcZR3\nJjoP/yUeez5AQBCI2svwQEAIbBwYHsz7bqY2RmDjBl+sy0ZamzQS56HeyFAhcPFFF270vnO//gXu\nvGU5w3PmMTpnR+buOB8zMpeffO0sJtfeT9YY4sjXnI7yAlMTeGuBAEGjZs9l4Z778usffY9nv/Qk\nLIqitBQu0FQSrSSmbyqrlNzItHaAmbi4bw14KAZWZS0uBIxS2BAoyyhvkKg4PDa4hwfH2NK1/Bkb\nkDdlVIR+Jlf6WAe0NtAtctaMd+lZh7eBFStu48x3vIbHH/9Kdtjv8TTG5vC4E09ndIcFGAkTRz+H\nZT/9Kt+++Mcc9KyXssvjjmaeUQTtufz870GAZzz/JcwaqjN7OAPvKS3oNGHHZsZQluKCo7SBRMtp\nLYWZ2Ij5Szz2pKAfjuPvBq8b/GzDr/SXKrTNFGzY4AtATWskgtJ7NIGaiIapTzr2qSxfdtP0+446\n7pkcdPjR3HfP3axceRc3X3MFN111OVUZh4TyTotffOafeNnpn8YLyX2r7iGtNUlTQ+ksRz77RM7+\n5Ht40nNeissS6qlhKq8IPjB3uIZRCtevZysh+kJYf71x7HY8gEHpjb6WjZJymoFV+oBAoI0iWBt1\nUKSktA4hJBngnUcqOSOSjRkbkDfN8EIQlNah+noRnaJgsmPpWIdzgpV3ruBD/3gSx774Ney43+Hk\n3scTnnq6XZAKkpGdOfLk01h9y01c9cMvc91F53L0S07mxxf9iJv/eCn7HngYSM1IwzCUpkgJ1nmM\nEgzX0ujsITQ1EwOx0TMvEA3wUOPJEH0HrQs45yh9LE94H2ttlYu1t3pqSHW8PR7shpd9CcyZtuXb\nEBtNAxpF6nUsudhYY0y05EMf+hBaSc7/6U849AnHcMLJpyClpJMXtLuWylr+4RkHb3Tc++9Y3p8A\n1ISiS1pvMplblISdd9uT2fMXctnPz+PQpzyb9SpHGEllHZUPzBdQSwzWg5Ga1IdYIkKgJSRaz7jF\nfSZjw50bgo0ao0qJPh01xhIXoKocEMtVtTT+sltaGtJMlzm3ZLIxY5fiTQVowKOVwKg4ZQUCSVz9\n1t57F2e89ZUc+4JXse+RT2e4OYyRkgC4MqBSKHPwFXTasGCPvXjV+z/LUc9/JT846/3cfNUlEALX\nX3kJnzjtzRipMFqSmGi/JDfJgLeWOt+AcZAOrIaIN6vzARenWKICmhDkNjZJjZYkicJ6T7vvQ9ju\nlf36fTSQna7NVTYGmhleTx5gIEI0XE8Za9YYylJSo/nABz7Ejy++lLe88900Ek1VVRipaGQSF2D+\nbnv+ybHuWXYNwZWU3S5JvUHwURVwotflwKe9kIvO+QohOPIyUJYOgSAvSyZ6UVA/9j8CvcoSRBy/\n11rh+nzp7fjLMNi5aaWmZXeFAO/jPZkZjRCCysahkDwvsM6hJNO0QyFi4rVhfyQEtsgzPmMD8kAs\nJnZJ+wG4v8UTUmB9BRJa42t4/5tfydHPfSmHP+N4SuvJXY8sTeK0nAgoIKvHOjQicvmHaym9qRa2\nr1cxwNWX/67fjxfYPosj7ztZD5oFpXXTgulbEwY3rw+hvz2L27TKOQT95leAovK084L7JlrctnqC\nNa1OvHlD1JzOq4rJXk5hXVw42TrpWkZFHWWjJc1UU08NjUwzWstIU4VUhkRL/vHjX+ZRSx+N0oZd\n9tyPV5z2cS78jw9z/e9+zuTEODprEARkSiFCYOel+6LSjKt/+3NS5UmNiqa7UtHtlXSKkm5p6RZx\noRtosWxnWfz1GDSvZV+WdfDMOh+QCBKt0FJSOYck4IkN3cp62t2ciW5O5TyTeUFeRSZXaR15ZbfI\nMz5jSxabeq7J/kl3PvRPOqy9fxXvOfmlHP3sF3LAcccz3i3wwVH1jUKlEtB3ApYShIaxDNbediM/\nOuPz1BpD7PGYQ1j+x8unP/fAww7HKIn1sf5qtCRRkmqDB2ZrweAcWhcF3UsXa9/WBbSWff62Z6Jb\nkRcl7cKhggOtESE2PYwSdAsobI9ZzYQQYnPVSBnroSGAAx9ihpH2BeO3hm23kpJQOXqlxTnHUGoY\nqmWUznLzveOURYFWkrFmxjs++RW6vYqJThcpJS9/7yf59hnvpOh1KTotLjjz7Tz3nR+nzKPtzNIn\nHM/Pzv4Khz7paQghyH2gbHUpg8ADs0dqpFJiRaDpwvT5mulloJmGQWlzcF9qrdBAWQVaeU5lA5WP\nJapUa4Kx+BB3i4ULSGcpS08QHgIYrfD9MpJGkCab9/vMyIC8UV0I6FVR11iJSMnSSjC5fg1v/PuX\ncPhxf8cRz3ox966dxHlHPU0phOb+iYLKCjoTnpGdIEvgvlWrufCcLzN+1808+SVv4KAnHkdiFN/5\n2Gksv+b3PPaQw3nfJz5LoiW6XyM1SkUXihDFYVKtSGZw7XiAaRpQ6JcniGpo1vlIt3KxCz3R7bGu\n1aNbltgq0LGOVrfNjiMJtSTFOkHQAmkL1jhLPTXxGqRJ1ADp16BrxsTGFDN3LHVD+H7ZJjH9CTqh\ncSGQKgV4Mq2Z06yBUkx1clqdCmEUo8MNQDC0ZC+GR2dx99r7AVh9+0389xlv5ZmnfowA7HrQoVz9\nk69x6a9/yUFHPIFaotFCMdwwCGDdZBEFrWopvbJkqBadqrezLP46DJrX1rm+XoWnVznyyqGJ17hX\nVLRKTyZLpovNxCQi1Ype6cgShXWBWqJpZFHHJHeOmg+b9T6ekQG5sq7/sIML8f/EKKx1PO0pR3PN\nH69GKcXzTnw1T3rRq1nf7mHxTHTyfjNPkFsQQlLqQFkWLPv591j26x+xxxHP4LiT38JYY5TgHUZo\nTv2XTzF/pEkjU2gVg61RkrQfeIWMzby4vVV9JsLMxqA8UVmP9Z5A3Bb3nMN7KKzFucB4q0tpPRMt\nh1IB5wXf/OzHuPGK33Dkk5/Ca095F1Wf2lc6S2I0LnjaeUVZxbq+kIJKRd2Gmc6+GMB535/iCljP\n9CLrvSevPGNNw5SSdPMSJQUjdU1hQZssvtcFVq24daNjrrljOQ5HI8swwCHPfDG/Ovfr7LD0AIaz\nlFmjTRpekVeBVDmEj+erU3qGa9tZFv8bDJrXRWUpbFR67JWWYC3jVexzWB8oyor1ZWCkpqhlCa28\nxFWesWaKD4HcehoqcsMHbvDe+c1+H8+4gOx91JFVShCItbQYnD3PPPbJXPn73wNQVfDz875Pbc4C\nCpFilcalTawbRqcNgoLOunv4/edOQUrFzvsdynPe8UnmzN+JkTRl9mgD7SVzR+vMH27QTBVCamYP\npzSTJGbCRm+Qafp+DXvreGgGu97CeZSM7efKBTq5I+DptAva1nLX6km00v3vo/jaZz/KhWd/CYDv\nfPEWKhd4+T+8DWsrRmtJ3D0oSZpGDejSeoxReBemOxJbw7bbutAfzIgeiL4/YjvQLh6pZ1iXU1hF\nmhjmpJpO1yKVZ6IbKKqSnZfsxZ3Lrp0+pkpSfvzRt3P0K97MjouWsPiQJ/K7//4Kdy2/jr32PQjl\n45BCr6xQmaHsDzLUNX3dDbVdhOp/wEPpfkgZ2RR97iadPDai75vsoWVA9X0WS2tJrGRyogP9XlS3\nqEikpLAW2a9Hx6ZftAPb3LfxjAvIznsGsW7wgJQ26ldce/XVG712zf2ruPqSX7J69WpaExMUnRZF\nZxIhJME7vItOwd5Z8qm11ObOQwlBVq+zw+gwaapQPiC0JEsNjSwhkYpkA66olAIdBNZFCp5g61Bu\nkyKeN9X/M52PmbJ1lsrFpa600C0s7V6PNDG4ELjiVxdtdJzLfvVznveqN+K8olkTtHJLI1MMq5gp\negSZijTAQY1pa9h2R5PUuItSUuKdx7oKKQRaCTq5p5HqOHZbVOR5hVACj0apuGs76f2f5jOnvoLV\nK1ewYMnePOOtH+HmS3/OeWeextLHH8NjnvFSDjj2eK78ydks3G1vOo2E0A3IIEiVop5EmdbUJHjf\n11zwHmZ4uWdL4M8NJlkXKKyjkxesWNtizUQHLySxGVKxvt3hom/+Bzf94bccdMSTeO5Jb0QpRbd0\nzGumKKWpnMNahbWeNItJyua+DDMuIPsQKSyV8zjnaJUVtnJ0csvSfffj2quunH7tLrvvw7Env4c1\n45NMdGFqkljT9AW/POMFGx137Z23UlWg6pohI0mNJJMSrwQNJWmkCVpKSusJSWxaDRgeSgoSvXWZ\nlUZyvOv7C8basXUe56K1Vek8nbxESk2nyJkocmwlGJo9l/vvum36OPscfBR5Fdhx2FDTml7lMBIq\na0mNYjhNkFLivd+owTLTdxBCyGmRn8GiWwSBRwzkLsAHmmmCGoGV4wHZHzYITpNnmkaZ8KzXv5uf\nfP6jnPzhz9EtHEsPO5bZSw7mj+f9J9/959dx8PNexT3Lr2PVPXdQT/dADw/RFQXSKNJEMtXrUVpL\nliiGydBCoJV6UPXARzIeajBpIPQ/0ctZ3epy/0TBmqkO492CVq9Hr4DUSC781n9w2Q+/AcDK25ZT\nVp4TXnsKibB0ck1mHLUszh74DYalNreG+Yx7agYPtfeeTmEp8oqiqKis5TNf/z7z5u+MkJLG0AiL\n9z+Essoh1SgFJoMgBYGM5rxFGx13bJclzK4r5jUaSKWwBKTRjKQKk8QFIHhPPdHTAjRKymkO79b2\ncEgpSHVc4aNLc8BVjlavoKosq9s5ayd6eCBLFWXhWHvPHdx760089snPIslq7HPoE3jWy15PUVas\n61nWTOXIvtV9YQPWRfaL6GfEgriDMFvBDkIrERu3PLDo1ozCKIGWmkZmCAhKFxiqp8wZzjBaEnxA\nq2jXldUS6vVhep02zksQnlrN0Bwb5YgTT+XQF7+ZK879CvWhUc75yKl88MRj+MTbXkHpHL4sMUrQ\n7lnWtXpM5ZZuUdGpLJ2ypCjtlj5FMwoDettAT6VXWFrdgtWtLms7PcY7PVatz2l3cjwxy51o5bS6\nU7R7Ocsuv3ij4113+cUkxlCvZ9RrGkdMXGqJQUuF855iC3DsZ1xAFghK63HBkyWRGzpZVHFrXToO\nPvLJvPC1b+E9n/0vLvvpdyknJxmSCiHAKDAJFAF2Pex5mPoQQml22G0pJ/y/MxlqZjSbCUM1jVGK\nkUzTzBKG6xmNRGK0IjOaRKsZKan518L0HZiD91Q+UDiP7/sMTrZyOs7hEAgvyCT88LMf4KjjT+KE\n15/GCa9/J9Y6Vrd6dAuPGmhOdyuqIEhUIDUK32dxNJKNXaBnOlS/AalVXHSjiqBABIELMRMeqaeM\n1A1GazItGU0T5o5kkWlSOda3CpzR5N02vbKLDIJmPWW0obAORhcewHFv+wyd1jh5ZwpnK+5cdh0f\nO/UkegGmupaetfgAnU7O6sleTD4qR7faeiVQHw5IAc756cGmynm6RUXhIp1zvFvRKUo8UOQlzkGa\nGbK0Tl5VVHl3o+M95vCjSUw0CtBKIhVkiSSOsQeUisNhtj+9urmuxYwLyIHIlfU+Wq5EyhsUlUVL\nwX2r7mbWDrswe8F8Dn/a87noO/9OEFCU8f0eaBrorr2N/Y55Hq/8t3N59fu/wFitzpCpMZRljA03\nGKsnzK4nzB2t00wTmrWMRpqQ9p2it5ZpvD8H7wO5s3igkWmUEYx3CoQWOOfIOz1KEfjZN/6dsXnz\nedyxz0YowT6HPpkVN/6RbnsSayvuX9tlfbegkxdI4dFGo5WKNKHUIGbcnfQ/Y9PBI0FcwIyWGCn7\njSNBagzgaaQptUwTHLR6Fe3SIXRgZHgWRbdDaWGyaHP/eJuydKQJDNdgYtUdFK2pjT77zltuJJOS\nIkStlPG8wiHwwdOzjk5pcfZP1fYeyVAyijFBZMY47whSxma1jV6aQUDpPGWIvRJfQSvvcsV532T2\nLruxYMm+NIbHOOq5r+CpL3sDda3IjIIQSLSimSUUVTx+XrmovV5tXg/ELf4YbSrpOGiexV8GproW\nKT0dC+s6OfffvZKddtkZHBzzghO586ZruPX6q3EeigqUBpXA+rtvZWjHJVhLP/PNMKmiV5a4qopb\nbKmYVa9FMfE+k2OwRdkaGlN/Ds57EAGj4o4g0YbhWtwhJH1d3sm84NrfXMSNv7+Y4171djyavKjI\nAyzZ/xBuvOyXjHd73Ds1ESVOfaBnwfWHTQRRuWxrXLw2HS3XKvKos9TQTA1ZotEyynY2MsO84Qb1\nTJFbh5YeKSS1LEUnCRQd2m2imIKRSAd33HgNv/ni+xneYeeNPnfR7ntjjMRIjQ2eTq+iU1RYGyjy\niqryePxWeU4fLsiBiJeIDTyEIDOK4D1F5TCJoaYVRenotAu6RUledVm57GZuuPhHHPP3b2W3xz6e\n3Q8+iqNe8EqmWh1anZzQ5+rXkgQjo3uQEJK8shTWopXYrB6IWzQgP5iGqfOevKxwPlD4gBQBrTRS\nenqFZfWqlYzOXUDlHVmtybEvewO//97nEd6DAlGBEp7WqtsxcxcjJXSKHCMdqdYoFzBpypxGRppE\n3mHlPa7fiBqMRjsXtnpT0oGMppIC6yxFZalsXPC8C3zzjH/iX175VM4563SeevK7qNfrKAIT3S7j\nvR57H/Zkrv/NBdSThJF6EyE10nmMCNMshdzOXLHvvxZqupEbSxZayVg2M5pUS+pZwmg9ZbSe0Kin\nSBXlV7N6k1ZrirI/6ZXnntuvv4xLvv5RnvCqd3H8e/+DuYuWALBwz3153+e/RaoMUni8i4M1oi8R\n6kKcMI31+S17PmYatIrXpJYoEiXRUpJbT68oCc7RqyxlVRKJnZY873Hptz7OIS94Hc4Mo+tDFO0W\ns4abzBpt4pUmCIkxitGaRilDavR0g7ryD8SAgQfiw40tGnE27Jz6PhPA+UArL/DeY5REKUWvsoyl\nCbZoo7VBJnWKoiQvKg446qkIKbn/2l+gAzSHIHRWkdSbNOvDDCeg06jeVjOGeXOb7DiUMtyskWlN\nUVUIAo3ETGsbS9H/t5U/EQN5TYC8Ckx1c9q9gvWTPT5x2j9y5a8uIO+2sVXJNRf9gJ6LziE1ralr\nyaJHH8jae+/i3rvvItOaylVYCwhJCAKp1PR129oXL4jXOzMaQqxXCh4wr6XPvgiIaF4AcRLMx4Dc\naU2ChPUtx7JLL+Kyb3+GI1/zPhoL9iO38Nx/+iSj8+bz/De8E1t4pqqKvLC0y4K60SA8DkhSheo7\nVm8L5/RviYHg2MDZvXIOEMgAVkDwAZNomlmKEpJLvvsl5izckwX7HkGWChojY/RakyRSMpwahjND\n3URFRC0FI7WELNVUPoCIzemoFBeZX5tjx7JlM+QNOqdFX83fE0WAChsFb4Rw1GQ0FF1/3z3M2XEB\n66ZaTLR6VD7W2o59+Zu49vyvY1SXqoLWfbcye5clNGuAEDSMptlMmD2cMJIlNFKNIFBPTX8oQqO1\nnN6+JkZPc2q3ZsQtXpxicj6Wg5RUSKW5aQP9DoAV119FKhOyTCNVbJI6J1hy8FFc95vz6ZUlAkGS\nqnitqpK8GBw3Nj62FtW3/wlaS+qpIetrctBfrFOjcc7hCdSTFJFo5ozUyRKBzmrYsmKsobjt0vO4\n7iff4IjXfZh0bHeKAjIJw40Gi/Y9kCsu+RXdPEchmCpKlJPUMs1ImtAwGld5elWgZrbLcAIbsSry\nssJaj7Vu2pbJ+4q8cky2CpQSsReUKe64/kruuv4KDn7xa/ESchtQ2RCtyfVMtgp6Lsac4KGRphgd\nM+PKegQeZx1qA9aQEGyWHcsWDcgDittgVJq+eE+iJaWLimOTHYcNlm7hWLniDsbmLUB6KIWk2y1o\n5QVjC5ewYM/9ufHCs/EFrFlxGzvtuoThEUGiUzKj8V5Slp4qyD77IKrHKRGms8gBtoX6MfSpb/36\nuBGCzEjqqWL2UMqjDzxs4xcLuPOW69BOkCUyliE0PPrwp7Dssl+QJpo0UZRlRZFX5FVAyqhT7Rlo\nZWydqm+bYlBbNv3auGewY4rb5NFGwsJZTeYM1+POqzGEcDnX/uS/uPniH/KkN/4LtbFdEBKGGyAU\n+MqyeP/HsfL6P+BQOB+oG0UtFYQQm8ihL4KldbSaeqRj2vOxtNMemoPyohACFwRGJiSJJtUK7yEv\nLffet4Yffe6jPP7lb8GbJs7BVAk5I7QnJ7l1zTruvG+c1VMdJvICpaIB7UB9TwQIQqG1nB6j3lyc\n5C161ZWUOBfoFOV0hlXZOP1VlDbSgMqCds/SygvuvvNO6rPmsb7bYaqTs6rVY6LbY82UZa+nvILb\nL7uAUK6idd9t7Lx4KYmKtkupUgzVNPV6grUV1joyPdCs0JEaNhDgCVuGEP5wIQYXTb2WMFSvxSEO\n4OT3/it7HXQ4AHsd/ASOeekb+eFZ7+X7X/hXWpMtlNYMpzXm7boXzlbcdtN1eOconEcbRb1mIPg4\nrRoEeVlR2s3bkX44seFkmBBxzHY6UGtFI1Eg4gj5fStu5pyPvZvLf/Rtjn3LR2mO7UBWg9Em1Gvg\ngImyoLloKffctozx8XGKssQKSR6g3c3jAkjcGhsZWUaPdExr2oTIExf9EWfnIy22mxeEYDFSMFQz\nCClp5wXf/cwH2eNxR5HOfTR5q3+sHrhshKI1zjff9kLOPvM9YAPtCmxlyYvIdJFSYIym1vdd7Bbl\ntFP45sDMiTp9EabCWXqlQ0uoJZpmmkAIdEvHyrtW0Jgzj04ZaUohwJr1kPdg9rzZPPa453L+J97C\nPcuu4Xff/yrDaZ3ZzUbkI/Y5xkopqj6LIpL8H4T+tBUMNvw5bMheAU+rV9LJc/LK0ykrur2KF739\nIyhtePab3sMRxz2Xt/7bt3HO8um3voSrf/kzyrwAIVh66NH88TfnY4FZIzWaWayzVj42oKoQsxYp\nN29H+uHEgwmfaymx3lGUjuFaynBm+PAbX0Z3ahII2LLgl59/Px7QPo6md3oxoDe0IUnqzF24hBuv\nuZKi8oxP9ZgYb+NC9LCwLk6IJlri2B6RK9efjERMK7mVNvKP8yrO0yllsD5Qufj73/3sB9x/9508\n+mkvRzgoCmj3IC/gj9/6KBAoex1uuOyXfP5DbyMUPe6dyimdQ6q44ArRH8f28fqniY4CWpth97fF\nm3pKCRppMq2vW5aeqiwoqkDNKBq1aCwqgmftqrsxjVm4fpffhUhzMwZGhhJWXHM5ZbcNwP0rbubL\np7+Bwll86ehUFkVg2AiyxCD6wWNA/N6ap/I2xYbsFQBC9BArKk9pK3pdS5ZpxoaaDI3Nprt+HCEt\n9foIT3nNW3nWm9/HNb/4AV/+4Cmsvec2DnziM7nxkgvpdnr0egVTuaVyFt3Xy7ADAaM+NldH+uHE\nhsLngwVbCIEWklRLssQwq5GwYvlNG71v3Ypb+PXnT2fFFT9D23FqBqQjei8KmLfnY7j5D7/j3jXr\nmZjs0ioczjmUiBZODtBbgbzr5kWIzKcQ8AS6ZUWvLJFAWZa0eiWT3Zx7717JT796Fk856W10fULl\nPFOrb+fmX3yfq755OutuuXSjo970xyvoBkFRxvu3ndtpwamyb147uK83l3nAFtWyGBiZmr6qknWO\n0tpozCRjZrGuW9AuLFZJJtesYmSH+dSNpigLuhVU7UnW3beMm+9exn133LzR8e+9fRm+9Jh6nEbL\nK4sWBiFC3ysvgIjTONJCmsw4aY//FTZkr1jnUEowVk8Zb3exNmBSweykDiJnbM48JsbvxzeGGKll\nzK5l+MVLeeFpZ3HNr37MOWe8kyUHH0WvPcXpLzqKHXZ5FF/875/T7lky48GVGBObXqGyfbGcza+S\n9bfGhp6OA2++yFc1mJqiW5aEAHvu82huuPoBfZUdd1vKgU96Grf+4Xf84QdfZvaCR7HogMPY/eAj\nmbPDfPZ87KGc8+FTuO7XP2X+bks55RP/iZCSTh5NFTLnqScKS7Qd2lrE/h8OGBV7SSEEbPAUZWzm\n2ehaQUDQLR2Jlvy/k1/ETdf+kaFZsylW38U1P/ov7r3lWkw2xNiu+zP/scfhy5yJlTdMH/9Rez+G\n7lTOaJaQW8+QchSVpZ4YSusZypKNSpebQ8Vwi0agwU0/aD6F4KmlmspahIOyrHB9EZwPvO4lTKy5\njwv+40Psf/QziyMjQwAAIABJREFUuH3ZDay5fRlFd4q5i/ZgwZKljO24C+Or7po+/qI99kanEhug\nDB4jJdooRIgXEgTNLEH2tyPpljsVf1Ns6NjtQ+S2IgSVF3GnIBStsqIoHPWR2eQTEyzYPSNLUqyT\nWJUQXI+Fhx3LnKWHcO77X9O3u4f7V67g5Oc+hf887yJMt4cSghETJ6YGItEhhI0y5q0RD+Xanfbd\nZLwP1I3m7B/+hBOe9VSWXX8tUghe/YHPUUtTjjzu71i3bj3XXXkpd153Ked84M00x+bQnZrAVXGs\n9O7l13HWqa/inz/3HYIA7wLS9N2/CXQLG3nOJrKAHmkYJGqFtZEhFHzs+RC7bNJIKi858fhnc9O1\nVwHQWr+WC7/zBY444TU87vhXYdO5lBV4C731qyna63B5m5323J9nv/l99HzAu1jSk0KQW4etuThS\nrzbeLW+OZv8WC8jehwe0j2Wf5wcYLekVEhEsnSrQyQs+8KaXc9fy6wBYd88dXPKDr7PvU09k8ROf\nz9i8XailkkZNcegzX8Jn/uHZCCHZZfe9eecnv8pQPekX6Q3oyDRwLjoEhH65YiAys61gI8fuEOjm\ncc5fiYCrPKsmWuR5wLmCobE5rFu7ip2sxZYWkyhSJZkMAlcF0iz5Ex2AVXevYLKT08wMo5lBSE0I\nnsQkMYNxnkSbLfTt/zZ4KNduETQT7S55UWF9IC8sZ3zpu0x1Cj7y1lez7JILOPSYv0MrjUo0ezz2\nEPY48DBaRcHdN93IBZ9+90afc89ty7DWMdmpoAFjJkGqyPBARP/DVl6QGYNW4hGVMQ8StdIKEikw\nSYaSksI5rHU46xiuZdx8/XUbva/odtjn8CeztmfplqAEJAba917P3k9/Obvu/wTqdUilZ4c5wxgj\n6RSWplKkzYxGajBSYUNAO49ScrOpGG6RZXe6g90/4QCdoqJXVCRSMVQzaCVp5QUSyYrlN270/rLb\nYb8jj2POTouoZ5JZQxnNNOP2637Pkv0P4V/O/g2nffrrKK2pbL9zrQXSgxKK0vcNPqViIOFntiGa\n0YaO3d4Hclv1p48kww2DQlGWPQoL2fAYrYn1ZCLuHvIqTo8J67n9kvP43vtORqe1jY4/b8EielVg\n9URO6QNaQrvPEZVi2wkam45WxwZT6Iv9O1pF1VcFFKSJ5O9OfD2/+O6Xoi2Ud3HkulZnqNZAS8XY\no/ZjZMeNVQh32nVP8srTznPGOz3auSMvy76QvaXq6yRHl7dtg1b410D2pW+zZOAAH2vxgzFq5x17\n7bvfRu/ZadHueKCuoG4glBAqz8RdN7L7PvuwwyzDSCIJUqGCQwqJDR7XZ2ukWpOlGiXi1ObmbPZv\nkSi0YY1TSkFiNFIE0n6HM4iAltG7TkrB4qX7bvT+2Qt3o5kJGinUDNHxN9HcfsWv2OeQJ2KlpCoD\nNRU5zd5W+CCRRpL0ubhaiP4kWwzOZhtqpGzYiKq8J1XRtqZnLSEI6g1Dkmi8gFmz55FPjhOUoVda\nptodLr/gXL727ldy9w1Xcfir389xb/23qB8MNMfmcNrnvkfei7KovbxCCYnqU4YGAu/bKoo+pS8z\nmtEsBQJF5cmtY8nS/dlxl0dxyfnnMt7t4a0gUYYsVSRpjbFhQ3P2DtRH5wIw51G785LTzyI4jxL9\ne98Hyio2ripro1uyFNNNxkeiK7VWUdDf+xDpaS4QfGzE+yD4zDe+T3NoBCEVteYw83bbnW7XgoRE\nxd3vxP0r0VmdbHgOeVkRpGaq02WqUzDRKzBIlJLUEtOfh4g0O6XUZm32b5GSxYY1zgGcF0gVRcMV\ngnW9ik5eMtWreMeZX+Yfnn0EZa9LUm+w49KDWTsVkAlMdaFR5ZTdNrdffyXPe+N7GK0lGC3J0gyp\n4wUZyiRjjZSRRoYSAqNlDCQy0pm2hYxuQwwaUalW+ETTLip8UCTas8NQnaooca2SK39xHncvv54i\nz9n1oMO57PtfIxse5ai/fztzFu+NqODKH3+DJY9/Bs3RBolS3L++xdyRBiarU4VAu1cw0kipnKWo\noJmauM3fxs4pDBxtBJWQgKdwgcQIJjugM8UJr/5Hzjr9FHZ8zOGkxjDe61G1SvLSs2LZray98xae\n+KYvsuyCf2fewoW4yjKVF1TeMl82KLzDVhVCGiQCraCsYv1+YFv0yMmPI1TfnVv3tUN8AOshLyND\npZVXLFy8O8848R+ozd2JM085kbElj2bBXodQqwWGGrBy5fXssHhfUiMIIuBDyVBtCG0UzcSQKEHw\nUYddTvtmbv4BsS2SIQ9qnBtCyQAhTut5F6icw0iJJOrWJlmNkz/6VU447VMs//UPcW6KykZ+Ya+E\n266+jPmL92K02UQrwdzhGnOGE+bUE+Y0DCO1lLqJwtWi7yqcJapfzth2suNNYZREK0W3tEhfkZfx\nBrZO8Y2PvpO7ll2D946br/gVv/zqWRzx4tdx/Kn/ym777IsMoFLHiisvYvEhT2HOzktYfeet5MGD\nUnjrSYIgL6PTb1lFK/VtdWs93fcoLZPtLmtaHe6b6jHZrRAaMq1Z+ugD2GnRYm7+3QUUuWW8nbO+\n7Vk9Adec/x0eddjzqQ0n7PzYo7j1yl/jTRxMklLjpcB5omBObukUZRy48QElHhC+2lxDCjMFcecF\nWkY3lcwoMqXxwcWJ3sLSak0hTErbGp70qrdz6Tc/TdVZS1HF6cc1t1/PvMX7MpTVqStFQNHIDCJI\nhAJpNIo+97nPppB9e6/N+l0366f1sWmNs6wsvu9b552jnfeoKUEjS2hkis7UBEW3Q210FvVZ81j0\nmCO47vxzCB5GhmDWsOSe63/PQU84jiRL8VWfKmMduYXRRspwljDSyBipp2TG9B1CwjYxBPI/YSC6\nr4XAeknlPcP1hLFhw/Jrrtzk1YK9H/N4tJFoHV1Y7rnhWurDw+y652LmPmoJq1fcgsTTy3Nya8lS\nSS2NjcA00SglsP3Bm21paz3oe0gEpQ20S0flBImCPK+w/YWulTuedMJJXPKDb9Etc5SAnoU1d9xG\n+97lLDrkOJSBBbs/mt74GjqrVkWnmkyjEBTWkiWxtBaHnwJaEDm429gC91ehb20lBZSVZ32vx/p2\nRTsvcXhaU1OsywPr2j2Gd96LxUc8g59/8WN0S0fpAmtvv4E5u+5LuyzIS89ImjEyVEdrQeVisPci\n9Ml0kPSNCzZ3bNjsAXngHGutp9MrmOgWVM5RM5qa0VTesbpT0a0cPsQO5/Kbb2SHRYvp2ihTuPSY\nF3HrpRfSWr8WKUFUltuv+T2HPPFYhlKDNoqyf/OODUUN4Cg8HutBWWrIEjXtWLutYnCuIeojDKeS\nhbMazG0kjNTr7POYQzZ6/a77HURmYgNUeBitZ9x++YUsPeIY8IGR0dlYW1FMjkdnjcqzqlPSzsu+\nB2JA94WJBi7Og79jQ83rrTGwDPoeCNAqMJxpGqmkZgxaSpyzlJVjYqrNrIVLmT1/EXdc8SvSVOEt\n3PHbb7Pw8cczNJxSM1CvKXZ/3FHc+odfExAYpdBGYW2gV3oSpWhkGfU+F3ZgaxZ1rLfde3ZTPHDv\nxB1DZT3dqqLbsxRFyfpuybqpkl6njcxqNFJDXsDOB5+AD4Lrf3Y2q+5YBUIwa4cdqBnN7KGMWqIo\niwolBcOJnBarr2lDZvS0UcXmxmYNyIMsw/koL0i/liuEpFtV9KxDIKiZKBCd20DpLHcuX8YOCxdT\nNxJbOurDs9j1sOO46effotWBG6+6nHmP2h2fNqhcYLhZY7SRMLtRI1WS0gcaaSzWD2hM24ojyENh\nw2k9rSV1kxC0IdEaJSR5WfKmD36Kxx75FGr1Jkprnn3SWyidpWsdhXW0pya564Yr2ePgJ6GNYmSo\nwYLdljK16i4atQwhFZ1+QO5VNop6VzEwdQuLcw5r/Z9oXm+N5YxpZUIERhtqaUozS2lmhgVzGtRr\nNTyeWprhcBz2nJdz+XnfxOY5nftvZ+re5ez1hKdGq/nIaGPXAw/nut9egJSeVEfhoqG6IdFxfDoz\n0YreaB1ZBv3FbhvOITbCphOnpXWMd3qsmewy3u0x3rOsHm9T5QVlr0ut3sSFaNytteKgF5zKbb/9\nMZd/+V3krfX85My3061ykJp6vQZCUDOa0gvKEGhkGVJAKy+2WOKwWQPyIMvw/fFERBwo6FWWblkx\n1etx/2Q31un6QaPX89y3Yjk7L9mHkeYYRkd2xT7HPJ9VN1xGd/xu7rz6dzzqgMNwpSVNNamRNBNN\ns5ZSTzSur3WzYUDYVkTVHwqbuvRmiaKmAhPdgtXdHCkF9Zrmdad/jA9+55ccetzx/PK/v0a7rEiV\nITjHTZf9goX7HsTI8GhsxAbJgiVRIKeqKqoQsNbStbEx6rxjsluwvt2jU1TTvmfTnGi2XqbAoO+h\nJSgR/j975x1uV1Xm/89qe+/TbkkntFATehcZBywwWAcRHccGOqNjb9jFXrD3UVEEex0b2AYRHUFA\nBARBKQKBBAjpyS2n7LLK7491zs29CNN+mtwb530engfIPfvc7LPOu9/yLdG13Ci0gERplgxnLBlu\nsXAkZdeRFsv2P4iRXfbghl/9jD9e8g2Wn3gazVpKLYPgoKpgj30PJu+2GbtvNUEGjJEMZQlZYqIe\nct+aPng/tWTamYSv/qsYnOEQoli8RDKRl2ya7FH0HEYDKNqdNibLGG3UaWSGVhOyDOoLFmBqTYrJ\nLRACm1bdygXvfQ1llZP1ryu1gmDRQsTOsI/c2FGFw/atkMOAzhv6GrqOblnSLi2TecmWdqy2tJAE\naylKz2RZcc/K29htr31xvqBwAQugmux3wmn88pOvYdVvf8mtV16CEhIfPEOZYV6zhhQBqSQ1LWJV\nPpUYdh5R9QeLwb2GvmiSkEhtSIxkJNFRrMWGeCDxHProp3Ljr35K6IwTEFQebrvyYo5+1ClIFUXT\ndSLZbZ8VrL3rVpxQiOCpApRFwUSvZLxX0SstciCQE4jMJzcz+c7F7mSw9zBKoZTGuxAF5Y1GCmjV\nEhaPZrTqKVlN00gzHnbas7n2O59hzR9+zb2/vYwsAy1jHTJ/SDM61ODgh57IDZdfQt6zJFLgrGPD\neI8tkzm9oiD4KJrTLaKLzl/TQm9whp33iBCiLo11kS0pYbIX6eYTY1tIaw26VQHBYRQkGjIZaG9a\nM+OaG+9eSW4FncIyf7iGsBZlUlIjSbSmtA4pwg4rHLZrRho4x7rg8SFglCIvHO1ewWS3oihKSgtj\n3ZwNEzkT3Zyy3WHT2ntoLdqVybzCOfAlpAmsu/nKKTGhDatu49NnPY+8U/bFh6Cd2+i3pXW8wdDf\nnu485IUHi+lIFuc9RkvqqcYYjRNgrUVLaBpDEIrW8Aj7H3cil17wNcZ6Pe656y4mx7ayePlBQKCV\nGcrcsnDPfbh35R/JjCJVCiUkWkjaeUVeRPqpUtugWUJEYZjpMRf1pgfYbqMlzdTQqhkyJUgTRb2m\naSSK0XqG9gEcaCO59OufIfS/0Jvvvo0Lz34pSRKoS6jXa9STOg991GO57lcX0ysq1k/02NKtgIAS\ngbyIRpsAmVbUUrPdVMdmQwzOcKT/BzpFNB0tQyCvPGOTOZWzdPOSrN5EC0mnWyCBVuq59psfRicz\nBREWL9uPkVpC2rfgClIxpLZxIqY7teyIwmG7JuSBc6wabEeIs2RbWqrKoaTGe8fGiZKN4x0muhW3\n3XYL85bsRhViQpUB2hVsWbOOzXffPuP69668lQBsnegx3snRyjOUJWi1jQILczMh/E9jBpKlf6iM\nkozUE+bVM0abNTKt6FnLlvE2vbLkkL97Mrdd9Qs2rN3CyqsuYdkxJ9K1Dhs8nggzag4voMh7rL57\nNWPtHkorvIx6IbkLSMBaR/Ahoi368v87g970gLnXqBnmDzXYdf4Qey0YYelIk8rDRCdHKInWEik0\n61fdNuP1m9es4otnPpNLvvhBbviPn7B241rqS/Zm68b1vOKUY3nz857GWF71iTUSqWWs1iSIaUli\nLo58/jcxdYa9Jy8tRsUiTvhAaS3jRclEu0CUHerNFo5AM1XUheMnH38LeMeT3vU1hFQIpdl1v4M5\n/R2fZt5wi7pWNFPNcE0jtKZbOZx3NIxB9u/1jsgT25UYMuUcS1S8iX9hgUD03RkCpQdUbA173nPf\nyttYvGw/tDEQoNu5m1t++E3uvOE3ZEPzyMc3T11/t71XkDUTnIe2dSxKGmRaY/vax9b7KFjiYzu0\ns5IXYKYWw6DNzbRGIGhLh8TjgsBWUb+iajtMa5S9jjmRn3/k5RTtMUZ33ZuyPJ2a8kgj8VLFJUvw\nfPTFp7Hn/gfzjs99E2cdwUa7IyGjpGmCxjuHEFDP0mndSdQD2Bnu+2DplGrNglaNTb5H7lzfiFOy\n2z4HsPqPN079/IJlKzj5BW9g9R+u5YYrL+VnX/5XCB7bFxtadeuNvPl5T+Xcb/wAJSxKSZQQKDdz\nkbc9VMe2dwwQQT5sI8AMznBRBgoXKf2JUfhCUBQOBSTGUOY90kYLnGdi6ya+9+E3sf9RD+Okp7+Y\nzd2oUP+iT13AcLOFUAIFWGdp9ypG6xlDdd3304xFopwmJvWX1q64f2x3pl60Wo8DdO8DtcSSp9F6\nvltFIZDMaGqJxuO5+Jvn0p7YytrVK1mwx97c+dsrWXHC4zntTZ8lSz1fes3pIASLly3n5R/8PMYY\nRpopRkiCd9GsEAEqgu4TINFqqsqQcuclhQzYegPlMiGgJjUj3nAfisL2sM4z0qgz0ckpOwUbV95E\n0R4DYOuaO/nuO1/Icac9h0QKhK+4/Htfpuh2ALjr1ht414uewUe/9B1SLXECuqWlnkblPuehliQ7\nDEL0lwzvA3lZ0S0tgYDWiqFGig+etQiKyvHP7z2Xj7zoNMY3rGX+nst5/Ks/xEg9oXXCY1hwyGMQ\nVFx41pNnXPfOW29iS7egltSorKWkj1Ge1lE456OnHDOT11yN6e4sUsZkWDmP6TfwHkEjUYx1XPzO\nAkoFrHN0exWbtmxCmZSVt/yOf//02Tz01NM5+MTHU7gSX/bQScK8VgMlDUaDq6BR14w2a6RaYENc\npoo+dt4ohWDHFA7bPSELRN90M1A5S1VFS5bKQwie0nsKa1FK8NFXncHkWKyAN66+nfb4Fk569bkk\njRbOwH23XcmyQ47mca94N8NZSlaLJqYL6ik+BJzvs8a8o6YNiRYzWHk7W5XxYDG9Wg5ApjTDTU07\nT9gqekgfGK6n9PKCsbWrZry2vWENN//qYpI0IUlSxjeunfHnd/3xJpxzJEmNRMSxiHMBqz01pWmk\nO2cyrpyncgHrt8kACAGFC2RGYpRA5J5HnP5yfnPh1zn+Re+hsLClU9JIJI2ap3CG1vzFTEy7p7vv\ns4LJToduPcF5R5Y0qJtkCnrs+q4ZiZZ/krzm6n2+PyJosHgfjBl98JEUIiNxpldGh+5GltKtLJd8\n43OMb1zLrVdfygnPfQt7H3Y07U7FxrzCtcfJ6k2C9YhaYLSWkmUJjbpBKoESkqE0JmClJJkx1NId\np0q8Xd95Gw7Z07OOXl5SuBBnRcLhnKcobeywleKum2+c8fp8chxvWpQl4OHe23/PXgcexmgto1FP\nolMvIZofqvihKiUp+waJjWSbJORfwxx5egyqZWs9becwQrFkuEYIjns39Wh6T62Ws3iv/Vl7xzZ1\nvXl7LuchZ7yV0Xp8uI2tv5f104wA9lp+INCfaaZJfOgpTdZX8ZuLs+L/KgZeb4W1eO8ARaA/qw8R\nUpmmCQ0bmDdvEb3JrWQG8hIKC13rmZ/CltW3UOVdFu6xDxvvuZPRRUt53ce+TG6hchXDjTrDjYRU\nK6x1WA8hOIzSKLVtpjxIXnO123sgbZvBWKZyHiUkhbPI/gNfyzhOwHnOe9MLp4qE4B03XvxNWrsf\nDRKEhzDRJm0MUYlA4kGnmiwzLB1qMFpPyGoGo2MarBm9w4Wxtuu3ZXCQA3EsgZB451BK0coSalkU\npSltQIfA/F12nfH60d32jR9KClkNNt5xM3secBTNumYoTZg/lNJKNJkGEEilyMsqbsdVhMTB3F4s\n/f9G2ReaT43GKM2ioQaLhlOGahrlBU9+44dZtGx/AEZ335+TXvahmIkVVA72OOYkskYLqTTaGN72\nyS+TpZpGYmikCikVRWUBQap0XzRy54mBhneU3YxGp+3C4lycPTbThHnNGq1U0StKhkfn0ZvYSlWC\nK6EqIR+Htes3ctn57+URz34lj3/tx3n4s17Gkr2XU5YVmZYYqWnVMupGUzqH1opaqpEy6oZP7+7m\nIoxwejyQts30gsl5j3cukmpEVIdUUuGF4J7bZ0rzjt97B2jwrt+x5JMk9QZS6QggcFA3EmMEboCs\nICBFZJnu6JywfRPyNNPCKRcGISmLqNdbWY8R4IKl1ysYWbSUoQVLABheuh/HPPdDICPkrWy3GVt/\nH3vst4LRWo0Fww0aicYLQWk9qVHUEt13CdYkfbPKncnI9H8T0bhRolRkSRol8RLy0rNgfosF9SGe\n9Y5Pscv+h3D4455Osw7NJhijkQpWX/0zTn3xm/nsxdez4vCHcPlFF2BtoPIRWSGIam+JkqTJ3Ldy\nun9Etbf47zGReCSevIxwtSzTLGhl1OopUivqjSFskeNthZBQS0GonMvOezf7P+KJ1PY6Buth70OP\nZvVN19OuHEFJcluRaDGl7jZo56MTy0ydkLne7U1HBMHMgkkJQV5Fbek0S6KkrPPUtUQpSGv1Gddq\nzN8lOqHL6LfpizbNoWFq0lCvpTSyuMArqjgKUULEJawxO0S74v6xgx4HEbZSOke7WzBZWsbykvFe\nQekDZQV33nMva+64hSe+8VPsftRJLDrwOIKIgnCFh8333MySvVewaHiIeSMNmvWoKTDa0NRqCbVU\nR9qwiMnHh5ikdxYj0/9u3F9HQhL6Bz9Kj9ZSgw6QKMXCVp2R4TpGanY/+ChW9W1xhmoJEs/k2tX0\nJraw/PBj8AhOfe7L+fbnP42rCvLc4WysjKWWFDa27rZydPOSiW5JNy+xdm7DtXyIhgfOBZwPeC9I\ntI6SYkQSg/UBE6LeBVJQHxohpc1QHYIM3Pi9TzCyyx4c+PDT8BV02iCa80iyGu11qxGVx7uI7x6Y\nyA6ovGrqfeY+jHAQ0/W7718wRYF6CSHOziWCynpsCPz+msupN4dYsvcKpNI0F+1O0ZlgfM1dBBE7\num6nTa0xhMkShuqGoSylniXMbyb9BCxpGkMjM7MiJ2zXT9Eo2WfoReZRN68Y6xb0Chv/uwpMdAva\nnR6/ueTH7HfU3yLTlOXHn8Ldv/kxRTfq7VYd2LjyD+xzyFF4At4FMiVoZJoFzSZD9RStIlts0DI7\nP7cP7f8mpmsBDGjjQsg+VEuAVATncEoxPJyhpYEQzQH2PPAINq38HR6YKEo2THpuvfIi9jvuJLou\nUBQlu+69nKV7Led73/gqJR6hVCT+WEeqJdYF2pXF9uGFhXVs7Rb08mrOLlSlYEpPQiuBliIa83qP\nEgFvA51eSc/DvOEG84ca1IdH6Y1tRhjJbT//Dt2tazn5n19Bo6GYPwStERipZex32LHccv3VbO0W\njBc5a7ZMsrnTI68imqNbxPum+64ZO1O390DuLBAfgIlWkRFaFhR9p3pfFnzj42fzhOe/jue86xye\n868XcMpZ53DYqc/n2q++g2JiA1JCZ3KSpNboy3dKWnVNM9EYpUh1X3LTu1mTG7ZvQtaK4APOOioX\nSJQkSQ3WBzZP5AhvkUi0Mtx0xcUceMLJdHvRIaQ+f1e23Ho5tTSSQ9bdcRPLVhyKJeC8Y8NkTiev\nqJzFVpa8il/4Mm5CSPXcP7T/03ig7bUxMpq9SkHNKJJE00oUznp6VU5pLZrAon32oeyMs2XtJibb\nkHcq7rzmUvY6+iTyvMe6rZNsGu9w6j+9mAu/dh7jWyfoFQ7nLL3+YrZyFiVii9ktK0oXCMHTq6o5\nyzabaq+JtOnUqP52XqO0pvSBVmoYqSUkWlJLNY3hUTZt2czd113F7b/6Icee8SYKp6kqj5DQSAwh\nCPY+5Ghuv/43KBmQXlBZS7fwdEpL5T2eQK+sCAGyxOz03d50tcLMaLRS9ApLbj1fOeejHHjksTz0\n+EewaHiI4WYGwL4PfTj7HH8KV3/x7ejQhrJNrdkE5ymqEm/jw6y0furaIcwe1cftS50eWPxoSaIk\ntcxQ15KRukEJgUCSZIa1q26lLHL22P9Q0gSMgP3+9oncffWFJCogZY/xtatp7ron1kbZx4DEWs9Y\nt6JbeRLd1x2QIMXOZdH0343pehaDEP2ZWWoMrSwBqRjONBaJLz1GSJQxUEmW7HcYa2+/nlYKEyt/\nzcjSvWgsXIIXAiEDQmjm7bE/yw44jO9968tsaXeZKGLiVVLiEVgf/RIhwoqElBRu7uolD9pr1ZcB\nEEKQKIXWEuscgoA2qj+uUFQeNtx1O78892x+cf7ZnPC8s9DpAnILmREYKSlsBTj2WH44d95yPUJA\nq26opQlGeIL3FGXJeK+kU5ZU/cXszhaD8VqviHToiV5BaS2doiIvK3pV5BWsuuUmLrvoB5z4jJcy\n0e5ROkvTGFoZpBIOPfFJ7HbQEVz1pbNZ9dvLufLCr/H1j5yFCoIqWGyI5CXfT8RKzp7CYLvX6bLP\niAmEqLiGwLrIoKt8oJcXXPnTCznyhMeQGc38VoIVsNuBx2CLNmP33sLYfbeycM99MVkN7yGvPFpr\nKiGR3hGcR4SYkIyUSPGnsJq/hniw7fVgXtctCwgBIyXzh1KCFDgRUAQaacqyQ45g4q4badRU1EV+\n2MkEB1u7ga2TOZsnx1m3ZYxjHvd0fvDV81mzeSs9GyisZd1Eh15Z0c0j+WQ6HVXLmXrJcy2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O6BWIWSQFFFWrT1EbqaW4+tPMJ5AoIt6+5FCEFz3pIoTVpEhMaqa3/Of3z8FaStkRnvs2DPfbEl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\nGO9pNnM6rkAKRZJkhFox2kmIhaRjYdNIm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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tqdm import tqdm_notebook as tqdm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.animation as animation\n", "from IPython.display import HTML\n", "from neupy import algorithms, utils\n", " \n", "utils.reproducible()\n", "\n", "gng = algorithms.GrowingNeuralGas(\n", " n_inputs=2,\n", " n_start_nodes=2,\n", "\n", " shuffle_data=True,\n", " verbose=False,\n", " \n", " step=0.1,\n", " neighbour_step=0.001,\n", " \n", " max_edge_age=50,\n", " max_nodes=100,\n", " \n", " n_iter_before_neuron_added=100,\n", " after_split_error_decay_rate=0.5,\n", " error_decay_rate=0.995,\n", " min_distance_for_update=0.2,\n", ")\n", "\n", "fig = plt.figure()\n", "plt.scatter(*data.T, alpha=0.02)\n", "plt.xticks([], [])\n", "plt.yticks([], [])\n", "\n", "def animate(i):\n", " for line in animate.prev_lines:\n", " line.remove()\n", " \n", " # Training will slow down overtime and we increase number\n", " # of data samples for training\n", " n = int(0.5 * gng.n_iter_before_neuron_added * (1 + i // 100))\n", " \n", " sampled_data_ids = np.random.choice(len(data), n)\n", " sampled_data = data[sampled_data_ids, :]\n", " gng.train(sampled_data, epochs=1)\n", " \n", " lines = []\n", " for node_1, node_2 in gng.graph.edges:\n", " weights = np.concatenate([node_1.weight, node_2.weight])\n", " line, = plt.plot(*weights.T, color='black')\n", "\n", " plt.setp(line, linewidth=1, color='black')\n", " \n", " lines.append(line)\n", " lines.append(plt.scatter(*weights.T, color='black', s=10))\n", " \n", " animate.prev_lines = lines\n", " return lines\n", "\n", "animate.prev_lines = []\n", "anim = animation.FuncAnimation(fig, animate, tqdm(np.arange(220)), interval=30, blit=True)\n", "HTML(anim.to_html5_video())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "widgets": { "state": { "8a9fe811f3c4492c84849e2e1f5d4c3b": { "views": [ { "cell_index": 2 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

bhermanmit/openmc

docs/source/examples/mg-mode-part-i.ipynb

1

53410

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark model using the `openmc.MGXSLibrary` class to create the supporting data library on the fly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate MGXS Library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "import numpy as np\n", "\n", "import openmc\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now create the multi-group library using data directly from Appendix A of the [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark documentation. All of the data below will be created at 294K, consistent with the benchmark.\n", "\n", "This notebook will first begin by setting the group structure and building the groupwise data for UO2. As you can see, the cross sections are input in the order of increasing groups (or decreasing energy).\n", "\n", "*Note*: The C5G7 benchmark uses transport-corrected cross sections. So the total cross section we input here will technically be the transport cross section." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a 7-group structure with arbitrary boundaries (the specific boundaries are unimportant)\n", "groups = openmc.mgxs.EnergyGroups(np.logspace(-5, 7, 8))\n", "\n", "uo2_xsdata = openmc.XSdata('uo2', groups)\n", "uo2_xsdata.order = 0\n", "\n", "# When setting the data let the object know you are setting the data for a temperature of 294K.\n", "uo2_xsdata.set_total([1.77949E-1, 3.29805E-1, 4.80388E-1, 5.54367E-1,\n", " 3.11801E-1, 3.95168E-1, 5.64406E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_absorption([8.0248E-03, 3.7174E-3, 2.6769E-2, 9.6236E-2,\n", " 3.0020E-02, 1.1126E-1, 2.8278E-1], temperature=294.)\n", "uo2_xsdata.set_fission([7.21206E-3, 8.19301E-4, 6.45320E-3, 1.85648E-2,\n", " 1.78084E-2, 8.30348E-2, 2.16004E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_nu_fission([2.005998E-2, 2.027303E-3, 1.570599E-2, 4.518301E-2,\n", " 4.334208E-2, 2.020901E-1, 5.257105E-1], temperature=294.)\n", "\n", "uo2_xsdata.set_chi([5.87910E-1, 4.11760E-1, 3.39060E-4, 1.17610E-7,\n", " 0.00000E-0, 0.00000E-0, 0.00000E-0], temperature=294.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now add the scattering matrix data. \n", "\n", "*Note*: Most users familiar with deterministic transport libraries are already familiar with the idea of entering one scattering matrix for every order (i.e. scattering order as the outer dimension). However, the shape of OpenMC's scattering matrix entry is instead [Incoming groups, Outgoing Groups, Scattering Order] to best enable other scattering representations. We will follow the more familiar approach in this notebook, and then use numpy's `numpy.rollaxis` function to change the ordering to what we need (scattering order on the inner dimension)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The scattering matrix is ordered with incoming groups as rows and outgoing groups as columns\n", "# (i.e., below the diagonal is up-scattering).\n", "scatter_matrix = \\\n", " [[[1.27537E-1, 4.23780E-2, 9.43740E-6, 5.51630E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 3.24456E-1, 1.63140E-3, 3.14270E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 4.50940E-1, 2.67920E-3, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 4.52565E-1, 5.56640E-3, 0.00000E-0, 0.00000E-0],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 1.25250E-4, 2.71401E-1, 1.02550E-2, 1.00210E-8],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 1.29680E-3, 2.65802E-1, 1.68090E-2],\n", " [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 8.54580E-3, 2.73080E-1]]]\n", "scatter_matrix = np.array(scatter_matrix)\n", "scatter_matrix = np.rollaxis(scatter_matrix, 0, 3)\n", "uo2_xsdata.set_scatter_matrix(scatter_matrix, temperature=294.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the UO2 data has been created, we can move on to the remaining materials using the same process.\n", "\n", "However, we will actually skip repeating the above for now. Our simulation will instead use the `c5g7.h5` file that has already been created using exactly the same logic as above, but for the remaining materials in the benchmark problem.\n", "\n", "For now we will show how you would use the `uo2_xsdata` information to create an `openmc.MGXSLibrary` object and write to disk." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Initialize the library\n", "mg_cross_sections_file = openmc.MGXSLibrary(groups)\n", "\n", "# Add the UO2 data to it\n", "mg_cross_sections_file.add_xsdata(uo2_xsdata)\n", "\n", "# And write to disk\n", "mg_cross_sections_file.export_to_hdf5('mgxs.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate 2-D C5G7 Problem Input Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the actual 2-D model, we will first begin by creating the `materials.xml` file.\n", "\n", "First we need to define materials that will be used in the problem. In other notebooks, either `openmc.Nuclide`s or `openmc.Element`s were created at the equivalent stage. We can do that in multi-group mode as well. However, multi-group cross-sections are sometimes provided as macroscopic cross-sections; the C5G7 benchmark data are macroscopic. In this case, we can instead use `openmc.Macroscopic` objects to in-place of `openmc.Nuclide` or `openmc.Element` objects.\n", "\n", "`openmc.Macroscopic`, unlike `openmc.Nuclide` and `openmc.Element` objects, do not need to be provided enough information to calculate number densities, as no number densities are needed.\n", "\n", "When assigning `openmc.Macroscopic` objects to `openmc.Material` objects, the density can still be scaled by setting the density to a value that is not 1.0. This would be useful, for example, when slightly perturbing the density of water due to a small change in temperature (while of course ignoring any resultant spectral shift). The density of a macroscopic dataset is set to 1.0 in the `openmc.Material` object by default when an `openmc.Macroscopic` dataset is used; so we will show its use the first time and then afterwards it will not be required.\n", "\n", "Aside from these differences, the following code is very similar to similar code in other OpenMC example Notebooks." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# For every cross section data set in the library, assign an openmc.Macroscopic object to a material\n", "materials = {}\n", "for xs in ['uo2', 'mox43', 'mox7', 'mox87', 'fiss_chamber', 'guide_tube', 'water']:\n", " materials[xs] = openmc.Material(name=xs)\n", " materials[xs].set_density('macro', 1.)\n", " materials[xs].add_macroscopic(xs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can go ahead and produce a `materials.xml` file for use by OpenMC" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate a Materials collection, register all Materials, and export to XML\n", "materials_file = openmc.Materials(materials.values())\n", "\n", "# Set the location of the cross sections file to our pre-written set\n", "materials_file.cross_sections = 'c5g7.h5'\n", "\n", "materials_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our next step will be to create the geometry information needed for our assembly and to write that to the `geometry.xml` file.\n", "\n", "We will begin by defining the surfaces, cells, and universes needed for each of the individual fuel pins, guide tubes, and fission chambers." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create the surface used for each pin\n", "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", "\n", "# Create the cells which will be used to represent each pin type.\n", "cells = {}\n", "universes = {}\n", "for material in materials.values():\n", " # Create the cell for the material inside the cladding\n", " cells[material.name] = openmc.Cell(name=material.name)\n", " # Assign the half-spaces to the cell\n", " cells[material.name].region = -pin_surf\n", " # Register the material with this cell\n", " cells[material.name].fill = material\n", " \n", " # Repeat the above for the material outside the cladding (i.e., the moderator)\n", " cell_name = material.name + '_moderator'\n", " cells[cell_name] = openmc.Cell(name=cell_name)\n", " cells[cell_name].region = +pin_surf\n", " cells[cell_name].fill = materials['water']\n", " \n", " # Finally add the two cells we just made to a Universe object\n", " universes[material.name] = openmc.Universe(name=material.name)\n", " universes[material.name].add_cells([cells[material.name], cells[cell_name]])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The next step is to take our universes (representing the different pin types) and lay them out in a lattice to represent the assembly types" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lattices = {}\n", "\n", "# Instantiate the UO2 Lattice\n", "lattices['UO2 Assembly'] = openmc.RectLattice(name='UO2 Assembly')\n", "lattices['UO2 Assembly'].dimension = [17, 17]\n", "lattices['UO2 Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['UO2 Assembly'].pitch = [1.26, 1.26]\n", "u = universes['uo2']\n", "g = universes['guide_tube']\n", "f = universes['fiss_chamber']\n", "lattices['UO2 Assembly'].universes = \\\n", " [[u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n", " [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, f, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n", " [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n", " [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u]]\n", " \n", "# Create a containing cell and universe\n", "cells['UO2 Assembly'] = openmc.Cell(name='UO2 Assembly')\n", "cells['UO2 Assembly'].fill = lattices['UO2 Assembly']\n", "universes['UO2 Assembly'] = openmc.Universe(name='UO2 Assembly')\n", "universes['UO2 Assembly'].add_cell(cells['UO2 Assembly'])\n", "\n", "# Instantiate the MOX Lattice\n", "lattices['MOX Assembly'] = openmc.RectLattice(name='MOX Assembly')\n", "lattices['MOX Assembly'].dimension = [17, 17]\n", "lattices['MOX Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['MOX Assembly'].pitch = [1.26, 1.26]\n", "m = universes['mox43']\n", "n = universes['mox7']\n", "o = universes['mox87']\n", "g = universes['guide_tube']\n", "f = universes['fiss_chamber']\n", "lattices['MOX Assembly'].universes = \\\n", " [[m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m],\n", " [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n", " [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n", " [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n", " [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n", " [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, g, o, o, g, o, o, f, o, o, g, o, o, g, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n", " [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n", " [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n", " [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n", " [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n", " [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n", " [m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m]]\n", " \n", "# Create a containing cell and universe\n", "cells['MOX Assembly'] = openmc.Cell(name='MOX Assembly')\n", "cells['MOX Assembly'].fill = lattices['MOX Assembly']\n", "universes['MOX Assembly'] = openmc.Universe(name='MOX Assembly')\n", "universes['MOX Assembly'].add_cell(cells['MOX Assembly'])\n", " \n", "# Instantiate the reflector Lattice\n", "lattices['Reflector Assembly'] = openmc.RectLattice(name='Reflector Assembly')\n", "lattices['Reflector Assembly'].dimension = [1,1]\n", "lattices['Reflector Assembly'].lower_left = [-10.71, -10.71]\n", "lattices['Reflector Assembly'].pitch = [21.42, 21.42]\n", "lattices['Reflector Assembly'].universes = [[universes['water']]]\n", "\n", "# Create a containing cell and universe\n", "cells['Reflector Assembly'] = openmc.Cell(name='Reflector Assembly')\n", "cells['Reflector Assembly'].fill = lattices['Reflector Assembly']\n", "universes['Reflector Assembly'] = openmc.Universe(name='Reflector Assembly')\n", "universes['Reflector Assembly'].add_cell(cells['Reflector Assembly'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Let's now create the core layout in a 3x3 lattice where each lattice position is one of the assemblies we just defined.\n", "\n", "After that we can create the final cell to contain the entire core." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lattices['Core'] = openmc.RectLattice(name='3x3 core lattice')\n", "lattices['Core'].dimension= [3, 3]\n", "lattices['Core'].lower_left = [-32.13, -32.13]\n", "lattices['Core'].pitch = [21.42, 21.42]\n", "r = universes['Reflector Assembly']\n", "u = universes['UO2 Assembly']\n", "m = universes['MOX Assembly']\n", "lattices['Core'].universes = [[u, m, r],\n", " [m, u, r],\n", " [r, r, r]]\n", "\n", "# Create boundary planes to surround the geometry\n", "min_x = openmc.XPlane(x0=-32.13, boundary_type='reflective')\n", "max_x = openmc.XPlane(x0=+32.13, boundary_type='vacuum')\n", "min_y = openmc.YPlane(y0=-32.13, boundary_type='vacuum')\n", "max_y = openmc.YPlane(y0=+32.13, boundary_type='reflective')\n", "\n", "# Create root Cell\n", "root_cell = openmc.Cell(name='root cell')\n", "root_cell.fill = lattices['Core']\n", "\n", "# Add boundary planes\n", "root_cell.region = +min_x & -max_x & +min_y & -max_y\n", "\n", "# Create root Universe\n", "root_universe = openmc.Universe(name='root universe', universe_id=0)\n", "root_universe.add_cell(root_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we commit to the geometry, we should view it using the Python API's plotting capability" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fefbdc521d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "root_universe.plot(center=(0., 0., 0.), width=(3 * 21.42, 3 * 21.42), pixels=(500, 500),\n", " color_by='material')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, it looks pretty good, let's go ahead and write the file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", "geometry = openmc.Geometry(root_universe)\n", "\n", "# Export to \"geometry.xml\"\n", "geometry.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now create the tally file information. The tallies will be set up to give us the pin powers in this notebook. We will do this with a mesh filter, with one mesh cell per pin." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tallies_file = openmc.Tallies()\n", "\n", "# Instantiate a tally Mesh\n", "mesh = openmc.Mesh()\n", "mesh.type = 'regular'\n", "mesh.dimension = [17 * 2, 17 * 2]\n", "mesh.lower_left = [-32.13, -10.71]\n", "mesh.upper_right = [+10.71, +32.13]\n", "\n", "# Instantiate tally Filter\n", "mesh_filter = openmc.MeshFilter(mesh)\n", "\n", "# Instantiate the Tally\n", "tally = openmc.Tally(name='mesh tally')\n", "tally.filters = [mesh_filter]\n", "tally.scores = ['fission']\n", "\n", "# Add tally to collection\n", "tallies_file.append(tally)\n", "\n", "# Export all tallies to a \"tallies.xml\" file\n", "tallies_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the geometry and materials finished, we now just need to define simulation parameters for the `settings.xml` file. Note the use of the `energy_mode` attribute of our `settings_file` object. This is used to tell OpenMC that we intend to run in multi-group mode instead of the default continuous-energy mode. If we didn't specify this but our cross sections file was not a continuous-energy data set, then OpenMC would complain.\n", "\n", "This will be a relatively coarse calculation with only 500,000 active histories. A benchmark-fidelity run would of course require many more!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# OpenMC simulation parameters\n", "batches = 150\n", "inactive = 50\n", "particles = 5000\n", "\n", "# Instantiate a Settings object\n", "settings_file = openmc.Settings()\n", "settings_file.batches = batches\n", "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "\n", "# Tell OpenMC this is a multi-group problem\n", "settings_file.energy_mode = 'multi-group'\n", "\n", "# Set the verbosity to 6 so we dont see output for every batch\n", "settings_file.verbosity = 6\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-32.13, -10.71, -1e50, 10.71, 32.13, 1e50]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Tell OpenMC we want to run in eigenvalue mode\n", "settings_file.run_mode = 'eigenvalue'\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's go ahead and execute the simulation! You'll notice that the output for multi-group mode is exactly the same as for continuous-energy. The differences are all under the hood." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%%%%%%\n", " ##################### %%%%%%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%\n", " ################# %%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%\n", " ############ %%%%%%%%%%%%%%%\n", " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", " Date/Time | 2017-03-09 08:18:02\n", " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections HDF5 file...\n", " Reading tallies XML file...\n", " Loading cross section data...\n", " Loading uo2 data...\n", " Loading mox43 data...\n", " Loading mox7 data...\n", " Loading mox87 data...\n", " Loading fiss_chamber data...\n", " Loading guide_tube data...\n", " Loading water data...\n", " Building neighboring cells lists for each surface...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Creating state point statepoint.150.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 1.3630E-01 seconds\n", " Reading cross sections = 3.0827E-02 seconds\n", " Total time in simulation = 8.9648E+00 seconds\n", " Time in transport only = 8.2752E+00 seconds\n", " Time in inactive batches = 2.4798E+00 seconds\n", " Time in active batches = 6.4849E+00 seconds\n", " Time synchronizing fission bank = 1.4553E-02 seconds\n", " Sampling source sites = 1.0318E-02 seconds\n", " SEND/RECV source sites = 4.0840E-03 seconds\n", " Time accumulating tallies = 4.2427E-04 seconds\n", " Total time for finalization = 1.7081E-02 seconds\n", " Total time elapsed = 9.1340E+00 seconds\n", " Calculation Rate (inactive) = 1.00813E+05 neutrons/second\n", " Calculation Rate (active) = 77101.8 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", " k-effective (Collision) = 1.18880 +/- 0.00179\n", " k-effective (Track-length) = 1.18853 +/- 0.00244\n", " k-effective (Absorption) = 1.18601 +/- 0.00111\n", " Combined k-effective = 1.18628 +/- 0.00111\n", " Leakage Fraction = 0.00175 +/- 0.00006\n", "\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run OpenMC\n", "openmc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Results Visualization\n", "\n", "Now that we have run the simulation, let's look at the fission rate and flux tallies that we tallied." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fefe4188d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load the last statepoint file and keff value\n", "sp = openmc.StatePoint('statepoint.' + str(batches) + '.h5')\n", "\n", "# Get the OpenMC pin power tally data\n", "mesh_tally = sp.get_tally(name='mesh tally')\n", "fission_rates = mesh_tally.get_values(scores=['fission'])\n", "\n", "# Reshape array to 2D for plotting\n", "fission_rates.shape = mesh.dimension\n", "\n", "# Normalize to the average pin power\n", "fission_rates /= np.mean(fission_rates)\n", "\n", "# Force zeros to be NaNs so their values are not included when matplotlib calculates\n", "# the color scale\n", "fission_rates[fission_rates == 0.] = np.nan\n", "\n", "# Plot the pin powers and the fluxes\n", "plt.figure()\n", "plt.imshow(fission_rates, interpolation='none', cmap='jet', origin='lower')\n", "plt.colorbar()\n", "plt.title('Pin Powers')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There we have it! We have just successfully run the C5G7 benchmark model!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

atlas-outreach-data-tools/notebooks

september_2018_v-2.0/ATLAS_OpenData_06-cpp_simple_cut_and_count_analysis_example.ipynb

1

22978

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><img src=\"../images/opendata-top-transblack.png\" style=\"width:30%\"></CENTER>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><h1>Simple ATLAS OpenData HEP analysis C++ notebook example</h1></CENTER>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "//%jsroot on" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TChain *dataset = new TChain(\"mini\");" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "//dataset->Add(\"mc_105986.ZZ.root\");\n", "//dataset->Add(\"mc_147770.Zee.root\");\n", "\n", "//This input is readed directly from the Internet. If you are ofline, you can use the line above\n", "dataset->Add(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\");" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "const int vs = 5;\n", "\n", "Int_t lepton_n = -1,\n", " lepton_charge[vs], \n", " lepton_type[vs];\n", "\n", "Float_t lepton_pt[vs],\n", " lepton_eta[vs],\n", " lepton_phi[vs];" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset->SetBranchAddress(\"lep_n\", &lepton_n);\n", "dataset->SetBranchAddress(\"lep_charge\", &lepton_charge);\n", "dataset->SetBranchAddress(\"lep_type\", &lepton_type);\n", "dataset->SetBranchAddress(\"lep_pt\", &lepton_pt);\n", "dataset->SetBranchAddress(\"lep_eta\", &lepton_eta);\n", "dataset->SetBranchAddress(\"lep_phi\", &lepton_phi);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TH1F *h_lep_pt_leptons = new TH1F(\"h_lep_pt_leptons\",\"Lepton pt in GeV\",20,0,200);\n", "h_lep_pt_leptons->SetFillColor(kRed);" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "int nentries, nbytes, i;\n", "nentries = (Int_t)dataset->GetEntries();" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total # events = 7500000. Events to run = 750000 corresponding to 10% of total events!\r\n" ] } ], "source": [ "// IMPORTANT: fraction events we want to run\n", "fraction_events = 0.1;\n", "events_to_run = nentries*fraction_events;\n", "\n", "std::cout << \"Total # events = \" << nentries\n", " << \". Events to run = \" << events_to_run\n", " << \" corresponding to \" << fraction_events*100\n", " << \"% of total events!\" << std::endl;" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for (i=0; i<events_to_run; i++)\n", "{\n", " nbytes = dataset->GetEntry(i);\n", " \n", " if(lepton_n>1) // Number of leptons in the events has to be at least 2\n", " {\n", " if(lepton_type[0] == lepton_type[1]) //Leptons of the same family, i.e. 2 electrons or 2 muons (those are the two most energetic leptons)\n", " {\n", " if(lepton_charge[0] != lepton_charge[1]) // The two selected leptons must have opposite charge\n", " {\n", " //PT\n", " float lepton_pt_inGeV = lepton_pt[0]/1000.; // The default value in the root file is in MeV, so, we divide by 1000 to get it in GeV\n", " h_lep_pt_leptons->Fill(lepton_pt_inGeV);\n", " }\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ALEEBAMgSFACALEEBAMgSFACALEEBAMgSFACALEEBAMj64+wC9leW5eL94zge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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TCanvas *cz = new TCanvas(\"cz\",\"cz\",10,10,700,700);\n", "h_lep_pt_leptons->Draw();\n", "cz->Draw();" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Your <b>first</b> histogram is done!" ] } ], "metadata": { "kernelspec": { "display_name": "ROOT Prompt", "language": "c++", "name": "root" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

dandtaylor/MetroShare

bikeshare_cleaning.ipynb

1

57775

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Get the bikeshare data ready for analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### import 2015-Q4 through 2016-Q3 bike data" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes5 = pd.read_csv('2015-Q4-Trips-History-Data.csv')\n", "bikes6 = pd.read_csv('2016-Q1-Trips-History-Data.csv')\n", "bikes7 = pd.read_csv('2016-Q2-Trips-History-Data.csv')\n", "bikes8 = pd.read_csv('2016-Q3-Trips-History-Data-1.csv')\n", "bikes9 = pd.read_csv('2016-Q3-Trips-History-Data-2.csv')\n", "bikes7.rename(columns={'Account type':'Member Type'}, inplace=True)\n", "bikes5.rename(columns={'Bike #':'Bike number', 'Member type':'Member Type'}, inplace=True)\n", "\n", "bikes = pd.concat([bikes5, bikes6, bikes7, bikes8,\n", " bikes9], ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Duration (ms)</th>\n", " <th>Start date</th>\n", " <th>End date</th>\n", " <th>Start station number</th>\n", " <th>Start station</th>\n", " <th>End station number</th>\n", " <th>End station</th>\n", " <th>Bike number</th>\n", " <th>Member Type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>166050</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:04</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>31105</td>\n", " <td>14th & Harvard St NW</td>\n", " <td>W21109</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>379172</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31314</td>\n", " <td>34th & Water St NW</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W20603</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>696038</td>\n", " <td>10/1/2015 0:01</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W01233</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>219423</td>\n", " <td>10/1/2015 0:02</td>\n", " <td>10/1/2015 0:06</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31121</td>\n", " <td>Calvert St & Woodley Pl NW</td>\n", " <td>W00218</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>253230</td>\n", " <td>10/1/2015 0:03</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>W21612</td>\n", " <td>Registered</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Duration (ms) Start date End date Start station number \\\n", "0 166050 10/1/2015 0:01 10/1/2015 0:04 31602 \n", "1 379172 10/1/2015 0:01 10/1/2015 0:07 31314 \n", "2 696038 10/1/2015 0:01 10/1/2015 0:13 31214 \n", "3 219423 10/1/2015 0:02 10/1/2015 0:06 31104 \n", "4 253230 10/1/2015 0:03 10/1/2015 0:07 31102 \n", "\n", " Start station End station number \\\n", "0 Park Rd & Holmead Pl NW 31105 \n", "1 34th & Water St NW 31237 \n", "2 17th & Corcoran St NW 31214 \n", "3 Adams Mill & Columbia Rd NW 31121 \n", "4 11th & Kenyon St NW 31102 \n", "\n", " End station Bike number Member Type \n", "0 14th & Harvard St NW W21109 Registered \n", "1 25th St & Pennsylvania Ave NW W20603 Registered \n", "2 17th & Corcoran St NW W01233 Registered \n", "3 Calvert St & Woodley Pl NW W00218 Registered \n", "4 11th & Kenyon St NW W21612 Registered " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bikes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create DatetimeIndex from `Start date`" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes['Start date'] = pd.to_datetime(bikes['Start date'], format='%m/%d/%Y %H:%M')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bikes = bikes.set_index('Start date')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.tseries.index.DatetimeIndex" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(bikes.index)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Duration (ms)</th>\n", " <th>End date</th>\n", " <th>Start station number</th>\n", " <th>Start station</th>\n", " <th>End station number</th>\n", " <th>End station</th>\n", " <th>Bike number</th>\n", " <th>Member Type</th>\n", " </tr>\n", " <tr>\n", " <th>Start date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>166050</td>\n", " <td>10/1/2015 0:04</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>31105</td>\n", " <td>14th & Harvard St NW</td>\n", " <td>W21109</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>379172</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31314</td>\n", " <td>34th & Water St NW</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W20603</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:01:00</th>\n", " <td>696038</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W01233</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:02:00</th>\n", " <td>219423</td>\n", " <td>10/1/2015 0:06</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31121</td>\n", " <td>Calvert St & Woodley Pl NW</td>\n", " <td>W00218</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>253230</td>\n", " <td>10/1/2015 0:07</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>W21612</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>655251</td>\n", " <td>10/1/2015 0:14</td>\n", " <td>31242</td>\n", " <td>18th St & Pennsylvania Ave NW</td>\n", " <td>31114</td>\n", " <td>18th St & Wyoming Ave NW</td>\n", " <td>W22093</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:06:00</th>\n", " <td>309212</td>\n", " <td>10/1/2015 0:11</td>\n", " <td>31280</td>\n", " <td>11th & S St NW</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>776195</td>\n", " <td>10/1/2015 0:20</td>\n", " <td>31226</td>\n", " <td>34th St & Wisconsin Ave NW</td>\n", " <td>31214</td>\n", " <td>17th & Corcoran St NW</td>\n", " <td>W20820</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>151604</td>\n", " <td>10/1/2015 0:10</td>\n", " <td>31017</td>\n", " <td>Wilson Blvd & N Uhle St</td>\n", " <td>31029</td>\n", " <td>N Veitch & 20th St N</td>\n", " <td>W00244</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:07:00</th>\n", " <td>809509</td>\n", " <td>10/1/2015 0:21</td>\n", " <td>31301</td>\n", " <td>Ward Circle / American University</td>\n", " <td>31113</td>\n", " <td>Columbia Rd & Belmont St NW</td>\n", " <td>W20352</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:08:00</th>\n", " <td>842712</td>\n", " <td>10/1/2015 0:22</td>\n", " <td>31276</td>\n", " <td>15th & L St NW</td>\n", " <td>31118</td>\n", " <td>3rd & Elm St NW</td>\n", " <td>W21327</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>171124</td>\n", " <td>10/1/2015 0:13</td>\n", " <td>31213</td>\n", " <td>17th & K St NW</td>\n", " <td>31267</td>\n", " <td>17th St & Massachusetts Ave NW</td>\n", " <td>W20487</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>241872</td>\n", " <td>10/1/2015 0:15</td>\n", " <td>31203</td>\n", " <td>14th & Rhode Island Ave NW</td>\n", " <td>31101</td>\n", " <td>14th & V St NW</td>\n", " <td>W22183</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>294746</td>\n", " <td>10/1/2015 0:16</td>\n", " <td>31212</td>\n", " <td>21st & M St NW</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>W20769</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:11:00</th>\n", " <td>590515</td>\n", " <td>10/1/2015 0:21</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>31312</td>\n", " <td>Wisconsin Ave & O St NW</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:14:00</th>\n", " <td>1217591</td>\n", " <td>10/1/2015 0:34</td>\n", " <td>31620</td>\n", " <td>5th & F St NW</td>\n", " <td>31615</td>\n", " <td>6th & H St NE</td>\n", " <td>W20668</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:14:00</th>\n", " <td>957676</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>31103</td>\n", " <td>16th & Harvard St NW</td>\n", " <td>W01031</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>1644646</td>\n", " <td>10/1/2015 0:42</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31617</td>\n", " <td>Bladensburg Rd & Benning Rd NE</td>\n", " <td>W21001</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>580130</td>\n", " <td>10/1/2015 0:25</td>\n", " <td>31610</td>\n", " <td>Eastern Market / 7th & North Carolina Ave SE</td>\n", " <td>31256</td>\n", " <td>10th & E St NW</td>\n", " <td>W00379</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>222180</td>\n", " <td>10/1/2015 0:19</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>W20592</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:03:00</th>\n", " <td>267251</td>\n", " <td>10/1/2015 0:08</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>W21206</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:15:00</th>\n", " <td>867784</td>\n", " <td>10/1/2015 0:29</td>\n", " <td>31600</td>\n", " <td>5th & K St NW</td>\n", " <td>31605</td>\n", " <td>3rd & D St SE</td>\n", " <td>W20970</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:17:00</th>\n", " <td>737845</td>\n", " <td>10/1/2015 0:29</td>\n", " <td>31104</td>\n", " <td>Adams Mill & Columbia Rd NW</td>\n", " <td>31509</td>\n", " <td>New Jersey Ave & R St NW</td>\n", " <td>W21281</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:19:00</th>\n", " <td>286638</td>\n", " <td>10/1/2015 0:24</td>\n", " <td>31017</td>\n", " <td>Wilson Blvd & N Uhle St</td>\n", " <td>31015</td>\n", " <td>Rosslyn Metro / Wilson Blvd & Ft Myer Dr</td>\n", " <td>W20617</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:19:00</th>\n", " <td>5628236</td>\n", " <td>10/1/2015 1:53</td>\n", " <td>31063</td>\n", " <td>S George Mason Dr & Four Mile Run</td>\n", " <td>31062</td>\n", " <td>S George Mason Dr & 13th St S</td>\n", " <td>W20439</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:20:00</th>\n", " <td>102044</td>\n", " <td>10/1/2015 0:22</td>\n", " <td>31049</td>\n", " <td>Utah St & 11th St N</td>\n", " <td>31037</td>\n", " <td>Ballston Metro / N Stuart & 9th St N</td>\n", " <td>W00900</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>504552</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31277</td>\n", " <td>17th & G St NW</td>\n", " <td>31239</td>\n", " <td>17th & Rhode Island Ave NW</td>\n", " <td>W21239</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>526336</td>\n", " <td>10/1/2015 0:30</td>\n", " <td>31277</td>\n", " <td>17th & G St NW</td>\n", " <td>31239</td>\n", " <td>17th & Rhode Island Ave NW</td>\n", " <td>W21241</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:21:00</th>\n", " <td>867396</td>\n", " <td>10/1/2015 0:36</td>\n", " <td>31312</td>\n", " <td>Wisconsin Ave & O St NW</td>\n", " <td>31306</td>\n", " <td>39th & Calvert St NW / Stoddert</td>\n", " <td>W00231</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2015-10-01 00:23:00</th>\n", " <td>278050</td>\n", " <td>10/1/2015 0:27</td>\n", " <td>31638</td>\n", " <td>1st & H St NW</td>\n", " <td>31627</td>\n", " <td>M St & Delaware Ave NE</td>\n", " <td>W21823</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:21:00</th>\n", " <td>933505</td>\n", " <td>9/1/2016 0:37</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>31509</td>\n", " <td>New Jersey Ave & R St NW</td>\n", " <td>W00906</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:20:00</th>\n", " <td>1574827</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W22628</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>8871227</td>\n", " <td>9/1/2016 2:47</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>W01127</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>1648284</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W00957</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>679655</td>\n", " <td>9/1/2016 0:30</td>\n", " <td>31264</td>\n", " <td>6th St & Indiana Ave NW</td>\n", " <td>31628</td>\n", " <td>1st & K St SE</td>\n", " <td>W00721</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>1633724</td>\n", " <td>9/1/2016 0:46</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31288</td>\n", " <td>4th St & Madison Dr NW</td>\n", " <td>W21699</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>8835027</td>\n", " <td>9/1/2016 2:46</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>31270</td>\n", " <td>8th & D St NW</td>\n", " <td>W00996</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:19:00</th>\n", " <td>191644</td>\n", " <td>9/1/2016 0:23</td>\n", " <td>31285</td>\n", " <td>22nd & P ST NW</td>\n", " <td>31234</td>\n", " <td>20th & O St NW / Dupont South</td>\n", " <td>W22575</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:18:00</th>\n", " <td>173292</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31110</td>\n", " <td>20th St & Florida Ave NW</td>\n", " <td>31234</td>\n", " <td>20th & O St NW / Dupont South</td>\n", " <td>W20893</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:18:00</th>\n", " <td>256249</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31109</td>\n", " <td>7th & T St NW</td>\n", " <td>31223</td>\n", " <td>Convention Center / 7th & M St NW</td>\n", " <td>W20689</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:17:00</th>\n", " <td>453588</td>\n", " <td>9/1/2016 0:25</td>\n", " <td>31278</td>\n", " <td>18th & R St NW</td>\n", " <td>31275</td>\n", " <td>New Hampshire Ave & 24th St NW</td>\n", " <td>W21256</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:15:00</th>\n", " <td>195777</td>\n", " <td>9/1/2016 0:18</td>\n", " <td>31257</td>\n", " <td>22nd & I St NW / Foggy Bottom</td>\n", " <td>31237</td>\n", " <td>25th St & Pennsylvania Ave NW</td>\n", " <td>W22398</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:15:00</th>\n", " <td>430642</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31267</td>\n", " <td>17th St & Massachusetts Ave NW</td>\n", " <td>31223</td>\n", " <td>Convention Center / 7th & M St NW</td>\n", " <td>W21480</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>1309689</td>\n", " <td>9/1/2016 0:35</td>\n", " <td>31240</td>\n", " <td>Ohio Dr & West Basin Dr SW / MLK & FDR Memorials</td>\n", " <td>31252</td>\n", " <td>21st St & Pennsylvania Ave NW</td>\n", " <td>W22661</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>513823</td>\n", " <td>9/1/2016 0:22</td>\n", " <td>31504</td>\n", " <td>10th & Monroe St NE</td>\n", " <td>31510</td>\n", " <td>18th St & Rhode Island Ave NE</td>\n", " <td>W20265</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:13:00</th>\n", " <td>1038905</td>\n", " <td>9/1/2016 0:31</td>\n", " <td>31279</td>\n", " <td>19th & G St NW</td>\n", " <td>31605</td>\n", " <td>3rd & D St SE</td>\n", " <td>W20524</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:12:00</th>\n", " <td>499112</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31111</td>\n", " <td>10th & U St NW</td>\n", " <td>31400</td>\n", " <td>Georgia & New Hampshire Ave NW</td>\n", " <td>W22865</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>1019381</td>\n", " <td>9/1/2016 0:26</td>\n", " <td>31290</td>\n", " <td>17th St & Independence Ave SW</td>\n", " <td>31281</td>\n", " <td>8th & O St NW</td>\n", " <td>W22637</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>680488</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31248</td>\n", " <td>Smithsonian / Jefferson Dr & 12th St SW</td>\n", " <td>W21299</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:09:00</th>\n", " <td>674944</td>\n", " <td>9/1/2016 0:21</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31248</td>\n", " <td>Smithsonian / Jefferson Dr & 12th St SW</td>\n", " <td>W21011</td>\n", " <td>Casual</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:08:00</th>\n", " <td>371207</td>\n", " <td>9/1/2016 0:14</td>\n", " <td>31628</td>\n", " <td>1st & K St SE</td>\n", " <td>31613</td>\n", " <td>Eastern Market Metro / Pennsylvania Ave & 7th ...</td>\n", " <td>W20941</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>1091877</td>\n", " <td>9/1/2016 0:23</td>\n", " <td>31228</td>\n", " <td>8th & H St NW</td>\n", " <td>31103</td>\n", " <td>16th & Harvard St NW</td>\n", " <td>W00785</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>259558</td>\n", " <td>9/1/2016 0:09</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>W21529</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>254785</td>\n", " <td>9/1/2016 0:09</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31602</td>\n", " <td>Park Rd & Holmead Pl NW</td>\n", " <td>W22528</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>878992</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31102</td>\n", " <td>11th & Kenyon St NW</td>\n", " <td>31403</td>\n", " <td>5th & Kennedy St NW</td>\n", " <td>W22689</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>283200</td>\n", " <td>9/1/2016 0:10</td>\n", " <td>31600</td>\n", " <td>5th & K St NW</td>\n", " <td>31264</td>\n", " <td>6th St & Indiana Ave NW</td>\n", " <td>W01320</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:05:00</th>\n", " <td>867363</td>\n", " <td>9/1/2016 0:20</td>\n", " <td>31280</td>\n", " <td>11th & S St NW</td>\n", " <td>31113</td>\n", " <td>Columbia Rd & Belmont St NW</td>\n", " <td>W22933</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:03:00</th>\n", " <td>4798819</td>\n", " <td>9/1/2016 1:23</td>\n", " <td>31258</td>\n", " <td>Lincoln Memorial</td>\n", " <td>31240</td>\n", " <td>Ohio Dr & West Basin Dr SW / MLK & FDR Memorials</td>\n", " <td>W22953</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:02:00</th>\n", " <td>763129</td>\n", " <td>9/1/2016 0:15</td>\n", " <td>31641</td>\n", " <td>2nd St & Massachusetts Ave NE</td>\n", " <td>31276</td>\n", " <td>15th & L St NW</td>\n", " <td>W22911</td>\n", " <td>Registered</td>\n", " </tr>\n", " <tr>\n", " <th>2016-09-01 00:00:00</th>\n", " <td>590832</td>\n", " <td>9/1/2016 0:10</td>\n", " <td>31200</td>\n", " <td>Massachusetts Ave & Dupont Circle NW</td>\n", " <td>31245</td>\n", " <td>7th & R St NW / Shaw Library</td>\n", " <td>W22867</td>\n", " <td>Registered</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3268722 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " Duration (ms) End date Start station number \\\n", "Start date \n", "2015-10-01 00:01:00 166050 10/1/2015 0:04 31602 \n", "2015-10-01 00:01:00 379172 10/1/2015 0:07 31314 \n", "2015-10-01 00:01:00 696038 10/1/2015 0:13 31214 \n", "2015-10-01 00:02:00 219423 10/1/2015 0:06 31104 \n", "2015-10-01 00:03:00 253230 10/1/2015 0:07 31102 \n", "2015-10-01 00:03:00 655251 10/1/2015 0:14 31242 \n", "2015-10-01 00:06:00 309212 10/1/2015 0:11 31280 \n", "2015-10-01 00:07:00 776195 10/1/2015 0:20 31226 \n", "2015-10-01 00:07:00 151604 10/1/2015 0:10 31017 \n", "2015-10-01 00:07:00 809509 10/1/2015 0:21 31301 \n", "2015-10-01 00:08:00 842712 10/1/2015 0:22 31276 \n", "2015-10-01 00:11:00 171124 10/1/2015 0:13 31213 \n", "2015-10-01 00:11:00 241872 10/1/2015 0:15 31203 \n", "2015-10-01 00:11:00 294746 10/1/2015 0:16 31212 \n", "2015-10-01 00:11:00 590515 10/1/2015 0:21 31278 \n", "2015-10-01 00:14:00 1217591 10/1/2015 0:34 31620 \n", "2015-10-01 00:14:00 957676 10/1/2015 0:30 31228 \n", "2015-10-01 00:15:00 1644646 10/1/2015 0:42 31258 \n", "2015-10-01 00:15:00 580130 10/1/2015 0:25 31610 \n", "2015-10-01 00:15:00 222180 10/1/2015 0:19 31270 \n", "2015-10-01 00:03:00 267251 10/1/2015 0:08 31306 \n", "2015-10-01 00:15:00 867784 10/1/2015 0:29 31600 \n", "2015-10-01 00:17:00 737845 10/1/2015 0:29 31104 \n", "2015-10-01 00:19:00 286638 10/1/2015 0:24 31017 \n", "2015-10-01 00:19:00 5628236 10/1/2015 1:53 31063 \n", "2015-10-01 00:20:00 102044 10/1/2015 0:22 31049 \n", "2015-10-01 00:21:00 504552 10/1/2015 0:30 31277 \n", "2015-10-01 00:21:00 526336 10/1/2015 0:30 31277 \n", "2015-10-01 00:21:00 867396 10/1/2015 0:36 31312 \n", "2015-10-01 00:23:00 278050 10/1/2015 0:27 31638 \n", "... ... ... ... \n", "2016-09-01 00:21:00 933505 9/1/2016 0:37 31237 \n", "2016-09-01 00:20:00 1574827 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 8871227 9/1/2016 2:47 31270 \n", "2016-09-01 00:19:00 1648284 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 679655 9/1/2016 0:30 31264 \n", "2016-09-01 00:19:00 1633724 9/1/2016 0:46 31258 \n", "2016-09-01 00:19:00 8835027 9/1/2016 2:46 31270 \n", "2016-09-01 00:19:00 191644 9/1/2016 0:23 31285 \n", "2016-09-01 00:18:00 173292 9/1/2016 0:21 31110 \n", "2016-09-01 00:18:00 256249 9/1/2016 0:22 31109 \n", "2016-09-01 00:17:00 453588 9/1/2016 0:25 31278 \n", "2016-09-01 00:15:00 195777 9/1/2016 0:18 31257 \n", "2016-09-01 00:15:00 430642 9/1/2016 0:22 31267 \n", "2016-09-01 00:13:00 1309689 9/1/2016 0:35 31240 \n", "2016-09-01 00:13:00 513823 9/1/2016 0:22 31504 \n", "2016-09-01 00:13:00 1038905 9/1/2016 0:31 31279 \n", "2016-09-01 00:12:00 499112 9/1/2016 0:20 31111 \n", "2016-09-01 00:09:00 1019381 9/1/2016 0:26 31290 \n", "2016-09-01 00:09:00 680488 9/1/2016 0:21 31258 \n", "2016-09-01 00:09:00 674944 9/1/2016 0:21 31258 \n", "2016-09-01 00:08:00 371207 9/1/2016 0:14 31628 \n", "2016-09-01 00:05:00 1091877 9/1/2016 0:23 31228 \n", "2016-09-01 00:05:00 259558 9/1/2016 0:09 31102 \n", "2016-09-01 00:05:00 254785 9/1/2016 0:09 31102 \n", "2016-09-01 00:05:00 878992 9/1/2016 0:20 31102 \n", "2016-09-01 00:05:00 283200 9/1/2016 0:10 31600 \n", "2016-09-01 00:05:00 867363 9/1/2016 0:20 31280 \n", "2016-09-01 00:03:00 4798819 9/1/2016 1:23 31258 \n", "2016-09-01 00:02:00 763129 9/1/2016 0:15 31641 \n", "2016-09-01 00:00:00 590832 9/1/2016 0:10 31200 \n", "\n", " Start station \\\n", "Start date \n", "2015-10-01 00:01:00 Park Rd & Holmead Pl NW \n", "2015-10-01 00:01:00 34th & Water St NW \n", "2015-10-01 00:01:00 17th & Corcoran St NW \n", "2015-10-01 00:02:00 Adams Mill & Columbia Rd NW \n", "2015-10-01 00:03:00 11th & Kenyon St NW \n", "2015-10-01 00:03:00 18th St & Pennsylvania Ave NW \n", "2015-10-01 00:06:00 11th & S St NW \n", "2015-10-01 00:07:00 34th St & Wisconsin Ave NW \n", "2015-10-01 00:07:00 Wilson Blvd & N Uhle St \n", "2015-10-01 00:07:00 Ward Circle / American University \n", "2015-10-01 00:08:00 15th & L St NW \n", "2015-10-01 00:11:00 17th & K St NW \n", "2015-10-01 00:11:00 14th & Rhode Island Ave NW \n", "2015-10-01 00:11:00 21st & M St NW \n", "2015-10-01 00:11:00 18th & R St NW \n", "2015-10-01 00:14:00 5th & F St NW \n", "2015-10-01 00:14:00 8th & H St NW \n", "2015-10-01 00:15:00 Lincoln Memorial \n", "2015-10-01 00:15:00 Eastern Market / 7th & North Carolina Ave SE \n", "2015-10-01 00:15:00 8th & D St NW \n", "2015-10-01 00:03:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:15:00 5th & K St NW \n", "2015-10-01 00:17:00 Adams Mill & Columbia Rd NW \n", "2015-10-01 00:19:00 Wilson Blvd & N Uhle St \n", "2015-10-01 00:19:00 S George Mason Dr & Four Mile Run \n", "2015-10-01 00:20:00 Utah St & 11th St N \n", "2015-10-01 00:21:00 17th & G St NW \n", "2015-10-01 00:21:00 17th & G St NW \n", "2015-10-01 00:21:00 Wisconsin Ave & O St NW \n", "2015-10-01 00:23:00 1st & H St NW \n", "... ... \n", "2016-09-01 00:21:00 25th St & Pennsylvania Ave NW \n", "2016-09-01 00:20:00 Lincoln Memorial \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 Lincoln Memorial \n", "2016-09-01 00:19:00 6th St & Indiana Ave NW \n", "2016-09-01 00:19:00 Lincoln Memorial \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 22nd & P ST NW \n", "2016-09-01 00:18:00 20th St & Florida Ave NW \n", "2016-09-01 00:18:00 7th & T St NW \n", "2016-09-01 00:17:00 18th & R St NW \n", "2016-09-01 00:15:00 22nd & I St NW / Foggy Bottom \n", "2016-09-01 00:15:00 17th St & Massachusetts Ave NW \n", "2016-09-01 00:13:00 Ohio Dr & West Basin Dr SW / MLK & FDR Memorials \n", "2016-09-01 00:13:00 10th & Monroe St NE \n", "2016-09-01 00:13:00 19th & G St NW \n", "2016-09-01 00:12:00 10th & U St NW \n", "2016-09-01 00:09:00 17th St & Independence Ave SW \n", "2016-09-01 00:09:00 Lincoln Memorial \n", "2016-09-01 00:09:00 Lincoln Memorial \n", "2016-09-01 00:08:00 1st & K St SE \n", "2016-09-01 00:05:00 8th & H St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 11th & Kenyon St NW \n", "2016-09-01 00:05:00 5th & K St NW \n", "2016-09-01 00:05:00 11th & S St NW \n", "2016-09-01 00:03:00 Lincoln Memorial \n", "2016-09-01 00:02:00 2nd St & Massachusetts Ave NE \n", "2016-09-01 00:00:00 Massachusetts Ave & Dupont Circle NW \n", "\n", " End station number \\\n", "Start date \n", "2015-10-01 00:01:00 31105 \n", "2015-10-01 00:01:00 31237 \n", "2015-10-01 00:01:00 31214 \n", "2015-10-01 00:02:00 31121 \n", "2015-10-01 00:03:00 31102 \n", "2015-10-01 00:03:00 31114 \n", "2015-10-01 00:06:00 31278 \n", "2015-10-01 00:07:00 31214 \n", "2015-10-01 00:07:00 31029 \n", "2015-10-01 00:07:00 31113 \n", "2015-10-01 00:08:00 31118 \n", "2015-10-01 00:11:00 31267 \n", "2015-10-01 00:11:00 31101 \n", "2015-10-01 00:11:00 31278 \n", "2015-10-01 00:11:00 31312 \n", "2015-10-01 00:14:00 31615 \n", "2015-10-01 00:14:00 31103 \n", "2015-10-01 00:15:00 31617 \n", "2015-10-01 00:15:00 31256 \n", "2015-10-01 00:15:00 31228 \n", "2015-10-01 00:03:00 31306 \n", "2015-10-01 00:15:00 31605 \n", "2015-10-01 00:17:00 31509 \n", "2015-10-01 00:19:00 31015 \n", "2015-10-01 00:19:00 31062 \n", "2015-10-01 00:20:00 31037 \n", "2015-10-01 00:21:00 31239 \n", "2015-10-01 00:21:00 31239 \n", "2015-10-01 00:21:00 31306 \n", "2015-10-01 00:23:00 31627 \n", "... ... \n", "2016-09-01 00:21:00 31509 \n", "2016-09-01 00:20:00 31288 \n", "2016-09-01 00:19:00 31270 \n", "2016-09-01 00:19:00 31288 \n", "2016-09-01 00:19:00 31628 \n", "2016-09-01 00:19:00 31288 \n", "2016-09-01 00:19:00 31270 \n", "2016-09-01 00:19:00 31234 \n", "2016-09-01 00:18:00 31234 \n", "2016-09-01 00:18:00 31223 \n", "2016-09-01 00:17:00 31275 \n", "2016-09-01 00:15:00 31237 \n", "2016-09-01 00:15:00 31223 \n", "2016-09-01 00:13:00 31252 \n", "2016-09-01 00:13:00 31510 \n", "2016-09-01 00:13:00 31605 \n", "2016-09-01 00:12:00 31400 \n", "2016-09-01 00:09:00 31281 \n", "2016-09-01 00:09:00 31248 \n", "2016-09-01 00:09:00 31248 \n", "2016-09-01 00:08:00 31613 \n", "2016-09-01 00:05:00 31103 \n", "2016-09-01 00:05:00 31602 \n", "2016-09-01 00:05:00 31602 \n", "2016-09-01 00:05:00 31403 \n", "2016-09-01 00:05:00 31264 \n", "2016-09-01 00:05:00 31113 \n", "2016-09-01 00:03:00 31240 \n", "2016-09-01 00:02:00 31276 \n", "2016-09-01 00:00:00 31245 \n", "\n", " End station \\\n", "Start date \n", "2015-10-01 00:01:00 14th & Harvard St NW \n", "2015-10-01 00:01:00 25th St & Pennsylvania Ave NW \n", "2015-10-01 00:01:00 17th & Corcoran St NW \n", "2015-10-01 00:02:00 Calvert St & Woodley Pl NW \n", "2015-10-01 00:03:00 11th & Kenyon St NW \n", "2015-10-01 00:03:00 18th St & Wyoming Ave NW \n", "2015-10-01 00:06:00 18th & R St NW \n", "2015-10-01 00:07:00 17th & Corcoran St NW \n", "2015-10-01 00:07:00 N Veitch & 20th St N \n", "2015-10-01 00:07:00 Columbia Rd & Belmont St NW \n", "2015-10-01 00:08:00 3rd & Elm St NW \n", "2015-10-01 00:11:00 17th St & Massachusetts Ave NW \n", "2015-10-01 00:11:00 14th & V St NW \n", "2015-10-01 00:11:00 18th & R St NW \n", "2015-10-01 00:11:00 Wisconsin Ave & O St NW \n", "2015-10-01 00:14:00 6th & H St NE \n", "2015-10-01 00:14:00 16th & Harvard St NW \n", "2015-10-01 00:15:00 Bladensburg Rd & Benning Rd NE \n", "2015-10-01 00:15:00 10th & E St NW \n", "2015-10-01 00:15:00 8th & H St NW \n", "2015-10-01 00:03:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:15:00 3rd & D St SE \n", "2015-10-01 00:17:00 New Jersey Ave & R St NW \n", "2015-10-01 00:19:00 Rosslyn Metro / Wilson Blvd & Ft Myer Dr \n", "2015-10-01 00:19:00 S George Mason Dr & 13th St S \n", "2015-10-01 00:20:00 Ballston Metro / N Stuart & 9th St N \n", "2015-10-01 00:21:00 17th & Rhode Island Ave NW \n", "2015-10-01 00:21:00 17th & Rhode Island Ave NW \n", "2015-10-01 00:21:00 39th & Calvert St NW / Stoddert \n", "2015-10-01 00:23:00 M St & Delaware Ave NE \n", "... ... \n", "2016-09-01 00:21:00 New Jersey Ave & R St NW \n", "2016-09-01 00:20:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 1st & K St SE \n", "2016-09-01 00:19:00 4th St & Madison Dr NW \n", "2016-09-01 00:19:00 8th & D St NW \n", "2016-09-01 00:19:00 20th & O St NW / Dupont South \n", "2016-09-01 00:18:00 20th & O St NW / Dupont South \n", "2016-09-01 00:18:00 Convention Center / 7th & M St NW \n", "2016-09-01 00:17:00 New Hampshire Ave & 24th St NW \n", "2016-09-01 00:15:00 25th St & Pennsylvania Ave NW \n", "2016-09-01 00:15:00 Convention Center / 7th & M St NW \n", "2016-09-01 00:13:00 21st St & Pennsylvania Ave NW \n", "2016-09-01 00:13:00 18th St & Rhode Island Ave NE \n", "2016-09-01 00:13:00 3rd & D St SE \n", "2016-09-01 00:12:00 Georgia & New Hampshire Ave NW \n", "2016-09-01 00:09:00 8th & O St NW \n", "2016-09-01 00:09:00 Smithsonian / Jefferson Dr & 12th St SW \n", "2016-09-01 00:09:00 Smithsonian / Jefferson Dr & 12th St SW \n", "2016-09-01 00:08:00 Eastern Market Metro / Pennsylvania Ave & 7th ... \n", "2016-09-01 00:05:00 16th & Harvard St NW \n", "2016-09-01 00:05:00 Park Rd & Holmead Pl NW \n", "2016-09-01 00:05:00 Park Rd & Holmead Pl NW \n", "2016-09-01 00:05:00 5th & Kennedy St NW \n", "2016-09-01 00:05:00 6th St & Indiana Ave NW \n", "2016-09-01 00:05:00 Columbia Rd & Belmont St NW \n", "2016-09-01 00:03:00 Ohio Dr & West Basin Dr SW / MLK & FDR Memorials \n", "2016-09-01 00:02:00 15th & L St NW \n", "2016-09-01 00:00:00 7th & R St NW / Shaw Library \n", "\n", " Bike number Member Type \n", "Start date \n", "2015-10-01 00:01:00 W21109 Registered \n", "2015-10-01 00:01:00 W20603 Registered \n", "2015-10-01 00:01:00 W01233 Registered \n", "2015-10-01 00:02:00 W00218 Registered \n", "2015-10-01 00:03:00 W21612 Registered \n", "2015-10-01 00:03:00 W22093 Registered \n", "2015-10-01 00:06:00 W00231 Registered \n", "2015-10-01 00:07:00 W20820 Registered \n", "2015-10-01 00:07:00 W00244 Registered \n", "2015-10-01 00:07:00 W20352 Registered \n", "2015-10-01 00:08:00 W21327 Registered \n", "2015-10-01 00:11:00 W20487 Registered \n", "2015-10-01 00:11:00 W22183 Registered \n", "2015-10-01 00:11:00 W20769 Registered \n", "2015-10-01 00:11:00 W00231 Registered \n", "2015-10-01 00:14:00 W20668 Casual \n", "2015-10-01 00:14:00 W01031 Registered \n", "2015-10-01 00:15:00 W21001 Registered \n", "2015-10-01 00:15:00 W00379 Registered \n", "2015-10-01 00:15:00 W20592 Registered \n", "2015-10-01 00:03:00 W21206 Registered \n", "2015-10-01 00:15:00 W20970 Registered \n", "2015-10-01 00:17:00 W21281 Registered \n", "2015-10-01 00:19:00 W20617 Registered \n", "2015-10-01 00:19:00 W20439 Casual \n", "2015-10-01 00:20:00 W00900 Registered \n", "2015-10-01 00:21:00 W21239 Casual \n", "2015-10-01 00:21:00 W21241 Casual \n", "2015-10-01 00:21:00 W00231 Registered \n", "2015-10-01 00:23:00 W21823 Registered \n", "... ... ... \n", "2016-09-01 00:21:00 W00906 Registered \n", "2016-09-01 00:20:00 W22628 Casual \n", "2016-09-01 00:19:00 W01127 Casual \n", "2016-09-01 00:19:00 W00957 Casual \n", "2016-09-01 00:19:00 W00721 Registered \n", "2016-09-01 00:19:00 W21699 Casual \n", "2016-09-01 00:19:00 W00996 Casual \n", "2016-09-01 00:19:00 W22575 Registered \n", "2016-09-01 00:18:00 W20893 Registered \n", "2016-09-01 00:18:00 W20689 Registered \n", "2016-09-01 00:17:00 W21256 Registered \n", "2016-09-01 00:15:00 W22398 Registered \n", "2016-09-01 00:15:00 W21480 Registered \n", "2016-09-01 00:13:00 W22661 Registered \n", "2016-09-01 00:13:00 W20265 Registered \n", "2016-09-01 00:13:00 W20524 Registered \n", "2016-09-01 00:12:00 W22865 Registered \n", "2016-09-01 00:09:00 W22637 Registered \n", "2016-09-01 00:09:00 W21299 Casual \n", "2016-09-01 00:09:00 W21011 Casual \n", "2016-09-01 00:08:00 W20941 Registered \n", "2016-09-01 00:05:00 W00785 Registered \n", "2016-09-01 00:05:00 W21529 Registered \n", "2016-09-01 00:05:00 W22528 Registered \n", "2016-09-01 00:05:00 W22689 Registered \n", "2016-09-01 00:05:00 W01320 Registered \n", "2016-09-01 00:05:00 W22933 Registered \n", "2016-09-01 00:03:00 W22953 Registered \n", "2016-09-01 00:02:00 W22911 Registered \n", "2016-09-01 00:00:00 W22867 Registered \n", "\n", "[3268722 rows x 8 columns]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bikes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "save this DataFrame with pickle" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pickle.dump( bikes, open( \"bikeshare_rides_all.p\", \"wb\" ) )" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3268722" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(bikes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

mne-tools/mne-tools.github.io

0.18/_downloads/012b7ba30b03ebda4c3419b2e4f5161a/plot_ssp_projs_sensitivity_map.ipynb

1

2421

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Sensitivity map of SSP projections\n\n\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nfrom mne import read_forward_solution, read_proj, sensitivity_map\n\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nsubjects_dir = data_path + '/subjects'\nfname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\necg_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'\n\nfwd = read_forward_solution(fname)\n\nprojs = read_proj(ecg_fname)\n# take only one projection per channel type\nprojs = projs[::2]\n\n# Compute sensitivity map\nssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show sensitivity map\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(ssp_ecg_map.data.ravel())\nplt.show()\n\nargs = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,\n hemi='rh', subjects_dir=subjects_dir)\nssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

AmericasWater/awash

docs/Tutorial 1 - Running the model.ipynb

1

265715

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Scope of this tutorial\n", "\n", "Welcome to the first America's Water Model tutorial! In this tutorial, you are going to learn how to (0) install the model, (1) run the model in simulation and optimization mode with different configurations, (2) look at the results, and (3) make requests of and get updates from the modeling team." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Some terminology\n", "\n", "We are going to use a bunch of terms in this tutorial, so it's important to clear up the confusion.\n", "\n", "* **The model**: A computer program that can provide information on how water is or could be supplied and consumed in the US.\n", "* **Julia**: The programming language used for the model. <img style=\"float: right; position: relative; top: -20px; width: 150px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Julia_prog_language.svg/2000px-Julia_prog_language.svg.png\"> From the [Julia website](http://julialang.org/):\n", "\n", " *Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.*\n", " \n", "\n", "* **Jupyter Notebook**: A program to make documents, like this one, that combine Julia programming and text.\n", "* **Git**: A program that saves all previous versions of the model, and lets the modeling team collaborate. All of these previous versions are stored on the website [Github](github.com/AmericasWater/operational-problem).\n", "* **Terminal**: A text interface to your computer, needed to use Git and Jupyter Notebook.\n", "* **Mimi**: The foundational code used by the model to provide its structure, developed by [David Anthoff](http://www.david-anthoff.com/).\n", "* **Simulation**: A way to run the model, where inputs (or *parameters*) are used to determine the value of outputs (or *variables*).\n", "* **Optimization**: Another way to run the model, where constraints on outputs (or *variables*) are used to determine inputs (or *parameters*)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Installing the model\n", "\n", "If you are an advanced user, you can follow the more succinct [installation instructions](install.md) and then skip to \"What you just got\" below.\n", "\n", "## Setting up your environment\n", "\n", "You will need some basic software to use the America's Water Model.\n", "\n", "* **Julia (command line version)** from http://julialang.org/downloads/\n", "\n", " Check your installation by running Julia and writing some arithmetic.\n", " \n", " \n", "* **Git** from https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\n", "\n", " If you're on Windows, also download PowerShell (https://msdn.microsoft.com/en-us/powershell/)\n", "\n", " Check that it's installed by opening a terminal/PowerShell and typing `git --version` and see a version number.\n", " \n", " \n", "* **Jupyter Notebook** from http://jupyter.readthedocs.io/en/latest/install.html\n", "\n", " Check that it's installed by opening a terminal/PowerShell and typing `jupyter notebook` and see a browser window pop up." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it's time to download the model. First, decide where you want to put the model, and open a terminal to this location.\n", "\n", "## Aside: How to navigate to the model in the terminal\n", "\n", "1. Opening a terminal. Doing this is different by OS:\n", " - On Mac OS: Open the Terminal program.\n", " - On Windows: Open the PowerShell program.\n", " - On Linux: Open up a shell window.\n", "2. To change directory, type `cd <directory>`. `<directory>` can be any of the following:\n", " - The name of a subdirectory.\n", " - Two periods (`..`) to go to the parent directory.\n", " - A single period (`.`) to not go anywhere.\n", " - Any combination of the above, separated by slashes (`/`).\n", " - A tilde (`~`) to go back to your home directory.\n", " - A slash (`/`) to go to the root of the server.\n", " - Either of the previous two, combined with any of the first four, using slashes.\n", " - Use the tab key to auto-complete the name you have started to type.\n", " - If there are spaces in your directory or filename, consider the life choices that led to this moment.\n", "3. The model will be created in a new folder, when you download it. From then on, for anything from this tutorial, you will want to go into the `docs` subdirectory of that folder.\n", "\n", "## Downloading the model\n", "\n", "In the directory where you want the model folder, paste the following command:\n", "\n", "```\n", "git clone https://github.com/AmericasWater/awash.git```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What you just got\n", "\n", "The model is organized into different folders. Here is what is in there:\n", "* `configs/`: Configuration files, that determine the basic operation of the model.\n", "* `data/`: Input data files. There is also a `data/cache/` folder, with saved preparation files. You can delete this to force the model to reinitialize.\n", "* `docs/`: Documentation, including tutorials like this one.\n", "* `prepare/`: Some source data files and the scripts needed to prepare them for use in the model.\n", "* `results/`: The standard location for output results, along with code for post-processing.\n", "* `src/`: The main source code for the model. All the magic happens here.\n", "* `test/`: Automatic testing scripts, to make sure changes don't break how the model should work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Opening the model\n", "\n", "For the purposes of this tutorial, the main interface of the model is Jupyter Notebooks, and the existing tutorials in the `docs/` subdirectory. To start the model, do the following:\n", "\n", "1. Open a terminal to the `docs/` subdirectory of the model.\n", "2. Type/paste the following command: `jupyter notebook`.\n", "3. In the browser window that opens, find the item `start.ipynb` and click on it.\n", "4. Click the \"<span style=\"font-family: FontAwesome\" class=\"fa-step-forward fe\"></span>\" button, which will run the line below.\n", "\n", "If this is the first time you're running the model, it is going to install some needed libraries and data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0;1;34;94m┌───────────────\u001b[0;34m────────────────\u001b[0;37m────────────────\u001b[0;1;30;90m───┐\u001b[0m\n", "\u001b[0;34m│\u001b[0m \u001b[0;34m▄▄\u001b[0m \u001b[0;34m▄▄\u001b[0m \u001b[0;37m▄▄\u001b[0m \u001b[0;37m▄▄\u001b[0m \u001b[0;1;30;90m▄▄▄▄\u001b[0m \u001b[0;1;30;90m▄▄\u001b[0m \u001b[0;1;34;94m▄▄\u001b[0m \u001b[0;1;34;94m│\u001b[0m\n", "\u001b[0;34m│\u001b[0m \u001b[0;34m████\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;1;30;90m████\u001b[0m \u001b[0;1;30;90m▄█▀▀▀▀█\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m│\u001b[0m\n", "\u001b[0;37m│\u001b[0m \u001b[0;37m████\u001b[0m \u001b[0;37m▀█▄\u001b[0m \u001b[0;37m█\u001b[0;1;30;90m█\u001b[0m \u001b[0;1;30;90m▄█▀\u001b[0m \u001b[0;1;30;90m████\u001b[0m \u001b[0;1;34;94m██▄\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;34m██\u001b[0m \u001b[0;34m│\u001b[0m\n", "\u001b[0;37m│\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m█\u001b[0;1;30;90m█\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m██\u001b[0m \u001b[0;1;30;90m█\u001b[0;1;34;94m█\u001b[0m \u001b[0;1;34;94m██\u001b[0m \u001b[0;1;34;94m▀████▄\u001b[0m \u001b[0;34m████████\u001b[0m \u001b[0;34m│\u001b[0m\n", "\u001b[0;1;30;90m│\u001b[0m \u001b[0;1;30;90m██████\u001b[0m \u001b[0;1;30;90m███▀\u001b[0;1;34;94m▀███\u001b[0m \u001b[0;1;34;94m██████\u001b[0m \u001b[0;34m▀██\u001b[0m \u001b[0;34m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n", "\u001b[0;1;30;90m│\u001b[0m \u001b[0;1;30;90m▄██\u001b[0m \u001b[0;1;30;90m█\u001b[0;1;34;94m█▄\u001b[0m \u001b[0;1;34;94m███\u001b[0m \u001b[0;1;34;94m███\u001b[0m \u001b[0;1;34;94m▄█\u001b[0;34m█\u001b[0m \u001b[0;34m██▄\u001b[0m \u001b[0;34m█▄▄▄▄▄█▀\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m██\u001b[0m \u001b[0;37m│\u001b[0m\n", "\u001b[0;1;34;94m│\u001b[0m \u001b[0;1;34;94m▀▀\u001b[0m \u001b[0;1;34;94m▀▀\u001b[0m \u001b[0;1;34;94m▀▀▀\u001b[0m \u001b[0;34m▀▀▀\u001b[0m \u001b[0;34m▀▀\u001b[0m \u001b[0;34m▀▀\u001b[0m \u001b[0;37m▀▀▀▀▀\u001b[0m \u001b[0;37m▀▀\u001b[0m \u001b[0;1;30;90m▀▀\u001b[0m \u001b[0;1;30;90m│\u001b[0m\n", "\u001b[0;34m└───────\u001b[0;37m────────────────\u001b[0;1;30;90m────────────────\u001b[0;1;34;94m───────────┘\u001b[0m\n", "\n", "Welcome to AWASH, the America's Water Model, version 0.7.\n", "\n" ] } ], "source": [ "include(\"../src/nui.jl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Configuration parameters\n", "\n", "The configuration file determines the general bounds of the model. The current configuration parameters are:\n", "\n", "* **`netset`**: The top-level decision of the world we are deciding.\n", "\n", " options: `usa` (full-country), `three` (3-counties), `dummy` (5-counties)\n", " \n", " \n", "* **`filterstate`**: Only include counties within this state (give as 2 digit FIPS).\n", "\n", " e.g., \"`10`\" for Delaware (3 counties), \"`08`\" for Colorado (64 counties)\n", " \n", " \n", "* **`startmonth`** and **`endmonth`**: First and last month of the simulation.\n", "\n", " e.g., 10/2000 - 9/2010\n", " \n", " \n", "* **`startweather`**: The weather entry to match to `startmonth`.\n", "\n", " e.g., 1 to use the first month in `VIC_WB`for the first month in the simulation.\n", " \n", " \n", "* **`timestep`**: The number of months for each timestep of the model.\n", "\n", " The larger this number, the faster the model will run. Currently agriculture is calculated separately for each timestep.\n", " \n", "# Standard configuration sets\n", "\n", "There are three configuration sets to know about:\n", "\n", "* `complete.yml`: A monthly run from 1949 - 2010. This is basically impossible on a laptop.\n", "* `standard.yml`: A 6-month timestep run from 2000 - 2010. This is about the maximum practical for a laptop.\n", "* `standard-1year.yml`: A 6-month timestep run for 10/2009 - 9/2010 (two timesteps). This is good for testing.\n", "* `single.yml`: A 12-month timestep run for 10/2009 - 9/2010 (one timestep). We will use this one here.\n", "\n", "Here is what the `single.yml` configuration looks like:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Only include counties within this state (give as 2 digit FIPS)\n", "# \"10\" for Delaware (3 counties), \"08\" for Colorado (64 counties)\n", "filterstate: null\n", "\n", "# Current options: usa (full-country), dummy (5-counties)\n", "netset: usa\n", "\n", "# First and last month of the simulation\n", "startmonth: 10/2009\n", "endmonth: 9/2010\n", "\n", "# First entry in VIC_WB to include\n", "startweather: 720\n", "\n", "# Months per time step\n", "timestep: 12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running a simulation\n", "\n", "There are three steps for running a simulation:\n", "1. Select a configuration: Create or select a file in `configs/`.\n", "2. Prepare the model: Call `prepsimulate(\"<config filename>\");`.\n", "3. Run the prepared model: Call `runmodel();`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading from saved region network...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: imported binding for edges overwritten in module Main\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading from saved water network...\n", "Loading from saved region network...\n", "Creating model...\n" ] } ], "source": [ "prepsimulate(\"single.yml\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This loaded up the model geography of the model, put together the sectors, and prepared all of the inputs. Now we can run the model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "runmodel();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Looking at the results of the model\n", "\n", "There are lots of ways to see the results of the model, but they all require that you know which variables you want to look at. Start by identifying the component that you are interested, as a box in this image:\n", "\n", "![Operational Problem Model](https://raw.githubusercontent.com/AmericasWater/operational-problem/master/docs/ModelDiagram.png)\n", "\n", "Let's consider the Agriculture component. Next, we need to know what variables are available within that component, which we do with the `getvariables` function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>name</th><th>dims</th></tr><tr><th>1</th><td>rainfedareas</td><td>(3109,9,1)</td></tr><tr><th>2</th><td>deficit_coeff</td><td>(3109,9)</td></tr><tr><th>3</th><td>precipitation</td><td>(3109,1)</td></tr><tr><th>4</th><td>water_demand</td><td>(9,)</td></tr><tr><th>5</th><td>logirrigatedyield</td><td>(3109,9,1)</td></tr><tr><th>6</th><td>irrigatedareas</td><td>(3109,9,1)</td></tr><tr><th>7</th><td>allagarea</td><td>(3109,1)</td></tr><tr><th>8</th><td>totalirrigation</td><td>(3109,1)</td></tr><tr><th>9</th><td>production</td><td>(3109,9,1)</td></tr><tr><th>10</th><td>cultivationcost</td><td>(3109,9,1)</td></tr><tr><th>11</th><td>water_deficit</td><td>(3109,9,1)</td></tr><tr><th>12</th><td>lograinfedyield</td><td>(3109,9,1)</td></tr><tr><th>13</th><td>totalareas</td><td>(3109,9,1)</td></tr></table>" ], "text/plain": [ "13×2 DataFrames.DataFrame\n", "│ Row │ name │ dims │\n", "├─────┼───────────────────┼──────────────┤\n", "│ 1 │ rainfedareas │ \"(3109,9,1)\" │\n", "│ 2 │ deficit_coeff │ \"(3109,9)\" │\n", "│ 3 │ precipitation │ \"(3109,1)\" │\n", "│ 4 │ water_demand │ \"(9,)\" │\n", "│ 5 │ logirrigatedyield │ \"(3109,9,1)\" │\n", "│ 6 │ irrigatedareas │ \"(3109,9,1)\" │\n", "│ 7 │ allagarea │ \"(3109,1)\" │\n", "│ 8 │ totalirrigation │ \"(3109,1)\" │\n", "│ 9 │ production │ \"(3109,9,1)\" │\n", "│ 10 │ cultivationcost │ \"(3109,9,1)\" │\n", "│ 11 │ water_deficit │ \"(3109,9,1)\" │\n", "│ 12 │ lograinfedyield │ \"(3109,9,1)\" │\n", "│ 13 │ totalareas │ \"(3109,9,1)\" │" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getvariables(:Agriculture)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `:` is important for both the component name and the variable names below.\n", "\n", "Then to see the results, call `getdata(<component>, <variable>)`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "3109x9x1 Array{Float64,3}:\n", "[:, :, 1] =\n", " 0.0 0.0 … 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 0.0 21570.7 … 0.0 33936.2 \n", " 0.0 37424.6 0.0 0.0 \n", " 0.0 0.0 0.0 32082.3 \n", " 0.0 32438.7 0.0 8336.67 \n", " 0.0 0.0 0.0 3.35729e5 \n", " 0.0 0.0 … 0.0 0.0 \n", " 0.0 9.73389e-23 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " ⋮ ⋱ \n", " 54033.4 42940.9 0.0 0.0 \n", " 1.10014e5 15292.0 0.0 0.0 \n", " 30610.1 5819.68 0.0 0.0 \n", " 78542.1 16184.3 … 0.0 0.0 \n", " 40925.4 30863.0 0.0 0.0 \n", " 78652.4 28882.8 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 42416.2 26897.1 0.0 0.0 \n", " 5701.34 12085.1 … 0.0 0.0 \n", " 0.0 0.0 0.0 0.0 \n", " 26212.3 3313.67 31500.0 0.0 \n", " 10993.2 1075.5 1.55547e-13 1.28253e-15" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getdata(:Agriculture, :production)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save the data to a file, call `savedata(<filename>, <component>, <variable>)`. The `savedata` function is best used on 2-D data (or less) and since production is of dimension COUNTY x CROP x TIME, we would like to drop one. We can do this with a fourth argument, which has the syntax for a Matlab index." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "savedata(\"../results/agprod.csv\", :Agriculture, :production, (:, :, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can plot it. This only works so far for results that describe nation-wide values, but it's a start." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: RCall.jl Warning: package ‘ggplot2’ was built under R version 3.2.3\n", "Need help? Try the ggplot2 mailing list:\n", "http://groups.google.com/group/ggplot2.\n", "WARNING: RCall.jl \n", "-----------------------------------------------------------\n", "PBS Mapping 2.69.76 -- Copyright (C) 2003-2016 Fisheries and Oceans Canada\n", "\n", "PBS Mapping comes with ABSOLUTELY NO WARRANTY;\n", "for details see the file COPYING.\n", "This is free software, and you are welcome to redistribute\n", "it under certain conditions, as outlined in the above file.\n", "\n", "A complete user guide 'PBSmapping-UG.pdf' is located at \n", "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/PBSmapping/doc/PBSmapping-UG.pdf\n", "\n", "Packaged on 2015-04-23\n", "Pacific Biological Station, Nanaimo\n", "\n", "All available PBS packages can be found at\n", "http://code.google.com/p/pbs-software/\n", "\n", "To see demos, type '.PBSfigs()'.\n", "-----------------------------------------------------------\n", "\n", "\n", "WARNING: RCall.jl Loading required package: maptools\n", "Loading required package: sp\n", "Warning: package ‘sp’ was built under R version 3.2.5\n", "Checking rgeos availability: TRUE\n", "Loading required package: foreign\n" ] }, { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.VecSxp}\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapdata(:Agriculture, :production, (:, 5, 1))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using RCall" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.62122279 0.64831833 -0.48935885 0.92764611 1.02611765 1.59837396\n", " [7] -0.96725780 -0.49321665 -1.10164360 -1.33944063 1.31890750 0.25991008\n", " [13] -0.36737477 0.08100751 0.01820744 0.57968764 -0.83115791 -0.30559390\n", " [19] 0.82804637 0.87722282 -0.36368721 0.29606504 -0.01717716 0.18193819\n", " [25] 0.98784232 -0.04729326 -0.76462815 -0.23869830 0.87341128 -0.56540542\n", " [31] 0.24752053 -0.93169866 -0.01312905 1.17846349 0.26131608 1.81919341\n", " [37] 1.72917614 0.14759056 0.88830378 -1.09978466 0.01376463 -0.23974337\n", " [43] 0.20255462 -1.12002824 -0.17669488 0.34420994 -0.46455188 1.14323986\n", " [49] 0.18923975 -0.17559747 -0.38363851 -1.08267915 1.09002894 0.94087581\n", " [55] -0.43349359 -0.44734339 3.37063110 1.36829580 -0.44395899 -1.16338533\n", " [61] -0.97981821 0.20694984 -0.06456482 0.47541359 0.52327356 1.63704958\n", " [67] 0.32423036 0.49562667 -0.33220034 0.54913155 -0.37544318 0.26960028\n", " [73] 0.55244578 0.89318511 -0.02618433 -1.09897372 0.95858630 0.87128071\n", " [79] -0.45338845 -0.44877374 1.78908111 -0.36408830 -0.88978697 -0.99465919\n", " [85] 0.33044888 -0.37553791 -1.42606459 -0.65207303 0.44036986 0.75407073\n", " [91] -0.75762170 -0.52891009 0.30297497 -2.11217177 -0.73390772 1.09533811\n", " [97] 1.34026477 0.26010075 0.97643020 0.18347344\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"x = rnorm(100)\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.VecSxp}\n", "$breaks\n", " [1] -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5\n", "\n", "$counts\n", " [1] 1 0 8 13 25 22 17 8 5 0 0 1\n", "\n", "$density\n", " [1] 0.02 0.00 0.16 0.26 0.50 0.44 0.34 0.16 0.10 0.00 0.00 0.02\n", "\n", "$mids\n", " [1] -2.25 -1.75 -1.25 -0.75 -0.25 0.25 0.75 1.25 1.75 2.25 2.75 3.25\n", "\n", "$xname\n", "[1] \"x\"\n", "\n", "$equidist\n", "[1] TRUE\n", "\n", "attr(,\"class\")\n", "[1] \"histogram\"\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"hist(x)\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myjuliavar = [1, 2, 3]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myjuliavar" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RCall.RObject{RCall.NilSxp}\n", "NULL\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R\"plot($myjuliavar)\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n", " [6] -0.625395697 0.788866194 0.503396481 0.806131966 -0.685962168\n", " [11] 1.409655399 -0.011001650 1.221350838 0.687594625 0.660120389\n", " [16] 0.323121302 1.121587552 -0.125611715 1.772632044 -1.576097599\n", " [21] -0.660744500 -0.429240447 -0.490280026 0.468150307 1.198544526\n", " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809 1.008367742\n", " [31] 1.042998949 -0.182158343 -0.198155519 0.064799689 1.055615930\n", " [36] 1.396022389 0.363787500 -0.265379634 -0.485115714 0.513091917\n", " [41] -0.340089246 -0.725946945 0.465066983 0.925172640 -0.499757044\n", " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050 1.549266436\n", " [51] -1.311331940 1.201508605 -0.271954598 2.534004825 -1.623282056\n", " [56] 0.001984485 -0.509082941 0.023470704 -0.710153768 -1.048219819\n", " [61] 0.950574020 0.223231617 -1.496654999 -0.660755363 -0.865973022\n", " [66] -1.125167386 -0.679917414 -1.014248154 1.170601061 1.133173229\n", " [71] 0.217915251 -0.863843341 0.858204953 -0.204352963 -0.521392645\n", " [76] 1.510171510 0.598071927 0.638948425 1.397193548 -0.639814179\n", " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727 1.295730250\n", " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995 0.781161860\n", " [91] -0.734598252 0.817520424 -1.157498298 0.815970762 -0.476418750\n", " [96] 0.162498461 -1.002618330 0.975990869 -0.457368921 0.461530679\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = R\"rnorm(100)\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RCall.RObject{RCall.RealSxp}\n", " [1] -0.396211276 -1.102864069 -0.273230592 -0.498711128 -1.688747229\n", " [6] -0.625395697 0.788866194 0.503396481 0.806131966 -0.685962168\n", " [11] 1.409655399 -0.011001650 1.221350838 0.687594625 0.660120389\n", " [16] 0.323121302 1.121587552 -0.125611715 1.772632044 -1.576097599\n", " [21] -0.660744500 -0.429240447 -0.490280026 0.468150307 1.198544526\n", " [26] -0.583805903 -0.911470531 -0.929171990 -1.592459809 1.008367742\n", " [31] 1.042998949 -0.182158343 -0.198155519 0.064799689 1.055615930\n", " [36] 1.396022389 0.363787500 -0.265379634 -0.485115714 0.513091917\n", " [41] -0.340089246 -0.725946945 0.465066983 0.925172640 -0.499757044\n", " [46] -0.496244776 -0.946024643 -1.095053418 -1.131125050 1.549266436\n", " [51] -1.311331940 1.201508605 -0.271954598 2.534004825 -1.623282056\n", " [56] 0.001984485 -0.509082941 0.023470704 -0.710153768 -1.048219819\n", " [61] 0.950574020 0.223231617 -1.496654999 -0.660755363 -0.865973022\n", " [66] -1.125167386 -0.679917414 -1.014248154 1.170601061 1.133173229\n", " [71] 0.217915251 -0.863843341 0.858204953 -0.204352963 -0.521392645\n", " [76] 1.510171510 0.598071927 0.638948425 1.397193548 -0.639814179\n", " [81] -0.860441210 -1.249230094 -2.199530514 -2.121140727 1.295730250\n", " [86] -0.072618465 -0.489912051 -1.028861139 -0.160846995 0.781161860\n", " [91] -0.734598252 0.817520424 -1.157498298 0.815970762 -0.476418750\n", " [96] 0.162498461 -1.002618330 0.975990869 -0.457368921 0.461530679\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Nothing to be done\n", "INFO: METADATA is out-of-date — you may not have the latest version of YAML\n", "INFO: Use `Pkg.update()` to get the latest versions of your packages\n" ] } ], "source": [ "Pkg.add(\"YAML\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"data-frame\"><tr><th></th><th>FIPS</th><th>County</th><th>ST</th><th>Neighboring</th><th>UrbanPop_2010</th><th>RuralPop_2010</th><th>MedianHouseholdInc_2008_12</th><th>Elevation_ft</th><th>TotalArea_sqmi</th><th>LandArea_sqmi</th><th>WaterArea_sqmi</th></tr><tr><th>1</th><td>1001</td><td>Autauga</td><td>AL</td><td>-1677436972259693513</td><td>31650</td><td>22921</td><td>53773</td><td>290.0</td><td>604.39</td><td>594.44</td><td>9.95</td></tr><tr><th>2</th><td>1003</td><td>Baldwin</td><td>AL</td><td>-7503806633868418079</td><td>105205</td><td>77060</td><td>50706</td><td>155.0</td><td>2027.31</td><td>1589.78</td><td>437.53</td></tr><tr><th>3</th><td>1005</td><td>Barbour</td><td>AL</td><td>-9162367971339582421</td><td>8844</td><td>18613</td><td>31889</td><td>220.0</td><td>904.52</td><td>884.88</td><td>19.64</td></tr><tr><th>4</th><td>1007</td><td>Bibb</td><td>AL</td><td>-3263215093836123959</td><td>7252</td><td>15663</td><td>36824</td><td>224.0</td><td>626.17</td><td>622.58</td><td>3.59</td></tr><tr><th>5</th><td>1009</td><td>Blount</td><td>AL</td><td>281814405218882823</td><td>5760</td><td>51562</td><td>45192</td><td>870.0</td><td>650.63</td><td>644.78</td><td>5.85</td></tr><tr><th>6</th><td>1011</td><td>Bullock</td><td>AL</td><td>-7528364842213094795</td><td>5307</td><td>5607</td><td>34500</td><td>455.0</td><td>625.14</td><td>622.8</td><td>2.34</td></tr><tr><th>7</th><td>1013</td><td>Butler</td><td>AL</td><td>7263807205192509739</td><td>6026</td><td>14921</td><td>30752</td><td>445.0</td><td>777.88</td><td>776.83</td><td>1.05</td></tr><tr><th>8</th><td>1015</td><td>Calhoun</td><td>AL</td><td>4089652745186409211</td><td>78617</td><td>39955</td><td>40093</td><td>565.0</td><td>612.29</td><td>605.87</td><td>6.42</td></tr><tr><th>9</th><td>1017</td><td>Chambers</td><td>AL</td><td>1546726499117356153</td><td>17399</td><td>16816</td><td>32181</td><td>745.0</td><td>603.11</td><td>596.53</td><td>6.58</td></tr><tr><th>10</th><td>1019</td><td>Cherokee</td><td>AL</td><td>3606844344126915727</td><td>3707</td><td>22282</td><td>36241</td><td>589.0</td><td>599.98</td><td>553.7</td><td>46.28</td></tr><tr><th>11</th><td>1021</td><td>Chilton</td><td>AL</td><td>2336210146209538225</td><td>5785</td><td>37858</td><td>40834</td><td>580.0</td><td>700.8</td><td>692.85</td><td>7.95</td></tr><tr><th>12</th><td>1023</td><td>Choctaw</td><td>AL</td><td>-6248320840704236021</td><td>0</td><td>13859</td><td>35123</td><td>350.0</td><td>920.86</td><td>913.5</td><td>7.36</td></tr><tr><th>13</th><td>1025</td><td>Clarke</td><td>AL</td><td>-2480994685536455671</td><td>6205</td><td>19628</td><td>30954</td><td>405.0</td><td>1252.57</td><td>1238.46</td><td>14.11</td></tr><tr><th>14</th><td>1027</td><td>Clay</td><td>AL</td><td>165275177167130605</td><td>0</td><td>13932</td><td>34556</td><td>745.0</td><td>606.0</td><td>603.96</td><td>2.04</td></tr><tr><th>15</th><td>1029</td><td>Cleburne</td><td>AL</td><td>-6945485711834679467</td><td>0</td><td>14972</td><td>37244</td><td>1175.0</td><td>561.01</td><td>560.1</td><td>0.91</td></tr><tr><th>16</th><td>1031</td><td>Coffee</td><td>AL</td><td>6132202507106331951</td><td>26375</td><td>23573</td><td>44626</td><td>195.0</td><td>680.49</td><td>678.97</td><td>1.52</td></tr><tr><th>17</th><td>1033</td><td>Colbert</td><td>AL</td><td>1077010790105928141</td><td>30537</td><td>23891</td><td>40158</td><td>536.0</td><td>622.13</td><td>592.62</td><td>29.51</td></tr><tr><th>18</th><td>1035</td><td>Conecuh</td><td>AL</td><td>1099010130103901053</td><td>2520</td><td>10708</td><td>27064</td><td>216.0</td><td>852.72</td><td>850.16</td><td>2.57</td></tr><tr><th>19</th><td>1037</td><td>Coosa</td><td>AL</td><td>6308886048509289181</td><td>0</td><td>11539</td><td>37425</td><td>660.0</td><td>666.35</td><td>650.93</td><td>15.42</td></tr><tr><th>20</th><td>1039</td><td>Covington</td><td>AL</td><td>-7456065813052386533</td><td>11461</td><td>26304</td><td>35321</td><td>260.0</td><td>1043.79</td><td>1030.46</td><td>13.33</td></tr><tr><th>21</th><td>1041</td><td>Crenshaw</td><td>AL</td><td>4065287195876681327</td><td>0</td><td>13906</td><td>37309</td><td>594.0</td><td>610.92</td><td>608.84</td><td>2.08</td></tr><tr><th>22</th><td>1043</td><td>Cullman</td><td>AL</td><td>6438249654864425607</td><td>21517</td><td>58889</td><td>39244</td><td>802.0</td><td>755.02</td><td>734.84</td><td>20.18</td></tr><tr><th>23</th><td>1045</td><td>Dale</td><td>AL</td><td>3596880842466902597</td><td>24679</td><td>25572</td><td>45247</td><td>470.0</td><td>562.7</td><td>561.15</td><td>1.55</td></tr><tr><th>24</th><td>1047</td><td>Dallas</td><td>AL</td><td>3774141952490839331</td><td>23821</td><td>19999</td><td>26178</td><td>147.0</td><td>993.8</td><td>978.69</td><td>15.11</td></tr><tr><th>25</th><td>1049</td><td>DeKalb</td><td>AL</td><td>-701081407554769413</td><td>7018</td><td>64091</td><td>36853</td><td>1040.0</td><td>778.69</td><td>777.09</td><td>1.59</td></tr><tr><th>26</th><td>1051</td><td>Elmore</td><td>AL</td><td>4377449246493266349</td><td>36330</td><td>42973</td><td>55514</td><td>290.0</td><td>657.04</td><td>618.48</td><td>38.56</td></tr><tr><th>27</th><td>1053</td><td>Escambia</td><td>AL</td><td>3957980972351392731</td><td>13982</td><td>24337</td><td>31075</td><td>85.0</td><td>953.14</td><td>945.08</td><td>8.06</td></tr><tr><th>28</th><td>1055</td><td>Etowah</td><td>AL</td><td>1525514402121635003</td><td>65286</td><td>39144</td><td>37660</td><td>565.0</td><td>548.63</td><td>534.99</td><td>13.64</td></tr><tr><th>29</th><td>1057</td><td>Fayette</td><td>AL</td><td>-6531450309154316941</td><td>3408</td><td>13833</td><td>33090</td><td>365.0</td><td>629.36</td><td>627.66</td><td>1.7</td></tr><tr><th>30</th><td>1059</td><td>Franklin</td><td>AL</td><td>7764312890763704205</td><td>9395</td><td>22309</td><td>37235</td><td>840.0</td><td>646.52</td><td>633.82</td><td>12.7</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></table>" ], "text/plain": [ "3229×11 DataFrames.DataFrame\n", "│ Row │ FIPS │ County │ ST │ Neighboring │ UrbanPop_2010 │\n", "├──────┼───────┼─────────────────┼──────┼──────────────────────┼───────────────┤\n", "│ 1 │ 1001 │ \"Autauga\" │ \"AL\" │ -1677436972259693513 │ 31650 │\n", "│ 2 │ 1003 │ \"Baldwin\" │ \"AL\" │ -7503806633868418079 │ 105205 │\n", "│ 3 │ 1005 │ \"Barbour\" │ \"AL\" │ -9162367971339582421 │ 8844 │\n", "│ 4 │ 1007 │ \"Bibb\" │ \"AL\" │ -3263215093836123959 │ 7252 │\n", "│ 5 │ 1009 │ \"Blount\" │ \"AL\" │ 281814405218882823 │ 5760 │\n", "│ 6 │ 1011 │ \"Bullock\" │ \"AL\" │ -7528364842213094795 │ 5307 │\n", "│ 7 │ 1013 │ \"Butler\" │ \"AL\" │ 7263807205192509739 │ 6026 │\n", "│ 8 │ 1015 │ \"Calhoun\" │ \"AL\" │ 4089652745186409211 │ 78617 │\n", "│ 9 │ 1017 │ \"Chambers\" │ \"AL\" │ 1546726499117356153 │ 17399 │\n", "│ 10 │ 1019 │ \"Cherokee\" │ \"AL\" │ 3606844344126915727 │ 3707 │\n", "│ 11 │ 1021 │ \"Chilton\" │ \"AL\" │ 2336210146209538225 │ 5785 │\n", "⋮\n", "│ 3218 │ 72137 │ \"Toa Baja\" │ \"PR\" │ NA │ 89609 │\n", "│ 3219 │ 72139 │ \"Trujillo Alto\" │ \"PR\" │ NA │ 74842 │\n", "│ 3220 │ 72141 │ \"Utuado\" │ \"PR\" │ NA │ 17593 │\n", "│ 3221 │ 72143 │ \"Vega Alta\" │ \"PR\" │ NA │ 39251 │\n", "│ 3222 │ 72145 │ \"Vega Baja\" │ \"PR\" │ NA │ 57170 │\n", "│ 3223 │ 72147 │ \"Vieques\" │ \"PR\" │ NA │ 8230 │\n", "│ 3224 │ 72149 │ \"Villalba\" │ \"PR\" │ NA │ 22564 │\n", "│ 3225 │ 72151 │ \"Yabucoa\" │ \"PR\" │ NA │ 32192 │\n", "│ 3226 │ 72153 │ \"Yauco\" │ \"PR\" │ NA │ 34303 │\n", "│ 3227 │ 78010 │ \"St. Croix\" │ \"VI\" │ NA │ 46601 │\n", "│ 3228 │ 78020 │ \"St. John\" │ \"VI\" │ NA │ 3090 │\n", "│ 3229 │ 78030 │ \"St. Thomas\" │ \"VI\" │ NA │ 50916 │\n", "\n", "│ Row │ RuralPop_2010 │ MedianHouseholdInc_2008_12 │ Elevation_ft │\n", "├──────┼───────────────┼────────────────────────────┼──────────────┤\n", "│ 1 │ 22921 │ 53773 │ 290.0 │\n", "│ 2 │ 77060 │ 50706 │ 155.0 │\n", "│ 3 │ 18613 │ 31889 │ 220.0 │\n", "│ 4 │ 15663 │ 36824 │ 224.0 │\n", "│ 5 │ 51562 │ 45192 │ 870.0 │\n", "│ 6 │ 5607 │ 34500 │ 455.0 │\n", "│ 7 │ 14921 │ 30752 │ 445.0 │\n", "│ 8 │ 39955 │ 40093 │ 565.0 │\n", "│ 9 │ 16816 │ 32181 │ 745.0 │\n", "│ 10 │ 22282 │ 36241 │ 589.0 │\n", "│ 11 │ 37858 │ 40834 │ 580.0 │\n", "⋮\n", "│ 3218 │ 0 │ 23572 │ NA │\n", "│ 3219 │ 0 │ 31354 │ NA │\n", "│ 3220 │ 15556 │ 14752 │ NA │\n", "│ 3221 │ 700 │ 17616 │ NA │\n", "│ 3222 │ 2492 │ 16462 │ NA │\n", "│ 3223 │ 1071 │ 16598 │ NA │\n", "│ 3224 │ 3509 │ 16014 │ NA │\n", "│ 3225 │ 5749 │ 17926 │ NA │\n", "│ 3226 │ 7740 │ 14728 │ NA │\n", "│ 3227 │ 4000 │ NA │ NA │\n", "│ 3228 │ 1080 │ NA │ NA │\n", "│ 3229 │ 718 │ NA │ NA │\n", "\n", "│ Row │ TotalArea_sqmi │ LandArea_sqmi │ WaterArea_sqmi │\n", "├──────┼────────────────┼───────────────┼────────────────┤\n", "│ 1 │ 604.39 │ 594.44 │ 9.95 │\n", "│ 2 │ 2027.31 │ 1589.78 │ 437.53 │\n", "│ 3 │ 904.52 │ 884.88 │ 19.64 │\n", "│ 4 │ 626.17 │ 622.58 │ 3.59 │\n", "│ 5 │ 650.63 │ 644.78 │ 5.85 │\n", "│ 6 │ 625.14 │ 622.8 │ 2.34 │\n", "│ 7 │ 777.88 │ 776.83 │ 1.05 │\n", "│ 8 │ 612.29 │ 605.87 │ 6.42 │\n", "│ 9 │ 603.11 │ 596.53 │ 6.58 │\n", "│ 10 │ 599.98 │ 553.7 │ 46.28 │\n", "│ 11 │ 700.8 │ 692.85 │ 7.95 │\n", "⋮\n", "│ 3218 │ 41.81 │ 23.24 │ 18.57 │\n", "│ 3219 │ 21.38 │ 20.76 │ 0.62 │\n", "│ 3220 │ 115.07 │ 113.53 │ 1.54 │\n", "│ 3221 │ 37.5 │ 27.73 │ 9.77 │\n", "│ 3222 │ 68.18 │ 45.86 │ 22.32 │\n", "│ 3223 │ 263.99 │ 50.77 │ 213.22 │\n", "│ 3224 │ 37.04 │ 35.64 │ 1.4 │\n", "│ 3225 │ 83.24 │ 55.21 │ 28.03 │\n", "│ 3226 │ 68.82 │ 68.19 │ 0.63 │\n", "│ 3227 │ 332.66 │ 83.32 │ 249.34 │\n", "│ 3228 │ 91.76 │ 19.69 │ 72.07 │\n", "│ 3229 │ 308.51 │ 31.31 │ 277.2 │" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ " countyinfo = readtable(\"../data/county-info.csv\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3229-element DataArrays.DataArray{Float64,1}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "areas = countyinfo[:, :LandArea_sqmi]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: replacing module _mimi_implementation_Brewery\n" ] }, { "data": { "text/plain": [ "initbrewery (generic function with 1 method)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The Brewery Component\n", "# This is how we make beer\n", "\n", "using Mimi\n", "\n", "@defcomp Brewery begin\n", " region = Index()\n", "\n", " production = Parameter(index=[region, time], unit=\"liter\")\n", "\n", " hopsdemand = Variable(index=[region, time], unit=\"MT\")\n", " waterdemand = Variable(index=[region, time], unit=\"1000 m^3\")\n", "end\n", "\n", "function run_timestep(c::Brewery, tt::Int64)\n", " v, p, d = getvpd(c)\n", "\n", " v.hopsdemand[:, tt] = .07 * p.production[:, tt]\n", " v.waterdemand[:, tt] = 2 * p.production[:, tt] / 1e6\n", "end\n", "\n", "function initbrewery(m::Model)\n", " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n", "\n", " brewery[:production] = repeat(production, outer=[1, numsteps])\n", "end\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = Model()\n", "setindex(m, :time, [1]) # Single period\n", "setindex(m, :region, length(dropna(readtable(\"../data/county-info.csv\")[:LandArea_sqmi])))\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n ...\nwhile loading In[31], in expression starting on line 1", "output_type": "error", "traceback": [ "LoadError: MethodError: `convert` has no method matching convert(::Type{DataArrays.DataArray{Float64,1}}, ::Array{Float64,2}, ::BitArray{2})\nThis may have arisen from a call to the constructor DataArrays.DataArray{Float64,1}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{S,T,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.DataArray{T,N})\n convert{S,T,R<:Integer,N}(::Type{DataArrays.DataArray{S,N}}, !Matched::DataArrays.PooledDataArray{T,R<:Integer,N})\n ...\nwhile loading In[31], in expression starting on line 1", "", " in call at essentials.jl:57", " in initbrewery at In[18]:27" ] } ], "source": [ "initbrewery(m)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3229-element DataArrays.DataArray{Float64,1}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 594.44\n", " 1589.78\n", " 884.88\n", " 622.58\n", " 644.78\n", " 622.8 \n", " 776.83\n", " 605.87\n", " 596.53\n", " 553.7 \n", " 692.85\n", " 913.5 \n", " 1238.46\n", " ⋮ \n", " 23.24\n", " 20.76\n", " 113.53\n", " 27.73\n", " 45.86\n", " 50.77\n", " 35.64\n", " 55.21\n", " 68.19\n", " 83.32\n", " 19.69\n", " 31.31" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ " brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "initbrewer (generic function with 1 method)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function initbrewer(m::Model)\n", " brewery = addcomponent(m, Brewery)\n", "\n", " production = readtable(\"../data/county-info.csv\")[:, :LandArea_sqmi]\n", "\n", " brewery[:production] = repeat(convert(Vector{Float64}, dropna(production)), outer=[1, numsteps])\n", "end\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "run(m)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 41.6108\n", " 111.285 \n", " 61.9416\n", " 43.5806\n", " 45.1346\n", " 43.596 \n", " 54.3781\n", " 42.4109\n", " 41.7571\n", " 38.759 \n", " 48.4995\n", " 63.945 \n", " 86.6922\n", " ⋮ \n", " 1.6268\n", " 1.4532\n", " 7.9471\n", " 1.9411\n", " 3.2102\n", " 3.5539\n", " 2.4948\n", " 3.8647\n", " 4.7733\n", " 5.8324\n", " 1.3783\n", " 2.1917" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:Brewery, :hopsdemand]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3225x1 Array{Float64,2}:\n", " 0.00118888\n", " 0.00317956\n", " 0.00176976\n", " 0.00124516\n", " 0.00128956\n", " 0.0012456 \n", " 0.00155366\n", " 0.00121174\n", " 0.00119306\n", " 0.0011074 \n", " 0.0013857 \n", " 0.001827 \n", " 0.00247692\n", " ⋮ \n", " 4.648e-5 \n", " 4.152e-5 \n", " 0.00022706\n", " 5.546e-5 \n", " 9.172e-5 \n", " 0.00010154\n", " 7.128e-5 \n", " 0.00011042\n", " 0.00013638\n", " 0.00016664\n", " 3.938e-5 \n", " 6.262e-5 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:Brewery, :waterdemand]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.0", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

fastats/fastats

fastats/maths/_notebooks/sum_squared_dev.ipynb

2

38868

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sum of squared deviations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TL;DR** Comparison of approaches" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import jit\n", "import numpy as np\n", "import pandas as pd\n", "from numpy import sum, power, mean\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "import matplotlib\n", "matplotlib.rcParams['figure.figsize'] = (16, 8)\n", "\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit(nopython=True)\n", "def sum_sq_dev(x):\n", " return sum(power(x - mean(x), 2))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit(nopython=True)\n", "def sum_sq_dev_experimental(x):\n", " population_mean = mean(x)\n", " total = 0.0\n", "\n", " for value in x:\n", " total += power((value - population_mean), 2)\n", "\n", " return total" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.random.randn(1000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.testing.assert_almost_equal(sum_sq_dev(x), sum_sq_dev_experimental(x)) # Basic sanity test" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 12.79 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 463 ns per loop\n", "The slowest run took 10.28 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 346 ns per loop\n", "The slowest run took 5.93 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 466 ns per loop\n", "The slowest run took 11.84 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 334 ns per loop\n", "The slowest run took 4.91 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 724 ns per loop\n", "The slowest run took 7.88 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1000000 loops, best of 3: 551 ns per loop\n", "The slowest run took 8.12 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "100000 loops, best of 3: 3.16 µs per loop\n", "100000 loops, best of 3: 2.93 µs per loop\n", "10000 loops, best of 3: 27.6 µs per loop\n", "10000 loops, best of 3: 26 µs per loop\n", "1000 loops, best of 3: 291 µs per loop\n", "1000 loops, best of 3: 257 µs per loop\n", "100 loops, best of 3: 5.69 ms per loop\n", "100 loops, best of 3: 2.62 ms per loop\n" ] } ], "source": [ "results = []\n", "\n", "for exponent in range(7):\n", " population_size = 10**exponent\n", " x = np.random.randn(population_size)\n", " timings_v1 = %timeit -o sum_sq_dev(x)\n", " timings_v2 = %timeit -o sum_sq_dev_experimental(x)\n", " np.testing.assert_almost_equal(sum_sq_dev(x), sum_sq_dev_experimental(x))\n", " results.append((population_size, timings_v1.best, timings_v2.best))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sum_sq_dev</th>\n", " <th>sum_sq_dev_experimental</th>\n", " </tr>\n", " <tr>\n", " <th>population_size</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.000463</td>\n", " <td>0.000346</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0.000466</td>\n", " <td>0.000334</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>0.000724</td>\n", " <td>0.000551</td>\n", " </tr>\n", " <tr>\n", " <th>1000</th>\n", " <td>0.003164</td>\n", " <td>0.002926</td>\n", " </tr>\n", " <tr>\n", " <th>10000</th>\n", " <td>0.027611</td>\n", " <td>0.025958</td>\n", " </tr>\n", " <tr>\n", " <th>100000</th>\n", " <td>0.291489</td>\n", " <td>0.257195</td>\n", " </tr>\n", " <tr>\n", " <th>1000000</th>\n", " <td>5.694846</td>\n", " <td>2.621306</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sum_sq_dev sum_sq_dev_experimental\n", "population_size \n", "1 0.000463 0.000346\n", "10 0.000466 0.000334\n", "100 0.000724 0.000551\n", "1000 0.003164 0.002926\n", "10000 0.027611 0.025958\n", "100000 0.291489 0.257195\n", "1000000 5.694846 2.621306" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x21f33ac0470>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x21f37305400>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lF5XSLiuDT57fP+koLZrFWJIkSZLaoJ17avjDwrV8/Jy+dOvg0K3DsRhLkiRJ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\ngKFc3/sW4B8AUkr/CkyNiElZ2cMppZ0ppQ2Uku7pB9nHs8B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"text/plain": [ "<matplotlib.figure.Figure at 0x21f37378358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(np.array(results), columns=['population_size', 'sum_sq_dev', 'sum_sq_dev_experimental'])\n", "df.population_size = df.population_size.astype(int)\n", "df = df.set_index('population_size')\n", "df.apply(lambda x: x * 1000)\n", "\n", "df.plot(logx=True);\n", "plt.ylabel('best time (ms)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Stanford-BIS/syde556

SYDE 556 Lecture 2 Representation.ipynb

1

658037

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## SYDE 556/750: Simulating Neurobiological Systems\n", "\n", "Accompanying Readings: Chapter 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NEF Principle 1 - Representation\n", "\n", "- Activity of neurons change over time\n", "\n", "<img src=files/lecture2/spikes.jpg width=800px>\n", "\n", "- This probably means something\n", "- Sometimes it seems pretty clear what it means" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/KE952yueVLA?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10185c990>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('KE952yueVLA', width=720, height=400, loop=1, autoplay=0)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/lfNVv0A8QvI?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10189b2d0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('lfNVv0A8QvI', width=720, height=400, loop=1, autoplay=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Some sort of mapping between neural activity and a state in the world\n", " - my location\n", " - head tilt\n", " - image\n", " - remembered location\n", " \n", "- Intuitively, we call this \"representation\"\n", " - In neuroscience, people talk about the 'neural code'\n", " - To formalize this notion, the NEF uses information theory (or coding theory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Representation formalism\n", "\n", "- Value being represented: $x$\n", "- Neural activity: $a$\n", "- Neuron index: $i$\n", "\n", "### Encoding and decoding\n", "\n", "- Have to define both to define a code\n", "- Lossless code (e.g. Morse Code):\n", " - encoding: $a = f(x)$\n", " - decodng: $x = f^{-1}(a)$\n", "- Lossy code:\n", " - encoding: $a = f(x)$\n", " - decoding: $\\hat{x} = g(a) \\approx x$\n", "\n", "## Distributed representation\n", "\n", "- Not just one neuron per $x$ value (or per $x$)\n", " - Many different $a$ values for a single $x$\n", "- Encoding: $a_i = f_i(x)$\n", "- Decoding: $\\hat{x} = g(a_0, a_1, a_2, a_3, ...)$\n", "\n", "## Example: binary representation\n", "\n", "Encoding (nonlinear):\n", "$$\n", "a_i = \\begin{cases}\n", " 1 &\\mbox{if } x \\mod {2^{i}} > 2^{i-1} \\\\ \n", " 0 &\\mbox{otherwise} \n", " \\end{cases}\n", "$$\n", "\n", "Decoding (linear):\n", "$$\n", "\\hat{x} = \\sum_i a_i 2^{i-1}\n", "$$\n", "\n", "--------------------\n", "\n", "Suppose: $x = 13$\n", "\n", "Encoding: \n", "$a_1 = 1$, $a_2 = 0$, $a_3 = 1$, $a_4 = 1$\n", "\n", "Decoding:\n", "$\\hat{x} = 1*1+0*2+1*4+1*8 = 13$\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear decoding\n", "\n", "- Write decoder as $\\hat{x} = \\sum_ia_i d_i$\n", "\n", "- Linear decoding is nice and simple\n", " - Works fine with non-linear encoding (!)\n", " \n", "- The NEF uses linear decoding, but what about the encoding?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neuron encoding\n", "\n", "$a_i = f_i(x)$ \n", "\n", "- What do we know about neurons?\n", "\n", "<img src=files/lecture1/NeuronStructure.jpg>\n", "\n", "- Firing rate goes up as total input current goes up\n", " - $a_i = G_i(J)$\n", " \n", "- What is $G_i$?\n", " - depends on how detailed a neuron model we want.\n", " \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"720\"\n", " height=\"400\"\n", " src=\"https://www.youtube.com/embed/hxdPdKbqm_I?loop=1&autoplay=0\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x10189b210>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('hxdPdKbqm_I', width=720, height=400, loop=1, autoplay=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rectified Linear Neuron" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1019bbad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Rectified linear neuron\n", "%pylab inline\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.RectifiedLinear()\n", "\n", "J = numpy.linspace(-1,1,100)\n", "\n", "plot(J, n.rates(J, gain=10, bias=-5))\n", "xlabel('J (current)')\n", "ylabel('$a$ (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Leaky integrate-and-fire neuron\n", "\n", "$ a = {1 \\over {\\tau_{ref}-\\tau_{RC}ln(1-{1 \\over J})}}$" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1040585d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "\n", "# Leaky integrate and fire\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate(tau_rc=0.02, tau_ref=0.002) #n is a Nengo LIF neuron, these are defaults\n", "\n", "J = numpy.linspace(-1,10,100)\n", "\n", "plot(J, n.rates(J, gain=1, bias=-3)) \n", "xlabel('J (current)')\n", "ylabel('$a$ (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Response functions\n", "- These are called \"response functions\"\n", " - How much neural firing changes with change in current\n", " - Similar for many classes of cells (e.g. pyramidal cells - most of cortex)\n", " - This is the $G_i$ function in the NEF: it can be pretty much anything\n", " \n", "## Tuning Curves\n", "- Neurons seem to be sensitive to particular values of $x$\n", " - How are neurons 'tuned' to a representation? or...\n", " \n", "- What's the mapping between $x$ and $a$?\n", " - Recall 'place cells', and 'edge detectors'\n", "\n", "- Sometimes they are fairly straight forward:\n", "\n", "<img src=files/lecture2/tuning_curve_auditory.gif>\n", "\n", "- But not often:\n", "\n", "<img src=files/lecture2/tuning_curve.jpg>\n", "\n", "<img src=files/lecture2/orientation_tuning.png>\n", "\n", "- Is there a general form?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tuning curves (cont.)\n", "- The NEF suggests that there is...\n", " - Something generic and simple\n", " - That covers all the above cases (and more)\n", "- Let's start with the simpler case:\n", "\n", "<img src=files/lecture2/tuning_curve_auditory.gif>\n", "\n", "- Note that the experimenters are graphing $a$, as a function of $x$\n", " - $x$ is much easier to measure than $J$\n", " - So, there are two mappings of interest:\n", " 1. $x$->$J$\n", " 2. $J$->$a$ (response function)\n", " - Together these give the tuning curve\n", " \n", "- $x$ is the volume of the sound in this case\n", "\n", "- Any ideas?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1041727d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate() #n is a Nengo LIF neuron\n", "\n", "x = numpy.linspace(-100,0,100)\n", "\n", "plot(x, n.rates(x, gain=1, bias=50), 'b') # x*1+50\n", "plot(x, n.rates(x, gain=0.1, bias=10), 'r') # x*0.1+10\n", "plot(x, n.rates(x, gain=0.5, bias=5), 'g') # x*0.05+5\n", "plot(x, n.rates(x, gain=0.1, bias=4), 'c') #x*0.1+4))\n", "\n", "xlabel('x')\n", "ylabel('a');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For mapping #1, the NEF uses a linear map:\n", "$ J = \\alpha x + J^{bias} $\n", "\n", "- But what about type (c) in this graph?\n", "\n", "<img src=files/lecture2/tuning_curve.jpg>\n", "\n", "- Easy enough:\n", "\n", "$ J = - \\alpha x + J^{bias} $\n", "\n", "- But what about type(b)? Or these ones?\n", "\n", "<img src=files/lecture2/orientation_tuning.png>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- There's usually some $x$ which gives a maximum firing rate\n", " - ...and thus a maximum $J$\n", "- Firing rate (and $J$) decrease as you get farther from the preferred $x$ value\n", " - So something like $J = \\alpha [sim(x, x_{pref})] + J^{bias}$\n", "- What sort of similarity measure? \n", "- Let's think about $x$ for a moment\n", " - $x$ can be anything... scalar, vector, etc.\n", " - Does thinking of it as a vector help?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Encoding Equation (i.e. Tuning Curves)\n", "\n", "- Here is the general form we use for everything (it has both 'mappings' in it)\n", "- $a_i = G_i[\\alpha_i x \\cdot e_i + J_i^{bias}] $\n", " - $\\alpha$ is a gain term (constrained to always be positive)\n", " - $J^{bias}$ is a constant bias term\n", " - $e$ is the *encoder*, or the *preferred direction vector*\n", " - $G$ is the neuron model\n", " - $i$ indexes the neuron\n", "- To simplify life, we always assume $e$ is of unit length\n", " - Otherwise we could combine $\\alpha$ and $e$\n", "- In the 1D case, $e$ is either +1 or -1\n", "- In higher dimensions, what happens?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UQvyZZVnfGjpGDYAmjmPDonuzXMjwRNhvDm13cSDE04jbZSuIhXtYiLdx4Fvw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"text/plain": [ "<matplotlib.figure.Figure at 0x1047ba0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#assume this has been run\n", "#%pylab inline \n", "\n", "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate()\n", "\n", "e = numpy.array([1.0, 1.0]) \n", "e = e/numpy.linalg.norm(e)\n", "\n", "a = numpy.linspace(-1,1,50)\n", "b = numpy.linspace(-1,1,50)\n", "\n", "X,Y = numpy.meshgrid(a, b)\n", "\n", "from mpl_toolkits.mplot3d.axes3d import Axes3D\n", "fig = figure()\n", "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", "p = ax.plot_surface(X, Y, n.rates((X*e[0]+Y*e[1]), gain=1, bias=1.5), \n", " linewidth=0, cstride=1, rstride=1, cmap=pylab.cm.jet)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- But that's not how people normally plot it\n", "- It might not make sense to sample *every possible* x\n", "- Instead they might do some subset\n", " - For example, what if we just plot the points around the unit circle?" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104e60490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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cnx8yzHv+L3A8ywvHlpS4O5RZu1wEzF7tBkKyng+cTrgJDCvucKeW1ycSNmyf\nQ7hJ/OKg90HYx3c+YUnqecA38DdxM7MsTGx5fghhZJGZmTXEboRvAXOB2cBmSaMxMzMzMzMzMzMz\nMzMzMzMzMzMzMzOzJvv/JtqorY/LcQsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x104e60b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import nengo\n", "import numpy\n", "\n", "n = nengo.neurons.LIFRate()\n", "\n", "theta = numpy.linspace(0, 2*numpy.pi, 100)\n", "x = numpy.array([numpy.cos(theta), numpy.sin(theta)])\n", "plot(x[0],x[1])\n", "axis('equal')\n", "\n", "e = numpy.array([1.0, 1.0])\n", "e = e/numpy.linalg.norm(e)\n", "\n", "plot([0,e[0]], [0,e[1]],'r')\n", "\n", "gain = 1\n", "bias = 2.5\n", "\n", "figure()\n", "plot(theta, n.rates(numpy.dot(x.T, e), gain=gain, bias=bias))\n", "plot([numpy.arctan2(e[1],e[0])],0,'rv')\n", "xlabel('angle')\n", "ylabel('firing rate')\n", "xlim(0, 2*numpy.pi);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- That starts looking a lot more like the real data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notation\n", "\n", "- Encoding\n", " - $a_i = G_i[\\alpha_i x \\cdot e_i + J^{bias}_i]$\n", " \n", "- Decoding\n", " - $\\hat{x} = \\sum_i a_i d_i$\n", " \n", "- The textbook uses $\\phi$ for $d$ and $\\tilde \\phi$ for $e$\n", " - We're switching to $d$ (for decoder) and $e$ (for encoder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoder\n", "\n", "- But where do we get $d_i$ from?\n", " - $\\hat{x}=\\sum a_i d_i$\n", " \n", "- Find the optimal $d_i$\n", " - How?\n", " - Math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving for $d$\n", "\n", "- Minimize the average error over all $x$, i.e.,\n", "\n", "$ E = \\frac{1}{2}\\int_{-1}^1 (x-\\hat{x})^2 \\; dx $\n", "\n", "- Substitute for $\\hat{x}$:\n", "\n", "$ \n", "\\begin{align}\n", "E = \\frac{1}{2}\\int_{-1}^1 \\left(x-\\sum_i^N a_i d_i \\right)^2 \\; dx \n", "\\end{align}\n", "$\n", "\n", "- Take the derivative with respect to $d_i$:\n", "\n", "$\n", "\\begin{align}\n", "{{\\partial E} \\over {\\partial d_i}} &= {1 \\over 2} \\int_{-1}^1 2 \\left[ x-\\sum_j a_j d_j \\right] (-a_i) \\; dx \\\\\n", "{{\\partial E} \\over {\\partial d_i}} &= - \\int_{-1}^1 a_i x \\; dx + \\int_{-1}^1 \\sum_j a_j d_j a_i \\; dx \n", "\\end{align}\n", "$\n", "\n", "- At the minimum (i.e. smallest error), $ {{\\partial E} \\over {\\partial d_i}} = 0$\n", "\n", "$\n", "\\begin{align}\n", "\\int_{-1}^1 a_i x \\; dx &= \\int_{-1}^1 \\sum_j(a_j d_j a_i) \\; dx \\\\\n", "\\int_{-1}^1 a_i x \\; dx &= \\sum_j \\left(\\int_{-1}^1 a_i a_j \\; dx\\right)d_j \n", "\\end{align}\n", "$\n", "\n", "- That's a system of $N$ equations and $N$ unknowns\n", "- In fact, we can rewrite this in matrix form\n", " \n", "$ \\Upsilon = \\Gamma d $\n", "\n", "where\n", "\n", "$ \n", "\\begin{align}\n", "\\Upsilon_i &= {1 \\over 2} \\int_{-1}^1 a_i x \\;dx\\\\\n", "\\Gamma_{ij} &= {1 \\over 2} \\int_{-1}^1 a_i a_j \\;dx \n", "\\end{align}\n", "$\n", "\n", "- Do we have to do the integral over all $x$?\n", " - Approximate the integral by sampling over $x$\n", " - $S$ is the number of $x$ values to use ($S$ for samples) \n", "\n", "$ \n", "\\begin{align}\n", "\\sum_x a_i x / S &= \\sum_j \\left(\\sum_x a_i a_j /S \\right)d_j \\\\\n", "\\Upsilon &= \\Gamma d \n", "\\end{align}\n", "$\n", "\n", "where\n", "\n", "$\n", "\\begin{align}\n", "\\Upsilon_i &= \\sum_x a_i x / S \\\\\n", "\\Gamma_{ij} &= \\sum_x a_i a_j / S \n", "\\end{align}\n", "$\n", "\n", "- Notice that if $A$ is the matrix of activities (the firing rate for each neuron for each $x$ value), then $\\Gamma=A^T A / S$ and $\\Upsilon=A^T x / S$\n", "\n", "So given \n", "\n", "$ \\Upsilon = \\Gamma d $\n", "\n", "then\n", "\n", "$ d = \\Gamma^{-1} \\Upsilon $\n", "\n", "or, equivalently\n", "\n", "$ d_i = \\sum_j \\Gamma^{-1}_{ij} \\Upsilon_j $\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.0244915143696\n" ] }, { "data": { "image/png": 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BTgPuSHLcKcATBFH3QpEd0T7VJ2JtPPdQwgC1g+2B5sBbqU85NolWEG2BoejT/hJUFdUA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\nmCHRBqIz2lOjpPEGIjNqqYEAMPeglb6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"text/plain": [ "<matplotlib.figure.Figure at 0x100cea7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1044c9e90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104e594d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 10\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates=Uniform(100,200)) #Defaults to LIF neurons, \n", " #with random gains and biases for\n", " #neurons between 100-200hz over -1,1\n", "\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqL2(reg=0)) #reg=0 means ignore noise\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "xhat = numpy.dot(A, d)\n", "\n", "pyplot.plot(x, A)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "figure()\n", "plot(x, xhat-x)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}-x$')\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)**2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What happens to the error with more neurons?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Noise\n", "\n", "- Neurons aren't perfect\n", " - Axonal jitter\n", " - Neurotransmitter vesicle release failure (~80%)\n", " - Amount of neurotransmitter per vesicle\n", " - Thermal noise\n", " - Ion channel noise (# of channels open and closed)\n", " - Network effects\n", " - More information: http://icwww.epfl.ch/~gerstner/SPNM/node33.html\n", "- How do we include this noise as well?\n", " - Make the neuron model more complicated\n", " - Simple approach: add gaussian random noise to $a_i$\n", " - Set noise standard deviation $\\sigma$ to 20% of maximum firing rate\n", " - Each $a_i$ value for each $x$ value gets a different noise value added to it\n", "- What effect does this have on decoding? " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.365644156802\n" ] }, { "data": { "image/png": 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kW76G8ATilG2ed7oVRdTPE2s2Fsin1C/1fRWSoYz528rqvhoxdasQU3UHLmWV\nx7GoGzDAdGKHc6KPR7RMAB7hwU457GupgvdrH8DDoZhgDdPNkT68LItfAat1nffC71Ilmvu0ZkAx\nSVgKElXwOUWyQIYi8kAhYmFMsjqVpW7jZHXrenkibuG8g4iRQMzZ03npTDcMBhkG957r8/iSIh3J\nMToJqiMwo7w85Vuapo589FGr94KT+PiQ6AikoRbIcGClrUBqfRhGJjCGTuX/AUqhz0DcCWQhMM4M\nz40MfWQlxiY3n4nTRLcPrDyGSPLgTiCO9bGA3LLkkJ4khuFDrCq3qhMx4VwTiBMyCDis7JJNNsGs\n6erbaMXpMT5qNES/dLRAzEghG4GozEqSOsdT/SjShxj8JKzOZDiRHehQzwLRDiA1pOrMRwWJSqNL\nciHdMj6qs6zCQSpvBiJGYpWwbkDq/Fio19rWzBLuiJDUZOoaaGlBFkbqVsorOtTLUak3Y1SixXbF\nfZYYDq8gBRadnInnEhOBXxvGefm+nO8wZ8fqWmRSsQEYEx9fXUD993IOMM5sJe2GcAQSKZkwGgf6\nNcCH6Ppp2bbDYNwkLF0/jLxTgyPsU3DR2GR2Hc7AuTOokw9kGCw9TuB6Y7ZAKpJYTbAF0hE4ga5X\nOGwfM873F0Lh0IM8ZH09/BxUJrdUwIjHeJyuOIfIdUfMQitmehJgxFPzT+DCZ4ehA/M/6MlvVIMI\nBAiRsUp6Mqg8A6pas77P1KjyREKtn1glrB7AGGU6FnWdU8g9S7Nt0xE4rutUIQQyx7auTsZK3QZl\nneo9LyuAjmZ0loU+wA4NYn5AzcZTHwC3xfrZJkYect9Hn+sT+bLhwIH8vps2jWyH+DiuBe2XZu0q\nh4mddhJ5pnTnvSmNs/OBRHagiw9wpvn3SojrQfgk2+jCef3E06ZjKq1aLkXqb4XCyQcyDFYoAjkr\nsRJILlKl294V0S5fTQT8tp+YcT4SiF2myUIysEGY3s7QnXExwx7zMe1B2u+Gz1dSwmzgDPUH9/HA\nQpvj6WJgrganF+Qxr0U1M4HpnU8RtzOd0ijO241AhoIUSjxwGX+vSqco+SBPAddGsc9QAok1Cqsr\ncv9usS0LlbHMJEKVgszQ7BFhdQSSvI+kM7m0tO/c9AsFO9sb5v+w4x/AdxSOMfHnCnmIRdVULYub\nKdSl7733/adOnuxwDBhshZeacPNNzsS9KVsmUGVWvXZCEZIDFurHxCSf8A50KU2iEwjjXwEVnWkM\nAoHeJLYxSSWDAAAgAElEQVRVtGj1Is4TJKf7MQwK0oDNZjWNTJzHvHoEYmD4gJxOhSwAOmMYllVn\nJ5D5NEMCmUGgNtKdSHVba/nNSNRPF2SmsdxpB6VdWTuZOa0JDuUN/XLs8pWUb++4biF+nrrhRsbe\nsp5K5efVPkcp/bBn8MDpAicCWUXAAvm/5CIyU7bzBlJw8TJT7gmHUAKJtZxJV+DPwG22ATmUQCwn\n4XhghdmcxsJKoD2oLonHaH2mM20cZJxQGetsCWQhktE/5iz20djIA/4GXGOX/zy4QaWAegZ4fvLk\nV+aOGfPhyyHPFbhL0KYfxDEJN5x8henb2ImzFZKN5HQ4zdQtjETKgFgS+kooaQNJblFYIARyYUQ/\nSEL6KHxJUFMzDbEIQscjiRatU0pUPFQMhNMZiDSXBRwJbiRVBycLJAM49ftP9BJk8mONhY3mQIdz\nTyCvISZWL2SgvAvJpJyMmKMXmf+DmHFvmr9nIeVHHCWsnd9jf1d25SdSMcBc5OQ8rovAAnrSYUMc\n3x/0MpB5pBWDEmp5Cbg//zjM7iaykWGgGQb5hsFthsFzhsEThoHF7K4S1ty5cVcD3876kFW+ajZo\nQgTrENksHEIIRKtEupeFHscNXZEkPR+B0iouFgiXUOf/qDueFRkzxVdDp4oOlFJfQpsNTDTDneEs\nCcS0CP8JfLuh+2gC5CITmSQCrQIiwjAYYhiMbG6+E8OgpWGw2jDqvnMbVHskz6EFMLBbt/UazoO2\nW3j9ZuQZ6OewLgKBAO4ylilfaeEk8ZB3QDsi7o8/OFynCV0/iLzPA1y3AUjqcAG15ac4erQceAcp\nbRKAhPaWEXi3h8DGQ8i9q8A9B8QKtAm1kuyO9XUE/CCNFsIL555AvoHMDBKRC3sROIbIST2RL9Qe\n9vYI4rvojQxcjjgxjHSFtmskyweaM5kgC0QJY7fEymad8oMH+fbYdDSeA24w+w7/P+Du9mW0LujG\neMPgVeQLXIZIGZuQL3u5YdAXZwIpiour4vDhnBeA6+LL6EpAz3yHyDKWUwRYVI50JdfXFnlR/wvc\naq5ys0BC/R8WLBkrt6JD/fa2mtQa20wgnPdsLRCQ6LtrVLCv5pzAMGiJnMchYuj1YgYnvIMEBhww\nDF4yDG40jGbRgXE04jge6rBuLFJL7i7Tp2GV2AiFi4SlKdyjscL5Pyy4OdKjcaCHTqLiodgH9+QB\nKOjsUi0hgoylHoW2o1DKsmReIfA+2mH3g/wQ3l1CZAd6DnDAoW2C3SqxmktBM7NAmgp5CVTPuYSP\nawgwrv1hvRBYqPkBP7+m7zvX8dETfvw8a5UJ0GBPZRsWqSQSd8eTX6uYi1hEmbrO9brOX4E7kDIo\nCz59n8uAtnbt3jC01gMHLkx+990fvDpRZxUyM7Kik94FrjLrb7mhjkBUoFlOtI70fKBAg1qEQG42\nj1XPAlm3btwZc59OxRw/TubMeAWtK9qzHeemPbOQ6KkM5BjhNOOI0GSwnkew7+ZcIRfYZ/axeBeJ\n0okGU4Ajuk5PpB/EckSS3WsYzDcM/scwIvZUOV8xHvF/OZX070dwYrBbiGm4/Cw3P0i4HBALESwQ\nFxhGOiLz2Dt7ZkByKaRY2ea/QlpKh2I+rvlLKhmt5mckJPckLskK2PkUmdyFWi1Whn5uW4qvnMJj\nbTk7B7q1vd0CabRKvNB8CSQf+ORrzKxGHujQQXNCrcwaHkdp1/PcimpW3/3v0J3s+AFLS3uyt1xx\naNJCFuo6280OgQDoOkrXeQG4qCaFn9ckQuFV4uQ2Z6Cvtm17cOO0af9zErMEgyYOfTT50vcQvpFL\nZ2C/khd1pwrMhKNxpHdFSk6jyaysALHsQpv5ZL/99v1ZwDzQHJIlteM92L71DC2LiatvgZiw/CAD\ngXWhGbHhYBg8YBg85rDqfJGx7C/op0C+2a8+Eu5D6pyh6xToOk/rOpcj391jSCvf8+H6GoILkT4u\nTkmk/ZBadBgGyUjIamgdKTDL6rjIewYwFFSotRaNhOVggSiNyBbIRcAidL3ctiwL4g4RkH8HIHWx\nQrEAGG/mWISiPx02HUDlx1Ha8pi5rBa5f6FWiEWq93+fZxalUnlJquS7QQNCeAmVsKSkiWeBRIE8\nYGF/NqS0pXgw9SWs8ZfezkXAGJ5e9zdKs1eaBRCDcHgS7dY+wbPIzGWE28F0nfXAiJpkKvZfxwJT\n0vodkLx48denIo70vtQvIR1GxlItgNY38OYh4E/IjK8X0eeC1BGIif8i0R/1LJD16y/ohbN8BcAE\nFqzdQ14NuBLICkSHvZIo5SvTn/Qb4Cc4yxVzgA4q+lpDTYW6F9HsqfIBktTqCsOgGzLovBG6Ttc5\no+vMBH4I/PLsWrtGB8PAbxhOeUeqM6inQPWvv851XwmIs3kqMM4hqKCOQJBBz+pCGARdpxzx5znk\n/GhnELK2lStSrREyijR7drJAugBlZm4WyPM2M2QbBx8g2VC+Cxh6A2/4cCMQXS+6hNmVVzPdyac5\nhB4zd5GQd4zP2vWyLf8vYmHbx+D9lKf1AL55P08c2QpxPwv4FhtCIHvN8zuCTFzzCN+6OmY0VwLJ\n16CkmHZ7pzBrIjYCWZZNbmkiXT7pQiowmSP9bweecdnPACTx6XMi9PfQdUoTTrK1zRpeRx7+W4Ab\ny8tbLQeG1aI5Eci7iNbv9D10Bore5KarkF7k7yIEEq2EFUogbwBXtFvEcWwEUlvryz5xouNIwhDI\nTbyxdwP9pVGNA4GYMtlsZEYdkUDMQedhJNFxFNAtVM4xQ4RfJLis/FnBMIg3DH4TYyRV7ptv/jQR\n1N/N/6cTWcb6PvCiOUg6QtfVH0+fTtvGF2OFjCfI0lVpoH6PaOOTEcs0WgwFduk6G5Ge4rZBUcWZ\n/1tWgtvgZiGcjBXqB+kFbA2bRS44AsSDsvsj7fJVFvAvJFxXfGwyM78URwKp2AMU92TbRKRoYT3C\nMwxS/4fH23Zl168dzmcI3T6uoWO7vWxO7WcVKEVkvuMEKxD7ONJHB2a0oXjgFqj9nvh8oWESln35\nWiRopxRdd6v8HTOaK4HkGAa+46QvHM1nAwnU22/10hBmrMuguNbH5fhVHjJbcSsp0h/5olcQxgKx\noEFxr7+wBHEkTtJ1jpjHVqdIG0aI+a3J/yUu+85JoHIf0pvjAYR8LAskJgnLPNZhYHG/hxkFtDcH\n0+SdOwemKqUdM1vxOmIMS7UC8vnnP3+vcPaBgMhYyUQgEHPw/iNSskbX9Topr5fD5i8DN9oadZ0t\nrkLi3TvG8Jm8DRsuiCMQFfQxMNwwnBt7mbLNnUh12XAY/txzjy0AftGUVogpEQ0DhuflbUgA9X0k\neKQz4gh/Cud77warejVI1Wq7jNUFOAKaVfLGzf9hIVyh05nAFFvIejT+D0wnfGhzKUu+8iEBGs8j\nhGL1Ku+OPGOhEzyrkOKKNpz4GkJ4ThLWbfFUl7fhxEgMI9TJPoTMtW1IzSgnUc3BqnIhsIJbfEAm\n81qmsevUgFb0P/ojGOSDox0DSZVnI2GBvJfWNTQamiuBnAQykyl7ayTLO/KScQpx7n5yyU6SRhby\nF/xUAN8D/glavdhqwyAdmaHsQR6+Ifgjtl0tBtrpOlt1nZ2ySFPAyhriBuPcBe0dnGe0OQ/xh0TE\n4vgIeel703AJC+AVXxW3EOjSmLVkyVWloIXOvILgQ+VWkbDm7bd/PADIddGtZyOSxEaHdUAdefwZ\nme1dZBIsCEnXk1E0ienfhcySGwP3IdEq0fbOBsgrLOwGJmnrOmeQdsdXuGx/M7Bc1+vdextUSyDt\n/ffvaY+Een83hvOJFT2U4lhlZVJiZWXyJiQLegpo3wRtH/JcNZRAFhPsSLfLV3BWFoi2G3mfrDyq\naPwfFkJlLMsC+QnQCpGXlxDINRL5StdDfXdWhOKK9hwdjXzvQQRiPtM/8KF+Ppg1FQRV11ZxxJ/p\nT3JxdxLSExlT/DxwGyjLB/kqEohTBqxlaYsH2LNb68zGbmlQlCAqRrcXBtMFIbjQuoHgnESYhkS2\nHrMtXmtep0cgUWAPkNeV3Z/0Y6N2VcHJAchgtOzqLVQl1GKYmde3IpnPThgAbNB1lBnWewAZwMPB\nKZSXOKpXplCai/ML8A5wXWjmdQcOd/0xf+0H/Nx0Slsv+kEiWCDmvroQKNNiYQYwJukwhxAZq9Py\n5Zf5CCNfmchNosIoL0+ZjDzE9XqKaHLt2VaQQMgZaQkJZXnAE0i44yRdx55NbO/fEgq3kMeYYBj0\nQQa4t4i+dzZA3tGjnRMJJu1w4bz3YjrPw8CapY5ALKIHTMulKTBi586BJ9evvyDh9tt//xowGbS6\nfhRLGNOpJ1udci7qwZw4jMPdAnEikHBlgCI1e7NHY8VCIDZHuvIBQ2FcFTL7vxWZRFhKATj7PyBA\nICszONQTce6nh0jOE5D37R8plNa04+g3zYKMAL3oMeskGmvwxXdg8IlNwEsErBCr/lUaqGzidp/m\ntsrqTfD5ozDtlEQiLuh2nOtwaCRlnodTy9pcYI9OECGuQ6Rwj0CiQAHiBynfQu9jk5lzPTC25A/8\nVhMNfzWSg/KpOQtzwgCCwxHDOtJNOBLI5Xy49xRpNZpzK8pVyGwh6CV+hAen7CV3mxbQbrcB3dM5\ndpjIFkgGcFojuHquJuXSZ2TPIA7oVFycmbdjx+BWSBhiOORewOL3gbGlpa1X4lKryNx/Pfh81Rck\nJZ3ZiEgGF+t60MwIwhPIm8DlSvTns8G9SGTXBqK0QAyDeCCrpKRNKtKrwpKaPgAmmTki9u1HIt+/\na46SiSzzPAbqutqAhPjeE+2FxIgRq1ZNio+Lq5k5ZcqLcfZEOgVtR/PZE29xfYfv8Fw0sl4fpG6a\nFaa9CWhnGHUTmr40mgUCBPtBehN9gU67BdILSo7C4meBHyFjA8BnwGgefTQRCcEN7UIKNgLJY0+7\nElLWIu9Uum2b+4C/6zq1PtSyCSxYDPzMXDeEAa+WgfYB8k4eRCLwbgdlTSKOIImCV1GRdgyttqwy\njgkIwQHMyShlMs5EnAUc16jna3Mi7q1AJY0YwgvRE0gfJK79UiLPws8H7MGsgruRfht7sF3HT2lK\nFeOApRqqBnF0ujnPoT6BROMHcSSQB5hasZF+PqfyDKZ1ERSNpaD9DUwb8ii/eN62XSlQvJohLZDy\nIuHkNCf5ysIrHT8hA+g0Z85t4zp23HsYtFNuOzKtmZwxfLYFWP7yyw9tQZzfUaN//8XfLC1t2+o3\nv5l2h67j1A/BlUA0ecEWcRZ1qAyDVGTm+Rz19fFw6AQcVirOIuyOAKb1tJL60tp9wDO6HrF3TGZb\ninchL3lfxAr5eSghxQIFbZRzVdjhK1dOTq+piZ9H/UCQn2jwVgH5Jx/kkXDvgoXxBKwPzOiqJQRk\nrH4Ey7SRfCCuFbVNLAZ6SrQYeUTu42HBHso7Ar6hkOZVr9u2OQwcYffum4AdZqRSKLKAAwrtTFd2\nacNZUYPIvx0ADIPOiGP6P+b2y2/lvzuBuzGMjlA7hK7z2tF21KdAmYQIawfN7e1Sl4ZYJX/yKfbH\n1zLKPF+AOdkljNDUWTnQQderkO/mC7NAuiD1f3YgDsE7kFIjzyFy0F+J0PP7HKIA89xWMszoyTar\n/MR4JGZ7JBISGE77tyKwLDTYAhnNZx020bcG53ayIBFW9nDeX73LNade45bQWl9b89jbHbFk5Dh+\neuCvm61YCEcgnyScILn1Gvpv3Dh2aJcuGze4bGehHVBuEtis6dPvaw1MiCWjuqYm/hKfr7p64cLr\n+7hsshPIMgxSXNbHJGMpGBAiCd4GzNd19iGz02h9ILnAnmt5O289/av+xd325LZ3sZGaYdAecdK/\nEGmnPmqyttPjoq/x4W5guK6zFhkwvhfleTnhDmCOss2ODYN4pRi0YcPY9KysXR8gzn8NxPpArLI/\n3M0Ln6Rz/AIlDdvCwe7/sGDKWHURWJvMY8dRvwVDKMI50TFL98xFQp73mP9HA3OSoDSYejMsTTP3\nEYqlFBffhPM4kIA8+4eBPofIKNlGr0HIhMbyg3wXeNVs+QywrC3H+yIlmv6HzsvH4auqoN/vThDc\n0fQx4E5QluU2zjzW9KEHOF4RT5kWKCG/tUYj/uJdTtKwq0ToRiy/RWSxRkM4ApkKvI9YHxMQyedm\n8+/eiHnplAB2PqDOAvkvt37QnqNpSmaPE5Bifd8HnnMLCTRfMisCy8JqoD/+sNnDjgTiQ/UpInsX\n7j3SFwNZCropGfxv/wWPxlPf3LT8IHZH+ihgNP4giceVQDSoPt2VJZkfM3rHjsE9Bgz4NJR8QmGP\n/phZWdlycm2tZiCDZURMn94upbCwe+ekpLK3cWkAZuZXbMG9ztQMYJSKIvpMSen6lZgOUvO7rEvq\nQyZE3aKsT5VXXR2/7yI+STtMx2M38/rDCl4xz+M94EpT5gIJN34vxLfjiCv4YFBbjqfcy9NxBCYl\nvwX+11ZbLVb0Q6zZ39qXVVQkHz1zpvXWW2/dVYT4p6wouvuB6RrsOkqHjXfzwgzgSeXSU9y8j04E\nshgZAM3Kz5pVuTobKNb1sGX99wOdI4RVz0TkvWj9H4B2PC6uqvzPf05/1ud7ZDJ8/yGo1wwNYAmH\nD4/EmUAyELKoBgYW024XYsEdQaIYE5HmZ3Z/13JgRAolU1F8h37zB1KTOBNffBZBBKIVIZOi/zUX\n/Bz4M2g1V25D7WgbIAUN1NIcDn97pWPUXzRZ6AHo+nR0/QuzQG5EnKtO1R+rkJt+Y2OeTCOiANMC\nKab9xk+5kDJaXAP0nsLMHcjM8cUwn88BTgcNBlLsbCfhi6Y5EgjQt4hse2XeIJg5D+8h0Vi/K6XV\ns4fITDT3Z8cW6ueCDJRdBNX8D2eBUDyK91ovi+t58kS7lClTXlwU5nog+GHcDKi5c29bTJTf/aZN\no75dWxtXWVaW+l/CW3DhZKwzCIlEmiGDhAhXIRMfEKszDomgQdcpRQIBwmnvFvKKirodHsei2if5\n4bJebP0tMmteP1HnKmrYgyTTxSHWQyTnOQCX8dGgQ3TccyGf5mPKSrrOOmQ271QqIxr0QyyKm1Tg\nGR2+bt2FR5DB/OeIDDvctFLuA/5gbrf1Ha5rjUQnvWErjGlHHjIr3xGy/HOgb2bm7iHE5v/AnLlX\nQ1hrdhaiFsTUoOzCC9868sILJ++65ZbauI8/fuxKw3Co2zVgwEZ27WoH9Sx4CO6FPqCCpBXI+2tJ\nWNcBm3Q9QGxmVOGx97mqJafiZ3JleRLJJ6YhE47QROWpwF2gdOS9+A/AxN2kfJob/N6/35PKcXvp\n6nCO0YbwNhmimYXtov5D/UETnEtjYg8SbqqBVraEscU+ah8CPv+IKbcAH5gd99wQ6v+wEEnGqkcg\nppTSdwP95+FugYD4QX4I6Jfx0TvAfofKoXYLxJqND0QGePsLEpZA9t3MgtO1reJv7fp8VUrKyUhO\nNRuBaAqY++STf1XAhWaoc1gUFXX7ZmrqsbXUJWM6lumG8I50kBlb2EZTSqyOMUiosGXN3Ac8bS9B\nQ/R+kLyiVX3Le7DdN49Jm/eTk66JVj0RuH7MzWS2X8D3gMuQ2XakZkUAjGJZ1+l8/aVUSjpkU9gP\nlDVg/xb4WaxWiPmM9UOsaz9iSWiLF1/5Nb//rQGI5Dkak0AQ6+M9LfCMWM/VU4jVO9XhMBcCn4bc\nRyujfM3o0R9eQmz+DwsRHOnaASTQxCkE3hVjxz6WtmtXcu2bbxZuTUiomgPMMAw+NAxbU7A//Smb\no0dr0XUn6dTeC31AJgfnAoOribMI5AfI/QrFcmAkTyd+To+hPgZMXYsjgWhWkdMZwJOglQH0P0zW\njN7Bk/Z3+5CScZqBDvlQkbPQmxjREEgV8sK8SGBmEs7xdc5hzmzKMbNGFzFuXRKVObVoC5GZYiSH\nYTgCCZeR7mSBZAKVnzNyPuEJ5BNkpvW7xYxrh7N27JQLMghJjoqaQFQChdNbXuG7o+WLiUQufBhq\nDn9SWpp+AaKlRirpkbF3b58+x45lTTedh6W4SCREJpBPgE7KJWfBHEQfRwrerQD6GgbZiKP7PyGb\nR+sHyWs/39d+Oz1OlZBWiHnPNZlp62WZPNnzCa7PeY0/EqX1oSCtF1vbPMUPZmvw8Q1MO4ppMeg6\nGxAf3X3R7MuGLKDaTBZ9HmjzDN/729SpL12VlbVzD1IKZyCwIuE4owm2PsCMWtKE3O8Gvq7qBy04\nyVcWFnXtum4EsYXwWojkSAexdt+KYl8AGAYdyss3dKyqupjKyrTlus7fkGTBD4A3DIM5hsFIkpIu\nJj19F86dJu0EMrALBZ8BRZvp4yvLog+iUrzv8LllwCiq3xtP0baTtB15L84WCEirir2Y45GCtmkV\ntJ6fb/MF+ok7mEqmpthJfQnYKQckAZHrG61cSThEQyBnEOlgMzLDyWvSM2o87MGUsRYxbkkprc48\nxQ9KEWKJpPu7EUikSKxTQAtFkJ+kL3Lv9gIJtiSiIGgSYjcCGQDcKmbuA9KzKTwGZOKnI9J3YQYm\ngSj5vz1hHqDJk8vbTjvxDd+gY5s1U9IJhyACGcrt40FNrKhoMY3IMtZNK1ZMLikrS11g/v85Ln4Q\nhEBc5UFT5nsdd2f6dUgm/CvIbLUvolG/ruv1wqejtUByc3Ydy9lIvyJCKgBooNY8ydQtD3AwYx59\ncah75YJJnzG6egMDCoAPb+KNGoInJZYVEkul3rr8Cw3FHfx701XM+N5TUy+sLirqkYZI0ZkzZ969\npdM7jFTwvpmkaV3NKcRKydFE3rsZeE4Fv+s2AlFxoF6RpkcALMrP39SFGCUsE5FCeQFtp1kfK1rc\nvnBh8tGamslJmAUUdZ1yXecZ5Ht/VwkhXYJMhJyal2UBRUomhK2Qd2DlUsa0Kc9gNPCs6bsLhVgg\n2StG8fnOmYh60xdHAtEKQesHmhXWPvp0Iuuq4oIINQs46hOXQV1tMHPC5HSPOwMHdHSnc2t0xJIH\n8hjwEHIh0ejH5xoFmC9ADfEbJjFv8U/4y2jgmQhNZaB+BJaFdUB3/M7hlmZI7jGCrRCzBpZkpOPc\nQ8H6/HZzoHTqA2LVnNp2OR/6kNnwACTDdL15XskIae419+OI6uqke1Iv2n28VZGyonHCwU4g2lbe\n/U4ip8q/853V+4EL7CU9VEj/jjNnUu44eDA/BZEgILwEuA9oaUYzueEV4NbQpEuTsKcCPzOvu0BB\nh7jT3IOzZRCpd7blNM7NKy3KW8XQbTjUINN11PFh/KNFEWUT9frvhIGRaGAEnWsNvikfcnk8Yi18\nNJRVGa0oHWXb5yZkcJ8Y7vxCYBKISgdmvswdHVLSjy2c+PHmkvLyVsnIILN5yZ/u6tHpPRL23sJ/\nHfZh+dfQJEfiz8BrCuINg47IQGaVqemIEPlIgAMH8j/r2nVd6uDBxjbb/qKVsCJEYsUG83v71oYN\nFZjRxUGyoq5TCTxTiy+lI4cSOHjwfQIJhXZkIz6QAcB6891euLZV3z74yENyipywSin6JuYsy2Jd\n7t+RpNPLcbZAQjE2TpktaOs6E9a9f3MIDhtvC9Ro1AuLdyt50iSIhkDsBcLmIiz4ZNOcTqOizgIB\nNixn1KBa4nRkEHKFWW20B06aq5Q/2YRzvL2FUBnLXkRxJeFlLAuOBGJi6yTmtUIGs0HAOvO8tiIP\nu1MGug0qCbhrytX/2lrahROIkzkc7A9kx9Oc9qWxumTfvt5jkOfhagAlxHBISbYrhkGvLVtGdqmt\n9a0xmF9hYHQkDIGY2voGnDvRWViNJF2FSg73Als0M0RRg5rqVA6lbuWgKQuFwq3pkB3tqaUiV+3N\nnc2la3CpQaYSeDiujBdxdvC/ga1MjQJNoX1tDpOPgVajwZFyWmy/mLmh30FQiHAU6LeTroWIfLIJ\nmLLzd+XzMmfTph8bdpiTl/UP8siPTvVl7+7vONYCCy1p8jgy8x6DRFktseW3WFb0pQC33LK7zfHj\nmVV/+ctFlqM3jka1QGLCiBMnaFFeXtkK+hcjE6wg6DqqkE6F41i0CaU+QyzA0L48loQ1kIAa8U6b\nsTtGJB2gRNc57HRwXaestCruQHdfe1j0y6WIVFhDlASSUsl8c3srsMB6/xYh4emtzeXn3IEO4Qlk\nGDJbLjJ/Wz/tkBDe8x0FBEzw7UjkyRu2Qm9u6AXs1XXcKlZGkrFCCcRegqFRCGQQa9sgg5m9+98q\n5PsJ6/9ApJ513buv3VHakwJcssohqF3mAYCWtOwvv+ckIv0T3kSKHfqQSUUcgeinW+fN+8Ym8C01\nz2sRcu8G26SPUIT1g5izQKssvXWO6cCDBCVmwam+tMz8qC4ZKxQ7gTxbCK4T8lK3cegEbSo3MGAn\nLlWQdR2libR2s8M+RhFscfauIS5uI/3qvtskKt6+iE9yzfpYFqYDX4+hFW6/p7m3LdLT/iegVZ/q\nR4/lffpsepG72gN0Yde28SycvO9GpuHsxwsiENPaXWBeQ6j/IxuZ+V5qHX/37v4HEaLph7S0jcUH\n0pgE8q2ZM1kELIeETpZzOhQrGB6vY5xBJLv91JdPs5CxbwDmO2YY2pGxV7+lOJoULuyYtYdT1MCE\nDidBq0XXdyHVjsPmWykhsBGI9We/J7mIolCO5ApZ76sbUfTmPLFAHjd//ozVfCl42fkOmwWiVSEO\nNKeoCQ0ZgKx7Ycv/UGmgQhOQIkZi1eBrZ4s2CrZAsj8fjT+ibBSWQDpRmIEMZg0hEMnAV+w92U9b\nRxgCQZybByw5LIWUofnkU82MNsBYw7jxY2BsaZe6KL1pQH9TRrh18eKrqpGHvh/QzWB+GfJyuFkZ\nkRzpIAXobrBFpDwEvKvZ9HfDoH9JL5I7fhKSfOXHh59n9QXUIqSYH+Y4eW2XU76C4acR8jgFJIYM\n9BY+A1KV7dwNjAxkELJf62U76L5a4aubjbagYsbVvFeDLQxb19mGDGwRK0BbEVgzuEoh0pyFEQ/5\nfiM/1BAAACAASURBVLe7GzuTFUx5jW/0msvFJ08MZTZREIiJZYi1F0ogWYgDua9ZNr3fnj191iME\n0h/of+gQNQ6+JydE40SPCmYm/w3TpnEKWAyacw7KP29M+ehYu/xebLUSAu2FFS0ESVjmsktr2tQe\njq+qTQ2VUe1Yfry2w/CM0kB1B12fj65Hqk4wENhjSlL1CMT82y5jOTnQByJ+v5cjHKvREI5AJiKD\ni47EfushP+c76pIJBdq1oDlViu2MmJnWzNn+wIwEfh8SehoxEusZvn8b8LCS2XsigXjyPVzys3SO\ndb3d/eNKIzyBbGnF6TxQ7aiN601gZhMFgShL4prBdW93OvqPlwqAPOVcohpC9FSFGjCe8eoo29JA\nbXv44Tf6aZV8cnIAf0BCkK2quqOV0ipPnuzQFyGQ3siz1pXwBByRQDSR57YCl5pJl3cBvwnZ7L7a\nBGb6auqV3clGktImE9mRntdmNUnzmaiAg6YM5GiFmLP1Nwm2QoYgM1g7gUz5kMt3EXgeANakcarm\na3w4JWS30fQdAXl+y3fQoy1m4ISZ0d9l6dqvZUzn648DfxvB51f+gkeTT59OWwUMMXNX7HAikM+U\nxmjk+7P7ErIRC38hMrvuu2HDWAMhkO4AS5ZERR7QuBbI9cDSEycYRHBr2hBoP9+++bX4eFU9wJSs\nlxLsB0kA2n5Xkgb7E3jHbq5MSvh7NfFxf+F+Z1+dn9z1J2uTe+XsjbU45gUEgnvsvdGjIhADIxWZ\nwP1ER48h6fLs0FyLKYKZTBhF8yCrV7DV2MVOIL0Qx7B90NiMOLla44ziWnzdkXLffYDNWl2LV02R\nsa6W411dHekENE63F3CbD9XTR2UJpRlF+Otm2euAvrUSJutmgXwf+IfB/MEcb3tn7ZHcDORFm+Cy\nfRCBVFLZK5/83emko1G4GpiU/2/iD1xBqSYDjEUAt61fP26mWH7aPuQ+WmXUIxHIgCi+M0vGegR4\nQjP1ZcMg2TB4CpiSsosnqZ/Znm/+vo3Ioby5KTvpMI9JLQno1+F6sbyO9J23zn0I8DaQY2AkK9Of\n8Dd+ZFV2BkSW20m3lZOY50Qg0fhB+iH3zR52OkQpNlRVtej3LV74F7DJh3p/K73LrrjiZIp5PaFk\nsQfoAMqeg7JLxZHSoogNIRnl1rE+QmSsfitWXLIA0FJTGZyWxo5ly6Ju2nUcSApTxiYW3H3qFP9B\nJlKfKScL0zDi8CV+l+rT8fvK2Ie8/6EWSCZw+DkZpIs1OGE+k5Paty969xRp5QuYcL3LOVy+d/0l\n5cktSlsahmNSsRvGEiA9q0ov1O9t3saMjqsjEDNQ43lggY4e1sfb2GjOBGJFJ0Sq2TQQiYixylLb\nI7B6hfwGP9WIxuvmyziaQFUWMGgfnUdhd8b76UDyiURKssP1QTetD+dIMU1KMhzPT9xYzrHuAaLw\nc1pT7FaaG4GoVODmRGr+iZRVn4v4agzcI36CCKSc8i455OzOIae8He+WpVByRed3GF3ajTQzempD\nbTz9gRufeuqJfYi0AzKDXYgM2Mtxd6QfQbTeSJLGtAoSry0n6SJEUsUwJM8BaDf26+zpaHAEyFYE\nRczlI7O4rx2pYC9hLJCkg/SIO0PLDfRvSaCvQrhukKuQiYL1XAw2r3Uncv0TgJWFdG4LHMTPTPyS\nXXyIjHdHsjy0TtgKIMUwIhYvtUJ47a1KR5w82X4zcBq0YiTc+h6s3tiBhEIbtBrzXOvuiQbqTA6H\nOxr1cpKsLO3ZCIH0rKlJ2AwsTkxkwIQJrNi0iTaEkXksmMEToW2WY4Zh0B3oe/vtFALbTSLfquo7\nx2+idFs8ULn4KIWIRLcV8aVZ3611ffbJZA/E0twBHKogybkVdU381bWbbmihaSpcyLoTxhKwQBwl\nLNPStawQuwXyPWSy+uMYjtcoCEcgT9p+OiGFFa3//9b0p3Z2MB/MAiIXfByI9AS50Kza2pFAjHwv\nnPuAuM6iq4g/lkxZOjDvAFmTCI7mEjkjrjILlNvAUF++MozWGMZLGIb1fW3tm/SZj4ODg3I9ck+y\nscqH0pytl1sBYzafjkVmw48jEpuBuyRpJ5BWVVS1ySe/MIOMQ+14tW018UOKyzs8ho9ZiNyy99hI\n0rVqdmzfPrQ7sNTAiEekpg+Rl3AN0BuUm4kfjYxVPI0byh7m16vmG5QbBvcjEViPXnAl9ySeZDwy\nMOwgeKbdBRk8P31qBx0JY4G0Xk/vkuTkLbXEHbHVTHO1QEwr0+5MH/IthvddQ2vL53MZMmPPJGPd\nKaS69RQAH7UvDmJtq4f5VV03O/P5jUbGciSQtWsnHMMc/DSo0CR6LQyBALZQXgvHh5HQYUG9nuaW\nBbIDGdSOmTWwFpWXk3P11ZTU1FBN9FWPG0PGugt45dQpRiEDcQ4iHwf8jYYRD/g5se408PbSYmqQ\n56QWmexYVohTBNYk4BNdR7XhxM54qseGWGv8f+reO0yq+uz/f52Z2d5778vuAkuVjpQDiA1R0ajR\nx0JM9LHEaExRn2gmap4UE03URJMYe69gowgcEOmwwLKw7AK7C9t7LzOzM+f3x+ecmTOzs8vmeb75\nfv3d18UF7J4zc+rnfZf3/b6xEoqkLuLMRSdMJnUf4wQQVZx7KB6ZGHE9hL5dMN6SRl4AoqBcgDYe\nWub/3Kja8dpYAHII8aAdRDBc9P8f0v78u60G8cAfRnhyIB6GrxDph82cP7rwqYP4tamIpqLgjRtZ\nDpQb6IqFiCY933B/tBeQA8w2J9NkB9YH4JiKtwhcMVBL0tF6RvcW/NU/HkWMSdXP92Rh4OFg6uZ5\nTShbVk19Q4S/mRyqBNwVhf0lRD/PjxGLYRzi+qaq/j1rI4AURhHVZsHSlkzy6RAa5xdTZsvjzDE0\nNpYEroYrGIg6xjeIl3EPYtFuRNzLApCGEAvVaFTocRTS1eibeTPi9+af5iHE9q4H5soybwT0uVlP\ns/A0FOqWjaihvHmgk7mMscBFnCL1rCunDG/65fnm0b8LXL+HtyKBtCrC5K9JGMIDIBuAFOb9KQZB\nTFgBcBkbu44xpbuAytt9Pm88dN7JTSSdQtCn27Sfzdq06RYYyfwpRXjVo9XxdKUDABSFoM5ZJIdV\nuQdg6aYtsJJOve4BqKzkkMNBSHY2sYmJHEUw9cZj/6tCulbPuRWhhLwQkQrSU0DG+t7NDA804hpM\nAF4/3U88HtAwDpjSAcTNwEIAyFaAYGwNBVRW4T2zHWAZPen1DMYfRO9IH5/NB3Z7Ut3uGkgGIwdJ\nfYWI+sJ3874N8e7dLSOPV+r+/6iNBSCvIiQyXhvl3/9uUxGplRl4kPwhxAUsQNzMh87zGTWMHYEE\nIRa4cuCbigpW4WFghSAWi02MBJBRIxAFOTKFRhvwVRLNSTYCfSOQbcSdHgBu0BgsvuYNIIpSCNyG\nWLx0/n5Fvqk8lLq5Xh7HFRX0VsT7Hbs7Hwj5kD3TgKMysoLwauI1htVO/KexjABSlERSN9CeRXJp\nNz3ZwEd9RCxFLIyzFYW8rhlERj6d1IFYiErwDAIy1hxGTWNhGG+roMxTUPy9hDMjI9tOhIT05lVW\nzjwBLDKMkJ2FeOn9AUgO4pn4bNBJcZuNFPc8ckUJQFGWiH8SHllO4KaBS/UGQt3GnEevMcG60vj4\nJuA4SFnVhA2EcXo2YiBWKZBCztZMBJtsqT4m+SCzSrM4e4XPR34NTFAU/4urrrP2AM90AI0gqZo+\nWfL+/ZcmMVJNQY9ADgNT/dCYfQvps3qKqDA5me5JBakWhOPRrG3TjpYmfPBBBlJTUc1mpsfHs4N/\nDUD+NxHISqBeljmOp5bgDSCKEgj8knNvvol4Hg8OOikcdhGvKCTgXUg3UniPaXRqGY8UeusCdp9g\npBLD5VSu6sTj9M4ZRz0PvNNX4KmBjKBCSyLKbFDhnJ2El4EvZOQPxvEd/xYbC0BeZmwa4VzGVrT9\nP2G+F381HvB6jfN7Z+eLQCYi0lU24JuGBubgnfOsQixCvumm00A01pHspf3MiU+kRe0kuimKblMc\n7cYc/GRAweyIRaQn/E2hy8BbB+uPCM2c02gAUh5PQ8FwnZmuHC/VVLkajiUS6Wd2+11x2N6woP4E\nj4R0GxCnFeC801hW5oQ/TDwCQHQwK8okcwDouI7ypHp6TQfJfg0x23wAASKvBTdSsavuotnAMS3a\nKEIsTHVAjIISzvglTe7wd43MZsecpUs/yAwL69l5552HSn0kJWYhCopTte8dGYEI4sG6DU30gFvl\ndCHwGYoSENxIXvgppOf5oe8ch/OOEwbejaDy+gHMpUBKLaGuWA5NAzZKwpFMJrK+CBHZ1qPVTLay\n/LNCKqaqhndSlnEgUn+jaY5lAr3v8t0IPOmrC4DDTmeAXlw3WjmQJ8uqHc8wK6P5Asii4Si24029\nTkSkrPRrro37VcP6+siNjqYdyMvOZj3imRpPnfV/CyC3I9arXARZ4xwjI5DbgXJq35HwNOadO97D\nCcRath8RFQcCqQni/chCXJPpQIthEmPrcrY2Aiu10dhoneOrKPleOHBY23YQ/Kro+povgOgU4GL8\n93R8Vc3tKiJq+6mf3/9fs7Fu7jMIamYlgvP9d0St4DPtZ3ehFTD/TaYiCr0HEdxmEBGB7vkYBQVH\nsxrGjkCMOc6dtbXk4s3AqkCASJphnClYcTFKGus4k1Oj6A6IpruoieT2fsJXaPtIiAdCARIx2f8E\n3AOqW/NIhaUSLs+irSiXAgXUBz9LbUgwLgEgv7kQy8SBVhdIXucfbSOtPpJOvBYBNQFY9VdK8oDX\n9VBXRtbZW6EYAUQc59upvVwnDsldTynKJ384nFNSBgcuTyLZAXO7gYmgRiNC6YXhp1l3jCnT8UxU\nKwJOysguBFjnMzYT6wQwUUtLTEFEoF6WnFxzRU5OWWNzc/ZbaGkgg81CCC/WIxyDidp5WRAvnA6I\nb25qJhBPGmsiIkqYn7SZZbYE+s6RFcPICOR8z9x74VTNOUZIHUAHgSHRHIl1EKkA0eAawuScjUhx\nbNGP/1Ou/LKTGLOf8x0rjeW3/uF0mg8hoj0fNQXJhnieJ+K/IbZC7Oemrev9H8Z0jJHtBZ4hUkuA\n/OBgTgH2e+7hCIJ8MNb4A93+x3Im1yt3TlHFNXwXT/pKRQDIEJCAooQg+oUeRYDBkHbclV+30Y6o\ng/QinLQZQOptgspbKQkx2WV4D2Jqi6Y7ArHo60PGpqDioGl6Dp60l9DFGsNUod9WjKbZBaClrOoQ\nwOIGEAUlUkG5aD+vBtfxnWTgOhl5rHkr/3YbC0COIaacTUH0SWxFpI+eRCy8t3Ge7sr/pS1E3MxL\nEeqhvswlFbxyg0azAta1a1m5d++Y0hjuRrwJEzjS1UXoe++5X0QNQCQHAojyffb125FeS0Z2MEMh\nwGQbQeV4eNvJiHRRLTDIY0FnEaG0MQx+P5ihPKBWC7mfAR7gP+YFUxIzi+bgCQDvFZMUax8yRdPp\nK8yY2xrGCby7n9fGYtuRiO1SxLwHo7UhCumlQLwqFod5QN7EVtHRasjLTiygwJTBu9cG0fJcHvnm\nYBqLEUCxGFEg/jh5Iy8fZ3KahEsHkEI8sxx0DaoTCFAeUcPSlJSb6Q3PR3jIRQqKl7CgzRY6MyKi\n82+4F2Cx4Gld6Yna9xzUzi1bFanKNKBVk30B2N5lx7y73c2+m4hY8C4Jq2HBQAa1iHvWjMfOG4FI\ncHqIRDWGz+KBwWAGk6M5QjmP1AMpxJ1qQ3jJddrxL9d2PfkZV9BJtC+7ZxMwbxTpfB1AjIv67BMn\n5tUC9aMIEI7FxOpCiKemKgrLEc+Rgj4/XJie3kFLZxUgoqmLgXynk/0IJQcXAsjHk8Y6XwTiK2Mu\nTFGiVKQD+5nTI6OAB0BAAEgpIgL5T+AAsqwpIRCI8PJNBzoIMJybTudNuULUG70K6IZv1qcSvo/n\n/b2c7sx9YDoLkl6HHE8dZBZQJjFC+aI2oy1j0U/W/yRDQXlRQSlFXPfHBsjqchG0VEau8f0wFaar\now9m87WlaGul9udftvGElzbEA/Qe4oLtY+QQ93+H6Vz5VoQXNgfvHHQK+NejQbsgr7zCf82bN6Y4\nnxtA/v53EoqKGP7b39xy43oEAv6brPymYewEFSJewnkJtO4AFoMagP6yC++iBeHJCoBAlVRIUiHB\nTmASAmTuQRR8vwTS6AqAzsDJAHYLU9qDgtomcSLD5+tz6yLZhxtAVBOodz7CyVTgCRm502f7diDO\nIFshI3ok6tJ7ycXj/ZiB/AnkhoZSO9eM7alUUjuiiV6IeLGWyzKDssw1zx76Zc1+5gT8Jy/qRT29\nBgJuDSppGJEnHo0KXUZ1znLtOlVhaMb79a9XLezvjwwKD+96HqQqoB9P0f0C4LBW19EXixoEaGVj\n1Aiz4pwQwR6lRSzgkd0U3/dnDgCXBDcypT+H44yU4dYikFFnmqCgBDZzUcAMNswADsoo2UMkdHQw\nNx1IIWebDdinPQdfA3OFCKbk3M7SkyqSF+tKlulHLOKXqRCoevcr+ItAZn344f0ODM6dCnNUz7XW\nC+mjEUEqFi78ZCHa/BVZpoOREYj+buZq1+czNAA5fJgv8BSXt+EByLGsLryS7DEWvjfxP5L29qtY\nN6QgHwQOEBi4DG8AKWmLjExFzHB5TEvtTkGsHc8CuQ1DZCDqd2Y8dZDUyWKbY5oq8kJgu+F7WxHO\nyTqEAxMBrOHI2jrEc63bfsS1v0xlVHVl3/QVAHduvjPi2ZefjZt1ZlY+IvX4fSBWRl4kI/9cRj7s\nu49mdyDSdeOx7WhrpaLwt3Hu42Xf1j6QUHCPaA1DFMmOITydW7Wf34q4gWNZG6JJKXKU3xtZFlNy\nc6lTVXekcz4A2Yt4+Q2LiRoLBEmobcCiBNoOIsLieRgktxELYyICHMIR0VVxO3EE4HCibA9FyKs8\ngCyrQCpdgWCX9HzqNJtJqszntFsUT3tAk48moeCJQFZEMKzOpDMc/D4gegQCoAxLLEd4VM8k9msM\nEGHZQHM4AclmhvZK0JtKarUT5wxEZOr2Mn+FNVdCdfyVe2IVlHjA/CqvtmifYSykj1UHOYbTfCHi\nnh82nA/d3fEPx8Q0n33ooQ36fGx3GgixIOqpAH2B1AvpegHdbXNi+KCki0lYMU05xtSr13HxtMNq\nfnADWd3T2MNIANGl78dqepvUyuKaSRyfEUr//hVsSegn5wji/ieTvcOEWJDBSg9C7G8hwGZWbgtl\nIMePMoBO570RbwKL3iWdBtQrCklA+M6daxLwLqDfiEfs8RjCcToCTFYU7+mDgYGDFffdd99/A3+R\nZXfa5hhCsSAK72hHf6aPADGQVzg8zClZRncetiOebf8RhMda8p8nWjXx36P8fhJizouHNqsollzO\nPBhPm+0hfnctNTV/xGzO48svCzVyQQZQ8qtbb50PbEeWjyGabFu1434eSHWq5PQP06x9x27teGOi\nxbalCOCs1IBUN22olNQJfMOk978PZLDz4SC8AeQgME0dOa/HaCMAREFZdcXBK4rv+sFd3PDjG34g\nI/9ZRt4vI9vFqVOgKPgSLnRLY/SZO35N6+EaITo5Hvu2AkgSIvd6BPGyfY6g7f4WkRKqRCxavx3r\nQzQu/WiF9EREakP33Kbk53MYWKR5mHrxF4QH7V1It1KPCDuNN6sQqJCEZ1+E8By+QgBgMSMARHIh\nmvp+DEw5R6YjnrYBRKrpDWRZ99xT6QoYRiJNy+VPirPb9uZSFSFmM4B2jnU9wRwCZmDFZEL97nXU\nRkrwoIzsbzSxUfhRGbJwqXbMuxL6ScADIBOBkyrmKDMDnwBkk106wEAe4oVJB1WvDczP53Stdr5F\nwMnXeG0lcKSV1ho8NQdRB7EyCSsP+BxXGaEDen3qMFpdQFGIaWtLW9bbG7PRsO1XjASQaPQZ9p5Z\n69n4qBSvSWNTuAUzIekXZdQSocLZX/yxYdhllgI6Z3II8RwaZ1mPKmdisOlDpB2oIbvvUZ6QVrLZ\nUssMBbFopZByKBIPLR0EAK8AGCBs/37mdKD1hxjsc+AiZzB3ot0vrdg+EQGQ+qI+GzioqibfcQRx\neBh8pcBUjfhwGh/K9K9/vXpCd3e8GTyLuSRSbiXa56dgHmrEyn/gBhDJBfZtcFE83gSQVsQ1H1NA\ndKnMhLAaVFSWqx7H0fD1ZCHWgnsNP7/6UjY4Axh+W5ZxsXZtLSbTAUJCnoj6/PO/2s1m+4nMzIE3\nVq4swpOemY5YO7qx0oxIuzbu6+AMwsmrQkTbzSZPfdRN3/U5Lx3k3yd36x3AuziDpmMAEFmmzzxA\ntSSctBGUcQ3oFuCpF6KgzABe+ceKfzzXFNME/gfL/QzvgWBGS2d8hXujPYmoIf3L9q8AiN8ZGP8m\nq0bc7OmIB/w32s87EC9bAWJR9tXC92ejAYjeZarn+KfMmcM2YCp8kQPYDINe/EUg4J0bBk+6ph0x\nIKoKT+OPkRWjRyAgpuUt3M/sBUeZVhYd1iohWDePGz43FYeplEBXHKIW0xA57CwtpsyB50EWGlhW\n2oAuurIKzKjXLKH1tIy8YZRrY4xAjg+biFlazRdAQ0I/0RgovMFYqkA1W+j7CKCQwoN27OEghSFS\nMUu1bednUHsEA4AgUhpRP+Nn6Yyk8l4OPI3Vq1BcRnRXJuL+lOApLH/vwIGVbb29cV8btlUQoB+I\nDiCRkaekjIwViHvfyygRSJCZupVJqAGq7e7CCvok+E1e+1G1c7JFUi2cw/8kuTGpvNqxHnmXG3q/\nz0tyCo3q89y5H5iMZTCN6LPxGAum3hHUwfe4PghPYRYQHfrmfio6ZlMIRKlikcsGOrSmUT2FpQOo\nQRAUEACig14tEAJqIj5pLEXhmsmTdxc99NCXZ7QahtH0NFYqV99SBLxBdNVM3E7R/qOwepCRc2jG\nk8b6XutiGh1RlCEkgIymS/vcDzyIe+aM+sAlbDThGeS1kP7+TcAsi9NZvObxxwML6+re+NGHH9oM\njth0hHSK7m2vB0w7WkXKGbEW7E4TABGIuKbL8K5/gFh3wlQIIKhrPblbCmiaug4hiOmVWgo/5U7f\n+mtaLQT6JQ0kFJR0RJblrvVz1u8EmrF6lwu0iHENkKvNafG1NCB3LLFHn8+bgYhuffXkxmXjAZAF\nCC9H98anA3/9n3zZ/yOrwT8Ty6hkC1AcG8sh4BhsWo3nfMENICNy38buVfAGkArNc9sFrsmokjGF\n1YwbQKR+4KW/cM/i7Sw50zQ9OBJ4DFk2gmMqTulrwoZDsYfN1I775ETKVTwLg1FEsYRDP7gpDltf\nBoNGb93X3BGIZCVqay6mtz+iG2hK6SN4wOL2foouJjrQQr/dgq0JwIKlIo20QcRiZVwk5mdybive\nAHIJ8FINNZcCAQpKrHasoQxFzkDQwf+GlUz39Y7pDCet7iTC85yq3HZLAHDPyZNzLHgtwFI7UDmL\nAxcD0RMiImrp64vHZntI2y4YTwRSYzx5WcYlJ1DltHdelFltV4FjqaZPj/ZNcZp+8Yuv+hC9D77y\n/+eLQGYAh1/meyFxtE/bzYKOdyiwAQmhmdsnYQ9v0VJXuu0FCjSF5tPruCrAhbRShWDjhyZ9xUDd\nGs4hACMGzxApCUh9mPIB+sLk7u64o4hF5LRhdwOASCp+6iCKQgHw4s6da+7q6EjxlwIRyrwBfWkU\nrb8BaCTp2DTcTK+fN8KiEK3eZ7QxC+mqSG/d0nQZpR2zOQp8x2cTXbKjHEEouB9FmVtAZWYY/U70\ndKAuRijLnQ3XXPPUbRs39lYnJ/OLN9/UdchArF0SHgDZCCQd7SYajyO4Z6HILJRuF+q++igCt2k1\nw3YgnodjCjHbB/nb4SVAt/Y8ui2mxJ3u89e0ugjhfKHR2z8DnpeRP0TcI39TLi/Vjn8bPgoS2rWM\nRTiv52ML6sO3ngMelWV866PjsvEAyJ8QC4De5XqE0cX3vo02WgTiBhCtgDYRscDvhCoZLwCRdOaM\n703Zw+gRiNaBLg0Rd6qE4SAXVrckgTECIYquv3zGFUkf516cbE4YMAH/9PmeVDoCjxPrcNGbIiMe\noIocqgODGNK7hL0BpH7uqsW0duENkr5mjEC+czyBspQ+5mLFkdWF+vp0NzNk4mqiskE1PmSVE5hg\nQQCFVgdRw4DCFWxZB0wGtWgHO7oQXuR/AZf106/pLUkqcFAL+19FUMLfwUoAsmKiMQWev9ekFf7b\nmHLs9vb25G6HIygUr3GsAHxlwnUDcOh0UdFUYmOhu3vqdnE9UoH8AOfIFBZAagjHo0Ii+89G7I0C\nyhMcFUkdWZHq9Ja4x/Co8Bpt1AhEQTEB0w4Qc7yBtDgVacNGLqmyYU4EKmJS9hYxEOfd3GfFjij8\nyiC5Gkg72EzSGcTcFkCMKc54nyndU8hQTW7Q1x2SWGBopdT4HKo0/4UXnuoFKjX2oG7GFBb4AIgm\ng/4h8NhvfvPGZ0CKH6mZvcBcae4zebgCSnCZPiLuVCbu53x3ArR3MHLY19eIWlcw/u1y4HTPJMpr\nbqMNWK5615gyCaYTK5GIcb/30d7+s7W8ckKC97U0dQACCPcABA0Pp1+7c+dQTG/v9wKcThOedNx0\nBPge1a59N1DS7WAqkKkoRAMvPyTuxzHEAn9IIzL4mlYH4T9om7gJ1fQgPtEHQOw+evqzsOE/AlkM\nfK2gmBEppEMIpQiRIrf6Vau4EdGE6lV71EwnFp1ifHWQGxE0Yt/1Ztw23hSWbzPL/5V5u/8LMxYh\na/AfgRgL6HmIRqFeYCecnYZ3BAL+01iHgSI8I251ADmKSK1oP11XQUd+P4CCcu3EuokShhe6i5jA\nZWwb6DuXdOGtJZ86VVn2fXlTqQmtJGTYhEm6ACiVoGeIYPsCduu1GQ+AOEKO0DBz0ioao/A/2103\nYw3k5poY/gnIKliS+pGeWIyE8NgmziN8motg43PQkEeeOYigCxALWQTasKpL2NwI9Ek4i1/nkccO\nZgAAIABJREFU9TSEp9cC7PmSLwdwv0yuAwT25WjX7A8Ib/9XwCQaUnuI7tbP7TDpdXevX3/3VyCV\nGLSpdNvSTNIi4CAhIfPJyYELL6y/R9zXGSrU5XWQgv988qlZMRmuzdM2qUuRhyUnubWFUW1pdayV\nUJv9bD9WBJIDdP+MaWFAvQn1yr9w7yEE4ByXko8mYIvc52c/I5334AvcVYl3A+XVIY0cxsy5jtnY\n8QaQ1FCGe0humokjwLJv8w1pjKTXxwGJhrSGXkgvRTzTL2n/flFjyFXjQ1uXoM4JzqyJz0QwHPxT\nmqc1knpwyEBZzYeKI3iGTOnWox2Pv7GxIBhD/+Tjq08PxQXerIqCsjGNlUUBkxBpm9NERHzFRx9e\nNof9eXg89BmIZ1/vWcoBkuJ6e9dp3/+dfalciVgsCxBOsG7vAJFNQxxDAF3rDHG9Rqt/6NbaE0gy\ncB2S6wlEPXUEgEScIrBjDiYVJvhJK+l9Ns8gAPYuGXm01gQ0MtDFCJVnf6lBPZV5hvPUQTTdv98D\n9xmkm/5lGw+AnENjiSDygj/BW9/p22jGC+svArHgiTjAW3VzF1SlQa+vtsxIABH5yTLgAi0HnwWc\nkeCfErzo3m7K2z3UzQ9XUKTf8btXM/ZlXIK3R1icvqiklmETS84dbAJ3Kke3VOzmeuymfqKcE9CA\nr5mktukc0amPHgB5fUuAKbLOnM5AFGOOt9UiECvZwKSdmfwD8SIs6A7C1hBJIhBvBnMMCcNOQtwS\nKzKymkZabQABF2heugI8jOYFOgk6rmJKrab6AkSXOsC7m9gUhx7OZ+w9ictiRvRnuBB9R7e8tuS1\nG2iLr0FvQpt1oJbwvrx33/1pH971A912tZCYUk7RCRyO6cTGdnDzzUEn4JIBmNQbyLkZTXRhxR+R\noHJ5aET4kewS08k4lttjOGePsZeWJwWSxUCYn+3HqoHMwDON76zkGWWaDJT1pZQGMxyi+NnPXUgn\n3lb62xdWrFQh30Br/QGiiXdd4+VEnL2RBY4IFp78KcX33/vDx4tyj6Qdve928wevhA3OUbuXY3Aa\nNAmSCAT1Xu+70Qvpui7ZVOBOzZuHUWp+B1IsgwtKiu38vv04pTc6SDlkXHgmwNHNjAQQGCWNpYoF\nb2EFD3zFcz+00hYf3LqE0xjTWKFMIhHPM/PUUz2mdR+bujpUEx5NPmP/B4hopFoStYoW4PHJrbwY\nNcRxhHNpjGA/BaTtrTTiiZ5059Jf/UO31s8KkYEqXt96BMG02u67keQi82Q0Na4AVAzPjSqekZDt\nbLsYsV5dOwrRxWhXAds1RlgZEKEoXmtbOgJAqjh/BPJfCHHIMeamnN/GAyB3IXoSdHSbof3/22zG\n4fM1QDaoL4KqNyvlI7jsOi3TCCDtkOaC+32vzUgmljC9DpIHnPM7BS2pNJbmqdIXBKzZwY4wc6u5\nAAOAOCWp+JN7pkVT1PPRdI6cwQtARI4baMTp6iAkOAQNFFpJqMumJk/zbDwAUrtgWWLqLpvD7Dil\ndYD7sznP8mwSwtu6CXj/9HPYES/BrR0h9CDu+cQM6OlnwjEDqQCAPPLKbNgKEN+/Vbs+ewC6mdzg\noq1LRV2EIBIArDvL2axOOkVfx4qf99E6UcKqrVtWWoBbPpj/wV313QGV6Ayh/3wxly0rmh2O4On4\nFfKUhuayz3k7/wxkcLCA4OBjZGXFYLEcfhnaz0Vhn1XvNw1BDxHVWQEdITktudVVsaztLaA5nrZD\nXywIDJxIT56C4jv3ZawIZAbCC83GU29pApJKE8/VD0Q2SVgGR3D+EYtVDFYy+eXxEEeRPe5kZuYG\n4A5VPKvFCCrve91TiKu7invMQ2S2zyd0sGzaxFCbqf2RJ4Zsf/+8KTCaffPxjkBiEQup8bjLENME\nzQjQX+2TphmhyouVjE+LXClyaYqo3xy5LZzos2EaKxAgH/75OaKT3ZeGPFod5Fbgg0ZWW0Gq453v\ndp66j9nACncaK5QLiMQGTEBRwiksvHbGNPXUH/9IswHwfAGkEE8GoBUoK0uk/p/rSQLKsBo8biu1\nQPu2FmKAeRpBYdKpe6lHgJaRMWe0ttooViBm0wDSHSBt991owELh407yOlJw4V0HWTRI8j6QfgWs\nkpHHQwjS01c6w9T3uqYhouwzjAEgisIEhFPy83F855g2HgApQBx4IgK9b8L/QvptspV4wsVmVSUy\nKKj/B3jokb4FdAOAqEGw1AxvZft85mhMLL0OYuy49jaJYnrS9jSx6+eDDGLpsaShegDkg6VLl/eE\nhjp5oeS6VBpP4x2BxAIDIA0g9XQzMF3VvHXaiK9Koz5N28YlQadoHmTNbEtdXX1sfesY1+jHG9l4\nq4oaD9yMZwymAnynJ4gWBHAVzYbQbqZU4y0rTRJJx8yYTQjPSvfU9gB0MK+vVPyoDM88ja5wwvdv\nZ7ugdWZ9k0hHvh1juG1l2/Jjy5t/krBtskulWFGIJKf6Qt66KQpUY5+H21RIWcEW5x7mT6O/P4Oo\nqBPAMVas2PIMhFTGET6jCRdWErDSiJXfYxWd3X/hHmdcaB0zqqdvjhpiUccchgJxVHX0hp5zRDib\nGak1dL4IxBdAmoHkF+Z+GZ/bWAB/LxmZ/hX3cyuwnMk9cxgyub67+o9hiKbO/wTekMAmy5xceDV/\nW3gd75oc1DnWKH8Z2L4iY399R+nAAA2zmPVyCU+mw6AvA6sNLxkWqRfhQOXLMpsMQpS6+XvWHz8l\n5e2d7ygTgDEYX4AjtE3bLlhck4ozCAfkIp99dyPeOXc/lkZD/t5ZbjyEeC+/w8ZLkh0xJNmjKEdn\noqnkYeZLBJCulXApP73fEbF3LxMROX8JA4BozlQi8LH2Va1AwtU3UL20htSbj+DvndhW088EYK7L\nItLZ9dcwG/hGlrH72Z6+ALqcElPwX+h2m91MYXU0al0W4cOhXo2Si2q5rgfYKiOPlSUAQOvxmYco\ntLuPG+9sizGFNVYE8gzwO4O21//YxgMg/uaI+/vZt8lUtBdAlnENDYW1JyWdAw/VdAQDC4/Xlgez\nW8Hmm7P1kro2mIhAJKex49pjVo2v3zpxfRv7pwcSeK57sFsFIrGKBqu/rV49e9LZsy8hyy5EytAY\nlnoat9QWG7aJaBRMmkkqT6cuAe8C+nygbUl7bG9ZZtlo0YcELB1kcF4ffQmINIc+/EkBIvoCxZCf\nJJgzHYJ6KezDB0BMmCpTSe1FAHADIp+7CtToTmaYDrInDE/6CgALltd3sztdE3Esoj+pGh9JmB9u\n+GFop3mw781zpAH3IKlbOlrSXZJYgPxNW7xgAbuPgrSCrq44EhJKgMM8+KCjHdSdcaQXtBOKmBmx\nB5HKqcDKT7YMz08zSw7phvaiTcUtRB+dSSRwljPhLQeXDrcDdykoRjnzsSIQvQ8gG8+wnyYguSLl\n1MTJdUWqn4hGN5HGkljF2bD3jyYWXeZCOojoQH7JsF07Ip9+HHhxH+FH7HwxG/hxNk8/PUyHyUK0\nsZAdp+1jYP4BnjqIP/MGECtTgMt2HnpjQz6nwzVpmMkMB5Vpn5Gjne/wFEp3Pc6j9/rk+wcRnrxR\njmixijRUzfcfAH4kI1fhtAzSlPxK3TVYgO9gxcwQsfTxPJgmoKo/+j4vbUhKwuZy8TLwMHy8Bswq\nHsCeiVjXdOZUK5DYGMGkB1dy+LkNzPfTa/KyQyU15BSDXcERzzWQUsvY9Q825pNa0E4TVr+ABMCM\nOygKGSakKZxXqiPpHshkpfH8W1maiBDLHI9dB3ym9e/othVYZlD8PW8NRFG4HBEJ/Wmc3zumjQUg\n8xG86wREo9uD2h/refb7Nthm8NyslpZM26RJezWpDlXCUEDXOjqj8BTNC2HhccSEQuNLcAah3+TV\nuYt4cSQSyy5wf4aVJKzuRSYT6P6vdtvJw+wNsOP8S4Pa0Bc4HNgPxKd88MH0kxkZEctLSp7Wtj+H\ndwTiAZChxhACMjq146eanMMZ1EbgDSDXAh/mtuSGHsw7ONpiVYjIie/YJ+0LjByIfA/PzIHTQL3N\nIhhMkTA/BvaCKRa8unEBTk1ggi4SeSsCiJYBNbuYfulezoQGE7DZuEMbbe+d4ITpHd6ZBBQxEFeC\noSNdQYm3uCzBLkm95qM6pLJuHkPiuRJizsVgrxllUuOsRexUQI2nMzyEKVP2AEewWKYVwNtfxJOS\n1Ees2cWdwG+xcgeCSXihq+7Tv1KXrk5JeDq3L5Duz5zkAWc5GzbUNaM/RxUMsccM36VFIN6UbgUl\nCeGJ6w5AjfarJiBZDW2fkdRQPACjarNtQTJfjKr2kNf3Kyb1BGwT2QmT5F1zbAeyuphmBkyV1CeA\n7TBw8F3mJ9/IAzYJ9c94+rZ0ANHlc3TTNbH8mS9t/TfAb5q754a3Ed++iZXLgXwC+/Zon5EPnFbB\n8g0XrvkRf56L6Bw3mm+65fuNXHoOpEo80UIZP32qrPEKclSJi4uqWcYgsIEdxF+oog53f5d3JiK8\n/t+CdBNc/DpcUo6nn+t6YEjyyC21dgSTAuS+NoOgjhD2AE/pB6FCsNNK8tcvY5r6IAk7gi/Mmsu+\nOM4DIEoO06Y2j+gR8rIgJ0/0BtLpNHHwcChtqkVQplVIdGFOdhA9Gx8Hawxzp68MVo2g7OqOrV4D\naUT0C3kpJmg9JH8C7h8tsvpXbSwgCEQgtVn7O1z704NYpL7NZhw+T03NpNClS98vxTOXeypQqigs\nRlDYVmvS2QCFMOkIaHlXt0kOBFh4iyqKhXcPafum44lAfoanEbAYKIvmq6v6sLvgl2dbaCFqIGoY\nSFIl6ZdrN2xoe+KVV3QGyegAMlgfRVhCu3b8vM2Nh2PoDEAASpWWvrrWjOvDqIGotNLM0pxRrs8S\nYDtm1m8zb5NWHVrlfog18cTnT8eyE0jrg9wowfqIxScCASonMjECpKkIcsXPQLoOyLNQZenCTjYl\nH4H6K1D1c+ovprj9EIfWAkXMSqknyn6nJsyHdi5ljicddZelcOiDOgaBnXuJ6y+i17cnQ7dZFpwH\nkbr2EbpaIivrNFoHexr8riqOwN5ABgvaGER0wIOVcqxclT58aelwQzKOyq7fflrI5zvbiAPO0W+O\nINY++OgTfAJ8R0HRngVpAPHS+srjzACOaCyabLxSWK4kQluLohqmNTEagFipxhxqpvPgHixqJdF2\ne0ds6FIVnKq3om2bk8DUFi5a8AmfWEHNhcv+oP2uOILFVZlkNol7AQiatr8IZAwAkdrRaetWlmjH\n/AKQcpasMztY8nfgSQIHDmEAEOCJMPr7JnHCocJa1VuTyZ1uUSFaRVpdzQ/mAvfKCgEoyvObVjJh\nR176rY3Rce/259Bx5TEeIIgewEnmjUgdez6VhCf+njifCW9BaChcYmTLXY73QLbWHdluWaJJH0/k\nPxHaVLergjpea4JbXphN45VPcvxHIU9H1ZE+saEhN4HRKPBWEk/FMiGvg5H1Ts82uTGDrIy0cQw4\nszOKQUsPqZq21oXtzD8J0hkZeUwQAlAUchEpqS3Gn2t1kK140lhpQL3Wp1LNyCjkfuCkLPsDrdH1\n3caysQBkByLamI+gVup/ngZ8GUrfNtuK4FgHghpcVTUlZtq0r4cBBc5cAiRs2EAEgvt+oyx7Fcr0\nh20nIxWA/RfSVWkvSccy8ABIEZ502WTgeBll14YyswZuLu6jLyyxJzGQiKKijsjIi+5ev95YWPUH\nIPVYMTPYkEBcdCfai19HRvMZ8nAhXYyIQGYDvVv4uk9Spf6u8K4ArCOmyaEd23ZupveI84h0ydZL\nvLwRCX577+VsiekmrROCE0ShMA6fCERG7sghx24mfinCw90FoLC94xS74uZAwx/4+dOImoG7oXE+\n84+eCzh3BZDK5WmxZA6EkDR0s/Zrdz0qPpBn97TjkHfAcSJDZtPhG/3pOW9RG4na3YS00oUs2xAp\nyYJ1itIeEIOrLJCgm46x3RBpAZBbH5eWvG+oM8HGkPVKlvY7UeUd5IGURIhz264LmY945n9v2M1Q\nB1F/AuoDuOsfqjYfQ6cMS0NE1wzhsphcnbmnGT0CgZjZw5x9oxdZdoXZBw/Zi23mT1ldijelt91B\ndKKDyPXP8uwtkDcEq3T66JRaQvf/gl80IsQHs/mfRSAAFZiGC4HfAb/QVIzT3uWG9IvZNLCRr18z\nfEb+rwRF9kYzru/Wk177Wx66G/i16umqP4BIdcWrSDd2MmvIQfTjskI/okEwI8DBZxG9TL6LF64/\nc3Vg+oLexOWEhrWgKLMITjZf1f1kIIL4orEn39cW1IvtIGphiOjPmEpuPZpMLmJBbfzJHup7mPhT\nBxHPIByBuRJc/M4UPjvYHZDV0pKRmJl5cv8XX3z/rJ9ufN2uHzajWFQ3Bd6fPbKyiq8DXZwGzpQk\nEBfSiCTZWQEsbmSVDaGFNx77LqLnxR9LayuwTHsPxFohzCuNpfX6PIjf+SGqhFjv/mUbTypqAMHR\n/xKRH1cYndr2bbE2BMjNA+b09MTVBgcPpALb4dgVQUGcDg7mC+AuWfZGdTwA8g0jAcSdG1ZQwhUU\nMWuibm4l6XvNhi7UIiAPK6nA5Nym3JYSShLayXwLMhe5cEmx3bGhQSFzbpEPHy5Pb2srMXxHHWLE\nrK5xpUcgeQz3dpOgDuN+8SVnFbk2kyguV6Glr4CpEpKvDIhuEgJAdpDNFWmmtMG3eXslI61t/lEi\nkqB3pYg6/UUgZJFd4aI3EwaeMqSXUvayV5qF4+DlfBmG0DDK0WeqXMRF27tiu7Jwcg5z0AKubGjC\nIekpDzeAPH+GdxwqTiC3laCMWSPl60F4XRJQR/K7EgMLAFVCloeA0ww1TneEQskAwXft88lXK4op\no86RUbDjeEj97RyOCyVhQSytqNwEJBNjX49oon0amKSg6FLrbln3H/PH/zjCtEejKP1+Ou+nvMv1\nt1hwtKnG7GfW1320TOluJrSC0QBEUdKImx9MT1kuwOrdu/lo8eLKH/HnKS6kG/Vu6hM8lB9Ip+Vu\nnn8LApZCeCAeyfniE0R+kU32LESq4g9AXHlm5uD+wsIBvAHkDJAI6mhCoydZ/tANiHrHO9oiM3cP\n81sW8Y0lCFf1x7//eACImhrCjJ8L6u11kqg5VD7CbwIQkjyvqkICxYF4p5Y6iPp5A1d0XrmOjYja\n2yFgzTKF12cepq+NhKRTsyN2LDvbGhhWND0R2EP7vm/mRDtWAO952FfTMsDVChN0qfvLEVpXxn6l\n1pNxpIJbuJIS/mraxaeh21F+I3lSv+929odFFxUdYM2a51yKcv0IZ8VgNwU4eRtGUfq2kgtcddth\nKhFZi9reIBJdFvpCa7kZWNzF1AzGUf/Q6hs3MTJ95d4EWDqYTDwidaez6XwL6bcCe2TZL9FnDviV\nRTmvjQdA3kIgei4iIqnBPxf/22Z6Gmtxf3/UAYQ3poSFHZm7aBG5wGOyzEd+9jNGIBf6/M5YXLwX\n2KKghLDu1UESy0xYCcJKMGLR34RIFU2+ZPclOSc5iYuk58A0C0yNqp2OuK4Q+akXX+zGwNuXROqs\nHdyRgw4gU3EOnSTSEYwY4mQBqCG7B8BGYBWike8jPItwCSOF7AqQJAebv/gJiStWz3LNOn2SkyO1\niqyoheU4gyzuYrDuyXpZP4mdwcS4IFHvqaGd9ilHOWq5SjgdxVr6rxqtgTCMsJOZSZm9dJhsQDYX\ntj2IRAqo8zEy4qwaVbEz52onUkAag5EKiq/XNws4KIFKwNF0LMMOPCmfI/RVyWEOhlrB3mwfMbY3\n88YdnzBsDm1oXUFEZiit8+OoQTXdhOQcIsG+CZgji9GvPwCeU1CiMTCaFrGzcDMrg/rJCY9lv9PC\n8A9mcTAW6FPhhAoXkL3DxbmFzlNE6Kq8/mwVUcWbQF10y1UE3bx5c/HWedPtZ8l+4zAzuoAbFJTg\nfnIfVTG5dtLyKCx6GqRWkJws/aWENFy8nUQFGHiZl9cDM1+HKXfff//sS3//+/tcXouE5ETIkBRj\nJRArhVhZhZUfY+VF7stfzJy/rgV+prHEfgVEVFD4XQkSA+gKiBmIuSzcxvG3h5ldCX+SPLIilUCB\n9v+1wHpV3Pttqwm62UVg+lNr5/+hJ4qvgaeQ5Z8gy05EZFGoyJj+0dm+oSpWVR/cs2sbMMFy5k9v\nTYxkCmKkhG6TwPQJnqzAKkT+3yuFdSqOGER9TpcwmYlw0BYbtttPYL+UM3l710UXvTGvqSk7E9SR\nNUSrGA1w7QnWAXGq/zX0EeCv0TaSgBqsDAO1/Waqg9q4bMCcVOAiOILxraNTEdHdHn+/lGUagcaW\nZSzDE32AoRdEU9r4McKh8Ge3En/yfzQWdzwAEodggdgRaa21jH/W8f9L0wvpi/r6or4CshRF6pkz\n50+hEH1Alr2YLZqp8YgHqwXxcsWCVwrImMJajfCq7qa9MJu+5E6Et5+PWCy/Qnj6RaYK07JIIo/A\nk63AQYgb6AkwDcc1NPdOqa7OZWTnsDGNlYYOIMP9pZjRFU8nANSQ3ayCK4uzcYjGNV2mohQhI/FL\nrHRjpRorJVxTtIVli1OxNX6X9Gv6l6vLjzbQMBsfyW0VggKbCWyJ4oTGmPJXRGcX8bnJJLVDv3th\n3Mzmy+KJb58pvEtd7bUcfTogVKYmpFqotaQA+wlyreP6Wgh2PoJHdDIIeIajDKCaVoF0UBILwHSf\nQ/BQewcGskmorcUjTngYe/t8FSyzXPSqPmA6u7x89rIzbwfUR8pPAQU5YXRFBtCGM6iXvK+6keU+\nBHtIlpG/RjSd/R53BKImFVAe+CHXDNiJiCjlD2uv5aMX9zL/fYRkxvvAXaTtD+bskoC9xJYC4QqK\nv+FQqwhO/hCoyunkh/NOnDjXGxY2gcndv3uCR+MGCPkR8FYUx2qbsElACHy5G33RKFx/B7cvDMUq\ndQA7c8iZC/z4UbiwISIiuCMiYsaHS5f61MRcpdw5/WmEd/4lor8rCyjj5JVv8mLJN1j5CtQ7EEVc\nqZfImiGS6yMp6wbXFevfIe5wJOY1eEmxu2X7JaEk/Atg4+VQdT2WlRVxC6pev8XyJHAjsvwPfSdt\nSmYdkL+5meKNYQxeG8UMZLnmvpzBoB4Hkix7EQomafck6znuiUBoQ9kxAEhnMG2VcYQiyEA6gMxA\nOIgr3J9kpZ/OnOHBCeuGQ0P76lVV+hr/TZE3Au/dUcIgwtv3HopmJQfR8PcnvNl4ZxqCaQyvYOjs\nrEmdIG2UkcfTAX4T8I6h58WfbRsOE3PhDT8zRiCrEe/uN747ghqE2XYdd0++YxzHMsLGAyB6frwJ\ngfAzwe90tG+b7QYmQvv8pqaczxEP0IbyctPQli2PjaZ9r08hVBGFqF14p7EqgMIN7EhCPLxXAz+L\nwTaFnowKRL1Il4HfDqwwuUwd5bbyrF5639Q+4yhSurM1IjgmrLNNRXiyvtpORgDRI5Bp2Nv3aefh\nzl9XUFjbS0R7M8lXAR9qxy68eCtfIMgQ2WTedBPzP+6mOTYWc8tnHH/sCcILwrIs+XVRRHXj7Y0B\nLC2x4OjKoAWtk1mfR+AxdXoFEXEFpHRjKPSWU74om+wS7TrkquIYjABy2hxjjqBBDaesrAxZHuTi\npnUmJ8vrCe6RkcO063cFu0knrPkCcB3CIO1uMA+A9PQkkd6+Dy8A6ZrdH0jXpSqhWRAeZpAv/++X\nXrqnlUVqY/3dHwL2qVFYqvsx0ThzBzP+qQPqRkQaC+Ah4LJCekKApCSa5jzCKVMjr/f3EFAvIw+j\nFdAlQV192W7iKmJPR3FuYZgTUyPCMfGOQhQlFBGtbsJl2va25Zr/Cu8b+jtwhucPp2/kkj84iZgY\nxdHUdF7Y/ilIefAoBGk5b1ViIO56Ug8GIArXX2v3c30qOOu3bMkOsdn+8Yvbb09CUTyOwrLHVMJa\nC4BYrORh5VKs/Agrz7P5j+/RXpQF6ipE9HEb0ArScDfFQ8lsKklj3aVFLUTduYC+03ixerTBYcIk\n4YC++gEhf7iE/uAbfndtILAEWfbHcipzSs6pwMUvdVBb0E9WXyZhK5KYvaUFE17zd5iE6Pw/m0P1\njQiA0AeyAbDgdsLjBpEklcnAUc0ZmolgYhkibzWJ2vlqWfCx6CEnr4H0KWLh9ZjVnU7Smge9ZN11\newR4ASsdeLPxzhxLxBZ7AHv7EnsE8KVIC6o3+bkGACgKJkT9Y7T0lW5bJZV5eEv1GGsgDwJ/GAWE\nrmDa6w2YXMf9/O68Nh4AeRKBsg8imDYvwYj5Dd9Gs0HcMfi8s7p6agtQ63JR1tTUJ8H1o+nEGIdI\nwYg0ltQGODsJ/C6wWZsKtvEWzq6kN8W3obAUSEzuTO7fwx7Vjv1T7UPOkJ0b2+ka6HExFKPCSWmk\nBLYGIKoZz0yKqdha9wEBRDrK0QBkA5eeuIwvX0bkoD/Uxr/mo1M/lygSS5SbyPn+eixRO/hmdzeb\nyx/koYZnkEzHT0wiYjazaxBgaLQrj0o4yHFrL41IXwE/s2N6tZhJoRgW5lOcKiikcKNGpTyLWEzc\nACIjD5xNOOsKip/Wz9/+JuRCIoffTC1uGnyaIAlRcBUvcDOFOMLMTH2zER8AMRTQDwFmuroiWGr7\nQNwzNQg4irMvyWWSdgZB+TAMTtDYSSrkzjtRPu+U5bZ+GbldVanMDSPjVB9hKNZS8jfGaunIjcCl\nKIokI3cD91xE8+UWXGmJPHTHp6hYOGE+Tbiee87WzhkJzu1PoyGwP6ofW1QYIrI9js8MDsRCdghZ\n7sw7MaPmVEZr9HK27gX2SC4WrGd/3EmulmL5c/NJhlf1g/20eywwTmAXAUOzaJ76AvCkMlnRhQDV\nF6C9/4sv0m0HDnyS1dSkhg8MiMl+VsKZ/8cr+PyFeqxevQW6VSGGMr2C8KhtaJMIO5gTHsv+Cdm8\nYn4obcL2gYwRxWbj4DDdnmgKnR+y22Th5OcfXmaQWPe1Y/Wx9cuB7lOdmDszGeiZzKPvh+v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7kOjyqKadSOUa/FOgXpIoIyIV8Dt2FjpgooGhtrHMGoDIAAtGzO5WzEdV+ILWz8cVgEooCxoDXJ\n0BwTOKkgDV6/fqn085/fgzqQqwyYoILH39Rzf+4pI4cIUyBKgpgGf/o+/6LlF3Ploqkv/uWpFQEz\nO0xdp1SOvobZD7cj/XPFc83+00UR/yc2G5EaWYdwPHEIh3oEpDrEIgzVGBoofRVpk/UozSeIzlTD\nxB/hF0fggkUycq/D4vhekZRYbBiHnBxylb/F7/UtvNxF7KBzgWnI8ibxMknlenREu+ltSGyoJGIH\nKIHi0XEyM3XdZNY+GVlE0wCkBLFop2B0bQXuNXlNjY9e/GjGirkrtJ1PAcGbeC4h9Q9FrI7zVj12\n92JdQDL4SHqqHl64BRIkATTDcFEC+F1GVyYC1JYioWNMZ7UqgqdZ+WhGm4HJk5ncToiUhAS+k3F0\nTa1jJ8XfJgPD0VsmdKXndBr8ZKisMQim3lZxVc1YwUxRpoB1CHyfhlMb+9pXSC8CuiRxPsDhyCY1\n9ZAde5FP56tPSd9poml0Kor/KGDGbs9A9KU8KcGGgiomDdIXuxXWfAq/C4C0DNhN/fgEfBYrAkC+\nBsZgIwd4lfr6y5Gk+4BbonD5KrA2Pb3oaV/AFziAsSKLjN1TCQeQqcD2nUyNiqM7kEeNGlWpTCy7\nPQtRO9jsx7zkEPe1ujHm+F3Jl5N+AIyO8QjnOR0RkX2q/n1rA+malHkX/SMQHE849s4sn9D87Liu\nVPWeOfjwIqbNqeI499RvRgwUehpjYCcweYBCeh5BB36HC0vq89w+nHAAyVNQ6sedGGfV+/WR9OrQ\nFNYyXdKPBwMSSldR1Sr0RNGf/qoxnRZEeaI2+vANJshmcwJHcrrZM/JW3E7MOXuYkOHFkIUgOqzz\nYD58gNGjCAeQccAJh4m2aB9JqHIzoVP/vEgbmjBrY5nBxgbgeuAjbNxMEEBmInxJH2PqN2dzdkBH\nPnC2mjoMtQLCayDj0zsK/EfSuqtOkDvd5YpxTJ/+xSy103yrXu+didhkZgGLTjFG91SWBdT/adXN\nAX3hRsjZ3rChxXfe9m0LPmdAWXfFSFztMtL3Z3KaNZZT2f+jAKJIqPOGEUXVKgSPvZwgXz0yjdUP\nQFQnG0qxu0CCz9TXU4t5j0tgKAdefPGcF6sy2zOVC3deeIlb71/U0+wyMeusanZd34oshxXUEojy\nSR66KtMqKxigiFaSQezQPbObqZkdyc/WCnf7sbSPQ0RO+7ERePOFN1+qT6p3rpy78jHsdgtil6ql\nmeYSXv+4B3hqyuEyW1c8yt2XfFymiDqIRucdDg914Y7rOGowzv4rhYupjn6YH60+zmqMTPOV50Xn\npXEHTIuaFkWErH1pGr7r9rKTpOPF6N0jUAJD/bGZmxGLXUvhaQCyA4NiZLDjI+ApcQnqDvM5TcCc\nI5lHSoERPqKnEezklejoSMyJHnoI+KIpoSlwaX1qHUbXJPVa7UU4k12o1zO/mvG9FqrhzIvhpvOA\nu0D6A81R1XhiohBTEt2Ic7IUWW7moYe6mD17B3A0kXbjAUtO89pxa480B5q3sUf6gIJN6Ri7G7ER\nhY3bEBHLBiDDgK8zgG6JdkoQEch5wBq7TNI+nv1TJVN7etD3vNRw2be447sZtH4esBOfbwzCUWrM\nv9bjDMoBTmKTdIgdfT+q593fzdyyP60ulpbNCqPpqMkh9/XP+1K0bwNGvvl+JoIpFCyk2+36dRMm\njP/zxRfrsdv1EnQfZXDPeWy4BwIWgk66GDiW3Zb9LZCJLUwmvRwYuoaNg4H8xpE/JCd0t7uR+JB0\njJzDvZGfF5HeDOS05Wxw4swm3AHvmljPsNZo3NF53/3tce6pMOD/FJFa/wIoq6RwEP0jkF6vnkrE\nmglNXwFwP6MakvHo7WwMihra+BYBGL/kD8ZL0LtH1ZArAx8CGQrosHHPjmymTztBBbYBKe5hEYiL\n1PlprcN0mwsbm3qImezxWN6RJMUIjDYaXduGD99+O4ISfH6fZLvttP1zjgJ12ykfPzMmuePuEe5V\nytm/8d957LEbFdGHFbk5WMDU53vQBT7AdnpRzqns/1EAoQBBQdYa9L5DKFGG5vrsBAUPYeAIZAKw\nJ0Su4ALEItbkrnWgGwK3XANM7dzXOTvKE1Ue47Y+WhNdPUTyxLbQ9uZ8/M5UwkUNj6Vj0nk8tJdl\nlzUQCSA2ZhxKJW5QycwA9Bv6okUgpWTuGY1CCeqo1vTO9KEPv//wJ8CF7Lr+d8AJVSJCoj+AXA7c\nFOdyfeAxSY6KIhYg0iXpsPIvkJACv/+KlqExRsmb7CvuOU6ms4th3ZeS4Y7UAKoqH1meTjJ0DenK\nQTiPPtuZheXsCgwoUgWF60aiKDETa3pLLZx0FvPcnxRBRxUAIorgq3jsgAHh9HeDspcy8oH65Tcv\nHwzUdDJqPkEAycASxUuP5d3YkNCw747r7zBe3JSYRnRTJs6kBkQue7x6/CSAzHqKqvyxPcAmePIH\nxIYjjyNP5yG1SyDdhriP3kYMdrqUykoP9903nLmNlgwaTHvjU10IcDpBlaeLphEelly6BHHfzQcu\nxMbXQKaDmBoPprPUXLqWwjr/ko84JOHbl8g+w/XMXA5SBUgKvSlHST00A1nuYceOOqKjGwkW51tO\nkqX1GBQBTdjoirgmDO4tPXHNj9d7OPbnCSzkrJgNVKf3kAygEivuRs/jWN1VLK1+ALv9dez2HUDX\nFfff//N7b7rpbGAJKEYvpngY/VksR5mLrK2HYuCYIWD4PKs9y0l4f0srQBOWG4H3O+KjJuvcrW7g\nS9r5BIVl2LAFGwOVaZQsuxH4VqfoSrvpTiJc02qneu2OYXDO+hvXb771XC54eSLRKb/hXn4xYuan\nZ/9QPO9njMHGNMTAsHGAxS9RxikAZBvWsaPp7CJS4cCmaunpfRn8fJLn/djZcxCbgc7su7gPuPGK\nUpaaAv2bqhVBqc8mBMxamXZRmsNS+32BzxJPVy5IqxGyQ0teeWXS0urqEWlNTTkXyrLKhrIxFjiE\njYHGKkda9jFjphIY/E30nZOrzIsyWZYT71zdfOndCU6izBAh+ij5r2HSq9H8k70foXY6AHIpgp4W\n+jiLf1J8619gCxA73KOceiSjGn30ifutRex0QwFkkzhOY/sMCCAjEd9zrB37IMRNqPUZDENwvTvh\ngQbgErYwvMJXYT+RUp1fmXo8EGjqeJtbNwXEZ2FOyOtWFBAwOjx0HMw72AlMUztktV3Hc5ndfJ3u\n70jmlAAidZL3oxNHZuhnHp3emb4LWEhv7R00rtV2F0WItNRx6JtFnUdwiFSjz8BsBG37U3h7GXAI\n9Blk7/o6XXL2Nl/RBCblb6R4tF6TPpORvd+O/9aX2Zb5/aZRm/wycnBXYyNqbyZR6T1M++x9n/tl\n/XVRJlO24a9/evraOMpNXYz0AfdLeCcQTMO8h9VzMZLyDAL89yAWvx2QdbhL3aReiKrwK+kNgzMN\nOXq91+NZdsey6a1xrb8wI9nNKWVOjlwQQzACOQQk7ikunp1dS9wOR1ohQdplHXA1hp024hpBx2hg\nJw/yNC7yyeBF3O5rMJlOsqBheRHH/SXxKdHAPqw0cx7nsHiZkbTSDOAP2LgQW5/Kc6aTmOPHKG5G\n0IsPKjBy0RfMv/VFflnI61uLeOUvTSTnoW16/MbtJFUK3bWvvnIycWJVyClvbSTdykD1jxDLYG33\nou3nm3SK3omOeucuzIQKKsryRuBL5KYs6qPGIZz0nUBmy0UX/XD12rXPA3djDKQDTTXcdDiGagdB\nqfjBCNLJNyNqR0RHu6JDUrGSAkp5M+Yrgbe7o6NH9ASa/EAlTrbzPZ8hxBZfUHtI7uTAlUsRvRy1\nTTTpr+Xa0N2xVqs5ijd6PPkbG/4yhQ9vOZ/7W2O4jMaxn/W6Ms07shmJYI3WINZ8tkFhH2LtahMj\nQ21eIT1bCJU10UyA8sXx7da999+4Ns5wP9anzsDVEs0vgLNu3UkpA6XiVAKCWndDAV0XI8eYvaY1\nJUkxhak060+QU4oAkHvz8w9J3d1JrUuWnAjdZM5BAPRAkVqkZa/PjhlBw7gfE038AHS1e7megk2u\nckua4XF+d2nwUCWZwg0LMDkaCTZE/tN2OgByPYLmdZX6eA2R/tgC/Ox/+gH+QdMjbo4FiLB9KUF5\njKAlu88iXJ74R0QnbchCk+oR6S2NwjgQgAxD3ATnoBY71Rnj2szo4BRCGydYhM/3beCS7UP36ZwJ\nPTpaWKm+zkZCoh0rLRVD6dG399LTFN9kQjj3AvXpnwGeWcdN32RSH42W4w+aFoFAwUYXFfM7oK+B\nSDgTG8cpvu1Vjj47FBsLyeB8JDYR7P84F1irNTlFOalE3ZmD9Qv4Lg56tNnjdfqAPqGiiPmI3Xgd\nEI09RM/JRlF9Ur3uT2/+qaokv8QUkcoo3J5DjQSlVicHnp4T5fPEFfrG//Wvg5qZ+3wT8xp6yX1U\nwmdNZWNo74iDDZvWgvQswbrHBkDOYE12G1NrJNUZLDBfdGehO0u54s7LDH69/z1sfAC8HhVfbeHI\nojzt79Xve29XdPSLRUfxVSsxbYTP0gZfeiXxnXBv1J+ATBR+QwUVXI4TG0vZdaMT469sZZc8rD90\n5nuDgUv4BU8RRyYrNnby7bM7gHuDO0dFQhTlq7cw/TCieazRZ8B01TsoJqVzTh6rpiOKp0VoABJ/\ncg3JR1PgqIm9e7NZvDi0Ua+1ibQE9Vr0q39oZqAnsTHT23Nh5TNfUcv5Hsh0Rm78ZPk2VucuZ2Na\nO7L8MrK8BVnuAvKfeumlj4FYLj9xHlAP+jFdDHsGuFMRTKVi4KiM3J7ekV6d3pm+MPSlU/C01hBt\nnMXC6pNWa3RHoNaCIHGU4aIQsSZGAu8g+WdwcmI6vckbZOQ4L15lGctC08elQGG6g2rMXcO56rzr\ngCew8SI2Doz56J4V7u8f0Xc9xuXYmISY15IGjMnrYKuHhHTETjwk/aqYgTMm0v4u4aNhg2YjsGlV\nx6eBNc/4/RJrnpxJyt8+5Q/YqEb00EgKxNixn2vHfo4du46I+ocfy6g2pui7zcY3fAalqIPEjhzq\nBiE2cMt1OuUSkLRal2ZTERIyy7FFzCCKsAZj7PCjhXVJONJvR2i13dZ2H050/muP55/wlxuzHgkR\nhryCmY+3ovO/GtIQ+U/b6QCIEeGkL1UfIxCOaCr/gqHs/6BNQdwAVYhmoVWIXUyYpU06eA5i16+Z\nEzFTYXPEoaIO8tyeSegDcYSLkYH43qsQLK4LCeagNVXe0DG2gxlJOYVTj3s8bZKhE1zNfYWwjYQA\nSAupKUlYFE9btBmJNLSxuMLxPgL8cjtndA/iuFftXg61ZiANu10iY18MO4uygEYdummE9oBkXaCQ\nd/Vq4FGu4zH+wBKEoOIP86/m/pnXE49NRBLxXVTGd2HBbh8E+1ogLgC+Q+r7nTQGouK74mlElg+q\nKabDhAP3slEnRh3M7Micn9OWU4sAKM2KG2M5IsFVM2/gtvJBOgPerDY1hXIUGLyT1/dFUeccyYPL\nANT3eI8gjbEEGEkDm/UB5sTrfhjcyhld4gLaF8Y69Qv2y7G9XdFd3QQn4X3jjT9pNNZOnaxer2zs\n9jhg5YHCQVF5tQFzB8anB5hwaELv9lM7VUYAzgZGcjbJPA0cwnlitZQ0MaqwsdDvPzHTCLzKNi5g\nld5F06hYDl2ejIhSH1ZfL159nVo7cj1w0VxkwxO/w37H8zwyk4sGAZWSYOIEJ0vGNG8ncw9Iqy/G\n63UwevQotekQoLWZ1FhEdHrKCARIOTLM33PmljQrb9MCHD8iJtUZI47bBUzUWIlqzjw/xu2uBp4h\nw3WD+l5jneRtRPRFvUNwkBTxzvjv/Dp/WH/YRNqTdpJcrsc1ubSwoBN3g4GgUOkIbHQCC2gZkoC5\nMwu9x8WTrQVAXhxxnXr0fSQXdTdfOv8YMZy/PJeu3L9g4y/a89uZioNYRdLk6YVzTAQCl5ZxyEFx\nKmLgV6jsyhlA2SB6vgLOsGO3MICNo2RsRtnMaj77271r3uKzK0tF170k/GCzg5nZVMUAACAASURB\nVKJsxOTKPwJHarnk535Mfb6kmdlXSgQcz0cnbQcsfp3/MDBElgnIMq/KMm6CZAnNpiLqb38kGCUP\naIcSoxa2uguaWP3Bfllmoyyrfs7GDx5D4MDgIc8E0MaQx9bfQP73VsRm8H9spwMguQTnDYC4AXIR\nOc5/yVjEf8DC8oqInHA/HZmE2TtT6F9UXAn9CkYbye9ZyOjOrXy0xYR941PY7eNCFupw4CUvcVMQ\nMzc0qp+WwhpO6BApyXCEB2wFcSclJb6TVjO8qy7W/UBaSB1kVICMHtrMSYhdkqajdS+wDhs7P+By\nX35YDVE1MefCyeFHh2Ds1VFXoeX2vzzIQZeMrDUyFZC3dA02JvAYLfyVccA8ay8PXl2CdW8GPYhI\nEgla8mqoAs6CnJGw9AACXDF5TfVmr9HYHdcXTUEogIgc9rIZh2d8C2QOqxu2mfCJlcFdtQ0nkslP\nZ4LWKVwODFEwjvIRuw54Qgmqm64CLsNuN6jXrSblZbKK29C/N7HqVQXDaDv20cDK73J2tzsKJB1w\npdqtj2yTLS5zj1/qzshEnpuCqDuMkSDw7qhlx/R4eIMFA8k3ZCD5O/DEze37jY1WbDyPjRe53/PS\n4O1z21uylro4fKkHG2/yHdsgKwOUZiCX3Tf+HliKjWkEG/AamkmLAY66jMaz1s9jYnMaqxER/usD\nnKsmAgY3qQ3X4/N9hCQZEOsOwFFPpi6BDk2RYMAIBLDunih582r62FAHS8SuOaKGJTUj+mw02m0C\nwjl2AG/i1g3XJ7qdCMA4KMFHPnBKIYAX64pd0RLfkv7AiAd0AHbshgm0Dy8hoQeYUp6dqeBpr1cd\n+wkEKzIJGy5eKllBUmU1cfWaEGa+BUs94SxJ9qVz+POh0vVsvx1eOPzfoc9ZcOfmU91DeHp1LFBi\nddLjYLBehzsSaM8C1qkaZ6VENPSF2EwrrV+w79qJ4xsoJ7wXpOUki5Yg6mxjgWt1eEfXcfH5duyv\n2LGP6WLkuRbqt7HRNpLOfJ8rvrWC8C59CNK1wUYKIjV2GNFjMhRb32CucLsr2xJlaM+wNuQNWM+Y\nf5y/5UtV0Qz5fC4oDzD2zWKkwKfYaD/Fd/2H7HQAxI7oxL0GwWT6HLGjjoF+1LV/t51WyFVTukGH\nWfcIYn7J3J84dCOzmmdQHvcj94xZi1hcnwIlxrVrf3vSai0A9jZxVo2Z5kOq3DQIDRw/otYSBJCc\nxYF0h6N7eW3gcHsejyPA9kG1CVBTSAUY7SW7iQ5dH4B0W7rPQUjl3wvwPksMqTQb1YJcpDXhapbx\nWfbC1gLg9+dy7sv3cE8CwWJgASJSEzS+kxzCRkXLk0g/209Jr4kHCQpFtg46TiNi8Y6FV95Bne0w\nrGW2uycafEbeC3n/UGXdKUBg4d6F3wHM3z9/NYLWqMmNBJ0iQKBbR0e3ttM7gUgrTHOTsQHBcBOj\ngGX5mPr8XPXYPVfCb6bU0XT3uS3diC7sdduLt7/YIbVnkFTwdQSVskBSqJ5Al2TBPw+xwMcBBFpz\nxvdaep3T2fqHAc5tBhI1GFynmNan6OZ9eDhh78wcC135GhB6oLgLXCeBEr54rRD4JfAG6fuKUAEE\nkcr6cO2kSbcA3YosdyCYgNqMi/Bz5cioIrV3GuKe7HMwEig15PlHJn3qQzizU00Ite6YZNDFOhhq\nx24GDu0Rqdz0AY7VitSgUnglUJDlXvYn7k0e0joKOC4juwD2wPfx4rV6AR4veXyH2Wv2d0d1a45u\nXibOGgfGbGBKQ3JSFJ4WralNIfQe8lumUj/xNU5OfhEVQBSUY4QW5W3kzrmOhfN3FTZSMV/BJkUK\nOOZmU9fMAAAigdLFcE8UdZURfzOP4Nzz0NGwfaYIGZ/cE+S+BpzvR1dHRDOhl/grgJdkZEVG3pzF\nFztT2XQ/IqPxTT0LRhnoWQUsxhNTvTeTXvqLTe5BEHNiEWtqFzb8KhPwl8Cz2Og/5Kpp5MM5nXrl\n0eP21/s9B1idHJp3nEouv1yHqfu/mPacF13gVfXpuQgfqT3+YTsdALkNkZ8dj7ggKxEhbA//+w2F\ndQR3Yag/10YelL/wXKQ/zUlFnJSNp345qYn5jRIr86M5HL8LWf4DYld1e3J395ThK1caJbv99XoW\netNYF9IkJCmoM5YJAshQMi8YfMfHH7c+uZ5nlUd4CrG7vE4JdoDPVY8d5SK3nG5fIpC2/Kbl7Saf\naUhGe8bPsImieRvWNAexvTDgSNomfF1n4HKUQKcRjh26m7udS1n6DaIIOYoggMyBfvWPrxALOBkb\nGUBLfjUO4EwkJZTnT3T69RO7Y90Kshyq9BkKIFcDb+sVfTnA2OqxOxARkTZLIegUP3oiCU+LnsCO\nBAA1nVCB6MY+gOhNWKIEQfADRNqUAqj0wkVVCTyqSJwJ2Nti2t6656p7bqLRoTBotD3iHBX4DL7y\noXTXZOK8lr46ipKZ0+1Ldetj1wNLlYhdLpBBQL+LqNacAc47QOGMtl2BHMf+FtwZIQt6RAe0tCEc\n8WQE5XMzN0/6mLszJ3L99PuIaRi9YBmu78eMOVvv83yhnrtPJOhW8/FhUuQ4zOWk98Yi0hvBHSpQ\nR7YUP+blTIRw5wBCeQBY25IMCYrEMUSv0cH9Ij11KgDRGgpDe0BgV1JtekJ3scscpGe/CkeGRviP\nxJ7EEz3mnivV/16ViWslKEVuyTCl1xITS29tNba+JkKRxhJ2hvod7cAsiCnooqsEGGnHrsNGNrAh\nr5OVZ64/J56UI176k3hyM6k/QXhqte9edjBYn8IPISlqJQGxTrQmXVUXq59NB3a0kHYIcDzDr6II\nyXp4SPLqcWYSVBMAKIiiYb+M/FAOq4vGcK8vg28FgJi7d3yfj0S/CERyIzY5U9RHcMy2jW/U83VX\n2J/Y0OvS9t+c2etRzHgiyTaaHU/rJRmDex23Dy4jurmDYH14I/8LABJAjEm9E9EdvJrTjAT+DbYL\nceILEHLJSxhAGVjevc+RoQxahi1cXKyf2e2jSfR62WadglZAl+UAsryp4dJL36m5/PI1Rg/D2w3F\nQ7L5JD/ir48gQFTclJbs0ZhTB/3yo4+GIaSgkUTq7wbgrbOPsZsggIzuYOwWeh0xKCQfyT7ygcPi\nqH7vufdCu+CzO0loY2Chu2a8XWPZGzDB7BYoGgmMvpIrP0LcZGupqkpB7HznEg6i5wFfqVHRFmAG\n0BrnIAqFToodEwgBkEBU+vwuc6tEuNpqGTAMG0bENXgHkSN/UP13NcE0Vl+eHFf9RRgTXVia9Cy+\nXHMe5Yi01QFJpEX/C3hBpU5/CZz3wDXXSI+BvAYafyjgfWDaonsW3X7pby6dSStvotcrFBZGpnEK\ngapsnH/txDgdLQIx+2/Lb/eS6DCtQyyaP0fw5NNxJa0nsdKC6MCPtHET2e1Nal7ZgjPKzF2H1Qhx\nhAuqetAcsdCluolHu3/P6vc+wuh6G3d87Jpi5n006wzzlK9+dbvTwC0E01cFwImwmldlQw+ZTYAS\nIAxAFH0rVr0/a28Bp05f4TSZrIokmXUBNgA36NEfPCIIFwMxKEM70sMBpNeQOLzWV//DLPp021aC\ndxjolZD7U5GUrV3RXXNUZYXzU/C8ZcDXvdM61oun1YXiOYfgbHkVQBQLIg23Sx0JXQ7nTHDgKEc0\nSeYj0jjvHXiJeyooSo6KqWqhfwooN5s6LbWs2VjEDJBYD1ZDLu+HprDnANtAcqn/34oYWxw+IEqs\nD612+sUbXD+ckAikk5F5CZRuV2fBaNbXA1LMi+lJ7GlMx14IJJJ8dMvGAmLoH4Fon+EM1AbUiOd+\nBfwaW8gGuq3o6rTG9GgdtEmnLifUAtYlpdxLXGMaet8r/4riuWanS+M9iriY3eqjH+f8f8l8iIho\nDeIGfB/CJpQBMOXw4fVSyiwPIahqx367HfsVIUquAFdSG71ObcKNZGANT+jtLb3raVZUDJL0rhhn\nshLuzA+Lv5EC2NCRcfbQoSeqf4hxuY5JIdRbSXQzf/rN29wuKaSPuoUcYPAPLNpIIE5BgPFOq8P6\nOeH9IFk9xDQgbsZwU/xNeDsHs5ehMHMfYgFqBfT3GDLkWe6+W4csFxECIIpYdLEIZwpiYcxEpOSs\ndBu2M6kNhPw82O3pBr9uTI+h0UM4XbECyMIQfz5wBBvH1fDdpnb5fgKcq6axctHkU3yOczDG11I3\ntQNLu1YgPwrUy8haM9briLrR1eo59qW3t/9ShpTjImLqAg73WHreB2p5kc/JylKIkGhBOOTK8bQ/\n70eyLLwiPwqFEeT2Li+uwGNxU4IQsktAgKBmGTSNOojB4yd3S79Z8UY8EwqpjC637EjF4vdT2KN+\nj6EKHPQTrqUEvqh0quce4pU9K/FF6yncdn9LUmp37AuH+NBAOkHnFCyga3aovpCM/RKCLr4L0b0e\nBaTF0ONpi/MM4xQFdAWimhMTDQh12oeAMV/y5fJmiG4foG6IkIQZq2rChQMIZE067um2y0xWG1Tx\nQVGsWHt9hIm65Lpva5NrrYg1ukVGbkqjqfWzlDMbKHvQhFAz0NaQFoFMAA6rkh4A62DOEASLSRsF\nMAQRqfl3Mal7jK+8mQEApJhjJQRrc0mIe+8wMNZMU4cBZ2g/xDyCNU1kZK2QPTfidWcQnOT3TQVF\nk1DPnx17vIOiwVa29jG71I1Pbsj5K0CAyWLgI3SBY6VppAOJqnxNqG2BwHTE/RMOIDaOAy8iiuqi\n9mhwPTRs96x9OpR+WRjNJJFqr161mkTEBuG5Ux37z9jpAMiTiAa6eEThK079+f/KvkEwoIoRMyP6\n2SWbf1jZmZAWjSn5YmyMsGPPRiyia4E6O/Y3v9PbF+p9LMWpewaB3uURLzMMKFuwhsHlQ6WS5Xfd\n1UJQlBGEU14NgDk9j4xzdc/85dUOBp6V8jsdDP/NZqrGN3A5UHOUIYeIMejw4UfssAQTK2hZbszV\nDBSBdJT4kMxeupgAi9dY8I9DOKAyUJK5+DMP51/TBLF7ITqGIHXxPOBrKRhB/ogagQApbE45wfRW\nV8g7LSmsZJ/L0NtDqNORZR9wHEn3cwZic9hoQIDZUkSTm5Cs93ZNRNLtp27SEeIatHN5hBAnqMqo\n3Ao8rshyQpTbvbYhOdmWDj9HyH8XIa7XGOAaYmJHkJurp3+/TCFQdRkznHn0HotpNNxOq8nDpScq\n8mvQAYfUxXU78MeQxZxBwNhAb0ojSccWRrwmoyid7pKMnV0WTEg0IHGreCbfDHuM6rmOByW97/WE\nxloAaMJhWGrq6fniex+WW/zESNY+mfjw+gdYaWUUxl4fhRvmIMu9CKc7EchOpMPRHE0RP1FAr09O\n7kSSmmXkdmC+BcvkdKyeH5GG9T9c6kTsVoXwZRiAKFmD3M7sykJK0MY7WBjnFymnRdpRXoN3X1lO\nmQsRib4DMIJS/99mbssi4JMQWYxIAJkOYVpv62BaOgJAhGBoyOc5xAj9ed17e6AftTX3XL7eCeSq\n6cDFwDfY8AATLNTXEb4JOotg/SPkvYN1EEUIKU4g2C/1owfT4FaSExWRAVkG0iETnVEhr5EBdKjy\nKyA2gNXq5/kQqAjo+q515HfYirV8Bgo92MKGQ2n2OIKtOZeAbi4+c/rNRxpWM0AaP8LEeFsbJ9Tz\n8S+z0wGQBgbY5f8nW2JPz/r5u3YrlswrNyBO+s+AD2XkBQhg2OWy8NRHl5Kz4dcdi//MnivsbMy2\nYy+0Y8+yY7f6iBnpIeEocEFaE7/6bMaMrB9Gjw5hF0nbQRIAln/1MrzdPQt37JiOKASHmXozXfnA\nRobMqmYJUMoDunmkOCRc+hpEoW4rgkqopVOyAuiOMRCAtG6Ow5ngBDZD9q48ehafxBJQxfcqqYq+\ng+HXOuDWLXCLn/71D812AiM/nfxpL5DCu3kKgx0JIWNPr564mwMuo6sTItKBnvaj+ByzCRaAI+0j\nRLe7AC+73YCnvQBP2yaOz/+BxKoxKoPrPeDGiPO1Sz2PDz/10ktpH8yd65YEYO9hKr9GBQds9BAf\nP5GUlHa14z7UClDTCArS+41YFkgH4wyp45qskoJPRm5S3+tHxGv/XnU88UAbAcNhYpumRLwmgzk6\ntjk2UAvso8dwFIdhCHb7CEiPgz0xan0stJ6gsbAAGnDrz2/bssUeC23R89hMJx8gQCESQM4D1tOd\nc5KMPRrYaimOrHg62ptjyOXUFF5rXUqKA0H7RkbuAM4uoMC/i4u0foVI09JY+fQBiGIEEpPw+JrS\neAy4C5tuAfHM25rMWmC2Qh9h4khbbJvFZXT5UHW7XHOfyGmPr48jdW4AEeUYECrF1UAKuGep30u1\nv+6A4SbY1g0c6IjuEMPUoA2UmHaSoi5pLTUwQASSQWMl4poPJnwY04Qo6iroAxAlC+HoI5sK1xNe\nB5kAHBU1KhB1Cun7rzm3y48pA7glitrPCGe1FRAuwVJgZ26X+t5b1Oey/FL42F/19evJ2+zBmXyI\ngUxMjrwL+DNO6x/ZfkfL5cpHXfRvP4i04wyoifU/t9MBkF2IVNFSgr0gl/w7Psy/yiTomr1/f1Wa\nZXomCqNdRtcvEEQAZORGGfn5879kk83GsxK0jKLrPoTDsiOkM45tYfWELXyyCeiZtp3NGW1tT/z+\nxhvn2ceO1fd7w4RRSwYd2XBYvPXA4yclOFCWyl9u2MOUQym0ICkv0DSjg65oHyInXaX+vVYbyIqh\n5yADAUhnaSqVihlRI9hyPvWf6FG2I3ZriSw/voqpbW/C4wvgKSMoaeoO+wxCwnZsuICSPy/88zAg\nylRrHoJLVw9MwW4fBmSP2U+rx+hpJTLtcWKVnuiCamyRkwr77GNEdKOlZcbSc9yLp3U3NbO24I4D\nGCUjO2XkgXZQvwcuv+6bb2YcycuLxm63kosDiasQznUsNqKQpBFYrQMVEAtQ01qlJHy4nwTL2Yd7\nN+XWMcgRG5aeAdFZfdMT/HYqQo49QFTrVmIbBqkOVDUlfRSllkMZrg5gL4pUT0nCRvzcCTFWOKTV\nTAYGELO/HYdhEK+80t0KP7ZM5V4upBP4CtrGEJ7CEuKJrsQDJFZrHd4agGSbEo926gUPKZRiH2rW\n2tRUFyqAAMjIXRKlXx2nNxF43Y498l7WmFihEUiGiUCXHkoCer5DURSSpr1MG+xfjBvhH8RUPxse\nRVKOrJyz8jYZuSfq9yw7PG5bYmD9J214OwIqdbQGkeLxg3QYpBmEAchNVtjrgqkzgdLGhMaxQLWa\ntx9qxHu8qNObR8juXQWwaEQqtoz0kumI+sc36iETYqk4RDACuQr4HKTITcc+IFXNWEB4+kqzNZ9x\nYaCC5QsBUzob7IRHNgWEC2rmP8OvMoCP1XS3Fzh5Mo6B0nAw+Js2qmf9VIngY6ARnW8E+655SifG\n4f49AAmdj/4vtdMBkATEDvpsRLi6CNGV/R9t83ft+qw5MWnK5duueqs9pj1lxZwV2/qeFDvsy/aN\n50UZ+SEZebKMPFRGLpCRM+dy5ujZLGyUOVOPcHJKVWbmE80JCb4nly69J+yN7PYczGmDH3znkzbg\nc+knCAZPzOSe90fh+eVCLgF+gTP/JB1RZiBNrR08Drw2l8YYIK6AqoEBxHkyn71tccCXIHkWUd+R\njns9SA3qDrgAqFIXyGaEtP08YHtwN9VnPwZ0gRlAWwLe8Tj1dkQYvwx4Vx8g2af3NREZgTRtGETm\neacCD9GZL2plIqJSAjNx1uoROekyjs/zMTDrBcQftQI3RHk81yuSZKd913KmcwH7OYiNvfgNB/GZ\nL8XtLiQtLZzGKna4JoJDsH7hReeevVafWlAFXmM4yEvCwT82jn2PICJuiOooIW2/m3CZ8nFT2d69\nqRAzwtk08m1mCa3my5ACneDUmFsDA0hBTxQVsQdxOrUZ8T8ymjaGsQKaZsPbWtojCnENviKg30RS\npVYH24NwjNmevB3uQe10/kRB1FqbluYlBEAA4nDvOcl3HkSUscKOPZQmvlMiMIXwWd5Z8XhdQAmy\nrFD/xT70ZyZgoocsChGbmL40liIpJatmrtJhY7ZXx/PPv59dS11REt4OTaxQG5YGjK4Gn4EIhwtb\nG9TvX9YW25YvBSSNmTbCjXmfyU9MqoMh9OloCTamuvYOk1RxMfAJNlxqc+BgK1tUORNFQkS8/fom\nVFagdv+DqA9GNh+vsSPHtTJpGfCyRCA4lbDv84dHIN8zewxaultYxZ5MHAxUSM/ZaubgFdH9fq+Z\nDYXdN9zNqk/8uBNXIOpjpwMg/2cRyLXq47qIx3+0jaqq+mRQfX3gggM3TFw/en3rSnnlZSFPnw2U\nI8uRhVfNhqOm7fqkn2XZ94c33/xs86hRv8VuDy1+XU/r1uZzD/cU8ndmxX/4IYErF/PBuiJewMaH\nEFNDhzmUFfM80HszFQ8C9VG4qogsotsw43MMwpviJ3ijhs4AgfBdkNZ/Epm+0mwzMDMAbbH4Mkn0\nrkY49mWIKYjJPp2vllAAsZGLpy2bjAUJA7xeqLWiOQtXw1koATdCubSC8kUWAvpzfuqPJfhSgq/o\nqd6Gz/F7TNxFL4MAiXe/0OGJeZ3izizy8iLrAAVApXCuypnAuX6kz+raEkaPPgAxPWzr92bwZwex\nI5Jo04q5R0g76Ce8uWz8aA4YNhaQiUh/NNBuiuNg/AbSvW4Em8xCXyFdsSD6pQTQDnZkcCChEu3+\nEs7/RZZIQ6FIgVtfQADOPPX1W0gt+4bUQzGgxLH9qkEEvAWgZHXn7VdGNRFar4o0a21Kip8IAEmD\nnZUEYhBOPx14KwRE9gEjXJgbpeBY2Mx0UcIqwUYix144h1qPjpTYBkS0/CWwKITJth+RofjwQTsr\nLqyv20inyUJPt3avauOagSvccKgpQg0gDzaWAfNkZGdNSk17siNZ25GPVNAd0sGuKXX4Ca6b0EmE\nZSj6KQTTV6OAo2baTiIc/SxE7Ssk6gmzNcDyH/g8noEjkKN6FG+ZKKavQJtKGLQCQgCxhDHFPcTE\nES6tVLG+EIiMQGyYiW3IoPy8n3b2X/x1OdVzVoHUjsgMnF4N5N9gPwUgmkzJnwd4PP/v+DD/Yttx\n8aYdupQWzirLLvsV8Ji6M4W/P6w+VKKkz67csOFvc/btcwGiAc1u1wM3pB7+wJzgIoNw+ZRT2c+w\n8aD40XKULl006kJQd0DXWvHcMJaOTkSjpk6BUEc9BofUxZSZ/rDfhefCCwjexJskAnMQAPL1AJ9n\nCzCtS5Jc6bhrsAQ2Ibriu9XXtAZ0gRrCU1hXIuk/Rm8pUs/BqcwATOSpwQZcJ2cg6dRzKvmomlMJ\nyswBm6NCzYaV0nuvJ/mMABOeWQEEYNlIKhYM5d3XP2KKz0jDa2cQLn0taiSiKeuvwM1u9F98T1rz\n3E2Q0MXuyLeRwLOBMzdOZbvWd3OM2MZY9O4Z2jExOCam0hx3QLBojiDSR+m8l/cjQ7pTkKSTQA5I\nJxFNdtPEMZKC3R5Pfm82B+M7CdmgAO/QnTkHSdcFXW8ghD+vRZv9Ed16mNgGhaGfyLhO/gV/r0Rs\nx6iuzKPmifV9UyoHspR6q1UiAkAmwY560N8hWrguQDhVVf5F6o2hp24rZ4RGlllZOGMQ1O4/oHg/\nw75pP+OnA4yQBPmkl6Cm3H5EivHe+34kPhrnNlLcUJGupRlDIpCFcbAhsoclH9bvAzJByaxMq+wo\nbC7Uvqc2hXCXXEUXQQccBJCxK520F8YRpK5rI2y1SOEm4LUBJGw0WwHsD2Da7iLVLfVzzpIynoaO\n7ejaVXJCOxAXIg/TF4EooHufJdk6Au9FpMsq1hQTTf8IZCxS4AieuBxQ+qn8ClOuQmwwfq3+4nRS\nWJVAgQI/tVb/KfspANEKObsiHruh/wL8TzMJvLPsUVWHh/uVLau2fIho9CnnkaR7UZTzOHXxF8IX\neKj98PLTT8dIinKDWiM4G8XfcumOIwmSwtchu7ZTW1jKwVhKly+KEF6+jFz7Hekr76OsYCP2KMIW\nHABTqFIMzJplxm7Xq+NC49Cikf5zQHaPY99gP7qBmGZgoxk4uTv7WFQ2zlpk2YFgnbylalJZ9QH9\ncbQIRKQNrkbx/g3hQAtP8T0lxMKuxpS8hJ5qHQF3cFJhb1opTmsDwsme6lxZgM9w1X+C3lzBuHHT\ngL2Qdh2wgdqLn+abXC8N/jPpyF2nHg9BAH0Mocr8DbChjLhEdRUPWKR8h6vKRlGao0AONpwougaS\nKmZpz4/k4OQuo77RY+CgmssW3eVH46KwBCpJS/MTbHTdiXDQ2nVYiDFQhltnRbAINRFOB9Vz1pNQ\n7QIeRSgMX4JGxrDhpzOvmaGfPwRswu9uxuAtdiQ3WKfUog3jGsisTSqNN/SXt0NPIQTiYLLaVX4d\ncJsd+2CAIipOrGNeH1PHhD8vC1fMstuX+RBqFH9g9+4AU+fkoDNpneKhaayNwHnYeAOYUjJo0H5y\ne6FsrBZFhNzPQ/Lg28goNh+81errnFWTWuMZWjdUYzlpALJz+gkori+eYMf+SBdDg5MIz7pvAq1D\nJWyKts7GI1J/zW0kpSLS72+d6qSp/Ry3JLH74G5eTbJjD5Wox45dPwNPwg7ijdDHGmwnKJlegLp5\n6yUq8x2uwoP5zYi3qThqJRMwKYTJwU9FYgfi3hlgXSgjEIzNxSprDk4DQCQB8G1EpqH/BfZTAPIF\nArHGILrPtccK9d//eIurmaL7doFPh92ej407gTNJO+sSOvaY2SRfhG1AmRA4RQQiQW9WW9vWc3bs\n+BQRid1M16GvLzuIS/d30lcDW2A3jl4TEY1df2RYZYPQAnqcSADp5Uxq0DFsWPsfHmIiQqH2tZAp\na2IudB8rSfIu4+2TBxhd+hP1mc278w8kFOLQnM1lBAXckqPd0eUEI5AxiIL8ZsI70iMtHVE7+xBf\nz8/p3F9P+Dkto2ZGPeHU6KDZGIVwIicQ0fBXiJ3tXnCcA3yBJBWTka9jhPenywAAIABJREFU07tL\nODl5Ol1ZpQgdoQJqZkiI4VjqaFzppB+pbg0ZD9Nf4RiADpIS3Zi3I6i9oPMeJOVILCg5oMQOpyzj\nRJKniWAfTaP6PQs4GfUpw4ZlYDJpgLoDASBa7v8iPLp14MxHOJxgoXTrnTvJ3mXFhglR0J9OaF+L\nK7EBo3M48Ct0xsN4jIkeizNrfEO/PoJQs7bHxZmJABCAYugJiGY1ZOQTiPvsRTt2aTpbOjYzoy8q\nTMQ7PAZfQ5217lHgj9howucbQ1FxJdGFadiIJRRAbLix8bUiUndFix57rJOMdj81M7S1plLTlRiI\nGgRbkwmyuEAdpYvaGd6Q2GAcenKoFZQoBDgf8xGzO63+/JTn33j+MWDcAR69qYthndiQiD95GYrU\nThDItRkgnW+zLFqPby1ILT9x3pCRldH8vjON9e8DG+zYzwx5+pzJNDYfoSBJfAdQz3GqmsbTPj//\nxX+fpyPgoT/b67giUYQqJhrye62BcCv9dLmUWEQd5XcglQAogjGoR8j6/D37t6Sx/l4NxI/IA0p/\n57j/OLNjH+QmNSVJv80rBQJCn8fGQYpvbUQJPI5glR3ExuX0n/x1qggEYO3H99/vReSqz8zY8Uj9\ntFqiUWdT/GN2bD893ToCUuTc4qzHGLYKuKiehQHCI5AZtLJh1o+63tnf8zVCZfPXIc8XEF6U5GI+\nMb3McvdPfJAfj2YfShhEj3BqstyozgEHsI6vHC96G0S66WrgHbWTPVKVN9SKETftanprptJd5iUc\nQA5TekX/QroNCzYeRhQzVwBXqe+lAkjMfqgbAnxJQsJYMjM9HBjyMZ+uGMuhy5LoST1KQD+LnbcI\nooLIE6smbfgjw7pDR5pGWMZmZnwI3KBAHBJHyN1yAsF8GjuNbW07cggQdAiavlUBpQmbiI9vZcwY\nra9jJ2LB1mO3m4EFlMd9AoFsIiOgk1NiiG6pR/QKKITm521EY+7Iw9TjwkY7rpQy4qsloCHGS9wp\ntNIArN3R0TEMACCDoK03nBzwnPo9Lj+Xr30HGdnXcKdHGaRENzcjjn8OEXH2kJa2k4TRmvbUj4hO\nylCJlAlAaW1aWgbpJwM0j9Ak/rUN0SSQDoDrKCIi00xLAa0D5nVFdSUMahqUh0j3VNjZOOxHvnir\nzX+u6cVF9+6Ukc9LZ11jCU/dPv3w9LOBAAHjfmCYHbsRUR8skVB4hZuVM9j60SnOV6TNHMwLzyGa\nTFfZsS9Tf39LGtUrRnPATXDGj1YHSQN6JVW09QdmLT6HNccGSJdVICYFho79hSCAbCGsJ0yREE2v\n20B6I+T4bILkgb9n/ycAAmK39RnCcfz/gsar2jWge3vBrq2u+N5eUUC321OAmSRP/hOCaXEb8Btg\nBzZBc1PVYOM4dWFqbZTHMw+hc3XPddsa59QkUCP9U8KSq730xPtAitQmymog6jhw3TFunekiXSww\nGwkYSbm6+mrH3X8iY/ViHpKRX45wiAWEAIgC1lxOWN/i6oJTfgxPzJa6nP1RuTjCUnDqvHJLWlda\nJyIVU0CwuA4/HYEUAceYY6/BlGzE3TyScFAu48gFqQhZ7yT1+81G3G8jgXHYeFkFDxBptWxGvJAO\nuxSQGjGZRpOSImisnvgjfPvsYLb8uhGYQHfWPpAio8INaJTTgS19DxMPIPoBbgCOkLOtB7EbHD+F\nHb5N+SQSjECaET08RUAVTU1fkJBwlqrmrE1L1ORkDrEu/SCYrPTfnBThTPoCtKbEMPtvYht2knQ8\nDhQ9x+K6KNjtRcKJuOdOkSvH6jSb4xgg2hoM9V0hkh8yshe4BXh6DjsS20myartrH1Jm9bA1ucBv\nVdr3REQKey+JY5zASFVG4zsgtPFS6DkpgTxSjxnoSdM2SXVAJgbnTARQlhGkrms7+BrgGDqv36/z\np6R3pCfn0HvGMLr0wAaQ3i6w3Lo9XirJBSjmJa+Jtq/u/PrOt7Pasj4R9GCGI77jCRm5G5jURXzg\nWxacSniyzxRRK8kASmX+v/bOPDyu+rz3nzPa992bvMgL3o3NZsBA4mEJ6yUlLTQNkDQkbUlyyVNy\nGwIJJNPQ9DY0S+E2lDQJaUKzQpNgwmJwIlYTvIPBK94kS7JW29q3mXP/eM/RnJk5MzqSJY3Gfj/P\nM4+lWX8+OnO+v3f3VyM9/75RTfV3gTXTeOHx63k2jXC/txbkNY4MLDNjF0vX3M4TG6Pf3+qm0N2c\nSz22BRKgFBHg3ci5vtrqCgAyimIFcr1y4iX+YTMutSBeBCQb8Z9dToqk8VpFUp8A48dXbN36Qk9W\n1kVWC4ibgefx+zuRXkUvISf6T4DXCbAay32VQNXfAQpMv78Fv//RS2tYvasiIsNiZPSVdoGZRQBn\nNesMoM6P/w95HN64m3tvqqbaKKktucaoN3y3BG9ZfNf/Y/3373R1xVQRaYFcA1R3k7cEzAKX58M3\nW81g2qDZOPX9aCErBdosgapH/OXbCAxZEsMJyAHgA5ScV4e0oXGmN+5lMGc+pvEm8GcE+D6S2HAf\nAT5CIOqLIS659Vyy5kY4YQLlDA4uoKLCkUlntPHGPWfzg7ce5LD/dpc1vQysAfMuyyUSjVU1zreA\nuwt72U/Frix5DasWsD//T7OoZChhwRhE3FHzgBo2bXqBlpYM4CrL8tmPCMhNSFC8QyZgVh6M+tx5\n7Lvht0jsJTxTI8DFwF+Rc/KvKTkIMzavYFdhLzPfDgLlpmS5leFCX0ZGedDny8FlY7MCDp6Iyu7z\n438DeL6OT5yTTe8+JHZAj2EUN1e+3YIUhkLYJbSd/LOyCV/8I9J5sQVk4ORSSvf3EcqYb/2f+oFm\nig+vRXbazqaKFUh/uS4wTCp2vUl/QVd6KH33ZTR/dy5d/cAKP/7HMszQG8ubmGnH2gpL7vzKy0tf\nznn80cevL6L/MPI9tuMfAH9zM0/W5tEdPY7ZjTXAm1anAvz430Msgg8C/1XJ0w3X8ZzpI2gLph2g\nryL83buykrquS9j4Nu4c2FwZkcob7sArPcHqgeXWGIkHkbhHd9R7jERAxqUW5HRN4/UDJ/z4t1e2\ntj67uKamC9kFxmZfiZBIPAOe/fVSPkaCyntLWF4ErjIh7aKjzKqemzAgPwz5rfRm9xEZB5mB1Zpj\nCf/07T6mlAP3Lj64+LHCY4Vd+eRfVjubI7g3xasiUkCuSyf4DLIjvsTl+RDMWlnQsOzEjqod0TuU\nMhgqFJSRr3YvHkGaKobnpzixBeQayi7+DdLA0ZGJYnQBjbSetQ3pfxUClhHgt65rBAjxLGf1XASZ\nO4Fz6OmpZNas9yKfZPTTcP5XrS9hFEYrYnleKWsz/4/Djw0iII2GuJ8Ov/44Z5HTNg1YXkrrpTlG\nd+6hYmoJRMyVaUSKD7sxzVoOHmwnPPL1B2QFtyDDyH4nroyWQbgmWvjn01+wD3gM2wqRhIDHgbsI\n0EB75Ulmv3YDewpDTNnlA8zOTHqIme0hNBcVlRumaQ/uimA1vN8plnZEvYGPni+1sqa0jMH3gQuy\nP3JLWa+ZkebLafu8I/nDFpAdZFVMwUhbZt3/PHCl1eLD+hg2EepfROGhY0CZ5ccHkxryG89HLBCn\ngDgq4IGzntvDydkh4J83MHXL80z/hh//MYDiPl67oB7jzs3MB4wVd3Wf++jVj+7JGsza8A/svSON\n0LLwWs184OY7eSzeCNpoYuo//PjrkdqeLxlgnsP2owZmKZhVhAXEWQNy60f5ZRtR7mQHB19YgEk4\nBnIhzg68Iq7XIck+nwEjNgFm5AIyoRZIKqfxfhKr8hz4w80vv5znC4U+h+yW17u+IsAzwLX1hXzy\nt4uHhhrF40Xg6u50LjlaiO/fL+SV0S81q56unBBxBCSHY+8v5evtwJd3zNphngye/JWVPdPMMAJi\npe1djXy5nfNIolmZ27Jw/+7K3dGt48sIF+NlITuyl4ce9ftbkMyz6BgOhLvwXkvJef9NgD93ec5u\nfrFuB3AOAT5DYJhg4P3Lj7CsPZv0njeByxgYyGHOnLjdaN0xtoDxYcTdchFwEMz7wJyOxBPs4Pa3\nljfxKQPyyWndvYTdZ7XkmvUh35D7yuYY4YtEDR0dBcAiqqvPBeNfeeE1acPh9+8HDDiaBp9yVBqb\ndn+5BiTl+COWOyMAvEvAKkDrnH6Q4ppLOZCXTvHuDODpgyXk4mKBmJDeUlSUbxqGa7JANjRMl//n\nEgL8DQH+iwCP3Hb3dffNMn4YvDrr4GWU77qlIu3kTwrpM9/69at29wID24Xl95/ADLWQO3ul9UAz\nsqH4gCnnZQmwH4w5DB4Xl5RdPd5T0krxoRAYR4kUkKEANADn/qiJ5iUFftauaya7nMjY0ZYL6jCK\nerkYqA36+BgGPwPunkXPplyCFyIbpu1IHOPVheyvwZuAODvwDuHHH7S77qYRqp/J0e3I98uOgVQh\n6eN5wA138lgGuE2FA+DAM4vIBhZaohvdgXcjYnmsA+MptzfAWxHh0OcxwQJi/7G2knppvDdgNXMz\noOlDmzcfDvl81wNP4ffHbyYWYMsd23jrf5ZwDgG+4xJct3kJ8Ldn8/EXzqKDwNDo2tFwiI6cNIbE\nwMxHcsrti2ldIXvLNuffP6VnSk8xsisF52x0JOfchKkZAwMLnrnvvnmm5Lt/B6gzJJPpFcJBv2hW\n5jUufW3/9P3R/vRSwgKyHNjhUvkcz401n4Vf7LfeY5vL4/La1kVVBIhn5kfyZvlaWjMbWLOmD7iZ\nioo+0tMPDPs6V4y3wbgZsVaXIQF+u5If4FkD8q7dTz1VL++7kLeadleYXcRm1DQSFpATQBrHjj2G\nxNbAbkkiTIHGEKx2jk6dCxwCwyRAE5Is8Ahi9YdjIn1FWyisXUp3WxnpJwzS8h/fU87UkLsLq+RY\naWkXhhETQLfXvAh6mMPVyAjl14ED5zaQlZb7/InZ2XXkdUxdVVlzQWU+QWfnglmIK1JSk420zeTN\nm8rQ/PchN9YFwGYDQqRlTae/ZR/OgHHzMoPp2+0Y435k555FdBV36YFiuqYcR9xHc3GkoRtQHzIY\nOLuJqwYN6qzj/Cs/fvMQeXf046Od9PORv5ddeR5dNR6DKV0AVhLbTj2auiv4w35EQKJjIDeC+eYM\nGipJICBHiqkE3gxJenR0B94XkWvYPW4vtvBSRGjTAmQ404ZNyDfhchPuN91rxIYlkYDcYv1bTOql\n8f7Bj38oVe/8ffueqTh+vAWIzseOobCf2Z/azl8g5u+TUbEJYGjOx+GKLm5bP/9UG0127qYjI4Ow\nGFjWh1zIrNqSxl9PfeM2fARZM5Sh04SkDvpNSfns7cnM3AmUX71580eRHc1JREhA3AUrwXRrk7Cy\nbN+HnmwqasokEGF9iQtLRrPmETsSGNwyseQ9sph2zQXAejc3ikWiGIobN3Iy43luvHEGsJhZswyi\nW6CPGGMXGLchO+uhIKWV3/+dB14hj7N/VvMhXqx7Y/ZQCxMnDYQFxARqCQQ2AFdRXT2XSAFZAl2t\nkOa02KKbKH4P6dV0tyUo1oKCL1BycBozNi2EuUHW/E9dSy68O8UxtS9MWV15eRcuGVgWTSshRDmf\nBe4nwA8J8PDvfsn6yi5z88qTJVcG+4qzFrz1qdZMTGevLTuALiJr+LZRtOIE4YD875H46GpgM9XV\nBmm5xXTX7EQu/uKuqVtdyLTttjD1I+fvQqJdWDCbYMZOJMB/2Bq6NMTRIo6sauDSPRUYwHasYWz/\nyPJQH77tv2LW5/2snY4I3wt4EBCkF9gug2E3hfV3891m4PJ2CtqIjIHcOp8DzwKdCd7HtggeHPTx\nQEaQHnv9glEDxsfBSNQ917MLy3K9HwA+a8oMnK3IdexBJB34PxO9Ph6JBOQ85GJ2B7KLjL5NZn7s\n/MVnmi/V3nLLPvz+2IwIB6bsgmb7D7MN2Vn0AtUEXKcCvtidQe8rVRF+y1FwcCedpDOYZX/GkPvK\nQc3RMm6nw27PAUBTYWfnPKTR5WeA/Nz16y8dyMg4mBkMXmfApw34qjHkVzW6keBvVIGSWQKULujK\n3bKobpGZ3Z99qeNB2wL5ovU5bsfBTQTmI+3eryXc0M6NEQiIOR1YyMyeR1m58mKglzlzMnFO8Dsl\njPfBiG718sTyZoquzPxt0Yd4sev12a4dXL+LFHfZ1LB3bymy430MqZmxM7KWWmNvnckK84gUkD8h\n8bpfRnxK5aaXKNuXQfneFaTP6sCXOStvgB0HS1wLMcvqohopRtG4LJ0iWslD3GY2s4GaG7ns7XL6\n6o6QdwWRPnw7/mGznYIlIcIuqHcQd8zHkPOuglC/SX/rXpwWSP15Mynf44yb2W6sSBcWzCGj5zUk\n+SWm+LMxj3cXtlL1+mxmENNZwtjzc+Z0IRuoH1sJD14E5IPgKSmmfjnvFQEHH+Ke6db7zrmHb3YA\nl/2Kv3yP+PEPsATEgNeb8uj4wsa4TTETMZIYCEg27fnIMb4LKDXgEgPuMcKbnBGRSEAeQ9IZFxF2\nW9m3LQleNxmIrsl4PWtw8Gxz+DkmC4AjBvQjMyxuQ1wKmwgMNcez+em/XcT2oC9mENUI6TlKV3eQ\nrooF1h2uAnK8hFVIbQQAX3niiYKy9vZlwKcMeMFKpawi8Unr5sY6G9j5E+YFl9Yt7ckczHTWZZRt\nnbsVJHbyOO6DiNwFxJd1ELkQvphgPdZrTS91RtcDL1A8sJ309AwKC2uoqDhuzSYZFwzo+VMlz3/+\nLa4OGazcOZUeAtFfdKMeDOfshlpkx/sI8v//nVXRD7AEBg8SGTMSsbWRpI5XYlyFD7V10l3RzcLf\nV5A7vQGYtaCN17syWUYgpk6r/Gh5eUwjRZs7r2dgFRRQS5cjTRocXXibyfrtW5SG2slwxphsC8Rm\nO7mzC8BYah0vE7FCFiACMou+phBiYVgCYhbSsngaua3OCnRbQGItkJlvPo+8b4yAtOTyWrqJsW06\nVUiXWie7kSysWwlPffQiIJcTOyfEDXs2+gu/54ZzkL9j8F+552rgufPYNoX47iuQ2FkBAfLvv5w9\nX9zInJG0GrFap5QRvxtzDAZ8zYCbDPiWARsNSFQb5olEAvIIcmH4MeJ/dN7GpTXwWBE1XtKex/Ey\nuAZynUQWEMqX+UHg88BzBLjV8Z7vfVUqCmIq1kdIPV0dMJhdZf0eIyCvQ/vAdHIpl12CCVVfePLJ\nHxytqOg3omYxk1hA3ALpQ3Ojlxxd0jaQNuCsgC370RU/ugDZTMgUwtiLlWRiRTKfKVf2Abvw+12y\noWyMVsTK89Ji4UbgGeti/Cx///edrFlzqsd+WL63mv/wH2LOgA8aCjzF/kRAZIb8l4h0DSwB3y5i\nBcRbHKd9Zi2z34CyabuBWRfXsrWkh3TCfahsyhrKy03iCMh/ns/9i4KECFIGOLPQhi7gfaRt7CfN\n10S2feG2A+hOC6QBwzdITuV5jvt+j8Td6gkNVNHTkG69p22BrKZ95g4Mc5bjNU4BkYtuwKoJmbZz\nLxLQjkmWaMkVv31nJtuQVvFO9iDek21gHLbuSyggVvxjNR4tEGRDtX4viy5FXnsEEayf4xhr64oI\n9yFg3k9XUWmYNBA5GXM4pgNNhsSkkoaXNN47x30VE8MPCccD4uHawsRKLb0ceJAA3yQ8H3wxsaNw\nR0oLXX0GhOyLaIyAPJxOuTEdgzQ2WdW+LxV1dj40kJGRbo8YtagisYC8AZxvDU6yWYUlIGcfOfto\nX0bfcqulBseKjk3fP23/ucC/W8VPJrFWXC1QRHW1c0c5n6lXlpLYfWXjwY1l5iK7efv9nsXvP5fK\nynEfdPa7JWz62dmEaoo5Tmz8w41w6xm//9/w+51NLpdA+XYiBSTahRWf7jJJNqhYtA2YnWbSuqCN\nNsTF46TsWEmJDzcBCXCxaXBdFtRlybniPPZOF5KzEBLkvPThdBn6/SahgZ3kz3fGYZ7Hbofe17SC\n/rYey5pvsV5/A93lrwE+ws1NdyEX7lzHmkuAkJWZ9+eE61CG+Pwm3t9dTrAzUxJmothtvZ+zbftw\nFsga4B3D28hu2wJ5s5ecBSGMjnYKGhGPzXpiB0u5cQBYYhqs6M7gASSY7eWaDCN3X40LXhd7OvAs\n0pFyWYLnxG9hEmAnkl1yAbCOALMR//ap/hFDdOd04eu1T+wYAXlxBefPaaLLlDqK9cB/p5vmI1iB\ndMdTq0goIEY7IpBOd9yQBVLUU9SY25fbiFVE9us1v14+7eS0lx1um3qirQUJkO8l0gpZQP7ChYyZ\ngHAFsMXRluSPiPl9igF0DwTofOBymm+/iV5i4x9u2C6saIrk5n+HIQEx063nHva0FiP0HN2l/eTN\n2WO9rnVmOwPALVGWYVlrYWEG0VXo0vvtP4B/SDNpLBexc34fnIOkDiBFkva5aMc/Il1rvqyN5FZV\n2MkmBoQMe1MV6l/CYLslCIaJWCG3gu9NInu87UNSUmsc7+8QM6PNimHEsOKzXPPMoiEXlZODSNzO\nOSG0DSh0dM6N5nLk3PJCAzDdxBgAqjvJ797MBXnAU1bgO7EFIhxAikwPzOxgHdIBezgviY0KyERi\nmXo/JrEVsoRELimZZXE1cnJuAfa6pLWOnO7MZjK77MZ40QJS0n4OM+/bRBfiHngFme8OsbUgVQx/\n0r7KUBzETEf+z7Z7oHV2y+zDwKUEyN2wYsPMK9+50rmDs3dd0USKQPa0haRlFeAtVrYbsYIScSPO\nZpV+fxciTu/Fe8FY0pzH7k0zWYw3CySegFjnVuExYKoV95mFFCF680Uv+v3PyG37gOMzWnMGyUN8\n585jWHYyPz+bWAvkc0hSxC+BxgXy+DIY8qlPZejcM0yk0aVtQUXHPwRf+lYKl3UT68YEfHMZOOlM\nctiP1EtEC0gP8p1y7tij5rK7E/w6G3Cd820MgPFRZxaTlVnXRpzqfUYgIJZbvNt6r/UNTE97hQ/O\nIxzMr2J4C+Qgcm6/ZcWPHsS7FaICkgR+BNxmQnb0A9YfLdxmOx4BBghwF3AfiWeKeKeTQ2R3Zlq7\nyEgBWcXfUs7AJ95jGnLC3e1osxJRC4I3AXmFcBxkEVAHhp2e27LyyMpjSCXuJ5ceXTrwiVc+4fR5\n237faMKpvAFyKFldAcYLCdJ3nTwNXAHm19yD6aa4PSJjPQAfwe+Pvm+82IukMHtxNdkX9+j/i2Xd\nGj3IxacYcV95t6ICDBLgLcdntBpQapg8SaQbq7wzJycfp4AEmIHMsfmctelpvFDSvIdGKCNV+I5+\naMZWMOy/YXQGls128hekO94nTFrWDAZOOHtP7UNqXo4RMVgKEDeWUzAia0LGDlc3lpVgs4L4g6bc\nsDdU63ewquQdzvYBb1hdeb2s/wASO7HrP55FinVv9PDZM/FeAzJunFECYsiXdTtiNkYzCzhheGuN\nDAF+RIDvjMnCBrv2M5ABrQtKkRMynNWziE9Tx/qMEDcjGVfOi3JYQGLngMTjdeBia9b3kPvKonXt\ne2vbgUsw+cLHXv+YDyJmnse6sASnBTKX8kv6MHweC5OMw0hq8fXAE9YUPyfnyxqMyIu3P25H3fFg\nLzKRz4sgdiNiE32RcrpH7S6+3gPokTQCRYYM8+q5pIbncLix+tPSyvrT03OI/Nt9G/i+o49Z09WS\nuWe7sIbb8ccTkPdJz08ne9q5MY+k5ZXS2+gcH7yR8ByeI0R2mX6TyKFoniyQURAvDvIBxBLocXks\nHtaGyjj4V/zi4DpufNwS3DJgwMO1xP7bvwVDWWwPAg+Yw3dAVwskSfwAdzeWewB9QuivpSsrxM6P\nXQoMgiFFVrdSRhXzaeKrBjxlxA6scsZAouaAxMNoQ7I/ziVWQFoWNizMBHp9pq/57JqzQ378zgZu\ndQyXypteuIiiFVkkTt+NXtMxJEieBWwA0/kFj3RfJYffIK1FvOLmxnITEO8BdCdi2dUhu9DWDT/l\nKHLBWQXQVFIyJS0UbOcV/0wC+AnwJUSkv+F4l8ZLw26rfBJfsKci2VqHYh7x+0MMdhykYPGFEfdX\nV2eSlpNNx17H+WVsAONe65foQWn/QmSLpOiU3rEinoCMJP5hM+TSNfH9nYnPrgfyEv8AOZ4biExR\nfhqppbnW9RVhVEDiEEBMs+3WzXkg70P8qHuQueaj4WlguWn35QmTaAbIeFNPty9Id5kfp/vKx1ep\n4zgvxW314XRhVeE1GBt2Y8VYIIiP+j+X1yz/LuE2JuF1ulsg7wOzqK7OYupVV9B/vA2/P14RWxyM\nbqyeRcCbYNo+dUnfTSYBagmwYfgnDjGcgDQStkBGmwgw5MbKknTcJ4HHCLD+YEXBAqO7rhjJuvtH\nZHP0VwRwbgaaMuVCutdaWyIBcQ+g25jBreTMWhR1byUDx0OEeuMJZLSARBNdVDhWjKWAOL4PRjUY\ndrJJFV7WHqCfAFc5m4xaHoZ/Ar46jBUyKQQkPdkLcMFEejhFu4eWIheYpcjB24DklXtxKwxhQJ8p\nLU0+DdzreGgx8K77q8aderoHg2CsxhaQAAbt3MZrCbrTioDYrqMqvAvIq0ivpZVEBoZbgDIC/NPD\nPLyKSBcIxLNA/P5+qqsPA2dRtOJiug97yVZywQgBXwZzP/CKNDlkGsP3JZpsRF8ccxBrwb6YWoH0\nUbuwwCEgiMvkEaA2LcShgeziy4N5lRsJcGmC19vTFF9Dvlf5RLqQnLgH0G3SC18hd/ZHkWFgvQAM\nnJxP7zEf8f30XgRkQiwQUzZNcxl5gXQ9kYO5bLxaIPF4CtlIX4n03YvAEpZJISCT0QIBd+X9MPAL\nxI1zGNn1rh7l+/8Q+OuodL5kWiB1dA2Y+ILLsE+KEJcxQAFbI9pkRDNaC+RVZMeVTWQrENsCgchG\nijbxLBCw3Vj5Zy2kfXfMST8yjB8jvdi+BTwLxjBuuUlHtAWyEBEK2wV5DCkEGwsBsUW/ngDfG/w6\nrzeVlAyYvvRjw7y+ETl3/i/wyXcloWI4C8SdtKwtFCwexDlZsPdsO52LAAARg0lEQVTYKvqPdxHR\nwj+COmAaAZeUWpl86SWeNxrcLBA/8JqLi3g44mUlVnEK1pMhgfRvAN8z4ZMuHTRKgV4P/brGnckq\nIHchrpUfwVBzvxlE7maO4u6PHxZDXGD7iByMlcQYCPV09KeTfaIQ2wLp4T620k4oYaqqM423Cs8C\nYjQhJ/jbRI7bbCEsIM5W7jYNyJfe7bzZA1xFekEu9U+PYrxvzBpfQSyk+079vSacaAGJ3pwcQyxp\nE2IqqEf6GdFDpcrrysu7id8Hy6YJsUAagIe/IrU/8QQksQUC75E9JZ3MsnAqcWhgGQPt8WePB6RJ\nKO4X4JlAfQLxORXcBGQ07iuIn5V4qhYISCfee5GNc60JPzfhWlO8RpPC+oDkCchLSO1B9O1GpNBp\nLhIQbECyR+IRLxMn4LitjfOcoWC6KYqeQ2wPqoniBF0hg7w6kC/ONDJZyzs8SeJ5x6O1QEDiINGx\nleNAYTXV6UQOkxKkovgk7j7k3Zjm7RzfDIMdo91VR2HUWgH2VCPaPeMmIGuAA8TOyx7JZ9gC4hwq\nVVZfXi5T/xLTilVUVw7f3gyFq9z/ruXIJi7+39Tv72fgZDNFK8NtcgzfPAY7hvs+xXNjjZf7CsZW\nQMbFAgHJyDLgN4Z0c56PxLMCyMbhnxkbAVlL5LVyxCQrBnKVx+f9kHAAtY7IXV2iYSoBD+/9FPBv\npuwWZpJ4jO14Y9JrtJFXOwURsU+zh3Y6iTdIxkYsEJkIWMXIBOSr0Xf48QerqT6JtJFwc2GB+Mwf\nIsAdUTvE3RhGNse3tQ35wc9c3CwQZ7fTRuT4emnal+gzZiNzHJw1GGX1ZWV259m4GBAy5e9b0Qzd\n34e+O+V780civwfnIsksiWONg527yZ0VTuX1Zc9ksCNmKFMU8QRkvDKwIEpATPnulxE//pOIJqDM\nhPSonlRjYYEMYYhn4HuIS2sRMhl0LLowvIxzQBx8baRvMBldWM6W4TcRrpJeB3wUSXGbi8wWGHUr\ndSvf++dIw7XFJC/+IfSajeQfDZJ9vJEQn2EjOUisIj5SkT2IfCFG6DM2mixXVjQt1nvFWiDC7YgJ\n/YTVGsNmD2ZogOObk3scJwd2y3b7+LhZIHBqF4F4LqyyhtJSk+g2Ju7YgfTZnxILoxhxmThJHP+w\nMYN/IntG1dDv6bnlDJwYrlNAIgtkPDKwILb9jx+oNkaYjAND3S1acPQ2M+UYpjF61+Rwn7nXgPuN\n8GC5pDIZBeSbyG7gbaTlxt3W/buQIqRdSBuLz3LqFsMPEAFZT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"text/plain": [ "<matplotlib.figure.Figure at 0x1019bba10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1040aefd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Have to run previous python cell first\n", "A_noisy = A + numpy.random.normal(scale=0.2*numpy.max(A), size=A.shape)\n", "\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "pyplot.plot(x, A_noisy)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)**2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What if we just increase the number of neurons? Will it help?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Taking noise into account\n", "\n", "- Include noise while solving for decoders\n", "\n", " - Introduce noise term $\\eta$\n", "\n", "$ \n", "\\begin{align}\n", "\\hat{x} &= \\sum_i(a_i+\\eta)d_i \\\\\n", "E &= {1 \\over 2} \\int_{-1}^1 (x-\\hat{x})^2 \\;dx d\\eta\\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i(a_i+\\eta)d_i\\right)^2 \\;dx d\\eta\\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i - \\sum \\eta d_i \\right)^2 \\;dx d\\eta\n", "\\end{align}\n", "$\n", "- Assume noise is gaussian, independent, mean zero, and has the same variance for each neuron\n", " - $\\eta = \\mathcal{N}(0, \\sigma)$\n", " - All the noise cross-terms disappear (independent)\n", "\n", "$ \n", "\\begin{align}\n", "E &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sum_{i,j} d_i d_j <\\eta_i \\eta_j>_\\eta \\\\\n", " &= {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sum_{i} d_i d_i <\\eta_i \\eta_i>_\\eta \n", "\\end{align}\n", "$\n", "\n", "- Since the average of $\\eta_i \\eta_i$ noise is its variance (since the mean is zero), $\\sigma^2$, we get\n", " \n", "$ \n", "\\begin{align}\n", "E = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$\n", "\n", "- The practical result is that, when computing the decoder, we get\n", "\n", "$ \n", "\\begin{align}\n", "\\Gamma_{ij} = \\sum_x a_i a_j / S + \\sigma^2 \\delta_{ij}\n", "\\end{align}\n", "$\n", "\n", "\n", "- Where $\\delta_{ij}$ is the Kronecker delta: http://en.wikipedia.org/wiki/Kronecker_delta\n", " \n", "- To simplfy computing this using matrices, this can be written as $\\Gamma=A^T A /S + \\sigma^2 I$\n", "\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE 0.0608650480064\n" ] }, { "data": { "image/png": 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C8NkCuyrpISAKip+C0jOQ/1PoaYGUoxUT/qJQIU0VKbPHi2TEpKaOvk0WPBlY0jDEZKfn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UwBLsIrIK5VeYbubldEVUhq2MUu5T6XQ2EnErVLlLhu7HtwtEQVQ6/L6Fro5N2yKqqBly+OqYNt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UTByDjt7ovYJzO5/0GB/Pwzsc1lLFqn/5nTR7OpbViJWNWNNX2tJi1mZuV6RURYWf9EHyM1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"text/plain": [ "<matplotlib.figure.Figure at 0x104166fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10435a290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 100\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates=Uniform(100,200)) #Defaults to LIF neurons, \n", " #with random gains and biases for\n", " #neurons between 100-200hz over -1,1\n", "\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqNoise(noise=0.2)) #Add noise ###NEW\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "A_noisy = A + numpy.random.normal(scale=0.2*numpy.max(A), size=A.shape)\n", "\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "pyplot.plot(x, A_noisy)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)')\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1)\n", "\n", "print 'RMSE', np.sqrt(np.average((x-xhat)**2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Number of neurons\n", "\n", "- What happens to the error with more neurons?\n", " - Note that the error has two parts:\n", " \n", "$ \n", "\\begin{align}\n", "E = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 \\;dx + \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$ \n", "\n", "- Error due to static distortion (i.e. the error introduced by the decoders themselves)\n", " - This is present regardless of noise\n", " \n", "$ \n", "\\begin{align}\n", "E_{distortion} = {1 \\over 2} \\int_{-1}^1 \\left(x-\\sum_i a_i d_i \\right)^2 dx \n", "\\end{align}\n", "$ \n", "\n", "\n", "- Error due to noise\n", " \n", "$\n", "\\begin{align}\n", "E_{noise} = \\sigma^2 \\sum_i d_i^2 \n", "\\end{align}\n", "$ \n", " \n", "- What do these look like as number of neurons $N$ increases? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"files/lecture2/repn_noise.png\">\n", "- Noise error is proportional to $1/N$\n", "- Distortion error is proportional to $1/N^2$\n", "- Remember this error $E$ is defined as\n", "\n", "$ E = {1 \\over 2} \\int_{-1}^1 (x-\\hat{x})^2 dx $\n", "\n", "- So that's actually a squared error term\n", "\n", "- Also, as number of neurons is greater than 100 or so, the error is dominated by the noise term ($1/N$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "\n", "- Methodology for building models with the Neural Engineering Framework (outlined in Chapter 1)\n", " 1. System Description: Describe the system of interest in terms of the neural data, architecture, computations, representations, etc. (e.g. response functions, tuning curves, etc.)\n", " 2. Design Specification: Add additional performance constraints (e.g. bandwidth, noise, SNR, dynamic range, stability, etc.)\n", " 3. Implement the model: Employ the NEF principles given the System Description and Design Specification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Horizontal Eye Control (1D)\n", "\n", "From http://www.nature.com/nrn/journal/v3/n12/full/nrn986.html\n", "\n", "<img src=\"files/lecture2/horizontal_eye.jpg\">\n", "\n", "There are also neurons whose response goes the other way. All of the neurons are directly connected to the muscle controlling the horizontal direction of the eye, and that's the only thing that muscle does, so we're pretty sure this is what's being repreesnted.\n", "\n", "- System Description\n", "\n", " - We've only done the first NEF principle, so that's all we'll worry about\n", " - What is being represented?\n", " - $x$ is the horizontal position\n", " - Tuning curves: extremely linear (high $\\tau_{RC}$, low $\\tau_{ref}$)\n", " - some have $e=1$, some have $e=-1$\n", " - these are often called \"on\" and \"off\" neurons, respectively\n", " - Firing rates of up to 300Hz\n", " \n", "- Design Specification\n", " - Range of values for $x$: -60 degrees to +60 degrees\n", " - Normal levels of noise: $\\sigma$ is 20% of maximum firing rate\n", " - the book goes a bit higher, with $\\sigma^2=0.1$, meaning that $\\sigma = \\sqrt{0.1} \\approx 0.32$ times the maximum firing rate\n", "\n", "- Implementation\n", " - Examine the tuning curves\n", " - Then use principle 1" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MSpaNHmmQytbG4GP7CU0YUpnREfBgCG+NiQnA4KRNk4g6vEKZmgDMHGCe/rjw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\n/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eCZhno/1EYztGsJKelBeAUIx+IKZBAeq8LcpJ7mOgete3rjWTRA3IbaMz7r/vsd6ATv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\nDCmo8N5SVKKgT0aNgyA9UF96U4MJlOM9Euhph+voBjj9m5cei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"text/plain": [ "<matplotlib.figure.Figure at 0x1043ce6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#%pylab inline\n", "import numpy\n", "import nengo\n", "from nengo.utils.ensemble import tuning_curves\n", "from nengo.dists import Uniform\n", "\n", "N = 40\n", "tau_rc = .2\n", "tau_ref = .001\n", "lif_model = nengo.LIFRate(tau_rc=tau_rc, tau_ref=tau_ref)\n", "\n", "model = nengo.Network(label='Neurons')\n", "with model:\n", " neurons = nengo.Ensemble(N, dimensions=1, \n", " max_rates = Uniform(250,300),\n", " neuron_type = lif_model)\n", "sim = nengo.Simulator(model)\n", "\n", "x, A = tuning_curves(neurons, sim)\n", "\n", "plot(x, A)\n", "xlabel('x')\n", "ylabel('firing rate (Hz)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- How good is the representation?" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE with 40 neurons is 0.0809303\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10435a290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Have to run previous code cell first\n", "noise = 0.2\n", "\n", "with model:\n", " connection = nengo.Connection(neurons, neurons, #This is just to generate the decoders\n", " solver=nengo.solvers.LstsqNoise(noise=0.2)) #Add noise ###NEW\n", " \n", "sim = nengo.Simulator(model)\n", "\n", "d = sim.data[connection].weights.T\n", "x, A = tuning_curves(neurons, sim)\n", "A_noisy = A + numpy.random.normal(scale=noise*numpy.max(A), size=A.shape)\n", "xhat = numpy.dot(A_noisy, d)\n", "\n", "print 'RMSE with %d neurons is %g'%(N, np.sqrt(np.average((x-xhat)**2)))\n", "\n", "figure()\n", "plot(x, x)\n", "plot(x, xhat)\n", "xlabel('$x$')\n", "ylabel('$\\hat{x}$')\n", "ylim(-1, 1)\n", "xlim(-1, 1);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Possible questions\n", " - How many neurons do we need for a particular level of accuracy?\n", " - What happens with different firing rates?\n", " - What happens with different distributions of x-intercepts?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: Arm Movements (2D)\n", "\n", "- [Georgopoulos et al., 1982. \"On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex.\"](http://www.ncbi.nlm.nih.gov/pubmed/7143039)\n", "\n", "<img src=\"files/lecture2/armmovement1.jpg\">\n", "\n", "<img src=\"files/lecture2/armmovement2.png\">\n", "\n", "<img src=\"files/lecture2/armtuningcurve.png\">\n", "\n", "- System Description\n", "\n", " - What is being represented?\n", " - $x$ is the hand position\n", " - Note that this is *different* from what Georgopoulos talks about in this initial paper\n", " - Initial paper only looks at those 8 positions, so it only talks about direction of movement (angle but not magnitude)\n", " - More recent work in the same area shows the cells do respond to both (Fu et al, 1993; Messier and Kalaska, 2000)\n", " - Bell-shaped tuning curves\n", " - Encoders: randomly distributed around the unit circle\n", " - Firing rates of up to 60Hz\n", " \n", "- Design Specification\n", " - Range of values for $x$: Anywhere within a unit circle (or perhaps some other radius)\n", " - Normal levels of noise: $\\sigma$ is 20% of maximum firing rate\n", " - the book goes a bit higher, with $\\sigma^2=0.1$, meaning that $\\sigma = \\sqrt{0.1} \\approx 0.32$ times the maximum \n", "- Implementation\n", " - Examine the tuning curves\n" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1046095d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "import nengo\n", "\n", "n = nengo.neurons.LIFRate() \n", "\n", "theta = numpy.linspace(-numpy.pi, numpy.pi, 100)\n", "x = numpy.array([numpy.sin(theta), numpy.cos(theta)])\n", "\n", "e = numpy.array([1.0, 0])\n", "\n", "plot(theta*180/numpy.pi, n.rates(numpy.dot(x.T, e), bias=1, gain=0.2)) #bias 1->1.5\n", "xlabel('angle')\n", "ylabel('firing rate')\n", "xlim(-180, 180)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Does it match empirical data?\n", " - When tuning curves are plotted just considering $\\theta$, they are fit by $a_i=b_0+b_1cos(\\theta-\\theta_e)$ \n", " - Where $\\theta_e$ is the angle for the encoder $e_i$ and $b_0$ and $b_1$ are constants\n", " \n", "- Interestingly, Georgopoulos suggests doing linear decoding:\n", " - $\\hat{x}=\\sum_i a_i e_i$\n", " - This gives a somewhat decent estimate of the direction of movement (but a terrible estimate of magnitude)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Higher-dimensional Tuning\n", "- Note that there can be different ways of organizing the representation of a higher dimensional space\n", "\n", "<img src=\"files/lecture2/semicircular_canal.png\">\n", "\n", "- Here, the neurons respond to angular velocity. This is a 3D vector. \n", "- But, instead of randomly distributing encoders around the 3D space, they are aligned with a major axis\n", " - encoders are chosen from [1,0,0], [-1,0,0], [0,1,0], [0,-1,0], [0,0,1], [0,0,-1]\n", "- This can affect on the representation\n", "\n", "<img src=\"files/lecture2/aligned_encoders.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Administrative Notes\n", "\n", "- Assignment 1 has been posted \n", " - [HTML](http://nbviewer.ipython.org/github/celiasmith/syde556/blob/master/Assignment%201.ipynb)\n", "- Due: January 25th at midnight\n", "- Total marks: 20 (20% of final grade)\n", "- Late penalty: 1 mark per day\n", "- It is recommended that you use a language with a matrix library and graphing capabilities. Two main suggestions are Python and MATLAB.\n", "\n", "- Tutoring Services\n", " - If you would like more personalized help for the assignments in this course, two of the PhD students in the lab (Xuan Choo and Travis DeWolf) offer tutoring services. They can be contacted at [[email protected]](mailto:[email protected]) and they charge \\$20 per half hour (or \\$15 per person per half hour for groups)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

CompPhysics/MachineLearning

doc/src/LectureNotes/chapter4.ipynb

2

198219

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression and more Advanced Regression Analysis\n", "\n", "\n", "## Why Linear Regression (aka Ordinary Least Squares and family)\n", "\n", "Fitting a continuous function with linear parameterization in terms of the parameters $\\boldsymbol{\\beta}$.\n", "* Method of choice for fitting a continuous function!\n", "\n", "* Gives an excellent introduction to central Machine Learning features with **understandable pedagogical** links to other methods like **Neural Networks**, **Support Vector Machines** etc\n", "\n", "* Analytical expression for the fitting parameters $\\boldsymbol{\\beta}$\n", "\n", "* Analytical expressions for statistical propertiers like mean values, variances, confidence intervals and more\n", "\n", "* Analytical relation with probabilistic interpretations \n", "\n", "* Easy to introduce basic concepts like bias-variance tradeoff, cross-validation, resampling and regularization techniques and many other ML topics\n", "\n", "* Easy to code! And links well with classification problems and logistic regression and neural networks\n", "\n", "* Allows for **easy** hands-on understanding of gradient descent methods\n", "\n", "* and many more features\n", "\n", "For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n", "Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n", "\n", "\n", "## Regression analysis, overarching aims\n", "\n", "Regression modeling deals with the description of the sampling distribution of a given random variable $y$ and how it varies as function of another variable or a set of such variables $\\boldsymbol{x} =[x_0, x_1,\\dots, x_{n-1}]^T$. \n", "The first variable is called the **dependent**, the **outcome** or the **response** variable while the set of variables $\\boldsymbol{x}$ is called the independent variable, or the predictor variable or the explanatory variable. \n", "\n", "A regression model aims at finding a likelihood function $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$, that is the conditional distribution for $\\boldsymbol{y}$ with a given $\\boldsymbol{x}$. The estimation of $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$ is made using a data set with \n", "* $n$ cases $i = 0, 1, 2, \\dots, n-1$ \n", "\n", "* Response (target, dependent or outcome) variable $y_i$ with $i = 0, 1, 2, \\dots, n-1$ \n", "\n", "* $p$ so-called explanatory (independent or predictor) variables $\\boldsymbol{x}_i=[x_{i0}, x_{i1}, \\dots, x_{ip-1}]$ with $i = 0, 1, 2, \\dots, n-1$ and explanatory variables running from $0$ to $p-1$. See below for more explicit examples. \n", "\n", " The goal of the regression analysis is to extract/exploit relationship between $\\boldsymbol{y}$ and $\\boldsymbol{x}$ in or to infer causal dependencies, approximations to the likelihood functions, functional relationships and to make predictions, making fits and many other things.\n", "\n", "\n", "\n", "## Regression analysis, overarching aims II\n", "\n", "\n", "Consider an experiment in which $p$ characteristics of $n$ samples are\n", "measured. The data from this experiment, for various explanatory variables $p$ are normally represented by a matrix \n", "$\\mathbf{X}$.\n", "\n", "The matrix $\\mathbf{X}$ is called the *design\n", "matrix*. Additional information of the samples is available in the\n", "form of $\\boldsymbol{y}$ (also as above). The variable $\\boldsymbol{y}$ is\n", "generally referred to as the *response variable*. The aim of\n", "regression analysis is to explain $\\boldsymbol{y}$ in terms of\n", "$\\boldsymbol{X}$ through a functional relationship like $y_i =\n", "f(\\mathbf{X}_{i,\\ast})$. When no prior knowledge on the form of\n", "$f(\\cdot)$ is available, it is common to assume a linear relationship\n", "between $\\boldsymbol{X}$ and $\\boldsymbol{y}$. This assumption gives rise to\n", "the *linear regression model* where $\\boldsymbol{\\beta} = [\\beta_0, \\ldots,\n", "\\beta_{p-1}]^{T}$ are the *regression parameters*. \n", "\n", "Linear regression gives us a set of analytical equations for the parameters $\\beta_j$.\n", "\n", "\n", "\n", "\n", "\n", "## Examples\n", "In order to understand the relation among the predictors $p$, the set of data $n$ and the target (outcome, output etc) $\\boldsymbol{y}$,\n", "consider the model we discussed for describing nuclear binding energies. \n", "\n", "There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model.\n", "Assuming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "BE(A) = a_0+a_1A+a_2A^{2/3}+a_3A^{-1/3}+a_4A^{-1},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have five predictors, that is the intercept, the $A$ dependent term, the $A^{2/3}$ term and the $A^{-1/3}$ and $A^{-1}$ terms.\n", "This gives $p=0,1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a \n", "$p\\times n$ matrix $\\boldsymbol{X}$.\n", "\n", "Here the predictors are based on a model we have made. A popular data set which is widely encountered in ML applications is the\n", "so-called [credit card default data from Taiwan](https://www.sciencedirect.com/science/article/pii/S0957417407006719?via%3Dihub). The data set contains data on $n=30000$ credit card holders with predictors like gender, marital status, age, profession, education, etc. In total there are $24$ such predictors or attributes leading to a design matrix of dimensionality $24 \\times 30000$. This is however a classification problem and we will come back to it when we discuss Logistic Regression.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "## General linear models\n", "Before we proceed let us study a case from linear algebra where we aim at fitting a set of data $\\boldsymbol{y}=[y_0,y_1,\\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\\boldsymbol{x}=[x_0,x_1,\\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. \n", "\n", "Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of $y$ which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree $n-1$ with $n$ points, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y=y(x) \\rightarrow y(x_i)=\\tilde{y}_i+\\epsilon_i=\\sum_{j=0}^{n-1} \\beta_j x_i^j+\\epsilon_i,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\epsilon_i$ is the error in our approximation.\n", "\n", "\n", "\n", "\n", "## Rewriting the fitting procedure as a linear algebra problem\n", "For every set of values $y_i,x_i$ we have thus the corresponding set of equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "y_0&=\\beta_0+\\beta_1x_0^1+\\beta_2x_0^2+\\dots+\\beta_{n-1}x_0^{n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0+\\beta_1x_1^1+\\beta_2x_1^2+\\dots+\\beta_{n-1}x_1^{n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0+\\beta_1x_2^1+\\beta_2x_2^2+\\dots+\\beta_{n-1}x_2^{n-1}+\\epsilon_2\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0+\\beta_1x_{n-1}^1+\\beta_2x_{n-1}^2+\\dots+\\beta_{n-1}x_{n-1}^{n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rewriting the fitting procedure as a linear algebra problem, more details\n", "Defining the vectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = [y_0,y_1, y_2,\\dots, y_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = [\\beta_0,\\beta_1, \\beta_2,\\dots, \\beta_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\epsilon} = [\\epsilon_0,\\epsilon_1, \\epsilon_2,\\dots, \\epsilon_{n-1}]^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the design matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\n", "\\begin{bmatrix} \n", "1& x_{0}^1 &x_{0}^2& \\dots & \\dots &x_{0}^{n-1}\\\\\n", "1& x_{1}^1 &x_{1}^2& \\dots & \\dots &x_{1}^{n-1}\\\\\n", "1& x_{2}^1 &x_{2}^2& \\dots & \\dots &x_{2}^{n-1}\\\\ \n", "\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n", "1& x_{n-1}^1 &x_{n-1}^2& \\dots & \\dots &x_{n-1}^{n-1}\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can rewrite our equations as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above design matrix is called a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix).\n", "\n", "\n", "\n", "\n", "## Generalizing the fitting procedure as a linear algebra problem\n", "\n", "We are obviously not limited to the above polynomial expansions. We\n", "could replace the various powers of $x$ with elements of Fourier\n", "series or instead of $x_i^j$ we could have $\\cos{(j x_i)}$ or $\\sin{(j\n", "x_i)}$, or time series or other orthogonal functions. For every set\n", "of values $y_i,x_i$ we can then generalize the equations to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_2\\\\\n", "\\dots & \\dots \\\\\n", "y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_i\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note that we have $p=n$ here. The matrix is symmetric. This is generally not the case!**\n", "\n", "\n", "\n", "\n", "## Generalizing the fitting procedure as a linear algebra problem\n", "We redefine in turn the matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\n", "\\begin{bmatrix} \n", "x_{00}& x_{01} &x_{02}& \\dots & \\dots &x_{0,n-1}\\\\\n", "x_{10}& x_{11} &x_{12}& \\dots & \\dots &x_{1,n-1}\\\\\n", "x_{20}& x_{21} &x_{22}& \\dots & \\dots &x_{2,n-1}\\\\ \n", "\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n", "x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \\dots & \\dots &x_{n-1,n-1}\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and without loss of generality we rewrite again our equations as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The left-hand side of this equation is kwown. Our error vector $\\boldsymbol{\\epsilon}$ and the parameter vector $\\boldsymbol{\\beta}$ are our unknow quantities. How can we obtain the optimal set of $\\beta_i$ values?\n", "\n", "\n", "\n", "\n", "## Optimizing our parameters\n", "We have defined the matrix $\\boldsymbol{X}$ via the equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n", "y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n", "y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_1\\\\\n", "\\dots & \\dots \\\\\n", "y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_1\\\\\n", "\\dots & \\dots \\\\\n", "y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we noted above, we stayed with a system with the design matrix \n", " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$, that is we have $p=n$. For reasons to come later (algorithmic arguments) we will hereafter define \n", "our matrix as $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors refering to the column numbers and the entries $n$ being the row elements.\n", "\n", "\n", "\n", "\n", "## Our model for the nuclear binding energies\n", "\n", "In our [introductory notes](https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html) we looked at the so-called [liquid drop model](https://en.wikipedia.org/wiki/Semi-empirical_mass_formula). Let us remind ourselves about what we did by looking at the code.\n", "\n", "We restate the parts of the code we are most interested in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Common imports\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from IPython.display import display\n", "import os\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"MassEval2016.dat\"),'r')\n", "\n", "\n", "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", " names=('N', 'Z', 'A', 'Element', 'Ebinding'),\n", " widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),\n", " header=39,\n", " index_col=False)\n", "\n", "# Extrapolated values are indicated by '#' in place of the decimal place, so\n", "# the Ebinding column won't be numeric. Coerce to float and drop these entries.\n", "Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')\n", "Masses = Masses.dropna()\n", "# Convert from keV to MeV.\n", "Masses['Ebinding'] /= 1000\n", "\n", "# Group the DataFrame by nucleon number, A.\n", "Masses = Masses.groupby('A')\n", "# Find the rows of the grouped DataFrame with the maximum binding energy.\n", "Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])\n", "A = Masses['A']\n", "Z = Masses['Z']\n", "N = Masses['N']\n", "Element = Masses['Element']\n", "Energies = Masses['Ebinding']\n", "\n", "# Now we set up the design matrix X\n", "X = np.zeros((len(A),5))\n", "X[:,0] = 1\n", "X[:,1] = A\n", "X[:,2] = A**(2.0/3.0)\n", "X[:,3] = A**(-1.0/3.0)\n", "X[:,4] = A**(-1.0)\n", "# Then nice printout using pandas\n", "DesignMatrix = pd.DataFrame(X)\n", "DesignMatrix.index = A\n", "DesignMatrix.columns = ['1', 'A', 'A^(2/3)', 'A^(-1/3)', '1/A']\n", "display(DesignMatrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With $\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p\\times 1}$, it means that we will hereafter write our equations for the approximation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "throughout these lectures. \n", "\n", "\n", "## Optimizing our parameters, more details\n", "With the above we use the design matrix to define the approximation $\\boldsymbol{\\tilde{y}}$ via the unknown quantity $\\boldsymbol{\\beta}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and in order to find the optimal parameters $\\beta_i$ instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values $y_i$ (which represent hopefully the exact values) and the parameterized values $\\tilde{y}_i$, namely" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or using the matrix $\\boldsymbol{X}$ and in a more compact matrix-vector notation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is one possible way to define the so-called cost function.\n", "\n", "\n", "\n", "It is also common to define\n", "the function $C$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{2n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "since when taking the first derivative with respect to the unknown parameters $\\beta$, the factor of $2$ cancels out.\n", "\n", "\n", "\n", "\n", "## Interpretations and optimizing our parameters\n", "\n", "The function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "can be linked to the variance of the quantity $y_i$ if we interpret the latter as the mean value. \n", "When linking (see the discussion below) with the maximum likelihood approach below, we will indeed interpret $y_i$ as a mean value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y_{i}=\\langle y_i \\rangle = \\beta_0x_{i,0}+\\beta_1x_{i,1}+\\beta_2x_{i,2}+\\dots+\\beta_{n-1}x_{i,n-1}+\\epsilon_i,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\langle y_i \\rangle$ is the mean value. Keep in mind also that\n", "till now we have treated $y_i$ as the exact value. Normally, the\n", "response (dependent or outcome) variable $y_i$ the outcome of a\n", "numerical experiment or another type of experiment and is thus only an\n", "approximation to the true value. It is then always accompanied by an\n", "error estimate, often limited to a statistical error estimate given by\n", "the standard deviation discussed earlier. In the discussion here we\n", "will treat $y_i$ as our exact value for the response variable.\n", "\n", "In order to find the parameters $\\beta_i$ we will then minimize the spread of $C(\\boldsymbol{\\beta})$, that is we are going to solve the problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practical terms it means we will require" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)^2\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_{ij}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or in a matrix-vector form as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretations and optimizing our parameters\n", "We can rewrite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and if the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ is invertible we have the solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} =\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note also that since our design matrix is defined as $\\boldsymbol{X}\\in\n", "{\\mathbb{R}}^{n\\times p}$, the product $\\boldsymbol{X}^T\\boldsymbol{X} \\in\n", "{\\mathbb{R}}^{p\\times p}$. In the above case we have that $p \\ll n$,\n", "in our case $p=5$ meaning that we end up with inverting a small\n", "$5\\times 5$ matrix. This is a rather common situation, in many cases we end up with low-dimensional\n", "matrices to invert. The methods discussed here and for many other\n", "supervised learning algorithms like classification with logistic\n", "regression or support vector machines, exhibit dimensionalities which\n", "allow for the usage of direct linear algebra methods such as **LU** decomposition or **Singular Value Decomposition** (SVD) for finding the inverse of the matrix\n", "$\\boldsymbol{X}^T\\boldsymbol{X}$.\n", "\n", "\n", "\n", "**Small question**: Do you think the example we have at hand here (the nuclear binding energies) can lead to problems in inverting the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$? What kind of problems can we expect?\n", "\n", "\n", "\n", "## Some useful matrix and vector expressions\n", "\n", "The following matrix and vector relation will be useful here and for the rest of the course. Vectors are always written as boldfaced lower case letters and \n", "matrices as upper case boldfaced letters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "6\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "7\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "8\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\log{\\vert\\boldsymbol{A}\\vert}}{\\partial \\boldsymbol{A}} = (\\boldsymbol{A}^{-1})^T.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretations and optimizing our parameters\n", "The residuals $\\boldsymbol{\\epsilon}$ are in turn given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\epsilon} = \\boldsymbol{y}-\\boldsymbol{\\tilde{y}} = \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{\\epsilon}=\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "meaning that the solution for $\\boldsymbol{\\beta}$ is the one which minimizes the residuals. Later we will link this with the maximum likelihood approach.\n", "\n", "\n", "\n", "\n", "Let us now return to our nuclear binding energies and simply code the above equations. \n", "\n", "## Own code for Ordinary Least Squares\n", "\n", "It is rather straightforward to implement the matrix inversion and obtain the parameters $\\boldsymbol{\\beta}$. After having defined the matrix $\\boldsymbol{X}$ we simply need to \n", "write" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# matrix inversion to find beta\n", "beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)\n", "# and then make the prediction\n", "ytilde = X @ beta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can use the least squares functionality in **Numpy** as" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n", "ytildenp = np.dot(fit,X.T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally we plot our fit with and compare with data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Masses['Eapprox'] = ytilde\n", "# Generate a plot comparing the experimental with the fitted values values.\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(r'$A = N + Z$')\n", "ax.set_ylabel(r'$E_\\mathrm{bind}\\,/\\mathrm{MeV}$')\n", "ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,\n", " label='Ame2016')\n", "ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',\n", " label='Fit')\n", "ax.legend()\n", "save_fig(\"Masses2016OLS\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding error analysis and training set up\n", "\n", "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of **Scikit-Learn** in the introductory slides.\n", "Since we are not using **Scikit-Learn** here we can define our own $R2$ function as" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def R2(y_data, y_model):\n", " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and we would be using it as" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(R2(Energies,ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily add our **MSE** score as" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", " return np.sum((y_data-y_model)**2)/n\n", "\n", "print(MSE(Energies,ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and finally the relative error as" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def RelativeError(y_data,y_model):\n", " return abs((y_data-y_model)/y_data)\n", "print(RelativeError(Energies, ytilde))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "Normally, the response (dependent or outcome) variable $y_i$ is the\n", "outcome of a numerical experiment or another type of experiment and is\n", "thus only an approximation to the true value. It is then always\n", "accompanied by an error estimate, often limited to a statistical error\n", "estimate given by the standard deviation discussed earlier. In the\n", "discussion here we will treat $y_i$ as our exact value for the\n", "response variable.\n", "\n", "Introducing the standard deviation $\\sigma_i$ for each measurement\n", "$y_i$, we define now the $\\chi^2$ function (omitting the $1/n$ term)\n", "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\chi^2(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\frac{\\left(y_i-\\tilde{y}_i\\right)^2}{\\sigma_i^2}=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\frac{1}{\\boldsymbol{\\Sigma^2}}\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the matrix $\\boldsymbol{\\Sigma}$ is a diagonal matrix with $\\sigma_i$ as matrix elements.\n", "\n", "\n", "\n", "## The $\\chi^2$ function\n", "\n", "In order to find the parameters $\\beta_i$ we will then minimize the spread of $\\chi^2(\\boldsymbol{\\beta})$ by requiring" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)^2\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}\\frac{x_{ij}}{\\sigma_i}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or in a matrix-vector form as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have defined the matrix $\\boldsymbol{A} =\\boldsymbol{X}/\\boldsymbol{\\Sigma}$ with matrix elements $a_{ij} = x_{ij}/\\sigma_i$ and the vector $\\boldsymbol{b}$ with elements $b_i = y_i/\\sigma_i$.\n", "\n", "\n", "\n", "## The $\\chi^2$ function\n", "\n", "We can rewrite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{A}^T\\boldsymbol{b} = \\boldsymbol{A}^T\\boldsymbol{A}\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and if the matrix $\\boldsymbol{A}^T\\boldsymbol{A}$ is invertible we have the solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} =\\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1}\\boldsymbol{A}^T\\boldsymbol{b}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "If we then introduce the matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{H} = \\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have then the following expression for the parameters $\\beta_j$ (the matrix elements of $\\boldsymbol{H}$ are $h_{ij}$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_j = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}\\frac{y_i}{\\sigma_i}\\frac{x_{ik}}{\\sigma_i} = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}b_ia_{ik}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We state without proof the expression for the uncertainty in the parameters $\\beta_j$ as (we leave this as an exercise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sigma^2(\\beta_j) = \\sum_{i=0}^{n-1}\\sigma_i^2\\left( \\frac{\\partial \\beta_j}{\\partial y_i}\\right)^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "resulting in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sigma^2(\\beta_j) = \\left(\\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}a_{ik}\\right)\\left(\\sum_{l=0}^{p-1}h_{jl}\\sum_{m=0}^{n-1}a_{ml}\\right) = h_{jj}!\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "The first step here is to approximate the function $y$ with a first-order polynomial, that is we write" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y=y(x) \\rightarrow y(x_i) \\approx \\beta_0+\\beta_1 x_i.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By computing the derivatives of $\\chi^2$ with respect to $\\beta_0$ and $\\beta_1$ show that these are given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -2\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_i\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The $\\chi^2$ function\n", "\n", "For a linear fit (a first-order polynomial) we don't need to invert a matrix!! \n", "Defining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma = \\sum_{i=0}^{n-1}\\frac{1}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_x = \\sum_{i=0}^{n-1}\\frac{x_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_y = \\sum_{i=0}^{n-1}\\left(\\frac{y_i}{\\sigma_i^2}\\right),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_{xx} = \\sum_{i=0}^{n-1}\\frac{x_ix_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\gamma_{xy} = \\sum_{i=0}^{n-1}\\frac{y_ix_{i}}{\\sigma_i^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_0 = \\frac{\\gamma_{xx}\\gamma_y-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\beta_1 = \\frac{\\gamma_{xy}\\gamma-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This approach (different linear and non-linear regression) suffers\n", "often from both being underdetermined and overdetermined in the\n", "unknown coefficients $\\beta_i$. A better approach is to use the\n", "Singular Value Decomposition (SVD) method discussed below. Or using\n", "Lasso and Ridge regression. See below.\n", "\n", "\n", "\n", "\n", "## Fitting an Equation of State for Dense Nuclear Matter\n", "\n", "Before we continue, let us introduce yet another example. We are going to fit the\n", "nuclear equation of state using results from many-body calculations.\n", "The equation of state we have made available here, as function of\n", "density, has been derived using modern nucleon-nucleon potentials with\n", "[the addition of three-body\n", "forces](https://www.sciencedirect.com/science/article/pii/S0370157399001106). This\n", "time the file is presented as a standard **csv** file.\n", "\n", "The beginning of the Python code here is similar to what you have seen\n", "before, with the same initializations and declarations. We use also\n", "**pandas** again, rather extensively in order to organize our data.\n", "\n", "The difference now is that we use **Scikit-Learn's** regression tools\n", "instead of our own matrix inversion implementation. Furthermore, we\n", "sneak in **Ridge** regression (to be discussed below) which includes a\n", "hyperparameter $\\lambda$, also to be explained below.\n", "\n", "## The code" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n", "import sklearn.linear_model as skl\n", "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "X = np.zeros((len(Density),4))\n", "X[:,3] = Density**(4.0/3.0)\n", "X[:,2] = Density\n", "X[:,1] = Density**(2.0/3.0)\n", "X[:,0] = 1\n", "\n", "# We use now Scikit-Learn's linear regressor and ridge regressor\n", "# OLS part\n", "clf = skl.LinearRegression().fit(X, Energies)\n", "ytilde = clf.predict(X)\n", "EoS['Eols'] = ytilde\n", "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, ytilde))\n", "# Explained variance score: 1 is perfect prediction \n", "print('Variance score: %.2f' % r2_score(Energies, ytilde))\n", "# Mean absolute error \n", "print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))\n", "print(clf.coef_, clf.intercept_)\n", "\n", "# The Ridge regression with a hyperparameter lambda = 0.1\n", "_lambda = 0.1\n", "clf_ridge = skl.Ridge(alpha=_lambda).fit(X, Energies)\n", "yridge = clf_ridge.predict(X)\n", "EoS['Eridge'] = yridge\n", "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, yridge))\n", "# Explained variance score: 1 is perfect prediction \n", "print('Variance score: %.2f' % r2_score(Energies, yridge))\n", "# Mean absolute error \n", "print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))\n", "print(clf_ridge.coef_, clf_ridge.intercept_)\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xlabel(r'$\\rho[\\mathrm{fm}^{-3}]$')\n", "ax.set_ylabel(r'Energy per particle')\n", "ax.plot(EoS['Density'], EoS['Energy'], alpha=0.7, lw=2,\n", " label='Theoretical data')\n", "ax.plot(EoS['Density'], EoS['Eols'], alpha=0.7, lw=2, c='m',\n", " label='OLS')\n", "ax.plot(EoS['Density'], EoS['Eridge'], alpha=0.7, lw=2, c='g',\n", " label='Ridge $\\lambda = 0.1$')\n", "ax.legend()\n", "save_fig(\"EoSfitting\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above simple polynomial in density $\\rho$ gives an excellent fit\n", "to the data. \n", "\n", "We note also that there is a small deviation between the\n", "standard OLS and the Ridge regression at higher densities. We discuss this in more detail\n", "below.\n", "\n", "\n", "## Splitting our Data in Training and Test data\n", "\n", "It is normal in essentially all Machine Learning studies to split the\n", "data in a training set and a test set (sometimes also an additional\n", "validation set). **Scikit-Learn** has an own function for this. There\n", "is no explicit recipe for how much data should be included as training\n", "data and say test data. An accepted rule of thumb is to use\n", "approximately $2/3$ to $4/5$ of the data as training data. We will\n", "postpone a discussion of this splitting to the end of these notes and\n", "our discussion of the so-called **bias-variance** tradeoff. Here we\n", "limit ourselves to repeat the above equation of state fitting example\n", "but now splitting the data into a training set and a test set." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "def R2(y_data, y_model):\n", " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n", "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", " return np.sum((y_data-y_model)**2)/n\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organized into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "X = np.zeros((len(Density),5))\n", "X[:,0] = 1\n", "X[:,1] = Density**(2.0/3.0)\n", "X[:,2] = Density\n", "X[:,3] = Density**(4.0/3.0)\n", "X[:,4] = Density**(5.0/3.0)\n", "# We split the data in test and training data\n", "X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", "# matrix inversion to find beta\n", "beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)\n", "# and then make the prediction\n", "ytilde = X_train @ beta\n", "print(\"Training R2\")\n", "print(R2(y_train,ytilde))\n", "print(\"Training MSE\")\n", "print(MSE(y_train,ytilde))\n", "ypredict = X_test @ beta\n", "print(\"Test R2\")\n", "print(R2(y_test,ypredict))\n", "print(\"Test MSE\")\n", "print(MSE(y_test,ypredict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## The Boston housing data example\n", "\n", "The Boston housing \n", "data set was originally a part of UCI Machine Learning Repository\n", "and has been removed now. The data set is now included in **Scikit-Learn**'s \n", "library. There are 506 samples and 13 feature (predictor) variables\n", "in this data set. The objective is to predict the value of prices of\n", "the house using the features (predictors) listed here.\n", "\n", "The features/predictors are\n", "1. CRIM: Per capita crime rate by town\n", "\n", "2. ZN: Proportion of residential land zoned for lots over 25000 square feet\n", "\n", "3. INDUS: Proportion of non-retail business acres per town\n", "\n", "4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", "\n", "5. NOX: Nitric oxide concentration (parts per 10 million)\n", "\n", "6. RM: Average number of rooms per dwelling\n", "\n", "7. AGE: Proportion of owner-occupied units built prior to 1940\n", "\n", "8. DIS: Weighted distances to five Boston employment centers\n", "\n", "9. RAD: Index of accessibility to radial highways\n", "\n", "10. TAX: Full-value property tax rate per USD10000\n", "\n", "11. B: $1000(Bk - 0.63)^2$, where $Bk$ is the proportion of [people of African American descent] by town\n", "\n", "12. LSTAT: Percentage of lower status of the population\n", "\n", "13. MEDV: Median value of owner-occupied homes in USD 1000s\n", "\n", "## Housing data, the code\n", "We start by importing the libraries" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt \n", "\n", "import pandas as pd \n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and load the Boston Housing DataSet from **Scikit-Learn**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", "boston_dataset = load_boston()\n", "\n", "# boston_dataset is a dictionary\n", "# let's check what it contains\n", "boston_dataset.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we invoke Pandas" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n", "boston.head()\n", "boston['MEDV'] = boston_dataset.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and preprocess the data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# check for missing values in all the columns\n", "boston.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then visualize the data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# set the size of the figure\n", "sns.set(rc={'figure.figsize':(11.7,8.27)})\n", "\n", "# plot a histogram showing the distribution of the target values\n", "sns.distplot(boston['MEDV'], bins=30)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is now useful to look at the correlation matrix" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# compute the pair wise correlation for all columns \n", "correlation_matrix = boston.corr().round(2)\n", "# use the heatmap function from seaborn to plot the correlation matrix\n", "# annot = True to print the values inside the square\n", "sns.heatmap(data=correlation_matrix, annot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above coorelation plot we can see that **MEDV** is strongly correlated to **LSTAT** and **RM**. We see also that **RAD** and **TAX** are stronly correlated, but we don't include this in our features together to avoid multi-colinearity" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", "\n", "features = ['LSTAT', 'RM']\n", "target = boston['MEDV']\n", "\n", "for i, col in enumerate(features):\n", " plt.subplot(1, len(features) , i+1)\n", " x = boston[col]\n", " y = target\n", " plt.scatter(x, y, marker='o')\n", " plt.title(col)\n", " plt.xlabel(col)\n", " plt.ylabel('MEDV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we start training our model" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n", "Y = boston['MEDV']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We split the data into training and test sets" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# splits the training and test data set in 80% : 20%\n", "# assign random_state to any value.This ensures consistency.\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n", "print(X_train.shape)\n", "print(X_test.shape)\n", "print(Y_train.shape)\n", "print(Y_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we use the linear regression functionality from **Scikit-Learn**" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "\n", "lin_model = LinearRegression()\n", "lin_model.fit(X_train, Y_train)\n", "\n", "# model evaluation for training set\n", "\n", "y_train_predict = lin_model.predict(X_train)\n", "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n", "r2 = r2_score(Y_train, y_train_predict)\n", "\n", "print(\"The model performance for training set\")\n", "print(\"--------------------------------------\")\n", "print('RMSE is {}'.format(rmse))\n", "print('R2 score is {}'.format(r2))\n", "print(\"\\n\")\n", "\n", "# model evaluation for testing set\n", "\n", "y_test_predict = lin_model.predict(X_test)\n", "# root mean square error of the model\n", "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n", "\n", "# r-squared score of the model\n", "r2 = r2_score(Y_test, y_test_predict)\n", "\n", "print(\"The model performance for testing set\")\n", "print(\"--------------------------------------\")\n", "print('RMSE is {}'.format(rmse))\n", "print('R2 score is {}'.format(r2))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# plotting the y_test vs y_pred\n", "# ideally should have been a straight line\n", "plt.scatter(Y_test, y_test_predict)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reducing the number of degrees of freedom, overarching view\n", "\n", "Many Machine Learning problems involve thousands or even millions of\n", "features for each training instance. Not only does this make training\n", "extremely slow, it can also make it much harder to find a good\n", "solution, as we will see. This problem is often referred to as the\n", "curse of dimensionality. Fortunately, in real-world problems, it is\n", "often possible to reduce the number of features considerably, turning\n", "an intractable problem into a tractable one.\n", "\n", "Later we will discuss some of the most popular dimensionality reduction\n", "techniques: the principal component analysis (PCA), Kernel PCA, and\n", "Locally Linear Embedding (LLE). \n", "\n", "\n", "Principal component analysis and its various variants deal with the\n", "problem of fitting a low-dimensional [affine\n", "subspace](https://en.wikipedia.org/wiki/Affine_space) to a set of of\n", "data points in a high-dimensional space. With its family of methods it\n", "is one of the most used tools in data modeling, compression and\n", "visualization.\n", "\n", "\n", "\n", "\n", "## Preprocessing our data\n", "\n", "Before we proceed however, we will discuss how to preprocess our\n", "data. Till now and in connection with our previous examples we have\n", "not met so many cases where we are too sensitive to the scaling of our\n", "data. Normally the data may need a rescaling and/or may be sensitive\n", "to extreme values. Scaling the data renders our inputs much more\n", "suitable for the algorithms we want to employ.\n", "\n", "**Scikit-Learn** has several functions which allow us to rescale the\n", "data, normally resulting in much better results in terms of various\n", "accuracy scores. The **StandardScaler** function in **Scikit-Learn**\n", "ensures that for each feature/predictor we study the mean value is\n", "zero and the variance is one (every column in the design/feature\n", "matrix). This scaling has the drawback that it does not ensure that\n", "we have a particular maximum or minimum in our data set. Another\n", "function included in **Scikit-Learn** is the **MinMaxScaler** which\n", "ensures that all features are exactly between $0$ and $1$. The\n", "\n", "## More preprocessing\n", "\n", "\n", "The **Normalizer** scales each data\n", "point such that the feature vector has a euclidean length of one. In other words, it\n", "projects a data point on the circle (or sphere in the case of higher dimensions) with a\n", "radius of 1. This means every data point is scaled by a different number (by the\n", "inverse of it’s length).\n", "This normalization is often used when only the direction (or angle) of the data matters,\n", "not the length of the feature vector.\n", "\n", "The **RobustScaler** works similarly to the StandardScaler in that it\n", "ensures statistical properties for each feature that guarantee that\n", "they are on the same scale. However, the RobustScaler uses the median\n", "and quartiles, instead of mean and variance. This makes the\n", "RobustScaler ignore data points that are very different from the rest\n", "(like measurement errors). These odd data points are also called\n", "outliers, and might often lead to trouble for other scaling\n", "techniques.\n", "\n", "\n", "\n", "## Simple preprocessing examples, Franke function and regression" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import sklearn.linear_model as skl\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "\n", "def FrankeFunction(x,y):\n", "\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n", "\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n", "\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n", "\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n", "\treturn term1 + term2 + term3 + term4\n", "\n", "\n", "def create_X(x, y, n ):\n", "\tif len(x.shape) > 1:\n", "\t\tx = np.ravel(x)\n", "\t\ty = np.ravel(y)\n", "\n", "\tN = len(x)\n", "\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n", "\tX = np.ones((N,l))\n", "\n", "\tfor i in range(1,n+1):\n", "\t\tq = int((i)*(i+1)/2)\n", "\t\tfor k in range(i+1):\n", "\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n", "\n", "\treturn X\n", "\n", "\n", "# Making meshgrid of datapoints and compute Franke's function\n", "n = 5\n", "N = 1000\n", "x = np.sort(np.random.uniform(0, 1, N))\n", "y = np.sort(np.random.uniform(0, 1, N))\n", "z = FrankeFunction(x, y)\n", "X = create_X(x, y, n=n) \n", "# split in training and test data\n", "X_train, X_test, y_train, y_test = train_test_split(X,z,test_size=0.2)\n", "\n", "\n", "clf = skl.LinearRegression().fit(X_train, y_train)\n", "\n", "# The mean squared error and R2 score\n", "print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test), y_test)))\n", "print(\"R2 score before scaling {:.2f}\".format(clf.score(X_test,y_test)))\n", "\n", "scaler = StandardScaler()\n", "scaler.fit(X_train)\n", "X_train_scaled = scaler.transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "\n", "print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n", "print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n", "\n", "print(\"Feature min values after scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n", "print(\"Feature max values after scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n", "\n", "clf = skl.LinearRegression().fit(X_train_scaled, y_train)\n", "\n", "\n", "print(\"MSE after scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test_scaled), y_test)))\n", "print(\"R2 score for scaled data: {:.2f}\".format(clf.score(X_test_scaled,y_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The singular value decomposition\n", "\n", "\n", "The examples we have looked at so far are cases where we normally can\n", "invert the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$. Using a polynomial expansion as we\n", "did both for the masses and the fitting of the equation of state,\n", "leads to row vectors of the design matrix which are essentially\n", "orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. \n", "\n", "\n", "\n", "This may\n", "however not the be case in general and a standard matrix inversion\n", "algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.\n", "\n", "There is however a way to partially circumvent this problem and also gain some insights about the ordinary least squares approach, and later shrinkage methods like Ridge and Lasso regressions. \n", "\n", "This is given by the **Singular Value Decomposition** algorithm, perhaps\n", "the most powerful linear algebra algorithm. Let us look at a\n", "different example where we may have problems with the standard matrix\n", "inversion algorithm. Thereafter we dive into the math of the SVD.\n", "\n", "\n", "\n", "\n", "\n", "## Linear Regression Problems\n", "\n", "One of the typical problems we encounter with linear regression, in particular \n", "when the matrix $\\boldsymbol{X}$ (our so-called design matrix) is high-dimensional, \n", "are problems with near singular or singular matrices. The column vectors of $\\boldsymbol{X}$ \n", "may be linearly dependent, normally referred to as super-collinearity. \n", "This means that the matrix may be rank deficient and it is basically impossible to \n", "to model the data using linear regression. As an example, consider the matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "\\mathbf{X} & = \\left[\n", "\\begin{array}{rrr}\n", "1 & -1 & 2\n", "\\\\\n", "1 & 0 & 1\n", "\\\\\n", "1 & 2 & -1\n", "\\\\\n", "1 & 1 & 0\n", "\\end{array} \\right]\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The columns of $\\boldsymbol{X}$ are linearly dependent. We see this easily since the \n", "the first column is the row-wise sum of the other two columns. The rank (more correct,\n", "the column rank) of a matrix is the dimension of the space spanned by the\n", "column vectors. Hence, the rank of $\\mathbf{X}$ is equal to the number\n", "of linearly independent columns. In this particular case the matrix has rank 2.\n", "\n", "Super-collinearity of an $(n \\times p)$-dimensional design matrix $\\mathbf{X}$ implies\n", "that the inverse of the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ (the matrix we need to invert to solve the linear regression equations) is non-invertible. If we have a square matrix that does not have an inverse, we say this matrix singular. The example here demonstrates this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "\\boldsymbol{X} & = \\left[\n", "\\begin{array}{rr}\n", "1 & -1\n", "\\\\\n", "1 & -1\n", "\\end{array} \\right].\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see easily that $\\mbox{det}(\\boldsymbol{X}) = x_{11} x_{22} - x_{12} x_{21} = 1 \\times (-1) - 1 \\times (-1) = 0$. Hence, $\\mathbf{X}$ is singular and its inverse is undefined.\n", "This is equivalent to saying that the matrix $\\boldsymbol{X}$ has at least an eigenvalue which is zero.\n", "\n", "\n", "## Fixing the singularity\n", "\n", "If our design matrix $\\boldsymbol{X}$ which enters the linear regression problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto1\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "\\boldsymbol{\\beta} = (\\boldsymbol{X}^{T} \\boldsymbol{X})^{-1} \\boldsymbol{X}^{T} \\boldsymbol{y},\n", "\\label{_auto1} \\tag{1}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "has linearly dependent column vectors, we will not be able to compute the inverse\n", "of $\\boldsymbol{X}^T\\boldsymbol{X}$ and we cannot find the parameters (estimators) $\\beta_i$. \n", "The estimators are only well-defined if $(\\boldsymbol{X}^{T}\\boldsymbol{X})^{-1}$ exits. \n", "This is more likely to happen when the matrix $\\boldsymbol{X}$ is high-dimensional. In this case it is likely to encounter a situation where \n", "the regression parameters $\\beta_i$ cannot be estimated.\n", "\n", "A cheap *ad hoc* approach is simply to add a small diagonal component to the matrix to invert, that is we change" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^{T} \\boldsymbol{X} \\rightarrow \\boldsymbol{X}^{T} \\boldsymbol{X}+\\lambda \\boldsymbol{I},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\boldsymbol{I}$ is the identity matrix. When we discuss **Ridge** regression this is actually what we end up evaluating. The parameter $\\lambda$ is called a hyperparameter. More about this later. \n", "\n", "\n", "\n", "## Basic math of the SVD\n", "\n", "\n", "From standard linear algebra we know that a square matrix $\\boldsymbol{X}$ can be diagonalized if and only it is \n", "a so-called [normal matrix](https://en.wikipedia.org/wiki/Normal_matrix), that is if $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$\n", "we have $\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ or if $\\boldsymbol{X}\\in {\\mathbb{C}}^{n\\times n}$ we have $\\boldsymbol{X}\\boldsymbol{X}^{\\dagger}=\\boldsymbol{X}^{\\dagger}\\boldsymbol{X}$.\n", "The matrix has then a set of eigenpairs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\lambda_1,\\boldsymbol{u}_1),\\dots, (\\lambda_n,\\boldsymbol{u}_n),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the eigenvalues are given by the diagonal matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\Sigma}=\\mathrm{Diag}(\\lambda_1, \\dots,\\lambda_n).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix $\\boldsymbol{X}$ can be written in terms of an orthogonal/unitary transformation $\\boldsymbol{U}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ or $\\boldsymbol{U}\\boldsymbol{U}^{\\dagger}=\\boldsymbol{I}$.\n", "\n", "Not all square matrices are diagonalizable. A matrix like the one discussed above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\begin{bmatrix} \n", "1& -1 \\\\\n", "1& -1\\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "is not diagonalizable, it is a so-called [defective matrix](https://en.wikipedia.org/wiki/Defective_matrix). It is easy to see that the condition\n", "$\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ is not fulfilled. \n", "\n", "\n", "## The SVD, a Fantastic Algorithm\n", "\n", "\n", "However, and this is the strength of the SVD algorithm, any general\n", "matrix $\\boldsymbol{X}$ can be decomposed in terms of a diagonal matrix and\n", "two orthogonal/unitary matrices. The [Singular Value Decompostion\n", "(SVD) theorem](https://en.wikipedia.org/wiki/Singular_value_decomposition)\n", "states that a general $m\\times n$ matrix $\\boldsymbol{X}$ can be written in\n", "terms of a diagonal matrix $\\boldsymbol{\\Sigma}$ of dimensionality $m\\times n$\n", "and two orthognal matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$, where the first has\n", "dimensionality $m \\times m$ and the last dimensionality $n\\times n$.\n", "We have then" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example, the above defective matrix can be decomposed as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& 1 \\\\ 1& -1\\\\ \\end{bmatrix} \\begin{bmatrix} 2& 0 \\\\ 0& 0\\\\ \\end{bmatrix} \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& -1 \\\\ 1& 1\\\\ \\end{bmatrix}=\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with eigenvalues $\\sigma_1=2$ and $\\sigma_2=0$. \n", "The SVD exits always! \n", "\n", "The SVD\n", "decomposition (singular values) gives eigenvalues \n", "$\\sigma_i\\geq\\sigma_{i+1}$ for all $i$ and for dimensions larger than $i=p$, the\n", "eigenvalues (singular values) are zero.\n", "\n", "In the general case, where our design matrix $\\boldsymbol{X}$ has dimension\n", "$n\\times p$, the matrix is thus decomposed into an $n\\times n$\n", "orthogonal matrix $\\boldsymbol{U}$, a $p\\times p$ orthogonal matrix $\\boldsymbol{V}$\n", "and a diagonal matrix $\\boldsymbol{\\Sigma}$ with $r=\\mathrm{min}(n,p)$\n", "singular values $\\sigma_i\\geq 0$ on the main diagonal and zeros filling\n", "the rest of the matrix. There are at most $p$ singular values\n", "assuming that $n > p$. In our regression examples for the nuclear\n", "masses and the equation of state this is indeed the case, while for\n", "the Ising model we have $p > n$. These are often cases that lead to\n", "near singular or singular matrices.\n", "\n", "The columns of $\\boldsymbol{U}$ are called the left singular vectors while the columns of $\\boldsymbol{V}$ are the right singular vectors.\n", "\n", "## Economy-size SVD\n", "\n", "If we assume that $n > p$, then our matrix $\\boldsymbol{U}$ has dimension $n\n", "\\times n$. The last $n-p$ columns of $\\boldsymbol{U}$ become however\n", "irrelevant in our calculations since they are multiplied with the\n", "zeros in $\\boldsymbol{\\Sigma}$.\n", "\n", "The economy-size decomposition removes extra rows or columns of zeros\n", "from the diagonal matrix of singular values, $\\boldsymbol{\\Sigma}$, along with the columns\n", "in either $\\boldsymbol{U}$ or $\\boldsymbol{V}$ that multiply those zeros in the expression. \n", "Removing these zeros and columns can improve execution time\n", "and reduce storage requirements without compromising the accuracy of\n", "the decomposition.\n", "\n", "If $n > p$, we keep only the first $p$ columns of $\\boldsymbol{U}$ and $\\boldsymbol{\\Sigma}$ has dimension $p\\times p$. \n", "If $p > n$, then only the first $n$ columns of $\\boldsymbol{V}$ are computed and $\\boldsymbol{\\Sigma}$ has dimension $n\\times n$.\n", "The $n=p$ case is obvious, we retain the full SVD. \n", "In general the economy-size SVD leads to less FLOPS and still conserving the desired accuracy.\n", "\n", "## Codes for the SVD" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "# SVD inversion\n", "def SVDinv(A):\n", " ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).\n", " SVD is numerically more stable than the inversion algorithms provided by\n", " numpy and scipy.linalg at the cost of being slower.\n", " '''\n", " U, s, VT = np.linalg.svd(A)\n", "# print('test U')\n", "# print( (np.transpose(U) @ U - U @np.transpose(U)))\n", "# print('test VT')\n", "# print( (np.transpose(VT) @ VT - VT @np.transpose(VT)))\n", " print(U)\n", " print(s)\n", " print(VT)\n", "\n", " D = np.zeros((len(U),len(VT)))\n", " for i in range(0,len(VT)):\n", " D[i,i]=s[i]\n", " UT = np.transpose(U); V = np.transpose(VT); invD = np.linalg.inv(D)\n", " return np.matmul(V,np.matmul(invD,UT))\n", "\n", "\n", "X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ])\n", "print(X)\n", "A = np.transpose(X) @ X\n", "print(A)\n", "# Brute force inversion of super-collinear matrix\n", "#B = np.linalg.inv(A)\n", "#print(B)\n", "C = SVDinv(A)\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix $\\boldsymbol{X}$ has columns that are linearly dependent. The first\n", "column is the row-wise sum of the other two columns. The rank of a\n", "matrix (the column rank) is the dimension of space spanned by the\n", "column vectors. The rank of the matrix is the number of linearly\n", "independent columns, in this case just $2$. We see this from the\n", "singular values when running the above code. Running the standard\n", "inversion algorithm for matrix inversion with $\\boldsymbol{X}^T\\boldsymbol{X}$ results\n", "in the program terminating due to a singular matrix.\n", "\n", "\n", "\n", "## Mathematical Properties\n", "\n", "There are several interesting mathematical properties which will be\n", "relevant when we are going to discuss the differences between say\n", "ordinary least squares (OLS) and **Ridge** regression.\n", "\n", "We have from OLS that the parameters of the linear approximation are given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix to invert can be rewritten in terms of our SVD decomposition as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{U}^T\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the orthogonality properties of $\\boldsymbol{U}$ we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{\\Sigma}\\boldsymbol{V}^T = \\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{D}$ being a diagonal matrix with values along the diagonal given by the singular values squared. \n", "\n", "This means that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}^T\\boldsymbol{X})\\boldsymbol{V} = \\boldsymbol{V}\\boldsymbol{D},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the eigenvectors of $(\\boldsymbol{X}^T\\boldsymbol{X})$ are given by the columns of the right singular matrix of $\\boldsymbol{X}$ and the eigenvalues are the squared singular values. It is easy to show (show this) that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is, the eigenvectors of $(\\boldsymbol{X}\\boldsymbol{X})^T$ are the columns of the left singular matrix and the eigenvalues are the same. \n", "\n", "Going back to our OLS equation we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will come back to this expression when we discuss Ridge regression. \n", "\n", "\n", "$$ \\tilde{y}^{OLS}=\\boldsymbol{X}\\hat{\\beta}^{OLS}=\\sum_{j=1}^p \\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y}$$ and for Ridge we have \n", "\n", "$$ \\tilde{y}^{Ridge}=\\boldsymbol{X}\\hat{\\beta}^{Ridge}=\\sum_{j=1}^p \\boldsymbol{u}_j\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{u}_j^T\\boldsymbol{y}$$ . \n", "\n", "It is indeed the economy-sized SVD, note the summation runs up tp $$p$$ only and not $$n$$. \n", "\n", "Here we have that $$\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T$$, with $$\\Sigma$$ being an $$ n\\times p$$ matrix and $$\\boldsymbol{V}$$ being a $$ p\\times p$$ matrix. We also have assumed here that $$ n > p$$. \n", "\n", "\n", "## Ridge and LASSO Regression\n", "\n", "Let us remind ourselves about the expression for the standard Mean Squared Error (MSE) which we used to define our cost function and the equations for the ordinary least squares (OLS) method, that is \n", "our optimization problem is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or we can state it as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have used the definition of a norm-2 vector, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert\\vert \\boldsymbol{x}\\vert\\vert_2 = \\sqrt{\\sum_i x_i^2}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By minimizing the above equation with respect to the parameters\n", "$\\boldsymbol{\\beta}$ we could then obtain an analytical expression for the\n", "parameters $\\boldsymbol{\\beta}$. We can add a regularization parameter $\\lambda$ by\n", "defining a new cost function to be optimized, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which leads to the Ridge regression minimization problem where we\n", "require that $\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\\le t$, where $t$ is\n", "a finite number larger than zero. By defining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have a new optimization equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n", "{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which leads to Lasso regression. Lasso stands for least absolute shrinkage and selection operator. \n", "\n", "Here we have defined the norm-1 as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert\\vert \\boldsymbol{x}\\vert\\vert_1 = \\sum_i \\vert x_i\\vert.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More on Ridge Regression\n", "\n", "Using the matrix-vector expression for Ridge regression," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})^T(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\right\\}+\\lambda\\boldsymbol{\\beta}^T\\boldsymbol{\\beta},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "by taking the derivatives with respect to $\\boldsymbol{\\beta}$ we obtain then\n", "a slightly modified matrix inversion problem which for finite values\n", "of $\\lambda$ does not suffer from singularity problems. We obtain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{X}^T\\boldsymbol{X}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{I}$ being a $p\\times p$ identity matrix with the constraint that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sum_{i=0}^{p-1} \\beta_i^2 \\leq t,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $t$ a finite positive number. \n", "\n", "We see that Ridge regression is nothing but the standard\n", "OLS with a modified diagonal term added to $\\boldsymbol{X}^T\\boldsymbol{X}$. The\n", "consequences, in particular for our discussion of the bias-variance tradeoff \n", "are rather interesting.\n", "\n", "Furthermore, if we use the result above in terms of the SVD decomposition (our analysis was done for the OLS method), we had" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can analyse the OLS solutions in terms of the eigenvectors (the columns) of the right singular value matrix $\\boldsymbol{U}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Ridge regression this becomes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T+\\lambda\\boldsymbol{I} \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\sum_{j=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the vectors $\\boldsymbol{u}_j$ being the columns of $\\boldsymbol{U}$. \n", "\n", "## Interpreting the Ridge results\n", "\n", "Since $\\lambda \\geq 0$, it means that compared to OLS, we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda} \\leq 1.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ridge regression finds the coordinates of $\\boldsymbol{y}$ with respect to the\n", "orthonormal basis $\\boldsymbol{U}$, it then shrinks the coordinates by\n", "$\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}$. Recall that the SVD has\n", "eigenvalues ordered in a descending way, that is $\\sigma_i \\geq\n", "\\sigma_{i+1}$.\n", "\n", "For small eigenvalues $\\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.\n", "Actually, calculating the variance of $\\boldsymbol{X}\\boldsymbol{v}_j$ shows that this quantity is equal to $\\sigma_j^2/n$.\n", "With a parameter $\\lambda$ we can thus shrink the role of specific parameters. \n", "\n", "\n", "## More interpretations\n", "\n", "For the sake of simplicity, let us assume that the design matrix is orthonormal, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^T\\boldsymbol{X}=(\\boldsymbol{X}^T\\boldsymbol{X})^{-1} =\\boldsymbol{I}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case the standard OLS results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{OLS}} = \\boldsymbol{X}^T\\boldsymbol{y}=\\sum_{i=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{I}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\left(1+\\lambda\\right)^{-1}\\boldsymbol{\\beta}^{\\mathrm{OLS}},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the Ridge estimator scales the OLS estimator by the inverse of a factor $1+\\lambda$, and\n", "the Ridge estimator converges to zero when the hyperparameter goes to\n", "infinity.\n", "\n", "We will come back to more interpreations after we have gone through some of the statistical analysis part. \n", "\n", "For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n", "Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n", "\n", "\n", "\n", "## A better understanding of regularization\n", "\n", "The parameter $\\lambda$ that we have introduced in the Ridge (and\n", "Lasso as well) regression is often called a regularization parameter\n", "or shrinkage parameter. It is common to call it a hyperparameter. What does it mean mathemtically?\n", "\n", "Here we will first look at how to analyze the difference between the\n", "standard OLS equations and the Ridge expressions in terms of a linear\n", "algebra analysis using the SVD algorithm. Thereafter, we will link\n", "(see the material on the bias-variance tradeoff below) these\n", "observation to the statisical analysis of the results. In particular\n", "we consider how the variance of the parameters $\\boldsymbol{\\beta}$ is\n", "affected by changing the parameter $\\lambda$.\n", "\n", "## Decomposing the OLS and Ridge expressions\n", "\n", "We have our design matrix\n", " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. With the SVD we decompose it as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X} = \\boldsymbol{U\\Sigma V^T},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{U}\\in {\\mathbb{R}}^{n\\times n}$, $\\boldsymbol{\\Sigma}\\in {\\mathbb{R}}^{n\\times p}$\n", "and $\\boldsymbol{V}\\in {\\mathbb{R}}^{p\\times p}$.\n", "\n", "The matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are unitary/orthonormal matrices, that is in case the matrices are real we have $\\boldsymbol{U}^T\\boldsymbol{U}=\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ and $\\boldsymbol{V}^T\\boldsymbol{V}=\\boldsymbol{V}\\boldsymbol{V}^T=\\boldsymbol{I}$.\n", "\n", "\n", "\n", "## Introducing the Covariance and Correlation functions\n", "\n", "Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about\n", "the definition of the covariance and the correlation function. These are quantities \n", "\n", "Suppose we have defined two vectors\n", "$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{y}] \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where for example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this definition and recalling that the variance is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{var}[\\boldsymbol{x}]=\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can rewrite the covariance matrix as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] & \\mathrm{var}[\\boldsymbol{y}] \\\\\n", " \\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The covariance takes values between zero and infinity and may thus\n", "lead to problems with loss of numerical precision for particularly\n", "large values. It is common to scale the covariance matrix by\n", "introducing instead the correlation matrix defined via the so-called\n", "correlation function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]=\\frac{\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}]}{\\sqrt{\\mathrm{var}[\\boldsymbol{x}] \\mathrm{var}[\\boldsymbol{y}]}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The correlation function is then given by values $\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]\n", "\\in [-1,1]$. This avoids eventual problems with too large values. We\n", "can then define the correlation matrix for the two vectors $\\boldsymbol{x}$\n", "and $\\boldsymbol{y}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{K}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} 1 & \\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", " \\mathrm{corr}[\\boldsymbol{y},\\boldsymbol{x}] & 1 \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example this is the function we constructed using **pandas**.\n", "\n", "## Correlation Function and Design/Feature Matrix\n", "\n", "In our derivation of the various regression algorithms like **Ordinary Least Squares** or **Ridge regression**\n", "we defined the design/feature matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n", "x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n", "x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n", "\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n", "x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n", "x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ refering to the column numbers and the\n", "entries $n$ being the row elements.\n", "We can rewrite the design/feature matrix in terms of its column vectors as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} \\boldsymbol{x}_0 & \\boldsymbol{x}_1 & \\boldsymbol{x}_2 & \\dots & \\dots & \\boldsymbol{x}_{p-1}\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with a given vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{x}_i^T = \\begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \\dots & \\dots x_{n-1,i}\\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these definitions, we can now rewrite our $2\\times 2$\n", "correaltion/covariance matrix in terms of a moe general design/feature\n", "matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. This leads to a $p\\times p$\n", "covariance matrix for the vectors $\\boldsymbol{x}_i$ with $i=0,1,\\dots,p-1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}] = \\begin{bmatrix}\n", "\\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & \\mathrm{var}[\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & \\mathrm{var}[\\boldsymbol{x}_{p-1}]\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the correlation matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{K}[\\boldsymbol{x}] = \\begin{bmatrix}\n", "1 & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & 1 & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & 1 & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", "\\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & 1\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Covariance Matrix Examples\n", "\n", "\n", "The Numpy function **np.cov** calculates the covariance elements using\n", "the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have\n", "the exact mean values. The following simple function uses the\n", "**np.vstack** function which takes each vector of dimension $1\\times n$\n", "and produces a $2\\times n$ matrix $\\boldsymbol{W}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 \\\\\n", " x_1 & y_1 \\\\\n", " x_2 & y_2\\\\\n", " \\dots & \\dots \\\\\n", " x_{n-2} & y_{n-2}\\\\\n", " x_{n-1} & y_{n-1} & \n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which in turn is converted into into the $2\\times 2$ covariance matrix\n", "$\\boldsymbol{C}$ via the Numpy function **np.cov()**. We note that we can also calculate\n", "the mean value of each set of samples $\\boldsymbol{x}$ etc using the Numpy\n", "function **np.mean(x)**. We can also extract the eigenvalues of the\n", "covariance matrix through the **np.linalg.eig()** function." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Importing various packages\n", "import numpy as np\n", "n = 100\n", "x = np.random.normal(size=n)\n", "print(np.mean(x))\n", "y = 4+3*x+np.random.normal(size=n)\n", "print(np.mean(y))\n", "W = np.vstack((x, y))\n", "C = np.cov(W)\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Matrix\n", "\n", "The previous example can be converted into the correlation matrix by\n", "simply scaling the matrix elements with the variances. We should also\n", "subtract the mean values for each column. This leads to the following\n", "code which sets up the correlations matrix for the previous example in\n", "a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\\times 2$ correlation matrix (since we have only two vectors)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "n = 100\n", "# define two vectors \n", "x = np.random.random(size=n)\n", "y = 4+3*x+np.random.normal(size=n)\n", "#scaling the x and y vectors \n", "x = x - np.mean(x)\n", "y = y - np.mean(y)\n", "variance_x = np.sum(x@x)/n\n", "variance_y = np.sum(y@y)/n\n", "print(variance_x)\n", "print(variance_y)\n", "cov_xy = np.sum(x@y)/n\n", "cov_xx = np.sum(x@x)/n\n", "cov_yy = np.sum(y@y)/n\n", "C = np.zeros((2,2))\n", "C[0,0]= cov_xx/variance_x\n", "C[1,1]= cov_yy/variance_y\n", "C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)\n", "C[1,0]= C[0,1]\n", "print(C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the matrix elements along the diagonal are one as they\n", "should be and that the matrix is symmetric. Furthermore, diagonalizing\n", "this matrix we easily see that it is a positive definite matrix.\n", "\n", "The above procedure with **numpy** can be made more compact if we use **pandas**.\n", "\n", "## Correlation Matrix with Pandas\n", "\n", "We whow here how we can set up the correlation matrix using **pandas**, as done in this simple code" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "n = 10\n", "x = np.random.normal(size=n)\n", "x = x - np.mean(x)\n", "y = 4+3*x+np.random.normal(size=n)\n", "y = y - np.mean(y)\n", "X = (np.vstack((x, y))).T\n", "print(X)\n", "Xpd = pd.DataFrame(X)\n", "print(Xpd)\n", "correlation_matrix = Xpd.corr()\n", "print(correlation_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We expand this model to the Franke function discussed above.\n", "\n", "## Correlation Matrix with Pandas and the Franke function" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "def FrankeFunction(x,y):\n", "\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n", "\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n", "\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n", "\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n", "\treturn term1 + term2 + term3 + term4\n", "\n", "\n", "def create_X(x, y, n ):\n", "\tif len(x.shape) > 1:\n", "\t\tx = np.ravel(x)\n", "\t\ty = np.ravel(y)\n", "\n", "\tN = len(x)\n", "\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n", "\tX = np.ones((N,l))\n", "\n", "\tfor i in range(1,n+1):\n", "\t\tq = int((i)*(i+1)/2)\n", "\t\tfor k in range(i+1):\n", "\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n", "\n", "\treturn X\n", "\n", "\n", "# Making meshgrid of datapoints and compute Franke's function\n", "n = 4\n", "N = 100\n", "x = np.sort(np.random.uniform(0, 1, N))\n", "y = np.sort(np.random.uniform(0, 1, N))\n", "z = FrankeFunction(x, y)\n", "X = create_X(x, y, n=n) \n", "\n", "Xpd = pd.DataFrame(X)\n", "# subtract the mean values and set up the covariance matrix\n", "Xpd = Xpd - Xpd.mean()\n", "covariance_matrix = Xpd.cov()\n", "print(covariance_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note here that the covariance is zero for the first rows and\n", "columns since all matrix elements in the design matrix were set to one\n", "(we are fitting the function in terms of a polynomial of degree $n$).\n", "\n", "This means that the variance for these elements will be zero and will\n", "cause problems when we set up the correlation matrix. We can simply\n", "drop these elements and construct a correlation\n", "matrix without these elements. \n", "\n", "\n", "## Rewriting the Covariance and/or Correlation Matrix\n", "\n", "We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\\boldsymbol{X}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}= \\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}].\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see this let us simply look at a design matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{2\\times 2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{00} & x_{01}\\\\\n", "x_{10} & x_{11}\\\\\n", "\\end{bmatrix}=\\begin{bmatrix}\n", "\\boldsymbol{x}_{0} & \\boldsymbol{x}_{1}\\\\\n", "\\end{bmatrix}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we then compute the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}=\\begin{bmatrix}\n", "x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n", "x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\\\\n", "\\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is just" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n", " \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n", " \\end{bmatrix},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n", "\n", "It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$.\n", "\n", "\n", "## Linking with SVD\n", "\n", "See lecture september 11. More text to be added here soon.\n", "\n", "\n", "\n", "\n", "## Where are we going?\n", "\n", "Before we proceed, we need to rethink what we have been doing. In our\n", "eager to fit the data, we have omitted several important elements in\n", "our regression analysis. In what follows we will\n", "1. look at statistical properties, including a discussion of mean values, variance and the so-called bias-variance tradeoff\n", "\n", "2. introduce resampling techniques like cross-validation, bootstrapping and jackknife and more\n", "\n", "This will allow us to link the standard linear algebra methods we have discussed above to a statistical interpretation of the methods. \n", "\n", "\n", "\n", "\n", "\n", "## Resampling methods\n", "Resampling methods are an indispensable tool in modern\n", "statistics. They involve repeatedly drawing samples from a training\n", "set and refitting a model of interest on each sample in order to\n", "obtain additional information about the fitted model. For example, in\n", "order to estimate the variability of a linear regression fit, we can\n", "repeatedly draw different samples from the training data, fit a linear\n", "regression to each new sample, and then examine the extent to which\n", "the resulting fits differ. Such an approach may allow us to obtain\n", "information that would not be available from fitting the model only\n", "once using the original training sample.\n", "\n", "Two resampling methods are often used in Machine Learning analyses,\n", "1. The **bootstrap method**\n", "\n", "2. and **Cross-Validation**\n", "\n", "In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular\n", "cross-validation and the bootstrap method.\n", "\n", "\n", "\n", "\n", "## Resampling approaches can be computationally expensive\n", "\n", "Resampling approaches can be computationally expensive, because they\n", "involve fitting the same statistical method multiple times using\n", "different subsets of the training data. However, due to recent\n", "advances in computing power, the computational requirements of\n", "resampling methods generally are not prohibitive. In this chapter, we\n", "discuss two of the most commonly used resampling methods,\n", "cross-validation and the bootstrap. Both methods are important tools\n", "in the practical application of many statistical learning\n", "procedures. For example, cross-validation can be used to estimate the\n", "test error associated with a given statistical learning method in\n", "order to evaluate its performance, or to select the appropriate level\n", "of flexibility. The process of evaluating a model’s performance is\n", "known as model assessment, whereas the process of selecting the proper\n", "level of flexibility for a model is known as model selection. The\n", "bootstrap is widely used.\n", "\n", "\n", "\n", "## Why resampling methods ?\n", "**Statistical analysis.**\n", "\n", "\n", "* Our simulations can be treated as *computer experiments*. This is particularly the case for Monte Carlo methods\n", "\n", "* The results can be analysed with the same statistical tools as we would use analysing experimental data.\n", "\n", "* As in all experiments, we are looking for expectation values and an estimate of how accurate they are, i.e., possible sources for errors.\n", "\n", " \n", "\n", "## Statistical analysis\n", "\n", "* As in other experiments, many numerical experiments have two classes of errors:\n", "\n", " * Statistical errors\n", "\n", " * Systematical errors\n", "\n", "\n", "* Statistical errors can be estimated using standard tools from statistics\n", "\n", "* Systematical errors are method specific and must be treated differently from case to case.\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "## Linking the regression analysis with a statistical interpretation\n", "\n", "\n", "The\n", "advantage of doing linear regression is that we actually end up with\n", "analytical expressions for several statistical quantities. \n", "Standard least squares and Ridge regression allow us to\n", "derive quantities like the variance and other expectation values in a\n", "rather straightforward way.\n", "\n", "\n", "It is assumed that $\\varepsilon_i\n", "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n", "independent, i.e.:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*} \n", "\\mbox{Cov}(\\varepsilon_{i_1},\n", "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n", "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2. \\end{array} \\right.\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The randomness of $\\varepsilon_i$ implies that\n", "$\\mathbf{y}_i$ is also a random variable. In particular,\n", "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n", "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n", "non-random scalar. To specify the parameters of the distribution of\n", "$\\mathbf{y}_i$ we need to calculate its first two moments. \n", "\n", "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n", "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n", "row number $i$ and perform a sum over all values $p$.\n", "\n", "\n", "## Assumptions made\n", "\n", "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n", "that there exists a function $f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n", "which describe our data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We approximate this function with our model from the solution of the linear regression equations, that is our\n", "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Expectation value and variance\n", "\n", "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*} \n", "\\mathbb{E}(y_i) & =\n", "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n", "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while\n", "its variance is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n", "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n", "[\\mathbb{E}(y_i)]^2 \\\\ & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n", "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n", "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n", "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n", "\\ast} \\, \\beta)^2 \\\\ & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n", "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n", "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n", "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n", "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2. \n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n", "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD). \n", "\n", "## Expectation value and variance for $\\boldsymbol{\\beta}$\n", "\n", "With the OLS expressions for the parameters $\\boldsymbol{\\beta}$ we can evaluate the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}(\\boldsymbol{\\beta}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that the estimator of the regression parameters is unbiased.\n", "\n", "We can also calculate the variance\n", "\n", "The variance of $\\boldsymbol{\\beta}$ is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray*}\n", "\\mbox{Var}(\\boldsymbol{\\beta}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n", "\\\\\n", "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n", "\\\\\n", "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "\\\\\n", "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "% \\\\\n", "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n", "% \\\\\n", "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n", "\\\\\n", "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n", "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n", "\\end{eqnarray*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have used that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n", "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n", "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n", "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n", "variance of the estimate of the $j$-th regression coefficient:\n", "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n", "[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n", "construct a confidence interval for the estimates.\n", "\n", "\n", "In a similar way, we can obtain analytical expressions for say the\n", "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n", "when we employ Ridge regression, allowing us again to define a confidence interval. \n", "\n", "It is rather straightforward to show that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see clearly that \n", "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n", "\n", "We can also compute the variance as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1} \\mathbf{X}^{T} \\mathbf{X} \\{ [ \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n", "\n", "With this, we can compute the difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference is non-negative definite since each component of the\n", "matrix product is non-negative definite. \n", "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below. \n", "\n", "\n", "## Resampling methods\n", "\n", "With all these analytical equations for both the OLS and Ridge\n", "regression, we will now outline how to assess a given model. This will\n", "lead us to a discussion of the so-called bias-variance tradeoff (see\n", "below) and so-called resampling methods.\n", "\n", "One of the quantities we have discussed as a way to measure errors is\n", "the mean-squared error (MSE), mainly used for fitting of continuous\n", "functions. Another choice is the absolute error.\n", "\n", "In the discussions below we will focus on the MSE and in particular since we will split the data into test and training data,\n", "we discuss the\n", "1. prediction error or simply the **test error** $\\mathrm{Err_{Test}}$, where we have a fixed training set and the test error is the MSE arising from the data reserved for testing. We discuss also the \n", "\n", "2. training error $\\mathrm{Err_{Train}}$, which is the average loss over the training data.\n", "\n", "As our model becomes more and more complex, more of the training data tends to used. The training may thence adapt to more complicated structures in the data. This may lead to a decrease in the bias (see below for code example) and a slight increase of the variance for the test error.\n", "For a certain level of complexity the test error will reach minimum, before starting to increase again. The\n", "training error reaches a saturation.\n", "\n", "\n", "\n", "\n", "## Resampling methods: Jackknife and Bootstrap\n", "\n", "Two famous\n", "resampling methods are the **independent bootstrap** and **the jackknife**. \n", "\n", "The jackknife is a special case of the independent bootstrap. Still, the jackknife was made\n", "popular prior to the independent bootstrap. And as the popularity of\n", "the independent bootstrap soared, new variants, such as **the dependent bootstrap**.\n", "\n", "The Jackknife and independent bootstrap work for\n", "independent, identically distributed random variables.\n", "If these conditions are not\n", "satisfied, the methods will fail. Yet, it should be said that if the data are\n", "independent, identically distributed, and we only want to estimate the\n", "variance of $\\overline{X}$ (which often is the case), then there is no\n", "need for bootstrapping. \n", "\n", "## Resampling methods: Jackknife\n", "\n", "The Jackknife works by making many replicas of the estimator $\\widehat{\\theta}$. \n", "The jackknife is a resampling method where we systematically leave out one observation from the vector of observed values $\\boldsymbol{x} = (x_1,x_2,\\cdots,X_n)$. \n", "Let $\\boldsymbol{x}_i$ denote the vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{x}_i = (x_1,x_2,\\cdots,x_{i-1},x_{i+1},\\cdots,x_n),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which equals the vector $\\boldsymbol{x}$ with the exception that observation\n", "number $i$ is left out. Using this notation, define\n", "$\\widehat{\\theta}_i$ to be the estimator\n", "$\\widehat{\\theta}$ computed using $\\vec{X}_i$. \n", "\n", "\n", "## Jackknife code example" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import *\n", "from numpy.random import randint, randn\n", "from time import time\n", "\n", "def jackknife(data, stat):\n", " n = len(data);t = zeros(n); inds = arange(n); t0 = time()\n", " ## 'jackknifing' by leaving out an observation for each i \n", " for i in range(n):\n", " t[i] = stat(delete(data,i) )\n", "\n", " # analysis \n", " print(\"Runtime: %g sec\" % (time()-t0)); print(\"Jackknife Statistics :\")\n", " print(\"original bias std. error\")\n", " print(\"%8g %14g %15g\" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5))\n", "\n", " return t\n", "\n", "\n", "# Returns mean of data samples \n", "def stat(data):\n", " return mean(data)\n", "\n", "\n", "mu, sigma = 100, 15\n", "datapoints = 10000\n", "x = mu + sigma*random.randn(datapoints)\n", "# jackknife returns the data sample \n", "t = jackknife(x, stat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling methods: Bootstrap\n", "Bootstrapping is a nonparametric approach to statistical inference\n", "that substitutes computation for more traditional distributional\n", "assumptions and asymptotic results. Bootstrapping offers a number of\n", "advantages: \n", "1. The bootstrap is quite general, although there are some cases in which it fails. \n", "\n", "2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small. \n", "\n", "3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically. \n", "\n", "4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).\n", "\n", "\n", "\n", "\n", "## Resampling methods: Bootstrap background\n", "\n", "Since $\\widehat{\\theta} = \\widehat{\\theta}(\\boldsymbol{X})$ is a function of random variables,\n", "$\\widehat{\\theta}$ itself must be a random variable. Thus it has\n", "a pdf, call this function $p(\\boldsymbol{t})$. The aim of the bootstrap is to\n", "estimate $p(\\boldsymbol{t})$ by the relative frequency of\n", "$\\widehat{\\theta}$. You can think of this as using a histogram\n", "in the place of $p(\\boldsymbol{t})$. If the relative frequency closely\n", "resembles $p(\\vec{t})$, then using numerics, it is straight forward to\n", "estimate all the interesting parameters of $p(\\boldsymbol{t})$ using point\n", "estimators. \n", "\n", "\n", "## Resampling methods: More Bootstrap background\n", "\n", "In the case that $\\widehat{\\theta}$ has\n", "more than one component, and the components are independent, we use the\n", "same estimator on each component separately. If the probability\n", "density function of $X_i$, $p(x)$, had been known, then it would have\n", "been straight forward to do this by: \n", "1. Drawing lots of numbers from $p(x)$, suppose we call one such set of numbers $(X_1^*, X_2^*, \\cdots, X_n^*)$. \n", "\n", "2. Then using these numbers, we could compute a replica of $\\widehat{\\theta}$ called $\\widehat{\\theta}^*$. \n", "\n", "By repeated use of (1) and (2), many\n", "estimates of $\\widehat{\\theta}$ could have been obtained. The\n", "idea is to use the relative frequency of $\\widehat{\\theta}^*$\n", "(think of a histogram) as an estimate of $p(\\boldsymbol{t})$.\n", "\n", "## Resampling methods: Bootstrap approach\n", "\n", "But\n", "unless there is enough information available about the process that\n", "generated $X_1,X_2,\\cdots,X_n$, $p(x)$ is in general\n", "unknown. Therefore, [Efron in 1979](https://projecteuclid.org/euclid.aos/1176344552) asked the\n", "question: What if we replace $p(x)$ by the relative frequency\n", "of the observation $X_i$; if we draw observations in accordance with\n", "the relative frequency of the observations, will we obtain the same\n", "result in some asymptotic sense? The answer is yes.\n", "\n", "\n", "Instead of generating the histogram for the relative\n", "frequency of the observation $X_i$, just draw the values\n", "$(X_1^*,X_2^*,\\cdots,X_n^*)$ with replacement from the vector\n", "$\\boldsymbol{X}$. \n", "\n", "## Resampling methods: Bootstrap steps\n", "\n", "The independent bootstrap works like this: \n", "\n", "1. Draw with replacement $n$ numbers for the observed variables $\\boldsymbol{x} = (x_1,x_2,\\cdots,x_n)$. \n", "\n", "2. Define a vector $\\boldsymbol{x}^*$ containing the values which were drawn from $\\boldsymbol{x}$. \n", "\n", "3. Using the vector $\\boldsymbol{x}^*$ compute $\\widehat{\\theta}^*$ by evaluating $\\widehat \\theta$ under the observations $\\boldsymbol{x}^*$. \n", "\n", "4. Repeat this process $k$ times. \n", "\n", "When you are done, you can draw a histogram of the relative frequency\n", "of $\\widehat \\theta^*$. This is your estimate of the probability\n", "distribution $p(t)$. Using this probability distribution you can\n", "estimate any statistics thereof. In principle you never draw the\n", "histogram of the relative frequency of $\\widehat{\\theta}^*$. Instead\n", "you use the estimators corresponding to the statistic of interest. For\n", "example, if you are interested in estimating the variance of $\\widehat\n", "\\theta$, apply the etsimator $\\widehat \\sigma^2$ to the values\n", "$\\widehat \\theta ^*$.\n", "\n", "\n", "## Code example for the Bootstrap method\n", "\n", "The following code starts with a Gaussian distribution with mean value\n", "$\\mu =100$ and variance $\\sigma=15$. We use this to generate the data\n", "used in the bootstrap analysis. The bootstrap analysis returns a data\n", "set after a given number of bootstrap operations (as many as we have\n", "data points). This data set consists of estimated mean values for each\n", "bootstrap operation. The histogram generated by the bootstrap method\n", "shows that the distribution for these mean values is also a Gaussian,\n", "centered around the mean value $\\mu=100$ but with standard deviation\n", "$\\sigma/\\sqrt{n}$, where $n$ is the number of bootstrap samples (in\n", "this case the same as the number of original data points). The value\n", "of the standard deviation is what we expect from the central limit\n", "theorem." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import *\n", "from numpy.random import randint, randn\n", "from time import time\n", "import matplotlib.mlab as mlab\n", "import matplotlib.pyplot as plt\n", "\n", "# Returns mean of bootstrap samples \n", "def stat(data):\n", " return mean(data)\n", "\n", "# Bootstrap algorithm\n", "def bootstrap(data, statistic, R):\n", " t = zeros(R); n = len(data); inds = arange(n); t0 = time()\n", " # non-parametric bootstrap \n", " for i in range(R):\n", " t[i] = statistic(data[randint(0,n,n)])\n", "\n", " # analysis \n", " print(\"Runtime: %g sec\" % (time()-t0)); print(\"Bootstrap Statistics :\")\n", " print(\"original bias std. error\")\n", " print(\"%8g %8g %14g %15g\" % (statistic(data), std(data),mean(t),std(t)))\n", " return t\n", "\n", "\n", "mu, sigma = 100, 15\n", "datapoints = 10000\n", "x = mu + sigma*random.randn(datapoints)\n", "# bootstrap returns the data sample \n", "t = bootstrap(x, stat, datapoints)\n", "# the histogram of the bootstrapped data \n", "n, binsboot, patches = plt.hist(t, 50, normed=1, facecolor='red', alpha=0.75)\n", "\n", "# add a 'best fit' line \n", "y = mlab.normpdf( binsboot, mean(t), std(t))\n", "lt = plt.plot(binsboot, y, 'r--', linewidth=1)\n", "plt.xlabel('Smarts')\n", "plt.ylabel('Probability')\n", "plt.axis([99.5, 100.6, 0, 3.0])\n", "plt.grid(True)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Various steps in cross-validation\n", "\n", "When the repetitive splitting of the data set is done randomly,\n", "samples may accidently end up in a fast majority of the splits in\n", "either training or test set. Such samples may have an unbalanced\n", "influence on either model building or prediction evaluation. To avoid\n", "this $k$-fold cross-validation structures the data splitting. The\n", "samples are divided into $k$ more or less equally sized exhaustive and\n", "mutually exclusive subsets. In turn (at each split) one of these\n", "subsets plays the role of the test set while the union of the\n", "remaining subsets constitutes the training set. Such a splitting\n", "warrants a balanced representation of each sample in both training and\n", "test set over the splits. Still the division into the $k$ subsets\n", "involves a degree of randomness. This may be fully excluded when\n", "choosing $k=n$. This particular case is referred to as leave-one-out\n", "cross-validation (LOOCV). \n", "\n", "\n", "## How to set up the cross-validation for Ridge and/or Lasso\n", "\n", "* Define a range of interest for the penalty parameter.\n", "\n", "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n", "\n", "* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n", "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n", "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n", "\n", "* Repeat the first three steps such that each sample plays the role of the test set once.\n", "\n", "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", "\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation in brief\n", "\n", "For the various values of $k$\n", "\n", "1. shuffle the dataset randomly.\n", "\n", "2. Split the dataset into $k$ groups.\n", "\n", "3. For each unique group:\n", "\n", "a. Decide which group to use as set for test data\n", "\n", "b. Take the remaining groups as a training data set\n", "\n", "c. Fit a model on the training set and evaluate it on the test set\n", "\n", "d. Retain the evaluation score and discard the model\n", "\n", "\n", "5. Summarize the model using the sample of model evaluation scores\n", "\n", "## Code Example for Cross-validation and $k$-fold Cross-validation\n", "\n", "The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import KFold\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# A seed just to ensure that the random numbers are the same for every run.\n", "# Useful for eventual debugging.\n", "np.random.seed(3155)\n", "\n", "# Generate the data.\n", "nsamples = 100\n", "x = np.random.randn(nsamples)\n", "y = 3*x**2 + np.random.randn(nsamples)\n", "\n", "## Cross-validation on Ridge regression using KFold only\n", "\n", "# Decide degree on polynomial to fit\n", "poly = PolynomialFeatures(degree = 6)\n", "\n", "# Decide which values of lambda to use\n", "nlambdas = 500\n", "lambdas = np.logspace(-3, 5, nlambdas)\n", "\n", "# Initialize a KFold instance\n", "k = 5\n", "kfold = KFold(n_splits = k)\n", "\n", "# Perform the cross-validation to estimate MSE\n", "scores_KFold = np.zeros((nlambdas, k))\n", "\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", " j = 0\n", " for train_inds, test_inds in kfold.split(x):\n", " xtrain = x[train_inds]\n", " ytrain = y[train_inds]\n", "\n", " xtest = x[test_inds]\n", " ytest = y[test_inds]\n", "\n", " Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n", " ridge.fit(Xtrain, ytrain[:, np.newaxis])\n", "\n", " Xtest = poly.fit_transform(xtest[:, np.newaxis])\n", " ypred = ridge.predict(Xtest)\n", "\n", " scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)\n", "\n", " j += 1\n", " i += 1\n", "\n", "\n", "estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n", "\n", "## Cross-validation using cross_val_score from sklearn along with KFold\n", "\n", "# kfold is an instance initialized above as:\n", "# kfold = KFold(n_splits = k)\n", "\n", "estimated_mse_sklearn = np.zeros(nlambdas)\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", "\n", " X = poly.fit_transform(x[:, np.newaxis])\n", " estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n", "\n", " # cross_val_score return an array containing the estimated negative mse for every fold.\n", " # we have to the the mean of every array in order to get an estimate of the mse of the model\n", " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n", "\n", " i += 1\n", "\n", "## Plot and compare the slightly different ways to perform cross-validation\n", "\n", "plt.figure()\n", "\n", "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n", "plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n", "\n", "plt.xlabel('log10(lambda)')\n", "plt.ylabel('mse')\n", "\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The bias-variance tradeoff\n", "\n", "\n", "We will discuss the bias-variance tradeoff in the context of\n", "continuous predictions such as regression. However, many of the\n", "intuitions and ideas discussed here also carry over to classification\n", "tasks. Consider a dataset $\\mathcal{L}$ consisting of the data\n", "$\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=0\\ldots n-1\\}$. \n", "\n", "Let us assume that the true data is generated from a noisy model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{y}=f(\\boldsymbol{x}) + \\boldsymbol{\\epsilon}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\epsilon$ is normally distributed with mean zero and standard deviation $\\sigma^2$.\n", "\n", "In our derivation of the ordinary least squares method we defined then\n", "an approximation to the function $f$ in terms of the parameters\n", "$\\boldsymbol{\\beta}$ and the design matrix $\\boldsymbol{X}$ which embody our model,\n", "that is $\\boldsymbol{\\tilde{y}}=\\boldsymbol{X}\\boldsymbol{\\beta}$. \n", "\n", "Thereafter we found the parameters $\\boldsymbol{\\beta}$ by optimizing the means squared error via the so-called cost function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{X},\\boldsymbol{\\beta}) =\\frac{1}{n}\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2=\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right].\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can rewrite this as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\frac{1}{n}\\sum_i(f_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\frac{1}{n}\\sum_i(\\tilde{y}_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\sigma^2.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The three terms represent the square of the bias of the learning\n", "method, which can be thought of as the error caused by the simplifying\n", "assumptions built into the method. The second term represents the\n", "variance of the chosen model and finally the last terms is variance of\n", "the error $\\boldsymbol{\\epsilon}$.\n", "\n", "To derive this equation, we need to recall that the variance of $\\boldsymbol{y}$ and $\\boldsymbol{\\epsilon}$ are both equal to $\\sigma^2$. The mean value of $\\boldsymbol{\\epsilon}$ is by definition equal to zero. Furthermore, the function $f$ is not a stochastics variable, idem for $\\boldsymbol{\\tilde{y}}$.\n", "We use a more compact notation in terms of the expectation value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}})^2\\right],\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and adding and subtracting $\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]$ we get" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}}+\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right],\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which, using the abovementioned expectation values can be rewritten as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{y}-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right]+\\mathrm{Var}\\left[\\boldsymbol{\\tilde{y}}\\right]+\\sigma^2,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "that is the rewriting in terms of the so-called bias, the variance of the model $\\boldsymbol{\\tilde{y}}$ and the variance of $\\boldsymbol{\\epsilon}$.\n", "\n", "\n", "\n", "\n", "\n", "## Example code for Bias-Variance tradeoff" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.utils import resample\n", "\n", "np.random.seed(2018)\n", "\n", "n = 500\n", "n_boostraps = 100\n", "degree = 18 # A quite high value, just to show.\n", "noise = 0.1\n", "\n", "# Make data set.\n", "x = np.linspace(-1, 3, n).reshape(-1, 1)\n", "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2) + np.random.normal(0, 0.1, x.shape)\n", "\n", "# Hold out some test data that is never used in training.\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", "# Combine x transformation and model into one operation.\n", "# Not neccesary, but convenient.\n", "model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", "\n", "# The following (m x n_bootstraps) matrix holds the column vectors y_pred\n", "# for each bootstrap iteration.\n", "y_pred = np.empty((y_test.shape[0], n_boostraps))\n", "for i in range(n_boostraps):\n", " x_, y_ = resample(x_train, y_train)\n", "\n", " # Evaluate the new model on the same test data each time.\n", " y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n", "\n", "# Note: Expectations and variances taken w.r.t. different training\n", "# data sets, hence the axis=1. Subsequent means are taken across the test data\n", "# set in order to obtain a total value, but before this we have error/bias/variance\n", "# calculated per data point in the test set.\n", "# Note 2: The use of keepdims=True is important in the calculation of bias as this \n", "# maintains the column vector form. Dropping this yields very unexpected results.\n", "error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n", "bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n", "variance = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n", "print('Error:', error)\n", "print('Bias^2:', bias)\n", "print('Var:', variance)\n", "print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance))\n", "\n", "plt.plot(x[::5, :], y[::5, :], label='f(x)')\n", "plt.scatter(x_test, y_test, label='Data points')\n", "plt.scatter(x_test, np.mean(y_pred, axis=1), label='Pred')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding what happens" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.utils import resample\n", "\n", "np.random.seed(2018)\n", "\n", "n = 40\n", "n_boostraps = 100\n", "maxdegree = 14\n", "\n", "\n", "# Make data set.\n", "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n", "error = np.zeros(maxdegree)\n", "bias = np.zeros(maxdegree)\n", "variance = np.zeros(maxdegree)\n", "polydegree = np.zeros(maxdegree)\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", "for degree in range(maxdegree):\n", " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", " y_pred = np.empty((y_test.shape[0], n_boostraps))\n", " for i in range(n_boostraps):\n", " x_, y_ = resample(x_train, y_train)\n", " y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n", "\n", " polydegree[degree] = degree\n", " error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n", " bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n", " variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n", " print('Polynomial degree:', degree)\n", " print('Error:', error[degree])\n", " print('Bias^2:', bias[degree])\n", " print('Var:', variance[degree])\n", " print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n", "\n", "plt.plot(polydegree, error, label='Error')\n", "plt.plot(polydegree, bias, label='bias')\n", "plt.plot(polydegree, variance, label='Variance')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Summing up\n", "\n", "\n", "\n", "\n", "The bias-variance tradeoff summarizes the fundamental tension in\n", "machine learning, particularly supervised learning, between the\n", "complexity of a model and the amount of training data needed to train\n", "it. Since data is often limited, in practice it is often useful to\n", "use a less-complex model with higher bias, that is a model whose asymptotic\n", "performance is worse than another model because it is easier to\n", "train and less sensitive to sampling noise arising from having a\n", "finite-sized training dataset (smaller variance). \n", "\n", "\n", "\n", "The above equations tell us that in\n", "order to minimize the expected test error, we need to select a\n", "statistical learning method that simultaneously achieves low variance\n", "and low bias. Note that variance is inherently a nonnegative quantity,\n", "and squared bias is also nonnegative. Hence, we see that the expected\n", "test MSE can never lie below $Var(\\epsilon)$, the irreducible error.\n", "\n", "\n", "What do we mean by the variance and bias of a statistical learning\n", "method? The variance refers to the amount by which our model would change if we\n", "estimated it using a different training data set. Since the training\n", "data are used to fit the statistical learning method, different\n", "training data sets will result in a different estimate. But ideally the\n", "estimate for our model should not vary too much between training\n", "sets. However, if a method has high variance then small changes in\n", "the training data can result in large changes in the model. In general, more\n", "flexible statistical methods have higher variance.\n", "\n", "\n", "You may also find this recent [article](https://www.pnas.org/content/116/32/15849) of interest.\n", "\n", "## Another Example from Scikit-Learn's Repository" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "============================\n", "Underfitting vs. Overfitting\n", "============================\n", "\n", "This example demonstrates the problems of underfitting and overfitting and\n", "how we can use linear regression with polynomial features to approximate\n", "nonlinear functions. The plot shows the function that we want to approximate,\n", "which is a part of the cosine function. In addition, the samples from the\n", "real function and the approximations of different models are displayed. The\n", "models have polynomial features of different degrees. We can see that a\n", "linear function (polynomial with degree 1) is not sufficient to fit the\n", "training samples. This is called **underfitting**. A polynomial of degree 4\n", "approximates the true function almost perfectly. However, for higher degrees\n", "the model will **overfit** the training data, i.e. it learns the noise of the\n", "training data.\n", "We evaluate quantitatively **overfitting** / **underfitting** by using\n", "cross-validation. We calculate the mean squared error (MSE) on the validation\n", "set, the higher, the less likely the model generalizes correctly from the\n", "training data.\n", "\"\"\"\n", "\n", "print(__doc__)\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import cross_val_score\n", "\n", "\n", "def true_fun(X):\n", " return np.cos(1.5 * np.pi * X)\n", "\n", "np.random.seed(0)\n", "\n", "n_samples = 30\n", "degrees = [1, 4, 15]\n", "\n", "X = np.sort(np.random.rand(n_samples))\n", "y = true_fun(X) + np.random.randn(n_samples) * 0.1\n", "\n", "plt.figure(figsize=(14, 5))\n", "for i in range(len(degrees)):\n", " ax = plt.subplot(1, len(degrees), i + 1)\n", " plt.setp(ax, xticks=(), yticks=())\n", "\n", " polynomial_features = PolynomialFeatures(degree=degrees[i],\n", " include_bias=False)\n", " linear_regression = LinearRegression()\n", " pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n", " (\"linear_regression\", linear_regression)])\n", " pipeline.fit(X[:, np.newaxis], y)\n", "\n", " # Evaluate the models using crossvalidation\n", " scores = cross_val_score(pipeline, X[:, np.newaxis], y,\n", " scoring=\"neg_mean_squared_error\", cv=10)\n", "\n", " X_test = np.linspace(0, 1, 100)\n", " plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n", " plt.plot(X_test, true_fun(X_test), label=\"True function\")\n", " plt.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n", " plt.xlabel(\"x\")\n", " plt.ylabel(\"y\")\n", " plt.xlim((0, 1))\n", " plt.ylim((-2, 2))\n", " plt.legend(loc=\"best\")\n", " plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(\n", " degrees[i], -scores.mean(), scores.std()))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More examples on bootstrap and cross-validation and errors" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.utils import resample\n", "from sklearn.metrics import mean_squared_error\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "\n", "Maxpolydegree = 30\n", "X = np.zeros((len(Density),Maxpolydegree))\n", "X[:,0] = 1.0\n", "testerror = np.zeros(Maxpolydegree)\n", "trainingerror = np.zeros(Maxpolydegree)\n", "polynomial = np.zeros(Maxpolydegree)\n", "\n", "trials = 100\n", "for polydegree in range(1, Maxpolydegree):\n", " polynomial[polydegree] = polydegree\n", " for degree in range(polydegree):\n", " X[:,degree] = Density**(degree/3.0)\n", "\n", "# loop over trials in order to estimate the expectation value of the MSE\n", " testerror[polydegree] = 0.0\n", " trainingerror[polydegree] = 0.0\n", " for samples in range(trials):\n", " x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n", " ypred = model.predict(x_train)\n", " ytilde = model.predict(x_test)\n", " testerror[polydegree] += mean_squared_error(y_test, ytilde)\n", " trainingerror[polydegree] += mean_squared_error(y_train, ypred) \n", "\n", " testerror[polydegree] /= trials\n", " trainingerror[polydegree] /= trials\n", " print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n", " print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n", " print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n", "\n", "plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", "plt.plot(polynomial, np.log10(testerror), label='Test Error')\n", "plt.xlabel('Polynomial degree')\n", "plt.ylabel('log10[MSE]')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## The same example but now with cross-validation" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Common imports\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "\n", "\n", "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", "\n", "infile = open(data_path(\"EoS.csv\"),'r')\n", "\n", "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n", "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", "EoS = EoS.dropna()\n", "Energies = EoS['Energy']\n", "Density = EoS['Density']\n", "# The design matrix now as function of various polytrops\n", "\n", "Maxpolydegree = 30\n", "X = np.zeros((len(Density),Maxpolydegree))\n", "X[:,0] = 1.0\n", "estimated_mse_sklearn = np.zeros(Maxpolydegree)\n", "polynomial = np.zeros(Maxpolydegree)\n", "k =5\n", "kfold = KFold(n_splits = k)\n", "\n", "for polydegree in range(1, Maxpolydegree):\n", " polynomial[polydegree] = polydegree\n", " for degree in range(polydegree):\n", " X[:,degree] = Density**(degree/3.0)\n", " OLS = LinearRegression()\n", "# loop over trials in order to estimate the expectation value of the MSE\n", " estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n", "#[:, np.newaxis]\n", " estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)\n", "\n", "plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n", "plt.xlabel('Polynomial degree')\n", "plt.ylabel('log10[MSE]')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation with Ridge" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import KFold\n", "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "# A seed just to ensure that the random numbers are the same for every run.\n", "np.random.seed(3155)\n", "# Generate the data.\n", "n = 100\n", "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n", "# Decide degree on polynomial to fit\n", "poly = PolynomialFeatures(degree = 10)\n", "\n", "# Decide which values of lambda to use\n", "nlambdas = 500\n", "lambdas = np.logspace(-3, 5, nlambdas)\n", "# Initialize a KFold instance\n", "k = 5\n", "kfold = KFold(n_splits = k)\n", "estimated_mse_sklearn = np.zeros(nlambdas)\n", "i = 0\n", "for lmb in lambdas:\n", " ridge = Ridge(alpha = lmb)\n", " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n", " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n", " i += 1\n", "plt.figure()\n", "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n", "plt.xlabel('log10(lambda)')\n", "plt.ylabel('MSE')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Ising model\n", "\n", "The one-dimensional Ising model with nearest neighbor interaction, no\n", "external field and a constant coupling constant $J$ is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto2\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = -J \\sum_{k}^L s_k s_{k + 1},\n", "\\label{_auto2} \\tag{2}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins\n", "in the system is determined by $L$. For the one-dimensional system\n", "there is no phase transition.\n", "\n", "We will look at a system of $L = 40$ spins with a coupling constant of\n", "$J = 1$. To get enough training data we will generate 10000 states\n", "with their respective energies." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import seaborn as sns\n", "import scipy.linalg as scl\n", "from sklearn.model_selection import train_test_split\n", "import tqdm\n", "sns.set(color_codes=True)\n", "cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n", "\n", "L = 40\n", "n = int(1e4)\n", "\n", "spins = np.random.choice([-1, 1], size=(n, L))\n", "J = 1.0\n", "\n", "energies = np.zeros(n)\n", "\n", "for i in range(n):\n", " energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use ordinary least squares\n", "regression to predict the energy for the nearest neighbor\n", "one-dimensional Ising model on a ring, i.e., the endpoints wrap\n", "around. We will use linear regression to fit a value for\n", "the coupling constant to achieve this.\n", "\n", "## Reformulating the problem to suit regression\n", "\n", "A more general form for the one-dimensional Ising model is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto3\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n", "\\label{_auto3} \\tag{3}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we allow for interactions beyond the nearest neighbors and a state dependent\n", "coupling constant. This latter expression can be formulated as\n", "a matrix-product" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto4\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{H} = \\boldsymbol{X} J,\n", "\\label{_auto4} \\tag{4}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $X_{jk} = s_j s_k$ and $J$ is a matrix which consists of the\n", "elements $-J_{jk}$. This form of writing the energy fits perfectly\n", "with the form utilized in linear regression, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto5\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon},\n", "\\label{_auto5} \\tag{5}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We split the data in training and test data as discussed in the previous example" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.zeros((n, L ** 2))\n", "for i in range(n):\n", " X[i] = np.outer(spins[i], spins[i]).ravel()\n", "y = energies\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear regression\n", "\n", "In the ordinary least squares method we choose the cost function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto6\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " C(\\boldsymbol{X}, \\boldsymbol{\\beta})= \\frac{1}{n}\\left\\{(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})\\right\\}.\n", "\\label{_auto6} \\tag{6}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then find the extremal point of $C$ by taking the derivative with respect to $\\boldsymbol{\\beta}$ as discussed above.\n", "This yields the expression for $\\boldsymbol{\\beta}$ to be" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = \\frac{\\boldsymbol{X}^T \\boldsymbol{y}}{\\boldsymbol{X}^T \\boldsymbol{X}},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which immediately imposes some requirements on $\\boldsymbol{X}$ as there must exist\n", "an inverse of $\\boldsymbol{X}^T \\boldsymbol{X}$. If the expression we are modeling contains an\n", "intercept, i.e., a constant term, we must make sure that the\n", "first column of $\\boldsymbol{X}$ consists of $1$. We do this here" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_own = np.concatenate(\n", " (np.ones(len(X_train))[:, np.newaxis], X_train),\n", " axis=1\n", ")\n", "X_test_own = np.concatenate(\n", " (np.ones(len(X_test))[:, np.newaxis], X_test),\n", " axis=1\n", ")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", " return scl.inv(x.T @ x) @ (x.T @ y)\n", "beta = ols_inv(X_train_own, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Singular Value decomposition\n", "\n", "Doing the inversion directly turns out to be a bad idea since the matrix\n", "$\\boldsymbol{X}^T\\boldsymbol{X}$ is singular. An alternative approach is to use the **singular\n", "value decomposition**. Using the definition of the Moore-Penrose\n", "pseudoinverse we can write the equation for $\\boldsymbol{\\beta}$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\beta} = \\boldsymbol{X}^{+}\\boldsymbol{y},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the pseudoinverse of $\\boldsymbol{X}$ is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}^{+} = \\frac{\\boldsymbol{X}^T}{\\boldsymbol{X}^T\\boldsymbol{X}}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using singular value decomposition we can decompose the matrix $\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma} \\boldsymbol{V}^T$,\n", "where $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are orthogonal(unitary) matrices and $\\boldsymbol{\\Sigma}$ contains the singular values (more details below).\n", "where $X^{+} = V\\Sigma^{+} U^T$. This reduces the equation for\n", "$\\omega$ to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto7\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{\\beta} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^{+} \\boldsymbol{U}^T \\boldsymbol{y}.\n", "\\label{_auto7} \\tag{7}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that solving this equation by actually doing the pseudoinverse\n", "(which is what we will do) is not a good idea as this operation scales\n", "as $\\mathcal{O}(n^3)$, where $n$ is the number of elements in a\n", "general matrix. Instead, doing $QR$-factorization and solving the\n", "linear system as an equation would reduce this down to\n", "$\\mathcal{O}(n^2)$ operations." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", " u, s, v = scl.svd(x)\n", " return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta = ols_svd(X_train_own,y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When extracting the $J$-matrix we need to make sure that we remove the intercept, as is done here" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "J = beta[1:].reshape(L, L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A way of looking at the coefficients in $J$ is to plot the matrices as images." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J, **cmap_args)\n", "plt.title(\"OLS\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is interesting to note that OLS\n", "considers both $J_{j, j + 1} = -0.5$ and $J_{j, j - 1} = -0.5$ as\n", "valid matrix elements for $J$.\n", "In our discussion below on hyperparameters and Ridge and Lasso regression we will see that\n", "this problem can be removed, partly and only with Lasso regression. \n", "\n", "In this case our matrix inversion was actually possible. The obvious question now is what is the mathematics behind the SVD?\n", "\n", "\n", "\n", "\n", "\n", "## The one-dimensional Ising model\n", "\n", "Let us bring back the Ising model again, but now with an additional\n", "focus on Ridge and Lasso regression as well. We repeat some of the\n", "basic parts of the Ising model and the setup of the training and test\n", "data. The one-dimensional Ising model with nearest neighbor\n", "interaction, no external field and a constant coupling constant $J$ is\n", "given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto8\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = -J \\sum_{k}^L s_k s_{k + 1},\n", "\\label{_auto8} \\tag{8}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins in the system is determined by $L$. For the one-dimensional system there is no phase transition.\n", "\n", "We will look at a system of $L = 40$ spins with a coupling constant of $J = 1$. To get enough training data we will generate 10000 states with their respective energies." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import seaborn as sns\n", "import scipy.linalg as scl\n", "from sklearn.model_selection import train_test_split\n", "import sklearn.linear_model as skl\n", "import tqdm\n", "sns.set(color_codes=True)\n", "cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n", "\n", "L = 40\n", "n = int(1e4)\n", "\n", "spins = np.random.choice([-1, 1], size=(n, L))\n", "J = 1.0\n", "\n", "energies = np.zeros(n)\n", "\n", "for i in range(n):\n", " energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more general form for the one-dimensional Ising model is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto9\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n", "\\label{_auto9} \\tag{9}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we allow for interactions beyond the nearest neighbors and a more\n", "adaptive coupling matrix. This latter expression can be formulated as\n", "a matrix-product on the form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto10\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = X J,\n", "\\label{_auto10} \\tag{10}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $X_{jk} = s_j s_k$ and $J$ is the matrix consisting of the\n", "elements $-J_{jk}$. This form of writing the energy fits perfectly\n", "with the form utilized in linear regression, viz." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto11\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}.\n", "\\label{_auto11} \\tag{11}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We organize the data as we did above" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = np.zeros((n, L ** 2))\n", "for i in range(n):\n", " X[i] = np.outer(spins[i], spins[i]).ravel()\n", "y = energies\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.96)\n", "\n", "X_train_own = np.concatenate(\n", " (np.ones(len(X_train))[:, np.newaxis], X_train),\n", " axis=1\n", ")\n", "\n", "X_test_own = np.concatenate(\n", " (np.ones(len(X_test))[:, np.newaxis], X_test),\n", " axis=1\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will do all fitting with **Scikit-Learn**," ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf = skl.LinearRegression().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When extracting the $J$-matrix we make sure to remove the intercept" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "J_sk = clf.coef_.reshape(L, L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we plot the results" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_sk, **cmap_args)\n", "plt.title(\"LinearRegression from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results perfectly with our previous discussion where we used our own code.\n", "\n", "## Ridge regression\n", "\n", "Having explored the ordinary least squares we move on to ridge\n", "regression. In ridge regression we include a **regularizer**. This\n", "involves a new cost function which leads to a new estimate for the\n", "weights $\\boldsymbol{\\beta}$. This results in a penalized regression problem. The\n", "cost function is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "3\n", "6\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_lambda = 0.1\n", "clf_ridge = skl.Ridge(alpha=_lambda).fit(X_train, y_train)\n", "J_ridge_sk = clf_ridge.coef_.reshape(L, L)\n", "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_ridge_sk, **cmap_args)\n", "plt.title(\"Ridge from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LASSO regression\n", "\n", "In the **Least Absolute Shrinkage and Selection Operator** (LASSO)-method we get a third cost function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div id=\"_auto13\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " C(\\boldsymbol{X}, \\boldsymbol{\\beta}; \\lambda) = (\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y}) + \\lambda \\sqrt{\\boldsymbol{\\beta}^T\\boldsymbol{\\beta}}.\n", "\\label{_auto13} \\tag{13}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finding the extremal point of this cost function is not so straight-forward as in least squares and ridge. We will therefore rely solely on the function ``Lasso`` from **Scikit-Learn**." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n", "J_lasso_sk = clf_lasso.coef_.reshape(L, L)\n", "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_lasso_sk, **cmap_args)\n", "plt.title(\"Lasso from Scikit-learn\", fontsize=18)\n", "plt.xticks(fontsize=18)\n", "plt.yticks(fontsize=18)\n", "cb = fig.colorbar(im)\n", "cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is quite striking how LASSO breaks the symmetry of the coupling\n", "constant as opposed to ridge and OLS. We get a sparse solution with\n", "$J_{j, j + 1} = -1$.\n", "\n", "\n", "\n", "## Performance as function of the regularization parameter\n", "\n", "We see how the different models perform for a different set of values for $\\lambda$." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lambdas = np.logspace(-4, 5, 10)\n", "\n", "train_errors = {\n", " \"ols_sk\": np.zeros(lambdas.size),\n", " \"ridge_sk\": np.zeros(lambdas.size),\n", " \"lasso_sk\": np.zeros(lambdas.size)\n", "}\n", "\n", "test_errors = {\n", " \"ols_sk\": np.zeros(lambdas.size),\n", " \"ridge_sk\": np.zeros(lambdas.size),\n", " \"lasso_sk\": np.zeros(lambdas.size)\n", "}\n", "\n", "plot_counter = 1\n", "\n", "fig = plt.figure(figsize=(32, 54))\n", "\n", "for i, _lambda in enumerate(tqdm.tqdm(lambdas)):\n", " for key, method in zip(\n", " [\"ols_sk\", \"ridge_sk\", \"lasso_sk\"],\n", " [skl.LinearRegression(), skl.Ridge(alpha=_lambda), skl.Lasso(alpha=_lambda)]\n", " ):\n", " method = method.fit(X_train, y_train)\n", "\n", " train_errors[key][i] = method.score(X_train, y_train)\n", " test_errors[key][i] = method.score(X_test, y_test)\n", "\n", " omega = method.coef_.reshape(L, L)\n", "\n", " plt.subplot(10, 5, plot_counter)\n", " plt.imshow(omega, **cmap_args)\n", " plt.title(r\"%s, $\\lambda = %.4f$\" % (key, _lambda))\n", " plot_counter += 1\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that LASSO reaches a good solution for low\n", "values of $\\lambda$, but will \"wither\" when we increase $\\lambda$ too\n", "much. Ridge is more stable over a larger range of values for\n", "$\\lambda$, but eventually also fades away.\n", "\n", "## Finding the optimal value of $\\lambda$\n", "\n", "To determine which value of $\\lambda$ is best we plot the accuracy of\n", "the models when predicting the training and the testing set. We expect\n", "the accuracy of the training set to be quite good, but if the accuracy\n", "of the testing set is much lower this tells us that we might be\n", "subject to an overfit model. The ideal scenario is an accuracy on the\n", "testing set that is close to the accuracy of the training set." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "\n", "colors = {\n", " \"ols_sk\": \"r\",\n", " \"ridge_sk\": \"y\",\n", " \"lasso_sk\": \"c\"\n", "}\n", "\n", "for key in train_errors:\n", " plt.semilogx(\n", " lambdas,\n", " train_errors[key],\n", " colors[key],\n", " label=\"Train {0}\".format(key),\n", " linewidth=4.0\n", " )\n", "\n", "for key in test_errors:\n", " plt.semilogx(\n", " lambdas,\n", " test_errors[key],\n", " colors[key] + \"--\",\n", " label=\"Test {0}\".format(key),\n", " linewidth=4.0\n", " )\n", "plt.legend(loc=\"best\", fontsize=18)\n", "plt.xlabel(r\"$\\lambda$\", fontsize=18)\n", "plt.ylabel(r\"$R^2$\", fontsize=18)\n", "plt.tick_params(labelsize=18)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above figure we can see that LASSO with $\\lambda = 10^{-2}$\n", "achieves a very good accuracy on the test set. This by far surpasses the\n", "other models for all values of $\\lambda$." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }

cc0-1.0

davidrglass/antidengue

src/fasta_to_patient_sorted.ipynb

1

9737

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from Bio import SeqIO \n", "import numpy as np\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import os\n", "import sys\n", "from subprocess import call" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_igblast(filename):\n", " '''Takes a file from antidengue/data/fasta/ and runs it through igblast\n", " \n", " Parameters:\n", " filename - the name of the fasta\n", " '''\n", " if filename.endswith('.fasta'):\n", " outfile = filename[:-6] + '_out'\n", " else:\n", " outfile = filename + '_out'\n", " \n", " fasta_path = '/Users/davidglass/antidengue/data/fasta/'\n", " ncbi_path = '/Users/davidglass/Documents/resources/ncbi-igblast-1.4.0/'\n", " blast = '/Users/davidglass/Documents/resources/ncbi-igblast-1.4.0/bin/igblastn'\n", " igblast_dump = '/Users/davidglass/antidengue/data/ig_parse_dump/' + outfile[:-4]\n", "\n", " call(['cp', fasta_path + filename, ncbi_path])\n", " os.chdir(ncbi_path)\n", " call([blast, '-out', outfile, '-query', ncbi_path+filename, '-num_alignments_V=1', '-num_alignments_D=1',\n", " '-num_alignments_J=1', '-evalue=1e-20', '-germline_db_V', ncbi_path+'database/IGHV_imgt.fasta', \n", " '-germline_db_D', ncbi_path+'database/IGHD_imgt.fasta', '-germline_db_J',\n", " ncbi_path+'database/IGHJ_imgt.fasta', '-domain_system', 'imgt', '-auxiliary_data',\n", " ncbi_path+'optional_file/human_gl.aux'])\n", " call(['mkdir', igblast_dump])\n", " call(['mv', filename, igblast_dump])\n", " call(['mv', outfile, igblast_dump])\n", " \n", " return outfile" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_sample_info_from_csv(csv):\n", " '''Puts the info from the sample_info csv into a pandas dataframe and returns it\n", " \n", " Parameters:\n", " csv - the csv file\n", " '''\n", " \n", " sample_info = pd.DataFrame().from_csv(csv)\n", " \n", " return sample_info" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_parse(fasta, blast_out, sample_info):\n", " '''Runs parse_igblast.py in the igblast_dump folder and sends the parsedfile to the by_patient directory\n", " \n", " Parameters:\n", " fasta - the fasta file\n", " blast_out - the output of igblast\n", " dataframe - the pandas dataframe containing patient info\n", " '''\n", " accession_num = blast_out[:-4]\n", " igblast_dump = '/Users/davidglass/antidengue/data/ig_parse_dump/' + accession_num\n", " destination_directory = '/Users/davidglass/antidengue/data/by_sample'\n", " patient_id = \"\"\n", " time_point = \"\"\n", " \n", " call(['cp', '/Users/davidglass/antidengue/src/parse_igblast.py', igblast_dump])\n", " os.chdir(igblast_dump)\n", " call(['python', 'parse_igblast.py', fasta, blast_out])\n", " \n", " # find the ID from the sample_info DataFrame\n", " patient_id = str(sample_info.loc[accession_num][0])\n", " time_point = str(sample_info.loc[accession_num][4])\n", " new_filename = patient_id + \"_\" + time_point + \"_\" + accession_num\n", " \n", " call(['mv', 'parsed_igblast.txt', new_filename])\n", " call(['mv', new_filename, destination_directory])\n", " call(['rm', fasta])\n", " call(['rm', 'parse_igblast.py'])\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def pipeline(filename, sample_info):\n", " '''Runs a fasta through igblast and then parses the output and properly sorts files\n", " \n", " Parameters:\n", " filename - the name of the fasta\n", " sample_info - the pandas dataframe containing patient info\n", " '''\n", "\n", " blast_out = run_igblast(filename)\n", " run_parse(filename, blast_out, sample_info)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample_info = get_sample_info_from_csv('/Users/davidglass/antidengue/data/sample_info.csv')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Runs the pipeline on all the files\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/fasta/')\n", " \n", "for file in dirs:\n", " if file.endswith('.fasta'):\n", " pipeline(file, sample_info)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Renames the files with proper labels\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "os.chdir('/Users/davidglass/antidengue/data/by_sample/')\n", " \n", "for file in dirs:\n", " name = file\n", " first = name.find('_')\n", " sec = name.find('_', first + 1)\n", " name = \"patient_\" + name[:first] + \"_time\" + name[first:sec] + \"_sample\" + name[sec:]\n", " call(['mv', file, name])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Replaces header with amended header.\n", "\n", "header = \"###sequence_id\tabundance\tV-gene\tD-gene\tJ-gene\tV_E-value\tD_E-value\t\" + \\\n", " \"J_E-value\tFR3_seq\tCDR3_seq\tFR4_seq\tconst_seq\tlen_FR3\tlen_CDR3\tlen_FR4\t\" + \\\n", " \"len_const\tsequence_boundary_indices:FR1_CDR1_FR2_CDR2_FR3_CDR3_FR4\tlen_boundary\t\" + \\\n", " \"stop_codon_present\tproductive_sequence\tAA_seq_whole_read\tmutation_positions\t\" + \\\n", " \"germline_bases\tderived_bases\tmutation_density\tV_germline_identity\tleader_seq\t\" + \\\n", " \"reads_per_molecule\tprimer_isotype\tsequence_reversed\tseq\tquality###\\n\"\n", "\n", "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "lines = []\n", "for file in dirs:\n", " if (file.startswith('patient')):\n", " with open (file, 'r') as f:\n", " lines = f.readlines()\n", " lines[0] = header\n", " with open (file, 'w') as f:\n", " f.writelines(lines)\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_seq_and_quality(filename):\n", " '''Takes a filename from a parsed_ig file, finds the corresponding fastq, and appends the\n", " input file with sequence and quality information for each sequence.\n", " \n", " Parameters:\n", " filename - the name of the parsed_igfile\n", " '''\n", " fastq_directory = '/Users/davidglass/antidengue/data/fastq/'\n", " by_samples_directory = '/Users/davidglass/antidengue/data/by_sample/'\n", " \n", " last_underscore = filename.rfind('_')\n", " fastq_file = filename[last_underscore + 1:] + '.fastq'\n", " fastq_lines = []\n", " parsed_lines = []\n", " \n", " with open (by_samples_directory + filename, 'r') as fi:\n", " parsed_lines = fi.readlines()\n", " with open (fastq_directory + fastq_file, 'r') as fi:\n", " fastq_lines = fi.readlines()\n", " for i in range(len(parsed_lines)):\n", " if i != 0:\n", " parsed_lines[i] = parsed_lines[i].strip('\\n') + \"\\t\" + \\\n", " fastq_lines[(i*4)-3].strip('\\n') + \"\\t\" + fastq_lines[(i*4)-1]\n", " with open (by_samples_directory + filename, 'w') as fo:\n", " for line in parsed_lines:\n", " fo.write(line)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_seq_and_quality('patient_148_time_ACUTE_sample_SRR2150126')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dirs = os.listdir('/Users/davidglass/antidengue/data/by_sample/')\n", "for file in dirs:\n", " if (file.startswith('patient')):\n", " write_seq_and_quality(file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

LeeYiFang/Carkinos

src/Untitled5.ipynb

1

139686

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pathlib import Path\n", "import pandas as pd\n", "import numpy as np\n", "root=Path('../').resolve()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "WindowsPath('C:/Users/user/Documents/GitHub/Carkinos')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "root" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "raw_path=root.joinpath('src','PCA_TEST.quantile_normalized.tsv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sample_raw_data_df = pd.read_table(raw_path.as_posix())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", 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<td>9.203023</td>\n", " <td>8.572035</td>\n", " <td>8.506107</td>\n", " <td>8.229169</td>\n", " <td>8.695021</td>\n", " <td>8.915824</td>\n", " <td>8.233599</td>\n", " <td>...</td>\n", " <td>8.776563</td>\n", " <td>9.155915</td>\n", " <td>9.336024</td>\n", " <td>8.824805</td>\n", " <td>8.772813</td>\n", " <td>8.802262</td>\n", " <td>8.678147</td>\n", " <td>8.625144</td>\n", " <td>8.695912</td>\n", " <td>8.629976</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>117_at</td>\n", " <td>7.612412</td>\n", " <td>8.043859</td>\n", " <td>7.603927</td>\n", " <td>7.714699</td>\n", " <td>7.742822</td>\n", " <td>7.551629</td>\n", " <td>7.504556</td>\n", " <td>7.720968</td>\n", " <td>7.730913</td>\n", " <td>...</td>\n", " <td>7.729079</td>\n", " <td>11.647400</td>\n", " <td>11.342093</td>\n", " <td>7.850625</td>\n", " <td>8.028290</td>\n", " <td>8.037324</td>\n", " <td>7.655276</td>\n", " <td>7.888281</td>\n", " <td>7.691653</td>\n", " <td>7.624577</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>121_at</td>\n", " <td>8.218106</td>\n", " <td>8.235331</td>\n", " <td>8.400500</td>\n", " <td>8.405479</td>\n", " <td>9.297814</td>\n", " <td>8.178474</td>\n", " <td>8.067798</td>\n", " <td>8.176426</td>\n", " <td>11.259210</td>\n", " <td>...</td>\n", " <td>11.123192</td>\n", " <td>8.063912</td>\n", " <td>8.138332</td>\n", " <td>8.213297</td>\n", " <td>8.035581</td>\n", " <td>8.203285</td>\n", " <td>8.021791</td>\n", " <td>8.373332</td>\n", " <td>8.081797</td>\n", " <td>8.261245</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1255_g_at</td>\n", " <td>7.392744</td>\n", " <td>7.395735</td>\n", " <td>7.453283</td>\n", " <td>7.493720</td>\n", " <td>7.474639</td>\n", " <td>7.436698</td>\n", " <td>7.388679</td>\n", " <td>7.508520</td>\n", " <td>7.432427</td>\n", " <td>...</td>\n", " <td>7.445189</td>\n", " <td>9.069289</td>\n", " <td>8.621514</td>\n", " <td>7.446887</td>\n", " <td>7.512241</td>\n", " <td>7.456834</td>\n", " <td>7.453321</td>\n", " <td>7.509484</td>\n", " <td>7.315354</td>\n", " <td>7.513548</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1294_at</td>\n", " <td>9.003722</td>\n", " <td>8.375226</td>\n", " <td>7.975448</td>\n", " <td>7.762306</td>\n", " <td>7.763070</td>\n", " <td>7.981557</td>\n", " <td>8.389011</td>\n", " <td>8.376507</td>\n", " <td>7.903314</td>\n", " <td>...</td>\n", " <td>8.412039</td>\n", " <td>7.796222</td>\n", " <td>7.722225</td>\n", " <td>7.956599</td>\n", " <td>8.201499</td>\n", " <td>8.151006</td>\n", " <td>8.215746</td>\n", " <td>9.143522</td>\n", " <td>8.090617</td>\n", " <td>7.830512</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1316_at</td>\n", " <td>7.909244</td>\n", " <td>7.878632</td>\n", " <td>7.903908</td>\n", " <td>7.741367</td>\n", " <td>8.015132</td>\n", " <td>7.772626</td>\n", " <td>7.807954</td>\n", " <td>7.809478</td>\n", " <td>7.864759</td>\n", " <td>...</td>\n", " <td>8.065336</td>\n", " <td>7.948937</td>\n", " <td>8.050267</td>\n", " <td>7.966506</td>\n", " <td>8.097026</td>\n", " <td>7.900293</td>\n", " <td>7.847609</td>\n", " <td>8.001678</td>\n", " <td>8.084945</td>\n", " <td>7.888374</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1320_at</td>\n", " <td>7.651478</td>\n", " <td>7.708905</td>\n", " <td>7.602916</td>\n", " <td>7.705086</td>\n", " <td>7.712262</td>\n", " <td>7.723882</td>\n", " <td>7.739506</td>\n", " <td>7.641239</td>\n", " <td>7.621617</td>\n", " <td>...</td>\n", " <td>7.761721</td>\n", " <td>7.783990</td>\n", " <td>7.627924</td>\n", " <td>7.675847</td>\n", " <td>7.875980</td>\n", " <td>7.544660</td>\n", " <td>7.588426</td>\n", " <td>7.574985</td>\n", " <td>7.583279</td>\n", " <td>7.611891</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1405_i_at</td>\n", " <td>7.834398</td>\n", " <td>7.697773</td>\n", " <td>7.633690</td>\n", " <td>7.601535</td>\n", " <td>7.726134</td>\n", " <td>7.614437</td>\n", " <td>8.776613</td>\n", " <td>8.449633</td>\n", " <td>7.636824</td>\n", " <td>...</td>\n", " <td>7.801481</td>\n", " <td>7.580300</td>\n", " <td>7.648297</td>\n", " <td>7.794772</td>\n", " <td>8.369012</td>\n", " <td>7.921049</td>\n", " <td>7.615833</td>\n", " <td>7.820004</td>\n", " <td>7.807366</td>\n", " <td>7.596185</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1431_at</td>\n", " <td>7.728028</td>\n", " <td>7.771812</td>\n", " <td>7.403523</td>\n", " <td>7.667112</td>\n", " <td>7.520539</td>\n", " <td>7.713980</td>\n", " <td>7.536457</td>\n", " <td>7.460594</td>\n", " <td>7.545543</td>\n", " <td>...</td>\n", " <td>7.672223</td>\n", " <td>7.619240</td>\n", " <td>7.575923</td>\n", " <td>7.509735</td>\n", " <td>7.758326</td>\n", " <td>7.674408</td>\n", " <td>7.513123</td>\n", " <td>7.458537</td>\n", " <td>7.479716</td>\n", " <td>7.611531</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1438_at</td>\n", " <td>7.657973</td>\n", " <td>7.839882</td>\n", " <td>7.600658</td>\n", " <td>7.751388</td>\n", " <td>8.271726</td>\n", " <td>7.653040</td>\n", " <td>7.639229</td>\n", " <td>7.651966</td>\n", " <td>7.630050</td>\n", " <td>...</td>\n", " <td>7.703543</td>\n", " <td>7.879641</td>\n", " <td>7.665347</td>\n", " <td>8.257526</td>\n", " <td>7.835643</td>\n", " <td>7.662081</td>\n", " <td>7.729477</td>\n", " <td>7.615180</td>\n", " <td>7.752624</td>\n", " <td>8.094068</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1487_at</td>\n", " <td>8.206241</td>\n", " <td>7.972470</td>\n", " <td>8.424972</td>\n", " <td>8.281678</td>\n", " <td>8.992934</td>\n", " <td>8.402891</td>\n", " <td>8.278146</td>\n", " <td>8.351512</td>\n", " <td>8.908842</td>\n", " <td>...</td>\n", " <td>8.344273</td>\n", " <td>8.172797</td>\n", " <td>7.982788</td>\n", " <td>7.879871</td>\n", " <td>8.472595</td>\n", " <td>8.059953</td>\n", " <td>9.227652</td>\n", " <td>8.028662</td>\n", " <td>8.095783</td>\n", " <td>8.198551</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1494_f_at</td>\n", " <td>7.858453</td>\n", " <td>7.830963</td>\n", " <td>7.711877</td>\n", " <td>7.809028</td>\n", " <td>7.815555</td>\n", " <td>7.735942</td>\n", " <td>7.664429</td>\n", " <td>7.825576</td>\n", " <td>7.818208</td>\n", " <td>...</td>\n", " <td>7.844457</td>\n", " <td>7.801330</td>\n", " <td>7.819480</td>\n", " <td>7.692542</td>\n", " <td>7.922304</td>\n", " <td>7.911433</td>\n", " <td>7.801025</td>\n", " <td>7.791079</td>\n", " <td>7.718452</td>\n", " <td>7.767680</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1598_g_at</td>\n", " <td>8.087957</td>\n", " <td>7.959245</td>\n", " <td>8.664610</td>\n", " <td>7.890980</td>\n", " <td>8.003252</td>\n", " <td>8.358760</td>\n", " <td>9.062693</td>\n", " <td>9.483724</td>\n", " <td>8.192223</td>\n", " <td>...</td>\n", " <td>10.170631</td>\n", " <td>9.618505</td>\n", " <td>9.566406</td>\n", " <td>8.119021</td>\n", " <td>7.823470</td>\n", " <td>8.055860</td>\n", " <td>8.487177</td>\n", " <td>9.481194</td>\n", " <td>8.711049</td>\n", " <td>8.056754</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>160020_at</td>\n", " <td>8.028948</td>\n", " <td>8.007019</td>\n", " <td>8.531796</td>\n", " <td>8.034021</td>\n", " <td>7.950786</td>\n", " <td>8.030606</td>\n", " <td>7.891479</td>\n", " <td>8.148135</td>\n", " <td>8.369209</td>\n", " <td>...</td>\n", " <td>7.966837</td>\n", " <td>8.077760</td>\n", " <td>8.133854</td>\n", " <td>8.968220</td>\n", " <td>7.858555</td>\n", " <td>8.259718</td>\n", " <td>8.591962</td>\n", " <td>9.294565</td>\n", " <td>8.483516</td>\n", " <td>8.049743</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1729_at</td>\n", " <td>8.096292</td>\n", " <td>8.180304</td>\n", " <td>8.388173</td>\n", " <td>8.260304</td>\n", " <td>8.831569</td>\n", " <td>8.349998</td>\n", " <td>8.593277</td>\n", " <td>8.616233</td>\n", " <td>9.019362</td>\n", " <td>...</td>\n", " <td>8.835704</td>\n", " <td>8.007914</td>\n", " <td>8.141308</td>\n", " <td>8.310294</td>\n", " <td>8.290844</td>\n", " <td>8.510494</td>\n", " <td>8.542961</td>\n", " <td>8.556058</td>\n", " <td>8.174816</td>\n", " <td>8.341683</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>177_at</td>\n", " <td>7.514178</td>\n", " <td>7.606598</td>\n", " <td>7.693805</td>\n", " <td>7.581838</td>\n", " <td>7.708548</td>\n", " <td>7.708384</td>\n", " <td>7.597449</td>\n", " <td>7.693610</td>\n", " <td>7.620039</td>\n", " <td>...</td>\n", " <td>7.656180</td>\n", " <td>7.591175</td>\n", " <td>7.567320</td>\n", " <td>7.698346</td>\n", " <td>7.841765</td>\n", " <td>7.630390</td>\n", " <td>7.571643</td>\n", " <td>7.708900</td>\n", " <td>7.706586</td>\n", " <td>7.618176</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1773_at</td>\n", " <td>7.677061</td>\n", " <td>7.844826</td>\n", " <td>7.771058</td>\n", " <td>7.596594</td>\n", " <td>7.590436</td>\n", " <td>7.767117</td>\n", " <td>7.812929</td>\n", " <td>7.795710</td>\n", " <td>7.775275</td>\n", " <td>...</td>\n", " <td>7.741154</td>\n", " <td>7.675076</td>\n", " <td>7.812365</td>\n", " <td>7.961307</td>\n", " <td>7.810738</td>\n", " <td>7.865056</td>\n", " <td>7.612419</td>\n", " <td>7.775011</td>\n", " <td>7.651543</td>\n", " <td>7.819192</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>179_at</td>\n", " <td>8.243753</td>\n", " <td>8.264494</td>\n", " <td>8.455341</td>\n", " <td>8.109603</td>\n", " <td>8.178008</td>\n", " <td>8.221881</td>\n", " <td>8.216369</td>\n", " <td>8.128763</td>\n", " <td>8.127175</td>\n", " <td>...</td>\n", " <td>8.079943</td>\n", " <td>8.025528</td>\n", " <td>8.051105</td>\n", " <td>8.290394</td>\n", " <td>7.986633</td>\n", " <td>8.172114</td>\n", " <td>8.114658</td>\n", " <td>8.182000</td>\n", " <td>8.141716</td>\n", " <td>8.375676</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1861_at</td>\n", " <td>7.947907</td>\n", " <td>8.087725</td>\n", " <td>8.533923</td>\n", " <td>8.077407</td>\n", " <td>8.486724</td>\n", " <td>8.214426</td>\n", " <td>8.467074</td>\n", " <td>8.454226</td>\n", " <td>8.380016</td>\n", " <td>...</td>\n", " <td>8.391886</td>\n", " <td>8.459854</td>\n", " <td>8.388810</td>\n", " <td>8.171772</td>\n", " <td>8.105932</td>\n", " <td>8.126414</td>\n", " <td>8.459284</td>\n", " <td>8.558124</td>\n", " <td>8.431172</td>\n", " <td>8.444880</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>200000_s_at</td>\n", " <td>10.943962</td>\n", " <td>10.554185</td>\n", " <td>10.010714</td>\n", " <td>10.351770</td>\n", " <td>10.582500</td>\n", " <td>10.918780</td>\n", " <td>10.779839</td>\n", " <td>11.012558</td>\n", " <td>10.666718</td>\n", " <td>...</td>\n", " <td>10.323892</td>\n", " <td>11.362956</td>\n", " <td>11.045315</td>\n", " <td>10.389930</td>\n", " <td>10.730466</td>\n", " <td>10.670736</td>\n", " <td>10.434048</td>\n", " <td>10.010854</td>\n", " <td>11.172518</td>\n", " <td>9.934250</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>200001_at</td>\n", " <td>10.147493</td>\n", " <td>10.029987</td>\n", " <td>9.968916</td>\n", " <td>10.557932</td>\n", " <td>11.251398</td>\n", " <td>10.456606</td>\n", " <td>11.179078</td>\n", " <td>11.440406</td>\n", " <td>11.391664</td>\n", " <td>...</td>\n", " <td>11.327322</td>\n", " <td>9.867762</td>\n", " <td>9.967675</td>\n", " <td>11.068072</td>\n", " <td>10.325408</td>\n", " <td>10.568185</td>\n", " <td>11.879458</td>\n", " <td>11.553767</td>\n", " <td>11.167581</td>\n", " <td>11.182587</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>200002_at</td>\n", " <td>12.598934</td>\n", " <td>12.884603</td>\n", " <td>12.851004</td>\n", " <td>12.440221</td>\n", " <td>12.163264</td>\n", " <td>12.102061</td>\n", " <td>11.971443</td>\n", " <td>11.775090</td>\n", " <td>11.991532</td>\n", " <td>...</td>\n", " <td>12.225448</td>\n", " <td>12.452044</td>\n", " <td>12.234126</td>\n", " <td>12.269134</td>\n", " <td>12.278390</td>\n", " <td>11.997784</td>\n", " <td>12.361425</td>\n", " <td>11.893040</td>\n", " <td>12.207024</td>\n", " <td>12.139081</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>200003_s_at</td>\n", " <td>13.962958</td>\n", " <td>13.866020</td>\n", " <td>13.939309</td>\n", " <td>13.421075</td>\n", " <td>13.495911</td>\n", " <td>13.614290</td>\n", " <td>13.357725</td>\n", " <td>13.273866</td>\n", " <td>13.261886</td>\n", " <td>...</td>\n", " <td>13.665884</td>\n", " <td>13.199544</td>\n", " <td>13.201468</td>\n", " <td>13.438959</td>\n", " <td>13.736109</td>\n", " <td>13.757036</td>\n", " <td>13.518426</td>\n", " <td>13.648311</td>\n", " <td>13.487201</td>\n", " <td>13.645801</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>200004_at</td>\n", " <td>11.638454</td>\n", " <td>11.515864</td>\n", " <td>12.092778</td>\n", " <td>11.388483</td>\n", " <td>11.110027</td>\n", " <td>11.023252</td>\n", " <td>12.139457</td>\n", " <td>11.722688</td>\n", " <td>10.971977</td>\n", " <td>...</td>\n", " <td>10.811857</td>\n", " <td>11.674871</td>\n", " <td>11.621797</td>\n", " <td>12.281479</td>\n", " <td>11.026658</td>\n", " <td>11.344845</td>\n", " <td>11.860932</td>\n", " <td>11.245582</td>\n", " <td>11.229407</td>\n", " <td>12.435254</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>200005_at</td>\n", " <td>11.789568</td>\n", " <td>11.780158</td>\n", " <td>11.265116</td>\n", " <td>10.932197</td>\n", " <td>11.208736</td>\n", " <td>11.723449</td>\n", " <td>11.019480</td>\n", " <td>10.959582</td>\n", " <td>11.198749</td>\n", " <td>...</td>\n", " <td>10.613178</td>\n", " <td>11.048126</td>\n", " <td>10.848619</td>\n", " <td>10.751546</td>\n", " <td>10.975890</td>\n", " <td>10.848473</td>\n", " <td>10.825055</td>\n", " <td>10.997519</td>\n", " <td>10.427742</td>\n", " <td>11.391999</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>200006_at</td>\n", " <td>11.829138</td>\n", " <td>11.970993</td>\n", " <td>12.057994</td>\n", " <td>12.046755</td>\n", " <td>12.081040</td>\n", " <td>12.305364</td>\n", " <td>11.943838</td>\n", " <td>11.873483</td>\n", " <td>11.524197</td>\n", " <td>...</td>\n", " <td>11.690109</td>\n", " <td>11.795945</td>\n", " <td>11.744640</td>\n", " <td>12.418210</td>\n", " <td>11.275798</td>\n", " <td>11.125740</td>\n", " <td>11.738490</td>\n", " <td>12.302239</td>\n", " <td>12.460312</td>\n", " <td>11.976653</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>200007_at</td>\n", " <td>11.819296</td>\n", " <td>11.439260</td>\n", " <td>11.757965</td>\n", " <td>11.494313</td>\n", " <td>11.563859</td>\n", " <td>11.501570</td>\n", " <td>12.002326</td>\n", " <td>11.871924</td>\n", " <td>11.536442</td>\n", " <td>...</td>\n", " <td>11.674007</td>\n", " <td>11.710358</td>\n", " <td>11.783797</td>\n", " <td>12.263241</td>\n", " <td>12.122391</td>\n", " <td>12.432593</td>\n", " <td>11.948162</td>\n", " <td>12.127104</td>\n", " <td>12.066296</td>\n", " <td>12.259765</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>200008_s_at</td>\n", " <td>10.676458</td>\n", " <td>10.690479</td>\n", " <td>9.862556</td>\n", " <td>8.386338</td>\n", " <td>8.236503</td>\n", " <td>9.655560</td>\n", " <td>9.618241</td>\n", " <td>8.874053</td>\n", " <td>7.748188</td>\n", " <td>...</td>\n", " <td>7.779491</td>\n", " <td>8.238553</td>\n", " <td>8.077361</td>\n", " <td>9.726048</td>\n", " <td>8.215377</td>\n", " <td>8.102943</td>\n", " <td>8.383057</td>\n", " <td>7.727232</td>\n", " <td>7.818090</td>\n", " <td>10.854761</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>200009_at</td>\n", " <td>12.241562</td>\n", " <td>12.123915</td>\n", " <td>11.613592</td>\n", " <td>11.436513</td>\n", " <td>12.077685</td>\n", " <td>11.656556</td>\n", " <td>11.656313</td>\n", " <td>11.804873</td>\n", " <td>11.717407</td>\n", " <td>...</td>\n", " <td>11.635394</td>\n", " <td>11.511827</td>\n", " <td>11.450963</td>\n", " <td>11.001656</td>\n", " <td>11.963714</td>\n", " <td>11.745106</td>\n", " <td>11.711815</td>\n", " <td>10.430322</td>\n", " <td>10.139406</td>\n", " <td>12.053892</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>22247</th>\n", " <td>AFFX-PheX-3_at</td>\n", " <td>7.587780</td>\n", " <td>7.739597</td>\n", " <td>7.637001</td>\n", " <td>7.572844</td>\n", " <td>7.566232</td>\n", " <td>7.626215</td>\n", " <td>7.597273</td>\n", " <td>7.609240</td>\n", " <td>7.693653</td>\n", " <td>...</td>\n", " <td>7.636286</td>\n", " <td>7.581094</td>\n", " <td>7.677399</td>\n", " <td>7.564076</td>\n", " <td>7.684905</td>\n", " <td>7.730319</td>\n", " <td>7.591921</td>\n", " <td>7.646766</td>\n", " <td>7.617262</td>\n", " <td>7.655340</td>\n", " </tr>\n", " <tr>\n", " <th>22248</th>\n", " <td>AFFX-PheX-5_at</td>\n", " <td>7.615105</td>\n", " <td>7.584478</td>\n", " <td>7.455959</td>\n", " <td>7.645467</td>\n", " <td>7.560949</td>\n", " <td>7.619625</td>\n", " <td>7.598112</td>\n", " <td>7.582925</td>\n", " <td>7.627934</td>\n", " <td>...</td>\n", " <td>7.525731</td>\n", " <td>7.579176</td>\n", " <td>7.570382</td>\n", " <td>7.418292</td>\n", " <td>7.732098</td>\n", " <td>7.588400</td>\n", " <td>7.546730</td>\n", " <td>7.637900</td>\n", " <td>7.641483</td>\n", " <td>7.602141</td>\n", " </tr>\n", " <tr>\n", " <th>22249</th>\n", " <td>AFFX-PheX-M_at</td>\n", " <td>7.500879</td>\n", " <td>7.558350</td>\n", " <td>7.423706</td>\n", " <td>7.425247</td>\n", " <td>7.467854</td>\n", " <td>7.400606</td>\n", " <td>7.364192</td>\n", " <td>7.400171</td>\n", " <td>7.531035</td>\n", " <td>...</td>\n", " <td>7.510141</td>\n", " <td>7.385669</td>\n", " <td>7.557117</td>\n", " <td>7.408859</td>\n", " <td>7.686879</td>\n", " <td>7.642765</td>\n", " <td>7.437861</td>\n", " <td>7.544820</td>\n", " <td>7.509462</td>\n", " <td>7.375522</td>\n", " </tr>\n", " <tr>\n", " <th>22250</th>\n", " <td>AFFX-r2-Bs-dap-3_at</td>\n", " <td>7.415058</td>\n", " <td>7.538497</td>\n", " <td>7.444206</td>\n", " <td>7.537508</td>\n", " <td>7.526306</td>\n", " <td>7.475735</td>\n", " <td>7.457193</td>\n", " <td>7.489438</td>\n", " <td>7.510553</td>\n", " <td>...</td>\n", " <td>7.641837</td>\n", " <td>7.473934</td>\n", " <td>7.497987</td>\n", " <td>7.439749</td>\n", " <td>7.762372</td>\n", " <td>7.470218</td>\n", " <td>7.395558</td>\n", " <td>7.626874</td>\n", " <td>7.440026</td>\n", " <td>7.339268</td>\n", " </tr>\n", " <tr>\n", " <th>22251</th>\n", " <td>AFFX-r2-Bs-dap-5_at</td>\n", " <td>7.510356</td>\n", " <td>7.599167</td>\n", " <td>7.576703</td>\n", " <td>7.430549</td>\n", " <td>7.595861</td>\n", " <td>7.407454</td>\n", " <td>7.450202</td>\n", " <td>7.493626</td>\n", " <td>7.640243</td>\n", " <td>...</td>\n", " <td>7.489692</td>\n", " <td>7.520011</td>\n", " <td>7.573883</td>\n", " <td>7.419995</td>\n", " <td>7.758236</td>\n", " <td>7.611355</td>\n", " <td>7.547459</td>\n", " <td>7.573749</td>\n", " <td>7.517581</td>\n", " <td>7.443672</td>\n", " </tr>\n", " <tr>\n", " <th>22252</th>\n", " <td>AFFX-r2-Bs-dap-M_at</td>\n", " <td>7.428785</td>\n", " <td>7.492951</td>\n", " <td>7.480116</td>\n", " <td>7.301982</td>\n", " <td>7.369849</td>\n", " <td>7.324410</td>\n", " <td>7.369699</td>\n", " <td>7.345805</td>\n", " <td>7.429821</td>\n", " <td>...</td>\n", " <td>7.307410</td>\n", " <td>7.394087</td>\n", " <td>7.483675</td>\n", " <td>7.442209</td>\n", " <td>7.438441</td>\n", " <td>7.523250</td>\n", " <td>7.365466</td>\n", " <td>7.517269</td>\n", " <td>7.419273</td>\n", " <td>7.410981</td>\n", " </tr>\n", " <tr>\n", " <th>22253</th>\n", " <td>AFFX-r2-Bs-lys-3_at</td>\n", " <td>7.541610</td>\n", " <td>7.804846</td>\n", " <td>7.405789</td>\n", " <td>7.458813</td>\n", " <td>7.653536</td>\n", " <td>7.562071</td>\n", " <td>7.451183</td>\n", " <td>7.573437</td>\n", " <td>7.857587</td>\n", " <td>...</td>\n", " <td>7.581681</td>\n", " <td>7.649899</td>\n", " <td>7.749900</td>\n", " <td>7.500011</td>\n", " <td>7.735787</td>\n", " <td>7.835627</td>\n", " <td>7.567267</td>\n", " <td>7.681973</td>\n", " <td>7.696307</td>\n", " <td>7.495174</td>\n", " </tr>\n", " <tr>\n", " <th>22254</th>\n", " <td>AFFX-r2-Bs-lys-5_at</td>\n", " <td>7.424384</td>\n", " <td>7.714397</td>\n", " <td>7.318250</td>\n", " <td>7.494257</td>\n", " <td>7.518036</td>\n", " <td>7.493577</td>\n", " <td>7.360679</td>\n", " <td>7.566592</td>\n", " <td>7.668429</td>\n", " <td>...</td>\n", " <td>7.515631</td>\n", " <td>7.450158</td>\n", " <td>7.508255</td>\n", " <td>7.357867</td>\n", " <td>7.726456</td>\n", " <td>7.789662</td>\n", " <td>7.445381</td>\n", " <td>7.663948</td>\n", " <td>7.512095</td>\n", " <td>7.388519</td>\n", " </tr>\n", " <tr>\n", " <th>22255</th>\n", " <td>AFFX-r2-Bs-lys-M_at</td>\n", " <td>7.598653</td>\n", " <td>7.533525</td>\n", " <td>7.550970</td>\n", " <td>7.556028</td>\n", " <td>7.639852</td>\n", " <td>7.378454</td>\n", " <td>7.558760</td>\n", " <td>7.513993</td>\n", " <td>7.536908</td>\n", " <td>...</td>\n", " <td>7.759471</td>\n", " <td>7.566438</td>\n", " <td>7.647331</td>\n", " <td>7.538462</td>\n", " <td>8.024200</td>\n", " <td>7.592782</td>\n", " <td>7.435744</td>\n", " <td>7.452174</td>\n", " <td>7.626060</td>\n", " <td>7.519610</td>\n", " </tr>\n", " <tr>\n", " <th>22256</th>\n", " <td>AFFX-r2-Bs-phe-3_at</td>\n", " <td>7.588818</td>\n", " <td>7.700379</td>\n", " <td>7.608957</td>\n", " <td>7.617782</td>\n", " <td>7.668815</td>\n", " <td>7.544640</td>\n", " <td>7.420475</td>\n", " <td>7.465998</td>\n", " <td>7.587814</td>\n", " <td>...</td>\n", " <td>7.579117</td>\n", " <td>7.570955</td>\n", " <td>7.700903</td>\n", " <td>7.527279</td>\n", " <td>7.805261</td>\n", " <td>7.738460</td>\n", " <td>7.566220</td>\n", " <td>7.714249</td>\n", " <td>7.558661</td>\n", " <td>7.564291</td>\n", " </tr>\n", " <tr>\n", " <th>22257</th>\n", " <td>AFFX-r2-Bs-phe-5_at</td>\n", " <td>7.626531</td>\n", " <td>7.575690</td>\n", " <td>7.474051</td>\n", " <td>7.700783</td>\n", " <td>7.609364</td>\n", " <td>7.330687</td>\n", " <td>7.302846</td>\n", " <td>7.577040</td>\n", " <td>7.630244</td>\n", " <td>...</td>\n", " <td>7.705097</td>\n", " <td>7.578413</td>\n", " <td>7.756379</td>\n", " <td>7.508484</td>\n", " <td>7.899927</td>\n", " <td>7.602541</td>\n", " <td>7.482776</td>\n", " <td>7.755496</td>\n", " <td>7.608196</td>\n", " <td>7.514778</td>\n", " </tr>\n", " <tr>\n", " <th>22258</th>\n", " <td>AFFX-r2-Bs-phe-M_at</td>\n", " <td>7.491488</td>\n", " <td>7.581517</td>\n", " <td>7.428292</td>\n", " <td>7.495536</td>\n", " <td>7.631848</td>\n", " <td>7.480835</td>\n", " <td>7.453392</td>\n", " <td>7.509254</td>\n", " <td>7.501456</td>\n", " <td>...</td>\n", " <td>7.597136</td>\n", " <td>7.554519</td>\n", " <td>7.478137</td>\n", " <td>7.444631</td>\n", " <td>7.700423</td>\n", " <td>7.555826</td>\n", " <td>7.438705</td>\n", " <td>7.460913</td>\n", " <td>7.608836</td>\n", " <td>7.501917</td>\n", " </tr>\n", " <tr>\n", " <th>22259</th>\n", " <td>AFFX-r2-Bs-thr-3_s_at</td>\n", " <td>7.576096</td>\n", " <td>7.616859</td>\n", " <td>7.621567</td>\n", " <td>7.633570</td>\n", " <td>7.607185</td>\n", " <td>7.675595</td>\n", " <td>7.595108</td>\n", " <td>7.778257</td>\n", " <td>7.824489</td>\n", " <td>...</td>\n", " <td>7.665649</td>\n", " <td>7.608371</td>\n", " <td>7.698390</td>\n", " <td>7.623490</td>\n", " <td>7.792992</td>\n", " <td>7.790727</td>\n", " <td>7.587139</td>\n", " <td>7.693699</td>\n", " <td>7.539833</td>\n", " <td>7.562464</td>\n", " </tr>\n", " <tr>\n", " <th>22260</th>\n", " <td>AFFX-r2-Bs-thr-5_s_at</td>\n", " <td>7.728724</td>\n", " <td>7.708178</td>\n", " <td>7.608102</td>\n", " <td>7.591521</td>\n", " <td>7.683940</td>\n", " <td>7.517711</td>\n", " <td>7.579549</td>\n", " <td>7.641952</td>\n", " <td>7.686149</td>\n", " <td>...</td>\n", " <td>7.659568</td>\n", " <td>7.594628</td>\n", " <td>7.740366</td>\n", " <td>7.635615</td>\n", " <td>7.985159</td>\n", " <td>7.910271</td>\n", " <td>7.584600</td>\n", " <td>7.754253</td>\n", " <td>7.666207</td>\n", " <td>7.649350</td>\n", " </tr>\n", " <tr>\n", " <th>22261</th>\n", " <td>AFFX-r2-Bs-thr-M_s_at</td>\n", " <td>7.586706</td>\n", " <td>7.730064</td>\n", " <td>7.519281</td>\n", " <td>7.657503</td>\n", " <td>7.557744</td>\n", " <td>7.527900</td>\n", " <td>7.502069</td>\n", " <td>7.569939</td>\n", " <td>7.534255</td>\n", " <td>...</td>\n", " <td>7.508732</td>\n", " <td>7.479604</td>\n", " <td>7.536840</td>\n", " <td>7.606430</td>\n", " <td>7.901393</td>\n", " <td>7.671241</td>\n", " <td>7.452707</td>\n", " <td>7.548825</td>\n", " <td>7.389860</td>\n", " <td>7.605472</td>\n", " </tr>\n", " <tr>\n", " <th>22262</th>\n", " <td>AFFX-r2-Ec-bioB-3_at</td>\n", " <td>10.184430</td>\n", " <td>9.529309</td>\n", " <td>8.619244</td>\n", " <td>10.147914</td>\n", " <td>8.838657</td>\n", " <td>8.555512</td>\n", " <td>8.452804</td>\n", " <td>8.597299</td>\n", " <td>8.768882</td>\n", " <td>...</td>\n", " <td>9.190201</td>\n", " <td>8.810104</td>\n", " <td>8.790299</td>\n", " <td>9.860041</td>\n", " <td>9.168162</td>\n", " <td>9.268455</td>\n", " <td>8.586246</td>\n", " <td>9.029980</td>\n", " <td>8.885536</td>\n", " <td>10.091491</td>\n", " </tr>\n", " <tr>\n", " <th>22263</th>\n", " <td>AFFX-r2-Ec-bioB-5_at</td>\n", " <td>9.998544</td>\n", " <td>9.456845</td>\n", " <td>8.516449</td>\n", " <td>9.808780</td>\n", " <td>8.703090</td>\n", " <td>8.361782</td>\n", " <td>8.357288</td>\n", " <td>8.425705</td>\n", " <td>8.778259</td>\n", " <td>...</td>\n", " <td>9.107011</td>\n", " <td>8.799799</td>\n", " <td>8.807281</td>\n", " <td>9.658986</td>\n", " <td>9.130400</td>\n", " <td>9.104295</td>\n", " <td>8.460702</td>\n", " <td>8.724103</td>\n", " <td>8.768013</td>\n", " <td>9.928022</td>\n", " </tr>\n", " <tr>\n", " <th>22264</th>\n", " <td>AFFX-r2-Ec-bioB-M_at</td>\n", " <td>9.886580</td>\n", " <td>9.316752</td>\n", " <td>8.414869</td>\n", " <td>9.810127</td>\n", " <td>8.594883</td>\n", " <td>8.288756</td>\n", " <td>8.271467</td>\n", " <td>8.283263</td>\n", " <td>8.720208</td>\n", " <td>...</td>\n", " <td>8.906124</td>\n", " <td>8.707507</td>\n", " <td>8.462430</td>\n", " <td>9.729706</td>\n", " <td>8.889075</td>\n", " <td>9.032003</td>\n", " <td>8.428403</td>\n", " <td>8.866616</td>\n", " <td>8.680978</td>\n", " <td>10.030972</td>\n", " </tr>\n", " <tr>\n", " <th>22265</th>\n", " <td>AFFX-r2-Ec-bioC-3_at</td>\n", " <td>9.496804</td>\n", " <td>10.373924</td>\n", " <td>9.122523</td>\n", " <td>9.469340</td>\n", " <td>9.079933</td>\n", " <td>8.593055</td>\n", " <td>8.660188</td>\n", " <td>8.902277</td>\n", " <td>9.101185</td>\n", " <td>...</td>\n", " <td>9.508558</td>\n", " <td>9.213596</td>\n", " <td>9.361792</td>\n", " <td>9.247763</td>\n", " <td>9.475856</td>\n", " <td>9.678426</td>\n", " <td>8.899431</td>\n", " <td>9.424195</td>\n", " <td>9.112191</td>\n", " <td>9.572530</td>\n", " </tr>\n", " <tr>\n", " <th>22266</th>\n", " <td>AFFX-r2-Ec-bioC-5_at</td>\n", " <td>9.791266</td>\n", " <td>10.589859</td>\n", " <td>9.408320</td>\n", " <td>9.824942</td>\n", " <td>9.469546</td>\n", " <td>8.960363</td>\n", " <td>9.020998</td>\n", " <td>9.076730</td>\n", " <td>9.407147</td>\n", " <td>...</td>\n", " <td>9.819895</td>\n", " <td>9.604219</td>\n", " <td>9.799778</td>\n", " <td>9.653474</td>\n", " <td>9.793151</td>\n", " <td>10.135800</td>\n", " <td>9.191924</td>\n", " <td>9.603816</td>\n", " <td>9.369659</td>\n", " <td>10.024467</td>\n", " </tr>\n", " <tr>\n", " <th>22267</th>\n", " <td>AFFX-r2-Ec-bioD-3_at</td>\n", " <td>12.953441</td>\n", " <td>12.664495</td>\n", " <td>10.985948</td>\n", " <td>12.976145</td>\n", " <td>11.643625</td>\n", " <td>11.059735</td>\n", " <td>11.240215</td>\n", " <td>10.678759</td>\n", " <td>11.696586</td>\n", " <td>...</td>\n", " <td>12.190513</td>\n", " <td>12.099303</td>\n", " <td>11.631617</td>\n", " <td>13.011969</td>\n", " <td>12.132080</td>\n", " <td>11.715813</td>\n", " <td>11.564594</td>\n", " <td>11.437572</td>\n", " <td>11.593778</td>\n", " <td>13.032810</td>\n", " </tr>\n", " <tr>\n", " <th>22268</th>\n", " <td>AFFX-r2-Ec-bioD-5_at</td>\n", " <td>12.661458</td>\n", " <td>12.620283</td>\n", " <td>10.741545</td>\n", " <td>12.682194</td>\n", " <td>11.454273</td>\n", " <td>10.980560</td>\n", " <td>10.843183</td>\n", " <td>10.394221</td>\n", " <td>11.313661</td>\n", " <td>...</td>\n", " <td>11.888910</td>\n", " <td>12.019156</td>\n", " <td>11.254002</td>\n", " <td>12.499611</td>\n", " <td>11.828679</td>\n", " <td>11.341177</td>\n", " <td>11.334232</td>\n", " <td>11.093700</td>\n", " <td>11.469527</td>\n", " <td>12.721535</td>\n", " </tr>\n", " <tr>\n", " <th>22269</th>\n", " <td>AFFX-r2-P1-cre-3_at</td>\n", " <td>14.346964</td>\n", " <td>14.126229</td>\n", " <td>13.314945</td>\n", " <td>14.450152</td>\n", " <td>13.692042</td>\n", " <td>13.304899</td>\n", " <td>13.486599</td>\n", " <td>12.848590</td>\n", " <td>13.609245</td>\n", " <td>...</td>\n", " <td>13.908696</td>\n", " <td>13.938217</td>\n", " <td>13.516265</td>\n", " <td>14.690593</td>\n", " <td>13.816831</td>\n", " <td>13.493863</td>\n", " <td>13.490274</td>\n", " <td>13.326792</td>\n", " <td>13.702848</td>\n", " <td>14.523304</td>\n", " </tr>\n", " <tr>\n", " <th>22270</th>\n", " <td>AFFX-r2-P1-cre-5_at</td>\n", " <td>14.085980</td>\n", " <td>13.808825</td>\n", " <td>13.040669</td>\n", " <td>14.181299</td>\n", " <td>13.383539</td>\n", " <td>12.818351</td>\n", " <td>12.867753</td>\n", " <td>12.499426</td>\n", " <td>13.264427</td>\n", " <td>...</td>\n", " <td>13.662149</td>\n", " <td>13.547378</td>\n", " <td>13.314796</td>\n", " <td>14.407109</td>\n", " <td>13.565362</td>\n", " <td>13.300146</td>\n", " <td>12.954981</td>\n", " <td>13.003925</td>\n", " <td>13.351522</td>\n", " <td>14.308021</td>\n", " </tr>\n", " <tr>\n", " <th>22271</th>\n", " <td>AFFX-ThrX-3_at</td>\n", " <td>7.642534</td>\n", " <td>7.676568</td>\n", " <td>7.645716</td>\n", " <td>7.607399</td>\n", " <td>7.644112</td>\n", " <td>7.667333</td>\n", " <td>7.561026</td>\n", " <td>7.579219</td>\n", " <td>7.733833</td>\n", " <td>...</td>\n", " <td>7.692174</td>\n", " <td>7.644974</td>\n", " <td>7.750016</td>\n", " <td>7.645419</td>\n", " <td>7.866301</td>\n", " <td>7.810828</td>\n", " <td>7.567872</td>\n", " <td>7.703038</td>\n", " <td>7.648424</td>\n", " <td>7.679152</td>\n", " </tr>\n", " <tr>\n", " <th>22272</th>\n", " <td>AFFX-ThrX-5_at</td>\n", " <td>7.631033</td>\n", " <td>7.542400</td>\n", " <td>7.455327</td>\n", " <td>7.540580</td>\n", " <td>7.566635</td>\n", " <td>7.543538</td>\n", " <td>7.455010</td>\n", " <td>7.436875</td>\n", " <td>7.456654</td>\n", " <td>...</td>\n", " <td>7.611665</td>\n", " <td>7.523678</td>\n", " <td>7.454592</td>\n", " <td>7.464096</td>\n", " <td>7.792424</td>\n", " <td>7.527578</td>\n", " <td>7.494294</td>\n", " <td>7.534673</td>\n", " <td>7.519013</td>\n", " <td>7.507123</td>\n", " </tr>\n", " <tr>\n", " <th>22273</th>\n", " <td>AFFX-ThrX-M_at</td>\n", " <td>7.501928</td>\n", " <td>7.610702</td>\n", " <td>7.495555</td>\n", " <td>7.614399</td>\n", " <td>7.472724</td>\n", " <td>7.444107</td>\n", " <td>7.438042</td>\n", " <td>7.424471</td>\n", " <td>7.599155</td>\n", " <td>...</td>\n", " <td>7.550857</td>\n", " <td>7.478097</td>\n", " <td>7.575058</td>\n", " <td>7.438450</td>\n", " <td>7.684441</td>\n", " <td>7.549105</td>\n", " <td>7.424649</td>\n", " <td>7.584892</td>\n", " <td>7.525257</td>\n", " <td>7.490484</td>\n", " </tr>\n", " <tr>\n", " <th>22274</th>\n", " <td>AFFX-TrpnX-3_at</td>\n", " <td>7.513615</td>\n", " <td>7.633015</td>\n", " <td>7.515081</td>\n", " <td>7.546858</td>\n", " <td>7.547228</td>\n", " <td>7.525382</td>\n", " <td>7.396986</td>\n", " <td>7.511896</td>\n", " <td>7.630086</td>\n", " <td>...</td>\n", " <td>7.598619</td>\n", " <td>7.590259</td>\n", " <td>7.718625</td>\n", " <td>7.468810</td>\n", " <td>7.863286</td>\n", " <td>7.591056</td>\n", " <td>7.523585</td>\n", " <td>7.703311</td>\n", " <td>7.504957</td>\n", " <td>7.483636</td>\n", " </tr>\n", " <tr>\n", " <th>22275</th>\n", " <td>AFFX-TrpnX-5_at</td>\n", " <td>7.567607</td>\n", " <td>7.427258</td>\n", " <td>7.534118</td>\n", " <td>7.453376</td>\n", " <td>7.448552</td>\n", " <td>7.526502</td>\n", " <td>7.432864</td>\n", " <td>7.508867</td>\n", " <td>7.492580</td>\n", " <td>...</td>\n", " <td>7.532869</td>\n", " <td>7.484638</td>\n", " <td>7.444738</td>\n", " <td>7.487153</td>\n", " <td>7.535606</td>\n", " <td>7.609732</td>\n", " <td>7.415789</td>\n", " <td>7.611814</td>\n", " <td>7.523121</td>\n", " <td>7.502138</td>\n", " </tr>\n", " <tr>\n", " <th>22276</th>\n", " <td>AFFX-TrpnX-M_at</td>\n", " <td>7.561439</td>\n", " <td>7.654442</td>\n", " <td>7.551581</td>\n", " <td>7.494555</td>\n", " <td>7.597676</td>\n", " <td>7.508101</td>\n", " <td>7.503649</td>\n", " <td>7.559667</td>\n", " <td>7.629655</td>\n", " <td>...</td>\n", " <td>7.596008</td>\n", " <td>7.595698</td>\n", " <td>7.743632</td>\n", " <td>7.567720</td>\n", " <td>8.060336</td>\n", " <td>7.605666</td>\n", " <td>7.532778</td>\n", " <td>7.542190</td>\n", " <td>7.611075</td>\n", " <td>7.470764</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>22277 rows × 799 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 GSM1687570_5500024035100021608461.G01.CEL.gz \\\n", "0 1007_s_at 9.525619 \n", "1 1053_at 9.047948 \n", "2 117_at 7.612412 \n", "3 121_at 8.218106 \n", "4 1255_g_at 7.392744 \n", "5 1294_at 9.003722 \n", "6 1316_at 7.909244 \n", "7 1320_at 7.651478 \n", "8 1405_i_at 7.834398 \n", "9 1431_at 7.728028 \n", "10 1438_at 7.657973 \n", "11 1487_at 8.206241 \n", "12 1494_f_at 7.858453 \n", "13 1598_g_at 8.087957 \n", "14 160020_at 8.028948 \n", "15 1729_at 8.096292 \n", "16 177_at 7.514178 \n", "17 1773_at 7.677061 \n", "18 179_at 8.243753 \n", "19 1861_at 7.947907 \n", "20 200000_s_at 10.943962 \n", "21 200001_at 10.147493 \n", "22 200002_at 12.598934 \n", "23 200003_s_at 13.962958 \n", 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mit

crendl/ilearnpython

jupyter/Untitled.ipynb

2

88400

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab notebook\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) 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03qb46xZwBFHADvsADz6KOS6IGD99YFXXgH23LPeYmHDSYAEAhAwCY4AWTqRpK41gAMHDswIPxVhuSN2Oto3atSozHV4KuJUAOrvdGSva9eumfYsW7YMG220EfTqvFnyEF5fH7oSXn75ZXTq1AmTJ0/OCL9cAahiTW9ZyQYtU8WflkUBWJsAp4Cd+OiwEq4SmDIF8rABTjoJGDwYaNTIq6l80cTZZ3tTwgwkQAIk4JeAjQDU2VSdaYgzrLkmRISFL6EuAdihQwcsXLgQU/RBmhN0FLBjx4746KOPsP32268UgHPnzoXey5sNf/zjH1FWVobx48ev/J1OK7du3RpPP/00unTpslIAriHTMboTOTfodLLG0zQUgBSA4Xs5c6g3BF54AfjLX4DrrgNkiUu10L07sM46gHzBZSABEiAB3wRsBKBuNssZ0PJdhk1EuQJXpmptUtQdty4BuINMm3z22WeZ6eDcoFO4S5cuxRtvvIF99913pQD87bffMoIvGw444ACsvfbaePbZZ1f+7ptvvsmMDOr08Mknn7xSAG622WaZtYK5oX///pm1g1oWBSAFYPhezhzqBQFZOoK//Q0YMQI4/vjaTZb1znjtNWDMmHqBg40kARKIiICNACz2EcD99tsPlZWVeOihh1ZuDsnFqKKtmaypyU4BhxGAKjInTZpUzUu6xvAF+SbPEcC6O28EA78RfSqizYZrAKPlWW9y000essYY990H2bUG7LNP3U2XGQxZwAz5dltv0LChJEACERCwEYARFJdYFrrpYsCAAVi0aNHKMnUEbtCgQfj3v/+d2f2bL0QhAD/44APMnDkTW265ZaYYFZP685/+9KfMhpJ8weQPbgJJrAtFVhAFYGQo609Gv/8OnHkmZHGxLjQGttsuf9tlrTE23xyyvsXbFMJAAiRAAn4ImASHnzxcjDN69GiZLTket956K/bee+/MyJ6KPv1ZdwnrejzdlKECUXfsTpw4MTM6pyEKAfjtt99mytQpX90FfMstt2RuJnn//fcz6wwpAGsT4Aigi58k1ilxAj/+CBx7LOThBDlyANhww8JV0KkZXZvz5puQIwsSry4LJAESKFICpSoAl8v0yV//+tfMOjy9DWTjjTfOTL1qe6+ThdQqEHWTh57vp2f0nSQ7684991yjAFxHFlvnru3TNYCa98MPP1xtDeA222yT2ViiI5E6apc9B/Dggw8u2FNM/uAIYPF90DgCWHw+S63GeqCzHvMiR1Th8ceB1Vf3V5X27YGLLvJ2CDOQAAmQgB8CJsHhJw/GqU5Aj4NRAfjYY49ZozH5gwLQGmnqCSgAU3dBcVRAjqfCkUd6o3933LHqmBc/tdfpYlnDjH79/MRmHBIgARLQayR/zpxV95Nsv9WDkhnCE6AADMaQU8DBuDFVCRDQg5xPOAFyTRFw2WX2Z2HJYfayxgR46qkSgMEmkAAJJEKAAjB6zO3atcuMAD6qJ/VbBpM/OAJoCdSB6BwBdMAJLldh+HDIyfPetW561l+Q8OKLwOWXQw4zDZKaaUiABOojAZPgqI9M0myzyR8UgGl6J1jZFIDBuJV8Kt28ITcTZaZ7//EPyBEBwZusV8TJWubMTuAmTYLnw5QkQAL1h4BJcNQfEm601OQPCkA3/GRTCwpAG1r1JO6SJd71bXL4fOaYF73bN0yQ802h1yjpOkIVggwkQAIkYCJgEhym9Px7tARM/qAAjJZ3ErlRACZBuYjK0OuOjjsOcjwBoFO3cpNQJKFtW28NYdWVlJHkyUxIgARKl4BJcJRuy91smckfFIBu+q1QrSgAi89nsdVYD23WY15atQKefBKQO8MjC6ec4o0kXnVVZFkyIxIggRImYBIcJdx0J5tm8gcFoJNuK1gpCsDi81ksNZYbiDLi7/DDgbvvhlxKHm0xctMRZsyA7D6LNl/mRgIkUJoETIKjNFvtbqtM/qAAdNd3+WpGAVh8Pou8xnpfr9xMlNmpq/f7Nojh0CM53F5OuYdcNxR59ZkhCZBACRLICo7Zs2fzHEAH/Kv+2HTTTfOey0gB6ICTLKtAAWgJrNSiP/QQ0KMHcM89wGmnxdc6uXscu+0G/Pqr3SHS8dWIOZMACbhMYPHixZk7cufPn+9yNetV3crLy/HFF19k7hKuGSgAi68rUAAWn88iqbEe8yJXQcpF4JD7I4GDDook27yZLFsGuXgccrk5sOWW8ZbF3EmABEqDgIrAJXosAYMTBMrKyuoUf1o5CkAnXGRVCQpAK1ylEXnpUuC884CxY4GXXgJ23jmZdu20EzBoENCpUzLlsRQSIAESIIFkCFAAJsM5ylIoAKOkWQR5/fIL8Oc/A/PmeeJvE+0BCQW9Tm7PPb21hgwkQAIkQAKlQ4ACsPh8SQFYfD4LXOOvvwaOPBJYf33g6achC6sDZxUood4s8tVXwIMPBkrORCRAAiRAAo4SoAB01DEFqkUBWHw+C1RjvYdXj3k58EBg2LB0rmQbNQoYPBh4++1ATWAiEiABEiABRwlQADrqGArA4nNMlDV+/XXvBo6ePb37feM45sVPfadPB/bZB3KMQHp18FNPxiEBEiABErAjQAFox8uF2BwBdMELMdbhsceAbt2AO+4AzjorxoJ8ZP37795O4C+/9G4bYSABEiABEigNAhSAxedHCsDi85mvGusxL7rjduBA4KmngI4dfSWLPdK22wJ33gkcemjsRbEAEiABEiCBhAhQACYEOsJiKAAjhOlKVnrm3gUXAGPGeDt99QBmV8IxxwAdOgAXXeRKjVgPEiABEiCBsAQoAMMS9JdeD9GoENtMTE/InCrWR2yyWAuxH8Vk/CcT9FIv/XkLMdl/WStQAPpjXjSx9KaNk04C/vtfT/xtpr3EodCrF/DDD8C99zpUKVaFBEiABEggFAEKwFD4fCfeS2L+LPaN2Gpi3cUuEdtIrLGYvF6xi9hnOTnmO0qdAtA3dvcj6o1JesiyHu/y7LNAy5bu1fnhh71dyBMnulc31ogESIAESCAYAQrAYNzCpjpdMtCT1bavEoU6AthG7FMfGVMA+oBUDFH0irXDD/d22Q4fDjRt6mat33vPW//3/ffcCeymh1grEiABErAnQAFozyxoCr259RGxNcRWrxKAXeXf7BSwjv7pbc2fi8mSe8h4UJ2BAjCoBxxK9/HHwL77Auee693vm9YxL36QLFwonVZ67Tcyfr3BBn5SMA4JkAAJkIDrBCgAk/NQEylqbbFysVPEPhSTyTXouI9cuIV3xXTtn9z7ANkHijPEHq+jehSAyfkstpIukQUA330nHUB7QBGE1q2BESO8zSAMJEACJEACxU+AAjAdH+q6P5321YM1JtVRhfvkd/LKxSEUgOk4yG+plZWVULMJetzLtts2wZAhy+Smj+zeH5scgEaNGmUsqaA3kuhaxR49wpUYhFe4Er3USfOKos7MgwRIgATiJEABGCfd/HnraOACsfPERtYR7S75na4PlEvAaoXMCGA3OSl4DZ2Xk9BRDoxTY0iWgIqZoUMfx/z5i6wK/vrr9fH440fKLR8j0bjxcqu02cjl5c1FjP0lMRF46aWyfV22Jenh1EFDUF5By8tNlzSvKOrMPEiABEggTgIUgHHSXZW3HvkyWmy2mE4BXyN2tNjOYirodERwRlV0GWeBCsALxWRrQK3AKeBkfGYsZYkoon79RqC8/BQRYqrp/YV//KMRFoj8r6iwGznM5l5ZuVRE56NyRVwFysrK/BUaMpZuUtEbSl59NXhGQXkFL9FLmQavsHVmehIgARKImwAFYNyEvfyfEPujmC6h1yNfZF8lrhObIqZCsL9YazEdDtLNILoJZESeqlEAJuMzYylZQbPJJhUykudPiOn079VXA8cdB+y+u7GIOiMsW7YEc+eOSFQATpKFCno38bx5weqsqYLwCl7aqpRp8Iqi3syDBEiABOIkQAEYJ9148qYAjIerda5BBM1sGQO++Wbg73+HjN5ZF5lJkIag0RHLtWULkx4Irf8GCUF4BSmnZpo0eEVRb+ZBAiRAAnESoACMk248eVMAxsPVOtcggua55yDTt8A551gXtzJBWoJm442Bp5+WoWwdyw4QgvAKUEytJGnxiqLuzIMESIAE4iJAARgX2fjypQCMj61VzkEETd++cs6PHPSzl94NEzCkJWgOPti7su7ss4NVPAivYCVVT5UWryjqzjxIgARIIC4CFIBxkY0vXwrA+Nha5WwraHT9XH9Z7anTv830yO+AIS1Bc8EF3rT1LbcEq7gtr2Cl1E6VFq+o6s98SIAESCAOAhSAcVCNN08KwHj5+s7dVtC8+CLw5Zfhz9JLS9DcfTegU9gvv+wbUbWItryClUIBGBU35kMCJFDaBCgAi8+/FICO+MxW0Fx/vZzsLUd7t28frgFpCcA33gBOl1usVcQGCba8gpRRV5q0eEVVf+ZDAiRAAnEQoACMg2q8eVIAxsvXd+42gkavfbv2Wm/6t3lz30XUGTEtQfPtt8CGGwK//OLdDWwbbHjZ5l0oflq8omwD8yIBEiCBqAlQAEZNNP78KADjZ+yrBBtBM3Ys8OmngK6jCxvSEjR6huH66wOvvALsuad9K2x42eeeP0VavKJsA/MiARIggagJUABGTTT+/CgA42fsqwQbQTNwILD//sA++/jKumCkNAWNtkF3AetUsG2w4WWbd6H4afKKsh3MiwRIgASiJEABGCXNZPKiAEyGs7EUv4JGD0++6irvAOggU6c1K5KmoOneHVhnHUAFrW3wy8s2X1P8NHmZ6sa/kwAJkEBaBCgA0yIfvFwKwODsIk3pV9D861/A9OnAxRdHU3yagmbIEOC114AxY+zb4peXfc6FU6TJK+q2MD8SIAESiIoABWBUJJPLhwIwOdYFS/IraG66CfjDH4A//SmaiqcpaMaN846x+UxvrLYMfnlZZmuMniYvY+UYgQRIgARSIkABmBL4EMVSAIaAF2VSP4JG79Dt1QsYNAho0SKa0tMUNHPmAJtvDixcaH+YtR9e0RCqnkuavOJoD/MkARIggSgIUABGQTHZPCgAk+WdtzQ/gub114F33wUuuyy6SqcpaHQnsArZN98EdtnFrk1+eNnl6C92mrz81ZCxSIAESCB5AhSAyTMPWyIFYFiCEaX3I2gGDwZ22w048MCICpVs0hY0epD1RRd59wLbBD+8bPLzGzdtXn7ryXgkQAIkkCQBCsAkaUdTFgVgNBxD52ISNHpg8uWXAwMGAGuvHbq4lRmkLWjOPBPYdFPguuvs2mTiZZeb/9hp8/JfU8YkARIggeQIUAAmxzqqkigAoyIZMh+ToJk4EXjrLeDKK0MWVCN52oJGj7N55x3gqafs2mXiZZeb/9hp8/JfU8YkARIggeQIUAAmxzqqkigAoyIZMh+ToNEjU7bfHjj00JAFOSYAX3zRG9n86CO7dpl42eXmPzYFoH9WjEkCJFB/CFAAFp+vKQAd8VkhQaO7ZHXjh06TrrdetBVOW9DMmgVstx2waBHQpIn/tlEA+mfFmCRAAiQQNwEKwLgJR58/BWD0TAPlWEjQTJoEvPoq0KdPoKwLJkpbAFZWAmuuCUydCrRp4799FID+WTEmCZAACcRNgAIwbsLR508BGD3TQDkWEjR33QVssQVwxBGBsnZaAGrl2rb1xG2XLv7bRwHonxVjkgAJkEDcBCgA4yYcff4UgNEzDZRjPkHz22/A3/4GXH01UF4eKGvnBeCpp3qjfzYjnBSA0fcF5kgCJEACQQlQAAYll146CsD02FcrOZ+g0R2yL78MXHttPBVNewpYW9W/PzBjBvDoo/7bSAHonxVjkgAJkEDcBCgA4yYcff4UgNEzDZRjPkFz773AxhsDRx0VKFtjIhcE4OjR3gaX9983VndlBApA/6wYkwRIgATiJlBAAMo5D6gQ20xsiZis+IauaJ9cVSe9B+oOsb3EfhC7T6xf3PW1yb+BTeQiiksB6Iiz6hI0v//uTf/q2X+bqKdiCC4IwJkzvRtOfv0VaPDz6jwAACAASURBVNTIXyMpAP1xYiwSIAESSIJAAQGowu5nsW/EVhPrLnaJ2EZiqq0+FXtGrLfYDmLPi8m8EOTwMzcCBaAbfijZWtQlaHRn7D/+IV+F5LtQg5h6oAsCcNkyoHlz4JNPgC239OdiCkB/nBiLBEiABJIgYDEFfLrU50ExOdkWe4vp6N8GYour6nmN/Hua2DZJ1NtPGTG9fv0UHWscjgDGitd/5nUJmvvvB9ZdFzj2WP/52MZ0QQBqnXfaCRg0COjUyV8LKAD9cWIsEiABEkiCgEEAHiR1eERsDbHVqwRgV/lXbrjPiEC1bDhcfnhBrIWYzAulHygA0/dBSdegpqBZutSb/r1EBso33zy+prsiAE84AdhzT+9WED+BAtAPJcYhARIggWQIGASgHvOvt9jrWRaniH0o9rCYDHNAB6JU9GVDe/lBLj6F3BKPr5OpfeFSSloA3iUHzfXo0SNDYPHixejduzcGDBiAZs2aZX43atQotGzZEh07dsz8f/78+Rgid5MNHDhwJbV7ZbfCrrvuivbt1Xc6nfcJxowZgyuuuGJlnBtvvBGdO3eWIz+8E38nT56MadOmoXt3XRLghV69eqFnz55y5Il35snYsWOxYMECnHjiiSVdP/XBlCmz0a7ddWjcuAzjx3v8Bg++YuX07yuv3Chr5ToLG4/frFmTMWfONOy//yp+o0f3woEH9kSLFh6/jz4aK7dsLMBee3n8li5djNGje8uo4gC5eaMZVACOG/dXdO3aWUbfvOG3NPw7Y8aJ+Oor4O67/fW/wYMHS/23lrWRFRleEybci1atdpUpZK//zZ//CT74YAwOO2xV/4uC32+//YzHHusizJ7FWmutlSmLnw9+fvn8K+3nM/1bt3/1/Xz77bejadOmMlu1Lu7XaSugldjclS/12j80ll/9KKYXm/5ZTB/af8yJxhHAAvCi/BOngKOkGSKvmiNaI0bIWLkMlh9/fIhMfSR1ZQRQvmOI2AXefttHpSUKRwD9cWIsEiABEkiCgMUaQB0NXCB2XlW9smsAZdtjJsipt9B1glwDGLPjKABjBuw3+1xBA5Rl7v49/3xgq6385hAsnisCcPp0YJ99gJ9+8rfhhQIwmL+ZigRIgATiIFBAAOqRL3LYF2aL6dSUbvI4Wmxnsf+JyTkQeFJM4+n01otiOr3IXcBxOConTwrAmAH7zT5X0MycWYaRI+UTIB+Bhg395hAsnisCUI+80Z3AX34p8wc6gWAIFIAmQvw7CZAACSRHoIAAfEJqoVO8utNXz/l7T+x6MbnmIBNkCyCGiskq8MzI4N1Vf0+u8oaSSnoN4OzZs+Wl6+Ot64w7Sq8iuYLm8cfLZH0bcNJJ8bfTFQGoLd12W+DOO2VhiK4MMQQKQBMh/p0ESIAEkiNgMQWcXKUiKokCMCKQzKZuAllBU15egauuKsM553iCKO7gkgA85higQwfgoovMraYANDNiDBIgARJIigAFYFKkoyuHU8DRsQyVU1bQLFx4Jh54oAluuin+6V+tsEsCUDaA4weZINDr70yBAtBEiH8nARIggeQIUAAmxzqqkigAoyIZMp+soHnzTT0bsxFO0ZOSEgguCcCH5VSoYcOAiRPNDacANDNiDBIgARJIigAFYFKkoyuHAjA6lqFyUkHTt+8IDB3aDRUVDbCD3oiYQHBJAL4nS4N1/d/335t3AlMAJtA5WAQJkAAJ+CRAAegTlEPRKAAdcYYKmrPPfhnPPns0br65ARo1SqZiLgnAhQu9sw+/kSvDN9D9YgUCBWAy/YOlkAAJkIAfAhSAfii5FYcC0BF/qKDZb79P5NiXHXHmmQmpP2m7SwJQXdG6NaCHYOtmEApARzonq0ECJEACBgIUgMXXRSgAHfHZ778vkVGv32XtXzO57k0PSk8muCYAjzgCciUd5GpCCsBkegBLIQESIIHwBCgAwzNMOgcKwKSJ5ynv//5vqdzhuwJyXTJWW60ssVq5JgAvvVSveQPu0MuBCgROASfWRVgQCZAACRgJUAAaETkXgQLQEZdcdlklXn55lox8bY7GjeuvABw+HHjsMeDVVykAHemarAYJkAAJGAlQABoROReBAtABl6xYAWy99Qrsvvs/ZRTwgHotACdNArp0AebNowB0oGuyCiRAAiTgiwAFoC9MTkWiAHTAHdOmAfvss0JG/x6QTRCn1WsBuEBuglx7be9AaP03X+AUsAMdl1UgARIggSoCFIDJdIXLpZgKsc3EZLUUpor1EZtcVfwu8q+uoNpLTC9evk+sX56qUQAm47OCpVxzDTBjRiW22244Ntmkol4LQAW18cbA00/L7eF6fXieQAHoQMdlFUiABEiAAjDRPqDC7mcxOS0Nq4l1F7tEbCMxvbP4U7FnxHqL6XHCz4v1FxtSRy0pABN1Xd2F6aHPffosw0cfPUABKIgOPhg46STIuYgUgA50T1aBBEiABIwEOAJoRBRLhNMl1wfFthfbW0xH//QY3cVVpcn4Ek4T24YCMBb+oTL9+GOgbVtg7twluOWWERSAQvOCC4Ay2Qdzyy0UgKE6FxOTAAmQQEIEKAATAi3FHCT2iJjcm4DVqwSgXiI7uEoEqhDMhsPlhxfEWoj9WqOKHAFMzmd1lnTDDcC77wJPPrkE/fpRACqku+8GnnsOsiuaAjDl7sniSYAESMAXAQpAX5giiaQnBesS+XKxU8Q+FHtY7H4xFXUq+rKhvfzwltimYl9TAEbCP7JMdtsN+NvfgBNOcFcAVlZWQi2pMGFCA3Tt2hizZi2XK/HqvhWFawCT8gbLIQESIAEzAQpAM6M4YjSWTH8UO1Tsz2Iq+HKXzxtHALt16yZ3sOpgItCxY8eMMcRP4PPPZd5eJu6//RZo3txNAajCb+jQxzF//qL4gVSVsHBhMwwZcrrcifwELr74z3WKQArAxNzBgkiABEjASIAC0Igolgg6GiiHZ+C8qtyzawB/r/r/1fKvrhPkGsBY8AfP9KabgPHjgZde0tsv3BSA2XqVl58iQiyZK+r0XMQrrmgio6Kjcc89nWQ9YO2DsV3lFbw3MCUJkAAJFC8BCsBkfKdHvowWmy2mU8C6yeNosZ3F/ic2U+xJMY3XRuxFsYFi3AWcjH98l9KuHXDuucBZZ7kvAJM+nuamm5Zjhx3ekONg9qEA9N2jGJEESIAE0iFAAZgM9yekGJ3i1Z2+es7fe2LXi71TVfxO8u9QsT3FdGRQltRn/l5X4CaQZHxWq5SvvgK22goytQqsuy4FYE1AI0dWYvny6TJCugMFYEp9lMWSAAmQgF8CFIB+SbkTjwIwJV/cdpsMzcrY7LhxXgVcndJMq17jxi3DtGlz8OGHG1MAptRHWSwJkAAJ+CVAAeiXlDvxKABT8sW++wKnnupNAVMA1nbC9OlL8eiji2SEdDUKwJT6KIslARIgAb8EKAD9knInHgVgCr6YN0/O5JFDeebMkUWcuoqTI4C1vPDdd0tw9dWNsWDBMqy1FjeBpNBNWSQJkAAJ+CZAAegblTMRKQBTcMVQWaE5ahTwxhurCk9rqnXZsiVyC8kIXHtthVMjbUuXLsElcsHh//1fA+yxR+3dx67ySqE7sUgSIAESSJ0ABWDqLrCuAAWgNbLwCQ48EDjmGODCCykA89FUYdq//4+48cZ1Zapcj7qsHigAw/dD5kACJEACURGgAIyKZHL5UAAmxzpT0nffARtvDHzxBdCqFQVgIQF4771foFOnrUUI1r4NhAIw4Y7L4kiABEigAAEKwOLrHhSACfvsfrmsb/hwYNKk4hjRSlNoPfPMVLRs2Q7PPNOQI4AJ91MWRwIkQAI2BCgAbWi5EZcCMGE/HHYYcPDB3v2/uSFNoeXiGkCdAn7jjVcxfXpHzJhBAZhwN2VxJEACJGBFgALQCpcTkSkAE3TDj3Jj84YbylUtclfLFltQABZCrwLwo4+ewbBhJ2HRogZoUmMfiKuCOcHuxKJIgARIwBkCFIDOuMJ3RSgAfaMKH3HkSLmPTy7ke0/vbqkRXBU0adZrzpwRuO22bpg6tQHa6KWGOSHNehUaMQ3fS5gDCZAACRQfAQrA4vMZBWCCPjtabmxu3x7o3ZsC0IQ9ezzNSy+dLecBNkSXLhSAJmb8OwmQAAmkRYACMC3ywculAAzOzirlL78A668Pud4M2G47CkATvKwA/OKLrthhh0bo04cC0MSMfycBEiCBtAhQAKZFPni5FIDB2VmlfOIJyHEmkE0NdSdzdUoz7Xo1bXqWrJlsLNfCUQBadThGJgESIIEECVAAJgg7oqIoACMCacrm+OOBnXYC+valADSx0r9nRwB32+1MDBjQBO+/TwHohxvjkAAJkEAaBCgA06AerkwKwHD8fKVeuBDYYANg8mRg550pAP1AywrAk06qQLt2Zfj1V6BRznnQaY9M5rs6z0/bGIcESIAESo0ABWDxeZQCMAGfPfOMt/Hjk0+ABg0oAP0gzwrAq66qkMOgyzLsttxyVUoKQD8UGYcESIAEkiFAAZgM5yhLoQCMkmaevE4+GWjdGjKVmb8wVwWNC/Vq27YMgwZBroWjAEygu7IIEiABErAmQAFojSz1BBSAMbtg8WJv+nf8eKBtWwpAv7izI4A61XrqqWXYc0/g8sspAP3yYzwSIAESSJIABWCStKMpiwIwGo55c3n+eeDCC4FZs/JP/2piF0baysrKarXDhXr171+Gr74CHnyQAjDm7srsSYAESCAQAQrAQNhSTUQBGDP+igpgvfWAv/+9cEEuCC1XBeDo0WUYPBh4+20KwJi7K7MnARIggUAEKAADYUs1EQVgjPiXLPHu/n35Ze8GkEKBArA6ndwp4Jkzy7DPPsBPP60aRXWVV4zdiVmTAAmQgLMEKACddU3eilEAxuizsWOBrl2Rmb5s2JAC0AZ1rgBcsaIMzZsDX34JtGrl5UIBaEOTcUmABEggXgIUgPHyjSN3CsA4qFblec45QLNmwO23mwtxVdC4Uq9ttwXuvBM49FAKQHNvYgwSIAESSJYABWCyvKMojQIwCop15LFsGbDxxsDTTwP7728uxBWhVbOmrtTrmGOADh2Aiy6iADT3JsYgARIggWQJUAAmyzuK0igAo6BYRx6vvw6cdBLw9dfVb7DIV5wrQstVAdirF/DDD8C991IAxtRlmS0JkAAJBCZAARgYXWoJKQBjQn/++XqfLXDPPf4KoACszil3DaDuTn74YWDYMGDiRApAfz2KsUiABEggOQIUgMmxjqokCsCoSObks3y5t1lh5Ejg4IP9FUABWFgAvveet/7v+++9ncCu8tJWVFZWZizp0EguS1ZjIAESIIGkCVAAJk08fHkUgOEZ1srhrbeAo48G5s8HmjTxV4CrgsaVei1cCKyxBvDNN97NKq7Uq6Z3VfgNHfq4+H6RP8dHGKu8vDl69PgLRWCETJkVCZCAPwIUgP44uRSLAjAGb1xyCbBgAfDAA/4zd1XQuFQvvU95xAhvM4hL9cr1crZe5eWniBDzqf79d5O8MSsrl4rofBR6dV5dB3pHUASzIAESIIG8BCgAi69zUABG7LMVKwAVKnffDRxxhP/MXRc0m2xSgcaNa18V57+FdjFrrgHU1MqzUyfIKJf7AtAFXnbEGZsESIAEghOgAAzOLq2UFIARk58yxVv39+23QNOm/jOnAKzOqi4BeOmlKvyAO+6gAKzZs+ri5b/3MSYJkAAJhCNAARiOXxqpKQAjpn7llcDcucjsWrUJFIBmATh8OPDYY8Crr1IAUgDafLoYlwRIIG4CFIBxE44+fwrACJnq9K/eWHHzzYAeXGwTKADNAnDSJKBLF2DePApACkCbTxfjkgAJxE2AAjBuwtHnTwEYIdNp04B99gG++w5YbTW7jCkAzQJQN9asvbZ3IPTqqy9Bv34j4NpaO1f9aNcbGZsESIAE7AhQANrxciE2BWCEXrjmGmDmTGDUKPtMXRUOrtUre73enntSAOb2Mq4BtP/MMQUJkEB0BCgAo2OZVE4UgBGS3nFHyDEcwAkn2GfqmtDKtsC1eukGG71i7/TTKQApAO0/Z0xBAiQQDwEKwHi4xpkrBWBEdD/+GGjb1pv+1QOLbYNrQstVAXjBBd7h2oMGUQBSANp+yhifBEggLgIUgHGRjS9fCsCI2N5wA6DXlY0eHSxDCsDq3PJNaer5is89B4wZQwFIARjss8ZUJEAC0ROgAIyeadw5UgBGRHj33QE9p+7UU4NlSAHoTwC+8YZO/wKffUYBSAEY7LPGVCRAAtEToACMnmncOVIARkD488+B7bf3Dn9u2TJYhhSA/gSgMt5wQ+B//1uCW27hLuAsNW4CCfa5YyoSIIFoCFAARsMxyVwoACOgfdNNgI5Mvfhi8MwoAP0JQD1rcf31geefX4oXXniQx8BUYaMADP7ZY0oSIIHwBCgAwzNMOgcKwAiIt2sHnHsucNZZwTOjAPQnADXW/vsDFRXL8PnnD1AAUgAG/9AxJQmQQGQEKAAjQ5lYRhSAIVF/9RWw1VbA/PnAuusGz4wC0L8A7N4dWGutSpSVDacApAAM/qFjShIggcgIUABGhtKYUT+JcbTY1mK/io0Xu1xstlgLsR/FZLIsExpU/byF/CtypVqgADSiLhzhttu8qd9x48JlRAHoXwAOGQL861/Lscsu91MAUgCG++AxNQmQQCQEKAAjwegrk2ES62mxf4vJ5Vi4S0zHn3atEoByWRZ2EfssJ7cldeRMAegLd/5I++7r7fzVKeAwgQLQvwBUsX3eeStw4on3UQBSAIb52DEtCZBARAQoACMCGSCbDpLmVbF1qtLqCGAbsU8NeVEABoCdTTJvHrDppsCcOUB5eYiMJCkFoH8BqLw333wF/va3B+Tf09C4cVk4+BapTZstXPWjRRMZlQRIgASsCVAAWiOLLMHVktNpYtuKZaeAdfSvmZgcUoI7xZ6tozQKwBAuGDrUu/dXdwCHDa4KBxfrpTuBW7TQEcBnsPvuR1MASuczCdOw/ZPpSYAESKAQAQrAdPrHUVLsI2JHir0p1lRMb6N9V0zXAervB4qdIfZ4jSpSAIbw2YEHAsccA1x4YYhMqpK6KLS0aq7W6w9/WI6NNnodhx66HwUgBWD4DyBzIAESCEWAAjAUvkCJT5RUOrr3Z7HxBXK4T/7WWuyQugRgt27d5P5a7wLbjh07ZoyhMAG983fjjYEvvgBatQpPy1Wh5Wq9Kioq8Z//fICTT96ZApACMPwHkDmQAAmEIkABGAqfdeLzJYVO/R4hJrfQFgy6SUTuqoCMWVULHAG0xu4luP9+YPhwYNKkgBnUSOaq0HK1XgMHLsPIkV/hggtaUQBSAEbzIWQuJEACgQlQAAZGZ51Qj4GR09BwuNiMnNS/y89/FGuc8/tO8rMKQJ2oFMlCAWhNu44Ehx0GHHwwZCNCFLm5O9XqqgD8xz+Wolu3X9Cv3xoUgBSA0XwImQsJkEBgAhSAgdFZJWwksZeKZc/508TZs/52lp/1bMD+Yq3FlovpZhCdJh5RRykcAbRC70X+UfZY6320M2cCW+jpihEEV4WWq/WaOXMJdtihMW67bRmaNuUuYG4CieBDyCxIgAQCE6AADIwutYQUgAHQjxwJ6GHE75km3i3ydlVouVqvxYuXYM01G6JXr+WyBpMCkALQ4sPGqCRAApEToACMHGnsGVIABkB8tNzB0r490Lt3gMR5krgqtFyu12ab/Yyjj26BPfdsEp0jDDmZhJarvBIDxIJIgATqJQGDAIzq9rJU2OoUaykGCkBLr/7yC7D++nIFi9zBsq2euhhRcFU4uFyvtm2/lCn4LXDUUbrkNZlAAZgMZ5ZCAiRQXAQMAjCq28tSgUIBmAp29wp94glZYCkrLKdPj7ZuLgutfv1GOHnl2qGHTsPChW1lM4gui00mUAAmw5mlkAAJFBcByyngDtK6ILeXpQKFAjAV7O4VevzxwE47AX37Rls3CsDqPP0IrZNPHo+33z4YV1/dMFpnFMjNT71cFMyJAWJBJEAC9ZKApQAMentZKmwpAFPB7lahixZ5079vv+2JwCgDBaC9ALzwwtF44IETcPvtDdAwIQ0YpwCsrJSt/bK3v3GAGW1TvaLsq8yLBEiABGoSsBCAYW4vSwV8SQvAu+66Cz169MiAXbx4sWxu6I0BAwagWTO9Sljvux2Fli1brrwhZP78+bILdggGDtQb5rxw7733Ytddd5XNEbI7QsInn3yCMWPG4IorrlgZ58Ybb0Tnzp3Rpk2bzO8mT56MadOmoXt3PdLQC7169ULPnj1RXl6e+f/YsWOxYMECufdVLz1Jt36vvw4cf3wvfPhhT7mGLNr6qQ+mTJmNdu2uy5xrN3/+J/jggzE47LBV/F555UbstltnYePxmzVrMubMmYb991/Fb/ToXjjwwJ5yV65Xv48+GotFixZgr708fkuXLsbo0b1x7LED0KRJs8wdsuPG/RVdu3ZGp056ZCSk7FX+zQrTWbOWYvPN98CWW3r+jbt+Rx3VF99++wSuvbZC6ju6Vv8bPHgwGjXaBjfffLacBdgAH398r+wG3jX2+v3228947LEuwuxZrLXWWhkWuZ8P5XXZZUOk/3+H4467aWW/njDBXD/9fLRo0Rnnnmvv3913PxZz546Qz9tJMjrdl59fIV9szxfXn3+sX2m/34L4V9/Pt99+uxzF1RTrrruuXJAgNyTI5Vhic1c+/Kr/EPb2sjzZxvvrkhaAs2fPlpdnBPeZxesDJ3L/7TdgtdWirwpHAKszNY1oZXk9+ODZ6NKlIXbZJXqf1JWj33ptskmF1QHVurlIvvtgnXWA666zb4upXvY5MgUJkAAJ+CfgYwQwitvL/FcowpgUgBHCZFa1CVAABhOAL7/cVXYCN5LR6WR6lUloBfXj8897O8vluxhuvdX+S4apXsnQYSkkQAL1lYCPY2CiuL0sFbwUgKlgrz+FBhUOYQmZhIPr9Xr//a6yRKARKirCkvCXPg5eMmucGf3r2hV4+GHgjDMgyyT81Scby1Qvu9wYmwRIgATsCBQQgFHeXmZXqYhiUwBGBJLZ1E3AdaFlO6UZ1s8mQZPlNX/+WXjttcYZAZVE8FsvG15vvAFMmAD06QMMk9OyNt8csvbTrjWmetnlxtgkQAIkYEfAxxSwXYYOxaYAdMgZpVgVCsDqXjUJmiwv4EwMHtxE7gSWC7ET+JT6rZdfAbhcbuu+5hrIBhzvdplXXgG+/BKyMcqul5vqZZcbY5MACZCAHQEKQDteLsTmTSAueEHqQAEYTABusEEFLr64THakA2uvHb8zTULL1o9TpwJPPukdLt5IJkpmzgQefBAYNMiuLaZ62eXG2CRAAiRgR4AC0I6XC7EpAF3wAgVgLS+YBE2u0OrXrwx/+Quwww7xO9OmXnqcT6GgZ/7JyS/YYw/gkEO8mLrL/OKLgZvkBJmqU2Z8NcpUL1+ZMBIJkAAJBCRAARgQXIrJKABThJ9btO3IUVTVNgmHYqjXsGFlmXuZDz44Kir584mS13/+A9xxhzfal3u00LXXQs4QhNXRNqZ6xU+GJZAACdRnAhSAxed9CkBHfFYMQss0ohUlSpOgyeX1/PNlcicwcOqpUdag7rxs6mXiNXQosOGGntjLDToFLGeq4uij/bfHVC//OTEmCZAACdgToAC0Z5Z2CgrAtD1QVT4FYHVHmARNLq933y3DxImQGzjid6ZNvQoJQLlsBddfD9xwQ+21i6+9pje4ABdc4L89pnr5z4kxSYAESMCeAAWgPbO0U1AApu0BCsA6PWASNLkCcO7cMrmaELjllvh3AtvUq5AA1PP+9O7fus4vnDULkJsB8fe/+2+PqV6OdHNWgwRIoEQJUAAWn2MpAB3xGUcAg48AVlaW4cILIfcC222cCOJ6k9Dy48eff4bct+0d/ryJfgJrhKVLkWmPjhCut56/Wprq5S8XxiIBEiCBYAQoAINxSzMVBWCa9HPK9iMc4qiqSTgUS71UUOkNGtttFwelVXlGwWvMGO+sPxV5+YJODR9+uLdD2E8w1ctPHoxDAiRAAkEJUAAGJZdeOgrA9NhXK7lYhFZSuEyCpiYv3U27885Ahw7x1tC2XjVr8/vv3sjfOecUvu7tkUe8ncE1N4jka52pXvFSYe4kQAL1nQAFYPH1AApAR3xGAVjdESZBU5PXU08By5Yhcx5gnMG2XjXr8vrrwP/9nzcFXOjmkjffBN5+G7j0Un+tMdXLXy6MRQIkQALBCFAABuOWZioKwDTp55RNARhOAKpgeucd4JJL4nWoSWgV8qNu+tBr3445Bthrr8L1nD3b2wRy661Aw4bmNpnqZc6BMUiABEggOAEKwODs0kpJAZgW+RrlUgCGE4Cffw7cc4+3ESTOYBJahfz47rvAs896mzv02rdCQcViz57AVVcBG21kbpGpXuYcGIMESIAEghOgAAzOLq2UFIBpkacALEjeJGhqCq1Fi7wr1AYPBlZfPT6n2tYrWxO99k3vK/7DH4CDDvJXP70Obr/9gL33Nsc31cucA2OQAAmQQHACFIDB2aWVkgIwLfIUgJEKQM3s8suB7t2BrbaKz6kmoZVvBPDTT4G77/ZEYLNm/uo3ahSwfLm/dY2mevkrkbFIgARIIBgBCsBg3NJMRQGYJv2csjkFXN0RJkFTFy9dL6dr6/bdNz6nBqmX1ubOO70z/4491n/ddBOIbhq58kpzGlO9zDkwBgmQAAkEJ0ABGJxdWikpANMizxHAyEcAH3/cW1t3wgnxOdUktOoSpl9/DfTvDwwYALRo4b9u33wDXHcdMrecNG5cOJ2pXv5LZUwSIAESsCdAAWjPLO0UFIBpe6CqfI4Ahh8BfOMNYNq0wgcsh3W3SWjV5ceRI71STz/drnSd/tVdzWqbbUYBaEePsUmABJIkQAGYJO1oyqIAjIZj6FwoAMMLQF1n9+CD3jq7uIKtAPzpJ+/M3SIH5wAAIABJREFUvz59/O3mrVlvndbW20D2358CMC6fMl8SIIHwBCgAwzNMOgcKwKSJ5ymPAjC8ANQ7di+7zJsy9bvRwtb9tgJw9Ghg7lzg/PNtS/Lia/pffjGPHprqFax0piIBEiABfwQoAP1xcikWBaAj3qAADC8A9agVvTlD79ht3Toex5qEVq4fly0ry1z7dt55wLbbBqvP1KnAiy8CV1/NEcBgBJmKBEggCQIUgElQjrYMCsBoeQbOjQIwvADUHPQgaN0F7OfsvCDOshGA48eXYcoUbxdvoWvfCtXjhx+8w6B1VLOsLH9MU72CtJVpSIAESMAvAQpAf6T0FLBdxNYRk8c7/i222F/SyGNRAEaONFiGFIDRCMBHHgGaNwe6dAnmB1Mqk9DK+rG8vAJ9+5bh+OO9NXxBg45qZs833HprCsCgHJmOBEggXgIUgIX56ulkfxPrKNZUbImYfqf/XWysmNz8CbnRNNFAAZgo7vyFUQBGIwBffRWYORPo0SMex/oVgF9/fRZeeKFx5to3P3f5FqqtniHYpg1w8MEUgPF4lbmSAAmEJUABmJ/gE/KndmKyRxGviOmonwo/FYI7ix0mdqaYTBjhpLCOsEhPAWgBK86oFIDRCMAZMwA9D1CFVxzBjwDs23cEHnnkbOyzT0MccED4WrzwAqBnAnbtSgEYniZzIAESiIMABWB+qufIn4aLyRXveUND+Ys+4u+Lwzl58qQATBB2oaIoAKMRgD/+iMzGizvuAJo0id65fgRgRcU/8dxzR8pxNA3QVL/ihQwffgjotXCFRK2pXiGrwOQkQAIkUJAABWDxdRAKQEd8RgEYjQDUNXMXXeQdB9OqVfTONQkt9eP223+D7bbbGMccI9eSRBB+/dXb3Tx4MLD66nVnaKpXBNWILYvKykqoJR0aybUxagwkQALhCVAA2jFcX6KvWSPJLLssQsemAAyNMJoMKACjEYCay6BBwEEHefcCRx1MQuu995aiffsGuOGG5Vh77QLbdi0rpjuBTzkF2GGH0hKAKvyGDn0c8+cvsiQSPnp5eXNZK/oXisDwKJkDCYAC0F8n2E+i6eVQerlTg5wkMnaBpL+OUgD681nssSgAoxOADz0EEV/A0UdH7zaTADzttEp8+OGn6NZtK7m/NzoBOGyYN6J5xBGlJQBX7Zo+RYRYDHP2ebpAZeVSEZ2P4tprK+R4nej8FH2PY44kUBwEKAD9+UmWqeNJMXmkY2GNJHJxVKKBAjBR3PkLowCMTgCOlT31//0v0L179M4tJADnzAG23noFKiqexC67HBupAPznP4HPP/cOla4rmIRp9CSiydHVfh9N65gLCdQfAhSA/nwtK3og4xNY6i96rLEoAGPF6z9zV1+ExViv6dOBZ56BnMPnn7/fmIWElp7XN3Pmcuy00/3YZJOKSAWg3nM8XLaR3XgjBaBfXxWKV6yCOYq2Mw8SiIMABaA/qnKoA24Qm+wveqyxKABjxes/82IUWv5bZx/T9IIuxOv7772r0/T8vKjX+Oerl95DvOmmwJgxSzFu3IORC8DFclS8bm5RAdiiRW2eJl72Hkgmhav9PpnWsxQSKB0CKQpA3U/RSUxXfWcv2HhXflat9W0UhHPX6oXNbyPJ4Cmx18Rk0qha0GlhP6GfRNIVTno3gI4ojheT8QfMrkqsN43IQRgZIHrbiB4to2lqBgpAP7QTiOPqi7AY67V8uXcfcJ8+QHl5tM7LJ7T+Lse4P/usfBDHL0G/fiMiF4DaCh3RPPZYYNdda7eJAtDOz8XKy66VjE0CyRFIQQBuIK0bKHaymG6g1fOVdRmdfkXW85W3FJNTYdFbTE5SDR6iFIAq1AaIyaROtTWAuglED4v2E1QoPl3VYJ1OvktsXTF9NaxRlbdMgmUarvsGnxfrLyY3ilYLFIB+aCcQpxiFVpxYTC9oE68bZIxdN0y0bRttLeuql5z8gi3lUaNnDx55ZHwCcMQIb3NL586122TiFS2F6HIz+TG6kqrnVKy84uLBfEkgLIEUBOCXUmd5KmbOWP6qjvrLnAy6iZ0u1jpM+6IUgDJBhRPEdAQwqtBBMpJLsDLDn8eI6eifquPsHcPXyM+niW1To0AKwKg8EDIfV1+ExVovXS+no39HHhnSMTWS1yUcHn4YuO464JNP5KT3yvgE4Ouvy1dc+Y7bs2ftNhWroHG1f0Xba5gbCZQ+gRQE4HpCVfWUKejg2P9MkQr9PUoBqFOyOg2sV8FFFWTFU0bgbSsmx8Vi7yrL5n+4/KDz4To0qlPG2UABGJUHQubj6ouwWOv10kvAvHmFr08L4rKaQksPntYpWb17+Nxz5YJvGQ6Mawr4iy+8UcZbbpHzo2o8kSgA7bxZrLzsWsnYJJAcgRQEYGKNi1IA3iq1lsudMsOWUYSjJJNHxHSs402x+8VU2Knoy4b28sNbYjok+nV9F4Au3jwQp3Ao1MlML8Jirdf77wMvvuitA4wy1OSlR86ceqrMP8gExGqrxSsAl8q5ATr6109W866vy55zgsmPUTKIMi9X+1eUbawvebn4XK0v7F1oZ0oC8BBpux74dXwdDHSZ3D1i/wrLJ0oBKEvFM+JMXlG1NoHo1LBNOFEiy15H/FlsfFVCHQFUwffHnIwKjgB269YNa6yhSweBjh07ZqxUg6s3D7j6IizWes2fL1vtZR3g7bcDDfWW7YhCTaF1iDx+9t0XcqCwV0DcvPrLSt5DD619ywkFoJ2Di5WXXSuTi+3qczU5AiwpJQGoM5sPiKmuqhmOk19UiOkgWagQpQDUXSv5glxj7zucLzF16lfvBngvJ5UueMyuAcxOM2s8/X29XwPo6s0DcQuHfL3K9CIs1nrp1bIXXFD3aJnvT1gdEXN5ffRRGfbZxxv9W09Xo0iIm9ejjwJNm8rX3Rrfd01+DNPmONPGzStov4+zzaWYt6vP1VJk7WqbUhKAepKKnnqiS+tqBl37pzuDdUY0VIhSAIaqSFViPdJFhz11ZE9vFskGFXw6lDdTTG8b0QmwNmIyGZbZLl3vdwG7+sJhvap/LEyCxg8vnSrVY1N20cdDRCG3XmeeWYaWLWULvu7Brwp+6hWmKm/JQo5Jk4C//c2OV5gy40wbN698dTf1rzjbXIp504+l6FW7NqUkAHWjqx72taCO2srTGTIXhGZ2LakdOw4BqKt41qxRlJ5lYwp6X7DeIqLHxmSD1k//r2ffqCDcSWyo2J5VYO6Wf6+vI+N6twnE1QcV61W9d5pe0H546f25m2+uyxpMHyn/f8/W68wzK7DjjmWYIZ+2rbZald5PvfyXVjvm3LneYdC33VZ9atvEK0yZcaaNm1e+uhcrrzh9ESZv+jEMvdJIm5IA/EzodRWbUAfF/eV3Oj2s5yWHClEKwP2kJiPFNhPLzVcFnIq7JAMFYEK0TS8cVx+gxVyv556TeQGZGKioiM7JWT/++mtXzJ3bCE/rMuOcEDcvndrWG0F6yWKRjTdeVbCpf0VHINqc4uZFARitv/LlRj8mw9nlUlISgPJVOHPhxUFi2WPvFJOO+o0TmyJ2SVhuUQpAHaHT6Vk9zHlhjYrpKdZJBgrAhGibXtCuPkCLuV5T5KP/L9n/pWIpqqB+/PzzxzBs2BmSdwP84Q/Vc06C1803I7P28I8527xM/Suq9kedTxK86qpzsfKKmn9U+dGPUZEs3nxSEoC6zk/3QOgA2igxvQ1NTzvJbqhVcRjqDED1SJQCUM/h09s7dBo37UABmJAHTC8cVx+gxVwvnS696SZvurTmuXlB3a5+fPrp97F06V6YOLH29uIkeD0pXx+XLZP7j/QCpKpg6l9B2xt3uiR4UQDG7cX4Nz/la0Gx9vv4PZJ8CSkJQG3ohmK6xE03xOoFGHr/r+570AswQl0Bl6UYpQDUbctyQAUmJ++iWiVSACbkBNODytUXYTHXS8/N053AA2X7k16hFkX47bclcrbgUowYUYbjjmtSK8skeL3zjlz7I/f+5I5smvpXFG2PI48keFEAxuG56nnSj/Ezdr2EFASgfgOXm9+NwW+8vBlFKQD1FpCnxPQqON3CnBt0WjjJQAGYEG3TC9rVB2ix1+tqOQDpL3+RC7H1RuwIwsSJy/Dyy79gzpzV0axZWSoC8Fv5fqs7nIfInv7Gjb0qmPpXBE2PJQtX+1csjS3hTOnHEnauz6alIAA/kqr1FRutj8A6qql7KvTALD0GTzfGBg5RCsDLpRYDxD4Vy10DqHPY7QLXMFhCCsBg3KxTmV7Qrj5Ai71eQ2Uv/LZyQeLBB1u7rFaC5fJds1+/5WjbdiJGj94bZWXpCEC9fu4SWdast4K0bu1V09S/wrc+nhxc7V/xtLZ0c6UfS9e3fluWggDU9X165vGWYnJTeuaGNd1HoVfe7ih2gNjnYheKveu3HXXFi1IA6uXFukBRRwDTDhSACXnA9IJ29QFa7PUaLd8Nf5VVt6fpTdkhw/TpwEMPrcB55z2A668/LTUBqM3QdY277w786U9eo0z9K2TTY0vuav+KrcElmjH9WKKOtWhWCgIwWzs9WUVOfEVbMV3so4dC68YQvR3k/yyakDdqlAJQK6fTwNlbOqKoX9A8KACDkrNMZ3pBu/oALfZ6TZaVthMnApddZumwOqLfcguw3XbLsPPOD8jVbxWpCsB//EO+6sp33TPOoAAM4lnT5zFInvU5javPifrsk6TbnqIAjL2pUQrAW6W2OlQ5PPZamwugADQziiSG6YXj6gO02Ov15ZfeaNlguSE7zE7g//4XUAF4ww1LsGDBiNQF4AcfAGPGrLqD2NS/IunEMWTiav+KoaklnSX9WNLu9dU4CkBfmDLDknqF2/tiNTeBZM+u8ZdT+FgUgOEZ+srB9IJ29QFa7PX6XcbZL5QVIHp23lpr+XJVnZHuu0+u7ZF7e44/fokcAJ2+AFwgFx9deaW3EUTvBjb1r+Atjzelq/0r3laXXu70Y+n51LZFFID+iOmdvPlChEfW+qoMBaAvTOEjmV7Qrj5AS6FevXt7U6XbbRfMj9/Lqt1rr/V23rZs6YYA1JZccQVw9tnANttQANp61vR5tM2vvsd39TlR3/2SZPspAPPT9nsOjd94UfmVAjAqkoZ8TC8cVx+gpVCvO2Sf2M5yS3aHDsGc/cQTwC+/AN26mYVWkrx0h7OKv0MOMdcrWMvjT5Ukr9zWmD6P8be8tEqgH0vLn0FaQwGYn5qf82qOk+R6cnWo82osHUcBaAksaHTTC8fVB2gp1OspOXVTb87Q8wBtw0I5qEmnWi+91DtyxSU/vvQSZDranzC1bXdS8V3tX0m1v1TKoR9LxZPB20EBmJ9dYufVWLqPAtASWNDoLgkHm5EQVx/sNvV6801Ab8/Qs/Nsg4qsjz/2BKAGl/w4Q24Vf/RRoH9/c71s251UfBs/Rlknkx+jLKs+5EU/1gcvF24jBaC5D8R+Xo25CtViUABaAgsa3fTCcfUBWgr1+lyOAr3nHm8jiE3Qq+R0/eDpp3tTyK4JQB2dVFGru5ObNSu8NtGm3UnGdbV/JcmgFMqiH0vBi+HaQAEYjl8aqSkAE6JOAWgHOkpeixYBF1/sHQWz+ur+66HnB+qdu7oBJHuETJT18l+T/DH79PGmtrfbjgLQhqfJjzZ5MS5AAcheQAFYfH2AAjAhn5leOK4+QEulXpfLBYzduwNbbeXP4XrtW9++QMeOwD77rErjmh/vv19OlZdj5Tt2pAD051l/I7k2eTEuBSD7gJxpN2cONt10U0XRSkxWJ5dOiPIgaJeoUAAm5A3XhEO22fWlXrfK8et7yUrcfff15/Bp01atr2vSxF0BOG4c8NlnwDnnUAD68ywFoA0nv3Fd/aLot/6MF54ABWB4hknnQAGYEPH6IrSiwhk1r8cfBxo1kku4fR61rusFdd3fYYdVb1HU9QrLS8WfHlI9YAAFoA1Lkx9t8mJcjgCyD3AEsBj7AAVgQl4zvXBc/QZdKvV64w1AR/X0VhBTmDXLuz5u0CCgeXO3BaDedNKzp3dF3cKF+W8oMbU5rb+72r/S4lGs5dKPxeq56OrNEcDoWCaVEwVgQqQpAO1AR83r00+BBx8EBha6h6eqivfeC6y9dt2jhVHXy45K3bH1hpJOnZZivfUezHtHcRTlxJEHhUMcVJPPk35MnrlrJVIAuuYRc30oAM2MIonhonDQhtWXev38M3DZZd7duc2a5Xfpt996V75dfz2wzjq147nI66GH9J7iSuyxx3AKQJ+fVpMffWbDaFUEKADZFSgAi68PUAAm5DPTC8fVB2ip1GvFCu8wZ50C1hs98oXHHgN++w3o2rXuGC76Uae3p05dji5d7qcA9Pl5NvnRZzaMRgHIPlBFgAKw+LoCBWBCPjO9cEpFaEWFMw5eurFDdwHvvXfdtfz1V+/atyuugBxnUDwC8L//1ZHNFbIW8D45uqYCZWVlUbkh9nxc7fexN7zECqAfS8yhAZpDARgAWspJKAATckAcgiaKqtenej3yiLepo0uXusk9/zygt4ZcdFF+si7y0nuOe/ZcgbPPHiVCsAsFoI8PhsmPPrJglBwCFIDsDhSAxdcHKAAT8pnphePqA7SU6qW3esycCfToUdvpS5YAvXoBZ50F7LhjcQlAre2AAcux226v48kn96MA9PGZNn0efWTBKBSA7AM5BCgAi687UAAm5DPTC6eUhFYUSOPgNWMGoOcB6gaPmkHX0U2YAOjVatlr3+pqRxz1ioLXo49WYvHijzBxYhsKQB9ATX70kQWjUACyD1AAFnUfoABMyH2mFw4FYHVHxMHrxx+9Ub477gByb/fQa9/0vt8jjwTaty/cIeKoVxRdcOLEZRg//juZwl6XAtAHUJMffWTBKBSA7AMUgEXdBygAE3Kf6YVDARi/ANSdwLq+T4+DaaW3VVaFqVOBUaN0GtW7LaRQcNWPX321FDfdBCxYsELWOXITiOljbfKjKT3/Xp2Aq88v+ik5ApwCTo51VCVRAEZF0pCP6YXj6gO01Oqlt3scdJB3L3A23HgjsPvuwKGHmjuDq35UP11ySQNMngy0bZtzebG5SanGcLV/pQqlCAunH4vQaRFXmQIwYqAJZEcBmABkLcJV4VDf6qWHJustH0cf7Tn+P//xpoRVGK62mrkzuMxr0KDvZQ3jBjjnnMbmhjgSg8LBEUeErAb9GBJgCSSnACw+J1IAJuQzl4XD3Ln575B19cEetF5jxwJ6bl737p7jhw4FNtwQOO44fx3BZT+OHPkxdtllJwwbZpjH9tfURGIF9WPYypn8GDb/+paefqxvHq/dXgrA4usDFIAJ+cz0wnH1AVpq9Zo+HXjmGciBycD8+d6O4Btu8EYF/QSX/Thu3AR89dWBeO+9hn6a4kQcV/uXE3CKqBL0YxE5K6aqGgSgXLAJnXfZWkyO3Md4scvFZldVZxf5V+ZioItzfhC7T0zTOBEaOFGL6CtBARg90zpzdFk41KcRwO+/B66+GrjzTu9ImKVLgTPP9N8JXPbjhx8+g/vvPwm//NIATZv6b1OaMSkc0qQfXdn0Y3QsizUngwAcJu16WuzfYvp1+y6xdcV2FVtD7FMx+WqO3mI7iMmx/OgvJre3px8oANP3QSQ1cPVBxXpVd29cQkuPfNH7gNVuv907FmYT/RrkM8RVL5/F542m9ZozZ4RM/3bDSy81QLt2YXNMJr2r/T6Z1pdOKfRj6fgyaEssp4A7SDlyND/WETtGTEf/NhBbXFX+NfLvaWLbBK1PlOkoAKOkmWJerj6oWK9kBKCWolO+eiTMWmvpFWp2ndFlAagjue++e7ZscGmIv/7Vrl1pxXa136fFo1jLpR+L1XPR1dtSAMo8TEbgbSs2WExvaM+9pf1w+f8LYi3EdMo41UABmCr+6Ap39UHFeiUnAIcPB955xzsTcPvt7fqW6wIQ6Iq5cxthxAi7dqUV29V+nxaPYi2XfixWz0VXbwsBeJSUKjezQ47ex5ti94vpPIyKvmzQI/nfEttU7OvoahksJwrAYNycS+Xqg4r1Sk4AvvIKZKOELDaR1SaFrn2rq/O6LgD32ONMOQqmCfTau2IIrvZ7ZVdZWZmxpEMjOY1cLV9wsV4u+zFp/9XX8nwKwBOFj6zAxp/Fxlex0hFAFXx/zGHHEcAEOlJmE8hdd92FHj16ZIpbvHixvBh7y60IA9CsWbPM70bJNQktW7ZEx44dM/+fL9snhwwZgoEDB66s4r333otdd91VrtLy7tL65JNPMGbMGFxxxRUr49woJ+527twZbdq0yfxuspxaO23aNDmSo+pMDvldL1mU1VPm5crLyzNxxsq5HQsWLMCJJ2q/CV+/tm3bol+/EfKAbY8PP3wZhx22qn6vvHIjdtuts5Tt1W/WrMmyrmoa9t9/Vf1Gj+6FAw/siRYtvPp99NFYLFq0QA4W9uq3dOlijB7dG8ceO0CuG/P4TZkyShblry5pvpYrxyrwww8/1OKnPpgyZbas3boOjRuXCeNP8MEHY2KvnwqaceP+iq5dO6NTp061/Jt9sM+atRSbb74HttzS82/c9TvqqL749tsnMrxGjx5dq/8NHjxY+G4t6/cqMrwmTLhXbvfY1Vf91lmnDeTcZMnf3r+//fYzHnusizB7VqaQZQ5ZQu7nQ3lddtkQ6f/fydEycjVHVbCpX5D+t/vux8rI3wicccZJ2G47ZTcA66/v/udXeZ144l/Fj53lCBuv//3003y89toQ+Qyter5EzW/11dfJ8Np7742xcOHCWs+X62V7+AMPjJZ+vkjE9BR5FjaXvrVjpn6//vqTfFZfwwEHHLvSv1OnTpDjhFpJO7bM/O777+fjs88+kPwPWxln0qRXsM02u2G99bxnx9y5s/DNN3Pk4O79V8Z5/fXRciXhkbjyyvMyIrDm80/r2qnT8fKc6CT93jvwO6n6lZc3l+fhdFx88cW1ns/HHnts5rm6wQYn4fnn+9Z6/jVv3hI77ui9P6L075dfvid+aZJ5TpSVlcX+/nD9/ZZ0/bR/3i6LqZvKrrN1111XNqHpYB70nqW5Kzv1qh/Olx916vcIMfkKvjKcLj9l1wD+XvVbjae/5xrAOkBG9SvuAo6KpCEf10eOsg/Qms1w9Zs961XdU7n9a6utyvDww0CHDgl17hDFuO7H8vJTRIgld7NKZeVSEZ2PrhQ0+T6PrtYr+4UsRJewSmp6rlplxsihCPg4BkZHUnRkL3d+QgWf7gKeKfakWB8xHYF5UUy/AXIXcCivFE5MARgj3NysTQ8q11+Erj3YySu/ADzppDIZefLuPHY90I/5/agjWvxCVrgHm56rrvf/UqpfAQGo6xnkwC3I1ruVQZfV6f93rhKEO8m/ciw/9hRbIHa3mJzS6kbgGkA3/BC6Fnzh8IUTphOZXjgu9K9bbinD++/L12n9Pu14cIEXhZa5kxRDv6/Lj+aWMUZUBHyuAYyquETzoQBMFHd8hfGFQwEYpncVw4twwoQydOsGfPFFmJYmk5afR34ew/Q00+cxTN5Ma0eAAtCOV9DYXSShHGObOUFbz8hpLfZVVWb6/x/FskOt2WHWLXLi5JbLKeCgXrBMZ3pQ8UXIF6Fll6oWPbd/LVpUlrna7ttvIRtBwuQaf1r2e/b7ML3M9FwNkzfT2hGgALTjFTT2AZJQt5HpnPoosVxxpwJQ79HTe/U+yylA9jzWGSgAg3rBMp3pQcUXIV+Ell0qrwDUqbBt5XhV2aiPw3NP1gpTQExp2e/Z78N0LdNzNUzeTGtHgALQjlfY2DoCOLUOAagjgLqLRu/WMwUKQBOhiP5uelDxRcgXYZiuVrN/nXIK5DgY4Bq9UMnhwH7Pfh+me5qeq2HyZlo7AhSAdrzCxi4kAHX0Tw8B+1xMD118Nk9hFIBhveAzvelBxRchX4Q+u1Kd0Wr2r9tuk4s25abN5/VKdYcD+z37fZjuaXquhsmbae0IUADa8Qobuy4B2FQyPUHsXTFdB6hXrehZOmeIPV5HgRSAYb3gM73pQcUXIV+EPruSLwH4llyidNxxwLx59redhKmHbVr2e/Z72z6TG9/0XA2TN9PaEaAAtOMVNnZdArCuPO+TX7YWOySfAOwmWwbXWEPPYkTmto/sjR9hK+hier5w+MIJ0y9NLxxX+teiRZCbSrydwJvqbZqOBld41cTDepXGc8LRbl+S1aIATNatfgXgXVItvfL+wHwCcPbs2XKNlt7eUvqBD/bSeLDTj2Y/ys2McqME0EXPDXA00I9mP+bGIC87Xo52+5KsFgVgMm7Vo130iHgVgJPFdMPHl2K601fO/0djsexVK3q5pgpAPTZmOAWgQJK7R/XOSt5s4fWGYhnRqtl36Ufzi/Dss71jYHKu7E7mCWVRCv1o9iMFYP4OZXp+WXRFRg1JgAIwJECfyU+UeLqeL/daFU2qBz7oxo/+Yq3FlovpZhDdBDIiT95cA+gTethopgcVX4R8EYbpY3X1r3vvBZ56CvjXv8LkHG9a9nv2+zA9zPRcDZM309oRoAC04+VCbArAhLxgelDxRcgXYZiuWFf/miqHRB0oCz9+kJNBGzYMk3t8adnv2e/D9C7TczVM3kxrR4AC0I6XC7EpABPygulBxRchX4RhumJd/WupHBW/5prAv/+NzMHQLgb2e/b7MP3S9FwNkzfT2hGgALTj5UJsCsCEvGB6UPFFyBdhmK6Yr3+1bw9ccAGgB0O7GNjv2e/D9EvTczVM3kxrR4AC0I6XC7EpABPygulBxRchX4RhumK+/qXir1EjQA+GdjGw37Pfh+mXpudqmLyZ1o4ABaAdLxdiUwAm5AXTg4ovQr4Iw3TFfP1r5EhAN4PowdAuBvZ79vsw/dL0XA2TN9PaEaAAtOPlQmwKwIS8YHpQ8UXIF2GYrpivf338MbDHHsDPP8v5UHpAlGOB/Z79PkyXND1Xw+TNtHYEKADteLkQmwIwIS+YHlR8EfJSvGflAAAgAElEQVRFGKYr5utfy+UwqJYtgYkT5eBQPTnUscB+z34fpkuanqth8mZaOwIUgHa8XIhNAZiQF0wPKr4I+SIM0xUL9S89CubkkwE9GNq1wH7Pfh+mT5qeq2HyZlo7AhSAdrxciE0BmJAXTA8qvgj5IgzTFQv1ryuuABYs8NYCuhbY79nvw/RJ03M1TN5Ma0eAAtCOlwuxKQAT8oLpQcUXIV+EYbpiof719NPAgAGAHgztWmC/Z78P0ydNz9UweTOtHQEKQDteLsSmAEzIC6YHFV+EfBGG6YqF+teXclP41lt7G0FWWy1MKdGnZb9nvw/Tq0zP1TB5M60dAQpAO14uxKYATMgLpgcVX4R8EYbpioX61wq5NXzDDYExY4C99w5TSvRp2e/Z78P0KtNzNUzeTGtHgALQjpcLsSkAE/KC6UHFFyFfhGG6oql/HXkk0LEjcOGFYUqJPi37Pft9mF5l6vdh8mZaOwIUgHa8XIhNAZiQF0wPKr4I+SIM0xVN/atvX+Dzz4GHHw5TSvRp2e/Z78P0KlO/D5M309oRoAC04+VCbArAhLxgelDxRcgXYZiuaOpfL74IXHop8MknYUqJPi37Pft9mF5l6vdh8mZaOwIUgHa8XIhNAZiQF0wPKr4I+SIM0xVN/evbb4HycuDHH4EWLcKUFG1a9nv2+zA9ytTvw+TNtHYEKADteLkQmwIwIS+YHlR8EfJFGKYrmvqX5r355sCDDwJ6MLQrgf2e/T5MX/TT78Pkz7T+CVAA+mflSkwKwIQ8YXpQ8UXIF2GYrmjqX5r38ccDe+0F6MHQrgT2e/b7MH3RT78Pkz/T+idAAeiflSsxKQAT8oTpQcUXIV+EYbqiqX9p3jfeCEyZAujB0K4E9nv2+zB90U+/D5O/a2n/8x/gjjuA3r29o51cChSALnnDX10oAP1xCh3L9KDii5AvwjCdzNS/NO/XXgPOPBPQg6FdCez37Pdh+qKffh8mfxfS6jme48cDt94KjB3rjeTrzT66pMOlQAHokjf81YUC0B+n0LFMDyq+CPkiDNPJTP1L8/7pJ2DttYH584ENNghTWnRp2e/Z78P0Jj/9Pkz+aaZdvBh4/HHgttuAOXOA7t2BHj2AVq3SrFX+sikA3fRLoVpRACbkM9ODii9CvgjDdEVT/8rm3aYNcMstgB4M7UJgv2e/D9MP/fb7MGUknfabb4C77/ZsnXWAiy4CTjsNaN486ZrYlUcBaMfLhdgUgAl5wfSg4ouQL8IwXdHUv7J564tkq60APRjahcB+z34fph/67fdhykgq7bRp3mifjvp16ABcfDFwyCFAw4ZJ1SBcORSA4filkZoCMCHqpgcVX4R8EYbpiqb+lc379tu9dUR6MLQLgf2e/T5MP/Tb78OUEWfaykrvs6jCb/Jk4PTTvesad9ghzlLjyZsCMB6uceZKARgn3Zy8TQ8qvgj5IgzTFU39K5v3pElA586ATjM1aBCmxGjSst+z34fpSX77fZgy4kj7yy/emZz6hey334DzzwfOOQdYd904SksmTwrAZDhHWQoFYJQ0C+RlelDxRcgXYZiuaOpf2bz1ZbPWWoAeJ+HCLkL2e/b7JPp9mDKiTPvf/3rHuNx/P7Dddt40r+7qbdIkylLSyYsCMB3uYUqlAAxDzyKt6QXNFyFfhBbdqVZUU//SBJUy36TWrl1jXHllJbp0kfMlEgiNGjWCWl2B/Z79PkwX9NPvw+QfRVo9xuWtt7xp3uefB44+Wqd59XNYmcoofKHPY5j2UgCGoZdOWgrAhLibHlR8EfJFGKYrmvqXCr+hQx+XI2AW4aWX9kOzZr/LlXDvhCnSd9ry8uZyfMVf6hSB7Pfs9747Uh0RTf0+TN5h0y5ZAjz1lCf8PvsM6NbNm+pt1WrVZzFsGUHSF/o8Bskvm4YCMAy9dNJSACbE3fSg4ouQL8IwXdFv/yovP0UWmzfFu+82RM+ey8IU6SttZeVSEZ2P4tprK1BWVlYrDfs9+72vjpQnkqnfh8k7aNrvvweGDQPuuss7uqVnT6CiAlhjDS/HbJ/Xz2KjRsnO/Zo+j0HbrOkoAMPQSyctBWBC3E0PKr4I+SIM0xVt+te8eWX4+9+9mwXiPmLCpl6NG9cWiGGYFErLetmRLVZedq0MF3vGDG+075FHgL339tb3HXFE7c9YWs96bZ3Jj2EIUACGoZdOWgrAhLibPnhpPRRYL7sOUAq8GjQoy4xK9OkDlJfbtd82dinwojA1CwdXn1+2/dU2/vLl3rFKKvwmTABOPtkb8dtll/w5pcWKAtDWu6viO3BgQvDKF0hJARgL1tqZ8kVoB5q84uV1003A/vsD7dvblWMbm360I0Ze0fKyy81/7IULgYcfBoYMAX780bui7dxz/V2xSAHon7MrMSkAXfFEyHqk9eHjg93OceQVL69RowDdnXjSSXbl2MamH+2IkVe0vOxyM8fWO3nvvNNb46fHKOk1bfoZatrUnDYbI613EEcA/fuoZkwKwODsnEqZ1oePD3a7bkBe8fJ6+23g9dchx8HYlWMbm360I0Ze0fKyyy1/7Hdkw7yumX32WW9dnwo/HUEPcph6Wu8gCsDgvYECMDg7p1Km9eHjg92uG5BXvLz0JpDrr/emsPIc0WdXgTyx6Uc7jOQVLS+73KrHXiab5FXw6fq+6dOBrl2BCy7w7tIOE9J6B1EABvcaBWBwdk6lTOvDxwe7XTcgr3h56eL1Sy7xbLPN7MqyiU0/2tAq3s0Wrj5X7eh7sXVNn97UoTd26JcjvZv3rLOAFi2C5FY7TVqsKACD+48CMDg7p1Km9eHji9CuG5BX/Lx0SmuPPbyprLgC/WhHlryi5WWT26efenfzjhgBtG3rTfPqvdlRj5Cn9Q6iALTpDdXjUgAGZ+dUyrQ+fHyw23UD8oqf1+jRgF5Kf/rpdmXZxKYfbWhxBNCOlpmX5pe9ArGuvHUj1OuvNxDh1wivvdZA7uVdLrd1LBcBGP6axHxXrqX1DqIAtO1dq+JTAAZn51TKtD58fBHadQPyip/X1KnAiy8CV19tV5ZNbPrRhpZZ0PD5VZ2nqX/lXoGYm3LZskb48MOtMWXKzvj119VE8H0sNgNrrrnIzmEFYue7ci0tH1IABnctBWBwdk6lTOvDZ3pQsV52D3byCs/rhx+Aq67yNoLUcUtbJJ9b9ns7jOQVD6/stWs//QRMnNhIrKGIvRU44IDl2Guv5ZH3/0JXrqX17KIAtOtbubEpAIOzcyplWh8+PtjtugF5xc9Lp78uvxzo3h3Yemu78vzGph/9kvLikVc8vJYvPxPjxzeRO7CBNm2Agw/2/g1yjIufGhbyY1rvID/9y0/b8sXhVXBh6KWTljeBJMSdD3Y70OSVDC891Db7QrQr0V9s+tEfp2ws8oqOV2WlHuOyFJde+h2++WYjuZ+3AQ48MP7rD01CiwLQzscuxOYIoAteiKAOaX34+GC3cx55JcPrhRcgL0fvjLM4Av1oR5W8wvP6+WfggQe8Hb1Ll67ANtu8g8MP312OcSmzyzxEbI4AYm4IfM4ldUkAdhE6cjIRdhXTk4lai32VQ0yvoZYTjLCXmKzywX1i/fIQ5QhgQl2ND3Y70OSVDK8PPwT0Wjg9FDqOQD/aUSWv4LzmzCnLnN03fDiw447eMS6dOi3BgAEjsMkmFWjcmALQ1L/s6FePzSngMPT8pz1AopaLLRWTRze2yBGAa8jPcpoRnhHrLbaD2PNi/cVkqXetQAHon3uomKYPHkcmq+MlL7vuFpTXr79Cpsi8a66aN7cr00/soPXyk3eYOKyXHT1XeS1dugSTJ7+Cn3/uhFdeaYhjj/WEX/v2XvtcfK6mVSflYfKjXa+oHpsCMAw9+7Q6AigHOVQTgGfI/3X0bwOxxVVZXiP/nia2DQWgmw8EVx9UrFftT4zpAZrWwz1MvXrLV8XT5Amx/fb2DyFTijD1MuUd5u+slx0913jpNW26oeNf/1qO775bImf3NZEbOxph002rt8vFz2NadaIAtOvzubFdmgLO1qsuAThY/rh3lWXjHS4/yEqfzHSxfN+vFjgCGLxPWKV07QGarTzrZeVG4zfotB7uYfw4bBgyL87D9UkRcQhTr4irUi071suOriu89ODyCRMgO3qB1VYDOnRYJn13JPr3P1WOcqk9xevi5zGtOlEA2vX5YhSAcoMhVNTlPsp1MPwtMf1u9DUF4BL06+fWmhD1SVoPBVce7DU/mqyX3cMqDK+xY4FZs4DzzrMr00/sMPXyk3/QOKyXHbm0ec2VLQWvvgq88453ZNFBB3nr/JYvX4K5c0fg2msrKAB9uNTkRx9Z5I3CKeAw9OzT5hsBVMH3x5zsjCOA3bp1wxpr6PJBoGPHjhkr1UChVd2zpgcCeZU+r5kzgQcfBAYNiv5Tz/5lx5S8VvFavhz46CNP+P3nP8Af/oDMMS6b6BBHVShGXmk9UxWZiZddb60emwIwDD37tHUJQL3VM7sG8PeqLPWiJ/091wAKhLQ+fKYPHutV+kLL/iPuP0WY/rVYVgvrwvkbb5R1IrpQJMIQpl4RVqNWVqyXHd0keWl/nDQJcjevLGSXnzt0APbfH3JzR+06J1kvG2KF6pXWs54C0MaD1eO6tAZQ66KLHVQAThaT88zxpWobsdXF5Ps8nhTrU/U3ue0TA8W4C5gC8P/bOxMwK6orjx/ZNOK4xgVxwSXGgEpEJRo3nNFxiQYwGYmahbiMC5PRYIyjmYlN4hLjiMERM0RNMJoYdZQwLpHoJ7gjKhEVRRNxAaRxwSUSmW6WOf9XXXb1e/266ry6r+6rV//6vvN1Q5+quvWrc+/9111ZEdZeBpTOzGOFg3THVTotLVKaQTkUpYrDo1l5OUTU5VJF5oWtCWfOFHnkEZFPfzro5t17b9HlW6rTziOvuLxYr9hKUn6luTdbANPQS37uGHW9WU03cupyoKv3j2q7qV2jpllH3lf7uVq1Vb44CSQ591SeeSyokgiHVFB6OJm8bGTT8kIX8GabiXz5y7b7xnmnTVfc9Wv9O9NlI1dPXq+8EnTzzpsnsvvugfDDOL8k27TVM102Ql292QLIhaDTxE9W51IAZkQ6jwUVBWBlcDTre0TLy3PP6QrzWGLe4dGsvBwi6nKpovDCNm1zdREzCL+lS0X23z8Y34eWP8uRR15sAbS84cbwbaQuYJdEKABd0mSLljOaeSzY8yyYX31VBw/r6OErrkjW8pL0RfM9JiUV+DU7rxUrRB5+OOjqRdcuRN8XdcoilnSp5cgjLwrAWt6033MoAP3yd3Z3X5kvjwVVngWNs4Apu1Czvsd23VcIrX/YEs7aCtMT62blxfjqSiCuXG1tDVr7Zuuo9UGDgm7ePXTT0l690pHMY3zFsUpHpOez43iluTfHAKah5+dctgBmxD0u4/kqFJguWwA0M6+LdcNIrACFgfeujmbm5YpR9DrNxGutjlJ/8cVA+C1YoJvT6+70aPHbbjt35PLIy1dZn6SFOc2boQBMQ8/PuRSAGXHPY0HFFsDK4Gjm9/ib34isu67IV7/qLlM0My93lDqv1Ay81qzpJ088EQg/7DWNJVwOPtj9EkNJBI0vscVJIJwEUo/ywfU1KQBdE61yvWYo2Pv0qdxqqV74yMtG1gWvR3W/IHTRnXOO7d49ebtIl7vUNJfQarT8+L3v3Sovv3y8LuPSWzbeOOjmHT5cpG/ferzB4Jp5jC9fojQJrzRvii2Aaej5OZcCMCPueSyo2AJYrBZAbLeFxaB/9rP0Y7NCcox7WwGTR15PPSUyceJquVVXnx08eB059NBe8tnPup1MVI1iHnlRANryRCN4cxJII7wFB2nwlfnyWFBRABZLAGJpDuwIcv75Iltv7SCz5bSFhnEfH/erVolMny5y5ZUizzyjW019c7WsXXubruN3rM7uZU8Bu4DZBeymBK3vVdgCWF++n1ydAtAGmrz88Lr8cpEDDhDZbz/b/ZuphYYCsLoAPPvssfLrX/crLRmESR6YOX7yySLrr98mEyZM1X16x1IAxnz4+GqEYBdw7WUaWwBrZ9dQZ/rKfBQ0tjAgLz+80I2HlsDjj7fdnwKwuXm9+Wab3H33yzqbd4huF7hOqaV41KjObdpYrnZ9/2wBZAugmxKhvldhC2B9+bIFsEa+FIA2cK54zZkTzN5EN7CLw1W6XKQleg2mK54oWvhefjmIh/nz1+q4vr/IlCmDdNeOylkdFIAUgJwEEp+nGs2DAjCjN8IKxwaavPzweust0a48kUmTOlt3bClJXhHCk8Kh8XhhUfAnnwyE33vviRx4IKxNVqyYKhdeOFb69asc48f3mPw9+mKFFMaVq2nyeowAPFavjY0mh6ptpDZI7Y2O++HfGmminxylAz2u+H2HiE+apKU+l13AqRE2xgV8Zb64jMd0JS9AKRwq85Kr+EKrz/jxImedFezYkPZwla606Sg/n+mqJPrhhyIPPhjYBhsEizbvu6+o4IsXDiy/kpdfvlghhXFxnyafxQjAQ/TaW6np54XcUibuIACXq+neMPLnSBra0qTH5bkUgC5peryWr8wXl/GYruQFKAVg/QQgroxlYPbcM1i8N+3BuLcR9MFr0aKgtQ+tfli+Bev3DR7cdRkXH+lKQi6P6fJV1nsWgOHrRAvg3G4EIFoAd1XTQQeNd1AANt47qSlFvjJfHgsqCq36Cq2aArjKSS7j6/e/F/ngA5FvfSt9Cl2mK31qOq9Q9HStWSPy3HOB8Fu4MJj1fYi20VRb/qfovKyxx0kgVSeB9CQA0fq3ntoraler3WHlXi9/CsB6kc34uhSAXYGzYLcFYBF4YV03rPF24YU2Nt15F4FXekrZCdOVK0Uee0zkgQe0L0474yD6sOwPunx7OvgebW+ZAtAkAHUDSjlOTZcUL439+5LapWr4BL3ZRr4+3hSA9eGa+VUpACkA0wRdESpCDPzHLGBMBMHewGmOIvBKw6f83HrxeucdkZkzRbdp04FYOhIL3bx77SXSu3ey1NcrXcnuXt0rj+nyVQeBYhyvNO8j4Szg7loAu7vttfqfg9QOS5MmV+c2tQCcPHmynHnmmSVWK/UT8YILLpBLLrlE1lsPrbE6YvOWW3Rvx43l8MMPL/27tbVVK4dJcumlEOnBMWXKFF0faqgOGtZRw3osWLBAWxGmy3nnnfeJz2W6z9TIkSNl113R1Y99R2fLvHnz5LTTTvvE53ytec7SEehboZTSY8aMGfL+++/LmDFjnKRv2LBhpQVLe/feV55//g9yxBGd6bv33svk858fqfcO0rdw4WxZvHiebmbemb5p087XwdFnyUYbBembP3+G/O1v78s++wTpa29fKdOmXSCjR1+ie2AG/J588hatSPvrOW+WZtEtX768gh/ewZNPLtK9M39UWki1tXWBrrA/ve7pQ4Fw333jdDHXkXL00UeX0ht9v2FhtXBhu2y//V6y447B+613+o45pkXeeut3JV7Tpk2riL+JEycq350/WXj2oYemyDbbDK17+j7++EP57W+PVWZ3yIYbblhiEc0f4HXuuZM0/t+Wr3zlp5/Edb3Tt+eeo2XJkqma374mLS0tFfm3f//+8vjjb5Z4rVixXFuAJmmMdubf8vSdc84CHQc4Xb7+9XT549ln79J0TVdGk0uzR8vLF/AaM2acpmuk7LFHEH8ffNAam7608de//6YlXvvtt7XyWFFRvoDhZZf9rsTrT3+apgsdbyxDhgTlXz3Td/vt39fycXO5/PKzSrzKy78PdabGYYcdKyeccId86lNB/KF86S59o0ZdKq9oZxq6eZ95Zop85jNDtfzdV/OIyLJltvIlFA7t7X/RSULjK8rn0aNHl8rVLbb4mtx5Z0tF+Vcvfq+//rQ+T99PZieX1x933XWXXH/9dGU2uVSuViufXacPvObM+aHWC9vKuHHjPikHkL4zzjhD68t7S7H10kszE9UfrtK3225H6jqfs0u85s6dm7r+RXxeddVVWr+tK5tttplcd911eNZt1HRjyW6PpAJwsp79OTWdiuT/aGoBuEhHAW+zDd5Z8x++vr7ivryYrq6xR162vOia1zXXiAoG/fxO+f3tOl02KtW9mzld2Kbt6acD4YdlfdDFi65erZ9rPpqZV81QejiRXcAVAhAaCusHQQDOVkMry+tqmOmLfYf6qL3QgRRfghCAWDbm+nq8H+s1KQCtxBrUn0KLQitNaBalIrz7bpGlS0VOOSUNrfguJ+ZHd/nxo49EHnpIZNasoOsey7hgckdHR06qF1mUuE8FKXIyBWCFAEQXGcbzhWv9hbSO1F/QVXax2iA1nZ5UWgoGk0Cmunofaa9DAZiWYIOczwrHXYVTz1fKCsdG1zWv+fO1tNbi+qKLbOko93adrnSp6Ty7mdL15ptBa98TT4jstFMwvm+33UR69XJFi0LeSpICkFvBWWPGhz93AsmIejNVOFkgIy8bZde80Jp0zjkiOtRSdPhgzYfrdNWckLIT856uAQPG6jZt/UrC78/aXjJ8eCD8BqJEr8ORd14Yb4cxgFkdFIAUgFnFWpr7UACmoWc4lwWoAZa6kpd/Xj/4gciJJwaLAtd68D3ayMXxeu+9Np0A8oROTDlAPv54ndJi3QcdJDohyXYfq3dcutiz0pUoBSAFoDWP+fCnAMyIOgtQG2jy8s/rWl2IAS1KRx1lS0vUm+/Rxq4ar8WLdVCUjor6xS/W6uzgd3VFho211a+PzoS3Xb9Wb75HGzkKQApAW8T48aYAzIg7C1AbaPLyz+u++4LuxY4VomwJ6vDme7RhK+eFcX3Ymu8O3RMBQnzcuHZdIudXumpD43Rp4gnZAsgWwITrANoyRIN4cxJIg7yItMlgQZW8oGLBXhltRRI0L+uunNfrIgy6fGfNR5F41QwpciJ4LVp0g+y++7e1xa9Pabu2k08W+c53ggkeLL/yX375eocgF5cf08QwBWAaen7OZQtgRtzjMp6vQoHpsgVAkXhh27Czzxb5yU9EF+K2cQq9i8SrNkKdZ+l61LqMyypt4Vspm2zSXxfEX0dOOkl0AflOH5YTFIBp4iwuP6a5NgVgGnp+zqUAzIh7XMZjwZ7/gr0ZW0wnTBDdPUJ0h5zaMgrjPp7bsmXB3ryPP65bKGyzRlnfLzfeOEJ3+qictcpyIv/lhK93yBbA+LxYzYNdwLWza6gzfWU+VoS2MCCvxuA1dWrQ+jdqlC09bAHsmddaXQ5Xd8ssLePy4osie+8dLNw8cGBbaYs6bNWFreDKD5ZfFIC15cTgrLhyNc212QKYhp6fc9kCmBH3uIzHgj3/BXsztgA++CD2kRXtjqwtozDuu3LTrY91j9hA+Om2vqUlXLCUS9jFTl62OMsjL19lPQWgLbai3mwBrJ1dQ53pK/PlsaBqRkGTNhiL9h5fe010s3eRK64QWaeGUrBovKrF1wcfiEBMw7BmHxZtxuLN5Y185GXLoXnk5asOogC0xRYFYO28GvZMX5kvjwUVBWBlGBftPa5aFbT+tbSIbL65PVsXjVc5oTfeCFr7nnpKZNddRQ49NPhZTUwXnZc1wvLIy1cdRAFoja5O/xq+fWu/WYZnsgs4I9h5LKgoACkAQeDSSwPhss8+9sxSxLhfo9vZz5sXCL/XXxfZb79gfN9WW8XzKyKveCrVPfLIiwIwzRv3cy4FoB/uzu/qK/PlsaCiAKQABIHbbhN59lnR3SdEvvAFMe0+UaS4//hjkUcfFZk5U2T1apFDDhE54ADbXspF4uWicM8jL191EFsAa484CsDa2TXUmb4yXx4LKgpACkAQaG8XwY4UaNH66187Jy5E16erlsmLEPdvvx2IPoi/AQOC8X3Dhon07m0v+orAy06FLYCumMXFV5r7cBZwGnp+zmUXcEbc4zIehWnXF0FetsDMgheWLsGSJRCCWMIES5dA7Gy3XXNV0Ek+fMAC2+SBxfPPB+skgsWOO9reW7l3Fu+xlhQyXTZqPfHyVdazBdD2DqPebAGsnV1Dnekr87EAtYUBeTU2r9bWzsWLt98+GCO4xx4ivXo1t5BHaygmdED4vfuuyIEHiowYIbLpprb3VeQWUzekgqvksZzwVQcl4ZXm3bAFMA09P+eyBTAj7nksqJK0hNQLH3nZyPrihe3LHnkk6AJFlyfGve2/v+guFvmtoLuLe6zZ9/DDIrNmBc+G1r599xVZd13be4rz9vUema44Ara/swVQltiINbY3WwAb+/0kTp2vry8W7IlfUW6/7IssmDHpYe7coGVs6dJABEIMbrJJvne2EPm2rt3Xt7R48847B8JvyJDKlk5bdFf3ZjlhI5lHXr7qILYA2mIr6k0BWDu7hjrTV+bLY0FVZEFTLWj5HuOz88KFgRDEDiJDhqyR3Xe/S6677ghtLcvH1mZYxmX69HYZP36Zbss2UGc+r9OxTVv8s6f1YHzZCOaRl686iALQFlsUgLXzatgzfWW+PBZUFICVYcz3mDxrL1+OcYKrteu0XRc/7qeCqpeMGdN194tGyo8ffSRyww0ikyaJfPTRWtlll6fkyCOHaitmpXBNTsHmyfhqfl6+Yp4C0BZbFIC182rYM31lPhbstpAgr+bh9eqrN+lM4W/K5Ml9BFuinXmmyOmnBzuLNEJ+bG3tJ1dfLXLttcEs3u9+V2TUqDZdAHuqDBw4Vvr0oQBkfnSXH33FPAWg7R1SANbOq2HP9JX5WIDaQoK8mo8XhNSMGSJXXhlMqDjxRIjBdrn99l9lLrTa29t0XN89smLFMXLnnb3l6KMD4Yexi9imjeVE1/hjfnSXH33FFgWg7R3mVQBq8Sq6e6foSlWCsYv4qR0bclI3j89ZwLXHhOlMFqAmXLlc3gFP6Ktwz1t8Ye28q64SuemmtbLllku0q3VLXUamb8UyMraoiffGZJWnnxa5//410tq6Slsie+tex71lhx26nsv3SAEYH2kDqewAABhiSURBVE3VPTgLmLOA08RPmnMhAHU9evlGhwDEtbTYK1n5QQGYhrTh3LxV0OGjsSJkRWgI8wrXuLhfsqRNWwLn6YSRvXWSSDDZAnvnrrdemrtWnovlatDqiOVq+vTB2n2rZPvtb5RLLjlR+vXLx+QUfmBUvte4+GrE8stXmkAvjleaXMd1ANPQc3cuBOCWaickuCQFYAJILlziMp6vQoHpsr1d8qoPry23HKsisF9p9vA77wR76GIZmbQLLGNJmgceEJk9W2TQoGAZFyxYvWZNvpen4djEIA7zmB99lfVJeNlyd1dvCsA09NydCwGoo2tkpdr7ag+p/VhtWTe3oAB0x73HK+WxoMID+SqsyMsWmM3CC1usvfIKumhFnntOZOjQQLTttFNyHrjGCy8ES9G89JLIPvsE19h2285rNAuv5FTSeZKXjR+7gNkFbIsYd94H6aUw7m+xmm7SJBerbaCmO3eKbmTU5aAAdMedAtAhS1Y4NpjNyAstgeiyxU4jA3RQC0TcsGHBjiPdHW1tQUsfWvywpMvBB4scpKXhRhtVejcjL1vE2LzJyx0vXx/VeIK492h7yq7ebAFMQ69+50LkLVLTXSvl0e4E4KmnniobbACNKHL44YeXrFkPX5kvLuMxXV0jjrxsObCZea3UvozHHguEHfbiRdcw9uDt3z9g9N57ort1aFeH9nVsskkgFNHq17dvdYbNzMsWOcm8ySsZp9CLLYBsAbRFTP28t9BL69btMkIN3cHRgy2A9ePe5cosQG2gyYu8yglghw50C6N7+LXXgr14IQ6xBd3gwSKHHiq6eHOwjEvcwfiKI8QPMhuh5Lx8fewjhXFxn+aZ2QKYhp67c0/RS+m3srytNkjtp2q6vKnoaJrSuEAKwAlc4DUMgrgCwVdhxXTZCoSi8VqkfRqzZgW7iowYobPeMO3NcBSNlwFNt67kZSPIFkC2ANoixp33dL3UcDXtDJG31HQUjfxQ7fVubsEWQHfce7wSC1AbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjB9vCsCMuLPCsYEmL/KyEbB5M77Iy0bA5k0BSAFoixg/3hSAGXFnhWMDTV7kZSNg82Z8kZeNgM2bApAC0BYxfrwpADPizgrHBpq8yMtGwObN+CIvGwGbNwUgBaAtYvx4UwBmxJ0Vjg00eZGXjYDNm/FFXjYCNm8KQApAW8T48aYAzIg7KxwbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjB9vCsCMuLPCsYEmL/KyEbB5M77Iy0bA5k0BSAFoixg/3hSAGXFnhWMDTV7kZSNg82Z8kZeNgM2bApAC0BYxfrwpADPizgrHBpq8yMtGwObN+CIvGwGbNwUgBaAtYvx4UwBmxJ0Vjg00eZGXjYDNm/FFXjYCNm8KQApAW8T48aYAzIg7KxwbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjB9vCsCMuLPCsYEmL/KyEbB5M77Iy0bA5k0BSAFoixg/3hSAGXFnhWMDTV7kZSNg82Z8kZeNgM2bApAC0BYxfrwpADPizgrHBpq8yMtGwObN+CIvGwGbNwUgBaAtYvx4UwBmxJ0Vjg00eZGXjYDNm/FFXjYCNm8KQApAW8T48aYAzIg7KxwbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjB9vCsCMuLPCsYEmL/KyEbB5M77Iy0bA5k0BSAFoixg/3hSAGXFnhWMDTV7kZSNg82Z8kZeNgM2bApAC0BYxfrwpADPizgrHBpq8yMtGwObN+CIvGwGbNwUgBaAtYvx4UwBmxJ0Vjg00eZGXjYDNm/FFXjYCNm8KQApAW8T48aYAzIg7KxwbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjB9vCsCMuLPCsYEmL/KyEbB5M77Iy0bA5k0BSAFoixg/3hSAGXFnhWMDTV7kZSNg82Z8kZeNgM2bApAC0BYxfrwpADPizgrHBpq8yMtGwObN+CIvGwGbNwUgBaAtYvx4UwBmxJ0Vjg00eZGXjYDNm/FFXjYCNm8KQApAW8T48aYAzIg7KxwbaPIiLxsBmzfji7xsBGzeFIAUgLaI8eNNAZgRd1Y4NtDkRV42AjZvxhd52QjYvCkAKQBtEePHmwIwI+6scGygyYu8bARs3owv8rIRsHlTAFIA2iLGjzcFYEbcWeHYQJMXedkI2LwZX+RlI2DzpgCkALRFjHvvCXrJU9Q2VntSbZza/LLbUAC6597tFVnh2ECTF3nZCNi8GV/kZSNg86YArBCAxyrBf1UbqraR2iC1NyJU99Df/0ttH7XlateqQcM0xLFOQ6QieSLOVdfvqR2jtkDtUrWRaruo/S1yGQrA5ExLnvPnz5AhQw43niVS7wqH6bK9EvIiryVLpsqFF46Vfv36VcBoa2uTCROmysCBY6VPn8q/x9FjfMUR6vr3IvFKG1v1rIdsb62r9+LFi2XbbbfFf26jtqTsWofov7dSa1e7RW0HtVAAbqC/v6x2u9oFaoPV7lS7WG1SmjS5OjdvAnChPvgv1S7qALC+/lymdrrabyJQKACNEXLrrePluOMmGs+qvwBkumyvhLzIq54CkPHF+KoWXy4EYL3iy/bWTAIwdEYL4NwyAfgt/Tda/7ZQW9nh+EP9+Q21z6RJk6tz8yQAN9SHfl/tSLUZEQBP6O8Pq6FlMDwoAI0RUq+Ml7ZQYLpsL5K8yIsCMHkM1LsHo0j5MW1Zj7dWL17JI6LSM6YFsCcBiBaV/Tos9IN+uUsN3cUfpUmXi3PzJADR/IqmVQCF6AuPezv+/5/LBeCcOXNkwIABLjg1/DXa29tl4sRbZfPNR0vv3n3N6b3nnovkqKP+3Xze6tXt8vbb02T8+OOkb9/K+zJdXZGSly3EyIu8khBg+ZWEUqdPPXilLeuRunqky0am0nvp0qUyfPhw/KG7LuDwhO5aAK/TP6IxCqIvPPbVXx5VQ5/ym2nTlvb8PAnAsAXwCH3oP0YevLsWwGH696fTwuH5JEACJEACJEACJKAE9lJDN293R3cCEC2AEHxfjJzAFsAUoYQxgNerYRAljk+phWMAfxu5LoTtnh1/S3E7nkoCJEACJEACJFBwAlvq8/9Jba1BAH5TfcMxgP/Xcd5/6E/8P8cA1hBQGOd3jtqX1DC7BpNBvqL2WbXoLOAaLs1TSIAESIAESIAESCAxATQ2YTo9WgBnq+2q9rpam1p/tZfUblXD+Cr87W41rF7CWcCJEXd1bNF/nqaGLuFq6wDWeGmeRgIkQAIkQAIkQAKJCIxRr5vVylsG0dWLoWq7qV2jtrcaJrH+XO3Hia6cgVOexgBmgIO3IAESIAESIAESIIHmJ0AB2PzvOHzC+/QX2E8b6JExhhPrNx6ohoU0t45J2+P69xs7vqga6DGYFEcEfqDX2V/tKEfXa8SYL380SxrB5wC16KxCR6h4GRLInMDbescT1aKTOjNPRJFvSAFYnLeP3VMWqT3TQI98qqblX9Qwa3t1gnRRACaAlGMXxAHWbcI4GRdHI8Z8+XNZ0uhaILtgnKdr9NbE4kMT3XHVZnNm8Tz/oDf5vdrfZXGzBr4HBaDnl0MB6PkFFPz2GAy7k9pxCTnkWQBikURUPo10oEJMIrwbKc1FTgsFYLq330dPx2xM7MvqUwAeqve/Qw3j2It8UAB6fvsUgJ5fQIa3j3Y1Xa33xUzqT6u9pXaD2o8iaYHQekQNs6u3V1tXDTOub3KYXgyM/ZoaCuVWNXwRY2bUFWroEoY4mal2thqW+sGBdD2ghpYirK2EhTSxsfbvHKQL10ZXBK7992pL1c5Uw2rtk9UwbX+OGros8De0XmIT8O06fO7Xn99Vw4bfOCBusRE4fFHhYAmj0SnT2d09wee9jvt9Xn+CJdKKleax7yRmn61Rwzv8WA0z6SG4N+54BuyiU+vhmpnrLs4w5jHMAK3f2LMT8Y4Ds/awrAPiD3zwvjBge7EaVn0Fv/9VG18rnITnWdJYLwHYU1xh83p8uCAvhMc0/eU5NWxrhQPro6FM+Zzaa2rI2/+ttp4aZkNaDyzqjzjdTA0fKFg4F3kH12tR+6rapmrPq+H9PNVxA7zDnvIAYh3lBt4xhOAf1M6yJi7iv4n+/p9q2EQdH3jIDygDXlVD+YW0jVXDor/Io1gXDsIPfohFlAk4WtSiy5jVmqS4cnukXhjvDB/dKDtRrsFwPKaGlvdwiTX8H1bYwKQFDM1ZVWuiOs5DXYOYQNn6gRreFe4VdgFjg4cwD/5Vf8fECqQV90Us4D2FEy0w6xbbq/luyU2JxP/pFID+30FWKYgKwJP1pqjc8AWGwhsZ/zw1CEEcYQGF8UbY/PoLarPUsNE1BIar43K9EAQmBAkK92fV7lFDRYeC/yo1iNCDI+nCv7+sBoGKghfCcYRadHeYWtKHZ4aY+yc1VCjgge5p/H6GGoTd/6hB0H1bDRXSC2qY5o8CEiL0lY6/4f4ozM5VQxcfKhoXR0/3xP2+rxbOPsMSBBDQSDPGfYYCEAIaaYIYTHu4ZuZa4ETFFXYRQvdyVACiFQgVdygAscTUP6rNUkPlDpEDkY9Ku16HJY2u+YTP1FNcxQlACOW/qF2mdmUHY+RJrMOKMb5WAQixBGGEcgnlAd4PPqAgUPABip0VsJcqyqWxahBgu6i9qxaXB8IuYFwb4j/tgXyNax6vhmXIfqY2Qm13NXxQj+r4G+4FwbqzGj4i0QUMEe26BbCncnuQ3u9BNcxaRdkPptiSDOXcL9VOUkN8QRyGB8piiGyUK2kPxDlEN1ihbMd6vhCYR6vh4wzlLIQ/hB/EMfhgl68Ly24MzXK7GrrPUdalFaZpnyvX51MA5vr1mRLf02BzFN4QMShYcaAgQeZD61p44Kv5lI7/N924B+eoAIQIhOBD5g8PZHJMnd9RDWsrIV0oKL4T8UHrzgq16FaAtaQP14ZguqDjZHxhQuyisMb/4zhBDYXkkG5ugMLoF2qowHCgMoJwja4CX0u6ejonek/cD11LKNjD4+v6C76iUUGGAhCiGbHg4nDNzLXAiWtdKxeAWLX/kAgYCA7E/b+5gFXlGpY0uuZT7bGicRUnALGoLcTOoMjF0EJ3i1otAhDlEAQgPrqmq4Ut6mF+hLiaH7kXBBVasfDxGpcHXI4BDLcmHaz3XdCRHjwvPqohcvBBiB4OtMKXH/UUgOXlNsQVWnjxTlCeQgCGB8QVeoLQ4r2+GloFj1XDR2IoxFHWYc3dNAeuhfIbH+9/7rgQynmIeMQaWibxfvHxHR6YCIaW5EFlN8a73l8NvURoKeSRggAFYAp4OTs1KgDHadpRcGN1czSr40sUwgriAAcq9vLZtsi4qICwqKWrIyoAIbxwfRQUiEukCz/RVYlCCWmC3aaGrpTwwJpKED3YIjDNUf7MaEFDAYPFO8MCEAUVVnZHS+Fhamjhw1d9LzV0S6DLCkILByoj/C1aqKVJH87t6Z64HyojpDE8UNHgKx8VUygAyyvQNGlyzcy1wLGIK/ArH4/anfhJw6u7cy1pdM0nTE9PcRUnAJFvUW6ErfS4JlrYIMxqEYA4H6IAH3mo6CEG0b38ohpa/SG2ouXDBvpvDB1BS2BcHnApANFlifSUPyPKyV+poVxCfkQPQflRTwFYrdxGrw+2Rw0/cJGmE9Uw5Cb86MYHLMo9/D/EIT4oIbTSHvgIRvd72NoeXg/lK1oB0cWLrvt31MJ3i6EZEKxotQ8PfIidroYPNZc9UWmfL7fnUwDm9tWZEx5WNGjSx9cyWjrwOw4UnCi00f2Fw4cARBcECh10CVc7umsBRCsNujPxlZvmsIgZjO1DAYQWU3RHoAsRlSC+9iEEQ6aWCS5xaYcQ7umeeIcQf6h0wgOtKBiHhDGBoQDEODd0Xbs4XDNzLXDCmMfYI7QkYwgDPjBwoHKboRbtAvYpAJOk0TUfcIiLK3xsoTscwx7CA5U5WsXRuow8ALEzKPJ3fPSgBaxWARheCh9W+LBDlyVaqbDwP2I5HDsXuWXp17g8gOuhy9DF2DG0ACKWkJ8gTnGELYBo+cPzoyWwuxZAlL1o3axHF3A1AYiyvacWQKQfjGepocUODQIoj39dDrmGf+N6r3VcF62MOPDsyJN4v+ipwIFGiWoHfNDFjmFJYYtrDUnhKVECFIDFiYewMkQGbFFD0z7GZKA1C4OsUYj5FIAoECBIMWYNE07Q9bO5Gr6Ww0keEBzozsQYNowJQnox3ggDi7ENT5rDImbwRRt2aWD8E7pPME4M6aiXAAwrHHSjdHdPVH4QexivgxYTtD5iDA++6tHF71sAJmHmWuBEW73REoPuIwwzQEWNOEPl0ygCEOM049Lomg/yS1xcYdgDButj0syHasiP6GZEvEEAhmMAkf5wDCDGb9U6BhAiHLGL8Wor1fBhiryOyVmXqKGF6jQ1jLdF6x9a4tAljHItLg/geZeqIZ/cjH+kPDAGEHUoxAk+QtEKCT4QhRDFKKfAD0tvoXcAz4aWUfQqIM0oe9EN6uro6cMdXcEQ7Rhqg14BiGD8PF8N4/HCY57+grLtIDW0vrkYK4xrIy+iJwW9Tzh+ooYeFLT2YlIIup3xwYqhAxg3io81lHVgDKYoX9FdjVZXHo4IUAA6ApmDy4SVIZr8p6ihewUD4tHsjp8onEIBGA64xhiM8EDmxYzSenUB4z4D1VDZ4AsZLRNIFwoGFPg4kC78G5UCBAVaxDBO0cUMuvJnRkWDCg8zG7vrAsbkABRmSCMqKhSuLWr1EoB4fnSLYWJK9J74Soe4Q+WHAhMVIcb5oBDF1zveGQZdwwcD1dEF7KoF0DUz1wInKgAhFCAAUeah6wkfDhiC0EgCMC6NrvkgpqrFVRjLaDWDaEYLNz7KMG4SrfRoIYrOAgZb5BXMgEW3MVprkBfKt8gK71ntJz5MUT6hJRvnIo9DWKFbFRPFMGkBogrdlniPmPyFGaIYqxmXB3BPtFjiAxOtYWiFi7ZsxqWt/O9oGUV5ig8JdFniIxSz8tFCie5mdG3i+ijXUM6iNRVDSHCg2zpcBQEfbVERZk1H6B9XbqOHoEUN4gpCGOV7mJ7wGihj8O7wDqIzv2tNU3gePuYxoxgtuHhv+DgFK4hnrL6A1ke8F3QFgyVEKOIIacSwH4g/MMQRdhNjnGDYi5U2fYU8nwKwOK8dwgldKcjcPJqPACo/l13OzUAoDzGfhzRaYwEfIGjhgdDI8mAeyJI275V7AhSAuX+FiR4AX7to9UETO5r9eTQfAVZ+Xd9pHmI+D2lMklPQRYeWmGVqaOFBFzDsP5Kc7NCHecAhTF6q+QlQADb/O8agWYg+DEbGIFtrl0zzE2qOJ2Tl1/ke8xDzeUhj0pyBMXXo7kQXLRY8RpcduoetawAmvV81P+aBtAR5fqEIUAAW6nXzYUmABEiABEiABEggGEzJgwRIgARIgARIgARIoEAEKAAL9LL5qCRAAiRAAiRAAiQAAhSAjAMSIAESIAESIAESKBgBCsCCvXA+LgmQAAmQAAmQAAlQADIGSIAESIAESIAESKBgBCgAC/bC+bgkQAIkQAIkQAIkQAHIGCABEiABEiABEiCBghGgACzYC+fjkgAJkAAJkAAJkAAFIGOABEiABEiABEiABApGgAKwYC+cj0sCJEACJEACJEACFICMARIgARIgARIgARIoGAEKwIK9cD4uCZAACZAACZAACVAAMgZIgARIgARIgARIoGAE/h95JmuqJBtF/gAAAABJRU5ErkJggg==\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rain = (20, 35, 30, 35, 27, 12, 21, 16, 16, 20, 11, 12)\n", "\n", "temp = (25, 32, 34, 20, 25, 14, 13, 14, 15, 16, 17, 18)\n", "\n", "i = np.arange(len(rain))\n", "fig, rr = plt.subplots()\n", "\n", "#bar_width = 1\n", "\n", "opacity = 0.4\n", "#error_config = {'ecolor': '0.3'}\n", "\n", "\n", "rr.bar(i, rain, alpha=opacity, color='b',\n", " label='rain', align='center')\n", "\n", "tt=plt.twinx()\n", "tt.plot(temp, linestyle='-', linewidth=1.0, label='temp')\n", "\n", "plt.xlabel('months')\n", "rr.set_ylabel('(mm)')\n", "#plt.ylabel('(mm)')\n", "tt.set_ylabel('(C)')\n", "plt.title('rain and temp')\n", "plt.xticks(i,('jan', 'feb', 'mar', 'apr', 'mai', 'jun', 'jul', 'aug', 'sept', 'oct', 'nov', 'dez'))\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "\n", "rr.set_ylim([0,40])\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

selimnairb/2014-02-25-swctest

lessons/swc-setdict/setdict-aggregate-instructor.ipynb

1

13527

{ "metadata": { "name": "setdict-aggregate-instructor" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Sets and Dictionaries in Python: Aggregation (Instructor Version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objectives\n", "\n", "* Recognize problems that can be solved by aggregating values.\n", "* Use dictionaries to aggregate values.\n", "* Explain why actual data values should be used as initializers rather than \"impossible\" values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lesson\n", "\n", "To see how useful dictionaries can be, let's switch tracks and do some\n", "birdwatching. We'll start by asking how early in the day we saw each\n", "kind of bird? Our data consists of the date and time of the observation,\n", "the bird's name, and an optional comment:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!cat some_birds.txt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2010-07-03 05:38 loon\r\n", "2010-07-03 06:02 goose\r\n", "2010-07-03 06:07 loon\r\n", "2010-07-04 05:09 ostrich # hallucinating?\r\n", "2010-07-04 05:29 loon" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rephrasing our problem, we want the minimum of all the times associated\n", "with each bird name. If our data was stored in memory like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " loon = ['05:38', '06:07', '05:20', ...]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the solution would simply be `min(loon)`, and similarly for the other\n", "birds. However, we have to work with the data we have, so let's start by\n", "reading our data file and creating a list of tuples, each of which\n", "contains a date, time, and bird name as strings:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def read_observations(filename):\n", " '''Read data, returning [(date, time, bird)...].'''\n", "\n", " reader = open(filename, 'r')\n", " result = []\n", "\n", " for line in reader:\n", " fields = line.split('#')[0].strip().split()\n", " assert len(fields) == 3, 'Bad line \"%s\"' % line\n", " result.append(fields)\n", "\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function follows the pattern we've seen many times before. We set\n", "up by opening the input file and creating an empty list that we'll\n", "append records to. We then process each line of the file in turn.\n", "Splitting the line on the `'#'` character and taking the first part of\n", "the result gets rid of any comment that might be present; stripping off\n", "whitespace and then splitting breaks the remainder into fields.\n", "\n", "To prevent trouble later on, we check that there actually are three\n", "fields before going on. (An industrial-strength version of this function\n", "would also check that the date and time were properly formatted, but\n", "we'll skip that for now.) Once we've done our check, we append the\n", "triple containing the date, time, and bird name to the list we're going\n", "to return." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the function that turns that list of tuples into a dictionary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def earliest_observation(data):\n", " '''How early did we see each bird?'''\n", "\n", " result = {}\n", " for (date, time, bird) in data:\n", " if bird not in result:\n", " result[bird] = time\n", " else:\n", " result[bird] = min(result[bird], time)\n", "\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, the pattern should by now be familiar. We start by creating\n", "an empty dictionary, then use a loop to inspect each tuple in turn. The\n", "loop explodes the tuple into separate variables for the date, time and\n", "bird. If the bird's name is not already a key in our dictionary, this\n", "must be the first time we've seen it, so we store the time we saw it in\n", "the dictionary. If the bird's name is already there, on the other hand,\n", "we keep the minimum of the stored time and the new time. This is almost\n", "exactly the same as our earlier counting example, but instead of either\n", "storing 1 or adding 1 to the count so far, we're either storing the time\n", "or taking the minimum of it and the least time so far.\n", "\n", "Let's test what we've written so far:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "entries = read_observations('some_birds.txt')\n", "result = earliest_observation(entries)\n", "print result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'loon': '05:29', 'goose': '06:02', 'ostrich': '05:09'}\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, what if we want to find out which birds were seen on particular\n", "days? Once again, we are [aggregating](glossary.html#aggregation)\n", "values, i.e., combining many separate values to create one new one.\n", "However, since we probably saw more than one kind of bird each day, that\n", "\"new value\" needs to be a collection of some kind. We're only interested\n", "in which birds we saw, so the right kind of collection is a set. Here's\n", "our function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def birds_by_date(data):\n", " '''Which birds were seen on each day?'''\n", "\n", " result = {}\n", " for (date, time, bird) in data:\n", " if date not in result:\n", " result[date] = {bird}\n", " else:\n", " result[date].add(bird)\n", "\n", " return result\n", "\n", "print birds_by_date(entries)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'2010-07-04': set(['loon', 'ostrich']), '2010-07-03': set(['loon', 'goose'])}\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we start by creating an empty dictionary, and then process each\n", "tuple in turn. Since we're recording birds by date, the keys in our\n", "dictionary are dates rather than bird names. If the current date isn't\n", "already a key in the dictionary, we create a set containing only this\n", "bird, and store it in the dictionary with the date as the key.\n", "Otherwise, we add this bird to the set associated with the date. (As\n", "always, we don't need to check whether the bird is already in that set,\n", "since the set will automatically eliminate any duplication.)\n", "\n", "Let's watch this function in action for the first few records from our\n", "data:\n", "\n", "<table>\n", " <tr>\n", " <th>Input</th>\n", " <th>Dictionary</th>\n", " </tr>\n", " <tr>\n", " <td><em>start</em></td>\n", " <td><code>{}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  05:38  loon</code></td>\n", " <td><code>{'2010-07-03' : {'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  06:02  goose</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-03  06:07  loon</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-04  05:09  ostrich</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}, '2010-07-04' : {'ostrich'}}</code></td>\n", " </tr>\n", " <tr>\n", " <td><code>2010-07-04  05:29  loon</code></td>\n", " <td><code>{'2010-07-03' : {'goose', 'loon'}, '2010-07-04' : {'ostrich', 'loon'}}</code></td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our last example, we'll figure out which bird we saw least\n", "frequently\u2014or rather, which *birds*, since two or more may be tied for\n", "the low score. Forgetting that values may not be unique is a common\n", "mistake in data crunching, and often a hard one to track down.\n", "\n", "Our first strategy is simple: figure out how many times we've seen each\n", "bird, then find the minimum of those counts and get the set of birds\n", "we've seen that many times. The function below implements this fairly\n", "directly:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def least_common_birds(data):\n", " '''Which bird or birds have been seen least frequently?'''\n", " \n", " counts = count_by_bird(data) # need to write this\n", " least = min(counts.values())\n", " result = set()\n", " for bird in counts:\n", " if counts[bird] == least:\n", " result.add(bird)\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`least_common_birds` depends on a function `count_by_bird`, but this is\n", "yet another example of using a dictionary to aggregate values (in this\n", "case, to sum the number of birds we have seen). Just for variety's sake,\n", "we'll use a slightly different strategy that we've used before: whenever\n", "we see a new kind of bird, we'll set its count to zero, and then always\n", "add one to the stored count:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def count_by_bird(data):\n", " '''How many times was each bird seen?'''\n", " result = {}\n", " for (date, time, bird) in data:\n", " if bird not in result:\n", " result[bird] = 0\n", " result[bird] += 1\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll test our function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print least_common_birds(entries)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set(['goose', 'ostrich'])\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This does the job, but is somewhat inefficient: we do one pass through\n", "all the data while counting birds, then another pass through all the\n", "birds to find those that we've seen the least number of times. We can\n", "actually do the whole job with a single pass through the data, but as\n", "we'll see in the challenges, the resulting code is significantly more\n", "complex than what we have written so far. Unless we're sure that the\n", "second pass is really a performance bottleneck, we should stick with\n", "this simple implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Points\n", "\n", "* Use dictionaries to count things.\n", "* Initialize values from actual data instead of trying to guess what values could \"never\" occur." ] } ], "metadata": {} } ] }

bsd-2-clause

slundberg/shap

notebooks/tabular_examples/tree_based_models/Census income classification with LightGBM.ipynb

1

2193199

null

mit

eoinmurray/icarus

notebooks/Fidelity verus Fine structure splitting.ipynb

1

110619

{ "metadata": { "name": "Fidelity verus Fine structure splitting" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<script type=\"text/javascript\">\n", " on = \"View input\";\n", " off = \"Hide input\"\n", " function onoff(){\n", " currentvalue = document.getElementById('onoff').value;\n", " if(currentvalue == off){\n", " document.getElementById(\"onoff\").value=on;\n", " $('div.input').hide();\n", " }else{\n", " document.getElementById(\"onoff\").value=off;\n", " $('div.input').show();\n", " }\n", "}\n", "</script>\n", "<input type=\"button\" class=\"ui-button ui-widget ui-state-default ui-corner-all ui-button-text-only\" value=\"Hide input\" id=\"onoff\" onclick=\"onoff();\">\n", "\n", "_(You should click this button, you dont need to see the code)._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fidelity versus Fine Structure splitting." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import sys\n", "sys.path.append('../')\n", "from mpl_toolkits.axes_grid1 import Grid\n", "from Icarus.Classes.QuantumDot import QuantumDot\n", "from constants import Constants\n", "from Experiments.utils.plot_fidelity_curves import *" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "constants = Constants()\n", "qd = QuantumDot(constants.xtau, constants.xxtau, constants.ptau, constants.FSS, constants.crosstau)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## No decoherence." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 0.\n", "qd.bg_emission_rate = 0.\n", "fss = np.linspace(-10, 10, 500)*1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_no_dec = np.loadtxt('../out/fidelity_vs_fss/xtau1/fss_v_fidelity.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_no_dec[:,0], numerical_data_no_dec[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.text.Text at 0x106018550>" ] }, { "output_type": "display_data", "png": 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G/l1tEPkX10MkIvOl6FZ7REQWJz4eKLUkRmXu6JODNnFHAfgZpyYiIgNjCyIRUW3U1III\nwDeoJcbZ/mikgoiIDI8tiEREtaFHQIS3N1DFWmdEpszR0dHos2XJsBwdHQ1yHgZEIiI9nT8PrImc\njvc9PKo/0NsbuHjROEURGVAa9xKnm9jFTESkp/+OFeBcrjtQakuvSrVrB1y/DmRnG6cwIiIDY0Ak\nItLT+X8z0cH+MmBtXf2BGg3g5cVWRCIyWwyIRER6io7MQ/u2mXodG9l6MJa/X0OQJCIyUQyIRER6\nir6oQQf3fL2OLXT1wKZ9NXRFExGZKAZEIiI9RV9qiva++r1t+nRzwMUUB+h0ChdFRKQABkQiIj2t\nG7genrfZ63WsvZ8Wza2vIylJ4aKIiBTAgEhEpKchugOw8dLqd7C3N9pbxyA6WtmaiIiUwIBIRKSv\nuDigpjUQi3l7o33eaUSfF8rWRESkAAZEIiJ96bOLSrEWLTDX7jMM7syFh4nI/HAnFSIifWRnA3l5\nQKtWej8l0DcbEOcB6P8cIiJTwBZEIiJ9xMcDWq1cBFtf3HKPiMwUAyIRkR4efLolzrXqW7snMSAS\nkZliQCQi0sO+o45o4dGidk/y8QEuXFCmICIiBTEgEhHVIDsbyLlhjTa+LWv3RLYgEpGZYkAkIqpB\nbCzgYZcCjaeeS9wU8/bGq//ch3//VaYuIiKlMCASEdUgNhbwsknQf4mbYp6eiMtqiciT3G+PiMwL\nAyIRUQ1iYgDPwgu1D4iNG8OjWSriTmYoUhcRkVIYEImIajDpAYE38ubXPiAC8HDOQ9yZHAWqIiJS\nDgMiEVENnDTpcGuUDDRvXuvnenoCsRe53R4RmRcGRCKimtRmi71yPDo2QdxlWwMXRESkLAZEIqKa\n1CMg+vZ0wPqeHxu4ICIiZTEgEhHVJC6uzgGxSScv9M46YOCCiIiUxYBIRFST+HjAo5ZrIBbz9uZu\nKkRkdhgQiYiq8c8/wH2bx9W5BRGurkB6OpCba9jCiIgUxIBIRFSNmBhA5ObWPSBaWcnWx5gYQ5ZF\nRKQoBkQiomokJADa/Doskl2ajw/3ZCYis8KASERUjfg4Ae31s4BWW+dz/NZoOOa+52rAqoiIlMWA\nSERUjYToG3Bvlgo0bVrnczTWtsHfZxwNWBURkbIYEImIqpEQUwhtu6J6ncOzWwvEXqv9LixERGph\nQCQiqsauuaHo1TGjXudo17kNUvPskZdnoKKIiBTGgEhEVI1W16LR2Lt+4wetPbVwsbqCS5cMVBSR\nCVq4cCE++OADtcvQ2+rVq9GhQwdYWVkhLS2tyuM2btyIjh07omPHjti0aZMRK1SXjdoFEBGZtHps\ns1eiXTtodWGIv9gO3t582yXLpNFo6vV8nU4HKyvjtVsNHDgQo0ePRnBwcJXHpKWl4a233sKxY8cA\nAD179sSYMWMQERGBl19+Gc7OziXHWltbY+fOnUqXbTRsQSQiqk5sLODpWb9z2Nhgc9vnEaS9bJia\niIwkJCQEPXv2RPfu3TF06FAAMjTde++96NatG/r164cTJ06UHB8ZGYk77rgD7du3x0cffVRy/1df\nfYU+ffogMDAQTzzxBHQ6HQDA3t4eL774Irp3746///672uMWLFiA7t27o1+/frh69SoA4MqVKxg3\nbhy6d++O7t274/Dhw9Ver7Tu3bvDs4af7Z9//hl33XUXWrZsiZYtW2LYsGEICQmBRqPBggULsHv3\n7pI/3t7e9fiXNj2KBsSQkBD4+fnB19cXy5cvr/B4eno6xo0bh27duqFPnz44deqUkuUQEdVeTAzg\n5VXv03h5Ac1S4+t9HiJjSU5OxsyZM7Fjxw5ERERg+/btAIA333wTPXv2xPHjx7F06VJMnToVACCE\nwJkzZ7Bv3z6EhYVh0aJFKCoqwunTp/HNN9/g0KFDCA8Ph5WVFTZv3gwAyMnJQd++fREREQEnJ6dq\nj+vXrx8iIiIwePBgfP755wCAOXPm4I477kBERATCw8MREBBQ7fVq69KlS9CWWuJKq9UiMTGx5PVa\nMsX6OoqKijB79mzs378fbm5u6NWrF8aMGQN/f/+SY5YuXYoePXrg+++/x9mzZ/H0009j//79SpVE\nRFR7BgqI0Gpld3W/fvU/F5ERHD58GLfffntJK1vLli0BAH/99Rd27NgBALjjjjuQmpqKrKwsaDQa\njBo1Cra2tmjVqhXatm2Ly5cv48CBAzh27BiCgoIAALm5uWjXrh0A2S07YcIEAKj2uEaNGuGee+4B\nILt5f/nlFwDAb7/9hq+++gqA7OJu3rw5Nm3aVOV5SH+KBcSwsDB06NABXjffWCdNmoQffvihTEA8\nffo0Xn31VQBAp06dEBMTg+TkZLRp00apsoiI9Pbq83lwz3wYT7dtW/+TabVyWxYiM6HRaKpsJavq\n/kaNGpV8bW1tjcLCQgDAtGnTsHTp0grHN2nSpMzYxaqOs7W1Lfnaysqq5LxV1VLVeWrLzc0NoaGh\nJbfj4+MxZMiQep/XHCjWxZyYmAj3UgO7SzfLFuvWrVvJp5CwsDDExsYigW+gRGQiYk7nwqmNNVDP\nwfcAGBDJ7PTp0we///47Ym7uI14803fQoEElXbahoaFo06YNHBwcKg1qGo0Gd955J7Zv347k5OSS\n88TFxVU4Vt/jyj/n008/BSB7LjMzM+t0nqoC7/Dhw7Fv3z5cu3YN6enp+OWXXzB8+PBqz2UpFGtB\n1Gc206uvvopnn30WgYGB6NKlCwIDA2FtbV3psQsXLiz5Ojg4uNpZR0REhpAQq4NWa4BwCMiAGBZm\nmHMRGUGbNm3w2WefYfz48dDpdHB2dsbPP/+MhQsX4tFHH0W3bt1gZ2eHjRs3ApC/9yv73e/v74/F\nixfjrrvugk6ng62tLT755BN4eHiUOV7f40pfZ+XKlZg5cybWrVsHa2trrFmzBn369KnyPKWtWrUK\n7733Hq5cuYKuXbvinnvuwWeffYZ//vkHa9euxeeffw5HR0e8/vrr6NWrFwA5/rK4q93UhYaGlmn9\nrC2NUGiU5eHDh7Fw4UKEhIQAAJYtWwYrKyu88sorVT7H29sbJ06cgL29fdkiq2nmJiJSiqdTJkJH\nvgvvzYvrfS7xx5/wvcsLkRlalOqFIyIzdPDgQVy7dg1jx44tuW/u3LlYsWKFilVVr7ZZSrEWxKCg\nIJw7dw4xMTFwdXXFtm3bsGXLljLHZGRkoGnTpmjUqBE+//xz3H777RXCIRGRGoqKgKSMZnALaGGQ\n82nctSgoAC5dMsycFyJSj52dHZYsWYINGzaU3Ofi4qJeQQpQLCDa2Nhg9erVGD58OIqKijBjxgz4\n+/tj7dq1AIBZs2YhMjISjzzyCDQaDTp37ox169YpVQ4RUa1cvQo42WahkW8910As5uoKre4fxMe4\nwMur8qE0RGQegoKCsG/fPrXLUJRiXcyGxC5mIlLDjR790WTNh0Dv3gY536QmOzH2f7dj8lOOBjkf\nEZG+apuluJMKEVEVmsRFGbQ/WNsyCwlnsgx2PiIipTAgEhFVJisLyMkBDLguq7ZNPuKj8w12PiIi\npXDXeCKiysTGytZDQ6yBeNNj/U5B45sHoIPBzklEpAQGRCKiyly8aPDpxvbtnYHkGIOek4hICexi\nJiKqRP75OMOvR8PdVIjITDAgEhFVwvfNybjYvJthT8qASERmggGRiKicoiIgKdsBrl1aGfbEDIhE\nZCYYEImIyrl6FXC0zkRjX4+aD64NNzcgMRGiSGfY8xIRGRgDIhFROQkJgLtQYAxikyZ4VLMeWz/n\nWohEZNoYEImIykmIyoGbSDToGojFHJsXcbFsIjJ5DIhEROWknkuDV4t0g66BWEzbJo+LZRORyWNA\nJCIq57Ge4fiwzxZFzu3upkNCvCKnJiIyGAZEIqLyYmKg8fZS5NRaH1skXLVV5NxERIbCgEhEVF5M\njOEnqNyk7WSPxGt2ipybiMhQGBCJiMpTMCC6dXFCTO8HFDk3EZGhMCASEZWnYEDUuGthmxijyLmJ\niAyFAZGIqJSCAiDtwjXFAiLc3ORCi0Ioc34iIgNgQCQiKuX4oWzcmbFDkTUQAQD29kCTJkBamjLn\nJyIyAAZEIqJSEiJS4W6XpsgaiCW4JzMRmTgGRCKiUhIiM6F1ylH0GsJNixsXLil6DSKi+mBAJCIq\nJeFCHrQuRYpeY0fhGDz0didFr0FEVB8MiEREpSQkaKD1slb0Gm7etki4wsWyich0MSASEZUirl+H\nl38zRa+h7WiH+HR7Ra9BRFQfDIhERKVsbjMXg+9WNry53OaElBv2KChQ9DJERHXGgEhEVJqCi2QX\ns/bUwtk6BUlJil6GiKjOGBCJiIplZgI3bgCtWyt7Ha0WvrooXL3CxbKJyDQxIBIRFYuNla2HSq6B\nCAAtWuDXdg8iqHWMstchIqojBkQiomJG6F4u0bs3cOSIca5FRFRLDIhERDddPXEFqe1uM87F+vQB\nwsKMcy0iolpiQCQiuunDH7zwaeJo41ysd28GRCIyWQyIREQ3JVy2gXv7xsa5WM+eQEQEuNYNEZki\nBkQiopsS0ppBe1tzo1xLNG+BJJcewKlTRrkeEVFtMCASEd2UkOMEbWBbo1yrqAjwvPArCg4dNcr1\niIhqgwGRiAiAuJaBBJ0r3Do7GuV6NjZA2+Y3cOngOaNcj4ioNhgQiYgA5JyNQ4+mZ2DvoPAaiKW4\nuwMJR7mdChGZHgZEIiIAdlcu4s87XjfqNbW+zZCQqAGysox6XSKimjAgEhEBxl0k+yZ3TyvEt+sF\nHDtm1OsSEdWEAZGICJAB0dvbqJfs2BEo1HpxPUQiMjk2ahdARGQSYmKA/v2NesknngDQPAvYwYBI\nRKaFLYhERIAqXcwAuKMKEZkkBkQiIgDh5x2Q3drL+Bdu3x64fh1I4mxmIjIdDIhERBkZmJz9GeKu\ntzL+tTUatiISkclhQCSiBk/ExCIOHvDwNN4aiGUwIBKRiWFAJKIGL+W/S2hqUwB7e+NfOy4OyLit\nPwMiEZkUBkQiavBij1+DZ4sMVa79wgvAzxl9gaNHAZ1OlRqIiMpjQCSiBi/27A14Ot9Q5dpaLRCf\n2QJwdASiolSpgYioPAZEImrwGqckYnBQjirX1mqBhAQAgYHAiROq1EBEVJ6iATEkJAR+fn7w9fXF\n8uXLKzyekpKCESNGoHv37ujcuTM2bNigZDlERJUalfcd5s4uUOXa7u5AfDxKJUUiIvUpFhCLioow\ne/ZshISEIDIyElu2bMHp06fLHLN69WoEBgYiIiICoaGheOGFF1BYWKhUSURElVNrkWyUyoVaLZCY\nqEoNRETlKRYQw8LC0KFDB3h5ecHW1haTJk3CDz/8UOYYFxcXZGZmAgAyMzPRqlUr2Nhw9z8iMqKM\nDCA/H2ilwhqIADw9gbZtAbi5MSASkclQLCAmJibC3d295LZWq0ViuTe/xx9/HKdOnYKrqyu6deuG\nlStXKlUOEVHlYmNl66FGnTUQ3dyAXbvALmYiMimKNddp9HizXbp0Kbp3747Q0FBER0dj2LBhOH78\nOBwcHCocu3DhwpKvg4ODERwcbMBqiajBUrF7uQy2IBKRAYWGhiI0NLTOz1csILq5uSE+Pr7kdnx8\nPLRabZljDh06hPnz5wMA2rdvD29vb5w9exZBQUEVzlc6IBIRGUrqqcs4aTsUt6tdiJsbcOkSIIRq\nrZlEZDnKN6YtWrSoVs9XrIs5KCgI586dQ0xMDPLz87Ft2zaMGTOmzDF+fn7Yv38/AODKlSs4e/Ys\nfHx8lCqJiKiCsDBgSeS9apcBNG0K2NkBKSlqV0JEpFwLoo2NDVavXo3hw4ejqKgIM2bMgL+/P9au\nXQsAmDVrFubNm4fp06ejW7du0Ol0ePfdd+Hk5KRUSUREFcTF6ODpbiI7mBSPQ2zTRu1KiKiB0wgh\nhNpF1ESj0cAMyiQiMzSv3To0vXcEXl/jploNqakyF3Z77W7gqaeAUaNUq4WILFNtsxR3UiGiBi02\nvTk8uzRXtYZ//wWefx6cyUxEJoMBkYgarowMxBZq4RFgr2oZXl5yMjVnMhORqWBAJKKGKzYWfR3P\nwLejurOGPTxkw2GRqztbEInIJDAgElHDFRuL9/tsh5t6ww8BAI0bA61bA4mNfdiCSEQmgQGRiBqu\nuDjZfGc0Gkb2AAAgAElEQVQCvL2BGJ07AyIRmQQGRCJquGJj5WbIJmDkSMDKuQ27mInIJNQYEFet\nWoX09HRj1EJEZFwm1II4fz4wcIQDUFAAZGWpXQ4RNXA1BsQrV66gV69euP/++xESEsL1CInIcphQ\nQAQgt9jTatnNTESqqzEgLlmyBFFRUXj00UexYcMG+Pr6Yt68eYiOjjZGfUREivkxqgNirbzVLqMs\nLnVDRCZArzGIVlZWaNeuHZydnWFtbY309HTcd999eOmll5Suj4hIGfn5WJL2JOLyndWupCwulk1E\nJqDGvZhXrlyJTZs2oVWrVnjsscfw/vvvw9bWFjqdDr6+vnjvvfeMUScRkWElJuKCpj18fBXbkr5u\n2IJIRCagxnfGtLQ07NixA57lZvpZWVlh9+7dihVGRKSk62cTkIHecHFRu5JbfvoJ6OvUHo4x4WqX\nQkQNXI1dzNHR0RXC4ZQpUwAAAQEBylRFRKSwi+Hp8LJPgZUJLfa1bBlwPM+PLYhEpLoa3xpPnTpV\n5nZhYSGOHTumWEFERMZw4dQNtG9jWsvJeHkBMYVuHINIRKqrMiAuXboUDg4OOHHiBBwcHEr+tG3b\nFmPGjDFmjUREBudy/RweDo5Xu4wyvLyAi1lt2IJIRKqrMiDOmzcPWVlZePHFF5GVlVXyJy0tDe+8\n844xayQiMrheuX9g0vgCtcsoo317IPqKHZCWBuTnq10OETVgVU5SOXPmDPz8/DBx4kT8+++/FR7v\n0aOHooURESnK1BbJBtChA/Dpp1aAszOQlGQy2wASUcNTZUD84IMP8Pnnn+OFF16ARqOp8Phvv/2m\naGFERIoRwiQDYqdOQHAwgIM3d1NhQCQilWiEGeydp9FouMUfERlOaqpsrjPVfebvuw+4/375h4jI\nAGqbpapsQfzuu+8qbTksNn78+NpVRkRkKkyw9bAMLpZNRCqrMiDu3r2bAZGILFLU36n4W/MIpqld\nSFW43R4RqazKgLhhwwYjlkFEZDyH/xb4JXeA6QZENzeA680SkYpqXCj78uXLmDFjBkaMGAEAiIyM\nxLp16xQvjIhIKReiBXzcTWuJmzLYgkhEKqsxID7yyCO46667cOnSJQCAr68vVqxYoXhhRERKiU5s\nAh/fGreiV0VyMvB/h7jdHhGpq8aAmJKSggceeADW1tYAAFtbW9jYmOYbKxGRPqJSW6Fj92Zql1Gp\n/HzgtRVtgEuXAJ1O7XKIqIGqMSDa29sjNTW15Pbhw4fRokULRYsiIlKKEMDZHC069W+ldimVcnUF\nsrM1yLRzAVJS1C6HiBqoGpsCP/jgA4wePRoXLlxA//79kZycjO3btxujNiIigyvKycNiqzfRKuAD\ntUuplEZzc8u9vD4ITEwE2rZVuyQiaoD0Wii7oKAAZ8+eBQB06tQJtra2ihdWGhfKJiKDiY4Ghg4F\nLl5Uu5IqjR8PTI5ZhomLOgOjR6tdDhFZAIMvlC2EKLMeYlRUFACug0hEZsrUF8mG3OTlfAInqhCR\nempcKPvq1as4dOgQhgwZAkDuwdy/f38GRCIyT2YQEO+9F8i9msWlbohINTUulD1s2DBERkbCxcUF\nAJCUlIRp00x2eVkiouqZQUDs3x9AlA7YH6N2KUTUQNU4izk+Ph7t2rUrue3s7Iy4uDhFiyIiUkxs\nLODpqXYVNevaFfjvP7WrIKIGqsZZzEOHDsXw4cPx4IMPQgiBbdu2YdiwYcaojYjIoAoLgRl778f6\n8bqaPx2rLSAAOHcOuHEDaNJE7WqIqIGpcRazEALff/89fv/9d2g0GgwePBjjxo0zVn0AOIuZiAwj\nOhq40y8RMcczZAAzdV27AuvXAz17ql0JEZk5g81iLn3C8ePHc1IKEZm9s2cEOukiAfe+apein8BA\nICKCAZGIjK7KXpYBAwYAkDupODg4lPnTvHlzoxVIRGQoUeHX0dH2IuDgoHYpNfr9d2Bz0SQgPFzt\nUoioAaqyBfHrr78GAGRnZxutGCIiJZ2NyEVA63S1y9BLSgqw9UIvPGS1RO1SiKgBqrIFsfQ4wwkT\nJhilGCIiJUVFCXRyz1G7DL34+wOnLzvKmcw6ndrlEFEDU2VALD2Q8cKFC0YphohISQuDD6J3l1y1\ny9BLhw5AwiVr3HBykbNriIiMyORXeiAiMpRBhb+hZYCr2mXoxdYW8PYGonxGchwiERldlQHxv//+\nK5mUcuLECU5SISLzd/Qo0KuX2lXozd8fON1qoJzJTERkRFVOUikqKjJmHUREysrLA06dkkvHmIlX\nXgHaHG8J7GRAJCLjqnEdRCIii3D8OODrCzRrpnYleuvTB4BLR+BNdjETkXFxDCIRNQxm1r1cwt0d\nyM8HLl9WuxIiakAYEImoQRi6JBjpAQPULqP2NBqge3eOQyQio2JAJCKLl5ICHL3igZbB3dUupW4Y\nEInIyBQNiCEhIfDz84Ovry+WL19e4fH3338fgYGBCAwMRJcuXWBjY4Nr164pWRIRNUCnjuagM05C\n06Wz2qXUTWAgl7ohIqPSiNIrYhtQUVEROnXqhP3798PNzQ29evXCli1b4O/vX+nxe/bswYcffoj9\n+/dXLFKjgUJlElED8PFz5/DflpNYe2VczQebmM2bgeyoRMzaOgQ4e1btcojITNU2SynWghgWFoYO\nHTrAy8sLtra2mDRpEn744Ycqj//6668xefJkpcohogbs5JHr6Oxnnkt3aTTALyddgIQEIDtb7XKI\nqIFQLCAmJibC3d295LZWq0ViYmKlx+bk5ODnn3/mns9EpIiT5xujc3/zXOC/Wzfg+H9WQECA3JeZ\niMgIFFsHUaPR6H3s7t27MXDgQLRs2bLKYxYuXFjydXBwMIKDg+tRHRE1JNuaPAKnSZvULqNOOnUC\nEhOB7IF9YR8RAfTvr3ZJRGQGQkNDERoaWufnKxYQ3dzcEB8fX3I7Pj4eWq220mO3bt1aY/dy6YBI\nRKS35GS4Zp0FuviqXUmd2NjILfdOtApGv/AQtcshIjNRvjFt0aJFtXq+Yl3MQUFBOHfuHGJiYpCf\nn49t27ZhzJgxFY7LyMjA77//jrFjxypVChE1ZP/8A/TsCViZ76pe3bsDxzVc6oaIjEexFkQbGxus\nXr0aw4cPR1FREWbMmAF/f3+sXbsWADBr1iwAwM6dOzF8+HA0bdpUqVKIqCEz1x1USnnrLcBOOAMf\nnwKKigBra7VLIiILp9gyN4bEZW6IqM5GjwYeeQSwhElwXl7AgQNA+/ZqV0JEZsZklrkhIlKdECg8\ncszsWxBLBAQAkZFqV0FEDQADIhFZrIxTCWiXcgI6N/eaDzYHDIhEZCQMiERksSJ2XEDHFldhZa3/\nslsmzd+fAZGIjIIBkYgs1rGDWejRMUvtMgxG+LMFkYiMgwGRiCzWv6eaoOeAJmqXYRD79gHjl/YE\nTp8GdDq1yyEiC8eASEQW61iKB3re46J2GQbh5wccOtoIonkLoNQmBERESmBAJCKLlJeQjCydHfwH\ntVa7FINwd5drfcf5BMtWRCIiBTEgEpFFanzhNOL7TIRtI8uYoKLRAL17A0eaD+U4RCJSHAMiEVmm\nyEhobgtQuwqD6tMHCCvowYBIRIpjQCQiy3T6tFwWxoL07g2cy9UyIBKR4hgQicgyRUbKhaUtyJ13\nAju/F/K1cftRIlIQAyIRWabISItrQdRoAE2b1kCjRkBSktrlEJEFY0AkIouTEJmJ+HR7wMND7VKU\nwS33iEhhDIhEZHE+WZ6Fz1u8KNeFsUQMiESkMAt99ySihuzQESsMCEhXuwzlBARwLUQiUhQDIhFZ\nlIIC4J8LTug7wFrtUhRzsmkv5J44r3YZRGTBGBCJyKJERAA+TS6hRY/2apeimCc/D8QfJ1qqXQYR\nWTAGRCKyKIcOAf1xyOKWuCnt9qG2OJjfF0hOVrsUIrJQDIhEZFEc7fIw9sY3gI+P2qUo5vbbNTho\nyy33iEg5DIhEZFGm9ozEyE4XABsbtUtRTP/+QERuJ+RERKldChFZKAZEIrIsFrjFXnl2dkAXtzQc\nCc1VuxQislAMiERkWSxwi73KPHpvOkRMrNplEJGFYkAkIsvSAFoQAeDxF5pjyOWv1S6DiCwUAyIR\nWZYG0oIIrRa4fh1It+AFwYlINQyIRGQRzp8HVq0oBC5eBDp2VLsc5Wk0MghHRKhdCRFZIAZEIrII\ne/cCJw5lAR4eQOPGapdjHHffDezcqXYVRGSBGBCJyCIcOAAMdY9qEOMPS0ycCGzfDuh0aldCRBaG\nAZGIzF5hIXDwIDCk0Z8NY/xhMX9/zC9YiPSfj6hdCRFZGAZEIjJ7//wDeHoCbeKONawWRAARjsH4\nZdUZtcsgIgvDgEhE5qmwEMjJAQDs3w/ceScazgzmUkbe74CQg03YzUxEBsWASETm6f33AUdHYOhQ\nTL7+BWYPOwNERQF+fmpXZlQjprVDSMGdEH/+pXYpRGRBGBCJyDyFhQEffww88wzaXzsGnydHAG5u\ngL292pUZVYcOgF1zG/z3KQMiERmO5e5mT0SWLTwceOcduebh2LGAEEBentpVqWLESA1+2lWEbjod\nYMXP/URUf3wnISLzk54OpKbK5rNiGg3QpIl6NanoxSWOmKINBf5iKyIRGQYDIhGZn4gIoGtXtpbd\n5OkJuD0UDHzzjdqlEJGF4LsrEZmf8HAgMBDXr8ueZYJcNPu774CiIrUrISILwIBIRObnZkCcOxdY\ns0btYkxEx45A27bsZiYig2BAJCLzEx6Ooq6B2L0bGDpU7WJMyH33Ad9/r3YVRGQBGBCJyLzk5gLR\n0fg97Ta4uAC+vmoXZDqKBgUj+dcTapdBRBaAAZGIzMuJE0CnTvj620aYPFntYkzLN7G98eip54Hr\n19UuhYjMHAMiEZmX8HDkde2FHTuASZPULsa03D22EQ7idmT+dkztUojIzDEgEpF5CQ9HotcATJwI\nuLurXYxpadECCPaKwY7/u6Z2KURk5hgQici8hIfDZ1h7zl6uwrTx2dh40FPtMojIzGmEMP1VxDQa\nDcygTCJSWmGhbCZLSgKaN1e7GpOUF3cFbl42OHrOEd7t2QZARFJtsxTfPYjIfERFAa6uDIfVaOzh\njDdbfYz0YxfULoWIzJiN2gUQEent5gLZVL1n7o4G0g8A6FDjsURElWELIhGZDwZE/QwYwB1ViKhe\nFA2IISEh8PPzg6+vL5YvX17pMaGhoQgMDETnzp0RHBysZDlEZOYW7eiMfTpunVIjBkQiqifFJqkU\nFRWhU6dO2L9/P9zc3NCrVy9s2bIF/v7+Jcdcu3YNAwYMwM8//wytVouUlBS0bt26YpGcpELU4OVc\nF3B3SMe/YYXwDGqrdjmmTacDWrcGIiOBdu3UroaITIDJTFIJCwtDhw4d4OXlBVtbW0yaNAk//PBD\nmWO+/vprTJgwAVqtFgAqDYdERADwzZpU9G30L8OhPqysgH79gEOHwM/WRFQXigXExMREuJdaxVar\n1SIxMbHMMefOnUNaWhruuOMOBAUF4csvv1SqHCIyY0IAH6+xxhOd/1S7FPPRvz+Sf4lA585Afr7a\nxRCRuVFsFrNGo6nxmIKCAvz77784cOAAcnJy0K9fP/Tt2xe+vr4Vjl24cGHJ18HBwRyvSNSAHDwI\nZKYX4Z6JhWqXYj4GDECbV1+Fs/Nb+O47cN9qogYmNDQUoaGhdX6+YgHRzc0N8fHxJbfj4+NLupKL\nubu7o3Xr1mjatCmaNm2KwYMH4/jx4zUGRCJqWH77TeCltptg1aO72qWYj969gRMnMGd9Ht75X2MG\nRKIGpnxj2qJFi2r1fMW6mIOCgnDu3DnExMQgPz8f27Ztw5gxY8ocM3bsWPz5558oKipCTk4Ojhw5\ngoCAAKVKIiJzJAQWZT6PGY2+BIYNU7sa89GsGXDbbRjdNgypqbIVlohIX4q1INrY2GD16tUYPnw4\nioqKMGPGDPj7+2Pt2rUAgFmzZsHPzw8jRoxA165dYWVlhccff5wBkYhu0emAp54CwsOh+e1Xuc0e\n6a9/f1gf/guvvTYIb78N3H672gURkbngXsxEZJoKC4EZM4CLF4E9e7i9Xl18+y2waRMKduzGrFnA\n6tWyYZGIGp7aZikGRCIyPTqdnFWRng7s3MlUU1fJyUDXrsCLLwLPPw/oMXmQiCwTAyIRmb/jxyHu\nHQfN6UigSRO1qzFvcXHAuHFAx47AF18AdnZqV0REKjCZhbKJiOrsyBFM0XyFX/5gOKw3Dw/gzz+B\nRo2A/v2BCxfUroiIzAADIhGZnGM/Xsavad0wYIDalViIpk2BDRuAxx+XO6ycOaN2RURk4hSbxUxE\nVBdCAM/vH4k3nk1Fs2bsDjUYjQaYPRsAEDv1dbj+sQ22jdlGQESV4xhEIjIpX6/Lwfszo3D0+m2w\nbmKrdjmWp6gIY1ofwtAR1pizpb/a1RCRkXCSChGZrcxMwN/nBra7Pot+/61VuxyLdWrHWQTf1wr/\nhevg0q2t2uUQkRFwkgoRma28POCNgb+h3zB7tUuxaLeN74TH+57Ac6Oj1S6FiEwUAyIRmYw2bYBZ\nYg3Qt6/apVi81/f0xbEkF+xZeFTtUojIBDEgEpHpEAI4cgTo00ftSixeU6emWLskFS8scUJRRrba\n5RCRieEYRCIyHbGxsvXw0iXu+mEkVybOhrOrNbBypdqlEJGCOAaRiEzfZ58BkZEV7z98WLYeMhwa\njfPat4DvvgMOHFC7FCIyIQyIRGR8770HvPEGkpOB++4D8vNv3s/uZeNzcpJb8E2fDly7pnY1RGQi\nGBCJyLiysoDERBQd/BMPjbuOjh3lLnAAZEDkBBXjGzECGDUKmDNH7UqIyEQwIBKRcf33H9C5M94K\n2Ir8Cwl4662b9xcUAMePA0FBqpbXYL33HvD33zj41kFwyDcRMSASkXGFh+Nr+5nYeHEQtuaMhU3K\nZXn/f/8B3t6Ag4O69TVUdnbI/7+v8OzbrbBqcSaQnAxs2gTcf7/8vhw+rHaFRGREDIhEZFSnQy/j\nuaMPYs9ea7R7eOit2bMcf6i6RoP6YOesELyzMBe7PJ8BfvhBdj+/9hrwyCNAbq7aJRKRkXCZGyIy\nKtGjJ86/+gV87w8ELlwAeveWf8+eDQwaBDz+uNolNmyFhTi6+SzufjEA33+vwcCBN++fNAlwdQX+\n97+6nXf6dGDkSNkiSURGx2VuiMh0FRRAc+Y0fO/pKG/7+ADDhsllbzhBxTTY2KDXtNuwebMGEybI\nnn8AwMcfA1u3An/8UftzFhYCO3eWOhkRmTobtQsgogbk9GnAwwOws7t138svy5alnBwgIEC92qiM\nu+4C1q4FmjW7eUerVsCnn8qWwOPHy34Pa3L4sFxCJ5p7PxOZC7YgEpGiyvRoREQAgYFlDwgMBLp2\nBXr2BKytjVobVe/ee4EOHUrdMXYs0L8/8MortTvRTz8BwcEMiERmhC2IRKSY8HBg7lzgl18AW1vI\ngNi9e8UDV6yQ2+uR6Vu5UgZ6QI5JdHSUi2337FkuTZby00/A/PnAzJnGq5OI6oUtiESkiL17geHD\ngWeeuRkOAZkYy7cgAsBtt8mxiGT6HB3lN9fTUy56fvw48M03soWwsLDi8UlJwMWLwJgxcssc7tZC\nZBbYgkhEBiWEbGRavlzOS+jfv9QDVbUgktlYtQpITu6CRYu6wKp0E8PAgXJZnAkTyj4hJAQYOlR+\nSmjfXs5Y79HDqDUTUe2xBZGooRACSEkx/HkLCkq+LCoCHn4YWLcO+PvvUuEQAGJj5YyHtm0NXwMZ\nzaRJciLz6NFAenqpB55+Ws50Lu+nn+QkJEDOWuc4RCKzwIBI1FB89pnsBqyN8+eBjRurP2by5JJg\nYG0tg8ORI4CXV7nj2HpoEdq2lWNKfX3laIGDB28+MGGCnKV++vStgwsLgf375WLbgGxBZEAkMgsM\niEQNQW4u8PbbQFSU/rthCAE8+ijw3HNlWgnLyMmR49FWrgR0OgCyhalkaZTSwsMZEC2ErS3w4YfA\nJ5/Izwfr1gFo1Ah47DF5Z7HDh+VYRVdXeZsBkchsMCASNQSffipnmfr7A5GR+j1n40YZJr29q14c\n+ddfgV69ZCI8cKD681W2xA2ZtbvvlnNUihsIMWsWsHmznLwClO1eBhgQzVl4OBAWpnYVZEQMiESW\nLitLzhh5+225PIk+u1mkpsq17tasAcaNk5MPSrlxA3j9dSDs/07KPuWnnirbclQZtiBapDZtADe3\nmze0WuCOO2RIBGTrcvmAeOGC0WskA/jsM7kNJre9bTAYEIks3cqVwJ13ynCob0B85RXZV9yzp1wc\nedcuQAjodMBXXwGdOgFnzgho//5WBsQHHwR+/x2Ij6/8fKmpcnkTHx/DvjYyPU89BXz8MVJPJaHw\nYjzQr9+tx9zd5bI3+fnq1Ud1c+aMHKKyf7/alZCRMCASmaKMDMN8Uk9Pl4PFFi2St/UJiH/9JZcm\nefttebtLFwgBHPjiInr1Alavlg1E386LgKt9JtCxI2BvDzz0kNybrTLHjwPduqHsuihkkYYMAQoL\nserhMHTWHce339sUD0+Vgxe1WiAmRs0KqS7OngXmzQPef1/tSshI+G5NZGr+/VcO7H/ppfqf6733\nZBexr6+83a2bDGtVhc+CAuCJJ+TOJs2by/s0Gly/eyJeW9QYr7wil68ZOBDAnj2y9VCjkcc9+STw\nxReVtw5VtUA2WR6NBnjqKSyMuBcfzTyJ5cvlMNXvv5fLIHEcohnKyAAyM+V70smT+vVCkNljQCQy\nJSdOyJH/K1bIAf71+bR+5Yps0Xv99Vv3OTvLVrykpMqf88knckDZffeVudt+4kiEtRuL+++/lQex\nZw8watStg/z95Y4oO3ZUPC+XuGlYpk6FxscHw17oiqNHgQULgGXLgN69AZ1PB2UDYnIybjVZkkGc\nPSvHlTRpIrdG+uADtSsiI2BAJDIVZ87Ivek+/BCYPh34+Wfgo4+ATZvqdr4PPpDdvh4et+7TaKrt\nZk76cj/+m7CoVAq8aeBAuV1aQoK8ffmyHI80cGDZ46qarMIWxIalRQu5hqaLCzQa2Yh95AiwfTtg\n1UHBxbILC4GgIJlGyXDOnAH8/OTXs2YBu3ffei8gi8WASFQbsbG3lvAwpOhouRfxsmVycgggx2qF\nhAAvvwz8+GPtzpebC6xfL9cwLK9cQLxxA/j2W2DM3QUIOLYJ+9Mr2QbN1lbORt21S97euxe46y65\n9l1pY8bI13LihPwFsm4dMHEicOkSEBBQu9dA5q3chwyNRq6YBB+fCjOZL14Erl83wDV/+EEOjVix\nwvizpQsKLLflsnRAdHQEpk6VH17JojEgKiktDXjggaoXGSbzIoQMSSNHygWiDSU1Ve5Vu2ABMG1a\n2cf8/eWGxo88Igf/6eubb+TAr8pmDd8MiNeuyVUrXF1lT/SEjicRFzwNz79sW/k5i2czAxW7l4vZ\n2gIzZwKDB8vxjvv3y3GKp08DjRvrXz9ZrkrGIK5bJ0c2PPigbJyq8yTnVavkkIoXX5Rb/9V1otfJ\nk/JDTm3MmgX87391u56pKx0QAfnB84sv5LhEslzCDJhJmRXt3SsEIMSGDWpXYjg6ndoVqCckRIgu\nXYR4+GEh7rlHiPx8w5x32jQh5syp/pgffxSibVshTp3S75y9ewuxe3fljx07JkTnzqKwUIgVK4SI\nj795/5w5QrzzTtXnzMwUwsFBiKtXhWjeXIjk5MqPy84W4uhRIQoL9auVGpbMTCGaNq3wXnL1qhAf\nfyzEwIFCtGolf8wq/BfLz5fPr0x4uBBubvKYvDwhAgKE+PbbutU4YYIQd9yh//HFr+mee+p2PVPn\n7y/Ef/+Vve+BB4T43//UqcdU5OUZ7veAEdQ2S5lF8jLbgLhkifxF7esrREGB2tXU39atQowdq3YV\n6hkxQoj/+z/5hjBypBBTpghRVFS/c+7fL4SHhxBZWTUfu3GjEO7uQsTFVX/c0aNCeHoKXUGhOHlS\niA8/FCIxsdTjublCNGkixI0bZZ8XECCfW50RI4SYMUOIAQNqrpeoKm3blvtPWVZ8vBBr1pT73ZuV\nJUNbQIAQ169XfNKjj8r33GIHD8rAmJFRu9ry84Vo2VL+OX9ev+ds2CDf61u1srwP0fn5QjRuLN83\nSjt8WIj27S3v9dbk0iUhvvhCiHHjhLC3lx/wzURtsxS7mPVVWCjHg9XGv/8Czz4rZ45u26ZMXca0\nY4cc43PsmNqVGN/p03KixeTJshv122/lIPyXXqp7N1ZuruyW+uQTuY5gTaZOBebMkRNZ0tIqPSQ6\nGvjiuZOY4rQHru7WGD1a7qyXl1fqoCZNZNfzmTO37ktKkhNPappIMnas7A+srHuZSF9VLXUTHg4U\nFUGrlT8atsWjHa5dk2NefXyQ1PF2jOschZUr5YpNOh2AlBT5/vT447fONXiwfM4bb9Sutr//lj8f\n06bJcbz62LRJjhW2swPOnavd9Wrryy+Vv0ZpFy/K/v8mTcre37u3/L146pTxalHbtGlypYZ9+4B7\n75X/X3/8Uc7yVpNOB7zzjhxQbkgKBVWDUr3MggIhJk6U3cXlm9mr4+MjxOnTQuzbJ4Sfn3l3uRUW\nCuHkJMQLL8juF0u0d68QM2dW/n2aNUuIN98se19qqhC33SbEypV1u96rrwpx//21f96LLwrRr1+l\nrShLX88RD9tuFWvfzxDR0dWcY9IkITZtunX7yy+FGD++5msnJMifgxMnal83UbGHHhJi/fqy9x0/\nLoS1tWwh3L79VstUcrIQgYFyCERRkchKuCa+bjNHzLzrgujYUQhHRyHu8o0Wn/bfWPE6ycmytfLY\nMf1re/VVIebPl+/1bm41v2/Hxsr3xhs3ZLerkkOKrl6VXdmzZtXvPH//LcTmzfod+8MPQtx9d+WP\nPfOMEIsX168Wc5GRIVsMs7PL3r94sfz/rKZ9++T78sKF1R5W2yzVMALi9etCpKXV7bnF4XDECPnG\n8YNKy94AACAASURBVOST+j0vLU3+ZyoslG90ffvKLlpzFRYmw1BWlhBt2sjga0mOHROidWshevQQ\nYt68so+lpMjupsuXKz7v5EkhXFxqH/6PH5f/jklJNR6q08lctmePEG+/LcSECTqxIuhLIe66q+L/\n6xUrhHjwwZqvv3SpDJrFpk0T4pNP9Ks9IqLhdSuRYb3xhhCvv172vkcflb9s9+6VgbBHD/meGRAg\nxGuvlf0/d+iQDH4JCSIpvkDsav2I+P5/Fyq9VNKy9eLsHbP0/xHt1k2IP/+UX/fqJcf/Vmfp0luB\n7cMP6x/eqjN/vhCjRslAWr7LtzbGjBGiUSMhQkNrPnb5ciGef77yxw4ckP9GVYmKqnYogUFcuSJf\nz6pVyl5n924hhgypeH9GRu1+J27cKMTvv1d/TEJC7WqbMEH+3mrVSv6bV4EBUQg5ZuLPP4V46y0h\nbr9dfuLq2rX2v9QKCmQLz4gR8ocxMVF+XNVnTMuvv5Ydp/XTTzJg1XfMmlrefluIuXPl1wsXCjF9\nurr1GFJcnGwp2L5dfkL39Cwb5pcuFeKRR6p+flCQED//rP/1Cgvlm+rnn9d46M6dMpu2bi3EsGFC\nvPyyEFu2CBFzLl+2qHh4CPHHH/LgoiI53rX4l1t19uyRJxRC/lxotdW+sRAZ1MaNZT/IXL0q/6Nf\nvSpvFxXJCSZBQfLnrzJvvSXHJG7dKsTgwVVe6tsvc4SXVYxo1rRI9Owpf5Tff19+zqmg+D2+eMz4\nmjXVt6zrdLJ36K+/5O0jR+TvmrrKyRHi3LnKH8vIkAHg/Hkhhg6te4NDWpqcZLZ9u/xwW1MYmT5d\niM8+q/yx/HwZVqsKgf366d+oUhdHjshx2XPnCtGhgxCLFin34fXZZ6v+v7h0qRCTJ1f/fJ1O/h5t\n1qz6sfzXrsnwfuSIfnUlJcmfnYwM+R972LAq/w0YEHU62bLSubPsDt27V84w8/PT79NSsYIC2V0w\nfHjZT2oTJwqxenXNz//gAyFmzy5bV69e8oeyNnQ6Ib7+Wr5xqGnQIBlyhZBdq46ONU+WEEJ+op48\n2XRbnDIy5Mzk9967dV94uExk4eFylpqraxW/TW5atUrvLobcXCEiXtgkdgTMFx+8XyRmz5a9N08/\nXXV5xb8zK7VrlxDOzvKXZUiI/h+E4uLk84QQ4swZ+SZrqt8jsjx//ilEnz63bi9eLFsQa6OwUL4v\n2dvX/L46a5bImPeOOHRIiLVrZc9opROc160TYUNfFTt3yh//1IsZQte8hWylqszRo2UnauTlCWFn\nV/VM65rMmSPfW2NiKj727rtyaIgQckjIyJF1u8YXX9waJrRkiezdysur+vh+/apv8XroISE+/bTi\n/f/+K7837dvXrc7q6HTyG9mmjfwULYQMSl26yNZOJd7LOneuOrRlZsoW7ZMnq6537lxZ36lTcjWI\nyiZaCSG7/q2sZCDVx9KlQjz2mPw6P1/+DtiypdJDGRA3bpTdE+VnDa9eXbuxc3PmyCRevhn/t9/k\nlP+a/gM+9JCc8Vra7t3ym3f2rOyy/eUX+S5V3WCxxYvl2IIqvuFGUTz2ovR/6OefF+K556p/3tWr\n8hNvp07yTak+qvphqo/8fPlh4sknK34/t26VLYkffljzchdXrwpd8xYiPS5TnDolh4MUZ+nywr6N\nEbdZR4rRQ7LFnDmyR3jnTvlfos4SEoQIDpYzk9es0e85Ot2tbvPVqy2rRZhMX1KS/BAmhPw5dHOT\nwy5qKzZWhqaaVok4dkz+PNfUz3zffeLLxw+KUaPk7/IWLYSwt8kRAc7JJTmkjGeeEbkL3i779jFg\ngFydoLYiI+W/ySuvyFBWegp3bq5s7Sv+oJqdLX9+L12q/XWGDBHiu+/k10VFsnu2qk+oOp0MrNV9\nSv3mG9nLVt5jj8kWM2dn/WeD6+P6dflhIiCg4htnaqoMvDNmGHbMf3ErXXXnXL688jHl/9/evcdF\nVW7/A//McFHymjc0SSEuchVQQQv9aiqZmqSoZGqgpn6zy8m8ZJ46x8wjaKmpWXbqlyfNStNfpiFS\nIZiWKSl4RVMLvKJ4QBDkMjCzvn8sBmaGGS46wyCs9+vlq5jZs/fac9l77Wc/z3pKS/n42q8fx0fE\nn4HRLxRxnrJwIVHnzjV/r9VqIhcXzie0Dh7k78qtW1UWb9oJorZD8pEjVZ+7fZubwmvT6rV1Kw8w\nMdZvUaPhL2ZiYvXr8PbmS1DD144axU3hvXvzlyQsjK+Cfvyx6jo2bOAPPyaGaPTomuO2lO++41sa\nuq5c4QOHqVp4RNyC+sorfFXVvr3pWyc1SUggsrXllkjdH0JdlJXxe+3nV/nPxYWb70z8CEtef4uu\nogsdW7OPDh0yvtoLF/iu7gM2RdSqeQl5evLHumCBkYVLS7nV5MMP724fqlNWRrR5c91amgcO5IuU\nMWP4tULUF42Gb7Xl5fHF76BBlt9m796mr9yI+PfZtm2VfsG5u3+hEy5hlHnN4CKypISoY0d6PiKP\n7O25t0dwMNHTbifpf4OOUEqK6c0YNWIE1xVUq7l1UPcg8vHHVQeKTJvGrYp1ce0aH7d1Gz5yc/kg\npjtoTSsri8+b1TWIaOuj6na9ysmpvAB97jnjLYx34+hRbnCYPNl0abD8fD4I11Rbti6+/LLmc3BB\nASfD2oGsGRk8YGnoUG5s0h3csnat8W5Ld+5U1pgNCqq569IPP3CDmOHnM3OmftKvVhNdu9bEE8TI\nyMp+csb87W+cmVfnzBm+iqtu1Nu6dUTjxpl+vqCA+z1W12yv6+ef+Yv1739XPrZ7Nz929iz/2Ax/\ngPXpxReNH4imT+fO5sb88QcnhdoEcvVqTo7uph7koEFEH3zA/Su6deNKujt21Oo2glrNx7hz6xMo\n2SuSfvz4T9r6bgZ9vTyDWywMPqPLl/lY2bYtka2thhzbFJKvr8Zk95KiIv7K3N60w3gHZl3R0Xyw\naCj9UF95hT9XIydFISzO15dvQ/brR/Ttt5bfXk39Cffv55OtIY2GDwoHD+o/vnMnH4uIjwPp6bzI\n/5//G33os874XYGzZ2nsWA098AD36ggM5EPChIFXKaXb05XHo5s3uV9wXBydSyulNKdQurrjMBUU\n6Bz2fv6Z+7XX5Xbq6tXG6/adOMGJo+Gt8f37iR57rOb1PvkkN6xorVpV2cd08+Z7b+BQq/lY1aFD\n7UZf37zJ+2OuATJTp9aua9mKFUQeHkTOztxYFRHBybFhzdmLF3lfDM+HO3TOI6Y+K13h4cbvGmVn\ncwtkaCgn1M2bE3Xq1IQTxJ9+4lsI1RUc/uMPbq0z1cpSUMA/OFMdcrXy8vjLZ6pz78GDfLVaF+fO\n8Rdr7lwuQdChA/9Xa+RI67XyuLkZv/1z7hwngbpxaoWHc8unllrNX9bFiysf02h4gMXUqVR66izd\nvs1dfTIydBobf/2Vf2zlt1vyb5XSv545QQtafUgvPX2ZoqJ4U9quOYYyMzlEV4cr1NvlvzR0KOf2\npgblqVSck2dn1zGPKyqqvoX6+HH+TC9erMNKLezTT7ml29fX2pGIpujpp3nUlbNz/ZQAy8ur/mJo\n4cKqFQy0YmK4Ve/zz7kb08aNfOFq7Fxx9arxVrezZ4mUStKMj6D86wWUkcE3u+JjS2nzQ/Pp0oaf\n9Jcvbzh4pf9R8nC4SI6OfJ63teVjWtJeNd/pMrhj9tFH3Pi4ZAnnaZ98wjlVZiZxE6dBq1RJSfmx\nbuzYqknQJ5/Urm/o+vWVCaFazecM7cCd69f5Xv3dzjhy8yYnTSEhnIXX1iuv8PfrXmk0nM2fPVvz\nskVF/B1JS6s5cQ8MrDou4rnnKj8D7W1tU/nKtWuVg1OMOX2aG5pOn67ootWgEsQ9e/ZQjx49yM3N\njZYZmcIrKSmJWrduTQEBARQQEEBLliwxHmRNO1VYyB1hY2NrDko7G4YhjYabraOiandFNmtW1bp4\nWh9+WNlptC6ys/mgY2/PV6e6Nm3i29NmpNHwBUxBgdHuCkREpDr7J+1/MIwSftJQXBxf4GzdqnOx\n+P33nPjs2EFEvK7ZEVfof1t8QZGTSmn8eK7KMGYM8YGzUye+Kt20iahXL/rvI0FkqyglJcqoRQsN\ndejAv8Xg4PL1P/WU3u2JggI+jkePPEBrgzbShg3cjfMng2OrntOn+WrK8CrO3GbO1E+KtUpKuHSG\nYd03azt8mPu31rYztBDmNGcOH+tWrKi/bT7/vPHfKBFRQEBlRQBD//0vl6+JjOST+KRJ/Hs3dXLu\n1q1q/7hJk4jeeovPMQEBlQNR1q41PfJ0yRL+jeqU2iku5gvpoiLiihK6gyGJx+tER3Ou++qrvMsT\nJhAdj73Ex1+DVqsnnuBNNLNXUxtlHnXurCEXl/Lr/jlzuG+dDu2YiJdfJpo/n6sVRb+RRxfb+HIS\nGB/P+1e+P8nJRImu0+mXj45TcjJ3o0xLq0O38mef5fe6rnef0tM5Uc/NrdvrDJ07x31kzT3wZfFi\n/X78JSVVWz1DQ/VbZnUtXUo0Y0adNlnXBFFR/iKzU6vV6NGjBxISEtC1a1cEBQXh66+/hpeXV8Uy\n+/btw6pVq7Br165q16VQKFBtmH//O1flr81sJXv2AG++ybOBKBQgAgpua6Be/QHUW7dDs+cHqJs9\nACKgSxdj+8UzU6j/uAD1rJeg2bUbaoUtFAqgb9/yhaZPB3r3BmbNQmkpF1ovK6v8V1oKKJVclN1Q\n8W0Vls7LQVn7znrL22lK8P7mjsClS0DbthXL37kDRERUXX/z5kBiYtX15+UB3boBJcUaqEoVUCoV\nsLcHOnTgVRvKX/0ZRsT0h71vD9jbo+Jf27bAp5+WL3TkCBAWBixciJIZL+NDzw/gMDAIDo8/CgcH\nwMGBJwoZPBjA9u3A+PFAaCjw6qugJ4fz/g0dCMWEZ4CXX67c+PHjwPDhwF9/Va3if+MG0KMHcPky\n0KpVdZ848OKLQMeOwOLF1S93r379FZg5Ezh1ClAo+LGyMuC114CLF3kWGu3jDcGdO/ze7dolM6OI\n+vfRRzwT0dWresc0i0pO5tmQzp/ng7BWZibPkJGVBdja3vt2JkzgY5f2IH/mDDBwIM++1KoVsHYt\nz3zx0Uc8ZUxSEm/fkFrNszY984zxY0d6Os9ocvUqH5irs3Qp7+e6dVWe0mgAVQmhqHd/FL+9DEV9\nBqBzZ+CB8SM5vrCwimXj4/mwW1TEE3cUFfG/GXFj4Pr+y8CaNbz89OkAgLlzgZRtf0IFO6gcu0Gl\nAlQqYONGDh0XLvDMOuX7N3AgcOhQ+blGoYJ9YS7sH+qAbduVvLyBuXP5nGxrq/9v0SLAc8kkwN+f\nZ7kpt349x2+4/MSJgJNT1fUnvLoLt45fhu2rL+ktHxRk/Gt77hy/H0olYGPD/5RKXreDg86CJ04A\no0cj/9if0JACysQE2CxbCpv9SRWvU2zaCOzYAXz3XdUPzNWVvxt9+pj8yA3VmEsZMMMvwbjk5GS4\nubnB2dkZADBhwgTs3LlTL0EEUOtg/f35t6LR8H8dHIBjxwBkZ/OP7MwZAEB+PuDszMvoLt+iBS+K\nYcN4+ruDB4GQEBTE7cdDYb2hVDwPm5Yvw6aPDZRKoE0b/qANFRXxF0mpdINN8QewmZQLZccOaNUK\nSEgoXyg1tWLKp9JS4PPPq34ZW7Y0niAqmtnD/uHOeMBgeQeHZsDVx/lkHhlZsXyzZpz/GK6/WTPj\n72Pr1sClPadh//Rw2CvLYPPN1/yLNKHVz7E4sKo5MKmH6Q+nTx9OjkaMQLPvv8ectlnAhpcAGyPL\njhsH3LzJGSkABQB7GwCf/T+gf39OVMq/M4iJAebMqZocAjx94YABPL2WsTdSKy8P2LKFkzZLe+wx\nPmKmpPAFwpEj/D3o2BHYvLlhJYcA/ygWLQIef9zakYimaOhQTibqKzkE+KzesiUnZEOGVD4eH8/x\nmCM5BIBHH+Up+7THpnfe4QvF1q3571df5YQwIoITVmPJIcBZwoQJprfj4sKv3b0bGDPG9HJEwFdf\n6VzV61MqgeYOCjR/NRL4ehUQMYCfOHsW8PTUW/bJJ01so3UQJ74HD/Ixt9zKlQBGXuSGnEOH9F9z\n8iQQEMDTIS5aBIA/mtJSQJVbCFXfAVCtfw+q/oPRqZPxzU6YwOd2w0aSdu3AieHw4fx+l58UW7fm\nr4B22eLi8u2pjK//wE/FSGv5FMo26a///feNf3VjYvgUYJiDbNlikMv5+QEAIkbk49cTraEp6g81\nfoS6FS//889A/zFjeHrVnJzyHeL3P3l/CZSqVNiMbFuRgH77LYwm0FFR3NaivJuJlevU3lgH27Zt\no+k6t1m/+OILetmgKXzfvn3Url076tmzJw0fPpxOnz5tdF0AKPWbc3TiBN8tPHNGp4/amjV6BVfV\nar4bkJPDrf8FBXwHWm8swurV3HT79NM8knXr1rtrPt67l/ta6DZ9l5TwABVLlGX58kvui1iTrCwe\nkGNs6O2lS3wP96uvuNSBt7fpviEqFfcdMTaDiDHZ2dwZuS71JnXFxPD9Do2Gb8906FB9PbFt22ou\nQfP++zUXMDWnt9/mezqvvcaDjL74QuoLCtGQrFvHfdr+8x8uv/Xvf3P/NmNdj+6WbsHsU6f41q6x\nY1lW1r13ffn6a+6/Xl3/5uPHuY9+Tcei/Hy+LZuRwfewmzWrfd/BU6f4XrWxgaLFxTzQUlvmhYhj\nGTKE71G7ulYd6bxwIdcivlfDhtVcZk2t5pnSpk3TP59rp5i11Gwwr73Gt5rLyvg7Yqwc0PjxlQNY\n1Wq6/bc3KdstmLJ+z6DMTA7t0iXTE+ucP88FVY4caUB9ELdv315jgnj79m26U55IxcXFkbu7u/Eg\nAVoUEkKLFi2iRYsWUVJSEj+h0fCPcO/eugWXm8tlTt59996mKyLiPoO6B5aUFB7oYgm3b/MQ+Oqm\nDTx2jDt9R0VxgjJvXmUn11u3ODbtiGSNhjtemyqV8Msv3JekvqhU3HH3P//hJMtUH08t7cAQY0Vl\nifhH7+padfShJV24wEVOIyOrLwEkhLCOvDyiF17g3+jUqdyhbvbse++rpku3YPb48VX68ZndqlV8\n4W+ikYXmzDFRe8uI2bM5WTpxgmv+1pZGww0EpmoeDh+uX5181y5ev0rFr+nSpbLg+cmT3EBwN3Ue\nDSUm8kheU6MOi4u5k2ZICA8pnzSpctDUkSN1ew/qat8+Puft38/91I3ZsYNLkhUV8Xepf3/9RLsa\nSUlJFXnTokWLGk6C+Ntvv9GwYcMq/o6OjjY6UEWXs7MzZRvZcQDcSdTwAz56lJMha5YN2b+fWyG1\nV1mffcaDXSxlzBjTV7rffss/Km1R7aws/uK7u/MojkGDeGSX7lXkhQs8JM7Y6Ns33qj9QcVctLOY\ntGtXux/BCy+Yniw+Npan6qrvFjxTsy4IIZqOkBC+g+HoqF8Dz1K++IK3pXtBvH8/t5a6uNS+WPW5\nc1ztY9Om8tGFZrJ6deWgipISPi/p1qVMSeHtJiZyaR1z1U7UaPg8UD6QUs+tW3xeDA/nhpQ7d/iu\n1JQpnFcsW8bnTEspLeXzXXi4foUPXcXFfD4MCuKyOffQqNVgEsTS0lJ65JFHKD09nUpKSsjf35/S\n0tL0lrl+/Tppyk/ehw8fpu7duxsPEuCWLMOWwhdfNP2m1qfQ0Mom4Jde4qs5S9mypWrV+rIyHu3m\n5MRTPxnasYNH8YaHGy8n8fbb+vXBsrO5Ba9rV9NTB1lSTIzppM/Qb7/xgcZYEjhsGJejEEKI+jZv\nXv2P0t6zh5Os1as5MXzkEW5QqGuJmSef5Fa3muoG10VaGo/u1mj4HGls9pW9e7nltV8/8zb8bNvG\n5bzWr+eWyyNHuJXS15e7Y+meFwsKeG7v6dP5FripGU/MZcoUvjVf3bn2rbe4weYe35MGkyAS8W1j\nDw8PcnV1pejySa4//vhj+ri8sOO6devIx8eH/P396dFHH6XfjNXTo/KdWrlSv/J4YSFn1Q2hrtxv\nv3HzfnExX/ncbR+82sjP59vM16/zj+mFF/iqceDA6vtJFBaarjVWVMS3Ynfv5lqLnTtzDQNrFeau\nC20RW8Pvzu7d3KfD0qVthBDCmB07+Nhsif7o1Tl0iBObu0kMtWJjOWkx5wW2RsONGL/8wq1mpm6H\nJyUR/fWX+bZLxOe+FSu4BXPkSG5weughovfeM964cPs2n8ttbMzb9cCY2FgeC1APd7rqmiBarMyN\nOSkUCtC1a4C3Nw/nf+AB4OuveXjwDz9YOzw2ciSPkP773znGNm0st62ICCA2lt+P8eOBsWMBN7d7\nW2d8PPD00zxi7ZNPdGr23AeWLgWuXOH6BaWlwD/+waOGv/oK+J//sXZ0QoimSKPhclzG6qU1dGo1\n4OvLpeN69jTfep9/nkvNjR0LfPCB+dZrCbdvc6zPPGP5bd25w1UlLKyuZW7unwSRiBOwqVN5XHto\nKNdZqo8PrzaOHOFyIY6OXNfJkrKy+Avl4mLe9R44APTrB9jZmXe9lnbpEhAYyGUlpkzh2gMbN3J5\nGSGEEHWn0dxlbZRqfPMN11W8cAFo39686xY1atwJ4hdf8BXNunVcUOjKFeM18qxl9GhOrrZts3Yk\nTc/gwcDhw1wMe84c8x/YhBBC3BuNhqtUd+9u7UiapMadIBYUcDnyiRO5oOnatdYOTd+NG1yp+15v\n94q6O3mSby/36mXtSIQQQogGp3EniAAweTLw5Zc8W0lAgHUDE0IIIYS4DzSYqfYsZsYM4Pp1SQ6F\nEEIIISzk/mtBBHheyYY2r60QQgghRANV1xbE+7MnvySHQgghhBAWc38miEIIIYQQwmIkQRRCCCGE\nEHokQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHok\nQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHokQRRCCCGEEHokQWxg9u3bZ+0Q6pXsb+PVlPYVaFr7\n25T2FWha+9uU9hVoevtbF5IgNjBN7csq+9t4NaV9BZrW/jalfQWa1v42pX0Fmt7+1oUkiEIIIYQQ\nQo8kiEIIIYQQQo+CiMjaQdREoVBYOwQhhBBCiPtaXVI+WwvGYTb3QQ4rhBBCCNFoyC1mIYQQQgih\nRxJEIYQQQgihp8EmiNu2bYOPjw9sbGyQkpKi91xMTAzc3d3h6emJH3/80UoRWk5ycjKCg4MRGBiI\noKAg/P7779YOyaI++OADeHl5wdfXFwsWLLB2OPVi5cqVUCqVyMnJsXYoFjV//nx4eXnB398f4eHh\nyMvLs3ZIZhcfHw9PT0+4u7tj+fLl1g7Hoi5fvozHH38cPj4+8PX1xdq1a60dksWp1WoEBgZi1KhR\n1g7F4nJzczFu3Dh4eXnB29sbhw4dsnZIFhMTEwMfHx/4+flh4sSJKCkpsXZIZjVt2jQ4OjrCz8+v\n4rGcnByEhobCw8MDTzzxBHJzc6tfCTVQZ86coT/++IMGDRpER48erXj89OnT5O/vTyqVitLT08nV\n1ZXUarUVIzW/gQMHUnx8PBERxcXF0aBBg6wckeUkJibS0KFDSaVSERFRVlaWlSOyvEuXLtGwYcPI\n2dmZsrOzrR2ORf34448Vv88FCxbQggULrByReZWVlZGrqyulp6eTSqUif39/SktLs3ZYFpOZmUmp\nqalERJSfn08eHh6Nen+JiFauXEkTJ06kUaNGWTsUi4uMjKTPPvuMiIhKS0spNzfXyhFZRnp6Orm4\nuFBxcTEREUVERNDnn39u5ajMa//+/ZSSkkK+vr4Vj82fP5+WL19ORETLli2r8XjcYFsQPT094eHh\nUeXxnTt34tlnn4WdnR2cnZ3h5uaG5ORkK0RoOV26dKloacnNzUXXrl2tHJHlrF+/HgsXLoSdnR0A\noGPHjlaOyPLmzJmDd99919ph1IvQ0FAolXyY6du3L65cuWLliMwrOTkZbm5ucHZ2hp2dHSZMmICd\nO3daOyyL6dy5MwICAgAALVu2hJeXF65du2blqCznypUriIuLw/Tp0xv9YMm8vDwcOHAA06ZNAwDY\n2tqiTZs2Vo7KMlq3bg07OzsUFhairKwMhYWFje48O2DAADz44IN6j+3atQtRUVEAgKioKHz33XfV\nrqPBJoimXLt2DU5OThV/Ozk54erVq1aMyPyWLVuGuXPnolu3bpg/fz5iYmKsHZLFnD9/Hvv370e/\nfv0waNAgHDlyxNohWdTOnTvh5OSEnj17WjuUerdhwwaMGDHC2mGY1dWrV/Hwww9X/N0Yj0emZGRk\nIDU1FX379rV2KBbz2muv4b333qu4yGnM0tPT0bFjR0ydOhW9evXCjBkzUFhYaO2wLKJdu3YV59iH\nHnoIbdu2xdChQ60dlsXduHEDjo6OAABHR0fcuHGj2uWtWuYmNDQU169fr/J4dHR0nfp73I91Ek3t\n+9KlS7F27VqsXbsWY8aMwbZt2zBt2jT89NNPVojSPKrb17KyMty6dQuHDh3C77//joiICPz1119W\niNJ8qtvfmJgYvX6zjaFVoja/46VLl8Le3h4TJ06s7/As6n489phDQUEBxo0bhzVr1qBly5bWDsci\nYmNj0alTJwQGBjaJ6djKysqQkpKCdevWISgoCLNnz8ayZcvwzjvvWDs0s/vzzz+xevVqZGRkoE2b\nNhg/fjy+/PJLTJo0ydqh1RuFQlHj8cuqCeLdJD1du3bF5cuXK/6+cuXKfdk0XN2+T548GQkJCQCA\ncePGYfr06fUVlkVUt6/r169HeHg4ACAoKAhKpRLZ2dlo3759fYVndqb299SpU0hPT4e/vz8A/u72\n7t0bycnJ6NSpU32GaFY1/Y4///xzxMXFYe/evfUUUf0xPB5dvnxZ7w5HY1RaWoqxY8di8uTJGD16\ntLXDsZiDBw9i165diIuLQ3FxMW7fvo3IyEhs2rTJ2qFZhJOTE5ycnBAUFASAzz3Lli2zclSWcRuB\nRQAACFRJREFUceTIETz22GMV55nw8HAcPHiw0SeIjo6OuH79Ojp37ozMzMwazzv3Rbu5bitLWFgY\ntmzZApVKhfT0dJw/fx7BwcFWjM783Nzc8PPPPwMAEhMTjfbFbCxGjx6NxMREAMC5c+egUqnu6+Sw\nOr6+vrhx4wbS09ORnp4OJycnpKSk3NfJYU3i4+Px3nvvYefOnWjevLm1wzG7Pn364Pz588jIyIBK\npcLWrVsRFhZm7bAshojw/PPPw9vbG7Nnz7Z2OBYVHR2Ny5cvIz09HVu2bMHgwYMbbXIIcP/Shx9+\nGOfOnQMAJCQkwMfHx8pRWYanpycOHTqEoqIiEBESEhLg7e1t7bAsLiwsDBs3bgQAbNy4seYLPEuN\noLlX3377LTk5OVHz5s3J0dGRnnzyyYrnli5dSq6urtSjR4+K0b6Nye+//07BwcHk7+9P/fr1o5SU\nFGuHZDEqlYomT55Mvr6+1KtXL0pKSrJ2SPXGxcWl0Y9idnNzo27dulFAQAAFBATQrFmzrB2S2cXF\nxZGHhwe5urpSdHS0tcOxqAMHDpBCoSB/f/+Kz3TPnj3WDsvi9u3b1yRGMR87doz69OlDPXv2pDFj\nxjTaUcxERMuXLydvb2/y9fWlyMjIikoajcWECROoS5cuZGdnR05OTrRhwwbKzs6mIUOGkLu7O4WG\nhtKtW7eqXcd9MRezEEIIIYSoP/fFLWYhhBBCCFF/JEEUQgghhBB6JEEUQgghhBB6JEEUQgghhBB6\nJEEUQgghhBB6JEEUQgghhBB6JEEUQpiFjY0NAgMDERgYiF69euHixYsICQmx2Pby8vKwfv36Bru+\ne6U7hZ32fTSM0fBvS77faWlpCA4OxnPPPYebN28CAFJTU+Hj44O4uDiLbVcIYR1SB1EIYRatWrVC\nfn5+vW0vIyMDo0aNwsmTJ6s8pz2s1WWu5OrWZ8rdbKe2jL2fhjHeTcz3YvHixejevTumTJkCADh2\n7Bjs7e2bxCwUQjQ10oIohLAYbStYRkYGvLy8MHPmTPj6+mLYsGEoLi4GAGzevBl9+/ZFYGAgXnjh\nBWg0mirruXPnDkaOHImAgAD4+fnhm2++wcKFC/Hnn38iMDAQCxYswMWLF9GjRw9ERUXBz88PBw4c\ngJ+fX8U6VqxYgcWLFwMANm3aBH9/fwQEBCAqKgoA8MYbb1RZn7HXG27n8uXLNe6Dsfi174unpycm\nT54Mb29vjB8/HkVFRSbfR90YX3/9db334PXXX0erVq1qfL8BYMmSJfD09MSAAQMwceJErFy5slaf\np5OTk97c06dPn5bkUIjGyuLzvQghmgQbG5uK6dfCw8OJiKhly5ZERJSenk62trZ0/PhxIiKKiIig\nzZs3U1paGo0aNYrKysqIiGjWrFm0adOmKuvevn07zZgxo+LvvLw8ysjIIF9f34rH0tPTSalU0uHD\nhyv+1n1+xYoV9Pbbb9OpU6fIw8OjYprDnJwcIiKj6zN8/eLFiykjI0NvO7XZB2Pxa7ehUCjo4MGD\nREQ0bdo0WrFihd57p/v/hjEa/l3T+01ElJycTAEBAVRSUkL5+fnk7u5OK1eurPKex8XF0apVq2jd\nunWUmZlJRETx8fE0c+ZMIiJKSEioeFwI0fjYWjtBFUI0Dg4ODkhNTTX5vIuLC3r27AkA6N27NzIy\nMpCbm4ujR4+iT58+AICioiJ07ty5ymt79uyJefPm4Y033sBTTz2F/v37Iycnp8py3bt3R3BwcLVx\nJiUlISIiAu3atQMAPPjggwAqbxdXR7uM7nb27t1b4z4Yi1/r4YcfxqOPPgoAmDx5MtauXYu5c+dW\nu31Tf+sy9n4DwK+//orRo0fD3t4e9vb2GDVqVJX1XLx4EdHR0Thw4AASExNRUFAAoLIFUa1WIysr\nC0OGDDH9Zgkh7muSIAoh6kWzZs0q/t/GxgZFRUUgIkRFRSE6Orra17q7uyM1NRW7d+/GW2+9hSFD\nhiAyMrLKci1atKj4f1tbW71bvbq3bmuTDFb3et3tAKhxH4zF/49//AOAfv9FIoJSaZ6eP8beb+32\ndPff2Hvx3Xffwd3dHbGxsWjRogXc3NwAcIJ45coV7Ny5E2FhYWaJUwjRMEkfRCGE1QwZMgTbt2+v\nGBWbk5ODS5cuVVkuMzMTzZs3x6RJkzBv3jykpqbWOCjG0dERWVlZyMnJQUlJCWJjY6FQKDB48GBs\n27atogVS+1/D9Zl6/d3sg2H8KSkpFc9dunQJhw4dAgB89dVXeq2LhgxjvJuBQSEhIfj+++9RUlKC\ngoIC7N69u8p+OTg4ICwsDE899RQGDBiArKwsAECbNm2Qk5MDpVJZJUkWQjQu0oIohDALY8mT7mOG\nzysUCnh5eeFf//oXnnjiCWg0GtjZ2eGjjz5Ct27d9JY9efIk5s+fD6VSCTs7O3z88cdo164dQkJC\n4OfnhxEjRuDFF1/U24adnR3++c9/Ijg4GF27dq0YTOHt7Y0333wTAwcOhI2NDXr16oUNGzagffv2\neutbvny50dcb7ktt9sFY/Fo9evTAhx9+iGnTpsHHxwezZs0y+d4Zi1H79/Dhw2t8vwGgT58+CAsL\nQ8+ePeHo6Ag/Pz+0adNGb9lnnnkGa9asgZ2dHXJzczFu3LiK50JCQqT1UIgmQMrcCCGEldR3mRqt\nO3fuoEWLFigsLMTAgQPx6aefIiAgoF5jEEI0bNKCKIQQVmSJGoo1mTlzJtLS0lBcXIwpU6ZIciiE\nqEJaEIUQQgghhB4ZpCKEEEIIIfRIgiiEEEIIIfRIgiiEEEIIIfRIgiiEEEIIIfRIgiiEEEIIIfRI\ngiiEEEIIIfRIgiiEEEIIIfRIgiiEEEIIIfT8H4NCegrDYbJ6AAAAAElFTkSuQmCC\n" } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoherence time of 2.5 ns, coherence of 71%." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 2.5\n", "fss = np.linspace(-10, 10, 500)*1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_with_dec = np.loadtxt('../out/fidelity_vs_fss/cross_dephasing2.5ns/fss_v_fidelity.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_with_dec[:,0], numerical_data_with_dec[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "<matplotlib.text.Text at 0x10643d6d0>" ] }, { "output_type": "display_data", "png": 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qtRoJCQnGLt/gGBCJiIjoualUqufaX6vV6qmSgvn8888xZswYhIWFwdraGosWLcq2TYcO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\nfS1gmQAAAABJRU5ErkJggg==\n" } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoherence time of 2.5 ns, background emission of 0.05." ] }, { "cell_type": "code", "collapsed": false, "input": [ "qd.crosstau = 2.5\n", "qd.bg_emission_rate = 0.05\n", "fss = np.linspace(-10, 10, 500)*1e-6\n", "fidelities = [qd.ideal_fidelity_lorentzian(f)[0] for f in fss]\n", "ghv = qd.ideal_fidelity_lorentzian(f)[1]\n", "\n", "numerical_data_dec_and_bg = np.loadtxt('../out/2013-08-07/fid_v_fss/fid_v_fss.txt', delimiter = ',')\n", "\n", "plt.figure(figsize = (16./1.5,9./1.5))\n", "plt.plot(numerical_data_dec_and_bg[:,0]/1e-6, numerical_data_dec_and_bg[:,1], 'r-', fss/1e-6, fidelities, 'b--')\n", "plt.legend(['Monte carlo', 'Theoretical']) \n", "plt.text(6, 0.9, ' coherence ' + np.array(ghv).astype('|S4').tostring())\n", "plt.xlim([-10, 10]) ; plt.ylim([0.45, 1.05])\n", "plt.xticks(np.linspace(-10, 10, 11))\n", "plt.xlabel('Fine structure splitting $eV$') ; plt.ylabel('Fidelity')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 6, "text": [ "<matplotlib.text.Text at 0x106383c10>" ] }, { "output_type": "display_data", "png": 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REZEMczgcGXp+fHx8JlWSOi+//DLPP/88u3fvpkSJEkyZMiXRMe3atWPjxo1s3LiRX3/9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\nEEIIIUz8fwfPJ7wWf8KCAAAAAElFTkSuQmCC\n" } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

hbwzhsh/pyDataScienceToolkits_Base

Scikit-learn/.ipynb_checkpoints/(1)getting_started_with_iris-checkpoint.ipynb

8

51025

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##从iris数据集入门scikit-learn\n", "iris数据集是由三种鸢尾花，各50组数据构成的数据集。每个样本包含4个特征，分别为萼片(sepals)的长和宽、花瓣(petals)的长和宽。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](Image/iris_petal_sepal.jpg)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#from IPython.diasplay import Image\n", "#Image(filename=\"Image/iris_petal_sepal.jpg\", width=400, height=432)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "##1. 载入iris数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "你还可以通过python的csv模块，或者NumPy的loadtxt函数，或者Pandas的read_csv()函数读取从[UCI Iris dataset](http://archive.ics.uci.edu/ml/datasets/Iris)下载的csv文件。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sklearn.datasets.base.Bunch" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "type(iris)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", "['setosa' 'versicolor' 'virginica']\n" ] } ], "source": [ "print iris.feature_names\n", "print iris.target_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在scikit-learn中对数据有如下要求：\n", "- 特征和标签要用分开的对象存储\n", "- 特征和标签要是数字\n", "- 特征和标签都要使用numpy的array来存储" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'numpy.ndarray'>\n", "<type 'numpy.ndarray'>\n" ] } ], "source": [ "print type(iris.data)\n", "print type(iris.target)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4)\n", "(150,)\n" ] } ], "source": [ "print iris.data.shape\n", "print iris.target.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# store features matrix in \"X\"\n", "X = iris.data\n", "\n", "# store response vector in \"y\"\n", "y = iris.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###回顾iris数据集" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###绘制iris的2d图" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x554f850>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fjs/jITooRy8+PafxYepUGMjNmFUhXi/eqVPzt+v1rim8I3EPAz7nWIQMBAwD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\nOE5zgPeAPyNjZi5D/mlyVftYlRKTmwM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"text/plain": [ "<matplotlib.figure.Figure at 0x5541b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "X_sepal = X[:, :2]\n", "plt.scatter(X_sepal[:, 0], X_sepal[:, 1], c=y, cmap=plt.cm.gnuplot)\n", "plt.xlabel('Sepal length')\n", "plt.ylabel('Sepal width')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x5705e90>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DNAYdPgvBJ3Vee0zQzoD6Z8xDck6YxohDMuBw2jBjWh/TIxE+3n13mubMoSkW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\nREREREREpBz8f4IdEqXfU0s0AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x5556d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_petal = X[:, 2:4]\n", "plt.scatter(X_petal[:, 0], X_petal[:, 1], c=y, cmap=plt.cm.gnuplot)\n", "plt.xlabel('Petal length')\n", "plt.ylabel('Petal width')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##2. 使用K近邻进行分类" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "KNN分类的基本步骤：\n", "1. 选择K的值\n", "2. 在训练数据集中搜索K个距离最近的观测值\n", "3. 使用最多的那个标签作为未知数据的预测" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面给出KNN的演示图例，\n", "分别是训练数据、K=1时的KNN分类图、K=5时的KNN分类图\n", "![Example training data](Image/Data3classes.png)\n", "![KNN classification map(K=1)](Image/Map1NN.png)\n", "![KNN classification map(K=5)](Image/Map5NN.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###scikit-learn进行模型匹配的4个一般步骤" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第一步：载入你要使用的模型类" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第二步：实例化分类器" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# looking for the one nearest neighbor\n", "knn = KNeighborsClassifier(n_neighbors=1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')\n" ] } ], "source": [ "print knn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第三步：用数据来拟合模型（进行模型的训练）" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###第四步：对新的观测值进行预测" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.predict([3, 5, 4, 2])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 1])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_new = [[3, 5, 4, 2], [5, 4, 3, 2]]\n", "knn.predict(X_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###使用不同的K值" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn5 = KNeighborsClassifier(n_neighbors=5)\n", "\n", "knn5.fit(X, y)\n", "\n", "knn5.predict(X_new)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###依照同样的流程，使用不同的分类模型" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 0])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import the class\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "# instantiate the model (using the default parameters)\n", "logreg = LogisticRegression()\n", "\n", "# fit the model with data\n", "logreg.fit(X, y)\n", "\n", "# predict the response for new observations\n", "logreg.predict(X_new)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

elivre/arfe

e2014/020-rede2014_converte_receitas.ipynb

1

34728

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 020-rede2014_converte_receitas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ano_eleicao = '2014'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dbschema = f'rede{ano_eleicao}'\n", "dbschema_tse = f'tse{ano_eleicao}'\n", "table_receitas = f'{dbschema}.receitas_{ano_eleicao}'\n", "table_receitas_do = f'{dbschema}.receitas_do_{ano_eleicao}'\n", "table_receitas_candidatos = f'{dbschema_tse}.receitas_candidatos_{ano_eleicao}'\n", "table_receitas_partidos = f'{dbschema_tse}.receitas_partidos_{ano_eleicao}'\n", "table_receitas_comites = f'{dbschema_tse}.receitas_comites_{ano_eleicao}'\n", "table_candidaturas = f\"{dbschema}.candidaturas_{ano_eleicao}\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append('../')\n", "import mod_tse as mtse\n", "home = os.environ[\"HOME\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "mtse.execute_query(f'CREATE SCHEMA IF NOT EXISTS {dbschema};')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CRIA TABELA RECEITAS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "query_create_table_receitas = f\"\"\"\n", "DROP TABLE IF EXISTS {table_receitas} CASCADE;\n", "CREATE TABLE {table_receitas} (\n", " tabela_id varchar,\n", "\n", " eleicao_ano varchar,\n", " eleicao_turno varchar,\n", " prestador_contas_sq varchar,\n", " prestador_contas_cnpj varchar,\n", "\n", " --RECEITA\n", " receita_fonte_cd varchar,\n", " receita_fonte_ds varchar,\n", " receita_origem_cd varchar,\n", " receita_origem_sg varchar,\n", " receita_origem_ds varchar,\n", " receita_valor numeric(18,2),\n", "\n", " --RECEPTOR\n", " receptor_id varchar,\n", " receptor_tipo_cd varchar,\n", " receptor_tipo_ds varchar,\n", " receptor_candidatura_id varchar,\n", " \n", " receptor_esfera_partidaria_cd varchar,\n", " receptor_esfera_partidaria_ds varchar,\n", " receptor_uf varchar,\n", " receptor_ue varchar,\n", " receptor_ue_nome varchar,\n", " receptor_cnpj varchar,\n", " receptor_nome varchar,\n", " receptor_sq varchar,\n", " receptor_cargo_ds varchar,\n", " receptor_candidato_nr varchar,\n", " receptor_candidato_cpf varchar,\n", " receptor_vice_candidato_cpf varchar,\n", " receptor_partido_nr varchar,\n", " receptor_partido_sg varchar,\n", "\n", " --DOADOR\n", " doador_id varchar,\n", " doador_tipo_cd varchar,\n", " doador_tipo_ds varchar,\n", " doador_candidatura_id varchar,\n", " \n", " doador_cnae_cd varchar,\n", " doador_cnae_ds varchar,\n", " doador_cpf_cnpj varchar,\n", " doador_nome varchar,\n", " doador_nome_rfb varchar,\n", " doador_originario_tipo varchar,\n", " doador_esfera_partidaria_cd varchar, \n", " doador_esfera_partidaria_ds varchar,\n", " doador_uf varchar,\n", " doador_ue varchar,\n", " doador_ue_nome varchar,\n", " doador_candidato_nr varchar,\n", " doador_candidato_cargo_ds varchar,\n", " doador_partido_nr varchar,\n", " doador_partido_sg varchar \n", ");\n", "\n", "CREATE INDEX ON {table_receitas} (tabela_id);\n", "CREATE INDEX ON {table_receitas} (receita_origem_sg);\n", "CREATE INDEX ON {table_receitas} (receita_origem_cd);\n", "CREATE INDEX ON {table_receitas} (receita_origem_ds);\n", "\n", "\n", "CREATE INDEX ON {table_receitas} (receptor_id);\n", "CREATE INDEX ON {table_receitas} (receptor_candidatura_id);\n", "CREATE INDEX ON {table_receitas} (receptor_tipo_cd);\n", "CREATE INDEX ON {table_receitas} (receptor_tipo_ds);\n", "CREATE INDEX ON {table_receitas} (receptor_cnpj);\n", "CREATE INDEX ON {table_receitas} (receptor_candidato_cpf);\n", "CREATE INDEX ON {table_receitas} (receptor_uf); \n", "CREATE INDEX ON {table_receitas} (receptor_candidato_nr); \n", "CREATE INDEX ON {table_receitas} (receptor_sq);\n", "\n", "\n", "CREATE INDEX ON {table_receitas} (doador_id);\n", "CREATE INDEX ON {table_receitas} (doador_candidatura_id);\n", "CREATE INDEX ON {table_receitas} (doador_tipo_cd);\n", "CREATE INDEX ON {table_receitas} (doador_tipo_ds);\n", "CREATE INDEX ON {table_receitas} (doador_cpf_cnpj);\n", "CREATE INDEX ON {table_receitas} (doador_nome);\n", "CREATE INDEX ON {table_receitas} (doador_nome_rfb);\n", "\n", "\n", "DROP TABLE IF EXISTS {table_receitas_do} CASCADE;\n", "CREATE TABLE {table_receitas_do} (\n", "LIKE {table_receitas}\n", ");\n", "\n", "\n", "\"\"\"\n", "\n", "mtse.execute_query(query_create_table_receitas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS DE CANDIDATOS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_candidatos = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'RC' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor,\n", " \n", " --RECEPTOR \n", " get_candidato_id(cpf_do_candidato) as receptor_id,\n", " 'CD' as receptor_tipo_cd,\n", " 'Candidato' as receptor_tipo_ds,\n", " get_candidatura_id(uf,numero_candidato) as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " nome_candidato as receptor_nome,\n", " sequencial_candidato as receptor_sq,\n", " cargo as receptor_cargo_ds,\n", " numero_candidato as receptor_candidato_nr,\n", " cpf_do_candidato as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatra_id, \n", " \n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_candidatos}\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_candidatos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS DE CANDIDATOS DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_candidatos_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select \n", " 'RCDO' as tabela_id,\n", " \n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor,\n", " \n", " --RECEPTOR \n", " get_candidato_id(cpf_do_candidato) as receptor_id,\n", " 'CD' as receptor_tipo_cd,\n", " 'Candidato' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " nome_candidato as receptor_nome,\n", " sequencial_candidato as receptor_sq,\n", " cargo as receptor_cargo_ds,\n", " numero_candidato as receptor_candidato_nr,\n", " cpf_do_candidato as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candidatura_id, \n", " \n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_candidatos} \n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_candidatos_doador_originario)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS PARTIDOS" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_partidos = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'ROP' as tabela_id, \n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_diretorio as receptor_nome,\n", " sequencial_diretorio as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatura_id, \n", "\n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg \n", "from {table_receitas_partidos}\n", "--where \n", "-- cpf_cnpj_do_doador_originario in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_partidos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS PARTIDOS DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_partidos_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select\n", " 'ROPDO' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_diretorio as receptor_nome,\n", " sequencial_diretorio as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candidatura_id, \n", "\n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_partidos} as rpdo\n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_partidos_doador_originario)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS COMITES" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_comites = f\"\"\"\n", "insert into {table_receitas}\n", "select \n", " 'ROP' as tabela_id, \n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_comite as receptor_nome,\n", " sequencial_comite as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador) as doador_id, \n", " get_doador_tipo(cpf_cnpj_do_doador) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador) as doador_tipo_ds, \n", " '' as doador_candidatura_id,\n", " \n", " cod_setor_economico_do_doador as doador_cnae_cd,\n", " setor_economico_do_doador as doador_cnae_ds,\n", " cpf_cnpj_do_doador as doador_cpf_cnpj,\n", " nome_do_doador as doador_nome,\n", " nome_do_doador_receita_federal as doador_nome_rfb,\n", " '#NE' as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg \n", "from {table_receitas_comites}\n", "--where \n", "-- cpf_cnpj_do_doador_originario in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_comites)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## INCLUI RECEITAS COMITES DOADOR ORIGINÁRIO" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "query_insert_receitas_comites_doador_originario = f\"\"\"\n", "insert into {table_receitas_do}\n", "select\n", " 'ROPDO' as tabela_id,\n", "\n", " {ano_eleicao} as eleicao_ano,\n", " '#NE' as eleicao_turno,\n", " '#NE' as prestador_contas_sq,\n", " cnpj_prestador_conta as prestador_contas_cnpj,\n", " \n", " --RECEITA \n", " '#NE' as receita_fonte_cd,\n", " fonte_recurso as receita_fonte_ds,\n", " '#NE' as receita_origem_cd,\n", " '#NE' as receita_origem_sg,\n", " tipo_receita as receita_origem_ds,\n", " valor_receita::numeric(18,2) as receita_valor, \n", " \n", " --RECEPTOR \n", " public.get_orgao_partidario_id(cnpj_prestador_conta) as receptor_id,\n", " 'OP' as receptor_tipo_cd,\n", " 'Órgão Partidário' as receptor_tipo_ds,\n", " '' as receptor_candidatura_id,\n", " \n", " '#NE' as receptor_esfera_partidaria_cd,\n", " '#NE' as receptor_esfera_partidaria_ds,\n", " uf as receptor_uf,\n", " '#NE' as receptor_ue,\n", " '#NE' as receptor_ue_nome,\n", " cnpj_prestador_conta as receptor_cnpj,\n", " tipo_comite as receptor_nome,\n", " sequencial_comite as receptor_sq,\n", " '#NE' as receptor_cargo_ds,\n", " '#NE' as receptor_candidato_nr,\n", " '#NE' as receptor_candidato_cpf,\n", " '#NE' as receptor_vice_candidato_cpf,\n", " '#NE' as receptor_partido_nr,\n", " sigla__partido as receptor_partido_sg,\n", " \n", " --DOADOR \n", " get_doador_id(cpf_cnpj_do_doador_originario) as doador_id,\n", " get_doador_tipo(cpf_cnpj_do_doador_originario) as doador_tipo_cd,\n", " get_doador_tipo_ds(cpf_cnpj_do_doador_originario) as doador_tipo_ds,\n", " '' as doador_candiatura_id,\n", " \n", " '#NE' as doador_cnae_cd,\n", " setor_economico_do_doador_originario as doador_cnae_ds,\n", " cpf_cnpj_do_doador_originario as doador_cpf_cnpj,\n", " nome_do_doador_originario as doador_nome,\n", " nome_do_doador_originario_receita_federal as doador_nome_rfb,\n", " tipo_doador_originario as doador_originario_tipo,\n", " '#NE' as doador_esfera_partidaria_cd, \n", " '#NE' as doador_esfera_partidaria_ds,\n", " '#NE' as doador_uf, \n", " sigla_ue_doador as doador_ue,\n", " '#NE' as doador_ue_nome,\n", " numero_candidato_doador as doador_candidato_nr,\n", " '#NE' as doador_candidato_cargo_ds,\n", " numero_partido_doador as doador_partido_nr,\n", " '#NE' as doador_partido_sg\n", "from {table_receitas_comites} as rpdo\n", "where \n", " cpf_cnpj_do_doador_originario not in ('#NE','#NE#', '','#NULO','#NULO#')\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_insert_receitas_comites_doador_originario)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-10-20 22:44:03.586346\n" ] } ], "source": [ "import datetime\n", "print(datetime.datetime.now())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

Ttl/scikit-rf

doc/source/examples/metrology/One Port Tiered Calibration.ipynb

3

7960

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# One Port Tiered Calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A one-port network analyzer can be used to measure a two-port device, provided that the device is reciprocal. This is accomplished by performing two calibrations, which is why its called a *tiered* calibration. \n", "\n", "First, the VNA is calibrated at the test-port like normal. This is called the *first tier*. Next, the device is connected to the test-port, and a calibration is performed at the far end of the device, the *second tier*. A diagram is shown below," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import SVG\n", "SVG('oneport_tiered_calibration/images/boxDiagram.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook will demonstrate how to use [skrf](www.scikit-rf.org) to do a two-tiered one-port calibration. We'll use data that was taken to characterize a waveguide-to-CPW probe. So, for this specific example the diagram above looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "SVG('oneport_tiered_calibration/images/probe.svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some Data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data available is the folders `'tier1/'` and `'tier2/'`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(if you dont have the git repo for these examples, the data for this notebook can be found [here](https://github.com/scikit-rf/examples/tree/master/oneport_tiered_calibration))\n", "\n", "In each folder you will find the two sub-folders, called `'ideals/' ` and `'measured/'`. These contain touchstone files of the calibration standards ideal and measured responses, respectively. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/tier1/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first tier is at waveguide interface, and consisted of the following set of standards\n", "\n", "* short \n", "* delay short\n", "* load\n", "* radiating open (literally an open waveguide)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls oneport_tiered_calibration/tier1/measured/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating Calibrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tier 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First defining the calibration for *Tier 1*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from skrf.calibration import OnePort\n", "import skrf as rf \n", "%matplotlib inline\n", "from pylab import * \n", "rf.stylely()\n", "\n", "\n", "tier1_ideals = rf.read_all_networks('oneport_tiered_calibration/tier1/ideals/')\n", "tier1_measured = rf.read_all_networks('oneport_tiered_calibration/tier1/measured/')\n", " \n", "\n", "tier1 = OnePort(measured = tier1_measured,\n", " ideals = tier1_ideals,\n", " name = 'tier1',\n", " sloppy_input=True)\n", "tier1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we saved corresponding *ideal* and *measured* standards with identical names, the Calibration will automatically align our standards upon initialization. (More info on creating Calibration objects this can be found in [the docs](http://scikit-rf.readthedocs.org/en/latest/tutorials/calibration.html).)\n", "\n", "Similarly for the second tier 2," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tier 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier2_ideals = rf.read_all_networks('oneport_tiered_calibration/tier2/ideals/')\n", "tier2_measured = rf.read_all_networks('oneport_tiered_calibration/tier2/measured/')\n", " \n", "\n", "tier2 = OnePort(measured = tier2_measured,\n", " ideals = tier2_ideals,\n", " name = 'tier2',\n", " sloppy_input=True)\n", "tier2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error Networks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each one-port Calibration contains a two-port error network, that is determined from the calculated error coefficients. The error network for *tier1* models the VNA, while the error network for *tier2* represents the VNA **and** the DUT. These can be visualized through the parameter `'error_ntwk'`.\n", "\n", "\n", "For tier 1, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier1.error_ntwk.plot_s_db()\n", "title('Tier 1 Error Network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly for tier 2, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tier2.error_ntwk.plot_s_db()\n", "title('Tier 2 Error Network')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## De-embedding the DUT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As previously stated, the error network for *tier1* models the VNA, and the error network for *tier2* represents the VNA+DUT. So to deterine the DUT's response, we cascade the inverse S-parameters of the VNA with the VNA+DUT. \n", "\n", "$$ DUT = VNA^{-1}\\cdot (VNA \\cdot DUT)$$\n", "\n", "In skrf, this is done as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dut = tier1.error_ntwk.inv ** tier2.error_ntwk\n", "dut.name = 'probe'\n", "dut.plot_s_db()\n", "title('Probe S-parameters')\n", "ylim(-60,10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to save this to disk, for future use, \n", "\n", " dut.write_touchstone()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls probe*" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

fabriziocosta/GraphLearn_examples

notebooks/Evaluation/EvaluateProbabilityOfSamples.ipynb

1

16876

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from eden.util import configure_logging\n", "import logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from itertools import tee, chain, islice\n", "import numpy as np\n", "import random\n", "from time import time\n", "import datetime\n", "from graphlearn.graphlearn import GraphLearnSampler\n", "from eden.util import fit,predict\n", "from eden.graph import Vectorizer\n", "# get data\n", "from eden.converter.graph.gspan import gspan_to_eden\n", "from itertools import islice\n", "def get_graphs(dataset_fname, size=100):\n", " return islice(gspan_to_eden(dataset_fname),size)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fit_sample(graphs, random_state=42):\n", " graphs, graphs_ = tee(graphs)\n", " sampler=GraphLearnSampler(radius_list=[0,1],thickness_list=[2,3],\n", " min_cip_count=2, min_interface_count=2,\n", " vectorizer=Vectorizer(5), random_state=random_state)\n", " \n", " sampler.fit(graphs, nu=0.25, n_jobs=-1)\n", "\n", " logger.info('graph grammar stats:')\n", " dataset_size, interface_counts, core_counts, cip_counts = sampler.grammar().size()\n", " logger.info('#instances:%d #interfaces: %d #cores: %d #core-interface-pairs: %d' % (dataset_size, interface_counts, core_counts, cip_counts))\n", "\n", " graphs = sampler.sample(graphs_,\n", " n_steps=5, n_samples=4,\n", " target_orig_cip=False,\n", " probabilistic_core_choice=False,\n", " score_core_choice= False,\n", " max_core_size_diff=3,\n", " burnin=1,\n", " omit_seed=True,\n", " max_cycle_size=6,\n", " improving_threshold=0.25,\n", " accept_static_penalty=0,\n", " n_jobs=-1,\n", " select_cip_max_tries=200,\n", " keep_duplicates=True,\n", " generator_mode=True)\n", " return graphs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fit_and_evaluate(original, sampled, local_estimator):\n", " outputs = []\n", " for desc,train in [('original',original),\n", " ('sample',sampled)]:\n", " train,train_ = tee(train)\n", " size=sum(1 for x in train_)\n", " logger.info( \"-\"*80)\n", " logger.info( 'working on %s'%(desc))\n", " logger.info( 'training set sizes: #: %d'%(size))\n", " if size == 0:\n", " logger.info( 'WARNING: empty dataset')\n", " outputs.append(0)\n", " else:\n", " start=time()\n", " predictions = predict(train, \n", " estimator=local_estimator, \n", " vectorizer=Vectorizer(4), \n", " mode='predict_proba',\n", " n_jobs=-1)\n", " avg_score=np.mean(predictions[:,1])\n", " logger.info( 'avg score: %.5f' % avg_score)\n", " outputs.append(avg_score)\n", " logger.info( 'elapsed: %.1f sec'%(time()-start))\n", " return outputs" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evaluate(data_fname, size=None, percentages=None, n_repetitions=None, train_test_split=None):\n", " # initializing \n", " graphs = get_graphs(data_fname, size=size)\n", "\n", " # train/test split\n", " from eden.util import random_bipartition_iter\n", " train_global,test_global = random_bipartition_iter(graphs,train_test_split)\n", "\n", " original_repetitions = []\n", " sample_repetitions = []\n", "\n", " for percentage in percentages:\n", " originals = []\n", " originals_samples = []\n", " samples = []\n", " for repetition in range(n_repetitions):\n", " random_state = int(313379*percentage+repetition) \n", " random.seed(random_state)\n", " \n", "\n", " train_global,train_global_ = tee(train_global)\n", " test_global,test_global_ = tee(test_global)\n", "\n", " from sklearn.linear_model import SGDClassifier\n", " estimator = SGDClassifier(average=True, class_weight='auto', shuffle=True, loss='log', n_jobs=-1)\n", " local_estimator = fit(test_global_, \n", " iterable_neg=None,\n", " vectorizer=Vectorizer(4),\n", " estimator=estimator, n_jobs=-1, n_iter_search=1)\n", " \n", " # use shuffled list to create test and sample set\n", " train,train_reminder = random_bipartition_iter(train_global_,percentage)\n", " train,train_ = tee(train)\n", " sampled = fit_sample(train_, random_state=random_state)\n", "\n", " #evaluate the predictive performance on held out test set\n", " start=time()\n", " logger.info( \"=\"*80)\n", " logger.info( 'repetition: %d/%d'%(repetition+1, n_repetitions))\n", " logger.info( \"training percentage:\"+str(percentage))\n", " perf_orig, perf_samp = fit_and_evaluate(train, sampled, local_estimator)\n", " logger.info( 'Time elapsed: %.1f sec'%((time()-start)))\n", " originals.append(perf_orig)\n", " samples.append(perf_samp)\n", "\n", " original_repetitions.append(originals)\n", " sample_repetitions.append(samples)\n", " \n", " return original_repetitions, sample_repetitions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot(dataset, percentages, original_repetitions, sample_repetitions):\n", " gc={'color':'g'}\n", " rc={'color':'r'}\n", " bc={'color':'b'}\n", " ws = 0.02\n", " o = np.mean(original_repetitions, axis=1)\n", " s = np.mean(sample_repetitions, axis=1)\n", " plt.figure(figsize=(18,8))\n", " plt.grid()\n", "\n", " plt.boxplot(original_repetitions, positions=percentages, widths=ws, capprops=rc, medianprops=rc, boxprops=rc, whiskerprops=rc, flierprops=rc)\n", " plt.plot(percentages,o, color='r', marker='o', markeredgewidth=1, markersize=7, markeredgecolor='r', markerfacecolor='w', label='original')\n", "\n", " plt.boxplot(sample_repetitions, positions=percentages, widths=ws, capprops=bc, medianprops=bc, boxprops=bc, whiskerprops=bc, flierprops=bc)\n", " plt.plot(percentages,s, color='b', marker='o', markeredgewidth=1, markersize=7, markeredgecolor='b', markerfacecolor='w', label='sample')\n", "\n", " plt.xlim(percentages[0]-.05,percentages[-1]+.05)\n", " plt.title(dataset+'\\n',fontsize=17)\n", " plt.legend(loc='upper right',fontsize=16)\n", " plt.ylabel('Likelihood',fontsize=16)\n", " plt.xlabel('Dataset size (fraction)',fontsize=16)\n", " plt.savefig('%s_plot_probability_of_samples.pdf' % dataset)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def save_results(result_fname,percentages, original_repetitions,sample_repetitions):\n", " with open(result_fname,'w') as f:\n", " f.write('dataset sizes list:\\n')\n", " for perc in percentages:\n", " f.write('%s '% perc)\n", " f.write('\\n')\n", " f.write('AUC scores:\\n')\n", " for repetitions in original_repetitions,sample_repetitions:\n", " f.write('%s\\n' % len(repetitions))\n", " for repetition in repetitions:\n", " for auc in repetition:\n", " f.write('%s ' % auc)\n", " f.write('\\n')\n", " \n", "def load_results(result_fname):\n", " with open(result_fname) as f:\n", " comment = next(f)\n", " line = next(f)\n", " percentages = [float(x) for x in line.split()]\n", " comment = next(f)\n", "\n", " original_repetitions = []\n", " size = int(next(f))\n", " for i in range(size):\n", " line = next(f)\n", " repetition = [float(x) for x in line.split()]\n", " original_repetitions.append(repetition)\n", "\n", " sample_repetitions = []\n", " size = int(next(f))\n", " for i in range(size):\n", " line = next(f)\n", " repetition = [float(x) for x in line.split()]\n", " sample_repetitions.append(repetition)\n", " \n", " return percentages, original_repetitions,sample_repetitions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#setup\n", "dataset_names = !cat NCI60/names\n", "random.shuffle(dataset_names)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************************************************************************\n", "Working with dataset: HCT_15_t\n", "Working with dataset: HCT_15_t\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 1/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99622\n", "elapsed: 0.7 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 112\n", "avg score: 0.99699\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.4 sec\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 2/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99586\n", "elapsed: 0.6 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 110\n", "avg score: 0.99692\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.0 sec\n", "graph grammar stats:\n", "#instances:56 #interfaces: 96 #cores: 32 #core-interface-pairs: 235\n", "================================================================================\n", "repetition: 3/3\n", "training percentage:0.2\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 56\n", "avg score: 0.99576\n", "elapsed: 0.6 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 112\n", "avg score: 0.99688\n", "elapsed: 1.3 sec\n", "Time elapsed: 8.2 sec\n", "graph grammar stats:\n", "#instances:112 #interfaces: 157 #cores: 43 #core-interface-pairs: 400\n", "================================================================================\n", "repetition: 1/3\n", "training percentage:0.4\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 112\n", "avg score: 0.98841\n", "elapsed: 1.1 sec\n", "--------------------------------------------------------------------------------\n", "working on sample\n", "training set sizes: #: 212\n", "avg score: 0.99597\n", "elapsed: 2.0 sec\n", "Time elapsed: 14.7 sec\n", "graph grammar stats:\n", "#instances:112 #interfaces: 157 #cores: 43 #core-interface-pairs: 400\n", "================================================================================\n", "repetition: 2/3\n", "training percentage:0.4\n", "--------------------------------------------------------------------------------\n", "working on original\n", "training set sizes: #: 112\n", "avg score: 0.98852\n", "elapsed: 1.0 sec\n" ] } ], "source": [ "%%time\n", "for dataset in dataset_names:\n", " #logging\n", " logger = logging.getLogger()\n", " if True:\n", " logger_fname = '%s_probability_of_samples.log'%dataset\n", " else:\n", " logger_fname = None\n", " configure_logging(logger,verbosity=1, filename=logger_fname)\n", " \n", " #main \n", " start=time()\n", " print('*'*80)\n", " print( 'Working with dataset: %s' % dataset )\n", " logger.info( 'Working with dataset: %s' % dataset )\n", " dataset_fname = 'NCI60/' + dataset + '_orig_pos.gspan'\n", " #pos_dataset_fname = 'bursi.pos.gspan'\n", " \n", " percentages=[.2,.4,.6,.8,.95]\n", "\n", " original_repetitions,\\\n", " sample_repetitions = evaluate(dataset_fname,\n", " size=400,\n", " percentages=percentages,\n", " n_repetitions=3,\n", " train_test_split=0.7)\n", " plot(dataset, percentages, original_repetitions, sample_repetitions)\n", " \n", " #save and display results\n", " result_fname='%s_probability_of_samples.data'%dataset\n", " save_results(result_fname,percentages, original_repetitions,sample_repetitions) \n", " percentages_l, original_repetitions_l,sample_repetitions_l = load_results(result_fname)\n", " plot(dataset, percentages_l, original_repetitions_l, sample_repetitions_l)\n", " \n", " print('Time elapsed: %s'%(datetime.timedelta(seconds=(time() - start))))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#display\n", "for dataset in dataset_names:\n", " result_fname='%s_predictive_performance_of_samples.data'%dataset\n", " percentages_l, original_repetitions_l,sample_repetitions_l = load_results(result_fname)\n", " plot(dataset, percentages_l, original_repetitions_l, sample_repetitions_l)\n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

zzsza/TIL

python/pyecharts.ipynb

1

157838

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## pyecharts\n", "- [HTML](http://htmlpreview.github.io/?https://github.com/zzsza/TIL/blob/master/python/pyecharts.html) 에서 확인하면 이쁜 그래프도 보입니다!!!\n", "- Baidu에서 데이터 시각화를 위해 만든 오픈소스인 Echarts의 파이썬 버전\n", "- 다양한 그래프 제공\n", "- [공식 문서](http://pyecharts.org/#/en-us/)\n", "- Dynamic\n", "- 단, 옵션의 단추가 중국어\n", "- 그래프를 그릴 때, echarts와 echartql을 로컬에서 찾으려고 함\n", " - 따라서 nbconvert를 사용해 HTML으로 저장한 후, 쉘에서 수정\n", " \n", " ```\n", " sed -i \"\" \"s|/nbextensions/echarts/echarts-gl.min|https://cdn.jsdelivr.net/npm/[email protected]/dist/echarts-gl.min|g; s|/nbextensions/echarts/echarts.min|https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min|g\" 파일이름.ipynb\n", " ```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pyecharts\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "attr = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", "v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]\n", "v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]\n", "bar = pyecharts.Bar(\"Bar chart\", \"precipitation and evaporation one year\")\n", "bar.add(\"precipitation\", attr, v1, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.add(\"evaporation\", attr, v2, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.render()\n", "bar.height = 500\n", "bar.width = 800" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"4c6948eec4b548ae8750f0d9ecd60f0d\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_4c6948eec4b548ae8750f0d9ecd60f0d = echarts.init(document.getElementById('4c6948eec4b548ae8750f0d9ecd60f0d'), 'light', {renderer: 'canvas'});\n", "\n", "var option_4c6948eec4b548ae8750f0d9ecd60f0d = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar chart\",\n", " \"subtext\": \"precipitation and evaporation one year\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7954956,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"precipitation\",\n", " \"data\": [\n", " 2.0,\n", " 4.9,\n", " 7.0,\n", " 23.2,\n", " 25.6,\n", " 76.7,\n", " 135.6,\n", " 162.2,\n", " 32.6,\n", " 20.0,\n", " 6.4,\n", " 3.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 7954956\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"evaporation\",\n", " \"data\": [\n", " 2.6,\n", " 5.9,\n", " 9.0,\n", " 26.4,\n", " 28.7,\n", " 70.7,\n", " 175.6,\n", " 182.2,\n", " 48.7,\n", " 18.8,\n", " 6.0,\n", " 2.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 7954956\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"precipitation\",\n", " \"evaporation\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"Jan\",\n", " \"Feb\",\n", " \"Mar\",\n", " \"Apr\",\n", " \"May\",\n", " \"Jun\",\n", " \"Jul\",\n", " \"Aug\",\n", " \"Sep\",\n", " \"Oct\",\n", " \"Nov\",\n", " \"Dec\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_4c6948eec4b548ae8750f0d9ecd60f0d.setOption(option_4c6948eec4b548ae8750f0d9ecd60f0d);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c4c2e8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"f01f0ae4fa2746a99fe3ccb43af4a333\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_f01f0ae4fa2746a99fe3ccb43af4a333 = echarts.init(document.getElementById('f01f0ae4fa2746a99fe3ccb43af4a333'), 'dark', {renderer: 'canvas'});\n", "\n", "var option_f01f0ae4fa2746a99fe3ccb43af4a333 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar chart\",\n", " \"subtext\": \"precipitation and evaporation one year\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3700519,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"precipitation\",\n", " \"data\": [\n", " 2.0,\n", " 4.9,\n", " 7.0,\n", " 23.2,\n", " 25.6,\n", " 76.7,\n", " 135.6,\n", " 162.2,\n", " 32.6,\n", " 20.0,\n", " 6.4,\n", " 3.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 3700519\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"evaporation\",\n", " \"data\": [\n", " 2.6,\n", " 5.9,\n", " 9.0,\n", " 26.4,\n", " 28.7,\n", " 70.7,\n", " 175.6,\n", " 182.2,\n", " 48.7,\n", " 18.8,\n", " 6.0,\n", " 2.3\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": [\n", " {\n", " \"type\": \"max\",\n", " \"name\": \"Maximum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " },\n", " {\n", " \"type\": \"min\",\n", " \"name\": \"Minimum\",\n", " \"symbol\": \"pin\",\n", " \"symbolSize\": 50,\n", " \"label\": {\n", " \"normal\": {\n", " \"textStyle\": {\n", " \"color\": \"#fff\"\n", " }\n", " }\n", " }\n", " }\n", " ]\n", " },\n", " \"markLine\": {\n", " \"data\": [\n", " {\n", " \"type\": \"average\",\n", " \"name\": \"mean-Value\"\n", " }\n", " ],\n", " \"symbolSize\": 10\n", " },\n", " \"seriesId\": 3700519\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"precipitation\",\n", " \"evaporation\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"Jan\",\n", " \"Feb\",\n", " \"Mar\",\n", " \"Apr\",\n", " \"May\",\n", " \"Jun\",\n", " \"Jul\",\n", " \"Aug\",\n", " \"Sep\",\n", " \"Oct\",\n", " \"Nov\",\n", " \"Dec\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": []\n", "};\n", "myChart_f01f0ae4fa2746a99fe3ccb43af4a333.setOption(option_f01f0ae4fa2746a99fe3ccb43af4a333);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c99630>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "attr = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n", "v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]\n", "v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]\n", "bar = pyecharts.Bar(\"Bar chart\", \"precipitation and evaporation one year\")\n", "bar.use_theme(\"dark\")\n", "bar.add(\"precipitation\", attr, v1, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.add(\"evaporation\", attr, v2, mark_line=[\"average\"], mark_point=[\"max\", \"min\"])\n", "bar.height = 500\n", "bar.width = 800\n", "bar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In pandas" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"eee57829df8d461f9d1cc8ebc388a707\" style=\"width:800px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_eee57829df8d461f9d1cc8ebc388a707 = echarts.init(document.getElementById('eee57829df8d461f9d1cc8ebc388a707'), 'light', {renderer: 'canvas'});\n", "\n", "var option_eee57829df8d461f9d1cc8ebc388a707 = {\n", " \"title\": [\n", " {\n", " \"text\": \"bar chart2\",\n", " \"subtext\": \"Profit and loss situation\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7257727,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"profit\",\n", " \"data\": [\n", " 0.1263776233106191,\n", " 0.8478258644254499,\n", " -0.7564831723889021,\n", " 0.5878566074181374,\n", " 0.841223198034568,\n", " 0.9710967562559695\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7257727\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"loss\",\n", " \"data\": [\n", " 0.8531044872139661,\n", " 0.20747839963171455,\n", " 0.7117874354985663,\n", " 0.5497325372182054,\n", " 0.07878415027622787,\n", " 0.7974613893080286\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7257727\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"profit\",\n", " \"loss\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"2018-08-31\",\n", " \"2018-09-30\",\n", " \"2018-10-31\",\n", " \"2018-11-30\",\n", " \"2018-12-31\",\n", " \"2019-01-31\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_eee57829df8d461f9d1cc8ebc388a707.setOption(option_eee57829df8d461f9d1cc8ebc388a707);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c99f28>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "title = \"bar chart2\"\n", "index = pd.date_range(\"8/24/2018\", periods=6, freq=\"M\")\n", "df1 = pd.DataFrame(np.random.randn(6), index=index)\n", "df2 = pd.DataFrame(np.random.rand(6), index=index)\n", "\n", "dfvalue1 = [i[0] for i in df1.values]\n", "dfvalue2 = [i[0] for i in df2.values]\n", "_index = [i for i in df1.index.format()]\n", "\n", "bar = pyecharts.Bar(title, \"Profit and loss situation\")\n", "bar.add(\"profit\", _index, dfvalue1)\n", "bar.add(\"loss\", _index, dfvalue2)\n", "bar.height = 500\n", "bar.width = 800\n", "bar" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"461b39c851d14ee99387f227aafdbd9b\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_461b39c851d14ee99387f227aafdbd9b = echarts.init(document.getElementById('461b39c851d14ee99387f227aafdbd9b'), 'light', {renderer: 'canvas'});\n", "\n", "var option_461b39c851d14ee99387f227aafdbd9b = {\n", " \"title\": [\n", " {\n", " \"text\": \"Line Bar\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3925989,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"bar\",\n", " \"data\": [\n", " 10,\n", " 20,\n", " 30,\n", " 40,\n", " 50,\n", " 60\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 3925989\n", " },\n", " {\n", " \"type\": \"line\",\n", " \"name\": \"line\",\n", " \"symbol\": \"emptyCircle\",\n", " \"symbolSize\": 4,\n", " \"smooth\": false,\n", " \"step\": false,\n", " \"showSymbol\": true,\n", " \"data\": [\n", " [\n", " \"A\",\n", " 38\n", " ],\n", " [\n", " \"B\",\n", " 28\n", " ],\n", " [\n", " \"C\",\n", " 58\n", " ],\n", " [\n", " \"D\",\n", " 48\n", " ],\n", " [\n", " \"E\",\n", " 78\n", " ],\n", " [\n", " \"F\",\n", " 68\n", " ]\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"lineStyle\": {\n", " \"normal\": {\n", " \"width\": 1,\n", " \"opacity\": 1,\n", " \"curveness\": 0,\n", " \"type\": \"solid\"\n", " }\n", " },\n", " \"areaStyle\": {\n", " \"opacity\": 0\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 3925989,\n", " \"xAxisIndex\": 0,\n", " \"yAxisIndex\": 0\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"bar\",\n", " \"line\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_461b39c851d14ee99387f227aafdbd9b.setOption(option_461b39c851d14ee99387f227aafdbd9b);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.custom.overlap.Overlap at 0x110c99a20>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Bar, Line, Overlap\n", "\n", "attr = ['A','B','C','D','E','F']\n", "v1 = [10, 20, 30, 40, 50, 60]\n", "v2 = [38, 28, 58, 48, 78, 68]\n", "bar = Bar(\"Line Bar\")\n", "bar.add(\"bar\", attr, v1)\n", "line = Line()\n", "line.add(\"line\", attr, v2)\n", "\n", "overlap = Overlap()\n", "overlap.add(bar)\n", "overlap.add(line)\n", "\n", "overlap" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"1610b0c60e594bb3aba3d9ac043d6b19\" style=\"width:600px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_1610b0c60e594bb3aba3d9ac043d6b19 = echarts.init(document.getElementById('1610b0c60e594bb3aba3d9ac043d6b19'), 'light', {renderer: 'canvas'});\n", "\n", "var option_1610b0c60e594bb3aba3d9ac043d6b19 = {\n", " \"title\": [\n", " {\n", " \"text\": \"pie chart\",\n", " \"left\": \"center\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 4535433,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"pie\",\n", " \"name\": \"A\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 10\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 20\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 30\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 40\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 50\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 60\n", " }\n", " ],\n", " \"radius\": [\n", " \"30%\",\n", " \"75%\"\n", " ],\n", " \"center\": [\n", " \"25%\",\n", " \"50%\"\n", " ],\n", " \"roseType\": \"radius\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"outside\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " }\n", " },\n", " \"seriesId\": 4535433\n", " },\n", " {\n", " \"type\": \"pie\",\n", " \"name\": \"B\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 38\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 28\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 58\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 48\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 78\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 68\n", " }\n", " ],\n", " \"radius\": [\n", " \"30%\",\n", " \"75%\"\n", " ],\n", " \"center\": [\n", " \"75%\",\n", " \"50%\"\n", " ],\n", " \"roseType\": \"area\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"outside\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " },\n", " \"formatter\": \"{b}: {d}%\"\n", " }\n", " },\n", " \"seriesId\": 4535433\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": false,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#444693\",\n", " \"#905a3d\",\n", " \"#61a0a8\",\n", " \"#749f83\",\n", " \"#b2d235\",\n", " \"#1d953f\",\n", " \"#c23531\",\n", " \"#f47920\",\n", " \"#6d8346\",\n", " \"#f05b72\",\n", " \"#6950a1\",\n", " \"#ac6767\",\n", " \"#ca8622\",\n", " \"#ef5b9c\",\n", " \"#c4ccd3\",\n", " \"#d48265\",\n", " \"#f6f5ec\",\n", " \"#546570\",\n", " \"#2a5caa\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#726930\",\n", " \"#2f4554\",\n", " \"#918597\",\n", " \"#fab27b\"\n", " ]\n", "};\n", "myChart_1610b0c60e594bb3aba3d9ac043d6b19.setOption(option_1610b0c60e594bb3aba3d9ac043d6b19);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.pie.Pie at 0x10cf6c4a8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Pie\n", "\n", "attr = ['A','B','C','D','E','F']\n", "v1 = [10, 20, 30, 40, 50, 60]\n", "v2 = [38, 28, 58, 48, 78, 68]\n", "pie = Pie(\"pie chart\", title_pos=\"center\", width=600)\n", "pie.add(\"A\", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius')\n", "pie.add(\"B\", attr, v2, center=[75, 50], is_randome=True, radius=[30, 75], rosetype='area', is_legend_show=False,\n", " is_label_show=True)\n", "pie" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 가로 그래프" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"ac7aa1a3028c490db76818513967b325\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_ac7aa1a3028c490db76818513967b325 = echarts.init(document.getElementById('ac7aa1a3028c490db76818513967b325'), 'light', {renderer: 'canvas'});\n", "\n", "var option_ac7aa1a3028c490db76818513967b325 = {\n", " \"title\": [\n", " {\n", " \"text\": \"\\uac00\\ub85c \\uadf8\\ub798\\ud504\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 7986890,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"A\",\n", " \"data\": [\n", " 10,\n", " 20,\n", " 30,\n", " 40,\n", " 50,\n", " 60\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7986890\n", " },\n", " {\n", " \"type\": \"bar\",\n", " \"name\": \"B\",\n", " \"data\": [\n", " 38,\n", " 28,\n", " 58,\n", " 48,\n", " 78,\n", " 68\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 7986890\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"A\",\n", " \"B\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"A\",\n", " \"B\",\n", " \"C\",\n", " \"D\",\n", " \"E\",\n", " \"F\"\n", " ]\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_ac7aa1a3028c490db76818513967b325.setOption(option_ac7aa1a3028c490db76818513967b325);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c89cc0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar(\"가로 그래프\")\n", "bar.add(\"A\", attr, v1)\n", "bar.add(\"B\", attr, v2, is_convert=True)\n", "bar.width=800\n", "bar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 슬라이더" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"86daa120791743a4b00ca98406bf4054\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_86daa120791743a4b00ca98406bf4054 = echarts.init(document.getElementById('86daa120791743a4b00ca98406bf4054'), 'light', {renderer: 'canvas'});\n", "\n", "var option_86daa120791743a4b00ca98406bf4054 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar - datazoom - slider \",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 8740746,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"data\": [\n", " 20,\n", " 20,\n", " 22,\n", " 7,\n", " 16,\n", " 30,\n", " 25,\n", " 24,\n", " 2,\n", " 18,\n", " 15,\n", " 23,\n", " 26,\n", " 22,\n", " 27,\n", " 17,\n", " 11,\n", " 19,\n", " 20,\n", " 15,\n", " 14,\n", " 30,\n", " 27,\n", " 22,\n", " 16,\n", " 19,\n", " 15,\n", " 4,\n", " 10,\n", " 27\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 8740746\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"0th\",\n", " \"1th\",\n", " \"2th\",\n", " \"3th\",\n", " \"4th\",\n", " \"5th\",\n", " \"6th\",\n", " \"7th\",\n", " \"8th\",\n", " \"9th\",\n", " \"10th\",\n", " \"11th\",\n", " \"12th\",\n", " \"13th\",\n", " \"14th\",\n", " \"15th\",\n", " \"16th\",\n", " \"17th\",\n", " \"18th\",\n", " \"19th\",\n", " \"20th\",\n", " \"21th\",\n", " \"22th\",\n", " \"23th\",\n", " \"24th\",\n", " \"25th\",\n", " \"26th\",\n", " \"27th\",\n", " \"28th\",\n", " \"29th\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ],\n", " \"dataZoom\": [\n", " {\n", " \"show\": true,\n", " \"type\": \"slider\",\n", " \"start\": 50,\n", " \"end\": 100,\n", " \"orient\": \"horizontal\"\n", " }\n", " ]\n", "};\n", "myChart_86daa120791743a4b00ca98406bf4054.setOption(option_86daa120791743a4b00ca98406bf4054);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar.Bar at 0x110c89978>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "\n", "attr = [\"{}th\".format(i) for i in range(30)]\n", "v1 = [random.randint(1, 30) for _ in range(30)]\n", "bar = Bar(\"Bar - datazoom - slider \")\n", "bar.add(\"\", attr, v1, is_label_show=True, is_datazoom_show=True)\n", "# bar.render()\n", "bar" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"154e53f451da4d009a405f450aa79202\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_154e53f451da4d009a405f450aa79202 = echarts.init(document.getElementById('154e53f451da4d009a405f450aa79202'), 'light', {renderer: 'canvas'});\n", "\n", "var option_154e53f451da4d009a405f450aa79202 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Bar - datazoom - xaxis/yaxis\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"series_id\": 2716897,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"bar\",\n", " \"data\": [\n", " 15,\n", " 28,\n", " 27,\n", " 25,\n", " 6,\n", " 10,\n", " 15,\n", " 22,\n", " 8,\n", " 23,\n", " 21,\n", " 20,\n", " 14,\n", " 21,\n", " 30,\n", " 20,\n", " 14,\n", " 3,\n", " 4,\n", " 7,\n", " 23,\n", " 22,\n", " 8,\n", " 19,\n", " 2,\n", " 28,\n", " 23,\n", " 26,\n", " 28,\n", " 8\n", " ],\n", " \"barCategoryGap\": \"20%\",\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 2716897\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"0th\",\n", " \"1th\",\n", " \"2th\",\n", " \"3th\",\n", " \"4th\",\n", " \"5th\",\n", " \"6th\",\n", " \"7th\",\n", " \"8th\",\n", " \"9th\",\n", 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\"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ],\n", " \"visualMap\": {\n", " \"type\": \"continuous\",\n", " \"min\": 0,\n", " \"max\": 20,\n", " \"text\": [\n", " \"high\",\n", " \"low\"\n", " ],\n", " \"textStyle\": {},\n", " \"inRange\": {\n", " \"color\": [\n", " \"#313695\",\n", " \"#4575b4\",\n", " \"#74add1\",\n", " \"#abd9e9\",\n", " \"#e0f3f8\",\n", " \"#ffffbf\",\n", " \"#fee090\",\n", " \"#fdae61\",\n", " \"#f46d43\",\n", " \"#d73027\",\n", " \"#a50026\"\n", " ]\n", " },\n", " \"calculable\": true,\n", " \"splitNumber\": 5,\n", " \"orient\": \"vertical\",\n", " \"left\": \"left\",\n", " \"top\": \"bottom\",\n", " \"showLabel\": true\n", " }\n", "};\n", "myChart_e242e555d23f4588b6b5f71d4a11c3e1.setOption(option_e242e555d23f4588b6b5f71d4a11c3e1);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.bar3D.Bar3D at 0x10cf8ddd8>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Bar3D\n", "\n", "bar3d = Bar3D(\"3D Graph\", width=1200, height=600)\n", "x_axis = [\n", " \"12a\", \"1a\", \"2a\", \"3a\", \"4a\", \"5a\", \"6a\", \"7a\", \"8a\", \"9a\", \"10a\", \"11a\",\n", " \"12p\", \"1p\", \"2p\", \"3p\", \"4p\", \"5p\", \"6p\", \"7p\", \"8p\", \"9p\", \"10p\", \"11p\"\n", " ]\n", "y_axis = [\n", " \"Saturday\", \"Friday\", \"Thursday\", \"Wednesday\", \"Tuesday\", \"Monday\", \"Sunday\"\n", " ]\n", "data = [\n", " [0, 0, 5], [0, 1, 1], [0, 2, 0], [0, 3, 0], [0, 4, 0], [0, 5, 0],\n", " [0, 6, 0], [0, 7, 0], [0, 8, 0], [0, 9, 0], [0, 10, 0], [0, 11, 2],\n", " [0, 12, 4], [0, 13, 1], [0, 14, 1], [0, 15, 3], [0, 16, 4], [0, 17, 6],\n", " [0, 18, 4], [0, 19, 4], [0, 20, 3], [0, 21, 3], [0, 22, 2], [0, 23, 5],\n", " [1, 0, 7], [1, 1, 0], [1, 2, 0], [1, 3, 0], [1, 4, 0], [1, 5, 0],\n", " [1, 6, 0], [1, 7, 0], [1, 8, 0], [1, 9, 0], [1, 10, 5], [1, 11, 2],\n", " [1, 12, 2], [1, 13, 6], [1, 14, 9], [1, 15, 11], [1, 16, 6], [1, 17, 7],\n", " [1, 18, 8], [1, 19, 12], [1, 20, 5], [1, 21, 5], [1, 22, 7], [1, 23, 2],\n", " [2, 0, 1], [2, 1, 1], [2, 2, 0], [2, 3, 0], [2, 4, 0], [2, 5, 0],\n", " [2, 6, 0], [2, 7, 0], [2, 8, 0], [2, 9, 0], [2, 10, 3], [2, 11, 2],\n", " [2, 12, 1], [2, 13, 9], [2, 14, 8], [2, 15, 10], [2, 16, 6], [2, 17, 5],\n", " [2, 18, 5], [2, 19, 5], [2, 20, 7], [2, 21, 4], [2, 22, 2], [2, 23, 4],\n", " [3, 0, 7], [3, 1, 3], [3, 2, 0], [3, 3, 0], [3, 4, 0], [3, 5, 0],\n", " [3, 6, 0], [3, 7, 0], [3, 8, 1], [3, 9, 0], [3, 10, 5], [3, 11, 4],\n", " [3, 12, 7], [3, 13, 14], [3, 14, 13], [3, 15, 12], [3, 16, 9], [3, 17, 5],\n", " [3, 18, 5], [3, 19, 10], [3, 20, 6], [3, 21, 4], [3, 22, 4], [3, 23, 1],\n", " [4, 0, 1], [4, 1, 3], [4, 2, 0], [4, 3, 0], [4, 4, 0], [4, 5, 1],\n", " [4, 6, 0], [4, 7, 0], [4, 8, 0], [4, 9, 2], [4, 10, 4], [4, 11, 4],\n", " [4, 12, 2], [4, 13, 4], [4, 14, 4], [4, 15, 14], [4, 16, 12], [4, 17, 1],\n", " [4, 18, 8], [4, 19, 5], [4, 20, 3], [4, 21, 7], [4, 22, 3], [4, 23, 0],\n", " [5, 0, 2], [5, 1, 1], [5, 2, 0], [5, 3, 3], [5, 4, 0], [5, 5, 0],\n", " [5, 6, 0], [5, 7, 0], [5, 8, 2], [5, 9, 0], [5, 10, 4], [5, 11, 1],\n", " [5, 12, 5], [5, 13, 10], [5, 14, 5], [5, 15, 7], [5, 16, 11], [5, 17, 6],\n", " [5, 18, 0], [5, 19, 5], [5, 20, 3], [5, 21, 4], [5, 22, 2], [5, 23, 0],\n", " [6, 0, 1], [6, 1, 0], [6, 2, 0], [6, 3, 0], [6, 4, 0], [6, 5, 0],\n", " [6, 6, 0], [6, 7, 0], [6, 8, 0], [6, 9, 0], [6, 10, 1], [6, 11, 0],\n", " [6, 12, 2], [6, 13, 1], [6, 14, 3], [6, 15, 4], [6, 16, 0], [6, 17, 0],\n", " [6, 18, 0], [6, 19, 0], [6, 20, 1], [6, 21, 2], [6, 22, 2], [6, 23, 6]\n", " ]\n", "range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',\n", " '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']\n", "bar3d.add(\n", " \"\",\n", " x_axis,\n", " y_axis,\n", " [[d[1], d[0], d[2]] for d in data],\n", " is_visualmap=True,\n", " visual_range=[0, 20],\n", " visual_range_color=range_color,\n", " grid3d_width=200,\n", " grid3d_depth=80,\n", ")\n", "bar3d.width=700\n", "bar3d.height=500\n", "\n", "bar3d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boxplot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"922d391f322e43a1b0addf5a632c1b92\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_922d391f322e43a1b0addf5a632c1b92 = echarts.init(document.getElementById('922d391f322e43a1b0addf5a632c1b92'), 'light', {renderer: 'canvas'});\n", "\n", "var option_922d391f322e43a1b0addf5a632c1b92 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Box plot\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 2083032,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"boxplot\",\n", " \"name\": \"boxplot\",\n", " \"data\": [\n", " [\n", " 650,\n", " 850.0,\n", " 940.0,\n", " 980.0,\n", " 1070\n", " ],\n", " [\n", " 760,\n", " 800.0,\n", " 845.0,\n", " 895.0,\n", " 960\n", " ],\n", " [\n", " 620,\n", " 840.0,\n", " 855.0,\n", " 880.0,\n", " 970\n", " ],\n", " [\n", " 720,\n", " 762.5,\n", " 815.0,\n", " 875.0,\n", " 920\n", " ],\n", " [\n", " 740,\n", " 802.5,\n", " 810.0,\n", " 870.0,\n", " 950\n", " ]\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": false,\n", " \"position\": \"top\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " },\n", " \"markPoint\": {\n", " \"data\": []\n", " },\n", " \"markLine\": {\n", " \"data\": []\n", " },\n", " \"seriesId\": 2083032\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"boxplot\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"xAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"category\",\n", " \"splitLine\": {\n", " \"show\": false\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"data\": [\n", " \"expr1\",\n", " \"expr2\",\n", " \"expr3\",\n", " \"expr4\",\n", " \"expr5\"\n", " ]\n", " }\n", " ],\n", " \"yAxis\": [\n", " {\n", " \"show\": true,\n", " \"nameLocation\": \"middle\",\n", " \"nameGap\": 25,\n", " \"nameTextStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"axisTick\": {\n", " \"alignWithLabel\": false\n", " },\n", " \"inverse\": false,\n", " \"boundaryGap\": true,\n", " \"type\": \"value\",\n", " \"splitLine\": {\n", " \"show\": true\n", " },\n", " \"axisLine\": {\n", " \"lineStyle\": {\n", " \"width\": 1\n", " }\n", " },\n", " \"axisLabel\": {\n", " \"interval\": \"auto\",\n", " \"formatter\": \"{value} \",\n", " \"rotate\": 0,\n", " \"margin\": 8,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_922d391f322e43a1b0addf5a632c1b92.setOption(option_922d391f322e43a1b0addf5a632c1b92);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.boxplot.Boxplot at 0x111688588>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Boxplot\n", "\n", "boxplot = Boxplot(\"Box plot\")\n", "x_axis = ['expr1', 'expr2', 'expr3', 'expr4', 'expr5']\n", "y_axis = [\n", " [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880,\n", " 1000, 980, 930, 650, 760, 810, 1000, 1000, 960, 960],\n", " [960, 940, 960, 940, 880, 800, 850, 880, 900, 840,\n", " 830, 790, 810, 880, 880, 830, 800, 790, 760, 800],\n", " [880, 880, 880, 860, 720, 720, 620, 860, 970, 950,\n", " 880, 910, 850, 870, 840, 840, 850, 840, 840, 840],\n", " [890, 810, 810, 820, 800, 770, 760, 740, 750, 760,\n", " 910, 920, 890, 860, 880, 720, 840, 850, 850, 780],\n", " [890, 840, 780, 810, 760, 810, 790, 810, 820, 850,\n", " 870, 870, 810, 740, 810, 940, 950, 800, 810, 870]\n", "]\n", "_yaxis = boxplot.prepare_data(y_axis) \n", "boxplot.add(\"boxplot\", x_axis, _yaxis)\n", "boxplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 퍼널" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"2547fda17d11453b90bee930d3fe169c\" style=\"width:700px;height:500px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_2547fda17d11453b90bee930d3fe169c = echarts.init(document.getElementById('2547fda17d11453b90bee930d3fe169c'), 'light', {renderer: 'canvas'});\n", "\n", "var option_2547fda17d11453b90bee930d3fe169c = {\n", " \"title\": [\n", " {\n", " \"text\": \"\\ud37c\\ub110 \\uadf8\\ub798\\ud504\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 3591842,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"funnel\",\n", " \"name\": \"\\ud37c\\ub110\",\n", " \"data\": [\n", " {\n", " \"name\": \"A\",\n", " \"value\": 20\n", " },\n", " {\n", " \"name\": \"B\",\n", " \"value\": 40\n", " },\n", " {\n", " \"name\": \"C\",\n", " \"value\": 60\n", " },\n", " {\n", " \"name\": \"D\",\n", " \"value\": 80\n", " },\n", " {\n", " \"name\": \"E\",\n", " \"value\": 100\n", " },\n", " {\n", " \"name\": \"F\",\n", " \"value\": 120\n", " }\n", " ],\n", " \"label\": {\n", " \"normal\": {\n", " \"show\": true,\n", " \"position\": \"inside\",\n", " \"textStyle\": {\n", " \"color\": \"#fff\",\n", " \"fontSize\": 12\n", " }\n", " },\n", " \"emphasis\": {\n", " \"show\": true,\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " }\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"E\",\n", " \"B\",\n", " \"F\",\n", " \"C\",\n", " \"A\",\n", " \"D\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_2547fda17d11453b90bee930d3fe169c.setOption(option_2547fda17d11453b90bee930d3fe169c);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.funnel.Funnel at 0x110c89b38>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Funnel\n", "\n", "attr = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]\n", "value = [20, 40, 60, 80, 100, 120]\n", "funnel = Funnel(\"퍼널 그래프\")\n", "funnel.add(\n", " \"퍼널\",\n", " attr,\n", " value,\n", " is_label_show=True,\n", " label_pos=\"inside\",\n", " label_text_color=\"#fff\",\n", ")\n", "funnel.width=700\n", "funnel.height=500\n", "funnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gauge" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>\n", " require.config({\n", " paths: {\n", " 'echarts': 'https://cdnjs.cloudflare.com/ajax/libs/echarts/4.1.0/echarts.min'\n", " }\n", " });\n", "</script>\n", " <div id=\"50e7288ced72497f84ed17005f045e18\" style=\"width:800px;height:400px;\"></div>\n", "\n", "\n", "<script>\n", " require(['echarts'], function(echarts) {\n", " \n", "var myChart_50e7288ced72497f84ed17005f045e18 = echarts.init(document.getElementById('50e7288ced72497f84ed17005f045e18'), 'light', {renderer: 'canvas'});\n", "\n", "var option_50e7288ced72497f84ed17005f045e18 = {\n", " \"title\": [\n", " {\n", " \"text\": \"Gauge Graph\",\n", " \"left\": \"auto\",\n", " \"top\": \"auto\",\n", " \"textStyle\": {\n", " \"fontSize\": 18\n", " },\n", " \"subtextStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"toolbox\": {\n", " \"show\": true,\n", " \"orient\": \"vertical\",\n", " \"left\": \"95%\",\n", " \"top\": \"center\",\n", " \"feature\": {\n", " \"saveAsImage\": {\n", " \"show\": true,\n", " \"title\": \"\\u4e0b\\u8f7d\\u56fe\\u7247\"\n", " },\n", " \"restore\": {\n", " \"show\": true\n", " },\n", " \"dataView\": {\n", " \"show\": true\n", " }\n", " }\n", " },\n", " \"series_id\": 6762080,\n", " \"tooltip\": {\n", " \"trigger\": \"item\",\n", " \"triggerOn\": \"mousemove|click\",\n", " \"axisPointer\": {\n", " \"type\": \"line\"\n", " },\n", " \"formatter\": \"{a} <br/>{b} : {c}%\",\n", " \"textStyle\": {\n", " \"fontSize\": 14\n", " },\n", " \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n", " \"borderColor\": \"#333\",\n", " \"borderWidth\": 0\n", " },\n", " \"series\": [\n", " {\n", " \"type\": \"gauge\",\n", " \"detail\": {\n", " \"formatter\": \"{value}%\"\n", " },\n", " \"name\": \"\\uc774\\uc6a9\\ub960\",\n", " \"min\": 0,\n", " \"max\": 100,\n", " \"startAngle\": 225,\n", " \"endAngle\": -45,\n", " \"data\": [\n", " {\n", " \"value\": 66.66,\n", " \"name\": \"\\uac00\\uc6b4\\ub370\"\n", " }\n", " ]\n", " }\n", " ],\n", " \"legend\": [\n", " {\n", " \"data\": [\n", " \"\\uc774\\uc6a9\\ub960\"\n", " ],\n", " \"selectedMode\": \"multiple\",\n", " \"show\": true,\n", " \"left\": \"center\",\n", " \"top\": \"top\",\n", " \"orient\": \"horizontal\",\n", " \"textStyle\": {\n", " \"fontSize\": 12\n", " }\n", " }\n", " ],\n", " \"color\": [\n", " \"#c23531\",\n", " \"#2f4554\",\n", " \"#61a0a8\",\n", " \"#d48265\",\n", " \"#749f83\",\n", " \"#ca8622\",\n", " \"#bda29a\",\n", " \"#6e7074\",\n", " \"#546570\",\n", " \"#c4ccd3\",\n", " \"#f05b72\",\n", " \"#ef5b9c\",\n", " \"#f47920\",\n", " \"#905a3d\",\n", " \"#fab27b\",\n", " \"#2a5caa\",\n", " \"#444693\",\n", " \"#726930\",\n", " \"#b2d235\",\n", " \"#6d8346\",\n", " \"#ac6767\",\n", " \"#1d953f\",\n", " \"#6950a1\",\n", " \"#918597\",\n", " \"#f6f5ec\"\n", " ]\n", "};\n", "myChart_50e7288ced72497f84ed17005f045e18.setOption(option_50e7288ced72497f84ed17005f045e18);\n", "\n", " });\n", "</script>\n" ], "text/plain": [ "<pyecharts.charts.gauge.Gauge at 0x10cf8d9e8>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyecharts import Gauge\n", "\n", "gauge = Gauge(\"Gauge Graph\")\n", "gauge.add(\"이용률\", \"가운데\", 66.66)\n", "gauge" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

colour-science/colour-ipython

notebooks/adaptation/cmccat2000.ipynb

1

796

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# !!! D . R . A . F . T !!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CMCCAT2000 Chromatic Adaptation Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliography" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

nick-youngblut/SIPSim

ipynb/bac_genome/fullCyc/trimDataset/rep4_DBL-comm_bw_ALL_FINAL.ipynb

1

1461974

null

mit

hetaodie/hetaodie.github.io

assets/media/uda-ml/deep/azjc/卷积神经网络的例子/dog/dog_app_zh.ipynb

1

546585

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 卷积神经网络（Convolutional Neural Network, CNN）\n", "\n", "## 项目：实现一个狗品种识别算法App\n", "\n", "在这个notebook文件中，有些模板代码已经提供给你，但你还需要实现更多的功能来完成这个项目。除非有明确要求，你无须修改任何已给出的代码。以**'(练习)'**开始的标题表示接下来的代码部分中有你需要实现的功能。这些部分都配有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示。\n", "\n", "除了实现代码外，你还**需要**回答一些与项目及代码相关的问题。每个需要回答的问题都会以 **'问题 X'** 标记。请仔细阅读每个问题，并且在问题后的 **'回答'** 部分写出完整的答案。我们将根据你对问题的回答和撰写代码实现的功能来对你提交的项目进行评分。\n", "\n", ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。\n", "\n", "项目中显示为_选做_的部分可以帮助你的项目脱颖而出，而不是仅仅达到通过的最低要求。如果你决定追求更高的挑战，请在此 notebook 中完成_选做_部分的代码。\n", "\n", "---\n", "\n", "### 让我们开始吧\n", "在这个notebook中，你将迈出第一步，来开发可以作为移动端或 Web应用程序一部分的算法。在这个项目的最后，你的程序将能够把用户提供的任何一个图像作为输入。如果可以从图像中检测到一只狗，它会输出对狗品种的预测。如果图像中是一个人脸，它会预测一个与其最相似的狗的种类。下面这张图展示了完成项目后可能的输出结果。（……实际上我们希望每个学生的输出结果不相同！）\n", "\n", "![Sample Dog Output](images/sample_dog_output.png)\n", "\n", "在现实世界中，你需要拼凑一系列的模型来完成不同的任务；举个例子，用来预测狗种类的算法会与预测人类的算法不同。在做项目的过程中，你可能会遇到不少失败的预测，因为并不存在完美的算法和模型。你最终提交的不完美的解决方案也一定会给你带来一个有趣的学习经验！\n", "\n", "### 项目内容\n", "\n", "我们将这个notebook分为不同的步骤，你可以使用下面的链接来浏览此notebook。\n", "\n", "* [Step 0](#step0): 导入数据集\n", "* [Step 1](#step1): 检测人脸\n", "* [Step 2](#step2): 检测狗狗\n", "* [Step 3](#step3): 从头创建一个CNN来分类狗品种\n", "* [Step 4](#step4): 使用一个CNN来区分狗的品种(使用迁移学习)\n", "* [Step 5](#step5): 建立一个CNN来分类狗的品种（使用迁移学习）\n", "* [Step 6](#step6): 完成你的算法\n", "* [Step 7](#step7): 测试你的算法\n", "\n", "在该项目中包含了如下的问题：\n", "\n", "* [问题 1](#question1)\n", "* [问题 2](#question2)\n", "* [问题 3](#question3)\n", "* [问题 4](#question4)\n", "* [问题 5](#question5)\n", "* [问题 6](#question6)\n", "* [问题 7](#question7)\n", "* [问题 8](#question8)\n", "* [问题 9](#question9)\n", "* [问题 10](#question10)\n", "* [问题 11](#question11)\n", "\n", "\n", "---\n", "<a id='step0'></a>\n", "## 步骤 0: 导入数据集\n", "\n", "### 导入狗数据集\n", "在下方的代码单元（cell）中，我们导入了一个狗图像的数据集。我们使用 scikit-learn 库中的 `load_files` 函数来获取一些变量：\n", "- `train_files`, `valid_files`, `test_files` - 包含图像的文件路径的numpy数组\n", "- `train_targets`, `valid_targets`, `test_targets` - 包含独热编码分类标签的numpy数组\n", "- `dog_names` - 由字符串构成的与标签相对应的狗的种类" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 133 total dog categories.\n", "There are 8351 total dog images.\n", "\n", "There are 6680 training dog images.\n", "There are 835 validation dog images.\n", "There are 836 test dog images.\n" ] } ], "source": [ "from sklearn.datasets import load_files \n", "from keras.utils import np_utils\n", "import numpy as np\n", "from glob import glob\n", "\n", "# define function to load train, test, and validation datasets\n", "def load_dataset(path):\n", " data = load_files(path)\n", " dog_files = np.array(data['filenames'])\n", " dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n", " return dog_files, dog_targets\n", "\n", "# load train, test, and validation datasets\n", "train_files, train_targets = load_dataset('/data/dog_images/train')\n", "valid_files, valid_targets = load_dataset('/data/dog_images/valid')\n", "test_files, test_targets = load_dataset('/data/dog_images/test')\n", "\n", "# load list of dog names\n", "dog_names = [item[20:-1] for item in sorted(glob(\"/data/dog_images/train/*/\"))]\n", "\n", "# print statistics about the dataset\n", "print('There are %d total dog categories.' % len(dog_names))\n", "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n", "print('There are %d training dog images.' % len(train_files))\n", "print('There are %d validation dog images.' % len(valid_files))\n", "print('There are %d test dog images.'% len(test_files))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 导入人脸数据集\n", "\n", "在下方的代码单元中，我们导入人脸图像数据集，文件所在路径存储在名为 `human_files` 的 numpy 数组。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 13233 total human images.\n" ] } ], "source": [ "import random\n", "random.seed(8675309)\n", "\n", "# 加载打乱后的人脸数据集的文件名\n", "human_files = np.array(glob(\"/data/lfw/*/*\"))\n", "random.shuffle(human_files)\n", "\n", "# 打印数据集的数据量\n", "print('There are %d total human images.' % len(human_files))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step1'></a>\n", "## 步骤1：检测人脸\n", " \n", "我们将使用 OpenCV 中的 [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) 来检测图像中的人脸。OpenCV 提供了很多预训练的人脸检测模型，它们以XML文件保存在 [github](https://github.com/opencv/opencv/tree/master/data/haarcascades)。我们已经下载了其中一个检测模型，并且把它存储在 `haarcascades` 的目录中。\n", "\n", "在如下代码单元中，我们将演示如何使用这个检测模型在样本图像中找到人脸。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of faces detected: 1\n" ] }, { "data": { 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa0c1014b38>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import cv2 \n", "import matplotlib.pyplot as plt \n", "%matplotlib inline \n", "\n", "# 提取预训练的人脸检测模型\n", "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n", "\n", "# 加载彩色（通道顺序为BGR）图像\n", "img = cv2.imread(human_files[3])\n", "\n", "# 将BGR图像进行灰度处理\n", "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", "# 在图像中找出脸\n", "faces = face_cascade.detectMultiScale(gray)\n", "\n", "# 打印图像中检测到的脸的个数\n", "print('Number of faces detected:', len(faces))\n", "\n", "# 获取每一个所检测到的脸的识别框\n", "for (x,y,w,h) in faces:\n", " # 在人脸图像中绘制出识别框\n", " cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n", " \n", "# 将BGR图像转变为RGB图像以打印\n", "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", "\n", "# 展示含有识别框的图像\n", "plt.imshow(cv_rgb)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在使用任何一个检测模型之前，将图像转换为灰度图是常用过程。`detectMultiScale` 函数使用储存在 `face_cascade` 中的的数据，对输入的灰度图像进行分类。\n", "\n", "在上方的代码中，`faces` 以 numpy 数组的形式，保存了识别到的面部信息。它其中每一行表示一个被检测到的脸，该数据包括如下四个信息：前两个元素 `x`、`y` 代表识别框左上角的 x 和 y 坐标（参照上图，注意 y 坐标的方向和我们默认的方向不同）；后两个元素代表识别框在 x 和 y 轴两个方向延伸的长度 `w` 和 `d`。 \n", "\n", "### 写一个人脸识别器\n", "\n", "我们可以将这个程序封装为一个函数。该函数的输入为人脸图像的**路径**，当图像中包含人脸时，该函数返回 `True`，反之返回 `False`。该函数定义如下所示。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# 如果img_path路径表示的图像检测到了脸，返回\"True\" \n", "def face_detector(img_path):\n", " img = cv2.imread(img_path)\n", " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", " faces = face_cascade.detectMultiScale(gray)\n", " return len(faces) > 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **【练习】** 评估人脸检测模型" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "---\n", "\n", "<a id='question1'></a>\n", "### __问题 1:__ \n", "\n", "在下方的代码块中，使用 `face_detector` 函数，计算：\n", "\n", "- `human_files` 的前100张图像中，能够检测到**人脸**的图像占比多少？\n", "- `dog_files` 的前100张图像中，能够检测到**人脸**的图像占比多少？\n", "\n", "理想情况下，人图像中检测到人脸的概率应当为100%，而狗图像中检测到人脸的概率应该为0%。你会发现我们的算法并非完美，但结果仍然是可以接受的。我们从每个数据集中提取前100个图像的文件路径，并将它们存储在`human_files_short`和`dog_files_short`中。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percentage of detecting human faces in human files is: 1.0\n", "The percentage of detecting human faces in dog files is: 0.11\n" ] } ], "source": [ "human_files_short = human_files[:100]\n", "dog_files_short = train_files[:100]\n", "## 请不要修改上方代码\n", "\n", "## TODO: 基于human_files_short和dog_files_short\n", "## 中的图像测试face_detector的表现\n", "human_files_short_detect = 0\n", "dog_files_short_detect = 0\n", "\n", "for i in range(100):\n", " if (face_detector(human_files_short[i])):\n", " human_files_short_detect += 1\n", " if (face_detector(dog_files_short[i])):\n", " dog_files_short_detect += 1\n", "\n", "print(\"The percentage of detecting human faces in human files is:\", human_files_short_detect/human_files_short.size)\n", "print(\"The percentage of detecting human faces in dog files is:\", dog_files_short_detect/dog_files_short.size)\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='question2'></a>\n", "\n", "### __问题 2:__ \n", "\n", "就算法而言，该算法成功与否的关键在于，用户能否提供含有清晰面部特征的人脸图像。\n", "那么你认为，这样的要求在实际使用中对用户合理吗？如果你觉得不合理，你能否想到一个方法，即使图像中并没有清晰的面部特征，也能够检测到人脸？\n", "\n", "__回答:__\n", "\n", "不太合理，因为图片的来源不同，不能保证所有的图片的脸部都是清晰的。如果脸部特征不太清晰，应对图片进行前期的预处理。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='Selection1'></a>\n", "### 选做：\n", "\n", "我们建议在你的算法中使用opencv的人脸检测模型去检测人类图像，不过你可以自由地探索其他的方法，尤其是尝试使用深度学习来解决它:)。请用下方的代码单元来设计和测试你的面部监测算法。如果你决定完成这个_选做_任务，你需要报告算法在每一个数据集上的表现。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "## (选做) TODO: 报告另一个面部检测算法在LFW数据集上的表现\n", "### 你可以随意使用所需的代码单元数" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step2'></a>\n", "\n", "## 步骤 2: 检测狗狗\n", "\n", "在这个部分中，我们使用预训练的 [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) 模型去检测图像中的狗。下方的第一行代码就是下载了 ResNet-50 模型的网络结构参数，以及基于 [ImageNet](http://www.image-net.org/) 数据集的预训练权重。\n", "\n", "ImageNet 这目前一个非常流行的数据集，常被用来测试图像分类等计算机视觉任务相关的算法。它包含超过一千万个 URL，每一个都链接到 [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中所对应的一个物体的图像。任给输入一个图像，该 ResNet-50 模型会返回一个对图像中物体的预测结果。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n", "102858752/102853048 [==============================] - 1s 0us/step\n" ] } ], "source": [ "from keras.applications.resnet50 import ResNet50\n", "\n", "# 定义ResNet50模型\n", "ResNet50_model = ResNet50(weights='imagenet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 数据预处理\n", "\n", "- 在使用 TensorFlow 作为后端的时候，在 Keras 中，CNN 的输入是一个4维数组（也被称作4维张量），它的各维度尺寸为 `(nb_samples, rows, columns, channels)`。其中 `nb_samples` 表示图像（或者样本）的总数，`rows`, `columns`, 和 `channels` 分别表示图像的行数、列数和通道数。\n", "\n", "\n", "- 下方的 `path_to_tensor` 函数实现如下将彩色图像的字符串型的文件路径作为输入，返回一个4维张量，作为 Keras CNN 输入。因为我们的输入图像是彩色图像，因此它们具有三个通道（ `channels` 为 `3`）。\n", " 1. 该函数首先读取一张图像，然后将其缩放为 224×224 的图像。\n", " 2. 随后，该图像被调整为具有4个维度的张量。\n", " 3. 对于任一输入图像，最后返回的张量的维度是：`(1, 224, 224, 3)`。\n", "\n", "\n", "- `paths_to_tensor` 函数将图像路径的字符串组成的 numpy 数组作为输入，并返回一个4维张量，各维度尺寸为 `(nb_samples, 224, 224, 3)`。在这里，`nb_samples`是提供的图像路径的数据中的样本数量或图像数量。你也可以将 `nb_samples` 理解为数据集中3维张量的个数（每个3维张量表示一个不同的图像。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from keras.preprocessing import image \n", "from tqdm import tqdm\n", "\n", "def path_to_tensor(img_path):\n", " # 用PIL加载RGB图像为PIL.Image.Image类型\n", " img = image.load_img(img_path, target_size=(224, 224))\n", " # 将PIL.Image.Image类型转化为格式为(224, 224, 3)的3维张量\n", " x = image.img_to_array(img)\n", " # 将3维张量转化为格式为(1, 224, 224, 3)的4维张量并返回\n", " return np.expand_dims(x, axis=0)\n", "\n", "def paths_to_tensor(img_paths):\n", " list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n", " return np.vstack(list_of_tensors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 基于 ResNet-50 架构进行预测\n", "\n", "对于通过上述步骤得到的四维张量，在把它们输入到 ResNet-50 网络、或 Keras 中其他类似的预训练模型之前，还需要进行一些额外的处理：\n", "1. 首先，这些图像的通道顺序为 RGB，我们需要重排他们的通道顺序为 BGR。\n", "2. 其次，预训练模型的输入都进行了额外的归一化过程。因此我们在这里也要对这些张量进行归一化，即对所有图像所有像素都减去像素均值 `[103.939, 116.779, 123.68]`（以 RGB 模式表示，根据所有的 ImageNet 图像算出）。\n", "\n", "导入的 `preprocess_input` 函数实现了这些功能。如果你对此很感兴趣，可以在 [这里](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py) 查看 `preprocess_input`的代码。\n", "\n", "\n", "在实现了图像处理的部分之后，我们就可以使用模型来进行预测。这一步通过 `predict` 方法来实现，它返回一个向量，向量的第 i 个元素表示该图像属于第 i 个 ImageNet 类别的概率。这通过如下的 `ResNet50_predict_labels` 函数实现。\n", "\n", "通过对预测出的向量取用 argmax 函数（找到有最大概率值的下标序号），我们可以得到一个整数，即模型预测到的物体的类别。进而根据这个 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a)，我们能够知道这具体是哪个品种的狗狗。\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from keras.applications.resnet50 import preprocess_input, decode_predictions\n", "def ResNet50_predict_labels(img_path):\n", " # 返回img_path路径的图像的预测向量\n", " img = preprocess_input(path_to_tensor(img_path))\n", " return np.argmax(ResNet50_model.predict(img))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 完成狗检测模型\n", "\n", "\n", "在研究该 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 的时候，你会注意到，狗类别对应的序号为151-268。因此，在检查预训练模型判断图像是否包含狗的时候，我们只需要检查如上的 `ResNet50_predict_labels` 函数是否返回一个介于151和268之间（包含区间端点）的值。\n", "\n", "我们通过这些想法来完成下方的 `dog_detector` 函数，如果从图像中检测到狗就返回 `True`，否则返回 `False`。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def dog_detector(img_path):\n", " prediction = ResNet50_predict_labels(img_path)\n", " return ((prediction <= 268) & (prediction >= 151)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【作业】评估狗狗检测模型\n", "\n", "---\n", "\n", "<a id='question3'></a>\n", "### __问题 3:__ \n", "\n", "在下方的代码块中，使用 `dog_detector` 函数，计算：\n", "\n", "- `human_files_short`中图像检测到狗狗的百分比？\n", "- `dog_files_short`中图像检测到狗狗的百分比？" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percentage of detecting dogs in human files is: 0.0\n", "The percentage of detecting dogs in dog files is: 1.0\n" ] } ], "source": [ "### TODO: 测试dog_detector函数在human_files_short和dog_files_short的表现\n", "human_files_short_detect = 0\n", "dog_files_short_detect = 0\n", "\n", "for i in range(100):\n", " if (dog_detector(human_files_short[i])):\n", " human_files_short_detect += 1\n", " if (dog_detector(dog_files_short[i])):\n", " dog_files_short_detect += 1\n", "\n", "print(\"The percentage of detecting dogs in human files is:\", human_files_short_detect/human_files_short.size)\n", "print(\"The percentage of detecting dogs in dog files is:\", dog_files_short_detect/dog_files_short.size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='step3'></a>\n", "\n", "## 步骤 3: 从头开始创建一个CNN来分类狗品种\n", "\n", "\n", "现在我们已经实现了一个函数，能够在图像中识别人类及狗狗。但我们需要更进一步的方法，来对狗的类别进行识别。在这一步中，你需要实现一个卷积神经网络来对狗的品种进行分类。你需要__从头实现__你的卷积神经网络（在这一阶段，你还不能使用迁移学习），并且你需要达到超过1%的测试集准确率。在本项目的步骤五种，你还有机会使用迁移学习来实现一个准确率大大提高的模型。\n", "\n", "在添加卷积层的时候，注意不要加上太多的（可训练的）层。更多的参数意味着更长的训练时间，也就是说你更可能需要一个 GPU 来加速训练过程。万幸的是，Keras 提供了能够轻松预测每次迭代（epoch）花费时间所需的函数。你可以据此推断你算法所需的训练时间。\n", "\n", "值得注意的是，对狗的图像进行分类是一项极具挑战性的任务。因为即便是一个正常人，也很难区分布列塔尼犬和威尔士史宾格犬。\n", "\n", "\n", "布列塔尼犬（Brittany） | 威尔士史宾格犬（Welsh Springer Spaniel）\n", "- | - \n", "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n", "\n", "不难发现其他的狗品种会有很小的类间差别（比如金毛寻回犬和美国水猎犬）。\n", "\n", "\n", "金毛寻回犬（Curly-Coated Retriever） | 美国水猎犬（American Water Spaniel）\n", "- | -\n", "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n", "\n", "同样，拉布拉多犬（labradors）有黄色、棕色和黑色这三种。那么你设计的基于视觉的算法将不得不克服这种较高的类间差别，以达到能够将这些不同颜色的同类狗分到同一个品种中。\n", "\n", "黄色拉布拉多犬（Yellow Labrador） | 棕色拉布拉多犬（Chocolate Labrador） | 黑色拉布拉多犬（Black Labrador）\n", "- | -\n", "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n", "\n", "我们也提到了随机分类将得到一个非常低的结果：不考虑品种略有失衡的影响，随机猜测到正确品种的概率是1/133，相对应的准确率是低于1%的。\n", "\n", "请记住，在深度学习领域，实践远远高于理论。大量尝试不同的框架吧，相信你的直觉！当然，玩得开心！\n", "\n", "\n", "### 数据预处理\n", "\n", "\n", "通过对每张图像的像素值除以255，我们对图像实现了归一化处理。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 6680/6680 [01:25<00:00, 78.01it/s] \n", "100%|██████████| 835/835 [00:09<00:00, 86.10it/s] \n", "100%|██████████| 836/836 [00:09<00:00, 86.80it/s] \n" ] } ], "source": [ "from PIL import ImageFile \n", "ImageFile.LOAD_TRUNCATED_IMAGES = True \n", "\n", "# Keras中的数据预处理过程\n", "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n", "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n", "test_tensors = paths_to_tensor(test_files).astype('float32')/255" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【练习】模型架构\n", "\n", "\n", "创建一个卷积神经网络来对狗品种进行分类。在你代码块的最后，执行 `model.summary()` 来输出你模型的总结信息。\n", " \n", "我们已经帮你导入了一些所需的 Python 库，如有需要你可以自行导入。如果你在过程中遇到了困难，如下是给你的一点小提示——该模型能够在5个 epoch 内取得超过1%的测试准确率，并且能在CPU上很快地训练。\n", "\n", "![Sample CNN](images/sample_cnn.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='question4'></a> \n", "\n", "### __问题 4:__ \n", "\n", "在下方的代码块中尝试使用 Keras 搭建卷积网络的架构，并回答相关的问题。\n", "\n", "1. 你可以尝试自己搭建一个卷积网络的模型，那么你需要回答你搭建卷积网络的具体步骤（用了哪些层）以及为什么这样搭建。\n", "2. 你也可以根据上图提示的步骤搭建卷积网络，那么请说明为何如上的架构能够在该问题上取得很好的表现。\n", "\n", "__回答:__ \n", "我选择根据上图提示搭建卷积神经网络。首先，搭建三层卷积层可以检测更高级的特征，以达到狗狗品种分类的目的。同时，两个卷积层之间的池化层有效降低了数据的复杂度，使得训练效率得到有效提升" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_9 (Conv2D) (None, 223, 223, 16) 208 \n", "_________________________________________________________________\n", "max_pooling2d_10 (MaxPooling (None, 111, 111, 16) 0 \n", "_________________________________________________________________\n", "dropout_8 (Dropout) (None, 111, 111, 16) 0 \n", "_________________________________________________________________\n", "conv2d_10 (Conv2D) (None, 110, 110, 32) 2080 \n", "_________________________________________________________________\n", "max_pooling2d_11 (MaxPooling (None, 55, 55, 32) 0 \n", "_________________________________________________________________\n", "dropout_9 (Dropout) (None, 55, 55, 32) 0 \n", "_________________________________________________________________\n", "conv2d_11 (Conv2D) (None, 54, 54, 64) 8256 \n", "_________________________________________________________________\n", "max_pooling2d_12 (MaxPooling (None, 27, 27, 64) 0 \n", "_________________________________________________________________\n", "dropout_10 (Dropout) (None, 27, 27, 64) 0 \n", "_________________________________________________________________\n", "global_average_pooling2d_3 ( (None, 64) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 133) 8645 \n", "=================================================================\n", "Total params: 19,189\n", "Trainable params: 19,189\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n", "from keras.layers import Dropout, Flatten, Dense\n", "from keras.models import Sequential\n", "\n", "model = Sequential()\n", "\n", "### TODO: 定义你的网络架构\n", "model.add(Conv2D(filters=16, kernel_size=2, input_shape=(224, 224, 3), activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "model.add(Dropout(0.2))\n", "model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "\n", "model.add(Dropout(0.2))\n", "model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))\n", "model.add(MaxPooling2D(pool_size=2))\n", "model.add(Dropout(0.2))\n", "\n", "model.add(GlobalAveragePooling2D())\n", "model.add(Dense(133, activation='softmax'))\n", " \n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "## 编译模型\n", "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 【练习】训练模型\n", "\n", "\n", "---\n", "\n", "<a id='question5'></a> \n", "\n", "### __问题 5:__ \n", "\n", "在下方代码单元训练模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n", "\n", "可选题：你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)，来优化模型的表现。\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8817 - acc: 0.0105Epoch 00001: val_loss improved from inf to 4.87374, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 22s 3ms/step - loss: 4.8816 - acc: 0.0106 - val_loss: 4.8737 - val_acc: 0.0120\n", "Epoch 2/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8539 - acc: 0.0120Epoch 00002: val_loss improved from 4.87374 to 4.84636, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.8536 - acc: 0.0120 - val_loss: 4.8464 - val_acc: 0.0168\n", "Epoch 3/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.8110 - acc: 0.0195Epoch 00003: val_loss improved from 4.84636 to 4.80888, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 22s 3ms/step - loss: 4.8109 - acc: 0.0195 - val_loss: 4.8089 - val_acc: 0.0168\n", "Epoch 4/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.7768 - acc: 0.0201Epoch 00004: val_loss improved from 4.80888 to 4.77889, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.7767 - acc: 0.0202 - val_loss: 4.7789 - val_acc: 0.0251\n", "Epoch 5/5\n", "6660/6680 [============================>.] - ETA: 0s - loss: 4.7451 - acc: 0.0243Epoch 00005: val_loss improved from 4.77889 to 4.75447, saving model to saved_models/weights.best.from_scratch.hdf5\n", "6680/6680 [==============================] - 21s 3ms/step - loss: 4.7449 - acc: 0.0243 - val_loss: 4.7545 - val_acc: 0.0240\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fa0ac6f8390>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from keras.callbacks import ModelCheckpoint \n", "\n", "### TODO: 设置训练模型的epochs的数量\n", "\n", "epochs = 5\n", "\n", "### 不要修改下方代码\n", "\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "model.fit(train_tensors, train_targets, \n", " validation_data=(valid_tensors, valid_targets),\n", " epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "## 加载具有最好验证loss的模型\n", "\n", "model.load_weights('saved_models/weights.best.from_scratch.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 测试模型\n", "\n", "在狗图像的测试数据集上试用你的模型。确保测试准确率大于1%。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 2.6316%\n" ] } ], "source": [ "# 获取测试数据集中每一个图像所预测的狗品种的index\n", "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step4'></a>\n", "## 步骤 4: 使用一个CNN来区分狗的品种\n", "\n", "\n", "使用迁移学习（Transfer Learning）的方法，能帮助我们在不损失准确率的情况下大大减少训练时间。在以下步骤中，你可以尝试使用迁移学习来训练你自己的CNN。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 得到从图像中提取的特征向量（Bottleneck Features）" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')\n", "train_VGG16 = bottleneck_features['train']\n", "valid_VGG16 = bottleneck_features['valid']\n", "test_VGG16 = bottleneck_features['test']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 模型架构\n", "\n", "该模型使用预训练的 VGG-16 模型作为固定的图像特征提取器，其中 VGG-16 最后一层卷积层的输出被直接输入到我们的模型。我们只需要添加一个全局平均池化层以及一个全连接层，其中全连接层使用 softmax 激活函数，对每一个狗的种类都包含一个节点。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "global_average_pooling2d_4 ( (None, 512) 0 \n", "_________________________________________________________________\n", "dense_4 (Dense) (None, 133) 68229 \n", "=================================================================\n", "Total params: 68,229\n", "Trainable params: 68,229\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "VGG16_model = Sequential()\n", "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n", "VGG16_model.add(Dense(133, activation='softmax'))\n", "\n", "VGG16_model.summary()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "## 编译模型\n", "\n", "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 11.8988 - acc: 0.1303Epoch 00001: val_loss improved from inf to 10.57918, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 308us/step - loss: 11.8949 - acc: 0.1307 - val_loss: 10.5792 - val_acc: 0.2132\n", "Epoch 2/20\n", "6500/6680 [============================>.] - ETA: 0s - loss: 9.6765 - acc: 0.2878Epoch 00002: val_loss improved from 10.57918 to 9.51589, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 253us/step - loss: 9.6727 - acc: 0.2880 - val_loss: 9.5159 - val_acc: 0.2862\n", "Epoch 3/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 8.9693 - acc: 0.3641Epoch 00003: val_loss improved from 9.51589 to 9.13973, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 248us/step - loss: 8.9632 - acc: 0.3645 - val_loss: 9.1397 - val_acc: 0.3341\n", "Epoch 4/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 8.7415 - acc: 0.4006Epoch 00004: val_loss improved from 9.13973 to 9.10053, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 255us/step - loss: 8.7140 - acc: 0.4024 - val_loss: 9.1005 - val_acc: 0.3485\n", "Epoch 5/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 8.4844 - acc: 0.4242Epoch 00005: val_loss improved from 9.10053 to 8.91824, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 250us/step - loss: 8.4887 - acc: 0.4241 - val_loss: 8.9182 - val_acc: 0.3593\n", "Epoch 6/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 8.2685 - acc: 0.4466Epoch 00006: val_loss improved from 8.91824 to 8.75737, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 255us/step - loss: 8.2920 - acc: 0.4454 - val_loss: 8.7574 - val_acc: 0.3701\n", "Epoch 7/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 8.1670 - acc: 0.4601Epoch 00007: val_loss improved from 8.75737 to 8.58505, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 250us/step - loss: 8.1624 - acc: 0.4605 - val_loss: 8.5851 - val_acc: 0.3904\n", "Epoch 8/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 7.8733 - acc: 0.4834Epoch 00008: val_loss improved from 8.58505 to 8.57290, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 253us/step - loss: 7.9312 - acc: 0.4799 - val_loss: 8.5729 - val_acc: 0.3892\n", "Epoch 9/20\n", "6660/6680 [============================>.] - ETA: 0s - loss: 7.8558 - acc: 0.4898Epoch 00009: val_loss improved from 8.57290 to 8.37072, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.8589 - acc: 0.4897 - val_loss: 8.3707 - val_acc: 0.3952\n", "Epoch 10/20\n", "6640/6680 [============================>.] - ETA: 0s - loss: 7.7289 - acc: 0.5017Epoch 00010: val_loss improved from 8.37072 to 8.36277, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.7267 - acc: 0.5016 - val_loss: 8.3628 - val_acc: 0.4084\n", "Epoch 11/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.6539 - acc: 0.5122Epoch 00011: val_loss did not improve\n", "6680/6680 [==============================] - 2s 250us/step - loss: 7.6913 - acc: 0.5097 - val_loss: 8.3630 - val_acc: 0.4048\n", "Epoch 12/20\n", "6480/6680 [============================>.] - ETA: 0s - loss: 7.6779 - acc: 0.5133Epoch 00012: val_loss improved from 8.36277 to 8.26086, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 259us/step - loss: 7.6780 - acc: 0.5133 - val_loss: 8.2609 - val_acc: 0.4240\n", "Epoch 13/20\n", "6660/6680 [============================>.] - ETA: 0s - loss: 7.6599 - acc: 0.5171Epoch 00013: val_loss did not improve\n", "6680/6680 [==============================] - 2s 255us/step - loss: 7.6636 - acc: 0.5169 - val_loss: 8.3361 - val_acc: 0.4204\n", "Epoch 14/20\n", "6520/6680 [============================>.] - ETA: 0s - loss: 7.6194 - acc: 0.5199Epoch 00014: val_loss improved from 8.26086 to 8.22236, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 251us/step - loss: 7.6497 - acc: 0.5181 - val_loss: 8.2224 - val_acc: 0.4180\n", "Epoch 15/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.5786 - acc: 0.5213Epoch 00015: val_loss improved from 8.22236 to 8.12911, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 254us/step - loss: 7.5671 - acc: 0.5222 - val_loss: 8.1291 - val_acc: 0.4263\n", "Epoch 16/20\n", "6500/6680 [============================>.] - ETA: 0s - loss: 7.4843 - acc: 0.5260Epoch 00016: val_loss did not improve\n", "6680/6680 [==============================] - 2s 252us/step - loss: 7.4813 - acc: 0.5256 - val_loss: 8.1297 - val_acc: 0.4251\n", "Epoch 17/20\n", "6580/6680 [============================>.] - ETA: 0s - loss: 7.3097 - acc: 0.5305Epoch 00017: val_loss improved from 8.12911 to 8.00258, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 259us/step - loss: 7.2970 - acc: 0.5314 - val_loss: 8.0026 - val_acc: 0.4311\n", "Epoch 18/20\n", "6480/6680 [============================>.] - ETA: 0s - loss: 7.1743 - acc: 0.5424Epoch 00018: val_loss improved from 8.00258 to 7.90895, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 256us/step - loss: 7.1896 - acc: 0.5416 - val_loss: 7.9089 - val_acc: 0.4251\n", "Epoch 19/20\n", "6540/6680 [============================>.] - ETA: 0s - loss: 7.0790 - acc: 0.5492Epoch 00019: val_loss did not improve\n", "6680/6680 [==============================] - 2s 249us/step - loss: 7.0858 - acc: 0.5488 - val_loss: 7.9667 - val_acc: 0.4335\n", "Epoch 20/20\n", "6460/6680 [============================>.] - ETA: 0s - loss: 7.0128 - acc: 0.5554Epoch 00020: val_loss improved from 7.90895 to 7.80676, saving model to saved_models/weights.best.VGG16.hdf5\n", "6680/6680 [==============================] - 2s 249us/step - loss: 7.0278 - acc: 0.5543 - val_loss: 7.8068 - val_acc: 0.4335\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fa08fc43d68>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## 训练模型\n", "\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "VGG16_model.fit(train_VGG16, train_targets, \n", " validation_data=(valid_VGG16, valid_targets),\n", " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "## 加载具有最好验证loss的模型\n", "\n", "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 测试模型\n", "现在，我们可以测试此CNN在狗图像测试数据集中识别品种的效果如何。我们在下方打印出测试准确率。" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 43.4211%\n" ] } ], "source": [ "# 获取测试数据集中每一个图像所预测的狗品种的index\n", "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 使用模型预测狗的品种" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from extract_bottleneck_features import *\n", "\n", "def VGG16_predict_breed(img_path):\n", " # 提取bottleneck特征\n", " bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n", " # 获取预测向量\n", " predicted_vector = VGG16_model.predict(bottleneck_feature)\n", " # 返回此模型预测的狗的品种\n", " return dog_names[np.argmax(predicted_vector)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step5'></a>\n", "## 步骤 5: 建立一个CNN来分类狗的品种（使用迁移学习）\n", "\n", "现在你将使用迁移学习来建立一个CNN，从而可以从图像中识别狗的品种。你的 CNN 在测试集上的准确率必须至少达到60%。\n", "\n", "在步骤4中，我们使用了迁移学习来创建一个使用基于 VGG-16 提取的特征向量来搭建一个 CNN。在本部分内容中，你必须使用另一个预训练模型来搭建一个 CNN。为了让这个任务更易实现，我们已经预先对目前 keras 中可用的几种网络进行了预训练：\n", "\n", "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n", "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n", "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n", "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n", "\n", "这些文件被命名为为：\n", "\n", " Dog{network}Data.npz\n", "\n", "其中 `{network}` 可以是 `VGG19`、`Resnet50`、`InceptionV3` 或 `Xception` 中的一个。选择上方网络架构中的一个，他们已经保存在目录 `/data/bottleneck_features/` 中。\n", "\n", "\n", "### 【练习】获取模型的特征向量\n", "\n", "在下方代码块中，通过运行下方代码提取训练、测试与验证集相对应的bottleneck特征。\n", "\n", " bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')\n", " train_{network} = bottleneck_features['train']\n", " valid_{network} = bottleneck_features['valid']\n", " test_{network} = bottleneck_features['test']" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "### TODO: 从另一个预训练的CNN获取bottleneck特征\n", "bottleneck_features = np.load('/data/bottleneck_features/DogXceptionData.npz')\n", "train_Xception = bottleneck_features['train']\n", "valid_Xception = bottleneck_features['valid']\n", "test_Xception = bottleneck_features['test']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 【练习】模型架构\n", "\n", "建立一个CNN来分类狗品种。在你的代码单元块的最后，通过运行如下代码输出网络的结构：\n", " \n", " <your model's name>.summary()\n", " \n", "---\n", "\n", "<a id='question6'></a> \n", "\n", "### __问题 6:__ \n", "\n", "\n", "在下方的代码块中尝试使用 Keras 搭建最终的网络架构，并回答你实现最终 CNN 架构的步骤与每一步的作用，并描述你在迁移学习过程中，使用该网络架构的原因。\n", "\n", "\n", "__回答:__ \n", "\n", "Xception_model = Sequential()\n", "这一步是调用Xception的预训练模型\n", "\n", "Xception_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))\n", "这一步添加一个全局平均池化层避免过拟合\n", "\n", "Xception_model.add(Dropout(0.2))\n", "这一步是添加Dropout层避免过拟合\n", "\n", "Xception_model.add(Dense(133, activation='softmax'))\n", "这一步添加133个节点的全连接层，使用softmax激活函数输出每个狗狗品种的概率\n", "\n", "使用该网络架构的原因是由于Xception具有如下优点：\n", "1.相比传统的卷积神经网络如VGG复杂度降低，需要的参数数量下降。\n", "2.可以做到更深，不会出现梯度消失的问题。\n", "3.优化简单，分类准确度加深由于使用更深的网络。\n", "4.Xception在众多图像识别领域中拔得头筹。\n", "因此，选取Xception网络可以比之前的VGG网络取得更好的预测效果。\n", "\n", "- 为什么这一架构会在这一分类任务中成功？\n", "\n", " 这四个架构都是经过反复多次实验确定的，非常有效果的架构。以Inception net为例，inception net是多层特征提取器，通过分别多次同时提取特征，然后叠加，就可以学到不同层次的特征，所以效果非常好。\n", "\n", "- 为什么早期（第三步）的尝试不成功？\n", "\n", " 第三步中，第一，使用的网络在架构上，非常浅，学到的特征非常少，其次学习库非常小，上面四个网络是在Imagenet上经过大量训练在不同种类的训练集上得来的，这是这个小库无法比拟的。\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "global_average_pooling2d_5 ( (None, 2048) 0 \n", "_________________________________________________________________\n", "dropout_11 (Dropout) (None, 2048) 0 \n", "_________________________________________________________________\n", "dense_5 (Dense) (None, 133) 272517 \n", "=================================================================\n", "Total params: 272,517\n", "Trainable params: 272,517\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "### TODO: 定义你的框架\n", "# 调用Xception的预训练模型\n", "Xception_model = Sequential()\n", "\n", "#加一个全局平均池化层避免过拟合\n", "Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))\n", "\n", "#添加Dropout层避免过拟合\n", "Xception_model.add(Dropout(0.2))\n", "\n", "#添加133个节点的全连接层，使用softmax激活函数输出每个狗狗品种的概率\n", "Xception_model.add(Dense(133, activation='softmax'))\n", "\n", "Xception_model.summary()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "### TODO: 编译模型\n", "Xception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】训练模型\n", "\n", "<a id='question7'></a> \n", "\n", "### __问题 7:__ \n", "\n", "在下方代码单元中训练你的模型。使用模型检查点（model checkpointing）来储存具有最低验证集 loss 的模型。\n", "\n", "当然，你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) 以优化模型的表现，不过这不是必须的步骤。\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 6680 samples, validate on 835 samples\n", "Epoch 1/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 1.1255 - acc: 0.7239Epoch 00001: val_loss improved from inf to 0.52573, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 475us/step - loss: 1.1172 - acc: 0.7256 - val_loss: 0.5257 - val_acc: 0.8335\n", "Epoch 2/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 0.4270 - acc: 0.8647Epoch 00002: val_loss improved from 0.52573 to 0.49305, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.4260 - acc: 0.8647 - val_loss: 0.4931 - val_acc: 0.8347\n", "Epoch 3/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.3540 - acc: 0.8870Epoch 00003: val_loss improved from 0.49305 to 0.48966, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.3586 - acc: 0.8864 - val_loss: 0.4897 - val_acc: 0.8431\n", "Epoch 4/20\n", "6620/6680 [============================>.] - ETA: 0s - loss: 0.3193 - acc: 0.9047Epoch 00004: val_loss improved from 0.48966 to 0.47889, saving model to saved_models/weights.best.Xception1.hdf5\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.3186 - acc: 0.9046 - val_loss: 0.4789 - val_acc: 0.8587\n", "Epoch 5/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2913 - acc: 0.9126Epoch 00005: val_loss did not improve\n", "6680/6680 [==============================] - 3s 409us/step - loss: 0.2898 - acc: 0.9129 - val_loss: 0.5032 - val_acc: 0.8503\n", "Epoch 6/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9162Epoch 00006: val_loss did not improve\n", "6680/6680 [==============================] - 3s 409us/step - loss: 0.2683 - acc: 0.9165 - val_loss: 0.4957 - val_acc: 0.8467\n", "Epoch 7/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2508 - acc: 0.9262Epoch 00007: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2489 - acc: 0.9268 - val_loss: 0.5211 - val_acc: 0.8539\n", "Epoch 8/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2384 - acc: 0.9277Epoch 00008: val_loss did not improve\n", "6680/6680 [==============================] - 3s 408us/step - loss: 0.2373 - acc: 0.9278 - val_loss: 0.5091 - val_acc: 0.8599\n", "Epoch 9/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2300 - acc: 0.9330Epoch 00009: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.2286 - acc: 0.9334 - val_loss: 0.5349 - val_acc: 0.8575\n", "Epoch 10/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2114 - acc: 0.9398Epoch 00010: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2109 - acc: 0.9398 - val_loss: 0.5300 - val_acc: 0.8575\n", "Epoch 11/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.2026 - acc: 0.9383Epoch 00011: val_loss did not improve\n", "6680/6680 [==============================] - 3s 412us/step - loss: 0.2020 - acc: 0.9385 - val_loss: 0.5516 - val_acc: 0.8659\n", "Epoch 12/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1893 - acc: 0.9433Epoch 00012: val_loss did not improve\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.1884 - acc: 0.9434 - val_loss: 0.5614 - val_acc: 0.8587\n", "Epoch 13/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1849 - acc: 0.9450Epoch 00013: val_loss did not improve\n", "6680/6680 [==============================] - 3s 407us/step - loss: 0.1841 - acc: 0.9452 - val_loss: 0.5743 - val_acc: 0.8575\n", "Epoch 14/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1761 - acc: 0.9489Epoch 00014: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1770 - acc: 0.9487 - val_loss: 0.5765 - val_acc: 0.8551\n", "Epoch 15/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1657 - acc: 0.9514Epoch 00015: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1688 - acc: 0.9509 - val_loss: 0.6255 - val_acc: 0.8515\n", "Epoch 16/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1554 - acc: 0.9539Epoch 00016: val_loss did not improve\n", "6680/6680 [==============================] - 3s 411us/step - loss: 0.1581 - acc: 0.9536 - val_loss: 0.6189 - val_acc: 0.8695\n", "Epoch 17/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1474 - acc: 0.9582Epoch 00017: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1476 - acc: 0.9581 - val_loss: 0.6452 - val_acc: 0.8575\n", "Epoch 18/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1457 - acc: 0.9568Epoch 00018: val_loss did not improve\n", "6680/6680 [==============================] - 3s 410us/step - loss: 0.1461 - acc: 0.9569 - val_loss: 0.6144 - val_acc: 0.8599\n", "Epoch 19/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1396 - acc: 0.9606Epoch 00019: val_loss did not improve\n", "6680/6680 [==============================] - 3s 414us/step - loss: 0.1408 - acc: 0.9603 - val_loss: 0.6590 - val_acc: 0.8563\n", "Epoch 20/20\n", "6600/6680 [============================>.] - ETA: 0s - loss: 0.1361 - acc: 0.9605Epoch 00020: val_loss did not improve\n", "6680/6680 [==============================] - 3s 415us/step - loss: 0.1349 - acc: 0.9608 - val_loss: 0.6394 - val_acc: 0.8683\n" ] } ], "source": [ "### TODO: 训练模型\n", "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception1.hdf5', \n", " verbose=1, save_best_only=True)\n", "\n", "history = Xception_model.fit(train_Xception, train_targets, \n", " validation_data=(valid_Xception, valid_targets),\n", " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "### TODO: 加载具有最佳验证loss的模型权重\n", "Xception_model.load_weights('saved_models/weights.best.Xception1.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】测试模型\n", "\n", "<a id='question8'></a> \n", "\n", "### __问题 8:__ \n", "\n", "在狗图像的测试数据集上试用你的模型。确保测试准确率大于60%。" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 84.4498%\n" ] } ], "source": [ "### TODO: 在测试集上计算分类准确率\n", "Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]\n", "\n", "# 报告测试准确率\n", "test_accuracy = 100*np.sum(np.array(Xception_predictions)==np.argmax(test_targets, axis=1))/len(Xception_predictions)\n", "print('Test accuracy: %.4f%%' % test_accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 【练习】使用模型测试狗的品种\n", "\n", "\n", "实现一个函数，它的输入为图像路径，功能为预测对应图像的类别，输出为你模型预测出的狗类别（`Affenpinscher`, `Afghan_hound` 等）。\n", "\n", "与步骤5中的模拟函数类似，你的函数应当包含如下三个步骤：\n", "\n", "1. 根据选定的模型载入图像特征（bottleneck features）\n", "2. 将图像特征输输入到你的模型中，并返回预测向量。注意，在该向量上使用 argmax 函数可以返回狗种类的序号。\n", "3. 使用在步骤0中定义的 `dog_names` 数组来返回对应的狗种类名称。\n", "\n", "提取图像特征过程中使用到的函数可以在 `extract_bottleneck_features.py` 中找到。同时，他们应已在之前的代码块中被导入。根据你选定的 CNN 网络，你可以使用 `extract_{network}` 函数来获得对应的图像特征，其中 `{network}` 代表 `VGG19`, `Resnet50`, `InceptionV3`, 或 `Xception` 中的一个。\n", " \n", "---\n", "\n", "<a id='question9'></a> \n", "\n", "### __问题 9:__" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "### TODO: 写一个函数，该函数将图像的路径作为输入\n", "### 然后返回此模型所预测的狗的品种\n", "def Xception_predict_breed(img_path):\n", " # extract bottleneck features\n", " bottleneck_feature = extract_Xception(path_to_tensor(img_path))\n", " # obtain predicted vector\n", " predicted_vector = Xception_model.predict(bottleneck_feature)\n", " # return dog breed that is predicted by the model\n", " return dog_names[np.argmax(predicted_vector)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "<a id='step6'></a>\n", "## 步骤 6: 完成你的算法\n", "\n", "\n", "\n", "实现一个算法，它的输入为图像的路径，它能够区分图像是否包含一个人、狗或两者都不包含，然后：\n", "\n", "- 如果从图像中检测到一只__狗__，返回被预测的品种。\n", "- 如果从图像中检测到__人__，返回最相像的狗品种。\n", "- 如果两者都不能在图像中检测到，输出错误提示。\n", "\n", "我们非常欢迎你来自己编写检测图像中人类与狗的函数，你可以随意地使用上方完成的 `face_detector` 和 `dog_detector` 函数。你__需要__在步骤5使用你的CNN来预测狗品种。\n", "\n", "下面提供了算法的示例输出，但你可以自由地设计自己的模型！\n", "\n", "![Sample Human Output](images/sample_human_output.png)\n", "\n", "\n", "\n", "\n", "<a id='question10'></a> \n", "\n", "### __问题 10:__\n", "\n", "在下方代码块中完成你的代码。\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "### TODO: 设计你的算法\n", "### 自由地使用所需的代码单元数吧\n", "from IPython.core.display import Image, display\n", "\n", "def dog_breed_algorithm(img_path):\n", " if dog_detector(img_path) == 1:\n", " print(\"hello, dog!\")\n", " display(Image(img_path,width=200,height=200))\n", " print(\"Your predicted breed is ... \")\n", " return print(Xception_predict_breed(img_path))\n", " elif face_detector(img_path) == 1:\n", " print(\"hello, human!\")\n", " display(Image(img_path,width=200,height=200))\n", " print(\"You look like a ... \")\n", " return print(Xception_predict_breed(img_path))\n", " else:\n", " display(Image(img_path,width=200,height=200))\n", " return print(\"Could not identify a human or dog in the chosen image. Please try again.\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<a id='step7'></a>\n", "## 步骤 7: 测试你的算法\n", "\n", "在这个部分中，你将尝试一下你的新算法！算法认为__你__看起来像什么类型的狗？如果你有一只狗，它可以准确地预测你的狗的品种吗？如果你有一只猫，它会将你的猫误判为一只狗吗？\n", "\n", "**上传方式：点击左上角的Jupyter回到上级菜单，你可以看到Jupyter Notebook的右上方会有Upload按钮。**\n", "\n", "<a id='question11'></a> \n", "\n", "### __问题 11:__\n", "\n", "在下方编写代码，用至少6张现实中的图片来测试你的算法。你可以使用任意照片，不过请至少使用两张人类图片（要征得当事人同意哦）和两张狗的图片。\n", "同时请回答如下问题：\n", "\n", "1. 输出结果比你预想的要好吗 :) ？或者更糟 :( ？\n", "2. 提出至少三点改进你的模型的想法。\n", "\n", "\n", "1.结果比我预想的好。该算法可以准确识别出图片中是否含有狗或者人\n", "2. 1）对训练集进行数据增强以优化模型的表现\n", " 2）优化神经网络结构\n", " 3）增大数据集数据" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "filename = images/1.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "83689472/83683744 [==============================] - 1s 0us/step\n", "in/045.Cardigan_welsh_corgi\n", "\n", "\n", "filename = images/2.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "in/005.Alaskan_malamute\n", "\n", "\n", "filename = images/3.jpg\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Could not identify a human or dog in the chosen image. Please try again.\n", "\n", "\n", "filename = images/4.jpg\n", "hello, dog!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Your predicted breed is ... \n", "in/006.American_eskimo_dog\n", "\n", "\n", "filename = images/5.jpg\n", "hello, human!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "You look like a ... \n", "in/056.Dachshund\n", "\n", "\n", "filename = images/6.jpg\n", "hello, human!\n" ] }, { "data": { "image/jpeg": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "image/jpeg": { "height": 200, "width": 200 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "You look like a ... \n", "in/050.Chinese_shar-pei\n", "\n", "\n" ] } ], "source": [ "## TODO: 在你的电脑上，在步骤6中，至少在6张图片上运行你的算法。\n", "## 自由地使用所需的代码单元数吧\n", "for i in range(1, 7):\n", " filename = 'images/' + str(i) + '.jpg'\n", " print('filename = ' + filename)\n", " dog_breed_algorithm(filename)\n", " print('\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**注意: 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出File -> Download as -> HTML (.html)把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。**" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

ES-DOC/esdoc-jupyterhub

notebooks/uhh/cmip6/models/sandbox-1/seaice.ipynb

1

99801

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: UHH \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Seaice \n", "**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \n", "**Properties**: 80 (63 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:41" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'uhh', 'sandbox-1', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --> Model](#1.-Key-Properties--->-Model) \n", "[2. Key Properties --> Variables](#2.-Key-Properties--->-Variables) \n", "[3. Key Properties --> Seawater Properties](#3.-Key-Properties--->-Seawater-Properties) \n", "[4. Key Properties --> Resolution](#4.-Key-Properties--->-Resolution) \n", "[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n", "[6. Key Properties --> Key Parameter Values](#6.-Key-Properties--->-Key-Parameter-Values) \n", "[7. Key Properties --> Assumptions](#7.-Key-Properties--->-Assumptions) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid --> Discretisation --> Horizontal](#9.-Grid--->-Discretisation--->-Horizontal) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Seaice Categories](#11.-Grid--->-Seaice-Categories) \n", "[12. Grid --> Snow On Seaice](#12.-Grid--->-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --> Energy](#14.-Thermodynamics--->-Energy) \n", "[15. Thermodynamics --> Mass](#15.-Thermodynamics--->-Mass) \n", "[16. Thermodynamics --> Salt](#16.-Thermodynamics--->-Salt) \n", "[17. Thermodynamics --> Salt --> Mass Transport](#17.-Thermodynamics--->-Salt--->-Mass-Transport) \n", "[18. Thermodynamics --> Salt --> Thermodynamics](#18.-Thermodynamics--->-Salt--->-Thermodynamics) \n", "[19. Thermodynamics --> Ice Thickness Distribution](#19.-Thermodynamics--->-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --> Ice Floe Size Distribution](#20.-Thermodynamics--->-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --> Melt Ponds](#21.-Thermodynamics--->-Melt-Ponds) \n", "[22. Thermodynamics --> Snow Processes](#22.-Thermodynamics--->-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --> Model \n", "*Name of seaice model used.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Overview of sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Variables \n", "*List of prognostic variable in the sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *List of prognostic variables in the sea ice component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Seawater Properties \n", "*Properties of seawater relevant to sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If using a constant seawater freezing point, specify this value.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Resolution \n", "*Resolution of the sea ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Tuning Applied \n", "*Tuning applied to sea ice model component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *List any observed metrics used in tuning model/parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Which variables were changed during the tuning process?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Key Parameter Values \n", "*Values of key parameters*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "**Is Required:** FALSE    **Type:** ENUM    **Cardinality:** 0.N\n", "### *What values were specificed for the following parameters if used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P*) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.N\n", "### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Assumptions \n", "*Assumptions made in the sea ice model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.N\n", "### *General overview description of any *key* assumptions made in this model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.N\n", "### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.N\n", "### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "*Conservation in the sea ice component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Provide a general description of conservation methodology.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *Properties conserved in sea ice by the numerical schemes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Does conservation involved flux correction?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *List any variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --> Discretisation --> Horizontal \n", "*Sea ice discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Grid on which sea ice is horizontal discretised?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the type of sea ice grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the advection scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *What is the time step in the sea ice model thermodynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *What is the time step in the sea ice model dynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Specify any additional horizontal discretisation details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "*Sea ice vertical properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *If using multi-layers specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Specify any additional vertical grid details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Seaice Categories \n", "*What method is used to represent sea ice categories ?*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Set to true if the sea ice model has multiple sea ice categories.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *If using sea ice categories specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *If using sea ice categories specify each of the category limits.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the sea ice thickness distribution scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --> Snow On Seaice \n", "*Snow on sea ice details*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Is snow on ice represented in this model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "**Is Required:** TRUE    **Type:** INTEGER    **Cardinality:** 1.1\n", "### *Number of vertical levels of snow on ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe how the snow fraction on sea ice is determined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Specify any additional details related to snow on ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "*Sea Ice Dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the method of horizontal advection of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Which method of sea ice strength formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *Which processes can redistribute sea ice (including thickness)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Rheology, what is the ice deformation formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --> Energy \n", "*Processes related to energy in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the energy formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What type of thermal conductivity is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the method of heat diffusion?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Method by which basal ocean heat flux is handled?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the method by which the heat content of precipitation is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --> Mass \n", "*Processes related to mass in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the method by which new sea ice is formed in open water.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the method that governs the vertical growth and melt of sea ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the method of sea ice lateral melting?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the method that governs sea ice surface sublimation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *Describe the method of frazil ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --> Salt \n", "*Processes related to salt in sea ice thermodynamics.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Does sea ice salinity impact the thermal properties of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --> Salt --> Mass Transport \n", "*Mass transport of salt*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *How is salinity determined in the mass transport of salt calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --> Salt --> Thermodynamics \n", "*Salt thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *How is salinity determined in the thermodynamic calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "**Is Required:** FALSE    **Type:** FLOAT    **Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --> Ice Thickness Distribution \n", "*Ice thickness distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *How is the sea ice thickness distribution represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --> Ice Floe Size Distribution \n", "*Ice floe-size distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *How is the sea ice floe-size represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Please provide further details on any parameterisation of floe-size.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --> Melt Ponds \n", "*Characteristics of melt ponds.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.1\n", "### *Are melt ponds included in the sea ice model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What method of melt pond formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *What do melt ponds have an impact on?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --> Snow Processes \n", "*Thermodynamic processes in snow on sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.N\n", "### *Set to True if the sea ice model has a snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe the snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "**Is Required:** TRUE    **Type:** BOOLEAN    **Cardinality:** 1.N\n", "### *Set to True if the sea ice model has snow ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "**Is Required:** FALSE    **Type:** STRING    **Cardinality:** 0.1\n", "### *Describe the snow ice formation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "**Is Required:** TRUE    **Type:** STRING    **Cardinality:** 1.1\n", "### *What is the impact of ridging on snow cover?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "*Sea Ice Radiative Processes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.1\n", "### *Method used to handle surface albedo.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "**Is Required:** TRUE    **Type:** ENUM    **Cardinality:** 1.N\n", "### *Method by which solar radiation through sea ice is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

caganze/wisps

notebooks/manjavacas data.ipynb

1

2151175

null

mit

MartinThoma/LaTeX-examples

presentations/causality-presentation/pynb/Interventions.ipynb

1

15400

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import bernoulli\n", "import matplotlib.pyplot as plt; plt.rcdefaults()\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sympy import latex" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def generate_data(n):\n", " \"\"\"\n", " Generate n datapoints for 3 variables A, H, B\n", " with the model\n", " A -> H -> B\n", " \"\"\"\n", "\n", " data = []\n", " for i in range(n):\n", " N_A = bernoulli.rvs(1./20)\n", " N_H = bernoulli.rvs(1./3)\n", " N_B = bernoulli.rvs(1./2)\n", " A = N_A\n", " H = (A + N_H) % 2\n", " B = (H + N_B) % 2\n", " data.append((A, H, B))\n", " return data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc41c826cd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "p(H=0|A=0) = 0.67\n", "p(H=0|A=1) = 0.32\n", "p(H=0|B=0) = 0.65\n", "p(H=0|B=1) = 0.65\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "\n", "data = generate_data(100000)\n", "\n", "# Example data\n", "people = ('(0,0,0)', '(0,0,1)', '(0,1,0)', '(0,1,1)', '(1,0,0)', '(1,0,1)', '(1,1,0)', '(1,1,1)')\n", "get_bin = lambda x, n: x >= 0 and str(bin(x))[2:].zfill(n) or \"-\" + str(bin(x))[3:].zfill(n)\n", "y_pos = np.arange(len(people))\n", "# ('(0,0,0)', '(0,0,1)', '(0,1,0)', '(0,1,1)', '(1,0,0)', '(1,0,1)', '(1,1,0)', '(1,1,1)')\n", "count = [sum([1 for d in data if map(int, list(get_bin(i, 3))) == list(d)]) for i in range(8)]\n", "\n", "error = np.random.rand(len(people))\n", "\n", "plt.barh(y_pos, count, xerr=error, align='center', alpha=0.4)\n", "plt.yticks(y_pos, people)\n", "plt.xlabel('Anzahl fur (A, H, B)')\n", "plt.title('Ergebnisse')\n", "\n", "plt.show()\n", "\n", "from sympy.interactive import printing\n", "printing.init_printing(use_latex=True)\n", "p_1 = (count[0] + count[1]) # h=0 and A=0\n", "p_2 = (count[4] + count[5]) # h=0 and A=1\n", "p_3 = (count[2] + count[3]) # h=1 and A=0\n", "p_4 = (count[6] + count[7]) # h=1 and A=1\n", "print(latex(\"p(H=0|A=0) = %0.2f\" % (p_1/float(p_1+p_3))))\n", "print(latex(\"p(H=0|A=1) = %0.2f\" % (p_2/float(p_2+p_4))))\n", "\n", "p_1 = (count[0] + count[4]) # h=0 and B=0\n", "p_2 = (count[1] + count[5]) # h=0 and B=1\n", "p_3 = (count[2] + count[6]) # h=1 and B=0\n", "p_4 = (count[3] + count[7]) # h=1 and B=1\n", "print(latex(\"p(H=0|B=0) = %0.2f\" % (p_1/float(p_1+p_3))))\n", "print(latex(\"p(H=0|B=1) = %0.2f\" % (p_2/float(p_2+p_4))))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

hanhanwu/Hanhan_Data_Science_Practice

sequencial_analysis/after_2020_practice/ts_1DCNN.ipynb

1

119915

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Forecast with 1D CNN\n", "\n", "* We can combine the speed and lightness of convnets with the order-sensitivity of RNNs is to use a 1D convnet as a preprocessing step before a RNN. \n", " * This is especially beneficial when dealing with sequences that are so long that they couldn't realistically be processed with RNNs, e.g. sequences with thousands of steps. The convnet will turn the long input sequence into much shorter (downsampled) sequences of higher-level features. This sequence of extracted features then becomes the input to the RNN part of the network.\n", "* 1D CNN means the kernel slides only need to move along 1 dimension\n", " * 2D means wdidth and height\n", " * 3D means wdidth, height and depth" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import datetime\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "from sklearn.preprocessing import MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(43824, 13)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>No</th>\n", " <th>year</th>\n", " <th>month</th>\n", " <th>day</th>\n", " <th>hour</th>\n", " <th>pm2.5</th>\n", " <th>DEWP</th>\n", " <th>TEMP</th>\n", " <th>PRES</th>\n", " <th>cbwd</th>\n", " <th>Iws</th>\n", " <th>Is</th>\n", " <th>Ir</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1021.0</td>\n", " <td>NW</td>\n", " <td>1.79</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-12.0</td>\n", " <td>1020.0</td>\n", " <td>NW</td>\n", " <td>4.92</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-11.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>6.71</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>-21</td>\n", " <td>-14.0</td>\n", " <td>1019.0</td>\n", " <td>NW</td>\n", " <td>9.84</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>2010</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>NaN</td>\n", " <td>-20</td>\n", " <td>-12.0</td>\n", " <td>1018.0</td>\n", " <td>NW</td>\n", " <td>12.97</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " No year month day hour pm2.5 DEWP TEMP PRES cbwd Iws Is Ir\n", "0 1 2010 1 1 0 NaN -21 -11.0 1021.0 NW 1.79 0 0\n", "1 2 2010 1 1 1 NaN -21 -12.0 1020.0 NW 4.92 0 0\n", "2 3 2010 1 1 2 NaN -21 -11.0 1019.0 NW 6.71 0 0\n", "3 4 2010 1 1 3 NaN -21 -14.0 1019.0 NW 9.84 0 0\n", "4 5 2010 1 1 4 NaN -20 -12.0 1018.0 NW 12.97 0 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/pm25.csv')\n", "\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train: (33096, 15)\n", "Shape of test: (8661, 15)\n" ] } ], "source": [ "df.dropna(subset=['pm2.5'], axis=0, inplace=True)\n", "df.reset_index(drop=True, inplace=True)\n", "\n", "df['datetime'] = df[['year', 'month', 'day', 'hour']].apply(\n", " lambda row: datetime.datetime(year=row['year'], \n", " month=row['month'], day=row['day'],hour=row['hour']), axis=1)\n", "df.sort_values('datetime', ascending=True, inplace=True)\n", "\n", "scaler = MinMaxScaler(feature_range=(0, 1))\n", "df['scaled_pm2.5'] = scaler.fit_transform(np.array(df['pm2.5']).reshape(-1, 1))\n", "\n", "split_date = datetime.datetime(year=2014, month=1, day=1, hour=0) \n", "df_train = df.loc[df['datetime']<split_date]\n", "df_val = df.loc[df['datetime']>=split_date]\n", "df_val.reset_index(drop=True, inplace=True)\n", "print('Shape of train:', df_train.shape)\n", "print('Shape of test:', df_val.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def makeXy(ts, nb_timesteps):\n", " \"\"\"\n", " Input: \n", " ts: original time series\n", " nb_timesteps: number of time steps in the regressors\n", " Output: \n", " X: 2-D array of regressors\n", " y: 1-D array of target \n", " \"\"\"\n", " X = []\n", " y = []\n", " for i in range(nb_timesteps, ts.shape[0]):\n", " X.append(list(ts.loc[i-nb_timesteps:i-1]))\n", " y.append(ts.loc[i])\n", " \n", " X, y = np.array(X), np.array(y)\n", " return X, y" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of train arrays: (33089, 7) (33089,)\n", "Shape of validation arrays: (8654, 7) (8654,)\n" ] } ], "source": [ "X_train, y_train = makeXy(df_train['scaled_pm2.5'], 7)\n", "X_val, y_val = makeXy(df_val['scaled_pm2.5'], 7)\n", "\n", "print('Shape of train arrays:', X_train.shape, y_train.shape)\n", "print('Shape of validation arrays:', X_val.shape, y_val.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of arrays after reshaping: (33089, 7, 1) (8654, 7, 1)\n" ] } ], "source": [ "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n", "X_val = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n", "print('Shape of arrays after reshaping:', X_train.shape, X_val.shape)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.layers import LSTM, Conv1D\n", "from tensorflow.keras.layers import Dense, Dropout, Input, ZeroPadding1D, AveragePooling1D, Flatten\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "\n", "from sklearn.metrics import mean_absolute_error\n", "\n", "tf.random.set_seed(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1D CNN Only\n", "\n", "* ZeroPadding1D layer is added next to add zeros at the begining and end of each series. \n", " * Zeropadding ensure that the downstream convolution layer does not reduce the dimension of the output sequences.\n", " * Pooling layer, added after the convolution layer is used to downsampling the input.\n", "* In Con1D\n", " * param 1 determines the number of features in the output\n", " * param 2 indicates length of the 1D convolution window, moving window size\n", " * param 3 is strides of the moving window, representing the number of places to shift the convolution window\n", " * `use_bias` as True, adds a bias value during computation of an output feature\n", " * 1D convolution can be thought of as generating local AR models over rolling window of three time units.\n", "* AveragePooling1D is added next to downsample the input by taking average over pool size of three with stride of one timesteps. \n", " * The average pooling in this case can be thought of as taking moving averages over a rolling window of three time units.\n", "* The Flatten layer reshapes the input to `(number of samples, number of timesteps*number of features per timestep)`, which is then fed to the output layer" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_11\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "zero_padding1d_7 (ZeroPaddin (None, 9, 1) 0 \n", "_________________________________________________________________\n", "conv1d_18 (Conv1D) (None, 7, 64) 256 \n", "_________________________________________________________________\n", "conv1d_19 (Conv1D) (None, 5, 32) 6176 \n", "_________________________________________________________________\n", "average_pooling1d_7 (Average (None, 3, 32) 0 \n", "_________________________________________________________________\n", "flatten_4 (Flatten) (None, 96) 0 \n", "_________________________________________________________________\n", "dense_12 (Dense) (None, 32) 3104 \n", "_________________________________________________________________\n", "dense_13 (Dense) (None, 16) 528 \n", "_________________________________________________________________\n", "dropout_4 (Dropout) (None, 16) 0 \n", "_________________________________________________________________\n", "dense_14 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 10,081\n", "Trainable params: 10,081\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(ZeroPadding1D(padding=1,\n", " input_shape=(X_train.shape[1:])))\n", "model.add(Conv1D(64, 3, strides=1, use_bias=True))\n", "model.add(Conv1D(32, 3, strides=1, use_bias=True))\n", "model.add(AveragePooling1D(pool_size=3, strides=1))\n", "model.add(Flatten())\n", "model.add(Dense(32))\n", "model.add(Dense(16))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(1, activation='tanh'))\n", "\n", "model.compile(optimizer=Adam(amsgrad=True), loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "1655/1655 [==============================] - 16s 9ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0129 - val_mae: 0.0129\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 2/50\n", "1655/1655 [==============================] - 16s 10ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 3/50\n", "1655/1655 [==============================] - 19s 11ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0124 - val_mae: 0.0124ss: 0.0145 - m - ETA: 0s - loss: 0.0145 - mae: \n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 4/50\n", "1655/1655 [==============================] - 22s 13ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 5/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 6/50\n", "1655/1655 [==============================] - 19s 11ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 7/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 8/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 9/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 10/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 11/50\n", "1655/1655 [==============================] - 27s 16ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0124 - val_mae: 0.0124 mae\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 12/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 13/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 14/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 15/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 16/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 17/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 18/50\n", "1655/1655 [==============================] - 29s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 19/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 20/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 21/50\n", "1655/1655 [==============================] - 29s 17ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 22/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 23/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0131 - val_mae: 0.0131\n", "Epoch 24/50\n", "1655/1655 [==============================] - 18s 11ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0131 - val_mae: 0.0131 1s - loss: 0.0143 - m - ETA: 0s - loss: 0.\n", "Epoch 25/50\n", "1655/1655 [==============================] - 28s 17ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 26/50\n", "1655/1655 [==============================] - 30s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 27/50\n", "1655/1655 [==============================] - 29s 18ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 28/50\n", "1655/1655 [==============================] - 18s 11ms/step - loss: 0.0143 - mae: 0.0143 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 29/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0133 - val_mae: 0.0133\n", "Epoch 30/50\n", "1655/1655 [==============================] - 20s 12ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 31/50\n", "1655/1655 [==============================] - 24s 14ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0135 - val_mae: 0.0135014 - ETA: 0s - loss: 0.0\n", "Epoch 32/50\n", "1655/1655 [==============================] - 24s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 33/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0129 - val_mae: 0.01290142 - \n", "Epoch 34/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0126 - val_mae: 0.0126\n", "Epoch 35/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0123 - val_mae: 0.0123\n", "INFO:tensorflow:Assets written to: 1dcnn_model\\assets\n", "Epoch 36/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 37/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125\n", "Epoch 38/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 39/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0124 - val_mae: 0.0124\n", "Epoch 40/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0125 - val_mae: 0.0125ae: 0.01\n", "Epoch 41/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0129 - val_mae: 0.01290142 - mae: 0.0\n", "Epoch 42/50\n", "1655/1655 [==============================] - 22s 13ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0124 - val_mae: 0.0124loss: 0.0\n", "Epoch 43/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0126 - val_mae: 0.0126s: 0.0141 - ETA: 0s - loss: 0.0141 - mae: 0.014\n", "Epoch 44/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0135 - val_mae: 0.0135.0142 - mae - ETA: 2s - lo - ETA: 1s - loss:\n", "Epoch 45/50\n", "1655/1655 [==============================] - 26s 15ms/step - loss: 0.0142 - mae: 0.0142 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 46/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 47/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0124 - val_mae: 0.01240s - loss: 0.0141 - mae: 0.\n", "Epoch 48/50\n", "1655/1655 [==============================] - 26s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0125 - val_mae: 0.0125: 0.0140 - ETA: 3s - loss: 0.014\n", "Epoch 49/50\n", "1655/1655 [==============================] - 27s 16ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0123 - val_mae: 0.0123\n", "Epoch 50/50\n", "1655/1655 [==============================] - 25s 15ms/step - loss: 0.0141 - mae: 0.0141 - val_loss: 0.0125 - val_mae: 0.0125\n" ] } ], "source": [ "save_weights_at = '1dcnn_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=20, epochs=50,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 12.2419\n", "MAE for the scaled validation set: 0.0123\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load the best model\n", "best_model = load_model('1dcnn_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))\n", "\n", "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add LSTM after 1D CNN" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_13\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "zero_padding1d_9 (ZeroPaddin (None, 9, 1) 0 \n", "_________________________________________________________________\n", "conv1d_22 (Conv1D) (None, 7, 64) 256 \n", "_________________________________________________________________\n", "conv1d_23 (Conv1D) (None, 5, 32) 6176 \n", "_________________________________________________________________\n", "average_pooling1d_9 (Average (None, 3, 32) 0 \n", "_________________________________________________________________\n", "lstm (LSTM) (None, 32) 8320 \n", "_________________________________________________________________\n", "dense_15 (Dense) (None, 1) 33 \n", "=================================================================\n", "Total params: 14,785\n", "Trainable params: 14,785\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential()\n", "model.add(ZeroPadding1D(padding=1,\n", " input_shape=(X_train.shape[1:])))\n", "model.add(Conv1D(64, 3, strides=1, use_bias=True))\n", "model.add(Conv1D(32, 3, strides=1, use_bias=True))\n", "model.add(AveragePooling1D(pool_size=3, strides=1))\n", "model.add(LSTM(32, dropout=0.1, recurrent_dropout=0.5, activation='tanh'))\n", "model.add(Dense(1, activation='tanh'))\n", "\n", "model.compile(optimizer=Adam(amsgrad=True), loss='mean_absolute_error', metrics='mae')\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "2069/2069 [==============================] - 24s 9ms/step - loss: 0.0218 - mae: 0.0218 - val_loss: 0.0140 - val_mae: 0.0140\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 2/20\n", "2069/2069 [==============================] - 18s 9ms/step - loss: 0.0164 - mae: 0.0164 - val_loss: 0.0141 - val_mae: 0.0141\n", "Epoch 3/20\n", "2069/2069 [==============================] - 26s 13ms/step - loss: 0.0158 - mae: 0.0158 - val_loss: 0.0133 - val_mae: 0.0133 0.0158 \n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 4/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0155 - mae: 0.0155 - val_loss: 0.0145 - val_mae: 0.0145\n", "Epoch 5/20\n", "2069/2069 [==============================] - 28s 14ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0143 - val_mae: 0.0143\n", "Epoch 6/20\n", "2069/2069 [==============================] - 28s 13ms/step - loss: 0.0155 - mae: 0.0155 - val_loss: 0.0140 - val_mae: 0.0140\n", "Epoch 7/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0151 - mae: 0.0151 - val_loss: 0.0139 - val_mae: 0.0139\n", "Epoch 8/20\n", "2069/2069 [==============================] - 29s 14ms/step - loss: 0.0150 - mae: 0.0150 - val_loss: 0.0128 - val_mae: 0.0128\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 9/20\n", "2069/2069 [==============================] - 24s 12ms/step - loss: 0.0149 - mae: 0.0149 - val_loss: 0.0130 - val_mae: 0.0130\n", "Epoch 10/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0152 - mae: 0.0152 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 11/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 12/20\n", "2069/2069 [==============================] - 28s 13ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0129 - val_mae: 0.0129\n", "Epoch 13/20\n", "2069/2069 [==============================] - 28s 14ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 14/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0148 - mae: 0.0148 - val_loss: 0.0127 - val_mae: 0.0127\n", "Epoch 15/20\n", "2069/2069 [==============================] - 30s 14ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 16/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0145 - mae: 0.0145 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 17/20\n", "2069/2069 [==============================] - 30s 15ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0128 - val_mae: 0.0128\n", "Epoch 18/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0147 - mae: 0.0147 - val_loss: 0.0133 - val_mae: 0.0133s - ETA: 0s - loss: 0.0147 - mae: 0\n", "Epoch 19/20\n", "2069/2069 [==============================] - 26s 12ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0126 - val_mae: 0.0126\n", "INFO:tensorflow:Assets written to: 1dcnn_lstm_model\\assets\n", "Epoch 20/20\n", "2069/2069 [==============================] - 27s 13ms/step - loss: 0.0146 - mae: 0.0146 - val_loss: 0.0126 - val_mae: 0.0126\n" ] } ], "source": [ "save_weights_at = '1dcnn_lstm_model'\n", "save_best = ModelCheckpoint(save_weights_at, monitor='val_loss', verbose=0,\n", " save_best_only=True, save_weights_only=False, mode='min',\n", " save_freq='epoch')\n", "history = model.fit(x=X_train, y=y_train, batch_size=16, epochs=20,\n", " verbose=1, callbacks=[save_best], validation_data=(X_val, y_val),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE for the validation set: 12.4811\n", "MAE for the scaled validation set: 0.0126\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# load the best model\n", "best_model = load_model('1dcnn_lstm_model')\n", "\n", "# Compare the prediction with y_true\n", "preds = best_model.predict(X_val)\n", "pred_pm25 = scaler.inverse_transform(preds)\n", "pred_pm25 = np.squeeze(pred_pm25)\n", "\n", "# Measure MAE of y_pred and y_true\n", "mae = mean_absolute_error(df_val['pm2.5'].loc[7:], pred_pm25)\n", "print('MAE for the validation set:', round(mae, 4))\n", "\n", "mae = mean_absolute_error(df_val['scaled_pm2.5'].loc[7:], preds)\n", "print('MAE for the scaled validation set:', round(mae, 4))\n", "\n", "# Check the metrics and loss of each apoch\n", "mae = history.history['mae']\n", "val_mae = history.history['val_mae']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(len(mae))\n", "\n", "plt.plot(epochs, mae, 'bo', label='Training MAE')\n", "plt.plot(epochs, val_mae, 'b', label='Validation MAE')\n", "plt.title('Training and Validation MAE')\n", "plt.legend()\n", "\n", "plt.figure()\n", "\n", "# Here I was using MAE as loss too, that's why they lookedalmost the same...\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and Validation loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note\n", "\n", "* 1D CNN only is faster when there is same amount of epoches and batch_size\n", " * In this case, both experiments got same performance whe epoch and batch_size were the same\n", "* Both experiments appear to be a little bit underfitting" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

zklgame/CatEyeNets

test/two_layer_net.ipynb

1

628786

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing a Neural Network\n", "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "os.chdir(os.getcwd() + '/..')\n", "\n", "# Run some setup code for this notebook\n", "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from utils.data_utils import load_CIFAR10\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# Some more magic so that the notebook will reload external python modules;\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from classifiers.neural_net import TwoLayerNet\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / np.maximum(1e-8, np.abs(x) + np.abs(y)))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a small net and toy data to check implementations.\n", "# set random seed for repeatable experiments.\n", "input_size = 4\n", "hidden_size = 10\n", "num_classes = 3\n", "num_inputs = 5\n", "\n", "def init_toy_model():\n", " np.random.seed(0)\n", " return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n", "\n", "def init_toy_data():\n", " np.random.seed(1)\n", " X = 10 * np.random.randn(num_inputs, input_size)\n", " y = np.array([0, 1, 2, 2, 1])\n", " return X, y\n", "\n", "net = init_toy_model()\n", "X, y = init_toy_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute scores" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scores: \n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "correct scores:\n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "Difference between your scores and correct scores:\n", "3.68027204961e-08\n" ] } ], "source": [ "scores = net.loss(X)\n", "print('scores: ')\n", "print(scores)\n", "print\n", "\n", "print('correct scores:')\n", "correct_scores = np.asarray([\n", " [-0.81233741, -1.27654624, -0.70335995],\n", " [-0.17129677, -1.18803311, -0.47310444],\n", " [-0.51590475, -1.01354314, -0.8504215 ],\n", " [-0.15419291, -0.48629638, -0.52901952],\n", " [-0.00618733, -0.12435261, -0.15226949]])\n", "print(correct_scores)\n", "print\n", "\n", "# The difference should be very small, get < 1e-7\n", "print('Difference between your scores and correct scores:')\n", "print(np.sum(np.abs(scores - correct_scores)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute loss" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference between your loss and correct loss:\n", "1.79412040779e-13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "classifiers/neural_net.py:74: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if y == None:\n" ] } ], "source": [ "loss, _ = net.loss(X, y, reg=0.05)\n", "corrent_loss = 1.30378789133\n", "\n", "# should be very small, get < 1e-12\n", "print('Difference between your loss and correct loss:')\n", "print(np.sum(np.abs(loss - corrent_loss)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Backward pass\n", "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b2 max relative error: 4.447687e-11\n", "b1 max relative error: 1.555470e-09\n", "W1 max relative error: 3.669858e-09\n", "W2 max relative error: 3.440708e-09\n" ] } ], "source": [ "from utils.gradient_check import eval_numerical_gradient\n", "\n", "loss, grads = net.loss(X, y, reg=0.05)\n", "\n", "# these should all be less than 1e-8 or so\n", "for param_name in grads:\n", " f = lambda W: net.loss(X, y, reg=0.05)[0]\n", " param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n", " print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Train the network\n", "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.2." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Final training loss: ', 0.017149607938731846)\n" ] }, { "data": { "image/png": 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s7c2xqB8AAMyLQNYgW3ty2ndiUu4edykAAKDJEMgaZFtvuyZLFR0bK8ZdCgAA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\nMyVMSiUSSqeCW1syoUz4MZU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"text/plain": [ "<matplotlib.figure.Figure at 0x10e936550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net = init_toy_model()\n", "stats = net.train(X, y, X, y, learning_rate=1e-1, reg=5e-6, num_iters=100, verbose=False)\n", "print('Final training loss: ', stats['loss_history'][-1])\n", "\n", "# plot the loss history\n", "plt.plot(stats['loss_history'])\n", "plt.xlabel('iteration')\n", "plt.ylabel('training loss')\n", "plt.title('Training Loss history')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load the data\n", "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((49000, 3072), (1000, 3072), (1000, 3072))\n" ] } ], "source": [ "# Load the raw CIFAR-10 data\n", "cifar10_dir = 'datasets/cifar-10-batches-py'\n", "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", "# Split the data\n", "num_training = 49000\n", "num_validation = 1000\n", "num_test = 1000\n", "\n", "mask = range(num_training, num_training+num_validation)\n", "X_val = X_train[mask]\n", "y_val = y_train[mask]\n", "\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "mask = xrange(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]\n", "\n", "# Preprocessing: reshape the image data into rows\n", "X_train = X_train.reshape(X_train.shape[0], -1)\n", "X_val = X_val.reshape(X_val.shape[0], -1)\n", "X_test = X_test.reshape(X_test.shape[0], -1)\n", "\n", "# Normalize the data: subtract the mean rows\n", "mean_image = np.mean(X_train, axis=0)\n", "X_train -= mean_image\n", "X_val -= mean_image\n", "X_test -= mean_image\n", "\n", "print(X_train.shape, X_val.shape, X_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a network\n", "To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 1 / 1000: loss 2.302957\n", "iteration 101 / 1000: loss 2.302440\n", "iteration 201 / 1000: loss 2.298435\n", "iteration 301 / 1000: loss 2.262676\n", "iteration 401 / 1000: loss 2.200829\n", "iteration 501 / 1000: loss 2.114582\n", "iteration 601 / 1000: loss 2.080862\n", "iteration 701 / 1000: loss 2.099089\n", "iteration 801 / 1000: loss 1.999965\n", "iteration 901 / 1000: loss 1.961439\n", "('Validation accuracy: ', 0.28599999999999998)\n" ] } ], "source": [ "input_size = 32 * 32 * 3\n", "hidden_size = 50\n", "num_classes = 10\n", "net = TwoLayerNet(input_size, hidden_size, num_classes)\n", "\n", "# Train the network\n", "stats = net.train(X_train, y_train, X_val, y_val,\n", " learning_rate=1e-4, learning_rate_decay=0.95,\n", " reg=0.25, num_iters=1000, batch_size=200, verbose=True)\n", "\n", "# Predict on the validation set\n", "val_acc = (net.predict(X_val) == y_val).mean()\n", "print('Validation accuracy: ', val_acc)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Debug the training\n", "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n", "\n", "### One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n", "\n", "### Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rHWFW8dDteLaFB5lYuq0a9721whSJQljWR9tnlNbjxoSJnTVN7XL/fOGe6Stw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\nI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"text/plain": [ "<matplotlib.figure.Figure at 0x117e3d150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the loss function and train / validation accuracies\n", "plt.subplot(2, 1, 1)\n", "plt.plot(stats['loss_history'])\n", "plt.title('Loss history')\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Loss')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(stats['train_acc_history'], label='train')\n", "plt.plot(stats['val_acc_history'], label='val')\n", "plt.title('Classification accuracy history')\n", "plt.legend()\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Classification accuracy')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PTeMs6/+duJ/8psk6ipqXajyEJyn4/mqfiz2LQlwqzhyBZaWbRZu+X41N7MpbyzZGYlvb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Y83UDNh3cgvNKTHIBHF61OtNpzxfFe5AOBPpp34ww3qdVA/hsQBx0A//L8DADy1V3FbIPCIXoqX\n2mQqOss2LFj5jvjzfO9sq46mqP6U0WTwWJV/v5a9jJhYn7HXeZwgH3iAbyloH6ffex3Xs7QH//bR\ndXzwDtnB+yVR9cgyidrzrMgkXTXiswdEmIkXr6G2VHiobJWCBv0zZAfzWQmKCj637aCO3NEtwzzI\nkOzB+1rY/GTCtosaJKWcg3qdejYGKyQ2siW/zkswvklbqRt1YQH8+/gqYXAmGMeOOL44MVVBLkI5\ngs1fYzNARjVbZEBbw7qEgmA9zc+cAlK0UUOdd5Cs/1cY1dzsUClk8SAUmqsAgHK5F1JxjufwFVC/\nTYNmG6DBSDftGDWRzulkFVBXuBhS/QVYhfK1TJzoqtGNTpWbV09xFxUtlcxVV6LHQ1o0WhBl3UoG\nBFQ0BNeadhjv8X0yWwUFIzf6niAn0ph3oVPl31qG7tcAHt/mEH/gl+F2mgr72XQU3zlKgz9fBsz7\npGnU97jRXR6Koc/KzV39gQLDR0kJrTmiUK6TPjdPcaO72vkRBtPc3FJH07h9j8Y339RBISIClQc0\nokX9HO70c/P66uttlCY4fvm7x3D3OA1i8CqvV1RfWETtGssUZgt3u8rWW0ugFuWm5m6IKERpGyNy\njvWNoA/HNrmIU6PzWLLQQVDu8hxa6rmFR2s0rvXELPI1bmpnRk5g4RqNvNLJxaZaM6Cze4Gf/beg\n04mNIbGD5RwpdXOBFJh/ZwJLcT5X5QuhFqE+jA9tobnP5+pUpNFDv7Z0le2o1gzpc9TF7RuMeiyE\nt+H3UA9f+Pou/nbuOD+Pfhd2cKziOv6m+s4/RTryQwCAcdqBzh/yuY0fr6I8yHfa9KT9Ou4ZaI2k\ny6KuQcjNoiTfm1UcH6Ox/vUq5yU3MI4Xt3gNaSc8C8VJynmzGsTzNRq8VTc3HHV7tKtsVkUV9TQN\nhS5A0ktV6ceqnUa/J+OD5AlSdf3NKDbqXC8dNc8S++Qt3BaOrFOmQFXD+W5BAkeBjrPUKKI85ZMw\nD4ryicMKyN4mVTpRV0Amzk+viSLq04k82m1RaNw6h7iPBrWc8cAVEskV8GMAQH2ip6tsNUkKJSWf\n0QxxzE4FDNgq8fjJbpejleCYNg0l5Iu0PcolkbgnGIdXzvUIXQ2SDjf63v49tKqifB+XHtSWNEwZ\nrqGMuge1GJ3spM+N5hY39HqJ/191aaAWm/FWfQ/NFm1IVmqBNUnZyu086u1gV7kA4AuGLD44oK7X\nQ7/CmdPUucrf8az3pkOJKS/pz+n3prDyHOczb72PE3VRJs/KzTS6GMaInvYhXjyOhlhDGvWvsHDu\n9wAAbp/YCKV9OCcSvNzoexafnqauhjZGceRPOa6teyEAQPiyG9EPGel8qryMFZco5XlzDPu1YSHJ\nP3xMNrfCDLmVtrJWvYexNN+3JqP9KH34KEY73GylT+yiucikFqWJXbjStN3WBN+1XynDtE476J2Q\nYSfN9TIVc2L4DO39zoG40dE4hzN1OrU721t41Pt5AMB2fhvmA+r4Ude3AACB+WeQHCZwqWb0kJio\nG5XSMzD+/9t3D6nmw3bYDtthO2yH7bfZPhGIV+RegCRaQU5OakJitMOuI9JTbftR4p+hEAFKydwe\n7KLIclRSx5RWUFDxKipe4YEX6fHZB8xou4XXHjdAWebfjcYO5iP0qJx+otzUbh/kJdLGjp4a5Ep6\n7kX7PEoZoou8g8/PGxrQtPgsbSPRVbZrZpHMQPoaWgFSvwszSjwW4dAnnS5oRRBDUkdvzHWlH71/\nSO/6husejrXpjbttTeSiDKzZWCNaGDDZIFkmstH4NzAU5R3Rgd7z+Mkg3+fLccxu67w4vUivfPHr\ncly8SQR6+fkUfv86+5D10HW7f+kpVIIMJOh9yOVwgzQNjZZe7IaUVPVKWAlZL2mckV9L0OkjtVOq\n70G1TSSWFIEI52wzuHyLlPHmF3+OyXXKHL2ah13J7/Qo6Ykn2w0kRFKGgKUf0jADlKLVDlo5opZN\nPRkFi6qAUpDjK6n50bnAqOy+zSOoD3Es5psMzmp7P57mEwAy6veR3CbSPRkg6r4daKI+H6I8KSe+\nVmHSjPn3L6L/KGmnyk0qqmvwGt7tJztxbs2IQJ3Micr7PpZnLvAlMREd38xBWqBuud/6AZQv0Fvv\noI77a0RGs+OkuxSqIlp5jrn78wXIE6Qblb6LuKvn+O69QfYikUl3la2c7UMDZGhyy+L++0gVo4KZ\n3i02MJqknsQGfICcyDPS5BGOuViBMk3UYzUvw7JNxLHn7MW+hkyNW1CBE3o3YhGyE7h5HQ4p4aLq\nWBgLgkg5lSf7E/LsYnCf341oTTCniER81bsYXW6J73Cuo9XulGxJZsRQh4g2L/q9Hs9j0Mc+5jVK\nmNMCWalKUGmJ4nUGzkWqo0SvlLon102g5uX69eSBfIEDNCHiHvNqLeoe/l6dVsI4SvRXXC4i7uP6\n9EEg6nIH1ZwYYNsAlCE+pKqpoi3SUlaTSlj6uh99AMCRqBIP1vi7noYL8z9m37yfI5194j9XsP6l\nCwCAYGkZ61IyQFP+P0buCJHw2i+o731ONR6skDUwf2kZ9j8nC/Po0H+PmXmyWN8WFHixJMX4EbJG\n1our+PXfEK8Vh5KofIcMx0aMLEFjwoBgkmv6rNGLJSefu1mdw0sSyvkfu8gmaQ0jd5dHN+qaBgk5\nEW2uh6jTbdvD2E4vAKBS68GKl+v/zBUpNqrcJ9RjXNPXnVXYfsk1+2jejffzfwAAKL+whMZtfsdt\nCgEAFPsD2BrkOGqGBrA0R9kzQ25ohziu+cV/xnEMbkJWpX6HDQnYYpz7+8dkCG+Q4bjQRbZu7RDx\nHrbDdtgO22E7bL/F9olAvJvX6Fnoe8KQ7wkP09hBuCoSxAdLCMvoTVpFZpd+B1AS+dR72jmkUgKZ\ntlSQ0ymBN0XvuRlooLNCzywyWoE/wvdppGWY++iF5sXDHI44oiWiZ4VUg6iSCAkFB6YK9Ir3JfT+\n9BINAhJ6oKWqHcDax2RLivMx47IJqxkR9HHehFSN9+pyK6/Ar+T73OK+Y995DWwNplN65IgJe1d6\nAQDrlhpmJezvBbwPAIhXH8V955sAgI0jp2E3MWT/HYMHo68The6f5Tg+GVHjbQXH5PidJZTLRMre\nN9/Hlp9BNpoZjv+Y5OeYczGgYkWcU/1mqxfSSI6LBONq/l5iuIO+JFHfvYAeNRU9yLGmAgkr5fSH\nOKat/evQtInenrobRFWc79Wm3kNeThqkESZStPU1UDKRlSiGdGiK7EEaXREDRqI+aUvc+X1ZhWOv\nEXHtTO+j/69YSKDm28LcAOfZVxE0i7u7bMXk1zFpYHGJkpnerGW/B1Yj78oe/MSEseM8J5ocfgSp\n7xNlNf6IKDP0Uy3+5SC99fueON6T8BneyCt47uf07G9IOWYegxWl60y55+0dRUJClHBx5gz+8qPM\narcY+HT8lSqWVERTwcZp1K5Sv9ZOVDGxwy8/+SWi5EKo+51QR78a0iRZnaE+moB2QQOdm7qlhgWF\nIf7dv1nA7AN+t3ZasDSlJppVohqlswVlL9fWaC2MTIPzUhMxD8lFORSDnHftQi+UgwyAkb4PDHoo\nR85AeT0pN1LimpinJEGiwPepM/0ojpI52QoTEY7P+wHc+5hsMmUbUQEa2xGBjo1SWJsMqGoUXUjm\nOS8nXHXExVVGpZHfVbQlKBaFTdBvI1ilPBK00ZGyn6pJZnZyZpwoFajL2gBQFNfDckEzzlTJ+uxL\nibxiJgfUJepnOymHxi6CujI2lAXadvRrEDF+/KrNR+07qjTaA5SjpJ/BXIXMm1EUKJk878VUhuzO\npVwQI+rnAQA9U5fR2OagRMuU81OtDVx7WmRhut6DpUmRLjT1KhphFlJ4zMdAotBzLVy6Spy62v8Z\nPGfg2sp7KzAWGa8R05ElOD8QQfUS18C7IzYkvRy/seRT+JbnzYfKdjQcg2Sctn0legKJDm3BhRjj\nPu7uTmFfzQIH+94W6teJiA8sVhSmGFeR+y7X5jPn92CepW4t3jNjoI8xE+p2APZBynTVwTv4cm8T\nVjnXUDoFZI5yjrSNBBxboiiDicj2/cazGNCRadn2fwBLi4Eb5vQSPHblQ2Xr1j4RG69jjIreaI7D\nIwxhqW2ERS9SBEYtMMup4JmOuI9nb2CiKtLP7RlRsZFCsVkc8CyTJlvpcEDthQYUEiq3K+uEpkzF\nSOn7oBSRhkY1/1+U+tFM0aBWNA04CzQgcXkTzX4qp26LtHKnVUPEIi7Tj3UP0vlAz6jTk0olfk/F\ndyxv98EAbjgTn1Fg7XXmy11vkRrWtpexV6MBGu98Dio3F9ZwVArIKX/5LI1hOqaAr8SNVTO/gBt6\nbjJf3NrB3bOk1wxBKltj+CxGfk5+b3Eoh6CdhqL1zhmc1NOIXYqSrqzXMhjcYr+O6nu73JgEIn2z\nsM9zvpI+Pje59DvovEIK9uBSAzPibuP24DTGYtwgo3YumnJ1HQOTpJ9WFTWc3KMBDzVPQL4t7mtq\naLhmdsdQmKBj06uyIJ7ngraqiogt0Kise2jMmpcS0NtpEAwHMkh6GQC3MvEM+kKkz/emGalcW7B2\nkQxYOv9NjGhJfdvfo4GTzC6gpvr37Lv/f8bKNqnt4aUfwvcCgy4exDlXlbO/gyvGVwEAmn/oh+0l\nUVmpkMcDHfVd9gH19MqXfKhdoLOyVH8Vjuu8G/2z9j24vQx0WX2Cc5mSROEunAcA3D2Iw+Hi+KtC\neUBCZ+6W+kUAgP6Ks6tsbbMJ9hzXVmmNjk3T2oItQUNTNe+gM08a8sMeH3ofoVGR2Llhae9egtdJ\nanEl2sQZOZ+hqMrRnuV6ad+hfuZNUQy0OVflHjXiWeqv1VsGMnR+Qhrq+p6hhq+nuKEvKi0oevmM\non4du2k6jNoxOhM3b3W/f51Rl2GMsA9Wca9zRNtELsM+Zht7GBWR+Nt6I3okdHZTOhpX/0Eeqd5e\nAMBYKYKaiKxuynLoSEVCkhSPECTqNGAVNqphRafATc1byaESpP7qyrQLzj2g7WK/DjIJGESwk9y1\nj3pdVPDJNODNPDyBxqQkjX0VdXLk0mX0mhkItLclgr7Oj8BR5uY054tD5xe5y6+08d1jXwAAfHWI\nm9A/1vah2qWTXjWt4nc8XOELm5O4F+QGeU7k09X9tRsnTExDevxBD745xbE8+/YijGN0Ir94jOkg\n/8O1NzHyDdq2l/7OBhzQYVw1DeMPS9wYX+si221TE+MHtBUL1SU8ZmGa200LKfLe+SrsJ6nriTvT\nyEto71OWDdRDnLu4h5viY1I57m9yXRuOVLF2n07BlHIJG8JJPL3KMW9qTZC3ae9b5QDOXOW8bD/p\nQbQhkvgIB/FG9B1M36POBod7ENNRkmQpCFu+u/1/WDukmg/bYTtsh+2wHbbfYvtEIN52kp6Mol+K\n2EdZ7iQZFBv0C9SVA0gV9DokWqIbc7WOxSa9l/ZEGeoc/9/qlLBmoqfsN9CTbqUL6DhF2rp9NdJD\noh5nMQpLjV6dqkwvNoZVtIsi/aRSg7SNVHLRIEVbQcRl7ScaMKQVWJfR03GEu3uqT88QAekvD2FX\nxee6JiUIJXmH0/bru5AHSDn2bjClvenZ00jdIx1+tSeOY8eIAsx7dih+SRR/XWR0KtvOoQ/0yo8M\nHmBEhMBntkPQieo6gR/8PgDgcu1n0DxGpDJdUmEnyRRuO84AnHUG71Q0If7e5IXlgB5qbqd7AJJ7\nQ47OCaKHgXvM3KSa+hC1TSIcbX4DaxNEBLm7K5DZ6TXHdskYeGHHQZvv6Lm3jqVeopjF1AbGRaDU\nxjGisOgvilCsE/WYz7uRVtOLTdR08JnJOmwXOZeOwSJyi4L2m1SibuJ3DeUc2rOkAE+WOYdh95Wu\nsvnbs5DfJXuQNdGzfd5/Ad9c/3cAgKc39Lgcp/6tDAXRY+Pc6rNE83Xj93H8fSKKtfF59LfZx2sV\nFZzi3UvfoJ5qUnaUL1PPPoULWLjIkkIryS/gsR2Or7RHULuLX0BGxTrJvpwb9nxHaVUAACAASURB\nVHNkVCpv30fNTYpwvEy6cnN8B+jC7snWMril4xpQCzrc315DTCST348oUIO4Ohfbg0NcxUt4icr1\nrscQDXNMp+t+zA2T/hwdaaKxqhSfiSjerN2Bd6sXAGCaiEFap5z5uBb1Y2RqbG8Q1RwdseK6jPqp\nU66jmmMfNY0XEdOTfXm6I46aTjSBv/64bM4GoBF3tWtV9mu3pobeSPRbhROJjrha2AAiHa4dtaiE\n1HHGMbgvzGJBjXw/GZkDvQn2Vf694wsBAHIHXvQUiWyltRAq4qhIorQhmqD8oyIzWqN3Hztqzrsv\nm0VGSjm0e0Z4hf7eaeng0zk+LpRokvek6BGpYrW/58b1bR6FNMWVsKD9f8Tq+h8BAM6+vYvhl8nC\nSMILuOB+n9/dozzaR19E4oe0N4o//iy+/12yVEXtSUyqiGgfyMiySEZ+juS1ZwAAc3+8jd/997RN\nv/rdFE6+yit1i42/AAAMjo3D8edE4Lon34O/QB33qdI4CCseKptRMYmKqH0+HtJj6z5t7UiQfdlX\njOIXUc7h6WYZzSoR7aY7BbuCa6RPQpp44WYI6haz9bU678PVR7sRbydhjPFus0scsby9U4ddSnas\nqtrB6qfE3uGMQ7rEPSF5j3N83tDG/WHuVY5/HEFdZK7qMa6j0nvmobJ1a5+IjddY5yIvKnMomoVy\nbuzCICqkHATLcEaoqGURjRY+0KPXQOXeWm5C5xFl/1oS7GlIOWazvQAAlTIOdZUUgckdRXGDG4Or\nkQREPtOwl4ZEUlUjlhZnXZk26qIwt3azjLaTnyNZsYGOSoEE+5isdI+ylDzg5GSbfZA3SLWGLmnQ\n76dS11e2cCrIhXNthEanuHgaX1FxQ/hFwwrlAimqPWsM2q/yGfJNGvjhyxsovsLFtPHqUVi/TKNd\n2dHAqaZCTfh4n/SyRQ2vyGfcuPcoVI9wQ53ODeG1FW4uZ810AnKWMgrUURRXN7vK1turxd6uyIks\nEo00dk2INkkvdRwbsJUp04bPAmebtJTeQ0q9uL0F7zo3y92gBLIezotqRYJikGd6pncou29Ciswg\nqbxrmw9gXeNiqmnVCFm50fSIMmie1/owP8hzOG27F9khftdUW0F5W2zkHRo4baZ7qrfMthY4wV3r\nIEYnQGO8g15QP+dPptC3zQ1wwiSBRxwNvCbnwnzsnRTWTvBzsRPCfFyUR7M8j586SMvL3+XGMOa+\nghfkzPF8ZUqNjTLpQEXwdVwe+RoA4DPbPOZ4tfRtTE7RaTty+S7CTjoSGscZ2C183rXvU+fkI093\nlU3nMEG9yQ1SKxJRbA5G0RAR1B6fBQaRyCHXMiB2lA7GsQek6V3GIFSj4t5x0oKRHOc+M6/Dtpzj\nuQs6iNa6CW0/+xWrTKGcY1WYVKOB6TKp0IQ4W77aSEI5SsNXf8OOtkyc+fs2MNzg+G44ROKMuKur\nbCWZFRJxw8BooiHWZKrYadNWDCvCaA18lOymCqWgfN0GOs71SC8qbsoQhhGmfa5vT66MapBOJCJ0\nGFqdLGIiXak+2gt7jOswN1JFn8gGk57h2l47sMBcJqpISWTQygTlbtlFNUuq1NnWQFl/eMrI5gtB\nvL3AmIkn589hyMJNqSbKbTrn/h2qRlFJ6g/MsITZH88JG0bXubHetHFDaywacGSA94AX3vwFFDaO\n53r7B6ipSWH7rLwXnrOPQF7jOyr/WxM7j1Gmnk0v6l/l50yM46ea30fNQRC08sMy9j5PivtE6QeQ\naR5/qGybgSLk21zzd9wNnNXxufsqAptU6y5c7wogdkSKR3W0j6/enMT2EY7DVIeOiGYEeMP9twCA\ns5HHMaPoBQD83F6FLErb/SBC/ZYOV7C9SNmPbbWwIO7Nr7xtx5CFx29Sx6c4zu0EWiKf/PhQGXeO\ni9KsmynUHmL/H9YOqebDdtgO22E7bIftt9g+EYg35ST14942Qy3q7WZdaqS1olh8vI1FFWmwvii9\nY61Ri70qPTpFGejk6XXvqAF1iSjKYiRSq8YVCDdEFit5A+ZBernxpBHpA3qACb3I5JPLYEhQwjvq\nFFoWerdNrQKNXQ7X+DgR3dqOFo1tIj5dn6qrbDsZ0j1juqsY6CNyWvAWcL/IYAarr4lshmioHuM4\n2EcUeEtLr1v3nh7FING+qu3GHRH0cnaY3qFhvwH/e/SS29Emtr/N/gxMLGPnp/QEb3+WHr7i9a9i\nyUl02L74j+hdJU2kMUtwRk5EIBV3DdsqLVp6IofJmhHf7yJbPajByk2RUcnIfo3LbsG2Lqq4zH4d\nVRsDF/piJjiiHDeTCDKRZ5X4qYWfR3J7aK0SsT5mUmKzzWw81wLfAADIHDlE71POEzIr4h2yGtvS\nCJQeUl/WB/zbml2ByCCR9oWdEvKz/F1uT43eANFbbJlotNHojpyOu0rQZsUzwIjjnRu98Gfoq+5W\n+vCPScr5x6UF7NUZTaopfA4AkPR6kEyyP/62HdlePiNl2cBTfymihE9Rf9X3XkTkHPV3IOHDF9PM\nLnRvZwdGDVH1B2Git5efHcDeFaKeO6enERGRy6qjNyA3Cl08Rz1yCGruN9tm2w3zGc7zXolrLJM9\nivEWn2XL11A0EY0abG3YBTKN9ol+l1Vo3aPnb2zYELFwbWVSMnj87EN7lcyCTC3Flji+UJgBl44o\nIZ3dxrUt9kGT5prX2uvoLIn79oMTKH/ERrR6oDdRT8oiOrdk/njtawBoNdpQWvjc/ZagDdtV+EWK\n1VrNhXKY75MZC5CLu7lZFVFnsVBH9iOaXZaCzkJboS5lUCiL4uo1rseSXIlam7pnNq8hLI4Q0tEY\n7EpS+PsF9lOZViLf5H1TS7wOiZMIPrerh8khik+0q8h2Hk417zcDeK71MwDAu32zuPBtjsnci6y7\nmzWOwbzGPvhXYiiKqlE3kr9EdZZUqCRE5uTG0TfQH2YfLjoGkHuLet37FTfmlxigJVWJowvXrxC1\nciz/yfBTWL9BXV8NtLG5zvWjW+fYPC6pY3NUjOV+Hw4eUH+/I03iyeruQ2Xr299CpMa5OFoPI7/O\nY6mUOCsJqQegi3LeFPok7umoBxaXCfoKx08ZIzpODlUw42fEsctyCXd3OJ/ZqwmcPCcqf21zPxlK\ntKH18RjjbtOEgjUEAND3F9EsU+YbKcoQHBhFapuI+Cf6KCaj7KMsYAc2ow+VrVv7RGy8Wgk5+n1L\nG2E5B8kcyyEnIVWKwDJMIlQ/LPK7SvIxfBRkaG5r0C4JBS+XIBPnnGlR1k3qL0ObFSHjhhqqIioZ\nqjoMZhqK0j4XxV6lhbqOG/dBs4I+KT9HSxJYdKIQdkhU/rYCHbcodG3sHk7ud9GY1cpO3F4mFYKw\nAQOjpHmehg435KKMW5QKu65XQaZn3/vNasy1SKusVz+HF9VU3swdGt9deLFS5gY7ds6FiqDBbimr\naGh5znF6jcqbkbyLuE8knkidwLqBm/CRmysIp/9bAEBvgPTKoMGKvTUavhXFTlfZ8ttVNHtIdfqV\n4iy3cgbbPo6TufwGmnEa4FHNKG7puOlp17kZ95pMmN3n3+JGFwoiEcaKooqeDs9XvqqlvLczZchC\njEbfLHrgc3He1KEOZGPcGFwj/H05VsHYCqnm9pGLmNnkprVo20e7wE1JauPCMw92p9HvGtMYMlO2\nuE4kKNkbR2eclN1jqys4+ymO78qbDdjHKMceuwJ1vYWF7/2An7/yPFx/xUUaefQAm8PUqVr5SQDA\nxVAZ5SnOy2WtEXPzPFsebpYh8dMg1qVcFx9+WMfYtIjKD7QwNE6ju3rjf4Fk9JcAgLMK6uK1se5n\n837lFm5e5jo6BnFU4o8j1SM2lvk0QoM0bMq1NpoWLjSHg8cj63sJaB3iuMV1E5oMjyeOqFJYVtOI\n+YOcv03kYS2JzWRDi9slsfkUmhgZEwkyRqjLf708jtkmN4Zb5gNMixKghYkYHoQ4sM8rGHXfUlzq\nKlvd1kA7yTUwLDbKkDOHcYlIXWo2QSelfqodEuQEnV0t00kqq3Pwi7PubKuBzYSI0bClod7nUchd\nEVNhtSuhrfC5knYD8TjjLppOBTYFBW1NiNSlzTDCInmISRMBxG2JjlqJRFmcoZsKCIi4lG7tuDaG\nv7PymMGxmcPVM3zfyTI3yMXAdfTdoC2IDO2gUeEmMf/ECOIH/PwVMW996zZcNXJewjdG4BHB/dFL\nG/AbCRBWrnH895+oon6dOjnnWISmxCOH6fwYFt7m3A2K8/pthRy/SlG2M4kinn2Z77hvUmF5/uEV\nfA7sEhgF/R7dOAODOC4Mi+ua2JFA+3naydyNx6HM8bgmakpDZ6eu3TFTP7ULcczcYWrRPelTqPVR\njhmzGvObHJ+gSD1csOmwdcC+X9lfxIslApBYRYtNPWMaRiLUNWUBcFl4bFXwlDEnbjCMl2SoqPoe\nKlu3dkg1H7bDdtgO22E7bL/F9olAvDtZeswu9Q1IU/Qgy6YKOjl6jYVtDcoGeo5yUWPT6NcgryfN\no1TloN4lzSDpUaLt5XfkIkOb5CANpbgkba2qEdcQhTmsJWRTpAisTT7LJncjEicilvXYsbtGT9sy\n0I+MlKjOXxJVXOJBuEQwyN3NYlfZcrukNpwdPU6f4DvSpZegMjPQ6L0DGVoCjfeI+sHy1usIWOjZ\nX29EEbtH5PKSdw3JMKdMIdJiDhtdsG4R0TmuziM6Q3rpgVcKl4KRsCtrRFsbRzIYShC9tUczsIcY\nEXtgHYAmLqIdRZDaLU0OyiV6nQXnqa6yLceWYWwRHewe0DOtm9s4I+Wz7hbMkGiInN5S3MXpfc5h\nwsvgqiu1Ocy6QwCAiqQHLQ//r9NuY0ukUKwLFJxOKuERQXiK2QPsF38BAChVTuPsMtFmUVSNcVnU\nWPOTAsuWq0g4iXDKDjPsolB7rYdUc3rJ1FW2560B/FDFqPDpB88BACzDIYQvcZ6/az6G33+VjIFy\n9x7uTLFvx29zHNY0Nbxw9LOUt/MBMic57vdUbbiL1C+X6acAgP/prAlWM3Xy8ZWfoXKCz1iOnMHY\nInWmfpGo849rdnxTMCCW7/cgNkPqVnXyexiq837vooW60WvXdJVNuRXClIn9XStSfkU7iuQGf6eq\n2RCpU+8D+mn0Z6lfSSXpZZuthEeKRH+35/VoniSTs3rrGDwaoofdBNeIq+pCuM21qZzIYVjCNRTa\nUCAd4XzdbzBFZruVQd5BRsEmD6K8RbbIaJCg4xW0aps2YUDyFICVj8nmKzggVBLFKNeVu9lG2UMd\nqSar0LlCAABdXYpGQTBVgq5smKoop0RQnEmGqkCNlqQZ1TzHROsTDN2GERZRBKANJYoiPaIsn4Y+\nyXlRjxDhy+UrsK8yIDKqycHa5jFGrLODgSZt3p65BWX04cFV+kIBj4tI4+kRO75Z51hnsrQxfRtG\nRM9QtvL0n0BxmbS0ORqEYu//BADcnGfEpPmcDxU5503SvIsVx0sAgNCWAkY9de3WMFHh40UJvDaG\nkMf2/gj6APVqT+bAuVe4Tt+REIHaf97Cy3/ECOg9vIXYnf8GAHDjyzo8UhLRml0OrpyJUbTCpHRL\nE2uw7BJtKreYtlXtuoqsqGGutoTRAPtoVQ5i/xqLjeQf4c2M5tt5hI/wtsR6Tx6fkZDduVy/C1+R\nR4rLd7jG5F+SwfwO4f6Jo05kkjxGuBXqwJ0n2xY7QXsl1ZqhMXGsPZka5AWyltDMwS90FRc/JlrX\ndoh4D9thO2yH7bAdtt9i+0Qg3sAUA4XqCQ/sZZ4DtOUdFEVGkWpzBKpdnmtpffTUt5o1aCv8nc7k\nhEIE2NxvxzDeItrRKOhdNwatSO/Sx9Bo5ChERZaq7QzMI/RS1WqeRWyojahI6Nkqc2o4TfT6crIS\naqJEWESgY3tNhfw+kcqgr3s93uPP8L03Wh5o7vNdinoNFjnlaR9MQeukl7++RGT7uMODxctEIs12\nFFUdZf5xfAstCb1xv5mosrVhQFrLQC2nTAcPj9jQydQhbmDAoeCZ92T2Km7WiaQfv30WqVP0iI2x\ncfRsEqHs3iKScV+YQ3CW5zqL3+p+xuvudSOUJir+KDOYoZRHUsvzDmkpAus2kcpSuYpNI+U3p3lG\n1Cc7g806g3vG20BYFCC4+bQV56tEpndMHBNZxIcPrEQfUxv92J/m/2cK69hdIVpJOokA3HYj1HNE\nBsGxCJYMVPPAgRR3dCxmMFjqBQCkVONdZQvVEzBUPgMAkNQZQCa/GsL2Cufe98+VaE9xDk2yL8JX\nIPpdGOBVoMaVU0g8znG7tfcIDMviQq3tHIoiFeqt14jYXnp0FbdX2I8fxeOYlRAZnIjGIRVpAdUf\nEL3dHBrEeIvXhFojY9CJuc0tXMX7vdS16WXq/9Kzoa6ybQ74UcpROfoaQrZyCjEp0cB2FUjFOG/e\n2lXMm4kCrGk+L2E14MOEKL2n74Okxnn1B5LYCDMwrG+CcxUp5GCR9QIA4tvvIN4kkhk2S7AmdNhb\nYr8NVTcMIs3jQX8Lhgl+N6a0wLLFMRtwEB0qt7unVkxiC4YKz+l0BiJ0TbOEzh4/OzR6NHM8pyvl\n09BVqF8lNRFxLeKBPidqYXeakFSZOS2f1yDXpJydPFF3WppARVxrMqg60BSIVjt2GwoVLsSOCGAy\ndkZR7ic6dK7okSiR8bJW9Sgd4xqalvWhqO9eQxkA/j7sxfEaYzD+c9SOf5kh2o7NUDfeW5Ci54Cf\nk5K/h+8mr6jph/8CHR9jIowhrgtPsYjIIv+/Z/bCoiGLInnsKOxP0cb0XmPa2ntzs9iTEsWOHv8e\nPF4iyNXIfYRv8Uzeb6Uelp58gO03xVjHvoH0K/8rAOBi5Av4TJ1r9htdZKtaOmjc7QUAmKZuYCfC\neVH5uIYSngkU73MuNKZtPBbhHC9bkthWkFUbXOXYKC4akFRy3EcSKrxW5Fn4qO4UKhMcayPNINaX\nldCe5jsyIT0OhhkzMXBkCp7rZASvVZjvQDuoRmqV61ERLUP9HJ+ri5oQH+ki1P9H+0RsvCpBFe4o\nm9BZuAhXNR4YslzcNlMeigKh/LycG501okPdJajmghmlBhfiiMQNuYI0RFslaoPW9ZDaqXhWZRJV\nkfe5OgO08lwMezpOdKzYgcnCBVbTFBA9ENWFyhkYlRwum5RUU8kmxbqMvz9Vy3SVrdymAswm5qH0\ncpGqvvshdBe4wIYDBuyHaXTls3Qk9jLbQILKW5so4UyOfV/L/whbRt6FM5QZkRwy70CnJhWaLr0N\nmaBVptzvQpZkRLXKQUPiqj0BdZJjWj9bxdC7NLR3PEV4LnBBduyk3i9UnsQvRGTqyEuBbmlx0amV\nMSKSnERzfG6oPgRZnuOfGzXB20v5R+d7UWhSpjMKJtu4G1UiEhZ1W48ZMHyehisCD6J6bkoSCymj\nTm4PLyg4F+HxEUzfI9WcLcrh93LRR2zc/PMtN+CnM7LrGoMXNJQKxSxaRo77gyp146Tj43QlAOxs\nlnDEQdkUp0TO676nsfcB+3BsrgKzl9RurnkZ+V1xfDFLIyCLFfChyOMb8DcwZ+RdwLqhjGjhJwCA\nPx1iLuw12VG4RO3cf/NkHj/6OY2D7rwKiSY3opqODqBx2Iw1UI8UQTUmFkVgjmcWpgU6fy0NdXFS\npOv7zWZNOGGsiDvlYmMv2OuYzlG/bwfqmAE3uuhgApI99qEtoe4FihGgj3PZvuOGvca1lx3TQ36E\nNPqOSLYx/gslEmoqT875NEbXSK8fmB5FY586kw7QgamkcsiJ65CxlSaGT3FNGu6nYarQOEZFnnWj\n2JR/s6nqNhjipMaLfaKIvbyJHhG93JFJUO/QUavk69DlSTkWRJ5vy7QG20r+zfNBE+tizUMqg0JU\n12nTd0OfrQ2poM7DjjbUYi1YiiVUI6Jm9wDHV5kvQi3GN2LUQ27id7W7KkRFsK9KAtQlDw+uujiy\njuwaadHhwG28f5d2qqQIUZ6WC8mjXFvexD4OHNzc3Sk7HK+L1Jd/Sj0t/7kK73toF76QbaHtol4H\nby3i+nd62Z9XqN8GyWV8uZebUDr3JBJv0nGeOnESy15+t1GiEzB1oMQ7Burf7MBNvN9glP/vG3X4\nLz0Pz2e8iy30iFrE629N4BE7wUZEScq++MGHKLkZbKuLqHFjgfrQb7JCIVI6JoIc66GwFm017cpk\nYA26qEh56utD9hbnRWbn2B1tXke9wzWr8fbj997lOvsLfxktA8fPUKZz0TmIYVzc6S+flWAgxrla\nqD7AmdzUQ2Xr1g6p5sN22A7bYTtsh+232D4RiDfXQwpXc6CCrCnu6Sb34XhAr2XvERfkI0Qz1jpp\nIr0tDLdCeHzqPNRK0qL7njTGNPReC0V6JM6yESo9PRmLBti1ihq8LQvKRlIzeQEOXKYdOERmqlAk\nAKWKXq5peBCtXXpy2T56nf6QEg2V8CBF6snfbNIb9LDS3joicXqYM3+gxsglyta2L2PQLq4RlShb\ntL0C1wuMEDFlcyhtMIPMiOXLqKl57aQSp+e/sxBEfx//5s48i6Mn+N1W8STuqxh00BYBRd4jbURm\n6BX2/VgO3X/HdzS//S62tETH1m3KsaVoYkBkFLLdH+4q21HtIKJ5yiQTVZ7CuwrEhog0Zi4XsfQk\n4YG7BaAiEtLH6aFmNGpYzxNpyzbDSEXo8U4fLyGRItIdEogkUnHi3iBR2GD1R4g8xYCv0XtL+MBN\n3TC2KedUbg3LeuqGt7KESofXfqT9UYxUSVFdNhKFtebNXWX70pgGv7rNsdKkmfrRJ7+Pl7c5R3KD\nFzeOc3w160a0y5xDS4n6VJtaRM97vPrRaf0Yj0noEb+9s4beb/wLAMDKz3nEMKu24tI7nMPvNWYg\nO0+vWmr/AFMgklCUGZiz1sggOk+dOvYuMD/JTFBG+QZ2x3hccjxOvb+333153xqQY3CLiN8nUMha\nfh81B9eNecCAxjrH348WJBpSzUUdEbzOWEZmj1ydPJAFrJwL60YeV/3iqCJLNmWjv4CqlzDWu1/A\n3jGyO+a1JvJq6kk2SRkjjR74bOzzeXkTy29zbQWVZWBAFGXYFmh0SdZVtqYGKIrjIWeb61iWc6Jk\nIarWd5Sol7im/bIIrsqELfCIrEgZQCNyCSxk21CKu81ZiwIZP9F8ucp+OSUmeIrvAwB6TE8jlSO9\n3pR0YHbyuZuigISq5sHQMHUD+1KkBLTPB8ywtIm4qvV9JKXda18DQOXtUQQf5/ieeOMv8c0R2oCJ\nci8AIPSiAyOv80oZkmYYJ8mMLAYnMZonQ6R/nbYr9JIaX89QZwfkWuyHKJtcdQzVac6z+bbIQjY7\ni/xV6kDSXkPP08ymlrOl0HuN86APkMkoeo4h4OaYvZrS4MUor/X9qnkRXzh466GyNZI9CIvUuAPZ\nKCoNMhoVwRptvejF6Ju0v82RMGxtyhYqRCDTUIdnxVKOqAsIiGDXuawVKg9tW8r+DmwvkbWR7tBO\nRgxnUEvQdgWyCbx7kuMwnNBgd5Pv0w+SRdQfHEH/s9S/xLUCGs+LeudhYLUgsl89VML/d/tEbLwl\nYaxkyGC/yMVvkvahdYaU27ixgXiGi1RV4+RoTSXUROSqoapGURQaHy0noRLpxpoFGg+FqwCNlHRj\nqaPEkKhEUtUDSRGMZhjigLqkkwh7adiCqzmkm9wMOrICjC5SmvUWv1vrbGJAxk1pVdXsKpu0xb8P\n5J7AKQf7fr8ygo1eLoTdvBcDHW6AJ9usZnNr24Erx1hpxv/hOPQa0kRz7gw2lkUe1DapQlOwgcBR\n/m3orXvYqvN88HpBi6ciIsL5BRqBPpUavn2R/3ZAh/nLpBDNU4/Co+eYbNV5NmU68GH1FKlf4+3u\nm1O89SEuL9C4+c5yofRaU1jb5UaYPa6GaY2bbH2gH60QqcO5SRqMpPI+vPf4bKNbAXi5cZT6NOjV\n89337/UCAJTGOlzFfwAAZHYn8agoS7fg8uHTazyn3HTSaMkLUuwPiVR+qSE4NNQdfbUHZSmjLwd/\nyok3ne5+OLPYOIqyS+hEilxg/e1TGM2Tir88+UOMXOZY6w6aKMZJ81aUIQCAzeVCvcBz3X39GE7J\nWbpMeV6DP1zkufZ7B9y8MvW3cfIUS7glGwYMiM3UnHkZ19xcD7oe6uSzPzTCM8M5rF55DYPqLwIA\nUrosBr9HuvqHL1I3Hst2N+K9t4GkSBJTFSXprAPTWBaXkI0NOfTiknyo7Ia1KZyjGMe5XB2AboCb\nj6+lRQ0iJ7p+FcchCs63ef5lVBtR3KR+1lVpGOU01NvBGHqEQyiycMISl2KXouHupg7P9nOOHjRU\naIQ5b0fcXI93Pd3vXzviNSjNXP/xDt9l8u8hKHI8Y1kPtbhPXuiUMSioZHlB3FnP1tAw8btKbwj5\nHOfVYGlCMc/fjQ2KCmlzNeQCNOpGWRgdrYjeLihh9tIeVStcu+ZgDtkmn6VuyiHXirvAJTlKOq7D\nRCWAUXCer3WRzfX4Mn64QCr5iac/i+M3OH4bPq5dw1v/BleffJlD+tOfYVJF51KTOYGMk6kbVUo+\nubzdhExDu3vLeBEBpchLrNCifJzRxZWdYTE2j2Cpww3dYksgrHofADA014Fxik5MyMS1MHCgwUEP\nZfvqYAHtcdrMsTdsiJ5NdJGKbTh7BftgH/zxSVzNMM2t+TMMEz5624mkl3o9WHchoaZulJtGjCa4\n1r3XGQGtcOWQFgmT8p/SY+gez22tH55AcZJ21VondR6P2BExMbq7oi5goUFb6R4zwFrmd1qTpJxl\nc3X8tNILABjVd+BfIVp7oG/juPE9IcnXHirj/7MdUs2H7bAdtsN22A7bb7F9IhBvS9TPVGhdmGnQ\nk922pdCWExnlclIoXQIZZYhyq80qpNui4LdfhnJHpBMzjiBoo8daERmdqqUO5D7+TpZOIuKgh27d\nAvx6oqy4mZRGs5nCqJy0VjiwBn9Q3CfNFuES1W8acpEY3NALeVMUsU92znGq+QAAIABJREFUr7yR\nCpPa2crPwyNnVF7tM2vQrBPlvuvzYLRI9PBmm2ndeo5pYXyHMuS+eBS+vxXjNGTAs7fpNW8E6ZVK\n13rRvsLfz005kX1dVPB4fBg7DiK18SwDiGJRB0ZrDBS4dtKA0RVxL7HVRNbMvw/rOQ47th64fkDU\n+Qt7dzXZy/fh6FF6xektIhy1OYyalaiklmvDpCVtapDo0fLxO3IVKRrHfRVyAc7xrgFwinqmtmoc\n8YooqP4M+/NYSIXXQK/961Y1QoLhiLvbqAm0qFAyMrOVsWKkQ50aLN9B2U0vVBFZhs7NoLRbj1DP\nJKnVrrL9/EEKk6Oc77iBfXHIG8gJqtp5x427LxIx+GM6RJ7+DwAAXZ60oX69gf0/IaI985MfQeZk\nBGnPdgU3ymQVbpdJVTd+8Fn87ldJPf7Nqcu4n2cWsXvv3cXnFfS6MUKknX/iQ5Suc44DvqfR8tJb\nt5X82AnSA/ebiYr0c93T2FWVcZiV7FtbxZsDtbur8I8SmSp2WtgRwVySUBVKgcI6k0RYFfTCui8C\nAT0F1KT87LL6YBWpTreWyID0ybZQHOCYKcJLGJ8mgrdfnsEvB6ifvjK/uzYeRnBLFAwwKjEPpi48\n4h7EYpAUf3GJ/XrGZMSfdZEtjCJcovCJyinqWCddCEs5hyX/MpSSXgDAQb4DlYyUZMdGNKurpGAp\ncE3H81pk+kQhgmgDDXG/9SArisb3JeEV8ULt2Cia4hlRawE5DRGZpkb7UpVKYGjz/4pAFjNpcXRQ\nyMHS5jqrdmSQVrozZwBwN+rG1B1GzZv0TrzXpA6fXidDsvHov4bvXcrsO/0UVjepn4PTCbxqIloc\nsVIPX7lmRvgE7Qb+7EPEnmTwX9o2h+Amg/6CamalWpgexOxTtFf+hWvYuU3btPzpFPzVfwUAGLjD\nyN/oZBFfm6c+/NTxOP6pqJz0l8NzuFCwP1S2fO4J1KQhjm9AjpaefRj4kLZH1ihh30lEO7exhJFh\nslHaug6RKm8fIClSGdZnMe7lOOiybWzd5Lg7XiohouKxk8UtItSXc5BW+HlbpsfTl8XaOmfFpoXv\n9h5wPW1NtfFohqjd75CgpuY4eHp8UO6uPVS2bu0TsfHKNFR+U16PLSMnZ6jiRcdHGiNvtEPaEAWn\nfaQr1ZkhGPykGKSFQShEdLFKkUV2h5x9v46rIuWrogYadbUyCHuVNJvFu4i4iKQbEec2mY4FqTo3\nCFdNCbmgorPtfmyLqy0OkdYSBgnycfYr6OieQEM3JarSDPSgtE/6o7ZmRvU4jcqTBi0uXScd29Pi\nhn+gv4kzw1SGg7/ZRFXQH6OmGuQHXAyyEVHIfSwN7wqN1ZrECNcL7Ec18i56JmjwaoIeyd6SIj3O\njSH25hKmgoKCPq7F1l0a4Amxmez+4/+FRJEbwFnlq7jcRbbbgV6Mr/H7bSMXm8yiwSkVDZ+pP4W5\nVW6cxT0d0iIN4fg++7h59mUcqfKcM9JxQCXyp0olLUiNIs1bkvpQ6tjweQfnaF7xAMYON/yBaA0u\nPQ1PXlCrw9o8XB0eM6zMXIDIbYGyehBb/VycT+/wXXMfhZT+Rhs7sogRE894zd9i/EDn5V9AF+Bi\nuyPdw8kIabCdI1twmmjY+/6cjkTqmWfxR7s8TpiPvYLQy9TVXGETv47R+H1tkjSc25LG60b+bvKO\nHVUTx1T6TA7boM59McgrRH93aRsZE42KoZ5ETZT3W2qVcfY85R9fIy17xdhdNqOuBomL/6ulhCOB\nXqzVeY3krPM+TDEaG0kwg5SP/VHtU0eMtSqMs3xXUlVC732evWePZyGZ55o7Ns2zfXWtDL+Bc1Hz\nPYGl1RAAQHu2hTMGOhvNKGW3GK0wPkJDHUk3YdcxivWWTApVhpuTUuRgCGXrXWWzGZyQtgVdWKLT\nsuPYR0MiEtmUnMiJxCKWAw2adjoC5U32UW9MotoWeZ2tDgzp+J5EuQ6ZuBZlKIooWZ0b6hbHZi+Q\nx0SVxuKgFkApykhvjZJ6rCgCsnHOVaAxAYlwcPVyN+R26kbfgRuLje7XpADAvpqC5RTf/ffGOmYr\ndFhcTtqPwuolaE+R8pRGRjFm4nlmZcWIF8We98g9ztXOU3cQWeHm9VygDx9epj6U/laH9nd5xJcs\ncMzOn3oNhT/jRnj/CS8mvHTaLPdXsb0kSPFHaLuazjyulhjb0BftwVtJcS5r/x4WzusfKlvbmYW/\nLo4GY9fhq/YCAHpEJPllmx2+cXHsstXEyAad96tuNaatdJ4/cPJd4+ocloXOue9ewsQXqA9zO1Ko\nvkDn1PgO94ilWg79bs7LsVoWmXP83a86GfgGCXJyEur9WOY5GDyk3O+pg+i7K870r1yH7OmHn813\na4dU82E7bIftsB22w/ZbbJ8IxFtQ0uOV6fSwCm8zZejALBdFDuJ+GFWMSqx36AEdd8WBMKmxRW8Z\nrjxRlnpYgfGESP5tJ0LqWdMjTaYKI/0a3BXJ9lWyaQy2+Tkn0k8adDFYEkRTSmsD6jZRqswphaxG\n6NRuinuWtRI64t7d9kOqilQMjORbi5xFwElUVE04YDLRY9PpwmgIqk6hZV/GYzHU7PS2hr4yh9Sr\ngs59exwZcQl/Wk6q+VrrFB5M0NMzHWRgsrwIAPB6LuHOHXpvI0+QBpF8PYNqkZT7Y6nLuO9+FgCQ\nScwiYCINfv09kX5SexJ7T7BGrzL2OIBvfUy2ryzlcTNOr887QQS5t2dFx8tIRmlkFP1l0jzLk/s4\nkyZcWbUT5QayOgQrohBGexNbF+mNttaOw2UW9XgdRP5LhQVYDEIfNBdh0hHVNUoGWNepByUHxyYZ\nGYYqSC9V3TpAOklGQFbeRTEn+gD2q2WzAPjex2RLd07Dkeby+OUFcZxw1IHFN/muad84bouECuZs\nDrZdxjNe/08cD/9b7+M7DaLGmSf/CqerfK97zo59B1GLSkN0kzoIYibFI4nKqa9h4TxpwUdffwY7\nQaL5uJTje/HcA3wnRlrQlE1Dt0lEbG+fRFgExeB96v1TWhv+7cckA4xaM6oSIjVdi+NU75FjGEzd\nWG49gmySSK/q0sMQYfRTW+S4tzuMyKeIspSSfrikRKOKNQe2tWSATgvKfSNcxPgkn/XTtgpn5aRp\nY+kqZH0hAED2MnXO36tHPko9lI0pUb/PeTOWdSiLIlIqcQQhkXVnmA6SKQSn5GLMuC6GlANINdlH\nqbkMo0qYPb8RQRGQlxIFzks1DRLirqzD4kFljXPhm/QhOMd+5tVcp1aVCZUJjv/pNJAyUuaApIOy\nmmjUoKI9a7UG0ZFSP2XyGtaN7Nugu4rtXdLOkUoOtZa3q1wA4PLn0FCT+fh6sQ+Kf8bx+9kHlGFS\n2ofxGJHptn0Bu1c5F4FPvQ256hUAwI0oqWqn6QbMPyQiftOmxokXOUe7H1QReI/0eulf0Wju/R8q\nbMxQZyff6sG7z3LsX7RHUf8fRIDrCtd0Lr2CZ2aIeP+j6TbOXWLwmUfvwO7W8kNlO10s4KaPjMHm\nnAryU7SrvxB3rmflFly/z7lonFLi8g7nSzUjR1rkeHh2l2usqWpBlhH3zDt+ZGKCWTIoUHpDJMzR\nESUPJzxQHeU4Vdd12NhggNfRV+6jJHIe6Hc4J9YBDQri7vnkSABzWtLOvYNBNOvd8zg8rB0i3sN2\n2A7bYTtsh+232D4RiFeaJd+uk+6iLEpDTZcjOBgnupAqViCvEG3mRcj5vrWCgaxAIvIapu2Cs5c5\nUQHPLtpJev5ShwOtKs8rEmU1xl30utcaB7CIYAYJiFh7y/uIlulNqfUOfORXaxT7aCfo+TTEFYFG\n1YhCje+SSrsHRchfY+Juo86GHZvIenSiDoMIxFDc6IP7Wb773F+zX42JKNDPc4eF6BF4LrDs15p/\nGfMZeoCKbaLVpwoarATZy5rHg7SJsESxOoSh0/TMkyWiscB+FPsiVVrPiRkM/UQEDzxSx36eXndb\nT49Y96s9mHUMRKjudC/Blo9uo9KgHJ0a0bWs5w4GQsx4U1LsozDA/8/sDaFSCAEADCJjky0sw5yf\nKKJffgLe+zwXtOYiWPCLdGx7RFutih+5Fv1Eb3gbmwP0fjuyMVh6xflKP1GwTWGG0sDxqytzyJQ4\nZlOek+jZptedMhJBFhKprrI5ZItIOogKP53jsxK/6uCemO/doBqzZYFuzTLccfEQbfBHPEPeT7rx\nuYvs1/xbx7H4HnVR9RUPTv+awVUyD4OVOs8/hbW7RN1NtxbxBV4D2R4KwLhNvX2vzpiAs74q+urM\n2tNIJtESdaALw1swvs3JbXyaKKyS7c7CJFYy2HTyXLGnyvPOliMApzoEAJBrJKiIYRmo+5AQhTNk\nR8SYDRnQCHEcy/kRbAwJ9Ht9F5YhrtlOlA/o6ZnGHQlRu7dXiViM+mBr7yC0y/V0MM71JE1YYQ+w\nz75VCQ5KZJtGNX7cDhN9lQVBYpV0xwwdXQXlPOUP9BPBp4wdWNeJoDSBEhpFPjfo6kFIpIw0bnD+\nqgY/rBoiPkUjDrOT46uSNhE7wrVlKorAnE4b0Qbn0NNYh0PDcWgGZfBSrVE30F6p9WFU9rnGCoMl\neA44TtWGFBUT0WI7roBa372GMgAsJm5j3Mh1djf4AM/8Pcct0CN04GtzeHuV46J/0IfgeWZv2+i8\njLbI+Nd6mdeNpFs6NL9GtPrl0gLezlBvd2R9cH6BtuzITa7H9UEdNKMsoqA41od/AZ4Hv6u9h+x9\n9kdjFPXFS8N4VQDbc76L0KtoO66EAzhq+QjNv/4x2a7oOrD83+y9aZBc2XUe+GW+XF7u+1KZVVn7\nChR2NNAN9MLe2AtJiSIpyiZFW9ZIlkxbsscKj2c8MRGembAiZMWExaDDUowkaqFEiaRIqpvNZi/o\nRgPdQAONQqNQKNSCWrK23Pc9873MnB/fbUXMIKGwY2J6+kfeP0Bk5ct3z7nnnnvOd89SJv8eDVjw\noTgHAmvUYYtDkzjnJ0qYX9pA8BQ919sXMvCIe2/jkrhPP1mDs8k1Ttpd2NSIu+PQItx13q1v3BA5\n6eNDqCaJ+miPzGPaQC+2UvZDMnB/T7mJBmxF0sjEiDhUpGEYrYy3qdUasHwo7uafuo+0nuMTcfB6\nRJEKf/kIzAOEmnYNbuCu6LoxewTVAUZovlhhwMuOLYzkPDfLTC2KbT8FTptzIzvI/3cz/N3CvR3M\njHHR4vI2sgUydFo7g60O4SyzhcK0rZdxwsSAkx1tG60iBdLWkDEoDteFIJ/v7GhgrxISret6B3sU\nh7lZh5xpDJ0iPesJD1p5CqFdu4IZUa5u6Usip7LmwtIbfJdjoIi3898GABxtjOGQykOrtcaDaeW5\nAGYuUXkU/F0suAjHnjiZQS0vSmo2qKhrhhn47rA+80rxNM49TOGLN65iuE7hrNYI967lQxht8v/V\nidmetMmKE3MhrlfVJjoH7TmQr3E+sXAXjjWugVQahOFnGMjmf48K497MFk5mqNSLkgpfk/xpWbXw\niybepQIP2E/p9XhD5qHnH9vEqQwjwLetWfhELWVphfS+bzLh0VHCS5G39rE1zf+X8pvQ6kUpTpC2\nZ0Z696zNGrxIxXjQjIm+xZcfy+JrFwijyW9dx7f+ESFJ3esV2CXO5+E5rmFdvoZ7V6iUh04ex9Yv\niQDCN1/C2SkeyO85CY3VCpdw4iuUAeu7XaDxVf7G499F3EYFPabyEPngsx/i7B+S59ul5zCUp9JY\nVy4gqCEMe0lEPR+O96YtY7HBLfZRcIS/q8CDpkr4rdgcQ/g0D/9WOQhjhWukE32UrTvnkNaSp3Ln\nBnR2wqou1YSomwdnNsW1qs6XYFrn39VuGpUkZc7n7sBbIkSo84mCNHodtnZF4ZiTccxXKcu30zFY\nCtzThQ1hGNqHe9KmsRtRE1cESo1yHzilg1FPmdtLueEQQYWle004w+SvoqfStrjjMKX4fLOuhcPB\nfdFppxAQ/ZkdKg+xnK8KIU4ojZnhinO+hlIV7RIPNa+G614wDMAp8k2NfieSIDRpSZSh1qjHKo48\ntBvRnnQBgNv628gcZ5eh3ZIK3RcI46LB5w9dDeL95ylHptwNpG5wjQpHOpAzXNsjLkLuXpcWL3S5\nt/7E/CxyL/O9n3nSiAj4ecL7zwEAY+pLiImucbnb21g8JoqXXHPhC0dI0yXL/8ipvJuAfYh8n373\nPZTGuW5nB3JYfau3jgSAptaIygFl5+DhEp65xn10/QXKw1zMA4gOc5XR40glGe4ZGHoEExFR/vR5\nziWRncOQmfrDH2qhsU/aMxYzcqIiZ93ChetaJvDsPs+G7bl3MX5Ao2shNACvcPY2x/iZoZrB0xHS\nsHR1HZnDNMjDwQKsPx5/IG29Rh9q7o/+6I/+6I/++BjHJ8LjrTRofe8auzA3aVUa56rQbNGy3Gps\nYbJBL6HcFvl2DQPc4N8LZj+6ooNKQSngeI7WpDRCD2iz00WxSC9ArtYR8NA6STiqmO+QBeYDWt9b\n0GLbSXjDF3TBSGMc+oqCNRDS8K4ICzV7C14XveN1VQbw+n206cHf2h4wIbxCc+vZaT++3+V7H9I9\njt23+f91Oy2s47UfYapJeDmXkjAqLMigFMfyW/R08/+GqRIPv2zG6w/Rsp3fN+FxhRbtWl6Fp0Fr\n3XaVsN/cYwo2OoRKzjVu41aMlp7xaAnZBD3/CRs9fMuwilSK87lpVu6jCwA+HPPBMM/fVkXf4kH5\nGKqnCZ1N6sahOUFr0dQoIFEkj8c9Ip2rokPORc9lxlyFWaWH+NdtL6aaRDi0h+nNptN2PJbgd5cG\ntMhOirQdYxYahdiW5Tpl5zOzJVRFYM61z/ngepvvzXd8UIIiNaZMOUraeqc4ZF5aRWtc9CO2iu5O\n79ixfpr5ipuXHsfkAqHtZtCBzwps9m9TTPWZlFrIf5Gfdf/oNk7tcg7vHf4n+M4SvbbDg4TyFuod\nnLxNjMqsK+HlDhO3Td6v4USdBelXRZW2p741hm6NcNju6AJ0KTZcmLv7LNqPETb+xQLRkrL9dE/a\nxoYNuKujbGirlM+Yso5DBvK/qm8DLlbBGmzYsOdmoJ8rTS827ndgglsWhWgVioneidU7iGPv0KON\nznCPaOJNTNdF/mtxEiGJtn7LJMPmIFqkLNOL2JlrwC7QL2fbh8k60aILOwE4nFEAwKEsafww1/uK\noBavIyAqYblFJ7NWHsjbOYdRaRgtMZ/mkA8oCHhY5P625DasZsqsJ3wYtTLluuNywJ6i11efFp3O\nDFVkcqJaVbONhlVAnrILKSvXS1tg1aMKKtAG6fmXd1To2yIlyWqCLk6kx96WsOE41JMuALjh/wsE\n3mTO+lz1VXxTpJI9PEpv/n3lMpT3qCd3pk8hEiKdz69uQN1ncNWWlR5zWx5FXWZOu0WVMPMUUZTX\nrTMoFlgi9bMKZf1CcQJDvosAgIjjBN68Tcj8XDONt0vUTc+liZD8eGwfRS+FI4NjWOrS47+WWcFT\nx3tXwAMAXa0G1xj3jqurw2sqEUjt90W1sK+koBMlLs36InbbvAYraD4ArovKaoPk3ZmRMDZEqc+0\nZgOTw7xqCyvX0DBQoRvPcy4LxQKuniZM7v1bOzpHqWPk8hKsomLbQIrymayEkZykd90414VBx31x\n640RdDTcp72L6/ag97/ye/+fDnFuYL1cR9NJWMu+rkAf4p3nYEJFeUS49S1uckfDAl1LlEob3EVR\nT4FzZfdQ84gotgI37oRtFMURKgTj/gAaligA4HB1AJui9Jg6QGFRdCUoJTJflyrDZSNzUyk9XGYK\nmWmYMHHX68f2HQq3IVjsSVtA5KgvVBWc8RBK+dt6B8YU89+a1Vlog1yuh+u8M2gdnsCdLcIjX7QF\ncaATykZywTpIRfDrN6gw/mh6DTNH+PwdScFZE+HLtnoWndZ/5HNHeB+S0U3CK7MU2xJOY0Qc/u0V\nFw40FK56mryz6rS4UBF1cW/13jBheQuJRc4tLJE3HbkGg5F3vGlpB0+KRuJ3kyPQeHiQtf2ixGBJ\nxREjIb5yaguJMSq0+bVhJKe4zpEWlUop4EVN5d+/0qlhV6HS1kgD0Ih6uxA505VYFlZQmc28NYKy\nNwoA6A5VUbrHg8bS4AbcDvQufHIu/HX86DAbdvsNNFD02k1c2BNRmg+/hHicv3vunYewPE2lUXRS\n4UbmPEiIkntnHrmMnTd5wB8xXsbeMA+9O0M83I5e8UESUdrrT0r40iKh7eVYB3ERsX0qyUjxAiy4\notCIfMwE5I5/nfxNv4bD738FALC//S0AQPM5S0/a5NIl1Dq8LM1ZCbnPWVxoDfC9lp3zsNup8Ap1\nDQIKjSDvGOV+rNLGnogatU25YNjne9pmF8pTND6tYP3mlqpCL/LpDcEqSn7+Vu2mCeNmUSdalBJ0\nXbUjfJKyGnjlDq6f5npGpmooV8V3VfJ3LN87pqLi3kUzRQW8ZaFsObxW6NrUFblSFoYh/t+aM6Jq\nFjEaVn53pDiKtpuyE62mIXt5CA8pIaRkwrjixgNppw02UeS9rNQw7OV6V9pGRHI0IFp+cdju5TEo\nOjrVPSp209RnxmoW6QHRWH53E+regw+nUdNxeP08yB+KHIfFSTnKLlNfSbIfL6ZpmIcvOPCXD1G3\nnf6KFv/hTb7j0T3mnhebAdzS8LBNz8TRtVPX/sxcCU9+g8bupd/kXglejuCNDRq6cx495F/mgXz1\nLy2wpMlrgyhEEzhyGIUCnRFV/d+gHGJk/+BlCba7dx5IW1U9QMLGNUiUpzEqk7bbj1BHrVyrQ5ol\nDZLSwlEP5aGpPg6rAG7347wK2XFV4NIyV15x7cL/l+JqwKpH5itcz7uLvDI6N7SHFT91+DOj57Ea\n4Zlh/okdrrOidKgsunMdD2BNFHsy7pWRXaEM+r+wjNvxB3de6jX6UHN/9Ed/9Ed/9MfHOD4RHu/d\njPBY6nEY3bS8dI4awnVajeqADv76R1YzvaVA0YLMEC2ZnZIHU1VaXLGIH9a8iOYVUXSWjh4+E62p\nTQ8wfpcW7+YJA4JlWi2qTkQIlg8hKyKDi7YmdDcIJUV8JizpRWRgjJ5bop5FdZAesbrbO0dtw0JY\n5kTlEpa1Iuhl/tNI6Fj+TH4fyGs4X9lEO2g7Cdg/xcCYty/4sHuE8MZTjThUAZ/9lSS6KlUVnHiD\nc4yXPsTSKM3x4/aXYYz9JtmqY2lC32gSp9Msqv/27X1cH6MlNxEfh+40PZHr20QOmq5dzC7R8p8Y\nbOKve9BWHbZgaJ8BDa0qIbDWfAePLXMOP7Y4cHtINGXwlxB205ov79CLK7U2kDTTsxq4dRJKml7z\n8lATI8KrsLZouerUXZSP0LuILgxhvcO5e08mkJXIy8ldWuIdbQQVN3mqZGWoR+hR1V8KIjdE+Ehd\np2yN2HoHfDTlGj5/gbKxGf/PAAB5fBqfGeHab932InienvCKz4xBUb60fZ7yuRBzwN7gfH8clDDx\nCGl3m5t46CJhtE3RRWfA7MMVUSWo+OeHgbP/FAAQ8Hwb+/azAICUnYiN42gdgxcoG3m3B3t1zl/t\n/gaUIOG++Sr7M//7P631pG1LexbHhYzrmvS6m/YBaAtci6Cji9QO+avX3oFHz3mu2EbIB0saR230\nchcWk2iKHM/gxeuI2vh7J3yi+049D9EDBScOtrHppKed9C2gJKrGjcUpI6mRCcQWRF79U250iwzq\nczcWkbYKCFCJknfoTZt+zYi8QEuaRe5pXy0DgygTm/OaYBCV6xquItp66oJQjvOtt6rYEvUDJk1N\nKAIZyek1kFSuccfBgB9LcAvoUJ7CWRnJLoXWVbWhFCDPRA8KaOUR3DPwHU5LGdUG56OpauFeIYpV\n2bNj353pSRcAxJxtSI/xucXveXHwFHkwpjJgsqI8if89x3X9lXP3IKvE3K9truLLomTkT1eJRKhf\n8GAq/e8AAOf+7LeweJR66mj2Gv6Xo1ywT7/K66zI09s4IQpU6YO3EfhbcVU3eRchB+dzbYVIRfe7\nN9ARzeSXzo/imfe4hredi8iZmw+kTe8ahbpI2r1OHYZnKSeZ9hucY+Mh7Aj90e1kobFxjh6PAYgK\n/1Hk6zun7Khf5VoUjWZon+bvjtjMKG9yPR8XPbory3ZMCNnYtG2iwZs6VJ4CHBKfa+QZUe/Ox3F1\nk7JzZsgFQ5v7e/ogCsVx5IG09RqfiINXo6NilKxBuAQk5PP7UBSRhpqqHwhSKasiYlaRAN0dwk6T\nMzXEWkKBRzX4QNSZjRjIGMPeAHQWQhfGVhIbE0IANu1oTEYBAPq7VB6ZoykYRdJ7M+qE20KBjWUL\n6Nq4+bMaCryrXYe69ZEC6Pak7XCBcE45FEJAtFW78fJPkA9xoQxPrGL7BptFF2dYbCPQPooV0QB+\n9l/HoRfZPO1cEbtJzvPYHAXv7PY/RnpWlNHUGeGNi2bcchgHKote+CbIm6F1ExY+FHDvYzm8UKPB\n80eNAzh3KHH+Cu/5JHUP++fIp9U7vVseziSKOBCVSQxjhEIHkmewPM51M64mkD6gUTDh0EARZ1xb\nwM+TEQPqS4Sw9hx5zDb4W/nKDTzv4eFxxU/+2W4WUF4jhF0v3ML4GPlQXjDjGTNLM3YMVLSNphaX\nIMofPlqFZl9UXxj342aL7w6IiG6p1Lus4p3SGiziznT4dZaMVMbzsLZEwYTjn8Jry1EAwJlDU1hx\nsHjF+Q8J1bsevoqfLHBzD20+ibUQDyT9ygE2nifNgwIu33PVkR7g5j/l6yJboyJO3upg5C6hxdtf\n+GUAwOXrP8VElfNacKygpiWc+PTlHSx6xSF8lnP4BfsYXvv2/bRZcIBIh/KaMZMfWnMHVQvVQVnT\nAmTuyeHuMXRENLMrzv3krRmwtUs5Oz4cwc0r5HVzYASRZfG7Jcpc57iKgplzvBgbhksUrBnx2HFT\nwK0zDdLbtNyEw0LDr1sA0iZevYQ066huEHa+p/D5gNtzP2EAmuZgJ9rrAAAgAElEQVQYDGnyQQ7y\nt/aCXfhbNLQ0MTfkIOVEl+hAK5PmqpY8bTUbmCjz+VR1BAZRulRvS6IdEh2ohAzIu3pApGPVw210\nszRG9vV5uBo0+suSSFcyJ+Ha5ymcrbQgy5SN7YMuNG0e9DnbHhT5welEDnUTpktR/p7XgNIFFstp\nemkg6uJ/g88ZuX8NplkEJO6L95bcSLt4aD1zhPu09EYYqa/9GwDAzkAWg1XeUf6O/Aq+7Pu3AIDd\nMA+91stfgH2GnbZS21Mwi1aSZzJG/HaVRu+zzxGGDygPYSPFDkAn44/hSoq0FZUkJqXcA2kbytxE\nocI7Z38oht06ZeZxlTKSfTwL/Qr5N9rwY0+0tYx2F5ANMR5mVKQTarJdNAVif3jPioxoep/OpnHc\nxX12wchD1W2QoCwy5VOzn4f7JPWqvmKFU0tn5I6G6z63GsXDInOg6lzDZJoZJrc3VzHxESGTDyTx\n/zb6UHN/9Ed/9Ed/9MfHOD4RHm+xS09HktNw1emBZHcl2AOiT29gF/VtWrzKGL+r1nMoy/yuUhtH\nuUGLq1XL4biezxViojFCwIhKlTCcOeuCQdRbcLqXUNijZZX08HlbLIKWRlid603shGib+IzDKMVo\nperrIkCk3UZHRCJDcwjAm/fRtm1hsEPU1MR8gdao7UU7jm7QSg3qwsg8zIIKtjyj7+7Ul/Ez64w+\n1l27AZ+LHvy9g2nknyGc6i8xiGr/2XfwpR/Tessf0yB1hNbdhzU3qpO0NvXLnOMHWglfGmVf2Ljd\nj9cK/Pxpk4IR0Ux7TUC8jZ1huEUw01uH795HFwBkZRk1Nz3lE6Iw/UFyBS1R329kehStDueQvXcL\n1hZhOdMu+Xd3W4NpEbDSsDSx+SijobUXTiM1yHcaF+kxTDgc6PiJCHRqBlSrhNFGn13CYpK86O6S\np8sBCS/u8e9rtRKsoryhYk9CD3r8a2byzD8U7Elb59QmnrlNT64iul2pzQI+sNGjCywV8IgoGflB\n7r/ghYlfJV+1pPH3fj+KJ/4lg0ymfxhHo8LApXz1IQw2RSDghyyKcWXej6/EKLM/MmRx5m3yT/Oo\nCdFBzu/cu/8TAKBg/VmMDxK+00bfgq9BT2X7UR2KTgb3XHmZwTEDG6s9aTPIwHsHRG8mjojgsk0V\nVq2IJA26ESwxKOiG+x7CVlr8g0bSfmukgYd89AoP6nrknVxj3WYD2rPk9ViKVz/Jmw8jP8K959oI\nQhFedcVcRrPNtb0zIIIgl19EYIp8Wkq0gCb34fI9L1InuBZDIhGzVOn0pM2T8KIxTI+sYyA9kS0J\nu6NcC2e3iMZNoihweaBPcw5h0YmmZJYQS9NzNdtrKHUonyMpFXbRxzctvNx024iqCM7UZTRQRC9m\n10EERRMRulKdPLOafdgVXqVB9UMvAhd1VQ9200QSZJ0e+YyrJ10A8EQ9h80Cvbt08Dq8Oj6n/yvK\n3LEBO5ad9Ao/KP4Uhy1EBc5kExjV0Jv8szz1qK25jPg3yeufzQKY5br9nO4zSBQYgOm7xUBC/cBt\nVOqkx7+XxuvTlFWX5pfwaPpHAIDuDuVhOzAM3xjh7NWX70I+TxfwsfppXL71EYy+fB9tBUMXBoF8\nWituyJ/hO1brXO9DuS5qou/z9cNXYXzvswCA8JFPYaTGPV1pMNCrMV2EVRGo20oM+SGu27GVAcgz\nDPAa2iBt69P3EKnTX/UViqhURcMO/T3cbHPfj4eIOn1g1WGgQf6VOz/EnU3Ot9OsQ4n0aiPz4NH3\nePujP/qjP/qjPz7G8YnweG0aWn/GKoBRWm8WqYzSLi/CNTEdJvT0ZpoxWk2dtgZmUVTbkLXD6qZF\nlpsqoFqileoWgQ8fZHMYEUE8kieOikiVsOxH0BRVWgIDtIg1iznEB2mlukIGGETf13hDA12e74jJ\ntOICtQMgy3dVZnvnFa526PF212fRcYhyg+/soGhh3matrMXTLt71vefne0+9PIK/CdIbOmboIn5M\ntMuz78B6me9e79DaClvn8ZoIvqq/boMyLCzTBR/Kv+AWvPoeACAU/lXE47xbflN6DU9s845iyR6G\nXdxXvjpIa/8JjYyGkZ5TPtc7j7esLyIAemfvVilKpk4HzSa9KUcTiN3jnVJkeApSle8YmaB3XNQn\n0F2gx5Fuu2GM0mtM6K+glaEFbnlyBABwYSOLgV1ao/6wE7JKeXj52nk8a+XvRYu0pKdqOrxm4/20\nLtqBRWHcwOFsB+UP+HnjPPMA3bvv96Ttq+8quHKUVrN5lV6ECzaYRFDHpm8fPjPX0LpcxpsG3ocd\ncdGi/uJX/iM2X2OTiYy/i1thBp88sbiExcsCiZgmbcPeH+JPFoXVjRvQnmLx+tvVETjdXMNbV2ih\nW4plXP4MPcjU6ixCojmFVLoJxwb3hv7nKBs696PAI/fTpstYMOohTwpRyqT3/AhsMcq6tKTD0nH+\nlmu4CKNoYoA93ltqbljRPENZbVT34RRpHq5MFVZx93ZP4nyNcgMeAZiM6RRsu8nLG/YOppZ5v7xs\nFVXl5l5BTMhTI+9AxEP+q20v9le4v1YVyvoRy+H7CQPQclShaYkC+QLN2gjUIO+Iu0KpCK+IQYhs\nORFzi/KvJsqQTllCS+gV5UYNIVHCcsNQhEnlfHVm0lsstNDOiaYNQ1q0i+TJqkbFYFPkB1u5nyqd\nbSgSUYRMGTDliCKU5BTsTc5hW1uGy0v6e4WOXd2dhd8kvPjKGVxc4ToffJ7ojjXzHLY69ED30v8C\nzvpF8t2UxoU478v/yRPcN6+sGPCkmffeK9UZdGX2su7In8KhCb691HkVAJBKDiOZ47z+4cA+ruSo\nV/esXTiXSJ8lzPXZmbYi+AMGmsZP5dE+4P5fGPs8xj/Hu198837a5o1H8U6bfJDbANKiWlqTv/9D\naxdPeLn2odKz2BOyYdG/ibVbzwEAzhwlbZWGglKUcQOup0fR1lLHfDiuonGd8RqlCBGZ+e8bYJ//\nUwBAzPA0tGYieGU1BX3m1wEAdhORONtGFkYX32Ed/jqKnigAwB9bwC0jgyBP3E9az/GJOHglDSFc\ntT2EusrIv+2lHLQBFkTwzu1hX08F1GqIQKFaCvkGBa5WeRMFUSZOq9PDIKISa3sCqp5OYk+lgqrE\nGnCL/qGh4S1Eb4uUZw2Z626YUUtwk8KXQkPUD00gilEB3ZY+5BybIxYMdPj3WKz34ZTSU0F8TjuL\niwoX2906Dd0botPOWSNu53hgNO5SyGLjSRhSPIwT0mFc/wGh2eGmAd45Cl/SQ8hJib+M1CKhR+sj\neYQy5IM7GEf0OpWccYRRiJeWX4N9lX//SmQKq3bOfaITx20QippsMCAj1R6GVwTgzGdfxDb+8D7a\nsp0xtI2ETRtrfwUACIROQ3YR4px+o4H0i1TswfgibjVpNMQFPG/xnIP2MfJdrbQxV6XSqGsn4LhK\npRKP8plaSIVH1FW+NiGjHePfxzJ3UCiMAADu+Xi4Ha+VcDzL+sChegNVH+Xku84Mjp4XyfYFwn7a\n7NB9dAFAyjeA4iZpG0nxQFKnVrC/IaInwmboktykheI6XCKiUp8kJByX9vBsmc/95LoW/2yScNgr\n7jGM/Tx7pv7NVfL/ufVTSNhFsQ33KG74qbgeq72IV97m58d+nsrDf9GOeIv0OGduIaTywHii/AS+\n42foaWabBRDGlt7tSds1jx4/n6VyTQxT1i3XtqAOUal0xyqw1kSE6B0dMhbCn80ID2D13i628zRg\n1EYW1p+KurkZM+ZsDKCpT0cBAJ6UHYsf1Tt2rCKap4ERaVih7RCenBR7rGJzoB2nQs26G9i8Qsh4\nwraNccMIACCv54F1s5DuSdumvonBNOfWCfDQNJV9yLs4L1vSDOMB53ZNrsKuoZFYECUVPVYD4kJp\nhxxJtJOiN63RDb+b8yyt8d3BgBsHOdExp5LAQY0AoqaSgqheCnOee7ft16Jo5P7u7G5DNYq61+oB\ndKJ/ta6jg7Hw4C43hndUrP4jHvQ+mwNfmKSR9+4S1yWjJODJ0jhqPfsDhHb53eVj85h4izrr1S7n\n88jmFjKW4wCAyvR7kDa5hgVzGUNOGn6pOULZm1ENnhnkofmt+acQFgFKrffXUBonXyt2GvHJKyYM\nTlMfmWILiNtF9HBShfbGjx5I22Ikg0+VqG9SjmnU3qPctxXyqXmmiOyKKPZyeBf6LcrB2sAxZIZp\niC5kqOcq+hBMfspvsWoG2uSpsVyFVQQgFlPkTTPQwJ1V0n4CZtwUNUAfHjqP7QJz0l+fFMVQJioY\naFNOjLsNQBJ5vufOYW6999XHg0Yfau6P/uiP/uiP/vgYxyfC422ptLBaph0kk7S6R0Y6UNO0cN5p\nt/GQKLpuLhCOeC8UxOiCKOc2WoTSpufSLLWQd/K7viCDKFJJC+SmgNG6WTgthIwuxo0wufmdZpSs\naOgtUA4Is5X2TXAN0kIarAZRttEC3LbS4jPHGqgV6YnERT7v/3PMgJ7w4vAbGL4mOvgElpF/htbq\nZMoKo7DOLCKJ7JW3T+GpQ3zvKwdv4VPCUs5oW9ircm7aZXrqRdMKXBHO3dBZRHKTnvBd0zYOd+gZ\nbaXp/Z0zPYu908z5a93+HNaa9Ay621soOegdpF8SZe/+QQ1LcVrHfk3vwAHj6jKMKqH0k7qvAQCG\ntq/icpvzfeVEFdhncNCSzQNHlmiFLIs0hd06lkq0PGc67+KWQDDk+jISU/TIikWmMZiyDrRCtGi7\nV6JAjnw/N/MoYrZN8lL6Afm09yw8wkqNZzXI5mhf/qrGgDiBBIRkykPhIWNP2pLrSzBPMh/x5gTh\n+YjzLHy6iwAAZeIEUtss/Sid18PRFgXVJ/89ACD2jg0vDQo0ZSCG7wYIpw4e1GH5T5SVF54j/Ld5\nzwqpRT6FD+fgfY1Q53eDFZTOMkXC+m3yZup3Arj8u/QKjxZGUPb8FwDAojSLuTS9N7fINy/OnQLw\nrftoc+pk7I6JMo/bwgM4Z0NwW8hhtYZ9URVODoSRDIjeyBoiMxXZi7UEvaUXqjexZeeVhDKoQ6pG\nJEcVAUP35A7sIqhwvz4BR5L7zSJJ+HCYcjLT5O/nl0uY1TN/M5hq4Kft7/J3C8/DoJJmnCTPzCu9\nOy+1WzW0ZaIamQPqErlVRj3G52tqByU9/26o7aDaFA0RRJpiJe5DRDTFuKKtY6ApujcpSRS6Avmw\nj/Df8jWky6ShKUuQRK0BJduA2qL81vPkrznWQFqktajbdViDDNhpqSEYtMJrLmmxZZR60gUArqOr\n6Ao4O+SL4cBBmRgSgWYdZxrJIcLHkUtubAwSgn1q8TsIZ7gHFhd5xbL8G+fw4U+pC8KyCzFRDjT8\naA0v/QlRr88+z+fXbt/AO27qPO/iNB6XeYXyhy/PY+RFev9n7Px9R1yHxTXuPbv1Q3TOEfF7Jvs2\nDj746N7jx/fRlvowhLdkytwZpQ7FQr5HJCIO/kwcEDn3QakKaZZolNZiRspBnrVy3DeV2TW4XheI\nWXYMNtG4Y+/QAeZESlzou6L86ecPITInenO/V0bYwHW7F9VAZDfisRQRqDcPAjCe5Z7ff30YelFT\nYddawzHs3UfT3zc03W7v/NOPc2g0mv//J9Ef/dEf/dEf/fH/YnS7Xc1/zff6UHN/9Ed/9Ed/9MfH\nOPoHb3/0R3/0R3/0x8c4+gdvf/RHf/RHf/THxzj6B29/9Ed/9Ed/9MfHOD4RUc0vvcloNUN5F+qk\nKMtWCWMwxQjmuisDNceIX9XMSNH6ug7uo0zOLmcGUdXnxG9ooBNlv+wm5mS17THYdMxT3VzeRPUs\nowut+SrG3Mx70+aYe2ptaXHPxSg4R7GKO6Jk5JCiwKQyiq0wwug7w10DZAujjANZG85//v7C7f/s\nf2WBBovNhFSBOWCNrBMjDkbtLXermBR5jjvXuBy2wSqqgyKacq2OcoWRk5PhReTLfIfcYjxa3mGB\nRRI5zvYkzEnOt2irw5hmURCzm0n3rS2gOsToRJ9HQmedOZWtThQ1HyPEbQo/G/HvYVc0OHBNOvF/\n/Ksz99F2/j9cwMQa1y7eYk7bIIqQJjj3W0oAHpV5fI12FhqTyO9TGcVZaJjgFQVKNIoeyDHSsBvU\nIj/AvENzlPxN2X3w1Eh7WdIioGeOd8N+F/Us+eewMVrVGD9AeYAxDtKOAxYf6S9a9ZBLlJ9QnXK2\nZVHw+jcfuo+2f/dHR3F4WXSj6bBDUkJpQTtG/tnWtpAziZKHM1HMqezukhTdT4YKH6DlYxS7pvgh\nfKLI/6FTetyTmDfolRh5eWXWjPkN0aEnYcbeFL/rH7kC3feYx9hwjPD3R+/gnJvysLV9F9vg30eN\nw8jp2YVKMpKeYbMR/92vvXwfbV/7vS10RSeoPQ35NGgZhq/JKOC82YRCm1HP5noDUpslHbMqv9vO\nd6DKojuM0Q41zeeMnSryg+L/otSfq5NAEqSno9VgzMQo9tRWFqUxRpDq0ly34WAbyQbfYWxUoY3w\n73vZCdgTlGvPGGkPGhL4nf/hmfto+9nf+hGaen5HJyKAZaMJTQvlqaOa4JUpL3lfDrp1fmdNNDUf\nRg5tDddQ2wG6Wsq3ki7APsDPsyXRpUxrgFNmtHQr2ULbJPKV5Q7gFCUwCvz9ts2FOigv1oIRelH+\nsGIpIdxlhK7a0kE3xr3xZ7/58/fR9vX/+V/jMyHK8p9Ha5g4wb1R3yK9thETlDcYga8uL8DyRUbK\nD2+pcNeY6yqfYImHn+xewq6PBWB8+QzmzzASeSv1HWS3mCFiC3AfD/xxBl9+jNHJv5c4hrsnmD0w\na5Tg2aH+HBI9mWfnCrha4d5q7FuQfVr0T2/akdwjj7/5n/78Ptp+6wuH4RyjTHWjJkiiPVmxxDNg\nQFnHvXvUQSefbOFylXz61OIwti3ksVPhHjp4MoXDXZHnmx6C70PqIOmwDfUl6mBLlnKYnrGj5GMU\nuxkzWHNSHx1TyrBHRV0FUT9gctSFTpk82TLdQFtPfRaw7mAt+eBSn73GJ+Lgjcs8VH0JDYyLZN6o\nXMa2ws1ibDuwP8XDbmJXdB45lsNBjQeLoi9CzlEIk4odxjAVmkbHzVqNGmHQsbiAcdANwy5/d92o\ngWuf4eUlUbJXVwAsoqnzUteL8QPRnWTLj9Q5KmLjJpmvcerQEMqo3LvkL9pGKqJSvQlZw/l6PDbc\njHMx7boS7spMwRgUtanTsg3OBabptHxOaEuiiEcrhHaRtNWMomKOoQFFYTpNeG8MB07+v5ZRUJMo\nUDoNhaLoLkHqiqo0GQkm+QYAoFnwoinAD4+gI1uzQAkzMb9ULPekbdzSxd48aZJ3qIxqZQl1A9OQ\npkp1yDUq1fhgDR0dFbh3UxyQEwW07nHTmBwtZKyi5q8RaOZJp2zjZnNnu9g1cu5HKkVonVw3DRzo\nikbrjQY3Y3k6CNs6DQnlTAtKnESZO0bU21wPaYBKMjKW7Emb/+AElieYdua5Qnqcj0uw3eb/h848\njMsq6TmvmuF+i/QbPsdnDOUxHE7xkI/PG+DyMhVKGQ/hoPV/AgBeO8r0iqk7SWylyWvzUwkMp5gS\nYvyGD0szVIInypSHUccO3higcXTMHkFkncph2SvDW+Lhjx3KutKd70nbyD0TDnQ8sN1hprXZ4wHk\nRM1kQ2UG+jZThIrOMOQCD86m4H/AV4emzYOjnJRRH+K+qOXd6Iq0PZNoAVdMD8NoolwXaxJ2wO8W\nIoDZwjnYinzvXjEAq8LfrdpaGN+ncVXzVxAws1CIXqaBXLKHetJmlR3olMXaGzjvtlKGXk8+1pU9\nZOvcD91tC2wOKuVpIQbWoSE0M9zzJX8BqAil7raiJFKLPCahl7QmxMukQR88gEmlbjKVrJDaoqiF\nmf+2DQqGa9yzLW0ILZ1oVad3QjW0xdzcaETHetIFAIafVvDOWRqf3vAiTDfIn/i84OOlEtrJEb7j\nG+/C9B3ycuzuFH40wwPsxCrX+4UXv4S3/pi15ZORp5FepIPgTX8Rj5+k3P7E+vvkzRNh/CTPAhmH\nP3sNmnV283nSrYNO6IjvDFNe3vGfwNRtykA5cAHh978AAPjGuAOHte89kLbKUwF89i/Ik02rFzrR\nMtPr4972vO+FwU7+7e0VYD7JIjFqJwVL7qMm9JQj90oIGS/LpT3kcqFuJO01pwGmL4qCPDf4d+2I\nhON7lBNrrgFtiHrhhVYRS0dFXfYu+bGS6GDqCOU6mXgEjxwwhcjanMfgfuKBtPUafai5P/qjP/qj\nP/rjYxyfCI/XnRR1dU0SIFz5tL8O+2Vank1vFdWPap6Ker82rQ0uF5/bW0jDY6XVk9Vo4LXQEmmL\nUmq2hhlvisbUwXwSDTstxEMbWVz3EdLw36KlaRvKo2zg33WlJJoJWkAHjgo6t2kBdRq0bjJGN6yi\nxKV+rncDa7NIuk/Wt2GWCMuslYoIOzg3SzeAtTK9sJboBmRrZ5D3iUT6qh5tO81xV8qCgpdzS5WE\ndxKX4NPRst0wrMMpSlcaHRIKReXveAIACUmLsCgk0HAZobUROquZnLCaRSEQUXJ6MttCIyw8g1is\nJ23JrQbMIfJa4yYiketW0K3TQuw4Cziw0EO0yga4NKQpOcjvBqMd6K3k/1bQBdMmvZqWtoxQm3S6\nbPQgsyYV4SrnK3XsqLvpORWzMVQs9GgNdsqOHUU0Q6RT3h6EIvo6lyU77EfIy73LooCGPNKTNm/O\nCa2woO2iZ3NaN4vVGL2Md040MWymFzD1ZhmvnOO1R2iJlr/1Zx7GdpIeYn4VuNcgitIeOsDZ3yOs\nl63Qq584CKIV5LtCF6dRHKRMrb6YxNPiCuVOnLDqiZYGTx5wL4y+VETrWfJBv9bFtpe8PnWEHol2\nu7dMliJL6OaFl7BHlCEp7yNc4LzyuRjaYo10lQBcHa5Rp0DkQ63tAD4WxdCbNYgXxHssdcjZj+ST\n8u1DF3UX5a/WNsPyDlEJY0SCp8X9uxsgAoJMC7U2v2vvBpEWe1bbaGJfFInweigjho3e6ZItOYWC\nTtRiFz14E/kIjnQ4932lC9lKnuVtTpTr9JwVLb/blZpoayhH3WYImg55Why0Qd7LCjroQbXRgt7G\ntTC6A2h3RW1pkwrFwL3ubtFLq1S3UdBSHoyWRbQVFjupFHLoSIRF9ZooDM7eva8B4FNn3oM+9BsA\ngI2fa2Dtt+lhj4rOV9ZaDjhLSFj949Oo2Vh85ftfiuEfXuL1xrdECVepMQ2bh97+4ZwDhh2iJZYJ\nI1bucI887/o1AMDV0R/Ctsum4LX3n4Y1xIISu54ASl3+Xkjoiie2XsZPj7Fz0NglP8xzPGJ+d6eF\nN3y969kDwOD1LNKfJ39uLW0hsEE6PKJIReaRUZg6nNfwzjSG7PTQK1MF2Fos5lIUyKAXJtT8RIW2\nFMA9S32kt2xA2uS1kTzFeQUHhiGnWLZ2+XE9xkUf3+XtQ5gUtb4/EGdLZKKBO4u8ovn08RlUddyn\nyc4p1A7d33Hp7xt9j7c/+qM/+qM/+uNjHJ8Ij7eioeV77x4QHqBVFCqVcGucFtB414T5Ku9+2yo/\ni5YsaG/QOmxb8qhqaBEfr2Wws0uLVyvTi4OiwLNAr1ANauG08p5jsSVjRPRc7Rynpb7fMWK8RXuk\nudvE3hgtLqfBhK6RXkm1RU9Sr9uHKUuLOfOetSdtJY0oPJ8qoG4mDY5CCelp/m79XRM8TwuPNyYC\nT5ot+BUuTcK3DmeM1vO+oQatlt5d18JnLNk8boU5H0uxhTkNLfu44oJb3LMZsqR9YiCGjOgtC0mD\nVonelMeYgCFFj78g7otv+hwoVOjpnhl7QO9TsxXlPREwIrpH2ewqNhV6QDqDAXqFv6uPxlAUd7Fy\nlfNZdZYha0VQQqeAhp8eQ72bg17H76aEs+3U+JE18e/asBOyid5i06DCsSd6yoper41SDLKNz/uL\nZWRc/BF93oVd0eAiNExr1pbtfcd7azKLn3XSM7pjmCH/VtxoHKMMzJe2kdugHN06NIz2JD2KSRFr\nsPqOGV1xN+41ROA4T094+IoHxV/mfa/vGvnaHV2DWeI90yuNFn5unLIxgMegLjCYJnSaZTZza8O4\nU7hIOs+MYNrMvs2Z8+9j9k0Gc90YITJzzHAOwAf30ZbMudFtcu1zWu4Vs66BdT15Yw8B+hZ52smu\nI93iPf5+mB6WR5lBa5ceotWiQcNO2fclfGgNkz/OKL3G5qwBhQ/p/Qbb+1BGuPbasBsaUWYzrJIP\nOksG0SzXUPV34SiJUoCKF3ZROnAP9A6Drdx9dAGAMeWHXjRSqUrcx8OhBBIK5xjUO2EUZQHrGjec\ndiI2pSa9/UKljWEdZbrgMKDVoBy1EzoYQc9cMZC/iluHgbZonGAG7OLuWKNvQRLNU+DkHtQkzNBZ\nSWexGoBOBLIpLgOUFOfjNntQTT242H5o4uv48y7LLd7983nMPUdPd6TCWIGLxjqe2VwAADQP5ZF3\nsrxhrfmL+N6/ZAeep1P0RpUPLmKvQZ4U37iKi9/kHD6zLqF5lXJQf4j34q5lCcXzLEWbr13B1hmW\n9fwH2QLeFWs3Lu51M1+fwelXWNbzztjnMC26WF01/hBTm4UH0jZk8yKh4xpIHfvfNaTZ3+PvyqoV\nzWPUTUMBBaktdr+yB+/CkyDqMDD1AgBgZ/EibKPCY24bkTlLOdvbVDExI5CyBuUsVSnj7DSRK5/x\nA4Ty9PzvGoywmEVsi4UyO6voUfDRu9Yv6xHLMZAt8bU9jN98QJDPA8Yn4uDNS9yYHqsWm0UqroRW\nDzcIM67YBqGtkGGGLBdStWXhCvLQNBrSKIo6yFcOpWCti7rJexQQ1XMbZqH01WYZu6JmsCanxbKb\nC3hUJiyjSYWxLokAnIgbAcH02r00tOJwkcIiujNhQEdE7XZHAj1pq0Q5R43/CAYL3IzJ0RzKdykM\ntZkcSptc4MNJQqndoSLSeio+adOJkiTmq2nBrlCBl2KiocCmbY8AACAASURBVPioHqNpHqZa5AET\nIUBHSwtbVkA7fm7+OE7D2CZ0Jndr0Dr5u+u7Xky5KeBtPQ9/d7GG0QSf2yhFetJ2K1PCgJZCr6kI\nharvwCXgdaWrwqvQKGjP1pHPCANDTxo8BQP0kwKqj+vRGeK7bXkD9CucZ/Mk393Yt0Bj4QG65rJh\nTiK/y8oheCOEuzRdKvuGtQ5skb/bsgRNkwpR0urgGRTt5HY5r4ajd7VSydjGlV1GLZ61UulvBAso\nJ/hcqPQQyidIh72kQyDH/28epqHhLN7BuouH8VTzDAoFykzSqkVRIiwVqVAmY84gDiSu52RuBde9\nnOP47fewbRCw5weiQbfjNMyiadxCswZdlrWPx16V8O405efMdRoK8aMHvWmz72Jnm3yfdDKidqNk\nRKjLoKSUOwJ/jTKpt5jgcDAiNifg40rHh6iN6x3U6BARxlwllYHa5jxVDedoylhg81Hhdpruv2vz\nVjE3oRH1kbOipaS7nIBUZzPzdgWoyYSlJ9It5EUd30iachEfn+hJW8Wsg4uvQBVc20zBgZqJ+0mt\n1VETNaJNuTQyEmXOL6L62ykt4i3yxJzehuyn7NjqWnR0NPRDbRog5WoTjazIZHA20BU0q7KKhjDa\ndKJ+cM3rQFWlXmq2UxgxcV21LRuyYRoItUoZJvnB0bHfjt3CQy6uwYGygPBfUT6bX4kCAM6GVrCr\nZ/DQ5VoY5yq/BAAID/wQtb/4VwCAtedpaKxNr+OFJvdW+FPD8L5Co1YXHsbW/LfEPEX3t8O/huEB\nBiMdtOOY2iRN9eA+5A9oMI6cInSe+oEfhhXqUnNjH1sF8uSZ6hMoR64LSpbuoy2brsEJBgMeDV6C\nIUpHKDJLeTBvbMCyzD29friMoGj5GjJ9DncbNIBlF/XrfNePxKBo97jawFyS++wRZwjdAuWn6iH/\nl+o2pDwM+nKrX4ZxhXrlS8ZlXI9w/wzd4KG65h3A+DAh7ltnojj8U8qO/oaE5VjxPpr+vtGHmvuj\nP/qjP/qjPz7G8YnweEWrTJQ0TWhttFrMewkUpgix2mq7KDVpoafEZftcWUFeoDnJkhdWPy0ZT8KO\nZdEZZaJAi6VV96I0QSs1X2/BJyza5GAb5lVelt+o0Wr32AqwjBNG296twLZDD0Z5NAjbKt8hiX69\nebmBSouQyMRCsydtui69vL3oXZSMAlq8lwIUWlaG1BjmRKPmbpBzcRQApcbn2h4N2sKzz98z4cMM\nrfjjg3wmWZmAt06rMuM4Cq9LdJ3Jy0gK61ht0mNzZxOoQqRHDRugiDw1kysNxUmPwrrF99btO9gN\n0iqccWz2pK0w1MXRKr2AbQ29E0fNCV9QpJ+0M0hX6CHO7M6jGiIdHmHvSaUDVPPkr2ytoSqRJld9\nFp1jXPt6g3Ms+/cwPC+6zqyY0NaJjixdBZo0abbRSEbTakRGEik5DiO0HUKlVU8UFi3n6z0kenvu\nmHrSNhNKwpSk/HQG6Wm3Lv4ivvgUrfby8tsIZ5gT2fXasCtgMn+ciM1cYAB3BLLyfuQnONalzNTK\nHrgF3y16se5jQcwvcj5Hn3Pi7QV6c9V7Vfy6kZ70q0cJh+VTu3jkLOk9eNeDAwu927F/HMXzKvnu\nFh2+op3e0J52twirgPjvCa9QluuIy/TeNO195IOco8tuw3ZFpK5FRdqQUcGwn56Vt7aHRoIeUMHt\nQiDDucldesQ7ZSsGHdy7BX0VhQ6/q10twmwS+fLCO8w4Q/C6iHKl2nZU21zvu4N5eBqE3Asl7l3d\n9lZv2rpGtMvcpxovPUyLRYeIlrJXsgXgqYk0OYsD+g7npnYob40G4J+gjjF3p6Cte8R3S7CIPN2C\nlWupr5XgN1GWkxYLKiLFSpcvoyV6MZtF4FRb6cIt0l7aFqDkFd7SahYt0XHMrvMgY671pAsABh7R\nQNp+HADwZF6PzafZ11nf5loaDp7C+yKn9TH9GiaCPwQAxC/74PqqSFVscY7+2j9FeZo8/cPJ72I+\nRg/R3dDhxtSLAICvaznHrvYtlF7j8y+0p7F7iOhAfucY9v97bjrNSyLAS7OF5pf53OG1MlJLvHpY\nrRbhWXlWUPLafbRpjg/COsQ9oEtOIXeS6+HP05OMnTChsEkd60/40PZyPuvWOIKMJYQKro/huXl8\ndpue/U71MSzMEhXqZGoYzVBup0S6UTFbR3zzNwEA9dPvoxnkHPYGTGhb+Xv+ElP5sNVB2sfnz98p\noKaINKQqMCv/t/X5+UQcvJsVLqSz04TeTbc+t5+HKUqFlwsbIIu7n25WQMbDa8jcFQrKmkNNHHAN\nVY9xPZV5fI4b2pTUQ+GfUVvVoy5xE5o1M6hoeQc2bRSHhZqBJsONG8nOQgpzI+Su7aAKoWAMPGR8\ntXE4xIbenVV60pbO8M7FbD8Fg4iGLppM6Bzi5p+800XawM0AAxW1UmmhHqZCaLTG4dVSGSVcJszV\nxN1mkfyYsCeh1XJz55p5xOPchNp2CbtCkQZ1PDjzZhNcHSqKTsGKhjiopHQX5SANE4eP89IcaDEq\ncz6m/d6tyk7sGJEycJ6OCtdCb28i3yGdRsWPsRHyJamxo1SmUBtBi8nkmIGYOvTVDsIG3ilVQtuw\nG3iQdZLkb0BnQ7pEPgXkEZSEUWWvb6M2QkL2tDyk5JthaAUCqNEmYRNR4Uo0hEaLm2hN3Bercu+2\ngC9ctOBPPk3odlsRUP+pS7iZ5WdTT3hReY/zKZhLOHOL37k9Q/5vb4cQGeBm9J0OwnyBkaB1cxdy\nghCe5QkaOydvzSJg5fPdKws4e5e0bR+J4A/qhPAiCc57xLaJG8ssriBLmxjqiHgFzzI6lwg9PjPI\nORY00z1py2qGEBtllPXUIudQd6lQ6twvvqAD2jg3jDNTQ7NN46RkFVcBFTukBte97C2gqXK/ebQF\ndAa4j+riDtMfbSLm4SHkrrhRMlMeImkXSiIfdkqhci1bWkiLqw5HzoKcsGXtQQOyCg/hlsgDtoV7\ng3XN7j5aMo0rjWhMr9O7oVhpEDQ1BUDmHL21OnL7PMirVuogi9kCR00Up7G4ofO3xXvdMOWoCzRd\nTkzx1lER+fShugVZshK1nBEelbpLkfhet9mNVoGfOTQ2VIUh0XAokBzibjhlRsjeOxIdAHIzecg/\n5IFimBuCQ/s0AGD9D/4zAOD4U0fwJSfft/rSUbyvoRz5zk/hCy/zvvevT5K2p+UtXNzhPv1segav\nj1OObKmjGNzh/rycpd5xP1TH5Ff53OL31nFsl/yrDL+Kf+Flru+ORSyW4ShK18hL1ZhGeoQbcTh3\nActnRST9n95Pm10/A3eJVxrv5oLwtcgffZN7ZXJEi63jnJehPICxKg9OR7WNpVFC5qc6fCZe1+D2\n1BMAgDu6OzDeoZKZCj+OfJv6/rUKDVnT+Br8adYzUMoDcDj4juiKFp1HKHOpUa6PLGvhd3MvZHYe\ng0em4SPPdxF9578NPO5Dzf3RH/3RH/3RHx/j+ER4vF0B/UTUGNrCu0pZG0gI61dT1EInyutZPPSE\nFmJzqEu0pI/mi1jz06qxlxK41qGlZt+ktZT2FmCIiohaZwj5Ni0cGXrIAo7arxNqiXT8aOnoMfil\nKg6KImcvVIMhR8t+QDyjjcWQF1GL7kpvqNlUpjWWaN6Fy8EouCZS0F+nFXXHG4fiFJWcIpx3K9GE\nrcHPZN0msE3oJ2JeQm5IQMHLDAqDK4M9UfkmVG8gNSgg96QBx1UR9NJl7mmnk0OxSJ5JIQ10Rnrw\nadmM41fJ94NZWvXOjhn39AK69PW2wrVqCx3wOdXA70yghrRRlDmsJZFx0eJVmiswVgVsbOG6NgfK\n8H1UAcnvg3aT/28OuxEokceVaUJYuU0PTBlC8QFrDV0L6Riue5AWOZoO8UwkYodhnFb35YMJaLbo\n8es9ZZQEpDYg8vWscgA3etD2I98YnrpLOHO/TM+xpo/CZRIR5n9wFPY5NmrPwAbdGL23oSS9iObk\nLHw2cQ1xOY41id6odvcWUk5W41LzlIFppPFtEXDimbSjbCI9k8NNeFZfBQAk/c/z+cu/gqnn6RlE\nWg1cbpGnnsUTGNIxmGv/Avnk/wVnD8oAXbUO0yW6Z1lh4es1gzCLnHSbVkZcXHXoPHYk9fxOzsn5\nDusTKNSIkIQ6J5EWsH7TJKMoms9rLJzXcY2EqkC8ux4/xquUr2KgDF2H3ykWuafNyT10/Hw+l5Yw\nMUY6y4UI9quUM6uZ+9hu6k1bpaJCN0Sv2rRN76RiSfxdPr3OGIBL6IeyswOjKEs5IKpcafwlZNoC\nBlZyfxfJrFU7MIvsASXN7+rNVuh13HvVoAvBNn+rcKiKWp5IikjdhWxqoz4kyuHq8zBWyf9WswtL\nk+td1Glhiw30pAsAzL/vwMAEPbZ2vYXXRBW6sd+h/6R/tYjc1j8HADw2/Q18/2F6gBavFq+tU/7O\nvEeVXzx6HfEIS0ZWT5sx94a4AnDmcCrAdWk9wX08cW0RrYLI5W4NQfWNAACGXHNY+QHzig3dr/Kz\nwG3cuXkJALA1+BnMhPnd0Y4Gi3/54Khm7U4cy/OMGO5YDyBZ+F1tguiFpeTHcECU3/TcQs1M/Wdq\nJTAhcokrAhXd+KAB2c7PIp+pINPgb5T3NjBlY+ng4iOigtqVEMYnuWcbO20siGDGuH4P4wnKYlUg\nB3LxZ5GX+f9JNQF9kLogsWuBMdIb8XzQ+EQcvJ17hOGSYR9KGREB6YmhW+Ims8KMkpsK3LnPZGdn\newgBAxmzL4+jsEulUDMsAVUyTxoieVqnHzYRQVlMLsA+yYNIVazQxAhDtgXjysEE3AUKf7rig11P\ngUzlDqCt8d2aCg90r1VFXBaFGPbcPWlTjCKFSDJDF+RGcFYbMM9zY1aSQKvKg6GR4IY3ulRUReqB\nnB5BUhSpsOoDMOp4wH908OQ6KrRhHu7dzD1YhbJx63RoWEWhj+YI3yuXYBUwm7ZaRj0p7nA1DVSG\nKdTlEmkzyBUEhilk9c3eSs4yqqCyz7kFdSLlxKaBLs5Dr2pwYFikU1U7egzpCe+UjVSo7a4TSSfn\nPupIIStRyQU1VmhEcRSrhodJZqwBJS7gYWkIUptrUW2b0PKKdIJ9GmdRk4xQlBtvOKHCFfIL2jKw\nDXA+uzXKi0nqfad2PeWG5mv87vzrNAje8spoy5yXfewaWh4elp+vPIvX/HxHR2JE8s8kLyB5i8n6\nK8ddGHcQZl/aGcNAm6X65vI0xG5PJjAYJVQfen8ZxtOcW35VQb4wAgDwixSW/ZO/hUubXwYAfG5T\nwYiIXE6MBFEqEH7TiQOgfqd3NHrNV8OAMAo6qkjJ07ZQ9HAPhLUJdCR+vpPOwBDmPbJtn0ZQdzCI\nqSb3Zs6uwGxjepRZLkF1kmeOA/IpO1NCYZuy6qrUkAnzkFE6HXhEvMGOuDO1h/xwggd6J1RHThSZ\ngCphyib0gk2k5BR7F2PQWAbQEJG0EQ/3VRkTEMH3sHRyKIhCC+HAJIpGHpzpFhVxKarFIR95nTMM\nwlwT1yyDCXS6XCN/R1zLODNILHEP1fQqqhp+bmlLMOq4jyrgPBWDATbwsNXUuiiJE1nra0LWcL9I\nJRui0oOLTHx6cAvfeI5z+OxbtxBZZWGI4+/yek6N6ND2/y4AYM/1JdiuRwEAPw4v4d/uk86N54RB\nu/o4HrFRP1y1NvG5J3gPqnvfjWiM+/DzJ1j7+2/dLeyVuHfPuRax1H0JAGAty/irFRqRXx2h4R1L\nJnDEyJSli7GfYv2A10fbwQyOfFXI4zv303asuImFJPdQyCGhkX+YvBLpaalMHg6zML5KbuyNvQsA\n6KpHkFulbMzO8l0zzyTw/javA0/c6mB6kHK9WMhDm+CePXyY+qpiiWIrLyLMJRM0KfLp1NQ4bjnI\nk8gieeb1vgX9GuVv67QFlgrPiaPbftw9vnY/UX/P6EPN/dEf/dEf/dEfH+P4RHi8TlEmrp5R4PEJ\nODI1hK6eVmFHzsMaozVTKhL2k9oa3AvSepYsKfhdogB/dAZpUVxBVHBDM15AIUSP2WfQISusFr03\nhZSF0ON4jl+uZk1ID9F609oLMBTIIpNRhslLa7NRpDe1XamjciBgqXbvhH51mtasddmIuOjkM4oR\ntDKkrabTwSsibZN62kFy/SQ0RgZiNOYrCBX5uaqvwVzmfBshWrk1swmprOhOYvdhtE2rsChpsdil\nd+sSeZTW2iAaNXqofqcHVpF6rKy14CjTKpzV0+KOGYDAJt+Vf0AgSyLdgcZHr6QVJ9wIwzhqAl3w\nGlaRztJ7sxgHkRoV+XIZEQBmLSFsJuy3u6+B30bvzejroCWge6OWPB807yGr0PKM2/fQbTG6sF2V\nUBVlRi2jwrPSGmAWnYzWg2ZIt+jBFAMKWiV6NmaVv1/ttnrS9isv3Mbru5S5tkTZekjSYb9Ii1kT\nBJQq8yQvFtvYrZBXFtArXGmdgLFEK3hgOIqdVxkBfSoMFIy8sti5Qw81eGoKTwQJgd0q2qAxsfxk\noxbCkJee04hXeLa7Z/FYTsC5+hlcLRC9mZJacGrFfhjnep3f6e3NV2taNOuUS53oBjQ6ZMNAh57r\nXmETNZl8sjoCqKv0HI0DorFC04S8V0DRRhlSnjKVs1thrNMTrjkpezoUMTPL64+7/xd7bxok2XWd\niX35MvO93Petsvatu6p3dAMNNDYCIEASJEUNQWqhZInUjCZGtkMOe8YRtmU7wvvYMbId4dB4hhOy\nRElDUhqJFEWRIggBxL6j0Xt39VJ7Ve77/jLf4h/faYRHzFb4FwM/6v6pjKrK9+4599xzzzn3nO/k\nTEzsc60yPgvmFNclc0UAG5xddCU5aNaVRr4utdaZFoLVOQCAu0L5HGiJsbTBr8BZ45qu6fQwT6wa\nyEtVRH8Qw5RApbbrDQwU0h8TLzbum8VOgvs73fbBlPCwa6hCCzBiZbgZYmwV0sBh8X6LNTRDkhns\nDMGwqE+iCa5Fq+v9qFtayXSjtUk6I4qCZkKSq8J9eALjI2cAkD/7RTyT/x4AoHj/CZy9ySsFn/UL\nAICt7vcxkOujE8U1pDc436XGM5h8hu8eCijR+f07OCkJoQ+f1hB/+dMAgO92fNCnGZG59MZXAQDH\nN/8tnjtH7/ft7pNQztJz7Tjfwb/UKH9v7WwBAJIzh/Bvcn8DAHjW+zXseJkUaFdvIzhzbz+vObeI\nuo9ygkAVxiXReV8lrz2pJqxd6uLc9gjqFQJ67DxUw4Lo5Qs5huF/oWkhE6M+MtVZbO2R5hcCSaQ+\nwbOhsE3P9tgHn8O3zrwFAPiNmTKWJQFutzqDE7vcA5djlLXUMARnkGeD//2LGEn293XXHyBdH19X\nfq9x4PEejINxMA7GwTgYP8PxsfB416KcxmS5C8sW+MNIFDVN0vY7k8i6aPErcmda80xD8QuyTSyM\nZp8eYDtQg9NNKzUo6CSdPtCNyd3RIAzvFP/e0ZuY89AyaoJeY8rpQleSdTyNx+AXkP+WPwfXvpTi\nSIJX0HsNftCzurHTH0ub8i7f21qowmUTjqxsNaE4ON9AchuWQa8mJDWO3UQPI4Xe29ylCIyz0m93\nx4m+JJn5QGvWawUQyNAyU3QNpRh5lmgEMNcif2IlqT8MzSCUo/WnH+mgVyft7dkleIfSms3DO++J\nzVlYUhNst8YnjoXrJvqSGNcOcV2KuxcRCbDW0FnTEdC5nq2HgCWp+R1Jva5vR4GWJT3TAT/KJp/R\nGmqYkPq9SZUWaNHTR3BV+qhuZ+DT6MEoMQNzA97FrKe4Fg/kS7htcc6z3igGabF+9z0YpBiZSPi5\nXh7XeKhPdQuYfYglGCfiXwQA/O0rlxEISz/jWgmxMj2chr0P9RF+L/sWn/vNkwv41YyUye0+hPcH\nfG+jkMChGba2q6Xo+fZ/UkAxwIsv5RdvQpfyh2VjD+/fptd97Qx/RorrcCcoUz3XZShheh/hkAO2\n3EdWqpzMh9nx8IO1ho4TDvLy2rzcdzY30FTprTZVwGdJsojDQgzkkdqjF9ezFWiaNLSwu/DM0etx\nbZYRi0pinZT9uEw3hoJqFrdNKBOkrT1bRegW99PODOlZdCjw3ua+sHw9DN1S4lPQUHLRu4tIQhbc\n4/ebNXTBKZCuISnv2dofoS6NE2bDNvoG5+YJashKolC9yeeaRgNBqXUNm0PcrUPsN0JoSO1o/Cpl\nvjfTB0xJPIwkEVKkRt6pQwtQp+V1RsSiZRMlKS3ylW1Meig7jl4PoyrlPtBWYJj3RkAa5Uf4myqL\nVr+4vonYl8j3ArjOufUFnMnyWVdfPQr/GSY8xW/+Mb75Ot995FXSMPdEAab7a+TPzntwCgLfpL+G\nc6okga6xFLL65BP4UFrj9Qa38LagSk31Mrh2mBGg1/ivUF69hn84ZOLTaxMdpL7MMqVr3z+G2J/e\n+/7a9mvw+/iQ1etPovJpfm4XGdma39lH72W5hz89C22ZCFLW1SnceYS19e53qFcG9y/hkpQe+uNb\n6AeoQx7OzODQe+RZPc4IVOTUS/gPkuSf1o3j2if4++p6EZrA3R6pU/+aE3ewJn2xl4fAMCeJudYJ\nLFfm70nbuPGxOHhDZSbbVPU0Rks81Lq1KtJObsLS7gb2Tkt4TXplTjhVBPO8IHcXy0g4JNSXMdGU\nLDavgHEMAh48sM4w2t7iNkpNKuXJRQdcAuAQlE4p3qANrciNZeEifDWGw4bNJpQEF7DbFkzgYhNG\nnO94wK3jW2Noc0mN6eiOifgxHuI+XUHVzw07ND0I+qhg0hZDTq2EB4MmlZ12tAXfVT5DfyoEr+C6\ntkdUFIOlCpz7VL5zUFEVhdR2FXEoyWe4pZFzVysDPQpRxKnD8jCE4rD2MNBJ86Tg3I70PJx5ClZY\n2xpDGaBrIZgNqfs1qLQj2SwCczxYHGsxuNzkVUTLoxWggRFxUDnkm4OPlFXF40UYVDrRYgBToCK4\nKv87mVIQavHvdrSNocKNVUz6EduVetgceVrtWPB1+L3KcBthB+efCHnhu8b59BcZIncOymNpe++S\nD/4oZfG6+g0AwOcGftxIM9lr+JADtT0qHd/FIoz3mDV6VWpdn2wVkP++dIqafRrBGcLwOfxHsOOm\nrD21QJ61dxbR3RGlcfERRO/wkH7rnIGIzH3zBSrOzv0GJOkZF5xZ/FqXBkg5PoLrguCNR3mgTejj\nDaYJo4O8yF90nclg1bSBvkEDJqUuwuGnkkNqhIz0nN6QEC5CCWRNrts+vNip8hD3eJcQkIQUd4Fy\naHmdsHx3Q7BDmHXZOwML3iGfsWxxj2l6BiGfXOM0Q4j6+TmoGOiPaDyFmjSuShiPjeuvl+Dzcm+U\npePQrOaH3aUsO/Q9ODXKodV3o1sXaMwIjVSfFUNfMIz3S12oceqdqCeE4DXKemOWRq9Tc2B0i2s4\nMdtGrkGjNaksoBOkXC0KfnMnUMeExfB72bEOzcm16RteGF5et9RSUaij8YAuAGCoLQTvZ+i2cP5p\n/Mj6Bt/9MpN86u4yircoA5Or17D5JmnaGbqgOqTH+K9SNqqv2ph6m1n5vuM5FH001r781Ax+IEmK\nSx1CP+70A0hKwupS5hTMEPdmI+SFdoX68/NZ6jA3HsFLj5Dvqau3kXmdRvgDj9Xx3T1m6OOFn6bt\nquc61ItM4Go+fQGlPPfn6Dxp6ByfRdjB/TTlPYXWFq83+q4W1B3Kl2uFxsGlxgDDCNdo9vgc3G8J\nf+I5jN6iUWBWuJbbD2rIuxjOTnR6OPETyucNO4grs5R3xcPzaaoXw+mbrIEoPjSNdID8qbiPI/Dm\nAWTkwTgYB+NgHIyD8bEdHwuPt+uiNeo0XRhuS+1u5gY2i/S+nIEZzIoFsy2WbisVQHCPdZZrth/p\nOVonQctGdZ4WdLBIryi7NI2KgL5nrjmBk7RSs8V5dPJS4ymp6vWuH5UQLfiFfBYOBy3MicJR7Km0\nuCLrTH5pakBbUGzyzvF1XLUyv69OAjWp7bOsINoOziGmtdBq0hIeRTjfR80ybgu6k8OTgLZI78O+\nGYItkVFHSkKe+2dwXqOlnch54ArQIzikRjBq0fKsBMiPdLAOuOgdd60ulKH0t1Sd2JekDFeJnuIo\naaLlkeYDa+OB2wtaFTPSvckrocuGPgWjSes34WvClSRflN059ASh57J4fLMYwFT4uVeowSUoWNnW\nEtbd/F6yR4u3WwxjmJU+qmsDzCcm5XtbGE6SPrVCkHWY76MrNY6OTgGmn9ZztFlBY5Ueq98nyUMS\nkvq7Y+ac56PesM5dRlb+NjmLWJbr+eKfZhHI8LMy14VaptcSDEuf1fp7uPIb5F9m7yWkVIbMfOkG\nNiXp740t8r9dW4RyH8ujPMYidkRWF0YNXA9wDkcWGaloXh7CK+Hs3/ndVVx7RRITd7owZrcAANM6\n55ULG2Npq2aSWG7S4i+q9G7U8BCxOj3Fyj7QFeSvdKCHpkCsemf495CyD83N/RYYLCDW5T40nH7U\nO4LANaRMR7xleKVnbV2tw6NzHyda7o+gFyNSh9k2+6g1uO6dRA3ZosDw6aGPErtqXc677mmNpS3i\n9MGjSCShRy/NcHVgS+RFNyPwq1K+o9YQqDNZsyQds8LeAHw5gUQM9jFoMhJRH01jWkoHXSLfujEB\ny8G/7+ZseJe4Obtbu9AGlPV2RzqaNfOQCizA00PbIIPtZAXBHvdOq92G2xUfSxcAzI66eOxV8n3v\nS2/hsXfp4TXEAy3uWPB9mrze/uEAy9OUr62ZIkrzTwEA/LuMurk3p3BKSsaarlt4L8ZrlSv2k+iu\nMZHK+WlJ5OydRXta6uJfewsntS8AACorRRxWJNRc4/pcWK/iCy9RBlpPHcX2HufgNrpIHfm0UPIn\nP0VboKujL8lV1ctL2HD8NQDg8TAjScWbAxgBynPJex2dFUa5wk1gWcrn3lilH7n1bQNLT0u9cnGA\njni0S20d3RMMUZ8oUw5vqI8jeZV7F751lIKSfDm7/gfJkwAAIABJREFUjJM3qHvWNc5BXSrjxi7l\nunqrhbUIv3dq4wY2l++NODZufCwO3kSXG6yVHmDUEaxhTxRuFzdhz2ug/CrDQ4uzkj1c1NA89gQA\nYLp4A7Ma78C6fgcWpCVZZlEAKbomhpJx2Ey5EanzGWYqBteIIWi3WzpmVFJYtgSj9HgJ7hoPKmum\ng4UdCl8+yfCKz2oDLd5huIPhsbSZIdIW0lfgmJHG3mUHYkJGZiaLloBPKAo37q2qHydneWdQcltQ\n5L5XzXRgSkG/lSONw+kWviLhKWPVi57cn1Z7HWhDhkrdI9KeLdq4GeTdicftgVeyj9GehJqiArZa\nEvYqjxAJCLhCYLyS808A+RwVV0Kj8qy617Ba5RzrKQWzeSqVgdmGx8eM30RZspB9E5BrXzyqtbEm\nV0CK810YGhXIntwrxrJOdBhdwqI1gzsxbjb1dhLBLjdc3U+mOtBDW7JV06U4OosCyqB24TUZCq3V\nyZu0LzSWtoI7gYk3yIvLJ9ghRcnuYWNPcJBjbcw2JTv71goSOifvXmR9oXMAZHYZ1qpec6F29tsA\ngODVT8Hq8X+ddSrO5blN7HZ5MF+fuoJVD5Vrfi2EmyppC25LrgGAh75E5fzNF65Cy/DviZIDwTUa\npZOfpMJ4ff6XAXz/p2iLOzRsuaX2WaASffkE2lJ7OvLWkJQuQ+mNAQoLpDnTET5bFrZi5N/cSAF8\nlPEdJYxZCe/uSD2v0x2CtcX3Jld66N/pyd8PYa7HqxtLgHDMlAEIVvi0p4eCAMNYvQ46XVn8KGuK\nD3d2cbfXzf93tJMh6JtU/NlFOdxcVRzr8o54yzcPzSPN6YMKGl2usWaQhqJjC5kg5+jux+BucF8r\nnk3kezQSo1F+v9/dhyJ5GZ5mFdZ1/q9qldGQ/TAUrHEHqghUpDtR1w1N8M91dQ59B+Uh6I5hFLi3\nAu++lYbyJPWRo/kDfMPk3vuVHHmzfOwI1n5CZp/O3EZjmvLQibcR+gHnvj/JA/Lo499A4xrvgDd+\n5MTb/znxin/rm3+C1iFCUbq6b/M7jhvw/BnvfSNPbSJ2mZx/unwGL58jTOuLXtLwW7e9+BcJHtx+\n/UF8Rq7B0qMtjAbde9JWv53Fqo+H9G19F2GDtP24zGzh5NQmsjb3CPQ2Ju6IzotU8Po05ejQe2cB\nAFsrPnQsytHJnSb+OsCD8z5nGZuHqQsfSlBGnu7ZeGH1FfJ0P4LeLI3Bwl4e7hDPhrsdsXz9dXgP\nCeDKqzG80yQvKygg0V65J23jxkGo+WAcjINxMA7GwfgZjo+Fx7vdpnWYNCwMJKu2nVuAv0xrMZ2+\nCXOVFnZpm95A1B9FtEEraza2DMu+DADI2segH6cFGDRo1ZiahbhAE+ZSKVT6tFK9VQvuCK0WZ47W\njappcHrpmSl7DpTaUocb6MNOkF2ZgnQv6rVgGAwt1ra3xtIWz0nf3VgFPQHYT84aCHb4+33VgUVT\nkH/anFdnNYtygSGqdlZD1kXPyR2uojhgRMBw0buLDgIoeWilDeJdZDfpJXhdzY/68KYMetJVj4Gj\nYnWHVzzYyUvmX6gDe4u8qqr8zkp0iL6Ea6u+8WIyaZ5GISrQbCE+Sw0G0XdIpvLaLBo+1qr6J230\nWqTfd4ZeRtHagydHb/q8J4qEQjqu2NOI35b+y27yeq/bxFHJrt1Pt9HI0UtaOD1E6zKt30Sf87S6\nwFyIdOxEokg1+YwqFqGZfJ/nGOd9e/Nu/O/fH45YFrFzgtx1h1a794oXfoki3I8buBWlu6727mDw\nS1w7XbJgo80o1Pc5381T65gyvwIAeK3egTJNT9kVl4S/RguR3X8LAHjosoZbZ+gJd384hPIl8uqR\nV/kdY2UZf9bhO5LnbyH1xG8CAJzRbwDLrJM2Hfy+f2ccGCbg6+/CNki/Wmd4Pu/ZhSYISpmJJtpV\n8vfNQAjHJQHOcsl32jaiOv/eGvUwkO44UxMl2DXa8hmBUuwX28hMSQLdXhzWHL3F6VYFpvRMdVUY\nyvftdjAxQT5u2oCvxfkMgzHog7sdgxix2PCNjzDBWYQmnm7F4BzSozCaYLjc4ymitSP/O+OEJold\nDkF0c1p5FGx6ig2zAI80g+h2VOjSRax+h3tQ83nh8khfYstEUWqMK6obDrmm6SncV349jBzkeih5\nFeWW1OE3Skh5GAptKypqw/F15QDQ+FwLw3fJS9u5gs9pnPvGMe7pqXer+PATnO+e/XksNlif6ruQ\nwduCzPfFsnj2wSR2H/ljAED81Ofw+ItsdhA+O4P+LN9xzcv9VN6LIXM/E6MeGJzDi4ktAMD8iobA\nxjMAgDPSne0vvlrCP7/I6o3/6cdB3DzHddIv68h9+YhQ8uJP0ZZOr2BTMqNv/7yGQol765NyJRRd\nCqO0LZ2kFC/Kkk2+4goi/gMmhg0PMUIXUtqYlAjGS9oRfE7qcW9YIZzJ8py4aDL03sQODIlqrtbd\nSOUoyxvhEZrS6EIaTWFqR8XbUofuvQ9w7pJPhWkX2tKj+P/v+FgcvKdnuQH3duegyT3KnENHU1oA\ntgZxSPcuKPMUskgrByVDpVntVuCC4MgadcQUbv47grkcb/pwyU3mp0wTczaFIe+24JSC6b4AbGi3\nGshL5m9UVbEU5997ZRttAa9Yd3CO3YaNkEKB9qXG06YlSEN5M4OUhEQO702gKgAEaBpILVJo1we8\nKzvidKImKe4RzwQcQ4Z2uo4ApuOcQydMRWHu9j5qPN/fa8MIc/M3aicRnhaM0asU4oh5HbagPxbr\nNtot/r4RbUIRWLVAiyJRt8LoNvgu53B8YETx5aFI+VKpKELfKWGyTyXXjuQwm+bhY1dUtCsCJXlB\n7uAGPrji/N9O7QM43ZyPs5ZHY8TwzkDhWnluKdg8RmMjpCxAEl4xuu1G0yvPldIgXxCISbbqHAxs\nu/jZ63Ng6OAGSV7h/+ZG4xfuSO992G4pJdkWmMhHgGSLRt0PyjFkJFS66PRC/SZlxn6aB+Vo1MPt\nz/8FAGDprT7eqzHcdcQdR97munT3GRJe2XNi7TmGyRqjt3DhVR7uJ58Z4rN18v6SX9rsjf5XZKLE\nbX78ky3ULr3CCadXsGhT1ko3uG4L0eRY2sySH5otEKEuhgqTsSwMyQoPOOyP5GTV8sAaku894ZVn\npMMU2EWX5UJQYsnWdS/aKR4cubbg/IZttE3K51CLwC1Z5K5IF4bNtdAKckXgvIpGk4ewI+1CTuQu\noTSQCIqqGs4BAPJGYSxtno0IHBkekEGVc3EYQ4w60tXI9MOULOJk0wPT4Bq3bYaia1UDHks6gEWq\ncKyJQp3uIlyiolU0ycy2d+Hp8QC9YQ0BJ+cbCfZQECjOWI+nvCeQxoSTQrtjpDAcSOefQA9dhes2\n6kUxM/p7Dt73nsfpyD8AALxQ28XIEBjNT/IQ2a4V8VSP+ScbM3+IQ03K5L/Lmpio00HwLTJkHC0+\nBM8W73W/WQ/jayPO4bXcC6gFmF18v0B9PvNuD7uRXwUArH2lCrUiNH/4DaRmaDSYs3xXdhP4Yf8U\nAOBQ+QYmP+BeX9E0TH6L/Pv6GNr2bBPKo/z7aPgWHl3mYbjkohy98VoQh5e4VtX8Tfg+Q6fA+HeH\nMJQUlE2dRk783FHs6oQhDi/XURagkYh/C/kX/yF5dopXi9qogTM16pjvRNSPYHIjl23MeLgnXaPn\nAQBdVwqLFvXrXj2JTldyfq6EES+OB1C61zgINR+Mg3EwDsbBOBg/w/Gx8HivG5xGamIHtljHtXYc\nqoCOq80elDg9lMUevYG+vgWzQKtnqv8wWiF6rPXpEVxDuRQHQ4z7Ix0qaGF63A4MyrTsy24LyWnp\nxlOl5TY8C+gf0vLyh/LYH9B7QDSFRJFu99I857XdSiCkMjR+2RqfjdgN03NbyPQxmJ+U720DAYbB\n5jsxFMvS9Bm0GtWpNmb3pZNJMwfvrHRDuaCjnOAcrCQ9jnDXA12awg+GCgZe0j4KFhC8JbWUPfJO\nnxhCovNwXyzBHeHvM0MPbpr06lTxDFLhOqo7UlMdGoylbdtswivdm+4mqdWtTeT95E+q5UddAOu9\nSzegtem19UoCKpDpoy+F+8NSEOtOfobixUqd88kZ5F87FYB7jZ7Ijt+PSfE6OhUdmTCjILb0D3X4\nvFjvS/anbeNBkZk9pxsBCYk3G9IAfnh9LG2bsyfgkq4kMPn8o8Mf488FrGA+HUC6wOc2rRF6YXoM\nV9foxc7aaSznCOUXXT2PWoTh3/0r6zgp/UxrHcrA+1oSmYvSTGP1l+COMzmlfySCNzeZZFJ5kjB8\n/sF/hNXb/P4Vq4anHQwvXwn8HNbqtNCrR8i7+wWu7++OoamjX5YoyLxk1zvysJ0u+bsXzn3ObTqQ\nQ0kTMAypnfZbQ7glUaaod1CcZDJdVsmhN6DHGhHQgnLXgfBNyrI+fxPTTen+suGEd56RiFJIOmYN\nkxh1JYy7q2BFpWyVHD442lzbsosCnM2kcGkcbfMeeCQMPlQkOSsMtKXuOKC10LFJ+2C4gVFLei1L\nP+RotIdykbLj3m2hL6Ainl0PmtJ7exgUIIyWEzFpXN+1uvBJc2l/3YOwZNqaAjSSb7TgsbjGtmLA\nLZUGGCbhn+S+r+kGnPZ40BMAuBCaR3j2r/i/zk8hviRRpj+hR/Yfzw/x7RXWZbsuTePNhlRRTPwI\niSF1nn6NURiPKw2Pk2vxa1od2Qi9WL1zHNHLlLk71/n9xcgdXJogtx2lGWznmPj59OgQBtOSMKlS\ndq7/3hsfJQf+1WMRrF5l0W79WBhT/wOTUvF//zRtJ966inefoEzF12Pol6lP3gtwjrp2GS+obJww\nFa/g6NfJ19LhHlJT0rlOeo0vfngR9eyTAID+DQMfCkzsUzM+GI+QD/kII21O5xw2PfSOzwxPQ99i\neP61rIpT7zD7Oi+wuBuObahR6sxW+A5S71K+/A93se9I/zRRf8848HgPxsE4GAfjYByMn+H4WHi8\nHrmu6aomXA5aES27j4iPVvN9syb0Oi2cTo0ekK4mkR1w+i53HzMavRNfPott5RUAgOakZTY3G0ZT\n7oY8PR0QOLz43hB6iJ+NopSZVIaI+HmX4Hf0YGxzDgip2BTYud4t8ZZ8QxRADzTlHm/DOAV2bF93\nYfIa73iUbAjqNp+1GTYwLbWqRU2Qpn6yi8wxaQ4QGWKtS4suHgjBEljFmINWdyuiwFDpiYx8KqZa\nRI1y1nSsDWmBL3h5x4G2CWee3mY5VsCE1C5W212Ybi7CqEna9GYEVoi07UvJxU+Nfgcti1ZoOEFL\nMZVbQNZJS/nCdB9T0r4teHkGl8UDiyalH+rIhe0G+TOvNGG7OZ9y7RZ2EvSc1CEt3mppFqrN6ELA\n30DNpoXpCARhN7fI1ySt/U6rhaxYvwl/DDdNSZJSE+iIV/KJIaMht7L2WNImX61gzc07tGicMndz\ny4WQj9GJUxfvYOMBwjzu9n8ZSlfKQCakDOdqE1tuAtpv3zqEtotJHaeeXMarNygH3UneF59bcsA5\nz8STzZc6uNSdAwAs/tGbODVFD+h69bMAAO9j25j8gDRY5xS8XpUEQnsAz1eZtPLI80xuaZnj66/9\nmTuoC0xpW5L8al0fekHyRCk2IEY+bvQiUCv0sG1pRNJQpxGSZCWjX0fQ5rqt92NYBvfvrjQSmchl\nUY7ze/6cik0fZTHrstAs8BmRJu/H9voaElLLqd+J4uYqPV5lpwV1lh6Z3+bedBvje7squoWyLZ6T\nNK6o+WOYc3GP9SJeJMv0yNrOGIY2n9OTqIavDgyG5IlRBhxu3tF2+x4MJMqUbdObLSCOWof0dAMm\nBh7qqO5+AOmIoNwNZK3mKrBLvC8OmQryHilFC3oxlN7H6YkU+rfHo40BwIN7PgyNhwAARxd+jFdf\n4t55IMH8gX90sYPDXYlOhd+Fb8C9vpQ6jWO7c6Q5RxQmTHoxanDv7jxzB+X3yYeHB4+gtsR74lKP\nd597R7OYrv/PAAD3S1/GocPst7s/+kXoUe7lTw+YAPr6Shq9PuX6jOXG4U9SR/93nov4+e/cIxEG\nwAuTM0CVc/cp02gNrgEAkmkm/9U7aRyXfrzGy4/jVppzTGbiMEoCmzpNXbM/G8J0nXkfmlKFFZKI\nX8ODrpP7dPkCPfWAcwX7NyjsV5VrsDtSHxy4jsuHOd8ZF3XQs5EQzr9BPp45XoL5Oerg8xcbCDvv\n3JO2ceNjcfAGLRJeMd5DVGNox7c7hOrm5n3LBUx4WEupTZHYVKWK1oCbsaDdQHifysZhdDGdJaOu\nSHLMzOUOrCw3UM3phN7gAeA1JlG+xsXua5Jx2MzDvE1FcCvkRuluN6CrXZg+LlpXNmmgOYB3lZtx\nNzWela4h3xWfiGLo4zv2zDewKoaAr7wIJUxFMTngpqs/nEC7Kb9rTcHb5+eBa4hIipu3uSWJI842\n5i0eLKOkhlKDAmd20phwSaJGiwJZrRahDCXxJGKgO+Sm0QteqLrgJKd42DrNFuqS8T2ndXF5DG2h\nRhpqgM9W78JozllY2xac5YiBli39TuM2YvyIbS/5d3hfR2nE5B6to0E1OIdkKgyrIwlj0vVpaiaE\n0ZCKTStkYUyQl13PLhZGAsUn9eBRfYgZUep6RoEevAtD6oTpoUy8d7eRenG8ostNRBAWTJRAnYfU\nOl6EP8B3eR8Kw/cAa8ezX7cRilEuT7gFH3w5BGuCSVCPtBuoZKgcl27bsPOUoyee5rx++MYjqHxI\n4yod2Mch6TLk+fUnUfg2E+4WV6jgZy+bePEY534isIbkh0x60Y6/BmxQAb38Wam7vTb+ikAfzmHO\n5no1ApLQ0nJiECRPZ71O9OTASA5LKBRZXeC2BRZUaaGUpMw57SSUa9LLNZKBx+Z6+nwMnxZjuwj5\n+A5nuYS4kzzbq4+ghKQ2VxNs5WIRdUmIVLURgmLvtZIqMlKXaWj8ZX9wjy4+jhEyAqBhGZSdJcuD\nfpQGitr1oRwSrIDhAHXRFVM1/u52p4dJg2vRQgdXBKc87HWjWxZoTIVyZidGyEtnsUMNPxx96W2s\nbqM/oiz3ZE8P16PwCp3wxxBrCK6z3sJMjLy+Xa7DjJvj6QJQ+p0mKl9lWHTmhc9A/xoPJ1NwwA+t\nHIdz9J8BAFz1/xEhF/9eDc3itXkeuNFJrqWv7kHL9Ud8sNuNo58mz179syP4+Tl2ynpd5d+P7pxA\n0fnb/N4vtqD+U9b51r5jYyD1tD+eYbj75KvzmFyV6o+AjR8uMOv5ydp3EfD770lb330eWocGzbTq\nwZUMM5V7b3BejpQN8zLls//cHrRbNDoKBR0zgt9dj1K3tW/auDxPPfYr76Uw8Tj30A1o6DQpqw/H\nGMq+M+lGtMe/Z+q3oT9E/Zp414UdkXeU+a7LnddhfY5rWNm8jG05sJOOk2gsjTdy7zUOQs0H42Ac\njINxMA7Gz3B8LDxefU7qcZUHcf4GrY9jniouCQzh0WICRQGqTgpQdqUTwUyblqflSyGfIYJUdhhD\nQwDRVYXWtzuWQE7qRRO9OAI52hu77g4ifr5bE7D5/ZaOLbGY1XebCIUILt70AqUuk0iWpfNKPZPG\nlQ1a3mdu18cTl2bIuFW6BWOdHonTmoIpHVlG/QpKbb4vLj1468Eq5nz05jcafUyK4eWwI9gT6zhh\nStjPmsHbEOSrwjwSggxU1ZqI+eltb0qHlHgtjasOhhPV1iLSNj2OQroFzy7nb7uk1AdeOKRxQq09\nPnFs1G8jPqRlb6RJj9PhxKx0xdnXJ5EWb3OoX4f0QIBD5XsbZgbuOJ+dt++g0mWYJ2IuAkN6Hc4k\nvQi7nIfbRa9nd7KH+QLXRQmv4k6NYaMlKWkYXptC5ZP0AmpNDcEW5Wg568a6YCGaFf4upp4fS1sj\nehmlnzDaEZ9iqGpVKyHo5xq+rcVgSe1e8FEfjHfoMe2foqetbYQwAXo1t6Ma5qWRgJkrwT3D8Nno\nImX2iHoFGyl6gmeMFbwi4dapD3voHWFphls6oZiT1/CgypDy5sCH0qnvAAC+3P1VdF8mzYVXybsT\nk86xtNXrKno25ScgkY62u4ZhhWvY8KjQq5SjslJH3Ef5cYU4h53+NuZMQSfzb8DQ+dmrF7Gpkr/D\nCvfISLUg0Wykm25sWJS/jqnD3uDes6WNUEQNw9ij19KZDgI2+eAsLqKyQG/HuSulXcvjZVLPV4Ao\n/5aVfQxjhK5Au87ZITTCpGfQKSPeoVAOTK5xNNxBu0YvrhxWsSjXXf1BB15V4Dl1qk2z54Eakvr3\noBdzAeqmZsdCXZL3/NN8VqAxREHqvld9FhqSpOdQ59FoUR9l3RvYbY5HUgOAa787ROUf07sbPnYb\no+dJh9pjX1g7ksfMy5SttvI64lVJ6FvdgOtbvHa7/9f4/GvBGhyD/woAsJR6Aef/lrKzfN9F/GCL\n/PuVmyy9aSjvoSlRmMkXPaj/L7wG6v+uiftnOfd/keFcnv38CTz8POX6nz9xFI/UuL8C6j/D1oZc\neeG1n6LNGcrgVI779+LhLjSp2R1KUtcxzxJyJuVh+oaBkp8NCpJT98Mn/XaHb/D7h84C7TL1w99E\nHsRncuT77IIfV6XHdukcnx/9k3W0n6CO3pzOY16ahrwXbSJQpcw0JOkzYTjgchPNq1x5Fp4Z0rOT\nM3D4ynh0v3uNj8XB67yL89vZhE/wVTspDxIObt79nQaM0N16TyqK2WNulAskvOPrwF0hk25mdGTK\nVMQ+g4fXza0cIGGDDzMuJAu8H2j75+GSRvddQ5p5j1bhLfO5uruC83In6NzahSEJh1uHOMf+nQqO\nnqTSKdfG4+LWN/n7kXsJ3kUekEf2/LizKSFOu4/ggMqmsSr3TecnEH2Kgr5jFOG3qHQqDgve17m5\nS0uct3t7D21peZjurWHbQ14m9k2UMlQUjpu8G22GVYT6JKJt7uB6gXPzxAroyH2gb0tqXrMK7LI0\nNV+8xx2vnUJsXoTeyVD/KKFip0rcYXVYRL4u7fuiEZgDCrDeWBaeFBGS+bon4gh1BPRC2UNCQo7V\nkmQOmiW0wfutuagBPUy+F/N1HAbXqCV1lvpkGdkm6xXDKQBebsJyNwj0OZ+gZCFf0ce3BYzt1LC4\nTGXVTTNkl37rOQwfJQ3B9+bRGFLZtMwfY/oLlMv9LX4/euYCwnHKn+tdBfkJvrfo6kOJ8z78oufX\nAQC1Nwu4/wjn3ny9i7ksQ/ytk3twf4tXGZl/QCPgg81VfFmyjG9dXsSJKGu8lUNFeIpcz99KUb63\n4p8aS1s4bUHJSKb8O/yOPRdEyCVZ4Rs+hFTOx+twYTNApRwr8hCqKDOwN6kQg4em0Zb7bZfHQqDA\nA8eUbl/2yESvIPCJMS+akmE6Ed5G00H+KRISdqg++GZE1rsjaL64zHeAgipN300aco3u+G4w7aQJ\nQV5F2OY+dSU9mJduXy20ES0K9GUrAI+0etJFHmLDGLqCJXDYFUBF7vfU3gBdMd57khbgiwOmg8/q\nt6qoiYExiJmIC9hO1eLBfHTRRMAhh3vcRqRD+c17hshKS9K3OxPQXbmxdAHAz7mc2MlxDwRfehm6\nyVDnW9Il59AohOD7Uu3wm+v4Xo06M/6Ta5j4bx8DALz2OuX3hFWC50WGrWc/fw3nszx89J0R7CUa\nZTvPkdDE11u4E+NVX/MhG5aL4drMahl7S4Sd/C8v/EsAgHN6Cd/7BRpUybffx6kKr2levu821Mq9\n73j3izYWD1NvDB54Fkv/F+WreD/D3oN4C06Te+D6+ruwHmarTnW3gm8e5z57eIcGZ7RRRrVB/j6R\nLGFTY16GcrOKabmTv/VDrk9o9TA8VA9YXJuH9igNw9H7bgyVLQDAZJ175XZ8EnMXmPHtdyfRbpN/\nx4Yu6Jt796Rt3DgINR+Mg3EwDsbBOBg/w/Hx8HhDDN+VHQXMGLR418xNoMAQVLVaQ3hPQjcZehaX\nr8cxIw2294INZB0CqVfoINehB7QoDcMHegVqiuGR4OYHGIboyWz126hLA4FFqdezCnfQktDtOhoY\nSDOCQV+FIrV3Sw3+77GVIQZRWsGuxPJY2uyIgPyXDLQl0WLHdkFr8Lmav4Khyrk3KoJ2YyrovCuW\n+LSFC12a0hOuNgY5WsSDEC3bgCsKb5Pv2En0EZReovndNdQkkmCn6CV7mwE4dHocQ0cffoGCrIZb\nGJr0MD0Cjq8kQxi2yb/s6PBY2sqjJkp+EaG7jec3bLiD0g1oOgR7SDqadhlNkxb4pEZrtDUXwDBH\nj0s/lIBnl/Q/MGjAP6DF+6GD3oCdnoEKekZOM4XA3QxztYWCl89dCdIq73R2AEGV8YwSsD1ZoTOC\nkZsWr1/A77P+8R5veXkE55u0/FMam2PnUpcwtU5P+sHom/jQ5tzO9x9D6jWu55kU53hTfwyxLfJk\nPtSD2uLvz3iOoS/dh94acg7Pzf3v+OsSk0kOZSJwmKTNtTeP7melHlm6RhXDJfzgYe6X6XYfiwI7\nWXLuItyjF7iVoSyG1PGh5rYahCodKawTlJGIHUS3SdlyLGjolBjWMxxZxHRBxBpJxyzXJpTRHADA\na9fQi5K/XsMFq0I6W4u8ugjeSiEYFeSrkuejLOId9wloe/wfNSEesbuDsjQBOT4XhMemB77XcmBC\nmtMrs9wrdWN8NrreiWKUlYRJiYREAlX0utyzroEXyiF+NnI2opL9rjkke74/hDHFv2tKD7Em16ht\neBBMiiwOGEka+L1AU+p85xxw2+R32m9BD/LzvDSNH2hAQmqYlR5gSlSjb3dhSiVBKNrHvVvFA6XH\nvopOg2mO9dBX4Bfsy7l1RvjmPvUo6r9MsP76jefQSf8QAPBFdQGX/pp75DnnSwCAYryN2mP8/JdG\nEg+tC+Sr7kJ6jzpt5x0iWOk/70BoTypINkyUO0w26q66cVL9W/JyhXK6HvgAN6Sm9dixGL4tlQFf\nuf5JNI/8xT1pe+g+H4o9ym33vfdRmOQ6OywV3AGUAAAgAElEQVTybO2iB+ohepX3H3sINUkc7GlD\nfEY6wFWucL++9Mxp/Ifulzmf27MoG9R5K1EN+XPUMWc36CXHq3X8pUL53TtzHu7rrApxBX4bQemf\njiuUz6WHq8i+Rz5e2LoKX5vnSMvI471znPs/vSeF//74WBy8mrSWmuyfwVXJigziGNYFjjGqKWiL\nEmlUBcbPU8VFjZ8TWzZ23RTZgBmBWwqq39njppm121BiXEAzb8PlpPItGSP4pcHzhrQKe7c7wFmd\nQjTs1eDvcGN5XX7UetJarE9BH8RPweOgEDoFD/jvDq/AthlqH2Za7pYKXZQ8XODZ7Dw0jcIZyfFd\n+/4t6DWBc/SH4O1ScMJDP3YeJE2OGxS8Rqj2EXyfuRmCvch3OF0qok7Oc3RVumj4bsLWJSzricKS\nMo5kawajFBVQe4X0BPJ7UPwCyzYenQ9GuAB0eKeUFaMkEOkhIdCFG+UeHJLtnbgVhrFK48csM9zl\nVYBAioZPwe5jsslwvxkOQ3uMQr3wvBwmVhoZuRs24kk0Tb53ARqcJd7/6xkqDLMXxEC6jxi6G6sm\nN5kvABQ9XO9cmyFs2xxPXHP0LBSTa1BfYMehdi8Nh5Q8bD00ieNvkD/KQhNZS0Agjr4PAEjlz8Gw\nacRczjrx3AT5s/38Ov7oNO/kVheoGL/9/nM4N095OJQ7jZd87GQ0+9azWMtw8080qOzrx2J4+AJ/\nt5G0sOHhc09rJ3Fp8hUAQKgq+MOO8VjN/koVbqe03NTJfzsURrXFzOtRYw+KGCvF4QQiDSq8TJyH\nf8OaRvwow2yl3QxGUp7X9hpwaZQvxZD74oyOUYV88E914G4y7KcrTUQTnKf7bkZ9rgXvHL9f1C24\nBcPYSpRg7HFv1P1872Q3MpY2O7CDsBjkEQmfhnpOVOLcY0bFC4+U8k0rKswUD/c7Da5fMrmEZYGX\nrZoROFXKamAugWZOGsu3BWyjPcBERgAe9AEaSSl1agaQlVwCS7qTeRxZOH08kEYhCxAc6VnNgaqH\nc7AsFa3q+DaVAGDvvY9gigfv3pqCsE7dcr3GPfaT7dv4tQ8I2hI78yfYneLB+XpmCyc07o0/HXJd\nv3g5geTv8Arl8R+ewTsp6t3Eq8+g8uh3AQApgyHste//PgJO3n1OPfgMHvlT8n7+dAjvmARw0W7z\n4HnggRrU1H0AgNL+a0j1n+McPRdwdOfetI2+W8DOw+TPwqsxXP0vGN5NX6QTFNYW4YhQpoxCHLEn\n+dlfPoLKBumfatIgTW3V8W+kA9XR+zyo1NgJqmF+Cvo17omflHlezNc80I/9JQCgGZrC6pChbcfy\n8xisC+Sqn/ps+HwI61JqemLext+eZz7SCecyHkyN32v3Ggeh5oNxMA7GwTgYB+NnOD4WHq/bwczN\nYqKCmQwtwZ39IPxOWp6FkB8z67Q8OylaxF7VRL9KC6ncGcAfo2dkD51QMwwJ6Q5ax6NiFO2BJJFo\nd+CQxvO90D70PL0aX0DCS50G1iVBWSsnMTpEi7Vq3UCsx5Cj+ahAvEX7iF7jHOfPLI6lrSnhxNVg\nDftVqQENujHv4TP8BS+u++mxWQIu7u8uoCL9LQNDwFVhCPDDtg6PeDgDJ/kw0ddQESusoxYQvcqw\niwonjBr50PZK1rOjjaoqIdbWBiyPNFTwTcAWEPWwU1q3mFGYYUk887XH0ubFDEYNWtK3pFH5qeEI\n7iY9GK/ewdSQlmB1aQqIkLG9BsEiYuYuVJUW+EL7OvZOiTe+dgJGkd56N0HP4UFPGDvSJzU58qAZ\noRU7WUvgsljKM1dI52BuGkcrnNdGrIhchNZv93oFPoGPjKqMsty87R1L2zIuYmuCa5tZZ+LYmjmE\ntk4vYS6+gp0oLfjORAIuST1vrTOJ5WTVxHBSrgPeuYLteWZT4qFn8d+3+O4/fImewWr2DjoSCvzf\nqtfx1aefAAAUD/fg3CDP+h162ou+GazMktfFSx3ct8k5XFaqOLnD9e7PUkZcq0tjadP6c7BWJWmt\nRU/aZxeREBAYPZ5F5hYjAVokCj3OvTfQxEuuN9DeYUhYjZmYGnFu9YaGQpJRlBSkm1j5Olp3G2Vs\nDWF2meiHMNDsHZX3cS1HwTAS4okPCxXoCYbqzagFt1OuinzSjN453uN1jSxYHvKhKpnVSjCKWent\nm5sKIiiNC2wtioHAmKY1n/BDhalKNrUrCtN912vuIKjxmsEWoHwtZUL38V3OngqfJfCbuoV9qT5I\nqtLdyKFg5OY+9psWulIhUek5MJC5KdsDhBz39gofuXYBP3aT/s8047ixSH3x6fs4n35rEcufZMRl\n7c6D8EtJsDpp4MI6vbPYzBwAwPufLuLH3xNI3ftCePYF6sGd9ItYv/zzAIBsnpFBp/E0HNNcT9+t\nl6D+KvXY+tWn4ZZa9OdnSPuDoymof0465076kdYZLYqEPNj/f+62hfrp0V6ZRHiXumv9rAP188zI\n/uSC4ASsvAS19CAAoBZLwdPkPvT85CgC57jOO5/gvmh4P8Djb9ADv1zexnyQsvNS6h0cf55MmZgm\nVGxRzyBSI9Tqkz8cwj4tV4slBYdrjJyuWaTx+MkYNqrk2atvTsD/IP/X8b19GPOZe9I2bhx4vAfj\nYByMg3EwDsbPcHwsPN7lCK3usDnAtSl6gqZ6A3aN1mKgGEBzTpBBJIHB1XOhmaEn1r1eQ0RQjaJ2\nH7Ud2hPBMr27O7ERRlKadNhI4A5owfdcPdg2rZq9Ei2WxKgPPUXvo5wooiN3gSdcT0Hx8S6m1eZc\ndH8CwUdpKRea49t5uV20NG9lkwiNOF9XyYONLr+nKxtId+gR7CV4V5bpWegM6S3587sYJWndx7IG\nGl0p03CQT4VRD3tu3tMlR060InxfsRVCRMDMdfEAXN44Qgo9kjvlBFqCJJPqDpF2kNe7Ki31ProI\nB2iVoz++5ZXlHKDWo0d2vM37lX5AR7lDq9AfBnZAT89MDjGZnwMAdJe2OIf9Plak1vp60wdIskLy\nhAGzyAjClFQgWKYHxkgA6ff7OOXjeu+6LUTzvHsbeeidJPfy2Ne4RtnQEG2dFvj6nB/TguLVyMm9\n4/T4PppbG1MISPOF/iz5/9lL7wJfliS+tUvoPM37tIfffQGxWULmveen7L1x9kl8ocvklbZ1Ftrj\n9CC3//U+3lukLC6FSWN+/RQmzzJi83jYjc0LfEfyUAwPf55ypfXYDm730tfxVpXzymhZ/HiWf//C\nMScuvcioRWede+R0ZDyMXf1QBx5JNrKk53Kj78HILchBTQO3V4R/N0ooSQvKlEbamtky+vZdqMkA\nIkHKh9uvwTtN+dz6gL/zReYR2aVcj0JFaKp4o/UEHGl6Z4pFOXOUdLRVenQebxhHJRpyrdKH3ec+\nNKSPbXNQGUvbMD8D+KV8bp5zybhtXBfvL9JTUBHYTy90KHLv6htxjoNmA2ZIICNzPXSnObcpZLHr\nJH8c6l2ZcWDYFGhCO4KQoGv5V4Zw73GNS1KWG3WYaHQ4r6A9i6yDcqgPgKYkPFreFmqD8QlxAPCH\nXxwh8O5vAABe8T2Pk2/SI20lGWXZDVyA5yw9vdfzDSx/wIifZy6Go1Ey4FCZd8Qf/J6GcIL5HMrz\nBWwLEtnuYgeP32DtbVPu27defA4TsX8FACgPDMz/zWcAAC/mf4Bff5C6sneGfNj7q4cwdT/L5cxC\nAoEL3NP7KwPY//hrJOSfvfNTtKm7u4gsSnJVpY2noiyJu9rkHgl55/GJPvX5IHQV7SLlM/2ZBmqb\n1DeayPRcMwwjLDXTmo2eSR2ULGxj8nHu+w9U6t/cWgefkMTFrfgIt1XqkE/fehmvkTTMS8/x8+8s\nQSvSaz/+8B3Ed9mI4YOZiwiNzvwUTX/f+FgcvMoMs2btDQ1TWQpk7dohxAJczPPKVTgNKr+0NJPf\niZaQk843C0EPDFOST7xV+EV4N4NUSkd9C6g7GN77cHMRlbhgpm4a0OsUvkGfzK2pGlak04aNNA4F\nGP64deM8Yp9jgsEpW0KiaQsJhf87szI+1ODISF/dUQKV61RG+uEuLINCorcSqElXooAkCe0vrKC7\nzo2y7ZpGXOptA8oIuyMqrmCSoRLfMIPkiIdmYSeNSojhMKORx7TgOSPC0GM+BAS3pGfonAt6kWGt\nQTsKHJcOSdv8zvZqEqbC53r18VBv+b0dIEsBLzj5v7NQYczREOgOhxjWJRx7ewgFUtP8PtflgXAG\n1SA3UzRQhUvWrWOVoYYp1KqHSqB9tYHwJN+FyQAaLtIR7rqxVxaQgqMCHvLBEN5TlJ2N6gRSXc5/\nTnfiypBKriGZkGgmxtIWa61BcTMUOlrj/2783EkYX6cyt07oeEI6G93oh1C+SPqPz1K2TlYuYcZJ\no+7/8MXw9GsSkozfwZfOMrRVb9EgmD8yxM0G+f/ozXexLZB9evIilA3yshFg6D0eegqNMD+re32c\nOUX5fWv/D5EvUWkEj/BZ35qfGUubtzKCT7LRB22Gic2RA5YlWe4jE0n5XI+YCEglwUiatPd7HoSl\nTnJu3o2S8H+0GoVzj/soGOW6ZiwDBS8Tx7KuFeQ95NOyY4CqIj2wB1xXwzLgmuAB6NxVcMsl+OEZ\nBwICcOFSyVNPPDuWtoprHZqT/3tUQBgaAcAZJv8dagUV6dY1bwHmgEq7GWA4ciIcQaNPozceGcAj\nxrbmGiFuUOaSYtSZvT46Gveuo9aDEeCBPXBE0XFQTnz75Jml6Qj7mLxm9zy42pI6aWsLzraENzUn\nfLv3xmpO/qsUIk+ym63xwSReP821rzsp657md/Dg2v8JAOjrmwgneWgNbvqQivwAALA7x0PakXgd\ny1uUQ2tVwy37awCAB1K/h9x/wz177LfJs9CDb2PWfpjPXXgbGwJWdOTW/fjjN5hcdegIdczJ2Tew\nf4XzcUwE8PtHud/+iXMbS8r4CgIAqD91GKUy9eMMVlFcY7h/R7rHGbkiJrO86ktvRtCRm5tI04Xd\nCGWtKUBMc7stXI3xs/JBHAFFzoNAFM4hDY9zO9wrH5x1obdB/hvONk7u0Fi+kzqKYI17uTYlXaDO\nvonFH5EPfz4ycSbGQ7ipfhn6jd170jZuHISaD8bBOBgH42AcjJ/h+Fh4vEaeVo3PZaC9RyvDc9oH\nV5dWy9Stc3AusK5TlxIit+2GJklSAyuIcJcW0iDYRVkSFDQJH7e0TXQEAF4JdeEUsvUFP3xdSdgJ\n87mDVyOwW/yd7fSg5qfVmPpEB1qDllN3ih7vbNyE10WrU3er44lz0TPob+7BAc6h3azDLRf24X4V\nBYNWbsTHUIuz1kckQQ+/1qzALWUDjfAInop0bEne7ZUbwqzkB5XbLcSLCXluGfkJ0qFZjJnE9ruA\n8M/RA8w5QaTqdZETeMjEnNSI5lqwsvydSx+fXBUzNKQjtAaN/BYAoGTHYdZp+TvTTfjFg5ntxlAT\nFvXi9BjcoQUkqgyHVi0dloeelSOZQu+qlDcIH4bpJOIdWpXrywkc3WUkYbe3C+UI3+GQRCHHpB95\nmxZ4WilD7/N92702sk3ypGhwjnut8ZWTPvMUypKblHqbDE795QYiMVrXrSDQ+AGt+fuO6BgluEaW\nhx7Z5jc3UPhNyt+58mFsj+hZDRK7WPtAah+95HV0Jw7/s/QU98KfQyXwYwCAq+KBdYuykThES73y\nVASzrz7KORz5HnyChvSF3i/g/RlC8Lym8ju3SuNrXQM20JfuQaEsw9PGYA+70hDAGRwACvk74zBh\nqZSTuiJXLLuAY4l7oaQ7EO5zPzkrLTQm+Nxojt/vmjZGktCWTzUQkuhD1arAK71qdU1CiFYD8Qq/\nZ6ym0avzc0hxw5rkFcpQQs0eazzCk6PYAkA6CimuW7ibhjLgvDzJANKSzNROumFLAwhPXzpmhUOI\nCZxj3z2BgZThDEY+DKMMb9vSCzq03UPVz/0YDsQxyov8jepIBKX0yiH9sT13kJCGSoXYPkbSTGOQ\ncyJYY0g41/ABiXugxAE499ldXHzjk5zDyauISRnX/YIiNjcfwe+O+PnB00lMTv0xef32p+CRTm23\n11kiNLeUwkURD//3FHh/g57y/CsLaP0i+ZN5kdGm+NkjmHYwAe673x3iRpLz/aorhPDZLwEAdtcJ\nYzqVDiAR+hoAoBvex+fyRFbbP/l9fL987zD69CsXEXiGG27o/BBHZG+sLkqkyFOEkZNk2aMKam65\npnHsIeMmHxZuSLTvWBN4R2TumZeQvk7P9P7uNOz2U/z9kvR6L9nYSXPvRhPbCEgzjf7FHawu8vcv\nCpJhQI9iN0WPOKncj8558sedu4PGs+/ek7Zx42Nx8KblnrTzPQ/0GBm9UBigtc/DdHh/B+4eN5zD\nKzWtowDMRyROf6mG0aSEtkZetEQgMwtU6pGCD4qAVNy0iwinuCiRfSdcPkrfaJ8C77nfQliRzT1y\nQDGo2JzJXYTKcgcp91BB5wJCMYaM2q3xhxPqki0ZdaETkJpMLKGrMXy5GxthyqIC6kvjan2wg4KE\nsOeyDYxucJPWOzGocQHscJNPtXYHzm2eaEroOuxZfh7VJzDboXCV0nxv2D+HRoUh92Ffh/M23+dd\n8AMTpN+V5z3U3tkwMjs8sJW53ljSdn13oNZ50Iem+az6/iEcCfCQGFoRFPbI95yrgtMDXtgaCcaJ\n8sqHsAyuxcB1GLPz3Fi3c0n4M3LP7qEicXeicM9SyWXzbuSW5V47GEK9Rf5ciJBnR9UgNuqko9M3\nMMpSYcZ39nG5ySCPKvXgcauJcbeFFW8Ih3WGOkOH+b9mp4d3MpS/E94gFp5gdvbWnT4mNrn+iQif\nf8f3EIJVYtImrBEeGjH8VplawssCSLLaZsh4J1nB51/6JQDAlew3YczSoNzqJuA/zbXrC/jK4iUd\nb04x1FcLLuGfDHgX9tf1KBISsp2PUUkecmbwB2NoK5pNDAUiVJGQJ0w/zCF56mx6cThDroxmfSjJ\nlYQ2oNybsRQ6I75jK5jFmS6NL60TR0dwVS2/GGS7TjiXpROPy0TNIh+USAraLsO7gwT36exUBMMd\nGkKVkQ6lQ5kcOZLIOeXOWQBK2oXx2b8uw4kg+L81F59lDrzQ+tK5qg/0+1zPXqGPiTjn2ZB2cKqz\nDEtqsoNeFUm5W25hHzmpyc+4ZC2VKWgOKuVK34IvKPjU190oLYgxaHGtjFYUmosyOaja0KV1Xt9Q\nUC4JnOUQsDvj764B4F/fOIVf+QyfoWsOTL9Gxf/9Ca77/s1/hPinedD91xcfwS+1eDh99XoH6/Pc\nG5uHGO7eKNRx7jY/V/+TbXReIYBL8vBF6H9AqNGbT3KNe/sO/H6fB9aho6fwWIJ78rszGgIR8ip1\njvvY8/tRvH/8R/zd0ghL67yuOXn9LThyBPf41hjaNhYm0X2Fz3gw5kf2NA3u/kUa4KWzWRyXO/S9\ni0fRi9OQdZ2uwbjCta34aFSU3wxi8UHpONQ7icC6XF/aQPoss6HdLeqgaZeFikU+tlIDOKTbUsiV\nwOtyd/yJCKsa0FhHS2QD3m2sz1O3PzbXxg3j6Biq7j0OQs0H42AcjINxMA7Gz3B8LDze+h1alcWV\nKGw/LcX+j2ron2TyxSO5Ia4MaW3GJUGnlsrB2aDdMFw2cNii91HM3cT9Pj6jEWMYOD8wEJ+k9byg\nL8Ic0BN0DdzQjjDxoyQN60/1/HC6OZ8tVf8IXSfTXEZrRrrVrNAbW9rvoyqe+PGz47sTuab4nVxR\nR0dCyW1rD1Xp3RnwqtjP0/PxLPB3g5IbLkU8ncsedIO0whIOA1aU1vFQFwu+G0E1RMs0MFqCZdGC\nzweAmpceeshBK9qr9hHP3M1a9sAOCYxe1ANnTeA1hc/hvgpVPD6jNz6M7m9H0S8Lukuda5EKbeCt\nOOd4/E4XuQCfN3/YxkWb4ahilaHSmcQcbkpv5EfcDdy8zjCPL9zHwJBOJh2uRbXxJrpeJldE9yuo\nm1sAAEfvLIwwPWVzl9/fLo/glc5V4WgR6yPJrG57MCFXC9IjHaV7hL+m7TYC3+Sabp2VJgHeM8jZ\n9GDqb2cxfPh1ztexi4sPE7w+cJ21ufHFBt4vMAHoK91lfD9G79fvtTCRl1psP3l30rWN73jpB8wq\nQYR2ye/p+Lfgv/Z5AEDmqCTxXDRxyMc9cP3oCG/WGEUI9l34sUHEq6dnHgcA1N642w3m74zoBELy\nJ3ecnnTLU0dGQvKuTBP7NUYyLN0H20X57GlS/76nwaCzijRUVCJyJdGuoKGz/jIxZNh7LzCNpPSe\n1WFClY5BSqmJ7hGGf1M5rsFWN4KwdB9TGh2oU+Rff1DG7JARnvxt/pwQGNW/O0aw0O9xjVtNaRgy\nsw89J9cNhgFFGtb7vF1s7tNLNaVXrhsBJCzKr2lV4RGsAMtqYDZNOjdK3CtxqwVXil5P1G+jKNvE\nZ7bgqkvCmEDD/r/svWeQZNd1Jvhl5nvpva/KMlnedLWp7kY7mAa6CRCGBCiCRqRIitTITWhWmp0Z\njXalCO3OyuyuZnZWK2k00ogxIiWKFiAIgAAJ3wCaaLR3VV3VZbNMeu8zX7r98V1od7azN2JiI7D4\nkfdPVWRlvXfPveece853j8l1csi3iFYlq3VoHVwTlcOKgl1cQ7Sr0MTaXekCgIeHSvjzZ9gLd/7k\nGq4e5vPkzme4JrsrWF6njHzd3sQ9LUKvbw4H8OA8+Wv+fRFM+KAOiVgIAKDZSSKX5LO+9bYHj36W\nDQ+2Fggjn7LlsXeCSNm3zl6G+0l6jfc/Pw7dp+k1v/0edbXK28IpkWu9dOmLSD31PwAAXnvOifb7\nd+81PFkKo7mH+spYXsWrImB0UgTNpqVBxJxECZIBEwZt1GmFjRGsbZMvbePU5VPJSRRXRGe60GvY\nsB4HAOzod7G98yDXqp/0lC7GEBTBoBnjMZQk6l2rN4dTo9QbOxGhgyznECxS9+9uj8AQI7KnHllA\ndmrfXWnrNj4SB69KlExs5AbR7DCs3TTfB3uEDL6aL2N6mgJX9nAj4sYWbDkyfaBgR6zNjW/2DaAo\nWpO1RMMgt9GALR9TTgzVClCistr3uIxUhYxjHGAyOhq3UG7xEJ6IF6EX1VPd40G4fVwufUd0JDlo\nAkT7tOxu90IMzTKVirM6gI6WMJHcasArijpEN+uIinSVAVHcwtkuQGfkHBcmIwjsUlhi1l2YKvxO\no0pmcWjV0JZ5NxJzVuCqUdkMyDrUS7wHa5SoJctJLeT9orxkAbCIJt6q7SxqMhWMLkgFXw3VEBEF\nFfY0u7OJJlFAIsA96G9SsZVuZdD0CKiv3w1NlcJQu9SCsp/v8Imay770AgwZzm1VW4bkE/de21pU\nBaSoPkd6VFMDiKR5EJZabuh2OCetegVhAcPKIip6SddCf4qGVimrhbLNva82Szh7k59bDpO2mrc7\njC5pd7Eq6sWmg6LLTWgTfhEFfzhhQbjMDinFtWsw2hlJrHEyMr1lK+JYhzy17YuhkiHs524UkfJQ\nqTwYY8nI+pNHMfeGgE6NAdRCvC+6Z+hLKD7Cz1fKQpEcPA7ta7y7G6scQdIrIlt3X8W+cULY1SXR\nftLX/eCVW0WoRA3xqjjQVB0TVKJLTlydQj1L4RkulJEJUp4sEG04gyk0RNEbfSmNqqiv3mzYYPDy\nkM4Kg0x25hHWk+f6ohW0B0SLQdkAK6jQwqJ8YkOfQ0K03Byy2hFSswWbhD0Iiq5aFSP3q6ruzpNb\nnToaH/SbL1NX7Cw1YdTxHY6BIpK7hCENRjtaooSor0Fdo09WsOESl59KA349113VdKK9Q2VfanHt\nNM0apDB/L1glyBC6oCmh0+Rdv1LiFYTJ5EKpQV6u1otohzmfVjKElJB7Xb2JSPvuh1M2VsB+UWzk\n5E4QMSch+sDiPwcA7Bp+A05ZRBQbOpBuM8WtOvQODjTJE98RZVudUQkngrQUvvdtPTz7ede/PPI6\n/kFhtPOnHyU/XNiIYfV18vfcx+1of19EsZ+yIFJ+AQAwH+G+Xlbtg36KOlxz9TvQXKOR3V5RcOmX\nRXnWf34nbfWdTyHUEKV8hxxwLNJJsXh5EBYvpJC7h+vvPHcdg32iHakuD8sBnh8rizyYt/t2sZVn\nsY2Oew8OiDv5ewb1aGRZw7mspk7VH5vGK0JPDl1axtScqKGfBKox8m34Op+vte9DWRjsqtwGmico\nx1ejXsy+K/T/z99JW7fRg5p7ozd6ozd6ozc+xPGR8HhbbVHIQZ9DWXiIir4F3R5aZyfaekSGCCea\nVugJql2jsDlpqbTiwLCdnmnaNAuNWpSdjBDG1O1XYyZEb6t/3oeiaHpftA/AkaTtYWzSE0rU++EV\nReY1HjUcMi17i1+GIyZK54m8xmZ5BAYBh9si1a60JTZpXXuHq7BG+Y4mzCiK3LuyK4z5LP+3aKLn\nkOw0YSyKEm/WIXREVxjvmoSEhxZ2x/pBucI62irh/RXUkERUqN1eQGEPLdpSkZakp+1H+jbXoWAy\nYFTDNVMGZEgiR7HjpkXtVAagNQl4036XaES9BTaFz6626DHUfRJcq5xbrpOAQOqgFMooXObcDCZ6\nIgvyIHwQ/S8jDWgbhPgSqhYcZhGRepBexFaritENzjGh7GLdK5CEZgLZXZqhJivnMLJjR7nJPb7d\n8mEsSku45mrCM8m9U0TjCcnQvaG6oeZAJ8/nDosGBSltFoE0eav2UBs7DSIYKfMIDlbJn6Oi+0n7\nphUhJ4M9cooNI2V6oxu6Moaq3PtvWf8bAID37BokAXXOLNcQ6yNtjUYW4z8SzUEOE5bdU1zEwl7R\nMeewBXNvEcJ+a6yIIxfIt9l5zmvrAzz9/zGikhr9oj91VMP1GKqlURKRwqaUA1bRP7iiUcG4w7WS\nnYTZdEoWWj/XLRmPw2kSczSrIIkI+rqFKIS3BjQKosGBswFJ9OstlTTIC+/VmyOkp5cj2Gnz6qae\n24VfeNVKxYSQQ3TamiCErd7urrp0uiD9pVQAACAASURBVDKkqChmI+DorKUARVxRFS4MwiDy2lul\nNhRRQGSnTN7LO5LwiTKSarUGqwI2Lfu08CrcN3uH619qJJCJct90Jiva4ndtfQM7Buo0k0CgooUc\nWgbKsTpdR6sqckQtaiBL77iuWKBR7t6fyFyT8PgRAbe+lEZ5hrpAmiS9P7t9Dr+lp6e4OVrCTflP\nAQAfCxzA6te5rsNOIlDnghuwz57kfL/oRjlE/v0d7z/FCzXybfxHjK7H/kl4T3I/JzbVuHyaa2/Z\nmMbKMD3h+x8i/Pz0Hyh47yFe+8Vsk9jO8rnj9iAqunfvStvQ8AJCVsr3SKEPl+e4L60CFciOuY2J\nDH9v7d2DfInrlGiqML7IK4lokLxn385gyM+/1/bXkLvGc0B1exqvGsnLx67RQ497K3g8SnoiUhWh\nSRb3sEr3AykGkbn3c79zuiZq18mr9bkM9FdY1yEyIiOvrN+Vtm6j5/H2Rm/0Rm/0Rm98iOMj4fH2\nSZxGabQflqbwGK4V4RSh+slAFUENLTmXhXcVF5zrsH+QEuHRwtQQ/SQVGwb19JTdJ0V/20gKxVla\nYZJnBYYbIiDCvAmtuJuL9NGiO5TvgyyaC+gMHZQG6HndoxjwumjxZTUGAQByoIzBG7Q282PdC9Ib\nRPWYrBJBUwQ2ORFDRVhZBqMJug4v5msd5pupnRboFd5BdHQmmIfpfejGW3DXaVVnW+LuyO1EK8w7\nTLnjQdlP6y9V3IFDIU06EQCWlIGyKADvVNmQqot0i6wWgSCDYipVWt/hlhEzoi1gvNrdPstoQtCl\nuCZ5cQdvkIzIRZjSoBQccNZp2Uf1CtRl3tEootnEoOU61o2k02kqoRajV1dBCqkbYg/tAgXIhVEQ\neanl+QY8q5zTjskMe4meyOIigzNkXxTqlrgLVG5jwyZSE+IyXCImJyySiEy57iJwWZ2EqyoqNt3D\nefW9rIZzmihA4dxFRA6xis1DchJ7stz/C99mJR9lZAqIkTd8rgDiGXoauxO3YE9+AQBwKkcPU5rf\nwtJ3REOKOTVy7SAAYPacCj/rp7c49CqtdtdcEMeq3NfLLy+iKnIQ7bf60TnB+br66X1Ml8bxr7vQ\nZq90AJGa4amQJ7PaFFrCq2wl6xg2c81WsquAnd5tpsx790BfAHifnufE9BCKUfKR0TsDS4PBZ7ug\nl5CqKeibEnEJF/qQcvDOtOMIQbPMe8WERJ7TrLRw3cc18ZQGMGAWrdv0cSyIXOCRBcr0bWO0C2UA\nSkBBSxmxtikXhXoblQJ5x6lZxXKN8jRQL6BjpHzqDfQkzfkWwmWiaqVKGbpREXexu4mQmXvU90Ej\ng7oGS6AcmpfSaLRFg4iShKKBv2fEfbOrLGNLXB1bzFpIDeGBb6igt/APzVoK2Xr3EqYAcDHqQ+tp\n3jf2d4bgvUW+73T+Jee7/R7e2OS+zKUNkNJEPF7e1uCTR/iO6yNM6bknMgbte9QV3vp3cU0ij788\n44V7kfJ7zU7emjfGUThAROCF5604coNyVnqqjuFngqRjid78ld9+Dz+okOhftsWxrOVaZu9/C7Va\n92pjAHBbWkFcT72wbHkVwQr1UV6hnM/c0CA2w73aN1+AIu7Qp759A51P8T7X/zrfm7pvEx4tU4DK\n6V2EQP1qkTrIRYg6Rn2MRbA5B7EiSnaaFRnRZ/mOkDcFlUyP1lCmF+xuzKC+IlBAhwGwEm1qr47B\nEeyOLt1tqDqd7kn2H+ZQqVT//0+iN3qjN3qjN3rj/8PodDp3t5z+b6MHNfdGb/RGb/RGb3yIo3fw\n9kZv9EZv9EZvfIijd/D2Rm/0Rm/0Rm98iKN38PZGb/RGb/RGb3yI4yMR1fxXP2THh4BVwo6OkW+e\nvAFxK6Mah1ItOERT+7DI1x00JhErMfJS75ER64jKSbt2uDrMHVP7GDno1+mRECXjJG8aCDPidWBy\nAOedzHncd4E5YKVGA9kg7ZFgoomoj1GlpkYVnRxjwIaKnOPN0TY0G4yucx67iY/v//IdtP3Rl1jO\n7d3tPOyieb1t4XuQpz4NAFjfNOHjx5gbqrcwSjFxZh3OKVaPuTUhw2xmVGmfxoCilmUXD6kZ5fne\n7ccwffrrAIDz3/RB+5vMF/We2YF5h7+n51lFqO+Pvo7USfa5Xd0fwbROVLnZCGDbwSjMtomRuJub\nKjx2g2uSmPsu/vC5O/OUf/tzKkRUjOZz1xm9aFMlUHKSraS0Gx/EzeVNDgRECcrtAc69b6UFlchL\nrml24DBzj1tbTmj2Mje0cp1R5SNDKlwrMiK2nTfAJYqz5xt+yBZGZKrSIh+yYYek8L1aF9AWAYd1\nxGGoMWrR2BTlJa0Z/M3X7yxM//QvfhZr/awMtOcco4gT/hysW6LRwy0XavdxrR5Wj+CKhVHdaxX2\nPXX+2AD/nMg7tszhtIMxF6+0VJBHGb3uqZMPx6Z/HuuXWfGqsXYLq4WvAAD2HbuAhIvPMFVFc4AH\nNHj9r7iv/6R6Fen7RPeclSD6O8wxNErMv3xlUMLCny3dQdvvf+cKlJLI2xaND6y2BtqivKEJG8jK\njISvGAqAiI6VFX631lFBYyfvGNdGUJJFibihEtQVfifdZH62QzOERolzHzdqsFYTDQrkcdhFVThI\nXN9OKQezTnQvMnjhTTLatGnVw9gRTTzUXP+G04zf+7WRO2i7/o1nkLGKCkkN/r2pryEveuzm1Ycw\n8IDocJbMoX6TUck2FXmn3a/BhmCHgN2ENTDroerNYmqNfJsxi2j0pAWnB0jnhetapPcy79itSyAW\nJh0uDzMzdmpxjIsc+kiogCOP87nJsILOCfZ1zm/YIatZcvSJh2fuoO1v/0U/VJJoDDPcgi3DSHdj\ngRHAbX8VxaKowtTwIGykPCnNAhQ/s0EKWRI3bLfDkeL6RzbqkC3kRUu/DIPora0tU8/tyhlIojyv\nyqJGQUO+b8U8kPXkZamf363FMtDoGQGt1bpRV/MdlkIDWht16S/9zp0R6b/6h8cwtUE9JzmTUC1Q\nL3hHyWfvaICHkuStuGsMN73cL497DsO3WOntpsiNdnpnMRI5AwCQ14/ioiiLes99xxD/6bOkQ5Sf\nLZ5wwvATRspnHLswzZFXs5EKdKPU16M/ZnT8Wb8BBwdJT0z1aeTW+N6seRwHTvzX5fF+JA5eRXQe\n0XnzmL7OKUUbKfiiFJb+mA3rh8kYgSaVQ6jSh+qwUK43GvCZhQLSJ6Dd5AbECkyPCJsb8BRZfi53\nUYb1JAVgZakJS4WH93WfSP3QdaCouek3M9fRNPIZ9p1xNI7yHenLorF6OYeoUEbhi8NdaVuUgwCA\nrz6ax/t/ynJlJ5+04o3iYwCAe6f/F7yW4xx+ZfGrAIDVvf8TfnadtD3V+A7O2/m52TYGk51FPM7b\nKeSeqe/gp39AwRv8xBBa36Qh4fSo8X6EynzWRkHY+VdPQGlRmSnrUUT1PLAWXCroRb3YwPd52OoP\nWfBGgIeUrvAkgO/dQZvKMI5RUU5Sped8C60anKKjS0I2I+ChgNhzBWhqVODiVdjdb8RIhIzeMHjQ\nTHHd1dMSSqIIB0a4lxF3Fj6rOFgCelSEEFobNaDGNALFR8GdjtmwNcD5mDoV2LdEbWPVAAx6vu+6\nmsI4mOjenFt7/T08uMU9mtv3DADgby+dQFG0IAx+1QHPBA+ny39cRG6Vwhmc4HpM/9osOlnyS/zc\nNl4wkY/MpTGU36FyPDVDGla2rsHl3wsAOFcs4X9Us23gay/PonyUaUhPjPFdz55x4KtBpmD9xBbA\nEz9i2tWbX7iFwCW++3qD/DkT0mKhC22GWg4drejQJVrzVas5lMUBGVGNw6qmgvZLMrb1VOBtUbRA\nrSugusv3+rGEiADO2lE33CYWfjFXxaFYySKv4aF5veyEBvzdZQ1DZaZ8JxT+HLBOoGoVbRDXbqHh\nI81VSxt5hfwwpHAvlWT3bmAFqwmVVaYeLo6RtqCygWaTfA1lAfY3yFOJ/El0Jn8K4P9KzxvVHEWu\nynedvfESpsD0m/6vhVFycY+9opZ2QcnifQ3X2q5Vo9NgSpKyWIU8SzpVCvl/JKdDvkU5rz0aQewe\npv08tz6GY8ssq+jQNtG4eLkrXQAwFDRC0YsStgUDYi52xGnURN12rQc6R5Bz6OwgsIc6rZO0oCpk\nSwrygKyU4/CruK+dIxJk0UJRF1MjU6MO0fZz7q7YBAqDpDkgReAuc+9TvhzSDq7PkRrXZts5gEqL\n/BBoV1HV8PBqDntQjmcEJXcevA84C7hZ5ncPLhrwn9zUp6MDTPtxV9so5Fgb+c3IfpwaFnXtNU3k\n06Jc8BiNlQAaWDnJbktNlwsmFff4zPkm7tlH3ri0Rd1o2p1F6TjrNtfP3Yu+ZcpmVCtB2+CaLHip\nd6oDGci1QwCAcvo5DOX43sGBbajfFcXL/8UdpHUdPai5N3qjN3qjN3rjQxwfCY/XUKAVm76ihToR\nAgCo9AZ0rLS41kZMSCq0kiQLIcLdkBr5Eq2sfb4SFjK0gKZUI8iN02pRLdGbicfGYK6IJOnhJWxk\naJkezY7jmrDmAylao+HYT6AJiYYJsgGmKmFaZcoNJGjFagdprTo6wNsCYgkUCl1p8w4xyfp9qw/y\nU+xg86q+hkuJf+AcSsexv0No7I0aoc3bT/4KtINMlN+6/lmUhfe25AnBs8W5Te68BwB40fcoPvZb\n9IIjV7Pol1k0f+PqNSzPkLYjKVEW7/t6XPgaaf/CdhrRT34JAHDP+TbCDpaJOzxIL+Md25vIn6R1\nHfc/Bbxxp8dr9mhR3eCz/aIhuN5kgaHMuXesesiihGBODZg8LNZg3UOvaDzTj6IoMOLMNhA/xrUe\nS3ZQHROlJEVhlGDFjhui/J7UKMF3iHyQ3UmiLNHD7ssShqwEVdCLJhINXQkbXlrSY+ooMka+YzZP\nyM4yqgJeSNxB25rFA6vC/fhf0+xusjezgLXXWDRjOmvH2v8hynMeM2NslPtvbPw+ACD59o9w+VHS\nFvAnsfcXyeNWZQ2uv2bx+sgYe5zOvJHDC/wIX26MYHuUexF5tIahMtGMF/6ONKbuVdCQOa+PLdWx\n9Bj/cf9SGLokeeIxx2e5Tpld/AAX7qBN3ZCRqxACrZS4jkmNBg5RIEJubGK3SXnIJJqwFvl5QyJv\ntApFNJqUsUjTBKuG3mIRFcRyXOuSVhR6QAMeIXuRhh8+iZ83G3Vk4nxeR5QrjbSWoTJwPlLfIMpq\nIjLV3SZsZXrCIT9hdoulezMBRVrD7rTosSuuhGrFNh600ov7cesWKqvUC65DKagM9GZqPyRf5Gau\nwv4gf/+FTj/+02EWT5h/xY0LdvJfn2CXow/JOHNVdDXb+xLaEnVQa/442tmzAABdk3PQVFU46mVT\njDeKo0i+T6/7RDaJqlo0aGiVMT033ZUuAAi1H4TKQ494tNWAc4D8pytS/hu5eXhV5OukbgbFXe7L\nHtmMNT11qTUnrt8GTMhq6SG25ZvQpzgfz4AOcokweEvhZ46xPPQhQtWKyQPTPN+rT/TDbyDft2P0\n/C3qMThS1GcpvR6yh3OwS/n/1+Igt2IBBIYo6683qwiEWQTGdVY0dTnlR17NrlvDsz68uUO9c1rR\nYdjG4jWRcaI4ysKPsTfDq7olbxTGKvXum3Y/Tk/yeQ94SM/Vn4XROHg/AEB16DVUclz/A7oFZCLk\ntcF+7vvB/h3UX6YX/Mn9s/jrDvXYvZoWAtJ/XQGNnsfbG73RG73RG73xIY6PhMdrd9Ea1UdtKPp5\nFxFXD8AwzuCBVqSMwVVa0nEbral9KRd+ILyzxuYG5nS0qq9EV/BB9S6dJOq1tS9idZQWVCBch6Sm\nlfueZQutGC3ANyXeKRwPTMAYF71pDTpoVmnhrLluYkrcK6oTtNy2DBUMufmyaGqzK215sBB56/UX\nMD33KQDA6tUFHJmn53nowrt4fowBT58ZZqBC+S8qmPlFmtVLxX34Iq8asflyApYB3gdpHuddw8m4\njEaU1pu5L4YLW7wH0Y1O4nOn/hgAcHvhvyPtg5fxqRjvBzdPPA2TCCgpztaRv0FW2EnyM2l+FL7/\nKDzM0jtdabPEWigP8juFqmikYGoiqjoMAPC0G9C1iBIMDRWQlWnRWkXPVn2rCtlNz0qlU8EnmmXU\nzHroarz3z1loMcc6HbhEr9aq0QpvnPtS7ZRhEZ6TVifaCtr1sFr5f9mGC+oc51ayHkVrQxQ+7+M6\nFBIf3Dv9l2Padxibe/gM59v0vCaecGHuIn9vhWrYa6FnunLhNzF8H++J8pZvAgD2ze3DoSUGX5yf\nO4bmOdF2LbmGAYX3tuP1nwAAXnpiHh1xX1SePws59LsAgMHY11CSPgcAcDwimnzkLkISjQ0CJ6zY\nyrLwvCaYQGnsSQDAm5d4fzV4/hSAH95BW/xaHm4zvT5Z9L9OdNTIpikLNp0Mf4FyWPJ7kQIte7tK\ntO+rdaCyc1/b9QrWm/ScPKEMtAbKiE2hR5wI+GAyc9/19SRSBcpOs3T7H8t3ZvLcd/ibcOjpbZYR\nAcTnfW4HVpuUySGL0A/xyB10AUB+RIa+RH44liFvqY23cMZI/m76+nBtLETaL5Tg1XFdEw8RRYjn\n9uKhN+iZ/qX/Pjz4ukDCZi/i56+QrxUX3+163QmH9wzX5N97kHiCCNGAJY3OFoN3kk6WEG1YI1iN\nMnBqdHoT7jU+43vHj2JGJq97bqbwqvPufV3dhbehoopAXTagbaWnJw2Qt1xKFnUH99OdMECtcL9K\nVhX2yaIUopX7o42robNSZ82UxpG3ca01mWUobiJTGtF8xV6eRXOYeqFTV0Nb5nxb7QKKeerjnEwv\nebAt4fY0Ebp+jQllEQC73snDp9fflbY+jYLC2/RiDw7psd/Geb4pfxIAYFrcxKLwmPvKt3DiKPdC\ne8aMtpnBaYG3GJcgjz+JfJQIabA1gHY/vfL5+Tbks5Sz1EnR7/y4GlMZ/l4w3gOlTnmKHp/FvjR5\nLnOFAYq3qo+g/zhl69ncPJwprvVc04J/sBGRefquFP6X4yNx8LY6hAi3VE3EhcA6ojtob1MotgfC\ncEtBfp7gZ7dGovAti6jHmQ5WxX29RtPCtlh0Wx+hH/twHbXbIlpaX0YhIwJ+HD5kVYyaGxFKO5IM\nYyhHjVAz69ESsPWBW33Y8lJhaufFARE3o71MyG7UfKgrbbOJ5wEAmeAnUdglzHYso0Km/TgA4O0T\nXpy8xYPt5c9TUE65nka5RGV3cPoc0s8/AQC4YroEb4YK4pFl9oJVu34br4T5++MHv4v5MdYBvpWK\nQrn66wAAy3SI871ZQHSOStIfuYGsEMziu23EHyEjR8CDKWAq4axoVO4eXQD++E7aan4nhpoUkISL\nCtFp92LoNg8sw3gHq3UKnr3lgFvE29RFRyiVqQ2X6KG7645guk1op2xbR6lCiHpWIzo3yVZYdHxW\nKB5DQUuhsIzUUPFSmPJV0SlKlUdbw/1W1S4hMExFUVEUzHoo/GsBGkzt1fqdhAHwyXGEztM4OmIT\ntXv1s0hNih6y0ZOYcXMtW82/xbX9hKAbohtLs7KN/R7Weh17IQndo9z78ayE15+iIhjbYGS7PbaA\nmXFGbL+uG8PHnqLi133jMRwS8Nl641Wuc+djMElcs7X3N2HcdxoAsHXzm/iKn8bl7j4qTmU9CoTu\npE3R7CDloWyoO6Jer1RFNEOt3qn50NCSx2Wlho6oe50tiR6yViuqde6LtdmBvU15SzgrMIu+tnab\n6NRjzGA7RTXjbqnREbW5y5omLFXRyN3Lz7T1MMI5zl0fsMClJp3RxQiG69yvRTWfu9dsvZMwAEOS\nCusRGqIzbsqrXLThRpxynmvL6KuKnrSN/ZBqlLPdMwxUmry3hktjrPP72IUN3NzD6xr19XFcEYa8\n0Uj4vtn/OYxlGM1qPOmAeZEHYGXwZ1BE1P1rZ7i+J04p2G0SEr69dj9MBgZajifsqILP3Xb1Q6Vs\ndKULAHL7h+FIkyfHfFZcXuS6my3UXZn2GFTiHS6TDXonDala24iKRPmcYIwU9MfCaO9wbjsWDdIF\nOjYjpik4drmHxv3k33AkBbuDxkzGJgNxzrdh8sLtowG3tcUHxx1mBPVc/2x1G5os9/awRYvM3bqc\nAdjdOou2lgGGzc5+vBTmFYrjGKH+SsWPUxny3Ms7c/jSC5SzhV+z4mFR9liv498v1bLYKwLELuhj\nsNQINR91teCfosGYS/IQ73ccRGmazoqk8uLSM5TJh9ZWENlhaKJpjMZQIvEOBtSs+e/RrGL2QfLv\nq1t5TMrlu9LWbfSg5t7ojd7ojd7ojQ9xfCQ83hshhowP1CSMS6L/qlkDWcDOLr0dm3FaOOEEIY0p\nUxsehRZ6490GOj5afYbcDrIqeigQ3qxx3YN8gFbaa0oZ3hVaZAnrLkKiA83eAOdgSfrQPk3LXn0z\ng+YSn5Vo7yDTpPfhUQhtWCodFI/RM9vdvDMXFADyNlrX6sr72DpIiNV8bxCal2k173Ptge0BBgLk\nf4PWbvrPnkM7S4jRsBmHXON8BvsHseujBX5j+AwA4N4ffh4h0Yd2KfkwlsHAr1HNV3Bo/7cBAOUa\nvbp06gHgkEi1Cn0VIwKiGX4gA/8lrlXmCa5zvfAJfG6U7zgfvhfAT+6gTfYHIGUJ1UrCq5GVPOJW\nrrVZZYO/w8+L1jqkAn/Xie4v8NZQEN7b3OYhKH6uoVQMoCkCGvQKn59vSuiIoI3pKT3WihbxiCom\nRP7phpFepUGuI2/lZy5pDokNkatpbiBiF12kmqTX7BsFcOc1gTrshsVA2DPrE15uNYFgh/PyHPpL\ntF96BABgPB3EySq9nY3rtGUveKv4RpXoxJf/nQnz36cX9bprB7+wTQu8eor7ti/qwO11/n20nsTG\nzb/gWjY/jRcVoiHBBnnvtf0pBMX1iNUwhns1RGTK+/xYSlN21KsiwOYzEeDf3UEaJFMGKQ09//4C\nPe282gGbyKHfbFfgq9LDgVKHz8D9iusojwvrLRyTyddx1S5KAsb12z3Qr4tuUiZelWjzOrhEFyxJ\nqiJa5lq6jUa0VOTFWpp/r+v80GvosXlSTuglPqvlN6IU5d66ivQk29nuHsbNK/3wWkhTdI17tT1Z\nRVkmD+hapxFeJPy7pNuAK0/+Mx5nyolr3QWscx2XAsexHeYejaWicMbodSedhG705WUs3CYfTu57\nC3GZV1jhwT1wNQXs/CmiLeFyFfVJ8tM9Ny7jVo5rejhoxhU953t7yoOR5a5k8Vk6FdR9lP9orAOj\nYOX2CpEetbuIaEukP1kjiGdFLnHLhWpJoBkTAr6PDkC1S9q9UhlDcggAkDVaYe6nvsgJVMPrMAFq\n8vWoZgkJLdGGtjIOlwiqkkb5LO2GhKiRfNJYt2FE5GjfHGxBSbTvSpvK/nk4R8jj5p8loHrkbwAA\nUzI930LldVzL0au+fzCA6iHC9tYrebxxiHtrEQjJcDWBip4IxpHjGbSi/HzTnkFL6IgJEYhpK24g\nIRBOfT2Brz1C3ZW37cdGjsG7LdHBzFrfj5W95M/hhV9CcZtzNBvaGLCcvitt3cZH4uC1pCmAqlQd\nO3vI9O5sGdUyFyxzrYy+Mbr1GjuZXyftYrculP2BNsoXyJDWe/RQFgjH3LhA+Nd7WI/U+lX+n0GN\nsorv6GQ6OLhKBVPWkbH0G2WoinzWeikJyzAXut4qw2FiDlgjRIWw2gxiLsrDQjNxZ6ECAPixikJx\nejWB9jqZMDClxsJ+XtxuvrWDEdEA+pf3kGEvv/jbODDPQgw3409gfOoiAKC6NIiTWsIq6eTbAICv\nzz6KxyqcY6j8EIZVVPbq7PN48xLvR/ZoCHc3ZjT42LcJtdzsvIhohXQ8Mh+A1UVFcfI675C/u7aK\nW2DU7eP3XsU3u9BWUurwZymEWisVrUrbgspHeKlV6GCqj4rnVqsIV5WCpQvwsKhv+CAFxMP6yyiY\nqMTcShutLIW0LvIOJ7YTWBJFKvy1Gia0PHxS26NQfB9c6hP2zzb7IO1SaWRbYThACBuOEqrbFKx+\nE4U4Wap1oQyYnKhgc5W0PV7nu8prRfx0gMq1vTiOT8nUki+MPYAXvs6o7z3/hHdpn3lnDNZ+rsOz\nf3Uc0QEaQZPpBxGfOwMAyMcZEdsKHUCr+rcAgKnX5pD9lT8BALgOXYJxicr6hRr56NfqOzCaqfgX\n/UPIagkvN2sBhMJczHGZe7x9Pd+VtrRiRbBCPlpUeEj7iw0UJK75hFVBRE2lY08YsdVHSDMWpXEx\nYMwjXuZeFXX9kLXk33rdjPIQjaNSgnvVL9dxW8iTwViDRsvfC4UlBDziwAiTDwtzVZgL3JdisYyU\nlfPXxXVQTDxo6xq+q1mydaUtcCQF92XO/VaZ36ltpWBtcC8rE+8h8AFMnTmPsjAmTBIVcawqI2Ym\nnUr9EubD5J11OQ+1T0RW13jl0dzUQz1GQ3fj6mFoh0X8wLvbyHVoYMxOMGI2b7yCi9+n/lCbVLDs\n58Hxo9ArePpjDwIANt+Ko3Vf9z0DgNFMA1eMfMZwogiVhXTUfSL6WAnBI4pX3HJ48TE951uVsrgg\nosUnDVxfrQqQRYvROkpQqYUcKw3UbdyXKdGaVNK9jzU7DcN6LIW2igedNKUgWhKHc4Hf3TUrMIgU\n68aUCiF3EAAwcSmHvPPuAGtHVqB5nTx1fa4N7yplLq4hT1o6QYwNCSj/igZSnXpuamgW15ycw9Ek\n/z8c/DR0N2hIxdd/ERk3ZeTwxSwqD5G2jRUaKNrUMkriWsTvtOKnCa6To/oWgjMfBwBczdMw159w\nAzcfBADMF78BRSE8/6ohBaN67a60dRs9qLk3eqM3eqM3euNDHB8Jj9dcpDVbnrShqqJnu2uahHuB\nnszYTAznt0XenkxIOBUfQ8BOz3b1BpCaCQEAotsuoCFyc+dYXnH7hg0TFkIPieYGJAFtmQ1OJH30\n1CZTtARTY16YnDTZ2ok+DIqmVTHtxgAAIABJREFU49paHnk/52Yy0ktQrZfQOkroohXt3pjbnSMU\nvXmggjkd/++KNg/tKwwa2Ht/PxLn6am9/UlaUJqlv8H7N2jxeaf+N6RXmb82NmmGqc2ouuithwEA\n6n+1i9Vn+NxP9F/Fvyky4GfEU4Bt9vsAgEiOkdUTgwpeGmCk7QOZPbi8Qi/9TXkD9Xf4f+kWoV3d\n/r14+n3+/vzFO0vzAcBhpYrkKD2y0aII8GhYMFjnmmXaXiwJeNjt88Co5vq10rTKW3tS0OXoGRVM\ngLZMyzMnN+Hx0iaUCvTe1Bo3hoy0bONFCxxD3C9zoYZmm88r5ujx9WnDiFXoySj6/ZD03GN5xwC9\nl3yULtLjG/Z3z78+H+rD7ASt5mdFBSplKwD3PsJSM7eyeFkEqpivRvH4HCPTNc9wLsuP+nHpCtfv\ncXkBfzLN9Z1UNqA20rta+zN6ek+d+iE27+c6/uTCefxqi171M85B7LO/DAA4VGPgSUZdxeoyEZu+\n/v8dF1/hd+/7pxI0r9PzSX+WcuFL7QCv3UlbLe1GVKYV36cm4lPqWAGBBKUyDVgc5EVF7cBmlmt5\nQKKc7hhsSAr4XUk54G2HAADZVgvWNp/nmuL/bC+r4ZwQgVbhAGSZ79BpZ1HVkjc0HvKvLbcGXYOe\nU1rWoS6LqnG+4j9mHzTdnENxsHvepD7XQFoEYu5MENKX9eeg1RJqXcgWMZmkR9w2ViBn6b1WnyGP\nrA9l4dwgvOyxj8Ggpkc7ZiohISJ3R4W+2hnLw56kR3xNuoCxFOXEabwAu4XrsBbm9VHGqeCIiIhd\nLzwMv/USAKAABbFVzk2JZZDK3dOVLgAo+qYR7HAto0HA5qHMDSf4WckwhaKRKMxYyoWk0IONTj/2\nirxqV4D/ozVqMaslWrfWTiMrk5ldlg4c8aRYN/LTxqQR7rPkF++eftysU3Ysaj1WNsknZiGvhZIC\nnfBsLXo1ag0+IzXih9+wc1faLMcLKMbJrIeNQ8gEiLZZ/p77PPF0H8orfO/osTSuuClDwUYC4zqi\naq8cIYIUeGYTDi892vgrZ5E6xNxn39ceR/T7lJfgF+kd2761i8Ag51VSAxMrDJ5qan2IvEeUdIqi\njWj8Oh4rMih20T6MZ/1c0181JaFfDN+Vtm7jI3HwrrqpUPvcdRi3udCesTJGylTKK2U/pArhVrte\nFGTQxrGVJ8OqVH7sVfPOKXY+hBSvTBFTuPiTjiquZMnoJfMk5v18X7i9AJOJYf/LRTJIPVtEx0Zh\nsTW9qDioKK6GJtFa5sY7DTw09TYNylvcyAHVYFfaPNsU6FZ4G5phEU1Zl7GZ4LOqszYMZynojiwN\ngs+bPoFolfV2z/sHoTbwsCw7nChuUpn4x8lMk39xABrBOIlQCZ/Vk7Yfqe/FF+O8U67uUqhiqhCm\n7RSUxZcm0D5KRRJrXcbEacJkUy9S6FbzQez+gqg/enmoK205jR4qkUYUkUQ9aM0iChIh5Vk0kNdT\nkSareXQ6ZPa2SEsJNNpo1Pl7WyNDEQaNsRpDVSYfqMX/WNt6DNmpfFXaJqQSIagtWQdJpASNjIg7\nwR0dTDbOS+NQoFSpYOKpPnjbXOtmn7iLNBi70hb/7D5ob4n7++TvAAD2948gmaACKlqcmDvOqM93\n1nfxFwdpxNw7RQPveiQLzy1ewtX/UI1Hv8m/jz8wioVN3q3v/QxTkFZwAposI5m/PHsM58Qc55/f\nA09GREYfIBT4Y70MtyjHWF+5jK0AoUz/zbMo/5YoafhDKuLW8buIt7GCxhr3tCLq2Fp9MmqiqHWk\nYka/lodLwqKFRy+Kd+T5d6+zjVqVstnW11ETsQ9Wixdyh/+Xj4qoaX0UclVAov5ttPT8vK9iQ6lB\nGWhZKHuZzgDqZVE4xT0Ju458rbdKaOWoXB0lvjdi7G4wRWJDqEu8yniwSsP7vZ0JbMbIAyO3gfZD\nPNAHC8PY3cu9WHmP0G9ybBFWUfO6lovhNcFzUzULyhlxwA3ws85zDkQf/g4AILA+iqJIu2q1clg8\nQuP94VukcSvvRaqf8xpZuonOQfJG/roH5gPkxY1DekzpuhcGAYCUnIFG5loaDVlIy5SB7X18x0RY\nB6NeQMKtFCDuta0WC3KdEADAVaf8W3LAiqjLbs+4YPCSV1oLRRTVPMBc4vpo7F0rws4gACARicIq\n7m3VbSAorgsaGh5COmzDpaHcJA3z0JaZcmcuurFduTtt3v+gwnY/9eOiewDTy9zv61/5EQDAjaNo\nmsj3m4ZnYJ5jpPHrb9XwORsdF4+dOjH0oAzHLdI5+fAuNNs0WsNv6eFepvxmX+YetuoGXHqXf3/s\nSAp6mXNPuYowuMhrcxvcH3najGcLjA3p17yBL5p5XXg2eQj3yncv9dlt9KDm3uiN3uiN3uiND3F8\nJDze7XXmXFl1MzglohXfvaHDqpaBBtbUEiyiW0oiIoryaxOwN0RuWT2ENRHBZ3nIA8cuPbmAgxfe\nO7UoBt0M0slls0gM8Rnl2D4oBULEATODYiq6DdjyDDCqN29ASdFCN9RVCKwR9izPBwEAqY6EgkKo\nylyJd6XNO0Qv5CHvZ/BnJRYzeMzy2+h8hSXlYhdWcP8wvZpci3NZvPgzXDjOvNBg4n0oLlp6lXM/\nRtv5KABg8xKh5uGH3kCrQo/PZZ7EikToy3frBYTaDA4oTtAaPb1TwM80hGgCx/2ILtOTbjvW8LyB\nnnRjD79rTN+COctghuFa94jtkk4Hq5aIwHSFsFZKcqEp8rJvGNsIBuhdBHNF7KTphbqc9IJv6bcw\n0Ob7tIoZtgAt4trtWagleg+ylV6WSZdArkSLWMk0oNfyua5WDc05WqHlNRE0Z1UjI8oNqhMytDLX\nb1AqoWIk1CbipaAvdi9j1/+d16Gd454Ot2jZhs0VTK5yjsc+U8HPXuM7jvV7cPUG+eeIlZ5tWn8D\njs+TtshKENUhogeL55pIH+Q8DUX+fe18C1KQXuMP7BM4ZXgTAHDTeRMxN2keHiMvZ94ZRP8Qr1tC\njuN4TKZX0jSOY/d7XKumm8Q1klsAFu+gTSpU0XETGmua6N3J4TwqMtGSpqRFZ5PyYnOpUBPRxbY4\nkROHoYlOjTJUsGghuemdWDMx5HL8rlkWmQEWO3Jq/p9XJSG9y8/X+uMYTFPWVaLkp7FkRUuhJ563\nl+AQDRVCMRfmDKJzV1kUdSh2L8ZgHk7BK6KSi0eIwnS+/yM8RFFAfHIPzgodM6pIkDbpLZaHyeOP\n7VawNv4iAEBzzQNjhJBnKaPG5Bz3YPclenx1TRy1/0g5dX3pMjJvCGj3U3aMv8d3T06T5wt1C/IF\nUfRhjx/SddL54PgWNl4nna0BP8qeWFe6AMCccMPlppeayJign+GzR9rkkYKvg3KKiFW1LMFl496W\nVEUU3fTkiuvUjdGBdXjsoniKPYeRGPdw6+gUpJtEF5NmBnIZXQPIigwIra8D1TXyl1qq4HqD+zJh\noP5tWT2IHOTeWC4vw6QTubuOJnzhzl1pW9wzhgdErnbiXQXhI3zer+aISIViNWT9RC1j5TYK3yNs\n/+i8Hy+JxiUnvkPduLPPjObH/xoAUC8egt0oGm+4SmiN0ZtXpoSuSMURkJmvezY6hvv76bk+vy3j\n/jHO97nTjDq//4V5NIapu9S1L8JSphy2d7ToOE/clbZuo+fx9kZv9EZv9EZvfIjjI+HxHhmmFyJt\nrOOagb/nKlsYE63JrPVJlF30gCxL9HJrHy8j/zr/HmwPQ5+l1RjV66FV8fNdEeBwZcuL8b2873CE\n81BVaNnX5BUU7LRUDkXp+awWJKT6RRnBNlDfoaXXdyQGlVrcy5ZpCVp1aVgK9CgSJ/zAn99J24zM\nAvsvvpyG/X7ex4XqF2H6ZhAAoP4NFzo36HlCxcvp6B+dwdZ/S09m55NTeMTFexuP9wzOJmj1Ff+U\nFt/Jr5iQ+fJX+X/FZ1A8zL9/fvMgIj6WP9ut8K77275lGC20jvuMb2F7moEY0xuH8asrtN5++uhv\nAQCmai9hYJPvsM+fBl58/g7arFkJ2UHeOYWFV1Rq6zCR4/qG4ibENbRGZeihddHKLNVJe2ClD50h\nEdiwW0MzxTurii+EQJ4ebb5MbynttiNZFb2Pj2iQEgFPqh0N4kvkmUqQP52NOkwJeh9aWwVrIv3D\nNgTURTOLiki98bm6V0AyaSKIp7jfxXnRxGNuEvd+gx7HP6jNePBr5LPtn7Tg8zB46ntRBrKtzb+I\n+QLXN2M14MD1pwAAQV8Sr4teowY31+YR31WkCmx+cTb9DG57iT6MD95GxEmadpbIc5bBIcTFXWwn\ndAmFDu+3BrxA3Ur+0TnZLOHQ2nKXgpFATlVHwMZnGNv06gs2HxxNehdV8wp2LEQt7JoarLwug0Z4\nNbGcF5VBytuUpQAlxGel3B7YJXoH9Q49A1tcD4uR6+huqtHy0/PqhNMo2ull2Tz0QKulNOxx0Uau\n3kR1l/MxGRXERWlLVYEoQ1nunnbzyLs+/InMgCjtWZGK8oX9iP4d/35+YheaFcr0tmkOZoX8Zdvg\nz+9l3Pi4XqSJHHRhpkDvL6NL468TTE98LE90wqX6GUynKafJ3XswpWI1qjduNHB0m23pzsuM2zAN\nTME8SRlbaSRxYIW6KWxuwvAkaVuRgU/YZrvSBQCdegNZEfxY3DOHRodrsLtLpGxU60JO3MeblBra\nDq5DztyG4wzpi0xwv4fQwE6W+zYq29AB90WztQlzH73xelqgGt4V2FUCRYlp0Hbw/1aTbfjNfK4q\nTH4xW9rwvkMvd7uoR93JYMNwWsLJQeWutE2cjKIqqpZZM06UNogUnvFSL2/ObmK6xHvxsdAaqi56\nrqrCFdguUMeemiGqJKurcF6hrr18QMEJUeegfc2NKyepx6Qac/Bnxu2YHxP53iOr2MoR7TQ7U3j3\nGs+GfT8QcR0HytDfZMDVtfVZJPYwx372wCNo5Lq3F73b+EgcvGURNWluatCgboa0CsRtIm9Lu4OV\nIqE867SIjIv5oRols+SVGOIiz8+o1iBW4++BC9yck3MFKMsUlprGAX2QwuDzmqHJEj5KVgRM5yuj\nI/rxtlrbMDipqKPyHLIiEvQ+vyjx1rqBvJEwsf+t7sEe75U4X+kTR3DowhkAgDvwSZz7lzwgn/pG\nAj/9DJXyQ2YG26y+fALzv0uFmnvrIAZkKoq36/fD/+tUJidfJjwS/k096n7CI0NXpqFtcY7f25vF\n10SAS2yJwvpbpqP4t5vsXKM1/QaOWnlIXP0FPTrP8aB6PEnF+aLlR8hVuX52fXeBKU9qMFwh1JSM\ncl59bhdyDbKVZziNnQoV8Igpj0qS8FtM+wYAYG+zH0ahXNvjddRiDNQommvY7HA9HWo+qx4xY0Ao\nleiOCd40D6RSeQv5Ee6XNkyFmTG20YQIAKmo4Fb43FK6CleT3xnwk0fU+vGutO26VegXhpbKwUNV\ne34Kpc9T0Xzi9/5nbPRTSQaenEZuSQR2GZlULwVGoL3C72bDA1gVUKmhr4bDUa5JSsDzGOzHczmu\n332Zx+CQmaN962IMrn2EfNcPUCH83PYrOG/nZ2seCdYEfx8rWuEDDZNzLvLk1u2TAP7gDtpkuwa7\n3GZo9VzffpsKEXEIjfv2QpsR698ywKKmbNysco4BTQHVAg2XYsqJD9SIrLSg7tDIM0E0m5+QUBbB\nYvGmFT4hmyp3EzFQWamb3CtfxYC4gXulzy/C6OV787kiXKLec73N72bNjTvoAoDnDoXh11EvWLeo\nUDWhGKIjnPtQfAbVpmiyvhnFu9YgAGBQBPaNOqLYSXN/jvy4jYSPBo9N08HhOuH558uc16QWsFyn\nbF7SDKDY4DvG3A7oC/y8KWjTJG9B3SKvyad1qNgp35Z+D+I3CDwevMeBM0vf7koXAGgPJzC8yQO7\nsr6FtkRDyLpEudC7o9AYeBhnFBtuR/jce71GZGYpL40Q96Va0cDoIm3lShFZO/lTpXMgGSF9dbuA\nvev9qIsrJY89BwcoF3FYoBNBVQkb97WieFAx0sDQdvxYcIjCG0YjzhcG7krbK+/68fh+0tHU2nG/\nk2utJOgI6E/8PSZ+yPW9cOQ0dNcYOBo+MYGpW+Sv12zUL1brIt6PUXdN56N4KUBe/ngoiJqK58Cg\niEw/94aE+08Rkne+X8aymnrZ0XgEW17m5rvSNLh2vlVFeD/fsW8kj/gVZpvUT+gRM98dRu82elBz\nb/RGb/RGb/TGhzg+Eh5vRxTd3g3dgrKXXm51tAW/CK4Y0GvQLy7eizFRnN2zgriGFpROW8K+KuHE\n5YEsBjP0UBwH6VGYJ1WIm+n1OaJWlJu0iPsbRdzM00uYFgFBq4ofJtEX16nzwX4gCACQb6cQFJZ0\n/iatWWd1AlqLgJpMpq60XZ36KgDglwo38doM6Zm79m28FxX5aT83iyfstEavNxhUc3XhD/H4Bi0o\nTf0iLgY4h/lWBjvvMyXh70TVlH2rbWS3CLEsP7CA1CV2QPpc+CDOpenWBGY43/+wMwbNAfaLPXbx\nMxhRcT7rSSva898FAKTM9Fg+/sKX8FM1IXDn9rWutBkLGkTahIdKVrJSuxiHpcW1UG/WIBv4joLH\nDkkhBGVvMxUg42wgGWdKmLzrRFFLJMKdsMAs0olqTVroBo0GG6L6kJRqYE30cA33aWEVgT5ZJ386\nbmsQHSUMbN5Wwx0QwXtmExI7onuLi5Zvq9qVNBgnfJAbnM/iGdLzoO4tqETO4HP//TQOnKfH8Gox\nhIcl8uK6nelpvp/MIaFhOstcRYeC4Kn31g9Dzv4brsNe7uGlyBdw1M6c4UGPDVc9ogKVuoidBuc7\n9v43AADb+haaSa7pfcoMXm2xytVa8EEUzYTl7t2gx5s+vQKcuZM2Wy2GHR3X16nmHLL6JgwiuKqR\nVEFt5OeqfA4x0bBiPkivcFcjoe+Dko2SDLOZHoXWbEetyO84NJyLLleCSSbyklQc6LToAdoGfDBm\n6ZEuVUUQpUYLf5PrFNU6oKuT1421PG5fpbdj9NEjNkS6q65B1TbieXooZg+vp3JxHSZs9Jh/MFLD\nSFJ0x9JswL9J5Kg5LeYQ1eI1IrQ48HQT6g3K5kqsAslHfmgN0usZyk1h00Pv+KC7CO+7/NwWUuPm\n/Zz7nrNcx3T7bZjPEbWru/U4r1A2Z0xNOOuEVZdXjAhMPCAoubPWZz6xB+tOUX5zJ4yMl4iLfYTX\naAtNFVICeTowW0c7w/W9vlkCfPzcM8F1yySskBL0TJsOLfIR7v0RfweXy/xuf517GQs2MFCm9xuR\naojXCMHqlYvYFnUOilGRi+3VIyPz/zWtOvq3Kb/50ToG7HeQ9I/jE6oWJi6xWcGlfcvIhtnn58Y4\n7++0L4/j2b30RvsHnoKVKg31cytox7m3KQfRH7zZj9EJpmPWIscweJQyqV9bx2iIk7jppIf/pPcQ\nLmkpY1bPEDIZXtPs2xrGYB916ZUMS4y+PSzj8Rnu8bU3lzEjmiQEc6dwPblwd+K6jI/EwRsoi5ZT\nVj3yHlG6cCuI3TwXZDmggdwSRStEs2lVcQD+mqj1qtEjKpqsBzZHkByhohwV7auuLPqQaIv7TLMT\nA+JeLLwRgH+ICviCgEqGBixQbZNhVeUt7IguFhP6FNQKn1c38l3qTgXhPGEOs747eHDgu4Qe45+Q\ncfq8qA888q/xicM8ZD3hAuLbzKFdKHOj/9nkNZjTVIzfeNOJ9jgVfHAoitYWt+z+6/y77feT+Pwt\nRjI//+Y0DONBAECq8oc4kPpLAMCmOPzKE8/gAd/vcc06NfyVhULTd/snwDtU9s3jY2Id+hG8xnft\nHj0I4MU7aCsay7BWSH+lQfpll4RSi4JXc+6iWid8qVRq0Ie4xnYt4bL4SBINcE0nMjW0RBETZbCE\nnIjehJ5rvZgJYaDG55Y6TmhkQlymTB5pUU7RYiDvGEdqcNSoMFNqJ5QCf7fKTWj6REGUnFgTTfe7\nmbGrVdzUUUHf42ME+h69F2VQOVx/ZwzXyiEAgGSaxHkTjZT7xF3v7dsXEZshtLjnB2dxeZTQl/aT\nWxhY5/1T4QYhrvTtVTxwijDbDcNlqH/EO7b+fX3wiIh1lTjYm84IQiHmCv/noBtP1L8MAJittrFV\nFofhELXSyIXuOcpbKQn9op62BMHLGIS1TNpgH4WzIwwXqxvNNuemKVBpTTXbsFEMkdUY/nEP69UC\nxNmMhkLDUbGrURdFM0ymADorVFY1bRYmM+8m94vynzp1C5mkyEk1VpCy8lBT5RQ4TOIOapEHRN3Z\nPRrdv+MBCucAAO1TvGddfnUTuzpxR+xehkG0BXXYzZCKos75MucwIb2IJ46w1GpxZRWSX9C+roPl\nJnlOJXJ70+HzuGnj+v1xq4mLAl4v7Mzg9vPCUTBxbe6f/AoyUzwg0so2DghIfd+iDv0PcV31Vwo4\nsu7oShcAZDc30dRxvuWWAcomDUpJFLrQdICGheu7eG0OviFG2O+my+jzUaetiNiJCaRRF3KYUa/C\n7eKBEork0RCR9MttrrURBZxXBP9uZtCy0BBvG08i1OJh6BkV7TLzt5AXWRi6ih41cb86tKnHmv+u\npGEwN4WfnKD89//QiMS9rGXfCFEGY9pbGFziwTpd/RautX8NAFDpyOg3cJ6Vvm8AAEqB47jt4b2v\ndmMVfT+lTltQ7cVUkAZPbpvrf/WeS8iIaHNVpIxYUtTCPv1dHEpTfu2DdA6eVAbhHCDPlj8+g6VF\nniOqgWu4J2m4O3FdRg9q7o3e6I3e6I3e+BDHR8Lj7VhpNdY2BmF+mRbS7uw2GhK9h4aUxqTEoKHr\nwkKf8hexIJoV+Bx2+Dr0JtUDQYxn+flig17u0LgbrTVaOKp+GWXRfCE32kSgIKw6o6hOciEDS+SD\nQJgmdkV7TGnMj0qVHsVuWOS6pnQoico/RU2lK23947RG2+7T2JknfJLL/RWGt2lB3rY9AVOVHm/w\nMi3Jm/r7kbJxXnu+0IS2xeovt1f8COwlJP69MXph+965gVSJgTcZnQVqhX1btQsfg97ybwEAb07Q\nEjeG5uESwSB//rsLeOrXvwoAWEi8g0eOsafvs8OM4mw7P4d7/fQ43t9d70qbWS1jW3SYGRFVclZX\n1Cj2i7J0W2MwiuAoxSfDKqAmxUtLXC4OAxLX8h2NGeOiYYJaXYVFeNAtYXUb1B7s5Gnhd2xVmEv0\nVLXRGjp7iHB0CtzXYjsOSc15TVrDWDPxxXLbgpZalDrUiQjqRLErbRjJYWaRlr9KxSjjzblNKAXC\nVif2lNDJk2cuVpfQEv1/E19g4I6+qYVnndb69oFrmJcYAJf79+MICmjQHmXgXfE31fjht/mOzwzn\ncNlKqzy0tR9aiwim6+c6ns9qERay8ItFA2pFRtWf2XwE7/ezscbn8+wpGh1c7UqaXa1FukrPZ1L0\nbw3LgMNBC7/UKcEropptpiLaZn53WEQ9y4Ys8pJYU62MmrgC8Hv1kEWCtMrAv2dNHczmeB1TKreg\n8/BZRY0RrQ+aGMjkr3LBDJXoMINWAj7RaGHVoIKuLBCKEc7BdZciSO2DA6jd4nfT4grG84gPkp66\nwL84jHyHsrp6rwl60U0qtkUX3hP5eVwXiELN7ML+21zrnCTjpkJkrrVBXaKbsCNZ5l7+ZHMH0SlW\nQNp2bGP/FvlB9KCAekuN0ZP8bGR4F6llzvG9YAp9GXp6wQNW/HRhqzthAMyNBnICwk/ommg2xN6J\nwnK2d1swWTk3szGHiijNajTIUNeInLh19Ng2c3a4BwiHt9RGNMXV15onj4LC/QwK2cimvCjYiCa5\n8jUURP9f+/Y1TBjJn6sRyrHGMQgpwf02VVuwin2qjvXBneze2AIArkxfQv9Z1iYIeL6PkQsiYKpF\nfnj6i0P4+3Wuv+ndp/DpSXrEK2EjXjtBlCD4LPdSMcVw8iB1ddppR2uMlQY99Xvx/Cb3WTNBZMC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i2cCKBzl8I5opqAfUBMWSr2wdjgJTpbFGGQgX3YPk883Ycb8HlFBUPIgh7q2Cj6eHHX5rT/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\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FRJEQfW9XfIx8J2VSaAZEkln0BAkTkytdjSLiZ3lFZbD9CNlgT7YAAObOBHxyRoKB9r+G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\nhKrfx9oWI8/G+50j3iM+Jk7di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"text/plain": [ "<matplotlib.figure.Figure at 0x11a0fb190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils.vis_utils import visualize_grid\n", "\n", "# Visualize the weights of the network\n", "def show_net_weights(net):\n", " W1 = net.params['W1']\n", " W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n", " plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n", " plt.gca().axis('off')\n", " plt.show()\n", " \n", "show_net_weights(net)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Tune your hyperparameters\n", "\n", "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n", "\n", "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including **hidden layer size, learning rate, numer of training epochs, and regularization strength**. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n", "\n", "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n", "\n", "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can, with a fully-connected Neural Network. For every 1% above 52% on the Test set we will award you with one extra bonus point. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 1 / 5000: loss 2.409961\n", "iteration 101 / 5000: loss 2.158140\n", "iteration 201 / 5000: loss 2.009294\n", "iteration 301 / 5000: loss 1.979639\n", "iteration 401 / 5000: loss 1.942002\n", "iteration 501 / 5000: loss 1.827258\n", "iteration 601 / 5000: loss 1.835251\n", "iteration 701 / 5000: loss 1.704696\n", "iteration 801 / 5000: loss 1.769029\n", "iteration 901 / 5000: loss 1.675095\n", "iteration 1001 / 5000: loss 1.689062\n", "iteration 1101 / 5000: loss 1.786561\n", "iteration 1201 / 5000: loss 1.822278\n", "iteration 1301 / 5000: loss 1.721067\n", "iteration 1401 / 5000: loss 1.730675\n", "iteration 1501 / 5000: loss 1.686564\n", "iteration 1601 / 5000: loss 1.544546\n", "iteration 1701 / 5000: loss 1.619521\n", "iteration 1801 / 5000: loss 1.678234\n", "iteration 1901 / 5000: loss 1.666344\n", "iteration 2001 / 5000: loss 1.637763\n", "iteration 2101 / 5000: loss 1.687455\n", "iteration 2201 / 5000: loss 1.479651\n", "iteration 2301 / 5000: loss 1.572565\n", "iteration 2401 / 5000: loss 1.578272\n", "iteration 2501 / 5000: loss 1.612838\n", "iteration 2601 / 5000: loss 1.767758\n", "iteration 2701 / 5000: loss 1.624772\n", "iteration 2801 / 5000: loss 1.643853\n", "iteration 2901 / 5000: loss 1.544022\n", "iteration 3001 / 5000: loss 1.699063\n", "iteration 3101 / 5000: loss 1.603541\n", "iteration 3201 / 5000: loss 1.684917\n", "iteration 3301 / 5000: loss 1.572763\n", "iteration 3401 / 5000: loss 1.582653\n", "iteration 3501 / 5000: loss 1.643857\n", "iteration 3601 / 5000: loss 1.476511\n", "iteration 3701 / 5000: loss 1.603652\n", "iteration 3801 / 5000: loss 1.518479\n", "iteration 3901 / 5000: loss 1.536813\n", "iteration 4001 / 5000: loss 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1.570371\n", "iteration 4201 / 5000: loss 1.623433\n", "iteration 4301 / 5000: loss 1.589593\n", "iteration 4401 / 5000: loss 1.700008\n", "iteration 4501 / 5000: loss 1.610730\n", "iteration 4601 / 5000: loss 1.647724\n", "iteration 4701 / 5000: loss 1.635629\n", "iteration 4801 / 5000: loss 1.684117\n", "iteration 4901 / 5000: loss 1.735978\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n", "\n", "\n", "best val_acc: 0.511000\n", "\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 7.000000e-01, train_acc: 0.500429, val_acc: 0.485000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 8.000000e-01, train_acc: 0.500510, val_acc: 0.482000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 9.000000e-01, train_acc: 0.494612, val_acc: 0.486000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 1.000000e+00, train_acc: 0.494184, val_acc: 0.487000\n", "\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 7.000000e-01, train_acc: 0.533082, val_acc: 0.495000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 8.000000e-01, train_acc: 0.529490, val_acc: 0.499000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 9.000000e-01, train_acc: 0.523714, val_acc: 0.489000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 1.000000e+00, train_acc: 0.514755, val_acc: 0.487000\n", "\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 7.000000e-01, train_acc: 0.530490, val_acc: 0.506000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 8.000000e-01, train_acc: 0.527694, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 9.000000e-01, train_acc: 0.517122, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 1.000000e+00, train_acc: 0.520612, val_acc: 0.472000\n", "\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 7.000000e-01, train_acc: 0.518082, val_acc: 0.511000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 8.000000e-01, train_acc: 0.516796, val_acc: 0.491000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 9.000000e-01, train_acc: 0.505898, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n" ] } ], "source": [ "input_size = 32 * 32 * 3\n", "num_classes = 10\n", "\n", "hidden_layer_size = [50]\n", "learning_rates = [3e-4, 9e-4, 1e-3, 3e-3]\n", "regularization_strengths = [7e-1, 8e-1, 9e-1, 1]\n", "\n", "results = {}\n", "\n", "best_model = None\n", "best_val = -1\n", "\n", "for hidden_size in hidden_layer_size:\n", " for lr in learning_rates:\n", " for reg in regularization_strengths:\n", " model = TwoLayerNet(input_size, hidden_size, num_classes, std=1e-3)\n", " stats = model.train(X_train, y_train, X_val, y_val,\n", " learning_rate=lr, learning_rate_decay=0.95,\n", " reg=reg, num_iters=5000, batch_size=200, verbose=True)\n", " \n", " train_acc = (model.predict(X_train) == y_train).mean()\n", " val_acc = (model.predict(X_val) == y_val).mean()\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))\n", " \n", " results[(hidden_size, lr, reg)] = (train_acc, val_acc)\n", " if val_acc > best_val:\n", " best_val = val_acc\n", " best_model = model\n", " print\n", "print\n", " \n", "print('best val_acc: %f' % (best_val))\n", " \n", "old_lr = -1\n", "for hidden_size, lr, reg in sorted(results):\n", " if old_lr != lr:\n", " old_lr = lr\n", " print\n", " \n", " train_acc, val_acc = results[(hidden_size, lr, reg)]\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hidden_layer_size: 50, lr: 3.000000e-04, reg: 7.000000e-01, train_acc: 0.500429, val_acc: 0.485000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 8.000000e-01, train_acc: 0.500510, val_acc: 0.482000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 9.000000e-01, train_acc: 0.494612, val_acc: 0.486000\n", "hidden_layer_size: 50, lr: 3.000000e-04, reg: 1.000000e+00, train_acc: 0.494184, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 7.000000e-01, train_acc: 0.533082, val_acc: 0.495000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 8.000000e-01, train_acc: 0.529490, val_acc: 0.499000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 9.000000e-01, train_acc: 0.523714, val_acc: 0.489000\n", "hidden_layer_size: 50, lr: 9.000000e-04, reg: 1.000000e+00, train_acc: 0.514755, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 7.000000e-01, train_acc: 0.530490, val_acc: 0.506000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 8.000000e-01, train_acc: 0.527694, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 9.000000e-01, train_acc: 0.517122, val_acc: 0.496000\n", "hidden_layer_size: 50, lr: 1.000000e-03, reg: 1.000000e+00, train_acc: 0.520612, val_acc: 0.472000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 7.000000e-01, train_acc: 0.518082, val_acc: 0.511000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 8.000000e-01, train_acc: 0.516796, val_acc: 0.491000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 9.000000e-01, train_acc: 0.505898, val_acc: 0.487000\n", "hidden_layer_size: 50, lr: 3.000000e-03, reg: 1.000000e+00, train_acc: 0.496857, val_acc: 0.477000\n" ] } ], "source": [ "for hidden_size, lr, reg in sorted(results):\n", " train_acc, val_acc = results[(hidden_size, lr, reg)]\n", " print('hidden_layer_size: %d, lr: %e, reg: %e, train_acc: %f, val_acc: %f' % (hidden_size, lr, reg, train_acc, val_acc))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3aiO5xybo0DMEfdxFKN/K70duCJNkOIUyQlqXQ2VNbxMtUSey3qUZYGTLfp+P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8ds6zRJx2R2fUgS5DFJUP4SHQkpRZZiRnjYYT9zWxwwiyS6y/oS2wN7IvUKHdVZLEJj89UM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\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hocvk70/CEKD+MpQSkyu52MfsJ0dpAseTf47zAg3dekSngHs3KlrQHM5rg2D9/AK2keFP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\nbeS9ttsVNo+SYcdhiPfvJMN29HywEycIHdlBvi5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"text/plain": [ "<matplotlib.figure.Figure at 0x11e31cbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize the weights of the best network\n", "show_net_weights(best_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run on the test set\n", "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%.\n", "\n", "**We will give you extra bonus point for every 1% of accuracy above 52%.**" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Test accuracy: ', 0.504)\n" ] } ], "source": [ "test_acc = (best_model.predict(X_test) == y_test).mean()\n", "print('Test accuracy: ', test_acc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

sueiras/training

tensorflow_old/02-template_class/template_class.ipynb

1

8425

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Standar usage of TensoFlow with model class\n", "\n", "Tipically use 3 files:\n", " - data_utils.py: With the data access and batch generator functions\n", " - model.py: With the class model. A constructor with the graph definition and method to manage model needs\n", " - train.py: With parameters. Access to the data, instance the model and train it. Optionaly add a parameter to train or inference.\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## data_utils.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#! /usr/bin/env python\n", "\n", "import tensorflow as tf\n", "\n", "# Access to the data\n", "def get_data(data_dir='/tmp/MNIST_data'):\n", " from tensorflow.examples.tutorials.mnist import input_data\n", " return input_data.read_data_sets(data_dir, one_hot=True)\n", "\n", "\n", "#Batch generator\n", "def batch_generator(mnist, batch_size=256, type='train'):\n", " if type=='train':\n", " return mnist.train.next_batch(batch_size)\n", " else:\n", " return mnist.test.next_batch(batch_size)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## model_mnist_cnn.py" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#! /usr/bin/env python\n", "\n", "import tensorflow as tf\n", "\n", "class mnistCNN(object):\n", " \"\"\"\n", " A NN for mnist classification.\n", " \"\"\"\n", " def __init__(self, dense=500):\n", " \n", " # Placeholders for input, output and dropout\n", " self.input_x = tf.placeholder(tf.float32, [None, 784], name=\"input_x\")\n", " self.input_y = tf.placeholder(tf.float32, [None, 10], name=\"input_y\")\n", " \n", " # First layer\n", " self.dense_1 = self.dense_layer(self.input_x, input_dim=784, output_dim=dense)\n", "\n", " # Final layer\n", " self.dense_2 = self.dense_layer(self.dense_1, input_dim=dense, output_dim=10)\n", "\n", " self.predictions = tf.argmax(self.dense_2, 1, name=\"predictions\")\n", " \n", " # Loss function\n", " self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(self.dense_2, self.input_y))\n", " \n", " # Accuracy\n", " correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))\n", " self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n", " \n", "\n", " def dense_layer(self, x, input_dim=10, output_dim=10, name='dense'):\n", " '''\n", " Dense layer function\n", " Inputs:\n", " x: Input tensor\n", " input_dim: Dimmension of the input tensor.\n", " output_dim: dimmension of the output tensor\n", " name: Layer name\n", " '''\n", " W = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1), name='W_'+name)\n", " b = tf.Variable(tf.constant(0.1, shape=[output_dim]), name='b_'+name)\n", " dense_output = tf.nn.relu(tf.matmul(x, W) + b)\n", " return dense_output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## train.py" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Parameters:\n", "BATCH_SIZE=256\n", "DATA_DIRECTORY=/tmp/MNIST_data\n", "DENSE_SIZE=500\n", "LEARNING_RATE=0.001\n", "LOG_DEVICE_PLACEMENT=False\n", "NUM_EPOCHS=20\n", "\n", "Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz\n", "0 0.91909\n", "1 0.881748\n", "2 0.777025\n", "3 0.937195\n", "4 0.783779\n", "5 0.353136\n", "6 0.256104\n", "7 0.28514\n", "8 0.277642\n", "9 0.225344\n", "10 0.301154\n", "11 0.249453\n", "12 0.324219\n", "13 0.202852\n", "14 0.244397\n", "15 0.211011\n", "16 0.199964\n", "17 0.246017\n", "18 0.289519\n", "19 0.280718\n" ] } ], "source": [ "#! /usr/bin/env python\n", "\n", "from __future__ import print_function\n", "\n", "import tensorflow as tf\n", "\n", "#from data_utils import get_data, batch_generator\n", "#from model_mnist_cnn import mnistCNN\n", "\n", "\n", "# Parameters\n", "# ==================================================\n", "\n", "# Data loading params\n", "tf.flags.DEFINE_string(\"data_directory\", '/tmp/MNIST_data', \"Data dir (default /tmp/MNIST_data)\")\n", "\n", "# Model Hyperparameters\n", "tf.flags.DEFINE_integer(\"dense_size\", 500, \"dense_size (default 500)\")\n", "\n", "# Training parameters\n", "tf.flags.DEFINE_float(\"learning_rate\", 0.001, \"learning rate (default: 0.001)\")\n", "tf.flags.DEFINE_integer(\"batch_size\", 256, \"Batch Size (default: 256)\")\n", "tf.flags.DEFINE_integer(\"num_epochs\", 20, \"Number of training epochs (default: 20)\")\n", "\n", "# Misc Parameters\n", "tf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\n", "\n", "FLAGS = tf.flags.FLAGS\n", "FLAGS._parse_flags()\n", "print(\"\\nParameters:\")\n", "for attr, value in sorted(FLAGS.__flags.items()):\n", " print(\"{}={}\".format(attr.upper(), value))\n", "print(\"\")\n", "\n", "\n", "# Data Preparation\n", "# ==================================================\n", "\n", "#Access to the data\n", "mnist_data = get_data(data_dir= FLAGS.data_directory)\n", "\n", "\n", "# Training\n", "# ==================================================\n", "\n", "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333, allow_growth = True)\n", "with tf.Graph().as_default():\n", " session_conf = tf.ConfigProto(\n", " gpu_options=gpu_options,\n", " log_device_placement=FLAGS.log_device_placement)\n", " sess = tf.Session(config=session_conf)\n", " with sess.as_default():\n", " \n", " # Create model\n", " cnn = mnistCNN(dense=FLAGS.dense_size)\n", " \n", " # Trainer\n", " train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(cnn.loss)\n", "\n", " # Saver\n", " saver = tf.train.Saver(max_to_keep=1)\n", "\n", " # Initialize all variables\n", " sess.run(tf.global_variables_initializer())\n", "\n", " # Train proccess\n", " for epoch in range(FLAGS.num_epochs):\n", " for n_batch in range(int(55000/FLAGS.batch_size)):\n", " batch = batch_generator(mnist_data, batch_size=FLAGS.batch_size, type='train')\n", " _, ce = sess.run([train_op, cnn.loss], feed_dict={cnn.input_x: batch[0], cnn.input_y: batch[1]})\n", "\n", " print(epoch, ce)\n", " model_file = saver.save(sess, '/tmp/mnist_model')\n", " print('Model saved in', model_file)\n", "\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

dariox2/CADL

session-1/.ipynb_checkpoints/session-1-checkpoint.ipynb

1

32768

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Session 1 - Introduction to Tensorflow\n", "<p class=\"lead\">\n", "Assignment: Creating a Dataset/Computing with Tensorflow\n", "</p>\n", "\n", "<p class=\"lead\">\n", "Parag K. Mital<br />\n", "<a href=\"https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\">Creative Applications of Deep Learning w/ Tensorflow</a><br />\n", "<a href=\"https://www.kadenze.com/partners/kadenze-academy\">Kadenze Academy</a><br />\n", "<a href=\"https://twitter.com/hashtag/CADL\">#CADL</a>\n", "</p>\n", "\n", "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Goals\n", "\n", "* Learn how to normalize a dataset by calculating the mean/std. deviation\n", "* Learn how to use convolution\n", "* Explore what representations exist in your dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Outline\n", "\n", "\n", "\n", "- [Assignment Synopsis](#assignment-synopsis)\n", "- [Part One - Create a Small Dataset](#part-one---create-a-small-dataset)\n", " - [Instructions](#instructions)\n", " - [Code](#code)\n", "- [Part Two - Compute the Mean](#part-two---compute-the-mean)\n", " - [Instructions](#instructions-1)\n", " - [Code](#code-1)\n", "- [Part Three - Compute the Standard Deviation](#part-three---compute-the-standard-deviation)\n", " - [Instructions](#instructions-2)\n", " - [Code](#code-2)\n", "- [Part Four - Normalize the Dataset](#part-four---normalize-the-dataset)\n", " - [Instructions](#instructions-3)\n", " - [Code](#code-3)\n", "- [Part Five - Convolve the Dataset](#part-five---convolve-the-dataset)\n", " - [Instructions](#instructions-4)\n", " - [Code](#code-4)\n", "- [Part Six - Sort the Dataset](#part-six---sort-the-dataset)\n", " - [Instructions](#instructions-5)\n", " - [Code](#code-5)\n", "- [Assignment Submission](#assignment-submission)\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Notebook</h1>\n", "\n", "Everything you will need to do will be inside of this notebook, and I've marked which cells you will need to edit by saying <b><font color='red'>\"TODO! COMPLETE THIS SECTION!\"</font></b>. For you to work with this notebook, you'll either download the zip file from the resources section on Kadenze or clone the github repo (whichever you are more comfortable with), and then run notebook inside the same directory as wherever this file is located using the command line \"jupyter notebook\" or \"ipython notebook\" (using Terminal on Unix/Linux/OSX, or Command Line/Shell/Powershell on Windows). If you are unfamiliar with jupyter notebook, please look at [Installation Preliminaries](https://github.com/pkmital/CADL/blob/master/README.md#installation-preliminaries) and [Session 0](https://github.com/pkmital/CADL/blob/master/session-0/session-0.ipynb) before starting!\n", "\n", "Once you have launched notebook, this will launch a web browser with the contents of the zip files listed. Click the file \"session-1.ipynb\" and this document will open in an interactive notebook, allowing you to \"run\" the cells, computing them using python, and edit the text inside the cells." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"assignment-synopsis\"></a>\n", "# Assignment Synopsis\n", "\n", "This first homework assignment will guide you through working with a small dataset of images. For Part 1, you'll need to find 100 images and use the function I've provided to create a montage of your images, saving it to the file \"dataset.png\" (template code provided below). You can load an existing dataset of images, find your own images, or perhaps create your own images using a creative process such as painting, photography, or something along those lines. Each image will be reshaped to 100 x 100 pixels. There needs to be at least 100 images. For Parts 2 and 3, you'll then calculate the mean and deviation of it using a tensorflow session. In Part 4, you'll normalize your dataset using the mean and deviation. Then in Part 5, you will convolve your normalized dataset. For Part 6, you'll need to sort the entire convolved dataset. Finally, the last part will package everything for you in a zip file which you can upload to Kadenze to get assessed (only if you are a Kadenze Premium member, $10 p/m, free for the first month). Remember to complete the additional excercises online, including the Gallery participation and the Forum post. If you have any questions, be sure to enroll in the course and ask your peers in the \\#CADL community or me on the forums!\n", "\n", "https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n", "\n", "The following assignment breakdown gives more detailed instructions and includes template code for you to fill out. Good luck!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-one---create-a-small-dataset\"></a>\n", "# Part One - Create a Small Dataset\n", "\n", "<a name=\"instructions\"></a>\n", "## Instructions\n", "\n", "Use Python, Numpy, and Matplotlib to load a dataset of 100 images and create a montage of the dataset as a 10 x 10 image using the function below. You'll need to make sure you call the function using a 4-d array of `N x H x W x C` dimensions, meaning every image will need to be the same size! You can load an existing dataset of images, find your own images, or perhaps create your own images using a creative process such as painting, photography, or something along those lines.\n", "\n", "When you are creating your dataset, I want you to think about what representations might exist in the limited amount of data that you are organizing. It is only 100 images after all, not a whole lot for a computer to reason about and learn something meaningful. So <b>think about creating a *dataset* of images that could possibly reveal something fundamental about what is contained in the images</b>. Try to think about creating a set of images that represents something. For instance, this might be images of yourself over time. Or it might be every picture you've ever taken of your cat. Or perhaps the view from your room at different times of the day. Consider making the changes within each image as significant as possible. As \"representative\" of the thing you want to capture as possible. Hopefully by the end of this lesson, you'll understand a little better the difference between what a computer thinks is significant and what you yourself thought was significant.\n", "\n", "The code below will show you how to resize and/or crop your images so that they are 100 pixels x 100 pixels in height and width. Once you have 100 images loaded, we'll use a `montage` function to draw and save your dataset to the file <b>dataset.png</b>.\n", "\n", "<a name=\"code\"></a>\n", "## Code\n", "\n", "This next section will just make sure you have the right version of python and the libraries that we'll be using. Don't change the code here but make sure you \"run\" it (use \"shift+enter\")!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First check the Python version\n", "import sys\n", "if sys.version_info < (3,4):\n", " print('You are running an older version of Python!\\n\\n' \\\n", " 'You should consider updating to Python 3.4.0 or ' \\\n", " 'higher as the libraries built for this course ' \\\n", " 'have only been tested in Python 3.4 and higher.\\n')\n", " print('Try installing the Python 3.5 version of anaconda '\n", " 'and then restart `jupyter notebook`:\\n' \\\n", " 'https://www.continuum.io/downloads\\n\\n')\n", "\n", "# Now get necessary libraries\n", "try:\n", " import os\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", " from skimage.transform import resize\n", "except ImportError:\n", " print('You are missing some packages! ' \\\n", " 'We will try installing them before continuing!')\n", " !pip install \"numpy>=1.11.0\" \"matplotlib>=1.5.1\" \"scikit-image>=0.11.3\" \"scikit-learn>=0.17\"\n", " import os\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", " from skimage.transform import resize\n", " print('Done!')\n", "\n", "# Import Tensorflow\n", "try:\n", " import tensorflow as tf\n", "except ImportError:\n", " print(\"You do not have tensorflow installed!\")\n", " print(\"Follow the instructions on the following link\")\n", " print(\"to install tensorflow before continuing:\")\n", " print(\"\")\n", " print(\"https://github.com/pkmital/CADL#installation-preliminaries\")\n", "\n", "# This cell includes the provided libraries from the zip file\n", "try:\n", " from libs import utils\n", "except ImportError:\n", " print(\"Make sure you have started notebook in the same directory\" +\n", " \" as the provided zip file which includes the 'libs' folder\" +\n", " \" and the file 'utils.py' inside of it. You will NOT be able\"\n", " \" to complete this assignment unless you restart jupyter\"\n", " \" notebook inside the directory created by extracting\"\n", " \" the zip file or cloning the github repo.\")\n", "\n", "# We'll tell matplotlib to inline any drawn figures like so:\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style> .rendered_html code { \n", " padding: 2px 4px;\n", " color: #c7254e;\n", " background-color: #f9f2f4;\n", " border-radius: 4px;\n", "} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Bit of formatting because inline code is not styled very good by default:\n", "from IPython.core.display import HTML\n", "HTML(\"\"\"<style> .rendered_html code { \n", " padding: 2px 4px;\n", " color: #c7254e;\n", " background-color: #f9f2f4;\n", " border-radius: 4px;\n", "} </style>\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Places your images in a folder such as `dirname = '/Users/Someone/Desktop/ImagesFromTheInternet'`. We'll then use the `os` package to load them and crop/resize them to a standard size of 100 x 100 pixels.\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# You need to find 100 images from the web/create them yourself\n", "# or find a dataset that interests you (e.g. I used celeb faces\n", "# in the course lecture...)\n", "# then store them all in a single directory.\n", "# With all the images in a single directory, you can then\n", "# perform the following steps to create a 4-d array of:\n", "# N x H x W x C dimensions as 100 x 100 x 100 x 3.\n", "\n", "dirname = ...\n", "\n", "# Load every image file in the provided directory\n", "filenames = [os.path.join(dirname, fname)\n", " for fname in os.listdir(dirname)]\n", "\n", "# Make sure we have exactly 100 image files!\n", "filenames = filenames[:100]\n", "assert(len(filenames) == 100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read every filename as an RGB image\n", "imgs = [plt.imread(fname)[..., :3] for fname in filenames]\n", "\n", "# Crop every image to a square\n", "imgs = [utils.imcrop_tosquare(img_i) for img_i in imgs]\n", "\n", "# Then resize the square image to 100 x 100 pixels\n", "imgs = [resize(img_i, (100, 100)) for img_i in imgs]\n", "\n", "# Finally make our list of 3-D images a 4-D array with the first dimension the number of images:\n", "imgs = np.array(imgs).astype(np.float32)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Plot the resulting dataset:\n", "# Make sure you \"run\" this cell after you create your `imgs` variable as a 4-D array!\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(imgs, saveto='dataset.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-two---compute-the-mean\"></a>\n", "# Part Two - Compute the Mean\n", "\n", "<a name=\"instructions-1\"></a>\n", "## Instructions\n", "\n", "First use Tensorflow to define a session. Then use Tensorflow to create an operation which takes your 4-d array and calculates the mean color image (100 x 100 x 3) using the function `tf.reduce_mean`. Have a look at the documentation for this function to see how it works in order to get the mean of every pixel and get an image of (100 x 100 x 3) as a result. You'll then calculate the mean image by running the operation you create with your session (e.g. <code>sess.run(...)</code>). Finally, plot the mean image, save it, and then include this image in your zip file as <b>mean.png</b>.\n", "\n", "<a name=\"code-1\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First create a tensorflow session\n", "sess = ...\n", "\n", "# Now create an operation that will calculate the mean of your images\n", "mean_img_op = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# And then run that operation using your session\n", "mean_img = sess.run(mean_img_op)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting mean image:\n", "# Make sure the mean image is the right size!\n", "assert(mean_img.shape == (100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(mean_img)\n", "plt.imsave(arr=mean_img, fname='mean.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have seen the mean image of your dataset, how does it relate to your own expectations of the dataset? Did you expect something different? Was there something more \"regular\" or \"predictable\" about your dataset that the mean image did or did not reveal? If your mean image looks a lot like something recognizable, it's a good sign that there is a lot of predictability in your dataset. If your mean image looks like nothing at all, a gray blob where not much seems to stand out, then it's pretty likely that there isn't very much in common between your images. Neither is a bad scenario. Though, it is more likely that having some predictability in your mean image, e.g. something recognizable, that there are representations worth exploring with deeper networks capable of representing them. However, we're only using 100 images so it's a *very* small dataset to begin with.\n", "\n", "<a name=\"part-three---compute-the-standard-deviation\"></a>\n", "# Part Three - Compute the Standard Deviation\n", "\n", "<a name=\"instructions-2\"></a>\n", "## Instructions\n", "\n", "Now use tensorflow to calculate the standard deviation and upload the standard deviation image averaged across color channels as a \"jet\" heatmap of the 100 images. This will be a little more involved as there is no operation in tensorflow to do this for you. However, you can do this by calculating the mean image of your dataset as a 4-D array. To do this, you could write e.g. `mean_img_4d = tf.reduce_mean(imgs, reduction_indices=0, keep_dims=True)` to give you a `1 x H x W x C` dimension array calculated on the `N x H x W x C` images variable. The reduction_indices parameter is saying to calculate the mean over the 0th dimension, meaning for every possible `H`, `W`, `C`, or for every pixel, you will have a mean composed over the `N` possible values it could have had, or what that pixel was for every possible image. This way, you can write `images - mean_img_4d` to give you a `N x H x W x C` dimension variable, with every image in your images array having been subtracted by the `mean_img_4d`. If you calculate the square root of the sum of the squared differences of this resulting operation, you have your standard deviation!\n", "\n", "In summary, you'll need to write something like: `subtraction = imgs - tf.reduce_mean(imgs, reduction_indices=0, keep_dims=True)`, then reduce this operation using `tf.sqrt(tf.reduce_sum(subtraction * subtraction, reduction_indices=0))` to get your standard deviation then include this image in your zip file as <b>std.png</b>\n", "\n", "<a name=\"code-2\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a tensorflow operation to give you the standard deviation\n", "\n", "# First compute the difference of every image with a\n", "# 4 dimensional mean image shaped 1 x H x W x C\n", "mean_img_4d = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "subtraction = imgs - mean_img_4d\n", "\n", "# Now compute the standard deviation by calculating the\n", "# square root of the sum of squared differences\n", "std_img_op = tf.sqrt(tf.reduce_sum(subtraction * subtraction, reduction_indices=0))\n", "\n", "# Now calculate the standard deviation using your session\n", "std_img = sess.run(std_img_op)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting standard deviation image:\n", "# Make sure the std image is the right size!\n", "assert(std_img.shape == (100, 100) or std_img.shape == (100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "std_img_show = std_img / np.max(std_img)\n", "plt.imshow(std_img_show)\n", "plt.imsave(arr=std_img_show, fname='std.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have plotted your dataset's standard deviation per pixel, what does it reveal about your dataset? Like with the mean image, you should consider what is predictable and not predictable about this image.\n", "\n", "<a name=\"part-four---normalize-the-dataset\"></a>\n", "# Part Four - Normalize the Dataset\n", "\n", "<a name=\"instructions-3\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to normalize your dataset using the mean and standard deviation. \n", "\n", "<a name=\"code-3\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs_op = ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs = sess.run(norm_imgs_op)\n", "print(np.min(norm_imgs), np.max(norm_imgs))\n", "print(imgs.dtype)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting normalized dataset montage:\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(norm_imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(norm_imgs, 'normalized.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We apply another type of normalization to 0-1 just for the purposes of plotting the image. If we didn't do this, the range of our values would be somewhere between -1 and 1, and matplotlib would not be able to interpret the entire range of values. By rescaling our -1 to 1 valued images to 0-1, we can visualize it better." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm_imgs_show = (norm_imgs - np.min(norm_imgs)) / (np.max(norm_imgs) - np.min(norm_imgs))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(norm_imgs_show, 'normalized.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"part-five---convolve-the-dataset\"></a>\n", "# Part Five - Convolve the Dataset\n", "\n", "<a name=\"instructions-4\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to convolve your dataset with one of the kernels we created during the lesson, and then in the next part, we'll take the sum of the convolved output to use for sorting. You should use the function `utils.gabor` to create an edge detector. You can also explore with the `utils.gauss2d` kernel. What you must figure out is how to reshape your kernel to be 4-dimensional: K_H, K_W, C_I, and C_O, corresponding to the kernel's height and width (e.g. 16), the number of input channels (RGB = 3 input channels), and the number of output channels, (1).\n", "\n", "<a name=\"code-4\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# First build 3 kernels for each input color channel\n", "ksize = ...\n", "kernel = np.concatenate([utils.gabor(ksize)[:, :, np.newaxis] for i in range(3)], axis=2)\n", " \n", "# Now make the kernels into the shape: [ksize, ksize, 3, 1]:\n", "kernel_4d = ...\n", "assert(kernel_4d.shape == (ksize, ksize, 3, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll Perform the convolution with the 4d tensor in `kernel_4d`. This is a `ksize` x `ksize` x 3 x 1 tensor, where each input color channel corresponds to one filter with 1 output. Each filter looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(5, 5))\n", "plt.imshow(kernel_4d[:, :, 0, 0], cmap='gray')\n", "plt.imsave(arr=kernel_4d[:, :, 0, 0], fname='kernel.png', cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform the convolution with the 4d tensors:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "convolved = utils.convolve(..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "convolved_show = (convolved - np.min(convolved)) / (np.max(convolved) - np.min(convolved))\n", "print(convolved_show.shape)\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(convolved_show[..., 0], 'convolved.png'), cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we've just done is build a \"hand-crafted\" feature detector: the Gabor Kernel. This kernel is built to respond to particular orientation: horizontal edges, and a particular scale. It also responds equally to R, G, and B color channels, as that is how we have told the convolve operation to work: use the same kernel for every input color channel. When we work with deep networks, we'll see how we can *learn* the convolution kernels for every color channel, and learn many more of them, in the order of 100s per color channel. That is really where the power of deep networks will start to become obvious. For now, we've seen just how difficult it is to get at any higher order features of the dataset. We've really only picked out some edges!\n", "\n", "<a name=\"part-six---sort-the-dataset\"></a>\n", "# Part Six - Sort the Dataset\n", "\n", "<a name=\"instructions-5\"></a>\n", "## Instructions\n", "Using tensorflow, we'll attempt to organize your dataset. We'll try sorting based on the mean value of each convolved image's output to use for sorting. To do this, we could calculate either the sum value (`tf.reduce_sum`) or the mean value (`tf.reduce_mean`) of each image in your dataset and then use those values, e.g. stored inside a variable `values` to sort your images using something like `tf.nn.top_k` and `sorted_imgs = np.array([imgs[idx_i] for idx_i in idxs])` prior to creating the montage image, `m = montage(sorted_imgs, \"sorted.png\")` and then include this image in your zip file as <b>sorted.png</b>\n", "\n", "<a name=\"code-5\"></a>\n", "## Code\n", "\n", "<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a set of operations using tensorflow which could\n", "# provide you for instance the sum or mean value of every\n", "# image in your dataset:\n", "\n", "# First flatten our convolved images so instead of many 3d images,\n", "# we have many 1d vectors.\n", "# This should convert our 4d representation of N x H x W x C to a\n", "# 2d representation of N x (H*W*C)\n", "flattened = tf.reshape(convolved...\n", "assert(flattened.get_shape().as_list() == [100, 10000])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Now calculate some statistics about each of our images\n", "values = tf.reduce_sum(flattened, reduction_indices=1)\n", "\n", "# Then create another operation which sorts those values\n", "# and then calculate the result:\n", "idxs_op = tf.nn.top_k(values, k=100)[1]\n", "idxs = sess.run(idxs_op)\n", "\n", "# Then finally use the sorted indices to sort your images:\n", "sorted_imgs = np.array([imgs[idx_i] for idx_i in idxs])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Then plot the resulting sorted dataset montage:\n", "# Make sure we have a 100 x 100 x 100 x 3 dimension array\n", "assert(sorted_imgs.shape == (100, 100, 100, 3))\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(utils.montage(sorted_imgs, 'sorted.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What does your sorting reveal? Could you imagine the same sorting over many more images reveal the thing your dataset sought to represent? It is likely that the representations that you wanted to find hidden within \"higher layers\", i.e., \"deeper features\" of the image, and that these \"low level\" features, edges essentially, are not very good at describing the really interesting aspects of your dataset. In later sessions, we'll see how we can combine the outputs of many more convolution kernels that have been assembled in a way that accentuate something very particular about each image, and build a sorting that is much more intelligent than this one!\n", "\n", "<a name=\"assignment-submission\"></a>\n", "# Assignment Submission\n", "\n", "Now that you've completed all 6 parts, we'll create a zip file of the current directory using the code below. This code will make sure you have included this completed ipython notebook and the following files named exactly as:\n", "\n", "<pre>\n", " session-1/\n", " session-1.ipynb\n", " dataset.png\n", " mean.png\n", " std.png\n", " normalized.png\n", " kernel.png\n", " convolved.png\n", " sorted.png\n", " libs/\n", " utils.py\n", "</pre>\n", "\n", "You'll then submit this zip file for your first assignment on Kadenze for \"Assignment 1: Datasets/Computing with Tensorflow\"! If you have any questions, remember to reach out on the forums and connect with your peers or with me.\n", "\n", "<b>To get assessed, you'll need to be a premium student which is free for a month!</b> If you aren't already enrolled as a student, register now at http://www.kadenze.com/ and join the #CADL community to see what your peers are doing! https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\n", "\n", "Then remember to complete the remaining parts of Assignemnt 1 on Kadenze!:\n", "* Comment on 1 student's open-ended arrangement (Part 6) in the course gallery titled \"Creating a Dataset/ Computing with Tensorflow\". Think about what images they've used in their dataset and how the arrangement reflects what could be represented by that data.\n", "* Finally make a forum post in the forum for this assignment \"Creating a Dataset/ Computing with Tensorflow\".\n", " - Including a link to an artist making use of machine learning to organize data or finding representations within large datasets\n", " - Tell a little about their work (min 20 words).\n", " - Comment on at least 2 other student's forum posts (min 20 words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure your notebook is named \"session-1\" or else replace it with the correct name in the list of files below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "utils.build_submission('session-1.zip',\n", " ('dataset.png',\n", " 'mean.png',\n", " 'std.png',\n", " 'normalized.png',\n", " 'kernel.png',\n", " 'convolved.png',\n", " 'sorted.png',\n", " 'session-1.ipynb'))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

power-system-simulation-toolbox/psst

docs/notebooks/validation/Validation13-5Bus.ipynb

2

31982

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Validation 13 - 5 Bus" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.case import read_matpower" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "case = read_matpower('./../../psst/cases/case5.m')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>GEN_BUS</th>\n", " <th>PG</th>\n", " <th>QG</th>\n", " <th>QMAX</th>\n", " <th>QMIN</th>\n", " <th>VG</th>\n", " <th>MBASE</th>\n", " <th>GEN_STATUS</th>\n", " <th>PMAX</th>\n", " <th>PMIN</th>\n", " <th>PC1</th>\n", " <th>PC2</th>\n", " <th>QC1MIN</th>\n", " <th>QC1MAX</th>\n", " <th>QC2MIN</th>\n", " <th>QC2MAX</th>\n", " <th>RAMP_AGC</th>\n", " <th>RAMP_10</th>\n", " <th>RAMP_30</th>\n", " <th>RAMP_Q</th>\n", " <th>APF</th>\n", " <th>STARTUP_RAMP</th>\n", " <th>SHUTDOWN_RAMP</th>\n", " <th>MINIMUM_UP_TIME</th>\n", " <th>MINIMUM_DOWN_TIME</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GenCo0</th>\n", " <td>Bus1</td>\n", " <td>40.00</td>\n", " <td>0</td>\n", " <td>30.0</td>\n", " <td>-30.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo1</th>\n", " <td>Bus1</td>\n", " <td>170.00</td>\n", " <td>0</td>\n", " <td>127.5</td>\n", " <td>-127.5</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>170</td>\n", " <td>170</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo2</th>\n", " <td>Bus3</td>\n", " <td>323.49</td>\n", " <td>0</td>\n", " <td>390.0</td>\n", " <td>-390.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>520</td>\n", " <td>520</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo3</th>\n", " <td>Bus4</td>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>150.0</td>\n", " <td>-150.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>200</td>\n", " <td>200</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo4</th>\n", " <td>Bus5</td>\n", " <td>466.51</td>\n", " <td>0</td>\n", " <td>450.0</td>\n", " <td>-450.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>1</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>600</td>\n", " <td>600</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GEN_BUS PG QG QMAX QMIN VG MBASE GEN_STATUS PMAX PMIN \\\n", "GenCo0 Bus1 40.00 0 30.0 -30.0 1 100 1 40 0 \n", "GenCo1 Bus1 170.00 0 127.5 -127.5 1 100 1 170 0 \n", "GenCo2 Bus3 323.49 0 390.0 -390.0 1 100 1 520 0 \n", "GenCo3 Bus4 0.00 0 150.0 -150.0 1 100 1 200 0 \n", "GenCo4 Bus5 466.51 0 450.0 -450.0 1 100 1 600 0 \n", "\n", " PC1 PC2 QC1MIN QC1MAX QC2MIN QC2MAX RAMP_AGC RAMP_10 RAMP_30 \\\n", "GenCo0 0 0 0 0 0 0 0 40 0 \n", "GenCo1 0 0 0 0 0 0 0 170 0 \n", "GenCo2 0 0 0 0 0 0 0 520 0 \n", "GenCo3 0 0 0 0 0 0 0 200 0 \n", "GenCo4 0 0 0 0 0 0 0 600 0 \n", "\n", " RAMP_Q APF STARTUP_RAMP SHUTDOWN_RAMP MINIMUM_UP_TIME \\\n", "GenCo0 0 0 40 40 0 \n", "GenCo1 0 0 170 170 0 \n", "GenCo2 0 0 520 520 0 \n", "GenCo3 0 0 200 200 0 \n", "GenCo4 0 0 600 600 0 \n", "\n", " MINIMUM_DOWN_TIME \n", "GenCo0 0 \n", "GenCo1 0 \n", "GenCo2 0 \n", "GenCo3 0 \n", "GenCo4 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.gen" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>MODEL</th>\n", " <th>STARTUP</th>\n", " <th>SHUTDOWN</th>\n", " <th>NCOST</th>\n", " <th>COST_2</th>\n", " <th>COST_1</th>\n", " <th>COST_0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GenCo0</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.005</td>\n", " <td>14</td>\n", " <td>56.90</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo1</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.006</td>\n", " <td>15</td>\n", " <td>0.11</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo2</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.010</td>\n", " <td>25</td>\n", " <td>2267.53</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo3</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.012</td>\n", " <td>30</td>\n", " <td>5.19</td>\n", " </tr>\n", " <tr>\n", " <th>GenCo4</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0.007</td>\n", " <td>10</td>\n", " <td>1391.16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " MODEL STARTUP SHUTDOWN NCOST COST_2 COST_1 COST_0\n", "GenCo0 2 0 0 3 0.005 14 56.90\n", "GenCo1 2 0 0 3 0.006 15 0.11\n", "GenCo2 2 0 0 3 0.010 25 2267.53\n", "GenCo3 2 0 0 3 0.012 30 5.19\n", "GenCo4 2 0 0 3 0.007 10 1391.16" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.gencost" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>F_BUS</th>\n", " <th>T_BUS</th>\n", " <th>BR_R</th>\n", " <th>BR_X</th>\n", " <th>BR_B</th>\n", " <th>RATE_A</th>\n", " <th>RATE_B</th>\n", " <th>RATE_C</th>\n", " <th>TAP</th>\n", " <th>SHIFT</th>\n", " <th>BR_STATUS</th>\n", " <th>ANGMIN</th>\n", " <th>ANGMAX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Bus1</td>\n", " <td>Bus2</td>\n", " <td>0.00281</td>\n", " <td>0.0281</td>\n", " <td>0.00712</td>\n", " <td>400</td>\n", " <td>400</td>\n", " <td>400</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bus1</td>\n", " <td>Bus4</td>\n", " <td>0.00304</td>\n", " <td>0.0304</td>\n", " <td>0.00658</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Bus1</td>\n", " <td>Bus5</td>\n", " <td>0.00064</td>\n", " <td>0.0064</td>\n", " <td>0.03126</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Bus2</td>\n", " <td>Bus3</td>\n", " <td>0.00108</td>\n", " <td>0.0108</td>\n", " <td>0.01852</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Bus3</td>\n", " <td>Bus4</td>\n", " <td>0.00297</td>\n", " <td>0.0297</td>\n", " <td>0.00674</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Bus4</td>\n", " <td>Bus5</td>\n", " <td>0.00297</td>\n", " <td>0.0297</td>\n", " <td>0.00674</td>\n", " <td>240</td>\n", " <td>240</td>\n", " <td>240</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-360</td>\n", " <td>360</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " F_BUS T_BUS BR_R BR_X BR_B RATE_A RATE_B RATE_C TAP SHIFT \\\n", "0 Bus1 Bus2 0.00281 0.0281 0.00712 400 400 400 0 0 \n", "1 Bus1 Bus4 0.00304 0.0304 0.00658 0 0 0 0 0 \n", "2 Bus1 Bus5 0.00064 0.0064 0.03126 0 0 0 0 0 \n", "3 Bus2 Bus3 0.00108 0.0108 0.01852 0 0 0 0 0 \n", "4 Bus3 Bus4 0.00297 0.0297 0.00674 0 0 0 0 0 \n", "5 Bus4 Bus5 0.00297 0.0297 0.00674 240 240 240 0 0 \n", "\n", " BR_STATUS ANGMIN ANGMAX \n", "0 1 -360 360 \n", "1 1 -360 360 \n", "2 1 -360 360 \n", "3 1 -360 360 \n", "4 1 -360 360 \n", "5 1 -360 360 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.branch" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.case.utils import solve_pf\n", "\n", "results, _ = solve_pf(case)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " <th>16</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.00281</td>\n", " <td>0.0281</td>\n", " <td>0.00712</td>\n", " <td>400.0</td>\n", " <td>400.0</td>\n", " <td>400.0</td>\n", " <td>0.0</td>\n", " 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<td>240.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-360.0</td>\n", " <td>360.0</td>\n", " <td>-238.188688</td>\n", " <td>32.149384</td>\n", " <td>239.905028</td>\n", " <td>-15.659987</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 \\\n", "0 1.0 2.0 0.00281 0.0281 0.00712 400.0 400.0 400.0 0.0 0.0 1.0 \n", "1 1.0 4.0 0.00304 0.0304 0.00658 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2 1.0 5.0 0.00064 0.0064 0.03126 0.0 0.0 0.0 0.0 0.0 1.0 \n", "3 2.0 3.0 0.00108 0.0108 0.01852 0.0 0.0 0.0 0.0 0.0 1.0 \n", "4 3.0 4.0 0.00297 0.0297 0.00674 0.0 0.0 0.0 0.0 0.0 1.0 \n", "5 4.0 5.0 0.00297 0.0297 0.00674 240.0 240.0 240.0 0.0 0.0 1.0 \n", "\n", " 11 12 13 14 15 16 \n", "0 -360.0 360.0 249.773373 21.599095 -248.006760 -4.637368 \n", "1 -360.0 360.0 186.500142 -13.612148 -185.437396 23.581606 \n", "2 -360.0 360.0 -226.273515 22.738212 226.604972 -22.549636 \n", "3 -360.0 360.0 -51.993240 -93.972632 52.118657 93.394588 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<td>0.9</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.000000</td>\n", " <td>4.112031</td>\n", " <td>230.0</td>\n", " <td>1.0</td>\n", " <td>1.1</td>\n", " <td>0.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 \\\n", "0 1.0 2.0 0.0 0.00 0.0 0.0 1.0 1.000000 3.273361 230.0 1.0 \n", "1 2.0 1.0 300.0 98.61 0.0 0.0 1.0 0.989261 -0.759269 230.0 1.0 \n", "2 3.0 2.0 300.0 98.61 0.0 0.0 1.0 1.000000 -0.492259 230.0 1.0 \n", "3 4.0 3.0 400.0 131.47 0.0 0.0 1.0 1.000000 0.000000 230.0 1.0 \n", "4 5.0 2.0 0.0 0.00 0.0 0.0 1.0 1.000000 4.112031 230.0 1.0 \n", "\n", " 11 12 \n", "0 1.1 0.9 \n", "1 1.1 0.9 \n", "2 1.1 0.9 \n", "3 1.1 0.9 \n", "4 1.1 0.9 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(results['bus'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from psst.model import build_model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "model = build_model(case)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FICO Xpress-Optimizer 64-bit v29.01.10 (Hyper capacity)\n", "(c) Copyright Fair Isaac Corporation 1983-2016. All rights reserved\n", "Using Xpress-Optimizer [/Users/dkrishna/opt/xpressmp/lib/libxprs.dylib]\n", "\n", "Reading Problem /tmpgRr1VC.pyomo\n", "Problem Statistics\n", " 106 ( 0 spare) rows\n", " 87 ( 0 spare) structural columns\n", " 239 ( 0 spare) non-zero elements\n", "Global Statistics\n", " 5 entities 0 sets 0 set members\n", "Minimizing MILP /tmpgRr1VC.pyomo\n", "Original problem has:\n", " 106 rows 87 cols 239 elements 5 globals\n", "Presolved problem has:\n", " 16 rows 31 cols 61 elements 5 globals\n", "Will try to keep branch and bound tree memory usage below 5.5Gb\n", "Starting concurrent solve with dual\n", "\n", " Concurrent-Solve, 0s\n", " Dual \n", " objective dual inf\n", " D 20013.800 .0000000\n", "------- optimal --------\n", "Concurrent statistics:\n", " Dual: 13 simplex iterations, 0.00s\n", "Optimal solution found\n", "\n", " Its Obj Value S Ninf Nneg Sum Dual Inf Time\n", " 13 20013.80036 D 0 0 .000000 0\n", "Dual solved problem\n", " 13 simplex iterations in 0s\n", "\n", "Final objective : 2.001380036213346e+04\n", " Max primal violation (abs / rel) : 2.274e-13 / 2.274e-13\n", " Max dual violation (abs / rel) : 0.0 / 0.0\n", " Max complementarity viol. (abs / rel) : 0.0 / 0.0\n", "All values within tolerances\n", "\n", "Starting root cutting & heuristics\n", "\n", " Its Type BestSoln BestBound Sols Add Del Gap GInf Time\n", "k 22255.36973 20013.80036 1 10.07% 0 0\n", " *** Search completed *** Time: 0 Nodes: 1\n", "Number of integer feasible solutions found is 1\n", "Best integer solution found is 22255.36973\n", "Best bound is 22255.36973\n", "Uncrunching matrix\n" ] } ], "source": [ "model.solve(solver='xpress', verbose=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "s1 = (model.results.angles / 2 / pd.np.pi * 360).T[0]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case.gen['PG'] = model.results.power_generated.T[0]\n", "\n", "from psst.case.utils import solve_pf\n", "\n", "results, _ = solve_pf(case)\n", "\n", "s2 = pd.DataFrame(results['bus'])[8]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np = pd.np" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rms(x):\n", " return np.sqrt(x.dot(x)/x.size)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.052620045089577835" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rms(s1.values - s2.values)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

arcyfelix/Courses

17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/04-Visualization-Matplotlib-Pandas/04b-Pandas Visualization/03 - Advanced Matplotlib Concepts.ipynb

2

854839

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced Matplotlib Concepts Lecture\n", "\n", "In this lecture we cover some more advanced topics which you won't usually use as often. You can always reference the documentation for more resources!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Logarithmic scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact only one application of a more general transformation system in Matplotlib. Each of the axes' scales are set seperately using `set_xscale` and `set_yscale` methods which accept one parameter (with the value \"log\" in this case):" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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nHT2Z0GICBXJZX0hm8ep2PSLyFlAmWYF8V6Cjqtb3PM8PHABqqup2EYkBxqvq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\npETLYz1ObWmWsmABvPEGzJxpiZYxvpa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"text/plain": [ "<matplotlib.figure.Figure at 0x10b54ab10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10,4))\n", " \n", "axes[0].plot(x, x**2, x, np.exp(x))\n", "axes[0].set_title(\"Normal scale\")\n", "\n", "axes[1].plot(x, x**2, x, np.exp(x))\n", "axes[1].set_yscale(\"log\")\n", "axes[1].set_title(\"Logarithmic scale (y)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Placement of ticks and custom tick labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explicitly determine where we want the axis ticks with `set_xticks` and `set_yticks`, which both take a list of values for where on the axis the ticks are to be placed. We can also use the `set_xticklabels` and `set_yticklabels` methods to provide a list of custom text labels for each tick location:" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "image/png": 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WN5i1YRbrDqwrbu+Z2JOf9/k5Y3qNoUOzDg5WKNKweL1w4MCZAa0opO3Z4zvb\nVpH27aFrV98yb17gxzbW2qrWHRKMMbah/4wi0rgVeAp8w6UbZ7Fw20IKvAWAb7j0tgtvI6VPChd1\nuEjDpSJV4PXCoUNln0VLS/OFtPz8ivfRtu2pkNat26nXXbtC584QE3NqW2MM1tqA/mfVmTgRkRC1\n6dCm4rtLD+ceBnzDpcO6DyOldwo3nHsDMRExlexFpHGz1hfSyjqLlpYGu3eD213xPlq3Lj+kdekC\nsbV01YJCnIhICMnIy+CNzW8wa+Msvj7wdXH7uUnnFg+Xtm/a3sEKReoXa+HIkbIDWtHiclW8j6Sk\nikNaXFwt/gAVUIgTEanHslxZrNi9gtS0VFJ3p7L+wHosvktEWsS0KB4u7d++v4ZLpVGyFjIzKw5p\neXkV7yMhofyQ1rUrxMfXXv3VoRAnIlKPHHcdZ8Uef2hLS2X9wfV4rbf488iwSK4860pS+qQwoucI\nDZdKg2ctHDtWfkBLS4OcnIr30aJFxSGtWbPaq782KcSJiDjouOs4n+/5vPhM29cHvj4jtA3sNJDk\nLskkd01mQKcBeui8NCj5+b4pONLTfdNtFK137z4V2rKzK95H06anwllZIa1Fi9r+KZyhECciUodO\nuE+cCm1pqaw7sO600BYRFsGAjgNI7uoLbQM7DVRok5BVXkAruT50yHe2rSJxceWHtG7dfCGtMV5N\noBAnIlKLst3Zp51pW7d/HR57ambPiLAILul4SfGZtoGdBhIX5dBV0iJBqKmAFhbmmyetY0fo1Mm3\n7tjRd8NAUUhLSGicIa0yCnEiIjUoJz/ntDNtX+3/6rTQFm7CuaTjJQzuOrg4tMVH1dOrpqXRqs2A\nVjKoderkm0MtMrJufq6GRiFORKQacvJz+GLPF8Vn2tbuW3tGaLu4w8XFoe3SzpcqtImj6iKgFa3b\ntYMIJY1ao/+0IiJByM3P5cu9X7I8bTmpaams3b+WQu+pZ+qEm3Au6nDRqdDW6VKaRjd1sGJpTGo6\noJU+a6aAVr/oP7+ISAXyCvJ8oe2H5aTuTmXNvjWnhbYwE0b/9v1J7prM4K6DubTzpTSLDtH5CqRe\nq62AVlZQU0ALDfqKRERKyCvIY+XelcVn2tbsW1P8LFLwhbZ+7fuR3CWZwd0Gc1nnyxTapFoKC33h\n68ABOHjQty75Oj29+gGt5LptWwW0hkJfo4g0aicLTrIyfWXxmbbV6avPCG192/UtPtN2WefLaB7T\n3MGKJVT0xxJAAAANU0lEQVTk5JQfzEq+zsioPJyBL6B16FD+8KYCWuOjr1pEGpWTBSdZlb6q+Ezb\n6n2ryffkF39uMPyk3U+Kp/y4vMvltIhpoDOFStC8Xt8jnioKZUWvK3uKQBFjoE0b3xBmu3a+IFby\ndYcOCmhSNv1xEJEGzVXoYlX6KlLTUlmetpxV6avOCG0/bvvj4sl1L+98OS1jWzpYsTjB7T4VwsoK\nZkXrQ4d8w5+BiIkpO5SVft2qlcKZVI3+2IhIg+IqdLE6ffVpoc3tcRd/bjD0adun+EzboC6DFNoa\nKGvh+PHKhzMPHPA9mzNQCQmVB7O2baF5c01QK7VLIU5EQpq70M3qfadC28q9K08LbQC92/QuPtM2\nqMsgEmITHKpWakJhIRw5UnEwK1q7XIHtMyLi1JBmRQGtbVuIjq7dn08kUApxIhIycvJz2J65nW0Z\n2/g241s+3/M5K9NX4io8/W/qXm16nXamLbFJokMVSyCshawsXzDLyPCty1qK7uA8csR3bVog4uMr\nPltW1JaY6LtxQCSUKMSJSL3i8XrYc3wP2zK3sS1jm2/tf70ve1+ZfS5sfWHx5LqDugwiqUlSHVct\nJXk8vov/ywtjpYNaRkbg15kVadWq4uHMotfxejiGNGAKcSLiiCxX1qmQViKs7cjcccZwaJGo8Ci6\nJ3SnZ2JPeiT2oF/7flzR5QpaxbWq4+obF7e78iBWcjl2LLApM0pq2tQXzEouSUmnvy8a7mzdWs/a\nFAGFOBGpRQWeAn7I+qHMsHY493C5/drFt6NnUk96JvoX/+suLboQEaZfW9VhrW/qi0DCWNFn2dnB\nHcMY3/BkWUGsrJCWlOS7k1NEgqPfhiJSLdZajuQdOS2obT/qu25t17Fdpz2iqqTYiFh6JPY4I6z1\nSOyhJyAEwev1nfkKJIwVLe6yT3SWKyKi/DBWVntCgqbMEKkL+t9MRALiKnSx8+jOM65T25a5jSxX\nVrn9OjfvfMYZtZ5JPenYrCNhRleSF/F6fdNhHD3qC2UVLSVDWWam7xq0YMTGVh7ESi6aKkOkflKI\nE5Fi1lr2Z+8v86aC3cd347Vl3xLYNKppmcOf5ySeQ5PIJnX8UzgnmCBWejl+PPjryIo0b155ECv5\neVxczf7cIuIMhTiRRig3P9c3VUepsLY9czs5+WU/KyjMhHF2y7PLDGtt49tiGsipGqeCGECzZtCy\nZdlLQsKp10XXmxWFs6iomvv5RSR0KMSJNFBe6/VN1VHGTQXpJ9LL7ZcQm3D60Kf/9dktzyY6IjRm\nOa1vQaxkACtvad5c15GJSHD0K0MkhHmtl2Mnj7Hr2K4zhj93HN1xxiS4RSLDIn1TdZRxVs3piXEL\nCnx3Q5444VsXLWW9z8qqmyAWSAhTEBORuqZfNyL1hLWW7PxsMvIyAl4yT2aWe50aQNv4tmXeVNC1\nRdcam6rDWt+jjSoKW+W9L+uzYO+cLE9REAs0gCmIiUioqfNfVcaYW4AhwE7gIuBla+2yuuovUldO\nFpwsN3wdyTtSZnuBtyDo4zSLbkbXFl3PCGs9EnvQPKZ5mX2K5gqrLFAFGr6CnW2/IuHhvolfi5Zm\nzcp+3bQptGhRdhBr0cK3HxGRhszY6ow5BHswY+4EpgA9rbUuY0wH4BvgGmvtl7XR3xhj6/JnlIYp\n35NPZl5m+WfFTp7ZlleQF/Rx4iLjSGqSdNrSMjqJZhFJNA1PIi4siTiSiPH6loj8RArckUGHr5yc\n6g03lhYdXX7wqiyIlX4fG6vpLESk8TLGYK0N6LdgnYU4Y0w8sAd4zlr7SIn2N4Ae1tq+tdFfIU5K\n83g9HD15NKhAdsJ9IujjRBBFfFgrmpBErE0i2pNEVGESEflJhLuTMCeTIC8Jb04SnuwkCo4n4s6N\nJS8PTp70LXl5NRu2SmrSpHphq+R73R0pIlIzgglxdTmcOgxoDqwu1b4KuMUYc761dmst9pcQk5qa\nSnJycvF7r/XiLnTj9rhxF7pxFbqKX+fmu8h1uzl8/BgHT2RwMDuDI7kZZORmkOnK4Jg7g+P5GRwv\nzCDPewxLkMnIG14cumyub13ZUpgfRxaG8qfBDUxUlC9wxcaeuS5agj0LFh9f+8ONpb8/CS36/kKX\nvrvGoy5DXB//ek+p9t2AAfoDFYWw6vaXAHk8kJ/vu0vQ7bbkuPLJdbnJcbnIcbnJc7vJdbvIc7vJ\ny3eTl+/iZL6bkwUuTha4cRX4Apar0B+2PC7yPb7wle91UeB1k+91U+B1UWDdFOKi0LopxPfaY9x4\njRv354cJGxSNN8yNN8wF4cFfL1Yma+BkYkBBrHhxN8PaU08XKDNYJUBsh7LDVlXXsbGhe22X/iIJ\nbfr+Qpe+u8ajLkNckn9d+lHKRTOLtqmt/q8uXYPXa/FYi9d7avF4Ld6ittJrr8Xj9eK1p7azZWxz\nWj//a2stHustbrdlbYP/s5Lt1uL1en3b21N9bYnX3lKvrS2xL+stbiv0FlBg3RTY04OSBzce4wtK\nHuMLR17jxoa5seEubLgbIlwQ7oYI/1JTDBDuXwIRAd7oUl93YbR/iQHP6a+NN5rwghZE5icRVdiK\nGK9vGDPOJBEf5ruurFlEEi1iWhIXG06TeIht1biClYiINBx1GeKK0kDpcayi+RHya6v/nasurrQ4\nx4TCoyM9kRhvNMYTQ5g32rfYGMJtNOE2hnCiiSCaCGKIMNFEmmgiTAxRJpqosBgiw6KJCo8mOjyG\n6PBoosOjiYmMISYimpiIGGIio4mNjCY2KoYmUdH+JYaFu55jwpA/EB8TQ1x0NHExUURHGyIjfWfC\noqIofq1QJSIijU1d3tjwB2AG0Ntau6VE+whgAXCrtfbtmu5vjNFdDSIiIhIy6uONDV/jG1DrBGwp\n0d4Z39m1DbXRP9D/ECIiIiKhpC4H8z4CMoHSY5sDgLXW2h213F9ERESkwaizEGet9QDTgXHGmGgA\nY0w74DpgWtF2xpjfG2M2GWMSq9JfREREpDGo08duWWufNsa4gJeMMVuAvsBYa+3iEpslAK2BmCr2\nFxEREWnw6vSxWyIiIiJSM+r0TJyINA7+Sx7uBrLwXbcaBtxtrfVW2FFEqsUY0wr4A9AFSAY+B8ZY\na0vPsSoNgM7EiUiNMsZEAouAX1prd/v/UjkE9LHWbnK2OpGGzRjzF+ARa22+MeZq4E1ggLV2m8Ol\nSS0IhalmRSS0TATWW2t3+99fCKQD250rSQJhjHnbGLPVGBPlf3+OMcZtjHnC6dokYIOAbgDW2mXW\n2gQFuIZLIU5Eaox/GHUiMM//vhXwP8D11lqXk7VJxfxnbeYBB4Af+5v/AuwCJjlVlwTtWWCVMWa8\n04VI7dM1cVKvGGNigUfwXUsVAXQHfgVsAz611k5wsDyp3A1AIXDAGLPM37bZWrvRwZokMOnW2q3G\nmI5ArDHmcuB64DJrbQ0+RFlqk7X2dWPMT4BXjTFD8V0Pp2tRQ4D//70HgKPAPmAk8Bdr7cry+uhM\nnNQbxpg2wHrgoLX2r9bamfie0PFPoD3wqJP1SUBGAB9aaw9ba68GxgIPGGNucbguqYS1dqv/5XHg\nBPAE8JS1drVzVUkwjDFXGmM+BD4ELgWGobOoIcEYkwCsBXZZax+z1s4CuuL7h3G5dCZO6pN/ARnW\n2qdLtG0F7gf+bq3d60xZEghjjAGGAv+vRHOsf9297iuSKooFrgCa4jsrLiHAGDMS3+/QftbaXf62\npfhCwHQna5OA9MU3R+7mEm0XWWvzK+qkECf1gjHmQnz/aryp1EdNgRxgZp0XJcHqB7TEdxagSF//\nesuZm0s9lQD8Ehhd2V8gUj/4n3D0L3z/2N1V4qNCINyZqiRIa4GNwLvGmI+BFtba4ZV1UoiT+mIw\nYIHUogZjTBPgWuD/rLVHHKpLAncVsKnUd3UHsBf4wJmSpAraA29Za9c4XYgE7FagGTCnVPulwPK6\nL0eC4b8W/HN8135fbq3NDbSvQpzUF/GAx1p7tETb74AkYLv/X5onrLUFjlQngRgKLC16Y4zpi+/C\n+JuttYWOVSUBM8aE45uior/TtUhQOgH5Jc/CGWOuBzoCT5fbS+qLm4DzgJuCCXCgGxuk/lgPhPun\npMAY0w+4Hfgvvhn/xynA1V/GmHjgEmCZ/30i8Bow2Vq7wMnaJCgpQFMNo4aczfh+fzYBMMYk4Qtv\nD+nO8JBQdPd3p6IGY8wA/3WOFdITG6TeMMY8hW9I4DPgAuBxfAHuFeA2a+3HDpYnFTDGjMA3x9hM\nfE9nuAyYZ639sMKOUm8YY8LwDeccstZe5nQ9Ejj/TUVPAYnAKnxnUt+x1i5ytDAJiP8M+HP4RjNW\n4Zur8SNrbaWXoSjEiUi1GWOeA7pZa69xuhapGmPMEOAjfNegTnS6HhGpnIZTRaQmXE2Jm1IkJLnx\nTTD6D6cLEZHA6EyciFSLMaY7vueiXmSt/crpekREGgudiROR6hoNpCnAiYjULZ2JExEREQlBOhMn\nIiIiEoIU4kRERERCkEKciIiISAhSiBMREREJQQpxIiIiIiFIIU5EREQkBCnEiYiIiIQghTgRERGR\nEPT/AT0NOs5Aq2cMAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b3de690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "\n", "ax.plot(x, x**2, x, x**3, lw=2)\n", "\n", "ax.set_xticks([1, 2, 3, 4, 5])\n", "ax.set_xticklabels([r'$\\alpha$', r'$\\beta$', r'$\\gamma$', r'$\\delta$', r'$\\epsilon$'], fontsize=18)\n", "\n", "yticks = [0, 50, 100, 150]\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18); # use LaTeX formatted labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a number of more advanced methods for controlling major and minor tick placement in matplotlib figures, such as automatic placement according to different policies. See http://matplotlib.org/api/ticker_api.html for details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scientific notation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With large numbers on axes, it is often better use scientific notation:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "image/png": 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wzs0H9oWyvJmlAGOAe51z+c65j4E3gavqXKmISIRbsGkBaYlpDDtlmN+lnCBcB2gzgGLn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\n+P+EBLR7ohLfjwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b7a0f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x**2, x, np.exp(x))\n", "ax.set_title(\"scientific notation\")\n", "\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "from matplotlib import ticker\n", "formatter = ticker.ScalarFormatter(useMathText=True)\n", "formatter.set_scientific(True) \n", "formatter.set_powerlimits((-1,1)) \n", "ax.yaxis.set_major_formatter(formatter) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis number and axis label spacing" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x106d4c710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# distance between x and y axis and the numbers on the axes\n", "matplotlib.rcParams['xtick.major.pad'] = 5\n", "matplotlib.rcParams['ytick.major.pad'] = 5\n", "\n", "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x**2, x, np.exp(x))\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "ax.set_title(\"label and axis spacing\")\n", "\n", "# padding between axis label and axis numbers\n", "ax.xaxis.labelpad = 5\n", "ax.yaxis.labelpad = 5\n", "\n", "ax.set_xlabel(\"x\")\n", "ax.set_ylabel(\"y\");" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# restore defaults\n", "matplotlib.rcParams['xtick.major.pad'] = 3\n", "matplotlib.rcParams['ytick.major.pad'] = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Axis position adjustments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, when saving figures the labels are sometimes clipped, and it can be necessary to adjust the positions of axes a little bit. This can be done using `subplots_adjust`:" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "image/png": 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sh0v0u3Z5narHHFP/lJ5e/dq7t+5/I7FBhUAOUekqOVB6gD0H9zSY3INH8IWl\nhXRL6eYl8lrJveZr99TudEnpQitr1ewYKyq8h44c7mi99lRWVp3AG5rS06FrV10zL/EnJguBmXUC\n5gDnADuBO51zz9VaJ2YKQXllOQUlBRSUFlBQUkB+SX7V+4LSwOeay0vzD7v+gbIDJCcm0ym50yHJ\nvHu77nWSe3pqOp3bdm5ScnfOu/HYvn3etH9/9fv65tVM/Hv3eqNZjya5H3OMd4Sva+RFDi9WC0Ew\n6U8GTgJeB8Y653JrrBPWQuCco7yynJKKEorLiykuL6ak3HtfUlHCgdIDDSfyBhJ7SUUJqW1SaZ/U\nnrQ2aaQlpZHWJs37HHhf53NSGptWbGLcaeMOWT+1TepRNclUVnoP/G4okTeU4Pfvh+RkL6HXnjp0\nqDuvU6fqxN+lS+guk8zOziYrKys0O4tC8f77QX8DiMGH15tZCvATYIhz7iCwxMxeBiYCd9Zcd9V3\nq6oSce3kXHN+ffOOanmNRF9cXgxA28S2JCUmkZyYTHJiMkkJ3vu2rdtWJ/JAom6f1J70dun079y/\n3kQeXD+ldUqDl0WWl3sP/CgqqvFaBK++PY1hXUezraju8uDR+uESeUGB11HaUBLv08d7AHh9ib19\ne2gdAQ/bivckEO+/H/Q3aKyoKATAQKDMOZdXY94q4PTaK056aVJVIk5OTK5K0IfMq/E+LSmt7vJ6\nknpSYhJJCckkkkyiJdE68GoukYoKr427vJyq9xUVUFpaT7LeW/15axF8WXt5Pa/1zauo8K5cSUk5\n9HXXLu8Sx9rzU1KgXTtvRGp9SbxjR6+5JZ4f9i0Sr6KlEKQC+bXm5QN1Bth3X7CqKhEXV8CBwyTp\nwyXvhuY75yXK+qbExLrz2rSpm5DrS96dOkHPnvUn7/pe27b19l3fCcO0ad4kInK0oqKPwMxGAB85\n51JrzLsDOM05d1GNeZH/Y0REwiSm+giA9UCimWXWaB4aDnxRc6Wj/dEiIlItKs4IAMzsWcAB1+Fd\nNfQq8P2aVw2JiEjjNX8kUPjcDKQAO4CngRtVBEREmi9qzghERKRlRNMZwWGZWSczW2BmhWa2ycx+\n6ndM4WRmN5vZUjMrNrM5fsfjBzNrY2azzOwrM9tvZjlm9kO/4wo3M3vKzLaZ2T4zW2dmU/yOyQ9m\nNsDMDprZk37HEm5mlh347flmVmBmR2w5iYlCADwCFAPdgJ8BfzWzwf6GFFZbgT8As/0OxEeJwGbg\nB865DsDUTOOzAAADVElEQVR/AC+Y2bH+hhV2fwT6Oec6AhcC95jZiT7H5IeHgc/9DsInDrjJOdfe\nOZfmnDtiLoz6QlBj1PHvnHMHnXNLgOCo47jgnHvJOfcKsMfvWPzinCtyzv3eOfdN4PPrwCZgpL+R\nhZdzbq1zrjjw0fCSQqaPIYWdmV0J7AXe9TsWHzXqCsqoLwQcftTxUJ/ikQhgZunAAGpdYhwPzOz/\nm9kBIBf4Fljoc0hhY2btgf8HTKWRyTDG/NHMdpjZYjOrcweG2mKhEBz1qGOJD2aWiHdl2ePOufV+\nxxNuzrmb8f5fjAP+DpT4G1FY/R6Y6Zz71u9AfPQrIAPoBcwEXjWzfg1tEAuFoBBoX2teB6DAh1jE\nZ+bdqe9pvOR3q8/h+MZ5Pgb6AP/qdzzhELgDwdnAX/yOxU/OuaXOuQPOuTLn3JPAEuBHDW0TLSOL\nG3JUo44lbswGugI/cs5V+B1MBEgkfvoITgf6ApsDBwSpQIKZDXHOnexvaL5yHKGZLOrPCJxzRXin\nv783sxQzGwf8GHjK38jCx8wSzCwZSMAriklmFnf3ETWzR4FBwIXOuVK/4wk3M+tmZleYWTsza2Vm\n5wJXAu/4HVuYPIZX9EbgHQw+CrwGjPczqHAysw5mNj6YA8zsKuAHwJsNbRf1hSAg3kcd/w4oAn4N\nXBV4f5evEYVZ4DLR6/GSwPbA9dP5cTamxOE1A32DdwXZvcBtgSuoYp5zrtg5tyM44TUbFzvn4ulq\nutbAPXi5cCdebrzIOfdlQxtpZLGISJyLlTMCERFpIhUCEZE4p0IgIhLnVAhEROKcCoGISJxTIRAR\niXMqBCIicU6FQEQkzqkQiIjEORUCEZE4p0IgEgJmlmFmuwO3QsbMegYeDHKa37GJHIkKgUgIOOc2\n4j0Q5GkzawvMBeY65z70NzKRI9NN50RCyMxewns6VCUwyjlX5nNIIkekMwKR0JqF97zsh1QEJFro\njEAkRMysHbAKeA84DxjmnNvnb1QiR6ZCIBIiZjYbaOucm2BmjwEdnXNX+B2XyJGoaUgkBMzsQrxH\nIt4UmDUVODHOnpAmUUpnBCIicU5nBCIicU6FQEQkzqkQiIjEORUCEZE4p0IgIhLnVAhEROKcCoGI\nSJxTIRARiXP/B7XhPC7uO4akAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b7b4950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", " \n", "ax.plot(x, x**2, x, np.exp(x))\n", "ax.set_yticks([0, 50, 100, 150])\n", "\n", "ax.set_title(\"title\")\n", "ax.set_xlabel(\"x\")\n", "ax.set_ylabel(\"y\")\n", "\n", "fig.subplots_adjust(left=0.15, right=.9, bottom=0.1, top=0.9);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the `grid` method in the axis object, we can turn on and off grid lines. We can also customize the appearance of the grid lines using the same keyword arguments as the `plot` function:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "image/png": 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s22fzwCUPVOi+dstFgKx1aE+Si+/M2z6PGz6/gYycDO7qdBcLhiygVmits97P\nbnn4i1lL8AghKuBk7knu/PpOFu1YRO3Q2swfMp9rW19rdVhCCFEh0zZNY8SiEbi1m0cufYS3b3ib\nICXXbM5EGi2TBeq6emVxSi52ySM1O5WbvryJn/b9RHR4NN8N/Y7Lml/m1WPYJRchRPUzcfVEnlz+\nJAAvXP0C468eL8MdKkAaLSH84GjGUfp93o/4I/E0j2zOsr8uo33D9laHJYQQZ6W15h8r/sFrq14D\n4J1+7/Bot0ctjipwyPU+kzlpDI1TcrE6j4SUBHpM70H8kXjaRbdj1X2rKt1kWZ2LEKJ6yXfn88Ci\nB3ht1WuEBIUwY/AMabK8JFe0hPChbYnbuG7GdRxKP0SXJl1YcvcSGtVuZHVYwlQJVgdgmrLWggtU\nkkvVnco7xdC5Q5mzfQ41Q2ry9e1fM+C8AZV+PCc9J96QebSE8JG1B9bS//P+nMg+Qc+WPVk4ZCF1\na9a1OizbkHm0hLCv9FPpDJ41mBV7VlC3Rl2+uesberToYXVYtuFN/ZIrWkL4wLLdyxg8azBZuVkM\nPH8gM2+dSXhouNVhCSHEWR3POk7/z/uz/tB6GtduzNK7l9K5SWerwwpYMkbLZE4aQ+OUXPydx+xt\ns7nxixvJys3ins73MOeOOaY1WU55ToQQ9nQg7QA9p/dk/aH1xETF8PN9P0uTVUXSaAlhoim/TmHI\n10PIdecyuttopg+aTkiQXDgWQtjfH8f/4MppV7L9+HY6NurIqvtW0Ta6rdVhBTwZoyWECbTWvPrz\nq4z7YRwAL/d+mWeuekbmmDkDGaMlhH1sPLyRfjP6cSzrGJefcznf3PUN0eHRVodlW7LWoRB+5NZu\nnlj2BON+GIdCMXnAZMb1HCdNVrUhax3akeRSca4EF70+6cWxrGNc3+Z6lv91uU+aLCc9J94wrdFS\nSrVTSp1USn3msa+PUmq7UipDKbVCKdXCrPPZlZPG0DglF1/mkefO474F9zFp7SRCg0KZedtMRnYd\n6bPzOeU5saPK17AYP0bpW056IZRcKmbB7wvoN6Mf6Tnp3NnhThb+ZSG1w2r75FxOek68YeYVrXeB\nXwo3lFINgDnAOCAa2ADMMvF8QlgqOy+b22bfxqebP6VWaC0W/WURd3S4w+qwROVJDRPVyqfxn3Lr\n7Fs5lX+KUV1H8fktnxMWHGZ1WI5jSqOllBoCnABWeOweDGzVWs/VWucALwCdlVLnmXFOu3LSWnRO\nycUXeaRU0hEyAAAeoUlEQVSdSqPfjH4s+GMB9WrWY8U9K7i+7fWmn6ckpzwndiM1TFQ3k9ZMIm5B\nHPk6n+d6Psd7/d8jOCjY6rAcqcqNllKqDvAiMBbwHJTSAdhcuKG1zgJ2FewXImAlZibS+9Pe/Lj3\nR5pGNOW/w/5L93O6Wx2WqCSpYaI60VozbsU4Hl/2OABvXf8WE3pPkDGlPmTGFa0JwFSt9aES+yOA\n1BL70oBIE85pW04aQ+OUXMzMY2/KXq6afhUbD2+kbXRbVt23io6NOpr2+GfjlOfEZqSGiWoh353P\nqG9H8crPrxCsgvns5s94rPtjVofleFWa4EcpFQtcC8SWcXMGUKfEvrpAelmPFRcXR0zBQkhRUVHE\nxsYWfUxS+OIi2/7dLmSXeCq7HR8fb8rjNe7QmOtmXMeBzQdoE92Gnx//mcYRjS3PL1C2C39PsNGI\nWHNqWBJxcS8UbcXG9iI2thcxMWWv7ZaQUPagYDscf+QIFD5ddoinKseHlPPqFijxex7veb/KPn5u\nfi6v/PwKrj2/Exrcl/fveoq/dr7WL/F77rPD32dljne5XLhcLlJSICWl9PFnUqV5tJRSjwEvYxQe\nhfEOMAjYDnwAxGmtexQcWxs4BsRqrXeUeByZh0bY2i8Hf6H/5/1JOplEjxY9WPSXRUTVjLI6rIBm\nh3m0zKhhUr+E3WXkZHDr7FtZtnsZdWrUYdFfFtGzZU+rwwpo3tSvqjZaNSn+ju9JjEllRmIUq53A\nfcBi4CWgh9b6ijIeRwqVsK2lu5Zy6+xbyczNZEC7Acy+fTa1QmtZHVbAs0mjVeUaJvVL2FliZiKD\nZg5i7YG1NKrdiCVDl9ClaRerwwp4fpuwVGudrbVOLPzBuNSerbVO1lofB24FXgGSga7AkKqcLxCU\n/NgtkDkll8rmkX4qnYe+fYh+n/cjMzeTuy+6m3l3zrO0yXLKc2IXUsOEU2mtmbl1Ju3fa8/aA2tp\nWbclPw/7WZosC5i6CJvW+sUS2z8AF5p5DiH84fs/v+f+hfezN3UvoUGhPNfzOcb1HEeQksUUnExq\nmHCCIxlHGPXtKOb/Ph+Aa1tfyyeDPqF5neYWR1Y9yVqHQnhIO5XGk8ue5MONHwJwcdOL+WTQJ3Rq\n3MniyJzHDh8dmkHql7ALrTVfbPmCR797lBPZJ4gMi2TS9ZMY3mW4TN9gMm/ql6lXtIQIZEt3LWXE\nohHsT9tPWHAYz1/9PE9e8SShwaFWhyZszVlrHZb1zaxAVN1yOZR+iJHfjGTRjkUA9Gvbjw9v/JBz\n657r8/gqyknPiTfkcxCTOWkMjVNyOVseKdkpDF8wnH6f92N/2n4ubXYpGx/YyDNXPWO7Jsspz4mz\nxFgdgGlsNPNGlVWXXLTWfBr/KR3e78CiHYuoW6Mu0wZOY/Fdi23VZIGznhNvyBUtUa19u+NbHvzm\nQQ6mH6RGcA0m9J7A2MvHEhIk/zWEEPZ2IO0ADyx6gO92fQfAgHYDmHLjFBmLZTPyamIyJ61F55Rc\nysrjxMkTjFk6hk83fwpA93O6M33QdC5ocIGfo/OOU54TIUTlaa2ZtmkaY5eNJe1UGlE1o3in3zvc\nfdHdMhbLhqTREtXOwj8WMvKbkRzOOEzNkJq83PtlRncfLQuqCiFsb1/qPkYsGsGy3csAGHj+QD4Y\n8AFNI5taHJkoj4zRMpmTxtA4JZfCPJKykrh77t0MmjmIwxmHufLcK9k8cjOPX/F4wDRZTnlOhBDe\n0Vrz4YYP6fh+R5btXkZ0eDSf3/I58++cL02WzckVLVEtzNs+j1HfjuJo5lHCQ8J5tc+rPHLZIwHT\nYAk7S7A6ANM46RthTsolJPoAff8Tx4o9KwC45cJbeL//+zSOaGxpXN5y0nPiDZlHSzjascxjPPrd\no8zaNguAni178vHAj2kb3dbiyITMoyXEmbm1mw9+/YCnlj9FZm4mDWo14L3+73F7+9tlLJbFZB4t\nIYCvtn3Fw4sf5ljWMWqH1uaf1/6Thy59SGZ3F0LY3p8n/mT4wuG4ElwA3NHhDt694V0a1m5obWDC\na/KKYzInjaEJ1FwSMxO5/avbuePrOziWdYzYk7FsGbWFRy57JOCbrEB9ToQQFePWbv697t90mtwJ\nV4KLRrUb8fXtXzPrtlnSZAUouaIlHENrzaxts3hk8SMknUwiIiyC1/u+znnp59GqXiurwxMekpKs\njkAI+9mZtJPhC4fz076fALir01283e9tGtRqYHFkwlNOjnfHyxgt4QhlLaL60U0f0TLKOcujBKr8\nfNi6FdasOf2zcyeAjNESAiDfnc87695h3A/jOJl3kiYRTfhgwAcMumCQ1aEJ4ODB4vVr40Y4dUrG\naIlqQmvN51s+52/f/U0WUbWJpCRYu/Z0UfrlF8jIKH5MeDicPGlNfOZzTjPvpLXoAiWXP47/wX0L\n72P1/tUA/PWiv/JWv7eIDo8uOiZQcjmbQMgjJwfi443atXq18ef+/VV7zCo3WkqpMOB94FqgHrAb\neEZrvaTg9j7Au8C5wDpgmNZ6X1XPa1cul8sxs3fbPZeKLqJq9zy8Ybdc8vNh27bi7/Z27Ch9XKtW\ncPnlp38uugjCwvwfb0nm1K8Y/wXsY4HwQlhRds8l353PpDWTGO8aT3ZeNs0imzHlxinceN6NpY61\ney4VZcc8Dh8uXr82bIDs7OLH1KkD3bqdrl/dukF0dNmPVxYzrmiFAPuAq7TW+5VSA4DZSqmOQCYw\nB7gP+AZ4GZgFXG7CeUU1pbXms82fMXrpaFKyU6hboy5v9XuLezvfK1exfCw5ufTVqvT04sfUrAmX\nXnq6KHXvDk2aWBNvBUj9En63/dh2hi0YxrqD6wAYFjuMSddPIqpmlMWROZvn1ao1a4xatndv6eMu\nuKD4G8MLL4TgKky56JMxWkqpzcALQAPgXq11j4L9tYDjQKzWeofH8TLGQZyV1poVe1bwz5//WTRx\nnyyi6jv5+fDbb8Xf7f3xR+njYmKKF6XOnSE09OyPb9d5tLyvX7201i4LIjWfywU2umBaJXbMZW/K\nXt5Z9w7vrX+PU/mnOKfOOUy9aSr92vY74/3smEtl+DuPI0eK169ffy19tSoysnJXqyydR0sp1Rho\nB2wDHgI2F96mtc5SSu0COgBlfMAgRGmn8k7xxZYveHPtm2xJ3AJAvZr1eLvf27KIqolOnCh+tWrd\nurKvVnXtWvxqVVMHrf4h9Uv4wroD65i0dhJzfptDvs4H4P4u9zPxuonUrVnX4uicITcXNm8u3lgl\nJJQ+7vzzi78xbN++alerKsLURkspFQLMAD7RWu9QSkUAiSUOSwMizTyvndhtDE1VWJ3LscxjTP51\nMu+vf5+jmUcBaBLRhEcufYSRXUdSv1b9Cj2O1XmYyaxc0tJg0ybj2zMbNxrv9H7/vfRxLVuWvlpl\nh7FVviD1S5gpz53H/N/nM2nNJNYcWANASFAIQzsOZUz3MVzS7BKLIwxceXlGvdqwwahfhX+W/IJN\nRETpq1X1K/ayYSrTGi1lXFaYAZwCHi3YnQHUKXFoXaDE+2SIi4sjpmCUXFRUFLGxsUUvKIWTNMq2\nf7cL+fv8n8z/hK+2fcUKvYJ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Ltgv2F/8xbne73eTk55CVn05Wfjon3emc0sZPrkonNyid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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1084dc3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n", "\n", "# default grid appearance\n", "axes[0].plot(x, x**2, x, x**3, lw=2)\n", "axes[0].grid(True)\n", "\n", "# custom grid appearance\n", "axes[1].plot(x, x**2, x, x**3, lw=2)\n", "axes[1].grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axis spines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also change the properties of axis spines:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAACUCAYAAACQh5KRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10b7d5c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6,2))\n", "\n", "ax.spines['bottom'].set_color('blue')\n", "ax.spines['top'].set_color('blue')\n", "\n", "ax.spines['left'].set_color('red')\n", "ax.spines['left'].set_linewidth(2)\n", "\n", "# turn off axis spine to the right\n", "ax.spines['right'].set_color(\"none\")\n", "ax.yaxis.tick_left() # only ticks on the left side" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Twin axes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it is useful to have dual x or y axes in a figure; for example, when plotting curves with different units together. Matplotlib supports this with the `twinx` and `twiny` functions:" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "image/png": 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Jb9Iky/iysqw85quvhizwDR8Ozz5rg3rjDdhtt6BH5OBrIGGbXmIJfpcAr6vy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\nBTuswDwC7KOqOwDnAQ+KyJEBjylIzwI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"text/plain": [ "<matplotlib.figure.Figure at 0x109b88690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "\n", "ax1.plot(x, x**2, lw=2, color=\"blue\")\n", "ax1.set_ylabel(r\"area $(m^2)$\", fontsize=18, color=\"blue\")\n", "for label in ax1.get_yticklabels():\n", " label.set_color(\"blue\")\n", " \n", "ax2 = ax1.twinx()\n", "ax2.plot(x, x**3, lw=2, color=\"red\")\n", "ax2.set_ylabel(r\"volume $(m^3)$\", fontsize=18, color=\"red\")\n", "for label in ax2.get_yticklabels():\n", " label.set_color(\"red\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Axes where x and y is zero" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1071d7110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.spines['right'].set_color('none')\n", "ax.spines['top'].set_color('none')\n", "\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.spines['bottom'].set_position(('data',0)) # set position of x spine to x=0\n", "\n", "ax.yaxis.set_ticks_position('left')\n", "ax.spines['left'].set_position(('data',0)) # set position of y spine to y=0\n", "\n", "xx = np.linspace(-0.75, 1., 100)\n", "ax.plot(xx, xx**3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other 2D plot styles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the regular `plot` method, there are a number of other functions for generating different kind of plots. See the matplotlib plot gallery for a complete list of available plot types: http://matplotlib.org/gallery.html. Some of the more useful ones are show below:" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = np.array([0,1,2,3,4,5])" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "image/png": 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uYDtwFDCY4NuRr5nZwwSB9FeAJ4HvEMzafKxDnZYuYfnG5ThOj7zOTaSI7Gpm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\nnP/bmdyd3Qd2s+fgHgA+OOyDnH3q2Yw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"text/plain": [ "<matplotlib.figure.Figure at 0x10a72ed90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 4, figsize=(12,3))\n", "\n", "axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx)))\n", "axes[0].set_title(\"scatter\")\n", "\n", "axes[1].step(n, n**2, lw=2)\n", "axes[1].set_title(\"step\")\n", "\n", "axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n", "axes[2].set_title(\"bar\")\n", "\n", "axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n", "axes[3].set_title(\"fill_between\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text annotation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Annotating text in matplotlib figures can be done using the `text` function. It supports LaTeX formatting just like axis label texts and titles:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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5AbhHa937lgEoNQD4P611i2xekwFuQgiRR7YOcLvlHYPWuuMtAvAB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\nQgghMpHEIIQQIhNJDEIIITL5f/mSKrvCXrVbAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109b0aa50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(xx, xx**2, xx, xx**3)\n", "\n", "ax.text(0.15, 0.2, r\"$y=x^2$\", fontsize=20, color=\"blue\")\n", "ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=20, color=\"green\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figures with multiple subplots and insets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Axes can be added to a matplotlib Figure canvas manually using `fig.add_axes` or using a sub-figure layout manager such as `subplots`, `subplot2grid`, or `gridspec`:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### subplots" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1070b7e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2, 3)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### subplot2grid" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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fwP8jTZZZAfwwIq5vsk3nS4+0my+FKjVIuhsI4HJS0qwgHUgak+YI4EngbGA1\nMB/4MjA9Inbm2jlheqQbg9TOl8HRyXwpMwGmydcuAGZFxGeavOd86ZFuVWqYR6o1toE0LXd3bbLs\n/pGpABHxImma7y2kT8lnAWfnDy42JjhfrIgyE2AanUKqi2gDxLXsxhhP47UyOnyGNJN02e0juXX/\nA7ggIoZ9UKOkS4ElwDERsbHJ+86XHmk3Xzybycx6pcwEGAAkzQGuIT0z632d0RBPgumOqifBFB1D\nKjQTpuFrHgZOBcY3zoTxJ5je6dIYkvNlQHRhDKnQBJjsvTOBO4FPRcRvRtiu86VHujWGVKgUTC6o\nC0hnX86Kscn5YqMq8zBHSbNJ45GfG6kzsv5Waemg7L1JwGPAXOBXwAR/4q2POpUOyt5zvtRYF/Il\nfzb9NvCNiLhXDQ9zlPQIMJP0QUekDy//NyL+tck2nS890o0xpOFmwgxXf2wZ6RPy+laDsr7mfLHC\nImIT8Nkm618nN7400iQHGxyVlg6SdDxwEnBT+6FZn3K+mFlLinRIhWbCSBJwMzA/O1/uq6nFVhnn\ni5m1pMgluxeB8ZKm5S7DHM37b0qbBBwH3JsdbMaRDjJrJZ0bEWvyjT0tszt6UMvO+dLHepAvZrtV\nXTpoSu7lR0mD1R8B3nYpmHqoWekg50vN+UZqK6NupYM2DC2k+08C2OBSMGOO88XMSnPpoDHGn3it\nDOeLldGtMyQzM7OOcodkZma1UKhDkjRZ0v2S3pX0iqTPD9NurqQnJG2R9Jqk6yS50xtjnC9m1oqq\na9ntQ3rI2gHAx4HTgK9XEKf1F+eLmZVWeS27hq9t+lRHDzr2Tt1q2TV8rfOlZjypwcroxqQGP9XR\nynC+mFlLilRqKFybLC97quNxwGWthWZ9yvliZi0p0iF15KmOLgXTHT0oBeN86WMuHWS9VHQMqdKn\nOvoab+90aQzJ+TIgPIZkZbSbL1XXspsN/BCYExGPjrA9J0yP1KyWnfOl5twhWRm1qmUHLCJdrlkp\naWv23gOtBmd9y/liZqW5lt0Y40+8VobzxcpwLTszMxsI7pDMzKwWKq1ll7VdIGmdpM2SbpU0obpw\nrR84X6wo54rlVVrLTtIZwELgVOBQYBpwZTWhNlflPRNjYVtd4nwZkG11QW1zBer7e6nrtto1aoeU\n3VdyDrAoIrZHxBpgOXBRk+Zzgdsi4vmI2AIsBS6pMuBGdf3F1HVbneZ8GaxtdVLdcwXq+3up67ba\nVXUtuyNWrWZFAAAEgElEQVSz9/Ltpkia3HqI1mecL1aUc8X2UKRDKlObbF9gS0M7DdPWBpPzxYpy\nrtieImLEBTgGeLdh3deA5U3aPgX899zrA4D3gMkN7cJL75bRfuftLM6XwVv6KVecL71f2smJIsVV\nXwTGS5qWO7U+muaPCXg2e+9H2etjgPURsSnfqJ0bp6z2nC9WVOW5As6XfjbqJbuI2AbcByyVNFHS\nTOAs4LtNmt8FXCZpenZtdxFwR5UBW705X6wo54o1qrSWXUT8FLgeWAW8ArwMLKk8aqs754sV5Vyx\nv+vQteHJwP2kZ+O8Anx+hLYLgHXAZuBWYEIr2yJNC32CNPD5GnAd8IFW48p9zcPArna2BRxGqnj9\nDukP79o2tnU1sJb0mPBHSI8Kz78/D3icdG/H7aP8bCPu+24tzhfni/Ol/vnSjVzpVML8IFv2AU7O\ngprepN0ZWdD/SHqI2ypgWYvb+mL2/njgoCx5FrayrVz7C4BfkAZPGxOmaFwTgJeA+cDewD8AR7W4\nrfOyZDmUNMNoGfCbhjZzgLOBm0dKmiL7vluL88X54nypf750I1c6kSwTgb8A03Lr7mwWEPB94Orc\n61OBda1sq8m2F5CbrVN2W6THIjwP/FNjwpT8GS8HflHR/loI3JN7PQPYNsx2rxolaUbc991anC/O\nF+dLf+VLJ3OlE8VVq7zZrcy2Gp3CnrN1ym5rGamsyfom75XZ1onAq5JWSnpL0iOSjmpxW/cA0yQd\nntXxuhh4cJj4R1OXGw2dL3tyvozM+bKnOuZLy7nSiQ6pypvdymxrN0mXAscBN7QSl6TjgZOAm4b5\nFmXimgqcD9xIOtVfCSyXNDTlvsy21gFrgBeAPwOfA746TIyjqcuNhs6XPTlfRo/D+fJ3dcyXlnOl\nEx3Su6TT0bz9ga0F2u5Purlq6zDvj7QtACTNAa4BzoyIjWXjkiTSNdL5kc43m93TUCau7cCjEfFQ\nROyMiBtIN/UNFZAss63FwAnAwaTrxUuBVZL2btJ2NKPt+25xvuzJ+VIujqFYnC/1yZeWc6UTHdLu\nm91y60a72W1I481uZbaFpDOBW4BPR8TvWoxrEunTz72S1gGPkZJmraSTW4jrt6RfxnDKbOto0jXe\ndRGxKyLuJM2gmTHC9ocz2r7vFufLnpwvI3O+7KmO+dJ6rhQZaCq7AHeTBrYmAjNJUwiHmwXzR1Jv\nPpk0G+OaFrc1G3gbmFlBXFNyy/GkaZkfBsa3sK0jSJ8YZpM+ACwAft/itq4AfpnFJVJV5K3ApFyb\ncaRPN8tINxPuBYxrZd93a3G+OF+cL/XPl27kSqcSJj/v/Q/A+dn6Q0jXE6fm2n4FeJNi9wkMuy3S\nnPm/Zuu2Zv8+0Gpcua85lObTMsv8jHOyJNmcxTm9xZ9xL9J15z9m23oCOL1hW4tJCf5ebrki29bW\nMvu+W4vzxfnifKl/vnQjV5R9sZmZWU91YgzJzMysNHdIZmZWC+6QzMysFtwhmZlZLbhDMjOzWnCH\nZGZmteAOyczMasEdkpmZ1YI7JDMzq4X/D+G9BA2wwBRtAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a08ce10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)\n", "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)\n", "ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)\n", "ax4 = plt.subplot2grid((3,3), (2,0))\n", "ax5 = plt.subplot2grid((3,3), (2,1))\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### gridspec" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.gridspec as gridspec" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10a341c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "gs = gridspec.GridSpec(2, 3, height_ratios=[2,1], width_ratios=[1,2,1])\n", "for g in gs:\n", " ax = fig.add_subplot(g)\n", " \n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### add_axes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manually adding axes with `add_axes` is useful for adding insets to figures:" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "image/png": 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Nfn7AL7+I50faTkYJ6QmY7z8fO5Q78G2/b+Hp6qmzD3+ZYbp9W7QWrF5dsmR0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\nju3K7YhPi8eoNqMwus1ouNR00e6BWYX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"text/plain": [ "<matplotlib.figure.Figure at 0x10a0a39d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(xx, xx**2, xx, xx**3)\n", "fig.tight_layout()\n", "\n", "# inset\n", "inset_ax = fig.add_axes([0.2, 0.55, 0.35, 0.35]) # X, Y, width, height\n", "\n", "inset_ax.plot(xx, xx**2, xx, xx**3)\n", "inset_ax.set_title('zoom near origin')\n", "\n", "# set axis range\n", "inset_ax.set_xlim(-.2, .2)\n", "inset_ax.set_ylim(-.005, .01)\n", "\n", "# set axis tick locations\n", "inset_ax.set_yticks([0, 0.005, 0.01])\n", "inset_ax.set_xticks([-0.1,0,.1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Colormap and contour figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Colormaps and contour figures are useful for plotting functions of two variables. In most of these functions we will use a colormap to encode one dimension of the data. There are a number of predefined colormaps. It is relatively straightforward to define custom colormaps. For a list of pre-defined colormaps, see: http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "alpha = 0.7\n", "phi_ext = 2 * np.pi * 0.5\n", "\n", "def flux_qubit_potential(phi_m, phi_p):\n", " return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "phi_m = np.linspace(0, 2*np.pi, 100)\n", "phi_p = np.linspace(0, 2*np.pi, 100)\n", "X,Y = np.meshgrid(phi_p, phi_m)\n", "Z = flux_qubit_potential(X, Y).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### pcolor" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "image/png": 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cDZ3wbUDePCya8OUxySN4OKqkm8/iWDvEyUMzI87IyDgSyIxYwSO7o0h4zm6s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\num5ZRQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107305f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "p = ax.pcolor(X/(2*np.pi), Y/(2*np.pi), Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", "cb = fig.colorbar(p, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### imshow" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "image/png": 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VzgefYYc9st+GqYl5gG3NIakD0AYkQUBobnoFoPkru/5Eo1xSY8LSKMFtafZ5ZYY0gTQG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\nNqdMbaINDmhK3MG64bMMkFxkw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hJALoKRmKNclCNFTJPlDT0607A0GvwasInxkzoEYC7s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\ntUUWWeS6kv8HUacFWVNPEqAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a083b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "im = ax.imshow(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])\n", "im.set_interpolation('bilinear')\n", "\n", "cb = fig.colorbar(im, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### contour" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "image/png": 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Zg4DDNVSgSjbT3q/ItDWL++lgGKCrCF0FbajWk1oCtWjWf3L7zMHF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"text/plain": [ "<matplotlib.figure.Figure at 0x10b038c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "cnt = ax.contour(Z, cmap=matplotlib.cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D figures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use 3D graphics in matplotlib, we first need to create an instance of the `Axes3D` class. 3D axes can be added to a matplotlib figure canvas in exactly the same way as 2D axes; or, more conveniently, by passing a `projection='3d'` keyword argument to the `add_axes` or `add_subplot` methods." ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d.axes3d import Axes3D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Surface plots" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "image/png": 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/8Ad/gC984QsDJ23bT+bOlU4B/qyALMueSHo6nW7Y0JvPW/jN33wNd965F9u2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\neOOXr/cTAACLF6dx8GAO73znfBQKBvb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P4aGHHmppDRtkG01yv/R19bkL8advv9D9agGG\n4J6CHQDWct+/A8BJxlhodgCYBQ4B0LvNCBllSmH2QobTshhKpfA3bCwWNL3YwDnn1Eebzz1Xxo4d\nWaRS9dGUFSu8msXlci0ybhg2ZLnauDwxYeIHP3ijnUuogyJZJK/XqIZ0qpUPyEHgN36U3WlUW/63\nf6vAtiUoigPbrn+9isXgTMOKFZZnvsTx44B/fTt61OuUj4xUoKr196VcdvDudy/Cjh21z6xlMaxc\nWcsYvfpqe/KjNBxG13XXQeD7MQahB2E64B0ETdOgKAoSiQSWLFmC7du346abbsKaNWtw66234syZ\nM6HH+dSnPoWrrrqq4XNdc801yGazyOVyyGazeO9739vryxFMM504BPQ3jDFXqaeZbGe7z6P6ItS8\nyo2eru+TkVQF0YXzagonDc6lWQmN57jctZL9TaVSPR2A2ep58GuDX/mmkSymHJI1VBLxwH4MfwOy\nHI16HAUAUNIpd8FQ53t7AZV5Dcu9vceetPN8ZL0VpyeIQVQsoqZkKlFbtmwZTp8+jUceeQQXXngh\nPvvZz4ZWAwyyjW5HZUiSpIwkSe+XJCkiSZIiSdL/A+A6AD8POPSDAD4mSdJFk30DdwL4p2bnM6Md\nAl6WrNsMAakckFJEqynMZs+9a5eFLVsYLr88gkym/kM2Nlas+xljEi680BuhiURkbNs2CtNkuPji\n+XV/Y1neY4+MjEPm3lCOU/1/LKbj7/9+e8f3i0qELMtqafDbdNNq6cgTT1QXpnicIRarf52OHg02\nNgsWeMtNjh1zcMUVtYV2eFjBsWPeCdMTEzZWrw5OGQc5CplMbVF/9dXTIVfaGnw2pZmDMFUDwZox\nVU3Lw8PD+MxnPoMlS5Zg9+7dePDBB7Fy5crQfoIf/vCHGBoawq//+q/37dwEsxtqSu5WqUdLRD0b\nCC0RDdyk8vjlMeWwzTm30XLVhiajIPpj32j5HMkZaCULMhV2x698o6oqbNtGoVCA9vQ/QuI273Is\nRGRg8p7TYyVVg9Kkrp13CoJUhgBAnjefTrLVy6n9bUBkna5rKmc49AtZlnH55Zfj93//93Hbbbfh\nxz/+Md761rcGOtKDbqP9GQL/lw8NwD0ARgGcAvBJAB9ijO2VJGmJVJ1NcD4AMMaeBHAvgF8AOABg\nH4AvNj2fnl3ZNNKNQ8BHLaLRaNtyos2e+7XXqoZz0yYb8+apWMgpgup6tSQkiMOHvRvNtWsTmJgw\nJ5+z3tDv2ZPzLCb5vIVVq3jHgbSybezePYbR0ca6+WFks9m25EoHjSAHoVKJ4NAhBYCDXE6GYXiN\n8MKFdt1gOIKx+ibi48eZG1hbtix40eM3+UQkIiObrS8JM4ya8e7EIWj0/mzkIBiG0VTRaRCjSZ3A\nX4NhGMhkMrj88stx++23B2pdZ7NZ3H333fj617/e1PZs3rwZw8PDWL16Ne65554ZvRgL6pEm56q0\nuwaRaAXVTLckK9rgefhSIM/PJ9WFSHLUj55JuKVDgb8fykDPpKAPZaBlamXJkq5Xy1tagG+IbSUL\nMtWQc0B2kJBjMUiKCmaZ7qZfTiTqSoWAqjMQBJUcyYnOlMu0J77b0d8B9ddFjbtk26ciM9yvNaJU\nKiEej+Oyyy7DJz7xibrfzwQb3Y7sKGPsNGPsKsZYhjE2nzF2DWPs2cnfHWHV2QRHucd/kzF2DmNs\nHmPsj1jQZsXHzNvRBdCpQ0ByooyxUBWhbtm8uVZ7fugQw/CwDtpfrFypoFIJPu9Dh0ysWVOLTDJW\nS8++8Uau7jzHxioe2UoAWLiQN1rVx1fLYST8z/+5vuVrIIcJABKJRMdypfzfDELkGaie0/e+p6FQ\nkKEoDLJsw1+St3hxeGr8zJn6z9jhww4uv7y6COh6cO/BiRP1P1+7Nolt27JQVe/H8tChmvO2Y8cY\nisX21Uxafb38DgLfsN2Kg9BrpuJ94n8O2qQ14q677sItt9yC8847r+Hjrr/+emzfvh2jo6N49NFH\n8fDDD+O+++7r+pwFg0c771VSvlIUpafyzzrXC6Alom4/Ae8UKBHvxlXmotSRed7MND9B120s5jbD\nrZQNkcABgLaGl01XoMHzvJLXFvOZAn8Jkb+nQo5GAvsPpIgOyZ+dSYUPJe0VvNwn9R3Qa9MrmdZ+\nwzsXzeYQzAQb3aHsaN+Y0Q5BNxvMcrnsRru7kWds5IwwxrBpkzfiu3OngyuuqBpnf7mJn6GhqjFP\nJhVs3VoLUY+NmVi9ul6JaHjYW9qQzfo3jtX7lU5H8NhjIw2fm6BJmaTE0m4DWND9GbSI8r//e/Wa\nHEcJjAwkEsHRAkVhrpyoH8oynD0b3Jy6b18F55zjjTzbtgnDsHHhhd7F4fTpMs47LzH5GIbNm7sr\nG1q/voQ77xzDj340gQMHmkjpNVB0IjUnxljfHYSpblpuZA+2bNmC//iP/8Btt93W9LjLly/HsmXL\nAABr1qzBXXfdhUceeaS7kxUMHO28P8vlMvL5PKLRaE8Gafq/V0MyBQDcSbhaqnG0WgqL/LOqLVSS\nCTDTgqTrcB79euhxqMSUersGzfaHYnvXTjnObf5ZbT0I6yuoUyCKBqgNBSgNSaoGeWhB3c/b4fn5\nV7hf6xcH1877m5J5xSKy6YPSlBxGI4dgpthoWZUbfk01M1fAlqMdI0MDPizLQiqVcuvrulWICHqe\nfL6A7dvrN/3r19u49FINlUpwuRCxe3cFsgxcckkcL73kbWxcsCAOwDt/IZ/3blx37x6HriuoVOgc\nZAA2ymUH5XIFL754FO9+9/mhz0+KBdFoFNFotKt5D4OKbQOHDsmIxx0UiyqqTfteLCvYIVi61MSB\nemUwAMD27TZWr9axd2/45n3FigROnKg6jJmMgq1bzwIAFiyIwf/ann9+AsePVzMFr712Btdee06T\nK6vn+HELN9xwFkePVlBVH2OQJOCTn5yPe+55i6fnJAgqjSAngRYNSjv7Z0KQ1v+M2QS0yHPPPYdD\nhw5h6dKlYIwhn8/Dtm3s3LkTr77aXNZ3kBdZQWe0kqWmaCw11JIj3Q2MMRjfuL3u59Q/oER1WAUD\nSiwK2yjVPc7TZDyZHZABOJaNyIJ51ayAbzAOs21IqgIlnQazrVDngZwBVVWhaZqbJRh0tOf+GYhE\nAaO1slolnYZTqD5WjscCB5cRUjQCSdPqMytUbkR/K0sA1KpjkmneXMwYw5aL3weLCwKq6ep7YMPy\nq6GlFFy5LbgqwD/DpVKpDp1rZRZAK+fVS/jjlctlzJs3L/BxM8VGT0cWoBGDdTYd0mrJEE3SlSQJ\nmUzGjXZ304MQ9AGh59m7lyFMvXB83DuQLIjRURuXXJIKrFM/fbr+Z2+8kYWu14yzYdhYvbq+j6Bc\ndhCJqPjSl34V+LyktkQDmiil3W3z9iDy0EMySqXqV5X613N0NNghGB5uXGc4PKzBNMPvl2nWnmvV\nqhhMs3o8w6j/Gz4zs2lTexmC48cZvvKVMq69dmLSGciCHB/GgG996yz+/M9PtnVMAJ6oX1gGwV9i\n1C5T0aPAP0crMqp/8id/gn379mHLli14/fXX8fGPfxw33ngjnnrqqbrH/vznP8fo6CgAYGRkBPfc\ncw8+/OEP9+EqBIMMlV1WKpWupDZ5G0wbbqA+KyBzswXod0osCiXqLVXR0rUodViDq4vv88tsq1pC\nE1A7T5nldvojBo5oNYsixQOyAFLvtk7ygkW1b9K+zH+mXkAkDGY6kDQJkiYhvjwKfb4KLaVAS7Wn\n5ESDQBvNAmjXlvfy9adjlUql0DkEM8VGt9lU3P/zmfJn7APNNqq8TGY8Hu+JnGjQc/ifZ8+ecAOb\nzwNLlzYf4pVOR7BjR/0GcPfuPDIZ7/ENw8aqVd5oQiYTnBq2LODll4/Btr0fbDLk7aottcIgOhMP\nP6xA1xkcRwbgwO8QRKMOjhwJPm9VbVzLb1k2YrHwj9ju3WUoSvX5KpWag7dvX31kamysFlF67bXW\nHYKvftXEu94Vxb33RnDmTAZr1mjIZKrnNDys4NprVZx3XgGPPXYETz4ZLq/ZCv6ZEEEOAmlkd+og\n9IN235fRaBTDw8PuVzKZRDQaxfz583HkyBGk02kcPVrt7XrmmWdw6aWXIpVK4cYbb8RHPvIR3HHH\nHf24DME00SxYQhFKsqlUJtRNgIWcAYrqSrLscQpkTa3b/BNKVK9XGPLZeW0ofDgmAM9EY1QqYM98\n3/3WsizkcjlEo9GO+yOmda2QpGqkJOhX8QSkRKrOSZATCdehcpuIY/Ha12RpkZysZmGkdgZ4tuB8\nvLzsGjhW7ZyViAwlIkPPqFBiMpSYjK3vXdf6c04SplhULBanXbHIMAzE48H7mxljoyWp8dcUM6NL\nhlrpIbBtG/l8HrIsI5PJBNZs9iJDQB8SAO7zvP56eAZg+XIJ27ebSCblulIfHsMACoX6jafjACtX\nZuqixfPmeT8g4+P+GnEJAIOuqzCMEr797U34sz+7EkA1BVcsFnveYM0Yc6UsqV9jUJyD7dtlLotT\n31C8bJmNN0LGNmSzjdP9lmXj0kuTePnlbMjfO7joogSOHDGwc2etRGh83MTy5SkcPFh7/+zfn4eq\nyrAsBwcP5nHmTAkLFjQedvPNb9r4ylcU7poK2LFDxapVC7BixQQOHz6D9etr74/PfnY/1q0bQiTS\nepyg0esYVGJEaiOWZaFcLnvS1UG9PFOlYtTNgMO7777b/f+SJUuQzdZe7/vuu080Ec8h/O9XfuPe\ni0AUDdIrFAqIRCKIRqPgk9BqPAarWP2JpCgNy1faRZ1fDTZJqganWA1asEqlujmuVMuByBmgptVO\noPW4UCi49mPagge08bdMSIlUraQHgJRMghVrwRspnnC/l2MxwPHaEn44maRVHQJmNujhygy5z6dt\n+BeY1/xfgQ975e3XQtJkyKjeo9QFcSi6AsdmkBUJktzeeyDM5vKzAHg7bhiGa8dp8BtvT3tpv/nj\nlcvlhk3FPINqo6cjC9CIWZMh8EP1mtlsFpFIpKGcaC9KYXK5XJ0c59at4RvGRMLB2JiDSy9t3NwV\nizGsWRNcQxik4zw25n3O3bsnEI3yfl/13EqlqpPxz/+83TW+hmEglUq5g7x6geM4sCwLjuN4JggD\nCBwQNpW8/LIE25ZQqdD9qV905s8PzwIcOdLYITh2rIhisfFHbN48DatWxVCpeJ/73HO974ty2cEF\nF9SajZuVDT3zDMPdd0uoOQMVANVrURQVyaSDs2e9i9HBgyU8+OCJhscNoh3VEH/EiRor+anSU51B\n4N97rSgMCQR+gj4DfNlMkDPQybpDkpGxWCw0+q76GlqVyY0o/VxNVje5lCWQIzrUTE1dSE2nIUnV\nibk024CcAWByUrF/NoeqwTRN5HI5d9ZLp9fqOA5M03QFP6jHr90BW52gvvpvgBYBopP3kGr9I1GP\nM0D4MwVu5N9pco569f6QYwCgpV4BPy9fdA3MnA1ZlaDEqvsBRVegRhVoUQWKJiOS1qEnNOgJDSO/\n+RttP0cQQYpFAKZUsahRydBMoZ3BZFPBrHEI+DdetaE3j3K5jHQ63dMNLg+lgoFgOc5t28I3k8Zk\ng9f27Q4S/z97bx5lx1Xei/52zXXGHjS1pJZkTZYlyxLGNh7AgRdfcmO4N9yE5CUvwH0syEtWyLuX\n3IEbB8cMyytwcZ6BPLh2IDyDSVZIFr7JNcRgVoyNsS3Llq3JraGlltRqqzX2dLrPWMN+f1Ttql1V\nu845PVot+rfWWT2cOlW7hvPt77e/7/t92fTbMDhYQqEgdlAGB5NFWsePl2CaIQGo1x1s3ZqsI/Au\nF8HJk2M4deoCKKWRugoRpjuBNRoNVKtVSJKEbDYb6P+zL7GoQdhCEoRHHpGDlB0PyWNKKasrq1Y5\nGB9Pd1i7uyWcO1fH4cM1rF6d7mCWSuJjUJp8Xr1iYw/NCMHwMMWHPuTG9uGtGq5ZQzA8XMKLL8rY\nuDG5uvKVr7wJ214YcibqCREnCLVaLXAQ5pMgsO9tuVxODUMvYQnNwNtHVkfWzHGfiT11HAeapkUc\nbubgi4p7AxLg/5Q5B0rSxXYpLp+pdAocVZeGyju2BdRrQef62RBq27YDOVbWRZzNE6KuwvO6aCDL\nYYRgumAr10LZUc6JzXOpWZLs1WPIMmjHctA2fBY1L0Pm0lKXva0DihE+B2rGS2Mi8vw5lyLFIkII\n6vU6qtXqnCoW8ftoJTu6GLBUQzCHEHUqbjQamJiYgCzLLbsh8vuZ7sNqWRYmJiaCtIj4cS5edHDx\nYrqxGhry0otKJYrdu8WdUHt7NQwNlXHmjDisePFiA+vXR3WjLcuNNSQDikUxi9Z1FZWKhUceOTyn\ndRWsloI1exPJzYmcQTaRMHUjlmY0XysNe/dKmJgIvwL8LczlKO6804GiuNi0CbjlFuDmm8MxrF7d\nPAwbNiQj2Lgx3cE8ebKBwcGk2lS8uzEQXaB6/fX0fP/f+R2gXOavtwXAASFAPl/D+LgDSolQoWFo\nqI4f/nB2sqYzheiZYK3q4xEEFnWaaywRgiVMF/HU1UajETjHM02biYN1EVcURbhoo+Siz6ySMRN1\nAnFIAse9ZWFxfHsWKdANdBz44axqzliEQdO0xHwa7yqsqur85rK3WzQsyYCZDQurTY5A8KTAaGFT\n0khUE//l0N3v5qIDXp2ApMhQTRWKrgRkQNEVqKYKNaMG/5sv8DacRQ4IIZE5fbZRHr6oeLETgqUI\nwTyAEALXdSPKONNRNpgOIWChS75AWZQT3yw6sHw5wcWLYX+C06ddKEpyrKyfxvBwHVu3ihuXrF2b\nT/wvn48SgPHxeGoLy+/z/vrpT4fmNEVocnIy6EgpmrzSehMoihKsMjCjTylFvV6PEIS50Lu/fBmY\nmiKglH0FXC9MDmDHDheFgouXXiLYv9/FwACwbx/w+usEu3YRdHYCptk8XSiTCSenZsRw82YTPT1J\nozY0VENnZ9SZOH8+zBbev/+K8Br8zd9Q7N/vNZ8L4ZGLO+4gOHYsJBqvv06EUYLHHms/bWg+c/xZ\nipEkSYkIgmVZgTMw26gSfw6VSiXSqXQJS2gXbFW0XC4jn8+3dI7blSplkTK2wBVJcfvG/cHvcq59\nIstHBxRfD5+oCuRCdD6RRdEBpsPv+isUzBmWZr6iaVkWpqamgsLVZuDTVfjuu3NBDqjM3bOIc5/1\nogXsFQef+qOnpLFkY3O1nzYkUmmKwyl0J54Vu+ZEogMAIt1tZU2BrHlkAIBHCkwVZz/0a02PNZc2\nnS0Gsjl9LqM81wQhkOWmr4XGNUEIWEoBS3uZ7ipFu4SAdTa2LAvFYrFpaPSNN9IJwbp10S/b8LCD\nt789GSXgvygrV4qdlHo9Oe54HcGZM5O4/voO6Lr3gOk6W4n39n/ixChOnRpLHS9Dq+vEojOqqjat\n2WgHcYKQyWSgKEqk7Xparnk79/Ib3+CLbQHAhW0T3Hyzg/5+iuFhglWrHJRi9cAHD1IUi4DrNlcY\nKpdDwnfihIX168WTRGcnQTYrfo42boySwKGhCvJ579m+dKmG/v7LkSiKbbv4kz9hdQMUXpG0DcDC\nihUEBw5MxI5AsHx5VE2kp0fDmTM1DA6m6OUuMPjJSRRBmOu0syVCsISZgBXMz1ZWNL7PSqUCy7La\njnbLuUzQgyD4n9k6ShFXGZoWFAXQTdAZnjMfUWFzarvf3TklB0ef946txWy1KbAHzVKJ4sSIjzbo\nRpIYAKC5MFrr5qMRfrvgNSrjc/MP3f1uyJoENSNDyXmv7m3ePhTdIwLeUDzbqWXnJlI1G8xFlIef\nD64JQjDDTsWEkC2EkCoh5PGU9/89IcQmhJQIIZP+z7tbjWdREwJ+9USWZeRyuXlbreQ7G/OFw4DY\nUX7jjfQVZH71mCHUwfcgScDx46GTfvmy2AHt759KNJTq7y/BMBSYpox3vasHrutA01TU696KTtio\nDCBEgmW5+OpXX00dbyuw+1AulyN9C7z9z03vAl7OktVriOQs2cTcCs8+K8Gy+GvuYMsWB2+8EfYH\n6OkRpwWdOePl+Jtm+rM2OFiJ/N3bKyYEFy9WcOWK+N5Gi8G9iM7GjaEDf/x4NRJF+ZM/sVEquX5d\nhArABCuU3rTJQaWSvC4HDkgoFGTceGMGW7cSnD8/itOnr+CJJ4ZTz+1qwVwRBH6SKZfLS4RgCdMC\nqyWjlAa67dP9fNo+WaSVn2/47eVsJjW1QMqYQcdc2dS9VyyKIGlaqgSm0tXtRU0lEnFqSbETyPuL\nFVxjNWI14Oz7oXBfafOAKKIy0zlcRA5YRL9SqbRMPSV0mivVGc6xV9RolAAI04TSIgY5cdQ/DfnT\nLwdRKOoXLbtOeD6KrkDRvTlDy2pQTRWypkAxfDlURYaa0aBmFk40Ie16pxE5Npe3U5R8TRACRW76\naoKvAXilxe5fopQWKKV5/+fzrcazqAmB67pwHGfWjU/a0ZBmCjyiAjHR5/v60leQ6/V64n8HD9bR\n2xuu0mzZYqDEdR08fryM5cuThqVUsrFtWzQXvNFwcfPNy9Hbm8XPf34O5bIdrCx750QAeKE8MP30\nVgAAIABJREFU0/SMw49/PJA63mZgKUK2bc8oOjNTxPXuWSoJEFU6EDmClAInTxJUq+HjrygUo6NA\noxHe22xWTAgyGYpXXrFw003iVZeeHhmjo1FCODycnGyWLVNw4sQkTp6soFBIrq6VSsnj5/OhMd+/\nfySIoui6ie98RwegwHFUhF/tKtasAfbuFUvg1usEd93VhTfeuIT+/nCbJ544L9w+joWSBW0HMyEI\n8WdjKUKwhOnCcZzg2ZvOdyFtW9d1gwaa+Xw+sp3oM1LseZV4lSGBMo5k6EGaEA+54DuospwoLA6O\nzyvh5AoAs/e+fCZpJqMZA7PT+Xx+TiIqPOIRZl3Xg+LWNIdTshsgfhqUq5te+lDcyQ829p21bIwU\nAB4BYFEFnhTwkYc0kuCD+vu3c9GULU3TcPrf/hsoRvJ6yZoCLWcEBABAkC4kKVLk/wuJVt+JuGIR\nEx0RzeP8/bomCMEMaggIIb8NYAzAM3M9nkVNCBRFQT6fT+RVThdphMC27aBwuJUCDw/HoTh6NJ0Q\nnDtXEfyXYMOG0LCvWBHXYwe2bBE3i+nujq76ZLMKslkV/f2htv3UlEAyjZCgO+7gYAmHD19KHbMI\nrLBaFDVZSPCOoCRJMAyjqSP49NOsfoB94ShyOYqRkegXkF2bONatc+C6wN69Dm69NUkK1qxJfpFP\nnbLQ2xvddtMmz/C5LrBpUzKMPDBQTkR/6vVwTHyDsvvvJ6jXCQB+VaEBwMGqVY14k9EAd96p4tSp\n5CR++PAkTp5MNkhbaMyGcLRDEKp+EwrHcYIJaIkQLGE64FMkpzsPxeceJlWqqmqq0IPoGIEzz/bL\nJEWDJllix0mOEYOguVYxKThARLKY3Co51QxQub05slqtBnURc00G4mB2gMmYihxOxwmJAIBoXn+r\nXg6CFKBUxNORALhZb16nqg67uDwsJJYUUEFxM1MRUgwleK24YXl0Gz2sHVB0JZEyNPLHH0od4lu5\nyMMrFvHKjawo2XXdIJJwNS1GzRTTrSEghBQAfA7Af0K8aVISbyOEXCKEHCOE3E9I60r5RU0IGOYq\nLYWBhRlZg5VWCjzx4w8MOKglRWIAAIUCESrIAEBfnwVVZcWNyShCNMUlxORk1GDdeGMBFy9Gc8AH\nBkqQZf7zElyXRsLbDz74gnjQPth58oXV8RShqwGtFIy++10KTePHa0c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8hsmGEjyolvJ9VZJEUGUWTIajJCoJh60EGXFRxLmgIla2L5zg3BdjQmTuDU06Po7Ps5V98dh0av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D8/P/j5+aF79+4A2tfvoLmmZ5cuXUJERASSk5NF27mh8oVmQbLnVnlA65SJzZkzByqVCuvW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\nIYQQQgi/KDtMOhXKEBBCCCGEEGLB/g9AJazGUaVqWAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a0bd650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(14,6))\n", "\n", "# `ax` is a 3D-aware axis instance because of the projection='3d' keyword argument to add_subplot\n", "ax = fig.add_subplot(1, 2, 1, projection='3d')\n", "\n", "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0)\n", "\n", "# surface_plot with color grading and color bar\n", "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0, antialiased=False)\n", "cb = fig.colorbar(p, shrink=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Wire-frame plot" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "image/png": 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XKhgGDBvmYvfu9K/Len+J7kFNDJK2h31dLlekUjT2Gq3FJp1+xsPNiIZCcO+9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24//yzO99o13ASkM2ck6tSxAiLLnDHDRkaGxIABBj/9pNCihTBaDg11kZtEnxWgRQuDI0dEj7hh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\nQvpNAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109e0e890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", "\n", "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Coutour plots with projections" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "image/png": 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tZs8eN88/v4uGhoaE7+HesbXguS7qvvcAT8y4mU/fVYbFPDq2QbyAxg/mfv81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\nP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C7kchlarYFkMrn1mwdzXwZDLQ0NrTvyT319B3jvvZdoa+tBpdKSyaSw26d55JHH8fu9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DaOt+j+3Dn8sQLMg4r7OtnH3qOdpffJPYiQZcNZW4ZlRgK8hDdtoRSZ1kNIba7ife2EzsZAPW\nPB+eJfPwLJ1P3jWXkzOvJsNuTgQ7SO7dQvLAduSKSiyXX49cary/0JOIjkZE5xm0nGJUXyluj0Hs\nCUbhVDvkumFagdFbBkgmYcMBmVkVOlOz3JuJDHMsZjifSyEEL730Ej/84Q95/PHHWbhw4Rgc6QWN\nSZbsYBgrQ2foKb/KskwoFMrou423Ck56VnkhMQqHArMPapJSzHk8SZJIJpOUf/MB9l52O11nmnEU\n5fclr1jtkNT6vK5IRJFzS/t8PxiF4n6EnHYdl6gqE6MSLAGmFApe2ykTCkeR6MtmznNDR6hvwJQc\nLiRvAaLlOJTPzngWLVXzYM1tqH98HEvt1ViWXIlky2SROypKmPnVzzPzq59HC4aIHqsnduoMmj+I\nrqhIFhnZ5cReXICjrBhXzQysXmOTmi5qnvC3kzh5EMvJg0j+VqQ5S7B+7G+Q84uNz6qeRPibjGDp\n8hErnoXVlYPH6URNSjS0G3OxlUVGwDTR3gX/+66FHKdg+ey+101RdNo6FKaUT6zxmHMdi1FVlW99\n61s0Njby6quvDksoZRKZmAyYaRirgJk+KuLxeDKGlXuXDcdSRi6d8HIxZZW9ke4D2Lu/5Xa7cZQW\noncESOb3lOktFgsWXUPyt0J+pn+jUGKgxMCVuRBFExBTjcDUG11R2HJY5lNrRq9v5LGrFHkFhxod\nLJ/b9xkqyOlRJ8rvVVSRymrQT+2BluNQWp0ZNGddilw8BW3jCyhPfhfLvMuQZy9GKpnS5z2suV58\nSxfgWzq4jZeIRxFN9UiNx5EbjmELtBtEnsVXok+fhSYgkUwi+duxRzuxhDvAnYtWOgtFMvp1ssVG\nU8AY5yn2GsHSzCp1HbYfldh8SObqBTpLqnt0dE00tcT59g+Ps2BuDvffPWPoF3uU0bsEO9RNbHNz\nM/fccw8f/OAH+dd//deLavM7HpgsydLjiXkuUnXDwVB7leMhI5c+p+ZyuS7KD9ZQhBaU1g62Lv0w\nq46uw+I0VHRSjMMd66G5HmXNRzPvRcsxJLsLuaQyn2GIFQAAIABJREFU7b2grsmQyOvNkNWS8Mzb\nFmrKdVbNG/mPWHomElI8/H6TnbuvS5KXpdMQihsiC3PK6GNDJpIaeuNB0HXkitkZYyYm9I4W9APb\n0I/vRyTiyOXTkYoqkPIKkXJywekGmwNJlo3NqKZCIo6IhSHShQh2Ivxt6O1NEI8ilU1DrpiJPH0W\nUnllqlcs9CQi1IEItEAshO4tQs0pQsX4uSRb6FIctIWteJ0wtUDCmZa5twbg5fcsWC1wy7Ik+Vmk\nC7ftCvBvj53kztsq+PBNJRNms3guykRCCDZu3MjXv/51fvSjH7Fy5coxONKLCpNKP/0hPTgNR6pu\nODgfAYL+7LRGYv4wY4xiEA/ACxnpQgsD6Woe+sI3sOZ6mfW9/5PxfdHVifKb/8B2x/1IBaU92p9d\n7dj8jcTL5mK1O1L342xAIpyQmFOWqfSj6/D8VuNe3XaF3ifDOV+kS6I5nU5kWWbbEYndJ2TuWpPM\n6s3ZGYH6dmNuNNeV+TMhBKLzLKK9HimnEKlwCpIzu7uOCAXQm08j2psQwQ5EOAiJuMEg1nVDW9Zi\nBbsTye0Bjw/JV4CUV2T0hHMLMkZ2hJpARPyIUAdEAuDyIeWVIHmL0JLG/bRYHQTiNlpDEm6bTqE7\ngU1SU58PTbew9Yid/fUWrlmos7iqb1YphOAPL7bw7EvNfP2L1cyfOzFsx9I/m8MpwSaTSX74wx/y\n7rvv8utf/5ri4uLB/2gSvTEZMPtDum5jKBQiLy9v8D8aIkbDVaQ/14PhzB8CKcm3izmrhJ4d+mD2\nTY0/+w1nnniWpW8+neqpgUGCUX/3E+T5y7DWXt3z/XgYvX4f0rT56HZ36n74oxJtUSdVBXGc9p4N\njRASL26XCcfhY6sHdiwBQz/2RH2UoyejNJ6NE0/oOB0yU8udzJuTw/QpPeeSTtLqvfERAt7aK3Oi\nWeLjVyXxuvq+V1fM0MAt7HZcsfR6FERSNQKnvwksNiRfEZInH1w5A4oGDBVC6JCIImIhiIUQ0SAk\nVSRPHuQUInkLkCy27lGKOKG4IKw6CcZl8t2GmpLLbr6WQNWS7Dsp8c4hG9OKVFbOjuJ1y314Akld\n8JMn6zl4OMy3/u+sIWvzjjbOVRy+s7OT+++/n6VLl/Lwww9PaFb7BMdkwOwP6U4BwWCQ/PzhS2/1\nRrr4wGgLEAymw9qbuJJesruYs8p0t4aBROGFrnPqez+n+bcvsvjFX+Ca0cOAFbEI6vO/RC6bjuWa\nD/dcw3gE/fQ+5LJqJF/PDr4jDA2dgppiHbulZ0OjqDqv7/OR1CU+coWG09F/X7q5NcHLb7SxbmM7\nTruFubM8TKtw4nZZiMV1Tp+JsfdgCLtN5qa1Rdy0phBZMvSQ+1tchYAtdRI7jsl8aEWS6VmSDjUJ\npzuMMm15nkEG6hM4hYBoEBHqRET8Ru/W4TbKtXYXWB1IVpuRSUqWtPRaGPOdySQiqYKmgKYglLjx\nGmrcKN26vODyIrlywenJCPrheJK2oEaXYsMiSxR7JQpzDM3e9PM81iSxfp+M3QbXLUpSXpDdFQRk\nfvxEE12hJN/4Sg3enIkhCWfOEA9Xn3nnzp18+ctf5tvf/jY33HDDRfmZHkNMBsz+kG567Pf7h2Qi\nPRDG26sy3TYoXbDZpOhrmpZyMXi/Z5Wxk43U/e23EEmN+b9+BEdpz4yB3t6M9sKvkGsWYll9S8/i\nHQmgNx5CKqtGzi1J/X5rl0GimV0G7jTiaDgGv39HJi9H5/pLYyCSWfvSpxri/Pa5s+zaH+LaKwu5\naU0RM6dnYQxh3ONDRyP86c/NbN8d5Jbrivj4h6aQ4xm4bHfsrMRL78ksmCG4cr6e1S0lkjDOIxw3\niEGFHvA4+orIg9HnJBFBJKKgxI0gmFSMuQ09Sc8SIoFsAdlijKpY7UaAtDnB7gS7u48dmJqEUAyC\nMUEwCqCT54Yir4zHIfUpdR85axB6kjpcvUBnVkXf8mvP7+s88pMT+IMqX31gKrKk9zHaHmvhkHP1\nrtR1nSeffJLnnnuOp556imnTpo3ykb4vMBkw+0N6VtbZ2XnOAXOiCxCYxs4mG3gshMzHGkOl3qv+\nLhoefYqzv/oD0x78NNP/9lM9BBMh0PdtRdv8CtYr/8LQlu3+vvA3IdrqMwTXhYCGTghEjWCZTjY5\n0wHPbbGwaKbO6nk9C3h6X7qlLc7Tzzaz92CUD99UxAeuK8bndQz4/KRnz8GQzG+fb+G93UE+eXsF\nt1xXgsXS/72MxGHdbpnTbRJXzddZUCn6ZJJgzI92hI0eZ1I3VIu8TiN4umzZA+i5QAgjOMZUiCoG\nuziSAE2HHIfAaVFwW1XyvC6sverY0YTh87njmIzHCSvm6Mye0n+gNPE/f2pi0zY/j3xjDk6HZdzd\nctKZ28MpwYbDYb74xS9SUlLCI488MmpGEe9DTAbM/pDuien3+8nNzR125jXeWeVAMEs8ptKH3M1Y\nvFANnftD+nn25wEYPV7PmSd+T/NvXqT4lmuo/If7cE4tS/1c72hGe/OPoCpYb7wTudCYrRSait50\nBNQ48tR5SHajEahoRu9PkqC6uEdkXQjYeVxi4wGZD1ymZ3UkUVSdP7zYzB9eauED1xZxx1+UYreJ\njAU7mw+ieZ69s+cT9VF+/lQDHX6Fz//VdJYtHpi81tgO6/fLBCISy2bpLKwUqT5gb8RVo88ZihtB\nLaGB0woOG9itRlnUKhslXIvcvdqkNgegCyMLTOpGIFSTxrVTNOO1ZMnoQbrsRnae4wCrpBGL9T1P\nRYPjTRIHT0ucapWYVSFYWqMzpTD7sfdGV0jj7gf38rNH5g/Ys+wdQEfS3Dkd/d3PwVBXV8ff/M3f\n8KUvfYk77rhjQq05FwEmA2Z/SA+YgUAAr9c75Gb5RM4q07OtwUo8o8nEHW0Mdp6x+rN0vLqB1j+8\nSvT4aco/8UGm3PMxnNN6VHlEKID27jr0Y3uxXn498qJVqVEI0dWGaDmBlFuCVFyZKh36I4aXZInX\nIMqYtz2uwMvvyXSGDZWdgiyky32HQvzH46eoKHNw/93TKS/NHJLvr6xuVgccDkfW/pYQgq07gzz+\n9GnKSxz89SenUTUje1nXxJkO2H5E5nizxMxSwdxpgupBBBWSuqFUlOgOeGrSGJdJdgdGIXoWD1ky\nro1FBotkbCps3f8cVrDbjGCbfg4mO9ToPdtoC0J9q8TJFonTbRIVhYJ50wRzpwqcw0yqdu3v4snf\nNvLod+YN6+/6+4ycaxn3XEuwQgieffZZfv7zn/OrX/2KOXPmDP5HkxguJgNmf0g3kQ4GgykB76H8\n3UT0qoSh9/D6Q3qJ6nyYuCON0J46HOXF2IoLUtlWytzZbkdr9xM72Uh4/xHCuw8R2LwTrStM4fWr\nKP7QdRRcuxLZ3rMw6Z0tJHdsQD+6B8uCy7EsvzblyCHiYfTm45DUkMtnIbmNgUotCac7jR7fzGKj\nTGmivhVe2GZhVoXg2kV9mbCRaJInftPA1h0B7r97OquWD638b46LACnfw4GqAqqm89Lrbfz2ubPU\nXprLXbdXDKpcE0tAXaPE4TMSje0SJXkwrUgwpVBQli/wukauDNsfEqpOU1uczrCFYMxJc0CiyS/h\nssP0YkFlqaCqrP9MeCjoCms8+PBB5s/2cvPaImZVe7Dbhr8ZHKiMO5hDixCCaDSa8pMd6mY0kUjw\nta99jWg0ymOPPYbbPfBm6HxRX1/P/fffz5YtW3A6ndx+++386Ec/mrCb5xHEZMDsD+kBs6ura0jZ\n2ETNKk1NXNP8eqScV3o7vw/GxB0tHPjsV+l8cyt6LI61IBfJbjB89UgMNdCF1ZeDq3Iqnnk1eBfN\nJfeKJXguqc4glAhVQT++n+SBbYj2JiwLr8CyeDWS25gvFIkYor0eEfYjFc9Ayi/vzuy6WbB+Q8R7\nan4Pi1TVYMN+mQOnJT6wTKemvO9HZ9f+Lv79ZydZssDHvZ+aNihBBwbW9B1KxhOLC57/cyt/eqWF\nJQt9fPQvyphdNbhWsqpBY4dEQ5tEUyc0ByS0JBR4Ic8j8LnB6xS4neC0g9MmsFmN7NEiG1mlcYw9\nZVhNg4QmoagQU4x/kbhEOAZdMYlgRBBNSOS6BcW5UJInKM2DigJBTpZRmPNBsEvlxdfbeGebn8az\ncYoL7ZQU2ynIs+HzWsnxWHE7ZVwuCy6njMNhwWmXcThkHHYZp8P42umQcTktqZ5x+mfEHP3qHUDN\nYGm2SIb6mWloaODee+/lk5/8JPfcc8+YrDm33HILJSUlPP744/j9fq677jruvfdeHnjggVF/73HG\nZMDsD+kBMxQKpRam/n73Ys0qh4tsPZ7z9T8cCpLJJKGOTnR/F3bZgixLWDxubPm5yI5+7ls8il5/\nBP34PvRTdUhl07HMW4ZccymSaZwcCyM6GhCRAFJBBVLBlJT4eDhuZJUImFFkEF9MNLTDS9stlOYJ\nbqzV+4gDKIrOE79tZOPWTr54byXLlwxtzneoYgupcxwg41FUidfW+3nhtXYK8mzcuKaIq1YU4M0Z\n+oYqmgB/GPxhia6oEewiCaMEnVAlFK27LNtdkgUjI5W7y7BWCzhsAofVCLIuB3gcAo9T4LAouO0K\nJflO7Nmou6OIhKLT3JqgpS1BIKgRDKmEI0misSTxuE48kSSe0EkoOomEbnyd0IkruvHzeBKbXcbj\ntpDjtuDNsZLrs1KQZzP+5VspLrBQUmQl19vjqpOuaTzQ50QIweuvv853v/tdfvazn1FbWztm12b+\n/Pn84Ac/4KabbgLgoYceIhQK8dhjj43ZMYwTJgPmQEgkDNNC0/PR4chc9SYyqSfdaWWgecOxOI5s\nu+tzZeKm1GHM/x+iKpEQOgT96K2NiOZ69MYTCH8r8pQq5Kr5yDULkNze7vdIIkLtiM4mUONGkMwv\nTwXKmAJn/Ian5dR8Y7A/vVf59j6ZI2ckbqjVmTu178elvjHGv/zoOBXlTr54byW+IQaoc5FD63sd\n+s4fqlqSvQdjvL2li937w8ybncOKpfnULvQxpfzc3ud8cDFIMgohiMV1orEkobBGKJwk0KUSCKp0\n+FXa2hWa2xKcaYoTiyeZMc1FzQwXl9fmMH+Oa8CNpqZp/Mu//AuHDx/miSeeGJEZ8eHgF7/4BZs2\nbeKxxx6js7OTm266ie985zt88IMfHNPjGAdMBsyBYAbMSCSSkhWDsRUgGC7StVHHKqscDs6Xiau+\n9j+IMyeQCkoRvnw0mxOcbuzu7oF2IUBNIBIxiIQR4SAi2I4ItIPDhVw6Fal0OvKUmUhlM3oySaFD\nJGiQebraweVFzi8Db2FKtSaSgKagMQdYlgelXsPw2fh7wzrrzT0yNRWCNZfqWXtqr61v5xdPN/DZ\nO6dy09qiIWuAnosjxVCR7gbSFUqwY2+I3fuj7K2LkEzC3BoPs2Z6mDndxdQKF+WlDhz20QliiqL0\nsRy7GJEuV6glbZxqiHP0ZITSYgerl+f32Wju37+fr3zlK9TW1rJ3715uvPFGvvWtb43LZqKuro67\n7rqLPXv2oOs6n/70p3nyySfH/DjGAZMBcyCYrhSmI4DL5ZrwWaVZrhvPrHI4GKzn1puJK3Qd0dmC\n2noWrbMVqxJH1hRIqsaTKclgtyPZneD2IuX4kHILkfJLkBy9WKfduqSE/YiwH+wuQ94ttwTJ5ug+\nPvBHDQGCuGrIrRV7M9Vumjrh9d0WtCTcWJvMOsqgqDo/+dVp9h8K8fCXqvsVH+iN9IW1v7GYkYZ5\nT1RVpbk1zuFjEU42JGhsUmlqUWhpV/B6rBQX2inIt5Hns+LzWvHmWPF6rHjcFpxOo49n9PrMHp8F\nh0PGbuv7uUmfIR1KqXmsoevdzN9u9m8yaYzFCGFUF0zGr80Ctu7RmmxzrNCzKXB2m1kPBfF4nN/9\n7nc8//zzhEIhDh8+jNfrZdWqVTz66KNjlmUKIZg5cyb33XcfX/nKVwiHw3zmM59hzpw5fP/73x+T\nYxhHTAbMgWAGTDMIOZ3OCUnqGYrjxoWC3kxcPRFFyNaUiLksy8Tj8dQGZsijPppqqM/Ew4YuaSwE\nugbuPKScfKScglSQBKPs2hGG9rAx5lDiM+yu5LTL2hkyDIdPt0pctUDn0kpBtg2/P6DyTz84SkGe\nnb+/fyZu1+DH3LvUPJ7D5737oIqi4g9qBII6wZAgFNHpCicJh5OEo0ki0STxeJJorKfXp6T1+rSk\nwGGXcXUHVbdLxu2SyPVZKS5yUV7ioHKaizk1HpyO0Q+cWhI6w+APSfjDEIxKhKIQjktEE0afVk0a\ns6XmfKksG//Mx0E3iUxJI6gmVKM/63FCjhN8bkGuR+BzKuS5VKaUOHDYh3Zuuq7z05/+lHXr1vHU\nU09RVlaGEILDhw+zefNmPv3pT4/ZBqOjo4OSkpLUqB3An/70J77+9a+zd+/eMTmGccRkwBwIqqqm\neoGmH6QsyxOqpzJcEsiFhuSZOuhqR1jtJC12dIsNYbEhW+3IVhuyxYIsm8oAerc2qQpJFaEqoCZA\njRmpgMOD5PSA02v0K+3uDF3SmGoo8/gjxgJZ4DGyyd6lVX8YNh8y+pTLZuksny2w90Ogbjgb42vf\nPcL1VxVx10crkOXBNzLnKrI9luhvgH8ovemkLkgkdGLxJMFgnEAwhqJZiUSho1PhbEuCk6ej1DfG\nWLksn3s+OY38vJHRdNV1aAlAQ7tEU6dEc0AiGAGfGwq9gjwPRmBzQ45T4HYYFmeOYaoYCWEEzUgC\nwjGJQFinNaARjFrpDFsJRKCmXPCRlfqArxMMBnnggQeYPXs23/72tydE1aimpobPf/7zfPnLXyYU\nCvHZz34Wj8fD008/Pd6HNtqYDJgDQVGUVKaTSCQmzNwhZI4WXOhZ5WBIJjUSXQFQ4zgkAUkVXVMQ\nSQ2hJ0EIJFlG6tYklax2Q+zbajeyRrvLcNNIuz5CgJI0VGpCMUOxBskwds53G3OUfYyEO+HdwzIn\nWyRqawTLZ+m4BjCyOH0mxkPfPszdH5/CTWsGt1NKrxRcaD28bL1pU+QiW296KLJvobDGr/67kbYO\nhW//39nnfGzhGBw9K3G8SaK+TcLrMmZJKwoFZXmCQh+DusRkP+eerwe6TSZZK/2eqprxzBUO4Bq2\nb98+/u7v/o6HH36YW2+9dcI8C3v37uXBBx9kz549WK1W1q5dy6OPPvp+sAybDJj9Qdd1vvrVr7Js\n2TJWrlyZsvfKNneYTapstI/NzECGU5a8ENARNno/bjtYZYGmDV5qHoyJK8tWEkmJePesXyRhSLkJ\nYQRGr9PQRHVmySIUFQ41Gt6RoRhcVqOzpHpgxRuASFTjb756kDtvK+fGIQbLidzDGy6y2c2ZoxOS\nJKEoCjabbdC+7In6KP/4r0d5+v8tGtb7KyocapDYVy/REpCoKhPMqhDMLBV4BtBq0HWjVx3vLqsq\nmrGxUpM9JVe9W9YvHRJGidYiGwpFNgvYrQILGhZU8nLsuBzWIWWpQgieeeYZ/uu//otf//rXVFVV\nDevcJzFqmAyY/UHXdTZs2MDbb7/Npk2biEajLFmyhFWrVrFq1SoKCw1mR2+psnQB85EOoAMNrF8s\naA5CMApRRaALsFt0HDYZh1VKDcBbpB6ihQDDJUoYEmzJJGi6QFFF2kInYbMIHFaB2w4eh0SOU8Zu\nzZ4ZqBqcaJGoa5A41iQxvViwuMqQhhtqdfRnT50mGk3y5ftmDvq756obeiHBDKCJRCIl9g8DC5kL\nIfj2vx9n5nQXn7pjypDep6MLth+VOXhaYlqxYGGloKZcZM0gdWFsnMLxnk1UQjN61k6b8V+H1SDx\n9NbFNSvrJjHb1MbVugUZFFUnHFNRkzKasBJTjT9YNG3gbDQWi/H3f//3OBwO/uM//iPFzJ/EhMBk\nwBwqEokE27ZtY8OGDbzzzjsEAgEWLVrEqlWrWL16NSUlJYa6TD+D++frbpBMJonFYsDFl1X2hlnC\nkq12hOxATRolLK1bqDupGw9h70F4Wepe1NJ0Se1WsMkCXU9mVAbSmbiybKWtS+J0m8ypFomGdony\nAkOTdO7UgTOS/nDfQ/t54LMzWDC3/5rbcHR9L3SYVRGTrCXL8qB90Of/3M4bGzv48T/Pwz7IGEuz\nH945KNPYLrGkWlBbpePNQkROqBCIGZuycNx4PnK63VY8DiNQDtZmFkIY/XKMXVvvz3O2EqzoDqa2\nAT62x48f5wtf+AL33Xcfn/zkJy/KjdMFjsmAea5QVZUdO3awfv16Nm7cSHt7O/Pnz2fVqlVceeWV\nVFRUpAJo+kI9XOWbizmrTCZh40GZigJDlzTHKYjHBzd3Ph/EEtARgtaAoDUALQGJ1qAFryvJlEKd\nyhKdqjIJt/P8KgPP/OEsr69v56N/UcbltXkUFWT2UC+G4fyhIlsAyYb0Puhzr7Ty4usdfPuhGZSX\nuvoV+28LGu4qZzslVszRWVJlyPGlQ9F6LMkUDXLdkOsyyvC9A5jQ9TQvT8PEWqgJw9w6qRn/ED1p\nohCAZJhjW6zoFhtJ2YrV7cNiGl9bBn6OhRC88MIL/PjHP+bxxx9nwYIFw7i6kxhDTAbMkUIymWTP\nnj28/fbbbNiwgaamJubMmZMKoDNmzOg3gKaXcNMDaHpWOVHZkueDhApbD8uc7TACl5YU5OcICnIk\ng6XoMliKLjvYbcIoi5mUfrMURnfW2V1+VVSjVxlXIJqQulmK0BWVCEaNv8nPgWKfoChXUJYPpXk6\nDmtmH7S3N+hwKwNCCHbvD/Hi663sPRhC1wVlpQ4K8+14cyTcTvB5HeR4bLhcFmNO0Wlok9rtEk6H\nBaepS+oyvh4Kw3YiwcygNU0b8gZI1wX/+bszbNzq57tfm0VRgTXjvoDBGUhoFrYcdnL4jMwVc3WW\n1mQGSiEgGDPmZ8MJg8hVmNOXzGXM4gYgGjRGjZSYMY/rcBtkMbsTyeoAmx0sNpCtGVml6K7H6ppC\nPBxC1lXskkBSYoh4GLlwCpKv/x62qqr84z/+I62trfz85z9PjWpMYkJiMmCOFnRdZ//+/axfv54N\nGzZQX19PTU0Nq1evZtWqVVRXV2d4UPYmrIARMM0ZvIslq+wNk+yiaRqSxU0wZiUYkeiKGXT8qKlL\nqhlC32qye3okbZNvkXvsoexWw9rJYTPGATxOQ6Tb5zJGBdyOoY0HjLQmrj+o0twS52xzmGBII6HI\nxOKCWDxJLKaTUIx5RVOTVFGMr2PxJLG48f9Op0yOxxAI8Hmt5PmsFBbYKS6wUVrsoKLMSUWpY9Dy\n5VjgXAQX4okkP3jsJG0dCv/0f2aR68ssUQsh0JI6u45LbDpkZXa5wrLqGB6XnDbKYqEjItEcNJ6L\n0u75WVNEQAgB8RCiqwMR7jAyR3cekjvXcJ5xeDKkF4cCswc9XLZ6U1MT99xzDx/5yEe4//77L7oN\n8UWIyYA5VtB1ncOHD/P222+zfv16Tpw4wYwZM1i9ejWrV69mzpw5yLLMrl27sFgsVFZWIstyH+m4\n4WqvTmQMxdx5ouB8NXHPVwc2qQtisSShiKFN2hXSenRJOxSaWxOcbUnQ2pagpMhBzUw3c2o8LJzr\npXqmG8sYZafpLYSBtH17o6UtwT/92zGmT3Xy5c/PzBr0W4Pw8nYLsgw31SYpyeu5L4qq0RGWaY/a\ncNl0SrxJfC45dV+EEkcEmhHBViND9BYheQuNkmk60UhTEe1NiI5mRGcrItiJCAcgFjHkFjXVoNJK\nMthsCLsT4fRgyS9GLipDnlaDXDZ90Gu0fv16vvnNb/LjH/+YFStWDP9CT2I8MBkwxwu6rnPixIlU\nAK2rqyOZTHL69Gm+8Y1vcPfdd2O1WjOk44Yy3zZR8ewLzWzbHWBOtYeqGW7KimWKCiTycj0XJNll\nqPclfVxkLOQKVU2n8WycYyejHDoaZt+hEJ0BlcsW5bJqWT6XL80bNR3YdD/H4YzGbNsV4AePneT/\nt3fmgVFVZ///3DtLJvsekkBCYoiyhy0gJNS9ilYtLlgsbrwuuC+1u0VqlV99xfZ1qUuLVbBV1BbF\nKlWgNUH2LWEHQ1iSkJCQfZbMes/vj0smGSYJASGJcD7/QObOzD33zsz93vOc5/k+N1+Xwo3X9AsS\nWJ8Ga/cobCxRuWi4vk7Zfgmx3g4VDXopUmq0wGw4FhnweKClCbOtFtVtR0QmoMYko4ZFtYVUvR5E\nRanetaaiFFFfjRKbiJKQghKXhBIVhxIRA+GRut2iyQSqAc3rxWltRjgdWDQPirUBUXcEImIwjr2o\n02P1+Xy8+OKLbN68mbfffpuEhIRTO9mS3kAKZl9g/fr13HXXXaSlpXHttdeyZcsWdu3aRWJioj+E\nO3LkyCABbe+92tcF1GrzsrvExp59Nkr2Wzlc5aa61kNUhO5JGh9rIi7WTHSUkehII+HhBsLDjISH\nqoSFGvQ1PYvuSRoSomIy9h1rQui47rD18dawZPsSI00joPVV+7o+5Vi2r/FYlu+3/Thr692s39LI\nynUNlOy3M/nCOK65PLFbPTC7y6mUxni9Ggs+PMx/vq7jFw9nMXJo8PpdnRU+XW/AYhJck6sR1S7z\n1eGCQ3X6uUuPg8hj/TGFEGCrRzt6CDQfIiYVX3gsXk3/jPB6MVbuw1C6A8r3oSSmomZcgDogC6Vf\nmm560Y1jPdloQV1dHffffz8TJkzgV7/6VY9kui9atIhnnnmGsrIyUlJSeOedd8jLyzvj+z1LkYLZ\nF7jjjju4+uqrmTZtWkAyweHDhyksLKSwsJDt27cTExPjL2MZPXo0JpMp6ELdXkA7yyzsDdp7o7aW\nUPg0QV29m6N1burqPdQ3emi2emmyerHZvdgdev/BFqe+lud06b0G3W4NTQi9Ya9Z9yO1WHRhjYww\nEhVhJC7WRGK8mbRUC9nnhXfLv/V0oGnQaBdU13uob9awu03YnQpWp0KLW8HlUXF59PXYgObKnfiS\nur36c0LNetlDxLG12NgIQXwkJEYLIkO7b9velPsVAAAgAElEQVRWW+9meWEtny0/Sr9EM9OnpjIu\nJ+qUbz46+ly7Q+URJ8+/up/wcCM/fSCT2Ojj1yth6wGFr7arfG+YxpistlmlpsHhRqi1Qv9Y3b7Q\nP+N0NKNV7wfNi5o4ECLbOsKIxlq8xavQdm+G+BS0QSPwpp2PZrYErU8DuI8cxVlehbumHp/dgeb2\noKkKmsVMZHp/IocMwhjRPRP9TZs28eSTT/Lcc89x+eWX98jN3vLly7n33nv58MMPyc3NpaqqCoCU\nlJQzvu+zFCmY3xWEENTU1PgFtKioiPDwcCZNmkR+fj7jxo0jJCQkyLy8vcNKR90/eoIzUULh9R5r\n3utuTZzRxdVm99HU7KWu0UPNURdlh1soPdTCqGGR3PWjAZw3sHsXuO7g06C6AQ7XKxypV6hpUqhr\nhlCzICbCR1ykSkw4RIbpTZEtJo0Qow+j6kHFB5w4E1cIXTRbXHpxvbVFodkO9TaFeivUNOnP7x8v\nSEsQZCYLkqJPLKA+n6BwbT3vf1yJxWLgrlv6M2Zk9Ekd/6l43goh+LKglrf+XsGPpqYwdUq/oOxf\nlwf+vVnlaJPCDy/0kdhuWA4X7D+q10sOTGgrCxFeD6J6P8LegJKUgRLdFtrVaqvwrVuOVrEPw/AJ\nGEZMRImOCzgOd7ON+sINNH69EVvxblp2l2KwhBCSlkJIvwQMkeFoioLmdoOtBdeRGlr2lzPyw1eI\nnTyuy3P01ltv8emnn7JgwQIGDBjQ3dP7rcnLy+Puu+/mrrvu6rF9nuVIwfyuIoSgvr6elStXUlhY\nyKZNmwgJCeHCCy8kPz+f8ePH+11Cjs/4hJ7xw+1uc+czjdPl44v/1rLokyre+uNwwsNOfR2xzgr7\nKhUOVCtU1CpEh+tilRKrERfhJcLkICK8e/WyXRXudzcTVwhodsDhOoWyowr7jyhoAgYPEAwfqJF8\ngq5PmiZYua6edxYdJjXFwj0/HtCt1mOnksR0tM7N//35IPWNHn72YGaH+znaBP9cYyA9QXDFaM1f\nKiIE1FihsgHS4iE+vN2ssrkWrapEb8uWONBf9yhszXhXL0U7uBvD2IsxjJyEYm4z/9Vcbo5+9hXV\nH3xO45otRI4aQuzFFxI1bhihw7JRIsMDfjOqqmI2m/3fY3Es7K52si5ts9l45JFHSE1N5fnnn+/R\ntfrWFn/PPPMM8+fPx+Vycf311zNv3jxCQrowQJZ0hRTMswUhBE1NTaxatYqCggI2bNiAwWAgNzeX\nyZMnM2HCBMLD9TWrnvDDba0hbe/s0pt4vRrT7i1m/h9GEHeSnS8abLDzkMKuchWnGwalCs5LFqQn\n6nWinXWMaZ0dOr3HPEmP9VL0+tpci1p7KopWjz9AUQQKAlXRMKl66NZiUgkLUQk1K5gMnc8ghYDa\nZthVrrLjoF7vOf58jSEDurb183g1PltWw3sfV3HRxDhuv7k/UZHBQnAqzax9muDz5Ud596PDXHdl\nEj+amoLJGDyYvYcVlm5SuXSkRk5m22VG0+BgLTg8MChJn10CCM2HOFKKcDSipg7Wy0KOjVHbvg7v\nmn9jGDYew/jLA3qhehqaqfjz+1TO/4iwwVmkzLiOhKu+hzE6cA21NeO3tXclEFQP2tlvZvfu3Tz0\n0EM88cQT3HTTTT1+o1hVVUX//v0ZN24cn332GUajkeuuu45LLrmE3/3udz06lrMIKZhnK0IIbDYb\na9asoaCggHXr1uHz+Rg3bhz5+flMnDiRqKi2C0yreLYK6Kn64fZVZ6J//OsI64saeWH24G4936fp\nF/CiUoWjTQpD0gRD0zUGxAeKVetMy2g0oakW7O5jPRTduom3QW3zJW31JG31JVXb++Ieez9B+zVM\ngbs19OzVa1HdPv2zCDMLokIhJkzt0DQedKEpPaKwdo+KwwUXj9C4oL/oMlzbbPWy8KPDFK6t59Yb\nUrn2ikSMx8TtVELru0tsvPZ2GSaTwiN3Z5CRFhr0HCH0LNjN+1RuzPOR2hYtxeODkmr9/GUktKun\n9LjQyneimENRUrLbZpUtNrxfLkI4bBi//yPUhOS28+HxUPH6+5T939vET7mI9EduJ/yCjo3NuzLD\nb132aJ/gpWkaP/nJT8jIyMBoNLJs2TIWLlzI+eefepeVb0NjYyNxcXEsXLiQGTNmALB48WKee+45\nNm/e3CtjOguQgnmu0Jryv379egoKClizZg1Op5MxY8aQn5/PpEmTiI2NDfDDPVnXm77qd1uwpo43\nFpbzf88MITmp63CUywNbShU2lajERMDYQRoXpAqOPxRNEzTaXDS1gMtnxu5WCTG2eZKGmfVEHcNp\nnFjrNzY+nG4fVqfA5lKxe4yoCsSFaSREKljMwTc3QsDBaoUVW1XCQwRTxmnERnS9r4PlLby5sIzq\no27uvKU/40eH43Z3/yaorKKFv/2zku27rdw1fQBXfC++k04z8MVmlaoGhWmTff5MV9Bn5HuP6C49\n/WPbhWBdDrSy7SgxKSgJaW1rlXVH8HzyFobskRjyrkZp96HZ95Sy6+5fY06KJ/t/f0bYoIGdjv1U\nTBd8Ph+ffPIJH330EUVFRdTX1zNixAjy8/OZMWMGo0aNOuF7nG7S09OZO3euXzA//vhjnn32WSmY\np44UzHMZp9PJ+vXrKSwsZPXq1VitVkaNGuXPxE1ISOjSUL59OMrj8fS5WSXAsoJa/vp+BXN/dX6X\nCT8uD2z4RhfKzGTBhRcEr/8Jodus1Vk1Ghy6PV9MmEJUqEKkJbinohACPE5wtyDcTr2ZtdeN8Hl0\nT1JNT/xBHLMtUlRQDXrvTqMZTCG6RVtIuP7/ICEU+HwazS0+6u0KTU4jEWYP/SJ8hIaoQdEBTdOP\nce1ele+P0hg28MQ/5U3Fjfz1/XJcbo0bf5DMJXkJhFo6vhHyaYKibc38a3kNu76xccPV/fjhlH6d\nPt/rg0/WqXi8cOMkLaAJt9sLe6r0DNiUmHbH7HKgHdqGkpSBGtNu9lhTgefj+Rgn/wDD0MAknNp/\nF7LnwTmc9/TDpNw+tcvvZvsQrNls7vR5x3Pw4EFmzZrFHXfcwcyZM2lpaWHjxo2sWrWKvLw8Lr74\n4m6/1+ni6aef5osvvvCHZK+//nouvfRS5syZ0+NjOUuQgilpw+PxsHHjRr+hfOtdcqsfbnJycqd+\nuIA/IeJUO7KcTjRN8LdjBujP/fJ80vsHhwJBv2hv2aewZo/KecmC/KEacceVA7q8eglDrU1fW4ww\nuUmINBARavRneLaKo3A0Q0szosUGLrvuPxoSimKygClEb2ptNOvCqBp0kfT3iNJA0w2+hcetv5/b\nAU673iQ7PBoi4lAi41EMweuwHq+gullQY1WIDfUSZ3GCCO6WU9OksHiNgcEDBBeP0DoN0bYPwe4q\n8fDplzVs320jZ1gk558XTmK8GYNBoabWxcHyFrbutJIQZ2LKZYlcNjkeS0jnEQafDxavVVEU+OGF\nWsDNhk+D3ZUQFwGp7cXS60Y7UIySkIYa21YaIRrrcH/wCsZLb8CQPTJgP7VLC9j72HOMeP+PRI3t\n3NT8VPuRCiFYtmwZv//973njjTcYPXp0t17XE3i9Xh599FHee+89QkNDueWWW3j++edP6kZAEoAU\nTEnneL1eioqK/AJaXV3NkCFD/D1BCwsLWbFiBW+++SYGg8FfD9reNu5UfFe/LXaHj3mv7ae+0cOc\nJ7OJ7SDJRwh9jfK/W1USogQXj9RIig7cbnVCdbP+b1y4INzQgsWoER6ul1AIzQf2BoS1HmFv0EUt\nLFq3W7NEgiX8hJ0quotwOxGORoS1HuwNKBFxKPEDUEKDi/3dXr2g3+WFrEQNoxJs9u/RjHyyPoyU\nOMGVYwLXNY+3t2t/gW22etmyvYnSgw7qGjx4vYKEeDMZA0IZNjiC/skn7oUmBHy6XsXthRsmaQFh\nayFgX42+zjuw3XqxEAKtbAeKJQK1X1uPUeHz4ln0CuqQsRjHfC9gPy37y9l8+e2M/OhVosYO63Q8\npxKCBf338dxzz1FaWsr8+fP9TeYlZy1SMCXdx+fzsX37dv71r3/x+uuvYzAYuPTSS5k4cSL5+fl+\n/9v2bZpa/z0Z39Vvw4EyB7/7YykjhkTy4F3pmE3Bi4i1zbCsSMXWonDFaI3Mfm1faSGg0QGVjXry\nTb8oiDS78bidmM1mzGYTiq0B0VyDsDWAJUKf8UXE6V0uAhoga9BUj2is1T1J7U0Ihw2OeZIKTUM5\n5klKSChKeJRuxZaQjBKfgtJJBqrweXVf1LoKCI1CTc5CMQWuzQqhi/2RJrggWV9P1R9v+2zsLV4W\nr49gULKHCy/w+kO4TqcTIcQZ65Dz9U6F/UdUbr3IF9SKq6YZjlphSGpgX0qtoQrRcAQ1c1TAOfZu\nLkArK8H0w7uDvlM7bnuSyNFDGfjEzE7H0j4EezJlT9XV1dx3331ceeWVPP74472eBS7pEaRgSk6O\nZcuWcfvttzNz5kx+85vfBPjhHjx4kMzMTL+hfHZ2tl9Aj/ddbW8ofzrs/IQQfL7iKAs+OMy9t6Vx\nxUXBHp1eH6zepbKlVCFvqMa4QW2lFq3toCoa9F9FagxEh7b15wwzqijN1YjGaggJ02v+IhMCbNRE\ni133Iz18AO1IGaK2CiyhujdpdDxKRDRKaARYQsGoe5KiaXrHDKcDYW9GNNUhao8gmupQ+qWhZg3H\nMHg0SljwTFJoPkRdBaK+EjX1ApTIuKDn1FrhcAMM7d9x82KrQ/DX5UauGddCvygXmqahKIo/OnC6\njS4OVit8ukFl5uU+Io6Lknt9sL0CLkjRk6b8xyk0tJINqGnDAmbUQvPhnv8spqn3oCamBryXp6GZ\ntSOuJm/vcgzhHWXmtrUeO5kQLMDq1av59a9/zYsvvsjkyZO7/TrJdx4pmJKTY/v27djt9g47LGia\nRklJid+NaN++faSlpfmTiIYMGRIkoKfDD7fZ5uXF1w9QU+vml4+c1+F6ZXktfL7RQEKU4MrRGpHt\n8n/sLiiv10sYBsRCTBj4fMf8Qn0uTNYaaGlCiUnWMzND2t5fq69GK9mGVroT0XAUNTUDpf95qCkD\nURJTUSyn5iwk3E608lK0fdvQSnegZo/EOPEqlIhgNx7haEIr34Wako0SFXyjUF6vJzUN6tfxvnaV\nwepdCtMmNhEaavF3yTm+5vDbGl34fPDnLw1cMVpjUErwZaSyQQ8jZx7XPlI016LVH8aQkRPwuHZ4\nP96CTzD/+Img97IW7WLPw78ld9UHQdtaHYoURSEsLKzbx6JpGq+88gqFhYUsWLCAfv06OaGSsxUp\nmJIzhxCCAwcOBHRkSU5O9s9Ahw8fjsFg+NZ+uDa7lyVf1HDzdclBIViPFwp3qOwqU/j+GI3BA9q+\nvl6fPqNscED/GD0jE3R3Iq+tAYutBsXj1NcKY5JR1GOGBM4WtN2b8O3aiLBbUbNHYMgajtL/vC7X\nLIXPh+b2gCZQLeaAsocuz6PTgW/TV/h2rMd4yQ0YLgguURAtVrSyHagZOXpmbTs0DbZVwPn99H6g\ngds07HYH730dyZVjNTL6BWfiduQUdSp1usX7FXaXK0y/SOtw+/YKOC9RL8sJGGNVCZhDUeMDbeV8\nOzeglZdiump60Hu5qmrYMHEaeftWBDjxtNbNnmw2d2NjIw899BBDhw5lzpw5Z7zrjKRPIgWzN3C7\n3TzwwAOsWLGChoYGsrKymDt3LldddVVvD+2MIoSgvLzcPwPdsWMH8fHx/hloTk6O31D+dPnhvleo\nEmqGK8doAWJRb4eyOn02OSBWLwnx+Xy0NDdibqrE4LajJKbrQqno+xKNtXg3F6LtLULNuADD8Ako\naYP821tx19bTtG4r1qJd2Pfsp+VAOe6qo3ibrCgmI6gKwuXBEBGGZWB/wodmETNpDAnXXIw5ITis\n2opWU4FnyV8xTroKw7DxwdtryxEtVgxpQ4O2Veg5SaS1NwVoJx7rSyz4NIVLRnYsZgH7Oe6z6W5z\n7Xf/a+DCwRrZqcGXEM+xcOzo9GATBt+h7ahx/YNCzr49W9D27cD0g9s7HGfx9bOIvWg8A5+YeUoO\nRa1s3bqVxx57jNmzZ3P11Vf3ega4pNeQgtkbOBwO5s2b52/p9fnnnzN9+nR27NhBenrXzWfPJoQQ\nVFVV+f1wt27dSlRUFJMmTSIvL4+xY8diNps7neV0J0xoc0LEcYmbB47q9ZQZCRBpOZYV6nKi1VZg\nsh1FjUvVC+JbZ5RNdXjXfol2YDeGkZMw5OShREQFHIdt6x5qlqyg7ouVOCuOED1+JJFjhhE+OIuw\nQQMJSU3CFBftn1UKTcPb2EzLgcPYtu+loXA99f9ZQ/yUizjvNw9hGZBMR2j11Xg+eBXz9EdRYgLD\nr0LzoX2zDjV7fFDZSZMDqppgcErg+l1rj86dhxS+qVSYOvHEghk0pg7KjI5P8hJCYd7HBh67zhdQ\nb9mK3QUHamF4/+BtvrIdqDHJQeFm0ViL+4NXMd/9VIcze2dZJUXX3E3SzVeT+MB0DKGWk7JpFELw\n7rvv8v7777NgwQIyMjK69TrJWYsUzL5CTk4Oc+bMYerUqb09lF5DCEFtba1/BrplyxZCQ0P9Wbi5\nublYLJYga7KT9cO1tuhhP1XVL/bO5kZMR0tRTSGoqdkoZn2NUnhc+NYtx7djPYZR+RjGXBTgSept\nslL1tyVULliM5nSTNPUKEn5wCZGjh3ZqyN0V3iYrZa++S9U7ixm28AViJnZc0+dd8wXC6cB06Q1B\n23wHilGTMvWazXbYXHCoFgYnd1xCUbxfobxW4drxJy+Yx9NRlrRPGJj/n2gev87Z4Rp1i1svJxnR\nQTMPrfoAKKAmZQZt83wyHyU1E+P4yzoci62skpKfPY+jeDepd95I4nWXETF00AmPweFw8MQTTxAV\nFcWLL74oDcslIAWzb1BdXU1mZibFxcW95j3ZFxFC0NjY6J+Bbty4EZPJxPjx45k8eTLjx48nLCzM\n/9yT9cP1d9wwqpjdtsCWUOX78Cz7ADU1A+PkawNmlN5mG+Wvvsvhv3xI7CUT6H/3NKInjj5tobq6\nFavZc//TjF//D0xxwbV9WnU53mUfYL7tyaBtvkPbUOMH6GUu7ai3CWqaNVIj7B2WUCzdpBIfKZhw\nwen/ebda+v3x0xDuvqwZgxK8Ro2iUnQIctI6cExy2vT12UG5/lm/f1tzA+5FL2PMuxrDsNyAfbZ2\nygkLC8O5u5TKdz/Bc7SeYW8/3+V4S0pKuP/++3nooYeYPn26DMFKWpGC2dt4vV6mTJlCdnY2r732\nWm8Pp08jhKC5udlvKL9+/XoAcnNzycvLY+LEiURERHTLD9ftdvtLCjpaz/LtLQKzBUPmkID9V3/w\nOaWzXyL2kglk/mIWoZlnpr/hrvueImr0UAbMujX4PDTW4v7nm4T8z6+Dx12yATV9GEpIeMC491d7\nURWN9ARjUAmFywOvfW5g5hU+osOPf8fTx/u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"text/plain": [ "<matplotlib.figure.Figure at 0x109e08610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "ax = fig.add_subplot(1,1,1, projection='3d')\n", "\n", "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.25)\n", "cset = ax.contour(X, Y, Z, zdir='z', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n", "cset = ax.contour(X, Y, Z, zdir='x', offset=-np.pi, cmap=matplotlib.cm.coolwarm)\n", "cset = ax.contour(X, Y, Z, zdir='y', offset=3*np.pi, cmap=matplotlib.cm.coolwarm)\n", "\n", "ax.set_xlim3d(-np.pi, 2*np.pi);\n", "ax.set_ylim3d(0, 3*np.pi);\n", "ax.set_zlim3d(-np.pi, 2*np.pi);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further reading" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* http://www.matplotlib.org - The project web page for matplotlib.\n", "* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n", "* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n", "* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n", "* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

anthonybu/api_sdk

DEMO.ipynb

1

41839

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SDK for the Brandwatch API: Demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this notebook is to demonstrate the capabilities of the Python Software Development Kit for Brandwatch's API. The SDK was designed to address many of the challenges involved in building complex applications which interact with RESTful API's in general and Brandwatch's API in particular:\n", "<ul>\n", "<li>The SDK's object hierarchy roughly mirrors the API's resource hierarchy, making the code intuitive for those familiar with the Brandwatch platform</li>\n", "<li>All required parameters are enforced, and most optional parameters are supported and documented</li>\n", "<li>Typical Brandwatch workflows are supported behind the scenes; for instance, one can validate, upload, and backfill a query with a single function call</li>\n", "<li>The SDK is designed to support simple and readable code: sensible defaults are chosen for rarely used parameters and all resource IDs are handled behind the scenes\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the user's perspective, the basic structure of the SDK is as follows. One first creates an instance of the class `BWProject`; this class handles authentication (via a user name and password or API key) and keeps track of project-level data such as the project's ID. (Behind the scenes, the user-level operations are handled by the class `BWUser` from which `BWProject` is inherited.) One passes `BWProject` instance as an argument in the constructor for a series of classes which manage the various Brandwatch resources: queries, groups, tags, categories, etc. These resource classes manage all resource-level operations: for example a single `BWQueries` instance handles all HTTP requests associated with queries in its attached project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically, you'd import only the classes you plan on using, but for this demo all classes are listed except for superclasses which you do not use explicitly)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from bwapi.bwproject import BWProject, BWUser\n", "from bwapi.bwresources import BWQueries, BWGroups, BWAuthorLists, BWSiteLists, BWLocationLists, BWTags, BWCategories, BWRules, BWMentions, BWSignals\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SDK uses the Python logging module to tell you what it's doing; if desired you can control what sort of output you see by uncommenting one of the lines below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "logger = logging.getLogger(\"bwapi\")\n", "\n", "#(Default) All logging messages enabled\n", "#logger.setLevel(logging.DEBUG)\n", "\n", "#Does not report URL's of API requests, but all other messages enabled\n", "#logger.setLevel(logging.INFO)\n", "\n", "#Report only errors and warnings\n", "#logger.setLevel(logging.WARN)\n", "\n", "#Report only errors\n", "#logger.setLevel(logging.ERROR)\n", "\n", "#Disable logging\n", "#logger.setLevel(logging.CRITICAL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you use the API for the first time you have to authenticate with Brandwatch. This will get you an access token. The access token is stored in a credentials file (`tokens.txt` in this example). Once you've authenticated your access token will be read from that file so you won't need to enter your password again.\n", "\n", "You can authenticate from command line using the provided console script `bwapi-authenticate`:\n", "\n", "```\n", "$ bwapi-authenticate\n", "Please enter your Brandwatch credentials below\n", "Username: example@example\n", "Password:\n", "Authenticating user: example@example\n", "Writing access token for user: example@example\n", "Writing access token for user: example@example\n", "Success! Access token: 00000000-0000-0000-0000-000000000000\n", "```\n", "\n", "Alternatively, you can authenticate directly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BWUser(username=\"[email protected]\", password=\"YOUR_PASSWORD\", token_path=\"tokens.txt\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you have authenticated you can load your project:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "YOUR_ACCOUNT = your_account\n", "YOUR_PROJECT = your_project\n", "\n", "project = BWProject(username=YOUR_ACCOUNT, project=YOUR_PROJECT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we really begin, please note that you can get documentation for any class or function by viewing the help documentation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(BWProject)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Queries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create some objects which can manipulate queries and groups in our project:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries = BWQueries(project)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check what queries already exist in the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also upload queries directly via the API by handing the \"name\", \"searchTerms\" and \"backfillDate\" to the upload funcion. If you don't pass a backfillDate, then the query will not backfill.\n", "\n", "The BWQueries class inserts default values for the \"languages\", \"type\", \"industry\", and \"samplePercent\" parameters, but we can override the defaults by including them as keyword arguments if we want. \n", "\n", "Upload accepts two boolean keyword arguments - \"create_only\" and \"modify_only\" (both defaulting to False) - which specifies what API verbs the function is allowed to use; for instance, if we set \"create_only\" to True then the function will post a new query if it can and otherwise it will do nothing. Note: this is true of all upload functions in this package." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload(name = \"Brandwatch Engagement\", \n", " includedTerms = \"at_mentions:Brandwatch\",\n", " backfill_date = \"2015-09-01\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're uploading many queries at a time, you can upload in batches. This saves API calls and allows you to just pass in a list rather than iterating over the upload function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload_all([\n", " {\"name\":\"Pets\", \n", " \"includedTerms\":\"dogs OR cats\", \n", " \"backfill_date\":\"2016-01-01T05:00:00\"}, \n", " \n", " {\"name\":\"ice cream cake\", \n", " \"includedTerms\":\"(\\\"ice cream\\\" OR icecream) AND (cake)\"},\n", " \n", " {\"name\": \"Test1\",\n", " \"includedTerms\": \"akdnvaoifg;anf\"},\n", " \n", " {\"name\": \"Test2\",\n", " \"includedTerms\": \"anvoapihajkvn\"},\n", " \n", " {\"name\": \"Test3\",\n", " \"includedTerms\": \"nviuphabaveh\"},\n", "\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Channels will be shown as queries and can be deleted as queries, but must be uploaded differently. You must be authenticated in the app to upload channels.\n", "\n", "In order to upload a channel you must pass in the name of the channel, the handle you'd like to track and the type of channel. As with keyword queries, we can upload channels individually or in batches.\n", "\n", "Note: Currently we can only support uploading Twitter channels through the API." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.upload_channel(name = \"Brandwatch\", \n", " handle = \"brandwatch\", \n", " channel_type = \"twitter\")\n", "\n", "queries.upload_all_channel([{\"name\": \"BWReact\",\n", " \"handle\": \"BW_React\",\n", " \"channel_type\": \"twitter\"},\n", " {\"name\": \"Brandwatch Careers\",\n", " \"handle\": \"BrandwatchJobs\",\n", " \"channel_type\": \"twitter\"}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can delete queries one at a time, or in batches." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "queries.delete(name = \"Brandwatch Engagement\")\n", "queries.delete_all([\"Pets\", \"Test3\", \"Brandwatch\", \"BWReact\", \"Brandwatch Careers\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that a lot of the things that were true for queries are also true for groups. Many of the functions are nearly identical with any adaptations necessary handled behind the scenes for ease of use.\n", "\n", "Again (as with queries), we need to create an object with which we can manipulate groups within the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups = BWGroups(project)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And can check for exisiting groups in the same way as before." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's check which queries are in each group in the account" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for group in groups.names:\n", " print(group)\n", " print(groups.get_group_queries(group))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily create a group with any preexisting queries.\n", "\n", "(Recall that upload accepts two boolean keyword arguments - \"create_only\" and \"modify_only\" (both defaulting to False) - which specifies what API verbs the function is allowed to use; for instance, if we set \"create_only\" to True then the function will post a new query if it can and otherwise it will do nothing.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.upload(name = \"group 1\", queries = [\"Test1\", \"Test2\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or upload new queries and create a group with them, all in one call" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.upload_queries_as_group(group_name = \"group 2\", \n", " query_data_list = [{\"name\": \"Test3\",\n", " \"includedTerms\": \"adcioahnanva\"},\n", " \n", " {\"name\": \"Test4\",\n", " \"includedTerms\": \"ioanvauhekanv;\"}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can either delete just the group, or delete the group and the queries at the same time." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groups.delete(\"group 1\")\n", "print()\n", "groups.deep_delete(\"group 2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Mentions (From a Query or a Group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can download mentions from a Query or from a Group (the code does not yet support Channels)\n", "\n", "There is a function get_mentions() in the classes BWQueries and in BWGroups. They are used the same way.\n", "\n", "Be careful with time zones, as they affect the date range and alter the results. If you're using the same date range for all your operations, I reccomend setting some variables at the start with dates and time zones. \n", "\n", "Here, today is set to the current day, and start is set to 30 days ago. Each number is offset by one to make it accurate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "today = (datetime.date.today() + datetime.timedelta(days=1)).isoformat() + \"T05:00:00\"\n", "start = (datetime.date.today() - datetime.timedelta(days=29)).isoformat() + \"T05:00:00\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use get_mentions(), the minimum parameters needed are name (query name in this case, or group name if downloading mentions from a group), startDate, and endDate" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\",\n", " startDate = start, \n", " endDate = today)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are over a hundred filters you can use to only download the mentions that qualify. see the full list in the file filters.py\n", "\n", "Here, different filters are used, which take different data types. filters.py details which data type is used with each filter. Some filters, like sentiment and xprofession below, have a limited number of settings to choose from.\n", "\n", "You can filter many things by inclusion or exclusion. The x in xprofession stands for exclusion, for example." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today, \n", " sentiment = \"positive\", \n", " twitterVerified = False, \n", " impactMin = 50, \n", " xprofession = [\"Politician\", \"Legal\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To filter by tags, pass in a list of strings where each string is a tag name.\n", "\n", "You can filter by categories in two differnt ways: on a subcategory level or a parent category level. To filter on a subcategory level, use the category keyword and pass in a dictionary, where each the keys are the parent categories and the values are lists of the subcategories. To filter on a parent category level, use the parentCategory keyword and pass in a list of parent category names.\n", "\n", "Note: In the following call the parentCategory filter is redundant, but executed for illustrative purposes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today,\n", " parentCategory = [\"Colors\", \"Days\"],\n", " category = {\"Colors\": [\"Blue\", \"Yellow\"], \n", " \"Days\": [\"Monday\"]}, \n", " tag = [\"Tastes Good\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWCategories object by passing in your project as a parameter, which loads all of the categories in your project.\n", "\n", "Print out ids to see which categories are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories = BWCategories(project)\n", "\n", "categories.ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upload categories individually with upload(), or in bulk with upload_all(). If you are uploading many categories, it is more efficient to use upload_all().\n", "\n", "For upload(), pass in name and children. name is the string which represents the parent category, and children is a list of dictionaries where each dictionary is a child category- its key is \"name\" and its value is the name of the child category.\n", "\n", "By default, a category will allow multiple subcategories to be applies, so the keyword argument \"multiple\" is set to True. You can manually set it to False by passing in multipe=False as another parameter when uploading a category.\n", "\n", "For upload_all(), pass in a list of dictionaries, where each dictionary corrosponds to one category, and contains the parameters described above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's upload a category and then check what's in the category." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.upload(name = \"Droids\", \n", " children = [\"r2d2\", \"c3po\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's upload a few categories and then check what parent categories are in the system" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "categories.upload_all([{\"name\":\"month\", \n", " \"children\":[\"January\",\"February\"]}, \n", " {\"name\":\"Time of Day\", \n", " \"children\":[\"morning\", \"evening\"]}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add children/subcategories, call upload() and pass in the parent category name and a list of the new subcategories to add. \n", "\n", "If you'd like to instead overwrite the existing subcategories with new subcategories, call upload() and pass in the parameter overwrite_children = True." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.upload(name = \"Droids\", children = [\"bb8\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To rename a category, call rename(), with parameters name and new_name." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.rename(name = \"month\", new_name = \"Months\")\n", "categories.ids[\"Months\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can delete categories either individually with delete(), or in bulk with delete_all(). \n", "\n", "You also have the option to delete the entire parent category or just some of the subcategories. \n", "\n", "To delete ALL CATEGORIES in a project, call clear_all_in_project with no parameters. Be careful with this one, and do not use unless you want to delete all categories in the current project." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's delete just some subcategories." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.delete({\"name\": \"Months\", \"children\":[\"February\"]})\n", "categories.delete_all([{\"name\": \"Droids\", \"children\": [\"bb8\", \"c3po\"]}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "categories.delete(\"Droids\")\n", "categories.delete_all([\"Months\", \"Time of Day\"])\n", "\n", "categories.ids" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tags" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWTags object by passing in your project as a parameter, which loads all of the tags in your project.\n", "\n", "Print out ids to see which tags are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags = BWTags(project)\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to upload tags: individually and in bulk. When uploading many tags, it is more efficient to use upload_all. \n", "\n", "In upload, pass in the name of the tag.\n", "\n", "In upload_all, pass in a list of dictionaries, where each dictionary contains \"name\" as the key and the tag name as the its value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.upload(name = \"yellow\")\n", "tags.upload_all([{\"name\":\"green\"}, \n", " {\"name\":\"blue\"}, \n", " {\"name\":\"purple\"}])\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the name of a tag, but mantain its id, upload it with keyword arguments name and new_name. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.upload(name = \"yellow\", new_name = \"yellow-orange blend\")\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with categories, there are three ways of deleting tags. \n", "\n", "Delete one tag by calling delete and passing in a string, the name of the tag to delete\n", "\n", "Delete multiple tags by calling delete_all and passing in a list of strings, where each string is a name of a tag to delete\n", "\n", "To delete ALL TAGS in a project, call clear_all_in_project with no parameters. Be careful with this one, and do not use unless you want to delete all tags in the current project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tags.delete(\"purple\")\n", "tags.delete_all([\"blue\", \"green\", \"yellow-orange blend\"])\n", "\n", "tags.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Brandwatch Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: to avoid ambiguity between the python data type \"list\" and a Brandwatch author list, site list, or location list, the latter is referred to in this demo as a \"Brandwatch List.\"\n", "\n", "BWAuthorLists, BWSiteLists, BWLocationLists work almost identically.\n", "\n", "First, instantiate your the object which contains the Brandwatch Lists in your project, with your project as a the parameter. This will load the data from your project so you can see what's there, upload more Brandwatch Lists, edit existing Brandwatch Lists, and delete Brandwatch Lists from your project\n", "\n", "Printing out ids will show you the Brandwatch Lists (by name and ID) that are currently in your project." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists = BWAuthorLists(project)\n", "authorlists.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To upload a Brandwatch List, pass in a name as a string and the contents of your Brandwatch List as a list of strings. The keyword \"authors\" is used for BWAuthorLists, shown below. The keyword \"domains\"is used for BWSiteLists. The keyword \"locations\" is used for BWLocationLists.\n", "\n", "To see the contents of a Brandwatch List, call get_list with the name as the parameter\n", "\n", "Uploading is done with either a POST call, for new Brandwatch Lists, or a PUT call, for existing Brandwatch Lists, where the ID of the Brandwatch Lists is mantained, so if you upload and then upload a list with the same name and different contents, the first upload will create a new Brandwatch List, and the second upload will modify the existing list and keep its ID. Similarly, you can change the name of an existing Brandwatch List by passing in both \"name\" and \"new_name\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.upload(name = \"Writers\", \n", " authors = [\"Edward Albee\", \"Tenessee Williams\", \"Anna Deavere Smith\"])\n", "\n", "authorlists.get(\"Writers\")[\"authors\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.upload(name = \"Writers\", \n", " new_name = \"Playwrights\", \n", " authors = [\"Edward Albee\", \"Tenessee Williams\", \"Anna Deavere Smith\", \"Susan Glaspell\"])\n", "\n", "authorlists.get(\"Playwrights\")[\"authors\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add items to a Brandwatch List without reentering all of the existing items, call add_items " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.add_items(name = \"Playwrights\", \n", " items = [\"Eugene O'Neill\"])\n", "\n", "authorlists.get(\"Playwrights\")[\"authors\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To delete a Brandwatch List, pass in its name. Note the ids before the Brandwatch List is deleted, compared to after it is deleted. The BWLists object is updated to reflect the Brandwatch Lists in the project after each upload and each delete" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.names" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "authorlists.delete(\"Playwrights\")\n", "\n", "authorlists.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only difference between how you use BWAuthorlists compared to how you use BWSiteLists and BWLocationLists is the parameter which is passed in. \n", "\n", "BWAuthorlists:\n", "\n", "authors = [\"edward albee\", \"tenessee williams\", \"Anna Deavere Smith\"]\n", "\n", "BWSiteLists:\n", "\n", "domains = [\"github.com\", \"stackoverflow.com\", \"docs.python.org\"]\n", "\n", "*BWLocationLists:\n", "\n", "locations = [{\"id\": \"mai4\", \"name\": \"Maine\", \"type\": \"state\", \"fullName\": \"Maine, United States, North America\"}, \n", "{\"id\": \"verf\", \"name\": \"Vermont\", \"type\": \"state\", \"fullName\": \"Vermont, United States, North America\"}, \n", "{\"id\": \"rho4\", \"name\": \"Rhode Island\", \"type\": \"state\", \"fullName\": \"Rhode Island, United States, North America\"} ]\n", "\n", "*Requires dictionary of location data instead of a string\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rules" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWRules object by passing in your project as a parameter, which loads all of the rules in your project.\n", "\n", "Print out names and IDs to see which rules are currently in your project. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules = BWRules(project)\n", "rules.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every rule must have a name, an action, and filters.\n", "\n", "The first step to creating a rule through the API is to prepare filters by calling filters(). \n", "\n", "If your desired rules applies to a query (or queries), include queryName as a filter and pass in a list of the queries you want to apply it to.\n", "\n", "There are over a hundred filters you can use to only download the mentions that qualify. See the full list in the file filters.py. Here, different filters are used, which take different data types. filters.py details which data type is used with each filter. Some filters, like sentiment and xprofession below, have a limited number of settings to choose from. You can filter many things by inclusion or exclusion. The x in xprofession stands for exclusion, for example.\n", "\n", "If you include search terms, be sure to use nested quotes - passing in \"cat food\" will result in a search that says cat food (i.e. cat AND food)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters = rules.filters(queryName = \"ice cream cake\", \n", " sentiment = \"positive\", \n", " twitterVerified = False, \n", " impactMin = 50, \n", " xprofession = [\"Politician\", \"Legal\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters = rules.filters(queryName = [\"Australian Animals\", \"ice cream cake\"], \n", " search = '\"cat food\" OR \"dog food\"')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second step is to prepare the rule action by calling rule_action().\n", "\n", "For this function, you must pass in the action and setting. Below I've used examples of adding categories and tags, but you can also set sentiment or workflow (as in the front end).\n", "\n", "If you pass in a category or tag that does not yet exist, it will be automatically uploaded for you." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "action = rules.rule_action(action = \"addTag\", \n", " setting = [\"animal food\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last step is to upload!\n", "\n", "Pass in the name, filters, and action. Scope is optional - it will default to query if queryName is in the filters and otherwise be set to project. Backfill is also optional - it will default to False.\n", "\n", "The upload() function will automatically check the validity of your search string and give a helpful error message if errors are found." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.upload(name = \"rule\", \n", " scope = \"query\", \n", " filter = filters, \n", " ruleAction = action,\n", " backfill = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also upload rules in bulk. Below we prepare a bunch of filters and actions at once." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filters1 = rules.filters(search = \"caknvfoga;vnaei\")\n", "filters2 = rules.filters(queryName = [\"Australian Animals\"], search = \"(bloop NEAR/10 blorp)\")\n", "filters3 = rules.filters(queryName = [\"Australian Animals\", \"ice cream cake\"], search = '\"hello world\"')\n", "\n", "action1 = rules.rule_action(action = \"addCategories\", setting = {\"Example\": [\"One\"]})\n", "action2 = rules.rule_action(action = \"addTag\", setting = [\"My Example\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When uploading in bulk, it is helpful (but not necessary) to use the rules() function before uploading in order to keep the dictionaries organized." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rule1 = rules.rule(name = \"rule1\", \n", " filter = filters1, \n", " action = action1, \n", " scope = \"project\")\n", "\n", "rule2 = rules.rule(name = \"rule2\", \n", " filter = filters2, \n", " action = action2)\n", "\n", "rule3 = rules.rule(name = \"rule3\", \n", " filter = filters3, \n", " action = action1,\n", " backfill = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.upload_all([rule1, rule2, rule3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with other resources, we can delete, delete_all or clear_all_in_project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rules.delete(name = \"rule\")\n", "rules.delete_all(names = [\"rule1\", \"rule2\", \"rule3\"])\n", "\n", "rules.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Signals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a BWSignals object by passing in your project as a parameter, which loads all of the signals in your project.\n", "\n", "Print out ids to see which signals are currently in your project." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals = BWSignals(project)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we can upload signals individually or in batch.\n", "\n", "You must pass at least a name, queries (list of queries you'd like the signal to apply to) and subscribers. For each subscriber, you have to pass both an emailAddress and notificationThreshold. The notificationThreshold will be a number 1, 2 or 3 - where 1 means send all notifications and 3 means send only high priority signals.\n", "\n", "Optionally, you can also pass in categories or tags to filter by. As before, you can filter by an entire category with the keyword parentCategory or just a subcategory (or list of subcategories) with the keyword category. An example of how to pass in each filter is shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.upload(name= \"New Test\",\n", " queries= [\"ice cream cake\"],\n", " parentCategory = [\"Colors\"],\n", " subscribers= [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 1}])\n", "\n", "signals.upload_all([{\"name\": \"Signal Me\",\n", " \"queries\": [\"ice cream cake\"],\n", " \"category\": {\"Colors\": [\"Blue\", \"Yellow\"]},\n", " \"subscribers\": [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 3}]},\n", " {\"name\": \"Signal Test\",\n", " \"queries\": [\"ice cream cake\"],\n", " \"tag\": [\"Tastes Good\"],\n", " \"subscribers\": [{\"emailAddress\": \"[email protected]\", \"notificationThreshold\": 2}]}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Signals can be deleted individually or in bulk." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.delete(\"New Test\")\n", "signals.delete_all([\"Signal Me\", \"Signal Test\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "signals.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Patching Mentions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To patch the metadata on mentions, whether those mentions come from queries or from groups, you must first instantiate a BWMentions object and pass in your project as a parameter. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions = BWMentions(project)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filtered = queries.get_mentions(name = \"ice cream cake\", \n", " startDate = start, \n", " endDate = today,\n", " parentCategory = [\"Colors\", \"Days\"],\n", " category = {\"Colors\": [\"Blue\", \"Yellow\"], \n", " \"Days\": [\"Monday\"]}, \n", " tag = [\"Tastes Good\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "if you don't want to upload your tags and categories ahead of time, you don't have to! BWMentions will do that for you, but if there are a lot of differnet tags/categories, it's definitely more efficient to upload them in bulk ahead of time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example, i'm arbitrarily patching a few of the mentions, rather than all of them" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[0:10], action = \"addTag\", setting = [\"cold\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[5:12], action = \"starred\", setting = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mentions.patch_mentions(filtered[6:8], action = \"addCategories\", setting = {\"color\":[\"green\", \"blue\"]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dsacademybr/PythonFundamentos

Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb

1

3693

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.8.8\n" ] } ], "source": [ "# Versão da Linguagem Python\n", "from platform import python_version\n", "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Herança" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Criando a classe Animal - Super-classe\n", "class Animal():\n", " \n", " def __init__(self):\n", " print(\"Animal criado\")\n", "\n", " def Identif(self):\n", " print(\"Animal\")\n", "\n", " def comer(self):\n", " print(\"Comendo\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Criando a classe Cachorro - Sub-classe\n", "class Cachorro(Animal):\n", " \n", " def __init__(self):\n", " Animal.__init__(self)\n", " print(\"Objeto Cachorro criado\")\n", "\n", " def Identif(self):\n", " print(\"Cachorro\")\n", "\n", " def latir(self):\n", " print(\"Au Au!\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Animal criado\n", "Objeto Cachorro criado\n" ] } ], "source": [ "# Criando um objeto (Instanciando a classe)\n", "rex = Cachorro()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cachorro\n" ] } ], "source": [ "# Executando o método da classe Cachorro (sub-classe)\n", "rex.Identif()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Comendo\n" ] } ], "source": [ "# Executando o método da classe Animal (super-classe)\n", "rex.comer()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Au Au!\n" ] } ], "source": [ "# Executando o método da classe Cachorro (sub-classe)\n", "rex.latir()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obrigado\n", "\n", "### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

johntanz/ROP

.ipynb_checkpoints/ROP Data Analysis v1-checkpoint.ipynb

1

467433

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ROP Data Analysis 0.1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Call ROPtimes dict, and clean data up" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Import Modules and Identify Low Signals\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pylab import *\n", "import pandas as pd\n", "from datetime import datetime, timedelta\n", "from pandas import Series, DataFrame, concat\n", "import scipy\n", "import scipy.stats\n", "import scipy.fftpack\n", "\n", "#Define any string with 'C' as NaN. 'C' is an indicator for the Masimo as a low signal reading\n", "def readD(val):\n", " if 'C' in val:\n", " return np.nan\n", " return val\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import ROPtimes as rt\n", "\n", "Baby = 'ROP015' #change depending on which baby you're doing!\n", "BabyDict = getattr(rt, Baby) " ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "\"\\nParse_dates tells the read_csv function to combine the date and time column\\nInto one timestamp column and parse it as a timestamp.\\nPandas is smart enough to know how to parse a date in various formats\\n\\nIndex_col sets the timestamp column to be the index.\\n\\nUsecols tells the read_csv function to select only the subset of the columns.\\nNa_values is used to turn 0 into NaN\\n\\nConverters: readD is the dict that means any string with 'C' with be NaN (for PI)\\n\"" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfNIRSpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/NIRS/Clean/' + Baby + 'NIRS.csv',\n", " parse_dates={'timestamp': ['Date',' Time']},\n", " index_col='timestamp',\n", " usecols=['Date', ' Time', ' Ch2 %StO2'],\n", " na_values=['0'])\n", "\n", "dfPOpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/Pulse Ox/' + Baby + 'PO.csv',\n", " parse_dates={'timestamp': ['Date','Time']},\n", " index_col='timestamp',\n", " usecols=['Date', 'Time', 'SpO2', 'PR'],\n", " na_values=['0'])\n", "\n", "dfPIpre0 = pd.read_csv('/Users/John/Dropbox/LLU/Projects/Retinopathy of Prematurity/Pulse Ox/' + Baby + 'PO.csv',\n", " parse_dates={'timestamp': ['Date','Time']},\n", " index_col='timestamp',\n", " usecols=['Date', 'Time', 'PI', 'Exceptions'],\n", " na_values=['0'],\n", " converters={'Exceptions': readD}\n", " ) #make a PI df as well to clean it better\n", "\n", "dfNIRSpre = dfNIRSpre0.rename(columns={' Ch2 %StO2': 'StO2'}) #rename NIRS column\n", "\n", "#Clean the PI dataframe to get rid of rows that have NaN\n", "dfPIpre = dfPIpre0[dfPIpre0.loc[:, ['PI', 'Exceptions']].apply(pd.notnull).all(1)]\n", "\n", "dfPOpre = dfPIpre.combine_first(dfPOpre0)\n", "#combine PI dataframe with the rest of the Pulse Ox dataframe after cleaning\n", "\n", "'''\n", "Parse_dates tells the read_csv function to combine the date and time column\n", "Into one timestamp column and parse it as a timestamp.\n", "Pandas is smart enough to know how to parse a date in various formats\n", "\n", "Index_col sets the timestamp column to be the index.\n", "\n", "Usecols tells the read_csv function to select only the subset of the columns.\n", "Na_values is used to turn 0 into NaN\n", "\n", "Converters: readD is the dict that means any string with 'C' with be NaN (for PI)\n", "'''" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Both NIRS and PO indices are even.\n" ] } ], "source": [ "# Phone almost always equals Philips monitor\n", "\n", "# NIRS date/time is 2 mins and 10 seconds slower than phone. Have to correct for it.\n", "# Pulse ox date/time is 1 mins and 32 seconds faster than phone. Have to correct for it.\n", "\n", "#This code is to make the combined dataframe come in even seconds.\n", "#The corrected is 1 second, + for NIRS and - for PO.\n", "\n", "TCcorrect = timedelta(seconds=1)\n", "\n", "ncorr = dfNIRSpre.index+TCcorrect\n", "pcorr = dfPOpre.index-TCcorrect\n", "\n", "\n", "if dfNIRSpre.index[:1].second % 2 == 0:\n", " if dfPOpre.index[:1].second % 2 == 0:\n", " print '\\nBoth NIRS and PO indices are even.'\n", " elif dfPOpre.index[:1].second % 2 != 0:\n", " print '\\nNIRS even, PO odd. PO index corrected.'\n", " dfPOpre = dfPOpre.set_index(pcorr)\n", " else:\n", " raise NameError('Indices are messed up')\n", "elif dfNIRSpre.index[:1].second % 2 != 0:\n", " if dfPOpre.index[:1].second % 2 == 0:\n", " print '\\nNIRS odd, PO even. NIRS index corrected.'\n", " dfNIRSpre = dfNIRSpre.set_index(ncorr)\n", " elif dfPOpre.index[:1].second % 2 != 0:\n", " print '\\nBoth NIRS and PO indices are odd. Both corrected'\n", " dfNIRSpre = dfNIRSpre.set_index(ncorr)\n", " dfPOpre = dfPOpre.set_index(pcorr)\n", "else:\n", " raise NameError('Indices are messed up')\n", "\n", "TCnirs = timedelta(minutes=2, seconds=10)\n", "TCpo = timedelta(minutes=1, seconds=32)\n", "# NIRS is slower than correct time, need to add TCnirs to catch it up\n", "# PO is faster than correct time, need to subtract TCpo to slow it down\n", "\n", "dfNIRS = dfNIRSpre.set_index([dfNIRSpre.index+TCnirs]) #NIRS Time Correction\n", "dfPO = dfPOpre.set_index([dfPOpre.index-TCpo]) #PO Time Correction" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Both machines on\n" ] } ], "source": [ "#for babies that only had one machine\n", "\n", "dffakePO = pd.DataFrame({'SpO2':0, 'PR':0, 'PI':0}, index=dfNIRS.index)\n", "dffakeNIRS = pd.DataFrame({'StO2':0}, index=dfPO.index)\n", "\n", "if len(dfNIRS) > 5:\n", " if len(dfPO) > 5:\n", " df = dfNIRS.combine_first(dfPO) #Combine two DataFrame objects and default to non-null values in frame\n", " print 'Both machines on'\n", " elif len(dfPO) < 5:\n", " df = dfNIRS.combine_first(dffakePO)\n", " print 'Only NIRS on'\n", "elif len(dfNIRS) < 5:\n", " df = dffakeNIRS.combine_first(dfPO)\n", " print 'Only Masimo on'\n", "else:\n", " raise NameError('Check your files')" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total (h) Baseline recording = 15.748345932\n", "Total (h) After Exam recording = 25.46390926\n" ] } ], "source": [ "a = -(df.index[0]-BabyDict['ExamStart']).total_seconds()*0.000277778\n", "print 'Total (h) Baseline recording = %s' % a\n", "b = ((df.index[-1])-BabyDict['ExamEnd']).total_seconds()*0.000277778\n", "print 'Total (h) After Exam recording = %s' % b" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#Convert to Relative Time and Time Windows\n", "def reltimecalc(ROPNumber, df):\n", " ROPreltime = {\n", " 'BaselineStart' : (df.index[0].to_datetime()-ROPNumber['ExamStart']).total_seconds(), #should be negative\n", " 'BaselineEnd': (ROPNumber['EyeDrop1'] - ROPNumber['ExamStart']).total_seconds(), #redundant, just for labeling sake\n", " 'EyeDrop1' : (ROPNumber['EyeDrop1'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'EyeDrop2' : (ROPNumber['EyeDrop2'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'EyeDrop3' : (ROPNumber['EyeDrop3'] - ROPNumber['ExamStart']).total_seconds(), #neg\n", " 'ExamStart' : (ROPNumber['ExamStart'] - ROPNumber['ExamStart']).total_seconds(), \n", " 'ExamEnd' : (ROPNumber['ExamEnd'] - ROPNumber['ExamStart']).total_seconds(), #positive number\n", " 'DataEnd' : (df.index[-1].to_datetime()-ROPNumber['ExamStart']).total_seconds()\n", " }\n", " \n", " TimeWindows = {\n", " 'tw0' : -10800.0, # three hours before 0\n", " 'tw1' : 10800.0, # three hours after 0\n", " 'tw2' : 21600.0, # six hoours\n", " 'tw3' : 32400.0, # nine hours\n", " 'tw4' : 43200.0, # 12 hours\n", " 'tw5' : 54000.0, # 15 hours\n", " 'tw6' : 64800.0, # 18 hours\n", " 'tw7' : 75600.0, # 21 hours\n", " 'tw8' : 86400.0 #24 hours\n", " }\n", " \n", " return ROPreltime, TimeWindows\n" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "tw = timedelta(hours=3)\n", "twend = timedelta(hours=24)\n", "df = df[(BabyDict['ExamStart']-tw):(BabyDict['ExamStart']+twend)]" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "Data, tw = reltimecalc(BabyDict, df)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df = df.set_index(np.linspace(tw['tw0'],tw['tw8'],len(df)))" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df = df[df.PI.between(0.02, 20)]\n", "df = df.drop('Exceptions', 1) # Drop exceptions now that you don't need it anymore\n", "#filter PI values to only allow =< 0.02 and >= 20\n", "\n", "dfen = df.dropna(how='any') #df for no NaN" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "psa = dfen['SpO2'].values\n", "prs = dfen['StO2'].values\n", "\n", "FTOE = pd.Series(((psa-prs)/psa)*100)\n", "\n", "dfen = dfen.assign(FTOE=FTOE.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analyis Start" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Linear Measures:\n", "\n", "Avgs, Std Dev, and Coefficient of Variation\n", " - Avg, std dev, and CoV for every time window\n", " - Avg every 10 sec for 4 mins of ROP exam, std dev, CoV.\n", " - O2 DeSat Counts" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = df[tw['tw0']:0]\n", "b = df[0:tw['tw1']]\n", "c = df[tw['tw1']:tw['tw2']]\n", "d = df[tw['tw2']:tw['tw3']]\n", "e = df[tw['tw3']:tw['tw4']]\n", "f = df[tw['tw4']:tw['tw5']]\n", "g = df[tw['tw5']:tw['tw6']]\n", "h = df[tw['tw6']:tw['tw7']]\n", "i = df[tw['tw7']:tw['tw8']]\n", "\n", "dflst = [a, b, c, d, e, f, g, h, i]\n", "\n", "tlst = [tw['tw0'], 0, tw['tw1'], tw['tw2'], tw['tw3'], tw['tw4'], tw['tw5'], \n", " tw['tw6'], tw['tw7'], tw['tw8']]\n", "\n", "#dfen for no na values\n", "\n", "ea = dfen[tw['tw0']:0]\n", "eb = dfen[0:tw['tw1']]\n", "ec = dfen[tw['tw1']:tw['tw2']]\n", "ed = dfen[tw['tw2']:tw['tw3']]\n", "ee = dfen[tw['tw3']:tw['tw4']]\n", "ef = dfen[tw['tw4']:tw['tw5']]\n", "eg = dfen[tw['tw5']:tw['tw6']]\n", "eh = dfen[tw['tw6']:tw['tw7']]\n", "ei = dfen[tw['tw7']:tw['tw8']]\n", "\n", "dfenlst = [ea, eb, ec, ed, ee, ef, eg, eh, ei] #basically same dataframe but all NaNs dropped\n", "#some functions will return NaN if there's even one NaN present so this is why this is necessary\n", "\n", "#entropy functions replace original data, so need to make a copy\n", "dfenlstcopy1 = [] #use this for entropy measures\n", "dfenlstcopy2 = [] #use this for cross entropy meaures\n", "dfenlstcopy3 = [] #use this for baseline cross entropy measures\n", "for i in dfenlst:\n", " cpy = i.copy(deep=True)\n", " dfenlstcopy1.append(cpy)\n", " dfenlstcopy2.append(cpy)\n", " dfenlstcopy3.append(cpy)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#AVERAGE DURING ROP EXAM FOR FIRST FOUR MINUTES\n", "\n", "\n", "#tensec needs df['y']\n", "\n", "def tensec(a, b): #a = baseline values, b = next time epoch\n", "\n", " blavg = np.nanmean(a.values)\n", " blstd = np.nanstd(a.values)\n", " blcv = scipy.stats.variation(a.values, nan_policy='omit')\n", " \n", " r = np.arange(0, 250, 10)\n", " \n", " avg = [blavg]\n", " stdev = [blstd]\n", " cv = [blcv]\n", " \n", " ind = list(np.arange(0, 250, 10))\n", " ind.insert(0, 'Baseline')\n", " \n", " for i in r:\n", " x = b[i:(i+10)].mean()\n", " y = b[i:(i+10)].std()\n", " z = scipy.stats.variation((b[i:(i+10)]).values, nan_policy='omit')\n", " \n", " avg.append(x)\n", " stdev.append(y)\n", " cv.append(z)\n", " \n", " dftensec = pd.DataFrame({\n", " 'a_mean': avg,\n", " 'b_std dev' : stdev,\n", " 'c_CoV' : cv,\n", " }, index = ind)\n", " \n", " return dftensec" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIten = tensec(a['PI'], b['PI'])\n", "PIten.columns = ['PI mean', 'PI std dev', 'PI CV']\n", "\n", "PRten = tensec(a['PR'], b['PR'])\n", "PRten.columns = ['PR mean', 'PR std dev', 'PR CV']\n", "\n", "SpO2ten = tensec(a['SpO2'], b['SpO2'])\n", "SpO2ten.columns = ['SpO2 mean', 'SpO2 std dev', 'PR CV']\n", "\n", "StO2ten = tensec(a['StO2'], b['StO2'])\n", "StO2ten.columns = [\"StO2 mean\", \"StO2 std dev\", \"StO2 CV\"]\n", "\n", "FTOEten = tensec(ea['FTOE'], eb['FTOE']) #e means dropped nan\n", "FTOEten.columns = ['FTOE mean', 'FTOE std dev', 'FTOE CV']" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def linearmeasures(avglst): \n", " \n", " #mean\n", " \n", " PImean = []\n", " PRmean = []\n", " SpO2mean = []\n", " StO2mean = []\n", " FTOEmean = []\n", " \n", " #std dev\n", " \n", " PIstd = []\n", " PRstd = []\n", " SpO2std = []\n", " StO2std = []\n", " FTOEstd = []\n", "\n", "\n", " #coefficient of varience\n", " \n", " PIcv = []\n", " PRcv = []\n", " SpO2cv = []\n", " StO2cv = []\n", " FTOEcv = []\n", " \n", " \n", " for i in avglst:\n", "\n", " ax = np.nanmean(i['PI'].values) #mean\n", " PImean.append(ax)\n", " az = np.nanstd(i['PI'].values) #std dev\n", " PIstd.append(az)\n", " av = scipy.stats.variation(i['PI'].values, nan_policy='omit') #CoV\n", " PIcv.append(av)\n", " \n", " bx = np.nanmean(i['PR'].values) #mean\n", " PRmean.append(bx)\n", " bz = np.nanstd(i['PR'].values) #std dev\n", " PRstd.append(bz)\n", " bv = scipy.stats.variation(i['PR'].values, nan_policy='omit') #CoV\n", " PRcv.append(bv)\n", " \n", " cx = np.nanmean(i['SpO2'].values) #mean\n", " SpO2mean.append(cx)\n", " cz = np.nanstd(i['SpO2'].values) #std dev\n", " SpO2std.append(cz)\n", " cv = scipy.stats.variation(i['SpO2'].values, nan_policy='omit') #CoV\n", " SpO2cv.append(cv)\n", " \n", " dx = np.nanmean(i['StO2'].values) #mean\n", " StO2mean.append(dx)\n", " dz = np.nanstd(i['StO2'].values) #std dev\n", " StO2std.append(dz)\n", " dv = scipy.stats.variation(i['StO2'].values, nan_policy='omit') #CoV\n", " StO2cv.append(dv)\n", " \n", " ex = np.nanmean(i['FTOE'].values) #mean\n", " FTOEmean.append(ex)\n", " ez = np.nanstd(i['FTOE'].values) #std dev\n", " FTOEstd.append(ez)\n", " ev = scipy.stats.variation(i['FTOE'].values, nan_policy='omit') #CoV\n", " FTOEcv.append(ev)\n", " \n", " \n", " PIlin = pd.DataFrame({\n", " 'a_PI mean': PImean,\n", " 'b_PI std dev' : PIstd,\n", " 'c_PI CoV' : PIcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " PRlin = pd.DataFrame({\n", " 'a_PR mean': PRmean,\n", " 'b_PR std dev' : PRstd,\n", " 'c_PR CoV' : PRcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " SpO2lin = pd.DataFrame({\n", " 'a_SpO2 mean': SpO2mean,\n", " 'b_SpO2 std dev' : SpO2std,\n", " 'c_SpO2 CoV' : SpO2cv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " StO2lin = pd.DataFrame({\n", " 'a_StO2 mean': StO2mean,\n", " 'b_StO2 std dev' : StO2std,\n", " 'c_StO2 CoV' : StO2cv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " FTOElin = pd.DataFrame({\n", " 'a_FTOE mean': FTOEmean,\n", " 'b_FTOE std dev' : FTOEstd,\n", " 'c_FTOE CoV' : FTOEcv,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " dfCoV = pd.DataFrame({\n", " 'PI CoV' : PIcv,\n", " 'PR CoV' : PRcv,\n", " 'SpO2 CoV' : SpO2cv,\n", " 'StO2 CoV' : StO2cv,\n", " 'FTOE CoV' : FTOEcv\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " \n", " return PIlin, PRlin, SpO2lin, StO2lin, FTOElin, dfCoV" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIlin, PRlin, SpO2lin, StO2lin, FTOElin, dfCoV = linearmeasures(dfenlst) #USE dfenlst" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_PI mean</th>\n", " <th>b_PI std dev</th>\n", " <th>c_PI CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>1.400518</td>\n", " <td>0.607776</td>\n", " <td>0.433965</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>1.470180</td>\n", " <td>0.408768</td>\n", " <td>0.278039</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>1.328343</td>\n", " <td>0.666908</td>\n", " <td>0.502060</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>1.516548</td>\n", " <td>0.507242</td>\n", " <td>0.334471</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>1.880443</td>\n", " <td>0.617186</td>\n", " <td>0.328213</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>2.044417</td>\n", " <td>0.810196</td>\n", " <td>0.396297</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>1.722643</td>\n", " <td>0.592473</td>\n", " <td>0.343933</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>1.780041</td>\n", " <td>0.529919</td>\n", " <td>0.297700</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>1.238639</td>\n", " <td>0.605162</td>\n", " <td>0.488570</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_PI mean b_PI std dev c_PI CoV\n", "-3:0 1.400518 0.607776 0.433965\n", "0:3 1.470180 0.408768 0.278039\n", "3:6 1.328343 0.666908 0.502060\n", "6:9 1.516548 0.507242 0.334471\n", "9:12 1.880443 0.617186 0.328213\n", "12:15 2.044417 0.810196 0.396297\n", "15:18 1.722643 0.592473 0.343933\n", "18:21 1.780041 0.529919 0.297700\n", "21:24 1.238639 0.605162 0.488570" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_PR mean</th>\n", " <th>b_PR std dev</th>\n", " <th>c_PR CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>147.459532</td>\n", " <td>15.811006</td>\n", " <td>0.107223</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>148.137215</td>\n", " <td>17.468538</td>\n", " <td>0.117921</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>146.487619</td>\n", " <td>15.656282</td>\n", " <td>0.106878</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>154.990661</td>\n", " <td>15.570040</td>\n", " <td>0.100458</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>150.472088</td>\n", " <td>14.765096</td>\n", " <td>0.098125</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>164.673462</td>\n", " <td>16.413125</td>\n", " <td>0.099671</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>151.246552</td>\n", " <td>14.890699</td>\n", " <td>0.098453</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>156.654985</td>\n", " <td>14.957723</td>\n", " <td>0.095482</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>153.593688</td>\n", " <td>17.143984</td>\n", " <td>0.111619</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_PR mean b_PR std dev c_PR CoV\n", "-3:0 147.459532 15.811006 0.107223\n", "0:3 148.137215 17.468538 0.117921\n", "3:6 146.487619 15.656282 0.106878\n", "6:9 154.990661 15.570040 0.100458\n", "9:12 150.472088 14.765096 0.098125\n", "12:15 164.673462 16.413125 0.099671\n", "15:18 151.246552 14.890699 0.098453\n", "18:21 156.654985 14.957723 0.095482\n", "21:24 153.593688 17.143984 0.111619" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_SpO2 mean</th>\n", " <th>b_SpO2 std dev</th>\n", " <th>c_SpO2 CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>98.768316</td>\n", " <td>1.969275</td>\n", " <td>0.019938</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>97.741483</td>\n", " <td>2.303449</td>\n", " <td>0.023567</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>98.665524</td>\n", " <td>1.748614</td>\n", " <td>0.017723</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>98.020172</td>\n", " <td>1.972344</td>\n", " <td>0.020122</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>98.063082</td>\n", " <td>1.734714</td>\n", " <td>0.017690</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>98.151674</td>\n", " <td>2.144752</td>\n", " <td>0.021851</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>97.956578</td>\n", " <td>2.278130</td>\n", " <td>0.023257</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>97.970015</td>\n", " <td>1.944758</td>\n", " <td>0.019851</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>98.817949</td>\n", " <td>1.833986</td>\n", " <td>0.018559</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_SpO2 mean b_SpO2 std dev c_SpO2 CoV\n", "-3:0 98.768316 1.969275 0.019938\n", "0:3 97.741483 2.303449 0.023567\n", "3:6 98.665524 1.748614 0.017723\n", "6:9 98.020172 1.972344 0.020122\n", "9:12 98.063082 1.734714 0.017690\n", "12:15 98.151674 2.144752 0.021851\n", "15:18 97.956578 2.278130 0.023257\n", "18:21 97.970015 1.944758 0.019851\n", "21:24 98.817949 1.833986 0.018559" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_StO2 mean</th>\n", " <th>b_StO2 std dev</th>\n", " <th>c_StO2 CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>78.951285</td>\n", " <td>4.364783</td>\n", " <td>0.055285</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>79.807937</td>\n", " <td>4.488576</td>\n", " <td>0.056242</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>78.421524</td>\n", " <td>3.891876</td>\n", " <td>0.049628</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>78.064625</td>\n", " <td>3.017061</td>\n", " <td>0.038648</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>79.340901</td>\n", " <td>3.176529</td>\n", " <td>0.040036</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>78.302226</td>\n", " <td>3.366731</td>\n", " <td>0.042997</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>81.875885</td>\n", " <td>3.955571</td>\n", " <td>0.048312</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>77.831709</td>\n", " <td>3.228982</td>\n", " <td>0.041487</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>81.162525</td>\n", " <td>5.011192</td>\n", " <td>0.061743</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_StO2 mean b_StO2 std dev c_StO2 CoV\n", "-3:0 78.951285 4.364783 0.055285\n", "0:3 79.807937 4.488576 0.056242\n", "3:6 78.421524 3.891876 0.049628\n", "6:9 78.064625 3.017061 0.038648\n", "9:12 79.340901 3.176529 0.040036\n", "12:15 78.302226 3.366731 0.042997\n", "15:18 81.875885 3.955571 0.048312\n", "18:21 77.831709 3.228982 0.041487\n", "21:24 81.162525 5.011192 0.061743" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a_FTOE mean</th>\n", " <th>b_FTOE std dev</th>\n", " <th>c_FTOE CoV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>-3:0</th>\n", " <td>20.027616</td>\n", " <td>4.778722</td>\n", " <td>0.238607</td>\n", " </tr>\n", " <tr>\n", " <th>0:3</th>\n", " <td>18.295492</td>\n", " <td>5.087889</td>\n", " <td>0.278095</td>\n", " </tr>\n", " <tr>\n", " <th>3:6</th>\n", " <td>20.489832</td>\n", " <td>4.247597</td>\n", " <td>0.207303</td>\n", " </tr>\n", " <tr>\n", " <th>6:9</th>\n", " <td>20.323137</td>\n", " <td>3.546801</td>\n", " <td>0.174520</td>\n", " </tr>\n", " <tr>\n", " <th>9:12</th>\n", " <td>19.063551</td>\n", " <td>3.603959</td>\n", " <td>0.189050</td>\n", " </tr>\n", " <tr>\n", " <th>12:15</th>\n", " <td>20.174427</td>\n", " <td>4.068943</td>\n", " <td>0.201688</td>\n", " </tr>\n", " <tr>\n", " <th>15:18</th>\n", " <td>16.356134</td>\n", " <td>4.786585</td>\n", " <td>0.292648</td>\n", " </tr>\n", " <tr>\n", " <th>18:21</th>\n", " <td>20.521799</td>\n", " <td>3.698035</td>\n", " <td>0.180200</td>\n", " </tr>\n", " <tr>\n", " <th>21:24</th>\n", " <td>17.821530</td>\n", " <td>5.556991</td>\n", " <td>0.311813</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a_FTOE mean b_FTOE std dev c_FTOE CoV\n", "-3:0 20.027616 4.778722 0.238607\n", "0:3 18.295492 5.087889 0.278095\n", "3:6 20.489832 4.247597 0.207303\n", "6:9 20.323137 3.546801 0.174520\n", "9:12 19.063551 3.603959 0.189050\n", "12:15 20.174427 4.068943 0.201688\n", "15:18 16.356134 4.786585 0.292648\n", "18:21 20.521799 3.698035 0.180200\n", "21:24 17.821530 5.556991 0.311813" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display\n", "display(PIlin)\n", "display(PRlin)\n", "display(SpO2lin)\n", "display(StO2lin)\n", "display(FTOElin)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4b/748WOaNm0aXbt2jV68eEH29va0Y8cOkslktHPnTrK3t6cXL17ktZGWlkbW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\noVyqUEHshNWpk/g9w4gI8Q170yZg9Gi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"text/plain": [ "<matplotlib.figure.Figure at 0x103c09c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfCoV.plot() #CV graph\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Pearson Correlation Coefficients of 3 Hour Epochs with graph to see trend" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ar = dfen[tw['tw0']:0].corr()\n", "#excludes NaN values\n", "#pearson correlation coefficeint\n", "\n", "#pearson, kendall or spearman?\n", "br = dfen[0:tw['tw1']].corr()\n", "cr = dfen[tw['tw1']:tw['tw2']].corr()\n", "dr = dfen[tw['tw2']:tw['tw3']].corr()\n", "er = dfen[tw['tw3']:tw['tw4']].corr()\n", "fr = dfen[tw['tw4']:tw['tw5']].corr()\n", "gr = dfen[tw['tw5']:tw['tw6']].corr()\n", "hr = dfen[tw['tw6']:tw['tw7']].corr()\n", "ir = dfen[tw['tw7']:tw['tw8']].corr()\n", "\n", "dfcorrlst = [ar, br, cr, dr, er, fr, gr, hr, ir]" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 0 1 2 3 4\n", "# PI, PR, SpO2, StO2, FTOE\n", "# PI aa ab ac ad ae\n", "# PR ba bb bc bd be\n", "# SpO2 ca cb cc cd ce\n", "# StO2 da db dc dd de\n", "# FTOE ea eb ec ed ee\n", "\n", "def corrlst(dflst):\n", " \n", " a = [] #ab\n", " b = [] #ac\n", " c = [] #ad\n", " d = [] #ae\n", " e = [] #bc\n", " f = [] #bd\n", " g = [] #be\n", " h = [] #cd\n", " i = [] #ce\n", " j = [] #de\n", " \n", " for x in dflst:\n", "\n", " ab = x['PI'][1] #ab\n", " a.append(ab)\n", " ac = x['PI'][2] #ac \n", " b.append(ac)\n", " ad = x['PI'][3] #ad\n", " c.append(ad)\n", " ae = x['PI'][4] #ae\n", " d.append(ae)\n", " bc = x['PR'][2] #bc\n", " e.append(bc)\n", " bd = x['PR'][3] #bd\n", " f.append(bd)\n", " be = x['PR'][4] #be\n", " g.append(be)\n", " cd = x['SpO2'][3] #cd\n", " h.append(cd)\n", " ce = x['SpO2'][4] #ce\n", " i.append(ce)\n", " de = x['StO2'][4] #de\n", " j.append(de)\n", " \n", " rgraph = pd.DataFrame({\n", " 'PI:PR': a,\n", " 'PI:SpO2' : b,\n", " 'PI:StO2' : c,\n", " 'PI:FTOE' : d,\n", " 'PR:SpO2' : e,\n", " 'PR:StO2' : f,\n", " 'PR:FTOE' : g,\n", " 'SpO2:StO2' : h,\n", " 'SpO2:FTOE' : i,\n", " 'StO2:FTOE': j,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return rgraph" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rgraph = corrlst(dfcorrlst)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIg = rgraph.ix[:,0:4]\n", "PRg = rgraph[['PI:PR', 'PR:FTOE', 'PR:SpO2', 'PR:StO2']]\n", "SpO2g = rgraph[['PI:SpO2', 'PR:SpO2', 'SpO2:StO2', 'SpO2:FTOE']]\n", "StO2g = rgraph[['PI:StO2', 'PR:StO2', 'SpO2:StO2', 'StO2:FTOE']]\n", "FTOEg = rgraph[['PI:FTOE', 'PR:FTOE', 'SpO2:FTOE', 'StO2:FTOE']]" ] }, { "cell_type": "code", "execution_count": 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TAFdXlotXIim6vEkZE/So0wOHQoseBuN8gIgNwc6fD5TXbiwSlWjfni3pGTFC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"text/plain": [ "<matplotlib.figure.Figure at 0x1186cdf10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PIg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/abvety/w/ff+GDas4H4/LXFdm+uZNbf3O5fvwBgzur6sz2FhYTAUuWcb7du3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\nMMOJQose43jb5sD8csYMhvs7q4WP2NzQwOVbt7Jj5ky/zev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"text/plain": [ "<matplotlib.figure.Figure at 0x113ef4610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PRg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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29oaTkxPq168PAMjIyICnpyecnJywceNGtV5buSgok2tyg5Z/M2SMMW0DHS5p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\nNVGPHhXQKU0d8fFEa9YQNWxI1LixuBJ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"text/plain": [ "<matplotlib.figure.Figure at 0x103b39610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SpO2g.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zcyBa3bpb5XDm97+0jDTptaCXTN402a7x5eWoCbtp06bi7u4unp6eUr9+fRk7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\ngy4WebRYZGpb2+AY9MJCgX18Qo3jpEJ/Wfj86R0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"text/plain": [ "<matplotlib.figure.Figure at 0x118692250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "StO2g.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LlxoJ4eRJo9k8Ph7queBKM3f//SxRG3Hm5hp9zB99BP/7n5G0HVWVOG31c198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\nHuDrsgWKm8+PKqXaF2/qD+xx8rhCCCG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"text/plain": [ "<matplotlib.figure.Figure at 0x11342a510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "FTOEg.plot(grid=True)\n", "plt.hlines(0,0,8,colors='k', lw=5)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plots comparing baseline and 3H after ROP Exam" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def savitzky_golay(y, window_size, order, deriv=0, rate=1):\n", " r\"\"\"Smooth (and optionally differentiate) data with a Savitzky-Golay filter.\n", " The Savitzky-Golay filter removes high frequency noise from data.\n", " It has the advantage of preserving the original shape and\n", " features of the signal better than other types of filtering\n", " approaches, such as moving averages techniques.\n", " \n", " Parameters\n", " ----------\n", " y : array_like, shape (N,)\n", " the values of the time history of the signal.\n", " window_size : int\n", " the length of the window. Must be an odd integer number.\n", " order : int\n", " the order of the polynomial used in the filtering.\n", " Must be less then `window_size` - 1.\n", " deriv: int\n", " the order of the derivative to compute (default = 0 means only smoothing)\n", " \n", " Returns\n", " -------\n", " ys : ndarray, shape (N)\n", " the smoothed signal (or it's n-th derivative).\n", " \n", " Notes\n", " -----\n", " The Savitzky-Golay is a type of low-pass filter, particularly\n", " suited for smoothing noisy data. The main idea behind this\n", " approach is to make for each point a least-square fit with a\n", " polynomial of high order over a odd-sized window centered at\n", " the point.\n", " \n", " Examples\n", " --------\n", " t = np.linspace(-4, 4, 500)\n", " y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)\n", " ysg = savitzky_golay(y, window_size=31, order=4)\n", " import matplotlib.pyplot as plt\n", " plt.plot(t, y, label='Noisy signal')\n", " plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')\n", " plt.plot(t, ysg, 'r', label='Filtered signal')\n", " plt.legend()\n", " plt.show()\n", " \n", " References\n", " ----------\n", " .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of\n", " Data by Simplified Least Squares Procedures. Analytical\n", " Chemistry, 1964, 36 (8), pp 1627-1639.\n", " .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing\n", " W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery\n", " Cambridge University Press ISBN-13: 9780521880688\n", " \"\"\"\n", " import numpy as np\n", " from math import factorial\n", "\n", " try:\n", " window_size = np.abs(np.int(window_size))\n", " order = np.abs(np.int(order))\n", " except ValueError, msg:\n", " raise ValueError(\"window_size and order have to be of type int\")\n", " if window_size % 2 != 1 or window_size < 1:\n", " raise TypeError(\"window_size size must be a positive odd number\")\n", " if window_size < order + 2:\n", " raise TypeError(\"window_size is too small for the polynomials order\")\n", " order_range = range(order+1)\n", " half_window = (window_size -1) // 2\n", " # precompute coefficients\n", " b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])\n", " m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)\n", " # pad the signal at the extremes with\n", " # values taken from the signal itself\n", " firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )\n", " lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])\n", " y = np.concatenate((firstvals, y, lastvals))\n", " return np.convolve( m[::-1], y, mode='valid')" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "ypsg = savitzky_golay(dfen['SpO2'].values, 31, order=4, deriv=0, rate=1)\n", "yrsg = savitzky_golay(dfen['PR'].values, 31, order=4, deriv=0, rate=1)\n", "ytsg = savitzky_golay(dfen['StO2'].values, 31, order=4, deriv=0, rate=1)\n", "yi = dfen['PI'].values #don't filter PI values\n", "yf = dfen['FTOE'].values #don't filter FTOE\n", "\n", "dfgraph = pd.DataFrame({'SpO2':ypsg, 'PR':yrsg, 'StO2':ytsg, 'PI':yi, 'FTOE':yf}, index=dfen.index)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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Kq1bJ+ZsCvMcecv2906bn50e2oDMyRMjWrBH3y2OPiUCNGSOi75eAOCPDX7gA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\n6o47bPfLd77j/7hJk1gkhIYG9gXrRT0BXvFedFFwlJacHL29tsUjqyQ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4vBgwgL8H+W7k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"text/plain": [ "<matplotlib.figure.Figure at 0x11ac6d990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['StO2'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline StO2');\n", "dfgraph['StO2'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam StO2');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VqhXd99/fPwZYD4JDfADz94gjuAKGE1BAYuXZCaGgRu7ui6RB1avHj/9FWB5y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\nYNy1a+R1nT+/tCp+pVXrb745eDP27QtW2LIqj5vx4ysLVKf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"text/plain": [ "<matplotlib.figure.Figure at 0x11204bf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['SpO2'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline SpO2')\n", "dfgraph['SpO2'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam SpO2');\n", "\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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L6apVfkE8/HDJb6etJbWkKiokEub884P7mDFDPq+7TvajyRgzhZkn7YNyBUpf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"text/plain": [ "<matplotlib.figure.Figure at 0x102320d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['PR'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PR');\n", "dfgraph['PR'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('3h After Exam PR');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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77jmvK6XhrLOYn33W3X/hhcF5vvCCe86vv8p5qVxLzTXM+g03yPbtt7v7vv8+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\nsyduPsbddpPl/vu7xw44QJbmBTadnc8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lj65bVz72L7+UvppjjvGma9Mm+GO2vXH8ZTzu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\nyEt1xx3B5h+TrkOH5LA0/mvZlV9QH5R5aXpZs4X4TQdBlXT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"text/plain": [ "<matplotlib.figure.Figure at 0x118477d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['PI'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PI');\n", "dfgraph['PI'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('After Exam PI');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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I+p0AhoWcy4D07dx6a/TssOYBi1Kg27Yxb9gQfMwVUE8+KffPyZEXy9igr7tO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\nf/+NIX4TaiyYudg/AKMBPO7bNxzAnZH1OwEMCzm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"text/plain": [ "<matplotlib.figure.Figure at 0x11af6a0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=1)\n", "\n", "dfgraph['FTOE'][-10800:0].plot(ax=axes[0]); axes[0].set_title('3h Baseline PI');\n", "dfgraph['FTOE'][0:10800].plot(ax=axes[1], color = 'r'); axes[1].set_title('After Exam PI');\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Information Theory\n", " - Fuzzy Entropy\n", " - Cross Fuzzy Entropy" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from entropy import *\n", "\n", "#def fuzzyen(x, dim, r, n, scale=True):\n", "# return entropy(x, dim, r, n=n, scale=scale, remove_baseline=True)\n", "\n", "def FuzzyEnLst(dfenlstcopy1):\n", " \n", " PI_FE = []\n", " PR_FE = []\n", " SpO2_FE = []\n", " StO2_FE = []\n", " FTOE_FE = []\n", " \n", " for i in dfenlstcopy1:\n", " a = fuzzyen(i['PI'].values, 2, .2*np.nanstd(i['PI'].values), n=1)\n", " b = fuzzyen(i['PR'].values, 2, .2*np.nanstd(i['PR'].values), n=1)\n", " c = fuzzyen(i['SpO2'].values, 2, .2*np.nanstd(i['SpO2'].values), n=1)\n", " d = fuzzyen(i['StO2'].values, 2, .2*np.nanstd(i['StO2'].values), n=1)\n", " e = fuzzyen(i['FTOE'].values, 2, .2*np.nanstd(i['FTOE'].values), n=1)\n", "\n", " PI_FE.append(a)\n", " PR_FE.append(b)\n", " SpO2_FE.append(c)\n", " StO2_FE.append(d)\n", " FTOE_FE.append(e)\n", " \n", " FuzzyEnDf = pd.DataFrame({\n", " 'PI FuzzyEn': PI_FE,\n", " 'PR FuzzyEn' : PR_FE,\n", " 'SpO2 FuzzyEn' : SpO2_FE,\n", " 'StO2 FuzzyEn' : StO2_FE,\n", " 'FTOE FuzzyEn' : FTOE_FE\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return FuzzyEnDf" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf = FuzzyEnLst(dfenlstcopy1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['PI FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['PR FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['SpO2 FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['StO2 FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf['FTOE FuzzyEn'].plot(grid=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FuzzyEnDf.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#def cross_fuzzyen(x1, x2, m, r, n, scale=True):\n", "# return entropy([x1, x2], dim, r, n, scale=scale, remove_baseline=True)\n", "\n", "def CrossFuzzyLst(dfenlstcopy2):\n", " \n", "#Cross Fuzzy Entropy between variables\n", "\n", " a = [] #ab\n", " b = [] #ac\n", " c = [] #ad\n", " d = [] #ae\n", " e = [] #bc\n", " f = [] #bd\n", " g = [] #be\n", " h = [] #cd\n", " i = [] #ce\n", " j = [] #de\n", " \n", " rlst = [a, b, c, d, e, f, g, h, i, j]\n", " \n", " # 0 1 2 3 4\n", " # PI, PR, SpO2, StO2, FTOE\n", " # PI aa ab ac ad ae\n", " # PR ba bb bc bd be\n", " # SpO2 ca cb cc cd ce\n", " # StO2 da db dc dd de\n", " # FTOE ea eb ec ed ee\n", " \n", " for x in dfenlstcopy2:\n", "\n", " ab = cross_fuzzyen(x['PI'].values, x['PR'].values, 2, .2, n=1) #ab\n", " a.append(ab)\n", " ac = cross_fuzzyen(x['PI'].values, x['SpO2'].values, 2, .2, n=1) #ac\n", " b.append(ac)\n", " ad = cross_fuzzyen(x['PI'].values, x['StO2'].values, 2, .2, n=1) #ad\n", " c.append(ad)\n", " ae = cross_fuzzyen(x['PI'].values, x['FTOE'].values, 2, .2, n=1) #ae\n", " d.append(ae)\n", " bc = cross_fuzzyen(x['PR'].values, x['SpO2'].values, 2, .2, n=1) #bc\n", " e.append(bc)\n", " bd = cross_fuzzyen(x['PR'].values, x['StO2'].values, 2, .2, n=1) #bd\n", " f.append(bd)\n", " be = cross_fuzzyen(x['PR'].values, x['FTOE'].values, 2, .2, n=1) #be\n", " g.append(be)\n", " cd = cross_fuzzyen(x['SpO2'].values, x['StO2'].values, 2, .2, n=1) #cd\n", " h.append(cd)\n", " ce = cross_fuzzyen(x['SpO2'].values, x['FTOE'].values, 2, .2, n=1) #ce\n", " i.append(ce)\n", " de = cross_fuzzyen(x['StO2'].values, x['FTOE'].values, 2, .2, n=1) #de\n", " j.append(de)\n", " \n", " XFuzzyEnDf = pd.DataFrame({\n", " 'xf PI:PR': a,\n", " 'xf PI:SpO2' : b,\n", " 'xf PI:StO2' : c,\n", " 'xf PI:FTOE' : d,\n", " 'xf PR:SpO2' : e,\n", " 'xf PR:StO2' : f,\n", " 'xf PR:FTOE' : g,\n", " 'xf SpO2:StO2' : h,\n", " 'xf SpO2:FTOE' : i,\n", " 'xf StO2:FTOE': j,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return XFuzzyEnDf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xengraph = CrossFuzzyLst(dfenlstcopy2) #error up to 4 decimal places" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIxg = xengraph.ix[:,0:4]\n", "PRxg = xengraph[['xf PI:PR', 'xf PR:FTOE', 'xf PR:SpO2', 'xf PR:StO2']]\n", "SpO2xg = xengraph[['xf PI:SpO2', 'xf PR:SpO2', 'xf SpO2:StO2', 'xf SpO2:FTOE']]\n", "StO2xg = xengraph[['xf PI:StO2', 'xf PR:StO2', 'xf SpO2:StO2', 'xf StO2:FTOE']]\n", "FTOExg = xengraph[['xf PI:FTOE', 'xf PR:FTOE', 'xf SpO2:FTOE', 'xf StO2:FTOE']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(PIxg, PRxg, SpO2xg, StO2xg, FTOExg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PIxg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PRxg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "SpO2xg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "StO2xg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "FTOExg.plot(grid=True)\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def OrderDetn(x1, x2):\n", " \n", " x1\n", " \n", " #Takes the length of two arrays and\n", " #arranges them from least to greatest\n", " #return (smaller x, bigger x)\n", " if len(x1) <= len(x2):\n", " return x1, x2\n", " else:\n", " return x2, x1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#def cross_fuzzyen(x1, x2, m, r, n, scale=True):\n", "# return entropy([x1, x2], dim, r, n, scale=scale, remove_baseline=True)\n", "\n", "def CrossFuzzyLst(dfenlstcopy3):\n", " \n", " #cross entropy of baseline across different time epochs\n", " #comparison across the same variable\n", " \n", " #len(x1) <= len(x2) for it to work\n", "\n", " PIxbl = []\n", " PRxbl = []\n", " SpO2xbl = []\n", " StO2xbl = []\n", " FTOExbl = []\n", " \n", " for i in np.arange(0, 9): #9 time epochs\n", " \n", " a1, a2 = OrderDetn(dfenlstcopy3[0]['PI'].values, dfenlstcopy3[i]['PI'].values)\n", " b1, b2 = OrderDetn(dfenlstcopy3[0]['PR'].values, dfenlstcopy3[i]['PR'].values)\n", " c1, c2 = OrderDetn(dfenlstcopy3[0]['SpO2'].values, dfenlstcopy3[i]['SpO2'].values)\n", " d1, d2 = OrderDetn(dfenlstcopy3[0]['StO2'].values, dfenlstcopy3[i]['StO2'].values)\n", " e1, e2 = OrderDetn(dfenlstcopy3[0]['FTOE'].values, dfenlstcopy3[i]['FTOE'].values)\n", " \n", " a = cross_fuzzyen(a1, a2, 2, .2, n=1)\n", " b = cross_fuzzyen(b1, b2, 2, .2, n=1)\n", " c = cross_fuzzyen(c1, c2, 2, .2, n=1) \n", " d = cross_fuzzyen(d1, d2, 2, .2, n=1)\n", " e = cross_fuzzyen(e1, e2, 2, .2, n=1)\n", " \n", " PIxbl.append(a)\n", " PRxbl.append(b)\n", " SpO2xbl.append(c)\n", " StO2xbl.append(d)\n", " FTOExbl.append(e)\n", "\n", " XblFuzzyEnDf = pd.DataFrame({\n", " 'BL X FuzzyEn PI': PIxbl,\n", " 'BL X FuzzyEn PR' : PRxbl,\n", " 'BL X FuzzyEn SpO2' : SpO2xbl,\n", " 'BL X FuzzyEn StO2' : StO2xbl,\n", " 'BL X FuzzyEn FTOE' : FTOExbl,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return XblFuzzyEnDf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph = CrossFuzzyLst(dfenlstcopy3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xblengraph.plot(grid=True)\n", "plt.title('Cross Fuzzy Entropy', color='black')\n", "plt.tight_layout()\n", "plt.axvline(1, color='y', linestyle=':', lw='4') #vertical line at ROP Exam time\n", "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chaos Theory\n", " -Lempel-Ziv complexity\n", " -Lyapunov largest exponent\n", " -Hurst exponent" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Scripts for Chaos Theory based variables\n", "# Import from other scientists\n", "#\n", "\n", "# #1\n", "from analysis import *\n", " #Source: https://github.com/thelahunginjeet/pydynet/blob/master/analysis.py\n", " #def lz_complexity(s):\n", " #Aboy 2006: turn into two bit data based off threshold (threshold = median)\n", " #turn data into a string of 0's and 1's\n", "\n", "# #2\n", "def LLEcal(data):\n", " #Source: http://systems-sciences.uni-graz.at/etextbook/sw2/lyapunov.html\n", " result = []\n", " lambdas = []\n", " \n", " # loop through data\n", " for x in data:\n", " #take the log of the absolute of the data\n", " result.append(np.log(abs(x))) #np.log = ln\n", " # take average\n", " lambdas.append(mean(result))\n", " \n", " return max(lambdas)\n", "\n", "\n", "# #4\n", "def hurst_mod(data):\n", " #Source: http://pyeeg.sourceforge.net/index.html?highlight=hurst#pyeeg.hurst\n", " #modified lstsq line to to start from third iterable\n", " X = data\n", " N = len(X)\n", "\n", " T = array([float(i) for i in xrange(1,N+1)])\n", " Y = cumsum(X)\n", " Ave_T = Y/T\n", "\n", " S_T = zeros((N))\n", " R_T = zeros((N))\n", " for i in xrange(N):\n", " S_T[i] = std(X[:i+1])\n", " X_T = Y - T * Ave_T[i]\n", " R_T[i] = max(X_T[:i + 1]) - min(X_T[:i + 1])\n", "\n", " R_S = R_T / S_T\n", " R_S = log(R_S)\n", " n = log(T).reshape(N, 1)\n", " \n", " #remove NaNs from both R_S and n\n", " rrr = np.ndarray.flatten(n)\n", "\n", " dfxxx = pd.DataFrame({\n", " 'n': rrr,\n", " 'R_S' : R_S,\n", " })\n", " \n", " dfxxx = dfxxx.dropna(how='any')\n", " \n", " N = len(dfxxx)\n", " \n", " n = dfxxx['n'].values.reshape(N, 1)\n", " R_S = dfxxx['R_S'].values\n", "\n", " H = lstsq(n[1:], R_S[1:])[0]\n", " return H[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ChaosVars(dflst, col): #or dfenlst if it doesn't work. \n", " #make sure you put in a['PI'] etc.\n", " #tau = 1 + n #embedding lag\n", " # n = m from fnn function, embedding dimension\n", " fs = 0.5 #sampling frequency, 1/2\n", " \n", " \n", " LZlst = []\n", " LLElst = []\n", " Hlst = []\n", " \n", " for i in dflst:\n", " \n", " data = i[col].values\n", " \n", " datalz = []\n", " \n", " thresh = np.nanmedian(data)\n", " for i in data:\n", " if i < thresh:\n", " datalz.append(0)\n", " else:\n", " datalz.append(1)\n", " \n", " strlz = ''.join(str(x) for x in datalz)\n", " \n", " a = ((1.0*lz_complexity(strlz))/random_lz_complexity(len(strlz),p=0.5)) #normalize\n", " LZlst.append(a)\n", " \n", " b = LLEcal(data)\n", " LLElst.append(b)\n", " \n", " try:\n", " d = hurst_mod(data)\n", " Hlst.append(d)\n", " except ValueError:\n", " Hlst.append(np.nan)\n", " \n", " dfchaos = pd.DataFrame({\n", " col + ' LZ': LZlst,\n", " col + ' LLE' : LLElst,\n", " col + ' H' : Hlst,\n", " }, index = ['-3:0','0:3','3:6','6:9','9:12','12:15','15:18','18:21','21:24'])\n", " \n", " return dfchaos" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfcPI = ChaosVars(dfenlst, 'PI')\n", "dfcPR = ChaosVars(dfenlst, 'PR')\n", "dfcSpO2 = ChaosVars(dfenlst, 'SpO2')\n", "dfcStO2 = ChaosVars(dfenlst, 'StO2')\n", "dfcFTOE = ChaosVars(dfenlst, 'FTOE')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display(dfcPI, dfcPR, dfcSpO2, dfcStO2, dfcFTOE)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

DistrictDataLabs/yellowbrick

examples/examples.ipynb

1

1497985

null

apache-2.0

ML4DS/ML4all

U_lab1.Clustering/Lab_ShapeSegmentation_draft/LabSessionClustering.ipynb

1

347350

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab Session: Clustering algorithms for Image Segmentation\n", "\n", "Author: Jesús Cid Sueiro\n", "Jan. 2017" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.misc import imread" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Introduction\n", "\n", "In this notebook we explore an application of clustering algorithms to shape segmentation from binary images. We will carry out some exploratory work with a small set of images provided with this notebook. Most of them are not binary images, so we must do some preliminary work to extract he binary shape images and apply the clustering algorithms to them. We will have the opportunity to test the differences between $k$-means and spectral clustering in this problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1. Load Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several images are provided with this notebook:\n", "\n", "* BinarySeeds.png\n", "* birds.jpg\n", "* blood_frog_1.jpg\n", "* cKyDP.jpg\n", "* Matricula.jpg\n", "* Matricula2.jpg\n", "* Seeds.png" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select image `birds.jpg` from file and plot it in grayscale" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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fx+VysXHjRiorKymVSni9XpxOJxs2bMBgMBAOhwkGg/o7hemYmpriySef5JVX\nXtEmLFHwys/5/X4SiQThcBiz2UwikdBz53Q6FdAWtXBzc7PiSYJ7JJNJ7SeKRCKMjo6qJPud2mnh\nICQ1c7lc2Gw2Tb36+vq0nrbb7VitVu1AFC66r6+PgYEBbUwqlUosLi5q5AFU1Sf1sdFo5PXXX1+V\nqYiJWnHnzp2Ki7hcLlKplGIHDQ0NHDhwAIfDwalTp3A6ndhsNv0z4erLy8uJxWIqNZbS5OjRoyps\nkeaxeDyueEMmk9EDYDKZVqHuK2tyu93OoUOHtEFKSpazzjpLv0sikaC+vl4zJvmZlRmJOLTnn39e\nuXuRrUutvJLpEGe7detWgsEgZ511llLUhUIBi8Wi3ZGS8gom0NnZqRdEdBRyGQAOHTrE4OCgqgfv\nvvtuRkdHefzxxykWi0obippWFIQ+n4+hoSHWrl2LyWTC5/MRjUb5sz/7M5qbm5UNkQxI6O0DBw7w\n2GOP6WWUfZifn8dutysdHo/HmZiYIBaLUVVVRT6fp7+/X8/P2rVrFTAEaGxsZN26dbS3t3P8+HFy\nuZxqX2pqaigWizQ2NiqwHIvFtFGwpaVF16Ompobq6moVfsnfL9oa6QWSICZl7x9jS/9WKzsdHs5Z\ns2bNUnV1NalUCofDoZSYz+fj3e9+t4pGqqqqqK2tXVV/DwwM8MILLzA5Oak1HCyr0Obm5jCbzVRW\nVjI+Ps7atWuZnp6moaGBWCzGE088QW9vL6lUShu0hFKzWq386Ec/4s477ySXy3HxxRczOjrK6Ogo\nW7du1S4/0U1IdAiHwzQ3N7N161aef/55RbltNptmCw6Hg/PPP59vfetbAKr0FHAvnU7z53/+57hc\nLq644go+//nPa5otQK44oPPPPx+Hw8Hg4CAej4dEIkEmk+GCCy5g3759VFdXk81mqaqq4h//8R9V\nuSlCq0gkQjab5SMf+QilUokjR47gcrlobm6mqqqK2dlZfvWrX1FRUYHBYFgVldLpNO9+97uJRqMs\nLi4SDoe1fdxisTAwMMC2bdvUia1du5aamhpcLhcHDhygpaWF0dFRZW3EYQtNabVatddGmubOO+88\n9uzZow4ym83S1dUFLGMSolqUQPPZz36Wv/qrv1LQ9pVXXuHrX/86o6OjWuqsWbNG+0IikYg6yPb2\ndkKhkOpw7HY7dXV1TE5OAqjTNpvNVFRUMD8/v6q1vbW1lVAopA6wvLycf/zHf+T+++/npZdeUmxI\nqG/R90iDWzgcxm63a7PXyuCQy+U0uxW85dSpU3pPKioqiMVijI+P/9/f7p3NZkmlUsTjcaLRqEao\nu+66iy996Uv8p//0n2htbaXrP8DeAAAgAElEQVSqqkqdg1zmLVu28Ld/+7fcfffdbNmyRVH+gYEB\nfD4fiUSCQqGgPfey+YlEgieffFIdyspSQyLmhz70ITZv3kw6nWZ8fJxQKER1dTX79+9XAKy7u1s9\nuKDcc3NzCmb19vZis9nIZDJ6KaVUEWcgjkNAurGxMWKxmIJQUmuK+Ec0+sPDw/q7JCpLqbZv3z69\nQOl0mvPOO0/raikV5Du//vrrnDhxglAopH9mNBo5cOAAuVyO6elpzcpWmtFoxOl06neTSL20tKQZ\nVzqd1s7VaDTK4OAgR44cUbxA9hJQJa3oBLLZLPX19cBbbEM4HFaVqtvtZufOnTz++OM8/vjj/PSn\nP+WGG25Y1Wuyb98+hoeH+eEPf8iOHTu44447FDAVKly+19zcnGah+Xxe9/DkyZP4/X6am5uZnJzU\nDFFKr4qKCqLRqP47YbakX8bv9wNwxx13YLfb+dCHPqQYgWQgHR0dRCIRZmdnicVidHd3Kz4l5Ux1\ndTUtLS1Kt1qtVpW4S9YUj8dJpVL/R/ow4DRxEHa7nVgshtPpVMowHo8zNzfH7Oyslg8C5qzU9Iu4\naPPmzTz55JN8/etfZ926dVqmrGQo/H4/VqtVAa0DBw6wZ88edSrwVlSQoSO33XYbFouFsbExpegE\nIDSZTOrFJfNIpVKaoTQ3N3Pq1Cna29tVhGUymdTjS1s3wPz8vP7OV199Fa/Xi91up7m5WR2JmFBd\ngmnY7XYqKyvVgbW0tGhnojT9XHLJJavwDlmDfD7Pz372M53zIIIwWTuLxcL09PS/wh9kHUS+Lp9x\nampKsYErrriCmZkZ3WMZmCIXciWQ6XK5tBvR4/GoaC4ajSrQLF2SXq+X3t5ennjiCa677jplWvx+\nP//1v/5Xenp6NNAcO3aMD3zgA3zta1/j2LFjwHL7dVlZGfPz87rfor1Yuc6iExF8ZHh4mFwux5o1\na4DlMk2ys1KppA1mglmk02l1ytu3b+fiiy/GZDLh9/uVhvR6vWQyGcbGxuju7lbnJg5elKSHDx8m\nm80q1SkY2ejoKGVlZVoyiehQ2Ld3aqeFg5CSYmU7blNTE48++ij9/f1ks1ncbrcyBvX19VitVnK5\n3Cq1n8Vi4QMf+AA/+9nPsFqt6qUDgYA6lEgkQn9/P2bz8syIH/zgBypwEVptZb2/ZcuWVbQhoFhJ\nTU0Ng4ODACoTFkHP3NwcVVVVxGIxAoGAqvGqq6u1N0DwB5k1YbVa2bdvHz/+8Y918Eg4HCaXy2lU\ny2azzM3NYTQa2b17t2YBdXV1eDweisUiS0tL2o+yEs2WXovFxUUVOQWDQV5++WUqKipIp9MqQZZO\nyGQyqZSZ/C6p4+fn5wkGg9hsNt07o9HI/v37SafTnDx5koWFBW3+CgaDdHR00NDQoFqTYDCoEmiZ\n8GQ2mxkbG2Pjxo16iQR7mp2d5cILL+Thhx/WKWIul0uxE2FvYLnkmJ+f1/kIkrHMzs4qqD0zM6Nl\nGaAdnzIXQ2aCGAwGrFarOik5k4JTeTweDh8+zMzMDOl0mkAgoGXKV77yFb785S9r16XgYRaLhXg8\nrhniwYMHCQaDKuRyOBw4HA4qKio08xRHeezYMe17iUajBAIBDVIrHfc7tdPCQTgcDk2XhDI8evQo\nx44d45577mFubk7VdJKaSzaxUl4sF9vr9fLUU09pyiUZSjAY1AMgTUQDAwM888wz2gNQKpVWaQny\n+TxdXV0sLCwocyIAkQh2PB4PDodD9Q9Sx6ZSKZqamvD5fFitVk0NZ2dnGR8f59ixY1q39vf3c+ut\nt/Kxj31MI5s4FWDVBTUYDMzPzxMIBLTUmZycVGBWFJArB+h873vf4+/+7u90wpIAds8++6w2f0m2\nJeDnzMwM4XCYeDyuDhneGvBTKpVIpVJ0dHRQW1tLIBCgrq5O12lmZkalzg0NDUozHj16VAG5P750\ngK67jBBMpVJK3e7YsYMvfelLKhSS7E+anebm5piZmVE61OPxYLFYOOecc/B6vbS3t69qapImQJnq\nJBSkZDPSIyRzOiorK7WkGxoaoqqqShknaQgUh3zTTTfxwAMPqNQ8lUrp8JzJyUktv4SylQ5h0TWI\nQvfkyZPawyPOCCAQCLBx40bWrFmzSlkrKtiVtPy/1U4LByHpnQiIhEoyGAxMT0/z6U9/WkdsiTjF\n6XRiMpk0hZPoL3bRRRfxzDPPcPXVV5PNZvH7/aokTCaTCh5arVZ+/OMf65gyYRFEh2+1Wrn44ou1\nlJBORp/Pp6PZenp6CIfD9PX1UVNTo2CddIJOTk5is9mUGxcl57333ksikeC+++5jx44djI6Osm3b\nNuCtEmBgYGAVfy8a/X/5l39hZmZmVe+Fz+cjl8sxMTHBli1bmJycpLW1FbPZzPDwME899RRf+MIX\nuOKKK/jkJz/JXXfdxQ9+8AO9KNL919TUhN1ux2azUVdXpw4P3sItpLSx2+3s3buXqakpXC4Xs7Oz\n2jyXz+e1gzMcDuu+OZ1OddKwDCiLSrahoUFp78rKShXCyZm44YYbtEwRZ9TQ0EBDQwNWq5WhoSGO\nHj0KoDqFqqoq9uzZg9lsZmBgQHEcKY2kGQ3Q8YCAZp0rxViS7crnl7kLQh2vnN953XXXabpvMpk0\na4hGo+qMJVPJZDIsLCzgdDqV9hXmQtrIRTUq/S+wTHfPzc2xbt26VdSm0Knv1E4LFqO/v3/pYx/7\nmF7yWCzGWWedxfHjx6mpqdHZgn6/nzvvvJOuri7y+bwiv+KxhUaU5hzpnINlxP2rX/0qb7zxhnr6\n5uZmQqEQ69atw2g0snfvXm3dlcMvHXyf/exnefHFF8nn89TV1TE8PEx9fT319fUcPnwYt9tNXV0d\nmzdv5le/+hXnnnsu+/bto6KiglQqRW9vLxMTE4yMjCi1VVZWRk1NDalUCli+KK+//rqO1vN6vbjd\nbt7//vdz2223qSbE6/XywQ9+kGPHjtHa2qodm+FwmNbWVmZnZ9mwYQMHDhzA5/MRCAQ0KgolKBJz\noXEHBwfp6+tjZmYGn8+nvS5+vx+TycR3v/tdVRPKFKhYLMall16qkviysjIF66RD02g0UlNTQzAY\nBJYdbkNDA+eeey433XQTFRUVWK1WHY4jA2ikhHrwwQcJBoP4/X5+/etfK1UouNLKORq7d+/WXpqG\nhgbtdWhsbOTUqVN68aSEymQydHV1aTkgWZEoeEX1ms8vjwbs7OxkdHRUmRIpfYRhkDLV6XTy1FNP\n0dzcrIyY4FRut5tbb72VAwcOEI1GtS9FLrSMvRM5tcFg4IknnuDcc8/V8/3HtmfPHq6++mqd2To/\nP8/CwgI1NTWMjo7+389iGI1Gdu3aRTwex+Vy4XA4KC8v17R/z549wDKavGvXLv7bf/tvBAIBKisr\ntU1WpL2w7Cjm5uaUwZANvfvuu/nIRz5CZWWlRqdSqUShUNC5k3JpVlo+n+fKK6/UGl6yCZPJRKFQ\n0PLi0ksv5W//9m/5/ve/T7FY1C5OEbGUl5erDFku9bFjx1hYWFh1WGw2Gw6Hg7POOgur1cr3v/99\nbUUX0EwUfMAqLGZmZkbRe5Es+/1+jEajOiJpm66srNTmqba2NgYHB8nlcoqAyzqcOnWKgwcP6uWR\nUkPYJ4fDwdTUFOXl5fr/EfGVfNaWlhZaW1u56aab+Pa3v83dd9+t3YYS0VeqWw0GA5dddhmf/vSn\nMZlMfOpTnyIej+sMCenAhWXnMDExwQMPPKC///jx4zo45cCBAzq3c9OmTXoZhTUpFosKHMvMSovF\ngsPhIJFIKPYk+IFkR9L4trIDV7p5ZZCOZApSNuVyOfr7+4lGo6sG+MTjcSorK1UZLDTtBz/4QXUO\n0rkr6yP/rK6uplAokE6nFRP7Y2D733w3Zfrzv6dNTk7eI6PBdu/erWIYAdskUqTTaaqqqhgZGWHv\n3r0cP36cSy+9VDUCAOXl5QAsLS0xPj6uF3FxcZFCocCWLVv4h3/4B+XepUYvLy/H6XSydetWnV2w\ncgzc+Pg4v/rVr1izZo0qOaXTcd26dbS0tPDFL34Rj8fD+vXrueiii4jFYpw8eRKLxcKRI0cUVFtc\nXKSyspLFxUXtNWlra2NmZgabzUYwGKS8vJx4PE5TUxOpVIrDhw+zd+9eYrEY3/jGNwiHw4ojiMR3\nZmZGJeAi6ioUCiwuLq6amrSwsKAAqdTDS0tLdHV1YbFYGBkZIZlM6nwF0ZKcd955LC4uKh4QDAb5\n7ne/S3V1NVarldnZWZVO+3w+PdRbtmzh2muv5XOf+xyXXnqpov/iQGG5pBIwrlAoUFZWRrFY1MlY\nn/zkJxWElO+4cmjrF77wBYaHhzEYDKRSKbxerzpCaYWenp7WC2uz2bRWlzHyMnRFwGXBWqqqqpib\nm9PPZDAYqKmpIZfLYbPZdB8rKipYXFwkFotx22236dktLy/XABaJRPje975HWVmZ9vDIXjQ2NmrZ\nWCgU2LlzJ7fffrtOoFo5i6JYLOqci29+85scP35cHd34+Dh9fX3Mz8/zuc997t53cjdPiwxCUqcb\nbrgBWO6H8Pl8+P1+7aKDZVXZ3Nyc1oLPP/889957L6FQiHQ6zfz8vJYppVKJ5uZmamtrmZ+fB5br\nMimpmpqaKC8vp7a2VlPbRx55hDfeeINgMMjg4CCBQIBIJMKnPvUp7r33Xn33QehAmTUhCr+6ujr9\nTnV1dXzta1/jpptu0jHnEs3j8bgCohaLhdbWVk6dOqURVaJ7sVjUmY0Ak5OTPPnkk4qUd3d365+F\nw2Hq6+tpb2/XcfqRSESVkJL+CrgnbzVI1PP7/Rw5coRsNktPTw/ZbFYxiZaWFkKhkNb+AjyeOnVK\nm75kpL/ZbMblctHW1obJZOKaa67hm9/8Ju9///upqqpifn4eo9Gok7NlrySLADQbguVIeOutt+pl\nFvGU/HmpVOLpp5/m6NGjquOQyC0S97Vr1+qo/Xg8TjgcZnJykng8rvJocbiSGco+w/L8R+kEln1b\nCRhLpiZPKIiQaSXoC8uZsjBotbW1FItFnbi+cuy+0+lkx44d7Ny581+tBbwFWBcKBZVyi7RdShzZ\nr3dqp8U8CPHMkkKXl5frZB5AdQWC7K5fv56xsTHa2tr4xS9+wT//8z/z4Q9/WOswSROLxaKOspMS\nolQqsXnzZiKRCCMjI2zevFnbju12OzfccIOOqJcILTMW3G43k5OTOvHH5XJpWSQS5pVmNBrZuXMn\nnZ2d3HvvvRw8eFCFONlsloWFBZ2OLRRjoVAgm83S1NREf38/CwsLdHR06OGTTZeZFclkkqmpKQWz\nEokEQ0ND+P3+VQdL+i7EKisrlRqrq6sjGAzqRPH6+nqcTqc+Q1AsFunv7ycQCGiLvdFo5He/+x1O\np1OHqcDy4W1ra+OSSy7h4osvxuv1qgBM5kFKQBAgVpyMZBVywaVZq7OzU3sRpKyQnzebzTzyyCN0\ndnYSi8V0CHB1dTWLi4tEo1Hq6+t5/fXXlYYWJyJveQA6PcpsXp4SVV9fryIzWeuVk7/EEZ44cUJx\noHA4zNzcnJaXgDpNETPt379fSzKfz0cwGFRAfGRkhIaGBm6//XauuuoqrFbrv3IOK+3JJ5/krrvu\nUiZH9CTpdFoDxju10yKDqKioUE75+uuvV1oLWOUUADZu3Kjocnl5OQ0NDczPz/O73/2Oa6+9lu98\n5ztEo1F9PUrSUaG7pM17ZmZGZzQKCi7pcUVFhR7aUqlEWVmZotnDw8MUi0WOHTtGIBBQufDKl7XE\nJBXctm0bX/va1zRbEVGPDKRpaWlhcHBQB+F0dXVpy69oNwDFCfL55ZmRc3NztLS0EAgEVPwTCoV0\nNJ7oCpLJpKoCzWazovQya1ImEgleMDk5qdOpFxYWCAaDVFZWsmfPHs2WgsEge/bsYcuWLTQ1Nal+\nQTCGq666Sp2sMBdSc0vJKFFYSkSJzNFolO985zvcd999Skuu7CpdGcHlsr766qvamSmTplZiKfX1\n9dTW1mpH5VlnnaWUoWQOInHO5/OaZQCK90jpVl9fTywW48SJE1rSyAyN6upqurq6VukWxPEDvPnm\nm0SjUQWKm5qalJJ2u918/vOf59prr9XhQfIdV/4Tlp3BnXfeqQFCWKxIJILP51OA+J3aacFiFIvF\npZU19aFDh7j55pupqalhaWlJeW3ZREnL5JBEIhHWr1+vLbmRSASbzcb555/Pu9/9bt71rndpBA2H\nw1xzzTXa6CJvVUgK2tnZqZdV2nydTidut5vKykoymQzF4vJE5qNHj+qbHldffTW33XYbdXV1b4s2\nx2Ix7r//fvbu3avDZCTtb2xs1KfWpG4WpxOJRPRnBcEXSlCewZMaWoaHxONxNm/eTCAQUFpM1svl\ncmn/yP+7/tTV1WmpViqV9Pk70St4vV4CgQDPPfcchw4d4uGHH8ZkMumjL0ajkc9//vNs375da2FB\n5MUkNZZ0WOTYMnfxpZde0o7Uvr4+vvzlLyu7IVO9VwLIoVCID3/4w6RSKaW6XS4XnZ2dBAIBnWgl\n2hB5DkDOj1CnNpuNjRs30tnZSVtbG2NjY9x///1UVVUxODioXbdVVVWq7RAh2djYmL51ITjZX//1\nX3PDDTeouEoETHa7XaX50odx1llnMTExwX333cdVV12lZc0fmwDE8Xic+++/nyeffFIdhkjaxSwW\nCy0tLfT397O0tPSOWIzTosQwGo1Kz8RiMXp6erjkkkt49dVXNbUqlUqMj48r2COAj8vl0tez5FIJ\n9XTgwAF+8pOf0NXVRV1dnYp0UqmUagYkvQuHw8pQiN7daDTS0NCAzWbTZ9GSySSxWAyr1arjwmB5\nfsMPf/hDPvGJT+Dz+f6k93a73Xz5y1/m8ccfJxAI0N3dzWuvvYbb7Wb9+vU6mk3mRkoZAstlljTh\nyLODl1xyCevXr2fNmjU6PUiUp0LJyppks1l9l0Pq7lgsxpYtW3j55Zd1jFpnZyfHjh3TLEJYA1nT\n66+/XpkOGaB78cUX80//9E/6wJAoEAVTEfBPLozY0NAQL774Iv/0T/+kHZPyXbdv365Yh91uV5R+\nJaX41a9+leHhYe2ilGxiYGBAVYypVIq5uTktKSQTuPTSS9m6dStNTU3aPSn4Snd3N4VCgW984xva\ndTk4OKhDdWD5fQrJGkQcJeyTMBgrnwoEdG0qKip0gtbU1BQAV1555dtGfCnRhoeHueOOO5idnV31\nnkY8HteRA/LIT0dHx/+ReRCnhYOQmq28vFxnNnzmM5+hv79fRUUymSgcDuP1erUOllmQo6OjKrLx\n+/36tJnP5yMcDnPixAncbrc+/CoqPGEWpGvO4/EQCAR0jLpo7ROJxKoBpILAT09P09jYyMmTJ3VM\n3He+8523/a4Wi4UbbriB6upqHnroIdra2hgfH6dYXB6QW1dXRzgcJhKJKM6yadMmwuGwNvB0dnay\nc+dOBcHkfQ1AXwi76qqrWFhYYGpqiomJCX70ox8xPT1NoVBQhWksFmP//v0qOpNhN42NjRrd5U0Q\neQRIHI7X69Xp3P39/VRUVNDV1UVFRYU6Xek+TSQSeolEIfmHP/yBL37xiwSDQUql0qrDXF1drQI4\nycZEFSo/++Mf/1jBOcGHROkouIqwUxdeeCFms5nu7m66urp05gSguIecC8kkm5ub9TPLYFzprxFF\nI6DrKGI1l8tFU1PTqvO8kh6XUlC6asPhsGYO/1/t2W+88Qb33nuvlrPSN7LS+cj6uN1unTr1Tu20\ncBAid7Xb7eosbDYbN998M5///OcVrJEIIRN3vF4vDoeDkZGRVWKb9vZ24vE4w8PD9PX1EQgEtDtQ\nOjvPOecc9u3bR19fH8FgkAsuuID+/n6tJ1e+sJzNZgmFQpxzzjkcPHgQu91OVVWVNglJB6PVauU3\nv/kNQ0NDelH/lJnNZq666irq6+v5yle+gsPhUKRcgFoR6WQyGTo6OggEAvo69vXXX69gm7wVKZnW\nyotms9no7Oxk8+bN/MVf/AWzs7McPnyY3bt38/vf/15ReZH4SjOVCHTkd0xPT+sMg9bWVgYHB1e1\nfReLRc477zwqKir0oohzEN5ewL/du3fz7LPPalu5ZHErey4GBgZ47LHHuO+++1Q8JKm62bw8wfuh\nhx5ShaHMJK2srKS8vJy+vj42b97M9u3blRb94+icTqfJZDLaeSrlnWgxYDnzWbduHRMTE5opitOQ\n8lOyl5XNa7Ke8NaQHxGPRaNRBdAl0u/atettz0qpVGLv3r184Qtf0HZ2odnlLiQSCQ1Y4iiy2Sxt\nbW3/+8v3v7HTwkEIui7RWca8vec97+Gcc85hbm5OX9QWhPraa6/lve99L7AcwR555BFNQUdHRxVk\nFCpr06ZNOtuyqqqKsbEx7ZtYWZvPzs7S0NCgE5ByuRxbt27lxIkTOgA1Ho+zf/9+1QYcPHhQ5dkm\nk4krr7yShx56iPPPP/9t8QiACy+8kOnpae677z5GR0fp6+tjz549nH322UxPT2t28NprrwHLMuvN\nmzcruClScZE2rxSLCSiWyWT0vcmqqiouuOACtm3bpn0o119/PSaTibGxMVpbW0mlUlx44YUEAgH2\n79+vmI4cenmhS/j2iooK6uvrue2221ZNp4K3JjiNjY3x7LPP8uqrr3L48GGV0cvwHikjqqurlVX4\nwx/+QDgcprOzc9X3Bbjnnns0y5N26u7ubrZv305XV5c6r5XIvygoJaKv1FCIQ4O3IvGbb76J2bw8\nRFcYFPnMgl9JNiF7LJmctPXL3ydMxvj4uDIaMlDnpptuUsHbnwIli8WitqhLGSPzIWQsYnNzMxMT\nE8oQmc1m1q1bx/Hjx///XcQ/YacFizExMaElgzx6InTXpz71qVU4RE1NDeedd96qF6E2btzIP/zD\nP3D55Zev4tbNZrMOjZGOOUBFRTabjWw2q92FK4e6HDx4UDOXgYEBBdzm5ubo6elRWnZkZERBPAGy\nFhYWuO2223j00Uc18rydXX755artiEQiWK1WfWnKbDZTV1enF9zpdLJx40ZCoRCNjY3U1dVhMpm0\nPIK3DrjD4VCMQNgJmW4lQ0y6urpoaWlRfCKRSJBMJnnzzTcJh8O0tLSsonthuRPSbF6eHC78/Wc+\n8xmcTqemu+Kc8/k8e/bs4brrruO5555jenpawWWJpH19fdrtKWCq9IJIc5uUUmazmZ/85CcKZEpZ\n9vDDD7Nr1y7FQFZOxbJarVRVVVFdXU1VVZWeK8kOJTiITFmG7zzxxBOKd8jFa2lpwev10tDQoGvo\n8/lWMRbye0XpKH9mNBqZmJhQ3Gpubo66ujouvvjiVesrJs7h2WefVbm20MJCa8raye/2+/0UCgV9\nxe0/DAZxySWXcMUVV3DLLbeo95UD0t7ezu23386dd96p0VtYBEkRpW/+2muvZefOnczNzfGxj31M\neWZR/dntdlVnJhIJrak3b97MwMCAvhg+Pz+vzTPCJV944YW8+OKLdHR0MDQ0pNy3dFOWlZXpuxOt\nra0cPHiQZ555hpdffhmfz8euXbu052OlScuvNFfJ61tiMkC1p6eHkZERjh07pvMRxXms1Gus7GoF\nlMYTJD2bzaqaM5/P88Ybb3DXXXfxyiuvEAqFMBgMDA4O4na7qa+v196NmZkZ6urqFIMQenh6eprP\nfe5zNDY2aqo9MDCg8zdyuRyBQED3taGhgaGhIZqamshmsxw+fBin00ksFmPjxo2Mjo5iNBqJx+Pq\n9OSgv/vd79YSr7KyEo/Hw9NPPw28VV4IViEmzXdCpcrAHMkypOkqn89z5MgRbr75ZgAVOkknq1xO\nh8OhlLE4POnXcblcOu5PfvfKMXG/+MUvdNL41NSUlqfCBElwMpvNPPbYYzz44IM6l2P9+vX09PRw\n/fXX6zMD0WiU2tpadbiA7vGtt96qAsF3YqdFBiFTeD/+8Y/r+wCiX08mk/T09GjDTDKZ5PXXX2d2\ndpb6+np9qUkk1blcjrq6Onp7e3Uw7dq1a1V/kM1mOXr0qCrezGYzx44dw+Vy0dHRAaA9+XKpisWi\nvtWxtLTE1NSUqiLNZjObNm2iUCjQ0dFBeXk5mUxG3widmZnh6NGjXH/99ToWXkafi4kuA9561k8o\nSJPJRE1NjYqsvvnNb3Lo0CF1DBLdRGH4p2gyOYgul4u6ujo6Ojp0lL7BYOArX/kKt9xyi0Y6eblM\n0n0ZmirzJozG5TdE5DuEQiEOHDigQ2aF8Th06BBlZWV0dXURi8WoqakhEAjQ3NyslKC0nkuLurwv\nkk6nOXr0qDq3l156iYGBAc0Q3W43gUCAG2+8UedjSMSWQTmBQEBpXilnBOMRheLw8DDPPvsst912\nG3fccQdtbW3azCfviUg2IAwQLOsoLBaLgqOyxvJ9RJQna7q4uMjg4KCOuXe5XExPT/Pqq69qD4uA\nmd/+9rf57ne/q4DvX/7lX/LAAw/wwAMPsH79es2GTSaTZm3SX9TQ0EBvb6+O4Xundlr0YnzhC1+4\nJ5FIMDIyoh2OXq8Xs9lMWVkZRqORBx54QKdES6168cUXazYBrHrI9n3vex8DAwM6L7Guro62tjYV\nI1VVVWG32znnnHNIJpNMTk4yOTmJyWQimUxSX19PWVmZ1nxCwckFkRe8Zmdn6e3tZXx8nNraWoaH\nhzWKJZNJysvLtff/4MGDVFdXU1lZqfRhPp/nwQcfxGg06jzHzs5OpRgXFhaora1l3bp1etD/8Ic/\nMDMzw8TEBOXl5dovIbr/lSaHVUok+Y9MsxIHI0N09+zZo2m3MBfz8/N6gXt6epiZmSGVSukUrMXF\nRRVAFYtFzVoKhQKpVEp7Qc4991wikQjT09OUlZVpZBYJtpQEohrdv38/+/bt46WXXuK///f/rs8Q\nCq0nA2FeeeUVfve739HY2IjFYmF2dlYDQnl5uTpQcdivvfYa3/jGN/j+97/Pk08+yYEDB5ifn2dp\naYnnnnsOn8/H/v37lQWE1rsAACAASURBVKqVTNHn8+kbH+l0ms7OTg1M0icyPT3NX/zFX2A2m1lc\nXKS8vJxCocDMzAxPP/00BoOBsrIystks5eXl7Nu3j9bWVvx+P6FQiAcffFBpX4DLLruMv/mbv9E1\nXVpa0uxPBjQLNudwOLR8/va3v83i4iJ33HHHO+rFOC2EUuvXr1+qqKhgZGRk1YMhf/VXf0Vvby+v\nvfYaN998My6Xi1AoxIYNG5ienmbXrl1cdtllCgJNTExoPb2wsMDS0hI33nijUp6ixZcmnkgkQnd3\nt7IcKzEL2XQRzoyPj+sm9vT06DQiqU0nJyd10wTXGBkZYdu2bRr5TCYT09PT9PX1sW3bNqxWKydP\nnmT37t0kEgm6u7u54YYb2LZtm9a/oVCI1157jV/+8pdEIhGmpqaUopM5DjMzM2zfvp1wOMwFF1zA\n2WefTVNTk07eErDzT/Hsgm9Izbtjxw6dRRkKhbj66qt58cUXWb9+vb6HWVlZSSwWUwcigiRYzjrk\nnU3JwqREkMGu8llisZg+UFNTU6PpvMxVSKVS+pSeOFMBqeXZRdm7dDrNWWedxWWXXcaHPvQhnWwV\nDAY5efIkwWBQg4Df7yeTyTA7O6vMTV9fH9dccw1XXHEFAD/60Y/40pe+hNfrZWJiYtX36O7uprm5\nmS996Uu8733v0+Ah7eC33HILH/jABzR4VVdX8+KLL3LLLbcoluR0OpU+dTqd+oLc5OQk1dXVbN++\nne3bt+P3+xUHklJSdBHRaFQBS0Cxj49//OOaPS0uLr4jodRp4SDe9773LR04cECHk4jKTARH8hCv\naBEaGxsJBAJ0dXXx85//HHjrRS5pWJF6NBgMcuONN6oIS4Qs/f39Sp1VVlbq5OR4PE5vby/Dw8OU\nl5czOztLZ2cnw8PDbN26lZdeekl7I8LhsDY6mc1mbe7yer24XC6CwSAXXXSRPhP405/+lGAwuOqy\nFotFfD4fPT097Nq1S7MI6QNYqcYbGBjg8ccf5/Dhw5SVlVFeXk5dXR2///3vaWlpUYq2qamJZDJJ\nTU0NBoOB3t5eLrjgAn3+T57hk0Y4cRDz8/N88pOf5Pjx49rBKCj/xo0bee211+js7FRAVYBDWfuq\nqiqGh4d1Vqi8YymyXxnPL+IgoQvlPQ15AX1qaop4PE5tbS2Li4v6+WQkvbylIW9Unn322dqc5HA4\naGxs1HF6MuZNLo+Mt5+fn9dgFIlE+MpXvsKGDRtUT5LL5fj+97/PXXfdhdFopL6+nuT/w92bh0dZ\nnu3DZ2YymckksySZZLJOErKSEIiBGEwRpFJFfaVU61IWFUSr4uGGu9WXioqIStEKKmqlKqV1Qygv\niAJqIxJZGgghezKZLJNtJttkkskkk++P+Z0XM337fcf3lf5Bv+c4ONRIZnme+77u6zqv8zwvlwvp\n6enYsmULoqKiEBYWhmPHjuGee+7BwMCA+Fjo9Xp8/PHHGB8fl/GGDz/8MD777DMpI5hxEeh0uVwo\nLi7G/PnzUVJSIgAwAOFQBJYrgaI7rpGJiQn86U9/wvr168Wpu7+//z+fSenz+eByuWCxWERiTKYk\nuf95eXmwWq1QKBTiWjQwMIAjR44IEYZpNgDx5ktLS8Prr7+OBx98EA6HQ/ragXwDvV4v+IVWq4XT\n6RTats/nE8OVlpYWlJSUiAUYMxe73S5uP4HIcW5uLq644gqUlJQAAN577z2xWe/p6UFBQQE6Ozvh\ncrnw9NNPCwrO1yBwxYWQn5+PN954A83Nzbj11lvh8XjgdDqDLNtoaabVasWGrru7G/v27RPreQJz\nWq0WpaWlMuS3oaFB5oEODg7CbDZjfHxcJjZRw8HT3Gw2CwnK5XIhISEBs2bNQmVlpXBZiJVER0ej\nt7dXAhlPvdHRUZhMJoyNjYngy2KxoKqqSrCIgoIC9PT0oL29HSkpKbIZAQgpiAY58fHxKC8vh1br\nn6dCTCAvLw+1tbUyDZsZmtvtxtatW5GWliafiTL/RYsWweVyYdOmTeINev/990sQUalUmDNnDlau\nXImXXnpJVLsDAwNCOAOAHTt24PDhw0LUY2Bj9uJyubB+/XrMmjVLMqhAdSvFdsA5TwoGTWqSTp8+\njeeeew4nTpyQw+r/qcX+//a6IDKI2NjYSRKTqNnPycnB2bNnMWXKFLS0tCAvL0/AGZ1OJ6dDZGQk\nbrvtNjzyyCNB5rIksQDnUq8NGzbgtddeQ3FxMY4ePSonIE8ZpVIp75uQkCA03tLSUjgcDjQ1NcFi\nsaC2tlbIOSMjI0hNTUVDQwPi4+MRGhoKo9GIZ599FsnJyZLCO51OPPHEEygoKMCePXtkvoLP54PF\nYsGaNWuwePFiuSecpUB8hZwCBg3iBDU1NdizZw927tyJhIQE2O12OJ1O6cbQrl6r1YpLF2eDkJUK\nICgjIPClUChkwvSTTz6JkpIS7N+/H2vXrsX06dNx4sQJ4WHw9wg+FhQUoLy8XJ4BcM4FOj09PciT\nIj09Xdp9n3/+uciVA7sztHVjLW4ymVBTU4PMzEyMj4+js7MTOTk5MktTo9GI/D4+Ph5VVVUyhDcs\nLAw33XQT7rzzTjEo4makwMvpdGJkZARHjhzB008/LfycnTt3yuQ3Ep58Ph+ef/556aiEhYWhq6sL\noaGhGBkZEftBlgZ8tgUFBVi9ejVSUlJgNBqltCXgSl4E2/48LBgcnnjiCezdu1fWEundPPBGRkbQ\n0NDwn+8oxYhLa67w8HCh25LJWFNTI5p9zkLkbIBdu3bhwIEDcgJoNBpERUXBbrdjcHBQUtPVq1dj\n2rRpqKioQGZmpnhGqFR+p+DU1FRERETA6/UKiJiYmIiKigr09PTIBCmKhhISEhAaGorBwUFJbcfG\nxvDwww8jJSVFPk9YWBgiIiLQ3t4uDEJ2EJg+fvXVV9i9ezdGR0clrabmBDhnx89WJmm1+fn5ePzx\nx/H5559j3rx54kTU3d0dNLatr68PHR0dIl9mLUsVJz9Hd3e3ALgej0dOwuuvvx4ajQZFRUVYunQp\nWltbRRnKzTJ9+nQRlDkcjiCPUcAf5AYHB6FSqUSLoNfr8ctf/hK333477rrrLmzfvl0IV9wI7FrM\nmzdPAqPb7ZbgxtaezWaDy+VCSUkJRkdHxb16aGhIAgHgx24eeOABKbf+sS1K+vU333wjLlVKpVKy\nD/534LO59dZbodVqZXg0xxYAkIyJztkEPB944AHpdhmNRjE/DmRyejyeIA2Iy+XCnj17cMcdd+D7\n77+XUXsKhQJTpkyRMZU0PTrf64IIECTDUGzCDMFoNMrEI7aSvF6vjKVj772npwdvvPEGurq6hBWn\nUqnEpIWppEKhwPr16xETEyOuTnwYrIE5cKetrU10Gunp6Whvb0dLS4uwFrk56ChMMpJCoUBRUREm\nJyeFz8B+Pof/ABANBGXS33zzDV5++WXcddddaGpqEsMSCp0CiTQkQnGjU3z2yCOPYObMmZgyZYoE\nBpZd7CqwxUZtC1WQpLvTvZk9fJVKJa5P5AUsWbJEPCOGh4fFAayurk42G7EbArekg8fExCAkJASZ\nmZkSxDjCzuPxID4+HqWlpUHfnQGOGFRmZqZ4h9KBicOZ9Xo9fvjhB9mggTaCnLHxyCOPiP/HP16j\no6NwOBzYuHEjNm7cKIcVAeyzZ88KGxM4N881PT0dS5YsEeCUwCXgz84IApOMtWrVKpn3CkAyUq6X\nQFDS7Xbj8OHDeP7553Hddddhw4YNOHz4MMbGxsRUiVlUSkoKzp49K0Su870uiABB9Vt4eLjMCaiq\nqpLWl8lkEpIUU2NSoSMiIoRO/MorrwgJimkv62XAH32Liork5EpPT8ezzz6LTZs2iUKOqlBiGrz5\nhYWFiIiIED68VqtFWVmZOAnl5uZCrVbj17/+tYBiUVFRYhZLz4SKigpcfPHFAM5RzAMl0MeOHRPH\n6UBXIMrMAy+CfGxtDg0NYfPmzUK2Yg1Os1maqdC0xmQyQa/Xy4QwGquyzo2OjpYhNcw4+M8bbrhB\nGJOU4Y+OjkrdTRt8Mvx4L5OTk3H69Gkxd+GcEhLgfD4fnn76adxxxx1BXo1qtRrNzc1BvA8CxEzJ\n2Z2iVoYlE5W5cXFxePTRR3HDDTcE8UW4CRlENm/ejA8++AAJCQlobW0VxiYAaX+yDON7A8DNN98s\nPwvsujAYcV6GRqNBYWGhZCX8DgBkvTEA1dfX484778S9996Ljz/+GH19fejp6QEAmbdCKwKXyyVl\nI4WG53tdEAFCq9WKnTvra6aPPHGJBtMGbWhoCImJiVKTAsCnn36Kb7/9VmizgYuTD9HlciE/P1+k\nzomJiZg/fz5efvllqNVqXH755UGW7IDfALW/v1/al9HR0dJeUigUwp2YMWMGlixZgvDwcIyPj4uy\njoNVz5w5A5VKhZ6eHqjVauE2sPPBEioxMRFffvklHn74YdhsNgGl/lk2oVKpgoYbq1QqoRyzXAL8\nFnhnzpwRJiCt/ylR7ujokAlaZArSlHbGjBmw2Wxob2/H6Kh/cM+sWbOEzMXsrq+vTyzqeb9oPEMO\nA2dRJicnSzDq7e1FWVkZoqOjpcNz3333YcmSJbIOKIKKjIxEXV1dUMsawP8ydCVXwev1W++rVCo8\n+OCDWLRoUVDmQJCQZe69996LAwcOAIDcB+IGxFd4OvPecm0lJSXhqquuEu0IJ2ARF2PmwWfGAVEM\nzJyGVl9fj+3bt2Pp0qW4/vrrUV5eLl0LTvGKjIxEXFycuIDze/Ow5aS6870uiABBiq/ZbJZ0ltGV\nff6kpCR4vV5xm4qLi0NjYyOqq6uhUCjQ0tICj8eDd999VzYV09r09HQ5TWgUOjg4KJFdoVBg9uzZ\nghFwYUVERAhAxFmLtKvLysoS5yB6aN57772S2huNRuj1ekRHR0OpVAojMiMjA52dnTLHgNlGamqq\n1NsdHR0yQfqXv/wlXnzxRTQ2Nkqbi0IsXsyU2L50u92Ij4+X2ptDiZilAX7grre3V0qK0dFR8XDU\n6/Vy6gIQ/4ukpKSgE3PRokXQaDRCWyZuwe4RHZoImgHn3K65ydntYCcoIiJCypsnn3wShYWFcloT\nYwD8AcPpdEKv12NgYEBYj9nZ2bJBWfJZrVY89dRTuPnmmwWHAPyHBbMOhUKB48ePo7OzEwBkqhi9\nI1tbW8Vi7uDBg//Lwo9l5LJly4JwEmIxCoVCND9utxt79uyR9q/H48GpU6ewbds23HvvvfjVr36F\nV155BVVVVaL/IPuWQ3pGRkZkTkxqaqrM2uC4hH+2Tv6V64LoYsyePXuypqZGFHsqld9w9MyZM4iJ\niYHL5YLb7RYyCYGYQBchgmPj4+PIysrCX//6VzmNJiYmEBYWJv3jKVOmIDMzEz/96U9x3333YWRk\nBGNjY0Jmcjgc+P3vfy8lBEGp+Ph4jI+PS/uVrbtHH30Ut99+u+AfNJMJXIxutxvZ2dmC0BcXF+PY\nsWNyCtOwhik7EXKz2SwneVJSEtra2rB48WKsX79ewD+CefxnV1cXfvGLX4imghssKSkJra2t0s1g\nAGXWEhISgqamJqhUKim3SkpKMGfOHKxYsUI6OwzoMTExePnll/HnP/9ZKMcmk0kIYTRGCezT33vv\nvTLxm61Bs9mMzz//XPAYnrbcaIcOHcLDDz+M5ORk/PDDD0hLS4PT6RSuiVbrn0w1MDAgc0YiIiJk\nTN/vfvc7CVDEZKjM9Pl8+MMf/oAtW7YEkaHYHuV8CnbZBgcHkZSUhPfffx8ZGRni6wBAysFdu3bh\n8ccfl4OCjmjEwtiCJd8nPDwcg4ODuPTSS1FVVYXFixdj1apVwi/54osv8Mwzz8jEN/58ZGREDlSS\n1ojZEF/q6Oj4z+9ifPTRR7j11ltF0GMwGIJMSnlCkaBDJiFTay4ktsI8Hg9eeOGFIEdszqVgq5CC\nIJ6GZFY6HA5ERkbi7rvvxsDAAKKjo5GUlCQeFGwHch5GcXExbrrpJtmgXGDc7ABElUfNCevplJQU\naZ8Ftgqzs7NFXzA8PCzmuw6HAxMTE9i7dy9eeeUVdHR0yAYHgo1cH330UXCUAO/n5OQk3G63BL1A\nwhQp6ETCWT+3tbVh//796O3tFcGWTqcTi/drrrkGarVaJlxbrVbx52Caz9ahTqfDrbfeKsNt+Zkt\nFotoUOhwzSwCAK688kosXboUVVVVAPylT19fn4CwvG80mFGr1TIkJyMjQwBfYgcsk0ZGRrB27Vrs\n3r1biEtut1s6LQUFBRgfHxfzGK5Jt9sd1DXjpVarERoairlz58qaZWuX3SJyE5RKJdrb24PKsqqq\nKjz44IN49NFHRQPi8/mQmZkpgYqyezqN0VyYpYvBYJCg8e+4LogA4fP58Mgjj2DLli0IDw9HQkKC\nyKppvx44Daqnp0cAGfaHGckTEhLQ39+Pr7/+Ghs3bpSHaDAYZH4BT5Pa2loR2QQKbgD/1KTc3FwM\nDQ0hIiJCSFMkprB+fvnll4P8HrkgKJ32eDz4+9//jtdeew15eXky8IWiHaoICwoK5ATmTAYqVh0O\nh/gQcIHv3LkTN954I95++210dXVJ54an42WXXYZ58+YJCzMwq6F5LwlYBF05m4IbSaFQwGazYXJy\nEmVlZbIBOWhYqVRi6tSpyMrKktYscaSuri4YDAaUlpYiIyMDWq0Wc+fOlUAAQHCigYEBNDc3i5gu\n0JqO7d4HHnhA9BU8RDjbcmJiQijJgdgDNzNwbuAwA/fw8DCeeOIJfPLJJ9K6zMjIkG4DxVmkldPD\ngrL4Xbt2STYbeCmVSphMJtjtdskgyayl2xkH8rDFrtPpYLFY8NJLL2HJkiXy/cnrSUxMlIDNA0Ol\nUgnWxTYwBxHHx8cHkcnO57ogAgT7vaWlpViwYIGcUswG6PJDPwi2gJjasQNA3oDH40FJSQl27tyJ\nF198UQgmHO6q1+tF6DMwMCBzD/l+XFTz589Hd3e3sNK6urqQn58v9d7q1avFbZvcBJ78PM2dTice\nfPBBtLW14ccff0RHR4f4NAS6HdGrwmAwCM4SOBODpzLRaqvVit7eXuzcuRO333471q1bh8rKSkxM\nTCAiIkL655yTQE2Bz+dDc3Mzpk+fLicPKeajo6Noa2sToRkDX0hICI4fPy4LNhAnUqvVuPLKKwU4\nJUWcztd1dXXijnXPPfdIhkNOBbGEV155Jch4lT4XfB9yVeLj44OmTF1//fXIyMgQDIMBlthOWVkZ\nbDYbBgcHMTg4KEHujjvuwIkTJ0QR6Xa7pUvCrlJnZ6eY+dbX12N8fBxWq1UGMZWVlUlA4cXnxcOk\nqalJjJZZXrKEamxsFB7Ghg0bMHfuXMlumUERNGb5zAORJZ7D4YDH4xGA2efzoampSajp53tdEGrO\nyMjItQwSixcvFsemuXPnorW1FT09PUhPT0djYyPCw8PF+oyyYrZ6QkNDMW3aNMTGxuKbb76BSuW3\nJ7PZbJg2bZqAQrW1tbDZbIiIiEBmZiZycnIwMjIi7siBHIC9e/fKiHkyE0NDQ5Geno4XXnhBFgSB\nPqaLSqXfcn/9+vX45ptvRFEZGxuL9PR0OfUdDgdSUlKQn5+P+vp6LFy4EK2trUIGow8iAAHdUlNT\nBQOhb8WxY8dQVVWFEydO4H/+53/wpz/9CX//+98ldfZ6vejp6ZFWMgBBzilTZpbFdiRLKafTCbVa\njXnz5sFsNkOj0WBwcBAKhQKTk5OIjo7Gxx9/LLZ7brdbyGE8Ra+66ir88pe/xOTkpPzxer04dOiQ\ndFa8Xi9KS0sxOTkp5YVK5R92xOzwyy+/lGG/ExMTOH36tLQGWQYQU9Lr9XC73bBarcjLyxOPkQ8+\n+ADNzc0IDQ1FaGiouF1T50DlK8FZYgec/MUsyePx4LrrrgMAOaw4FWzr1q3o7e0VJy+OPOAYhcnJ\nScl6jUYjfvOb34hbVyDl2+PxYPv27fjuu+8Ej5sxY4a8ltFoRE5ODtra2qR7x5a9VqvFAw888J8/\nWYt2Y7zxK1euxObNm3Hw4MGglhQXus/nk0gcHR0tmERubi5Wr16NzZs3S49cq9XiwIEDWLx4MbZv\n3w6r1SoU39TUVOzfvz9IyBMIONHCfurUqVJLp6enS3oemMlwQTFgaLVa7Nu3D2+99ZYsVMDPqtu3\nb5+MducpffToUZFb0+SEsmcAgo14vV7U1tYK447gIJWh5eXl2Ldvn4jZXn75Zbz66quSffD0ASAL\nnpJoGsUmJSVJBsA0dXh4GM3NzZK6ms1meR7JycnSSqQrFi8yUpcvXy7lQ2RkJGJiYpCXl4ekpCQM\nDg5Co9Fgx44d+Nvf/iakKQBBjklXXXUVbrzxRgkmZAyGhoaiu7tbGJDM6ngS/+1vf8OGDRtw9913\n44477sCOHTtEsk6pusViEWyD2AwJfOwuMbvIyMhAeHg4zp49K+uRWQTvV35+PgCINoPfITMzE0ql\nUliVF110EbZt2yaBJ3BEIl/v+++/F1fz8fFxtLW1wWq1SmZcXV0tJRcHPJvNZsF+zue6IAIEVZY0\nMe3v78esWbNw/PhxLFmyBEqlEi0tLUhLSxOnIb1eD4VCgfr6ekkJN23ahMLCQqnl2RkJDw9Ha2sr\nnnnmGVx55ZX49ttv4XK5UFVVhZMnT2Lv3r1iUkPQk9O11qxZg9TUVAwMDCAtLQ2HDx9Gamoquru7\nMXPmTHz44Yfo7e2VOpadhVtuuQX33HMP8vLy0N/fjylTpiAkJAQej0coviQeRUVF4dJLL0V6ejpa\nW1vF78JsNiM7Oxvt7e1CfuGgVs795BSlkJAQAfxoJf/yyy+jpKQERUVFYnATHh4ualTax1GYZbfb\nkZKSImAtAUMK0d544w3Y7Xb5zIFTtmm8GhUVhZiYGKSlpUGj0aCjo0MG9/I5cbOkpqbioosuknkj\no6OjuOuuu6Q844wOlhSRkZF47rnn8O2334qhL+AfScjTNTMzU0xp6Gil1WqxZMkSbNu2DfPmzROM\nZXBwUExWzp49KzJsdji0Wi3q6+tlBgbVnD/88AO6urrEz5R4E8tclUqFa6+9FkqlUlrN1N20trYC\n8I+ZPHjwILZs2SLPhUAksSalUonf/e536Ovrg9vthtPpRGRkpDiXqdVqKaf4TNLS0kSoF2gs/K9e\nF0SAYCptMBiCgBev14sVK1aIYSgX18jICNra2jB//nw53QsLC4UhCAC33nqrnI4mkwkxMTFCXKJi\nkjjEd999Jyg3eQMEiYqKimC1WgH460r27OmQ9Mc//hE7d+4Mitbbt28XzgQ59qmpqUEAKGvmwcFB\nsbmnOIw1a0REBCoqKtDd3S0GKZzQrdFokJiYKAuSqemrr74qmQI5DyQLEayk/brBYJBTEfAj4nRT\nIi7T3t4OwO8b2t7eLkEOgGxSgoQkuHHRhoWFYcqUKYKrECgkBqFQKJCVlSWqTQaG7du3y7Pm6Q2c\nI0NlZGRIm5fanalTp0Kr1aK6ulqsBmmHn5mZKczFxx57DHfccYeAtg6HI4iJGR4ejs7OTvl/M2bM\nEGDTYDDI4UR26okTJyQQsG1K9aVarUZsbGwQUU+p9I9jfOyxx6DT6SQAB2bHzEq3b9+ODz74QDIh\nu92OiIgI6HQ66YqwNevz+dDZ2Ym+vj5kZWVBqVT+/4coRaZab2+voOhcNBqNRkw8uKkYMDo6OqQ2\n1el0QslVq9VYuHAhLr/8cgwPD6O6ulpO4NzcXGHsESBqbGwUgJKBgicDx8JzhqbBYEBzc7NM4Xa5\nXNi+fTt2794tqfp7772HiYkJNDc3IzY2Fq+++io2bdoUxNAkUMm0mz8zGo0YHh6WgGA0GpGVlYXa\n2tqgDCUyMhLDw8Ow2WwICwtDXl6eTFtiu8toNMpr2+121NbWiseFxWLBxMSEtHJJYKKCk6xMDiwe\nGxsTtWqgdyKt7nn6DQ4OyqzRtrY2EcsRlxkeHpbn7fP5pDulUCiQnJwMn8+H9evXo7KyUjaAy+US\n0g9fZ968eXj66adRVFQkLkqAn78Q+NqhoaEYHR0Nsse/7777ZL4qmZ7EMRwOh2SgvEdsf5IdGfg+\nP/74o6wXYhVkPapUKuksMEg8/fTTuPvuu+X+sVQkOE7y1VtvvYVnn30WLpcL8+fPB+AvW3t6egS7\n4HNgAGV2wesfqfn/ynVBBAgKoPhFKRHmTef8R6of2T6cPXs2amtrMTQ0FGQuA0B63Jdddhk8Hk8Q\npRXw+07GxMRISVFRURHEbAw0gaXysLm5Genp6RgZGYFWqxXTW6/Xi61bt6K6ulpEXlqtFgUFBXjp\npZeQnp6Ojo4O5OXlQalUChLO9iLgp3M3NTUJCs+TmPMyhoaGZBgMzWPz8/ORmJiI22+/HVu2bEFW\nVhYMBgNiY2ODTo/Ozk709vZCr9djcHAQarUa0dHRSE5Oxvj4uIwGoDyapz0zA7pAjY6OSu3OS6vV\nyqYnKYvtSr1ej7i4OAFkiSewg6NWq5GWlhZkEMss6ze/+Q2am5vlswRySvhnyZIlsFgsUKvVQTNK\n+NzKy8tFnBYYaIaGhrBt2zbcc889kqVw7gczsebmZgwNDYmdn9vthtFohN1uF/csn8+HTz/9FDU1\nNQAglGqfz4f29nZ0d3dDq9UiLi5O/lx77bWy3gOJdIHU7XfeeQcvvPACdDod0tLS0NjYiPHxcfke\nZIuSHAhAgNvAe/TvcLW+IALELbfcgqqqKmg0GgwPD8viYY0eGRmJ/v5++blarRYQkEw8RnuTyYTk\n5GTZuOvWrcOsWbNw+vRpuFwuNDY2SrpoNBrR3t4Oh8OBr776StJM1m7UP8TExMBoNEKj0aCmpgZq\ntRpGo1FqYMD/gLdu3YrbbrsNXq/fYPeKK64QYMnn8+Hqq6+WqE5zGc7SoPMStQUkR4WGhqKyslLQ\n7kCvA4fDgfXr12PVqlVCt2ZmQtEa4J/KxOyDGUhFRYWg7AaDASaTSTY+NxnFVMzkYmNjsX//fuGA\nABBSE+3YCeB2l3oodAAAIABJREFUd3dDo9HIgFsufuCcmMlkMiE+Pl4ymcAWcVVVFTZu3Cj1NbOC\nwN6+SqXCk08+icHBQSFKkRSlVqthsVjgdrtRW1srQHLg795yyy3YsmWLyNKBcxZ8dM7m8GStVouW\nlhbhXpCib7PZ8M4770j3hpvz2LFjso4mJycxPj6Ohx9+WLgm/8iyZeawY8cOvPfeewgPD5dD4tSp\nU0EzT8rLy4VExW4XeSdk5up0Osmqzue6IALE0NAQVqxYgZ07d4rAKFCnz02dkZEhakrWvACk28B6\nzufzIT4+XmrW5cuXCxW1vb0dbrdbakMqEY8cORJEdSYRiFqOtrY2Ado4PdpgMIi2gwub7b/S0lJc\neuml6OnpkQnjaWlpKC0tlVM3Li4OJpNJ6nGmyqR00x6vpKQEV1xxhegEMjMzUVpaij/84Q+YM2eO\n6DBYywIQgDEiIgLff/+9BFVmZdxUxBlUKr8LEadpk0/B14uMjERVVRUqKytFVAYgKPNLSEiQzgg3\nDCdYMWAESqUZ9KKjo4M6H2SsHjp0CBs3bpTnwLYsOwvAuZMzMTERQ0NDGBgYQG9vrwB94+PjGBsb\nk7KQfhlutxv9/f1ISkpCcXExkpKSBJOhOphelATQA0VbbGkrFAp8//33qKiokDa5z+fDkSNHxPIv\nPDwcq1atwhVXXCGZwz/Ss5VKJXbt2oXXXntNHK/orMYOGGnWMTEx8llVKr8cn6bCJpNJfEB4j87n\nuiACRGNjIyIiIrBlyxYZvEregM/nwyeffCJEHtbxQ0NDMjy1rq5OggNPTRrOkAjEDUgA76KLLsIP\nP/wgdTd1AOQvsLZmqdDV1RVkuUZVX3h4eFDLiVyLJUuWiMqTJ/PExISQYQh8dnd3i6U+xWFErGNj\nY2VWKL0QFixYgIaGBjz55JOwWCyIiooKSiW5MDg45+jRozIlitmRVqtFenq6tJaZ3rtcLrGcS0hI\nECAP8AulRkdH0djYiPLycrnPAIQPUVBQIKIlErrIFGRQ4f3mH41GIxlfdna2nIBMp//617/inXfe\nEWo8MyFmdwCCDgBOgWf7lyc6FZEEARmkvF4vfv7znwdJp8ld8Pl8QopjiQVAGJRer98ox2g0YsOG\nDVKiBA5rCg0NRWlpKZYtWyadBmIOXNMejwc7d+7EunXrBMth4O3r68NFF10El8slfq30xEhMTITd\nbkd/f7+QwLq6umQeBkuh87kuCE/KiYkJ1NfXQ6VS4Re/+IVIvCcnJ0UmDEAGliYlJcFqtWLatGnC\naSe+wOgeOOwE8G/qwJRr//79iIyMhMlkQn19PY4cOSIlTH9/P2JjY9HT04OJCf9058TERBw9ehRm\ns1mszvr7+xETEyPuxHa7XUDTtrY2zJgxQ1pp3DgLFizAjz/+iI8//hh2ux3d3d1IS0uD2WxGXV0d\n5s6di4MHDyI9PR3l5eWSzvb19eHee+/FLbfcgoiIiCArt8CL9+i1115DWVmZlAhk4kVFRaGpqQlK\npd8VmWSgqKgoREREyOTv2tpaSecDMY/+/n5s2rQJZrMZ1113nQyJ7erqwg033IB9+/bB6/VK21Kn\n02FoaEhwHF7Ei8gvUKlUkkoTaGVK/dZbb+Gjjz7CgQMH4HQ6JRMi+/W5556DzWYT1+rk5GRUVlbK\nGDyXy4Xdu3dj2bJlQWaxRqMRRqMRV155JW688Ubs3r1bcCH6NHR1dUn7MiMjQ7JPbl4+QwAoLS2F\n1+vFnDlzEBsbi9DQUDz33HP42c9+Jveyq6tLTnYqUHfs2IG1a9eKGtZisUj263Q6pbsVHR0t2URd\nXZ2U4AxMBoNBDk5yVc73uiAyCCLmHOxC/QFPtZkzZ0r9TUEXQT6ezqQlc9w7x9kxuGi1/ulblJTT\nGsxms2HKlCmCPBMHCSTGOJ1OmbnBxTw8PIz8/HwMDw9j7ty5UKlUYtZht9slbSdlOLD/n5SUJJoT\ni8USZErCEoW+Fz6fD2azGY8//jgeeOABGI3G/9vgAACVlZVYunQpzp49K6IjwL8YOZqNHgsjIyOw\nWCzCKaEwTqvVSl/ebDYLyMjvpdFo8PXXX8tJSoAtKioK4eHhUiaSfMQTLRADYMlAAhd1N1qtVkRs\nDPJstX744YdBeEaglR0PAK/X7zhGUJCdi/b2dlRVVcnEbb62QuGfDbJixYogunhLS4usoUDgGvC7\nZYWFhQkRLzw8XMpi0tadTifCw8NRWFgoh0MgqYqB6O2338bzzz8v68PtdqOrqwuNjY2y5li+smRj\nh43AMwMt2/Qmkwlms1kGIJ3PdUEECN6wqKgooYxmZ2fDYDAgKioKra2t8kDpnsOxb6GhoYLyA5Ab\nSqIPATEAMkWbP6OohZTt9vZ22RjAuWGqp06dAgCpQ+kBQYNU9qXpyB0eHo6TJ09KJ4SkG0px58+f\nLzRmpvu9vb1iwZaamgqPx4Pc3FwsWLAA77//Pn71q1+JgOsfL+IKfX19eOihh1BXVyf3hi5SHMRD\nh+5Auz3yPmpra8XrkQFPrVZDp9MhNjYWQ0NDUtcfO3ZM3KuJLbCEC7wCFzcvpvrcLEajEVVVVYiK\nihJDW256nrLd3d346KOP8Mgjj8jpycBUVVUlsy+4QaKiotDZ2SmqR5VKhUOHDgE41zXj81UoFCgo\nKEBpaen/Onm1Wq3QtFni0Fymp6cnyE2LhsqcVB44s4L3gPjIxMQENm7ciN///vcAIGAsWbeBeFJk\nZCSmT58uKlwS5rjO+fcJFC9atAjbtm3Dm2+++a9tyIDrgigxyFLjUNpAXoJWqxWRkVqtltbR1KlT\nMTQ0JJRi+v4FmngQ3IqOjsbAwIBQcIllBP5dlUqFgwcPYtmyZQDOzWY0GAwijIqLixPhE2nIHo8H\ndXV1iIyMlFO/s7NTPClZ+lDNp1QqkZWVhaioKKFmU1iTl5eHxsZGzJw5U1yYX331VVkMgScwcG7K\nl9vtxtmzZ7F27VrhbjQ1NYlNH3EVslBdLpdYwxmNRpw6dQqpqalSa7MrYTKZgjIEBlq2Snfs2IEn\nn3wSUVFRknUxu0tKShJbuLq6OnR3dws+wlOdG5Tfz+fzobKyEoB/Y3J0ImX7Op0OZ86cwapVq5CT\nk4P58+dDrVbjxRdfFNo6BXPp6elQKBTo6+tDdnY21Go1/va3v8lIAIKwgRyFm2++GeXl5bBarYiM\njITdbkdmZiYMBoP4bZLUx2HNLNGokCV/hwHqT3/6E6699lpUV1fj6NGj4nhGl63R0VHRAtXX10Op\nVAofIzIyEklJSRgeHhZsTqPRwGq1IjMzU9Ztbm4uent7YTKZMH/+fKxdu1Z8Sc73uiAyiPr6emRl\nZQlmcMkll8BsNksayrRKoVBIxDxy5EgQmMk6mzeRF1Fjr9eL7u5u1NbWor29XbwkOd5eoVBg7969\nguBTm8EFQGuy5uZmaZ8NDAxgxowZ0vunNoCnAQfR8uIC0+l0mDp1KmbPni2nSVxcnLAB6XuwcuVK\nKVP+8WKaPTAwgB07duDRRx8VIxuCYaxlOV7earWKSUlLSwv6+vrEAp/OR4A/wBG7CZy7QY0Cr5qa\nGnzyySeIjo5GYmIient7ZX7F6Ogo6urq0NbWJuY3AEQHw4sqTdbRxB/MZrM4UhGIHRgYgMPhwMjI\nCCoqKrBp0yZs3LhRngWDcGJiImpqapCcnCyZk9PphM/nQ01NjQSjQIKS1+vFJZdcIocGBXL9/f1o\namqS78/xCJStM8vl+mCZZrfbMTY2hnfeeQcLFy7EnXfeiX379gFA0PBfi8WC6upqcfBi1kQqO20B\ngHNdDJPJBIfDgfDwcLEMuPbaa/H+++9j7dq1QS3l870uiABBcNHr9YqzNB+owWBAQkKCnDoxMTHS\nb6d0lxuDLkCBvg78b55CcXFxGB8fF+oxAFHqqVQqvPPOO4IZHD16FMuWLUNKSorYhQH+E89kMiEp\nKQnXX3893n33Xezbtw/33Xcf0tPTpTY9fvx4kPCGKkGqFhsaGqBQKDA4OAifz4e9e/dK6vv666/j\n5z//+T89BRgs+/r68PDDD2Pt2rVicEsxGQBRbWZnZwuvgkYkJpNJ0m9OdwrshrDU4ucDIJwJj8eD\nnp4e9Pf3Y+/evXjqqafgdDrxwAMPCEah0WhEsxIWFiZWfsA57IDvzYDACdWBHQOXyyWlg9FolC4P\nuRn8Z6AxC01dKaZqaWmBw+FAc3MzvvjiC8mUAj8D03RiQGfPnpX7QTo24A/y6enpsNvtEthY3sbG\nxsLhcMj9JCbGEqS5uRmdnZ0YGBgQ7MxqtcLn8wmwSFZpTEyMZC8EkxncvV6vZF1FRUVYu3Ytnn/+\neaSlpUkgJr5yvtcFYTkXHR09GchBz8rKgt1uR0xMDDo7O6UOttvtiI2NlahJcNPtdqO4uBhvv/22\neEiS+MKHfPXVV0v9TR3GNddcg2+//VaITbNnz0Z1dTWSk5PFsObkyZNQq9UoLCxETU0NVq1ahQUL\nFoi7NtFj8i8ol/Z6vbjnnnuwbdu2oFYiaeQKhQL3338/2traAEC6B2azGc899xwyMzOFJGY0GmVK\nFTGNmTNninszR9WlpKRIO9BgMKC+vh7p6elwOp2CkQwMDKC4uBh1dXXCK6F7OIf+8MRyOp2IiYmB\nRqNBVlYWcnJykJaWhvb2dnzwwQcAIGpK1uaBxjURERFoaWlBRkYG1Go1du7cieTkZNmgDOzl5eW4\n5ZZb5Oesp+mixdo/NTUVw8PDwqaNjIyE2WzGqVOnoNVqJetwOByIi4sTzgKp3+3t7Zg2bZo4W/M5\nsZRRqVSYPn26tCh5ILDcZP2vUvnnwObm5sqoBRrLcD3OnDlTxhfYbLYgv1BqRfg9pk2bhoGBAVHv\nkojGQ66vrw9JSUlBQDmzaLPZLIclvwNJZf8noP/nW85Nnz5dFjcASfO5UAM9CsfGxmSOJutaGo8w\ndWRNCJwT+ISHh4uPAk8M1rtsq544cUKisFarxbFjx6BSqZCeno7S0lKxxmNUJ9BIRSVt7Yi0d3V1\nob29XT67TqeTMop0ZwYGWp/39vbizJkzAnBx03AxHzp0CEuXLsXExISYuHJTdnZ2or29XViRFotF\nauzAcXvsxFDQwzKMGz08PBzLli3Dpk2b8Ic//AGffvopXnnlFaxYsQKXX3457rvvPrz++uvCNlQq\nleKgFB0dLWDZ2NgYcnJyJLupr68P4h/wM9AKMCEhQcRrLAnZHaGuoqSkRIhxg4ODqKmpQXx8PMLD\nwzE5OYni4mLo9XpMTExIlsjSjq3ow4cPB3UwyIp0uVwoLCxEX18fEhMTheBFti7g36x9fX2YMWOG\nWP6FhoaKWC82NhYGgwHV1dXC/6DGRqXye60ODQ0JZsEWNjEHZngM1mTIElgFICA+Sy+SsQCIaGx4\nePjfYnt/QYCUq1atwn333ScDUTlli9OVmHqxri4rK0NCQoJgEi0tLYiPj5c6ldE/0A6sr69PNtKl\nl16KiooKoaeOjY1Bo9HIKW2321FSUgKv1z/ibevWreKe3Nvbi5CQEKG+EnjjpuTQ4fb2drS3t+PY\nsWPifs0NysB38cUXo66uDg6HA4ODg9L62rlzJ0ZHR7FkyRJxhyorK8P777+PH374AREREYLuk4zE\n9F2j0YgLFklVNL7lCUerPavVCpPJhNzcXBQWFiItLQ35+fkyFOcfa1kGK6/XK0GV1O/ExEThfJAp\nOjExISSw0dFRlJWV4fLLLw8SNgEQD4RAC3eDwSDpMrPFzZs3Izc3F6dOncLy5csB+FP+Dz74AFVV\nVTh48KDU6YDfiwLwtyX1ej2Sk5MxPDyMiooKCVQsPfleBQUFKCsrEwYjae3sWDC4U77N3zl+/LgA\nv5QDECSOjY2F0+kUfQqnmXu9XsTExIglAV3UBgcHUVhYKMbCLL/i4uLQ2dkJt9uN4eFhOTRCQ0Mx\nPj4uP+Oz+kdQ+1+5LogAMWPGDKFP04yUlt4JCQkCAmo0GiGNcCOyvUfyCvEGBgeCUSEhITIufe/e\nvcjJyYHNZoPJZEJ3dzemTZsmCDfr7FWrVmHRokUCgoaEhEjPGzgnKuOGJIOP/XwSuniR2cdoP3v2\nbLzwwgsinGLNPDAwgL/85S/YvXs3Zs+ejW+++UbszngCjYyMSHs0LCwMIyMjmDZtGmw2GxQKBSwW\nC6xWK6Kjo4UUREt7ACguLkZ0dDRWrVolIwzZdgxEwAM7JUyxq6qq8MQTTwi1lzZ1Go0GDocD4+Pj\nSEhIQH19PWJiYqS0OXbsmDBSeQ/JcCVHg+9F3gER/ieeeELk7bNmzUJWVpaMN0hOTkZxcTGWL1+O\nmpoa7NixA0eOHBHfx+7uboyMjMi94dhFBjt2bQAgLy9PaOfEbZjaZ2dn4/Dhw+LoxCnldXV1cliZ\nzWYkJCTg7NmzAPwbtaOjQ9rZZFmGh4eLvoWAMTkvZMAyexgcHJT74HK5xN/k4MGDQkbzer3IysqS\nDJjl9/leF0SA+Pjjj2EwGHD27FlJtTmvgekh21hUy7EG7evrQ1FRkbwWxTaBElxSgVl20JXZ7XZL\n2kud/9jYGFauXInrrrtOAgXBRd5wnlBWq1VOAYqEAD8t+cMPP4ROp0NVVZWMieNrREREyBAe6jr4\nmtxMISEhaGhoQHd3N7q7u1FSUoKCggJ8/fXXkhFwIXM4UH9/P6ZOnYqTJ09ixowZaGtrQ29vL6Ki\nojBlyhRccskl+MlPfoLMzExERUUFKT7JS2AmRYfwQIIQN/Pvfvc7of1yyC5T/L6+PsyaNUtIPA6H\nA52dnYiPjxeQ8Fe/+pXU2QqFf4ZFU1OTOE0RPzIYDMjJycGaNWsE4yGIy2DHIbncKIWFheIKbrPZ\nMDw8jA0bNkg3gi1LHibMKonv0PMS8GcnBEUNBoOI5jh9DfCXFF1dXdLJ6O3txcDAANLT02Gz2aTT\nwAnpdL4mdkNAlUGqp6dHyipyURQK/9wXql/ZtVm7di0MBgOcTidCQ0MxOTmJOXPmYM2aNdi/fz8O\nHTokA4X/1euCCBBbt25FRkYGKisrERISgpKSEpw+fRrAuYUbKDQqLi5GdXU13G439Ho9qqurhQ3J\nm09QiVeg3x/JNSaTSRaDXq/HnDlzcMMNN4h6lO1KzqkgAERQixkEQVRSxpctW4aWlhYhdXV3d0v6\nyXqbqtWrrroK7733nsi8GTgSExPR0dEBlUqFkpISAfToQ5iQkCDmvkNDQ5gxY4a0LwNNbTIyMpCR\nkYHnn39eUtiJiQlx5uYpBZzTcQAQPIe1NWt1q9WK2tpaCQxhYWFSh7MTwE1FvIOLf2JiAps3b0ZK\nSgrmzZsn4N/JkyeD5oQWFxejqqoK69atw09+8hPRJfT29iIyMhKnTp2C1WpFbm4uIiMj5XNSCEZ5\nemxsLCwWC7Zs2YKHH35YZn5Qy8MAyVScQYYgt8ViQUdHB3JycjA4OCjdCt4TUrEBfxnkcrkkMw0P\nDxdzIrVajeHhYVnDLAljY2MlKyWeZrFY8Pe//13YpMw2srOzUVdXF1RCEfj2+XwiSzh8+DC+/fZb\n4eSc73VBdDGSk5MnyUBTq/0zFvr7+4VTT+SYtZtKpZKBrrm5uXA6nYiOjsbBgwcFMPvH07+0tFTe\nj+nctGnT0NjYiJ/+9Ke45pprBCtgEElKSgoa7UfBFNuo9fX1OHjwIPbt24ezZ88GDUfJysqC1WpF\nVFQU4uPjsWfPHgCQxUFhmVKpxM9+9jOhfDc1NaGoqAgVFRUoLS0VkhHt1rmYOYdi5syZaGhokO4F\n+RucY7pp0yZYLBaYTCbJxvgaxER4cZGTYk6QKywsTMCwn/70p+IYxWCclpaGzs5OUZsS5O3s7ER6\nejrGxsZk87GOHx0dRXZ2NioqKqRrAfgxjT/+8Y8oLS2V7oXT6URERAQUCv90qptuugknT56Ue5Kc\nnIxt27bJ4COPx4PIyEhYrVYYjUb5PM3NzXjhhRfgcDjw0EMPiS0cW5Uulwtnz57FzTffLE7ZBHc5\nQ4WZAclytLLnM83IyMCqVauwePFivPPOO1i3bp0ESGJTer0eRqNRukcsfQsLC1FRUSFGxeySBfIj\n6BnKNc9OTVRUlLh3M2vJyMjAmTNn/vO7GBTPkNBDdZrdbkdRUZEsHsqwo6OjxTyFN4q8eabzPL2U\nSqUImFJSUkSlp9fr0djYCK1Wi6ysLCxcuDCoP06gjukcswYu8M8++ww333wzPv/8czQ2NkKv10ut\nTxyBvIHa2lqZ0jU0NCSdFABSGrCnzpkJHo8HJ06cCFInajQaxMXFweVyCWegra1N+A/URlCDkpyc\njNjYWGmhtbW1wW63o66uDo2NjfKdAP8E6t7eXoyNjQmD1Wg0ikdjTU0N3nnnHSH5DA8Pw263Q6/X\ni48D/TioAlWr1RgbG0NYWJiUiFSmRkdHw2azYWRkRBilXANz5syBTqeD2WyGy+WS1i6AIJzEZrOJ\nfT9nqwae1AS62RosKirCY489BgDYt2+fgJPEAkwmEyorK6FSqSTY9/X1wWq1yuRwGuZqNBqYTCaZ\ny0Fsafny5SgoKJAymFaHfC7MlNiVcLvdCA0NhU6nw8mTJyXgEpcB/AGXHqU07SFWxmd3/Phx6HQ6\naUur1Woh3J3PdUEECKa2nJeZkJCA1NRU6ZUDEGk3a1eOkHe73VLHEcXlQydH4ciRIzLBqKOjQ6jD\nIyMjiIqKwqxZs6BSqaR7EtiWomhLq9WisbERv/nNb3D11VfjscceExSeOhLOokhISJDTlAy5rVu3\nQqn0+2nSq4DUcBLCGhoa4Ha75YRSqVQYHx/HmjVrsHXrVvzlL3/BunXrMHfuXPh8PsyePRtOpxMZ\nGRmwWq2CadDtu7e3F/39/VIGMPMhJZmpMu3TuLH1ej1sNhv27duHdevW4fbbb8ett96KdevWAYDM\nc2DgbmxsxPDwMHQ6Herr66WbpNVqYbPZpCVMMNPr9YpDNgf5UFy0ZcsWCUo0agnMZAKzHqVSierq\nang8Huzfvx8ff/yxbEDiCSwLuc5mz56N9evXo7GxETabTUhxgL8D9Omnn0KpVAroPGXKFAEBAwlj\ntMQzm82SPdx222245JJLAPizMbvdjtbWVqhUKhmAzEyELdfs7GwhsrEjMX36dMkM6H3JUphsV2Y7\n/Pf8/HzZR/+M+PavXhdEgAgPDxcknmzI2NhYLF26FMuXL8f8+fPl1CePvru7W+YnMGPo6OgIUvsR\nEd++fTsyMzPR29uLmTNnIioqComJiTJUNjo6Go2NjUhOTpaeO99Hq9Viz549uOWWW3DjjTfi66+/\nFporI7harRbPRCL7/F1SmLu6ugQMY8uRKTSVkgTIBgYGZCDtkiVLsGjRItkYeXl5ePHFF2EwGMSF\ni0g3AKlJeVrv3btXuiYul0uMYiYnJxESEiIKSKPRCLVajcbGRrzxxhu4+eabsWHDBuzatQtHjx7F\nxMQEcnNzBUAj/uN0OpGcnIyxsbGgtl2gOMvj8SAjI0PAx5GRkf81/DY8PBzr169HcXGx4A1UgnID\nG41GeDweOUji4uJE/ajT6fD888/jxIkTYp4C+OeRkg3K9uBll12GwcFB/PnPf0ZXVxecTicOHz6M\npUuXymAlYl8DAwNCRiJISrCZWatKpcKCBQuwePFi4TaQEcn1SIo3P3ttba3oNQoLC3HppZdKEGBZ\n2dPTI1PkCgsLAZzTLblcLuFuREVFyd/TarXIz8+XQHy+1wUBUi5btgyHDx9Gf3+/UFBXr14tfoLz\n58+HyWQSf4P4+HhphfK0MZlMqKurQ1ZWVlCb7O2330ZNTY3YzweqJ+lkRKFLUVFREHvOZrPhs88+\nw7p160Q6y5PXYrHA5/Ohvr4eRUVF0u/nZmUayrTbbrejvLwcRUVFgoAzDY2NjcXw8LB4QHi9XqSk\npKCzsxPXXHONZFEMeIDfGbu2thZJSUkoLy9HQkKCYC8ARE/yySefwOv14vHHH5fgEUgi83g8OHbs\nGE6cOIGKigo0NDRgcnJSsjFawdNpm4Qmm80m94JZndlsDlIcBqoMqUsxGo2C1HMR5+fn47e//a20\nAUnxpj3b6OgowsLCAABr165FdXW1BFK+B3UzO3bsQGxsLOLi4kQ8lpSUhJ6eHvk7KpUKd911F958\n802xhuNUMo1GI/eRfwLH+aWlpaG+vh6lpaXCl6BjFIMKPT4CDVtYutJqQKFQIDs7G6tXr5YZGgkJ\nCfjLX/4iTNljx45JqVNdXQ0Aki0z+JBlydERKpUKdrsdISEh/1T5+//1uiACxLXXXotPPvkEo6Oj\n0kf3+XwoKCiQB/frX/8aK1euxHvvvYfGxka0tLRAp9Ph4osvxt69exESEoI9e/bgqquuEhBuz549\n2LZtmywgt9uN06dPIz09XU7Ympoa2O12FBYWyg0vKyvD4cOHsWfPHilVaCem1+sFnSbSPz4+jubm\nZsyaNUucrGfMmCGnxPHjx6FWq/H000/joYcewpw5c6DRaNDc3Iw33nhDGIzx8fFQKpXIz8+XLggx\nCcCfnQROXuJFfwCyNUmCioyMRE9PD7788ksolUokJSWJFyazlp07d6K7u1sAuJiYGNTV1UlZ1dbW\nhtjYWFFsEtehHoFdFXp1khLPExeApMnkCrAbRZfwV199VUq0oaEhsf4nC5bA64oVK3D69Gkp+xIT\nE6VsqampQW5uLr766ivU19fjgw8+ECyFHQ2yKb1eL5YtWwav14tXXnkFRqNRshnep8LCQuzZs0cI\nTVlZWTh79iwaGxsRGxuL6upqkWY/9dRTyM3NFayHZc0ll1wSBHrPmDFDytjs7Gw8+eSTiI2Nlc29\ncuVK7NmzR7JRUrApd2c5Rk8Uku50Oh16e3slaEZERAhH43yvC6KL4fF4JpcvX46vv/5aTqiBgQFs\n2bIF+fn5OH36NHQ6HaKjo8XjYWxsDDt27MAXX3whvWWm7+yvM8VKTk5Gc3MzCgsL8d1330mWQYFM\nfHw8WltZoHl6AAAgAElEQVRbkZGRIX4RgWKmmJgYlJeXIykpCV6vV9yhHQ4HLr/8cthstiC3KmY1\nnZ2dcpIlJCTAZDKhoqICer1eZnSSXk2PTdaic+fOxY8//ojvv/8era2tcqKSi/Dtt9/ipZdeQnd3\nN0wmk7zG0NAQ0tPTERISgqqqKukWOJ1O2O12IaMVFRWhurpaTjneKwYbak0ASPAgLZwgKsseLmLe\nt5qaGigUCsycORNlZWViapKSkoKGhga5z/v27RPJPBWtnHYF+DGHmJgYHDhwAM888wz6+vowNDQk\n2gpK0FUqlXgxEOj1+Xw4ffq0gLmcJ8HATnu5vr4+PPjgg6itrUVLS4vIuMkQJbFs6tSp6OzsREND\ngzAyU1NTsW7dOsyePVtEWQDEhDgmJgbff/89brvtNmHp0kTn9ddfF5yLPicJCQl44YUXsHXrVqjV\napSWlor1QSAQzCwnLS0NkZGROHPmDDIyMqR1S+sDtVqNnp6e//wuRnNzMxobGyUAkH349ttvCzIL\nnAPTiPr++te/RkREhNT8JSUlonrjPEROJeIwHvoMcE4GufB0B+Z7kBzkcDgwPDwsszeoMbBYLHIi\nc1OSbcfUnQ7PlHiTaBOo0LTZbLLAAQiv/8cffxSqLzUZ/AP401GPxyOTrFgf+3w+lJeXS6bDFNlg\nMCAtLU2k7V1dXdL9GRgYQEZGhuAm06dPFzs/PpOqqiox/KVbNzkPlMOzs8BNb7VaMTo6KnUy4OeO\ndHR0YN68ebKReSjwXgOQ2Z5bt27FM888A51OhylTpgQRt+bNmyeBjXRudgwGBgawa9cuIdzR3j8s\nLEzahTR2eeSRRzA5OYnIyEjRf3i9XuTl5UlJYrVaJeBPTk7C7XZjbGwMM2fOlL9PH0xK/wPZqPx3\nt9uN1NRUxMbGAjgXmAE/DyMvL0+MhY4ePYquri5hglKTZDAYZISfw+FAWlqagO6RkZEoKSkJMqs5\nn+uCCBDPPvss6uvrhQhjMBjEUZmzEzkEJvBGezwevPXWW7jmmmswMjIiJiuM8PRu6OrqQmpqqpwa\ngYw8Cl1o08UoT4Q/MTER7e3t0Gr9sy4YAGgpV1tbC51OJ+ksbfLi4uKEQz916lRh7yUkJIjhCNNs\ncifoKEXlH9ul7Dxwk3GRAH4mX1NTE1JSUtDV1SVpeU1NjbRUA0FPdjJ4r/V6PSIiInDy5EmZb9nb\n2ytBmsYrxcXFMjj5hx9+EAk06172+pm5UerM/n12djYcDgcsFgsWLlyI//7v/xbORUxMjGQnxJVC\nQ0Px1FNPYePGjUhMTITNZpOuBuAHhjn1GjjHbo2Pj4fH45HS5ciRI3LftFqtrKfw8HAxgp0+fTpW\nrVolDl98TWIRxJN4DzkDlLgIsxK2ZjnGUalU4ssvvxR3cWqKOL2Nl0KhkLIiLS0NcXFxwkWhGI/P\nnR4RY2NjgpGEhoYKrjE8PCzy+H/HdUEEiO+//14eosPhkJrb6XSiqalJLObi4uLkxrP1Ex0djcWL\nF+Pll18WPQa5ETzBWZawk6BS+SeCT506VTwo2YKihX1YWJjoHniKU848MDCAhIQEqFQqocDW1dVJ\ngOCDAvyLp7+/X0oftgZpDx8ZGYmEhATk5ubKomVGQdMcouZsVU5OTkopwqlQ1K7QG4HgIuBH8t1u\nN6ZPnw6v1yuTu30+n6TGTHcBf1ZTW1sr5CKdToeOjg4RNgUqC61WKzIyMiSlNRqNYsvOEsRkMonp\nSmlpKZ588klp9ZFwBpxT8fb29uKee+7BX//6V4yOjqKpqUmwD7pc8XkAwLZt23D33XdLxkRAMCIi\nAi+++KLgMSzPAAjiT1/TFStW4JJLLpGOB8u2uLg4KSdJcuP7Z2VlYWxsTDJEHjjMEk+fPi3myC0t\nLQJiBo4TACBBm9wH4iGcyULsgSpkTp9jp4c4BHEiPqt/h5rzgggQExMTWLRokXD62Reuq6vD8uXL\n8dlnn0k7MtCTkiw32oBv2LAB9957r2xinsKsNUlsyc7ODrIwLygoEK68Xq9HTU0NxsbGUFdXJ+P0\nhoaGEBISgsrKSklXedKQowH4T5309HQh6dA/ITs7W4Lc3LlzMXfuXGRkZMipQO4DN2AgmYYdCV78\nWSBgOTQ0JD6TWVlZaG5uFkVfeno61Go1GhoaEBcXJ9+TrUm6bkdGRkr7jIpTq9UKs9mMyclJmeLt\ncDikOxMVFYWKigrpkACQVh7bp0ajEQ6HAwsXLsTKlSullKPNGvUYCoUCUVFRuPPOO2WEnEajkTkV\nbAsbDAZp5c6cORMZGRn4r//6Lzz11FMSmKljaG9vx1NPPSVYBNcbAOkI0b/08ccfl/vJ6W12u104\nGXzeHo8HRqMR9913XxClmepKzuh84403pG2flJQkzyssLEwYvsC5MoOliFarRWxsLNRqNRwOh2Sn\nxKkmJyeFbh8REYHw8HDJbkjOChxLcD7XBdHF8Hq9OH78uNBXOXyErLA333wTN9xwg5z+ROKBc+Qo\nntyLFi3CxRdfjLvuugsJCQlyEk5M+O3rXS4XWlpaAPhbRjExMWhvb4fNZkNqaipycnLgcrlQXFyM\nAwcOIDk5GfHx8WhsbJSFOmXKFNTX1yM8PBx5eXmyGbVa/2Qrh8MBo9EokT8tLQ0FBQVYsGCBGKn4\nfD7YbDbs2rULe/fuFf4/iWJtbW0iVw48bYil0Pk7KioKGRkZaGxsDJLFGwwGIYSRlEW6L8HHzs5O\n4WrQk+Ls2bPSBuU4u4aGBmlLEhgjE5Ktw+zsbAD+1J/AnsvlkhImKSkJ999/P0ZHRzE8PCzTxGkz\np9PpEB8fj++++06yIQYMYkeBJDIGlKuvvloC5jXXXIP9+/eLFwI7ElVVVVizZg22bNkiWQDvN1N5\nt9uNvLw8eDz+ocd6vV7apMwsybDU6XS45pprUFBQIJ2jQC8Tr9eLAwcOoKysTHghzGKZvfJe8fky\ngwgLC4PRaBTGKgDprvHvMJOgjH1kZEQ4NnFxcULD/3dgEBdEF8NoNE6SnDQwMCAybIJdFotFBqOs\nXr0a8+fPlwfT398Pr9c/cSnQnp7p9ccff4zdu3fDZrOJlwAAzJo1Cy6XCyaTCadPn0ZiYiKioqJQ\nWVmJhIQEKVM4x8Dn88k4ebZh3W430tLS4HK5cNFFF2HatGm44oorkJKSIvUpU076C7hcLmlJknQz\nNjYm5cqdd94pG6OzsxNHjhwRwImnkc/nw+7du/Hb3/5WtCmA33inurpa1JPk9NPRKDo6GvX19UhK\nShKDYAAiHCMfhPeQRBx2KTjRnJkZVYrkCURGRsrCZMlIRei7776L2NhYwUG4odhZeOutt7Bz505Z\nE3z/kZERGUMHQMRog4ODiI+Px759+4JmokxMTOCxxx7D4cOHBbNitud2u7F06VI89thjMqeT9564\nRX5+vtDaSb0m/tTX14ecnBzcfPPNWLNmTVBWp1QqBajlWuHvKJVK5OTkAPDLCPR6PXbu3BnkGsUO\nREJCAh599FF8+umnQXgb75tWq4XdbkdSUpKUdXxflrCXXHKJvN6hQ4f+87sYGRkZUt8TqeWVmpqK\nU6dOYXx8HL29vbjtttvwxBNPyEQhcvVZFzIwDA0NYXR0FMuXL8fmzZuxYMECNDc346KLLoJKpUJd\nXZ3UbPQzJFA0Pj4uqDGJTDk5Oejv7xdQMT09HUVFRVi0aBE++ugjvPbaa7j77ruRlZUlOgQAcmpw\ngzNNDnTIIpJP70IGDuoTmLozs2JrlKw5LuaysjJ0d3cjIyND+Bt0jOKJD5wb3hIaGgqn0ymbZWLC\nP+PR4XDAbrcLsEvTEp5+9MTIyckR8dA/emQE/szr9eLMmTPQarVi6krhVmdnJ+6//37s2rVLsB4a\nwPJ7U8TW3d0tLMeSkhLJighuEqC96qqrBDwN9OzQaDTYt28fPvvsM3nWvK+AP5gR55qYmJARDCqV\nChkZGSLrv+mmmyQ48LBgGUiiEvENHnT19fXw+XxiUhx48b+p6WCGHNhNI3BJrxF2mDgPxuFwwGAw\nICYmBlVVVejv75ehwudzXRABoq2tTdyRGChyc3NFn5CZmSnKNq1WK7TYd999V9iFer0e2dnZMjKO\ngijeuIceegiXXnopAATNMqAP4NDQEGprawFAWl7AuQdjNpuhVCrFtVqtVuPdd9/Fgw8+iNzcXDll\nKBLjvwOQQSiBBqt88GFhYYiKisLhw4exfv16SfVJjGH6y4AxMTGByclJDA0NiXEIU/KsrCyoVH5v\nSdKPq6qqkJ+fD7fbLZLx8fFxGSFAs96xsTEkJibKYCGWFgRGnU6niOVGRkbEDTwiIkJs1QKFVCTu\ncFO9++67KC8vlw0I+AfhLF26VFywWZ4YDAYZHKxWq1FRUSFaHdbo/J2enh45UBhQiouLkZmZCZfL\nhfr6egnIo6OjaG9vx9atWxEbGytYD7suAGSEHfEEtsJPnjwJjUaDlStXwmw2y3uxewBAuCrbt28P\n6vDwszQ3N8NoNGJ4eFhEWuxgBGZV1NPodDpkZ2fD4/HA4XBAp9PJzNa+vj5hs5Jjws/PQPTvuC6I\nAEE2Ik+4uLg4/PDDD7JAOIOCbjyAny9QVlaGZ555BidPnkRfXx/a2tqgVqsxZcoUREdHIyIiAmFh\nYXIK33XXXRgZGZFWFn0LzGaznGxFRUUYGhoS0hFZdt9995281vz587F+/XqZbcmAQEbk4OAgmpub\n0dzcDKvVGpRWE1yMiIiQenHZsmVYu3ateDT09PRgdHRUFiK/M1+DFG0CjZwiRXUmuQkEYhUKBZxO\npwBltHajFyIA2Wx2ux1ms1mmnKlUKhkwRG9GllmdnZ3isszPmZGRIcY+rPWp0fjwww9x/fXX44kn\nnsCaNWvw3nvvQavVor+/X54X53bMnj0bGo1GJm7R6Zm4CL/nmTNn5PTmBtXr9cjLy4NarZaAxMzE\n6/Wivb0d+/fvlxSer88OCLMzblyyU4uLi3HddddJdkgHLJagPp8PbW1t+OKLLwQ87+7uRlNTE9LT\n00UQODk5KYGNa4F4E+DPvpRK/3wPMlTZ/g+0HgAgnTo6X3E+qdFo/Ld4QlwQICVPMG5akowoIOrq\n6kJ4eDh6enoQFRUlNTzTxuPHjyMtLQ0LFy7EnDlzEBcXJ3Z0BI08Ho9w9Ovq6kRTTxq1QqGA1WoV\n/CIkJER0BCQxXXTRRQD8vA06+TDqswbkTAOWAryIOYSGhorM98CBA3j33XdFW9DV1SWS3qSkJMky\nPB5PUNahUvmdpMjYGxgYQHNzM/Lz86W9ZbFY0NbWhpycHDQ1NYnpCrETsgQZbAAI4NXb24vU1FT0\n9PQIaMvNTi0Fuy3sBFgsFpEdx8XFiY1eRUWFdCDo6alSqaS1mZqaitOnT0Ol8luzGQwGMQFKTk5G\na2urdKsAiEpUrVajuLgYP/74I2699Vap/fkdf/azn2H79u0CMpJyrdVq0dvbixdffBGrV6/GZZdd\nJpL6gwcPwuPxCAWchr82mw35+flYvXq18At4nwMtAjweDz788EN0dnZKq5qWhgAETyEuEvh7AGQu\naUJCgvAlGNSoFKZQjt+H1GtmklarFXPmzMGJEydE43E+1wURIIqKilBeXg4AUqurVCoxBiVt99tv\nv5W5Azk5OeJ/kJycjIaGBrzyyivYuXMnZs+ejRUrVsjwVhJOtm/fjrKyMgD+HrnVasX8+fNRXl6O\nOXPmCMg2OuqfYq3RaJCTk4PJyUl0d3ejsrIS69evl3KIrUjgnHs2AOl2sK1FcKmzsxPl5eU4dOgQ\nKisrYbPZBByLi4uTTdPe3o5Zs2bJCcP6mu0xLj4yETlDxGw2w2azSTnBtFStVmNychKJiYmiD+nt\n7RXhWGhoKDo7OyVQ8H6RaEXDGvo6Av7SiGSvQDYqh8GcOnVKBskya2LZVFtbKxb1DQ0NQdZ3fG9u\nRGaF5ATQv9TpdIqcnVkcSzAAyMzMFAwGOOeMzvLG6XTizTffxFdffYXLL78c7e3t+PTTT+UZVlZW\nihGMxWLBddddh+LiYnl9Bh4+34mJCdTU1OCDDz4QzxASmQA/oY3tZafTKZ0HAOJgRtDUYrEIAYrz\naOmArtVqEREREVRqsJzgBLmGhgaoVCppFZ/PdUF0MQYGBibLy8vx85//XOzsk5OT0dXVBYvFguHh\nYbS2tsqkZr1eLycWCS80bGlubha+gfX/OAr19/cjMzMTTU1NwmpkrV9WVoZp06YJ2Mnhs5WVlYiL\ni4PD4RCzkcrKSrz99tuIiYmBw+FASkpKkJU8y4vD/xd37x4ddX3mj78yk8kkc0kmk3smmUwYciEh\nEAM0ahFElIK0luJaLerxUrwVi1qv6Fe7xSq2gFZta+2yWlfb1dNtpSjVUpCCFMwGKSEEcptcZzK5\nz2RmMjPJTDK/P6avh8+43/M757v0j+x+zvHUIoS5fD7P+3let+fwYbjdbhw9elQCWLiajtQa6Uam\nAnEzuN/vx/z58xGLxbBhwwYJlU1JSZGZU6/XY9OmTWhra0N5ebmImajBoHbfbrejra1N2lClU5Xj\nAqXQNptN5O4UINHk5vV6EwKCI5GIGKj4HZ09exYajUY+G9KEVKVSxToxMYEFCxYkJEtzXmcRIec/\nNDQk0nnSfGR3ONcnJyfjxIkTIlRiYZuamsK1114rC4yVWhV+Z3q9HiMjIyJZZwdCCpShO319fZLv\nweQut9stuZKZmZk4evQonnnmGQBx+vLs2bPIzc1FSUkJBgYGhBkim/LQQw9h8+bN0g3w3khLS4PZ\nbMa9996LgwcPwm6344c//CHsdrsIpNiZulwuDAwMoKmpCadOnUJ3dzdGR0dRXV2NgYEBuN1uOJ3O\ni2Ix5kQHoVarUVNTIwKPmZkZ0ZUzVmxwcFAqcjAYxNmzZ7FixQoMDQ2hqalJ1II8NRwOh7ARMzMz\nOH/+vKjpuIE5GIxv2po3bx4+/fRTkcC63W4UFhZicnISixcvRlNTE9rb2xEMBvFv//ZvuPXWWyUz\nIhgM4vTp0+jq6sKRI0cE6KRgB4DYf5ctW4YjR47ITcbxhiAau5CysjLZxajT6ZCSkoLp6ekEWs3r\n9cJoNKK7uxulpaWyktDpdAp9CcTHhurqaoyPj8sDzpFlaGgIhYWFEt7LANaSkhLEYjFZRlxUVCSf\ncWZmpox2FC3xZAMgqUncnM4NUPQ0RCIRDA4OSltfVFSEEydOyGkJXAjuJVhIyTMFb3a7XQxsFKSR\neeBnFgwGcckll+Djjz+GTqeTtDIqMOm7KC8vR0tLiwTIshviuMvUMT6UxBXIgmg0GoyMjGD79u2S\nJUFBHsNmzWazsBNc9sx9KQR1mQjO7A673Y7x8XHs2rULpaWl/9f1izqdDvPmzUN9fb0A7ufOncPx\n48dlA93FXnOiQKSlpUGj0WDBggUSgqLRaGS5KrljJjQD8Va0ublZgBi6DanA5D6DaDSK0tJSOBwO\nCQcNh8MYHByUL51/J3GDvr4+LFy4UFaecWlKTk4ODhw4IIEm7EwaGxvFTs5TMz8/H8FgEOPj43IT\nNDY2IhQKoaSkRGbrSCQeTMpfIzgFQNpTsjE84dhiVldXo7+/XzaDcxfnyMgIysvLE7ZpkcpkviQB\nOWWiNgDJZuT3kp2djWg0KoWarzkpKQk+n0+Ww/DXOUP39PRIrJ8SM5qampLVBjTSUdhFBeL4+Ljo\nHDjGMRdSpVIJ6MxW/uTJk1i1apUIllh0169fj2PHjklRLCoqEszE5/NBp9OhubkZExMTEs/f09Mj\njABfGx2eMzMzYlhj8K9KpcLTTz8tJi+v15uQvJ2bm4uBgQGYzWYBPTMzM9Hd3S24CcdopZbBZrPB\nZrMhOztbsiyIO/C+VWJcxOxKS0uxYsWKBGbmYq45wWKwGi9evDjBRZmUlCRxZhTZEGQjthAKhSSQ\nlXFcNTU1MjMHAgFhB0wmE5KSkiQXgPRoOBxGSUmJ4AlsaXmzMhGKqr/u7m5hTpjSZLVa5QQLBAI4\nefKkAIjcCkU5MwA51XhikfdnVHxmZmYCOEfvBQChMjs6OiSUlRw+jUIEENkVcAlPKBQSxJ4/i/oT\nAGI80+v1MJlMuOSSS5CXl4fs7GyYzWYxYE1PT2PevHly6vPmpk2a7TIzIywWC8LhMGw2m4Byubm5\nyMnJEeZgeHgYJpNJRj2OELSSE/fhw0gJ8heXLpO6pYSeGE9nZ6doCzweD6qrq6FSqcSKD0AYA44d\noVBIlLYOh0M6z6mpKcRiMfzsZz/D4cOHodFoBFeguY8AMMVNAwMDQm/29vbK/UbVJztngtQcjTwe\nD4aHhzE6OiphRF/UUhCvIrWqUqn+9wTGAPEbrLq6WpB0zpoARFij/GCILaSlpeG6667Dd77zHUxP\nT+PAgQMSAkIAqaWlRbhzRsJxVmYiUyAQkDRpLmGl2KWtrQ0qlQoDAwOYN2+exM1RK8BQEQp3WCgA\niJCot7cXkUgEtbW1Esve2toq7ryamhoAkCRvbvvi+MEHWJmhyM5qdnZWwLurr74an332meQtOp1O\nXHrppbjppptQX1+P1157DadPnxYqNC0tDUNDQyIzrqqqgtPpxNNPP40vf/nLAOKga3d3Nz7++GN8\n9NFHqK2txcTEBLxeLwAI6p6eng673Y7u7m7JzCgoKBBTEUcMruuLRCJob2+XZG7y/Hq9XjQIDEYZ\nHx+XEzQUCiE/P1/CiE+ePCmUOKlnntS1tbU4depUgsmPojGn04msrCzEYjGcP39eqFqGwVAOzsJM\n7QOL0PT0NP7whz8gOztb/olGo2IKZO6k3W4XXQMAiUbkPQZAwF5eOTk5+M1vfoNFixYhKSlJOieK\n5/geeS+o1er/Mob8r+kgiFJXVlbKTcs06YyMDLhcLnR2dqKyslIW5tBFl5qaik2bNolBa82aNaiu\nrk5Y+pqcnAybzSaGraSkJNmrweQmntShUAh6vR6BQEDmViLS0WgUOTk5Ei5DFJn5kAUFBcJwUONA\nfwlv+OnpaWEvZmZmxNvf0tKCoqIiEY0xmUipp6fe4s033xTBFvdfeL1eBINBkSvzhhkfH0ddXR0y\nMzNRWlqKhx9+GIsWLZLRx2QyCTNCn8TU1BS+9KUvIRKJZ4OWlZVhzZo12LFjhwBrBIgjkYj4MMje\nTE3Fd3OGQiHBJ8giEMnnaMiHCoB879wV2t/fL1vLiLEAkO+UoJ/f78cHH3wgRVrpqrzmmmtgsVgA\nQLaecWFyT08Pent7pePh98FDiAt2MzMzsWPHDvT39wOIF4rjx49jy5YtgjW53W6hm6lBIFhOzQLv\nMcbp055PypjfG9WShw4dwvPPP49YLCb3AkcRRgEwcX1kZETWPRK/+UeIpeYEixGJRGL8kDZs2IC2\ntjbhgFko5s+fj/T0dHR0dEjUF1OItVqtRGzp9XocPnwYO3fuhFarRVFRkSgSfT4fFi9ejNOnT4sQ\nibp2j8eD8fFxObXr6+vR3NwsLbfX64XJZILP50NpaSkmJyeh1+vhdrsTcgrYsnM2r6qqQlNTExYu\nXIiOjo6EPQp5eXkS5jI8PCxYDIG3H//4x+I7UalUePDBB+F0OtHX15ewRo8gIGlHinQYSvKDH/wA\n69evF4wEgBSoq666Cnq9Ht3d3bIMNiUlBe+8846cqPn5+Qn7JYD4fO52u3Hw4EEcOnQInZ2diEaj\nglOQZl28eDG6u7sRCoVEnMVluwRUWaTnzZsnC5OoXuWmKo5jLpdLRiJ6JiKReLr0008/jQ0bNgij\nxOXGu3fvlrj6pqYmYZEY9U/mjN8f9S/ABW0INQcs/uwm+F75mS9btgybN29OiO/7+te/LntS8vLy\nJOxWp9Ph6quvxsMPPyzY2tRUfN/I2NgYbrrpJhEHajSaBONYdXU1Vq5cCZvNJvZ9sjMs3H8PUfqf\nz2JQAZeamore3l55YHQ6HYqLi+XkZYoSb2ZSWykpKQmR6LQh/z1ySxD8vLw8fPrppyJhraysRF9f\nn9ygdHw6nU709PTA7XZLW8hEps8//1xoV4PBgOrqanR1dUksHb8wpjYHAgFJJsrIyJAdFwT2dLr4\nAh2DwYDR0VFxZlosFpw4cQKXX345ZmZmsHv3bjQ2NgK4EEhL2zPpPeoNIpF46C2ZA76HL1qZuX6Q\npxJb+vz8fOj1evGpcJRguhSLTF5eHm6++WbccMMNaG1txQMPPACXy4W0tDTMzs7K8hmG3nKdQCgU\ngs/nk5OaeykoGFPulWTIDkVLpGwZCe/1ejEzM4ORkRG8++67uPLKKwW95/2wfPlyfPLJJ9LFzczM\noKenJyGIhfJmFiN2fZzt+TmxyBkMBmGjqBT96le/is2bN8tOFY1Gg2PHjgmFydeekZEhgrHf/e53\naG9vl9xQsnj8JysrS9gVGuIGBweh1+sFx8jKyhJzWGlpKZYsWZKQ73Ex15woEEoBC09YCo04FkSj\nUTQ0NODqq6+WVpA3PltktpX8IkmJMuWIQJnVasXLL7+M2dlZHDlyBM8884ygwFxvxhQkxsKz2NDe\nTLnt6dOnxZFYXV0tRqZjx45JJJ3X68Xo6Kig0319fVi1ahUOHjwoUXYulwtLliwRd+qll16KY8eO\nibBrZGQEOTk50ikRSAUgngci4nS3sgUndQZAJNjcHcGiQI0C051oImPXEAqFRPPAVp7FSK1WY9Gi\nRXj55Zfxxz/+Ec3NzWhvbxfpOfd0hsNhify32+1C0/KBov7B6XSKKIzFi2Y9RsuRWVKpVJI50dzc\njI6ODtTW1iItLU0YjcLCQmRnZ0u8H0Fdajv4PnixOITD4YTdHfzMACRIt61WK+655x5ce+210nlw\nPD569Cg0Go24K7lwmJjD6Ogo+vr6cPDgQaxbt050MRSZcaykcdBqtQpFnZ2djVAoBKfTiebmZmRn\nZ+PkyZN45513kJ+fj+Li4ovezTknMAgAMosR8QUg5hyPxyNR6Z999hkmJiYQDoexfv36BKWiXq+X\nG//IAj8AACAASURBVL6kpASTk5MC8DFXsbS0VMJp/H4/li1bJotS+YGTWaCpZurvuxi6urqQkpIi\n9F52djasVquIbEZGRuD1euWmcjgcaGlpkQwHnvBK8JEnF7sNjhzHjx/HyMgI/H6/0J6U4mo0GmRk\nZEinRBk3PzeLxYKOjg7k5uYKg6M8mWjWIpgaDodht9sBxBmlcDgsy4fJTpCSoy+GBZ2sAAvkE088\ngV/84hf40Y9+JBvEiehzzQC1CBqNRkJuqIjkA6TX6+Hz+aSQEFfg50SZOQDZzpWWlob33ntP2C3K\nk/V6vWQ48iHn6+e4QuaIegoGCtMpSR8Ld27y8KmsrMT/+T//Bxs3bpQkL7IXo6OjOHjwoHSMPCTY\nuRBnGh0dxTvvvIN33nkHfr9fdCkM4SkoKBDgvq+vD4WFhQAgqlZ2FsPDw2htbcXIyAg8Hg8+/PDD\ni38uL/on/IMuatuZWGyz2aRoMPFXq9WKzt1oNGLFihUyewEXWmgWkJGRETnpqcJUqVS47LLLxIZt\nsVhw5513Qq1Wo6WlRdJ5SJsSeVfiGIxmd7lc6OnpEW09RySi2PRexGIxXH755dDpdOjq6pLXsXDh\nQmk1mSLEk9nv96OgoEAWzvDEKi4ullmUHhE6U7mqjQYshtICSFj4AlwY60gVms1mCeUFgPfffx9/\n+ctfkJmZKYnhpBKVRiwuECZNR0HXypUr8eSTT+InP/mJBOKyIyRmwuLr8XikU2OxoZaBJ/y1116b\n0GaHw2H4fD4JsmXU/ocffojTp0/LnM/OoLKyUt67Mt+Tzkf+N8bvK8cu3nfABSUli+yOHTtwxRVX\nSMfh9/slCOfMmTNwuVwYHh6WODkyOPwO6JA9fvw43nzzTWzduhWHDh3CgQMH5Pcwe5NrENra2uSz\no74FiB8MykwVvseLueZMgaDXffny5QleA61WK3mRTGrOzc1FcXGxVHzmDHC0oIWbF09qIN4+MhOQ\nlOHmzZuxbNky6HQ65ObmQqVSySJf4EI7SSAqNzdX2j/OohQe8f9rNBqRzZIOLSsrg1arxfLly9HY\n2CjjBANwW1tbxVDGVpIPhMlkQjAYRCgUkuJQXFyMsbExeT3k8skQkHfnScvXRTqMD6TRaERjYyNS\nUlLQ0dEhp+0vfvEL/OIXvwAA4dR5qirVhWq1WpgJFh2KnrhO75577pHPhA8f0Xqe6hwFZ2dnJXdT\nrVbj/vvvxzPPPIObbrpJurfBwUHRKZAWDYVCEvVGepmv0W63y2Gi3DNC1oI4A3CheDL/ksWRVLvd\nbodWq8Xq1atht9ulCJHdUY6k7DaysrIklYzJUPz76NkZHR1FY2Mj7r//fuzdu1cYMbPZLDgTcCEX\nhL+mdNPSwUon7sVec4LFABALBALwer1wuVz4yle+gqKiIiQlJYkvg9RUdXU1mpub8eGHH0Kv16Oi\noiLB1BWNRnHttdfC6/Xi8ssvx759+7By5UoMDQ1J9T137pycnEpH3SOPPIKDBw/CYrFIjgTpJD6o\nOp0Od911Fx5++GFhDEhNkafu7++Hw+GQOP+enh74/X4MDQ1JJB1b2dnZWYyOjqKqqgoOhwOLFi3C\nzEw8Pp4iIwKOdrsdHR0dEvnG7oAdDvl9+iRYRG6++WbccsstMoIRtI1EIrj22mtF5UgcgvM2i6Hf\n70d1dTWWLVuGJ598MiHEhp8fcy54kvIieMqbGAB+97vf4fnnnxeL/Bft7Fu3bsVXvvIVWbUXCoUw\nPT2NX/7yl3jjjTcS0q343RG9p+LSarVi//79ks85NDQkydUsquFwWMBknvLKB1EZnUeZf21tLerq\n6nDfffcJCErPiHJ8sFgs+Otf/yrdKT/vhQsXyqJe3jfsVEl/cqxj4Zk3bx76+/sTNBMqlUr2q9DL\nwSJOde7flbz/81kM4MKNZrVasWDBAnR1dckp2tfXJ/MyaUmz2SymHM6WrMiTk5PIzs5GS0uLjA4E\noniq8MPkYlsAqK+vx29/+1tcddVVOH78ONRqtbSLhYWFGBoawoIFC7B69Wp5SHiTZ2RkyKxtMpmw\nePFimYF9Ph/OnDmD559/XpbCVFdXy2bulJQUtLW1ITc3F06nU6TJFosFPX9PZGYWIgNkeWNUV1fj\n6NGjshKOfg66AAcHB4Wx4GdMSXkkEpHPSavVSnEYGxuDwWBAVlaW0Kfd3d3w+/3QarXYsGEDbDab\n/CyOHUoBD6P0uDAmGo2Kd4IPdygUEvkxtRNFRUW44YYb5IHie1Gr1Th48CAqKirQ39+PrKwsKQoE\nrJljCcTHzIceegiPPvooampqcPDgQSQnJ+N73/secnNz8d3vflfGFHaeXCBMZSQdlBqNRkDRNWvW\nYOPGjTIKUcZOEx7NWADEFs8ixDG35+8rBXJzc4UiJUtDnIVjJw89HoDsEtjB8jPiKExXLXDBRn4x\n15wpEHyDRqMRq1evxsDAAIxGo0hduY8zGo3KmjxWYN6AarVaMgVIWX3ta18Tw05SUpK04VNTUwJ+\nEajT6eKhs4zqYtI1AFRUVGDt2rW45557kJWVJfw0TxGePsrgEspemTW4aNEiHD9+HDt37oTT6URH\nRwdKSkokxIW4BiW2hYWF0s2YzWZxQDJGTq1WixqTLSaLQXd3N2w2G4aHh0UUxdempBHpveB7VRYR\njgpURc7OzuKNN97Ab37zG1RXV2PDhg1YvXq1dFocgTga8LPlCefxePDee+/h448/htFolPY4LS1N\nxqgHHnhATkjeE8oTkbgACxzB1oGBARQVFUlbbzab0dnZiW3bton2YOfOnbjyyisBQAq8VqsVj4/b\n7ZYiyCVDxD1yc3OxdetW1NXVyYGixAHo6CXm1dPTg9dff11GCQLhlJAzCJmYm3Ic4L3M70zJfPAK\nBAJySLATYpe2atUq7N+//38PzckrGo2KC++nP/0pTp06JZujOEdPTk4KPsB2UpnJ0NbWJg9vIBDA\n6dOnAcQLUHFxMUZGRqT68yZUml4ASML1mTNnUFJSgscffxxZWVkSRU6qjug7t44zB1Kp5AMgHg6L\nxYK1a9fKNidiB9QE0P1IMdjk5CQyMzMlgYj+ALbE7FIAiOORRid6AygC4kPGz4Jtv9JoZbFY5Obl\nz6CGIT09XcDSSCSCgYEBbN++Hb/73e9QWlqKtWvXYuXKldJNcDRQqVTYs2cPPvroIxEvFRYWCiND\nB21mZiYuueQS1NbWysPB7zs1NRVJSUlSEIgdEJg0m80YGxsTRyjfD4tde3s7nnvuOVx11VVivSeN\nTsl5RkYG+vv7hR53uVyYP38+otEo3G43nnzySdTU1IhcWxnmw5GMAKTBYMANN9wgngx2B8q4PoLa\nfKh9Ph9sNptQuJ2dnbJkiVkYoVBInLRarVbs5lzDwA1mhw8fls7vYq85VSDoK6ioqJBTh18WwUii\n9eFwGElJSdLOJSUlITk5WTZEWSwWpKenw2w2o7q6Gq2trejv75dCw8LAL3t2dlYeetqVY7EYUlJS\nUFlZCbfbLUG5bNeVgB8xCD6ELGC0avPvUavVWLZsGXbu3Ikf/OAHEilmsVjQ1NQkp3tWVhaGhoZQ\nVFSE1tZWeajZbhOh5kPCh5dtMRf3cs+pMkeAFx2zJpMJPT09yM/Ply3lPKGzsrIkK4OaA3oJ+MA3\nNDTg0KFDuPLKK7F8+XLU1dWJ3Xn//v3Ys2eP5HhoNBrYbDa0tLQAuLARq6qqCt/5zncEPObIEovF\nJPS1pqZGbPCkkunKVHYaFJsZDAZYrVbccsst+MpXviKjm9frTdhSZrVapUhHIhGRx/f39yMQCGDN\nmjUiJ+foRCqTOQ8EGlNTU3Hq1Cm5B9LT0+H3+4V25XumY7SzsxMLFy6E1+uVxdVMNSOmpbT5M0Wd\nxS0ajYoXSGm8Y0z/xV5zhsXgzarRaJCTkyP6+cnJSdmjyTFAKXAhohuLxRCNRtHR0QGNRiO7I4H4\nPNrT0wObzSby4y8aXUipEg3nNqxgMIif//znQrWaTCYAkBuSGgmCrPx3UoF+vx8+n0+ceLzxa2pq\ncOedd8rsy7Vv6enp0qqqVCqZgYG425WjCwCZZTUaDaqrq0WWzKRnAAlbsXjqKQVlJpNJ9BMMdeEc\nzZ/l8XiQkpIiTtZ58+aJlJtKwlgshr179+Kxxx7DXXfdhbvvvhs333wzdu7cKQAvN3+dOnVKWCEA\nKC8vx6ZNmyRmj69tdnZWov/27duH06dPi7KSD9vMTHwJT3FxsaxLYBdx8803Y8eOHairq5P0bqpC\n6dadmppCR0eHdA7ENdiprFu3Dk8//XTCQwpAxiMyQ+yEjh07JopSOoZJFVPIBkBWE6hU8TWO0WhU\naHHqMAhU8mczZZwOVEoD5s+fL0pd+kr+EUYtYI50EDwNNBqNyEpXrlwpKcQZGRkiGlHuNCTqzpGh\no6MDjY2NElp66tQpORl4onIWVG43ikQiUpwoh6Yxh8XlwIEDcLlcqK+vB3Bh4zXFMrQUp6amorKy\nUmZRZRFil5GUlCTpzWp1fCUgJb7cHt3a2gqLxSKWaeok1q5di1OnTgmWwCLm9XplTl2wYAHOnz+P\nyspKEYdRyw9c6G7UajWqq6sldYodFHMl6R9YsGABOjs7UVVVhdbWVumUKFpisDAQR+/PnTsnLtVF\nixZJ+Am3c7M1pipzx44dolNgKAwfplgshldeeQX79u0DEF/dd+zYMQn94eLaQCCA8vJyJCUlyUi2\nZcsWGXco7mIuZGlpqXQMU1NTcDgcyM/Pl9GFn1d3dzcCgYDsex0cHJSHj+FDKpUKv//97/H2228L\nZsWHmypVl8sl+ZQM1enu7kZ2drakdJHWVCaaM3xmcHBQOkgC0n6/HxkZGTh48KCMlzpdfO8oD5iL\nveZEgWhtbRW7M6t6RkYG2traUFBQIK1UZmYmxsfH5eYmSMgb/9SpU3JK/u1vf0NfXx8qKirgcrlg\nMBjQ1dWFNWvWJDAX/BIp8KFwpqysDA6HQ6y6paWl8Pl8OHfuHMLhsOQI9vb24vTp0/D7/bJop7Cw\nEDabDT09PZLrUFBQgJqaGtFenDx5EsePHwdw4UQn0BWLxSSyjW0mN0QRwGT6FqPvGOzL/7548WKc\nO3cOKpVKxiqqKKl+5OfMOV6v18Pr9YoXhiAeW2jusuAKP5fLhTVr1qCrq0vcluwWcnNz4Xa70dbW\nhqysLAlcnZiYQGlpKQYGBhCNRrFp0yZUVVUldHRsk2lF//jjj1FUVISJiQmMjY0Jc8EiPTIygsHB\nQXi9XkxNxdcwUpkJQNSr/HcWIgqQZmZmJJezpKRE5nluRt+2bRvee+89WVjMkZRd1e7du/HOO++I\nvNtkMiEajcLlcokxj+MdAV9KqjkuE+RVGq4IqLPrZJfAbewcZfizOZ7y4FFK7P+715woEJs3b8a1\n116LjRs3QqfTCSpPIU8gEJCgWlq8Cfwp04WPHj0qjMLY2BiqqqoAIOFG6e3tFUxAuTyFRYZfVFJS\nkjAmjY2NkgCk0WhExp2SkoL09HTpgEZHRwXFd7vdGBkZkcQmlUqFAwcOSMcxPDwsnY/P55OMRRqF\nGOo6b948XHHFFTh//rykYalU8QRuinSYzlRZWSkndXZ2NtLS0qR1ZRGiQImF0e12izErPz8fQ0ND\nEuTidDoF5OQDRQER1/Rx/2VtbS3cbreMePQ66HQ6WYVnNBpFwBaNRpGfn49NmzYlKDyBCzszp6am\n8N5778HpdIoac2BgAGVlZTKb9/f3i16BmhLqCniAKEN/ea8UFxeju7sbZrNZNCanTp2Cy+USRon3\nEQCcO3dOHnYg/jCaTCbccccdaGxsRFlZmdj4mVBOgd6pU6eEhQMgyexKXwzveXZyFAmSzmR3QaPX\n6OioyPSpzyFQ/qUvfQmlpaWCm1zMNScKRH9/P37961+joaEBr776qtzIbNnJUjCliP+fSkSetGfO\nnMG8efMkOchut0v7FolEpNWjDgBAQgfCv6OkpEQcpIyTGxgYkBuZFmDG1DOQJBgMYuHChXC73QL6\nRSIRAfZIRdKgxCRpttUMszEYDFi4cCG6u7vR1dUlFmez2YyRkREYDIaEaPSenh7ZxUldCO3LTC9S\nApR8/3ygmKTsdDpFc8EsBCoiKRijxfrcuXNQq9Voa2sTdJ6dSW5uLgKB+BLZ3t5eMSqlp6fL6Jae\nno7bbrsNVqs1IeiEnZ1GE09l/vDDD4XiJAC7atUq7N27V3QaycnJ0pmRcqWSkDQrL+IzsVhMvg9l\nchgTswgSUjj12GOP4amnnkJlZaVQsx9//DEGBweh1WqFRfL7/XA4HDLWNjU1oaCgAElJSbBYLEJj\nU33LMYIjmjIkR0ltUobPPxMOh0VUl5eXB5vNhurqahQXF6O6ujrBb3Ix15woEIFAQJJ/yAu///77\nkotA3MDv96OoqEjkqlwXBwBdXV3ieGMAzNmzZ2UPAeksBrbyUvLrAIRjHxgYgN1ux+HDh7Fu3TpJ\nJqZklq+Tey9TUlJEV0/E/tJLL8XJkyel9YtEIhIOQ36c6c1UTDocDuTl5aGjo0MAtWg0ikAgIF3F\n/PnzkZ+fj5aWFlitVglC5cPHGyMajcJischpplTo8cajSpSfbVNTkzgRCwsL0dnZKScyaTnevCwy\nKpVKWInc3FxYLBa0tbUhFosJK6BWq+FyuVBQUAC32w2bzYavfe1rwiQpLwKVH3zwgdj++ZmrVCop\nDjyhz549K7oFdp006imFWwS2+Z0TB+CCXeIRo6Oj0nWwiLjdbjz66KN48cUXkZ2djeeeew6NjY3o\n7e2FVqsVrCs9PR05OTmSnE5wOBAICL1J3MzhcIh2hSOlRqMR/MFkMkGlUolqtqKiAiaTCevXr4fd\nbhd/DzsIfo7/iKg5XnOiQKhUKtEr/OQnP8Hp06clbp4yWj5g3FsBXGhFVSoVGhsbhfcl4EMqiy1b\nNBpFSUlJQtfAm0IpxjGbzejq6hJQ8/z587LyjXP5l770JblBKioqkJKSgnnz5uHUqVMinOn5e+x+\nT08PysrKoNfrMTo6KspLblOqqKjA6Oio0KtcmZeWliZOQuYyMGqNAbDnzp2T2L1f//rXUiDdbjcK\nCgpEd0AFZzgcljEoEolIMlQgEBDGSFm4lDsvSZ0pmRWi9JzLDQYDenp6kJaWlrCuj98TO5JoNAqj\n0SioPS+ORO3t7Thy5IiAiyxqPIVZ8Kk0ZaFkAVJ2hSxq/I7pcA2FQvL9A5CxwuFwiJyb4xIf9oce\nekjs1hQ+ccwlNU13rfKe4r1Miz49KHxfLArBYBC1tbXo7OyU5PJVq1ahpqYG5eXlwuip1WoZtXn9\nI2jNL15zokDU19fD4XDAarWisbERzc3NciNQ9EK+GQA6Ojpwww03AABOnDgBl8uFd955B8AF0FEp\n/U1LS4PJZJIlLDw9lVRQNBrFmTNnxKZtMBjQ29srm7ACgQBKS0slrObQoUMwm82ys6Kjo0NStRke\n29XVJcAeEHcRLl68GLW1taiqqoLZbBaQjCccXxe7pJUrVyIWi0mXwRNHpVKhuLgYw8PDMBqNeOut\nt7B06VL09fXJqcuFN9PT02J5VyLwbFfT09Ml6n98fByFhYUS1Q5AEo7y8vLkASfgWFVVJQ8mANk2\nRuk7uz9qUyYmJlBSUoLdu3cnMCt83+Pj4/jLX/6Cp59+GhMTE5IqBUAKYkFBgSSel5WVIRAI4MSJ\nE6IHYAo11Zsci6iDoOtSObaQViQQSK8DO9tAIIDKykrZ/g7EMS29Xo+WlhYxdmm1WukG+D2w4HZ2\ndoroidiCRqORGETa2J1OJyKRCBYtWoRf/vKXYvgDkDAa81KCu1+8vtid/b9ec6JAMNmJAR2k93Q6\nHWpqatDQ0CBS0mg0is7OTvmz2dnZ2L9/P3Jzc9HX14f8/Hz09/cLeANAbNgMolFeyiJBteLU1FQC\nIMUFraRFMzMzJV2YoR+bNm3C22+/jWXLlgkot2zZMhk1rFarpCgDF5gLCp/IMAAQIQ7BxMnJSfh8\nPpSVlYlmgSMWOW+ezj09PXKTMiY+EongxIkTWL58uch6NRoNDh06JH82IyNDWIyhoaGEPaher1dM\nSdRq9PX1yYOYnZ0tiUnshAYGBpCbm4u0tDRMT0+LpNlgMKCiogL19fX/5cQjPvDWW28BgITXEGzk\naa5SqeB0OpGTk4O2tjZxYyYnJ8NkMklmBZOngAubs7nCkJ/5zMwMCgsLxWIPAMXFxcjPz5f3Tg8F\naUmVSgWLxSJbzHS6+BoBh8OBpUuXyoY0SvlTU1PhcrlkyTPxCeUaQzpiKVIbHx9HT0+PbEdTjsXK\nDviLHZjyUmJt/91rThSIXbt2obW1Fb///e/xpz/9SYA/k8mEiYkJoYS4Hamrq0uqdVpaGv7whz8g\nEomIyiwrKwvDw8MSScYbsbS0VEYZ4MJNEw6HEyzQ6enp8Hq9yMnJQWpqqmgT0tLSsHjxYlE2pqSk\nyAP3+eefo7S0FFNTU7jqqquwdOlS4fa5Uo3mLc7zbHuBCwWDBjReKpVKQCxKdisrK7FmzRrs3btX\nTmGdToempib5/zy12Cb/+Mc/RmZmJubPn4/R0VG8++67+M///E9psefPn4+Ojg6UlZWhq6tL/n6e\nwkofwZo1a/DBBx9gyZIlOHz4sGwDZwhMLBZDRkYGlixZggMHDgiDQXZkw4YN/wVA43eRkpKCwcFB\nkSITD6GsXrkYmIGxpGuHhoYwOTmJQCAguhaORV8EaalcTE1NxcDAgFCfO3bswGWXXSaW+enpaXR3\nd+OXv/ylFB6+Noa59PT0SPw/3anEJRjhNzw8jOTkZNjtdsGgmA5Gap33NoHKoaEh7N+/H3fffXeC\nSpcXxVe8T/iz+Gvs4C7mmjNKypqaGjz55JO4/PLLZU29VqtFa2srjEYj0tLSBHDs7OzE0NAQAODk\nyZNy8hJwI/BFzpxWZAJHwIVRhP87Ozsr/52zfywWg1qtRm5urowgjJPX6/Uil2WK8Y033oinnnoK\nt956q6xtp0OSWgLy98yW4L+TBeDFbAkgnjxUU1MDj8cjVuMNGzZgzZo1AC4oKSlFBy6oInU6HUwm\nEzweDx599FHceeed2Lp1K/bu3SsbqXgyz87OikiHCkmtVosFCxZIUKtWq0VjY6PkHfA040OiLBSf\nffaZ5HdQmWkwGFBfXy+UHxAvDsxyoLyaOQ0cqVhMr7nmGvGWkKHid8/ugtJ1shNKPQk7JqVdnONS\namoqvv71r0toDr//iooK7Nq1C7fddptoaJia3tPTI+NFbm6uhO7SN8NlTWq1GqOjo2htbZX3SeyH\nh4zf7xcmiL9OFoffD+9ZdtO8qB+i/HtgYEBMhxdzzYkOgjOhWq3G008/jb6+Prz55ps4cuQINJq4\n6YrR37Tovvfee7jjjjuwe/duAPEHijsi2QbTn0BgcGJiAsXFxfIQMbWIFZezNMccv9+PpKQkaDQa\nsZ5rtVr09PRIkOr111+Pyy+/XLh5Rscr3YwsWmyBgfjJxJuNNzll40SkTSaTAF509NXU1KCgoACp\nqam4/vrrceDAAbjdbpw/f1648vz8fJE1B4NB2O12NDc3w+VySYoVN0LzvSvBXyCOl+Tn58v+z97e\nXlgsFkQiEQmmHR0dTeDnOZIxhIYBwnScAvGHMT09XdYW8GRnSvgnn3wiowxBYbIvQBxzAi607yqV\nSkBSADLbd3Z2isw9Go2KaEipi1ACrUB8v8fk5CSSk5MFVFSpVIjFYjAYDLjxxhuxYsUKvPnmm/jr\nX/8qm8yId+Xl5cnYWFlZiXPnziE9PV1YirGxMSn8VAbz8yLdyQhB3usdHR1wOByorKxMwB2mpqYS\nFu6Ew2F4vV4MDAzgnXfeEe/Q2bNnL+rZnBMFQq/Xy81JpPoHP/gBurq68Pbbb+Pw4cPixSAi/Prr\nr2Pfvn2yFIfx9NTQKxFi8uPKDdT8IpSeeka5ARCJc3l5OVatWoXZ2Vl0dXXhxIkT0ma++OKLyMvL\nSwAAaZji6Ucgi7kRvIh18CIlRwWcRqPBBx98IN6T9vZ2ZGZmYtmyZTAajRgfH4der8fll1+O48eP\nS0iKsuDwQaNAiVLfrKws+Sw1Go0UO6r0gsEgCgoK0NvbC4PBgIaGBulEOjs75b3RF+FwOFBUVCSJ\n4yqVSqzItGNTIcjsy0AgIEWE3cPIyIh4N/Lz89HZ2SnCocrKSil6DCsOBAJYuHAhAKCpqUns7cQJ\nGLjLEZJ+F3Yl/G7ofOR3z/tCacBiB6NSqfD444/j0KFD2LNnjyyAtlqtshYhEomgu7tblJVUt7Jg\nK9kwdpHZ2dmCL3H8IqvywAMP4Ic//CFqa2tlDKO1oL29HQ0NDXA4HMKscC8tIwgv5poziVL8F44X\nKSkpIp0lN3zs2DHs3LlT9O50WfJBUHYMFMjMzs4iLy9PGIyJiQls3rwZ27dvl5a3p6cHDz30ELxe\nL7Ta+CbvjRs3or6+XqhA4gfNzc342c9+hmAwiIMHD8Jms0mXwN9XUFAgJwCLlDJVSWmWAuKnd2tr\nK06dOiX0Hh/AvLw8ES5dffXVgu7n5+eLTbi7uxtPPvkkXC6X7AhV8udmsxmtra1YvHgxurq6pIiW\nlZVhcnJSouNDoRDq6+sxNDQEt9udYLKqq6uTMBPuA6mrq8OxY8ekGDMchiYm8vMTExNS6Orq6vDY\nY49JQeJD2NTUhNtvvx06XXyLFilTjjw+ny9hqzrp356/7wBlcef8XlxcjH379kkR5ihXWFgIlSq+\nGYsPIABRpL766qtYu3YtNBqN6BCAxBmfuI3f78e+ffvw8ssvw2QyoaOjA7OzswJ2KvEhBsYw6Ytj\nDr+v9PR0RKNRoTt1Op0AwRqNRtYGkEWanZ2VAsQDip0J8adgMIiRkZH/+YlSypNfGSY6PT0tX1Iw\nGMTKlStRVFSEq666SgxVvb29yM7OxtjYGCoqKgRB5mmp0VzIReDP/eijj1BUVISqqiq43W68Vcok\nPwAAIABJREFU9tpr0Ol0sFgsWL58OW677TbpMCgiogWXy3I1Gg3uuusu1NTUYOPGjaipqYHFYoHb\n7ZYoOCVazhsxFArB7/djenpaKNrGxkb09fXhuuuuw969e7F8+XKRaI+Pj8PtdqOoqAh2u126ECUt\nunDhQlx55ZV47bXXpPBw2S/HG2IDfOB5eun1ejidTjH4dHd3y7xLHYXdbheGyOPxSAT94OAgrFYr\nxsfHMTw8LLRtW1sbbDabiL5I9zmdTkxMTODrX/86CgsLpWB6PB48++yzMi7QdETxEh9uj8eDFStW\nSMHjQ8cHMDU1VYBppViI8u4vov18gClKAoDnnnsOx44dw8MPPyxaA/4+FgkW75mZGVx33XVYsmQJ\nXnjhBVnRyA7AYDCIVDsSiUgSOnEpmtY4QjFHc2hoSNSn1GowlRyAfC/KhHTKsu12O0ZGRgBA6OGL\nueZEgVCpVCIwISKdkZEhQa3kr9meAfEHhHMxHXijo6PC0StDYdRqNcrLy3HkyBEB215//fX/Yh+/\n8cYbsXHjRrlZxsfH5SHSaOJe+08++USoPSCer8gRaPPmzVi7di2ACycW+fC+vj588MEHaG9vR1JS\nEvr7+wV8I1jY2NgolZ9mHd6MQNwpSWCKEnRSr2VlZQkxaJx9gQsLaV0uF0pKSuRzZJfAm5+JWsrt\nVdnZ2UhNTYXD4RBBD6nU3t5e6VbKysrEecuCTlaGHRYf+G3btmHbtm2or6/HxMQEdu7ciZaWFlRU\nVODMmTPQ6XQoLy9HQ0ODLGFmtxCJRPDJJ5+goqJCqG3KymkKm5mZEWEYO7kv+j1IGzJNy+/3izhs\n37598Hg82LJlCxYvXizFmF4H3n88vbOzs/Hiiy/igw8+wMsvvwwA8l45YjH0xul0SlRgf3+/OFdp\nUefnRdyCDljeb0ppttJGUF9fjxMnTmBkZARqdXzF4T9ixFD/8z//80X/kIu9XnvttX8uKSkRn0Us\nFpMPzmg0IiUlRapiamoqfvvb3wKA8O9GozEhOiwpKQlqtVoeslgshszMTPT29soMyv0GfEAmJyex\na9cumT2TkpIwMTEhLs+srCy88cYbOHjwoCz/9Xq9iEajiEajGB8fR0NDA/bv3w8A2L9/Pz766CO8\n9dZb+Nd//Vf09vbi008/xfDwsGyRMhgM0o5WVFSgoaEhIeyEn0dWVhZUKhXuuusuod+KiooE5NJq\ntTh+/DhaW1sFjAXiJ5her0dSUpK8DwarFhUVYXx8HLFYTDab00FLzCYnJ0fYIAbAaLVaARSnp6dl\n/+fMTHzPpzLkJC0tDcXFxcjNzYXD4RBsZmxsDA0NDeLBaWpqksyJoaEhMctxTR+X1xKnMJlM6O3t\nhcfjwczMDPR6PWKxGKanpyVaMCUlBTfeeKMUVxbFtLQ0xGIxvPDCC4jFYiKOmp2dFQET9QPvv/8+\njEYj5s2bh0gkgunpaUxOTkqoCwsE2Y4lS5bgqquuQkdHh8j3rVarAOzRaFTuxfPnzwsQy5zNoqIi\n0V0w9GV8fBzJycmyOS4Wi6GzsxPJycmymAmIiwe5A4TdeDQaxbZt235wMc/mnKA5X375Zdx11114\n8cUXZTMVq+jAwIBs8WbbuGrVKvh8Pvh8PixZskSMRRpNfOU7EV5KlGdmZmT5L09NBrPodDqMjY3h\nvvvuE2qV7TgftMzMTPz1r3+VTAJSrYyES05Olravt7cXH3zwAV555RXs3bsX7e3tyM7ORkNDg7TU\nZ86ckdBXnky8MZRtIakxWt15WrBrYMAJEI/JYy6FRqNJSIIiA1JXVyfmJvo0+NBTLEVrOi3LBoMh\nwXZMhocnqc1mg8ViEUl1JBLfNEX1n9frlYwPdlQEQ//93/8dDocDgUAAS5YskTxRvne26Ex7Vqpg\nGeLr8XhEYBcMBkVmzcW1XG4LJI4YHBvYVWg0GrhcLhkrSKM///zzePzxx2VcUwLgxMZIYU9NxfeW\nPPfcc0K7siB6PB55rT6fD1NTU/Lwe71eFBQUYHx8HBkZGRI/x70u7EJCoRDy8vLEok89BztEhizx\n8/ritu//zjUnRoyBgQH4fD44HA7odDp8+ctfRmVlJUKhkLxJzoqhUAibN2+Gw+GA3+9Hd3c3gHhC\nTzgcRktLi+gKaBoyGAyYmppCeXk5nE4nZmZmJFG6rKwMGzZswI033ijzZjAYFHHQ1NQU2tvb8dJL\nL8lszFxLUnBarRZWq1Xsw7w4V9rtdtEG5ObmwufzJWABTJKurq5GS0uLaAC46Ts5OVkUhWwxgfhN\nTUajtbVV5OoApNDy5KRlnoVlZGQEq1evJpAl0nAqCnk6ciamzDwjIwNWq1W+K260Sk1NFbOY3W5H\nd3e3AMYsggScqbrkrwWDQVlMQ1Czp6cHS5cuxblz52RlHT8/6jUuueQSuN1u5OTkiEGKBTMpKSlB\nNET6kuMjL4a+8PskMMtQY4/HgyNHjuDw4cNYuXJlgv+H2ZgMEOZWNQB4+OGH8corr2BsbAx2u13C\nZnj4ZGZmIi8vDypVPISYG7k4YqakpEj6GdkZAt1UBGs0GlH/ErTk+PtFncR/95oTLIbZbI5xSenU\n1JSkEy9fvhzp6em49dZbUVtbKycob/yhoSFce+21uOmmm/Duu+/KXE502uPxYMGCBQgEAoImU4Ov\n0+lw8803Y8uWLSI9JpbB+TEpKQljY2O4+eabYTKZMD4+jsrKShw7dkzwgb6+PtTV1YkTlZQWeW92\nBaQgXS4XbDYb+vv7UVZWJh4LdjPDw8NYtmwZzp8/LyG5o6OjKC8vx89//nPMzMygtLQ0waTT0NCA\nRx55BO3t7RgbG0NpaSm8Xq9YuNPT02VEY54Ai1AoFJLsgeTkZCxevBj79++XhC/amInt6HQ6tLa2\nJmy45s+kq5QdCb8Po9Eo8XWcq61WKwoKCmSHKd2qXq9XFJR5eXlISkqSzWFE6gcHB2GxWCSngiAw\nR0xuVX/33XelQLGwkmallJq5pQ6HA5dffjk2bdqEH//4x8jIyEB3d7cUeXZwlZWVuPPOO7Fq1Srx\n6RCHmZycFN9LRkYGnE6njDlkx9asWYPrr78eBoNBgl04+inZLiVrQlpZCZj6/X75TNn1ckP9s88+\nKysffT7fRbEYc2LEUKvVYlBJS0uDz+dDTk4OGhsb8fHHH+NnP/sZdu/ejTNnzgiFlpKSIrQmzV18\naCgWoh6frWFfXx9SU1ORl5eHm266Cd/73vcEkzAajRgaGkrwRAwMDOCRRx7B7Gw8GzEjIwPNzc0i\nCqIt2Ol0yumuNIlR0UcBDK3mvGHcbrfw5qmpqRgcHJTdGEajEZOTk8IqaDQaEfB88QoGg2hvb5c9\nocnJyTAYDGhtbcXw8DAmJyflz/EEoi+APhPggmfFZrMJUEoqkoWVJySBO4K3NTU1SE1NRVpampyy\nwWBQkHuOdfxv4XBYjG8Ep61Wq+wDValUwudT1Wm320W6TD1Menq6FBTiJQRaGxoaZISgHoSvi7oa\ntvqMAqivr8ePfvQjmM1mGR1mZmYkAt/pdOK5557Ds88+i+HhYRE8TU1Nib2doyS3pPEBPn36NNat\nWydUPosDx2NqN/iaqZ9RsikA5Pdx1wi/18zMTFitVlx//fXi6r3Ya04UCGIG2dnZMoMy0CM1NRWN\njY3Ys2cP7r33Xtx+++3Ytm0btm7digceeADZ2dlwOp246qqrJOyW/HlVVRVGRkawYMEC+QCDwSDW\nr1+PLVu2iPJRqazjzsVAIIBnnnkGbrcbHo8HZ8+ehclkwujoKEwmkxSJ3NxcmatJ0/LfNRoN2tra\nYDKZ0NfXJyeEyWSSEzo9PV3mac7oyogxtrqciXkjKS8Cg9XV1dL+8+RTq9UylxcVFSEcDmN0dBTt\n7e0oKSmRU+zSSy/F448/jsLCwoQEcc7z3PhEzwLfH3EB7s8wGo0oLCyERqPB/PnzpdBotVpceuml\n0okASIjR58gRjUbldSuNdFSTUmsCQAJfWUAZuFtbW4vR0VHs2rVL9BYshpFIBEePHk1geBgxyNyK\noqIi3HHHHdBqtaiurgYAKVxkTD755BM88MADQmPz79fr9WIszM/Pl86XWo5gMIiBgQE0NzejubkZ\nQ0NDcLlcApaTMeM9qSwMHCWU6l/+eiAQENxs79698rou9poTBQKA8N4Eu/jvkUhEQB2fzydMwcmT\nJxM45/Pnz0uICOkffmjkmSmz3rJli3QO/EKGhobk7xkaGsKzzz6LpqYmXHrppZJORVuv1+uF3+/H\nmjVrxNnJ10Hqkl4IlUolRq1IJG5FpsqQDwgBTqo9lRQZ3X8EH79I1wEQxeCRI0fE0ERshJ0D22+N\nRiPLeMbGxlBQUIDq6mo8+OCDWLt2Lb7//e+joqJCJMGciRmtD1xIAaffIS0tDR0dHUhNTZXYPcao\nARCF35EjRySRmic2PQy5ubn49NNPhSEiBc2kJZ6aVG8yx4L3Ch9QfpfUrGzbtg1Hjx7F7Gw8Q7Kx\nsRE7duxAcnKypEGHw2G43W5cc8018Hq9ki51zz33wOl0wmq1yg4Tjk1ULL7xxhuy+YqUOLvESCSC\nr33tawDiO2EZ+5+SkgKNRoPJyUkBMBkORFqenzNwYfEyOxF2vDyosrKyMDMzg48//hgPPvggGhsb\nUV9f/w/JpJwTBYJI+ejoqOxN1Ol0sjo+KytLcgftdrsAPGxxgTjNwweOIJ7BYJD1c4yJY+AHEF+7\nx/xAfsEzMzN44YUX0N3dDYvFgs8++wwajQZJSUnIy8tDfn6+dBrj4+NwuVyyVRmAtMdsZ9lNUHas\nPCnC4bCcdkr+m3Nmamoq8vPzYTAYYLPZRDSjvIgjMGaOMfGMJwMgFmKn04lAICC5kf39/cjOzsYN\nN9wAm80mMuZrrrlGPnsWPLIKnOlJDTOlmkIdpXKR9CcL9sTEhGzyGh8fh1arhd1uF/EZEAebSTfO\nmzdPEsWI63ADGL9n4h5AfA2ASqWSsN6pqSm0tbVh+/btuOmmm/CNb3wD27dvx9mzZ+HxeIQ1YXZm\nSUmJ3BszMzNYsWIFamtrYTQaJSqOSdxAvAPas2cP7rvvPkka52fBA2D58uW4+uqr5aE+cuQIbDab\ndBpkIGgs4zjF16D8R9k9KEVbf/vb3/Dggw/i7bffljWKZ86cEdzpYq45USCU4ST8AlwulwA+tAoD\nFz60pUuXYmpqSk7e2dlZJCcnIykpCVarFYWFhbIZubOzE0ajEUVFRfjGN74B4AL4Q1ByYmICsVgM\nr7/+Oo4ePSoP7ezsLKqqqmRJsM/nE9PS7OyszKbEN9jp0KPBDAiCoKTfuru7UVdXhxMnTsBoNMJu\nt0Oj0Yh0m1Jej8eDrq4uhMPh/98oc9K2lOSaTCYRO1GhSLs8QVGOM1deeaU4DJOTk1FVVYXS0lKo\n1WpMTk4KdsDwG3ZuPp8PoVBIfm9ycjKWLl2KYDAIvV4Ph8Mhfovq6mrpKDjW8SGy2WyC5FPnwYef\nnxsAwXpGR0ehVqvlJKdGgIWZxYw6ErIeQ0NDaGxslGJOcLWwsBATExMSVsviGIlEcPfdd4tupKCg\nAN3d3fD5fEKtezwetLa2YuvWrXjxxRfhdDoTsKJIJILNmzdLy0/6OzMzE5mZmdLl8fVTGEjszO/3\nCyDp9/ulw2EhGR4exve+9z2cP39e8il4wPzf8Kr/12tOsBglJSWxcDiM8vJyeL1enD9/XoAottSk\nxlQqFWprayWk44knnhAQLBKJoLGxEd/+9relRTcajdBoNLjhhhtw9913S+w7/QEAJOCzvLxc2m+y\nDxaLBZdeeqmoMDkCEKBMT08Xd2Vzc7OYmhgASwMP28W6ujq0traKpZw3WW5uLvr7+3HJJZego6ND\nUpbZRZSXl2PXrl2YmppCTU2NfPnBYBA7d+7ECy+8gKVLl8LpdGJsbAzLly/H8ePH5eEiZ085LjMJ\nPv/8c8EIOCvPzs5i0aJFmJiYSAh9YVDqkSNHsG7dOhw5ckSYn7q6OgwNDUn6lHJfJ8c+xt3TAcoR\njAAmPSGpqalCUVMgVV9fLwuEyVhQg8BxkjhDZWWlWJ3JaE1NTQkADsSXEHH/B++Fp556Crfddpuw\nTUNDQ+K6/ad/+idhSDQajXQ2zKLk98oAodtuuw0rV65EcXEx/vznP+ONN96Qz4KekoqKCng8HtTW\n1qK+vh5Wq1WwHypSmTiuxCJYbBobG/Hmm2+itbVVCoNOF9+fEgqFsGzZMrz//vv/870YShML2/H5\n8+dLGjMDS7OyspCfn4+tW7dKGAuBPV5sw2pqatDa2gqPx4Pq6moMDQ0hGo1KNeaNydZv//79sgtR\npVIJ3djV1YWTJ0+KwrGurg7Hjx+Xm8NsNqOtrQ35+fmSwk3FnkajkcW7BNMYWU7QLysrC0uWLMHB\ngwdloStj6GgvN5vNMnoQkwAuRPQz/NTpdApecP78eVHmUR1oMBjEG6FWq1FTUyMAZn5+PkKhkMy4\nJpNJOhkA4mlpbGxEZmYmTp8+LfszOPfPzs5KYfR4PCgqKhKzFdkVsgJkliigYviLx+OBxWKB1+tF\nSUkJQqEQ+vr6MDIygkgkIjgA1Y7stADImjv6GACgsLAwYXUhEO9Cm5ubJXeBgN+7776LO+64I2GU\n4OvKysrCxMSECNB4ry1btgz9/f2iAK2vr8exY8fw1ltvYd++fYJPcU0DOziVKr66gPfDJ598IgK0\njo4O6baqq6vR3t4uupS6ujp57bx/6fPIysqS/aKk8C/2mhMjBuO8GDuXm5uLhoYGCXlVzojf//73\nsXjxYkFsGazBlmz//v3Q6XQyEzL3gCAPmQQgXonNZjP+9Kc/iUpSmWnQ2dkpKD65aSoby8vLodFo\n5MtkYaI8myYkfoE0W7W3t8vvBeJZmFygwz8zOzuLtrY2FBUVwWaziWCKnQvn9fHxcYTDYbS3t0v8\nGxO19Ho9RkZGErYs6XQ6SeemPoGfBylKgoF8IHljm83mBPMUMx8ACJhMVogz+OTkpLwnu92eAAQT\nBM3KypLtUhT5ZGdnY2RkRHIn2aWQGuTrpKgtNzdXAEqbzSa7LXiRLeLpTaqcHgyC2Lz/eGVlZQk2\ndddddwn4SmZpamoKn3/+ufgxmH1JJoujM5kuZlXS/0Nql8K+rq4u8RMBECPf2NiYBPI4HA54PB4M\nDw/D5/NJASZlq9Vqcfvtt+O73/2udI8Xc82JAsGLX0Z5eTlmZmaQlJQkJ05paSm2b98uHywX2/Bh\nU6vVaG5uxoEDBzB//nxB3fnF1dXViXGHD2dmZib279+P3bt34/Tp0wkcPzdqGQwG3H777di6dSue\ne+453HDDDVi9erW4P7OysiSrgHPx8PCwUIqkq4C4upGoM2dItsdUvzHQJRwOSzyd2WyWk5FFiKCV\nx+NBX1+fPPQ6nQ6xWCyhq+DF/aCkCvnz+J55avFk1mq1olAdHx8XXwWTukhXRiIX0saXLl0qSP/E\nxAQuu+wyAJCN2pyPuduDSkTl6U4glElNlBATqOSfd7vd8nBRycnUaLIoVNbS9MTxkZ+XkgIlhkNs\nIycnB1arVVLM165dK1odi8Ui70WtVguIefz4cczOzmLBggVITU2FzWYTQRMA0b/wsx8dHRUQl3EE\nycnJQlsrHZ+zs7NwOBzIzMwUDC0jIyNByFVaWootW7YILXux15woEIzlstvtyMjIQEtLC3JzcxGL\nxWA0GmG1WvHyyy/DYrEgFArJzFpcXCwP39TUFPbs2YOsrCxotVrJUeAXvnz5cgl4NZlMMJvNeOWV\nV7Br1y7o9Xr5kCsrKwFAHuDU1FSsX78elZWVWL16NbZu3Ypf/epX+NWvfoUVK1bAaDSivb1d2tto\nNJqgrwiHwxgbG8PQ0BBisZhEtjPpSdn9sIUmAzE6Ooqmpib4/X5RmXKs4InKjU8cdwBg0aJFQvlS\n/UhMgGCv1WoVpkTZAisvZVvO16c0bZ08eTIhRo9AJ29cCosYPpORkYG0tDRhlMhyUBZOGpCtc2tr\nKzIyMlBVVSW5CNxpmpWVJa9dmaHAB5FRd6mpqcJqcbkQgVPiQjxpPR4P3njjDQCQ90hfilqtxt13\n3y3z/fj4uGAoeXl58veyeLe1taGlpUU+M75n6kCoIaFlm3kQfG2BQEC6OAq4KioqYDAYxHrPxC4+\nDxqNBg8++CAAiJjwYq85USBCoRAyMzMlDZozpcPhEKFJWVmZOO74wShdi6+88orEk1MPoFarEYvF\nsHjxYmRnZ6O3txfhcHyB7rZt2/Dmm2/CZDKJ+k+5NJWt4cTEBJqbm8UvwPZep9Phsccew8MPPwyz\n2Swaf6vVioqKCgAQ5oAPmZIFoFWZdB3pz6KiIpSWliakG1FG63K5YDabpfMA4nZzt9stfgiLxYI/\n/vGPKC4uxrp166BSqSTEld0KENcm8KTkTA9cWOxrMBgEI5iampJ0ZaYyER+x2WziKWEHQo3BFVdc\ngaKiIik0er1eTlzSs729vULTckSigpMRgGNjY1L4qXJV5iwo/S9UrxqNRllgxJ2oLJS0tVONSWl0\nJBLBf/zHf8j9AyBBX2E0GnHvvfeKvoDiOxYLiqjY2Sq9JozdZ7Q9U814aHEUdbvd0v2wgAWDQZw8\neRJdXV2iiaB5jaydxWLB9u3bsXr16oSog4u95gSLUVZWFqONmBjA1NQUFixYgC1btiRkLNBIQ/de\nd3c3vvnNbwKIF5rly5fj2LFjgvLPzs5i3rx5+OY3vwmDwYBDhw7h3LlzSE5OhsvlQlVVlRi+Zmdn\nYbfbMTY2hoGBAVE7RiIRPPXUU6irq0uYvSsrK+V1NDQ04JlnnkFRURFOnz4t7WpaWprsduAppzyt\n+QCPjo6ivr4e586dk8KSlZWFvLw8tLe3IzU1FXq9Ho8//jjWrFmD3/72t3jttddQUFCAlpYWVFdX\no7W1VYqRRhNPIWJWJ0cT5XKZ+vp6/PznP5eCy05GrVajtrYWarVaVgRqtVoBN1lEqfSj2Wzp0qVY\nuXIlHn30UdFddHd3w+PxYPfu3dIRcCbv6uqScYunJoVCZJBYOPkwcd7nQ0U/Bt/f7OwsbDab6C8W\nLFiAUCiEQCCAnp4ebNy4Efv37xeA2+PxwGazYXBwUHaZpqenY9GiRXj11VdhNpuh1WrR1dWFgYEB\nTE9PY2xsDC+88IK4ZNklKIsKCxHZluHhYdFc9PT0YNWqVTh+/LhgKXSW8nNmoV60aBEGBwdlq7hy\nSREQL4gPPPAAbrnlFgAQASAgY+P/fC+Gz+eD1+uV04NKvY0bN+LKK6+UjoLbsfjh6HQ6PP3006Jc\n1Gq1+Pzzz5GWlia5hNzh8Prrr+PVV1+Fw+FALBZDR0eHuCQZEccvdGRkRCjNQCCAUCiEf/mXf5Eo\nPN4UShq1rq4OL730kti2ac4yGo1wOBwIBoPIyckR6TZxBrbu3IaVmZkpi1kAyN5L+vvfeust3Hvv\nvXjppZcQiUTgcDhk83d+fr5kEfp8PjFUsQAAkNm5oKAA0WhUshZ4WnGdYSgUQkZGhvD9pHKpoKRE\nW6/XyzjW0tKCyy67TDoG/npRURHuu+8+ubnb2trQ09MDAPJ9c6wguwJAioPNZpORi3gBFZAcT+kx\nIRNE+fsjjzyCt956C1u3boXFYsGRI0eEGuV9xDHPbDZDpYpvQz9z5gyOHTsmBZeZHEBct7Np0yYp\nUgSp58+fD4vFIvmUyoef+RosGGfOnBG2hN1Qbm6ueF84GnZ1dYmTc8WKFfLwU99y55134lvf+haA\nC1vMyUT9r8EglGg6577rrrtO3jjnMtKUjDenB6C0tBQA5ERgaAhBMWrgmfpbUFAArVaLwsJCDA8P\nw+12IxAIwO12o6amBjU1NdIWciTo7+/HW2+9JQYwAGLbtlgsyMzMRFVVFX7605/KuML9liwmNpst\nIauR+g4+dB6PB4WFhVIohoeH4ff7MX/+fGlXW1pa0NLSIvM3DV8sZspTMBgMioaBfgGezikpKf/F\nSs/PkP9LHwUQ327NB6KtrU3MZ6FQCNFoVDotXtSvsN2tqanBV7/6VXk4bTabvMZgMCjdDu31AGT8\nikajsrGcdCwLQ3l5ORYuXCgjEXMeCXJyJFu5ciXuv/9+hEIhGfHKysqkcwEgDAwL0EsvvSQjZUZG\nBkpKSpCTk4O0tDTU1tbilltugcFgQE5ODiKRCLxeL3w+HyYmJkRMVVxcnNA5KscHdo9UCUejUUmC\nIn3MrmnNmjVobm6WlYsajQZLlizB9ddfL6IoiswAyGhzsdecKBBMD8rLy5OgkSeeeEJuOt7cPG15\nIz7xxBNwOp3w+/0iz+YODafTKQIdzmMsMu3t7dBqtVixYoXw2lqtFtnZ2WhubsbJkycBxHl1rv9T\nqeJLY/fs2SOnQnd3t2AdbLULCgqwYMECBINBVFZWorOzU/ZqnD9/XgrbsmXL5IErLy+Hz+cTMJKF\nIRgMoqmpKcGzQYVfTk4OSktLE8xbycnJkg/BLAOKckjV8mRlTP/09HQCkxGJxHMEysrK5NeU3gpq\nPOhIzM7ORlpaGioqKmSNHV8Lt3Wx89q4cSP8fr/Yu5lNwROPxZIPOaXvXq8XKSkpIgICIOzHuXPn\nMDExAZPJJJvDgHjnZTabUVBQIPTounXrsHHjRmRnZ0Ov1yfoDXJzcwVDUKvjS4k7Ojqwfft2CZwh\nFsF7cd26ddBoNOjs7ITFYpFcUsriCdpGIhHJ1eB4ScCYqtBIJAKn0ynFcPHixdJhpqam4vTp0zCZ\nTAmLhpmdSrCZaswvfq8Xc82JAkHlHRmAJ554ApWVlbLwhhJm5Un4/e9/H2fOnJHWnFkKfCDC4bDk\nMtpsNkmbJl+s0+nw5z//WR6+8vJyAHHuub6+PqHtZfhqKBTC0aNHsX//fvmi+/r6ROXJeXXXrl1Y\nuXKlgIIMzaUMempqCmfOnJGgEwq6CgoKkJaWJi7AgoICwVx4GvOkBYDPPvtMYvyZ3Uhtk19xAAAg\nAElEQVRsgK89OTlZqFAKmzweDz777DORIXOs4KmvUqnQ19eHmZkZoQTz8/NFAcnuh2lVgUAAXq8X\nkUgETU1NUmQAyNo4rVaLsrIyXHHFFQDiCViUuWs0mgTgTzmi8L/zAWA0G2lYalvC4TDOnz8vPh0+\nyEzJAuIt+P333y/aEB5AdI8qhWFpaWlYtGgRDh06hB/+8IfyupT+FDIkLMonT57E0NAQzp49K5Sz\n1+uVlYnskDgesdulWIwAZl9fn4Qvs3j4fD4MDAzISKtWq/HlL3854eDge6Ixje/lYq45USCY0RCN\nRrF+/XqsWrVKKEYiz7yBTSYT3n33Xbz77rvCrfMfOg+j0agIhwBIQCyrdnZ2NpYtWyZRXxTJ0BVH\ngDMjIwMWi0WKRTgchtVqxZ49eyRrIDU1FSMjIzLrm81mVFZWYseOHaitrZWf6Xa7hVqk9ZlSXlJ1\nWq1W3IvcMF1cXCyybo4ORqMRNptNHmzejNQ28OHIzc2Fy+USIRG3iQeDQXmIVCpVwn5H0nv0WgAQ\njIedjclkktZ4dnYWa9askSwE2topQCLrBMRv9vXr18upSIaA2gUlLqAMBk5OTobP5xM8BrhgigsG\ng/I9Dw8PIzU1VdbfVVZWwmg0ykjJ4vutb30Lbrc7Qd1IHEqr1aK4uFjSx30+Hw4dOiQCJp1OJ2E3\nBoMB1dXVMkL4/X6htinMo86B4irKstlF0MFLmr20tFRYCXZtxcXFklpGhS4ZHaXLlfjczMwMWltb\n8Z3vfOein805wWLU1tbGOLM3NjZifHxcjDcsDj6fD+3t7fj2t78tVCgVeQCkGgeDQaxatUqcbDQg\nRSIRbNiwAbfeeisMBgOeffZZ7N+/X6g8yp+5CcpoNMLv9ye09XwdRPF37dolbS27HCLoSnHMtm3b\n0NTUhPz8fElv7vz/qPvSuDjLc/0LZoFh2GZhgGFYBggQCCEbzT9pNEZba4y2RuvRpoutRqtHjVbr\nbtRqNdaqtXU/qU3dcqoeNdr6U1OTmJgmchLJQiCEsMPADAPD7Pvy/0CvO0PPt5PzIc4XfyowM+/7\nPs9z39d9Lb29s96LM3W6T6tUKsyfPx/Hjx+Xk46MRlKqGWnHh23OnDk4cuQIVq9ejS+//BJ1dXXo\n6uoShJ0VQHFxMVKpFCYmJvDpp5/K2I++E/F4HMuXL5eeuaysbBbDkBUTACEODQwMwGAwoLq6Go8/\n/jjq6uqk3GViNiuDCy64QFig6ZgBNwxS4dl2mM1m4XnQbYxAqkajQX9/v9x7siWzs7Mltk6v10Oj\n0Yhbd15eHs466yxpk6xWq/htsPqcN28e7Ha7eJXU19fDbDZj/fr1WLJkCQBIJskdd9wBp9OJ8vJy\n2Gw2IX5xfM9Kkj4jHEkHg0FYLBbBlOhCVl5eDr1eL9b2bD+5mXLEX1ZWJmHVc+fOlWdlenpacI6h\noaGv/xQDgOy67PdYPXCkMzk5iVtvvVUYeiqVCq2trYIr0CiG8/NwOIzCwkJkZ2fDarVi06ZN2LBh\ngzx4w8PDgs4vWLAAAMSlh4uVLwJtFotFQEkakuzfv18YmsBMQjhLUeIaDz74IKqqqtDR0YF4PC42\nb/RJAGb4E4ODg8jOzsb09DQWL16M48ePzzoRS0tLhRlJWXIoFEJDQwOysrKkpz98+DD0er04SVVW\nVopxDB2rCJymOxqx3KVBcDr7j2M2TmDoKF5YWIj8/HwYDAYUFRVhaGgIXV1ds7waAcj7aLVaCeEh\nRkDgl+U1uRJcVKFQCNXV1Xj77bfxu9/9Di+++CKuvfZaIUypVDMKz+rqagE90zfO9GsDzGxqxEjo\nCJauOQEg7NbGxkYoFArxTL3mmmvws5/9DM888ww+/vhjfPjhh5iampJrw4zVoqIiqeaA2XT0+fPn\ny4bo8/lQVVU1i+HLsTc3vfTpREVFhVSuNpsNJ06cQCwWE5Up21FyJU73dUZsEJQ7V1VVyUVi+Uv6\n9RNPPAGr1YpgcCY1iCah1BDwgaLjNUv3wsJC3HHHHVi0aNEsVDd9dEoZ8fLlyyUNnDkFExMTAqjR\nlJaGrXa7HVu3bsXAwAAsFguAUy5JlC9nZs6kc2/YsEEYnixXeTOzsrJEb5FMJmEymXDgwAHxeIhG\noxgZGRHj2tWrV0u+BU8dhUIhIBgwI5fu7+9HZmYmTpw4IaNf9vvEcni906MPWUonEgnxfuTvsf3g\n4nM4HLKoKVhqa2ubdQ+BU710OBzGeeedh6mpKVlABJc5TSAekZubC7PZjKmpKXz7299GRkaGsGtX\nrlyJF154AWazWUyGyOKkIlKj0cwyruWGkJmZibPOOmsWGQ2AtKHU6NCtPL3lDAaD6OzsxJYtW/Dy\nyy9j//79Ur1GIhGpXmgyxPKfoUXU+PC9m5qaZDFTVczPxGeUB97ExIRoNzhqvuyyy3D11VfLRkLg\neHh4+P8kvPeM2CDcbjfcbvesUpOnD3f2jz76SEpJ7ow2m00EMgBQXV2NkydPirdkRUUFnnjiif+x\neDkWpNhlcHAQCoUChw8fFqozX1xIJpNJnKHZL2ZmZmLfvn24//77sX37dmErsg8nYAQACxcuFM0A\ne27SiAsKCjA1NQWLxYLu7m6xOSM2AczM4fkZDh48CLVajenpaRQVFcHtdqOqqgperxf5+fmiUiSd\nmJoNbsDcaLmR8TOm6yGMRiNMJpP4StBgNx19V6lmVJ9cHNxwaJ4KnPJVJCAaDofR3NwsJyr9I+mP\nwPtJIJQV0+TkpLg1sSLIycnBPffcI1wEr9eLiooKWK1WIZYBpzYAAPLsXHrppRLQRG4IK5Y1a9ag\nq6sLExMT2Lt3r7SYTDvzeDxCgONCT+fwsEJ1uVyzAnupX0nHH8LhsJgRFRQUSOi0Xq+H3++XTYuj\n/r6+PtHTxGIxvPXWW/jLX/4irSMnLDU1NbPSxf63rzNig+CYkOIfAi986D799FNotVp0d3ejrq4O\nw8PDyM/PF1m22WwW7YPP50N1dTXOOussrF+/XsZlRJGnp6cxMjIi7DcSrOhyvGLFCpko+P1+sTdj\nfBx9D61WK5YvXy4o+K9//WscPHhQTGFGR0fF34AbRW5urtw0ysr7+voErV+0aJGE4QIQSzF6ILDv\nrK6uhsPhQFlZGQKBgJxSZDSqVDNuTlR/FhUVIScnB8eOHUMwGEQ0GpUKJz8/X1oLgpsA5EEbGxub\ntVFTlJZIJMSFuqKiQjI7rVYrDh8+jO7ubpn1U9lJT0WTyYSmpiYhYnFTIwDJ9yH1PDMzU7Aak8kk\nPhv8zEajUTYIn8+HgwcPwuPxYHh4WHQefPHwWbBgAaxWK2KxmGATVVVVQrajjwQ3TcrQAcg9JxaU\nnZ0Ni8UCk8k0CztLt6fz+/2wWCzSVnH6NjExISYz/NuJRGJWG0j9jMlkErCblZdGo5FxNqvEhoYG\nnH322bjssstOe22eERvE2rVrZ6no0mnIHR0duP/+++Xh6ezsxODgoOyuk5OT4hvBUZtGo8E111wj\nYyZWJwQ/7Xa7tBAcvyWTSQFCVSqVAHN1dXUSWd/c3Izh4WHo9XoMDAzg2LFjUKlUGBsbg91ux69+\n9Sv09PRIsA2RcAKq69at+x+gIxmCCxcuxM6dO2e5TLMPLSsrk5JXo9Ggo6NDTl+qX7nhpMvMWT0E\nAgGUl5cDgEx5VKqZUBu73S6fFzgVRQ9AqgFyCdjKTU5OwmazySiU1cjQ0BB8Ph/Gx8dx6NAhub/8\nPpzrJ5NJnHfeeTJ9oqxaqVQKFwKYqT7Gxsag0+nQ09Mj5X9OTo4kSKlUKvz7v/87YrEZk1i2nCqV\nCl988YUs0vSRHzfEiy++GD6fT0bjZIcCpwRcZLEmEgn09/fLhpFMJmGz2aSaoNAqGAwiEAiIy7rR\naJxFx87IyMD4+Dj0er2wJomrxWIxMTFmW0PcAQCKi4vlOSYOZ7VacdVVV+GBBx7AE088gVdffRXP\nPfccNm7ciNtvv/201+YZMcWYnJxMhcNh3Hbbbdi8eTOAmTn5zTffDGDGnm1iYkJ2y8suuwwHDhwA\ncGrH1ev1mJycRFNTE+6//35UVlYK8svQksLCQiGRrF27Vk719FaFWgWVSiWkH54OAwMDAhoCwLFj\nx0RuS2FXTU0NNm3ahIaGBni9XqRSKRkvajQanHvuuaI9IM02Go2KZV1JSQksFossvkcffRRVVVWi\nTuWLG0wyOZPr6Ha7MTU1hd27d2NwcBCJRAKdnZ2yCAHIWJLTBD6Q999/P7773e8iPz9fQnkvu+wy\noVlzXKpQKFBVVYXu7m7RSXDDIOo/OTkJk8kEi8WCTz/9FAUFBVLNDA8Piw+oQqHA8uXLYTabEY1G\nBZchhtDa2orPPvtMph+ZmZno7OyUk5ObIcfPn3/+OR555BE4HA5pBauqqvDAAw/gggsuELEYNxXi\nTjfeeCO++OILaW+58Ng60KsikUigqakJY2NjUmWx9OdnZxtRU1MDt9st1zpdN0KyGUejLpdLxtVs\nlaxWK/bv3y+UfTpVceKWSCQwf/583HXXXWKxR9o/eSHUyZSWln79pxiU4D7wwAMYHh7Gnj17cMst\nt2BychKHDx+WEpD+hZRAc7qQnZ0t0uErrrgCdXV1QowiT4EPQG5uLvbv3y9gEBFz9qP0dGB8mcfj\nwdjYGCYmJuS05RiJ3pS1tbVSDno8Htxzzz3C1iQwRxqvWq1Gbm4uVq5ciampKSnLAYiUme/9u9/9\nDosXLxYbeeCU0zHbD51Oh7KyMtTV1WHZsmW4++678fzzz+PJJ5/E5s2bsWbNGil9ObMnlTgSiaCk\npARbtmzBO++8Iz4RrAoAzAJOMzMzZfJCII9kpFQqJeQh6k/a29vl9OTvcCqVn58Ps9kMp9Mp4Tzc\nAHNzc6WS4wZYWloKm80m1eW/Oj4vW7ZMiFMsyf1+P959911pV9PbHOIzl156qfBmqKTkM8FRJGXj\nAITPwCqLPhIGgwGTk5Mi++dImpvB5OSkgNSlpaVCxWflTHUpNS0cEbONYUvl9/sFFGWbyNaQkyJS\nzPnMnM7rjAjvHRoaeogl8913342XXnoJQ0ND4lNYVVWFoqIi2O12lJSUIJmcsVofGxtDIpFAbW0t\nxsbGsGHDBlx55ZUC3qlUKrFjB2Yo3SaTCRs3boTD4RCrM0q1eWr7fD60tLTMMkc9//zz8dVXXwmB\nKCMjA1VVVQJCRaNRWK1WdHV1IR6P49ChQ6iurobVapXwVq/Xi7/85S9Qq9XCjCssLJyVF+r3+6FW\nq7Fp0yYsXrxYhDsUVVFYlZeXJ+QkmopotVrRRjDcdd68edi2bZswMJn/SFJXZmYmnE4n9u3bh/7+\nfsmI/OCDDwS1d7lcYorLh5PUeLL/OJrjBIK9/qpVq2bpV1hma7VavPTSSyJXttvtsulmZGTA4XCg\nubkZIyMjyMjIQEZGBvLy8iRbQ6FQSGXG7+v3+9HX1yfJ3hyPt7a2ori4GNFoVEa7arUa8XgcBQUF\n2LZtG4LBmRBeUsPD4bBUO6w8+CJAy2cjHA6jsrJSpguDg4OIRqMSDsxNmSrU8fFxaUGpzaFlvsVi\nkemO2+2W1jCVSiEzM1M4FFNTU6iursbChQsFCC4oKEBeXh4yMjJEGa3T6b7+4b3B4IyrtNPpFOYZ\njWPC4TBsNpuwy+i0RJS2tLQUbrcb69atw1VXXQWv1ys3kyVpuobe7XYLYEk3ZlYTpBarVCoZEXLW\n/vHHH0vpydOjs7NT+mev14vh4WH5TuFwGL/85S/x17/+Vf7+W2+9JeX/8PDwLOk6W5VEIoFzzjlH\nnJj4uzwl1Gq1jOp4YjPbgz/H70KQdfXq1SgrKxMcgbJvn8+HkydPSibDli1b8NOf/hQ///nPxZyX\nSdY2m02MU1gZke0HQOjKzc3NMqXZuXOnLKZQKIRoNCqVC6uKUCgkzmFTU1MytVAoFLJ46urqEIvF\n8Pbbb8v1T8epWHavWbNGKghS3cPhMN58881Z3gp0c6KmZdmyZeLqxGeA157TAXJlCMhSFsCWZ3h4\nWIBRqmhZiaXngLA1ogSc6lESuhhhSMzN5/MhJ2cmH4RuVJFIBDabDa+//jq2bNki7RPbRvqq/KsB\n0P/mdUZUEH6//yG2CPX19XjrrbckSp7AmMPhECNbi8UiRp5ZWVn41re+hTvvvFMAomQyKdwAnmg0\nBPnFL34hCke2Fna7HcXFxRJZx9OuqKhIcImMjAwYDAa43W4JXWXbwNwNpVKJOXPmwO/3Y2pqStoZ\n0pNffPFFOJ1OOfnY27P10Wg0KCwsxIMPPiibUH5+PjQaDTQajZCvON/nScjSWqlUSmugVCqhVCqR\nSqVQW1srm5NSqZQEb4fDAY/HA7PZLGG7JJox4SoUCqGyshJut1tOdypBeUJyMZOgRCwmEolg/vz5\nsyTNAOSz7ty5EwMDA8jMzBSwkLL/rKwsNDY2SiAvuQBWqxW1tbWzlKiJREKqg8HBQezevRtjY2NC\na3c4HDjnnHOg0+lk4VBJmpmZicLCQvGIaGlpwZEjRwDMJk1NTk6K1yi5OPRtoL0+gc3GxkYZCaen\nqOXl5cHtdiMajaKurk58Nkhbz87OxsTEBAoLCwW7oisXAedkMinGOxMTE+js7MQHH3wgEY2MIggE\nAvB4PKioqDitCuKM2CBsNttDLJXLysqwZ88eMV+lWxIFKLy5J0+ehFqtxne+8x3cdtttsnvq9Xro\ndDqJRU+lUtBoNMjLy8OvfvUrdHV1wel0QqlUyg1WqWaCcQhQKZVKuFwu5ObmQqlUSotjsVgQjUal\n9eHoampqShybU6kUlEqlIOpOpxOpVApvvvkmAoGAvA/zF9jT5+fnIxAICNOwpqZGNjeOAEkEYgnJ\nE48ncjweFxCMI0YAkuR06NAhaDQaIZhRUm4ymRCNRqHRaOB0OlFUVAS9Xi/J3WzZ2Ec7nU5pM4qK\nimQzJnuTUnsyFs8991wRKiUSCWlX3n33XTidTsmlYO/OicTBgwcFZOamMj4+ju9973sIhUL/w1It\nGo3CZDLh73//O5RKpdxnVp3f+ta3pCrSarVIpVIIh8MoKSnBzp07YbPZZLyq1WrFaYou2QQrFQoF\nysrKYLPZxLE7HVPgpIPPKw8cpVKJZDIJo9GIrq4uYaDywLJarVJBZWRkoKSkBFVVVRgaGgINldhq\nssphhdHb24u///3v6OjowAsvvIBdu3bhww8/xL//+7+f1gZxRkwxtm/fnuKFz8vLw5/+9Ce89dZb\nAgguWbJEPBvoyLNixQq43W68/fbbclOoNQgGg4LoGo1GfPzxx+JoxD5ap9OJ+Mlut6O8vFxOzhUr\nVogrFNl95FAQLGtpaUFOTg7a2tqk76QsnGo/LrB4PC49cDA4E6YTiURE/0/tfn19Pfr7+wUwvfzy\ny/GNb3xjVogrTyn2otwYCa6x4krnNBDp93q9WLVqlRCzeNLxehBPUalUqKmpkSQuj8eD2tpaiYmr\nqamBy+WaNRqltT8/m9frxbx58zA9PY2vvvoKwEzv7nA4UFxcjJycHFx44YVwu92w2WzicsXoAQCz\nxn2s5Kanp7F+/Xr86le/kvaQLE2CkG1tbfj5z38OYGZMycnIihUr8OSTT0qFki6R9nq9+O53vyu0\nedrh87nkoiezk3wQLlTqVuhkxf+e7tthNBplQXM6Qs1OY2OjCPRycnJgsVjw2WefoaWlRX6uqKhI\nDHF4zR0OBxSKGYduh8MhFHhOoAKBwNc/F4MaiEgkIig+MFM+5uXliU7faDTC5XLBYrHA5XJh48aN\nchPJYEwnXBmNRnz11Vd44IEHxHfRYDBIv0/aNOnK9JXYt2+f9H3kSgSDQSxfvhwHDhyQk9lut8si\nIVrNPpOLUqPRQKlUIhqNCgmqt7cXAASAYq/IENfm5mYcOXIE77//Pv70pz/JdYnFYhI9Z7VakZOT\nA5PJhJqaGjQ0NEiGZPp15SuZTAp5idmRo6Oj0g/HYjGxuOvu7hZXbPJF2LoxSIdcCpoNE6zj+6tU\nKskL4cIn06+goAA7duxARkaG8DnS9SucBlGMRryAY9nu7m44nU6xsP/XZ2nRokVYvHgx2traZOQH\nAHv37sV7772HNWvWCNWd1PG8vDzU1dXhwIEDwsWoqakRzQ7fx2g0YmBgQIJt+LPcdPPz8yVGkq5k\nZHSSURqLxeRA4bPlcDiEocu2E5hRqJaVlWF8fFyEdNQQ8f5xakRRHqtsHm6n8zojWozR0dGHiPoy\n75DyZgJF7Dmj0ShKSkpwzTXXSBWRTM5kV6RSKeTn54tY5sSJE7jjjjvgcDgkFOass86CQqHAyMiI\n8Pjz8vLEjZilNW8yK5HR0VFkZGQgGo2ioqIC4+Pj0laMjIzIf9Pr9cjIyBA8gIxEYAaJnp6ehlKp\nlIdz7ty56O7uRnFxsYTCDA4Oor6+HmNjY5ienkZlZSV6e3ul1waAo0ePIhQK4csvv0R7ezsGBgbw\n4Ycf4vPPP8eePXskrq+3txft7e147rnn8OabbyIzM1M0Jaxi6LK0YMECwQRisZikOjHljBvI0NCQ\nMAb5edJ1DFlZWcjPzxfAuKioCNXV1TJ5iEajePLJJ4WSzr6+sLBQTmECywQ3Achsv7y8HAqFQsa2\nfFZ4yHDTaWtrg9vtRkZGBkKhEJqbm7Fjxw4sXLhQWg/qSsh72bNnj+BBBJKJGVDZqdVq5doAkM2C\n5jaBQECYmGR40n6ACeB6vR5er1dMfymzp7kP/yZp7JFIBJWVlSJT9/l8cDgc4pdCrIpO2PF4HNnZ\n2bjzzjtPq8U4IyoIznMB4Gc/+5lQRwnEkdhjMBgAABdddBEuuOACUa8FAgFpT/hwTU5O4pe//CUG\n/xnoWlRUBJfLhf7+frS3t6OyshIDAwNoamrCrl27kJeXJ+SfmpoaqSaamppEtEUEvqamRtBmvV4v\n9N958+ahvLwcBw4cENKWy+WSVikUCs0KbnG5XBgbG5MFSX4+H8T01Gh+f8qAgVPOzpFIBEeOHJFE\nqtHRUbFBt1gskikaDAZFd0CdRUdHh7RSo6OjmJiYkFaJWEl6bkU4HEYoFILFYkEgEBABGEFmltN0\n+FKpVPj444+xcOFCybekQziDd9NNalk9Go1G2Gw2oUoTH0omkxJWs3r1avGDBE45QZG2ng70Tk9P\n49ixYwCAxx57DK+99ppMkIjXMO0svUVghXXixAm5d9yAPB4P5syZg5UrV2J0dBS7du2CTqcTCTa1\nN/T+JN+BVVkyOROQpFQqEQqF0NjYiLa2Nuh0OgwNDSE3NxcjIyNYsWKFTPPSI/+4CdHnksZELpdL\nANrTfZ0RFcQVV1zx0NjYGP7+97+LtTfn7NQCUAs/b948/PKXv5RZtcfjkcVACW9paSmuuOIKMYhd\nuXKl6Buoe8jNzRXsYXR0FCUlJUilUjKWU6lUuPrqq3HjjTdi1apV0Gg0+Mc//oGWlhbRBQQCAaE5\np1Ip8YugZya1IXq9XhyWeAOpTGRiFacLWq0W0WhUmJ98ANge9ff3CybBh5s9LfkGZCqyvHa73WLL\nRj8Er9cLpVIpn4uTFSLn0WgUGRkZQt02GAyYmJgQsDAcDiOVSkGtVksVR0UtadME+EKhEEKhEObP\nny/YyZYtW0QFyyzRgYEB8TGoqKiQZyDduIbXlSU8qwGi/hzvJpNJvPbaa/KZeK2Lioqg1WolGpA4\nRzgcxp///GcEg0Go1WpkZGQITpWRkSGBzLQ0HBkZgU6nw1NPPYVzzjkH559/vqSGU7hFsJMmyKlU\nCiaTCRkZGejv7wcwUxU1NjZiampK2jifz4fi4mKpwkZGRoSUx0qJG04qlUJhYeEs4ZhGo5G/cdNN\nN339eRAdHR1oa2vDtm3bpI+PxWKykAm2JZNJ1NXViVMPtRUsEfPy8mA0GjE4OCghvRaLBfv27Zu1\nofAkys/Pl9AbAkG5ubkwGo149NFHcd1114moZs2aNWhsbJS+jjPu9vZ2NDQ0iNyWEm36LqQTeigq\nYpxccXExysvLpULiwmWlFIvFJH1KpVKJOSpVqbRTo9UcH0riDLSX5/Xh3yF/gb/H6k2pVAoJhzTq\n4uJiOUUpOQZmqhdmVvDU5rgzvbfm9fjyyy8lvYwbVmlpqWSD9PT0YGpqSvIvBwYGEIlEMDg4OMuQ\nhyYs4+Pj+Oijj/6HECsnJ0ccqFtbW0V1y1KeVgCvv/467rnnHtxwww348Y9/jB/96EdCUwcg1Q2r\nSmIMAwMDMs3Jzs5GXV2daGQaGhrELIgTCk6Q0unUNMLlYUWpfzgchtlsloqNMQMFBQVwu93IzMyU\nfBCdToeWlhYhVZFlSv4Kw6dP93VGTDGam5tTLJn8fj8mJiawZs0afPrpp9Dr9bLgsrOzkZGRgVtu\nuQXNzc2CQvM0fu+99/DGG28gHo+jpKREXKUY/ktMgOq6xsZGEUT19fVh8eLFePrpp8UVKhabiZTj\nWNBms+Huu++GVquFy+XC9PQ0Ghsb0d3dDaPRKIg6WWzsj1ny8oGYnJyUlKnjx4/L6cDPRsdmmo5w\nMyJyTs4/05rKyspE8anValFVVSWnMasPAor19fXy38455xzs2LEDoVBIUHh6CnChnzx5ElarVcx3\n2fbREm5yclIWH41kJicnpfydmJhAfn6+5FfQX4KgLnEKvjfFXBqNRkDksrIyefjZctLFesGCBXjt\ntdcECCSJy+fzYdWqVULOouCKAikAsqmWlpbKhkVaNCcGHR0dsz7b+vXrsWLFCixdulSYpmxrr7/+\nevT09GDBggX47LPPUFZWhrPOOgvr1q3Drl27sH37dnR2dopkYHBwELW1tTh+/LiQvWiDB0AWenZ2\nNm6//XZcfPHFQpyjiNHv94u9QVNTEzo7O6W9mJ6eRjQa/fpPMY4fP478/HwRMeXn58PhcKCurk54\nA+mquTfeeAOZmZmC3NtsNhw+fBh9fX0oLS1Fd3c3ksmknDTsqflQejweVFZWSlpqyccAACAASURB\nVDYGT9dnn31WJgsApNVhdRIOhzE4OCgOQIlEAgMDA0KTPnbsGObNmweFQoGpqSlBsfmgsUog5kAt\nCQEqStc5TqVxqdlslokK/193dzcikQguuOACSXG22WzYsWMHjh49irq6OkngHh8fF7t2qg5jsRh2\n7NghrQsfSlqiJRIJ0RaQ50BlJHGKwcFBaXv4YKdvwCSAcdJDAhtJPqQqp5OWKEiqqanB4OAgGhoa\n0N3dLV4WrPaAmQXucrmgUqkEh+LEQq/XC8+BDmC0byOQ6vf7hazGZ6W8vFwWIX1HOeWpr6/H1Vdf\nLZsT34vXc2xsDC6XC7t370ZJSQkikYjkuF5wwQWor6/HLbfcIlUP3bL4d1ip8CDjZms0GrF06VJ4\nvV4EAgFRFt900014+eWXJVF9YGBAnlMeOKf7OiNaDC7AWCwm5CVOBMbGxoSVR3rr8ePH0d3djQ8/\n/BCvvPIKdu3ahc7OTjn9IpEIJiYmEI/Hhc5KMUt6PidBtYyMDLS2tkpJDpwaW+bl5QlbsaOjQ+bx\ner1eFk1NTY2U3un9ckFBAWpqaoQ4lW7rztEYszIoNyYDkNMPLlyqAhOJBFwul5x2TU1NKCwsFFeu\n6667Dk8++SQuueQSLF++XBZDVlaWhACT7ZieVs3rQ6Yh2aIESVtaWmZRiwFg3rx50jPzb1CuTuGV\nSqWaJV3mKQ7MLAjO7emjQc8NbswEOisrKwFAjHDIH5iYmJADgTqadCo2wWlqeDh5orlPcXGxxAfQ\nCJZ6G743r9H3v/99GAwGoU6zdaC+Ynx8XNotMmlra2slWby4uBhr164Vd3UAUlmxWmC1Q+ZoOu6S\nHkuZTCaxZs0abN68GRs2bBClstFolPejTPx0XmfMBuHz+cSSi9JgxrbR+oz0VfbgLP+BGcEOcxkS\niQRKSkqEV88QE55uq1atkl6T1uUPP/ywbFTsyXmT+IB/8sknwiwkmWh6eloWCBWPl19+OTZv3ow7\n77wT5513HhobGyVMt6mpCc3NzfD7/ejq6pLPyWQn+mqePHkSOp0OBoMBbAMp6U1PAmMbw1NjfHwc\nsVgMzc3NuPHGG/Hggw9i7dq1kvhFuvLk5KQg4gDkpOeDT2UnKwOeahMTE7I5sQKi6W66k7jX6xWv\ni1gsJlMFTg744iIk65K29VwILpcLsVgMvb298n3TpxMejwdPP/20TIbY0hH1DwQC8Pv9iMfj4vhk\ns9nk1J2cnMTo6OgsV3AyOrlJE8M4++yzRZtCDw2Kv7q7uwXEZZtz7rnnQqvVys9lZ2dj/fr1YtvP\nTSk/P18A0UQiIfci3U2KfzM9M5WTl9tvvx0vvfQSWltbpWUjoHm6rzNig2DkmNVqFccmUm6JZp88\neRJNTU2yw9IncXp6GmNjY/D5fBgeHhYaLLM3SSbiOI39flbWTLJWQ0MDXnrpJZSUlIhCj6UrTwed\nTodPPvlEWIDEM9IXEHDqlP/hD3+IpqYmkV8//PDDMJvN0Ol0aG9vBwDh6Lvd7lmCMVKqqcwrLi4W\nL0r+fbowqVQq7NmzR+zPgFNGJ6wIVCoVLr30Uvz85z+Xn6FpDePdxsfHUVhYCK1WC5PJJPgLWwJm\nYrBcByCs03SXb45piVMYDAZUVFQgEonA6/VKpcDPRYoy8RqFYibWgJoHXluG2tDLATgl9w4Gg9i9\nezfeeOMNAKdMadiG0IGbDtZ+vx9lZWWzTmU+czzJGThcWFgo3harV6/GnDlzxHg3vXxXq9XYv38/\nysrKkJ2dLWZGjCEETmEjALBhwwapxjwej0zAfD4f6uvrhbOjUqnQ3NwsuAkJbnQX43MXi8VQU1OD\nZ599Ftddd51obdIduv63rzNig2BpNTo6irq6OrHw4qlC+ilPBspmCeDp9XrhQLBUa21tlXJweHgY\nSqVS8jAXLFgAjUaDhQsXYtOmTaKbYPo2KwmNRgO1Wo2pqSk8//zzssBqampE78ExmdFohE6nw7nn\nnitWcwBkUT311FPyHaLRqJjjpvebtNKbnp5J8ubDnpOTIxRvJivZ7XYkEgkMDQ3hjTfegNFolHyR\noqIi8SiwWCzIyppJEbv11lvxyCOPYO3atVi0aNEsu//R0VHRMpC1SfKPUqnEkSNHkJOTg/nz58s9\nI6Wb1z3dW7KgoABDQ0OykUajUdnguGjZ0gAQG7lIJCLSZt73/Px8aQdo5ZauJE0mk/jTn/6Ebdu2\nyaJJryKJI/Czsm0KBoNS6UxMTEirQSozQe7R0VEsWbJk1kZOCwEACAQC+PzzzyVfhNUUN0ceTsCp\ntopK01gsJmNUXod0w11KDPbs2SPVHiu39EMDmDkUfvCDH6Cqqur/bG2eETyIjz766KFEYiZcRqvV\nwm63y6zf5XLB5/NJBLzdbofZbBZvh1QqJb+XTCZRVFQEm80mKkCNRiNEI1YHIyMjmDdvHh577DFZ\nWNT88+QMh8MIBALIzc3Fvffei4GBAdE5qNVq9Pb2isFMPB5HU1MTwuEwLrroIrS0tIi6kxsF6cNd\nXV3iAcFTIx6Py2ZEj0mTyQS3243p6elZPpxGo1GUi1RQ9vb2oqGhQYA8ajkoQiOVnXb2zc3NWLFi\nhZDFWKXwlAqFQnA6nZL4zUXF6mB0dFSYgORSKJVK2VD8fj/y8vLEiDccDovprNPplIqjtrZWkHuO\n8cg05biVTEQyankdyW+or6+XtqCvrw8ajUbyTf/2t79hampKmJQMqcnNzUUqlYLL5Zp12LDs93g8\nmDt3rmSY+nw+3HvvvSIOU6vVwlcBgO7ubmzduhWJRAIjIyMAgP/3//4fLrnkEqRSKSgUCqRSKdkE\nhoeH8fnnn0smyOTkJAoLC6W1Y2wgAd2CggIcPXoUl156KdRqtRycpLur1WphD2dnZ+Pss8/G4sWL\nMTExgcsvv/zrL9bSarWp6upqGfmZzWYcP34cDQ0NaGtrQ21tLZxOp4BmAERcBEDGftXV1dBoNEil\nUujo6BC5d11dnTgUZWdn4/3335c2Jt2wlQxGnp7JZBJPPfUU3n33XcyfPx/79u0TH0gufJZ8rACu\nuuoq3HHHHSgqKhKgjuw8/s5dd92FvXv3SoYmCVbAzAlTUlIieEFvby+qqqowMTGBhoYGdHR0oK6u\nTkROVBVWVFRg5cqVuP322wUVT3fFJqGJbFCO5vhPn8+Hw4cP47333hNH6vHxcVit1llhwrFYTHgf\nNGc9cOAADAYD4vE4XC6XnK7MOq2oqJDRYfq4lG2QQqGA1WrF7t27sWTJEpw8eVKqq+rqamkXWFqz\n8qmvr5dKihsIR6RerxcajQZWqxUDAwOor6+H0+mEy+WSg2NoaEgmXFyorG5yc3Mx+M+ckvz8fHR2\nds4aRfPk9ng8uP7667F7924BSktLS/Hoo49i6dKlovVg25OZmYnf/va32Lp1q7R409PTqK6uxpEj\nR+Rvk/tBM2ZWD1dffTWuu+46aZPTs0d4PV0ul+AXDQ0NpzXmPCMqiD/84Q8P+Xw+QV8BYGxsTFBt\nlr5cUJwi0PORXoEkqZSWliKVSiEQCAiPwmq1wul04qyzzsLq1atn+SnQq5GBp2TSvfLKK3jllVdk\nLEmJMfn4JFsxhYv+lj/96U8Ri8UESOIYk8ShpUuX4sSJE7JY58yZg4GBAVFJcrTHTSojIwMajQbB\nYFCs31mac/Sp1WrR0dGBzs5OMWChbD0ajSIajaKgoEDMb/lKJpPCzDMajTjnnHOwePFi8bNkhcON\nef369Vi3bh2+973v4cILL8Ty5cvR0tICnU4nIUKpVEp4KH6/H4lEAt/+9rexd+9e0SLQh4PlPqsD\n3ov04BuDwQCbzSafg/dOqVRK5UGA1Gw2z/pdYKbNI3uVeAargWg0KroKv98vlevQ0JDwYRQKBTZs\n2IB4PA61Wi0y7kQigePHj+Ppp5+WVowM1F/84hcCJBOXiEajiMfjePHFF+Hz+dDa2ipUa/4eGaHp\n4GRhYaGQschzaG1tlUqXmyyrPfpBqNVqGAyGr78fxPHjxx8iyzCZTMLlcsFsNiORSMgCIAJNgpDH\n40F5ebkwGzlpoJXY+Pi4iGaYXlRYWIjf/OY3ErfOsp4eD5ypq9Vq/PnPf8brr78+y+jDarViampK\n/j0QCEiP39/fLxXIDTfcIDZ07FlZfnOxNzc346uvvhKPQaVSKWrHZDIJt9stblZOp1NCfQoLC9HX\n14dYLDbL+CYUCqG2thZfffUVjhw5gk8++QQulwtVVVUoKCgQOjLHfHR+DgQCMhqMx+PiC0HsgPyB\ncDiMRYsW4eabbxYQkSVuZWUlFi9eLK2Dy+US49iWlhYJ1CG5isAkq5qCggIEAgEolUr4fD7BIMh8\npZ0bFZ6BQAAajQYWi0XGo8lkUkbiIyMjwkOgrRsl97m5uXA6nfI9maBFQ9l4PC7UdypxzWYz/u3f\n/m1WJgcroPfeew+7d++Wqo0t7c0334x4PC7AKg+zyclJbNu2DVarVQ5B5mJQDcv7wepDr9fPotUf\nPXoU7e3tuOiiiwRI53uzpQTA6Miv/wZRX1//0Le//W2sXbsW3/rWt9Da2gqz2SwPPsv+goICRKNR\nGAwGhEIhFBYWCgEKgOQr0vSDJVwikcDChQtx5513CqcilUqJP6JarZaNJjs7WzYHt9sNp9OJ6upq\nOJ1O8W+YP38++vr6ZEOw2+2SIZqZmYni4mLZ4UnZ5d9mRZGfn49Fixbh4MGDsjGNjIygsrISg/+0\npUufCJCOy0zNaDSK5cuXo7+/X1yUOUak9NrpdOLgwYOSLRkKheTnAEhvTBMSTiJCoRCWLVuGL774\nQrwiJycncccdd8gDyFEfK5xUKoXly5fjrLPOgtlshsPhEG1Bum+l1+sV+jIXaH5+PtRqtfgb0DxH\nqVQiIyMDwWAQRUVF8Hq9qK6uht1uFwyBuSbs29mCpI8pTSaTaBgoWvP7/SgpKZEsTlaP3CwoslIo\nFJg7dy5Wr14NrVYr35XK0ccff1wMgzIyMuSA+fGPfyyYDze7jIwM9PT04O2334ZarZYWhtdBrVaj\nuroakchMxiuxJvqIEHBPJGZyM4qKijB37lxhtrKaiMfjSKVSvCZf/w3CZrM9xPJLr9ejqqoKixcv\nxoUXXoiLLroIq1atgkqlkl033W2YfSVPiYaGBgGdqqurxe5tw4YNaGhogFqtlkkHpyLRaFS4FS+/\n/DKefPJJYSySlswNhOUbKd78LGRklpeX49ChQygrK0NTU5OUuuzLuagospk/fz62bdsmfXMikRBn\nKY5AMzIyYLfbUV9fD5vNJmlaTLzmSUlwkDoAj8cDpVKJTz/9VKYa6SNkCtxYCrO902q1MJvNOHHi\nBNrb28Xde/369SLQ4kJhScsWDQDq6uqwatUq6HQ6DAwMIBAICAMQmGE5UulIdSPDfbg5ADMbPQ2F\nuVgY2EsXanIyfD4fSkpKcPLkSQFX4/E4DAaDVFypVEqmKelWbyrVjKMXWaYEdNmaMXGeY3Auwlgs\nhhdffFFAXdrXaTQaXHXVVeIWxU24qKgIX375JT766COJGODipkiOpi8k+KnVasHJuFGTWn/o0CEc\nOHAAVVVVs/g0fMb+qe78+ou1eCKRHOL1euWkKygoQEtLCzZu3Ii7774bzz33HC655BKRBpNNSL3G\niRMnRCyzatUq+P1+XHjhhZg7d64ATgSNAMwi7mzZsgWPPvqoLBjO3unkTBlzKBRCaWmphKPU1dXJ\nfJ/5EQ8//DB27NghYBbfD4CQaUicoqEscQomQtPpiOQtKvYYMEyviH8l6JAp6vV6ZQr0+uuv4/77\n78djjz0m+AIAmZVzE9Pr9TAYDDh48CDef/998Xyge1ZTUxMsFgssFgusVquMHXktfT6fXM9LLrkE\nv/3tb2EymWTknJOTIy7fLJvJuxgYGBDJNclCWVlZwkLleNfn86G/v18IdpTSs1okKSoUCkmkQHoW\nBcfZrEQ5LfD5fJicnITBYBBA0WAwiAgr/cWqll4jCoUCc+bMQSwWQ3V1tbSYAOTeJxIJnDx5ErHY\njFlPWVmZTKKYt8JWNF3QxVyMcDgsQG0kEsH09DTa2tpwzTXX4J133pE2h9YEfP/TeZ0RUwyHw5Ei\nUJRMJuWBS78pLLEikYj00clkEtdeey26u7uhUqmwYMECfPjhh8jJycGcOXMQiURw5ZVX4vrrr5ey\nkmYuvNAkybzyyivYvHkz5s6di3379ol5BzcUAEJUufLKK7FixQqMj4/jww8/RFtbGyKRCIqLi9Hb\n2yv5ix6PBytWrMCDDz6I5uZmaLXa/5GXyMW0Z88ePPfcczhw4ADKyspkqqFWqzE2NobW1lYR9YyM\njIh7EDDTKhB841SCfpYs6akM5YPHNoPmJyqVSkRbk5OTgj2Qnn3eeefhk08+wY9//GPccMMNcnKT\nUcmTniE5AASg5Pvv2LEDr776KkZHR6Wf589QwMVqhc5aFEMxgoDKRcrGydQkUGwymeTesoe32+2S\nnpWVlSUj4Ly8PIm444bFqRIB6KKiIrz55puoqKiYNbniodba2gq/34+ioiIMDAxAp9Nh48aNuPTS\nS4WVyhYyFovh+9//vrxncXGx+FpmZWXJNMrlcqGmpgaHDh2SjYHv29TUhJ6eHrl+6SngRqMRxcXF\nwtmZnp7GsmXLvv5TjMHBwYc4cmPgB0c26XTR7OxspFIp0RDE43Gce+65OHr0KA4dOgS73S4hp+wL\nn3jiCZSWlorhp1KpFGCOqP6LL76Ip556Shyt9Hq97OgajWZWKEoikcDll18OjUaDmpoatLa2IhgM\nYmRkRGbURUVFmJiYELHQ3r170dLSgsLCQsE8+OLDbLFYYDAYsH//fiiVSiLQGB0dFU8FKiOpLm1t\nbYXNZkNJSQkCgYA8kFarVcRdqVQKdXV1mJycFFYkNQM0g/X5fBgdHRUVqsvlQigUElovuSmpVArt\n7e34/PPPkUgkUFlZKVqWSCQi5sB0ZCK4SaVpRUUFli9fDpfLBbvdDpVqxsUpOztbAGUCeVw8wMwm\nSbEe81SBmbYjfZy8aNEijI2NycibGw89JbRarXhCEssiv4bCLeIF1FksXboUF198seBH5GiQ8PXq\nq69Kbofb7UZDQwNuuukmGAwGuTYAhGPxxBNPIBqNStA0q0ulUolYLAan0ylWd8ReWFWxTaqoqEA0\nGoXT6URpaalUIdXV1ejs7MSXX36JSCSC2tra03a1PiNaDJbxzBWgKo4tBmfApORyxyVP/v7770dt\nba2MylguFxcXy2nO8otgHP0Pn3nmGfz+978HACm7w+EwHA6H2M+xFB4eHsbKlStRWloqiro5c+Zg\n/fr1uOyyy9Db2yvzdjoPx2Izkub77rsPOTk5kv3B04gKxmAwiBUrVuD+++9Hf3+/kMPoEsTSmN+N\n3ojAbH4AUXByBbKysjA8PIypqSnBKHJzc4UVylKZACfDf3k9OFbu6+uTjbOrqwsvv/wyrrnmGuzY\nsQMARMuQrlQkYMcJCUVO9913H66//nqhxpPmTiOa9ArE5XIJ1ZwTAgKkvIbFxcVScvN09/l8Qu1m\ndcNrxbaGi5DPVDKZhNVqlWeFY1eFQoFoNCqVBe8bDX+pTqVZERPEeX3TVa587k6ePIlwOIyKigqx\nuyf9nON83oNAIIBQKITzzjtPpjUUB/L7qlQz2agqlQrd3d14/vnn8bOf/ey01+YZsUEQ/U8HgHiS\nDQ8PSzRbUVGRbBYkKLF/+8EPfgCdTgeLxSITgHA4jN27dyMWm/FXpNBKr9fD5XLhrrvuwnPPPScu\ny7FYDH19fYKOky+Q7kOwatUqwQTot1hYWChBN+lW+larVdhyNptNHnS6JvMBz8nJkdN71apVWLdu\nnbQ1ubm5Yi1PujidkNjTc/zHDY5AFmncZG2SYKXVaqXV4AkFAAaDAfn5+ZgzZw5qa2vF7p0LhuYk\nBQUFcDgc6OzsxJ133olrrrlGTHm42XBUR22F2+2Gz+eTCctll10mG9jhw4cFeE03/SHdmFkRtKIv\nLi5GXV2dkIwY29fY2CilNu9DJBJBT0+PXB+mwnPCQa+KvLw8jI+Pi3yfzMp0VSu5Blz0xAbS3cyX\nLl0qzycnC4lEQngzBHUJ2Pb19c1qIfhPtr+khBsMBvT09ACAiNJoaENA3efzCfDr8/kwNjZ22mvz\njPCD+OMf/4iamho0NjbCbDYLRkDAKRQKSX4EX8QpAoEAVCoVGhoakJubi9HRUTF8dblceOyxx/DR\nRx/JiJI3WKFQCMhJmnG6mpN6CKr1srOzUVFRgYaGBmRnZ4s6k4Eqg4ODgi5zYXq9XtE5xGIxPPXU\nU9i4cSMAyAIyGo1C92XC0sqVK/Huu++KhoAPLTdHg8EgylaWx3xRtUq1KnDqdFcqlRgcHJTNpbq6\nWvJB04lFlDtTJEQcg4zQ6upq8YAIBoPYtWsXRkdH8dxzz6GpqUk2CoqWGIjDBZZeabC1ZM4HOSvB\nYFAs641GI8bGxuT60nqeRjNMXKd0mjZ+ZMfyvQBIJWKxWNDV1TULvwEgtG6OYfPy8sQBnVUEX11d\nXVCpVBgeHpZrtWLFCgCQViX9vScmJgRr4QSKmwOnT3SxTvfLJDlscHBQxHicwvHw5Hc3GAwCMnMq\ndTqvM6KC2LVrF+666y5cf/31eOSRR/Db3/4WH330Eex2O4BT5SofYnIjCGiyRFMoFPB6vbDb7bO8\nFPbs2YMvvvgCbW1tUmq3t7fLorNYLFJKk+vPEyYWi4mhyYIFC+QUUKlmTFGcTieSySQOHDgg5CEK\nhXp7e+HxeKTd2L59O55//nkAM9MDujABkNIzEolg9+7dMl3gYqa4Kjc3F3q9Hnq9flYVxcWUSCTE\nXJUS69LSUmHkcYGksympyOSDODk5iTlz5siDbDKZUFpaCgCzRFlWq1XGwx6PBzfccANefvnlWc5X\nOTk5kkuZzrUAIL01VbkApOIijhGLxURMx/4bwKzJg0ajgdFoRHt7OxYsWACXyyULiExXbsacogwP\nDwtISo8QlvW0+QNOnerpeZ+8X0yzon9DZWUlioqK5LpyM+TYdteuXTCZTAJY0lpQoVBg3bp1Eshs\nt9uFGJhIJCS5i89D+u/y3gEQotv4+Lg8s6f7OiOmGBqNJsUxHXDqS3O0SFUfFYQtLS2wWCwwmUzw\n+/344IMPxCKe4z/mGjAvQKWayT4kHVur1WJ0dFTKOArDKioq0N/fj/r6epw4cQI+nw81NTVQqVR4\n+umn5QFobm7GyZMnEQgE0N7ejscff1zi46amphCPx+Hz+SSijZsE4/WuvfZaXHzxxQAgvS0NZu+7\n7z5s375d2I7JZFLGukTlWU3xOqX7D3o8HlH08QQtLCzE0NCQ5Go0NjYiHo/LNUgmk5IrQlEaTYOL\ni4vhcDhEGJWRkSHWelNTU4ILcJT5z8g3NDU14Uc/+pGU6eQRkDz09NNP47nnnsOKFSskh4OmwcDM\nZsRRIL8X3a6pQSHfIx6Pi4UgLf34+5s2bZLKLxgM4vjx47jxxhsRi83YCXg8HnHdor7HYDAgEAhg\n5cqV+PWvfy24VTA445N68uRJfO9730Nubi60Wi0cDgeefPJJrFq1SrglxERoF/D888/Pqgb5GdOT\n2qqqqmA0GhGPx/Hll1+Kj+nIyIhwP9LVzX6/H3PmzJnVFrGaGRwcRCqV+vpbzmVlZQliTtPWZDIp\nAhoAYszC8aHP54PFYhHjE+7szMWge1O6r19eXh6OHz8uGwVl2pT3sqflglUqlSLkWrNmDcxms7wH\nN7RUKoUDBw7I2IvakM7OThEpNTY2or29fRbjc/PmzXC5XFi3bp3MtZnBUFZWJpsDORh8RSIRWRy0\nhuODQTPTWCwmoBdf5DLQlJfhLP/q9ZiVlSUjPbvdLoxCAPLvDodDjGu0Wi2OHDki42JWfX19fRgc\nHMThw4fxwgsvCChKRS3HdzqdDkeOHBH3JNquVVVV4dixY7I4WEWma15YBbFqo2V+Q0MDenp6kJeX\nh7Vr18Jqtc5ygVqwYAGuuuoqvPzyyzJF0Ol0UCqVkm/i9XqRTCbR0dGB48ePY/78+TI5UygU2L9/\nv1RDqVQKOp0O5eXlUnWyOlGpZkx2/vCHP0icYzAYlJi9UCgkJi8qlQoOh0OeeVY+bJnou0kZOD0+\n+fwXFxfL2JeY3um+zogWA4AwIcvLy6XMJXWaizGZTIoTMqm3HNlRg88Stbm5GRdddBEqKytlvEWE\n3uVyCY2ZElkuRvbw7e3tsjBozApANhIAolX44osvZIETHadwKJlMyrSBUXzMyXjjjTfwySefzFIG\nxmIxnH/++bIxEfk+cuQI8vLyJEiWGEhra6uAXuSJBIPBWUawNAKmZoQPHEtVlqhc5PR35CZLK7aJ\niQmpsogbMAyGC95gMAjvgfbr99xzD/7yl7/A5/MJpRkAXn31VfksHCNS6dnf3y+cj1gsJuna7OHZ\nQpLGnUwmYTabEQ6H0dPTIwcKrxM3Am7+dLOORqNyoIyPj2NwcFAWPq/F5s2bAczgRoFAAMPDw9i6\ndau4f/HAUigUIj1nEFNmZiZ+//vfyzPBait9bM5nKRaLiVsZ3z8SiaCjo0NyM9KTy+hiFovFBFvL\nzc0VHg3NfU7ndcZsEOwlCT7SVo1AGBc03ZZIc+bEYGpqCuPj4zI7Xr16Nc477zw8/fTTEvvm9/tR\nWFiI6elpdHV1IRaLiZcl3YPoJUnXaZ5mTHECIKO7rKwsHDlyREhNHo9HpNNZWVk4ceIEGhsbheWX\nn58vNGifz4eBgQFs2rRJFjRxj4KCAtTV1UkFwPel4pViq0gkIii4Xq+XB50u0HzQOT7jZ6ZZjdFo\nFOt8tgfUv4yPjyMzM1Peh60YT1FmTdDZKBgMitaBdnEGgwFerxd9fX3YunUrbrrpJmzbtg0Oh0M4\nDjabTfrsvLw8MXGlryVPYUYeNjY2So9NN+rp6Wnk5+fjq6++EmUowcX+WHm+5AAAIABJREFU/n6p\nxniNCTzfeuut+OY3vym4Ei3cWI2SnHf48GG8+uqr4lr2yCOPoKenBxUVFYK/JJNJSQUnqJ2Xl4et\nW7fiP//zP6Vq8ng8KCkpEbyLG2lWVpaY9fAZJoZA3Gj37t2C6RQUFGD+/Pk4cuSIVEes3tjSplee\n/9vXGbFBsMzlAkmnDZeVlaGgoAAulwv19fXCFiSQk06bZhXS2dkplNXGxkb85je/kWRnhp2yLGep\nSvPUJUuWwOPxCEil1+tRWVkpbsHkxkciMxmcbW1t8oCRx88ELXIoWEpzjKvVapGXlydu1Y8//rgw\n4lpbW1FUVCS2/kuXLkUsFpPqiHgJUX2FQoH6+nrhSdCFmj8XCoVEthyPx1FVVSWnMcN/GCbDU4/X\nlsrHqakpKXs5DeEYOhgMory8HNPT0zIaDoVCqKioEJMdLuJjx47h9ddfx09+8hP85Cc/kYqLmz/1\nNWx9CGCyeuSomPwMTipUKpVgQxRuFRcXIysrCwcOHAAAqZIUCsUs8dytt94qmhliIPw8FosFwAwg\n+dZbb2H9+vW49957sX37dgAzRjGs+iwWC37/+9+ju7sbwIwL1SOPPII//vGPUKlmskh4uBEYJXhL\nFS+/T0FBAcrLy2GxWDBv3rxZ4850Xc/Q0JDksgaDQZn+8DMT2Dyd1xnBpHzmmWceYsZDupiGeZ00\nJ9Hr9QBmFu3IyIgsyHA4LKNJhsuUlpaiqakJwExaVHV1Nerr66WkI62YZerU1JSctrRG58bxne98\nB/X19eLuQ1Xf4OAgnn32WSn3yEcoKiqC0+mUERWdqRQKhaRQUQTm8/kwMTGBtrY2OJ1OOJ1ObN26\nFTt37hQ9AXkcdECiUKiiogJ2u12Q8uLiYllcXMic04+NjQlD0Ol0IitrJk4+HA5jfHwcNTU18t/5\n0FOBSVYhJx10vSIGQHIPS16O3EhBpoM02YLsmclJ4MPP0jsSiUCr1UKtViOVSsnkiozNjIwMwQrI\nbky3e8vIyBDsZnp6GvX19aitrUUkEoFarUZubq5sckVFRSgvL8fOnTuRTCbldCcDlGE2w8PDoobl\niZ0uy/d6vXA6ndi7dy/++te/4u233xZ8oaamBj09PWJMw0OQCk+1Wi2O60ww4z1lu0GFaX5+vsTs\nUd1MPwm6UFGANjo6io0bN379HaWWLVuWWrhwId555x05NegJSSPT3t5eaDQauN1uQZInJiZQUVEh\nDxwXHFOlHn74YeGsc7rBctloNOKdd97Biy++KPH20WgUNpsNS5YsQUdHh0wQ9u3bJ74LJpMJer0e\nbrcbd9xxBw4cOCCaCPb7nBQsXboUBw4ckNPfZrNh3rx56O3tlTHV1NSU9N7pgTLMxUjvc/+VFUra\neE1NjZxcmZmZEqbDz7Br1y60tLQgEAhgaGhIcByCYBaLBWNjY7Kos7KyYLVacfDgQdkU6BHJhzUd\nkCPbMy8vT5y7OIKtqqqSVGq2DNx4WBmxXyazklTjcDiM0tJSqUzoWk7MqbKyUr5PR0eHEL7ITeBk\nRaFQ4IUXXsCiRYuktSIhjwfG+Pg4br75Zhw9elTGxqxieSqzsiA3A5gp52tra3HixAmZfLF1I0PW\n4/GgpqZGXKXIdOV1ZQWUnZ2NoaEhmEwmcZIKBoPQ6/UIBAKw2+0Ih8Pyt0jGMhgMKCkpgc/nw3XX\nXYeysjJMTU1h7969eO21177+Uwy3242Ojg4x9LBYLDj//PNFLJSbm4tPPvkEmzdvRiQSkQkHtfyU\nAFNXwN1/cHAQ3/jGNwBAph5DQ0NC5Lnyyiuxf/9+HDhwAA6HQ0aRJKhQsAWcYm6m4wKdnZ0oLS0V\nWjh7YjIm29vb5fc4OcjNzRVvyRMnTsgmSO4FT75wOIyGhgZ84xvfgNlsFskvH7hYLAa73S6nDyso\njjIJIHJ8SLVrXV2dWMplZWWJ8S032nTKdX5+PhYsWCDze34v8iyo1/B6vTJeTPdhYL/M7+T3+2V0\nSkt8vrjgCPISD2CvThCRVQr1L36/XzZSAMKnoI08Zc//8R//geeff14IXPz/XGgqlQrr1q3D9u3b\nRRVKAtKiRYvQ0dEhit3p6WmMj4+jublZ4hI5WeF9isVm3McbGhok6JmaC95HtkdGoxEOh0PsCVas\nWIGxsTF4PB45NOhdUVJSgomJCWnduNHE43EsW7YMq1atQjAYRG1tLc4+++zTXptnxAaxceNGIQoV\nFhbKDs85byKRwKWXXoolS5Zgy5YtOHz4sIym+P85HiUarlKp8Nlnn+Gb3/ymkI7oiE0gLScnB5dc\ncon0qXyw+/r6xOuvvLxccinIjuNGEQwGxQ7v2LFjQuLhKUlkmsnQOp0Ohw8fRklJiRidJJNJMTrh\niVZWVgaLxYJbbrkFra2tYmab7uIMQIhEROmTySR2796NnTt34tChQ0IW4rUdHh4W4IrgGisEUoBJ\nvMrKysLixYvF/JeOzzxN+d7j4+MCijLtmpRsKi+JlaS/B/vyZHLG1p4VAwARZKXjEBwPVlVVCReA\nYzz28nSestlsszCbRCKBr776Ch988AGuuOIKuN1uWcTAKT0D6fKcvuTl5YmehClX5IdwHMlqNz8/\nHyMjIzLZAiDAL/0p0tPI2GJRbxQMBqWq47QEgGRsUiNCpyxWkbxG4+PjuPbaawXj+L96nREYREVF\nxUNE0ysqKsS8A4CcVG63G+Xl5bjggguwdu1aoRtTpWmxWMSGnj1/NBrFkiVLxJYsFovBarXC4XBA\nqVRCq9WitrYWbW1tkrYMzGAW9LC0Wq246KKLhAZNJWYgEMBrr70mgh2SvGjHptPpxOSEvSVdp4uK\nigRctNvtworkBOOSSy7Bvffei9bWVhQUFECtVouylGpVqhhZkeh0OuTl5QnA+c1vflNi8WKxGKLR\nKFwu1yxlak5ODrRarWA/gUBAjEeYr0mz3L6+PmRmZkqVUV5eLhtgaWmpmKXyROU4NycnR2z9uHFQ\nhcnKiW1GVlaWMFOVSiXKyspkQsCRJau/yspKjI+PIxqNwmw2S7XHqqW4uFg8Ky0WCzweD3p6erB6\n9WqZgtGijVWpRqPB3/72N0SjUfkepaWlOHTokIDWtA2kSpN8A+ouyA7lxM3hcIhzGZPg6bJtMpnE\nZs9sNgvo7nA4UFNTI0Y6brdb1L2sxBiHmJmZidraWrEcPO+882TD+yej9+uv5iRJhmEy6cAVv2gq\nlYLdbhcT1lWrVuEXv/iFmJQ4nU55AHmyZGVlYdu2bdDr9QJOkR/P+TcArFmzBjk5OWJ4qlKphEi1\naNGiWXFnwMxDsnXrVoyOjgKABJ4QLG1paZGbTYJQMjkTBGO32zE6Oiry4OnpaRw/fhyxWAytra24\n7777cPvtt2POnDmSGsbvQjdm8jYAiMkIQUmFYibAt6SkBJdffjk2bdqEmpoaqW44XzcajQIKEiXn\nhkA68PDwsEw2SBlnLofNZkNpaSnMZjPcbjfKysqEHh2JRFBYWCi/Q04FgUGyP+neTSctqiGp4CUd\nOn2en24zyFZBrVYLwS3dcYzfNyMjQzQyr7/++iydhEqlkuvp9/tx5513znIgJ4WfxDMqjzkK52fi\n5kj/VB54xBcSiYSIu0ipJvhO0Jp0dk7XSMUnU3Z8fFwEW6SOZ2VlYWRkBAqFAt3d3QgEAqioqBDD\n5NN9nREVRDgcfoj2ZTabTRSc09PTUlrTADQUCgnYU1hYiC1btogtW/rUgAvIbrejtbVVkHTqOBiJ\nlkgkUFFRgZGREZw4cUIQdAKfra2tqKyslBudm5uLrq4uPPzww6JGpFsyPRgKCgrQ398vyDpdp3hC\nMpyYjEiVSoXzzz8fDzzwABYuXAi/3y96EprjMrae9OaRkRGRjk9OTsLlcgndnCGvXPQtLS34/PPP\nZdFMT08jHo/LpIU6B4YVJ5NJ1NXVIRAIiAckqyqv1yv4APMcRkZGUFRUNCvJiZMULhSn0ymVXGVl\nJbxeLxwOBwoLC2VBuVwuabdyc3PFn2FsbGxW+HBmZqZQ6A0Gg0wAOM0wGo1SXZSUlMwC/IaHh7Fs\n2TIp81lFsEWrqqqCx+OBy+WSSgiYof+z0lOr1eJ6Rpdxv9+PefPm4eTJk1JllJSUQKPRwG63y3SO\nG5xOp4PT6ZQJCT05R0dHhRMRicwEDlksFgFnTSYTmpqaMDg4CI/HgwULFmBgYEA+Az1BeWgYjcav\n/xRj165dKUpqA4GA0FlZPQAzo83MzExMTk7iH//4B95//31MTU1Bo9GgtbUVu3btEs2FQqGATqcT\nxWFmZibefPNNGT/W1dWhra1NTGHMZjNGRkZw8803S/YD+16LxYK3334bAHDw4EE888wz+PLLL2W3\nj8Vi4khcUFAgHo4FBQVoamrC9u3bBViiMOzIkSNYunQp2tvbhe4dDofxwx/+EA0NDSgvLxdcgb08\nX+kZCHx/lt28VrTmp9R4ZGQEt912m6gFOTFhSzcwMCDCLAAS1NvR0SHioMWLF2Pfvn3i2MT4v/z8\nfAwNDSE7OxuVlZUIhULSmxPQM5lMmD9/vtje5+fn45FHHpHJETDDKXjiiSekEhoZGUFpaaloJAhA\nc/LR39+P6upqnDhxAiUlJTLVYjYG8SkKxKhK5VTjv//7v2dVEPR8YKvT19eH6667DoFAQHQwdLBi\n9UKPUP4dunklEglpv6jUtVqtUimmK085oaE9vkajwcjIiMgDwuGwcIQmJyelHSspKYHRaERXV5e4\nZAWDQcyfPx//9V//JQxcAF//KQbBokQiIbb1RNrZ2z377LOYnJzEwMCA9HdLliyBzWZDW1ubjAI5\nfaBztEKhEFrzhg0bZOERBGW1UF5ejsHBQZSWlootu06nw+joqES29/T0SFnLSD0SjEixBSBW6gQk\nqcwLBoMyliTbMjc3V0Zyf/7zn6FSqVBbW4tYLAaz2SxtD522AIiDdjpxht+Ji2JychKBQABdXV0A\nIJsdQUqdTifXwOv1SnpXVlYWOjs7Z4W10BBXpVLBbrejpKQEAMT+n5UQKwFyGXJzc+Xk7unpkfFg\nU1MTKioqkEqlhHS2aNEi/PGPf8QTTzyBtrY2ARHp7ch2q7i4GPF4XExfORIln4WtAn83KysLpaWl\ngqFwysLvQYIW2wU+c3PmzMFtt92G559/XioHVoWMKyQb1O/3w2KxQKvVikCLhrjEaIaGhuSeulwu\nsYvjBswgqNLSUvT396O4uFis/MiKZYRfS0sLVq5ciTfffBMqlQpmsxljY2Mwm80SRMwW93RfZ8QG\nQabd8PAw2tvbMTw8DAAYGBgQHQB7VxqCUO7N36+rq0NZWRm6uroE5SWKXlJSgo8//hhr1qwRwKqu\nrg5dXV3izMyymTsxx1Y6nQ7d3d0YHBzEnDlzRJOfmZmJ+vp67Ny5U8Ci6upqWSR0u+L40mAwoLi4\nGKOjoygvLxfTVfo2VFVVyWjRbrdjZGQEvb29mDt3Lg4fPoza2locPXpUFKx8URBGsGpgYEA4/cXF\nxdJu1dbWorOzE8uWLZO0K5vNJoClw+GQE3t6ehpGoxElJSWw2+1oampCf3+/6F8ASHYGAEHnWX2R\n78AFAEAqMofDgeXLl4uLdrpRUFlZGX7zm99g8eLFAjaS9UjCFzcxYIZDQpHV+Pi4kNFI0ybGQj5D\naWmpTKG2bduGW2+9VQxo+F6kp/v9flx66aXo6urCxx9/LAZEZDFyvM7QX7Z+JFCFw2FYLBahpUci\nEeHHsPWqqKiQg62wsBB2u102i9LSUuzbtw8Wi0Wo/SaTSVzXPvroI0laZ34tcRtO9jjBOp3XGQFS\n3nnnnXj44Yfx6KOP4uOPP0YkEsHY2JgAQvQiZGpWYWEhqqqqMDo6OsulqaurS0p/GoAAQHl5OSYm\nJvDqq68CgFCpWZKTW8ANhdRvAkjcQGhdRl+Bzz77TCqSvLw8ybOsqKiQmTctziiPZs/OBy07Oxs5\nOTnSQgGQLAq73S4jOer7WUnQ+bm7uxtGo1GckziOpJ8lS3NSpzs7OwFAWjg+RASEOVUg4QoARkZG\nMDExIZMchgdxE+aJmk4q4ncjAEg/DWBGzcoqiwpTnqQkSpG/QADSZDJhYGBAqrFIJIKSkhKo1Wqh\nKvOe1tXVCfM0JycH8XgcFRUVMBgMUnrv3bsX27ZtE/Ee7xEwu3278MIL4fP5ZBpBMJVEvnQgnSc3\nae6UcHM8TV9Kthl9fX2YmpoSjkRxcbHkrZw8eVIW97x58wTX4EY3NjYGk8mE5uZmyRtlC3b06FFh\n957u64zYINIXc3Z2tgiqmFNAMgiZcTQm9Xq9qKqqEoAwJycHfX19sqPSIKW3txd5eXno7OxEX18f\nAoGA9IAMJUm37aI6kH/H4/GgrKwMTqcTJpNpVqDs/2/vamPbLK/2FduP7cRx4iR2HCe2Eydp2jRN\nW9qF0hC+BusqBh1rhbaxaWysCAZj2tgGY5tG2Sid2rFNSEgDMdpOjI0hPqZOgkEFAkpW1DRyCGnT\nOE3SxLHj2HGcD9tx/JH3R97r1Nn75s/b/SivnvMLUEnqx8997nOuc13XyRdZRaNRsRKjSWwisezO\nHIlEBDADlndHABCMgVMbKjaJKfh8PlRXVwtRh4YwmUwGzc3NKCsrQygUEjYncYdEIiFIPRfylpeX\nS/Vht9tlbMbynG2QoiiyvYxVG+3ty8rKRHdCJuDExASKioqwdu1aufGZWGkeMz09LQj8mTNnZAJU\nXl4uP5s3/X333QcAQgcnpZtu1JwmVFVVib8CsSfS2Wm3xgNMxSP1OoODg3jmmWekTeCfyVfQsmUA\nloFdTmo4DeMkgy0y/T50Oh02btwoSZnrBPg+K4oitPNMJoPS0lL09vYKRZxjUZrtnjhxArlcDiaT\nSSYjrEx4wVBg53a78cILL8jU7FLjskgQ1dXVQiipqamRxTMs00dGRgQQIyuPL4VOpxO23ebNm2G1\nWrF161aZy+evjh8bG4PX6wUAhMNhOJ3OFUa13/rWt6REZMlLJmBpaakAZufOnYPBYBDLczo88yAM\nDw+jsrISbW1tSKfTImo6ffq0ZHyv1yto9vz8/ArePXDRd1Cj0cgez6KiInzyyScyE3/jjTeQTqdF\nO5IvG6eBLfEJ7qbUaDRwu904efKkrLhjcgiFQjJ1IE/ihhtukMObSCQwNDQk/TpNfKi+5I3P6oJl\nOcVq/D3RaBSHDx9e4clJkK6oqAjXX3899uzZI5VSUVGRlOb5ozvqIujTyVaLwjH+PegTQTNh4gHp\ndBpPPfWUVACKoojnAslPPT09gmXNzs7KFI3vHslfPKzl5eWCv9BTc2xsTLa08Vl4PB7BZwg+Tk1N\nrWBw8jPyoEcikRWb3nO5HN555x0xizGbzSgoKEBvby+8Xu9/xA/ishhz7t+/f19FRYX4CdAjgMpJ\n0qd1Oh3C4bCUpZTQxmIxbNiwAf39/bDb7Th9+jQcDoc8OBqrGI1GxGIxfOUrXxHgh+Wkoii4/vrr\nRQre3t6OkZERWVLj9/vR0NAgZincVK3RaODxeHD69Gnp0V0uFwoLC3H27Fm43W4B2GhrZzKZBJDl\nhi32+kw258+fR3Nzs/D/M5kMIpGIGO8CkBeUmgeNRoOpqSl4PB5MTk5i48aNGBsbg8vlknEsABkb\nG41G2f+p1+sRCoXQ2NiI0dFRWS/Hm41jS8rVw+EwjEYjotEonE4n4vE4wuGwgHgswbm/kwcDgJCe\nOjo6ZBEu+R1sxTo6OuB0OnHixAl58YPBIFpaWlbYuJWWlor3BisEErmIBXA1Ac2BOblYWlqCz+fD\nsWPHoNPpsGHDBiwtLYlfpM/nw759+5DNZlFZWSmGyLFYDHV1ddKOMgnHYjFYrVbMzc3JLg5yfNju\nkGQ1MDAgW7fsdrsY85CL4vF4MDg4CJfLJWeAAi8SzwCIvR5l/ufOnUMwGMT58+exdu3a/x+29zzo\nNIDhdCDfFMNqtUqpy/+u0Wgk04bDYQAQoRbNYQwGg0h/W1paMD4+LiUtdxmSVZnL5fCFL3wBMzMz\n8Hq9UBQFbrdbNmjxZS8sLJT+nJRegppr1qyBoiiyYZt9K4OeDgSxFhYWBGyzWCyYm5uTGT9NWvLB\nvnwyzebNmwXFJnuULEgAsqCGUu/h4WGpMvLZqrwNafhKTwX+Hnor0CaN5CGdTiceDtSsABDWJ7Un\n7KW5g5LP4cUXX5TvgZUTCWupVAo7d+5EU1PTiqkAt5MTk+IKBLYv1EXw55KFyZs/EomgvLwcdrsd\nJSUlsFgsSKVSOHr0KDo7O+XSmJubwyuvvLKipaAlHw8qsFz98n0k3qEoijyThYUF+Qwej0dwNYfD\nAbfbjVtvvRV33nknrr32Wqmi3G43GhsbpZXIZDKiDwmFQjKyJc2b29KTyaQwVKPRKF5//fVLPpuX\nRYKw2+0YHR0VGev58+extLQkjD6z2YyGhgZs2rQJAMQUlpoKWnBRDs6emrp67rak1DYQCKxwbOKt\nQ+R/06ZNwr6kzNZgMEgLEY/HEY1GUVlZKYaqROVpRjsxMSFKVJfLBaPRCL1eD4/HI8tOtFqt3Er0\nliCFGVg+4PkuSxQ0sW3iXD2VSsHv92N4eFiQc2D5dmFJTpcojhr5eSwWCxYWFlZY0HNMDCxPSTo6\nOvDlL38ZN954IywWi5TSpEqTjcmDUl1dLTcc6enz8/NobW2VvjwYDOLYsWOSiPMBNf5+RVFwzz33\nSElfXV0tI1DiQ+QIABDKN2954OL+CoKlnDDp9Xr4fD4ZOS8uLuLQoUNCkwaAwcFB1NbWYmFhQXwz\nkskkFGV5B0VpaekKa3leBJwo0ECns7MTxcXFGBgYQG1tLdavX4877rgDv/3tb3Hfffdh+/bt+N73\nvoe9e/fKiLizs1OqKVbQbBkotyflPBwOi9U9N5/PzMzgvffeu+SzeVm0GEePHt3HEV8ul8PExISI\nf0pKSkTgMjAwIAeIPa+iKLDb7QiHw6iqqkJvby+WlpZQX1+PQCAgBi4FBQUCTjY2NuIzn/kM5ufn\nwaXB+RuRz5w5g3Pnzsk4k4KaSCQCnU6HkpISuN1uTE1NYXZ2Vmz2q6qqxL+AFmQU5RBE5WHfsmUL\nrr76auHlt7a2YnZ2FjabDZlMBnq9HtnsxWWsdEWam5uDRqMRlSt3MDKREtQjKaqkpATDw8PQ6/Vo\naGgQt6Mrr7wS3/jGN7Bp0yZx3+aiIC7Qvfrqq/Hoo4/i5ptvxjXXXIPrrrtOxr78vfSPKCkpwcaN\nG8Uhipu++Pfn5AGAVHYkFH32s5+Vfycdm/TsxsZGDAwMCEuVqwpIxiIturGxUTQxlK8zyQCQcl+n\n0yEajcrBj0ajiMViqKysRCAQQGNjo7xjL774Irxer2z2Iv+FDFdaxhmNRiwuLgqlu6CgAC6XCxMT\nE8LgZdXa0tKCAwcOYNu2baIDoRZmw4YNeOWVVwTcJnVbq9WisLBQlkaTXavVakWcxaqUxkRkBN9z\nzz2f/u3e+/bt2xePx1FdXS1ZkE6/ZrMZFRUV4k7EG8lkMsnh1Wg0Qm0FLgpoyGcIBoPQarXQ6/VC\nk73mmmvkMOdyOVkoW1hYiA8++ACjo6OynToQCMjLTBYccRLam01OTkq259SALzCNSkwmE+rq6vC1\nr30Ne/fuxbZt29DR0QFFUdDQ0IAbbrgB1113HXbv3o2uri4Eg0GRc/NQjY6Owm63i8xcr9dDURTB\nKVjeUsXICQ/t37LZLO68807cddddqKurE+0DDWvYUzscDtx///3iL0mn5g0bNqCtrU14GktLSzJl\niMfjCAQC4o/JCYnBYMD27dvR39+PTCYDi8UiRj/0+3S5XFhcXBRiFBmRGo0Gra2t+OCDD2Q9H1cX\n2O12IT45nU50d3fDYrEI2EqRUzKZFAUtkxZp3TyMJOh9+OGH0Ov1OH/+PN577z3E43E0NjbKXhUC\nkMAygcxisSAQCOCKK67A4uKiTHk4iaJ6uLS0FFVVVfjd734nFVMymRSl8NLSEgwGA/7xj3+IkU4s\nFhPfioKCAnn+dEEjT4Vj19nZWRkVkxH63e9+99OPQRQVFcHhcKwgOHGcSPp1IpGAxWIRMGlkZASJ\nREL6+DVr1kgioG0ZfQ0ASH9OrsOJEydWjIEotaZQRqvVys/jvF6j0aCpqUls1YqLizE1NSXehMDy\nSzMzMyOGMGvWrBF/wgcffBAHDx7ETTfdJJMFGqjedddduOGGG9DU1IRNmzbhiiuuQGVlJSwWi5ST\nlKgvLCyIM1UisbzgpbGxETabTYxqiRl4PB5YrVZ4PB5otVrs2rULX/3qVwFghcszgTZOK8xmM5qb\nmwFcXFfHNmndunU4cOAAbr/9dklcdEeihJstF5+31+uV6oSCKmISr7766oqtW7Rtp/ampqYGd9xx\nh2ALNNGhZ4ZWq0VfX5/wSegl6Xa7Bd/iaNztdsukg4mEXgu8hf/0pz/hj3/8o7wbsVhMJh1sUdju\nsf2JxWJi6ptIJMTtikmttrYWP/zhD+X/yadlU98xNzcnAGm+7SHH+1QYkxFKYZxOp5NxLfkqdB+/\n1LgstBgWi2WJ/R11FKOjo3Lr8UWmDwBv5pqaGrFgSyaTGBoaQkVFBSYmJsSAdmRkRHwRiouLhS1X\nXFyML33pS9i9e7esprPb7Th06BC6urpgsVgw8t9bqEhq4Zyeq/sURUFzczMGBwcFSHO73cK4YwJy\nOp342c9+JotseAMxgsGgrNYrLi7GSy+9hCNHjgBYJk3NzMxg06ZNiMVi4gqdTqflViwrK5PdDPlU\n8traWvT19UmCyGazOHLkiFRYNNgNBoO46667BFQkd2LPnj2499575ZYmSYeEoHg8junpaRw7dgwH\nDx5EIrG8QHZwcHDFoeOtT/8L7hWtqamRG9DhcODIkSMyZiWHgnTyoqIifO5znxPAlYa5TBr9/f1w\nOByyhJejcQC45pprcPz4cZFXU+sTDAZlkxsrP066SKk3mUwIBAIZ7/MXAAAVr0lEQVQi1iOVma3N\n1q1bMTg4CEVRRGzlcrlw4cIF2O123Hvvvfj85z+/onXSarUCzBIoHx8fx8svv4xIJIJYLCbVQCQS\nwY4dO/DWW2/J5m8yRDnNA5ZXKIZCIUkYnJjFYrFP/3bvZ555Zh/lz9RSWK1WxGIxJJNJOBwOORiU\nFxuNRqRSKREfDQ4OCsFEr9eL6QnHdwUFBXxgIgHu7++Hx+OBxWKBxWLBE088gffff19eCgJqHC22\ntrZifHwcBQUFWFpaEmdt9qMkTfHlLS8vx9q1a/Hkk0/KTUcGZX5QSlxUVIQ333wThw8fhtPpRCgU\nEjUo+2sCg0ajEQUFBYhGo6iqqpIyOZvNCoOSZC4+g127dqGjo0Oei06nw9LSEkKhEA4fPoySkhIU\nFBSsSLhLS0vYsGGD4ByLi4srJkxMkmazGV1dXfId0USXOAqnS+Pj4/ISs1znmDSXy+Gqq67C/Py8\nqCgpespkMjh+/DguXLggExcAYpZCAJIVFFu80tJSSU7EoojdsK3gIiICmvlrDRRFQTwel3aUDND8\n1haAbJ2nK9ratWvxox/9CNddd51YC+STo1it0iRp3759MBqN6OrqktbV4XBIRcpJE30l6APKNX35\nkzXSzfV6PR588MFPf4vBG5H7FQKBgMzSrVYrenp65Ia3WCyyl4GHhuM7Timamprkn4Hl9oIvLb0F\neHs+++yzOHToEL7//e/LLcCszJ0RoVAIa9asQXd3t9xKwDKvwel0CrBIqztyEzweDx5//HExk/13\n4kr+C1daWooXXngBzz33nJSdS0tLmJ+fR1NTkySF0dFRcdyi38L4+DgGBgZEBJTL5dDS0iLiMe5p\nYJvBZMQKhzP2iooKAQGbm5uRTCbFt5P2balUSiThnAJpNBrs2bNHfh4ZkLFYTNijhYWFMinhsyU5\njtvOjx8/Ljoc2giSLJZOp7Fz504BJDk54XjUarXKAiCTyYSysjLY7Xa0tLQgHA5LywlgxeSBGgdi\nVhxf2mw2uN1uGSeyNeHoMZlMikWcxWKRpEJc5dFHH8XmzZtlQmUwGARbYIvA1jMQCAjWReyIo1WK\nFevr69HY2Ija2lpMTEwgHo/LcyatHIAsNWbldalxWSQIah9SqRSampqkpaCtOHdvAsujP5byZLjx\nSw6FQrBarUJgIihF/UB+yc8Xnl8KPQ85hweArVu3YnJyEuPj44jFYjJqIqfCaDTi448/hsPhwMTE\nhOzySKfTaG9vxy9+8QsxjPl30cz8/LyYsSQSCfz85z8XBBuAeBLyYJWVlWHjxo2STIhUs30iGQlY\n5mb09fWJVT8ZhS6XCwD+h5EITU6YqFkdENB75513cOTIESSTSeEVcGEtb8JUKoW7774bAAQIVhQF\nXq9Xensefhr4EsxjrxwOh3Hs2DGk02mR/eePom+55RbY7XYMDw+DLug8tGazWfaXcFERKfM02+Fa\nAX7f1I5QHs/EqSiKVG/coE0gmE7iiqKIRRwFcmSpPvTQQ8LIpEiNOzeZZCnRDofDeOqpp2RUTz4D\np0233nqr4E/Dw8OCbZFUNjIygsXFRTknvCzJZ7nUuCwSBCcSGo0GAwMDSCQSKCgoQEtLCxobG4Ws\nQsEMzTzzHZ6pIMw3B6XFm91uF+CLQiODwYDa2lqZPmi1WvFfPHnyJAwGA86cOQObzSYkLrpd5XI5\nnD17FtFoFJOTkzh79uwKwKilpQWPP/441qxZI+5H+UFJMF/ahx9+GK+99hpMJpOU8rOzs2hsbBTe\nBAAZ3bE/ZYKkkUgqlUI0GoXJZJJkQWMWh8MhJf+/Jwj28vm7SrmXlB4I//znP7F//35JCPPz8+jv\n74fFYgEX+ezevRt33323cFN4k8fjcfFVIIeDI7rh4WEB46g7IOBLB3P+TpPJJPL46elpBAIBGSGy\nWiKfhgf55MmTcLvdQqgiz4MHNJ1eNhHiciMyGUmtT6eXzWd9Ph8SiYRUoG63W5ymOA5vb2/HT3/6\nU+GLMDFwvM6ETRp6d3c3HnnkEVH0ktKfTqfFSIeWe6Ojo1AUBefOnUMsFsPU1BQaGhoEQOcIfH5+\nXkax/4m4LBJERUUFbDab0HPXrVuHWCyGgoIC9PX1wW63o6ioaIVYCQA2b94sM2DSVNlexGIxYbDx\ngbW1tUGn02FwcFBKYAKP/CKoljSbzTLCs1qtSCaTaGtrkwTEMo7TF2C5XF27di1+85vfCOU2Pzmw\nLQIg9NvHHnsMw8PDqK+vF/ORYDAonIuZmRl0dXXBZrOhu7tbnIhYtpN5SpLY5OQk/H4/SktLUVlZ\nKcpMJjrePAxqFQhscQJBn0aOPvV6PT755BMZVfLz9vf3S1tGYHPv3r2w2+0iQONkhOM3PhOTySQH\nNR6Pw2q1wufzrdCj5BOXPvzwQySTSbHLs9vt0m+zGqMYihMK0uJ9Pp+Ae2azGRs3bpRxLq3jOaVg\nVcfvicStVCqFvr4+uazcbrcQtnb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GmiDUUEONVUNNEGqoocaqoSYINdRQY9VQE4Qaaqix\naqgJQg011Fg1/gvLlHywlhiLRgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11814a8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "name = \"birds.jpg\"\n", "name = \"Seeds.jpg\"\n", "\n", "birds = imread(\"Images/\" + name)\n", "birdsG = np.sum(birds, axis=2)\n", "\n", "plt.imshow(birdsG, cmap=plt.get_cmap('gray'))\n", "plt.grid(False)\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Thresholding\n", "\n", "Select an intensity threshold by manual inspection of the image histogram" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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yACSpowwArRp3DUlnFgNA0rK4q+fMZwBoTfSvDNwykEaTAaChmh0MOjP477YxGQAaCYOs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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11814a550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(birdsG.ravel(), bins=256) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the binary image after thresholding." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQgAAAD8CAYAAACLgjpEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10febbfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if name == \"birds.jpg\":\n", " th = 256\n", "elif name == \"Seeds.jpg\":\n", " th = 650\n", "\n", "birdsBN = birdsG > th\n", "\n", "# If there are more white than black pixels, reverse the image\n", "if np.sum(birdsBN) > float(np.prod(birdsBN.shape)/2):\n", " birdsBN = 1-birdsBN\n", "plt.imshow(birdsBN, cmap=plt.get_cmap('gray'))\n", "plt.grid(False)\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Dataset generation\n", "\n", "Extract pixel coordinates dataset from image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(h, w) = birdsBN.shape\n", "bW = birdsBN * range(w)\n", "bH = birdsBN * np.array(range(h))[:,np.newaxis]\n", "pSet = [t for t in zip(bW.ravel(), bH.ravel()) if t!=(0,0)]\n", "X = np.array(pSet)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 17 0]\n", " [ 18 0]\n", " [ 19 0]\n", " ..., \n", " [214 253]\n", " [215 253]\n", " [216 253]]\n" ] } ], "source": [ "print X" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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UF6U4UtkiqRPDshymry6nLvXj0wnnzFa/IA8ALAfcump30O3t58xW1UGeljZHl586ZW+h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"text/plain": [ "<matplotlib.figure.Figure at 0x11ac70290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X[:, 0], X[:, 1], s=5);\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. k-means clustering algorithm\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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pJ5rWYLp/GRkzyMqaidORbVzZIiq97eduYQI/sQ9z/zA4g1/0NycYjTpuzk5F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"text/plain": [ "<matplotlib.figure.Figure at 0x11b099510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.cluster import KMeans\n", "\n", "est = KMeans(50) # 4 clusters\n", "est.fit(X)\n", "y_kmeans = est.predict(X)\n", "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=5, cmap='rainbow',\n", " linewidth=0.0)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Spectral clustering algorithm\n", "\n", "#### 5.1. Affinity matrix\n", "\n", "Compute and visualize the affinity matrix" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4206, 2)\n" ] } ], "source": [ "from sklearn.metrics.pairwise import rbf_kernel\n", "\n", "gamma = 5\n", "sf = 4\n", "Xsub = X[0::sf]\n", "print Xsub.shape\n", "gamma = 0.001\n", "K = rbf_kernel(Xsub, Xsub, gamma=gamma)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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5Ang4vTLw3Si5ae7qwcBibpaHuDsUJ9eHVJtcMgPZJc0c4M+oPKopZQn5QT3O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\nugIFCqz2KARdgQIFVnsUgq5AgQKrPf4/E++LvuzEvWQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b9f0f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(K, cmap='hot')\n", "plt.colorbar()\n", "plt.title('RBF Affinity Matrix for gamma = ' + str(gamma))\n", "plt.grid('off')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import SpectralClustering\n", "\n", "spc = SpectralClustering(n_clusters=50, gamma=gamma, affinity='rbf')\n", "y_kmeans = spc.fit_predict(Xsub)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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hQkVZ6d/n5PEN/RmMlJIQIQx0bBy6iWFiYGHTRQ8fMm4bs3MPTMgCWnBQ7jbbBv5IAsLk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\n/JRXwT2T9IYLlO3dC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KQ5d3vG27dJmva4HfGfZEfSkC8v+/obe+XWMWbWlwU4CDzZpH07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\nfRAEQcWJRB8EQVBx/gYchZR3iC2iNAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13a5d8bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Xsub[:,0], Xsub[:,1], c=y_kmeans, s=5, cmap='rainbow', linewidth=0.0)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 0 0 ..., 253 253 253]\n" ] } ], "source": [ "print X[:,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

ogaway/Econometrics

Dummy.ipynb

1

54300

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#構造変化、理論の妥当性のテスト\n", "『Rによる計量経済学』第9章「構造変化、理論の妥当性のテスト」をPythonで実行する。 \n", "テキスト付属データセット(「k0901.csv」等)については出版社サイトよりダウンロードしてください。 \n", "また、以下の説明は本書の一部を要約したものですので、より詳しい説明は本書を参照してください。 \n", "\n", "##ダミー変数(Dummy Variable)\n", "###例題9.1「定数項ダミー」\n", "以下のようにモデルを設定して回帰分析を行う。 \n", "$Y_{i} = \\alpha + \\beta X_{i} + \\gamma D_{i} + u_{i}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding:utf-8 -*-\n", "from __future__ import print_function\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>i</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>9</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " i X Y D\n", "0 1 1 2 0\n", "1 2 2 5 1\n", "2 3 3 7 1\n", "3 4 4 3 0\n", "4 5 5 7 1\n", "5 6 6 5 0\n", "6 7 7 9 1\n", "7 8 8 5 0\n", "8 9 9 7 0\n", "9 10 10 10 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データ読み込み\n", "data = pd.read_csv('example/k0901.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>const</th>\n", " <th>X</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " const X D\n", "0 1 1 0\n", "1 1 2 1\n", "2 1 3 1\n", "3 1 4 0\n", "4 1 5 1\n", "5 1 6 0\n", "6 1 7 1\n", "7 1 8 0\n", "8 1 9 0\n", "9 1 10 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 説明変数設定\n", "X = data[['X', 'D']]\n", "X = sm.add_constant(X)\n", "X" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2\n", "1 5\n", "2 7\n", "3 3\n", "4 7\n", "5 5\n", "6 9\n", "7 5\n", "8 7\n", "9 10\n", "Name: Y, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 被説明変数設定\n", "Y = data['Y']\n", "Y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.948\n", "Model: OLS Adj. R-squared: 0.933\n", "Method: Least Squares F-statistic: 64.02\n", "Date: Sun, 19 Jul 2015 Prob (F-statistic): 3.17e-05\n", "Time: 04:03:42 Log-Likelihood: -8.0050\n", "No. Observations: 10 AIC: 22.01\n", "Df Residuals: 7 BIC: 22.92\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 1.1650 0.491 2.374 0.049 0.005 2.325\n", "X 0.5777 0.071 8.143 0.000 0.410 0.745\n", "D 3.3155 0.408 8.136 0.000 2.352 4.279\n", "==============================================================================\n", "Omnibus: 3.054 Durbin-Watson: 3.236\n", "Prob(Omnibus): 0.217 Jarque-Bera (JB): 0.973\n", "Skew: 0.000 Prob(JB): 0.615\n", "Kurtosis: 1.472 Cond. No. 16.9\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ダミー別データ\n", "data_d0 = data[data[\"D\"] == 0]\n", "data_d1 = data[data[\"D\"] == 1]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1060c7550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# グラフ生成\n", "plt.plot(data[\"X\"], data[\"Y\"], 'o', label=\"data\")\n", "plt.plot(data_d0.X, results.fittedvalues[data_d0.index], label=\"D=0\")\n", "plt.plot(data_d1.X, results.fittedvalues[data_d1.index], label=\"D=1\")\n", "plt.xlim(min(data[\"X\"])-1, max(data[\"X\"])+1)\n", "plt.ylim(min(data[\"Y\"])-1, max(data[\"Y\"])+1)\n", "plt.title('9-1: Dummy Variable')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "###例題9-2 「係数ダミー」\n", "以下のようにモデルを設定して回帰分析を行う。 \n", "$Y_{i} = \\alpha + \\beta X_{i} + \\gamma D_{i} + \\delta D_{i} X_{i} + u_{i}$ " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>i</th>\n", " <th>X</th>\n", " <th>Y</th>\n", " <th>D</th>\n", " <th>DX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>5.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>7.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>7.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>7.5</td>\n", " <td>1</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>8</td>\n", " <td>7.2</td>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>9</td>\n", " <td>7.0</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>8.4</td>\n", " <td>1</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " i X Y D DX\n", "0 1 1 1.0 0 0\n", "1 2 2 1.5 0 0\n", "2 3 3 5.0 0 0\n", "3 4 4 7.0 0 0\n", "4 5 5 7.5 0 0\n", "5 6 6 7.0 1 6\n", "6 7 7 7.5 1 7\n", "7 8 8 7.2 1 8\n", "8 9 9 7.0 1 9\n", "9 10 10 8.4 1 10" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# データ読み込み\n", "data = pd.read_csv('example/k0902.csv')\n", "data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>const</th>\n", " <th>X</th>\n", " <th>D</th>\n", " <th>DX</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " const X D DX\n", "0 1 1 0 0\n", "1 1 2 0 0\n", "2 1 3 0 0\n", "3 1 4 0 0\n", "4 1 5 0 0\n", "5 1 6 1 6\n", "6 1 7 1 7\n", "7 1 8 1 8\n", "8 1 9 1 9\n", "9 1 10 1 10" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 説明変数設定\n", "X = data[['X', 'D', 'DX']]\n", "X = sm.add_constant(X)\n", "X" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1.0\n", "1 1.5\n", "2 5.0\n", "3 7.0\n", "4 7.5\n", "5 7.0\n", "6 7.5\n", "7 7.2\n", "8 7.0\n", "9 8.4\n", "Name: Y, dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 被説明変数設定\n", "Y = data['Y']\n", "Y" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Y R-squared: 0.946\n", "Model: OLS Adj. R-squared: 0.918\n", "Method: Least Squares F-statistic: 34.73\n", "Date: Sun, 19 Jul 2015 Prob (F-statistic): 0.000346\n", "Time: 04:03:43 Log-Likelihood: -8.6672\n", "No. Observations: 10 AIC: 25.33\n", "Df Residuals: 6 BIC: 26.54\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const -1.1500 0.779 -1.475 0.191 -3.057 0.757\n", "X 1.8500 0.235 7.872 0.000 1.275 2.425\n", "D 6.7300 2.062 3.263 0.017 1.684 11.776\n", "DX -1.6200 0.332 -4.874 0.003 -2.433 -0.807\n", "==============================================================================\n", "Omnibus: 1.187 Durbin-Watson: 2.627\n", "Prob(Omnibus): 0.552 Jarque-Bera (JB): 0.835\n", "Skew: -0.432 Prob(JB): 0.659\n", "Kurtosis: 1.878 Cond. No. 75.1\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# OLSの実行(Ordinary Least Squares: 最小二乗法)\n", "model = sm.OLS(Y,X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ダミー別データ\n", "data_d0 = data[data[\"D\"] == 0]\n", "data_d1 = data[data[\"D\"] == 1]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x109489890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# グラフ生成\n", "plt.plot(data[\"X\"], data[\"Y\"], 'o', label=\"data\")\n", "plt.plot(data_d0.X, results.fittedvalues[data_d0.index], label=\"D=0\")\n", "plt.plot(data_d1.X, results.fittedvalues[data_d1.index], label=\"D=1\")\n", "plt.xlim(min(data[\"X\"])-1, max(data[\"X\"])+1)\n", "plt.ylim(min(data[\"Y\"])-1, max(data[\"Y\"])+1)\n", "plt.title('9-2: Dummy Variable')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###例題9-3 「t検定による構造変化のテスト」\n", "例題9-2において$\\gamma = 0$に関するP値は0.017であり、$\\delta = 0$に関するP値は0.003であることから、標準的な有意水準を設定すれば、いずれのダミー変数も有意であるといえる。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###例題9-4 「F検定による構造変化のテスト」" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ダミー変数を加えない時のOLSモデル作成\n", "X = data[['X']]\n", "X = sm.add_constant(X)\n", "model2 = sm.OLS(Y,X)\n", "results2 = model2.fit()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 8 19.316242 0 NaN NaN NaN\n", "1 6 3.314000 2 16.002242 14.486037 0.00505\n" ] } ], "source": [ "# anova(Analysis of Variance)\n", "print(sm.stats.anova_lm(results2, results))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "F値は14.486、それに対応するP値は0.005より $\\gamma$ , $\\delta$ のうち少なくとも1つは0ではないと分かる。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

SatoshiNakamotoGeoscripting/SatoshiNakamotoGeoscripting

Lecture 11/.ipynb_checkpoints/Satoshi Nakamoto Lecture 11 Jupyter Notebook-checkpoint.ipynb

1

15229

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Team: Satoshi Nakamoto <br>\n", "Names: Alex Levering & Hèctor Muro <br>\n", "Lesson 10 Exercise solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import standard libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import mean\n", "import os\n", "from os import makedirs,chdir\n", "from os.path import exists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import non-standard libraries (install as needed)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from osgeo import ogr,osr\n", "import folium\n", "import simplekml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional directory creation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "if not exists('./data'):\n", " makedirs('./data')\n", "\n", "chdir(\"./data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is the ESRI Shapefile driver available?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ESRI Shapefile driver IS available.\n", "\n" ] } ], "source": [ "driverName = \"ESRI Shapefile\"\n", "drv = ogr.GetDriverByName( driverName )\n", "if drv is None:\n", " print \"%s driver not available.\\n\" % driverName\n", "else:\n", " print \"%s driver IS available.\\n\" % driverName" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function which will create a shapefile from the points input and export it as kml if the option is set to True." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def shpFromPoints(filename, layername, points, save_kml = True):\n", " spatialReference = osr.SpatialReference()\n", " spatialReference.ImportFromProj4('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')\n", " ds = drv.CreateDataSource(filename)\n", " layer=ds.CreateLayer(layername, spatialReference, ogr.wkbPoint)\n", " layerDefinition = layer.GetLayerDefn()\n", " \n", " point = ogr.Geometry(ogr.wkbPoint)\n", " feature = ogr.Feature(layerDefinition)\n", " \n", " kml = simplekml.Kml()\n", " for i, value in enumerate(points):\n", " point.SetPoint(0,value[0], value[1])\n", " feature.SetGeometry(point)\n", " layer.CreateFeature(feature)\n", " kml.newpoint(name=str(i), coords = [(value[0],value[1])])\n", " ds.Destroy() \n", " if save_kml == True:\n", " kml.save(\"my_points2.kml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the file and layer name as well as the points to be mapped. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filename = \"wageningenpoints.shp\"\n", "layername = \"wagpoints\"\n", "pts = [(5.665777,51.987398),\n", " (5.663133,51.978434)]\n", "shpFromPoints(filename, layername, pts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a function to create a nice map with the points using folium library." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mapFromPoints(pts, outname, zoom_level, save = True):\n", " mean_long = mean([pt[1] for pt in pts])\n", " mean_lat = mean([pt[0] for pt in pts])\n", " point_map = folium.Map(location=[mean_long, mean_lat], zoom_start = zoom_level)\n", " for pt in pts:\n", " folium.Marker([pt[1], pt[0]],\\\n", " popup = folium.Popup(folium.element.IFrame(\n", " html='''\n", " <b>Latitude:</b> {lat}<br>\n", " <b>Longitude:</b> {lon}<br>\n", " '''.format(lat = pt[1], lon = pt[0]),\\\n", " width=150, height=100),\\\n", " max_width=150)).add_to(point_map)\n", " if save == True:\n", " point_map.save(\"{}.html\".format(outname))\n", " return point_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call the function specifying the list of points, the output map name and its zoom level. If not False, the map is saved as an html" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;base64,
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mit

wilomaku/IA369Z

dev/Autoencoderxclass.ipynb

2

620811

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Corpus callosum's shape signature for segmentation error detection in large datasets\n", "\n", "## Abstract\n", "\n", "Corpus Callosum (CC) is a subcortical, white matter structure with great importance in clinical and research studies because its shape and volume are correlated with subject's characteristics and neurodegenerative diseases. CC segmentation is a important step for any medical, clinical or research posterior study. Currently, magnetic resonance imaging (MRI) is the main tool for evaluating brain because it offers the better soft tissue contrast. Particullary, segmentation in MRI difussion modality has great importante given information associated to brain microstruture and fiber composition.\n", "\n", "In this work a method for detection of erroneous segmentations in large datasets is proposed based-on shape signature. Shape signature is obtained from segmentation, calculating curvature along contour using a spline formulation. A mean correct signature is used as reference for compare new segmentations through root mean square error. This method was applied to 152 subject dataset for three different segmentation methods in diffusion: Watershed, ROQS and pixel-based presenting high accuracy in error detection. This method do not require per-segmentation reference and it can be applied to any MRI modality and other image aplications." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 2.7.13\n", "Numpy version: 1.12.1\n", "Scipy version: 0.19.0\n", "Matplotlib version: 2.0.2\n" ] } ], "source": [ "## Functions\n", "\n", "import sys,os\n", "import copy\n", "path = os.path.abspath('../dev/')\n", "if path not in sys.path:\n", " sys.path.append(path)\n", "\n", "import bib_mri as FW\n", "import numpy as np\n", "import scipy as scipy\n", "import scipy.misc as misc \n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from numpy import genfromtxt\n", "import platform\n", "import torch\n", "from torch.autograd import Variable\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "%matplotlib inline\n", "\n", "def sign_extract(seg, resols): #Function for shape signature extraction\n", " splines = FW.get_spline(seg,smoothness)\n", "\n", " sign_vect = np.array([]).reshape(0,points) #Initializing temporal signature vector\n", " for resol in resols:\n", " sign_vect = np.vstack((sign_vect, FW.get_profile(splines, n_samples=points, radius=resol)))\n", " \n", " return sign_vect\n", "\n", "def sign_fit(sig_ref, sig_fit): #Function for signature fitting\n", " dif_curv = []\n", " for shift in range(points):\n", " dif_curv.append(np.abs(np.sum((sig_ref - np.roll(sig_fit[0],shift))**2)))\n", " return np.apply_along_axis(np.roll, 1, sig_fit, np.argmin(dif_curv))\n", "\n", "print \"Python version: \", platform.python_version()\n", "print \"Numpy version: \", np.version.version\n", "print \"Scipy version: \", scipy.__version__\n", "print \"Matplotlib version: \", mpl.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "The Corpus Callosum (CC) is the largest white matter structure in the central nervous system that connects both brain hemispheres and allows the communication between them. The CC has great importance in research studies due to the correlation between shape and volume with some subject's characteristics, such as: gender, age, numeric and mathematical skills and handedness. In addition, some neurodegenerative diseases like Alzheimer, autism, schizophrenia and dyslexia could cause CC shape deformation.\n", "\n", "CC segmentation is a necessary step for morphological and physiological features extraction in order to analyze the structure in image-based clinical and research applications. Magnetic Resonance Imaging (MRI) is the most suitable image technique for CC segmentation due to its ability to provide contrast between brain tissues however CC segmentation is challenging because of the shape and intensity variability between subjects, volume partial effect in diffusion MRI, fornex proximity and narrow areas in CC. Among the known MRI modalities, Diffusion-MRI arouses special interest to study the CC, despite its low resolution and high complexity, since it provides useful information related to the organization of brain tissues and the magnetic field does not interfere with the diffusion process itself.\n", "\n", "Some CC segmentation approaches using Diffusion-MRI were found in the literature. Niogi et al. proposed a method based on thresholding, Freitas et al. e Rittner et al. proposed region methods based on Watershed transform, Nazem-Zadeh et al. implemented based on level surfaces, Kong et al. presented an clustering algorithm for segmentation, Herrera et al. segmented CC directly in diffusion weighted imaging (DWI) using a model based on pixel classification and Garcia et al. proposed a hybrid segmentation method based on active geodesic regions and level surfaces.\n", "\n", "With the growing of data and the proliferation of automatic algorithms, segmentation over large databases is affordable. Therefore, error automatic detection is important in order to facilitate and speed up filter on CC segmentation databases. presented proposals for content-based image retrieval (CBIR) using shape signature of the planar object representation.\n", "\n", "In this work, a method for automatic detection of segmentation error in large datasets is proposed based on CC shape signature. Signature offers shape characterization of the CC and therefore it is expected that a \"typical correct signature\" represents well any correct segmentation. Signature is extracted measuring curvature along segmentation contour. The method was implemented in three main stages: mean correct signature generation, signature configuration and method testing. The first one takes 20 corrects segmentations and generates one correct signature of reference (typical correct signature), per-resolution, using mean values in each point. The second stage stage takes 10 correct segmentations and 10 erroneous segmentations and adjusts the optimal resolution and threshold, based on mean correct signature, that lets detection of erroneous segmentations. The third stage labels a new segmentation as correct and erroneous comparing with the mean signature using optimal resolution and threshold." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"../figures/workflow.png\">\n", "\n", "The comparison between signatures is done using root mean square error (RMSE). True label for each segmentation was done visually. Correct segmentation corresponds to segmentations with at least 50% of agreement with the structure. It is expected that RMSE for correct segmentations is lower than RMSE associated to erroneous segmentation when compared with a typical correct segmentation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mask List [ 0 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18\n", " 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36\n", " 37 38 39 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55\n", " 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73\n", " 74 75 76 77 78 79 80 81 82 83 84 85 86 88 89 90 91 92\n", " 93 94 95 96 97 99 100 101 102 103 104 105 106 107 108 109 110 111\n", " 112 113 114 115 116 117 118 119 120 122 123 124 125 126 127 128 129 130\n", " 131 132 133 134 135 137 138 140 141 142 143 144 145 146 147 148 149 150\n", " 151 152 153]\n", "Label List [0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0\n", " 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0\n", " 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1\n", " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", "Correct List [ 0 2 3 4 5 6 8 9 10 13 14 15 16 17 18 19 21 22\n", " 23 24 25 26 27 29 30 31 33 36 37 38 39 41 42 43 44 45\n", " 46 47 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 65\n", " 66 67 68 69 71 73 74 75 76 77 80 82 83 84 85 86 88 89\n", " 90 91 92 93 94 95 97 99 100 102 104 105 106 107 108 109 110 111\n", " 112 113 116 117 118 119 120 122 123 124 125 126 127 128 129 130 132 133\n", " 134 135 141 142 143 144 145 146 147 148 149 150 151 152 153]\n", "Erroneous List [ 1 7 11 20 28 32 34 35 48 64 70 72 78 79 81 96 101 103\n", " 114 115 131 137 138 140]\n" ] } ], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_Watershed/watershed_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]\n", "print \"Mask List\", list_masks\n", "print \"Label List\", list_labels\n", "print \"Correct List\", ind_ex_cor\n", "print \"Erroneous List\", ind_ex_err" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb0cc2bf550>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e379c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask_correct = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex_cor[10]))\n", "mask_error = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex_err[10]))\n", "\n", "plt.figure()\n", "plt.axis('off')\n", "plt.imshow(mask_correct,'gray',interpolation='none')\n", "plt.title(\"Correct segmentation example\")\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.axis('off')\n", "plt.imshow(mask_error,'gray',interpolation='none')\n", "plt.title(\"Erroneous segmentation example\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Shape signature for comparison\n", "\n", "Signature is a shape descriptor that measures the rate of variation along the segmentation contour. As shown in figure, the curvature $k$ in the pivot point $p$, with coordinates ($x_p$,$y_p$), is calculated using the next equation. This curvature depict the angle between the segments $\\overline{(x_{p-ls},y_{p-ls})(x_p,y_p)}$ and $\\overline{(x_p,y_p)(x_{p+ls},y_{p+ls})}$. These segments are located to a distance $ls>0$, starting in a pivot point and finishing in anterior and posterior points, respectively.\n", "The signature is obtained calculating the curvature along all segmentation contour.\n", "\n", "\\begin{equation} \\label{eq:per1}\n", "k(x_p,y_p) = \\arctan\\left(\\frac{y_{p+ls}-y_p}{x_{p+ls}-x_p}\\right)-\\arctan\\left(\\frac{y_p-y_{p-ls}}{x_p-x_{p-ls}}\\right)\n", "\\end{equation}\n", "\n", "<img src=\"../figures/curvature.png\">\n", "\n", "Signature construction is performed from segmentation contour of the CC. From contour, spline is obtained. Spline purpose is twofold: to get a smooth representation of the contour and to facilitate calculation of\n", "the curvature using its parametric representation. The signature is obtained measuring curvature along spline. $ls$ is the parametric distance between pivot point and both posterior and anterior points and it determines signature resolution. By simplicity, $ls$ is measured in percentage of reconstructed spline points.\n", "\n", "In order to achieve quantitative comparison between two signatures root mean square error (RMSE) is introduced. RMSE measures distance, point to point, between signatures $a$ and $b$ along all points $p$ of signatures.\n", "\n", "\\begin{equation} \\label{eq:per4}\n", "RMSE = \\sqrt{\\frac{1}{P}\\sum_{p=1}^{P}(k_{ap}-k_{bp})^2}\n", "\\end{equation}\n", "\n", "Frequently, signatures of different segmentations are not fitted along the 'x' axis because of the initial point on the spline calculation starts in different relative positions. This makes impossible to compare directly two signatures and therefore, a prior fitting process must be accomplished. The fitting process is done shifting one of the signature while the other is kept fixed. For each shift, RMSE between the two signatures is measured. The point giving the minor error is the fitting point. Fitting was done at resolution $ls = 0.35$. This resolution represents globally the CC's shape and eases their fitting.\n", "\n", "After fitting, RMSE between signatures can be measured in order to achieve final quantitative comparison.\n", "\n", "## Signature for segmentation error detection\n", "\n", "For segmentation error detection, a typical correct signature is obtained calculating mean over a group of signatures from correct segmentations. Because of this signature could be used in any resolution, $ls$ must be chosen for achieve segmentation error detection. The optimal resolution must be able to return the greatest RMSE difference between correct and erroneous segmentation when compared with a typical correct signature.\n", "\n", "In the optimal resolution, a threshold must be chosen for separate erroneous and correct segmentations. This threshold stays between RMSE associated to correct ($RMSE_E$) and erroneous ($RMSE_C$) signatures and it is given by the next equation where N (in percentage) represents proximity to correct or erroneous RMSE. If RMSE calculated over a group of signatures, mean value is applied.\n", "\n", "\\begin{equation} \\label{eq:eq3}\n", "th = N*(\\overline{RMSE_E}-\\overline{RMSE_C})+\\overline{RMSE_C}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments and results\n", "\n", "In this work, comparison of signatures through RMSE is used for segmentation error detection in large datasets. For this, it will be calculated a mean correct signature based on 20 correct segmentation signatures. This mean correct signature represents a tipycal correct segmentation. For a new segmentation, signature is extracted and compared with mean signature.\n", "\n", "For experiments, DWI from 152 subjects at the University of Campinas, were acquired on a Philips scanner Achieva 3T in the axial plane with a $1$x$1mm$ spatial resolution and $2mm$ slice thickness, along $32$ directions ($b-value=1000s/mm^2$, $TR=8.5s$, and $TE=61ms$). All data used in this experiment was acquired through a project approved by the research ethics committee from the School of Medicine at UNICAMP. From each acquired DWI volume, only the midsaggital slice was used.\n", "\n", "Three segmentation methods were implemented to obtained binary masks over a 152 subject dataset: Watershed, ROQS and pixel-based. 40 Watershed segmentations were chosen as follows: 20 correct segmentations for mean correct signature generation and 10 correct and 10 erroneous segmentations for signature configuration stage. Watershed was chosen to generate and adjust the mean signature because of its higher error rate and its variability in the erroneous segmentation shape. These characteristics allow improve generalization. The method was tested on the remaining Watershed segmentations (108 masks) and two additional segmentations methods: ROQS (152 masks) and pixel-based (152 masks)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean correct signature generation\n", "\n", "In this work, segmentations based on Watershed method were used for implementation of the first and second stages. From the Watershed dataset, 20 correct segmentations were chosen. Spline for each one was obtained from segmentation contour. The contour was obtained using mathematical morphology, applying xor logical operation, pixel-wise, between original segmentation and the eroded version of itself by an structuring element b:\n", "\n", "\\begin{equation} \\label{eq:per2}\n", "G_E = XOR(S,S \\ominus b)\n", "\\end{equation}\n", "\n", "From contour, it is calculated spline. The implementation, is a B-spline (Boor's basic spline). This formulation has two parameters: degree, representing polynomial degrees of the spline, and smoothness, being the trade off between proximity and smoothness in the fitness of the spline. Degree was fixed in 5 allowing adequate representation of the contour. Smoothness was fixed in 700. This value is based on the mean quantity of pixels of the contour that are passed for spline calculation. The curvature was measured over 500 points over the spline to generate the signature along 20 segmentations. Signatures were fitted to make possible comparison (Fig. signatures). Fitting resolution was fixed in 0.35. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to get a representative correct signature, mean signature per-resolution is generated using 20 correct signatures. The mean is calculated in each point." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Signature configuration\n", "\n", "Because of the mean signature was extracted for all the resolutions, it is necessary to find resolution in that diference between RMSE for correct signature and RMSE for erroneous signature is maximum. So, 20 news segmentations were used to find this optimal resolution, being divided as 10 correct segmentations and 10 erroneous segmentations. For each segmentation, it was extracted signature for all resolutions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (123, 50, 500)\n", "Erroneous segmentations' vector: (24, 50, 500)\n" ] } ], "source": [ "smoothness = 700 #Smoothness\n", "degree = 5 #Spline degree\n", "fit_res = 0.35\n", "resols = np.arange(0.01,0.5,0.01) #Signature resolutions\n", "resols = np.insert(resols,0,fit_res) #Insert resolution for signature fitting\n", "points = 500 #Points of Spline reconstruction\n", "\n", "prof_vec = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " #Loading correct mask\n", " mask_pn = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(mask))\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec[ind] = refer_temp\n", " if mask > 0: #Fitting curves using the first one as basis\n", " prof_ref = prof_vec[0]\n", " prof_vec[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(123,)\n", "(123,)\n" ] } ], "source": [ "print(ind_rel_cor.shape)\n", "print(ind_ex_cor.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_ex = 15\n", "#for ind_ex, ind_rel in zip(ind_ex_cor, ind_rel_cor):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_Watershed/mask_wate_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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RhzMscYWiffTPKPoScbjl0PqKCRMmyE3xQTWF4vMSqKyk5tLLMBYXk//sM2gm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\nGPvJqg7en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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e2cfa90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Vbeq/ThAYx4/HvX8/0tfTS1rn9vB6XSvLU+JIMOhD7r8Ln8+N3bEX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NAAAg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\nrMhYwaCwQfjr/FvdryVMg8IwVHQBSVBavInengb6BPnxzItvcTI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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c4b00d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoencoder" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(model,train_loader,loss_fn,optimizer,epochs=100,patience=5,criteria_stop=\"loss\"):\n", " hist_train_loss = hist_val_loss = hist_train_acc = hist_val_acc = np.array([])\n", " best_epoch = patience_count = 0\n", "\n", " print(\"Training starts along %i epoch\"%epochs)\n", " for e in range(epochs):\n", " correct_train = correct_val = total_train = total_val = 0\n", " cont_i = loss_t_e = loss_v_e = 0\n", " for data_train in train_loader:\n", " var_inputs = Variable(data_train)\n", "\n", " predict, encode = model(var_inputs)\n", " loss = loss_fn(predict, var_inputs.view(-1, 500))\n", " loss_t_e += loss.data[0]\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " cont_i += 1\n", "\n", " #Stacking historical\n", " hist_train_loss = np.hstack((hist_train_loss, loss_t_e/(cont_i*1.0)))\n", " print('Epoch: ', e, 'train loss: ', hist_train_loss[-1])\n", "\n", " if(e == epochs-1):\n", " best_epoch = e\n", " best_model = copy.deepcopy(model)\n", " print(\"Training stopped\")\n", " patience_count += 1\n", "\n", " return(best_model, hist_train_loss, hist_val_loss)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class autoencoder(nn.Module):\n", " def __init__(self):\n", " super(autoencoder, self).__init__()\n", " self.fc1 = nn.Linear(500, 200)\n", " self.fc21 = nn.Linear(200, 2)\n", " self.fc3 = nn.Linear(2, 200)\n", " self.fc4 = nn.Linear(200, 500)\n", " self.relu = nn.ReLU()\n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def encode(self, x):\n", " h1 = self.relu(self.fc1(x))\n", " return self.fc21(h1)\n", "\n", " def decode(self, z):\n", " h3 = self.relu(self.fc3(z))\n", " return self.sigmoid(self.fc4(h3))\n", "\n", " def forward(self, x):\n", " z = self.encode(x.view(-1, 500))\n", " return self.decode(z), z\n", " \n", "class decoder(nn.Module):\n", " def __init__(self):\n", " super(decoder, self).__init__()\n", " self.fc3 = nn.Linear(2, 200)\n", " self.fc4 = nn.Linear(200, 500)\n", " self.relu = nn.ReLU()\n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def decode(self, z):\n", " h3 = self.relu(self.fc3(z))\n", " return self.sigmoid(self.fc4(h3))\n", "\n", " def forward(self, x):\n", " return self.decode(x.view(-1, 2))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "autoencoder (\n", " (fc1): Linear (500 -> 200)\n", " (fc21): Linear (200 -> 2)\n", " (fc3): Linear (2 -> 200)\n", " (fc4): Linear (200 -> 500)\n", " (relu): ReLU ()\n", " (sigmoid): Sigmoid ()\n", ")\n" ] } ], "source": [ "net = autoencoder()\n", "print(net)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "314.804579605\n", "torch.Size([147, 500])\n" ] } ], "source": [ "res_chs = res_ex\n", "trainloader = prof_vec[:,res_chs,:]\n", "val_norm = np.amax(trainloader).astype(float)\n", "print val_norm\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training starts along 20 epoch\n", "('Epoch: ', 0, 'train loss: ', 0.0095215536444923105)\n", "('Epoch: ', 1, 'train loss: ', 0.0050852037164411147)\n", "('Epoch: ', 2, 'train loss: ', 0.00376094494262064)\n", "('Epoch: ', 3, 'train loss: ', 0.0035085569192864455)\n", "('Epoch: ', 4, 'train loss: ', 0.0036567833340333989)\n", "('Epoch: ', 5, 'train loss: ', 0.003376171098575376)\n", "('Epoch: ', 6, 'train loss: ', 0.0031902957710613286)\n", "('Epoch: ', 7, 'train loss: ', 0.0030486840006561165)\n", "('Epoch: ', 8, 'train loss: ', 0.0030420855841746704)\n", "('Epoch: ', 9, 'train loss: ', 0.0028730589579862107)\n", "('Epoch: ', 10, 'train loss: ', 0.0027359282127882474)\n", "('Epoch: ', 11, 'train loss: ', 0.0026264062204174889)\n", "('Epoch: ', 12, 'train loss: ', 0.0025942252269196861)\n", "('Epoch: ', 13, 'train loss: ', 0.00255845210631378)\n", "('Epoch: ', 14, 'train loss: ', 0.0027449630140675371)\n", "('Epoch: ', 15, 'train loss: ', 0.0026573979062761567)\n", "('Epoch: ', 16, 'train loss: ', 0.0025321919434200214)\n", "('Epoch: ', 17, 'train loss: ', 0.0023269568295550664)\n", "('Epoch: ', 18, 'train loss: ', 0.00239070575228151)\n", "('Epoch: ', 19, 'train loss: ', 0.0024925716195396482)\n", "Training stopped\n" ] } ], "source": [ "loss_fn = torch.nn.MSELoss()\n", "optimizer = torch.optim.Adam(net.parameters())\n", "epochs = 20\n", "patience = 5\n", "max_batch = 64\n", "criteria = \"loss\"\n", "\n", "best_model, loss, loss_test = train(net, trainloader, loss_fn, optimizer, epochs = epochs, \n", " patience = patience, criteria_stop = criteria)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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3uHuXmc1Ochkipy0SEUlHyW6RPG9m3yEIkq1mVgSMNqjUKqDe3Q+6ezfwNEEAJFoHPOmB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\nERGRlChIREQkJQoSERFJiYJERERS8v8BgQWtuespTfIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb080cb3a10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Loss')\n", "plt.xlabel('epochs')\n", "plt.ylabel('loss')\n", "plt.plot(loss, label='Train')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((147, 500), (147, 2), (147,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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5P5FM0NjaQG5V1wYf2xJGU2sjxe4in/3+xzn8pDmDiSoSin6Ly8zuAjatsut0\nd7+x5zWnA0Xgt+s5zlxgLsCUKVM2KqzIcPT4PU+ud6JE+9JVHP61Odzwk9sol8sDPm46m+KEn3+B\nSVtvyrSdt9S9WhIb/RaXux+8vv1m9hlgDnCQr+fhXu5+MXAxVJ7HtWExRYavydtO4ok/P9XnUk6N\nrQ188Mvv5dZf3klXx8CfgvzuI/dh9mcPDCqmSGgGO6twNvAN4APu3hlMJBFZ04dOPHS9Dyn98Elz\n2HTqBH5453+QbRzYE7izTRkO+8p7g4ooEqrBziq8EGgF7jSzx8zsFwFkEpE1/GvhIlLpZNV9Mw+c\nwae/eyQA0/fclkxT/8WVbcyw0/7vZLu2rQPNKRKWwc4qnBZUEBF5W7FQ5A+//iPzLvsTLz29iO6u\nwjqvSWfTnPjzL6x1NTZ52kSeWvpc1WM2j2pi9CajOPSYg/jQCYfWLLtIrWmtwgFyz4PnwEaud9hG\nZLDcndPf9wMWPvAM+c6+P7NKZ1LkVq89k/BTZ36Ub84+u+rrU+kUv3nqgkCzikRBSz71wz1HeeWp\n+Gu74q/vg79xEJ6/L+pYUsceu3sBT/7l2fWWFlSmwW+5w9ozdHc5eIc+X796+eDv9RIZClRc/fAV\nX4XcbUA3UIDSInz5cXjhqaijSZ164s9P0dXR9z1ZiWSCbFOGr/36S6TSaw+aJBIJttyx+u0m27Zt\nFWhOkaiouNbDS69C/n6g92++3XjHr6OIJMPA6AkjyVaZZJHKJNl65lTmHHsIFz18Dvt+eI+q//5X\nfnYM2aYMlqgMaSeSCRqashz3k8/WNLdIWPQZ1/qUXgZLg/curjIUX4wkktS//T+6N7867X/W2Z5p\nyHD+PWf1e6PwDvu+g58+8J9c+YPreeGJl5g2c0uO+taH2GL65rWKLBIqFdf6pLYGX3c2F6Qhs3Po\ncWR4GDGmlXNuP4OzjjivstSTO61jWvjOdV+nqbWR7nyBq865gdsv/T9KhRL7fWQvjv7uEYwY0/rW\nMbbacQtOv/KrEf4pRGrH1rPYRc20tbX5/PnzQz/vxii3nw2dvwPeXCvOwJqxcbdgyUlRRpM6Vy6X\nefGJlzAzttxhCmaGu/ONWWfx5APPvDVFPpVJMWHKOH71xPlksumIU4tsPDN71N3b+nudPuPqh7V+\nC1pPgeTmYCMgexA29jqVltRcIpFg652mstWOW7x1C8YzjzzP0w89t9Z9XcXuIssXr+DP1z4YVVSR\nUMVyqNB96P/3AAAHKklEQVS9DN0PQXkxpHfEUrVbAcAsgTUfDc1H1+wcIgP17PwXKFdZszC3uouF\nDzzNQZ/YN4JUIuGKXXF5aTG+7CgoLwccvIRnD8JGnYdZ9WVxROrFJlPHk0yt+/c825hhs20mRpBI\nJHyxGyr0FSdC6VXwDvBOIA/5u/HOPp+oIlI32g7ZidYxzSSSa//opjIpZh29f0SpRMIVq+Ly0htQ\nWAiUeu3JQeeVUUQSCVUyleQnfz6bGe/anlQmRSqTYssdpnDen85kxNjW/g8gUgdiNlSYp8+u9Q1/\n+qtIHI2fPJbz7j6TjpUdFAslRo4bEXUkkVDFq7gSkyAxFsov99qRgYbZkUQSiUrzyOaoI4hEIlZD\nhWaGjToXrBF4c0mcRkhuirUcG2U0EREJSbyuuADLtMG4eXjn1VB6CcvsCY3vx6wh6mgiIhKC2BUX\ngCU3xVpPjDqGiIhEIFZDhSIiIiouERGJFRWXiIjEiopLRERiRcUlIiKxEstZhSJx5O489eCzPP+3\nfzJxqwnsMmtHkkktDC2yoVRcIiHI5/Kc9p6zeXb+PyiXnVQmyegJo/jxn7/H2Imjo44nEisaKhQJ\nwS9OuZwF9z9Nd1eBYneRrtV5Fr/4Gv/1uYuijiYSOyoukRpzd267+I/gvbfDX+96gq7OfDTBRGJK\nxSVSYy8+8RLlUrnqvnK53Oc+EalOxSVSY6uWrSaVrj4Jo6GpgabWxpATicSbikukxrZt24pEqnpx\nzfniwSGnEYk/FZdIjTW2NPKFH36SbFPmrW3JdJKJW03g6O8cGWEykXjSdHiREHzwy+9lqx234Pc/\n+wPLX1vBXh/YjTlfnKVhQpGNoOISCcmO+01nx/2mRx1DJPY0VCgiIrGi4hIRkVhRcYmISKyouERE\nJFZUXCIiEisqLhERiRUVl8gwU+gusOjZV2hftirqKCIbRfdxiQwjv7/wNn5zxlWUy06pUGLPObvy\n9cuOp7G5IepoIgOmKy6RYeCfC//NZ7Y7gYtO+A2d7Tm6VndRyBd48JZH+dGnL4w6nsgG0RWXSJ1r\nX7aKr+77bVav6FhnXyFf4KFb/8rKN9oZOW5EBOlENpyuuETq3J1X3EMhX+hzfyqTZPlrK0NMJDI4\nKi6ROrfomVfI57r7foHDpGmbhhdIZJBUXCJ1bvs9tqGhOVt1X6Yhzed/cBSZbDrkVCIbT8UlUufe\n/dG9GTluBMleT2FuGtHIGVd/jcOOf29EyUQ2jopLpM5lG7Nc+PAPmPWp/Wkd08LoTUbysdM+yDWv\n/oq93t8WdTyRDWbuHvpJ29rafP78+aGfV0REhi4ze9Td+/1tSldcIiISKyouERGJFRWXiIjEyqCK\ny8y+Z2aPm9ljZnaHmU0KKpiIiEg1g73iOtfdd3T3mcAtwH8EkElERKRPgyoud29f49tmIPwpiiIi\nMqwMepFdM/s+8ClgJXDAoBOJiIisR79XXGZ2l5ktqPJ1GIC7n+7umwO/Bb68nuPMNbP5ZjZ/yZIl\nwf0JRERkWAnsBmQzmwLc5u4z+nutbkAWEZHeQrkB2cy2WePbw4CnB3M8ERGR/gz2M65zzGw7oAz8\nCzh28JFERET6FslahWa2hErRDXXjgDeiDlFH9H4GS+9nsPR+Bmtj3s8t3H18fy+KpLjiwszmD2S8\nVQZG72ew9H4GS+9nsGr5fmrJJxERiRUVl4iIxIqKa/0ujjpAndH7GSy9n8HS+xmsmr2f+oxLRERi\nRVdcIiISKyqufpjZEWa20MzKZqYZRxvJzGab2TNm9ryZnRZ1njgzs0vN7HUzWxB1lrgzs83N7G4z\ne7Ln5/zEqDPFmZk1mNnDZvb3nvfzzFqcR8XVvwXAh4F7ow4SV2aWBC4C3gtMBz5uZtOjTRVrlwGz\now5RJ4rAye4+HdgTOF5/NwclDxzo7jsBM4HZZrZn0CdRcfXD3Z9y92eizhFzuwPPu/sL7t4NXEVl\niTDZCO5+L7As6hz1wN1fdfe/9vzvVcBTwGbRpoovr1jd82265yvwiRQqLgnDZsC/1/h+EfqPgwwx\nZjYV2Bl4KNok8WZmSTN7DHgduNPdA38/B/08rnpgZncBm1bZdbq73xh2HhEJl5m1ANcBJ/V6QK5s\nIHcvATPNbBRwg5nNcPdAP49VcQHufnDUGercy8Dma3w/uWebSOTMLE2ltH7r7tdHnadeuPsKM7ub\nyuexgRaXhgolDI8A25jZlmaWAT4G3BRxJhHMzIBLgKfc/fyo88SdmY3vudLCzBqBWdTgcVcqrn6Y\n2YfMbBGwF3Crmc2LOlPcuHuRytOx51H58Psad18Ybar4MrMrgb8A25nZIjP7fNSZYmwf4GjgQDN7\nrOfr0KhDxdhE4G4ze5zKL6x3uvstQZ9EK2eIiEis6IpLRERiRcUlIiKxouISEZFYUXGJiEisqLhE\nRCRWVFwiIhIrKi4REYkVFZeIiMTK/weVXPd5PW/EWAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb07975da10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing in new datasets\n", "## ROQS test" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_ROQS/roqs_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (136, 50, 500)\n", "Erroneous segmentations' vector: (16, 50, 500)\n" ] } ], "source": [ "prof_vec_roqs = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " mask_pn = np.load('../../dataset/Seg_ROQS/mask_roqs_{}.npy'.format(mask)) #Loading mask\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec_roqs[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting using Watershed as basis\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec_roqs[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec_roqs[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "#for ind_ex, ind_rel in zip(ind_ex_err, ind_rel_err):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec_roqs[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_ROQS/mask_roqs_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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wFMn/1JJ7w0SsP31uALee4nhQAr3ilBF/uUY9nk8aUZm12OcW0mPSsuSJPahU\ngjOvHcagYTp4bCoxTLT0/pW9ohPz4nvZkZ+GR2+kvXgIlUkraUr3IWJwjsfMb7q6MAsXEKMrdimV\ngSns8uQQ0PRRnrWaUcYw2prpxCLJqNjC8Lw3GBdrxBL14der2DfEgrkmidecMzC2Rng5bQY5qq2c\nWb+PpDIvhac1EWtMJ0wym1tHkLx5Nyop6Z6QRoVKsDdzG0U+C3safkoEAxNFOS/q/kC3Wc+ukWZW\nfpRHyJjETeFvY1TbedewnBUGLTtLz8RrMjFobwWeRh/X1r+JVKnoTg6gyZiKKpLFdyMjeRoPs8Y/\njP1P9ViNYQqWbAGj0lZ/KlECveKUIGOS3tcq8e3oxDQ+A8d5xXR1+Hjnwe1YUwyc973RWJMN8O4C\n2PYszqlLeGy5l4+9jRjFAWwZW6jO7MSrjZDjdTCr/kIs7jJ0IkCRYSM6fQX3y7kM8RUwOKzBnLYf\nw2mP8ly3jqZYiOtaVVhqvkdUPYhoqBxH6lJ6B2dxaee7ZIf7aMwwYPGFafereSRcREFlGrFBLsae\n2U6go4SGVQtosUfJrn2BEE7abWewsbAHV+57DA+GGdc1jo5MIzs7RxL1JjFR7uY+7ULWpudTqzbS\n/nE6aqFlfMpsiqxl9EVcbOzbzMPTx9OSkcO4HS/R0VbEdysW4THpac2IYE6ezKhwIcOjRexJX0NB\n67PY3tVQfO8V6C+5e6A3qeJLdKyBXnnWjeKk5l7egG9HJ7bZBSRdMgSpVbHs6X3oTRou+P6YeJBv\n3gpbn4bTbuO+5V08ToDO7K3Uj1vM3twWpoTc/LFexaV7F+DwDGe4cS06XR17/TPY5bydc12DGBRW\ns9oQ5lG1gfUVFzKh5gpssWE8mx3j4ZkbWDMkhEo7jFb/deyrS2fW6Od4JaWQnPYAhpBkkzGVooo0\nbIN6GHtGF762EVRu/A4CNemt6wgKJ2rzbMyGEVgslRSHosxuH8PYsJOhtT7sLjXTNPV8wnjej05k\nUnszjcEhvPmta3jy0lv5/ThB9f7H8GlCzLKfzcK9ZvJdAXaVXc4kNrIj53ySPT6y2tQ4PZvZpa2j\nVd3Ors7xBCaakWrofeV1OAkqdooT7zNr9EKIPOA5IIP4/XtPSCkfEkIkA68ChUAdcJmUsjcxzc+B\nG4Ao8H0p5dKjLUOp0SsOJ9TspeNv2zGNzyDpkiEIIdj6QR0b3qlh/u2jKCxL3PX50uXEGjexMOUH\n3FdVTE7yeRp6AAAgAElEQVTWizgd+zirz8c1TRFW9YzApr6DWExPqa6Lu2QYp1bHgr6XSCq0EbNY\nMGfsYnPrcF5pmUcEFephJvSmILNXv0BmpxuVFAR1JoyqkbhTx+PXbaLJZmCSejHfjpRjDUVoyDBQ\nmZtF2+7LcDVMIqQRmDoX0UcNLlU6O/JnMjVgxBpMQRoaabME0YkuYqoQCGi1J7Fo9BnY+tx8vO07\nxKKCm/XfoyClh0xngPT2Ts78cDP6mT9BZx1Eiy7Gt6ZaMQaDXPLe40SC40l3/gu/TsOBEg1p2vGM\nDpeQVLwO6+qPMG+FIW8/i3rIaQO7YRVfmi+t6UYIkQVkSSm3CSGswFbgQuBbQI+U8o9CiJ8BSVLK\nu4QQpcDLwCQgG1gGlEgpo0dahhLoFYfTuXA34WYvmXdORGXQEApEeO4X68ga7GD+baPiiVp30fjK\nLL6fmU1VUCLD6QhdBzf0ePlmS5Sn3KWMMNxGZ9DCiOhG7jGm0GxK48fjHmVwai0Rv4G29mKaWkfg\naHLhaOhgrzmXTqOeIcEKdCod1qRs1iTvweqC/HYTQpgIpMylodBEbst6pL+XOVlNRFyj2OM/h4jU\nsbVYx8Qt/yAgnQTVZl4Ydhrm/KdJjcD51Vfi65uMIRJ/vEFMRDGrO0jXVVKb5mJl2kiKtE3cV/sw\nVTKHm0ILuEizltNVFWhaIwyONqLPSCasKuNVw2x+M2U809v8nL70NfxyMDr/+xjDIXaOsZLJGByq\nXMbk30fa/VoyLhlH8r3K3bKnii/tzlgpZSvQmvjuEUKUAznAN4CZiWTPAiuAuxLDX5FSBoFaIUQV\n8aC//vOvhuLrKljrInjAif284n8/771yQxtBX4TxcwsAkDJG7ZYf8H95qVT3ScLhIrS6Ws53Rrmx\n28PfvGWUpV5CW7eFvOhO7nNYaNBk8oOhT5DZ6+dA43TaPAVkyXbm+PyIXdVonc3E8npIEWa0AcH6\nrIlcYcvj59FZPFu0jCXFG5i+MxNr11sM94xBmuehVqnY2qpGIik0rWOy6WUKNmVQJYzEhI49w+2Y\n8p8iLRJhassEHhBlzMhaxfmyiU+ybmH2mvX06Kw0Rk5HVx/jAddfKDZsRAKDVU0s1f+MPbEihtCM\nI7sPZ8xMU1+UAstyrgpvYHv5rbwxYi7jxp2Hcfs6NLqpeNUbmLy5nRUzqhji04C1mFB+Cz0fbSfp\n7jBCqx3Aras40T7XIxCEEIXAWGAjkJE4CQC0EW/agfhJYEO/yZoSwxSKY9a3qQ2hV2OelAnE+8/v\nXN5IRpGNzGI7sViIfdtv4YChgfXdJjSBMUQs9WT5NNztbOD1nhGY86fS11iMTvp42iaoVBdyq+1F\n7JXJbNdPwCo9zAhvJkNOp2vbRqKRXjaPLUEbCTLC3UZWbYDZlVXU2LNoMtq5Vm3kCn0ZH5UeYF+a\nhuH1OwjK/bRkTKVBP4wqkxqNrpS7N6ZTL0zYRIDQxH3sSbNQHI5xpdZK+4huHvbcy+yqKvauzuOu\n/OtZlTWeJsNIkvs0qCLJLHHeRWFWE17fFrbkq7lV/SZZ7jbWB4axKjyKRZHT8WlNDAk18bLuXhZ0\nPMdqVRGPjhjCvW05dHSpMboy6DFLhuzbTfvQZPZXT2b43JfRPaGl96lHSb55wQBvYcWJdMyBXghh\nAd4EFkgp3UL85wl+UkophPhcV3mEEDcBNwHk5+d/nkkVpzgZjuHf24VpTDoqXfzZ7u21blydfibM\nL0RKSWXl3bS7VlJZJ4jqwJekwuh38peuFnZ355CaOYjs9tmUx2JsVHezXTuEa/I+wt9RQp1eR550\nYFCPYHfaJWze8zp56l5aCjPwGQxUF0hqOq2MFjaK2/yEhRrpc9Me7SQp6OXcmiBFRWN5erSNCc2N\nFDUuw2FewhCNgSxnlDq1BVU0wvMz3fRYrJwRtnFeYSt+aWBabR1lrW28pLuQ+jNy0QXT0AVzSfY6\nQMYwhZoIGDOpa80D8jjDb+DDrCDakv0UUsfZ1HOWfJdgRIsnbGV1Zy7fqC/n5y0v8fPIHTw6dTTf\nfedZ2pLmY+l9mozuFLaIdVgD55Ax3Ia+IErn489gu/gaNKnKky2/Lo4p0AshtMSD/ItSyrcSg9uF\nEFlSytZEO/7B19o0A3n9Js9NDPsUKeUTwBMQb6P/gvlXnIKCNU5kKIahNOXfww5sbketUVE8Oo2W\nlldoaX2Ngno/9/myiBiT0Qe2cWOvB4fTiDM0jDzPd1gRiFKtDrHeZuPi0vfYpDqT5sIcevV6sjqa\nGFxXwYgtb2H2e2hJstKRnIHR18qYPSqk2oihKJm+oeNx7HyX7Zo83ig5k3ZjEtft+4BLq1Zws7OA\nmuHzsKi3EqCT1F4fae4wXQ4PC+cHGeEfwgVNYwj1WTlQkY0jFKBG9rGKZDQRPUnShkBFVO2nz1KD\nVVOPaK/H0R6hJ+VyMFhprAmQ7P8WvcOj/MzZSaavicm9K7m9czlmXxtPZ51OXa6RK5pWsLjtND4x\nT2PtGZcw8oOnURmm41St4oK1HhafVUF+9enoLl+H5UE/rb/+Dbl/f5T+FTbFqeszA72I7wkLgXIp\n5YP9Rv0LuB74Y+Lvon7DXxJCPEj8YuwQYNOXmWnFqS1wwAkaFYZB8Zt7YjFJ1dYOCspSQO2mqvoB\nLKEkuj6J4ZkSxOrvpiAW5apOH5/UTGREzndY1BdGi5ptjh4mTapjhe9chh/Yy4TWZdjd3WgiQWJC\nkOL1k+z1U5tqJ7W3jdbkIGXJPcwwd3Dj6L+xIaWIiVOmcdfSxUzb8Hf8ERXtxiQ6k3IpdjVTvOFJ\nVJZMYp4WAIJlc3APnchTB0yUq1oh3ExuqJ2wdOEVdvoCaWjUekLaGDp1F+NUqfTkuHiRj8kKZ5Fs\nH0o4GEYTqsbRM5qw1k1Xc4zQ0x7sY6vZXDiL3fph1DOae5qf4sqW7fxu9JX8tvsZ/sRjzDowmA8m\nF5BROhFjg4lUvxWXPkpmwwEqU/PJGRqifbKV9E8+wfn66yRddtlAbmrFCXIsvW6mAauB3cDB19v/\ngng7/WtAPlBPvHtlT2KaXwLfASLEm3qWHG0ZSq8bRX/tj2xHaNWk3xzvWdNywMnbf97GnO+OIGZ9\njKbmFxn6Lx+/tI2hz+SmJsPJS00dbKkqw5H9Q14NqJns01Kn241MqSRjfzvqaAgAXTiKPRRDxCSl\njS1oYpLVJfnsGhVjc34rD0g7Z7dW8vzQh/n9PgfeyWmoDWqiwANGO6NfXk+nPohWYyXga0NVtRqL\nqwv0NrpGzCBktdMjvPT4mnjqzPkM767l/PpVqKSZtlgaARFFL7WMjxQz0lKMdXoulqnZbO/czq/X\n/poGTwPj7GXctXsPFTVzadXPxeDfhd84kqjGy76iFlaWlOK0OCjwN7N0242IWIyNWdnMbq7mpdgM\n7lHfSmRyOte8/RCpkflEPc+R4vXy2hzBRZZUuvQBrlxiwl9exaD3F6PNyhrAra34Xyh3xiq+kmKh\nKC33rMM6Iw/7OYUAbHinmm0fNnD9/WPYtHUakf0akh8TvDjTzkdjevhxdy9p5dlE7T/g/0QSF7h8\nCN9SRLiVmFCR6fSQ6vHhsA/GF6rF3N2HMRShLTkJzXnnsG+qkSdqnuWOsIGbmqvhkoUw4iIae3ws\n+HAfa9PVaMKSiE7FPXob573bjMsSZGtaHfW+TnzuAOZAGLvbTVpnJ7q+EHfd8hNCOiOX7ixncrSX\nWHYKeoOevJw8SocPR2+Kv1hc9HtblT/i59WKV1lat5RRHdX8rH4/LwT/jteZzlDPGirMU9FIJ10p\nFbjMaqZs28D8EesJ6QU6EaY9VUduW5DLg79mc+EkMlJinP3xStJ7PYRiFaBupmb8GM4t3UKSZz4p\n9y7BNn8+2ff9YYC2tuJ/pQR6xVdSsN5N52M7SbmuFGOijf61P2xGo1Mx4bKdHKj6f6Q+oOb1oeNZ\nNnovQ8N+flqpoiVwB/fbBjGmp4Fs1weEtSr2F49mwZuvIhDsGz2BpmgXGhFjTFIzI3JjJE+8iNUi\nwB0tSzinL8ADrgDi4oVQMudTebptcxVveb0ITxhp1TLbDT/Z6yfFHX/yZUAFbSLKXmpZnRxlZdlo\n5jZ2sSDQwtDLLkCTlvb5CyIWhb+fhi/m4NWmu0HAuOk2NrzfgpARpKOdLjyM713HjPR1/E1/FqGk\nKD9yL8YfMHNu4H4ap5UwsW4Hs3dlEu59EkvQz/JJTi4uVbHGnc0vmifS+/LLDF72EdrMzP952ylO\nPOUNU4qvpHCrFwBtlhkAnztEZ4OHYTNdVGx+AL1f4BcOGgftIibgm015tPvPZpG9hDTXfrJdHxIw\nprLo7Av4x0O/QarUrByaRyjajdBqGX/J1Zw+CMTmhVRvfZy7MlMZHonxu5zZiGt+Dbb/bsZ4ePwg\nundVsxIvulCMj2wqPpqoxxHVM9QZwaJWszlJjVs9lulaPR+MzGfEHPP/VhAqNUz/Caa3b+KC87pZ\nvCSZte+1k6ptwS9S8fYW4EBFNaOo7rgFC2FWxCpYO2w7M/e18JjuL9y+9bdsmj6B7LZVZEfOQKpW\nMH+dio8ybIy2efBddCY8/zyudxaResvN/1t+FSc15Vk3ipNKuLUPYdCgdugB+HhFAwB7tn2AOimM\nqVzP5ul9bLOr+EbLYIZ4S+mxn8ZuXwMTez4iqkvnhW9ew4Mv34uuL8LGogwiunRyhpVy+xMvMu3C\nSxBll9B5xXPcPmQMBmMqD12+DOM3/n7YIA+gUQleHDWIHxZkoNLHu3uqNCqcehUbM3SsSdcwLcPO\n4nFDeH3acEY4/scgf9DIiyGjjJQ1t3Gp44eMtb6HJqOYmDEZUKEmRHJGLTHrLkqNH3JOaw5dkUIq\nhpoZrzrAD8NPkFPt5aPTJ6M2FRLCgtOYjHV3B+m2Bt7qfQ/j+PG43nv3y8mv4qSl1OgVJ5Vwuw9t\nhgkhBC5/mCUf1zAYwQTtKkIRiV9YeD7LS6nHwSjXWYRSHfzFU8eFnUtBZWPJ3G/y6Dt3k1Ldx478\ndExpedhyM7jkl79Fo9MB0OHr4NZlt9IT7GXhnIVk2j77fj6NSnBXcRYLCjNwhqOkatXUBUJEJOQa\ntJjV6i+/MNQauOIFeHcBxqCHKVdeCfnx59S4Onx89OD7dLbnMnrQGio8Xuabf03Ftvm0n7OfWJ6R\nyxtX0FKXyUM5N7Joqpkxa+YwtOtNxu638v7kGOaelZjO/jbd9/+JUGMjury8z8iQ4qtKqdErTiqR\nbj+aVCMAb2zaRkZIhc1fS2hyhOQKB//M60HEVJxT/UNKzen82XyAs5s3oY6FKR91PvcufpusdV3U\npNkpHuollmzjort+hUanIxwNs6R2CVe+dyWNnkYeOvMhytLKPlf+9CoVGXotapWKQSYDQ82G4xPk\nD0oqhOvegRuX/zvIA9jTTZz3y/OxmCNU146mrK2Zp7iYfcl2OurG0FUY5WP7WH6ofYWbNy2hISuL\nriEWXJpUuqw2zFsF+bowHyetAcC7atXxWwfFgFMCveKkEQtGiHnCaFKNeL2VLNmwiZSYijT2oFVH\n2BbpZLvBwIzGeZylS+GD3A+R5XbsoSbc6TO4etWrJG9bQ1OSFfMIP/U2M55Li/nV5nu4aNFFTHpx\nEneuuhO7wc4L815gSvaUgV7l/4nBqmfej2YQFMl0m89m1qaVuPuCtHtGEotpqSsxU6HKY4F8mKv3\nbGfDqMHsyT2dmBBM2G9ndcTL4uBG1Jlp+LduHejVURxHSqBXnDQi3QEAwqYOtm67hr6uIgDkmHZC\nu808mmynrM3ILf55dNh28UrVLMa41hHT5TJj1/tk1u6jPjuX5iEWFpf28fuSXv5R/iR7uvaQa8nl\n+hHX89CZD/HG+W9QklQykKv6pUnJsTD1kiHU+0ehKyzkzGXLiXgEbW2DyNMf4E9DrsWNie93/pG0\nPj81EwtRCQNug5Vws4qAhL5CiW/HjoFeFcVxpAR6xUnjYKCvaP8lbb58ivpiqGJhQrZunncE0Ufg\nDvedBCX8WJiY17QZIaOMbGoio7MV59h57E3Xs290Mx/Yjdw06iY+uewTlly8hL+d9TcWjF/ArPxZ\nqMSptduXzcylYGQKWzXXYU8SzHn3XWLhMxDEmBCt44A2jwJVG/ds+wdtqensKR5FRK3i7L1mmsJq\nPrSGibS0Em5vH+hVURwnp9YefxJo8jQRk7HPTqj4L67mnQDokm2s672LQcEwMXMd+zv62GY0cF37\nmaRGk/mVuo0JdQF0oWrS/EZyO+sxTrmDPeou1LndLEnV8ZPRt3PH2DtINZ76D+4SQjDruuHozAZ2\nDb4dTSTMtDdX4XelUJz+CR2WPHbFipgvF3POvgMsnTUffSSGo8eKNwYfl8TvB/BvV2r1pyol0H+J\nXq54mXPfOpdvLvom65rXDXR2vlK83kq66lYg1WFGTXiC1e0+dFodTUnVvJfhZnyf5FzPN/lHMEDM\npyfHvRINZsbt34lu0o20GCVhg4eXS91MN+Vx3eivV79wk03H2d8uxRnNYs/k2/E0dTHkhS7UmjAh\ng2CoqCeGikvankYdFrjtGbgNeqb1SLrsYXx6gW/H9oFeDcVxogT6L8na5rXcv+l+xmeMJyqj3Lzs\nZv62/W+cDHcefxXsP/B7tMF0NA4z67d4yOnqwGtvoMGyipAQ/KhjBs8ZdrNODdN71oP0M7mykrYx\nkzCklbHHuYb9I+qQKhW/mv3I1/KpjPmlKcy9uYxezSA2Tf0t1ZFLiW7PIrlgB31qB50qB/NV65i8\ndS8rJ84EYGJlKiEZ463TdPTtUJ49eKpS+tH3EwqFqKyspKenB4fDQX7JUCqDEZoDITzRGEaVCrOQ\nZFe+ifbAUrQqwe682bwZy0Gz5s/MdOYgDA7Khk/DmVPNE7ueoMvfxa9P+zUalVLUR+Lx7KW3dz1p\nsevQOIy8vKmRcV2NOAeVszvZy/UuD57IeN4J27nU1YYM7SW/p4+YQUtJ/repdG3BlNPI8uQwPx18\nBdmO4oFepQFTNDqNK35zGmvfqKKeOcgDczA596HNfo8z29cTFirmBhfzO/sdnB+OIjtMZGs72TxS\nzVXP1CCl/FqeJE91p3z0iUQiLFu2jObmZlwuF7FYDJVKhcPhID09nbycTDoCbdS0+eiuqCYUDP57\nWp9Oz9pBZVSn5wIwwbWHPxz4C2XeKhqMWcioJHdbOeO6C1DFTPhMFgg5Yek72LQ6bph8Fs9UvI03\n5OWPZ/wRreq/X99W46zhk8ZPKLAVcFb+WV/Lg6y17R2E0KL22/CnqmkNSHKSQ1Sat2GNwvW9KVys\n8zLOlYHO+yY6BENbOgicdTsRGaTF/wnPTuim1FLAVVPuGujVGXCOdBPzbxtFnytIxdpmNr/vprz7\nh6TmhEmLHuAy9Qoeq7yYiM6MO+pjoi7KIpuKDm2Eko4OtBkZn70QxVfKVz7Q9zmDmBO3y/+XWJQP\n33uHTTv2kJ2VSWZmJgaDASEEvb297Ny+hfiz1CQgMKp8VOcXsqpwClPDXobu3cJ1y/9FblcHBNvo\nMJlYpsrkQ1UGndYwZj9kuqA538fotMlc4T+PGDEqkg6w2bMG1ZoqLrcUsap7NXdxF/dPvx+tOh7s\nw9Ewj+96nH/u/ifRxHvTry29ljsn3nlCyu1k0tW1jOSkacS8EaqdfoL6XoK5m6g2h/lptwtP9Az8\nfcWMdG0kGnMzrqqZltKRjDSVsbnzfWrGuunRqXlk5v8p/zn1Y7brGT+vmMLJFt7+wxLWti3g8tQ7\nsIgAs8KbqEsbRF7HXkbtKGXRkANsGywYV74Te8acz5654gsLBSJUbe2go86N2aGneEwaKTmW47rM\nr/RR0VrtYtFftnPahcWMPiuPUChEV2sDvTs/wNe0i8auPnbLEqawhXNaV9PXaqSbJMKGNKTWyMfR\nIlrIJKpWoSGCP2piUH07k3Y9SvG+SjLanbTaLVSlO4hYUrH7AhR5nYQ1GtRRC6qYZFhrG6fXRNCl\nVRMrfYoVmZkUBEdwvfUGyg272dSzhLO3pNPQuIMbWq7ilhk/xB1y8/iux6lyVnHBoAtYMG4B/9j5\nD57f9zzziuYxMnXkQBftCRMItOD3N5Cbcj3EJDUdfZQY91Fpr8UegMs8Hn6jzuRMj5tocDs5rj50\nVklp0Q20+etwZdfxVpKTm8puojSldKBX56SUkpLOrNkOlizSs0x1DTNi/+QS9UruNN9MntyDoSqZ\n9KGS7YMEXTs+wj5TCfTHJBICtRY+x3/hrdUuPnh8Nz53CL1ZQ8gXobvZy9ybPt8d2p/XVzrQ94pW\nnKYm1r4RY8WyVTgNVf3G5qAmSpboIi2nhHrjSHS+Nrpam9ntyaRWU4xQSz4eNIYat5302mbObVvB\njJo9pPV6aEm2snZIHhG1Co1QE8oppMVkI9rWQV5jAxPrO9mdk8r/Z++9o+M8y7z/z1Om99GMNOqS\nJduy5N6T2I7jJE4lgRQIEEhgaYHd5QWWFlgSQltYILu/XWApAUJLJZ3ETk/sOHbcLRdZlqxepvf6\ntN8f8rLs+y4LhMiKE33O0dHxoynXdY/Pd+7navfuOXXUlGDZyX7UZ8psaF/MyIrdbLWFWFh5N9fL\nn+LR4n3UxgZoeKTAz/d9noPtaYI1Dfzref/KpqZNAHxixSfYMriFXxz5Bd8691szs6AzQCq1GwC3\nvJwcaRIVFaenl+NOjRvG7Zh1E6/kz+KK7FOASMdkGNOmD1AxFA4WnuSOZSfpquriI4s/MrOOvM5p\nvegKah69jfDEBootd7Oq0IuCiKOsEpXjdApedjakmdi1h7aZNvb1TjEFD34Eep8ATyN0XAbtF4DF\nDeUM6Co0rAbHqaMwdR0MjX3PjbLzgQEsTpGOS21MyCc5OXacsreGi5kV+j9KshSjZD+GXSlgSrWz\nxH0Iq72HwyzgUGAx9kqOciZAvKeAKd6HUIgjYAGSOKRudLOTTf3jvCs5SX0yjlVVibnsHKkLoAtQ\nZWtgqW8jAUs9FRSO62Oka6o5EBQ4ZhrlSOMoH3ixijAOHl+/nPUnY9B3kOrJGuYsn4u/5puEzWfz\nVuE69rd085hyP/NGXXRMeFl20eWs9f3XGGmn2cmlrZfyYN+DZCtZXGbXzC3saSSbO4ogmLFo9eRI\nk3YUiRuHMKkG7yyVGKWFNZkkhjpG51iC9AUuWqWlPDNxFw8uP4nT6eHfz//334fEZvmfEQSBuQEH\n4bDMPtOFNPMb3irtQBesZMQKC5OLeN69nbFUbKZNfX2j63DP9TC8E9Z+DJIDsOdn6Dv+A0MXECUD\nBBFFbKTb10RJ7WVZJsPO9I0cK25GNo8xah1iZN9U74IZkZIan3azz2ihT73wIN6eo/gsL1G0Xsxg\nYSN7GlfgUGO0xU5QkWTK5Sze0WMgQMVfjW6xY00lsGWTaJUisiSRt0Gvberb1yRYCLaU8XYmyXAW\nO1L1CEmNXZUi/UYASQsQoh2LsYaWicMEw69QceSYjMPW+XVU1i5g89btVL94N4ca2yg0ZVjk+il7\nOj/EnrqVjA5+mcUDOvsef4Qjzz3N8sveSkNHJ8GWOVzSegl3H7+bl8dfZnPLm+P2OZc7jtMxF6Mw\n1WSm2bo54izSOe6hjqP8XLuCmvxBRF2mOhBHq72RXUe28FLoOLWdnXz1nK++KZqiXgvmveNSDvzr\nHuLh1UT993Ft7gV+Jm/AJYZpH5gPS7YzbtXRVQVRnv3i/L8p5RRe+dkTDPW8i4rp4zh22LDlxrGM\ndmAk46iShZ2dTbywbC4xrwtRq+CJpfDnZdZUbGy0Psw6zy8YlqvYGerCFC9R7C2jJaZfhs9ood/n\nWchIp5mmkT6sqe24NzzF25tT5PNeNLsd++EMgxEXeZefBakK3p4hvMkkzlIBAF2QyIbmI9Z3kQ+0\ncCLoxTL/QWr9B1A0G2OFvdwXbiJS9GNIYLalCJZMjKg2xivVGJVNHFh2Hi4px5r0HuZP9pDKFXjg\n3LXURRK0TIzRvG8barHCnOEC+nXvp9n5eQ7P+wqlOoWLuq28fN+vAXAFgrzjK9/CZXKxfWz7m0jo\ne6jyb0BLTJ3paontIFMNC9UFCMJhBlOrcKtbaEgVib/PhL5D4KBwgHWXX89713/gDTfOYDqxdnQQ\nHP8pA8KljLTXsrwwTMHhxFUKo8UmsWsiw1U64z3P0rDwopk293VFLlni4dv3kYnYqJcGEYs5cmNl\nos46yv514J+K0/tUgfYJEyvT27kp/2vmF4coimYe7jqPb/ov558tK4ha/FiiaS45dB+KycPJ+i5u\nmGb7z2ihN+cP84m2u4kuMXPkuTlkt1uRDT/uZIl4j0REddEQz7DwYD8iMOYIsDs4n/EaHz3eZg7a\n5oMos6kisqAsIY+IaLnNPF57Do8b9RQmRJAF1LkutGYHZameLGBRVGpjKo6syoiikk+beV7dyHFh\nDisy+6kf6EVCZ9RtYtTdiFk0s6r3JD/5xhf43Ec/zd8nPs1P/N/hZxdkuGxnHXOC9RT7j/G77/4T\nKzeuYG/4zTFJUFWzVCpR7I420icK5IUs49II1jK8RZfRRRFPJoJhCATWjpFMraY3uY2zmy/lknM+\ngDAr8n8RgihSF9QYAAYrq1nCMG3VEdQ+hbAxQovmYTCUoHfPY7NC/wdUyioPfWMH+XSZZYf+DU9y\nALm6mtyFN7Iz7MNA45nFEimrk01HJlh10oVJ6CLsWkeVO4ODLNfEn+TK1FNM2Hz06UGO7K3CbNFZ\nuuY4C0xR4JZp9eGMFnrL6BALhBzteQkhorJbM5HcJpHECWg4lDqOOES2b3Ji8yssz42yKjLEyeoU\n7q4wm+R9KAfejlj0cMyskhM0WjK1zEmKXC+qDJkmaSsVcJt1bKVeMv4YkwEH0fwCiLfhj9q4NC1i\nLcuAjG40MemtYreUok9wYNUrbNAOE4j2s2teiHUn03z9+9/i6x/7EjcP/S2fa/4mv1ub5JqXJRae\nfWm8EaIAACAASURBVB7Dz21hflMXz1mGSZVSeK3emV7iaaVYHAXAZm1gLJInpTxFT4uKJ95IKwPs\nz50L6jBWw0TxLJHh+0q0BaysqF2HIM2K/Kuh+S3nsntLgezICuLBh7lQ38ezSjMpKUNrpYGnggny\n3f0zbebrBiUcZuvn7ydtms/ynh9S3Zok/plv0D2kU+jxoJoybFmocmBuO1f0bOEK30850jAfaeIc\nuhPXcCx7GXWuPdT6dhOSj2NJ5unr8+IUK1xXdxDXZIWjpvy0+3FGC3083MRdk5/EMAdI+eYhq6Po\nhe1oSCiOhWjyQprUe2muvR/JrlDyG5xsNHDqOmeNuekN34Jh2Di8bJSIX2X52BHOSu4hWHTTnX87\n3nIDftMgxViWRxOb8aMwRzFTr03V7ZtdE1hrhrB4xkAwKMbaEccXU2d4WGIf436zm3uETVTVLuWa\n8QfZ1u5jw7EiH7vzdm7/0Ff49vFP89E5t7F1Mch9J2nvWsLki0dwniPRHetmfcP6GV7h6aVUGgHA\nZmsknywwmThAcpFAQ3oFFvFXHJn8IIb2Mt6FCSaGOkj5RrhKvg1Lq3+GLT9zca5Zje+e+4gInUQ6\nXCzMTPIk86lIOk3ZJirObvRIeqbNfF2gF4sc+tgtDAfejt8zSvfGEFtZSuilIbTcIkKWQzSG7uH2\ntn/hrSfuJpeCDxS+yM35u3mb+Rs851vPwcxmBjMrGM6sR1MGUfKPAQKaZQW/mLwISlAW80x3YfAZ\nLfS1FSuJmrMpiwmG/bsZCj3LkGsSRRDA2Mmlxz5MKHcFd4UOUTRnf/88SZe57OhN1OgBts/7Hn79\nCOvGK8xXFByyiUnRimH/Pi55JbH8WYiVFjYBFQnG3b3s9x1h2HuUssvLEsnPvNFGFpb8tDYcJ7b4\nfmK73g3JBfxdbJJX5ChPVjfxePVFXBl+jBe7mrlw9xGWvXgPX7vwRr5w/BPc3PRPFEsOItVLEASR\nFb2+N4XQ/35Hb2tEzu9lXJuqPrhY9VBKl0hLMqjgXJDn2B4nV0+UEZqtmBvfHBVJ04Gpro5Q4hBh\n7yIG9cV08jwuu0IeiZpJM9RCVivMjkIATn7tG+z2bUQXyxy3DFIrmukyWojkmmi1vYAQuI+Pt30a\nz4EneTa+mrnCGPeZv0wTMX5jW8F3Cm/HK6ToLD9DlZLCpURRJB9W5yZMehW6aMVwydjzPdPuyxkt\n9MPtWZ6o/QckwYqtYiUkxLguUwDRTb1SodH5I7rT3+bynr+hOzBOONjASG0dl+8VqctKbJ3/BCO+\nASyagx02O4bwhwPI8sALiMbzLMxbWFAy6NAjiMU2usurqRRWoVj3sLcyxGhjlFLPebTueQet7a2E\nNv0LgROXMN59BYuNEBdNTrDfyLPdfzbrEzvYvrCdy7Y/yzOr13HLig7OH/5bnlp8H1f2jNHctRRt\n/yscP7Ibls7Uyp4eiqVRJMmBJHkw51+iN6RhLtnZrCQ4dnINqnsMrCYS+VZiVVFch1ZBM7NC/1dS\n3+rkEAbJ8cVEnC+z0D/GZLIJPRYBYNKsMDK5j6baFTNs6cyxb+tWnip5cUrVVM1Pc0nkSXbnP0Ek\n76PB/Shfa+pmovQZHIdUlusSNzq/zbnKETRR4LbQe7EeEHlX/F4AdESSZj+9/sVUTWS59MC3MQSD\nJ5ea2bpCo+Rs5yN8dFr9OaOFfs5kmNsrf8tABkqFFvz2B1nR8BjzMoNkTFa+5P4ACbuVNSdamVi+\niLw3x/v2pKma8ODPPcr/2foCL8118MRCBUSFZaMXUp1rpigVyCgDNKROMiosZnlVnA+bfoeVCnCQ\njwsHUJMik1Ervwg6uM8Y4dn6O9lgf5p9yXPZPeKlteoprl4TxnfgQ4SppYFaEhaF3V6NValdPLeg\nmY/c/T1+8P7Pcc/81VRnfORPPs9gScdqk7Fvn0B/59RcnjcqlUoUi6WGSLaCFt7DsbUClWI71dk+\nnhNXo6sH8DQnGB1bxfrcNiT/RkS7hOT7IyMvZvmzcJ+zEvu2POXwEjIrZRalhnlpspasFsWmSYRd\nChO/+B5Nn/3pTJt62jEMgxdeeIHnX36ZQH4BNrdElznK0/F/xCwWOdv3Hd5f48I7/la+I93L5dJO\nTLKGpsKw2cdHgh9g83P70QSFgWYdp7mZhmyISZ/EW17cQtt4nJcWCPzyXCsILQy2nc8HTzqm3a8z\nWuhdHTaeze0m1HYCYcfN5OSzeCXeRlLdT3f4KuaoRdodj6KLa3jntqnnGIYTpfgM48oJxj112CNw\n2TaNF5fG2N/4JItPtrB65Aok0yrU4hYClWPUlNI87lxIWrViFBRCliz19jQ+c5GPhHNclUvz3YCH\nRwMTELgbgKNFmT7xCFct+xYnj9/A6phIZ6mamHQWB4N+VsSeYiAgcfEjP2Czp4FHzr6AOy+4kU3H\n9tFuGSdwYJzdL/6ONRvfMoMrPL1UylHM5gDHR1OoyTApp4Brcg6ZgS1Eq66EwssIDhMxo8Q1gy6k\n0DzMTZ43fUjhr8W+Yjm+x55mVFrEqD3EPLkfu6qSlUsENTsxd5rgkcMzbeaMsGPHDp5//nkaB1OU\nrEEcLjPbD7YTMo2huB/m3eLV1IR1HjT/I1axwniNmXJR5mbXBeytuYh3PraVnEnlibUj5OwyMElT\n+gq+eOfdyJrGd95SxQtV59NcWsPJ5TUsyhj4so9Mu19ntNBXL2nDX7wTWYRox+NkxpaSKfrZm7kA\np5imQcohV2rJiScpiyFshpOQScDlXI3AWkr0YxO2IBvHuXzUwvfxsWPOIO6q+7k4upHdai35cgwl\nAqYY6IJAzmVld22Z/Q0mypKJs3p8tB92sdSksqpLZI2xj7aKRr9h4guNfu6Sx3jb8n+iY8xM5ZkO\njtfdgE/voKfexTt2fJ+0WWQClau2/JrnLnkPT3etIToEq/oG2fPA/azecBnCG3RXX1FiOJ0LmDwW\nRiEHSKwoOohEgyiBqYRgXqwFVKQxDbG5CnPD9A5/ejNgnT+fuvF/ZNS/iPHoYgZNaaqkInHJRqDg\nJebO4KokMFQVQT6jJeIvor+/n6eeeoqWfB53oYFJO8TGigRsCX5izXFMex8+KcnPHF/ATpGXO/wU\n+7x8cs7lJIJXse7Ay1SlYjy9MkLZfTaGdTMNg1/gS7+6h7JJ4N+ubKDxWCOCYyWHzwmBLHJj7AXE\nlb8DbptW387oT7GpvIzu6FM0RS+gLbIWWRTBbMWomkokaYaGKBgIf+Cm5DEjB2wolQpiWEJKN2A2\nKlRZ3Xwx5uZeYyu/DD7Gds9vABB1CW+hGlHPkXBm0UUIVPwszq3BWi7wUsdhjjbmuHhvG5b9RQ7Y\nOmlr6KYNhSu3BXloWZYHqGCfk2Fj3TbOubfAk9UfozNfz76Ot3Dxy7+iKZXihQVzeefz28hfpXKo\naRXlZS/xlm1x+vbuYu6qs2ZqiaeVcjmK378e06E99Aenrl01MELMsxBdmQDJIFVupNlRxlDtIAiY\n6maF/q9FkGWqGhyYBIPc2GJizS/R5ozSq/qpH7NwfA6opQqllx7Ddu5bZ9rc04KiKDz66KP4HQ4W\nPbSVXWu/iqGD1znIl00+JNHPhQ3Pcr36BC3xFNv9IWIvVPPFdetJBq/ivOEUZ+15jnFvhaLLzlX7\n5+KbeIBlvQKGIHLH1SI3vTBC1cAIqUubeFhu5Jx4AW/THcQHQ9Pu3xkt9C2dq3AdfBdquAvBLoEK\nhqLjWFaDa10Q+acL0AUzJx+rAWsLgrMOydOE5G9BMLuxyQ4wyagOg+PeCDvEZ+g296ElNiEJHs5u\nbuer51/G0bt6Odidx+4pstJjxlMwI6hT4YPjhR6+Hvo5d53Xw/yhamyJeoZT1fy99wWuaTvBgzW3\nEoz8il8bCSw+gbk3HmHz1gM8yGL8lrWM1+ynNnyEdi1CT0HjCy908MlLJ+mbfyOFwz9g98P3vyGF\nXtPKaFoOizmId/wgY1UCQsVF9WAP+xqvR9G3Y/MoZA0zCz0tSJ4cAKbQ9Mcz3ww4167E11Mhmuyg\ntEZlnj8PEfBHFPJdAhMlJ5ZdP3nTCP327dtJpVJccPgIEwvehoGA1TnBrY5q7HqGdy64nyNj7ZxT\nGmfY5KB3Tx1PdLaSClzD2oks79m1m2Nqge62HGsnzsMVfo5l/RGcRYNtV8l86okSckQgcYPBwtA+\nHjMuZ2HuYfYfu5J3brp42v07o4XeFAphnX8RufAYgiSDoBP80GLMDaeqMq75CaKzDmf5IQSLFTkY\nxCiX0LK9iA47tsVLcG7YhCDLtALn69dRUAo4TI7/Ntd83UfWULMnzLN3HmOvYGLzB7qo5GOMPXGM\nttQ8fjTyFX5Z8wDbqvcQbT7EfkPixNAmfpTfwj19N/MBfydqzsWv1Bx/X1fEvP7nLN99O4dyBkfn\n30gwfiutvQn6ukJE4j1cdyzPv3Ru4unVi7BvfYX46AhVDY0zs8jTRKUyNTzLbA7gTAww2i7iTvhQ\n42HS8xsRMnF0qxlNUKiOJxC9jQiW2UTsa4V9xQqq9x4l4mgnUZrDhH8Cx0gFRZ8KE45jpjOzd2qI\n1xs0dPifVCoVdu3aRath4D3aQ/fGm0DX+XZNEC1e5j0L7+GOQ+/hnuZPIOUNnu2bh0tXOdD5Xmpz\nJW4+VOTZ7IvEfSWatMWcsMa4cCSNJ51m97wqNjwcRzAJaF9cTjm0iwXlIX64ZycJbwfXffbdmM3m\naffxTwq9IAg/BS4HIoZhLDx17Vbgg0D01MNuNgzj8VN/+zzwN4AG/L1hGFunwW4AtLxC7uVxBLuM\nUVKwNI4R/8lW9HwByeXCMn8+tmUuQrfd9mcl8EyiCY/F8z/+be7KGnwhO1t+eJiHvjs1A//sz16O\nUVTJvTLJh196Lx+cvI6Y3MfHa+5iX2s374ou4fuFg/xH7BDfF2/kt6Hn+OmIg8+2Zamr+RVPaW/H\nnbfRM+9aFh39OTVilrFCH5uH1nJXay99DedgSHs5/PxTnHv9+1/r5ZtRFGWqZr6CHzkRZcIvcOFO\ng7S3DcPIIhg6RZMPCQnX0WPIoUsx1TpmE7GvEbalS6ga/RHMb2d8rIuYswq/OsGkPBUaS0kVzEIB\nDv8WFl87w9ZOL/tffplSqcScp58hdfVn0KPQ41EoxjUuaHqBZ4+fT6f7ZboiGU7Kbnx6ie+d/SHy\nDjef2zXBXcKPaagY5FurWdxXZt3O3ciaxtFWJ6uPx1AaDWy3Xk3r4psxHrkN+8AyLPl6TFb7aZu6\n+ud8Vf8c+J/uLW43DGPpqZ//FPlO4Dqg69Rzvi8IgvRaGft/o6XKCAIYBZXCS/9B9Lu3kn1iC8X9\n+0k/8giTt9zCwBVXcvKyy4n9+Mdo6b+u4y/Q4OLtN69iztIALz/Qz+M/6KZigHtjI7WfXYXvmrmE\nvJ38Yuxm2mLncdyf5cPeueiSxM3qT/n8pEzcqvHbITPFuS9yra3CKxaVaPUqEp52OsfHAINwup9z\nR1NopioGl9Zz+PnH0XXttVm01wmKMvVZxJMOiuU8RYvB2QMpUtXtoIUBKAs1uO1ulMOHEa3Vs2Gb\n1xDJ5cIiRfCIUJzsoidYS7UphyJJOAsiCRfkwj6MLZ+FzMRMmzttFA4f5qXHHsOXTNL50Y/SW2wG\n4HlrGYdcoMZIclSZw6ed9yIAO8aa2R9aSvf8DpYP3M1PArcQGiyDaNBQbmDt7n3ImoYmQOdAjuwi\nG4lPSzSv/ATpB4fwH30LjddfQuDdC1DDBWJ3dFMenP5O5D8p9IZhvAgk/szXuxK42zCMsmEYA0Af\nsPqvsO9/pbD/MEbFQBl9Cd+159D29NPM27WT9qefYt7uV2jbuoXQrbcg+XxEv/Nd+s7bRPib30KJ\nRF71e5ptMhd9cCHr3zGX4SNx7v36biJDGQRZxLEyRM0nV1O9Gf4lsZSNvV/nwPiX+GXuP+j3VXGR\nFuHDyTS7ZJnc8SLLLH9Hh20/eVT62q9GnjAw22QGc4d4+7AfUU2wp2M9pWyJnj13vYYrN/OoagaA\n9MkyCauKo2jQMpki52tFF4YBA91iI+hxAzZAxlQ7K/SvJc6zVhOUDEzpOVj9Bezuqc1E55CZ8YCZ\n2PAKqOThgQ+Cps6wta89aizG7s/fTMbh4OzNF5FfdhHFnErUVCZbltnQsIPfjV7IJu+jrElkGPA6\nMWPhzvVnExz9FEOmJ7h6WzWyJnLW8TGufOQxrJUK0aAfyYDInBpyH87gmzyP6C1HKB2N4zy3DkuL\nB2tXFYJVonwyTfL+/dPu618TfPs7QRAOCYLwU0EQfKeu1QMjf/CY0VPX/h8EQfiQIAh7BEHYE41G\n/6eH/Enc5y9DcgzScPsHCdx0E+aG/3orQRAwNzfju+46Wn79K1ofehDnpk0k7ryT/gsuZPKrX0MJ\nh1/V+6IZdC4NcuVNi8GAB/55H0dfGp96X1HAtmkjLZdmWdH0GBvEEW43bHxz4gd8LnobUs+3qc2Y\nuCVYRdhS5mbLN0k5TpBzNRGrWsLcSpSCmsOV12lIH2fIOZeKycy+5/6D5KnTmN4IKOrULkY9ESXm\nFljeZ6CLZlKmFnTCCFYDRIl6XUdyTx3OPiv0ry321asIaBkEQ6ImaeJwzXwwDNrHrQyH7CiKnWjL\nu2FwGzw9vdMVZ4Lov/07x+tqsVutLLtoM7t+OTU1dq84gSyouMtFNEPgFv0hShaRHalGvr1+M47k\nv1BdcfAP+96JUBYIFBWKHh+iYRAJBnj+3PPovWgZ1r9zImpWPAfXYKgqhmGQefh51GTyVAhyEgA1\n9jrY0f8RfgDMYapJfwL4zl/6AoZh/MgwjJWGYawMBoOvygjRZqH2H9+DKVj1Jx9r7eig/tv/zJzH\nHsd1+Q3kXoky+n/uYOwL9xP90T7id/eQfKiP9JZBMs8Nk90+Rm7XBNlto6S3DJK49ziRHx5k4hu7\nGPvHl5j85m70Xx1lvWgQcMg898senvt1D6pyKsRyzse5cf1Grm94mvdJW9lmKDznDBLWTazruR5V\nF/lcYxUJuZZbHV/C0NIMtFxGy8jU3cZQ/giXTFgwBJn0ynXkx90cOPC+N4zYq+rU7CF5ZJSYG5ad\nNIgF5mEgIugJVIuMaAjURKOIVS0ggKlmVuhfS2zLluPJDGIIGuXxEI8EN+JWynhyZiI+kTR5ehIW\nWP0hePnfofv+mTb5NUNLpRjbupXxUC2r1qwhM5EjkpTRMDhh97Ci5gBl3cRD7i9Qr5Q4NtfJC4EL\nKWiP4tcCfLrnRhLhl3CUFRLN8/Ck0hybP59XNpzFDfYn6Tq/mZzlGFWHL8LW2IhgMmNu0hFdc5n8\n8o+J/fDnaDEDUKj51AXT7u+rqroxDOP3W2FBEH4MPHbqn2PAH5aHNJy6Ni1omTLJh/ox1zmwLQz8\n0RiuXtEodscoHIhQPpkGfRXWzlVABTWdQD1wHMntR7A4MBRAN/77C4gCktOE5LdiafMi+61IbguG\npqOEC5zVHeVwSeTotnHC/Wku/vAivDV2hEVXsbnjEtbdvpir9QPYUrcx6rJyKNvJkv429szr44dz\nrdx0zMdS54MclG4krizAbjcYzR/juvH1/GhBkYE5XVTveh6TWEt390dZs+YJLOYz+1QlVUkjCCZM\n4RNEfbD+iMHIvA4wymhFFdXuwyTIOAb6EUPrkP1WRMu0pXvelJjq6xCyA9iq2ilH53KoM8C7hRIT\ngou8nCOvpcn2H4GP/RbG98MTn4H288Hm+9Mv/jon+8wzDNbXgQDLly9n579uA8PKsCNMyfCyzrmP\nG5PbcSgahzrdbFXO5rk6sGfS3NR7HQcmH8Cm6rSlSkw6k+Q9Hg4tW8p7uR9jzgaGrQ9gS8ylZeVN\nZJ6YwHluA57NzYzf9hS6sYbiiQqiw4R1YTXlvjSm4PRuYl6V0AuCUGsYxn9maN4G/Ge/9CPAbwRB\n+C5QB8wFXvmrrfwjaFkFNVqgdCxO5plhXBsbcW9u/n1lhqHoZF8aI7dtDD2vIPmtOM+px9rmwdTg\nQnKYUMbGiP34x6QffAijXMbc1oZz43nYVqzG2tGF7HcjWKT/tdrDe/kc3DsnCDzaz96JPPd89RU2\nXt/B/DUhMNmwb/wMCx//B25vHOXakWYedeksjt3ISOuXeFwssFG8jlWuH3Ikew2jDefRqf+aPWqI\nSilOQzbFflcTq3SdoO0TjOU+ycn+77BgwTema1lPC6qaQZbdyOkhtCoJT0Gl19WIVTxByRAwzF6s\nNivKiT5sZ187m4idBgRBQPZDjSxSytdQb85QtjlQVImW8QoZhxUtVgHJBJffDj/cAM99HS7955k2\n/a+mePgIg61zaGlpwSzaODlqwhAMjvpTeCtwfWYH1oLBwS43g/4AvyjdiD36JdaGFzMw9CIWQ2Xt\niTGOLl7CsgMH2X3OWcw3HSO9wEvCuxVLuoV5nq+TeyGKXGPHc2EzgiQSvGkV47c8iuRpAkGi3JtE\ndk9/yfCfU155F7ARCAiCMMrUUSgbBUFYChjAIPBhAMMwjgiCcC9wFFCBjxmGMW3lIuZ6J6FPrUTL\nK6SfGCD73FR6wHNRC5WxHIm7e1CjRSzzfLjObcAy5/+dk2Kqr6f21lup/tSnyGzZQuaRR0n84k64\n4ycAyMEg5uZmTA0NmOrqMNXXTf0+9SOYTAiyiGtdPYvn+/DfeZRdA1me/tlRxo8n2fDu+UjLb4Ad\n/x/vTn2Tk5bvsdmQucMbY03fRh5d8Az3Ne5g4uQG5qnPcLTqLXQNg+CG/vwh1seW8RuvmaSniuxk\nibp572B8/F7mtH3qjN7VK2oGk8mNKZsgkAFdEClIjZhMTwOgma14/W7URBoEx2x8fppwbFjBnKN2\nhjBYlFLY6V9Ma+QYCwdkxkNeWq0pdENHDC2C5e+FPT+DDZ8GZ/VMm/5XMTI8RK6xkfOXLOHwg/vQ\nRROCI0JvoZ5bPD/Dl1H4Qu0FGKk+9lj/AUU9gEkv0XBYQiZPUzaHRQdbWQG7B09zA9VLtpGQy/hP\nXEZN8Z2U8xUESSBwQxeCLFIeypC8tx/ZNwdLmxvXec1YWj0I0vSXDP9JoTcM453/w+U7/pfHfw34\n2l9j1F+K5DDhu3ouqDrZF0Yx1ztJ3Hsc0SYTeP9CrPP+9K2m5HLhu/ZafNdei14oUNi3n9KRI1SG\nhqgMDZHfuRM1HAbjv8I6gt2OfcUKXOdvwn3JJZiCHlo+vhzP4yfZ8+woR3dMkOxLseltbVjmfg37\ny/fg0AZpEFpZSCOPzPkVCwWJbVKERmEjm7y/4FjxcgbkzQTtO4nke7l08kJ+0w6J5jYiQwN0nPdu\nxsZ+RTSyhYaG66dzWacVVc2glOuxZ/fTGtYZqWlEw4xkmcpRGCYLIasV0VUHCLNCP01433IZhUOv\nkLYlaRh38zP/OdwUPkJdwsKJJU7MMQtjPY/RuOAKOOtvYe/PYe+dcO6nZ9r0V41hGAyUy4jAggUL\nuP+OpxBUhXBDN1p4LW9RdzJs9fFbV4WVEzdwqK2LuQO/Zm5vI85ygt45Opc8Mslgaxsdx47iWHIF\nwWX3g+6geffNWLPN6KhIfiuB93YieS1knh0m8/QQktf6Z2vSa8kZ3Rn7hwiCgH15DYUDUeK/OoYp\n5CDw/i6kV3FbJNrtONedg3PdOf/tulGpoITDKGPjKGNjlI4cIb9jB5O3fpnw17+B64IL8F73DnxX\nrOLcJUG8d/eyZzDL/T86zLkuLzbxI/hQMDC4AQtbSrUEzCqHhSSDgV50sUxg/CDj1WtZyiNsV+2E\nkknMqo1oTZBoz2EcjrnYbE3E4s+d8UJfjCwkUHyFtkk4uKAdAF1MggiGJNNYqSB5TlXczIZupgU5\nEMAoRilWRwhFajHNAbOggWHBEAt4CgUGn/71lNAH5kLbJthzB6z7P1MhnTMQZWyciaoqam12spMl\n0qoDXRilFwvr5W68Sokf+zcRSkZ5edFGlob7mGSUtuE55OxOzikdQtYMVGcASRzj6MXP4CRP3YNr\nsVY1I4ccuNbXY18cRCsoxH7STflkGtuSIL63tSNaT7/svmGEHsDc7P594jTw/oVI7te2tVgwmzE3\nNmJuPJVvvvoqDMOgdPQo6QcfIv3II2Qefxxzexu+d1zHio9dScNwhYe/d4jhdh8br6hC/ekG0rn3\nkTfOwpFdxG7vIZZaNXYa3TwfW0ir9iJR03IqqbmAwsnSIdozXoar6omPPY6mKPj9G5icfADD0JjG\nfrRpRVXzMO6mYAZnCQquBqxCAU0tgMmHIAj4x8apVLUgmCUkn3WmTX7DIkg5bNapWULzFJFxe5Bq\nLUHn8AmaVBiOtWDkogjOIKx4H9z7HhjcDm3nzbDlr4549yFSPh+LW5o5+sQR0FWsgUkOJTr4luUO\ndB3u9tbQULmWE5JA7dg2nHknVk2l0JFjwcMycb+Lht5uIgtcOOpG8T7sxLfkb1BjGsEPLERymike\niZH87QkMVcd37Tzsy6tnrLP7DTXEQrRIVL27g8AHF73mIv/HEAQBW1cXoS9+gbkvPE/t176GaLMT\n/trXOLF+A5Wvf5wmpYfjeyYZ37ID/mYLJf0lTIaJy4sFcrpEhxigJOi84PLR7tqDuZxmUDyfGmuW\nicIRzk5YGPE0UJLNxEeHcbsXoWkFCoXB0+LjdKBpecRJg/KpTaFoCuEyj6KmJSSTC6tkQe3vRwq2\nTY0+EGdHH0wXlrk11ApOYo4I87Ow17EYTRKpGyoyIdWwQdjF4w+eqqOfeyGYHHD04Zk1+q+g7+hR\nAOavWsXo8RRyeYxUdZK84mC1cJhxhxu5PMHehlYuHo4xbOlnwYAPxexjnecYzkSRaKgRR7GAtH4C\nKSIw55I7UEbLuC9oQrRIJB/qI/7LY0g+K9V/twzHipoZHd/xhhJ6AFtXAFPQPiPvLdpseK++itb7\n7qXlvvvwXnMNGAatmVcQ0NnzcC9GSqOn5SJyUp6leiuWci1jqoVGk0a3bwRHsEQo/AoZVwfVImRg\nkAAAIABJREFUNoVcMc+iaA5DEJkINhAe6MflWghANnvmHg6haUXkaBYEyFlERD0E9jiVnAndbMXt\ndlM+cQLRVo0pNDOf55sF+6oOGso1nPTvpzGlkzHVAaBVLIhoVJNA7R8iWoiCyTa1k+975r/lq84k\nhuIJrJUKvkA9qaKVspBmSAYPOYJqjoO2Btr1K1FFga7eg5SNcbwFAbOvFtOLDhRZxp6JUw7KqAs0\nGt3XUjoMctCGpd1L5HsHye+cwLmhnuqblsyYHv0hbzihf71gW7SQ0Be/QMtvfk3nPT+j85xaJqpX\n0f/pW1i84Xz6cv206u3ImTnsqUyy3qkRN6d4xFdDTWoXCCLxympAwDp5BIB4oJrE0Akc9nYEwUwu\nN/2HCk8Xul5Ay0SwKNDTXIthmCnaY2AIlMwigYAPvQhgmk3ETjP2xW20lusY9O1DAOplL4pkkLJb\naE6MklOsNBgpfrf3Z1NPaDsP0sMQ759Ru18tEUOnWtMYPTAGgoAsV+jN13CBZaozdqe9ikO181kW\nTjMs6rSE7RhAXdsQnu4Kow0NNIyNUTmrCHkTQceHUWNFrAuqiH7/AFqmTNWNXXgvnYMgvz4k9vVh\nxZuApZfOxRAkBpVGjLvuoWQbQ0DAVV5JwRCoK7fiFOB+j4ca5yCO/BhxVuA2lZgoHMJfVkkGq0kN\nHUcUZWy2eorFkT/9xq9DdF1F1xTU4jj2MozXTOU8irapkUqa2UKd2YTonhppMZuInV4klw1LRcEr\nSaTtGi26iahLYNJnxTxS5JVKF3OFAfJPnWqJad049Xtw24zZ/GopFApkLBZq7XaGdg0gaGUcNSX6\n062sk/djAJPqOUStEuv7DzDqPU7LhBPDXEVtvBdZ0cg4nOiyTm6zTrVjE9lnx5G8FnIvjmKqc1L9\n8eXYOvwz7ep/Y1boTxOeoI2WxQEm5lxA4v6HmO93ETEynFVpwaRW05P3scapcNSRxwhW8CeOIZhC\nBJw6+VyUzkSFWFUjqfDUfAybtZFiaXiGvXp16HoRqejHnIshAFlnEyahiCJNDTrTzRZ8mcxsxc1p\nRJALzC+0MlSVpq4kE7HbqIhmVFXCMlzCTR5bxUmsGIOqNrBXweiZN45j5PhxAOpqagiPlBCUUUZ9\nOpohs0AeIGeRUUwrMWs6dZOQMJ/El5ORHI1YX4asy0nzyDClLgnDEAiG34VR1tBSZVwbGwh+cDGy\n5/V3ZsKs0J9GFm9soKyZSK58K+Z7HyCWeIVNFRFTaj67lFHW2gwM4KU6GX/yOAgSOVrAgAWDJ5jw\nBIlnKxi6jtXWdMbu6DWtgJFpxlYoAiALjfjkIfSMAggYshn7yAiSvwXJZ5mRcrQ3G5LPREexhWHf\nOBZNoGwKISCQD8oEBhIYBlRbUuzsfxEEARpWwci0Nb1PG8O9vWAY1Da3kSlbUIwYJwUTsqBSJySJ\nWO0cqK5iYThKlGbcuan/o0F3DPdIieHGJryZDOkrNczpNtSDBgjge/s8PBe3npbmp1fDrNCfRhoW\n+PCF7IzPvQSA0MBOmhEoZlYSNaUwJTqYb5L4dbMdb+oEgqGSVJciSiqeiW5UUSLiCZJNxLDZGqea\njpTpn3z3WqNpBbR0G7aCSswt4y5XY1gzSHEVyWTHarJCXx+Sv3l2N3+aMDf66Ci1Mu4+AYDFmOpr\nGGisQs8IjCf9NAoTTDzx7NQT6ldAvA/K2Zky+S/C0A0yzwwzfGwQTzpNxRrAEESsokpvMUibZQJH\nWSUsNjJpE1kxfpSwc5DauBVNkmmKdqMLAhVRQqmzIdQohPquAaDqvZ04ltfMsIf/O7NCfxoRBIGu\nDfVEx0tY/uErmEaHKSd6WViux0SIoXgrG7x5RlwSmq2CtTSGJNcScuYg0Y+g60SrakkO9GG1TB0o\nXC5PzrBXfzmaVkTK1WNSVQZq/Vg0J2mnjpEFw2zF7/FR6R9EMHtnE7GnCeuCRhoqNUiEibpF6pQ6\nChaVYZsH0ayT6bXRJIyTTp7q26hZCBgQPjqjdv+5FPaFyTw1RFQp4kskiQ1NfUHZfTKjuVpWSEeR\nDBiWlgHQFS0w5uolFLcimkJ4jqSZDIVoGR0hvVpDztViS87Hc2kLtgV/enruTDMr9KeZ+WtCSCaR\nIaMV92WXIRx/kpVI2HNz6ckLdJhEvKrMcI2BN9UP+KiySQiqQtPEENlAM5GduzCfmnPzn2evnklo\nWgFb3IFowFhwaicU8UpoJQnFJFMT8GHodkCc3dGfJqwd9YiILM66GQmYqKl4iPjKUFYQW0QY0nHm\nc5hlHcMwILRo6onh7pk1/M8k/8okao1MyQTeTJbJfRNgKExWgYHIYnEqdn/AtZzabI5Sbh5x8zDO\nkoxHlLBkNMbq6/CmUmgrcvhHLsDa7sO14cw4y3lW6E8zVoeJ9uXV9O6axP+Zz0FlgqWVMkZ0AePW\nSQqTi1im2ThQK1EVPwGIjFa6MASDpX3HyPrrSJ04iNk8NcO/UonPrEOvAlUtYBufuhNJeKd2Q2G3\nDrqAYjHjEwxE92wi9nQi2U0YapaV6SbCXgOLLpN2yUgVgd45LWBAqt9Bgxhh7/E94GkAixsir/8S\nX72kUhnNkjt1LpFPg0RaQ9fiDJpkBHQ6rL0AbK2eS2NqkKzuxVaeqgJrKPSjiwIV2US52oLuseIO\nr6Hqhs6ZcukvZlboZ4DOdXVUShqD/RVqv/wlmod2Yik1kXCqZEZWsaaqwEhQwJMZBCArtKJ7cjSO\n9BD1+MjmJ5H1qaFIZ+SOPlmiGDsGQMkawBAqZMUKALrJgiuVQvLUgywgV9lm0tQ3FaK1wtLcPOKu\nqeonzTQ1ofK4UI8jVCY1aCcgRtn73JNTCVn/HEi8/mvpK8NZ0CFlLwEQ8LvJmN0IepKTqp1aawK/\nuUBBtjBhdTM3dZKkPUwgI6MLAjWD40zWhGgcHUVZUsI9cRbVVzQjms6c8SOzQj8D1LZ78IXsHN0+\njnvzZuSAxnmCA5laYpFWapwl2qwKlkoGjByyXEWNK4UtlyCvl0hpKtqJFIJgplJ5dccwziTahIKa\nmcQw2bCp1ejmCM5kCgDDbMExNo7kb8FU43jdVjG8ETHVOWhSaslbwqgi2LV5KLKBUqog1wmoeYma\nRIRkeGouDlVtZ0TTlBItAJCopLEoCraqWjTJjlWsMJCvoUlO4VJUJs1THcELJkUmHUMEUmZE0YUt\nrTBWX08oHKbcpSKPd2Jbeebs5mFW6GcEQRDoXFfH5MkM8bEcdbd9kvOzMVzJOsLWKFqkkzWiFV0A\nXRvA0AIExKndQ+N4P2FHG9qhJzGb/Wfmjn7SwJ5LUvLW4y3WULEkcZ06N1OyODD19SF6GmbDNqcZ\nW1cjoiASKuSY9EnU5FuIegzMpRTxoB9BNHD2Z6kYEoqigL8N0iOgVmba9P8VNVZEsEpEEjHcqRQl\n81Qzk+KDomajXopgK+uMWJrwFnOY0nMYco7gz1jwqToAKa8Xk6Eg+Fox1ydm0p1XxazQzxAda2sR\nZYEj28cx1QRotOhUpUPEXBGyw2sRHBYqTh1zcQBBczKc60KXBFpG+kj751EcfAmzOXBGCr0yrGIr\n5UkE63CX/WRsZayJPIJsIeAPUhmeRJDssxU3pxnr4hYAVqRtjPtlAoUQkx4dvaBz1NaGs65EfsRC\nNXH6+/qmQjeGDqmhmTX8T6DGikhVViLhMJ5kikxlapJexDV1txhydGMp6xy3NeAqjpLTQiiMIusC\n1bkIabebQDZHpUUnEZ9H67I1M+nOq2JW6GcIq9NE27KppKxS0XBuWsNSzUfKoZAYX4zh0qlyVqiJ\nT53Y2O3XCKZyNI/1o0gySrEPM9UoamaGPfnLMAwD/cTU4SLRgAsBkbBbwpyroJssVHncCKIXYHaY\n2WlG9low9DIXxKqZ8MmYDBNZ51Sz2ojqxlVfQitJtCSHeXn7s1OhG3jdh2/UeImiS6OiKLgzGSKF\nqWFsJ2UJpylPvec4AtDjrCOQnURHx12aCiXWjMeYrA1RPTJEpU0gEi9ha183g968OmaFfgbpWldH\nuaDSvzeCbZ6fCxULEiqKZsZNCItbpSEyCkDc5eZ3awvYykUyQomiGsGaDKGeYUKvjOepxKbOi0+d\nGiXdF/AhVzQUsxm/ZMzOuJkhBEFAtFZozzvJ26dCFpLZhyGCrCVRqu0gGAQnIoyMR6Z29PC6Tsga\nhkHf6B4e3PIVrGMncWazJCsmDL1An+KmyRrDK08laUctNcyJqSRsYbw5CQMBR0lhMhSiKp4gFmyg\n0aKdkYejzwr9DFI3z4svZKf7+VHMjS58JgfBtIOSlCefXYvZpWIvJDBQWD6+BO1UAUqWfWQzJqxj\nxTOuM7Z0LE45NYxhcVGRzRjojLqdCIaOYbbgOTXjRnTISM7Tc6bALP+Fqc6BKEiYpSKaoOMvNpD2\n2BFSBQYt9VirFByjOXRD4MRYAiweSJycabP/KEqmwL7IUxiGjimTIC+oFA0HCHkmSj4CYp6QPnWs\n9Zilhtawh37XCL6cCasuggBFtxdLpcQrtrk019fOsEevjlmhn0EEQWDRxgYiQ1kik3mkgJUGtZaY\nc5x0bBEFjxkBENUYZr2GpaNLKFpE/KkIe0UbtvgkqpqdamA5Qyj2JDAyQ0ieZkTVQdaSpCafBKaG\nmTnHxpB8zZjqnDNs6ZsT++IWBEGkoZAj7lIJ5BvotwdQCyKDehXu+hJGEuyFPHffcw/7rGe/rkM3\nfTt3ohoVGtdegma1M+p1oMpeKhYV3ZCwyDE82pTQpwUT1lwD445hfFkz3lKZtNePu5hGrYX/n703\njbElPe/7fm/tdarq7Kf3232XudusImfIIS1RpkxSoRzYUoQkkvzBjBNAsaIAQRAgcBDYQAJY8Bcj\ncIAEiIFEUZAgRuJNcizJEilZ4jIkZ0bD2efuW3ef7j5rnXOqTu1vPlQPOZIoiZy7dPed8wMu+vS5\nS/3fma7/eet5nvd5DoJTbF5+/ohX9OFYGP0Rc/FTK+iWypu/v425WeWSqBOafdLAI2yWZqdmPbTM\nQbMN/EaD5ZHNv7qgY4Y9pEwoiuiIV/GDkU8SkjtDrNk+Wv0UStpibO+zNCiNXjEr6NdvINzlRSL2\niLAulLXzl0ch3bqgHa7zrlsezhsnKs5K+bO2tHdAnuf868kFJqPjWxBw7+030BWTxHXQG0uEpgmK\nR2iWoSnPuY4V5fT1OkrcR0iTyLiHmSo0xhMO2i1a3S7DFYe1zKdy4cePeEUfjoXRHzGGpXH506tc\nf/WAvG1zPquSazOCXKA4KwitwEq7KFJnUIkZeeDOFfachJHoguTExOmj94bk/jZCFih1F5ms4ls9\nmoOyXK3eXCHbnSKEtojPHxFqywJSnh/DQd2gklYpTIFSLYinMUoNMOGJ/XIXn0uFdyfHN2l+cPcm\nDWOZcTilqtuouAihMNbA1uZ4zi3UWOWetYI7GSEpcLLytHl1HtHvtGntDXi3vslTVh/cpSNe0Ydj\nYfTHgGc+u0GRS653Q6qygqpkBDk4ydMYbo432QWgMt/kXr3c/XbGJt+sjNGixomJ08/fHZBG5VpG\nnkRIk7E9ojrykYpGp9FEWOWNZKwvQjdHgRACrSVYi1xcvRzIvpSrDJ060RjuROdwl+fU90cgCwwV\nruZrEB2/zUaeZQwO7lE3lhlNxniTCZpaVnRtC5UNZ5e6HqNHCjvmMlt9Qc/qUQ/Kv+9FCf12m5rv\n82blApvLtSNczf2xMPpjQH25wuZTTd57vQ8S2mKZqTZjPn8Gw83o9G8D4KZLdBu3yRWF5VGFV00d\nb+yeiB29THPi62OiyS3QK8y0sntgvzLDDaYUhkFDA7W+CSpox2DO5kcV68k16lR5vmzFTiezeFe5\nBMB7YRNnOUGNCqqTKVkB26xSHG5GjhPj/S55nmGbdfI8x+j3yQ6N/lpRoWGMWNYLvDhi21zm4n6d\nG9W71Gc6agHoJhgqGilX9AusbZ472gXdBwujPyY889kNwknCnqGyaS0xtvcY5mvoXk7TH5CJEC3z\n8AqPca1Fa+LyhmHiTTWy7Pj3BI9u+Mi0oOjdRa1uUJABMHAj7CSiMCzqswlKbRN9pYJQFq0Pjgrr\n0jICwScHIyaWTyfxuK48gbMScm+q4C6XcfrlvT0KCTEmw+3jl5D1Dw5beDtlGDCNAoRaQ8qcgdTR\nFZ9lI8MoMgZaldZ0hV33Ho2JiRfn+M06bjgm6Bg4Ss7ShZOZiIWF0R8btp5q4TUttlPJZuISGz7z\n1Cb3TJRCEmpT1MxhbdphVK1Rnwq6hkoWpidiRx+9NwRdoI33ENU1cuGSqCFzU6Cm5WEpb7usuDE2\nT+4j8uOAccoDJFkxYmKP6URNxmoVvZ0wjxR6SgXDy9jY3wHKiq/dneM37Wy8Vxp9WilH+4WiQKVK\nos5BgBS7eMQA9IWFgopfuUtjqlMNZvTrDWrDMd1Ggy1xgLr6zJGt5X5ZGP0xQSiCJ55fYt9PcCcm\nusgICoFeKQeMZCJAyypsTdoMXR0zLXDnKgdFQJYHR6z+z0dKSfTuEL2TouQxWdViWpSJWCe2EIBq\nOei3ewjVXMTnjxjFUFFrCpGIcdQ59aiJTsF+sYSiFbw1XqGyFNM6GCCKFJD0B8evXbZ/sIemGoSG\nRFMUxqaKqlRJtbLipqXdQ41LC+znGpICQ+yhFQIvShg0GtQPJlyvnOKiPQLj5BYILIz+GHHu+aXy\nUTiqYAnJLJdolTIuqAd9BCrVZI1ePQWg5Zvsijl5NjtK2X8h2UFI7seQHfZEqY8YZesM7QNqh9Lr\n9SWKw5zyoob+6LEudFCFyrLQECh0RMBr6cepn5twZdpBtAVqVtAYlWHD/XF4xIr/NP7BHq7VYCpC\nHEUh01QKrUGogK2FLGshSeQBkIQuQ+uA2mF7BC9KGDUa1HyfN8zzPNs52aHEhdEfI5a2PLy6yV4K\nllthSExunkOoktq47HmjFW0GtSGFUGj7Bnf0+Njv6OPrZd+Q4Go5jUit7REWTUaVLk2/3F112sso\nlRUQEn15kYg9asxzDWxpsUZ5HHtZxHyn+THq5yakhcpOpQpA56AHCPaO4V5jNhxQ0TzGeYAahQhM\nUCwGqmC5ckDHzGFuEyg2jWGNO94dGtPyNLYlC+YVm+pkwk17g/ObJ2OS1J/FX2j0Qoj/TQhxIIR4\n6wPvNYUQvyuEuHb4tfGB3/tvhBDXhRBXhBD/zsMS/jgihOD0c236mWTVbjFRfTK5heFmrI2vA6Dm\nFZpRwrDeoj2pcE+HIhwdsfI/n+iGj9q0iG7tIOwmuVImYkf2Po1ZihSCZdtGqW+iNlSEuth/HDXm\n2RrV3GUrbxOpAcu5IEoNXvFexLESrs3r4MFS7wCQTDKN/PCE6XFhNhpiKS7TLCCdTagm5YfWriKo\n2Qd0bIkZqexYS5wa1tl1t2lMTYysYN70MIsITaZErk3n/MePeDX3xw9yR/3vwBf/xHt/B/iKlPI8\n8JXD7xFCPAn8PPDU4d/5n4UQJ2cMyzFg8+kWOVCLHTLDZ5Kvobs5q9MusZagpTZnBgaDxhLVmU5X\nU9H2j29/bFlI4ps+5tkacnRA4a2R5+Xj8tjepzbLKHSTxmyMWtvE3Dp5DaMeR9SqiSl15uqckdOl\nk1io04TftP46K2dG7MxrFMuSdq+HViRIBOPx+Khlf5cizwnHY1RMclmQzAPcpHxSvIuGoQ9w3Qwv\nDjnQ6tTjNkNnm7Zv480jhrUmXugzbjosKVO09eeOeEX3x19o9FLKPwT+pJP8NPBrh69/DfiZD7z/\nT6SUsZTyFnAd+OQD0vqRYP1CA0VAOjJJ9SnTwkB1TJxgzkSPUVObTlQnsD2cCHpSRxsc3/LKdHeG\njDKMUxbK9ICs6jAtVikomJoD3DBFGhbOnX2E4WCcXhj9cUGuGtwz9onNMe24jkihq6wRPlkBJAc1\nGyPNqPvlE2XvYP9oBX+A0B8jZQFqGYpR0hhdlLkfX5Ho8QSlktBKRvTUCpKCyN7BC8tEbK/epDac\ncLfaYVMfg1U9yuXcNx/2GXlZStk9fL0HLB++Xgc+WGe1ffjen0II8YtCiFeEEK/0eidvHN7DQjdV\nOi2LqW+RGQHTokB1myg5DNMCLXewZI1UK3+AtblJPDq+J2OTu+WHULH7HZA5ohkwyjaYaSGFKKjM\nU0zLo+iWybxFxc3xwT7t0jV6mHqCJnUahUD3I/5N6/Os2VNuiXJSU6tXGv3dG1ePUu4fYzYsq4By\nreynb4Uhc7tBLjJSAY18yCyv0sx8DnAY2wc4cY4iy0TsuNakfjDhqr3JhWp2lEt5INx3MFSWrRN/\n6PaJUsp/LKV8QUr5QqfTuV8ZjxXrp6sEuaDi1hjKBOGUsyyLeIpAR6VCUQ7JoT7VGc+Pb4w+2Z6i\nuDr+y68CYLX3GGYbDM0AO1ZRiwKv3qaITaBY9Lg5RphnakyVgNrh3N6loqC12+UV8SlO1QNGqc2w\nUaG9X5rqzu7OUcr9Y8zG5T2RaqXFLQ/GBNUlUiERSFbUAeO4bLcRxjV2vJs0puVN5SQJM9elNvG5\nYp/mRza8o1nEA+TDGv2+EGIV4PDrweH7O8AH09Mbh+8t+CFYu1SGL1pGk4k6QVTKAQ9L87Lvt5pV\nUFRJqqo0pgaj4hiWPByS7MwwNjwmV/cBgesdMMlXmdpjvLDcbbUanbJtsVMgtEUi9rhQWW2gZAW2\nmpOLjBUxw+pPGYgO7koBSLrLLp1eD2TBcHx8Du7NJ+VT7lRNEGlCaxaSGG1mqqBpDVk2UuKoDMeI\naYd7tWu0JuUmQ7UVpKJQ9Sfcqy7zsScvHtk6HhQf9q76DeBLh6+/BPz6B97/eSGEKYQ4A5wHvn1/\nEj96rD7TRgHMeQVfH5Lbl0CRXI6+A4CWmLhZxqDRojHVGcv50Qr+MyjinOwgRF93kSMfWWmh5ToS\nFb8y/K7RL9sGav00xubJ3zk9TliehznLuW3tMKx0WS4kWVJaxk67zao1ZWwbmElCyx8QRvERK/4e\n82n5oTNRYkSaUA1ThKzRVyR1+4BORYV5GSbUp2c4cO+wNKphpTlB1cMoIvQ8JXVMauc+cZRLeSD8\nIOWV/zfwEnBRCLEthPhPgH8AfEEIcQ34/OH3SCnfBv4f4B3gt4FfllIer5qrE4BRM2kYCsnIxNcG\nJHIDw8k5E9wiFBISCy+V9Jsr1Gc6E+V4Gn3anYEEbdVGmw0onCrTrJzQM3aGeKGJBJbGCUK3sJ9c\nO1rBC/4YumGiTENe8l4ntPp0Yo+ZsKgnQ173LnDaHeEXBomq0B72yAuIouMxG2E+naBpBqGWo8UR\nqlLOJ76nFFhqj0o1w4glGQrzvMPcGtCcqnhhhO/VqIY+/ZrHmj6BSvOol3Pf/CBVN78gpVyVUupS\nyg0p5f8qpRxIKT8npTwvpfy8lHL4gT//96WU56SUF6WUv/Vw5T++LNVN5mOLQs+YFCaqq+BO5gyU\ngkLWqGYF/cYKlVhjnAtIjt/JxGS7DCkNh9eQswPwBOOszM33vSHV0ATdRN8rzcE4XT8yrQu+PyIK\nmWgB0phh5xVUXDZ7N/hq5UXOuENAsNus0eyXFnD79u0j1fs+88kE03ZJ1XIaVlBpAdBXwchHiOoc\nL5kx0Dx2vdsoBZhJUp6IdZtURxNueaucrxy/++rDsAiIHlOW1x1AoV5bwidBdTzwBYExx8xd1KIg\nsstQhx+55P720Qr+PqTdAMXV6f3R65DH6LUp/fwUgZITWnOqgYZu2BQzHWSC1raPWvKCP4Fdsamn\nHjOrTMMty4Tmzj5vVy6xZAWYIueg7dA8rLy5c+fOUcr9LvOpj6abIAS12QS/2gZgrBS4ic+e3mYt\n7nGgVLnbeIf6xAEkXpTgew3qBz5X7S2eXtaPdiEPiIXRH1NWzpWJoqbRYSpmqE4HmSm0nOuYUiXP\ndAq9LLGM5g55//gNaE73A/QVh/n1snzWqfXZy84QGTNyIqqBglupIKwVFCdFiJPdT+RxxG006ERV\ndr3SwJeVCUZ3TqIYDPUmy3rEVFNxpyFmFLF32DHyqJlPJ0jKn6fGeMq4toREMhOwIobcZYuNeI9h\nUeVO4x02++WTpvt+xY0/4Y63wicvbB3lMh4YC6M/plTWPGoqMHQYqROEU/7AXaIsU/SjOoqikmiQ\nRxb5we2jE/t9kIUk2w9Rlyz0Qfn4W3d7TLMVpDnGikOMDFquiVJdQ19b9Lc5jthelepMY986YGoO\nWC4y9GIVS865Zy+z5k6JFY1IU2kN+4xGx6PUdz6d8H71e2vkM3VWSBUw1JSLesE9uclq3OMaLpE+\nY3PYBAmKDYWqUvUn9KoNnnj2xSNdx4NiYfTHFK1j01IVgq7GUBshnQsAnIveBiDImzh5ysgz0EKN\n9Mp7Ryn3T5GPImRaMK7EVKYTpKIiLAukhdTH1KdlArmjghAK9tMbR6x4wffDdqtYfkKgzskMn07i\nklnrbGR3ueqeYssrB4OPHIvO8IDZ7Hg02JtPJ2SyAClp+FMKfYWJIqmZA5ZdnSBuocucNywNIRWa\nUx07ywhqHnoRoaUJWlVBra0c9VIeCAujP6ZoTYuWoVDkCrGjU9hPgpBUB2MyJUUr6njzkHGtghOq\nFLvHIzb6Pul+uYvvzrvosyE4LuOiNPPEmFGdla2WO/NyKIT9zMnuDvi4YrkutWF5HlLTImpxi26l\ny2awyxvOBVbtMYooGFcsOt0eWZYeeXOzPMuIg4BU5qhpjECg0GBPLbC0AyrVjDy2yYBXan3qk3OI\ndEI1jPCrdarzCftekyec4/Gh9SBYGP0xRagKS51yOHOneYpYWGgVgdZTEG6Peq6RTCXDmocTaQTp\nhPKQ8vEg3S9vkoO9OxD2URyFflqa+Z4NtUBDAmbUQeZTVOfxSHo9blheleZER0iBtGYIFArzJudm\nM65UzqIKScOKGdYsauMpSEm32/2L/+GHSDQr224kqoIaz/hnn+kACneVAlMOCGqSWhJzycbIAAAg\nAElEQVTwm67D1JizOXgKWYzx5gmjSpPqyOeGu85z7ccnZ7Qw+mOMu+riGQp22GJCjOJWYKRQa9yi\nVQiGgcGwVp6iPchjoitXjljx90j3QpSawaQbUAQDDC/kTn6BTOTcdUy8UEcaAsU4j+qd/F4ijyu2\n66EVCp24yXu1MmzYEiErPlyvbAKwbCZMTAMlz7Hnc27dunWUkr9r9Llm8trSVb59uQy/DNUCLwq5\nVamxNL/NP2rU6MzWuDQpq9e+V3Ez5Y63wqcurB7ZGh40C6M/xuhLFVoChrcyBiJEcdpkE5WKvoMj\nFQZpnYlb1p7vJzGT3/zqESv+Htl+QNQWWNMC8hjHHbOXniGzfHzHLEsrzRxhdTDPLDpWHlcs73DA\nyLTBd2pvIEVOO1zjLXmbgV5lLiwaRoZEITR16qMxu7u7R6p5PilPxYYVhatLe1zeLUOG4dq/JFx9\nj18fjPgdvoavqPzYjb9BNSn/vBMnTD2Pmu/TrzY498zjkYiFhdEfa7TlCm1VkMY5fT1BcbYoUoVG\nUTYItaXNXFQphGSUaczfOB619DIvSHtzDqwpzVkCQMUNifIlNGOPoKJQDTU8o0zIVp4/e5RyF/w5\nWG7ZJmBt3KRQCmJjSmtynm+6r7Gav8tda5WWWbY+mNgGzeGQ4e7Rhm7eb3+wUx9QKJJmskmoT8jr\nr1EYcyZ5wbkU/uE9DSteRsRzhATNKrtdVv0Jcc3Gaj8epZWwMPpjjb5UoaUdxgktF+lcAqATlobe\nzgXKRGdayZhEFchNsn7/qOR+l6w/h1yyX4yozkozFxUVtahgs42UCWaqsqxnyCLDeowekR83bPfw\nPEdQ4Wn/DFcbr9OM2kgp0P3f4EZlgyXTByEZV23a/T7REfelf9/ou16fxlQQay4Tc0R265f5lehH\nUVb+S/6n/R6dYItcm5EnMyppStBw0YsYPY5pNBV4jM51LIz+GKN1bCxVUPN0TLWJcMpH0EZvgKJF\nrBURxcRk7KaEsYHRbjH7xktHrPp7FTcHsx6Vw3K70CpHFiTZjOq0PC6/pBoI/EXHymOM7ZXxayVN\n+fytZ1gVFjqCjf3PMg2v8o1KA1cbYdoFk5pJzfdJ8vxICwPKw1KSgT2kPpVUoyXGuUdDpOTeDD9r\nUM9mjLNT6PqIPPWphhFjr051PmbPbfPM8uNj8rAw+mONYqioDYuOp9PrJsztMpZtHIDhdVkhJw5s\nfCclj1VUKyH4+mtHrLrc0RcU7A96mOEYYapscw6AA3Qaftm/fFntoDUfrxvqccPyPIRQ0LKMvgj5\nmcmny/cHn8QSBn+g76CIHM+QBLqGFUVkqsJ0enRTz+ZTn2klJ1YTwkoLM69wD5fzmcUNL2czKp84\n/GyVXPWhmOHNE4ZOi9pgwm1vhU+eWzoy/Q+DhdEfc/SlCi0kaZyzrygIu0I+UbGtXWq5SZZbjF2J\nkBCkXeIb4yMvs8z6cyZuip5rEA5QHcmt9BKSgp1KlfrhjsvRljEvPl431OOGoqjY1SpaURAbBnpW\n9nlXM5eflJcYyx4vWyZNNSfONXJF4AYhd64c3bSp+WRCr1Ge07CLMiy4rxZ8uljhrYrHmXkZ+vTz\nVURePl2+X3HT2BuzX2ux9dTjNQF1YfTHHG2pQmNelh9OhETxVol9nVqxg55VMAsYuWVf90l+gDDX\niK9dO0rJZP05Q3dOVVYogh6mO6eXbYI1ZtTsUJ+GxGaOEEu4n3nqSLUu+Itx6g30oqAwDIrxDSQS\nr9D57OgJEDb/pOrR0goAppZBzfd596vfODK9M99nVM1AQivYQCLpq5I1Yq6YW1wKbgDgF030pOyc\n6sQJk2qVuu8zbVSprp/8YSMfZGH0xxxjzcGU0FqpkEkFpXqWeKLRodyVtHIY2+XBqnE+Qu1cYva1\no7vJoDT6gTqllpvI+RDLSYjyDqrZZVxrUZsmYMekUkdvn+yhyx8FKrU6SiFBURmN30ZTBY1CcDCp\noTsf4/cqNql1aPS2QdWfML55dE32Rr0DfDelntbYmJ6lUCAVsJv9U65xieem7xEWNRKtQI1ihJSo\ntiDXNGq+j7diPFaJWFgY/bFHPxyWvdqxmYSgVjegEKzNypYHZ9OAVNiEpmQQz1GsGvOX3zkyvUWY\nUoQZB+mY9kwBWaC6BWrhURQDJl6dagC6GZPZ5pHpXPCD49QbyLwsk90uungKNHLB9VBn1dskF4Jv\ntwNQJH7dpub7yOjo+rhPx2Pa2iq5ktEO19nVA1R1wrwyoCeWuRzcop+eJtInyHiGm6bM6i52OiNU\nTZ49Yx2Z9ofFwuiPOVrLRlgqS6bCLIPYK6cwuaMeQknYkAmkNmO3YDIvQzjJXoosiiPRmx4mYnvB\noNy5A6lTR5Eq2WzCXJFYqULFjFC3FmWVJwGn3iBNQpCSiefgyZiqFNzMFc6oM9ZzjT/yIlRbIXA0\n6v6YXFMYh8mR6E3SgC/OPkNMgZ663NETdCnYXT+FInOWkxHDbItEHyMTn/osYuQ1qI98btdX+djF\nx+9cx8LojzlCERjrLrUgRdUV9itLIBSSsYZjdmkoCkVi47spkwTQMxR3k/j69SPRmw0ixiIky3Oc\noKxn3jXLipt4nlCZHZ4BMHLsT1w4Eo0LfjicepOiyCHPmXkuRtpHIJgpOpuxz6Zs8Y5doOgqoaZS\nCeekus5Lb95+5FqzNEXzPKSQdGZlb6WurODOI24sX+RSeAOVgkG2SS76iCKjGsYMnQ7tbp/deofV\np3/0ket+2CyM/gSgb3gUByGr52r4aCjOErOJQ0u5h104yLyC70RkskCuK6jtC4SvvHokWrP+nIEy\nBQnmfAgCbqtlYmtUN2mPSqNf13Tsc4vWxCeBarsDgJ4kxKaN6L8BgCxczoQJjnaOQggCKyPKdXIh\nUIqCq19/+ZFrjaYTTnmXuWN2WZqVJ1u7qqST9njHe5a/MvkaANOig0jKUZfePMGv1WgORyTtClrj\n8eukujD6E4Cx7kIuWVtzCBKBUl0nHlmsiDuoqYeWekyc8hh62IxQzCrhHx1N5U3WnzO2IyoYEPbQ\nKjm9YguhBQzaDVqjfTKlYFO10PVFIvYk4B0avZXkFIYB3W8CYGZVlscZM/M8S1nGbi0EBIGp481m\nzK++/ci19vojNo0nuGLfZmm2SaDkxArUW3N8pcEnJ28ipcAXLfTDQeZeFDP1POqjEWvnnccuEQsL\noz8RmGdrIKCjCka5RNY2IUioH1beNKIWvlvWDfujuwAkO/Mj0Zr154y1kFpeRYZ7GG5GUrQQao9x\nvUNzPGbiZDiGhqIYR6JxwQ9HtVOedbBTkLpJqqZIIfFyA39aIbR1XohirjfLQ1LvV97Y0z6TKH2k\nWnev7LFSLHPH2GV5doZ7qgQkB2dPYcmQy4Nt/HyFhAlqHGLmOUnVxEwiDrwWP/F0+5HqfVQsjP4E\noLoG+oaH3Z1huDr9avloqc3Lgc2duEZg5eSKwt6d2wg9ReirpI+4i6CUsqyhz6c0kg5F0Ed3Msjq\nFOkQ32vQnIRklZioerTDKRb84NheFU030LIcqZv4tokiMuqF4MasgrRSXogiutUYCfgNi9rExyXi\n1TuPdrSg+2aCJlWSQqGSemxrBaoteOfUc/wEX6YRzhmmG7zw+7+KGvjU53OG9Sbt3oC3Ns6x8eTj\n07HygyyM/oRgX2yQ7sw4db5O1y77gKdj0ETEqcQEATPHZv+gi7HlorUvELzyR49UYzFLieOYaRrQ\niE1kPCf3XJTCIk5CAt3ECyW2NSdYPpqKjAU/PEIIqq0OpBFSURhXXVQ5pZELbmYGDb3PxVQlVyWp\nBYGnUfV9FBVeuT18ZDqzYURlWtDTR7SD8h7ZVQvS5QqVPOBn5/8UV58yyVr4joEsclp+RK+xTH00\nYvfUCuLU43Ui9n0WRn9CsC6Vw4uXqzoDYSOcDtl+k7Z2i61MBcD3LGbzCZWPbSIMh/DVRzuIJOvP\nGYuyiVl9Xia69p0z5e9pEUpcVuF0jJCsUX+k2hbcH82NTdLDvu2B42LG21Slwq7Q2Si2kdoS9Uxh\nVskJdA0nCEkNg3duPrpOlrNv7DJTQm6ZOyzNNslEeSJW1nT+09H/yKl75c/k3Gjw0vPPAdAI5oya\nDfxajedXQ9Afvxp6WBj9iUFfc1GbFo3BnFRCVN2EQURLv0UlaYAUDKsGRRYhOmXsO7nzaA+tZL05\nQ6W8mSrzcie3bZUllJGdYh2WVp7WZ+S15UeqbcH90d7cJEqnUBQUuoE+K3NBGTXORDPuWWs8F+f0\nvYh5Vo6FlEIwufIeRfHwey/JtCD49g6xjPl/V3dYnm1xYBVkAv7jb/5/fKz+Gs7QoZAqse1Qd26h\nihwvShjXa7yz9QT/7nNrD13nUbEw+hOCUATuiyuo2zMqdYOutwnzIRWlh5QmtahN3ysPTO1du4nQ\nI1CWyMfjR6Yx7c8ZqQHkCua8B8BAP4UQIX7Tozm6SyEkT4sCxWo+Ml0L7p/WxiYgUZKIwjBRpuWH\ntpF2WJ4UbFvLfCyaclCdI6VCaGgYScraaJtbg4c/ZDt84wCZwMiyeK0a0plt0s0LqgjWlS6KJnET\nQVjUUZ7/DYpxjnd4IjbWdRKpUrvw+NXPv8/C6E8QzqfWUFydNVXQt8uErDktd0/t6RYjpywLu/b6\n6xhbDmrrPMHLj65tcdYLGRtzvGQJEd5D0QsilsjzEb7XpDXqMbNzWpqNpi1KK08SS2eeAMCKYlTd\nIqMM42jzBunEYmJZfDwOv1v9NbVNqpMJG+mA1+89/M3G5HffZZ5M+e1THVqTHL0w2VMLLkUqtc5V\nhJS05R5CjegNTjEfmbSHcw6aSyhS0FYmsP7CQ9d5VCyM/gShmCrVn9yiFWUEXplscg40FDKWgw18\nq0xw7mxfx/n0OYSqE7786E7IZv05IzmjlXbIwh00J0dmTTJmjG2Hph+SmxlzS0PTvEema8H901hd\nwzarGOEMYVcYW5JUpjiZwXZgk5qSy0nK1Ck7rfoNk9rEpyEDvvOQjT4PU7KR4E68z5dXDU6NytBl\nV5OsE2GsBli7JrqIGVsOV1+5jABWRzP2l1cQAp5ds0HVHqrOo2Rh9CcM54UVVreq5JrNuLJEMRpR\n17ZZDtYoRECqm4yn+1jn28giJdl+NNUtMpfMhhPCPKJeOOTBEFmrIHKLTA8ZazNqgaClzwicDH2x\noz9RCCFYX71IMR2SqxqJoZPJKY1CcDMxEFaEJSWtrEpsFMxqKlV/gqlkvL07eajaJr/7BkKo/H57\nzsBU6ExaREqCLyQ/rvxznNU5zT0VCRzwDMpkgCYFTpRwsFzWzf+lFz/9UDUeNQujP2EIRbD0cxdo\naIJh7TTp8AZ1c5/GfBUlDwhthyyZEU1CFHMGyjLF4QnAh0k+ihgVZSK2KjUIQ4bV8qkjs1LU4AoC\nwTPahFl1jm60HrqmBQ+Wp5//K8giRb/5FgC6DKjngnuawLLKgSSbqc3YTQh1HXc2o1A13t31H2pC\nNvjmbbLI5/ZaG5HPWZptsG/6bOYC7AmqUbAaDhDA/mwJbT5jZRoyaVaZWjZSSlrPfO6h6TsO3JfR\nCyFuCyHeFEJ8RwjxyuF7TSHE7wohrh1+bTwYqQveR192OHWuRuydRUkCvDzFyKrUkoKJW0NJIm6/\ncQ3zbBXFXWb20usPXVPanzNSZiAFbhogcjg4LK1M7BzPL4c9PGcOCSsCQ18Y/Ulj7cnLrDc/TqEZ\nSEXDSXtUpSA0KqylAQd6kycyychNCfIyfCIVgTUbcWf4cCrAijBCZjV8xWev1eaM36cZrtLVYz5e\njMg2fPSJpCZDcim4sT1CKioX7+3TXVojzzUaroGiqg9F33HhQezof0JK+SNSyvczGX8H+IqU8jzw\nlcPvFzxgzn1hk0mt7AppHZR9blaDFsPGEqIouP7uu3g/fhmA8OXbD11P1pszFAFq4uFE5YndobWJ\nlBH9yoDWOCTRFFpmTOCoGIsd/YlDbdlcqr9IeO5pTNWkiLoIBGq4QWcsuGct81QWMHZTikIl1lTU\nPOdSsM273YcTvpn87isI1SRUFW5XPS51fRRUuopkKWpRPd3Fu2txddLm/7r7caJ5gKdYmFnO9qkN\nKkrGx1741EPRdpx4GKGbnwZ+7fD1rwE/8xCu8ZFn5XKTubdCrFnYO68hRUo7WGenvQLA9s51jLNL\nyGRM2s0eup6sHzLQAtSkhh6/C0CgrJMpY67X79KcmORmhdReplAE+mJHf+LQmhZVWQFAVzRCUSZZ\ni9kKYVBhYFV5Ju0T2GWYZmobOEHIE1GXdx5SnD54+SZSFsycJoGuszRIkBTE0qB55teZ3LT5+jc3\n+Vc7lzmIq4Srm1zY3mfmVdjv1AA4+8Tj3y77fo1eAl8WQrwqhPjFw/eWpZTdw9d7wPc9GSOE+EUh\nxCtCiFd6vd59yvjooagKa1t1xtUzyPE20txhaXaa3UbZaXA8OyDLMhR7BuoSRfxwm0ulByEDZuhC\nI4zfQqqCIm8TG1P2zBs0pga2kpI2yw8iw1jU0Z80FFOl0nKxFRNh2uSyjMvLsMXeXGFmGaxmfYzD\nW95vlCWW69mIdx7Cjl4WBdlQIGTAl1dLLe7Eol/ZZT11MJQr3PvDNZbNGV/YugEXLmIYLsv7+9zZ\nPM2wcNFUhbW1x/eg1Pvcr9H/mJTyR4CfAn5ZCPHjH/xNKWXZOu77IKX8x1LKF6SUL3Q6nfuU8dHk\n7AvLBNVzqHGKqu3TCTZJSZEIRDJn+9odzPMNhGYRfP3hjhcc90cUZLQKF/w+SWcJhMa95m3qMwVV\nwproM297qKqDqtoPVc+Ch4OxWaUqbdJqDWSEJKeWq1zNLFKrQJc59WKJRC2Y1ssZrBUlfiihm/lr\nb6C4p8AxeKkR4SQFXtim697jyZWv479psGxP+fTaNs9Wdlk3B5y/eg0pBN1nPWp5zNbpM6iPeXwe\n7tPopZQ7h18PgH8BfBLYF0KsAhx+PbhfkQu+P1vPtfFr5diz+rCLXhh84uAOqaajJBG33rmO95ef\nRhY5wat3H5qOIsoYBGOklCzlHto4wm+WQx/ebr/GqWH5eksfEnj6IhF7gjG2PBpphWmlgpkVKMxp\nFIJdYaDpZYhwiWo58cwoK2+kUNjzQ6YPuGXx5MvfRigqsVTYcSq8sJOiSY2u3WMteoUs0vjc8nWu\nVMpGZbfSgCeuX6d3vsbbrTaWKrl06dID1XRc+dBGL4RwhBDe+6+BnwTeAn4D+NLhH/sS8Ov3K3LB\n96fatpmvdJAIVu5eBeD8MCR0XJQ4Ymd3F/PMBsXsLunBwxumkHYD+mJKDHi166jTgr53llwk9J1d\nOpMlZk6NJXuGb6eY1mJW7EnFfqpNHZdYValmEi0b0M4FgezgHva4WVPAd1PCwkCREiWXNAi50Xuw\nrRCSm30ArojbxMYq53diCgr65oh0AJ4VUVciVAt81ePs61P0LGP6xZxaX0cIFkb/A7AMfE0I8Trw\nbeBfSyl/G/gHwBeEENeAzx9+v+AhsXlxj7ndprF/l0D3aYYOo1YTkSXsjcpUid5KEWqN5CFVPqS7\nM+4wIy10dO2bgGBsbtF3dmhGNRrDIaHboO5ZTBlgW+sPRceCh4/qGayeK///6VKQZfu4UmEebkBk\nkQiNjnbA3AaZa6SKgpblnI+6XD+YPTAdUkryQAEyvlG7TaY51HsZe94tlsUe832Ly06P3fknOJe8\nydvhKZ555ya3zm5ypb5KXUouXLiI5300Tmh/aKOXUt6UUj53+OspKeXfP3x/IKX8nJTyvJTy81LK\nR9eQ+iNI59nbjL0tzFRy4N5Gj1YZrrQRwHzu4/s+zotlPfv0K28+FA1JN2CkBKiZTRJcJVc05soa\n3epNzo0uYUchqYDaxeeJkwOshdGfaM78dNniF90iTsvmZulkg/1Yp2u1WBZdhFKeOJ04BvY85FKw\n/UCNPt3ZRamsIHT4ZmNCY5pjJ3Cr9TrPzH2kFGzYU8b1v8l60EX9ajmw/M5nO1S2PWJh8alPPf5l\nle+zOBl7wtHrkn7nPALI5C1IV9D1MtGpJBH3rt/B/eyL5JMdoisPp+dIvDsjV0JOaSlqN2HmnUKg\nMbZ32ZyWpl7LfeSppwCJZS2Ggp9kvEYN2zBIKg4yKyvmaqnJtdhiUHHZTPYwZNl0b9ixqI99NtIe\nN3oPzujnb72NUt1gpqbccTUuHbb6uN14k86+jqvFRP6TdO132f1mHa8f861PvQidKeNgk7Nti9On\nTz8wPcedhdGfcFS9gjxdJjuX+zcB2PLL5kxKXBq96joIcUCRuBThg02IyVxyb3+PuRqwuXoXZzfD\nXzoNQEWC6Y8YVxs8Ed9j3ilN37IXO/qTjBCCztISk2qNmgwR5HRyhXupx9zWOB3touhPkCkFg7pG\nfTymIufc3n1wdRnR69cQmsl3tCuk+jofvx7RNWaE+ohs32XFm6OkX+DUb/0G022b1z/2LL3NJpP9\nU6gi5699/jOIx3AI+J/FwuhPOKrqUFvvkwuFyzfvkilzTo1UCl1Bm4fs7u4AYF2oI4TC7OU7D/T6\nWT/klWzMTI2prL+G1UvZbT2Bb/ZYnxqoo7t0l0+zogf4Vvkh4zjnH6iGBY+e5dVV/EadltAwZcBK\nAWOWUISFITMqhsawmuJrJkaaomU56XRAkhUP5PrxrQEAV+3bnBmcpRlK3qjeZr1nUUiVIGkTvPF/\nYBzE7H2uzZWLlzHNACPU+ZL4lzSe+MQD0XFSWBj9CUfX6uT1a4RmhXN7KQPnJmrQJm5YiDhkb9Kn\nKAq8zz9PEfkE37z5QK8f354wEFNca44234cYptZZ/Moedpyj5ilTr0Gj0yEIr6PrzUV55WPA2toa\nmaZhqgbkA9q5QjfcIE/LUXyOs03gCNLUQAJqVNARM24/gCEksijIxuURnRvGDj/+bhVflYza3+LM\nno0mcp789k20LGbrswNeO/1xBAXG7oD/YuMNVlaWQTPuW8dJYmH0JxxNr2I032Vaq6AVEMurzPMl\n8qqDyBLSIqV30MN68kmK2U2ygYp8QLsqgNn1ESgzTjcO4D2HwFlFpUqhzfBCnUzVkEVK85nPMguu\n4TjnP1KPzI8r6+tl+C0rBEl6B1UqMF3mWlImYZtmn0xvohQKgamhJxlrxfiBJGSTO3dQKuUJ6zQ6\nzdJE8odmyqb5Dpv7FTqjOVLA8KdbRGsW06DKueImv/Sf/RLK4Bqsffy+NZw0FkZ/wtH1Oqq3Q6V5\njkJAa1D2mbGyBgIQWcrdd28hhMA66yIUg/DNnQdybSklf/DmPqHZZ7NzB/Ndj0GzrEuuxRPkeMjN\nzQss93ZpvPjXmc3epuo9/UCuveBoabfb6ELgKyoyKs9wnMpUvjNfw9crrBZDDMqke6/l4AQhp+e7\nXOvef0HA/M23UBunGSoBz977MXY9wbY+pd6XqLlKM0z4ztObfEZ7gz+wPoGC5Kc+dRnDrUHsw9Zf\num8NJ42F0Z9wdK0GQiLqTzO14MmbO5hqn0pY9htRo5B7N24DUPtrn0JmCdOvvP1Arp0N5twNJ2Sd\nq1iOj707Z2f5MjNjiBPfo0gDrpx9ijPDOxTulKJIqNc/WrHRxxVFUVhxXYatJquujikytnLYFS16\ndoMng+sI9Qy5kPQaBo3RiKoccufm1fu+9vy1txF6hdeTADep8OW25Gf0r3H+bQ81L7h98Sn+8qVr\nBKrFO/OL/FXxVVqf/SW4/dXyH9h8vIeMfD8WRn/C0fSyA9+4s0Smwrl9kMYVlKRsjaAGPve6Za2z\n9fRliuAm6R7IBzAI4vof7IA2YaW9A4GGNsiYO+eJzQEizslVjWm9xVbbZTR+CRDU64/vXM6PGuub\nm4zrdZbqLSzpcypTuBOuMjFqXJ7dYm7rjL2UkW1Qmc+phBHT/Xv3fd3o6pCwkPizGm+vzzgII/79\n9HfQEgdNpKimxNET/of6f8Rn82/x/Kd+DCpNuPpvoHMJ6qcewOpPFgujP+HoWmn0k/Uah61GkNPr\nSJpIVaDGM0bZjCROEEJgP9VEaA7T37v/YSTf+PouB+5NztfGGN86zbR6BiEMrLQH04zbpy7QGOyz\n9CM/Qe/gt6nXXkDXF3NoHhe2nn6aQlXBtCHroksVMVtiJ/XQyKm3dpm5KmleJmStUYKZh/c1bSob\nDBDmOW7EZZ7pq2dtzibbRFclsa6xs1Fnz2jwmWd+lb+pvcePmtfgM/8VhEO483W48MUHtPqTxcLo\nTzi6Xi9fLCcYus5uA9Zuv4euxGDU0QhAwN03ywlPzV/4HDKLmHzl/rpZ7rw9YB5mhFtfxdFTxJuS\ng/ZlJDnV2R+RZzFvnX+W1f1tqusOs+AKS0s/db/LXXCMOHPmDEJK9iZT5PwtQHIpVfn6vMzDrBl9\nUqOFUiiMHQtrmtBSZtzp+R/6msFLr1A0z3M3LthtXGGY2Pxc8fvsTlogJfPGEjc7a/y3t/4hZ/Z/\nD77w35W7+bf/ORQZPP2zD2j1J4uF0Z9wVNUBFMzOALXe4t0thdM7E7a0V1HlGjIBpOTKt8r2B1qr\njmKOkXGD5N6HT8p+6/98jbExYL29TZ4Z6N09uktPEusD1DwF1eTm5gW2dm+SGi+hKDYrK//eg1n0\ngmOBZVmsaBrbikKtLmmrkidThTezcwyMKmfibRRRVsd0Ow618YSmvs9rb733oa8ZfLvLdq6QAd8+\n/Qp6L+CL3W+x7zmMvYhTvYRPyDf50vAP4VO/DM//LZASXvlVWH4aVp59QKs/WSyM/oQjhIKu12nU\nuxTuEgdVUCV4s7dRlDXIFJTQ587u9+rna3/1YwjDpf+//LMPdc3uS+/QHSm8uv4HPFNJka9fxIhd\nCuMUJDeYzQV7py5TSwJW8gg//i3W138eXa8+qGUvOCacPXOGUaNBe3UDlx5uoRJMN9kzW7wweYvE\nbDA3cvq1sjd9PRhw+51XPtS1iiQnny1xLykwRIjVkfyE/zLTfZ3QNLh6Kubi9Zw2EJgAABhFSURB\nVB1+9uqXmfwHvwpf/BUQAq5/Gfbfghf/dvn9R5CF0T8GmOYSVW2HsXcOMy3oe6Dd20bXyrCOxzZD\nEZIMygHNzotngYT4Hszf/uEqcJLbt/nGP/odcpngbrxERQVegYN2uVNyw1coZMEfPvs8W927VNcT\nNK3C6a2//SCXvOCYcOnHfgyEIAwisvgqKDlPRyavJ+usxz3aKwX9espUM1GkpLodMh5+uD6H/m++\nQaC6jHOYu3uos8v8h8XvczNuApJRQ7DmJ9zsL1N96jBEk2fwO38XGmfg2Z97cAs/YSyM/jHAMDok\nyQH9lWfYGAi+eUng7g45Z10FFHR1QqYpvPfr3wZAaArOi+toK8/S/Xu/gkySH+g68Y0bXPlb/zn7\ntad4uXmTZ6oz4sTCvX2TuxvPUSgjlCLFMFvcWT3Nxo23Mdr3OHfuv8Yw2g/xv8CCo2J9cxNXSq77\nPpIeG1bCxVTl3wafAeCMfoe57SIynYlt4u5F6MqcMAx/qOvIvCB4ech2UB642jv1L5jtN3nx4B32\nPJfASlkdBDiaR/vZD5RPvvqr0HsXvvDff+ROw36QhdE/BpjmMkl8QHjxPFs9wb99VkEpJBvjbyDU\nDoxdVDXh3bdfReZlxYP7mS2EUJHZKt2/+/copz7+2cy+9nVu/8Lf4F7jeaRQuLX0Mk/bOZPXL+EF\nOon9BEX0BpMM7v7/7d15fJvVne/xz9EuS14l7/saZyX7RgLZCZAU2hQm0L7KnbZDOwxTOnB7h06X\n29LSaZkOLR3oAkyntAUCcwl7eJFQQgKBhKxO7DiJ992WZMm2ZEnWdu4fFr1pp1wYiKPkyXm/Xnr5\neY70eny+5xX/cnT06Hkqp6NLxCnva6d4VjnFRVvPxzAoKaDT6Zg9YwZDTie2NEklApDo/HM4ZS9j\nvWcfMrlk15efQY7bR1ZmH50d/71LcYwfcSFjRvoSBrJ0CZwFA6wcPY6nz8ZYmplTZQEqBicQcUnu\n/Mn/ZPB2wK7/DVWrYPrmc5r7YqMKvQaYTblEoh4ypzlIz8hk1CbocKZBiwezMZ2JkQQFec10MYD/\n2CAARqcVy7RszDOvZfSlVxj45jdJ/IWZfSISwf3gQ/Tceiu6gmL6i1cxYZDUFjRg1oHhcACXcxag\nxzLRCAhemTuPmv4z5GT7mLvo+wih/plp2YK1a5E6HSP9Q3hCp4mme5g+YWZPdAGV4X5K8xKMm2O4\nsvSkhcLkDXo4c/jND318mZCMvdrKyOgAAQzojd1E4+XcKPfQGpp8p9hZGKLAJ0lMjGKdORMi4/B/\nPg86PVz30CW7Nv8e9ReoASZzHlLGqSjTI4srqO1LsHu2QDcSo8owADKGPTbOhBU6/vOdP87q09eU\nQcJA1mfuZvSZ7XRs/gS+J58k1NhE8MhRhh99lParr8Hz4INkXHstkTv+lYmoZE9mO/My/IyHrBR2\n9NJVNhfJKNF4DHNmJZ6sfOpPHqNiXi3p6dNTPDrKVHM6nZQUFtJZU4Pfc5x5BhMC6HVdxYjBzmrZ\nwaAjQkBnJi4EOU0h2ns//FVUQyfcJAKSbr8PAUTrniDizaDMM8SQ3QZ6PX5bjEKvQDKKubYGnvki\n9B+F638Bmer+B6rQa4DZPHm5g5J0H8OORczqkuyeHyZuNFHRN3m+vLulnoppJ2n2HcK/d/Lbieby\nDMw1WchYBcX/9iuExcLgd++h89Ofpuvmm3H9+F8xFBRQ+sgjFN33Ixp2D5Cmg9G8/Uy3JOh/uxx7\nOI1w2kwI72VCxtk7ux5jNEh1z2mWXP21lI2Jcn6tuPJKxi0WIvpxiiIW+u1e8kJ5vGi7hhUjjUSy\nEoi4gZ4CJ85OH2Z7L36//wOPKxOS0VfbiY0NMGArI8cQJbemk+neYQZ70/HaLLgLjYgEFEdsWJfN\nR+z8Jzi9A66+D6ZvOg/pL3yq0GtAmrUCgGikm97MeUxzGZgwCfZNq4KuGCZDGjF3hNxsF77iRgZ2\nnCDimrxcbNamKhITcWLefCqfe5aqHTso/tkDlPzi59TseYOKx3+PfeUK+s+M4POE8enGWVZ8FAHk\nHBuku2QBYMAY6cRiymbvjMXMbT5I5cxicgqqUzYmyvlVV1eHw+HAtXQ5naOHyNNNnuve1rcOtzGb\nMoeLuJAM5+mxTExQ1uem9Z19H3jcUPMwcW+UgcFGJnQWDM59mESCtZEjtARyQQh6KlwUjIA9YSZ/\nngkOPgLL/x6W3Dr1wS8SqtBrgNVaBkAo2EW8OpvsHAdFw5Jti8ZBp6M06ELGBjh5/Gaql56iKfE8\nnodPkAhGMRbYSF9VSvCoi8C+fsxVlWRs2ED66tUY8yffKUgpObK9FaOAFzO7WZIRoLWlgvqOIEPF\nS5GxkwTiUU7XzUEAC4/tZ+WNX0nhiCjnm06nY9WqVbhHRgjX21kTz+CkNURuIJdHMr/I+oCHfmcI\nl8VE0GIhb1+I5pcfY6K19X2PKaVk5JnjJMbd9GZVYRQS29ztyNYczN4orrQ0rFhot49QPZDAaPJi\n7X4cZn4S1t1zHtNf+FSh1wC93orZXEAo1EXt7DzSapdy5YkEnoIBPHWLyO0eBeLo2owE/YVYrthL\nz+hhXL9oIBGKkbG2DOtMB6MvtTPySgeJiTgw+bY5dMpLx8+O0t3lxyTHWDTtFdINCYLvhvBn1jNh\nKMMa3odOZ+KFhcuYeeYIFQVZFNbUp3ZQlPNu5syZFBUV0Sok7lADVp0gjCTQOZ9M8ugvGEeGjXSV\n5pLhcVNizKDjhhvxPvEEMvqnt7iUUjL8Hy+RCBoJhlpwG0vIdrRiyghRPhSmrTOP4XQr0pFLQCep\n75EUTHdD2XK4/pegU6XtbGo0NCLNWsF4sIOldXlY5ZXMdtsREh5anI4jEcEajREPN9Fw5KsYrHGG\n5v4ST18XroeOkghEydlaj21RAYE9vfR/bz+D9x+i/553GP5NE6c6RhAywW5LE2vy2zjRVc2iEz7a\natcj4l2MRP201c4nZtCz8sAfuPpLam3+UqTT6di4cSN+vx/v/Byuj8fYb4lQHLDwa+udFGV4iesS\n+Ir0hM1mMg50E1+2hqF7vkfrhqsYvPcHeJ94As/Dj9B182cIvO1CxgI0BQRSCGzzf0e0tZBq2UOP\nPwMpBO5pk5831fdITBVO2Po4GC0pHokLjyr0GmFPn0Eg0IzZIAno7STqqlnQkuB07luwaA2lnlGI\n9ZLjkjQe+Ry2wiAncv+Fwe4zDP74bSa6h8jeUkvu316GfUkBBqcFQ3aYYP9OusYjGBLNbFi5CwH0\nH3UjzXPwp9VjDr8GOhMvLVvD8oNvkFmeSV5FVaqHQ0mRsrIylixZQnNvN15bMxkGgUcXx9FSglNf\nTFtRkBG/jpbqaszuTszhIjq2fJpIdSUjTz3F0D3fw33//STiGRicdXjGDjCYOZ/s0kPozcOYu+wM\nD9rxplkwCTNNjh5sYT0V4RiGLzw/eQEz5b8wpLoDyrmRkT6bnkSY8fEWbA4rWVzFpuNnOFwT4d76\nGN/2jdKSyCEa2ovs20yg9QS5iw6z/9lfM3NoM4mHQkw0/B064xhSGoj29xOzR+hftQRn+RDmsgOk\nmYK88m4laxt8nJq2FUPsCCORUQ7MX03O2BAzzuzlph8/muqhUFJs7dq1nDlzhp5EggXtx9iWNoe/\nCljoGfo8Z8p+Sl2vn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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07968c210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zN521GzeCq4Q2LTdjDCrBXBNG/u7eSHsAUTU1mGtyMWv86ZJXilutRu7ewN7d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hqF9++aRvUo5Jpcgx7MWWhGmKJSIItOW1VpBexBjZvDDX26mGbKMjHSOl6f60g1zTs72lSWDf0s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\n/htwfY/y5YqidCiKcgYopKttLiuKolQoi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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e2df9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec_roqs[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec_roqs[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([152, 500])\n" ] } ], "source": [ "trainloader = prof_vec_roqs[:,res_chs,:]\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((152, 500), (152, 2), (152,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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bmWMHjhf5ulablfN6nlus0AOY/r/Z+L159/B0p7k5tOff/88un7mau9o+zpwv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\ny07NitxUmXQsmVtbPkTKidTMXrE2h5V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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07e3d4450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pixel-based test" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading labeled segmentations\n", "seg_label = genfromtxt('../../dataset/Seg_pixel/pixel_label.csv', delimiter=',').astype('uint8')\n", "\n", "list_masks = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 0] #Extracting segmentations\n", "list_labels = seg_label[np.logical_or(seg_label[:,1] == 0, seg_label[:,1] == 1), 1] #Extracting labels\n", "ind_ex_err = list_masks[np.where(list_labels)[0]]\n", "ind_ex_cor = list_masks[np.where(np.logical_not(list_labels))[0]]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct segmentations' vector: (131, 50, 500)\n", "Erroneous segmentations' vector: (21, 50, 500)\n" ] } ], "source": [ "prof_vec_pixe = np.empty((len(list_masks),resols.shape[0],points)) #Initializing correct signature vector\n", "for ind, mask in enumerate(list_masks):\n", " mask_pn = np.load('../../dataset/Seg_pixel/mask_pixe_{}.npy'.format(mask)) #Loading mask\n", " refer_temp = sign_extract(mask_pn, resols) #Function for shape signature extraction\n", " prof_vec_pixe[ind] = sign_fit(prof_ref[0], refer_temp) #Function for signature fitting using Watershed as basis\n", "\n", "ind_rel_cor = np.where(np.logical_not(list_labels))[0]\n", "ind_rel_err = np.where(list_labels)[0]\n", " \n", "print \"Correct segmentations' vector: \", prof_vec_pixe[ind_rel_cor].shape\n", "print \"Erroneous segmentations' vector: \", prof_vec_pixe[ind_rel_err].shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "#for ind_ex, ind_rel in zip(ind_ex_cor, ind_rel_cor):\n", "# plt.figure() \n", "# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", "# ax1.plot(prof_vec_pixe[ind_rel,res_ex,:].T)\n", "# ax1.set_title(\"Signature %i at res: %f\"%(ind_ex, resols[res_ex]))\n", "# \n", "# mask_correct = np.load('../../dataset/Seg_pixel/mask_pixe_{}.npy'.format(ind_ex))\n", "# ax2.axis('off')\n", "# ax2.imshow(mask_correct,'gray',interpolation='none')\n", "#\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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cXljfRarzx3MQXM5GSrwNfDAomeZwIyF+PS7HHBpZhdllZFzjOMKNHcSoLCQ3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\nvuc6o9DPUXeVLwDg3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c37cad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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BfsG+dCgrYmbOVu4bPp4kqSEw0Q8Av8g4qq/6gIaf7sK16BE63buO8rkVFNWb\nCd9ZhqW6iURtF3LctrF+xZeM6jSF6Bt64trBH2mX1K3KxbC1BO+JL8H30yh8+GGiZn/C5526cuXO\ngzyS68JT+mjKK1YRFjbx5B2l8I+jVYpeCKHFoeS/llIudgaXCSFCpZQlQohQoNwZXoTj6jhMhDNM\n4SKlvLycmpoaBg4ceMZ5O3bsyOrVq9m/fz+DBg06fYY/SXX1ZkpKFxEdfS+eB7dBUw31chB1S94n\n4L57ce/XD4CKbZl09RtOtaaJDIOOQ36SjIxG/m9Me+79ejuHypsR2mr8Q/IZHeLOqAPfkR7tTqPN\nFdUCb/YNHM7BZg0VtWq+2VYM0he4DTaATmUmrH8jIdVG7CoD6hIj2cUGvvPxYXz3CNQqQcLgq9hx\n8AG65L2DfdG1DJ+yjJ//W0ykAFuDBbWLhqFDbufHTe+xoXAhN4R1x02rRgA+18YjtCoaNxZhuPFF\n6t55BMOU+3hq+MO4to1lV5IXH6kfZEr1s1gs9Wi1XqfsM4V/Dq2ZdSOAz4B0KeXbLaKWAbc7928H\nlrYIv0EIoRdCxAJtgO1nT2SF801GhmMd9sTExDPO6+3tTXh4OOnp6WdbrCPYbM0cyHgBV9doYqPv\nhx2fYgvsQdnHC9EnJRFw770AWCobsfxUToOlmk2ljVjKU1h4oJRuqkZe/TGNnJoyvCK/5/8m2fh9\nWAKvHniFtkEq4qMO0S63kGt9TNzUP4LhuiymBFbQr/8C3BPe4JqBeTx/RQjDo7bj71tJpW8AyT5t\n0B6oQ1Vv5cnv99LhxZ+4+v1NPPHdHnYl3MhHTKa0vhkxZyhdLrNisksaEAQ/2I2I23txzbPTMFjs\nzHvpWUqyHP0vhKC2VxCZrgLXbVV81PseVGo101MXcIW3O14FRtbZYvld9iKv6Odz1t8KFx+tsegH\nALcCqUKIw87W54A3gIVCiLuBPGASgJQyTQixENiPY8bO/VJK2/HFKlwsZGRkEBYWhqfnn1vzvF27\ndqxZs4ba2lp8fHzOsnSQl/8JTU35dOs6F3XhLqjKpMowAWv5ViLe+y9Cq8WUV0DZ25tBF8CW8mUI\njxv5IiYOL7uJXXgwvGAnV1jW0Wf4o0SW7UJsegei+5PhEwIUE7Adwj9+k6zMTADqaup4vctrfF38\nNd9mfIivrpjH7Qnkd38TqY3nLsvLXL5pPVds28pzHW8lJsQfHzct6w9W8P3OQmAIbzMEfaMZ/a8V\nqFRu2Cte5gaMAAAgAElEQVQbML+wCrPNjl0CAdeDlHw6azvd/X6iV4iRbbsa8bJreMh3CK/6h+H9\nxnRKHn6YJ8s28sqDDzJwYxpzmIJ292xu9R1DqLfrWe9vhYuP0yp6KeUm4GTrn444SZ7pwPS/IJfC\n34TGxkaKiooYNuzPf5s0KSmJNWvWcODAAfr27XsWpXN8szUv72OCgq7Ez68/rJuCxepD9S+78Lp6\nDK5dumAuKKD4hblowwex37SJRqmh3L2WSuEKQkeQz4/cVZtG0PYaDPc+Ra6vGfeu/ZCug2hwnYss\n0dP33x+ii4nBduAAarUaIQT79u7juaueI9QllPClAo0phrbD3uGTsp8wq/WMjdhH/IL9fNj0NpNV\nT3Gbu5YnusdAgDeN3hoOlteTsjMFS8Ee3DTeSNGVuiYDAe5rwc2M8LHQYHcjLbctaxtiWdcoGZm0\nlh7Gvew4WMdAOYG9v2ThN3ok1XPm4DNxAt/0TmTE9jS+9Z7EL/N/Z9ldg3HTKR+S+6ejnAEKpyQ7\nOxuAhISEP11GQEAAQUFBpKenn1VFX2ooZWvKFNxsZt7Jzcc99xbe3reSg4fiEbYm6m8fg3d1OeWP\nz0AXdx3qNoK037ZhVbfnO60egH6Ry3lff5AAbTn2OA21pfHUlyXQUCRoqs9D38uEpnIg7s6XxKxW\nK3q9nvj4eFJTUxk5ciRjMwZgbCrn9fDPsBToKPF9gAhjFS5xm6m9XkXot5X8K2MJ/3MdzJOuH6Gq\nlqhVZuK0FXSIrsSSANKuJWrLh+Tb/VAbLfRTzYdSsKjUGDxc+aGiPd/KK/ipeCQHvRJ4acznmPeG\nEyv7sLEunTZuLpTPfIu2/32XlyOtvFAQQV1kHa/8uJ83xnc+a32ucHGiLIGgcEqys7NxdXUlNDT0\nL5WTlJREfn4+BoPh9IlPg5SSL9O+5IEVo/E0pbPbEoSLSzi9KvIQRoltbwNrOtqZtOM+Xnt9EvbY\nMWTqc7nW+ghmm2RxYHusqHAJ/5Z9Hlu4ysXI7Ul9eCt6Emm+t6KJnIC+/Xjo65gVHFlwJfVr8rA1\nmrHZbKjVanr27InJZGLbl79i3F2O12VRjLxiLJvLs9jRaGeQMYji9f8h2+MG6nuEcs2BLSRm5pNu\nD8PfvwKdSzP1tgCqzO1w07YjyKszqtAyIlwkuw0TKW/zOJXRfdjn4kWTaOJO/+28r3+P0RU/k1cX\nydOp93Ow2/dIbQE9fS5nf0I4dT//jCk7m7viujFVzMbg5s3yslr+DkuRK1xYFEWvcFKklGRnZxMT\nE4NK9ddOlcTExCPl/VWZ/rPjP7yVPIMbA1RotIE8MXI10wdOxxPBt+XhaG2CH3ur0KDhMvfJWNV2\nZoctomdOf34JHEGJxocu/mVMkD3ol3ctnUp60FBQzbymRdwX9SoP93iH/fc0kR/0C7LJA0+/9tSv\nyafk9e0YDlWjsgl8C9T4qTzZU7gfj+GRuAwMp7t5ED3UrwAQsLoC78BYhl7zGL3mrMK1Rw8eS/mO\n3bt70GXoei4ft5YOiXMp2vosO1Y8g3Cfhd/Qy9FINSFeOhbs685IKrk10I8bIsex7IppuPTsyTP+\nSxlXuoxKox+v772DPYMX4aLxIM5nJPmB3lR//jkqlY6xfipCZSnVUa5kVRv/Up8rXPworhuFk1Jd\nXU19ff1ZmRYZFhaGq6srWVlZdOrU6U+X882Bb5iXPo+HE3rj3byeet97ue+3R9lavBWNzcZHKXby\nOwdzy+Cb6PBRCcEB0RgSLAxIfoi1+hIOufvQycvO0icnY6lsonpxJpbsOgwNajY0VLPLayepoRt4\nZvNjTA9roiQ3gczdlbhJSZxehVk0IVRW6n/MIU4VRrIugwXr0rD+kI2UsPMqb+KMZlYnvsaBqGBG\nxHyISqcn4r3/cnDcRB5aO5uvf+zA1Il9iesWSGC0J798msYvn6bReVAoMUJS75mKqqAdPbLuIi8o\njh+mXo67RlBf2YaaHBtXFO6lWbiwktHMWTqAB6Lfpy2PsjtxGFVLfiDosccIDhzG3ZUf8KrrKzy0\nN5dVw1r/opvCpYdi0SuclMPWd1xc3F8uS6VSER8fT1ZWFvZjPofXWtKr0pmZPJPLIgbg3pzK/6r8\neG7nl2TVZHG3f3fmbarFwygZ9OQMhn9XT5D/MGx+ku27JIWeJSS7+tC5bi+vj+5E7bJDlL27E2tR\nIz7XxJP4cj9ue200T0+8n7ciP+EWU2d0KljuUUjRoK0kXRmNx5VxNAfqkW56irsH496pE2qhwepb\nRu8xscTek0SVh5oHusXz/BVPsq9yH/9a/S/qzfVo/P2J+3gWnnYTbV9/gqpDeQB4+rkw9vFuRA10\nZ+/GEkrsTXSwRJLta6BbVXtebtce9cED5F43ieLHn8DWaKLTxOuYqMlgSNUGdnsmseb3KEqL3qGz\n/+XUx/enfs7r+PsPoR3p9DWlsxsLT+3K+cvHUOHiRVH0CiclOzsbb29v/Pz8zkp5CQkJGAyGP7V0\nsZSS17a9hqfWk0DRwBuFTVTZdEzrP42fJvzEQxVluGT7oIuNp25TM5LeCCFQVwt8vVQsVPkQ21TN\ngNrd+CwsoHFbCW5dggh5vCce/cMQKoHeVUNMpwD6XBVPgq4Mu01NXOholpq+Ya7rO8QP8cHdT4eH\nvxu9JyUy7MaO9OrTk6qmQtoM8GWTpx0XlWBMkA8jo0fy1tC32F+9n5tW3ERKeQou7dqhfed/eJgM\n5N1yK80HDlDcWMy/t73CC/YpbO6wkFqrjtBmX6wGPW6hevYsPsT2+1/DUlFO2IwZxK34kaDHn2LI\nrGWM98ygfUM689uOZF8+1B34iIjEm6nbbkTbZMLbuxtP+i7Au9bCV3V1zD5UelaOo8LFh6LoFU6I\n3W4nNzeX2NhYWi538VeIj48HICsr64zz/pz7MykVKcR4hjG/YC+9fUNYPm4F49uMR2usonnvdprL\nbQiP7lhLbYCkVFPFz4YmXtQY8RCCkVU78PcNwHdSIqHP9MbvukTUXrrj6qopLUbtUYxWJvD64Jk8\n3+d5Nhdt5sYfb6TCVoFa/cdirP2cb9xu/P13lpTVMCrAGy+NI3541HBmj5yN2Wbm1lW3MnX1VPaF\nF/LlpPswNpnIvG4CHz05ilUZS7kx6UY+nPImYnQUABNq8+iy+Gk863LY1/5uqh/6GK8xVyGcYyVC\n58boaV8woXk9geYKZva5gdqCPCqyvkQTez0V767Fz2MozcZ9LOsXgLbWzIu5JWypbjjjvlc4Pc0G\nC5u+z6SysPH0iS8AiqJ3IqXEVluLtaYG+SddC5cSlZWVNDU1nfBzgX8WT09PQkJCzljR26WdWSmz\n8NJ5sbsyjVFeNt4d8Sk+Lo6Xr2T6KuqyXUGo0MV1QQg1Vu9StlR6ssDHgFGouKJBi55qQpNice8e\njNrzeAV/mPTfV+DiayY0ciRCCG5IuoE5o+fQaGlkPvMpV5cfSevt7U2nTp34IbeIGquN60KOfvrp\nGdKTH679gYe6PUR2XTavbnuV34IXcv+wR0kJD+bGtVa+/FDLrQsqqZ0xi/yZL2JrriVSGtAGBeDi\n7Q4Idv5ayicPr+eXz/ZRmFGDlBKtlx/XvTiDa6t+xoSW1wffjC5jG9mFH2I2BMNShyyy9jPeCliO\nl62GW/YcJMvQfEb9r3B6Utbks2dNAQumbydnz99vkcaLejC2rL6Z5XuKOVjWQF6VkbomC2abHReN\nGn8PHXEB7sQHedArxo+2wZ6oVEdbps0ZGTT8shrDpk2YsrKwO6f+Ca0Wl/bt8bhsBD4TJ6I5xdeU\nLjS1tbVUVFTQpk2bs1puQYFjAdLIyMjTpDwzEhIS2LJlC83Nzbi4uLQqz/qC9eTUO3zMl3nZmNx+\nIu7uMQBIqx3Tr4uoy3VH374fdpMela6anSX17HB35aBFx4BQTyLKmzBZ6/EJDjllXdJup+DQcgK6\nQ1jE5UfCuwV1Y+4Vc7lx0Y0sFAvpX9ifwRGDAcenE6dv3I0fdob6Hv/2sLvWnXs638PkTpMpNhRT\na6rlP8tqmOnzDL8970HzksUYd++iubySNlp3bB1r0EX3oMPnD9JRraK6xMD2Zdnk7K0ic0c5mTvK\n0btpiGzvR1gbH/r3GUfezo2sDhzB24OfY4jRToapEnWllfBmT3Iy1uCtduV5l428Kqdxa/Im1g0Y\nhF6jb1X/K5ya9C0l7P4ln7A2PljNNlZ9vI+rH+hCZPtTuzztdsneojoE0CXy7L8x3pKLWtFXNJh4\ndUU6/u464gLdifRzQ6dR0Wy2Ud5gYtGuIhpNVgD83XUMaRvINZ1D6ZqbQu0XX9CUkgJC4NqlC95j\nx6KNjECo1FjKSjEmJ1Px1ttUzvqQgPvuxf+OOxBa7QVu8fG8++67ALz88stntdyCggLc3NzOmn/+\nMAkJCWzatImcnBzatWvXqjyzUmYB0MMnmDFeRcTGPAA41muvXrAPbfYebCYPdL5dEHoPTNpF7DRc\nw2atmas6h1KdXIG0G0Da8Q46taLP3bML4V6IwB0Pjz/kKzNZ+KXeFZXvk1iMs7n/1wfxCJ1Kv6ir\n6OvtTqFfMP0LM7FbO4LuxE8LQgjCPcIJ9wjn4RE1jJ+1hWWqUO58ayYbDlZw25ztPDg8gV5h/lTN\nS8eS34A+zgf/MA+umNqZZoOFfRuKSN9cTH1lM1nJ5WQllwMBdCGCgoYM1nklktBYQFx9PfZwLcaS\nznhE7iBz6cuEtA2iu9fvrIsewLSURbzW86ZW9b/CibHb7Py+JJuU1flEJPky6p6OqNSCxTN28vOn\n+5j4dE98gt2Oy1dYY2Tu1jyWpxRTXNfMiKQgPruj1zmV9aJW9Ekhnux84TL8PU5smUgpKaptYmt2\nNb8dqiQ1JZUOn04nOH8fhoBQ/B95nMhJ49GcRJmZMjMp/+9/qXjrbRrXryfinXfQBAaeyyadESaT\n6ZyVXVBQQGRk5Fnzzx8mMjISnU7HoUOHWqXoM2szOVBzADeNKxM9iggPnYBeHwRA3coc7GkbaSxQ\noQ5JQhPQFZ1qE2vruvKTp4VwX1f83HR4mtW4+TRjrgef4FO/+LVr1VK8OjXjHzAEIVSk1Bt5L6+M\nVZV1SMDPJYhI3aM0m76iumQWK5tK+cZzLAhBSFkh27dvb9Uqn92jfOkZ7ctnm3KY0D2c55ekEhfo\nzv3DEtDZJKgFzRk16OP+sPRc3LX0vCKGnlfEYLPaSN9SypZFmQiV4PIHbiNxwUM8ZQjm22A/Pvnl\nU2i20ibKzB6NmcTwTHKyXRlobkeReynzZDx3lm+nTZDyTaA/Q1OjmV8+TaPwQA0dh4RjuiyYu7Ly\nkcCYG2Pgo4OsmLWXiU/3QO/mMBCLa5t465eDLEkpQgCDEwN5YlRbhiSee51yUfvoNWrVSZU8QL3V\nxsK6emY217A4EFJHduL5Z59n3MzPGHfbS4woDufBlTnszKs+YX59mzZE/u9/hM2YQXPafnJvvgVL\ncfG5as4Zc+DAgSP7Z/PtR4PBQFVV1Vl32wCo1WpiYmI4dOhQq9LP3DETgDtjuuAqLERF3Q2AcXc5\njZuK8AjZT0OxG/p214G9HrP9ZxZpwqlFMu2aDixLLiTCpsIvyAKAdwvXjc1mIz8/n+TkZLZu3cqm\nNaspyk9G625C592X+/bnMXrnQTbXNvJgVBAbeidxT/o2ppoaWHPtZ1wbfy2auh/RYEaDZFW3QXy/\nO5Xm5tb5wO8ZHEdhTROPLtxDQXUTr43rhItWjcpFgz7Gi+aME5+XAGqNmo6Dw7n+hd54BbiyclYq\ncVdM41H7YgxqV14ecDsuey3Y88pRocEvaj+j4ryI6ejPyG0uWNDx1sHdyluzZ4iUkpw9FSx8bQcl\nWXXob45laoSZ2/blkmk0kdtk4vGCEtZPDKaypolfPkvDbLHxwdoshr+1jh/3FnNH/xg2PDWMOXf0\nYnz3iFPqsLPFRW3RnwwpJfNLq5meVUyV1Ub3g/sZtX8vMZ070DR4KOsNnqx3b0CX4M3atFpWffg7\n3aJ8uGdQHKM6hKA+xpfvffUYdJER5N8zhbxbbiV67ldow4/7aNZ5Z8+ePUf27Xb7UbNB/gqFhYXA\n2ffPHyYuLo6DBw9SU1OD7ynGP4wWI1uLt+Kl86SDfR+e/kNxc4vFXGKgZnEmulgvzHt+Qx0+BLV3\nJH6a15kj/8UenY07+0eTWlRPYJNESNDpDai1Wjx8/ZBSsnv3btatW0d9ff1RdQZ3dTzdTc72p0DU\n8mh0MPdFBeHpnEmzzGZDo9GgVWn594B/U6jtwU9GPd1N31HmMprFST3p9vt2bh82+LT9MLJdMGE+\nLvyaXs71PSPoG/fHh1lc2vpRtzIHa50JjffJFYF3oBvjHuvOill7+XluAVeOup6b1q5knvfVfNZl\nFNfsXkNQDwONYamIfQ2MmNyJ/F9sdMwzsyKyJ/mVe4gO7HpaWRWgptTApoWZ5O+vxjfEjfop0Uyr\nqiTKRcddEQE8GBWMTiWYlV/Oa9kllI8Pot+iEt57Yx2ZhmZGdwjhhTHtiPA93p1zrrnkFH2D1cYj\n6fmsqKyjU34Or8/9hK5J8fg8+jimoDAMJiuD3axcIfTMyiyh2F1NTIw3GcUN3Pf1LnRqQYSvG92i\nfOgQ5k3fUEG7LU/gqtYQdUcS+Z+mUHDbJKKn/wt1YCSEdQPXPz+QUmuxsqveSKXFir9WQ08vN7y1\npz8s9fX1ZGdno1arsdlsZ1XRFxQUoFKpCAsLOyvlHcvhaZaHDh2iZ8+eJ033zs53sGPn5tiBmI2L\nCAt9GXuzlap5+xE6FdaDn2HJV6FvNxadKo0GHz1flXrj56bmvmEJXP7OBq718EBrtWM11+AdGIzV\nZmPZsmWkpqYSHh7OZZddhht2Ni+YS5rBhGGghjq8MNbqebg5g9s7hh9R8uBY1OxwP5ulZK8tkUR9\nAxVFv+Cu3YzB/Q5es7RhVH0DIV6nXtbZLiWHDerL2x89duDS1pe6lTk0H6jGo8+p3U06Vw1jHuzC\nTx/vY+VP8GBnG/sPpbEychgxhiL6Z2zD1KcEa0AVjesLuf3uzqyeuRZLTCDvrylg5o2Koj8VpiYr\nySty2PtbIRqdioHXtSE5Uc/0rGJG+nvxSYcYXNUq7HZJs8XGA9HBhLnoeGB/HmlDPXFLruLxjpE8\neMuFW1zuolf0VrONuoomasuN7G9s5iVjGWU1FtrvSMdi0/Fy/weps4Jt9n4cS+QfjQYoUAt83HXE\ne7tQ3mAiu9JATqWBRbscH8aKFFdxg/d+blL/SkS/cvLXmil69iUiB1Uj1CqIGwZ974OEEdBKn/bO\nOgP/zSvj1+p6bC2enl1UghtD/Xk+LhQPzckVd2pqKgDdunUjOTkZm82G9iwNFhcUFBAaGnrWyjuW\ngIAAPD09yc7OPqmil1Ky/NBy1ELNALdG6ix+BAQMo2ZBJraqJpq2v4+bfidmr1vRal3xdZvLK0yn\nXGPg3THtWbG3hBqDhQjcCEvypTK7BA8/f+Z+9in5pWWEaAWqtGR+XfsjJgTb+4xkW+f+vCUfQO/R\njQ9cPFi/PpmPP85kzJgxRz6KbnNa9ADflFRTbLKwoEsHwtvN54n1T1Bb9TZG96FM3WpkyeUjT9kP\nczbnUFLXjIdew9fb8xnRPvhInCbIDbWPnuaMmtMqegCtTs2V93Zizef7WbprLB+EvsBVjU8wN2Es\n/nsqiOqTj6VzBqbfAvBttNCrezgHjTWsV4eQ/ns+7fpFtfbw/WOQUnJwexmbv8+kqdFC+/6h9Lk2\nnjXNRp5Py+Vyfy9md4xB73y34fkl+5i/PZ8QLxd83bSoVVYsHX0J7h+K7adK1sp0+o1LwMVDe1w9\nZ3ss7FguakVfklXL4pm7kMD+8CaW9w5Et7saba2NMuFPuN6Fzu5uxMT60KSvwFOvoY0tBE1BI67l\nTfgarfigYkZ7V1aGabnb7sr9vWPZU2Pg07XprEqvQoWkWevDjNqhvKseQlLXOkYErWbUt9v4tqEP\n+f382JPjw5PzHqFbfAJc+wF4n9ytU2ux8uqhEuaVVOGnVXNfZBBD/TwJ1esoNpn5oayGL4oqWVdd\nz6KuCYS5HD+DQ0rJnj17iIiIINA5OGyznZ1vu9hsNoqKik5paf9VhBDEx8dz4MAB7Hb7CRdM+73k\ndwxWA32Ce1JdvZ7Q0IkYNufTtKcSU/pyXBIDcA8ZjN04FK3pV2rHPcOixQaSPF24ukc4w99ez6AQ\nb0wZZqCYyoI8LHpXmmxaXCqLcdFr8IuKpnnwKOb6RlOMmlsDJYHlpSSE3El0VD86dGjP4sWL+eGH\nHygqKmL06NFHLPpai5UZOSX09/FgsK8HQngyf8x83tv1HvPSvyHTuI2H127j2d6TCXE/fqZPfpWR\nt1cf5LJ2QbQP9eK937I4VNFIfKDHkT5ySfLDuKsMabUjNKcfTlNrVIy8uwPrXdT8vv0uPvb9L9eb\n/o+vAifwdMX7NHguxY0BGPdWclvfGL5bnk5BZDxLlh0iPDEQL3/lIyWHsZpt/PZVOpnJ5QTHejHm\ngS4ERXuxt8HIw+l59PJyP0rJW212Vu0rcUyTlJI9hXWoASRkdvLlu9G+iF9KOLCtlJBYb1w9tDQb\nLDRUNxOR5MewW5LOaXsuakXvHexK2w4NfOZiYm1CIm12H6Sg1p27uoQyLjKEkgPV5KRW0pxXTG3S\nIsZXjCbcokK4aXBpE4A2xA2hVTOz0Yy5oY43PIwEf7qLK4KqeKHicXoGqlnk24U8mY+bTY+legCp\n2T1IFePZPDyWezf8zPcBJRyMgNvsGno3FvLsp4NIGDML2o4+Tt6UeiN378uh1GzhvsggHo8Nxr2F\nuyXOTc9AX08mBPtxW2o2E1MOsaJHG3yPceUUFhZSXl7OmDFjjgym/dn1Y46ltLQUq9V6zvzzh4mL\niyMlJYWSkhLCTzDe8cneTwC4Kbob9pIN+Mqe1C7Nxt5YRuADo/EaOYKiqW+Cp8Svr4kXdgVjVFXw\n3NXtWZtRQV6VkUl+GkBL+vovQEqsPoHERUVy0/PPUWKDV7KKWV5RS7yrnu/bRpBo2UxqOfh4O25y\n3t7e3HbbbaxevZqtW7dSX1+PlBK1Ws2bOaXUWGz8u034EWvMVePK072fZkjw5dz1+7v8lr+Q3/IX\n0MG/A31C+5Dgk0CIewhuGjdeWpqB2qWS8f09MNtrcPHZzYu/HWBIO08azA00mBsIs/typfn/2Xvv\nKKnq+///cae3nZ3Z3vuyfVl2l16UoqACYguiktj9qFhiEo2amERNLInBEo29FySKioUuRTq7LGzv\nvZfZmZ1e7++PRZQICIqFfH/Pc/ac3Tvv+y575j7v6/18v0ohL3/6DMMxLgwqA0alkXBNOAlBCcTq\nYpFLj7QOJRKBM6/IZJ9BiXbbGv6geIP7tb9iRf0ilk58j+hYL86DA8TNjCdbEkcPUBcpY/vKBs69\n8f/PWw/gcfn45F8H6WmyMPH8FArnJiKRCJi9Pq6qaCFELuPF7Hiad26jce8uzP29WBUGtJZY0jIn\n8P7+LubmRHLXvEzWVvXycks/TYlqHp8TxNUdIiFWP84eDyqdnIjEIKJifni37dOa6PcdLOP61HA8\ncgWzq9rZM6gjNUzDbbnxBBrMSAYcDPgCTNbLuKj7l7TL+3g45mVaovo5P/N8CiMKCVGFIAgC11us\n1NX7uTtXR9yuYPz+P7Ey9kWc2i5mh09A7cukTRFFyYgEb0BgX1AR+2ePY1r1QcJlYdiCq9ij3sVF\nGi1L1tzIxeXXQPJZxIzJRKMPZlXfML+ubSdMLuOTwjGM0x/7QGaKUcfb+SlcdKCJu+o7eS478Yit\n3d69e1EqleTl5VFVVQWcOov+hwqU+m98mSitqanpG0Tv8Doo6y9DIVEQRS/9Ui3uVx0IqAlZkkXQ\nGcV4aqoJBE9CGFjP8Ozf88E/djJWo2JGQTSXPrsDg+DGX9eLXBXBmUsvZcvrLyLTBnH2hRfzRKeJ\nf7WP5tu5MzmKm+IjUEkl1DeUIJEoCQrKPjwXqVTKvHnzMBgMrF27FoByUcYrXYNcGxdGju6bVvCk\nxHEsbrqO5/xOFklKGXFX8nrV6/hE31eNZCCJg7t2jv4pj4ZyF5SXgVKqRCfXoUHFHCGPQKOdlbb3\ncfqcR4wjESREa6PJCskiNyyX/PB8skOz0cq1TFiQQm3HEpZ038H+QBqf2GcwZqCZ4I6/ESX5E95+\nB0sLEinpqaYjxUnLZ146a03EZZ7auInTDX5fgLXPV9LbPMLZ1+SQXvyVnHZ3fSd9Hi9PSUb49O4n\nGO7pRhcSiiQ0BlNDFRcGSqlZV8v8GZew/LJC5FIJN52Zxo1npPJEZSf/CAzydCZo+50s0fhIH6xn\n/bCN9AYtWbNSf9B1ndZEPzY/l9mf78bY6WFlp5QZWhW/H5ZifasWFBKcPpHpOhmeAOzyD5PqCyGn\nL5U6WnjK+dQ3+vNLQ3FH3c9NE3y8uzuaF7r+QuiMLDSZX3lDuLx+Krss7KgfZPO2KnbE5COaJMyT\n59HkPJdW4X3eMu5lbc8HzP54O/qAht5LruEVfSyTDVpezEkm9ARKu00w6PhdchR/a+5hQbiBBRGj\nB742m43q6mqKiopQKpWHDwZPJdEHBwej1+tPSX/Hgk6nIzIykubmZmbMONJDZVvnNgJigOKoUdlG\n78lDQgxSYztBZ8wBYPjt3eCLQ5cv8PDqJjzAb+dlUlLdyu5WMzPMFchV08iZEY/VUQLA4JRZzKps\nx+T1syDcwJ/SYoj7mjRmMZei149FIvmmXDZp0iSkUikrNn7Ou4KWdLWSe1OOfVj92ynFvLN5P7uF\n6ZTMuxM/ftpH2mkZ7uU37+0lXC/h3nMK0Ck0aGQaeofh2lcr+c2csSyb+dU2ftBRyYLBWVx3+V24\nfC7MbjO99l7are20jbTRNtJG1WAVG9s3AiATZBREFDA9bjozz8qn9Z5w/pn9LLX2NF6pugxt+IvM\n3xsHJOkAACAASURBVL8DV10KsyZHY6zfRaMhGlWIm10fNnPxXcYfXC/+OWP3h010VJuYuTTzCJJf\n0dTBB/1mZlfvpnnbJ4QnJLHwt/cyoEvlvlf2k5w8k9De3WTa9qLa+hIfD7jRBoehVMvQauWcZ1Qw\nqbqaZ31ONuXm86JcA8H5qLwe9J7vX4zn23BaE/3w/gbidou8gp25yLnVakeTEYKxMA3z1k6ieh3U\nSUysjN1KXs1s+v0Q6ZnIA/YC9lpX0BpkIjOihegIA2Gh+UTqz6FCHc8NbgtvXWBg2WYTpteqCSxM\nRTd59KFWSiVkmbzE7jdzkS+Mrq4NvOnzspap+AWBYP98LCN5SONfY9v0diJlV7NRH0vBUBevT5pJ\n0EnU77w5IYIP+oZ5qLmHc8KCkUkEysrK8Pv9jB8/Gkn3pb59qqSbjo4OEhJ+nIO51NRUdu/ejcfj\nQfG1aNKVdSsBuCRlOu72jRhqz8JvaSfq7gUAeCrK8XrT8TR+iHjH7/joqf3kyBQUpKtY+rcPkUlj\nuX7+fA5+YsGWqGbtunpipTLe0IQxK0jDHUlRFAdrj5iL3+/AaqsiMeH6Y843Pr+APT4Nfq+fF3OT\nUUuPrZtrVSp+aVTxpFPCigNVXFaYR6ohlafXWXGYM3h66XQyor7yyskJg8lJVt7c1cUNM8YgP9S3\nKjME80dNeAedqMLURMmiiNJGURBxpKfMsGuYysFKSvtK2d61neWly1kO5BUquUA08pz3r1wseYBX\nrJcRPvAKs3bkEDR9AdmqKFoFFc4JvbjW+ultshCd9sOG4/8c0dfcyLZ3VtJRVY9c4Wf7WzK2vuHD\n6XUyIpHw2gW3EW0ZIu7gB+zK9REmC6brvTZ0FjsXMPrd9SmnoFBG4xj+jNaSZwjSLwRJFJ7D4UrR\nnOHoY8He3Xgi9YQbYknxqOj298P8H3Z9pzXRv9PexivIyRabiIv/kD1eJwn9BYgrL4GAjL2eEd7I\nfoLJPS5kiko0MXkMtM+kz6UlWLuEhOFd2Hs1kDSN2M6x4BYpFNu5JEvNC5gpjpIzSaYbfdB67SgS\n9di+6MLbY0ceq8N48RhiEiYStugCLvpiD+9MvJbPlcGIjnTMjb/Hl/AiXfKPmW0Oo+D9VWzprGT+\nbXeesMUkFQTuSo7mysoWVvaaWBxpYN++fSQnJx8+hD2VFr3ZbGZkZOQHl22+REpKCjt37qStre1w\nrh6P30PZQBlSQUqaMkAToDZlIw9tRRY8usuwflyC6ItF6trNs1v7cCJy9ZQEnnzmWcpl+YxJ0bOu\nQyRMAtdbB1lot+LVG1k/PpP8oKNLZpaRg4iin+DgoqN+fmDEwZUVLYz4/byel0yG9tvz9NxalM/z\nWw/wQmsfF+dmsLvVwqqyLm6ZlXYEyX+Ja6Ylc81rJXxW0cP5BaNyliojBGjCVWtCPu3Yh/xGlZHp\ncdOZHjed24tup8fWw+qm1bznfon7lRrSPB6W2F/j2b6beTvvbNI/Xk74TTO5fEwaa9o7qAjuY7w2\nhAMbO/6fI/qa7VtY+8xyRFGOQhNPSkE8Pc5uKgYP4gl4qBm7FJdKzfmOAaJSbiWhdVSuG9S1UxW3\nkbagRoY03TjlVmSilHRTBBNKFVjN71DQ1keoPcBQxBiGInIwB6fRq8kCtxRTr5tXFb3EeHr49ljq\n74fTmugn6vswyxRkSkVkI7NIVsqIkAUzpGlnm2eEwcLXuSVsGFkqQD8CrcgnVNJXE4+5YRZy9Swk\nmmn4xP2Y5I8SZSwjIBO5zh3FXvdj3G008G6VCyNg39OLfU8vEqOSkEszUOeHI0gE/D4fTbnpxK1e\nxzLbVu6Y8mteLO1glV+HreVWVDHv4XM8xKSZv2Dvpi8oG5NJ4bnnn/Aa54bpydepebZjgKy+DkZG\nRliwYMHhz78k+lNh0f9Y+vyXSExMRCqV0tTUdJjoD/QfwBfwkR2SjXVoP1K3HmobMd47+j/zHtyD\n05KKp2kt6nHpvFHZjTRYwa1qO0RNR95o5WC0hunb7XjjNdwkjCDrbSc1O+eYJA9gMZcAAsHBhd/4\n7MO+YW6vbSdMIeOTselkH0WXPxp0chmLjFpWChI+3PoFT5UJh9McHA0zMyJIDtPy8vYWFo6NQRAE\nZCEqZBFqXLUmgo5D9P+NaF00N4y9gYsqdXz0n7+yaqGK1WGdnKPeyKctc/k4tpLIPz7AmcsfJqT5\nAC0yFb+aGsOBDe04rR7Ux8nu+b+E3qYG1j7zOOrgRALiOSz+43Q+Hn6Px0tfIT83n0vy72V1g4ez\nzQLhG5ORa+WMmWOkxfdnpoaX8e+S66ntvZSrcl5DHjyI4qCGvA1dRAwJ7E6NpCwpiq4wOwfSGxgI\nrkSQSNCgJXkkn96+GbT4DIwP+2FlUjjNiV6XrWau/i+H//YAnYd+HwtIvCJKi4jBIcHoVRHqUKBw\ndyFa94HuPfqV6dS5ZtBgnU63ZRJKyQipqi/IVW/iZfPvmFf0HPdOaeKuNgeatlxCAyLuYTcMutAc\nIvlPn3iUpvYWks9eRpBiDELpALehYjEK/oSDqu5fUG6I58Ogd5maPZ8v3nmdlKKJ35pF8UsIgsCV\ncWHcUdvBiuoKkiIjSUv7iii+lG5OhUXf0dGBXC4nMjLy2xufAsjlchISEo6oI/ul1nxW4lmY+19C\nPZyOaNmPOvceAKxrDgCpeJs28vmUm3AMBZAk6yjc/wWN5lQyU0L598Rs3v90D5PnRPN52bsIXjdh\ncceXo8yWUrTadOTy4MPXAqLI31t6Wd7Wx4RgLS/lJhGuODkPiZsyk1mxt5YVLT2Yh9W8eN2ZqORH\nj4+QSASumprEfR9VUdo2THHS6MGoKjME245uAm4/EuXJBcXpxo9nygMiF1y8lDeGnkSXtoEtHVPY\nEjWZyaWvYti4gVh8tJJIZJYHcT00HxggZ/pPH/n9Q0MURTa9/G+UmiB8/rkUnTuG3e6tPFb6GHOT\n5vLQtIc4b1cDOleAcVstjD8vieRJej7fsYiE4F4+2HAupeRyHUqKGs9npOsZ8r5w4gsTGbxCh3FC\nOhHlu0mP8jLDIwWpgETlo9FpZOXwRPp8ei5O/4jcmDLg6h90rac10edpx9FRdyUSczwycQCD9E2Q\ndeKWSrEqlbi0clwagd5IkR6pCyHgwug1EqFcQETITCLCxxIZmspUeRAd1SaqdnRSXRFEteM85NYm\nrtVv4N8Z57EvfDnFGW+hCtyEfEM83g2tNBsllK17nv6qRs7PvhWFQ4G3ax8j5gp+dcNNXCDX8vTO\nYf7ldvCeeTKbfHom6F9HIslk00vPcNE995/wOs+PMHBfXQc71AYuLxp7hPRzKqWbjo4OYmNjT1mE\n7YkgNTWVjRs3YrVaCQoK4ovOLwCYbsimc7CfoNZUtBNHrX1fYw0OcwaiaTsymZk3B8IRNFKSvV2k\ndpmpUArcfmYaQ3VmACR6Bw7TENpAgPDE5GPOQRT9WCxlREUtPHzNHQiwrLqdjwfMXBoVwiMZcYd9\npk8GY7QqkmVSGsJjWWSpY+LX0hwcDRcVxvGPdXW8tL3lK6LPCMG2rQt34zDqnLCTGl+ZloYkOBhX\nrZnbMiewbqSMs5M280HjQrYlZhHz4AMU/vVvlPuCKLMfIDg8mab9/f9PEH3jvl30Ntajj5qPKshI\nzBkK7ljzIOMixvHw9Id5cWsb5Xi4sNFJVlYHTfvW0NL+OVHpQ4jbQymUDaD1t5MdryC64gsSv3AS\nfMlF+K8ew2DH86gcO3ClK7F6tQz5jLSZEzgwlEebIwGD1MKvlG+T0jJIhnbht0/2e+K0JvrB0g7U\nrTPwCVtIUv4LQSHDNf5O/tx9Bh9VDjE9PYx/zi1AN9BK138eY8iyFctYM6bwHdSZ9hKpOI843VKC\nVQUk5YWRkKHHvLOEqk8qqB8KIfRAKqkGG0+G3siqg9czlHEPqvxziCq/jI1PPYxakHJe8g0IVg+O\n/c9SFq8hv2E3l22KYuFlt+CZZ+D2fT0ouod425bD48Jl/CVlKxUH99NaXkZS/rgTWqdGIiHb3M/+\n8BiSM4/M+HiqDmM9Hg+9vb0nlHnxVOJLN8vm5mZSs1LpsnWhkWlQHkp6Jt3Xhm7ZVQBYPy0BYnGU\nfUZvSixtEgFvvJbw9jJqoyeTKlUwIz2MtZsr0RmVdA40I/eOZvg8HtHbbPX4/bbD+rzd7+fqila2\nDlv5Y2oMN8WHf2dPFI8vgKvNzkCsEZvXSn19PWPGjDlme61SxpKJCbywrZkOk4P4EA3KJD2CUoqr\n9uSJXpBI0BQW4iwphWvuYeIHi/Dmbmd181wOGnLINrUxfedeXp0wmxJTF+cXTqRsQzsum/cbEZz/\nSxBFkV3vr0Ctj8DtSuPs6zN4qvwh/KKfBybdz5pnN/GvmDBC8ZC84zFqZCK6sT5yEgfI3eckSDAx\nVdqMVLaegY7LKCntYPfFN1MjVVDzggZ74O6jjpsRKuVPWU6WSPcxcjAYm2cpngPdcOkPu97Tmuhj\nM5Ws/ugfdDh8XL7ol4TOvxuVNozHRZGJezv4y8dVnPPEFzy+uIBpdz1PQmcXQy+/xMCr/8E+3kXf\n5I/o7f0QzWA4xn3RsLEN0ekkXKMhadYcTLlz8dYFeHySgltiH2fVjuvpjhpNPTA1vRCdNR2P14T7\n88fY8vDdPCCN4un+IeZtWM/Wkcn45KNRjrMitAx7bKyxjuV5fYBlkSr2vLWCxNyxh0vDHQ91dXVE\nttbjGzuNbRY754V/dVh2qiz6rq4uRFH80fT5LxEVFYVGo6GpqQlziBkRkbywPMzdZRApQdoyjGbi\nJPwmC/aeGOTyOgK2Yd6PvhyJAP44DWmDWt40efnLwjEEAiKdNSbSiiPYX7ebEKUcp0xGSEzcMedg\nsZQCYAguwhMI8MvyFnaZbTyeGc+l0ce3wL8NT29uxNRohtgoepLSWbNmDcnJycdNL/GryUm8+EUL\nr+1s5Q/zsxGkElRjjDhrTRgCIoLk5F46muIibJs349NlotfmoBO6yDDUURdIp0cfROTq95CMP5MO\nEZLHhrF/XRuddcOkFUV8r7X/nNHbWM9AazOKoLNIL4rEE2Nifcl6lqZexucPrmBX+DQGs6T8weNg\n8SOPsb61imj77eQedODzyjjffT8XxWeTXPE4D/kyqZ51DnKflxRVOxPCS4h2DZNiMaMPONEILkKw\nki1pQ2cNwnFwGsP+BfjFUHTyeoLGnFgm1++D05ropWlnMueyKl5/eycba+X84hehCIzq2pdNTKAw\n0cCyt8tY+vIebjwjlTvOGkP0ffcRblrGyNq12NZvZyjoAObiQbrOGUA3MY5U412ETFmIRK0mHsj1\nBvBtaeSxMAl3hL/A/Z0PAT501nTskZ/TLa7jvfmpvC+NIHbITV/EQsY0P8rEvQ+ycVYc/RkT8Dfn\nk+1Q0Kr3UTEyjipjLwtc42n/104i5+WgTDMc8+ENBAJs3ryZHEmA7TIpnw5YfhCi//IgNi7u2IT4\nQ0AikZCcnExzczNV0aPBX+dq5uCUv4PCYkCTmYZUp2Xk7TWAjs6aWmRyKZ8H5aEMlxNk6cUSWYja\nZuOCwlj6mkfwuPzoogSsjVaCnXYiklORyo79VTdbSlEqIlGp4vhtXQc7zDaeykr4RlnAk0VNzwhP\nb27kwrExVAep6VekMFx7gF27dn0jduDriDGoOTcvmnf3dXD7WWPQKWWos0NxVgzi6bCiTDy5wztN\n0ehOxbG/DH3RVYQ2/pEZsfuoNuXTog8nKNxNvKmPfmMY7iCQKaV01/9vE335prUIUgVyVSZTL07n\ngYr7UMvUKN5pxepfxM5ZanK1Km4+cxYlzc0obfeRU29F7vZxrfsPCPIsagJ+/qT/P0J8Tm5RNpI9\n7j2U2n4iyhYT3HsuiuQ4JDIvEq8J0TGC067F5h19dpXSKoz6d1AmyhGyFhx/sqcAp3U+eiRSgmbf\nyrRLf0lndSWNe3cd8XFmlJ7Vy6byi6J4ntnSxKXP76bb7EQWEkLIZZeRsPwZxt2/kxnnHSQt9U6c\n4SYqpPfT3vcqojhKnFK5hN/OSWdukI7Pcw28xL0MevYRKr+XMNtrlOXF8NEZtxJvdbB0vw3pryS0\nn52Nwmvl3HU1aO07eHfs36gO385EhxQ58JEnnBr7bvzdLgZfrmTw5Ur8lqMXEamqqqKvr4/ZM2cy\nLzyYDYMWPF+TaU6VdNPR0UFYWBgazY+fQjU1NXU0EKxtNOncWFMabn0bskYXqrw8Al4/5goF/V4f\niuadHMgYgy0gwxKvZ4rHyfpGK4vGxaJXyWmvGkKQCIwEeiAQwNrbRVxW7nHHt1j2ExxcyEf9Zt7q\nMXFrQsT3JnmfP8Dv3juIQaPgvvnZnB0aTI0nQGx2Ltu2bcNsNh/3/mumJWN1+1i5b/QFrMoKAamA\ns2LwpOeiys5GUKlwlJZA3sWEWQRyw2uREmBPbCQOpZyozk76AjHsbaoiJjWY7kbLd1r36QCvy0XN\n9q1IpGMoPi+DgNbNprZNZPYakXqn0pCsY1Aj5fbGZ/A9WYDj4HlkDfcRaXHxln8ppWIa7fhY0eFl\n0Ugnz0X1UTj1OdQyC9EfzcUwlIl6bAoSjQJRVOH1R+JXpaPISiP4vBSi7hxP+F//D9Vd7yJc+iaM\nXfyDr/n0JvpDyJs1l7D4RLa9/QqB/7JsNQoZj1yczxOXFlDTM8K5T37Bxuq+I9pIpWoSE29g0sT1\nhIaeSVPzP9hfthS3e7SdIAgsH5tEmFLOZ9M0bHEXs6l3IUbfMK6uZGICPfxBt4yF13Rw3cxfctZf\nXkCq1SINCmLxW52sifkLd//mapKnecgVLNSLUtYGx/NRy1MM5jvwtI3Q98R+nP9VaMLv97NlyxYi\nIiLIycnh7FA9Vn+A0hHH1+b+/S36QCBAZ2fnjy7bfIkvdXrvgBe5RI6ibQi/woq8yY0iJ5fd/yxB\nIipwiJ3IHDY2xRajlAUIGBUkhqTj9gW4YtKoV017tYmoFD3tXa2Eq2QE/H7iso9N9G53Hy5XJ17d\nRH5f30mhXsOdyd+eLfLb8Ny2Ziq7RnhwUQ5GrYKzw/SIgKRoIgDr168/7v0F8QaKEo28srMFf0BE\nopKhSjfirBw86WIhgkKBeuzYUZ1eGURo1FzUMjcpKhOOQAoWrQeV08KANJyGnnIikvSYeux4Pacm\n2vrnhsbSvfi9HvQRBYybk8C61nV4Ah7S67JBEktppp10RxtzPU3siJUiU7lIbbaziQIe8J6NDwgZ\n7OTJPcu5alw9lsLHkXZ5Sdw6iaTrryXukcsI+1Ue4dfkEXFTAVG/LiLy9iJCL8siaHosspATq5V8\nKvE/QfQSqZSpi5di7u2hqWTPUducXxDLJ7dOJ9ag5trXS3jgk2o8viOtYJUqmvy8p8nOepSRkXL2\n7J3P8PBuAELkMh7PSqBbK2f/mGHapIW85nyI37S8wmOtQySGTqSl/UFaWp5CajAQcuWVBKxW5LGx\ndC27Bdkjz7G4IJM/LzqTaKmNzX4D6tAiXt31KBvPqkESrGTo1SosG9oQA6MPcklJCUNDQ8yaNQuJ\nRMIUgw4J8MWw9fCcTwXRDw0N4XQ6fzKiNxgM6I16whxhZKnGYPfWASDrENjTEo5+wI1PHCYzqR2z\nQstOdS7+cDk5XjvrqocpSjAQYu+lp7GdgXYrcZkGOjs7UTtsKNQaEnLGHnNss2U/AC9Y83D4AzyV\nlYDsJDXw/8bBDjPLN9QzPz+aebmjL41cnZoYpZydTh/Tp0+nurr6CLfSo+Hqqcl0mJxsOGSYqHPD\n8JvdeDttJz0nTVERrtpa/DYbipxL0Y94SQupxeuJZH+qjSDbMKIgoXekk4gkPWJAZLDd+u0dn2bw\n+53s+3gNCBpm/Wo2UrmED2reI8SiJkwyg4H4Ppr1kdycmcHGZAN+o5nhg5Hc476ea12/QyKK3NDw\nGU+UPEb8HVIG0lajqYlk3JgXSP7786jzf56J4U5rjf7rSB5XjEyhpPqLz2nct4uexjp8Hi/BkZFE\np2cSl5lDTEYWq26awkOf1fLS9hb2tZp4ask4EkOPDIePjr4IvX4sFZXLKDtwJdlZjxIVtZCZoXqu\niQ3jJaA9zMMlX4xhs3gXc82PIiZvoFYRTHPL4yBISLjyV5jefBN5dBS66dMxvfUWlvdXIY+NZfak\ns3mTHNr0E8nqbOLf1f+ibdwF3NyzGOumdrydVlQL49m8eTPJyclkZGQAECyXkR+kYfuwjTsPOZGc\nCunmxw6UOhpkYTLCGsMoFDNxBbWDCCbJdMyNLvKCZBh0qzFVStieNhYfUtzJoUzR6VnRX8PF/i28\nvbURAIkiCzSXEnA5sbQ2kD39TGTHKNYNowexzUI2HwzBLQnhpGq+n7Vlc/u4bUUZkXoVf12Ud/i6\nIAicERLEZwMWnp48mbKyMj777DNuvPHGY7qzzs2JJNag5uXtLczLjUKdHcKwRMBROYgi/vhFTf4b\nmvHF8EwAZ1kZuilnELpLSnJYLf7uabQFhzCmZ/Rlou0fwacfpYWBDuv/VJSszVbH3j2XM9QRgTEh\niaT8cIacQ1RaajirdR49Eh0bUkMI6R5hV/M79NrSaRiYi8kXihI/oQ4Lj+94khCNiZE/B2FXthBa\ns4BISwxBU8/8qZd3XPxPWPQAUpkMfVg4jft207BvN2EJScTn5OHzeCj95AM+eOQvPH3NEt69+zam\nDWzj0SKRrr5h5j+5nU/Kv1kHVqtNo6hwJcHBhVRV/5rOrrcBuCDSiBSoi1GgWhhPk7WIHX0LEN78\nBVlJdxMVuYjm5n/SZ1tH6FVXYd++g+ALFpG++XOi/nQfyqxMlm54H4OiiQ/xkRl1CUv7i/lPy3us\nzNiMYVEarkYza55ehdvlZu7Zc49w7Ztu1LF/xI7dd+gM4RRY9B0dHajVakJDv5+HyfdBp7ITmSgj\ndTgBj74b2YiS1tiFZBsVCIIVVboSx569bI0rIFxqRhYko6tphDmWnbi7mpl55Q2EJ88k4Kll1+uP\noGmrQxBg4qJfHHdci7mUD2RXESqXcVvi9w8U+9NHVbSbHCxfXECw5kjPmhnGICw+PzUuH/PmzWNw\ncJC9e/cesy+ZVMJVU5PY22pif/swEo0cZZoBZ8XJyzfqsWNBJsNRUgpSOUbDJBKDR8MLI0bGYdK1\nAeB2BnFg0IpSI8PU4zhel6cd6hsewNwqI+CTEBW6j/ZlN7Fr/lIyt17OavkZvKZ3033AiqPCykcN\nMyjry6Ew0MTvpJ+yrOxD3lj/IMYkM0N/lOJUeog9cAuaFW2EXXn5T720b8X/jEUPkHPmHPZ88C6X\n/235Ee50XreLnoZ6umqr6KytonrrJrxuF1dIpQwFxfPE8zXsOvMM/nh+/hFRi3K5nnEFr1BRsYy6\nuj/S5Ra5siufCKUMURR4XO7m3jkxHNw4D0N7J7nvLCHr8nfweAaorb2XgnOfR3heg+nlV4h55GGM\nS5ZgXLIE0e/n9+s/4PdbYbtMxbiuLO6yDbHc/28mnjeRyMVxVK/aRIYvBlZ04ZgpQZ0XjiAVmG4M\n4qn2fvZY7MwK1Z+SFAgdHR3ExcUdtQDIj4VasZYCChgZsqNK6MPfoQWpjhCfH510La5ACv1CKZWa\nFKKjHExVaygv38sSczVFCy6kYO589m/4gjFT8miuW4HM62HRzb/GEHVsvd3vd1Jmc7OfFO5NCT9u\nRa8TwUcHunh/fye3zk5nQvI3D3OnGUfdbbcPW1mWkUFqaipbtmwhPz8frVb7jfYASyYk8PTmRv71\neSMvXzkeTW4Yw6sa8HbbUcTqTnhuEo0GVXb26IEsoE/7BaGdv0UpeDE4MmiN/gydzUK7NhZdbQsF\n0VpM3ScvEf1cYbc3MTy8i/6DU5EIZhJes/Jpuou/F/8fctFHvkeGOWKEluQo/r38PuQ+F9HyIaQu\nCQGXBLdEhumG8XgK9iGXhJHwxU0IlfUoipN+FvWjvw3/MxY9wITzL+bml1Z8w2darlSRkJvP5IuX\ncMkfHuTml1ew+E8PU3TeIhIVLuYNbES56m8s++O/qO850htCIlGSl/cvpPopjLT+iWJhHx+OS2dl\nQSoB4JFoP+pxIXxhvYaeZguSd64gL+NRVKoYqjvuQb/oPCyffoq3r/9wn4JUyqXnXIze0MlbOEg1\nFKFvSuefL4msfONe1u/fjEKpYM6CswEwraij958lOA4OUKTXIAFKR+yH5vf9UiA4HA4GBwd/UtlG\nFEW6XF2YlWa6XP24FV3YbYmkaQQEQCtfj63Zwc7YUSmkMzGO0H4PeZZypHI5E86/mL5mC26Hj4wp\n+VhjUsi88HKSCo6eoOxLjIyU86F4AcHSAFfFnlwg0n+jvs/K3asqKEo0cuuso+eyCVfIydKq2DZs\nRRAE5s6di8fjYfPmzcfsV6uUcc20ZD6v7aei04IqJxQkAo4D/ce851jQFBXhKq8g4PEgTZ6FweYl\nVtuH12fEpPcQYhmgIzIG6f5NGGO0mHp++PS5PxYGBtYhiuDqdxJmsbGzqIBHsy5Epm3lElkX09Uy\nGiaP4ZJYLZWZxcijhwnSelFm5/L62IvYeNdkXGN3oNcXkHTwfpTOCNzl7xN2/bGznf6c8K1ELwjC\ny4Ig9AuCUPm1a38WBKFLEIQDh37O/dpndwuC0CgIQp0gCHN/qIkfC5ITCN+XymTEZecy4/KruPaJ\n57nonvuJjIsnq2UDr995K++s2X1E+x1mDzfYbqVbksZVvuWE+JoYo1Xxn4JUXIEAy3NkDCdpWet8\nEHtLDbJ3ryZ3zCN4vSb6JzaAz4flg1XfmMf1MxJwIOV9wU1c4lQsMYuYsRHaWtuYWVxM2MREIm8r\nJPSKLCRKKaZ3anGvaiRTq2L/Ic+b7yvddHaObt9/SqLvd/TjCXiQaEQGJVY8PgGrO4cEpQyVpglZ\nXBxDGz9nZ0IuSdI+FMEaaivbybY3kDNjNuogPW2Vo26VyjAfPp/vhOIBSgZq2S+M59rYkO9lJsxR\nOwAAIABJREFUzVucXm54oxSNQsbTlxUiO0764mlGHfssdjyBABEREUyYMIHS0lJ6e3uPec8vpySh\nV8l48vMGpFr5aInBsn5E/8nt4jTFRYgeD66KClAFYwyEE63vpAuIdoehdPXSFxpBfPMBNCEq3HYf\nLrv3pMb4uWJwcDu2rkT8ohMlap5K/gUxQR3oo1YSPZxF27QYvCKkqEIx5e5hRnEv6utmsCH9BoyL\nqhkXv4nYmCWM8T8K3TJcB95Bd8Y0VFlZ3z74zwAnYtG/CnyzLh4sF0Wx4NDPZwCCIGQzGsybc+ie\nZwRB+PESp3wHCBIJSWMLuf6Rxzjj5t+jxU37aw/xh0dexO72sX7QwhUVzURrgjir+CUUcj0Hy6/H\n6x0mW6fmo8J0VFIJz09QUxahZoP8WcS23ehX/5n0xN8wJN+LtCAZ83vvI/6XvHLDpDkoNL28I7UQ\nrjDij5rMvgkTCRkcRH/7r+m+5148ba2oc8OIuLkA7eRoHPv7yfdI2D/iIHCorB18Jd34/X5qa2tZ\nv349H3zwAZs2baKnp+eY6+/o6EAQhKOW8/uxUGuqBSBeMxqgYzZHobdmIguA1reSbl8GI0M2KvUp\nJOsHmSTKMbaXIgn4KDx3NE9IW9UQ0anB9A+OrvVEiP7tQRkKPFyb8N2DxAIBkTvePUCHycG/rygk\nKvj4h7kTg3W4AiIV1tFqUWeccQYqlYq1a9ceU3fXq+RcPS2ZDdV9VHePoC2OJGDz4qobPqm5qgtH\nM3M6Sg5FAhsnEK4eYgiItWXiE/pxqTSE2i0MMWo4WIdcJzXGzxGBgI+RkYOM7DcCsG7SUtw+KVPT\n3iLJkolfkLBR52dWSBCmrffwoL2czogx+GU3ohj7CIUR5aSn3cuYtPuxbelFUDrxNu0g7KYbf+KV\nnTi+lehFUdwGmL6t3SGcD6wQRdEtimIL0AhM+B7z+9EgCALFM6ax7KnnkMWkYdz/Idf99UmurGgh\nU6ti1bg0YnSx5Oc9i8czSHXN7xFFkTFaFZ8VpZMbpGblRC2vheopTXkd2ncRu+V9QnRFmIra8XZ2\n4th95E5BLpFzdp4aq1/LpzjI1iqRImNYmYtr0YWMfPopzeeeR+dtt+OqrcGwMBV5lJasFjsWn58m\nh/sI6ebgwYM8/vjjrFixgj179tDS0sKOHTt47rnnWLVqFR6P5xvrbm9vJyoq6ojCHz829vXtA6BY\nlodMEDEPRxPjiUCiEREC+yhd08jO6BwCggQxXI6sfYR8axXxeQWExiVgG3Yz2GEjMTeUzs5OtFot\nBsPxvUXMHi+fu9OYo+78Rk3ek8FfP6thU20/f5yfzfikbw+ymnCo4Mkey6gsotFomDlzJq2trdTW\n1h7zvqumJBOklPHPDfWoMoxIdHLspX3HbH80yIxGFGmpOEoO6fSJiwhTDwEQZ8nDJRvtb1gdTId5\n9CViGXAevbPTCHZ7PSIuvD1eHGIQnzuDuFK5mZ4+J6mmsbRmaBj0+5lX8wb32D7loDYM7ex/c1D1\nayKCunDpHiAh4Wqc5QP4TS5cpe+inT4ddV7etw/+M8H30ehvEQSh/JC0Yzx0LRbo+FqbzkPXvgFB\nEK4XBKFEEISSgYGB7zGNU4sgg4FbHnmIkYwCiuo+J7dkD4uccoyHtvZ6fR5pqXcyOLiRrq63gFHt\nddW4NK6MCWVXlpplykjqZryG0L6b/PJBPLl+RLUUyyeffmO8u2bNAYmb5xU9aFEwU1+M3hXPTus0\n+Od/CL3uOuw7dtB60cUMPPEE6nHhZLeOyjZfl2/27t3LBx98QFBQEEuWLOGee+7hjjvu4Le//S3T\np0+nvLyc119/Ha/3q624z+ejq6vrR6sodSxUD45GxCbYIwgLcmE2xRMulaONbmfLQDK6PhN7EsaS\nQB+myBSsJdvR+uxMWHgRAO1Vo2T1JdHHxcV9axKyNzsacKPkiojvTvIvftHMS9tbuHJKEr+cnHhC\n90Qo5SSrFey1fHXQWVRUREREBOvWrcPr9eINiPS4PQx7fYet/GCNnBvOSGFjTR+lHWY04yJw1Zjw\n27758j4eNEXFOMvKEP1+5PHTiZWMPnth7gRc8lHdvz0ykv66GgBGhk5/ojcPVxHwCVgDfsqipqKR\nBZhvW0e3X0GcJYOKZD9x7n6uqH2cN/TB6C57mfLWG/HIHLxccwdnFy1GDIhYN3cgKN14GncSduPp\nY83Ddyf6fwMpQAHQAzx2sh2Iovi8KIrFoigWf1kt6ecAi9fH9bWdvDBjEe6UTM4u/ZRX39/Cta+V\nYLKPPlTx8VcRGnoGDY0P43SOvtcUEgkPZ8Tzj6QYOkNlnO+OZ9/8N5B2lTGhVYkrz8PIhrWI/2VV\nxwdHkBjdj8lrZA0uEiw6TGftY0jVzab/dHAg+GziP1tP8EUXMvTsc9g3ryDRHkCLwAHrV0TvdruZ\nM2cO1157LRkZGYclHY1Gw+zZs7nkkkvo7Oxk9erVh8fu7e3F5/P95ETfbm0HQDEsYAjpx+VRMyI4\nGR76hOrhCILdIgdCUpghK6erXEnu0H50cckk5o2W02urHEJnVKINlTE0NERMzLFrucJonvnXe2yk\ni7VMivpuVtnHB7v562c1nJMbxR/nZ59UdssJwTr2WuyHSVwqlTJn7lx2qYKZs/0gydsOMm5nNVnb\nK8nfWcVtNe3ss9i5eloy4UFKHllbi6Y4EgIi9j3H1vaPBk1xEQGbDXddHSg0pMhGidwvqNB4RtMe\n9ISF42+qQ6mRYR08/aWbvs4q3LUaRuQaDmqSWKLaTb8kHg0JjKgUVOrUzLXs5dKYSBqyb6C3/vf4\nRA8PlyxjVv5sFDIJrloTvgEnroOr0EyahKbwxDLP/lzwnYheFMU+URT9oigGgBf4Sp7pAr5+qhd3\n6NppgXKrg7NL6tk4NML9mYnc8Yc/ExwaxqX2Leyp7eKcJ7axq2kIQRDIzHgQQZBQV3ffEdrqFckR\nvBQaCd4AF1jieOvcd1H2txET7ka0OrBu3/qNccfrPSDKeVHeheAVWeSZxvvZy0maraWptJ/Vzzeg\n/90fCbnySswrXkaqEslwQ7Vt9CE999xzueqqq5g2bdoxXSRzcnI488wzqaiooL6+HhiVbeCnPYgV\nRZEh5xBaQUPA7iXI2DA6N3kfmyoH0CKyLiELjyAjXjpAWusugn0jzF5yBYIg4PP6aa8xkZgbSn//\nqEUaFXX8oi67zXbavQrmSnegVied9Jw/q+jh9ncPMD4xhOWLC5CeZCTt+GAtJq+fVufoS7/C6uCa\nQQ9bM8Yx4HBydaSBR8bE8efUGGYYg/hswMyC/Q1cU9PGL2cks691mG1DNlQZRmy7uhG9J34oqyku\nBr7S6RN1YcgEH/1SF2EuBULAT39IKCG9raiDldjMR8/BdDphxNyAo9ZIrS4DPwJLPO+j0DYQZU2j\nPFkGgsBq7046hVTO0q7B73Gypea3DLrjuWzCqBFk292DIPfhqdt62lnz8B2JXhCErzsnXwB86ZGz\nGrhUEASlIAjJQDpw7IiQnwm8AZGn2vqYX9qAVxT5YFw618aFo9LqOO+23yHazPxeX4FWLuWyF3fz\nz/V1yORRpKb8hiHTNvr6Pj6iv7njYvjrgILEPi+/sUWwcu7rRGrMoArQt+q5I9rW19ejbh5EphjE\nJLjZKbqJrNGgEGV0jjnAecvysQw4+eSpcow334oiLRVfdyXpQ16qbE4CokhBQcEJWeXTpk0jLCyM\nNWvW4PP5aG9vx2g0otf/8KXMjoVB5yA+0UeyNAG/YgSDJxitqKRV6MDsVWMXJOyKyUEW8GJptDPB\nXEp00TTSikdzxnRUm/C5/aSMCz986BwdffxcNf/pNaHCxdkhspPOM7+moodb3iljXLyBl68af8xq\nUcfDl/VmGx0uPh0wM39/AzZ/gKcSw1hcupkJTZX8KjaM/0uI4OnsRA5OzeW+1Bj2Wuws99sIM6h4\nZG0t6qkxBGzek3K1lEdHI4+JwVE6SvTBYRPQK0cYkA8R64hD6R7GpA8h2d5FQCXBcYxke6cTvIFW\nHCYlNbosxmv7SZb00anxEjWSSnm6hhhhEJm/kzsS3fg8w4SV3MEqUzgL8mMI1sjxDTpx1w/jadmG\nelwBmgnjf+olnTROxL3yHWAXkCEIQqcgCNcAjwqCUCEIQjkwE/g1gCiKVcBKoBpYC9wsfpkG8meK\nAyMOzi2t56/NPZwVpmdDcQbjg78KXokZk8XUxUvpO7iHv491c1FhHE9+3siSF3Yj6C4iKCiPxqZH\n8PuP1DLnXTSGpSVOMq0iv3HFUzrpTvSRLry7KvG6R7fIDoeD1atXExURSWGqiNcTw2sSGzj9LPEv\nYGf3ThKyQznn+lxM3Ta2vtdM+C234u2sIt3kw+YP0O46cY1WJpMxd+5choeHKS8vp6Oj4ye15gFa\nR1oByCINj7YbQ89UYvxG+kUnIHK2R0qfJpGxgQbcSg0FF17Bpb/53eH7m8sGUGpkxI4x0tvbi0aj\nISjo2OkBHP4AHw8MM1HcQYzx+H72/42V+zpY9k4ZBfEGXr16Ajrld9P3U9RKAF7sHOT6qlbydGo2\nFGdwSUoc44uLKS0tZXDwqyyVGqmEmxIi2DIhk3HBWrqTNDT021jRZ0YepcW6vetwfqQTgbq4CEdp\nKaIoEhQ3D4PSwqDETbojEZl3iJEgA3rvCHaJeNpb9A6rC0ExSL0slmGFgV94PkQUJGxwJGLXpDCs\nlGAdeI+LtUEYZa1EVVzHbmMWdq+fJRMPWfN7e0AQcVeuIeyG679zEZqfEifidbNEFMVoURTloijG\niaL4kiiKS0VRzBNFMV8UxYWiKPZ8rf1fRVFMFUUxQxTFNT/s9L87au1ObqhqZV5pPX0eLy/lJvFS\nbjKhim8+vBMWXkRCbj7b33yBe6aG8vjiAqq7RzjvyZ0MS/8Pt7uX9o6Xj7hHG6xk+qJUFmwwEyZK\nuClkEYG8WAS7QMea0TKCn332GQ6HgwsvvJDrphQCEpplLbRLAswdmMLB/oNYPVYSckIpOjeJ+j19\nWBOLEKR2xlhH35+V1pM7LEtLSyMiIoLt27djt9t/cn2+YXhUqsn1jcGt7SOorxi1C0SpjNhQge5O\nM31yPReodmGbfimzF196OFbC7w/QUj5IUl4YUpmEnp4eoqKijvsgrhu0YPOLTGMrBsOJOYSJosjy\nDfXc+X45U1JDee17kDxAqEKGUSZl67CVgiANK8amEnboezdjxgzkcjmbNm36xn3xKgXvjk3ll+Pi\n8IcqeWh9Hf4Jkfj6HDjLT9yhQVNUjH9wEG9bG7KoQkJkIwyJcrJcSUj8Q1h1BvwSAbPLjnPEQ+Ak\n/fV/TuhpbsNnFajRpSEV/XwS1cK5cUmMKOMpT1GjCHgYH9jPpJBeoj1XEDRQzGqvi4zIIMbFGxB9\nARwlfQTsLcjC9WinTz/qOL6AyPZhK4+29LC0vJlZe2sp2FHF2B2VjN9VzYLSBm6sauUfLb1sGLQw\n4Plx4xNO68hYq8/Pa12DlI04cJ9ACgCT18eKniEuLmvkzL11rB8c4deJkWyfmHVEMY//hiCRMO/m\nO5AplHz6xN+ZnxvBp7dOJyFEww0r/Qx4J9Ha9hxuz5G5wrOnxpAUH8T5u2x0urw8e+m/QBCRvreK\nA2U7qays5IwzziA6Opo5aXmo1H34CGJlwI3BoibJFU35QDkARXMT0RmV7FjVjHbaWFJsAaQiVNlO\njugFQWDy5MmYTKMesz810VcOjqp+ad4EpBIFEr8a6+DoGYLOEElJxGhCtyJJPYtnHVnmsLvejNvh\nI2VcOD6fj/7+/m+VbVb2moiQ2MiT9aLVpn/r/OxuH7etOMATmxq4pCiOl68c/71I/ktk69TEKuW8\nmpd8RLCWTqdjypQp1NTUHE4293XIJAIPZ8Rz3VnpeH0BLqlsQRatxbK29YS1ek3xoUIkJSUgkRIu\nszPs05Lpi0JkEKs2CJtSgW14EFEEp/X0DZoa6G7C1aCmUZtCtL+B/TqRbpkHjT+JulgFGvsOFhut\neIVM9Lvm0Jmmp7xnhCUT4hEEAWeNiYDDh+vAaoyLf/GNinDdLg9/aewif2clFx9o4vHWPtpdHuLV\nCmaGBnFWaDATg7UoJAKlIw4ea+1laUULeTuqKN5Vxc3VbazuP35tglOB0zrXTYXVyV31o5GdMgEy\ntWoSVAqilXKC5VIkCFj9fvrco3p2o8ONCMQq5dyTEs3l0aFHteCPhqCQMObecCsf/eNB1v37cc5d\n9hvev3EKf19Xy/I9c3hg6j4O1jxBUc591O3aTv3u7QR8PiKSCwjfrOeswmBeE7zMHp9DUsMBLDvv\nJCHh6sM1WgVBYHyKny+qYtjMML8W1MyyTKRysJKpsVORKaRMWJDM56/XYjtvKopN/SS51FSeJNED\n5Obm8vHHHyMIAmFh3y/0//ui2TKaqlfv1OBxheGV2RhyNiBzZdCFgbKYBGKFASzyECYmGI+8t2wA\nmUJCQnYIAwP9BAKB4x7E9rq9bDVZuUCynVDjhG/dgjf227jxzVKaBmz8bm4GN52Zesq27c/mJCJB\nOOr3b/Lkyezbt48NGzZw1VVXHXXM+woSqawbZG9ZL89NCOKavXZsu7oJmvHtwV+KlBSkRiOOklIM\nF19MlErE5tMiEwRkPjOiREpvSCjuwV7QhGK3uNEalKdk3T82RsztNPUnYVXoMajX8jdvMPfILbg0\neXhlAufItyMTIIt7wB1gvVpELhVYNG7UK9xR1g+Ch4C5ieALLzzcr9Xn5x+tvbzUOYAInBtmYFGk\ngTONQWiPE2Vt9/kptzk5MOKgZMTOtmErConAwogfNkvoaU30kw1a9kzKotzqpNzqoPIQmW8btmI7\ntN1USwQiFPL/j733DozjPK+9fzPbK7BYYAEuKgEQhRUEQLB3UhIlSqK6IttSpNiKk9iOb+7NjZOb\nOPf7krik2HHTdSyrWZasSlEkJUoyexN7RyMAopcFsNjed2fuH0OCothAEZQoX51/JO7OzL4zmDnv\nO89znvNQYdZzT7aN5XYr082GT/TAls6aw4KHH2X3K79BliRWPvkN/tcdk5lXmsnBQx8wQ3qVX/6q\niehwhDRHNiqNlvZjz2Bz1jDt/aXsXZPB0/f+Gd/9zjeodTUy/S+WX2BR+yfz57KrvhM0HRxIOVgW\nmM2vhs5HvybVZrPnjVbaBgxURN0Ue22ctl27/E2j0aDRaEgkEqRSqc/UzKwvqDiHqgMghgrok08j\nI+NM9tFlmMQJWyH3C9tRF9Re8DeTJZkzx4YonGpHrVWNWghcaUW/1uVBAuak3iHddnmPEkmS+c2H\nHfzwvWaMWhUv/sls5peO74SYpb18z1idTseSJUt45513OH369KhN9cfx7JoZzD7t5pmGAVbl2RG2\ndGGYnok6/crVuYIgYKipHk3ITjAr5miDxi6s8RgewJVhxxhwgxHC/mvT6t9MiIR7OJqYhqhJMSH9\nBBO094D8Ov2ZhRjjYW7XneDo0BqqXHqC2RH2tzazaoKWjtMNGNV6pOY+tB17sdyyEnWGUhC3ze3n\nL5u6GIoneWRCBn9ZmE2BYWwToUmtYm66mbnpyjWXZZnINeRXPik+10QvCAKFBh2FBh13fmxGlGUZ\nCSU2NZ7Jk7o1D4AgsOfVF2k/dpjc8kpikQhGlwfVAynUFW50aX/FE19ahSCK7H39Jfa9+Qp6aw7L\nhufxdkYmj+bkkdvnQbPvf8Hdvx099uKJU9Bp9xOVLbwrxZkrmYl3+JFlGUEQUGtVlM/J4dTOXsoN\nIhN9KTZH44RTEsYr+Kt8HKFQiFhMSbK1tLQwefLkcbs+14KUlMIb86IR1EihEGJKT6dXCdtUpVo4\nKFQTEzUsVJ1iWvW3Lth34IyPsD9O8UylBmNgYACNRkNGxqWrU2VZ5rWBEabpIzgjfdjSZ19yu7ah\nIH+39iT720dYXJbFD++bflVbgxuB6upq9u3bx+bNmyktLb2kZ71Zp+Zn98/giRcO8Q1bmNckkZbf\nHcJfrcHr9ZJKpTAajeTk5FBcXHxB9bNp1iyCm7eQ6Osjx66E74YMbeSE1XQCbpuNAtcw2CEa/PyF\nbhKJBG1tbQRooV57N8WBTvJTGWw8I6HJt9HhNLFQ2k4sYiTSpOdX0gYQYBbAMKxbd0w5kAYMhTHK\nSsqY3t7O71Jaft49RLlJz2+mFVNlvb7Wm4IgYFTd+OTu55rorwRBELgRJjuCIDB7zQMUTqvi+O83\n4WpvRaPVMWPxw+it+yiavJP/sSNI82sn+Lf7FcfM9qOH8A3uo2R7Geq70nj5tjVUHOuk8PhGWNgO\nGRNHjz0ty82h3koa1D6SSQPlnnxcYRc5JiUkUTnPyYmtPYQzMyiOapCBtnCUaZax33DnYr86nY7G\nxsbPjOiHIkPIyGSrHEhiHJkUfZ52NALkhWRcehMgM0dsQFdYe8G+LQddqDQiRVOVlfbAwADZ2dmX\nfTupD0ZoCkX5lukIOl3ORfF5XyTBT7e08MLeDgwaFf9633QeqL16he2NgkqlYvny5bz22mscP36c\n6rM+NR/Hssps1kzLpK3xBM/q3IiuKGxS3tpUKhXRqPLGp9FoqK6uZuHChZjNZiWp+P0fENy5k5zy\nqUAvbr2bkrCd/YDHkkZtuAEfn68Y/eDgIPv27ePUqVPE43GMThU+TRqLXbu4NRzCl2Fhk3oJSbXA\nAnkLra3VyLIKEzpOqzKoD1tYpm1BLcgsZirhuJ9+30EaBgc5/sILeIxm/njyDP5u7iKs+s9POOsP\nluhvNHJKJpFTciFZhEIL2bf/9/z90ia+84EFly/Krx6toe7u+9nw4x+gE1qZ2TuZ7bVzGNy3ngIG\niW/+G7QPvjZ6jAdnlnCoV0StaudEKovawFRODZ8aJXp7rgmzTceIoKI4qISnToeujei7urpQqVSU\nlZXR2tqKJEmfSfjmXNimSMxHTOkZ0pxEQqJQ5Sc2ZGDIaiJTE0KjN4H1fLVrKiXRcmiQ4hmZaA1q\nZFnG5XIxderle8O+PuBBIwjMiLxMhmPhKIH7Igl+d6CLX+08gycc5+FZ+fz3W8rJNH/2D3FlZSW5\nubls376dadOmodFcGO5JpVIcOHAAR+d20tUx+qU05uhLmRtIZ+ITdegKrMTjcXp6ejh+/DgHDx7k\nxIkTrF69msmTJ6PJyyO4fQcZS24DevFoo0z2p4Es4Tel4YwM4hEgco02C58FXC4Xv//972ltbUWt\nVjN16lSyzPn8+sBGBFliUuQUK3PMvNI5gCZrKWmSl7xIH7tHFvNo5izSYiZWB0ZYXuXg6/MX0X6y\nhYk71BxSB+mc4GRHzRLwurl1qJvEoT38V+MxFi9eTHV1NWr1zU+jN/8IP0cwmUqw25ei8b/Lzx5+\njL96rYn7f/kh//XwVFBrSMrHqW6s4GChmvdyp5OT3kBB4wcwdBqyygC4Z/YdfOe9N/FJOvbISb4Z\nd/J+dwMUrgCUVX/BFDsDh13U6CXUskxz6Nri9F1dXTidTsrKyjh58iR9fX1jcnscb/QGlaLpmugU\nRElDp/sMIDNJ52KkyUJvmY3JoouhrPl8tC1Hd/0I0VCCstnK5Ofz+YhGo2RnX7pDVEKSedPlYUma\nhN4zgD1jIX3eCM/ubueVg90EY0kWlGbynVUVTM1NG7fzGxkZoampiZ6eHjweD7GYYkJnNptxOBzk\n5eVRWlqK0XjpSVoQBJYvX85vfvMbDh48yLx580a/Gx4e5o033mBgYIDS0lL0RVU8v7GTvROsvJxQ\n4f5tI9nfnInWoqW4uJji4mLmz5/P22+/zeuvv87ixYupWLQI39q12DXKJO8VVJQlbWjiAYImC+aY\nn6gIkZs4dBMOh9m6dSuHDx9Gr9ezbNkyampqMJlMnNjWzclgMXnRXjLVI5B7JwN72ulenMk8YQcH\nO+cwofoObHtGODpZh38oyV0zcsnLc5DeKeDlDPYDG9j+7W/TqjWw3O2iKCOd8rmzOXnyJO+++y57\n9+5lyZIlTJ8+/TPNdV0NXxD9OKMg/3GOHnuUasdhXnhiKU++eJB/f/olCs3pGCIu8hMyxf4kGxcu\n4wFPO5K4BXH79xAeeB4AjUZPnv40XaEqTmqSkADOROAjysLCKXY69vah0YnkB8OcDo+d6GOxGL29\nvSxYsIDi4mIAWltbP1Oin+IvRkam190LCGRLAT4wVSAJIrmih2ahlKKP7Ne8fwC9WUP+ZCUe73Ip\nrouXI/rtI36GE0lWaE4BIv++3cJbx7chA6unT+BrC4vHleC7u7vZvn07bW1tgNL8PCsrC7vdTiqV\nIhAIcPToUQ4cOIAoipSWljJ37lyKioouChWdI+ldu3ZRXV2NXq/n5MmTrF+/HrVazUMPPURFRQWC\nILCjO8E7J/v5xpQMXmhM4H6pkayvTkNQKwTkcDh4/PHH2bhxIzt27CBUUEBxNIrm5FEEZLyyAZtk\nRJP0ETJakOUUITlF8Catjj1z5gxvvfUWwWCQuro6Fi9ePDppjoTi/OO+NoalDJYFTpChD+M1ldNc\nYiWm0jI1cYrneu9hbY0VJDc7ozHMOjXzSpV2muFTbmTJx3/cdy9tBgs/Kslhguhn//79tLS0UFlZ\nyV133cWBAwdYt24de/fuZcWKFUyaNOmmLKj6gujHGTbbPMymcrq7n2POrPv4xxo4dXiENvMkKrx7\nKa6SmNIUZUOdk46OyRideyisXwdLWyBTCQWtzArwTFCLbO5lxDMJi+vCMEJehY0Yys000e3ntOPq\n9rjn0NXVhSzLFBUVYTKZyM3Npa2tjSVLlozbNRgr2n3tAGQFrAToJyFEEGUjBLWctBeDLFMnNtHk\nmcQtZxPSsUiS9hPDTJ7vRHU2AX2O6B0OxyV/57WBESyiiNj1O9qi+bxbH+axeUU8Pr+IPNv1JdM+\ning8zgcffMChQ4cwm80sWbKEqqqqS1omp1Ip+vv7qa+v58SJE7zwwgvk5eWxatWqi3p/Ne3VAAAg\nAElEQVQDLF++nKeffpq9e/eiUqnYtm0bhYWF3HfffRfYV/zz3VM53DHCUEeA/68ijX865ce78Qy2\nNec7XqnVau6++240Gg0HDx4kNWUKGbt2YVFPJpQwYVBHEZMpgsZMYhoVCTmBz3tzGZtJksTWrVvZ\nvXs3drudr33taxcZ2b34YSdHz1pBT/a14LAneL85RNfEIlRyArvLhF5joDAm40Vma7+XJeVZ6NQq\nUoE48Q4fezR+9pVN4YdlefxRbiYU5DB37lz27t3L3r17aW5uZtasWcyZM4cdO3bw8ssvU1hYyMqV\nKz+ThdOV8AXRjzMEQSA//49pbPpbDh9+hVOHmympmMqWZi3l7MUf72CGZxLvpiR+r8rBVjSBgt5O\nhH1PweofA/An08p5piOJN+bhBCnKvPnEU3G0KkU1oTWoSXeaSAQiFLmDbI/EiKYk9GNQ3nR0dCCK\n4qj1QVFRER9++CHxePxT96TvDnSjk7Togio6Ap3obXFUg9mEvekcLqskS/QxQ2hmYzCfwcFBsrOz\naTnoIpWQKJ9zXi/vcrlIT09Hr79QHRNPSrx6rIeNPi+6Xi85uafx6L/E3u8sv6hx9/UiEAjw0ksv\nMTAwwNy5c1myZAk63eXj/CqViry8PPLy8li2bBnHjh1jx44dPP3009TV1bFy5crRmHxubi6VlZXs\n3r0bSZKYMWMGd95550Wx4TSjhh89VMUfPb2PvYEI60qNrNnXj9ZpxlR3/noJgsCqVasIBoMck2XS\njx8nfUYlgYSJpK4PUUojZJxITK1GkuKEb6JkbCwWY+3atTQ3N1NdXc1tt912yfv2RI9ShFQRbiQz\n4idqLqSpd5ie2ZOopIGm7vnUFtlI9gRpNKtwB+PcMkW5RpEGN8jw89nl/DExHvtIm0mdTsfSpUup\nra1l27Zt7N+/H4vFwq233ko4HGbHjh38+te/prKykuXLl3/mdSrncPMGlT7HyM5ejSAYaWl9lqKi\nIv7o/jW8+BfL8evt7PjwMBOnZ1I8kGRzyWSslmUMZBuQj/0OQoqvurNoNiZtB+64nUYxiTOVhnvo\nQuMqR6GFiAQTgykk4ExkbK/X7e3t5OXljT4chYWFSJJEb++nbzLqCrmYGi5FQGAg1IVanyAtLuEb\nSNFuzmGmtpNyq5IIbGpqQpZl6nf1kplvxlF43s/G5XJdUCgVjid5Znc7i/9tG3+7/wyyKPDfi/sQ\nBZlba++9IST/7LPP4na7eeSRR7j11luvSPIfh0ajYdasWXzjG9+grq6OAwcO8Nxzz412DZNlGY1G\nM1oQtmbNmssmAOcU2/nTRSXEOoP80JSgw6nH83YrsS7/BduJosg999xDlsHI7rJJWAWZUNxEwtSO\nIRUhbDARVatQSTES0ZvDrioQCPDcc89x+vRpVq1axV133XVJkk+mJA60jzBbFeKWwc2oRA3brEuR\nzCYG9dlMDnVx1J3B7Il24t0BduuVIqml5YpU13V8kG6DgL2rnn+aM/2SY7FYLNx111189atfxWQy\n8cYbb9DS0sJjjz3GkiVLaGtr4xe/+AUbNmwYrUL/LPEF0Z9FKpVicHCQlpYWDh8+zL59+9i/fz+N\njY0MDQ1dts3bpTA8HGBgoJDMzE7uu+821Go1RZkmZlRNJTM6xA9aepjSE2fIZqNzcBpdTjVCMgKH\nzvrlOCopUzURT2YzYFVeP/1tF3qZOAqtRCQoiiikdXoMCdloNEp/fz9FRUWjn51b2Xd2do75/MYD\nsiwzEh1haqgEWZaI2ZtJRlXkhDy0WnNJiSpuUR3FkldJXl4eDQ0NDHYGGO4OMmWBczQOmkgkcLvd\nZGdn448m+MW2Vhb8cBv/tLGBggwjRTOyqDTpWWTZhU6Xg9Uyvl2BotEov/3tbwkGgzz22GOUlZV9\n4mPp9Xpuv/12Vq5cSV9fHz6fYn63c+dOTpw4gcPhYHBwcPTzy+GvVpYxNdeKqcnPE0UCMbOakd81\nIUWTF2yn1Wp58I8eRhYEwuEY0YSelKkXSzKOLIqMpKVjEWMICema7v8bgUAgwPPPPz86mc6efek6\nCID6Pj+BWJJKqR1JVuMqrUAUBUbsyqQwsV3HiEpmbo6FpDfKjnCEeSWZWPQa/OE4Qoefw2lJvt90\nCI3BcMVx5eXl8bWvfY1bb72Vjo4OnnnmGaxWK9/61reYNWsWR48e5ac//SmvvvrqaNj0s8D/k6Eb\nSZJwu9309fXR19dHb2/vaBOOy8FisTBjxgxmz559RXdEj8fDSy+9hE43hezsRrze97FYHgNgYkU5\nHft2kEgE0EVEVCkT7/szuCfXSnBCGuaDT8OCb4Nax2pzD0fDMKAaIIodX0sA5p//HUehhTYZChJ6\nxDEqbzo7O5FlmYkTJ45+ZjAYyM7OHvWm/7TgjrpJSklmD5fhTQyRNbuPtg2l2MJDbMtSNONLUvsg\n938wtWAq7733Hge2NKDWipR9JAwxODiILMucGoFv/3AbvkiCJeVZfGNpKWlZRhYeaOK7+emMtO/C\nOeEBBGH81jaSJPH6668zNDTEI488Mm5x2XNvJz6fj97eXrZt28b06dNZtmwZP/vZz9i+fTtr1qy5\n7P5atch/PjST1T/bhbrNz19PTuen+0J4N5wh44ELJ6LMwkLmBgJsNqeIJEzIFjd2v0wr4E5LxyaH\nSciQjEtodJ9N+2e/388LL7xAIBDgy1/+MoWFV+7m9eEZ5c24inoabQ5i6TbWTEzyt2YzGfIw2jNl\npCwiJQmBU0h0h+M8WelAlmWe3n2GhyQo27uWCffMHdP4VCoVc+fOpaKignXr1rF+/XomTZrEnXfe\nycKFCzlw4AAHDx6ksbGRzMxMZsyYweTJk7Hb7dd9bcaKzzXRR6NRmpqasNlsZGRkYDabL8h4S5JE\nIBDA4/Hg8XhwuVz09/fT398/2j9Vo9EwYcIEamtrmTBhwqg/u1arRZIkfD4fLpeL5uZm9uzZw4ED\nB1ixYgWzZs26KLvu8/l44YUXiMfjPPLIf6Oru5m+vlfJy3sUQRDInqgkxf7/BWn87x0JSvoTfGBz\n8IRtEZ2OHUzpd0HL76Hidh5wpvPPIz4GwxItpLB9LLJid5ppBPQqI3mxMC1jUN50dHSMxoY/ioKC\nAo4dO0YqlbpkBeaNQH+wnxmtaeSRx4ixGbNFi8wENOF29mdPxijGyBT84KxmSuYU3n//fZpPN1I9\naw5aw/nbdttRxf3yV4dGqCrN43/eWsG0PEVB8y9tfagEWKptoE+KkpW1clzPYc+ePbS1tbF69WpK\nS0uvvsMYkZamjL+lpYX9+/dTUFDAXXfdhVqtpq6ujn379jFv3rzLJp8BSh1m/tcdk/mHdac4kqHj\n95VmVh52YZhixzD5QoKZuWABtj2DDIsOJGMA+5Cy6nSn2XBG/bi14PfFsDvGL3E9VoTDYX7zm9+M\nkvxYTPiOd3vJtejRBAeIOWZjGx6gcmUNZ6LFlMXa8MoTqSkyI/UG2S+kQIZFZVmsdXmg1UtClMk/\ntQ3zj//mmsZqs9l47LHHOHDgAJs3b+app57i9ttvZ9myZSxYsICTJ09y4sQJtmzZwpYtW7DZbJSU\nlDBp0qTL2lyMFz7XRD84OMi6desu+OxcRWAymbxoha5Wq8nJyWHGjBk4nU6cTidZWVlX1L+azWZy\nc3Oprq5meHiYTZs28e6779LT0zP68MH5VUckEuHRRx8lJyeHVOohmpr/Hr//OGlpVTiKikEQMAcG\n+P7jt/D8awc4nZfLkPYWROsmKo0ZiEdfhIrbsU6YRtrp07gjU2kRwtwWNI9aIQCoNCJqixZVLEmh\nZ4SWMawOOjo6yMvLu6jwJj8/n4MHDzI0NHTV7kzjhca9O1jcUYomT4dY2IbsS0MrpyNFk5xJczLd\n6IKUCM6ZWHRm7JYcRhIupi9VJqnhYIzvvn2K4cYGytUiP3l0AUsrHOdDOpJiebAsw4rk+S1qddqY\nbYnHgu7ubrZu3cqUKVOoqbk2X/ur4RzR79mzB5PJxIMPPjh6ny1cuJAjR46wdetWHn744Sse58uz\nC9ja6GLX6WG+W6dhToYW1cYz6CfZEDTn73nrypUUrv1XThrzkbVJMuPKc+NJT8Pc6cOthTP9gU+d\n6BOJBK+88goej2fMJA9K6KYkzUBbJA8QmHr6EEfkhfhEGwXDMQakFLMnZhBvC3BQK1NkNmK0aPn7\nfW287JZQRXrQFheiyb1ku+srQhRF5syZQ2lpKevWrWPt2rU0NjZyxx13UFtbS21tLR6Ph5aWFlpb\nWzlx4gSJROILor8ScnNz+cY3voHH42FkZIRwOEw8HieZTKLRaNBqtZjNZmw2G+np6aSnp1/XijUz\nM5Mvf/nL7Ny5k23bthGPx3nggQcIBoO8+OKLBINBvvKVr4zK47Kz76Sl9Xv09b9GWloVGr2eDGce\nrjOt3PPAl2gxJtkEPLvfxJ/kq/BPnER6w/sQcEFWBZOFl9krz+K41sW9cSuR4TDGrPOlQ1qbDgaS\nFA642O8sICnJqC/T1i4SidDf38/ixYsveR0Bent7PxWid51ppfu1D7DblTBC0HGEZL+FjHCSHouD\nuErDCv1pMFaCzkwiliLlSkcy9BPFx/v1Ib7z5glCsRSPZQk4TBNYVnmhhv79YR+ueJIvT0hjuHEr\nmZlLEcXxScImk0nefvttrFYrq1evHnfdtEajwWg0Eg6HufvuuzGfNR0DpQfwvHnz2LZt22gj9MtB\nEAR+eP90bvvPXcSa/Px9STo/ORQhsKsH67LzpKm22cjMtJGU1SQkNVkpJS/kNVsxRHvAAu19fmbN\nuHSdwo2AJEmsXbuWrq4u7r///gvCjVeCL5ygayTMHIuaEVU+mhEXmQkf7ySUc3K2ZdEkyswqtBHc\nMcjhRJwHywr4bksvGYEkWaEU0dYPMc+dd5VfujIyMzN54okn2Lt3L9u2baOzs3O0Itlms1FXV0dd\nXR2SJI36Tt1IfK6TsSqViszMTCZNmsTs2bNZunQpt956K3fccQe33HILS5Ysoba2lpKSEux2+7iE\nJQRBYPHixaxatYqmpibWrl3LM888g9/v50tf+tIFHZvUajNZWbcwOPguqZTyx3QUFTPU2QHAo48u\nJHc4SpPeSFJVSXdmEuQUHP8dZFVwj3wMkOgUgwC8vX3nBWPR25VEUW7vIHFZvmK3qfZ2RbN+qQcm\nIyMDvV7/qShvUskE7z31Y2S9mlS2nbg6TtzSQ9JrIsPvpjGjCIClsd2Qp6yUG3b3IfptiKKKZ9bv\n4E9fPEx+hpGN35yPOua/5OT0fO8weXoNM+SjJJM+sh2rx+0c9uzZw/DwMKtXr8ZwlWTdJ8XUqVNZ\nsmTJJZO7c+bMwWg0snnz5qsm9xwWPT+4dxohT4zjngiHcnX4t3WT+lgRlL1CCT1FEnpMmmHUiQgB\nkxVtWJEp9g2Gx+nMxoYdO3bQ2NjILbfcckVri4+jvl9JVMve0whyEv1QLxatxJGUCqvkI20gE79K\nptKo53gsTlSSseYYeWvQy9+klL9lsvcopjmXT/aOFaIosmDBAp588kmsViuvvfYab775JpFI5IJt\nbtQ9dMFYbvgv/IFi9uzZTJs2jfr6eqLRKI8//vglk0Q5OfeQTAYYdm8FIDO/kIB7iFg4jDYzg9rO\nbvqyDKxvqGJQPoOUVwPHX4GMYlbhRtT145HNSMi0N/UQiAdGj22eoKzuczwKwbdeIU7f1taGVqu9\nZOtAQRDIzc2lr6/vuq7JWHBw/VqGuztxLUgnP+kkZPGDIBP3GrH5eziYXYEoyEyKN0NuLamkxLHN\nXWRPzMCrsRNydfDYnAJe//pcsg0ykUjkoorY1nCU3d4gX5mQydDg22g0GWRkLLjMiK4NbrebnTt3\nMmXKFCZNunrjkk+K22+//bJFbDqdjsWLF9PR0TFafXsl3DIlh4dq80m1+fmnTAkpJRHY0XPBNpkz\npgAQSRgwGH0Y4lH8RivqkAeAQfe19z34pGhqamLHjh1UVVUxd+7YEqLnUN+ryEjFaD/qcAf6WAy9\nRc8ZMZuCUCcCYM7Qox6OcoAkalHg5ViIcpOeOUNJBHUUOTIy2kR9PJCdnc3XvvY1Fi9eTH19PU89\n9RQtLS3jdvyx4Aui/4Q4fPgwp06dQq1WTLUuNytn2Oai02YzMKDkEux5yiuzu0dRudyZGgRBYCA1\nH5Bx51XAUCOMtGHOLMWi62QkZqdHlCiMZrO+bf3osS1OhegnRBTN9uUklrIs09raysSJEy/7VuN0\nOnG5XKNJ6huBZCLBkU3rKa6eRZvNQ1HMSdKsaIwTI2bMoT6abQXkGkKIggx5tZw+4CLoibE2EuB4\nOA2jkOBLk3Xo1KrLWh883zuMRhB40KFleHgz2dmrxy1s88EHH6BSqbjtttvG5XifFDU1NaSlpbFl\ny5ZRvf2V8N07J1NgNxJu9bMhR01gfz8p//lVfbpdqd6VwlrM2gimRIqg2Yo6pKzoRz6l6tjh4WHe\neustJkyYwB133HHNYbFTfT4sqiQZYhLZP4AhkcJTkk4/TjJHlHPJz7eS6AuynyTZ2Sb6Uin+daKT\neLuPlLcF/eTJqNLGzxIDlOjD0qVL+epXv4per+ell15i/fr1o+6iNxqfa6KXZZmOjo5PVZsaj8fZ\nsGEDGzZsoLi4mCeeeAKAjRs3XnIcgqAiO+dO3O7txOMj2PPPEn2vQvTzK/OxhOP40m34o1Y+CI8g\nI0D9OsiqYJJ4Bhk1JwxQnLKypWP76LEtOQrRG0UzWQK0hC8d63O73fh8visqQ3Jzc5FlebSBx41A\ny77dRPw+Zt52JwlPFKOkJ2XuRxWzonMnSAgibr2V2eYB0JqR7eV8+E47bo1MEwm+/8e3YDKZOHy2\nYcaliN4dT/JS3whrstORvb9HkuLk5Fxeingt6OzspLm5mQULFlxRYvtpQK1Ws3TpUvr7+2lsbLzq\n9iadmh89WEUsnOA14kiSzMj2860KLXolXZeMaLHqAhjiEULmdMSYEjYMBuOkbnCDjFgsxquvvopK\npeKhhx66SDQwFpzoHCJNDjJd9CHERbSJFCfKi5EFEbtLREamssRGX5eXNiR6LSJ3OdKZ7k5CUiZW\nvx3jFTT61wun08mTTz7J/PnzOXLkCL/4xS9oaGi4Yb93Dp9rom9vb+f555/nueeeo6en5+o7XCe6\nu7v55S9/yeHDh5k/fz5f+tKXcDqdLFu2jNbW1su+juXk3IMsJ3ENvkOaIxu1Rou7WyF6U3UNM5ub\nactUE/HehdHYyEBaFTQoRL9KPgJIHEt6mYDI0Y4hQmcTS9o0pQBE0lkpScUvG7ppbW0FoKSk5LLn\n9tGE7I3C8c2bsE1wYisvJjuktAVMmFrRhiZgCPhoSc8HQWCxfAKcM9m+vYeIO0pjusCbfz6PupIs\nqqqqOH36NH6/H5fLRVpa2gXWB7/uGSIqSXyzIJv+gbcwGIqwWi5d3XgtkGWZDz74AIvFwpw5c677\neOOB6dOnk5WVxdatW0mlrl69WlNo48lFJXT2BXk3QyS0f4DUWQtiq14h1WREj8rkh+QAYb2R7sJ8\nBLWAJgU9nhsbp9+0aRNDQ0Pcf//9l/QHuhoSiQQ93hjZRpHyVDuqpAptSuKkQwlX5g1MJCDIzCi0\nseesRQJZev6hxEm0eQRUkHQ1jkt8/krQaDSsXLmSr371qxiNxtHn80bic030hYWFrF69Grfbza9/\n/WveeOMNhoeHr77jNSIUCvHOO+/w7LPPkkqlePTRR1m5cuWoLLOurg673c77779/yQfOYq7AbK5g\nYGAdoqgiIzd/NHSjyXWy+MxxIjqRwcgirNogb4TyYbAB9FYWSW5EfR+nYmHUCGQEKkYbagsqkRSA\n1kxRJEhLOHrJt4rW1lYyMjIu230JlIIwq9V6w+L0YZ+X3uZGKhcuZSDsYmJMmVii5pPoQrmYAiPs\nm6A0QKkLbmfQOpXtb7cRUMMPvjWbkixFeVJdXY0syxw9epT+/v4LErGBZIpne4dZlZmGU+7C6z2A\n0/nguKhi6uvr6e3tZdmyZZ+6J9DlIIoiy5cvx+12c+zYsTHt8+0Vkyi0G9kYjaBOyZzcqVREn1vR\nJ6I6BF0UITVAXK3hxIwZiJoEOlnpvnWjUF9fz7Fjx1i4cOGoq+q1YuveQyRkkdlTSzAm+1EnVGiT\nSRoNudgSfqwRAwEVlFn0bJYTyBqBr0/OJU+nIdrsQVB7QZAxVI+vXPZyyMvL48knn+TWW2+94b/1\nuSZ6lUpFbW0t3/rWt1i4cCFNTU38/Oc/57XXXqO7u/u6QzrhcJidO3fy05/+lEOHDlFbW8uf/dmf\nXXQjqlQqVqxYgdvt5tSpU5c8Vk7OGvz+Y4TD7djzCxg+S/SCILDYrEWQZfZFjUgJPUGnEQmBxHAb\nxYkEOmMHfZiJIeMMl3Jy+OTocSWVgKAxUeR1409KDMUvrB1IJBJ0dHSMqaDH6XTesBV9x/EjIMsU\nz5xFb7CXomguLs0wktaLxp+NJdDP8cxSdJoU2fIwPznkxJEQmL96IoUfkZTa7XaKi4s5dOgQbrf7\nAqfHZ3qG8CVTfLMwm97elxFFLc4JD1z32JPJJFu2bMHhcDBjxozrPt54ory8nLy8PLZv304icXXz\nMb1GxffumUZ9KMYxA0gHBhmJJkZX9JG4MqGmiYp6xWe2EjK0o5cF2gZDN+QcvF4vGzZsIDc39xO7\nqEajUd7bfQiA6rICUvIQAgK69DhnhGJyA8rYTRoVgifKISGFxqbnm0XZJIcjpEaipIYa0JeXozKb\nrvRT4wqVSnVNvkifFJ9roj8HnU7H8uXL+fa3v82CBQtoa2vjmWee4amnnmL37t3X5FUjSRLt7e2s\nX7+eH/3oR2zdupXCwkL+/M//nDvuuOMih8RzqKioIDs7m507d14yOZaTfRcg0j+wDntuPkH3MLGw\n8irsnD6Vkp5eWh1qEiOrmVM+yDGpBFf9LlSimtK0QSRUnCJFfiyXk0PnJxNZo0LQ6CgYVGLrH/em\n7+rqIplMXjFscw65ubmMjIxcIP8aL5w5eghjWjqOomL6Q/0UxifgMiiTiqpTQJOK02NxUGZSVBPq\nYBk6i4Z5yy9WMtXV1REIKOqjc/a0Q/EEP+sa5LZMK9OMEv0Db+Fw3I5WO3YL58vh0KFDeDyeC97i\nbhYIgsCKFSsIBAIcPHhwTPvML83k/po8Xo9GcEQlXtjWgvnsij4uK9crU1aur2nEQ0DTj1kToXVw\n/Ff05/TykiRx3333fWIJ9N69exk+e+s7teATlXyVlAeDZJPlVv5uBr2al9qHSMZS3DrRjkmlItqs\nKIuixzdjqLq5JvLxwue6YOrjMJvNrFixggULFlBfX8/Ro0fZvHkzmzdvxmq1kp+fP9oAwmAwjFbQ\nhkIhPB4P/f39dHd3E4lEUKvVTJ8+ndmzZ1+2ocVHIQgCixYt4vXXX6ehoeEi7a9Ol02GbR4DA2/j\nyPsBoChvnGUVGGtqmP3My7x82xpcR+ZTkL2WYP7tVPc+T8hcyCx9DyeR2CdHyUfLW/3n3fAEvQpN\nQCSnqw9mQms4xgLb+URhW1sboiheYGR2OZwjzb6+vjFNDGOFlErRefwIJbVzEESR/kAfc+M1DKSf\nQgeIXV68WhNRtY6y+FEa4jNxxLVUry5ApbmYWMvKykYLiiZMmADA98/0E5Mk/qHEycDAG6RSQfJy\nv3LdY49EIuzYsYPi4uJxtTkYTxQVFVFSUnJBc5Kr4W9XVbDs5ABDKSg66eWtmR5MWhVRlWKrm4ky\n4Rq8HhJGMxpjK02D4+/Nsnv3brq6ulizZs0VQ4tXgt/vZ+/evZgdU6EXbOEBTstKeM1VkoUsiGQM\nGegVQ1QsL+DH/Up492s1ijAi2jyCKk1Fyt2D4SZ7Yxsv/EER/Tno9XpqamqoqanB4/HQ1tZGW1sb\nfX191NfXX3Y/u91OeXk5kyZNorS09JpfqSorK8nMzGTXrl1MmTLlothwTs7dNDT+NYYc5SE6R/S6\nSZOY097CS4LA4ZgNZ9TE1NmlsBa6giqmmVyI+l6OxfKYJ4t4uvTEUjF0Kh0qgxqNAHKfF5NKpOVj\nEsvm5maKiorGdC43iuiHujqIhoIUTlMeouCwD62sAcsQQkIPrh725yjx+WXJfTTLX0FnVDN14aVL\n0EVRJC0tjXA4zMDAAL02By/3j/Dn+Q4m6tXs634Gi2UaVuv1P7S7du0iEomwcuXKm7Jz0DksX76c\nX/3qV+zdu5dly5ZddXu7WcefLy9l7aYz/OmInoeOdKLXqQmJNsQkZAtK6MZvNFMQtuEyDxMb6uAC\nZ73rRE9PD9u2bWPq1KnXFRLbvn07kiRhceRhdbvReLpwSwrR9+QpC4Esn8S7hiRpOTqGmqOYRIGp\nuWlI8RSxMz7UduUNxlBVdf0ndhPiD5LoPwqbzTbqMQGKPNLr9RKNRkkmk6jVaoxGI+np6dfd5FcU\nRebNm8f69evp6Oi4qAo1K+sWxOZ/IBjfiVqrw92jJMIEUaTGnoYxFqMtW0OodyHuom4yjNloglEq\nRtyojO20RPNxygLWwWI6fB2UZ5SjtmhRCwKekEypUXeBudnQ0BBut5u6urF5vBgMBmw227gnZPtb\nmgFwllcCkHIrY9SaB1H5s5A8XeyregCQqUoNsc5TRM2q3AvMyz6OUCiEWq1m054PebZ0JqVGHX89\nMYfBwQ1EIl1Mn/Z/rpuYR0ZG2L9/P1VVVaNvDjcrnE4nU6ZM4cMPP6Suru4C24TL4Y/nF/HQh11I\nXpk1fUl+iYRXMGOIpsjWKETvsaYxY1CNW2+hUtVBn9uH0379GvNoNMqbb76J1Wr9RHr5cxgaGuLo\n0aPU1dXx1hBMSDMguU/jkzUIKomejFxEOUVGUMJjFXnd78fgjlFj1KNWiUROuyElkxpuRJWejmaM\nfjqfN9xcAcdPAVqtFofDQUFBAcXFxRQUFJCZmTlundynTZuGwWDgwIEDF313zhq0b5kAACAASURB\nVBLBNfguGXlOhrvPWwNbZ86kpuE47blagr2LcI/sQl1+K0VqN6WJGFpjN0lgUJCJ46SxUYnHaq1a\nNAKEExomGXS0fkRL39ysEOy1GCbdiArZ/tONGNPSsWYpITCtV3modZZedF02xHiY1vQ8rJooTeE7\nUKsFZiy7uIL3HEKhEH6/n4KSUp6x5DAcS/CLyYXoRWjveAqTqYzMzBXXPe7NmzcjiuKYVsg3A5Yt\nW0YymWTnzp1X3xjQqVV8fXUFB0lxZ0+cpEqgLabBGE1iM4TQxSJ4zWloY1HM3lL0JHh30/vjMtZN\nmzbh9Xq59957r8sCYPPmzWi1WhYtWoTLHyU7TQ/eLgKSGr0tRq+Qjz3iQ0jF0Jen0xOIkggnqc1S\nwpvRZg+CViR6cgeGGTNu6re268H/c0R/o6HRaKiurqapqQmv13vR9zk5d5NM+sgql3H3ni9YMdbW\nUNtwAq9e5Ewoi5A3TjR/CmophgYwi0pc/iQp9Fo77YdOKL9nVog+qrVRIkj0xRIEk4rEs6mpiZyc\nnGvSJDudTnw+H8Hg+CXe+lubmTBJaWAdToSxhc1ExRhq/TCGVkXtMaK3Mlk9xOnIYirnOzFYLi9h\n7OvrI65S82rhFPrT7KzuPc00k57BofcIh1uZWPQX1+0739XVRUNDA/Pnz7+gL+vNDLvdTnV1NYcO\nHRpzV6Nbp+TQmKnBHJWYrNUylNSij0oYDWGMkQA+ixVNPIYmaaEhOYGe1oYxFWhdCSdPnuT48eMs\nWrToqt7yV0J7ezvNzc3Mnz8fk8lEvy9KjlWHEBogIOswZMToIZ9Mb4yEEMafZ6T8bCnArCIbsiwT\nbR5BW2gm3nL6DzYRC18Q/Q3BrFmzAEWt8XFk2Bag0djRT+g+q7xRZF/6qVOpa1Eq5NomaAj0VjNo\niYGovGnkpRLo1H5OkiJHUNNxRlHGiAYVoiCQ1FmZGFc+aw3HCAaD9PT0UFFRcU1j/2icfjwQ9vvw\n9PfhLFPGMRAaIDfuYFA3BAJou2P0WBxIooqCiISMSNXKyz/8sizzTu8gr9cs5Wgsxd9YVThaGzh6\n9ACtrf+KyVSGw7HqusacTCbZuHEjFouFefOuz8Xw08bixYtRqVRs2rRpTEozQRBYcsckAshkBJLo\nBB2GaAqVKoUxHsBntqJPKEVVXXIeGG1s2LDhql2uLgePx8PGjRvJy8tj0aJFn+gYoKh13nvvPdLS\n0pg7dy4pSWY4GCPbqkcVHSIia1DZkwziwO5W4zZpiKkFyj0ptEBVWSbJoQgpTwxRfzY+/weaiIUv\niP6GID09nbKyMo4cOXKRJ74oqsnJvpOUpgmVNjUavhF1OibmOSn0uOkuNBDuX8BwYD/k1YGoYYFe\nRDS0K0SPwFAkjUQ0inBWFifp0pkYVlbhLeHoaNjmWon+XCx6vIh+oO20ctxSxYWxL9SHM+7Aqztr\ntdA3yLZcJQGmDhYwsdBH0KzioC/EpiEvr/S7+a/uQX54pp//2dzNwgNN/EA2oVaJrJtZyrdrppGf\nn8+pUz8mGu2mbNLfIwjX51K6Z88eBgcHueOOO26a4qixwmq1snTpUlpaWsa88p5f4eCkWSQ9kkKS\nVBjO9og1pXwEzFb0SUWfX5xmoSd9GqlUildeeeWSvkiRSOSyE0wqleLNN98EuC4pJcDRo0dxuVyj\nTdS94TiSDHaTFk18mJikIuhIRxZUZAZEBrLSmaLR0j0QZCoqTDlmpRoWkHxKRbt+ypRPPJ6bHV8Q\n/Q1CbW0t4XCYpqami77LybkbSJJW7Md15rxtgrG6mprjhzhjE/EM5+F2nSZVNBekBHWSH8nUhhcZ\nKylcumwGO9sRzyYsZZ2FPN8IagFaQlHq6+ux2WxjkoZ+FDqdjqysrHEj+qEOxR7ZMVFR8fT7+8hJ\n2IkbXeASIezlWJbiArmvKpP/NqeY6g8buPNIC4+f6uDbTd38Y2sf/9np4u1BL06dhhWtJ/h+wk1d\nutJRbNWqOUxwHiMarcRmu74V+ODg4Kg75bVOkjcLzkmCN23aNCavc0EQKFmYTxoC/qiEIaLUgVjw\n4jdZCKkUQi+06mn0wL333kt/fz9vv/32BTUj4XCY//iP/7hkfgoU6+Genh5Wr16NzWb7xOcXjUbZ\nunUrBQUFTDlLziMhZYwZZh2apJuUJOA7+xu2kJqgSc3XszJo9keo0mgRDWqizR7UDgPx1lNocnPH\n3cjsZsIXRH+DUFJSQnp6+iXDNxbLNIzGYrImh0cVKQDGujpmnTpGXIAuu4ZAz1R8mUp8vdTdicrY\nAUBIkBjQOXCdaUE8u6JHa0b0eplo0NHi9tDe3s7UqVM/UXLpXIXseJjFDXV1YM1yoDMq1YaeoWFU\nqNCaXahO6vAbTTQVlSDrVZwp0LDSkc4Py/J4aXoxH9SWsX9OJc0LptK7ZAbNC6fxnw4jpb1nKD6r\njpBlGdfgv6FSiZw8UX7J6z1WxONx3njjDXQ6HatWXV/457OESqXizjvvJBAIsGXLljHtM3NBPmpB\nICmDHFd0+FbRQ0xn4IxeUUmdMYu0VaezU29j5cqV1NfXs379+lHbj6GhIZLJJHv27LnICqSjo4Nd\nu3ZRVVXFtGnX16B9+/bthEIhbrvtttH7232W6O0GNTrZi1ot49ZlAZARlJAiYdJTIAE1GSakWJJY\nuw99RQbRpiZ0lZ/PSX2s+ILobxBEUaSmpoaOjg6GhoYu+E4QBHJy1mDI8jPUc96jxDirlqquNrRS\nip5iA4HeOlzqXlDrcMQjpBlDGAWZAVkgpDbTfKYDQa+8/goaI/ERD1PMBvztrciyfE0NGz4Kp9M5\nqmy5Xgx3dZBVeF5mGhk+W3FpHmJoKJ+vf+dfSEoiNkQ2nHqZn02ZyGO5mSy3W5luMVJo0JGmUSOe\nfaDPNTE/l8Tr6fkNIyO7KJv0d+TlzWTTpk2cOXPmmscpSRJvv/02g4OD3HvvvWOSJ97MyMvLY9as\nWRw4cGBM10NUiegyFfXLsFCEGFVjUykVo9lnG9qoUzKoRZ7uGWLevHksWbKEY8eO8fLLLxMKhUZ9\npvx+/wVWIKFQiLVr12Kz2a57Au3p6WH//v3U1taO5pPgIyt6MUgSiTRzgkGy0SeiGOIytlCQw51e\n1MDM3HRiLV5IyeiKTMTb29FXVF7XuG52XJXoBUF4VhCEQUEQTn3kswxBEH4vCELL2f/aPvLd3wqC\n0CoIQrMgCDferecmxsyZMxFFcdRW96NQLBFAZWvD61Li1aJej72qiuldZ2jP1REeLMM1cAQ5ZwYC\nMMnooEAbpePsMY70+EZDNxoBwu4QU8wGMnu7sGdlXXPY5hzGKyGbjMcZ6eshq6Bo9DNPn5J8HjCJ\nfPfOvyGgNSKGksz0BZgy/erqoM7OTtLT00lLS8PjOUBL6/fItC8jL+/L3H///djtdn73u9+NdtQa\nCyRJYtOmTdTX17NixYqbtgL2WrFy5Ursdjvr1q0bk61FyVRlBdwZm4M2KmLXKESfUilvdo+npaNu\n8NIVT9AQirJkyRLuvPNO2tvb+fnPf86RI0dQq9VkZWWxc+dOUqkUkiTxxhtvEAqFuP/++6/L1yWZ\nTLJ+/frRCviPYnRFjxefKGIzJxgkh7RgGAHItho40DZMJSosOSYiTSMIehVytA9kGf0XK3qeBz7e\nZeE7wBZZlicBW87+G0EQJgMPA1PO7vOUcL2Zsc8xzGYzlZWVHDt27CLDKYMhH5NhGhllPtqPn58I\nzAsXUHt4P90qCa9Ojbs9h3ih4qZXIouYtS46kTHLMm1+EVmrXF6NIBD2xSiRk0zwu7GVXtyCbqzI\nyclBFMXrJnp3TxeyJJFZoKzod7cMYwiJeNUS/6z/CyREZr9/HIDZ6oOIRVeOr0uSREdHB4WFhQQC\njZw4+acYDPlMmfIjBEFAr9fz6KOPkp6ezosvvsiBAweu2pQjEonwxhtvcPDgQebNm8f8+eNX+flZ\nQ6vVcs899xAIBNi0adNVt7fnKTJSb2I6uoiEQ+cGwKcDkDGLImpXBFGGNweUSaCmpoavf/3r5OTk\n0Nvbi8PhGHXUPNfEvL29ndWrV1+wAv8k2LZt22iS/OM2DyNn7ZbTk4P4VCJWawKXnI0lqIghMp02\nTvT5mYEKVaaeaLMH/SQbsdNK6FT/Oc3HjBVXJXpZlncCHxfl3g28cPb/XwDWfOTzV2RZjsmy3A60\nAmMry/wDRW1tLdFo9JLWC3n5D6K3xWk7/vboZ6YFC5nVoGjke0t0BHqqcZ91byzxuxF0Suu4LCGM\nW2Xjhf3KvzUChP0JaFeSu8G8ok88Zo1Gg8PhuG4ny6GuDmWshUUM+qP85asHcaTMfHe6lhHBzr/8\nn38nrFZsDpbr3oeCK7eN6+/vJxKJUFiY4uixR1GpjFTNeB61+ry3j8Vi4fHHH6e4uJh3332X733v\ne/zkJz9h165dDA8PjxJ/IBBg3759PPXUUzQ2NrJy5cqb3ubgk+CcjPHEiRMcPXr0ittaDUpNQ4p8\n9FEZh1Z57EeMOtRAKpaiwKwnOybz1qCH1NkcjsPh4LHHHuOb3/wmDz74IOXl5RQUFLB582Z2795N\nTU0NM2fOvK7zaG1tZc+ePdTU1FwySe4Jx7Ho1YgBF15RhSktwRAO0s/Wg6jSLSQlmRmoQAYpEFfi\n841NiFYr6uuchG52fNJy0GxZlvvP/v8AcC5GkAvs+8h2PWc/uwiCIDwJPAlQ8AdadgyK4ZTdbufQ\noUNUfcxHIzv7Dpqb/zdJ3REOrn+TWXfdh3ZiEWWiTFYkRF+JlXDDFPqiv8Mpaij0DRA1tSIOL0ct\npOjRZfGr3x/lNm0hGgEiEYmWE8cZtGXhV12f9anT6aShoQFJkj6xY+NwVztqrQ5zVjZfefYQ4ZSb\n4zkV7M0y8MSR5yjr7CReYcAgx8kSAe2V7WFbW1vJmXAar+819Lpsqqqew2DIu2g7o9HII488wsmT\nJzly5AixWIwtW7awZcsW1Go1oiiOSgPz8/N5+OGHL7A7/kPDokWL6OzsZOPGjWRnZ192ZX2O6EMI\nGCO5pAnKG53bYiYrJBALJijJMtHgitCpF9jrCbIw4/wka7efNz2rrq5m3bp1GI3G647Le71e1q5d\ni8PhuGwLR284js2oJTXSj1clEks3kRS1ZHl8yLKVkUQKEZguakj0hUAAfbmNwaZG9BUVf3AT/Mdx\n3clYWZFmXLM8Q5blX8myXCvLcm1WVtb1DuOmhSAI1NbW0tPTc1GbPs3/Ze+9A+wq6/z/13POuf3e\nuXd67zOZ9ElPCAkJCSCCoLQ1IgKKdXVFd7/6dZvruqs/XXXXXct+RUDBIChKFzBIKKb3NjNJpk+m\n19v7Oc/vjzNJiCkEQknIff0zM+feOfeZM+d+7uf5tLfFS0HB+8mdGmbb0w+TTiYRQpjhm/27OeAw\n0KVG76EkhreUqvA4I7ZBqlCIC42Y6sQrEoSkRBMwpJoNKYnqKTSHz02LsqKigng8flIi+S+RUkfK\nU4dHRrq7yCuv4H/Wd7C1c5wbV+TyeFUJC/zDfOjZdRyuvJwx1WCaOELSeuYxDeHwYULhb1Ffv5Xs\n7EUsWPAoTmfVaZ+vKAqNjY18/OMf57Of/Sx333031113HYsWLWLevHlcddVVfOYzn+Guu+56Txt5\nMKtwbrnlFlwuF4888shpk+xHxUfCWhIlshALaRzJCBOeLNORGI9RV+BmoiOAV1N5aGDslOcZHR3l\nj3/8I3a7nWg0ek6dtNFolLVr16LrOrfccstp5QWD8TRZDg0jMMiY1cGoZvqe2aEkkiTNgyEa7Fa8\nuQ4ShyewlHlQHCqJQ4ff8/F5ePOGfkgIUQww+XV48ngf8NohJWWTxy5qGhsbUVX1lEnZsrKPomgp\nnMWDtG3fDIBn1SoW7NtJSEpGSjSCvbOIFJdTnE6T0BI0CMGYND32RZU+AoaORUj6fSp2u52y+ikc\nisSOba3fDEd3WUerXP4Sf2Anu3Z/jJdfmc3Lr8xm1+6PMTZ2fMaKlJKR7k5S3iJ+8nIbtywoZbPV\niyoFnxnagGsA+ouWMKJK5qlNJH2n7kqMRrtpavo7tm67BoulB2l8mDmN92O15r2hvyc7O5v58+dz\n1VVXcfXVV7N06dLzflDZW4nL5WLNmjXEYjEeeuihUyZnPUflBHMlRM0wmjsdYizLhwWD2HCA2nw3\nyaTB1T4Pz44EGPsLoRu/38/atWsRQnDXXXdRUVHBU0899abyPfF4nIcffpiJiQk+8pGPcCaHMBib\nFE8JDTLhdDCK+VxvFAzS7O7x0yg01Gw7yd4QjoZskt3dyHgc23u84gbevKF/Crhj8vs7gCdfc3yN\nEMImhKgG6oFTd09cRDidTmbOnMnevXtPamDx+RbidNZSMDvMgZf/ZD5/8WIW9nQgpGR0to/IwGwG\ncj1oQJlip9KaIipVhNSJpEFzWNBFEr8XZs2YwXSfh5gh6TiNWPjZkJ2djcfjobu7+6THurvvYefO\nvyIaaae0ZA0lJX9FNNrBnr0fp6np70inQ0T8E8RCQZ7rEzQUeliytILmhJO/bk1QGughbstn3JGH\nLqBe6UMrOrEUNB4foOXgP7Jl61UMjzyPw/Ehtm+7gYaGz53zHJuLlZKSEtasWcPIyAgPP/ww8fiJ\nuz63zfTo4zkaaiIXQ1fx6n4msrxoUicRTlJbYJadzsdCUkoeHTyevvP7/fzyl78kFovx0Y9+lPz8\nfG655RacTie/+tWv3pDwfCgU4he/+AV9fX3ceOONr6unEJxUyYqFh0g6NcYxZ9tnRVXSKiTSBrMT\nIAQgmYzPmzuNjEcPCCEeBjYDDUKIXiHEXcB3gCuFEK3AFZM/I6VsAn4LNAPPA5+XUr6+avFFwPz5\n80kmkydJDQohKC39CPacEEO9mxnv70WxWilZMI9pvV0czlEx0nbaR+1IoCKRINtmDktz6xFa/Qal\nhW6OWPuRCkyrqmWG26xIaAq/eaUoIQQVFRX09PSc0DjV3f0z2tq/S0HBtSxZ8gJTpvwzDVO+ztJL\nXqK6+m6Ghp9m+44b6W59GYBxWy4/uW0e/9kzRL4e4paeFMrwGAOFCxlSzZCPS9hxFppvzGRylMOt\n32LzllUMDPye0pKPsPSSl+g9sgi7Pfc9H2Z5u6mtreWmm26it7eXBx98kGj0uOC3qgjcNo2o3YFF\nDKDEcvEKP36PF2HESOqC2lwnAEl/ggVZTtYOjCGlZGBggPvuu494PM7tt99+7P/k8Xi444470DSN\n+++//4x6EEdpa2vjZz/7GePj49x6663Hul/PRDBmhm7i8SEsVhgz8nBFI1gNG4nJUQuzDQU9kkLx\nWLCUuEkcPAgWC7Y3qVF7IXE2VTcfkVIWSyktUsoyKeV9UsoxKeVqKWW9lPIKKeX4a57/LSllrZSy\nQUr5+jVdFwnl5eUUFBScsnOzuOgGhLCSPzPArmefAsCz+goW7NtFczpJ0iUZ66oibbFQGQ2Q1LpM\nZSaSdMYtCJug09qPI+LgwfVt1DttWIVgX+jcJAErKysJBoNMTJildKNjL9PW/j0KCq5h5oz/QtOO\nJ08VxUpN9ReZO+dXpNNB+gJ/j682wP9Zs5Jd6STtsQTLRw6hArJrjIGixXRZUthIosWLEDkq7e3f\nZ9Pmyzly5JcUFl7PJUv+REPDN1CUbFpbW2loaDjvpPwuRGbMmMGHP/xhhoaG+PnPf36Cp+2xa+bk\nR+XPuKIl+FTT0JOOkBYajvaD5ListA2Hua0kl7ZogrV7DnD//fcjhODOO+886cM4JyeHT33qU+Tl\n5fHoo4/y0EMP0dXVdULpq67rdHR08Otf/5q1a9ficDi46667zrqnIRhPEULiS4zhtqYZS+eRGxgH\nxU5YVanzOfChkB6OYm/IQSiCeMtBbHV1iAtsntGb4T0vPHK+cDQp++yzz9LX13fCm8Fi8VFYeC2G\n/gwtD6/j0jUfw73iMhbecx8PXnsTwXlZOLbNwT+zkMqgn32WYaag0qVYiEmNbZFuEiJFXricDfu7\n+M22I8z0ONgVPDcx56PCKR0dHbjdkubmr+B2NzB92n+cdnBYdvZiupWfoI19jqor+ilyr+WuzpuZ\n6XYwfWgQgzri/R6SjXn4tTEalH70sj52DH8SQ8YpKHg/NdVfwuU6rnDV1tZGKpVi2rT3fiz1naKh\noYE77riDRx99lHvvvZcrr7yShQsXmoY+JbBrm7HGFpGbN05MdZCQYdIUE3jxZWrzF9E+EuZrWXY8\nhs6P2gf5VEEBa9aswePxnPL1srKyuOuuu9i8eTMbN27kl7/8JTabjZycHKSUjI+Pk0wmsdvtrF69\nmiVLlpw28fqXpHSDaFJnRziA04jjsaYZk7nk+SeQSjbjUjA/ywn+FDJp4Jhp5nfiBw/iXr78Lbum\n5zMZQ/8OMnv2bF544QV27NhxktdTXn4ng4OP460ZYv+Lf2TRB29mfkEu7niMnsoc8v4cotU2l4rU\nOl5wjzEdlWbhBiRN/kPkGG6EXsCluWH+7ZlmVtwwhRfDUdKGRFPeXOlYXl4eXq+X9vZ2bPYHSKdD\nzJu7FlU9vVDE+oNDfP3pIT45OhPPyhEeoZcukeI/ig6iGnEiln5Gq6biLt1KIF7PlKJBbFP3kJ97\nDVW1f4PbVX/SOfft24fL5TpJsSvDuVFRUcFnPvMZHn/8cZ577jn27NmDJusIxtJgDSOiTryKuZsL\nama+J7BpD3VrltPavJ+f/fjPTC2oYHvVNJa/bzkez5nLY1VVZdmyZSxatIhDhw7R1dVFMBhECEF5\neTlVVVVMmTLlrA38UYIxsxkxmQqiK+Cy6fjTOTQEukGUEEHncosV1DRCU7DX+UiPjKCPjl4U8XnI\nGPp3FLvdTmNjI7t372blypV4XzMtL8szk2zfEvR5ezmw7nkWXn8TvlWXM79pLzsWXsI8VefISB3L\n088yZBljGSq6UClnjGQ6zBx9JvsVKx+ZmsO6cSevbusjNs3LwUiMmR7nm1qvEILa2lp6jrzA8PCz\n1FR/Cbf79GWQTf0BvvDr3cwocuHonsAhbuFZ60zq9D5KBr6KmAP9gPNyiMayif75X6kiwMRv7+SK\ne/7plOeMxWIcPnyYBQsWnNNY2wynxu12c9ttt9HU1MS6desIjg4yOmEnbLejxf14J0XCx21pvCnY\nmFeEveVZZkqdvIJqvnPFZVzbMcp9/eN8r+HMhv4oVquVWbNmnfNws6P0h80PoWm2JFGHii40whYP\nef4AQigkhM6shDyWhBWaciwRa3uPd8QeJRPwfIdZtmwZUko2bNhw0mMVFZ9EtcXAeYix3h48q1ax\n5MBuhgxJbKaBv28GuYbOhGbO7AAoUCNEhZtqvQAFkMEID3x8Ec6oWfb2ysi5DSarqamivHwzmlZI\nRcWnT/u8tuEwd9y/HZ/DwveuLMLQ0+wsq6cvpfKNGcuwVv1/uFrW4N63goHtt/PE1k8A4AtVoYZO\nP0CsubkZXdeZPXv2Of0dGU6PEIKZM2dy9913U1NeQlpYGEspuJLN+DAT/wNZprMw4sumVLXyTGIa\ns1Zex4zyUm4pyuG3g+MMJlJnepm3jXs6hwC43mcQdahMkI0UCtnhyQF6LhXvWAIMiWOG2dQVbzHH\nh7/XRx8cJWPo32F8Ph9z585l165dJ6n05OauwGGvpmDuGIe3bcBSVMTqVBSLnqatIY90NJceawP5\nRhifGsdBiiAOtibLkJhjEGL+GOU5Th748DxE0uAnu48cm+z3ZnB79uN2T5BMXIt6mm7bztEIt/58\nCyB58K5FGKP9pBWVh4SL+VlOVudmMRiTlBy5Cg6sJNC+iHgqhYKBL1WDTTt1YZaUku3bt1NQUHDO\nc1IyvD6qqlJelIewOCiunE9erJ8sad6jfs10LFaNerns8EFGpZtDQ6Yh/UJFAbqU/Hf30Du+5pZw\njCf6zMatejVEwKkxcbS0MmIWI1TkO5ExHRSwN5iPxVtasJSVoV4gMpHnSsbQvwssX74cKSUvv/zy\nCceFUKip+SKOnAR93Y8DULJ8GQsP7GGzpiGFzmG5goqUTq9liFwlwnDaRbfu4QgGmoBY0KyNbizP\nZkGWE79dcOvPt7wpY59ORzhy5Eckk2U0N7tOOZ++bTjMrT/fQtqQPPTJJdQVeBjqbKdp1hIG05Kv\nVhcjhMA/PIKCwqg1n6AxABiUKSPkhvI53UDDvr4+BgcHWbhw4Xu+Rf18wWO3EIynIKsQtwjiiaUQ\nUhJxmbsumVuOvmsHhZpOc7+5W6x02FhTlMtD/WP0xt+8U/FGMaTkK4eO4DDMe8ObHCbg1BhImclW\nd9TcYTTkmWu3lLhRbOYHVrylGftFlNzPGPp3AZ/Px+LFi9m9eze9vb0nPFZYeC2KXoCzsonwxCju\nVZdz+c7NDOmSsYYwg+NzKU8b7BFHyBcRApiJ0RZ0NAHx8PHt83VlORhOjfZQjBt/upHO0TdWhdPd\ncw/J5Ai5OZ9jYsJ/UsPL9q5xbvrfTaR0g7V3LaahyKy46O3uYsu8FSzxurgs23yTxQbM3w0JOy02\nnWGyybbHsaUkdsepY+/bt2/HarVmwjbvIB67RkqXpNxFKMIgGZZkyyByUmA+KVyQSnN1qp/mgeNh\nwS9VFSKBH3a9c179g/1j7AhGuS7b9MqzkkNEHFYGkwUA2OOmeZs9qbt8NGyjh8Okunve82IjryVj\n6N8lVqxYgdvt5plnnjlBV1YIleL8T2DPTtLadB+2+npWjA1hS6dob8giHirCGZxNSKZYLN0gBBYk\nBzGwCEE8drw2eanPNLKfvmE6wXiaG366kZcPDZ+0llMRj/fT03MvhQUfYMaM6xBCHGv2klLywKYu\nPvrzreS6rDz2uUuZXmK+2aRhsM7mJWhz8pXqomOeuD5hbqNjepq4O0ifzMdld5CWYHedXBMQiUQ4\ncOAAjY2N5zTDPMMbI2ty3k3MVQRAOAI5jBCbDHEkkiCcbhaMHubgQJC0HZN58QAAIABJREFUbt5v\nZXYrt5fk8uuBsXNq1Dtb+uNJvtXez/JsN3WTVTrueC9pB4zouVhSSTDMvIJvxNzlOheY828Sk3rK\nGY8+w9uO3W7n2muvZXBwkPXr15/wWN2MO4iNORiPPoxhxClcvoxL9u9iqyObmDXMSKoRQ01yjWHG\nrT0izSF0LEgSyePhleluBz5NZUCTPP7XSynw2LjzF9v55tPNRBInzij5S9rbfwAY1NZ+FZfLRX19\nPXv37qV3LMynHtzJvzzVxLL6PH7/uaVU5B6v6hkY6GfjzEuYQ5JLs4/XVNvCdgwpMYwOylTzwybL\nUkJKShzek8s1t23bhq7rLFy48A1f2wxvnqMTLGNOc1ZMIqaRI4YZd9vQFElUlzgXr6S84wCJtHHC\nLvH/VBfhs6j8w+Het0SG8nQYUvLFlh7SEv5jSjmheBpNEajRHoQGEzKPrLCfuGWyqm0shrAoaB7T\nYYg3T44+mD79bVvj+UbG0L+LTJs2jQULFrBp0yb27DkuKahZrKQGloIWorvn57hXr+LaV18kIBX2\nNIygSI1+bw85qHhlBEWmOYyORdVJpI97x4oQLPG52DARpiLHyVNfWMbtl1Ry/8ZOVv3gZX67/QiJ\n9MmJ0EBwL4NDT1BefhcOh1nv3zCjkXA4zJ0/fIoNbSP807XTuPf2BWS7TuwqvKe9l6jTw5eKj6tF\nRZJhKgOlxAwYdA/iEWbb/SzdTVqCw3eioY/H42zZsoWpU6dSUFBw7hc6w1lz1NAHVFM0Lh23kMsY\nQ3aFEpvCgJTYZy7GNnCEguj4CeGbbIvGP9aUsDUQ4fdDE2/bGv/3yAgb/GH+vb6UaqfNnHPjsBAz\nTCnDgMgmKxwkYXGTpQJJAzX3uFBJvKUFNScH7SK6tzKG/l3m6quvprq6mieffPIEY19cuoqJdg/d\nXf8PMb2Q6mgQbyxMU2kVWWNzCEwayxJCRAyVBDCuGSSlBfmaUNCKnCx64uYIArtF5ZsfnMnvP3cJ\nRVl2vvr7fVz6nZf49rMtbGobJRRPIaWk9fC/YbXm4cy5k+cPDPK3v9nDx37XQ0RaWOie4IUvr+CT\ny2tQ/qIRK5LWeSipUt3bxpV1x5ubevq2UJooIWpIdmQlCEknLjXOvKgkJcGZd2I35bZt20gkElx2\n2WVvwxXPcCZynOYHtx8z7GfENHIZJaEq5DtVxqWBpdhsals42nYsIXuUjxTnMC/LyT+39jGQeOsT\ns9v8Yb7TMcC1+V5uLTYraIKxNFl2jZhqioyELDn4gn7SmoMyq3mP2qqPV9fEWy6OGfSvJWPo32U0\nTePDH/4wlZWVPPHEEzz22GOMjY1ROnU6/VsKMaRk45Yv8OIli2jsbmXAm8uYO4uioBm2maqEiSnm\nm7NXg5TFje73Hzv/6klhiBfHjr8h51fm8PhfX8oDn1hEY5mX+zd0cuu9W5n1jXV8/H+/TSC4m1/u\nex9Lv7uVz67dyYsHh7musZRLFi/GFhtBjfs5Fff1jRJSLXxwqB1VO97d2H1kEx7FSyoVokAGOSgr\nsLqgNCpJS4mz4HjjWCKRYPPmzdTX12dKKt8FsicN/XjKio4FJaaSg+kpR5wquYogmrKhFRayPNB+\ngkcP5i7yR9MqiBuSu1t6MN7CEE5fPMldTV2U2S38oKH8mKEOxlN4rRB1KKQMhYjdiy/oB9VG2aTU\nprXYfB/IZJJEWxv26RdPfB4ynbHnBXa7nY997GO89NJLbN68mX379uF2u4kUzaOtNYe6+m3MnVLK\n5T/6E1u+/SN2T0+zdP90wtYgi9B5XIIN6FGg1OJGn5hAyzNLzCocNqY47bw4FuQz5ce3qooiWDEl\nnxVT8gkn0mztGKNtaJSy9L8STNdQXnYL/zrHw7TiLOZW+LCoCvF4nMP7dvDqq6+yZs2aE/4GfyrN\nj7uHqOk+xLKyEw1038EuZqqXo1jHmJseZr28El+2ByUIejqOJee4hMGOHTuIxWIZb/5dwucyP6D9\n0RQJ4cOZAm9iAuzQa5NM01Re6Z1g5aWX0vDcOr7f50dKeYJ3XOu08836Er5yqJf/6R7iS1VF57yu\nYFrnzv2dxHSD382pw2c5brqCsRRZmk7UqdKfyEU6VXIDE+TbynBMrkvLN8ODibY2SKWwXUSJWMh4\n9OcNqqpyxRVX8MUvfpGrr76a2tpaHFJHjE3F7V5AVukmchwh3j/Uxe4yH6QbGLL4WSBtKFLHh6Ad\ng6TFRXr8xPjoFblZbPZHCJ0iHg/mHPLV0wq5suJP2JUxVi76Nt+4fhZ3LK1iUXUOFtW8Tex2OzMq\ny2jbsoGuzs4TzvGjnmFCus7yreson35ia7t6cFLgo0KnRARIoVHpNUMDaiKIOlm6l0ql2LRpEzU1\nNZSXl5Phncdj09AUwUQ0SVLNJYs07gmzMeqgGqbCorBpNIxz0ULssTDugR4Ggyermd1WnMuNhdl8\np3OQJ84xXh/RdW7b10FLJMb/m1FFg+tEYfBgPI1HNccfdOtmZU1OYJwKh+eY+M5RQx/btx8Ax8wT\n9Q/e62QM/XmG1+tlyZIl3HDDDUwvzIO+LmbP+gFCUfF/QeUjTzyCIWDzNBcTMoWLIgpTI0gBXdIg\nbnGeZOivzssiJSUvjJ1+HEI40kpX9/9SWHgd2b5TV7oMtB7i8BOP4Bjo4sn/+s6xMbN98ST39o6w\nPDpBUWCMkimvqU82DNxRs7qhr9BNUpge43yXEyklSsKPmm0m/nbu3EkkEsl48+8iQgh8TisT0SRp\nay65RgLreAxVpmlTQwig0hAY00xFsNmjHezpOTmUJ4Tgv6aWs8Tr4ostPaw/w713JiZSaT66t4Md\ngQg/nV7FFbknd7IGYymyRISYXaVHN2vlcwMBShwuAgIUp4YyuVOJ7duH6vNheQ/rVJ+KjKE/jyms\nqSM0OoJMeZgx/T9JZIewzt7DFakD7Ky1MqBmY8gCpske/FKSBkY0hfjIiYZ+gddFic3Ck8On9qyk\n1Glp+Rqa5mZK/T+fdj0bf7sWp9dH+fwlpIf6ePaRhwD4ftcgUsKyHS9SVDcFi/24xxXpacVuMT36\niL+NZqMSoUhWKBbSgDUVQnG5SKfTbNy4kYqKitdVE8rw9pLttDARSZG25pItIiQnrOQwxpjHYDAt\nuQYLr/ToaCUlNI53sKvn1PeVTVG4f1Y1U1x2bt/fcYIa1dnQFo3zgZ2t7ApG+en0Sq4v8J3yecF4\nCgfjSEUwMDn+oMrIwqaoBAGtwHkstBTbtxd74+yLKhELGUN/XlNYbYouDHe0kZd3ORX5nyB2icFt\nI4+hkeZ3U4qRwGJtiKP1DYOqQWQsdMJ5FCG4Lt/HS2MhgqcI33T33EswuIcp9V/Has095Voi/gl6\n9u9l1qr3ccPdX0F1umj+49O8cKiN3wyMc2u2g1TzXuoWLD7h95r+sAmnqmCg4xvfzj6jBumxUBcz\nK27sIoEQgj179hAKhVixYsU5X7cM50a208p4NIl05OERYRJ+K/kME/Q66EgYeIVC/85BXAsXMHu8\nk13dpw/N5Fg0Hp9bxxKvm79p6eGrh46cNoR4lLQh+d+eYa7YfoiJdJpH59TyocLsUz43kdaJpwzs\n0hSxH8WHoutUOepJSUksJbEUmn0eejhMsr0Dx0XYaZ0x9OcxBdWmxNlQZzsAdTP/HvfBbNSSA3ww\n/Xuaiqw8XaJxqdUcPGXHNPSx8ehJ5/pggY+klDwzfOI2e2JiGx0dP6Cg4BoKC6877Vo6d+9ASoOG\nS5ZhsdlY+dGPo8RjfLW5E6+qsOLgDhCChktOFHJob0rjkUmS9ggz4gdoklV4XApE0qQkOGwSXdfZ\nsGEDpaWl1FwEsm7nO3keK2PhBNJVgF1NEZ+wUcggE+5sRtOSMcNgrj+NMXch7liIiYOtJNPGac/n\n0VQeaqzhc+X5/Kp/jMVbmvlB5yBt0fixxiopJT2xBPccGebSrS38a3s/K3M8rF84lcW+0083DcbM\nUmKbMA19CB/uaAhf3iwGUhJNl1gKTEMf378fpMQx+9RC9O9lMob+PMbucuMrLGaoow0wh55Vpz+K\n7YDCddbHqAn38t3pdsLuCjyk8AjBoGYQC54sCj43y0m908avB8aOHYsnBjnQ9EUcjkqmTf32Gbez\nvS1NODxZ5FVUATB71ZW0Lr2SAV8+S5q30/rc49QvvISs/OOVPcl4mol0BS4lie4xUKRBDDvV2Rb0\nSIqUlDhdCvv27cPv97NixYqLbkt9PpLvtjESSoC7EJuqk4poFKQHiVg8xFXJIFCBSluyEoCpQ600\n9QfOeE6bovAvdaU8v2AKjR4n3+saZNnWg9T/eT8LNjcxdcMBFm1p4ett/eRaNR6cVc0vZlZTZDuz\nCEkwbs52sqhjKAmVlJpLbiyFqjk4kjTHgmiTHn1ssk/FMfutmYN/IZEx9Oc5BTV1DHW2HfvZs3I1\nOfeo2AJW/tb1L7hlhE/MvZEadZCUhFFFEoicPBdcCMFHi3PZEYxyMBIjlZpgz5470fUos2b+GE07\ntQTcUfoONlE6dfoxQ9yX1Hmx8TIq+zqY8dLjJBNJqi9bfcLvdLzailQs2K12EBGapNlEtaTchx5N\nT3bFWtmwYQNFRUXU15+sLpXhnSffYyMYT5NyF2FVdEBQGDTHVoy74iQ1lQ50cloTiPxCZo12sOsU\nCdlT0ehx8nBjLduWTOP7DeV8uCiHJV43NxRm8+36Uv68aCrPzp/CVXnes/rQP6oupVoC6DE7usVH\nSdJCKu5nNC2xCLAUmoIo0e3bsTU0oL5G8OdiIWPoz3MKq2sJjgwTC5lVC/aZM9G8eeQ9V8xIJM7X\n1L9HtyTZV1FHAIkhoE0/tdTfzUU5WIXgke5mdu76CLFYN42z7zmjahRAeGIc/9AApVNnAJAyJJ9r\n6gJF4UfzpzH32g9haVzMY88+z6uvvnqsGqd1fQuuVAirsKEk2thiTEMKWDOvHiNuhm4mvBpjY2Ms\nW7Ys482fJxR4zGR6wF2KTUkjkeSOmjvBcVeSiC5pFgY5KYll8U3MnuhiV/cbS7RWOGzcVpLLt6aU\n8ePplXxnShmfKMun/i9KJ1+PYNwM3ajOKONRF2lLDpVpB/7hJgBsdhXFbUEmk0R37cZ5kc5Oyhj6\n85yCalMke7irAwChKLhXrEDsGuNwXKdw3wq+qf8D2b4gEtCnZrGutuKU7ee5FpUvePcxZ/AuYvFB\nGhvvJzt7yeuuoe+g+aYpmzoDKSVfPXyEHcEo328oZ9G0aay+/ZN89st/x4wZM1i/fj1r167FPx6g\nb9xJbqoLgOz4NjbL6ShOQU2WC5I6SWlwUCTIzs5m+kU0YOp8J39y+FfQnotEQdF0ciYrZvxZKcYC\nKUrScIA0WGeRk4zT3dzxtg4yOx1HPXq7LU5bMg9dtVEQlwRGzF2wI8+BEILYgSZkPI5z0cVp6DOd\nsec5BVVmcnK4s53KWXMAcK9YQeCxx5jSZ9AcyuL6zV/i32p+yOf5vzj0CBsvrWPupmZyNYUCm0aO\nquOSE2jxQ9iSXRzkBuzea6ixzyb7L7oaT0VvSxMWm528qhq+3tbHwwPjfLmy8IRKCJvNxk033UR1\ndTXPPfccD/7HA6jKTGJZpifoNQ7RIT9BliNpdlKmJeNinOFknGuvXI2iZHyO84Vjhh6FqJGNZtFR\nRgyypJ+AWb2IPwIvFOh8K25FNFxLftdBesajVOaenW7sW8XRGL1Di9JkmM6CLTRKTDePOycTsdHt\n282fL1KPPmPoz3OcWV48ufnHPHoA16VLwaJRe1jhV9N3cHPvJWjbP06ZZxjnUJQbKv+bDqWeI6ly\ngikvQ3gJkUVEzCAiFiARMAGPbWkhS1OYn+ViodfFIq+LuVlOXH8hwt3XcgD7zLnc0dTDi+NBPlWW\nx1erT25rF0Iwf/58Kioq+MM/PkHciOEvlhhxnT4BKWmhtiiFTBoIoMfWg9NqY86cOW/3ZczwBijI\nMg39WCxJ2MjDrupEx83KG3+WaTLiEmpKvTzXEeSa2itYcuhJ/tw6+s4b+smqG6clRsQwd7/p8UPo\nFjPn5Cw11xPZshlbfT1a9qnLNN/rZAz9BUBBdQ3DkyWWAKrbjWvhQooO7qZjfg8AUlTRaLzCq8FG\nfrj+kzx6U5qcwhSGkcRiseDx1OByTcEAXp0I8ZG9HbwvN4tCm4XtgQjf6xxEAqqAmW4Hi7wuprjs\nBKIxHm1YQnvNdCz+EN+qL+UTpXln3AXk5uSSlCVkRZtQtHxSYpSXdbOkbfXUbGQ8TVBEmbAFWDFr\nPhbLmSsrMryz5LlsWFWFXn+MmFKIR5kgGLVTaAyyz9WIq7CX6FApq+12vihHuEo4WFE4i/9uHeG2\nJZXv6FqD8RSa0HHqCYqkmcyPj+4nbTXnLTlL3ejhCNEdO8m5/WPv6NrOJzKG/gKgoKqW9p3bSMXj\nx7pO3StXEvn2ZpyhJONqEK/qpiDtJqRawYDdXT7umH/pSedSgctzsrg238tL4yE2LZ5Gkc1CIJVm\nRzDK9kCErYEwa/vHiBlmzNVVVMFNToW/bZxKpeP11Z661+8npTqI5O5iWvJDuOjhJTkXhOSK6lKM\neJqgMFWIqqdePHJuFwqKIij22en3x0lYisgSQ4yqLkoifWzIWsmEvhcrpZQFddzZdv4QivKhnHqc\nzcPohkRV3rmkejCWwqnFccTAqxYjpMQdGka3NmARYC12E9n8CqRSuC/iZrxMYPQCoKC6FqRkuPv4\nILGjN+2lnZJuRw9ZahprzDSauUaELT1nni3y9doS0obk2x39AHgtGqtzs/haTTGPz63n0PJZ7Lpk\nOvcN7ucLv/5Pvj9v2lkZeYCWdQdR9AQbZxyiNFmAXfTQJKqx23SsURU9miY22cvrycl5w9cjw9tP\nqc9B30SUlL0Yp5oiraqUDh8BoFczG/KCw1FumlfGf6UhFujlk2kbTe1vrPrmXAnGUji0CCJcyrjd\ngjsexpZOo1s8WBSB6rESefVVFI8H59y57+jazicyhv4C4FhCtut4+MZaWYmlqpJ5bZJuey8eRcWT\nduCSCcqVcbaNpc9YBVHpsPHp8nx+OzjBVn/4pMetikKJ3UqweR+FtXVYrGdn5NPxJEcmXBQa7QTV\nXCzSgkI3E4aHfCXA7x75HYNHhogJ09C7XO9sTDfD2VHic9Dnj6G7SrArKQxFoaTLFLIf85rls6Fg\nkjXzykAIHgu041I0Qs93num0bzn+aASnJYY1NJ8uZwpHdBRNl+gWDzabgtR1wi+/guvSSxEXcYgw\nY+gvADy5edg9WQx3dpx4/PJV1HRBt9qLIlSKNSjTFcaxMZZWaB+JnPqEk3y5spBKu5W/aekhfIr5\nI6lkgsH2Vsom6+fPhsOPbyatOdFyd1MSKwNgIy6Q4CnqIp1O88zWPxIWcRSDjPD3eUpZtoPhUIK4\nqwynZlaw5LROYJcx/DkeVFUQMSAvJblyeiEPlU5jaGAHlX0x4u1n1zz1VjAUCOCwxHAGZ9HlTJEV\n8aOlDXTNic1tJbpzJ+mREbKuuvIdW9P5SMbQXwAIISisrj3BowczTq/qoI+YXlSlVacw5aCHAmrp\nZXP76BnP69JU/mdaBUfiSf6lre+kxwfbDmPo6WONUmfD4Q1HsKTCHKzbSVWiFEmapzCHszVUadx4\n442MRsZo1npxTP5tGc4/avPdSAkD9kJcmrn7sg6olHKE0exCXF6NiC5Jj8b42CWVBFU7W4ebGJA6\nY79vRaZOP/vmrWQsHMOpxRDhQkJWJ96wH2taJ63asGdZCT3/PMJux71y5TuynvOVjKG/QCioqmG0\npxs9fXy8gXPeXHSnhZr2UcKqH58qqZ58fy2ilfUHh1/3vIt9bj5fUcBDA+M88po5OABHmvYjhEJp\nw9k1M8VGAwyk8il2jDLgS1AdL0ETvexU60GNMqOokIaGBmrtVQBEMnffeUtdgTlIbEA40DTzw1hN\nWiiUQww5i1GcCSKGJDUSY2ltLjUuwe+qL+HhUBdyPE7wpZ63fY2j4QTxlMQtDJrUCRAWfIEAlrRB\nSrHiyLYR/OM63CtXojidb/t6zmcyb7ULhIKqGgw9zVjvkWPHhMUCi2Yzr03S5+7EomgsUdOoEmIy\nm63tQ0ST6TOc1eRr1cUsz3bz1UO9bA8cD/ccadpHQXUtdvfppwe+lv1rX8VQLDiK++iXgrpEGZro\nZhwfmqOTKTlm+dvUpJlzqMjE589bqvNcKAIGkikMzfz/p1WVPH2ckOplxDZCREJyOIIQgs+trKXL\nW0IwMcyeLIXQy72kBs8cOjxXfr21h6RhxWNY2eQyiw+8gQAW3SAlLGiREfSxMbKuef/buo4LgYyh\nv0A4OgrhtQPOAPKuvI7sCIwb7ajCSrUqKNVhuzGD5foONrSeOXwDoCmCe2ZUUWq3cMf+DlrCMVKJ\nOP2HD1Ix8+xmd0spOdSUwJMYYsK5kZGYi9x0DodJkE6C6uxmao5ZFaTFJDeHFnPD4sWvc9YM7xZ2\ni0pFjpPeaIKkalZGJTQVX8Ss0urxDmFICPabxvyGpfWUJAK0eor4RiiIsKlMPNaKNN6esQiheIrf\nbDxIyrCQnU7T7jKb/DzBIKpUkEIgD+1DzcnBc5GHbSBj6C8YsotKsDldDLYePuF43qqrMAAxaH4A\n2FU75ckkfYqFFXr4rMI3ANkWjV/PrsUqFG7e087WvXsx9DTlM87O0Pdt7yCo5lBTlmDCNUhp1Gyc\neR6zE9GXNU6+Ix8AkQYtpeOqfGebazK8MeoKPHQHYgRlMVbSJDWVVN9BhDQYyDJjhBMjZj+Epirc\n4Ril257LqJGmszGHZE+IyNaBt2VtD2zq4jKLWQWUn+5jzGEFICscQGB+bzTvxnv99Qir9W1Zw4XE\nORl6IUSXEGK/EGKPEGLH5LEcIcQLQojWya8XZ8/xW4xQFEqmTKXvUPMJx7XsbLrKVbzN3UiRQlMs\n1KZ6QcDO2ApaDzRjnKVXVe208fu5tagCfvbSqwhFpXTq2cXndz7ejJaO4mqwMpKTYGrMHEm8x1WG\nEDqN5TkIIUhEU2hSYKQiWIqL39hFyPCOUlfg5og/xmiqAqeaIm7RGB8epVj2MegxlciC0TT65Fjs\nG+eVUxIewYbkF/4Atjofgee70AMn6yOcC2PhBPe+0sFst5lTciYmCDksKHoMWzKOEGZToTXmx3fz\nTW/pa1+ovBUe/eVSyjlSygWTP38NeFFKWQ+8OPlzhreAkobpjPX2EAufKBXYMSObsj6dtNP0cCpT\n/dgMg3ZNY+VYhL29Z1/uVuu08/S8emq6D9JTVMlDY5HXnUo4PhChd8JFVaqF/vB+DgoX06M1KOII\nAUs2wt7L4pJ5AIz1hbEpoMhYxtM6z6krcJMyJB16PR4tQdyi4YlAafoIvfYqbC6FkC6PxeK9C+fx\nmf1PkhAqLx0aQV5VgdQlE4+3vaWTLb+/7jCXJRVCNjMs6bF6CDmd2BMjCADFTLx6qoux1dW9Za97\nIfN2hG4+CDww+f0DwIfehte4KDnqXQ8cPnjC8bF55s0cTe4DwCehImkwYIuhJKv543Otb+h1PBOj\nZI0NIWfM4e8P9/Kx/Z30xk8ee3yUrY/sR9ETzLqsmJ7kIXbH85keq2JADtPnT6I6ulhQZPoBo0fC\nWAXY7K+fJM7w7jKt2BwM1mOrwGOLE7Nq5ITBlx4ipHiJlo8QMiA1YBp6S0UFl+ijLNZHMSQ80DyA\n9+oq4gfHiWwbfEvWtLN7gie29fB5zcGozVS1yvPkErP7cMeGAJBWU0Q8/wNXvCWv+V7gXA29BP4k\nhNgphPj05LFCKeXRwNwgUHiqXxRCfFoIsUMIsWNkZOQcl3FxUFRbj6Kqx+bDH8U7fRajWRBt3wJA\ngb2c8lSKIexYrG249gQYPnLmkQivpX2HeZ5vXn8t36ovZcNEiEu3tvDv7f0nzbnvb/PTcShKRe96\nDjZO578bPklpNIXLcPOq3YVuQF72GLPyTPm28Z4gqhDYvBlv/nxnSqEHu0VhyGLF4UqRsGjU+HUi\nSbPyq7uwn5AhSfaZO0whBM558/h801MoAu7b2IltUaEZwnmmg9TIyVrGb4R4Sucrj+7lU3YXVmWM\noGGaL4+rlJQlh+yomY9KW7wgDfI/mKm2Ocq5GvplUso5wPuBzwshLnvtg9Lcr51yzyalvEdKuUBK\nuSA/P/8cl3FxYLHZKaytp+fA3hOOT8udzq5agePAKIZIkm8voyxuhnEU33YsIsrjP9xD4CzfaK1b\nN5FfVYO3oJC7yvL58+JpXJfv48c9wyzY3Myte9v5cfcQ6wb9PPurFqypILpniFtDCpb0YaZHJ+Pz\n7nxA50OzZqII81YLdU6Yf0tJJnVzvmNRFWaWeOmVaVS3+TYuCQtag7uwyThdWRq6hED38VCic8F8\n8juaWdOQRTxl8I9PNpHzV1MQFoXx3xxCnkFE/ExIKfnnJw4QGo1yc1ojObOPUNKFik6ztQiEQknI\njNmnLDnYVB3NfXHXzr+WczL0Usq+ya/DwOPAImBICFEMMPn17Mo+MpwVNXMWMNjeSsQ/cezYjNwZ\n7KwTqElBwmjCbcmmKNVNrpFgh/cybsz+Z5KJFI99bxdDnWf27Ie7OhhoO8T05ZcfO1Zut/Lj6ZVs\nWTKNz5UX0BVL8u/t/fzmwSYSQzGmtqzlxYWLuWF8L5cl7mV+ZCqSAFvCASzOPj7ZeDsAhiFJTJg7\nAltt2dtwdTK81TSW++hJJEm6zFEVrrgG6FQkD9NprwAkLeP9xwy4a+lSAO629aMpgkd39vJMxyjZ\nN9aT6g0z8cSbi9c/uLmbR3f28pPcXFQh6MxtIhjPwkeY3ZhrKwtOYNF1EpYs3PmZHo3X8qYNvRDC\nJYTwHP0euAo4ADwF3DH5tDuAJ891kRmOUzN/EQCde3YeO1biLuFApSBhg0T/OgAqbNnMDg3xZ38e\nXq0P3fMsiqbw2Pd2suXJdhLRkwXEAfas+wOa1cbMlSfPBqly2PjxQZwXAAAgAElEQVSn2hI2LprK\nr0aczOtIUOrpIy94iB98+jZWDTxAd0wyPzyVPrWfRDyHlVOKyXWYFRqB4ShWaXZZ2qdVv6XXJcPb\nQ2O5j5SUdNnNJreotDPfWYY12UK/Uk4yv5d+I0JqyNwtWmtr0QoKSG3bwp1LqwD429/uZZOq41lV\nTnTHEOGN/W9oDU/u6eMbTzfxlaIcSsaSZL2vimhyB6GolzxLnMMiDkDJ+Bi2lE7Kno2nMOutuwjv\nAc7Foy8ENggh9gLbgD9IKZ8HvgNcKYRoBa6Y/DnDW0R+ZTXunFw6dm07dkxVVFS7g33TFcS+dqSM\nUemeTtVEN5GUwcvZN3OT5UkSK3OpW1jAzue6eeDvN7Huvib2v9zLkYPjjPSE6DvcR8ufX6Z+8XJU\ni+OkZpdUQqdz3yi//95ODr/Ux6zLy5h54GGcCxcQTSYwfMNooXI8RhaveGyAwuqG0mO/P9wVxD45\nq1zL97wj1yvDuTG33Exs/sJ/LYo0CAobn9ZKmIi1ANDT0E84rZLqMyegCiFwLV1KdPMWPrWsCosi\n8DosfHbtTv6YrWCfkUvgDx1Etr9+clZKyS83dvKl3+zhxuJsPjiqY6v30Vnix2sdJRL3UOyQDFkk\nih7AEYxjS6VJ27NxZ2eG5b2WNy08IqXsABpPcXwMWH0ui8pweoQQ1M5fRNOr60lEI9ic5hbVa/Py\n4swYC3dDOnqIbNccapQUDk1hg+ca3ud/hJbtf+ATX/sSc1ZXsP/lXjr3jdK6fejYuZORZzGSKdr3\nltN59ysAqJqCogoQkIqbEy4dWVZW3zmNqvwYnf/SSc5HP0p30zb6vUnmDE8DYE+WGwJJlr0mRNPX\n6seJDhgo1hPlCjOcn5TnOPnYvHJ+tQtCzhxC4TjXH+llTn4+ozJOT56TqngWiZ4grkWmvKTr0qUE\nnniCrN5Obl5Qzu92HmFuRTZf/f1+bppdzN/VeJn4fSsybeC+pOSUrzsSSvCNp5v4w74B1tTm8zeD\nOorbSs6aqTy8/ofMcMF40sOcXEnA6cSaHCEdF1jTkgi2jKH/CzIKUxcgs1a9j70vPEfzq+uZe/V1\nABS5ithdOkA6RxJtexrXrBlUZdexJFfhT0Mu/sni46r4szzftIYPzC5h1e3TkFIS8SeZGAyz70+P\ncXDDQeoWX0/13EWkkwbplEE6qWPoEiTYPRbyKzyUNWSjagpj994LgGfV5fT/6bu0OFVuDs0mLfpo\nGbejudopcl93bN39LaPMNmIIeyZJdiHx5asbWLurh25PObmBUVLtLfzfZd/g4EAzBx3TucISZaxj\ngqMSMq5LLgEgsmkTn7nxVh7dcYTafBdLa3P50fo21msqP83xwpPtJHvD+K6rQbFrSCk5OBji8d19\n/HprD/GUzncXVrG8KQiKIO+umQyn08jYq8QtuYxJH15Hmqgjj8LAfpIJFVWaM+ddvoyhfy0ZQ38B\nUlhTR1FtPXvWPcuc930AIQR1vjr2juxlYoGFvBeO4K/vp9zVwPRwOy9FSji84LNcdeA/+NTL27l2\n1vUIIUinknTsfIldzz3FWG8P0y9bxfs+dxeKcnbedujF9dimT8NSUkI4voveZD7TYjW0Zrfin/CQ\nV96Lppi3WCSQIDCewmkDS+HZDUnLcH6Qk2Wj0hrlEDVMt+4n4k9R6rMx7cV2/rBgHpHaF2k/olGb\nSKPYNLS8PGwNDUQ2bqTq05/itiWVPLi5i+e/dBkfmF3Cf71wmDsODHInVm7bOUj/7kH+5IRnknHa\nkiksQnB7bT6325yoO8ZR8hzk3jEDS56D+5/exoKcQ1iiCwGIWnIwVAdTgwGkVFCEA4CsPMe7eMXO\nPzKzbi5Q5rzvA4z3HaF160YApueazVSHF5gxcXVwM6rQmDmSQBGwzrIKBUnj8OO82DJMy4aXufcL\nd/HCz3+MarFwzRe/wtV//eWzNvLp0VFie/bgWb2awPAQft8ojaFZCBQOVJhTKiuKjlf4DLSZzS02\nmwtree5bdh0yvDPMdEt6KSGiOhiNuHBFOqnrMQvq2qsS9Bpxkr3Hlcrcy5cR3bULPRzm7tX1uG0a\n3/pDC3UFbn7y0Xls+IdVTLllKo/PzGLcrnJzWPDLpINXFC8viSw+3hZHPTiBe3kpBV+YgyXPVLxq\n7noeTdFRR0wHot0wX3NmwOz+ljbz3vLmZwz9a8l49Bco05atZMczj7P+l/dQNm0mC4tMD6c518m8\nWQb2w5voL15Fg30qC0osPHU4ypfqruS2tpf4xs9+StXgToqnTOUDd3+Vsumz3rAASOill0BKPKtX\nc7h5A9sND9eH5mGIPl6ZKMfqGGRG0fH4a8+BEVwygVDcaLmZN+GFxqIiH8+Mp+lxVDAWbEUM7qOh\ntJTi2BGa3NXMJEGiO4i91kzeuleuZOze+4hs2ED21VfzxdX1/PsfWnh6bz/XNZZQ4LFz8/wymG/m\ncFIjURJtftITCYQArcCJY1oOivO4/N8P1h1iQeFONEs+Y0fM2v0jlhgYSeoCo/QChqsEzabizMo0\n5L2WjEd/gaKoKu///N+SCIf57Tf/AXdAIBC0xoNELtcR8TgT/W1YFRuLQ366x6Lsr/kkh0ayqBrc\niXvOZaz5xncpnzH7Tak8hV9cj6WkBFtDAwO96+ibKGJ2dApJ1yF29fjBtZ8ZuaYylWFIOncNURTq\nAsgY+guQxooqvNYAPY5yRtNO6HyVotopTGlv5rCYhqhsZ/zQ8d4Ox5w5qF4v4ZdeAuDOpVU0lvv4\n5ycPMBSMn3R+S74T9yUl+K6pxvv+alzzC08w8ts6x/lzyx5m5TZRlnUZ3SkfAslIlhVbshdLwuyu\n1z1VePMcGeWyvyBj6C9gCqpq+ND//TrRYIC1X/sSK/cXkhgIQmUBerGd3PY/Mhzu5gq/Hasq+PnO\nFJtHKynNTvDz9FxCiTfXpWhEIkQ2bcJ9xWqEEGwL93NZeCYSgx3FhUhA8zQfCycNdgSIJ6AQ0wvT\nCjLJ2AuN/LJCZuS00uMsZ0RzIvv2UFxdRd1+c5rqodoUHZ0jSN0syRWahnvlCsIvv4JMp9FUhf/8\nq0ZzjMHv/v/27jw8yupe4Pj3zJ7JTDJJJvseCJCEJUDYd0RABbnqraJ2UVu1itVqrVWL99b23tpW\nW2tF625dW62CuIFsogjIEgiQkAQSSCAb2SeTTDLbe+4fiYhUxVuFmOF8nmeevHNm5n3f33lmfjlz\n3jPn7CX4/5invscf5O4V+7gweys6nZ6UziiqZTxJESY6bU6Sutrx9HRhDATxWZJxxKv318lUoh/g\n0kfkc9UDjzDq3PNIqTczb1MMZasSqBpnINxTT9vRfcTorORLL+8f6SYjM45L47cT11PB71aXnfoA\nn6Nz82akz4d99jm0NtSwyhvPua6JCP1uVvsGY7f6sIS1HF9o5FBhA0ILEJOajLDo0at5bgYcZ4qN\nYbZaenQWDjjS8bTrSTA0Ee1qIdZ7hJ1hw6mzluKvP6GfftYsgi4X3UVFQO86tPcsyOXDA03c927p\nVz7271aVcbSlhcmJW4mLOw/zgY+oMmRiNeuQOgt5Lj8eV5BwbwCP34wzRf0q9mQq0YcAa6SD2Vdf\nj/uHI9me00q3B8qr43k/L4NmTyE1XeUsDNrx6K1EXnIHOqOF/03ayt+3H2HboZZTH+Ak7nXr0EdG\nYi0Yy+Or1pMfsOIMRCF1H7LpqI+ImAOMjBuBSW9CC2qUb64hurUUU0waxoRw9bV6ADJZDOTa3Ag0\nqsLTqGmOJKzsFRwJiYysq6ZSDKEj6yAdZa3HXxM+dSoYjb3Xc/pcOSGdqyZn8NRHh3l28+FTHndl\nUS1/21LFz6ZWgOwi1XkRWvVWKoIJtPt637tz66C7y4g52NvVE5OsRnWdTCX6EDI5Yxr7M914LhlK\nxtwaLI4w6hwm9rdsZIow4UDw8odNMGox+a2rGR3Vwy3/KKLxc/pMv4j0++nc+AG2WbOoavfy3GE/\nF7sL8OjrWWvPIKhJ3Kb1jInrnX++al8zPT4d6bpqgp06jImqtTVQRYU5ybIdodqaRl2XAyo2kDwk\nhxGFu9HJADuTY6nY9+m3RL3NRviECbjXrP3M/Db3LMhlbm489761n0c3fvHcN2tKGrjt1T2Mz3Qw\n3LGGiIhRRNbVcFSLoTOopy0mDJP3CCPEYQJSjwhzAirRfx6V6EPI+MTeeXD2BA4TG2dlxHfMTDyi\nY/yevdTq93ABRjY2dVCX+2OE5ufJ7O24uv388LmduLo/f+6bk3l27EDr6MAwYyY3vLCT/PBqhvVk\nYtO/yRv+CWTG6ZGmekbHjQZg35slmLztZF8wA+nTMGeoOUgGqsiYIYxMKKbRHMcBkQSBbnKi2zA2\nN5LgLWOTmMHR8JWfmaEy4rz5+I8epaf406m19TrBsivGsHBUEn9YXc6NL+2i0f3Zxsamg03c9PJu\nhidHct+8Srq7D5OaejXsX8k+Y+8P8rviEhjWWkOzuXeYp3QMISzChD3GcgZqY2BRiT6ExITFYNQZ\nqeqsxuYdTpehmNqh56ILBkmsLySpaQMSeGFrD+RdjLPsRR7/ThZlDR1c+dTHNLhO3bJ3r1uPsFi4\nrdLMgWPtXO3NpFPXjkfuZa/bRmZyMwLBqLhRtNa5qanVSOksxpTZm/hNaSrRD1RJ6XmMjutd3GZb\nVA5et56UIy+jQ+P8Lg+dIoJ9mUHqd5Yff419zhwwGulYteoz+zIZdDx0WT53nz+MtfuPMev+jdz7\nVglbKptZU9LAdc8XkhUbzhOXJ3K0+n6io6YSbxmDPLiW7Q0pCDSkzch/1BqprW9GSInflk/ioEjV\nNfg5VKIPMan2VDr9nZiM+Wg6D/FT9FSlzcW/ZRvDnV4KNMk/9zfgn/RT8HUyveV1nvheAYeauljw\n8EesLq7/wq/SUkra1q5jT8IwttR2cW38TvI82fRYlvOqnIZeB27T++TF5BFhimDb01vQaX7GLB6L\nr7IDfYwFvfpp+oAV5RxKsq2BONyU2wdT1ZSJvruZqRmdjK6ow+qr5V39Bew/vOz4a/SRkYRPnkTH\n6lX/8r7S6QTXTR/Emlunc05OPC99fIQrntzGdS8Ukhhp4flrxlFTtRTQkZNzH/537kegsd2Qh4zU\nYQpUMb3DjPtYN+HeIB6flcRBkWe4VgYGlehDzCd944fCrCAFMfmdVKXPxR8WQcKmbUz0HKZZ01he\nYoGcC2HLX5iVFGTlkik4bSZ+/OIuLvnrFpbvqqGxowcpJf6gxt6adh55eDk0NbIpPpcnrxzNOe3D\naTI0k2XczivabGYNi6asYzuz0mbRXtPO4Rodad4ynBfMpafSRdjQaNXaGsAslkSQYYy0H6XWksSu\njkSkhDFhxXgrPybfVEWNSKMotpn21k9b9RHnnUegrv746JuTZcXa+Mvlo9nxyzk8d814fn/JCF79\n8SS621+kvX0bQ7KXEtx1GFH0Ag2uBA5YkwjE2MlvOowt1ovbb8Lct3xgWp761fXnUYk+xCzIWgDA\nBrkbc0c67q5tpIhKduf8GK2ri3lH32eQhEc+OEjwnF+BFoAN/0N2vJ23fzKV3yzKo6nT23sR7Lfr\nGbJ0FUOWruLCZZvxrXqHgNHEPb+7EUfhDpK8SVTZnmN11yBcmoXsjN4VJGemzuSDhzYgtADjr55I\nz/5WCGiEDXf2Y80oX5cQOsKtuUxM3QFCsCEyh542C1JnYp6ziLuNmZh9rbwmLmPX9juQsrev3n7O\nOQiTiY633vrS/UdajcwYEstl49IwywoqK/9IbOw87IcScf32+xitATZP/V80CTLayy2VCZS3HUYK\nAc4x2GMsRCWoMfSfRyX6EJMfl49O6Chq34PdN5pOWcL4+Va8YTFUDr4YfXkpi5oPciQoeXWzByZc\nD0UvQd1uDHod35uUwQe3z2Llkin898Jcfjg1i5/MGszDl+RxfnMx0fPn4Qy3EVlupTC8mIkRdTwe\nXECO00CVdx3JtmTMe7zUuB0MsdYQO2siXdsa0EdbMKkLsQNejDOfjKS9JAU62Rkzitr6IQihI9rU\nTdLm35Md3EaVLouPTXYOVT4NgN5uxz5vHq633kbr7j7lMfz+dvYV/wSTMZpM/VXU3nwzzuEeZMxQ\nnmpKQJp1ZHoLSfE5ObJvF6ZAkC7yyS6IU98Yv4BK9CFGr9OTbEumpacFS+Q4EEFEQQJjKx7hmCOP\no8kzmP3x30iT8NiWKjxjb4HwWFh5EwS8QG/f6ahUB1dPyeTO84Zx29yhTG8sQbrdRCxcyNHnC0EK\nasOfZZd3NJUymSunp7K1fgvzUubywUvlmH3tTL7rInoq2vBVd2CfkoTQqQ/hQBcZOQah8zNa56LN\nFMV77jh8x3wckMNJ6i7iabMXS08DLwZ/RFn1X2h39a6EFnXpd9DcbjpWv/el+9c0H/v2LaGnp55h\nyffSsOROoob4MVk87B1zN6VVLmSCn19UxOIOa8at0xNujgFpYMiEhDNRBQOSSvQhaHpy7xrtu2zd\niKCJpuoPGDw/iQlbf0XqoimYx47je0d3Uy103PenrQQXPATHimH9rz93f1JKWp/9G6b0dGQwDUNN\nkCfj/8ksh4GHG/PJCu+h3bARTWoMX5+MWx/NhPFGLDFRtK+oQB9twTpOfQhDQVTUZEDP1MHlmDQf\n72ZNo7Y4Ec3nobzDSdqmPzO/9T1aDQ5e9X+fot3X0tl1kLCCAkxZWbS9+OIXXuzXtAD7S39BW/vH\nDBv8a9x3PInsbCFulBt/6mS+vycSqYNBYj2ju4dQWrIcAG/kfFJzHcQkqfHzX0Ql+hD0/bzexbhX\ntq0mvDOPtq6PsF/3X1gjujEtf4SsRx9kdk8RowIBVgb0vLE2CjnuWti6DIpe/pf9ebbvoKe4mMj/\nvB7X2iN8ZC/EYlxDoXExZTKNm2eksqLidRYGplNR5SAheIS86y6gbWUlgZYeoi7OVitKhQijMQKH\no4DE7J2M8rRQYh9MWbcT3SEXG1ty6dDH8ZfqV0hq3c4a0xzKejIoLFyMy1VI9NVX0VNSQtdHm/9l\nv35/B/v23cCxY2+SlXk72p+20FNcTMY1g5C+Dq6MX4rriAdDbAV3VE2jvvMgjVo3UcKOJpMYfW7G\nma+MAUQl+hCUZEvCYXZQ1lZGVMRMfKYGOrrbiZmVga/BRddHm0h/4jGWHFlPJ7Cmqo4tXddA5vTe\nLpy9rx7flwwGaXzgAQzpI/HWxNGkr+Px+BeYGj+EP++PpSCsHld0GS3uJoZumYRO8zP7Z7Nwr6nG\ns/MY9lmpWAY7+q0ulG9ectJlBGUN89PaMMgAT0+ah7bXzOjWal49mInRYOWFimUY/W4eCtxJW5eD\nXbuuoGlkOSIrluZHHz3eqte0AMeOvc32HQtoaf2QoUPuxfZWAPeq1aTceC6GYxv40ein2bbNBZZu\nEk3vkB2MorDhDUwBje7oS4lKdZMyLKqfa+XbTSX6EDUzdSZBGaTIEQ6ajtrS14m45i6M4QFal92P\nwelk1rKlLOqo4T2TheKNu9gX/wdInwzLr4V3fw5dLbS99DL+eh9hY2/AH2zhZ+mPMM/UxoqGxbTJ\ncG6fk8CTxU9w/Z5LcRsTmTROByU+3BtrCB+fQMTc9P6uCuUbFhd3HjZbDhkFjzMuWMhuywi2T8/E\nftBPSmU7++KvIdffyAOlD9NuNfDH9v/B5ZrC0dpnqftZA0dnbGfP6sXs3XcDm7dMobjkFgx6G2PH\n/B37rgiaH30U5yVTMbuXc+mwP7F+VxhC+hkT+ycuapzP7tpX6NIFsUdNAxHOxIvt6iLsKahEH6Ju\nyr8JgBePrMDuy6fFtw4tZRrRk2LprqjHs30rxrg47l26kEx/N3+NiObYGzs5mHQ/TLgBtj9Bx+2T\naVtZStiUW9HJOu5Jvg9haEEzzufdmjiWRG3nVf8GhhcnoXknkqarIcmcRteOBuyzUnFcNFh9AEOQ\nTmdi1MgnSEy4iEUZTUT4O/hr4tU0zdGT0dKB69EVeOc8yHfa1/Kzitc4mBDGo+0/ZlvpDxBRcxBR\nVtq7d9HpKsPhmMCI4Y8yfvxb6Ha5qLv7lzimD6YpZjPjkv/AjuIEdMEelsQ+TkV4F1nVbRwJ1hEd\nTKLLOJHYka+ROnhsf1fJt574ogsjZ1JBQYHcuXNnf59GyFmwfAHV7mpeGH4DLa4/Mkz/IInx4VRc\neiNh2cmkvr4BgL1l1VzxTBHRwsgyaSEy0I5O+sEQi9AbCbPt4cGo11hpa2GhMYl/7ruRseIgF1yo\n49VNb3FB+e1Eyi5mjcwi0NCNY9EgbBOTTnF2Sijo6erhrp/cxRux5zAxpZqbWh/C8Q89MjqKzCXn\nE37gfu5Ov4tnMuYztMZHXsUmyoe8Q3ZpK4NdYWQv+i4RKRm4PvyA2o/fpGWYgW3REezz/AJdnYHw\nSD8vJrzJff7NpB8cQ2J1B9ZgBAHnj4jPKSd10jtMnPBOf1dDvxFCFEopC071PNWiD2FL8pcA8GhL\nIfpABDVNz0PmDKJnZ9NZUk/XymcAGDksnYcX51GPn2uD7VR0e9B8QQyR7YRfkcKzEWWstLUwxDeC\nfxbfQCqNXDT5GM9ve50LS28gXEimZ6UQaOoh5socleTPIpZwC5fMn8bk1q1src3gaedVNC0JEnC7\nOPL716hrnclvq+/j2rrVHEw2sXHMTEbW3EFj6lCeK+hkae1j3LztTu4xr+KxGUb+bp9JSc1SdHUG\npuU72T12D3vb1uBvtJJ2yIUOK4GY75Ez2Un0iIeIdc7u7yoYEFSLPsRN/8d02r3tPJt7Me3uF8gL\nPE3c+BwOnTsLnSFIxquvoEvNB2BrSTXXvrALj9ST5y5npK4Zj/cw64ZryM6JdLpzyDEe4oKRu1l7\n6DALyq4n3OxgWmw4OoMB5w9yMWeouUbONsGAn8duuJaN+hx2OIaTF13BVYkvkP6Ehr2pm7BBdtJG\nl/PayJ/y2/j/pEELktbkZ6qmZ6g4hAw0U2Sws6E6El+rQG8zcu+iPL7rXk7Zxnv5vSeHgrJEevQ+\nzPbFTFs8mdjcQkrLbmdcwQoiIkb2dxX0m6/aoleJPsStrFjJ0s1LyYsazPXWCiztGRTMeIWerW9S\nc8e9RGb5SLznF4hxV4HBRIu7h7tf3My6Kg9B8ekXPovew4XxG+iObiRucyyx/gUkmwzkO8Ix2M04\nr8nDGK/mmj9bHSneyz9/czdlUXPY5ByCpnmZkfgRcz4+xMjCMgyRRlIm1NE2aTGvF9zFM9XN1GtB\ndE096Gs96Fu96PSQP8zJL0emY9v7Hsf2bKHIPRbNc4Sgrwhn+BxmzJlHxhU57N23BJdrF1OnbEaI\ns7djQiV65biFKxZS1VHFf2fPJarnDZKPLWHoZbfS8tD9ND3+LCZ7gLAEgbDHEQwY0Lp6OCgES6cP\npt1kYqG9h+kJxRx754f4/Nno9GbGWLwkWmyY0iOIuTIHfYRaHvBst+Fvz7B71XJ8MTOpHjeeNeXt\naOhxau0MaT2Gw92CzdFDR3wCR2y5lLaCBOxCI9dnYEKXCTMnXLyXGoaOjXRqRTjMucxLWkjczaMR\nsX4+2jyFpMRLGTr0V/0V7rfCV030hjNxMkr/WnbOMi5840J+U7GOB5PzqI19DOu6NNJuvQPziDG0\nPvYQXdVHoLYVnUnjaIKO+87VE7Ts4TaDIDO5k6bVCzF2GRkW4yEt3IYIGLFNTiLyvEyE4extUSmf\nmvX9q+hoaqVy50ZG72jm9tuu4Jn1j3CgK5UD+jR8UWkENXC0dZLi2s9NYaVMMxVTYKqBubficulo\nW7uapnI9+pZuWi1e9qU60RvTmZm6AFOyHVOSjarqx9E0L8nJl/d3yAOGatGfJT7pwjHrDPwqJpIw\nUx2pxmsZPO0WdLre1rg/6Oel0pdYVrQMk87ARZFd5Fs8lLYMZ3b3rURVmZHdAUypdhyLBmFKsfdz\nVMq3jdQ03ln2N8o3r0BvDGPa5Zfh0z2Mx3yU5v0OdJsiyK7TY26qBQHmKD8mawAtIPC0mpA+HV0m\nA3tTY2mzWdEbk8hJvZw8aSDmqjzMQ+xs3TqbsLA0xox5qb/D7Xeq60b5F8+VPMcDOx9AAPPN0QyN\nrCOaGAxR0zkgdbxV/REtXhfpBj1Xx3iI1OtJLP0RkXXjESY9lmFR2CYlYcqIUOPjlS/14d+3sPPt\n55CBWqyRDlJGWZHRmzGYJLXbYsmqLSDDLeluLcfTU0dQStwmI7VRNurjwjF3g96UTnTSJZwTYcVg\nMxF382hqap7nwMFfM2rkkzjViBuV6JXPt7VuKz//4Oe4vC44KVdnm4PMtvvJNemwH5tAfMvl2NMG\nY8mNwTLIobpolP+XHe8cZuvyjRj0e+juqIS+XKO3BNAbBAIjZs2GXhpxaW4CMoDOpyF0JvSm8aTk\nzmZasg1fSQtxN+bji6pjx85LcDgKyB/1rGpsoBK98iWklKytXsublW/S1tbCoM4YxuAk2WwnypFG\nfOI4rJmJ6CPVsn/K11O+rYGNL5WhaV0kD/Zgc3hwd+zF3X4Qb3c7WkCHFhDoDBp6s0ZYFITFOohL\nHEV8TxZil4OISRkERtZw4OBvEMLAuHErsJjVbKigEr2iKN8S7tYetq6opKKwEalJDGY9VrsRdB1o\n+sPoTa3E29xEW7vQzC689hq84TWgC35mP7bwoYwY8QhWa2Y/RfLtoxK9oijfKl0uL0f3t9J01E23\n24/BqMNsNeBMsRGfGUmE3YinuBl/fReYgmiD2+ixViFlkDBrOtFRkxBCTXd9on4fXimEmA88BOiB\np6SUvztdx1IU5dsvPNLMsEmJDJuU+IXPsY0/+bGJp/ekzhKn5eqa6P23+whwHpALXC6EyD0dx1IU\nRVG+3OkaRjEeqJBSHpJS+oB/AItO07EURVGUL3G6En0ycPSE+zV9ZYqiKMoZ1m8Do4UQ1wkhdgoh\ndjY1NfXXaSiKooS805Xoa4HUE+6n9JUdJ6V8QkpZIKUsiHw4EU8AAAPwSURBVI2NPU2noSiKopyu\nRL8DyBZCZAohTMBi4M3TdCxFURTlS5yW4ZVSyoAQ4ibgPXqHVz4jpSw5HcdSFEVRv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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb07c604ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(prof_vec_pixe[ind_rel_cor,res_ex,:].T)\n", "plt.title(\"Correct signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.plot(prof_vec_pixe[ind_rel_err,res_ex,:].T)\n", "plt.title(\"Erroneous signatures for res: %f\"%(resols[res_ex]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([152, 500])\n" ] } ], "source": [ "trainloader = prof_vec_pixe[:,res_chs,:]\n", "trainloader = trainloader / val_norm\n", "trainloader = torch.FloatTensor(trainloader)\n", "print trainloader.size()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((152, 500), (152, 2), (152,))\n" ] } ], "source": [ "decode, encode = net(Variable(trainloader))\n", "out_decod = decode.data.numpy()\n", "out_encod = encode.data.numpy()\n", "print(out_decod.shape, out_encod.shape, list_labels.shape)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb079740fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 6))\n", "plt.scatter(out_encod[:,0], out_encod[:,1], c=list_labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RMSE over the 10 correct segmentations was compared with RMSE over the 10 erroneous segmentations. As expected, RMSE for correct segmentations was greater than RMSE for erroneous segmentations along all the resolutions. In general, this is true, but optimal resolution guarantee the maximum difference between both of RMSE results: correct and erroneous.\n", "\n", "So, to find optimal resolution, difference between correct and erroneous RMSE was calculated over all resolutions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The greatest difference resulted at resolution 0.1. In this resolution, threshold for separate erroneous and correct segmentations is established as 30% of the distance between the mean RMSE of the correct masks and the mean RMSE of the erroneous masks.\n", "\n", "# Method testing\n", "\n", "Finally, method test was performed in the 152 subject dataset: Watershed dataset with 112 segmentations, ROQS dataset with 152 segmentations and pixel-based dataset with 152 segmentations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Discussion and conclusion\n", "\n", "In this work, a method for segmentation error detection in large datasets was proposed based-on shape signature. RMSE was used for comparison between signatures. Signature can be extracted en various resolutions but optimal resolution (ls=0.1) was chosen in order to get maximum separation between correct RMSE and erroneous RMSE. In this optimal resolution, threshold was fixed at 27.95 allowing separation of the two segmentation classes.The method achieved 95% of accuracy on the test Watershed segmentations, and 95% and 94% on new datasets: ROQS and pixel-based, respectively.\n", "\n", "40 Watershed segmentations on dataset were used to generation and configuration mean correct signature because of the greater number of erroneous segmentations and major variability on the error shape. Because the signature holds the CC shape, the method can be extended to new datasets segmented with any method. Accuracy and generalization can be improve varying the segmentations used to generate and adjust the mean correct signature." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

CrowdTruth/CrowdTruth-core

tutorial/notebooks/Multiple Choice Task - Person Type Annotation in Video.ipynb

1

112567

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CrowdTruth for Multiple Choice Tasks: Person Type Annotation in Video\n", "\n", "In this tutorial, we will apply CrowdTruth metrics to a **multiple choice** crowdsourcing task for **Person Type Annotation** from **video fragments**. The workers were asked to watch a video of about 3-5 seconds and then pick from a multiple choice list which are the types of person that appear in the video fragment. The task was executed on [FigureEight](https://www.figure-eight.com/). For more crowdsourcing annotation task examples, click [here](https://raw.githubusercontent.com/CrowdTruth-core/tutorial/getting_started.md).\n", "\n", "To replicate this experiment, the code used to design and implement this crowdsourcing annotation template is available here: [template](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.html), [css](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.css), [javascript](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/templates/People-Video-Multiple-Choice/template.js). \n", "\n", "This is a screenshot of the task as it appeared to workers:\n", "![Task Template](../img/person-video-multiple-choice.png)\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A sample dataset for this task is available in [this file](https://raw.githubusercontent.com/CrowdTruth/CrowdTruth-core/master/tutorial/data/person-video-multiple-choice.csv), containing raw output from the crowd on FigureEight. Download the file and place it in a folder named `data` that has the same root as this notebook. Now you can check your data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>_unit_id</th>\n", " <th>_created_at</th>\n", " <th>_id</th>\n", " <th>_started_at</th>\n", " <th>_tainted</th>\n", " <th>_channel</th>\n", " <th>_trust</th>\n", " <th>_worker_id</th>\n", " <th>_country</th>\n", " <th>_region</th>\n", " <th>...</th>\n", " <th>description</th>\n", " <th>descriptiontags</th>\n", " <th>hiddeninput_gold</th>\n", " <th>imagelocation</th>\n", " <th>imagetags</th>\n", " <th>keyframeid_gold</th>\n", " <th>selected_answer_gold</th>\n", " <th>subtitles</th>\n", " <th>subtitletags</th>\n", " <th>videolocation</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 18:40:05</td>\n", " <td>3990340198</td>\n", " <td>8/20/2018 18:39:51</td>\n", " <td>False</td>\n", " <td>clixsense</td>\n", " <td>1.0</td>\n", " <td>40712302</td>\n", " <td>GBR</td>\n", " <td>J8</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:07:38</td>\n", " <td>3990381441</td>\n", " <td>8/20/2018 19:07:29</td>\n", " <td>False</td>\n", " <td>clixsense</td>\n", " <td>1.0</td>\n", " <td>40925305</td>\n", " <td>CAN</td>\n", " <td>QC</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:19:02</td>\n", " <td>3990407780</td>\n", " <td>8/20/2018 19:18:52</td>\n", " <td>False</td>\n", " <td>imerit_india</td>\n", " <td>1.0</td>\n", " <td>44399792</td>\n", " <td>USA</td>\n", " <td>LA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:20:32</td>\n", " <td>3990410322</td>\n", " <td>8/20/2018 19:20:14</td>\n", " <td>False</td>\n", " <td>elite</td>\n", " <td>1.0</td>\n", " <td>44185847</td>\n", " <td>USA</td>\n", " <td>FL</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1856509899</td>\n", " <td>8/20/2018 19:27:03</td>\n", " <td>3990420566</td>\n", " <td>8/20/2018 19:26:19</td>\n", " <td>False</td>\n", " <td>imerit_india</td>\n", " <td>1.0</td>\n", " <td>42395899</td>\n", " <td>USA</td>\n", " <td>LA</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 26 columns</p>\n", "</div>" ], "text/plain": [ " _unit_id _created_at _id _started_at _tainted \\\n", "0 1856509899 8/20/2018 18:40:05 3990340198 8/20/2018 18:39:51 False \n", "1 1856509899 8/20/2018 19:07:38 3990381441 8/20/2018 19:07:29 False \n", "2 1856509899 8/20/2018 19:19:02 3990407780 8/20/2018 19:18:52 False \n", "3 1856509899 8/20/2018 19:20:32 3990410322 8/20/2018 19:20:14 False \n", "4 1856509899 8/20/2018 19:27:03 3990420566 8/20/2018 19:26:19 False \n", "\n", " _channel _trust _worker_id _country _region ... description \\\n", "0 clixsense 1.0 40712302 GBR J8 ... NaN \n", "1 clixsense 1.0 40925305 CAN QC ... NaN \n", "2 imerit_india 1.0 44399792 USA LA ... NaN \n", "3 elite 1.0 44185847 USA FL ... NaN \n", "4 imerit_india 1.0 42395899 USA LA ... NaN \n", "\n", " descriptiontags hiddeninput_gold \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " imagelocation \\\n", "0 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1 https://joran.org/ct/entity.admin.unit.2649/85... \n", "2 https://joran.org/ct/entity.admin.unit.2649/85... \n", "3 https://joran.org/ct/entity.admin.unit.2649/85... \n", "4 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", " imagetags keyframeid_gold \\\n", "0 industry__c0_###_grinder__c1_###_production__c... NaN \n", "1 industry__c0_###_grinder__c1_###_production__c... NaN \n", "2 industry__c0_###_grinder__c1_###_production__c... NaN \n", "3 industry__c0_###_grinder__c1_###_production__c... NaN \n", "4 industry__c0_###_grinder__c1_###_production__c... NaN \n", "\n", " selected_answer_gold subtitles \\\n", "0 NaN Italian astronaut samantha cristoforetti uploa... \n", "1 NaN Italian astronaut samantha cristoforetti uploa... \n", "2 NaN Italian astronaut samantha cristoforetti uploa... \n", "3 NaN Italian astronaut samantha cristoforetti uploa... \n", "4 NaN Italian astronaut samantha cristoforetti uploa... \n", "\n", " subtitletags \\\n", "0 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "1 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "2 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "3 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "4 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "\n", " videolocation \n", "0 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1 https://joran.org/ct/entity.admin.unit.2649/85... \n", "2 https://joran.org/ct/entity.admin.unit.2649/85... \n", "3 https://joran.org/ct/entity.admin.unit.2649/85... \n", "4 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "test_data = pd.read_csv(\"../data/person-video-multiple-choice.csv\")\n", "test_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Declaring a pre-processing configuration\n", "\n", "The pre-processing configuration defines how to interpret the raw crowdsourcing input. To do this, we need to define a configuration class. First, we import the default CrowdTruth configuration class:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import crowdtruth\n", "from crowdtruth.configuration import DefaultConfig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our test class inherits the default configuration `DefaultConfig`, while also declaring some additional attributes that are specific to the Person Type Annotation in Video task:\n", "\n", "* **`inputColumns`:** list of input columns from the .csv file with the input data\n", "* **`outputColumns`:** list of output columns from the .csv file with the answers from the workers\n", "* **`annotation_separator`:** string that separates between the crowd annotations in `outputColumns`\n", "* **`open_ended_task`:** boolean variable defining whether the task is open-ended (i.e. the possible crowd annotations are not known beforehand, like in the case of free text input); in the task that we are processing, workers pick the answers from a pre-defined list, therefore the task is not open ended, and this variable is set to `False`\n", "* **`annotation_vector`:** list of possible crowd answers, mandatory to declare when `open_ended_task` is `False`; for our task, this is the list of relations\n", "* **`processJudgments`:** method that defines processing of the raw crowd data; for this task, we process the crowd answers to correspond to the values in `annotation_vector`\n", "\n", "The complete configuration class is declared below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class TestConfig(DefaultConfig):\n", " inputColumns = [\"videolocation\", \"subtitles\", \"imagetags\", \"subtitletags\"]\n", " outputColumns = [\"selected_answer\"]\n", " \n", " # processing of a closed task\n", " open_ended_task = False\n", " annotation_vector = [\"archeologist\", \"architect\", \"artist\", \"astronaut\", \"athlete\", \"businessperson\",\"celebrity\", \n", " \"chef\", \"criminal\", \"engineer\", \"farmer\", \"fictionalcharacter\", \"journalist\", \"judge\", \n", " \"lawyer\", \"militaryperson\", \"model\", \"monarch\", \"philosopher\", \"politician\", \"presenter\", \n", " \"producer\", \"psychologist\", \"scientist\", \"sportsmanager\", \"writer\", \"none\", \"other\"]\n", " \n", " def processJudgments(self, judgments):\n", " # pre-process output to match the values in annotation_vector\n", " for col in self.outputColumns:\n", " # transform to lowercase\n", " judgments[col] = judgments[col].apply(lambda x: str(x).lower())\n", " # remove square brackets from annotations\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace('[',''))\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace(']',''))\n", " # remove the quotes around the annotations\n", " judgments[col] = judgments[col].apply(lambda x: str(x).replace('\"',''))\n", " return judgments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pre-processing the input data\n", "\n", "After declaring the configuration of our input file, we are ready to pre-process the crowd data:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>output.selected_answer</th>\n", " <th>output.selected_answer.count</th>\n", " <th>output.selected_answer.unique</th>\n", " <th>unit</th>\n", " <th>worker</th>\n", " <th>started</th>\n", " <th>submitted</th>\n", " <th>duration</th>\n", " <th>job</th>\n", " </tr>\n", " <tr>\n", " <th>judgment</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3990340198</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>40712302</td>\n", " <td>2018-08-20 18:39:51</td>\n", " <td>2018-08-20 18:40:05</td>\n", " <td>14</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990381441</th>\n", " <td>{'astronaut': 1, 'scientist': 1, 'archeologist...</td>\n", " <td>2</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>40925305</td>\n", " <td>2018-08-20 19:07:29</td>\n", " <td>2018-08-20 19:07:38</td>\n", " <td>9</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990407780</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>44399792</td>\n", " <td>2018-08-20 19:18:52</td>\n", " <td>2018-08-20 19:19:02</td>\n", " <td>10</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990410322</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>44185847</td>\n", " <td>2018-08-20 19:20:14</td>\n", " <td>2018-08-20 19:20:32</td>\n", " <td>18</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " <tr>\n", " <th>3990420566</th>\n", " <td>{'astronaut': 1, 'archeologist': 0, 'architect...</td>\n", " <td>1</td>\n", " <td>28</td>\n", " <td>1856509899</td>\n", " <td>42395899</td>\n", " <td>2018-08-20 19:26:19</td>\n", " <td>2018-08-20 19:27:03</td>\n", " <td>44</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " output.selected_answer \\\n", "judgment \n", "3990340198 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990381441 {'astronaut': 1, 'scientist': 1, 'archeologist... \n", "3990407780 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990410322 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "3990420566 {'astronaut': 1, 'archeologist': 0, 'architect... \n", "\n", " output.selected_answer.count output.selected_answer.unique \\\n", "judgment \n", "3990340198 1 28 \n", "3990381441 2 28 \n", "3990407780 1 28 \n", "3990410322 1 28 \n", "3990420566 1 28 \n", "\n", " unit worker started submitted \\\n", "judgment \n", "3990340198 1856509899 40712302 2018-08-20 18:39:51 2018-08-20 18:40:05 \n", "3990381441 1856509899 40925305 2018-08-20 19:07:29 2018-08-20 19:07:38 \n", "3990407780 1856509899 44399792 2018-08-20 19:18:52 2018-08-20 19:19:02 \n", "3990410322 1856509899 44185847 2018-08-20 19:20:14 2018-08-20 19:20:32 \n", "3990420566 1856509899 42395899 2018-08-20 19:26:19 2018-08-20 19:27:03 \n", "\n", " duration job \n", "judgment \n", "3990340198 14 ../data/person-video-multiple-choice \n", "3990381441 9 ../data/person-video-multiple-choice \n", "3990407780 10 ../data/person-video-multiple-choice \n", "3990410322 18 ../data/person-video-multiple-choice \n", "3990420566 44 ../data/person-video-multiple-choice " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data, config = crowdtruth.load(\n", " file = \"../data/person-video-multiple-choice.csv\",\n", " config = TestConfig()\n", ")\n", "\n", "data['judgments'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing the CrowdTruth metrics\n", "\n", "The pre-processed data can then be used to calculate the CrowdTruth metrics:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "results = crowdtruth.run(data, config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Video fragment quality\n", "\n", "The **video fragments metrics** are stored in `results[\"units\"]`. The uqs column in `results[\"units\"]` contains the video fragment quality scores, capturing the overall workers agreement over each video fragment." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>duration</th>\n", " <th>input.imagetags</th>\n", " <th>input.subtitles</th>\n", " <th>input.subtitletags</th>\n", " <th>input.videolocation</th>\n", " <th>job</th>\n", " <th>output.selected_answer</th>\n", " <th>output.selected_answer.annotations</th>\n", " <th>output.selected_answer.unique_annotations</th>\n", " <th>worker</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " <th>uqs_initial</th>\n", " <th>unit_annotation_score_initial</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509899</th>\n", " <td>24.95</td>\n", " <td>industry__c0_###_grinder__c1_###_production__c...</td>\n", " <td>Italian astronaut samantha cristoforetti uploa...</td>\n", " <td>Italian__0_###_astronaut__1_###_samantha__2_##...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'astronaut': 20, 'scientist': 3, 'other': 1, ...</td>\n", " <td>25</td>\n", " <td>4</td>\n", " <td>20</td>\n", " <td>0.919461</td>\n", " <td>{'astronaut': 1.0, 'scientist': 0.169109758893...</td>\n", " <td>0.865963</td>\n", " <td>{'astronaut': 1.0, 'scientist': 0.15, 'other':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509900</th>\n", " <td>30.00</td>\n", " <td>man__c0_###_soccer__c1_###_portrait__c2_###_pe...</td>\n", " <td>this phenomena is it's massive the</td>\n", " <td>phenomena__0_###_massive__1_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'celebrity': 2, 'journalist': 2, 'presenter':...</td>\n", " <td>29</td>\n", " <td>11</td>\n", " <td>20</td>\n", " <td>0.290902</td>\n", " <td>{'celebrity': 0.007285683902434039, 'journalis...</td>\n", " <td>0.239176</td>\n", " <td>{'celebrity': 0.1, 'journalist': 0.1, 'present...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509901</th>\n", " <td>32.15</td>\n", " <td>people__c0_###_man__c1_###_adult__c2_###_portr...</td>\n", " <td>around could the lights be coming from</td>\n", " <td>lights__0_###_coming__1_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'artist': 1, 'celebrity': 1, 'producer': 1, '...</td>\n", " <td>25</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>0.295147</td>\n", " <td>{'artist': 0.006139167492493724, 'celebrity': ...</td>\n", " <td>0.216495</td>\n", " <td>{'artist': 0.05, 'celebrity': 0.05, 'producer'...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509902</th>\n", " <td>46.20</td>\n", " <td>water__c0_###_no person__c1_###_ocean__c2_###_...</td>\n", " <td>when investigators map the coordinates onto lo...</td>\n", " <td>investigators__0_###_map__1_###_coordinates__2...</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'scientist': 5, 'none': 11, 'presenter': 2, '...</td>\n", " <td>24</td>\n", " <td>8</td>\n", " <td>20</td>\n", " <td>0.552869</td>\n", " <td>{'scientist': 0.15804046047341153, 'none': 0.7...</td>\n", " <td>0.334078</td>\n", " <td>{'scientist': 0.25, 'none': 0.55, 'presenter':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509903</th>\n", " <td>35.85</td>\n", " <td>sky__c0_###_no person__c1_###_power__c2_###_el...</td>\n", " <td>the bright lights are part of a</td>\n", " <td>bright lights__0_###_</td>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>../data/person-video-multiple-choice</td>\n", " <td>{'none': 15, 'other': 2, 'fictionalcharacter':...</td>\n", " <td>21</td>\n", " <td>6</td>\n", " <td>20</td>\n", " <td>0.963842</td>\n", " <td>{'none': 0.9830831242894289, 'other': 0.008259...</td>\n", " <td>0.557895</td>\n", " <td>{'none': 0.75, 'other': 0.1, 'fictionalcharact...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " duration input.imagetags \\\n", "unit \n", "1856509899 24.95 industry__c0_###_grinder__c1_###_production__c... \n", "1856509900 30.00 man__c0_###_soccer__c1_###_portrait__c2_###_pe... \n", "1856509901 32.15 people__c0_###_man__c1_###_adult__c2_###_portr... \n", "1856509902 46.20 water__c0_###_no person__c1_###_ocean__c2_###_... \n", "1856509903 35.85 sky__c0_###_no person__c1_###_power__c2_###_el... \n", "\n", " input.subtitles \\\n", "unit \n", "1856509899 Italian astronaut samantha cristoforetti uploa... \n", "1856509900 this phenomena is it's massive the \n", "1856509901 around could the lights be coming from \n", "1856509902 when investigators map the coordinates onto lo... \n", "1856509903 the bright lights are part of a \n", "\n", " input.subtitletags \\\n", "unit \n", "1856509899 Italian__0_###_astronaut__1_###_samantha__2_##... \n", "1856509900 phenomena__0_###_massive__1_###_ \n", "1856509901 lights__0_###_coming__1_###_ \n", "1856509902 investigators__0_###_map__1_###_coordinates__2... \n", "1856509903 bright lights__0_###_ \n", "\n", " input.videolocation \\\n", "unit \n", "1856509899 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509900 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509901 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509902 https://joran.org/ct/entity.admin.unit.2649/85... \n", "1856509903 https://joran.org/ct/entity.admin.unit.2649/85... \n", "\n", " job \\\n", "unit \n", "1856509899 ../data/person-video-multiple-choice \n", "1856509900 ../data/person-video-multiple-choice \n", "1856509901 ../data/person-video-multiple-choice \n", "1856509902 ../data/person-video-multiple-choice \n", "1856509903 ../data/person-video-multiple-choice \n", "\n", " output.selected_answer \\\n", "unit \n", "1856509899 {'astronaut': 20, 'scientist': 3, 'other': 1, ... \n", "1856509900 {'celebrity': 2, 'journalist': 2, 'presenter':... \n", "1856509901 {'artist': 1, 'celebrity': 1, 'producer': 1, '... \n", "1856509902 {'scientist': 5, 'none': 11, 'presenter': 2, '... \n", "1856509903 {'none': 15, 'other': 2, 'fictionalcharacter':... \n", "\n", " output.selected_answer.annotations \\\n", "unit \n", "1856509899 25 \n", "1856509900 29 \n", "1856509901 25 \n", "1856509902 24 \n", "1856509903 21 \n", "\n", " output.selected_answer.unique_annotations worker uqs \\\n", "unit \n", "1856509899 4 20 0.919461 \n", "1856509900 11 20 0.290902 \n", "1856509901 9 20 0.295147 \n", "1856509902 8 20 0.552869 \n", "1856509903 6 20 0.963842 \n", "\n", " unit_annotation_score uqs_initial \\\n", "unit \n", "1856509899 {'astronaut': 1.0, 'scientist': 0.169109758893... 0.865963 \n", "1856509900 {'celebrity': 0.007285683902434039, 'journalis... 0.239176 \n", "1856509901 {'artist': 0.006139167492493724, 'celebrity': ... 0.216495 \n", "1856509902 {'scientist': 0.15804046047341153, 'none': 0.7... 0.334078 \n", "1856509903 {'none': 0.9830831242894289, 'other': 0.008259... 0.557895 \n", "\n", " unit_annotation_score_initial \n", "unit \n", "1856509899 {'astronaut': 1.0, 'scientist': 0.15, 'other':... \n", "1856509900 {'celebrity': 0.1, 'journalist': 0.1, 'present... \n", "1856509901 {'artist': 0.05, 'celebrity': 0.05, 'producer'... \n", "1856509902 {'scientist': 0.25, 'none': 0.55, 'presenter':... \n", "1856509903 {'none': 0.75, 'other': 0.1, 'fictionalcharact... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of video fragment quality scores\n", "\n", "The histogram below shows **video fragment quality scores** are nicely distributed, with both low and high quality video fragments." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Video Fragments')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.hist(results[\"units\"][\"uqs\"])\n", "plt.xlabel(\"Video Fragment Quality Score\")\n", "plt.ylabel(\"Video Fragments\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `unit_annotation_score` column in `results[\"units\"]` contains the **video fragment-annotation scores**, capturing the likelihood that an annotation is expressed in a video fragment. For each video fragment, we store a dictionary mapping each annotation to its video fragment-annotation score." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "unit\n", "1856509899 {'astronaut': 1.0, 'scientist': 0.169109758893...\n", "1856509900 {'celebrity': 0.007285683902434039, 'journalis...\n", "1856509901 {'artist': 0.006139167492493724, 'celebrity': ...\n", "1856509902 {'scientist': 0.15804046047341153, 'none': 0.7...\n", "1856509903 {'none': 0.9830831242894289, 'other': 0.008259...\n", "Name: unit_annotation_score, dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"][\"unit_annotation_score\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ambiguous video fragments\n", "\n", "A low unit quality score can be used to identify ambiguous video fragments. First, we sort the unit quality metrics stored in `results[\"units\"]` based on the quality score (`uqs`), in ascending order. Thus, the most clear video fragments are found at the tail of the new structure:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>input.videolocation</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509905</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>0.146278</td>\n", " <td>{'other': 0.28844109454744976, 'presenter': 0....</td>\n", " </tr>\n", " <tr>\n", " <th>1856509931</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.194667</td>\n", " <td>{'engineer': 0.15692508334014804, 'journalist'...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509938</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.207788</td>\n", " <td>{'businessperson': 0.06949004651596365, 'prese...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509921</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2848/88...</td>\n", " <td>0.220392</td>\n", " <td>{'monarch': 0.42098378954483356, 'other': 0.27...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509944</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2784/11...</td>\n", " <td>0.242865</td>\n", " <td>{'politician': 0.006580944290157723, 'none': 0...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " input.videolocation uqs \\\n", "unit \n", "1856509905 https://joran.org/ct/entity.admin.unit.2649/85... 0.146278 \n", "1856509931 https://joran.org/ct/entity.admin.unit.2833/90... 0.194667 \n", "1856509938 https://joran.org/ct/entity.admin.unit.2833/90... 0.207788 \n", "1856509921 https://joran.org/ct/entity.admin.unit.2848/88... 0.220392 \n", "1856509944 https://joran.org/ct/entity.admin.unit.2784/11... 0.242865 \n", "\n", " unit_annotation_score \n", "unit \n", "1856509905 {'other': 0.28844109454744976, 'presenter': 0.... \n", "1856509931 {'engineer': 0.15692508334014804, 'journalist'... \n", "1856509938 {'businessperson': 0.06949004651596365, 'prese... \n", "1856509921 {'monarch': 0.42098378954483356, 'other': 0.27... \n", "1856509944 {'politician': 0.006580944290157723, 'none': 0... " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].sort_values(by=[\"uqs\"])[[\"input.videolocation\", \"uqs\", \"unit_annotation_score\"]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we show an example video fragment with low quality score, where workers couldn't agree on what annotation best describes the person in the video. The role of the person in the video is not directly specified, so the workers made assumptions based on the topic of discussion." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uqs 0.146278\n", "Name: 1856509905, dtype: float64\n", "\n", "\n", "Person types picked for the video below:\n", "other : 0.28844109454744976\n", "presenter : 0.144930232611664\n", "scientist : 0.22919915879815284\n", "journalist : 0.14534325083723273\n", "artist : 0.005696670688424697\n", "businessperson : 0.10163406871362905\n", "politician : 0.03781678033568675\n", "none : 0.21943896131257373\n", "lawyer : 0.020785177196552786\n", "philosopher : 0.0056418079486984535\n", "architect : 3.3953714172294374e-05\n", "astronaut : 3.3953714172294374e-05\n", "athlete : 3.3953714172294374e-05\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8576.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "print(results[\"units\"].sort_values(by=[\"uqs\"])[[\"uqs\"]].iloc[0])\n", "print(\"\\n\")\n", "\n", "print(\"Person types picked for the video below:\")\n", "for k, v in results[\"units\"].sort_values(by=[\"uqs\"])[[\"unit_annotation_score\"]].iloc[0][\"unit_annotation_score\"].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = list(results[\"units\"].sort_values(by=[\"uqs\"])[[\"input.videolocation\"]].iloc[0])\n", "HTML(\"<video width='320' height='240' controls><source src=\" + vid_url[0] + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unambiguous video fragments\n", "\n", "Similarly, a high unit quality score represents lack of ambiguity of the video fragment." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>input.videolocation</th>\n", " <th>uqs</th>\n", " <th>unit_annotation_score</th>\n", " </tr>\n", " <tr>\n", " <th>unit</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1856509933</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'militaryperson':...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509930</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'model': 0.000696...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509908</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2649/85...</td>\n", " <td>0.998529</td>\n", " <td>{'none': 0.9993036353660087, 'philosopher': 0....</td>\n", " </tr>\n", " <tr>\n", " <th>1856509909</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2643/86...</td>\n", " <td>0.998512</td>\n", " <td>{'none': 0.9992954829530591, 'fictionalcharact...</td>\n", " </tr>\n", " <tr>\n", " <th>1856509937</th>\n", " <td>https://joran.org/ct/entity.admin.unit.2833/90...</td>\n", " <td>0.998456</td>\n", " <td>{'none': 0.9992692794931864, 'journalist': 0.0...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " input.videolocation uqs \\\n", "unit \n", "1856509933 https://joran.org/ct/entity.admin.unit.2833/90... 0.998529 \n", "1856509930 https://joran.org/ct/entity.admin.unit.2833/90... 0.998529 \n", "1856509908 https://joran.org/ct/entity.admin.unit.2649/85... 0.998529 \n", "1856509909 https://joran.org/ct/entity.admin.unit.2643/86... 0.998512 \n", "1856509937 https://joran.org/ct/entity.admin.unit.2833/90... 0.998456 \n", "\n", " unit_annotation_score \n", "unit \n", "1856509933 {'none': 0.9993036353660087, 'militaryperson':... \n", "1856509930 {'none': 0.9993036353660087, 'model': 0.000696... \n", "1856509908 {'none': 0.9993036353660087, 'philosopher': 0.... \n", "1856509909 {'none': 0.9992954829530591, 'fictionalcharact... \n", "1856509937 {'none': 0.9992692794931864, 'journalist': 0.0... " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"input.videolocation\", \"uqs\", \"unit_annotation_score\"]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we show an example unambiguous video fragment - no person appears in the video, so most workers picked the `none` option in the crowd task." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uqs 0.998529\n", "Name: 1856509933, dtype: float64\n", "\n", "\n", "Person types picked for the video below:\n", "none : 0.9993036353660087\n", "militaryperson : 0.0006963646339910918\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2833/9012.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"uqs\"]].iloc[0])\n", "print(\"\\n\")\n", "\n", "print(\"Person types picked for the video below:\")\n", "for k, v in results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"unit_annotation_score\"]].iloc[0][\"unit_annotation_score\"].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = list(results[\"units\"].sort_values(by=[\"uqs\"], ascending=False)[[\"input.videolocation\"]].iloc[0])\n", "HTML(\"<video width='320' height='240' controls><source src=\" + vid_url[0] + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Worker Quality Scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **worker metrics** are stored in `results[\"workers\"]`. The `wqs` columns in `results[\"workers\"]` contains the **worker quality scores**, capturing the overall agreement between one worker and all the other workers." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3587109</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>12.840000</td>\n", " <td>0.470469</td>\n", " <td>0.603399</td>\n", " <td>0.779698</td>\n", " <td>0.215188</td>\n", " <td>0.371369</td>\n", " <td>0.579445</td>\n", " </tr>\n", " <tr>\n", " <th>4316379</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>24.040000</td>\n", " <td>0.511478</td>\n", " <td>0.668219</td>\n", " <td>0.765435</td>\n", " <td>0.280311</td>\n", " <td>0.436881</td>\n", " <td>0.641618</td>\n", " </tr>\n", " <tr>\n", " <th>6330997</th>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>1</td>\n", " <td>38.714286</td>\n", " <td>0.468025</td>\n", " <td>0.615484</td>\n", " <td>0.760417</td>\n", " <td>0.320348</td>\n", " <td>0.457103</td>\n", " <td>0.700822</td>\n", " </tr>\n", " <tr>\n", " <th>6339764</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>80.500000</td>\n", " <td>0.019778</td>\n", " <td>0.122949</td>\n", " <td>0.160866</td>\n", " <td>0.028307</td>\n", " <td>0.122603</td>\n", " <td>0.230880</td>\n", " </tr>\n", " <tr>\n", " <th>6367365</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>29.280000</td>\n", " <td>0.172523</td>\n", " <td>0.385336</td>\n", " <td>0.447722</td>\n", " <td>0.122195</td>\n", " <td>0.275177</td>\n", " <td>0.444062</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "3587109 25 25 1 12.840000 0.470469 0.603399 0.779698 \n", "4316379 25 25 1 24.040000 0.511478 0.668219 0.765435 \n", "6330997 7 7 1 38.714286 0.468025 0.615484 0.760417 \n", "6339764 10 10 1 80.500000 0.019778 0.122949 0.160866 \n", "6367365 25 25 1 29.280000 0.172523 0.385336 0.447722 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "3587109 0.215188 0.371369 0.579445 \n", "4316379 0.280311 0.436881 0.641618 \n", "6330997 0.320348 0.457103 0.700822 \n", "6339764 0.028307 0.122603 0.230880 \n", "6367365 0.122195 0.275177 0.444062 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of worker quality scores\n", "\n", "The histogram below shows the worker quality scores are distributed across a wide spectrum, from low to high quality workers." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Workers')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(results[\"workers\"][\"wqs\"])\n", "plt.xlabel(\"Worker Quality Score\")\n", "plt.ylabel(\"Workers\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Low quality workers\n", "\n", "Low worker quality scores can be used to identify **spam workers**, or workers that have misunderstood the annotation task." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>44656777</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16.0</td>\n", " <td>0.000267</td>\n", " <td>0.014584</td>\n", " <td>0.018305</td>\n", " <td>0.015075</td>\n", " <td>0.073468</td>\n", " <td>0.205196</td>\n", " </tr>\n", " <tr>\n", " <th>44606916</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>70.6</td>\n", " <td>0.000573</td>\n", " <td>0.022518</td>\n", " <td>0.025460</td>\n", " <td>0.006691</td>\n", " <td>0.057175</td>\n", " <td>0.117028</td>\n", " </tr>\n", " <tr>\n", " <th>31508822</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>573.0</td>\n", " <td>0.001310</td>\n", " <td>0.038485</td>\n", " <td>0.034045</td>\n", " <td>0.021185</td>\n", " <td>0.102832</td>\n", " <td>0.206015</td>\n", " </tr>\n", " <tr>\n", " <th>15965551</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>30.0</td>\n", " <td>0.008752</td>\n", " <td>0.088362</td>\n", " <td>0.099049</td>\n", " <td>0.010984</td>\n", " <td>0.091147</td>\n", " <td>0.120512</td>\n", " </tr>\n", " <tr>\n", " <th>6339764</th>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>80.5</td>\n", " <td>0.019778</td>\n", " <td>0.122949</td>\n", " <td>0.160866</td>\n", " <td>0.028307</td>\n", " <td>0.122603</td>\n", " <td>0.230880</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "44656777 1 1 1 16.0 0.000267 0.014584 0.018305 \n", "44606916 5 5 1 70.6 0.000573 0.022518 0.025460 \n", "31508822 3 3 1 573.0 0.001310 0.038485 0.034045 \n", "15965551 25 25 1 30.0 0.008752 0.088362 0.099049 \n", "6339764 10 10 1 80.5 0.019778 0.122949 0.160866 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "44656777 0.015075 0.073468 0.205196 \n", "44606916 0.006691 0.057175 0.117028 \n", "31508822 0.021185 0.102832 0.206015 \n", "15965551 0.010984 0.091147 0.120512 \n", "6339764 0.028307 0.122603 0.230880 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].sort_values(by=[\"wqs\"]).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from low quality worker `44606916` (with the second lowest quality score) for video fragment `1856509900`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF LOW QUALITY WORKER 44606916 FOR VIDEO 1856509900:\n", "celebrity : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509900\n", "other : 9\n", "scientist : 6\n", "none : 3\n", "celebrity : 2\n", "journalist : 2\n", "presenter : 2\n", "archeologist : 1\n", "fictionalcharacter : 1\n", "producer : 1\n", "psychologist : 1\n", "writer : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8561.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import operator\n", "\n", "work_id = results[\"workers\"].sort_values(by=[\"wqs\"]).index[1]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[0]]\n", "\n", "print(\"JUDGMENTS OF LOW QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[0]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[0])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[0]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[0]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from the same low quality worker (`44606916`) for a second video fragment (`1856509903`):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF LOW QUALITY WORKER 44606916 FOR VIDEO 1856509903:\n", "archeologist : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509900\n", "none : 15\n", "other : 2\n", "fictionalcharacter : 1\n", "writer : 1\n", "archeologist : 1\n", "scientist : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8570.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[1]]\n", "\n", "print(\"JUDGMENTS OF LOW QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[1]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[0])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[1]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[1]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## High quality workers\n", "\n", "High worker quality scores can be used to identify **reliable workers**." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>unit</th>\n", " <th>judgment</th>\n", " <th>job</th>\n", " <th>duration</th>\n", " <th>wqs</th>\n", " <th>wwa</th>\n", " <th>wsa</th>\n", " <th>wqs_initial</th>\n", " <th>wwa_initial</th>\n", " <th>wsa_initial</th>\n", " </tr>\n", " <tr>\n", " <th>worker</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6432269</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>23.36</td>\n", " <td>0.789141</td>\n", " <td>0.836564</td>\n", " <td>0.943312</td>\n", " <td>0.579480</td>\n", " <td>0.671050</td>\n", " <td>0.863542</td>\n", " </tr>\n", " <tr>\n", " <th>15176395</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>17.24</td>\n", " <td>0.784259</td>\n", " <td>0.832825</td>\n", " <td>0.941685</td>\n", " <td>0.571864</td>\n", " <td>0.664663</td>\n", " <td>0.860381</td>\n", " </tr>\n", " <tr>\n", " <th>39021485</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>17.24</td>\n", " <td>0.781853</td>\n", " <td>0.831344</td>\n", " <td>0.940468</td>\n", " <td>0.573010</td>\n", " <td>0.666604</td>\n", " <td>0.859595</td>\n", " </tr>\n", " <tr>\n", " <th>40712302</th>\n", " <td>25</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>16.56</td>\n", " <td>0.755567</td>\n", " <td>0.816770</td>\n", " <td>0.925067</td>\n", " <td>0.537336</td>\n", " <td>0.640505</td>\n", " <td>0.838926</td>\n", " </tr>\n", " <tr>\n", " <th>43620110</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>194.50</td>\n", " <td>0.752887</td>\n", " <td>0.772451</td>\n", " <td>0.974672</td>\n", " <td>0.637002</td>\n", " <td>0.674885</td>\n", " <td>0.943867</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " unit judgment job duration wqs wwa wsa \\\n", "worker \n", "6432269 25 25 1 23.36 0.789141 0.836564 0.943312 \n", "15176395 25 25 1 17.24 0.784259 0.832825 0.941685 \n", "39021485 25 25 1 17.24 0.781853 0.831344 0.940468 \n", "40712302 25 25 1 16.56 0.755567 0.816770 0.925067 \n", "43620110 2 2 1 194.50 0.752887 0.772451 0.974672 \n", "\n", " wqs_initial wwa_initial wsa_initial \n", "worker \n", "6432269 0.579480 0.671050 0.863542 \n", "15176395 0.571864 0.664663 0.860381 \n", "39021485 0.573010 0.666604 0.859595 \n", "40712302 0.537336 0.640505 0.838926 \n", "43620110 0.637002 0.674885 0.943867 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from worker `6432269` (with the highest worker quality score) for video fragment `1856509904`:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF HIGH QUALITY WORKER 6432269 FOR VIDEO 1856509904:\n", "scientist : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509908\n", "scientist : 10\n", "other : 6\n", "presenter : 3\n", "farmer : 2\n", "businessperson : 2\n", "journalist : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8574.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_id = results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).index[0]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[0]]\n", "\n", "print(\"JUDGMENTS OF HIGH QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[0]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[1])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[0]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[0]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example annotations from worker `6432269` (with the highest worker quality score) for video fragment `1856509908`:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JUDGMENTS OF HIGH QUALITY WORKER 6432269 FOR VIDEO 1856509908:\n", "none : 1\n", "\n", "ALL JUDGMENTS FOR VIDEO 1856509908\n", "none : 19\n", "philosopher : 1\n" ] }, { "data": { "text/html": [ "<video width='320' height='240' controls><source src=https://joran.org/ct/entity.admin.unit.2649/8583.mp4 type='video/mp4'></video>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_id = results[\"workers\"].sort_values(by=[\"wqs\"], ascending=False).index[0]\n", "work_units = results[\"judgments\"][results[\"judgments\"][\"worker\"] == work_id][\"unit\"]\n", "work_judg = results[\"judgments\"][results[\"judgments\"][\"unit\"] == work_units.iloc[1]]\n", "\n", "print(\"JUDGMENTS OF HIGH QUALITY WORKER %d FOR VIDEO %d:\" % (work_id, work_units.iloc[1]))\n", "for k, v in work_judg[work_judg[\"worker\"] == work_id][\"output.selected_answer\"].iloc[0].items():\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "\n", "print(\"\\nALL JUDGMENTS FOR VIDEO %d\" % work_units.iloc[1])\n", "sorted_judg = sorted(\n", " results[\"units\"][\"output.selected_answer\"][work_units.iloc[1]].items(),\n", " key=operator.itemgetter(1),\n", " reverse=True)\n", "\n", "for k, v in sorted_judg:\n", " if v > 0:\n", " print(str(k) + \" : \" + str(v))\n", "vid_url = results[\"units\"][\"input.videolocation\"][work_units.iloc[1]]\n", "HTML(\"<video width='320' height='240' controls><source src=\" + str(vid_url) + \" type='video/mp4'></video>\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Worker Quality vs. # Annotations\n", "\n", "As we can see from the plot below, there is no clear correlation between worker quality and number of annotations collected from the worker." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '# Annotations')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(results[\"workers\"][\"wqs\"], results[\"workers\"][\"judgment\"])\n", "plt.xlabel(\"WQS\")\n", "plt.ylabel(\"# Annotations\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Annotation Quality Scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **annotation metrics** are stored in `results[\"annotations\"]`. The `aqs` column contains the **annotation quality scores**, capturing the overall worker agreement over one annotation.\n", "\n", "There is a slight correlation between the number of annotations (column `output.selected_answer`) and the annotation quality score - annotations that have not been picked often (e.g. `engineer`, `farmer`) tend to have lower quality scores - this is because these annotations are less present in the corpus, therefore the likelihood that they are picked is lower, and when they do get picked it is more likely it was a mistake by the worker. However, it is not a set rule, and there exist annotations that are picked less often (e.g. `astronaut`) that can have high quality scores." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>output.selected_answer</th>\n", " <th>aqs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>astronaut</th>\n", " <td>21</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>none</th>\n", " <td>379</td>\n", " <td>0.858</td>\n", " </tr>\n", " <tr>\n", " <th>militaryperson</th>\n", " <td>20</td>\n", " <td>0.846</td>\n", " </tr>\n", " <tr>\n", " <th>celebrity</th>\n", " <td>157</td>\n", " <td>0.746</td>\n", " </tr>\n", " <tr>\n", " <th>model</th>\n", " <td>29</td>\n", " <td>0.457</td>\n", " </tr>\n", " <tr>\n", " <th>other</th>\n", " <td>176</td>\n", " <td>0.450</td>\n", " </tr>\n", " <tr>\n", " <th>presenter</th>\n", " <td>84</td>\n", " <td>0.430</td>\n", " </tr>\n", " <tr>\n", " <th>scientist</th>\n", " <td>65</td>\n", " <td>0.407</td>\n", " </tr>\n", " <tr>\n", " <th>monarch</th>\n", " <td>11</td>\n", " <td>0.319</td>\n", " </tr>\n", " <tr>\n", " <th>fictionalcharacter</th>\n", " <td>89</td>\n", " <td>0.306</td>\n", " </tr>\n", " <tr>\n", " <th>athlete</th>\n", " <td>15</td>\n", " <td>0.280</td>\n", " </tr>\n", " <tr>\n", " <th>artist</th>\n", " <td>59</td>\n", " <td>0.251</td>\n", " </tr>\n", " <tr>\n", " <th>criminal</th>\n", " <td>8</td>\n", " <td>0.127</td>\n", " </tr>\n", " <tr>\n", " <th>journalist</th>\n", " <td>31</td>\n", " <td>0.076</td>\n", " </tr>\n", " <tr>\n", " <th>businessperson</th>\n", " <td>29</td>\n", " <td>0.062</td>\n", " </tr>\n", " <tr>\n", " <th>writer</th>\n", " <td>22</td>\n", " <td>0.057</td>\n", " </tr>\n", " <tr>\n", " <th>politician</th>\n", " <td>12</td>\n", " <td>0.056</td>\n", " </tr>\n", " <tr>\n", " <th>engineer</th>\n", " <td>8</td>\n", " <td>0.036</td>\n", " </tr>\n", " <tr>\n", " <th>farmer</th>\n", " <td>4</td>\n", " <td>0.001</td>\n", " </tr>\n", " <tr>\n", " <th>producer</th>\n", " <td>13</td>\n", " <td>0.001</td>\n", " </tr>\n", " <tr>\n", " <th>philosopher</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>psychologist</th>\n", " <td>3</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>sportsmanager</th>\n", " <td>0</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>archeologist</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>architect</th>\n", " <td>8</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>judge</th>\n", " <td>2</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>chef</th>\n", " <td>1</td>\n", " <td>0.000</td>\n", " </tr>\n", " <tr>\n", " <th>lawyer</th>\n", " <td>2</td>\n", " <td>0.000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " output.selected_answer aqs\n", "astronaut 21 1.000\n", "none 379 0.858\n", "militaryperson 20 0.846\n", "celebrity 157 0.746\n", "model 29 0.457\n", "other 176 0.450\n", "presenter 84 0.430\n", "scientist 65 0.407\n", "monarch 11 0.319\n", "fictionalcharacter 89 0.306\n", "athlete 15 0.280\n", "artist 59 0.251\n", "criminal 8 0.127\n", "journalist 31 0.076\n", "businessperson 29 0.062\n", "writer 22 0.057\n", "politician 12 0.056\n", "engineer 8 0.036\n", "farmer 4 0.001\n", "producer 13 0.001\n", "philosopher 8 0.000\n", "psychologist 3 0.000\n", "sportsmanager 0 0.000\n", "archeologist 8 0.000\n", "architect 8 0.000\n", "judge 2 0.000\n", "chef 1 0.000\n", "lawyer 2 0.000" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[\"annotations\"][\"output.selected_answer\"] = 0\n", "\n", "for idx in results[\"judgments\"].index:\n", " for k,v in results[\"judgments\"][\"output.selected_answer\"][idx].items():\n", " if v > 0:\n", " results[\"annotations\"].loc[k, \"output.selected_answer\"] += 1\n", " \n", "\n", "results[\"annotations\"] = results[\"annotations\"].sort_values(by=[\"aqs\"], ascending=False)\n", "results[\"annotations\"].round(3)[[\"output.selected_answer\", \"aqs\"]]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "rows = []\n", "header = [\"unit\", \"videolocation\", \"subtitles\", \"imagetags\", \"subtitletags\", \"uqs\", \"uqs_initial\"]\n", "annotation_vector = [\"archeologist\", \"architect\", \"artist\", \"astronaut\", \"athlete\", \"businessperson\",\"celebrity\", \n", " \"chef\", \"criminal\", \"engineer\", \"farmer\", \"fictionalcharacter\", \"journalist\", \"judge\", \n", " \"lawyer\", \"militaryperson\", \"model\", \"monarch\", \"philosopher\", \"politician\", \"presenter\", \n", " \"producer\", \"psychologist\", \"scientist\", \"sportsmanager\", \"writer\", \"none\", \"other\"]\n", "header.extend(annotation_vector)\n", "annotation_vector_in = [\"archeologist_initial_initial\", \"architect_initial\", \"artist_initial\", \"astronaut_initial\", \n", " \"athlete_initial\", \"businessperson_initial\",\"celebrity_initial\", \"chef_initial\", \n", " \"criminal_initial\", \"engineer_initial\", \"farmer_initial\", \"fictionalcharacter_initial\", \n", " \"journalist_initial\", \"judge_initial\", \"lawyer_initial\", \"militaryperson_initial\", \n", " \"model_initial\", \"monarch_initial\", \"philosopher_initial\", \"politician_initial\", \n", " \"presenter_initial\", \"producer_initial\", \"psychologist_initial\", \"scientist_initial\", \n", " \"sportsmanager_initial\", \"writer_initial\", \"none_initial\", \"other_initial\"]\n", "header.extend(annotation_vector_in)\n", "units = results[\"units\"].reset_index()\n", "for i in range(len(units.index)):\n", " row = [units[\"unit\"].iloc[i], units[\"input.videolocation\"].iloc[i], units[\"input.subtitles\"].iloc[i], \\\n", " units[\"input.imagetags\"].iloc[i], units[\"input.subtitletags\"].iloc[i], units[\"uqs\"].iloc[i], \n", " units[\"uqs_initial\"].iloc[i]]\n", " for item in annotation_vector:\n", " row.append(units[\"unit_annotation_score\"].iloc[i][item])\n", " for item in annotation_vector_in:\n", " row.append(units[\"unit_annotation_score_initial\"].iloc[i][item])\n", " rows.append(row)\n", "rows = pd.DataFrame(rows, columns=header)\n", "rows.to_csv(\"../data/results/multchoice-people-video-units.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "results[\"workers\"].to_csv(\"../data/results/multchoice-people-video-workers.csv\", index=True)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "results[\"annotations\"].to_csv(\"../data/results/multchoice-people-video-annotations.csv\", index=True)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

NuclearTalent/NuclearStructure

doc/pub/mbpt/ipynb/mbpt.ipynb

1

24951

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Many-body perturbation theory and effective operators for the nuclear shell model\n", "\n", " \n", "**Morten Hjorth-Jensen**, [National Superconducting Cyclotron Laboratory](http://www.nscl.msu.edu/) and [Department of Physics and Astronomy](https://www.pa.msu.edu/), [Michigan State University](http://www.msu.edu/), East Lansing, MI 48824, USA\n", "\n", "Date: **Jul 19, 2017**\n", "\n", "Copyright 2013-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", "## Many-body perturbation theory\n", "\n", "We assume here that we are only interested in the ground state of the system and \n", "expand the exact wave function in term of a series of Slater determinants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle = \\vert \\Phi_0\\rangle + \\sum_{m=1}^{\\infty}C_m\\vert \\Phi_m\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have assumed that the true ground state is dominated by the \n", "solution of the unperturbed problem, that is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{H}_0\\vert \\Phi_0\\rangle= W_0\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The state $\\vert \\Psi_0\\rangle$ is not normalized, rather we have used an intermediate \n", "normalization $\\langle \\Phi_0 \\vert \\Psi_0\\rangle=1$ since we have $\\langle \\Phi_0\\vert \\Phi_0\\rangle=1$. \n", "\n", "\n", "## Many-body perturbation theory\n", "The Schroedinger equation is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{H}\\vert \\Psi_0\\rangle = E\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and multiplying the latter from the left with $\\langle \\Phi_0\\vert $ gives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\hat{H}\\vert \\Psi_0\\rangle = E\\langle \\Phi_0\\vert \\Psi_0\\rangle=E,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and subtracting from this equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Psi_0\\vert \\hat{H}_0\\vert \\Phi_0\\rangle= W_0\\langle \\Psi_0\\vert \\Phi_0\\rangle=W_0,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and using the fact that the both operators $\\hat{H}$ and $\\hat{H}_0$ are hermitian \n", "results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=E-W_0=\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is an exact result. We call this quantity the correlation energy.\n", "\n", "\n", "## Many-body perturbation theory\n", "This equation forms the starting point for all perturbative derivations. However,\n", "as it stands it represents nothing but a mere formal rewriting of Schroedinger's equation and is not of much practical use. The exact wave function $\\vert \\Psi_0\\rangle$ is unknown. In order to obtain a perturbative expansion, we need to expand the exact wave function in terms of the interaction $\\hat{H}_I$. \n", "\n", "Here we have assumed that our model space defined by the operator $\\hat{P}$ is one-dimensional, meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{P}= \\vert \\Phi_0\\rangle \\langle \\Phi_0\\vert ,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}=\\sum_{m=1}^{\\infty}\\vert \\Phi_m\\rangle \\langle \\Phi_m\\vert .\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "We can thus rewrite the exact wave function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle= (\\hat{P}+\\hat{Q})\\vert \\Psi_0\\rangle=\\vert \\Phi_0\\rangle+\\hat{Q}\\vert \\Psi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Going back to the Schr\\\"odinger equation, we can rewrite it as, adding and a subtracting a term $\\omega \\vert \\Psi_0\\rangle$ as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\left(\\omega-\\hat{H}_0\\right)\\vert \\Psi_0\\rangle=\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\omega$ is an energy variable to be specified later. \n", "\n", "## Many-body perturbation theory\n", "We assume also that the resolvent of $\\left(\\omega-\\hat{H}_0\\right)$ exits, that is\n", "it has an inverse which defined the unperturbed Green's function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\left(\\omega-\\hat{H}_0\\right)^{-1}=\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can rewrite Schroedinger's equation as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\frac{1}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and multiplying from the left with $\\hat{Q}$ results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\vert \\Psi_0\\rangle=\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is possible since we have defined the operator $\\hat{Q}$ in terms of the eigenfunctions of $\\hat{H}$.\n", "\n", "\n", "\n", "## Many-body perturbation theory\n", "These operators commute meaning that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}\\hat{Q}=\\hat{Q}\\frac{1}{\\left(\\omega-\\hat{H}_0\\right)}=\\frac{\\hat{Q}}{\\left(\\omega-\\hat{H}_0\\right)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these definitions we can in turn define the wave function as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\vert \\Phi_0\\rangle+\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\vert \\Psi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This equation is again nothing but a formal rewrite of Schr\\\"odinger's equation\n", "and does not represent a practical calculational scheme. \n", "It is a non-linear equation in two unknown quantities, the energy $E$ and the exact\n", "wave function $\\vert \\Psi_0\\rangle$. We can however start with a guess for $\\vert \\Psi_0\\rangle$ on the right hand side of the last equation.\n", "\n", "\n", "## Many-body perturbation theory\n", " The most common choice is to start with the function which is expected to exhibit the largest overlap with the wave function we are searching after, namely $\\vert \\Phi_0\\rangle$. This can again be inserted in the solution for $\\vert \\Psi_0\\rangle$ in an iterative fashion and if we continue along these lines we end up with" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\vert \\Psi_0\\rangle=\\sum_{i=0}^{\\infty}\\left\\{\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\right\\}^i\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for the wave function and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\sum_{i=0}^{\\infty}\\langle \\Phi_0\\vert \\hat{H}_I\\left\\{\\frac{\\hat{Q}}{\\omega-\\hat{H}_0}\\left(\\omega-E+\\hat{H}_I\\right)\\right\\}^i\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is now a perturbative expansion of the exact energy in terms of the interaction\n", "$\\hat{H}_I$ and the unperturbed wave function $\\vert \\Psi_0\\rangle$.\n", "\n", "\n", "## Many-body perturbation theory\n", "In our equations for $\\vert \\Psi_0\\rangle$ and $\\Delta E$ in terms of the unperturbed\n", "solutions $\\vert \\Phi_i\\rangle$ we have still an undetermined parameter $\\omega$\n", "and a dependecy on the exact energy $E$. Not much has been gained thus from a practical computational point of view. \n", "\n", "\n", "## Many-body perturbation theory\n", "In Brilluoin-Wigner perturbation theory it is customary to set $\\omega=E$. This results in the following perturbative expansion for the energy $\\Delta E$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "7\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1\n", "9\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{E-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This expression depends however on the exact energy $E$ and is again not very convenient from a practical point of view. It can obviously be solved iteratively, by starting with a guess for $E$ and then solve till some kind of self-consistency criterion has been reached. \n", "\n", "Actually, the above expression is nothing but a rewrite again of the full Schr\\\"odinger equation. \n", "\n", "## Many-body perturbation theory\n", "Defining $e=E-\\hat{H}_0$ and recalling that $\\hat{H}_0$ commutes with \n", "$\\hat{Q}$ by construction and that $\\hat{Q}$ is an idempotent operator\n", "$\\hat{Q}^2=\\hat{Q}$. \n", "Using this equation in the above expansion for $\\Delta E$ we can write the denominator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "1\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\left[\\frac{1}{\\hat{e}}+\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\n", "\\frac{1}{\\hat{e}}+\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\n", "\\frac{1}{\\hat{e}}\\hat{Q}\\hat{H}_I\\hat{Q}\\frac{1}{\\hat{e}}+\\dots\\right]\\hat{Q}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "Inserted in the expression for $\\Delta E$ leads to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\n", "\\langle \\Phi_0\\vert \\hat{H}_I+\\hat{H}_I\\hat{Q}\\frac{1}{E-\\hat{H}_0-\\hat{Q}\\hat{H}_I\\hat{Q}}\\hat{Q}\\hat{H}_I\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In RS perturbation theory we set $\\omega = W_0$ and obtain the following expression for the energy difference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\n", "4\n", " \n", "<\n", "<\n", "<\n", "!\n", "!\n", "M\n", "A\n", "T\n", "H\n", "_\n", "B\n", "L\n", "O\n", "C\n", "K" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)+\n", "\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "Recalling that $\\hat{Q}$ commutes with $\\hat{H_0}$ and since $\\Delta E$ is a constant we obtain that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{Q}\\Delta E\\vert \\Phi_0\\rangle = \\hat{Q}\\Delta E\\vert \\hat{Q}\\Phi_0\\rangle = 0.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inserting this results in the expression for the energy results in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\langle \\Phi_0\\vert \\left(\\hat{H}_I+\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I+\n", "\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}(\\hat{H}_I-\\Delta E)\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I+\\dots\\right)\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many-body perturbation theory\n", "We can now this expression in terms of a perturbative expression in terms\n", "of $\\hat{H}_I$ where we iterate the last expression in terms of $\\Delta E$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E=\\sum_{i=1}^{\\infty}\\Delta E^{(i)}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get the following expression for $\\Delta E^{(i)}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(1)}=\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is just the contribution to first order in perturbation theory," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is the contribution to second order.\n", "\n", "\n", "## Many-body perturbation theory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(3)}=\\langle \\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\Phi_0\\rangle-\n", "\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\langle \\Phi_0\\vert \\hat{H}_I\\vert \\Phi_0\\rangle\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "being the third-order contribution. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "\n", "In the shell-model lectures we showed that we could rewrite the exact state function for say the ground state, as a linear expansion in terms of all possible Slater determinants. That is, we \n", "define the ansatz for the ground state as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Phi_0\\rangle = \\left(\\prod_{i\\le F}\\hat{a}_{i}^{\\dagger}\\right)|0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the index $i$ defines different single-particle states up to the Fermi level. We have assumed that we have $N$ fermions. \n", "A given one-particle-one-hole ($1p1h$) state can be written as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Phi_i^a\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_i|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while a $2p2h$ state can be written as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Phi_{ij}^{ab}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and a general $ApAh$ state as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Phi_{ijk\\dots}^{abc\\dots}\\rangle = \\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_{c}^{\\dagger}\\dots\\hat{a}_k\\hat{a}_j\\hat{a}_i|\\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "We use letters $ijkl\\dots$ for states below the Fermi level and $abcd\\dots$ for states above the Fermi level. A general single-particle state is given by letters $pqrs\\dots$.\n", "\n", "We can then expand our exact state function for the ground state \n", "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Psi_0\\rangle=C_0|\\Phi_0\\rangle+\\sum_{ai}C_i^a|\\Phi_i^a\\rangle+\\sum_{abij}C_{ij}^{ab}|\\Phi_{ij}^{ab}\\rangle+\\dots\n", "=(C_0+\\hat{C})|\\Phi_0\\rangle,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have introduced the so-called correlation operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\hat{C}=\\sum_{ai}C_i^a\\hat{a}_{a}^{\\dagger}\\hat{a}_i +\\sum_{abij}C_{ij}^{ab}\\hat{a}_{a}^{\\dagger}\\hat{a}_{b}^{\\dagger}\\hat{a}_j\\hat{a}_i+\\dots\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the normalization of $\\Psi_0$ is at our disposal and since $C_0$ is by hypothesis non-zero, we may arbitrarily set $C_0=1$ with \n", "corresponding proportional changes in all other coefficients. Using this so-called intermediate normalization we have" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\langle \\Psi_0 | \\Phi_0 \\rangle = \\langle \\Phi_0 | \\Phi_0 \\rangle = 1,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "resulting in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "|\\Psi_0\\rangle=(1+\\hat{C})|\\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "In a shell-model calculation, the unknown coefficients in $\\hat{C}$ are the \n", "eigenvectors which result from the diagonalization of the Hamiltonian matrix.\n", "\n", "How can we use perturbation theory to determine the same coefficients? Let us study the contributions to second order in the interaction, namely" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\langle\\Phi_0\\vert \\hat{H}_I\\frac{\\hat{Q}}{W_0-\\hat{H}_0}\\hat{H}_I\\vert \\Phi_0\\rangle.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The intermediate states given by $\\hat{Q}$ can at most be of a $2p-2h$ nature if we have a two-body Hamiltonian. This means that second order in the perturbation theory can have $1p-1h$ and $2p-2h$ at most as intermediate states. When we diagonalize, these contributions are included to infinite order. This means that higher-orders in perturbation theory bring in more complicated correlations. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "If we limit the attention to a Hartree-Fock basis, then we have that\n", "$\\langle\\Phi_0\\vert \\hat{H}_I \\vert 2p-2h\\rangle$ is the only contribution and the contribution to the energy reduces to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta E^{(2)}=\\frac{1}{4}\\sum_{abij}\\langle ij\\vert \\hat{v}\\vert ab\\rangle \\frac{\\langle ab\\vert \\hat{v}\\vert ij\\rangle}{\\epsilon_i+\\epsilon_j-\\epsilon_a-\\epsilon_b}.\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpreting the correlation energy and the wave operator\n", "If we compare this to the correlation energy obtained from full configuration interaction theory with a Hartree-Fock basis, we found that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "E-E_0 =\\Delta E=\n", "\\sum_{abij}\\langle ij | \\hat{v}| ab \\rangle C_{ij}^{ab},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the energy $E_0$ is the reference energy and $\\Delta E$ defines the so-called correlation energy.\n", "\n", "We see that if we set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "C_{ij}^{ab} =\\frac{1}{4}\\frac{\\langle ab \\vert \\hat{v} \\vert ij \\rangle}{\\epsilon_i+\\epsilon_j-\\epsilon_a-\\epsilon_b},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we have a perfect agreement between FCI and MBPT. However, FCI includes such $2p-2h$ correlations to infinite order. In order to make a meaningful comparison we would at least need to sum such correlations to infinite order in perturbation theory. \n", "\n", "## Interpreting the correlation energy and the wave operator\n", "Summing up, we can see that\n", "* MBPT introduces order-by-order specific correlations and we make comparisons with exact calculations like FCI\n", "\n", "* At every order, we can calculate all contributions since they are well-known and either tabulated or calculated on the fly.\n", "\n", "* MBPT is a non-variational theory and there is no guarantee that higher orders will improve the convergence. \n", "\n", "* However, since FCI calculations are limited by the size of the Hamiltonian matrices to diagonalize (today's most efficient codes can attach dimensionalities of ten billion basis states, MBPT can function as an approximative method which gives a straightforward (but tedious) calculation recipe. \n", "\n", "* MBPT has been widely used to compute effective interactions for the nuclear shell-model.\n", "\n", "* But there are better methods which sum to infinite order important correlations. Coupled cluster theory is one of these methods. \n", "\n", "Codes for computing effective Hamiltonians for the shell model as well as many other codes can be obtained from the [CENS website](https://github.com/ManyBodyPhysics/CENS)." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

erikdrysdale/erikdrysdale.github.io

_rmd/extra_BCa/bca_python.ipynb

1

406413

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing the BCa Bootstrapping in Python\n", "\n", "The [bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) is a powerful tool for carrying out inference on statistics whose distribution is unknown. The non-parametric version of the bootstrap obtains variation around the point estimate of a statistic by randomly resampling the data with replacement and recalculating the bootstrap-statistic based on these resamples. This simulated distribution can be used to obtain (close-to) valid frequentist inference measures like p-values and confidence intervals (CIs). This post will show how to implement the bias-corrected and accelerated ([BCa](https://www.tandfonline.com/doi/abs/10.1080/01621459.1987.10478410)) bootstrap to calculate either one and two-sided CIs. A good discussion of bootstrapped p-values can be found [here](http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1127.pdf). Nathaniel Helwig's excellent class [notes](http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf) are used or paraphrased throughout this post.\n", "\n", "If you are want to use an existing `python` package, [`arch`](https://arch.readthedocs.io/en/latest) is an excellent choice (I have made use of it in other [posts](http://www.erikdrysdale.com/threshold_and_power)). There are two advantages for implementing a custom procedure. First, `arch` is limited to a number of sampling strategies, and I recently found myself needing to implement a stratified bootstrap, which I could not do with the package. Second, the `conf_int` method only allows inference for a specific level (i.e. type-I error rate) for every sampling run. If you are carrying out statistical simulations this is a huge waste since computing the 90% or 95% CI can be done from the same bootstrap simulation. Lastly this blueprint can modified for other uses.\n", "\n", "The motivating example for this post will be to estimate the threshold for determining the positive predictive value ([PPV](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values)) of a classifier. This statistic is a good target for the BCa bootstrap because its distribution is skewed, biased, and non-normal. Anyone developing machine learning tools for the real world will understand the importance of [this task](http://www.erikdrysdale.com/threshold_and_power/), since researchers want to ensure their algorithms obtain *at least* some level of performance in a generalized setting. For simplicity the scores of the algorithm will come from a normal distribution to allow for an easy comparison to the \"ground truth\". \n", "\n", "# (1) Background\n", "\n", "To begin I'll establish the notation that will be used through the rest of the post. Define the statistic of interest as $\\theta = s(X)$, where $X_i \\sim F$ from IID draws. The statistic $s$ can be anything like the mean, median, skew, PPV, etc. Of course, if we knew the cdf $F$, one could simply sample from it and estimate $\\theta$ by appealing to the law of large numbers. However researchers almost never know the distribution the data comes from, and if they do, the exact parameters of the distribution are unknown. Instead we can use an empirical draw of the data: $\\hat{X} = (X_1, \\dots, X_n)$ to approximate the actual CDF: $\\hat{F}(\\hat{X} \\leq x) \\approx F(X \\leq x)$. From the [Glivenko-Cantelli theorem](https://en.wikipedia.org/wiki/Glivenko%E2%80%93Cantelli_theorem) we know that as $n \\to \\infty$, the empirical CDF (ECDF) approaches the true CDF.\n", "\n", "Using the ECDF from a given draw of data as an estimate of $F$, means using the CDF of a discrete random variable. This non-parametric approximation naturally creates some estimation error, but for a reasonably sized $n$ it will be small. When bootstrapping is referred to \"sampling with replacement\", it is actually referring to sampling from the ECDF. Since this is a discrete distribution it means that some values can be sampled more than once. Draws for the ECDF, or bootstrapped samples, will be denoted as follows:\n", "\n", "$$\n", "\\begin{align*}\n", "\\hat\\theta &= s(X) \\\\\n", "X_1^*, \\dots, X_n^* &\\sim \\hat{F} \\\\\n", "\\hat\\theta^* &= s(X^*)\n", "\\end{align*}\n", "$$\n", "\n", "The PPV is the ratio of the true positive rate (TPR) to the predicted positive rate (which includes the false positive rate (FPR)), weighted by the prevalence of the binary outcome: $\\mu_y = E[y==1]$. In this post we will assume that the scores of the classifier come from a normal distribution: $x|y=i \\sim N(\\mu_i,\\sigma_i^2)$, for $i=[1,2]$. The distribution for the ground-truth PPV will be smooth and monotonically increasing over $t$ since $\\mu_y$ is fixed, as defined by \\eqref{eq:PPV_true}. However, its empirical estimate will necessarily by discontinuous, and not even necessarily monotonic! This is because the count of the number of TPs and FPs is used rather than the theoretical CDF and prevalence. Note that $\\Phi$ refers to the standard normal CDF.\n", "\n", "$$\n", "\\begin{align*}\n", "\\text{PPV}_\\Phi(t) &= \\frac{\\Phi_1(t)\\cdot\\mu_y}{\\Phi_1\\cdot\\mu_y + \\Phi_0\\cdot(1-\\mu_y)} \\label{eq:PPV_true} \\\\\n", "\\Phi_1(t) &= \\Phi([\\mu_1 - t]/\\sigma_1), \\hspace{3mm} \\Phi_0(t) = \\Phi([\\mu_0 - t]/\\sigma_0) \\\\\n", "\\hat{\\text{PPV}}(t) &= \\frac{NTP(t)}{NTP(t) + NFP(t)} \\\\\n", "NTP(t) &= \\sum_{i: y_i =1} y_i \\geq t, \\hspace{3mm} NFP(t) = \\sum_{i: y_i =0} y_i \\geq t\n", "\\end{align*}\n", "$$\n", "\n", "The codeblock below loads in the necessary modules and shows that the theoretical PPV aligns with the average empirical PPV across different thresholds ($t$)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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hiSeeYOrUqXz77be0adOmyPssqqK0Yf7ztmjRogIDqfPlf8HnP664Ay0Le97z9ztr1ixiY2MLfVxgYGCx9nOlorbhTz/9RNeuXalduzYzZ86kZs2amM1mNE3j8ccfLzDIt7h0Ol2Rf5wVp+7w4cOZPHkyCxYsYPz48axbt47ExEQmTZpUIRZcFJWTvHJuIP8X34EDB+jWrZvDffm/uK6UPyCysF/Z+b/2iqpu3bps2rSJ8PBwmjZtWqzHVgT5X3IJCQllvi+9Xo+vr+9NPe9Lly7FbDazbds2h+mlaWlpXLx4sUD9a30x5v861zTthh/woaGhmM3mQmdjZGVlcfz4capXr16k+P38/Ojduzdr1qzh7NmzLFmyBMChSyefyWSiW7du9tf0L7/8wu233860adNuqgvjZuQ/bwEBATd83vKT8n379hW4Ly4ujoyMjALTnm+0Xw8PjyJ9IRc3IapXrx5r16697nogAMuXL8dqtbJx40aHbkylFMnJyfj5+RVrv+UlMDCQvn37smLFCvbs2cP8+fMBCnQVCVEcVWrMSUncfffduLu7M3fuXNLT0+3l2dnZvPnmmwXq16tXD6DAWItt27YVe32R/MW9Jk+eXOivpgsXLhToeigNxZlKvHXr1gKnn/N9/vnnANec4lra6taty44dOxy6QbKysnjnnXeK9Hi9Xo9OpytwhuSll14q9Nevl5dXoclQbGwsjRo14oMPPij0ebTZbCQnJwOXT5n37t2bvXv38s033zjUmz17NpmZmUWKPd/IkSOxWCwsW7aMZcuW0ahRI2677TaHOvn7vlKjRo1wd3cvctdVWRg8eDAmk4kXXniBjIyMAvdnZmZy6dIlAFq0aEFkZCRLlizh5MmTDvVeeuklAPr371+k/Xbv3p3g4GBeeeUVkpKSCtyfl5fH+fPn7bfzx54V9bnKXy/kySefLDAd+srXVf7Zpatfa3Pnzi2TLszSlD+2ZPr06axfv5477rijTGZ9iapDzpzcQEBAADNmzODJJ5+kZcuWjBgxAoPBwPLly+1dLVf+kmrYsCFdunRh7ty55OXl0axZM/7880+WLl1K06ZN2b9/f5H33bJlS1588UVefPFFmjZtyn333Ud4eDhnz55l7969fPXVVxw+fPiap8Dz/fbbb/Yvvl9++QW4nDjkr1fQp08fh3EtxZlKPHv2bLZv307Pnj2JjY3Fz8+P5ORkvvnmG3744QeaNm3KAw88UORjvhn/+Mc/GD58OB07dmTYsGGkp6ezePFi+9iVGxk4cCBffPEFHTt2ZMSIEdhsNtavX8+RI0cK3UarVq1YsmQJL7zwAvXq1UOn03Hvvffi4eHBsmXL6NKlC82aNePBBx+kcePG5ObmcuzYMb744gvGjBnDlClTAJg5cyYbN26kf//+jB8/nrp167Jz507Wrl1b7Kmj3bp1IywsjJdffpm0tDRee+21AnW6dOlCYGAg7du3JyoqivT0dD7++GMyMzOdOu0zKiqKd999l7Fjx1KvXj0eeOABatasSWpqKgcOHODzzz9n7dq1tGvXDoPBwLvvvkufPn1o0aIF48aNIzg4mHXr1rF+/Xp69OhR5GnmXl5eLF26lL59+1K/fn1GjRpF3bp1uXTpEocPH2bNmjXMmTPHnmS0atUKgH/9618MHToUNzc3mjZtes0kfPDgwXz55ZesWLGCo0eP0rdvX4KCgoiLi+Ozzz7jt99+w8vLi/79+/Pvf/+bbt26MXbsWDw8PPj+++/ZvHlzqU8hLm2dO3emVq1arFmzBpCBsKIUOGuaUHnKn0p85ZTfa7nW9NAFCxaohg0bKpPJpMLDw9WkSZPs0+hmz57tUDcxMVENGjRI+fj4KLPZrDp27Kh+/PHHa04lvtF0wHXr1qm7775bBQQEKKPRqMLCwlTnzp3VnDlzVHZ29g2PKX/68LX+rpz2mP8cFHUq8a5du9RTTz2lbrvtNhUSEqIMBoPy9vZWsbGxavr06Q7Tr5W69lTiwvZ3+PDha7bbtR7z+uuvqxo1aiij0ahiYmLU7Nmz1caNG4s0lVipy89Vo0aNlLu7uwoODlbDhg1Tp06dKvR1cebMGdW3b1/l5+enNE0rML00Pj5ejR8/XtWsWVOZTCbl7++vmjZtqiZOnKgOHDjgsK2//vpL9erVS3l5eSkvLy/VrVs3tXfv3mJNF8339NNPK0AZDAZ15syZAve///77qlu3bqp69erKZDKp4OBg1bFjR7V69epi7Uepok8lLmza7bWObefOnap///4qJCREGY1GFRISotq0aaOmT5+uzp0751B3x44dqnv37srX11eZTCZVr149NX36dJWbm1vkOPLt379fjRgxQoWHhyuj0aiCgoJUbGysevbZZws8bubMmSo6OloZDAaH1+i1XldWq1W9++67qkWLFsrDw0N5enqqBg0aqIkTJzpMbf78889VbGysMpvNyt/fX917773qwIEDhT5XJZlKXNp1rzRjxgwFKF9f30Kn5AtRHJpSxRhxJxx88sknDB06lFWrVhX5FLIQQgghrk+SkyLIzs7Gzc3NofsmOzubtm3bsn//fk6dOkVQUJATIxRCCCFch4w5KYJt27bx5JNPMmDAACIiIjhz5gzLly/n0KFDvPTSS5KYCCGEEKVIkpMiqF27NvXq1WPBggWkpKRgMBho3LgxS5Yssc+oEUIIIUTpkG4dIYQQQlQoss6JEEIIISoUSU6EEEIIUaFIciKEEEKICkWSEyGEEEJUKJKcCCGEEKJCkeRECCGEEBWKrHPC5av7Fvfqr0IIIUrGbDbj5+fn7DBEBVblk5MLFy7YryBcEl5eXjRr1ow9e/aQnp5eytE5h06nsx+TzWZzdjilwtXaSdqocpB2KpzRaOTRRx+VBEVcU5VfhO306dN88MEH9OvXr0TL0BsMBvz9/Tl//jwWi6UMIhSlQdqp4pM2qhxutp1SUlJYs2YNY8eOJSwsrAwiFK6gyp85yRcUFFSiN4pSCovFQnBwsMOFASszm81GYmIioaGh6HSuMSzJ1dpJ2qhykHYSomRc490ihBBCCJchyYkQQgghKhRJToQQQghRoUhyIoQQQogKRZITIYQQQlQoMltHCCFEkdiys8k+dgSr1Yo5pg56s9nZIQkXJcmJEEKIG8o9c5r4V15GXbgAgObrQ9TTz2MKC3dyZMIVSbeOEEKIG4p763WsFy+iodBQWNPSiHvzNWeHJVyUJCdCCCGuy5qTgz7xNHr1vyX49UqhT0rEKtclE2VAunWEEEIUkJecRE78CfReXqjoWlh0egw2q0Mdq6Yj22DA00kxCtclyYkQQggHyT98z/mF76M00NlsWGvVZneLNrT8eYf97IlVp+PH29oywmh0crTCFUlyIoQQwi7r7FnOL3wPnVKQf1nY43HEhEbwbYe7aL5/D6DY07AZsX36yvV1RJmQ5EQIIaowZbORdWA/ltQUjNXDOHY2CYOmwRUXrDfYrBjjjnD31JfZffEerDbFnX5e1PeUqcSibEhyIoQQVZSy2Tjz9mwyf/sVzWBAWSxojW9BuyIxAbChkelh5nazB3U83LFYLBgM8vUhyo7M1hFCiCoqbesmMvf+Bkqh8vJAKUx//E6qfyAWnR4AG4AGl+68x6mxiqpFUl8hhKii0o8fw2a1OvxKtej1mFu04r/JKdQ4dZwMD09OdenOAy1bOi1OUfVIciKEEFVUnMFEkE6P7sopwkqR6+tL70FDOJmdQ5RBz11uJhn4KsqVJCdCCFFFZPz2Cxc2rseWnY3nLc040qIN3ru2o+Vmo7fZsOh0XPTx5XTTWG4z6GnkJQNehXNIciKEEFVA+s8/kjjvLfssnJwTcTRpepz3HxjPHTu34n/hPGeDq/F9686M8fFxcrSiqpPkRAghqoDU1SsdpgdjteK752ei2nXl67v6otM0rEpxm48nt/vImq/CuSQ5EUKIKiD70iX0hZQ/5u/JnoAQUvPyCDOZuM3HU8aXCKeT5EQIIaqA+OoRRB47hMF2efl5BeQZjVwKqEZ7P2/nBifEVWSdEyGEcDFKKdK+38KZt+eQ+P47ZB08wLq7+nHBxx+bpmHV6bDoDXzSewjZbiZnhytEAXLmRAghXEzqpx9z4du18PdZkvQfdxA7dDTvPfAYEaeOY7TkcSo0AouvH9VNkpyIikeSE8DLywuDwYC6asnmosh/TEkeW1EppezPh6scl6u1k7RR5eCMdrKkXeTC+q+vDoS2G7/mj7FPcqRmHTTATafxr6hQ3HRasWK72XaSZe9FUcirBGjWrBn+/v5YLJYSb8Nqtd64UiXi7++PzWbD9vcvL1fhSu0kbVQ5lHc75Z47V2i59dJFXqwRSlx2Lrk2G1HuJrz1+hJ/7pW0nfz9/Uv0OFG1SHIC7NmzhyZNmhAcHFzsxyqlsFqt6PV6lxnhbrPZSE1NJTAwEJ3ONYYluVo7SRtVDs5op9zgauTpDRit/0s6rDodqUEhxBiNNDAab2r7N9tOycnJN7V/UTVIcgKkp6djsVhu6gNR0zSX+UDVNM3+fLjKMeVzlWOSNqocnNFOqQYjn/UYyOCvV2LT6dCUItvNnVV396NNKcZQ0mO6mTPUouqQ5EQIISqxvKREkhbPJzfhFIaAQDwG3c+fdRvx9sjHqXkqjjyDkYO16uEtq76KSkSSEyGEqKSsaWmcevl5rJkZYLViTbtIzusz6Dv+Kb4MDCYl8HJXtQY8Uj3IucEKUQySnAghRCWV/t+fsGZlQv7gVKVAKTrt34Nfj378eikTk06jW4AvTeQifqISkeRECCEqKVtODjY0h9U0lc2GLTuLuwL9uCvQz2mxCXEzXGOYvxBCVEFJNWrCVQNMbZqOuBoxTopIiNIhyYkQQlRSh6qFsbZbb2xXzJr5sXlLfm1wixOjEuLmSbeOEEJUEspmI/voYWzplzBFRGHWu/HLLbexL6YBgRdSSfPyIc3Xn25G+WgXlZu8goUQohKw5eZy5s1XyfpzH+j0gKLpAw/hE1KTi55exHt6oQP0msadAb7ODleImyLdOkIIUQmc+2oNWQf/unzDZgWbjYuLP2Sap4HbfDwJMxlp5OnB9FoRhMuVhkUlJ2dOhBCiEsj+60+wXrW6ql6Pe8JJJrRq45yghCgjcuZECCEqAauXl8PAVwBltaLz9HRSREKUHUlOhBCiEtjZsgMANi4nKBadnjPBoZyRacPCBUm3jhBCVAJ/Vgtjz30P0XnHFrwy04kPi2Rzx7t50Kao7ezghChlkpwIIUQFlJNwkoxf/wtK4XlrLCEmN3aF12DxoAftdTQgSKYNCxckr2ohhKhgMvf9zuk3XkXTNEDj3Ber6PfIP/nNK5hsmw2bupyYtPL1op7Z3dnhClHqJDkRQogK5uwHc8FqRV1Rlr3wPV556wO2nE/jktVGTQ83Ovh5/53ACOFaJDkRQogKxJabizUtrWB5VhZ+uTkMDAl0QlRClC+ZrSOEEBWIzmRCMxcyPdhkQmc2l39AQjiBJCdCCFHB/NhrEDZNw6rTYdXpsKHxY88BaDr5yBZVg3TrCCFEBWJVim8iY/htyBgaHdyPhuJA7QYcj6zJAJsNd0lQRBUgyYkQQlQASimHwa0nw6I4GRblUEeGvoqqQpITIYRworykRBLn/ZucE8fRubsR0GcgLerdyp6MTCx/T9cxaNDQ0wM3OWsiqgh5pQshhJPYsrNJmPUyOfEnQNmwZWWRsvIjHjjxF828PO1nShp7mvlnZKhTYxWiPMmZEyGEcJLsIwexnD8H6ooVTWw2sv6zlSc7dMZiUyjAqJMOHVG1SHIihBBOoqw20DTH5ATAagXAIEmJqKKkW0cIIZzEPaYOmrsHtisGwiqdDq/bWzsxKiGcT5ITIYRwEqvZzIrBo0g3ewGggF23tuRA6zucG5gQTibdOkII4SQHM7PZFxjCvnH/wjMjgxyTG3kmE/XOX6KNv6+zwxPCaSQ5EUIIJ8m1KTTApulI9/J2KBeiKpPkRAghyomy2cj4ZTd5qamYQkOJadQUo04j54pkRA/Eess1dETVViGSk/T0dObOncuvv/6Kh4cHgwYN4p577ilQb9u2bcybN89+WylFTk4OkydPpk2bNvzxxx9MmbVk+lkAACAASURBVDIFNzc3e50BAwYwaNCgcjkOIYS4FmWxcHrOLLL++hNNr0dZrXjd1pKnh4/h9fhEMm02ANr4edG3WoCToxXCuSpEcvL+++9jtVpZtGgRZ86c4fnnnyciIoKmTZs61OvYsSMdO3a03/7ll1947bXXiI2NtZf5+vqydOnS8gpdCCGK5OLW78g6+BfYbKi/E5H0n38iKrYl77W4naRcC556Hf7GCvGxLIRTOX22TnZ2Njt27GDYsGGYzWZiYmLo3LkzmzZtuuFjv/vuO9q1a+dwpkQIISqi3IRTYLM6lGk6PTkJJzHpdES4myQxEeJvTn8nJCQkABAV9b8LXNWqVYsvvvjiuo+7dOkSu3fvZubMmQXKR4wYgdFopHnz5owYMQJvb2+HOikpKaSkpACQnJxMbm4uALa/f80Uh1IKm82GzWZzuGhXZZb/PJTk+aioXK2dpI0qhyvbSe/rB3o9WCz2+5VS6H39KlU7umI7iYrH6clJdnY2Hh4eDmWenp5kZWVd93Hbtm0jNDSU+vXr28siIiJ46623iIiI4Ny5c8ybN48333yTqVOnOjx29erVfPjhh/bbHTp0ACAxMfFmD8elJCUlOTsEcQPSRpVDUlISqsmt5GzZhD4jHYPNikWnxxIQSEZMHTLls0cIB05PTtzd3QskIhkZGQUSlqtt2rSJrl27OpT5+/vj7+8PQFBQEGPHjmXcuHHk5OQ4dP3079/fnpAkJyezfv16AEJDi39hLaUUFosFg8HgMr8ibDYbSUlJVKtWDZ2LXAXV1dpJ2qhyuLKd9mRk8d7w8bT7+Qf8L54nOSCY7be359nAatQxuzs71CK72XaSH4GiKJyenISHhwNw8uRJIiMjAYiLi6NGjRrXfMyxY8eIj4+nU6dO1922TqdDKYW66roVQUFBBAUFAeDj44PJZLLXLy6lFDqdDp1O5zIfqPnyj8sVuGo7SRtVDjqdjj8ysskye/Jth7vt5UZNY39mNvW8Ks/UYVduJ1FxOP1Tzd3dnbZt27J8+XIyMzOJi4tj8+bNdOnS5ZqP2bRpE7GxsfazJPn27t3L2bNnUUpx/vx5PvjgA2699Vbc3SvPrxIhhGsyahpXf5UrFCa5uJ8QBTg9OQF4+OGHARg5ciTTpk3j/vvv55ZbbgFg0KBB7N+/3143Ly+P77//vkCXDlw+o/LMM88waNAgJk6ciI+PD0888UT5HIQQQlzHHf6XB+bnpyI6wKBptPLxclpMQlRUTu/WAfDy8mLy5MmF3vfpp5863DYajSxfvrzQun369KFPnz6lHp8QQhRX5oH9XNi4nuy0i1yIvY2ou3sypWY4808ncS7PQojJyLjwEIJMRmeHKkSFUyGSEyGEcCUZv+/hzJuvwt/j3c4dP0ZewikajBnP7DrXHk8nhLisQnTrCCGEK0n9dLk9MQHAauXSjv+QlyxTv4UoCklOhBCilFkvXbpGeVo5RyJE5STJiRBClDK36Foovd6x0GDEGFL8tZSEqIokORFCiFJ2uM99XPD0xqrTYdHrsep0/NBvKDqzp7NDE6JSkAGxQghRyj63wLmRj1P7+BFMebnEh0dxzi+Qbrl5hLuZnB2eEBWeJCdCCFHKsm02ck1u/Fm3UYFyIcSNSbeOEEKUssaeHg6//DTAU6cj3CRnTYQoCklOhBCilI0KC6bWFRfzM+t0TIqujrtePnKFKArp1hFCiJugLBZSP/+MjD3/RTOa8LvzbnzadeDFmuHEZ2VzOjmFW8Kq42mUj1shikreLUIIcRPOfjiP9P/+BFYrAEkL3kPl5uLb+U6i3N0wGfV4yBkTIYpF3jFCCFFClgvnSf9ppz0xAUApzn21xnlBCeECJDkRQogSsmVmFl6enV3OkQjhWiQ5EUKIEjIGV0Pz9OKKq+hg0+lxq13XaTEJ4QokORFCiBJSBgNrBjxAjps7NjQUkOIfwJaeA50dmhCVmgyIFUKIEkrKtfBLcHX2j3mSsLOnsRgMJISG4650jHB2cEJUYpKcCCFECem0y/9mu3twrEaMvVxzUjxCuArp1hFCiBIKMhqIdjdx5fWH9Rq09/N2WkxCuAJJToQQooR0msbk6DDqmt3RuPyB2snPh/tDgpwdmhCVmnTrCCFEEeUknCTpw3fJPZOA3teP4GEP4tf0Vl6oFYHFptBplxMWIcTNkTMnQghRBJYLF0iY8SI58cdROTlYks5y5o1XyDpyCACDTpPERIhSIsmJEEIUQcae/2LLzQGbzaE87YdtzglICBcmyYkQQhSBystFu/rMiFKo3FznBCSEC5PkRAghisCjQWPUldfQAZSm4dn0VidFJITrkuRECCGKILt6GF/0HIRFf3nisAK2terI2aaxzg1MCBcks3WEEKIIfk7L4Ld6jdkbXQe/tAtc8vQmx8MMFy5R29PD2eEJ4VIkORFCiCLItSk0NHJNbiQFhdjLc64aICuEuHnSrSOEEEXQ0NMDi1IOZTqgqZfZOQEJ4cIkORFCiCKI9nBjfEQI+ism7PQM8pOl6oUoA9KtI4QQhVAWCxl7f8N6KQ23GtG4R9eivZ83zb3NJOXm4W8w4GeUj1AhyoK8swAvLy8MBgPqqlO2RZH/mJI8tqJSStmfD1c5LldrJ2mjsmXLziZh1kvknjyBptej8vIIGHAf/j3uxazTEe3uBtw4VmmnggwG+doRNyavEqBZs2b4+/tjsVhKvA3rVesfVHb+/v7YbDZsLjbYz5XaSdqo7Jxb8ym5p+LBZkP9/fyeW7USt4aNMUXWKNa2pJ0c+fv7l3IkwhVJcgLs2bOHJk2aEBwcXOzHKqWwWq3o9fqCq0dWUjabjdTUVAIDA9HpXGNYkqu1k7RR2co7fgyu+vLVDAasZ05jqBlT5O1IOxWUnJxcBlEJVyPJCZCeno7FYrmpD0RN05z+gVpaNE2zPx+uckz5XOWYpI3KlsE/AHQ6h+voKKsFg69vsWKTdiroZs5Ql0RRYly0aBEjR44s+2BEkUlyIoQQV3Hr0YcLv/6MTil0SmHR6cmIiiamYRNnhyaKadeuXQ63W7duzeOPP87QoUPtZTExRT8bJsqHJCdCCHGVNUYP/hg2njY//4B3+iVOhkXyn5Z38GJ2LrXN7s4OTxRDq1atCpRFRUUVWi4qDtfoBBVCiFIUn51LYlA11nTvz5KBI9nStgua0cTpHLkCsSvKzs4mMDCQ6dOnF7ivX79+tGnTBoANGzagaRrfffcdvXv3xtPTk/DwcF5//fUCj/vjjz/o0aMHPj4+eHl5ce+993L8+PGyPhSXIcmJEEJcpZrRiP6qMotSBMq6Ji7J3d2d4cOHs2jRIocp0snJyXzzzTeMHj3aof6oUaNo1KgRn3/+OQMHDuRf//oXixcvtt9/8OBB2rZtS1ZWFkuXLuWjjz4iPj6eO++8k7y8vPI6rEpNkhMhhLjKwJAA3HU69IAG6IEW3p40lAv8uawxY8Zw7Ngxtm3bZi9btmwZbm5uDB482KFu9+7d+b//+z+6devGm2++yYABA3jppZfs9z///POEhYWxYcMG+vTpQ58+fVi7di0nT55k2bJl5XVIlZokJ0IIcZVqJiOv1omiZ5AfHf28eaB6MBOjQl1uxo34n0aNGtG6dWsWLlxoL1u0aBGDBg3Cy8vLoW7fvn0dbvfr14+4uDiSkpIA2LhxI3379kWn02GxWLBYLFSrVo1GjRrx888/l/3BuABJToQQVV7OiThSV68kddUnZB85DECg0cCQ0CAejgihW6AvOklMXN6YMWNYvXo1Fy9e5KeffmLfvn0FunQAqlWrVujtM2fOYLVauXDhArNmzcJoNDr8/frrr5w8ebJcjqWykw5UIUSVlvH7Hs689drldU2A82u/ImTc43i3bOPkyER5Gzx4MBMmTOCTTz5hz5491K9f3z4Y9kr5Z0iuvl29enX0ej0+Pj4MGTKEhx56qMBjfX19yyZ4FyPJiRCiSkta8O7lxdauWHAtacH7eN3WCs1FVnUVRWM2mxk6dCjvvfcecXFxTJ06tdB6n3/+Od27d7ffXrNmDdHR0fYzKF27dmXfvn3ExsZKV2AJSXIihKiylMWCNS2tYHluDraMdPTePk6ISjjTmDFjeO+99zAajYwYMaLQOuvXr+fZZ5+lY8eOrF+/nlWrVrFgwQL7/dOnT6dly5bcc889jBo1imrVqnHmzBm2bt1Kt27d6N+/f3kdTqUlPwuEEFWWZjCg8/bh6uvraiY3dJ5ehT5GuLbmzZsTFRVFz549C4wtybdgwQJ+//13+vTpw8qVK3nllVcYNWqU/f4GDRrw008/4enpybhx47jrrrt47rnnyM3NpXHjxuV1KJWanDkRQlRpv/cdSsNlH6B0l0+/62w2futzHzHSpeOSrlzHpDB//vkn8fHxzJs375p1wsLCWLt27XW306BBA1atWlWiGIUkJ0KIKsymFKtCIgge/giNDu1HU4q/atfnVPVI7rXZcJMEpcpITk7m8OHDTJ06lbp16zqMKRHlT5ITIUSVpQCbgsRq1UmsVt3hPusNfmEL17J69WrGjx9P/fr1WbZsGTpJTJ1KkhMhRJWl1zQaenrwV0YW1vwyIMrdDbP+6gXsRWV0/vz5ItUbPHiww0qwhT2uZcuWnDt3Dr28NsqcJCdCiCrtn5GhvHLiNEezcgAIczPxVI3qN3iUqAwyMzM5duxYqW/Xzc1NBraWMUlOhBBVmo9Bz/RaEaTmWbABQUaDrAYrhJMVKzkZNmwYw4YN484775TTWkKISseakU7yskVkHfoLvdmTgL4D8Iq9HU3TCDIZnR2eKEN169YttW2dOHGi1LYlClesET9btmyhR48ehIWF8fjjj/Pjjz+WVVxCCFGqlMVCwisvk/7zT1jPpZJ7Kp7Ed94gY88vzg5NCHGVYiUnCQkJbNy4kZ49e7J8+XLatm1LTEwMU6dO5cCBA2UVoxBC3LTso4fJjY8Hq+V/hUpxft1XzgtKCFGoYiUnmqbRpUsXFixYQGJiIqtWrSI2NpY5c+bQuHFjmjdvzuzZs0lISCireIUQokRsWVmgKziWxJaV5YRohCi5H374gZiYmOvW6d69u8OS+iVx/PhxNE0jOzv7prZTEiWeyG0ymejbty+ffvopZ8+eZdGiRYSEhPDMM89Qo0YNOnfuXJpxCiHETXGLrolN5zhWzqrTo28osy5E6ejYsSPu7u54eXnZ/2JjY0t9P+3bt+fo0aPXrbN+/XpGjx5d6vsuL6WyyoyXlxcjRozgrbfeYsyYMSil+P7770tj00IIUSpSPLz4qPd95BqM9mvpHK1Ri69ad3JqXMK1vPnmm6Snp9v/fvmlfMc0Wa1WbFdcYbuyuunkJCEhgTlz5tCiRQsaNGjA4sWLGTRoEF9++WVpxCeEEKUiISeXI7Xq8drD/2L+fQ/x7wceZ2m/ERy1VP4PcnFzVF4emf/ZyqU1n5L5n62ovLxS3X5+98iSJUuIjo7G29ubKVOmcOrUKTp16oSPjw+dO3cmOTnZ/hhN03j77bepXbs2AQEBjBkzhpycy2vxbNu2jdDQUHvd6OhoZs2aRbNmzfD09CQpKYmOHTvy3nvv2essXbqUxo0b4+3tTe3atdmwYQNw+QxL8+bN8fHxITIykqlTp97w+kPloUTrnJw7d45Vq1bx8ccfs337djRNo2vXrixevJi+ffvi5SVX8xRCVCx+Bj02IMvDzImIaAA0wN8gyz1VZSovj3Nvvool4aS9LGv3LgL++S80Y+lOL//hhx84cOAAcXFxNG/enB07dvDuu+8SHR1Nr169eO2113j11Vft9VesWMHOnTsB6NGjBzNnzmTatGmFbvujjz7iq6++IiIiosBSH1988QWTJk1izZo1tG7dmlOnTpGeng6Ap6cnixcvpnHjxuzbt48777yTW265hQEDBpTqsRdXsc6cfPzxx/Tq1Yvq1aszbtw4cnJyeOONNzh9+jTr169n+PDhkpgIISqkWh5u3ObtSf7HtgboNRgSGujMsISTZe3afjkxsVrtf5ZT8WTt2l6i7T3xxBP4+fnZ/64c9zF16lQ8PDxo2LAhTZs2pWPHjtSvXx93d3f69u3Lr7/+6rCtyZMnU61aNapVq8aUKVNYvnz5Nff72GOPUatWLUwmU4Hk5L333uOpp56iTZs2aJpGZGQkDRo0AOCOO+6gadOm6HQ6mjZtypAhQyrEsIxiL8JWr149pkyZwv3330+tWrXKKi4hhChVmqYxISqUdSkX+CszC2+9nu5BftRwd3N2aMKJrCnJhZenppRoe3PmzGHcuHEOZcePHwcgJCTEXmY2mwvczj+bkS8qKsr+/xo1alx3JuyVda8WHx9P7dq1C73vp59+YvLkyezbt4/c3FxycnLo27fvNbdVXoqVnPz3v/+lefPmZRWLEEKUOqUU2t/L0es1jV7B/vTC38lRiYpCHxRceHlgUDlHUlB8fDy33HKL/f/h4eHXrHu9qyhHRUVx5MiRQu8bOnQojzzyCOvWrcPDw4OJEydy5syZmwu8FBSrW6d58+Z88MEHNG3aFG9vb+rVq8fUqVPJzc0tq/iEEKJEMvbuIW7CIxwdNZTjTz1O1kFZKFIU5NG6HYbwSNDr7X+GiCg8Wrdzdmi8+uqrJCcnk5yczIwZMxgyZEiJtjN27Fhmz57Njz/+iFKKU6dO8ddffwFw6dIl/P398fDw4L///S8ff/xxaR5CiRXrzMmiRYsYN24cdevW5Z577iEuLo4ZM2Zw/vx53nnnnRIHkZ6ezty5c/n111/x8PBg0KBB3HPPPYXW7d27N25ubvZfQg0bNuTFF1+03//NN9+watUqsrKyiI2N5bHHHsNsNpc4NiFE5ZMdd5Qzb74Gf0+ptKSmcPq1mUROfwVTaJiToxMViWY0EjBhElm7tmNNTUEfGIRH63YlHgw7YcIEnnrqKfttPz8/tm8v2fiVQYMG0bp1a1JTU+nXrx/PPfdcibbTr18/zp8/z4MPPsjJkycJCwvj7bffpn79+sybN48nn3ySCRMm0KFDBwYOHEhKSsm6tEqTpooxZyg2NpbatWvzySef2E8h5Y8ezsjIwFDCUe+zZ88mJyeHCRMmcObMGZ5//nmefvppmjZtWqBu7969mTdvHhEREQXu27NnD7Nnz+all14iNDSUN954A7PZzMSJE6+579OnT/PBBx8wduxYwsKK/6GllMJisWAwGOwJU2Vns9lITEwkNDT0uqcKKxNXaydpo+tLWbGMC99tuDzAMZ/BQECfAQT07HOTkRadtFNBN/uZWxyZmZn2S6uU9oX/Gjcu28X7NE3jwIED1K9fv0z3U1EV691y+PBhRo8e7fAmGzduHHl5efYBP8WVnZ3Njh07GDZsGGazmZiYGDp37symTZuKva0tW7bQpUsXatWqhdls5v7772f79u32ueFCiKpBWW0F1mpQ4JisCCEqrGIlJ+np6fj5+TmU+fr6ApCWllaiAPJHH1850rhWrVrXvST1lClTGD58OC+99BLx8fH28hMnTlCzZk377Ro1amCz2Th9+nSJYhNCVE4XGjRB2a5KTqxW0uvLUvVCVAbF7oc5ePCgQ/eN9e9fIvmDa65UlJk92dnZeHh4OJR5enqSdY2Lcc2cOZN69eqRl5fHmjVreP7555k3bx5ms5ns7Gw8PT3tdTVNw2w2F9hWSkqKvU8tOTnZPqC3JEv+KqWw2WzYbDaX6C6A/z0PrrAEcj5Xaydpo+vbFRpJcrd76bHpaww2K3kGA190H0A9vyAiyvE5k3YSJVURVml1pmInJyNHjnR40vJfnMOGDbP/P3/qnrUIp1Dd3d0LJA8ZGRkFEpZ8+f18RqORYcOGsXXrVg4cOEBsbCzu7u5kZmY61M/MzCywrdWrV/Phhx/ab3fo0AGAxMTEG8ZblSQlJTk7BHED0kaFS8/I5ZemLfi14a14ZmWQbvZCp9cTlp5OorX8u3mlnYQonmIlJ1u3bi31APLnbZ88eZLIyEgA4uLiqFGjRpEef2XmXqNGDeLi4uzJxokTJ9DpdAUGXfXv399eJzk5mfXr1wM4XKugqFxtoCVc/pWXlJREtWrVZBBfBSVtdH0dMrL44cQZbAYDad6+9vL21asRWo6Lrkk7FSQ/AkVRFCs56dChA/v37+f9998nLi6OsLAwBg4cSNeuXUscgLu7O23btmX58uX84x//4OzZs2zevJlJkyYVqBsfH09eXh7R0dFYLBZWr15Nbm4u9erVA6Bz587MmTOHDh06EBISwvLly2nXrh1ubo4fRkFBQQQFXV5gx8fHB5PJBFx/EZtrUUqh0+nQ6XQu8aV3pfzjcgWu2k7SRoVr5O3J+IgQPkxIIlcpPHQaj0aEUMtc+BnZsibt5Bx6vR5vb2+AUl1YTC7TUvaKlZxs376drl27kpeXR1BQEOfOnWP+/PnMnTu3wHK9xfHwww/zzjvvMHLkSPssm/xV8QYNGsQLL7xAo0aNuHDhAu+++y4pKSmYTCZq167NtGnT7C+UZs2aMXjwYKZNm0ZmZiaxsbE8/PDDJY5LCFF5tffzpq2vFxlWG556HboK/kUqSp+bm1upTiEW5adY65x07dqV1NRUvvrqKyIjI0lLS+PBBx/k+++/rxCLtpSErHNSkKzNUPFJGznKPZvIpe3fY8vJxtygEZ7NWpRRlMUj7VRQea5zIiqvYp052bt3L++99559bIiPjw+zZ8+mVq1aDmNGhBCivOSciOPUjBdQVhuguPjdBvzv7U9gH+de8l04X25ubpksJWEymSSxKmPFSk5SUlIKrMyan5CkpKRIciKEKHfJyxaiLBb7UvUA579Yhe8dnTAEBDoxMuFsFouF1NTUUt+um5ubJCdlrNhTiV3hlLgQwnXkJSc7JCb28tQUSU6EXWkvXy/KVrGTk06dOhXad9q+fXuHck3TuHjx4s1FJ4QQN5ARGIwx7SL6K4bPKSrGJe+FECVTrOTkhRdeKKs4hBCiRD7v0pO+S+ahrBYA9FYrGzrcTR+zF9LRLETlJMmJEKJSSwysxtsjH6fpgb2Y8nKJi6zJsRoxdLdV7eW/hajMXGNumxCiyrrF20yGjx8/tLyDze26ElcjBi+9jgg3k7NDE8Ju8eLFtGrVytlhVBrFHnMihBAVyYjqwSTlWtiXcfkaXZ56HZNrhOGul99eovxcuWpsVlYWRqPRfpHcZ599Vmb3FJMkJ0KISs1dp+O56DASc/PIsSmquxlxc5EFz0T5yFOK7elZJOdZCTbqaeflgbGYM1PT09Pt/2/VqhXjxo1j5MiR9rLFixeXUrTXp5TCarXaE6PKSt7BQohKJevwQVI//4xzX39OXtLli8hpmkZ1NxPRHm6SmIhiyVOKVxPPsfLcJbZcymTluUu8lniOvKIvnl4szz33HIGBgYSHh7N8+XJ7eW5uLs888wzR0dEEBQUxdOhQzp8/b79/3bp1NG3aFF9fX1q1asXu3bvt93Xs2JFnnnmGDh064OnpyY8//khAQAA5Of+7AveOHTsIDg4mLy+vTI6rtMm7WAhRaaT9ZysJM1/k/NovOfflauKnTCL76GFnhyUqse3pWZzMtWAF+198roXt6Vmlvq9ffvmFsLAwzp49y9tvv83DDz9MWloaAM888wx79uxh9+7dnDx5EpPJxGOPPQbA4cOHGTBgAP/3f/9Hamoqo0ePpnv37g7Jy5IlS3jrrbe4dOkSt912G+Hh4XzzzTf2+5cuXcrQoUMxGo2lflxlQZITIUSlYMvOJmnJfFAKLBawWFB5eZxd+L6zQxOVWHKetdDyFEvh5TcjPDycRx99FIPBQL9+/dDpdBw6dAilFO+//z5vvPEG1apVw8PDg5dffpnPPvsMi8XCypUrueuuu+jRowcGg4ExY8YQGRnJ2rVr7dseMWIEt956K3q9Hjc3N0aOHMmyZcsAyMnJ4dNPP+WBBx4o9WMqK5W7U0oIUWVYzqWC9aovDKXIS0pyTkDCJQQb9YWWBxkKL78ZoaGhDrfNZjPp6ekkJyeTkZFB69atHe7X6XScPXuWhIQEoqOjHe6Ljo4mISHBfjsqKsrh/mHDhvH888+TmprKli1biIiIoHnz5qV7QGVIzpwIISoFnZ8ftqsGKdqAdB9f5wQkXEI7Lw8iTQb0YP+LMhlo5+VRbjEEBQXh4eHBb7/9xoULF+x/2dnZhIeHEx4eXmDJ/OPHjxMeHm6/ffXK7SEhIXTp0oUVK1awdOnSSnXWBCQ5EUJUEqkGE+s63YMCrDodFp0em07H53fe6+zQRCVm1DQmhQYwOMCbLj5mBgd486/QgGLP1rkZOp2Ohx9+mIkTJ3LmzBkAkpKS+PLLLwEYNGgQ3377Ld9++y0Wi4WFCxcSHx/PPffcc93tjhw5krlz57Jp0ybuv//+Mj+O0iTdOkKISsGgafzYvDUpAUHUiTuMRW/g9wa3kBla3dmhiUrOqGl08jY7NYZZs2Yxc+ZM2rVrR1JSEiEhIQwePJh7772XunXrsmLFCp566ini4+OpV68ea9euJSAg4Lrb7NWrFw8//DCdOnWievXK9T6R5EQIUSkEGPQ0MLtzKLoOR6LrAJdPwffy93FuYEJc5ccffyxQNnLkSId1TwASExPt/3dzc2PatGlMmzat0G326tWLXr16FXrftm3bCi03Go2Eh4dXui4dkG4dIUQloWkaT9WoTqyPJyZNw6zT0SPIj0Ehgc4OTYgKaePGjSQmJtK3b19nh1JscuZECFFpeOr1PBFVuU5PC+EMXbt2Zc+ePcyfPx+TqfJdZ0qSEyFEhWS5cJ6U5UvIOXkCg38AgYPvxz26lrPDEqJS2LRpk7NDuCnSrSOEqHBsWZmcenkq6b/+TF7iGbL++pOE6c+Tc+qks0MTQpQDOXMihKhw0n/5GcuF8/9bdE0plM3GxU0bqDZyjHODE5XSoUOHSm1bbm5upbYtUThJIwgxkwAAIABJREFUToQQFU52Rjp5mobDVUBsNi6lpVHNWUGJSsdoNBZYlbU0VPYr/lYG8gwLISqcwyERVLNYHMqsOh1Hw2oQ46SYROWTP5VWVD4y5kQIUeGkRdZgQ5eeKLAvWf9HvSYcatH6+g8UQrgEOXMihKhw6prdWdqsFX/WrEvQuRQueXmTHBzKA95ezg5NCFEOJDkRQlQ4MR7uPFg9mEXABb8AFNDBz5uuAbIarBBVgSQnQogKqVugL829zZzOzcPfoCfSXWZICFFVSHIihHC6jN9+JeXT5VjT0nCLrknIqIcxBAQSZDISZDLeeANCCJciA2KFEE6V+ec+zrz1GnmnE7ClXyLrwH5OzXgBW1aWs0MTQjiJJCdCCKe6uGkDSqn/FVitWM6fJ3P/H84LSgjhVNKtA3h5eWEwGBw/IIso/zEleWxFpZSyPx+uclyu1k6u1EbnLqVz9WgSiwbpmRl4VvJjc6V2ynez7yVZwEwUhbxKgGbNmuHv74/lqkWfisOav8y2i/D398dms2Gz2ZwdSqlypXZylTY6ULMOTY4eQn/FcWhKcTQkgsCbeE9WFK7STlcr6XvJ39+/lCMRrkiSE2DPnj00adKE4ODgYj9WKYXVakWv16P9vVhUZWez2UhNTSUwMBCdzjV6/lytnVypjQ63ugPL6QRa7PsVgDy9gdU9B9EpJKTS/8p2pXbKd7PvpeTk5DKISriayv3OLyXp6elYLJab+tLSNM0lvvTg8rHkPx+uckz5XOWYXKmN2gf4Mffufmxr3QlzVibn/QLA7MljXuZKf2yu1E5XK+kx3cwZalF1SHIihHCqdn7eXLBYWKHBBV9/Ag16noiqjl8lP2sihCg5efcLIcqVLSuLrMMHwWbDvXZd9F5e9Azyp3uALxl5eXibTC53lkEIUTySnAghyk1u4hkSZr2ENe0iADoPD8L+NQX36JroNA0PFxmXIYS4OfJJIIQoN4lz38SSdhFsNrDZsGZmcuat11xmmq0QonRIciKEKBfKZiPnVDzaVVOGrefPYbt0yYmRCSEqGklOhBDlQgG5hoLXyVGAxc1U7vEIISouSU6EEOVD09jSpjO2Kwa7WnU6trdoR45eLu4nhPgfGRAr/r+9O4+Tor7zP/6qqu6Znp77HmaGGQG5b1AMh1yiRtCIEdlVEdwN0WASYvLzSLwPlKyJG92IEV2DMRp/i8ZrVSIexBgPREDkEBAYzrkv5j66qvaPgZZhBrnpnuH9fDzGh/Odqu5P1Zfufve3vlUlckqYhkHhuRN53RfFsC8/x3Ad1vYdzNfnjOFaS9+TROQbCicicsr8LKcL99vn8PnA4QDEWxZ3nJGJYRiaFCsiQQonInJSuK5L7Yrl1G/djBXlJ27sBNKSknm4Zw5b6xtxgW6+SKI0aiIiB1E4EZGTovSFP7P33b8BBhgGlW+/Rfbd84jIyKRvdFSoyxORMKavLCJywjXt2c3epW/tu56JDXaAQGMDJS/8OdSliUgHoHAiIifc3qJCHKP124vpOJTv2ROiikSkI1E4EZETriA2EdN1WrXZpklhYkqIKhKRjkThREROOG9mJn8/ZyyOYRCwLAKWRUNEJCsv/F6oSxORDkATYkXkhOsZ5eOPky4mPzOHrD07aYj08cWAofxbzx6hLk1EOgCFExE5bq7jULtyBc2lJXjTM4geOpw7u2XxZISXT3v1w2+aXJOWxKj42FCXKiIdgMKJiBwX17bJ/8//oP6rdWBZYNtEDx1Oxo9/zk25XUJdnoh0QJpzIiLHpeqD96jbuL7ltOHmZnAcalavpObTj0Ndmoh0UAonInJc9uRtx3EOOjPHMMjfnheiikSko1M4EZHjku+PxjUPeitxIT/KH5qCRKTDUzgRkeNS/p1zqY6OJWBaAARMi4qERKpGjApxZSLSUWlCrIgcl1FdMrh35g2M/OyfJFaWU5aUwqcjzuWBVF1wTUSOjcKJiByVuvVrqfzbG9j19fj7DyTrksu4qW9PnopPoKy5mbQIL7dlpZEe4Q11qSLSQSmciMgRq12zmvxHHgLXxQDq87bSuHsnvX7yC37bMyfU5YlIJ6E5JyJyxHYv/kswmACYtk3dyhU05euGfiJy4iiciMgRq6+qCgaTAxVVVpzyWkSk81I4EZEjtieza/CsnP0ClkVdSnqIKhKRzkjhRESO2Pap/0JFQmLL3YZNC9s0eenifyEzOTnUpYlIJxIWE2JrampYsGABq1atIioqiunTpzN58uQ2y23cuJEXXniBLVu2ANC7d29mz55NZmYmAGvXruWOO+4gMjIyuM60adOYPn36qdkQkU7EbW6m9KUXqF25AsPjIf68C7l6/Hk8MPtG/Fs2ExloYk96FrMG9iXOYx3+AUVEjlBYhJOFCxdi2zaLFi2ioKCAu+66i+zsbAYNGtRqudraWiZNmsQtt9xCREQEzz//PPPmzePxxx8PLhMfH8+zzz57qjdBpNPJf2oBtStXYNo2ACUvPEtyczMPfHcK67t2ocFx6BHlI02nDIvICRbywzoNDQ189NFHzJgxA7/fT48ePZg4cSLvvvtum2WHDx/OueeeS3R0NF6vl6lTp7J7926qqqpCULlI52VXVVH/2afBYAJgOA7Fb7xKhGkyNDaakfGxCiYiclKEfORkz56WUxBzcr65RkL37t159dVXD7vuunXrSExMJC4uLthWXV3NzJkz8Xq9DBs2jJkzZxIbG3viCxfpxCpra9ptd5uaTnElInI6Cnk4aWhoICoqqlVbdHQ09fX137peYWEhCxcu5Prrrw+2ZWdn8+ijj5KdnU15eTmPP/44jzzyCHfeeWerdUtLSyktLQWgpKSEpn1vuAffWfVIuK6L4zg4joNhtHeSZcezfz8cy/4IV52tn052HzXHJVAVE0tMbQ2m6wIQME12ZJ9B75P0nJ2tj0CvJZFjFfJw4vP52gSR2traNoHlQKWlpdx1111MmzaNMWPGBNsTExNJTEwEICUlheuuu44f/ehHNDY2tpok+9e//pWnnnoq+Pu4ceOAlsAj3yguLg51CXIYJ7KP7N07cYsKMWJjoUcvXp02i+8vXkRMXS0ApcmprP3eNAbpdXLU9FoSOTohDydZWVkA7Nq1i65duwKQl5dHbm5uu8uXlZVx++23c8EFF3DppZd+62Obponrurj7vvntd/nllwcDSUlJCUuWLAEgIyPjqOt3XZdAIIDH4+k03yIcx6G4uJi0tDRMM+TTkk6IztZPJ7qPSl9eTO0br+J4PJi2jbdnH2b/5Bc8nHQrEYW7sU2LmJxcbuqWRYx1cs7M6Wx9BHottUdfAuVIhDyc+Hw+Ro8ezfPPP8/cuXMpKirivffe45ZbbmmzbFlZGbfddhvjx49n2rRpbf7+5Zdfkp6eTlpaGpWVlTz55JMMGTIEn8/XarmUlBRSUlrumBoXF0dERATAMb15uK6LaZqYptlp3lD3279dnUFn7acT0Ud1m75i7xuvYQBWIABA45bNxL6/lIe/930KeuViGQYZEV7Mk7jvOmsfgV5LIkcr5OEE4Prrr+exxx7j2muvxe/3c/XVVzN48GAApk+fzt13303//v1ZunQpBQUFvPLKK7zyyivB9RcsWEBqairbtm3jkUceobq6mujoaIYNG8asWbNCtVkiHcLXmzcT4bHw7gsmAJZjs2vzJjJNg66+yG9ZW0TkxAuLcBITE8Mvf/nLdv+2ePHi4P9feeWVXHnllYd8nKlTpzJ16tQTXp9IZ1YVFU3aQRM2bdNkrz8mRBWJyOkuLMKJiJw6TmMDNZ9+TKBqL5G5ZxA37GxK3v5fUspK8Tg2tmES8HionXhBqEsVkdOUwonIacSurWXrvbfjlpXgGAYe2yZpwvl89qNfELHkdTKL8tkbF89XYy/g5t69Ql2uiJymFE5ETiNbX/r/OKUleByb/efcNLy/lCvO/g5bZ/47OxsaSfF6uDU+hohOMoFTRDoehROR00j59jxSHLtVW8DykLdjB8P79mN4XHSIKhMR+Ya+Gol0ck5TE05DAwB7E5KwDxoRsRybhviEUJQmItIujZyIdFJOQwPb/vBfsGZVy+89emFccjmNX60loqkRy3GwTZNtuT0YPGRoiKsVEfmGwolIJ7XxyQWYa9cEX+Ru3hYyX13Mxz+5Bc8H7xNTV0NheibDp1xCVpTvWx9LRORUUjgR6YRc18VaswrrgPklluNgbd/KzJxMymb/kKqAzYWRXuI9ehsQkfCidyWRTsKuraWpYA9WlB8zowvuIZarcSArMoIsXfhVRMKUwolIJ1C17ksK/uthrKZGAOy+A/i690B6blyLZ9/oScA02ZV1BhPj40JZqojIYelsHZEOrrlqL/mP/hZzXzABcDd9RXp8HOt7D8AxDBzDYFtuD+LmzMXSzdpEJMxp5ESkg9uxZQtmoJkDI4fHsbE2f8XIBx9meUUVjusyNCGW7pr4KiIdgMKJSAfTVFRI3gt/pjl/DzVZ2TQNP4dIt+0Mk4aISDIjI7gsIyUEVYqIHDuFE5EOpLm8jK33/AqzsRGf42CXFmNs3EBRagYpZSXB+SUuBkXjzg9xtSIix0bhRKQDWbv0bfxNTViOA7ScHuw2NdI0aBgb9uzmjN3baYiMZO3Y85lx/qQQVysicmwUTkTCWO3OHex852849XUk9B1ATXUV0QcfwnHBYzcz5hc3k1ffSKxl8gN/FB5TE19FpGNSOBEJU+WbN1HyH/dhOg4e16Vm5Qq83c7EOCicmK6DfUZ30iK8pEV4Q1StiMiJo1OJRcLUlueewXAcLNfFACzXIXXbZjb0HQwQvIHfqrNGM3z0uSGsVETkxNLIiUgYcBobWP/fC/Gs/QLXtLDGTsBTUY550CiJC3SdOIlPxk+ipqCQ2KxMLh48iDivXsoi0nnoHU0kDHzxu9/i3/xV8Gwbe+mbOHEJ2KYZnPwK4BoGqVlZXJ2eTmFhIRkZGZimBkBFpHNROBE5xVzHYc8//8HendvxJSSSOuxs4jaua7WM5Tj4amuoi/ITVV+Paxh47ABrJn+faRkZuO1c10REpLNQOBE5hVzXZc2jDxO1djUWBs0G5L3zFtHtLGu5Du5dD7L6nx/iNjQQ27sPlw0bhmEYCici0qkpnIicRK5tU7RmNbUVFSSf0Y3yigr8X65qPZekqpr6SB8RTY1Y+9oDpsXu7r04Pz2NgZdfHqLqRURCQ+FE5CQJNDay5sF7id2Zh21alNgBSnv3J8GyMAOB4HIe16E4PRNfVSVJleUA5HfJ5szrfhKq0kVEQkrhROQEqSzIZ+enn+DYNplDh7P904+I27UD03Ux7ZYwkrJpPe5BdwUOmCbN2Tl0n3Enm3fuxPR4GJyTQ7KuWSIipymFE5ETYOeG9dT+53wiXBcXg8o3X4HUjODZN/sFTIvylDRSSouAlrNvav0xdL9sGtn+KLL79A5F+SIiYUXhROQoNdbUsObphURu2YQd6cN/yWXUvvoSsQEbk2/mksSXFGIbRnAeCYDpuhjf/R7rq/bi7tmFGxfPkIum0C0lORSbIiISlhRORL6F6zhs/Ww51UWFxKZn0G3YcNY8eC9xhfktoyLVVTiLFhIHHHwnG8t1afZ4wLaxXJeAaVGalsGQ0aMYFRkZis0REekQFE5E9nFdl5rSUupra0nK7IJpeVj+m/kkb1qPz7JwbJsVOd1Iyd/Vaj2TlkvJm47TKqDYpknNL26n+M3X8VXvpTY7l3OuvIoYBRMRkW+lcCKnrYbqakzLIsLvJ9DUxGe/+y1pX30JQFGUn5qRY0navKFlQuu+s2sSd+bh0naUpC4qmui6GlriiYvpuuz63hVM6tcX+vU9pdslItLRKZwAMTExeDyeY7qw1f51OtNFsVzXDe6PzrBdTY2NlBcV4Y+NJTYhgYrSUjb/7qHgCEhR7/64ScmkbFofXCeyvg7PB++0eSwXcA4aJQmYFuX9B2OPHkvxxx+CYxM7ZDgTR448afuvs/UR6LXUURxvP3k8+tiRw9O/EmDo0KEkJiYSOODaE0fLtu3DL9SBJCYm4jgOzgH3dQl3jm2zdd1a6isrSc7JJatbN776bDmeZ54kormJGuDLc8/D+/VGEosLguslf70R27JanVljAoZt4xx83xrTZNfQc+iy5nMim5sAKMjtztAZ1xAb5W81SnKy/010xD46EnotdQzH2k+JiYknuBLpjBROgNWrVzNw4EBSU1OPel3XdbFtG8uyMIyDB/s7JsdxKCsrIzk5OWxuKldfU01ERCRWRAQAm79YRfE//g4Bm9hhZ9Fv9Bg+f+hBUvO+Jto0abZtPh07idR/LsPaFzoMIP3D9zh4izyOjem0faM1gDp/NFF1dXgcm4BpURMTz7CZ19LozGRX3nYi/dGM6tGdCOvU7qdw7KPjpddSx3C8/VRSUnISqpLORuEEqKmpIRAIHNcbomEYneYN1TCM4P44mdu0t7yM+tpaUjO6YHm9NDY08sVb/0tzcRGelFSGTLmE8uJi8h/9DQnlpbhA4chxxPTqQ/SfniQdMHBx163msw//Tsr2rfsueNYSNDL+8S62abWaH3KojwfHtMB1gpeVD5gmlSnp5Nx8G+teewWzpAgnNZ2h0/6FpLg4ALoMHXLS9s3hnKo+CoXOtE3qp7aOZ4RaTh8KJ3JE7OZmbNchIqLlTBPXdSnYsYPqinJSu3YlKSUVx3FY//FHVG3Pwxsfz4CJk4j0+fjspRexVi7HtSyscycweNL5fPrYI3T5chUAxVHRRM2ZS/lf/kR8cRGG6+AaJmuWf0RkfT2xtdVAy0hG2vIPCaz8pNX1RAzXJX3b5jaTVG3DxHRbD6U7QH1UNJGNDcHDOAHTIn/oCKycXGLffQtvUxOlud3pf/2PSUtOJnP2dSdln4qISPsUTo5DTU0NOzZuxAW69etLtD8a27bZsn49jbU1ZPY4k5S0NAC2b/maivx84tPT6d67DwDFBQXs3rwJr89Hz8FD8Pl8VFdX8fXKVTiBZnL6DyCtSxeam5tZ9+nHNJZXkJCbS58hQwFYt3w5e7dvwxsXz4Dx4/FH+fl6/ToKP18BpknOyFHkntmT3Tu2s+3tJRi1tUT06sPZ372Iyopy1v3lObxFBTSnptP3yhlE+aNZ9d9PELdlE80REey58GIGT5jI8j/8nowvPscAinO60funv2DDX/5Ml9WfYQKlhsG2K2ZQv20r6Ss/Icm0MFyXDe8vpb5HL9JWLcfad7zdefE5ln/2Mak7twf3o6++jub/+g3xjvvNvA/XJr6sBOug03Mtx8FsamrTFwbgGEarG+oZuDR7I/A0N2PtCykG0HTlNVT+4wO6bN2IY5jkDz2bUdf9CF9kJFw6Fdd1O923XBGRjsRwO8sU8mOUn5/Pk08+yXXXXUdmZuYRr7d96xb2PjyfqLo6DFyq4xJImnsT+c/8N6m7t+MYJq5hUD/rOqq3bibzH+8Fr4WRf9ZI4gcPw7foCQBM12FvUgoJ/349tY8/SlR9La5h4GLQ/IM51Lz1Osn5u3BME8u2KRw1HlyXjE8+aDls4bpUJyTiTLqIxMXP4VgmuC0fzhWX/Svxry3GtB1M18ExTQr7DyUu72v8dTVYjoNtmjRG+KhNTCKxqCAYEBzDoCSnG8m7dhwwymBSFxNHdE1VMHBAy4iEQetTbAOGieU6bUY0HNoeXtl/FsyBjxkwDDzt/PMMmBaG6wSvvOoCzR4vBi2HdPZf8GxvcgrJP/wxxYueJLGkkDp/NM73/5Wzx08AoDEQwMAgwmMdcb+HC8dxKCwsJCMjo1PNZQgEAng8nk4TDtVPbR3re66cXhROjuGF4jgOq3/+Y2KqKoMfkLZh0uDzEdnY2OqsD9swMKDVN3obA0yj9QexadEcEUFEU2Ow3QUCloXh0uoxDxUEDAiOEOxf37YsTMdp9fzQcoGw1s9v4mnnbAIHo9UhlEMJGCaGQavHPJT2rhPSfjgxaYr0EdnUEGy3TZP8c84lddVyPIFmXAxM16Huhz8hITWdHX/9H7x7K2jKOYPhV80kLi62ZTscB9u2O80Hnz70Ogb1U1sKJ3IkdFjnGFTtrSJ+b0WrNst18NfXtfnQNVwX1zShVThwsZzWH/gex8ZqqG+1vgF42zldzzH3BZEDPsg9bttQYACWbbcbBA7OG4brthsajEMEk7aHUMA4aJtsw8C2PHjsQHBZ2zSpjo0nrqqy1eTTvYnJNPujSdmzi33XMaMsM5vs2XPIf+L3pBTuwbYsCs89j7HXzKKsbBpbVnyKaztkDx5C79xcALrdelu79XaWDzsRkdOBwskxiPL7DvHt3wj+d799n7OtlzNMOChMtDdysJ+97/BI8DFdF8M9eJm2IzQtIy8eDNdpMyrSXugIeLxYgebgIZeAaVGVmERcRXmrwzp7k1JIKC/FccHEbTnU44+hJiOzZWKq6+IYJo0+H+6sHxJY9CS+xnoM16UmNp6Mm25jx6rPiV76JhFNTZR2PYO+N8wlPi6ele8upbG4iIjUNM4+/0IiIrzk/vph6puaiPB46LPv22daWippUy5psw0iItLxKZwcg8hIH/mjxpHx6YffHG4wTAoHD6fLms/BbQkoAdOiLCOTmMoKIhvq8Oyb39Hs8VKS0520bZuwHCcYEwpHjafLP5dh7As4tmFSMGwEyevXBA/3BEyLirQMAjExpG37GnP/Y0ZEUn3hxaS89lLLhcOMlpGMmitn4Xn1xeA8Fk8gQNHkqQRKS8j67CNsy8KybQqGnEXauPNo+sMj+BobACjJ7caAuf+PNc88TdaalgmxpVm59L3xJor37KH8f57DV11FTZds+v77D0lKSeGzt96gaccOjLg4+k+5hJTkZCr79mPHxo0YlkWfvv2I9keR3bUrXHpZm307cvKUdvd51L7rm4iISOencHKMxvzgOj6JS8C7annL5NWRYzn3e5eyYeVK9r7xCp66Whp69OLsWf9GbVU1G577E5HFBTQlpXLm1bPok57Gp//zAtbG9diRPhIvnMK4ESP4sv8A9i57FyMQwDNkOGMvvoTSoiK+evWvGBXl0DWXs6dNx+v18tn/vk7zzu0YsXH0nXIJA9PT2XhmT4pXrgDDInvUaM7u2ZOqEeew/p8fYtfWktK7D2MGDQJg09gJVBcWEJueztj+AzBNk5qHF7B7x3bqGxr4zuDBeD0exv/8JhqbmnAchzN9PgCSk5Nh0ENt9suoSy5t05YQF0fCiBEnt0NERKTT0ITY45ycpUl8HUNn6yf1UcegfmpLE2LlSHSOV4uIiIh0GgonIiIiElYUTkRERCSsKJyIiIhIWFE4ERERkbCicCIiIiJhReFEREREwoouwrZPaWnpMa3n8XhITEykpKSEQCBwgqsKrcLCwlCXcMJ01n5SH3UM6qdvHOt7rZxeTvtw4vf78Xq9vPzyy6EuJWw0NDSwY8cOcnNz8e27IqyEF/VRx6B+ap/X68Xv94e6DAljp/0VYgEqKyupq6sLdRlhY+vWrfz85z/nd7/7HT169Ah1OdIO9VHHoH5qn9/vJyEhIdRlSBg77UdOABISEvRCOUBVVRUAqampurx0mFIfdQzqJ5FjowmxIiIiElase+65555QFyHhJyoqirPOOkvHhcOY+qhjUD+JHD3NOREREZGwosM6IiIiElYUTkRERCSsKJyIiIhIWNGpxHJIL7/8MsuWLaO4uJjo6GgmTJjAVVddhWVZoS7ttFZTU8OCBQtYtWoVUVFRTJ8+ncmTJ4e6LDlAc3MzTzzxBGvWrKG6upqUlBSuuOIKxo8fH+rSRDoEhRM5JNd1mTt3Lt26daO8vJx58+bh9/u5/PLLQ13aaW3hwoXYts2iRYsoKCjgrrvuIjs7m0GDBoW6NNnHtm2SkpKYN28eaWlpbNy4kfvuu4+MjAz69OkT6vJEwp4O68ghXX755fTs2ROPx0NaWhrjxo1jw4YNoS7rtNbQ0MBHH33EjBkz8Pv99OjRg4kTJ/Luu++GujQ5gM/n4+qrryYjIwPTNOnXrx99+/blq6++CnVpIh2CwokcsfXr15OTkxPqMk5re/bsAWjVD927d2fHjh2hKkmOQENDA1u2bCE3NzfUpYh0CAonpynbtr/152BvvPEG27dv57LLLgtBtbJfQ0MDUVFRrdqio6Opr68PUUVyOK7r8uijj9KzZ0+GDh0a6nJEOgTNOTlN3Xnnnaxbt67dvyUkJPDss88Gf1+2bBkvvfQS8+bNIy4u7lSVKO3w+XxtgkhtbW2bwCLhwXVdHn/8ccrKyrjvvvswDCPUJYl0CAonp6kHH3zwiJb74IMPWLRoEffffz/Z2dknuSo5nKysLAB27dpF165dAcjLy9PhgjDkui5PPPEE27Zt4/7778fn84W6JJEOQ4d15JA++OADnnrqKe6++259+IUJn8/H6NGjef7556mrqyMvL4/33nuP8847L9SlyUEWLlzIpk2buPfee3VfHZGjpHvryCHNnj2bsrIyvF5vsK1fv37oXpGhVVNTw2OPPcaqVavw+/26zkkYKi4uZvbs2Xi93lbXBZo2bRrTp08PYWUiHYPCiYiIiIQVHdYRERGRsKJwIiIiImFF4URERETCisKJiIiIhBWFExEREQkrCiciIiISVhROREREJKwonIiIiEhYUTgROYBhGIf9eeaZZ/j73/+OYRh8/vnnIa13/PjxXHzxxSfksa699loGDBhw2OUGDBjAtddee0KeU0SkPbrxn8gBPvnkk1a/jxw5kp/+9KdcddVVwbYePXqwfv36U12aiMhpQ+FE5ADf+c532rTl5OS0234sXNelqamJyMjIE/J4IiKdkQ7riByH8vJyrrrqKmJjY8nNzeWhhx5q9ff9h0reeustBg8eTGRkJK+//joAlZWV3HDDDXTp0oXIyEiGDx/O0qVLW63/0UcfMXbsWOLj44mNjWXgwIH86U+CjgoeAAAEvklEQVR/alPHiy++SO/evYmJiWHixIls3bq1TZ2zZ88mNTWVqKgoRowY0ea52vPxxx8zfPhwfD4fAwYMYMmSJUe7i0REjppGTkSOw5w5c7jmmmt45ZVXePnll7n11lsZNGgQ3/3ud4PL5Ofn87Of/Yw77riDrl270rVrV5qamjj//PMpKirigQceICsri+eee44pU6awatUqBg4cSFVVFVOmTGHMmDG88MILREZGsmHDBiorK1vV8MUXX1BSUsKvf/1rbNvmxhtvZMaMGcFDVLZtc9FFF7Flyxbmz59PdnY2f/jDH5g8eTLvvPMOEyZMaHfbCgsLufDCCxk4cCCLFy+moqKCOXPmUF1dffJ2qIgIgCsihwS4v/nNb9q0L1u2zAXcm2++Odhm27bbtWtX9wc/+EGwbdasWS7gLl++vNX6f/zjH12Px+OuX7++VfuIESPcK664wnVd112xYoULuF9++eUh6xs3bpwbHR3tFhcXB9ueeuopF3B37drluq7rvvbaay7gvvnmm61q7du3rztu3LhWtfbv3z/4+6233urGxsa6FRUVwba3337bBdxZs2YdsiYRkeOlwzoix+GCCy4I/r9pmvTp04fdu3e3WiYlJYURI0a0alu6dCkDBw6kV69eBAKB4M95553HihUrgJaJt3FxccyZM4fFixdTUlLSbg1DhgwhNTU1+Hu/fv0AgnV8+OGHxMbGMnny5Fa1Tp8+nY8//hjbttt93OXLlzNhwgQSEhJabW9cXNxh94uIyPFQOBE5Dgd+cANERETQ0NDQqi0tLa3NeqWlpaxevRqv19vqZ/78+ezatQuAxMRE3nnnHWJjY7nmmmvIyMhg/PjxrF279rA1AME6KioqSE9Pb1NDRkYGzc3N1NTUtLttBQUF7dbeXpuIyImkOSciJ5lhGG3akpKSGDRoEE8//fS3rjtixAiWLFlCfX09y5Yt46abbmLq1KltJrx+m6SkJIqKitq0FxYW4vV6iYmJaXe9Ll26UFxc3Ka9vTYRkRNJIyciITBp0iS2bdtGZmYmZ511Vpufg0VFRTF58mTmzJlDXl5em9GZbzNmzBiqq6v529/+FmxzHIcXX3yRUaNGYVlWu+uNGDGCZcuWsXfv3mDb0qVLqaqqOootFRE5eho5EQmBmTNnsnDhQsaPH89NN91Er169qKysZPXq1TQ1NTF//nzefPNNnn76aS677DJycnIoLCzk97//PaNHj8bn8x3xc02ZMoURI0ZwzTXX8OCDD5Kdnc0TTzzBpk2bWLBgwSHXu/HGG1mwYAEXXXQRv/zlL6moqODuu+8mKSnpROwCEZFDUjgRCYHIyEjef/997rnnHh544AEKCgpISUlh6NCh3HDDDQCceeaZmKbJ7bffTlFRESkpKVxwwQXMnz//qJ7LsiyWLFnCzTffzK9+9StqamoYNGgQb775JuPHjz/kel26dGHJkiXMnTuXK664gh49erBgwQJuvfXW49l0EZHDMlzXdUNdhIiIiMh+mnMiIiIiYUXhRERERMKKwomIiIiEFYUTERERCSsKJyIiIhJWFE5EREQkrCiciIiISFhROBEREZGwonAiIiIiYUXhRERERMKKwomIiIiElf8DKhupmCgfTw0AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 450x350 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766715492109)>\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 450x350 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766715658605)>\n" ] } ], "source": [ "import os\n", "from time import time\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.utils import resample\n", "from numpy.random import randn\n", "from scipy.stats import norm, t\n", "import plotnine\n", "from plotnine import *\n", "from scipy.optimize import minimize_scalar\n", "from scipy import stats\n", "# !pip install git+https://github.com/retostauffer/python-colorspace \n", "from colorspace.colorlib import HCL\n", "\n", "def gg_color_hue(n): \n", " hues = np.linspace(15, 375, num=n + 1)[:n]\n", " hcl = []\n", " for h in hues:\n", " hcl.append(HCL(H=h, L=65, C=100).colors()[0])\n", " return hcl\n", "\n", "def dgp(n0, n1, mu):\n", " \"\"\"\n", " FUNCTION TO CREATE BINARY RISK SCORES\n", " \"\"\"\n", " y = np.append(np.repeat(0, n0), np.repeat(1, n1))\n", " score = np.append(randn(n0), randn(n1) + mu)\n", " return y, score\n", "\n", "def PPV_theory(thresh, mu0, mu1, prev):\n", " \"\"\"\n", " FUNCTION TO RETURN ASYMPTOTIC PPV FOR NORMALLY DISTRIBUTED SCORES\n", " \"\"\"\n", " tpr = norm.cdf(mu1 - thresh)\n", " fpr = norm.cdf(mu0 - thresh)\n", " ppv = tpr*prev / (tpr*prev + fpr*(1-prev))\n", " return ppv\n", "\n", "def PPV_fun(y, score, thresh):\n", " \"\"\"\n", " FUNCTION TO CALCULATE EMPIRICAL PPV VALUE FOR A GIVEN THRESHOLD\n", " \"\"\"\n", " ntp = np.sum(score[y == 1] >= thresh)\n", " nfp = np.sum(score[y == 0] >= thresh)\n", " if ntp == nfp == 0:\n", " return np.NaN\n", " else:\n", " ppv = ntp / (ntp + nfp)\n", " return ppv\n", "\n", "thresh_seq = np.round(np.arange(-3,3.1,0.1),2)\n", "theory_seq = np.array([PPV_theory(tt, mu0=0, mu1=2, prev=0.1) for tt in thresh_seq])\n", "\n", "mu0 = 0\n", "mu1 = 2\n", "n0, n1 = 90, 10\n", "prev = n1 / (n1 + n0)\n", "\n", "np.random.seed(1234)\n", "nsim = 5000\n", "\n", "mat = np.zeros([nsim, len(thresh_seq)])\n", "for ii in range(nsim):\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " mat[ii] = [PPV_fun(y, score, tt) for tt in thresh_seq]\n", " \n", "df_sim = pd.DataFrame({'thresh':thresh_seq, 'theory':theory_seq, 'emp':np.nanmean(mat,0)}).melt('thresh',None,'tt')\n", "\n", "plotnine.options.figure_size = (4.5, 3.5)\n", "gg_theory = (ggplot(df_sim, aes(x='thresh', y='value', color='tt')) + theme_bw() + \n", " geom_point() + labs(x='Threshold', y='PPV') + \n", " ggtitle('Figure 1: Simulated vs Theoretical PPV') + \n", " scale_color_discrete(name='Type',labels=['Empirical','Theory']))\n", "print(gg_theory)\n", "\n", "np.random.seed(102)\n", "y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", "thresh_seq = np.sort(np.unique(score))\n", "df_ex = pd.DataFrame({'ppv':[PPV_fun(y, score, tt) for tt in thresh_seq],'thresh':thresh_seq})\n", "yy, xx = tuple(df_ex.query('ppv<0.4').tail(1).values.flatten())\n", "yend, xend = tuple(df_ex.query('ppv>0.4').head(1).values.flatten())\n", "xstar = (0.4-yy)/((yend - yy)/(xend - xx))+xx\n", "\n", "plotnine.options.figure_size = (4.5,3.5)\n", "gg_ppv_ex = (ggplot(df_ex.query('thresh>0.5 & thresh<1.5 & ppv>0.25 & ppv<0.5 & ppv!=0.4'),aes(x='thresh',y='ppv')) + \n", " theme_bw() + geom_step() + \n", " labs(x='Threshold', y='PPV') + \n", " ggtitle('Figure 2: Empirical PPV interpolation') + \n", " geom_segment(x=xx,xend=xend,y=yy,yend=yend,color='red') + \n", " geom_hline(yintercept=0.4,color='blue',linetype='--') + \n", " geom_vline(xintercept=xstar,color='darkgreen',linetype='--'))\n", "print(gg_ppv_ex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Though the asymptotic PPV curve is smooth (see Figure 1), the empirical PPV has is non-smooth (as Figure 2 shows). Our statistic of interest will be the value of the threshold that is needed to meet a targeted PPV. For the PPV curve, this amounts to the following:\n", "\n", "$$\n", "\\begin{align*}\n", "t^*(p) &= \\Big\\{\\inf_t : PPV(t)=p \\Big\\} \\label{eq:tstar}\n", "\\end{align*}\n", "$$\n", "\n", "Note that we want \\eqref{eq:tstar} to find the infimum of thresholds since if there are two distinct thresholds that obtain the same PPV, the smaller one will have a higher senitivity. For the empirical curve, the threshold will need to be interpolated when the targeted value cannot be found. For example, if the targeted PPV is 0.5, but the closest values we can find are PPVs of 0.49 and 0.54, for threshold values of 1 and 2, respectively, then a linear interpolation will estimate a threshold of 1.2. In the case where the targeted PPV is larger than the largest empirical PPV, an interpolation will also be used. However it is not advised to pick PPVs that are generally beyond the range of the classifier because these estimates will be noisy.\n", "\n", "The `thresh_ppv` function below also has a built-it [Jackknife](https://en.wikipedia.org/wiki/Jackknife_resampling) estimate of the statistic, since these will be required later. The Jackknife calculates the statistic by leaving one sample value, so there are $n$ estimates of it. Because the empirical PPV is discrete, deleting an observation whose value is below a given threshold will not affect the PPV and hence the choice of threshold. Deleting a positive label observation whose score is at or above the threshold will necessarily lower the PPV, and so we can interpolate the new slope/intercept position to obtain the original PPV. Similarly, deleting a negative observation will make whatever the the next smallest threshold is to the current one the optimal choice.[[^1]] It is exceedingly quick therefore to approximate the leave-one-observation out statistic for the PPV. \n", "\n", "Note the `draw_samp` function, which draws data with replacement, can make use of the `strafify` argument. Normally one would want to make use of the variation in the label prevalence, however, since this post is inspired by a validation trial for a machine learning algorithm, it is likely that the label balance is already known with a high degree of confidence. Lastly, because there can be discontinuities in the data, the `thresh_ppv` function will always try to find the infimum of values, because we know on a statistical level the relationship should be monotonic (for reasonable assumptions about the distribution of $\\Phi_0$ and $\\Phi_1$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def draw_samp(*args, strata=None):\n", " \"\"\"\n", " FUNCTION DRAWS DATA WITH REPLACEMENT (WITH STRATIFICATION IF DESIRED)\n", " \"\"\"\n", " args = list(args)\n", " if strata is not None:\n", " out = resample(*args, stratify=strata)\n", " else:\n", " out = resample(*args)\n", " if len(args) == 1:\n", " out = [out]\n", " return out\n", " \n", "def thresh_interp(df, target):\n", " \"\"\"\n", " LINEARLY INTERPOLATES PPV TO FIND THRESHOLD\n", " \"\"\"\n", " idx = df.ppv.isnull()\n", " if idx.sum() > 0:\n", " df = df[~idx]\n", " df = df.assign(err=lambda x: x.ppv - target).assign(err1 = lambda x: x.err.shift(1))\n", " if df.ppv.max() < target:\n", " df = df.query('ppv == ppv.max()').sort_values('thresh1').head(1)\n", " else:\n", " df = df[((np.sign(df.err1)==-1) & (np.sign(df.err)==1)) | ((np.sign(df.err1)==-1) & (np.sign(df.err)==0))]\n", " df = df.sort_values('thresh1').head(1)\n", " thresh0, thresh1 = df.thresh1.values[0], df.thresh.values[0]\n", " ppv0, ppv1 = df.ppv1.values[0], df.ppv.values[0]\n", " slope = (ppv1 - ppv0) / (thresh1 - thresh0)\n", " tt = thresh1 - (ppv1 - target)/slope\n", " return tt\n", "\n", "def thresh_PPV(*args, **kwargs):\n", " \"\"\"\n", " FUNCTION TO FIND THE THRESHOLD THAT CORRESPONDS TO A TARGET PPV\n", " First two *args should be y and score. target (PPV) should be in **kwargs\n", " Other optional **kwargs include: jackknife (whether to use jackknife), ret_df (whether to return data_frame)\n", " \"\"\"\n", " # --- assign --- #\n", " jackknife = False\n", " ret_df = False\n", " if 'jackknife' in kwargs:\n", " jackknife = kwargs['jackknife']\n", " if 'ret_df' in kwargs:\n", " ret_df = kwargs['ret_df']\n", " assert 'target' in kwargs\n", " target = kwargs['target']\n", " assert len(args) == 2\n", " y, score = args[0], args[1]\n", " assert len(y) == len(score)\n", " assert np.all((y==0) | (y==1))\n", " # --- calculate --- #\n", " s0, s1 = score[y == 0], score[y == 1]\n", " u_scores = np.sort(score) # Useful for step function\n", " store = np.zeros([len(u_scores),2],int)\n", " for ii, tt in enumerate(u_scores):\n", " store[ii] = [np.sum(s0 >= tt), np.sum(s1 >= tt)]\n", " dat = pd.DataFrame(store,columns=['n0','n1']).assign(thresh=u_scores,tot=store.sum(1))\n", " dat = dat.assign(thresh1=lambda x: x.thresh.shift(1), ppv=lambda x: x.n1/(x.tot))\n", " dat = dat.assign(ppv1=lambda x: x.ppv.shift(1), tot1=lambda x: x.tot.shift(1)).iloc[1:]\n", " if ret_df:\n", " return dat\n", " tstar = thresh_interp(dat, target)\n", " # Do a fast interpolation with the Jackknife\n", " # Remember: all s[1]<t and s[0]<t do not impact calculation (i.e. False negatives and True Negatives)\n", " if jackknife:\n", " tmp = dat.query('thresh>=@tstar & thresh1<@tstar')\n", " n0, n1, tot0, tot1 = tmp.n0.values[0], tmp.n1.values[0], tmp.tot1.values[0], tmp.tot.values[0]\n", " thresh0, thresh1 = tmp.thresh1.values[0], tmp.thresh.values[0]\n", " ppv0, ppv1 = tmp.ppv1.values[0], tmp.ppv.values[0]\n", " holder = []\n", " holder.append(np.repeat(tstar,len(score) - tot1)) # Removing all false/true negatives\n", " # Slope for removing TP\n", " ppv1_new, ppv0_new = (n1-1)/tot1, (n1-1)/tot0\n", " slope_new = (ppv1_new - ppv0_new) / (thresh1 - thresh0)\n", " assert ppv1_new < ppv1 # Has to decrease\n", " holder.append(np.repeat(thresh1 + (ppv1 - ppv1_new)/slope_new, n1))\n", " # Note that becasue n1/(tot0-1) = n1/tot1, implies thresh0 will be be the new choice\n", " holder.append(np.repeat(thresh0, n0))\n", " tstar = np.concatenate(holder)\n", " tstar = tstar[np.abs(tstar)!=np.Inf]\n", " return tstar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation below will show the distribution of thresholds that are found for PPV targets of 25%, 50% and 75% when the scores and labels are generated from the true data generating process (DGP). An example of the distribution generated by the bootstrap will also be made. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 900x350 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766757080797)>\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 800x350 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "<ggplot: (8766756878801)>\n" ] } ], "source": [ "# Visualize a threshold/PPV curve\n", "np.random.seed(1)\n", "target_ppv = 0.5\n", "nsim = 100\n", "holder = []\n", "for ii in range(nsim):\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " # Get threshold for different PPV scores\n", " t25 = thresh_PPV(y, score, target=target_ppv/2)\n", " t50 = thresh_PPV(y, score, target=target_ppv*1)\n", " t75 = thresh_PPV(y, score, target=target_ppv*1.5)\n", " tmp = thresh_PPV(y, score, target=target_ppv, ret_df=True)\n", " tmp = tmp[['thresh','ppv']].assign(sim=ii,t25=t25,t50=t50,t75=t75)\n", " holder.append(tmp)\n", "dat = pd.concat(holder).reset_index(None,True)\n", "dat2 = dat.groupby('sim')[['t25','t50','t75']].min().reset_index().melt('sim',None,'target')\n", "dat2 = dat2.assign(target=lambda x: x.target.str.replace('t','').astype(float)/100)\n", "\n", "# Repeat visualization for bootstrap\n", "holder_bs = []\n", "for ii in range(nsim):\n", " ytil, stil = draw_samp(y, score, strata=y)\n", " # Get threshold for different PPV scores\n", " t25 = thresh_PPV(ytil, stil, target=target_ppv/2)\n", " t50 = thresh_PPV(ytil, stil, target=target_ppv*1)\n", " t75 = thresh_PPV(ytil, stil, target=target_ppv*1.5)\n", " tmp = thresh_PPV(ytil, stil, target=target_ppv, ret_df=True)\n", " tmp = tmp[['thresh','ppv']].assign(sim=ii,t25=t25,t50=t50,t75=t75)\n", " holder_bs.append(tmp)\n", "dat_bs = pd.concat(holder_bs).reset_index(None,True)\n", "dat2_bs = dat_bs.groupby('sim')[['t25','t50','t75']].min().reset_index().melt('sim',None,'target')\n", "dat2_bs = dat2_bs.assign(target=lambda x: x.target.str.replace('t','').astype(float)/100)\n", "dat_both = pd.concat([dat.assign(tt='DGP'), dat_bs.assign(tt='Example bootstrap')])\n", "dat2_both = pd.concat([dat2.assign(tt='DGP'), dat2_bs.assign(tt='Example bootstrap')])\n", "\n", "plotnine.options.figure_size = (9,3.5)\n", "gg_ex = (ggplot(dat_both, aes(x='thresh',y='ppv',group='sim')) + theme_bw() + \n", " geom_line(alpha=0.1) + facet_wrap('~tt') + \n", " geom_point(aes(x='value',y='target',color='target.astype(str)'),\n", " size=1,alpha=0.25,data=dat2_both, inherit_aes=False) + \n", " ggtitle('Figure 3: Empirical distribution of PPV tradeoff') + \n", " guides(color=False) + labs(y='PPV',x='Threshold'))\n", "print(gg_ex)\n", "\n", "target_seq = [0.25, 0.5, 0.75]\n", "thresh_seq = [minimize_scalar(fun=lambda x: (PPV_theory(x, 0, 2, 0.1)-pp)**2).x for pp in target_seq]\n", "tmp = pd.DataFrame({'thresh':thresh_seq, 'target':target_seq})\n", "tmp2 = dat2_both.groupby(['tt','target']).value.mean().reset_index().query('tt==\"DGP\"')\n", "title = 'Figure 4: Empirical distribution of PPV tradeoff\\nBlack vertical line is ground truth'\n", "plotnine.options.figure_size = (8,3.5)\n", "gg_bs = (ggplot(dat2_both,aes(x='value',fill='tt')) + theme_bw() + guides(color=False) + \n", " facet_wrap('~target',labeller=label_both,nrow=1) + geom_density(alpha=0.5) + \n", " labs(x='Threshold',y='Density') + scale_fill_discrete(name=' ') + ggtitle(title) + \n", " geom_vline(aes(xintercept='thresh'),data=tmp) + \n", " geom_vline(aes(xintercept='value',color='tt'),data=tmp2))\n", "print(gg_bs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 3 shows the range of thresholds that would be chosen by the `thresh_PPV` function for PPV different targets, along with the different realizations of the empirical PPV curves. The second figure takes the last draw of the data in the simulation and shows the variation in the threshold statistics generated in that bootstrapping instance. Unsurprisingly the latter has more variation, since the empirical PPV for this data is noisier. Figure 4 compares the distribution of a bootstrap instance with those of the point estimates from the simulation. Note these distributions are not statistically comparable, since the DGP is the true variation and the bootstrap distribution only comes from one example. The inference procedures needed to obtain statistically meaningful insights from the bootstrap distribution are be described below.\n", "\n", "## (2) Bootstrapping approaches\n", "\n", "The statistical quality of a bootstrap CI is determined by its coverage. For example, a two-sided confidence interval of level $1-\\alpha$ has the \"right\" coverage when:\n", "\n", "$$\n", "P(\\theta_l \\leq \\theta \\leq \\theta_u) = c = 1-\\alpha\n", "$$\n", "\n", "If $c > 1-\\alpha$, then the CI is said to be conservative because it under-rejects the true null. Though it is better to be conservative than to over-reject the null, it is still problematic because the test will have less power than expected. In the case of a one-sided CI, proper coverage will entail:\n", "\n", "$$\n", "\\begin{align*}\n", "P(\\theta_l \\leq \\theta) &= 1-\\alpha \\\\\n", "P(\\theta_u \\geq \\theta) &= 1-\\alpha,\n", "\\end{align*}\n", "$$\n", "\n", "Depending on whether it is an upper or lower bound. One-sided CIs are used when the direction of statistic is known. Using the running example of the PPV, if the researcher wants to establish that the PPV as *at least* some amount, say 50%, then it would be desirable to find a upper-bound of the threshold, $t_u$ such that: $P(t_u \\geq t^{*}(p)) = 1-\\alpha$. If $t_u > t^{*}(p)$ from \\eqref{eq:tstar}, then $PPV_\\Phi(t_u) > p$ from \\eqref{eq:PPV_true}. \n", "\n", "There are several reasons why the bootstrap distribution of statistics will not provide the right coverage. First, the ECDF is an approximation of the CDF which introduces sampling error. Second, the bootstrap statistic may be biased. When the average of the bootstrapped statistics will differ in expectation to the original statistic $E[\\bar\\theta^* - \\hat\\theta] \\neq 0$, the bootstrap is said to be biased. This phenomenon can arise for any statistic with [finite-sample bias](https://en.wikipedia.org/wiki/Bias_of_an_estimator). Since sampling with replacement is equivalent to generating a statistic with fewer observations, finite sampling bias will be seen indirectly through this mechanism.[[^2]] Third, the distribution of a (bootstrapped) statistic may be [skewed](https://en.wikipedia.org/wiki/Skewness), which will cause symmetric CIs to be erroneous. \n", "\n", "### Approach #1: CI with BS-standard error\n", "\n", "If the statistic of interest is normally distributed, then confidence intervals can be obtain using the well-known formula,\n", "\n", "$$\n", "\\begin{align*}\n", "[\\hat\\theta_l, \\hat\\theta_u ] &= \\hat{\\theta} \\pm t_{\\alpha/2} \\cdot \\hat{\\sigma}_\\theta\n", "\\end{align*}\n", "$$\n", "\n", "The variance of the statistic $\\sigma$ is simply estimated from the bootstrapped samples. Note that a student-t distribution with $n-1$ degrees of freedom is used for the quantile since the value of the standard error is estimated rather than known. The drawbacks with this approach are first that it assumes the statistic has a normal distribution, and second that it is symmetric. Many statistics (such as the PPV threshold) will neither be normal nor symmetric.\n", "\n", "### Approach #2: Quantile Bootstrap\n", "\n", "If we rank order the realizations of the bootstrap statistics: $\\theta^*_{(1)} \\leq \\theta^*_{(2)} \\leq \\dots \\leq \\theta^*_{(n)}$, then the quantile bootstrap simply returns the empirical quantiles for the $\\alpha/2$ and $1-\\alpha/2$ percentiles as the confidence intervals.\n", "\n", "$$\n", "\\begin{align*}\n", "[\\hat\\theta_l, \\hat\\theta_u ] = [ \\hat\\theta^*_{(n\\cdot\\alpha/2)}, \\hat\\theta^*_{(n\\cdot(1-\\alpha/2))} ]\n", "\\end{align*}\n", "$$\n", "\n", "The advantage of the quantile bootstrap is that is easy to calculate, intuitive, and can handle skewed data (i.e. the CIs are not necessarily symmetric about the mean/median). However if the quantile method will be unable to account for a biased statistic.\n", "\n", "### Approach #3: Bias corrected and accelerated (BCa) Bootstrap\n", "\n", "The BCa procedure largely remedies the problems of bias and skewness and will obtain coverage rates that are very close to their nominal level. \n", "\n", "$$\n", "\\begin{align*}\n", "[\\hat\\theta_l, \\hat\\theta_u ] &= [ \\hat\\theta^*_{\\alpha_1}, \\hat\\theta^*_{\\alpha_2} ] \\\\\n", "\\alpha_1 &= \\Phi\\Bigg(\\hat{z}_0 + \\frac{\\hat{z}_0+z_{\\alpha}}{1-\\hat{a}(\\hat{z}_0+z_{\\alpha})}\\Bigg) \\\\\n", "\\alpha_2 &= \\Phi\\Bigg(\\hat{z}_0 + \\frac{\\hat{z}_0+z_{1-\\alpha}}{1-\\hat{a}(\\hat{z}_0+z_{1-\\alpha})}\\Bigg)\n", "\\end{align*}\n", "$$\n", "\n", "There are two terms which need to be estimated when using the BCa: 1) the acceleration parameter $\\hat{a}$, and 2) the bias-correction factor $\\hat{z}_0$. The other terms are deterministic: ($\\Phi$ and $z_\\alpha=\\Phi^{-1}(\n", "\\alpha)$). Notice if the acceleration and bias correction factors are zero, then $l=\\alpha_1=\\alpha$ and $u=\\alpha_2=1-\\alpha$, which is equivalent to the quantile bootstrap.[[^3]] \n", "\n", "$$\n", "\\begin{align*}\n", "\\hat{z}_0 &= \\Phi^{-1}\\Bigg(\\frac{1}{B}\\sum_i \\hat{\\theta}^*_i < \\hat\\theta \\Bigg) \\\\\n", "\\hat{a} &= \\frac{\\sum_i (\\bar{\\theta}^{-i} - \\hat{\\theta}^{-i} )^3 }{6 \\cdot \\big[\\sum_i (\\bar{\\theta}^{-i} - \\hat{\\theta}^{-i} )^2 \\big]^{3/2}}\n", "\\end{align*}\n", "$$\n", "\n", "Also note that the bias correction term is a count in the median bias (rather than the magnitude of the bias). The acceleration factor is basically Pearson's skewness coefficient divided by 1/6th (see the original Efron paper for a discussion of this constant).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (3) Simulation of PPV CIs\n", "\n", "In this section I will define the `bootstrap` class which will carry the three bootstrap-CI approaches discussed above. A simulation will then be run using the DGP discussed above with $\\mu_0=0$, $\\mu_1=2$, and $\\sigma_0=\\sigma_1=1$. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "BOOTSTRAPPING SUPPORT FUNCTIONS\n", "\"\"\"\n", " \n", "class bootstrap():\n", " def __init__(self, nboot, func):\n", " self.nboot = nboot\n", " self.stat = func\n", " \n", " def fit(self, *args, **kwargs):\n", " strata=None\n", " if 'strata' in kwargs:\n", " strata = kwargs['strata']\n", " # Get the baseline stat\n", " self.theta = self.stat(*args, **kwargs)\n", " self.store_theta = np.zeros(self.nboot)\n", " self.jn = self.stat(*args, **kwargs, jackknife=True)\n", " self.n = len(y)\n", " for ii in range(self.nboot): # Fit bootstrap\n", " args_til = draw_samp(*args, strata=strata)\n", " self.store_theta[ii] = self.stat(*args_til, **kwargs)\n", " self.se = self.store_theta.std()\n", " \n", " def get_ci(self, alpha=0.05, symmetric=True):\n", " self.di_ci = {'quantile':[], 'se':[], 'bca':[]}\n", " self.di_ci['quantile'] = self.ci_quantile(alpha, symmetric)\n", " self.di_ci['se'] = self.ci_se(alpha, symmetric)\n", " self.di_ci['bca'] = self.ci_bca(alpha, symmetric)\n", "\n", " def ci_quantile(self, alpha, symmetric):\n", " if symmetric:\n", " return np.quantile(self.store_theta, [alpha/2,1-alpha/2])\n", " else:\n", " return np.quantile(self.store_theta, alpha)\n", " \n", " def ci_se(self, alpha, symmetric):\n", " if symmetric:\n", " qq = t(df=self.n).ppf(1-alpha/2)\n", " return np.array([self.theta - self.se*qq, self.theta + self.se*qq])\n", " else:\n", " qq = t(df=self.n).ppf(1-alpha)\n", " return self.theta - qq*self.se\n", " \n", " def ci_bca(self, alpha, symmetric):\n", " if symmetric:\n", " ql, qu = norm.ppf(alpha/2), norm.ppf(1-alpha/2)\n", " else:\n", " ql, qu = norm.ppf(alpha), norm.ppf(1-alpha)\n", " # Acceleration factor\n", " num = np.sum((self.jn.mean() - self.jn)**3)\n", " den = 6*np.sum((self.jn.mean() - self.jn)**2)**1.5\n", " ahat = num / den\n", " # Bias correction factor\n", " zhat = norm.ppf(np.mean(self.store_theta < self.theta))\n", " a1 = norm.cdf(zhat + (zhat + ql)/(1-ahat*(zhat+ql)))\n", " a2 = norm.cdf(zhat + (zhat + qu)/(1-ahat*(zhat+qu)))\n", " #print('Accel: %0.3f, bz: %0.3f, a1: %0.3f, a2: %0.3f' % (ahat, zhat, a1, a2))\n", " if symmetric:\n", " return np.quantile(self.store_theta, [a1, a2])\n", " else:\n", " return np.quantile(self.store_theta, a1)\n", " \n", "np.random.seed(1234)\n", "alpha_seq = np.linspace(0.05,0.4, 8)\n", "#nsim, nboot = 2000, 2000\n", "nsim, nboot = 2, 2000\n", "n0, n1, mu1 = 90, 10, 2\n", "\n", "stime = time()\n", "store = []\n", "for ii in range(nsim):\n", " print('Simulation %i of %i' % (ii+1,nsim))\n", " y, score = dgp(n0=n0, n1=n1, mu=mu1)\n", " sim = bootstrap(nboot=nboot, func=thresh_PPV)\n", " sim.fit(y, score, target=target_ppv)\n", " holder = []\n", " for alpha in alpha_seq:\n", " sim.get_ci(alpha=alpha,symmetric=True)\n", " holder.append(pd.DataFrame.from_dict(sim.di_ci).assign(tt=['lb','ub']).assign(alpha=alpha))\n", " holder = pd.concat(holder).assign(sim=ii)\n", " store.append(holder)\n", " nleft, rate = nsim-(ii+1), (ii+1)/(time() - stime)\n", " print('ETA: %0.1f minutes' % (nleft/rate/60))\n", "res = pd.concat(store).melt(['alpha','sim','tt'],None,'method')\n", "#res.to_csv('res.csv',index=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/edrysdale/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 550x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8766715698169)>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = pd.read_csv('res.csv')\n", "# Get ground truth threshold for 50% PPV\n", "thresh_ppv50 = minimize_scalar(fun=lambda x: (PPV_theory(x, mu0, mu1, prev)-target_ppv)**2).x\n", "res = res.assign(ppv=lambda x: PPV_theory(x.value, mu0, mu1, prev))\n", "res = res[res.value.notnull()].reset_index(None,True)\n", "cn_gg = ['sim','alpha','method']\n", "res2 = res.pivot_table('ppv',cn_gg,'tt').assign(lb=lambda x: x.lb > target_ppv, ub=lambda x: x.ub < target_ppv)\n", "res2 = res2.reset_index().melt(cn_gg).rename(columns={'value':'viol'})\n", "assert res.shape[0] == res2.shape[0]\n", "res = res.merge(res2)\n", "del res2\n", "res_agg = res.groupby(['method','alpha','tt']).viol.apply(lambda x: pd.Series({'mu':x.mean(),'n':x.shape[0]})).reset_index()\n", "res_agg = res_agg.pivot_table('viol',['method','alpha','tt'],'level_3').reset_index().assign(n=lambda x: x.n.astype(int))\n", "res_agg = res_agg.assign(ci_u=lambda x: x.mu+norm.ppf(0.975)*np.sqrt(x.mu*(1-x.mu)/x.n),\n", " ci_l=lambda x: x.mu-norm.ppf(0.975)*np.sqrt(x.mu*(1-x.mu)/x.n),\n", " alpha2=lambda x: x.alpha/2)\n", "di_method = {'bca':'BCa', 'quantile':'Quantile', 'se':'SE', 'arch':'ARCH-BCa'}\n", "di_tt = {'lb':'lower bound', 'ub':'upper bound'}\n", "res_agg = res_agg.assign(gg = lambda x: x.method.map(di_method)+', ('+x.tt.map(di_tt)+')')\n", "\n", "colz = list(np.repeat(gg_color_hue(3),2))\n", "shapez = list(np.tile(['+','x'],3))\n", "\n", "plotnine.options.figure_size = (5.5,4)\n", "xx = list(np.arange(0,0.25,0.05))\n", "gg_inf = (ggplot(res_agg,aes(x='alpha2',y='mu',color='gg',shape='gg')) + theme_bw() + geom_point(size=3) + \n", " scale_shape_manual(name='Method',values=shapez) + \n", " scale_color_manual(name='Method',values=colz) + \n", " geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", " labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", " scale_x_continuous(limits=[0,0.25],breaks=xx) + \n", " scale_y_continuous(limits=[0,0.25],breaks=xx) + \n", " ggtitle('Figure 5: CI coverage for PPV threshold'))\n", "gg_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 5 shows that only the BCa approach gets anywhere near the right coverage for the upper bound, and the other approaches are much too conservative. Interestingly even the BCa approach has a lower bound interval which is conservative. This likely stems from the obvious finite-sample bias that comes from the empirical PPV curve." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# res_agg2 = res_agg.groupby(['method','alpha']).mu.sum().reset_index()\n", "# res_agg2 = res_agg2.assign(gg = lambda x: x.method.map(di_method))\n", "# plotnine.options.figure_size = (5.5,4)\n", "# gg_inf = (ggplot(res_agg2,aes(x='alpha',y='mu',color='gg')) + theme_bw() + \n", "# scale_color_discrete(name='Method') + geom_point(size=3) + \n", "# geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", "# labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", "# scale_x_continuous(limits=[0,0.25],breaks=xx) + \n", "# scale_y_continuous(limits=[0,0.25],breaks=xx) + \n", "# ggtitle('Simulation'))\n", "# gg_inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (4) Compare to ARCH\n", "\n", "To show that our functions developed in the post will obtain (nearly) identical results to using the `arch` package's BCa approach, we will turn off the stratified sampling and calculate the variance of a standard normal distribution. To comply with the `bootstrap` class, the `var_calc` function will have a built-in Jackknife estimator, which is can calculated efficiently since the determintic formula for the sample variance can be vectorized." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from arch.bootstrap import IIDBootstrap\n", "\n", "def vfun(x):\n", " return x.var(ddof=1)\n", "\n", "def var_calc(*args, **kwargs):\n", " \"\"\"\n", " *args should be an IID draw\n", " \"\"\"\n", " jackknife = False\n", " if 'jackknife' in kwargs:\n", " jackknife = kwargs['jackknife']\n", " # Calculate stat\n", " x = args[0]\n", " n = len(x)\n", " vv = x.var(ddof=1)\n", " if jackknife: # Built in jackknife\n", " mu = x.mean()\n", " s2 = np.sum(x**2)\n", " mu = (mu - x/n)*(n/(n-1))\n", " s2 = s2 - x**2\n", " vv = (s2 - (n-1)*mu**2)/(n-2)\n", " return vv\n", "\n", "np.random.seed(1234)\n", "# nsim, nboot, n = 2000, 2000, 100\n", "nsim, nboot, n = 2, 2000, 100\n", "t1err = 0.1\n", "alpha_seq = np.arange(0.05, 0.25, 0.05)\n", "store = []\n", "stime = time()\n", "for ii in range(nsim):\n", " x = stats.norm(0,1).rvs(n)\n", " sim = bootstrap(nboot=nboot, func=var_calc)\n", " sim.fit(x)\n", " holder = []\n", " for alpha in alpha_seq:\n", " sim.get_ci(alpha=alpha,symmetric=True)\n", " holder.append(pd.DataFrame.from_dict(sim.di_ci).assign(tt=['lb','ub']).assign(alpha=alpha))\n", " holder = pd.concat(holder).assign(sim=ii)\n", " # Calculate with ARCH\n", " bs = IIDBootstrap(x)\n", " mat = np.zeros([len(alpha_seq), 2])\n", " for jj, alpha in enumerate(alpha_seq):\n", " ci_bca = bs.conf_int(func=vfun, reps=nboot, method='bca',size=1-alpha, tail='two').flatten()\n", " mat[jj] = ci_bca\n", " holder2 = pd.DataFrame(mat,columns=['lb','ub']).assign(alpha=alpha_seq,sim=ii)\n", " holder2 = holder2.melt(['alpha','sim'],None,'tt').rename(columns={'value':'arch'})\n", " holder3 = holder2.merge(holder,on=['alpha','sim','tt'])\n", " store.append(holder3)\n", " nleft, rate = nsim-(ii+1), (ii+1)/(time() - stime)\n", " if (ii+1) % 100 == 0:\n", " print('Simulation %i of %i' % (ii+1,nsim))\n", " print('ETA: %0.1f minutes' % (nleft/rate/60))\n", "res_all = pd.concat(store).reset_index(None,True)\n", "res_all = res_all.melt(['sim','alpha','tt'],None,'method').assign(viol=lambda x: np.where(x.tt=='lb',x.value>1,x.value<1))\n", "#res_all.to_csv('res_all.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 550x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8766715455761)>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_all = pd.read_csv('res_all.csv')\n", "res_all_agg = res_all.groupby(['method','alpha','tt']).viol.apply(lambda x: pd.Series({'mu':x.mean(),'n':x.shape[0]})).reset_index()\n", "res_all_agg = res_all_agg.pivot_table('viol',['method','alpha','tt'],'level_3').reset_index().assign(n=lambda x: x.n.astype(int))\n", "res_all_agg = res_all_agg.assign(gg = lambda x: x.method.map(di_method)+', ('+x.tt.map(di_tt)+')',\n", " alpha2 = lambda x: x.alpha / 2)\n", "colz = list(np.repeat(['black']+gg_color_hue(3),2))\n", "shapez = list(np.tile(['+','x'],4))\n", "\n", "plotnine.options.figure_size = (5.5,4)\n", "yy = list(np.arange(0,0.1751,0.025))\n", "xx = list(np.arange(0,0.1251,0.025))\n", "gg_comp = (ggplot(res_all_agg,aes(x='alpha2',y='mu',color='gg',shape='gg')) + theme_bw() + geom_point(size=3) + \n", " scale_shape_manual(name='Method',values=shapez) + \n", " scale_color_manual(name='Method',values=colz) + \n", " geom_abline(intercept=0,slope=1,color='blue',linetype='--') + \n", " labs(x='Type-I error (theory)',y='Type-I error (actual)') + \n", " scale_x_continuous(limits=[min(xx),max(xx)],breaks=xx) + \n", " scale_y_continuous(limits=[min(yy),max(yy)],breaks=yy) + \n", " ggtitle('Figure 6: Comparison to arch package'))\n", "gg_comp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 6 shows that the BCa approach yields (virtually) identical estimate to the one obtainable from the `arch` package. Since the estimate of the variance is skewed, it also shows the superiority of the BCa method over the simpler bootstrapping approaches since ther coverage for both lower and upper bounds is close to the expected level.\n", "\n", "Most researchers that use the bootstrap to carry out inference on a statistic should probably be using the BCa approach. When the Jackknife estimator can be implemented effeciently, there is almost no additional computational overhead for using the BCa and it obtains an empirical coverage much closer to the expected level. Beyond sample means, most statistics show some form of skew or bias meaning the symmetric standard-error or quantile approach will be suboptimal. The BCa not only helps to remedy these problems, it gives researcher estimates that are much more efficient." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

dnc1994/Kaggle-Playground

misc/santander/Explore.ipynb

1

173384

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "import pandas as pd\n", "import zipfile\n", "import sklearn\n", "from sklearn import cross_validation\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = zipfile.ZipFile('train.csv.zip')\n", "df = pd.read_csv(z.open('train.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Target Exploration" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xab67940>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x3f764e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.TARGET.value_counts().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0411987070619\n" ] } ], "source": [ "num_positive = len(df[df['TARGET'] == 1])\n", "num_negative = len(df) - num_positive\n", "print num_positive / float(num_negative)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reverse Feature Engineering" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drop_constants(df):\n", " constant_features = []\n", " for col in df.columns:\n", " if df[col].std() == 0:\n", " constant_features.append(col)\n", " ndf = df.drop(constant_features, axis=1)\n", " print len(constant_features), 'features dropped because they are constant.'\n", " return (constant_features, ndf)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def drop_lin_coms(df):\n", " with open('rfe_from_forum.txt', 'r') as f:\n", " lines = f.readlines()\n", " lines = [line.strip().split(' ')[0] for line in lines]\n", " lin_com_features = lines\n", " ndf = df.drop(lin_com_features, axis=1)\n", " print len(lin_com_features), 'features dropped because they are linear combinations.'\n", " return (lin_com_features, ndf)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def drop_high_corrs(df):\n", " high_corr_features = []\n", " safe = []\n", " for x in df.columns.values:\n", " for y in safe:\n", " if (abs(np.corrcoef(df[y], df[x])[0,1]) > 0.999):\n", " high_corr_features.append(x)\n", " break\n", " safe.append(x)\n", " ndf = df.drop(high_corr_features, axis=1)\n", " print len(high_corr_features), 'features dropped because they are in linear relation with other features.'\n", " return (high_corr_features, ndf)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34 features dropped because they are constant.\n", "40 features dropped because they are linear combinations.\n", "56 features dropped because they are in linear relation with other features.\n" ] } ], "source": [ "df = pd.read_csv(z.open('train.csv'))\n", "constant_features, ndf = drop_constants(df)\n", "lin_com_features, ndf = drop_lin_coms(ndf)\n", "high_corr_features, ndf = drop_high_corrs(ndf)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dropped_features = constant_features + lin_com_features + high_corr_features\n", "with open('dropped_features.txt', 'w') as f:\n", " f.writelines([s+'\\n' for s in dropped_features])\n", "import cPickle as pickle\n", "with open('dropped_features.dump', 'w') as f:\n", " pickle.dump(dropped_features, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downsampling Majority Class" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# not necessary if we use boosting" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_positive = df[df['TARGET'] == 1]\n", "df_negative = df[df['TARGET'] == 0].sample(frac=num_positive / float(num_negative))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ndf = pd.concat([df_positive, df_negative])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Importance" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x172de2e8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gpZVWbuH+zMzaj7OjrWJ2mb+NlrV2cP1Fc/3FKnP9zo4eIM6ZNjOzVnATrsE502Zm1gqd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\nJbttH/o/N3poRJwsaWyTWmu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"text/plain": [ "<matplotlib.figure.Figure at 0x162d5710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = df.drop(['ID', 'TARGET'], axis=1)\n", "y = df['TARGET']\n", "rfc = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=16, oob_score=True)\n", "rfc.fit(x, y)\n", "feat_imp = pd.Series(rfc.feature_importances_, index=x.columns)\n", "feat_imp.sort_values(inplace=True, ascending=False)\n", "feat_imp.head(20).plot(kind='barh', title='Feature importance')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Auc: 0.829 (+/- 0.013)\n" ] }, { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=16, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n", " oob_score=True, random_state=None, verbose=0, warm_start=False)" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cv = cross_validation.StratifiedKFold(y, n_folds=4, shuffle=True)\n", "scores = cross_validation.cross_val_score(rfc, x, y, cv=cv, scoring='roc_auc')\n", "print(\"Auc: %0.3f (+/- %0.3f)\" % (scores.mean(), scores.std()))\n", "rfc.fit(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Starter Submission" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import datetime" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def export_predictions(model, method, num=1):\n", " test = pandas.read_csv(zipfile.ZipFile('test.csv.zip').open('test.csv'))\n", " id_test = test['ID']\n", " x_test = test.drop(['ID'], axis=1)\n", " y_pred = model.predict_proba(x_test)\n", " sub = pandas.DataFrame({'ID': id_test, 'TARGET': y_pred[:,1]})\n", " filename = 'submission_{date}#{num}_{method}.csv'.format(date=datetime.date.today().isoformat(), num=num, method=method)\n", " sub.to_csv(filename, index=False)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": true }, "outputs": [], "source": [ "export_predictions(rfc, 'rfc-undersampling-tuned', 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Visualization" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def filter_feature_by_importance_percentage(imp, per):\n", " assert per <= 1.0 and per >= 0\n", " imp_sorted = imp.sort_values(ascending=False)\n", " total_per = 0\n", " for (i, v) in enumerate(imp_sorted):\n", " total_per += v\n", " if total_per >= per:\n", " return imp[:i+1]\n", " return imp" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "369\n", "110\n" ] } ], "source": [ "print len(feat_imp)\n", "filtered_feat_imp = filter_feature_by_importance_percentage(feat_imp, 0.95)\n", "filtered_feat_imp_list = list(filtered_feat_imp.index.values)\n", "print len(filtered_feat_imp)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x15de6b70>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Xc+9n3tsUfmN22+9ntNPX2ByukhfT4bbb7t+HX5CSZIuHwZpSuRe+NSinDS4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\ni7IRua7t4bq2h+vaHhs1J83tTq0Kcxo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"text/plain": [ "<matplotlib.figure.Figure at 0x11841128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='TARGET', y='var15', data=df)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xf73cf60>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb47b898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# How to scale this?\n", "sns.boxplot(x='TARGET', y='var38', data=df)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def top_k_feature_pairwise_plot(df, imp, target):\n", " df = df[imp + [target]]\n", " sns.pairplot(data = df, hue=target, vars=imp, size=10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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emxpLkjz59JkkyRf/7MWcPDGlZBT+C5yans/Hn3h659r3EgCvRUuzFwAAAPBG\nN7N4LpeX57OysVo3P7d4LrOXlpJcKxVdXt2ou396trpna4Q3khu/d3wvAfBa2IkOAADQYIeLh7Pc\neiFbtVr9vPdwVg/2JEl6Om/+8exYqbgn64M3mvEbvnd8LwHwWgjRAQAAGuzBY/flL84+l56+3gxP\nDqW6uphSdyml2ltzuW89f/uh8Rwb6U1bW0tKB3vqzkQHfnKTEwM5eWIqp2erOVYq+l4C4DURogMA\nADRQrVbLy2evpFY9mM2+K1lYvprh7lLar9yRMysrOXNhMWPDxfzV+0bSdt2Jm7VaLadeurkYUWEi\n/HiFFHJ8YtA56AC8LoToAAAADXRqej7nLy9ntf+F/MFzn9+Z/3z57+RTn13eud6q1fL2+w/Xfd6t\nihEVJgIA7C3FogAAAA10eraasxcXM79xoW5+YXm27vrMhR9dhLh9rTARAGBv2YkOAADQQOOlYjo7\nW7PWNlI3H+kuJfnhTvSx4eJNn3e97WJEhYkAAHtLiA4AANBAkxMD6elKFpbK+cjb3p/F9cUsri2l\nq9Cb//7xO/PSuWoOD/Wkv6c9W9lKyyu/MHx9MWJ/X0fOzS2mtZDUkvzcX7szB3o7M3aoO+VjChMB\nABpJiA4AANBAhRQyfX4l6Z/NywvT+fr0t1+589W89+gH85VvXs0vvrOc/+N3/3M+9vjxPDJZ2vm8\n7bPOt89Af2xqLE8+fWbnvU+emFIqCgDQYM5EBwAAaLCXzy9mZnEmKxurdfP59bkkyeylpSTJ9MzN\n55tff+b58urGrvcAAGgMIToAAECDHS0Vc7g4mq62rrr5QPuhJEnpYE+SZHz05vPNrz8Dvaez/peJ\nnYcOANB4jnMBAABosEfvG8nL5zvT3tKakfJIrqxUM9J5NJdO9+eX3nM0q6tr+djjx/Pw5PBNn3v9\n2eh3HC7mwXtGcnq2mmOlYu6dcB46AECjCdEBAAAabG15K6drz2d2dTal4nCqi11JLTl6qDsvnF3M\nyMHunJtbyjefm8tgX3vOrb+Yhc25DLYNp9QykXsnBnbOR09S9zEAAI0lRAcAAGiwby98J5859dmd\n60fHH8wT3/uj/PydJ/LFb1xOcq009Au//4N88P19+cLLv7Pz2od735uNrbcJzgEAmsSZ6AAAAA12\nbvFc3fV2wej55Zmd2XZp6HbZ6La11nkFogAATSREBwAAaLAjvYfrrrvaOpMkI92jO7PuV0pDB9vr\nz0Xv2BxcmkOtAAAgAElEQVRQIAoA0ESOcwEAAGiwB0YfSArJ7OJsRopDuTi/kg/d9ZEUV0fznkf6\nM3ywOxfnV/Irjx/Pwd6OfPCnTmRhcy4DrYdSar0j5WMKRAEAmkWIDgAA0GjL1/6nu/XaDvSN1mpa\nO4rpLVz7kay1pZC+3vbMXl5OLcnK1UN5a+mOTE4MpJDCTW9Xq9VyavraMS/jpeKurwMA4LUTogMA\nADTYdrHo++55Zz7zzOd25h++9wP54jcWk1wrFk2Szz35ws79kyemblkoemp6Ph9/4ukf+zoAAF47\nZ6IDAAA02Hax6OXl+VvOk2vFotvlott2KxS9ca54FACgcexEBwAAaLDtYtGD3fVnmx/uPZzk2k70\n7s62mw5k2a1QdPyGueJRAIDGEaIDAAA02AOjD6RQSBZW5vPh+x7P7NW5jPaOZnj9rrz7kYUcPtSd\n5dXNLK1s5GOPH8/C1bUcKxVz78StC0UnJwZy8sRUTs9Wf+TrAAB47YToAAAAjfZKsejK1mq6Njsy\n3HpHWi6Vsty5lf7e1iS1HDh8OdXq2bT2jeWdk/emJS2p1Wr57vTlmwpECynk+MRgjk8MXisZfUnJ\nKABAowjRAQAAGmy7WHTbo+MPZuPqQo6t3ZX2tva8UP1+npr9wnWf8dE8NPq2V1UgqmQUAKCxFIsC\nAAA02PUFokmysrGatdb5nL24mLMXF7PWWl84+vKVs0leXYGoklEAgMayEx0AAKDBtotFt3W1dWZj\ncyBHhnqTJC+v1u8cP3rgSJJXVyCqZBQAoLGE6AAAAA32wOgDaWlJZhZn09/Vl7bVwbT1jaanszUX\nLi/lzt67cvf4R3O2ejZjfUfy4OjxJK+uQFTJKABAYwnRAQAAGm25JV2Ld2SsrzXLm0u5UHsxh4fW\n0rZ0Z3q6OzN7aSmlwkjed/x4Wq8/dbP2ww9vrAqt1Wo5NX2tULS/rzNtrepEAQAaQYgOAADQYE89\nP5vWwy9kaW05n3/uj3fmH7r3/Xnh+cE8+fSZJMnW1mQee9sPj375UaWhN957bGosn/7K84pFAQBe\nZ4pFAQAAGuzMXDXnqudzebm+QHRmcSbLqxs71y+fX6y7/6NKQ2+8t/0+ikUBAF5fdqIDAAA02Nhw\nMW3F0Sxt1Ifko72jWer84Y9lR0d66+7/qNLQG+91v/I+ikUBAF5fQnQAAIAGe/j+Ur7/g/Z09L2Y\nDx5/b+aWLmW053D6l+7MXWNb6etpT+lgTx45PlL3eT+qNPT6e/19HVlcWs/JE1OKRQEAXmdCdAAA\ngAaq1WqZOb+Qq51nsrhyJV0dHanVaikUNjNfXctWrTV3jBbzl2ev5t9952wG+zqzcHU1Rw71ZnJi\nIMcnBm95xnkhhV3vAQDw+nEmOgAAQAOdmp7P6dWX8lvP/Va2WrbymWc+nz998Rv59DO/n5Reym9+\n8dlcvrqeL3/jpXzmK8/nuz+4lJfnFvMvnng6p16a//H/BwAANJQQHQAAoIFOz1YzsziTJDcVi56r\nnk+SzF5a2pktr24oCQUA2Ecc5wIAANBA46ViLraMJkkOdtefV364OJJkNaWDPTuz7uuKRpWEAgA0\nnxAdAACggSYnBnLuYvJ37/m7qW7N5cP3vS8z1Qs5XBxJbXYiv/Tu1vT1tOZdj0xkoNiZwWJHFqpr\nSkIBAPYJIToAAEADFVLIYE9/ZhY2kq5kaeNi2gqtKdTa09PdkfWNWmq15J7xwbS2FHJ6tprxUjGT\nEwMppJBarXbtXPUb5gAA7A0hOgAAQIM99fxsCgMzObP0vXx9+ts78w9Nvj9Xp4+ks701rS0b+dSX\nn9u5d/LEVI5PDObU9Hw+/sTTN80BANgbikUBAAAa7MxcNbNLs1nZWK2bzyzN5OLCSmYvLeXsxcW6\ne9ulojeWiyobBQDYW3aiAwAANNjYcDEtPaN5ef1q3Xy0ZzRX+7uu7URvrT+iZbtUdPyGclFlowAA\ne0uIDgAA0GAPv3Uk370ylzt6xtLzlq4UO3rS196btY3NHBqfT9fSkaRQy4f/9t2Zr67m8FBvLlxe\nyqkk90z05+SJqZyereZYqahsFABgjwnRAQAAGuxbF57JctuFfP67f7wze3T8wRzpG80nT/3fee/R\nD6a9eiRn5xbz5NNn8tjUWJLkk1+u7JyB7hx0AIDmcCY6AABAg80snsvl5fm62crGas4vziVJ5tfn\nMntpKcurG0mS5dWNnY+dgQ4A0Fx2ogMAADTY4eLhLLdeqJt1tXVmpPdQkmSg/VDaD/ZkfXMrSdLd\n+cMf1ZyBDgDQXEJ0AACABnvw2H05NfNcPnLf4zm/fCm97T0ptvdmZW0tJ+7+SDoWR1PrqaW/2JET\n77w7A70dWaiu5eSJKWegAwA0mRAdAACg0ZZr2WhdzvrWZvo6enNltZrulmLaLt+VQqGQs/NLOdjf\nlWJXe+avrqa749qPaoVbvFWtVsup6fmcnq1mvFTM5MRACrd8JXvFMwGANzYhOgAAQIN9e+E7OV19\nKUny9elv78x/7s73Zeb54STJfHUtTz59ZufeY1Nj+fRXnt8pFt12ano+H3/i6Z3rG++z9zwTAHhj\nUywKAADQYOcWz2VlYzUrG6t18/mNCzslottFott2Kxb9cdfsPc8EAN7Y7EQHAABosCO9hzNdXb9p\nPtA2nJXOWx/dsl0uemOx6PgN14pHm88zAYA3NiE6AABAgz0w+kA6LrRleWsx7598V66sVjPcVUrt\nwrHcdbSQufmVDPV35sihuzNfXc3oUE9WVzdvWSw6OTGQkyemcnq2mmOlouLRfcAzAYA3NiE6AABA\noy23pGNxIrWus6kW5q7NWtbTM3wpy3NDGR3qzpkLSxnq70pvd1vW1ms5UOzMubnFFJK6ospCCjk+\nMejM7X3EMwGANzZnogMAADTYU8/P5krLy/nL6nP5f099OX/ywtfyxDOfz9zWTF5Y/F4uLqzl6tJ6\nPvnF51KrFfJbf/RcTr14KS/PLeZfPPF0Tr003+w/AgDAm5YQHQAAoMHOzFUzszhzU7Ho5eX5rLXO\n5+LCyk6R6MWFlSSpKxtVVAkA0DyOcwEAAGiwseFiWnpH8/La1br5YPdA1uY6MtTfla1aLUky1N+V\n5IfFoomiSgCAZhKiAwAANNjD95fywosd6S62pXTvUK6sVDPUczC9WwPp6T2Y9taWrKxt5JfefU+W\nVtfzkZ8pp6+nPVeqa7csFwUAYO8I0QEAABptuZZq15mst1xNW9qyWdtKIYW0FFrS2d6aWmo5NNCd\n+SsreevRgboi0SSp1Wr57vTlnJ6tZrxUvOk+AACNI0QHAABosG9deCYvr1Uy1HMwn3/uj3fm77vn\nnenoWM3m5ZF8+o8rO/OTJ6ZyfGJw5/rU9Hw+/sTTu94HAKBxFIsCAAA02MziuaxsrOby8nzd/PLy\nfM4vz2T20lLd/MYi0R93DQBA49iJDgAA0GCHi4dzevVqDnbXn20+2D2QjpahbA711M1vLBIdv+Fa\n0SgAwN4RogMAADTYg8fuS+dMS9ZbruQX7ntvLixeykjvofTVDmZ97WDSk/y990zm8tVrZ6LfWCQ6\nOTGQkyemcnq2mmOloqJRAIA9JEQHAABotOWtbLYuZWVzNVdXF3Owuz8tWy3ZzFZqtaRWSy7ML6ev\npyOLKxvZqtXy3PR8XZHo8YnBfXMOeq1Wy6kb1qfoFAB4oxKiAwAANNi3r34rp6+8nK9Pf3tn9uj4\ng0mSwZY7s3W5lC/+2YtJksemxrKytpFP/OGzO6/db0Wiik4BgDcTxaIAAAANdq46m5WN1brZysZq\nVjZWM78+l4sLKzvz5dWNvHx+se61+61IVNEpAPBmIkQHAABosCPF0XS1ddXNuto609XWmYH2Qxnq\n/+G97s62HN3nRaKKTgGANxPHuQAAADTYAyMPpq3QktK9Q7m6upi+zmI60pnuHMjG/KHkQPKen74j\nfT3tGezrzAPlQxnq69y3RaKKTgGANxMhOgAAQKMtt6R1szcH2tvT0dKR2cULGek9lK31WjZ6ZnNx\n/XyOvKWUrcsjOTu3mGJXe+59pUy0Vqvl1Ev7q8SzkMK+KjoFAGgkIToAAECDfevCM9nqXEg2kt/5\n7hd25u+75525uHZpp3D04d735k+/upHkh2WdSjwBAJrLmegAAAANNrN4LucX53J+ca5ufnl5vq5w\ndK11fufj7bJOJZ4AAM1lJzoAAECDHS4ezlbHwk3zwe6BbNW2dq47NgeSXNuJvl3WqcQTAKC5hOgA\nAAAN9uCx+3Jq5vmkfTkfvu99mV28kOHeQ+laH0hvx3B6JwYz3DWSrcsjOfDXV/LWowM7ZZ1KPAEA\nmkuIDgAA0EC1Wi0LF65kteVKzl05l1LvcDpa2tPW0pr5rZkc6BzMSNtIzi+fT2/3Zga3DmdxZSN/\n+v+dTVdnWxaurmW8VMzPPHS0rlC0Vqvl1PT+KhwFAHgjEqIDAAA00Knp+VzqfD6fOfXZndmj4w/m\nj/7zk3nfPe/MD66+sFMsmlwrF906V8rwQHc+9eXKzvzGQlGFowAAe0OxKAAAQAOdnq3m3OK5utl2\nmeiNxaLJtXLR5dWNXFxYuel9fpJrAABeH3aiAwAANNB4qZhLnYfrZl1tnUm2i0Vrdfc6NgfS1tmW\nof6uuvmNhaIKRwEA9oYQHQAAoIEmJwZyYfnufOS+D+Tc4rmM9B7K5eX5/OLbHk91ZSk/1Xdn7rzn\n7mtnoudg2hcPp3ugLUvL6/nY48ezcHXtloWiCkcBAPaGEB0AAKCRasnmYpLCZtpa2lJI4dru81oh\n93U9mJn59Wxu1ZKLB3L06EA2+5MfnFtIcfRyZtYv5Oj4kUyOjt1UGlpIIccnBp2DDgDQYEJ0AACA\nBjo1PZ9L3afymWc+tzN7dPzBfPqZ38+H76tl9eodWV3fzOee/EEemxrLk0+fydvf0ZYvvfCFndfX\n8tE8NPq2ZiwfAOBNT7EoAABAA52ereZc9XzdbLtM9Fz1fGYvLe2UiC6vbiS5Vi56vZevnN2DlQIA\ncCt2ogMAADTQeKmYS92lutl2sejh4khWD/ZkdX0zSdLTee1HtM7N+iNajh44sgcrBQDgVoToAAAA\nDXTPeH8urkzmF992bef5SO9QWmot+an7x9OVztSOvJjW5d788nsnM9DbkTuOHMjFhZV88KdOZH7j\nQo4UD+fB0eN171mr1XJqej6nZ6sZLxUzOTFw05npP8pr/XwAgDcTIToAAEADPTu9kEvdz+Yzz/z+\nzuzR8QdzpG80C5vVXFy6lCTZWLySY5t35VNfem7ndR97/L/KQ6Olm97z1PR8Pv7E0zvXJ09M/UQF\no6/18wEA3kyciQ4AANBA185En62brWys5vziXC4vz2dlYzUrG6tZa53P2bnFutdNz1R3fc8fdf1q\n1vRaPh8A4M3ETnQAAIAGunYm+mjdrKutMyO9h7K6uZat2laSZGNzIGOHeus/d7S463te71jp1q/7\nUWt6LZ8PAPBmIkQHAABooMmJgcwt35OPvu0DmVk6n972nvS296SjpT3taUtXsSubS8V0FEczfKAz\nH333PZm9uJSJw315eHJ41/c8eWIqp2erOVYq5t6JgZ94Ta/l8wEA3kyE6AAAAA1USCHFdKVQSPo6\nenNltZreju7Mr15JR0tHCqt9qS0cylphMxevrmWi1J0kefHc1aysbaa1JanVChkd7MrdxwaS2rUz\nzZ8/PZ8DvZ1ZXNnIv/vOmRw51PuqC0ILKeT4xKBz0AEAXgUhOgAAQIN9++qf5/SVM/n69Ld3Zu+7\n55357VOfz6PjD2ZjbSFb89cKRFfX++rKRT/wjrtyYX45Pzh3JRvXTn6pKwV9bGosSfLprzyvIBQA\noAGE6AAAAA12rno+KxurdbPLy/NJrpWMbrXOZ311KEluKhe9uLCS5dWNJLcuAN2+t31fiA4A8PoS\nogMAADTYkeJIpq9s1M0Gu6+dQ97V1pmNzYG0dV778WxsuL5cdKi/K1u1WpJrBaA3HtbS3fnDH+sU\nhAIAvP6aHqKXy+V/lOR9SdqT/J9JnkzyiSRbSZ6pVCq/2rzVAQAAvHYPjPw36WhpS6l3KFdWqxnp\nHcry+mp+4d73JSvFrHccSkupkM6O1owNXSsXnbm4lNLB7rS1FlLs7ktpsDvlY9eC95Mnpl45E70j\nfT3tuVJdy8kTUwpCAQAaoKkherlc/htJHqlUKj9dLpd7k/yDJP8yya9VKpWvlsvlf1Mulx+vVCqf\na+Y6AQAAXpPlJIWWtLa2ZbO2la1aLb1tXWltaUtb1//P3p0Hx3nfd57/PH2fQOPoA1eDkig1QZCJ\nEdFmNJzhWN5Ytmyaiq0oEqXY8eyOq/ao2T/C3ezMbFVqa2qPqmyc/WO3dnbXmy2vvbEkO0pCSZZi\nJWs5cpT44IQbDwmqJUoicXfjavR9P/sHiCYa4AGIaDYBvl9VDvD7Pc+D/vXzI2Dg6yffj1OWDpfe\nm0rL57HLYhg6/ssRWWWRaZp6ZzKlmcW8Ll5JqV6XRoYDm0JBTdPU+ERKP/jZlKJh35YDRgEAAHBr\n7X4S/TOSzsdisT+X5Jf0u5L+eTwe//HV469L+rQkiugAAAAAdq2zmb9XvlrQy++80Zg7eeAxLeaX\n1O+PyLQU9frfLUtaDQqtVOs6frhP4xMp/fydpN46Ny1JekW6bnjo+ESqKWyUgFEAAICd0+4ieq+k\nqKQTku6X9LIky7rjGUmdW/lCwaB/xxeH7WEP2ov7337sQfuxB+3F/W8/9mDvY493r9kPkqqbtaa5\n5UJKxWpJydyCzGJOklPSalDo1HxOwaBfc+emm4JDJWluKa9PHok2z10tst/sHOwMvg93P/Zwd2P/\ndj/2ELtRu4voi5IuxuPxqqR3Y7FYUdLguuN+SamtfKH5+UwLloetCgb97EEbcf/bjz1oP/agvbj/\n7ccetN+d+IOQPd69+n0h5auFprkud0B1s66Qt1em4Ze0+iS622nTYNCr+fmM+ro9mkpmm66LdHs2\n/Vvo6/bc8hzcPn7W7n7s4e7G/u1+7OHud6/+jyDtLqL/jaT/XNL/FIvF+iV5Jf2/sVjsn8bj8b+W\n9LikH7ZzgQAAAABwux4O/YrOL57XU4dOaCG3pJC3V3ZZ1BXwy1rtkLUe1OOPdMrnsavTa9fRgyFJ\nq/3PLRZpMORTOlfWQ0OB64aHjgwHdPrUmCYTWQ2FfQSMAgAA7KC2FtHj8fj3Y7HYP4nFYj+TZEj6\nTyRdlvR/xmIxu6SLkv6kjUsEAAAAgNtXqKlSr2q5kFLA1SGbYZHd6pB1KapCuS6Hra6AzyaPx65C\nqabv/vAD7evr0NGRXsUGA6rVpUqlfsOoUEPGprBRAAAA7Ix2P4mueDz+L68z/ck7vQ4AAAAAaJWz\nmX/QC+fPNMbHokfU4+mWp6Oo988FFQy45bRblUqX9dKbl9ZdOaoOj4PQUAAAgDZqexEdAAAAAPa6\n2WyyaVyslrRcSGnFsKpQ6tLiSlFWi6Fa3Ww6b2Iuq06vo2luMpGliA4AAHAHUUQHAAAAgBbr94Wa\nxi6bU13ugDw2l/JOm3o6XXLarSpVak3nRSM+dXqai+hDYV/L1wsAAIBrKKIDAAAAQIs9HPqYLIct\nSuTn5XP45LG55LDYZVuO6qEhU3aboVSmpO4Ou577bEzJpYKG+/w6OhKUIYPQUAAAgDaytHsBAAAA\nALDnFSyqqy6LDLmsDi3kFlUxK/IFV2QEEpoy/kEKzMs0DaleU1+vV1OJjN76xZxe++kVGZIe+/iA\nJOkHP5vS+JVlmTJlmqYuXFnWX/xssjG33q2OAwAA4NZ4Eh0AAAAAWuxs5u/1wvkzOhY9ojfe/3Fj\n/plDJ/XCOy83xke9JzTk3K9vvXaxMXd8bEAvvXlOX3tiVN84c6Exf/rUmCTdNHR0fCJFKCkAAMBt\n4kl0AAAAAGixtWDRYrV03fk1ZWtKM4u5prlCqSppNWR0vclEVpOJzXPbGQMAAODWeBIdAAAAAFps\nLVjUZXNtmA83jR21gPp7vE1zbufqn23RiL9pfijsk7HhdTaGjkY3jAklBQAA2D6K6AAAAADQYg+H\nfkU6LC3ml/T0oZNayi8r6O1WyBHSbx14TnO5hHxGjzzlARWLZX3lcyNKLuUU7PIoXyzr9KkxjQx3\nqsOzOWD0ZqGjI8MBQkkBAABuE0V0AAAAAGi1gk0ui0dBj0XJ/IL8Tp8MGXp35ZICzh512yPK1Bbk\n7a2qmMvIZ+nVPme/llZKui/SqZHhgAwZGh3u2tTT/Hpz0mqo6PhESpOJrKJhX+NrAAAAYHsoogMA\nAABAi53N/Fw1s6rvXXi1MXcsekSS9NqlN3XywGNK55f0+vm/aBw/6j2hH7252g/9owSCEioKAACw\nMwgWBQAAAIAWm80mlMwtNM0Vq6VG0OhyIbUpdLRsTTU+/yiBoISKAgAA7AyeRAcAAACAFuv3RVQz\nK01zLpuz8XmXO6C6aTYdd9QCklafRP8ogaCEigIAAOwMiugAAAAA0GIPh44ovnhBzx3+4mpPdIdX\nLptTy4W0nh19UtWCWx2GRc+OPqlUPiOf0SN3aUChT5U+ciAooaIAAAA7gyI6AAAAALRaQXI7HSoU\ni/I6PMqUc3Lb3Qq7e1STqRVzUS4FVEv2qc9t1y8/0K13JlY0PZ/T3FJBNov00ND2gkFvFEQKAACA\n7aGIDgAAAAAtdjbzc0l1TaZn9PbE2cb8U6MnNJOZkyS9PfG6jnpPqCe/T9lCRd/8/sXGecfHBlSt\ni4I4AABAG1BEBwAAAIAWm80mJJmbwkOTuYWmubI1pcWlolay5abzCqWqJhNZiugAAABtQBEdAAAA\nAFqs3xeRVNdEeqZpPuTtVbVebYwdtYB6Ol3ye+xN57mdNoJBAQAA2oQiOgAAAAC02MOhI5rLTshm\nsSro7Va2nFPY0yu7bLqvM6rlbEFfHH5a9lyf/G67fvnBHvV0uPTuZEodXocGej2KDREMCgAA0A4U\n0QEAAACg1QolLZXTKterShXS6vOF5LF5NJWeUcDZI2P+fjm9DhlWQx/OZbSQLslpt2p0X0C1ujS9\nkNfFKynFhgIaGd5ewCgAAABuD0V0AAAAAGixs5l/UM2s6XsXXm3MPTV6QulKVq+//yP9RuyLev98\nt946N904fnxsQNlCRfOpQmP+FUmnT43RGx0AAOAOsrR7AQAAAACw181mk0rmFprm1oeKzhcSKpSq\nTccLpaoWV4qb5icT2dYuFgAAAE14Eh0AAAAAWqzfF1LNrDXNrQ8VDbrDKjib/zxzO23q6XTJNM2m\neQJGAQAA7iyK6AAAAADQYg/7R3W+cElfOvhZpYtZhby9clmccltdOnXoi7IUfep8YEL/4cGgFicC\ncjmtshiG/B67ejqdcjltCvidGg55CRgFAAC4wyiiAwAAAECLnc1c0GR6Wm9PnG3MHYseaYzXf35i\n8Cm9+KeZxnnHxwYaPdG/9sQooaIAAAB3GD3RAQAAAKDFZrPJRv/zNevH6z9PVZp7p6/viT4xRz90\nAACAO40iOgAAAAC0WL8vJJfN1TTnsjmv+3mXvbfpPPe6XunRCP3QAQAA7jTauQAAAABAiz0c+pgc\nFrvCvh6lS1l1uQJyWx3qtnfLbw3KkOSJdilg65Gz0K8vP17T3GJB9/X5ZbUYcjtsikZ8OjoSbPdb\nAQAAuOdQRAcAAACAFjJV1wfLH2q+sCif0yuLDDmsdhmm1OMLyKjYlE12yuuparmyoFCHTa6VoFwO\nqzo8Do0MB/TxWKjdbwMAAOCeRREdAAAAAFoonn5P/+u5b+lY9Ihee+/NxvxToyc0szKhHk+3TP+y\nXvnw5caxE4NP6ZW/yegVSadPjWl0uKsNKwcAAIBET3QAAAAAaKnpzKwkbQoWTeYWVKyWtFxIKVWd\nbzq2Plx0MkGYKAAAQDvxJDoAAAAAtNCAv0+SNgWLhry9qtar6nIHpFLzsYC9V1JGkjQUJkwUAACg\nnSiiAwAAAEALxToe1H965Ld1eWVCTx06oVRhRSFPt6yy6sHA/TKqbmUzAf169GllzUWF3GFZ0iF9\n4R8X9dBQQAeHA+1+CwAAAPc0iugAAAAA0EKGLLrfPqSyv6pcJatqvaqqWZfH7pVqhhZqC/L11DVg\n3K8PZ8OanS2rN2DKbjNkbOHrm6ap8YmUJhNZRcM+jQwHtLUrd+Z6AACAve4jFdFjsZhfUiUejxd3\neD0AAAAAsOeczfx7mTL14vlr4aEnDzwmj82lP4t/X8eiR1R21PTCXy41jh8fG9Cf/ujcLYNFxydS\n+vrz5xrj7QaR3u71AAAAe92WgkVjsdgfXf04GIvF/kbShKSZWCz2eiwWG2jlAgEAAABgt5vNJjWX\nTTbNLRdSmr06V6yWNJebazpeKFUl3TpYdOPx7QaR3u71AAAAe92WiuiSxq5+/F8kfTsej3fF4/Fu\nSc9L+lZLVgYAAAAAe0S/L6Q+X6hprssdaMy5bE5FvJGm427n6v/j8K2CRaMbjm83iPR2rwcAANjr\nttvOJRqPx//3tUE8Hv9WLBb7L3d4TQAAAACwpzwc+pgupd7XqcNPaC6TVNDXow67X0bNol+PfV5e\ns1tBY1DPfLpX6VxZvZ0uZQtlnT41dstg0ZHhgE6fGtNkIquhsG/bQaS3ez0AAMBet9UiejQWi/1X\nkpZjsdgX4vH4K7FYzJD0pKR065YHAAAAAHtAoa6qWZfdsKvX26P53JKsXqscFpt8Vq8sqaDOL6YU\nDDhl7UppsTqvSE9E5aJXP/jZ1M0DP81rn36UOFBDhkaHu+iDDtzjCBkGgBvbahH9i5I+Likp6XFJ\nr0j6V1fnv9KapQEAAADA3nA28wuZMlWoFvXyO2805k8eeEyL+SV1O4tayQaVsU3qp4lXG8dPDD6l\n7/0wI+nGgZ8EgwLYCfwsAYAb21IRPR6P/1jSjzdM/w/xePy/3/klAQAAAMDeshogaqpu1pvmlwsp\nFeNQiDkAACAASURBVKslpcx5FUpdsltTTcdTlQVJTkmrgZ/XK2hdLxiUwheA7eJnCQDc2HZ7ojfE\n43Hz1mcBAAAAAPp9ocaT6Ot1uQOqm3UFrEEVnTZZa80Fq4C9V9Lqk+g3CvwkGBTATuBnCQDc2JaK\n6LFY7Pdudjwej/+bnVkOAAAAAOw9D4c+pkvLl+S0OPTU6Akt5JcU9vbKabHLbwnIkhpUwVdQ0Ltf\n/cFnlKrOK+yJyFPs129+qnjTwE+CQQHsBH6WAMCNWbZ4nk3S70qyajWrZuN/AAAAAADXYZqmVjJl\n5WslZSpZLRdSinh75bG5dZ/rIdVV06T9pwo+kJTfY1NtOaT67IMqzvfI67Tp00cGlM6X9d03P9Df\nXUyqruaWMGvBoJ/9xJBGh7sIAgTwkfCzBABubKs90X8vFov1S8rF4/Hfb/GaAAAAAGDPGJ9Iack9\nrsn0tN6eONuYP3ngMeWr43ph/Exj7umDX9IL3803xsfHBpRIFfXN719c9xVH9chI+E4sHQAAANr6\nk+iS9DuSZlu1EAAAAADYiyYTWc1mkypWS03zy4XU1cDRa+Zyc03jQqmqqWSuaW5irjn8DwAAAK21\n5SJ6PB5PS1pq4VoAAAAAYM+Jhn3q94Xksrma5rvcAfX7Qk1zEW+kaex22jS4IdwvGiHsDwAA4E7a\nUjuXdX5f0vdbsRAAAAAA2ItGhgNaKByUw2JX2NujdCmrHk+3vFa3os77deqgTbO5OUW8EXXk79cz\nny5pJVtWl9+hwaBXDw51ym41NDGXVTTi09GRYLvfEgAAwD1lu0X092Ox2P8l6aeSCmuT8Xj8Wzu6\nKgAAAADYK0ypJqlsVpQr5xX09GguN68+f1DL9Tk5rDZFCh/X3FRReW9JNpuhYJdLhiGd/2BZUws5\nDYe8knxXW7kYOjrSK4ssMk1T4xMpTSayioZ9GhkOEAYIAACww7ZbRF+UZEj61XVzpiSK6AAAAABw\nHWvBoi+cP6Nj0SP6wftvNY49c+ikJtMz6vYW9PqrZUmrYaJr3jo3LUl69rGYvvNGfN1XXQ0XHZ9I\n6evPn2vMnj41ptHhrta+IQAAgHvMtoro8Xj8n22ci8Vi7p1bDgAAAADsLZOJrNK9qwGiG8NF1wJH\nU+a8pE5Jq2GiGyWW8k3jibmsHhkJazLRHDI6mchSRAcAANhh2yqix2KxJyX9niSfVp9It0pySwrd\n7DoAAAAAuFdFwz4tuVf/ZNoYLtrnC2kyPaOANShp9Ul0t3Pzn2nhHk/z17waLhrdEDo6FCZ0FAAA\nYKd9lGDRfy7ptKT/TtJnJPXu9KIAAAAAYK9YCxY9dVhayi/rmUMnlcgtKOILKuTolrPTpVKiX48/\nUlCH1yGb1ZDVapFhSD7XPnX6HdoX8ehrT4xuChcdGQ7o9KkxTSayGgr7dHA40OZ3CwAAsPdYtnn+\ncjwef1PSTyR1xuPx/0bSIzu+KgAAAADYIwwZ8skmq2FVXVLdNOW2OVU3TaUqGcmoK+n49wrvy8gb\nXlLGPy6jMyGbxZDfY5XPbdelydVA0Z4OpyRDP/x3Mxq/sixJOhgNaCjs02Qiq4tXUjJl3nJNpmnq\nwpVl/cXPJjV+ZXlL1wAAANyrtvskeiEWiz0k6aKkT8ZisR9qrXEfAAAAAOC6zmZ+oRfOn2mMj0WP\n6LX33tSx6BFJ0ttTZxvzb0+sfn7Ue0I92qdvnLnQuO742IDeOveujo8N6Dt/+a5OnxqTpG2HixJI\nCgAAsHXbfRL9v5b030p6RdJ/ICkh6c92elEAAAAAsJfMZpNN47WA0WK11BQ2uv7zsjWlxZVi03Vr\noaNrHycT2euGi97KR7kGAADgXrXdJ9G/rtUg0d+R9CVJ2Xg8vrzjqwIAAACAPaTfF2oau2zOpo8b\n5yXJUQuop7M5iHQtdHTt41DYJ2PDa20lXJRAUgAAgK0zTHN7ve9isdh+SackPSVpSdK34/H4H7Vg\nbdthzs9n2ryEe1sw6Bd70D7c//ZjD9qPPWgv7n/7sQftFwz6N9Yydxq/8+5iBeX0DwvnlcjNq9PV\noVw5J7/TL5fFLpmGJpeX1esMyWpYtFBOKGALypaLKJcvq8Pn0kqmrE6/Q7l8RV6PXbl8RX293kaQ\n6PiVVFO4qLGptN7MlLnta8DP2r2APdzd2L/djz3c/e7A77x3pe0+ia54PH4pFov9oaT3JZ2W9C8l\ntbuIDgAAAAB3pXq9rpVSWTaLTV3ugJK5BYW8PfLY3HLLoURxSQPefvmr/ZpK5lXN+2UE3EquFNXb\n5VapVFU07NPITQrdo8Nd2+ppbsjY9jUAAAD3qm0V0WOx2Je0+hT6UUmvSvoX8Xj8b1uxMAAAAADY\nC34an1e155Ly1YJefueNxvzJA4/JY3PppXe+r2PRIxp01PTiXy01jh8fG9Crb3+oJx/drz94/hzh\nnwAAAG2y3SfRn5P0bUnPxuPxSgvWAwAAAAB7ysRcVoYzqbpZa5pfLqS0YlgkrQaKzlXmJDkax9fC\nQ9fCRScTWYroAAAAbbCtIno8Hn+yVQsBAAAAgL0oGvGr5gspXy00zXe5A/LYVoNDXTanIo6IVmOn\nVq2Fh66FixL+CQAA0B7b7okOAAAAANi6oyO9WizYNZF/X08fOqlkbkG93m55rW4lcwt69tCvy1Ht\nVGWlR599xC+/xyG/266lTFFfeXxELrt0+tSYDg4HVDfrOpu4oKn0jMLePlXmexTq8ty0X/puZZqm\nxidWw09v1RP+bn4NXMP9BgDsVhTRAQAAAKCFLLLoncy4Xjh/RicPPKY3P/xbHYse0dsTZxvnPH3w\nS/rmmURjfHxsQINBn771+kV99fMjjTYuP0+c1/89/u3GeScGn9IfPJ/Zk/3SxydS+vrz5xrjVrzH\nO/EauIb7DQDYrSztXgAAAAAA7HWz2aSk1T7o0moP9PXmcnNN40KpqsRSXpI0lcw15qfSM03npSoL\nklb7pe81G99TK97jnXgNXMP9BgDsVhTRAQAAAKDF+n0hSVK3OyBJcl3thb4m4o00jd1Om8LdHknS\nYMjbmB/sGGg6L2DvlbQ3+6VHN7ynVrzHO/EauIb7DQDYrQzTNNu9hp1gzs9n2r2Ge1ow6Bd70D7c\n//ZjD9qPPWgv7n/7sQftFwz6W93Yl995d7GCcvr7+f9PmWJOHqdHy4WUejxdSmYXFfKEVZntl2Gx\nKbmUV4dvtSf6SrYkn8ehY4fDsl59/qmuus7OXe2J7omostCrYJdHB/dgb2lTpsavrPbPHgr7WvIe\nt/sa/Ky9PXdiT2+FPdzd2L/djz3c/e7A77x3JXqiAwAAAEALmaprKjMhj80ri9uqbCUnU6ashlX/\nOPKw3l2ZUqTLq4sTKYW6PVpIFeR22BTp9ii5XFT8ykojgNEiiz4ROayPhw+tBjSWs3usdH6NIUOj\nw10t7Zl9J14D66x7hm+v/rsFAOxNFNEBAAAAoIXi6feULC5KWu1pvj5Q9JlDJ/XC+Mt6+uCX9Orb\nqz3Qj48N6LW/vawnH92vl968JGlzACMBjdiN+HcLANit6IkOAAAAAC00nZnVXDapuWxyU6DoWuDo\n+mDRQqkqSVpcKTbmbhXISEAjdgP+3QIAdiueRAcAAACAFhrw98lhX/3TazI923Ss72rg6Gqw6OqT\n6G7n6rk9ndfCRzcGMBLQiN2If7cAgN2KIjoAAAAAtFCs40E5bDblSlnZAzYFvd3KlnOKeIJ6wBfV\nMwefVG/pQX3hWEpdnS4tpIr6yudG1OG26Tc/9WAjgHG9keGATp8aawpoBO52/LsFAOxWFNEBAAAA\noJVMQx6jWwv1JRXrZa0UM+rzh+S1eZSqZlSc7tOKr6TuBxKaLyQVCUVUmIrI1enSZz4xKGNdBKNp\nmquBoomsomHfpuO3XMqG69cCS++Udr8+2osgVwDAbkURHQAAAABaaHwipSX3uPLVgl5+543G/NOH\nTspQRvZ+KVWUXnnn5caxL9x3Un/wfHnHA0XbHezY7tcHAAD4KAgWBQAAAIAWmkxkNZtNarmQapqf\nyyY1m00qmZ9XqjrfdGxtvNOBou0Odmz36wMAAHwUPIkOAAAAAC0UDfu05A4pXy00zUd8IRmSajWp\nVGxuaRKwBSWVdzxQtN3Bju1+fQAAgI/CME2z3WvYCeb8fKbda7inBYN+sQftw/1vP/ag/diD9uL+\ntx970H7BoL/VjZ35nXeXMmVqoZDS5fwl5WoFLeQWFfGF5Lf55LY5dOVdrzp8ThX9H2i+kFTYu9oT\nfSDYoYMbeoabMjV+JdUUzLitnui3ef3tavfr3y5+1u5+7OHuxv7tfuzh7ncHfue9K/EkOgAAAAC0\nkKm6FmvTqpo1VWoV2Sw2WQ2LCtW8LqenFBru1WIuJa861V8+KmvF0FwuK4ejqF+8v6jZxYKiYZ8O\nRDsVn1xRYjmvbLGiuaWCbBbpoaGtF6LbHezY7tcHAAD4KCiiAwAAAEALnV38e5VqJU2mZ/T2xNnG\n/LHokcb45IHH9N13/lRfuO+kasmocsWqXn/top59LKbv/vA9SdLXnhhVfCKlt85NN77G8bEBVeui\nKA0AANBCBIsCAAAAQAtNp2c1m02qWC01za8fr4WOpqrzWlwpqlCqSpISS/nGORNz2cb8mkKpSjgn\nAABAi/EkOgAAAAC00GBnv0rVkibSM03zLpuz8XmXOyBpNVC01ulS/Wp2Vbjb0zgnGvGpWG4uorud\nNsI5AQAAWowiOgAAAAC00MPdY/ow+75sFquC3m5lyzmFPUEZkrwPeBTy9Go5v6KnDnxR9uV9snZa\nlC9W9JXHR9Tls+s3P/WghsI+jQx3qsvnUDTs02K6pC6/UwO9HsWGAu1+iwAAAHsaRXQAAAAAaCGL\nrOq3BpUwF5Ur59Xh8Mus1yWLIbvVrrppqq6a6kZJNV9SyoYV6XErsVSQxSoF/A5NJrIyJB2IdqpW\nl1LZsgyLoXS+or/6d9PyeexayZQVDfs0Mrz1oFEAO8c0TY1PpDSZyPK9CAB7DEV0AAAAAGixs5lx\nvXD+TGN88sBjevnCG43xsegRvf7+j3QsekTV7Ip6tE+v/e1lHR8baAoS/doTo/rGmQuN8fGxAUlq\nOuf0qTGCRoE2GJ9I6evPn2uM+V4EgL2DYFEAAAAAaLHZbLJpvBYkumYtZLRYLalsTWlxpShJm4JE\nJ+aaQ0QLpeqmcwgaBdpj4/ce34sAsHfwJDoAAAAAtFi/L9Q0XgsSXbMWMuqyOVWtBdTT6ZIkeZzN\nf7JFI80hom6nbVOzCIJGgfaIbvje43sRAPYOiugAAAAA0GIPhz4my2GLkvkF+R1euSxOPXf4CaVK\nWXU4/EoVUvrSyOdkK3XJ6gvLNOv63D/ap3CXS7HoqFKZciNctMMzpncnUwr4HPK67Upny/raE6Na\nuXrOwWGCRoF2GBkO6PSpMU0msnwvAsAeQxEdAAAAAFqobtY1mZ+SzWJT0NOjfLWo+eKSwt5ePeCP\nKlvNq6ROOQ2bFmtJuZ0VeUoDui/iUyZfUU+HQ0dHQpIpjV9ZDS2MDQVksUiXZ3c2wHCrwYgEKAKb\nGTI0OtxFH3QA2IMoogMAAABAC51NXFDJsqJ8tajF/JLenjjbOPbMoZPKV4tyuhx6/sK14NGj3hPq\ntz+gUqWmP3j+nE6fGpOkptDC9aGjOxVguNVgRAIUAQDAvYRgUQAAAABooan0jGazSS0XUo0A0TVr\n88ncQtN82ZpSYinfCBidTGQ3hRSuDxTdqQDDrQYjEqAIAADuJXfFk+ixWCwk6aykX5NUk/RNSXVJ\n5+Px+H/WxqUBAAAAwG0Z7BhQ2bKifLWgumk2HevzhVafRLc6muYdtYDC3R6VKjVJqwGFG5uluNeF\nju5UgOFWgxEJUAQAAPeSthfRY7GYTdL/Jil/deoPJf3reDz+41gs9m9jsdgT8Xj8zI2/AgAAAADc\nvY5EDupK7ooWiwvy2T0KjvQoW8oq7A2qx9GpjDWnlWxZpw4+qcVcWq56lzylAbkcVuXyq61S1gIK\n14cWWi1SpMuzowGGWw1GJEARAADcS9peRJf0B5L+raR/JcmQ9CvxePzHV4+9LunTkiiiAwAAANid\nTMltMWSx2GXWy8qWsupw+mWzWGUxrKrVLOqq7FdGM6rXTbmdNoU8Lpl1KZUpK50v6wc/m1L0arF6\nfe/x2GBA70ym9Ne/mFW5UleuUNFINKCaqeuGft4qEHSrwYgEKAIAgHtJW4vosVjsq5KS8Xj8L2Ox\n2L++Or2+T3tGUucdXxgAAAAA7JC1YFFT0ovnX27MH4se0VBHvybTMxrqKOq75689O3Ri8CklPuyQ\npEZ4qLQ5wHN8IqWfv5NsOm9lXeDoxmsIBAUAANi+dj+J/s8k1WOx2Kcl/bKkb0kKrjvul5TayhcK\nBv07vzpsC3vQXtz/9mMP2o89aC/uf/uxB3sfe7w7Tb83q4p1c/BmsVrSbDbZ+LheqrKgQsmz6Zq5\npbw+eSR6bXxuuilgVNKm8fpr5tYV16/39XBrfB/ufuzh7sb+7X7sIXajthbR4/H4P137PBaL/VDS\nfyzpf4zFYsfj8fhbkh6X9MOtfK35+UxrFoktCQb97EEbcf/bjz1oP/agvbj/7ccetN+d+IOQPd6d\nBvz9KltWZKo5VNRlc6rPF9JkekZ9vnDTsYC9VyWnbVOYaKTb0/TvoK/bo6lkc4He47Td8Jq+bs8N\nj+HW+Fm7+7GHuxv7t/uxh7vfvfo/grT7SfTr+S8kfSMWi9klXZT0J21eDwAAAAB8ZEciB7VYWNFs\ncULPHX5CidyiOpw++e0edTkCssslZ3pIv/nAKS2Vk+pxhBS27dN9B6WpZE5fe2JUK5nydQM8R4YD\nslikxHJBfb0PrfZEHw7oyIHQdUM/CQQFAADYvrumiB6Pxz+1bvjJdq0DAAAAAHaSRRb5JOVqBeWq\nRRmGIa/dK8Mw9G7qQ/XYB5QvlWTzlyWzrLJjWYtGVbbssP7Jr/Tr8tSKKrW6JpJZjV9eVm/Apf5u\nt0xJl2dXA0KP/1KfZK72PF+b+8wnBptCQyUCQQEAAD6Ku6aIDgAAAAB71dnMuCbT03p74mxj7lj0\nSGP8zKEn9MK6YNFj0SPqdpT0d7+oKVesaD5VaAoLPT42IOlamOjpU2OSRGgoAABAC1BEBwAAAIAW\nWwsQXW/9eGOwaLFaUsqcV20hrFrNvGV46GRic3DpZCJLER0AAGAHUEQHAAAAgBbr94U0kW4ufLts\nznXHw5uOBaxBuXp9yhUrMs3mUFL3hvDQobBvUwjpUNh3+wsHAAAARXQAAAAAaKVara6Y/6AshuS+\n/x+rw+mXx+aSzWJTt6tTHY4urWQLeu7QF5UsLMhr98pt8cmWGtZyqaieDpe6/A6FezxK58rq8jsV\n8DmUzpX12V8dVn+vVweGO2WRQWgoAABAC1BEBwAAAIAWens8ISP8waae55I01NGvPz7/J435p/c/\nq29+a0lSVtIFPfnofv0fZy7oq58f0Xf/6r3GeU8+ul8vvXmpMbZaDT0yEiY0FAAAoAUs7V4AAAAA\nAOxlU8ncdXueF6ulTfNzubmm8eJKsfE1rje/ZmJuc090AAAA7AyeRAcAAACAFhoM+2TxhZrm1vqh\nb+yFHvFGJC01xj2drsbXWG9tfk00Qv9zAACAVqGIDgAAAAAtdOxQSEsFp547LCVyi+pw+WSRVW6b\nUw949+u52HOazswp7IkoYo3qy4+HNLeYV9/VHuhfe2JUnxgJqsfvbPQ7t9ukZz79kFLZkqIhnz4x\nEmz32wQAANizKKIDAAAAQAuZVakmyWF1K+jt0XxuUZ0uv8q1sj7Mf6Capay+br8WMwmZtoLy/rzC\nHQHVV9yK9HiVzpYUv7Kig8MBjQ53qVav6Scz55XyzircG1GPrVuGDNXrdf00Pq+JuayiEb+OjvTK\nQgfPO8I0TY1PpDSZyCoa9mlkOCBDRruXBQAAdghFdAAAAABoob8ZT8ga+UA1s6bvXXi1Mb8+XPT5\n83/eNP/axF/oqPeE6onVdi/f+ct3dfrUmEaHu/STmfP6zrt/3Dj/xOBTKl/Zr3S+rG+cubDulUf1\nyEhzuxi0xvhESl9//lxjvLZXAABgb+CxBAAAAABooen5rGazSSVzC03zNwoXLVZLkqSyNaVCqapC\nqSpJmkyshodOZ2ebzk9VFjSZyG4KFyVs9M5Z25sbjQEAwO7Gk+gAAAAA0EKDQZ+svpBqZq1pfi1c\ntO8GoaOOWkA257U/2YauhosO+Pubzg/YezUU9qkzX2maJ2z0zoluCH4dCnPvAQDYSyiiAwAAAEAL\nHfulsJYLLk0WPtSpw08omVtUp8snu2GTy+pUqVLRqUO/rsVMVp1uv3LFgr607xkZ6bB8++xKZ8s6\nfWpMB4cDkqRfHRiVzOc0nZ1VyBVWv32fHhwIyJQpafRqT3SfjhI2eseMDAd0+tRYI/h1ba8AAMDe\nQBEdAAAAAFrIqJtaqiWUq+aVyC2ozx9SuVrVUnFFAWeHPHa37Kn75LdKK9UZ5cor6vBa5fbalM3X\nlC2uPmH+7lRKU/N5pXNl9fX2qTvXo4jHq0pV+sHPphQN+/SrIyE9MhJeDbq8cuugy70UiGmqrnj6\nPU1nZjXg71Os40EZW+xgerv3wZCh0eEu+qADALBHUUQHAAAAgBb6ycx51R0ZvXD+5cbcsegRvT1x\ntvH5oL8ilbx68dJ3Guc8vf9Zfeulpcb4yUf366U3LzXGx8cGNLWQ01vnphtza4GWWw263EuBmPH0\ne/qfz/5RY/wvjvxHOtAR29K1e+k+AACAnUewKAAAAAC00HR29obhoWufz2WTmsvNNZ2zcby4Umwa\nrw8dXbMWaLnVoMu9FIg5nZm96fhm9tJ9AAAAO48n0QEAAACghQb8/TLtmaa5tfDQtc8jvpBU8jad\nE/FGJF17Er2n09V03O20bWo4shZoudWgy70UiDng77vp+Gb20n0AAAA7zzBNs91r2Anm/Hzm1meh\nZYJBv9iD9uH+tx970H7sQXtx/9uPPWi/YNDf6kbS/M67S9VU0+XsZc3kZ1d7ovuCKtdqWild7Ylu\nc8uW2ieb1aKUdUpz+TlFPBF5ihFlC3WlsiU9OBiQ3SZNJq/2RO/xKJuvaDDkVa2upkBLQ4ZMXeuJ\nvn5+o62etxvcVk/0Ld4Hftbufuzh7sb+7X7s4e53B37nvSvxJDoAAAAAtJBRN+S1dsttW9H9gX3K\nVbJayC+qvyOkiDOoc/PjivhMpa9E5HaF5MgGVC85NJ0r6oGBTnlcNp3/YEnRiF/RsEcTCWl2Ma+H\nhgKSpHcnU+rwOmWzNIePxoYC+swnBm9aFN9LgZiGLDrQEdtyH/Tma/fOfQAAADuPIjoAAAAAtNBP\n4/Oq9lxSzazJYlj04rqA0WcOndSbl/9WkvSF+07q2y+WdXxsQC//+AMdHxvQHz5/TsfHBhrhoc8+\nFtN33og3rl9/7PjYgIIBdyN89BURkAkAALATKKIDAAAAQAtNzGVlOJOSNrfSXB84mqrOS+pshIVu\n/ChJiaV80/XrjxVK1U3ho5OJLEV0AACA20QRHQAAAABaKBrxq+YLNZ5EX6/PF2p8HrAFJZXldq7+\nmbbxoySFezxN168/5nbaNoWPEpAJAABw+yiiAwAAAEALHR3p1WLBrunCZRmGRc8cOqm57Lz6/CH1\nuYJ6dN8/UtjTp8yViL78uE0r2Yq+/PgBpXNl/c6pMeWLFbkdNkUjPgUDDj33mZjSubIeGgrIapE6\nvQ51eB0a6PXIYlHT8YPDgXa/fQAAgF2PIjoAAAAAtJBFFvkk5WtFJXLz6nT5FfT0yG6xqlQpK+Lp\nU3oiLF/fspYr8wr1hlVe6FW5Utf0fFZet00+j1WdHoce6Atof19ze5YDQ83jhwau377FNE2NT6Q0\nmcgqGvZpZDhw09BRAAAArKKIDgAAAAAtdjYzrhfOn2mMj0WPSJKGOvr1wvjLemrkpF68eC1w9Kj3\nhH70d6v9zo+PDUiSXnrz3G0FhY5PpPT15881xoSOAgAAbI3l1qcAAAAAAG7H+gBRSSpWSypWS435\nZH6+6XjZmmp8XihVGwGik4nsR17Dxmtv52sBAADcS3gSHQAAAABarH9dgKgkuWxOSdeCRUOe5uOO\nWkDSauF8fXjo7QSFRjdcS+goAADA1lBEBwAAAIAWezj0MVkOW5TIzavD5ZNFNrltDnVYfHrm4JPK\nTET09P5ntVyZV9AVUnkhqMcfKcrvtcvrsimdK+n0qbHbCgodGQ7o9KkxTSayGgr7CB0FAADYIoro\nAAAAANBCtVpdK+WyPHaPIr6Q8pW8UqWUnL6gDKuhpdK8/EMlOepdqlx+QMUOl/xumyzdDklWzczn\nFY34FRvq1E8uJjUxl1U04tfRkV4ZprHlsFBDhkaHu26rDzrhpNfHfQEAYG+jiA4AAAAALfT2eEJG\n+AOZMjWVntHbE2cbx54+dFJvvP+WpNWw0aozotf+oqonH90vj8umb79+sXFu6XMj+tZrF9d95VF1\neBx3NCyUcNLr474AALC3ESwKAAAAAC00lcxpNpvUXDapYrXUdGxuXeBosVpqBIourhQ1s5BrOnd6\nvjkIdGIue8fDQgknvT7uCwAAextPogMAAABACw2GfbL4QjJlajI923Qssi5w1GVzqno1ULSn0yWP\nq/nPtcFgcxBoNOJTp8fRNNfqsFDCSa+P+wIAwN5GER0AAAAAWujYoZCWCk4lSlNyBGwKe3uULmUV\n9vaqy9Gpxx44rg5Hh1y1Lk2XvHruM075PTaVyhV99fMjml3IKxrx6eMjQTnslqs90X06OhKUIeOO\nhoUSTnp93BcAAPY2iugAAAAA0EKGKWW1rEw1r0q1LKfVqWp9RXXTVKVW1YCvT6o4NVOeVfi+oJam\nOmXvmdeybV5+o0cRs089fofiV1a0kinr8P3dTcGVB6OrBdvJRFaG1HRspwMvdyKcdC/ivgAALJLL\nwgAAIABJREFUsLdRRAcAAACAFjqbuKCSZUUvnH9ZTx86qefPn2kce/rQSRWqRS3mlxqBo1+476Re\nvPRy45yj3hPKTu/TS29easytD668WaglgZcAAAC3j2BRAAAAAGihqfSMZq8GiK4PEl0bLxdSTYGj\nqep80zlla0qLK8WmufXBlTcLtSTwEgAA4PbxJDoAAAAAtNBgx4DKlhVJUt+6IFFpNVi0UC2qbtYb\nc122YNM5jlpAPZ2uprn1wZU3C7Uk8BIAAOD2GaZptnsNO8Gcn8+0ew33tGDQL/agfbj/7ccetB97\n0F7c//ZjD9ovGPR/9EbTW8PvvLtUtV7TUmlO08WkDNOiTDWjuey8+vwh9TgCKtZKssup95dn5DG7\nZctFZO9Z0GJhQV1+n5ZzaUU8EXVWB3V5djW40mqRLs+u9jk/MNypi1dWmkItGz3RZWr8SqpxbCTa\nqYsTKzvWI327drpH+512Oz9rd/t73yv478vdjf3b/djD3e8O/M57V+JJdAAAAABooZ/HF1TtuSzJ\n1GR6ptH7XJKeGj2hmcyc+v0RrSw4debNFUkrOj42oPB9HXpx/HuNc3/74Jf12U8c1oUry/r9P97c\n5/x6vc43Bl5euLLc1h7p93KP9nv5vQMAsNvREx0AAAAAWmhiLqvZbFKz2WRT73NJSuYWVKyWlMwt\nqGxNNeYLpapSlYWmc6fSM5Jur895u3ukt/v12+lefu8AAOx2PIkOAAAAAC0UjfhVu9oLfeJqIXxN\nyNurar2qkLdXxXlJqkqS3E6buuzNvdEHO/pXv95t9Dlvd4/0dr9+O93L7x0AgN2OnujYEfS0ai/u\nf/uxB+3HHrQX97/92IP2oyc6bqSuuhYLKyqaKc3kE8pW8kqXsgp7e2Q3HKqrJlvVr/JyjyaTOfk9\nDvnddi1liursX1aqOq9+X58eDo/KIsumPucHt9Fb+3au3Qntfv3bdVs90Xf5e98r+O/L3Y392/3Y\nw92PnugAAAAAgB1nkUU+SfHcjOay84p4g3JaSzIlLRaX1eXo1uwVrzo8pkainbqSyMrsWFLZPifD\nCCtU/mUtXinrneKKbHZppnxZyWJCgwP9eqivXz+5mNTEXFbRiF9HR3pluUnXzo090u+0dr9+O93L\n7x0AgN2OIjoAAAAAtNjZzLheOH+mMT4WPaLvv/emjkWP6JV3/0pfuO+kvvliWV9+/ICs3fN64dK1\nQNGj3hP60ZurbV6+/HSX/uTD5xvHqrXn9M0zi+teaVSPjIRb/n4AAADuJQSLAgAAAECLzWaTTeO1\ngNG1j6nqvCRpZiG3KVB0feBosjDXdGwuN9s0npgjrBIAAGCnUUQHAAAAgBbrvxosusZlczZ9DNhW\nQ0T7e72bAkUdtUDj85A70nQs4u1rGkcjhFUCAADsNNq5AAAAAECLPRz6mHRYmsvOK+TtVbaU0anD\nTyhbyunLh57SwodBfeVzTnX7bJpI9urp/c8pWZhT0BWSI9+n0KcqGgr75HRIT91/SsliQgO+Pn1i\n8JDsTyxc7Ynu09GR4K0XAwAAgG2hiA4AAAAALVSr1bVSLsthcarX062lwrJ8Dq8cFrushkU1S0UP\nDvi0aJnWB+VJ+YY9clg65JiOqeZxyHAaqpk1Jc3LSucXFAtGddz/mOKTK3rr3JzSubIO39+tkeGA\nDBk3XYtpmhqfSGkykVU07LvpNds5FwAAYC+jiA4AAAAALfT2eEJG+ANNpqf19sTZxvyx6BH1eLr1\nnV/8uZ459MSm4NHuSFH/z4tlPfnoflm65vUnH6yGjb5xRfrtg1/W+EWH3jo3LUl6RdLpU2MaHe66\n6VrGJ1L6+vPnGuObXbOdcwEAAPYyeqIDAAAAQAtNJXOazSYbIaJritWSlguroaHXCx5dCxtdXClu\nChudSs+oUKo2zU0mbh0quvGcm12znXMBAAD2Mp5EBwAAAIAWGgz7ZPGFNJFuLnq7bE51uVdDQ68X\nPBqwBiWV1dPpknVD2OhgR78yzuY/54bCtw4VjW4452bXbOdcAACAvYwiOgAAAAC00LFDIS0VnHJa\nner3h5Uqrsjr8KrD7lX6asBosHhAz8V8SlYm5XV45LN2aP6DXv3WZ+3yuGxaXA7qN+4/pXRtQQ8F\nhxTreEgdlRUNhnxK58p6aCigg8OBW65lZDig06fGNJnIaijsu+k12zkXAABgL6OIDgAAAAAtZDEN\n1SSV62Ut5Jc04IuoWCtqLjevsLdXDrm1ZJ3WVHpWIfegPJk+Fct1FUt5eV02VX1zKljm5DfCUuJB\nJbIOWbpX9NBgp2p1qV43lStW9MNzM0rnyooNBW4YAmrI0Ohw15Z6m2/nXAAAgL2MIjrQBrVaTZcv\nf7BjX2952aelpZ3rUblv3/2yWq079vUAAADuZeMTKS25xxvBoceiR5oCRk8eeEwvv/NGY/wb953S\nt/9sWZL0yUdt+um7rzaOHfWe0JnXqzo+NqClbFnfOHNBx8cGNL2Q23bIKAAAALaGIjrQBpcvf6Df\nffn35A36272UTXLzGf3+yX+jBx54sN1LAQAA2BMmE1mle68Fh24MGF0LF12TLMxJckqSytbmY6tj\nnwqlqibmVh+i2BgwuvaaFNEBAAB2BkV0oA1qtXq7l3BTd/v6AAAAdpNo2Kcl97XgUJfN1XR8LVx0\nTcgdkbT6JLqz1lwId9QCkqpyO22KRlaDPj3OzX/WEQIKAACwcyiiA21hKnX2PpX83e1eyCaFzJL0\nebPdywAAANgzRoYDWigc1HOHDc1kkxrwhdV3MKiVUkZhT69chke/deC3NJmeUcgVlqfUp69+PqK5\nxbx6HE6dGnhOc/k5BZ0hpaa79JXHHYp0u/XQUKc6PGOaXcipw+fYdsgoAAAAtoYiOtAGVqtVPYMj\n8nUNtHspm2SXp+mHDgAAsINM1ZVRUnWtPahgXP2/hiwWq2oqK1lJqq87oKXMgpw+i2olUw73vFzu\nkBaudKrXF9LKYkldfqeW0kWVq3UllotyOS1SIKHp+qJCwbCioaj2D2wOFTVNU+MTKU0msoqGfTcM\nHt3uuQAAAPcCiugAAAAA0EJnF/9epVpJL5x/WdL1g0VzlZzeeP+vG3PrzznqPaEzr1X15KP79c3v\nX2ycc3xsQOH70nr1yvcacycGn1L5yv5N/dDHJ1L6+vPnGuObBY9u51wAAIB7gaXdCwAAAACAvWw6\nPavZ7M2DRTfOrR+vhYsurhSbzimUqkpVFprmUpUFTSaym9awce5653yUcwEAAO4FPIkOAAAAAC00\n2Nmv0rqi+PWCRetmcyaNy+ZsfL4WJtrT2Xyd22lTlz3YNBew9143VDS6Ye5mwaPbORcAAOBeQBEd\nAAAAAFro4e4xJQrTOnX4Cc1l59W/Lli0zxuSxbCoWCnp6dEntJzNK+gKqVQy9eloQEFXWAsTHfrK\n406l82V99fMjWkoX5XHZ5bRb5DYD+uLw08rUFxV0hxUyonpwYHOo6MhwQKdPjWkykdVQ2HfT4NHt\nnAsAAHAvoIgOAAAAAC1Uqxgy5JfVsKrL3aFkfkEdTp8inqAcFqfS5YxUN+S0uOR315QoTinkGJJt\n6iFlbTb5PYY8LpuqNVNzS3n193glw1SuUNVSuqjeYZuyZUP5YlULlbIm5qbV3+vVgWin3pta0cxS\nQYnFvIb7OvTpjw/onYkV/eBnUzcMDTVkaHS466Z90AkfvbuxP7sXewcAdyeK6AAAAADQQm+PJ2Tt\n+0CT6emmQNFj0SPq8XRrMb+ktyfObgocPTH4lF7804yOjw0oGHDrpTcvNY49+eh+vfTmJX3yUZv+\n6r1XG/NHvSdUT4X1nb98V197YlRL6VLTdZXqSFM46UcNDSV89O7G/uxe7B0A3J0IFgUAAACAFppe\nyGo2m7xueOj6UNGNx9dCQwul6qZQ0bXxWujomrI1pUKpKkmamMtuum4qmWsaf9TQUMJH727sz+7F\n3gHA3Ykn0QEAAACghQaCPtl8IU2kq03zLpvzaqho/eq4OTg0YO+VlJHbadsUKro2dtaan1B11AKy\nOVf/zItGfFpKNxfmB0PepvFHDQ0lfPTuxv7sXuwdANydKKIDAAAAQAsd+6WwUgWXbBarwr4eZUo5\n+Z1eeaxuua0uuSxOPXbfJ9XnCylyIKxUIaOQc1CpqU795q9FZLca8nvsevaxmJYyRfX1eGQxpKd/\n7UHli1U9vf85LVWS8pjd8pUHlLdVdfrUmEaGO3VpekVffvzA1Z7ofn1iJKieDtdth4YSPnp3Y392\nL/YOAO5OFNEBAAAAoIUsNakmyZRUN1fnXBanHFa7HFa7MqWMul3dqi0Mylqpa9BpUypVVr5UUr/f\nqYpnVtPVBfk7e9Xv7FM6W5bbZVe+WFUw4Ja54lTYFtFKpizDb1GpUlUi9f+zd+/Rfd53neDfP0m2\nrJstX2Q5cWwnzeWJkrbgoUx64XgaaNOmLaWlZyhpSblsYYbhHLZDdtgtTLvnsMvsTGfKDjMMw3Lp\ncFpooFxKU0raUCiUlm2gEFhKkqeX1LHjxI580V2WdfntH7ZVSfaT2ImlR7Jfr3NyrOf6+/j3tRTp\n7cefz2S+fng4Gzvbc3zkZDb3bEhrS8vpIs4436jCCx1qWDV81FDE1eFChsOyOlk7gNVJiA4AALCM\nPv/wkTT6zx0s+sab70hn24aMTI/l/q/9Wb7zujdmdmR3xiZn5oeBvvL2tjx4ZPHg0K25Nh+6/9Hs\n27szH//c1/O2O4r8j0/84/w5+/buzGf/7LG85fYbFg0R3bd3Z06emnnGwaLPd6ihoYgAwOXIYFEA\nAIBl9MTT4+cdLHpicmjR/qGZwRwbPrloGOj5BoeePX52gOiR4xOLzjm7f+lQ0cmpmWcdLPp8hxoa\niggAXI48iQ4AALCMrunvTst5Botu7uhNZ9uGHBx5MknS29aX2SUDRM83OPTsUNGOMwNE+7d2Ljrn\n7P6lw0g72ttyzbMMLXy+Qw0NRQQALkdCdAAAgGX0ihduz/HJ9rS1tKava2vGTo2lr2NrNrSuT2fr\nhgyvG8lbB96cdUPX5lTHXDra2/K9r74pQ6NTubqrK9ds/94MzRxNd8vWbJi8OqPjp3L3nUWGRk/l\nB98wkEazmR/+rlszPHoqm3rW5/jIZN7+2iKTU9P5gTcMZPDEZLo2rMuWje35lmJbtva0Vw4tfL5D\nDQ1FBAAuR0J0AACAZdScOT1YtHN9Z1oarWlvXZfD44PZ3rU1jUZrWlpa09qaDHc8mo7OzTl0cFM2\n97Rnz1XdOXxsMt0z/Zke2pT2bZ3ZsL4t4ydnMnjiZHZs6Uj7+tYcH57K6MTJ9PVuyNae9dnYuT4H\nj4zlBTs2ZWBPb9LM/LDP8vHh3LKnN7fu2Xx6COjj5w4BvdihhkuHiZ69/3IzxHTtuJi1sq4ArEZC\ndAAAgGX0uYePpHXHY0mamZg5mfsefWD+2BtvviNDU8P5xFf+JK/Y/ZJ8/sD9ua3rDfn4/TN5y+03\n5MToVD722cfmz3/L7TfMDx1NkrfdUeQjf/KVyuP33LU3Sc477PNSDQGta5ioIaZrx8WslXUFYDUy\nWBQAAGAZHRocy1NjT+epsadzYnLxoNATk0Pzg0XP/np2mOix4ZPzQ0LPWjosdOlQ0aXHDx4Zqxz2\neamGgNY1TNQQ07XjYtbKugKwGnkSHQAAYBld09ed1u7tSZKJmclFxzZ39GauOZck2dDWnuT08NBk\nJls3bUiz2Vx0/tJhof1bOp/x+K7+7nMaYZwd9nmphoDWNUzUENO142LWyroCsBo1ln5TtkY1BwdH\n667hitbX1xNrcOG+9rWv5N2//IV0b95ZdynnGDtxKP/Xj7w0119/Y92lrCk+B+pnDerl/a+fNahf\nX1/Pcjft9T3vGjU5OZvBHMxT409nrjmX6eZMjowN5uqe7dnS3ptDo4fT1d6ViemJjEyNZVvn1myY\n7U1zZFsGhyaycc/TOTx+OFd17Uj7yJ6MjM9m8MRkdmzryomRyWzb3JGTJ2czPHYq23o3ZGjsVLo7\n23LNtq7cuHNT/uYrR3N8ZCpjk9MZ2LM5t5zpMd3MN3qi7+rvzsDuTXnkwHC+/tRweq4ayujc0dyw\ndVdu6rkxjxwYzpNHx9PduS7Do6cW9ao+e58vHxxK35YNmZlJnhwczzX93XnFC7enpdl4xh7XS3tg\n33ymjmfrib20/ltWoHf2s32tnZuby4PlYA4cHsvuHT25bWBbWq7wfwB+dn2/fHAoG7vas3NbR4pd\nz9AT/RKs6zOtg/9frm3Wb+1ba2toTsO5VuB73lXJk+gAAADL6K8Hv5TGhtEcHHkyWzu3LOqJ/r0v\nfGPWr2vP14cO5PMHvji//40335EjQ8eyfXtLfuvh++b3v3Xgu/Obv/+NFi779u7M8Ph0PvvQoUX7\n7vuLx/LD33Vr/qoczK987B/nj+3s65r/4X/pENF/fPxE3n/vQ3nl7W25/2t/ePqCryfff8vd+aV7\nB7Nv785Fr3O2V/XZ+338c1/P2+4o8uEHym/85pvNbN244Rl7XC/tgf3D33XropqremI/lyGoy+3B\nJe93cmteNtBfWz2rwfl6nD9TAHUp1tU6AJeKOQ2cdWX/lTgAAMAyOzz+VJ4aezonZ6bO6Yn+1NjT\nOXzm2EInJodyqnUoQzODi+81cXjR9uTUzDl9089uHzg8lgOHF/eTXrq90Nne02d7sp/1xMiTi+67\n9PyFHy/t0f7E0+PP2uN66fbSGtdST+yLeb+vFHX0OLcOwKViTgNneRIdAABgGV3VfVUa7WM5MPJk\ntnT0Ljm2Pc0kT4w8tWj/5o7enDq6PpvbWhft39G5I8k3guqO9rZznuntaD/9Y97uHd3JkqOn953f\n2V7U7bOLn7C7ZuPVSQbT2b74x8eFvarPXtu/dXGP9mu2d2XbxnP7tJ/vdatqXEs9sXfv6FmyvXZq\nXy519Di3DsClYk4DZ+mJziWx1npa1U1P9MuPz4H6WYN6ef/rZw3qpyc6VSYzm+HJp3Jg/FCm52Yy\nk9kMjh/Lju7t2b5hSw6NPZ2OtvaMz05m5ORotnZuScfc5pw6tjVHR05m83WH8/T4kfR37cjJQ1el\nra0tgycmsn1zV44OT6Z/S0eazeTpE5PZvLE9o+OnsmNrZ/7pzX1JkgcfOdsbuju3DfRV9ug+24v6\ndE/0ExmdO5Ybtl6TmzbelEceH85TR8fTdaYn+tJe1WevPTEymZlm43RP9O1decWL+tOSxjP2uD6n\nN/ueTXnk8eEV7XV+oZ61J3rmLvj9vlLU0bv+mdbB/y/XNuu39q21Nazja9hqd6X2RBeic0mstS+C\ndROiX358DtTPGtTL+18/a1A/ITrPZHJyLl/82tMZm5zO2MSp9Pa0Z2PnuoyOT2U2LWlpJJ3tbdmw\nviVpNHJ8ZConRqfS292e3du7kiT7nzLUrG6+1q591nBts35rnzVc+67UEF07FwAAgGX24JePZP/h\nkXMGgF7T153ffqDMvr2nH664pq87U9Oz+b3PfHXReUnmrzXUDABgZV3Z/64MAABgBRw6OnbeAaBn\nB3GeHRB65PhEjg2fPOe8hdcaagYAsLI8iQ4AALDMdvZ1Z2ZmZNG+jva29G/pnP84Sfq3dGZqevac\n8xYy1AwAYGUJ0QEAAJbZbS/uT1tLI9u3dGZ04lQ2d7enp2tdxsan8tZX3ZiWlkY62tvSsb4ljcb6\n3HXHTef0RN+xuXN+qBkAACtHiA4AALDMNjQb2bGtK3NHR9PSWJ/jo1NpX9eabb0dOTWbjI6fyuCJ\nyRS7eisHh968Sx90AIA6CNEBAACW2cMHhvLXjz6dJIuGi9792pszMTUzP0j04zE4FABgtTFYFAAA\nYJkdPDJ2zoDQJHny2Pg5g0QNDgUAWF1qfRK9KIq2JB9Icm2S9Ul+NsnDSX49yVySL5Vl+WN11QcA\nAHAp7O7vzpETE+fsv3prVyaWBOsGhwIArC51t3P5viRHy7J8R1EUvUn+PsnfJfmpsiz/oiiK/14U\nxXeVZfmxessEAAB47gb29Ka9vS1PHh3Njq03Zmj0VPq3dmZTV1u6O9vy9tcUGRk/lZt29RocCgCw\nytTdzuUjSd5z5uPWJDNJ/klZln9xZt/9SV5VR2EAAACXTDNpppHxidls3bQh27d05NDgWEbGZ/JN\nN2xL54a2nJqey8jEdJpp1l0tAAAL1PokelmWE0lSFEVPkt9J8tNJ/tOCU0aTbKqhNAAAgEvm4QND\nef+9DyVJ3nZHkQ8/UM4fazaTD97/yIKzb83LBvpXuEIAAKrU3c4lRVHsSvL7SX6hLMvfKorifQsO\n9yQZupD79PX1LEd5XARrcOFOnFjdfS63bOm2ns+B96x+1qBe3v/6WYPLnzVeuw4/dGj+4yPHF/dG\nP3R08SDRg0+P5Y37bliRurh4Pg/XPmu4tlm/tc8ashbVPVi0P8mnkvxYWZafObP7oaIo9pVl+dkk\ndyb50wu51+Dg6DJVyYXo6+uxBhfh+PGxZz+pRsePj1nPi+RzoH7WoF7e//pZg/qtxA+E1njtumpL\n5/zH/Vs7Fx3b2bf4AYtd27ut9Srla+3aZw3XNuu39lnDte9K/UuQup9Ef3eS3iTvKYrivUmaSf7n\nJP+1KIp1SR5J8rs11gcAAPC8DezpzU/9wD/NVw+cyObudbn7zpvz5NHx7Ozrzstf3J/2dS05cHgs\nu3d057aBvrrLBQBggbp7or8rybvOc+iVK1wKAADAsmnONXNseDKjk6eyYUNbhsemsnNbV44NT+YL\n//B0ToyeTG93e1pbWuouFQCAJep+Eh0AAOCy92A5mF/52D/Ob7/l9hvywfsfnd/et3dnPvvQY9m3\nd2dm5+YMFgUAWEWE6MBFm52dzf79j9VdRqVrr31BWltb6y4DAGDegcOLZ+IcGz65aHtyamb+1wOH\nx4ToAACriBAduGj79z+Wn7zvvelahcMkxgdH8743/kyuv/7GuksBAJi3e8fi75u2btqwaLujvW3+\n1907Fg8aBQCgXkJ04Dnp6utJz9W9dZcBALAm3DawLS0tL8qBwyPp6+3I0NhU7r7z5pwYOZmtvR0Z\nGp3K93zHjdmysT0vKbbVXS4AAAsI0QEAAJZZczY5NTOXZjOZmp7Llk3tmZtr5OT0XGbnmrllz6bc\nsLM3jTS+cU2zmYcPDOXgkbHs7u/OwJ7FxwEAWBlCdAAAgGX2+YeP5Nc/8cj89tvuKPLhB74xWPQt\nt9+QUzPJrXs2z+97+MBQ3n/vQ/Pb99y1d9FxAABWRkvdBQAAAFzunnh6fNH2keMTi7aPDZ/MwSOL\nh48+2zYAACtDiA4AALDMrulfPCy0f2vnou2tmzZk15Jzdi/ZXnocAICVoZ0LAADAMnvFC7enpdHI\nk4Nj2dS9Pu1tjbzjdQN58uh4+rd0ZFdfZ27cuXho+8Ce3txz194cPDKWXf3duWWPoe4AAHUQogMX\nbXZ2LuODo3WXcV7jg6OZnZ2ruwwAgEVamo30b+nM8ZHJdGxoy/jkTEYnprNt04akmQyPz+RP/uZQ\nrt7WNT9AtJFGbt2zWR90AICaCdGB56CZoS9el6meLXUXco7J0ePJ65t1lwEAsMjZIaH79u7M8ZGp\nfPahQ/PH9u3dOf/xh//4ywaIAgCsMkJ04KK1trZm6zUD6d6889lPXmFjJw6ltbW17jIAABY5OxR0\ncmrmnGNL9x08MiZEBwBYRYToAAAAy+zskNDO9nN/BOtYss8AUQCA1UWIDgAAsMwG9vTm3d//rXlk\n/7Fs7mlP/5bOjE5MZ1P3+rS2JBs712d47FTuuWuvAaIAAKuMEB0AAGCZNdLI3pv7MzJxMiNj0zk2\ncjI7+7rT2d7IocHJXLWlKy8p+tJIY/6aZrOZhw8M5eCRsezu754fOAoAwMoSogMAAKyAP/r8Y5md\nmcuHPvno/L67X3tzPv65r+fjyTkDRc8OIz3LwFEAgHq01F0AAADAleCJp8fy5LHxRfsWbp8dPnqh\n2wAArAxPogMAAKyA3du7Mz07t2jf1Vu75j9eOlB095JtA0cBAOohRAcAAFgBr9x7Tb702GDecedA\nDh0dy87t3WnOzuXOl12bq/u6cnR4Mh/93FCKXb0Z2HP6v3vu2puDR8ayq7/bwFEAgJoI0QEAAFbA\np794MM1mc74n+r69O/PZhw7NHz+7vbA/+tn/AACoj57oAAAAK+DQ0cU90SenZhYdX7it/zkAwOrh\nSXQAAIAVsLOvO81mc367s33xj2MdC7b1PwcAWD2E6AAAACvgztuuzf/7pSfyjtcN5Kmj47lme1eu\n3taVobFT2b2jKyenZrOp67rctKtX/3MAgFVEOxcAAIBl1mw286WvH83I5HSmTs2krbUlU9NzaWtt\npKUlaTYbmZ6eS7GrN60tyaf+6ok8/PiJNNN89psDALCsPIkOAACwzB4+MJT9h0czODQ5P0z0fINF\nP/zHX160/+yAUQAA6uNJdAAAgGV28MhYjg2fXDQ8tGqwqAGjAACriyfRAQAAltnu/u7MNpsXNFjU\ngFEAgNVFiA4AALDMBvb0prOjLQefbsuOrTdmZPxUtmxsz/e9tsiJ0ans3NaVsYnp3HPX3rS2JDs2\nd2ZXf7cBowAAq4AQHQAAYJk155oZHJ7KyVNzp7ebSUtLS67e1pHbv/nqNNJYdP7Nu/RBBwBYLYTo\nAAAAy+zBcjDlgaEkWTRM9C2335CZmRgeCgCwihksCgAAsMwOHB7L5NTMOcNEjw2fNDwUAGCV8yQ6\nAADAMtu9oycnT82cs3/rpg2GhwIArHJCdAAAgGV228C2rGtryYnRk/meV92YkbFT6du8IVdv68xN\nOw0PBQBYzYToAAAAy6zRbGRTd3sOHB7Jlk0bcnJdSxpp5NHHhzI9k0ycnM7XnxzN7h09uW1gW1p0\n3gQAWDWE6AAAAMvs4QNDef+9D81vv+X2G/LB+x+d3963d+eCgaO35mUD/StcIQAAVTxA3Kp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t5IPctvzvX4kWPZu3Pggea62p97NR+KbPQWNb3ygAgAAABYfUJ0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\niL604QmB2656h9aaT9rH0FHJ1fLqU9/7bHfasRv0qQ1PKZw4J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"text/plain": [ "<matplotlib.figure.Figure at 0xb47b240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top_k_feature_pairwise_plot(df, filtered_feat_imp_list[:2], 'TARGET')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

staeiou/wiki-stat-notebooks

botvbot/exploratory-botplots-samples.ipynb

1

81471

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bot-vs-bot reverts\n", "## Getting and processing data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-03-10 21:44:24-- https://quarry.wmflabs.org/run/161195/output/0/tsv?download=true\n", "Resolving quarry.wmflabs.org (quarry.wmflabs.org)... 10.68.21.68\n", "Connecting to quarry.wmflabs.org (quarry.wmflabs.org)|10.68.21.68|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [text/csv]\n", "Saving to: ‘enwiki-botvbot-pagetitles.tsv’\n", "\n", "enwiki-botvbot-page [ <=> ] 36.78M 3.61MB/s in 11s \n", "\n", "2017-03-10 21:44:35 (3.41 MB/s) - ‘enwiki-botvbot-pagetitles.tsv’ saved [38564767]\n", "\n" ] } ], "source": [ "# bot-vs-bot revert table: https://quarry.wmflabs.org/query/17237\n", "\n", "!wget https://quarry.wmflabs.org/run/161195/output/0/tsv?download=true -O enwiki-botvbot-pagetitles.tsv" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting ftfy\n", " Downloading ftfy-5.0.1.tar.gz\n", "Requirement already satisfied: html5lib in /srv/paws/lib/python3.4/site-packages (from ftfy)\n", "Requirement already satisfied: wcwidth in /srv/paws/lib/python3.4/site-packages (from ftfy)\n", "Requirement already satisfied: six in /srv/paws/lib/python3.4/site-packages (from html5lib->ftfy)\n", "Installing collected packages: ftfy\n", " Running setup.py install for ftfy ... \u001b[?25l-\b \b\\\b \bdone\n", "\u001b[?25hSuccessfully installed ftfy-5.0.1\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "!pip install ftfy\n", "import ftfy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "158606" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"enwiki-botvbot-pagetitles.tsv\", sep=\"\\t\", encoding='utf-8')\n", "len(df)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['rev_id', 'rev_timestamp', 'rev_user', 'rev_user_text', 'rev_page',\n", " 'rev_sha1', 'rev_minor_edit', 'rev_deleted', 'rev_parent_id',\n", " 'archived', 'reverting_id', 'reverting_timestamp', 'reverting_user',\n", " 'reverting_user_text', 'reverting_page', 'reverting_sha1',\n", " 'reverting_minor_edit', 'reverting_deleted', 'reverting_parent_id',\n", " 'reverting_archived', 'rev_revert_offset', 'revisions_reverted',\n", " 'reverted_to_rev_id', 'page_namespace', 'page_title'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rev_id</th>\n", " <th>rev_timestamp</th>\n", " <th>rev_user</th>\n", " <th>rev_user_text</th>\n", " <th>rev_page</th>\n", " <th>rev_sha1</th>\n", " <th>rev_minor_edit</th>\n", " <th>rev_deleted</th>\n", " <th>rev_parent_id</th>\n", " <th>archived</th>\n", " <th>...</th>\n", " <th>reverting_sha1</th>\n", " <th>reverting_minor_edit</th>\n", " <th>reverting_deleted</th>\n", " <th>reverting_parent_id</th>\n", " <th>reverting_archived</th>\n", " <th>rev_revert_offset</th>\n", " <th>revisions_reverted</th>\n", " <th>reverted_to_rev_id</th>\n", " <th>page_namespace</th>\n", " <th>page_title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>56161718</td>\n", " <td>20060531172522</td>\n", " <td>91310</td>\n", " <td>CanisRufus</td>\n", " <td>584516</td>\n", " <td>1lyohbi8ymubjfzdb4w4ssfiflfpgat</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>54170358</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>b3lf3olmh1hw99f68tjh187jl7dzi1j</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>56161718</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>54170358</td>\n", " <td>0</td>\n", " <td>Sharon_Pratt_Kelly</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>56161633</td>\n", " <td>20060531172452</td>\n", " <td>91310</td>\n", " <td>CanisRufus</td>\n", " <td>793703</td>\n", " <td>bxfv9z3d7uypsla28qfioc5qs4l963w</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>54169562</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>1nlk1pfhntoa3m0inrdpotrqb5ajxif</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>56161633</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>54169562</td>\n", " <td>0</td>\n", " <td>Anthony_A._Williams</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>103117636</td>\n", " <td>20070125105128</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>5202035</td>\n", " <td>6ruzx0gegy5xy0mw5yxsdnh1fir7mwd</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>102641383</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>c6uu9wkg34ko0nidqakdktfkkvcds3k</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104315087</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>102641383</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Core_topics_article...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>105582355</td>\n", " <td>20070204154028</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>6744678</td>\n", " <td>p14vq3lzxeu50upo1ax9h3ya2hrz1hv</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104618440</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>qaiydx752b8av3qp2zbnfpqspttpw91</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>105582355</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>104618440</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Medicine_articles_b...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>105627219</td>\n", " <td>20070204194135</td>\n", " <td>234358</td>\n", " <td>Mathbot</td>\n", " <td>8468041</td>\n", " <td>rebsad1ch2hvs85nhf0nwm2r8ukboil</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>104982162</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>3lda2892o6z3p29r8xipbqh780h2rph</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>105627219</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>104982162</td>\n", " <td>4</td>\n", " <td>Version_1.0_Editorial_Team/Soft_drinks_article...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 25 columns</p>\n", "</div>" ], "text/plain": [ " rev_id rev_timestamp rev_user rev_user_text rev_page \\\n", "0 56161718 20060531172522 91310 CanisRufus 584516 \n", "1 56161633 20060531172452 91310 CanisRufus 793703 \n", "2 103117636 20070125105128 234358 Mathbot 5202035 \n", "3 105582355 20070204154028 234358 Mathbot 6744678 \n", "4 105627219 20070204194135 234358 Mathbot 8468041 \n", "\n", " rev_sha1 rev_minor_edit rev_deleted \\\n", "0 1lyohbi8ymubjfzdb4w4ssfiflfpgat 1 0 \n", "1 bxfv9z3d7uypsla28qfioc5qs4l963w 1 0 \n", "2 6ruzx0gegy5xy0mw5yxsdnh1fir7mwd 0 0 \n", "3 p14vq3lzxeu50upo1ax9h3ya2hrz1hv 0 0 \n", "4 rebsad1ch2hvs85nhf0nwm2r8ukboil 0 0 \n", "\n", " rev_parent_id archived ... \\\n", "0 54170358 0 ... \n", "1 54169562 0 ... \n", "2 102641383 0 ... \n", "3 104618440 0 ... \n", "4 104982162 0 ... \n", "\n", " reverting_sha1 reverting_minor_edit reverting_deleted \\\n", "0 b3lf3olmh1hw99f68tjh187jl7dzi1j 1 0 \n", "1 1nlk1pfhntoa3m0inrdpotrqb5ajxif 1 0 \n", "2 c6uu9wkg34ko0nidqakdktfkkvcds3k 0 0 \n", "3 qaiydx752b8av3qp2zbnfpqspttpw91 0 0 \n", "4 3lda2892o6z3p29r8xipbqh780h2rph 0 0 \n", "\n", " reverting_parent_id reverting_archived rev_revert_offset \\\n", "0 56161718 0 1 \n", "1 56161633 0 1 \n", "2 104315087 0 1 \n", "3 105582355 0 1 \n", "4 105627219 0 1 \n", "\n", " revisions_reverted reverted_to_rev_id page_namespace \\\n", "0 1 54170358 0 \n", "1 1 54169562 0 \n", "2 3 102641383 4 \n", "3 1 104618440 4 \n", "4 1 104982162 4 \n", "\n", " page_title \n", "0 Sharon_Pratt_Kelly \n", "1 Anthony_A._Williams \n", "2 Version_1.0_Editorial_Team/Core_topics_article... \n", "3 Version_1.0_Editorial_Team/Medicine_articles_b... \n", "4 Version_1.0_Editorial_Team/Soft_drinks_article... \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:5]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['reverting_timestamp_dt'] = pd.to_datetime(df['reverting_timestamp'], format=\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df.set_index('reverting_timestamp_dt')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#df = df[df['page_namespace'] == 0].copy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'Series' object has no attribute 'find'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-21-427b3df18e94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usertext_pair'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mftfy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reverting_user_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" reverting \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mftfy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rev_user_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usertext_pair'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/paws/lib/python3.4/site-packages/ftfy/__init__.py\u001b[0m in \u001b[0;36mfix_text\u001b[0;34m(text, fix_entities, remove_terminal_escapes, fix_encoding, fix_latin_ligatures, fix_character_width, uncurl_quotes, fix_line_breaks, fix_surrogates, remove_control_chars, remove_bom, normalization, max_decode_length)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mtextbreak\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 156\u001b[0m \u001b[0mfix_encoding_this_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix_encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtextbreak\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/srv/paws/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 2742\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2743\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2744\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2746\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'find'" ] } ], "source": [ "df['usertext_pair'] = df['reverting_user_text'] + \" reverting \" + df['rev_user_text']\n", "df['usertext_pair'].value_counts().head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Cydebot reverting CanisRufus', 'Cydebot reverting CanisRufus',\n", " 'WP 1.0 bot reverting Mathbot', ...,\n", " 'Citation bot reverting Citation bot 2',\n", " 'AvicBot reverting EmausBot', 'DumbBOT reverting Lowercase sigmabot'], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['usertext_pair'].values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gp = df.groupby(['usertext_pair'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 83295\n", "4 31227\n", "14 27417\n", "1 10354\n", "3 3365\n", "2 1350\n", "10 890\n", "5 253\n", "11 204\n", "6 177\n", "109 28\n", "100 27\n", "7 5\n", "108 4\n", "101 3\n", "118 3\n", "12 2\n", "15 2\n", "Name: page_namespace, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['page_namespace'].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def botpair_ns_table(botpair_df):\n", " tablestr = \"Reverts by namespace<br>\\n\"\n", " for x, y in botpair_df.page_namespace.value_counts().iteritems():\n", " namespaces_dict={0: \"Article:\", \n", " 1: \"Talk:\",\n", " 2:\"User:\",\n", " 3: \"User_talk:\",\n", " 4:\"Wikipedia:\",\n", " 5: \"Wikipedia_talk:\",\n", " 6:\"File:\",\n", " 7:\"File_talk:\",\n", " 8:\"MediaWiki:\",\n", " 9:\"MediaWiki_talk:\",\n", " 10:\"Template:\", \n", " 11:\"Template_talk:\",\n", " 12:\"Help:\",\n", " 13:\"Help_talk:\",\n", " 14:\"Category:\", \n", " 15:\"Category_talk:\",\n", " 100:\"Portal:\",\n", " 101:\"Portal_talk:\",\n", " 108:\"Book:\",\n", " 109:\"Book_talk:\",\n", " 118:\"Draft:\"}\n", " \n", " tablestr = tablestr + namespaces_dict[x] + \" \" + str(y) + \"<br>\\n\"\n", " return tablestr\n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "namespaces_dict={0: \"\", \n", " 1: \"Talk:\",\n", " 2:\"User:\",\n", " 3: \"User_talk:\",\n", " 4:\"Wikipedia:\",\n", " 5: \"Wikipedia_talk:\",\n", " 6:\"File:\",\n", " 7:\"File_talk:\",\n", " 8:\"MediaWiki:\",\n", " 9:\"MediaWiki_talk:\",\n", " 10:\"Template:\", \n", " 11:\"Template_talk:\",\n", " 12:\"Help:\",\n", " 13:\"Help_talk:\",\n", " 14:\"Category:\", \n", " 15:\"Category_talk:\",\n", " 100:\"Portal:\",\n", " 101:\"Portal_talk:\",\n", " 108:\"Book:\",\n", " 109:\"Book_talk:\",\n", " 118:\"Draft:\"}" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"botvbot.html\", \"w\") as f:\n", " f.write(\"<h1>Bot vs bot reverts on enwiki</h1>\")\n", " f.write(\"<p>For bot-bot pairs with more than 20 reverts</p>\")\n", " f.write(\"<p>Jupyter notebook for this code <a href=\\\"http://paws-public.wmflabs.org/paws-public/User:Staeiou/wiki-stat-notebooks/botvbot/exploratory-botplots-samples.ipynb#\\\"> here</a></p>\")\n", " for botpair in df['usertext_pair'].unique():\n", " botpair_df = df[df['usertext_pair'] == botpair]\n", " total_reverts = len(botpair_df)\n", " if total_reverts > 20:\n", " \n", " first_revert = df[df['usertext_pair'] == botpair].index.min()\n", " last_revert = df[df['usertext_pair'] == botpair].index.max()\n", " sample = df[df['usertext_pair'] == botpair].sample(2) \n", " count = 0\n", " f.write(\"<h2>\" + ftfy.fix_text(botpair) + \"</h2>\\n\")\n", " f.write(\"<p>Total reverts: \" + str(total_reverts) + \"</p>\\n\")\n", " f.write(botpair_ns_table(botpair_df))\n", " f.write(\"<p>First revert: \" + str(first_revert) + \"</p>\\n\")\n", " f.write(\"<p>Last revert: \" + str(last_revert) + \"</p>\\n\")\n", " f.write(\"<p>Random examples: \")\n", " \n", " for revid in df[df['usertext_pair'] == botpair].sample(20).iterrows():\n", " page_title = namespaces_dict[revid[1].page_namespace] + revid[1].page_title\n", " \n", " f.write(\"<a href=\\\"https://en.wikipedia.org/w/index.php?title=\" + page_title + \"&diff=prev&oldid=\" + str(revid[1].reverting_id) + \"\\\">\" + str(count) + \"</a> \")\n", " count = count + 1" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-49-69398291b023>, line 30)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-49-69398291b023>\"\u001b[0;36m, line \u001b[0;32m30\u001b[0m\n\u001b[0;31m \"ex0\":ex{0},\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "output_df = pd.DataFrame(columns=[\"reverting_bot\", \"reverted_bot\", \"total_reverts\", \"ns0_reverts\", \"ex0\", \"ex1\", \"ex2\", \"ex3\", \"ex4\", \"ex5\", \"ex6\", \"ex7\", \"ex8\", \"ex9\", \"ex10\"])\n", "\n", "with open(\"botvbot.tsv\", \"w\") as f:\n", " for botpair in df['usertext_pair'].unique():\n", " botpair_df = df[df['usertext_pair'] == botpair]\n", " total_reverts = len(botpair_df)\n", " ns0_reverts = len(botpair_df[botpair_df['page_namespace'] == 0])\n", " if total_reverts > 20:\n", " sample = df[df['usertext_pair'] == botpair].sample(1) \n", " \n", " reverting_user_text_link = \"=HYPERLINK(\\\"http://enwp.org/User:\" + sample.reverting_user_text[0] + \"\\\",\\\"\" + sample.reverting_user_text[0] + \"\\\")\"\n", " rev_user_text_link = \"=HYPERLINK(\\\"http://enwp.org/User:\" + sample.rev_user_text[0] + \"\\\",\\\"\" + sample.rev_user_text[0] + \"\\\")\"\n", "\n", " first_revert = df[df['usertext_pair'] == botpair].index.min()\n", " last_revert = df[df['usertext_pair'] == botpair].index.max()\n", "\n", " ex = {}\n", " count = 0\n", "\n", " for revid in df[df['usertext_pair'] == botpair].sample(10).iterrows():\n", " page_title = namespaces_dict[revid[1].page_namespace] + revid[1].page_title\n", " ex[count] = \"=HYPERLINK(\\\"https://en.wikipedia.org/w/index.php?title=\" + page_title + \"&diff=prev&oldid=\" + str(revid[1].reverting_id) + \"\\\",\\\"diff\\\")\"\n", " \n", " count = count + 1\n", " \n", " output_df = output_df.append({\"reverting_bot\":reverting_user_text_link,\n", " \"reverted_bot\":rev_user_text_link,\n", " \"total_reverts\": total_reverts,\n", " \"ns0_reverts\":ns0_reverts,\n", " \"ex0\":ex{0},\n", " \"ex1\":ex{1},\n", " \"ex2\":ex{2},\n", " \"ex3\":ex{3},\n", " \"ex4\":ex{4},\n", " \"ex5\":ex{5},\n", " \"ex6\":ex{6},\n", " \"ex7\":ex{7},\n", " \"ex8\":ex{8},\n", " \"ex9\":ex{9}},\n", " ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>reverting_bot</th>\n", " <th>reverted_bot</th>\n", " <th>total_reverts</th>\n", " <th>ns0_reverts</th>\n", " <th>examples</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td><a href=\"http://enwp.org/User:FrescoBot\">Fresc...</td>\n", " <td><a href=\"http://enwp.org/User:Mathbot\">Mathbot...</td>\n", " <td>58.0</td>\n", " <td>58.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>1825.0</td>\n", " <td>154.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>34.0</td>\n", " <td>34.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td>294.0</td>\n", " <td>92.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " 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<td>467.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>148</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>207.0</td>\n", " <td>42.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>149</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:DarafshBot\">Dara...</td>\n", " <td>69.0</td>\n", " <td>2.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>150</th>\n", " <td><a href=\"http://enwp.org/User:Hazard-Bot\">Haza...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot I\">Cybe...</td>\n", " <td>71.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>151</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot II\">Cyb...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td><a href=\"http://enwp.org/User:Amalthea (bot)\">...</td>\n", " <td><a href=\"http://enwp.org/User:Cyberbot II\">Cyb...</td>\n", " <td>779.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td><a href=\"http://enwp.org/User:Hazard-Bot\">Haza...</td>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td>28.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>154</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>692.0</td>\n", " <td>195.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>155</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>141.0</td>\n", " <td>7.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>156</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Dexbot\">Dexbot</a></td>\n", " <td>83.0</td>\n", " <td>2.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>157</th>\n", " <td><a href=\"http://enwp.org/User:Chobot\">Chobot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>248.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>158</th>\n", " <td><a href=\"http://enwp.org/User:Addbot\">Addbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>5260.0</td>\n", " <td>3417.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>159</th>\n", " <td><a href=\"http://enwp.org/User:Xqbot\">Xqbot</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>31.0</td>\n", " <td>30.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>160</th>\n", " <td><a href=\"http://enwp.org/User:EmausBot\">EmausB...</td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>1761.0</td>\n", " <td>595.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>161</th>\n", " <td><a href=\"http://enwp.org/User:KLBot2\">KLBot2</a></td>\n", " <td><a href=\"http://enwp.org/User:Makecat-bot\">Mak...</td>\n", " <td>452.0</td>\n", " <td>195.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>162</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:Theo's Little Bo...</td>\n", " <td>136.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:Theo's Little Bo...</td>\n", " <td>41.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>164</th>\n", " <td><a href=\"http://enwp.org/User:タチコマ robot\">タチコマ...</td>\n", " <td><a href=\"http://enwp.org/User:VoxelBot\">VoxelB...</td>\n", " <td>111.0</td>\n", " <td>111.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>165</th>\n", " <td><a href=\"http://enwp.org/User:AvicBot\">AvicBot...</td>\n", " <td><a href=\"http://enwp.org/User:VoxelBot\">VoxelB...</td>\n", " <td>27.0</td>\n", " <td>27.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>166</th>\n", " <td><a href=\"http://enwp.org/User:RussBot\">RussBot...</td>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td>28.0</td>\n", " <td>10.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>167</th>\n", " <td><a href=\"http://enwp.org/User:Cydebot\">Cydebot...</td>\n", " <td><a href=\"http://enwp.org/User:ArmbrustBot\">Arm...</td>\n", " <td>318.0</td>\n", " <td>317.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>168</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:BracketBot\">Brac...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>169</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:BracketBot\">Brac...</td>\n", " <td>31.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>170</th>\n", " <td><a href=\"http://enwp.org/User:ClueBot III\">Clu...</td>\n", " <td><a href=\"http://enwp.org/User:MediaWiki messag...</td>\n", " <td>312.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " <tr>\n", " <th>171</th>\n", " <td><a href=\"http://enwp.org/User:Lowercase sigmab...</td>\n", " <td><a href=\"http://enwp.org/User:MediaWiki messag...</td>\n", " <td>458.0</td>\n", " <td>0.0</td>\n", " <td><a href=\"https://en.wikipedia.org/w/index.php?...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>172 rows × 5 columns</p>\n", 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href=\"http://enwp.org/User:Lowercase sigmab... \n", "145 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "146 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> \n", "147 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "148 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "149 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "150 <a href=\"http://enwp.org/User:Hazard-Bot\">Haza... \n", "151 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "152 <a href=\"http://enwp.org/User:Amalthea (bot)\">... \n", "153 <a href=\"http://enwp.org/User:Hazard-Bot\">Haza... \n", "154 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "155 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "156 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "157 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> \n", "158 <a href=\"http://enwp.org/User:Addbot\">Addbot</a> \n", "159 <a href=\"http://enwp.org/User:Xqbot\">Xqbot</a> \n", "160 <a href=\"http://enwp.org/User:EmausBot\">EmausB... \n", "161 <a href=\"http://enwp.org/User:KLBot2\">KLBot2</a> \n", "162 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "163 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "164 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... \n", "165 <a href=\"http://enwp.org/User:AvicBot\">AvicBot... \n", "166 <a href=\"http://enwp.org/User:RussBot\">RussBot... \n", "167 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... \n", "168 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "169 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "170 <a href=\"http://enwp.org/User:ClueBot III\">Clu... \n", "171 <a href=\"http://enwp.org/User:Lowercase sigmab... \n", "\n", " reverted_bot total_reverts \\\n", "0 <a href=\"http://enwp.org/User:Mathbot\">Mathbot... 58.0 \n", "1 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 1825.0 \n", "2 <a href=\"http://enwp.org/User:Chobot\">Chobot</a> 34.0 \n", "3 <a 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href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 31.0 \n", "19 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 223.0 \n", "20 <a href=\"http://enwp.org/User:タチコマ robot\">タチコマ... 528.0 \n", "21 <a href=\"http://enwp.org/User:SuggestBot\">Sugg... 296.0 \n", "22 <a href=\"http://enwp.org/User:SuggestBot\">Sugg... 38.0 \n", "23 <a href=\"http://enwp.org/User:MiszaBot\">MiszaB... 68.0 \n", "24 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... 1174.0 \n", "25 <a href=\"http://enwp.org/User:Cydebot\">Cydebot... 86.0 \n", "26 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 165.0 \n", "27 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 35.0 \n", "28 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 63.0 \n", "29 <a href=\"http://enwp.org/User:JAnDbot\">JAnDbot... 24.0 \n", ".. ... ... \n", "142 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 160.0 \n", "143 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 159.0 \n", "144 <a href=\"http://enwp.org/User:DPL bot\">DPL bot... 57.0 \n", "145 <a href=\"http://enwp.org/User:BattyBot\">BattyB... 22.0 \n", "146 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 53.0 \n", "147 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 825.0 \n", "148 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 207.0 \n", "149 <a href=\"http://enwp.org/User:DarafshBot\">Dara... 69.0 \n", "150 <a href=\"http://enwp.org/User:Cyberbot I\">Cybe... 71.0 \n", "151 <a href=\"http://enwp.org/User:Cyberbot II\">Cyb... 31.0 \n", "152 <a href=\"http://enwp.org/User:Cyberbot II\">Cyb... 779.0 \n", "153 <a href=\"http://enwp.org/User:Lowercase sigmab... 28.0 \n", "154 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 692.0 \n", "155 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 141.0 \n", "156 <a href=\"http://enwp.org/User:Dexbot\">Dexbot</a> 83.0 \n", "157 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 248.0 \n", "158 <a href=\"http://enwp.org/User:Makecat-bot\">Mak... 5260.0 \n", "159 <a 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"execute_result" } ], "source": [ "output_df" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting openpyxl\n", " Downloading openpyxl-2.4.5.tar.gz (180kB)\n", "\u001b[K 100% |████████████████████████████████| 184kB 382kB/s eta 0:00:01\n", "\u001b[?25hCollecting jdcal (from openpyxl)\n", " Using cached jdcal-1.3.tar.gz\n", "Collecting et_xmlfile (from openpyxl)\n", " Using cached et_xmlfile-1.0.1.tar.gz\n", "Installing collected packages: jdcal, et-xmlfile, openpyxl\n", " Running setup.py install for jdcal ... \u001b[?25l-\b \bdone\n", "\u001b[?25h Running setup.py install for et-xmlfile ... \u001b[?25l-\b \bdone\n", "\u001b[?25h Running setup.py install for openpyxl ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\n", "\u001b[?25hSuccessfully installed et-xmlfile-1.0.1 jdcal-1.3 openpyxl-2.4.5\n" ] } ], "source": [ "!pip install openpyxl\n", 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mit

samgoodgame/sf_crime

iterations/misc/Cha_Goodgame_Kao_Moore_W207_Final_Project_updated_08_19_2230.ipynb

1

74063

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kaggle San Francisco Crime Classification\n", "## Berkeley MIDS W207 Final Project: Sam Goodgame, Sarah Cha, Kalvin Kao, Bryan Moore\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Environment and Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Bryan/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/Users/Bryan/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "# Import relevant libraries:\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn import preprocessing\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.naive_bayes import BernoulliNB\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import log_loss\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import svm\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "# Import Meta-estimators\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "# Import Calibration tools\n", "from sklearn.calibration import CalibratedClassifierCV\n", "\n", "# Set random seed and format print output:\n", "np.random.seed(0)\n", "np.set_printoptions(precision=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### DDL to construct table for SQL transformations:\n", "\n", "```sql\n", "CREATE TABLE kaggle_sf_crime (\n", "dates TIMESTAMP, \n", "category VARCHAR,\n", "descript VARCHAR,\n", "dayofweek VARCHAR,\n", "pd_district VARCHAR,\n", "resolution VARCHAR,\n", "addr VARCHAR,\n", "X FLOAT,\n", "Y FLOAT);\n", "```\n", "#### Getting training data into a locally hosted PostgreSQL database:\n", "```sql\n", "\\copy kaggle_sf_crime FROM '/Users/Goodgame/Desktop/MIDS/207/final/sf_crime_train.csv' DELIMITER ',' CSV HEADER;\n", "```\n", "\n", "#### SQL Query used for transformations:\n", "\n", "```sql\n", "SELECT\n", " category,\n", " date_part('hour', dates) AS hour_of_day,\n", " CASE\n", " WHEN dayofweek = 'Monday' then 1\n", " WHEN dayofweek = 'Tuesday' THEN 2\n", " WHEN dayofweek = 'Wednesday' THEN 3\n", " WHEN dayofweek = 'Thursday' THEN 4\n", " WHEN dayofweek = 'Friday' THEN 5\n", " WHEN dayofweek = 'Saturday' THEN 6\n", " WHEN dayofweek = 'Sunday' THEN 7\n", " END AS dayofweek_numeric,\n", " X,\n", " Y,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS bayview_binary,\n", " CASE\n", " WHEN pd_district = 'INGLESIDE' THEN 1\n", " ELSE 0\n", " END AS ingleside_binary,\n", " CASE\n", " WHEN pd_district = 'NORTHERN' THEN 1\n", " ELSE 0\n", " END AS northern_binary,\n", " CASE\n", " WHEN pd_district = 'CENTRAL' THEN 1\n", " ELSE 0\n", " END AS central_binary,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS pd_bayview_binary,\n", " CASE\n", " WHEN pd_district = 'MISSION' THEN 1\n", " ELSE 0\n", " END AS mission_binary,\n", " CASE\n", " WHEN pd_district = 'SOUTHERN' THEN 1\n", " ELSE 0\n", " END AS southern_binary,\n", " CASE\n", " WHEN pd_district = 'TENDERLOIN' THEN 1\n", " ELSE 0\n", " END AS tenderloin_binary,\n", " CASE\n", " WHEN pd_district = 'PARK' THEN 1\n", " ELSE 0\n", " END AS park_binary,\n", " CASE\n", " WHEN pd_district = 'RICHMOND' THEN 1\n", " ELSE 0\n", " END AS richmond_binary,\n", " CASE\n", " WHEN pd_district = 'TARAVAL' THEN 1\n", " ELSE 0\n", " END AS taraval_binary\n", "FROM kaggle_sf_crime;\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading the data, version 2, with weather features to improve performance: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)\n", "\n", "We seek to add features to our models that will improve performance with respect to out desired performance metric. There is evidence that there is a correlation between weather patterns and crime, with some experts even arguing for a causal relationship between weather and crime [1]. More specifically, a 2013 paper published in Science showed that higher temperatures and extreme rainfall led to large increases in conflict. In the setting of strong evidence that weather influences crime, we see it as a candidate for additional features to improve the performance of our classifiers. Weather data was gathered from (insert source). Certain features from this data set were incorporated into the original crime data set in order to add features that were hypothesizzed to improve performance. These features included (insert what we eventually include)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#data_path = \"./data/train_transformed.csv\"\n", "\n", "#df = pd.read_csv(data_path, header=0)\n", "#x_data = df.drop('category', 1)\n", "#y = df.category.as_matrix()\n", "\n", "########## Adding the date back into the data\n", "#import csv\n", "#import time\n", "#import calendar\n", "#data_path = \"./data/train.csv\"\n", "#dataCSV = open(data_path, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allData = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#df2 = pd.DataFrame(allData)\n", "#df2.columns = csvFields\n", "#dates = df2['Dates']\n", "#dates = dates.apply(time.strptime, args=(\"%Y-%m-%d %H:%M:%S\",))\n", "#dates = dates.apply(calendar.timegm)\n", "#print(dates.head())\n", "\n", "#x_data['secondsFromEpoch'] = dates\n", "#colnames = x_data.columns.tolist()\n", "#colnames = colnames[-1:] + colnames[:-1]\n", "#x_data = x_data[colnames]\n", "##########\n", "\n", "########## Adding the weather data into the original crime data\n", "#weatherData1 = \"./data/1027175.csv\"\n", "#weatherData2 = \"./data/1027176.csv\"\n", "#dataCSV = open(weatherData1, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allWeatherData1 = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#dataCSV = open(weatherData2, 'rt')\n", "#csvData = list(csv.reader(dataCSV))\n", "#csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "#allWeatherData2 = csvData[1:]\n", "#dataCSV.close()\n", "\n", "#weatherDF1 = pd.DataFrame(allWeatherData1)\n", "#weatherDF1.columns = csvFields\n", "#dates1 = weatherDF1['DATE']\n", "#sunrise1 = weatherDF1['DAILYSunrise']\n", "#sunset1 = weatherDF1['DAILYSunset']\n", "\n", "#weatherDF2 = pd.DataFrame(allWeatherData2)\n", "#weatherDF2.columns = csvFields\n", "#dates2 = weatherDF2['DATE']\n", "#sunrise2 = weatherDF2['DAILYSunrise']\n", "#sunset2 = weatherDF2['DAILYSunset']\n", "\n", "#functions for processing the sunrise and sunset times of each day\n", "#def get_hour_and_minute(milTime):\n", " # hour = int(milTime[:-2])\n", " # minute = int(milTime[-2:])\n", " # return [hour, minute]\n", "\n", "#def get_date_only(date):\n", "# return time.struct_time(tuple([date[0], date[1], date[2], 0, 0, 0, date[6], date[7], date[8]]))\n", "\n", "#def structure_sun_time(timeSeries, dateSeries):\n", "# sunTimes = timeSeries.copy()\n", "# for index in range(len(dateSeries)):\n", "# sunTimes[index] = time.struct_time(tuple([dateSeries[index][0], dateSeries[index][1], dateSeries[index][2], timeSeries[index][0], timeSeries[index][1], dateSeries[index][5], dateSeries[index][6], dateSeries[index][7], dateSeries[index][8]]))\n", "# return sunTimes\n", "\n", "#dates1 = dates1.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "#sunrise1 = sunrise1.apply(get_hour_and_minute)\n", "#sunrise1 = structure_sun_time(sunrise1, dates1)\n", "#sunrise1 = sunrise1.apply(calendar.timegm)\n", "#sunset1 = sunset1.apply(get_hour_and_minute)\n", "#sunset1 = structure_sun_time(sunset1, dates1)\n", "#sunset1 = sunset1.apply(calendar.timegm)\n", "#dates1 = dates1.apply(calendar.timegm)\n", "\n", "#dates2 = dates2.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "#sunrise2 = sunrise2.apply(get_hour_and_minute)\n", "#sunrise2 = structure_sun_time(sunrise2, dates2)\n", "#sunrise2 = sunrise2.apply(calendar.timegm)\n", "#sunset2 = sunset2.apply(get_hour_and_minute)\n", "#sunset2 = structure_sun_time(sunset2, dates2)\n", "#sunset2 = sunset2.apply(calendar.timegm)\n", "#dates2 = dates2.apply(calendar.timegm)\n", "\n", "#weatherDF1['DATE'] = dates1\n", "#weatherDF1['DAILYSunrise'] = sunrise1\n", "#weatherDF1['DAILYSunset'] = sunset1\n", "#weatherDF2['DATE'] = dates2\n", "#weatherDF2['DAILYSunrise'] = sunrise2\n", "#weatherDF2['DAILYSunset'] = sunset2\n", "\n", "#weatherDF = pd.concat([weatherDF1,weatherDF2[32:]],ignore_index=True)\n", "\n", "# Starting off with some of the easier features to work with-- more to come here . . . still in beta\n", "#weatherMetrics = weatherDF[['DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed', \\\n", "# 'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY', 'DAILYSunrise', 'DAILYSunset']]\n", "#weatherMetrics = weatherMetrics.convert_objects(convert_numeric=True)\n", "#weatherDates = weatherMetrics['DATE']\n", "#'DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed',\n", "#'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY'\n", "#timeWindow = 10800 #3 hours\n", "#hourlyDryBulbTemp = []\n", "#hourlyRelativeHumidity = []\n", "#hourlyWindSpeed = []\n", "#hourlySeaLevelPressure = []\n", "#hourlyVisibility = []\n", "#dailySunrise = []\n", "#dailySunset = []\n", "#daylight = []\n", "#test = 0\n", "#for timePoint in dates:#dates is the epoch time from the kaggle data\n", "# relevantWeather = weatherMetrics[(weatherDates <= timePoint) & (weatherDates > timePoint - timeWindow)]\n", "# hourlyDryBulbTemp.append(relevantWeather['HOURLYDRYBULBTEMPF'].mean())\n", "# hourlyRelativeHumidity.append(relevantWeather['HOURLYRelativeHumidity'].mean())\n", "# hourlyWindSpeed.append(relevantWeather['HOURLYWindSpeed'].mean())\n", "# hourlySeaLevelPressure.append(relevantWeather['HOURLYSeaLevelPressure'].mean())\n", "# hourlyVisibility.append(relevantWeather['HOURLYVISIBILITY'].mean())\n", "# dailySunrise.append(relevantWeather['DAILYSunrise'].iloc[-1])\n", "# dailySunset.append(relevantWeather['DAILYSunset'].iloc[-1])\n", "# daylight.append(1.0*((timePoint >= relevantWeather['DAILYSunrise'].iloc[-1]) and (timePoint < relevantWeather['DAILYSunset'].iloc[-1])))\n", " #if timePoint < relevantWeather['DAILYSunset'][-1]:\n", " #daylight.append(1)\n", " #else:\n", " #daylight.append(0)\n", " \n", "# if test%100000 == 0:\n", "# print(relevantWeather)\n", "# test += 1\n", "\n", "#hourlyDryBulbTemp = pd.Series.from_array(np.array(hourlyDryBulbTemp))\n", "#hourlyRelativeHumidity = pd.Series.from_array(np.array(hourlyRelativeHumidity))\n", "#hourlyWindSpeed = pd.Series.from_array(np.array(hourlyWindSpeed))\n", "#hourlySeaLevelPressure = pd.Series.from_array(np.array(hourlySeaLevelPressure))\n", "#hourlyVisibility = pd.Series.from_array(np.array(hourlyVisibility))\n", "#dailySunrise = pd.Series.from_array(np.array(dailySunrise))\n", "#dailySunset = pd.Series.from_array(np.array(dailySunset))\n", "#daylight = pd.Series.from_array(np.array(daylight))\n", "\n", "#x_data['HOURLYDRYBULBTEMPF'] = hourlyDryBulbTemp\n", "#x_data['HOURLYRelativeHumidity'] = hourlyRelativeHumidity\n", "#x_data['HOURLYWindSpeed'] = hourlyWindSpeed\n", "#x_data['HOURLYSeaLevelPressure'] = hourlySeaLevelPressure\n", "#x_data['HOURLYVISIBILITY'] = hourlyVisibility\n", "#x_data['DAILYSunrise'] = dailySunrise\n", "#x_data['DAILYSunset'] = dailySunset\n", "#x_data['Daylight'] = daylight\n", "\n", "#x_data.to_csv(path_or_buf=\"C:/MIDS/W207 final project/x_data.csv\")\n", "##########\n", "\n", "# Impute missing values with mean values:\n", "#x_complete = x_data.fillna(x_data.mean())\n", "#X_raw = x_complete.as_matrix()\n", "\n", "# Scale the data between 0 and 1:\n", "#X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "# Shuffle data to remove any underlying pattern that may exist:\n", "#shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "#X, y = X[shuffle], y[shuffle]\n", "\n", "# Separate training, dev, and test data:\n", "#test_data, test_labels = X[800000:], y[800000:]\n", "#dev_data, dev_labels = X[700000:800000], y[700000:800000]\n", "#train_data, train_labels = X[:700000], y[:700000]\n", "\n", "#mini_train_data, mini_train_labels = X[:75000], y[:75000]\n", "#mini_dev_data, mini_dev_labels = X[75000:100000], y[75000:100000]\n", "#labels_set = set(mini_dev_labels)\n", "#print(labels_set)\n", "#print(len(labels_set))\n", "#print(train_data[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local, individual load of updated data set (with weather data integrated) into training, development, and test subsets.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37 37 37 37\n" ] } ], "source": [ "data_path = \"/Users/Bryan/Desktop/UC_Berkeley_MIDS_files/Courses/W207_Intro_To_Machine_Learning/Final_Project/x_data_3.csv\"\n", "df = pd.read_csv(data_path, header=0)\n", "x_data = df.drop('category', 1)\n", "y = df.category.as_matrix()\n", "\n", "# Impute missing values with mean values:\n", "x_complete = x_data.fillna(x_data.mean())\n", "X_raw = x_complete.as_matrix()\n", "\n", "# Scale the data between 0 and 1:\n", "X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "# Shuffle data to remove any underlying pattern that may exist. Must re-run random seed step each time:\n", "np.random.seed(0)\n", "shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "X, y = X[shuffle], y[shuffle]\n", "\n", "# Due to difficulties with log loss and set(y_pred) needing to match set(labels), we will remove the extremely rare\n", "# crimes from the data for quality issues.\n", "X_minus_trea = X[np.where(y != 'TREA')]\n", "y_minus_trea = y[np.where(y != 'TREA')]\n", "X_final = X_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "y_final = y_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "\n", "# Separate training, dev, and test data:\n", "test_data, test_labels = X_final[800000:], y_final[800000:]\n", "dev_data, dev_labels = X_final[700000:800000], y_final[700000:800000]\n", "train_data, train_labels = X_final[100000:700000], y_final[100000:700000]\n", "calibrate_data, calibrate_labels = X_final[:100000], y_final[:100000]\n", "\n", "# Create mini versions of the above sets\n", "mini_train_data, mini_train_labels = X_final[:20000], y_final[:20000]\n", "mini_calibrate_data, mini_calibrate_labels = X_final[19000:28000], y_final[19000:28000]\n", "mini_dev_data, mini_dev_labels = X_final[49000:60000], y_final[49000:60000]\n", "\n", "# Create list of the crime type labels. This will act as the \"labels\" parameter for the log loss functions that follow\n", "crime_labels = list(set(y_final))\n", "crime_labels_mini_train = list(set(mini_train_labels))\n", "crime_labels_mini_dev = list(set(mini_dev_labels))\n", "crime_labels_mini_calibrate = list(set(mini_calibrate_labels))\n", "print(len(crime_labels), len(crime_labels_mini_train), len(crime_labels_mini_dev),len(crime_labels_mini_calibrate))\n", "\n", "#print(len(train_data),len(train_labels))\n", "#print(len(dev_data),len(dev_labels))\n", "#print(len(mini_train_data),len(mini_train_labels))\n", "#print(len(mini_dev_data),len(mini_dev_labels))\n", "#print(len(test_data),len(test_labels))\n", "#print(len(mini_calibrate_data),len(mini_calibrate_labels))\n", "#print(len(calibrate_data),len(calibrate_labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sarah's School data that we may still get to work as features: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Read in zip code data\n", "#data_path_zip = \"./data/2016_zips.csv\"\n", "#zips = pd.read_csv(data_path_zip, header=0, sep ='\\t', usecols = [0,5,6], names = [\"GEOID\", \"INTPTLAT\", \"INTPTLONG\"], dtype ={'GEOID': int, 'INTPTLAT': float, 'INTPTLONG': float})\n", "#sf_zips = zips[(zips['GEOID'] > 94000) & (zips['GEOID'] < 94189)]\n", "\n", "### Mapping longitude/latitude to zipcodes\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)**2+(long1-long2)**2)\n", "# return abs(lat1-lat2)+abs(long1-long2)\n", "#def find_zipcode(lat, long): \n", "# distances = sf_zips.apply(lambda row: dist(lat, long, row[\"INTPTLAT\"], row[\"INTPTLONG\"]), axis=1)\n", "# return sf_zips.loc[distances.idxmin(), \"GEOID\"]\n", "#x_data['zipcode'] = 0\n", "#for i in range(0, 1):\n", "# x_data['zipcode'][i] = x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "#x_data['zipcode']= x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "\n", "\n", "### Read in school data\n", "#data_path_schools = \"./data/pubschls.csv\"\n", "#schools = pd.read_csv(data_path_schools,header=0, sep ='\\t', usecols = [\"CDSCode\",\"StatusType\", \"School\", \"EILCode\", \"EILName\", \"Zip\", \"Latitude\", \"Longitude\"], dtype ={'CDSCode': str, 'StatusType': str, 'School': str, 'EILCode': str,'EILName': str,'Zip': str, 'Latitude': float, 'Longitude': float})\n", "#schools = schools[(schools[\"StatusType\"] == 'Active')]\n", "\n", "### Find the closest school\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)**2+(long1-long2)**2)\n", "\n", "#def find_closest_school(lat, long): \n", "# distances = schools.apply(lambda row: dist(lat, long, row[\"Latitude\"], row[\"Longitude\"]), axis=1)\n", "# return min(distances)\n", "#x_data['closest_school'] = x_data_sub.apply(lambda row: find_closest_school(row['y'], row['x']), axis=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formatting to meet Kaggle submission standards: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The Kaggle submission format requires listing the ID of each example.\n", "# This is to remember the order of the IDs after shuffling\n", "#allIDs = np.array(list(df.axes[0]))\n", "#allIDs = allIDs[shuffle]\n", "\n", "#testIDs = allIDs[800000:]\n", "#devIDs = allIDs[700000:800000]\n", "#trainIDs = allIDs[:700000]\n", "\n", "# Extract the column names for the required submission format\n", "#sampleSubmission_path = \"./data/sampleSubmission.csv\"\n", "#sampleDF = pd.read_csv(sampleSubmission_path)\n", "#allColumns = list(sampleDF.columns)\n", "#featureColumns = allColumns[1:]\n", "\n", "# Extracting the test data for a baseline submission\n", "#real_test_path = \"./data/test_transformed.csv\"\n", "#testDF = pd.read_csv(real_test_path, header=0)\n", "#real_test_data = testDF\n", "\n", "#test_complete = real_test_data.fillna(real_test_data.mean())\n", "#Test_raw = test_complete.as_matrix()\n", "\n", "#TestData = MinMaxScaler().fit_transform(Test_raw)\n", "\n", "# Here we remember the ID of each test data point, in case we ever decide to shuffle the test data for some reason\n", "#testIDs = list(testDF.axes[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate baseline prediction probabilities from MNB classifier and store in a .csv file (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate a baseline MNB classifier and make it return prediction probabilities for the actual test data\n", "#def MNB():\n", "# mnb = MultinomialNB(alpha = 0.0000001)\n", "# mnb.fit(train_data, train_labels)\n", "# print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", "# return mnb.predict_proba(dev_data)\n", "#MNB()\n", "\n", "#baselinePredictionProbabilities = MNB()\n", "\n", "# Place the resulting prediction probabilities in a .csv file in the required format\n", "# First, turn the prediction probabilties into a data frame\n", "#resultDF = pd.DataFrame(baselinePredictionProbabilities,columns=featureColumns)\n", "# Add the IDs as a final column\n", "#resultDF.loc[:,'Id'] = pd.Series(testIDs,index=resultDF.index)\n", "# Make the 'Id' column the first column\n", "#colnames = resultDF.columns.tolist()\n", "#colnames = colnames[-1:] + colnames[:-1]\n", "#resultDF = resultDF[colnames]\n", "# Output to a .csv file\n", "# resultDF.to_csv('result.csv',index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note: the code above will shuffle data differently every time it's run, so model accuracies will vary accordingly.*" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.016 0.985 0.826 0.667 0.055 0.002 0. 0. 0. 1. 0.\n", " 0. 0. 0. 0. 0. 0. 0.514 0.405 0.375 0.661 1.\n", " 0.985 0.985 0. ]]\n", "['LARCENY/THEFT']\n" ] } ], "source": [ "## Data sub-setting quality check-point\n", "print(train_data[:1])\n", "print(train_labels[:1])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "MultinomialNB accuracy on dev data: 0.22347\n" ] } ], "source": [ "# Modeling quality check-point with MNB--fast model\n", "\n", "def MNB():\n", " mnb = MultinomialNB(alpha = 0.0000001)\n", " mnb.fit(train_data, train_labels)\n", " print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", " \n", "MNB()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining Performance Criteria\n", "\n", "As determined by the Kaggle submission guidelines, the performance criteria metric for the San Francisco Crime Classification competition is Multi-class Logarithmic Loss (also known as cross-entropy). There are various other performance metrics that are appropriate for different domains: accuracy, F-score, Lift, ROC Area, average precision, precision/recall break-even point, and squared error.\n", "\n", "(Describe each performance metric and a domain in which it is preferred. Give Pros/Cons if able)\n", "\n", "- Multi-class Log Loss:\n", "\n", "- Accuracy:\n", "\n", "- F-score:\n", "\n", "- Lift:\n", "\n", "- ROC Area:\n", "\n", "- Average precision\n", "\n", "- Precision/Recall break-even point:\n", "\n", "- Squared-error:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Prototyping\n", "We will start our classifier and feature engineering process by looking at the performance of various classifiers with default parameter settings in predicting labels on the mini_dev_data:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", " weights='uniform') Multi-class Log Loss: 21.0240643644 \n", "\n", "\n", "BernoulliNB(alpha=1, binarize=0.5, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.6947927812 \n", "\n", "\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.60974496429 \n", "\n", "\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 2.59547592791 \n", "\n", "\n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False) Multi-class Log Loss: 2.60265495281 \n", "\n", "\n", "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 15.5020995603 \n", "\n", "\n", "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best') Multi-class Log Loss: 29.8634820265 \n", "\n", "\n", "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False) Multi-class Log Loss: 2.62373605675 \n", "\n", "\n" ] } ], "source": [ "def model_prototype(train_data, train_labels, eval_data, eval_labels):\n", " knn = KNeighborsClassifier(n_neighbors=5).fit(train_data, train_labels)\n", " bnb = BernoulliNB(alpha=1, binarize = 0.5).fit(train_data, train_labels)\n", " mnb = MultinomialNB().fit(train_data, train_labels)\n", " log_reg = LogisticRegression().fit(train_data, train_labels)\n", " neural_net = MLPClassifier().fit(train_data, train_labels)\n", " random_forest = RandomForestClassifier().fit(train_data, train_labels)\n", " decision_tree = DecisionTreeClassifier().fit(train_data, train_labels)\n", " support_vm_step_one = svm.SVC(probability = True)\n", " support_vm = support_vm_step_one.fit(train_data, train_labels)\n", " \n", " models = [knn, bnb, mnb, log_reg, neural_net, random_forest, decision_tree, support_vm]\n", " for model in models:\n", " eval_prediction_probabilities = model.predict_proba(eval_data)\n", " eval_predictions = model.predict(eval_data)\n", " print(model, \"Multi-class Log Loss:\", log_loss(y_true = eval_labels, y_pred = eval_prediction_probabilities, labels = crime_labels_mini_dev), \"\\n\\n\")\n", "\n", "model_prototype(mini_train_data, mini_train_labels, mini_dev_data, mini_dev_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Features, Hyperparameter Tuning, and Model Calibration To Improve Prediction For Each Classifier\n", "\n", "Here we seek to optimize the performance of our classifiers in a three-step, dynamnic engineering process. \n", "\n", "##### 1) Feature addition\n", "\n", "We previously added components from the weather data into the original SF crime data as new features. We will not repeat work done in our initial submission, where our training dataset did not include these features. For comparision with respoect to how the added features improved our performance with respect to log loss, please refer back to our initial submission.\n", "\n", "We can have Kalvin expand on exactly what he did here.\n", "\n", "##### 2) Hyperparameter tuning\n", "\n", "Each classifier has parameters that we can engineer to further optimize performance, as opposed to using the default parameter values as we did above in the model prototyping cell. This will be specific to each classifier as detailed below.\n", "\n", "##### 3) Model calibration\n", "\n", "We can calibrate the models via Platt Scaling or Isotonic Regression to attempt to improve their performance.\n", "\n", "- Platt Scaling: ((brief explanation of how it works))\n", "\n", "- Isotonic Regression: ((brief explanation of how it works))\n", "\n", "For each classifier, we can use CalibratedClassifierCV to perform probability calibration with isotonic regression or sigmoid (Platt Scaling). The parameters within CalibratedClassifierCV that we can adjust are the method ('sigmoid' or 'isotonic') and cv (cross-validation generator). As we will already be training our models before calibration, we will only use cv = 'prefit'. Thus, in practice the cross-validation generator will not be a modifiable parameter for us.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### K-Nearest Neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning:\n", "\n", "For the KNN classifier, we can seek to optimize the following classifier parameters: n-neighbors, weights, and the power parameter ('p')." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For KNN the best log loss with hyperparameter tuning is 2.62923629844 with k = 2001 w = uniform p = 1\n", "Computation time for this step is 351.40 seconds\n" ] } ], "source": [ "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_log_loss = []\n", "\n", "def k_neighbors_tuned(k,w,p):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\", p, \"is:\", working_log_loss)\n", "\n", "k_value_tuning = [i for i in range(1,5002,500)]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " k_neighbors_tuned(this_k, this_w, this_p)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "Here we will calibrate the KNN classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively.\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For KNN the best log loss with hyperparameter tuning and calibration is 2.71372469963 with k = 1 w = uniform p = 1 m = sigmoid\n", "Computation time for this step is 49.62 seconds\n" ] } ], "source": [ "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def knn_calibrated(k,w,p,m):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " ccv = CalibratedClassifierCV(tuned_KNN, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\",p,\",\",m,\"is:\", working_log_loss)\n", "\n", "#k_value_tuning = [i for i in range(1,5002,500)]\n", "k_value_tuning = [1]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " for this_m in methods:\n", " knn_calibrated(this_k, this_w, this_p, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss], 'm =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Comments on results for Hyperparameter tuning and Calibration for KNN:\n", "\n", "We see that the best log loss we achieve for KNN is with _ neighbors, _ weights, and _ power parameter.\n", "\n", "When we add-in calibration, we see that the the best log loss we achieve for KNN is with _ neighbors, _ weights, _ power parameter, and _ calibration method.\n", "\n", "(Further explanation here?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multinomial, Bernoulli, and Gaussian Naive Bayes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Bernoulli Naive Bayes\n", "\n", "For the Bernoulli Naive Bayes classifier, we seek to optimize the alpha parameter (Laplace smoothing parameter) and the binarize parameter (threshold for binarizing of the sample features). For the binarize parameter, we will create arbitrary thresholds over which our features, which are not binary/boolean features, will be binarized." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For BNB the best log loss with hyperparameter tuning is 2.6247750866 with alpha = 1.2 binarization threshold = 1e-20\n", "Computation time for this step is 186.46 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_bs = []\n", "list_for_log_loss = []\n", "\n", "def BNB_tuned(a,b):\n", " bnb_tuned = BernoulliNB(alpha = a, binarize = b).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = bnb_tuned.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_bs.append(this_b)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a,b =\", a,\",\",b,\"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "binarize_thresholds_tuning = [1e-20, 1e-19, 1e-18, 1e-17, 1e-16, 1e-15, 1e-14, 1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 0.999, 0.9999]\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_b in binarize_thresholds_tuning:\n", " BNB_tuned(this_a, this_b)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For BNB the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'binarization threshold =', list_for_bs[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: BernoulliNB\n", "\n", "Here we will calibrate the BNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For BNB the best log loss with hyperparameter tuning and calibration is 2.61370308039 with alpha = 1.0 binarization threshold = 0.5 method = sigmoid\n", "Computation time for this step is 1066.40 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_bs = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def BNB_calibrated(a,b,m):\n", " bnb_tuned = BernoulliNB(alpha = a, binarize = b).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = bnb_tuned.predict_proba(mini_dev_data)\n", " ccv = CalibratedClassifierCV(bnb_tuned, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_bs.append(this_b)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a,b,m =\", a,\",\", b,\",\", m, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "binarize_thresholds_tuning = [1e-20, 1e-19, 1e-18, 1e-17, 1e-16, 1e-15, 1e-14, 1e-13, 1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 0.999, 0.9999]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_b in binarize_thresholds_tuning:\n", " for this_m in methods:\n", " BNB_calibrated(this_a, this_b, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For BNB the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'binarization threshold =', list_for_bs[index_best_logloss], 'method = ', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Multinomial Naive Bayes\n", "\n", "For the Multinomial Naive Bayes classifer, we seek to optimize the alpha parameter (Laplace smoothing parameter)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For MNB the best log loss with hyperparameter tuning is 2.60930490132 with alpha = 1.8\n", "Computation time for this step is 5.96 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_log_loss = []\n", "\n", "def MNB_tuned(a):\n", " mnb_tuned = MultinomialNB(alpha = a).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities =mnb_tuned.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with BNB and a =\", a, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " MNB_tuned(this_a)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For MNB the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: MultinomialNB\n", "\n", "Here we will calibrate the MNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For MNB the best log loss with hyperparameter tuning and calibration is 2.6145055659 with alpha = 2.0 and method = sigmoid\n", "Computation time for this step is 34.13 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def MNB_calibrated(a,m):\n", " mnb_tuned = MultinomialNB(alpha = a).fit(mini_train_data, mini_train_labels)\n", " ccv = CalibratedClassifierCV(mnb_tuned, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with MNB and a =\", a, \"and m =\", m, \"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2.0, 10.0]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_m in methods:\n", " MNB_calibrated(this_a, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For MNB the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'and method =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Tuning: Gaussian Naive Bayes\n", "\n", "For the Gaussian Naive Bayes classifier there are no inherent parameters within the classifier function to optimize, but we will look at our log loss before and after adding noise to the data that is hypothesized to give it a more normal (Gaussian) distribution, which is required by the GNB classifier." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with pre-tuned GNB is: 34.1076504549\n", "Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution is: 31.8040829494\n" ] } ], "source": [ "def GNB_pre_tune():\n", " gnb_pre_tuned = GaussianNB().fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities =gnb_pre_tuned.predict_proba(mini_dev_data)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " print(\"Multi-class Log Loss with pre-tuned GNB is:\", working_log_loss)\n", "\n", "GNB_pre_tune()\n", " \n", "def GNB_post_tune():\n", " # Gaussian Naive Bayes requires the data to have a relative normal distribution. Sometimes\n", " # adding noise can improve performance by making the data more normal:\n", " mini_train_data_noise = np.random.rand(mini_train_data.shape[0],mini_train_data.shape[1])\n", " modified_mini_train_data = np.multiply(mini_train_data,mini_train_data_noise) \n", " gnb_with_noise = GaussianNB().fit(modified_mini_train_data,mini_train_labels)\n", " dev_prediction_probabilities =gnb_with_noise.predict_proba(mini_dev_data)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " print(\"Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution is:\", working_log_loss)\n", " \n", "GNB_post_tune()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration: GaussianNB\n", "\n", "Here we will calibrate the GNB classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For GNB the best log loss with tuning and calibration is 2.67904020299 with method = sigmoid\n", "Computation time for this step is 1.36 seconds\n" ] } ], "source": [ "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def GNB_calibrated(m):\n", " # Gaussian Naive Bayes requires the data to have a relative normal distribution. Sometimes\n", " # adding noise can improve performance by making the data more normal:\n", " mini_train_data_noise = np.random.rand(mini_train_data.shape[0],mini_train_data.shape[1])\n", " modified_mini_train_data = np.multiply(mini_train_data,mini_train_data_noise) \n", " gnb_with_noise = GaussianNB().fit(modified_mini_train_data,mini_train_labels)\n", " ccv = CalibratedClassifierCV(gnb_with_noise, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with tuned GNB via addition of noise to normalize the data's distribution and after calibration is:\", working_log_loss, 'with calibration method =', m)\n", " \n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_m in methods:\n", " GNB_calibrated(this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For GNB the best log loss with tuning and calibration is',list_for_log_loss[index_best_logloss], 'with method =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logistic Regression\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Logistic Regression classifier, we can seek to optimize the following classifier parameters: penalty (l1 or l2), C (inverse of regularization strength), solver ('newton-cg', 'lbfgs', 'liblinear', or 'sag')\n", "\n", "###### Model calibration:\n", "\n", "See above\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decision Tree (Bryan)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Decision Tree classifier, we can seek to optimize the following classifier parameters: min_samples_leaf (the minimum number of samples required to be at a leaf node), max_depth\n", "\n", "From readings, setting min_samples_leaf to approximately 1% of the data points can stop the tree from inappropriately classifying outliers, which can help to improve accuracy (unsure if significantly improves MCLL).\n", "\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support Vector Machines (Kalvin)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the SVM classifier, we can seek to optimize the following classifier parameters: C (penalty parameter C of the error term), kernel ('linear', 'poly', 'rbf', sigmoid', or 'precomputed')\n", "\n", "See source [2] for parameter optimization in SVM\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural Nets (Sarah)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Neural Networks MLP classifier, we can seek to optimize the following classifier parameters: hidden_layer_sizes, activation ('identity', 'logistic', 'tanh', 'relu'), solver ('lbfgs','sgd', adam'), alpha, learning_rate ('constant', 'invscaling','adaptive')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'theano'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-99-746a0d71593d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m### All the work from Sarah's notebook:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msandbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrng_mrg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMRG_RandomStreams\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mRandomStreams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'theano'" ] } ], "source": [ "### All the work from Sarah's notebook:\n", "\n", "import theano\n", "from theano import tensor as T\n", "from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n", "print (theano.config.device) # We're using CPUs (for now)\n", "print (theano.config.floatX )# Should be 64 bit for CPUs\n", "\n", "np.random.seed(0)\n", "\n", "from IPython.display import display, clear_output" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features = 25\n", "Train set = 700000\n", "Test set = 78049\n", "['DRUG/NARCOTIC', 'RUNAWAY', 'DRUNKENNESS', 'LOITERING', 'STOLEN PROPERTY', 'MISSING PERSON', 'ARSON', 'FRAUD', 'SEX OFFENSES NON FORCIBLE', 'NON-CRIMINAL', 'WEAPON LAWS', 'RECOVERED VEHICLE', 'ASSAULT', 'TRESPASS', 'GAMBLING', 'SUSPICIOUS OCC', 'TREA', 'BAD CHECKS', 'VANDALISM', 'FAMILY OFFENSES', 'DRIVING UNDER THE INFLUENCE', 'WARRANTS', 'PROSTITUTION', 'SEX OFFENSES FORCIBLE', 'DISORDERLY CONDUCT', 'LIQUOR LAWS', 'ROBBERY', 'FORGERY/COUNTERFEITING', 'OTHER OFFENSES', 'EXTORTION', 'VEHICLE THEFT', 'SUICIDE', 'PORNOGRAPHY/OBSCENE MAT', 'LARCENY/THEFT', 'BRIBERY', 'EMBEZZLEMENT', 'SECONDARY CODES', 'KIDNAPPING', 'BURGLARY']\n" ] } ], "source": [ "numFeatures = train_data[1].size\n", "numTrainExamples = train_data.shape[0]\n", "numTestExamples = test_data.shape[0]\n", "print ('Features = %d' %(numFeatures))\n", "print ('Train set = %d' %(numTrainExamples))\n", "print ('Test set = %d' %(numTestExamples))\n", "\n", "class_labels = list(set(train_labels))\n", "print(class_labels)\n", "numClasses = len(class_labels)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classes = 39\n", "\n", " [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 1.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]] \n", "\n", "['BURGLARY' 'LARCENY/THEFT' 'OTHER OFFENSES' 'OTHER OFFENSES'\n", " 'SUSPICIOUS OCC' 'VANDALISM' 'DRUG/NARCOTIC' 'MISSING PERSON'\n", " 'LARCENY/THEFT' 'OTHER OFFENSES'] \n", "\n" ] } ], "source": [ "### Binarize the class labels\n", "\n", "def binarizeY(data):\n", " binarized_data = np.zeros((data.size,39))\n", " for j in range(0,data.size):\n", " feature = data[j]\n", " i = class_labels.index(feature)\n", " binarized_data[j,i]=1\n", " return binarized_data\n", "\n", "train_labels_b = binarizeY(train_labels)\n", "test_labels_b = binarizeY(test_labels)\n", "numClasses = train_labels_b[1].size\n", "\n", "print ('Classes = %d' %(numClasses))\n", "print ('\\n', train_labels_b[:5, :], '\\n')\n", "print (train_labels[:10], '\\n')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'theano' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-94-8c49129ca7e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnumHiddenNodeslayer2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mw_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumFeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumHiddenNodeslayer1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mw_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumHiddenNodeslayer1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumHiddenNodeslayer2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mw_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumHiddenNodeslayer2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumClasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'theano' is not defined" ] } ], "source": [ "###1) Parameters\n", "numFeatures = train_data.shape[1]\n", "\n", "numHiddenNodeslayer1 = 50\n", "numHiddenNodeslayer2 = 30\n", "\n", "w_1 = theano.shared(np.asarray((np.random.randn(*(numFeatures, numHiddenNodeslayer1))*0.01)))\n", "w_2 = theano.shared(np.asarray((np.random.randn(*(numHiddenNodeslayer1, numHiddenNodeslayer2))*0.01)))\n", "w_3 = theano.shared(np.asarray((np.random.randn(*(numHiddenNodeslayer2, numClasses))*0.01)))\n", "params = [w_1, w_2, w_3]\n", "\n", "\n", "###2) Model\n", "X = T.matrix()\n", "Y = T.matrix()\n", "\n", "srng = RandomStreams()\n", "def dropout(X, p=0.):\n", " if p > 0:\n", " X *= srng.binomial(X.shape, p=1 - p)\n", " X /= 1 - p\n", " return X\n", "\n", "def model(X, w_1, w_2, w_3, p_1, p_2, p_3):\n", " return T.nnet.softmax(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(X, p_1), w_1)),p_2), w_2)),p_3),w_3))\n", "y_hat_train = model(X, w_1, w_2, w_3, 0.2, 0.5,0.5)\n", "y_hat_predict = model(X, w_1, w_2, w_3, 0., 0., 0.)\n", "\n", "### (3) Cost function\n", "cost = T.mean(T.sqr(y_hat - Y))\n", "cost = T.mean(T.nnet.categorical_crossentropy(y_hat_train, Y))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'cost' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-ef1dde28bb64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mupdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_input_downcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'cost' is not defined" ] } ], "source": [ "### (4) Objective (and solver)\n", "\n", "alpha = 0.01\n", "def backprop(cost, w):\n", " grads = T.grad(cost=cost, wrt=w)\n", " updates = []\n", " for wi, grad in zip(w, grads):\n", " updates.append([wi, wi - grad * alpha])\n", " return updates\n", "\n", "update = backprop(cost, params)\n", "train = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)\n", "y_pred = T.argmax(y_hat_predict, axis=1)\n", "predict = theano.function(inputs=[X], outputs=y_pred, allow_input_downcast=True)\n", "\n", "miniBatchSize = 10 \n", "\n", "def gradientDescent(epochs):\n", " for i in range(epochs):\n", " for start, end in zip(range(0, len(train_data), miniBatchSize), range(miniBatchSize, len(train_data), miniBatchSize)):\n", " cc = train(train_data[start:end], train_labels_b[start:end])\n", " clear_output(wait=True)\n", " print ('%d) accuracy = %.4f' %(i+1, np.mean(np.argmax(test_labels_b, axis=1) == predict(test_data))) )\n", "\n", "gradientDescent(50)\n", "\n", "### How to decide what # to use for epochs? epochs in this case are how many rounds?\n", "### plot costs for each of the 50 iterations and see how much it decline.. if its still very decreasing, you should\n", "### do more iterations; otherwise if its looking like its flattening, you can stop" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Forest (Sam, possibly in AWS)\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Random Forest classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_features, max_depth, min_samples_leaf, bootstrap (whether or not bootstrap samples are used when building trees), oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Meta-estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AdaBoost Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "There are no major changes that we seek to make in the AdaBoostClassifier with respect to default parameter values.\n", "\n", "###### Adaboosting each classifier:\n", "\n", "We will run the AdaBoostClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bagging Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Bagging meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_samples, max_features, bootstrap (whether or not bootstrap samples are used when building trees), bootstrap_features (whether features are drawn with replacement), and oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Bagging each classifier:\n", "\n", "We will run the BaggingClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gradient Boosting Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Gradient Boosting meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_depth, min_samples_leaf, and max_features\n", "\n", "###### Gradient Boosting each classifier:\n", "\n", "We will run the GradientBoostingClassifier with loss = 'deviance' (as loss = 'exponential' uses the AdaBoost algorithm) on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final evaluation on test data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Here we will likely use Pipeline and GridSearchCV in order to find the overall classifier with optimized Multi-class Log Loss.\n", "# This will be the last step after all attempts at feature addition, hyperparameter tuning, and calibration are completed\n", "# and the corresponding performance metrics are gathered.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "1) Hsiang, Solomon M. and Burke, Marshall and Miguel, Edward. \"Quantifying the Influence of Climate on Human Conflict\". Science, Vol 341, Issue 6151, 2013 \n", "\n", "2) Huang, Cheng-Lung. Wang, Chieh-Jen. \"A GA-based feature selection and parameters optimization for support vector machines\". Expert Systems with Applications, Vol 31, 2006, p 231-240\n", "\n", "3) More to come \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

albertxavier001/graduation-project

pytorch/New Gradient Network NO DSN 1.ipynb

1

27417

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, glob, platform, datetime, random\n", "from collections import OrderedDict\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.utils.data as data_utils\n", "import torch.nn.parallel\n", "import torch.backends.cudnn as cudnn\n", "import torch.optim as optim\n", "from torch.autograd import Variable\n", "from torch import functional as F\n", "# import torchvision.datasets as datasets\n", "import torchvision.models as models\n", "import torchvision.transforms as transforms\n", "\n", "import cv2\n", "from PIL import Image\n", "from tensorboardX import SummaryWriter\n", "\n", "import numpy as np\n", "from numpy.linalg import inv as denseinv\n", "from scipy import sparse\n", "from scipy.sparse import lil_matrix, csr_matrix\n", "from scipy.sparse.linalg import spsolve\n", "from scipy.sparse.linalg import inv as spinv\n", "import scipy.misc\n", "\n", "from myimagefolder import MyImageFolder\n", "from mymodel import GradientNet\n", "from myargs import Args\n", "from myutils import MyUtils" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Configurations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myutils = MyUtils()\n", "\n", "args = Args()\n", "args.arch = \"densenet121\"\n", "args.epoches = 500\n", "args.epoches_unary_threshold = 0\n", "args.image_h = 256\n", "args.image_w = 256\n", "args.img_extentions = [\"png\"]\n", "args.training_thresholds = [250,200,150,50,0,300]\n", "args.base_lr = 1\n", "args.lr = args.base_lr\n", "args.snapshot_interval = 5000\n", "args.debug = True\n", "\n", "\n", "# growth_rate = (4*(2**(args.gpu_num)))\n", "transition_scale=2\n", "pretrained_scale=4\n", "growth_rate = 32\n", "\n", "#######\n", "# args.test_scene = ['alley_2', 'bamboo_2', 'bandage_2', 'cave_4', 'market_5', 'mountain_1', 'shaman_3', 'sleeping_2', 'temple_3']\n", "args.test_scene = 'alley_1'\n", "gradient=False\n", "args.gpu_num = 1\n", "#######\n", "\n", "writer_comment = '{}_rgb_no_dsn'.format(args.test_scene)\n", "if gradient == True:\n", " writer_comment = '{}_gd_no_dsn'.format(args.test_scene)\n", "\n", "offset = 0.\n", "if gradient == True: offset = 0.5\n", "\n", "args.display_interval = 50\n", "args.display_curindex = 0\n", "\n", "system_ = platform.system()\n", "system_dist, system_version, _ = platform.dist()\n", "if system_ == \"Darwin\": \n", " args.train_dir = '/Volumes/Transcend/dataset/sintel2'\n", " args.pretrained = False\n", "elif platform.dist() == ('debian', 'jessie/sid', ''):\n", " args.train_dir = '/home/lwp/workspace/sintel2'\n", " args.pretrained = True\n", "elif platform.dist() == ('debian', 'stretch/sid', ''):\n", " args.train_dir = '/home/cad/lwp/workspace/dataset/sintel2'\n", " args.pretrained = True\n", "\n", "if platform.system() == 'Linux': use_gpu = True\n", "else: use_gpu = False\n", "if use_gpu:\n", " torch.cuda.set_device(args.gpu_num)\n", " \n", "\n", "print(platform.dist())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# My DataLoader" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "train_dataset = MyImageFolder(args.train_dir, 'train',\n", " transforms.Compose(\n", " [transforms.ToTensor()]\n", " ), random_crop=True, \n", " img_extentions=args.img_extentions, test_scene=args.test_scene, image_h=args.image_h, image_w=args.image_w)\n", "test_dataset = MyImageFolder(args.train_dir, 'test', \n", " transforms.Compose(\n", " [transforms.CenterCrop((args.image_h, args.image_w)),\n", " transforms.ToTensor()]\n", " ), random_crop=False,\n", " img_extentions=args.img_extentions, test_scene=args.test_scene, image_h=args.image_h, image_w=args.image_w)\n", "\n", "train_loader = data_utils.DataLoader(train_dataset,1,True,num_workers=1)\n", "test_loader = data_utils.DataLoader(test_dataset,1,True,num_workers=1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Load Pretrained Model\n", "\n", "[Defination](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py)\n", "* DenseNet-121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)\n", " * First Convolution: 32M -> 16M -> 8M\n", " * every transition: 8M -> 4M -> 2M (downsample 1/2, except the last block)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "densenet = models.__dict__[args.arch](pretrained=args.pretrained)\n", "\n", "for param in densenet.parameters():\n", " param.requires_grad = False\n", "\n", "if use_gpu: densenet.cuda()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ss = 6\n", "s0 = ss*5\n", "s0 = 0\n", "\n", "args.display_curindex = 0\n", "args.base_lr = 0.05\n", "args.display_interval = 20\n", "args.momentum = 0.9\n", "args.epoches = 240\n", "args.training_thresholds = [0,0,0,0,0,s0]\n", "args.training_merge_thresholds = [s0+ss*3*3,s0+ss*2*3, s0+ss*1*3, s0, -1, s0+ss*4*3]\n", "args.power = 0.5\n", "\n", "\n", "\n", "# pretrained = PreTrainedModel(densenet)\n", "# if use_gpu: \n", "# pretrained.cuda()\n", "\n", "\n", "net = GradientNet(densenet=densenet, growth_rate=growth_rate, \n", " transition_scale=transition_scale, pretrained_scale=pretrained_scale,\n", " gradient=gradient)\n", "if use_gpu:\n", " net.cuda()\n", "\n", "if use_gpu: \n", " mse_losses = [nn.MSELoss().cuda()] * 6\n", " test_losses = [nn.MSELoss().cuda()] * 6\n", " mse_merge_losses = [nn.MSELoss().cuda()] * 6\n", " test_merge_losses = [nn.MSELoss().cuda()] * 6\n", "else:\n", " mse_losses = [nn.MSELoss()] * 6\n", " mse_merge_losses = [nn.MSELoss()] * 6\n", " test_losses = [nn.MSELoss()] * 6\n", " test_merge_losses = [nn.MSELoss()] * 6 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test_model(epoch, go_through_merge=False, phase='train'):\n", " if phase == 'train': net.train()\n", " else: net.eval()\n", " \n", " test_losses_trainphase = [0] * len(args.training_thresholds)\n", " test_cnts_trainphase = [0.00001] * len(args.training_thresholds) \n", " test_merge_losses_trainphase = [0] * len(args.training_thresholds)\n", " test_merge_cnts_trainphase = [0.00001] * len(args.training_thresholds)\n", " \n", " for ind, data in enumerate(test_loader, 0):\n", " input_img, gt_albedo, gt_shading, test_scene, img_path = data\n", " input_img = Variable(input_img)\n", " gt_albedo = Variable(gt_albedo)\n", " gt_shading = Variable(gt_shading)\n", " if use_gpu:\n", " input_img = input_img.cuda(args.gpu_num)\n", " \n", "# pretrained.train(); ft_pretreained = pretrained(input_img)\n", " ft_test, merged_RGB = net(input_img, go_through_merge=go_through_merge)\n", " \n", " for i,v in enumerate(ft_test):\n", " if epoch < args.training_thresholds[i]: continue\n", " if i == 5: s = 1\n", " else: s = (2**(i+1))\n", " gt0 = gt_albedo.cpu().data.numpy()\n", " n,c,h,w = gt0.shape\n", " gt, display = myutils.processGt(gt0, scale_factor=s, gd=gradient, return_image=True)\n", " gt_mg, display_mg = myutils.processGt(gt0, scale_factor=s//2, gd=gradient, return_image=True)\n", " \n", " if use_gpu: \n", " gt = gt.cuda()\n", " gt_mg = gt_mg.cuda()\n", " \n", " if i != 5: \n", " loss = mse_losses[i](ft_test[i], gt)\n", " test_losses_trainphase[i] += loss.data.cpu().numpy()[0]\n", " test_cnts_trainphase[i] += 1\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " if i==5: gt2=gt\n", " else: gt2=gt_mg\n", "# print(i)\n", "# print('merge size', merged_RGB[i].size())\n", "# print('gt2 size', gt2.size())\n", " loss = mse_merge_losses[i](merged_RGB[i], gt2)\n", " test_merge_losses_trainphase[i] += loss.data.cpu().numpy()[0]\n", " test_merge_cnts_trainphase[i] += 1\n", " \n", "\n", " \n", " if ind == 0: \n", " if i != 5:\n", " v = v[0].cpu().data.numpy()\n", " v = v.transpose(1,2,0)\n", " v = v[:,:,0:3]\n", " cv2.imwrite('snapshot{}/test-phase_{}-{}-{}.png'.format(args.gpu_num, phase, epoch, i), (v[:,:,::-1]+offset)*255)\n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " v = merged_RGB[i][0].cpu().data.numpy()\n", " v = v.transpose(1,2,0)\n", " v = v[:,:,0:3]\n", " cv2.imwrite('snapshot{}/test-mg-phase_{}-{}-{}.png'.format(args.gpu_num, phase, epoch, i), (v[:,:,::-1]+offset)*255)\n", " \n", " run_losses = test_losses_trainphase\n", " run_cnts = test_cnts_trainphase\n", " writer.add_scalars('16M loss', {'test 16M phase {}'.format(phase): np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'test 8M phase {}'.format(phase): np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'test 4M phase {}'.format(phase): np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'test 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'test 1M phase {}'.format(phase): np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'test merged phase {}'.format(phase): np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch)\n", " \n", " run_losses = test_merge_losses_trainphase\n", " run_cnts = test_merge_cnts_trainphase\n", " writer.add_scalars('16M loss', {'mg test 16M phase {}'.format(phase): np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'mg test 8M phase {}'.format(phase): np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'mg test 4M phase {}'.format(phase): np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'mg test 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'mg test 1M phase {}'.format(phase): np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'mg test merged phase {}'.format(phase): np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# training loop\n", "\n", "writer = SummaryWriter(comment='-{}'.format(writer_comment))\n", "\n", "parameters = filter(lambda p: p.requires_grad, net.parameters())\n", "optimizer = optim.SGD(parameters, lr=args.base_lr, momentum=args.momentum)\n", "\n", "def adjust_learning_rate(optimizer, epoch, beg, end, reset_lr=None, base_lr=args.base_lr):\n", " \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n", " for param_group in optimizer.param_groups:\n", "# print('para gp', param_group)\n", " if reset_lr != None:\n", " param_group['lr'] = reset_lr\n", " continue\n", " param_group['lr'] = base_lr * (float(end-epoch)/(end-beg)) ** (args.power)\n", " if param_group['lr'] < 1.0e-8: param_group['lr'] = 1.0e-8\n", " \n", "\n", "for epoch in range(args.epoches):\n", "# epoch = 234\n", " net.train()\n", " print('epoch: {} [{}]'.format(epoch, datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")))\n", "\n", " if epoch < args.training_thresholds[-1]: \n", " adjust_learning_rate(optimizer, epoch, beg=0, end=s0-1)\n", " elif epoch < args.training_merge_thresholds[-1]:\n", " adjust_learning_rate(optimizer, (epoch-s0)%(ss), beg=0, end=ss-1, base_lr=args.base_lr)\n", " else:\n", " adjust_learning_rate(optimizer, epoch, beg=args.training_merge_thresholds[-1], end=args.epoches-1, base_lr=args.base_lr) \n", " \n", " \n", "# if epoch < args.training_thresholds[-1]: go_through_merge = False\n", "# elif epoch >= args.training_merge_thresholds[5]: go_through_merge = '32M'\n", "# elif epoch >= args.training_merge_thresholds[0]: go_through_merge = '16M'\n", "# elif epoch >= args.training_merge_thresholds[1]: go_through_merge = '08M'\n", "# elif epoch >= args.training_merge_thresholds[2]: go_through_merge = '04M'\n", "# elif epoch >= args.training_merge_thresholds[3]: go_through_merge = '02M'\n", " go_through_merge = '32M'\n", " \n", " run_losses = [0] * len(args.training_thresholds)\n", " run_cnts = [0.00001] * len(args.training_thresholds)\n", " run_merge_losses = [0] * len(args.training_thresholds)\n", " run_merge_cnts = [0.00001] * len(args.training_thresholds)\n", " if (epoch in args.training_thresholds) == True: \n", " adjust_learning_rate(optimizer, epoch, reset_lr=args.base_lr, beg=-1, end=-1)\n", " if (epoch in args.training_merge_thresholds) == True:\n", " adjust_learning_rate(optimizer, epoch, reset_lr=args.base_lr, beg=-1, end=-1)\n", " \n", " writer.add_scalar('learning rate', optimizer.param_groups[0]['lr'], global_step=epoch)\n", " for ind, data in enumerate(train_loader, 0):\n", "# if ind == 1 : break\n", " \"\"\"prepare training data\"\"\"\n", " input_img, gt_albedo, gt_shading, test_scene, img_path = data\n", " im = input_img[0,:,:,:].numpy(); im = im.transpose(1,2,0); im = im[:,:,::-1]*255\n", " input_img, gt_albedo, gt_shading = Variable(input_img), Variable(gt_albedo), Variable(gt_shading)\n", " if use_gpu: input_img, gt_albedo, gt_shading = input_img.cuda(), gt_albedo.cuda(), gt_shading.cuda()\n", "\n", " if args.display_curindex % args.display_interval == 0: cv2.imwrite('snapshot{}/input.png'.format(args.gpu_num), im)\n", "\n", " optimizer.zero_grad()\n", " \n", " \n", " ft_predict, merged_RGB = net(input_img, go_through_merge=go_through_merge)\n", " for i, threshold in enumerate(args.training_thresholds):\n", " if epoch >= threshold:\n", "# if epoch >= 0:\n", " \"\"\"prepare resized gt\"\"\"\n", " if i == 5: s = 1\n", " else: s = (2**(i+1))\n", " gt0 = gt_albedo.cpu().data.numpy()\n", " n,c,h,w = gt0.shape\n", " gt, display = myutils.processGt(gt0, scale_factor=s, gd=gradient, return_image=True)\n", " gt_mg, display_mg = myutils.processGt(gt0, scale_factor=s//2, gd=gradient, return_image=True)\n", " if use_gpu: \n", " gt = gt.cuda()\n", " gt_mg = gt_mg.cuda()\n", " if args.display_curindex % args.display_interval == 0:\n", " display = display[:,:,0:3]\n", " cv2.imwrite('snapshot{}/gt-{}-{}.png'.format(args.gpu_num, epoch, i), display[:,:,::-1]*255) \n", " \n", " \"\"\"compute loss\"\"\"\n", " if i != 5: \n", " loss = mse_losses[i](ft_predict[i], gt)\n", " run_losses[i] += loss.data.cpu().numpy()[0]\n", "# loss.backward(retain_graph=True)\n", " run_cnts[i] += 1\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", "# print(epoch, go_through_merge, i)\n", " \n", "# print (merged_RGB[i].cpu().data.numpy().max(), merged_RGB[i].cpu().data.numpy().min())\n", " if i!=5: continue\n", " if i==5: gt2=gt\n", " else: gt2=gt_mg\n", "# print(i)\n", "# print('merge size', merged_RGB[i].size())\n", "# print('gt2 size', gt2.size())\n", " loss = mse_merge_losses[i](merged_RGB[i], gt2)\n", " run_merge_losses[i] += loss.data.cpu().numpy()[0]\n", " loss.backward(retain_graph=True)\n", " run_merge_cnts[i] += 1\n", " \n", " \"\"\"save training image\"\"\"\n", " if args.display_curindex % args.display_interval == 0:\n", " \n", " if i != 5:\n", " im = (ft_predict[i].cpu().data.numpy()[0].transpose((1,2,0))+offset) * 255\n", " im = im[:,:,0:3]\n", " \n", " cv2.imwrite('snapshot{}/train-{}-{}.png'.format(args.gpu_num, epoch, i), im[:,:,::-1])\n", " \n", " if go_through_merge != False and i != 4:\n", " if ((go_through_merge == '32M') or\n", " (go_through_merge == '16M' and i != 5) or \n", " (go_through_merge == '08M' and i != 5 and i > 0) or\n", " (go_through_merge == '04M' and i != 5 and i > 1) or\n", " (go_through_merge == '02M' and i != 5 and i > 2)):\n", " im = (merged_RGB[i].cpu().data.numpy()[0].transpose((1,2,0))+offset) * 255\n", " im = im[:,:,0:3]\n", " cv2.imwrite('snapshot{}/train-mg-{}-{}.png'.format(args.gpu_num, epoch, i), im[:,:,::-1])\n", " optimizer.step()\n", " args.display_curindex += 1\n", "\n", " \"\"\" every epoch \"\"\"\n", "# loss_output = 'ind: ' + str(args.display_curindex)\n", " loss_output = ''\n", " \n", " \n", " \n", " for i,v in enumerate(run_losses):\n", " if i == len(run_losses)-1: \n", " loss_output += ' merged: %6f' % (run_losses[i] / run_cnts[i])\n", " continue\n", " loss_output += ' %2dM: %6f' % ((2**(4-i)), (run_losses[i] / run_cnts[i]))\n", " print(loss_output)\n", " loss_output = ''\n", " for i,v in enumerate(run_merge_losses):\n", " if i == len(run_merge_losses)-1: \n", " loss_output += 'mg merged: %6f' % (run_merge_losses[i] / run_merge_cnts[i])\n", " continue\n", " loss_output += ' mg %2dM: %6f' % ((2**(4-i)), (run_merge_losses[i] / run_merge_cnts[i]))\n", " print(loss_output)\n", " \n", " \"\"\"save at every epoch\"\"\"\n", " if (epoch+1) % 10 == 0:\n", " torch.save({\n", " 'epoch': epoch,\n", " 'args' : args,\n", " 'state_dict': net.state_dict(),\n", " 'optimizer': optimizer.state_dict()\n", " }, 'snapshot{}/snapshot-{}.pth.tar'.format(args.gpu_num, epoch))\n", " \n", " # test \n", " if (epoch+1) % 5 == 0:\n", " test_model(epoch, phase='train', go_through_merge=go_through_merge)\n", " test_model(epoch, phase='test', go_through_merge=go_through_merge)\n", "\n", " writer.add_scalars('16M loss', {'train 16M ': np.array([run_losses[0]/ run_cnts[0]])}, global_step=epoch) \n", " writer.add_scalars('8M loss', {'train 8M ': np.array([run_losses[1]/ run_cnts[1]])}, global_step=epoch) \n", " writer.add_scalars('4M loss', {'train 4M ': np.array([run_losses[2]/ run_cnts[2]])}, global_step=epoch) \n", " writer.add_scalars('2M loss', {'train 2M ': np.array([run_losses[3]/ run_cnts[3]])}, global_step=epoch) \n", " writer.add_scalars('1M loss', {'train 1M ': np.array([run_losses[4]/ run_cnts[4]])}, global_step=epoch) \n", " writer.add_scalars('merged loss', {'train merged ': np.array([run_losses[5]/ run_cnts[5]])}, global_step=epoch) \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Visualize Graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from graphviz import Digraph\n", "import torch\n", "from torch.autograd import Variable\n", "\n", "\n", "def make_dot(var, params=None):\n", " \"\"\" Produces Graphviz representation of PyTorch autograd graph\n", " Blue nodes are the Variables that require grad, orange are Tensors\n", " saved for backward in torch.autograd.Function\n", " Args:\n", " var: output Variable\n", " params: dict of (name, Variable) to add names to node that\n", " require grad (TODO: make optional)\n", " \"\"\"\n", " if params is not None:\n", " assert isinstance(params.values()[0], Variable)\n", " param_map = {id(v): k for k, v in params.items()}\n", "\n", " node_attr = dict(style='filled',\n", " shape='box',\n", " align='left',\n", " fontsize='12',\n", " ranksep='0.1',\n", " height='0.2')\n", " dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"10240,10240\"), format='svg')\n", " seen = set()\n", "\n", " def size_to_str(size):\n", " return '('+(', ').join(['%d' % v for v in size])+')'\n", "\n", " def add_nodes(var):\n", " if var not in seen:\n", " if torch.is_tensor(var):\n", " dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')\n", " elif hasattr(var, 'variable'):\n", " u = var.variable\n", " name = param_map[id(u)] if params is not None else ''\n", " node_name = '%s\\n %s' % (name, size_to_str(u.size()))\n", " dot.node(str(id(var)), node_name, fillcolor='lightblue')\n", " else:\n", " dot.node(str(id(var)), str(type(var).__name__))\n", " seen.add(var)\n", " if hasattr(var, 'next_functions'):\n", " for u in var.next_functions:\n", " if u[0] is not None:\n", " dot.edge(str(id(u[0])), str(id(var)))\n", " add_nodes(u[0])\n", " if hasattr(var, 'saved_tensors'):\n", " for t in var.saved_tensors:\n", " dot.edge(str(id(t)), str(id(var)))\n", " add_nodes(t)\n", " add_nodes(var.grad_fn)\n", " return dot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# x = Variable(torch.zeros(1,3,256,256))\n", "# y = net(x.cuda())\n", "# g = make_dot(y[-1])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# g.render('net-transition_scale_{}'.format(transition_scale)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

LSSTC-DSFP/LSSTC-DSFP-Sessions

Sessions/Session13/Day2/02-Fast-GPs.ipynb

1

11479

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fast GP implementations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib import rcParams\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"figure.figsize\"] = 12, 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking GP codes\n", "Implemented the right way, GPs can be super fast! Let's compare the time it takes to evaluate our GP likelihood and the time it takes to evaluate the likelihood computed with the snazzy ``george`` and ``celerite`` packages. We'll learn how to use both along the way. Let's create a large, fake dataset for these tests:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "np.random.seed(0)\n", "t = np.linspace(0, 10, 10000)\n", "y = np.random.randn(10000)\n", "sigma = np.ones(10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Our GP" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def ExpSquaredCovariance(t, A=1.0, l=1.0, tprime=None):\n", " \"\"\"\n", " Return the ``N x M`` exponential squared\n", " covariance matrix.\n", " \n", " \"\"\"\n", " if tprime is None:\n", " tprime = t\n", " TPrime, T = np.meshgrid(tprime, t)\n", " return A ** 2 * np.exp(-0.5 * (T - TPrime) ** 2 / l ** 2)\n", "\n", "\n", "def ln_gp_likelihood(t, y, sigma=0, A=1.0, l=1.0):\n", " \"\"\"\n", " Return the log of the GP likelihood for a datatset y(t)\n", " with uncertainties sigma, modeled with a Squared Exponential\n", " Kernel with amplitude A and lengthscale l.\n", " \n", " \"\"\"\n", " # The covariance and its determinant\n", " npts = len(t)\n", " K = ExpSquaredCovariance(t, A=A, l=l) + sigma ** 2 * np.eye(npts)\n", " \n", " # The log marginal likelihood\n", " log_like = -0.5 * np.dot(y.T, np.linalg.solve(K, y))\n", " log_like -= 0.5 * np.linalg.slogdet(K)[1]\n", " log_like -= 0.5 * npts * np.log(2 * np.pi)\n", " \n", " return log_like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time to evaluate the GP likelihood:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "ln_gp_likelihood(t, y, sigma)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### george" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's time how long it takes to do the same operation using the ``george`` package (``pip install george``).\n", "\n", "The kernel we'll use is\n", "\n", "```python\n", "kernel = amp ** 2 * george.kernels.ExpSquaredKernel(tau ** 2)\n", "```\n", "\n", "where ``amp = 1`` and ``tau = 1`` in this case.\n", "\n", "To instantiate a GP using ``george``, simply run\n", "\n", "```python\n", "gp = george.GP(kernel)\n", "```\n", "\n", "The ``george`` package pre-computes a lot of matrices that are re-used in different operations, so before anything else, we'll ask it to compute the GP model for our timeseries:\n", "\n", "```python\n", "gp.compute(t, sigma)\n", "```\n", "\n", "Note that we've only given it the time array and the uncertainties, so as long as those remain the same, you don't have to re-compute anything. This will save you a lot of time in the long run!\n", "\n", "Finally, the log likelihood is given by ``gp.log_likelihood(y)``.\n", "\n", "How do the speeds compare? Did you get the same value of the likelihood?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import george" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "kernel = george.kernels.ExpSquaredKernel(1.0)\n", "gp = george.GP(kernel)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "print(gp.log_likelihood(y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``george`` also offers a fancy GP solver called the HODLR solver, which makes some approximations that dramatically speed up the matrix algebra. Let's instantiate the GP object again by passing the keyword ``solver=george.HODLRSolver`` and re-compute the log likelihood. How long did that take? Did we get the same value for the log likelihood?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp = george.GP(kernel, solver=george.HODLRSolver)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp.log_likelihood(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### celerite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``george`` package is super useful for GP modeling, and I recommend you read over the [docs and examples](https://george.readthedocs.io/en/latest/). It implements several different [kernels](https://george.readthedocs.io/en/latest/user/kernels/) that come in handy in different situations, and it has support for multi-dimensional GPs. But if all you care about are GPs in one dimension (in this case, we're only doing GPs in the time domain, so we're good), then ``celerite`` is what it's all about:\n", "\n", "```bash\n", "pip install celerite\n", "```\n", "\n", "Check out the [docs](https://celerite.readthedocs.io/en/stable/) here, as well as several tutorials. There is also a [paper](https://arxiv.org/abs/1703.09710) that discusses the math behind ``celerite``. The basic idea is that for certain families of kernels, there exist **extremely efficient** methods of factorizing the covariance matrices. Whereas GP fitting typically scales with the number of datapoints $N$ as $N^3$, ``celerite`` is able to do everything in order $N$ (!!!) This is a **huge** advantage, especially for datasets with tens or hundreds of thousands of data points. Using ``george`` or any homebuilt GP model for datasets larger than about ``10,000`` points is simply intractable, but with ``celerite`` you can do it in a breeze.\n", "\n", "Next we repeat the timing tests, but this time using ``celerite``. Note that the Exponential Squared Kernel is not available in ``celerite``, because it doesn't have the special form needed to make its factorization fast. Instead, we'll use the ``Matern 3/2`` kernel, which is qualitatively similar and can be approximated quite well in terms of the ``celerite`` basis functions:\n", "\n", "```python\n", "kernel = celerite.terms.Matern32Term(np.log(1), np.log(1))\n", "```\n", "\n", "Note that ``celerite`` accepts the **log** of the amplitude and the **log** of the timescale. Other than this, we can compute the likelihood using the same syntax as ``george``.\n", "\n", "How much faster did it run? Is the value of the likelihood different from what you found above? Why?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import celerite\n", "from celerite import terms" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "kernel = terms.Matern32Term(np.log(1), np.log(1))\n", "gp = celerite.GP(kernel)\n", "gp.compute(t, sigma)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "gp.log_likelihood(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"background-color: #D6EAF8; border-left: 15px solid #2E86C1;\">\n", " <h1 style=\"line-height:2.5em; margin-left:1em;\">Exercise (the one and only)</h1>\n", "</div>\n", "\n", "Let's use what we've learned about GPs in a real application: fitting an exoplanet transit model in the presence of correlated noise.\n", "\n", "Here is a (fictitious) light curve for a star with a transiting planet: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "t, y, yerr = np.loadtxt(\"data/sample_transit.txt\", unpack=True)\n", "plt.errorbar(t, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "plt.xlabel(\"time\")\n", "plt.ylabel(\"relative flux\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a transit visible to the eye at $t = 0$, which (say) is when you'd expect the planet to transit if its orbit were perfectly periodic. However, a recent paper claims that the planet shows transit timing variations, which are indicative of a second, perturbing planet in the system, and that a transit at $t = 0$ can be ruled out at 3 $\\sigma$. **Your task is to verify this claim.**\n", "\n", "Assume you have no prior information on the planet other than the transit occurs in the observation window, the depth of the transit is somewhere in the range $(0, 1)$, and the transit duration is somewhere between $0.1$ and $1$ day. You don't know the exact process generating the noise, but you are certain that there's correlated noise in the dataset, so you'll have to pick a reasonable kernel and estimate its hyperparameters.\n", "\n", "\n", "Fit the transit with a simple inverted Gaussian with three free parameters:\n", "\n", "```python\n", "def transit_shape(depth, t0, dur):\n", " return -depth * np.exp(-0.5 * (t - t0) ** 2 / (0.2 * dur) ** 2)\n", "```\n", "\n", "*HINT: I borrowed heavily from [this tutorial](https://celerite.readthedocs.io/en/stable/tutorials/modeling/) in the celerite documentation, so you might want to take a look at it...*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

thehackerwithin/berkeley

code_examples/SQL/SQL_Tutorial-0.ipynb

1

65862

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro to SQL - The Hacker Within (2018-02-21)\n", "This tutorial will show you the basics of SQL:\n", "* creating a table\n", "* inserting rows\n", "* querying with conditions\n", "* grouping and ordering\n", "* joining tables with a common field\n", "* aggregating rows using COUNT, MAX, etc.\n", "* alternates to SQL like pandas and Django\n", "* indexing frequently queried columns for performance\n", "* alter existing table to add, drop, or rename a column\n", "* drop a table\n", "* vacuum a database\n", "\n", "## SQLite\n", "We are going to use [Python](https://docs.python.org/3/library/sqlite3.html) because it comes with the popular [SQLite](https://sqlite.org/index.html) (_aka_ [`etilqs`](https://www.google.com/search?q=etilqs)) relational database builtin. \n", "\n", "## Requirements\n", "The following examples assume you have Python 3 installed on your computer. Any distribution will do. If you have [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://conda.io/miniconda.html), you can create a new conda environment with the requirements for this tutorial from a terminal. For example with miniconda on Mac OS X, you might use the following to activate the root conda environment and create a new one called \"py36-thw-sql\".\n", "\n", " ~$ source ~/miniconda/bin/activate\n", " (root) ~$ conda create -n py36-thw-sql python=3.6.3 psycopg2 django pandas\n", "\n", "Or on Windows with Anaconda, you might use the following in a CMD terminal.\n", "\n", " ~> %LOCALAPPDATA%\\Continuum\\anaconda3\\Scripts\\activate\n", " (root) ~> conda create -n py36-thw-sql python=3.6.3 psycopg2 django pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python DB-API 2.0\n", "[PEP 249](https://www.python.org/dev/peps/pep-0249/) specifies a `connection` to a database, and a `cursor` from the connection to execute SQL." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# imports\n", "import io # we'll need this way later\n", "import os\n", "import sqlite3 # this is the module that binds to SQLite\n", "import numpy as np # never know when you might need NumPy, oh, right, always!\n", "import pandas as pd # you'll see why we can use this later\n", "\n", "DBFILE = 'sqlite3.db' # this will be our database\n", "BASEDIR = %pwd # os.path.abspath(os.path.dirname(__file__))\n", "DBPATH = os.path.join(BASEDIR, DBFILE)\n", "\n", "# we may need to delete the existing file first\n", "if os.path.exists(DBPATH):\n", " os.remove(DBPATH)\n", "\n", "# open a connection to the database for this tutorial\n", "conn = sqlite3.connect(DBPATH)\n", "\n", "# get a cursor to the database\n", "cur = conn.cursor()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notes\n", "If a file with the same name doesn't already exist, then this creates a new database otherwise it connects to the existing database contained in the file. You can also use ':memory:' to create an \"in-memory\" database that has no file, but then you can't connect to that from another process.\n", "\n", "We'll have to close the connection and cursor later. Next time we could use a [`with` context to automatically close the connection](https://docs.python.org/3.6/library/sqlite3.html#using-the-connection-as-a-context-manager).\n", "\n", " with sqlite3.connect('sqlite3.db') as conn:\n", " cur = conn.execute('SQL QUERY ...') # e.g.: 'SELECT * FROM table_name;'\n", " output = cur.fetchall() # get the results\n", "\n", "Closing the connection automatically closes the cursor. [Other bindings may offer similar context managers.](http://initd.org/psycopg/docs/usage.html#with-statement) to commit and close changes or rollback changes and raise an exception." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating tables\n", "A relational database or SQL database is a tabular structure consisting of rows of data with columns of fields. The [data definition language or DDL](https://en.wikipedia.org/wiki/Data_definition_language) used to create the table is the same language used to query it, called [SQL or Structured Query Language](https://en.wikipedia.org/wiki/SQL).\n", "\n", "Although [the basic SQL commands](https://sqlite.org/lang.html) are nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/sql.html), the data types may be different. [SQLite only has 5 datatypes](https://sqlite.org/datatype3.html): `NULL`, `INTEGER`, `REAL`, `TEXT`, `BLOB`. For [boolean, use integer zero for false, and one for true](https://sqlite.org/datatype3.html#boolean_datatype). For [dates and times use text and ISO8601](https://sqlite.org/datatype3.html#date_and_time_datatype), _e.g._: `\"2018-02-21T17:05-0800\"`. By comparison, [PostgreSQL has too many to list here](https://www.postgresql.org/docs/10/static/datatype.html) including booleans, date, time, arrays, JSON, etc.\n", "\n", "## `CREATE`\n", "The [basic SQL command to create a table](https://sqlite.org/lang_createtable.html) is\n", "\n", " CREATE TABLE <table_name> (<field_name> <TYPE> <CONSTRAINTS>, <field_name> <TYPE> <CONSTRAINTS>, <CONSTRAINTS>, ...);\n", "\n", "Some languages enforce the semicolon, some don't. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-basics.html).\n", "\n", "### Constraints, Defaults, and Options\n", "Constraints are optional and set conditions, limitations, or options for columns and the table. The most common constraints are: `PRIMARY KEY`, `UNIQUE`, `NOT NULL`, `DEFAULT`, `FOREIGN KEY`, `REFERENCES`, etc. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-constraints.html).\n", "\n", "#### `PRIMARY KEY`\n", "The most important of these is `PRIMARY KEY` which is equivalent to `UNIQUE NOT NULL`. A [primary key](https://www.postgresql.org/docs/10/static/ddl-constraints.html#DDL-CONSTRAINTS-PRIMARY-KEYS) is a unique references that identifies each record in the table. Although it is not required, every table should have a primary key. Only one primary key is allowed, and it can be constructed from multiple columns, `PRIMARY KEY (<field_A>, <field_B)`, to create a unique together, non-null identifier. [In SQLite, if missing then a integer primary key named, `rowid`, is created by default](https://sqlite.org/lang_createtable.html#rowid). Also in SQLite, any integer primary key is automatically incremented, so [the _AUTOINCREMENT_ command is usually **not** needed](https://sqlite.org/autoinc.html). [In PostgreSQL the `SERIAL` command is used](https://www.postgresql.org/docs/10/static/datatype-numeric.html#DATATYPE-SERIAL) to create a corresponding [sequence](https://www.postgresql.org/docs/10/static/functions-sequence.html) for the primary key.\n", "\n", "## Practice\n", "The other constraints and options are also important, but we'll discover those as we learn. Let's create some simple databases with fictitious data to practice. Imagine you are testing several different materials with different properties $\\alpha$ and $\\beta$ under different stresses like different temperatures and light intensity and changing thickness. How would you organize this data? Take a moment to design a schema or structure for your data. The schema consists of the column names and data types and the column and table constraints." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# we can use Python triple quoted strings to span multiple lines, but use single quotes, since SQL only uses double quotes\n", "\n", "# first create a materials table\n", "cur.execute('''CREATE TABLE materials (\n", " material_id TEXT PRIMARY KEY,\n", " long_name TEXT UNIQUE NOT NULL,\n", " alpha REAL NOT NULL,\n", " beta REAL NOT NULL,\n", " material_type TEXT NOT NULL\n", ")''')\n", "conn.commit()\n", "# if you don't commit the changes, they won't be written to the file, and won't be visible to other connections\n", "\n", "# then create an experiments table\n", "cur.execute('''CREATE TABLE experiments (\n", " experiment_id INTEGER PRIMARY KEY,\n", " temperature REAL DEFAULT 298.15,\n", " irradiance REAL DEFAULT 1000.0,\n", " uv_filter INTEGER DEFAULT 0,\n", " material_id NOT NULL REFERENCES materials ON UPDATE CASCADE ON DELETE CASCADE,\n", " thickness REAL DEFAULT 0.005,\n", " UNIQUE (temperature, irradiance, uv_filter, material_id)\n", ")''')\n", "conn.commit()\n", "\n", "# and finally create a trials table\n", "cur.execute('''CREATE TABLE trials (\n", " trial_id INTEGER PRIMARY KEY,\n", " experiment_id NOT NULL REFERENCES experiments ON UPDATE CASCADE ON DELETE CASCADE,\n", " results BLOB NOT NULL,\n", " duration REAL NOT NULL,\n", " avg_temperature REAL NOT NULL,\n", " std_temperature REAL NOT NULL,\n", " avg_irradiance REAL NOT NULL,\n", " std_irradiance REAL NOT NULL,\n", " init_visible_transmittance REAL NOT NULL,\n", " final_visible_transmittance REAL NOT NULL,\n", " notes TEXT\n", ")''')\n", "conn.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `FOREIGN KEY`\n", "A [foreign key constraint](https://sqlite.org/foreignkeys.html) creates a relationship between two tables. The `FOREIGN KEY` is implied when the `REFERENCES` column constraint is applied. In the experiments table above, the column constraint on `material_id` is the same as adding this table constriant:\n", "\n", " FOREIGN KEY (material_id) REFERENCES materials (material_id)\n", "\n", "Specifying the referenced column in the table constraint isn't necessary, and if omitted defaults to the primary key of the referenced table. The syntax is nearly the same for [other relational databases](https://www.postgresql.org/docs/10/static/ddl-constraints.html#DDL-CONSTRAINTS-FK).\n", "\n", "You can use the same name for the foreign key and it's related field, but it may make joining tables more difficult because you will need to use the table name to avoid an ambigous column name. _E.G._: you can use `trials.experiment_id` and `experiments.experiment_id` to differentiate between them. You can also use `AS` to create a temporary name like `trials.experiment_id AS experiment`. Or you could just use different names for the foreign key and it's related field like `FOREIGN KEY (material) REFERENCES materials (material_id)` and then there's no ambiguity. Your call.\n", "\n", "### `DELETE` and `UPDATE`\n", "What happens if the reference of a foreign key is deleted or updated? That's up to you: in SQLite the default is to do nothing, but typically you want the action to cascade. Add the desired [`ON DELETE` or `ON UPDATE` action](https://sqlite.org/foreignkeys.html#fk_actions) to the constraint.\n", "\n", "\n", "### Bonus Questions\n", "1. What is the difference between a column constraint and a table constraint?\n", "2. What [other table constraint](https://www.postgresql.org/docs/9.1/static/ddl-constraints.html#AEN2496) is in the experiments table?\n", "3. What other constraints or defaults are applied in the tables?\n", "4. What part of the materials table schema is fragile and can be improved?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `INSERT`\n", "The [basic SQL command to put data into a table is](https://sqlite.org/lang_insert.html)\n", "\n", " INSERT INTO <table_name> (<field_name>, <field_name>, ...) VALUES (<value>, <value>, ...)\n", "\n", "[Other relational databases use the same SQL syntax.](https://www.postgresql.org/docs/current/static/dml-insert.html)\n", "\n", "Let's add some pretend data to the database" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add a EVA as a material\n", "cur.execute('INSERT INTO materials VALUES (\"EVA\", \"ethylene vinyl acetate\", 0.123, 4.56, \"polymer\")')\n", "conn.commit() # you must commit for it to become permanent\n", "cur.rowcount # tells you how many rows written, sometimes, it's quirky" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Placeholders\n", "You can use placeholders to loop over insert statements to add multiple records.\n", "\n", ">*WARNING*: Never use string formatters to in lieu of placeholders or you may be subject to [a SQL injection attack](https://xkcd.com/327/).\n", "\n", "SQLite uses `?` but [other relational databases may use `%s`](http://initd.org/psycopg/docs/usage.html#passing-parameters-to-sql-queries) or another placeholder.\n", "\n", "Also, in `sqlite3` [`executemany`](https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executemany) is a convenient shortcut, [but it may not be convenient for all database bindings](http://initd.org/psycopg/docs/extras.html#fast-exec)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rowcount = 2\n" ] } ], "source": [ "# add some more fake materials\n", "fake_materials = [\n", " ('PMMC', 'poly methyl methacrylate', 0.789, 10.11, 'polymer'),\n", " ('KBr', 'potassium bromide', 1.213, 14.15, 'crystal')\n", "]\n", "for mat in fake_materials:\n", " # must have same number of place holders as values\n", " cur.execute('INSERT INTO materials VALUES (?, ?, ?, ?, ?)', mat) # use place holders\n", "conn.commit() # you can commit all of the changes at the end of the loop\n", "\n", "# use the executemany shortcut\n", "fake_materials = [\n", " ('SiO2', 'silicon dioxide', 1.617, 18.19, 'crystal'),\n", " ('CaF2', 'calcium flouride', 2.0, 21.22, 'crystal')\n", "]\n", "cur.executemany('INSERT INTO materials VALUES (?, ?, ?, ?, ?)', fake_materials)\n", "conn.commit()\n", "print('rowcount = %d' % cur.rowcount) # with executemany, cur.rowcount shows total number of rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `DELETE` and `UPDATE`\n", "Oops I made a mistake. How do I fix it? The opposite of `INSERT` is [`DELETE`](https://sqlite.org/lang_delete.html). But don't throw the baby out with the bathwater, you can also [`UPDATE`] a record. [Other relational databases use the same SQL syntax to manipulate data](https://www.postgresql.org/docs/10/static/dml.html)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cur.execute('DELETE FROM materials WHERE material_id = \"SiO2\"')\n", "cur.execute('UPDATE materials SET alpha=1.23E-4, beta=8.910E+11 WHERE material_id = \"CaF2\"')\n", "conn.commit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Queries\n", "The way you [select](https://sqlite.org/lang_select.html) data is by executing queries. [The language is the same for all relational databases.](https://www.postgresql.org/docs/10/static/queries.html) The star `*` means select all columns, or you can give the columns explicitly." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('EVA', 'ethylene vinyl acetate', 0.123, 4.56, 'polymer'),\n", " ('PMMC', 'poly methyl methacrylate', 0.789, 10.11, 'polymer'),\n", " ('KBr', 'potassium bromide', 1.213, 14.15, 'crystal'),\n", " ('CaF2', 'calcium flouride', 0.000123, 891000000000.0, 'crystal')]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cur.execute('SELECT * FROM materials')\n", "cur.fetchall() # fetch all the results of the query" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditions\n", "You can limit a query using [`WHERE`](https://www.postgresql.org/docs/10/static/queries-table-expressions.html#QUERIES-FROM) and [`LIMIT`](https://www.postgresql.org/docs/10/static/queries-limit.html). You can combine `WHERE` with a conditional expression, `IN` to check a set, or `LIKE` to compare with strings. Use `AND` and `OR` to combine conditions.\n", "\n", "### Python DB-API Cursor Methods\n", "The Python DB-API cursor can be used as an iterator or you can call it's [fetch methods](https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.fetchone)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EVA is ethylene vinyl acetate\n", "PMMC is poly methyl methacrylate\n" ] } ], "source": [ "# limit the query using WHERE and LIMIT\n", "cur.execute('SELECT material_id, long_name FROM materials WHERE alpha < 1 LIMIT 2')\n", "for c in cur: print('{} is {}'.format(*c)) # user the cursor as an iterator" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('EVA', (0.123, 4.56)), ('PMMC', (0.789, 10.11))]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "materials_list = (\"EVA\", \"PMMC\")\n", "cur.execute('SELECT alpha, beta FROM materials WHERE material_id IN (?, ?)', materials_list)\n", "[(mat, cur.fetchone()) for mat in materials_list] # use the cursor fetchone() method to get next item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregates\n", "Your query can aggregate results like `AVG`, `SUM`, `COUNT`, `MAX`, `MIN`, etc." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4,)\n" ] } ], "source": [ "cur.execute('SELECT COUNT(*) FROM materials')\n", "print(cur.fetchone())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `GROUP BY`\n", "You can group queries by a column or a condition such as an expression, `IN`, or `LIKE`, if your selection is an aggregate." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('crystal', 2, 0.6065615000000001, 891000000000.0),\n", " ('polymer', 2, 0.456, 10.11)]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cur.execute('SELECT material_type, COUNT(*), AVG(alpha), MAX(beta) FROM materials GROUP BY material_type')\n", "cur.fetchmany(2) # use fetchmany() with size parameter, just for fun" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More Practice\n", "Add a fictitious experiment schedule and doctor up some data!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# use defaults, let primary key auto-increment, just supply material ID\n", "cur.execute('INSERT INTO experiments (material_id) VALUES (\"EVA\")') # use defaults, \n", "conn.commit()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sqlite3.IntegrityError: %s NOT NULL constraint failed: experiments.material_id\n" ] } ], "source": [ "# set up a test matrix for EVA\n", "temp = range(300, 400, 25)\n", "irrad = range(400, 800, 100)\n", "try:\n", " for T in temp:\n", " for E in irrad:\n", " cur.execute('INSERT INTO experiments (temperature, irradiance) VALUES (?, ?)', (T, E))\n", "except sqlite3.IntegrityError as exc:\n", " print('sqlite3.IntegrityError: %s', exc)\n", "\n", "# Oops! We forgot to specify the material, there is not default, and it is constrained as NOT NULL!\n", "conn.rollback() # undo any changes\n", "try:\n", " for T in temp:\n", " for E in irrad:\n", " cur.execute('INSERT INTO experiments (temperature, irradiance, material_id) VALUES (?, ?, \"EVA\")', (T, E))\n", "except sqlite3.IntegrityError as exc:\n", " print(exc)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1, 298.15, 1000.0, 0, 'EVA', 0.005),\n", " (2, 300.0, 400.0, 0, 'EVA', 0.005),\n", " (3, 300.0, 500.0, 0, 'EVA', 0.005),\n", " (4, 300.0, 600.0, 0, 'EVA', 0.005),\n", " (5, 300.0, 700.0, 0, 'EVA', 0.005),\n", " (6, 325.0, 400.0, 0, 'EVA', 0.005),\n", " (7, 325.0, 500.0, 0, 'EVA', 0.005),\n", " (8, 325.0, 600.0, 0, 'EVA', 0.005),\n", " (9, 325.0, 700.0, 0, 'EVA', 0.005),\n", " (10, 350.0, 400.0, 0, 'EVA', 0.005),\n", " (11, 350.0, 500.0, 0, 'EVA', 0.005),\n", " (12, 350.0, 600.0, 0, 'EVA', 0.005),\n", " (13, 350.0, 700.0, 0, 'EVA', 0.005),\n", " (14, 375.0, 400.0, 0, 'EVA', 0.005),\n", " (15, 375.0, 500.0, 0, 'EVA', 0.005),\n", " (16, 375.0, 600.0, 0, 'EVA', 0.005),\n", " (17, 375.0, 700.0, 0, 'EVA', 0.005)]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this list is hard to read\n", "list(cur.execute('SELECT * FROM experiments'))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>298.15</td>\n", " <td>1000.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>300.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>300.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>300.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>300.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>325.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>325.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>325.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>325.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>350.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>350.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>350.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>350.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>375.00</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>375.00</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>375.00</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>375.00</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "1 298.15 1000.0 0 EVA 0.005\n", "2 300.00 400.0 0 EVA 0.005\n", "3 300.00 500.0 0 EVA 0.005\n", "4 300.00 600.0 0 EVA 0.005\n", "5 300.00 700.0 0 EVA 0.005\n", "6 325.00 400.0 0 EVA 0.005\n", "7 325.00 500.0 0 EVA 0.005\n", "8 325.00 600.0 0 EVA 0.005\n", "9 325.00 700.0 0 EVA 0.005\n", "10 350.00 400.0 0 EVA 0.005\n", "11 350.00 500.0 0 EVA 0.005\n", "12 350.00 600.0 0 EVA 0.005\n", "13 350.00 700.0 0 EVA 0.005\n", "14 375.00 400.0 0 EVA 0.005\n", "15 375.00 500.0 0 EVA 0.005\n", "16 375.00 600.0 0 EVA 0.005\n", "17 375.00 700.0 0 EVA 0.005" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# not only is Pandas much nicer, it also executes queries!\n", "pd.read_sql('SELECT * FROM experiments', conn, index_col='experiment_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `ORDER BY`\n", "Does what it says; order the query results by a column. Default is ascending, but use `ASC` or `DESC` to change the order." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>300.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>325.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>350.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>375.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "5 300.0 700.0 0 EVA 0.005\n", "9 325.0 700.0 0 EVA 0.005\n", "13 350.0 700.0 0 EVA 0.005\n", "17 375.0 700.0 0 EVA 0.005" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Python's SQLite let's you use either '==' or '=', but I think SQL only allows '=', okay?\n", "pd.read_sql('SELECT * FROM experiments WHERE irradiance = 700 ORDER BY temperature', conn, index_col='experiment_id')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>uv_filter</th>\n", " <th>material_id</th>\n", " <th>thickness</th>\n", " </tr>\n", " <tr>\n", " <th>experiment_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>17</th>\n", " <td>375.0</td>\n", " <td>700.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>375.0</td>\n", " <td>600.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>375.0</td>\n", " <td>500.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>375.0</td>\n", " <td>400.0</td>\n", " <td>0</td>\n", " <td>EVA</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " temperature irradiance uv_filter material_id thickness\n", "experiment_id \n", "17 375.0 700.0 0 EVA 0.005\n", "16 375.0 600.0 0 EVA 0.005\n", "15 375.0 500.0 0 EVA 0.005\n", "14 375.0 400.0 0 EVA 0.005" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# descending order\n", "pd.read_sql('SELECT * FROM experiments WHERE temperature = 375 ORDER BY irradiance DESC', conn, index_col='experiment_id')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Dr. Data](http://www.startrek.com/database_article/data)\n", "![Dr. Data](http://www.startrek.com/uploads/assets/db_articles/26da32597d9bd37fde9da22660aa524f24fd725c.jpg)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Dr. Data\n", "start_time, end_time = '2018-02-21T17:00-0800', '2018-02-21T18:30-0800'\n", "timestamps = pd.DatetimeIndex(start=start_time, end=end_time, freq='T')\n", "# use http://poquitopicante.blogspot.com/2016/11/panda-pop.html to help you recall what offset alias to use\n", "size = len(timestamps)\n", "data = {\n", " 'temperature': np.random.randn(size) + 298.15,\n", " 'irradiance': np.random.randn(size) + 1000,\n", " 'visible_transmittance': np.logspace(np.log10(0.9), np.log10(0.8), size) + np.random.randn(size) / 100\n", "}\n", "results = pd.DataFrame(data, index=timestamps)\n", "duration = (results.index[-1] - results.index[0]).value # [ns]\n", "avg_temperature = results.temperature.mean() # [K]\n", "std_temperature = results.temperature.std() # [K]\n", "avg_irradiance = results.irradiance.mean() # [W/m^2]\n", "std_irradiance = results.irradiance.std() # [W/m^2]\n", "init_visible_transmittance = results.visible_transmittance[start_time]\n", "final_visible_transmittance = results.visible_transmittance[end_time]\n", "values = (1, results.to_csv(), duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance, final_visible_transmittance,\n", " 'this is doctored data')\n", "cur.execute('''INSERT INTO trials (\n", " experiment_id, results, duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance,\n", " final_visible_transmittance, notes\n", ") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', values)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>irradiance</th>\n", " <th>temperature</th>\n", " <th>visible_transmittance</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2018-02-21 17:00:00-08:00</th>\n", " <td>998.959655</td>\n", " <td>298.918741</td>\n", " <td>0.893836</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:01:00-08:00</th>\n", " <td>1001.346465</td>\n", " <td>298.553568</td>\n", " <td>0.888956</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:02:00-08:00</th>\n", " <td>1000.222570</td>\n", " <td>296.760235</td>\n", " <td>0.916820</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:03:00-08:00</th>\n", " <td>998.697424</td>\n", " <td>297.998634</td>\n", " <td>0.895405</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:04:00-08:00</th>\n", " <td>999.766542</td>\n", " <td>298.686229</td>\n", " <td>0.903194</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:05:00-08:00</th>\n", " <td>998.521437</td>\n", " <td>298.325881</td>\n", " <td>0.892011</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:06:00-08:00</th>\n", " <td>1000.991891</td>\n", " <td>297.175668</td>\n", " <td>0.896553</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:07:00-08:00</th>\n", " <td>1000.054543</td>\n", " <td>297.946657</td>\n", " <td>0.878752</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:08:00-08:00</th>\n", " <td>1002.449126</td>\n", " <td>298.814267</td>\n", " <td>0.887048</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:09:00-08:00</th>\n", " <td>998.311166</td>\n", " <td>296.710237</td>\n", " <td>0.883333</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:10:00-08:00</th>\n", " <td>1001.106463</td>\n", " <td>297.809162</td>\n", " <td>0.887446</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:11:00-08:00</th>\n", " <td>998.999252</td>\n", " <td>298.670059</td>\n", " <td>0.892166</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:12:00-08:00</th>\n", " <td>999.143503</td>\n", " <td>297.965134</td>\n", " <td>0.881345</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:13:00-08:00</th>\n", " <td>998.718983</td>\n", " <td>299.050471</td>\n", " <td>0.899170</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:14:00-08:00</th>\n", " <td>999.689110</td>\n", " <td>298.396573</td>\n", " <td>0.888901</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:15:00-08:00</th>\n", " <td>998.906026</td>\n", " <td>298.081031</td>\n", " <td>0.883191</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:16:00-08:00</th>\n", " <td>1001.441733</td>\n", " <td>300.027797</td>\n", " <td>0.889522</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:17:00-08:00</th>\n", " <td>999.874945</td>\n", " <td>298.704170</td>\n", " <td>0.880713</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:18:00-08:00</th>\n", " <td>999.763816</td>\n", " <td>299.457996</td>\n", " <td>0.866446</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:19:00-08:00</th>\n", " <td>999.328643</td>\n", " <td>297.217506</td>\n", " <td>0.869135</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:20:00-08:00</th>\n", " <td>999.746232</td>\n", " <td>297.342810</td>\n", " <td>0.900115</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:21:00-08:00</th>\n", " <td>1000.737338</td>\n", " <td>297.912401</td>\n", " <td>0.882068</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:22:00-08:00</th>\n", " <td>1000.274663</td>\n", " <td>297.772786</td>\n", " <td>0.878944</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:23:00-08:00</th>\n", " <td>1000.158990</td>\n", " <td>297.938545</td>\n", " <td>0.888503</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:24:00-08:00</th>\n", " <td>1000.922571</td>\n", " <td>298.359145</td>\n", " <td>0.877483</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:25:00-08:00</th>\n", " <td>1001.872280</td>\n", " <td>298.087752</td>\n", " <td>0.873660</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:26:00-08:00</th>\n", " <td>1000.656051</td>\n", " <td>297.877323</td>\n", " <td>0.866980</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:27:00-08:00</th>\n", " <td>1000.853954</td>\n", " <td>296.797606</td>\n", " <td>0.856736</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:28:00-08:00</th>\n", " <td>999.942882</td>\n", " <td>299.841713</td>\n", " <td>0.856718</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 17:29:00-08:00</th>\n", " <td>998.647786</td>\n", " <td>298.861976</td>\n", " <td>0.882361</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:01:00-08:00</th>\n", " <td>1000.933114</td>\n", " <td>297.728089</td>\n", " <td>0.836169</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:02:00-08:00</th>\n", " <td>999.142461</td>\n", " <td>298.791704</td>\n", " <td>0.842719</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:03:00-08:00</th>\n", " <td>998.136343</td>\n", " <td>298.775994</td>\n", " <td>0.813755</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:04:00-08:00</th>\n", " <td>1001.349441</td>\n", " <td>297.735862</td>\n", " <td>0.834132</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:05:00-08:00</th>\n", " <td>1000.251661</td>\n", " <td>299.644198</td>\n", " <td>0.817461</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:06:00-08:00</th>\n", " <td>1000.057671</td>\n", " <td>298.579453</td>\n", " <td>0.808162</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:07:00-08:00</th>\n", " <td>1000.744842</td>\n", " <td>300.871544</td>\n", " <td>0.819319</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:08:00-08:00</th>\n", " <td>1000.056866</td>\n", " <td>296.959531</td>\n", " <td>0.823236</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:09:00-08:00</th>\n", " <td>999.557475</td>\n", " <td>298.471216</td>\n", " <td>0.806808</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:10:00-08:00</th>\n", " <td>999.519186</td>\n", " <td>298.048904</td>\n", " <td>0.822102</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:11:00-08:00</th>\n", " <td>1000.148397</td>\n", " <td>298.552681</td>\n", " <td>0.806129</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:12:00-08:00</th>\n", " <td>999.258875</td>\n", " <td>293.823340</td>\n", " <td>0.827836</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:13:00-08:00</th>\n", " <td>1001.182033</td>\n", " <td>299.571102</td>\n", " <td>0.832395</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:14:00-08:00</th>\n", " <td>997.366496</td>\n", " <td>297.647049</td>\n", " <td>0.817866</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:15:00-08:00</th>\n", " <td>1000.163225</td>\n", " <td>296.983521</td>\n", " <td>0.818295</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:16:00-08:00</th>\n", " <td>1000.236309</td>\n", " <td>297.914401</td>\n", " <td>0.808314</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:17:00-08:00</th>\n", " <td>999.754813</td>\n", " <td>298.566226</td>\n", " <td>0.822418</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:18:00-08:00</th>\n", " <td>999.857364</td>\n", " <td>299.251087</td>\n", " <td>0.802571</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:19:00-08:00</th>\n", " <td>1000.809811</td>\n", " <td>296.530036</td>\n", " <td>0.810362</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:20:00-08:00</th>\n", " <td>999.982595</td>\n", " <td>296.600307</td>\n", " <td>0.831929</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:21:00-08:00</th>\n", " <td>998.040597</td>\n", " <td>298.726982</td>\n", " <td>0.809474</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:22:00-08:00</th>\n", " <td>1000.357286</td>\n", " <td>296.702854</td>\n", " <td>0.792977</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:23:00-08:00</th>\n", " <td>999.828010</td>\n", " <td>296.561526</td>\n", " <td>0.792677</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:24:00-08:00</th>\n", " <td>998.066797</td>\n", " <td>298.541645</td>\n", " <td>0.795909</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:25:00-08:00</th>\n", " <td>999.942954</td>\n", " <td>300.276747</td>\n", " <td>0.805300</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:26:00-08:00</th>\n", " <td>998.832891</td>\n", " <td>297.323483</td>\n", " <td>0.791858</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:27:00-08:00</th>\n", " <td>998.118673</td>\n", " <td>297.802544</td>\n", " <td>0.794021</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:28:00-08:00</th>\n", " <td>1000.149277</td>\n", " <td>299.374961</td>\n", " <td>0.811664</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:29:00-08:00</th>\n", " <td>998.827775</td>\n", " <td>298.726997</td>\n", " <td>0.796634</td>\n", " </tr>\n", " <tr>\n", " <th>2018-02-21 18:30:00-08:00</th>\n", " <td>1000.273102</td>\n", " <td>299.743819</td>\n", " <td>0.808884</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>91 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " irradiance temperature visible_transmittance\n", "2018-02-21 17:00:00-08:00 998.959655 298.918741 0.893836\n", "2018-02-21 17:01:00-08:00 1001.346465 298.553568 0.888956\n", "2018-02-21 17:02:00-08:00 1000.222570 296.760235 0.916820\n", "2018-02-21 17:03:00-08:00 998.697424 297.998634 0.895405\n", "2018-02-21 17:04:00-08:00 999.766542 298.686229 0.903194\n", "2018-02-21 17:05:00-08:00 998.521437 298.325881 0.892011\n", "2018-02-21 17:06:00-08:00 1000.991891 297.175668 0.896553\n", "2018-02-21 17:07:00-08:00 1000.054543 297.946657 0.878752\n", "2018-02-21 17:08:00-08:00 1002.449126 298.814267 0.887048\n", "2018-02-21 17:09:00-08:00 998.311166 296.710237 0.883333\n", "2018-02-21 17:10:00-08:00 1001.106463 297.809162 0.887446\n", "2018-02-21 17:11:00-08:00 998.999252 298.670059 0.892166\n", "2018-02-21 17:12:00-08:00 999.143503 297.965134 0.881345\n", "2018-02-21 17:13:00-08:00 998.718983 299.050471 0.899170\n", "2018-02-21 17:14:00-08:00 999.689110 298.396573 0.888901\n", "2018-02-21 17:15:00-08:00 998.906026 298.081031 0.883191\n", "2018-02-21 17:16:00-08:00 1001.441733 300.027797 0.889522\n", "2018-02-21 17:17:00-08:00 999.874945 298.704170 0.880713\n", "2018-02-21 17:18:00-08:00 999.763816 299.457996 0.866446\n", "2018-02-21 17:19:00-08:00 999.328643 297.217506 0.869135\n", "2018-02-21 17:20:00-08:00 999.746232 297.342810 0.900115\n", "2018-02-21 17:21:00-08:00 1000.737338 297.912401 0.882068\n", "2018-02-21 17:22:00-08:00 1000.274663 297.772786 0.878944\n", "2018-02-21 17:23:00-08:00 1000.158990 297.938545 0.888503\n", "2018-02-21 17:24:00-08:00 1000.922571 298.359145 0.877483\n", "2018-02-21 17:25:00-08:00 1001.872280 298.087752 0.873660\n", "2018-02-21 17:26:00-08:00 1000.656051 297.877323 0.866980\n", "2018-02-21 17:27:00-08:00 1000.853954 296.797606 0.856736\n", "2018-02-21 17:28:00-08:00 999.942882 299.841713 0.856718\n", "2018-02-21 17:29:00-08:00 998.647786 298.861976 0.882361\n", "... ... ... ...\n", "2018-02-21 18:01:00-08:00 1000.933114 297.728089 0.836169\n", "2018-02-21 18:02:00-08:00 999.142461 298.791704 0.842719\n", "2018-02-21 18:03:00-08:00 998.136343 298.775994 0.813755\n", "2018-02-21 18:04:00-08:00 1001.349441 297.735862 0.834132\n", "2018-02-21 18:05:00-08:00 1000.251661 299.644198 0.817461\n", "2018-02-21 18:06:00-08:00 1000.057671 298.579453 0.808162\n", "2018-02-21 18:07:00-08:00 1000.744842 300.871544 0.819319\n", "2018-02-21 18:08:00-08:00 1000.056866 296.959531 0.823236\n", "2018-02-21 18:09:00-08:00 999.557475 298.471216 0.806808\n", "2018-02-21 18:10:00-08:00 999.519186 298.048904 0.822102\n", "2018-02-21 18:11:00-08:00 1000.148397 298.552681 0.806129\n", "2018-02-21 18:12:00-08:00 999.258875 293.823340 0.827836\n", "2018-02-21 18:13:00-08:00 1001.182033 299.571102 0.832395\n", "2018-02-21 18:14:00-08:00 997.366496 297.647049 0.817866\n", "2018-02-21 18:15:00-08:00 1000.163225 296.983521 0.818295\n", "2018-02-21 18:16:00-08:00 1000.236309 297.914401 0.808314\n", "2018-02-21 18:17:00-08:00 999.754813 298.566226 0.822418\n", "2018-02-21 18:18:00-08:00 999.857364 299.251087 0.802571\n", "2018-02-21 18:19:00-08:00 1000.809811 296.530036 0.810362\n", "2018-02-21 18:20:00-08:00 999.982595 296.600307 0.831929\n", "2018-02-21 18:21:00-08:00 998.040597 298.726982 0.809474\n", "2018-02-21 18:22:00-08:00 1000.357286 296.702854 0.792977\n", "2018-02-21 18:23:00-08:00 999.828010 296.561526 0.792677\n", "2018-02-21 18:24:00-08:00 998.066797 298.541645 0.795909\n", "2018-02-21 18:25:00-08:00 999.942954 300.276747 0.805300\n", "2018-02-21 18:26:00-08:00 998.832891 297.323483 0.791858\n", "2018-02-21 18:27:00-08:00 998.118673 297.802544 0.794021\n", "2018-02-21 18:28:00-08:00 1000.149277 299.374961 0.811664\n", "2018-02-21 18:29:00-08:00 998.827775 298.726997 0.796634\n", "2018-02-21 18:30:00-08:00 1000.273102 299.743819 0.808884\n", "\n", "[91 rows x 3 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check the blob, is it really there?\n", "cur.execute('SELECT results FROM trials WHERE trial_id = 1')\n", "trial1_results = cur.fetchone()\n", "pd.read_csv(io.StringIO(trial1_results[0]), index_col=0)\n", "# yay! it works!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `JOIN`\n", "The foreign keys relate tables, but how do we use this relation? By joining the tables." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# add the results for experiment 17: T=375[K], E=700[W/m^2]\n", "experiment_id, temperature, irradiance = list(cur.execute(\n", " 'SELECT experiment_id, temperature, irradiance FROM experiments WHERE (temperature = 375 AND irradiance = 700)'\n", "))[0]\n", "start_time, end_time = '2018-02-28T17:00-0800', '2018-02-28T18:30-0800'\n", "timestamps = pd.DatetimeIndex(start=start_time, end=end_time, freq='T')\n", "# use http://poquitopicante.blogspot.com/2016/11/panda-pop.html to help you recall what offset alias to use\n", "size = len(timestamps)\n", "data = {\n", " 'temperature': np.random.randn(size) + temperature,\n", " 'irradiance': np.random.randn(size) + irradiance,\n", " 'visible_transmittance': np.logspace(np.log10(0.9), np.log10(0.7), size) + np.random.randn(size) / 100\n", "}\n", "results = pd.DataFrame(data, index=timestamps)\n", "duration = (results.index[-1] - results.index[0]).value # [ns]\n", "avg_temperature = results.temperature.mean() # [K]\n", "std_temperature = results.temperature.std() # [K]\n", "avg_irradiance = results.irradiance.mean() # [W/m^2]\n", "std_irradiance = results.irradiance.std() # [W/m^2]\n", "init_visible_transmittance = results.visible_transmittance[start_time]\n", "final_visible_transmittance = results.visible_transmittance[end_time]\n", "values = (experiment_id, results.to_csv(), duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance, final_visible_transmittance,\n", " 'this is doctored data')\n", "cur.execute('''INSERT INTO trials (\n", " experiment_id, results, duration, avg_temperature, std_temperature,\n", " avg_irradiance, std_irradiance, init_visible_transmittance,\n", " final_visible_transmittance, notes\n", ") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', values)\n", "conn.commit() # commit! commit! commit!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>experiment</th>\n", " <th>init_visible_transmittance</th>\n", " <th>final_visible_transmittance</th>\n", " <th>material</th>\n", " <th>temperature</th>\n", " <th>irradiance</th>\n", " <th>alpha</th>\n", " <th>beta</th>\n", " <th>material_type</th>\n", " </tr>\n", " <tr>\n", " <th>trial_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0.893836</td>\n", " <td>0.808884</td>\n", " <td>EVA</td>\n", " <td>298.15</td>\n", " <td>1000.0</td>\n", " <td>0.123</td>\n", " <td>4.56</td>\n", " <td>polymer</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>17</td>\n", " <td>0.917532</td>\n", " <td>0.690272</td>\n", " <td>EVA</td>\n", " <td>375.00</td>\n", " <td>700.0</td>\n", " <td>0.123</td>\n", " <td>4.56</td>\n", " <td>polymer</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " experiment init_visible_transmittance final_visible_transmittance \\\n", "trial_id \n", "1 1 0.893836 0.808884 \n", "2 17 0.917532 0.690272 \n", "\n", " material temperature irradiance alpha beta material_type \n", "trial_id \n", "1 EVA 298.15 1000.0 0.123 4.56 polymer \n", "2 EVA 375.00 700.0 0.123 4.56 polymer " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql('''\n", "SELECT\n", " trial_id, trials.experiment_id AS experiment, init_visible_transmittance, final_visible_transmittance,\n", " experiments.material_id AS material, temperature, irradiance, alpha, beta, material_type\n", "FROM trials\n", "JOIN experiments ON experiments.experiment_id = experiment\n", "JOIN materials ON materials.material_id = material\n", "''', conn, index_col='trial_id')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "cur.close()\n", "conn.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Epilogue\n", "Unfortunately that's all we have time for, but we covered a lot, even if we didn't get this far. If there's anything I hope you learned from this it's that:\n", "\n", "1. You can read the SQLite and PostgreSQL manuals or use [StackOverflow](https://stackoverflow.com/questions/tagged/SQL) to teach yourself SQL. It's not rocket science. I was a mechanical engineer, and I learned it.\n", "2. The basics of making a table, inserting data, and conducting queries.\n", "3. How to interact programmatically with a database.\n", "\n", "But there's still so much to learn! Hopefully you will continue on your own and try some of these intereting topics.\n", "1. Use what you've learned on a more advanced database management system like PostgreSQL, MySQL, or MS SQL Server.\n", "2. Use an Object Relational Mapper (ORM) like Django or SQLAlchemy to simplify your workflow, by creating and manipulating data in native Python.\n", "3. Explore a NoSQL database like Cassandra or MongoDB and see if the flexible structure and large scale cluster computing allow you to explore big data with tools like [Spark](http://spark.apache.org/) and [Hadoop](http://hadoop.apache.org/).\n", "\n", "Did you learn about everything you expected today? What did wish we had covered? Leave your comments or improve this tutorial by sending a PR to [The Hacker Within, Berkeley](https://github.com/thehackerwithin/berkeley).\n", "\n", "Thanks!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

vzg100/Post-Translational-Modification-Prediction

old/Phosphorylation Sequence Tests -MLP -dbptm+ELM -EnzymeBenchmarks-VectorAvr..ipynb

1

128979

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Template for test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from pred import Predictor\n", "from pred import sequence_vector\n", "from pred import chemical_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\n", "\n", "Included is N Phosphorylation however no benchmarks are available, yet. \n", "\n", "\n", "Training data is from phospho.elm and benchmarks are from dbptm. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 11, 12, 2, 10, 20, 11, 20, 20, 12, 10, 0, 0, -0.018181818181818136, 44.10909090909091, 0.0]\n", "Finished working with Data\n", "Training Data Points: 200363\n", "Test Data Points: 50091\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.013149454778704297\n", "Specificity : 0.996563320814002\n", "Accuracy: 0.8129204847178135\n", "ROC 0.504856387796\n", "Matthews Correlation Coeff: 0.0523747492729\n", "TP 123 FP 140 TN 40597 FN 9231\n", "\n", "\n", "\n", "None\n", "Cross Validation: [ 0.81553941 0.81579525 0.81242139 0.81593132 0.81531244]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.0625\n", "Specificity : 0.9971330275229358\n", "Accuracy: 0.9640486725663717\n", "ROC 0.529816513761\n", "Matthews Correlation Coeff: 0.156571693721\n", "TP 4 FP 5 TN 1739 FN 60\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 8, 19, 20, 10, 3, 8, 8, 10, 12, 10, 0, 0, -0.6636363636363637, 100.02727272727273, 0.0]\n", "Finished working with Data\n", "Training Data Points: 200363\n", "Test Data Points: 50091\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.02822405557967868\n", "Specificity : 0.9931749798184887\n", "Accuracy: 0.8157153979756843\n", "ROC 0.510699517699\n", "Matthews Correlation Coeff: 0.0803519307738\n", "TP 260 FP 279 TN" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"S\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"S\")\n", " del x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y Phosphorylation " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 3, 17, 20, 11, 15, 15, 10, 12, 2, 8, 12, 1, 0.13076923076923075, 21.20769230769231, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.064\n", "Specificity : 1.0\n", "Accuracy: 0.9536633663366336\n", "ROC 0.532\n", "TP 8 FP 0 TN 2400 FN 117\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95566112 0.95683168 0.95524752 0.95324881 0.95324881]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 20, 7, 18, 3, 19, 15, 12, 2, 20, 10, 13, 16, -0.30769230769230776, 18.723076923076924, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19360\n", "Test Data Points: 4840\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.945583926329008\n", "Specificity : 0.9951040391676866\n", "Accuracy: 0.9706611570247934\n", "ROC 0.970343982748\n", "TP 2259 FP 12 TN 2439 FN 130\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85953315 0.98347449 0.975 0.97396156 0.98140112]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 8, 19, 4, 10, 2, 15, 3, 2, 9, 8, 10, 17, -1.2000000000000002, 121.27692307692308, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19222\n", "Test Data Points: 4806\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9496157130657558\n", "Specificity : 0.9951298701298701\n", "Accuracy: 0.9729504785684561\n", "ROC 0.972372791598\n", "TP 2224 FP 12 TN 2452 FN 118\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89679567 0.98918019 0.9852268 0.98314256 0.98772112]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 8, 3, 6, 7, 2, 15, 10, 18, 2, 16, 1, 9, -0.7538461538461539, 32.21538461538462, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4661016949152542\n", "Specificity : 0.7142857142857143\n", "Accuracy: 0.5907172995780591\n", "ROC 0.5901937046\n", "TP 55 FP 34 TN 85 FN 63\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63445378 0.56779661 0.6059322 0.62288136 0.58898305]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 17, 3, 16, 3, 11, 15, 2, 3, 3, 8, 9, 12, 0.0076923076923075965, 82.15384615384616, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9236\n", "Test Data Points: 2310\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 0.9990888382687927\n", "Accuracy: 0.9528138528138528\n", "ROC 0.53432702783\n", "TP 8 FP 2 TN 2193 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94761905 0.95106107 0.95019489 0.95106107 0.95149415]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [16, 19, 5, 7, 5, 10, 15, 10, 9, 13, 18, 5, 8, -0.8538461538461539, 9.938461538461542, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5847457627118644\n", "Specificity : 0.7899159663865546\n", "Accuracy: 0.6877637130801688\n", "ROC 0.687330864549\n", "TP 69 FP 25 TN 94 FN 49\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.68487395 0.70762712 0.63135593 0.57627119 0.66525424]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 7, 7, 3, 4, 2, 15, 1, 1, 7, 3, 7, 15, -0.6230769230769231, -10.969230769230771, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.03418803418803419\n", "Specificity : 0.9991694352159468\n", "Accuracy: 0.9544554455445544\n", "ROC 0.516678734702\n", "TP 4 FP 2 TN 2406 FN 113\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95486936 0.95722772 0.95366337 0.95641838 0.9544374 ]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 395 FN 1\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 1, 11, 11, 11, 3, 15, 18, 20, 18, 16, 19, 5, -1.369230769230769, 14.969230769230771, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07462686567164178\n", "Specificity : 0.999163529903806\n", "Accuracy: 0.95009900990099\n", "ROC 0.536895197788\n", "TP 10 FP 2 TN 2389 FN 124\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95447348 0.95524752 0.95485149 0.9544374 0.95522979]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 396 FN 1\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 20, 15, 1, 19, 12, 15, 7, 2, 17, 18, 12, 16, -0.9615384615384616, -0.13846153846153825, 0.15384615384615385]\n", "Balancing Data\n", "Balanced 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"\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.87493696 0.97907211 0.98108926 0.9820888 0.98410696]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 18 TN 379 FN 1\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 18, 10, 12, 9, 19, 15, 7, 19, 3, 16, 9, 3, -1.176923076923077, 17.51538461538462, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19232\n", "Test Data Points: 4809\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.944136460554371\n", "Specificity : 0.9833603896103896\n", "Accuracy: 0.9642337284258682\n", "ROC 0.963748425082\n", "TP 2214 FP 41 TN 2423 FN 131\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89873155 0.98378041 0.9841963 0.98252548 0.98814229]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 392 FN 1\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 19, 10, 3, 17, 13, 15, 15, 13, 7, 18, 15, 12, -0.5076923076923078, 56.63846153846155, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5\n", "Specificity : 0.7394957983193278\n", "Accuracy: 0.620253164556962\n", "ROC 0.61974789916\n", "TP 59 FP 31 TN 88 FN 59\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56302521 0.53813559 0.5720339 0.59322034 0.55932203]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6523929471032746\n", "Accuracy: 0.6532663316582915\n", "ROC 0.826196473552\n", "TP 1 FP 138 TN 259 FN 0\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 12, 20, 1, 9, 10, 15, 1, 1, 13, 15, 12, 11, 0.09230769230769229, 16.430769230769233, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5254237288135594\n", "Specificity : 0.7478991596638656\n", "Accuracy: 0.6371308016877637\n", "ROC 0.636661444239\n", "TP 62 FP 30 TN 89 FN 56\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56722689 0.55508475 0.50847458 0.66949153 0.53813559]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6372795969773299\n", "Accuracy: 0.6381909547738693\n", "ROC 0.818639798489\n", "TP 1 FP 144 TN 253 FN 0\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 15, 10, 19, 15, 3, 15, 5, 2, 10, 10, 18, 7, -0.38461538461538464, 42.261538461538464, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9229\n", "Test Data Points: 2308\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06086956521739131\n", "Specificity : 0.9990880072959416\n", "Accuracy: 0.9523396880415944\n", "ROC 0.529978786257\n", "TP 7 FP 2 TN 2191 FN 108\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94932871 0.95188557 0.94971825 0.9514521 0.95188557]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 397 FN 1\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 18, 1, 4, 14, 8, 15, 15, 19, 2, 8, 14, 8, -1.0076923076923077, 2.1769230769230776, 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Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5423728813559322\n", "Specificity : 0.7226890756302521\n", "Accuracy: 0.6329113924050633\n", "ROC 0.632530978493\n", "TP 64 FP 33 TN 86 FN 54\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.67226891 0.67372881 0.63983051 0.57627119 0.57627119]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.4282115869017632\n", "Accuracy: 0.4296482412060301\n", "ROC 0.714105793451\n", "TP 1 FP 227 TN 170 FN 0\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 8, 19, 3, 5, 10, 15, 8, 2, 3, 15, 10, 15, -0.3538461538461538, 38.861538461538466, 0.3076923076923077]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5508474576271186\n", "Specificity : 0.7563025210084033\n", "Accuracy: 0.6540084388185654\n", "ROC 0.653574989318\n", "TP 65 FP 29 TN 90 FN 53\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63445378 0.65677966 0.69067797 0.55508475 0.65254237]\n", "Number of data points in benchmark 398\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.4332493702770781\n", "Accuracy: 0.43467336683417085\n", "ROC 0.716624685139\n", "TP 1 FP 225 TN 172 FN 0\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 1, 7, 13, 3, 5, 15, 18, 12, 12, 12, 9, 18, 0.3153846153846155, 24.500769230769233, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.049586776859504134\n", "Specificity : 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [14, 8, 18, 18, 7, 2, 15, 14, 15, 18, 1, 4, 14, -0.7461538461538463, 10.084615384615386, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9388682362660058\n", "Specificity : 0.996694214876033\n", "Accuracy: 0.9677752530468912\n", "ROC 0.967781225571\n", "TP 2273 FP 8 TN 2412 FN 148\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85398596 0.97892997 0.97624458 0.97747934 0.97809917]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 19, 12, 8, 19, 3, 15, 9, 2, 1, 9, 3, 7, -1.0000000000000002, 20.730769230769234, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19228\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9483126868859462\n", "Specificity : 0.993109039319011\n", "Accuracy: 0.971297836938436\n", "ROC 0.970710863102\n", "TP 2220 FP 17 TN 2450 FN 121\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89455075 0.98772879 0.98689684 0.98585102 0.98730753]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [14, 3, 10, 3, 17, 10, 15, 10, 2, 20, 13, 8, 2, 0.44615384615384623, 72.66153846153847, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4745762711864407\n", "Specificity : 0.5546218487394958\n", "Accuracy: 0.5147679324894515\n", "ROC 0.514599059963\n", "TP 56 FP 53 TN 66 FN 62\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.56722689 0.53389831 0.58898305 0.59322034 0.54237288]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 3, 10, 13, 18, 3, 15, 9, 3, 13, 7, 17, 18, 0.24615384615384622, 31.46923076923077, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05309734513274336\n", "Specificity : 0.9975124378109452\n", "Accuracy: 0.9552475247524752\n", "ROC 0.525304891472\n", "TP 6 FP 6 TN 2406 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95407759 0.95485149 0.95405941 0.9544374 0.9544374 ]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 5, 20, 10, 2, 17, 15, 13, 17, 3, 17, 5, 8, -0.5846153846153846, 18.24615384615385, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9439368770764119\n", "Specificity : 0.9934237566789971\n", "Accuracy: 0.9688080975005164\n", "ROC 0.968680316878\n", "TP 2273 FP 16 TN 2417 FN 135\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85997522 0.97149349 0.97376575 0.97334711 0.97995868]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 2, 3, 17, 20, 11, 15, 15, 10, 12, 2, 8, 12, -0.10769230769230778, 27.738461538461543, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19229\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9401964972234088\n", "Specificity : 0.9927036886907175\n", "Accuracy: 0.9671381031613977\n", "ROC 0.966450092957\n", "TP 2201 FP 18 TN 2449 FN 140\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88831115 0.98710483 0.99043261 0.98439775 0.98626717]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 13, 6, 12, 1, 18, 15, 10, 20, 20, 12, 7, 6, 0.3076923076923077, -12.576923076923077, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4745762711864407\n", "Specificity : 0.680672268907563\n", "Accuracy: 0.5780590717299579\n", "ROC 0.577624270047\n", "TP 56 FP 38 TN 81 FN 62\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55882353 0.56779661 0.5720339 0.58474576 0.55084746]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 2, 4, 1, 7, 2, 15, 16, 11, 1, 14, 5, 20, -0.4000000000000001, 9.976923076923077, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9230\n", "Test Data Points: 2308\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 0.9981760145918832\n", "Accuracy: 0.9519064124783362\n", "ROC 0.533870615992\n", "TP 8 FP 4 TN 2189 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9497618 0.95147314 0.95101864 0.9479844 0.95188557]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 11, 15, 18, 15, 10, 15, 13, 5, 7, 15, 13, 13, 0.2384615384615385, 12.484615384615388, 0.38461538461538464]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.576271186440678\n", "Specificity : 0.7310924369747899\n", "Accuracy: 0.6540084388185654\n", "ROC 0.653681811708\n", "TP 68 FP 32 TN 87 FN 50\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.68907563 0.73305085 0.63559322 0.5720339 0.69067797]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 17, 12, 3, 17, 8, 15, 5, 19, 13, 18, 18, 10, -0.46153846153846156, 57.36923076923077, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07258064516129033\n", "Specificity : 0.9975010412328197\n", "Accuracy: 0.952079207920792\n", "ROC 0.535040843197\n", "TP 9 FP 6 TN 2395 FN 115\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95368171 0.95683168 0.95405941 0.95562599 0.95562599]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9964788732394366\n", "Accuracy: 0.9964850615114236\n", "ROC 0.99823943662\n", "TP 1 FP 2 TN 566 FN 0\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 2, 3, 5, 20, 3, 15, 19, 19, 8, 12, 20, 5, 0.19230769230769224, 24.607692307692307, 0.23076923076923078]\n", "Finished working with Data\n", "Training Data Points: 10099\n", "Test Data Points: 2525\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05504587155963303\n", "Specificity : 0.9979304635761589\n", "Accuracy: 0.9572277227722772\n", "ROC 0.526488167568\n", "TP 6 FP 5 TN 2411 FN 103\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95407759 0.95485149 0.95445545 0.95364501 0.95602219]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9947183098591549\n", "Accuracy: 0.9947275922671354\n", "ROC 0.99735915493\n", "TP 1 FP 3 TN 565 FN 0\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 13, 8, 15, 3, 12, 15, 8, 4, 3, 7, 1, 3, 0.32307692307692304, -21.961538461538467, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19363\n", "Test Data Points: 4841\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9454545454545454\n", "Specificity : 0.9962825278810409\n", "Accuracy: 0.970873786407767\n", "ROC 0.970868536668\n", "TP 2288 FP 9 TN 2412 FN 132\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.85543164 0.97665772 0.98223508 0.97789256 0.97520661]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9964788732394366\n", "Accuracy: 0.9964850615114236\n", "ROC 0.99823943662\n", "TP 1 FP 2 TN 566 FN 0\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [16, 10, 17, 19, 11, 16, 15, 15, 19, 19, 3, 17, 10, -2.1461538461538465, 134.16923076923078, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19360\n", "Test Data Points: 4840\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9466221851542952\n", "Specificity : 0.9946764946764947\n", "Accuracy: 0.9708677685950413\n", "ROC 0.970649339915\n", "TP 2270 FP 13 TN 2429 FN 128\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.86304483 0.98058252 0.98243802 0.97210167 0.97458153]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 1, 15, 20, 3, 7, 15, 12, 2, 5, 18, 1, 20, 0.37692307692307686, -28.453846153846158, 0.23076923076923078]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19228\n", "Test Data Points: 4808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9414780008543358\n", "Specificity : 0.9971625456019457\n", "Accuracy: 0.9700499168053245\n", "ROC 0.969320273228\n", "TP 2204 FP 7 TN 2460 FN 137\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88997504 0.98668885 0.98960067 0.98314607 0.98564295]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9894366197183099\n", "Accuracy: 0.9894551845342706\n", "ROC 0.994718309859\n", "TP 1 FP 6 TN 562 FN 0\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 3, 13, 5, 10, 13, 15, 9, 10, 8, 7, 13, 12, 0.6846153846153845, 20.0, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 19237\n", "Test Data Points: 4810\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9404001702852277\n", "Specificity : 0.9922795611540024\n", "Accuracy: 0.9669438669438669\n", "ROC 0.96633986572\n", "TP 2209 FP 19 TN 2442 FN 140\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89189189 0.98877339 0.98336798 0.98939488 0.98564892]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9894366197183099\n", "Accuracy: 0.9894551845342706\n", "ROC 0.994718309859\n", "TP 1 FP 6 TN 562 FN 0\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 17, 13, 11, 19, 1, 15, 3, 9, 7, 3, 8, 13, -0.08461538461538466, 13.230769230769234, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5084745762711864\n", "Specificity : 0.6470588235294118\n", "Accuracy: 0.5780590717299579\n", "ROC 0.5777666999\n", "TP 60 FP 42 TN 77 FN 58\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5 0.58050847 0.55508475 0.59745763 0.58474576]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6919014084507042\n", "Accuracy: 0.6924428822495606\n", "ROC 0.845950704225\n", "TP 1 FP 175 TN 393 FN 0\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 13, 3, 16, 12, 3, 15, 20, 19, 14, 13, 17, 8, 0.16153846153846152, 32.330769230769235, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4661016949152542\n", "Specificity : 0.6638655462184874\n", "Accuracy: 0.5654008438818565\n", "ROC 0.564983620567\n", "TP 55 FP 40 TN 79 FN 63\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.57563025 0.54237288 0.57627119 0.61440678 0.56355932]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6619718309859155\n", "Accuracy: 0.6625659050966608\n", "ROC 0.830985915493\n", "TP 1 FP 192 TN 376 FN 0\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 9, 1, 4, 2, 15, 3, 9, 10, 7, 17, 5, -0.12307692307692299, 58.61538461538462, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9240\n", "Test Data Points: 2311\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05217391304347826\n", "Specificity : 0.9977231329690346\n", "Accuracy: 0.9506707053223713\n", "ROC 0.524948523006\n", "TP 6 FP 5 TN 2191 FN 109\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95023799 0.94935065 0.95194805 0.95021645 0.95238095]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 4, 13, 9, 10, 18, 15, 16, 10, 10, 18, 11, 15, -1.3384615384615386, 79.93846153846155, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 9225\n", "Test Data Points: 2307\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06956521739130435\n", "Specificity : 1.0\n", "Accuracy: 0.9536194191590811\n", "ROC 0.534782608696\n", "TP 8 FP 0 TN 2192 FN 107\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94974003 0.95143105 0.94969644 0.95056375 0.95143105]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 13, 1, 1, 9, 12, 15, 11, 11, 20, 12, 7, 19, 0.015384615384615228, 29.215384615384615, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5508474576271186\n", "Specificity : 0.7058823529411765\n", "Accuracy: 0.6286919831223629\n", "ROC 0.628364905284\n", "TP 65 FP 35 TN 84 FN 53\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.69747899 0.72881356 0.6440678 0.58898305 0.61864407]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.43661971830985913\n", "Accuracy: 0.437609841827768\n", "ROC 0.718309859155\n", "TP 1 FP 320 TN 248 FN 0\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 20, 15, 19, 11, 15, 3, 19, 12, 2, 3, 9, -0.46923076923076945, 15.884615384615385, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 945\n", "Test Data Points: 237\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5423728813559322\n", "Specificity : 0.6722689075630253\n", "Accuracy: 0.6075949367088608\n", "ROC 0.607320894459\n", "TP 64 FP 39 TN 80 FN 54\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.69747899 0.62288136 0.66949153 0.58050847 0.62288136]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.44366197183098594\n", "Accuracy: 0.4446397188049209\n", "ROC 0.721830985915\n", "TP 1 FP 316 TN 252 FN 0\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " try:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"bagging\")\n", " y.benchmark(j, \"Y\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"bagging\")\n", " x.benchmark(j, \"Y\")\n", " del x\n", " except:\n", " print(\"Benchmark not relevant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "T Phosphorylation " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 20, 13, 7, 7, 3, 20, 3, 8, 10, 2, 0, 0, -0.28181818181818186, 55.518181818181816, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0029411764705882353\n", "Specificity : 0.9981510243325198\n", "Accuracy: 0.8144864604064893\n", "ROC 0.500546100402\n", "TP 9 FP 25 TN 13496 FN 3051\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81859848 0.80014474 0.80838359 0.81785283 0.80874548]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 3 TN 1150 FN 43\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 16, 10, 3, 2, 12, 20, 1, 20, 3, 3, 3, 8, 1.1307692307692307, 14.715384615384618, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.00398406374501992\n", "Specificity : 0.9977153806470631\n", "Accuracy: 0.8172004101079549\n", "ROC 0.500849722196\n", "TP 12 FP 31 TN 13538 FN 3000\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81811603 0.81775419 0.81586248 0.81212304 0.81863691]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 4 TN 1149 FN 43\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [2, 2, 9, 7, 20, 12, 20, 2, 9, 18, 19, 16, 12, -0.6692307692307692, 2.7, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109784\n", "Test Data Points: 27446\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6471138620987477\n", "Specificity : 0.8769923258559622\n", "Accuracy: 0.7606208554980689\n", "ROC 0.762053093977\n", "TP 8991 FP 1667 TN 11885 FN 4903\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.54118847 0.76317995 0.79483349 0.79872472 0.80572053]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.32558139534883723\n", "Specificity : 0.8881179531656548\n", "Accuracy: 0.8678929765886287\n", "ROC 0.606849674257\n", "TP 14 FP 129 TN 1024 FN 29\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 16, 3, 8, 9, 20, 10, 11, 3, 17, 19, 2, -0.45384615384615395, 91.20769230769231, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109789\n", "Test Data Points: 27448\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6709561466570813\n", "Specificity : 0.826931599940907\n", "Accuracy: 0.747886913436316\n", "ROC 0.748943873299\n", "TP 9333 FP 2343 TN 11195 FN 4577\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51103906 0.77969251 0.79644406 0.78839217 0.79356578]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.46511627906976744\n", "Specificity : 0.8438855160450998\n", "Accuracy: 0.830267558528428\n", "ROC 0.654500897557\n", "TP 20 FP 180 TN 973 FN 23\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 14, 1, 9, 12, 20, 18, 15, 13, 9, 9, 7, -1.0307692307692309, 44.215384615384615, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75724\n", "Test Data Points: 18931\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8786101744612227\n", "Specificity : 0.5337492909812819\n", "Accuracy: 0.7822618984734034\n", "ROC 0.706179732721\n", "TP 11986 FP 2466 TN 2823 FN 1656\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.72866047 0.79463342 0.80154244 0.79809826 0.80036978]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.9069767441860465\n", "Specificity : 0.3217692974848222\n", "Accuracy: 0.342809364548495\n", "ROC 0.614373020835\n", "TP 39 FP 782 TN 371 FN 4\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 2, 2, 5, 1, 1, 20, 18, 12, 9, 11, 10, 12, 0.23846153846153836, 30.261538461538464, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102802\n", "Test Data Points: 25701\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9276861200032832\n", "Specificity : 0.3412487054297973\n", "Accuracy: 0.6192366055795494\n", "ROC 0.634467412717\n", "TP 11302 FP 8905 TN 4613 FN 881\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60968018 0.69440878 0.69081712 0.68105058 0.6711284 ]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.9302325581395349\n", "Specificity : 0.2419774501300954\n", "Accuracy: 0.26672240802675584\n", "ROC 0.586105004135\n", "TP 40 FP 874 TN 279 FN 3\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 10, 20, 11, 12, 12, 20, 10, 1, 3, 20, 15, 13, 1.0384615384615385, 17.51538461538462, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5782894736842106\n", "Specificity : 0.64\n", "Accuracy: 0.6088113050706567\n", "ROC 0.609144736842\n", "TP 1758 FP 1071 TN 1904 FN 1282\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5880984 0.60222739 0.60907882 0.5857998 0.60409046]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.627906976744186\n", "Specificity : 0.5758889852558543\n", "Accuracy: 0.5777591973244147\n", "ROC 0.601897981\n", "TP 27 FP 489 TN 664 FN 16\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 13, 2, 16, 3, 3, 20, 10, 11, 10, 15, 7, 12, 0.1769230769230769, 78.58461538461539, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7690789473684211\n", "Specificity : 0.4319327731092437\n", "Accuracy: 0.6023275145469659\n", "ROC 0.600505860239\n", "TP 2338 FP 1690 TN 1285 FN 702\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.58926197 0.59258644 0.59727303 0.58779514 0.59411373]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.8837209302325582\n", "Specificity : 0.3434518647007806\n", "Accuracy: 0.362876254180602\n", "ROC 0.613586397467\n", "TP 38 FP 757 TN 396 FN 5\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 19, 8, 8, 3, 17, 20, 2, 10, 3, 11, 2, 13, -0.4538461538461536, 83.30769230769232, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47212\n", "Test Data Points: 11803\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.05589638718473074\n", "Specificity : 0.9875972488442891\n", "Accuracy: 0.7559942387528594\n", "ROC 0.521746818015\n", "TP 164 FP 110 TN 8759 FN 2770\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75516774 0.75482887 0.74870796 0.74775462 0.73284189]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.06976744186046512\n", "Specificity : 0.9774501300954033\n", "Accuracy: 0.9448160535117057\n", "ROC 0.523608785978\n", "TP 3 FP 26 TN 1127 FN 40\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [6, 7, 12, 16, 12, 5, 20, 12, 12, 8, 3, 20, 14, 1.0, 2.7, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47218\n", "Test Data Points: 11805\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.1826235093696763\n", "Specificity : 0.9489289740698985\n", "Accuracy: 0.7584074544684456\n", "ROC 0.56577624172\n", "TP 536 FP 453 TN 8417 FN 2399\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75554803 0.7519695 0.75652321 0.74559471 0.74923755]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.16279069767441862\n", "Specificity : 0.9184735472679966\n", "Accuracy: 0.8913043478260869\n", "ROC 0.540632122471\n", "TP 7 FP 94 TN 1059 FN 36\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 9, 5, 3, 19, 1, 20, 17, 7, 20, 18, 18, 13, -1.353846153846154, 24.938461538461542, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5631578947368421\n", "Specificity : 0.7636974789915967\n", "Accuracy: 0.6623441396508728\n", "ROC 0.663427686864\n", "TP 1712 FP 703 TN 2272 FN 1328\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63796543 0.69381649 0.66544729 0.65696708 0.61805786]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.4418604651162791\n", "Specificity : 0.4787510841283608\n", "Accuracy: 0.4774247491638796\n", "ROC 0.460305774622\n", "TP 19 FP 601 TN 552 FN 24\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 10, 11, 8, 8, 1, 20, 17, 2, 2, 10, 13, 9, -0.9230769230769229, 114.74615384615386, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6194078947368421\n", "Specificity : 0.706890756302521\n", "Accuracy: 0.6626766417290108\n", "ROC 0.66314932552\n", "TP 1883 FP 872 TN 2103 FN 1157\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63048537 0.67536569 0.64466245 0.63917526 0.61972065]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.46511627906976744\n", "Specificity : 0.40763226366001737\n", "Accuracy: 0.4096989966555184\n", "ROC 0.436374271365\n", "TP 20 FP 683 TN 470 FN 23\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 2, 7, 15, 8, 8, 20, 19, 12, 18, 18, 10, 11, -1.7923076923076924, 57.846153846153854, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0032658393207054214\n", "Specificity : 0.9988164805089134\n", "Accuracy: 0.8149689403534166\n", "ROC 0.501041159915\n", "TP 10 FP 16 TN 13503 FN 3052\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81829695 0.81112049 0.8181544 0.81845597 0.8181544 ]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 702 FN 15\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 11, 20, 3, 3, 7, 20, 5, 9, 2, 3, 7, 5, 0.07692307692307689, 5.661538461538462, 0.15384615384615385]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.06833333333333333\n", "Specificity : 0.9693689713570429\n", "Accuracy: 0.8063446113020928\n", "ROC 0.518851152345\n", "TP 205 FP 416 TN 13165 FN 2795\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81823664 0.80937161 0.81851628 0.81121834 0.79764777]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.9559659090909091\n", "Accuracy: 0.9401947148817803\n", "ROC 0.577982954545\n", "TP 3 FP 31 TN 673 FN 12\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [13, 9, 9, 9, 9, 3, 20, 3, 20, 13, 3, 17, 8, -0.23076923076923084, 162.70769230769227, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109770\n", "Test Data Points: 27443\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.565138546351371\n", "Specificity : 0.9324888335652046\n", "Accuracy: 0.7479502969791932\n", "ROC 0.748813689958\n", "TP 7791 FP 922 TN 12735 FN 5995\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55290774 0.772984 0.78387144 0.79848408 0.78245026]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9147727272727273\n", "Accuracy: 0.8970792767732962\n", "ROC 0.49071969697\n", "TP 1 FP 60 TN 644 FN 14\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 12, 20, 12, 20, 12, 20, 18, 18, 20, 9, 7, 0, -0.5166666666666665, 0.4416666666666706, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109747\n", "Test Data Points: 27437\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6750180505415162\n", "Specificity : 0.8369029219106499\n", "Accuracy: 0.7551846047308379\n", "ROC 0.755960486226\n", "TP 9349 FP 2216 TN 11371 FN 4501\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51067862 0.76660106 0.80419886 0.80383438 0.76964572]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.4666666666666667\n", "Specificity : 0.8224431818181818\n", "Accuracy: 0.8150208623087621\n", "ROC 0.644554924242\n", "TP 7 FP 125 TN 579 FN 8\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 7, 8, 1, 17, 17, 20, 12, 19, 12, 19, 1, 16, -1.3000000000000003, 36.792307692307695, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75727\n", "Test Data Points: 18932\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9187613843351549\n", "Specificity : 0.4351834069521798\n", "Accuracy: 0.7857595605324319\n", "ROC 0.676972395644\n", "TP 12610 FP 2941 TN 2266 FN 1115\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74420324 0.7911583 0.80185938 0.79773916 0.79879563]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.8666666666666667\n", "Specificity : 0.27698863636363635\n", "Accuracy: 0.28929068150208626\n", "ROC 0.571827651515\n", "TP 13 FP 509 TN 195 FN 2\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 8, 8, 13, 3, 3, 20, 3, 8, 16, 3, 11, 3, 0.8692307692307693, 103.83076923076923, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102990\n", "Test Data Points: 25748\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7671746393936925\n", "Specificity : 0.5981301476589745\n", "Accuracy: 0.6786934907565636\n", "ROC 0.682652393526\n", "TP 9414 FP 5416 TN 8061 FN 2857\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60278069 0.68164518 0.69076009 0.69433332 0.69565386]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.5355113636363636\n", "Accuracy: 0.5396383866481224\n", "ROC 0.634422348485\n", "TP 11 FP 327 TN 377 FN 4\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 13, 15, 17, 9, 17, 20, 7, 14, 9, 7, 9, 10, -2.0769230769230766, 4.50769230769231, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6532894736842105\n", "Specificity : 0.5626890756302521\n", "Accuracy: 0.6084788029925187\n", "ROC 0.607989274657\n", "TP 1986 FP 1301 TN 1674 FN 1054\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.57480053 0.59275266 0.59760559 0.60093116 0.53092784]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.48011363636363635\n", "Accuracy: 0.4853963838664812\n", "ROC 0.606723484848\n", "TP 11 FP 366 TN 338 FN 4\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 9, 9, 2, 3, 3, 20, 2, 8, 8, 9, 17, 19, -0.8615384615384616, 63.938461538461546, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.44144736842105264\n", "Specificity : 0.7431932773109243\n", "Accuracy: 0.5906899418121363\n", "ROC 0.592320322866\n", "TP 1342 FP 764 TN 2211 FN 1698\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60787899 0.59524601 0.56451613 0.6005986 0.60192883]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.7333333333333333\n", "Specificity : 0.6789772727272727\n", "Accuracy: 0.6801112656467315\n", "ROC 0.70615530303\n", "TP 11 FP 226 TN 478 FN 4\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 9, 3, 2, 7, 7, 20, 7, 5, 18, 3, 14, 17, -0.4923076923076923, 9.230769230769232, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47275\n", "Test Data Points: 11819\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.027551020408163266\n", "Specificity : 0.9939182340353644\n", "Accuracy: 0.753532447753617\n", "ROC 0.510734627222\n", "TP 81 FP 54 TN 8825 FN 2859\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.7535533 0.74678511 0.75156541 0.72702657 0.75156541]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9758522727272727\n", "Accuracy: 0.9568845618915159\n", "ROC 0.521259469697\n", "TP 1 FP 17 TN 687 FN 14\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [15, 7, 17, 18, 12, 3, 20, 1, 3, 18, 3, 7, 15, -0.5692307692307693, 30.738461538461536, 0.15384615384615385]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47229\n", "Test Data Points: 11808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.07101399795151929\n", "Specificity : 0.9841198333145624\n", "Accuracy: 0.7576219512195121\n", "ROC 0.527566915633\n", "TP 208 FP 141 TN 8738 FN 2721\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74678184 0.75643631 0.75455238 0.75107987 0.74930126]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.9446022727272727\n", "Accuracy: 0.9290681502086231\n", "ROC 0.572301136364\n", "TP 3 FP 39 TN 665 FN 12\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 7, 17, 13, 11, 15, 20, 11, 1, 9, 13, 11, 7, -1.1384615384615384, 32.361538461538466, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7115131578947368\n", "Specificity : 0.6110924369747899\n", "Accuracy: 0.6618453865336659\n", "ROC 0.661302797435\n", "TP 2163 FP 1157 TN 1818 FN 877\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64311835 0.67985372 0.66195544 0.64166944 0.63801131]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.6\n", "Specificity : 0.3068181818181818\n", "Accuracy: 0.3129346314325452\n", "ROC 0.453409090909\n", "TP 9 FP 488 TN 216 FN 6\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 2, 17, 1, 1, 18, 20, 11, 11, 2, 3, 1, 19, -0.9769230769230768, 28.761538461538464, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7325657894736842\n", "Specificity : 0.6151260504201681\n", "Accuracy: 0.6744804655029094\n", "ROC 0.673845919947\n", "TP 2227 FP 1145 TN 1830 FN 813\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64178856 0.69863697 0.65829731 0.65862986 0.62520785]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.6\n", "Specificity : 0.2911931818181818\n", "Accuracy: 0.29763560500695413\n", "ROC 0.445596590909\n", "TP 9 FP 499 TN 205 FN 6\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 3, 16, 1, 15, 1, 20, 8, 7, 10, 10, 3, 7, -0.9230769230769232, -15.853846153846154, 0.07692307692307693]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Failed\n", "TP 0 FP 0 TN 13588 FN 2993\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81298999 0.81455795 0.8185766 0.81845597 0.81791315]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [11, 8, 10, 5, 20, 3, 20, 10, 1, 10, 2, 14, 1, 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"Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.668160462594868\n", "Specificity : 0.8574472775369241\n", "Accuracy: 0.7620244862264975\n", "ROC 0.762803870066\n", "TP 9244 FP 1940 TN 11669 FN 4591\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.51608672 0.78152669 0.79991984 0.7948475 0.79331706]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.07692307692307693\n", "Specificity : 0.8430379746835444\n", "Accuracy: 0.7957244655581948\n", "ROC 0.459980525803\n", "TP 2 FP 62 TN 333 FN 24\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 17, 2, 11, 11, 8, 20, 16, 3, 11, 10, 1, 2, -1.0769230769230769, 76.7769230769231, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109776\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test 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0.9620512820512821\n", "Specificity : 0.3363705391040243\n", "Accuracy: 0.7878211227402474\n", "ROC 0.649210910578\n", "TP 13132 FP 3496 TN 1772 FN 518\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.71260175 0.79025267 0.79605646 0.79901676 0.79991542]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.9615384615384616\n", "Specificity : 0.15443037974683543\n", "Accuracy: 0.2042755344418052\n", "ROC 0.557984420643\n", "TP 25 FP 334 TN 61 FN 1\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 20, 13, 11, 19, 19, 20, 11, 4, 14, 17, 0, 0, -1.0090909090909088, -20.9, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102995\n", "Test Data Points: 25749\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.738437653211309\n", "Specificity : 0.6266005477018726\n", "Accuracy: 0.6797545535748961\n", "ROC 0.682519100457\n", "TP 9037 FP 5045 TN 8466 FN 3201\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61984466 0.66897087 0.70428771 0.68339288 0.69356843]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.38461538461538464\n", "Specificity : 0.529113924050633\n", "Accuracy: 0.5201900237529691\n", "ROC 0.456864654333\n", "TP 10 FP 186 TN 209 FN 16\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 7, 19, 2, 8, 18, 20, 15, 2, 4, 3, 20, 20, -0.36153846153846153, 14.384615384615385, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5572368421052631\n", "Specificity : 0.6363025210084033\n", "Accuracy: 0.5963424771404822\n", "ROC 0.596769681557\n", "TP 1694 FP 1082 TN 1893 FN 1346\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59906915 0.58510638 0.58995677 0.59461257 0.60192883]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.5\n", "Specificity : 0.5848101265822785\n", "Accuracy: 0.5795724465558195\n", "ROC 0.542405063291\n", "TP 13 FP 164 TN 231 FN 13\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 7, 10, 16, 17, 17, 20, 10, 3, 11, 14, 13, 11, -0.8923076923076922, 60.0923076923077, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6480263157894737\n", "Specificity : 0.5653781512605042\n", "Accuracy: 0.6071487946799667\n", "ROC 0.606702233525\n", "TP 1970 FP 1293 TN 1682 FN 1070\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60388963 0.58427527 0.60691719 0.58629864 0.60093116]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.46153846153846156\n", "Specificity : 0.4936708860759494\n", "Accuracy: 0.4916864608076009\n", "ROC 0.477604673807\n", "TP 12 FP 200 TN 195 FN 14\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 12, 13, 10, 18, 11, 20, 8, 19, 19, 20, 2, 7, -0.5692307692307692, 4.469230769230773, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47210\n", "Test Data Points: 11803\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.18336741649625085\n", "Specificity : 0.9498252339609877\n", "Accuracy: 0.7592984834364145\n", "ROC 0.566596325229\n", "TP 538 FP 445 TN 8424 FN 2396\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74610302 0.7439634 0.75385528 0.71869175 0.7514828 ]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.11538461538461539\n", "Specificity : 0.8860759493670886\n", "Accuracy: 0.838479809976247\n", "ROC 0.500730282376\n", "TP 3 FP 45 TN 350 FN 23\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 7, 18, 11, 9, 10, 20, 12, 11, 13, 2, 11, 9, -0.4615384615384616, 75.9846153846154, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47257\n", "Test Data Points: 11815\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.11862678450033991\n", "Specificity : 0.9706976219993237\n", "Accuracy: 0.7585272958104105\n", "ROC 0.54466220325\n", "TP 349 FP 260 TN 8613 FN 2593\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75133305 0.75150233 0.75715253 0.74860335 0.75198917]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.07692307692307693\n", "Specificity : 0.9189873417721519\n", "Accuracy: 0.8669833729216152\n", "ROC 0.497955209348\n", "TP 2 FP 32 TN 363 FN 24\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 10, 19, 2, 14, 10, 20, 3, 11, 7, 10, 10, 17, -0.7769230769230769, 102.29230769230772, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6529605263157895\n", "Specificity : 0.6635294117647059\n", "Accuracy: 0.6581878636741479\n", "ROC 0.65824496904\n", "TP 1985 FP 1001 TN 1974 FN 1055\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61602394 0.68101729 0.67442634 0.65580313 0.61938809]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.4230769230769231\n", "Specificity : 0.3924050632911392\n", "Accuracy: 0.39429928741092635\n", "ROC 0.407740993184\n", "TP 11 FP 240 TN 155 FN 15\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [17, 1, 3, 12, 11, 10, 20, 17, 17, 9, 9, 17, 1, -1.6076923076923075, 94.93846153846154, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5305921052631579\n", "Specificity : 0.7852100840336135\n", "Accuracy: 0.6565253532834581\n", "ROC 0.657901094648\n", "TP 1613 FP 639 TN 2336 FN 1427\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.64976729 0.6200133 0.67924842 0.65147988 0.63518457]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.2692307692307692\n", "Specificity : 0.5139240506329114\n", "Accuracy: 0.498812351543943\n", "ROC 0.391577409932\n", "TP 7 FP 192 TN 203 FN 19\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [4, 1, 7, 13, 3, 7, 20, 16, 11, 7, 2, 12, 8, -0.37692307692307697, 2.2384615384615394, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.08374875373878365\n", "Specificity : 0.9685381668140289\n", "Accuracy: 0.8079729811229721\n", "ROC 0.526143460276\n", "TP 252 FP 427 TN 13145 FN 2757\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81775419 0.81811603 0.81785283 0.81803378 0.8185766 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.20689655172413793\n", "Specificity : 0.9776297001427892\n", "Accuracy: 0.9671361502347418\n", "ROC 0.592263125933\n", "TP 6 FP 47 TN 2054 FN 23\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 8, 12, 12, 20, 1, 20, 8, 18, 10, 9, 1, 8, -0.9615384615384616, -7.146153846153848, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0029605263157894738\n", "Specificity : 0.9989661029466066\n", "Accuracy: 0.8163560702008322\n", "ROC 0.500963314631\n", "TP 9 FP 14 TN 13527 FN 3031\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.78904837 0.81715113 0.81785283 0.81863691 0.81845597]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.06896551724137931\n", "Specificity : 0.9990480723465016\n", "Accuracy: 0.9863849765258216\n", "ROC 0.534006794794\n", "TP 2 FP 2 TN 2099 FN 27\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 2, 10, 2, 3, 19, 20, 13, 20, 17, 19, 17, 12, 0.16153846153846171, -8.130769230769232, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109772\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6610860699720971\n", "Specificity : 0.8575035271404173\n", "Accuracy: 0.7574697565952485\n", "ROC 0.759294798556\n", "TP 9240 FP 1919 TN 11548 FN 4737\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55983093 0.78636496 0.78570127 0.79492038 0.79957729]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.41379310344827586\n", "Specificity : 0.886244645406949\n", "Accuracy: 0.8798122065727699\n", "ROC 0.650018874428\n", "TP 12 FP 239 TN 1862 FN 17\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 3, 10, 18, 14, 17, 20, 10, 14, 11, 5, 8, 11, -0.7153846153846154, 70.00769230769231, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109776\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.639202705619918\n", "Specificity : 0.8693437661474865\n", "Accuracy: 0.7528057134528494\n", "ROC 0.754273235884\n", "TP 8883 FP 1770 TN 11777 FN 5014\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.5269448 0.77693569 0.78811398 0.77764822 0.80224465]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.8900523560209425\n", "Accuracy: 0.8845070422535212\n", "ROC 0.686405488355\n", "TP 14 FP 231 TN 1870 FN 15\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 17, 12, 2, 3, 19, 20, 3, 3, 7, 20, 12, 13, 0.9538461538461538, -8.861538461538462, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 75672\n", "Test Data Points: 18919\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9660905229236854\n", "Specificity : 0.34112060778727443\n", "Accuracy: 0.7921666050002643\n", "ROC 0.653605565355\n", "TP 13191 FP 3469 TN 1796 FN 463\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.734394 0.79380517 0.79955598 0.79818163 0.79362478]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.896551724137931\n", "Specificity : 0.2213231794383627\n", "Accuracy: 0.23051643192488264\n", "ROC 0.558937451788\n", "TP 26 FP 1636 TN 465 FN 3\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [12, 6, 20, 10, 16, 17, 20, 11, 7, 2, 20, 10, 12, -0.5846153846153845, -2.3307692307692305, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102901\n", "Test Data Points: 25726\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6959967320261438\n", "Specificity : 0.6523060952098473\n", "Accuracy: 0.6730933685765373\n", "ROC 0.674151413618\n", "TP 8519 FP 4689 TN 8797 FN 3721\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60180362 0.69785431 0.68894072 0.68691934 0.69372206]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.3448275862068966\n", "Specificity : 0.6506425511661114\n", "Accuracy: 0.6464788732394366\n", "ROC 0.497735068687\n", "TP 10 FP 734 TN 1367 FN 19\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 10, 20, 7, 11, 1, 20, 11, 1, 18, 10, 11, 10, -1.3230769230769233, 38.98461538461538, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.4832236842105263\n", "Specificity : 0.7132773109243697\n", "Accuracy: 0.5970074812967581\n", "ROC 0.598250497567\n", "TP 1469 FP 853 TN 2122 FN 1571\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59823803 0.5802859 0.59062188 0.60143 0.60276023]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.7339362208472157\n", "Accuracy: 0.7305164319248826\n", "ROC 0.608347420768\n", "TP 14 FP 559 TN 1542 FN 15\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [3, 3, 15, 18, 12, 2, 20, 2, 18, 3, 17, 11, 8, 0.0461538461538464, 53.67692307692308, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.3105263157894737\n", "Specificity : 0.8077310924369748\n", "Accuracy: 0.5564422277639235\n", "ROC 0.559128704113\n", "TP 944 FP 572 TN 2403 FN 2096\n", "\n", "\n", "\n", "None\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Cross: Validation: [ 0.59674202 0.59242021 0.61207183 0.58696375 0.60242767]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.41379310344827586\n", "Specificity : 0.8181818181818182\n", "Accuracy: 0.8126760563380282\n", "ROC 0.615987460815\n", "TP 12 FP 382 TN 1719 FN 17\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 2, 2, 2, 2, 17, 20, 8, 2, 11, 0, 0, 0, -0.2, 20.720000000000002, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47229\n", "Test Data Points: 11808\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.3496073745305565\n", "Specificity : 0.8558396215790066\n", "Accuracy: 0.7302676151761518\n", "ROC 0.602723498055\n", "TP 1024 FP 1280 TN 7599 FN 1905\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.74864499 0.75482724 0.75074109 0.74455831 0.74701448]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.8524512137077582\n", "Accuracy: 0.8474178403755869\n", "ROC 0.667604917199\n", "TP 14 FP 310 TN 1791 FN 15\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 3, 7, 12, 4, 8, 20, 19, 9, 10, 18, 7, 9, -1.6, 16.86923076923077, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47285\n", "Test Data Points: 11822\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.20950764006791173\n", "Specificity : 0.9359017686155232\n", "Accuracy: 0.7549484012857385\n", "ROC 0.572704704342\n", "TP 617 FP 569 TN 8308 FN 2328\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75097276 0.75241076 0.75188224 0.75137467 0.7508671 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.3448275862068966\n", "Specificity : 0.9300333174678724\n", "Accuracy: 0.9220657276995305\n", "ROC 0.637430451837\n", "TP 10 FP 147 TN 1954 FN 19\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 20, 20, 10, 1, 20, 1, 16, 18, 8, 20, 17, 0, -1.6583333333333332, 8.466666666666667, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7223684210526315\n", "Specificity : 0.5858823529411765\n", "Accuracy: 0.6548628428927681\n", "ROC 0.654125386997\n", "TP 2196 FP 1232 TN 1743 FN 844\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.61884973 0.68417553 0.65314267 0.64183572 0.6232125 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.7931034482758621\n", "Specificity : 0.3683960019038553\n", "Accuracy: 0.3741784037558685\n", "ROC 0.58074972509\n", "TP 23 FP 1327 TN 774 FN 6\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [1, 7, 13, 12, 12, 15, 20, 2, 14, 18, 18, 1, 10, 0.20769230769230784, -0.4538461538461551, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6243421052631579\n", "Specificity : 0.7075630252100841\n", "Accuracy: 0.6655029093931837\n", "ROC 0.665952565237\n", "TP 1898 FP 870 TN 2105 FN 1142\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.62965426 0.67869016 0.64715663 0.66711008 0.61240439]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.6896551724137931\n", "Specificity : 0.44407425035697284\n", "Accuracy: 0.44741784037558685\n", "ROC 0.566864711385\n", "TP 20 FP 1168 TN 933 FN 9\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [10, 19, 9, 18, 7, 2, 20, 1, 8, 1, 17, 9, 18, -2.3, 20.915384615384614, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.13507340946166393\n", "Specificity : 0.9426605504587156\n", "Accuracy: 0.7933779627284241\n", "ROC 0.53886697996\n", "TP 414 FP 775 TN 12741 FN 2651\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.81805572 0.81624653 0.81863691 0.81869723 0.8185766 ]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.10714285714285714\n", "Specificity : 0.9498069498069498\n", "Accuracy: 0.9276315789473685\n", "ROC 0.528474903475\n", "TP 3 FP 52 TN 984 FN 25\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 17, 10, 20, 10, 10, 20, 10, 10, 1, 10, 18, 10, -1.2384615384615385, 72.66153846153847, 0.0]\n", "Finished working with Data\n", "Training Data Points: 66323\n", "Test Data Points: 16581\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.002711864406779661\n", "Specificity : 0.9988995671630841\n", "Accuracy: 0.8216633496170316\n", "ROC 0.500805715785\n", "TP 8 FP 15 TN 13616 FN 2942\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.8121457 0.81847787 0.81103739 0.81694813 0.81302774]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1036 FN 28\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [9, 20, 11, 2, 12, 7, 20, 11, 20, 7, 2, 10, 7, -1.0384615384615385, 21.830769230769235, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109775\n", "Test Data Points: 27444\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6144630484988453\n", "Specificity : 0.8990285546070061\n", "Accuracy: 0.7553563620463489\n", "ROC 0.756745801553\n", "TP 8514 FP 1372 TN 12216 FN 5342\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.52745491 0.78367644 0.80523266 0.79328062 0.83081296]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.25\n", "Specificity : 0.917953667953668\n", "Accuracy: 0.900375939849624\n", "ROC 0.583976833977\n", "TP 7 FP 85 TN 951 FN 21\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 9, 5, 13, 3, 18, 20, 11, 20, 13, 20, 2, 3, 0.5384615384615384, 32.330769230769235, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 109760\n", "Test Data Points: 27440\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6762538456034914\n", "Specificity : 0.8212879744484884\n", "Accuracy: 0.7474125364431486\n", "ROC 0.748770910026\n", "TP 9452 FP 2406 TN 11057 FN 4525\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.52858861 0.7794541 0.79438776 0.78198914 0.80884872]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.35714285714285715\n", "Specificity : 0.86003861003861\n", "Accuracy: 0.8468045112781954\n", "ROC 0.608590733591\n", "TP 10 FP 145 TN 891 FN 18\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 20, 1, 9, 9, 3, 20, 10, 2, 13, 7, 12, 2, -0.0999999999999999, 8.915384615384614, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102932\n", "Test Data Points: 25733\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5642664045219956\n", "Specificity : 0.7691113411208044\n", "Accuracy: 0.6719387556833638\n", "ROC 0.666688872821\n", "TP 6888 FP 3123 TN 10403 FN 5319\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59928499 0.68578534 0.68950375 0.6805534 0.68634385]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.2857142857142857\n", "Specificity : 0.7799227799227799\n", "Accuracy: 0.7669172932330827\n", "ROC 0.532818532819\n", "TP 8 FP 228 TN 808 FN 20\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [4, 9, 17, 17, 3, 17, 20, 6, 3, 9, 17, 9, 3, -1.2923076923076924, 66.03846153846155, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 102978\n", "Test Data Points: 25745\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.7093996062992126\n", "Specificity : 0.6549841363535749\n", "Accuracy: 0.6807535443775491\n", "ROC 0.682191871326\n", "TP 8649 FP 4676 TN 8877 FN 3543\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59826769 0.69893183 0.67363269 0.69037446 0.69371504]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.4642857142857143\n", "Specificity : 0.6573359073359073\n", "Accuracy: 0.6522556390977443\n", "ROC 0.560810810811\n", "TP 13 FP 355 TN 681 FN 15\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [18, 3, 3, 10, 12, 17, 20, 2, 18, 10, 3, 9, 11, -0.11538461538461542, 24.04615384615385, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Done training\n", "Test Results\n", "Sensitivity: 0.6638157894736842\n", "Specificity : 0.5277310924369748\n", "Accuracy: 0.5965087281795511\n", "ROC 0.595773440955\n", "TP 2018 FP 1405 TN 1570 FN 1022\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59142287 0.58859707 0.59527769 0.6084137 0.56767542]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.6428571428571429\n", "Specificity : 0.553088803088803\n", "Accuracy: 0.5554511278195489\n", "ROC 0.597972972973\n", "TP 18 FP 463 TN 573 FN 10\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [7, 7, 19, 7, 17, 2, 20, 17, 1, 7, 7, 9, 7, -2.9769230769230766, 14.715384615384613, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.20296052631578948\n", "Specificity : 0.894453781512605\n", "Accuracy: 0.5449709060681629\n", "ROC 0.548707153914\n", "TP 617 FP 314 TN 2661 FN 2423\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.59790559 0.58793218 0.58546724 0.59843698 0.59860326]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.14285714285714285\n", "Specificity : 0.8996138996138996\n", "Accuracy: 0.8796992481203008\n", "ROC 0.521235521236\n", "TP 4 FP 104 TN 932 FN 24\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [20, 12, 5, 18, 3, 1, 20, 4, 19, 3, 0, 0, 0, 0.7699999999999998, 0.5099999999999998, 0.1]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47224\n", "Test Data Points: 11807\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.11885245901639344\n", "Specificity : 0.9724068025678567\n", "Accuracy: 0.7607351571101889\n", "ROC 0.545629630792\n", "TP 348 FP 245 TN 8634 FN 2580\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75209621 0.75192682 0.74606132 0.74733187 0.75459551]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.07142857142857142\n", "Specificity : 0.9594594594594594\n", "Accuracy: 0.9360902255639098\n", "ROC 0.515444015444\n", "TP 2 FP 42 TN 994 FN 26\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [8, 3, 8, 19, 13, 2, 20, 3, 2, 19, 8, 17, 17, -0.8846153846153846, 86.65384615384616, 0.0]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 47241\n", "Test Data Points: 11811\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0126193724420191\n", "Specificity : 0.9979727446784548\n", "Accuracy: 0.7533655067310134\n", "ROC 0.50529605856\n", "TP 37 FP 18 TN 8861 FN 2895\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75175684 0.74024215 0.75969517 0.75173582 0.75004234]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 1031 FN 28\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [19, 9, 3, 11, 8, 7, 20, 12, 19, 14, 20, 20, 6, -0.9230769230769229, 40.815384615384616, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.6983552631578948\n", "Specificity : 0.6285714285714286\n", "Accuracy: 0.6638403990024938\n", "ROC 0.663463345865\n", "TP 2123 FP 1105 TN 1870 FN 917\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63663564 0.68833112 0.66029265 0.63219155 0.631859 ]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.6785714285714286\n", "Specificity : 0.36003861003861004\n", "Accuracy: 0.3684210526315789\n", "ROC 0.519305019305\n", "TP 19 FP 663 TN 373 FN 9\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Sample Vector [5, 3, 18, 7, 9, 13, 20, 10, 12, 1, 10, 10, 7, -0.2307692307692308, 50.54615384615384, 0.07692307692307693]\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 24059\n", "Test Data Points: 6015\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5976973684210526\n", "Specificity : 0.7347899159663865\n", "Accuracy: 0.6655029093931837\n", "ROC 0.666243642194\n", "TP 1817 FP 789 TN 2186 FN 1223\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63181516 0.6775266 0.67342867 0.64516129 0.63119388]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.5714285714285714\n", "Specificity : 0.47586872586872586\n", "Accuracy: 0.4783834586466165\n", "ROC 0.523648648649\n", "TP 16 FP 543 TN 493 FN 12\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"T\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"T\")\n", " del x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ledeprogram/algorithms

class2/donow/schuetz_rebecca_2_donow.ipynb

1

1321

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import pi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sphere_vol(r):\n", " r = abs(r)\n", " return 4/3 * pi * r ** 3 " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4188.790204786391" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sphere_vol(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

crocha700/dp_spectra

altimeter/plot_spectra_altika.ipynb

1

214978

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n", "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n", "Vendor: Continuum Analytics, Inc.\n", "Package: mkl\n", "Message: trial mode expires in 15 days\n" ] } ], "source": [ "import numpy as np\n", "from numpy import pi\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import seawater as sw\n", "\n", "#import seaborn as sns\n", "#sns.set(style=\"darkgrid\")\n", "#sns.set(style=\"whitegrid\")\n", "\n", "from pyspec import spectrum\n", "from plots_aux import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams.update({'font.size': 12\n", " , 'legend.markerscale': 1., 'axes.titlesize': 12, 'axes.labelsize' : 12,\n", " 'legend.fontsize' : 10,'legend.handlelength': 3})\n", "\n", "plt.rc('xtick', labelsize=12) \n", "plt.rc('ytick', labelsize=12)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "color2 = '#6495ed'\n", "color1 = '#ff6347'\n", "color5 = '#8470ff'\n", "color3 = '#3cb371'\n", "color4 = '#ffd700'\n", "color6 = '#ba55d3'\n", "lw1=1\n", "aph=.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load SSH and geostrophic vel. spectra calculated by Sarah Gille\n", "\n", "column 1: wavenumber (in cycles per km)\n", "\n", "column 2: descending track spectrum\n", "\n", "column 3: ascending track spectrum\n", "\n", "column 4: descending track lower error bar\n", "\n", "column 5: descending track upper error bar\n", "\n", "column 6: ascending track lower error bar\n", "\n", "column 7: ascending track upper error bar" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "spec = np.loadtxt('spec/spec_altika_dp.dat')\n", "spec_vel = np.loadtxt('spec/plot_vdata1.dat')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = spec[:,0]\n", "kv = spec_vel[:,0]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## -2 and -3 slopes in the loglog space\n", "ks = np.array([1.e-3,1])\n", "Es2 = .1e-3*(ks**(-3/2.))\n", "Es4 = 1.e-9*(ks**(-4))\n", "Es5 = 1.e-11*(ks**(-5))\n", "rd1 = 22.64 # [km]\n", "Enoise = np.ones(2)*2.*1.e-4\n", "\n", "def add_second_axis(ax1):\n", " \"\"\" Add a x-axis at the top of the spectra figures \"\"\"\n", " ax2 = ax1.twiny() \n", " ax2.set_xscale('log')\n", " ax2.set_xlim(ax1.axis()[0], ax1.axis()[1])\n", " kp = 1./np.array([500.,200.,100.,40.,20.,10.,5.])\n", " lp=np.array([500,200,100,40,20,10,5])\n", " ax2.set_xticks(kp)\n", " ax2.set_xticklabels(lp)\n", " plt.xlabel('Wavelength [km]')\n", " \n", "f0 = sw.f(59)\n", "A = (9.81/f0)**2/(1.e6)\n", "slab1=np.load('../adcp/outputs/adcp_spec_slab1.npz')\n", "\n", "win = np.hanning(k.size)\n", "fac = (k.size/(win**2).sum())" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sBNRUY6ytJriuBmoriC/OQ6or0NdUYDUGUBkaysQ+0bzS9ySjTpSzef8QYkIq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\nWkXyjkez1LWI+ADvAg85vsAmNWmWem5PdKrRqDqWPHgAecDQqjVWIrI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"text/plain": [ "<matplotlib.figure.Figure at 0x115b20b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dk = 5.649717514124294\n", "\n", "lw = 1.\n", "\n", "Essh = fac*(slab1['Eu']+slab1['Ev'])/(A*((2*pi*slab1['k'])**2))/2.\n", "Esshu = fac*(slab1['Euu']+slab1['Evu'])/(A*((2*pi*slab1['k'])**2))/2.\n", "Esshl = fac*(slab1['Eul']+slab1['Evl'])/(A*((2*pi*slab1['k'])**2))/2.\n", "\n", "fig = plt.figure(figsize=(8.27/2-.25,11.69/3-.25))\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.fill_between(k,fac*spec[:,3]/dk,fac*spec[:,4]/dk, color=color1, alpha=0.25)\n", "ax1.fill_between(k,fac*spec[:,5]/dk,fac*spec[:,6]/dk, color=color2, alpha=0.25)\n", "ax1.fill_between(slab1['k'],Esshl,Esshu, color=color3, alpha=0.25)\n", "\n", "ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(k,fac*spec[:,1]/dk,color=color1,linewidth=lw,label=\"Descending\")\n", "ax1.loglog(k,fac*spec[:,2]/dk,color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "ax1.loglog(slab1['k'],Essh,color=color3,linewidth=lw,label=\"from ADCP\")\n", "\n", "\n", "ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "ax1.loglog(ks,Es2/4.,'-', color='0.5',linewidth=1.)\n", "ax1.loglog(ks,Es5/5.,'-', color='0.5',linewidth=1.)\n", "\n", "plt.text(0.0011, 3.1/10/2.,u'-3/2',fontsize=12)\n", "plt.text(0.005675/3.25, 16.51,u'-5',fontsize=12)\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'SSH variance density [m$^{2}$/ cpkm]')\n", " \n", "#plt_spec_error(sn=158)\n", " \n", "#lg = plt.legend(loc=(.35,.65), numpoints=1,ncol=1)\n", "lg = plt.legend(loc=3, numpoints=1,ncol=1)\n", "#lg.set_title(r\"SSH variance spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "plt.axis((1./1.e3,1./4.,1./1.e4,5.e1))\n", "\n", "plt.text(1./15, 18., \"AltiKa\", size=8, rotation=0.,\n", " ha=\"center\", va=\"center\",\n", " bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nJSXx6KOPsmHDBp588knGjx9Pv3798Pv9mBnr1q0jKyuLwYMHs3r16irjOPXU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ikmBeuRqMXu9iSvvjPI/kH3Ce+70B/lY+dnYnm8LMx3UCQrtgghxLxypncSRWs9emSZxfcd3zeb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\nSyR9ErgW2Eb6ewHTImJnuVGZg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"text/plain": [ "<matplotlib.figure.Figure at 0x1154d86d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12.,10.))\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.fill_between(kv,spec_vel[:,3],spec_vel[:,4], color=color1, alpha=0.25)\n", "ax1.fill_between(kv,spec_vel[:,5],spec_vel[:,6], color=color2, alpha=0.25)\n", "\n", "ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(kv,spec_vel[:,1],color=color1,linewidth=lw,label=\"Descending\")\n", "ax1.loglog(kv,spec_vel[:,2],color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "#ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "#ax1.loglog(ks,Es2,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es5,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es4,'--', color='k',linewidth=2.,alpha=.7)\n", "\n", "#plt.text(0.0011, 3.1,u'k$^{-3/2}$',fontsize=25)\n", "#plt.text(0.0047, 5.51,u'k$^{-5}$',fontsize=25)\n", "#plt.text(0.002, 5.51,u'k$^{-4}$',fontsize=25)\n", "\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'SSH variance spectral density [m$^{2}$/ cpkm]')\n", " \n", "lg = plt.legend(loc=(.5,.75), numpoints=1,ncol=2)\n", "lg.set_title(r\"SSH variance spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "#plt.axis((1./1.e3,1./4.,1./1.e3,1.e1))\n", "\n", "plt.text(1./15, 5., \"AltiKa\", size=25, rotation=0.,\n", " ha=\"center\", va=\"center\",\n", " bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc_vel',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps_vel',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc_vel.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f0 = sw.f(59)\n", "A = (9.81/f0)**2/(1.e6)\n", "slab1=np.load('../adcp/outputs/adcp_spec_slab1.npz')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tIHTGyc4d0otP+QC6cE7uc0aOl046Szr2NKl3n9LWBwAA0ApCZ1yteVN65l/Ss49Km95q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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112c8bc10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(11.,12.))\n", "ax1 = fig.add_subplot(111)\n", "ax1.loglog(k,(spec[:,1]),color=color1,linewidth=lw,label=\"Descending\")\n", "\n", "#plt.axis((1./1.e3,1.,1./1.e4,1.e0))\n", "\n", "add_second_axis(ax1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Mvet16tShQ4cOWGt59tlnOXXqlEdjDA0NpWvXrlhrefXVV7PtmVu3bh2fffYZ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\n7Xp+K8KtFI0p0FYTLux2iZ6Nd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"text/plain": [ "<matplotlib.figure.Figure at 0x114a70850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "fig = plt.figure(figsize=(12.,10.))\n", "ax1 = fig.add_subplot(111)\n", "\n", "#ax1.fill_between(kv,spec_vel[:,3],spec_vel[:,4], color=color1, alpha=0.25)\n", "#ax1.fill_between(kv,spec_vel[:,5],spec_vel[:,6], color=color2, alpha=0.25)\n", "\n", "#ax1.set_xscale('log'); ax1.set_yscale('log')\n", "\n", "ax1.loglog(kv,spec_vel[:,1],color=color1,linewidth=lw,label=\"Geostrophic vel.\")\n", "#ax1.loglog(kv,spec_vel[:,2],color=color2,linewidth=lw,label=\"Ascending\")\n", "\n", "#ax1.axis((1./(1000),1./4,.4e-5,10))\n", "\n", "#ax1.loglog(ks,Es2,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es5,'--', color='k',linewidth=2.,alpha=.7)\n", "#ax1.loglog(ks,Es4,'--', color='k',linewidth=2.,alpha=.7)\n", "\n", "#plt.text(0.0011, 3.1,u'k$^{-3/2}$',fontsize=25)\n", "#plt.text(0.0047, 5.51,u'k$^{-5}$',fontsize=25)\n", "#plt.text(0.002, 5.51,u'k$^{-4}$',fontsize=25)\n", "\n", "ax1.loglog(k,A*((2*pi*k)**2)*(spec[:,1]),color=color2,linewidth=lw,label=\"from SSH spectrum\")\n", "ax1.loglog(slab1['k'],slab1['Eu'],color=color3,linewidth=lw,label=\"ADCP\")\n", "\n", "\n", "\n", "\n", "\n", "plt.xlabel('Along-track wavenumber [cpkm]')\n", "plt.ylabel(u'KE psectrum [m$^{2}$ s$^{-2}$/ cpkm]')\n", " \n", "lg = plt.legend(loc=2, numpoints=1,ncol=2)\n", "lg.set_title(r\"Across-track KE spectrum\",{'size':25})\n", "lg.draw_frame(False)\n", "\n", "#plt.axis((1./1.e3,1./4.,1./1.e3,1.e1))\n", "\n", "#plt.text(1./15, 5., \"AltiKa\", size=25, rotation=0.,\n", "# ha=\"center\", va=\"center\",\n", "# bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "\n", "add_second_axis(ax1)\n", " \n", "plt.savefig('figs/spec_altika_asc_desc_vel',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc.eps_vel',format='eps',bbox_inches='tight')\n", "plt.savefig('figs/spec_altika_asc_desc_vel.pdf',format='pdf',bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Eul',\n", " 'Evl',\n", " 'Es2',\n", " 'k',\n", " 'kK1',\n", " 'Kpsi',\n", " 'ks',\n", " 'Eu',\n", " 'Ev1',\n", " 'Euu',\n", " 'Es3',\n", " 'Ew1',\n", " 'Ev',\n", " 'Kphi',\n", " 'Evu',\n", " 'Enoise']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

MarjorieH/camera_model

python/notebooks/g2i_collinear.ipynb

3

3300

{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import sin, cos, tan, pi\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ground point, should produce image point 400 samples and 500 lines\n", "X = 1116890\n", "Y = -1604470\n", "Z = 1459570\n", "\n", "# Hardcoded values from json\n", "# some set to 0 for simplicity\n", "omega = 2.256130940792258\n", "phi = 0.09433201631102328\n", "kappa = -0.9630375478615623\n", "focal_length = 549.1178195372703\n", "pixel_pitch = 0\n", "principal_point_x = 0\n", "principal_point_y = 0\n", "spacecraft_x = 1728357.7031238307\n", "spacecraft_y = -2088409.0061042644\n", "spacecraft_z = 2082873.9280557402" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rotation_from_opk(o, p, k):\n", " om = np.empty((3,3))\n", " om[:,0] = [1,0,0]\n", " om[:,1] = [0, cos(o), -sin(o)]\n", " om[:,2] = [0, sin(o), cos(o)]\n", " \n", " pm = np.empty((3,3))\n", " pm[:,0] = [cos(p), 0, sin(p)]\n", " pm[:,1] = [0,1,0]\n", " pm[:,2] = [-sin(p), 0, cos(p)]\n", " \n", " km = np.empty((3,3))\n", " km[:,0] = [cos(k), -sin(k), 0]\n", " km[:,1] = [sin(k), cos(k), 0]\n", " km[:,2] = [0,0,1]\n", " \n", " return km.dot(pm).dot(om)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample: 4337.42884664\n", "Line: -13809.8886083\n" ] } ], "source": [ "rm = np.empty((3, 3))\n", "rm[:3, :3] = rotation_from_opk(omega, phi, kappa)\n", "\n", "# Collinearity equations\n", "samp = -focal_length * ((rm[0, 0] * (X - spacecraft_x) + rm[0, 1] * (Y - spacecraft_y) + rm[0, 2] * (Z - spacecraft_z))/(rm[2, 0] * (X - spacecraft_x) + rm[2, 1] * (Y - spacecraft_y) + rm[2, 2] * (Z - spacecraft_z))) + principal_point_x\n", "line = -focal_length * ((rm[1, 0] * (X - spacecraft_x) + rm[1, 1] * (Y - spacecraft_y) + rm[1, 2] * (Z - spacecraft_z))/(rm[2, 0] * (X - spacecraft_x) + rm[2, 1] * (Y - spacecraft_y) + rm[2, 2] * (Z - spacecraft_z))) + principal_point_y\n", "\n", "# Image point\n", "print(\"Sample: \", samp)\n", "print(\"Line: \", line)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

unlicense

briennakh/BIOF509

Wk08/Wk08_Numpy_model_package_survey_inclass_exercises.ipynb

1

128890

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Week 8 - Implementing a model in numpy and a survey of machine learning packages for python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This week we will be looking in detail at how to implement a supervised regression model using the base scientific computing packages available with python.\n", "\n", "We will also be looking at the different packages available for python that implement many of the algorithms we might want to use." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression with numpy\n", "\n", "Why implement algorithms from scratch when dedicated packages already exist? \n", "\n", "The packages available are very powerful and a real time saver but they can obscure some issues we might encounter if we don't know to look for them. By starting with just numpy these problems will be more obvious. We can address them here and then when we move on we will know what to look for and will be less likely to miss them.\n", "\n", "The dedicated machine learning packages implement the different algorithms but we are still responsible for getting our data in a suitable format." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048ddecc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 20\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", "\n", "\n", "plt.plot(x, y, 'b.')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a very simple dataset. There is only one input value for each record and then there is the output value. Our goal is to determine the output value or dependent variable, shown on the y-axis, from the input or independent variable, shown on the x-axis.\n", "\n", "Our approach should scale to handle multiple input, or independent, variables. The independent variables can be stored in a vector, a 1-dimensional array:\n", "\n", "$$X^T = (X_{1}, X_{2}, X_{3})$$\n", "\n", "As we have multiple records these can be stacked in a 2-dimensional array. Each record becomes one row in the array. Our `x` variable is already set up in this way.\n", "\n", "In linear regression we can compute the value of the dependent variable using the following formula:\n", "\n", "$$f(X) = \\beta_{0} + \\sum_{j=1}^p X_j\\beta_j$$\n", "\n", "The $\\beta_{0}$ term is the intercept, and represents the value of the dependent variable when the independent variable is zero.\n", "\n", "Calculating a solution is easier if we don't treat the intercept as special. Instead of having an intercept co-efficient that is handled separately we can instead add a variable to each of our records with a value of one." ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.44929165],\n", " [ 1. , 0.30446841],\n", " [ 1. , 0.83918912],\n", " [ 1. , 0.23774183],\n", " [ 1. , 0.50238946],\n", " [ 1. , 0.9425836 ],\n", " [ 1. , 0.6339977 ],\n", " [ 1. , 0.86728941],\n", " [ 1. , 0.94020969],\n", " [ 1. , 0.75076486],\n", " [ 1. , 0.69957506],\n", " [ 1. , 0.96796557],\n", " [ 1. , 0.99440079],\n", " [ 1. , 0.45182168],\n", " [ 1. , 0.07086978],\n", " [ 1. , 0.29279403],\n", " [ 1. , 0.15235471],\n", " [ 1. , 0.41748637],\n", " [ 1. , 0.13128933],\n", " [ 1. , 0.6041178 ]])" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intercept_x = np.hstack((np.ones((n,1)), x))\n", "intercept_x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Numpy contains the linalg module](http://docs.scipy.org/doc/numpy/reference/routines.linalg.html) with many common functions for performing linear algebra. Using this module finding a solution is quite simple." ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([[ 3.8217988 ],\n", " [ 6.15768399]]),\n", " array([ 8.50971729]),\n", " 2,\n", " array([ 5.17434252, 1.146272 ]))" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.lstsq(intercept_x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values returned are:\n", "\n", "* The least-squares solution\n", "* The sum of squared residuals\n", "* The rank of the independent variables\n", "* The singular values of the independent variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "1. Calculate the predictions our model would make\n", "2. Calculate the sum of squared residuals from our predictions. Does this match the value returned by lstsq?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coeff, residuals, rank, sing_vals = np.linalg.lstsq(intercept_x,y)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((20, 2), (1, 2))" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "intercept_x.shape, coeff.T.shape" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6.5883948 , 5.69661904, 8.98926023, 5.28573784, 6.91535432,\n", " 9.62593075, 7.72575628, 9.16229289, 9.61131296, 8.44477158,\n", " 8.12956095, 9.78222488, 9.94500463, 6.60397395, 4.2581925 ,\n", " 5.62473192, 4.75995094, 6.39254797, 4.630237 , 7.54176534])" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(intercept_x * coeff.T, axis=1)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predictions = np.sum(intercept_x * coeff.T, axis=1)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTs6oVjv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"text/plain": [ "<matplotlib.figure.Figure at 0x2004a156ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'bo')\n", "plt.plot(x, predictions, 'ko')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20,)" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions.shape" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(8.5097172851712308, array([ 8.50971729]))" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum((predictions.reshape((20,1)) - y) ** 2), residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Least squares refers to the cost function for this algorithm. The objective is to minimize the residual sum of squares. The difference between the actual and predicted values is calculated, it is squared and then summed over all records. The function is as follows:\n", "\n", "$$RSS(\\beta) = \\sum_{i=1}^{N}(y_i - x_i^T\\beta)^2$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matrix arithmetic\n", "\n", "Within lstsq all the calculations are performed using matrix arithmetic rather than the more familiar element-wise arithmetic numpy arrays generally perform. Numpy does have a matrix type but matrix arithmetic can also be performed on standard arrays using dedicated methods.\n", "\n", "![Matrix multiplication](https://upload.wikimedia.org/wikipedia/commons/e/eb/Matrix_multiplication_diagram_2.svg)\n", "_Source: Wikimedia Commons (User:Bilou)_\n", "\n", "In matrix multiplication the resulting value in any position is the sum of multiplying each value in a row in the first matrix by the corresponding value in a column in the second matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The residual sum of squares can be calculated with the following formula:\n", "\n", "$$RSS(\\beta) = (y - X\\beta)^T(y-X\\beta)$$\n", "\n", "The value of our co-efficients can be calculated with:\n", "\n", "$$\\hat\\beta = (X^TX)^{-1}X^Ty$$\n", "\n", "Unfortunately, the result is not as visually appealing as in languages that use matrix arithmetic by default." ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 3.8217988 ]\n", " [ 6.15768399]] \n", " [[ 3.8217988 ]\n", " [ 6.15768399]]\n" ] } ], "source": [ "our_coeff = np.dot(np.dot(np.linalg.inv(np.dot(intercept_x.T, intercept_x)), intercept_x.T), y)\n", "\n", "print(coeff, '\\n', our_coeff)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 6.5883948 , 5.69661904, 8.98926023, 5.28573784, 6.91535432,\n", " 9.62593075, 7.72575628, 9.16229289, 9.61131296, 8.44477158,\n", " 8.12956095, 9.78222488, 9.94500463, 6.60397395, 4.2581925 ,\n", " 5.62473192, 4.75995094, 6.39254797, 4.630237 , 7.54176534]),\n", " array([[ 6.5883948 ],\n", " [ 5.69661904],\n", " [ 8.98926023],\n", " [ 5.28573784],\n", " [ 6.91535432],\n", " [ 9.62593075],\n", " [ 7.72575628],\n", " [ 9.16229289],\n", " [ 9.61131296],\n", " [ 8.44477158],\n", " [ 8.12956095],\n", " [ 9.78222488],\n", " [ 9.94500463],\n", " [ 6.60397395],\n", " [ 4.2581925 ],\n", " [ 5.62473192],\n", " [ 4.75995094],\n", " [ 6.39254797],\n", " [ 4.630237 ],\n", " [ 7.54176534]]))" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "our_predictions = np.dot(intercept_x, our_coeff)\n", "\n", "predictions, our_predictions" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048f5be10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'ko', label='True values')\n", "plt.plot(x, our_predictions, 'ro', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(12).reshape((3,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "1. Plot the residuals. The x axis will be the independent variable (x) and the y axis the residual between our prediction and the true value.\n", "2. Plot the predictions generated for our model over the entire range of 0-1. One approach is to use the np.linspace method to create equally spaced values over a specified range." ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048f62400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y - our_predictions, 'ko')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 2) (2, 1)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048d69d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, y, 'ko', label='True values')\n", "\n", "all_x = np.linspace(0, 1, 1000).reshape((1000,1))\n", "intercept_all_x = np.hstack((np.ones((1000,1)), all_x))\n", "\n", "print(intercept_all_x.shape, our_coeff.shape)\n", "\n", "#all_x_predictions = np.dot(intercept_all_x, our_coeff)\n", "all_x_predictions = np.sum(intercept_all_x * our_coeff.T, axis=1)\n", "\n", "plt.plot(all_x, all_x_predictions, 'r-', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types of independent variable\n", "\n", "The independent variables can be many different types.\n", "\n", "* Quantitative inputs\n", "* Categorical inputs coded using dummy values\n", "* Interactions between multiple inputs\n", "* Tranformations of other inputs, e.g. logs, raised to different powers, etc.\n", "\n", "It is important to note that a _linear_ model is only _linear_ with respect to its inputs. Those input variables can take any form.\n", "\n", "One approach we can take to improve the predictions from our model would be to add in the square, cube, etc of our existing variable." ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2.48528927e+05]\n", " [ -1.71103499e+07]\n", " [ 4.65315707e+08]\n", " [ -6.91480377e+09]\n", " [ 6.44385305e+10]\n", " [ -4.06133878e+11]\n", " [ 1.81054068e+12]\n", " [ -5.85359860e+12]\n", " [ 1.38391083e+13]\n", " [ -2.36324769e+13]\n", " [ 2.76241766e+13]\n", " [ -1.78960448e+13]\n", " [ -3.36195922e+12]\n", " [ 2.22108078e+13]\n", " [ -2.64916311e+13]\n", " [ 1.80609411e+13]\n", " [ -7.62315594e+12]\n", " [ 1.86509989e+12]\n", " [ -2.03646141e+11]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048da9e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_expanded = np.hstack((x**i for i in range(1,20)))\n", "\n", "b, residuals, rank, s = np.linalg.lstsq(x_expanded, y)\n", "print(b)\n", "\n", "plt.plot(x, y, 'ko', label='True values')\n", "plt.plot(x, np.dot(x_expanded, b), 'ro', label='Predictions')\n", "plt.legend(numpoints=1, loc=4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a tradeoff with model complexity. As we add more complexity to our model we can fit our training data increasingly well but eventually will lose our ability to generalize to new data.\n", "\n", "Very simple models __underfit__ the data and have high __bias__.\n", "\n", "Very complex models __overfit__ the data and have high __variance__.\n", "\n", "The goal is to detect true sources of variation in the data and ignore variation that is just noise.\n", "\n", "How do we know if we have a good model? A common approach is to break up our data into a training set, a validation set, and a test set. \n", "\n", "* We train models with different parameters on the training set.\n", "* We evaluate each model on the validation set, and choose the best\n", "* We then measure the performance of our best model on the test set.\n", "\n", "__What would our best model look like?__ Because we are using dummy data here we can easily make more." ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x20048d64048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 20\n", "p = 12\n", "training = []\n", "val = []\n", "for i in range(1, p):\n", " np.random.seed(0)\n", " x = np.random.random((n,1))\n", " y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", " x = np.hstack((x**j for j in np.arange(i)))\n", " our_coeff = np.dot(\n", " np.dot(\n", " np.linalg.inv(\n", " np.dot(\n", " x.T, x\n", " )\n", " ), x.T\n", " ), y\n", " )\n", " our_predictions = np.dot(x, our_coeff)\n", " our_training_rss = np.sum((y - our_predictions) ** 2)\n", " training.append(our_training_rss)\n", " \n", " val_x = np.random.random((n,1))\n", " val_y = 5 + 6 * val_x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", " val_x = np.hstack((val_x**j for j in np.arange(i)))\n", " our_val_pred = np.dot(val_x, our_coeff)\n", " our_val_rss = np.sum((val_y - our_val_pred) ** 2)\n", " val.append(our_val_rss)\n", " #print(i, our_training_rss, our_val_rss)\n", "\n", "plt.plot(range(1, p), training, 'ko-', label='training')\n", "plt.plot(range(1, p), val, 'ro-', label='validation')\n", "plt.legend(loc=2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient descent\n", "\n", "One limitation of our current implementation is that it is resource intensive. For very large datasets an alternative is needed. Gradient descent is often preferred, and particularly stochastic gradient descent for very large datasets.\n", "\n", "Gradient descent is an iterative process, repetitively calculating the error and changing the coefficients slightly to reduce that error. It does this by calculating a gradient and then descending to a minimum in small steps.\n", "\n", "Stochastic gradient descent calculates the gradient on a small batch of the data, updates the coefficients, loads the next chunk of the data and repeats the process.\n", "\n", "We will just look at a basic gradient descent model." ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lstsq [[ 4.07959732]\n", " [ 5.69608619]]\n", "[[ 4.08212025]\n", " [ 5.69136537]] [10215.151832573791, 151.49738175162241, 120.99886225509709, 104.87099032951339, 96.334352755009746, 91.815828439379132, 89.424130090614341, 88.158181167489815, 87.488102235174722, 87.13342301602863, 86.945687896436965, 86.846317893274772, 86.793720395534692, 86.765880034455108, 86.751143863756042, 86.743343866972538, 86.739215253707584, 86.737029939025462, 86.735873230960678, 86.735260974264634]\n" ] } ], "source": [ "np.random.seed(0)\n", "n = 200\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", "intercept_x = np.hstack((np.ones((n,1)), x))\n", "coeff, residuals, rank, sing_vals = np.linalg.lstsq(intercept_x,y)\n", "print('lstsq', coeff)\n", "\n", "\n", "\n", "def gradient_descent(x, y, rounds = 1000, alpha=0.01):\n", " theta = np.zeros((x.shape[1], 1))\n", " costs = []\n", " for i in range(rounds):\n", " prediction = np.dot(x, theta)\n", " error = prediction - y\n", " gradient = np.dot(x.T, error / y.shape[0])\n", " theta -= gradient * alpha\n", " costs.append(np.sum(error ** 2))\n", " return (theta, costs) \n", "theta, costs = gradient_descent(intercept_x, y, rounds=10000)\n", "print(theta, costs[::500])" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lstsq [[ 5.47554054e+00]\n", " [ -2.39855397e+01]\n", " [ -2.16608732e+03]\n", " [ 1.83443739e+05]\n", " [ -5.27245313e+06]\n", " [ 8.26516683e+07]\n", " [ -8.23118154e+08]\n", " [ 5.62639497e+09]\n", " [ -2.76438408e+10]\n", " [ 1.00543636e+11]\n", " [ -2.75843668e+11]\n", " [ 5.77141231e+11]\n", " [ -9.24891464e+11]\n", " [ 1.13282985e+12]\n", " [ -1.05057010e+12]\n", " [ 7.23919989e+11]\n", " [ -3.58600661e+11]\n", " [ 1.20492881e+11]\n", " [ -2.45402172e+10]\n", " [ 2.28151842e+09]]\n", "[[ 4.79516406]\n", " [ 2.15892204]\n", " [ 1.59022686]\n", " [ 1.12552832]\n", " [ 0.76583482]\n", " [ 0.50446167]\n", " [ 0.31909476]\n", " [ 0.18845052]\n", " [ 0.09618612]\n", " [ 0.0306512 ]\n", " [-0.01621254]\n", " [-0.04991523]\n", " [-0.07421911]\n", " [-0.09170582]\n", " [-0.10416419]\n", " [-0.11285053]\n", " [-0.11866168]\n", " [-0.12224985]\n", " [-0.1240988 ]\n", " [-0.12457462]] [10215.151832573791, 54.942826326885879, 49.019636887639798, 48.508526705309478, 48.258178349345279, 48.117210951931447, 48.030663705920169, 47.971678161563304, 47.927021249546534, 47.890143189067288, 47.857775390826845, 47.828262392661784, 47.800743812843862, 47.77475299941122, 47.750019966197222, 47.726374493746306, 47.70369835417678, 47.681901638557513, 47.660910919846714, 47.640663236621648]\n" ] }, { "data": { "image/png": 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uqDjIuS4f6INRhe/7pCRWLlxo+pCJiYnxew2u5tBSXbFtIKIuCGGioKCAHTt2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\n5+bmhq0miL+CTY0Vc/Lld09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"text/plain": [ "<matplotlib.figure.Figure at 0x2004a13f668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(0)\n", "n = 200\n", "\n", "x = np.random.random((n,1))\n", "y = 5 + 6 * x ** 2 + np.random.normal(0,0.5, size=(n,1))\n", "x = np.hstack((x**j for j in np.arange(20)))\n", "\n", "coeff, residuals, rank, sing_vals = np.linalg.lstsq(x,y)\n", "print('lstsq', coeff)\n", "\n", "theta, costs = gradient_descent(x, y, rounds=10000)\n", "print(theta, costs[::500])\n", "\n", "plt.plot(x[:,1], y, 'ko')\n", "plt.plot(x[:,1], np.dot(x, coeff), 'co')\n", "plt.plot(x[:,1], np.dot(x, theta), 'ro')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Machine learning packages available in the python ecosystem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Overview in the python wiki](https://wiki.python.org/moin/PythonForArtificialIntelligence)\n", "\n", "General\n", "* [scikit-learn](http://scikit-learn.org/stable/)\n", "* [milk](https://pythonhosted.org/milk/)\n", "* [Orange](http://orange.biolab.si/)\n", "* [Shogun](http://www.shogun-toolbox.org/)\n", "* [GraphLab Create (dato)](https://dato.com/learn/userguide/)\n", "\n", "There is a collection of field specific packages including some with machine learning components on the [scipy website](http://www.scipy.org/topical-software.html#science-basic-tools). Other packages can often be found searching the [python package index](https://pypi.python.org/pypi).\n", "\n", "\n", "Deep learning is receiving a lot of attention recently and a number of different packages have been developed.\n", "* [Theano](http://www.deeplearning.net/software/theano/)\n", "* [pylearn2](http://deeplearning.net/software/pylearn2/)\n", "* [keras](http://keras.io/)\n", "* [Blocks](http://blocks.readthedocs.org/en/latest/)\n", "* [Lasagne](http://lasagne.readthedocs.org/en/latest/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scikit-learn\n", "\n", "Scikit-learn is now widely used. It includes modules for:\n", "* Classification\n", "* Regression\n", "* Clustering\n", "* Dimensionality reduction\n", "* Model selection\n", "* Preprocessing\n", "\n", "There are modules for training online models, enabling very large datasets to be analyzed.\n", "\n", "There is also a semi-supervised module for situations when you have a large dataset, but only have labels for part of the dataset.\n", "\n", "## Milk\n", "\n", "Milk works very well with mahotas, a package for image processing. With the recent improvements in scikit-image milk is now less attractive, although still a strong option\n", "\n", "## Orange and Shogun\n", "\n", "These are both large packages but for whatever reason do not receive the attention that scikit-learn does.\n", "\n", "## Dato\n", "\n", "Dato is a relative newcomer and has been receiving a lot of attention lately. Time will tell whether it can compete with scikit-learn." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignments\n", "\n", "This week we will continue working on our project ideas. As you develop the outline some points you may want to consider:\n", "\n", "For projects developing the object oriented programming component of the course:\n", "\n", "* What will your classes be?\n", "* What will each class have as attributes and methods?\n", "* How will your classes interact?\n", "\n", "For projects developing GUIs or web applications:\n", "\n", "* What will your screens/pages be?\n", "* What components will each page need?\n", "* How will you store any data needed/produced?\n", "\n", "For projects developing machine learning models:\n", "\n", "* What will be your data?\n", "* How is your data structured?\n", "* How much data do you have?\n", "* Is your data labeled?\n", "* What type of machine learning task is it?\n", "* How good would the performance need to be for the model to be useful?\n", "\n", "You do not need to answer all these questions. Each answer does not need to be complete. Your final project will likely be different to your initial idea.\n", "\n", "The goal of the project description is to document your project as you currently envision it and to encourage planning for the earliest stage.\n", "\n", "__Your project descriptions should be sent to me by our class next week.__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

andrewzwicky/puzzles

advent_of_code/2021/12.ipynb

1

6520

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import requests\n", "from session import SESSION\n", "from common import print_problem, get_problem_input, submit_answer, neighbors\n", "from bs4 import BeautifulSoup\n", "from IPython.core.display import HTML\n", "from collections import Counter\n", "import re\n", "from collections import defaultdict\n", "from copy import copy, deepcopy\n", "import itertools\n", "from pprint import pprint\n", "from functools import lru_cache\n", "import numpy as np\n", "import colorama" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "DAY = 12" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raw Data:\n", "'bm-XY\\nol-JS\\nbm-im\\nRD-ol\\nbm-QI\\nJS-ja\\nim-gq\\nend-im\\nja-ol\\nJS-gq\\nbm-AF\\nRD-start\\nRD-j'\n", "Split Data:\n", "['bm-XY',\n", " 'ol-JS',\n", " 'bm-im',\n", " 'RD-ol',\n", " 'bm-QI',\n", " 'JS-ja',\n", " 'im-gq',\n", " 'end-im',\n", " 'ja-ol',\n", " 'JS-gq']\n" ] } ], "source": [ "raw_data, data = get_problem_input(DAY)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "data = [\n", "\"fs-end\",\n", "\"he-DX\",\n", "\"fs-he\",\n", "\"start-DX\",\n", "\"pj-DX\",\n", "\"end-zg\",\n", "\"zg-sl\",\n", "\"zg-pj\",\n", "\"pj-he\",\n", "\"RW-he\",\n", "\"fs-DX\",\n", "\"pj-RW\",\n", "\"zg-RW\",\n", "\"start-pj\",\n", "\"he-WI\",\n", "\"zg-he\",\n", "\"pj-fs\",\n", "\"start-RW\",\n", "]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "dd = defaultdict(set)\n", "for x,y in [d.split(\"-\") for d in data]:\n", " dd[x].add(y)\n", " dd[y].add(x)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "def recurse_paths(this_dd, current, visited, found_paths):\n", " visited = deepcopy(visited)\n", " visited.append(current)\n", " \n", " if current == 'end':\n", " return found_paths + 1\n", " \n", " for n in this_dd[current]:\n", " if (n != n.lower()) or (n not in visited):\n", " found_paths = (recurse_paths(this_dd, n, visited, found_paths))\n", "\n", " return found_paths" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "first = recurse_paths(dd, \"start\", [], 0)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3887\n" ] } ], "source": [ "print(first)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3887\n", "(\"You don't seem to be solving the right level. Did you already complete it? \"\n", " '[Return to Day 12]')\n" ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submit_answer(DAY, 1, first)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "def twice_recurse(this_dd, current, visited, found_paths, doubled):\n", " visited = deepcopy(visited)\n", " visited.append(current)\n", " \n", " if current == 'end':\n", " p = \",\".join(visited)\n", " return found_paths + 1\n", " \n", " for n in this_dd[current]:\n", " if n == \"start\":\n", " continue\n", " if (n != n.lower()):\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, doubled)\n", "\n", " else:\n", " #lowercase\n", " if n not in visited:\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, doubled)\n", " else:\n", " #already found:\n", " if doubled is None:\n", " found_paths = twice_recurse(this_dd, n, visited, found_paths, n)\n", "\n", " return found_paths" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "second = twice_recurse(dd, \"start\", [], 0, None)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "104834\n" ] } ], "source": [ "print(second)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "104834\n", "(\"That's the right answer! You are one gold star closer to finding the sleigh \"\n", " 'keys.You have completed Day 12! You can [Shareon\\n'\n", " ' Twitter\\n'\n", " 'Mastodon] this victory or [Return to Your Advent Calendar].')\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submit_answer(DAY, 2, second)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit