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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
yugangzhang/CHX_Pipelines	2018_2/Coralpor_LocalDisk/Mask_pipeline_2018_V1.ipynb	1	1084217	null	bsd-3-clause
james-prior/euler	euler-001-multiples-of-3-and-5-20160318.ipynb	1	12001	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9.\n", "The sum of these multiples is 23.\n", "\n", "Find the sum of all the multiples of 3 or 5 below 1000." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def foo(n):\n", " return sum(filter(lambda x: x % 3 == 0 or x % 5 == 0, range(n)))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=10\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 355 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(n):\n", " total = 0\n", " for i in range(n):\n", " if i % 3 == 0 or i % 5 == 0:\n", " total += i\n", " return total" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 251 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is surprising that the naive code immediately above is faster than the functional programming version." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(n):\n", " a = []\n", " for i in range(n):\n", " if i % 3 == 0 or i % 5 == 0:\n", " a.append(i)\n", " return sum(a)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 305 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(n):\n", " return sum([i for i in range(n) if i % 3 == 0 or i % 5 == 0])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 258 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def foo(n):\n", " return sum((i for i in range(n) if i % 3 == 0 or i % 5 == 0))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 261 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def foo(n):\n", " return sum((j for j in (i for i in range(n) if i % 3 == 0) if j % 5 == 0))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 219 µs per loop\n" ] }, { "data": { "text/plain": [ "33165" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, the nested generator expressions above were fastest yet, and also ugliest.\n", "\n", "The unnested list comprehension was faster than the unnested generator expression,\n", "so let's try nested list comprehensions." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(n):\n", " return sum([j for j in [i for i in range(n) if i % 3 == 0] if j % 5 == 0])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 195 µs per loop\n" ] }, { "data": { "text/plain": [ "33165" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I expected the nested list comprehensions to be a little bit faster than the nested generator expressions, \n", "so I was surprised by the big speed increase.\n", "\n", "Let's play with generators more." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def threes_or_fives(gen):\n", " for i in gen:\n", " if i % 3 == 0:\n", " yield i\n", " elif i % 5 == 0:\n", " yield i\n", "\n", "def foo(n):\n", " return sum(threes_or_fives(range(n)))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 254 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def threes_or_fives(gen):\n", " for i in gen:\n", " if i % 3 == 0 or i % 5 == 0:\n", " yield i\n", "\n", "def foo(n):\n", " return sum(threes_or_fives(range(n)))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 4.86 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000 loops, best of 3: 261 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "It is surprising that the naive verbose generator function with two if statements\n", "is faster than the generator function with the combined if statement.\n", "\n", "I thought of one more way later, using sets, that should be much more elegant." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(n):\n", " return sum(set(range(0, n, 3)) \| set(range(0, n, 5)))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 63.6 µs per loop\n" ] }, { "data": { "text/plain": [ "233168" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "%timeit foo(n)\n", "foo(n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Holy smokes! It is easier to read, but I did not expect it to be so fast.\n", "\n", "I can not resist the temptation to generalize." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def foo(n, divisors):\n", " all_multiples = set([])\n", " for multiples in (set(range(0, n, divisor)) for divisor in divisors):\n", " all_multiples \|= multiples\n", " return sum(multiples)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 60.9 µs per loop\n" ] }, { "data": { "text/plain": [ "99500" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=1000\n", "divisors = (3, 5)\n", "%timeit foo(n, divisors)\n", "foo(n, divisors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am surprised that the generalized version is faster yet. It sure is ugly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, this does not compare to what I did last year in\n", "http://nbviewer.ipython.org/url/colug.net/python/all-ipython-notebooks/euler-001-multiples-of-3-and-5-20150220.ipynb." ] } ], "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": 0 }	mit
nproctor/phys202-2015-work	assignments/assignment01/ProjectEuler1.ipynb	1	2162	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n", "\n", "Find the sum of all the multiples of 3 or 5 below 1000." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "233168\n" ] } ], "source": [ "numbers = [] # create an empty list to store all my numbers in\n", "for i in range(1000): #this give me i values from 0 to 999\n", " if i % 3 == 0 or i % 5 == 0: # if i divided by 3 or 5 yields a remainder of 0\n", " numbers.append(i) # then that number is a multiple of 3 or 5 and is added to the list 'numbers'\n", "answer = sum(numbers) # let the variable 'answer' equal the sum of the numbers in 'numbers'\n", "\n", "print(answer) # and finally print the answer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "6e498cbe102f8b3c1bc4ebd777bcc952", "grade": true, "grade_id": "projecteuler1", "points": 10 } }, "outputs": [], "source": [ "# This cell will be used for grading, leave it at the end of the notebook." ] } ], "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
steromano/BayesianMethodsForHackers	Chapter2_MorePyMC/Chapter2.ipynb	1	729155	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Chapter 2\n", "======\n", "______\n", "\n", "This chapter introduces more PyMC syntax and design patterns, and ways to think about how to model a system from a Bayesian perspective. It also contains tips and data visualization techniques for assessing goodness-of-fit for your Bayesian model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A little more on PyMC\n", "\n", "### Parent and Child relationships\n", "\n", "To assist with describing Bayesian relationships, and to be consistent with PyMC's documentation, we introduce parent and child variables. \n", "\n", "* parent variables are variables that influence another variable. \n", "\n", "* child variable are variables that are affected by other variables, i.e. are the subject of parent variables. \n", "\n", "A variable can be both a parent and child. For example, consider the PyMC code below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm\n", "\n", "\n", "parameter = pm.Exponential(\"poisson_param\", 1)\n", "data_generator = pm.Poisson(\"data_generator\", parameter)\n", "data_plus_one = data_generator + 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`parameter` controls the parameter of `data_generator`, hence influences its values. The former is a parent of the latter. By symmetry, `data_generator` is a child of `parameter`.\n", "\n", "Likewise, `data_generator` is a parent to the variable `data_plus_one` (hence making `data_generator` both a parent and child variable). Although it does not look like one, `data_plus_one` should be treated as a PyMC variable as it is a function of another PyMC variable, hence is a child variable to `data_generator`.\n", "\n", "This nomenclature is introduced to help us describe relationships in PyMC modeling. You can access a variables children and parent variables using the `children` and `parents` attributes attached to variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Children of `parameter`: \"\n", "print parameter.children\n", "print \"\\nParents of `data_generator`: \"\n", "print data_generator.parents\n", "print \"\\nChildren of `data_generator`: \"\n", "print data_generator.children" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Children of `parameter`: \n", "set([<pymc.distributions.Poisson 'data_generator' at 0x1184fa310>])\n", "\n", "Parents of `data_generator`: \n", "{'mu': <pymc.distributions.Exponential 'poisson_param' at 0x1109b9990>}\n", "\n", "Children of `data_generator`: \n", "set([<pymc.PyMCObjects.Deterministic '(data_generator_add_1)' at 0x1184fa390>])\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course a child can have more than one parent, and a parent can have many children." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PyMC Variables\n", "\n", "All PyMC variables also expose a `value` attribute. This method produces the current (possibly random) internal value of the variable. If the variable is a child variable, its value changes given the variable's parents' values. Using the same variables from before:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"parameter.value =\", parameter.value\n", "print \"data_generator.value =\", data_generator.value\n", "print \"data_plus_one.value =\", data_plus_one.value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "parameter.value = 0.0281609254306\n", "data_generator.value = 0\n", "data_plus_one.value = 1\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyMC is concerned with two types of programming variables: `stochastic` and `deterministic`.\n", "\n", "* stochastic variables are variables that are not deterministic, i.e., even if you knew all the values of the variables' parents (if it even has any parents), it would still be random. Included in this category are instances of classes `Poisson`, `DiscreteUniform`, and `Exponential`.\n", "\n", "* deterministic variables are variables that are not random if the variables' parents were known. This might be confusing at first: a quick mental check is if I knew all of variable `foo`'s parent variables, I could determine what `foo`'s value is. \n", "\n", "We will detail each below.\n", "\n", "#### Initializing Stochastic variables\n", "\n", "Initializing a stochastic variable requires a `name` argument, plus additional parameters that are class specific. For example:\n", "\n", "`some_variable = pm.DiscreteUniform(\"discrete_uni_var\", 0, 4)`\n", "\n", "where 0, 4 are the `DiscreteUniform`-specific lower and upper bound on the random variable. The [PyMC docs](http://pymc-devs.github.com/pymc/distributions.html) contain the specific parameters for stochastic variables. (Or use `object??`, for example `pm.DiscreteUniform??` if you are using IPython!)\n", "\n", "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the `name`.\n", "\n", "For multivariable problems, rather than creating a Python array of stochastic variables, addressing the `size` keyword in the call to a `Stochastic` variable creates multivariate array of (independent) stochastic variables. The array behaves like a Numpy array when used like one, and references to its `value` attribute return Numpy arrays. \n", "\n", "The `size` argument also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n", "\n", " beta_1 = pm.Uniform(\"beta_1\", 0, 1)\n", " beta_2 = pm.Uniform(\"beta_2\", 0, 1)\n", " ...\n", "\n", "we can instead wrap them into a single variable:\n", "\n", " betas = pm.Uniform(\"betas\", 0, 1, size=N)\n", "\n", "#### Calling `random()`\n", "We can also call on a stochastic variable's `random()` method, which (given the parent values) will generate a new, random value. Below we demonstrate this using the texting example from the previous chapter." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lambda_1 = pm.Exponential(\"lambda_1\", 1) # prior on first behaviour\n", "lambda_2 = pm.Exponential(\"lambda_2\", 1) # prior on second behaviour\n", "tau = pm.DiscreteUniform(\"tau\", lower=0, upper=10) # prior on behaviour change\n", "\n", "print \"lambda_1.value = %.3f\" % lambda_1.value\n", "print \"lambda_2.value = %.3f\" % lambda_2.value\n", "print \"tau.value = %.3f\" % tau.value\n", "print\n", "\n", "lambda_1.random(), lambda_2.random(), tau.random()\n", "\n", "print \"After calling random() on the variables...\"\n", "print \"lambda_1.value = %.3f\" % lambda_1.value\n", "print \"lambda_2.value = %.3f\" % lambda_2.value\n", "print \"tau.value = %.3f\" % tau.value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "lambda_1.value = 2.046\n", "lambda_2.value = 0.185\n", "tau.value = 10.000\n", "\n", "After calling random() on the variables...\n", "lambda_1.value = 1.611\n", "lambda_2.value = 0.798\n", "tau.value = 2.000\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The call to `random` stores a new value into the variable's `value` attribute. In fact, this new value is stored in the computer's cache for faster recall and efficiency." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Warning: Don't update stochastic variables' values in-place.\n", "\n", "\n", "Straight from the PyMC docs, we quote [4]:\n", "\n", "> `Stochastic` objects' values should not be updated in-place. This confuses PyMC's caching scheme... The only way a stochastic variable's value should be updated is using statements of the following form:\n", "\n", " A.value = new_value\n", "\n", "> The following are in-place updates and should never be used:\n", "\n", " \n", " A.value += 3\n", " A.value[2,1] = 5\n", " A.value.attribute = new_attribute_value\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Deterministic variables\n", "\n", "Since most variables you will be modeling are stochastic, we distinguish deterministic variables with a `pymc.deterministic` wrapper. (If you are unfamiliar with Python wrappers (also called decorators), that's no problem. Just prepend the `pymc.deterministic` decorator before the variable declaration and you're good to go. No need to know more. ) The declaration of a deterministic variable uses a Python function:\n", "\n", " @pm.deterministic\n", " def some_deterministic_var(v1=v1,):\n", " #jelly goes here.\n", "\n", "For all purposes, we can treat the object `some_deterministic_var` as a variable and not a Python function. \n", "\n", "Prepending with the wrapper is the easiest way, but not the only way, to create deterministic variables: elementary operations, like addition, exponentials etc. implicitly create deterministic variables. For example, the following returns a deterministic variable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(lambda_1 + lambda_2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "pymc.PyMCObjects.Deterministic" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The use of the `deterministic` wrapper was seen in the previous chapter's text-message example. Recall the model for $\\lambda$ looked like: \n", "\n", "$$\n", "\\lambda = \n", "\\cases{\n", "\\lambda_1 & \\text{if } t \\lt \\tau \\cr\n", "\\lambda_2 & \\text{if } t \\ge \\tau\n", "}\n", "$$\n", "\n", "And in PyMC code:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "n_data_points = 5 # in CH1 we had ~70 data points\n", "\n", "\n", "@pm.deterministic\n", "def lambda_(tau=tau, lambda_1=lambda_1, lambda_2=lambda_2):\n", " out = np.zeros(n_data_points)\n", " out[:tau] = lambda_1 # lambda before tau is lambda1\n", " out[tau:] = lambda_2 # lambda after tau is lambda2\n", " return out" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, if $\\tau, \\lambda_1$ and $\\lambda_2$ are known, then $\\lambda$ is known completely, hence it is a deterministic variable. \n", "\n", "Inside the deterministic decorator, the `Stochastic` variables passed in behave like scalars or Numpy arrays (if multivariable), and not like `Stochastic` variables. For example, running the following:\n", "\n", " @pm.deterministic\n", " def some_deterministic(stoch=some_stochastic_var):\n", " return stoch.value2\n", "\n", "\n", "will return an `AttributeError` detailing that `stoch` does not have a `value` attribute. It simply needs to be `stoch2`. During the learning phase, it's the variable's `value` that is repeatedly passed in, not the actual variable. \n", "\n", "Notice in the creation of the deterministic function we added defaults to each variable used in the function. This is a necessary step, and all variables must have default values. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Including observations in the Model\n", "\n", "At this point, it may not look like it, but we have fully specified our priors. For example, we can ask and answer questions like \"What does my prior distribution of $\\lambda_1$ look like?\" " ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "from IPython.core.pylabtools import figsize\n", "from matplotlib import pyplot as plt\n", "figsize(12.5, 4)\n", "\n", "\n", "samples = [lambda_1.random() for i in range(20000)]\n", "plt.hist(samples, bins=70, normed=True, histtype=\"stepfilled\")\n", "plt.title(\"Prior distribution for $\\lambda_1$\")\n", "plt.xlim(0, 8);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1184faf90>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To frame this in the notation of the first chapter, though this is a slight abuse of notation, we have specified $P(A)$. Our next goal is to include data/evidence/observations $X$ into our model. \n", "\n", "PyMC stochastic variables have a keyword argument `observed` which accepts a boolean (`False` by default). The keyword `observed` has a very simple role: fix the variable's current value, i.e. make `value` immutable. We have to specify an initial `value` in the variable's creation, equal to the observations we wish to include, typically an array (and it should be an Numpy array for speed). For example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = np.array([10, 5])\n", "fixed_variable = pm.Poisson(\"fxd\", 1, value=data, observed=True)\n", "print \"value: \", fixed_variable.value\n", "print \"calling .random()\"\n", "fixed_variable.random()\n", "print \"value: \", fixed_variable.value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "value: [10 5]\n", "calling .random()\n", "value: [10 5]\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is how we include data into our models: initializing a stochastic variable to have a fixed value. \n", "\n", "To complete our text message example, we fix the PyMC variable `observations` to the observed dataset. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We're using some fake data here\n", "data = np.array([10, 25, 15, 20, 35])\n", "obs = pm.Poisson(\"obs\", lambda_, value=data, observed=True)\n", "print obs.value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[10 25 15 20 35]\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finally...\n", "\n", "We wrap all the created variables into a `pm.Model` class. With this `Model` class, we can analyze the variables as a single unit. This is an optional step, as the fitting algorithms can be sent an array of the variables rather than a `Model` class. I may or may not use this class in future examples ;)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = pm.Model([obs, lambda_, lambda_1, lambda_2, tau])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling approaches\n", "\n", "A good starting thought to Bayesian modeling is to think about how your data might have been generated. Position yourself in an omniscient position, and try to imagine how you would recreate the dataset. \n", "\n", "In the last chapter we investigated text message data. We begin by asking how our observations may have been generated:\n", "\n", "1. We started by thinking \"what is the best random variable to describe this count data?\" A Poisson random variable is a good candidate because it can represent count data. So we model the number of sms's received as sampled from a Poisson distribution.\n", "\n", "2. Next, we think, \"Ok, assuming sms's are Poisson-distributed, what do I need for the Poisson distribution?\" Well, the Poisson distribution has a parameter $\\lambda$. \n", "\n", "3. Do we know $\\lambda$? No. In fact, we have a suspicion that there are two $\\lambda$ values, one for the earlier behaviour and one for the latter behaviour. We don't know when the behaviour switches though, but call the switchpoint $\\tau$.\n", "\n", "4. What is a good distribution for the two $\\lambda$s? The exponential is good, as it assigns probabilities to positive real numbers. Well the exponential distribution has a parameter too, call it $\\alpha$.\n", "\n", "5. Do we know what the parameter $\\alpha$ might be? No. At this point, we could continue and assign a distribution to $\\alpha$, but it's better to stop once we reach a set level of ignorance: whereas we have a prior belief about $\\lambda$, (\"it probably changes over time\", \"it's likely between 10 and 30\", etc.), we don't really have any strong beliefs about $\\alpha$. So it's best to stop here. \n", "\n", " What is a good value for $\\alpha$ then? We think that the $\\lambda$s are between 10-30, so if we set $\\alpha$ really low (which corresponds to larger probability on high values) we are not reflecting our prior well. Similar, a too-high alpha misses our prior belief as well. A good idea for $\\alpha$ as to reflect our belief is to set the value so that the mean of $\\lambda$, given $\\alpha$, is equal to our observed mean. This was shown in the last chapter.\n", "\n", "6. We have no expert opinion of when $\\tau$ might have occurred. So we will suppose $\\tau$ is from a discrete uniform distribution over the entire timespan.\n", "\n", "\n", "Below we give a graphical visualization of this, where arrows denote `parent-child` relationships. (provided by the [Daft Python library](http://daft-pgm.org/) )\n", "\n", "<img src=\"http://i.imgur.com/7J30oCG.png\" width = 700/>\n", "\n", "\n", "PyMC, and other probabilistic programming languages, have been designed to tell these data-generation stories. More generally, B. Cronin writes [5]:\n", "\n", "> Probabilistic programming will unlock narrative explanations of data, one of the holy grails of business analytics and the unsung hero of scientific persuasion. People think in terms of stories - thus the unreasonable power of the anecdote to drive decision-making, well-founded or not. But existing analytics largely fails to provide this kind of story; instead, numbers seemingly appear out of thin air, with little of the causal context that humans prefer when weighing their options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Same story; different ending.\n", "\n", "Interestingly, we can create new datasets by retelling the story.\n", "For example, if we reverse the above steps, we can simulate a possible realization of the dataset.\n", "\n", "1\\. Specify when the user's behaviour switches by sampling from $\\text{DiscreteUniform}(0, 80)$:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tau = pm.rdiscrete_uniform(0, 80)\n", "print tau" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "18\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\\. Draw $\\lambda_1$ and $\\lambda_2$ from an $\\text{Exp}(\\alpha)$ distribution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "alpha = 1. / 20.\n", "lambda_1, lambda_2 = pm.rexponential(alpha, 2)\n", "print lambda_1, lambda_2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "14.9003272127 37.9862424635\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "3\\. For days before $\\tau$, represent the user's received SMS count by sampling from $\\text{Poi}(\\lambda_1)$, and sample from $\\text{Poi}(\\lambda_2)$ for days after $\\tau$. For example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "4\\. Plot the artificial dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.bar(np.arange(80), data, color=\"#348ABD\")\n", "plt.bar(tau - 1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", "plt.xlabel(\"Time (days)\")\n", "plt.ylabel(\"count of text-msgs received\")\n", "plt.title(\"Artificial dataset\")\n", "plt.xlim(0, 80)\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x118aaa450>" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is okay that our fictional dataset does not look like our observed dataset: the probability is incredibly small it indeed would. PyMC's engine is designed to find good parameters, $\\lambda_i, \\tau$, that maximize this probability. \n", "\n", "\n", "The ability to generate artificial datasets is an interesting side effect of our modeling, and we will see that this ability is a very important method of Bayesian inference. We produce a few more datasets below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_artificial_sms_dataset():\n", " tau = pm.rdiscrete_uniform(0, 80)\n", " alpha = 1. / 20.\n", " lambda_1, lambda_2 = pm.rexponential(alpha, 2)\n", " data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]\n", " plt.bar(np.arange(80), data, color=\"#348ABD\")\n", " plt.bar(tau - 1, data[tau - 1], color=\"r\", label=\"user behaviour changed\")\n", " plt.xlim(0, 80)\n", "\n", "figsize(12.5, 5)\n", "plt.title(\"More example of artificial datasets\")\n", "for i in range(4):\n", " plt.subplot(4, 1, i)\n", " plot_artificial_sms_dataset()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/stefano.romano/DataScience/lib/python2.7/site-packages/matplotlib/axes/_subplots.py:69: MatplotlibDeprecationWarning: The use of 0 (which ends up being the _last_ sub-plot) is deprecated in 1.4 and will raise an error in 1.5\n", " mplDeprecation)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x118aa0d10>" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Later we will see how we use this to make predictions and test the appropriateness of our models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#####Example: Bayesian A/B testing\n", "\n", "A/B testing is a statistical design pattern for determining the difference of effectiveness between two different treatments. For example, a pharmaceutical company is interested in the effectiveness of drug A vs drug B. The company will test drug A on some fraction of their trials, and drug B on the other fraction (this fraction is often 1/2, but we will relax this assumption). After performing enough trials, the in-house statisticians sift through the data to determine which drug yielded better results. \n", "\n", "Similarly, front-end web developers are interested in which design of their website yields more sales or some other metric of interest. They will route some fraction of visitors to site A, and the other fraction to site B, and record if the visit yielded a sale or not. The data is recorded (in real-time), and analyzed afterwards. \n", "\n", "Often, the post-experiment analysis is done using something called a hypothesis test like difference of means test or difference of proportions test. This involves often misunderstood quantities like a \"Z-score\" and even more confusing \"p-values\" (please don't ask). If you have taken a statistics course, you have probably been taught this technique (though not necessarily learned this technique). And if you were like me, you may have felt uncomfortable with their derivation -- good: the Bayesian approach to this problem is much more natural. \n", "\n", "### A Simple Case\n", "\n", "As this is a hacker book, we'll continue with the web-dev example. For the moment, we will focus on the analysis of site A only. Assume that there is some true $0 \\lt p_A \\lt 1$ probability that users who, upon shown site A, eventually purchase from the site. This is the true effectiveness of site A. Currently, this quantity is unknown to us. \n", "\n", "Suppose site A was shown to $N$ people, and $n$ people purchased from the site. One might conclude hastily that $p_A = \\frac{n}{N}$. Unfortunately, the observed frequency $\\frac{n}{N}$ does not necessarily equal $p_A$ -- there is a difference between the observed frequency and the true frequency of an event. The true frequency can be interpreted as the probability of an event occurring. For example, the true frequency of rolling a 1 on a 6-sided die is $\\frac{1}{6}$. Knowing the true frequency of events like:\n", "\n", "- fraction of users who make purchases, \n", "- frequency of social attributes, \n", "- percent of internet users with cats etc. \n", "\n", "are common requests we ask of Nature. Unfortunately, often Nature hides the true frequency from us and we must infer it from observed data.\n", "\n", "The observed frequency is then the frequency we observe: say rolling the die 100 times you may observe 20 rolls of 1. The observed frequency, 0.2, differs from the true frequency, $\\frac{1}{6}$. We can use Bayesian statistics to infer probable values of the true frequency using an appropriate prior and observed data.\n", "\n", "\n", "With respect to our A/B example, we are interested in using what we know, $N$ (the total trials administered) and $n$ (the number of conversions), to estimate what $p_A$, the true frequency of buyers, might be. \n", "\n", "To set up a Bayesian model, we need to assign prior distributions to our unknown quantities. A priori, what do we think $p_A$ might be? For this example, we have no strong conviction about $p_A$, so for now, let's assume $p_A$ is uniform over [0,1]:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm\n", "\n", "# The parameters are the bounds of the Uniform.\n", "p = pm.Uniform('p', lower=0, upper=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Had we had stronger beliefs, we could have expressed them in the prior above.\n", "\n", "For this example, consider $p_A = 0.05$, and $N = 1500$ users shown site A, and we will simulate whether the user made a purchase or not. To simulate this from $N$ trials, we will use a Bernoulli distribution: if $ X\\ \\sim \\text{Ber}(p)$, then $X$ is 1 with probability $p$ and 0 with probability $1 - p$. Of course, in practice we do not know $p_A$, but we will use it here to simulate the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set constants\n", "p_true = 0.05 # remember, this is unknown.\n", "N = 1500\n", "\n", "# sample N Bernoulli random variables from Ber(0.05).\n", "# each random variable has a 0.05 chance of being a 1.\n", "# this is the data-generation step\n", "occurrences = pm.rbernoulli(p_true, N)\n", "\n", "print occurrences # Remember: Python treats True == 1, and False == 0\n", "print occurrences.sum()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[False False False ..., False False True]\n", "87\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The observed frequency is:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Occurrences.mean is equal to n/N.\n", "print \"What is the observed frequency in Group A? %.4f\" % occurrences.mean()\n", "print \"Does this equal the true frequency? %s\" % (occurrences.mean() == p_true)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "What is the observed frequency in Group A? 0.0580\n", "Does this equal the true frequency? False\n" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We combine the observations into the PyMC `observed` variable, and run our inference algorithm:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# include the observations, which are Bernoulli\n", "obs = pm.Bernoulli(\"obs\", p, value=occurrences, observed=True)\n", "\n", "# To be explained in chapter 3\n", "mcmc = pm.MCMC([p, obs])\n", "mcmc.sample(18000, 1000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 18000 of 18000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the posterior distribution of the unknown $p_A$ below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 4)\n", "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n", "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", "plt.hist(mcmc.trace(\"p\")[:], bins=25, histtype=\"stepfilled\", normed=True)\n", "plt.legend()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "<matplotlib.legend.Legend at 0x109862250>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ntn3bSUlJ2Lt3L958800888wzDo+HbbbZZptttp25nZOTg9LSUgBAfn4+EhMT\nYSvFs6N89dVXWLt2LXbs2AEAGDJkCDIzM+Hn54fCwkJMmDABR48eNVuHs6PIRa/Xm96Q1LHFx8dj\n79692LJlC6KiohwdDqmEx6hcmE+5MJ9yscvsKA02btxoGooCAJMnT0ZqaioAIDU1FfHx8TYFQkRE\nRETUWSgqwisqKpCeno6HHnrI9NjSpUuxc+dOhISEYNeuXVi6dGm7BUnOgZ/giZwbj1G5MJ9yYT7J\nkpuShXr27IlLly6ZPebj44P09PR2CYqIiIiISGatmh2FOreGHypQxxcUFITg4GBotVpHh0Iq4jEq\nF+ZTLswnWVJ0JZyI5JKSkgK9Xo/IyEhHh0JERNQpKZ4dpS04OwoRERERyUaN2VF4JZyIqJ0Yrlaj\nvMaoWn9e7m64qWdX1fojIiLHYRFOinGOU7kwn+0v70oVXv/nKdX6e29KSItFOHMqF+ZTLswnWeIP\nM4mIiIiI7IxFOCnGT/DyyM3NhVarRVlZmaNDIRXxGJUL8ykX5pMssQgn6oSSkpIQHR2N7OxsR4dC\nRETUKbEIJ8U4xymRc+MxKhfmUy7MJ1liEU5EREREZGcswkkxjmcjcm48RuXCfMqF+SRLLMKJiIiI\niOyMRTgpxvFs8ggKCkJwcDC0Wq2jQyEV8RiVC/MpF+aTLPFmPUSdUEpKCvR6PSIjIx0dChERUafE\nK+GkGMezyYX5lA9zKhfmUy7MJ1liEU5EREREZGeKivCSkhJMmzYNQ4cORWhoKH766ScUFxcjNjYW\nISEhiIuLQ0lJSXvHSg7G8WxyYT7lw5zKhfmUC/NJlhSNCX/hhRdw//33Y/Pmzairq0NFRQXeeust\nxMbGYvHixXjnnXeQnJyM5OTk9o6XiKjdFJRUoehqjWr9Hb9YqVpfREQkF40QQrS0QGlpKcLDw3Hq\n1Cmzx4cMGYI9e/ZAp9OhqKgI48ePx9GjR82WycjIQEREhPpRE5FNcnNzUV5ejqCgIHh6ejo6HKfx\n45lSvLHzlPUFHeS9KSEY3Keno8MgIur0srKyEBMTY1MfVoej5OXloU+fPnjyyScRERGBuXPnoqKi\nAgaDATqdDgCg0+lgMBhsCoSI7CcpKQnR0dHIzs52dChERESdktXhKHV1dcjKysKaNWtw66234sUX\nX2w07ESj0UCj0TS5/sKFC+Hv7w8A8PLyQlhYmOkXwg3jo9juGO21a9cyfxK1AeDIkSOIiopyinic\nof2LoQJ6TkAwAAAec0lEQVSALwCg7ORhAIBn8CinaWf9dBmDH5zYbPw5OTlYsGCB0+xPtm1rM59y\ntZnPjt3OyclBaWkpACA/Px+JiYmwldXhKEVFRRg7dizy8vJMgaxYsQKnTp3C7t274efnh8LCQkyY\nMIHDUSSn1+vNCjjquOLj47F3715s2bLFVIRTxx+OwmNULsynXJhPudhlOIqfnx9uueUWHD9+HACQ\nnp6OYcOGYdKkSUhNTQUApKamIj4+3qZAyPnx5EHk3HiMyoX5lAvzSZbclCz03nvvYdasWaipqUFw\ncDA++ugjGI1GTJ8+HRs2bEBAQAA2bdrU3rESEREREUlBURE+cuRIHDx4sNHj6enpqgdEzotfpckj\nKCgI586dg1ardXQopCIeo3JhPuXCfJIlRUU4EcklJSUFer0ekZGRjg6FiIioU+Jt60kxfoKXC/Mp\nH+ZULsynXJhPssQinIjo/zQz0yoREZHqOByFFON4NrnIkM+84mvIPn9Vtf5yCstV68sRZMgp/Qfz\nKRfmkyyxCCeiDqvwajXW7T/n6DCIiIhajUU4KcZP8PLIzc2FVqtFWVkZPD09HR0OtULh1epmnwse\neWuLz1vq7uYC7+5d1AiL2gHPuXJhPskSi3CiTigpKYl3zOyAnvvquKr9rZ4cwiKciMhB+MNMUkyv\n1zs6BCJqQdnJw44OgVTEc65cmE+yxCKciIiIiMjOWISTYhzPRuTcPINHOToEUhHPuXJhPskSi3Ai\nIiIiIjtjEU6KcTybPIKCghAcHAytVuvoUEhFHBMuF55z5cJ8kiXOjkLUCaWkpECv1yMyMtLRoRAR\nEXVKvBJOinE8m1yYT/lwTLhceIzKhfkkSyzCiYiIiIjsTFERHhAQgBEjRiA8PBy33XYbAKC4uBix\nsbEICQlBXFwcSkpK2jVQcjyOZ5ML8ykfjgmXC49RuTCfZElREa7RaJCZmYns7GwcOHAAAJCcnIzY\n2FgcP34cMTExSE5ObtdAiYiIiIhkoXg4ihDCrJ2WloaEhAQAQEJCArZu3apuZOR0OJ5NHrm5udBq\ntSgrK3N0KKQijgmXC8+5cmE+yZLiK+ETJ07E6NGj8cEHHwAADAYDdDodAECn08FgMLRflESkqqSk\nJERHRyM7O9vRoRAREXVKiorw77//HtnZ2fj222/x/vvvY9++fWbPazQaaDSadgmQnAfHsxE5N44J\nlwvPuXJhPsmSonnCb775ZgBAnz59MHXqVBw4cAA6nQ5FRUXw8/NDYWEhfH19m1x34cKF8Pf3BwB4\neXkhLCzM9JVMwxuS7Y7RzsnJcap42LatDQBHjhxBVFSUU8TTlvYRQzmA69/INRSgDUMyOmO78nxu\nq5bP+ukyhkya2Oz+Zdux7ZycHKeKh23mszO3c3JyUFpaCgDIz89HYmIibKURloO9LVRWVsJoNMLD\nwwMVFRWIi4vDG2+8gfT0dPTu3RtLlixBcnIySkpKGv04MyMjAxERETYHSUTqio+Px969e7FlyxZT\nEd4R/XCmBMt25jk6jA5r9eQQDPHt6egwiIg6nKysLMTExNjUh5u1BQwGA6ZOnQoAqKurw6xZsxAX\nF4fRo0dj+vTp2LBhAwICArBp0yabAiEiIiIi6iysFuGBgYE4fLjxOEMfHx+kp6e3S1DknPR6vdlQ\nBuq4goKCcO7cOWi1WkeHQioqO3mYM6RIhOdcuTCfZMlqEU5E8klJSYFer0dkZKSjQyGJGOtbHN3Y\nJq4u/NE/EcmJRTgpxk/wcmE+5ePoq+C/Giqw4dB51fpbeHs/hPTpvGPWeYzKhfkkSyzCiYhIFVV1\n9fjVUKFaf3XtcGWdiMhZKL5jJlHDlD0kB+ZTPpwnXC48RuXCfJIlFuFERERERHbG4SikGMezySM3\nNxdarRZlZWXw9PRsdrnSa3Wora9Xbbvd3VzQsxtPO+3F0WPCSV0858qF+SRL/GtI1AklJSUpulnP\nrxfK8Yc9+apt908PDEQwi3AiIiIW4aQc5zjtfIwCqKgxOjoMUqi184QXllWjxqjeNx0FJVWq9UU8\n58qG+SRLLMKJiDqpFZlnHB0CEVGnxR9mkmL8BE/k3DgmXC4858qF+SRLLMKJiIiIiOyMRTgpxjlO\n5REUFITg4GBotVpHh0Iq4jzhcuE5Vy7MJ1nimHCiTiglJQV6vR6RkZGODoWIiKhT4pVwUozj2eTC\nfMqHY8LlwmNULswnWWIRTkRERERkZyzCSTGOZ5ML8ykfjgmXC49RuTCfZElREW40GhEeHo5JkyYB\nAIqLixEbG4uQkBDExcWhpKSkXYMkIiIiIpKJoiJ81apVCA0NhUajAQAkJycjNjYWx48fR0xMDJKT\nk9s1SHIOHM8mj9zcXGi1WpSVlTk6FFIRx4TLhedcuTCfZMlqEX727Fls374diYmJEEIAANLS0pCQ\nkAAASEhIwNatW9s3SiJSVVJSEqKjo5Gdne3oUIiIiDolq0X4okWL8Mc//hEuLv9Z1GAwQKfTAQB0\nOh0MBkP7RUhOg+PZyFau//dtmlpcVO6vo+OYcLnwnCsX5pMstThP+Ndffw1fX1+Eh4cjMzOzyWU0\nGo1pmAoRUUs+/9mAm3p0Ua2/oxcrVeuLiIjInloswn/44QekpaVh+/btqKqqQllZGR5//HHodDoU\nFRXBz88PhYWF8PX1bbaPhQsXwt/fHwDg5eWFsLAw07iohk+FbHeMdsNjzhIP27bn88iRI4iKimp2\n+ZyicgDXv/VquMraMO64Le0tJ21bn23r7QbOEo8t7awDlxA6KRaA448XR7UbOEs8bDOfnbWdk5OD\n0tJSAEB+fj4SExNhK41oGOhtxZ49e/CnP/0J27Ztw+LFi9G7d28sWbIEycnJKCkpafLHmRkZGYiI\niLA5SCJSV3x8PPbu3YstW7aYivCm6E+XYHl6nh0jI/qPP08ahFCd1tFhEBE1kpWVhZiYGJv6aNU8\n4Q3DTpYuXYqdO3ciJCQEu3btwtKlS20KgjoGy0/y1HEFBQUhODgYWi0LHJlwTLhceM6VC/NJltyU\nLhgVFWW6Yubj44P09PR2C4qI2ldKSgr0ej0iIyMdHQoREVGnxDtmkmI3jiWmjo/5lA/nCZcLj1G5\nMJ9kiUU4EREREZGdsQgnxTieTS7Mp3w4JlwuPEblwnySJRbhRERERER2pviHmUQczyaP3NxcaLVa\nlJWVwdPT09HhkEpkGxPexdUFtcZ61frTaDRwc+k4N5fjOVcuzCdZYhFO1AklJSUpmiecyJHe2nUa\nnt1cVevvmXH9MbhPT9X6IyKyBYtwUuzGu2USkfMpO3lYqqvh58uqcV7F/urqFd2bzmnwnCsX5pMs\ncUw4EREREZGdsQgnxfgJnsi5yXQVnHjOlQ3zSZZYhBMRERER2RmLcFKMc5zKIygoCMHBwdBqtY4O\nhVTEecLlwnOuXJhPssQfZhJ1QikpKdDr9YiMjHR0KERERJ0Sr4STYhzPJhfmUz4cEy4XHqNyYT7J\nEotwIiIiIiI7YxFOinE8m1yYT/lwTLhceIzKhfkkSyzCiYiIiIjsrMUivKqqCmPGjMGoUaMQGhqK\nl19+GQBQXFyM2NhYhISEIC4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"text": [ "<matplotlib.figure.Figure at 0x109615390>" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our posterior distribution puts most weight near the true value of $p_A$, but also some weights in the tails. This is a measure of how uncertain we should be, given our observations. Try changing the number of observations, `N`, and observe how the posterior distribution changes.\n", "\n", "### A* and B Together\n", "\n", "A similar anaylsis can be done for site B's response data to determine the analogous $p_B$. But what we are really interested in is the difference between $p_A$ and $p_B$. Let's infer $p_A$, $p_B$, and $\\text{delta} = p_A - p_B$, all at once. We can do this using PyMC's deterministic variables. (We'll assume for this exercise that $p_B = 0.04$, so $\\text{delta} = 0.01$, $N_B = 750$ (signifcantly less than $N_A$) and we will simulate site B's data like we did for site A's data )" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm\n", "figsize(12, 4)\n", "\n", "# these two quantities are unknown to us.\n", "true_p_A = 0.05\n", "true_p_B = 0.04\n", "\n", "# notice the unequal sample sizes -- no problem in Bayesian analysis.\n", "N_A = 1500\n", "N_B = 750\n", "\n", "# generate some observations\n", "observations_A = pm.rbernoulli(true_p_A, N_A)\n", "observations_B = pm.rbernoulli(true_p_B, N_B)\n", "print \"Obs from Site A: \", observations_A[:30].astype(int), \"...\"\n", "print \"Obs from Site B: \", observations_B[:30].astype(int), \"...\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Obs from Site A: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] ...\n", "Obs from Site B: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0] ...\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "print observations_A.mean()\n", "print observations_B.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.056\n", "0.0306666666667\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "# Set up the pymc model. Again assume Uniform priors for p_A and p_B.\n", "p_A = pm.Uniform(\"p_A\", 0, 1)\n", "p_B = pm.Uniform(\"p_B\", 0, 1)\n", "\n", "\n", "# Define the deterministic delta function. This is our unknown of interest.\n", "@pm.deterministic\n", "def delta(p_A=p_A, p_B=p_B):\n", " return p_A - p_B\n", "\n", "# Set of observations, in this case we have two observation datasets.\n", "obs_A = pm.Bernoulli(\"obs_A\", p_A, value=observations_A, observed=True)\n", "obs_B = pm.Bernoulli(\"obs_B\", p_B, value=observations_B, observed=True)\n", "\n", "# To be explained in chapter 3.\n", "mcmc = pm.MCMC([p_A, p_B, delta, obs_A, obs_B])\n", "mcmc.sample(20000, 1000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 20000 of 20000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the posterior distributions for the three unknowns: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "p_A_samples = mcmc.trace(\"p_A\")[:]\n", "p_B_samples = mcmc.trace(\"p_B\")[:]\n", "delta_samples = mcmc.trace(\"delta\")[:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 10)\n", "\n", "# histogram of posteriors\n", "\n", "ax = plt.subplot(311)\n", "\n", "plt.xlim(0, .1)\n", "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", " label=\"posterior of $p_A$\", color=\"#A60628\", normed=True)\n", "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n", "plt.legend(loc=\"upper right\")\n", "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n", "\n", "ax = plt.subplot(312)\n", "\n", "plt.xlim(0, .1)\n", "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n", " label=\"posterior of $p_B$\", color=\"#467821\", normed=True)\n", "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n", "plt.legend(loc=\"upper right\")\n", "\n", "ax = plt.subplot(313)\n", "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n", " label=\"posterior of delta\", color=\"#7A68A6\", normed=True)\n", "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n", " label=\"true delta (unknown)\")\n", "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n", "plt.legend(loc=\"upper right\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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xmCskt61bt8LFxQV79uxBdnY2XnvtNQDAgQMHEBUVhczMTBgaGjbbt3HaRkND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"text": [ "<matplotlib.figure.Figure at 0x109207450>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that as a result of `N_B < N_A`, i.e. we have less data from site B, our posterior distribution of $p_B$ is fatter, implying we are less certain about the true value of $p_B$ than we are of $p_A$. \n", "\n", "With respect to the posterior distribution of $\\text{delta}$, we can see that the majority of the distribution is above $\\text{delta}=0$, implying there site A's response is likely better than site B's response. The probability this inference is incorrect is easily computable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Count the number of samples less than 0, i.e. the area under the curve\n", "# before 0, represent the probability that site A is worse than site B.\n", "print \"Probability site A is WORSE than site B: %.3f\" % \\\n", " (delta_samples < 0).mean()\n", "\n", "print \"Probability site A is BETTER than site B: %.3f\" % \\\n", " (delta_samples > 0).mean()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Probability site A is WORSE than site B: 0.003\n", "Probability site A is BETTER than site B: 0.997\n" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If this probability is too high for comfortable decision-making, we can perform more trials on site B (as site B has less samples to begin with, each additional data point for site B contributes more inferential \"power\" than each additional data point for site A). \n", "\n", "Try playing with the parameters `true_p_A`, `true_p_B`, `N_A`, and `N_B`, to see what the posterior of $\\text{delta}$ looks like. Notice in all this, the difference in sample sizes between site A and site B was never mentioned: it naturally fits into Bayesian analysis.\n", "\n", "I hope the readers feel this style of A/B testing is more natural than hypothesis testing, which has probably confused more than helped practitioners. Later in this book, we will see two extensions of this model: the first to help dynamically adjust for bad sites, and the second will improve the speed of this computation by reducing the analysis to a single equation. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An algorithm for human deceit\n", "\n", "Social data has an additional layer of interest as people are not always honest with responses, which adds a further complication into inference. For example, simply asking individuals \"Have you ever cheated on a test?\" will surely contain some rate of dishonesty. What you can say for certain is that the true rate is less than your observed rate (assuming individuals lie only* about not cheating; I cannot imagine one who would admit \"Yes\" to cheating when in fact they hadn't cheated). \n", "\n", "To present an elegant solution to circumventing this dishonesty problem, and to demonstrate Bayesian modeling, we first need to introduce the binomial distribution.\n", "\n", "### The Binomial Distribution\n", "\n", "The binomial distribution is one of the most popular distributions, mostly because of its simplicity and usefulness. Unlike the other distributions we have encountered thus far in the book, the binomial distribution has 2 parameters: $N$, a positive integer representing $N$ trials or number of instances of potential events, and $p$, the probability of an event occurring in a single trial. Like the Poisson distribution, it is a discrete distribution, but unlike the Poisson distribution, it only weighs integers from $0$ to $N$. The mass distribution looks like:\n", "\n", "$$P( X = k ) = {{N}\\choose{k}} p^k(1-p)^{N-k}$$\n", "\n", "If $X$ is a binomial random variable with parameters $p$ and $N$, denoted $X \\sim \\text{Bin}(N,p)$, then $X$ is the number of events that occurred in the $N$ trials (obviously $0 \\le X \\le N$), and $p$ is the probability of a single event. The larger $p$ is (while still remaining between 0 and 1), the more events are likely to occur. The expected value of a binomial is equal to $Np$. Below we plot the mass probability distribution for varying parameters. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 4)\n", "\n", "import scipy.stats as stats\n", "binomial = stats.binom\n", "\n", "parameters = [(10, .4), (10, .9)]\n", "colors = [\"#348ABD\", \"#A60628\"]\n", "\n", "for i in range(2):\n", " N, p = parameters[i]\n", " _x = np.arange(N + 1)\n", " plt.bar(_x - 0.5, binomial.pmf(_x, N, p), color=colors[i],\n", " edgecolor=colors[i],\n", " alpha=0.6,\n", " label=\"$N$: %d, $p$: %.1f\" % (N, p),\n", " linewidth=3)\n", "\n", "plt.legend(loc=\"upper left\")\n", "plt.xlim(0, 10.5)\n", "plt.xlabel(\"$k$\")\n", "plt.ylabel(\"$P(X = k)$\")\n", "plt.title(\"Probability mass distributions of binomial random variables\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KSmBubo6FCxdi0aJFTdr35cuXIZfL4ebmplzm4+Nz33fuFy1aVGefGnvMwsJC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\naTtlabtYSmQa2rLlovYiYrGkWYNGRVAIVgONKWI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"text": [ "<matplotlib.figure.Figure at 0x1097c4750>" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The special case when $N = 1$ corresponds to the Bernoulli distribution. There is another connection between Bernoulli and Binomial random variables. If we have $X_1, X_2, ... , X_N$ Bernoulli random variables with the same $p$, then $Z = X_1 + X_2 + ... + X_N \\sim \\text{Binomial}(N, p )$.\n", "\n", "The expected value of a Bernoulli random variable is $p$. This can be seen by noting the more general Binomial random variable has expected value $Np$ and setting $N=1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example: Cheating among students\n", "\n", "We will use the binomial distribution to determine the frequency of students cheating during an exam. If we let $N$ be the total number of students who took the exam, and assuming each student is interviewed post-exam (answering without consequence), we will receive integer $X$ \"Yes I did cheat\" answers. We then find the posterior distribution of $p$, given $N$, some specified prior on $p$, and observed data $X$. \n", "\n", "This is a completely absurd model. No student, even with a free-pass against punishment, would admit to cheating. What we need is a better algorithm to ask students if they had cheated. Ideally the algorithm should encourage individuals to be honest while preserving privacy. The following proposed algorithm is a solution I greatly admire for its ingenuity and effectiveness:\n", "\n", "> In the interview process for each student, the student flips a coin, hidden from the interviewer. The student agrees to answer honestly if the coin comes up heads. Otherwise, if the coin comes up tails, the student (secretly) flips the coin again, and answers \"Yes, I did cheat\" if the coin flip lands heads, and \"No, I did not cheat\", if the coin flip lands tails. This way, the interviewer does not know if a \"Yes\" was the result of a guilty plea, or a Heads on a second coin toss. Thus privacy is preserved and the researchers receive honest answers. \n", "\n", "I call this the Privacy Algorithm. One could of course argue that the interviewers are still receiving false data since some Yes's are not confessions but instead randomness, but an alternative perspective is that the researchers are discarding approximately half of their original dataset since half of the responses will be noise. But they have gained a systematic data generation process that can be modeled. Furthermore, they do not have to incorporate (perhaps somewhat naively) the possibility of deceitful answers. We can use PyMC to dig through this noisy model, and find a posterior distribution for the true frequency of liars. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose 100 students are being surveyed for cheating, and we wish to find $p$, the proportion of cheaters. There are a few ways we can model this in PyMC. I'll demonstrate the most explicit way, and later show a simplified version. Both versions arrive at the same inference. In our data-generation model, we sample $p$, the true proportion of cheaters, from a prior. Since we are quite ignorant about $p$, we will assign it a $\\text{Uniform}(0,1)$ prior." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm\n", "\n", "N = 100\n", "p = pm.Uniform(\"freq_cheating\", 0, 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, thinking of our data-generation model, we assign Bernoulli random variables to the 100 students: 1 implies they cheated and 0 implies they did not. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "true_answers = pm.Bernoulli(\"truths\", p, size=N)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we carry out the algorithm, the next step that occurs is the first coin-flip each student makes. This can be modeled again by sampling 100 Bernoulli random variables with $p=1/2$: denote a 1 as a Heads and 0 a Tails." ] }, { "cell_type": "code", "collapsed": false, "input": [ "first_coin_flips = pm.Bernoulli(\"first_flips\", 0.5, size=N)\n", "print first_coin_flips.value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[False True True True True False False True False False False False\n", " False True True False True True False False True True False False\n", " False False False True False False True False True True True True\n", " True False True False True True True True True False False False\n", " True True True True True True False True False False False True\n", " True True False False False False True False True False False True\n", " True False False False False True True False False True False True\n", " False False True False False False True True True True True False\n", " False False False False]\n" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although not everyone flips a second time, we can still model the possible realization of second coin-flips:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "second_coin_flips = pm.Bernoulli(\"second_flips\", 0.5, size=N)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these variables, we can return a possible realization of the observed proportion of \"Yes\" responses. We do this using a PyMC `deterministic` variable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "@pm.deterministic\n", "def observed_proportion(t_a=true_answers,\n", " fc=first_coin_flips,\n", " sc=second_coin_flips):\n", "\n", " observed = fc * t_a + (1 - fc) * sc\n", " return observed.sum() / float(N)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The line `fct_a + (1-fc)sc` contains the heart of the Privacy algorithm. Elements in this array are 1 if and only if i) the first toss is heads and the student cheated or ii) the first toss is tails, and the second is heads, and are 0 else. Finally, the last line sums this vector and divides by `float(N)`, produces a proportion. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "observed_proportion.value" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "0.42999999999999999" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we need a dataset. After performing our coin-flipped interviews the researchers received 35 \"Yes\" responses. To put this into a relative perspective, if there truly were no cheaters, we should expect to see on average 1/4 of all responses being a \"Yes\" (half chance of having first coin land Tails, and another half chance of having second coin land Heads), so about 25 responses in a cheat-free world. On the other hand, if all students cheated, we should expect to see approximately 3/4 of all responses be \"Yes\". \n", "\n", "The researchers observe a Binomial random variable, with `N = 100` and `p = observed_proportion` with `value = 35`: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "X = 35\n", "\n", "observations = pm.Binomial(\"obs\", N, observed_proportion, observed=True,\n", " value=X)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = pm.Model([p, true_answers, first_coin_flips,\n", " second_coin_flips, observed_proportion, observations])\n", "\n", "# To be explained in Chapter 3!\n", "mcmc = pm.MCMC(model)\n", "mcmc.sample(40000, 15000)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 40000 of 40000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 3)\n", "p_trace = mcmc.trace(\"freq_cheating\")[:]\n", "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30,\n", " label=\"posterior distribution\", color=\"#348ABD\")\n", "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n", "plt.xlim(0, 1)\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": 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"text": [ "<matplotlib.figure.Figure at 0x10971e0d0>" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "With regards to the above plot, we are still pretty uncertain about what the true frequency of cheaters might be, but we have narrowed it down to a range between 0.05 to 0.35 (marked by the solid lines). This is pretty good, as a priori* we had no idea how many students might have cheated (hence the uniform distribution for our prior). On the other hand, it is also pretty bad since there is a .3 length window the true value most likely lives in. Have we even gained anything, or are we still too uncertain about the true frequency? \n", "\n", "I would argue, yes, we have discovered something. It is implausible, according to our posterior, that there are no cheaters, i.e. the posterior assigns low probability to $p=0$. Since we started with a uniform prior, treating all values of $p$ as equally plausible, but the data ruled out $p=0$ as a possibility, we can be confident that there were cheaters. \n", "\n", "This kind of algorithm can be used to gather private information from users and be reasonably confident that the data, though noisy, is truthful. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Alternative PyMC Model\n", "\n", "Given a value for $p$ (which from our god-like position we know), we can find the probability the student will answer yes: \n", "\n", "\\begin{align}\n", "P(\\text{\"Yes\"}) = & P( \\text{Heads on first coin} )P( \\text{cheater} ) + P( \\text{Tails on first coin} )P( \\text{Heads on second coin} ) \\\\\\\\\n", "& = \\frac{1}{2}p + \\frac{1}{2}\\frac{1}{2}\\\\\\\\\n", "& = \\frac{p}{2} + \\frac{1}{4}\n", "\\end{align}\n", "\n", "Thus, knowing $p$ we know the probability a student will respond \"Yes\". In PyMC, we can create a deterministic function to evaluate the probability of responding \"Yes\", given $p$:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "p = pm.Uniform(\"freq_cheating\", 0, 1)\n", "\n", "\n", "@pm.deterministic\n", "def p_skewed(p=p):\n", " return 0.5 * p + 0.25" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "I could have typed `p_skewed = 0.5p + 0.25` instead for a one-liner, as the elementary operations of addition and scalar multiplication will implicitly create a `deterministic` variable, but I wanted to make the deterministic boilerplate explicit for clarity's sake. \n", "\n", "If we know the probability of respondents saying \"Yes\", which is `p_skewed`, and we have $N=100$ students, the number of \"Yes\" responses is a binomial random variable with parameters `N` and `p_skewed`.\n", "\n", "This is where we include our observed 35 \"Yes\" responses. In the declaration of the `pm.Binomial`, we include `value = 35` and `observed = True`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "yes_responses = pm.Binomial(\"number_cheaters\", 100, p_skewed,\n", " value=35, observed=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we add all the variables of interest to a `Model` container and run our black-box algorithm over the model. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = pm.Model([yes_responses, p_skewed, p])\n", "\n", "# To Be Explained in Chapter 3!\n", "mcmc = pm.MCMC(model)\n", "mcmc.sample(25000, 2500)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[*************100%***************] 25000 of 25000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 3)\n", "p_trace = mcmc.trace(\"freq_cheating\")[:]\n", "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30,\n", " label=\"posterior distribution\", color=\"#348ABD\")\n", "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n", "plt.xlim(0, 1)\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109766f50>" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More PyMC Tricks\n", "\n", "#### Protip: Lighter* deterministic variables with `Lambda` class\n", "\n", "Sometimes writing a deterministic function using the `@pm.deterministic` decorator can seem like a chore, especially for a small function. I have already mentioned that elementary math operations can produce deterministic variables implicitly, but what about operations like indexing or slicing? Built-in `Lambda` functions can handle this with the elegance and simplicity required. For example, \n", "\n", " beta = pm.Normal(\"coefficients\", 0, size=(N, 1))\n", " x = np.random.randn((N, 1))\n", " linear_combination = pm.Lambda(lambda x=x, beta=beta: np.dot(x.T, beta))\n", "\n", "\n", "#### Protip: Arrays of PyMC variables\n", "There is no reason why we cannot store multiple heterogeneous PyMC variables in a Numpy array. Just remember to set the `dtype` of the array to `object` upon initialization. For example:\n", "\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 10\n", "x = np.empty(N, dtype=object)\n", "for i in range(0, N):\n", " x[i] = pm.Exponential('x_%i' % i, (i + 1) ** 2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The remainder of this chapter examines some practical examples of PyMC and PyMC modeling:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "##### Example: Challenger Space Shuttle Disaster <span id=\"challenger\"/>\n", "\n", "On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1]):\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 3.5)\n", "np.set_printoptions(precision=3, suppress=True)\n", "challenger_data = np.genfromtxt(\"data/challenger_data.csv\", skip_header=1,\n", " usecols=[1, 2], missing_values=\"NA\",\n", " delimiter=\",\")\n", "# drop the NA values\n", "challenger_data = challenger_data[~np.isnan(challenger_data[:, 1])]\n", "\n", "# plot it, as a function of tempature (the first column)\n", "print \"Temp (F), O-Ring failure?\"\n", "print challenger_data\n", "\n", "plt.scatter(challenger_data[:, 0], challenger_data[:, 1], s=75, color=\"k\",\n", " alpha=0.5)\n", "plt.yticks([0, 1])\n", "plt.ylabel(\"Damage Incident?\")\n", "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Temp (F), O-Ring failure?\n", "[[ 66. 0.]\n", " [ 70. 1.]\n", " [ 69. 0.]\n", " [ 68. 0.]\n", " [ 67. 0.]\n", " [ 72. 0.]\n", " [ 73. 0.]\n", " [ 70. 0.]\n", " [ 57. 1.]\n", " [ 63. 1.]\n", " [ 70. 1.]\n", " [ 78. 0.]\n", " [ 67. 0.]\n", " [ 53. 1.]\n", " [ 67. 0.]\n", " [ 75. 0.]\n", " [ 70. 0.]\n", " [ 81. 0.]\n", " [ 76. 0.]\n", " [ 79. 0.]\n", " [ 75. 1.]\n", " [ 76. 0.]\n", " [ 58. 1.]]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "<matplotlib.text.Text at 0x109766650>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AUaNG6e9z7/aTJMngZDuFQlEuZ9V5YpwQAuPGjcOsWbPKzbv7w93dyp7bJ0+eNCgQy5R9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\nNSem76FIJEJDQwN8fHwwPT0NnU4HLy8vAEBwcDA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"text": [ "<matplotlib.figure.Figure at 0x109785fd0>" ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks clear that the probability of damage incidents occurring increases as the outside temperature decreases. We are interested in modeling the probability here because it does not look like there is a strict cutoff point between temperature and a damage incident occurring. The best we can do is ask \"At temperature $t$, what is the probability of a damage incident?\". The goal of this example is to answer that question.\n", "\n", "We need a function of temperature, call it $p(t)$, that is bounded between 0 and 1 (so as to model a probability) and changes from 1 to 0 as we increase temperature. There are actually many such functions, but the most popular choice is the logistic function.\n", "\n", "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t } } $$\n", "\n", "In this model, $\\beta$ is the variable we are uncertain about. Below is the function plotted for $\\beta = 1, 3, -5$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12, 3)\n", "\n", "\n", "def logistic(x, beta):\n", " return 1.0 / (1.0 + np.exp(beta * x))\n", "\n", "x = np.linspace(-4, 4, 100)\n", "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\")\n", "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\")\n", "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\")\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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P9afNZCG7qIFv8uvYVdbMusIG1hU2EOCh5tLEQKYmBzM06MxF4UDIpy40iHGr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\nDSy4t2TIC+BEzKdi0FO08idMf/OfUMxGGlavZ/u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"text": [ "<matplotlib.figure.Figure at 0x1097f02d0>" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But something is missing. In the plot of the logistic function, the probability changes only near zero, but in our data above the probability changes around 65 to 70. We need to add a bias term to our logistic function:\n", "\n", "$$p(t) = \\frac{1}{ 1 + e^{ \\;\\beta t + \\alpha } } $$\n", "\n", "Some plots are below, with differing $\\alpha$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def logistic(x, beta, alpha=0):\n", " return 1.0 / (1.0 + np.exp(np.dot(beta, x) + alpha))\n", "\n", "x = np.linspace(-4, 4, 100)\n", "\n", "plt.plot(x, logistic(x, 1), label=r\"$\\beta = 1$\", ls=\"--\", lw=1)\n", "plt.plot(x, logistic(x, 3), label=r\"$\\beta = 3$\", ls=\"--\", lw=1)\n", "plt.plot(x, logistic(x, -5), label=r\"$\\beta = -5$\", ls=\"--\", lw=1)\n", "\n", "plt.plot(x, logistic(x, 1, 1), label=r\"$\\beta = 1, \\alpha = 1$\",\n", " color=\"#348ABD\")\n", "plt.plot(x, logistic(x, 3, -2), label=r\"$\\beta = 3, \\alpha = -2$\",\n", " color=\"#A60628\")\n", "plt.plot(x, logistic(x, -5, 7), label=r\"$\\beta = -5, \\alpha = 7$\",\n", " color=\"#7A68A6\")\n", "\n", "plt.legend(loc=\"lower left\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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vTWfk+FhiBga6KVrBlUxNVtZ/nwnAhVcMFTPCCcI5nLUcYunSpaxdu5YPPvgA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\nXWniubW5PNLFZFhoX3WTjXe2FVHZZG3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HxYfqOX9WMpkXik4eQfCWM54sY9KkSRQUFFBS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"text": [ "<matplotlib.figure.Figure at 0x10aa12250>" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a bias).\n", "\n", "Let's start modeling this in PyMC. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a Normal random variable, introduced next." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normal distributions\n", "\n", "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the precision, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n", "\n", "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n", "\n", "$$ f(x \| \\mu, \\tau) = \\sqrt{\\frac{\\tau}{2\\pi}} \\exp\\left( -\\frac{\\tau}{2} (x-\\mu)^2 \\right) $$\n", "\n", "We plot some different density functions below. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.stats as stats\n", "\n", "nor = stats.norm\n", "x = np.linspace(-8, 7, 150)\n", "mu = (-2, 0, 3)\n", "tau = (.7, 1, 2.8)\n", "colors = [\"#348ABD\", \"#A60628\", \"#7A68A6\"]\n", "parameters = zip(mu, tau, colors)\n", "\n", "for _mu, _tau, _color in parameters:\n", " plt.plot(x, nor.pdf(x, _mu, scale=1. / _tau),\n", " label=\"$\\mu = %d,\\;\\\\tau = %.1f$\" % (_mu, _tau), color=_color)\n", " plt.fill_between(x, nor.pdf(x, _mu, scale=1. / _tau), color=_color,\n", " alpha=.33)\n", "\n", "plt.legend(loc=\"upper right\")\n", "plt.xlabel(\"$x$\")\n", "plt.ylabel(\"density function at $x$\")\n", "plt.title(\"Probability distribution of three different Normal random \\\n", "variables\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MV0MfAgCcO7cBJ+EAAC1amaJLT1sAwMN76VqLjzRd0dHR8Pf3BwC0atUKzs7O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\nnr3//jx7H/8VxrVz2Tl6NqBw0SV5klifwoQpGTQd6KS5qZs3f7eRBXd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"text": [ "<matplotlib.figure.Figure at 0x10a952d50>" ] } ], "prompt_number": 57 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Normal random variable can be take on any real number, but the variable is very likely to be relatively close to $\\mu$. In fact, the expected value of a Normal is equal to its $\\mu$ parameter:\n", "\n", "$$ E[ X \| \\mu, \\tau] = \\mu$$\n", "\n", "and its variance is equal to the inverse of $\\tau$:\n", "\n", "$$Var( X \| \\mu, \\tau ) = \\frac{1}{\\tau}$$\n", "\n", "\n", "\n", "Below we continue our modeling of the Challenger space craft:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pymc as pm\n", "\n", "temperature = challenger_data[:, 0]\n", "D = challenger_data[:, 1] # defect or not?\n", "\n", "# notice the`value` here. We explain why below.\n", "beta = pm.Normal(\"beta\", 0, 0.001, value=0)\n", "alpha = pm.Normal(\"alpha\", 0, 0.001, value=0)\n", "\n", "\n", "@pm.deterministic\n", "def p(t=temperature, alpha=alpha, beta=beta):\n", " return 1.0 / (1. + np.exp(beta * t + alpha))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have our probabilities, but how do we connect them to our observed data? A Bernoulli random variable with parameter $p$, denoted $\\text{Ber}(p)$, is a random variable that takes value 1 with probability $p$, and 0 else. Thus, our model can look like:\n", "\n", "$$ \\text{Defect Incident, $D_i$} \\sim \\text{Ber}( \\;p(t_i)\\; ), \\;\\; i=1..N$$\n", "\n", "where $p(t)$ is our logistic function and $t_i$ are the temperatures we have observations about. Notice in the above code we had to set the values of `beta` and `alpha` to 0. The reason for this is that if `beta` and `alpha` are very large, they make `p` equal to 1 or 0. Unfortunately, `pm.Bernoulli` does not like probabilities of exactly 0 or 1, though they are mathematically well-defined probabilities. So by setting the coefficient values to `0`, we set the variable `p` to be a reasonable starting value. This has no effect on our results, nor does it mean we are including any additional information in our prior. It is simply a computational caveat in PyMC. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "p.value" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "array([ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", " 0.5])" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "# connect the probabilities in `p` with our observations through a\n", "# Bernoulli random variable.\n", "observed = pm.Bernoulli(\"bernoulli_obs\", p, value=D, observed=True)\n", "\n", "model = pm.Model([observed, beta, alpha])\n", "\n", "# Mysterious code to be explained in Chapter 3\n", "map_ = pm.MAP(model)\n", "map_.fit()\n", "mcmc = pm.MCMC(model)\n", "mcmc.sample(120000, 100000, 2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 120000 of 120000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have trained our model on the observed data, now we can sample values from the posterior. Let's look at the posterior distributions for $\\alpha$ and $\\beta$:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "alpha_samples = mcmc.trace('alpha')[:, None] # best to make them 1d\n", "beta_samples = mcmc.trace('beta')[:, None]\n", "\n", "figsize(12.5, 6)\n", "\n", "# histogram of the samples:\n", "plt.subplot(211)\n", "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n", "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", " label=r\"posterior of $\\beta$\", color=\"#7A68A6\", normed=True)\n", "plt.legend()\n", "\n", "plt.subplot(212)\n", "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n", " label=r\"posterior of $\\alpha$\", color=\"#A60628\", normed=True)\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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h4SgoKMDChQsRFBSk935JSQm8vb11bS8vLxQXF7NA70IcVyaO0sdJ195swDdf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\nT7OQp1noVzYPmUKiQAcAAAAchQIdUUePq1nI0yy3PM+4uFv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"text": [ "<matplotlib.figure.Figure at 0x10aaeea50>" ] } ], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "All samples of $\\beta$ are greater than 0. If instead the posterior was centered around 0, we may suspect that $\\beta = 0$, implying that temperature has no effect on the probability of defect. \n", "\n", "Similarly, all $\\alpha$ posterior values are negative and far away from 0, implying that it is correct to believe that $\\alpha$ is significantly less than 0. \n", "\n", "Regarding the spread of the data, we are very uncertain about what the true parameters might be (though considering the low sample size and the large overlap of defects-to-nondefects this behaviour is perhaps expected). \n", "\n", "Next, let's look at the expected probability* for a specific value of the temperature. That is, we average over all samples from the posterior to get a likely value for $p(t_i)$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "t = np.linspace(temperature.min() - 5, temperature.max() + 5, 50)[:, None]\n", "p_t = logistic(t.T, beta_samples, alpha_samples)\n", "\n", "mean_prob_t = p_t.mean(axis=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 4)\n", "\n", "plt.plot(t, mean_prob_t, lw=3, label=\"average posterior \\nprobability \\\n", "of defect\")\n", "plt.plot(t, p_t[0, :], ls=\"--\", label=\"realization from posterior\")\n", "plt.plot(t, p_t[-2, :], ls=\"--\", label=\"realization from posterior\")\n", "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", "plt.title(\"Posterior expected value of probability of defect; \\\n", "plus realizations\")\n", "plt.legend(loc=\"lower left\")\n", "plt.ylim(-0.1, 1.1)\n", "plt.xlim(t.min(), t.max())\n", "plt.ylabel(\"probability\")\n", "plt.xlabel(\"temperature\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IIW2MatgJIYQQQgjpoARL2OfOnQsvLy9ER0c32eb5559HWFgYevfujTNnzgjV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\nDKx0LFRmZ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"text": [ "<matplotlib.figure.Figure at 0x10aae4d90>" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we also plotted two possible realizations of what the actual underlying system might be. Both are equally likely as any other draw. The blue line is what occurs when we average all the 20000 possible dotted lines together.\n", "\n", "\n", "An interesting question to ask is for what temperatures are we most uncertain about the defect-probability? Below we plot the expected value line and the associated 95% intervals for each temperature. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.stats.mstats import mquantiles\n", "\n", "# vectorized bottom and top 2.5% quantiles for \"confidence interval\"\n", "qs = mquantiles(p_t, [0.025, 0.975], axis=0)\n", "plt.fill_between(t[:, 0], qs, alpha=0.7,\n", " color=\"#7A68A6\")\n", "\n", "plt.plot(t[:, 0], qs[0], label=\"95% CI\", color=\"#7A68A6\", alpha=0.7)\n", "\n", "plt.plot(t, mean_prob_t, lw=1, ls=\"--\", color=\"k\",\n", " label=\"average posterior \\nprobability of defect\")\n", "\n", "plt.xlim(t.min(), t.max())\n", "plt.ylim(-0.02, 1.02)\n", "plt.legend(loc=\"lower left\")\n", "plt.scatter(temperature, D, color=\"k\", s=50, alpha=0.5)\n", "plt.xlabel(\"temp, $t$\")\n", "\n", "plt.ylabel(\"probability estimate\")\n", "plt.title(\"Posterior probability estimates given temp. $t$\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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5DbEJdJdfQgghfUclMYR0w+6woaziDIrOHkFURCwmXDHb1yERP3fxtJozJB5D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"text": [ "<matplotlib.figure.Figure at 0x10b08f110>" ] } ], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 95% credible interval, or 95% CI, painted in purple, represents the interval, for each temperature, that contains 95% of the distribution. For example, at 65 degrees, we can be 95% sure that the probability of defect lies between 0.25 and 0.75.\n", "\n", "More generally, we can see that as the temperature nears 60 degrees, the CI's spread out over [0,1] quickly. As we pass 70 degrees, the CI's tighten again. This can give us insight about how to proceed next: we should probably test more O-rings around 60-65 temperature to get a better estimate of probabilities in that range. Similarly, when reporting to scientists your estimates, you should be very cautious about simply telling them the expected probability, as we can see this does not reflect how wide* the posterior distribution is." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What about the day of the Challenger disaster?\n", "\n", "On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings." ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 2.5)\n", "\n", "prob_31 = logistic(31, beta_samples, alpha_samples)\n", "\n", "plt.xlim(0.995, 1)\n", "plt.hist(prob_31, bins=1000, normed=True, histtype='stepfilled')\n", "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n", "plt.xlabel(\"probability of defect occurring in O-ring\");" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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4Mlmch0hyMF9IKuYKycF8IWNhEU9EREREZGJYxJPJ4jxEkoP5QlIxV0gO5gsZ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\nLS0NpaWlOj/cVSqVmDdvHhYuXIi7d+8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"text": [ "<matplotlib.figure.Figure at 0x10b495690>" ] } ], "prompt_number": 68 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is our model appropriate?\n", "\n", "The skeptical reader will say \"You deliberately chose the logistic function for $p(t)$ and the specific priors. Perhaps other functions or priors will give different results. How do I know I have chosen a good model?\" This is absolutely true. To consider an extreme situation, what if I had chosen the function $p(t) = 1,\\; \\forall t$, which guarantees a defect always occurring: I would have again predicted disaster on January 28th. Yet this is clearly a poorly chosen model. On the other hand, if I did choose the logistic function for $p(t)$, but specified all my priors to be very tight around 0, likely we would have very different posterior distributions. How do we know our model is an expression of the data? This encourages us to measure the model's goodness of fit.\n", "\n", "We can think: how can we test whether our model is a bad fit? An idea is to compare observed data (which if we recall is a fixed stochastic variable) with an artificial dataset which we can simulate. The rationale is that if the simulated dataset does not appear similar, statistically, to the observed dataset, then likely our model is not accurately represented the observed data. \n", "\n", "Previously in this Chapter, we simulated artificial datasets for the SMS example. To do this, we sampled values from the priors. We saw how varied the resulting datasets looked like, and rarely did they mimic our observed dataset. In the current example, we should sample from the posterior distributions to create very plausible datasets. Luckily, our Bayesian framework makes this very easy. We only need to create a new `Stochastic` variable, that is exactly the same as our variable that stored the observations, but minus the observations themselves. If you recall, our `Stochastic` variable that stored our observed data was:\n", "\n", " observed = pm.Bernoulli( \"bernoulli_obs\", p, value=D, observed=True)\n", "\n", "Hence we create:\n", " \n", " simulated_data = pm.Bernoulli(\"simulation_data\", p)\n", "\n", "Let's simulate 10 000:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "simulated = pm.Bernoulli(\"bernoulli_sim\", p)\n", "N = 10000\n", "\n", "mcmc = pm.MCMC([simulated, alpha, beta, observed])\n", "mcmc.sample(N)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[**************100%***************] 10000 of 10000 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5, 5)\n", "\n", "simulations = mcmc.trace(\"bernoulli_sim\")[:]\n", "print simulations.shape\n", "\n", "plt.title(\"Simulated dataset using posterior parameters\")\n", "figsize(12.5, 6)\n", "for i in range(4):\n", " ax = plt.subplot(4, 1, i + 1)\n", " plt.scatter(temperature, simulations[1000 i, :], color=\"k\",\n", " s=50, alpha=0.6)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(10000, 23)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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LHMdh7NixiIiIwM6dOwGYv6gRsRxTeQDAli1bEBYWhkWLFtHXYW3g/v37cHd3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"text": [ "<matplotlib.figure.Figure at 0x10adccc50>" ] } ], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the above plots are different (if you can think of a cleaner way to present this, please send a pull request and answer [here](http://stats.stackexchange.com/questions/53078/how-to-visualize-bayesian-goodness-of-fit-for-logistic-regression)!).\n", "\n", "We wish to assess how good our model is. \"Good\" is a subjective term of course, so results must be relative to other models. \n", "\n", "We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use Bayesian p-values. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n", "\n", "The following graphical test is a novel data-viz approach to logistic regression. The plots are called separation plots[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf), but I'll summarize their use here.\n", "\n", "For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 \| t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "posterior_probability = simulations.mean(axis=0)\n", "print \"posterior prob of defect \| realized defect \"\n", "for i in range(len(D)):\n", " print \"%.2f \| %d\" % (posterior_probability[i], D[i])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "posterior prob of defect \| realized defect \n", "0.47 \| 0\n", "0.20 \| 1\n", "0.25 \| 0\n", "0.32 \| 0\n", "0.39 \| 0\n", "0.11 \| 0\n", "0.08 \| 0\n", "0.19 \| 0\n", "0.94 \| 1\n", "0.71 \| 1\n", "0.20 \| 1\n", "0.02 \| 0\n", "0.39 \| 0\n", "0.98 \| 1\n", "0.40 \| 0\n", "0.05 \| 0\n", "0.20 \| 0\n", "0.01 \| 0\n", "0.03 \| 0\n", "0.01 \| 0\n", "0.05 \| 1\n", "0.04 \| 0\n", "0.92 \| 1\n" ] } ], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we sort each column by the posterior probabilities:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ix = np.argsort(posterior_probability)\n", "print \"probb \| defect \"\n", "for i in range(len(D)):\n", " print \"%.2f \| %d\" % (posterior_probability[ix[i]], D[ix[i]])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "probb \| defect \n", "0.01 \| 0\n", "0.01 \| 0\n", "0.02 \| 0\n", "0.03 \| 0\n", "0.04 \| 0\n", "0.05 \| 0\n", "0.05 \| 1\n", "0.08 \| 0\n", "0.11 \| 0\n", "0.19 \| 0\n", "0.20 \| 0\n", "0.20 \| 1\n", "0.20 \| 1\n", "0.25 \| 0\n", "0.32 \| 0\n", "0.39 \| 0\n", "0.39 \| 0\n", "0.40 \| 0\n", "0.47 \| 0\n", "0.71 \| 1\n", "0.92 \| 1\n", "0.94 \| 1\n", "0.98 \| 1\n" ] } ], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can present the above data better in a figure: I've wrapped this up into a `separation_plot` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from separation_plot import separation_plot\n", "\n", "\n", "figsize(11., 1.5)\n", "separation_plot(posterior_probability, D)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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777xTb731lioreQY2nMuUZ5abgiv/5qJW4QTUKZyCfSCsS7uIOh6Pq7m5WaFQSKdPn1ZX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\nHQBPymDlAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10ab87e90>" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The snaking-line is the sorted probabilities, blue bars denote defects, and empty space (or grey bars for the optimistic readers) denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue bars should be close to the right-hand side, and deviations from this reflect missed predictions. \n", "\n", "The black vertical line is the expected number of defects we should observe, given this model. This allows the user to see how the total number of events predicted by the model compares to the actual number of events in the data.\n", "\n", "It is much more informative to compare this to separation plots for other models. Below we compare our model (top) versus three others:\n", "\n", "1. the perfect model, which predicts the posterior probability to be equal to 1 if a defect did occur.\n", "2. a completely random model, which predicts random probabilities regardless of temperature.\n", "3. a constant model: where $P(D = 1 \\; \| \\; t) = c, \\;\\; \\forall t$. The best choice for $c$ is the observed frequency of defects, in this case 7/23. \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(11., 1.25)\n", "\n", "# Our temperature-dependent model\n", "separation_plot(posterior_probability, D)\n", "plt.title(\"Temperature-dependent model\")\n", "\n", "# Perfect model\n", "# i.e. the probability of defect is equal to if a defect occurred or not.\n", "p = D\n", "separation_plot(p, D)\n", "plt.title(\"Perfect model\")\n", "\n", "# random predictions\n", "p = np.random.rand(23)\n", "separation_plot(p, D)\n", "plt.title(\"Random model\")\n", "\n", "# constant model\n", "constant_prob = 7. / 23 * np.ones(23)\n", "separation_plot(constant_prob, D)\n", "plt.title(\"Constant-prediction model\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "<matplotlib.text.Text at 0x10c392b90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10c11f2d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109861250>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109434bd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10c37a650>" ] } ], "prompt_number": 75 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the random model, we can see that as the probability increases there is no clustering of defects to the right-hand side. Similarly for the constant model.\n", "\n", "The perfect model, the probability line is not well shown, as it is stuck to the bottom and top of the figure. Of course the perfect model is only for demonstration, and we cannot infer any scientific inference from it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercises\n", "\n", "1\\. Try putting in extreme values for our observations in the cheating example. What happens if we observe 25 affirmative responses? 10? 50? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2\\. Try plotting $\\alpha$ samples versus $\\beta$ samples. Why might the resulting plot look like this?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# type your code here.\n", "figsize(12.5, 4)\n", "\n", "plt.scatter(alpha_samples, beta_samples, alpha=0.1)\n", "plt.title(\"Why does the plot look like this?\")\n", "plt.xlabel(r\"$\\alpha$\")\n", "plt.ylabel(r\"$\\beta$\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 76, "text": [ "<matplotlib.text.Text at 0x10c358750>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SSnkUFUFVlSGTkbrv0ahHNmvw+SAY9MhkLF1dBmN8hMMetbWWSy+Vkf5duyTQ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"text": [ "<matplotlib.figure.Figure at 0x10b092250>" ] } ], "prompt_number": 76 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "- [1] Dalal, Fowlkes and Hoadley (1989),JASA, 84, 945-957.\n", "- [2] German Rodriguez. Datasets. In WWS509. Retrieved 30/01/2013, from <http://data.princeton.edu/wws509/datasets/#smoking>.\n", "- [3] McLeish, Don, and Cyntha Struthers. STATISTICS 450/850 Estimation and Hypothesis Testing. Winter 2012. Waterloo, Ontario: 2012. Print.\n", "- [4] Fonnesbeck, Christopher. \"Building Models.\" PyMC-Devs. N.p., n.d. Web. 26 Feb 2013. <http://pymc-devs.github.com/pymc/modelbuilding.html>.\n", "- [5] Cronin, Beau. \"Why Probabilistic Programming Matters.\" 24 Mar 2013. Google, Online Posting to Google . Web. 24 Mar. 2013. <https://plus.google.com/u/0/107971134877020469960/posts/KpeRdJKR6Z1>.\n", "- [6] S.P. Brooks, E.A. Catchpole, and B.J.T. Morgan. Bayesian animal survival estimation. Statistical Science, 15: 357\u2013376, 2000\n", "- [7] Gelman, Andrew. \"Philosophy and the practice of Bayesian statistics.\" British Journal of Mathematical and Statistical Psychology. (2012): n. page. Web. 2 Apr. 2013.\n", "- [8] Greenhill, Brian, Michael D. Ward, and Audrey Sacks. \"The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models.\" American Journal of Political Science. 55.No.4 (2011): n. page. Web. 2 Apr. 2013." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "\n", "\n", "def css_styling():\n", " styles = open(\"../styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n", " }\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " font-weight: bold;\n", " src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n", " }\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " font-style: oblique;\n", " src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n", " }\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " font-weight: bold;\n", " font-style: oblique;\n", " src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 22pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\$\",\"\\\$\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x10cc9f550>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 } ], "metadata": {} } ] }	mit
danielfrg/pelican-ipynb	pelican_jupyter/tests/pelican/liquid/content/liquid-tag.ipynb	1	45741	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur purus mi, sollicitudin ac justo a, dapibus ultrices dolor. Curabitur id eros mattis, tincidunt ligula at, condimentum urna. Morbi accumsan, risus eget porta consequat, tortor nibh blandit dui, in sodales quam elit non erat. Aenean lorem dui, lacinia a metus eu, accumsan dictum urna. Sed a egestas mauris, non porta nisi. Suspendisse eu lacinia neque. Morbi gravida eros non augue pharetra, condimentum auctor purus porttitor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Header 2" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "a = 1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "b = 'pew'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "'pew'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "from pylab import " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "x = linspace(0, 5, 10)\n", "y = x * 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1082c6150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure()\n", "plot(x, y, 'r')\n", "xlabel('x')\n", "ylabel('y')\n", "title('title')\n", "show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1084b2050>]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CH4tFEyP4VBbNvcC5TbDWoA+C91k0dn/6qGStkUXjevBDB1mNB7/Hyp0uUfC+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\naAzB26QayqKpXujE4pDPqUKntijx4H1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CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108383350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_points = 130\n", "y = np.random.random(num_points)\n", "plt.plot(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is some text, here comes some latex" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", "\\end{align}" ], "text/plain": [ "<IPython.core.display.Latex object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%latex\n", "\\begin{align}\n", "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apos?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "text = 'foo bar\\t baz \\tqux'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "['foo', 'bar', 'baz', 'qux']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.split('\\s+', text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": 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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
rhancockn/MRS	ipynb/001-principles-of-MRS.ipynb	2	951065	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import MRS.api as mrs\n", "import MRS.analysis as ana\n", "import MRS.utils as ut\n", "import MRS.data as mrd\n", "import os.path as op\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/matplotlib/__init__.py:1256: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called before pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n", "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/matplotlib/cbook.py:133: MatplotlibDeprecationWarning: The matplotlib.mpl module was deprecated in version 1.3. Use `import matplotlib as mpl` instead.\n", " warnings.warn(message, mplDeprecation, stacklevel=1)\n" ] } ], "source": [ "import nitime as nt\n", "import nitime.analysis as nta\n", "import nitime.viz as viz\n", "import scipy.stats as stats\n", "import scipy.io as sio\n", "import os" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 1H Magnetic Resonance Spectroscopy (1H-MRS) \n", "\n", "## Measuring GABA concentrations in vivo in human \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Principles of MRS measurement" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Following a $90^\\circ$ square (broadband) pulse: \n", "\n", "$FID(R) = M_x(t) = M_0 sin[(\\omega_0 - \\omega)t + \\phi] e^{-(t/T^_2)} $\n", "\n", "\n", "$FID(I) = M_y(t) = M_0 cos[(\\omega_0 - \\omega)t + \\phi] e^{-(t/T^_2)}$ \n", "\n", "Where $\\omega_0$ is the resonance frequency of the nucleus and $\\omega$ is the center frequency of the RF pulse" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def FID(t, M0=1, omega_0=128, omega=150, phi=0, T2_star=1.0):\n", " Mx = M0 * np.sin((omega_0 - omega) * t + phi) * np.exp(-(t / T2_star))\n", " My = M0 * np.cos((omega_0 - omega) * t + phi) * np.exp(-(t / T2_star))\n", " return Mx, My" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def plot_fid(t, FID, omega=None, omega_0=None):\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111, projection='3d')\n", " x,y = FID\n", " ax.plot(t, x, y)\n", " ax.plot(t ,x ,ax.get_zlim()[0], 'g')\n", " ax.plot(t ,np.zeros(y.shape) + ax.get_ylim()[1], x, 'r')\n", " ax.set_xlabel('Time')\n", " ax.set_ylabel(r'$M_x$')\n", " ax.set_zlabel(r'$M_y$')\n", " if omega is not None and omega_0 is not None:\n", " ax.set_title(r'$\\omega_0=%s$ $\\omega=%s$'%(omega_0, omega))\n", " fig.set_size_inches([8, 6])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXd8HPWd///6TJ9VlyzZkjsu2DQXwNgUY4gNxtSEFhJC\n", "QkKOkPBNSH7JweWSS+5CgEByXAg1B1waCQmdGAi9h2IwYMCAcZHrWrJVZ9u0z+f3x2dGu1rtSqu6\n", "svV5Ph77kDQ7bbW785p3J4wxCAQCgUAg6B2p2CcgEAgEAsG+gBBMgUAgEAgKQAimQCAQCAQFIART\n", "IBAIBIICEIIpEAgEAkEBCMEUCAQCgaAAhGAKBAKBQFAAQjAFAoFAICgAIZgCgUAgEBSAEEyBQCAQ\n", "CApACKZAIBAIBAUgBFMgEAgEggIQgikQCAQCQQEIwRQIBAKBoACEYAoEAoFAUABCMAUCgUAgKAAh\n", "mAKBQCAQFIAQTIFAIBAICkAp9gkIBPsbhJD5AC5kjH0/Y9kXANQDWATgIcbYvcHy0wFMAmAA2MoY\n", "e7AIpxyeY67z3hScXzuAHzDG/hAsPwvAQQAogJ2MsT8W4ZQFghFFCKZAMIQQQr4H4FgAHRnLZgKo\n", 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cos \\phi - D(\\omega) sin \\phi$\n", "\n", " $I(\\omega) = A(\\omega) sin \\phi + D(\\omega) cos \\phi$\n", "\n", "where: \n", "\n", " $A(\\omega) = \\frac{M_0 T_2^}{1 + (\\omega_0-\\omega)^2 T_2^{2}}$\n", "\n", " $D(\\omega) = \\frac{M_0 T_2^{2} (\\omega_0-\\omega)}{1 + (\\omega_0-\\omega)^2 T_2^}$\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def fid_disp(M0=1, omega0=128, omega=np.linspace(0,1024,4096), T2_star=1.0, phi=0):\n", " A = (M0T2_star)/(1+((omega0-omega)2)T2_star*2)\n", " D = (M0T2_star*2(omega0-omega))/(1+((omega0-omega)*2)T2_star*2)\n", " return A,D\n", "\n", "def fid_fft(M0=1, omega0=128, omega=np.linspace(0,1024,4096), T2_star=1.0, phi=0):\n", " A,D = fid_disp(M0, omega0, omega, T2_star, phi)\n", " R = A np.cos(phi) - D * np.sin(phi)\n", " I = A * np.sin(phi) + D * np.cos(phi)\n", " return R + I1j" ] }, { "cell_type": "code", "execution_count": 7, 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], "text/plain": [ "<matplotlib.figure.Figure at 0x10883b290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phis = np.linspace(0, 2np.pi, 6)[:-1]\n", "fig, ax = plt.subplots(len(phis))\n", "for ii in range(len(phis)):\n", " ax[ii].plot(np.real(fid_fft(phi=phis[ii])))\n", " ax[ii].set_ylim([-1,1])\n", " ax[ii].text(0.5, 0.6, '$\\phi=$%s $\\pi$'%(phis[ii]/np.pi), transform=ax[ii].transAxes)\n", "fig.set_size_inches([8,10])\n", "ax[-1].set_xlabel('Frequency')\n", "ax[-1].set_ylabel('Power')\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "In small organic molecules (such as GABA), hydrogen atoms are free to receive and release energy in MR experiments" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Due to local shielding effects, their resonance frequency is shifted by some small amount" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { 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"<matplotlib.figure.Figure at 0x10a261650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "FID1=np.vstack(FID(t, M0=0.3, omega_0=128, omega=0, phi=0, T2_star=1.0))\n", "FID2=np.vstack(FID(t, M0=0.2, omega_0=128, omega=150, phi=0, T2_star=1.0))\n", "FID3=np.vstack(FID(t, M0=0.5, omega_0=128, omega=250, phi=0, T2_star=1.0))\n", "my_fid = FID1 + FID2 + FID3\n", "my_fid = my_fid[0] + my_fid[1] * 1j\n", "plot_fid(t, (my_fid.real, my_fid.imag))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/numpy/core/numeric.py:462: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return array(a, dtype, copy=False, order=order)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10a6e2ed0>]" ] }, "execution_count": 9, "metadata": 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"metadata": {}, "output_type": "display_data" } ], "source": [ "ft = np.fft.fftshift(np.fft.fft(my_fid))\n", "plt.plot(ft)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10a7bc650>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAE/JJREFUeJzt3X+s3Xd93/HnK3G8BMLiWVS2Y1tKJEgVT6tCaQ0qMC4r\n", "Sw2qnPyVgLQqolH/CaOodDQ2WxdPrdKAtNJOU6Zphcpk4MmiIgptpdpkuaKdVDxUmwQczzGqpTrD\n", "16hCCNpudZb3/jifS05O7rnn+t7z4977fT6kq/P9fr7f8/2+/fG5r/M53x/npqqQJHXLdbMuQJI0\n", "fYa/JHWQ4S9JHWT4S1IHGf6S1EGGvyR10IrCP8m2JF9M8nySs0nelmR7kpNJzic5kWRb3/qHk7yQ\n", "5FySuydXviRpNVY68v9d4I+r6k7gJ4BzwCHgZFXdATzd5kmyD7gf2AccAB5P4icMSVpHRoZykluA\n", 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shift (in Hz) depends on the magnetic field strength it is quantified in parts-per-million ('ppm')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The standard scale is relative to dimethyl sulfide (DMS), because its chemical shift is relatively stable in different temperatures. At body temperature, water is at 4.7 ppm " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "fname = '12_1_PROBE_MEGA_L_Occ.nii.gz'\n", "data_file = op.join(mrd.data_folder, fname)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 1400.\n", " warnings.warn(errors[info][0], RuntimeWarning)\n" ] } ], "source": [ "G = mrs.GABA(data_file)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "water_hz = 0.0 # The center frequency of our RF pulse\n", "water_ppm = 4.7 # Per convention\n", "hz_per_ppm = 127.68 # at our 3T\n", "sampling_rate = 5000. # Hz\n", "n_samples = 4096\n", "freq_hz = np.linspace(-sampling_rate/2., sampling_rate/2., n_samples)\n", "\n", "freq_ppm = water_ppm - (freq_hz - water_hz)/hz_per_ppm" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def plot_fid_fft():\n", " fig, ax = plt.subplots(2)\n", " fig = viz.plot_tseries(\n", " nt.TimeSeries(data = [np.abs(G.water_fid[0,0])],#, np.imag(G.water_fid[0,0])],\n", " sampling_rate=5000.), ylabel='FID', fig = fig)\n", " ax[0].set_xlabel('Time(s)')\n", " #ax[1].plot(freq_ppm, np.fft.fftshift(np.fft.fft(G.water_fid[0,0])))\n", " idx = np.where(np.logical_and(freq_ppm>3, freq_ppm<5))\n", " ax[1].plot(freq_ppm[idx], np.fft.fftshift(np.fft.fft(G.water_fid[0,0]))[idx])\n", " ax[1].set_xlabel('Frequency (ppm)')\n", " ax[1].set_ylabel('Power')\n", " fig.set_size_inches([10,6])\n" ] }, { 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"fragment" } }, "source": [ "- Water is present at very high concentrations drowning out signals from other molecules" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- After excitation with a broad-band pulse, a broadband 180 to induce spin echo" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- An additional frequency-selective 180 is applied to invert the evolution of water-bound protons" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Water suppression on" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def plot_fid_ft_w_suppressed():\n", " fig, ax = plt.subplots(3)\n", " fig = viz.plot_tseries(\n", " nt.TimeSeries(data = [np.abs(G.w_supp_fid[0,0])], #np.imag(G.w_supp_fid[0,0])],\n", " sampling_rate=5000.), ylabel='FID', fig = fig)\n", " idx = np.where(np.logical_and(freq_ppm>0, freq_ppm<5))\n", "\n", " ax[1].plot(freq_ppm[idx], np.fft.fftshift(np.fft.fft(G.w_supp_fid[0,0]))[idx])\n", " ax[1].set_xlabel('Frequency (ppm)')\n", "\n", " idx = np.where(np.logical_and(freq_ppm>0, freq_ppm<4))\n", " ax[2].plot(freq_ppm[idx], np.fft.fftshift(np.fft.fft(G.w_supp_fid[0,0]))[idx])\n", " ax[2].set_xlabel('Frequency (ppm)')\n", "\n", " fig.set_size_inches([10,6])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmYHNV9LvyeWrtbu5CERhIYkBBCaEMCsQiQBFpYbOyY\n", "OF4S+zrJvYFc49jOvdeJP3/+nvjJvblxEseOQ5xg4+vEiY2Nje0ANzYGGzDGgDFiEasQYhCSRvtW\n", "o+6u7Zzvj3PO9Oma6u7qZWa6Z877PP1opqarurtUfd76Le/7I4wxaGhoaGhoaNSHMdZvQENDQ0ND\n", 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[ "<matplotlib.figure.Figure at 0x10a3d5bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_fid_ft_w_suppressed()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Water-included data is used: " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Suppressing residual water signal" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- High SNR: used for coil combination and zero-order phase alignment" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## J coupling" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Different hydrogen atoms bound covalently are physically coupled (\"J coupling\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Resonance in one affects resonance in another" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- GABA has 3 resonance groups: at 1.9 ppm, 2.3 ppm and 3.0 ppm" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Problem : There are other resonances at these frequencies" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def plot_resonances():\n", " fig, ax = plt.subplots(1)\n", " fig.set_size_inches([8,6])\n", " ax.plot(G.f_ppm[G.idx], np.mean(G.echo_off, 0)[G.idx])\n", " ax.invert_xaxis()\n", " ax.annotate('NAA', xy=(2, 0.65), xycoords='data', xytext=(50, -10), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax.annotate('Creatine', xy=(3.1, 0.4), xycoords='data', xytext=(-80, 10), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax.annotate('Glx', xy=(2.3, 0.1), xycoords='data', xytext=(-30, 60), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax.annotate('Choline', xy=(3.3, 0.25), xycoords='data', xytext=(-60, 40), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax.annotate('Myoinositol', xy=(4.0, 0.2), xycoords='data', xytext=(-40, 30), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax.annotate('Lipids', xy=(1.4, 0.05), xycoords='data', xytext=(20, 30), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", "\n", " ax.set_xlabel('ppm')\n", " ax.set_ylabel('Power')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAfwAAAF/CAYAAACsdntlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xe8XFW5//HPl1BDL1IN0qVG6TY0CEgAaTZARcGGV7Hr\n", 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resonance at 3 ppm" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The experiment is repeated twice, with and without editing" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def plot_two_echos():\n", " fig, ax = plt.subplots(3)\n", " fig.set_size_inches([8,8])\n", " ax[0].plot(G.f_ppm[G.idx], np.mean(G.echo_off, 0)[G.idx])\n", " ax[1].plot(G.f_ppm[G.idx], np.mean(G.echo_on, 0)[G.idx])\n", " ax[2].plot(G.f_ppm[G.idx], np.mean(G.diff_spectra,0)[G.idx])\n", " ax[2].set_xlabel('ppm')\n", " ax[0].set_title('GABA included')\n", " ax[1].set_title('GABA edited')\n", " ax[2].set_title('Difference')\n", " ax[2].annotate('GABA', xy=(3.0, 0.03), xycoords='data', xytext=(-5, -40), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " ax[2].annotate('Glx', xy=(3.8, 0.01), xycoords='data', xytext=(-15, -40), textcoords='offset points', arrowprops=dict(arrowstyle=\"->\"))\n", " for a in ax:\n", " a.invert_xaxis()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAe0AAAH4CAYAAABnr7XTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmYHFW9//H3h7BDwhYIWyCssoMIYRMMCBK47KiAIIvL\n", "D0FwQWXTK/G6672KiggIiKKCsoPsCEFEtig7QYgSIRCWsIQdEvj+/jhnoNLpnumZmpnu6vm8nqef\n", "dFWdqjrdmZlvnV0RgZmZmbW/eVqdATMzM2uOg7aZmVlFOGibmZlVhIO2mZlZRThom5mZVYSDtpmZ\n", "WUU4aJsNIZJekjSm5DUmSDq7j+eOk/TYYJ9r1ikctM0GiKR9Jd0m6WVJT0m6VdJhddJNkPS2pLE1\n", "+w+W9FYOtC9J+pekz9Q5f9F8jyt6ylNEDI+IqaU+GHhyB7MWcdA2GwCSvgScCHwfGBURo4DPAFtJ\n", "mr+QTsCBwL3531o350A7HNgb+IGkjWrS7A08CoyTNKr/P81cNAj3MLM6HLTN+pmkxYBvAIdFxIUR\n", "8QpARNwVEQdExJuF5FsDI4DPA/tKmq/2cl1vIuIuYDKwVk2ag4DTgZuBA3rI29uSVs3vz5L0c0l/\n", 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RuntimeWarning: Number of calls to function has reached maxfev = 1200.\n", " warnings.warn(errors[info][0], RuntimeWarning)\n", "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:299: RuntimeWarning: Number of calls to function has reached maxfev = 1800.\n", " warnings.warn(errors[info][0], RuntimeWarning)\n", "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:83: RuntimeWarning: invalid value encountered in sqrt\n", " return lambda x: sqrt((x - lower + 1.)*2 - 1)\n", "/Users/arokem/anaconda3/envs/py2/lib/python2.7/site-packages/MRS/leastsqbound/leastsqbound.py:87: RuntimeWarning: invalid value encountered in arcsin\n", " return lambda x: arcsin((2. (x - lower) / (upper - lower)) - 1.)\n" ] } ], "source": [ "G.fit_gaba()\n", "G.fit_glx()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def plot_gaba_spec():\n", " fig, ax = plt.subplots(1)\n", " ax.plot(G.f_ppm[G.gaba_idx], G.diff_spectra[:, G.gaba_idx].T, color='b', alpha=0.3)\n", " ax.plot(G.f_ppm[G.gaba_idx], np.mean(G.diff_spectra[:, G.gaba_idx], 0), color='r', linewidth=4, label='Average')\n", " ax.invert_xaxis()\n", " plt.legend(loc=2)\n", " ax.set_xlabel('ppm')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWeQXed55/l7ACLnHBo5BwIESBAACWZREmnZspxkaSzL\n", "47TyTtm76/LWej9M2dza4C8zW1NeV3m0Xs+sPWFty5ZsJZpBEiQGRCIRIAAiNWI30Mg5NZ798H/e\n", 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"display_data" } ], "source": [ "plot_gaba_spec()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What can we do to improve the situation ?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## First order phase correction" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Strategy : fit the creatine peak as a Lorentzian, including phase" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Then, rotate the phase of the spectrum by that amount across GABA-including and GABA-edited spectra" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Outlier exclusion" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Strategy : use the creating model parameters to detect the presence of outliers in the data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "def show_creatine_model():\n", " fig, ax = plt.subplots(2)\n", " ax[0].matshow(np.real(G.echo1[:, G.cr_idx]), cmap=matplotlib.cm.Reds)\n", " ax[0].set_xticks([0,53])\n", " ax[0].set_xticklabels([str(x) for x in [3.2, 2.7]])\n", " ax[0].set_xlabel('ppm')\n", " ax[1].matshow(G.creatine_model, cmap=matplotlib.cm.Reds)\n", " ax[1].set_xticks([])\n", " fig.set_size_inches([10,8])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAl0AAAF/CAYAAABzDnyLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmYZGV59/Hv3bX0Mj0LMwMDzMK+iiwqBJBlQIRx31Gi\n", "JnGLeV9AjInBaF4dk7jFxMQEF1xCjCaCGlExIKIyCgIKsgoMMMDADMssMGtvVdV9v388p4ea6qru\n", 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "ax.plot(G.f_ppm[G.idx], stats.nanmean(G.sum_spectra[G._cr_transients, :], 1).squeeze()[G.idx])\n", "ax.plot(G.f_ppm[G.cr_idx], stats.nanmean(G.creatine_model, 0), 'r')\n", "ax.invert_xaxis()\n", "ax.set_xlabel('ppm')\n", "fig.set_size_inches([10,6])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Then - fit a model for the GABA part of the spectrum on the rephased difference spectra:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAQUAAAHaCAYAAAAJ/lxrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmQpedV5vmcu+W+V1XWriotpdVGsuUFDFSZsMHtAAEN\n", "bZahR00YmmCzocfdCDoGkM1iM0NjIliCoY0RhsEYuzFmGIOERirsBoRla7HW0lIl1ZaZlfueeZd3\n", 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"metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1)\n", "ax.plot(G.f_ppm[G.idx], stats.nanmean(G.diff_spectra[G._gaba_transients, G.idx], 1).squeeze())\n", "ax.plot(G.f_ppm[G.gaba_idx], stats.nanmean(G.gaba_model, 0), 'r')\n", "ax.plot(G.f_ppm[G.glx_idx], stats.nanmean(G.glx_model, 0), 'r')\n", "ax.invert_xaxis()\n", "ax.set_xlabel('ppm')\n", "fig.set_size_inches([10,6])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Quanfication" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Problem : the magnitude of these peaks is affected by all kinds of things" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The proportion of gray matter in the voxel" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Coil gain" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Editing efficiency " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The editing pulse is broad: GABA at 1.9 ppm is co-edited with a lysine resonance at 1.7 ppm" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This leads to contamination of the signal by lysine-containing macromolecules" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# \"Solution\" : measure relative concentration" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "$\\frac{[GABA]}{[Cr]} \\propto \\frac{AUC_{GABA}}{AUC_{Cr}}$" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "0.23434054869982721" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.nanmean(G.gaba_auc)/stats.nanmean(G.creatine_auc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly for the glutamate/glutamine peak at 3.7 ppm:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.76677718867328692" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.nanmean(G.glx_auc)/stats.nanmean(G.creatine_auc)" ] }, { "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
wathen/PhD	MHD/FEniCS/FieldSplit/ShellPrecond/Untitled0.ipynb	1	15486	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "\n", "#!/usr/bin/python\n", "import petsc4py\n", "import sys\n", "\n", "petsc4py.init(sys.argv)\n", "\n", "from petsc4py import PETSc\n", "Print = PETSc.Sys.Print\n", "# from MatrixOperations import \n", "from dolfin import \n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "import os\n", "import scipy.io\n", "from PyTrilinos import Epetra, EpetraExt, AztecOO, ML, Amesos\n", "from scipy2Trilinos import scipy_csr_matrix2CrsMatrix\n", "import PETScIO as IO\n", "\n", "m = 6\n", "errL2u = np.zeros((m-1,1))\n", "errL2p = np.zeros((m-1,1))\n", "l2uorder = np.zeros((m-1,1))\n", "l2porder = np.zeros((m-1,1))\n", "NN = np.zeros((m-1,1))\n", "DoF = np.zeros((m-1,1))\n", "Vdim = np.zeros((m-1,1))\n", "Qdim = np.zeros((m-1,1))\n", "Wdim = np.zeros((m-1,1))\n", "iterations = np.zeros((m-1,1))\n", "SolTime = np.zeros((m-1,1))\n", "udiv = np.zeros((m-1,1))\n", "nn = 2\n", "\n", "dim = 2\n", "Solving = 'Iterative'\n", "ShowResultPlots = 'no'\n", "ShowErrorPlots = 'no'\n", "EigenProblem = 'no'\n", "SavePrecond = 'no'\n", "case = 3\n", "parameters['linear_algebra_backend'] = 'PETSc'\n", "\n", "xx = 4\n", "\n", "nn = 2*(xx)\n", "# Create mesh and define function space\n", "nn = int(nn)\n", "NN[xx-1] = nn\n", "\n", "mesh = RectangleMesh(0, 0, 1, 1, nn, nn,'right')\n", "\n", "parameters['reorder_dofs_serial'] = False\n", "V = VectorFunctionSpace(mesh, \"CG\", 2)\n", "Q = FunctionSpace(mesh, \"CG\", 1)\n", "parameters['reorder_dofs_serial'] = False\n", "W = VQ\n", "Vdim[xx-1] = V.dim()\n", "Qdim[xx-1] = Q.dim()\n", "Wdim[xx-1] = W.dim()\n", "print \"\\n\\nV: \",Vdim[xx-1],\"Q: \",Qdim[xx-1],\"W: \",Wdim[xx-1],\"\\n\\n\"\n", "def boundary(x, on_boundary):\n", " return on_boundary\n", "\n", "\n", "if case == 1:\n", " u0 = Expression((\"20x[0]pow(x[1],3)\",\"5pow(x[0],4)-5pow(x[1],4)\"))\n", " p0 = Expression(\"60pow(x[0],2)x[1]-20pow(x[1],3)\")\n", "elif case == 2:\n", " u0 = Expression((\"sin(pix[1])\",\"sin(pix[0])\"))\n", " p0 = Expression(\"sin(x[1]x[0])\")\n", "elif case == 3:\n", " u0 =Expression((\"sin(x[1])exp(x[0])\",\"cos(x[1])exp(x[0])\"))\n", " p0 = Expression(\"sin(x[0])cos(x[1])\")\n", "elif case == 4:\n", " u0 = Expression((\"sin(x[1])exp(x[0])\",\"cos(x[1])exp(x[0])\"))\n", " p0 = Expression(\"sin(x[0])cos(x[1])\")\n", "\n", "\n", "\n", "\n", "bc = DirichletBC(W.sub(0),u0, boundary)\n", "bcs = [bc]\n", "\n", "(u, p) = TrialFunctions(W)\n", "(v, q) = TestFunctions(W)\n", "if case == 1:\n", " f = Expression((\"120x[0]x[1](1-mu)\",\"60(pow(x[0],2)-pow(x[1],2))(1-mu)\"), mu = 1e0)\n", "elif case == 2:\n", " f = Expression((\"pipisin(pix[1])+x[1]cos(x[1]x[0])\",\"pipisin(pix[0])+x[0]cos(x[1]x[0])\"))\n", "elif case == 3:\n", " f = Expression((\"cos(x[0])cos(x[1])\",\"-sin(x[0])sin(x[1])\"))\n", "elif case == 4:\n", " f = Expression((\"cos(x[1])cos(x[0])\",\"-sin(x[1])sin(x[0])\"))\n", "\n", "\n", "\n", "\n", "N = FacetNormal(mesh)\n", "h = CellSize(mesh)\n", "h_avg =avg(h)\n", "alpha = 10.0\n", "gamma =10.0\n", "n = FacetNormal(mesh)\n", "h = CellSize(mesh)\n", "h_avg =avg(h)\n", "d = 0\n", "a11 = inner(grad(v), grad(u))dx\n", "a12 = div(v)pdx\n", "a21 = div(u)qdx\n", "L1 = inner(v,f)dx\n", "a = a11-a12-a21\n", "i = pqdx\n", "\n", "tic()\n", "AA, bb = assemble_system(a, L1, bcs)\n", "\n", "A = as_backend_type(AA).mat()\n", "print toc()\n", "b = bb.array()\n", "zeros = 0b\n", "del bb\n", "bb = IO.arrayToVec(b)\n", "x = IO.arrayToVec(zeros)\n", "\n", "PP, Pb = assemble_system(a11+i,L1,bcs)\n", "P = as_backend_type(PP).mat()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "V: [ 2178.] Q: [ 289.] W: [ 2467.] \n", "\n", "\n", "0.0311899185181" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:UFL:No integrals left after transformation, returning empty form.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting missing element domain to Domain(Cell('triangle', 2), 'triangle_multiverse', 2, 2).\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:FFC:Adjusting element degree from ? to 2\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "(p) = TrialFunction(Q)\n", "(q) = TestFunction(Q)\n", "\n", "(u) = TrialFunction(V)\n", "(v) = TestFunction(V)\n", "p11 = inner(grad(v), grad(u))dx\n", "p22 = pqdx\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "P11 = assemble(p11)\n", "bc.apply(P11)\n", "P22 = assemble(p22)\n", "A = as_backend_type(P11).mat()\n", "n = A.size[1]\n", "n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "DEBUG:FFC:Reusing form from cache.\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "2178" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "class ApplyPC:\n", " def __init__(self,P):\n", " self.P = P\n", " def apply(self, _pc, x, y):\n", " \"y <- M x\"\n", " ksp = PETSc.KSP().Create()\n", " pc = ksp.getPC()\n", " ksp.setType('preonly')\n", " pc.setType('lu')\n", " ksp.setFromOptions()\n", " ksp.setOperators(self.P)\n", " ksp.solve(y, x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "del P\n", "# P = PETSc.Mat().create()\n", "# P.setSizes([W.dim(), W.dim()])\n", "# P.setType('python')\n", "# shell = apply(PP)\n", "# P.setPythonContext(shell)\n", "# P.setUp()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "P = PETSc.Mat().create()\n", "P.setSizes([W.dim(), W.dim()])\n", "P.setType('python')\n", "# # shell = ApplyPC(PP)\n", "# # P.setPythonContext(shell)\n", "# # P.setUp()\n", "# shell = ApplyPC(PP)\n", "# P.setPythonContext(shell)\n", "\n", "ksp = PETSc.KSP()\n", "ksp.create(comm=PETSc.COMM_WORLD)\n", "ksp.setTolerances(1e-10)\n", "ksp.setType('minres')\n", "\n", "pc = ksp.getPC()\n", "pc.setType('python')\n", "shell = ApplyPC(PP)\n", "pc.setPythonContext(shell)\n", "ksp.setOperators(A,P)\n", "OptDB = PETSc.Options()\n", "module = __name__\n", "factory = 'ApplyPC'\n", "# OptDB['pc_python_type'] = '%s.%s' % (module, factory)\n", "ksp.setFromOptions()\n", "ksp.solve(bb, x)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "pc = ksp.getPC()\n", "pc.setType('python')\n", "shell = ApplyPC(PP)\n", "pc.setPythonContext(shell)\n", "ksp.setOperators(A,P)\n", "OptDB = PETSc.Options()\n", "module = __name__\n", "factory = 'ApplyPC'\n", "# OptDB['pc_python_type'] = '%s.%s' % (module, factory)\n", "ksp.setFromOptions()\n", "\n", "ksp.solve(bb, x)\n" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "Error", "evalue": "error code 56\n[0] KSPSolve() line 424 in /home/mwathen/programs4/petsc/petsc-3.4.3/src/ksp/ksp/interface/itfunc.c\n[0] PCPreSolve() line 1435 in /home/mwathen/programs4/petsc/petsc-3.4.3/src/ksp/pc/interface/precon.c\n[0] No support for this operation for this object type\n[0] Cannot embed PCPreSolve() more than twice", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-a5507270b494>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mksp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetFromOptions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mksp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/mwathen/programs4/petsc/petsc4py/lib/python2.7/site-packages/petsc4py/lib/arch-linux2-c-opt/PETSc.so\u001b[0m in \u001b[0;36mpetsc4py.PETSc.KSP.solve (src/petsc4py.PETSc.c:115228)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mError\u001b[0m: error code 56\n[0] KSPSolve() line 424 in /home/mwathen/programs4/petsc/petsc-3.4.3/src/ksp/ksp/interface/itfunc.c\n[0] PCPreSolve() line 1435 in /home/mwathen/programs4/petsc/petsc-3.4.3/src/ksp/pc/interface/precon.c\n[0] No support for this operation for this object type\n[0] Cannot embed PCPreSolve() more than twice" ] } ], "prompt_number": 14 } ], "metadata": {} } ] }	mit
phockett/ePSproc	notebooks/classDev/ePSproc_class_demo_161020_Wigner_271020.ipynb	1	3176091	null	gpl-3.0
McWilliamsCenter/CMUDeepLens	notebooks/BLF_GroundBased.ipynb	1	177594	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Strong Lens Finding Challenge\n", "\n", "In this notebook, we illustrate how to use CMU DeepLens to classify ground based images from the strong lens finding challenge http://metcalf1.difa.unibo.it/blf-portal/gg_challenge.html " ] }, { "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": [ "import sys\n", "sys.path.append('..')\n", "%load_ext autoreload\n", "%autoreload 2\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download and extract data\n", "\n", "The training sets from the Strong Lensing Challenge can be downloaded from the challenge page with the following procedure:\n", "\n", "```\n", "$ cd [data_dir]\n", "$ wget http://metcalf1.difa.unibo.it/blf-portal/data/GroundBasedTraining.tar.gz\n", "$ tar -xvzf GroundBasedTraining.tar.gz\n", "$ cd GroundBasedTraining\n", "$ tar -xvzf Data.0.tar.gz\n", "$ tar -xvzf Data.1.tar.gz\n", "```\n", "\n", "See the details about the content of this archive in the README file.\n", "\n", "Once the data is downloaded, we use the following script to turn it into a convenient astropy table." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astropy.table import Table\n", "import pyfits as fits\n", "import numpy as np\n", "\n", "# Path to the downloaded files\n", "download_path=[data-dir] # To be adjusted on your machine\n", "\n", "# Path to export the data\n", "export_path=[data-dir] # To be adjusted on your machine\n", "\n", "# Loads the catalog\n", "cat = Table.read(download_path+'GroundBasedTraining/classifications.csv')\n", "\n", "ims = np.zeros((20000, 4, 101, 101))\n", "\n", "# Loads the images\n", "for i, id in enumerate(cat['ID']):\n", " print i\n", " for j, b in enumerate(['R', 'I', 'G', 'U']):\n", " ims[i, j] = fits.getdata(download_path+'GroundBasedTraining/Public/Band'+str(j+1)+'/imageSDSS_'+b+'-'+str(id)+'.fits')\n", "\n", "# Concatenate images to catalog\n", "cat['image'] = ims\n", "\n", "# Export catalog as HDF5\n", "cat.write(export_path+'catalogs.hdf5', path='/ground', append=True)\n", "\n", "print \"Done !\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data and separate training and testing sets\n", "\n", "We now load the astropy table compiled above, and we apply some very minor pre-processing (clipping and scaling)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astropy.table import Table\n", "\n", "# Loads the table created in the previous section\n", "d = Table.read('catalogs.hdf5', path='/ground') # Path to be adjusted on your machine" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We use the full set for training,\n", "# as we can test on the independent challenge testing set\n", "x = array(d['image']).reshape((-1,4,101,101))\n", "y = array(d['is_lens']).reshape((-1,1))\n", "# [Warning: We reuse the training set as our validation set,\n", "# don't do that if you don't have an independent testing set]\n", "xval = array(d['image'][15000:]).reshape((-1,4,101,101))\n", "yval = array(d['is_lens'][15000:]).reshape((-1,1))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Clipping and scaling parameters applied to the data as preprocessing\n", "vmin=-1e-9\n", "vmax=1e-9\n", "scale=100\n", "\n", "mask = where(x == 100)\n", "mask_val = where(xval == 100)\n", "\n", "x[mask] = 0\n", "xval[mask_val] = 0\n", "\n", "# Simple clipping and rescaling the images\n", "x = np.clip(x, vmin, vmax)/vmax * scale\n", "xval = np.clip(xval, vmin, vmax)/vmax * scale \n", "\n", "x[mask] = 0\n", "xval[mask_val] = 0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f89e14b99d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WUQLmbXbbPS+cPGfP5p7VGno44oIPwJ0MQ02l5ObR7VOL52gIe7VCc9iDuWiY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\nPzS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UXby4iT+0lEYRTESbfvmrCf3HIpa+OmPnp2uoUjG6LE80jb2c0SWf2ZrCTTSVr4j6\nDzf4eDc7mrnA2W9+x+f+6be8/0+Af/LOz/zd44NBYy8KzHIXU/v/2HuzGMuSM7/vF3H2e+5+b+5V\nWXtX791sNmeGo4XDkTQ2rJEseIElQIJl6MWADT/4zU9+M+AnP9kwYAgGbAOSLFmwZI8sDbWYZA9n\nuEyT7K3Y1dVV1ZWVlZWZd1/OfiL88F2WaEru6h5RYBFmABeNzjq53RMZJ+L7/v//z0MluaTSBT7U\nVtQeTV9QY4M2qjTo+Rr/bIUuavyPz/DvjzDDHnqRCEzgcEj+yiH4Ht68lB38o7HkUC/XIg8cL2ne\nnooRR0PrgRVJUyLHsGTfsrgckvc9rKvJtgL0KsddZNQNH3djBHKXuZQVlFBjsr5P7WnKSBh/6UBq\ng2j4s9fe59Cd8q3kGv90dJP2XUlQK2NHaPNFJTkhoYfKCnReURx0yfZbOPMUb5ygErHW67KmuL4n\ndffaUB700YmYcVRlcNYF1nVwRysJ5GqIjd9qhTqfgOtsoBByfdVtYPqtp9+sf7Gm/bOIZv0sDZ1f\nyFEHYAJN5+MMp7AUbc3oVQenkKZb/BDi0wqngOEPLItLLu7aECwM45cV+2+lzG74zC97jF8KQcH4\nxYDJi8I19RLLvb8CW2+v8FdivAmm4KaWsi1EdDeVtzfdrXH/2ESyrhWUbVhdVDSPJaPbutI0nb5i\niM4sxWY6JNsO4djyyb8rnoTmQ4PxINutqBqWH423ee3SQ4bhilkW8cmsR1E5mEcRVbvGOrC8Yogf\nlVSR5uyNiGAqgVlF1+fg62vylhZZYw2LQ490y6P/nsVLpCRifGg/yKkCRf+HC9YHoQAOLkdc+Gcl\n1pUTjL8yBO8fYZVY4vOOoM+eOv5oNe2fy3gmFm2USNBM5GJaIVWvgSpKnNEcXYim2DQDVF5uasoh\nai0uRxtH2DhCJxn1oIVphfgPRjhpRd0UtQdKrvMezzGXdym3W5h2g3LYxDqK7obz6M8tGKhDgz9T\nLC8p1juSlOcvKor9tiC7IjGi1HGwSSXUlC0Xf5LhFIYqFprG8tBh8rJi8VzNX/ztb/BfbH+TS27N\nW7Mb3Pr+JaxShGcJ8cMEd54/UaDopMSGPvkwxD9dSpMkLyTRrx1LxCrgjdfoZYazyHHPlwI10Brv\n0URkgHEInotarGh8NBIVy3iN2d9CzVeSUWJBz9fiQi0/41bjZ++IfGpD5xd1eCvD8sDl/PWIvCON\ntvjIMnneZXbNpzEyuKkc4WtPjDSjVz2Mo2icKtxljr+w7Lw1ZvjDFAz0P5TmY7KtiY9zBl/fpENu\nYABuallch/6tCn9DdAnPHKKDFfXX+9SRJZzIott6YFle0jSPLdlQFB6DP5SGo5NLo3O9r5g9B8FD\nj/WVivkVjX5jzgsvPCS+PuffufxD/u71r3EYTdluLOlEGd7XOziZ4sb/nOPP4cI/Naz3vCcPi2Bp\nWVzWmEBRdH0697MnmSfLy3D+hqL2FcFErPyDdxLqQE4Z519qU0YiNcy6mukNn+jMUPsioTz+S9dp\nPayITwp6H+U4uTAqnzr+CI7In8d4Jsoj1pVF0FkVYqqpLaYrmmpnskJlBcUV8TE4pzPy6zuUzS6N\nhyuqfiwSuTRDlQ1M6GJ6LWFBLlKs68h/QzEx5INQqDX3FtRbUn7ILvXwVzXKCtx25enNrsNSNhXT\n5wNaR5U8fS3405yy5VNHGuMGhGc5Vit56LiKoqlJdjTZlqU6yLmyP+JacMoHZcwH2QHfevsm7Xua\nxkhACqoymNjDXZfSLC1KTKdBeJqQ77cJ3jui3t+S+nwrQk0zTNAVJiSbQCtr8R9MpJad5aiqgW24\n1J6PCgfUTR8nq7GeLM71dg+1Ubfklwe4iZxInjaUBf2zl/r9feA/VUr9TeBXgfnPq579sx51oNl5\na8zjPznYzA0ljtYKwqlhceiQv+aw+52SxUWX1QWfxqk0LYc/SHj0G138ueWTvzAkOhMEV95R7H7t\nhPT6kMXlkM79nOSgIeFRQOuoJN0OyTv6ia56caPCu92m2LVUw5JZS+OPHUZfNOhc4SZaasQ9xex5\nCMea9r2aB/+GT3wEjRwJlJo55AODV2mO5x1uDM753+69zneml7kcj3nneJ9qFGFfLYjv+CyuRWJH\nz2pWL7vsfjujbIUsL2i6d2pmV12G7+ScvRFRhdB8aNl/SzwD8+sN6gDW25L6Z5WieSLCgdWBpop8\nnEKasG5myXsafyZJgmWsWV50sUrKOvPLn77U/Wua1/9axjOxaCsjCXnOymBdjTdZoeYryc7e62FC\nB//xUnbMjRAnqwjunIK1VFd3KHc6eCeGOvZFa+w7uI8mpC/uEZyuUVkpmuRFSjBKobZU+33CkxU2\n8OU4tLHmdu6VJLsuqgLlQbJn8GcaXcqTOpxqdOVtutgGd12zvBKJFtdXLC451D4kBzV6UPDqhUfs\nRAt+s3Gf/2786/ztr/0xOp9IyHz7wznryy2at8bU+x1sZch3mvjjBPXhJ9jrh4S3T6mu7MppoxWR\n78SERSULeDsSXfp0LezISrJR1KD7xJJexx7uLMe/e05xaYj6MeQ3ciXi9WwBze7nu2GfcxeyiWb9\nDaT+/RD4LwEPYJP09w+Afwu4AyTAf/T5vsOzO5SFx18ZiIEqMxQth/6HBcd/wqP5EDr3KpaFLEj+\nypJ3N7GpEThFgC6g+1GKvw4pGwIJULXi+Lf3iUaGxnnFeteX/PdlTTaUXXr7nsFfGlZas3ipQOUO\n7kphXlvivdsiu5ZTbFkwCpVq5m/m2ELTvqWpm4Yy14xf0VStmmzo4uQK41vC5+ZoZRk21wROxeud\nhxirOIyn3FluSfBSpYg+lr9Fq+VBNXkhoHFiWV4MUJWldSYGGSeDsu1uFl6ITwqWhz7eZmfcfCQl\nvcUlF+MJZadxarj4OxMWL3YJZhXLCx5WQzg2dD6Y4V9ssTgUmvt6zyfrO0+3scMzvbv+yfFMLNoo\nhbsQE4ifFqiqxraF82h9TXD3XHJEsoL8Uh9dGOqdrsB6a4OqN0E6eYUJXJyPHrL+tevE7z6iPBzi\ngLAnB01M4ODOMpzRGtMMMaGHf7YWtFe7iT8t6N52SbegbEF4ril6lunQEJ45GEckWV5iKZoueV/k\nU0VLkew4GB+KjiG+uGS/veAv7HyfF4Nj/qfZF/mdT16idRfaR6UAFg6aBOOCaquFLg0qr/HTEmpL\n/fJV3Mka02vifnKG7bZAKaJbj7GhL2De8RrrOBQHXalj+65MPKWgklq1l5foVYbNcrzzFaooqTpd\nIcpb6Rn4j5eS+Nf5DA4E+/kbNtbav/SUf7fAf/L5vuovxrAKeh9KY0xZxeD9nNl1n+5Hkied9h2i\nicSrWq2IH9dUgSJ+XDK/4tP/kZTCwlEJA5f4OEXXIcsLLllPE40q0qHGObGkWx5VqFi/FNB6WJNs\nOez9fspRFBFMIBtaen8/ZnrT4jz2CZ5b4Dk1605AHJbUf9hl+YWM4I4oQqxvUaWi6NVY19I4cvnq\nxY/4P77/Oo42/OWr3+V780t8eC6n4Fs/uITxDcFCkb2Ucu2/NSQHIdMbDsFEssW91OLkSv5eK0Xr\nWMhQ4VhTBw7JrodxIdnRuGuLVYqipYnODf6iZn7Fo3N7xeq5DqsDzey6T+92jT+rqCOHyes9Bt8d\nMbu6xcmXQ8KJ7MTnV91Ph9j9Eeb1z2s8G4u2MVJL7sWS7DdsbizoFu/xEtNtotYZpt0guHMmDcmi\nkuQ/a+F8Sv7SRbxJgjtPUL0O0dGS6sKAquHirHKc9+6Sf/kmwVmCSnKoa6mjBy7OJ6dUzx/QPEoF\nE5Ya3LWmUSjqSKIuMZpsr6boaIwvCWNOKuHv2UC66OWgQqcOnatTXhyechDNuJPt8DeOf4Xbd/do\n3fJoPygpmw7BOIdYdrvGFSKOCV2cpEAZgztZy0miE1FeaOGtK6gtrquxgYd3upBMlR/dxz8NMZd3\nUUbKLWW7gTvPpdRkLdZxsHfv4/Ta1MM2Oquo26I28MqaOhbbu/cZeem/KMfIZ2EYH9Z7HqqG+KQk\n2RE6kHGgjuXeWw2rfZfWUU04Lik6rrAYO4rRawH+3D7JAlldjCR/PpOPrfY9wqlkYNeBZIAUXcgX\nmvi0Zr0X4CZIbdsqag+8laJ8Y4V5u8N8u8YZ5nSijOLL56SPuhR9Q+fSnPlRB2+m6b8pxs/1vs9X\n2h/yla98yP/w8E/w1z/8MkXu8dLBCd+/c4noXIPS5AND9xsh6Z6hjDStI8N6T1OHCi+B1lHB7IZP\n3lPE36mZPt9A1RIH6y2tsCQ9Mf307pScfyHi4Bsps6sh0cRQNTcRyiVc+P0V45djlHGZ3HTo3a45\n/q0tnMKy/82UxdWIYF6LSe8p4xdlXj8Ti7b1JdTI1grrarFcN3yso1COpo593KKS5tomP6Ruh+gf\n3EZdu0T54gUJgkoL0XVHgWSOrAocV8vX3xkS3ptgujG2GVHHbbwPj+HKLnZ3gDtOQUu2dziusNoD\nZUW2dwZZX1HkDlVs8JYCNK1iSzEQxp2OS4Kg4vrNEWnlsShDvn/yElpb8lsd+p9AMDOUTYfGSY4J\nHLz1Jh4VsD+B+qrboRh7ajHWeI8LbOSL09Nz0KtMFuy8RO3vSJ06LSmHDXRew4ZWoyuDM16yfn6L\nyLxA1fDxTqaYbhN3KsFSKCXNyVaIczr7DDeLX5hj5LMw3FROYd27JZPnA+oQijZc/FpCshdQxpoq\nksU92XYYva5p3QcMFB2Lkyq8Jax3RGq39cOUvOvRvZMzvRnQOBd3ZN7WRBMBB4PUzYumpAf6c8vq\nokSpWgeKnsV9t0n9ygoyl3oU8Oh8mz/+Kx+Qly7Lok1WeNi4ovA159MWjmPwvJr//ugrPJq1yVKf\nmwen/OiDi3z07jV4Lid/ISV8PyJ+qLHakgw1XiISwot/75SjP79D0VbEjxXB3DJ4N6XoSV3azSx5\n30HXluUFh51vr0l2Ys5fiwjGML0RMnhvzeqwwfFvhAzeq9n73VPWzw3w1pasq9GlkG6aj2rSgSi+\nal/Qadvfnn/6jfoFmtfPhHpEVYa6FWykbiUAepHKLjTycKcJVT9+Eh9qtcZ4DnpnS2rg52tJthu2\nqPtNqk6I9RyqbogJHFnEek3YNCV1XqKLmvraHmgl39PV5LtNrKdx0or23TX+osZbG4K53E1dQjDW\nFP2aaj/HenJ8VFGF69fkq4Dbj7d48L0D3n+wR363Tf29Lu174K0tjdMCb13jLjKcdYn3aI4qK+q2\nkNyNLz+rnDDmErkaOCTXB8K/rAx1w0elOWoTVWsDVyzvGsk1aYjJxj2bS573QY/owRJV1niPJtRb\nHTAGG3ioZYLKRLOtiorqoP/0ewVo889fvxyfPqxWuCk8/E0Pf2FxMrj4j1POvtSgjDXDb51tUiIl\nJrV9B5rH0vRuHklOTfO4ov/BCl2IuqmKNKNXQ9mdNpSk2PUUo1ccsq4iHEP8uJS41//7Ebr6MakG\ngrnh6t9aEEzBea9J/5sBe9+E1n3NWx/ewHyrhzvX5GcNqDQq0/i3GpSZy7C5ZrxuEIfizrx7PgAD\nZdsSHPls/06Ak0uZQdfQelgRjWqCmWX2hS10CfHjmtrXNI9yvGlKHSisUhsDDDgF7H19zvFXY8Kx\npXlcs/P7c/yl5d6fj2nfXrD/jRw3MTz8czssDl2KtqL25W+se7fEWxvcFFCQDRThuCS50PzU+/TT\n8/pZntvPxE4b2GizPfKLXYLHK2zDpewE6MKglxnWVfgfnmB7bSgrdJJTD9uoWgKZvEWBdzTCdppS\nbqlqXCDfjqm6AcEnoqzIhxHh0Zx8K6Rxb06x3SS/1iN+94RwlVLudnBnotrwpwXWCfBWFfGJIKGc\nwhDMHMpY9NfZ0BJ8EpLuWLRnce/5eAvQDyOMI0E3ykDz/krs9kC636Rxf0bdi6U8syrQ6wzTjtDz\nFXXUE5DBMqfqhkRHS+q+0OgdrbCeC47owus4QGcVxTAm+OE96HWohy3pAZgN6cZzsI5LcaGNk1R4\ni5R8t4nnDCS7pRR5pHf8Gesjz/CEftaGVbDeV2x/r2a177D7zTnW0+y9tWT0WpPlS0NGrziEI8ni\nyAYQLDThzJAONP33LYvLLuOXWxgPVhcD/KVh+3sp45cbNI8LdFYTtzRFSyC3vT+YUrdCrOsx/vW9\njeRNU4dI3XinQ+9HOff+osLJfeY3NPExhHcCVtdL/FOX/vc1i6ubTJGOweYOp2/t/3NFfc9glyG+\nUaAsjUewuiAZIjvfGDF5Y8B6xyWa1qwuKLofGfb/yYTbf61LeO7g5C7Gi4jOZJddxPK5waTkkz/X\nIZhC6yjHP19z8tUBVsH+75VMX2rTvp9RB5qtH+Q8/pWA9n1LdF5iPM3ZGx473ytwSsv0hoObwtkX\nQpqPPsOk/QWZ18/Eom0VZPstorsT/A3zEGufAATqfow7TqkvbOGMl9T9JpiNk3K+xm1IoE290xV5\nX+SD48hCtIkbrTsxddsnfCQIrvBxQt0MZFe7EIiCqi0m2OR7JBVOWhKMIe8HBHPR0hZth+5HBUXH\nFd1trVC1pflAdgzduyVZzyE6l5r8j0nQZSekih3CcwEFL17q0/5AEvpwtTQWT2bUuz2yrRCv4eIf\nTVGxj2l46FUhYVCBgzNNJEGwyKiHTUzoEjyYUN28iDta4Z7OMXGEO1lTdyKcWUo1bEkTsxFSbjWJ\n7oyoezEqyeTaeYb1PwON/RdIGvUsDGUR4osCXVgmrwln0TgCL8g7DvFDi/GlPzI4EQBCsDAke1Kj\nVkZyRvKuogoVtefgpC5ODv7xHPvwhLa6wcOvxoRTy+Jmh8kLDu17Yt8u2pInYlzBkzVOa06/FBA+\nAFWBc2WFe7tJ1VCoTOMmisV1i5Mp/BksXilpv+eT9S1Vy9J7XxFMNcYTAjtIc56NJvzOfzgEoPFI\n0X5QE505eIlh+nqXC//EUDYh7Wu23844/kpI45HU3JM9hb/0qENL66ER6/6upHhGY8Nqz6V9v6Do\neLhJzdkXQzr3DOtdzfT5gN6PDK0j8R6oGgbvV1SR4vGXFXvfSj79Rv0CzetnpDxSE337DjbyxdVo\nDHq6EhfgiUSN4ij00Rnlbhfji9bYOiIB1GmWDrE1AAAgAElEQVSFKiU32zYC9CJB1TXuNMGZpxvG\n4xJnVWA2tWNnIl/fWRWycG4cid48F314ItBc7/FceJS1pJBF5wVoRTCtaD0s6Xxc0jqqad+vaB9V\nREdLwoks8P68QNeG4GRJcLoierTGWWTopKB5f0162JHF0pH8iPywj15lRA+WWK3ILg8kl2W0RK9T\nVF3jjJaoLJdEwNrgjde4iwwbBpId4jqYTiykdceh6PiYdoR3MhVjTpLh5DXp9aE8SLba4CiM/xmf\n3/bzR7P+PJBMz8qoPRi97DC7Lo2wzr1MNMbC0ODsz+YUXYXxFMEmiU5XsjMOpgI8COaWaFyx/49H\ntO8XNM4r0IrGqCK51ufxX32doutz6f+cEk4qkm0JZFJWOJSd+xVnb4pbcPB+ThVp4hOLt4LdbyeY\nj5ssrsvm6crfqwjHlvBcUQdWpIczl7IBg/cs29/ZlGTmFl1AdG6JziyDd1cbBQzsfMfQ/EROmaNX\nArzEkmy5NB8WKCM29sEHGSdfDtl7K0eXkne9/b2K1b5m/60NCFtL2SQcG3Qped9532N2zZMm+tKy\n3tN07lcM3quZX9e0jnKmN3ySHY1TGKY3HeJjzcOvfnp55Kfn9c9wbgdKqb+1+fdvPyWD5zONZ2LR\nxnFQ3Tb5Toz1PapeAzNoU7dCyt0udeiiz6aUN/ZxxyvZDTd90W17EuOKMZTDpqC12g05HvouxV4b\nPV9TbXdwTmcUvRC1Sqi22lLLNUaS77ISPZoLLHi8FCjuo1Pywz7hoyXesiT+eIqzKvAWBW5SivQw\n1EQna8JRhruqSS+28BYl7roUjuQ0JdtvkV5so/Ia0/BRSYa+9wh/llNsx7j3T3HPl7irAhP66CRD\n5zXBgwneaEVx0CO5uU3dCjGtBjYQwEPdj6GsqDohdTvAPZ5IVnZlcCZrcDXeokTlNfWwTTlsgCsA\nYXdZ4qTVJgpWU3UCgSs/ZSg+n9X354VkelaGdWHvDwr8xSbUycDOdzPRFZ8m3Pyv1rLovZeTDDWL\nyw7+smZ5qGkdVZy97tH+aEnRdJi+1ifd8lhedMl6Lqs9F+sqmo9rpjc85s93qH2xgw/fK/GXotVe\nHjj03pcApvlVn7KBaMFjWF0I6d2Cq393RXRuGb8kzc3msaH/vsVbQ/MTTePM0rq3ZvKSouhJWuH8\ntYLmSY2uYX49lpr5zNK8Lf2YyQsigdUVpFuy4HrritWBz+SFgHBsSXY8si3F8N2M8Cyl/6OKdOCS\nDIWk7q0Mwbwmb2v8GXjLWk6zA5fdf/SQ+MSw3HdZ70oaobssntTH3XX1BPa7/fanAz5+el7/DOf2\nXwOm1trrwH8D/NefexL91Hg2Fm1jMK0I4whN3WolTTmtcBcZ7qrAtpt4pwvqfkxw51To5GWNun8s\nxJokR5fS5FOJkFioLcH9EeQFziwhu7lLeLKi3O/jzFNMJyY9aKKKivWVNutXD0gPWmKH7zUxNw7x\nTxZYrZ/UhqtWQB2JGSJ4MKFxnGB8l8XVBk5pCMY5VeziPhzjrsRlGDxeE4wzbOCgk4Li4oD0zatC\nwFEKPKnlO2uR+1VD0W1XOx3QGv/hRJiT50v0KpXTxGxFHbngyg7OezSVU8d4IRnbjQC1zvAez6i6\nITotMY6mOOzjP15Kg1ZB1RBwhP9wSv0ZHJGyI7FPXp9h/FyQTM/K0AXUvtRr19uaZC/g0R8PaZwZ\nZs+3OP+1Abq2hB+d4q8se2+tOfrTDls/LNGFZfvtkvWlJlbLbrRsSK706HXF9GWLt6xwE4N1AQWN\nhyv8hdmETlnGL7n0P8xxc0vrSHJAGiMjdvX3a5aXNOPXLLObMSiIT2qcXCSEZayoGrC8LLzF8zea\nuCvF8J0aN4Hu2z55y8FbiyolGwg4OLnSZuvtNe27hu6dmtWeZGNnXc3Z6xHLS5pobGmcGZwS9r+x\nYvxyyOJajHVgfaAYvLdids0lHQq5BkRh46YCF659xcO/cFEMR5VosaNzQ3KhgZtA537N+Rca9G6L\ng7LoPOUk+VPz+mc4t3+SK/p3gD+llPpXQj49E4u2dR30eEH0aEUd+9JAS0qcqQRAVa2A5FqP5MYA\nZ0MZrxs+2W6MuXYBG4dU223ch2PUZE49bJHtxtjIo7g0wKZS5/YmGSbypPm2WeyiR2uoapofjGTS\n351K49Nx0PMEXIf0YksML7FkVuu8xlnm5Id96oZ8vehMTDHOMic8TcT8AziLDBu66EUqtBktWSbR\nwyVqleCPE2yS4I8lmrZuBsKkLCrc8yXphRYmjqg7MSYOWT8/JNuNqbe7+I8WqGVCPgioey2S6z1s\nFMjuecPbTK8NcZe5NB6nmUgMNwFS3vEEJ69RWUm515U4gM8wPmeozs8FyfSsDKeUBS3vanQNs2ua\nne8WUls+r3ByceJWGwJTPgho3dekA5flRZcq1gTTktUFTfNRTfNE9NrDH1gO/2HN6Zshi0OXnW8n\nzK5p0r2Y+RUX4yhUDb3bNenQw8mt5Jv4ivFLDjvfTQnPC6Izy/43DWUsUIVwXLG45ApvsavwFtC5\nrYjGFa1jodk4uZQrmo9rkh1xPBZNRXRucRMYv+hy/oWYvKN5/Gsab2WJxjJZerdLonNLsq2lJDQp\nefQnm6haSh1nb7gEE8viaow/swy+eUwRa3ofCrBkvefjrQQTGMylhp4NFLvfmuEva4JpyeKKomwo\n4pOaxWWH7d+fsNr718KI/Cxz+8k11toKmAODz/TV/z/GM9GIVHlJeWkLd7zGxj5OUpIdNHGzWqRx\n4zXeaY1ap9TbPVk4y1rqaosU0xSlxPqVffxFiTPPiBayQBtHYy/tSw08FqZicHRGdXUP93yBbQTU\n3QZl26dxW8AK5UEXVRrKXpfgdEXjvkS7msDBHwsNBlfjJGKPV0WJUxq8SSJKDaVwJivKK0N5g63F\nei6mHUlDKquouhF22MB7NMdcPZCc77QET4s0cZliWhHRx2NxiHouphnRuLeADWDYRj6qKAlGOTiK\n+Na5hEYh9UyUwk0q9NkUv7bodUpycxtdGOK3H1Be2cVZZtjQwzuZYRqfAez7LzZsnhbN+lmRTH/D\nWpsrpf5jZGfym0//YZ79IcYpy9bba86/GLPz3ZzJCwG92wVVpGWxzCE5aDB9Abo/cqSGWwNI43G9\n6xOOLd7aMLnp0T6qGb3soCtN73ZN7Sv84ynN44jlRffJOz56xSOYWVoPSsqWI3VaBfGxZXYjJJwZ\nFlfBHDkEM0vjrGB2LSCYW4bvpCwuh6RbmuYjyXsfvaZpf2w5fdNDl2AcB2Wldr247BKOavKWpv9h\nRRVoqkix/82axUUX40riob8oad8z5H0PZSzZwMNNALspY3w/FwlgbWgnlZw0V4b5tUjYkG1FfAJe\nYsT2fm9BNw64/VfbDN9WrHdcvDWEk5r1jkvz2HD8WwOC6VN2zv/yRuTPYm7/zBMsn4mdNo78GNnF\njiCvKoM/L6RM4jlUnQjTaUi6nzG4R+cY35EjjOuglwnGc4iOpYGnR1MAVF7jJiUqLcj2W+jpSgAI\nV/dwZwmm1RBEV+Tiz6XmVVzsodMKJynxz9ZU7VAgCq7GXZdyEtiYeJx1gc5KTCvEe+c+2X4Ltc4k\nJ6TdwB8nYpQJPbj7gKIjODFlDO6dR3jvP6Daagmw4fFMomiVWHxJUupmAIEv+SvdmPRAGox6Jjt2\njKE4kOAnE7hYz8W6Gqs1deRtom2hvLKDjTxxlJ4nEsx/YQtnmYtTTKlNE/Mz7Eb43NGsPxck07M0\nxi96mNDBW1nmV32hFCEYusa5QVlYHThc/FpBNlRMX1Cs9jXpUOjk5YZm46aS2rc8cBh8UOOmMLsm\njNHxr+8RzmqCuSUbiGrFn1u8taWKHabPOcyvuOz8wRJ/aQlnhtlVh9a9jXqrr1gcBjRGNaoG627i\nGpaWrKuY3nQYvGfpfbimf8vQvm+IJob+rRI3rVlesqx3HKJpzfySy/JQ42YWb1WR92RBbH9Ske4E\nzK77VIGiiDXTFzQY2P7DFeHMkHc95td8ptdDrKMoDvubXB+oI0U6VFSxw8PflJ36nb/cY/R6g/Zt\nSVC0Lmz/YcbkeQ8vsVSBJAU6xVNwY/xLd9r/ynP7J69RSrlAB4Eh/JHHM7FoS8JfTnC2RmUVKstx\nFhl14FC1PJx1jipryv2O2Nm3e7ijFXXoCpn9oIvOK/RkiTtNqQ63MYFHsROLZbsf488LQZkVNbqQ\nJ7iyomP2zteotJTmpLHUsYcJXapBRN1wBcowWbG61MBZ5aKTBigril6IXmZkX7pGcLaWB0E7IN9q\nYIJNbXuyhquHOJmoP/Rshd0dwFYf794p7jSh7rVQj89FxbJOKa8JpUiNZzjLHOdkgr8s5X3ptUBD\n1Ynwbj0QA8f5UhqcyxQbeeJ8bPobW7woY9Sx2JGju2P0bC2kd1cLf7MTS339qTfrc9e0fy5Ipmdl\n1D6EY8viMKBoKdxMpH3G18yvasJzeVb9WIYXTC3uWrH1w5zdP1hjNU8Youu9gHBq2Pv6hCpQuGvL\n1g8K4Zp2NeMXXJIdTXxsGb8oPYsyUnirGl1A/1bO6nKMU1p0YWkem40bURIFs4GU1RaXNaOXItGJ\n30qoGorhOxVuaig6PlZLc9EpLLNrHmXTITpV1AFMb7hs/TATD0NPkfU92p8YVnsOWd95ssccvwr+\n2uAtNpng/3aTKpKyBkbq17PnYlnEr0qJJT4xRCNL486U6LFmveNw46+fowx0Py4oWorh9xZyWphJ\nOaj9SU77QU4wfYoc5I9W037q3Ob/zRX994B/usna+SOPZ2LRVpUh345Fd71cy8eSDHdVEpwl6GW6\n2V0qbBw+yYp25/88ctWZrCRkKnTJt0Jp+hVG5INZhXM2Qxe1mFlOZxuMmbgQTRyQ7zdZHUY4SYW7\nKsn7Ac66JHi8wria4mKP9gdTqnZI1ZUyQt2PCc7WomJR8vDReYk7z/DHmQB3lcIGHnUrQFmwYUDd\nb0tA1W6L4tou2cUOaEjfvCrNwb0expegerPdoxzKKcMbbSAPgxDraPyPHkG/i7PIsY0A029R7nap\nmp7g2EZL1OMx7onAHszlPfnd8wJVGwEi5KU0KUOXOv4MOm3kD/bHr6eNTR3vx0imW8D/+mMk0wbD\nBIJken/DZPzP+BkgmZ6VoSwsrklpwGqYviDZG96qIphA0fVJthVlQ7P3rZTejxIu/uMlyws+x1+J\n0YUh3VJkPf2kGTl5rUc4q+l/mLG45FE2FO0HFb2PaspYwBq6lNJMNDEUbYf2A8PkxYDZNZlXWc8h\n3dKsLmqMA2dfinGEr8HwnZJ8IA5gXUgZcnbdxXiKszd8GqcFwayi9hRb30/QhWX4bkHvdkX7E8Pj\nXw2xGqKR4exLYi/v38pxCqgihS7h8GslRbwh1awN/Q+swIwf5qCFAdm+l7G45DJ8tyJYCPWncW4o\n9tsc/LMl7QcVo1/bQtXw+Fd9vMRy+uUOdSgAkvgo4eTXQ6rQIR0+vRL8k/P6Zzi3/zowUErdAf5z\n4F+QBX7e8UzUtK1W+NOMuhVgO6HUtrWmbrg4372FuXEFG3m4y0JyRKYrTKcBNdRxsCG3dPFGCc5o\nQZRVqLzALUpsswF1LQ7BVYrthdhGiAk8yk6AP0pQaUE0WuAPWk/gv8EITOBSdAP8aSa51/MVqiMk\nc5Xl2IaPCT2csiYYpeTDiMbtFcVhH+94Rt3sYkNfrPYPzqUeH7rCgFzk+LM1yliqbVnEg1MxADhn\nU5xOE9PwqXoRwT1JOay6EsXqTzOcyQqz00cluTwskpz8Qofg8QpGJcVBF3ddkt0cEoxzrKPReUV5\nOMSdpQJxCB2c1JFAKV/jP/gMpzbLZ2NJ/uSn/ByQTM/KUBUc/sOUKnbJ+i7eSmJKh+9mRBODPy9x\nU6EnOUlBtttgvSPk9MEtSa6LHxvSoaZ1L6Xo+syveaz3PazjgYXozJIMHaJJTfOhpQ414UhCzaY3\nHcKRqCu6H5VEx0uO/3SfaGTpfVhw/FWP9j1LODc4uSHrSUzE4L2axaHDyR9v07lXke64BJMSb+2w\nvBCAguWhwikDvFXN6GWf/W8uOf5qi7JpsR50P7ZEpwpvXVN0XIpY4a8lE9wqCJZGVCT3F5x/uUfz\nyLLaD+h8XFDGAQ9+K+T6//iI0z+1h1VSsgjmhvGLIf1binTgkHcU4cRy5W+dkVzrsTzUHPyzNfMb\nDcavNonOxVEaTp5e0/688xo+09zOgH//c3/hTxnPxE4bkLov4I5WmEYA1hKcJfDi9Sdh/SqrUGn5\nxBEp1y9xZyne2Uoaj+2GpN11Y6jFzm6VEqhvM8I/W1Psd9BZQXCyyegOPepBC2eygrKCokQnIikU\n0K4LFln4rUWVFaYb46zl3Gp92b2H3/oQG/p4j+ZkVwd44zX5xR5V0ye7uYcymx/aSkCUDQPK3Q7G\n1TiTteDWyhoCUZHodY53PAOtSQ87GG8jPXQ05W4XqzU28sl3GmSXuvina9R8JZLBXMxGjTtTVF7j\nncxwxvL7UpRYrQmPF+hVjn5wIrX+rfZT75Oyn/sI+f/7sb4gJ7PORyvcTN6zZEcYostLAeHEkOwq\nTr/cIdlyUUZ2yZObLpObLr3vnmE1PPxTMcmOy/Z3liK5uyOSunRHsbqkWO07JLsKNzWEM0M2VFz6\nmw8JFpbWccl61yW51Gb3D9ZSb16W9N+16NqSbGnmlzyiUU3zQcJ6R9N6WBNOpFbtLSDd9gmmcv3q\nQNG5awjPcqI7I7yVKD7cFbTuCzpNGfAWsmjmbQGLLC6LkzJ8vGZ+xSHZdhi/2ZM6+9SQbODG3TsV\nh/8oZfXiNkVbCYGmkAak8WFx2adoKZqPaqJJxeRLQ3Ein1lOfzVm+vyPTUqKOlDEx9mn3qOfntfP\n8tx+JhZtZcGdpehUYLNlPxQ4QSqZHCrJcM7nqEwWSWeVo/MS6ziU+x2qnphpVFmjFwnW1RswcFcS\n8PIC79EEKiMBVMsc67ukhx0x5xjQM8nXRmuq7TblVmPzc2W45wuhxbcjnGmCaQQSe5rkKAtVKyB9\ncY/s12+S77cxzQh/nKLmK7x5hn+ywJtmlP1IGoRKFDP5bgwWMdW0I8kFdxTJjSHOWvBi9bBF3W0S\nPl4TPJhQ9OU6d5GJISbwiD6Z4Y9TbOiSPr+LLircyZpsr0ndiVB1LTv+Rig7lg2ujKrGNALWX76O\ne/8UZ/3pBoQn9+sXgKP3rAynsERnJasDj+XVJu37GW5iKZpKGIcXRVrnpqIzDuYS7NT9uOTg6yvi\nE8P8C9u0jmppPmaWqukTP65RRhyPW98v6dwx9G4X9H9Ucf6ax/KCQ+duTXJzm8VlTRVJnknR1Bz9\nmZi8rXBnKfPrGlVD60GFU1rqUDF6tUn3TkEZawbfG1PFit4dab4Hy5pkR9N6YIgfFdQNl9mbu/Ru\n5zROC4oNT6PzwYyzL7g4JQzey8iGis7HBcFEMrJHb3TZ+kFOMBdNuFXgrWo6n1QsLvu4Sc3odQnV\nyvuW4z8R4uSW6XMew3dyrBYjT9kQaET/uyPmNzTKgJtYtn5gicY1zWPD+oLh9FcaT71Xv2REfp5h\nLeQFdcOnaniEH59Td0I4G1MNmtT9JuuXdih3O+jpApSQynVe4p0sJGvD36T6tRsUWzGmE4OrJUVv\n0KTe7rJ6roPz8JyiF5LtNWl8eIZOCup2QD0UyABagbH4D2foVYYezwS0kGQiydsAcXVZY0M5EThp\nSfTROdHRguCTCTZwBLqwXJHuxyLNK2uC24/Fcu5o9HSFP87kFGEtxnMwgUO+E+NPc9RkLk3OrBLi\n/GiOaUX4ozWL6y2M7+JNU/QqA2Nlx5wURHfHslt3NP44w5klcirZnCBUbSmubKPqmvywJyeOaYHZ\nHTyBBn/6vQJV2SevX45PH7WvmD4n+c/nr0vvw0s3u9CVxV9C49zQ/bgSvXaomN+AsulQxR5VpIiP\nUoqWwKJ1aZlf8UmHskCdvumwuuCiKwH+zq+I5K99VKOs5fRNHzeBYFYyuyYZIJf+wZLGec2dvzJg\n+22BEOjaoioIRgVeKoqT6Lxi+nqfYGpx0prGWcHpFx0G78s11lEyV2vL4nIg/7+A7sc5y+c6XPkb\njzfNyoDWkcFbFmz/3ohgWeOvDNMbPv68wnibjVtSg4VkR5H3XYq2YMUG71jCMQTzmnBsNzJKCdjK\n+4rZNY/1c306dwzRpMYpwJ9XpAOHOlCE5/rptvSfmtfP8tx+JhZt62jqYQtdVDhZxfqFHaGJH+7i\n3XuMqi2NuzMpZXRbmIaPu9Fhs2n21Q0XVRrQWsoexqDXOdnFDs4yp2p6NB4mFDf3CT+ZEt0ZsX5x\nGxO6uDPJlrauxoQ+7miJ6TRk5x0GmE5McaFPOWw8eTnLTS15mVG1AlGeJNmmdGMpOz7lq1dpPFiS\nbzfILrSoDgZPdvo4GmcmTU6rNWiFsy7xz1NhTQ66RB+eUjdEH17vdEUBkpW0P16h6hpVVLKTXiVP\nGqvSXKwk9Gq+ptxuUWzHYlH3N1AEV6PSAm+WSY8gr1B5SXH49GhWAF3bJ69fjk8fykoYUuu45PB3\nc6yniU8K4rMKN7O4iUj4Tr4smufRa4qt7xtWB5r5VZ/GqCY5CHFTy+QV4Y8qi+iVP1kxfEeAwNF5\nSfOkZuv7YgV310KuMYEVyV1eEz82NI4T0t2IOtDsfluuqUMHdy2dt9GrDayC0Ssu4XlKMBUJYB1o\nHv9qhJtKTkra0ywv+hx/pUUVKrCQDTzCiWF1wcdNDPWgSbIlS8z8imbyYpOj395i9IqLU1qcApys\npnO/pGwoRq9FLC659G7XGFdx6X8fUQdiUHJyS9ncnEqymrynBC92V0w/622H3rtzjKMoY0XZdGgd\nFRhXduTtB08PE/nJef0sz+1nYtHGWvSqQN26h3u+JDpeit65qChu7gupoqzw752BMTjnG+ee1k/K\nGf4kQ3/3FnXsY+IAtKbsNwhPVtStAO88QecV3qO5pNsZi7eoqFrBhuyu0fME62lMpyHlEk+L9tlR\n6Mrg3x9JtOkooexJ2aEaNnHnOc4yk3yTdYGy0Hj3GP9YUvyi22dE96Y4qxzjOaQHMdV2R36no7Hk\ne2fiqFTG4D2S388MJPnPf7TAGS8xzRBVVuiFaLhNw8eZrCmubGNDbxOs5YiN3RHVipPXghZTks/i\njFf4JwvqQROdlujxgrIdSF0+eXrLXG0aNj9+/XJ8+rBK6rhFy2F2LaBou1QNh9lVD38pjsJgbtn7\n/ZqipWh9Arqy6FyamOMXXXRpCWYVW29bVhcU0bhm/62ExY2W7NwTy+SFgCpUpDs+y32XZNsj7yh6\ntyQYavxKjJvJiU6Xks3RvDWRMKcdj2zo0zivaZzX9N8e03pgefBvtlkeSlN0/FJA525N52PJTxn+\ncInxYPiu+Ckao4ral7JCOtDML3uMX4qfOCGDqcCKo3PLwTdSVC351+4yZ3HRo3M3J5gZundKWvfW\noj3/4oBLf/uEItYsLyGE+jODcTQHv/OY9t2UrOuQtwW2kO43qUKxtTdOMpYXfTH1+IrVwaeTa356\nXj/Lc/uZWLSVBZUXmFevy24wK6l7sSw6awEWqLIC35MgqDSj7DdwzkS6553MSHcbqBeEq6hXGXXk\n4WQV2UEL586xOBVdTbnfQScl1pMdppOUFIdDcUfGodywssY2Arx7p9T9mKIXUkcu+ZUt6sABDd7Z\nEpXkuKdzMffEgbgohw3yfoAZdqTsEXpSn58vAfDGaxp3ZzizBNtpYpNMrvNddF5SN3yKC10h2Wwa\nsKYdSa7ITCz3qqpxFjkmcKmGoj0vhrGgybYkT7zoBlS9hkj68s1irBS2IZEAzkSkitWFAeHdc4ph\nAz1dPf1m/bI88rmGdaF1ZJk+51C2hSiT9RzaRxXzyx57v58RzmrcpCYaG5I9SAcOjfNajvoZzC+L\n8uT8dUUwBV1azt5oiKZZCR1H1VI2sApaxxX9H07Z/W5K60EmpQErnze72WB5waV1P2X2haEk9q0M\nVahZ74rRavlCn2hUMXi/JuuLY7N5YqgCxeqC5uxNj6PfatN/PyHruxStjWX+nRnLQ8322ymd+yXx\nWU0wq3HKzXuhpKRx9kbE6Zsu/rImPWhiPBi9GqIrePQnXZZXYhaHDllfMXtzh3BWc/i7m9KdhfHL\nAWdf2WF2s0H7k5xwVuOlBm8lEOD4tGZ1GNE6KjalooqtP1w/5Ub9sjzy+Ya1csQ3lnynSbnXpmr6\nVK1AjvKlwcaRBEsFLgx7pNs+9XZXShrtBvFHE9DCTzQNHxQ4owXeLEdFEc5oLh1hA3omC6g7WuKs\nC7zThdi/N9bvsheBEbWIVQonNwQ/eiSSwvWmAbrdQlW1kF/KCpVV6GWCfzQlfOsWKEV6fQtnnmFa\nIclrF4W40/Cpug1s6Ekkq++hViIJA3DeuYO7KmWBtpZyryuKmXYIvkdxeQsbeGKjX+R4dx/jrHL8\n0RonKXDyGut7hA+XYmTQGvd8gVpJtgm1xU1qVCHuTp2WWNeR7JPeU+Ir5WZ97t3IzyO+8lkZbmpZ\nXFZ4K4hPDLWvWO9r0p5D1RDuYtZ1mN7wSfuaw/9LTC5VpKkDjT+XaNXJy4r9tyqChWF606P/YUF8\nInXwoqnoflTgL83mexrKQYPpcyHLS6EkDD6uKWNN0VYYDyYvNihaiv4Ha9a7Ip0LFpbahyLWnL8m\ngN3oTD6WtxTxSYkysPOdgt6HNae/GuMUlsGtjKzvMP5Cj+0/zFlcCQlPE9K+Q3zrjNqXEpF1YPf3\nlhz87hhdQuPjKeFZSvduSRVJPf/a/zIVUHFuxaRTWaY3XLItH+tA+/aS7sclVUNRtBTZ0KNoapYH\nLmdfjAhnNdPrLsm2Jni8wslh9LLL7ObTGpH2F2an/YzotDXl1V3cswXZdkTjKHli51ZFRb7fxpsr\nlNZ44zWMZ3S+L1pk7UlN2zHmiTJDF5AhaWAAACAASURBVDXu+ZK618I9mwsR57BPcDTFW2eUh0PZ\nRe8PqCMP7/EcpgtUp0nVi/AfTiVAaVSjswobOFgjSDTvdL4hvwdkV7fwloXUpR2FN7XkWzGBMdSu\nNALLrQZWScMGR1G1fIwnz0on2Vjnr27hzwsBErx49ckOu44DvKMx5YUBTlo+qYdX/ViiZFc5thU/\nYUcKmd5SDZqSn71JJfTWGaYZyftp7ROTjrMuRA653UaXRk4RT71ZYob6rOMn4iv/DGLp/a5S6u9b\naz/4icuexFcqpf4iEl/5H3zmb/KMD38BO99eYh3N8Vclyc7NLW6qGL2q6N6W5L1wXLK4GrH3eytm\nN2LcxDB+2cF4Dlf+zgLrabyFg5cEFC2HZFs/UTmc/kpAdC7hU6sDh3BsSYeKwQ9XmMDl5NcjGo8t\n8WOD1eKUNB48/GoTZSRYanbdYfBeSTDO0HXM9HmHi19bsT6ISLY0blLSPBYHZBlr+h8UYsm38uBo\nPqqfZM8XvZBoUnP/Lx0QTCSVcPhDy/iVDWyhBtMKGX2hhd6IlpSBo9/u071Ts7gkaZ9pXzN4vyDZ\n9kh2FP4i5uyLLv0PpEbtLWvylvcku2S942zckJBeaGE19H9UU/w/7Z1pjCTned9/b91VfU/3nLuz\nJ3d5i6REXTYs2fEZAbETwHJiBLAROHE+5GOAIICBBAgQxJ8DGAlyGEqAwEmUwJBgG0Fk2YnsyJJJ\nSRQpUlwu956ds++ruq73zYend0mvSO6QorgzUv+BQs/0VPdWdz371lvP+z/K95mfvsu6fpA4EjNt\nVRRY04xsrYbfTVCTGdZBH2uvC9rg9mdi1epYwoAIhZpnpQUq1zi9qXhmlxycwQw1Ex618W3pHfcG\nONMM43sUjZIE2Q6G2N0x7v6I7ESd/MKGGEXlBiwLe5YTn65Lj9u10RvLGFuhqxH5ao2sWcIZp+JN\n3Z/i9mLM9S28rR66GsmCYFbgdmO8zhRVyF2CezDFyjROe4RKc6ZPnsA9mGAfDDCei3akt05XWCum\nWpIQhM5w7iUiwgTr5r5QBJWSdO5JinZt7J0u7t6AbKMBd8IVNhtY4/huUZrQfcPjBLAyzawVYB2y\naOXiINsh8EDsK48KClfRuCS8//7DEeVbhuUXciZrkiBTvqkIehqlYbzhUbkx4/rfkIF9cNbh3H85\noAgUs7WIK58ts/+RCKWNiG1uFbS+PSFsa+qXC6xM+uCrX59gZ5LgsvMTFdK6y/pXY7Fl3Z4xOCu+\nIM2XY6wMnCmMNm3yEoxOSXvCmRmWXimYnAjpPG7jjaRW2k8pwv2U6tWYg6c93FFKvOxSvZGx/RM2\nO5+MxM3wgjBmnFjc+Na+phmetuQYDjRBx9B9ogIGmt/okUdyh7D8QgYGqjdkUK6/njJddQl6hTj7\ndROCjvC1AZKGQ/PFIdqH1l+2mZxQTDYU0V5O0hB9ReFJEtD98Oa6PmRtPxAcjZm2bZOshIQ3B7KA\nuF7Hu90jW61hZQXWJBGvkCSTgcxz0WVffEQmCclGFVWUCXbGGMci3igTtKUHZvenZI+ewhnOwBKO\nN5ZCf+ghTFKArXAGCeRaxC95QVGPsLtjwmkqLYSskECBfWGV2IMYy3NQs0zef6UiA96T58lLLv7W\nAGcUCw+6VhYuOGD3JuhqiDNKSTYb+HtjWbXPcmnFuLbwyQuNWVumiFycgyFFs4LlOuITosG7sote\nb2KQDEyrMBSejzPJmT28JuEGQBE4uHtDVLMsCtJ5+8fqT9D1sviRZzlqa4cgPymeLPfBHRHCu8Bb\n2Vd+/O32McbkSqk79pXtd/MPHUVoT3JFO882ZAAJYO9ZyRet/9l14r91ju4jNqVtQ/31mL1nI2qX\nDdFejpXbXP+VFdb+ImF41mP5mwY3FmZF5WZOXrK4/eky9kwWI+0ZJMuK7U+VWH0+Ifc9yrdTuo/5\n1K8IA6P7eMjyixnTlkMR2IRtgzuVhc60auNMpf2SlcGeQfM7GavPa/rnXZwkZPW5gt4jAbUrKc2X\ncyYnI2kpVmxKW4qwLb3v2tWMYGdM95El1Lyf3nhN0qb8boaVOVRfPGD8WIutzyzhDWC2ZOFONWE7\npftIwLTlYeXy2bSnqNwqsNKC0m5BsD0lmqW8/ust2k9VqV41vP5rLda+nqNyyEsW9W8egGPTfWYJ\nZ/LO7JH3UNcPDEdjpq01we2x9F0DB5VrzJx6p2Y5OvBIGz7ZShljW8TnloSXPElIVyu4g0TyJJWE\n3Uavd8hLroh16hF2UpDXQlQ8jxtTCmuaogNn3tOVnEbty+KfPYjBcyHL5RiyAvXKVXQpwJokkqjT\nCEVkU4/Qvn3XkdCeFaQbVaYPr4ClxEvFlkmjCT3s9hBjKek9K4UzSshWq2RLEUXJo2hVMZUIHbr4\n19voUjjncLvYWwcY28LUK5IROcvwdye4B1PCmwPJtLzZAwP2NMMZiyRf9pUBWU1m9D+8gprMY85m\nCeb0BnZ7IL38+8EAuX5jm9tXvmn7zXtP79u8y7vd53jCwNZPeuKbsS+ZhfYMWi/l3P6V8wQ9SYex\nU1lc9IaGeFnRu+hSvp3ijsEdCnUtixSzmtTa/rMuw1M2J788pHYjp3Irxx8VrP/5hKBrGG16xCuS\nStR6aUYeiFWqlUL/vEvYLcgqNtFezmjTIm459C7a9B9yKW9JyEHllmZ0yiEPLfyBoXRzSlqyqF1N\naT/pc/vTNgdPSz88uj3DGxrcWO4ahmdcJmerWAWMTlqkVQsn1sRNRfspn97DttR9ZFHMedqrf9Yh\nLdvsfCIkOpCE+ehAuNuDMzYHz1h0nqyQliyGD1cwvovfkV7/bElx7n+OKF0b0b/g4MSaG7+8yvDR\nBt5ECwf8Pufpr9T1EW6VHI2ZtmOh0gxdK4NSkqoC5MsVQNSSXkdmqvlKVWKEpjN0syqcZ6VkVjkW\n9aQJPOxZjtrtYFdK6IosQkweaeF3U1SuSVsR/gvX0GfWUfO+NZYlfObCzA2ejCSgd0bkj58DQA1S\nVOji7Y+JN6syWPYmqDTDlEKKsi92sCUXXSkJK8azsScpRSXAGkywxwl5PcTq9CnWmtLe6MdggTWY\nUCzXZMG1USFtBgQ3+5jQI31oHSuTxc+iVZXFSgtRgUZiATtbi4he60gCkGtDp4elNUWrIpax9RKN\nv9yR2XucSZhvYTDlUC5eh8BdOb6gbYx59h12fzf2lVvvl33lUYGVQfUa9B5VuGMXK52H9FZtSjsF\n5RtTSjsOvQsBq18f0X66TGlHi9WBpWi+lLD/bIXS/jxya6KxZ4bqVRidshifKZFURe2YVC1yP8Tv\na0q3Z9QvKyYbPnrJJqkqwq5B23KBwED3EYflFzJq1wqmy5ZkPrY1g/P2/NgN0b5hvGFT3ikYnS2R\nlRWFb1G9VYiR0+6I7oeX2P50CWcCadWhslVgFYrOYw7L387kkmyg9K1bTNbO4k4M0V5G/0JI5XZG\nUnMJ+oZrv9yitGOIdmUh0InlGNxYY6cWQUdRvzwlaXqoAgYPV0FB+wmX8m3N9qeqVG8U1C9LZFnz\nlZzuIw5GQfm2DV9553N1T11/X1BKLQH/DTgDXAd+xRjTe4v9CuCl+a83jTG/eO8+9+JozLSnCSoR\nZaI1mMqAGyc4/RhnfyheIKMZOhKFoDNKIBMekQ5cCemd5ehyQN6qkNdDtG+jolBofEWBcRTRjSFO\ne0x8soyVFKQfOiOz6PnJsvoT+b0osCdC51PDMSb0KUouVpJjfJe0EUAh6TPGdzGhJwrMLMfZ6aED\nUSuqLJdkeCMXJnuS0v/4CYzn4OwPyU+v3G1JWKMJyVoZ3ahgbx1gTTOsNCe80kbXorkPSi6vW66i\nPVsixbKColGRC1+hia4PSU415nS/nPTJM9ICKQzWaIbVnzB+fHWexCM9flUU5Esl/Bud+58sY97t\nbOSB2FceJTRenXL+d2/TfDm76yBXuxpjbFHgTtZ88lCx/2yFeFkxWbNwEsNsySFZcqndyLATQ+O7\nU/oP2Yw2bdzYUNoxjNdtSjs5k1WLxqsTZg2FN5RIrmTJw06lLeIPpQXi9wuCfkH7SYfWSxnTZYfO\nYzbVGznlbUmCb1wqWHpVwnWTqkVlKyctW1SvTlj5Wp/BWZcsVOx/xGd8oU7lVkJpW/6d1efGDM7a\naEex/GJG4Yunid9NeOVfbM5FOApvkFK5nWFlmrCjcWKNO4bVL+8w2ZA8S1VA74JLFlmUdgpKO5rp\nRsDohMSQHXxYUb9csPKtlPqrY0q7spgbt2ziZUXnUYfGpQJvBJ2n7mcYZd7vmfY/Bb5sjLkAfJm3\nd/eL35SNet8BG47IoG0CD1OOcHcHmNDDKEVRLwvbQRuJE2uVUcaQ13zyig+R3MpbaUHeqmB3Rkw3\nQklicS20Y6FLoVwQZhn+q9tY4xlqPCW8McIZJULrUYqi7KMKg4mE2wxyMTCei15dkhlyIopJlCK8\n2kFXAsgL8R05WZFAXZjzxntCT6xKn9rKCsm0nMyoP78LxlC0KuIWOJ7idCckZ5cJbo9IWiFmqYYq\n5HYuPdEQ0c+coZK35O7DHidiGGVLCISaipxYRx7aEYWajny8232KlTr2bg+Vy0w72hpjTROctlAf\n1TgWrnj1/v4Msspe3N3uu/sDsq88KrAzw8HTJQYfWcfrplS2hKY3OhXgjjWdx0sEnYygK0ZIlVsi\nQrET4V0XnphHdR4T2trKN1OyikLNTZsqW9In1o6i86TMhCfrLrufiChd6dN/SMRhg3MWeWShPcXB\nh1zCfaF+jk8Ki8ROCrqPKkmzOW0zq0vSTdiV95+uKcanI6abFfyBpvkVCdWNbk5oPxmSRRD0NJOT\nIdUbYqPaP+9ip5qoLZ9z/cs2/ki8VUZnIuKWg5UUJDXhiLsTw/W/vUHzuznLL8wkJHhH4/cLxhs2\n0a74n1RvSkzbxX+7TVIVauTggghr8lDEQ87UULumySJFtFdw4k/vMwjfU9eHqe374M2L6/8J+Jvf\n7xvewZEYtEHUesaxscYznEGMNY7Jl0rMzjQpSp6ISSIP7Vh4t3vMzjax+mPysivUNduidGOMjjzc\nnSHBlQPyRkjeqlDUI8xSDeO75CeaqCTFag+kLeDa4ug3jrGGU9ytjkjf+1OZKSuF3ZvIomcss/t8\npYrKNXmzhJUVlF49QKU52WoVMx4zO78M3YFI6YcxxlJQSGCvsRR5NcC5sY8ajNGNCrrk420PMK5N\neGkP7btiOTud3b2QYVk4t7s4tw7mX5miKHtC61uuwmgi4hitcUfZnN9eiFdKkpGeW4Z4JmEPZU88\nWRoldOiQn1jCHiZY+99z9/YWMKD1G9thXmHMHxljLhpjzhtj/uX8uX9mjPni/OeZMeazxpiHjDEf\nM8ZcfQ9FdCShHfG6Hp+wGVyISMsW0X6OO9UobdAe4t6YQ2lXk4dQv1KQVmRRLqlb9C46uCOIWwp7\nmlO7IhFjtWua3sM2w1MOtes55e0cqwBvpKlfKWh/rEntWkHhiRKxdHWInRiWLhW0XhiS1F38nmHp\nFU3ScDnxf1OMBSe/cJvKrZSkbjE44xAvWZRvaaKdhNJrHTDQ/+RJ4qbF6EKFaF+TVURinweK0Umb\noJPhTAxJzWZ0UsIUZnXxTgk6WjjWDUssJLSoIys3c5SWlsh01SMrQbSTkFZsNv74gOmaR1Kz6J+X\nDMvxE6u4sWFWtylvJeSB4uAZl+mqpNpYmaHzpAia8vB+ZKR76lofar3mnbBqjNkBmD+uvM1+wfy9\nv6aUOtTAfiR62ijEQyPXWHFG1ghx90eSKjNPGTe2GPa7Q8jW61iJtAXsiezvjG3SZojbn1HUI5zt\nLt7NtlizTiboC6dQcSbsEW8eEjDLhfoGFEtl7NEMLIvw9TbGdchbFTFrmkkfXIeu0AGv7KA8l+LM\nilCD0oyiJFFp+vxJ3EFCcXoVuycqLHuSYnwHpzPGlEOcUUJ6fg330m0AkRa7DnnNx3YbWNMUuzuk\naFVFtVjxcHsz8vUGSSsgujEUSbqS6DJ7kkgSzjyu7K4Cck6RnD7UwG8nFCeXsfoTnJGiqJVQWYHT\nF0VmvFkhyvLv7TbfC2MgP4RD/AJ3oYxh/St9+o9WCbo5eWQzPG3jDQxhWy6y259yKN1S1K9m7H3E\nxZ1CaQcqWzn2TKLGrn8moPdwSBEo4lUzj/8SNeV0xSWpS+pL0Mk4eCbASkQDYacSuHvw0Qarf7oD\n8YydXzpLdKDxRoa0ovDGML7o03g95bV/uEHju+J3bWWayYYEGdz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8bc9ff8410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Illustration of a lens in the 4 bands provided\n", "im = x[0].T\n", "subplot(221)\n", "imshow(im[:,:,0]); colorbar()\n", "subplot(222)\n", "imshow(im[:,:,1]); colorbar()\n", "subplot(223) \n", "imshow(im[:,:,2]); colorbar()\n", "subplot(224)\n", "imshow(im[:,:,3]); colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the model\n", "\n", "In this section, we train the CMU DeepLens model on the dataset prepared above. \n", "\n", "Note that all the data-augmentation steps required to properly trained the model are performed online during training, the user does not need to augment the dataset himself" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: TITAN X (Pascal) (CNMeM is enabled with initial size: 95.0% of memory, cuDNN 5110)\n", "Using TensorFlow backend.\n" ] } ], "source": [ "from deeplens.resnet_classifier import deeplens_classifier" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = deeplens_classifier(learning_rate=0.001, # Initial learning rate\n", " learning_rate_steps=3, # Number of learning rate updates during training\n", " learning_rate_drop=0.1, # Amount by which the learning rate is updated\n", " batch_size=128, # Size of the mini-batch\n", " n_epochs=120) # Number of epochs for training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.fit(x,y,xval,yval) # Train the model, the validation set is provided for evaluation of the model" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saving to deeplens_params.npy...\n" ] } ], "source": [ "# Saving the model parameters\n", "model.save('deeplens_params.npy')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.97792608, 0.94681907)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Completeness and purity evaluated on the training set [Warning: not very meaningful]\n", "model.eval_purity_completeness(xval,yval)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Sq5HAkpz1RpIjjVyzgAuBHwLnAwMkDW1n35H5byDpcuBygOHDhzOidSv79RNP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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f89662d58d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot ROC curve on the training set [Warning: not very meaningful]\n", "tpr,fpr,th = model.eval_ROC(xval,yval)\n", "title('ROC on Training set')\n", "plot(fpr,tpr)\n", "xlabel('FPR'); ylabel('TPR')\n", "xlim(0,0.3); ylim(0.86,1.)\n", "grid('on')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Obtain predicted probabilities for each image\n", "p = model.predict_proba(xval)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Classify Testing set\n", "\n", "In this section, we test the model on one of the test datasets provided as part of the challenge" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.0/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.1/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.2/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.3/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.4/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.5/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.6/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.7/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.8/Public\n", "Classifying...\n", "Loading images in /data2/BolognaSLChallenge/Dataset3/Data_KiDS_Big.9/Public\n", "Classifying...\n" ] } ], "source": [ "from deeplens.utils.blfchallenge import classify_ground_challenge \n", "\n", "# Utility function to classify the challenge data with a given model\n", "cat = classify_ground_challenge(model, '/data2/BolognaSLChallenge/Dataset3') # Applies the same clipping \n", " # and normalisation as during training" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Export the classified catalog, ready to submit to the challenge\n", "cat.write('deeplens_ground_classif.txt',format='ascii.no_header')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from astropy.table import join\n", "\n", "# Load results catalog\n", "cat_truth = Table.read('ground_catalog.4.csv', format='csv', comment=\"#\")\n", "\n", "# Merging with results of the classification\n", "cat = join(cat_truth,cat,'ID')\n", "\n", "# Renaming columns for convenience\n", "cat['prediction'] = cat['is_lens']\n", "cat['is_lens'] = ( cat['no_source'] == 0).astype('int')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve,roc_auc_score\n", "\n", "# Compute the ROC curve\n", "fpr_test,tpr_test,thc = roc_curve(cat['is_lens'], cat['prediction'])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f896661be50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(fpr_test,tpr_test,label='CMU DeepLens')\n", "xlim(0,1)\n", "ylim(0,1)\n", "legend(loc=4)\n", "xlabel('FPR')\n", "ylabel('TPR')\n", "title('ROC evaluated on Testing set')\n", "grid('on')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.98228380093582546" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get AUROC metric on the whole testing set\n", "roc_auc_score(cat['is_lens'], cat['prediction'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a point of reference, our winning submission got an AUROC of 0.9814321" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python2", "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
mdastro/UV_ETGs	Coding/Model/ModelFit3D.ipynb	1	445402	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "new_probability = np.loadtxt('./Model/fit_results_3d.csv', delimiter=',', dtype=str)\n", "results = np.loadtxt('./Model/model_prob_3d.csv', delimiter=',', dtype=str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "my_probability = {}\n", "for i in range(len(new_probability[0, :])): # Converting numpy array into dictionary\n", " my_probability[new_probability[0, i]] = np.array(new_probability[0 + 1:, i], dtype=str)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "my_results = {}\n", "for i in range(len(results[0, :])): # Converting numpy array into dictionary\n", " my_results[results[0, i]] = np.array(results[0 + 1:, i], dtype=str)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "parameter = my_probability['parameter'].astype(str)\n", "quartile_1st = my_probability['25%'].astype(float)\n", "quartile_2st = my_probability['75%'].astype(float)\n", "quantile_25 = my_probability['2.5%'].astype(float)\n", "quantile_97 = my_probability['97.5%'].astype(float)\n", "mean = my_probability['mean'].astype(float)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "probability = my_results['pnew'].astype(float)\n", "redshift = my_results['redshift'].astype(float)\n", "sersic_gal = my_results['SERSIC_GALFIT'].astype(float)\n", "sersic_sex = my_results['SERSIC_SEXTRACTOR'].astype(float)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9611\n", "2401\n", "2401\n", "2401\n", "2401\n" ] } ], "source": [ "print mean.size\n", "print probability.size\n", "print redshift.size\n", "print sersic_gal.size\n", "print sersic_sex.size" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "etanew[640]\n" ] } ], "source": [ "print parameter[5449]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7210\n", "ok\n", "9610\n", "ok\n" ] } ], "source": [ "idx_beg = []\n", "idx_end = []\n", "for i in range(parameter.size):\n", " if (parameter[i]=='pnew[0]'):\n", " idx_beg = int(i)\n", " print idx_beg\n", " print 'ok'\n", " elif (parameter[i]=='pnew[2400]'):\n", " idx_end = int(i)\n", " print idx_end\n", " print 'ok'\n", " else:\n", " continue" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "new_q1 = quartile_1st[idx_beg:idx_end+1]\n", "new_q2 = quartile_2st[idx_beg:idx_end+1]\n", "new_25 = quantile_25[idx_beg:idx_end+1]\n", "new_97 = quantile_97[idx_beg:idx_end+1]\n", "new_param = parameter[idx_beg:idx_end+1]\n", "new_mean = mean[idx_beg:idx_end+1]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# interp_function_01 = s.interp1d(redshift, mean)\n", "# new_redshift = np.linspace(redshift.min(), redshift.max(), 200)\n", "# new_probability = interp_function_01(new_redshift)\n", "# print mean.size" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2401\n" ] } ], "source": [ "print new_mean.size" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2401\n" ] } ], "source": [ "print redshift.size" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection='3d')\n", "# plt.scatter(redshift, sersic_gal, mean, c = '#feb24c', alpha=0.7)\n", "ax.plot_trisurf(redshift, sersic_gal, new_mean, color='#feb24c', alpha=0.7, linewidth=0, antialiased=False)\n", "ax.set_xlabel(\"Redshift\", fontsize=15)\n", "ax.set_ylabel(\"Sersic Index\", fontsize=15)\n", "ax.set_zlabel(\"Probability\", fontsize=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": 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": [ "plt.scatter(redshift, new_mean)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": [ "plt.scatter(sersic_gal, new_mean)\n", "plt.show()" ] }, { "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": 2 }	mit
pgr-me/metis_projects	03-water_1/get_pums-h_data.ipynb	1	25215	{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "import urllib\n", "import urllib2\n", "import os\n", "import pandas as pd\n", "import zipfile\n", "import fnmatch\n", "import shutil\n", "import glob" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[['01', 'AL', 'Alabama', '01779775'],\n", " ['02', 'AK', 'Alaska', '01785533'],\n", " ['04', 'AZ', 'Arizona', '01779777']]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "states_url = 'http://www2.census.gov/geo/docs/reference/state.txt'\n", "response = urllib2.urlopen(states_url)\n", "cr = csv.reader(response)\n", "\n", "states_list_1 = []\n", "for row in cr:\n", " states_list_1.append(row)\n", "states_list_1.pop(0)\n", "\n", "states_list_2 = []\n", "for element in states_list_1:\n", " for string in element:\n", " split_string = string.split(\"\|\")\n", " states_list_2.append(split_string)\n", "\n", "states_list_2[:3]" ] }, { "cell_type": "code", "execution_count": 24, "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>id</th>\n", " <th>abr</th>\n", " <th>name</th>\n", " <th>num</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>01</td>\n", " <td>al</td>\n", " <td>Alabama</td>\n", " <td>01779775</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>02</td>\n", " <td>ak</td>\n", " <td>Alaska</td>\n", " <td>01785533</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>04</td>\n", " <td>az</td>\n", " <td>Arizona</td>\n", " <td>01779777</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id abr name num\n", "0 01 al Alabama 01779775\n", "1 02 ak Alaska 01785533\n", "2 04 az Arizona 01779777" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_states = pd.DataFrame(states_list_2)\n", "df_states.columns = ['id', 'abr', 'name', 'num']\n", "df_states['abr'] = df_states['abr'].str.lower()\n", "df_states.head(3)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# make directories\n", "\n", "data_dir = 'data'\n", "if not os.path.exists(data_dir):\n", " os.makedirs(data_dir)\n", " \n", "pums_dir = 'data/pums-h'\n", "if not os.path.exists(pums_dir):\n", " os.makedirs(pums_dir)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "scrolled": 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>id</th>\n", " <th>abr</th>\n", " <th>name</th>\n", " <th>num</th>\n", " <th>url</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>01</td>\n", " <td>al</td>\n", " <td>Alabama</td>\n", " <td>01779775</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>02</td>\n", " <td>ak</td>\n", " <td>Alaska</td>\n", " <td>01785533</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>04</td>\n", " <td>az</td>\n", " <td>Arizona</td>\n", " <td>01779777</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id abr name num \\\n", "0 01 al Alabama 01779775 \n", "1 02 ak Alaska 01785533 \n", "2 04 az Arizona 01779777 \n", "\n", " url \n", "0 http://www2.census.gov/programs-surveys/acs/da... \n", "1 http://www2.census.gov/programs-surveys/acs/da... \n", "2 http://www2.census.gov/programs-surveys/acs/da... " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# construct the urls\n", "# example url: http://www2.census.gov/programs-surveys/acs/data/pums/2014/1-Year/\n", "# http://www2.census.gov/programs-surveys/acs/data/pums/2014/1-Year/csv_hak.zip\n", "base_url = 'http://www2.census.gov/programs-surveys/acs/data/pums/'\n", "year = '2014'\n", "middle_url = '/1-Year/csv_h'\n", "end_url = '.zip'\n", "df_states['url'] = base_url + year + middle_url + df_states['abr'] + end_url\n", "df_states.head(3)" ] }, { "cell_type": "code", "execution_count": 27, "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>id</th>\n", " <th>abr</th>\n", " <th>name</th>\n", " <th>num</th>\n", " <th>url</th>\n", " <th>path</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>01</td>\n", " <td>al</td>\n", " <td>Alabama</td>\n", " <td>01779775</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " <td>data/pums-h/al.zip</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>02</td>\n", " <td>ak</td>\n", " <td>Alaska</td>\n", " <td>01785533</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " <td>data/pums-h/ak.zip</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>04</td>\n", " <td>az</td>\n", " <td>Arizona</td>\n", " <td>01779777</td>\n", " <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n", " <td>data/pums-h/az.zip</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id abr name num \\\n", "0 01 al Alabama 01779775 \n", "1 02 ak Alaska 01785533 \n", "2 04 az Arizona 01779777 \n", "\n", " url path \n", "0 http://www2.census.gov/programs-surveys/acs/da... data/pums-h/al.zip \n", "1 http://www2.census.gov/programs-surveys/acs/da... data/pums-h/ak.zip \n", "2 http://www2.census.gov/programs-surveys/acs/da... data/pums-h/az.zip " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# construct the paths\n", "df_states['path'] = 'data/pums-h/' + df_states['abr'] + '.zip'\n", "df_states.head(3)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# download zipped files and save to pums directory\n", "for index, row in df_states.iterrows():\n", " urllib.urlretrieve(row['url'], row['path'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%bash\n", "rm data/pums-h/as.zip\n", "rm data/pums-h/mp.zip\n", "rm data/pums-h/um.zip\n", "rm data/pums-h/gu.zip\n", "rm data/pums-h/vi.zip" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# unzip files\n", "\n", "rootPath = r\"/Users/peter/Dropbox/ds/metis/notebooks/projects/mcnulty/data/pums-h\"\n", "pattern = '.zip'\n", "\n", "for root, dirs, files in os.walk(rootPath):\n", " for filename in fnmatch.filter(files, pattern):\n", " #print(os.path.join(root, filename))\n", " zipfile.ZipFile(os.path.join(root, filename)).extractall(os.path.join(root, os.path.splitext(filename)[0]))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['data/pums-h/ak/ss14hak.csv',\n", " 'data/pums-h/al/ss14hal.csv',\n", " 'data/pums-h/ar/ss14har.csv',\n", " 'data/pums-h/az/ss14haz.csv',\n", " 'data/pums-h/ca/ss14hca.csv']" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make list of tuples of csvs\n", "\n", "base_path = 'data/pums-h//*.'\n", "csv_list = glob.glob(base_path + 'csv')\n", "\n", "csv_list[:5]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# make dataframe of all the csvs\n", "df = pd.DataFrame()\n", "temp_list = []\n", "\n", "for csv in csv_list:\n", " dataframe = pd.read_csv(csv, index_col=None, header=0)\n", " temp_list.append(dataframe)\n", "df = pd.concat(temp_list)" ] }, { "cell_type": "code", "execution_count": 33, "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>RT</th>\n", " <th>SERIALNO</th>\n", " <th>DIVISION</th>\n", " <th>PUMA</th>\n", " <th>REGION</th>\n", " <th>ST</th>\n", " <th>ADJHSG</th>\n", " <th>ADJINC</th>\n", " <th>WGTP</th>\n", " <th>NP</th>\n", " <th>...</th>\n", " <th>wgtp71</th>\n", " <th>wgtp72</th>\n", " <th>wgtp73</th>\n", " <th>wgtp74</th>\n", " <th>wgtp75</th>\n", " <th>wgtp76</th>\n", " <th>wgtp77</th>\n", " <th>wgtp78</th>\n", " <th>wgtp79</th>\n", " <th>wgtp80</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>H</td>\n", " <td>77</td>\n", " <td>9</td>\n", " <td>400</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1000000</td>\n", " <td>1008425</td>\n", " <td>22</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>24</td>\n", " <td>36</td>\n", " <td>24</td>\n", " <td>36</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>20</td>\n", " <td>22</td>\n", " <td>22</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>H</td>\n", " <td>315</td>\n", " <td>9</td>\n", " <td>200</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1000000</td>\n", " <td>1008425</td>\n", " <td>161</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>165</td>\n", " <td>156</td>\n", " <td>44</td>\n", " <td>43</td>\n", " <td>244</td>\n", " <td>212</td>\n", " <td>56</td>\n", " <td>137</td>\n", " <td>244</td>\n", " <td>164</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>H</td>\n", " <td>807</td>\n", " <td>9</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1000000</td>\n", " <td>1008425</td>\n", " <td>1303</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>1909</td>\n", " <td>2330</td>\n", " <td>463</td>\n", " <td>1339</td>\n", " <td>1384</td>\n", " <td>446</td>\n", " <td>1416</td>\n", " <td>381</td>\n", " <td>1443</td>\n", " <td>502</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>H</td>\n", " <td>1408</td>\n", " <td>9</td>\n", " <td>200</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1000000</td>\n", " <td>1008425</td>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>...</td>\n", " <td>62</td>\n", " <td>79</td>\n", " <td>92</td>\n", " <td>55</td>\n", " <td>45</td>\n", " <td>51</td>\n", " <td>49</td>\n", " <td>91</td>\n", " <td>56</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>H</td>\n", " <td>1508</td>\n", " <td>9</td>\n", " <td>101</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1000000</td>\n", " <td>1008425</td>\n", " <td>125</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>130</td>\n", " <td>35</td>\n", " <td>134</td>\n", " <td>228</td>\n", " <td>125</td>\n", " <td>36</td>\n", " <td>36</td>\n", " <td>119</td>\n", " <td>155</td>\n", " <td>108</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 235 columns</p>\n", "</div>" ], "text/plain": [ " RT SERIALNO DIVISION PUMA REGION ST ADJHSG ADJINC WGTP NP \\\n", "0 H 77 9 400 4 2 1000000 1008425 22 0 \n", "1 H 315 9 200 4 2 1000000 1008425 161 5 \n", "2 H 807 9 300 4 2 1000000 1008425 1303 0 \n", "3 H 1408 9 200 4 2 1000000 1008425 50 3 \n", "4 H 1508 9 101 4 2 1000000 1008425 125 4 \n", "\n", " ... wgtp71 wgtp72 wgtp73 wgtp74 wgtp75 wgtp76 wgtp77 wgtp78 \\\n", "0 ... 24 36 24 36 6 6 20 22 \n", "1 ... 165 156 44 43 244 212 56 137 \n", "2 ... 1909 2330 463 1339 1384 446 1416 381 \n", "3 ... 62 79 92 55 45 51 49 91 \n", "4 ... 130 35 134 228 125 36 36 119 \n", "\n", " wgtp79 wgtp80 \n", "0 22 38 \n", "1 244 164 \n", "2 1443 502 \n", "3 56 41 \n", "4 155 108 \n", "\n", "[5 rows x 235 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# remove unnecessary files in pums folder\n", "shutil.rmtree('data/pums-h')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pums_dir = 'data/pums-h'\n", "if not os.path.exists(pums_dir):\n", " os.makedirs(pums_dir)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# http://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMSDataDict14.pdf\n", "\n", "cols_list = [\n", " 'RT' # record type, H indicates house\n", " ,'SERIALNO' # housing unit / GQ person serial number...unique identifier\n", " ,'DIVISION' # division code\n", " ,'PUMA' # public use microdata area code, based on 2010 census defn, 100k people per puma\n", " ,'REGION' # region code\n", " ,'ST' # state code\n", " ,'WGTP' # housing weight\n", " ,'ADJINC' # adj for income & earnings to 2014 values, divide by 1 million to do conversion\n", " ,'ADJHSG' # adj for housing dollar amounts, divide by 1 million to do conversion\n", " ,'HINCP' # household income past 12 months\n", " ,'FINCP' # family income past 12 months\n", " ,'NOC' # number of own children in household (unweighted)\n", " ,'NRC' # number of related children in household (unweighted)\n", " ,'WORKSTAT' # work status\n", " ,'WATP' # yearly water cost\n", " ,'CONP' # condo fee / month\n", " ,'ELEP' # electricity fee / month\n", " ,'FULP' # fuel cost / year\n", " ,'GASP' # gas cost / month\n", " ,'GRNTP' # gross rent / month, use grntp to adjust to constant dollars\n", " ,'GRPIP' # gross income as % of hh income past 12 months\n", " ,'INSP' # fire/hazard/flood insurance amount / year\n", " ,'MHP' # mobile home costs / year\n", " ,'RNTP' # rent cost / month\n", " ,'SMP' # total payment on all second and junior morgages and home equity loans / month\n", " ,'SMOCP' # selected monthly owner costs, use adjhsg to adjust to constant dollars\n", " ,'TAXP' # property taxes per year\n", " ,'MRGP' # first mortgage payment / month\n", " ,'FS' # yearly food stamp / snap recipiency\n", " ,'TEN' # tenure: b n/a, 1 owned, 2 owned free & clear, 3 rented, 4 occupied w/out payment of rent\n", " ,'TYPE' # type of unit: 1 housing unit, 2 institutional group quarters, 3 non-institutional group quarters\n", " ,'BLD' # units in structure\n", " ,'FPARC' # family presence and age of related children\n", " ,'FES' # family type and employment status\n", " ,'KIT' # complete kitchen facilities\n", " ,'PLM' # complete plumbing facilities: b n/a, 1 yes, 2 no, 9 case from pr so plm n/a\n", " ,'MV' # when moved into this house or apartment\n", " ,'VEH' # number of vehicles available\n", "]" ] }, { "cell_type": "code", "execution_count": 54, "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>RT</th>\n", " <th>SERIALNO</th>\n", " <th>DIVISION</th>\n", " <th>PUMA</th>\n", " <th>REGION</th>\n", " <th>ST</th>\n", " <th>WGTP</th>\n", " <th>ADJINC</th>\n", " <th>ADJHSG</th>\n", " <th>HINCP</th>\n", " <th>...</th>\n", " <th>FS</th>\n", " <th>TEN</th>\n", " <th>TYPE</th>\n", " <th>BLD</th>\n", " <th>FPARC</th>\n", " <th>FES</th>\n", " <th>KIT</th>\n", " <th>PLM</th>\n", " <th>MV</th>\n", " <th>VEH</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>H</td>\n", " <td>77</td>\n", " <td>9</td>\n", " <td>400</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>22</td>\n", " <td>1008425</td>\n", " <td>1000000</td>\n", " <td></td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>H</td>\n", " <td>315</td>\n", " <td>9</td>\n", " <td>200</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>161</td>\n", " <td>1008425</td>\n", " <td>1000000</td>\n", " <td>000050660</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>H</td>\n", " <td>807</td>\n", " <td>9</td>\n", " <td>300</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1303</td>\n", " <td>1008425</td>\n", " <td>1000000</td>\n", " <td></td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " RT SERIALNO DIVISION PUMA REGION ST WGTP ADJINC ADJHSG HINCP \\\n", "0 H 77 9 400 4 2 22 1008425 1000000 \n", "1 H 315 9 200 4 2 161 1008425 1000000 000050660 \n", "2 H 807 9 300 4 2 1303 1008425 1000000 \n", "\n", " ... FS TEN TYPE BLD FPARC FES KIT PLM MV VEH \n", "0 ... NaN NaN 1 2.0 NaN NaN 2.0 2.0 NaN NaN \n", "1 ... 2.0 1.0 1 2.0 3.0 2.0 1.0 1.0 4.0 3.0 \n", "2 ... NaN NaN 1 3.0 NaN NaN 1.0 1.0 NaN NaN \n", "\n", "[3 rows x 38 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select desired columns\n", "df1 = df[cols_list]\n", "df1.head(3)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# pickle dataframe\n", "df1.to_pickle('data/pums-h/metadata.pickle')" ] } ], "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
sgkang/simpegAIP	notebook/StoltzaAndMacnae1998_simple_impulse.ipynb	1	66904	{ "cells": [ { "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": [ "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $A(t,T) = \\Sigma_i A_i e^{-t/\\tau_i} / (1 + e^{-T/2\\tau_i})$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def AofT(time,T, ai, taui):\n", " return ainp.exp(-time/taui)/(1.+np.exp(-T/(2taui)))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from SimPEG import \n", "import sys\n", "sys.path.append(\"./DoubleLog/\")\n", "from plotting import mapDat" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class LinearSurvey(Survey.BaseSurvey):\n", " nD = None\n", " def __init__(self, time, kwargs): \n", " self.time = time\n", " self.nD = time.size\n", " \n", " def projectFields(self, u):\n", " return u\n", "\n", "class LinearProblem(Problem.BaseProblem):\n", "\n", " surveyPair = LinearSurvey\n", "\n", " def __init__(self, mesh, G, kwargs):\n", " Problem.BaseProblem.__init__(self, mesh, kwargs)\n", " self.G = G\n", "\n", " def fields(self, m, u=None):\n", " return self.G.dot(m)\n", "\n", " def Jvec(self, m, v, u=None):\n", " return self.G.dot(v)\n", "\n", " def Jtvec(self, m, v, u=None):\n", " return self.G.T.dot(v)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple exponential basis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "$$ \\mathbf{A}\\mathbf{\\alpha} = \\mathbf{d}$$" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "time = np.cumsum(np.r_[0., 1e-5np.ones(10), 5e-5np.ones(10), 1e-4np.ones(10), 5e-4np.ones(10), 1e-3np.ones(10)])\n", "# time = np.cumsum(np.r_[0., 1e-5np.ones(10), 5e-5np.ones(10),1e-4np.ones(10), 5e-4np.ones(10)])\n", "M = 41\n", "tau = np.logspace(-4.5, -1, M)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = time.size\n", "A = np.zeros((N, M))\n", "for j in range(M):\n", " A[:,j] = np.exp(-time/tau[j])//tau[j]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mtrue = np.zeros(M)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(1)\n", "inds = np.random.random_integers(0, 41, size=5)\n", "mtrue[inds] = np.r_[-10, 2, 1, 4, 5]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "out = np.dot(A,mtrue)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ec801XHvttYwcOZKysjJOnDjByZMnOXHiBMHBwa4HuJwDkHSmoaGB/fv3s3nz\nZqKiopg1axYpKSmWOsnu3LmTa6+9lqlTp7Jz505fF0d1kaW7YeiIBn3fq6urc50A3Pu6d3aeFhQU\n5OrK2DklJCQQEhLi66J7lMPh4ODBg+zYsYPLly8zffp0Jk+eTFBQEMYYLl68yPHjx/nss88oKChg\n2LBhjBo1ilGjRnX6K6CxsZEDBw6wefNmwsLCmD17NmlpaZYI/vv27SMzM5OJEye6Wpso69Ogr7xi\n06ZNTJ48mYsXL7omZxfH4eHhrU4G8fHxfX68XGMMZ8+eZevWrZw9e5bp06czbdq0Zie5mpoaTpw4\nQV5eHnl5ecTExJCRkcGYMWMYMGBAu/tubGzk8OHDZGdnExwczJw5cxgxYkS3g78ncsaHDx9m7Nix\nZGRkcPjw4R7tSzXnzZy+Bn3lFe39p21sbKS0tLTZyeDixYtcunSJ6OjoVoOdDBgwoE/2cV9YWMiW\nLVs4fvw406ZN47rrrmvVKqexsZFTp05x+PBhcnNzsdvtrn5+oqOj29yvMYbc3Fw2btxIVFQUN998\nc4cPrbXHE0Hl2LFjjBw5krS0NI4dO9ajfanmNOi3QYO+f2loaODSpUutTgZlZWUkJSW5WtIMGzas\n24O2+0JxcTFbt27lyJEjTJ8+nenTp7eZ0zfGcOrUKQ4cOEBubi4JCQlMnDiRcePGtfn5hoYGcnJy\nyM7OJiMjg7lz53q0m+muOH36NMnJyQwdOpTTp0/36rFV92nQV5ZWX1/P2bNnOXXqFKdPn+bs2bPE\nxcW5TgDJyclERET4upidunTpEhs3buTUqVPMmjWLyZMnt/sLxuFwcOzYMfbu3cupU6cYO3YsU6ZM\n4Zprrmn12cuXL5Odnc3+/fuZMWMG1113Xa89k3HhwgWuueYakpKSOH/+fK8cU/WcBn3lFd76edrQ\n0MD58+ddfdyfPn0au93u+iWQnJxMdHS0JW50tuXcuXNs2LCB0tJSFixYQHp6eodlLS8vZ8+ePeze\nvZuIiAimTJnS5tV/cXEx69evp6ioiNtuu63VsJEteaJ+iouLiY+PJy4ujuLi4h7tSzWn6Z02aNC3\ntt56uMTZOsZ5Ejh16hQBAQGMGDGC9PR0UlJSLPk08okTJ1i3bh2RkZEsWrSo007YGhsbOX78OLt2\n7eL06dOMHz+e66+/vlVvpXl5ebz//vukpaWxcOHCdpvNeqJ+KioqiIqKIiIiQjtd8zAN+m3QoK/a\n4hxhKy+1fX/WAAAgAElEQVQvjyNHjnDx4kXS0tIYPXo0I0eOtNSzAw0NDezcuZOPP/6YSZMmMWvW\nrC41Zy0vL2fnzp3k5OSQmprKjBkzmqV+amtr2bBhA7m5uSxatIgxY8Z4pfy1tbWEhoYSHBxMbW2t\nV46hPE+DvvJrVVVVrhNAfn4+gwcPJj09ndGjR7fbQqa3VVZW8tFHH3HixAkWL17M6NGju7RdbW0t\nOTk5fPrppyQkJDBjxoxmD3CdPn2aNWvWkJCQwBe+8AWP3+h1djbonLdqSk01p0FfeYUV+96pq6vj\n+PHjHD16lLy8PKKjo1193FvhZnB+fj5r1qxh8ODB3HLLLV0O0s6nd7du3UpwcDDz5s1zPcDlcDjI\nyspi3759LFmyxDWsoafqJzAw0NXhoBXTaH2VpnfaoEHf2qwY9N0528jv27ePI0eOkJyczKRJkxg5\ncqRPnwuor69n06ZN7N+/n1tuuYVx48Z1eVtjDIcPH2bjxo1ERkYyf/58hg4dClw5obz99tuMHTuW\n+fPns2XLFo/UT3h4uKsfp77SO2hf0C+Dvohcy5UeN9OMMc+18b4GfeURtbW1HD58mD179lBcXMyE\nCROYNGmSV3u57ExBQQHvvPMOQ4cOZdGiRV3qr8epsbGRffv2kZWVxcCBA5k3bx5JSUlUV1fzt7/9\njZKSEu644w6PjOEbExPj6oajsy4llDVYOeg/Zoz5oYh8B3jXGHO6xfsa9JXHFRcXs3fvXvbt20dE\nRIRrABVfpC7q6ur44IMPOHv2LMuWLSMpKemqtnc4HOzatYstW7YwatQo5s+fT3h4OLt372bDhg0s\nXryYsWPH9qiMiYmJFBUVUVhY6NOTpOo6Kwf9Z40x3xSRrwEHjDE7W7yvQd/CrJ7e6YyzieSOHTs4\nf/68a/BzX6Qw9u/fz7p165g7dy5Tp0696humNTU1ZGdns2/fPmbPns3UqVN59913OXPmjCvd092R\n2QYPHsy5c+c4e/YsgwcP7tY+VGu+Su94bXw+EbGJyBMt1j0iIreLyE+bVpU1vcZwZZAVpXqNzWZj\n5MiR3HPPPdx7772UlZXx1FNPsXbt2l5/EGnChAncf//97N69m9dff53Lly9f1fahoaHcfPPN3Hff\nfRw9epTnnnuOuro6vvGNb3D+/HlWr15NdXV1t8qmfer7F2/1px8L3AfcY4yZ0rRuAXCDMeaXIvJz\nYGPTxwOBicaYJ9vYj17pq15VWVnJjh07yMnJYciQIcycOZMhQ4b02vEdDgcfffQRR44c4Y477nDd\npL0axhiOHDnCunXrSE1NZcGCBWzbto1Dhw5x1113XXXnbaNHj3Y1ie1qU1PlWz5L77QYGP1nwG5j\nzFoRuR2YZIz5RSfba9BXPlFfX8/evXvZsmVLsxulveXo0aOsWbOGm266iYkTJ3ZrH7W1ta4TyK23\n3orD4WDdunXccccdpKamdnk/48eP5+DBg+zfv5/x48d3qyyqd1llYPREroyeBVAFdOkbtHLlSoYP\nHw5caUWQmZnpyoNlZWUB6LKPlp988km/rY+goCCqqqpc7ftXr15NZWUlkyZNYsmSJV4/vvPp4uef\nf56lS5cyb948srOzr2p/f/zjH8nMzGTZsmWsWbOG4uJiUlNTeeutt1i4cCGlpaVd2p8zvfPJJ59Q\nXFxsifrxh2VPfn+ysrJYtWoVgCtetqc3r/SfBV4zxmSLyE3A7caYb3WyvV7pW1hWH7+RezXq6urY\nsWMH27ZtY9SoUcyZM6dXmi9WVVXx2muvERERwdKlS69qEBr3+nE+G3DgwAFmzZrFtm3bmDBhAnPm\nzOn0pvH06dPZvn07n3zyCdOnT+/JP0e58eb3xyc3cttQCDgfP4wCinrx2MoL+kvAhytXuzfeeCPf\n+c53iI6O5rnnniMrK4v6+nqvHtdut3PvvfcSGBjIqlWrqKys7PK27vUTFBTETTfdxNKlS9m8eTPD\nhw/nyJEjvP/++3R2YaU3cr3DV9+f3gz6W4AJTfPTuDJAulJ9SmhoKHPnzuWBBx6gqKiIZ555htzc\n3E4DZ08EBgaydOlSRo0axfPPP09hYfcbuqWmpvLggw9SW1tLfX09J06c4J133qGxsbHdbTTo+xev\nBH0RsYvI94AMEXlYROxcaa2TICLLAGOMWe+NY6ve48wp9kcxMTF8+ctfZsmSJWzatImXX36ZoiLv\n/XgVEWbPns3ChQt56aWXOHPmTKfbtFc/4eHhfPnLX2bGjBlUVVXx2Wef8frrr+NwONr8vDPoe/tX\nTX/jq++PV4K+MabKGPN7Y0ySMebJpmVjjPmBMeZNY8xPvHFcpXpbSkoKDz74IKNHj+bFF18kOzub\nhoYGrx1v3LhxLF26lL/+9a/k5+d3ez8iwpQpU1ixYgVBQUEcOHCAV199tc3A77yPoFf6/qE30zvK\nz/SnnH5HbDYb1113HQ888ABnz57l+eef9+rQgiNGjGDZsmW88cYbHQ5W3pX6GTRoEA8++CDp6el8\n+umnvPTSS60Cv6Z3vKM/5PSV8mvR0dHcfffdXH/99axevZqNGze2mzLpqZSUFJYvX84777zDkSNH\nerSv8PBwvvrVr7JkyRKys7P585//3OzXigZ9/6JBX3Vbf87pt0dEmDhxIg8++CAXL17k+eef91qu\nf+jQodxzzz2sXbuWgwcPtnr/aupHRLj55pv57ne/S1ZWFk8//bTr5q4Gfe/wq5y+Uv1dZGQkX/nK\nV7juuut48cUXycnJ8UoLn0GDBvEv//IvfPjhhxw+fLjH+5s8eTK/+tWv+PTTT/n973+PMcaV09cb\nuf5BB1FRysuKiop48803GTBgALfddptXxvC9cOECL7/8Ml/60pdIS0vzyP4efvhhpk2bxokTJ3j2\n2Wf5wx/+wLe+1eHzlMoirPJwllL9UkJCAl//+teJjIzkueee88pN3qSkJL7yla/w9ttvd6k5Z1f2\n99RTT7Fz504OHDgAaHrHX2jQV92mOf2uCwwMZNGiRSxcuJCXX36Z/fv3e/wYw4YN44tf/CKvvfYa\nhYWFPa6fxMREnnrqKT7//HPgSp/9ynM0p69UPzB27FhWrFhBVlYW69at83ib/pEjR3LLLbfwyiuv\nUFFR0eP9JSYmsnDhQgC2bdvmtdZIqvdo0Ffdpu30u2fgwIF8/etfp7i4mNWrV3v8CnrcuHHMmjWL\nkydPdnvgFHfx8fEAnD59mv/93//VG7oeou30lepHwsLCuOuuu0hMTOTFF1/0yFW5u6lTpzJ27Fhe\ne+21Hl+dO5tsTpgwgZycHF555RWvPnWsvEuDvuo2zen3jM1m45ZbbmH8+PG88MILrty5J/cfHh7O\n2rVre9Rc1Bn04+Pjuf3229m+fTtvvvlmh520qc7125y+iCwRkUG+LodSviAi3HjjjcyZM4dVq1Zx\n9uxZj+576dKlFBYWsnXr1m7vxxn0HQ4Hixcv5vrrr2fbtm1d6pZZWY9Pg76IJAErgI5HcVCWpDl9\nz8nMzGTJkiX85S9/IS8vzyP7nDNnDsHBwdx1113s2LGD3Nzcbu3HvcM1m83G8uXLGTlyJJs3b2bD\nhg0eKWt/1C9z+saYC8A+X5Z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"text/plain": [ "<matplotlib.figure.Figure at 0x109128850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(6,4.5))\n", "ax = plt.subplot(111)\n", "\n", "for i, ind in enumerate(inds):\n", " temp, dum, dum = mapDat(mtrue[inds][i]np.exp(-time/tau[ind])/tau[j], 1e-1, stretch=2)\n", " plt.semilogx(time, temp, 'k', alpha = 0.5) \n", "outmap, ticks, tickLabels = mapDat(out, 1e-1, stretch=2) \n", "ax.semilogx(time, outmap, 'k', lw=2)\n", "ax.set_yticks(ticks)\n", "ax.set_yticklabels(tickLabels)\n", "# ax.set_ylim(ticks.min(), ticks.max())\n", "ax.set_ylim(ticks.min(), ticks.max())\n", "ax.set_xlim(time.min(), time.max())\n", "ax.grid(True)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# from pymatsolver import MumpsSolver" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "27.023041276921603" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs(survey.dobs).min()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SimPEG.InvProblem will set Regularization.mref to m0.\n", "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " Done using same solver as the problem\n", "=============================== Projected GNCG ===============================\n", " # beta phi_d phi_m f \|proj(x-g)-x\| LS Comment \n", "-----------------------------------------------------------------------------\n", " 0 1.00e-04 5.23e+03 0.00e+00 5.23e+03 4.09e+03 0 \n", " 1 1.00e-04 4.00e+03 3.86e+00 4.00e+03 7.52e+01 0 \n", " 2 1.00e-04 3.29e+03 9.21e+00 3.29e+03 4.29e+00 0 Skip BFGS \n", " 3 5.00e-05 9.31e+02 6.13e+00 9.31e+02 5.51e+01 0 \n", " 4 5.00e-05 7.77e+02 1.05e+01 7.77e+02 4.58e+00 1 \n", " 5 5.00e-05 7.04e+02 1.57e+01 7.04e+02 5.60e+00 1 Skip BFGS \n", " 6 2.50e-05 5.79e+02 1.25e+01 5.79e+02 1.57e+01 0 \n", " 7 2.50e-05 5.63e+02 2.15e+01 5.63e+02 6.56e+00 3 \n", " 8 2.50e-05 5.35e+02 2.51e+01 5.35e+02 7.09e+00 1 Skip BFGS \n", " 9 1.25e-05 5.28e+02 2.34e+01 5.28e+02 2.58e+00 0 \n", " 10 1.25e-05 5.26e+02 2.79e+01 5.26e+02 7.47e+00 3 \n", " 11 1.25e-05 5.24e+02 2.69e+01 5.24e+02 2.34e+00 0 \n", " 12 6.25e-06 5.13e+02 5.97e+01 5.13e+02 1.65e+00 2 Skip BFGS \n", " 13 6.25e-06 5.12e+02 6.95e+01 5.12e+02 6.07e+00 4 Skip BFGS \n", " 14 6.25e-06 5.12e+02 6.85e+01 5.12e+02 1.78e-15 0 \n", "------------------------- STOP! -------------------------\n", "1 : \|fc-fOld\| = 2.6700e-01 <= tolF(1+\|f0\|) = 5.2354e+02\n", "1 : \|xc-x_last\| = 8.7988e-02 <= tolX(1+\|x0\|) = 1.0000e-01\n", "1 : \|proj(x-g)-x\| = 1.7764e-15 <= tolG = 1.0000e-01\n", "1 : \|proj(x-g)-x\| = 1.7764e-15 <= 1e3eps = 1.0000e-02\n", "0 : maxIter = 20 <= iter = 14\n", "------------------------- DONE! -------------------------\n" ] } ], "source": [ "mesh = Mesh.TensorMesh([M])\n", "prob = LinearProblem(mesh, A)\n", "survey = LinearSurvey(time)\n", "survey.pair(prob)\n", "survey.makeSyntheticData(mtrue, std=0.01)\n", "# survey.dobs = out\n", "reg = Regularization.BaseRegularization(mesh)\n", "dmis = DataMisfit.l2_DataMisfit(survey)\n", "dmis.Wd = 1./(0.05abs(survey.dobs)+0.05300.)\n", "opt = Optimization.ProjectedGNCG(maxIter=20)\n", "# opt = Optimization.InexactGaussNewton(maxIter=20)\n", "opt.lower = -1e-10\n", "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n", "invProb.beta = 1e-4\n", "beta = Directives.BetaSchedule()\n", "beta.coolingFactor = 2\n", "target = Directives.TargetMisfit()\n", "inv = Inversion.BaseInversion(invProb, directiveList=[beta, target])\n", "m0 = np.zeros_like(survey.mtrue)\n", "mrec = inv.run(m0)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x108c08510>]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107923810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogx(tau, mtrue, '.')\n", "plt.semilogx(tau, mrec, '.')" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107f12410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(6,4.5))\n", "ax = plt.subplot(111)\n", "obsmap, ticks, tickLabels = mapDat(survey.dobs, 1e0, stretch=2) \n", "predmap, dum, dum = mapDat(invProb.dpred, 1e0, stretch=2) \n", "ax.loglog(time, survey.dobs, 'k', lw=2)\n", "ax.loglog(time, invProb.dpred, 'k.', lw=2)\n", "# ax.set_yticks(ticks)\n", "# ax.set_yticklabels(tickLabels)\n", "# ax.set_ylim(ticks.min(), ticks.max())\n", "# ax.set_ylim(ticks.min(), ticks.max())\n", "ax.set_xlim(time.min(), time.max())\n", "ax.grid(True)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "time = np.cumsum(np.r_[0., 1e-5np.ones(10), 5e-5np.ones(10), 1e-4np.ones(10), 5e-4np.ones(10), 1e-3*np.ones(10)])\n", "N = time.size\n", "A = np.zeros((N, M))\n", "for j in range(M):\n", " A[:,j] = np.exp(-time/tau[j]) /tau[j]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mfund = mtrue.copy()\n", "mfund[mfund<0.] = 0.\n", "obs = np.dot(A, mtrue)\n", "fund = np.dot(A, mfund)\n", "pred = np.dot(A, mrec)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ip = obs-fund\n", "ipobs = obs-pred" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1.0000000000000001e-05, 0.016600000000000007)" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108a31690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.loglog(time, obs, 'k.-', lw=2)\n", "plt.loglog(time, -obs, 'k--', lw=2)\n", "plt.loglog(time, fund, 'b.', lw=2)\n", "plt.loglog(time, pred, 'b-', lw=2)\n", "plt.loglog(time, -ip, 'r--', lw=2)\n", "plt.loglog(time, abs(ipobs), 'r.', lw=2)\n", "plt.ylim(abs(obs).min(), abs(obs).max())\n", "plt.xlim(time.min(), time.max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Range of tau is really important to fit ..." ] }, { "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
chappers/Data-Science	Think-Stats/3 Cumulative Distribution Functions.ipynb	1	73156	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This HTML version of is provided for convenience, but it is not the best\n", "format for the book. In particular, some of the symbols are not rendered\n", "correctly.\n", "\n", "You might prefer to read the [PDF\n", "version](http://thinkstats.com/thinkstats.pdf), or you can buy a hardcopy\n", "[here](http://www.lulu.com/product/paperback/think-stats/12443331).\n", "\n", "# Chapter\u00a03\u00a0\u00a0Cumulative distribution functions\n", "\n", "## 3.1\u00a0\u00a0The class size paradox\n", "\n", "At many American colleges and universities, the student-to-faculty ratio is\n", "about 10:1. But students are often surprised to discover that their average\n", "class size is bigger than 10. There are two reasons for the discrepancy:\n", "\n", " * Students typically take 4\u20135 classes per semester, but professors often teach 1 or 2.\n", " * The number of students who enjoy a small class is small, but the number of students in a large class is (ahem!) large.\n", "\n", "The first effect is obvious (at least once it is pointed out); the second is\n", "more subtle. So let\u2019s look at an example. Suppose that a college offers 65\n", "classes in a given semester, with the following distribution of sizes:\n", "\n", " \n", " \n", " size count\n", " 5- 9 8\n", " 10-14 8\n", " 15-19 14\n", " 20-24 4\n", " 25-29 6\n", " 30-34 12\n", " 35-39 8\n", " 40-44 3\n", " 45-49 2\n", " \n", "\n", "If you ask the Dean for the average class size, he would construct a PMF,\n", "compute the mean, and report that the average class size is 24.\n", "\n", "But if you survey a group of students, ask them how many students are in their\n", "classes, and compute the mean, you would think that the average class size was\n", "higher.\n", "\n", "Exercise\u00a01\u00a0\u00a0 Build a PMF of these data and compute the mean as perceived by the Dean. Since the data have been grouped in bins, you can use the mid-point of each bin. \n", "\n", "Now find the distribution of class sizes as perceived by students and compute\n", "its mean. \n", "\n", "Suppose you want to find the distribution of class sizes at a college, but\n", "you can\u2019t get reliable data from the Dean. An alternative is to choose a\n", "random sample of students and ask them the number of students in each of their\n", "classes. Then you could compute the PMF of their responses. \n", "\n", "The result would be biased because large classes would be oversampled, but\n", "you could estimate the actual distribution of class sizes by applying an\n", "appropriate transformation to the observed distribution.\n", "\n", "Write a function called `UnbiasPmf` that takes the PMF of the observed values\n", "and returns a new Pmf object that estimates the distribution of class sizes.\n", "\n", "You can download a solution to this problem from\n", "`http://thinkstats.com/class_size.py`. \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "\n", "t = [7]8 + [12]8 + [17]14 + [22]4 + [27]6 + [32]12 + [37]8 + [42]3 + [47]2\n", "print \"The mean of the data above is %.2f\" % np.mean(t)\n", "pd.DataFrame(t).hist(normed = True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The mean of the data above is 23.69\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "array([[<matplotlib.axes.AxesSubplot object at 0x09107DB0>]], dtype=object)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x91344f0>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Exercise\u00a02\u00a0\u00a0 \n", "\n", "In most foot races, everyone starts at the same time. If you are a fast\n", "runner, you usually pass a lot of people at the beginning of the race, but\n", "after a few miles everyone around you is going at the same speed. \n", "\n", "When I ran a long-distance (209 miles) relay race for the first time, I\n", "noticed an odd phenomenon: when I overtook another runner, I was usually much\n", "faster, and when another runner overtook me, he was usually much faster.\n", "\n", "At first I thought that the distribution of speeds might be bimodal; that is,\n", "there were many slow runners and many fast runners, but few at my speed.\n", "\n", "Then I realized that I was the victim of selection bias. The race was unusual\n", "in two ways: it used a staggered start, so teams started at different times;\n", "also, many teams included runners at different levels of ability. \n", "\n", "As a result, runners were spread out along the course with little\n", "relationship between speed and location. When I started running my leg, the\n", "runners near me were (pretty much) a random sample of the runners in the\n", "race.\n", "\n", "So where does the bias come from? During my time on the course, the chance of\n", "overtaking a runner, or being overtaken, is proportional to the difference in\n", "our speeds. To see why, think about the extremes. If another runner is going\n", "at the same speed as me, neither of us will overtake the other. If someone is\n", "going so fast that they cover the entire course while I am running, they are\n", "certain to overtake me.\n", "\n", "Write a function called `BiasPmf` that takes a Pmf representing the actual\n", "distribution of runners\u2019 speeds, and the speed of a running observer, and\n", "returns a new Pmf representing the distribution of runners\u2019 speeds as seen by\n", "the observer.\n", "\n", "To test your function, get the distribution of speeds from a normal road race\n", "(not a relay). I wrote a program that reads the results from the James Joyce\n", "Ramble 10K in Dedham MA and converts the pace of each runner to MPH. Download\n", "it from `http://thinkstats.com/relay.py`. Run it and look at the PMF of\n", "speeds. \n", "\n", "Now compute the distribution of speeds you would observe if you ran a relay\n", "race at 7.5 MPH with this group of runners. You can download a solution from\n", "`http://thinkstats.com/relay_soln.py`\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.2\u00a0\u00a0The limits of PMFs\n", "\n", "PMFs work well if the number of values is small. But as the number of values\n", "increases, the probability associated with each value gets smaller and the\n", "effect of random noise increases.\n", "\n", "For example, we might be interested in the distribution of birth weights. In\n", "the NSFG data, the variable `totalwgt_oz` records weight at birth in ounces.\n", "Figure\u00a03.1 shows the PMF of these values for first babies and others.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import gzip\n", "\n", "def makeRecord(line, fields):\n", " obs = {}\n", " for (field, start, end, cast) in fields:\n", " try:\n", " s = line[start-1:end]\n", " val = cast(s)\n", " except ValueError:\n", " val = np.nan #make use of numpy's nan\n", " obs[field]=val\n", " return obs\n", "\n", "fresp = gzip.open('./data/2002FemResp.dat.gz')\n", "resp_fields = [\n", " ('caseid', 1, 12, int),\n", " ]\n", "\n", "fpreg = gzip.open('./data/2002FemPreg.dat.gz')\n", "preg_fields = [ ('caseid', 1, 12, int),\n", " ('nbrnaliv', 22, 22, int),\n", " ('babysex', 56, 56, int),\n", " ('birthwgt_lb', 57, 58, int),\n", " ('birthwgt_oz', 59, 60, int),\n", " ('prglength', 275, 276, int),\n", " ('outcome', 277, 277, int),\n", " ('birthord', 278, 279, int),\n", " ('agepreg', 284, 287, int),\n", " ('finalwgt', 423, 440, float)]\n", "\n", "respondents = pd.DataFrame([makeRecord(line, resp_fields) for line in fresp])\n", "pregnancies = pd.DataFrame([makeRecord(line, preg_fields) for line in fpreg])\n", "\n", "#recode\n", "pregnancies['agepreg'] = pregnancies.agepreg/100.0\n", "pregnancies['totalwgt_oz'] = pregnancies.birthwgt_lb 16 + pregnancies.birthwgt_oz\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# add column if they are first born\n", "pregnancies['first_born'] = pregnancies.birthord == 1\n", "pregnancies.query('totalwgt_oz < 250') \\\n", " .groupby('first_born')['totalwgt_oz'] \\\n", " .hist(bins=100, normed=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "first_born\n", "False Axes(0.125,0.125;0.775x0.775)\n", "True Axes(0.125,0.125;0.775x0.775)\n", "dtype: object" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xdf43c70>" ] } ], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Overall, these distributions resemble the familiar \u201cbell curve,\u201d with many\n", "values near the mean and a few values much higher and lower.\n", "\n", "But parts of this figure are hard to interpret. There are many spikes and\n", "valleys, and some apparent differences between the distributions. It is hard\n", "to tell which of these features are significant. Also, it is hard to see\n", "overall patterns; for example, which distribution do you think has the higher\n", "mean?\n", "\n", "These problems can be mitigated by binning the data; that is, dividing the\n", "domain into non-overlapping intervals and counting the number of values in\n", "each bin. Binning can be useful, but it is tricky to get the size of the bins\n", "right. If they are big enough to smooth out noise, they might also smooth out\n", "useful information.\n", "\n", "An alternative that avoids these problems is the cumulative distribution\n", "function, or CDF. But before we can get to that, we have to talk about\n", "percentiles.\n", "\n", "## 3.3\u00a0\u00a0Percentiles\n", "\n", "If you have taken a standardized test, you probably got your results in the\n", "form of a raw score and a percentile rank. In this context, the percentile\n", "rank is the fraction of people who scored lower than you (or the same). So if\n", "you are \u201cin the 90th percentile,\u201d you did as well as or better than 90% of the\n", "people who took the exam.\n", "\n", "Here\u2019s how you could compute the percentile rank of a value, `your_score`,\n", "relative to the scores in the sequence `scores`:\n", "\n", " \n", " \n", " def PercentileRank(scores, your_score):\n", " count = 0\n", " for score in scores:\n", " if score <= your_score:\n", " count += 1\n", " \n", " percentile_rank = 100.0 * count / len(scores)\n", " return percentile_rank\n", " \n", "\n", "For example, if the scores in the sequence were 55, 66, 77, 88 and 99, and you\n", "got the 88, then your percentile rank would be `100 * 4 / 5` which is 80.\n", "\n", "If you are given a value, it is easy to find its percentile rank; going the\n", "other way is slightly harder. If you are given a percentile rank and you want\n", "to find the corresponding value, one option is to sort the values and search\n", "for the one you want:\n", " \n", " \n", " def Percentile(scores, percentile_rank):\n", " scores.sort()\n", " for score in scores:\n", " if PercentileRank(scores, score) >= percentile_rank:\n", " return score\n", " \n", "\n", "The result of this calculation is a percentile. For example, the 50th\n", "percentile is the value with percentile rank 50. In the distribution of exam\n", "scores, the 50th percentile is 77.\n", "\n", "Exercise\u00a03\u00a0\u00a0This implementation of `Percentile` is not very efficient. A better approach is to use the percentile rank to compute the index of the corresponding percentile. Write a version of `Percentile` that uses this algorithm.\n", "\n", "You can download a solution from `http://thinkstats.com/score_example.py`. \n", "\n", "Exercise\u00a04\u00a0\u00a0Optional: If you only want to compute one percentile, it is not efficient to sort the scores. A better option is the selection algorithm, which you can read about at `http://wikipedia.org/wiki/Selection_algorithm`. \n", "\n", "Write (or find) an implementation of the selection algorithm and use it to\n", "write an efficient version of `Percentile`.\n", "\n", "## 3.4\u00a0\u00a0Cumulative distribution functions\n", "\n", "Now that we understand percentiles, we are ready to tackle the cumulative\n", "distribution function (CDF). The CDF is the function that maps values to their\n", "percentile rank in a distribution.\n", "\n", "The CDF is a function of \\\$x\\\$, where \\\$x\\\$\u00a0is any value that might appear in the\n", "distribution. To evaluate CDF(\\\$x\\\$) for a particular value of \\\$x\\\$, we compute\n", "the fraction of the values in the sample less than (or equal to) \\\$x\\\$.\n", "\n", "Here\u2019s what that looks like as a function that takes a sample, `t`, and a\n", "value, `x`:\n", "\n", " \n", " \n", " def Cdf(t, x):\n", " count = 0.0\n", " for value in t:\n", " if value <= x:\n", " count += 1.0\n", " \n", " prob = count / len(t)\n", " return prob\n", " \n", "\n", "This function should look familiar; it is almost identical to\n", "`PercentileRank`, except that the result is in a probability in the range `[0\u20131]`\n", "rather than a percentile rank in the range `[0\u2013100]`.\n", "\n", "As an example, suppose a sample has the values `{1, 2, 2, 3, 5}`. Here are some\n", "values from its CDF:\n", "\n", "$$CDF(0)\u00a0=\u00a00 $$\n", "$$CDF(1)\u00a0=\u00a00.2 $$\n", "$$CDF(2)\u00a0=\u00a00.6 $$\n", "$$CDF(3)\u00a0=\u00a00.8 $$\n", "$$CDF(4)\u00a0=\u00a00.8 $$\n", "$$CDF(5)\u00a0=\u00a01 $$\n", " \n", "We can evaluate the CDF for any value of \\\$x\\\$, not just values that appear in\n", "the sample. If \\\$x\\\$\u00a0is less than the smallest value in the sample, CDF(\\\$x\\\$) is\n", "0. If \\\$x\\\$\u00a0is greater than the largest value, CDF(\\\$x\\\$) is 1." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "\n", "x = [0,1,2,3,4,5]\n", "y = [0, 0.2, 0.6, 0.8, 0.8, 1]\n", "\n", "plt.step(np.array(x), y, label='post')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xd039b50>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Figure\u00a03.2 is a graphical representation of this CDF. The CDF of a sample is a\n", "step function. In the next chapter we will see distributions whose CDFs are\n", "continuous functions.\n", "\n", "## 3.5\u00a0\u00a0Representing CDFs\n", "\n", "I have written a module called `Cdf` that provides a class named `Cdf` that\n", "represents CDFs. You can read the documentation of this module at\n", "`http://thinkstats.com/Cdf.html` and you can download it from\n", "`http://thinkstats.com/Cdf.py`.\n", "\n", "Cdfs are implemented with two sorted lists: `xs`, which contains the values,\n", "and `ps`, which contains the probabilities. The most important methods Cdfs\n", "provide are:\n", "\n", "`Prob(x)`: \n", " Given a value \\\$x\\\$, computes the probability \\\$p\\\$\u00a0=\u00a0CDF(\\\$x\\\$). \n", "`Value(p)`: \n", " Given a probability \\\$p\\\$, computes the corresponding value, \\\$x\\\$; that is, the inverse CDF of \\\$p\\\$. \n", "\n", "Because `xs` and `ps` are sorted, these operations can use the bisection\n", "algorithm, which is efficient. The run time is proportional to the logarithm\n", "of the number of values; see `http://wikipedia.org/wiki/Time_complexity`.\n", "\n", "Cdfs also provide `Render`, which returns two lists, `xs` and `ps`, suitable\n", "for plotting the CDF. Because the CDF is a step function, these lists have two\n", "elements for each unique value in the distribution.\n", "\n", "The Cdf module provides several functions for making Cdfs, including\n", "`MakeCdfFromList`, which takes a sequence of values and returns their Cdf.\n", "\n", "Finally, `myplot.py` provides functions named `Cdf` and `Cdfs` that plot Cdfs\n", "as lines.\n", "\n", "Exercise\u00a05\u00a0\u00a0Download `Cdf.py` and `relay.py` (see Exercise\u00a02) and generate a plot that shows the CDF of running speeds. Which gives you a better sense of the shape of the distribution, the PMF or the CDF? You can download a solution from `http://thinkstats.com/relay_cdf.py`. \n", "\n", "## 3.6\u00a0\u00a0Back to the survey data\n", "\n", "Figure\u00a03.3 shows the CDFs of birth weight for first babies and others in the\n", "NSFG dataset.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pregnancies.query('totalwgt_oz < 250') \\\n", " .groupby('first_born')['totalwgt_oz'] \\\n", " .hist(bins=100, normed=True, cumulative=True, histtype='step')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "first_born\n", "False Axes(0.125,0.125;0.775x0.775)\n", "True Axes(0.125,0.125;0.775x0.775)\n", "dtype: object" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xdf536d0>" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure makes the shape of the distributions, and the differences between\n", "them, much clearer. We can see that first babies are slightly lighter\n", "throughout the distribution, with a larger discrepancy above the mean.\n", "\n", "Exercise\u00a06\u00a0\u00a0How much did you weigh at birth? If you don\u2019t know, call your mother or someone else who knows. Using the pooled data (all live births), compute the distribution of birth weights and use it to find your percentile rank. If you were a first baby, find your percentile rank in the distribution for first babies. Otherwise use the distribution for others. If you are in the 90th percentile or higher, call your mother back and apologize.\n", "\n", "Exercise\u00a07\u00a0\u00a0Suppose you and your classmates compute the percentile rank of your birth weights and then compute the CDF of the percentile ranks. What do you expect it to look like? Hint: what fraction of the class do you expect to be above the median? \n", "\n", "## 3.7\u00a0\u00a0Conditional distributions\n", "\n", "A conditional distribution is the distribution of a subset of the data\n", "which is selected according to a condition.\n", "\n", "For example, if you are above average in weight, but way above average in\n", "height, then you might be relatively light for your height. Here\u2019s how you\n", "could make that claim more precise.\n", "\n", " 1. Select a cohort of people who are the same height as you (within some range). \n", " 2. Find the CDF of weight for those people.\n", " 3. Find the percentile rank of your weight in that distribution.\n", "\n", "Percentile ranks are useful for comparing measurements from different tests,\n", "or tests applied to different groups.\n", "\n", "For example, people who compete in foot races are usually grouped by age and\n", "gender. To compare people in different groups, you can convert race times to\n", "percentile ranks.\n", "\n", "Exercise\u00a08\u00a0\u00a0I recently ran the James Joyce Ramble 10K in Dedham MA. The results are available from `http://coolrunning.com/results/10/ma/Apr25_27thAn_set1.shtml`. Go to that page and find my results. I came in 97th in a field of 1633, so what is my percentile rank in the field? \n", "\n", "In my division (M4049 means \u201cmale between 40 and 49 years of age\u201d) I came in\n", "26th out of 256. What is my percentile rank in my division?\n", "\n", "If I am still running in 10 years (and I hope I am), I will be in the M5059\n", "division. Assuming that my percentile rank in my division is the same, how\n", "much slower should I expect to be?\n", "\n", "I maintain a friendly rivalry with a student who is in the F2039 division.\n", "How fast does she have to run her next 10K to \u201cbeat\u201d me in terms of percentile\n", "ranks?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 3.8\u00a0\u00a0Random numbers\n", "\n", "CDFs are useful for generating random numbers with a given distribution.\n", "Here\u2019s how:\n", "\n", " * Choose a random probability in the range 0\u20131.\n", " * Use `Cdf.Value` to find the value in the distribution that corresponds to the probability you chose.\n", "\n", "It might not be obvious why this works, but since it is easier to implement\n", "than to explain, let\u2019s try it out.\n", "\n", "Exercise\u00a09\u00a0\u00a0 Write a function called `Sample`, that takes a Cdf and an integer, _n_, and returns a list of _n_\u00a0values chosen at random from the Cdf. Hint: use `random.random`. You will find a solution to this exercise in `Cdf.py`. \n", "\n", "Using the distribution of birth weights from the NSFG, generate a random\n", "sample with 1000 elements. Compute the CDF of the sample. Make a plot that\n", "shows the original CDF and the CDF of the random sample. For large values of\n", "\\\$n\\\$, the distributions should be the same. \n", "\n", "This process, generating a random sample based on a measured sample, is called\n", "resampling.\n", "\n", "There are two ways to draw a sample from a population: with and without\n", "replacement. If you imagine drawing marbles from an [urn](http://wikipedia.org/wiki/Urn_problem), \"replacement\" means\n", "putting the marbles back as you go (and stirring), so the population is the\n", "same for every draw. \"Without replacement,\" means that each marble can only be\n", "drawn once, so the remaining population is different after each draw.\n", "\n", "In Python, sampling with replacement can be implemented with `random.random`\n", "to choose a percentile rank, or `random.choice` to choose an element from a\n", "sequence. Sampling without replacement is provided by `random.sample`.\n", "\n", "Exercise\u00a010\u00a0\u00a0 The numbers generated by `random.random` are supposed to be uniform between 0 and 1; that is, every value in the range should have the same probability.\n", "\n", "Generate 1000 numbers from `random.random` and plot their PMF and CDF. Can\n", "you tell whether they are uniform?\n", "\n", "You can read about the uniform distribution [here](http://wikipedia.org/wiki/Uniform_distribution_(discrete)).\n", "\n", "## 3.9\u00a0\u00a0Summary statistics revisited\n", "\n", "Once you have computed a CDF, it is easy to compute other summary statistics.\n", "The median is just the 50th percentile2. The 25th and 75th percentiles are\n", "often used to check whether a distribution is symmetric, and their difference,\n", "which is called the interquartile range, measures the spread.\n", "\n", "Exercise\u00a011\u00a0\u00a0 Write a function called `Median` that takes a Cdf and computes the median, and one called `Interquartile` that computes the interquartile range.\n", "\n", "Compute the 25th, 50th, and 75th percentiles of the birth weight CDF. Do\n", "these values suggest that the distribution is symmetric? \n", "\n", "## 3.10\u00a0\u00a0Glossary\n", "\n", "percentile rank: \n", " The percentage of values in a distribution that are less than or equal to a given value. \n", "CDF: \n", " Cumulative distribution function, a function that maps from values to their percentile ranks. \n", "percentile: \n", " The value associated with a given percentile rank. \n", "conditional distribution: \n", " A distribution computed under the assumption that some condition holds. \n", "resampling: \n", " The process of generating a random sample from a distribution that was computed from a sample. \n", "replacement: \n", " During a sampling process, \u201creplacement\u201d indicates that the population is the same for every sample. \u201cWithout replacement\u201d indicates that each element can be selected only once. \n", "interquartile range: \n", " A measure of spread, the difference between the 75th and 25th percentiles. \n", "\n", "\n", "\n" ] } ], "metadata": {} } ] }	mit
ostwind/ML-fundamental	SVM/SVM data.ipynb	1	3503	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pickle\n", "import random" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random.seed(0)\n", "def folder_list(path,label):\n", " '''\n", " PARAMETER PATH IS THE PATH OF YOUR LOCAL FOLDER\n", " '''\n", " filelist = os.listdir(path)\n", " review = []\n", " for infile in filelist:\n", " file = os.path.join(path,infile)\n", " r = list(read_data(file))\n", " r.append(label)\n", " review.append(r)\n", " return review\n", "\n", "def read_data(file):\n", " '''\n", " Read each file into a list of strings. \n", " Example:\n", " [\"it's\", 'a', 'curious', 'thing', \"i've\", 'found', 'that', 'when', 'willis', 'is', 'not', 'called', 'on', \n", " ...'to', 'carry', 'the', 'whole', 'movie', \"he's\", 'much', 'better', 'and', 'so', 'is', 'the', 'movie']\n", " '''\n", " f = open(file)\n", " lines = f.read().split(' ')\n", " symbols = '${}()[].,:;+-/&\|<>=~\" '\n", " words = map(lambda Element: Element.translate(symbols).strip(), lines)\n", " words = filter(None, words)\n", " return words\n", "\t\n", " \n", "def shuffle_data():\n", " '''\n", " pos_path is where you save positive review data.\n", " neg_path is where you save negative review data.\n", " '''\n", " pos_path = \"C:/Users/Lihan/Documents/WORD/2017 Spring/Machine Learning/hw3/txt_sentoken/pos\"\n", " neg_path = \"C:/Users/Lihan/Documents/WORD/2017 Spring/Machine Learning/hw3/txt_sentoken/neg\"\n", "\t\n", " pos_review = folder_list(pos_path,1)\n", " neg_review = folder_list(neg_path,-1)\n", "\t\n", " review = pos_review + neg_review\n", " random.shuffle(review)\n", " \n", " pickle.dump(review[0:1500], open( \"train.p\", \"wb\" ) )\n", " pickle.dump(review[1500:2000], open( \"valid.p\", \"wb\" ) )\n", " return 0\n", "'''\n", "Now you have read all the files into list 'review' and it has been shuffled.\n", "Save your shuffled result by pickle.\n", "Pickle is a useful module to serialize a python object structure. \n", "*Check it out. https://wiki.python.org/moin/UsingPickle\n", "'''\n", "shuffle_data()\n", "train = pickle.load( open( \"train.p\", \"rb\" ) )\n", "valid = pickle.load( open( \"valid.p\", \"rb\" ) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "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
google/tf-quant-finance	tf_quant_finance/examples/jupyter_notebooks/Dates_in_TFF.ipynb	1	65777	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "7GxUxaCDn-BU" }, "outputs": [], "source": [ "#@title Install dependencies\n", "\n", "!pip install --upgrade tensorflow\n", "!pip install tff-nightly" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "executionInfo": { "elapsed": 7809, "status": "ok", "timestamp": 1593632280834, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "lUoalEum5l00" }, "outputs": [], "source": [ "#@title Imports\n", "import tensorflow as tf\n", "import tf_quant_finance as tff\n", "import numpy as np\n", "import datetime\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LCTMGpuSeP_n" }, "source": [ "# Date Tensor Essentials\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ySeShHlSsGep" }, "source": [ "## Constructing DateTensors\n", "\n", "\n", "\n", "There are 5 possible ways of constructing Date Tensors using tff.datetime.\n", "\n", "1. Sequence of `datetime.datetime`, `datetime.date`, or any other object with attributes or properties called `year`, `month` and `day`.\n", "2. A numpy array of `datetime64` type. \n", "3. Sequence of (year, month, day) tuples. Months are 1-based (with January as 1) and tff.datetime.Month enum may be used instead of ints. Days are also 1-based. \n", "4. A tuple of three int32 `Tensors` containing year, month and date as positive integers in that order.\n", "5. A single int32 `Tensor` containing ordinals (i.e. number of days since 31 Dec 0 with 1 being 1 Jan 1.)\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 488, "status": "ok", "timestamp": 1593632389436, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "6FFc4SzjDN_S", "outputId": "b4355588-6e8c-49ad-d6c8-f400c1f470a3" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2015, 4, 15],\n", " [2017, 12, 30]], dtype=int32)" ] }, "execution_count": 3, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#@title (1) Constructing Dates: sequence of `datetime.datetime`\n", "# Use Python's datetime library to construct a date as datetime.date(year, month, day).\n", "dates = [datetime.date(2015, 4, 15), datetime.date(2017, 12, 30)]\n", "# Then, convert this into a date tensor.\n", "date_tensor = tff.datetime.dates_from_datetimes(dates)\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 463, "status": "ok", "timestamp": 1593633099249, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "0r2g5j41DI16", "outputId": "297c01be-706d-4de5-eb89-cef521c327b6" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2, 2), contents=array([[[2019, 3, 25],\n", " [2020, 6, 2]],\n", "\n", " [[2020, 9, 15],\n", " [2020, 12, 27]]], dtype=int32)" ] }, "execution_count": 9, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#@title (2) Constructing Dates: a numpy array\n", "# You can also use a numpy array of dtype datetime64 (in this case, generated via Python's datetime library).\n", "dates_np = np.array(\n", " [[datetime.date(2019, 3, 25), datetime.date(2020, 6, 2)],\n", " [datetime.date(2020, 9, 15), datetime.date(2020, 12, 27)]],\n", " dtype=np.datetime64)\n", "# Again, convert this into a date tensor.\n", "date_tensor = tff.datetime.dates_from_np_datetimes(dates_np)\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 384, "status": "ok", "timestamp": 1593633100453, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "pw4M_-33FVQr", "outputId": "990408eb-13ec-48d8-9bd9-38f74de7a3c7" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2020, 2, 25],\n", " [2020, 3, 2]], dtype=int32)" ] }, "execution_count": 10, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#@title (3) Constructing Dates: sequence of tuples\n", "# You can start instead with a sequence of tuples.\n", "date_tensor = tff.datetime.dates_from_tuples([(2020, 2, 25), (2020, 3, 2)])\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 383, "status": "ok", "timestamp": 1593633101596, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "rTNr3TAZDejd", "outputId": "972a708e-816f-47f7-ce4e-7789ae856ce9" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2015, 4, 1],\n", " [2017, 12, 30]], dtype=int32)" ] }, "execution_count": 11, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#@title (4) Constructing Dates: a tuple of three tensors\n", "# Another way of using tuples is to first create a tuple of three tensors for the respective Day, Month and Year. You can do this by using TensorFlow's 'constant' function as follows:\n", "year = tf.constant([2015, 2017], dtype=tf.int32)\n", "month = tf.constant([4, 12], dtype=tf.int32)\n", "day = tf.constant([1, 30], dtype=tf.int32)\n", "date_tensor = tff.datetime.dates_from_year_month_day(year, month, day)\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "height": 221 }, "colab_type": "code", "executionInfo": { "elapsed": 365, "status": "ok", "timestamp": 1593633102661, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "CqI-JagLPHQ7", "outputId": "bcc95262-20cb-4830-e751-1dd12b730f34" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Invalid day-month pairing.\n", "Condition x \u003c= y did not hold.\n", "Indices of first 1 different values:\n", "[[0]]\n", "Corresponding x values:\n", "[31]\n", "Corresponding y values:\n", "[30]\n", "First 2 elements of x:\n", "[31 30]\n", "First 2 elements of y:\n", "[30 31]\n" ] } ], "source": [ "# Note that if the days don't represent valid dates with their respective months or vice versa, you will get an `InvalidArgumentError`, e.g.:\n", "try:\n", " year = tf.constant([2015, 2017], dtype=tf.int32)\n", " month = tf.constant([4, 12], dtype=tf.int32)\n", " day = tf.constant([31, 30], dtype=tf.int32)\n", " date_tensor = tff.datetime.dates_from_year_month_day(year, month, day)\n", "except tf.errors.InvalidArgumentError as e:\n", " print (e)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 535, "status": "ok", "timestamp": 1593633104460, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "XSNiLVISDvex", "outputId": "1749f6e2-e92c-4804-ef7b-c6ad81eab8b2" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(1,), contents=array([[1, 1, 1]], dtype=int32)" ] }, "execution_count": 13, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "#@title (5) Constructing Dates: a single tensor containing ordinals\n", "# And finally, you can create date tensors using ordinals. The ordinal value is\n", "# defined as the number of days since 1 Jan 0001. \n", "# So, for example, 1 Jan 0001 has the ordinal value of 1.\n", "ordinals = tf.constant([1], dtype=tf.int32)\n", "date_tensor = tff.datetime.dates_from_ordinals(ordinals)\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 456, "status": "ok", "timestamp": 1593633105612, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "HiEW0sgiQWPI", "outputId": "a8460954-af84-4bb2-9881-eccee73f7f9a" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(5,), contents=array([[2015, 4, 15],\n", " [2017, 12, 30],\n", " [1871, 8, 4],\n", " [2119, 9, 3],\n", " [1913, 5, 10]], dtype=int32)" ] }, "execution_count": 14, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# We can create more meaningful and numerous dates as follows:\n", "ordinals = tf.constant([\n", " 735703, 736693, 683219, 773829, 698473], dtype=tf.int32)\n", "date_tensor = tff.datetime.dates_from_ordinals(ordinals)\n", "date_tensor" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 385, "status": "ok", "timestamp": 1593633106786, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "8b9suIazS5AP", "outputId": "6ff8f895-35a7-420f-aa23-50d2216d963b" }, "outputs": [ { "data": { "text/plain": [ "736693" ] }, "execution_count": 15, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# You can identify the ordinal value of a date by computing the number of days since 1 Jan 0001.\n", "delta = datetime.date(2017,12,30) - datetime.date(1,1,1)\n", "delta.days + 1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_zgPFwQIUUbG" }, "source": [ "## Generating random dates\n", "\n", "To generate random dates from date tensors between specific start_dates (inclusive) and end_dates (exclusive), we can use tff.datetime.random_dates. The end_dates must be a tensor of a shape compatible with start_dates. In this case we've started with a pair and requested a size of 10, meaning that our tensor will be the shape (10, 2)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "height": 510 }, "colab_type": "code", "executionInfo": { "elapsed": 475, "status": "ok", "timestamp": 1593633109386, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "83ybZV4VWxKk", "outputId": "c622a4a2-6613-4605-a26a-b8072c8e36df" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(10, 2), contents=array([[[2020, 7, 12],\n", " [2020, 12, 5]],\n", "\n", " [[2020, 7, 1],\n", " [2021, 4, 15]],\n", "\n", " [[2020, 8, 19],\n", " [2021, 2, 28]],\n", "\n", " [[2020, 9, 29],\n", " [2021, 4, 14]],\n", "\n", " [[2020, 7, 16],\n", " [2021, 1, 20]],\n", "\n", " [[2021, 1, 11],\n", " [2021, 3, 26]],\n", "\n", " [[2020, 11, 27],\n", " [2020, 8, 8]],\n", "\n", " [[2020, 10, 27],\n", " [2020, 10, 7]],\n", "\n", " [[2020, 6, 29],\n", " [2020, 9, 8]],\n", "\n", " [[2020, 6, 7],\n", " [2021, 5, 16]]], dtype=int32)" ] }, "execution_count": 16, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Generate random dates\n", "start_dates = tff.datetime.dates_from_tuples([\n", " (2020, 5, 16),\n", " (2020, 6, 13)\n", " ])\n", "end_dates = tff.datetime.dates_from_tuples([(2021, 5, 21)])\n", "size = 10 # Generate 10 dates for each pair of (start, end date).\n", "random_dates = tff.datetime.random_dates(start_date=start_dates, end_date=end_dates, size=size)\n", "random_dates" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 483, "status": "ok", "timestamp": 1593633110629, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "gTbFjnBXVTm9", "outputId": "8246f35c-5c28-41ce-ba77-661c5660731b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Invalid Argument Error, Incompatible shapes\n" ] } ], "source": [ "# In the following case, the start_dates shape (4) and end_dates shape (2) don't \n", "# broadcast, producing an error.\n", "try:\n", " start_dates = tff.datetime.dates_from_tuples([\n", " (2020, 5, 16),\n", " (2020, 6, 13),\n", " (2020, 10, 31),\n", " (2020, 12, 1)\n", " ])\n", " end_dates = tff.datetime.dates_from_tuples([(2021, 5, 21), (2021, 10, 20)])\n", " size = 4 # Generate 4 dates for each (start, end date).\n", " random_dates = tff.datetime.random_dates(start_date=start_dates, end_date=end_dates, size=size)\n", " random_dates\n", "except tf.errors.InvalidArgumentError:\n", " print('Invalid Argument Error, Incompatible shapes') " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L913G28mtkRq" }, "source": [ "### Broadcasting" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "height": 340 }, "colab_type": "code", "executionInfo": { "elapsed": 385, "status": "ok", "timestamp": 1593633113114, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "UL7LU2mFXYzO", "outputId": "e2c73920-8d4b-4856-e98e-7142007cad40" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(4, 4), contents=array([[[2020, 10, 31],\n", " [2020, 6, 16],\n", " [2021, 2, 25],\n", " [2021, 8, 10]],\n", "\n", " [[2021, 3, 26],\n", " [2020, 8, 14],\n", " [2021, 7, 23],\n", " [2021, 9, 16]],\n", "\n", " [[2021, 4, 16],\n", " [2021, 6, 18],\n", " [2021, 5, 17],\n", " [2020, 12, 11]],\n", "\n", " [[2020, 11, 14],\n", " [2021, 5, 31],\n", " [2021, 4, 7],\n", " [2021, 8, 29]]], dtype=int32)" ] }, "execution_count": 18, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Instead, match the end_dates by using a scalar (single date), or a matching shape of (4)\n", "start_dates = tff.datetime.dates_from_tuples([\n", " (2020, 5, 16),\n", " (2020, 6, 13),\n", " (2020, 10, 31),\n", " (2020, 12, 1)\n", " ])\n", "end_dates = tff.datetime.dates_from_tuples([\n", " (2021, 5, 21), \n", " (2021, 10, 20), \n", " (2021, 12, 5), \n", " (2021, 11, 20)\n", " ])\n", "size = 4 # Generate 4 dates for each (start, end date).\n", "random_dates = tff.datetime.random_dates(\n", " start_date=start_dates, end_date=end_dates, size=size\n", " )\n", "random_dates" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "juqAkiMmTTFU" }, "source": [ "# Dates Exploration\n", "\n", "Now that we've constructed our dates, let's see what we can do with them." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 355, "status": "ok", "timestamp": 1593633115157, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "Ov-SE0D9WeOP", "outputId": "52428ed0-953c-45d6-a8b0-c1f59daecbaf" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[31, 16, 25, 10],\n", " [26, 14, 23, 16],\n", " [16, 18, 17, 11],\n", " [14, 31, 7, 29]], dtype=int32)\u003e" ] }, "execution_count": 19, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Return the day, the month or the year from date tensors.\n", "random_dates.day()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 420, "status": "ok", "timestamp": 1593633116269, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "lg66bmNRXAWr", "outputId": "eb20c630-e0a0-4c9d-e0f3-eb8c37f67664" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[10, 6, 2, 8],\n", " [ 3, 8, 7, 9],\n", " [ 4, 6, 5, 12],\n", " [11, 5, 4, 8]], dtype=int32)\u003e" ] }, "execution_count": 20, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "random_dates.month()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 363, "status": "ok", "timestamp": 1593633117358, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "MfM7lzUrXCJq", "outputId": "50bb25f8-13ea-4bb3-9bcf-07d5f0ec39dc" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[2020, 2020, 2021, 2021],\n", " [2021, 2020, 2021, 2021],\n", " [2021, 2021, 2021, 2020],\n", " [2020, 2021, 2021, 2021]], dtype=int32)\u003e" ] }, "execution_count": 21, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "random_dates.year()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 394, "status": "ok", "timestamp": 1593633118524, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "eeKG8uG9XHL3", "outputId": "159ad97a-4143-4f64-f5e2-a9540c3080e6" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[737729, 737592, 737846, 738012],\n", " [737875, 737651, 737994, 738049],\n", " [737896, 737959, 737927, 737770],\n", " [737743, 737941, 737887, 738031]], dtype=int32)\u003e" ] }, "execution_count": 22, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Or the ordinals of date tensors.\n", "random_dates.ordinal()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "height": 340 }, "colab_type": "code", "executionInfo": { "elapsed": 416, "status": "ok", "timestamp": 1593633119623, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "kq5Su9aGGkxE", "outputId": "c24088a6-f03e-4729-df32-ee5e1e2f7614" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(4, 4), contents=array([[[2020, 11, 10],\n", " [2020, 6, 26],\n", " [2021, 3, 7],\n", " [2021, 8, 20]],\n", "\n", " [[2021, 4, 5],\n", " [2020, 8, 24],\n", " [2021, 8, 2],\n", " [2021, 9, 26]],\n", "\n", " [[2021, 4, 26],\n", " [2021, 6, 28],\n", " [2021, 5, 27],\n", " [2020, 12, 21]],\n", "\n", " [[2020, 11, 24],\n", " [2021, 6, 10],\n", " [2021, 4, 17],\n", " [2021, 9, 8]]], dtype=int32)" ] }, "execution_count": 23, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# We can then use the days() function to return any multiple of days. For example,\n", "# what is the date 10 days from our date tensors?\n", "new_dates = random_dates + tff.datetime.day()10\n", "new_dates" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 380, "status": "ok", "timestamp": 1593633120765, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "JPpB7PLGXPWs", "outputId": "f0a4a2d3-12fc-4413-dc02-caee7543c6d0" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[5, 1, 3, 1],\n", " [4, 4, 4, 3],\n", " [4, 4, 0, 4],\n", " [5, 0, 2, 6]], dtype=int32)\u003e" ] }, "execution_count": 24, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# You can also identify the corresponding day of the week of your date tensors, \n", "# whereby Monday is \"0\" and Sunday is \"6\", according to Python dates convention.\n", "random_dates.day_of_week()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 476, "status": "ok", "timestamp": 1593633121925, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "p03f53pZXfAd", "outputId": "fd8edddc-369c-418c-9355-a37e4d382aad" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", "[[b'Saturday' b'Tuesday' b'Thursday' b'Tuesday']\n", " [b'Friday' b'Friday' b'Friday' b'Thursday']\n", " [b'Friday' b'Friday' b'Monday' b'Friday']\n", " [b'Saturday' b'Monday' b'Wednesday' b'Sunday']], shape=(4, 4), dtype=string)\n" ] } ], "source": [ "# To make this more intuitive, we can create a TF table with the assigned values \n", "# to then look up and print the corresponding day of the week.\n", "table = tf.lookup.StaticHashTable(\n", " initializer=tf.lookup.KeyValueTensorInitializer(\n", " keys=tf.constant([0, 1, 2, 3, 4, 5, 6]),\n", " values=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", " ),\n", " default_value='Monday',\n", " name='days_in_week'\n", ")\n", "\n", "input_tensor = random_dates.day_of_week()\n", "out = table.lookup(input_tensor)\n", "print(out)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 361, "status": "ok", "timestamp": 1593633122889, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "Pj5rY-x6leMA", "outputId": "40e0521f-3aff-4f92-e8b5-00537560f087" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[305, 168, 56, 222],\n", " [ 85, 227, 204, 259],\n", " [106, 169, 137, 346],\n", " [319, 151, 97, 241]], dtype=int32)\u003e" ] }, "execution_count": 26, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# What about the day of the year?\n", "random_dates.day_of_year()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 370, "status": "ok", "timestamp": 1593633123962, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "MSo6qR3UlmVL", "outputId": "8efe1ca8-4b65-4380-9084-16a68e18ea6a" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[490, 627, 373, 207],\n", " [344, 568, 225, 170],\n", " [323, 260, 292, 449],\n", " [476, 278, 332, 188]], dtype=int32)\u003e" ] }, "execution_count": 27, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# We can also calculate the number of days until a target date\n", "target = tff.datetime.dates_from_tuples([(2022, 3, 5)])\n", "random_dates.days_until(target)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 364, "status": "ok", "timestamp": 1593633125068, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "vqF6u8URl5vB", "outputId": "218d30b5-29d0-4184-d0e3-8b7e968bb630" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[-240, 658, 770, 1033],\n", " [-386, 599, 622, 996],\n", " [-407, 291, 689, 1275],\n", " [-254, 309, 729, 1014]], dtype=int32)\u003e" ] }, "execution_count": 28, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Or multiple target dates, but the shapes of the dates \u0026 targets tensors must broadcast.\n", "targets = tff.datetime.dates_from_tuples([(2020, 3, 5), (2022, 4, 5), (2023, 4, 6), (2024, 6, 8)])\n", "random_dates.days_until(targets)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "height": 340 }, "colab_type": "code", "executionInfo": { "elapsed": 357, "status": "ok", "timestamp": 1593633126083, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "BBni4eUirXlG", "outputId": "089d89cc-e1e1-4b49-bda1-b47b11476f02" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(4, 4), contents=array([[[2020, 10, 31],\n", " [2020, 6, 30],\n", " [2021, 2, 28],\n", " [2021, 8, 31]],\n", "\n", " [[2021, 3, 31],\n", " [2020, 8, 31],\n", " [2021, 7, 31],\n", " [2021, 9, 30]],\n", "\n", " [[2021, 4, 30],\n", " [2021, 6, 30],\n", " [2021, 5, 31],\n", " [2020, 12, 31]],\n", "\n", " [[2020, 11, 30],\n", " [2021, 5, 31],\n", " [2021, 4, 30],\n", " [2021, 8, 31]]], dtype=int32)" ] }, "execution_count": 29, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Let's now shift our dates to the end of their respective months.\n", "random_dates.to_end_of_month()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "d783OaVQmXEd" }, "source": [ "## Periods\n", "\n", "Now, let's think about periods. A PeriodType can be any of the following: day, days, week, weeks, month, months, year or years. Often, this is used in conjunction with 'quantity' to calculate the quantity of periods within another period (i.e. how many months in a year)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 439, "status": "ok", "timestamp": 1593633128843, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "6xnsA7bSnzFY", "outputId": "9b7bf34f-b2b1-432a-d5ab-ba551e886b19" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[30, 30, 28, 31],\n", " [31, 31, 31, 30],\n", " [30, 30, 31, 31],\n", " [30, 30, 30, 31]], dtype=int32)\u003e" ] }, "execution_count": 30, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# You can compute the number of days in specific periods, in this case the period\n", "# is months: How many days are in each month in our date tensors?\n", "random_dates.period_length_in_days(tff.datetime.month())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 413, "status": "ok", "timestamp": 1593633130059, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "H_vldVvJev01", "outputId": "21c53630-d56e-4ce0-f2a6-0edaa9efca20" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=int32, numpy=\n", "array([[365, 365, 365, 365],\n", " [365, 365, 365, 365],\n", " [365, 365, 365, 365],\n", " [365, 365, 365, 365]], dtype=int32)\u003e" ] }, "execution_count": 31, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# What about using years as the period?\n", "random_dates.period_length_in_days(tff.datetime.year())" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 428, "status": "ok", "timestamp": 1593633131184, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "eS-EPiV1qZXr", "outputId": "c7b9274e-894f-43e3-a3bb-89924c27e5c8" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(4, 4), dtype=bool, numpy=\n", "array([[False, False, False, False],\n", " [False, False, False, False],\n", " [False, False, False, False],\n", " [False, False, False, False]])\u003e" ] }, "execution_count": 32, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Looks like there aren't any leap years in our dates, let's confirm. This\n", "# function is in the 'utils' part of our library.\n", "years = random_dates.period_length_in_days(tff.datetime.year())\n", "tff.datetime.utils.is_leap_year(years)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 479, "status": "ok", "timestamp": 1593633435577, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "2pnCAELXYLaG", "outputId": "91e18052-1f3d-41ff-f399-50dd029b7b95" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(2,), dtype=int32, numpy=array([121, 153], dtype=int32)\u003e" ] }, "execution_count": 38, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# We can also specify the period. For example, how many days are there up to the \n", "# 4th and 5th month in 2020?\n", "dates = tff.datetime.dates_from_tuples([(2020, 2, 25), (2020, 3, 2)])\n", "periods = tff.datetime.months([4, 5])\n", "dates.period_length_in_days(periods)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zjBfpr8Lz4r5" }, "source": [ "# Holiday Calendar\n", "\n", "Up to this point we've been using a standard year for our dates. We can also create Holiday Calendars in order to calculate dates taking business days and holidays into account.\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GutS7OroskOi" }, "source": [ "## Creating Holiday Calendars - with Pandas\n", "\n", "The first step will be to create our own Holiday Calendar. This can be done completely manually, however, it would then be necessary to provide holidays for each year and also adjust the holidays that fall on weekends if required. To avoid that, we can use AbstractHolidayCalendar from Pandas." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "height": 119 }, "colab_type": "code", "executionInfo": { "elapsed": 438, "status": "ok", "timestamp": 1593633462654, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "LW0_b1vvsO2b", "outputId": "23356855-7e41-4c0b-9f77-c1abad2961cc" }, "outputs": [ { "data": { "text/plain": [ "array(['2020-01-01', '2020-12-25', '2021-01-01', '2021-12-24',\n", " '2021-12-31', '2022-12-26', '2023-01-02', '2023-12-25',\n", " '2024-01-01', '2024-12-25', '2025-01-01', '2025-12-25',\n", " '2026-01-01', '2026-12-25', '2027-01-01', '2027-12-24',\n", " '2027-12-31', '2028-12-25', '2029-01-01', '2029-12-25',\n", " '2030-01-01', '2030-12-25'], dtype='datetime64[D]')" ] }, "execution_count": 39, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Start with the necessary imports.\n", "from pandas.tseries.holiday import AbstractHolidayCalendar\n", "from pandas.tseries.holiday import Holiday\n", "from pandas.tseries.holiday import nearest_workday\n", "\n", "# Define the rules (i.e. holidays) for the Calendar.\n", "class MyCalendar(AbstractHolidayCalendar):\n", " rules = [\n", " Holiday('NewYear', month=1, day=1, observance=nearest_workday),\n", " Holiday('Christmas', month=12, day=25,\n", " observance=nearest_workday)\n", " ]\n", "calendar = MyCalendar()\n", "holidays_index = calendar.holidays(\n", " start=datetime.date(2020, 1, 1),\n", " end=datetime.date(2030, 12, 31))\n", "holidays = np.array(holidays_index.to_pydatetime(), dtype=\"\u003cM8[D]\")\n", "holidays" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "height": 102 }, "colab_type": "code", "executionInfo": { "elapsed": 402, "status": "ok", "timestamp": 1593633463793, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "bEXj8TWZwVYD", "outputId": "0817976d-69a6-49e5-c634-09ab90ff907e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", "[b'Wednesday' b'Friday' b'Friday' b'Friday' b'Friday' b'Monday' b'Monday'\n", " b'Monday' b'Monday' b'Wednesday' b'Wednesday' b'Thursday' b'Thursday'\n", " b'Friday' b'Friday' b'Friday' b'Friday' b'Monday' b'Monday' b'Tuesday'\n", " b'Tuesday' b'Wednesday'], shape=(22,), dtype=string)\n" ] } ], "source": [ "# As you can see, all of the holidays have been adjusted to a week day, as would be the case for that year's Holiday Calendar.\n", "date_tensor = tff.datetime.dates_from_np_datetimes(holidays)\n", "input_tensor = date_tensor.day_of_week()\n", "out = table.lookup(input_tensor)\n", "print(out)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NO9MnBH-xsfT" }, "source": [ "## Creating Holiday Calendars - manually\n", "\n", "Let's now create our own Holiday Calendar using the TFF Library. To do this, we need to specify:\n", "- A Weekend Mask: Boolean `Tensor` of 7 elements one for each day of the week starting with Monday at index 0. A `True` value indicates the day is considered a weekend day and a `False` value implies a week day.\n", "Default value: None which means no weekends are applied. The following enums for common weekend patterns are also accepted: `SATURDAY_SUNDAY`, `FRIDAY_SATURDAY`, `SUNDAY_ONLY`, `NONE`.\n", "- Holidays: In this case it will be necessary to provide holidays for each year, and also adjust the holidays to that fall on weekdays if necessary.\n", "- Start Year: the earliest year this calendar includes\n", "- End Year: the latest year this calendar includes" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": {}, "colab_type": "code", "executionInfo": { "elapsed": 385, "status": "ok", "timestamp": 1593633466275, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "SeQwp_pxh14-" }, "outputs": [], "source": [ "# Create a calendar\n", "cal = tff.datetime.create_holiday_calendar(weekend_mask=tff.datetime.WeekendMask.SATURDAY_SUNDAY,\n", " holidays=[(2020, 2, 25), (2020, 2, 26), (2019, 12, 25), (2019, 12, 26)], start_year=2019, end_year=2020)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 355, "status": "ok", "timestamp": 1593633468641, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "6LnNWT8ifmhq", "outputId": "d38db948-dcf3-438b-d295-ddd2a93bb533" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(2,), dtype=bool, numpy=array([False, True])\u003e" ] }, "execution_count": 42, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Now, let's test it. Is 'dates' a business day?\n", "dates = tff.datetime.dates_from_tuples([(2020, 2, 25), (2020, 3, 20)])\n", "cal.is_business_day(dates)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": {}, "colab_type": "code", "executionInfo": { "elapsed": 379, "status": "ok", "timestamp": 1593633469774, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "jRSUNsc70SrS" }, "outputs": [], "source": [ "# Rather than using a WeekendMask Enum, let's create our own for 4-day weekends.\n", "new_cal = tff.datetime.create_holiday_calendar(weekend_mask = (0, 0, 0, 1, 1, 1, 1),\n", " holidays=[(2020, 2, 25), (2020, 2, 26), (2019, 12, 25), (2019, 12, 26)], start_year=2019, end_year=2020)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 335, "status": "ok", "timestamp": 1593633470952, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "w-laBs5u2jF_", "outputId": "cd953445-218f-4dbd-ff31-f4353715654d" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])\u003e" ] }, "execution_count": 44, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Let's see if the same holds true - is 'dates' a business day?\n", "dates = tff.datetime.dates_from_tuples([(2020, 2, 25), (2020, 3, 20)])\n", "new_cal.is_business_day(dates)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "colab": {}, "colab_type": "code", "executionInfo": { "elapsed": 437, "status": "ok", "timestamp": 1593633472306, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "ftQ1V54c3tfJ" }, "outputs": [], "source": [ "# Great, now we have both days off!" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JVzYqiBPPOwK" }, "source": [ "##Roll Conventions\n", "\n", "Now that we have our holiday calendar and know how to work with dates, we can apply roll conventions to determine where the business days fall. The main argument is a `BusinessDayConvention` enum which determines how to roll a date that falls on a holiday (including weekends):\n", "\n", " `NONE`: No adjustment.\n", "* `FOLLOWING`: Choose the first business day after the given holiday.\n", "* `MODIFIED_FOLLOWING`: Choose the first business day after the given holiday\n", " unless that day falls in the next calendar month, in which case choose the\n", " first business day before the holiday.\n", "* `PRECEDING`: Choose the first business day before the given holiday.\n", "* `MODIFIED_PRECEDING`: Choose the first business day before the given holiday unless that day falls in the previous calendar month, in which case choose the first business day after the holiday.\n", "\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 354, "status": "ok", "timestamp": 1593633474421, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "QlgoiSccRLJ-", "outputId": "b906adb8-ee35-49a7-80f7-7a4a52f40e36" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2020, 3, 2],\n", " [2020, 3, 23]], dtype=int32)" ] }, "execution_count": 46, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Based on our four-day weekend holiday calendar, let's see what the next\n", "# business days are according to the `FOLLOWING` Convention:\n", "new_cal.roll_to_business_day(dates, roll_convention=tff.datetime.BusinessDayConvention.FOLLOWING)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 413, "status": "ok", "timestamp": 1593633475659, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "B_x-LkDiTIL-", "outputId": "f00f8ae1-e65a-4b33-9d59-a9fefdc8b6b7" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2020, 2, 24],\n", " [2020, 3, 23]], dtype=int32)" ] }, "execution_count": 47, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Since the first date transitions us to the following months, let's see how\n", "# `MODIFIED_FOLLOWING` works.\n", "new_cal.roll_to_business_day(dates, roll_convention=tff.datetime.BusinessDayConvention.MODIFIED_FOLLOWING)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 373, "status": "ok", "timestamp": 1593633476749, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "U7Faa1xIVV2V", "outputId": "f49e9ef0-58c2-497d-8ece-2394e510bbf9" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2020, 3, 16],\n", " [2020, 4, 6]], dtype=int32)" ] }, "execution_count": 48, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# We can also add or subtract business days using a roll convention, where \n", "# the second argument is the number of days we want to add/subtract, as follows:\n", "new_cal.add_business_days(dates, 6, tff.datetime.BusinessDayConvention.FOLLOWING)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "height": 51 }, "colab_type": "code", "executionInfo": { "elapsed": 404, "status": "ok", "timestamp": 1593633478048, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "t4oqEcW4VYWi", "outputId": "237eef9b-b844-4413-c3e2-93772589efdc" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(2,), contents=array([[2020, 2, 11],\n", " [2020, 3, 9]], dtype=int32)" ] }, "execution_count": 49, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "new_cal.subtract_business_days(dates, 6, tff.datetime.BusinessDayConvention.FOLLOWING)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lrlKem4-tQ8-" }, "source": [ "# Day Count Conventions\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VgAx5bBiNmQ5" }, "source": [ "Day count conventions are a system for determining how a coupon accumulates over a coupon period. They can also be seen as a method for converting date\n", "differences to elapsed time. The functions in this module of our library are based on the commonly used day count conventions: \n", "\n", "\n", "* Actual (ISDA)\n", "* Actual 360\n", "* Actual 365\n", "* Actual 365 fixed\n", "* Thirty 360 (ISDA)\n", "\n", "examples coming soon" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cws8Jbkc0L4v" }, "source": [ "# Scaling up\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "height": 136 }, "colab_type": "code", "executionInfo": { "elapsed": 377, "status": "ok", "timestamp": 1593633483447, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "AHf2SUTTfwKb", "outputId": "5fcfc939-2cb8-45e1-aeb0-0a7790da435f" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(1, 1827), contents=array([[[2015, 1, 1],\n", " [2015, 1, 2],\n", " [2015, 1, 3],\n", " ...,\n", " [2019, 12, 30],\n", " [2019, 12, 31],\n", " [2020, 1, 1]]], dtype=int32)" ] }, "execution_count": 50, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# Let's now consider 5 years worth of dates. We'll do this by using the \n", "# PeriodSchedule.dates function in the library, which is useful for creating \n", "# dates within a range.\n", "start_date=tff.datetime.dates_from_tuples([(2015, 1, 1)])\n", "end_date=tff.datetime.dates_from_tuples([(2020, 1, 1)])\n", "tenor = tff.datetime.day()\n", "date_range = tff.datetime.PeriodicSchedule(start_date=start_date, end_date=end_date, tenor=tenor)\n", "date_range.dates()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "colab": {}, "colab_type": "code", "executionInfo": { "elapsed": 356, "status": "ok", "timestamp": 1593633910168, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "gV-ZAcK-68jY" }, "outputs": [], "source": [ "# We can do this for an even bigger date range. Let's see how long that takes.\n", "start_date=tff.datetime.dates_from_tuples([(1001, 1, 1)])\n", "end_date=tff.datetime.dates_from_tuples([(2020, 1, 1)])\n", "tenor = tff.datetime.day()\n", "date_range = tff.datetime.PeriodicSchedule(start_date=start_date_alt, end_date=end_date, tenor=tenor)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 2563, "status": "ok", "timestamp": 1593633913539, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "uU8ZC4BZwMg2", "outputId": "7b836aa8-3511-42b6-f108-f2e1e9aebf14" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 53.3 ms per loop\n" ] } ], "source": [ "%%timeit\n", "date_range = tff.datetime.PeriodicSchedule(start_date=start_date, end_date=end_date, tenor=tenor)\n", "date_range.dates()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "colab": { "height": 136 }, "colab_type": "code", "executionInfo": { "elapsed": 396, "status": "ok", "timestamp": 1593633915385, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "Yin6gwre9uvz", "outputId": "47b49f8a-9016-47ac-d635-08aa1651767e" }, "outputs": [ { "data": { "text/plain": [ "DateTensor: shape=(1, 372183), contents=array([[[1001, 1, 1],\n", " [1001, 1, 2],\n", " [1001, 1, 3],\n", " ...,\n", " [2019, 12, 30],\n", " [2019, 12, 31],\n", " [2020, 1, 1]]], dtype=int32)" ] }, "execution_count": 73, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "dates = large_date_range.dates()\n", "dates" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "colab": { "height": 34 }, "colab_type": "code", "executionInfo": { "elapsed": 348, "status": "ok", "timestamp": 1593633920583, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": -60 }, "id": "XupiasmviPLf", "outputId": "bf002778-f5cc-4427-e738-7161f61034e2" }, "outputs": [ { "data": { "text/plain": [ "\u003ctf.Tensor: shape=(), dtype=float32, numpy=90403.0\u003e" ] }, "execution_count": 74, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "# How many leap years are within these dates?\n", "years = dates.year()\n", "leap_years_boolean = tff.datetime.utils.is_leap_year(years)\n", "tf.reduce_sum(tf.cast(leap_years_boolean, tf.float32)) # Count the number of 'True' values by casting the values to floats.#" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "41La2a7cXhpi" }, "outputs": [], "source": [ "# 90,403 leap years out of the 372,183 years we provided, sounds about right!" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "//learning/brain/python/client:colab_notebook_py3", "kind": "private" }, "name": "Dates_in_TFF.ipynb", "provenance": [ { "file_id": "1vcxG8Z4Iwr0lmDkwX8T_m2GiLHiEbL-6", "timestamp": 1593613346342 } ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
colour-science/colour-ipython	notebooks/volume/macadam_limits.ipynb	1	774	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# !!! D . R . A . F . T !!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimal Colour Stimuli - MacAdam Limits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliography" ] } ], "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 }	bsd-3-clause
bgruening/EDeN	examples/ExampleModel.ipynb	1	78675	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Sequence Modeling with EDeN\n", "##The case for real valued vector labels\n", "\n", "Aim: Suppose you are given two sets of sequences. Each sequence is composed of characters in a finite alphabet. However there are similarity relationships between the characters. We want to build a predictive model that can discriminate between the two sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Artificial Dataset\n", "\n", "Lets build an artificial case. We construct two classes in the following way: for each class we start from a specific but random seed sequence, and the full set is then generated every time by permuting the position of k pairs of characters chosen at random in the seed sequence.\n", "\n", "To simulate the relationship between characters we do as follows: we select at random some charaters and we capitalize them. For the machine, a capitalized character is completely different from its lowercase counterpart, but it is easier for humans to see them. \n", "\n", "Assume the similarity between chars is given as a symmetric matrix. We can then perform a low dimensionality embedding of the similarity matrix (e.g. MDS in $\\mathbb{R}^4$) and obtain some vector representation for each char such that their euclidean distance is proportional to their dissimilarity. Lets assume we are already given the vector representation. In our case we just take some random vectors as they will be roughly equally distant from each other. In order to simulate that the capitalized version of a cahr should be similar to its lowercase counterpart, we just add a small amount of noise to the vector representation of one of the two. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Auxiliary Code" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#code for making artificial dataset\n", "import random\n", "\n", "def swap_two_characters(seq):\n", " '''define a function that swaps two characters at random positions in a string '''\n", " line = list(seq)\n", " id_i = random.randint(0,len(line)-1)\n", " id_j = random.randint(0,len(line)-1)\n", " line[id_i], line[id_j] = line[id_j], line[id_i]\n", " return ''.join(line)\n", "\n", "def swap_characters(seed, n):\n", " seq=seed\n", " for i in range(n):\n", " seq = swap_two_characters(seq)\n", " return seq\n", " \n", "def make_seed(start=0, end=26):\n", " seq = ''.join([str(unichr(97+i)) for i in range(start,end)])\n", " return swap_characters(seq, end-start)\n", " \n", "def make_dataset(n_sequences=None, seed=None, n_swaps=None):\n", " seqs = []\n", " seqs.append( seed )\n", " for i in range(n_sequences):\n", " seq = swap_characters( seed, n_swaps )\n", " seqs.append( seq ) \n", " return seqs\n", "\n", "def random_capitalize(seqs, p=0.5):\n", " new_seqs=[]\n", " for seq in seqs:\n", " new_seq = [c.upper() if random.random() < p else c for c in seq ]\n", " new_seqs.append(''.join(new_seq))\n", " return new_seqs\n", "\n", "def make_artificial_dataset(sequence_length=None, n_sequences=None, n_swaps=None):\n", " seed = make_seed(start=0, end=sequence_length)\n", " print 'Seed: ',seed\n", " seqs = make_dataset(n_sequences=n_sequences, seed=seed, n_swaps=n_swaps)\n", " train_seqs_orig=seqs[:len(seqs)/2]\n", " test_seqs_orig=seqs[len(seqs)/2:]\n", " seqs = random_capitalize(seqs, p=0.5)\n", " print 'Sample with random capitalization:',seqs[:7]\n", " train_seqs=seqs[:len(seqs)/2]\n", " test_seqs=seqs[len(seqs)/2:]\n", " return train_seqs_orig, test_seqs_orig, train_seqs, test_seqs" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#code to estimate predictive performance on categorical labeled sequences\n", "\n", "def discriminative_estimate(train_pos_seqs, train_neg_seqs, test_pos_seqs, test_neg_seqs):\n", " from eden.graph import Vectorizer\n", " vectorizer = Vectorizer(complexity=complexity)\n", "\n", " from eden.converter.graph.sequence import sequence_to_eden\n", " iterable_pos = sequence_to_eden(train_pos_seqs)\n", " iterable_neg = sequence_to_eden(train_neg_seqs)\n", "\n", " from eden.util import fit, estimate\n", " estimator = fit(iterable_pos,iterable_neg, vectorizer, n_iter_search=n_iter_search)\n", "\n", " from eden.converter.graph.sequence import sequence_to_eden\n", " iterable_pos = sequence_to_eden(test_pos_seqs)\n", " iterable_neg = sequence_to_eden(test_neg_seqs)\n", " estimate(iterable_pos, iterable_neg, estimator, vectorizer)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#code to create real vector labels\n", "def make_encoding(encoding_vector_dimension=3, sequence_length=None, noise_size=0.01):\n", " #vector encoding for chars\n", " default_encoding = [0]encoding_vector_dimension\n", " start=0\n", " end=sequence_length\n", " #take a list of all chars up to 'length' \n", " char_list = [str(unichr(97+i)) for i in range(start,end)]\n", "\n", " encodings={}\n", " import numpy as np\n", " codes = np.random.rand(len(char_list),encoding_vector_dimension)\n", " for i, code in enumerate(codes):\n", " c = str(unichr(97+i))\n", " cc = c.upper()\n", " encoding = list(code)\n", " encodings[c] = encoding\n", " #add noise for the encoding of capitalized chars\n", " noise = np.random.rand(encoding_vector_dimension)noise_size\n", " encodings[cc] = list(code + noise)\n", " return encodings, default_encoding\n", "\n", "def make_encodings(n_encodings=3, encoding_vector_dimension=3, sequence_length=None, noise_size=0.01):\n", " encodings=[]\n", " for i in range(1,n_encodings+1):\n", " encoding, default_encoding = make_encoding(encoding_vector_dimension, sequence_length, noise_size=noise_size)\n", " encodings.append(encoding)\n", " return encodings, default_encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Artificial data generation" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from eden.util import configure_logging\n", "import logging\n", "configure_logging(logging.getLogger(),verbosity=2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#problem parameters\n", "random.seed(1)\n", "sequence_length = 8 #sequences length\n", "n_sequences = 50 #num sequences in positive and negative set\n", "n_swaps = 2 #num pairs of chars that are swapped at random\n", "n_iter_search = 30 #num paramter configurations that are evaluated in hyperparameter optimization\n", "complexity = 2 #feature complexity for the vectorizer \n", "n_encodings = 5 #num vector encoding schemes for chars\n", "encoding_vector_dimension = 9 #vector dimension for char encoding\n", "noise_size = 0.05 #amount of random noise " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Positive examples:\n", "Seed: dgbcefah\n", "Sample with random capitalization: ['DgBcEFaH', 'ghbCEFad', 'egBhDFAc', 'cdBgeFaH', 'DGbceFAh', 'bCdGEfAH', 'dFBCAgEh']\n", "Negative examples:\n", "Seed: afbdhgce\n", "Sample with random capitalization: ['AfbdhGce', 'afcdeGBh', 'dfBGhacE', 'aFBDhGCE', 'ahbcfgde', 'afGdEBch', 'efBdhgcA']\n" ] } ], "source": [ "print 'Positive examples:'\n", "train_pos_seqs_orig, test_pos_seqs_orig, train_pos_seqs, test_pos_seqs = make_artificial_dataset(sequence_length,n_sequences,n_swaps)\n", "print 'Negative examples:'\n", "train_neg_seqs_orig, test_neg_seqs_orig, train_neg_seqs, test_neg_seqs = make_artificial_dataset(sequence_length,n_sequences,n_swaps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Discriminative model on categorical labels" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictive performance on original sequences\n", "Positive data: Instances: 25 ; Features: 1048577 with an avg of 63 features per instance\n", "Negative data: Instances: 25 ; Features: 1048577 with an avg of 63 features per instance\n", "\n", "Classifier:\n", "SGDClassifier(alpha=0.000133951114782, average=True, class_weight='auto',\n", " epsilon=0.1, eta0=0.390045426813, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='constant', loss='hinge', n_iter=26, n_jobs=-1,\n", " penalty='l1', power_t=0.259604239911, random_state=None,\n", " shuffle=True, verbose=0, warm_start=False)\n", "\n", "Predictive performance:\n", " accuracy: 0.925 +- 0.160\n", " precision: 0.900 +- 0.300\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Python/2.7/site-packages/sklearn/metrics/classification.py:958: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " recall: 0.850 +- 0.320\n", " f1: 0.867 +- 0.306\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Python/2.7/site-packages/sklearn/metrics/classification.py:958: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " average_precision: 1.000 +- 0.000\n", " roc_auc: 1.000 +- 0.000\n", "Elapsed time: 13.0 secs\n", "Test set\n", "Instances: 52 ; Features: 1048577 with an avg of 63 features per instance\n", "--------------------------------------------------------------------------------\n", "Test Estimate\n", " precision recall f1-score support\n", "\n", " -1 0.92 0.85 0.88 26\n", " 1 0.86 0.92 0.89 26\n", "\n", "avg / total 0.89 0.88 0.88 52\n", "\n", "APR: 0.967\n", "ROC: 0.966\n", "\n", "\n", "\n", "Predictive performance on sequences with random capitalization\n", "Positive data: Instances: 25 ; Features: 1048577 with an avg of 63 features per instance\n", "Negative data: Instances: 25 ; Features: 1048577 with an avg of 63 features per instance\n", "\n", "Classifier:\n", "SGDClassifier(alpha=0.000502260979083, average=True, class_weight='auto',\n", " epsilon=0.1, eta0=0.731179927384, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=39, n_jobs=-1,\n", " penalty='l2', power_t=0.511234550646, random_state=None,\n", " shuffle=True, verbose=0, warm_start=False)\n", "\n", "Predictive performance:\n", " accuracy: 0.783 +- 0.140\n", " precision: 0.827 +- 0.186\n", " recall: 0.800 +- 0.245\n", " f1: 0.776 +- 0.151\n", " average_precision: 0.902 +- 0.116\n", " roc_auc: 0.869 +- 0.167\n", "Elapsed time: 12.8 secs\n", "Test set\n", "Instances: 52 ; Features: 1048577 with an avg of 63 features per instance\n", "--------------------------------------------------------------------------------\n", "Test Estimate\n", " precision recall f1-score support\n", "\n", " -1 0.70 0.81 0.75 26\n", " 1 0.77 0.65 0.71 26\n", "\n", "avg / total 0.74 0.73 0.73 52\n", "\n", "APR: 0.846\n", "ROC: 0.839\n", "CPU times: user 2.83 s, sys: 733 ms, total: 3.56 s\n", "Wall time: 26.3 s\n" ] } ], "source": [ "%%time\n", "#lets estimate the predictive performance of a classifier over the original sequences\n", "print 'Predictive performance on original sequences'\n", "discriminative_estimate(train_pos_seqs_orig, train_neg_seqs_orig, test_pos_seqs_orig, test_neg_seqs_orig)\n", "print '\\n\\n'\n", "#lets estimate the predictive performance of a classifier over the capitalized sequences\n", "print 'Predictive performance on sequences with random capitalization'\n", "discriminative_estimate(train_pos_seqs, train_neg_seqs, test_pos_seqs, test_neg_seqs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: as expected the capitalization makes the predicitve task harder since it expands the vocabulary size and adds variations that look random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Discriminative model on real valued vector labels" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#lets make a vector encoding for the chars simply using a random encoding \n", "#and a small amount of noise for the capitalized versions\n", "\n", "#we can generate a few encodings and let the algorithm choose the best one.\n", "encodings, default_encoding = make_encodings(n_encodings, encoding_vector_dimension, sequence_length, noise_size)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#lets define the 3 main machines: 1) pre_processor, 2) vectorizer, 3) estimator\n", "\n", "#the pre_processor takes the raw format and makes graphs\n", "def pre_processor( seqs, encoding=None, default_encoding=None, args ):\n", " #convert sequences to path graphs\n", " from eden.converter.graph.sequence import sequence_to_eden\n", " graphs = sequence_to_eden(seqs)\n", " \n", " #relabel nodes with corresponding vector encoding\n", " from eden.modifier.graph.vertex_attributes import translate \n", " graphs = translate(graphs, label_map = encoding, default = default_encoding)\n", " \n", " return graphs \n", "\n", "#the vectorizer takes graphs and makes sparse vectors\n", "from eden.graph import Vectorizer\n", "vectorizer = Vectorizer()\n", "\n", "#the estimator takes a sparse data matrix and a target column vector and makes a predictive model \n", "from sklearn.linear_model import SGDClassifier\n", "estimator = SGDClassifier(class_weight='auto', shuffle=True)\n", "\n", "#the model takes a pre_processor, a vectorizer, an estimator and returns the predictive model\n", "from eden.model import ActiveLearningBinaryClassificationModel\n", "model = ActiveLearningBinaryClassificationModel(pre_processor=pre_processor, \n", " estimator=estimator, \n", " vectorizer=vectorizer, \n", " fit_vectorizer=True )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#lets define hyper-parameters vaule ranges\n", "from numpy.random import randint\n", "from numpy.random import uniform\n", "\n", "pre_processor_parameters={'encoding':encodings, 'default_encoding':[default_encoding]}\n", "\n", "vectorizer_parameters={'complexity':[complexity],\n", " 'n':randint(3, 20, size=n_iter_search)}\n", "\n", "estimator_parameters={'n_iter':randint(5, 100, size=n_iter_search),\n", " 'penalty':['l1','l2','elasticnet'],\n", " 'l1_ratio':uniform(0.1,0.9, size=n_iter_search), \n", " 'loss':['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],\n", " 'power_t':uniform(0.1, size=n_iter_search),\n", " 'alpha': [10x for x in range(-8,0)],\n", " 'eta0': [10**x for x in range(-4,-1)],\n", " 'learning_rate': [\"invscaling\", \"constant\", \"optimal\"]}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Model Auto Optimization" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\tParameters range:\n", "\n", "Pre_processor:\n", "default_encoding: [[0, 0, 0, 0, 0, 0, 0, 0, 0]]\n", " encoding: [{'a': [0.61130350595572236, 0.97494329098637944, 0.76960857629201163, 0.26204322815157477, 0.74098952829502562, 0.55485929053033622, 0.90283039829360467, 0.20374427842050191, 0.025894317246876075], 'A': [0.64750085888744346, 1.0114911829929614, 0.7788359711138283, 0.31102929429331178, 0.77385259578416088, 0.59583083055497355, 0.93001756286211967, 0.23657107411903455, 0.060268410602284203], 'c': [0.6523471061473145, 0.31669718594387875, 0.50399780278157447, 0.51426329227193146, 0.54405855736034336, 0.79033509268205648, 0.27515862258756418, 0.64348631160522207, 0.88094348785592158], 'B': [0.72020470836554829, 0.36248323702076946, 0.37991267736254836, 0.33504139098078817, 0.62877132030170402, 0.68931340490102877, 0.60853039082880167, 0.22587295274994407, 0.047338929930690374], 'e': [0.12678263907856469, 0.46564311815607762, 0.50938167721932559, 0.42888900086117043, 0.85595328840676455, 0.75237430766001223, 0.1003829690368373, 0.44878814485212515, 0.79018523118941941], 'd': [0.47735044517183378, 0.38749295007351747, 0.46841040939336409, 0.0020780329581173707, 0.43854652330270394, 0.92161422392169823, 0.56424315528018099, 0.26630006183242128, 0.45978981167871535], 'g': [0.520513311943547, 0.88471129710259777, 0.94814958604248334, 0.35330548778795701, 0.29601150063308768, 0.49009130399904843, 0.8994550179191414, 0.63450919141928197, 0.17319716089252146], 'f': [0.028968751996938713, 0.21891100722122447, 0.8408713496300827, 0.23476823223883148, 0.84823197245570781, 0.50555143522829371, 0.18554367626929691, 0.81008971254565743, 0.47807324445662891], 'h': [0.30053732743398087, 0.6076873125366401, 0.85514292292895566, 0.64381727086384277, 0.90311938962877925, 0.75181672280978917, 0.31697134908675983, 0.69850536388395246, 0.34318144322637212], 'F': [0.054190513008265176, 0.26068424233980692, 0.88938821687162284, 0.28287706519863209, 0.8538353545045132, 0.52994723068937433, 0.22131500220361691, 0.84709544633385003, 0.52125724729945055], 'C': [0.67299253814317672, 0.35893516835972772, 0.52600707161303317, 0.52655825482486451, 0.58599408800839792, 0.80886766857265147, 0.32103548135764576, 0.67868076372205921, 0.9015109534227258], 'G': [0.56277891847944606, 0.92748142110553367, 0.97420582773140063, 0.36984358974379539, 0.30378111832680527, 0.5339479970693557, 0.90848725293466526, 0.65160409723679946, 0.21078999127737788], 'b': [0.6847901996472856, 0.32209277464470265, 0.33211684273344677, 0.32956336270907693, 0.62652415385585736, 0.65226574766841994, 0.59277224915442017, 0.18637177781520076, 0.034078812428796401], 'H': [0.33306000296574756, 0.63264242163522477, 0.90486009522243793, 0.68167693210096436, 0.94246878585978533, 0.75942312320023564, 0.34912084896666562, 0.70794269427034628, 0.36752500653732079], 'E': [0.13168632673374234, 0.49532101592442696, 0.51836444175187657, 0.43833477344394767, 0.87671166856216731, 0.77275466010113647, 0.13492700400886004, 0.46184382538981855, 0.82929099124065853], 'D': [0.4943253463608635, 0.40795128592391289, 0.48179850248573153, 0.038121415864375167, 0.48313339351348289, 0.95305019795433021, 0.6012941682412799, 0.31107492615592908, 0.46236246533782716]}, {'a': [0.23815907341298415, 0.65661783944593877, 0.62754085978986429, 0.55455838285382586, 0.89547073541153366, 0.1005940831955634, 0.87583628099434041, 0.37397194503388009, 0.90691174780194728], 'A': [0.27094956813745119, 0.66854965517399112, 0.64731876648776165, 0.56597168257791652, 0.9184081689872311, 0.14812304180519276, 0.91891341999805454, 0.38388227940484032, 0.91716908114880846], 'c': [0.69894077067577143, 0.82114815108198391, 0.47102983190546799, 0.060862260216037245, 0.92897929550986458, 0.83643100621726929, 0.94852547985335944, 0.38043984847691015, 0.13865982957021405], 'B': [0.76923488024089715, 0.43427489422370796, 0.077063760996661707, 0.97512015081348391, 0.23836900074180367, 0.96398203747848299, 1.0182039684618427, 0.18499072012518741, 0.23368788804673801], 'e': [0.76197970138712579, 0.66386242291300113, 0.65024271879372098, 0.6648116266623767, 0.61926504716157216, 0.83833886004181768, 0.43807657349749285, 0.29198108158626435, 0.22014142524966862], 'd': [0.49039181631807038, 0.027149338136071788, 0.22362323766749237, 0.75633025222176087, 0.69515077719251961, 0.31314515736718396, 0.5554060350199137, 0.40004020212281743, 0.35283788183161791], 'g': [0.82201737146882858, 0.32887177882147234, 0.97627690097932007, 0.58679594530630208, 0.53689819988622811, 0.98286174962355333, 0.94051516507680488, 0.39684566688607237, 0.1090569237937854], 'f': [0.48999166501559055, 0.42382746057548737, 0.038401490120500426, 0.38505929029781438, 0.22690623537677834, 0.76247068293243614, 0.99095534395521212, 0.55797496796257495, 0.18305853708902498], 'h': [0.039226445599407467, 0.97773387149804603, 0.24331730132590235, 0.53015034403421735, 0.3575930554554847, 0.21223608203787447, 0.73099955793668092, 0.49946462351054877, 0.66803760431678283], 'F': [0.52604666513812182, 0.42715922636140174, 0.051497957298214804, 0.40566672360936779, 0.26516790243857119, 0.81117831122568818, 1.0189122670481123, 0.5708239310069797, 0.19420880082938066], 'C': [0.71806388284536027, 0.82648572329440717, 0.51161292808371928, 0.074080240664160413, 0.97735974106117163, 0.84162335731270754, 0.96992647506308338, 0.39619711095643806, 0.15130136123792845], 'G': [0.86450046953655069, 0.37613856479504693, 0.99552660929838921, 0.62605248666224689, 0.56491150620947395, 1.0328035051782973, 0.96550196459715365, 0.39863333375917781, 0.12809970805427259], 'b': [0.73767428258106449, 0.42419345537560427, 0.028596330658275715, 0.92787646906787291, 0.23714376933131676, 0.94005045272110788, 0.97107151606877218, 0.17728455709792368, 0.22959537368142724], 'H': [0.046217636487528836, 1.0162090102004164, 0.25008989981235019, 0.54252153311399032, 0.39300865317092115, 0.25904611811848843, 0.75588291681985842, 0.50038234307994711, 0.69399624821731565], 'E': [0.78263365529553541, 0.6682305665603131, 0.68017947839564918, 0.71115385465959358, 0.64489965857333353, 0.87075495200887287, 0.47225430827096615, 0.34066960489048853, 0.2329668595002394], 'D': [0.52607115554042194, 0.065759952763707441, 0.23063308865386858, 0.75975457344051001, 0.70141235073998209, 0.33949491959361816, 0.56248033289355615, 0.42587612513217393, 0.36125969390335155]}, {'a': [0.2987247133961054, 0.27190738034915907, 0.12785402802252366, 0.47782068258232813, 0.49162216401336922, 0.04003107014890428, 0.55853387104158236, 0.40786258142098419, 0.60446889557742101], 'A': [0.31879631158089827, 0.30763385046175018, 0.16692890556991163, 0.5035563709858849, 0.54099365559662194, 0.049365229511249657, 0.56728609651593387, 0.43999020181791926, 0.62387819307846903], 'c': [0.053628609477675449, 0.67447668020193374, 0.90265826959923356, 0.82469794259360429, 0.36998538138583703, 0.22602735362194037, 0.15198765910689027, 0.030592694314823587, 0.099599886573785157], 'B': [0.7592025373705843, 0.14759468776850915, 0.9390481697507197, 0.7382657017099562, 0.11623280154353018, 0.47541818547550208, 0.32388386316034756, 0.63579470390567838, 0.30950501595190444], 'e': [0.72656334239666864, 0.80102785475165272, 0.40122466055463613, 0.64646621124894699, 0.91234719173185874, 0.7491335047671468, 0.91849139129995994, 0.60695238711347044, 0.033555215570581165], 'd': [0.82416265069164985, 0.78013164420248671, 0.54664510470831984, 0.27447440172796933, 0.3751725908511131, 0.19295389584346456, 0.45142089673645014, 0.11609476228326787, 0.64646174191759354], 'g': [0.093010944297106102, 0.4151497003497221, 0.87180721568186048, 0.038815233154226303, 0.78788927537856102, 0.35575271969719624, 0.7359439311379613, 0.65148789151081388, 0.87532473661052357], 'f': [0.84610597280850719, 0.34299346306040013, 0.0734915343936694, 0.29005988365533641, 0.71823013869206431, 0.36661155567691051, 0.22629595703339844, 0.96216361759917468, 0.49402222928933104], 'h': [0.92942210099585187, 0.76675108275750503, 0.25397703413953843, 0.7368513480296105, 0.80236733960959727, 0.1925417754286749, 0.99252780687704678, 0.82133890541385957, 0.10909627811357137], 'F': [0.85574714097764004, 0.36942138664002888, 0.11411905154874782, 0.30383513991405614, 0.75353161419995796, 0.38928216958196005, 0.25414009460408177, 0.97773556834839515, 0.52664074644950731], 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0.26158741186623335, 0.48092087274759154, 0.41357063759733326, 0.97617425021411353, 0.46690734510989629, 0.70937149736732596], 'F': [0.98017854623970746, 0.20451522479211923, 0.62415097600410097, 0.71322259285925416, 0.88821038008108888, 0.37406182016987299, 0.52958530069404153, 0.592737521860906, 0.061946855806887344], 'C': [0.11035565701091446, 0.13416755909792422, 0.47178065133608493, 0.10613284714380894, 0.12778090832607927, 0.70546271044013042, 0.12008373395016018, 0.50950972209035361, 0.87304290836706133], 'G': [0.65518192005894349, 0.77353812492534613, 0.045305342515199548, 0.8853756264557624, 1.0214818682217699, 0.35621809710394325, 0.34509134759007992, 1.0211711924918285, 0.94349173563108535], 'b': [0.68291917989216899, 0.40137435891334117, 0.051076899341218174, 0.52334985225893271, 0.35759449079167627, 0.93162969223337566, 0.18450548109408094, 0.4676614543353631, 0.43716736497234254], 'H': [0.59504111892223044, 0.2279556584847803, 0.67274655011891027, 0.27834306524802299, 0.51329038722915443, 0.44554774661393459, 0.98626928290177618, 0.47843782157956305, 0.75310961639995], 'E': [0.82238516840941944, 0.57996760890211452, 0.13837771707998367, 0.19863608029049956, 0.10811592122275931, 0.57489411029450388, 0.2252486912541925, 0.28928235466177354, 1.0309988470653586], 'D': [0.45173053195320445, 0.6162858196976887, 0.55828062244478183, 0.82869809868112332, 0.14666318712388321, 0.64519586222548586, 0.42541884577860145, 0.50191640272397808, 0.45189452010573739]}]\n", "\n", "Vectorizer:\n", "complexity: [2]\n", " n: [ 7 6 7 10 3 14 17 12 10 6 11 15 10 15 9 7 12 13 6 8 6 9 5 14 16\n", " 10 7 7 17 4]\n", "\n", "Estimator:\n", " alpha: [1e-08, 1e-07, 1e-06, 1e-05, 0.0001, 0.001, 0.01, 0.1]\n", " eta0: [0.0001, 0.001, 0.01]\n", " l1_ratio: [ 0.69401474 0.21691935 0.12566055 0.14792885 0.34631221 0.60410889\n", " 0.26875281 0.88864925 0.13967705 0.28149049 0.33301708 0.12832015\n", " 0.65081721 0.81773401 0.17992341 0.45952326 0.69241378 0.81186173\n", " 0.70407805 0.22751279 0.48599393 0.35632968 0.10499169 0.432556\n", " 0.87667584 0.10395859 0.18042836 0.47513962 0.63789491 0.78261129]\n", "learning_rate: ['invscaling', 'constant', 'optimal']\n", " loss: ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']\n", " n_iter: [16 83 51 99 21 41 43 17 50 79 86 19 61 44 16 90 64 44 74 15 85 61 7 61 91\n", " 29 91 79 9 26]\n", " penalty: ['l1', 'l2', 'elasticnet']\n", " power_t: [ 0.82300339 0.41651703 0.33042655 0.94469532 0.76612548 0.72719732\n", " 0.51277805 0.53472338 0.51695298 0.75824188 0.31087143 0.4207361\n", " 0.28806641 0.73981136 0.95521723 0.84616751 0.55245076 0.23070303\n", " 0.92653635 0.22306297 0.64264247 0.12909405 0.43464252 0.11551154\n", " 0.67776543 0.9601002 0.17717625 0.47586947 0.90488517 0.57767511]\n", "iteration: (1/5) 1/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "\n", "\n", "\tIteration: 1/30 (after 1.2 sec; 0:00:01.222671)\n", "Best score (roc_auc): 0.745 (0.845 +- 0.100)\n", "\n", "Data:\n", "Instances: 50 ; Features: 1048577 with an avg of 44 features per instance\n", "class: 1 count:25 (0.50)\tclass: -1 count:25 (0.50)\t\n", "\n", "\tModel parameters:\n", "\n", "Pre_processor:\n", "default_encoding: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " encoding: {'a': [0.9188519320909152, 0.55845416915042867, 0.77315347580299965, 0.87531251311946456, 0.14573990255029534, 0.99645812320804461, 0.92914630880805305, 0.607966839325773, 0.28684531991335627], 'A': [0.93173074404799028, 0.60120720084931167, 0.7775379793479088, 0.87557115783492867, 0.17309321127455443, 1.041383723457014, 0.95835967896248331, 0.62051984521756054, 0.28930949880010987], 'c': [0.092769706238625016, 0.084968601523610188, 0.42702499215940615, 0.079985087204747063, 0.098260186846615194, 0.68649798080837154, 0.11634844249020992, 0.46938537856429652, 0.86229246295173989], 'B': [0.68579091603982634, 0.4225295089406822, 0.065931442863455883, 0.55639651405745272, 0.40568631547904499, 0.97366728330305774, 0.22141919665137885, 0.49114964390010302, 0.47536341428906054], 'e': [0.81917551305554859, 0.53103834785521897, 0.11494393003579373, 0.15755890454452337, 0.071842060423706666, 0.53465322563335171, 0.20506265189137263, 0.26631405879283165, 0.98808703390324393], 'd': [0.43571268738361835, 0.58317368253574542, 0.53207682209881002, 0.80276387560718521, 0.10772481046508486, 0.64004812123713528, 0.3774548121721768, 0.46309258214386284, 0.43608786386779264], 'g': [0.64270975878404657, 0.72355128829273718, 6.2381882274142875e-05, 0.85924047654600544, 0.99746616958595402, 0.31255459158943155, 0.31653266877195374, 0.99502191197333656, 0.93126119234835203], 'f': [0.97822762252826134, 0.19893255803413956, 0.62199871651835004, 0.6742599775328022, 0.87182434582191626, 0.35798536447002927, 0.48598866595488288, 0.57384361920863369, 0.044682672049866756], 'h': [0.57216406130470909, 0.20624613978909012, 0.66000941288650961, 0.26158741186623335, 0.48092087274759154, 0.41357063759733326, 0.97617425021411353, 0.46690734510989629, 0.70937149736732596], 'F': [0.98017854623970746, 0.20451522479211923, 0.62415097600410097, 0.71322259285925416, 0.88821038008108888, 0.37406182016987299, 0.52958530069404153, 0.592737521860906, 0.061946855806887344], 'C': [0.11035565701091446, 0.13416755909792422, 0.47178065133608493, 0.10613284714380894, 0.12778090832607927, 0.70546271044013042, 0.12008373395016018, 0.50950972209035361, 0.87304290836706133], 'G': [0.65518192005894349, 0.77353812492534613, 0.045305342515199548, 0.8853756264557624, 1.0214818682217699, 0.35621809710394325, 0.34509134759007992, 1.0211711924918285, 0.94349173563108535], 'b': [0.68291917989216899, 0.40137435891334117, 0.051076899341218174, 0.52334985225893271, 0.35759449079167627, 0.93162969223337566, 0.18450548109408094, 0.4676614543353631, 0.43716736497234254], 'H': [0.59504111892223044, 0.2279556584847803, 0.67274655011891027, 0.27834306524802299, 0.51329038722915443, 0.44554774661393459, 0.98626928290177618, 0.47843782157956305, 0.75310961639995], 'E': [0.82238516840941944, 0.57996760890211452, 0.13837771707998367, 0.19863608029049956, 0.10811592122275931, 0.57489411029450388, 0.2252486912541925, 0.28928235466177354, 1.0309988470653586], 'D': [0.45173053195320445, 0.6162858196976887, 0.55828062244478183, 0.82869809868112332, 0.14666318712388321, 0.64519586222548586, 0.42541884577860145, 0.50191640272397808, 0.45189452010573739]}\n", "\n", "Vectorizer:\n", "complexity: 2\n", " n: 12\n", "\n", "Estimator:\n", " alpha: 0.001\n", " eta0: 0.0001\n", " l1_ratio: 0.103958588145\n", "learning_rate: constant\n", " loss: hinge\n", " n_iter: 44\n", " penalty: l2\n", " power_t: 0.115511537599\n", "iteration: (2/5) 1/30 score (roc_auc): 0.697 (0.787 +- 0.090)\n", "iteration: (3/5) 1/30 score (roc_auc): 0.511 (0.614 +- 0.103)\n", "iteration: (4/5) 1/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (5/5) 1/30 score (roc_auc): 0.596 (0.727 +- 0.131)\n", "iteration: (1/5) 2/30 score (roc_auc): 0.868 (0.918 +- 0.050)\n", "\n", "\n", "\tIteration: 2/30 (after 3.4 sec; 0:00:03.380852)\n", "Best score (roc_auc): 0.868 (0.918 +- 0.050)\n", "\n", "Data:\n", "Instances: 50 ; Features: 1048577 with an avg of 44 features per instance\n", "class: 1 count:25 (0.50)\tclass: -1 count:25 (0.50)\t\n", "\n", "\tModel parameters:\n", "\n", "Pre_processor:\n", "default_encoding: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " encoding: {'a': [0.26146804506497245, 0.0052347524304895421, 0.47133068781824405, 0.64408416868938378, 0.24885570235617993, 0.96091793910443479, 0.04366763576900623, 0.39128005275241495, 0.90230838937964042], 'A': [0.29918594612887728, 0.023749062270889316, 0.47185738685526801, 0.65314384191149133, 0.29054332063095323, 0.9686936912627222, 0.083218503112858555, 0.41387702110138547, 0.95080741756348619], 'c': [0.37749768081707913, 0.83670224597595189, 0.68731571102004485, 0.70904979581969008, 0.13131844000697634, 0.2054540261618597, 0.51402689773556176, 0.27096154237138537, 0.76967475183095946], 'B': [0.89958060063938106, 0.29845785300171729, 0.69740252787807688, 0.56044654881955736, 0.67929318604711686, 0.78890767012910068, 0.16529488944833912, 0.8524376578748224, 0.74572687296213236], 'e': [0.6406909407003275, 0.63485071920178748, 0.56626546891203966, 0.40517753800169043, 0.6086218416346979, 0.69318641219979737, 0.58918028757213059, 0.69384101378910978, 0.12003556895979539], 'd': [0.82511269940608589, 0.25712338929487577, 0.71347021022822033, 0.77736051549718455, 0.88094176718951034, 0.57603356515605419, 0.22407471828546299, 0.73737929532921453, 0.16493163822814605], 'g': [0.27441422335956145, 0.017265091654779186, 0.84299363084387213, 0.75796907860815077, 0.4472997217508301, 0.76888818911496448, 0.83387883320948475, 0.2646971493925605, 0.15098240844106792], 'f': [0.33374969704213919, 0.59752653452693594, 0.59719203039447988, 0.24191723555876599, 0.81616963590455249, 0.51541075499976652, 0.36362282506801202, 0.81603931754036529, 0.74484208840039856], 'h': [0.52345396706360858, 0.7242703508783912, 0.25852082352992412, 0.15389699119208722, 0.026167335908178102, 0.83234782350240399, 0.29581907791685336, 0.71237218584539863, 0.43111721868486286], 'F': [0.36764711546608891, 0.60762861364201604, 0.61763267259016863, 0.26882283231830095, 0.84553503948121222, 0.52159018572118665, 0.37856948535573448, 0.84943807896462431, 0.78932186779615954], 'C': [0.3854259685042446, 0.87375746925996312, 0.71088235445101844, 0.75710124864192396, 0.16468861306936641, 0.22136819392668777, 0.55798269957782476, 0.31930592481049958, 0.79371064393593216], 'G': [0.28884527150994704, 0.055805979879880591, 0.84446745235903553, 0.75804646601554226, 0.49014504307596535, 0.81796491558766449, 0.84288023022904446, 0.29647782278656698, 0.19821525449330052], 'b': [0.85334623900933249, 0.29502433813319151, 0.69546393582900234, 0.5268772398196806, 0.62981020967399493, 0.78761266412690678, 0.14255397443310269, 0.81689877894708585, 0.7028577984890676], 'H': [0.55231529441474525, 0.73446904386099399, 0.30808084207467878, 0.17354098682502531, 0.033518658896534256, 0.85275720487225926, 0.31457571315998356, 0.76169396600878259, 0.46988978209700344], 'E': [0.66223035361177762, 0.64584620681323757, 0.6084204225289791, 0.42679733119839369, 0.64788954380324415, 0.7070187700557734, 0.606521036890595, 0.71606824104069344, 0.16001279576494509], 'D': [0.87339228003838543, 0.29232360726305151, 0.73834173328731434, 0.79212550755106825, 0.90747547508458482, 0.59895502721047866, 0.25826421988668574, 0.74845124597550339, 0.18268446646580203]}\n", "\n", "Vectorizer:\n", "complexity: 2\n", " n: 10\n", "\n", "Estimator:\n", " alpha: 1e-06\n", " eta0: 0.01\n", " l1_ratio: 0.459523255283\n", "learning_rate: constant\n", " loss: perceptron\n", " n_iter: 85\n", " penalty: elasticnet\n", " power_t: 0.230703034999\n", "iteration: (2/5) 2/30 score (roc_auc): 0.853 (0.914 +- 0.062)\n", "iteration: (3/5) 2/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (4/5) 2/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (5/5) 2/30 score (roc_auc): 0.672 (0.749 +- 0.077)\n", "iteration: (1/5) 3/30 score (roc_auc): 0.485 (0.494 +- 0.009)\n", "iteration: (2/5) 3/30 score (roc_auc): 0.472 (0.541 +- 0.069)\n", "iteration: (3/5) 3/30 score (roc_auc): 0.471 (0.546 +- 0.075)\n", "iteration: (4/5) 3/30 score (roc_auc): 0.494 (0.497 +- 0.004)\n", "iteration: (5/5) 3/30 score (roc_auc): 0.469 (0.540 +- 0.070)\n", "iteration: (1/5) 4/30 score (roc_auc): 0.512 (0.575 +- 0.063)\n", "iteration: (2/5) 4/30 score (roc_auc): 0.321 (0.399 +- 0.078)\n", "iteration: (3/5) 4/30 score (roc_auc): 0.337 (0.386 +- 0.049)\n", "iteration: (4/5) 4/30 score (roc_auc): 0.472 (0.541 +- 0.069)\n", "iteration: (5/5) 4/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (1/5) 5/30 score (roc_auc): 0.468 (0.530 +- 0.062)\n", "iteration: (2/5) 5/30 score (roc_auc): 0.470 (0.551 +- 0.081)\n", "iteration: (3/5) 5/30 score (roc_auc): 0.407 (0.530 +- 0.123)\n", "iteration: (4/5) 5/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (5/5) 5/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (1/5) 6/30 score (roc_auc): 0.783 (0.862 +- 0.079)\n", "iteration: (2/5) 6/30 score (roc_auc): 0.897 (0.950 +- 0.053)\n", "\n", "\n", "\tIteration: 6/30 (after 12.4 sec; 0:00:12.427268)\n", "Best score (roc_auc): 0.897 (0.950 +- 0.053)\n", "\n", "Data:\n", "Instances: 50 ; Features: 1048577 with an avg of 44 features per instance\n", "class: 1 count:25 (0.50)\tclass: -1 count:25 (0.50)\t\n", "\n", "\tModel parameters:\n", "\n", "Pre_processor:\n", "default_encoding: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " encoding: {'a': [0.61130350595572236, 0.97494329098637944, 0.76960857629201163, 0.26204322815157477, 0.74098952829502562, 0.55485929053033622, 0.90283039829360467, 0.20374427842050191, 0.025894317246876075], 'A': [0.64750085888744346, 1.0114911829929614, 0.7788359711138283, 0.31102929429331178, 0.77385259578416088, 0.59583083055497355, 0.93001756286211967, 0.23657107411903455, 0.060268410602284203], 'c': [0.6523471061473145, 0.31669718594387875, 0.50399780278157447, 0.51426329227193146, 0.54405855736034336, 0.79033509268205648, 0.27515862258756418, 0.64348631160522207, 0.88094348785592158], 'B': [0.72020470836554829, 0.36248323702076946, 0.37991267736254836, 0.33504139098078817, 0.62877132030170402, 0.68931340490102877, 0.60853039082880167, 0.22587295274994407, 0.047338929930690374], 'e': [0.12678263907856469, 0.46564311815607762, 0.50938167721932559, 0.42888900086117043, 0.85595328840676455, 0.75237430766001223, 0.1003829690368373, 0.44878814485212515, 0.79018523118941941], 'd': [0.47735044517183378, 0.38749295007351747, 0.46841040939336409, 0.0020780329581173707, 0.43854652330270394, 0.92161422392169823, 0.56424315528018099, 0.26630006183242128, 0.45978981167871535], 'g': [0.520513311943547, 0.88471129710259777, 0.94814958604248334, 0.35330548778795701, 0.29601150063308768, 0.49009130399904843, 0.8994550179191414, 0.63450919141928197, 0.17319716089252146], 'f': [0.028968751996938713, 0.21891100722122447, 0.8408713496300827, 0.23476823223883148, 0.84823197245570781, 0.50555143522829371, 0.18554367626929691, 0.81008971254565743, 0.47807324445662891], 'h': [0.30053732743398087, 0.6076873125366401, 0.85514292292895566, 0.64381727086384277, 0.90311938962877925, 0.75181672280978917, 0.31697134908675983, 0.69850536388395246, 0.34318144322637212], 'F': [0.054190513008265176, 0.26068424233980692, 0.88938821687162284, 0.28287706519863209, 0.8538353545045132, 0.52994723068937433, 0.22131500220361691, 0.84709544633385003, 0.52125724729945055], 'C': [0.67299253814317672, 0.35893516835972772, 0.52600707161303317, 0.52655825482486451, 0.58599408800839792, 0.80886766857265147, 0.32103548135764576, 0.67868076372205921, 0.9015109534227258], 'G': [0.56277891847944606, 0.92748142110553367, 0.97420582773140063, 0.36984358974379539, 0.30378111832680527, 0.5339479970693557, 0.90848725293466526, 0.65160409723679946, 0.21078999127737788], 'b': [0.6847901996472856, 0.32209277464470265, 0.33211684273344677, 0.32956336270907693, 0.62652415385585736, 0.65226574766841994, 0.59277224915442017, 0.18637177781520076, 0.034078812428796401], 'H': [0.33306000296574756, 0.63264242163522477, 0.90486009522243793, 0.68167693210096436, 0.94246878585978533, 0.75942312320023564, 0.34912084896666562, 0.70794269427034628, 0.36752500653732079], 'E': [0.13168632673374234, 0.49532101592442696, 0.51836444175187657, 0.43833477344394767, 0.87671166856216731, 0.77275466010113647, 0.13492700400886004, 0.46184382538981855, 0.82929099124065853], 'D': [0.4943253463608635, 0.40795128592391289, 0.48179850248573153, 0.038121415864375167, 0.48313339351348289, 0.95305019795433021, 0.6012941682412799, 0.31107492615592908, 0.46236246533782716]}\n", "\n", "Vectorizer:\n", "complexity: 2\n", " n: 17\n", "\n", "Estimator:\n", " alpha: 1e-07\n", " eta0: 0.0001\n", " l1_ratio: 0.125660550016\n", "learning_rate: optimal\n", " loss: hinge\n", " n_iter: 91\n", " penalty: elasticnet\n", " power_t: 0.552450762423\n", "iteration: (3/5) 6/30 score (roc_auc): 0.777 (0.879 +- 0.102)\n", "iteration: (4/5) 6/30 score (roc_auc): 0.487 (0.531 +- 0.044)\n", "iteration: (5/5) 6/30 score (roc_auc): 0.842 (0.914 +- 0.072)\n", "iteration: (1/5) 7/30 score (roc_auc): 0.381 (0.461 +- 0.080)\n", "iteration: (2/5) 7/30 score (roc_auc): 0.470 (0.551 +- 0.081)\n", "iteration: (3/5) 7/30 score (roc_auc): 0.347 (0.431 +- 0.084)\n", "iteration: (4/5) 7/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (5/5) 7/30 score (roc_auc): 0.473 (0.536 +- 0.063)\n", "iteration: (1/5) 8/30 score (roc_auc): 0.539 (0.671 +- 0.132)\n", "iteration: (2/5) 8/30 score (roc_auc): 0.728 (0.832 +- 0.104)\n", "iteration: (3/5) 8/30 score (roc_auc): 0.728 (0.832 +- 0.104)\n", "iteration: (4/5) 8/30 score (roc_auc): 0.725 (0.827 +- 0.102)\n", "iteration: (5/5) 8/30 score (roc_auc): 0.725 (0.827 +- 0.102)\n", "iteration: (1/5) 9/30 score (roc_auc): 0.749 (0.851 +- 0.101)\n", "iteration: (2/5) 9/30 score (roc_auc): 0.684 (0.816 +- 0.132)\n", "iteration: (3/5) 9/30 score (roc_auc): 0.749 (0.851 +- 0.101)\n", "iteration: (4/5) 9/30 score (roc_auc): 0.749 (0.851 +- 0.101)\n", "iteration: (5/5) 9/30 score (roc_auc): 0.684 (0.816 +- 0.132)\n", "iteration: (1/5) 10/30 score (roc_auc): 0.688 (0.700 +- 0.012)\n", "iteration: (2/5) 10/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (3/5) 10/30 score (roc_auc): 0.725 (0.827 +- 0.102)\n", "iteration: (4/5) 10/30 score (roc_auc): 0.711 (0.811 +- 0.101)\n", "iteration: (5/5) 10/30 score (roc_auc): 0.622 (0.703 +- 0.081)\n", "iteration: (1/5) 11/30 score (roc_auc): 0.824 (0.888 +- 0.064)\n", "iteration: (2/5) 11/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (3/5) 11/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (4/5) 11/30 score (roc_auc): 0.783 (0.862 +- 0.079)\n", "iteration: (5/5) 11/30 score (roc_auc): 0.840 (0.909 +- 0.069)\n", "iteration: (1/5) 12/30 score (roc_auc): 0.409 (0.447 +- 0.038)\n", "iteration: (2/5) 12/30 score (roc_auc): 0.560 (0.617 +- 0.057)\n", "iteration: (3/5) 12/30 score (roc_auc): 0.461 (0.516 +- 0.055)\n", "iteration: (4/5) 12/30 score (roc_auc): 0.421 (0.441 +- 0.020)\n", "iteration: (5/5) 12/30 score (roc_auc): 0.432 (0.536 +- 0.104)\n", "iteration: (1/5) 13/30 score (roc_auc): 0.375 (0.434 +- 0.059)\n", "iteration: (2/5) 13/30 score (roc_auc): 0.471 (0.546 +- 0.075)\n", "iteration: (3/5) 13/30 score (roc_auc): 0.463 (0.532 +- 0.069)\n", "iteration: (4/5) 13/30 score (roc_auc): 0.500 (0.592 +- 0.091)\n", "iteration: (5/5) 13/30 score (roc_auc): 0.397 (0.457 +- 0.059)\n", "iteration: (1/5) 14/30 score (roc_auc): 0.500 (0.500 +- 0.000)\n", "iteration: (2/5) 14/30 score (roc_auc): 0.394 (0.506 +- 0.111)\n", "iteration: (3/5) 14/30 score (roc_auc): 0.388 (0.424 +- 0.036)\n", "iteration: (4/5) 14/30 score (roc_auc): 0.404 (0.453 +- 0.049)\n", "iteration: (5/5) 14/30 score (roc_auc): 0.424 (0.451 +- 0.027)\n", "iteration: (1/5) 15/30 score (roc_auc): 0.728 (0.832 +- 0.104)\n", "iteration: (2/5) 15/30 score (roc_auc): 0.677 (0.734 +- 0.057)\n", "iteration: (3/5) 15/30 score (roc_auc): 0.517 (0.562 +- 0.045)\n", "iteration: (4/5) 15/30 score (roc_auc): 0.728 (0.832 +- 0.104)\n", "iteration: (5/5) 15/30 score (roc_auc): 0.685 (0.738 +- 0.053)\n", "\n", "\n", "\tParameters range:\n", "\n", "Pre_processor:\n", "default_encoding: [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]\n", " encoding: [{'a': [0.9188519320909152, 0.55845416915042867, 0.77315347580299965, 0.87531251311946456, 0.14573990255029534, 0.99645812320804461, 0.92914630880805305, 0.607966839325773, 0.28684531991335627], 'A': [0.93173074404799028, 0.60120720084931167, 0.7775379793479088, 0.87557115783492867, 0.17309321127455443, 1.041383723457014, 0.95835967896248331, 0.62051984521756054, 0.28930949880010987], 'c': [0.092769706238625016, 0.084968601523610188, 0.42702499215940615, 0.079985087204747063, 0.098260186846615194, 0.68649798080837154, 0.11634844249020992, 0.46938537856429652, 0.86229246295173989], 'B': [0.68579091603982634, 0.4225295089406822, 0.065931442863455883, 0.55639651405745272, 0.40568631547904499, 0.97366728330305774, 0.22141919665137885, 0.49114964390010302, 0.47536341428906054], 'e': [0.81917551305554859, 0.53103834785521897, 0.11494393003579373, 0.15755890454452337, 0.071842060423706666, 0.53465322563335171, 0.20506265189137263, 0.26631405879283165, 0.98808703390324393], 'd': [0.43571268738361835, 0.58317368253574542, 0.53207682209881002, 0.80276387560718521, 0.10772481046508486, 0.64004812123713528, 0.3774548121721768, 0.46309258214386284, 0.43608786386779264], 'g': [0.64270975878404657, 0.72355128829273718, 6.2381882274142875e-05, 0.85924047654600544, 0.99746616958595402, 0.31255459158943155, 0.31653266877195374, 0.99502191197333656, 0.93126119234835203], 'f': [0.97822762252826134, 0.19893255803413956, 0.62199871651835004, 0.6742599775328022, 0.87182434582191626, 0.35798536447002927, 0.48598866595488288, 0.57384361920863369, 0.044682672049866756], 'h': [0.57216406130470909, 0.20624613978909012, 0.66000941288650961, 0.26158741186623335, 0.48092087274759154, 0.41357063759733326, 0.97617425021411353, 0.46690734510989629, 0.70937149736732596], 'F': [0.98017854623970746, 0.20451522479211923, 0.62415097600410097, 0.71322259285925416, 0.88821038008108888, 0.37406182016987299, 0.52958530069404153, 0.592737521860906, 0.061946855806887344], 'C': [0.11035565701091446, 0.13416755909792422, 0.47178065133608493, 0.10613284714380894, 0.12778090832607927, 0.70546271044013042, 0.12008373395016018, 0.50950972209035361, 0.87304290836706133], 'G': [0.65518192005894349, 0.77353812492534613, 0.045305342515199548, 0.8853756264557624, 1.0214818682217699, 0.35621809710394325, 0.34509134759007992, 1.0211711924918285, 0.94349173563108535], 'b': [0.68291917989216899, 0.40137435891334117, 0.051076899341218174, 0.52334985225893271, 0.35759449079167627, 0.93162969223337566, 0.18450548109408094, 0.4676614543353631, 0.43716736497234254], 'H': [0.59504111892223044, 0.2279556584847803, 0.67274655011891027, 0.27834306524802299, 0.51329038722915443, 0.44554774661393459, 0.98626928290177618, 0.47843782157956305, 0.75310961639995], 'E': [0.82238516840941944, 0.57996760890211452, 0.13837771707998367, 0.19863608029049956, 0.10811592122275931, 0.57489411029450388, 0.2252486912541925, 0.28928235466177354, 1.0309988470653586], 'D': [0.45173053195320445, 0.6162858196976887, 0.55828062244478183, 0.82869809868112332, 0.14666318712388321, 0.64519586222548586, 0.42541884577860145, 0.50191640272397808, 0.45189452010573739]}, {'a': [0.26146804506497245, 0.0052347524304895421, 0.47133068781824405, 0.64408416868938378, 0.24885570235617993, 0.96091793910443479, 0.04366763576900623, 0.39128005275241495, 0.90230838937964042], 'A': [0.29918594612887728, 0.023749062270889316, 0.47185738685526801, 0.65314384191149133, 0.29054332063095323, 0.9686936912627222, 0.083218503112858555, 0.41387702110138547, 0.95080741756348619], 'c': [0.37749768081707913, 0.83670224597595189, 0.68731571102004485, 0.70904979581969008, 0.13131844000697634, 0.2054540261618597, 0.51402689773556176, 0.27096154237138537, 0.76967475183095946], 'B': [0.89958060063938106, 0.29845785300171729, 0.69740252787807688, 0.56044654881955736, 0.67929318604711686, 0.78890767012910068, 0.16529488944833912, 0.8524376578748224, 0.74572687296213236], 'e': [0.6406909407003275, 0.63485071920178748, 0.56626546891203966, 0.40517753800169043, 0.6086218416346979, 0.69318641219979737, 0.58918028757213059, 0.69384101378910978, 0.12003556895979539], 'd': [0.82511269940608589, 0.25712338929487577, 0.71347021022822033, 0.77736051549718455, 0.88094176718951034, 0.57603356515605419, 0.22407471828546299, 0.73737929532921453, 0.16493163822814605], 'g': [0.27441422335956145, 0.017265091654779186, 0.84299363084387213, 0.75796907860815077, 0.4472997217508301, 0.76888818911496448, 0.83387883320948475, 0.2646971493925605, 0.15098240844106792], 'f': [0.33374969704213919, 0.59752653452693594, 0.59719203039447988, 0.24191723555876599, 0.81616963590455249, 0.51541075499976652, 0.36362282506801202, 0.81603931754036529, 0.74484208840039856], 'h': [0.52345396706360858, 0.7242703508783912, 0.25852082352992412, 0.15389699119208722, 0.026167335908178102, 0.83234782350240399, 0.29581907791685336, 0.71237218584539863, 0.43111721868486286], 'F': [0.36764711546608891, 0.60762861364201604, 0.61763267259016863, 0.26882283231830095, 0.84553503948121222, 0.52159018572118665, 0.37856948535573448, 0.84943807896462431, 0.78932186779615954], 'C': [0.3854259685042446, 0.87375746925996312, 0.71088235445101844, 0.75710124864192396, 0.16468861306936641, 0.22136819392668777, 0.55798269957782476, 0.31930592481049958, 0.79371064393593216], 'G': [0.28884527150994704, 0.055805979879880591, 0.84446745235903553, 0.75804646601554226, 0.49014504307596535, 0.81796491558766449, 0.84288023022904446, 0.29647782278656698, 0.19821525449330052], 'b': [0.85334623900933249, 0.29502433813319151, 0.69546393582900234, 0.5268772398196806, 0.62981020967399493, 0.78761266412690678, 0.14255397443310269, 0.81689877894708585, 0.7028577984890676], 'H': [0.55231529441474525, 0.73446904386099399, 0.30808084207467878, 0.17354098682502531, 0.033518658896534256, 0.85275720487225926, 0.31457571315998356, 0.76169396600878259, 0.46988978209700344], 'E': [0.66223035361177762, 0.64584620681323757, 0.6084204225289791, 0.42679733119839369, 0.64788954380324415, 0.7070187700557734, 0.606521036890595, 0.71606824104069344, 0.16001279576494509], 'D': [0.87339228003838543, 0.29232360726305151, 0.73834173328731434, 0.79212550755106825, 0.90747547508458482, 0.59895502721047866, 0.25826421988668574, 0.74845124597550339, 0.18268446646580203]}, {'a': [0.61130350595572236, 0.97494329098637944, 0.76960857629201163, 0.26204322815157477, 0.74098952829502562, 0.55485929053033622, 0.90283039829360467, 0.20374427842050191, 0.025894317246876075], 'A': [0.64750085888744346, 1.0114911829929614, 0.7788359711138283, 0.31102929429331178, 0.77385259578416088, 0.59583083055497355, 0.93001756286211967, 0.23657107411903455, 0.060268410602284203], 'c': [0.6523471061473145, 0.31669718594387875, 0.50399780278157447, 0.51426329227193146, 0.54405855736034336, 0.79033509268205648, 0.27515862258756418, 0.64348631160522207, 0.88094348785592158], 'B': [0.72020470836554829, 0.36248323702076946, 0.37991267736254836, 0.33504139098078817, 0.62877132030170402, 0.68931340490102877, 0.60853039082880167, 0.22587295274994407, 0.047338929930690374], 'e': [0.12678263907856469, 0.46564311815607762, 0.50938167721932559, 0.42888900086117043, 0.85595328840676455, 0.75237430766001223, 0.1003829690368373, 0.44878814485212515, 0.79018523118941941], 'd': [0.47735044517183378, 0.38749295007351747, 0.46841040939336409, 0.0020780329581173707, 0.43854652330270394, 0.92161422392169823, 0.56424315528018099, 0.26630006183242128, 0.45978981167871535], 'g': [0.520513311943547, 0.88471129710259777, 0.94814958604248334, 0.35330548778795701, 0.29601150063308768, 0.49009130399904843, 0.8994550179191414, 0.63450919141928197, 0.17319716089252146], 'f': [0.028968751996938713, 0.21891100722122447, 0.8408713496300827, 0.23476823223883148, 0.84823197245570781, 0.50555143522829371, 0.18554367626929691, 0.81008971254565743, 0.47807324445662891], 'h': [0.30053732743398087, 0.6076873125366401, 0.85514292292895566, 0.64381727086384277, 0.90311938962877925, 0.75181672280978917, 0.31697134908675983, 0.69850536388395246, 0.34318144322637212], 'F': [0.054190513008265176, 0.26068424233980692, 0.88938821687162284, 0.28287706519863209, 0.8538353545045132, 0.52994723068937433, 0.22131500220361691, 0.84709544633385003, 0.52125724729945055], 'C': [0.67299253814317672, 0.35893516835972772, 0.52600707161303317, 0.52655825482486451, 0.58599408800839792, 0.80886766857265147, 0.32103548135764576, 0.67868076372205921, 0.9015109534227258], 'G': [0.56277891847944606, 0.92748142110553367, 0.97420582773140063, 0.36984358974379539, 0.30378111832680527, 0.5339479970693557, 0.90848725293466526, 0.65160409723679946, 0.21078999127737788], 'b': [0.6847901996472856, 0.32209277464470265, 0.33211684273344677, 0.32956336270907693, 0.62652415385585736, 0.65226574766841994, 0.59277224915442017, 0.18637177781520076, 0.034078812428796401], 'H': [0.33306000296574756, 0.63264242163522477, 0.90486009522243793, 0.68167693210096436, 0.94246878585978533, 0.75942312320023564, 0.34912084896666562, 0.70794269427034628, 0.36752500653732079], 'E': [0.13168632673374234, 0.49532101592442696, 0.51836444175187657, 0.43833477344394767, 0.87671166856216731, 0.77275466010113647, 0.13492700400886004, 0.46184382538981855, 0.82929099124065853], 'D': [0.4943253463608635, 0.40795128592391289, 0.48179850248573153, 0.038121415864375167, 0.48313339351348289, 0.95305019795433021, 0.6012941682412799, 0.31107492615592908, 0.46236246533782716]}]\n", "\n", "Vectorizer:\n", "complexity: [2, 2, 2]\n", " n: [12, 10, 17]\n", "\n", "Estimator:\n", " alpha: [0.001, 1e-06, 1e-07]\n", " eta0: [0.0001, 0.01, 0.0001]\n", " l1_ratio: [0.10395858814476898, 0.45952325528318061, 0.1256605500160137]\n", "learning_rate: ['constant', 'constant', 'optimal']\n", " loss: ['hinge', 'perceptron', 'hinge']\n", " n_iter: [44, 85, 91]\n", " penalty: ['l2', 'elasticnet', 'elasticnet']\n", " power_t: [0.11551153759903279, 0.23070303499941752, 0.55245076242345781]\n", "iteration: (1/5) 16/30 score (roc_auc): 0.662 (0.760 +- 0.098)\n", "iteration: (2/5) 16/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (3/5) 16/30 score (roc_auc): 0.629 (0.759 +- 0.129)\n", "iteration: (4/5) 16/30 score (roc_auc): 0.646 (0.753 +- 0.107)\n", "iteration: (5/5) 16/30 score (roc_auc): 0.587 (0.717 +- 0.129)\n", "iteration: (1/5) 17/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (2/5) 17/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (3/5) 17/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (4/5) 17/30 score (roc_auc): 0.857 (0.913 +- 0.056)\n", "iteration: (5/5) 17/30 score (roc_auc): 0.833 (0.902 +- 0.069)\n", "iteration: (1/5) 18/30 score (roc_auc): 0.902 (0.939 +- 0.037)\n", "\n", "\n", "\tIteration: 18/30 (after 36.9 sec; 0:00:36.927737)\n", "Best score (roc_auc): 0.902 (0.939 +- 0.037)\n", "\n", "Data:\n", "Instances: 50 ; Features: 1048577 with an avg of 44 features per instance\n", "class: 1 count:25 (0.50)\tclass: -1 count:25 (0.50)\t\n", "\n", "\tModel parameters:\n", "\n", "Pre_processor:\n", "default_encoding: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " encoding: {'a': [0.61130350595572236, 0.97494329098637944, 0.76960857629201163, 0.26204322815157477, 0.74098952829502562, 0.55485929053033622, 0.90283039829360467, 0.20374427842050191, 0.025894317246876075], 'A': [0.64750085888744346, 1.0114911829929614, 0.7788359711138283, 0.31102929429331178, 0.77385259578416088, 0.59583083055497355, 0.93001756286211967, 0.23657107411903455, 0.060268410602284203], 'c': [0.6523471061473145, 0.31669718594387875, 0.50399780278157447, 0.51426329227193146, 0.54405855736034336, 0.79033509268205648, 0.27515862258756418, 0.64348631160522207, 0.88094348785592158], 'B': [0.72020470836554829, 0.36248323702076946, 0.37991267736254836, 0.33504139098078817, 0.62877132030170402, 0.68931340490102877, 0.60853039082880167, 0.22587295274994407, 0.047338929930690374], 'e': [0.12678263907856469, 0.46564311815607762, 0.50938167721932559, 0.42888900086117043, 0.85595328840676455, 0.75237430766001223, 0.1003829690368373, 0.44878814485212515, 0.79018523118941941], 'd': [0.47735044517183378, 0.38749295007351747, 0.46841040939336409, 0.0020780329581173707, 0.43854652330270394, 0.92161422392169823, 0.56424315528018099, 0.26630006183242128, 0.45978981167871535], 'g': [0.520513311943547, 0.88471129710259777, 0.94814958604248334, 0.35330548778795701, 0.29601150063308768, 0.49009130399904843, 0.8994550179191414, 0.63450919141928197, 0.17319716089252146], 'f': [0.028968751996938713, 0.21891100722122447, 0.8408713496300827, 0.23476823223883148, 0.84823197245570781, 0.50555143522829371, 0.18554367626929691, 0.81008971254565743, 0.47807324445662891], 'h': [0.30053732743398087, 0.6076873125366401, 0.85514292292895566, 0.64381727086384277, 0.90311938962877925, 0.75181672280978917, 0.31697134908675983, 0.69850536388395246, 0.34318144322637212], 'F': [0.054190513008265176, 0.26068424233980692, 0.88938821687162284, 0.28287706519863209, 0.8538353545045132, 0.52994723068937433, 0.22131500220361691, 0.84709544633385003, 0.52125724729945055], 'C': [0.67299253814317672, 0.35893516835972772, 0.52600707161303317, 0.52655825482486451, 0.58599408800839792, 0.80886766857265147, 0.32103548135764576, 0.67868076372205921, 0.9015109534227258], 'G': [0.56277891847944606, 0.92748142110553367, 0.97420582773140063, 0.36984358974379539, 0.30378111832680527, 0.5339479970693557, 0.90848725293466526, 0.65160409723679946, 0.21078999127737788], 'b': [0.6847901996472856, 0.32209277464470265, 0.33211684273344677, 0.32956336270907693, 0.62652415385585736, 0.65226574766841994, 0.59277224915442017, 0.18637177781520076, 0.034078812428796401], 'H': [0.33306000296574756, 0.63264242163522477, 0.90486009522243793, 0.68167693210096436, 0.94246878585978533, 0.75942312320023564, 0.34912084896666562, 0.70794269427034628, 0.36752500653732079], 'E': [0.13168632673374234, 0.49532101592442696, 0.51836444175187657, 0.43833477344394767, 0.87671166856216731, 0.77275466010113647, 0.13492700400886004, 0.46184382538981855, 0.82929099124065853], 'D': [0.4943253463608635, 0.40795128592391289, 0.48179850248573153, 0.038121415864375167, 0.48313339351348289, 0.95305019795433021, 0.6012941682412799, 0.31107492615592908, 0.46236246533782716]}\n", "\n", "Vectorizer:\n", "complexity: 2\n", " n: 10\n", "\n", "Estimator:\n", " alpha: 0.001\n", " eta0: 0.01\n", " l1_ratio: 0.459523255283\n", "learning_rate: optimal\n", " loss: hinge\n", " n_iter: 44\n", " penalty: elasticnet\n", " power_t: 0.230703034999\n", "iteration: (2/5) 18/30 score (roc_auc): 0.902 (0.939 +- 0.037)\n", "iteration: (3/5) 18/30 score (roc_auc): 0.868 (0.918 +- 0.050)\n", "iteration: (4/5) 18/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (5/5) 18/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (1/5) 19/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (2/5) 19/30 score (roc_auc): 0.878 (0.929 +- 0.051)\n", "iteration: (3/5) 19/30 score (roc_auc): 0.870 (0.934 +- 0.064)\n", "iteration: (4/5) 19/30 score (roc_auc): 0.855 (0.908 +- 0.053)\n", "iteration: (5/5) 19/30 score (roc_auc): 0.865 (0.924 +- 0.058)\n", "iteration: (1/5) 20/30 score (roc_auc): 0.855 (0.908 +- 0.053)\n", "iteration: (2/5) 20/30 score (roc_auc): 0.868 (0.918 +- 0.050)\n", "iteration: (3/5) 20/30 score (roc_auc): 0.843 (0.920 +- 0.078)\n", "iteration: (4/5) 20/30 score (roc_auc): 0.878 (0.929 +- 0.051)\n", "iteration: (5/5) 20/30 score (roc_auc): 0.868 (0.918 +- 0.050)\n", "iteration: (1/5) 21/30 score (roc_auc): 0.855 (0.908 +- 0.053)\n", "iteration: (2/5) 21/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (3/5) 21/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (4/5) 21/30 score (roc_auc): 0.837 (0.897 +- 0.060)\n", "iteration: (5/5) 21/30 score (roc_auc): 0.855 (0.908 +- 0.053)\n", "iteration: (1/5) 22/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (2/5) 22/30 score (roc_auc): 0.845 (0.894 +- 0.049)\n", "iteration: (3/5) 22/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (4/5) 22/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (5/5) 22/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (1/5) 23/30 score (roc_auc): 0.740 (0.840 +- 0.100)\n", "iteration: (2/5) 23/30 score (roc_auc): 0.619 (0.754 +- 0.135)\n", "iteration: (3/5) 23/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (4/5) 23/30 score (roc_auc): 0.698 (0.781 +- 0.083)\n", "iteration: (5/5) 23/30 score (roc_auc): 0.684 (0.816 +- 0.132)\n", "iteration: (1/5) 24/30 score (roc_auc): 0.672 (0.760 +- 0.088)\n", "iteration: (2/5) 24/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (3/5) 24/30 score (roc_auc): 0.619 (0.754 +- 0.135)\n", "iteration: (4/5) 24/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (5/5) 24/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (1/5) 25/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (2/5) 25/30 score (roc_auc): 0.698 (0.781 +- 0.083)\n", "iteration: (3/5) 25/30 score (roc_auc): 0.672 (0.760 +- 0.088)\n", "iteration: (4/5) 25/30 score (roc_auc): 0.670 (0.769 +- 0.099)\n", "iteration: (5/5) 25/30 score (roc_auc): 0.670 (0.769 +- 0.099)\n", "iteration: (1/5) 26/30 score (roc_auc): 0.902 (0.939 +- 0.037)\n", "iteration: (2/5) 26/30 score (roc_auc): 0.890 (0.934 +- 0.044)\n", "iteration: (3/5) 26/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (4/5) 26/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (5/5) 26/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (1/5) 27/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (2/5) 27/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (3/5) 27/30 score (roc_auc): 0.817 (0.887 +- 0.070)\n", "iteration: (4/5) 27/30 score (roc_auc): 0.845 (0.894 +- 0.049)\n", "iteration: (5/5) 27/30 score (roc_auc): 0.884 (0.944 +- 0.060)\n", "iteration: (1/5) 28/30 score (roc_auc): 0.556 (0.709 +- 0.153)\n", "iteration: (2/5) 28/30 score (roc_auc): 0.740 (0.840 +- 0.100)\n", "iteration: (3/5) 28/30 score (roc_auc): 0.670 (0.769 +- 0.099)\n", "iteration: (4/5) 28/30 score (roc_auc): 0.745 (0.845 +- 0.100)\n", "iteration: (5/5) 28/30 score (roc_auc): 0.619 (0.754 +- 0.135)\n", "iteration: (1/5) 29/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (2/5) 29/30 score (roc_auc): 0.882 (0.940 +- 0.058)\n", "iteration: (3/5) 29/30 score (roc_auc): 0.873 (0.943 +- 0.070)\n", "iteration: (4/5) 29/30 score (roc_auc): 0.773 (0.857 +- 0.084)\n", "iteration: (5/5) 29/30 score (roc_auc): 0.837 (0.912 +- 0.075)\n", "iteration: (1/5) 30/30 score (roc_auc): 0.883 (0.939 +- 0.056)\n", "iteration: (2/5) 30/30 score (roc_auc): 0.884 (0.944 +- 0.060)\n", "iteration: (3/5) 30/30 score (roc_auc): 0.849 (0.917 +- 0.068)\n", "iteration: (4/5) 30/30 score (roc_auc): 0.870 (0.934 +- 0.064)\n", "iteration: (5/5) 30/30 score (roc_auc): 0.872 (0.939 +- 0.068)\n", "Saved current best model in my_seq.model\n", "\n", "\tModel parameters:\n", "\n", "Pre_processor:\n", "default_encoding: [0, 0, 0, 0, 0, 0, 0, 0, 0]\n", " encoding: {'a': [0.61130350595572236, 0.97494329098637944, 0.76960857629201163, 0.26204322815157477, 0.74098952829502562, 0.55485929053033622, 0.90283039829360467, 0.20374427842050191, 0.025894317246876075], 'A': [0.64750085888744346, 1.0114911829929614, 0.7788359711138283, 0.31102929429331178, 0.77385259578416088, 0.59583083055497355, 0.93001756286211967, 0.23657107411903455, 0.060268410602284203], 'c': [0.6523471061473145, 0.31669718594387875, 0.50399780278157447, 0.51426329227193146, 0.54405855736034336, 0.79033509268205648, 0.27515862258756418, 0.64348631160522207, 0.88094348785592158], 'B': [0.72020470836554829, 0.36248323702076946, 0.37991267736254836, 0.33504139098078817, 0.62877132030170402, 0.68931340490102877, 0.60853039082880167, 0.22587295274994407, 0.047338929930690374], 'e': [0.12678263907856469, 0.46564311815607762, 0.50938167721932559, 0.42888900086117043, 0.85595328840676455, 0.75237430766001223, 0.1003829690368373, 0.44878814485212515, 0.79018523118941941], 'd': [0.47735044517183378, 0.38749295007351747, 0.46841040939336409, 0.0020780329581173707, 0.43854652330270394, 0.92161422392169823, 0.56424315528018099, 0.26630006183242128, 0.45978981167871535], 'g': [0.520513311943547, 0.88471129710259777, 0.94814958604248334, 0.35330548778795701, 0.29601150063308768, 0.49009130399904843, 0.8994550179191414, 0.63450919141928197, 0.17319716089252146], 'f': [0.028968751996938713, 0.21891100722122447, 0.8408713496300827, 0.23476823223883148, 0.84823197245570781, 0.50555143522829371, 0.18554367626929691, 0.81008971254565743, 0.47807324445662891], 'h': [0.30053732743398087, 0.6076873125366401, 0.85514292292895566, 0.64381727086384277, 0.90311938962877925, 0.75181672280978917, 0.31697134908675983, 0.69850536388395246, 0.34318144322637212], 'F': [0.054190513008265176, 0.26068424233980692, 0.88938821687162284, 0.28287706519863209, 0.8538353545045132, 0.52994723068937433, 0.22131500220361691, 0.84709544633385003, 0.52125724729945055], 'C': [0.67299253814317672, 0.35893516835972772, 0.52600707161303317, 0.52655825482486451, 0.58599408800839792, 0.80886766857265147, 0.32103548135764576, 0.67868076372205921, 0.9015109534227258], 'G': [0.56277891847944606, 0.92748142110553367, 0.97420582773140063, 0.36984358974379539, 0.30378111832680527, 0.5339479970693557, 0.90848725293466526, 0.65160409723679946, 0.21078999127737788], 'b': [0.6847901996472856, 0.32209277464470265, 0.33211684273344677, 0.32956336270907693, 0.62652415385585736, 0.65226574766841994, 0.59277224915442017, 0.18637177781520076, 0.034078812428796401], 'H': [0.33306000296574756, 0.63264242163522477, 0.90486009522243793, 0.68167693210096436, 0.94246878585978533, 0.75942312320023564, 0.34912084896666562, 0.70794269427034628, 0.36752500653732079], 'E': [0.13168632673374234, 0.49532101592442696, 0.51836444175187657, 0.43833477344394767, 0.87671166856216731, 0.77275466010113647, 0.13492700400886004, 0.46184382538981855, 0.82929099124065853], 'D': [0.4943253463608635, 0.40795128592391289, 0.48179850248573153, 0.038121415864375167, 0.48313339351348289, 0.95305019795433021, 0.6012941682412799, 0.31107492615592908, 0.46236246533782716]}\n", "\n", "Vectorizer:\n", "complexity: 2\n", " n: 10\n", "\n", "Estimator:\n", " alpha: 0.001\n", " eta0: 0.01\n", " l1_ratio: 0.459523255283\n", "learning_rate: optimal\n", " loss: hinge\n", " n_iter: 44\n", " penalty: elasticnet\n", " power_t: 0.230703034999\n", "\n", "Classifier:\n", "SGDClassifier(alpha=0.001, average=False, class_weight='auto', epsilon=0.1,\n", " eta0=0.01, fit_intercept=True, l1_ratio=0.45952325528318061,\n", " learning_rate='optimal', loss='hinge', n_iter=44, n_jobs=1,\n", " penalty='elasticnet', power_t=0.23070303499941752,\n", " random_state=None, shuffle=True, verbose=0, warm_start=False)\n", "\n", "Data:\n", "Instances: 52 ; Features: 1048577 with an avg of 43 features per instance\n", "\n", "Predictive performace estimate:\n", " precision recall f1-score support\n", "\n", " -1 0.85 0.85 0.85 26\n", " 1 0.85 0.85 0.85 26\n", "\n", "avg / total 0.85 0.85 0.85 52\n", "\n", "APR: 0.897\n", "ROC: 0.894\n", "CPU times: user 17.1 s, sys: 5.76 s, total: 22.9 s\n", "Wall time: 1min 7s\n" ] } ], "source": [ "%%time\n", "#optimize hyperparameters and fit a predictive model\n", "\n", "#determine optimal parameter configuration\n", "model.optimize(train_pos_seqs, train_neg_seqs,\n", " model_name='my_seq.model', \n", " n_active_learning_iterations=0,\n", " n_iter=n_iter_search, cv=3,\n", " pre_processor_parameters=pre_processor_parameters, \n", " vectorizer_parameters=vectorizer_parameters, \n", " estimator_parameters=estimator_parameters)\n", "\n", "#print optimal parameter configuration\n", "print model.get_parameters()\n", "\n", "#evaluate predictive performance\n", "apr, roc = model.estimate(test_pos_seqs, test_neg_seqs)" ] } ], "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-3.0
vivekec/datascience	tutorials/cpp/Ipy notebooks/Binary search.ipynb	1	1769	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#include <iostream>\n", "#include <stdio.h>\n", "using namespace std;" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "int binarySearch(int arr[], int l, int r, int x)\n", "{\n", " while (l <= r)\n", " {\n", " int m = l + (r-l)/2;\n", " \n", " // Check if x is present at mid\n", " if (arr[m] == x)\n", " return m;\n", " \n", " // If x greater, ignore left half\n", " if (arr[m] < x)\n", " l = m + 1;\n", " \n", " // If x is smaller, ignore right half\n", " else\n", " r = m - 1;\n", " }\n", " \n", " // if we reach here, then element was\n", " // not present\n", " return -1;\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int arr[] = {2, 3, 4, 10, 40,8};\n", "int n = sizeof(arr)/ sizeof(arr[0]);\n", "int x = 10;\n", "int result = binarySearch(arr, 0, n-1, x);\n", "result" ] } ], "metadata": { "kernelspec": { "display_name": "C++17", "language": "C++17", "name": "xeus-cling-cpp17" }, "language_info": { "codemirror_mode": "text/x-c++src", "file_extension": ".cpp", "mimetype": "text/x-c++src", "name": "c++", "version": "17" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
jlandmann/oggm	docs/notebooks/wgms_refmbdata.ipynb	2	22187	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<img src=\"https://raw.githubusercontent.com/OGGM/oggm/master/docs/_static/logo.png\" width=\"40%\" align=\"left\">" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Processing WGMS mass-balance data for OGGM" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In this notebook, we use the most recent lookup table provided by the WGMS to prepare the reference mass-balance data for the OGGM model.\n", "\n", "For this to work you'll need the latest lookup table (available through official channels soon), the latest WGMS FoG data (available [here](http://wgms.ch/data_databaseversions/)), and the latest RGI version (available [here](http://www.glims.org/RGI/))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Read the WGMS files" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "idir = '/home/mowglie/Downloads/Links/'\n", "df_links = pd.read_csv(os.path.join(idir, 'WGMS_FoG_GLACIER_ID_LUT_v2017-01-13.csv'), encoding='iso8859_15')\n", "df_mb_all = pd.read_csv(os.path.join(idir, 'WGMS-FoG-2016-08-EE-MASS-BALANCE.csv'), encoding='iso8859_15')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "'Total number of links: {}'.format(len(df_links))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_links = df_links.dropna(subset=['RGI_ID']) # keep the ones with a valid RGI ID\n", "'Total number of RGI links: {}'.format(len(df_links))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Select WGMS IDs with more than N years of mass-balance " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_mb = df_mb_all[df_mb_all.LOWER_BOUND.isin([9999])].copy() # remove the profiles\n", "gp_id = df_mb.groupby('WGMS_ID')\n", "ids_5 = []\n", "ids_1 = []\n", "for wgmsid, group in gp_id:\n", " if np.sum(np.isfinite(group.ANNUAL_BALANCE.values)) >= 5:\n", " ids_5.append(wgmsid)\n", " if np.sum(np.isfinite(group.ANNUAL_BALANCE.values)) >= 1:\n", " ids_1.append(wgmsid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print('Number of glaciers with more than 1 MB years: {}'.format(len(ids_1)))\n", "print('Number of glaciers with more than 5 MB years: {}'.format(len(ids_5)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Number of glaciers in the lookup table with at least 5 years of valid MB data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "'Number of matches in the WGMS lookup-table: {}'.format(len(df_links.loc[df_links.WGMS_ID.isin(ids_5)]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# keep those\n", "df_links_sel = df_links.loc[df_links.WGMS_ID.isin(ids_5)]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Duplicates?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Yes:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_links_sel.loc[df_links_sel.duplicated('RGI_ID', keep=False)]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Careser is an Italian glacier which is now disintegrated in smaller parts. Here a screenshot from the WGMS exploration tool:\n", "\n", "<img src=\"https://dl.dropboxusercontent.com/u/20930277/do_not_delete/wgms_1.jpg\" width=\"80%\">\n", "\n", "We keep the oldest MB series and discard the newer ones which are for the smaller glaciers (not represented in RGI)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# We keep CARESER as this is the longest before they split\n", "df_links_sel = df_links_sel.loc[~ df_links_sel.WGMS_ID.isin([3346, 3345])]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The two norwegian glaciers are part of an ice cap:\n", "\n", "<img src=\"https://dl.dropboxusercontent.com/u/20930277/do_not_delete/wgms_2.jpg\" width=\"80%\">\n", "\n", "The two mass-balance time series are very close to each other, unsurprisingly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_mb.loc[df_mb.WGMS_ID.isin([3339])].set_index('YEAR').ANNUAL_BALANCE.plot()\n", "df_mb.loc[df_mb.WGMS_ID.isin([3343])].set_index('YEAR').ANNUAL_BALANCE.plot();" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Since there is no reason for picking one series over the other, we have to remove both from the list." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# The two nowegians glaciers are some part of an ice cap. I'll just remove them both\n", "df_links_sel = df_links_sel.loc[~ df_links_sel.WGMS_ID.isin([3339, 3343])]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "'Final number of matches in the WGMS lookup-table: {}'.format(len(df_links_sel))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# add some simple stats\n", "df_links_sel['RGI_REG'] = [rid.split('-')[1].split('.')[0] for rid in df_links_sel.RGI_ID]\n", "df_links_sel['N_MB_YRS'] = [len(df_mb.loc[df_mb.WGMS_ID == wid]) for wid in df_links_sel.WGMS_ID]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Write out the mass-balance data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "odir = '/home/mowglie/Downloads/WGMS'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Annual MB" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "for rid, wid in zip(df_links_sel.RGI_ID, df_links_sel.WGMS_ID):\n", " df_mb_sel = df_mb.loc[df_mb.WGMS_ID == wid].copy()\n", " df_mb_sel = df_mb_sel[['YEAR', 'WGMS_ID', 'POLITICAL_UNIT', 'NAME', 'AREA', 'WINTER_BALANCE', \n", " 'SUMMER_BALANCE', 'ANNUAL_BALANCE', 'REMARKS']].set_index('YEAR')\n", " df_mb_sel['RGI_ID'] = rid\n", " df_mb_sel.to_csv(os.path.join(odir, 'mbdata', 'mbdata_WGMS-{:05d}.csv'.format(wid)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Profiles" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "for rid, wid in zip(df_links_sel.RGI_ID, df_links_sel.WGMS_ID):\n", " df_mb_sel = df_mb_all.loc[df_mb_all.WGMS_ID == wid].copy()\n", " df_mb_sel = df_mb_sel.loc[df_mb_sel.LOWER_BOUND != 9999]\n", " df_mb_sel = df_mb_sel.loc[df_mb_sel.UPPER_BOUND != 9999]\n", " if len(df_mb_sel) == 0:\n", " df_links_sel.loc[df_links_sel.RGI_ID == rid, 'HAS_PROFILE'] = False\n", " continue\n", " lb = set()\n", " for yr in df_mb_sel.YEAR.unique():\n", " df_mb_sel_yr = df_mb_sel.loc[df_mb_sel.YEAR == yr]\n", " mids = df_mb_sel_yr.LOWER_BOUND.values1.\n", " mids += df_mb_sel_yr.UPPER_BOUND.values[:len(mids)]\n", " mids = 0.5\n", " [lb.add(int(m)) for m in mids]\n", " prof = pd.DataFrame(columns=sorted(list(lb)), index=sorted(df_mb_sel.YEAR.unique()))\n", " for yr in df_mb_sel.YEAR.unique():\n", " df_mb_sel_yr = df_mb_sel.loc[df_mb_sel.YEAR == yr]\n", " mids = df_mb_sel_yr.LOWER_BOUND.values1.\n", " mids += df_mb_sel_yr.UPPER_BOUND.values[:len(mids)]\n", " mids = 0.5\n", " prof.loc[yr, mids.astype(int)] = df_mb_sel_yr.ANNUAL_BALANCE.values\n", " prof.to_csv(os.path.join(odir, 'profiles', 'profile_WGMS-{:05d}.csv'.format(wid)))\n", " df_links_sel.loc[df_links_sel.RGI_ID == rid, 'HAS_PROFILE'] = True" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Links: add some stats" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Handle various RGI versions\n", "df_links_sel.rename(columns = {'RGI_ID':'RGI50_ID'}, inplace = True)\n", "df_links_sel['RGI40_ID'] = df_links_sel['RGI50_ID']\n", "df_links_sel['RGI40_ID'] = [rid.replace('RGI50', 'RGI40') for rid in df_links_sel['RGI40_ID']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Get the RGI\n", "import geopandas as gpd\n", "import glob, os\n", "frgi = '/home/mowglie/Documents/rgi50_allglaciers.csv'\n", "if not os.path.exists(frgi):\n", " # one time action only\n", " fs = list(sorted(glob.glob(\"/home/mowglie/disk/Data/GIS/SHAPES/RGI/RGI_V5//_rgi50_.shp\")))[2:]\n", " out = []\n", " for f in fs:\n", " sh = gpd.read_file(f).set_index('RGIId')\n", " del sh['geometry']\n", " del sh['GLIMSId']\n", " del sh['Name']\n", " out.append(sh)\n", " mdf = pd.concat(out)\n", " mdf.to_csv(frgi)\n", "mdf = pd.read_csv(frgi, index_col=0, converters={'GlacType': str, 'RGIFlag':str, 'BgnDate':str, \n", " 'O1Region': str, 'O2Region':str})\n", "mdf['RGI_REG'] = [rid.split('-')[1].split('.')[0] for rid in mdf.index]\n", "# add region names\n", "sr = gpd.read_file('/home/mowglie/disk/Data/GIS/SHAPES/RGI/RGI_V5/00_rgi50_regions/00_rgi50_O1Regions.shp')\n", "sr = sr.drop_duplicates('Secondary_').set_index('Secondary_')[['Primary_ID']]\n", "sr['Primary_ID'] = [i + ': ' + s for i, s in sr.Primary_ID.iteritems()]\n", "mdf['RGI_REG_NAME'] = sr.loc[mdf.RGI_REG].Primary_ID.values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Read glacier attrs\n", "key1 = {'0': 'Glacier',\n", " '1': 'Ice cap',\n", " '2': 'Perennial snowfield',\n", " '3': 'Seasonal snowfield',\n", " '9': 'Not assigned',\n", " }\n", "\n", "key2 = {'0': 'Land-terminating',\n", " '1': 'Marine-terminating',\n", " '2': 'Lake-terminating',\n", " '3': 'Dry calving',\n", " '4': 'Regenerated',\n", " '5': 'Shelf-terminating',\n", " '9': 'Not assigned',\n", " }\n", "\n", "def is_tidewater(ttype):\n", " return \n", "\n", "mdf['GlacierType'] = [key1[gtype[0]] for gtype in mdf.GlacType]\n", "mdf['TerminusType'] = [key2[gtype[1]] for gtype in mdf.GlacType]\n", "mdf['IsTidewater'] = [ttype in ['Marine-terminating', 'Lake-terminating'] for ttype in mdf.TerminusType]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# add lons and lats and other attrs to the WGMS ones\n", "smdf = mdf.loc[df_links_sel.RGI50_ID]\n", "df_links_sel['CenLon'] = smdf.CenLon.values\n", "df_links_sel['CenLat'] = smdf.CenLat.values\n", "df_links_sel['GlacierType'] = smdf.GlacierType.values\n", "df_links_sel['TerminusType'] = smdf.TerminusType.values\n", "df_links_sel['IsTidewater'] = smdf.IsTidewater.values\n", "df_links_sel['RGI_REG_NAME'] = smdf.RGI_REG_NAME.values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_links_sel = df_links_sel[['CenLon', 'CenLat',\n", " 'POLITICAL_UNIT', 'NAME', 'WGMS_ID', 'PSFG_ID', 'WGI_ID', 'GLIMS_ID',\n", " 'RGI40_ID', 'RGI50_ID', 'RGI_REG', 'RGI_REG_NAME', 'GlacierType', 'TerminusType', \n", " 'IsTidewater', 'N_MB_YRS', 'HAS_PROFILE', 'REMARKS']]\n", "df_links_sel.to_csv(os.path.join(odir, 'rgi_wgms_links_20170217_RGIV5.csv'.format(wid)), index=False)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "source": [ "## Some plots " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_context('poster')\n", "sns.set_style('whitegrid')\n", "pdir = '/home/mowglie/Documents/git/fmaussion.github.io/images/blog/wgms-links'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_links_sel['N_MB_YRS'].plot(kind='hist', color='C2', bins=np.arange(21)5);\n", "plt.xlim(5, 100);\n", "plt.ylabel('Number of glaciers')\n", "plt.xlabel('Length of the timeseries (years)');\n", "plt.tight_layout();\n", "plt.savefig(os.path.join(pdir, 'nglacier-hist.png'), dpi=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import cartopy\n", "import cartopy.crs as ccrs\n", "\n", "f = plt.figure(figsize=(12, 7))\n", "ax = plt.axes(projection=ccrs.Robinson())\n", "# mark a known place to help us geo-locate ourselves\n", "ax.set_extent([-180, 180, -90, 90], crs=ccrs.PlateCarree())\n", "ax.stock_img()\n", "ax.add_feature(cartopy.feature.COASTLINE);\n", "s = df_links_sel.loc[df_links_sel.N_MB_YRS < 10]\n", "print(len(s))\n", "ax.scatter(s.CenLon, s.CenLat, label='< 10 MB years', s=50,\n", " edgecolor='k', facecolor='C0', transform=ccrs.PlateCarree(), zorder=99)\n", "s = df_links_sel.loc[(df_links_sel.N_MB_YRS >= 10) & (df_links_sel.N_MB_YRS < 30)]\n", "print(len(s))\n", "ax.scatter(s.CenLon, s.CenLat, label='$\\geq$ 10 and < 30 MB years', s=50,\n", " edgecolor='k', facecolor='C1', transform=ccrs.PlateCarree(), zorder=99)\n", "s = df_links_sel.loc[df_links_sel.N_MB_YRS >= 30]\n", "print(len(s))\n", "ax.scatter(s.CenLon, s.CenLat, label='$\\geq$ 30 MB years', s=50,\n", " edgecolor='k', facecolor='C2', transform=ccrs.PlateCarree(), zorder=99)\n", "plt.title('WGMS glaciers with at least 5 years of mass-balance data')\n", "plt.legend(loc=4, frameon=True)\n", "plt.tight_layout();\n", "plt.savefig(os.path.join(pdir, 'glacier-map.png'), dpi=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "df_links_sel.TerminusType.value_counts().to_frame()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "ax = sns.countplot(x='RGI_REG', hue=\"TerminusType\", data=df_links_sel);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "md = pd.concat([mdf.GlacierType.value_counts().to_frame(name='RGI V5').T, \n", " df_links_sel.GlacierType.value_counts().to_frame(name='WGMS').T]\n", " ).T\n", "md" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "md = pd.concat([mdf.TerminusType.value_counts().to_frame(name='RGI V5').T, \n", " df_links_sel.TerminusType.value_counts().to_frame(name='WGMS').T]\n", " ).T\n", "md" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "area_per_reg = mdf[['Area', 'RGI_REG_NAME']].groupby('RGI_REG_NAME').sum()\n", "area_per_reg['N_WGMS'] = df_links_sel.RGI_REG_NAME.value_counts()\n", "area_per_reg = area_per_reg.reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sns.barplot(x=\"Area\", y=\"RGI_REG_NAME\", data=area_per_reg);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "area_per_reg['N_WGMS_PER_UNIT'] = area_per_reg.N_WGMS / area_per_reg.Area * 1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sns.barplot(x=\"N_WGMS\", y=\"RGI_REG_NAME\", data=area_per_reg); # , palette=sns.husl_palette(19, s=.7, l=.5)\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "plt.title('Number of WGMS glaciers per RGI region');\n", "plt.tight_layout();\n", "plt.savefig(os.path.join(pdir, 'barplot-ng.png'), dpi=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sns.barplot(x=\"N_WGMS_PER_UNIT\", y=\"RGI_REG_NAME\", data=area_per_reg);\n", "plt.ylabel('')\n", "plt.xlabel('')\n", "plt.title('Number of WGMS glaciers per 1,000 km$^2$ of ice, per RGI region');\n", "plt.tight_layout();\n", "plt.savefig(os.path.join(pdir, 'barplot-perice.png'), dpi=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "nmb_yrs = df_links_sel[[\"RGI_REG\", 'N_MB_YRS']].groupby(\"RGI_REG\").sum()\n", "i = []\n", "for k, d in nmb_yrs.iterrows():\n", " i.extend([k] * d.values[0])\n", "df = pd.DataFrame()\n", "df[\"RGI_REG\"] = i\n", "ax = sns.countplot(x=\"RGI_REG\", data=df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
pagutierrez/tutorial-sklearn	notebooks-spanish/16-metricas_rendimiento_evaluacion_modelos.ipynb	1	16025	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluación de modelos, métricas de puntuación y manejo de conjuntos de datos no balanceados." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En los cuadernos anteriores, hemos detallado como evaluar un modelo y como escoger el mejor modelo posible. Hasta ahora, hemos asumido que nos proporcionaban una medida de rendimiento, algo para medir la calidad del modelo. Sin embargo, no siempre está claro cuál debería ser la medida a utilizar.\n", "Por defecto, en scikit-learn, se utiliza el ``accuracy`` para clasificación, que es el ratio de patrones correctamente clasificados y el $R^2$ para regresión, que es el coeficiente de determinación.\n", "Estas medidas son razonables para muchos escenarios. Sin embargo, dependiendo de la tarea que estemos tratando, estas no tienen porque ser las mejores opciones (y a veces pueden ser opciones muy poco recomendables).\n", "Vamos a centrarnos en la tarea de clasificación, volviendo de nuevo al problema de clasificación de dígitos manuscritos. Scikit-learn tiene métodos muy útiles en el paquete ``sklearn.metrics`` para ayudarnos a entrenar un clasificador y luego evaluarlo de distintas formas:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "np.set_printoptions(precision=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.svm import LinearSVC\n", "\n", "digits = load_digits()\n", "X, y = digits.data, digits.target\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " random_state=1,\n", " stratify=y,\n", " test_size=0.25)\n", "\n", "classifier = LinearSVC(random_state=1).fit(X_train, y_train)\n", "y_test_pred = classifier.predict(X_test)\n", "\n", "print(\"CCR: %f\"%(classifier.score(X_test, y_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vemos que hemos predicho alrededor de un 95% de patrones de forma correcta. Para problemas multi-clase, a veces es muy útil saber qué clases son más difíciles de predecir y cuáles más fáciles o incluso qué tipo de errores son los más comunes. Una forma de tener más información en este sentido es la matriz de confusión, que muestra para cada clase (filas) cuántas veces se predicen qué clases (columnas)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "confusion_matrix(y_test, y_test_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A veces un gráfico es más fácil de leer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.imshow(confusion_matrix(y_test, y_test_pred), cmap=\"Blues\")\n", "plt.colorbar(shrink=0.8)\n", "plt.xticks(range(10))\n", "plt.yticks(range(10))\n", "plt.xlabel(\"Etiqueta predicha\")\n", "plt.ylabel(\"Etiqueta real\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos ver que la mayoría de valores están en la diagonal principal, lo que significa que predecimos casi todos los ejemplos correctamente. Las entradas que no están en la diagonal principal nos muestran que hay bastantes ochos clasificados como unos, y que los nueves son fácilmente confundibles con el resto de clases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Otra función muy útil es ``classification_report`` que nos proporciona los valores de precisión, recall, puntuación f y el soporte para todas las clases. La precisión nos dice cuantas de las predicciones de una clase, son realmente de esa clase. Sea TP, FP, TN, FN \"true positive\" (verdaderos positivos), \"false positive\", (falsos positivos),\"true negative\" (verdaderos negativos) y \"false negative\" (falsos negativos), respectivamente:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Precision = TP / (TP + FP)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El recall representa cuantos ejemplos de la clase fueron clasificados correctamente (accuracy considerando solo esa clase):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall = TP / (TP + FN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El valor F1 es la media geométrica de la precisión y el recall:\n", "\n", "F1 = 2 x (precision x recall) / (precision + recall)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Todas estas métricas están en el intervalo $[0,1]$, donde un 1 es una puntuación perfecta." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "print(classification_report(y_test, y_test_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estas métricas son especialmente útiles en dos casos particulares:\n", "1. Clasificación no balanceada, es decir, una o varias clases son mucho menos frecuentes (hay menos casos en el conjunto de entrenamiento) que el resto de clases.\n", "2. Costes asimétricos, esto es, algunos tipos de errores son más \"costosos\" que el resto." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a ver el primer caso. Imagina que tenemos un ratio de 1:9 para un problema de clasificación (lo cuál no es muy exagerado, piensa por ejemplo en la predicción de clicks sobre banners de publicidad, donde a lo mejor solo un 0.001% de los anunciados son visitados):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.bincount(y) / y.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para probar este escenario, vamos a clasificar el dígito 3 contra el resto (el problema de clasificación es un problema binario, ¿es este dígito un 3?):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X, y = digits.data, digits.target == 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora vamos a aplicar validación cruzada con un clasificador para ver que tal funciona:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.svm import SVC\n", "\n", "cross_val_score(SVC(), X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nuestro clasificador tienen un 90% de acierto siempre. ¿Es bueno o malo? Ten en cuenta que el 90% de los dígitos no son un 3. Vamos a ver que tal funciona un clasificador simple, que siempre predice la clase más frecuenta (ZeroR):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.dummy import DummyClassifier\n", "cross_val_score(DummyClassifier(strategy=\"most_frequent\"), X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También un 90%, como esperábamos. Por tanto, podemos pensar que el clasificador SVC no es demasiado bueno, ya que funciona igual que una estrategia que ni si quiera mira los datos de entrada. De todas formas, esto sería sacar conclusiones muy rápido ya que, en general, el accuracy no es una buena medida de rendimiento para bases de datos no balanceadas." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.bincount(y) / y.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Curvas ROC\n", "=======\n", "\n", "Una medida mucho mejor se puede obtener utilizando las llamadas curvas de operación características (ROC, Receiver operating characteristics). Una curva ROC trabaja con las medidas de incertidumbre de un clasificador, por ejemplo la función de decisión de un ``SVC``. En lugar de utilizar el cero como umbral para distinguir ejemplos negativos de ejemplos positivos, la curva ROC considera todos los posibles umbrales y almacena el ratio de ejemplos de la clase positiva que se predicen correctamente (TPR) y el ratio de fallos para la clase negativa (FPR).\n", "\n", "El siguiente gráfico compara la curva ROC de tres configuraciones distintas de nuestro clasificador para la tarea \"tres vs el resto\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import roc_curve, roc_auc_score\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n", "\n", "for gamma in [.01, .095, 1]:\n", " plt.xlabel(\"False Positive Rate\")\n", " plt.ylabel(\"True Positive Rate (recall)\")\n", " svm = SVC(gamma=gamma).fit(X_train, y_train)\n", " decision_function = svm.decision_function(X_test)\n", " fpr, tpr, _ = roc_curve(y_test, decision_function)\n", " acc = svm.score(X_test, y_test)\n", " auc = roc_auc_score(y_test, svm.decision_function(X_test))\n", " plt.plot(fpr, tpr, label=\"gamma: %.2f (acc:%.2f auc:%.2f)\" % (gamma, acc, auc), linewidth=3)\n", "plt.legend(loc=\"best\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si el valor de umbral es muy bajo, tendremos muchos falsos positivos y por tanto un TPR muy alto y un FPR muy alto (porque casi todo lo clasificamos como positivo). Si usamos un umbral muy alto, habrá muy pocos falsos positivos (casi todo se predice como negativo), y por tanto el TPR será bajo y el FPR también. Por lo que, en general, la curva va desde arriba a la derecha hasta abajo a la izquierda. Una línea diagonal indica que el rendimiento es aleatorio, mientras que el objetivo ideal sería que la curva se desplace arriba a la izquierda. Esto significa que el clasificador daría siempre valores más altos de la función de decisión a los ejemplos positivos que a los ejemplos negativos.\n", "\n", "En este sentido, esta curva solo considera el orden asignado a los ejemplos positivos y negativos según la función de decisión, pero no el valor asignado. Como puedes ver a partir de las curvas y de los valores de accuracy, aunque todos los clasificadores tengan el mismo accuracy, uno de ellos tiene una curva ROC perfecta, mientras que otro se comporta igual que un clasificador aleatorio.\n", "\n", "Para realizar búsqueda en rejilla y validación cruzada, nos gustaría que la evaluación se guiase por un único valor numérico. Una buena forma de hacer esto es considera el área bajo la curva ROC (area under the curve, AUC). Podemos usar esto en ``cross_val_score`` especificando ``scoring=\"roc_auc\"``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score\n", "cross_val_score(SVC(), X, y, scoring=\"roc_auc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compara el rendimiento con el DummyClassifier:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "cross_val_score(LogisticRegression(), X, y, scoring=\"roc_auc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funciones de rendimiento por defecto y personalizadas\n", "=======================================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hay muchas medidas de rendimiento, que son útiles para problemas muy distintos. Puedes encontrarlas en el módulo `metrics` y obtenerlas por su nombre con la función `get_scorer()`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import get_scorer\n", "print(get_scorer('accuracy'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También es posible escribir tu propia medida de rendimiento. En lugar de una cadena, puedes pasar un nombre de función como argumento ``scoring``, esto es, un objeto con un método ``__call__`` (o lo que es lo mismo, una función). Esa función debe recibir un modelo, un conjunto de características ``X_test`` y un conjutno de etiquetas ``y_test``, y devolver un valor real. Los valores más altos deberían indicar que el modelo es mejor.\n", "\n", "Para probarlo, vamos a reimplementar la medida estándar de accuracy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def my_accuracy_scoring(est, X, y):\n", " return np.mean(est.predict(X) == y)\n", "\n", "cross_val_score(SVC(), X, y, scoring=my_accuracy_scoring)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EJERCICIO</b>:\n", " <ul>\n", " <li>\n", " En las secciones anteriores, normalmente usábamos el accuracy para evaluar el rendimiento de nuestros clasificadores. Una medida relacionada de la cuál no hemos hablado aún es el accuracy medio por clase (average-per-class accuracy, APCA). Como recordarás, el accuracy se puede definir como:\n", "\n", "$$ACC = \\frac{TP+TN}{n},$$\n", "\n", "donde n es el número total de ejemplos. Esto puede generalizarse para multiclase como:\n", "\n", "$$ACC = \\frac{T}{n},$$\n", "\n", "donde T es el número total de predicciones correctas (diagonal principal).\n", " </li>\n", " </ul>\n", " ![](figures/average-per-class.png)\n", " <li>\n", " Dados los siguientes arrays de etiquetas verdaderas y de etiquetas predichas, ¿puedes implementar una función que utilice la métrica accuracy para conseguir el APCA?\n", " </li>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y_true = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2])\n", "y_pred = np.array([0, 1, 1, 0, 1, 1, 2, 2, 2, 2])\n", "\n", "confusion_matrix(y_true, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3.9.4 64-bit ('venv': venv)", "language": "python", "name": "python394jvsc74a57bd0aa41ab814fffd34efff3c20848600797b001dae600799b4bbaa134a787d72b57" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 1 }	cc0-1.0
alazareva/291Project	src/data_processing/merge_ids.ipynb	1	2197	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pattern.web import URL, DOM, plaintext, Element, extension, Crawler, DEPTH\n", "import re\n", "import pickle\n", "import random\n", "import PIL\n", "import time\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15368\n", "2758\n", "15268\n", "2739\n", "15368\n", "2758\n" ] } ], "source": [ "tried_ids_sharath = pickle.load(open(\"sharath_ids/tried_ids_final.p\",\"rb\"))\n", "recipe_list_sharath = pickle.load(open(\"sharath_ids/recipes_final.p\",\"rb\"))\n", "\n", "print len(tried_ids_sharath)\n", "print len(recipe_list_sharath)\n", "\n", "#Change the location to Other Ids and Recipes files\n", "tried_ids = pickle.load(open(\"tried_ids.p\",\"rb\"))\n", "recipe_list = pickle.load(open(\"recipes.p\",\"rb\"))\n", "\n", "print len(tried_ids)\n", "print len(recipe_list)\n", "\n", "merged_ids = tried_ids_sharath.union(tried_ids)\n", "\n", "z = recipe_list_sharath.copy()\n", "merged_recipe = z.update(recipe_list)\n", "\n", "print len(merged_ids)\n", "print len(z)\n", "\n", "pickle.dump( merged_ids, open( \"recipes_merged.p\", \"wb\" ) )\n", "pickle.dump( merged_recipe, open( \"tried_ids_merged.p\", \"wb\" ) )" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
balarsen/pymc_learning	BayesianAnalysisWithPython/Multi Parametric model.ipynb	1	454834	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi parameter models\n", "startingt his ch3 NMR example" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import stats\n", "import seaborn as sns\n", "import pymc3 as pm\n", "\n", "%matplotlib inline\n", "sns.set(font_scale=1.5)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = np.array([51.06, 55.12, 53.73, 50.24, 52.05, 56.40, 48.45,\n", "52.34, 55.65, 51.49, 51.86, 63.43, 53.00, 56.09, 51.93, 52.31, 52.33,\n", "57.48, 57.44, 55.14, 53.93, 54.62, 56.09, 68.58, 51.36, 55.47, 50.73,\n", "51.94, 54.95, 50.39, 52.91, 51.5, 52.68, 47.72, 49.73, 51.82, 54.99,\n", "52.84, 53.19, 54.52, 51.46, 53.73, 51.61, 49.81, 52.42, 54.3, 53.84,\n", "53.16])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a38391450>" ] }, "execution_count": 3, "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": [ "sns.kdeplot(data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "NUTS: [sigma, mu]\n", "Sampling 4 chains: 100%\|██████████\| 6400/6400 [00:01<00:00, 4296.69draws/s]\n" ] } ], "source": [ "with pm.Model() as model_g:\n", " mu = pm.Uniform('mu', 40, 75)\n", " sigma = pm.HalfNormal('sigma', sd=10)\n", " y = pm.Normal('y', mu=mu, sd=sigma, observed=data)\n", " trace_g = pm.sample(1100, chains=4)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9a386ede50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f99fb2a4fd0>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99fb642e50>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a0a6c4690>]],\n", " dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace_g)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9a387da910>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a387f5fd0>],\n", " dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ctngkh082svVApdFxRERkAO04VE17l4PFk43pRT8tNNjKiukZ7D1ay8kq9aaLiHySinSRAeZwunh1XSHJsaGG3whdiQUTUxmZFsnfPjqmbXJERHzIx/vKSIkLZUyG8VOwlk1LJyQogLc2Hzc6ioiIx1GRLjLANuwvp7K+ndsXjyTA4n1/xcwmE59ZOZa2Dgf/WF9odBwRERkAJ6taKCpvZtGkNEymodl27VJCg60sn5bO7iM1lFS3Gh1HRMSjeF8FIeLBOroc/HNTMWOHRTNpVLzRca5YRmI4y6als2FfuRb2ERHxAZvyKwiwmJg9xNuuXcqyaRkEB1p4S3PTRUTOoSJdZACt3nGSlvYebl88yiN6Kq7GjXOzCA+18tKaI1pETkTEizmcLrYfrGLiqHjCQ6xGxzkjPMTKsmnp7LLXUFqj3nQRkdNUpIsMkJb2blbvLGGaLYHhKZFGx7lqocFWbl04kmOlTWw/VGV0HBERuUIFRfW0tPcwNzvF6CjnWTF9GEGBFt7ectzoKCIiHkNFusgAeW/7Sbq7ndw0f4TRUQbMvJwUMpMieHVdId09TqPjiIjIFdhcUEFEqJXsEbFGRzlPeIiVpVPS2XmomvLaNqPjiIh4hACjA4j4gsbWLj7aXcqsCcmkxYcZHeeKvPbaq/z3f//0gs+NuuaHrN1TyrUzM6mvr+N3v/s1O3Zso62tjZycXB5++IuMHTt+iBOLiMjltHb0sP9YLYsnp3vsYqYrZmSwZncJb285zsM3TrjgMVfb9rz++t/5xz9eoby8jKSkJG688VPcccensVgsA3kpIiIDQkW6yAB4d+sJnC43N83LMjrKFSsuLiIyMoovfemr5z2X35DIu1tPMNMWy7/92xeoqanijjvuJioqijfe+Adf/OJD/P73zzB69BgDkouIyMXsPFSFw+lmjgctGPdJkaGBLJ6cxoc7S/nUghHER4ec83xnZ+dVtT1/+MNvef75p1myZDm33XYnu3bt4De/+SWtrS089NAXBvPSRESuiIp0kavU1NrF+v3lzJ6QTGJMqNFxrlhxcSEjRoxk5cpV5z03rqqFHzyzk/9+6jmOHy/iv/7rf5g9ey4AS5eu4I47buaFF57hySd/MtSxRUTkErYfqiY1PoxhSeFGR7mk5dMyWLOrlA92lnD38nOL7rfffuOK254TJ47z0kvPcf31N/HNb34XgJtvvpVvf/vrvPzyC3zmMw8SFBQ8eBcmInIFPHPck4gXeX/HSRxOF9fNyTQ6ylUpLi4kK2v4BZ8blhTBrPFJHDpew7jxOWdukgBiYmLJzMyisPDoUEUVEZE+qG/u5GhJIzPGJXr8jiOxkcHMGp/EhrxyWjt6znmuu7ubnJzcK2p73n//HQAeeeTRcx6/5577ueee+2lr0zx4EfE86kkX6YfbbruBOXPmMXq0jZdeep7q6kosoYksvuGzBLja+e53n2Tbti2EhYVx7bXX89BDX8Bs7v0ubPPmjbzwwjMcPWrHag1k6tRpPPLIYwwb9n/FvcPh4OWXX2DNmg8oLT2J2w0ZGcO4/fa7uP76m84cN29e72tjYsJ5/vkXqKmpJiNjGA888BBLliwDoKKinNtvv/GS1/Ptb3+fVatuoL6+jqampjNFemdnJ4GBgWeyA9y8YAQ7Dy9iXu65qwN3dHRQWlrC+PEXnkcoIiJD45NtVGVlJZawRGIWPU5dXQS//OXPhrSNsloDeO21V/vdRl3zeu/vp9uou+/+DHff/Zlzjulr25Ofv5+RI0cRE9O7aN7p9m3ChGwmTMju3wcsIjJEVKSL9NPGjetZv34dd9zxafIKa9my9h9sf/cpDm16kREjRvLYY19m/fqPeOGFZxg2LJNrr72ed999i5/85EmmTp3OF7/4b7S0tPD663/n4Ycf4A9/ePbMTdCPf/wEa9d+wC233MZtt91JY2Mjb731Ov/xHz8kLi6O2bPnncnxxht/x2SCm266jeDgYF555WW+//1vkZWVxYgRo4iOjuG7333ykteSnZ0LQFFRIQAHDhTwyis3UV5eRkhICCtXruKxx75CcHAwidEhLJqcxro9ZayYnkFEkJvCwqP88Y9P0dHRzr33PjA4H7iIiPTZ2W3U2t0lFO59j1/85/cJCwsb9DbqxhuvPZPjjTf+jtvt5pZbbu9XG/X+jpNUN3Rw97LRTJw46bznW1tb+9X2lJScZPz4CWzatJ7f/e7XHD9eTEhICNdffzOPPvolAgJ0Kywinkf/Mon0U21tDc8++zJpGVls/c0WbJMbObzrPSZNmsQTT/TOi1ux4lpWrVrCjh3bWLBgEf/zPz9jyZLlPPHEj8+c54Ybbubee+/gqad+xU9+8jPq6mr58MP3ueee+/n85x87c9zChYu4++7b2L596zlFenNzEx9++CHQO5du/PhsHnnkAT78cDWPPDLqTJHdF8XFRQAcPFjA3Xd/hpiYGLZt28Ibb/yD0tISfvGL32AymbhhThab8it4fUMRJ7Y9zebNGwG47ba7yM09/2ZKRESG1uk2KjIujbUlW5k+1822j//J0qXLB72NOrtIb25u4q9/fZ24uHig721U1rgGfvqXvYQmjyEtLf2853/4w+/2q+1pbW2hsPAYP/jBd/j0p+9j1KjRbN26mVdffZmWlmYef/yJfny6IiJDQ0W6SD+lpaUzcuQo1uwqob3Lwdwp4zi86z0WLFh85piQkBBiYmKpq6tl587ttLW1sWDBIhobG88cY7EEMHXqNLZu3YzD4SAuLp7Vq9efM8Tc7XbjcDgAaG9vPydHbu5kEhISqKlpATizum19fR0ALpeL5ubmS15LaGgogYGBjB07js985rPcfvuniYmJAWDhwiVERUXz4ovPsmXLJubOnU9kWCArp2fw5ubjXDdvJatW3cjOndt57bVXqKqq4Cc/+fmVfqwiIjIATrdR72w9DsDUHBvbPsaQNup0gQ59b6MSIyAtxszbGw4xe3wCwcFB5zx/ww239Kvt6enpoby8jMcff4JrrrkO6G3f3G4377zzJnff/RlGjBh50deLiBhBRbpIP8XExOJyuflwVwkj0yJJiWs78/jZzGYzLpeLsrJSAL7//W9f9JyNjY3Ex8cTGBjI6tXvsmPHVkpKTlJaWkp7e+/53W73Oa+Jjo4+58+BgYFA740PQFVVZZ/npOfkTCQnZ+J5z99006d48cVn2bNnF3Pnzgdg5YxhrN1dSllXPLcuzGXhwsVERETwwgvPsHv3TqZOnX7J9xQRkcFzui3aZa9hRGokURHe20b9OeIrPPov95zz2Lx5CwD63PYEB4fgcDhYseLacx6/5prreOedN9m7d7eKdBHxOCrSRfrJYrGw92gNNY2d3L5oFNVFFZc8/vQNyTe+8R1SUlIveExERARdXV08+uhDHD1qZ/LkqUybNoM777yHSZOmcOut15/3mrN7My4kNjaOX/ziN5c8ZvjwS9+YnL6p6+j4vx6SkKAAVkzP4PWNxZyobCEzOYLFi5fywgvPYLcfVpEuImIgi8VCbVMHJypbuH3xSNx13tdGud1unn73EBXdSbjd7ouuTN+XtichIYGOjo7z8pxu3z45AkBExBOoSBe5Aqt3lhAfFcyUMQm8X3TpY5OTe1dDj46OYfr0mec8t2fPLlwuF4GBgbz//jscPnyQb37zu+eskltbW3NFGYOCgs57v4v5yU+exG4/zDPPvHTOzdCJE8cBSElJA+Bb3/oazc1N/NfPn+L9HSW8teU4j30q58xNTlBQ0HnnFhGRobXH3ttuTB2TwK6tlz7WU9uoNmsGL6y2c6SkkWef+hHNzU385jd/POeYvrQ9Nts41q79gObmZiIjI888XlFRBkBSUvIV5RcRGUzaJ12knzq7nRwrbWL5tAzM5svvOzt9+iwCA4N4+eXnz8zdA6ipqeZb3/oqv/vdrzGZTDQ3NwGQlTXinNe/8srLADidzgG8inPFxsZx7NgRNmz4+Mxjbreb5577MxaL5cyWOQkJCezfv5eiYwdZPi2dPUdqOFnVwt/+9hcsFguzZs0ZtIwiItI3u4/UkJEYTmJM6GWP9dQ2ak52MmHBAazZVXqm7SkoyDvzvNvt7lPbs3z5SpxOJy+//MI5r3311b8RFBSkdktEPJJ60kX6qb65k5SggPP2C7+Y6OhoHnnki/zqV7/gkUceZOXKa3E4HLz22qt0d3fz6KNfAmD69JlYLBb+/d+/x6c+dQcBAQFs3ryRHTu2YrVaz8z7Gwz33HM/a9as5sknH+fWW+8kKSmZDRvWsXv3Th555LEzK+w++ODDrF+/jq9//cvcdPPttJXU8+Uv/ZnqUjuf+9znL7gSr4iIDB2n082x0iZumje8T8d7ahsVZLWwYFIq728/ybfvuu9M23PbbXcSHR3NunVr2bdvz3ltz+rV7xIbG8v06bMAmD17HosXL+OFF56hrq6WceMmsGnTBnbs2Mpjj335nN51ERFPoSJdpB+cLjetHT3cNSmVkKC+//W58857SEhI4q9/fZHf//43BAUFY7ON5Xvf++GZ7WNGjBjFj370nzz99B/4/e9/TWho7562v/jFb3j99VfZu3cPDodjUPZ0DQ8P59e//gNPPfUr3n77n3R2dpCZmcV3vvMDrr32/+YaxsTE8Nvf/omnnvoVb7z+Ch2dnQSEJfLYVx7nrltvHvBcIiLSPy0dPQQCU2wJfX7NQLVRPT09A3otS6eks3p7CbsL28+0Pa+++le6u7vIyhrB977376xYcc05r/nhD7/HpElTzhTpAN///r/3rnj/zlusWbOatLT089o3ERFPYvrkapxnq6lpufiTfZSQEHFmiyhfouvyLgN1XX9de5S1u0v56ednExsZPADJro7R/79aO3r4+lNbmDgyjs/flD2g5zb62gaLrsu7GHldCQkRl59PcwkD0Yb3h6/+DPSVp1z/z/+2j9rGDn788KyLLrg2WAbjM/jtGwUcLK7n54/OJSjQMqDnHmie8jNgJH//DPz9+kGfQUJCBMBV/+OrOekifdTR5WDD/nKmj030iALdE4SHWFk6JZ2dh6qpqBu84fgiInJ57Z0ODp9oYPKYhCEv0AfL8mnptHc52FJw6VXqRUR8iYp0kT7adrCKzm4nS6dp3vXZVszIwGo18/aW40ZHERHxawXFdThdbiaNijc6yoAZlRZFZnIEa3aX4rrE6E8REV+iIl2kD9xuN+v2lDEsKZwRKVpk5myRoYEsmpTG9oPV1DZ2GB1HRMRv7T9WS3iIlVFpUUZHGTAmk4kV0zKoqGvnYHG90XFERIaEinSRPigsb6a0ppVFk9N8ZgjhQFoxPQOTqXf/eBERGXpOl4u8wjpyR8b1aXtQbzJ9XCJRYYF8uKvU6CgiIkNCRbpIH3y8t4zgQAszxyUZHcUjxUYGM3tCMhv3l9Pc3m10HBERv3OstIm2TodPDXU/LcBiZvHkNPKL6rT+iYj4BRXpIpfR2tHDjkPVzJ6Q3K9t1/zNNTOH0e1w8dFu9XSIiAy1fcdqCbCYmDA81ugog2Lh5DQCLCbWqo0RET+gIl3kMjbnV+Bwulg0Oc3oKB4tNT6MyaPjWbu7lM5uh9FxRET8yr5jdYwdFuOzXyZHhQUyc1wSm/Mrae8c2P3YRUQ8jYp0kUtwu918vLeMUWlRZCSGGx3H462alUlbp4MN+7VVjojIUKmoa6Oqvp2JPjjU/WzLpmXQ1eNUGyMiPk9FusglHD7RQFVDB4smpxodxSuMTItiTEY0q3ecxOF0GR1HRMQv7D9WB+CT89HPlpkcwZj0KD7aU4rLpe3YRMR3qUgXuYR1+8oJCw5gmi3R6CheY9WsTBpauth+sMroKCIifmHfsVoyEsOJiwo2OsqgWzYtg9qmTvYerTU6iojIoFGRLnIRTa1d7D1Sw9ycFAKtFqPjeI2cEbGkJ4Tz7rYTuNzq6RARGUytHT0cLW30+V700yaPiScuMpi1u7Xlp4j4LhXpIhexKb8Cp8utBeP6yWQyce2sYVTUtZNfWGd0HBERn5ZfWIfbDZNG+0eRbjGbWTwljcMnGymr1XZsIuKbVKSLXIDb7WZTXgVjMqJJjg01Oo7XmT42kZiIIFbvOGl0FBERn7b3WC1R4b/IUZ4AACAASURBVIFkJkcYHWXIzM9NIcBiZt0ebccmIr5JRbrIBRSWNVPV0MHcnGSjo3ilAIuZZdPSOXyykROVLUbHERHxSQ6ni4KiOiaOjMdsMhkdZ8hEhAYyfWwiWwoq6ejSlp8i4ntUpItcwKb8CoKsFqaP1YJxV2rhxFSCAi18sFO96SIig+FISSOd3U6/mY9+tiVT0+jsdrLtQKXRUUREBpyKdJFP6OpxsuNQFdNsCQQHBhgdx2uFBluZn5vCjkPV1Dd3Gh1HRMTn5BfVEWAxMS4zxugoQ25ESiSZSRF8tLcMtxYpFREfoyJd5BP2HKmhs9vJ3JwUo6N4veXTMnC53azVvEERkQGXX1TPmIxoggL9bwcSk8nE4ilplNW0cbS0yeg4IiIDSkW6yCdszq8gPiqYMcOijY7i9RKiQ5hqS2T93nI6uzVvUERkoNQ1dVJe20bOiDijoxhm5vgkQoMC+EhfBIuIj1GRLnKWuqZODh1vYG5Oil8twjOYVk7PoL3Lwca8CqOjiIj4jPzi3i0us/24SA+yWpiXm8Juew1NrV1GxxERGTAq0kXOsqWgAjcwJ1urug+UkWlRjEqL4sOdJbhcmjcoIjIQCorqiYsMIjXOv7cJXTw5DafLzfr95UZHEREZMCrSRU5xu91szq9k7LBoEqJDjI7jU1bOyKC2qZM9R2qMjiIi4vUcThcHj9eTPSIOk5+P+kqKDWXC8FjW7yvH6XIZHUdEZECoSBc55WhpE9WNHVowbhBMHp1AQnQwq7Udm4jIVSssa6Kz2+nX89HPtmRyGg0tXeQdqzM6iojIgFCRLnLK5vwKggItTLNpb/SBZjabWD4tg8KyZo6VaRVeEZGrkVdUh8Xsn1uvXUjuqDiiwwM15F1EfIaKdBGgu8fJzsPVTLcl+uVWNkNhXm4KoUEBfLBDvekiIlejoKie0elRhAQFGB3FI1jMZubnppJfWEddU6fRcURErpqKdBFg37FaOrudzNaCcYMmODCARZPT2H2khprGDqPjiIh4pYaWLkqqWzXU/RPmT+ydqrYxT73pIuL9VKSLANsOVBETEYRNe6MPqqVT0zGbTHy4s8ToKCIiXqmgSFuvXUh8VAg5I+PYsF8LyImI91ORLn6vpb2b/KI6Zo5P0t7ogywmIogZ4xLZlF9BR5fD6DgiIl4nv7ie6PBA0hPCjI7icRZOTKWxtZu8Qi0gJyLeTUW6+L1dh6txutzMnqCh7kNh2bQMOrudbMqrMDqKiIhXcbpcHCzW1msXkzsqjqjwQNbv05B3EfFuKtLF7209UEVaQhgZieFGR/ELw1MiGZUWxdrdpbhcbqPjiIh4jaLyZtq7HORqqPsFnVlArkgLyImId1ORLn6turGDY2VN6kUfYsunZ1Dd2MH+wlqjo4iIeI38ojrMJhPjs7T12sUsyE0BtxaQExHvpiJd/Nr2A5UAzByXZHAS/zJlTDyxkUGs2VVqdBQREa+RX1TPyLRIQoOtRkfxWPHRIUwYEcvGvAotICciXktFuvgtt9vN1gNV2DKiiYsKNjqOX7GYzSyZks6hEw2UVrcaHUdExOM1tXVzorJFq7r3wcKJaTS0dJFfVG90FBGRK6IiXfzW8coWKuvbtTe6QRZMTCUwwMyHu7Qdm4jI5Zzeek3z0S9v4ukF5PaWGR1FROSKqEgXv7XtQBUBFhPTbAlGR/FL4SFW5mQns+1gFS3t3UbHERHxaAXF9USGWslI0iKnlxNgMTM/N4W8ojrqm7WAnIh4HxXp4pecLhfbD1UxcWS85vYZaOm0DHocLm2XIyJyCS6Xm4KiOrJHxGHW1mt9Mj839dQCctruU0S8j4p08UuHjjfQ3NbNrAlaMM5IafFhTMiK4aM9pTicWuBHRORCiiubaet0kD0i1ugoXiMhOoQJw2PZsL9c232KiNdRkS5+advBKkKCAsgdqbl9Rls2LYPG1m522auNjiIi4pHyC+swAdnD1Wb1x8JJqacWkKszOoqISL+oSBe/093jZPeRGqbZErAGWIyO4/dyRsaRFBOi7dhERC6ioLie4amRhIdoelZ/TBwVT1RYoKZUiYjXUZEufmd/YR1d3U5mjddQd09gNplYNi2DovJmCsuajI4jIuJRWtq7KS5vJkeruvdbgMXM3JwU8grraGrtMjqOiEifqUgXv7PtQCVR4YHYhsUYHUVOmZOdTEiQRduxiYh8woHj9bhBRfoVmpebgsvtZsuBSqOjiIj0mYp08SttnT3kF9Uxc1wSZrNWyPUUIUEBzM9NZbe9hoYW9XaIiJyWX1hPeIiVrOQIo6N4peTYUEanR7EprwK3WwvIiYh3UJEufmW3vQaH081MDXX3OEumpuNyufl4b5nRUUREPILL7aaguI7s4bH6YvkqzMtNoaKuncLyZqOjiIj0iYp08SvbDlSSFBuqHgkPlBgdQs7IONbvL9d2bCIiwMmqFlrae7T12lWaPjaRIKuFTXlaQE5EvIOKdPEbDS1d2E82Mmt8EiaTeiQ80ZIp6TS3dbPbXmN0FBERw+UX9m4dpq3Xrk5wYADTxyWy/VA1Xd1Oo+OIiFyWinTxGzsOVeEGDXX3YNkjYkmMDmHtHm3HJiKSX1xPZnIEkWGBRkfxevNzU+jqdrLLXm10FBGRy1KRLn5j24EqspIjSI4NNTqKXITZZGLxlDSOlTZRXK7t2ETEf7V19lBY1qRV3QfIqLQokmJD2bhfQ95FxPOpSBe/UFLVwomqFu2N7gXm5aYQGGDmnc3FRkcRETHMweMNuN2Qo/noA8JkMjE/N4UjpU1U1rcbHUdE5JJUpItf2LC3DBMwfZyKdE8XFmxl5vgkPt5TSntnj9FxREQMkV9YR2hQACNSI42O4jPmZCdjNpnYnF9hdBQRkUtSkS4+z+12s35vKWMzY4iJCDI6jvTBkinpdHU72ZRfaXQUEZEh53a7yS+uY/zwWCxm3aoNlOjwIHJGxLI5vwKnS7uIiIjn0r/84vOKK1qoqG3TUHcvkpkcwdjMGD7aU4rL7TY6jojIkCqpbqWptVtD3QfBvNxUGlu7OVBcb3QUEZGLUpEuPm/bwUoCLGam2hKMjiL9cN3c4VQ3dHBQN1Ii4mfyi7T12mCZOCqOiFArG/M05F1EPJeKdPFpLpebHYeqmT4+idBgq9FxpB/mTkwlMtTKR3vKjI4iIjKkCorqyUgM1xStQRBgMTN7QjL7jtbS3N5tdBwRkQtSkS4+7dDJBprbulk4Od3oKNJP1gALCyalsv9YLbWNHUbHEREZEh1dDo6VNZGtoe6DZl5uCk6Xm20HqoyOIiJyQSrSxadtP1BFSJCFaZqP7pUWTUoDE6zbp950EfEPB4834HS5ydFQ90GTnhDO8JRINuWV49a6JyLigVSki8/qcTjZfaSaKWMSCLJajI4jVyA2MpjJoxPYuL+CHofT6DgiIoOuoLiO4EALo9KjjI7i0+bnplBa08bxyhajo4iInEdFuvis/cfq6OhyMmt8stFR5CosmZJGa0cPOw5VGx1FRGRQud1u8ovqGJ8VS4BFt2iDaca4JKwBZjZpATkR8UBqAcRnbT9YRWRYIGMzo42OIldhXGYMKXGhWkBORHxeeV079c1dmo8+BEKDA5hmS2DbwSq6ezRSS0Q8i4p08UntnT3sL6xjxthELGb9mHszk8nEoklpFFc0c7JKwxJFxHcVnNp6TfPRh8a83FQ6uhzsOVJjdBQRkXOoehGftPtIDQ6ni1kTNNTdF8zOTibAYmb9/nKjo4iIDJr8ojpS48OIiwo2OopfsA2LJj4qWHumi4jHUZEuPmnbgSoSo0MYnhJhdBQZAOEhVqaPTWDbgUq6ujUsUUR8T1e3kyMljWQP11D3oWI2mZiXm8KhEw3a6lNEPIqKdPE5DS1dHD7ZwIzxSZhMJqPjyABZOCmNji4nOw5rX1sR8T2HTjbgcLrJGamh7kNpbnYKJmBTvnrTRcRzqEgXn7PtYCVuN8zJ1lB3XzI6PYqUuFA2aMi7iPiggqI6Aq1mxmjrtSEVFxXM+OGxbM6vwOXSnuki4hlUpItPcbvdbMmvZGRqJMmxoUbHkQFkMplYMDGVwrJmSqtbjY4jIjKgCorqGTssBmuAxegofmd+bgp1zV0cOtFgdBQREUBFuviYk1WtlNW2qRfdR83JTibAYtICciLiU6rq26lu7CBnhIa6G2Hy6ATCggPYmKe2RUQ8g4p08SlbCiqxmE1MH5dkdBQZBBGhgUy1JbK1oJIu7WsrIj4i//TWa9of3RDWADOzJiSz50gtrR09RscREVGRLr7D4XSx/WAlk0bFEx5iNTqODJKFE1Np73Kw63C10VFERAZEQXE9STEhJMZompZR5uemnLqP0OKkImI8FeniMw4U19Pc3qOh7j7ONiyapJgQLSAnIj6hx+Hk8IkGsjXU3VDDkiLITIpgo9oWEfEAKtLFZ2wpqCQ8xKrta3ycyWRiwaRUjpY2UVbbZnQcEZGrYi9ppNvh0lB3DzAvN4WT1a2cqGwxOoqI+DkV6eIT2jt72Hu0lpnjkgiw6Mfa183NTsFiNrFhn3o8RMS75RfWE2AxYxsWY3QUvzdrQu89xKY87ZkuIsZSNSM+YefhahxOF3NyNNTdH0SGBTJlTAJbCirocWgBORHxXgXFddiGRRNk1dZrRgsLtjLVlsC2g5VqW0TEUCrSxSdsKagkJS6UrOQIo6PIEFk4KZW2Tge77DVGRxERuSK1jR1U1LVr6zUPMi83hbZOB3uO1BodRUT8mIp08XrVDe0cLW1iTnYyJpPJ6DgyRMZmxpAYHaIh7yLitfKL6wFtveZJxmXGEBcZzCbtmS4iBlKRLl5vS0ElJmDWeA119ydmk4n5E1OwlzRSUacF5ETE+xQU1REXGUxyrLZe8xRmk4l5uSkcPN5AbVOH0XFExE+pSBev5nK52ZRfwfisGOKigo2OI0NsXk7vAnIb92uRHxHxLg6ni4MnGsgZGadRYB5m7qn1bTbnVxqcRET8lYp08WoHj9dT39zF/ImpRkcRA0SFB5E7Mo4tBypxOF1GxxER6bOjpU10dTvJGa6h7p4mPiqE8VkxbMqrwOV2Gx1HRPyQinTxahvyKggPsTJ5dILRUcQg83JTaG7rJr+ozugoIiJ9VlBUh8VsYmymtl7zRPNyU6lr7uTQiQajo4iIH1KRLl6rpb2bvUdqmDUhCWuAfpT9Vc6IOCLDArWvrYh4lfyiOkanRxESFGB0FLmAKWPiCQsOUNsiIoZQZSNea+uBKpwuNwtyNdTdnwVYzMyZkExeYR1Nbd1GxxERuayGli5Ka9q09ZoHswZYmDU+md32Glo7eoyOIyJ+RkW6eCW3283GvHKGp0SQnhhudBwx2NzcFJwuN1sLtMiPiHi+glPTc1Ske7b5E1NwOF1qW0RkyKlIF69UXNFCWU2bFowTANLiwxiZGsmm/ArcWuRHRDxcflEd0eGBpCWEGR1FLmFYUgQjUiNZv79cbYuIDCkV6eKVNuaVE2g1M3NcktFRxEPMzU2hvLaN4ooWo6OIiFyU0+XiwPEGskdo6zVvsHBiKuW1bRwtbTI6ioj4ERXp4nW6up1sP1jFdFuiFtyRM2aMTSIwwMymvHKjo4iIXFRReTMdXQ5yNdTdK8wYl0RIkIX1+9S2iMjQUZEuXmeXvZrObqeGuss5QoMDmGpLZPuhKrp6nEbHERG5oPyiOswmE+OztPWaNwgKtDBrQjI7D1drATkRGTIq0sXrbNhfTlJsKKPTo4yOIh5mfm4KHV1O9hypMTqKiMgF5RfVMzItktBgq9FRpI8WTkzVAnIiMqRUpItXKa1u5WhpEwsnpmoun5xnzLBo4qOCta+tiHik5rZuTlS2kK2h7l5FC8iJyFBTkS5eZd3eMgIsZublphgdRTyQ2WRiXm4Kh040UNvYYXQcEZFzFBSf3not1uAk0l9aQE5EhpKKdPEaHV0OthyoZOa4RMJDNExQLmxudgomYFO+etNFxLMUFNUTGWplWFKE0VGkn7SAnIgMJRXp4jW2Haikq9vJ4inpRkcRDxYXFcz4rBg251fi0rBEEfEQLpebguJ6JgyPw6zpWl5HC8iJyFBSkS5ewe1289HeMjKTIxieoh4IubR5uanUNXdy+ESD0VFERAA4XtlCa0cPOSM11N1bnV5AbosWkBORQaYiXbzC0dImymraWDw5TQvGyWVNGRNPaFCAFpATEY9RUFSHCZiQpSLdWw1LimBkWiTr9pZppJaIDCoV6eIVPtpTSkhQADPHJxkdRbyANcDCzAlJ7LLX0N6pYYkiYrz8ojqyUiKJCA00OopchSVT0qmqb+fg8Xqjo4iID1ORLh6vqbWL3fYa5uWkEGS1GB1HvMT83BQcThfbD1UbHUVE/FxrRw9FFc1a1d0HTLMlEhlq5aPdZUZHEREfpiJdPN6GvAqcLjeLJqcaHUW8SGZSBOkJYRryLiKGKyiuw+2GHO2P7vWsAWYWTEpl/7FabfUpIoNGRbp4NKfLxfp9ZYzPiiElLszoOOJFTCYT83JSKK5opqym1eg4IuLH8grrCA+xMjwl0ugoMgAWTUoDE6zbp950ERkcKtLFo+07Wkd9cxeLJ6cZHUW80KzsZCxmk/ZMFxHDuFxuCorqyRkRh9mshU99QWxkMJNHJ7BxfwU9DqfRcUTEB6lIF4/24a4S4iKDmTQ63ugo4oUiQwOZOCqerQWVOJwuo+OIiB8qKm+mtaOHiaM01N2XLJ2SRmtHDzu07omIDAIV6eKxTlS2cKSkkaVT07GY9aMqV2ZeTgrN7T3kF9UZHUVE/FBeUS1mk4kJw7VonC8ZmxlDSlwoH+0pNTqKiPggVT7isdbsKiHIamHBxBSjo4gXyxkZS2RYoBaQExFD5B2rY1RaJGHBVqOjyAAymUwsmZJOcUULReXNRscRER+jIl08UlNrF9sPVTEvJ4VQ3djIVbCYzcyZkExeYR3Nbd1GxxERP9LQ0sXJ6lZyR2nKli+ak51MUKBFvekiMuBUpItHWre3DIfTzdJp6UZHER8wNzcFp8vN1gOVRkcRET+SV1gLQO5IzUf3RSFBAczNTmbHoSqaWruMjiMiPkRFunicHoeLj/eWkTsyjuTYUKPjiA9Iiw9jRGokm/IqcLvdRscRET+RV1hHXGQQafHaQtRXLZuWgdPp5qM92o5NRAaOinTxODsOVdHc3sPy6RlGRxEfMi8nhbLaNo5XthgdRUT8QI/DxcHjDeSMjMdk0tZrvio5NpSJo+JZt7eMzm6H0XFExEeoSBeP4na7+XBnCWnxYYzPjDE6jviQGeOSsAaYtYCciAyJIyWNdPU4NdTdD6yckUFrRw/rdmtuuogMDBXp4lGOlDRysrqV5dMz1PMgAyo0OICptgS2H6yiu8dpdBwR8XH7C2uxBpgZpy+cfd6YjGgykyP45/pCXJpSJSIDQEW6eJTVO0oID7Eya3yS0VHEB83LSaG9y8GeozVGRxERH5dXWMfYYTEEWS1GR5FBZjKZWDk9g7KaVvIK64yOIyI+QEW6eIyKujb2HatlyZQ0AnVTI4NgbGYMcZHBbNaQdxEZRJX17VQ3dGioux+ZNjaR+KhgPthx0ugoIuIDVKSLx1i9owRrgJklU7XtmgwOs8nE3JxkDh5voK6p0+g4IuKjTvemqkj3HwEWMzfMH8Hhk42c0AKlInKVVKSLR2hq62ZLQSVzc1KIDA00Oo74sLk5KbiBLQXqTReRwZFXWEtKXCgJ0SFGR5EhtGJWFkGBFj7YWWJ0FBHxcirSxSOs3V2C0+lipbZdk0GWEB3C2GHRbMqv0AI/IjLgOroc2E82MnFkvNFRZIiFh1iZn5vCjkNVNLR0GR1HRLyYinQxXGe3g3V7ypgyJoGk2FCj44gfmJebQk1jJ0dLGo2OIiI+5uDxBpwut4a6+6nl0zJwu+GDnZqbLiJXTkW6GG5jXgVtnQ6umTnM6CjiJ6baEgkOtGjPdBEZcPlFtYQEWRiVHmV0FDFAQnQIM8Yl8vHeclo7eoyOIyJeSkW6GMrpcvHhzhJGpUcxMk03NDI0gqwWZoxLYqe9mo4uh9FxRMRHuN1u8grrmJAVS4BFt1j+atWsTLp6nHy0u9ToKCLipdSCiKF2Ha6htqmTa2eoF12G1rzcFLp7XOw8XG10FBHxESerWmls7SZX89H9WnpiOBNHxrFmdyld3U6j44iIF1KRLoZxu928v/0kybGhTBytGxoZWiNTI0mJC2VjXrnRUUTER+QV1gKQo/nofu+6OVm0dvSwfr/aGBHpPxXpYpiDxxs4UdXCNTOHYTaZjI4jfsZkMjE/N5XCsmZKa1qNjiMiPiCvsI6s5AiiwrSVqL8blRaFLSOa1TtO4nC6jI4jIl5GRboY5p2tx4kOD2T2hGSjo4ifmpuTTIDFxPp96ukQkavT0NxJUXkzkzQyTE65bnYmDS1dbC2oNDqKiHgZFeliiMLyJg6fbGTljGFYA/RjKMaICA1kqi2RLQWVdPVo3qCIXLkdB6twA5NHJxgdRTzEhOGxDEsK593tJ3G53EbHEREvoupIDPHu1hOEBQewYGKq0VHEzy2alEpHl4Odh7SAnIhcue0HKoiLDCY9IczoKOIhTCYT183Ooqq+nT1HaoyOIyJeREW6DLmy2jb2Hq1l6dR0QoICjI4jfm5MRjQpcaGs319mdBQR8VJd3U72H6lh8uh4TFpjRc4ydUwCSbGhvLXlOC63etNFpG9UpMuQe2/bCQKtZpZNyzA6iggmk4mFE08tIFetBeREpP8KiuvpdriYrPno8glms4kb5mRSUt3KXvWmi0gfqUiXIVXb1MH2g1UsnJhGeIjV6DgiAMzJSSHAYubjfepNF5H+23eshrAQK6Mzoo2OIh5o5vgkkmJD+eemYvWmi0ifqEiXIbV6ewkAK2d4dy/6/v37ePTRh1i6dC4333wtP//5T2lsbLzka44dO8qiRbP4859/f87j7e3tPP74N1i6dC733/9p9u/fd95rX3/979x11y04HI4+5fvzn3/PvHnT2LNn1wWfr6goZ968afzoRz847zVn/5o/fzrLls3jgQfu5m9/ewmXy3XJ4xctmsVNN63k29/+Ovn5+/uU1ROEh1iZNjaBrQcq6erWAnIi0ncul5v9x+qYNjaJAItn3lapzTK2zbKYzdw4N4vSmjb22NWbLiKXpwnBMmSa27rZkFfO7OxkYiODjY5zxfbs2cVXv/qvhIdHcN99D2KxWHjllZfZs2cnTz31NJGRkee9xuFw8OMf/+CCNywvvPAMu3bt4KGHvsDevbv55jf/P1555Z9EREQA0N3dzQsvPMPDD3+RgIDB/yt7330PkpU1HAC3201nZwcbN27gV7/6BeXlZXzlK9+46PFdXV1UVVXy3ntv8+ijD/H440+yYsU1g555ICyalMa2A1XsOFzF/FwtaCgifXOsrInWjh5mZnvmdqJqszyjzZo5Lom3Nh/nn5uLmWJLwKy1C0TkElSky5D5cFcJDoeLa2cOMzrKVfnlL/8Ls9nM7373NGlp6QAsWLCY+++/i+eff5rHHvvyea958cVnKS4uuuD51q79gJtvvo277rqXG2+8hVWrlrJ16+YzNwpvvvkaQUFBLF8+NMXu9OkzmTJl2jmP3Xjjp/jCF/6F11//O/fe+wAJCYmXPP6uu+7lC1/4LP/xHz8kN3cSycmeefN6ttHpUb0LyO0rV5EuIn2272gtFrOJqWMTaWvpNDrOedRmeUabZTabuHFuFn946yC77TVMH5t4+ReJiN/yzHFZ4nM6uhx8tKeMqbYEUuK8d3uaiopyiooKWbly1ZmbHYDMzCzmzp3P+++/fd5rCguP8dxzf+b++//lguesra0hJaW3KAwNDSMqKpqamiqg91v+F198jgcffAiLxTIIV9Q3ZrOZxYuX4nK5OHiw4LLHR0RE8PWvf4fu7i5effUvQ5Dw6plMJhZNSqOovJkTlS1GxxERL+B2u9l7tIaxmTGEBnveOitqszyrzZoxLomUuFDe1Nx0EbkMFekyJNbtLaOjy8Gq2ZlGR7kqNTW9e2mPHDnqvOfS0jJobGykqqryzGO9QwafYPr0maxcueqC54yKiqatrXdVcZfLRVtbK1FRvYsP/fOf/yAsLIxly1YO9KX0m9nc+8+Fw9G3OdsTJ04iKSmZ7du3DmasATU3J5kgq4U1u0uMjiIiXqCirp2qhg6PXdV9qNusl19+WW3WJTOZuHHucMpq29h1uHrQ3kdEvJ+KdBl03T1OPthZwoSsGLKSz5/75k1CQkKA3oVzPqm5uQmA+vq6M4+99NJzlJae5Gtf+9ZFzzlp0hTeffctiouL+Nvf/kJPTw+TJ0+lq6uTl156jgce+NyZm43+amtrpbGx8bxfLS3N/T7Xrl07AbDZxvb5NSNGjOT48WJ6enr6/X5GCA22Micnme0Hq2hu6zY6joh4uH3HagGYNMozi/ShbrP+9Kc/qc26jOljE0mND+td6d2l3nQRuTDNSZdBtzm/gua2blbdOMHoKFctK2sEYWFhfPzxR9x77wOYTi380tXVxY4d24DeRXMAiooKefbZP/GVr3yDxMQkKirKL3jOhx76Al/5yqPcd98dmM1mHnvsy6SlpfPyyy8SERHJ0qUrrjjvt771tX6/5vRNEoDb7aKqqor33nuLLVs2smDBYtLT+74yf0RE75cyzc1NxMV55k3sJy2bms66PWV8vK+MG+cONzqOiHiwvUdryEyO8NjFUIe6zYqMVJt1OWaziZvmDeepNwrYeqCSuTkpg/I+IuLdVKTLoHK6XLy3/SQjUiMZO8z794+1Wq3ceec9PP30H3jiice5774Hcbmc/PGPT9HR0QGAxWLB6XTy4x8/QW7uJG688ZZLnjM1NY0XX3yVwsJjJCQkEBcXT0dHB3/5y/N8+ctfw2w28/777/D880/T1dXFqlU3jJAYpAAAIABJREFU8OCDD/Wpp+LRR7/MqFGjz3u8oaGeJ5/87gVfc6GbJIvFwvLl1/C1r33zsu95ttMrA5u8aBXblLgwsofHsm5vGf9/e3ceH0V5P3D8s7u57/sg5IAEhjvciMiNgICoeFBUvLXWqq1orW39VW21/Wmt2lqttf68xQMPFA8UFAUEue9jAiHhDDnJfW/298dsQgg5dpPdzO7m+3698trN7uzs853nmZ35zjzzzJwLkl32lkpCCH2VVNRy5GQpl13kugfzunub9cc//o9ss2wwSokmJS6Y5euOMHZgDN5e+l2/L4RwTZKkC6fafCCPgpJqFk3v51aJWntuuuk2ysvL+PDD91m9+msAJkyYyHXX3cBLL/2LkJAQli59i8zMQ7z44itNR/gbu+vV1FRTXFxMSEhI006Lt7c3AwYMbPqOjz56n/DwcKZNu5gjRzJ54olH+fWvf0Pv3ok8+ugfiI6O6XBHCrRufi1HsQXaPEMC5+4kGY1GAgICSE7uQ0BAgI1L6KySkhJMJlPT2Ql3MWN0Is8t28XWg3lcMNj1R6YXQnS/7Rn5WICR/aP1Lkq7unObdckll7B58y7ZZnXAaDBw1ZRUnn5vJ2u2n2TmWPe+640QwvEkSRdOY7FY+PKno/SKCiTdRQfV6Qyj0ci9997P9dffxPHjx4mNjSUuLp7//OcFTCYTsbHxbNq0gbq6Om6//cbzPr906VssXfoWy5Z91jRCbnOVlZW8++5bPPDA7zAYDKxZs5qEhN5ceeU1AEydOp1vv/3Gph2ezmhrJ8leFouFzMwMkpNT8PZ2vVGP2zOkbwSxEQGs2npCknQhRKu2HswjLiKAhGjXvmOJbLNs093brEEpEQxOCWfFhmwuGtaLAD/ZJRdCnCW/CMJpdmUWcjK/gtvmDcToIWfRAVatWklkZBQjR44mIiKy6fVdu3bQv/8AfH19ufvu+84b6Kaxu96sWXOYPXvuOZ9t7sMP3yMqKpopU6Y3fS48PLzp/dDQMHbt2uGEyBxr+/atFBcXM3/+Ar2LYjejwcCMUb15Z1UGmSdLSE0I1btIQggXUlpZy8FjZ5g7Ptnle4nJNss2emyzrpqSxmOvb2Hl5qMsmJTabd8rhHB9kqQLp7BYLHyxMZvIED/GDozVuzgO9cEHS6murua115bi5aWtQhs2rGf37p384Q+PApzTDbBRY3e9Xr0SGDNmXKvzrqgo57333uG3v/1D045fZGQUGzasx2KxYDAYOHXqJFFRMU6IzHEqKsp5/vln8ff3Z8GCa/QuTqdcOCSOj9dmsnrbCUnShRDn2JGRj8UCoxXX/i0G2WbZQq9tVnJcMGMHxvDNluNMG9mbsCDfbvtuIYRrkyRdOMWBo2fIPFnK4pn9PW7greuuu5GHH/4tDz54H5MnT+X06Rzef/8dxo4dz8yZl3Rp3h988C4xMbFMmjS16bWJE6fw6qsv87//+2d69Upg7do1LFny266G4TBbtmxquhdvXV0tJ0+e5JtvvqKgIJ9HHnmCqCj3vNTB39eLicN68e22E1wzNY3wYNl5EkJotqr5xIT7kxgTpHdROiTbrHO52jZrwaS+bFPz+ezHbG6YpXTrdwshXJck6cIpVvyYTViQDxcN87xbi0yZMp1HH32Ct99+g+eff4bw8EgWLVrM4sU3YzJ1foTW8vJy3n9/Kb///SPndJ9MS+vHQw/9D6+99l9+/HEdixYtZu7c+Y4IxSHeeuu1pucmk4mIiEiGDUtn0aLFDBgwSMeSdd20Ub1ZteU4320/wZWTpSuiEALKq+o4kH2G2eOSXL6rO8g2qyVX22bFhAcweXgvvt9xipljEomLsH/AOyGE5zFYLJY238zPL2v7TRtFRweTn1/W1dm4HImrbeqxMzy5dAeLpvfj4jG235/UmaS+3I+rxPbCJ3vYn32Gv/3iQocM7OMqcTmaxOWU7+5SBuiIbbg9PLUNtLRu1yle++ogf7xpNClxZ0cB7ynxt6enL4POxl9SUctD/9nI4JQI7l4w1Akl6z7SBnp2/CDLIDo6GKDLR3A9qx+ycAkrNmQTEuDNpOHnjwIrhLuZOz6Zqpp61uw4oXdRhBAuYKuaT1SoH8mxwXoXRXiI0EAf5l6QzPaMfA4ePaN3cYQQLkCSdOFQmSdL2J99hlnjkvD17nw3OiFcRUpcCEP6RvDNluPU1Jn1Lo4QQkcV1XXszy5i9IAYt+jqLtzHzDGJRIb48t63h2ho6NZOMEIIFyRJunCoFRuyCfL3ZuqIBL2LIoTDzBufQlllHet35+hdFCGEjnYeKsDcYHGLUd2Fe/HxNnHVlDSO5ZXz4x7Z1gjR00mSLhwm+3QpuzMLmTkmET8fGZNQeI7+iWH06x3KV5uOUm9u0Ls4QgidbD2YR0SIL33ipau7cLyxA2NITQjho7VHqKqp17s4QggdSZIuHGbFj9kE+HoxfVRvvYsihMPNHZ9CUWkNP+3L1bsoQggdVFbXsy+7iNGKdHUXzmEwGFg0vT+lFbV8+dNRvYsjhNCRJOnCIY7nlbPjUAEzRvfG31fOogvPM7RvBEkxQXz501G5XlCIHmhXZgH1ZgujB0hXd+E8fXuFcMHgWL7efJyCkiq9iyOE0Ikk6cIhVmzIxs/H5DK3XBPC0QwGA3MvTOF0USXbM/L1Lo4Qoptt2p9LeLAvfXuFdDyxEF1w1eRUjAZYtiZT76IIIXQiSbrospMFFWw7mMf0Ub0J9PPWuzhCOM2o/tHERgTw+YZsLBY5my5ET1FaWcveI0VcMDgWo3R1F04WEeLHJRcks+VgHgeyi/QujhBCB5Kkiy77YkM2Pt4mZspZdOHhjEYDl16YzLG8craqcjZdiJ5i8/5cGiwWxg+O07soooe4ZFwS0WF+vL0qQwYsFaIHkiRddEluUSWbDuQydUQCwQE+ehdHCKe7YFAcCdGBfPRDpuw4CdFDbNyXS2JMEL2jg/QuiughfLxNXDujPzmFlazaelzv4gghupkk6aJLPvsxC2+TkVlj5Sy66BmMRgNXTU4l70wV6+S+6UJ4vNNFlWTllMpZdNHt0tOiGNEvis/WZ1NUWq13cYQQ3UiSdNFppwoq+GlfLtNG9iY0yFfv4gjRbYalRtK/dyifrc+iptasd3GEEE60ce9pDMC4QbF6F0X0QIum96PBYuG97w7rXRQhRDeSJF102qfrs/DxNjH7giS9iyJEtzIYDFw1NY2Silq+kW6IQngsi8XCxn2nGZgSTniwHIwW3S8qzJ9545PZejCPfVkyiJwQPYUk6aJTjueVs+VgHjNG9yZErkUXPVBaQigj+kWxctNRyipr9S6OEMIJMk+WUlBSLV3dha5mj0smJtyft1dlUFcvY6EI0RNIki46Zfm6I/j7mpg1Vs6ii57rysmpVNea+WLjUb2LIoRwgo37TuPjZWRk/2i9iyJ6MG8vI9df3J/cokq++km2N0L0BJKkC7tl5ZSy41ABs8YkEeQv90UXPVevqEAuGhrPd9tPUFBcpXdxhBAOVG9uYPOBXEb0j8bf10vv4ogebkjfSMYNiuXzjdmcKqjQuzhCCCeTJF3Ybfm6LAL9vLhY7osuBJdd1AejwcAHa2RQHyE8yZ7MQiqq6xk/WAaME67hZ9P74ett4s2VB2mwWPQujhDCiSRJF3Y5fLKEPUcKmT0uSc4sCAFEhPgxd3wyW9V89mfLoD5CeIqN+04THODNoJQIvYsiBAChgT5cMy2NjBMlrN11Su/iCCGcSJJ0YTOLxcLHP2QSHODN9FG99S6OEC5j9rgkosP8eGdVBvVmGdRHCHdXXlXHzsMFjB0Yi5dJdpWE67hoaDwDksJYtiaT4vIavYsjhHAS2fIIm+05UsTBY8XMG5+Cn4+cRReikbeXiUXT+5NTWMnqrSf0Lo4Qoos27D1NvdnCpPReehdFiHMYDAZunD2AuvoGlq7K0Ls4QggnkSRd2KShwcKH3x8mOsyPqSMT9C6OEC4nPS2S9NRIlq8/IoPICeHGLBYLa3edok98CIkxQXoXR4jzxEYEMH9CClvVfHYcyte7OEIIJ5AkXdhkw97TnMiv4MrJqdL1T4hWGAwGrp+pYMDAW99kYJFBfYRwS5knSzlVUMHk4XIWXbiu2eOS6B0dyJtfq1RU1+ldHCGEg0m2JTpUW2fmk3VH6BMfzJgBMXoXRwiXFRnqxxWT+rLnSCFbDubpXRwhRCes3XUKX2+TbO+ES/MyGbl17iDKK+uk27sQHkiSdNGhVVuPc6ashmumpmEwGPQujhAubcao3qTEBfPOqgxKK2v1Lo4Qwg6V1fVsPpjLuEExcgcT4fKS44KZOz6Zjfty2Z4h3d6F8CSSpIt2lVXW8uVPRxmeFoWSFK53cYRweUajgVvmDqSyup63v5GzG0K4kw17c6ita2DycBl7RbiHeRemkBQTxJsrD1ImB4aF8BiSpIt2rdiQTXWtmSunpOpdFCHcRu/oIC6f2IetB/PYfCBX7+IIIWxgsVhYs+MkfeJD6BMfondxhLCJl8nILXMHUlFdzzvS7V0IjyFJumhTblEla7afZOKwXiREBepdHCHcyuxxSfSJD+atr1WKSqv1Lo4QogMHjp4hp7CSaXIHE+FmkmKDmT8hhc0H8tgq46EI4REkSRetslgsvLMqAx9vI1dM7KN3cYRwOyajkdsvHUy92cLLK/ZjbmjQu0hCiHZ8t/0kQf7ejB0oA8YJ93PJBckkxwXz5tcqJeU1ehdHCNFFkqSLVm3PKGBvVhGXX9SX0CBfvYsjhFuKiwhg8az+ZBwvZsWP2XoXRwjRhqLSanYcymdiejzeXia9iyOE3bxMRm6bN4iaOjP/9+UBuQ2oEG5OknRxnppaM+99m0Hv6ECmjZJuf0J0xYVD4rlwSBwrNmRz8OgZvYsjhGjFt9tPADBVBowTbiwhKpCF09LYe6SIb7ed0Ls4QogukCRdnOfzjdkUltZw/UwFk1GaiBBddf3M/sSEB/Dyin3SDVEIF1NdW88PO04xSokhKsxf7+II0SVTRyQwLDWSD9ZkciKvXO/iCCE6STIwcY6T+eWs3HSMC4fE0T8xTO/iCOER/Hy8+MVlgymvquO593ZIN0QhXMiPe05TWVPPrDGJehdFiC4zGAzcMmcgAX5e/GfFPurqzXoXSQjRCZKkiyYWi4X/fLwbH28jV09N07s4QniUpNhgFk7rx9YDuXy+8ajexRFCAA0NFlZtOU5qQgipCaF6F0cIhwgJ9OHWuQM5mV/Bsu8z9S6OEKITJEkXTbap+ezIyOeKiX0JDfTRuzhCeJxpIxOYPKI3n6w9wo5D+XoXR4geb8ehAvKKq5g1JknvogjhUEP7RjJjVG9Wbz3B7swCvYsjhLCTJOkCgPKqOt5elUHfhFCmyj1ihXAKg8HAPQuHkxwXzMsr9nMyX64XFEIvFouFL3/KJirUjxH9o/QujhAOd/XUVBJjgvjviv0UllTrXRwhhB0kSRcAvLv6EBVVdfxq4QgZLE4IJ/L1NnHPgqH4eZv450e7Ka+q07tIQvRI+7PPkJVTxpzxybLdEx7J28vEXZcPwdxg4d+f7qXe3KB3kYQQNpKtkmDX4QI27jvNnAuS6SvX5AnhdBEhfty9YChnymp58ZM9suMkhA4+35BNeLAvE4bE610UIZwmNiKAW+YM5MipUj5Yc1jv4gghbCRJeg9XXlXH6ysPkhAdyKUTUvQujhA9RmpCKDfOVjh4rJilqw/JiO9CdKOM48Wox4uZNTYJby/ZFRKebfSAGGaM1q5P33owT+/iCCFsIFumHsxisfDGyoOUV9Zx+7xBeJmkOQjRnSYMjeeSC5L4fsdJPt+QrXdxhOgxVmzIJjjAm8npvfQuihDd4pqpaaT2CuHVLw+QW1Spd3GEEB2QrKwH27D3NNvUfBZM6ktSbLDexRGiR7pycirjB8fxybos1u46pXdxhPB46rEz7MsqYva4JHx9THoXR4hu4WUycudlQzAZDfzrkz1U1dTrXSQhRDskSe+hcosqeXtVBv0Tw5g1Vm49I4RejAYDN88ZwNC+kbyx8iA7MuTWbEI4i8Vi4ZO1RwgN9GHayN56F0eIbhUZ6sedlw0hp6CSVz7fT4NcZiWEy5IkvQeqqzfz4vK9eJuM3HHpIIxGg95FEqJH8zIZuevyIaTEhfDSZ/vIOF6sd5GE8Ej7sorIOFHCvAtT8PWWs+ii5xncJ4KF09PYcaiA5euO6F0cIUQbJEnvgd5dfYjjeeXcNm8gESF+ehdHCAH4+pj49dXDiAjx458f7ubo6TK9iySER2mwWPho7REiQ/yYPFyuRRc914xRvZmUHs/nG47y0/7TehdHCNEKSdJ7mPW7c/h+5ykuGZfEsNQovYsjhGgmOMCH+xem4+9r4un3dnAsVxJ1IRxl075cjp4u4/KJfWSgVNGjGQwGrp+p0L93KK99eZCsnFK9iySEaEG2Uj3IkVOlvPm1ysDkcBZM7qt3cYQQrYgK9ec3i0bg423i6fd2ciK/XO8iCeH2aurMfPhDJilxwYwfEqd3cYTQnZfJyF0LhhIa6MPzH+2mqLRa7yIJIZqRJL2HKCmv4YVP9hAW5MOdlw3GZJSqF8JVxYQH8OCiEZhMBp5+dwenCir0LpIQbu3rTcc4U1bDz6b3w2iQcViEAAgJ8OHeK4dRU2fm7+/vpLyqTu8iCSGsJFPrAapr63lu2W4qquu4e8FQggN89C6SEKIDsRFaoo7BwN/e3UFOoSTqQnRGUWk1X246yiglmv6JYXoXRwiX0jsmiHuvHEZ+cTX/WLaLmlqz3kUSQgBeehdAOJe5oYGXPt3Hsbwy7r1yWKv3Q7/99hs4cGD/ea9PmTKNxx9/CoBt27bwyisvcfjwIQIDA5k6dQa33/4LAgICnB6DEO7KlnWro2l+s2gEf1u6nf99ZztLrhlOctz567AQom3vfnsILHDN1DQANm3ayBtv/B+qegCj0cigQUO5/fZfMGTI0Hbns2vXTl5++QUOHtxPcHAIEydO4dZbf05YmCT+Ql+HDx/ittsWs3jxzdx668/bnbat/bmfzx/Mi8v38O9P93L3gqEyboMQOpMk3YNZLBbe+lpld2YhN8xSSE87f6A4i8VCdnYWEydOYcqUaQQH+1FWpl2XFBcXD2g/6Pfd90sUZQB33nk3eXm5LFv2HgcP7ueFF/6LUbrOC3GelutWo+BgPwIDw9udBs6ufwlRgTx0/Siefm8HT727nV9dlS5nA4Ww0e7MQrap+SyY1JfoMH927NjGAw/cS58+fbnjjrswm8188smH3HPPHbzwwn8ZNGhIq/PZvn0r999/D0FBwSxefDMmk4kPPniX7du38O9/v0pISEg3RyaEpr6+nr/85VHq6+s7nLaj/bkbZim8sVLl9a8OcsvcgXJpiBA6kiTdQ1ksFpatyWTtrhzmXZjClBEJrU6Xk3OKqqoqJk6czKxZc4iODiY//9wRpV944R/Exsbxr3+9jK+vdsu22Ng4nnnmSTZt2sj48ROcHo8Q7qblutWo+TrW1jQtxUUE8PvrR/H0ezt55v2d/HLBUIb2jXR6DEK4s9o6M++sUomLCGDW2CQA/vnPvxMTE8vLL7+Bn5+2PZs9ey7XXXc1L7/8Is8992Kr83ruub9hNBp56aVXSUjoDcCkSVO58caf8eabr3L33b/unqCEaOHtt18nK8u2+513tD83efwESitq+WRdFv6+Xlw7ox8GSdSF0IWcAvVQKzZks3LzMaaP7M0VE/u0OV3jD3tycuvT1NTUEBYWzqWXXt70gw4wfPhIADIzDzmw1EJ4jo7WLVunaRQR4sdD140kLiKAf364m80Hch1TUCE81Kfrs8gvrmbxzP54exkpLS3l8OFDTJt2cVOCDhAREcnw4SPZu3d3q/PJyTnFkSOZzJo1pylBB0hOTmHChImsXPm502MRojWZmYd5443/48Ybb+1wWlv35+ZdmMKssYl8u+0Eb3+TQYPF4pzCCyHaJWfSPdCn67P4dH0W4wfHseji9o+CZmVlApCSkgJAZWXlOe/7+vryzDPPn/e5w4czAO0IrBDifC3XraqqKvz9/e2eprmQQB8evHYk//hwF//5dB9FpTXMGpsoZzqEaCHzVAkrNx9jUno8A1MiAAgMDGTp0o9aXcdKSooxmUytzis/Pw+A1NS0895LSEjkhx/WkJt7muhoGS9CdB+tm/tjjBkzjlmz5vDKKy+1O72t+3MGg4FrpqZhNBr46qdjmBsauGH2AOn6LkQ3kyTdg1gsFj5Zd4TPNxxlwpA4bp7T8fVEWVmZBAQE8vzzz/Ltt6uoqqqkV68E7rjjLmbMmHXe9KdP57B9+1b+9a/n6Ns3lUmTpjorHCHcWlvr1gMP3M/YsZPanaat9Q8gwM+L+xcO55UvDvDBmsPknank2ov7yyA/QljV1Zt59YsDhAf7snBav6bXTSYTiYlJ501/+PAh9uzZxdix41udX2NS3/IgNkBpaQkARUWFQL/z3hfCWd555w1OnDjGX//6NGaz/SOyt7c/ZzAYuGpyKiajkc83ZGNusHDzJQMxGiVRF6K7SJLuIcwNDbz1tcraXTlMHBbPjZfYdtQzK+sIlZUVlJeX8fDDj2Ew1PHqq6/z6KN/oL6+ntmz5zZNW1pawlVXXQqAn58fv/71b/D19XVaTEK4s5brVnl5GcuWvceSJUt4+OHHmD17bpvTtLb+NefjbeLOywbzSbg/X2w8Sn5JNb+4bAgBfvKTLsQna7PIKaxkyTXp+Pu2v05UVlby+OOPAHD99Te2Ok1KSl8CAwP5/vvvuP76m5p6rtTU1LB5808A1NbWOjACIdp35Egmr7/+Cvfd9yAxMbHk5Jyy6/O27M8ZDAYWTOqLl9HA8vVZmM0Wbpk7UA4IC9FNZI/OA9TUmnnp073syixk7vhkFkzqa3P31/nzr8BsbuDKK68BtEGtxo2bxOLFC3nxxX9y8cWzm3UBNPDYY3+hrq6ODz98n/vu+yWPPfYXpkyZ7qTIhHBfLdctgBkzZnLTTYua1q22pml9/TuX0WDgysmpxIT58+bXKn99Zxv3XDmMmLC2u8sL4en2Hilk5eZjTBmRwJAOBlesrq7moYeWcPhwBosX38yIEaNanc7b25uFC6/j1Vdf5rHHHmbx4ptpaDDz3//+m6qqKoA211MhHM1sNvOXvzzGsGHDmT//ik7Oxfb9ufkX9cFkMvDRD0coqajll1cMIcDPu2tBCCE6JIfD3FxpZS1/e28Hu48Usnhmf66cnGrX9amXX37VOQkCgK+vH7NmzaGoqJDs7Kym10NCQpg+fSazZ8/lhRdeJjY2jn/+8xmHxSKEJ2lr3brsssua1i171r+2TEzvxX3XpHOmtIY/v76F3ZkFDo1DCHdRUl7DK5/vJyE6kJ9NO//68ebKysq4775fsn37VubOnc8dd9zV7vQ33XQb11yziO++W8WNN/6Mm2++DoPBwHXX3QAgt2AT3Wbp0rfIzDzEnXfeTXFxMcXFxZSVlQJQU1NNcXExDQ0N7c7D3v25ueNTuHXuQDKOF/PEW9soKK5yaExCiPNJku7GThdV8te3tnE8r5xfXjGUqSN7d/whG4WHawPtVFWdfw0eaInEhRdOJC8vl+LiYod9rxCeLiKi/XULOl7/WhqUEsEfbx5DZIgf/1i2m+XrjsiIvKJHMTc08PKK/VTVmrlz/mB8vNs+s33mTBH33vtz9uzZxfz5V/DQQ//T4cFto9HIvffez/LlX/HCC6/w4YcrePLJZ6moqMBkMhEbG+/okIRo1aZNG6irq+P2229k3rwZzJs3g1tuuR7QEvh582aQm3va5vnZuj83YWg89y8cTkl5LY+/uZUjp0q7HIsQom2SpLupHYfy+fMbW6ioruc3PxvByP7Rds8jPz+P66+/htde++957x07lm19ZuCqqy7l44+XnTdNZWUFBoMBHx/p9iREc+2tW1lZ2tnx2Ni4Dte/+PheNn9nTJg/v1s8iguHxPHZj9n8Y9luyqvqOheAEG5m2ZpMDhw9w+KZCgnRQW1OV1lZwZIld3PoUAYLF17Lgw/+wabeZ6tWrWT79q1ERESSnj6cuDgtKd+1awf9+w+Q8VlEt7n77vt49tkXzvn74x//DMCsWXN49tkXiIg4/1KPo0ezu7w/NyA5nD/cMAofbxNPLd3Oxr22HwwQQthHknQ3U29uYNn3h3n+oz3EhAfwyE1jSOsd2ql5RUfHUF5exooVy6moKG96/fTp03z55eeMHDmaAQMGUl5ezqeffkRdXV2zaXL4/vvvGD58JAEBgV2OSwhP0t669fHHHzNy5Gib1r/IyCi7vtfX28QtcwdywyyF/dlFPPLqZg4cPeOwuIRwRRv25vDNluPMGNWbi4a1f0b7739/kkOHMrj66kXcc88Sm7/jgw+W8uyzT1FfX3/2ezesZ/funSxYcHWnyy6EvQYMGMiYMePO+Rs6NB2AXr0SGDNmXKsHjRISejtkfy4+MpCHbxhNSnwI//18P69+cYCaWvtHlxdCtM/06KOPtvlmZWVt22/aKDDQl8pKzxv1VI+4cgoreO6D3WxV85mU3ou7Lh9CcIBPl+bZq1dvPvvsE378cS319fVs3bqJJ574ExaLhSeeeIqIiEiio2NYvvwjtm7dTG1tLZs2beDJJx/HYmng8cefbOqa68qkHbofd4+t5bq1ZcsmnnrqCQwGePzxpwgLC29zmsb1Lyws3O7vNRgMpMSHMLRvJDsPF7Jqy3Fqas30TwzD5MTb57h7fbVFz7gCA30f68rnHbENt4cey0o9doZ/L99L/8Qwbrt0ULu3iMrOzuJvf/sLQUHBzJkzjyNHMsnMPHTOX1paP06ePMHGjevx9/dvutY8JCSEjz9exv79+6iuruaHH77j+eefYdSosfziF/dgNBo9dh2wR09fBnrFr90Z5F1GjBjFyJHgCOdTAAAgAElEQVSjAc5rx0aj0WH7c74+JsYPiQULfLvtBNsy8lGSwggJ9JE20MPjB1kGgYG+AF3afgMYLO1ct5ifX9blixqjo4PJzy/r6mxcTnfGZbFYWLvrFO9+ewhvk5GbLhnIKMX+7u1tWbfue9588zUyMw/h5+dHevpI7rzzbpKTU5qm+fbbVbzzzhtkZWXi5+fPqFFjuOOOu0hKSnZYOZxJ2qH78YTYmq9bPj6+jBgxit/97kFCQmLanabl+tdZNbVm3l9zmO93nCQxJog7Lh3UblfgrvCE+mqNnnFFRwd36aiKI7bh9ujuZXWyoIK/vrWN0CAffnf9KIL82++qu3z5hzz99P+2O8369Vv58ssV/OUvj/H73z/CnDmXNr23evXXvP22dm/q8PBIZs6czeLFN+Pn5wd47jpgj56+DPSKPyfnFFdfPZ+bb76dW2/9OUCb7djR+3P7s4u08SBq6vnZtDSuungAhYXlHX/QQ/X0dQBkGURHBwN0+ayIJOmd1F1xFZZU886qDHYeLmBQSji3zh1EeLDzrn2T+nIvnhoXeG5sesS183ABr315gKqaeuaNT+GSC5Lx9nLs1U5SX075bknS21BUWs1f395GvdnCHxaPIsoFbj3oqeuAPXr6Muip8ZdU1PLK5/vZl1XE4L6RXDs9jfjInnkpZE9tA8319GXgqCRd7pPuourNDazeeoJP12dhsVhYOC2Ni8ckYrTj9mpCCAEwPC2KP906jndXZ7B8fRabDuRywywFJcn+7vRC6O1MWQ1PLd1BZY2ZBxeNcIkEXYieLDTQhyXXpLN+Tw7L1mTyyKubmTc+hTnjk/EyyfBXQnSGJOku6PCJEt78+iAn8itIT43kuov7y06IEKJLQgN9uPOyIVw4pJC3v1F5cukOJg6L56opqV0e20KI7nKmrIan3t1BaWUt9/9sOMlxwXoXSQiBNh7KxGG9mDo2mX+9v4Pl67PYfDCPa6amMrRvpE13URBCnCVJugvJKazgk3VZbD2YR0SIL3cvGMqIflHywyaEcJhhqZH8+bZxfLY+i683H2ermsecC5K5eHRiu/eWFkJvuUWV/P39nZRV1XHf1emk9urcnU2EEM4THuxnPSBcwNJVh3hu2W76J4Zx9ZRUUhNknRXCVpKku4CC4io+/TGLDXtP4+NtYt6FKcy5IAk/H6keIYTj+XqbuHpqGhOGxvPh95l89MMRvtt+kism9uXCIXHtjpAthB6Oni7j2Q920mCBBxeNoE98iN5FEkK0Y1hqFINSIli76xSf/ZjNE29tY2T/aOZPSCEpVnrACNERyQJ1dLKgglVbjvPjnhwMBgMXj05kzvhkQqTrqRCiG/SKCuTeq4ahHjvDB2sO8+qXB1i5+RhzLkhi7MBYuZZQuITNB3J59YsDBAd4s2Th8B47IJUQ7sbLZGTayN5cOCSOb7YcZ+WmY2zPyGdgcjizxiYypG+kjLUkRBskSe9mFouFfdlFfLPlOHuPFOHtZWRiei/mjU8mIsRP7+IJIXogJSmch28YzZaDeazYkM0rnx/gk7VZzB6XxEXD4vGVbvBCB/XmBpavy+LLn46S1juUX14xlNBAOYgthLvx8/Fi/oQ+TB/Vmx92nmL11uM8t2w38ZEBzBjVm7GDYgn0a/8WikL0NJKkd5Pi8hp+2pfL+j05nCqoIDTQhysm9mHKiAQZtEkIoTuDwcDYgbGMGRDD7sxCvvjpKO+syuDT9VlMHBbPpOG9iA0P0LuYoocoKK7iP5/tI/NUKZOH9+LaGf0dfttAIUT3CvTzZs4Fycwck8iWg3l8s/k4b32TwbvfHmJ4WhQXDolnSN8I6cUlBJKkO1VNnZldhwvYsPc0e44UYrFAaq8Qbp07kLEDY2WHQwjhcgwGA+lpUaSnRZFxvJivNx/j683H+WrTMQalhDN5eAIj+kXJTpRwigaLhR92nGTZ95kYDHDnZYMZOzBW72IJIRzIy2Rk/OA4LhgUy7Hccn7cm8Om/blsVfMJ8vdmeFoU6WmRDEqJwN9XUhXRM0nLd7DSilp2Hi5g56EC9mUXUVffQHiwL3MuSObCIXFyLZ0Qwm30Twyjf2IYZ8pqWLf7FGt3neLfy/cS6OfF6AExjBsYS/+kMLmmUDjEsdwylq7KIONECYNSwrlp9gC5/agQHsxgMJAcF0xyXDDXTE1jb1YRP+07zfaMfNbvycFkNDAgKYzBfSLplxhKcmywHCAWPYYk6V1UVVPP4ZMlHDx2hoNHi8nOKcUCRIb4Mim9FyP7RaEkhctoyUIItxUe7Mv8CX2YNz6FvVmF/LQvl5/25fLDzlOEBfkwZkAsk0cnEhPsIztQwm6FJdV89mMW63fnEOjvzc2XDOCiYfFy+1EhehAvk5HhaVEMT4ui3txA5skSdh0uZFdmAR+sOQyAj5eRPvEhpPUOJSk2mN7RgcSGB8g+tvBIkqTboaHBQk5RJdk5peSWZLEvs5Cjp8tosFgwGQ306RXC/Iv6MKJfFIkxQbKDIYTwKEajgWGpUQxLjaKm1szOwwVs2p/Lmh0nWLX1OP6+JganRJCept16JzzYV+8iCxd2uqiSrzcfY/3uHAAuHpPI/AkpBMgAUkL0aF4mI0pSOEpSONdMS6OkvIZDJ0qsf8V89dMxGiwWQEvcE6IDiY8MJDbcn9iIAGLDA4gJ95eu8sKtSettRUODhaKyak4XVnKqoIKTBRWcKqzgRH4FNbVmAPx8TCTFBDFnfBJKUjhpvULx9fGsEZDffPNVXnrpX1RWVupdFCE8TkBAAHfeeTc33HCL3kXpFF8fE+MGxTJuUCzVtfWcPFPNuu3H2ZVZyFY1H4D4yAAGJIczMCmc/klhcntJQV19A7szC/lh50n2ZhXhZTIwaXgv5oxLJjLUde5wIts/4SncfVsDEBrky+gBMYweEANAXb2ZUwWVHM8r53heOSfyyzlw9Awb9p4+53P+viYigv0ID/ElMsSPiGBfIkL8CA3yITTQl9BAH4L8veVMvHBJBov1SFRr8vPL2n7TRtHRweTnl3V1Ng5XWV1PQUkV+cVV5BdXWx+1v4KSaswNZ0MPDvAmISqQhOggUuKCSYkPYagSS1FhuY4ROEfz+rr44onk5+frXCIhPFd0dDSrVq3TuxgO0fjbYbFYOJar7TAdPHYG9Xhx08HN6DA/+vYKpW98CH0TQkiKCXb5ATT13IZFRwd3ac/REdtwe7S1rCqq69iffYY9RwrZkZFPRXU9YUE+TBmRwOThCS55WzXZ/glP0p3bGj1/M2tqzeQVV5FbVEl+SRVFpTUUlVZrj2XVlFXWnfcZgwFCAnwIDfQhJND6GORDaID1MdC36fVAP68Oe8m6at7TnXr6MoiODgbo8pEfjzyTXltn5kyZdcW0Pp4pq2l6XlRaQ2VN/TmfCfTzIirMn8TYYEYq0USH+hMfGUB8VGCrZ39MPeCo2+LFN8uZBCGcJCAggMWLb9a7GA7XfCCg2eOSqDc3kJ1TxqGTxRw5VUrG8WI27c8FwMtkIDEmmMSYIBKiA5sOhoYEeMvlQm6qwWLhTGkNx/PKycopZX92EUdySrFYwN/Xi/TUSMYPiWNQSjgmo+seoJHtn/AUnrqtaY2vj4nEmCASY4Jafb+u3kxRWQ0l5bWUVtRSYv0rrbC+VlnLqcIKSsprzzlZ18hkNDQl7E1JfbNEPiTAm+oGqK+pI8DPSwZVFV3iVmfS6+rN1pWpjtJKbQUrrajlTHkNZ0rPJuXlVecfKQvy927q5tLY7SUmzJ/oMH+iwvwItPMaOE89SiRxuRdPjQs8NzaJC86U1XDkVAmZp0rJOlXKifxyKqrPHjgN8td6L8VHBhAZ6kdUqL/10Y+QQJ9u3fGRM+lnNTRY2LjvNMXlNdSbLdSbG6g3N1BeWUdhaTUlFXXkF1dSb9a+1gD06RXCkD4RDOkTSZ9ewS6dmHeVp67b9ujpy6Cnxw+esQwsFgsV1fXNEvkaSivqtMfyWkoqa7XHCi2xby2VMhoMBAd4ExzgQ0igNyEBPk3PgwN8tP+tr4cE+HjUJbOe0Aa6wm3PpOedqWRXZiENDRYaGiyYrY8NFu15VU299c9MZU09ldXa/xXVdVRbu0y2FOjnRXiwHxEhvvTtFUJ443UnjUl5sC8+3p7T+IUQwp2FB/sySolhlKJdX2ixWCitqOVkQQUn87VxQE4WlLPlYN45yTtoZ98jQvwIC/QhKEC7nrDxLzjAm0A/b3y8jfh4mfDxNuLtdfa5yWjEYMD6Z0DrEGU45zUs2tlgbafLQnVtPdW19VgsWjkbT640TmOxWAgN9OkRZ/4rqutYujqDqhptW2wyGjAZDQT6exMZ4ke/xDCGp0USFepHYkwwCdGBMnCTEMLtGAyGpu1Kr6j2b53c0GChvKquKWE3mEycPF1KaWUtZZXaicWyyloyi0soraxruvyrJS+TkQA/LwJ8vZoe/Zs9D/DzwtfbhI+3CR8vI95eJny9jfh4m7TtnPX1xkcvkxGTySBn891Yt289f9h1iq9+OtbqeyajAT8fk9YorY0zKtQPf18vAv28tK4kgdoRJ+25dgRKEnAhhHBfBoOB0CBfQoN8GZQScc571bX1FJZUU2D9KyzVHssqasktqiSzqo7yqrpWuyZ2l/kTUrh8Yl/dvr+7BAf48I97J2Kx0OrOX08/eyKE6HmM1i7wIdaxNTr6HaypM1NWWUtZZR2lFdbHyloqquqaTk5W1tRTUV1Pfkk1VdXa6409lOxlMIDJqCXsXtYDqyaTsekg67nPDdq0jc8NBoxGAwaD4ZyD28Zm/5//uoGAAB+qq+vAAhYsWI95Wx+t/2tPAesB76ZpLE3TNr3f7PNNB8utB8rPHlRvdvCcswfRz3mk+XPtMSTAh19cPhhvL9fLJbs9Sb9qciqXjEvGZNQq3mjQGkbTWQwhhBDCys/Hi4ToIBKiW7/GELSNblWNmfLqOiqq6qitM1NX30BtfQO1deamR3PD2Y1z8zPmzTfcTTseaM+Dg/yorKw97/WmM/EGA+mpkd20NPTnZfLc7upCCOFsvt4mfEP9iQr1t+tztXVmaqzbtsbH2roGaurN1NU1UFtvprbZY725AXODBXNDA2az1ltZe2ygvtnzs6+fnbauvoHqWu158+1jgzVB1v63nNPDrHnvMjBgNjfQmNY15ncGg7UPuHVbqr2IdbvaYhprL7ezr51/YODc15tvoxu3z9bn1pkYDWCwXnJltL4X6O+FA3qmO0W3J+mNXUiEEEIIRzAYDFqXQD8vCLNvx6cjcnZYCCGE3nysXd3dgWw3HUMOiQshhBBCCCGEEC5CknQhhBBCCCGEEMJFSJIuhBBCCCGEEEK4CEnShRBCCCGEEEIIFyFJuhBCCCGEEEII4SIkSRdCCCGEEEIIIVyEJOlCCCGEEEIIIYSLkCRdCCGEEEIIIYRwEZKkCyGEEEIIIYQQLkKSdCGEEEIIIYQQwkV4tfXGY4895nX99bd2+Qu8vc2UlpZ3eT6uRuJyLxKX+/HU2CQu96JnXC+++EwKcOKRRx6pt/ezjtqG28NT24Ctenr8IMugp8cPsgx6evwgy8Db28w//vGPFDq5/W7UZpIO9H777f/r7HyFEEII0TVZQB8guxOflW24EEIIoY+ubL+B9pP0E9aZd1VjIT2NxOVeJC7346mxSVzuRe+4TnThc91dbr2Xld56evwgy6Cnxw+yDHp6/CDLoDH+zm6/NRaLxal/jz76qMXZ36HHn8TlXn8Sl/v9eWpsEpd7/XlqXLKsJH5ZBhK/LAOJX5aB68bfHQPHPdYN36EHicu9SFzux1Njk7jci6fG5Qw9fVn19PhBlkFPjx9kGfT0+EGWgUPiN1gsFkfMRwghhBBCCCGEEF0kt2ATQgghhBBCCCFchCTpQgghhBBCCCGEi2hvdPdzKIriBZQBfi3eqlBVNajFtAnAPmCeqqrrO5jvRcC6Vt76QlXVebaWr7NsiUtRlKuBB4EBQDGwGvitqqp5Hcz7V8A9QAJwAPiDqqpfOTaCNr/bKXEpitIbON7KW/tUVR3iiLK3x8a4rgMeAlLRyvoC8Lyqqm1e22Gd7yPATUAksA24X1XVzY6OoZ3vd0ZcLr9+tZj+WeAeVVXb/W1yh/pqMb2tcelaX9Yy2NIWD6O1w5aiVVUtaGe+Ll1nnYxL9zpzNkVRjMAdwF1AXyAX+BR4RFXVsjY+41HLRVEUA/ArtGWQCGQAT6qqurSdzwQBTwJXAkHAWuBXqqoecn6JHauT8V8PvNXKWy+oqnq3UwraTRRF+RgYpqpqWjvTeEz9t8bGZeBRbcDebb/1Mx7TDjoZv0e1AQBFUSYBfwFGouVPHwG/U1W11ZvDd7YN2JykAwpapdyI9uPcyNyiIPHASiDUxvmmAxXAjBavn7GjbF3RblyKoiwE3gP+A/wBiAf+BKxWFGWMqqo1rc5UUX4D/BV4FG1n9FbgM0VRJqqq+pNzQjm3CDghLrT6ApiJtqI2qnRc0dvVUVzXAm8DzwBfAuOsz/2Ap9qZ7z/QkoffAkeBJWjLYriqqkccG0KrnBWXS69f50yo/ej9CmiwYb4uXV/nTGhfXHrXF3TcFoPQErWHgB9afLa4nfm6dJ11IS5XqDNnexB4HPgb8C3QH/gzMAiY3cZnPG25/A5tG/kI8BNwCfCOoij1qqp+0MZn3gfGAL9B214+AqxRFGWwqqol3VBmR+pM/OnAYWBxi9dPO62U3cCadFwBZHYwqSfV/znsWAae1gZs3vY340ntoDPxe1QbUBTlAmAVsAKYD6Sh5XvRwM/a+Fin2oA9SXo62k7mh6qqnpeQWY+yLgSeA7ztnO/ebkpc2/r+NuNC2zB9qarqnY0vKIpykLMbqeUtP6AoSiBa4vu0qqqPW19bCWxAq5hLHB1EKxweV7P55qqqusrB5bVVR3H9HvhcVdX7rf9/qyhKf7QeDa0ms4qipAA/B+5WVfUl62vfoP0A/Qb4hUMjaJ3D42o2X1dev4CmBOk14CTaAaM2uUl9YS2XzXE1m6+e9dVYhvZiGwYYgE9VVT1oywzdpM7sjqvZfPWuM6exbtsfBP6jqurvrC+vVhSlEHjPepBlZysf9ZjloiiKN/AA8G9VVZ+wvvytoiij0X6Dz0tSrT0J5gCXqKq60vraOrT7596JdlbFLXQmfqt0YJsntIFGiqL0Av5JB/c/9qT6b8nWZWDlaW3Apm1/Iw9sB3bF3+wzntQGnkTLla629mRdrSiKCViiKEpAy+XSlTZgzzXpw4HMdiolGXgT7cf6Bjvnu9uO6R2tzbisOyergZdbvNW4A9dat0jQznKGonV/AMBakR8DMxRF8elqoW3gjLga5+uS9WW1AO2sZXO1nN81p7lpgIlz66sG+BxtxeoOzoircb6uXF+N/oZ2VPU1G+bpDvXVyJ64GuerZ301lqG92IYDVYA9XfXcoc46E1fj5/SuM2cKRuvF07Jbc0fbC09aLmZgMtrZkuba+w1u7G3WdEBbVdV8tF4a3dXmHaUz8YO2c+4pbaDRK8A3aD1K2uNJ9d+SrcsAPK8N2Lrtb+Rp7cDe+MGD2oCiKFHARLQDlk2Xmqqq+oKqqqltLJdOtwF7z6TXWM8IXwTUoSXkD1ivSSsA+quqmq0oyhRbZmg98jAEKFAUZbv1+Wm0bpHPtHetrQN1FNcDrXzmcuvjvjbmOcD6qLZ4/TDaMu/L2R0cZ3FGXI3zzVMUZT0wGigBXgX+qKpqnaMK38H3txmXqqpN3W8URYlAS25vAP7ezjwHAGesK01zh4EkRVH8VVWtcmQQrXB4XG6yfqEoysVosYwArrVhni5fX2B/XC5SX9BxbOlAEfCuoigz0X7TPgd+rapqW93X3KHO7I7LherMaVRVLQXubeWtNrcXnrZcVFVtAPZA00HuGOBmtK78P2/jYwOAw6qqtuwGehit16Hb6Ez81ksfY4AR1l56qcAR4HFVVVu7PtXlKYpyGzAKGAw83cHkHlP/zdmzDDyxDWDDtr8FT2sHdsXvgW1gKFqPuyJFUd4H5gH1aAexl7SxH9PpNmDPmfR0tIX7JVrm/2dgEbBCURSDqqrlqqpm2zE/gH6AP9o1Dk+iXdv2CdrZp0ftnFdntRtXy4kVRUlF+2HajnYksTWN1+O3bLCN/4d0scy2cHhciqIEoF17MQD4P2AW8BLataWvOD6EVtkUl6Ioo4BC4L9oOxftJemhQGkrrzfWV3DXi90hZ8Tl8uuXoiihaG3pj80PRHTA5eurk3G5Qn1Bx20xHYhDS84uBe5DO8u2RlEU/zbm6fJ1RuficpU661aKooxDu3Z/eRuXBnjyclmAdsDhr2ht6e02pmuvzXfHPoCz2Bp/4/g1fdEul5gLbAHeVBTlZmcX0tEURUlGGwfmLrWNQSRb8Lj678Qy8Kg2YGXXvjWe1w7sjd/T2kC09fF1tJPTl6Jt024A/t3GZzrdBuw5k74QKFJVdY/1/7WKouSi/UDPoNlpfDucRLv+eWezMxXfWZPB3yqK8nQbR6Ycyea4FEUZgJbA1gPXWI8ut6a1htqcLQNIdZUz4qpH67aRrapq42AhPyiKUgs8rijK46rzR6u0Na4TwFQgBW3Ao/WKooxq4yiXO9WXPXG5w/p1LdpI9c/aMU93qK/OxOUK9QUdx3YPYFRVdZP1/XWKouwH1gPXox1Aaskd6qwzcblKnXUbRVEmoPUwyAJua2MyT14u29EO3gxD20H9QlGU6a30DmivzXdHe3cWW+PfirYD+0Ozuv5GUZRY6+dsvQRId9bk41W0cXw+6mh6K4+q/04uA49pA83Ymwt5VDvA/vg9rQ00Xq68QVXVX1qff2ddP55WFOVP6vkD4Xa6DdicpKuq2nK0W4AvrI/pdCJJt1bYyjbmexvaUfit9s7XzjLYFJe1C//HQDkwtVmS2prGkfqCOPdsekiL953GGXGpqlpL69cgfYGWMKZj//WcdrE1LlVVc9FuE4SiKEfQrv24gvOvqwStPlo7k9dYX60dAXMoZ8TlBuvXOLSRMEcDRkW7zZMRmm7z0dDGASNXr69OxeUK9WUtR7ttUVXV87o4qqr6o6IoJZw9at6Sq9dZp+JylTrrLop2V5DX0Qb8m62qamFr03nyclFVNQvtAMVaRVFKgTeA8WgDwzZXgnb2qKUQumEfwFlsjd96pvXzVmbxBdrYPFE2no11Bb9EOygx1PobDtYdb+v/5lYOUnha/du9DDysDQCdyoU8qh3YG78HtoHGnO7LFq9/jdazdShad/7mOt0GbOrurihKjKIotymK0vJLGrsAdmoBK4oyVFGUXyjayKEOm68d329TXNYdk6/RzmKOb6N7X3ON16K3vHdkGlCDdvshp3FWXIqi9FEU5Q5FGzihzfk6iw1xlSuKskhRFKXF+9utj73amLUKRCiKEt7i9TQgy3pwwmmcFZcbrF+3oQ06tBftuqY64H/QBhirA/7Yxqxdvb46FZfe9WUtQ0exVSmKcrOiKOktPmdEO8LcVhldvc46FZcr1Fl3URRlCfAusBGYpKpqTjvTetRyURQlQlGUxYo2qnVz7f0Gq0DfVrqApnH+eDUurTPxK4oyXlGUW1uZnT9arzx3SlCuAqKAHM7+pt+A1u23Du12VC15TP1b2b0MPKwNdDYX8ph20Jn4Pa0NcPZEpG+L1xvPsLc23kqn24Ct16Q3oN1Pu+VN5xeijfq53sb5tNQPeJHzb0m2EO1IrVOTWWyIS1GUWcA7aDsmF6mqetKG+W5Auz/sVY0vWCtnAbDW2TukOC+ucOt8Ww6CtRBtRdvRlULboKO41qC1p9+3eH+m9XEPrWs88te8vnzRBoRY3YXy2spZcbn6+jUD7b6Rzf/+a31vDOfffaCRq9dXZ+PSu76g49hWo12T+EiL9+ejbXS/b2O+rl5nnY3LFerM6aw7WX9HGyBottrxvX09bbkY0c4Ytxwkrb3f4G+AMJrdJ15RlGhgEt3T5h2pM/GPB15RFGVY4wvWg15XAT+q3TPQrKP8nPN/0z9HO8ExBu1+yS15Uv1D55aBJ7UB6Fwu5EntoDPxe1obOIC2/Wp5P/TGAeQ2tvKZTrcBg8Vi2yCriqL8E7gLrVvzOmAC2r3A/62q6q9bTDsFLbGYqKrq+mavhwCD0Ibvz1e0W5FtABKt8zoOXId2/d+Vqqp+YlPhuqC9uIDfAplAIHAN5w8Ed1xV1ZPWnc0RwAlVVU9Y5/so2tmzx9Hup3cL2mi4U1RV/dHJYTkzrk+B6Wix7UUbOOJXaKMaPqdnXKqq/lpRlAfQBid6Em3FSEfb8d4MzFJV1dKyHVrn+zraD83v0Y6ULUHrrjxSVdXD7hiXq69fLX83rNM/CjysqqpXs9fcrr5amf5ROojLFerLWq6O2uIStITteeAztJG7HwPWqKp6eWuxWV97HReus87E5Sp15kyKosSgJdZ5wGK0HZHmDqPdisujl4uiKP8CbkfrCbMVbWTj3wFvqap6u3WnKxXYr2oj4qMoyhq0LsIPot054FEgEhiqquqZbg+iC+yN39prZidae3kYbT/jLrT9h0nNxn5wS9bfs4tUVU2z/u/R9d+ajpaBJ7YBG7YjHt0O7I3fQ9vAQrReZUvRLv8aBfwJeEFV1SWObAP2jO5+P9rO1c/QriW4ES1JWGLHPEaiHWWYC03XOM8Gllvn9Snahv6KbtyItxfXOCAB7QjIN9ayN/9rHJkw3vp/80F0/mSdz81o13z3BeZ3R4Ju5ay4rgX+hTbI0gq0I+l3dEeCbtVuO7ReV3ob2sGDL6yvv4S27BuPSJ3TDq1+bp3uIeB9tPEaLu6O5MHK4XG5wfplK7erLxu5Yn1Bx23xGbS2OAUtmX0ArS4WNZuH29VZZ+JyoTpzptlAANpgles4f3sxm56xXO5DOzh9C1r7WYyWsDaeXZ6LtgxGNvvMArS29DTazqYbMVMAAADNSURBVNwJYLq77Zhb2RW/NcZJaAeSn0XrhRGEFr/b7ZjbwNPr3xY9oQ10tO339HZgV/ye2AZUVX0frU4HofUm+SVaztd4a2uHtQGbz6QLIYQQQgghhBDCuew5ky6EEEIIIYQQQggnkiRdCCGEEEIIIYRwEZKkCyGEEEIIIYQQLkKSdCGEEEIIIYQQwkVIki6EEEIIIYQQQrgISdKFEEIIIYQQQggXIUm6EEIIIYQQQgjhIiRJF0IIIYQQQgghXIQk6UIIIYQQQgghhIv4f4bcTlOA4pVUAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 993.6x331.2 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.plot_posterior(trace_g)" ] }, { "cell_type": "code", "execution_count": 12, "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>mean</th>\n", " <th>sd</th>\n", " <th>mc_error</th>\n", " <th>hpd_2.5</th>\n", " <th>hpd_97.5</th>\n", " <th>n_eff</th>\n", " <th>Rhat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mu</th>\n", " <td>53.498401</td>\n", " <td>0.527612</td>\n", " <td>0.007463</td>\n", " <td>52.489643</td>\n", " <td>54.550422</td>\n", " <td>4125.384536</td>\n", " <td>1.000013</td>\n", " </tr>\n", " <tr>\n", " <th>sigma</th>\n", " <td>3.552864</td>\n", " <td>0.380939</td>\n", " <td>0.006333</td>\n", " <td>2.857691</td>\n", " <td>4.305856</td>\n", " <td>3649.234419</td>\n", " <td>1.000209</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean sd mc_error hpd_2.5 hpd_97.5 n_eff \\\n", "mu 53.498401 0.527612 0.007463 52.489643 54.550422 4125.384536 \n", "sigma 3.552864 0.380939 0.006333 2.857691 4.305856 3649.234419 \n", "\n", " Rhat \n", "mu 1.000013 \n", "sigma 1.000209 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.summary(trace_g)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/anaconda3/envs/python3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: sample_ppc() is deprecated. Please use sample_posterior_predictive()\n", " \"\"\"Entry point for launching an IPython kernel.\n", "100%\|██████████\| 100/100 [00:00<00:00, 973.91it/s]\n" ] }, { "data": { "text/plain": [ "Text(0.5, 0, '$x$')" ] }, "execution_count": 19, "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": [ "y_pred = pm.sample_ppc(trace_g, 100, model_g, size=len(data))\n", "sns.kdeplot(data, c='b')\n", "for i in y_pred['y']:\n", " sns.kdeplot(i.flatten(), c='r', alpha=0.1)\n", "plt.xlim(35, 75)\n", "plt.title('Gaussian model', fontsize=16)\n", "plt.xlabel('$x$', fontsize=16)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 48, 48)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred['y'].shape" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "85.31093750000001" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(stats.iqr(data)+data.mean())*1.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Look at students-t dist" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.15427608081517086" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(stats.t(loc=0, scale=1, df=1).rvs(100))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (8 chains in 4 jobs)\n", "NUTS: [nu, sigma, mu]\n", "Sampling 8 chains: 100%\|██████████\| 84800/84800 [00:30<00:00, 2818.68draws/s]\n", "The acceptance probability does not match the target. It is 0.8830519721214525, but should be close to 0.8. Try to increase the number of tuning steps.\n", "The acceptance probability does not match the target. It is 0.8787489954578583, but should be close to 0.8. Try to increase the number of tuning steps.\n" ] } ], "source": [ "with pm.Model() as model_t:\n", " mu = pm.Uniform('mu', 40, 75)\n", " sigma = pm.HalfNormal('sigma', sd=10)\n", " nu = pm.Exponential('nu', 1/30)\n", " y = pm.StudentT('y', mu=mu, sd=sigma, nu=nu, observed=data)\n", " trace_t = pm.sample(5100, chains=8)\n", "chain_t = trace_t[100:]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f99fc088850>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a2a330b10>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9a18990d10>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f99fb408a10>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9a187cf5d0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a0b48ed10>]],\n", " dtype=object)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace_t)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f9a0bc70150>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f99fb5ef4d0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f9a187d2e90>],\n", " dtype=object)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1490.4x331.2 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pm.plot_posterior(trace_t)" ] }, { "cell_type": "code", "execution_count": 34, "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>mean</th>\n", " <th>sd</th>\n", " <th>mc_error</th>\n", " <th>hpd_2.5</th>\n", " <th>hpd_97.5</th>\n", " <th>n_eff</th>\n", " <th>Rhat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mu</th>\n", " <td>53.013970</td>\n", " <td>0.385711</td>\n", " <td>0.001518</td>\n", " <td>52.270750</td>\n", " <td>53.786008</td>\n", " <td>55043.116055</td>\n", " <td>1.000071</td>\n", " </tr>\n", " <tr>\n", " <th>sigma</th>\n", " <td>2.192517</td>\n", " <td>0.396714</td>\n", " <td>0.001810</td>\n", " <td>1.454511</td>\n", " <td>2.989008</td>\n", " <td>40335.554727</td>\n", " <td>1.000055</td>\n", " </tr>\n", " <tr>\n", " <th>nu</th>\n", " <td>4.634511</td>\n", " <td>4.198292</td>\n", " <td>0.023733</td>\n", " <td>1.069904</td>\n", " <td>10.041432</td>\n", " <td>29163.158997</td>\n", " <td>1.000147</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean sd mc_error hpd_2.5 hpd_97.5 n_eff \\\n", "mu 53.013970 0.385711 0.001518 52.270750 53.786008 55043.116055 \n", "sigma 2.192517 0.396714 0.001810 1.454511 2.989008 40335.554727 \n", "nu 4.634511 4.198292 0.023733 1.069904 10.041432 29163.158997 \n", "\n", " Rhat \n", "mu 1.000071 \n", "sigma 1.000055 \n", "nu 1.000147 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.summary(trace_t)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/anaconda3/envs/python3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: sample_ppc() is deprecated. Please use sample_posterior_predictive()\n", " \"\"\"Entry point for launching an IPython kernel.\n", "100%\|██████████\| 100/100 [00:00<00:00, 1018.80it/s]\n" ] }, { "data": { "text/plain": [ "Text(0.5, 0, '$x$')" ] }, "execution_count": 36, "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": [ "y_pred = pm.sample_ppc(trace_t, 100, model_g, size=len(data))\n", "sns.kdeplot(data, c='b')\n", "for i in y_pred['y']:\n", " sns.kdeplot(i.flatten(), c='r', alpha=0.1)\n", "plt.xlim(35, 75)\n", "plt.title('t model', fontsize=16)\n", "plt.xlabel('$x$', fontsize=16)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
hcp4715/AnalyzingExpData	HDDM/Run_models_3.ipynb	2	74904	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Note:\n", "This script is for parallel processing the HDDM processing for '5', which share the same experimental design." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Notebook is running: /home/hcp4715/miniconda3/envs/py_ddm/bin/python\n", "The current HDDM version is 3.7.7\n", "The current HDDM version is 0.8.0\n", "The current IPython version is 7.13.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/hcp4715/miniconda3/envs/py_ddm/lib/python3.7/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated since IPython 4.0. You should import from ipyparallel instead.\n", " \"You should import from ipyparallel instead.\", ShimWarning)\n" ] } ], "source": [ "import sys\n", "import hddm, IPython\n", "print('Notebook is running:', sys.executable)\n", "from platform import python_version # further check your python version\n", "print('The current HDDM version is', python_version())\n", "print('The current HDDM version is', hddm.__version__) # 0.8.0\n", "print('The current IPython version is', IPython.__version__) " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Preparation\n", "import os, hddm, time, csv\n", "import kabuki\n", "from kabuki.analyze import gelman_rubin\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from datetime import date\n", "import random" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# define a function to run model in parallel for experiments share the same design\n", "def run_model_5(id):\n", " import hddm\n", " \n", " exp_name = '5'\n", " print('running models %i'%id, 'for for exp', exp_name)\n", " dbname = 'df' + exp_name + '_chain_vtaz_%i.db'%id # define the database name, which uses pickle format\n", " mname = 'df' + exp_name + '_chain__vtaz_%i'%id # define the name for the model\n", " fname = 'df' + exp_name + '.v.hddm_stim.csv'\n", " df = hddm.load_csv(fname)\n", " m = hddm.HDDMStimCoding(df,\n", " include='z', \n", " stim_col='stim', \n", " depends_on={'v':['match','val','domain'], 't':['match','val','domain'],'a':['match','val','domain']},\n", " split_param='v', \n", " drift_criterion=False,\n", " p_outlier=.05)\n", " m.find_starting_values()\n", " m.sample(10000, burn=5000, thin=5, dbname=dbname, db='pickle')\n", " m.save(mname) # save the model\n", " return m" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "run model for exp 5\n", "Running 4 chains for exp 5 used: 69315.499958 seconds.\n" ] }, { "ename": "NameError", "evalue": "name 'gelman_rubin' 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-4-acb8e539ad0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Running 4 chains for exp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"used: %f seconds.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\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 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mgelman_rubin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_models\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'gelman_rubin' is not defined" ] } ], "source": [ "start_time = time.time() # the start time of the processing#\n", "print('\\nrun model for exp', '5')\n", "from ipyparallel import Client\n", "rc = Client()\n", "jobs = rc[8:12].map(run_model_5, range(4)) # 4 is the number of chains\n", "df_models = jobs.get()\n", "print(\"Running 4 chains for exp\", '5', \"used: %f seconds.\" % (time.time() - start_time))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'a(Emotion.Match.Bad)': 1.0000969463420162,\n", " 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1.000398112653179,\n", " 'a_subj(Scene.Mismatch.Good).5236': 1.0001865570208879,\n", " 'a_subj(Scene.Mismatch.Neutral).5236': 0.9997746458383434,\n", " 'a_subj(Scene.Mismatch.Bad).5237': 0.9995901212368137,\n", " 'a_subj(Scene.Mismatch.Good).5237': 1.0017645181319803,\n", " 'a_subj(Scene.Mismatch.Neutral).5237': 0.9999941199046442,\n", " 'a_subj(Scene.Mismatch.Bad).5238': 0.9995291649169457,\n", " 'a_subj(Scene.Mismatch.Good).5238': 1.0008533970858804,\n", " 'a_subj(Scene.Mismatch.Neutral).5238': 1.0002104238218128,\n", " 'a_subj(Scene.Mismatch.Bad).5239': 1.0008753433418824,\n", " 'a_subj(Scene.Mismatch.Good).5239': 0.9998739570834915,\n", " 'a_subj(Scene.Mismatch.Neutral).5239': 1.000328608482655,\n", " 'a_subj(Scene.Mismatch.Bad).5240': 0.9997225985410695,\n", " 'a_subj(Scene.Mismatch.Good).5240': 1.0002306881140965,\n", " 'a_subj(Scene.Mismatch.Neutral).5240': 0.999983095388324,\n", " 'a_subj(Scene.Mismatch.Bad).5242': 0.9997978094406755,\n", " 'a_subj(Scene.Mismatch.Good).5242': 0.999910667784461,\n", " 'a_subj(Scene.Mismatch.Neutral).5242': 1.0001118536159532,\n", " 'v(Emotion.Match.Bad)': 1.0000642958945962,\n", " 'v(Emotion.Match.Good)': 0.9997753464117928,\n", " 'v(Emotion.Match.Neutral)': 1.0000540982542212,\n", " 'v(Emotion.Mismatch.Bad)': 1.000076222854999,\n", " 'v(Emotion.Mismatch.Good)': 0.9995483216814139,\n", " 'v(Emotion.Mismatch.Neutral)': 0.9999846070541725,\n", " 'v(Morality.Match.Bad)': 1.0012380679290493,\n", " 'v(Morality.Match.Good)': 1.0019767961599277,\n", " 'v(Morality.Match.Neutral)': 1.0009601724459953,\n", " 'v(Morality.Mismatch.Bad)': 1.0000779422373367,\n", " 'v(Morality.Mismatch.Good)': 1.0009538304220178,\n", " 'v(Morality.Mismatch.Neutral)': 0.9996300902503424,\n", " 'v(Person.Match.Bad)': 0.9999146719797574,\n", " 'v(Person.Match.Good)': 0.999604770192551,\n", " 'v(Person.Match.Neutral)': 0.9999462667903255,\n", " 'v(Person.Mismatch.Bad)': 1.001399069239444,\n", " 'v(Person.Mismatch.Good)': 0.999851933510106,\n", " 'v(Person.Mismatch.Neutral)': 1.0001004925679902,\n", " 'v(Scene.Match.Bad)': 0.9996111989730565,\n", " 'v(Scene.Match.Good)': 1.0019511947419726,\n", " 'v(Scene.Match.Neutral)': 0.9995749259245608,\n", " 'v(Scene.Mismatch.Bad)': 0.999966938662014,\n", " 'v(Scene.Mismatch.Good)': 0.999571124867881,\n", " 'v(Scene.Mismatch.Neutral)': 0.9997154981711647,\n", " 'v_std': 1.0013451427906408,\n", " 'v_subj(Emotion.Match.Bad).5201': 1.000134312364855,\n", " 'v_subj(Emotion.Match.Good).5201': 1.0000032524503724,\n", " 'v_subj(Emotion.Match.Neutral).5201': 1.0002401896887878,\n", " 'v_subj(Emotion.Match.Bad).5202': 1.0006606278448817,\n", " 'v_subj(Emotion.Match.Good).5202': 1.001248409168158,\n", " 'v_subj(Emotion.Match.Neutral).5202': 1.0040647572035555,\n", " 'v_subj(Emotion.Match.Bad).5203': 0.9998604954685973,\n", " 'v_subj(Emotion.Match.Good).5203': 0.9999784999232918,\n", " 'v_subj(Emotion.Match.Neutral).5203': 1.0000166559852375,\n", " 'v_subj(Emotion.Match.Bad).5204': 0.9998059448692529,\n", " 'v_subj(Emotion.Match.Good).5204': 0.9996836574021986,\n", " 'v_subj(Emotion.Match.Neutral).5204': 1.0003563016288832,\n", " 'v_subj(Emotion.Match.Bad).5205': 1.0002296407486435,\n", " 'v_subj(Emotion.Match.Good).5205': 1.0017218436393578,\n", " 'v_subj(Emotion.Match.Neutral).5205': 1.0000086303787439,\n", " 'v_subj(Emotion.Match.Bad).5206': 1.0002166941163855,\n", " 'v_subj(Emotion.Match.Good).5206': 0.999885179031082,\n", " 'v_subj(Emotion.Match.Neutral).5206': 1.0000206999360706,\n", " 'v_subj(Emotion.Match.Bad).5207': 0.9999968331666932,\n", " 'v_subj(Emotion.Match.Good).5207': 1.0000734230996908,\n", " 'v_subj(Emotion.Match.Neutral).5207': 0.9999079097438379,\n", " 'v_subj(Emotion.Match.Bad).5208': 1.0004406069772365,\n", " 'v_subj(Emotion.Match.Good).5208': 0.9996911942780717,\n", " 'v_subj(Emotion.Match.Neutral).5208': 0.9998758970533006,\n", " 'v_subj(Emotion.Match.Bad).5209': 1.001067423208293,\n", " 'v_subj(Emotion.Match.Good).5209': 0.9998512796566277,\n", " 'v_subj(Emotion.Match.Neutral).5209': 0.9997216505495274,\n", " 'v_subj(Emotion.Match.Bad).5210': 1.0000498802343862,\n", " 'v_subj(Emotion.Match.Good).5210': 0.9997078110758287,\n", " 'v_subj(Emotion.Match.Neutral).5210': 1.0005757903959043,\n", " 'v_subj(Emotion.Match.Bad).5211': 0.9998000255389046,\n", " 'v_subj(Emotion.Match.Good).5211': 1.0004657449147885,\n", " 'v_subj(Emotion.Match.Neutral).5211': 1.0003954720697095,\n", " 'v_subj(Emotion.Match.Bad).5212': 1.0005776868089047,\n", " 'v_subj(Emotion.Match.Good).5212': 0.9996856594528766,\n", " 'v_subj(Emotion.Match.Neutral).5212': 0.9995153254032547,\n", " 'v_subj(Emotion.Match.Bad).5213': 1.0006265518866364,\n", " 'v_subj(Emotion.Match.Good).5213': 0.999604892623801,\n", " ...}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gelman_rubin(df_models)" ] } ], "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }	cc0-1.0
augustot2/HPFEM.jl	augusto/lifting HPFEM-Copy1.ipynb	1	4346	{ "cells": [ { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: replacing module HPFEM\n" ] }, { "data": { "text/plain": [ "HPFEM" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include(\"HPFEM.jl\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Jacobi\n", "using PyPlot" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ψj (generic function with 1 method)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function ψj(p,E,Q)\n", " if(p == 1)\n", " return (1-E)/2\n", " elseif(p == 2)\n", " return (1+E)/2\n", " else\n", " return (1-E)(1+E)/4 . jacobi(E, p-3, 1, 1)\n", " end\n", "end " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ϕ_matrix (generic function with 1 method)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function ϕ_matrix(ψj,Q,M)\n", " ϕ = zeros(Q,M)\n", " ξ = zglj(Q)\n", " for i in 1:M\n", " for j in 1:Q\n", " ϕ[j,i] = ψj(i,ξ[j],M)\n", " end\n", " end\n", " return ϕ\n", "end" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "integrateϕ (generic function with 1 method)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function integrateϕ(w,Q,i,j) \n", " m= 0.0\n", " for q in 1:Q\n", " m = m + ϕ[q,i]ϕ[q,j]w[q]\n", " end\n", " \n", " return m\n", "end" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2x2 Array{Float64,2}:\n", " 0.0 0.0\n", " 0.0 0.0" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrateϕ(w,10,2,2)\n", "\n", "Ahh = zeros(2,2)\n", "for i in 1:2\n", " for j in 1:2\n", " Ahh[i,j] = \n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2x2 Array{Int64,2}:\n", " 3 1\n", " 1 2" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "#elemento\n", "b = [1,2] #modos de fronteira\n", "i = [3] #modos internos\n", "lnum = HPFEM.LocalNumSys1d(b,i)\n", "Nel = 2\n", "nnodes = Nel + 1\n", "\n", "idir = [1]\n", "\n", "\n", "dof_map = zeros(Int, 2, Nel)\n", "for i = 1:Nel\n", " dof_map[1,i] = i\n", " dof_map[2,i] = i+1\n", "end\n", " \n", "ii = [nnodes;1:(nnodes-1);]\n", "nb = Nel+1\n", "nd = 2\n", "for e in 1:Nel\n", " dof_map[1,e] = ii[e]\n", " dof_map[2,e] = ii[e+1] \n", "end\n", "dof_map" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
wonkoderverstaendige/telemetry	doc/PlotTelemetryData.ipynb	1	43306	{ "cells": [ { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import sqlite3\n", "import time\n", "import sys\n", "import os\n", "sys.path.append('..')\n", "from datetime import datetime\n", "from util.Stopwatch import Stopwatch\n", "import socket\n", "%matplotlib inline\n", "\n", "DB_PATH = '../local/db/telemetry.db'\n", "assert(os.path.exists(DB_PATH))\n", "IMG_OUT = 'plot_repair.png'\n", "HOST = 'chuck'\n", "LOCAL_HOSTNAME = socket.gethostname()\n", "\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def get_sensors(db_path, host):\n", " with sqlite3.connect(db_path) as con:\n", " cur = con.cursor()\n", " cur.execute(\"SELECT * FROM sensors WHERE host='{}';\".format(host))\n", " sensors = {s[2]: s[0] for s in cur.fetchall()}\n", " return sensors\n", "\n", "sensors = get_sensors(DB_PATH, HOST)\n", "sensors_rev = {v:k for k, v in sensors.iteritems()}" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def read_df(db_path, host=None, sw=None, from_id=0, deduplicate=False, table='telemetry', immediate=False, *kwargs):\n", " # TODO: incremental reads\n", " # TODO: Select only current host\n", " if sw is None:\n", " sw = Stopwatch('Dataframe loading')\n", "\n", " with sqlite3.connect(db_path) as con:\n", " if not immediate:\n", " df = pd.read_sql_query('SELECT FROM {table} where id>{id};'.format(table=table, id=from_id), con)\n", " else:\n", " df = pd.read_sql_query('SELECT * FROM {table} where id>{id};'.format(table=table, id=from_id), con,\n", " index_col='timestamp', parse_dates={'timestamp': {'unit':'s'}})\n", " sw.event('SQL raw data into df' + '' if not immediate else 'with timestamp to dates')\n", "\n", " # timestamps as datetime array\n", " if not immediate:\n", " df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')\n", " sw.event('convert timestamps to datetime')\n", " \n", " # type as category (sensor type)\n", " df['type'] = df['type'].astype('category')\n", " newcats = [sensors_rev[c] for c in df['type'].cat.categories]\n", " df['type'].cat.categories = newcats\n", " sw.event('type as category')\n", " \n", " #return df\n", " if deduplicate:\n", " df.drop_duplicates(subset=['timestamp', 'type'], inplace=True)\n", " sw.event('De-duplication')\n", "\n", " pivoted, _ = prepare_df(df, sw, kwargs)\n", " \n", " return pivoted, sw" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def prepare_df(df, sw=None, resample=False):\n", " if sw is None:\n", " sw = Stopwatch('Pivot and adjust data frame')\n", " \n", " pivoted = df.pivot(index = 'timestamp', columns='type', values='value') \\\n", " .tz_localize('UTC').tz_convert('Europe/Amsterdam')\n", " sw.event('pivot table')\n", " \n", " if resample:\n", " pivoted = pivoted.resample(resample, how='mean')\n", " sw.event('resampling')\n", "\n", " # adjust time zone\n", " pivoted = pivoted\n", " sw.event('adjust timezone')\n", "\n", " # Get rid of false temp values\n", " pivoted.temp[(pivoted['light'] > 250) & (pivoted.index < '2016-03-10 07:03:57.603722+01:00')] = np.NaN\n", " sw.event('Removing false temp values')\n", " \n", " return pivoted, sw" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def make_plot(db_path, host, sw=None, kwargs):\n", " if sw is None:\n", " sw = Stopwatch('Make Plot')\n", " \n", " df, sw = read_df(db_path, host, sw=sw, *kwargs)\n", " plot(df, sw=sw)\n", " \n", " return sw" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Timings for Make Plot\n", "2.64s (2.64s): SQL raw data into df\n", "4.54s (1.90s): convert timestamps to datetime\n", "4.56s (0.02s): type as category\n", "5.28s (0.72s): pivot table\n", "5.38s (0.10s): resampling\n", "5.38s (0.00s): adjust timezone\n", "5.38s (0.00s): Removing false temp values\n", "5.63s (0.25s): plot pivoted dataframe\n", "5.64s (0.01s): annotate axes\n", "6.87s (1.23s): save plot file\n" ] } ], "source": [ "def plot(df, sw=None, width=None):\n", " if sw is None:\n", " sw = Stopwatch('Plotting')\n", "\n", " # Throwing the data into a plot\n", " axes = df.plot(secondary_y=['temp', 'ds_temp'], mark_right=False, style=['r', 'g', 'b', 'c'])\n", " sw.event('plot pivoted dataframe')\n", "\n", " fig = plt.gcf()\n", " \n", " # limits\n", " axes.set_ylim((0, 1024))\n", " axes.right_ax.set_ylim((10, 40))\n", " \n", " # Grid lines and ticks\n", " rticks = axes.right_ax.get_yticks()\n", " rmin, rmax = min(rticks), max(rticks)\n", " lmin, lmax = axes.get_ylim()\n", " lrange = lmax - lmin\n", " rrange = rmax - rmin\n", " factor = lrange/rrange\n", " axes.set_yticks([(t-rmin)factor for t in rticks])\n", " axes.set_yticklabels([])\n", " \n", " axes.right_ax.grid([])\n", " axes.grid()\n", " \n", " # Fiddling with axis labeling\n", " axes.set_ylabel(u'Raw ADC values')\n", " axes.right_ax.set_ylabel(u'Temperature °C')\n", "\n", " axes.set_xlabel('') \n", " fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')\n", "\n", " # Messing with the legend\n", " axes.legend(loc='upper left', shadow=True, fontsize='x-large')\n", " axes.right_ax.legend(loc='upper right', shadow=True, fontsize='x-large')\n", "\n", " # Annotation at bottom\n", " elapsed = sw.elapsed()\n", " axes.annotate('in {elapsed:.1f} s on {hostname}, {timestamp}'.format(elapsed=elapsed,\n", " hostname=LOCAL_HOSTNAME,\n", " timestamp=datetime.now().strftime('%Y-%m-%d %H:%M'),),\n", " xy=(1, 0), xycoords='axes fraction', fontsize=10, xytext=(0, -55),\n", " textcoords='offset points', ha='right', va='top')\n", " sw.event('annotate axes')\n", "\n", " # resampled to 6 minutes, 240 points per day\n", " # 1 inch = 2.54 cm, * 0.944881889764 = 2.4cm = .1mm/data point * number of days plotted\n", " if width is None:\n", " delta = (df.index[-1] - df.index[0]) / pd.Timedelta('1 Day')\n", " width = delta0.944881889764\n", "\n", " fig.set_size_inches(width, 5.5)\n", " fig.savefig('plot.png', dpi=150, transparent=False, bbox_inches='tight', pad_inches=0)\n", " sw.event('save plot file')\n", " plt.close();\n", " return sw\n", "\n", "timings = make_plot(DB_PATH, HOST, resample='6min')\n", "timings.report()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "receiving incremental file list\n", "telemetry.db\n", " 69.50M 100% 13.53MB/s 0:00:04 (xfr#1, to-chk=0/1)\n", "\n", "sent 56.17K bytes received 5.38M bytes 473.09K bytes/sec\n", "total size is 69.50M speedup is 12.78\n" ] } ], "source": [ "!rsync -avh --progress VersedSquid:~/code/telemetry/db/telemetry.db ../local/db/\n", "#timings = make_plot(DB_PATH, HOST, resample='5min', table='telemetry', from_id=int(1.2e6))\n", "#timings.report()\n", "#!scp plot.png lychnobite.me:~/projects/static/" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "need more than 2 values to unpack", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-26-f92d302058a4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDB_PATH\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHOST\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mValueError\u001b[0m: need more than 2 values to unpack" ] } ], "source": [ "p, s, d = read_df(DB_PATH, HOST)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Id type value\n", "timestamp \n", "2016-02-12 17:50:43.231437 1 3 NaN\n", "2016-02-12 17:50:43.231437 2 2 116.00\n", "2016-02-12 17:50:43.231437 3 1 25.38\n", "2016-02-12 17:50:48.292462 4 3 NaN\n", "2016-02-12 17:50:48.292462 5 2 114.62\n", "1 loops, best of 3: 986 ms per loop\n" ] } ], "source": [ "with sqlite3.connect(DB_PATH) as con:\n", " df = pd.read_sql_query('SELECT FROM {table} where id>{id};'.format(table='telemetry', id=0), con,\n", " index_col='timestamp', parse_dates={'timestamp': {'unit':'s'}})\n", "print df.head()\n", "#df.pivot(index='timestamp', columns='type', values='value')\n", "%timeit df.value[df.type==1]" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.42 s per loop\n" ] }, { "ename": "KeyError", "evalue": "'timestamp'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-135-eb4e309aaacf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#%timeit make_plot(DB_PATH, HOST, resample='30Min')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu\"timeit make_plot(DB_PATH, HOST, resample='6min')\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu\"timeit make_plot(DB_PATH, HOST, resample='6min', immediate=True)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/reichler/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[1;34m(self, arg_s)\u001b[0m\n\u001b[0;32m 2305\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2306\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2307\u001b[1;33m 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key)\u001b[0m\n\u001b[0;32m 1795\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1796\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1797\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1799\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/reichler/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1802\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1803\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1804\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/reichler/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1082\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1083\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1084\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1085\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m 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method)\u001b[0m\n\u001b[0;32m 1570\u001b[0m \"\"\"\n\u001b[0;32m 1571\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1572\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\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 1573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1574\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3824)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3704)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'timestamp'" ] } ], "source": [ "#%timeit make_plot(DB_PATH, HOST, resample=False)\n", "#%timeit make_plot(DB_PATH, HOST, resample='30Min')\n", "%timeit make_plot(DB_PATH, HOST, resample='6min')\n", "%timeit make_plot(DB_PATH, HOST, resample='6min', immediate=True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "receiving incremental file list\n", "./\n", "2016_w13-chuck.csv\n", "chuck_2016-03-28.csv\n", "\n", "sent 503 bytes received 17.91K bytes 12.28K bytes/sec\n", "total size is 67.80K speedup is 3.68\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/reichler/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:115: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._setitem_with_indexer(indexer, value)\n" ] }, { "ename": "NameError", "evalue": "name 'spans' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-31-bead4cd609e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;31m# night background\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mspan\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mspans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\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 17\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxvspan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspan\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspan\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'0.2'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'spans' is not defined" ] }, { "data": { "image/png": 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}, "output_type": "display_data", "metadata": {} } ], "source": [ "!rsync -avh VersedSquid:~/code/telemetry/local/db/ ../local/db/\n", "df = pd.read_csv('../local/db/chuck_2016-03-28.csv', names=['timestamp', 'type', 'value'])\n", "df.timestamp = df.timestamp.astype('datetime64[s]')\n", "df.set_index('timestamp', inplace=True)\n", "df = df.drop(['timestamp'])\n", "df = df.tz_localize('UTC').tz_convert('Europe/Amsterdam')\n", "df.type.loc[df.type=='ds_temp'] = 1\n", "df.type.loc[df.type=='light'] = 2\n", "df.type.loc[df.type=='soil'] = 3;\n", "df.type.astype('uint8');\n", "\n", "fig = plt.figure()\n", "axes = fig.add_subplot(111)\n", "\n", "# night background\n", "for span in spans(df):\n", " plt.axvspan(span[0], span[1], facecolor='0.2', alpha=0.3)\n", "\n", "groups = df.groupby('type')\n", "groups.get_group(2).value.resample('6min').plot(ax=axes, style='k', label=r'light')\n", "groups.get_group(3).value.plot(ax=axes, style='b', label=r'$\\theta_{soil}$')\n", "\n", "axes.set_ylim((0, 1023))\n", "# handles, labels = axes.get_legend_handles_labels()\n", "# handles.append(ep_artist)\n", "# axes.legend(handles=handles, loc=2)\n", "axes.legend(loc=2)\n", "axes.set_xlabel('')\n", "axes.set_yticks([])\n", "axes.set_ylabel(u'raw ADC values')\n", "\n", "# temp\n", "axes_r = axes.twinx()\n", "groups.get_group(1).value.plot(ax=axes_r, style='r', label=r'$T_{ambient}$')\n", "\n", "axes_r.set_ylim((15, 35))\n", "axes_r.set_ylabel(u'Temperature (°C)')\n", "axes_r.legend()\n", "\n", "\n", "delta = (df.index[-1] - df.index[0]) / pd.Timedelta('1 Day')\n", "width = delta0.944881889764*8\n", "\n", "fig.set_size_inches(width, 5)" ] }, { "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": 2.0 }, "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
jbwhit/jupyter-best-practices	notebooks/06-Interactive-Splines.ipynb	2	10343	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-06-02T00:23:23.485630Z", "start_time": "2020-06-02T00:23:21.437985Z" }, "execution_event_id": "aa730533-01d9-4a27-b437-667bd82f9205", "last_executed_text": "%matplotlib inline\n\nfrom matplotlib.collections import LineCollection\nimport matplotlib.pyplot as plt\nfrom scipy import interpolate\nimport numpy as np\nfrom numpy.random import rand\n\nfrom ipywidgets import FloatSlider, interactive, IntSlider", "persistent_id": "1711e28e-fa0a-43f2-95e9-a9e952504584" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import functools\n", "from matplotlib.collections import LineCollection\n", "import matplotlib.pyplot as plt\n", "from scipy import interpolate\n", "import numpy as np\n", "from numpy.random import rand\n", "\n", "from ipywidgets import FloatSlider, interactive, IntSlider\n", "import seaborn as sns\n", "sns.set_context('poster')\n", "sns.set_style('whitegrid') \n", "# sns.set_style('darkgrid') \n", "plt.rcParams['figure.figsize'] = 12, 8 # plotsize \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-06-02T00:23:45.730773Z", "start_time": "2020-06-02T00:23:45.723478Z" }, "execution_event_id": "ebd63c20-b897-4129-8ca0-05611db607bd", "last_executed_text": "def simple_example(amplitude=2.0, phase=0.0):\n plt.rcParams['figure.figsize'] = 8,6\n plt.figure()\n x = np.linspace(-2np.pi, 2np.pi, 1000)\n y = amplitude * np.sin(x + phase)\n plt.plot(x, y)\n plt.xlim(-3, 3)\n plt.ylim(-2np.pi, 2np.pi)\n plt.show()\n return", "persistent_id": "f15cc174-d5c7-4e0a-978d-5739a5bacd39" }, "outputs": [], "source": [ "def simple_example(amplitude=2.0, phase=0.0):\n", " \"\"\"Simple sin function.\"\"\"\n", " x = np.linspace(-2 * np.pi, 2 * np.pi, 1000)\n", " y = amplitude * np.sin(x - phase)\n", "\n", " fig, ax = plt.subplots(figsize=(8, 6))\n", " fig.patch.set_facecolor(\"w\")\n", " ax.plot(x, y, label=\"Example\")\n", " ax.set_xlim(-3, 3)\n", " ax.set_ylim(-2 * np.pi, 2 * np.pi)\n", " ax.axhline(0, color=\"k\")\n", " ax.legend(loc=\"best\")\n", " ax.set_xlabel(\"x label\")\n", " ax.set_ylabel(\"y label\")\n", " fig.tight_layout()\n", " return fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-06-02T00:23:46.671973Z", "start_time": "2020-06-02T00:23:46.165694Z" }, "execution_event_id": "a147b3d0-976d-475a-b783-26ef4342429e", "last_executed_text": "amplitude_slider = FloatSlider(value=2.0, min=0, max=6.0, step=.1)\nphase_slider = FloatSlider(value=0.0, min=-np.pi, max=np.pi, step=.10)\n\ninteractive(simple_example,\n amplitude=amplitude_slider,\n phase=phase_slider\n )", "persistent_id": "daeb2423-4a57-4633-b39d-7fc846aca80f" }, "outputs": [], "source": [ "amplitude_slider = FloatSlider(value=2.0, min=0, max=6.0, step=.1)\n", "phase_slider = FloatSlider(value=0.0, min=-np.pi, max=np.pi, step=.10)\n", "\n", "interactive(simple_example,\n", " amplitude=amplitude_slider,\n", " phase=phase_slider\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "history": [ { "cell": { "executionCount": 1, "executionEventId": "aa730533-01d9-4a27-b437-667bd82f9205", "hasError": false, "id": "139fe120-6698-4380-a9cc-6187284fb054", "outputs": [], "persistentId": "1711e28e-fa0a-43f2-95e9-a9e952504584", "text": "%matplotlib inline\n\nfrom matplotlib.collections import LineCollection\nimport matplotlib.pyplot as plt\nfrom scipy import interpolate\nimport numpy as np\nfrom numpy.random import rand\n\nfrom ipywidgets import FloatSlider, interactive, IntSlider" }, "executionTime": "2019-09-09T21:58:47.473Z" }, { "cell": { "executionCount": 2, "executionEventId": "ebd63c20-b897-4129-8ca0-05611db607bd", "hasError": false, "id": "64c4ebdd-f6a2-48a2-8db0-723881391937", "outputs": [], "persistentId": "f15cc174-d5c7-4e0a-978d-5739a5bacd39", "text": "def simple_example(amplitude=2.0, phase=0.0):\n plt.rcParams['figure.figsize'] = 8,6\n plt.figure()\n x = np.linspace(-2np.pi, 2np.pi, 1000)\n y = amplitude * np.sin(x + phase)\n plt.plot(x, y)\n plt.xlim(-3, 3)\n plt.ylim(-2np.pi, 2np.pi)\n plt.show()\n return" }, "executionTime": "2019-09-09T21:58:48.154Z" }, { "cell": { "executionCount": 3, "executionEventId": "a147b3d0-976d-475a-b783-26ef4342429e", "hasError": false, "id": "f233cb84-ee17-4e69-9873-2a7b827a3f0d", "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "49b241ffa49842bda80e67662bfb47ea", "version_major": 2, "version_minor": 0 }, "text/plain": "interactive(children=(FloatSlider(value=2.0, description='amplitude', max=6.0), FloatSlider(value=0.0, descrip…" }, "metadata": {}, "output_type": "display_data" } ], "persistentId": "daeb2423-4a57-4633-b39d-7fc846aca80f", "text": "amplitude_slider = FloatSlider(value=2.0, min=0, max=6.0, step=.1)\nphase_slider = FloatSlider(value=0.0, min=-np.pi, max=np.pi, step=.10)\n\ninteractive(simple_example,\n amplitude=amplitude_slider,\n phase=phase_slider\n )" }, "executionTime": "2019-09-09T21:58:49.282Z" }, { "cell": { "executionCount": 4, "executionEventId": "de398124-5310-4cc5-a752-5c004c7a6bfc", "hasError": false, "id": "f09757c6-3179-4688-9ea3-086cfbea5121", "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "113f938420b34ef59fe01c107d64ad0e", "version_major": 2, "version_minor": 0 }, "text/plain": "interactive(children=(IntSlider(value=8, description='num', max=20, min=4), FloatSlider(value=0.0, description…" }, "metadata": {}, "output_type": "display_data" } ], "persistentId": "c2809b5d-f933-41eb-9ca5-cac9b6283487", "text": "def spline_demo(num=14, smooth=0, seed=10, brush_strokes=30, alpha=0.5):\n a = np.random.RandomState(seed=seed)\n x = a.rand(num)\n y = a.rand(num)\n t = np.arange(0, 1.1, .1)\n plt.rcParams['figure.figsize'] = 8, 8\n plt.figure()\n for brush_stroke in range(brush_strokes):\n tck, u = interpolate.splprep(\n [x + a.rand(num) / 10.0, y + a.rand(num) / 10.0], s=smooth)\n unew = np.arange(0, 1.01, 0.001)\n out = interpolate.splev(unew, tck)\n plt.plot(out[0], out[1], alpha=alpha, c='black', linewidth=3.0)\n plt.xlim(-1.5, 2.)\n plt.ylim(-1.5, 2.)\n plt.axis('off')\n plt.show()\n\n\nsmooth_slider = FloatSlider(value=0, min=0, max=20.0, step=.1)\nnum_points_slider = IntSlider(value=8, min=4, max=20)\nseed_slider = IntSlider(value=4, min=4, max=20)\nbrush_slider = IntSlider(value=1, min=1, max=20)\nalpha_slider = FloatSlider(value=.5, min=0, max=1.0, step=.05)\n\nw = interactive(\n spline_demo,\n smooth=smooth_slider,\n num=num_points_slider,\n seed=seed_slider,\n brush_strokes=brush_slider,\n alpha=alpha_slider)\nw" }, "executionTime": "2019-09-09T21:58:56.425Z" } ], "kernelspec": { "display_name": "dspy3", "language": "python", "name": "dspy3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.11" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "12px", "width": "252px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "uuid": "c4ab12b9-2054-4f48-939f-a7536e39dd2e", "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 }, "widgets": { "state": { "2d5c1a38b6fa4f8cb0fc51837d1a1096": { "views": [ { "cell_index": 2 } ] }, "2f0955e5054942bf9031854eaea14d2a": { "views": [ { "cell_index": 3 } ] }, "5205ad7666ac454c8dededcc76c0eae7": { "views": [ { "cell_index": 7 } ] }, "859981a099934515887f41069f22d992": { "views": [ { "cell_index": 8 } ] }, "9a8f2c644d434e69b76ac69358781a9f": { "views": [ { "cell_index": 7 } ] }, "bf8ee1c1ae2f4b69bd83960aac95c562": { "views": [ { "cell_index": 8 } ] }, "e0baa1ed1cec4014b008642cb9e7ac18": { "views": [ { "cell_index": 8 } ] }, "f2754076a4014a358fa7065d2a3da65d": { "views": [ { "cell_index": 7 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
drcjar/pypf	notebooks/.ipynb_checkpoints/pypf_maps-checkpoint.ipynb	1	13156	{ "metadata": { "name": "", "signature": "sha256:4330dc77f1ee8d13d92a02a88c6650aef5af1824cc0953bad2ded14ee83d4137" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import pandas as pd\n", "from pandas import DataFrame\n", "import pickle\n", "import folium\n", "from IPython import display\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_pickle('.././data/pickle/pypf_prep.pkl')\n", "regionlookup = pd.read_csv('.././data/maps/oldnewlookupregions.csv', names=['GOR10CD', 'Area_code', 'Region'])\n", "regionlookup['Region'] = regionlookup['Region'].str.upper()\n", "regionlookup['Region'] = regionlookup['Region'].str.strip()\n", "regionlookup['Region'] = regionlookup['Region'].str.replace('EAST OF ENGLAND', 'EAST')\n", "#or_geo = '.././data/maps/gor.json'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "maps = {}\n", "\n", "for cause in df.Cause.unique():\n", " grp = df[(df['Cause'] == cause) & (df['Sex'] == 'Male')].groupby('Region')['Rate per 100,000 (standardised)'].mean()\n", " grp = DataFrame(grp).reset_index()\n", " maps[cause] = pd.merge(grp, regionlookup, on='Region')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "maps['All Mesothelioma']" ], "language": "python", "metadata": {}, "outputs": [ { "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>Region</th>\n", " <th>Rate per 100,000 (standardised)</th>\n", " <th>GOR10CD</th>\n", " <th>Area_code</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> EAST</td>\n", " <td> 7.861819</td>\n", " <td> E12000006</td>\n", " <td> C000G</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> EAST MIDLANDS</td>\n", " <td> 6.482373</td>\n", " <td> E12000004</td>\n", " <td> C000E</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> LONDON</td>\n", " <td> 8.220906</td>\n", " <td> E12000007</td>\n", " <td> C000H</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> NORTH EAST</td>\n", " <td> 17.543943</td>\n", " <td> E12000001</td>\n", " <td> C000A</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> NORTH WEST</td>\n", " <td> 8.965068</td>\n", " <td> E12000002</td>\n", " <td> C000B</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> SOUTH EAST</td>\n", " <td> 9.205414</td>\n", " <td> E12000008</td>\n", " <td> C000J</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> SOUTH WEST</td>\n", " <td> 7.618154</td>\n", " <td> E12000009</td>\n", " <td> C000K</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> WALES</td>\n", " <td> 6.184247</td>\n", " <td> W92000004</td>\n", " <td> A0004</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> WEST MIDLANDS</td>\n", " <td> 6.525374</td>\n", " <td> E12000005</td>\n", " <td> C000F</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> YORKSHIRE AND THE HUMBER</td>\n", " <td> 8.646171</td>\n", " <td> E12000003</td>\n", " <td> C000D</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ " Region Rate per 100,000 (standardised) GOR10CD \\\n", "0 EAST 7.861819 E12000006 \n", "1 EAST MIDLANDS 6.482373 E12000004 \n", "2 LONDON 8.220906 E12000007 \n", "3 NORTH EAST 17.543943 E12000001 \n", "4 NORTH WEST 8.965068 E12000002 \n", "5 SOUTH EAST 9.205414 E12000008 \n", "6 SOUTH WEST 7.618154 E12000009 \n", "7 WALES 6.184247 W92000004 \n", "8 WEST MIDLANDS 6.525374 E12000005 \n", "9 YORKSHIRE AND THE HUMBER 8.646171 E12000003 \n", "\n", " Area_code \n", "0 C000G \n", "1 C000E \n", "2 C000H \n", "3 C000A \n", "4 C000B \n", "5 C000J \n", "6 C000K \n", "7 A0004 \n", "8 C000F \n", "9 C000D " ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "maps['IPF'].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "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>Rate per 100,000 (standardised)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 10.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 13.155275</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 1.394424</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 11.214911</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 12.187088</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 12.841522</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 14.435973</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 15.200417</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ " Rate per 100,000 (standardised)\n", "count 10.000000\n", "mean 13.155275\n", "std 1.394424\n", "min 11.214911\n", "25% 12.187088\n", "50% 12.841522\n", "75% 14.435973\n", "max 15.200417" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "map = folium.Map(location=[54.2, -2.45], zoom_start=5)\n", "map.geo_json(geo_path='gor.json', data_out='GorIPF.json', data=maps['IPF'],\n", " columns=['GOR10CD', 'Rate per 100,000 (standardised)'],\n", " key_on='feature.properties.GOR10CD',\n", " threshold_scale=[11, 12, 13, 14, 15],\n", " fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,\n", " legend_name='Male IPF deaths by region per 100,000 population (1975-2012)')\n", "map.create_map(path='IPF_death_per100000.html')\n", "\n", "map = folium.Map(location=[54.2, -2.45], zoom_start=5)\n", "map.geo_json(geo_path='gor.json', data_out='GorMES.json', data=maps['All Mesothelioma'],\n", " columns=['GOR10CD', 'Rate per 100,000 (standardised)'],\n", " key_on='feature.properties.GOR10CD',\n", " #%%! threshold_scale=[6, 7, 8, 9, 10, 16],\n", " fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,\n", " legend_name='Male Mesothelioma deaths by region per 100,000 population (1975-2012)')\n", "map.create_map(path='Mesothelioma_death_per100000.html')\n", "\n", "map = folium.Map(location=[54.2, -2.45], zoom_start=5)\n", "map.geo_json(geo_path='gor.json', data_out='GorASB.json', data=maps['Asbestosis'],\n", " columns=['GOR10CD', 'Rate per 100,000 (standardised)'],\n", " key_on='feature.properties.GOR10CD',\n", " # threshold_scale=[11, 12, 13, 14, 15],\n", " fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,\n", " legend_name='Male Asbestosis deaths by region per 100,000 population (1975-2012)')\n", "map.create_map(path='Asbestosis_death_per100000.html')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "display.IFrame('IPF_death_per100000.html', '100%', 500)\n" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"100%\"\n", " height=500\"\n", " src=\"IPF_death_per100000.html\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "<IPython.lib.display.IFrame at 0x3fd3610>" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "display.IFrame('Mesothelioma_death_per100000.html', '100%', 500)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"100%\"\n", " height=500\"\n", " src=\"Mesothelioma_death_per100000.html\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 67, "text": [ "<IPython.lib.display.IFrame at 0x3fd3510>" ] } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "display.IFrame('Asbestosis_death_per100000.html', '100%', 500)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"100%\"\n", " height=500\"\n", " src=\"Asbestosis_death_per100000.html\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 68, "text": [ "<IPython.lib.display.IFrame at 0x3fd3210>" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	agpl-3.0
arielgatech/py_for_engineers	pandas_basic.ipynb	1	161646	{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from pandas import read_csv\n", "import os\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID emissionQuant\n", "0 0 90 42 2 2017 18192.50\n", "1 1 90 42 2 2017 9065.73\n", "2 11 90 42 2 2017 11845.50\n", "3 12 90 42 2 2017 33361.00\n", "4 13 90 42 2 2017 58868.60\n" ] } ], "source": [ "os.chdir('C:/Users/arielxxd/Documents/GitHub/py_for_engineers') # access to the file directory \n", "sample_emission_df = read_csv('sample.csv', sep = ',') # load data as data frame\n", "print sample_emission_df.head() # print top 5 rows" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpModeID 1.00\n", "pollutantID 90.00\n", "sourceTypeID 42.00\n", "fuelTypeID 2.00\n", "modelYearID 2017.00\n", "emissionQuant 9065.73\n", "Name: 1, dtype: float64\n" ] } ], "source": [ "# Query data from dataframe, check the difference between iloc and loc here: https://pandas.pydata.org/pandas-docs/stable/indexing.html\n", "print sample_emission_df.loc[1]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "0 0 90 42 2 2017 \n", "24 0 90 42 3 2017 \n", "\n", " emissionQuant \n", "0 18192.50 \n", "24 9542.36 \n" ] } ], "source": [ "# select dataframe based on column value\n", "print sample_emission_df.loc[sample_emission_df['OpModeID']==0]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 18192.50\n", "24 9542.36\n", "Name: emissionQuant, dtype: float64\n" ] } ], "source": [ "# select one column based on another column value\n", "print sample_emission_df.loc[sample_emission_df['OpModeID']==0, 'emissionQuant']" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "0 0 90 42 2 2017 \n", "1 1 90 42 2 2017 \n", "2 11 90 42 2 2017 \n", "24 0 90 42 3 2017 \n", "25 1 90 42 3 2017 \n", "26 11 90 42 3 2017 \n", "\n", " emissionQuant \n", "0 18192.50 \n", "1 9065.73 \n", "2 11845.50 \n", "24 9542.36 \n", "25 7636.18 \n", "26 9998.75 \n" ] } ], "source": [ "# select dataframe if column values fall into a set\n", "target_opmode = [0, 1, 11]\n", "print sample_emission_df.loc[sample_emission_df['OpModeID'].isin(target_opmode)]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID emissionQuant\n", "3 12 90 42 2 2017 33361.0\n", "4 13 90 42 2 2017 58868.6\n", "5 14 90 42 2 2017 84858.5\n", "6 15 90 42 2 2017 106570.0\n", "7 16 90 42 2 2017 144355.0\n" ] } ], "source": [ "# select dataframe if column values NOT fall into a set\n", "target_opmode = [0, 1, 11]\n", "print sample_emission_df.loc[~sample_emission_df['OpModeID'].isin(target_opmode)].head()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "0 0 90 42 2 2017 \n", "1 1 90 42 2 2017 \n", "2 11 90 42 2 2017 \n", "3 12 90 42 2 2017 \n", "4 13 90 42 2 2017 \n", "\n", " emissionQuant rate \n", "0 18192.50 5.053472 \n", "1 9065.73 2.518258 \n", "2 11845.50 3.290417 \n", "3 33361.00 9.266944 \n", "4 58868.60 16.352389 \n" ] } ], "source": [ "# for most of time, you can manipulate the dataframe without a loop, let's see how we can do that\n", "sample_emission_df['rate'] = sample_emission_df['emissionQuant'] / 3600.0\n", "print sample_emission_df.head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "0 0 90 42 2 2017 \n", "1 1 90 42 2 2017 \n", "2 11 90 42 2 2017 \n", "3 12 90 42 2 2017 \n", "4 13 90 42 2 2017 \n", "\n", " emissionQuant rate rate_2 \n", "0 18192.50 5.053472 5.053472 \n", "1 9065.73 2.518258 2.518258 \n", "2 11845.50 3.290417 3.290417 \n", "3 33361.00 9.266944 9.266944 \n", "4 58868.60 16.352389 16.352389 \n" ] } ], "source": [ "# if you want to apply a function to each row, try this:\n", "def try_this_function(x):\n", " y = x/3600\n", " return y\n", "sample_emission_df['rate_2'] = sample_emission_df.apply(lambda row: try_this_function(row['emissionQuant']), axis=1)\n", "print sample_emission_df.head()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "0 0 90 42 2 2017 \n", "1 1 90 42 2 2017 \n", "2 11 90 42 2 2017 \n", "3 12 90 42 2 2017 \n", "4 13 90 42 2 2017 \n", "\n", " emissionQuant rate rate_2 new_rate \n", "0 18192.50 5.053472 5.053472 5.05347 \n", "1 9065.73 2.518258 2.518258 2.51826 \n", "2 11845.50 3.290417 3.290417 3.29042 \n", "3 33361.00 9.266944 9.266944 9.26694 \n", "4 58868.60 16.352389 16.352389 16.3524 \n" ] } ], "source": [ "# under rare conditions, you really need a loop, then you can try this:\n", "sample_emission_df['new_rate'] = None\n", "\n", "for index, rate in enumerate(sample_emission_df['emissionQuant']):\n", " sample_emission_df.loc[index, 'new_rate'] = rate / 3600.0\n", "print sample_emission_df.head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before: \n", "1 9065.73\n", "25 7636.18\n", "Name: emissionQuant, dtype: float64\n", "After: \n", "1 13598.595\n", "25 11454.270\n", "Name: emissionQuant, dtype: float64\n" ] } ], "source": [ "# if you only want to change selected values, then try this\n", "whatever_factor = 1.5\n", "print \"Before: \" \n", "print sample_emission_df.loc[sample_emission_df['OpModeID']==1, 'emissionQuant']\n", "sample_emission_df.loc[sample_emission_df['OpModeID']==1, 'emissionQuant'] = whatever_factor\n", "print \"After: \"\n", "print sample_emission_df.loc[sample_emission_df['OpModeID']==1, 'emissionQuant']" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean count\n", "modelYearID fuelTypeID \n", "2017 2 131880.332292 24\n", " 3 122041.903333 24\n" ] } ], "source": [ "# processing category data? try groupby!\n", "group_result = sample_emission_df.groupby(['modelYearID','fuelTypeID'])['emissionQuant'].agg(['mean', 'count'])\n", "print group_result" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OpModeID pollutantID sourceTypeID fuelTypeID modelYearID \\\n", "count 48.000000 48.0 48.0 48.000000 48.0 \n", "mean 34.708333 90.0 42.0 2.500000 2017.0 \n", "std 57.002037 0.0 0.0 0.505291 0.0 \n", "min 0.000000 90.0 42.0 2.000000 2017.0 \n", "25% 14.750000 90.0 42.0 2.000000 2017.0 \n", "50% 24.500000 90.0 42.0 2.500000 2017.0 \n", "75% 33.500000 90.0 42.0 3.000000 2017.0 \n", "max 300.000000 90.0 42.0 3.000000 2017.0 \n", "\n", " emissionQuant rate rate_2 \n", "count 48.000000 48.000000 48.000000 \n", "mean 126961.117812 35.218650 35.218650 \n", "std 109542.231871 30.480780 30.480780 \n", "min 9452.280000 2.121161 2.121161 \n", "25% 31694.175000 8.803937 8.803937 \n", "50% 101874.450000 28.298458 28.298458 \n", "75% 199422.750000 55.395208 55.395208 \n", "max 375836.000000 104.398889 104.398889 \n" ] } ], "source": [ "#want some descriptive stats? lets try this!\n", "print sample_emission_df.describe()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fuelTypeID 2 3\n", "OpModeID \n", "0 18192.500 9542.36\n", "1 13598.595 11454.27\n", "11 11845.500 9998.75\n", "12 33361.000 22977.60\n", "13 58868.600 64232.50\n", "14 84858.500 105750.00\n", "15 106570.000 147023.00\n", "16 144355.000 206835.00\n", "21 9452.280 13149.30\n", "22 42155.600 26693.70\n", "23 68486.900 68712.90\n", "24 97998.900 80494.70\n", "25 125315.000 112071.00\n", "27 169997.000 157229.00\n", "28 237997.000 220120.00\n", "29 305995.000 283012.00\n", "30 373994.000 345903.00\n", "33 37111.400 42663.40\n", "35 110333.000 85325.10\n", "37 170835.000 140681.00\n", "38 239169.000 196952.00\n", "39 307502.000 253224.00\n", "40 375836.000 309497.00\n", "300 21300.200 15464.10\n" ] } ], "source": [ "# pandas pivot table\n", "out_emission_df = pd.pivot_table(sample_emission_df, index = 'OpModeID', columns = 'fuelTypeID', values = 'emissionQuant')\n", "print out_emission_df" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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SZJTr168zb948unXrhk6nIz09nUWLFvHXX3/Rq1cvU0cXQgihEqoudElJSURHR5Obm4uZ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\nLi4uuLi4ULt2bTw8POjSpQvbt2/XxH5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"text/plain": [ "<matplotlib.figure.Figure at 0xa0a68d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# some data visualization\n", "import matplotlib.pyplot as plt\n", "plt.style.use('fivethirtyeight')\n", "out_emission_df.plot(kind=\"barh\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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ewez+Te4dzO6/2sElLCyMo0eP1rjI0aNHrxgHUJNLQxbLy8v57LPPaNeuHVOm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\nNLiIiIiI39DgIiIiIn7j/wH7ZhKVf0oD2wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb1f2a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#don't worry about the layout! try this\n", "import seaborn as sns\n", "plt.style.use('fivethirtyeight')\n", "out_emission_df.plot(kind=\"barh\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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vbx577LESH7yowMS6devyyiuv0KhRI1auXMnixYsZPXp0iY8hIiIilYNNQ0v/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\nS0REBP7+/nTt2pW7776bvXv3Fttv27Z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"text/plain": [ "<matplotlib.figure.Figure at 0xbad6860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# want some decoration, here you are:\n", "out_emission_df.plot(kind=\"barh\")\n", "plt.legend(['Diesel','CNG'])\n", "plt.xlabel('Emission rate (gram/hour)', fontsize = 14)\n", "plt.ylabel('Operating mode bin',fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "image/png": 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O0aVLlwIfPLfAxCpVqhAREYGzszOurq6Ehobi7e1t1f5sHZiocEEREZHiY9XQ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\nwYMHs3fv3hI9bvfu3Xn++ed54okn8PP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"text/plain": [ "<matplotlib.figure.Figure at 0xdf54a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#change plot face color\n", "plt.rcParams['axes.facecolor'] = 'white'\n", "out_emission_df.plot(kind=\"barh\")\n", "plt.legend(['Diesel','CNG'], fontsize = 14)\n", "plt.xlabel('Emission rate (gram/hour)', fontsize = 14)\n", "plt.ylabel('Operating mode bin',fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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LCxUXF6clS5aooqJC06dP13PPPSdJqqys1KxZs3Trrbe26L4BuFdb5ItExgC2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\nwvYJ9Z78/v170br/+fl8Ph9NTU24XC6CwSA6nY7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"text/plain": [ "<matplotlib.figure.Figure at 0xe462128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# split plots? sure!\n", "ax = out_emission_df.plot(kind='barh',subplots=True, layout=(1, 2), figsize=(8, 8), sharex=False)\n", "# change label for one plot\n", "ax[0][0].set_xlabel('emission rate')\n", "ax[0][0].legend('d')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fuelTypeID 2 3\n", "OpModeID \n", "0 909.62500 477.1180\n", "1 679.92975 572.7135\n", "11 592.27500 499.9375\n", "12 1668.05000 1148.8800\n", "13 2943.43000 3211.6250\n", "14 4242.92500 5287.5000\n", "15 5328.50000 7351.1500\n", "16 7217.75000 10341.7500\n", "21 472.61400 657.4650\n", "22 2107.78000 1334.6850\n", "23 3424.34500 3435.6450\n", "24 4899.94500 4024.7350\n", "25 6265.75000 5603.5500\n", "27 8499.85000 7861.4500\n", "28 11899.85000 11006.0000\n", "29 15299.75000 14150.6000\n", "30 18699.70000 17295.1500\n", "33 1855.57000 2133.1700\n", "35 5516.65000 4266.2550\n", "37 8541.75000 7034.0500\n", "38 11958.45000 9847.6000\n", "39 15375.10000 12661.2000\n", "40 18791.80000 15474.8500\n", "300 1065.01000 773.2050\n" ] } ], "source": [ "# plot error bar, assume error = 0.05 value\n", "error = out_emission_df * 0.05\n", "print error" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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iIiISeNosfObMmeN6bDAYgPN3ZJnNZkwmE/X19fTq1YvQ0FD++7//2/eZioiI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\nQRAEQWg3RMdHEARBEIR24/8+y8QeWafjRgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xdc1dbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "out_emission_df.plot.bar(yerr=error, error_kw=dict(lw=1, capsize=2, capthick=1))\n", "plt.legend(['Diesel','CNG'], fontsize = 14)\n", "plt.xlabel('Emission rate (gram/hour)', fontsize = 14)\n", "plt.ylabel('Operating mode bin',fontsize=14)\n", "plt.xticks(rotation=0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
nkmk/python-snippets	notebook/pandas_str_extract.ipynb	1	3338	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A [email protected]\n", "B [email protected]\n", "C [email protected]\n", "D ddd\n", "dtype: object\n" ] } ], "source": [ "s_org = pd.Series(['[email protected]', '[email protected]', '[email protected]', 'ddd'], index=['A', 'B', 'C', 'D'])\n", "print(s_org)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2\n", "A aaa xxx com\n", "B bbb yyy com\n", "C ccc zzz com\n", "D NaN NaN NaN\n" ] } ], "source": [ "df = s_org.str.extract('(.+)@(.+)\\.(.+)', expand=True)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2\n", "A aaa xxx com\n", "B bbb yyy com\n", "C ccc zzz com\n", "D NaN NaN NaN\n" ] } ], "source": [ "df = s_org.str.extract('(.+)@(.+)\\.(.+)', expand=False)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0\n", "A aaa\n", "B bbb\n", "C ccc\n", "D ddd\n", "<class 'pandas.core.frame.DataFrame'>\n" ] } ], "source": [ "df_single = s_org.str.extract('(\\w+)', expand=True)\n", "print(df_single)\n", "print(type(df_single))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A aaa\n", "B bbb\n", "C ccc\n", "D ddd\n", "dtype: object\n", "<class 'pandas.core.series.Series'>\n" ] } ], "source": [ "s = s_org.str.extract('(\\w+)', expand=False)\n", "print(s)\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " local second_LD TLD\n", "A aaa xxx com\n", "B bbb yyy com\n", "C ccc zzz com\n", "D NaN NaN NaN\n" ] } ], "source": [ "df_name = s_org.str.extract('(?P<local>.)@(?P<second_LD>.)\\.(?P<TLD>.*)', expand=True)\n", "print(df_name)" ] } ], "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
landmanbester/fundamentals_of_interferometry	8_Calibration/8_2_1GC.ipynb	2	14983	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", " [Outline](../0_Introduction/0_introduction.ipynb)\n", "* [Glossary](../0_Introduction/1_glossary.ipynb)\n", "* [8. Calibration](8_0_Introduction.ipynb)\n", " * Previous: [8.1 Calibration as a least-squares problem](8_1_Calibration_Least_Squares_Problem.ipynb)\n", " * Next: [8.3 2GC Calibration](8_3_2GC.ipynb)\n", "\n", "*" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Import standard modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import section specific modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (18, 6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "HTML('../style/code_toggle.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8.2 1GC Calibration<a id='cal:sec:1gccal'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The preceding chapters of this course have hopefully made it clear that an interferometer does not perform error-free measurements. On the contrary, an interferomter's measurements will be corrupted by a variety of effects. These include environmental effects such as the atmosphere, as well as faults and inaccuracies in the system itself. Naturally, doing good science requires good data. This is the purpose of calibration - given a signal which has been corrupted in a variety of ways, we can restore it to something resembling the truth.\n", "\n", "The starting point of this discussion is 1GC or, to give it its full name, first generation calibration. This form of calibration is usually the initial step in a series of calibration efforts. 1GC is performed using calibrator observations. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8.2.1 Calibrator Observations<a id='cal:sec:calobs'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are observations of a source with known parameters such as flux, shape and spectrum. Observations of calibrators are interspersed with observations of the target field. This is done so that the calibrator observations track changes in the observational parameters. Thus, it is possible to solve for calibrator gain solutions which can then be transferred to the target field. This is usually an effective method of removing large-scale errors in the visibilities. The requirements placed on calibrator sources vary based on which quantity is being calibrated. \n", "\n", "<p class=conclusion>\n", " <font size=4> <b>Calibration quantities</b></font>\n", " <br>\n", " <br>\n", "• Absolute flux calibration is used to determine the true flux of sources in the field. This is necessary to ensure that the flux values obtained are correctly scaled. This type of calibration requires a very bright, invariant calibrator which is either point-like or well-modelled. <br><br>\n", "• Bandpass calibration is used to correct for errors along the frequency axis of the observation. These errors can be introduced by both the system and the atmosphere. Performing bandpass calibration requires a very bright, invariant point-like or well-modelled source with a known spectrum. The number of calibrator sources which satisfy these conditions is low; they are often very far away from the target.<br><br>\n", "• Delay calibration is used to remove the phase delay error which manifests as a linear ramp in the bandpass. This is often done before bandpass calibration, as it can be fit once to all the data. This is usually performed using the same calibrator source as the bandpass calibration.<br><br>\n", "• Gain calibration is used to determine the complex valued gains. This is done regularly throughout the observation. The calibrator source needs to be relatively bright but, more importantly, it needs to be close to the target. This is because the gain calibrator is used to track the evolution of local effects, such as the atmosphere. As such, the closer the calibrator is to the target, the more likely it is to be subject to the same effects. \n", "</p> \n", "\n", "In practice, absolute flux, delay, and bandpass calibration can all be performed using the same calibrator. Gain calibration could also be performed using this calibrator, but only if it was sufficiently close to the target. This is not usually the case and a unique calibrator is required for determining the complex gains.\n", "\n", "Below is a schematic representation of how an observation might be broken up. Note that this is not accurate by any means, but merely hopes to convey the idea behind calibrator observations and 1GC." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='figures/observe.png' width=100%>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 8.2.1: A typical observation time schematic.<a id='cal:fig:time'></a> <!--\\label{cal:fig:time}-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One point has been neglected in the above discussion: how do we actually calibrate using the calibrator observations? The answer is, fortunately, relatively simple. As you have seen in [$\\S$ 8.1 ➞](../8_Calibration/8_1_Calibration_Least_Squares_Problem.ipynb#pos:sec:lst)<!--\\ref{pos:sec:lst}-->, it is possible to treat calibration as a least-squares problem. 1GC is no exception. One helpful fact is that the model will usually contain only a single source. For a point source at the centre of the field, the model reduces to the amplitude of the calibrator multiplied by the antenna gains. For more complicated calibrator sources, this will not be the case. However, given a sufficiently accurate source model, complicated calibrators can still be used.\n", "\n", "Once the calibrator solutions have been found, the final obstacle is transferring the solution to the target field. In the simplest case, this can be done by applying the results from a calibrator observation to the subsequent target field observation/s. It is also possible to do more complicated solution transfers by interpolating between values or fitting curves across the solutions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8.2.2 Closure Quantities<a id='cal:sec:closure'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other methods of calibration which predate the use of least-squares solvers. One such method makes use of closure quantites as presented in [<cite data-cite='Jennison1958'>A phase sensitive interferometer technique for the measurement of the Fourier transforms of spatial brightness distributions of small angular extent</cite> ⤴](http://mnras.oxfordjournals.org/content/118/3/276.short). Whilst this method of calibration is no longer widely used, the quantites it introduces are of interest. These quantities are the closure phase and closure amplitude. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='figures/triangle.png' width=60%>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 8.2.2*: A closure phase triangle.<a id='cal:fig:triangle'></a> <!--\\label{cal:fig:triangle}-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to define closure phase, we imagine that we have a three antenna array. We can think of these antennas as the vertices of a triangle. The sides of the triangle then represent the baselines. Each baseline observes a visibility as given by the following expression: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\tilde{v}_{pq} = g_pv_{pq}g_{q}^{}.$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above expression, $\\tilde{v}_{pq}$ is the measured visibility, $v_{pq}$ is the true visibility, $g_p$ is the gain of antenna $p$ and $g_{q}^{}$ is the conjugate of the gain of antenna $q$. The indices $p$ and $q$ are simply placeholders for the antennas. It is possible to write this out in terms of the amplitude ($A$) and phase ($\\phi\n", "$) of the various contributions to the visibility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "\\tilde{v}_{pq} &=& A_{p}e^{-\\imath\\phi_p}A_{pq}e^{-\\imath\\phi_{pq}}A_{q}e^{\\imath\\phi_q} \\\\\n", "&=& A_{p}A_{pq}A_{q}e^{\\imath(-\\phi_p-\\phi_{pq}+\\phi_q)}\n", "\\end{eqnarray} \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These expressions are all that is required to reach the definitions of closure phase and amplitude. For our three antenna case outlined above, we can substitute in the indices for baselines $ij$, $jk$ and $ki$. Note that this is equivalent to moving clockwise around the triangle from the $i$ vertex. For closure phase, we are only intrested in the argument of the above expression. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "\\tilde{\\phi}_{ij} &=& arg(\\tilde{v}_{ij}) = -\\phi_i-\\phi_{ij}+\\phi_j \\\\\n", "\\tilde{\\phi}_{jk} &=& arg(\\tilde{v}_{jk}) = -\\phi_j-\\phi_{jk}+\\phi_k \\\\\n", "\\tilde{\\phi}_{ki} &=& arg(\\tilde{v}_{ki}) = -\\phi_k-\\phi_{ki}+\\phi_i\n", "\\end{eqnarray} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\tilde{\\phi}$ represents the total phase of the relevant baseline. Adding these three expressions together we obtain:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p class=conclusion>\n", " <font size=4> <b>Closure phase</b></font>\n", " <br>\n", " <br>\n", " \\begin{equation}\n", " \\tilde{\\phi}_{ij} + \\tilde{\\phi}_{jk} + \\tilde{\\phi}_{ki} = -\\phi_{ij} - \\phi_{jk} - \\phi_{ki}.\n", " \\end{equation}\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This relationship is known as closure phase. What makes this interesting is that the phase contributions of the gains cancel, and we are left with the sum of the true phases. We are free to swap either all or some of the indices on the true phases provided we also swap their sign. This is equivalent to taking the conjugate of the orginal $\\tilde{v}_{pq}$.\n", "\n", "Closure phase is still a useful observable as it can be used to measure the performance of an interferometer. For a point source at the center of the field of view, the phase closure should be close to zero as the individual true phases should be zero. Naturally, in the presence of noise, this will never be perfect. However, if the closure phase is large, it is a good indicator that something is wrong in the sytem. By taking the closure phase for each group of three antennas, it is possible to identify faulty antennas - if every closure phase involving antenna $i$ (for example) is significantly more or less than zero, it is likely that that antenna needs to be flagged." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Amplitude closure is also a useful observable. Its derivation is rather simple, as simple substitution can show that it is true. For four antennas (the minimum required for amplitude closure) $i$, $j$, $k$ and $l$, we can write the following expression: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\frac{\\lvert \\tilde{v}_{ij} \\rvert \\lvert \\tilde{v}_{kl} \\rvert}{\\lvert \\tilde{v}_{ik} \\rvert \\lvert \\tilde{v}_{jl} \\rvert}.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we substitue in equations of the following form:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\tilde{v}_{pq} = A_{p}e^{-\\imath\\phi_p}A_{pq}e^{-\\imath\\phi_{pq}}A_{q}e^{\\imath\\phi_q}, $$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we obtain this expression, noting that the exponential terms fall away in the absolute value: " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\frac{\\lvert A_iA_{ij}A_{j} \\rvert \\lvert A_kA_{kl}A_{l} \\rvert}{\\lvert A_iA_{ik}A_{k} \\rvert \\lvert A_jA_{jl}A_{l} \\rvert}.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The amplitude terms which belong to the gains cancel, and we are left with a closure amplitude relationship." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p class=conclusion>\n", " <font size=4> <b>Closure amplitude</b></font>\n", " <br>\n", " <br>\n", " \\begin{equation}\n", " \\frac{\\lvert \\tilde{v}_{ij} \\rvert \\lvert \\tilde{v}_{kl} \\rvert}{\\lvert \\tilde{v}_{ik} \\rvert \\lvert \\tilde{v}_{jl} \\rvert} = \\frac{\\lvert A_{ij} \\rvert \\lvert A_{kl} \\rvert}{\\lvert A_{ik} \\rvert \\lvert A_{jl} \\rvert}\n", " \\end{equation}\n", "</p>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As for the case of closure phase, we can eilminate the contribution of the gains and obtain a measure of the true amplitude. For a point source at the center of the field, we expect each baseline to measure the same amplitude, and the above expression should be unity. This can once again be used as a diagnostic." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", "Next: [8.3 2GC Calibration](8_3_2GC.ipynb)" ] } ], "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
acmcu/resources	kmeans/Untitled0.ipynb	2	969	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "var = 1\n", "print var" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "var = var + 1\n", "print var" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
davidadamsphd/spark-vs-dataflow	src/k-means.ipynb	1	101924	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/davidada/apps/spark-2.0.0-bin-hadoop2.7\n", "Welcome to\n", " ____ __\n", " / __/__ ___ _____/ /__\n", " _\\ \\/ _ \\/ _ `/ __/ '_/\n", " /__ / .__/\\_,_/_/ /_/\\_\\ version 2.0.0\n", " /_/\n", "\n", "Using Python version 2.7.10 (default, Oct 23 2015 19:19:21)\n", "SparkSession available as 'spark'.\n" ] } ], "source": [ "import os\n", "import sys\n", "\n", "spark_home = os.environ.get('SPARK_HOME', None)\n", "print(spark_home)\n", "sys.path.insert(0, os.path.join(spark_home, 'python'))\n", "sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.8.2.1-src.zip')) # for Spark 1.4\n", "sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.10.1-src.zip')) # for Spark 2.0\n", "\n", "src_dir = os.environ.get('SRC_DIR', None)\n", "if not src_dir:\n", " print('SRC_DIR must be set to the src directory in the git project, e.g.,\\n'\n", " 'SRC_DIR=~/git-projects/spark-vs-dataflow/src; export SRC_DIR')\n", " sys.exit(1)\n", "class_path = os.path.join(src_dir, 'PtAgg.py')\n", "sys.argv = ['python/pyspark/shell.py', '--py-files', class_path]\n", "\n", "execfile(os.path.join(spark_home, 'python/pyspark/shell.py'))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XrJjctceXPPx/kb6+yTZKqZtebKjGPX2YA+DrAL6gxO1gJv7DJr5553adGDem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\nX6nkXgBPA4gD+FsAS/PcnwKY3Jfx1ClWBTx82Fb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"text/plain": [ "<matplotlib.figure.Figure at 0x103fedf50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "np.random.seed(1000111)\n", "class_one_size = 500\n", "class_two_size = 500\n", "mean = [0, 0]\n", "cov = [[1, 0], [0, 5]]\n", "class_one = np.random.multivariate_normal(mean, cov, class_one_size)\n", "x, y = class_one.T\n", "mean2 = [5, 5]\n", "cov2 = [[5, 0], [0, 1]]\n", "class_two = np.random.multivariate_normal(mean2, cov2, class_two_size)\n", "x2, y2 = class_two.T\n", "plt.plot(x, y, 'x', color='red')\n", "plt.plot(x2, y2, 'x', color='blue')\n", "plt.axis('equal')\n", "plt.show()\n", "both_classes = np.concatenate((class_one, class_two), axis=0)\n", "\n", "np.set_printoptions(suppress=True)\n", "np.savetxt('binary_sim_data_{0}_{1}.csv'.format(class_one_size, class_two_size), both_classes, fmt='%5.4f,%5.4f',\n", " header=\"x,y\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "new centers: 2.0143092295,2.90425952949 1.34917859683,0.172265408248\n", "new centers: 4.24317371901,4.6551,1 -0.0204415189873,-0.686950632911,1\n", "new centers: 4.78803824627,4.91731865672,1 -0.015825,-0.19545862069,1\n", "new centers: 5.02929289941,4.99276804734,1 0.0186494929006,0.0277010141988,1\n", "new centers: 5.06339681909,5.00051848907,1 0.0244609657948,0.0598173038229,1\n", "new centers: 5.07343366534,5.00037749004,1 0.0244618473896,0.0698805220884,1\n", "final centers: 5.07343366534,5.00037749004,1 0.0244618473896,0.0698805220884,1\n" ] }, { "data": { "image/png": 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rHNHfdZez4w0NcQybNjEbUjTm5ma36Ki7t0tTBNGtpRmCaPCyKCnatGjO4j7R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\npEbaZhM0GIqGzwF4a8jrpVX4qxV6IU9+19mMuoe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"text/plain": [ "<matplotlib.figure.Figure at 0x1041be090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import copy\n", "import math\n", "import sys\n", "\n", "from PtAgg import PtAgg\n", "\n", "# I originally wrote this without classes and it was a pretty confusing to follow.\n", "# So, I created a PtAgg class that has an x,y location and a counter (because we\n", "# also need to 'add' points together).\n", "\n", "def convert_binary_line(line):\n", " assert len(line) == 2\n", " return (float(line[0]), float(line[1]))\n", "\n", "\n", "def pick_closest_center(p, centers):\n", " d_min = sys.float_info.max\n", " idx_min = -1\n", " for idx in range(len(centers)):\n", " c = centers[idx]\n", " d = p.DistSqr(c)\n", " if d < d_min:\n", " d_min = d\n", " idx_min = idx\n", " assert idx_min != -1\n", " # Make an empty array of center points, then only add to the closest center point.\n", " closest_centers = [PtAgg() for x in centers]\n", " closest_centers[idx_min] = p\n", " return closest_centers\n", "\n", "\n", "def center_reduce(a, b):\n", " # Combine the lists of center point accumulations.\n", " return tuple([el1 + el2 for el1, el2 in zip(a, b)])\n", "\n", "\n", "def random_point_location():\n", " return PtAgg(np.random.uniform(min_max[0], min_max[1]),\n", " np.random.uniform(min_max[2], min_max[3]))\n", "\n", "#np.random.seed(1000111)\n", "NUM_CLUSTERS = 2\n", "\n", "\n", "input_rdd = sc.textFile('binary_sim_data_{0}_{1}.csv'.format(class_one_size, class_two_size))\n", "header = input_rdd.first() # Remove the first line.\n", "parsed_input_rdd = input_rdd.filter(lambda x: x !=header).map(\n", " lambda x: convert_binary_line(x.split(',')))\n", "\n", "\n", "# Find (x_min, x_max, y_min, y_max) over all data; We'll use that as the bounds for our centers.\n", "# We start by saying that each point's x and y are the largest and smallest x's and y's they've seen.\n", "# Next, we compare x,y points via reduce and keep smaller x (and y) and the larger x (and y). \n", "#\n", "# Note that don't use our class yet -- as it's not actually helpful here.\n", "min_max = parsed_input_rdd.map(lambda p: (p[0],p[0],p[1],p[1])).reduce(\n", " lambda p1,p2: (min(p1[0],p2[0]), max(p1[1],p2[1]), min(p1[2], p2[2]), max(p1[3], p2[3])))\n", "\n", "# Pick a random start location for every cluster center\n", "c_centers = [random_point_location() for x in range(NUM_CLUSTERS)]\n", "\n", "# Side note: pt_rdd would be a good thing to cache.\n", "pt_rdd = parsed_input_rdd.map(lambda x: PtAgg(x[0], x[1], 1))\n", "MAX_STEPS = 100\n", "MIN_DELTA = 0.001\n", "delta = 1.0\n", "step = 0\n", "while delta > MIN_DELTA and step < MAX_STEPS:\n", " step += 1\n", " c_centers_old = copy.deepcopy(c_centers)\n", " b_c_centers = sc.broadcast(c_centers_old)\n", " s = ' '.join([str(x) for x in b_c_centers.value])\n", " print('new centers: {0}'.format(s))\n", " # For every point, find the cluster it's closer to and add to its total x, y, and count\n", " # Let the reader note that for large inputs (many partitions) it may make sense to\n", " # replace reduce with treeReduce to lessen the burden on the master.\n", " totals = pt_rdd.map(lambda x: pick_closest_center(x, b_c_centers.value)).reduce(\n", " lambda a,b: center_reduce(a,b))\n", " # Now update the location of the centers as the mean of all of the points closest to it\n", " # (unless there are none, in which case pick a new random spot).\n", " c_centers = [t.Normalize() if t.cnt != 0 else random_point_location() for t in totals]\n", " \n", " # compute the distance that each cluster center moves, the set the max of those as\n", " # the delta used to the stop condition.\n", " deltas = [math.sqrt(c.DistSqr(c_old)) for c, c_old in zip(c_centers, c_centers_old)]\n", " delta = max(deltas)\n", " \n", "s = ' '.join([str(x) for x in c_centers])\n", "print('final centers: {0}'.format(s))\n", "c_centers_old = copy.deepcopy(c_centers)\n", "b_c_centers = sc.broadcast(c_centers_old)\n", "\n", "# Now, take the centers and use them to label all of the points.\n", "possible_colors = ['blue', 'red', 'green', 'orange', 'purple', 'brown']\n", "pt_labeled = pt_rdd.map(lambda x: pick_closest_center(x, b_c_centers.value))\n", "for c_idx in range(NUM_CLUSTERS):\n", " labeled_pts = pt_labeled.map(lambda x: x[c_idx]).filter(\n", " lambda a: abs(a.x) > sys.float_info.min or abs(a.y) > sys.float_info.min).collect()\n", " pts_x = [pt.x for pt in labeled_pts]\n", " pts_y = [pt.y for pt in labeled_pts]\n", "\n", " plt.plot(np.array(pts_x), np.array(pts_y), 'x', color=possible_colors[c_idx])\n", "plt.axis('equal')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([ 0.02446185, 0.06988052]), array([ 5.07343367, 5.00037749])]\n" ] }, { "data": { "image/png": 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G3WX4Ua4kAd12Gz+3Z4/TtG++mSRaW+tS9+NxnrtE7rGYk2n0NZdqhX7jBBmzHFuIv74+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\nhQujhw8D111HV4nfX/PUKY5LWrABPG4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"text/plain": [ "<matplotlib.figure.Figure at 0x1042abf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Or we could just do it using mllib\n", "# Code modified from: http://spark.apache.org/docs/latest/mllib-clustering.html#k-means\n", "from pyspark.mllib.clustering import KMeans, KMeansModel\n", "from numpy import array\n", "from math import sqrt\n", "\n", "def matchCluter(clusters, pt, cluster_center):\n", " pt_center = clusters.centers[clusters.predict(pt)]\n", " for idx in range(len(pt_center)):\n", " if pt_center[idx] != cluster_center[idx]:\n", " return False\n", " return True\n", "\n", "# Load and parse the data\n", "input_rdd = sc.textFile('binary_sim_data_{0}_{1}.csv'.format(class_one_size, class_two_size))\n", "header = input_rdd.first() # Remove the first line.\n", "data = input_rdd.filter(lambda x: x != header)\n", "\n", "parsedData = data.map(lambda line: np.array([float(x) for x in line.split(',')]))\n", "\n", "# Build the model (cluster the data)\n", "clusters = KMeans.train(parsedData, NUM_CLUSTERS, maxIterations=10)\n", "print(clusters.clusterCenters)\n", "for c_idx in range(NUM_CLUSTERS):\n", " c_center = clusters.clusterCenters[c_idx]\n", " labeled_pts = parsedData.filter(lambda x: matchCluter(clusters, x, c_center)).collect()\n", " pts_x = [pt[0] for pt in labeled_pts]\n", " pts_y = [pt[1] for pt in labeled_pts]\n", "\n", " plt.plot(np.array(pts_x), np.array(pts_y), 'x', color=possible_colors[c_idx])\n", "plt.axis('equal')\n", "plt.show()" ] } ], "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 }	apache-2.0
catherinekuhn/ee-python	define_google_maps_interactive_widget.ipynb	1	25286	{ "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" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tyler Erickson's Map Widget Code" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html import widgets\n", "from IPython.utils import traitlets\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "class GoogleMapsWidget(widgets.DOMWidget):\n", " _view_name = traitlets.Unicode('GoogleMapsView', sync=True)\n", " value = traitlets.Unicode(sync=True)\n", " description = traitlets.Unicode(sync=True)\n", " lat = traitlets.CFloat(0, help=\"Center latitude, -90 to 90\", sync=True)\n", " lng = traitlets.CFloat(0, help=\"Center longitude, -180 to 180\", sync=True)\n", " zoom = traitlets.CInt(0, help=\"Zoom level, 0 to ~25\", sync=True)\n", " bounds = traitlets.List([], help=\"Visible bounds, [W, S, E, N]\", sync=True)\n", " \n", " def __init__(self, lng=0.0, lat=0.0, zoom=2):\n", " self.lng = lng\n", " self.lat = lat\n", " self.zoom = zoom\n", " \n", " def addLayer(self, image, vis_params, name=None, visible=True):\n", " mapid = image.getMapId(vis_params)\n", " self.send({'command':'addLayer', 'mapid': mapid['mapid'], 'token': mapid['token'], 'name': name, 'visible': visible})\n", " \n", " def center(self, lng, lat, zoom=None):\n", " self.send({'command': 'center', 'lng': lng, 'lat': lat, 'zoom': zoom})\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:2: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", " from ipykernel import kernelapp as app\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:3: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", " app.launch_new_instance()\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:4: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:5: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:6: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:7: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n", "/Users/catherinekuhn/miniconda2/envs/ee-python/lib/python2.7/site-packages/ipykernel/__main__.py:8: DeprecationWarning: metadata {'sync': True} was set from the constructor. Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "%%javascript\n", "\n", "require([\"widgets/js/widget\"], function(WidgetManager){\n", " var maps = [];\n", " \n", " // Define the GoogleMapsView\n", " var GoogleMapsView = IPython.DOMWidgetView.extend({\n", " \n", " render: function() {\n", " // Resize widget element to be 100% wide\n", " this.$el.css('width', '100%');\n", "\n", " // iframe source; just enough to load Google Maps and let us poll whether initialization is complete\n", " var src='<html style=\"height:100%\"><head>' +\n", " '<scr'+'ipt src=\"http://maps.googleapis.com/maps/api/js?sensor=false\"></scr'+'ipt>' +\n", " '<scr'+'ipt>google.maps.event.addDomListener(window,\"load\",function(){ready=true});</scr'+'ipt>' +\n", " '</head>' +\n", " '<body style=\"height:100%; margin:0px; padding:0px\"></body></html>';\n", " \n", " // Create the Google Maps container element.\n", " this.$iframe = $('<iframe />')\n", " .css('width', '100%')\n", " .css('height', '1000px')\n", " .attr('srcdoc', src)\n", " .appendTo(this.$el);\n", " \n", " var self = this; // hold onto this for initMapWhenReady\n", "\n", " // Wait until maps library has finished loading in iframe, then create map\n", " function initMapWhenReady() {\n", " // Iframe document and window\n", " var doc = self.$iframe[0].contentDocument;\n", " var win = self.$iframe[0].contentWindow;\n", " if (!win \|\| !win.ready) {\n", " // Maps library not yet loaded; try again soon\n", " setTimeout(initMapWhenReady, 20);\n", " return;\n", " }\n", "\n", " // Maps library finished loading. Build map now.\n", " var mapOptions = {\n", " center: new win.google.maps.LatLng(self.model.get('lat'), self.model.get('lng')),\n", " zoom: self.model.get('zoom')\n", " };\n", " var mapDiv = $('<div />')\n", " .css('width', '100%')\n", " .css('height', '100%')\n", " .appendTo($(doc.body));\n", " self.map = new win.google.maps.Map(mapDiv[0], mapOptions);\n", " \n", " \n", " // Add an event listeners for user panning, zooming, and resizing map\n", " // TODO(rsargent): Bind self across all methods, and save some plumbing here\n", " win.google.maps.event.addListener(self.map, 'bounds_changed', function () {\n", " self.handleBoundsChanged();\n", " });\n", " \n", " self.initializeLayersControl();\n", " }\n", " initMapWhenReady();\n", " },\n", " \n", " LayersControl: function(widget, controlDiv, map) {\n", " var win = widget.$iframe[0].contentWindow;\n", " var chicago = new win.google.maps.LatLng(41.850033, -87.6500523);\n", "\n", " // Set CSS styles for the DIV containing the control\n", " // Setting padding to 5 px will offset the control\n", " // from the edge of the map.\n", " controlDiv.style.padding = '5px';\n", "\n", " // Set CSS for the control border.\n", " var $controlUI = $('<div />')\n", " .css('backgroundColor', 'white')\n", " .css('borderStyle', 'solid')\n", " .css('borderWidth', '1px')\n", " .css('cursor', 'pointer')\n", " .css('textAlign', 'center')\n", " .appendTo($(controlDiv));\n", " \n", " // Set CSS for the control interior.\n", " var $controlContents = $('<div />')\n", " .css('fontFamily', 'Arial,sans-serif')\n", " .css('fontSize', '12px')\n", " .css('paddingLeft', '4px')\n", " .css('paddingRight', '4px')\n", " .css('paddingTop', '0px')\n", " .css('paddingBottom', '0px')\n", " .appendTo($controlUI);\n", " \n", " this.$controlTable = $('<table />')\n", " .append($('<tr><td colspan=2>Layers</td></tr>'))\n", " .appendTo($controlContents);\n", " },\n", "\n", " initializeLayersControl: function() {\n", " var doc = this.$iframe[0].contentDocument;\n", " var win = this.$iframe[0].contentWindow;\n", "\n", " // Create the DIV to hold the control and call the LayersControl() constructor\n", " // passing in this DIV.\n", " \n", " var layersControlDiv = document.createElement('div');\n", " this.layersControl = new this.LayersControl(this, layersControlDiv, this.map);\n", "\n", " layersControlDiv.index = 1;\n", " this.map.controls[win.google.maps.ControlPosition.TOP_RIGHT].push(layersControlDiv);\n", " },\n", " \n", " // Map geometry changed (pan, zoom, resize)\n", " handleBoundsChanged: function() {\n", " this.model.set('lng', this.map.getCenter().lng());\n", " this.model.set('lat', this.map.getCenter().lat());\n", " this.model.set('zoom', this.map.getZoom());\n", " var bounds = this.map.getBounds();\n", " var playgroundCompatible = [bounds.getSouthWest().lng(), bounds.getSouthWest().lat(),\n", " bounds.getNorthEast().lng(), bounds.getNorthEast().lat()];\n", " this.model.set('bounds', playgroundCompatible);\n", " this.touch();\n", " },\n", " \n", " // Receive custom messages from Python backend\n", " on_msg: function(msg) {\n", " var win = this.$iframe[0].contentWindow;\n", " if (msg.command == 'addLayer') {\n", " this.addLayer(msg.mapid, msg.token, msg.name, msg.visible);\n", " } else if (msg.command == 'center') {\n", " this.map.setCenter(new win.google.maps.LatLng(msg.lat, msg.lng));\n", " if (msg.zoom !== null) {\n", " this.map.setZoom(msg.zoom);\n", " }\n", " }\n", " },\n", " \n", " // Add an Earth Engine layer\n", " addLayer: function(mapid, token, name, visible) {\n", " var win = this.$iframe[0].contentWindow;\n", " var eeMapOptions = {\n", " getTileUrl: function(tile, zoom) {\n", " var url = ['https://earthengine.googleapis.com/map',\n", " mapid, zoom, tile.x, tile.y].join(\"/\");\n", " url += '?token=' + token\n", " return url;\n", " },\n", " tileSize: new win.google.maps.Size(256, 256),\n", " opacity: visible ? 1.0 : 0.0,\n", " };\n", " \n", " // Create the overlay map type\n", " var mapType = new win.google.maps.ImageMapType(eeMapOptions);\n", " \n", " // Overlay the Earth Engine generated layer\n", " this.map.overlayMapTypes.push(mapType);\n", "\n", " // Update layer visibility control\n", " var maxSlider = 100;\n", " \n", " function updateOpacity() {\n", " mapType.setOpacity($checkbox[0].checked ? $slider[0].value / 100.0 : 0);\n", " }\n", " \n", " var $checkbox = $('<input type=\"checkbox\">')\n", " .prop('checked', visible)\n", " .change(updateOpacity);\n", " \n", " var $slider = $('<input type=\"range\" />')\n", " .prop('min', 0)\n", " .prop('max', maxSlider)\n", " .prop('value', maxSlider)\n", " .css('width', '60px')\n", " .on('input', updateOpacity);\n", "\n", " // If user doesn't specify a layer name, create a default\n", " if (name === null) {\n", " name = 'Layer ' + this.map.overlayMapTypes.length;\n", " }\n", " \n", " var $row = $('<tr />');\n", " $('<td align=\"left\" />').append($checkbox).append(name).appendTo($row);\n", " $('<td />').append($slider).appendTo($row);\n", "\n", " this.layersControl.$controlTable.append($row);\n", " }\n", " });\n", " \n", " // Register the GoogleMapsView with the widget manager.\n", " WidgetManager.register_widget_view('GoogleMapsView', GoogleMapsView);\n", "});\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "\n", "require([\"widgets/js/widget\"], function(WidgetManager){\n", " var maps = [];\n", " \n", " // Define the GoogleMapsView\n", " var GoogleMapsView = IPython.DOMWidgetView.extend({\n", " \n", " render: function() {\n", " // Resize widget element to be 100% wide\n", " this.$el.css('width', '100%');\n", "\n", " // iframe source; just enough to load Google Maps and let us poll whether initialization is complete\n", " var src='<html style=\"height:100%\"><head>' +\n", " '<scr'+'ipt src=\"http://maps.googleapis.com/maps/api/js?sensor=false\"></scr'+'ipt>' +\n", " '<scr'+'ipt>google.maps.event.addDomListener(window,\"load\",function(){ready=true});</scr'+'ipt>' +\n", " '</head>' +\n", " '<body style=\"height:100%; margin:0px; padding:0px\"></body></html>';\n", " \n", " // Create the Google Maps container element.\n", " this.$iframe = $('<iframe />')\n", " .css('width', '100%')\n", " .css('height', '1000px')\n", " .attr('srcdoc', src)\n", " .appendTo(this.$el);\n", " \n", " var self = this; // hold onto this for initMapWhenReady\n", "\n", " // Wait until maps library has finished loading in iframe, then create map\n", " function initMapWhenReady() {\n", " // Iframe document and window\n", " var doc = self.$iframe[0].contentDocument;\n", " var win = self.$iframe[0].contentWindow;\n", " if (!win \|\| !win.ready) {\n", " // Maps library not yet loaded; try again soon\n", " setTimeout(initMapWhenReady, 20);\n", " return;\n", " }\n", "\n", " // Maps library finished loading. Build map now.\n", " var mapOptions = {\n", " center: new win.google.maps.LatLng(self.model.get('lat'), self.model.get('lng')),\n", " zoom: self.model.get('zoom')\n", " };\n", " var mapDiv = $('<div />')\n", " .css('width', '100%')\n", " .css('height', '100%')\n", " .appendTo($(doc.body));\n", " self.map = new win.google.maps.Map(mapDiv[0], mapOptions);\n", " \n", " \n", " // Add an event listeners for user panning, zooming, and resizing map\n", " // TODO(rsargent): Bind self across all methods, and save some plumbing here\n", " win.google.maps.event.addListener(self.map, 'bounds_changed', function () {\n", " self.handleBoundsChanged();\n", " });\n", " \n", " self.initializeLayersControl();\n", " }\n", " initMapWhenReady();\n", " },\n", " \n", " LayersControl: function(widget, controlDiv, map) {\n", " var win = widget.$iframe[0].contentWindow;\n", " var chicago = new win.google.maps.LatLng(41.850033, -87.6500523);\n", "\n", " // Set CSS styles for the DIV containing the control\n", " // Setting padding to 5 px will offset the control\n", " // from the edge of the map.\n", " controlDiv.style.padding = '5px';\n", "\n", " // Set CSS for the control border.\n", " var $controlUI = $('<div />')\n", " .css('backgroundColor', 'white')\n", " .css('borderStyle', 'solid')\n", " .css('borderWidth', '1px')\n", " .css('cursor', 'pointer')\n", " .css('textAlign', 'center')\n", " .appendTo($(controlDiv));\n", " \n", " // Set CSS for the control interior.\n", " var $controlContents = $('<div />')\n", " .css('fontFamily', 'Arial,sans-serif')\n", " .css('fontSize', '12px')\n", " .css('paddingLeft', '4px')\n", " .css('paddingRight', '4px')\n", " .css('paddingTop', '0px')\n", " .css('paddingBottom', '0px')\n", " .appendTo($controlUI);\n", " \n", " this.$controlTable = $('<table />')\n", " .append($('<tr><td colspan=2>Layers</td></tr>'))\n", " .appendTo($controlContents);\n", " },\n", "\n", " initializeLayersControl: function() {\n", " var doc = this.$iframe[0].contentDocument;\n", " var win = this.$iframe[0].contentWindow;\n", "\n", " // Create the DIV to hold the control and call the LayersControl() constructor\n", " // passing in this DIV.\n", " \n", " var layersControlDiv = document.createElement('div');\n", " this.layersControl = new this.LayersControl(this, layersControlDiv, this.map);\n", "\n", " layersControlDiv.index = 1;\n", " this.map.controls[win.google.maps.ControlPosition.TOP_RIGHT].push(layersControlDiv);\n", " },\n", " \n", " // Map geometry changed (pan, zoom, resize)\n", " handleBoundsChanged: function() {\n", " this.model.set('lng', this.map.getCenter().lng());\n", " this.model.set('lat', this.map.getCenter().lat());\n", " this.model.set('zoom', this.map.getZoom());\n", " var bounds = this.map.getBounds();\n", " var playgroundCompatible = [bounds.getSouthWest().lng(), bounds.getSouthWest().lat(),\n", " bounds.getNorthEast().lng(), bounds.getNorthEast().lat()];\n", " this.model.set('bounds', playgroundCompatible);\n", " this.touch();\n", " },\n", " \n", " // Receive custom messages from Python backend\n", " on_msg: function(msg) {\n", " var win = this.$iframe[0].contentWindow;\n", " if (msg.command == 'addLayer') {\n", " this.addLayer(msg.mapid, msg.token, msg.name, msg.visible);\n", " } else if (msg.command == 'center') {\n", " this.map.setCenter(new win.google.maps.LatLng(msg.lat, msg.lng));\n", " if (msg.zoom !== null) {\n", " this.map.setZoom(msg.zoom);\n", " }\n", " }\n", " },\n", " \n", " // Add an Earth Engine layer\n", " addLayer: function(mapid, token, name, visible) {\n", " var win = this.$iframe[0].contentWindow;\n", " var eeMapOptions = {\n", " getTileUrl: function(tile, zoom) {\n", " var url = ['https://earthengine.googleapis.com/map',\n", " mapid, zoom, tile.x, tile.y].join(\"/\");\n", " url += '?token=' + token\n", " return url;\n", " },\n", " tileSize: new win.google.maps.Size(256, 256),\n", " opacity: visible ? 1.0 : 0.0,\n", " };\n", " \n", " // Create the overlay map type\n", " var mapType = new win.google.maps.ImageMapType(eeMapOptions);\n", " \n", " // Overlay the Earth Engine generated layer\n", " this.map.overlayMapTypes.push(mapType);\n", "\n", " // Update layer visibility control\n", " var maxSlider = 100;\n", " \n", " function updateOpacity() {\n", " mapType.setOpacity($checkbox[0].checked ? $slider[0].value / 100.0 : 0);\n", " }\n", " \n", " var $checkbox = $('<input type=\"checkbox\">')\n", " .prop('checked', visible)\n", " .change(updateOpacity);\n", " \n", " var $slider = $('<input type=\"range\" />')\n", " .prop('min', 0)\n", " .prop('max', maxSlider)\n", " .prop('value', maxSlider)\n", " .css('width', '60px')\n", " .on('input', updateOpacity);\n", "\n", " // If user doesn't specify a layer name, create a default\n", " if (name === null) {\n", " name = 'Layer ' + this.map.overlayMapTypes.length;\n", " }\n", " \n", " var $row = $('<tr />');\n", " $('<td align=\"left\" />').append($checkbox).append(name).appendTo($row);\n", " $('<td />').append($slider).appendTo($row);\n", "\n", " this.layersControl.$controlTable.append($row);\n", " }\n", " });\n", " \n", " // Register the GoogleMapsView with the widget manager.\n", " WidgetManager.register_widget_view('GoogleMapsView', GoogleMapsView);\n", "});\n" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Javascript object>" ] } ], "prompt_number": 3 } ], "metadata": {} } ] }	cc0-1.0
google-research/ott	docs/notebooks/OTT_&_POT.ipynb	1	115794	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "OTT & POT", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "C4nZjbMHWcm_" }, "source": [ "# OTT vs. POT\n", "\n", "The [Python Optimal Transport (POT)](https://pythonot.github.io/) toolbox paved the way for much progress in OT. `POT` implements several OT solvers (LP and regularized), and is complemented with various tools (barycenters, domain adaptation, Gromov-Wasserstein distances, sliced W, etc.). The coverage of `OTT` is currently far smaller than of `POT`.\n", "\n", "With that disclaimer in mind, the goal of this notebook is to compare the performance of their Sinkhorn solvers. `OTT` benefits from just-in-time compilation, which should give it an edge. `OTT` is also differentiable w.r.t its inputs, but since `POT` is not that aspect is not considered here.\n", "\n", "The comparisons carried out below have limitations: minor modifications in the setup (e.g. data distributions, tolerance thresholds, type of accelerator...) could have an impact on these results. Feel free to change these settings and experiment by yourself! \n", "\n", "This NB was run on [colab](https://colab.research.google.com/drive/1IwoIPC4nRHmRRtExAY9j-3EkrZinVEGb?usp=sharing) using a GPU." ] }, { "cell_type": "markdown", "metadata": { "id": "dpTlNSRqXevL" }, "source": [ "## Installs toolboxes\n", "\n", "We install the 2 toolboxes first..." ] }, { "cell_type": "code", "metadata": { "id": "IO2KLVZ1KWvq" }, "source": [ "!pip install ott-jax\n", "!pip install POT" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "02VJX2uXYHDX" }, "source": [ "... and import them, along with their numerical environments, `jax` and `numpy`." ] }, { "cell_type": "code", "metadata": { "id": "ysURew0UKhHE" }, "source": [ "# import JAX and OTT\n", "import jax\n", "import jax.numpy as jnp\n", "import ott\n", "from ott.geometry import pointcloud\n", "from ott.core import sinkhorn\n", "\n", "# import OT, from POT\n", "import numpy as np\n", "import ot\n", "\n", "# misc\n", "import matplotlib.pyplot as plt\n", "plt.rc('font', size = 20)\n", "import mpl_toolkits.axes_grid1\n", "import timeit" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rzeoMqPlcGMT" }, "source": [ "## Regularized OT in a nutshell\n", "\n", "We consider two probability measures $\\mu,\\nu$ compared with the squared-Euclidean distance, $c(x,y)=\\\|x-y\\\|^2$. These measures are discrete and of the same size in this notebook:\n", "\n", "$$\\mu=\\sum_{i=1}^n a_i\\delta_{x_i}, \\nu =\\sum_{j=1}^n b_j\\delta_{y_j},$$\n", "\n", "to define the OT problem in its primal form,\n", "$$\\min_{P \\in U(a,b)} \\langle C, P \\rangle - \\varepsilon H(P).$$\n", "\n", "where $U(a,b):=\\{P \\in \\mathbf{R}_+^{n\\times n}, P\\mathbf{1}_{n}=b, P^T\\mathbf{1}_n=b\\}$, and $C = [ \\\|x_i - y_j \\\|^2 ]_{i,j}\\in \\mathbf{R}_+^{n\\times n}$.\n", "\n", "That problem is equivalent to the following dual form,\n", "$$\\max_{f, g} \\langle a, f \\rangle + \\langle b, g \\rangle - \\varepsilon \\langle e^{f/\\varepsilon},Ke^{g/\\varepsilon} \\rangle.$$\n", "\n", "These two problems are solved by `OTT` and `POT` using the Sinkhorn iterations using a simple initialization for $u$, and subsequent updates $v \\leftarrow a / K^Tu, u \\leftarrow b / Kv$, where $K:=e^{-C/\\varepsilon}$.\n", "\n", "Upon convergence to fixed points $u^, v^$, one has $$P^=D(u^)KD(v^)$$ or, alternatively, \n", "$$f^, g^* = \\varepsilon \\log(u^), \\varepsilon\\log(v^)$$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OhSVMQUWYZiY" }, "source": [ "## OTT and POT implementation\n", "\n", "Both toolboxes carry out Sinkhorn updates using either the formulas above directly (this corresponds to `lse_mode=False` in `OTT` and `method=sinkhorn` in `POT`) or using slightly slower but more robust approaches:\n", "\n", "`OTT` relies on log-space iterations (`lse_mode=True`), whereas `POT`, uses a stabilization trick , using the `method=sinkhorn_stabilized` flag, designed to avoid numerical overflows, while still benefitting from the speed given by matrix vector products. \n", "\n", "The default behaviour of `OTT` and [POT](https://github.com/PythonOT/POT/blob/f6139428e70ce964de3bef703ef13aa701a83620/ot/bregman.py#L413) is to carry out these updates until $\\\|u\\circ Kv - a\\\|_2 + \\\|v\\circ K^Tu - b\\\|_2$ is smaller than the user-defined `threshold`." ] }, { "cell_type": "markdown", "metadata": { "id": "yjjlf297b-WF" }, "source": [ "## Common API for `OTT` and `POT`\n", "\n", "We will compare in our experiments `OTT` vs. `POT` in their more stable setups (`lse_mode` and `stabilized`). We define a common API for both, making sure their results are comparable. That API takes as inputs the measures' info, the targeted 𝜀 value and the `threshold` used to terminate the algorithm. We set a maximum of 1000 iterations for both." ] }, { "cell_type": "code", "metadata": { "id": "cM2cM87nZ6XU" }, "source": [ "def solve_ot(a, b, x, y, 𝜀, threshold):\n", " _, log = ot.sinkhorn(a, b, ot.dist(x,y), 𝜀, stopThr=threshold,\n", " method='sinkhorn_stabilized', log=True,\n", " numItermax=1000)\n", " f, g = 𝜀 * log['logu'], 𝜀 * log['logv']\n", " f, g = f - np.mean(f), g + np.mean(f) # center variables, useful if one wants to compare them\n", " reg_ot = np.sum(f * a) + np.sum(g * b) if log['err'][-1] < threshold else np.nan\n", " return f, g, reg_ot\n", "\n", "@jax.jit\n", "def solve_ott(a, b, x, y, 𝜀, threshold):\n", " out = sinkhorn.sinkhorn(pointcloud.PointCloud(x, y, epsilon=𝜀),\n", " a, b, threshold=threshold, lse_mode=True, jit=False,\n", " max_iterations=1000)\n", " f, g = out.f, out.g \n", " f, g = f - np.mean(f), g + np.mean(f) # center variables, useful if one wants to compare them\n", " reg_ot = jnp.where(out.converged, jnp.sum(f * a) + jnp.sum(g * b), jnp.nan)\n", " return f, g, reg_ot" ], "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bPWuBwYvC-y-" }, "source": [ "To test both solvers, we run simulations using a random seed to generate random point clouds of size $n$. Random generation is carried out using [jax.random](https://jax.readthedocs.io/en/latest/jax.random.html), to ensure reproducibility. A solver provides three pieces of info: the function (using our simple common API), its numerical environment and its name." ] }, { "cell_type": "code", "metadata": { "id": "kchT2nnMKl2q" }, "source": [ "dim = 3\n", "\n", "def run_simulation(rng, n, 𝜀, threshold, solver_spec):\n", " # setting global variables helps avoir a timeit bug.\n", " global solver_ \n", " global a, b, x , y\n", " \n", " # extract specificities of solver.\n", " solver_, env, name = solver_spec \n", "\n", " # draw data at random using JAX\n", " rng, rngs = jax.random.split(rng, 5)\n", " x = jax.random.uniform(rngs[0], (n, dim))\n", " y = jax.random.uniform(rngs[1], (n, dim)) + 0.1\n", " a = jax.random.uniform(rngs[2], (n,))\n", " b = jax.random.uniform(rngs[3], (n,)) \n", " a = a / jnp.sum(a)\n", " b = b / jnp.sum(b)\n", "\n", " # map to numpy if needed\n", " if env == 'np': \n", " a, b, x, y = map(np.array,(a, b, x, y))\n", " \n", " timeit_res = %timeit -o solver_(a, b, x, y, 𝜀, threshold) \n", " out = solver_(a, b, x, y, 𝜀, threshold)\n", " exec_time = np.nan if np.isnan(out[-1]) else timeit_res.best\n", " return exec_time, out" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DxySanaEOwYX" }, "source": [ "Defines the two solvers used in this experiment:" ] }, { "cell_type": "code", "metadata": { "id": "EWmVxyysvEKT" }, "source": [ "POT = (solve_ot, 'np', 'POT')\n", "OTT = (solve_ott, 'jax', 'OTT')" ], "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "szWoHukXOz08" }, "source": [ "## Runs simulations with varying $n$ and $\\varepsilon$\n", "We run simulations by setting the regularization strength 𝜀 to either $10^{-2}$ or $10^{-1}$.\n", "\n", "We consider $n$ between sizes $2^{8}= 256$ and $2^{12}= 4096$. We do not go higher, because `POT` runs into out-of-memory errors for $2^{13}=8192$ in this RAM restricted colab environment. `OTT` can avoid these by setting the flag `online` to `True`, as done in the tutorial for grids, and also handled by the [GeomLoss](https://www.kernel-operations.io/geomloss/) toolbox. We leave the comparison with `geomloss` to a different NB. \n", "\n", "When `%timeit` outputs execution time, notice the warning message* highlighting the fact that, for `OTT`, at least one run took significantly longer. That run is that doing the JIT pre-compilation of the procedure, suitable for that particular problem size $n$. Once pre-compiled, subsequent runs are order of magnitudes faster, thanks to the `@jax.jit` decorator added to `solve_ott`.\n" ] }, { "cell_type": "code", "metadata": { "id": "9VWhb6B3VFJN", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7b85bed2-903e-465f-b7eb-cf8479554c4e" }, "source": [ "rng = jax.random.PRNGKey(0)\n", "solvers = (POT, OTT)\n", "n_range = 2 np.arange(8, 13)\n", "𝜀_range = 10 np.arange(-2.0, 0.0)\n", "\n", "threshold = 1e-2\n", "\n", "exec_time = {}\n", "reg_ot = {}\n", "for solver_spec in solvers: \n", " solver, env, name = solver_spec\n", " print('----- ', name)\n", " exec_time[name] = np.ones((len(n_range), len(𝜀_range))) * np.nan\n", " reg_ot[name] = np.ones((len(n_range), len(𝜀_range))) * np.nan\n", " for i, n in enumerate(n_range): \n", " for j, 𝜀 in enumerate(𝜀_range): \n", " exec, out = run_simulation(rng, n, 𝜀, threshold, solver_spec)\n", " exec_time[name][i, j] = exec\n", " reg_ot[name][i, j] = out[-1] " ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "----- POT\n", "10 loops, best of 5: 43.7 ms per loop\n", "100 loops, best of 5: 11.9 ms per loop\n", "1 loop, best of 5: 230 ms per loop\n", "10 loops, best of 5: 41.4 ms per loop\n", "1 loop, best of 5: 33.4 s per loop\n", "10 loops, best of 5: 155 ms per loop\n", "1 loop, best of 5: 2min 13s per loop\n", "1 loop, best of 5: 367 ms per loop\n", "1 loop, best of 5: 6min 21s per loop\n", "1 loop, best of 5: 1.22 s per loop\n", "----- OTT\n", "The slowest run took 66.78 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1 loop, best of 5: 11.2 ms per loop\n", "1000 loops, best of 5: 1.04 ms per loop\n", "The slowest run took 128.37 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1 loop, best of 5: 6.12 ms per loop\n", "1000 loops, best of 5: 1.08 ms per loop\n", "The slowest run took 94.84 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1 loop, best of 5: 8.95 ms per loop\n", "1000 loops, best of 5: 1.42 ms per loop\n", "The slowest run took 33.90 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1 loop, best of 5: 24 ms per loop\n", "100 loops, best of 5: 3.47 ms per loop\n", "The slowest run took 8.19 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "1 loop, best of 5: 112 ms per loop\n", "100 loops, best of 5: 14.3 ms per loop\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "zruxPCbHN1HY" }, "source": [ "## Plots results in terms of time and difference in objective.\n", "\n", "When the algorithm does not converge within the maximal number of 1000 iterations, or runs into numerical issues, the solver returns a NaN and that point does not appear in the plot." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 540 }, "id": "xj_9C6-3uHMH", "outputId": "736a26fd-3a0f-4b0f-b107-843a84503bc7" }, "source": [ "list_legend = []\n", "fig = plt.figure(figsize=(14,8))\n", "\n", "for solver_spec, marker, col in zip(solvers,('p','o'), ('blue','red')):\n", " solver, env, name = solver_spec\n", " p = plt.plot(exec_time[name], marker=marker, color=col,\n", " markersize=16, markeredgecolor='k', lw=3) \n", " p[0].set_linestyle('dotted')\n", " p[1].set_linestyle('solid')\n", " list_legend += [name + r' $\\varepsilon $=' + \"{:.2g}\".format(𝜀) for 𝜀 in 𝜀_range]\n", "\n", "plt.xticks(ticks=np.arange(len(n_range)), labels=n_range)\n", "plt.legend(list_legend)\n", "plt.yscale('log')\n", "plt.xlabel('dimension $n$')\n", "plt.ylabel('time (s)')\n", "plt.title(r'Execution Time vs Dimension for OTT and POT for two $\\varepsilon$ values')\n", "plt.show()" ], "execution_count": 24, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x576 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "ma5sqdMtzggT" }, "source": [ "For good measure, we also show the differences in objectives between the two solvers. We substract the objective returned by `POT` to that returned by `OTT`.\n", "\n", "Since the problem is evaluated in its dual form, a higher objective is better, and therefore a positive difference denotes better performance for `OTT`. White areas stand for values for which `POT` did not converge (either because it has exhausted the maximal number of iterations or experienced numerical issues)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "id": "RCoef_sbzyFn", "outputId": "42851943-d6e7-4765-9432-5f95237a4b4f" }, "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = plt.gca()\n", "im = ax.imshow(reg_ot['OTT'].T - reg_ot['POT'].T)\n", "plt.xticks(ticks=np.arange(len(n_range)), labels=n_range)\n", "plt.yticks(ticks=np.arange(len(𝜀_range)), labels=𝜀_range)\n", "plt.xlabel('dimension $n$')\n", "plt.ylabel(r'regularization $\\varepsilon$')\n", "plt.title('Gap in objective, >0 when OTT is better')\n", "divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)\n", "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n", "plt.colorbar(im, cax=cax)\n", "plt.show()\n" ], "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZxwbScNpz3LR", "outputId": "36eed4e2-4fca-4cb1-fe00-cf10c4291c09" }, "source": [ "for name in ('POT','OTT'):\n", " print('----', name)\n", " print('Objective')\n", " print(reg_ot[name])\n", " print('Execution Time')\n", " print(exec_time[name])" ], "execution_count": 45, "outputs": [ { "output_type": "stream", "text": [ "---- POT\n", "Objective\n", "[[-0.00862313 -0.79116929]\n", " [-0.02666368 -0.93283839]\n", " [ nan -1.07958862]\n", " [ nan -1.22432204]\n", " [ nan -1.36762311]]\n", "Time\n", "[[0.04367424 0.01185102]\n", " [0.22960342 0.04137421]\n", " [ nan 0.15465033]\n", " [ nan 0.3669143 ]\n", " [ nan 1.21968372]]\n", "---- OTT\n", "Objective\n", "[[-0.00783848 -0.79117149]\n", " [-0.02610656 -0.93283963]\n", " [-0.05083928 -1.07959068]\n", " [-0.06328616 -1.21402502]\n", " [-0.07956241 -1.35710597]]\n", "Time\n", "[[0.01124264 0.00103751]\n", " [0.00612156 0.00107929]\n", " [0.00895449 0.00142238]\n", " [0.02404206 0.00346715]\n", " [0.11208566 0.01432985]]\n" ], "name": "stdout" } ] } ] }	apache-2.0
stijnvanhoey/course_gis_scripting	_solved/05-the-power-of-gdal.ipynb	1	663199	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import ogr\n", "import subprocess\n", "\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GDAL command line" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: reprojection" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "GDAL is a really powerful library for handling GIS data. It provides a number of functionalities to interact with spatial data. As a typical example, take the reprojection of a shapefile to another CRS:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "!ogr2ogr ../scratch/deelbekkens_wgs84 -t_srs \"EPSG:4326\" ../data/deelbekkens/Deelbekken.shp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is this combination of commands?\n", "\n", "* `!` this is a jupyter notebook-thing, telling it we're running something on the command line instead of in Python\n", "* `../scratch/deelbekkens_wgs84` the output location of the created file\n", "* `-t_srs \"EPSG:4326\"` the CRS information for to which the data should be projected\n", "* `../data/deelbekkens/Deelbekken.shp` the source file location" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The documentation is a bit overwhelming:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Usage: ogr2ogr [--help-general] [-skipfailures] [-append] [-update]\r\n", " [-select field_list] [-where restricted_where\|@filename]\r\n", " [-progress] [-sql <sql statement>\|@filename] [-dialect dialect]\r\n", " [-preserve_fid] [-fid FID]\r\n", " [-spat xmin ymin xmax ymax] [-spat_srs srs_def] [-geomfield field]\r\n", " [-a_srs srs_def] [-t_srs srs_def] [-s_srs srs_def]\r\n", " [-f format_name] [-overwrite] [[-dsco NAME=VALUE] ...]\r\n", " dst_datasource_name src_datasource_name\r\n", " [-lco NAME=VALUE] [-nln name] \r\n", " [-nlt type\|PROMOTE_TO_MULTI\|CONVERT_TO_LINEAR]\r\n", " [-dim 2\|3\|layer_dim] [layer [layer ...]]\r\n", "\r\n", "Advanced options :\r\n", " [-gt n] [-ds_transaction]\r\n", " [[-oo NAME=VALUE] ...] [[-doo NAME=VALUE] ...]\r\n", " [-clipsrc [xmin ymin xmax ymax]\|WKT\|datasource\|spat_extent]\r\n", " [-clipsrcsql sql_statement] [-clipsrclayer layer]\r\n", " [-clipsrcwhere expression]\r\n", " [-clipdst [xmin ymin xmax ymax]\|WKT\|datasource]\r\n", " [-clipdstsql sql_statement] [-clipdstlayer layer]\r\n", " [-clipdstwhere expression]\r\n", " [-wrapdateline][-datelineoffset val]\r\n", " [[-simplify tolerance] \| [-segmentize max_dist]]\r\n", " [-addfields] [-unsetFid]\r\n", " [-relaxedFieldNameMatch] [-forceNullable] [-unsetDefault]\r\n", " [-fieldTypeToString All\|(type1[,type2])] [-unsetFieldWidth]\r\n", " [-mapFieldType srctype\|All=dsttype[,srctype2=dsttype2]]\r\n", " [-fieldmap identity \| index1[,index2]]\r\n", " [-splitlistfields] [-maxsubfields val]\r\n", " [-explodecollections] [-zfield field_name]\r\n", " [-gcp pixel line easting northing [elevation]] [-order n \| -tps]\r\n", " [-nomd] [-mo \"META-TAG=VALUE\"]* [-noNativeData]\r\n", "\r\n", "Note: ogr2ogr --long-usage for full help.\r\n" ] } ], "source": [ "!ogr2ogr --help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but there are [great online resources](https://github.com/dwtkns/gdal-cheat-sheet) with good examples you can easily copy paste for your own applications..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: Accessing online webservice data (Web Feature Service - WFS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of expensive terminology... \n", "\n", "Let's illustrate this with an example: The information about municipalities is available as [open data on geopunt](http://www.geopunt.be/catalogus/webservicefolder/1/11c37274-f9db-526e-2067-6606-b324-23d1-f285dbe7) (coming from informatie Vlaanderen). The publication is provided as a [WFS service](http://docs.geoserver.org/stable/en/user/services/wfs/reference.html)... \n", "\n", "Take home message -> GDAL can handle WFS web services ;-)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Downloading the province boundaries from the WFS service provided by informatie Vlaanderen/Geopunt to a geojson file is as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "!ogr2ogr -f 'Geojson' ../scratch/provinces.geojson WFS:\"https://geoservices.informatievlaanderen.be/overdrachtdiensten/VRBG/wfs\" Refprv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can start working with this date..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f44bfa6f748>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OmqD3nC7UbF+LtiPb6lpKnHB3Uueg+/LdHBERwcGDB6lfv36Szj6UjfBkRkrJ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\ngPZ5X8seAFimurwKcC8LeZV05AWRvNA5ozryHSlliJQyWUq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bfa79550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "provinces = gpd.read_file(\"../scratch/provinces.geojson\")\n", "provinces.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually, GDAL can directly query the WFS data:\n", "\n", "Let's say I only need the province of `Antwerp`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "!ogr2ogr -f 'Geojson' ../scratch/antwerp_prov.geojson WFS:\"https://geoservices.informatievlaanderen.be/overdrachtdiensten/VRBG/wfs\" Refprv -where \"NAAM = 'Antwerpen'\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f44bef43630>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PqnvXxMRYfblRig4pJQvWz8W+rC2r16wukglYJd8CtHfvXnbv3s2pk6fYt3cf\npW0cGfLyCE0fWKgoRZUuTceC9fOwcbJmw8b1mJlp86j7p6VmOxSgZs2a8cEHH7Dyz5WE3Aih36C+\nfLpoMofOBmgdmqIUOSbGJgztPJLgC8EPHghaHKnkm8+MjY2ZOHEiO3bt4LdtSwi9FZLzSYqiPMTY\nyBgnB2eioqK0DsVgVPI1kLp16/Lp1E+Z/MNYEpMTtQ5HUYocCzNLoqOjtQ7DYFTyNaCRI0fSpHET\n/E+qvUsVJa8sTFXyVZ6SEILxE8Zz4vIxrUNRlCLH0sxSDTsoT++FF14g6PolitusEkUxNCsLK+7e\nicy5YhGlkq+BOTg4kJB4r8hvFqIoBc3KwpqoSJV8laek0+kwNjbWOgxFKXKsLKzVsIPy9GJjY7G0\nKFmbRCtKfrCysHqmG25SykJ9w04lXwO7dOkSLmVctQ5DUYocKwtrYmLy/pTtlJQULl26xKiRo3Bx\nceGLL74gNbVgtoDNC5V8Deyff/6hikd1rcNQlCLHysIqT8n3woULvPP2u7g4u9D8BT+O7j/OlDem\n8uey1dSrW49jxwrXrCOVfA1s1Z+rqVWhjtZhKEqRY2VhTXRsNPHx8dnW27RpE02bvECTRs9z9UQI\nUwZOZdrw2Qzv9i7lXb14r/c4mvi8SKsWrRg3bhyJiYVj0ZNKvgaUkpLChYsXqOxZVetQFKXIsTS3\npG5VX8q6lqVdm3bMnTuXy5cvPzienp7OlMlTGDRwMHXcfPn67e/o2bIPTg7OD7UjhOCF2s35eMg0\n9m31p2b1muzevbuAP83j1K5mBpSYmEhph9J8N3aBQff7VZTiLD4xnjPBJzlz7RSngo5ja2dH+w7t\nCQ0JJehsECO6v4ettV2u2zt6PpDfty+l48sdmTlrJg4ODjmflAdqS8lCon5dX9rU6kA175pah6Io\nRV66TCfk5nVOBZ0gJS2ZTk27PlXHJjE5gTX/ruRE8FHmzptL9+7d820uvnqMUCHxUpvWnDt4ViVf\nRckHRsKI8q5elHf1eqZ2LM2t6NfmdRpcb8yYd8awetUafvt9ef4EmUtqzNfAOnfpzLFLh9XyYkUp\nhCp7VuX1Dm9x6NChAr+2Sr4G1qRJE8wtzTh+8ajWoSiKkoVL18/TqlXLAr+uSr4GJoTgpwU/sWzL\nIrWxuqIUQkHhF2n9UusCv26OyVcI4SGE2CWEOCeEOCOEeFdfPkMIcV4IcVII8ZcQwj7TOROFEEFC\niAtCiLb8keGEAAAgAElEQVSZytvpy4KEEBMylXsLIQ4KIS4JIf4QQpjpy831Pwfpj3vl54cvKC1a\ntGD2t7P4+rfP+ffoTlJ1hW+1jaKURGnpaZy7fIbmzZsX+LVz0/PVAe9LKasBjYGRQojqwDagppSy\nFnARmAigP9YHqAG0A+YJIYyFEMbAXKA9UB14VV8X4CtgtpTSB4gCBuvLBwNRUspKwGx9vSLptdde\nY+v2rQTHnOe9b4bx49o5HDi9n7iEWK1DU5QS61r4FcqVdcPZ2Tnnyvksx+QrpQyXUh7Vv48DzgFu\nUsqtUkqdvtoBwF3/vguwQkqZLKW8AgQBDfWvICnlZSllCrAC6CIy5ne0BFbpz18CdM3U1hL9+1VA\nK1GE92b09fVl97+7CQoO4vURA7gYeZYJ80Yz9vtRnAo+oXV4ilLinL92jlatC368F/I45qv/2l8X\nOPjIoUHAZv17NyDz4GaovuxJ5Y5AdKZEfr/8obb0x2P09Ys0FxcXhg4dyvad24iNi2X578tZtPFH\n9hzbpXVoilKiBIVdoFXrVppcO9fJVwhRClgNvCeljM1UPpmMoYn7k+Sy6pnKpyjPrq1HYxsqhDgs\nhDh8+/btJ3+IQkgIwUsvvcR+//38e3o7y7f+gi5Nl/OJiqI8k1RdKuevnKVFixaaXD9XyVcIYUpG\n4l0upVyTqXwg0AnoJ/+byBoKeGQ63R0Iy6b8DmAvhDB5pPyhtvTH7YDHtraXUs6XUvpKKX2dnJxy\n85EKnapVq3L46GHuiVi2B27ROhxFKfaCQi9SqWIlSpcurcn1czPbQQALgXNSylmZytsB44HOUsqE\nTKesA/roZyp4Az7AISAQ8NHPbDAj46bcOn3S3gX01J8/EPg7U1sD9e97AjtlMV6tYGdnx6DBgwiL\nDNU6FEUp9s5cPkW79u00u35uer5NgdeAlkKI4/pXB+B7wAbYpi/7EUBKeQZYCZwF/gFGSinT9GO2\no4AtZNy0W6mvCxlJfIwQIoiMMd2F+vKFgKO+fAzwYHpacRUREUEpcxutw1CUYu9CyFlNk2+OeztI\nKfeR9djrpmzO+Rz4PIvyTVmdJ6W8TMZsiEfLk4BeOcVYnOze9S/eZXy0DkNRirX4xHhu3AqlSZMm\nmsWgVrgVIlu2bOHokaM0qN5Y61AUpVg7d+U0jRs1wdzcXLMYVPItJFavXs2rfV7lrS6jMDNVe/8q\niiGdvXaaDp3aaxqDSr6FwKZNm3hzyFDe6z2OKuWraR2OohR7tta2/Lb8N+7cuaNZDCr5aiwkJIT+\nffszqsdovMpW0DocRSkRujbrhZtNeRo3asy1a9c0iUElX429OeRNWtZvSyX3ylqHoiglhhCCHn6v\nUMerAU0aN0GnK/iFTepJFhpITk5m27ZtBAYGcvTIMaYNn611SIpS4iQmJ3Lq8nH69OmDiUnBp0KV\nfDUwfNgItv+znRretXiryyhMjNUfg6IUpFRdKvPWfMMLLZ5n5qyZmsSg/tVrICoykpebdqNRzee1\nDkVRSpz09HQWbvgRryqezF8wP98enJlXasxXA/v27yM5NVnrMBSlxJFS8vv2pRiXgj9W/oGxsbFm\nsajkq4E7d+8QHHpJ6zAUpcRZv+8vbsSEsGHjBiwsLDSNRQ07aMDf35/nn38eO1s7ujd/RetwFKVE\nSE5NZv2+v7h27Rp2dnZah6N6vlpo0qQJa9eu5cYd9UBNRSkoKanJWFla4erqqnUogEq+milfvjyR\nsY9tTawoioGk6lI13cvhUSr5aiQlJYWwm2E5V1QUJV+k6lIxN9N2nDczlXw1Uq1aNVJ1KSSnJGkd\niqKUCKm6VMzMTLUO4wGVfDViY2NDOddyRMdHax2KopQI6elpmqxkexKVfDVkZ29PdFyU1mEoSomQ\nlp6GaSHarlUlX41ERUVx+swplXwVpYDo0nSYmqphhxLPzs6ON998k4Cz+7QORVFKhLS0NMzMCk/P\nt/AMgJQwRkZGfP3115QrW47E5AQsza20DklRirW0dB2mpoUn5amer4ZsbW1p1KARJ4NOaB2KohR7\npiZmJCQkaB3GAyr5aqxXn14cv3hE6zAUpdhzLu1K8OXLSCm1DgVQyVdzXbt2xf/kXqLi1Go3RTEk\nO2s7kJLw8HCtQwFU8tWcq6srzz//PDsOb9E6FEUp1oQQeLlVYMqUKZw+fVrrcFTy1VJKSgqDBw8m\nKSGJ9XvWah2OohR7PZu/Svj5W7zc8WVSUlI0jUUlXw2lpaWxaNEiaparw1ej1HPcFMXQPFw8ebXN\nABxLOfHNN99oGkuOyVcI4SGE2CWEOCeEOCOEeFdf3kv/c7oQwveRcyYKIYKEEBeEEG0zlbfTlwUJ\nISZkKvcWQhwUQlwSQvwhhDDTl5vrfw7SH/fKrw9eGFhaWtK6VWt06WlExUayfMsv6NIK/imqilLS\n9G7Zl/HjxzNz5kwSExM1iSE3PV8d8L6UshrQGBgphKgOnAa6A3syV9Yf6wPUANoB84QQxkIIY2Au\n0B6oDryqrwvwFTBbSukDRAGD9eWDgSgpZSVgtr5esTL1s6n89s8Sftu1hIAz+zhy7pDWISlKsefq\nWI4JAz5g5ZJVeJX3YsWKFQUeQ47JV0oZLqU8qn8fB5wD3KSU56SUF7I4pQuwQkqZLKW8AgQBDfWv\nICnlZSllCrAC6CIynl7XElilP38J0DVTW0v071cBrYRWT7szkEaNGrF27VpOnjrJpEmT1Io3RSkg\n1bxrMrL7aIZ3eZfhw0awf//+Ar1+nsZ89V/76wIHs6nmBmR+REOovuxJ5Y5AtJRS90j5Q23pj8fo\n6xcbQgi6dOmCnZ0dr7zyCtfCrxSaeYiKUhJUcKvEoI5v0b1rd0JDQwvsurlOvkKIUsBq4D0pZWx2\nVbMok09Rnl1bj8Y2VAhxWAhx+Pbt29mEVrh5eXlhbmHBzcjCMQ9RUUqK2j51aV67NZ06vkxSUsHs\nsZ2r5CuEMCUj8S6XUq7JoXoo4JHpZ3cgLJvyO4C9EMLkkfKH2tIftwMeW40gpZwvpfSVUvo6OTnl\n5iMVSkIIfH19uRJ2RetQFKXE6di0M1ZYM+j1QQXy7TM3sx0EsBA4J6WclYs21wF99DMVvAEf4BAQ\nCPjoZzaYkXFTbp3M+JS7gJ768wcCf2dqa6D+fU9gpyzG38n379/Pjh3bsbJQm+woSkETQvBGx6Ec\n2H+Q2bMNP/UzNz3fpsBrQEshxHH9q4MQopsQIhRoAmwUQmwBkFKeAVYCZ4F/gJFSyjT9mO0oYAsZ\nN+1W6usCjAfGCCGCyBjTXagvXwg46svHAA+mpxVHK1euxMOlPLV96modiqKUSOZmFozsMZrPp37B\njh07DHotUdw6kr6+vvLw4cNah/FU9u7dS5s2bTEWxtSsWIvufr1wdSyndViKUuKcCj7Bqj2/ceVa\n3ocAhRBHpJS+OdVTK9wKkWbNmnH8+DEmTp7A6SsnOH7xmNYhKUqJVM2rBqFhhp35UHh2FlYAqFKl\nCpMnT+bUiVNYp1hrHY6ilEjGRsbodDqklBhqaYHq+RZSkZFRWFuo5KsoWhBCYGJsgk5nuOX+KvkW\nUjEx0VhZquSrKFoxMTEx6M5nKvkWUomJSZiZFJ6H/SlKSWNsbEJqaqrB2lfJt5CysbUhMVmb3ZYU\nRQFTE5V8SySnMmWIT4zXOgxFKbHMTA37wE2VfAspJ2cn4hPitA5DUUosa6tSxMZmt43Ns1HJt5By\ncnYiIfme1mEoSollbWlNTEyMwdpXybeQcnR0JClVjfkqilYszS1V8i2JypQpw70U1fNVFK1YqORb\nMjk6OhJ/T435KopWLEwt1ZhvSZSSkoKRkfrjURQtSCkhHe7dM9y3T/Wvu5AKDw/H1spO6zAUpcRJ\nTk1m0o/vc+DMflq0aGGw66jkW0jduHEDGwtbrcNQlBJns/96GjZpQGxcLPXq1TPYddSuZoVUyLUQ\n7Es5aB2GohQLMfHRXA2/gpWFFS6ly2JrnXXHZsuBTRy84E/AAX+Dx6SSbyEVHBzMCz4ttQ5DUYq8\nK2GX+XjBRACMjY1JS0ujoqcPgzq+hbvzf4+V3Lj/bw5c2I9/wH48PT0NHpcadiiEkpOTOXHyBBXc\nKmkdiqIUeenpadSoWhMpJTqdjqSkJN4YOpB5a74h7M4NpJSs27uGw8EH8Q/YT/ny5QskLtXzLYQO\nHDiAp5uXepCmouSD29G3qFip4oOfzc3NmTRpElevXmXi3DE0fu55opLvst9/H66urgUWl+r5FkK7\ndu2iuncNrcNQlGLhVuRNqlSt/FCZEIIFCxawYcMG2vVow37//QWaeEEl30Ln9u3b/PjDj9T0rK11\nKIpSLGw/vOWhnm9mHTt25KOPPsLR0bGAo1LJt1A5fPgwzV/04/kaL1LR3UfrcBSlyDsZdBx7ezsG\nDRqkdSiPUWO+GpNS4u/vz3dzvmP7th10fbEnzWr7aR2WohRpKakprN//F5v917NhwwZMTU21Dukx\nKvkamJSSAwcOsGTJUu7evkNCQiIpKckkJyeTnJxCxM0ISIfnazTj87dmYGmubrIpSnZ0aTpMjP9L\nXY8+Yfh08El+3bqYJk0bc+XKFdzc3LQIM0cq+eZCeHg4A/oPIOhSEB6enlTyqchLbV6ia9euWFpa\nPvG8sLAw+vbpy6WLQTSt+SKOtq642phiamKKiXHGf618rXF39jDY46kVpSi7cO0cNyMjaFC9MQlJ\n9/ht2xKOnT+CjbUN1b2f49iFwyQmJVK3an1srG3Yc2Q3VpZWrFq9ivbt22sdfraElDL7CkJ4AEsB\nVyAdmC+l/FYIURr4A/ACrgK9pZRRIiOLfAt0ABKA16WUR/VtDQSm6Jv+TEq5RF9eH/gFsAQ2Ae9K\nKeWTrpFdvL6+vvLw4cO5/z+Qg4SEBOrXrU9l1+o0qvE8d2PucDvqFqeuHCciKpwffpxH165dHzon\nOTmZwMBA+vXtRwOfJnRs2gVjI+N8i0lRSoKbkRF89suHNGncmD1795Cens64ceOYNHkSERERrFix\nglq1alG7dm2WL1+Ovb095cqVw8/Pj1KlSmkWtxDiiJTSN8d6uUi+ZYGyUsqjQggb4AjQFXgdiJRS\nThNCTAAcpJTjhRAdgLfJSL6NgG+llI30ifQw4AtIfTv19Qn7EPAucICM5DtHSrlZCDE9q2tkF29+\nJl8pJQP6D+DKueu8+fKIx3qn56+eZek/C6lZuwZt27UlOjqaP1as5OrVK5RzceMl3/Y0rfVivsSi\nKCXNuatn2H1uKwEHA0hJSSEuLk6TWQl5ldvkm+Owg5QyHAjXv48TQpwD3IAugJ++2hJgNzBeX75U\nZmT1A0IIe30C9wO2SSkj9QFuA9oJIXYDtlLKAH35UjKS++ZsrlEgxo8bz8H9gYztOynLYYGqXtX5\nePCXBJzax5ZV2zE1NqPH833w7lVRPfZdUZ6R/6m9NHqhMQBmZmZFIvHmRZ7GfIUQXkBd4CDgok/M\nSCnDhRDO+mpuQEim00L1ZdmVh2ZRTjbXMLgVK1awbMmvTHn9UyzMLJ5Yz8zUjOb11B4MipKfQm+F\nsPfYbjbs/lvrUAwm18lXCFEKWA28J6WMzeYGUVYH5FOU55oQYigwFHjmDTHS09P59NNPmfvdPN7p\n+T42VmpbR0UpSMcvHmHxpgX8+OOP2NjYaB2OweQq+QohTMlIvMullGv0xTeFEGX1PdKywC19eSjg\nkel0dyBMX+73SPlufbl7FvWzu8ZDpJTzgfmQMeabm8+URRsEBATw4QcfcuNaOB+8PhV7G7Wlo6IU\nlBRdCmv/XcXGfX9z8OBBGjZsqHVIBpXjCjf97IWFwDkp5axMh9YBA/XvBwJ/ZyofIDI0BmL0Qwdb\ngDZCCAchhAPQBtiiPxYnhGisv9aAR9rK6hr56vbt2zR/0Y+eXXtS1tyTsX0mqcSrKAUoLT2NH/76\nFmx1nD17ttgnXshdz7cp8BpwSghxXF82CZgGrBRCDAauA730xzaRMdMhiIypZm8ASCkjhRBTgUB9\nvU/v33wDhvPfVLPN+hfZXCNf9Xu1H9bptnwxbJZ6bpqiFLDrEVdZsvlnKlWtyF9r/yqUq9EMIcep\nZkVNXqeaxcTEUK5sOb4d/RNmpmqGgqIUpHSZzkc/T2DshPcZNWpUsVhslNupZiW+mxcYGEgFj0oq\n8SqKBrYd3IxzWWeGDx9eLBJvXpT45cUhISE42hav+YOKUhREx0Wxfv9aDh8JxMSk5KWiEt/zLVeu\nHDdu3yAtPU3rUBSlRLkcFkzdOnWoXLlyzpWLoRKffFu3bk0FHy++XPox/if3kqpL1TokRSm20mU6\nNyMj+GXTfBZv/In+A/prHZJmSl5f/xHGxsZs3rKZtWvXMmvmLA6e9+e9XuNK3PiTouQ3KSXhd25w\n7upZ/j2+kzvRt7mXEI+9nQMjRgznh2VzqVKlitZhaqbEz3bILC0tjedqPEeXRj2p5l0znyNTlJIj\n+EYQizfMJyTiGu3atuO5Ws/RoEEDGjZsWGBPB9ZKvm2sU5IYGxvTtXtXzgWcU8lXUfJAl6bjbswd\ngkMvcfb6KQ6dPsCIESMYPXo0Hh4eOTdQAqnk+whHR0dSUk9rHYaiFDpXwoIJuxOGfSk7PFzKE3b7\nBtdvXuP4paOcCT4JQMcOnejUuwNrt66hdOnSGkdcuKnk+4ioqCisLK21DkNRCp3pv35GhQoVOH3m\nv86JTSkbBgwYyPqxa/Hw8CiRU8aelvo/9Yhr165hZ2WvdRiKUuiUd/NizndzaNGixWPPTVPyrsRP\nNXvU9ashOKhNdRTlMUIakZycnPFeJd5nppLvI0JDQ3FQK94U5SHJKUlcuHquROw2VlBU8n3EzVsR\nONiqGwWKktmtqFtU8K6obqLlI5V8M0lOTiYpKQmK2dxnRXlWt6Nv4e3lrXUYxYq64ZaJubk5Q4a8\nyXvfDMfB1oE2jTrS2ret1mEpiiaklGzyX8et6AhOXDrOW8OGah1SsaJ6vo/48acfSEhIYO36tazc\ntpzitgJQUXLrXmI8K7f/Rq83enAw8ABfTvtS65CKFdXzzYKxsTHr162nVuU66q6uUmJtPbiZfq/2\nZ8SIEVqHUiyp5JuFJUuWsHDBIiYN/ETrUBRFE/GJ8ew6uo1ji49pHUqxpYYdHjFnzhzGjR3PO73G\nYl9KLbZQirejFw5z+UbQY+VbDmykS9euVKhQQYOoSgbV833EzK9nMar7aNyd1WYgSvEVnxDHHzuX\nc+LSUSq4V+K9XuP+O5YYz+5jOzj+i+r1GpLq+T7C28uLiMiIHOslpSTx4fzxHDoTQIouJcs6d2Pu\n8NWyqaze9Qe/bvmFb1ZOJyU167qKUlBSUlMY8+1InLwcCQoO4tK1i8TERz84vvXgJrp364a3t5pa\nZkiq5/uIocOG8r8x4zgWFEgb3w5UcKv0WJ2Iu2H8vm0ZqSKFDYfWcuh8AKN6jHms3r9Hd3L28mna\ndG6Ns7MzkyZNYkLEaMqX88KhVGl83KrSsEZjjI2MC+KjKQUgXaZz7spp0qXkuYq1NYsjRZeClJL0\n9HSEEEgpuR11k1tRN/lz1+907tyFP1auQAhB2zZt+SdgA8LYiE371lHKuhQnfzmpWewlhUq+j+jZ\nsydJSUnEx8fzyUef8HbP9x9KwInJCYz/fjRTpkxh8uTJpKSkYGdnx8UG56nsWRWAmPhoDp4JYM+J\nnezevZvmzZsDUL9+fVJTUzEyMuLKlSv8vGAhf+9bRbPn/GhYowlODs6afGbl2SWnJLHn+G52H9+O\njW0pwsPD+Wjwl5QuwNWSEXfDOHg6gGu3r3Dy4nFSU1MfJF4A93LuuLi48PPiBXTo0OHBTJ4Ro4Yz\n8LWB9Ordi417JT169MDLy6vA4i6p1JMssrF+/XoGvjaQYd3eoZJ7ZW5FRjD/77mE3bmRsRJO78sv\nv2Tm1zPxKluBI2cCAbCytGLeD/MYOHDgE9uXUhIQEMCihYv4e+3ffPD6Z9irTX2KnKSUJOasmoFH\nBXcmTZ7ECy+8wIDXBiDvmtCmUfsCiSHibhiz/5hOrz49qVWrFv369cPMzAwhBCEhIbi5ueW43aOU\nkjFjxjBmzBi1AfozyO2TLFTyzcH69et5a+hbhEeEA/DOO+8wbdo0LC0tH6qXkJDA77//jp2dHd26\ndcPYOG9DCZMnT2bH+l2M6P5enmO8/9BPUxPTPJ+rPJuU1BS+XzOT+s/XY8GCBRgZZdxG2bBhAxPG\nTGRc3w8MHsO18CvMXDGNKVMmM/Z/Yw1+PSV7KvnmsyNHjpCSkkKTJk3yvW2ApKQkfCr60Kt5P+pU\nrpfr89LT0xkzZyTJKUm0atCG7s1feZAAFMNK1aUy769v8HmuIsuXL3/oF25SUhI+lSrjW6kR9as2\nwNPVy2BxfPvnDIa+M4S33nrLYNdQci+3yTfHf6VCiEVCiFtCiNOZymoLIQKEEKeEEOuFELaZjk0U\nQgQJIS4IIdpmKm+nLwsSQkzIVO4thDg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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bef3e048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "antwerp = gpd.read_file(\"../scratch/antwerp_prov.geojson\")\n", "antwerp.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I do know that the [Meetplaatsen Oppervlaktewaterkwaliteit](http://www.geopunt.be/catalogus/webservicefolder/4/435c1aae-6619-0801-c317-2bf9-9f58-ce7f-e262a96c) are also available as a WFS web service. However, I'm only interested in the locations for fytoplankton:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "!ogr2ogr -f 'Geojson' ../scratch/metingen_fytoplankton.geojson WFS:\"https://geoservices.informatievlaanderen.be/overdrachtdiensten/MeetplOppervlwaterkwal/wfs\" Mtploppw -where \"FYTOPLANKT = '1'\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/html": [ "<iframe src=\"data:text/html;base64,<head>
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if (gjData.features.length != 0) {
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</body>\" width=\"100%\" height=\"240\"></iframe>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mplleaflet\n", "fyto = gpd.read_file(\"../scratch/metingen_fytoplankton.geojson\")\n", "fyto.head()\n", "fyto.to_crs('+init=epsg:4326').plot(markersize=5)\n", "mplleaflet.display()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "run_control": { "frozen": false, "read_only": 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>BACTERIO</th>\n", " <th>FYSICOCHEM</th>\n", " <th>FYTOBENT</th>\n", " <th>FYTOPLANKT</th>\n", " <th>MACROFYT</th>\n", " <th>MACROINV</th>\n", " <th>MAP_MEETNT</th>\n", " <th>MEETPLNR</th>\n", " <th>OIDN</th>\n", " <th>OMSCHR</th>\n", " <th>UIDN</th>\n", " <th>WATBODEM</th>\n", " <th>ZUURSTOF</th>\n", " <th>geometry</th>\n", " <th>gml_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>335000</td>\n", " <td>160.0</td>\n", " <td>Gewad, opw brugje, thv Club Vrije Vissers</td>\n", " <td>160.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>POINT (198585 204048)</td>\n", " <td>Mtploppw.120</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>336000</td>\n", " <td>177.0</td>\n", " <td>Schoor; thv spoorweg (zandweg langs spoorweg),...</td>\n", " <td>177.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>POINT (207151 205751)</td>\n", " <td>Mtploppw.137</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>340500</td>\n", " <td>183.0</td>\n", " <td>RV: Battenbroek; Walem, ter hoogte van Blaren...</td>\n", " <td>183.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>POINT (155148 195239)</td>\n", " <td>Mtploppw.143</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>969012</td>\n", " <td>2622.0</td>\n", " <td>Reninge, Steenw Op Reninge-Noordschote</td>\n", " <td>2622.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>POINT (39450 183350)</td>\n", " <td>Mtploppw.511</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>973075</td>\n", " <td>2665.0</td>\n", " <td>Westouter, Langedreef zijweg</td>\n", " <td>2665.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>POINT (35218 165357)</td>\n", " <td>Mtploppw.553</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " BACTERIO FYSICOCHEM FYTOBENT FYTOPLANKT MACROFYT MACROINV MAP_MEETNT \\\n", "0 0 1 0 1 0 1 0 \n", "1 0 1 0 1 0 1 1 \n", "2 1 1 0 1 0 1 0 \n", "3 0 1 0 1 0 1 1 \n", "4 0 1 0 1 0 1 0 \n", "\n", " MEETPLNR OIDN OMSCHR UIDN \\\n", "0 335000 160.0 Gewad, opw brugje, thv Club Vrije Vissers 160.0 \n", "1 336000 177.0 Schoor; thv spoorweg (zandweg langs spoorweg),... 177.0 \n", "2 340500 183.0 RV: Battenbroek; Walem, ter hoogte van Blaren... 183.0 \n", "3 969012 2622.0 Reninge, Steenw Op Reninge-Noordschote 2622.0 \n", "4 973075 2665.0 Westouter, Langedreef zijweg 2665.0 \n", "\n", " WATBODEM ZUURSTOF geometry gml_id \n", "0 0 1 POINT (198585 204048) Mtploppw.120 \n", "1 1 1 POINT (207151 205751) Mtploppw.137 \n", "2 1 1 POINT (155148 195239) Mtploppw.143 \n", "3 0 1 POINT (39450 183350) Mtploppw.511 \n", "4 0 1 POINT (35218 165357) Mtploppw.553 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fyto.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually, the same type of subselection are also possible on shapefiles,... \n", "\n", "Extracting a specific DEELBEKKEN from the deelbekken shapefile:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "!ogr2ogr ../scratch/subcat.shp ../data/deelbekkens/Deelbekken.shp -where \"DEELBEKKEN = '10-10'\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(If you're wondering how I know how to setup these commands and arguments, check the (draft) `introduction_webservices.ipynb` in the `scratch` folder. <br>\n", "I use the python interafce of GDAL/OGR and the package [`owslib`](https://geopython.github.io/OWSLib/) to find out how to setup the arguments.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GDAL command line, but inside Python..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No problem if this is still unclear... an example application!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clipping example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example we will use is to clip raster data using a shapefile. We use a data set from [natural earth](http://www.naturalearthdata.com/downloads/50m-natural-earth-1/50m-natural-earth-i-with-shaded-relief/), which we will unzip to start working on it (off course using Python itself):" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import zipfile\n", "zip_ref = zipfile.ZipFile(\"../data/NE1_50m_SR.zip\", 'r')\n", "zip_ref.extractall(\"../scratch\")\n", "zip_ref.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The GDAL function that support the clipping of a raster file, is called [`gdalwarp`](http://www.gdal.org/gdalwarp.html). Again, the documentation looks rather overwhelming... Let's start with an example execution:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating output file that is 13P x 3L.\r\n", "Processing input file ../scratch/NE1_50M_SR/NE1_50M_SR.tif.\r\n", "0...10...20...30...40...50...60...70...80...90...100 - done.\r\n" ] } ], "source": [ "!gdalwarp ../scratch/NE1_50M_SR/NE1_50M_SR.tif ../scratch/cliptest.tif -cutline \"../scratch/subcat.shp\" -crop_to_cutline -overwrite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `!` this is a jupyter notebook-thing, telling it we're running something on the command line instead of in Python\n", "* `gdalwarp` is the GDAL command to use\n", "* `../scratch/NE1_50M_SR/NE1_50M_SR.tif` the source file location\n", "* `../scratch/cliptest.tif` the output location of the created file\n", "* `-cutline \"../scratch/subcat.shp\"` the shape file to cut the raster with\n", "* `-crop_to_cutline` an additional argument to GDAL to make the clipping\n", "* `-overwrite` overwrite eventual existing file with the same name" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f44bedf3400>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bfabf828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "%matplotlib inline\n", "img=mpimg.imread('../scratch/cliptest.tif')\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is off course a dummy example (to keep runtime low), but it illustrates the concept." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## the `subprocess` trick..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doing the same using pure Python code and `from osgeo import gdal` is actually not so beneficial, as the command above is reather straighforward... However, the dependency of the command line provides a switch of environment in any data analysis pipeline. I actually do want to have the best of both worlds:\n", "Using Python code, but running the command line version of GDAL...\n", "\n", "...therefore we need `subprocess`!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import subprocess" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Doing the same as above, but actually using Python code to run the command with given variables as input:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inraster = '../scratch/NE1_50M_SR/NE1_50M_SR.tif'\n", "outraster = inraster.replace('.tif', '{}.tif'.format(\"_out\")) # same location, but adding _out to the output \n", "inshape = \"../scratch/subcat.shp\"\n", "subprocess.call(['gdalwarp', inraster, outraster, '-cutline', inshape, \n", " '-crop_to_cutline', '-overwrite'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remark when GDAL provides a zero as return statement, this is a GOOD sign!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f44be6b69e8>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABwCAYAAAA+CZrQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f44be6d0a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "%matplotlib inline\n", "img=mpimg.imread('../scratch/NE1_50M_SR/NE1_50M_SR_out.tif')\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence, the result is the same, but calling the command from Python. By writing a Python function for this routine, I do have a reusable functionality in my toolbox that I can load in any other Python script:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "def clip_raster(inraster, outraster, invector):\n", " \"\"\"clip a raster image with a vector file\n", " \n", " Parameters\n", " ----------\n", " inraster : GDAL compatible raster format\n", " outraster : GDAL compatible raster format\n", " invector : GDAL compatible vector format\n", " \"\"\"\n", " response = subprocess.call(['gdalwarp', inraster, outraster, '-cutline', \n", " invector, '-crop_to_cutline', '-overwrite'])\n", " return(response) " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inraster = '../scratch/NE1_50M_SR/NE1_50M_SR.tif'\n", "outraster = inraster.replace('.tif', '{}.tif'.format(\"_out\")) # same location, but adding _out to the output \n", "inshape = \"../scratch/subcat.shp\"\n", "clip_raster(inraster, outraster, inshape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More advanced clipping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider the data set of the provinces we called from the WFS server earlier:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "run_control": { "frozen": false, "read_only": 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>NAAM</th>\n", " <th>NISCODE</th>\n", " <th>NUTS2</th>\n", " <th>OIDN</th>\n", " <th>TERRID</th>\n", " <th>UIDN</th>\n", " <th>geometry</th>\n", " <th>gml_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Antwerpen</td>\n", " <td>10000</td>\n", " <td>BE21</td>\n", " <td>2.0</td>\n", " <td>357</td>\n", " <td>6.0</td>\n", " <td>(POLYGON ((178133.91 244025.6, 178125.41 24402...</td>\n", " <td>Refprv.1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Vlaams Brabant</td>\n", " <td>20001</td>\n", " <td>BE24</td>\n", " <td>4.0</td>\n", " <td>359</td>\n", " <td>7.0</td>\n", " <td>(POLYGON ((200484.928 193540.963625, 200484.79...</td>\n", " <td>Refprv.2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Oost-Vlaanderen</td>\n", " <td>40000</td>\n", " <td>BE23</td>\n", " <td>5.0</td>\n", " <td>356</td>\n", " <td>9.0</td>\n", " <td>(POLYGON ((142473.938 226522.156, 142360.719 2...</td>\n", " <td>Refprv.3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Limburg</td>\n", " <td>70000</td>\n", " <td>BE22</td>\n", " <td>1.0</td>\n", " <td>355</td>\n", " <td>10.0</td>\n", " <td>(POLYGON ((231635.609 218998.547, 231477.484 2...</td>\n", " <td>Refprv.4</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>West-Vlaanderen</td>\n", " <td>30000</td>\n", " <td>BE25</td>\n", " <td>3.0</td>\n", " <td>351</td>\n", " <td>11.0</td>\n", " <td>(POLYGON ((80189.158375 229275.081, 80038.5202...</td>\n", " <td>Refprv.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NAAM NISCODE NUTS2 OIDN TERRID UIDN \\\n", "0 Antwerpen 10000 BE21 2.0 357 6.0 \n", "1 Vlaams Brabant 20001 BE24 4.0 359 7.0 \n", "2 Oost-Vlaanderen 40000 BE23 5.0 356 9.0 \n", "3 Limburg 70000 BE22 1.0 355 10.0 \n", "4 West-Vlaanderen 30000 BE25 3.0 351 11.0 \n", "\n", " geometry gml_id \n", "0 (POLYGON ((178133.91 244025.6, 178125.41 24402... Refprv.1 \n", "1 (POLYGON ((200484.928 193540.963625, 200484.79... Refprv.2 \n", "2 (POLYGON ((142473.938 226522.156, 142360.719 2... Refprv.3 \n", "3 (POLYGON ((231635.609 218998.547, 231477.484 2... Refprv.4 \n", "4 (POLYGON ((80189.158375 229275.081, 80038.5202... Refprv.5 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "provinces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can actually use a selection of the provinces data set to execute the clipping:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inraster = '../scratch/NE1_50M_SR/NE1_50M_SR.tif'\n", "outraster = inraster.replace('.tif', '{}.tif'.format(\"_OostVlaanderen\")) \n", "invector = \"../scratch/provinces.geojson\"\n", "subprocess.call(['gdalwarp', inraster, outraster, '-cutline', invector, \n", " '-cwhere', \"NAAM='OOST-VLAANDEREN'\", \n", " '-crop_to_cutline', \n", " '-overwrite'])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f44be7702e8>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44be75c908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "%matplotlib inline\n", "img=mpimg.imread('../scratch/NE1_50M_SR/NE1_50M_SR_OostVlaanderen.tif')\n", "plt.imshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By having it as a Python call, we can do the same action for each of the individual provinces in the dataset and create for each of the provinces a clipped raster data set:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Antwerpen\n", "Vlaams Brabant\n", "Oost-Vlaanderen\n", "Limburg\n", "West-Vlaanderen\n" ] } ], "source": [ "import ogr\n", "\n", "inraster = '../scratch/NE1_50M_SR/NE1_50M_SR.tif'\n", "invector = \"../scratch/provinces.geojson\"\n", "\n", "# GDAL magic...\n", "ds = ogr.Open(invector)\n", "lyr = ds.GetLayer(0)\n", "\n", "lyr.ResetReading()\n", "ft = lyr.GetNextFeature()\n", "\n", "# clipping for each of the features (provincesin this case)\n", "while ft:\n", "\n", " province_name = ft.GetFieldAsString('NAAM')\n", " print(province_name)\n", "\n", " outraster = inraster.replace('.tif', '_%s.tif' % province_name.replace('-', '_')) \n", " subprocess.call(['gdalwarp', inraster, outraster, '-cutline', invector, \n", " '-crop_to_cutline', '-cwhere', \"NAAM='%s'\" %province_name])\n", "\n", " ft = lyr.GetNextFeature()\n", "\n", "ds = None" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f44be8cf8d0>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bed1f8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "%matplotlib inline\n", "img=mpimg.imread('../scratch/NE1_50M_SR/NE1_50M_SR_West_Vlaanderen.tif') # check also Antwerpen,...\n", "plt.imshow(img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "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": "3.5.3" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
DCPROGS/R-PROGS	pyScripts/RSOS2014.ipynb	2	95254	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python code for a paper \"An investigation of the false discovery rate and the misinterpretation of p-values\" by David Colquhoun published in Royal Society Open Science 2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Some general settings" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from pylab import\n", "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as stats\n", "from statsmodels.stats.power import TTestIndPower" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two mean simulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Set mean and SD for Sample1 and Sample2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "mu1, sd1 = 0.0, 1.0\n", "mu2, sd2 = 1.0, 1.0\n", "n1 = 16 #number of obs per sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot distribution of observations for Sample1 and Sample2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "xmin = mu1 - 4 sd1\n", "xmax = mu1 + 4 * sd1\n", "increase = (xmax - xmin) / 100\n", "x = np.arange(xmin, xmax, increase)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "dist1 = stats.norm(mu1, sd1)\n", "y1 = dist1.pdf(x)\n", "dist2 = stats.norm(mu2, sd2)\n", "y2 = dist2.pdf(x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y1, 'b-')\n", "plot(x, y2, 'r-')\n", "xlabel('distributions of observations')\n", "ylabel('probability density');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot distribution of means for Sample1 and Sample2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sdm1 = sd1 / sqrt(n1)\n", "sdm2 = sd2 / sqrt(n1)\n", "dist1m = stats.norm(mu1, sdm1)\n", "y1m = dist1m.pdf(x)\n", "dist2m = stats.norm(mu2, sdm2)\n", "y2m = dist2m.pdf(x)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, y1m, 'b-')\n", "plot(x, y2m, 'r-')\n", "xlabel('distributions of means (16 observations)')\n", "ylabel('probability density');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run simulations" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def run_simulation(mean, sigma, n, nsim):\n", " #initialisations\n", " pval = np.zeros(nsim)\n", " diff = np.zeros(nsim)\n", " #loCI = np.zeros(nsim)\n", " #hiCI = np.zeros(nsim)\n", " \n", " for r in range(nsim):\n", " s1, s2 = np.random.multivariate_normal(mean, sigma, n).T\n", " sd = s2 - s1\n", " t, p = stats.ttest_ind(s1, s2, equal_var=False, nan_policy='omit')\n", " diff[r] = np.mean(s1) - np.mean(s2)\n", " pval[r] = p\n", " #low, high = stats.t.interval(0.95, len(sd)-1, loc=np.mean(sd), scale=stats.sem(sd))\n", " #loCI[r] = low\n", " #hiCI[r] = high\n", " \n", " return diff, pval" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "mean1 = np.array([mu1, mu2])\n", "#set covariance matrix\n", "cor = 0.0 #correlation = 0\n", "var1 = sd12\n", "var2 = sd22\n", "sigma1 = np.array([[var1, cor], [cor, var2]]) #matrix(c(myvar1,mycor,mycor,myvar2),2,2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "nsim1 = 10000 # number of simulation jobs to run\n", "diff1, pval1 = run_simulation(mean1, sigma1, n1, nsim1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(pval1, bins=20);\n", "xlabel('value of P')\n", "ylabel('frequency');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(diff1, bins=20);\n", "xlabel('difference between means')\n", "ylabel('frequency');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#set min and max P values for \"significance\"\n", "Pmin, Pmax = 0.0, 0.05\n", "# nsig: counts number of pval between and myPmax =0.0\n", "nsig = pval1[(pval1 > Pmin) & (pval1 <= Pmax)].size\n", "#mean observed difference for expts with Pmin<P<=Pmax\n", "meandiff = np.sum(diff1[(pval1 > Pmin) & (pval1 <= Pmax)]) / nsig" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "effect size= 1.0\n" ] } ], "source": [ "effect_size = (mu2 - mu1) / sd1\n", "print('effect size=', effect_size)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Power = 0.7813977845875587 for P = 0.05\n", "(alternative power calculation = 0.7791 )\n" ] } ], "source": [ "# calculate test power\n", "Psig = 0.05\n", "nrej = pval1[pval1 <= Psig].size\n", "power_analysis = TTestIndPower()\n", "pwr = power_analysis.power(effect_size, n1, Psig)\n", "print('Power =', pwr, 'for P =', Psig)\n", "print('(alternative power calculation =', nrej / float(nsim1), ')')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "n001 = pval1[pval1 <= 0.001].size #counts number of P<0.001\n", "n01 = pval1[(pval1 > 0.001) & (pval1 <= 0.01)].size #counts number of 0.001<P<0.01\n", "n05 = pval1[(pval1 > 0.01) & (pval1 <= 0.05)].size #counts number of 0.01<P<0.05\n", "ns = pval1[pval1 > 0.05].size #counts number of P>0.05 \"non sig\"" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of (P <= 0.001) = 2284 (= 22.84 %)\n", "Number of (0.001 < P <= 0.01) = 3058 (= 30.58 %)\n", "Number of (0.01 < P <= 0.05) = 2449 (= 24.49 %)\n", "Number of (P > 0.05) = 2209 (= 22.09 %)\n", "Number of (P <= 0.05) = 7791 ( 77.91 %)\n", "\n", " Observed difference between means for 'sig' results = -1.127876302739111 True value = -1.0\n" ] } ], "source": [ "print(\"Number of (P <= 0.001) = \", n001, \"(=\", 100n001/nsim1, \"%)\")\n", "print(\"Number of (0.001 < P <= 0.01) = \", n01, \"(=\", 100n01/nsim1,\"%)\")\n", "print(\"Number of (0.01 < P <= 0.05) = \", n05, \"(=\", 100n05/nsim1,\"%)\")\n", "print(\"Number of (P > 0.05) = \", ns, \"(=\",100ns/nsim1,\"%)\")\n", "print(\"Number of (P <= 0.05) = \", nsim1-ns, \"(\", 100(nsim1-ns)/nsim1,\"%)\")\n", "print(\"\\n\",\"Observed difference between means for 'sig' results = \", \n", " meandiff, \" True value = \", mu1-mu2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot P value ditribution for a case with 4 observations per group" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "nsim1 = 10000 # number of simulation jobs to run\n", "n2 = 4\n", "diff2, pval2 = run_simulation(mean1, sigma1, n2, nsim1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(pval2, bins=20);\n", "xlabel('value of P')\n", "ylabel('frequency');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate P and observed difference for two samples with equal means" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "mu, sd = 0.0, 1.0\n", "n = 16 #number of obs per sample" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "mean = np.array([mu, mu])\n", "#set covariance matrix\n", "cor = 0.0 #correlation = 0\n", "var = sd*2\n", "sigma = np.array([[var, cor], [cor, var]])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "nsim1 = 10000 # number of simulation jobs to run\n", "diff, pval = run_simulation(mean, sigma, n, nsim1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(pval, bins=20);\n", "xlabel('value of P')\n", "ylabel('frequency');" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(diff, bins=20);\n", "xlabel('difference between means')\n", "ylabel('frequency');" ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
dnjohnstone/hyperspy	hyperspy/tests/doc_docstr_examples/docstr_examples_EDS.ipynb	12	28737	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib qt4\n", "import numpy as np\n", "import hyperspy.api as hs\n", "import matplotlib.pyplot as plt\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Doc-string examples for EDS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading signal examples" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot(True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.plot(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## eds" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "data": { "text/plain": [ "array([1000279])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sum()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.sum(0).data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<EDSSEMSpectrum, title: EDS SEM Signal1D, dimensions: (\|1024)>\n", "<EDSSEMSpectrum, title: EDS SEM Signal1D, dimensions: (\|512)>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#rebin()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "print(s)\n", "print(s.rebin([512]))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Al' 'C' 'Cu' 'Mn' 'Zr']\n", "['Al']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#set_elements()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "print(s.metadata.Sample.elements)\n", "s.set_elements(['Al'])\n", "print(s.metadata.Sample.elements)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Al' 'C' 'Cu' 'Mn' 'Zr']\n", "['Al', 'Ar', 'C', 'Cu', 'Mn', 'Zr']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#add_elements()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "print(s.metadata.Sample.elements)\n", "s.add_elements(['Ar'])\n", "print(s.metadata.Sample.elements)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Al_Ka line added,\n", "C_Ka line added,\n", "Cu_La line added,\n", "Mn_La line added,\n", "Zr_La line added,\n", "['Al_Ka', 'C_Ka', 'Cu_La', 'Mn_La', 'Zr_La']\n", "Cu_Ka line added,\n", "Al_Ka line added,\n", "C_Ka line added,\n", "Mn_La line added,\n", "Zr_La line added,\n", "Cu_Ka line added,\n", "['Al_Ka', 'C_Ka', 'Cu_Ka', 'Mn_La', 'Zr_La']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#set_lines()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.add_lines()\n", "print(s.metadata.Sample.xray_lines)\n", "s.set_lines(['Cu_Ka'])\n", "print(s.metadata.Sample.xray_lines)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Al_Ka line added,\n", "C_Ka line added,\n", "Cu_La line added,\n", "Mn_La line added,\n", "Zr_La line added,\n", "['Al_Ka', 'C_Ka', 'Cu_La', 'Mn_La', 'Zr_La']\n", "Al_Ka line added,\n", "C_Ka line added,\n", "Cu_Ka line added,\n", "Mn_Ka line added,\n", "Zr_La line added,\n", "['Al_Ka', 'C_Ka', 'Cu_Ka', 'Mn_Ka', 'Zr_La']\n", "Al_Ka line added,\n", "C_Ka line added,\n", "Cu_La line added,\n", "Mn_La line added,\n", "Zr_La line added,\n", "['Al_Ka', 'C_Ka', 'Cu_La', 'Mn_La', 'Zr_La']\n", "Cu_Ka line added,\n", "['Al_Ka', 'C_Ka', 'Cu_Ka', 'Cu_La', 'Mn_La', 'Zr_La']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#add_lines()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.add_lines()\n", "print(s.metadata.Sample.xray_lines)\n", " \n", "\n", "\n", "\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.set_microscope_parameters(beam_energy=30)\n", "s.add_lines()\n", "print(s.metadata.Sample.xray_lines)\n", " \n", "\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.add_lines()\n", "print(s.metadata.Sample.xray_lines)\n", "s.add_lines(['Cu_Ka'])\n", "print(s.metadata.Sample.xray_lines)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mn_Ka at 5.8987 keV : Intensity = 52773.00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "data": { "text/plain": [ "[<EDSSEMSpectrum, title: X-ray line intensity of EDS SEM Signal1D: Mn_Ka at 5.90 keV, dimensions: (\|1)>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get_lines_intensity()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.get_lines_intensity(['Mn_Ka'], plot_result=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mn_Ka at 5.8987 keV : Intensity = 53597.00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "data": { "text/plain": [ "[<EDSSEMSpectrum, title: X-ray line intensity of EDS SEM Signal1D: Mn_Ka at 5.90 keV, dimensions: (\|1)>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get_lines_intensity()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot(['Mn_Ka'], integration_windows=2.1)\n", "s.get_lines_intensity(['Mn_Ka'],\n", "integration_windows=2.1, plot_result=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n", "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mn_Ka line added,\n", "Mn_Ka at 5.8987 keV : Intensity = 46716.00\n" ] }, { "data": { "text/plain": [ "[<EDSSEMSpectrum, title: X-ray line intensity of EDS SEM Signal1D: Mn_Ka at 5.90 keV, dimensions: (\|1)>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get_lines_intensity()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.change_dtype('float')\n", "s.set_elements(['Mn'])\n", "s.set_lines(['Mn_Ka'])\n", "bw = s.estimate_background_windows()\n", "s.plot(background_windows=bw)\n", "s.get_lines_intensity(background_windows=bw, plot_result=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka line added,\n", "Pt_La line added,\n", "Fe_Ka at 6.4039 keV : Intensity = 3710.00\n", "Pt_La at 9.4421 keV : Intensity = 15872.00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "data": { "text/plain": [ "[<EDSTEMSpectrum, title: X-ray line intensity of EDS TEM Signal1D: Fe_Ka at 6.40 keV, dimensions: (\|1)>,\n", " <EDSTEMSpectrum, title: X-ray line intensity of EDS TEM Signal1D: Pt_La at 9.44 keV, dimensions: (\|1)>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#estimate_integration_windows()\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.add_lines()\n", "iw = s.estimate_integration_windows()\n", "s.plot(integration_windows=iw)\n", "s.get_lines_intensity(integration_windows=iw, plot_result=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka line added,\n", "Pt_La line added,\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka at 6.4039 keV : Intensity = 2754.00\n", "Pt_La at 9.4421 keV : Intensity = 15090.00\n" ] }, { "data": { "text/plain": [ "[<EDSTEMSpectrum, title: X-ray line intensity of EDS TEM Signal1D: Fe_Ka at 6.40 keV, dimensions: (\|1)>,\n", " <EDSTEMSpectrum, title: X-ray line intensity of EDS TEM Signal1D: Pt_La at 9.44 keV, dimensions: (\|1)>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#estimate_background_windows()\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.change_dtype('float')\n", "s.add_lines()\n", "bw = s.estimate_background_windows(line_width=[5.0, 2.0])\n", "s.plot(background_windows=bw)\n", "s.get_lines_intensity(background_windows=bw, plot_result=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "data": { "text/plain": [ "37.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get_take_off()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.get_take_off_angle()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "57.00000000000001" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get_take_off()\n", "s.set_microscope_parameters(tilt_stage=20.)\n", "s.get_take_off_angle()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka line added,\n", "Pt_La line added,\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka line added,\n", "Pt_La line added,\n" ] } ], "source": [ "#plot()\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot()\n", "\n", "\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot(True)\n", "\n", "\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.add_lines()\n", "bw = s.estimate_background_windows()\n", "s.plot(background_windows=bw)\n", " \n", "\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s.plot(['Mn_Ka'], integration_windows='auto')\n", "\n", "\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.add_lines()\n", "bw = s.estimate_background_windows()\n", "s.plot(background_windows=bw, integration_windows=2.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## eds_sem" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "0.01\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#get_calibration_from\n", "ref = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "s = hs.signals.EDSSEMSpectrum(\n", "hs.datasets.example_signals.EDS_SEM_Spectrum().data)\n", "print(s.axes_manager[0].scale)\n", "s.get_calibration_from(ref)\n", "print(s.axes_manager[0].scale)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Default value 130.0 eV\n", "Now set to 135.0 eV\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#set_micro\n", "s = hs.datasets.example_signals.EDS_SEM_Spectrum()\n", "print('Default value %s eV' %\n", "s.metadata.Acquisition_instrument.\n", "SEM.Detector.EDS.energy_resolution_MnKa)\n", "s.set_microscope_parameters(energy_resolution_MnKa=135.)\n", "print('Now set to %s eV' %\n", "s.metadata.Acquisition_instrument.\n", "SEM.Detector.EDS.energy_resolution_MnKa)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "133.312296\n", "135.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#set_micro\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "print(s.metadata.Acquisition_instrument.\n", "TEM.Detector.EDS.energy_resolution_MnKa)\n", "s.set_microscope_parameters(energy_resolution_MnKa=135.)\n", "print(s.metadata.Acquisition_instrument.\n", "TEM.Detector.EDS.energy_resolution_MnKa)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## eds_tem" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "0.020028\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "#get_calibration_from\n", "ref = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s = hs.signals.EDSTEMSpectrum(\n", "hs.datasets.example_signals.EDS_TEM_Spectrum().data)\n", "print(s.axes_manager[0].scale)\n", "s.get_calibration_from(ref)\n", "print(s.axes_manager[0].scale)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe_Ka line added,\n", "Pt_La line added,\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fe (Fe_Ka): Composition = 15.41 atomic percent\n", "Pt (Pt_La): Composition = 84.59 atomic percent\n" ] } ], "source": [ "#quant\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s.change_dtype('float')\n", "s.add_lines()\n", "kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La\n", "bw = s.estimate_background_windows(line_width=[5.0, 2.0])\n", "s.plot(background_windows=bw)\n", "intensities = s.get_lines_intensity(background_windows=bw)\n", "res = s.quantification(intensities, kfactors, plot_result=True,\n", "composition_units='atomic')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res[0].axes_manager.signal_size" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n", "/home/to266/dev/hyperspy/hyperspy/signals.py:126: VisibleDeprecationWarning: The Signal class will be deprecated from version 1.0.0 and replaced with BaseSignal\n", " VisibleDeprecationWarning)\n" ] }, { "data": { "text/plain": [ "array([False, False, False, True], dtype=bool)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Simulate a spectrum image with vacuum region\n", "import numpy as np\n", "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "s_vac = hs.signals.BaseSignal(np.ones_like(s.data, dtype=float))0.005\n", "s_vac.add_poissonian_noise()\n", "si = hs.stack([s]3 + [s_vac])\n", "si.vacuum_mask().data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scaling the data to normalize the (presumably) Poissonian noise\n", "\n", "Performing decomposition analysis\n", "Auto transposing the data\n", "Undoing data pre-treatments\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/to266/dev/hyperspy/hyperspy/signals.py:54: VisibleDeprecationWarning: The Signal1D class will be deprecated from version 1.0.0 and replaced with Signal1D\n", " VisibleDeprecationWarning)\n", "/home/to266/dev/hyperspy/hyperspy/signals.py:126: VisibleDeprecationWarning: The Signal class will be deprecated from version 1.0.0 and replaced with BaseSignal\n", " VisibleDeprecationWarning)\n" ] } ], "source": [ "s = hs.datasets.example_signals.EDS_TEM_Spectrum()\n", "si = hs.stack([s]*3)\n", "si.change_dtype(float)\n", "si.decomposition()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## utils.eds" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.8766744984001607" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Electron range in pure Copper at 30 kV in micron\n", "hs.eds.electron_range('Cu', 30.)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.9361716759499248" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# X-ray range of Cu Ka in pure Copper at 30 kV in micron\n", "hs.eds.xray_range('Cu_Ka', 30.)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7.6418811280855454" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# X-ray range of Cu Ka in pure Carbon at 30kV in micron\n", "hs.eds.xray_range('Cu_Ka', 30., hs.material.elements.C.\n", "Physical_properties.density_gcm3)\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Conda Python3", "language": "python", "name": "conda_py35" }, "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" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
napsternxg/ipython-notebooks	Dictionary_lookup_with_default.ipynb	1	6366	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dictionary handle missing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'0': 0,\n", " '1': 1,\n", " '2': 2,\n", " '3': 3,\n", " '4': 4,\n", " '5': 5,\n", " '6': 6,\n", " '7': 7,\n", " '8': 8,\n", " '9': 9}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = {f\"{i}\": i for i in range(10)}\n", "a" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def handle_missing():\n", " return 11\n", "handle_missing()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "68.8 ns ± 1.94 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit\n", "handle_missing()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "119 ns ± 3.45 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(\"11\") or 11" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "192 ns ± 6.18 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(\"11\") or handle_missing()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "123 ns ± 5.15 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(\"11\", 11)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "185 ns ± 10.8 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(\"11\", handle_missing())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "360 ns ± 33 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "try:\n", " a[\"11\"]\n", "except KeyError:\n", " handle_missing()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "119 ns ± 9.23 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "if \"11\" in a:\n", " a[\"11\"]\n", "else:\n", " handle_missing()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def dynamic_handle_missing(key):\n", " return int(key)\n", "key = \"11\"\n", "dynamic_handle_missing(key)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "278 ns ± 6.97 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit\n", "dynamic_handle_missing(key)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "414 ns ± 7.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(key) or dynamic_handle_missing(key)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "416 ns ± 9.55 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "a.get(key, dynamic_handle_missing(key))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "556 ns ± 15.5 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "try:\n", " a[key]\n", "except KeyError:\n", " dynamic_handle_missing(key)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "337 ns ± 37.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%%timeit \n", "if key in a:\n", " a[key]\n", "else:\n", " dynamic_handle_missing(key)" ] }, { "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
soumith/thnb	is there a simple way to extract the index of a particular value in a tensor.ipynb	2	4845	{ "metadata": { "language": "lua", "name": "", "signature": "sha256:d44865d54cb8b0a9b07829d58cbb3878932e79bd4ecf39c9b65e11f77ce2ef62" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###Q. is there a simple way to extract the index of a particular value in a tensor?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A. Lets work this out.\n", "\n", "Let's define a tensor with 10 integers from 1 to 10 randomly permuted." ] }, { "cell_type": "code", "collapsed": false, "input": [ "t=torch.randperm(10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "t" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " 8\n", " 3\n", " 6\n", " 1\n", " 5\n", " 7\n", " 4\n", " 9\n", " 10\n", " 2\n", "[torch.DoubleTensor of dimension 10]\n", "\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, find all instances of \"7\" in the tensor.\n", "\n", "Let us do two methods. \n", "\n", "Method (1) is a simple for-loop." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i=1,t:size(1) do \n", " if t[i] == 7 then\n", " print('Found 7 at location: ' .. i)\n", " end\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "Found 7 at location: 6\t\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Method (2) which is more efficient and faster:\n", "\n", "We use the :eq function, which is part of the logical operators.\n", "\n", "https://github.com/torch/torch7/blob/master/doc/maths.md#logical-operations-on-tensors" ] }, { "cell_type": "code", "collapsed": false, "input": [ "t:eq(7)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", "[torch.ByteTensor of dimension 10]\n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see, it retured a mask, where there is a value 1 wherever there was the value 7.\n", "Now, let us extract a new tensor with just the value." ] }, { "cell_type": "code", "collapsed": false, "input": [ "t[t:eq(7)]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " 7\n", "[torch.DoubleTensor of dimension 1]\n", "\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, but let's say you actually want the Index number of all locations of 7.\n", "You can technically do this with a linspace:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "idx = torch.linspace(1,t:size(1), t:size(1))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "idx[t:eq(7)]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " 6\n", "[torch.DoubleTensor of dimension 1]\n", "\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hope this helps make it clear." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	unlicense
OSGeo-live/CesiumWidget	GSOC/notebooks/Projects/IRIS/ne2-cache-test.ipynb	1	60632	{ "metadata": { "name": "", "signature": "sha256:4e422f56d0f376606c56845e11e9a4efa7aa2ea95761c7386362ffc3e7abb2a1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cartopy\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "## Natural Earth 2 cache test\n", "## Live 8.5 * darkblue-b\n", "##" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = plt.axes(projection=ccrs.PlateCarree() ) \n", "\n", "ax.add_feature(cfeature.LAND)\n", "ax.add_feature(cfeature.OCEAN)\n", "ax.add_feature(cfeature.COASTLINE)\n", "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", "ax.add_feature(cfeature.LAKES, alpha=0.5)\n", "ax.add_feature(cfeature.RIVERS)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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1bNtk6Sw4ONizevUSrKyskEgEbsQe5d2pX3E9Pouy0kIun9qPnYNlt+3GLduz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\nbxydG+Hm5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"text": [ "<matplotlib.figure.Figure at 0x7fb238512a90>" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	apache-2.0
python-practice/practice-makes-python	numeric-types/.ipynb_checkpoints/ex 2.1 number guessing game-checkpoint.ipynb	1	3121	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Write a program that chooses a random integer between 0 and 99 (inclu-\n", "sive). Then ask the user to guess what number has been chosen. Each time the user enters a guess, the program indicates whether the user guessed correctly (and exits), or if the guess was too high or too low.\n", "If you didn’t already know this, then you can tell Python to choose a random integer in any range with the randint function in the random module. Thus, you can say:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "number = random.randint(10, 30)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "and number will contain an integer from 10 to (but not including) 30." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Try to guess a number between 0 and 99:33\n", "Nope! Try a larger number!66\n", "Nope! Try a larger number!83\n", "Too much! Try a smaller number!74\n", "Too much! Try a smaller number!70\n", "Too much! Try a smaller number!68\n", "Too much! Try a smaller number!67\n", "Bingo!\n" ] } ], "source": [ "import random\n", "\n", "def guess_number():\n", " min = 0\n", " max = 99\n", " \n", " answer = random.randint(min, max)\n", " \n", " ok = False\n", " \n", " prompt = \"Try to guess a number between {0} and {1}:\".format(min, max)\n", " \n", " while not ok:\n", " \n", " try:\n", " guess = int(raw_input(prompt))\n", " if guess == answer:\n", " ok = True\n", " print \"Bingo!\"\n", " break\n", " elif guess > answer:\n", " prompt = \"Too much! Try a smaller number!\"\n", " else:\n", " prompt = \"Nope! Try a larger number!\"\n", " \n", " except Exception as ex:\n", " prompt = \"Apparently, it's not an integer number. Try once more.\"\n", "\n", "guess_number()\n", " \n", " " ] }, { "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 }	unlicense
googledatalab/notebooks	samples/ML Toolbox/Regression/Census/6 Cleanup.ipynb	1	2496	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Removing files\n", "This is an optional notebook in the census sample series. Run any cell if you want to remove the files or models that were made in previous notebooks. This assumes you did not change the default locations or names." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing files from the local file system" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "workspace_path = '/content/datalab/workspace/census'\n", "!rm -fr {workspace_path}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing files from cloud storage\n", "\n", "This removes the files in the 'census' folder in the bucket that was used. It does not delete the bucket that was used. To also remove the bucket, make sure it is empty and run \n", "`!gsutil rb gs://BUCKET-NAME`\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import google.datalab as datalab\n", "import os\n", "storage_bucket = 'gs://' + datalab.Context.default().project_id + '-datalab-workspace/'\n", "workspace_path = os.path.join(storage_bucket, 'census')\n", "!gsutil -m rm -rf {workspace_path}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing the deployed model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model_name = 'census'\n", "model_version = 'v1'\n", "\n", "!gcloud ml-engine versions delete {model_version} --model {model_name}\n", "!gcloud ml-engine models delete {model_name}\n" ] }, { "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": 2 }	apache-2.0
datahac/jup	UX_analytics.ipynb	1	104639	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# iPython notebook - SS analysis - User groups\n", "Google Analytics data\n", "\n", "\n", "## 1. Import libraries" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline \n", "\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib as mpl\n", "import matplotlib.cm as cm\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "pd.set_option('display.width', 500)\n", "pd.set_option('display.max_columns', 100)\n", "pd.set_option('display.notebook_repr_html', True)\n", "import seaborn as sns #sets up styles and gives us more plotting options\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "URL = \"tmrw.co\" # User-entered website\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " 2. Settings" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Time period 17th Jan - 16th April (arbitrary )\n", "\n", "# API credentials\n", "# Email address [email protected]\n", "# Key IDs 948ee8e2a420ef14a5d5a29bd35104fe2f1e6ed4\n", " " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# open file. It is requested via API explorer using request parameters:\n", "\n", "#Account: TMRW Tech Hub\n", "#Property: TMRW\n", "#View: All Web Site Data\n", "#ids: ga:123303369\n", "#start-date: 2017-01-15\n", "#end-date: yesterday\n", "\n", "#metrics\n", "#ga:sessions\n", "#ga:percentNewSessions\n", "#ga:bounceRate\n", "#ga:pageviewsPerSession\n", "#ga:avgSessionDuration\n", "#ga:goal1ConversionRate\n", "#ga:goal1Completions\n", "\n", "#dimensions\n", "#ga:city\n", "#ga:userAgeBracket\n", "\n", "#sort\n", "#ga:goal1ConversionRate\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Skipped step:\n", "Check statistical validity \n", "Filter off spam traffic and own dev/marketing IPs\n", "\n", "\n", "\n", "\n", "Algorithm of actions:\n", "\n", "\n", "\n", "1. Determine buckets\n", "\n", "### Are there lines that can be grouped by each metric:\n", "Bounce Rate\t\n", "Avg. Session Duration\t\n", "Goal 1 Completions\t\n", "Goal 1 Conversion Rate\t\n", "Pages / Session\n", "\n", "\n", "\n", "### Acceptable spread = 10%\n", "\n", "\n", "Take Key_metrics and check volume of traffic and conversions. Which is the most extreme?\n", "Conversion bucket = new array \n", "TMRW_users_city.max=TMRW_users_city.max()\n", "\n", "2. Define key metrics\n", "Key_metrics = location/age. Can be location/gender or age/gender also. For them other API call needs to be made.\n", "\n", "3. Open file\n", "\n", "4. Visualise" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": 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>City</th>\n", " <th>Age</th>\n", " <th>% New Sessions</th>\n", " <th>Sessions</th>\n", " <th>Bounce Rate</th>\n", " <th>Avg. Session Duration</th>\n", " <th>Goal 1 Completions</th>\n", " <th>Goal 1 Conversion Rate</th>\n", " <th>Pages / Session</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>(not set)</td>\n", " <td>35-44</td>\n", " <td>43.750</td>\n", " <td>16</td>\n", " <td>62.500</td>\n", " <td>53.500</td>\n", " <td>0</td>\n", " <td>0.000</td>\n", " <td>1.688</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>London</td>\n", " <td>55-64</td>\n", " <td>77.778</td>\n", " <td>36</td>\n", " <td>47.222</td>\n", " <td>92.472</td>\n", " <td>0</td>\n", " <td>0.000</td>\n", " <td>2.333</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>London</td>\n", " <td>45-54</td>\n", " <td>70.909</td>\n", " <td>165</td>\n", " <td>53.939</td>\n", " <td>135.079</td>\n", " <td>2</td>\n", " <td>1.212</td>\n", " <td>1.915</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Croydon</td>\n", " <td>45-54</td>\n", " <td>65.854</td>\n", " <td>82</td>\n", " <td>48.780</td>\n", " <td>167.707</td>\n", " <td>1</td>\n", " <td>1.220</td>\n", " <td>2.049</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>London</td>\n", " <td>35-44</td>\n", " <td>69.014</td>\n", " <td>426</td>\n", " <td>53.756</td>\n", " <td>119.549</td>\n", " <td>12</td>\n", " <td>2.817</td>\n", " <td>1.962</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>London</td>\n", " <td>25-34</td>\n", " <td>66.623</td>\n", " <td>758</td>\n", " <td>59.235</td>\n", " <td>103.788</td>\n", " <td>22</td>\n", " <td>2.902</td>\n", " <td>1.856</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Croydon</td>\n", " <td>35-44</td>\n", " <td>64.216</td>\n", " <td>204</td>\n", " <td>43.137</td>\n", " <td>158.848</td>\n", " <td>6</td>\n", " <td>2.941</td>\n", " <td>2.284</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Croydon</td>\n", " <td>55-64</td>\n", " <td>74.194</td>\n", " <td>31</td>\n", " <td>51.613</td>\n", " <td>62.323</td>\n", " <td>1</td>\n", " <td>3.226</td>\n", " <td>1.677</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Croydon</td>\n", " <td>18-24</td>\n", " <td>69.767</td>\n", " <td>86</td>\n", " <td>43.023</td>\n", " <td>101.384</td>\n", " <td>3</td>\n", " <td>3.488</td>\n", " <td>2.140</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>(not set)</td>\n", " <td>25-34</td>\n", " <td>75.000</td>\n", " <td>24</td>\n", " <td>62.500</td>\n", " <td>75.542</td>\n", " <td>1</td>\n", " <td>4.167</td>\n", " <td>1.583</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Croydon</td>\n", " <td>25-34</td>\n", " <td>58.194</td>\n", " <td>299</td>\n", " <td>47.157</td>\n", " <td>198.344</td>\n", " <td>15</td>\n", " <td>5.017</td>\n", " <td>2.261</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>London</td>\n", " <td>18-24</td>\n", " <td>75.484</td>\n", " <td>155</td>\n", " <td>51.613</td>\n", " <td>172.226</td>\n", " <td>8</td>\n", " <td>5.161</td>\n", " <td>2.187</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>London</td>\n", " <td>65+</td>\n", " <td>85.714</td>\n", " <td>14</td>\n", " <td>71.429</td>\n", " <td>116.857</td>\n", " <td>1</td>\n", " <td>7.143</td>\n", " <td>1.286</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Hove</td>\n", " <td>35-44</td>\n", " <td>75.000</td>\n", " <td>12</td>\n", " <td>16.667</td>\n", " <td>69.417</td>\n", " <td>1</td>\n", " <td>8.333</td>\n", " <td>2.167</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " City Age % New Sessions Sessions Bounce Rate Avg. Session Duration Goal 1 Completions Goal 1 Conversion Rate Pages / Session\n", "0 (not set) 35-44 43.750 16 62.500 53.500 0 0.000 1.688\n", "1 London 55-64 77.778 36 47.222 92.472 0 0.000 2.333\n", "2 London 45-54 70.909 165 53.939 135.079 2 1.212 1.915\n", "3 Croydon 45-54 65.854 82 48.780 167.707 1 1.220 2.049\n", "4 London 35-44 69.014 426 53.756 119.549 12 2.817 1.962\n", "5 London 25-34 66.623 758 59.235 103.788 22 2.902 1.856\n", "6 Croydon 35-44 64.216 204 43.137 158.848 6 2.941 2.284\n", "7 Croydon 55-64 74.194 31 51.613 62.323 1 3.226 1.677\n", "8 Croydon 18-24 69.767 86 43.023 101.384 3 3.488 2.140\n", "9 (not set) 25-34 75.000 24 62.500 75.542 1 4.167 1.583\n", "10 Croydon 25-34 58.194 299 47.157 198.344 15 5.017 2.261\n", "11 London 18-24 75.484 155 51.613 172.226 8 5.161 2.187\n", "12 London 65+ 85.714 14 71.429 116.857 1 7.143 1.286\n", "13 Hove 35-44 75.000 12 16.667 69.417 1 8.333 2.167" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Open file\n", "TMRW_users= pd.read_csv(\"files/TMRW_geo_loc_API.csv\")\n", "#TMRW_users[TMRW_users.Age=='55-64']\n", "TMRW_users" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [] }, { "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>city</th>\n", " <th>age</th>\n", " <th>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>London</td>\n", " <td>45-54</td>\n", " <td>70.909</td>\n", " <td>165</td>\n", " <td>53.939</td>\n", " <td>135.079</td>\n", " <td>2</td>\n", " <td>1.212</td>\n", " <td>1.915</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Croydon</td>\n", " <td>45-54</td>\n", " <td>65.854</td>\n", " <td>82</td>\n", " <td>48.780</td>\n", " <td>167.707</td>\n", " <td>1</td>\n", " <td>1.220</td>\n", " <td>2.049</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>London</td>\n", " <td>35-44</td>\n", " <td>69.014</td>\n", " <td>426</td>\n", " <td>53.756</td>\n", " <td>119.549</td>\n", " <td>12</td>\n", " <td>2.817</td>\n", " <td>1.962</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>London</td>\n", " <td>25-34</td>\n", " <td>66.623</td>\n", " <td>758</td>\n", " <td>59.235</td>\n", " <td>103.788</td>\n", " <td>22</td>\n", " <td>2.902</td>\n", " <td>1.856</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Croydon</td>\n", " <td>35-44</td>\n", " <td>64.216</td>\n", " <td>204</td>\n", " <td>43.137</td>\n", " <td>158.848</td>\n", " <td>6</td>\n", " <td>2.941</td>\n", " <td>2.284</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Croydon</td>\n", " <td>18-24</td>\n", " <td>69.767</td>\n", " <td>86</td>\n", " <td>43.023</td>\n", " <td>101.384</td>\n", " <td>3</td>\n", " <td>3.488</td>\n", " <td>2.140</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Croydon</td>\n", " <td>25-34</td>\n", " <td>58.194</td>\n", " <td>299</td>\n", " <td>47.157</td>\n", " <td>198.344</td>\n", " <td>15</td>\n", " <td>5.017</td>\n", " <td>2.261</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>London</td>\n", " <td>18-24</td>\n", " <td>75.484</td>\n", " <td>155</td>\n", " <td>51.613</td>\n", " <td>172.226</td>\n", " <td>8</td>\n", " <td>5.161</td>\n", " <td>2.187</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city age new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "2 London 45-54 70.909 165 53.939 135.079 2 1.212 1.915\n", "3 Croydon 45-54 65.854 82 48.780 167.707 1 1.220 2.049\n", "4 London 35-44 69.014 426 53.756 119.549 12 2.817 1.962\n", "5 London 25-34 66.623 758 59.235 103.788 22 2.902 1.856\n", "6 Croydon 35-44 64.216 204 43.137 158.848 6 2.941 2.284\n", "8 Croydon 18-24 69.767 86 43.023 101.384 3 3.488 2.140\n", "10 Croydon 25-34 58.194 299 47.157 198.344 15 5.017 2.261\n", "11 London 18-24 75.484 155 51.613 172.226 8 5.161 2.187" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rename columns\n", "TMRW_users.columns=['city','age', 'new_sessions','sessions','bounce_rate','asd','goal1','goal1CR','PPS'] \n", "TMRW_users=TMRW_users.sort_values('goal1CR')\n", "\n", "TMRW_users_filter = TMRW_users[TMRW_users.sessions > 80]\n", "TMRW_users_filter" ] }, { "cell_type": "code", "execution_count": 24, "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>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>8.000000</td>\n", " <td>8.000000</td>\n", " <td>8.000000</td>\n", " <td>8.000000</td>\n", " <td>8.00000</td>\n", " <td>8.000000</td>\n", " <td>8.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>67.507625</td>\n", " <td>271.875000</td>\n", " <td>50.080000</td>\n", " <td>144.615625</td>\n", " <td>8.62500</td>\n", " <td>3.094750</td>\n", " <td>2.081750</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>5.122239</td>\n", " <td>227.124533</td>\n", " <td>5.641801</td>\n", " <td>35.132391</td>\n", " <td>7.28869</td>\n", " <td>1.479211</td>\n", " <td>0.161107</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>58.194000</td>\n", " <td>82.000000</td>\n", " <td>43.023000</td>\n", " <td>101.384000</td>\n", " <td>1.00000</td>\n", " <td>1.212000</td>\n", " <td>1.856000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>65.444500</td>\n", " <td>137.750000</td>\n", " <td>46.152000</td>\n", " <td>115.608750</td>\n", " <td>2.75000</td>\n", " <td>2.417750</td>\n", " <td>1.950250</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>67.818500</td>\n", " <td>184.500000</td>\n", " <td>50.196500</td>\n", " <td>146.963500</td>\n", " <td>7.00000</td>\n", " <td>2.921500</td>\n", " <td>2.094500</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>70.052500</td>\n", " <td>330.750000</td>\n", " <td>53.801750</td>\n", " <td>168.836750</td>\n", " <td>12.75000</td>\n", " <td>3.870250</td>\n", " <td>2.205500</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>75.484000</td>\n", " <td>758.000000</td>\n", " <td>59.235000</td>\n", " <td>198.344000</td>\n", " <td>22.00000</td>\n", " <td>5.161000</td>\n", " <td>2.284000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "count 8.000000 8.000000 8.000000 8.000000 8.00000 8.000000 8.000000\n", "mean 67.507625 271.875000 50.080000 144.615625 8.62500 3.094750 2.081750\n", "std 5.122239 227.124533 5.641801 35.132391 7.28869 1.479211 0.161107\n", "min 58.194000 82.000000 43.023000 101.384000 1.00000 1.212000 1.856000\n", "25% 65.444500 137.750000 46.152000 115.608750 2.75000 2.417750 1.950250\n", "50% 67.818500 184.500000 50.196500 146.963500 7.00000 2.921500 2.094500\n", "75% 70.052500 330.750000 53.801750 168.836750 12.75000 3.870250 2.205500\n", "max 75.484000 758.000000 59.235000 198.344000 22.00000 5.161000 2.284000" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_filter.describe()\n", "# will need to convert dnumbers into tim\n", "#TMRW_users_filter_new =pd.to_datetime(TMRW_users_filter['asd'], format='%H:%M')\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "164.85714285714286" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check if the number of sessions is enough for analysis\n", "\n", "if TMRW_users.sessions.sum() < 80:\n", " print(\"Error\")\n", "\n", "#if sessions are too small remove\n", "#if one conversion - remove\n", "\n", "# 1. Too small sessions\n", "TMRW_users.describe().loc['mean', 'sessions']\n" ] }, { "cell_type": "code", "execution_count": 26, "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>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " <tr>\n", " <th>city</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>Croydon</th>\n", " <td>63.9805</td>\n", " <td>192.5</td>\n", " <td>45.090</td>\n", " <td>149.864</td>\n", " <td>9.0</td>\n", " <td>4.2525</td>\n", " <td>2.2005</td>\n", " </tr>\n", " <tr>\n", " <th>London</th>\n", " <td>75.4840</td>\n", " <td>155.0</td>\n", " <td>51.613</td>\n", " <td>172.226</td>\n", " <td>8.0</td>\n", " <td>5.1610</td>\n", " <td>2.1870</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "city \n", "Croydon 63.9805 192.5 45.090 149.864 9.0 4.2525 2.2005\n", "London 75.4840 155.0 51.613 172.226 8.0 5.1610 2.1870" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#algo for bucketing into varios secsions: \n", "\n", "#buckets by goal1CR\n", "\n", "TMRW_users_goal1CR = TMRW_users_filter.nlargest(3, 'goal1CR')\n", "TMRW_users_goal1CR_gCity = TMRW_users_goal1CR.groupby(['city']).mean() #this is not too correct- average of proportion, but ok for now\n", "TMRW_users_goal1CR_gAge = TMRW_users_goal1CR.groupby(['age']).mean() \n", "TMRW_users_goal1CR_gCity" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'18-24'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_goal1CR.loc[11,'age']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "age\n", "18-24 5.5\n", "25-34 15.0\n", "Name: goal1, dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_goal1CR_gAge.loc[: , 'goal1']" ] }, { "cell_type": "code", "execution_count": 35, "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>city</th>\n", " <th>age</th>\n", " <th>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11</th>\n", " <td>London</td>\n", " <td>18-24</td>\n", " <td>75.484</td>\n", " <td>155</td>\n", " <td>51.613</td>\n", " <td>172.226</td>\n", " <td>8</td>\n", " <td>5.161</td>\n", " <td>2.187</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Croydon</td>\n", " <td>25-34</td>\n", " <td>58.194</td>\n", " <td>299</td>\n", " <td>47.157</td>\n", " <td>198.344</td>\n", " <td>15</td>\n", " <td>5.017</td>\n", " <td>2.261</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Croydon</td>\n", " <td>18-24</td>\n", " <td>69.767</td>\n", " <td>86</td>\n", " <td>43.023</td>\n", " <td>101.384</td>\n", " <td>3</td>\n", " <td>3.488</td>\n", " <td>2.140</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city age new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "11 London 18-24 75.484 155 51.613 172.226 8 5.161 2.187\n", "10 Croydon 25-34 58.194 299 47.157 198.344 15 5.017 2.261\n", "8 Croydon 18-24 69.767 86 43.023 101.384 3 3.488 2.140" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_goal1CR.loc[]\n", "'age'.max" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "bad operand type for unary +: 'str'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-30-70d1487a1078>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# The most converting audience\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\" is most converting Demographic category\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTMRW_users_goal1CR_gAge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary +: 'str'" ] } ], "source": [ "# The most converting audience\n", "print(+\" is most converting Demographic category\")\n", "\n", "#x = TMRW_users_goal1CR_gAge.index\n", "\n", "y = TMRW_users_goal1CR.[: , 'goal1CR']\n", "\n", "plt.hist(y)\n", "\n", "plt.title(\"Top converting buckets\")\n", "plt.xlabel(\"Conv rate\")\n", "plt.ylabel(\"Frequency\")\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "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>city</th>\n", " <th>age</th>\n", " <th>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>London</td>\n", " <td>25-34</td>\n", " <td>66.623</td>\n", " <td>758</td>\n", " <td>59.235</td>\n", " <td>103.788</td>\n", " <td>22</td>\n", " <td>2.902</td>\n", " <td>1.856</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>London</td>\n", " <td>45-54</td>\n", " <td>70.909</td>\n", " <td>165</td>\n", " <td>53.939</td>\n", " <td>135.079</td>\n", " <td>2</td>\n", " <td>1.212</td>\n", " <td>1.915</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>London</td>\n", " <td>35-44</td>\n", " <td>69.014</td>\n", " <td>426</td>\n", " <td>53.756</td>\n", " <td>119.549</td>\n", " <td>12</td>\n", " <td>2.817</td>\n", " <td>1.962</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city age new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "5 London 25-34 66.623 758 59.235 103.788 22 2.902 1.856\n", "2 London 45-54 70.909 165 53.939 135.079 2 1.212 1.915\n", "4 London 35-44 69.014 426 53.756 119.549 12 2.817 1.962" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_bounce_rate = TMRW_users_filter.nlargest(3, 'bounce_rate')\n", "TMRW_users_bounce_rate\n", "#if the_largest traffic source = ('not_set') then output error ''" ] }, { "cell_type": "code", "execution_count": 70, "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>city</th>\n", " <th>age</th>\n", " <th>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>Croydon</td>\n", " <td>35-44</td>\n", " <td>64.216</td>\n", " <td>204</td>\n", " <td>43.137</td>\n", " <td>158.848</td>\n", " <td>6</td>\n", " <td>2.941</td>\n", " <td>2.284</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Croydon</td>\n", " <td>25-34</td>\n", " <td>58.194</td>\n", " <td>299</td>\n", " <td>47.157</td>\n", " <td>198.344</td>\n", " <td>15</td>\n", " <td>5.017</td>\n", " <td>2.261</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>London</td>\n", " <td>18-24</td>\n", " <td>75.484</td>\n", " <td>155</td>\n", " <td>51.613</td>\n", " <td>172.226</td>\n", " <td>8</td>\n", " <td>5.161</td>\n", " <td>2.187</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city age new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "6 Croydon 35-44 64.216 204 43.137 158.848 6 2.941 2.284\n", "10 Croydon 25-34 58.194 299 47.157 198.344 15 5.017 2.261\n", "11 London 18-24 75.484 155 51.613 172.226 8 5.161 2.187" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_PPS = TMRW_users_filter.nlargest(3, 'PPS')\n", "TMRW_users_PPS" ] }, { "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>new_sessions</th>\n", " <th>sessions</th>\n", " <th>bounce_rate</th>\n", " <th>asd</th>\n", " <th>goal1</th>\n", " <th>goal1CR</th>\n", " <th>PPS</th>\n", " </tr>\n", " <tr>\n", " <th>city</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>(not set)</th>\n", " <td>118.750</td>\n", " <td>40</td>\n", " <td>125.000</td>\n", " <td>129.042</td>\n", " <td>1</td>\n", " <td>4.167</td>\n", " <td>3.271</td>\n", " </tr>\n", " <tr>\n", " <th>Croydon</th>\n", " <td>332.225</td>\n", " <td>702</td>\n", " <td>233.710</td>\n", " <td>688.606</td>\n", " <td>26</td>\n", " <td>15.892</td>\n", " <td>10.411</td>\n", " </tr>\n", " <tr>\n", " <th>Hove</th>\n", " <td>75.000</td>\n", " <td>12</td>\n", " <td>16.667</td>\n", " <td>69.417</td>\n", " <td>1</td>\n", " <td>8.333</td>\n", " <td>2.167</td>\n", " </tr>\n", " <tr>\n", " <th>London</th>\n", " <td>445.522</td>\n", " <td>1554</td>\n", " <td>337.194</td>\n", " <td>739.971</td>\n", " <td>45</td>\n", " <td>19.235</td>\n", " <td>11.539</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " new_sessions sessions bounce_rate asd goal1 goal1CR PPS\n", "city \n", "(not set) 118.750 40 125.000 129.042 1 4.167 3.271\n", "Croydon 332.225 702 233.710 688.606 26 15.892 10.411\n", "Hove 75.000 12 16.667 69.417 1 8.333 2.167\n", "London 445.522 1554 337.194 739.971 45 19.235 11.539" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TMRW_users_agcities=TMRW_users.groupby([\"city\"]).sum()\n", "TMRW_users_agcities\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "City\n", "(not set) 1\n", "Croydon 26\n", "Hove 1\n", "London 45\n", "Name: Goal 1 Completions, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selected=TMRW_users_agcities.loc[:,'Goal 1 Completions']\n", "selected" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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o/1HYatRTOw7dIuu+bXAUlW8uGI4A+o5I60X06tAEI//zEXQ6zh1ExBFAIgAZ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"text/plain": [ "<matplotlib.figure.Figure at 0x116328940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = selected.index\n", "sizes = selected\n", "# colours are taken from http://tools.medialab.sciences-po.fr/iwanthue/\n", "colors = ['#1f394d','#2a7585', '#163c45', '#004a6e']\n", "explode = (0, 0, 0, 0)\n", "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n", " autopct='%1.1f%%', shadow=False, startangle=90)\n", "plt.axis('equal')\n", "plt.title('Cities by conversion')\n", "plt.show()\n", "\n", "# Conversion traffic" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " segment converts best\n" ] } ], "source": [ "# Generate text\n", "print (\" segment converts best\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Mobile analytics\n" ] }, { "cell_type": "code", "execution_count": 1, "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>sessions</th>\n", " <th>%news</th>\n", " <th>new_users</th>\n", " <th>bounce_rate</th>\n", " <th>PPS</th>\n", " <th>ASD</th>\n", " <th>goal1CR</th>\n", " <th>goal1</th>\n", " </tr>\n", " <tr>\n", " <th>device</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>desktop</th>\n", " <td>2976</td>\n", " <td>70.93%</td>\n", " <td>2,111</td>\n", " <td>53.36%</td>\n", " <td>2.01</td>\n", " <td>00:02:19</td>\n", " <td>3.76%</td>\n", " <td>112</td>\n", " </tr>\n", " <tr>\n", " <th>mobile</th>\n", " <td>1502</td>\n", " <td>70.51%</td>\n", " <td>1,059</td>\n", " <td>60.19%</td>\n", " <td>1.83</td>\n", " <td>00:01:35</td>\n", " <td>1.73%</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>tablet</th>\n", " <td>171</td>\n", " <td>73.10%</td>\n", " <td>125</td>\n", " <td>56.73%</td>\n", " <td>1.74</td>\n", " <td>00:01:33</td>\n", " <td>1.75%</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sessions %news new_users bounce_rate PPS ASD goal1CR goal1\n", "device \n", "desktop 2976 70.93% 2,111 53.36% 2.01 00:02:19 3.76% 112\n", "mobile 1502 70.51% 1,059 60.19% 1.83 00:01:35 1.73% 26\n", "tablet 171 73.10% 125 56.73% 1.74 00:01:33 1.75% 3" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "input_mob = pd.read_csv('files/TMRW_mob.csv')\n", "input_mob.columns=['device','sessions','%news', 'new_users','bounce_rate','PPS', 'ASD', 'goal1CR','goal1']\n", "input_mob = input_mob.set_index('device')\n", "\n", "def p2f(x):\n", " return float(x.strip('%'))/100\n", "\n", "input_mob" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conv_increase = float(input_mob.loc['mobile','sessions']) * p2f(input_mob.loc['desktop','goal1CR']) - float(input_mob.loc['mobile','goal1']) \n", "conv_increase=int(conv_increase)" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Have 30 more conversions per month by optmiising mobile UX\n" ] }, { "data": { "image/png": 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KkiRJDQYlSZKkBoOSJElSg0FJkiSpwaAkSZLUYFCSJElqMChJkiQ1rDXsAkwv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\njwO26P5+DxwCbFFKuYiZUU+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"text/plain": [ "<matplotlib.figure.Figure at 0x117ed2ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check if data is valid\n", "\n", "is_valid = True\n", "output_chart_data = input_mob.loc['mobile',['sessions','goal1']]\n", "\n", "# convert percentages into float\n", "\n", "\n", "\n", "mob_CR = p2f(input_mob.loc['desktop','goal1CR'])\n", "desk_CR = p2f(input_mob.loc['mobile','goal1CR'])\n", "\n", "def funnel_cart():\n", " return \n", "\n", "# calculate difference\n", "\n", "mob_dif = mob_CR / desk_CR\n", "\n", "# if mobile goal1CR is less by more than 20% then reult is failed \n", "\n", "if mob_dif > 1.5:\n", " #funnel_chart()\n", " output_chart_data\n", "\n", " \n", "else:\n", " is_valid = False\n", " \n", "\n", "# if not then build chart\n", "\n", "# http://stackoverflow.com/questions/21397549/stack-bar-plot-in-matplotlib-and-add-label-to-each-section-and-suggestions\n", "# http://pandas.pydata.org/pandas-docs/stable/visualization.html#bar-plots\n", "\n", "#mob_chart = output_chart_data\n", "x = {1}\n", "y = {100,10}\n", " \n", "\n", "#ou = pd.DataFrame([100,10], columns=['sessions', 'goal1'])\n", "\n", "output_chart_data.plot.barh(stacked=True)\n", "#plt.barh(x,y,'stacked')\n", "\n", "mob_conv = input_mob.loc['mobile','goal1CR']\n", "\n", "\n", "plt.xlabel('Visits')\n", "plt.title('Only %s of all mobile visitors end up completing a conversion' % mob_conv)\n", "\n", "#need to specify analytics time perdiod\n", "print(\"Have %s more conversions per month by optmiising mobile UX\" % conv_increase)\n", "\n", "\n", "plt.show()" ] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
t-peters/meetup-doc	Homeworks/Homework 2.ipynb	2	2220	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TextBased Adventure Game\n", "\n", "The Goal: Remember Adventure? Well, we’re going to build a more basic version of that. A complete text game, the program will let users move through rooms based on user input and get descriptions of each room. To create this, you’ll need to establish the directions in which the user can move, a way to track how far the user has moved (and therefore which room he/she is in), and to print out a description. You’ll also need to set limits for how far the user can move. In other words, create “walls” around the rooms that tell the user, “You can’t move further in this direction.”\n", "\n", "## Concepts to keep in mind:\n", "\n", "1. Strings\n", "2. Variables\n", "3. Input/Output\n", "4. If/Else Statements\n", "5. Print\n", "6. List\n", "7. Integers\n", "\n", "The tricky parts here will involve setting up the directions and keeping track of just how far the user has “walked” in the game. I suggest sticking to just a few basic descriptions or rooms, perhaps 6 at most. This project also continues to build on using userinputted data. It can be a relatively basic game, but if you want to build this into a vast, complex word, the coding will get substantially harder, especially if you want your user to start interacting with actual objects within the game. That complexity could be great, if you’d like to make this into a longterm project. *Hint hint.\n", "\n", "\n", "\n", "I have a project for you: Try to make a plagiarism checker where you give the program a txt file, it sends sentences to google and reports to you what sentences you plagiarised." ] } ], "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 }	mpl-2.0
maubarsom/ORFan-proteins	2017.08_scirep_rev1/2_benchmark_method/1_get_hits_from_tools.ipynb	2	24643	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".\r\n", "├── 1_get_hits_from_tools.ipynb\r\n", "├── 1_merged_viruses.tsv\r\n", "├── 2_set_intersection_viz.R\r\n", "├── README.md\r\n", "├── find_viruses_in_families\r\n", "│ ├── 0_virus_fasta\r\n", "│ │ ├── blastdb\r\n", "│ │ │ ├── viruses.fasta\r\n", "│ │ │ ├── viruses.nhr\r\n", "│ │ │ ├── viruses.nin\r\n", "│ │ │ └── viruses.nsq\r\n", "│ │ ├── crassphage.fasta\r\n", "│ │ ├── enterobacteria_phage_phi92.fasta\r\n", "│ │ └── parvovirus-nih_cqv.fasta\r\n", "│ ├── 1_blastn\r\n", "│ │ ├── asn\r\n", "│ │ │ └── orfan_to_viruses.asn\r\n", "│ │ ├── plots\r\n", "│ │ └── tsv\r\n", "│ │ ├── blast_tsv_columns.txt\r\n", "│ │ ├── orfan_to_viruses.tsv\r\n", "│ │ └── orfan_to_viruses_filt.tsv\r\n", "│ ├── 1_orfans_to_viruses.sh\r\n", "│ ├── blastn.mak\r\n", "│ ├── plot_blastn_hits.py\r\n", "│ └── plot_blastn_tsv_hits.py\r\n", "├── kaiju\r\n", "│ ├── kaiju_greedy\r\n", "│ │ ├── 454_seqs_kaiju_greedy.family.report\r\n", "│ │ ├── 454_seqs_kaiju_greedy.filt_species.tsv\r\n", "│ │ ├── 454_seqs_kaiju_greedy.genus.report\r\n", "│ │ ├── 454_seqs_kaiju_greedy.names.txt\r\n", "│ │ ├── 454_seqs_kaiju_greedy.names.virus.txt\r\n", "│ │ ├── 454_seqs_kaiju_greedy.species.report\r\n", "│ │ ├── 454_seqs_kaiju_greedy.txt\r\n", "│ │ ├── kaiju_greedy_summarize_virus_hits.ipynb\r\n", "│ │ └── summarize_virus_hits.ipynb\r\n", "│ ├── kaiju_mem\r\n", "│ │ ├── 454_seqs_kaiju.genus.report\r\n", "│ │ ├── 454_seqs_kaiju.names.txt\r\n", "│ │ ├── 454_seqs_kaiju.names.virus.txt\r\n", "│ │ ├── 454_seqs_kaiju.report\r\n", "│ │ ├── 454_seqs_kaiju.species.report\r\n", "│ │ ├── 454_seqs_kaiju.txt\r\n", "│ │ └── kaiju_mem_summarize_virus_hits.ipynb\r\n", "│ ├── poophage_in_kaiju.ipynb\r\n", "│ ├── run_kaiju_greedy.sh\r\n", "│ └── run_kaiju_mem.sh\r\n", "├── kraken\r\n", "│ ├── 454_seqs_kraken.out\r\n", "│ ├── 454_seqs_kraken_filt.out\r\n", "│ ├── 454_seqs_kraken_filt.report\r\n", "│ ├── _filt.virus.report\r\n", "│ ├── kraken.log\r\n", "│ └── run_kraken.sh\r\n", "└── metaphlan2\r\n", " ├── 454_reads_mpa2.bowtie2.bz2\r\n", " ├── 454_reads_mpa2.sam\r\n", " ├── 454_reads_mpa2.txt\r\n", " ├── get_marker_taxon_annot.ipynb\r\n", " ├── markers_to_taxons.csv\r\n", " └── run_mpa2.sh\r\n", "\r\n", "12 directories, 50 files\r\n" ] } ], "source": [ "!tree" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "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>pct_sum</th>\n", " <th>reads_sum</th>\n", " <th>reads_assigned</th>\n", " <th>tax_level</th>\n", " <th>taxid</th>\n", " <th>taxname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>S</td>\n", " <td>1341019</td>\n", " <td>Parvovirus NIH-CQV</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>S</td>\n", " <td>93678</td>\n", " <td>TTV-like mini virus</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>S</td>\n", " <td>1211417</td>\n", " <td>uncultured phage crAssphage</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>S</td>\n", " <td>948870</td>\n", " <td>Enterobacteria phage phi92</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>-</td>\n", " <td>196894</td>\n", " <td>unclassified Siphoviridae</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_sum reads_sum reads_assigned tax_level taxid \\\n", "5 0.0 3 3 S 1341019 \n", "8 0.0 1 1 S 93678 \n", "11 0.0 2 2 S 1211417 \n", "16 0.0 1 1 S 948870 \n", "18 0.0 1 1 - 196894 \n", "\n", " taxname \n", "5 Parvovirus NIH-CQV \n", "8 TTV-like mini virus \n", "11 uncultured phage crAssphage \n", "16 Enterobacteria phage phi92 \n", "18 unclassified Siphoviridae " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kraken = pd.read_csv(\"kraken/_filt.virus.report\",sep=\"\\t\",header=None,names=[\"pct_sum\",\"reads_sum\",\"reads_assigned\",\"tax_level\",\"taxid\",\"taxname\"])\n", "kraken[\"taxname\"] = kraken[\"taxname\"].apply(lambda x: x.lstrip(\"\\t \").rstrip(\"\\t \"))\n", "kraken[kraken.reads_assigned >0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16, 2)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>species</th>\n", " <th>read_count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Enterobacteria phage phi92</td>\n", " <td>52</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>uncultured crAssphage</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Parabacteroides phage YZ-2015b;Parabacteroides...</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Parvovirus NIH-CQV</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Phytophthora parasitica virus</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Gokushovirinae Fen672_31;Gokushovirinae Fen787...</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Sewage-associated gemycircularvirus 11;Sewage-...</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Chimpanzee faeces associated microphage 2;Chim...</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Salmonella virus SP31</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>unclassified NA</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Pseudomonas virus NP1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Rhizobium phage RHEph10;Rhizobium phage vB_Rgl...</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Croceibacter phage P2559Y</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Pseudomonas phage PAJU2;Pseudomonas phage phiP...</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>TTV-like mini virus</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>unclassified Siphoviridae</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " species read_count\n", "0 Enterobacteria phage phi92 52\n", "1 uncultured crAssphage 34\n", "2 Parabacteroides phage YZ-2015b;Parabacteroides... 24\n", "3 Parvovirus NIH-CQV 18\n", "4 Phytophthora parasitica virus 18\n", "5 Gokushovirinae Fen672_31;Gokushovirinae Fen787... 10\n", "6 Sewage-associated gemycircularvirus 11;Sewage-... 5\n", "7 Chimpanzee faeces associated microphage 2;Chim... 4\n", "8 Salmonella virus SP31 4\n", "9 unclassified NA 4\n", "10 Pseudomonas virus NP1 3\n", "11 Rhizobium phage RHEph10;Rhizobium phage vB_Rgl... 3\n", "12 Croceibacter phage P2559Y 2\n", "13 Pseudomonas phage PAJU2;Pseudomonas phage phiP... 2\n", "14 TTV-like mini virus 2\n", "15 unclassified Siphoviridae 2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kaiju = pd.read_csv(\"kaiju/kaiju_greedy/454_seqs_kaiju_greedy.filt_species.tsv\",sep=\"\\t\")\n", "print(kaiju.shape)\n", "kaiju" ] }, { "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>full_tax</th>\n", " <th>rel_ab</th>\n", " <th>clade</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>k__Viruses\|p__Viruses_noname\|c__Viruses_noname...</td>\n", " <td>100.0</td>\n", " <td>s__Parvovirus_NIH_CQV</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " full_tax rel_ab \\\n", "6 k__Viruses\|p__Viruses_noname\|c__Viruses_noname... 100.0 \n", "\n", " clade \n", "6 s__Parvovirus_NIH_CQV " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpa2 = pd.read_csv(\"metaphlan2/454_reads_mpa2.txt\",sep=\"\\t\")\n", "mpa2.columns = [\"full_tax\",\"rel_ab\"]\n", "mpa2[\"clade\"] = mpa2[\"full_tax\"].apply(lambda t: t.split(\"\|\")[-1])\n", "mpa2_sp = mpa2[mpa2[\"clade\"].apply(lambda c: c.startswith(\"s__\"))].copy()\n", "mpa2_sp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Homogenize tool output" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import functools\n", "replacements = {(\"crassphage\",):\"uncultured crAssphage\",\n", " (\"parvovirus\",\"nih\",\"cqv\"): \"Parvovirus NIH-CQV\"\n", " }\n", "\n", "def replacement_fx(clade_name):\n", " new_name = clade_name\n", " for r in replacements:\n", " name_matches = functools.reduce(lambda x,y:x and y, [word in clade_name.lower() for word in r ])\n", " if name_matches:\n", " new_name = replacements[r]\n", " break\n", " return new_name" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mpa2_sp[\"std_cladename\"] = mpa2_sp[\"clade\"].apply(replacement_fx)\n", "mpa2_sp = mpa2_sp[[\"std_cladename\",\"rel_ab\"]].copy()\n", "mpa2_sp.columns = [\"clade\",\"abundance\"]\n", "mpa2_sp[\"tool\"] = \"metaphlan2\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kraken[\"std_cladename\"] = kraken[\"taxname\"].apply(replacement_fx)\n", "kraken = kraken[kraken.reads_assigned >0][[\"std_cladename\",\"reads_assigned\"]].copy()\n", "kraken.columns = [\"clade\",\"abundance\"]\n", "kraken[\"tool\"] = \"kraken\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kaiju[\"std_cladename\"] = kaiju[\"species\"].apply(replacement_fx)\n", "kaiju = kaiju[[\"std_cladename\",\"read_count\"]].copy()\n", "kaiju.columns = [\"clade\",\"abundance\"]\n", "kaiju[\"tool\"] = \"kaiju\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "orfan_hits = [[\"TTV-like mini virus\",1],\n", " [\"Phytophthora parasitica virus\",1],\n", " [\"uncultured POOphage\",1]]\n", "\n", "orfan_method = pd.DataFrame.from_records(orfan_hits,columns=[\"clade\",\"abundance\"])\n", "orfan_method[\"tool\"] = \"ORFan\"" ] }, { "cell_type": "code", "execution_count": 11, "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>clade</th>\n", " <th>abundance</th>\n", " <th>tool</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Parvovirus NIH-CQV</td>\n", " <td>100.0</td>\n", " <td>metaphlan2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Parvovirus NIH-CQV</td>\n", " <td>3.0</td>\n", " <td>kraken</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>TTV-like mini virus</td>\n", " <td>1.0</td>\n", " <td>kraken</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>uncultured crAssphage</td>\n", " <td>2.0</td>\n", " <td>kraken</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Enterobacteria phage phi92</td>\n", " <td>1.0</td>\n", " <td>kraken</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>unclassified Siphoviridae</td>\n", " <td>1.0</td>\n", " <td>kraken</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Enterobacteria phage phi92</td>\n", " <td>52.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>uncultured crAssphage</td>\n", " <td>34.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Parabacteroides phage YZ-2015b;Parabacteroides...</td>\n", " <td>24.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Parvovirus NIH-CQV</td>\n", " <td>18.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Phytophthora parasitica virus</td>\n", " <td>18.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Gokushovirinae Fen672_31;Gokushovirinae Fen787...</td>\n", " <td>10.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Sewage-associated gemycircularvirus 11;Sewage-...</td>\n", " <td>5.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Chimpanzee faeces associated microphage 2;Chim...</td>\n", " <td>4.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Salmonella virus SP31</td>\n", " <td>4.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>unclassified NA</td>\n", " <td>4.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Pseudomonas virus NP1</td>\n", " <td>3.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Rhizobium phage RHEph10;Rhizobium phage vB_Rgl...</td>\n", " <td>3.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Croceibacter phage P2559Y</td>\n", " <td>2.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Pseudomonas phage PAJU2;Pseudomonas phage phiP...</td>\n", " <td>2.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>TTV-like mini virus</td>\n", " <td>2.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>unclassified Siphoviridae</td>\n", " <td>2.0</td>\n", " <td>kaiju</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>TTV-like mini virus</td>\n", " <td>1.0</td>\n", " <td>ORFan</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Phytophthora parasitica virus</td>\n", " <td>1.0</td>\n", " <td>ORFan</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>uncultured POOphage</td>\n", " <td>1.0</td>\n", " <td>ORFan</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " clade abundance tool\n", "0 Parvovirus NIH-CQV 100.0 metaphlan2\n", "1 Parvovirus NIH-CQV 3.0 kraken\n", "2 TTV-like mini virus 1.0 kraken\n", "3 uncultured crAssphage 2.0 kraken\n", "4 Enterobacteria phage phi92 1.0 kraken\n", "5 unclassified Siphoviridae 1.0 kraken\n", "6 Enterobacteria phage phi92 52.0 kaiju\n", "7 uncultured crAssphage 34.0 kaiju\n", "8 Parabacteroides phage YZ-2015b;Parabacteroides... 24.0 kaiju\n", "9 Parvovirus NIH-CQV 18.0 kaiju\n", "10 Phytophthora parasitica virus 18.0 kaiju\n", "11 Gokushovirinae Fen672_31;Gokushovirinae Fen787... 10.0 kaiju\n", "12 Sewage-associated gemycircularvirus 11;Sewage-... 5.0 kaiju\n", "13 Chimpanzee faeces associated microphage 2;Chim... 4.0 kaiju\n", "14 Salmonella virus SP31 4.0 kaiju\n", "15 unclassified NA 4.0 kaiju\n", "16 Pseudomonas virus NP1 3.0 kaiju\n", "17 Rhizobium phage RHEph10;Rhizobium phage vB_Rgl... 3.0 kaiju\n", "18 Croceibacter phage P2559Y 2.0 kaiju\n", "19 Pseudomonas phage PAJU2;Pseudomonas phage phiP... 2.0 kaiju\n", "20 TTV-like mini virus 2.0 kaiju\n", "21 unclassified Siphoviridae 2.0 kaiju\n", "22 TTV-like mini virus 1.0 ORFan\n", "23 Phytophthora parasitica virus 1.0 ORFan\n", "24 uncultured POOphage 1.0 ORFan" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_viruses = pd.concat([mpa2_sp,kraken,kaiju,orfan_method],ignore_index=True,axis=0)\n", "merged_viruses.to_csv(\"1_merged_viruses.tsv\",sep=\"\\t\",index=False)\n", "merged_viruses" ] }, { "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": 2 }	mit
zipfian/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers	sandbox/GithubUsers.ipynb	8	22363	{ "metadata": { "name": "GithubUsers" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##### Example: Number of Github users\n", "\n", "\n", "This is a fun example. Suppose we wish to predict how many sign-ups there are on Github.com. Officially, Github does not release an up-to-date count, and at last offical annoucment (January 2013) the count was 3 million. What if we wish to measure it today? We could [extrapolate future numbers from previous annoucements](http://redmonk.com/dberkholz/2013/01/21/github-will-hit-5-million-users-within-a-year/), but this uses little data and we could potentially be off by hundreds of thousands, and you are essentially just curve fitting complicated models. \n", "\n", "Instead, what we are going to use is `user id` numbers from real-time feeds on Github. The script `github_events.py` will pull the most recent 300 events from the [Github Public Timeline feed](https://github.com/timeline) (we'll be accessing data using their API). From this, we pull out the `user ids` associated with each event. We run the script below and display some output:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%run github_events.py\n", "\n", "\n", "print \"Some User ids from the latest events (push, star, fork etc.) on Github.\"\n", "print ids[:10]\n", "print\n", "print \"Number of unique ids found: \", ids.shape[0]\n", "print \"Largest user id: \", ids.max()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'id'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Python27\\lib\\site-packages\\IPython\\utils\\py3compat.pyc\u001b[0m in \u001b[0;36mexecfile\u001b[1;34m(fname, glob, loc)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mevent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mids\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m \u001b[0mevent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"actor\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"id\"\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 25\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'id'" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Some User ids from the latest events (push, star, fork etc.) on Github.\n", "[1524995 1978503 1926860 1524995 3707208 374604 37715 770655 502701\n", " 4349707]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Number of unique ids found: 300\n", "Largest user id: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2085773151\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "figsize(12.5,3)\n", "plt.hist( ids, bins = 45, alpha = 0.9)\n", "plt.title(\"Histogram of %d Github User ids\"%ids.shape[0] );\n", "plt.xlabel(\"User id\")\n", "plt.ylabel(\"Frequency\");" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'figsize' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-2-e83805e8eaea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfigsize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m45\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Histogram of %d Github User ids\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"User id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Frequency\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'figsize' is not defined" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some users with multiple events, but we are only interested in unique `user ids`, hence why we have less than 300 ids. Above I printed the largest `user id`. Why is this important? If Github assigns `user ids` serially, which is a fair assumption, then we know* that there are certainly more users than that number. Remember, we are only looking at less than 300 individuals out of a much larger population, so it is unlikely that we would have sampled the most recent sign-up. \n", "\n", "At best, we can only estimate the total number of sign-ups. Let's get more familar with this problem. Consider a fictional website that we wish to estimate the number of users:\n", "\n", "1. Suppose we sampled only two individuals in a similar manner to above: the ids are 3 and 10 respectively. Would it be likely that the website has millions of users? Not very. Alternatively, it is more likely the website has less than 100 users. \n", "\n", "2. On the other hand, if the ids were 3 and 34 989, we might be more willing to guess there could possibly thousands, or millions of user sign-ups. We are not very confident in an estimate, due to a lack of data.\n", "\n", "3. If we sample thousands of users, and the maximum `user id` is still 34 989, then is seems likely that the total number of sign ups is near 35 000. Hence our inference should be more confident.\n", "\n", "\n", "We make the following assumption:\n", "\n", "Assumption: Every user is equally likely to perform an event. Clearly, looking at the above histogram, this assumption is violated. The participation on Github is skewed towards early adopters, likely as these early-adopting individuals have a) more invested in Github, and b) saw the value earlier in Github, therefore are more interested in it. The distribution is also skewed towards new sign ups, who likely signed up just to push a project. \n", "\n", "To create a Bayesian model of this is easy. Based on the above assumption, all `user_ids` sampled are from a `DiscreteUniform` model, with lower bound 1 and an unknown upperbound. We don't have a strong belief about what the upper-bound might be, but we do know it will be larger than `ids.max()`. \n", "\n", "Working with such large numbers can cause numerical problem, hence we will scale everything by a million. Thus, instead of a `DiscreteUniform`, we will used a `Uniform`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "FACTOR = 1000000.\n", "\n", "import pymc as mc\n", "\n", "upper_bound = mc.Uniform( \"n_sign_ups\", ids.max()/FACTOR, (ids.max())/FACTOR + 1)\n", "obs = mc.Uniform(\"obs\", 0, upper_bound, value = ids/FACTOR, observed = True )\n", "\n", "#code to be examplained in Chp. 3.\n", "mcmc = mc.MCMC([upper_bound, obs] )\n", "mcmc.sample( 100000, 45000)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ZeroProbability", "evalue": "Stochastic obs's value is outside its support,\n or it forbids its parents' current values.", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroProbability\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-3-4a6014825d95>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m 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270\u001b[1;33m \u001b[0mStochastic\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[0mlogp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogp_partial_gradients\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogp_partial_gradients\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m\u001b[0m\u001b[0marg_dict_out\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 271\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 272\u001b[0m \u001b[0mnew_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, logp, doc, name, parents, random, trace, value, dtype, rseed, observed, cache_depth, plot, verbose, isdata, check_logp, logp_partial_gradients)\u001b[0m\n\u001b[0;32m 714\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcheck_logp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 715\u001b[0m \u001b[1;31m# Check initial value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\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 717\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Stochastic \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'s initial log-probability is %s, should be a float.\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 718\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\Python27\\lib\\site-packages\\pymc\\PyMCObjects.pyc\u001b[0m in \u001b[0;36mget_logp\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 846\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrmsg\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"\\nValue: %s\\nParents' values:%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m,\u001b[0m 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mcmc.trace(\"n_sign_ups\")[:]\n", "\n", "hist(samples, bins = 100, \n", " label = \"Uniform prior\",\n", " normed=True, alpha = 0.8, \n", " histtype=\"stepfilled\", color = \"#7A68A6\" );\n", "\n", "quantiles_mean = np.append( mquantiles( samples, [0.05, 0.5, 0.95]), samples.mean() )\n", "print \"Quantiles: \", quantiles_mean[:3]\n", "print \"Mean: \", quantiles_mean[-1]\n", "plt.vlines( quantiles_mean, 0, 33, \n", " linewidth=2, linestyles = [\"--\", \"--\", \"--\", \"-\"],\n", " )\n", "plt.title(\"Posterior distribution of total number of Github users\" )\n", "plt.xlabel(\"number of users (in millions)\")\n", "plt.legend()\n", "plt.xlim( ids.max()/FACTOR - 0.01, ids.max()/FACTOR + 0.12 );" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'mcmc' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-2ca0106ec6de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmstats\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmquantiles\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmcmc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"n_sign_ups\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\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 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m hist(samples, bins = 100, \n", "\u001b[1;31mNameError\u001b[0m: name 'mcmc' is not defined" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we have plotted the posterior distribution. Note that there is no posterior probability assigned to the number of users being less than `ids.max()`. That is good, as it would be an impossible situation. \n", "\n", "The three dashed vertical bars, from left to right, are the 5%, 50% and 95% quantitle lines. That is, 5% of the probability is before the first line, 50% before the second and 95% before the third. The 50% quantitle is also know as the median* and is a better measure of centrality than the mean for heavily skewed distributions like this one. The solid line is the posterior distribution's mean.\n", "\n", "So what can we say? Using the data above, there is a 95% chance that there are less than 4.4 million users, and is probably around 4.36 million users. I was wondering how accurate this figure was. At the time of this writing, it seems a bit high considering only five months prior the number was at 3 million:\n", "\n", "\n", "<blockquote class=\"twitter-tweet\"><p>Last night @<a href=\"https://twitter.com/github\">github</a> crossed the 3M user mark <a href=\"https://twitter.com/search/%23turntup\">#turntup</a></p>— Rick Bradley (@rickbradley) <a href=\"https://twitter.com/rickbradley/status/291284133483802626\">January 15, 2013</a></blockquote>\n", "<script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n", "\n", "\n", "I thought perhaps the `user_id` parameter was being used liberally to users/bots/changed names etc, so I contacted Github Support about it:\n", "\n", "<blockquote class=\"twitter-tweet\"><p>@<a href=\"https://twitter.com/cmrn_dp\">cmrn_dp</a> User IDs are assigned to new users/organizations, whether they\u2019re controlled by humans, groups, or bots.</p>— GitHub Support (@GitHubHelp) <a href=\"https://twitter.com/GitHubHelp/status/331523721527427073\">May 6, 2013</a></blockquote>\n", "<script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n", "\n", "\n", "So we may be overestimating by including organizations, which perhaps should not be counted as users. TODO: estimate the number of organizations. Any takers?" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
notconfusing/WIGI	Make-POB-Chi-Square-Test-Data.ipynb	1	1813239	null	mit
ctroupin/OceanData_NoteBooks	PythonNotebooks/IndexFilePlots/read_CMEMS_indexfile.ipynb	2	112478	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to use an index file.<br/>\n", "This example uses the index file from the Mediterranean Sea region ([INSITU_MED_NRT_OBSERVATIONS_013_035](http://marine.copernicus.eu/web/69-interactive-catalogue.php?option=com_csw&view=details&product_id=INSITU_MED_NRT_OBSERVATIONS_013_035)) corresponding to the latest data.<br/>\n", "If you download the same file, the results will be slightly different from what is shown here." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "indexfile = \"./datafiles/index_latest.txt\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To read the index file (comma separated values), we will try with the [genfromtxt](http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html) function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "dataindex = np.genfromtxt(indexfile, skip_header=6, unpack=True, delimiter=',', dtype=None, \\\n", " names=['catalog_id', 'file_name', 'geospatial_lat_min', 'geospatial_lat_max',\n", " 'geospatial_lon_min', 'geospatial_lon_max',\n", " 'time_coverage_start', 'time_coverage_end', \n", " 'provider', 'date_update', 'data_mode', 'parameters'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Map of observations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lon_min = dataindex['geospatial_lon_min']\n", "lon_max = dataindex['geospatial_lon_max']\n", "lat_min = dataindex['geospatial_lat_min']\n", "lat_max = dataindex['geospatial_lat_max']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We import the modules necessary for the plot." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.basemap import Basemap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the projection, centered on the Mediterranean Sea in this case." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = Basemap(projection='merc', llcrnrlat=30., urcrnrlat=46.,\n", " llcrnrlon=-10, urcrnrlon=40., lat_ts=38., resolution='i')\n", "lonmean, latmean = 0.5(lon_min + lon_max), 0.5(lat_min + lat_max)\n", "lon2plot, lat2plot = m(lonmean, latmean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we create a plot showing all the data locations." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HRupUxYI0Etiey/tEoFTaX7rKtxOIBFapLWEWJIvVWzS9K1CXjsUSBSM2NpaA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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x5550850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,8))\n", "m.plot(lon2plot, lat2plot, 'ko', markersize=2)\n", "m.drawcoastlines(linewidth=0.5, zorder=3)\n", "m.fillcontinents(zorder=2)\n", "\n", "m.drawparallels(np.arange(-90.,91.,2.), labels=[1,0,0,0], linewidth=0.5, zorder=1)\n", "m.drawmeridians(np.arange(-180.,181.,3.), labels=[0,0,1,0], linewidth=0.5, zorder=1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Selection of a data file based on coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's assume we want to have the list of files corresponding to measurements off the northern of Lybia.<br/>\n", "We define a rectangular box containg the data:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "box = [12, 15, 32, 34]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "then we look for the observations within this box:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([ 278, 632, 978, 1341, 1720, 2062, 2408]),)\n" ] } ], "source": [ "import numpy as np\n", "goodcoordinates = np.where( (lonmean>=box[0]) & (lonmean<=box[1]) & (latmean>=box[2]) & (latmean<=box[3]))\n", "print goodcoordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The generation of the file list is direct:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151120/GL_LATEST_PR_PF_1900948_20151120.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151124/GL_LATEST_PR_PF_1900948_20151124.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151128/GL_LATEST_PR_PF_1900948_20151128.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151202/GL_LATEST_PR_PF_1900948_20151202.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151206/GL_LATEST_PR_PF_1900948_20151206.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151210/GL_LATEST_PR_PF_1900948_20151210.nc'\n", " 'ftp://medinsitu.hcmr.gr/Core/INSITU_MED_NRT_OBSERVATIONS_013_035/latest/20151214/GL_LATEST_PR_PF_1900948_20151214.nc']\n" ] } ], "source": [ "goodfilelist = dataindex['file_name'][goodcoordinates]\n", "print goodfilelist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to the file names, we have 7 profiling drifters available in the area. <br/>\n", "To check, we replot the data only in the selected box:" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m2 = Basemap(projection='merc', llcrnrlat=32., urcrnrlat=34.,\n", " llcrnrlon=12, urcrnrlon=15., lat_ts=38., resolution='h')\n", "lon2plot, lat2plot = m2(lonmean[goodcoordinates], latmean[goodcoordinates])" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x5d6fa90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,8))\n", "m2.plot(lon2plot, lat2plot, 'ko', markersize=4)\n", "m2.drawcoastlines(linewidth=0.5, zorder=3)\n", "m2.fillcontinents(zorder=2)\n", "\n", "m2.drawparallels(np.arange(-90.,91.,0.5), labels=[1,0,0,0], linewidth=0.5, zorder=1)\n", "m2.drawmeridians(np.arange(-180.,181.,0.5), labels=[0,0,1,0], linewidth=0.5, zorder=1)\n", "plt.show()" ] } ], "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 }	gpl-3.0
fmeynadier/allantools	examples/noise-color-demo.ipynb	2	29513	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Allantools demo\n", "\n", "Allantools tests with various noise types. We test ADEV etc. by calculations on synthetic data with known slopes of ADEV.\n", "\n", "#### Import packages and setup notebook" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import matplotlib.pyplot as plt \n", "import allantools\n", "from allantools import noise" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plotallan(plt, y, rate, taus, style):\n", " (t2, ad, ade, adn) = allantools.oadev(y, rate=rate, data_type=\"freq\", taus=taus)\n", " plt.loglog(t2, ad, style)\n", "\n", "\n", "def plotallan_phase(plt, y, rate, taus, style):\n", " (t2, ad, ade, adn) = allantools.oadev(y, rate=rate, taus=taus)\n", " plt.loglog(t2, ad, style)\n", "\n", "\n", "def plotline(plt, alpha, taus, style):\n", " \"\"\" plot a line with the slope alpha \"\"\"\n", " y = [pow(tt, alpha) for tt in taus]\n", " plt.loglog(taus, y, style)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Colors: http://en.wikipedia.org/wiki/Colors_of_noise\n", "\n", "* Pink frequency noise - should have constant ADEV\n", "* White phase noise - should have 1/tau ADEV\n", "* White frequency noise - should have 1/sqrt(tau) ADEV\n", "* Random Walk frequency noise - should have sqrt(tau) ADEV" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "image/png": 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bV1JicYQyqfKj9hcReTjeixljHgEKgc7GmE+AX4rIAmPMdcDzBNNx/+xGNMJY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\nKLfYi0q64yaZmBrcGIXDDq8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URYkLFQ5FURQlLlQ4FEVRlLj4/5tI0VBMu0gz\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4bc7254b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = numpy.logspace(0, 3, 50) # tau values from 1 to 1000\n", "plt.subplot(111, xscale=\"log\", yscale=\"log\")\n", "N = 10000\n", "\n", "# pink frequency noise => constant ADEV\n", "freq_pink = noise.pink(N)\n", "phase_p = numpy.cumsum(noise.pink(N)) # integrate to get phase, color??\n", "plotallan_phase(plt, phase_p, 1, t, 'co')\n", "plotallan(plt, freq_pink, 1, t, 'c.')\n", "plotline(plt, 0, t, 'c')\n", "\n", "# white phase noise => 1/tau ADEV\n", "phase_white = noise.white(N)\n", "plotallan_phase(plt, phase_white, 1, t, 'ro')\n", "freq_w = noise.violet(N) # diff to get frequency, \"Violet noise\"\n", "plotallan(plt, freq_w, 1, t, 'r.')\n", "plotline(plt, -1, t, 'r')\n", "\n", "# white frequency modulation => 1/sqrt(tau) ADEV\n", "freq_white = noise.white(N)\n", "phase_rw = noise.brown(N) # integrate to get Brownian, or random walk phase\n", "plotallan(plt, freq_white, 1, t, 'b.')\n", "plotallan_phase(plt, phase_rw, 1, t, 'bo')\n", "plotline(plt, -0.5, t, 'b')\n", "\n", "\n", "# Brownian a.k.a random walk frequency => sqrt(tau) ADEV\n", "freq_rw = noise.brown(N)\n", "phase_rw_rw = numpy.cumsum(noise.brown(N)) # integrate to get phase\n", "plotallan(plt, freq_rw, 1, t, 'm.')\n", "plotallan_phase(plt, phase_rw_rw, 1, t, 'mo')\n", "plotline(plt, +0.5, t, 'm')\n", "\n", "plt.xlabel('Tau')\n", "plt.ylabel('Overlapping Allan deviation')\n", "print(\"Done.\")\n", "plt.grid()\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": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }	lgpl-3.0
BuzzFeedNews/2014-09-tuition-and-minimum-wage	notebooks/charts.ipynb	1	193010	{ "metadata": { "name": "", "signature": "sha256:171813d26dea1212d8aa7227028bc7ac14b901d949a69456690ef38b641694c2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib as mpl\n", "import mplstyle\n", "import mplstyle.styles.simple\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "mplstyle.set(mplstyle.styles.simple)\n", "fonts = mpl.font_manager.findSystemFonts()\n", "arial_narrow = mpl.font_manager.FontProperties(filter(lambda x: \"Arial Narrow.ttf\" in x, fonts)[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "mplstyle.set({\n", " \"figure.figsize\": (12, 8)\n", "})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Increases in Average Tuition+Fees vs. Minimum Wage" ] }, { "cell_type": "code", "collapsed": false, "input": [ "min_wage = pd.read_csv(\"../data/federal-minimum-wage.csv\", parse_dates=[\"effective_date\"], index_col=\"effective_date\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "min_wage[\"highest\"] = min_wage.max(axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because we're talking about summer earnings, we use the minimum wage as it was the July 1 of each year." ] }, { "cell_type": "code", "collapsed": false, "input": [ "monthly_min_wage = min_wage[\"highest\"].resample(\"D\").fillna(method=\"pad\")\n", "julys = [ i for i in monthly_min_wage.index if i.month == 7 and i.day == 1 ]\n", "july_min_wage = monthly_min_wage.ix[julys].shift(-1, freq=\"AS\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "avg_tuition = pd.read_csv(\"../data/average-tuition.csv\", parse_dates=[\"year\"], index_col=\"year\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Limit timespan to years for which we have tuition data\n", "start = \"1970\"\n", "end = \"2011\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate and chart the number of hours require to pay for avg. tuition" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hours_required = (avg_tuition[\"public_4yr_instate\"].resample(\"AS\").ix[start:end] * 1.0 / \\\n", " july_min_wage.ix[start:end])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = hours_required.plot(drawstyle=\"default\", color=\"red\",\n", " markersize=0, lw=3, alpha=0.75)\n", "ax.minorticks_off()\n", "ax.set_ylim(0, ax.get_ylim()[1])\n", "ax.set_yticklabels(map(\"{0:,.0f} hours\".format, ax.get_yticks()), fontsize=\"large\", color=\"#888888\")\n", "mpl.pyplot.setp(ax.get_xticklabels(), fontsize=14, fontweight=\"bold\")\n", "ax.set_xlabel(\"\")\n", "ax.set_title(u\"\"\"Minimum-Wage Hours Required To Pay In-State\n", "Tuition and Fees at an Average 4-Year Public University\n", "\"\"\", fontsize=20)\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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TJmRmZmLKlCmoWbOmUrZu3bqIjIwsZQT0M2DAALRq1Up5nZaWhk2bNqFz58549dVXVWWD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w8//QTz5sEbb8ATT8Btt0GPHmaVkd69zVVUD6lXz46gy3HHDhg2rGxBf955pn8+SAv6oMswRClH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\nREJCAosXLyY8PJzXX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"text": [ "<matplotlib.figure.Figure at 0x1067435d0>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "hours_required.apply(round).head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "year\n", "1970-01-01 272\n", "1971-01-01 268\n", "1972-01-01 314\n", "1973-01-01 321\n", "1974-01-01 256\n", "Freq: AS-JAN, dtype: float64" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "hours_required.apply(round).tail(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "year\n", "2007-01-01 1154\n", "2008-01-01 1079\n", "2009-01-01 1022\n", "2010-01-01 984\n", "2011-01-01 1062\n", "Freq: AS-JAN, dtype: float64" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chart the nominal-dollar values for tuition and minimum wage summer earnings over time" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = (july_min_wage * 12 * 40).ix[start:end]\\\n", " .plot(drawstyle='steps', lw=3, markersize=0, color=\"teal\", alpha=0.75)\n", "avg_tuition[\"public_4yr_instate\"]\\\n", " .resample(\"AS\")\\\n", " .dropna()\\\n", " .ix[start:end]\\\n", " .plot(drawstyle=\"default\", color=\"red\", markersize=0, lw=3, alpha=0.75)\n", "ax.minorticks_off()\n", "ax.set_yticklabels(map(\"${0:,.0f}\".format, ax.get_yticks()), fontsize=\"large\", color=\"#888888\")\n", "mpl.pyplot.setp(ax.get_xticklabels(), fontsize=14, fontweight=\"bold\")\n", "ax.set_xlabel(\"\")\n", "ax.set_title(u\"\"\"\n", "Average Tuition and Fees \u2014 4-Year U.S. Public Universities (Red)\n", "vs.\n", "Minimum-Wage Summer Earnings \u2014\u00a012 Weeks @ 40 Hours/Week (Blue)\n", "\"\"\", fontsize=20)\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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K48aNg0KhwBdffKF15sC9e/cwZswYAMBbb72lt176YGpqit69eyMuLg6rVq2C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\n4+NJTk7mzJkzDB8+HLB6tzMyM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"text": [ "<matplotlib.figure.Figure at 0x109fa9150>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## School-Specific Calculations\n", "\n", "Here, we compare the cost-coverage of a summer's worth of minimum-wage work, in 1976\u201377 vs. 2014\u201315, at four prominent state universities." ] }, { "cell_type": "code", "collapsed": false, "input": [ "schools = pd.read_csv(\"../data/specific-schools.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "schools[\"coverage_1976\"] = schools[\"farm_min_wage_1976\"] * 40 * 12 / schools[\"cost_1976\"]\n", "schools[\"coverage_2014\"] = schools[\"state_min_wage_2014\"] * 40 * 12 / schools[\"cost_2014\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = schools[[\"coverage_1976\", \"coverage_2014\"]]\\\n", " .rename(columns=dict(coverage_1976=u\"1976\u201377\", coverage_2014=u\"2014\u201315\")).plot(kind=\"bar\",\n", " width=0.8,\n", " color=[ \"#0077ee\", \"#ee3322\" ],\n", " edgecolor=\"white\")\n", "mpl.pyplot.axhline(1, zorder=0, lw=2, color=\"#ffee00\")\n", "ax.set_ylim((0, 1.6))\n", "ax.set_yticks(np.arange(0, 1.6, 0.25))\n", "ax.set_xticklabels(schools[\"university\"].apply(lambda x: x.replace(\", \", \",\\n\")),\n", " rotation=0,\n", " fontsize=\"x-large\",\n", " fontweight=\"bold\")\n", "ax.set_xlim((-0.5, 2.5))\n", "ax.set_yticklabels([ \"{0:.0f}%\".format(y*100) for y in ax.get_yticks() ],\n", " fontsize=\"x-large\")\n", "ax.xaxis.grid(False)\n", "ax.legend(loc=\"upper right\", fontsize=18)\n", "ax.set_title(u\"\"\"\n", "Summer Minimum Wage Earnings (40 Hours/Week for 12 Weeks)\n", "as a Percentage of Tution and Mandatory Fees\n", "\"\"\", fontsize=18, linespacing=1.25)\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KDg4mXV1dys/Pl/ZdEARZYC0IAo0bN05a5uXLl1SjRg1ycnIiIqLCwkJyd3cnHx8fev78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"text": [ "<matplotlib.figure.Figure at 0x1067509d0>" ] } ], "prompt_number": 16 } ], "metadata": {} } ] }	mit
hpssjellis/forth-tensorflow	skflow-examples/broken/a21-neural_translation_word.ipynb	2	964	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [ ], "source": [ "%autosave 0\n", "!python /home/ubuntu/workspace/tensorflow/tensorflow/examples/skflow/neural_translation_word.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load /home/ubuntu/workspace/tensorflow/tensorflow/examples/skflow/neural_translation_word.py" ] } ], "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 }	mit
chengsoonong/mclass-sky	projects/peerjcs16/sandbox.ipynb	1	923581	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "import warnings\n", "from time import time\n", "from mclearn.experiment import ActiveExperiment, load_results, save_results\n", "from mclearn.tools import log\n", "from matplotlib.ticker import FuncFormatter\n", "%matplotlib inline\n", "sns.set_style('white')\n", "warnings.filterwarnings('ignore') # Ignore annoying numpy warnings" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "uci_sets = ['glass', 'ionosphere'] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thompson Sampling: testing effect of mu and tau/sigma" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for dataset in uci_sets:\n", " data_path = os.path.join('data', dataset + '.csv')\n", " data = pd.read_csv(data_path)\n", " X, y = data.iloc[:, 1:], data['target']\n", " for mu in [0.001, 0.005, 0.05, 0.5]:\n", " for var in [0.001, 0.005, 0.01, 0.02]:\n", " print(dataset, mu, var)\n", " save_name = 'thompson-{}-{}'.format(mu, var)\n", " expt = ActiveExperiment(\n", " X, y, dataset, 'thompson', scale=True,\n", " n_splits=10, n_jobs=10, ts_sigma=var,\n", " ts_tau=var, ts_mu=mu, save_name=save_name)\n", " expt.run_policies()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testing effect of information density" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for dataset in uci_sets:\n", " data_path = os.path.join('data', dataset + '.csv')\n", " data = pd.read_csv(data_path)\n", " X, y = data.iloc[:, 1:], data['target']\n", " for policy in ['w-confidence', 'w-margin']:\n", " for gamma in [50, 60, 70, 90, 95, 99]:\n", " print(dataset, policy, gamma)\n", " save_name = '{}-gamma-{}'.format(policy, gamma)\n", " expt = ActiveExperiment(\n", " X, y, dataset, policy, scale=True,\n", " n_splits=10, n_jobs=10, gamma_percentile=gamma,\n", " save_name=save_name)\n", " expt.run_policies()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "titles = {\n", " 'f1': 'F1',\n", " 'accuracy': 'Accuracy',\n", " 'mpba': 'MPBA'\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def plot_learning_curves(measure):\n", " format_as_percent_plot = lambda x, pos: \"{:.0f}%\".format(x * 100)\n", " fig = plt.figure(figsize=(15, 20))\n", " \n", " selected_methods = []\n", " for mu in [0.001, 0.005, 0.05, 0.5]:\n", " for var in [0.001, 0.005, 0.01, 0.02]:\n", " selected_methods.append('thompson-{}-{}'.format(mu, var))\n", " for (i, dataset) in enumerate(uci_sets):\n", " initial_n = 10\n", " \n", " learning_curves = {}\n", " for method in selected_methods:\n", " learning_curves[method] = load_results(dataset, method, measure, True)\n", " \n", " maximum = load_results(dataset, 'asymptote', 'asymptote_{}'.format(measure), True)\n", " sample_size = learning_curves['thompson-0.05-0.02'].shape[0] + 9\n", "\n", " ax = fig.add_subplot(4, 3, i + 1)\n", " for method in selected_methods:\n", " xticks = np.arange(initial_n, initial_n + len(learning_curves[method]))\n", " method_label = 'exp3++' if method == 'exp++' else method\n", " ax.plot(xticks, learning_curves[method], label=method_label, linewidth=1)\n", "\n", " ax.legend(loc='lower right', frameon=True)\n", " ax.get_yaxis().set_major_formatter(FuncFormatter(format_as_percent_plot))\n", " ax.set_title(dataset)\n", " ax.tick_params(top='off')\n", " ax.set_ylabel(titles[measure])\n", "# ax.set_xscale(\"log\")\n", "\n", " ax.plot([initial_n, sample_size], [maximum, maximum], ls='--', color='#377eb8')\n", " ax.set_xlim(initial_n, sample_size)\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", "# fig.savefig('figures/learning_curves-thompson-{}-{}.pdf'.format(measure, data), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a0b8486d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_learning_curves('mpba')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def calculate_strength(asymptote, passive, policy):\n", " n_trials, n_samples = passive.shape\n", " asymptote = np.repeat(asymptote, n_samples).reshape((n_trials, n_samples))\n", " deficiency = np.sum(asymptote - policy, axis=1) / np.sum(asymptote - passive, axis=1)\n", " strength = 1 - deficiency\n", " return strength" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def plot_strength(measure='mpba', data='small'):\n", " fig = plt.figure(figsize=(10, 8))\n", " fig.subplots_adjust(hspace=.6)\n", " methods = []\n", " method_names = []\n", " \n", " for mu in [0.001, 0.005, 0.05, 0.5]:\n", " for var in [0.001, 0.005, 0.01, 0.02]:\n", " methods.append('thompson-{}-{}'.format(mu, var))\n", " method_names.append('μ={}, σ=τ={}'.format(mu, var))\n", " \n", " for i, (dataset, part) in enumerate(zip(['glass', 'ionosphere'], ('A', 'B'))):\n", " results = {}\n", " for method in methods + ['passive']:\n", " results[method] = load_results(dataset, method, measure, mean=False)\n", " results['max'] = load_results(dataset, 'max', 'max_' + measure, False)\n", " strength_dict = {}\n", " for method in methods:\n", " s = calculate_strength(results['max'], results['passive'], results[method])\n", " strength_dict[method] = s\n", " strength_df = pd.DataFrame(strength_dict)\n", " strength_df.columns = ['μ={}, σ=τ={}'.format(x.split('-')[1], x.split('-')[2])\n", " for x in strength_df.columns]\n", " sorted_cols = (-strength_df.median()).sort_values().index\n", " strength_df = strength_df[sorted_cols]\n", "\n", " ax = fig.add_subplot(2, 1, i + 1)\n", " strength_df.index.name = 'trial'\n", " strength_df = strength_df.reset_index()\n", " strength_df = strength_df.melt(id_vars=['trial'], value_vars=method_names)\n", "# strength_df.loc[strength_df['variable'].isin(methods_al), 'type'] = 'single'\n", "# strength_df.loc[strength_df['variable'].isin(methods_bandits), 'type'] = 'bandit'\n", "# strength_df.loc[strength_df['variable'].isin(methods_rank), 'type'] = 'rank'\n", "# strength_df.loc[strength_df['variable'] == 'baseline', 'variable'] = 'explore'\n", "# strength_df.loc[strength_df['variable'] == 'exp++', 'variable'] = 'exp3++'\n", " sorted_cols = list(sorted_cols)\n", "# sorted_cols[sorted_cols.index('baseline')] = 'explore'\n", "# sorted_cols[sorted_cols.index('exp++')] = 'exp3++'\n", " # We could use hue here, but I think there is a bug in seaborn that squishes\n", " # the boxplot\n", " \n", "\n", " sns.boxplot(data=strength_df, x='variable', y='value', order=sorted_cols,\n", " width=0.4, linewidth=1, fliersize=3,\n", " color='#3498db')\n", " ax.set_title('({}) {}'.format(part, dataset))\n", " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, rotation_mode='anchor', ha='right')\n", " ax.xaxis.set_visible(True)\n", " ax.set_ylabel(titles[measure] + ' Strength')\n", " ax.set_xlabel('')\n", " ax.axhline(linewidth=1)\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", " \n", " # set bar width\n", " new_width = 0.5\n", " for bar in ax.patches:\n", " x = bar.get_x()\n", " width = bar.get_width()\n", " centre = x + new_width / 2.\n", "\n", " bar.set_x(centre - new_width / 2.)\n", " bar.set_width(new_width)\n", " \n", " fig.savefig('figures/strengths-thompson-params.pdf'.format(measure, data), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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km0kNNCLiNcAXI2KCpC263baZmZnZaNV1Iw0gIp4PvA3YDVieNPxpZmZmZouo2xMHdiE1zF4CTAP2k3RnD3KZmZmZjWrd9qR9D7gFuAF4BfCFiABA0u5dbtvMzMxs1Oq2kbZdT1JU5OuAnghsDMwC9pY0o/L8PsAHgdnAUZIu7HUGMzMzs37rdjHbX/QqSMXOwDhJW0bEFsCxwFsBImI88FHS0h/jgF9FxGWSZtWQw8zMzKxvSrwo+tbAdABJ15AaZC2bA1dJmiXpYWAGsFHzEc3MzMzqNWZoaKjfGZ4lIk4Ffijp4nz/b8A6kmZHxHuAV0j6ZH7uTOBMSZdXt7H57h8f+udLXtd0dDMzM7OFtsx5Bx0paVL74z1ZgqPHHgFWqNwfK2n2MM+tADzUvoH3brkWBxzw5vm+0Z5778PDDz6w0AFXXGVVTj/1lIX+O2dZvLMMZ3BwkGnTpjXyXvPjLJ01nWVwcJAntv/EQv/duCuO6XlOZyk/i1mcd1DHx0tspF0FvAU4N89Ju6ny3G+Bz0fEOGAZYD3g5kV9o3lV4k0f1J2l/CxmZmZNKrGRdh6wQ0S0rgW6Z0QcBMyQdEFETAF+SZpPd5ikJ/qY1czMzKwWxTXSJM0B9mt7+JbK86cAzYxjmZmZmfVJiWd3mpmZmY16bqSZmZmZFai44U4zs8XdiqusClccs2h/5yyjLovZcNxIMzPrsZLOSnaW8rOYDcfDnWZmZmYFciPNzMzMrEBupJmZmZkVyI00MzMzswK5kWZmZmZWIDfSzMzMzArkJTjMFsHEiROZOXPmM/cHBwefuT1hwgQmT57sLM5iZtYVN9Ky9gM6zD2o97tycZbyspRUwTtLZyVlMTNbFG6kZSUd0J2ls5KymJmZ1c1z0szMzMwK5EaamZmZWYHcSDMzMzMrkBtpZmZmZgVyI83MzMysQG6kmZmZmRXIjTQzMzOzArmRZmZmZlYgN9LMzMzMCuRGmpmZmVmB3EgzMzMzK5AbaWZmZmYFKuoC6xGxLHA28ELgUeD9ku7r8Lp1gfMlbdhwRDMzM7NGlNaT9iHgJknbAGcCh7e/ICLeC3wXWK3hbGZmZmaNKa2RtjUwPd++GHh9h9f8C3htY4nMzMzM+qBvw50RsRdwYNvD9wIP59uPAiu2/52kC/Pf15rPzMzMrJ/61kiTdBpwWvWxiJgGrJDvrgA81HQuMzMzsxKUNtx5FbBTvv0m4Jd9zGJmZmbWN6U10k4CNoiIXwH7AkcCRMSXI2LzviYzMzMza1BRS3BIehzYpcPjB3d4bHwjoczMzMz6oKhGmpnZSDNx4kRmzpz5rMcGBwcBmDBhApMnT3YWZzHraMzQ0FC/M/TcCSecMHTAAQf0O4aZmZnZfEXEkZImtT9e2pw0MzMzM8ONNDMzM7MiuZFmZmZmViA30szMzMwK5EaamZmZWYFG5NmdEXEqcFe/c5iZmZktgDUl7d3+4IhspJmZmZkt7jzcaWZmZlYgN9LMzMzMCuRG2mIgIsb0O4OZ2Wjg462VxI20DiLiBf3O0BIRy0rq+8TBiFi/lINXRLyy3xls8RARRR3jSvkOlSQilu13hpaIWAYY1+8cLS4vVtQBrAQRcTIwsd85ACLiRODjlft9+cJGxBTgTYU0FicD+/Q7B0BE7BYR2/U7B0BE7BQRG/Q7B0BEfCoiVi8gxzuBwYhYooAsX42ILwEnRERfGwER8YWIOCwiPh4RS/U7CzA5Ig7pZ46c5VjgVODLEbFFn7O8E0DSUL8bahGxcz/fv6qU41yTxzg30ipyo+hRSYdHxEsjYo2IWLJPWaYADwFfiIjnR8RK/WgkRcSZwOOSjo2I/8r/9eXAHhHHAQ8D+0XEBv1sIOWKf2fgvRGxTR9zjImI5wMHAbtExHr9ylKxA3BeRKzR5xxvB3YFdurX9xhSAw1YCTgLmAPs1McsU4HVgZuAlwD/08csRwEvBr4K7BsR7+pjlmOBlYHPA7cBA/3Kkn0jIj4HqaHWrxC5Z/HUiPhUvzLkHKUd5xo7xrmRlkXEZsDLgSNyb9oxwLnAXhGxYsNZdgC2lnQocCLwdeDnEfHm/Hwjv6zyL4UXAZdHxNeAo4BDgNPyl7cxEbEW8FpgOmnfHEj6BX5ERCzfZJZsPLAucDfwfxGxbR8ytA7gKwNr50xvjoj1+5EFIB+0HgX+CPwiIl7chwxj83d2PPBPYDPS59KvhtpqwGck/RH4F7BhP0LkYcVVJO0p6QJgGaAvPUZ5/2wIfFnSLaTv9Ev6kSVbEpiUszwGvKOPIxdrAn/PGSb3I0PFS3OW90fECf0KUdJxruljnBtpmaTrgBsBAXdK2hWYBLwBWLPhLJcBN0bEX4G7JO0GfBn4ZES8uKlfVpLuITVUPwo8IukdwCeBR0ifS2Mk3QkcBvwYWDYv+rcDsDWwS1M5KnOc7ic1Wr8D/BV4a0S8tqkcbR4jDYufCqwKvKWPDbVHge9L2ovUc/SzphtqkuZIehj4FOk7/E/g1TTco1bpcZ5NahAB3EzqDSYi1m644TgWGB8RL833fw88kLOs0eSwcN4/VwAr5IfmAEvnLC9t6kdgpSH2GKlBAvAn4J481BgR0XSv2tPAJyStB2weEce3nuhDw3EccISk/wK2rmbpg1KOc40e4/o2BFCK3I37b+AOSR+PiMeBPwNIujwi3gCsR2o1153lw8B/gN8C+wOHA9fkLOfkeRLrkH7Z1JnjUOCJ/D7nA+sDF+Qc90bEP4Hl6sxQyXI4qTfvt8CvgN2AVSNiiZzlWzQ00TcijgDGRcQ9wCnAdEn/iYjHgDcB74qIsZJ+1kCW1j56SNI3I+JKSQ9GxNPAIOlX+A9z703dWQ4HXgBcC/xG0tkAkj4bEbOBayNic0m1XgUkT1d4glTZ7g38QdLjka5AsiewPbAEcF6dOXKWI4BlI+IGUi9a69/+X8BtEbEhcCypR/hPNWc5FJhFOq7tJOmJSpafRsRGpGPNIcDtNWc5jPR9/jXwe0nX5KfWBX6fK91vAu8mDTvWmeVE4MmIWAn4mKSH8lNrALfm3vsppM+lVrkeegS4D5hG+nwAXg9cHBEnS9qviR/ouR56jFQufwfcm5/ahlReTpa0X905cpYijnP9PMaN6p60/KsgSHO/3hAR3wSOlfSjSJPCNyV9SWo9WOQsJwObk7pz9yb1AkzJDcXXR8QmpC/JgzXnOArYhFQYPwW8gzRn5PcR8aqIeA1p2HFmnTlylmNJc0Omkw6cRwJDkr4DvC8i3g98AvhLA1k+Q2qsfhd4I2luxAQASX/NGf8I/KOBLNV9tH9E/D/SQRVJfyA1rJcnNfjrztLaR5eS5hd9utqjKOkLpOH6l9WcY39gRdIv7ZnAt8g94JJm5ft/If8AqzlLtay8F9gjIl6en36c1PN7POlYU3cDrVpWPg98tNLD9y/gLcBxwGmS6m6gHUs63l5K2jcfrEwTeJB0rD0aOFJS3Q20Vnk5kNSzeVZbj9l7SD02x+XvVJ1ZWvXQo8BWpLL6OICkf5N+AG7aRK9RpR5aF9idNFrwr5zlMeB1wFbRwNn+pRzn+n2MG9WNNNIOPkrSmaRfkg8Bx+U5ATuThvkOb+BLujSpZ2pvSZ8BzgCWAvaJiI2BfYHPAofW+Ysh0sTMtUmfyS+Bt+X/3pnzfY50ZuVXJV1dV46KR4BjJP0EOBu4HNg1NxRXA7YEDpR0ZZ0h8v55EXCCpBtI++PFpAYs8Mxw7KmSam0ERMTzSPvoc3kfDZJ6iD5ayfI70j6stdLN/sWz99EVwHuqFZ6kIyX9ouaD+srATZKGJH2M1ANwXOT5irn36Gt5zlFt2srK9aTvyxqkExggDeu9mtQQubTmLNWyciXpu7wd8LH8klVJJ1ccIemSOrNkrbJyIXAOqay8O9KSR/8mHWe+KuniBrJUy8tEUkPg+IhYDngS2Jj0uUxvIEu1HjqC1HN1UqsxnRtqWzTQoO9UDy0NfCoiloiIMbmhtomkP9XZq9ehLurnca69Hmr0GDcqG2mVX5L/Ic1pas2R+CJph7xU0juBj0i6qIFIT5HO/mqd6vxH4Aek+RpP5vlxe9Z98JL0CGlIcfeIWFPSHcCHSI2SkPQGSe/NhbU2lXlfT5LmFCHp78AvSMMB4yQdA+zfxNCipCdJ83cOiIh1c5bPk04Y2L3yuieG20YPszxGqlDeHRFr5Mbhh0knuPxf5XWP1p0lWwH4dH7PvwM/J83XWxuePYemjoN6ZfvTgZdEROv7/Fngb6T9VNv7t5tPWdkZuAx4d6546s4yXFn5QKQzo78PfEDSr+rOknUqKw8Cm5K+29vX/X1egPLyGaWTKraWdFXNWYarh75Eqoe2qmRuYh5yez30J1IZWRlYW3OXA2nie9ReF91Jw8e5edRDP6fBY9yoa6TluSKfi4h3kyahHxwRbwGQdB+pm3mtfP/fNWc5NCIOAt5M6laeHBH/K+kpSb8FVmFuF2ptw5wRcXhETMkNjqdJBfCNETEhN9SmkE7br12eJ3JsRJwBTCXNAfsePDOs+BD5gEYDB4vKF/Ai4AbSWU6tyvck5k58bioHwNWkf/ubIp1IcgcwmQa/z60DmKRPAiu17aMHSb2ctTeMKtu/E7gL2LYyhHYuqbw0Yj5l5URgdUk3K50Y1EQO6FxWppDO8rxK0gUNDFvNq6w8ALxS0g2SftMhf0/Np7x8j3T8ozVSUFeWBayH1mhlrrnXan710AqklQ+ayDK/uqiR49wC1EONHeNGVSOtba7Ie0jzIk4AjoqIAyLiPaR5X39rIEtrvP060i/tlwH7ASdHxHsjnbAwQJ7jVFdBaBtvHw+8ijSUtyLw2Yh4L6mL+f463r8tS2ueyEGk+RknAgeQviA/jIi9SfN5LgeQ9HSNWcZExDKtX49KZ7peQpoTcUpEfJA0RK66MrSp/lr7LemEkrVJB/r3koawHqg7RESsnDPMqeR5A7nyzftoV+CnNed4QUQ800DOFdt3SZOM9420XMCxpO9XrRairNQ61Foxv7IykdQj3XpNXceWBSkruwDP6lWsqcd1QcvLr6t/V1OWBa2Haj3RJmdZkHooqJSXGrMsSF1U+3FuAeuh2o9xLWOGhvq+iHwj8nj7ccB3JP0q0ryz1tmT1wPbktbpmd7QXJGvA1+SdFNErJ3vn0Y6RX8v4HnAjxoYWjw8v89NETGB1Eu1DWk4YHnS3Izpkn5eZ46c5dPAU5K+lO9/jnQ26wdIcxKeRzoL9/Kac5xIWjZhddLcoZsrzy1DOnFgLdLZg7UOW0XEMcztMTxNkirPvZx0INuQBvZRPoiuRmp8nA7cXB3izQevJYDb6txHkSZar0k6WN8u6ejKc8uReiBeBUjSdbnhVFdDxGWlc5Yiykp+r5LKi+uh4fMUUReVUg+1jJpGGkBE7EVa3+swSTNyQZhGmlh7QcNZPkb6tTBF0t0RsQ5wMbCPKhPh6zxg5O0fTZpvsGu+3zq79EpJl0TEUpKequv983uOyb0QryQtlXBOa55MRJwC3C+pkRWv837ZlDThey/gYGBnpUngRMR44LE836fuLJNIy0kcDexImpPxdkk35edXlvSvSMt+zBl+Sz3J8kHSxN33kOYVLUWa73ShpFmRFnj8RwM5PkQ6w2x30oHzLOAXkj6Rn58A/FtSrWdB5/dyWemcpYiykrMUU14qmVwPdc7S17qopHqoalQMd8bwc0VmksabV48GFgmMec8VuZ10Wv6q1b+p8RfdcPNE7iCdkr5VfmltQ4ot85kncg5p4mZThoBbJc2SdCJpCOC8SJehGkP68q7SUJbZwKWSbpF0PGk45tsR8fLcA3BIRLywicqO9Ovx10oT448EZgCvBNbIZekDQBPX1LuNtIzGnNxTtAOwZUS0rre7D23foRq5rHRWSlmBgsqL66Fh8xRRFxVWDz1jRDfSFmKuiOr+lZAtyFyRWud+LcQ8kSvaX1dDlgWdJ3JtXRkqWVoLO98ILB8Rm+YycwrpoPX+XEZOlVTrnMVKlruBDSJiFQBJXycdLHaQ9DhpmYd/1pyltQr95aSFI/9X0mzSWk4rks4OnAN8pdVrU3OO1srwG0Va0Phh0tpoL8rPf1FSrevmuawMm6WIstKWpYR/qxK0AAAgAElEQVTy4nqog1LqopLqoU5GbCMtzxWZDJwdERu2Cr/S2irHk+YFLEM6cPy85izHRMSXgS9FRLQel/Qj0lo0PyX9stxPNc5biTRP5PiI+FpEbBYRz6zUL+mNpOUBlgA+pfrXHjuedHbkVyLik5Uct5Mman6GdPDYV9KFdf7CzAfv2fnuWNLaQP9HWtQR0skBrc+q7kZ0NctNpFXhPxQRL8yP3UuaTAv1X3liCUlPR1oq4HmkobT3RMRbc8ZzSY2UpVXj8iN5iK71K/pJ0tIAn2HuxcGXBzaJuWtc1cZlZZ5Z+l5WcpaSyovroc5ZiqiLSqqHhjMi56SF54p0yuF5Ip2ztCqXsaQVrKcCfwD+H6myXYZ04DpaaRHOprL8GPgCcAfpV9xdpPWUtgU+r5oXH23L8m3SPJULSRXdccBPSOspTVSNC3625bgA+KGk0yPiYNKyAEOkM+U+r5rXEXRZWaAsfSsrHbL0u7y4HuqcpYi6qKR6aF5Gak+a54o8l+eJdFA5oJ9B+vX26/z+Z5F+YV0OHNRAb96YtizTSb0jO5N+zV1CutzRYQ1UutUsZ5E+k++TFnv+I/BW0mf1zror3Q6V/w8i4nTSUN6XSGt+7V93hduWxWWlc5a+lhUoq7zgemg4pdRFxdRD8zKiGmnhuSKdcnieyLyzQLpY+o2ka/WdSfr1dHM+oP1A0rVQ70kclW2fRjoV/1RSBbyMpL9IulzSKQ0MQ1SzfI/U1X8K8B3SBbHvlnRbznN9jTmq++cw0mWevkn6fG6QdFfOcXOdOTpkcVnpnKVvZSVnKam8uB7qnKWIuqikemhBjJhGmueKDJvD80Sem+WZIZGI2BU4Bvga6TT4iyRNbmWuM0cly5xIk4t3JfXInES6LNhFko5rMMuSlSybkT6T00gH84vygb397LA6crT2z5g8ZHQ+6TOZBlyRewJqz9GWxWXl2VmKKCv5PUorL66HOmfpe11UUj20oEbEnDTPFZlvDs8TmZultRbOWNLncYekj0S6ruKqkk6rvq7BLOeRFjydFOmsJiSd2lSWSqaxpAnEF0j6aqTLs6ws6WtNZGn7TC4C/iVpt4jYClhT0rlN5OiQxWXluZn6Wlaq71FIeXE9NP8snt+6kEZEIw2eOWCcAfyW1H16AGlHzCatsv1XSdfW+WVtO2C0spxBmiB5GalrdW3gljqHItpynAX8hjQMcTxpDsCyOcf9dXf9VzK1vqC/Ig0TTSFNGl0m51mygWGIZybFRsRU0rXXfgccoXRafOt1TVYuY0jldVPgbFIPwGOVnE1kqX4uZwFvB86UtF+nzA3lOJ60wvg1wCWqnPnWh8/EZWVuliLKSocsfS0v1Uy4HhouS9/rohLqoYW1WDfSWi3jfPs00rXxppK63H/eGopoKEv1gHE66bIaJ+YsV7aGIhrO8X3gStKcle8AF7eGIRrKUt0/nyb9evsa6YBxdWsYosksuaL7P9JB85fAvqRft1+Q9JeGf3GPAbYjrfh9I6nCgzRX5e99+PW/PfA3SYqI6aQzrPbIr6u7gVb9THYAZpGuJ7gL6dqCP1NaKqB2LivzzdLXstKWpZjykm+7HuqcpW91UUn10KJYbBtpbQeMd5AOXH8DfghMU5qk+ayC0kCWMaSDxJ2kL8f3gfObyhJpnsjsnON/SGP/vyV3MUv6Rn5d0wfRiaQFCW8nzZ85X5U5Kw1XLt8HzpZ0Xn7uxaSzrNYjXYLkiYYaI2NJc2ZOVL5GX0RsQer2f4p0iZjG5uaRFm/8Gemaj0/mx64E7pT0ngZznE/6Dp9DWmH8BaTr5a0PfFZpocmmsrisdM7St7LSIUtJ5cX10NwsRdRFJdVDi2qxbKS1daF6rsiz83ieSOdMra7/a0nd7UcBJyldO29NYGmlBQybzHJdzvIJ4GRJ9+bP6D41sAp6ZR+dQ+r+/ybpYthHSLolv25zpVXJa1UZDrk6Z/gacJykGyNiddIwxMy6c1SyuKzMzVBUWcnv1ffy4npovpk8v7UHlpz/S8rS9itgCul02bsiYnVJ51de1/RckdNJC+JdHRHPB76phuaKtH0mZ5DOJLoFQNI57XnrytEhy1dJY/13RsS2an5e0TPd3KRfcL9j7hIBl0qaASDprjpz5CzVz+XbpEr3GznLZZLuzVmubjjL14GrSGfmnUU6G+6W1mdXZ6Xbtn8+Q+qF+CZprsiVkm4EUGUOWENZXFY6Z+lbWclZSiovroc6ZymiLiqpHurWYtWTFp4rMq8cnify7Cyt7vbW53I7aa2gaTx7GKKJz6Xa9b8T6eyqW5nb5d6vLK8lXcfvWlJFd0lT3f9t++djpF///8z//4GaHZp3WZl/lr6VlbYsJZQX10PzzuL5rT202KyT1lYAfgCMlfQTSY+QfmHeBkyKiGUbznIeqWv9HEk3k1b7XhbYL9KaL0010L4LrEs6zRrSAX7diDgb6ltcc5gsPwJWJw1F/Id0yrWA10fEC+rMUckyu5JlF0m3AdvQfEVXzfJdYCtJN5Dmr1zQxyw/BXaS9DPSqfjTG2ygVXNMA3aUdAdpHafT+lDhuqzMO0vfykqHLCWUF9dD887St7qopHqoVxa3njTPFZn7/p4n0jlD9XP5GWmxxntIl8i5sv11DWb5BfASUs/Ie6vltMku95i7RtCmpH31d9KE9NlNZsk5fkKqSFYETpc0pfJ80/vHZeW5mYooK5UsfS0vbVlcD83NUFRdVEI91EvF96TFsy/38W3gBubOFblNlbkidX8x4tkreX+bdEmWb5AK5v2qzBWpuYFWvRxLdZ7ImaRTvm9pfW51fyna9k91nsgZpEvDPDNPpIkvRuVzOZ50sdx1SetJHRsRL+nwuiaynELaLy8lXT5nekSs3GSWiqOBayWtQaqA1yevtt1kRQd8mbRO07bAm4EDImK31pMN7x+Xlc5KKSvQ5/Liemj4LCXURaXVQ71UdCMtz0NoXZJlB+Bw5l6S5SdKF60ljz03kaV1GZQ3ky7WeyKpy/snqpyx0mCObUlfzNNJX9bLJJ2UX1r7hXLb9s9BpC/EVFI382WSTsivq33/dPAE6UDamrD6GyD6kAPSNeLOy1mmkHpt1mo6RKTr+K1E6gFA0lWkVbZflO83WeneAfw2V/Z3kyrhfnXru6y0KaysQB/Li+uhBcqyLX2qiwqvh7pWbCPNc0Xmm8PzRDrnGpu/jHcD60XEqyPiv4FXk65V15h88FqaNOH6NRGxRkRsCmxGahg0Kg9TXU66ft+rI2JtUu/IP5rOQrou3puBbSLiTcA+1Hz9wHYuK8MrrKxAn8qL66EFyuL5rTUqck6a54rMM4/niSxYtgmks61WJU1iPVY1X6NuHlkGgCOAB4D/Ji0+emmfsjyPtNjnvsB9pFP0f1zze3YsBxHxCWBlYCPgeEmX15ljOC4rw2ZpvKzk9y2ivLgemm+mIuqikuuhnhgaGir2v4GBgSkDAwOfzbd3HxgYuHZgYOAlfcpy2sDAwOfy7Y8ODAzcOjAwsHIfchwzMDBwRL796oGBgZMHBgZele+PaTjLVwYGBo7Lt9cYGBj4y8DAwG4NZxjb4bEx+f/LtrI1kGPdeeUbGBhYaWBg4HkDAwNrN5FlfmVhYGBgxYGBgRVrzrFjpxzt+2xgYOB5Liuju6yUWF4q7+d6qHOWIuqiEuqhOv8rdrgz81yRCs8TmSsi3hcRq+Y5Ec8qx5XPYVb+f62LW0bEV0hDDs/Me2hl0twFFR+W9Fjuiq8zy4nAHsP0RLSyjZH0sKSHa8xxHLBdNUcuv9XPpOXfdeXI7+uy0jlLEWUlv08x5aUD10NtCquLSprf2nNFXnEgH7SGqMwVAR4nzRX5RsNZxgBLMXeuyD9ICwY2Plckj7235ok8TPp81idN1uyH1jyRv0fE8qR5Ih+v+00jYjngXcAmEXGUpAfj2RfzXVrSk637Nc+JOIk0f+noiFiFdJD6cyXLWElzGur2/zrpepKHR8QmpMrsX5Luzy9ZmrmNkTpzfAu4K+fYBlgNuFDSU/n5ccCs1mdS8/5xWemcpYiykrN8i0LKS1su10PDKKwu6ks91JQi56S1eK5Ixxyjep5Ifs9XkOZA/BxYHjhE0gP5uTVJq0xfq7SoY5051iddWHp7YC/SRYRfBXxL0jERsVa+P72BnoiXA58DJpFWZF+VVNFeTzol/jHgkJztTzVnuQy4DFiTNE9keeBlwGtIFc1BwHdU8/IAOYvLynOzFFNWcp5iyssw+VwPdc7i+a0NKGK4s30IIj82Rmk9ky9I+jCpW77WL0ZErDtcPkm3Ah8BDgXeV+cXIyLWbQ03tMvDIGeSfjns0cCXYsdOX4zKEM0xkg4F3tXgF+MJ0unex5N6Jr4QEavm55YEliBdq602+TP5E+kX22nAbEkfIF0mZt+IeH3OsglpwnGtcgX2G+D7wJ2SdiEdzFcjDYf8m7TI5IZ1Zah8jz9MuozQcpL2kbQ7abjoUEmPkfbZmnXlaOOy0qaEsgLllRfXQ52zlFAXFVoPNaKvjTTPFemYw/NEOmfZKyIOjoj1gBmkX7N3At8idbUfFxEvyI99Nz9WWxbg4IgI0qKJZwPn5/1yG2kNpaeUFrj8CulXb11Zjsqfy0ckHUf6t98OIOl60i/c9STNknQB6dIodeQYUykTd5MWs/xBzF1k8kLgkZzr6+Q5PjVlcVnpnKWIspKzlFReXA91zlJEXVRSPdQPfRvuzHNFfgDcAgw7V6ShLK25IkeSukxfJOnPlefHdigMdeRozROZ2GmeSEQsI2nWcF2+Pc7yLRZinkjNWU4knVp9M+nX/a+AiyXdmQ8W6wLvB74n6aaGsvyRtBbPL4E/Sboq0rpW40krXn9M0jU1Z/kCsAZpZe13k07L/1+lhR33JVVsJwAHSPpN/puel52IWFbSf9oeW45Use1I2j+DpO/5Zb187w5ZXFY6ZymirOTtllReXA91zlJEXVRSPdQv/Wykea7Is3N4nkjnHMsDUyTtle/vQCobtwBnVb6szznwN5DljcB2pIv23k6q/F4KHNNEl3tEfA04V9Iv8v2TgXWAtwGnAv8ELpF0UY0ZPksqF78FJGl6fnwsqRGyJ3MPrFfUlSO/p8vK8Hn6Xlby+xZTXvL7uh56bpZi6qJS6qF+6udwp+eKVMjzRDqS9G9gmdwrQf5l/TNgV9JBvfW6WivdYbJcQlpYcpB0XcF9gcG6K92YO0fkDtJZi8vnPPuRGgDbS9oN+HjNDbQjgAHSNR7/A7wzIt6Xs8wBHpX0eUkHNlHhuqw8VyllJWcpqrxkrofalFAXlVYP9VOjjTTPFemYw/NEOmdpfS4fIg0JPRoRh+f3vpjUI7F5Xe+/kFn+SrpMDKTKp1aVrv3LScNDb4uIFVpPA6vn182uOcq9pDO6bgR+SLog+Ksj4rWRLnF0TkSsEx0mZPeSy8rwCiorUE55cT3UOUsRdVFJ9VAJGmuk5V+UryP9Mjkc2A+YkJ++jfRr5m/kX7xKEyNrmaTZluUw0loz1ypd6uO/I2In0tpKs3KWByT1/Fp+eZ7ImsA1wEYRcTHwJUnnRsS+EfEqYCdSN3PLY73OkbMsWx3Xzz0BPyZ1Ne8YERNJv/RurLzmwZqyVD+XDUgX7L0QWCkizoqI15CGIW4ffiuNZnkdqQx3msjayyzLVu/nA+fngF2Aj+V99A5SxVObysES4IsRsVweIvod6Tu8ltI8nt0k3V7zZ+Ky0jlLEWUlZympvLge6pyliLqopHqoFI3MSeswP8NzRfA8kXnkaf9cppC61ncFDiYdtH5d9+dSUpbh9lF+bkPSQpIbAVcoXei4rhzVSdXLkCY57wBsI+nxPHy1FbA/1N9DU8r+KSlLKWUlv18x5cX10Dzz9L0uKq0eKkUjPWmeK/Js4Xkiw2UZ7nP5KHArsIWkg4EjJF0Uw6zfMwKzDLuPsrslnSvp8JobaEsoL1MQEZcA75R0CKm36IqI+DhwIOlgP7vmCrek/VNSliLKSs5STHkB10OdlFIXlVQPlcZzReZmaWyuiDxPpKMF+FxenF9X+yV8SsrCvPfRUsAZde+jSPNEns7v8W3gR6S5M6dIOgL4EvAn4KOSflpXjpaS9k9JWSigrEBZ5cX10PAKqouKqYdKU+dB3XNFnpvD80Q6Zynpcykpy4Lso6eoeR/Fs9dCOo30PTqdNAxyC4CkH0m6WNIv68hQyVLS/ikpSxFlJWcpqby4HuqcpYiyW1I9VKra5qR1GOMe1XNFhhtvz8+N5nkiJX0uJWUpZh+15dqCtDjr2cBFSmdXtVfMdb13SfunpCxFlpWcp2/lJb9P34/9pWUppeyWXG5L0vNGWuvLFxH/D3gKODWPv7cmI14o6cLWDqrzy1pKlkjj7esBXwC2ALYEfqZ03TMiYhU1dIZKpHkirWGIi4FvSzozIo4E3kBaffs9pDkIdQ9DlPS5lJSlpH3U+g6dCiwl6f0RsQewpKRTq6+pOUdJ+6ekLMWUlZynlPJSxLG/wCxFlN3Sym3J6uxJ2wT4Ink+gqRHI+JA4PHWr6mm9DtLROwH3C7p0kgLJb6SdNbMOcDVwDRgImnhwNqHIWLuPJFfkn7hHitpn4h4K/Ak8FjdwxA5TxGfS0lZStlH7RVFHh75OfArSR9vvQZqn2PVev8i9k9JWUopK9Uslft9LS+VHK6Hnp2h72W3pHK7OOjZnLQoZIy7pCzheSLDZSnpcykpSzH7qJUjIiZFxP8pnfH2GmCniDi79ZoGekRK2j8lZSmmrOT3KqW8FHHsLzBLEWW3tHK7OOhJIy3SGPepETExInZsPS7patIifQJeAByu+pdxKCJL7rp+Ot89nbQY3y/zl+M+YCawZUQsSbo0Sa3aDo5fJ11o+bukeQfH5sy1LQ3QUtLnUlIWKGcftUSaJ/IP4F0RsaOkWaQz0t4WES9r4P2L2T8lZYHyykp+v36XlyKO/QVmKabsllhuS9f1cGcpY9wlZSltvD3KmSdSzOdSUpacp4h91CHXsqRf+3sDPyUtVXDsaNo/JWXJeYosK/l9+1Veijj2F5ilmLJbcrktWS8aaX0f4y4pS0nj7e0FPvo4T6TEz6WkLJX7Rczlacu4DPB+0llWZ0k6ps5MJe6fkrJU7hdXVnKGRstLfs++H/tLy1JK2V1cym2pFrmRVmmh7wfsw9zTZl8A7AvcJemMiHie0tXqa1NKlmphjIjTSdc5+wZwBvCbVndu0yJiEvB7SRfkA+j1wO8kvaeh9y/mcykpS1uuSfRxH81PRIwj9ZDsCnxF6fqCdbxPMfunpCxtuSZRcFmBRstLEcf+ArMUV3YXh3JbokWak1bSGHdJWVTgeHv0eZ4IlPW5lJSlpYR9ND+SniD1AJxDXmyzpvcpZv+UlKVlcSgr0Ex5KenYX1IWKK/sLi7ltkQL3ZNW2Bh3SVmKHW+PPs0Tye9dzOdSUpYO2fq2jxZGnZ9PSfunpCwdsi0WZQXq+4wKO/YXkyXnKbLsLk7ltiQL1UgrZYy7pCyLy3h79GleUeV+3+fDlZBlXpreR6Uoaf+UlGVeRmtZgXKO/aVmqdwvruyO5nK7qBa4kVbSGHdJWSqZJlH4eHs0NE+k7T0nUcjnUlKW4fRjH5WipP1TUpbhjMayUtKxv6QsbbkmUXDZHY3lthsLPCetpDHukrLk91osxtubmlfUUtLnUlKWeWl6H5WipP1TUpZ5GY1lpaRjf0lZWhaHsjsay203FqgnraQx7pKytOVabMbbm/x8SvpcSsoyP/2a79RPJe2fkrLMz2gpKyUd+0vK0iHbYlF2R0u57dY8G2kljXGXlGUeGT3e3kFJn0tJWey5Sto/JWUZzUo69peUZV5cdkeOBe1Jm0QhY9wlZenE4+2dlfS5lJTFnquk/VNSltGupGN/SVmG47I7Msx3TlpJY9wlZRmOx9s7K+lzKSmLPVdJ+6ekLKNZScf+krLMi8vuyLCgPWnFjHGXlGVePN7eWUmfS0lZ7LlK2j8lZRmtSjr2l5Rlflx2F28LswRHMWPcJWUxM7NmlHTsLymLjVwLu5htMWPcJWUxM7NmlHTsLymLjUwLde3Oksa4S8piZmbNKOnYX1IWG5kW+tqdUNYYd0lZzMysGSUd+0vKYiPLIjXSzMzMzKxeCzXcaWZmZmbNcCPNzMzMrEBupJmZmZkVyI00MzMzswK5kWZmZmZWIDfSzMzMzArkRpqZmZlZgdxIMzMzMyuQG2lmZmZmBXIjzczMzKxAbqSZmZmZFciNNDMzM7MCLdnvAGZm7SLiaOAK4FbgNuCm/NQSwOPAQZKuiogVgHOBQUn/6bCdi4D/J+lPzSQfXkT8HJgq6Qf9zmJmiwf3pJlZUSJiC2A9SZfmh/4jaZP83yuAY4FvAUh6FPgO8LlO25K0UwkNNDOzReGeNDMrzSRg6jyeXxW4p3L/XODoiDhG0r3VF0bEncA7JF0XEfsCHwWeBu4F9pd0a0R8C3gEeAUwAbgReJ+kxyJiG+AYYDngSeBwSdMjYjxwJrBafqufSPp0ROwB7EL6AfxS4O/A+yXdnV/31oj4BDAeuBzYR9KciNgKOBpYPuc7UtKFeXt75ccflrRdROwFfDi/xwP533HL/D5UM1v8uCfNzIoRESsB2wCXVh5eNiKuz//9FZgMfLH1pKQngGuBneax3dcBBwPbSdoYOAc4PyLG5Jf8D7AjsB6wFrBLRKwK/ACYKGkj4P3A2RGxNrAPcLukTXPel0fEinlbr81/sz7wO2BKJcoKwFb5fd4EvDoiVgZOB96bt/dW4KSIeEn+mw2AbXMD7bU5xzaS/hv4MnDevD9VM1tcuZFmZiVZF7hH0pOVx6rDnS8lNca+nxtLLXcAMY/t7gh8T9J9AJK+BbyY1CADmC5plqSnSPPfVgFeBcyQ9Jv8N38ErgK2BaYDb89z3j4IHCLp4bytSyXdmm+fAryxkuN7kp6W9DjwF+CFwJbA6qRG4/XARcAQsFH+mxslPZJvvzl/Rlfn134ZWDkiVpnHv93MFlNupJlZSYaYz3FJ0uXADGDzysNPkYYJh7NE3nbVGGCpfLt60sFQfq7T34wFlpJ0LbA28A1SQ++3EfE/+TWz215fzfXUMO/z50pDdBNgC+CS/LrH2v4dZ1VetymwGfCvYf7dZrYYcyPNzEpyG/CiiBg33AsiYoDUMPpD5eG1gXnNy5oOvCsiXpC3sSdpPteMefzNr4H/iojN899sALwG+HlEfAn4tKTzgYnAH4EN899tHxEvzrf3A348j/cAuIY0XPqa/D6bkHrZXtzhtZcAu0XE6pXtXzGf7ZvZYsqNNDMrhqSHgF8C21Uers5Ju540T2zf1pBiRCxN6nkatjEk6TLgOOCnEfFH0ryu/5U0Zx5/cz/pJIATIuIm0jy2PfP7Hg9sEhE3A9eRhlu/m//0LuCsiPgzqTH5sfn8m+8D3g4cExE3AGeR5qfd2eG1l5JOMLgsIm4EdictP9Le42dmI8CYoSF/t82sHPlMx8MkvXkBX78HsIGkT9QabMGzvEPS//Y7i5kt/tyTZmZFkXQ1oIjYcX6vjYjnkXqTJtWdy8ysae5JMzMzMyuQe9LMzMzMCuRGmpmZmVmBRuRloQ477LCh8ePH9zuGmZmZ2XxNnTr1NEl7tz8+Ihtp48eP54ADDuh3DDMzM7P5mjp16l2dHvdwp5mZmVmB3EgzMzMzK5AbaWZmZmYFciPNzMzMrEBupJmZmZkVaESe3WlWt4kTJzJz5syOz02YMIHJkyc7i7OYmXVlRF4W6oQTThjyEhzWlMHBQaZNm9bvGICzDKekLGZm7SLiSEmT2h/3cKeZmZlZgdxIMzMzMytQcXPSImIscCKwMTAL2FvSjA6v+QnwI0knN5/SzMzMrF4l9qTtDIyTtCVwCHBsh9ccBazSaCozMzOzBpXYSNsamA4g6Rpgs+qTEfEOYA5wcfPRzMzMzJpR3HAn8Hzg4cr9pyNiSUmzI2JDYHfgHcBn+pLOzMysJiUtHeMs/c9SYiPtEWCFyv2xkmbn2+8DXgz8FFgLeDIi7pQ0vdmIZmZmvVet4Pu9dIyz9D9LiY20q4C3AOdGxBbATa0nJB3cuh0Rk4B/uIFmZmZmI1GJjbTzgB0i4mpgDLBnRBwEzJB0QX+jmZmZmTWjuEaapDnAfm0P39LhdZMaCWRmZmbWByWe3WlmZmY26rmRZmZmZlYgN9LMzMzMClTcnDQzs5FktK7v5Cxm3XMjzcysRu0VfD/XeHKW8rOYVXm408zMzKxAbqSZmZmZFciNNDMzM7MCuZFmZmZmViA30szMzMwK5EaamZmZWYHcSDMzMzMrkBtpZmZmZgVyI83MzMysQG6kmZmZmRXIjTQzMzOzArmRZmZmZlYgN9LMzMzMCuRGmpmZmVmBlux3gFJMnDiRmTNndnxuwoQJTJ482VmcxczMrDFupGXtFfzg4CDTpk1zFmcxMzPrCw93mpmZmRWouJ60iBgLnAhsDMwC9pY0o/L8R4A9gCHgs5Iu7EdOMzMzszqV2JO2MzBO0pbAIcCxrSciYjXgw8BWwPbASRExpi8pzczMzGpUXE8asDUwHUDSNRGxWesJSfdHxMaSZkfEWsBDkob6lNNGkT333oeHH3xg2OcHBwc7Pr7iKqty+qmnOMsoy2K2oEoqt85SXpYSG2nPBx6u3H86IpaUNBsgN9D2B44EpvQjoI0+Dz/4AE9s/4mF/8MrjnGWUZjFbEGVVG6dpbwsJQ53PgKsULk/ttVAa5E0FVgdeE1EbNdkODMzM7MmlNiTdhXwFuDciNgCuKn1REQE8EXg7cBTpBML5vQjpJmZmVmdSmyknQfsEBFXA2OAPSPiIGCGpAsi4gbg16SzOy+W9Is+ZjUzMzOrRXGNNElzgP3aHr6l8vyRpPloXfPERGcxMzMrVXGNtCZ5YqKzmJmZlarEE+AVMGUAACAASURBVAfMzMzMRr2e9KRFxIrADsByrcckndmLbZuZmZmNRr0a7jwP+Cvwj3zfC8yamZmZdaFXjbQxkvbs0bbMzMzMRr2uGmkRsXS+eXtEbAn8ntyLJunJLrOZmZmZjVrd9qSJ1CgbA7yu8vgQsE6X2zYzMzMbtbpqpElaGyAiXinp2tbjEbFtl7nMzMzMRrVuhzu3BtYHDoqIr+aHxwL7Axt2mc3MzMxs1Op2uPMh0oXOl8n/h3QtzYO73K6ZmZnZqNbtcOfNwM0R8Q1J9/Qok5nZYq2ky5k5S/lZzIbTqyU4rouIFwL3AasBTwD3Ah+WdFmP3sPMbLFQ0uXMnKX8LGbD6dVloa4ENpS0BrAecD7wJuBzPdq+mZmZ2ajSq0bampIEIOk24CWSZgCze7R9MzMzs1GlV8Od90TEl4Crga2Af0TEDoAXtDUzMzNbBL3qSXsfcDdpiPNvwB7AY8BuPdq+mZmZ2ajSq560WcA1wPX5/uaSruzRts3MzMxGnV410qaRzuqcSbpE1BDpZAIzMzMzWwS9aqS9SNJWPdqWmZmZ2ajXqzlpt0TEGj3alpmZmdmo16uetK2Bv0XE/aShzqG8ZpqZmZmZLYKeNNIkDfRiO2ZmZmaW9KSRFhEbACcDKwHfBm6WdGEvtm1mZmY2GvVquHMKsCdwCnAacDGwSI20iBgLnAhsTFraY+989YLW8wcC78p3L5J0ZBe5zczMzIrUqxMHyA2pIUn3AY92samdgXGStgQOAY5tPRER6wDvJl3VYEvgDRGxURfvZWZmZlakXjXSHoyIDwLLR8S7gIe62NbWwHQASdcAm1WemwnsKOlpSXOApYAnungvMzMzsyL1arhzL+BQ4H5So2qvLrb1fODhyv2nI2JJ/f/2zjzcjqrY22/CEEaZ5MqooLhL0KvIp4giildRxKsCV1AQRWQwDsh0RaYrYRDBGIYwKgmziKhMIjMiKCiDyiRSyKQCDqgIBGQIyfdHrW06J/ucHLJPd1dyfu/z5Mneuzvdv3RXd9VaVWst9+nu/jzwNzMbA0wEfu3u9/RxLiGEEEKIlIxUkHaiu398hI71BLB05ftYd5/e/WJmiwGnECnVz43QOYUQQgghUjFSQdpipTbsHmAGgLs/N4/Huh74IHCumW0A3NHdUHrQLgR+7O5H9Cc5WOzqiSNxmBFBWnqTSYsQQgjRFCMVpHWI4KnLTOCV83is84FNzOwGYh3QHcxsT+BeYCHgncA4M3t/2X9fd//5PJ6LZ979pRf9b+oKGqSlN5m0CCGEEE0xUkHap9395u4XM9t4Xg9UBgSMH/Dz3ZXPi83rsYUQQggh5hf6CtLMbCNgHWAPMzuy/DwW+ALwuj61CSGEEEKMWvrtSXsMWAkYB6xcfpsB7N3ncYUQQgghRjV9BWnufidwp5md7O6PmNmywAvu3s9ktkIIIYQQo56+JrM1s/XM7NfAo2a2JeDALWb2wRFRJ4QQQggxSuk33flVYHt3f97MDgXeT4zCvBT4Yb/ihMhEphGj0tKbTFqEEKJf+g3Sxrr77Wa2CrCku/8KwMxm9C9NiFxkmgpEWnqTSYsQQvRLv2t3dv/9psBVAGY2jtlXDBBCCCGEEC+SfnvSrjKz64HVgQ+Z2auAE4Hv9q1MCCGEEGIU01dPWlmaaSfgje5+a/n5RHf/Wt/KhBBCCCFGMX2vOODuv618vg+4r99jCiGEEEKMdvqtSRNCCCGEEDVQS5BmZqvWcVwhhBBCiNHCSC2wDoCZvYtYt3NDYrkoIYQYlWSa2kNaepNJixC96DtIM7MlgU8BnyUCs12Bbfs9rhBCzM9kmrNNWnqTSYsQveh3WahjgZuIxdU3B2529++4+7MjIU4IIYQQYrTSb03a24FfAjcC9wMz+1YkhBBCCCH6niftjcBJQHdx9Y6ZvWYkhAkhhBBCjGb6Ht3p7je4+w7AesAk4Cwzu6VvZUIIIYQQo5gRm4LD3Z8ETiFSoDuN1HGFEEIIIUYjfY3uNLMO0Xv2APB94HyiLm0P4NYh/qkQQgghhBiCfqfgOAU4CFge+BGR8nwUuAw4s89jCyGEEAs8mab1kJbetKWl3yBturtfCWBmu7n778rnaX0rE0IIIUYBmeZrk5betKWl3yBtRuXzM5XP88WaoMssvwLMw0VcZvkVpGUUahFCCCGapN8g7bVmdjYwZsDndeb1gGY2FjgBeAPwLLCTu987YJ8VgRuA/3T3Z+Y8yvA4dcrJg27bcsstOe+88+b10NKyAGoRQgghmqTfIG3ryueTBvn8YtkcWMzd32pmGxADEz7c3Whm7wMOB17WxzmEEEIIIVLTb5B2C7ADMA04w91nzGX/4fB2YuAB7v4LM3vTgO0zgPcQKx0IIYQQQiyQ9Fs7djqwGrABcGj/cgB4CfB45fsLZvbvYNLdr3T3v4/QuYQQQgghUtJvT9pL3f0jpY7sipEQBDwBLF35Ptbdp4/QsYUQQggh5gtGZHSnu88ogdpIcD3wQeDcUpN2xwgdVwghGiHTqGRpya9FiMHoN0gba2aLEGnT7ucxAO7+3Dwe83xgEzO7oRxrBzPbE7jX3S/qU68QQtROplHJ0pJfixCD0W+QtgbglMAMuKf8PRN45bwcsAw+GD/g57t77LfGvBxfCCGEEGJ+oK8gTYGSEEIIMe9kSrtKSz4t/S6wfspg29z90/0cWwghhFjQyZR2lZZ8WvpNd74JWAI4i1gBYMzQuwshhBBCiOHQ14hMd389ZYUAYB/grcB97n75CGgTQgghhBi19NuThrvfSQRomNk7gK+Z2eruvkG/xxZCCCGEGK30HaQBmNlLgC2AbYAlifSnEEIIIYSYR/odOLAVEZi9HDgPGO/uD46ALiGEEEKIUU2/PWnfJeYwuw34T+AwMwPA3bft89hCCCGEEKOWfoO0d42ICiGEEEIIMRv9TmZ77UgJEUIIIYQQsxipRdGFEEIIIcQIoiBNCCGEECIhCtKEEEIIIRKiIE0IIYQQIiEK0oQQQgghEqIgTQghhBAiISOyLJQQCzrLLL8CXD1x3v6dtIw6LUIIMRIoSBNiGJw65eRBt2255Zacd9550iItQggxoijdKYQQQgiREAVpQgghhBAJUZAmhBBCCJEQBWlCCCGEEAlRkCaEEEIIkZB0ozvNbCxwAvAG4FlgJ3e/t7J9Z+AzwHTgUHe/uBWhQgghhBA1krEnbXNgMXd/K7APMKm7wcxWAr4IbAi8D/iamY1rRaUQQgghRI2MmTlzZtsaZsPMjgRucvdzyveH3X3V8vlDwGbuPr58Px84zN1vrh5j/W33mvnXl/9Xw8qFEEIIIV48487f8yB3nzDHhpkzZ6b60+l0pnQ6nfdXvv+h0+ksXD5v1+l0jqhsO6PT6bxn4DEmT548s1+22GKLvo8xUkhLb7JoyaJj5kxpGQxp6Y209CaLliw6Zs6UlsEYKS2dTmfCzB4xUcZ05xPA0pXvY919+iDblgb+2ZQwIYQQQoimyBikXQ9sBmBmGwB3VLbdBGxkZouZ2TLA2sCdzUsUQgghhKiXdKM7gfOBTczsBmAMsIOZ7Qnc6+4Xmdlk4KdEgLm/uz/TolYhhBiS3XbbjT/+8Y+z/bblllsCsPrqq3PMMcdIi7QI0ZN0QZq7zwDGD/j57sr2k4HBV1IWQohEZHLw0tKbTFqEqJIx3SmEEEIIMepRkCaEEEIIkRAFaUIIIYQQCVGQJoQQQgiREAVpQgghhBAJSTe6sy0yDcGWlvxahBBCiLpRkFbI5OClpTeZtAghhBB1o3SnEEIIIURC1JMmxDwwMPXaTbtC+2lgacmnRQgh5gUFaULMA5kcvLT0JpMWIYSYF5TuFEIIIYRIiII0IYQQQoiEKEgTQgghhEiIgjQhhBBCiIQoSBNCCCGESIiCNCGEEEKIhChIE0IIIYRIiOZJE0IIIZKQaRJmaWlfy5iZM2eO2MGycOyxx87cdddd25YhhBBCCDFXzOwgd58w8HelO4UQQgghEqIgTQghhBAiIQrShBBCCCESkmrggJktDpwF/AfwJLC9uz/aY7+1gAvc/XUNSxRCCCGEaIRsPWmfBe5w942AM4ADBu5gZp8AzgFe2rA2IYQQQojGyBakvR24rHy+FHhPj30eA97ZmCIhhBBCiBZoLd1pZjsCewz4+S/A4+Xzk8AyA/+du19c/v2gxz7uuOOmHnfccQ+NjFIhhBBCiFpZrdePrQVp7j4VmFr9zczOA5YuX5cG/jmPx96pP3VCCCGEEO2SLd15PbBZ+fx+4KctahFCCCGEaI1sQdqJwGvN7GfALsBBAGb2dTNbv1VlQgghhBANskAuCyUWfMxsjLvLeIXoAz1HQuQmW09aCsxsTNsaslHmsEuBmY0DFmtbRxfZixguZrZi2xq6mNniGQI0M1snyzNkZm9uW4OYPzCzRuInBWkVzOxIMzscONbMWg0CzOwwM9vfzPYys0Xa1gIcY2b7tKmjaJkETAG+bmYbtKzlowDuPrNtJ2Nmm7d5/ipmtpmZvTaBjn3NbOW2dXQxs5OA3drWAWBmJwB7Vb63Yr9mNhl4f5Jg8Rhg57Z1AJjZNmb2rrZ1QJ7nGfI80+Xdv6WZLVT3uRSkFczsSGBZ4ExgBrMGMLSh5ThgZeAO4OXA/2tRy6HAqsCRwC5m9rEWtUwClgO+CtwHdNrSUviWmR0CEai1JaL0LE4xs33b0lB0jDGzlwB7AluZ2dpt6gE2Ac43s1Va1tENip509wPM7BVmtoqZtTK6vgRG/wQOM7OXmNmybdivmZ0BPO3uk8zsNeVPKw1SMzuKmP5pvJm9ts0AqTj+zYFPmNlGLerI9jxDnmf6f4Ctgc3qfo4VpM3ipcBX3P03xIS5rSw5VdKKy7v7Du5+ETAOaKXHyMyWIa7D1939bmAiETS2xcLAhKJlGvCRFnsAVgMeLhqOaUNDhVcULdub2bFtiSiOfjlgTWAl4ANmtk4bWspL/EngN8C1ZrZqGzqKljcBrwYOLL1pE4FzgR3LM9aklk2At7v7fsAJwDeBn5jZB8r2Rp6n0hvyMuAqMzseOBTYB5haGh2NYWZrEBOkX0bcmz2IzMGBZrZkk1oKKwFrAY8AHzKzjVvQkOp5hhzPtJmNLc/sSsBfgTcR16W2QG3UB2mVltt0IiACuJMyqa6Zrdlwi3cssJKZvaJ8/xXw96JllSa6V7u4++PA1cyau24GsGjR8oqmXqYVxzGNCEgA7gL+VFKNZmZN96q9AHzJ3dcG1jezo7sbWggcFwMOdPfXAG+vammBaUQabQqwAvDBll7sTwLfc/cdid7xa9oK1Nz9FuB2wIEH3X1rYALwXgaZwLJGLVcCt5vZ74GH3H0b4OvAl81s1aZ61Nz9T0Sg+kXgCXf/CPBl4AniujSGuz8I7A/8EFi8zLO5CbECzlZN6ajUOP2NCFq/A/we+LCZtbXKTpbnGRI80+4+o/jFfYln+K/AhtTYo5ZqgfWmMbMDgcXN7DaiF627SsFrgPvM7HXAJKJldVfNWvYDngV+C2zm7s9UtPzYzF5PrGW6D3B/zVr2J1q5Pwd+5e6/KJvWAn5VHtJTgI8Tacc6tZwAPGdmywK7u3t3guNVgHtKK3gycV1qpaQTnwAeBc4jrg/E8mWXmtlJ7j6+CUdnZp8jXqB3Ab8kVusA2Iiwl5PcfXzdOoqW/YBngH+6+ylmdp27/8PMXgC2JHobf1B6qevUcQCwInAzcKO7nwXg7geb2XTgZjNbv/Kc10qxl6eAB9x9LzN7mni+cferzOy9wNpEz0DdWj4H/Au4CfgC8S75RdFytkV95yuJHtk6dXRt5WHgAmAd4KKi4y9m9ldgiTo1VLQcQLznbgJ+BmwDrGBmCxUtp9HQAKXiixYzsz8BJwOXufu/zGwaMWfox8xsrLtf04CWFM9z0ZLimS5+6Bmik2An4Nfu/rSZTQF2AN4NLAScP9LnHrU9aWb2FeIFcQ7wCeBTZvbqsvlpogV1NDDJ3esO0A4F1iUM8avAFys9fI8BHwSOAqa6e90B2iTAgCuIVv5nKt3t/yACkiOAg9y97gDtC8TSYHsQPZtnDugx245o4R3l7r+uWcvRxHV5EngbcBphJ7j7U8SLdL0mWpklZbY+ETRvS7S6HytapgH/BbzNGhg1N8B2v2Bm/0sEj5R7cgGwJBEg1KljElGjeAVRQ/l/1d4Hdz+MSO29qk4dFT1de/kn8F4zO4V4l1xoURS+HvEs1foMFS1de1mTcDD7ApNLoPgeM1uXCO7/UbOOqq3sC3yEqHX9lZm9xczeQaQd/1injqKlay+XEQ2+g4CZ7v4d4JNmtj3wJeB3DWip+qL3ETVgqwO4+++Lxt8Af25AS4rnuWhJ8UxX/NBehG2eRukBd/dny/ffURpgI82oDNLMbFGiBXWsu99KjOhZhSgEhEjrbUgEIlfUrGUp4uV5iLtfB2wBvAvYveyyAlGkeKC7X16nlsJjwMSyRurZRLrz4xZTBzwFfBQ40t0vbUDLcsAd7j7T3XcjXhxHm9kSwHPAG4jrclkDWpYEDnX3M4ADiZ6rE7vBdAnUNmggoF+U6GnYyd2/ApxOpKD3NbOFLOa9mgas6+531dmrZ1FUvCZxXX5KtLLfTaSwAHD3X5bttTYuiB7Oie7+I+Aswm63qwb17n6Qu1/bUDq6ai8HEMHaURa1jJsT1+iABhoXvexlEWBnM3sDMWn4wcB+dfaM9LCVLcqfjxZ9hxDv4SPd/Ya6dFQYaC9XAVuXQPGlwFuBPco7uTYG+KLbiPuxKhHAAv9Ox05x91qCgIqWqi9q+3mGWb6o7We66od2JzIXR1mpVyxZr+NLrfSIMyqDNHd/jqj12tXM1nL3h4kerA9ZTGVwJfDxYqh1a5lGBB8fN7NVygP5OeDTFiOMvgd82t1/VreWwtLA/xVtDwM/IVrY6wHXAu+uu8u98sBdBrzczN5e9BwM/IFITV9EFEBfX7OWbo/mv4galW6t3uHEi/5tFc1N1PM8T4xC7k65cRdhI8sBa1amA6ldi7s/QaSJtjWz1Sq2u6OZfaiy35N1aajU8TxH1IhU7fZvhNOZrU6w5sB1MHv5GmEvr3D3jwKfd/dL6tJRYaC9/Ab4PvGcP1fq43aou9HVw1YeAD5LBCXm7u91908Uh1wbQ9jLtUQZw2LuPhH4QhOpxbn4om0r+z0z2DFGUMtgvqix53kAvXxRY8/0MPzQV+s8f5dRF6RVLvwlwG3EiLjuw3ECsLK73+lRYNuEDoAbCKf6fovi3QeIOqvl3f16d7+ogbTVWAB3/zKwrJl9t3z/PTFw4c3ufpu739hD/4hSMfgHgYeAjSsp1+8SRft0W9x1aSl1IoeY2ceJouK9zeyD5dyPEunOVbqaa3b++5nZnsAHiPTmMWb23+7+vLvfRLzQXt2QlgPMbHJxIi8QL873mdnqxXaPoYF3S6kTmWRmpwPHETU9Vbv9B9Ej0sgUKcO0lzXK96dq1jI3e1meWWmi2tKcw7CVycR0Q7UzDHv5JyWwpoFGzlx80YnMGrDVlA7o7YsaeZ4reobyRY0903PxQ+cS9lI7oyZIs5jzZVy3p8FjdNHlRL79ZDP7DJGWqKXLsgfVVsBNRBHvmsRL/hPEpJePVvapxSDNbLly/BmVc72X8nCY2U5Efd5svYo1tVxWNLN/v5iKYzuHKNjcxWJ6iUnMKtivU0u1TmQ7ogbhWOBQM9vVzLYj6nhqL0Kv1IncQrTeXgWMB04ys09YFKAbFXupUUu1TmQl4C1EemYZ4OBiu7tTRiTXqKNbJ7InUSd4ArAr4Xh/UOx2a+DHdeqo6BmuvfyhAS3DsZcOpcapxnfLcGzli0TgVivDtJetiLQn7v5CjVqG64u8Lg0DmJsvqv15hmH7otqf6Rfhh26pU0eXUbF2Z2lBTSdabAe5+52VbeOIYs01iBEbtaY4zWwis1ppU93dK9teTbzIXkeM7vlJzVomEfUX04BTgTur3erloVgIuM/dr6pZy9GEY/s7cL+7H1HZtgTRY/UWwN39FqtxzcFSJ3IU8B13/5lFHVF3NNytwMbEfHGXeTM1i98EDnf3O8xszfJ9KjFVzI7AUsCFdaeKip4DyrnuMLPViZ6HjYiUwJJEnWATtvt/wPPufnj5fggxOvHTRC3NUsSoylrttpxb9tJbSwpbKVpS2It80aBaUviiTH6oywIfpJnZ7kQ91c7EC2pvYHOPAQOY2UrAtJKPr1vLBGII7xHApkS+/3/c/Y6yfTl3f8xiqPWMwY80Ilo+QxSFbkfk/Rch6hEudvdnLSYO/HPdOoqWzxIjErclXpxnAte6+5fK9tWBp9y91tFnAzTtSMzXtL+731s0nEcU1V7UlI6iZXeiJ2Kyuz9iZq8ELgV29kphcxMvDDM7gqh927p8744YvM7dLzezRdz9+RrPP6b0QLyZGPp+tpd6TTM7Gfibuze+8oLspaeOVm2lnDONvcgXDaolhS/K6IdgdKQ7ZwL3uPuz7n4C0f1/vsXSH2OIB3f5hrRMB65w97vd/Wiiy/TbZvbqEqXvY2b/0URgRLQcf+5RuHoQcC/wZmCVUhPwaaCp9druI4YvzyituU2At5pZd53DnYlRrrVjg9eJ/JGoY1nZGhgdaEPXidxPTA8z2zWp2eEOVifyADE9ytvKrrWlicr5hqoTOZsoCG8M2UtPLSlspZwzk73IF/Umiy9K44eqLLBBms2a/fd2YEkzW6+0qk4mXljblwd4irvXWidS0fII8FozWx7A3b9JvCg2cfeniWHYf61ZS3fFgquISQn/292nE3O9LEOMJJ0BfKPbqmpAS3clgddbTCT5ODEnzcvK9q+5e63zFb2YOpG6e6sKw6lZbKKWZ7g1i1cP3G+EdQy3TuTmOs7fQ4/sZQBZbKVoSWMv8kWDaknhizL5oV4skEFaucDTy9exxDxSHyImdIQoyOzOJF33i6uq5Q5iBYHPmtl/lN/+QhTTQv2zfS/k7i9YTBOwFNHVvZ2ZfbhoPJd4iSzqNQ/5Lt3o3Zb0c8RUEl9h1mLySwLr2qw50erUcgIxguksM3td16l6zAl0NFFvNI54afykZi0TzezrwOFmZt3f3f1CYo6rHxOtyvFef83KJGJeuuPN7E1m9u/Z1939fcRUNQsB+3qN80lZ1ImcCHzDzL5c0XA/UQD+FSIo2cXdL66750r20lNHClspWtLYi3zRkFpa90WZ/NBgLHA1aZWbP5aYHfk44NfA/xIPwzjipXWEx4StTWn5IXAY8ADRgnuImEtpY+CrXvNEtQO0fJuoUbmYMMajgB8Rcynt5jVPDjtAy0XAD9z9VDPbm5hGYiYxUu6rXvP8TaY6kcG0qE6ktx7Zy5w6UthK0ZLGXuSLhqWlNV+UyQ8NxQLXk1a56KcTrbefEy/UM4nW1VXAng20oMYM0HIZ0XrZnIjULyeWmNi/gYeiquVM4pp8j5hg8zfAh4lr9dG6AzSY7R51H9Dvm9mpRHf74cTcSV9o6MFQnUhvVCfSG9nLnGSxFUhkL/JFc9XSqi9K5ocGZYEJ0ip5ZYgFam8n1nU8g2g53VleZt9395uh3sLZyrGnEsPwpxAPyDh3/527X+XuJzeQsqpq+S7RzX8y8B1i8fRH3P2+oufWmrVU79H+xPIapxDX6DZ3f6houbMBLaoT6a1FdSK99che5tSRwlYGaGndXuSLhqWlNV+UyQ8NhwUiSKt2W5rZ1sBE4HhiCPwl7n5M2a+JWdAXcvcZFoXFWxMtphOJpVgucfejGtSycEXLm4hrMpV4kV9SXuoDR4bVpaV7j8aUlNEFxHU5D7i69AQ0qUV1Ir21qE5kTj2yl946WreVoiWNvcgXDaolhS/K5IeGy3xfk2az5sEZS+S1H3D3z1uswbmCu0+t7teglvOJCQknWIxqwt2nNKWlomksUTx8kbsfabE8y3LufnxTWgZcl0uAx9x9GzN7G7Cau5/boBbVicxdi+pEeuuRvcypQ/Wts7TIFw2tqVVflMkPvRjm6yDNKgWxZnYcsabXL4EDPYbEd/dr8qEYQ3T3rwecRUTp0yo6m9BSvS5nAv8DnOHu43tpblDL0cQs478ALvfKyLcWXhanAzcR92pX4gU/nZhh+/fufnOdmga8MLpaTieKr68kUjZrAnc3kIaoajkTuJFIQxxN1BgtXrT8rYnu/4rz/xmRIppMFKSPK1oWbjINIXsZVEfrtlI0tW4v8kWDaknhizL6oeEy3wZp1W5LIvUwnVhfcheiZXuYu/+u4d6ZMcC7iNm+bycMEqJO5eEWeoreDfzB3d3MLiNGWH2q7Nf0ddkEeJZY72wrYm3BazymCqidrpbyeSqxRutxRHf7T7ppiIa0VF8YpxLL9ZxQtFzXTUO0oOV7wHVEzcp3gEu7aYgGdFTvz/8RvULHE4HIDd00RFPIXuaqozVbKedPYy/yRXPV0qovyuSH5oX5MkgbcPO/B5zl7ueXbasSI6zWJpYgeaYhAxhL5LVP8LI+n5ltQHT7P08sD9NYPQQxceM1xJpsz5XfrgMedPft6tTRQ8sFwA8Ip/ICsCKxXt46wMEeE002peUjxEvrD0XTeR7F37M5oQa0jCFeEg8STvd7wAUNa1nY3acXLf+PqC26iZLCcvdvlf2afInuRkx2ej9RO3OBV+pVmmjlyl566khhK+UcaexFvmhYWlrzRZn80LwyXwZpMFsK4maiu/1Q4ESPdfNWAxb1mLywSS23FC1fAk5y979Y5Lsf9QZmzK+kIc4muv5PIRarPdDd7y77re8xI3ntVFIiNxQdxwNHufvtZrYykYb4Y80aVCcytCbViQyuR/Yyux7Vt/bWJF80u4ZUviiDH+qHhee+Sx6qXdxEC+6XzBrCe4W73wvg7g81oKXacv028VB8q2i50t3/UrTc0LCWbwLXEyNnziRGrNzdvXZ1PxQD7tFXcMD5qwAAEVxJREFUiF6IU4hakevc/XYAr9Rp1Kilel0mE8PwHzKzld39gsp+TdeJnEpMtHmDmb0EOMVbqhMhXujrE+k83P3sgZob0nEkUUP0oJlt7C3UicheeupIYSs9tLRqL/JFw9LSmi/K5If6Zb7pSat0t3fz2/cT8wSdx+wpiCYe0GrX/2bEyKp7mNXd3paWdxJr+N1MGOPlDXf9V+/R7kTr/6/l7++3mBJRncicWlQnMrge2Qt5bGWAltbtRb5oWFpa80WZ/NBIMF/Mk1Ye0O5FvxDYyt3vAzaieUOsajkHeJu730bUrlzUopYfA5u5+zXEMPzLGg7QqlrOAzZ19weIeZymthSgjSVeVmPd/Ufu/gTRurwPmGBmi9epo4eW84mu9bPd/U5ipu/FgfEW80k1eV3OAdYipnCAeMGvZWZnQX2Ta/bQcSGwMpGK+BcxnYMD7zGzFevSMIQe2cucOlqzlR5aWrUX+aJhaWnNF2XyQyNF+p60Srf/WKL4cFXgT8QSFtcN3K9BLdcCLydaLp/wSs1BkwZgs+YHWo/oUn6YKBid3pKWHxGOZBngVHefXNmuOhHViXT1pKoTkb3Mdv5UtlLO1bq9yBfNVVMKX5TJD40E6XvSKhfzaGKh3LWI+V4mmdnLe+zXhJaTiWH4ryCWt7jMzJZrUkuFI4Cb3X0V4gFZhzLTdgvG+HVinqaNgQ8Au5rZNt2NTbTmKl+/DdzGrDqR+7xSJ1K3w7XZZ/H+NrEcy7cIp/c3r9SJNBCgVZdjqdaJnEHY8d3da1en0x1wf6p1IqcTy8L8u06kiQBN9tJbRwZbKVpS2Yt80VzJ4ota9UMjTfogrcIzxEu0W7B6I2AtaZlGpCIoEfo1wBpNi7BYw29ZovWPu19PzLD9svK9aWN8ALipPJCPEA9LU624hbspETPbBDiAWcux/MhjMWxKvUQTWrpLoHyAWKz3BCKV9iOvjIZrWMvGhNM/lQgErnT3E8uuTUz30b0/exKO9jgiZXSlux9b9mtkORbZy1x1bExLtlLRksZeBiBfNIBkvqg1P1QH6YO08pCOIQoz1zazDc3sjcCGxDp1TWoZY2aLEgWR7zCzVcxsPeBNxIPbKKUb+Spi7b4NzWxNovXy56a1FJ4jWi4bmdn7gZ2peb1JUJ3IMLWoTqS3HtnLnDpU39pbl3zRICTzRa34obpIX5PWxcxWJ0ZarUAUsU7ymtczHEJLBzgQ+DvwRmJywCta0rIUMSHfLsCjxPD8HzZw3p4vSDP7ErAc8HrgaHe/qgkdqhMZVJPqRGbXIXsZXE8KW6load1eBtEmX9RbS+O+KIsfqpWZM2em+tPpdMb2+G1M+Xvx8vcqDehYayh9nU5n2U6ns1Sn01mzCS3dazDEPst0Op1lGtCyaS8tA+9bp9NZqmG7mdzpdA4un7ftdDo3dzqdlzepoaJlaqfTOaR8/mKn07mn0+ks15KWiZ1O58DyecNOp3NSp9N5S/k+pE2NsI5vdDqdo8rnVTqdzu86nc42bVwT2UtuW8liL/JFvbVk8EVZ/VAdf9KkO83sk2a2QqmJmE1XJVJ+tvxd6wR0ZvYNIt3w75qHriafNVHf4+4+rXTD16nlBOBTg7QWutrGuPvj7v54zVqOAt5V1VJqEarXpctTdWrpgepEBqA6kSGRvVRIZivQbn2rfFFvLSl8UXI/NOKkWHHAzJYAPgasa2aHuvs/bPbFfBd19+e632uuzTiRqC84wsyWJ15Sv61oGevuMxpKP3yTWO/tADNblzC4x9z9b2WXRZn1sqhby2nAQ0XLRsBLgYvd/fmyfTHg2e51aTgtMpNKnQjwNFEn8q0mNFS0jAEWYVadyJ+JiUhbqxMxs26dyOPENVqHKAZvmm6dyMNmtiRRJ7JX0yJkL71JZivQkr3IFw2qJYUvyuqH6iRFTZqZ/SdRA/ETYElgH3f/e9m2GjHD9M0eEzrWqWMdYuHXdwM7EosIvwU4zd0nmtka5ftlDfRavRo4BJhAzJq8AvEg3EoMiZ8G7FO03VWnlqLnSuBKYDWiTmRJ4FXAOwhHsyfwHa95Ookh9KlOpLcW1Yn0QPbSU8eorm8t55QvmlNLGl+U3Q/VQZZ05zPEcO+jiZbDYWa2Qtm2MLAQsU5bbZQXxV1Ea20qMN3dP00sEbOLmb2naFmXKDaulWJkNwLfAx50962IF/lLiVTIU8Qkk6+rU0elu/9zxFIfS7j7zu6+LZEu2s/dpxH3bbU6tQzQU/1tjMc8SYe5++eILvlaHa6ZrTWYPne/B/g8sB/wybodrpmt1U03DKSkQc4geiU+VafTNbNNezncSnpmorvvB3ysqQBN9jKnjgy2UrSksxfki+Yggy/K5oeapLUgzcx2NLO9zWxt4F6iJfsgcBrR1X6Uma1Yfjun/FabFmBvMzNiwsSzgAvKw3IfMX/S8x6TW36DaPHWpeXQcl0+7+5HEf/3+wHc/Vaihbu2uz/r7hcRy6LUpWVMJcf/CDGh5fdt1iSTFwNPFG3fpNT41KRFdSK9tahOpLce2cucOlLYSjlPGnuRLxpUSwpflMkPtUEr6c7yslgGuJOIvn8GXOruD5aXxVrA9sB33f2OhrT8hpiH56fAXe5+vcW8MysRs13v7u6/qFnLYcAqxKzaHyeG5P+3x6SOuxDGdyywq7vfWP5NLUPRzWxxd//XgN+WIBzbpsQ92hI41N2vHOnz9zjv94G7y/l61onUqaGipVsnchCRinmZu/+2sn1sDydTl5ZunchuvepEzGycuz9bl41UdJzGi6gTqRvZS08dKWylnOs0ktiLfNGgWlL4okx+qC0aD9IsikAnu/uO5fsmRJ7/buDMyoM6x81pQMv7gHcRC/beTxjnK4CJTXS3m9nxwLnufm35fhLwSmALYArwV+Byd7+kZh0HE3n+mwB398vK72OJF8UOzHqxXl2nlnJe1YnMqUV1IoPrkb3MriONrRQ9KexFvmhIPa37omx+qC0aT3e6+1PAuNJqoES/1wBbExe+u1+tD8UgWi4nJpXcklj7axdgy7ofCptVI/IAMapoyaJnPPGAvtvdtwH2aiBAOxDoEOuw/Qv4qJl9suiZATzp7l919z0afDBUJzIAV53IUMheKmSwFchnL/JFc5LFFyX1Q63QWJBWyW9/luiyfdLMDgBw90uJFsP6SbT8nlgiBsJAaqXSRXwV0YW7hZkt3d0MrFz2m163FuIFeYrH4sU/IBbt3dDM3mmxDMnZZvZK61GQPZKoTmRQLaoT6a1H9jKnjhS2UrSksRf5osFJ5ItS+KEMNPIfLPnt1YBfAK8lFuy9GFjWzM40s3cQ3cz3D36URrX8F3Af9CxiHUkti1e/lxfnIcBWwO5mthtRcHxvXRoqWhaqfP2amS1RUkS/BP4ArOFRx7ONu99f83U5gbgHCxELX48HVi+b7yN6Sf5Aae16FFzXUvw9QMv+xBxWN3ssIfRGM9uMmFfp2aLl7+5eyzp+A2z39WZ2KXC4u59rZruY2VuAzYg0VpdpNehYvFp7UnoBfkiksDYtdrsXEaB09/nHSOuo6JG9zKkjha0ULWnsRb5oUC0pfFEmP5SFRmrSeuS3JxNd61sDexMvrJ/XncrLpGWwfHvZ9jpiIsnXA1d7LHRcp5ZqUfU4osh5E2Ajd3+6dDO/DfgC1NuKMtWJDKVHdSJz6pG99NbSuq2U82azlxTv/0xasviiTH4oE3WnrAbLb38RuAfYwN33Bg5090tskPl7FkAtg+bbC4+4+7nufkADAdpCXqYpMLPLgY+6+z5Ei+5qM9sL2IN4mUyv+8Fw1YnMwRC2O+rrRGQvs5PFVoqWNPaS7P2fSUsKX5TND2Wi1iDN557fXrXsV/sSG5m0MHS+fRHg9Cby7RZ1Ii+U83wbuJConTnZ3Q8EDgfuAr7o7j+uWYvqRAZhGLY76upEZC+9SWQrkMheMr3/M2khgS/K5IcyUsuFz5LfTqhlOPn252mm7qs6p81Uoi7iVCIVcjeAu1/o7pe6+0/r0lG0qE6kt5YUtjtMu22sTkT20lNHClspWtLYS7LrkklLCl+UyQ9lZcRr0rLktxNqSZtvN7MNiAkUzwIu8RhdNfABqvP8KWozMmnJYrsZ7TbLPcqiJYutlPOlsZdk1yWTljT3aICuVv1QVkY0SCv57bWBw4ANgLcC13isB4eZLe81jvJKrGWhSnfupcC33f0MMzsIeC8xO/p2RJ1I7d25XaM3synAIu6+vZl9CljY3adU92lIx/8CzwNTPOp6uoXOF7v7xd2XSp2akmlJYbuJ7TbDPUqhJYutlHOlsZdk1yWTlkz3KIUfys5IB2njgfvd/QqLySPfTIwmOhu4ATgP2I2YULHulEgKLRVD7Obbf0q0FCa5+85m9mHgOWBaA2nF2Qy+dL//BPiZu+/V3Qdqr4MYqGtd4GuUegR3f9LM9gCe7ramRpOWDLabyW57aGv9HmXRksFWio5U9pLlumTSkuUeZfVDWRmRmjRLkt9OqCVVvr2rxcwmmNmHPEa8vQPYzMzO6u7TQA9aptqMTFpS2G42u012j1JoyWIrRUsae0l2XTJpSXOPsvih+YW+g7TSnf9C+XoqMUHhT4tBPgr8EXirmS1MLNdSG5m0wBytgG8Si9WeQ9QdTCqaaxte3QuLGoQ/Ax8zs03d/VliRNoWZvaqBs5/MDDFzHYzs027v7v7DcTknw6sCBzg9Q/Lz6Qlje1msttk9yiFlky2AnnsJdN1yaQF8tyjLm37ofmJvtKdyfLbabQUPWnz7aU34CPATsCPiakKJjVwjzLVZmTSksZ2M9ltsnuUQksmWyl6UthLpuuSSUvRk+Ie9dDVih+a35jnIC1Lfjurlsr3lPn20pLZnhjBc6a7T6xblyWpzcikJYvtZrTbLPcoi5YstlLVUvnemr1kvC6ZtFS+t/5M99DYuB+a35inIK16883sVGL9t28BpwM3drtPmyCTlgG6JgC/cveLiiHeCvzS3bdrQ08vzGwxoiWzNfANj/UF6zhPt2U5HtiZWUO9VwR2AR5y99PNbCl3r2X9wKRa0tluBrtNdo9SaMloK0XLBFq0l0zXJZOWAbomkNgXNeWH5lfmqSYtU347k5YuNp/k2939GaIH4GzKZJsjjSWqzcikBfLZbga7zXSPMmnJZivlfK3bS6brkklLlwz3aG404YfmZ150T1qm/HYmLT20zTf59rquUabajExaip6Uttum3Wa6R8m0pLSVct427SXNdcmkpYe2+cIXtXV9sjPsIC1TfjuTlqEYzfn2jLUZmbRUvqez3TbsNuM9alvL/GArRUOj9pLpumTSMhSj2RfN78xLT9oEkuS3M2kZjNGYb89Um5FJywBdE0hsu03abaZ7lElLRdMEEtsKtPOey3RdMmkZjNHoixYEXlRNWqb8diYtQzEa8+2ZajMyaekyP9huk3ab6R5l0lLOld5WoPn3XKbrkknLUIxGX7QgMC89aWny25m0zI3Rkm/PVJuRSUsPbfOF7dZ9fTLdo0xaBuiaL2wFmr0+ma5LJi1zY7T4ogWFeZ2CI01+O5OW0Uym2oxMWoZiNNtupnuUScsQGketrQxFpuuSSYtYcOhnMts0+e1MWkY7mWozMmkZjNFuu5nuUSYtvRjttjIYma5LJi1iwWCe1+7MlN/OpGU0k6k2I5OWoRjNtpvpHmXSMhij2VaGItN1yaRFLBj0tXYn5MpvZ9IyWslUm5FJy9wYrbab6R5l0jIUo9VW5kam65JJi5i/6TtIE2IgmWozMmkRvcl0jzJpEUIIBWmiFjLVZmTSInqT6R5l0iKEGN3Mc02aEEORqTYjkxbRm0z3KJMWIcToRj1polYy1WZk0iJ6k+keZdIihBidKEgTQgghhEiI0p1CCCGEEAlRkCaEEEIIkRAFaUIIIYQQCVGQJoQQQgiRkP8PnHbXepz9pZwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1159c7780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_strength('mpba')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "colors = {'passive': '#9b59b6',\n", " 'entropy': '#3498db',\n", " 'margin': '#95a5a6',\n", " 'qbb-margin': '#e74c3c',\n", " 'qbb-kl': '#34495e',\n", " 'confidence': '#2ecc71'}\n", "\n", "lc_line = {'passive': ':',\n", " 'entropy': ':',\n", " 'margin': '-',\n", " 'borda': '-',\n", " 'qbb-margin': '-.',\n", " 'qbb-kl': '--',\n", " 'confidence': '--'}" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def plot_selections(datasets):\n", " fig = plt.figure(figsize=(15, 20))\n", " \n", " for k, dataset in enumerate(datasets):\n", " for i, mu in enumerate([0.001, 0.005, 0.05, 0.5]):\n", " for j, var in enumerate([0.001, 0.005, 0.01, 0.02]):\n", " method = 'thompson-{}-{}'.format(mu, var)\n", " order = k * 16 + i * 4 + j\n", " ax = fig.add_subplot(8, 4, order + 1)\n", " result = load_results(dataset, method)\n", " arms = ['passive', 'margin', 'confidence', 'entropy', 'qbb-margin', 'qbb-kl']\n", " total_n = sum(result['T'][0][-1])\n", " sample_sizes = np.arange(10, total_n + 10)\n", " trials = np.arange(1, total_n + 1)\n", " props = np.mean(result['T'], axis=0)[1:] / np.repeat(trials.reshape(-1, 1), 6, axis=1)\n", " df = pd.DataFrame(props, columns=arms)\n", "\n", " ordered_labels = df.iloc[-1].sort_values(ascending=False).index\n", " for label in ordered_labels:\n", " curve = df[label]\n", " inital_n = sample_sizes[0] - 1\n", " n_selections = sample_sizes - inital_n\n", " ax.plot(sample_sizes, curve, label=label, color=colors[label],\n", " ls=lc_line[label], linewidth=1)\n", "\n", " if order in (28, 29, 30, 31):\n", " ax.set_xlabel('Training Size')\n", " else:\n", " ax.xaxis.set_major_formatter(plt.NullFormatter())\n", " \n", "\n", " if order % 4 == 0:\n", " ax.set_ylabel('Frequency of Selections')\n", " format_as_percent_plot = lambda x, pos: \"{:.0f}%\".format(x * 100)\n", " ax.get_yaxis().set_major_formatter(FuncFormatter(format_as_percent_plot))\n", " else:\n", " ax.yaxis.set_major_formatter(plt.NullFormatter())\n", "\n", " if order == 0:\n", " ax.legend(loc='upper right', frameon=True)\n", " ax.set_title('{}, μ={}, σ=τ={}'.format(dataset, mu, var))\n", " ax.set_ylim((0, 0.3))\n", " ax.set_xlim((10, total_n + 10))\n", " ax.set_xlim((10, 235 + 10))\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", " \n", " fig.savefig('figures/selection-thompson-params-{}.pdf'.format('-'.join(datasets)), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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sbCyFhYWAb1bQhx56iKysLLxeL9OnTycjI4PRo0dz6qmnoqoqiqLw2muvYbVa+fe//82GDRtwuVzceuutDB8+nOeff55Vq1ahaRpTpkxh5MiRLfzqHT+9Xofd4cTpctU6I+0hXxauoshl4+qEs5o5Qklq/RRFwePx4na7WzoUSWrTjAYDVXLMnXSSaExfqruAi4DsAMfSKAa9HqfT1y2zd/ejZ+d7LfdbOphjuSC6f3OHJjWhjz76iOTkZF588UW2b9/OkiVLiIyM5Nlnn8VmszFu3DgGDRoEQK9evbjvvvt48cUXWbRoEbfccgsLFy7knXfeYd26dQCUl5czZ84cvvzySxRFYdy4cQDMnz+fyMhInnjiCYqLi5k8eTKLFi2ioqKCCy64gAceeIDbb7+dZcuWYTKZKC4uZsGCBeTn5/PBBx9gNBrJycnho48+wuFwcNlllzF06FDCwsJa7LVrCnq9vnpsq8fjwWCo/U/Ftsp9xBnDmzM0SWozDt00cbpkcidJgaQ3GMi3yeROOjk0JrnLVFV1Z8AjaSSDwUClfxzQ2UMHHLV/Z9V+0i0JzR1Wmzcr91te37+4+ucPu84AYOK2F6u33Zg4gpuSzmf4hofJd5UB0C04hY+63cajWZ/wacHv1WW/P+Uh4kx1JwWZmZkMGzYMgC5duvDhhx8yZMgQAEJCQkhLSyM723e/oXv37gAkJCRQUFBQ5/nS09MxmUyALyEE2L59O6tXr2bDhg0AuN1uiouLa5w3MTERh8PBvn376NOnDwCxsbHMmDGDt956i82bN3PllVdWH5+bm9vqk7vIsFAGDBnE/qL9OF2uOpO7XGcxvUM6Nm9wktSG9BRpeDxyzJ0kBZLBbCYhKb2lw5CkZtGY5K5SCPENsA7QAFRVvTegUdXDbDRSXlEBwLrN21n6+xqmXTehen+eq5RY2ZLQ5G5KOp+bks4/avv6/i8ctW1Jr4eP2vZgh8t4sMNljX6+tLQ0Nm7cyPDhw8nOzmbRokWYTCbOPfdcbDYb27dvJyWl8ZPmtGvXjp07d2K32zEajWzdupWLLrqI1NRUEhISuPHGG7Hb7cyaNYvwcN/758iuiKmpqXz77beAryVw+vTpTJo0iYyMDB577DG8Xi+vvfba34rrRBUeFsL5Zw3mg/99idPlIthiqbWcHh3JpoYXOpckqXZ9ewrcMrmTpICKCrUw7MxeLR2GJDWLxiR3Xwc8ir9Br9fj1TQcTicer5fN23bV2B+qt5BgkmtutXYTJkzg3nvvZfLkyXg8HmbPns3cuXOZOHEiDoeDW265hejoxicVUVFRTJs2jQkTJhAVFYXFn6xMmDCB+++/n8mTJ2Oz2Zg0aRI6Xe2TyJ5zzjmsWLGCiRMn4vF4uPnmmxk2bBh//PEHkyZNorKykuHDh7eJSVwKikq49YHnGDakZ/UY19o8l3Z1M0YlSW3PJ18uqbUXiiRJTafU7uXfL3+APqI9PdvHEh8iZ3iW2q7GvLvnAjcA3YHtwKyARtQARVEItQZTXlFBRFgIxUdMqDKr8w0tFJnUlMxmM88//3yNbYe6Uh7u/fffr348ceLE6sc//vgjABkZGWRkZAAwatQoRo06erKdZ5555qhth44HuOOOO6ofP/DAA0eVveeee+q8jtassKgEk8mIs47kzuV185/cb5mecmEzRyZJbYfd7sThH0cuSVJghJr1RFjNrN65n+TYSJncSW1aY9a5ewNIBb4HOgKzAxlQY4QGW7FVVBITFYHH89fir6XuCmbuO6EaGiWpVTL4J1QxGU3VS48c6aCrlG+L1zZzZJLUtigouORsmZIUUAa9QpA1mMLCIn7NrmzpcCQpoBpz66KzqqrD/I8/F0L8FsiAGiPUaqW8soKOKcksnP1Xq8s+RxHLSrdyi1wKQZKOi9FooFO7JExGI05X7a0KB50lxMvxrZJ0XBLiYvB6vQ0XlCTpmBVXedGHxVBoh1GdQ1s6HEkKqMa03AUJIYIBhBAWQB/YkBoWFmKltNzXHXPBoh8oLC4FIM9VRqxRVlpJOl7WYAvPPTjNn9zV1XJXQrwc3ypJx+Xq8RdgNBood3iocMokT5ICIcZq4Krz+jL+3AyyS+seRy5JbUFjkruXgfVCiM/wzZj5UmBDalhMVBT5Rb7p6r/7+XeycvYDUOQuJ9rYuqegl6QTgdvt4ZnX5mA2GavXuzvSmeE9uavd2GaOTJLalh9++RNbRSVfbCvjgg92t3Q4ktQmVbk0Fv25mx8XL2FzvlzvTmrbGkzuVFWdC2QAjwNDVFX9MOBRNSA2MpKC4mK8Xi8xURHkF/oSvVFR/bg9ZXQLRydJbcP/Fi+rt+Vur6MAr291FEmSjtEWNROH08loEcaNA+WyIpIUCG4NrDoPP6/axNV9Ils6HEkKqDqTOyHE/f7/PwRmArcBrwoh5jVTbHUKMpuxBAVRUl5ObHQE+UUlAOy251HplbOOSdLx0ut1OF1ujPUkd6/nfsd6257mDUyS2hi9Xo/H62XBllIu7npi9Txx2d1omryBI7V+ERYDiRFBdA7z8tTy/JYOR5ICqr6Wuy/9/7+Ob8bMw/+1uKjwcIpLy7hi3EhGnT0UgHcP/MSa8swWjkwKlCuvvJJdu2qua7hy5UpmzJjRQhHBsmXL+Pjjj1vs+QNFURQMej0Gnb7eMXdyTUlJOj5GowGP14sOuGJBNt4AJVP2cie5WwqP2n5k8lZV6mD7zzlomsb/Hvqd8rwqtizOojy/KiBxSVJzyLO52VuukVNQzrAO1pYOR5ICqr7ZMjcJIUzANOByQME3mcoi4OxmiK1ewZYgqhx24mNiOHCwgKiIMIrcNqLlhCpSMxo2bFjDhVqpr+a8iK3SVudsmQecckIVSTpet02dxPv/+x9T+kYyPC0EpYnPX7S3HLfDjU6vY+/qPJK6/9X1U9M0vn1qFUOv7cnetXnojTo6ZSRQWeoAYNyTQ1F0CkaLAZ1BoTinnOx1+fS6MLWJo5SkwIq0GEjrFIXIuIuByRY0TUNRmrq2SdKJob7k7lrgXiABUP3bvMAvgQ6qMSzmIKrsDvbszeWZWe8z5+WHKXSVE2UIaenQpCZQUVHB7bffTllZGenp6axdu5aIiAheeeUViouLMZlM1YuPZ2Vlcd1111FcXMzEiRMZP358jXMtXLiQn376CbvdTn5+PldddRU//PADO3bs4F//+hfDhw/ngw8+YPHixbjdbkJDQ3n11Vf56quv+PTTT/F6vfzzn/8kJyeHuXPnEh4ejtForF4QPTMzkwkTJnD77beTkJBAdnY2p5xyCo888kizv25NadvOPaQkxda5zt2tyaOIlvVNko7LrtwiyrRgrvs8h7uHxeH0aJgNx/+l0+v2+r7A6uDg9hJOuaATManhFGWXE9UutPr/IVN6EBpnofNpybidHoJCTPS5OM13En8YnU9PBkDzakS1kzdQpdZHr1NwuDx4stYzbnsa71/SjiiLXMhcapvq7Japqupbqqp2Am5WVTVVVdVUoLuqqlOaLbp6WIKCqLLbSUqIJfdAHgA3JI4g2RzVwpFJTWHevHkIIZg3bx5jxoyhoqICgBEjRjBnzhzOOuss3njD10PY5XIxa9Ys5s2bx+zZsykqKjrqfBUVFbz11ltMnTqVDz/8kJkzZ/Loo4+ycOFCvF4vJSUlvPvuu8ybNw+3283GjRsBCAsL48MPP0QIwezZs/nwww955513qKo6uovSnj17ePzxx5k/fz7Lli0jP7919+t/95Ov2LUnp9ZumVVeJ6OjBqBTGjPhriRJdXn3zzz2OS08cHok76wporDK0yTnzdlYwM+zNhCZEsopF3QCwOPy8Nu7W7CXOfl9zlbsZU7CE60oioI5xIg1Kqjec4ZEW0jpHcuuFftZ+/nOJolTkprDgSqFykob777/EfMvSTyhE7v/5H7Dl4V/1ti2rXIfpe4KtlRkc/uud3l139e4vG4+zvuVA87iFoq0JpfmYVfVgRrbNE2rddxuXdvkGN+m0Zh3t0EI8aKqqjOAr4QQ76uq+n6gA2uIJcjM/jw7keGhOF1uysptnBXRE5PuxK2wrVn5vHeo+PC/R23XRUUT997nlM97B4DQSdeSd/UYvEVHj+2wTrymen/0C7PRR8fU+Xw5OTmcfvrpAPTr1w+TyQTAgAEDqrctXboUgD59+lTvT0tLIzMzk2nTpgEwZMgQ4uPj6datmy++0FDS0tJQFIXw8HAcDgc6nQ6j0chtt91GcHAwBw4cwO12A9Cpk+9L0d69e0lLS8NisQDQt2/fo2Ju3749ISG+lqzY2FgcDkfdL2gr0LenYOuOPVhCj65TC/JXkGXP5/4Ol7ZAZK3Xoa5A+8pchJt1hJj11dv+bjchl0djeVYFZ3S0otfJ7kWt1Rkilm9X51NS6eKpcxOP+3zF+2zsWJZD74vSSOkdW2Of3qjnwgcyABh136nH/BztesfgsIXLrm1Sq9EtLpjoiFDCrRZ+2JbP4C6JxFhPvO+LuY4ivi5cg0Nzsca2i7vajeXZ7C/4vng9YQYLOnRcGjuYtbbdbKzYS76rjEeyPmFm+lT0zXizdbf9ID8Ub+SC6P7YPHae2ruQg64SbB4HC7rfQZY9n4/yf2FV+U4iDSG83vkGdIqOeXnL+K1UZa+jgDldb8WqC+LZnC+INFj5rmgdz6ReydDwbs12HW1VY97ZNwJD/I8vAJYBLZ7cBQf5xtwpisKM6yeR7yrj+o3P8kPv1t0V7kQVOulaQiddW+/+Q+Le+7zeczW0H0AIwZo1axg+fDiqquJ0+sZ9bdy4kfj4eP788086d+4MwJYtW3C73TidTnbt2kV6ejrvv//XW3ThwoX1fgHZtm0bS5YsYf78+VRVVTFu3Ljqu0c6ne+PZfv27cnMzMRut2MymdiwYQOpqTXHnbS1Lzl9e3bhvx9/Sb9+6Uft+7ZoLTcnj2yBqFoHt0djw0E7/ZIsrMypxKz3vTf+u7aYl0clsWBzKQOTLfRPsjDuoyy+mNSRWX8Ukhhq5NIe4Ty+NI8bBkSh04EOhQiLno0H7XSPNZNV6mLd/irGdgtjZU4lZ3aSkwO0ZmW6EIq9FuZvKmK3TUevhCDSo8zHdC5npYtf39lMco9oTBYDSoCSflOwEaPFwOLnVtN3bDr2cid6o47knnXfsDvE7fSwd00eqYOOP5GVpMY6YHNhc4SA3sDOA2X07BhPzAn0p1PTNJ7P+R/XJJzNwh7/wqN5yXOVokeHQdEzq/MNJJoiURSINIRwZdwZKIrCKdb23LJzNldte4WZnf/B7qo8vir6k6mJ55JZdYBBYYL1FXtQUKj02FGrcrkm/my2Ve1DWJLq7X1T7Lbh1TSijaF4NS8KCuWeKp7MXsiq8p2cE9ELTdPQoXBl/JlEGILpZe2ATtFR4XEwLLwH05NHk+MoJEQfxH5nCXr03NVuLPGmCPSKQqjewhnhPdhjz+PznncTY5DdvptCY5I7j6qqdgBVVV1CiBOizdRiDqLS7msZGXv+mawr302CSa5d0laMHz+e++67jyuuuIKkpKTq7UuWLOG9997DarXy9NNPs23bNsxmM1OnTqWsrIxbb72ViIi/N8lHhw4dsFgsjBs3DpPJRGxsLHl5eTXKREVFMXXqVCZNmkRERAQOhwODwVDdwtcW9enehWsnXMSabZvweDzo9XoADjpLyHYUcGro0UnfyW7B5lJ0CpzR0cqCLaX0S/K19GpAn4QgHh+eAMC0wb4vwV5N48Px7THoFG4cGI3H6/vzeloHK9HBer7aXk5hpYcpfSOZu6GYGYNj8Xg1ksOMKIrCPcPiWuQ6paaTFGYkI9rBmJ4mcrwGggzHfvd9yUtrOXWiIC498BMdKYrC6VNPwRJuIn9nCV6PhtvpIX9XKYndfMMjNE2jsshOcFQQ2WvzKcm1kX56Mnk7SkgdlEh5fiUhMZY2d2NMOvF4vZAcE8kFI4Zy3ekdCQ0xtXRINay27eLTgt+ZlnIhRsX3WdtR7/v7fk/7cUeVP1RnjDoDMztPZUWZSqjeQowxFLPOyPgtz5IWlMiQsK6Uu6t4KOsj4ozhXBxzKoqi8PK+RQhLEjckjsCAnh32/Xy8YjmRBiubk/bxj4ThdAyKZfx2U3xgAAAgAElEQVSW54gzhpNpP8jTqVdyamhnog2hfNXzXoJ0f72GaZaEGvF1CIqlQ5Cv50CyOQq7zUlqUBy31HJTeEzMsfcikGrXmOTuCyHEcuAPoB/wRWBDahxLkJkqux2AWXM+JdtbSMLpcua+tsJoNFZPmOJwOBg5cmSN1rhDMjIymDev/qUXx4376w/jsGHDqme47NatG2+//TYAc+bMqfccbrebvLw8Fi5cCMAVV1xBYmIiAwcOrC7zySef1Pq4tQoKMtNDpLF++1acLhcWf3Ln1rxMT7kQg/8D6GTlcHtRCxz0SrDw1uoiLu4axrCOVnQKRAcbeMKfyGWkBFcfE2Kq+SVWpyiEmX2vo1GvYPS38J3R0XdLebT4a92zQ1324kNOvK5E0rELM+tZWNwR9/o87hrd7rjONfLugQ0XakLBEb4WxrjOvhuruZsL2bpnL/8xfsulBwfxTNQXXPLFAIZf3Y+49Ais0UEEh5sZdGU3NE1j+ZubGHpdD8ITTqAmFKlNSgozMrRTBBvsESzcZqNjjMYZHVt+QrAydyUGRc9b+5dwc9L51Ynd32FU9AwL7w5A+6BY7mo3lrvaja3ef0ZED34MfwQFpTopfKLjFdy44w3m7/+Nmz8+l/OeGECvsvaUCweTYk9HBCcRaQhhYY+7OOAsposlCYOiZ+W7W5mUcRpauUZRWXmDEyy5nR4MJj1LZ22g79h0giPN6HQKwZFBFOeUE5kSyoZFu4lMDiGqfSgbv97NoMmyW+bxavAWoaqq/wZuxZfcTVdV9emAR9UIZpMJp8uFx+slOSGWkv2lDA3v2tJhSW2UwWCgqqqKsWPHctlll9GtW7fq8X9t2eoNW6mqsteYVCXZHMW4mEEtGFXLsDm9AGzJt5Nf4WZznoPfsisB6BBuxKxXiLMaiAmWyZfUeD/vruCCZDur//iDmSsO8vGmkmM6z2/vbaGyxBGwrpiNsT35AM+1/4oOSixKEfQJ6ciCi1dREeUgKMxEdIe/blYoisLIewcSnmBl0ze7Kc4px+vxkrvZN167qtSBs8qN16vhsNU+Y68kNdbGg3ZWH/Sw6PvfcGZtpFts/ZMH1WXnr7m4ncc/6ZHd66TcU8UjWZ/w7sGf6BAUy6Wxg4/7vHXRKboaLeRRxhBetVzLV33u48I7MogxhXHpuNOY0v1s7LOqUA6CraCKja9n0jO4PVu/3IsOhZ6jOhGbGo693MmWxVkAzL9jGZWlDiqK7VQUVuF1eynPq0Tzaiy4czmVxXbOu3MAcekRlOTY2PL9XrxejV9mb8ZR4SK5ZzTRncKwRJjpd0nngL0GJ5MGkzshRDJwB76lEU4VQmQEPKpG0Ol0WMy+1rtO7ZOw7bedlF84TwZms5kff/yxpcPgtttu47PPPuOTTz7h/vvvPym6EsVGReJ0uWskd9eqM9lQkdWCUTW/gko3dy3ej6ZprNpXRXapi35JFm4c6FszbER6KOFBJ3dLpnRsLu0RRliQnvZBVZyT6GFM17CGD6pFSq8YzCHGJo6ufpUeB/scvtmJN9j28HzOFzzU4XKmpp1L79Gp3JkyhvOj+nHrztkA2Dz2Gscf+hsamRKKzqDDZfew2f+FcfWCHeRtL6ayyM6Pr64FIG9HMVVlrXuiKqllpEWZGNgujKAgE66KMizHsNyIx+0lf1cJP81cz9LXN7D7jwMNH3SYMnclD+35iL32fD4t+J2z1z/Er2XbmBx3Bve2v6RGN8dA8bi9bPgqE3uZk9Uf7iAcS42Wc0VROPPm3oTGWTAE6ek7Ng1nhRuDSY/H5SUsPhhjkIHIlFBOu64nAKMfHoQl1MTO5fvI21lCzsYC1n+ZiaJTmPDymQRH/pVIp/SOZcBlXdDpFC58KAOz1Uh0hzCCw30teqYTeBbT1qQxr+KbwPPAA/gmU3kPOCGyqFCrlQ05xWwuC6Uy2sM62276hHRq6bAkqc2IiQqnyu6okdwVuMoJ0R3bXc/WJqfMxZdqGTcOiOL+M+JQFIWr+8ixvVLTsbs13ttppnt4Vw4UloI16qhWhZ9KNvHk3oUs7vVgredwOz0k9YjGYGq+Gwyl7gombX2JfFcpM1JGMzHudBZ2/xfGw2asVhSFq+PP5LLYIbg0DxO2Ps/NSSMZGdWvxrmST/lrIpZzZ/j2Db22R/Ussmf+X28Ati/bR58xaXKWTulv0zTfElomk4F1e/JRNpRwXZ8wdIqCwdDwV2HNq+GqdDP4qu4U7C7FWeUmMvnvdev81+73STJFEmUM5Yq4YUyMPY0qrxOrvuHPU7fDw6Zv9tDrolR0x9g6r2kamtf3zxRsYNS9tY91Cw73dbfWG/UE+ccm9hzZsc7zHirT+yLf+piaV6N934bHg8s6HDiNGbkdpKrqj4CmqqoK2Bs6oLlUYea3zEL6p4ShHx/Jnu37WjokSWpToiLDSYqPxXHYQuZlnkrCDcH1HNW6eDWNgza3bwxQVgWapjFzZQFL91QQbdEjos0oikJiaPO2ikgnh082lfLwqXrOStHwWiJYnlVxVJkYY6h/mvHaP373by5k2RsbAx1qDVsqcxgSLvio2+0MCusCUCOxO5xFZ8Ko6Hk29Wpe2beIhQW/N3j+Q1/8FEXB4v+yedp1PQmJtrD8zY1sX5ZDeX4lu37LBWDdF7twVrqoKnNQUXj0OqTSyW17oYO1B91ERoYy6lTBTQOjefLVd3ngmdcbdXzhnjJ+nrUBgJhO4SR1j+bg9mI2f7enUcdvqMhijz2Pe9pfQog/mdMpugYTO82rsWPZPrxeDb1JR235kMf1VzdRTdPYtWJ/9c/bl+bw+/tbcVa6+PKh31F0Cr0vSkN3HBM3NaQlu4ZLPo357TqEEOcBeiHEIBpI7oQQRiHE+0KI5UKIP4QQFwkh0oUQv/i3zRJC6Pz/PhdCrBRCnOs/NlUI8XJjg0+JDmVAnMIpiSYy1U48PWs5lVUnTO4pSa2eQa+nY0oiDv9SFADdglMIa+XJndur8fnWUiqcXr7dYeObHeXYnF5+yLTh8cKYbuFkJFuwGHWcndryg+6ltuuRs+OZtcmLEmTlrK7xXD8gusZ+TdNob45hcJjg97LtRx2fu7mQ0Lhgzrqld3OFDMDgMMF97S8l1RJPp6D4Rh3TLTiFF9KuqV6g+aCzhP3OYopcNryat9HPffr1p5A+NInKYgd6ow6vV0OnVzCY9OSsKyBz5QG8Xo3f3tuCy952ZzSWGq9vooWRXcJIToyl14D+LNllIytnP5dccE6Dxx7qfjnizv41tkckhdBxQOPe+0mmSO5rf0mjJ0zRvBpbvs/CWemmcG8ZxiA9p4zqRP6uUnb/cYDKUgffv7AGj9vLFw+uoKLIzh8fqpQdqOTg9mLcDg+7fsulU0YCAy7vginYyBk39UIfwKROOnE05rd8PXANEINv7N1NDZSfDBSqqno6MBKYCbwA3O/fpgAXA32APcD5wC3+Y+8Hnmhs8IoxCI+zigPOYjp3KkCEa2zZvruxh0uS1Ai//LGe7Ny/xhbM6nzDMc3odSLYU+xk8c5yDDqFwioPHk1jWIdgruoTQahZz8NnxWPQK6SEGQkyyg9BKfA+3FiCQa+wK7+Kl2Z/zIM/HsTh/ivR2Va1j6u2vcr97S9lSJjg9dzvyC7KY9vOPQBUFNqx21wB6+Lk9LrZ7yyuse3nkk3M3PfNMZ2vW3AK/xW+j/yX9n3F1dte5aLNT/CvzMYvn6soCjq9jvgukXQcmIBOp9DrwlR0Bh2dhyXT4/yOaF6N2E7hGMx68nYU46hw+ZZmKPGN2dvyfRYel4fM3/ez4xdfr59F/15JVZkDl91N6QFfC2pBZilux/FPoCG1rMxiJ1vz7eCFBx59npIqFzv35LBk+UoW/fBLjbJlBytqtIbp9AqKXjmqO2REcgguuwdbQc2WYk3T0DSNOQd/ptxTxWNZ8wnRB3G6f0bLhlSWOPB6NUr3V2AOMTJocrfq+m0w6TEYdVjCTPQe3Qm9QccF92UQHGEmItFKaJyFIVd3x2V3c3BHCYpOqe6uHZ4oZ6U9WdT57UUIYRJCmIA84CqgLzAJaKjv43x84/MOcQP9gaX+n78BhgM2wOr/VyGEGArsUFX1YGODX7lfI6/URjtzDO/0/AcpnXuwcdvOxh4utTJXXnklu3btqrFt5cqVzJgxo97jFi5cyHPPPdfgNql2Br2BUpsNgHxXGU/s/bSFI/r73B4Nm8NDeJCeYrvvQ/u6flGEmfWEmPXoZN9/qYWY9QrPDY8h0VBOYXExZx+xKP3Sks2cFt6VFHM0wXozK8q2s3T7ep55bQ7OKjedhyWTIAIzDvTDvOVMUV9l3OZn+K1MJd9Vxpv7v+fJvQvpbk057vM/2Wkyi3s9yE+9HmVS/OkAPJr1Cb+WbqPSc+wTp+h0Cnp/oqcoCnvX5lO6v4KCzFKWv7URTdNwVrhxVLiJTAkhLs23jNKgq7oRFGpi12/7yVlfAMCmb/dQVSoncWnt9AoYdAphoSHs2p3FhenB3Hb9JMLDQtm3P7+6XMHuUj6ZsYyS3L+6R3foH19jptfD7V2TR8k+W/XPlR4H/9j+GgddJXxbtJaLNz2FXtFhVhru1v/nJ9vZumQvS2etp7LIzuCrjk4Go9qH0q6vb/z3oSVIzCFGFJ1ClzNT0Ol9X+st4WaGXN29WcfhSieO+kaRqvjW3lX8/3PY49Q6D1JVG4AQIhRYgK817jlVVQ+doxwIV1V1uxAiB3jJX+Yx4C4hxCygGF9LX739NK4blMziX/bwZeEqYrQEiuN60r/rCbHGuiS1GcGWIFau2cT4kSPIKc9nQ0UWOaUuHvn5IG9dfPxf8Jqa3eVlV7GTHnFBOD0aJr3Cwq2llDq8TO0fxeU95XqY0oljXPdwBr25k/MswezOzqEsczW2yP5MuvMxALwjrNx13gQqKquwBlvoUBXJx3O/oySnlO+eX0XG5V2rv+Q1lRJ3BeH6YIQlmZA4C8nmKNBgv7OYA85iHugwntPCm24tKqPOQL8Q39eKBFMkM3O/xqIz8VzqFKKMx98tesBlvjGBmqYx4o7+KIpCnzG+yR8OrdUHEN3e9wW+69l/rTd4aDIXqXXrEGGic7SZXdZgQkOsXDj9OZa8fh+fLvqRrf5WcE3TmPLQw/zn/ruqkzmvx8snty3jsueH1TpOrddo3/vWWenCFGzkyeyFJJmjSDBF8laXm8hxFNLZknhUy7rL7kZv1HFALSY2NRxjkIFTRnVC82p0G94+sC+G1ObVmdypqlpj2kkhRBRQfFiSVichRDvgM+A1VVXnCSGeOWx3KFDif45H/OUn4VscfSrwNnAmcA7wfV3P4fJozN3iwFxRwff5K7k+aQT/GduFclsFbre7UbMfSSeuiooKbr/9dsrKykhPT2ft2rVERETwyiuvUFxcjMlkql7kPCsri+uuu47i4mImTpzI+PHjaz1nUVER//d//8e0adOa81JaraGzd/HVFR05a8gAcg4cpLC4lBk3PUuXR3sTYtbxj/5RLRKX06OxvcBBz3jfQHRN05i9upjLeoZj0isUVXmYv7mUHnFBPLU8j0u6hzOuezhyjLd0Irr2s2w+ubw9S77fQJ+Lz2FRfjjafjfPPzSdZSvW8M3mP+g+NoVb7nyWiWNGEFyhx23VSIyLocuERGLTmvZmxby85by8bxGPdZzAiMg+9DviXm4va4cmfb4jXZ94Lv9IOIdX9n1NlddJgauM3fY80i0JbK/cz6mh6cDRM+15NC96pf6u1HJ2vpPXL1kV9M2qICTYwv3Tp2AISwSgfXIC2ft9HcYURcFkNFJgL2b3awc57bqe6E06Rj88qN4JSD7Z8CuGzzXKznWwzZTDe33+CYBVH4QITq4uV5JrY+cvuQy4rAvfv7CGAeM7k702n6BQEwW7SknsEUVobOsezy6dGBqzzt0wIcQm4FfgESHEdQ2UjwcWA3epqvqOf/NaIcSZ/scjgeWHlQ8CLgHmAsGAB1/rYL236zyaRlJ4ENagIPLKSkgNiufbHeVMeeZj1m3e0dBlSSe4efPmIYRg3rx5jBkzhooKXxeJESNGMGfOHM466yzeeOMNAFwuF7NmzWLevHnMnj2boqKio85XWFjITTfdxD333MPgwYFbKLQt6RBhxOb0EBIcjMlkJDoyHJ1eh7lch9urEWRQWJPb/LPS/Z5dydI9FeSWu7j/hwN4NYiw6PFoGhPm7yU5zMjDZ/kGuV/eM4IecUEYdIrseimdkJ44N4GkUCOhIVZKy8sYZs3h4m4RdExJ5MJzT+eu8VdQfKCMTeouZs35FGu+nlHdMpg6biz2fe4mTVjm5//GnIM/81n3fzEisk+Tnffv0ik6pqdcSLI5ij/Kd/BSzlecs/5h3ty/mAJ3OTMy/8udme/xeu53rCnPpMrr5MptL7PGlvm3n6vIZcOt1RxT5/C6WFW+kxxHYVNdktTC+idZ6J9kISTYik6v0DE5jjKHh4F9ujP9HxMB+GPdZnQeHb99t4m0wYmgQHGODa+r7k5kTq+bFz1fsfyy7aRkRvGIdjlZP/01ushld1NRWIXH5SE4wlzdhXrUvacS1zmSjCu6EtUu1Le8h7wDKTWRxjRv/RsYBnyKb7KTX/G1rtXlXiASeEAIcWjs3TTgFf8Yvq34umseMh14RVVVTQjxX+ANoAwYU19QQQYdl/YI5/M8K1a7nlhjGKlRTk5Nj2fF6g0M6N10XUYkePODz3hr3ufVP895+WEArpr2cPW2qZPGcP3ksYycPI2CohIAuqZ34P1XHuXxV97h82+XVpf9+v2XiI2uuytRTk4Op5/uG4PRr18/TCbfOioDBgyo3rZ0qe98ffr0qd6flpZGZmZmdevckCFDiI+PZ/ny5cTGxuL1Nn5GtpOZpmncNyyOduEmsiqMOF2+2TJ7d0lnpL0P9/9wkIHJFnrGBTXrmlN2l5dhHa0MbR+My6NxRa8I9DqF8T3CAVhwec1WBRFjru00knTCKKjwEBtsINRqJb+oiLJKWJVjo1eciVx3IS/avyH3sR1cO+Ei5i78BrfTgyktmK4dO7F9bRYOpxOzqWkWP7boTLzR+UaSzC3TKl+bUVH9GRXVnyqvE4t/kecH2o/nl9KtbKnM4ZviNfQLTWVKwlncu3suQ8IE05NHY9Lp0St6Ps1fwU8lm5jV+Xrm56/go/xfmBB7GtHGUIZH9uKxvfPJdRTxTOpVtDNHU+6p4tItzxFvCifHUcjjHSfRNySVYneFr3uq1CrZ3Rour0ao1cr+/Dw+2ljCGR2t9IzW89LbH9G7e2eemvkeA3t3JzTNQkqfWIpzbBRklmIw6wmNq71FbVX5DrpYEnkkfSJhXS04Klxs/zkHu83Jlw/9zikXdMJhc6L+lMNFjw0mpXdsrecRZ7WrdbskHYvGJHdeVVWLhBCaqqp2IUR5fYVVVZ2GL5k70hl1lH/qsMfrgIxGxMTSPTZ+3l3B8PBIbjaMQFEUukSbGdpb8J833+XWay9vzGmkRrp+8liunzz2qO2rvn7vqG3ffHD0ahb3/fNa7vvntY1+PiEEa9asYfjw4aiqitM/Ff/GjRuJj4/nzz//pHPnzgBs2bIFt9uN0+lk165dpKen8/77f828tnDhQsaMGcOYMWOYNm0a8+fPb3QcJyu3F6Z8lsOnE9pjMhpx+te565bchfx15bx+i6+rSX6Fm8mfZvPBJe0CmuC5PRoGvcIVn2bznwuSSAg1otcpRy32bNTLO59S6/KfPwp5cWQioVbfl0ePIYgdeTZMtlI27ttFUDsziz98FUVR+Py7pYw6ewgPlM6nm6sTX21YRnFQKeefOZhV67eQ0bcnwZaGF0R2eF1oaGjA/wpW0TEols8L/+DJTpMDfLXH7lBiBxBtDOXimFO5mL8WYR4R2YchYV15JvtziioK2FW4m9lbPua8nXbuzdVRUHg5A19+mfQtq1kd9hs5nVMZtKWQJy3dWZHzJz/Nv4nIjl0ZHtqDt867nvg1WynNUtF1S8Lz5++Q1hFkctdqbSuws6fYSYI1mPKKCm4f6UuyvF4v3y9dyfZdWdxzyxS6haYSlmjF6/ay4r0tnH/3wHqXDzg1rAvplkTCDBYAzFYjp1zgGzt3wYMZ1QuCdx/RAWOQHC4kNY/GvNN2CiGeBKKFEHcDWQGOqVGGtrPSO8HM3M0rSCuPqd4+e5eZ/hmD8Xq96HRyKvPWavz48dx3331cccUVJCUlVW9fsmQJ7733Hlarlaeffppt27ZhNpuZOnUqZWVl3HrrrURE1D4GJT09nYsuuognn3ySvn37NteltEpGvcLj58SjVxTMRlP1IuZ7e5TSsSiaHzJtnJMaQqzVwMsjkwKS2O0pdvLGn0X8c1A0d31/gDnj2vHphMCO95Gk5jZrtO9GSajVikGvp1+ilc7RTvbuL6ayspIkc2R1/bpuwkWEWINJLYnnR8cmLhs/gieefYeYyAhee28BK1Zv5N5br6lx/gqPnVXlO1lty6R7cDvW2jJZUryBe9qP48zwHvxStpUn1nzC0M3t4PbaYzxYUITm1UiIiz5q374D+SQnxLJ+83YcLhddUjsQERaYtSFd27fiKS1GH5eAVl6G488VeAoLiLj9Aap+/p6gtM7cXy4oun0KpwRbeTE4mOCM0zEN6I6pXwZxwcE4e55Fn7gEtKoqyr5+HqdOTx9LMP27X4SWn4fHXUgHUzRar77Edu+FzhhG6W+/EBeT6FsQSmqVBrez0ivBQpVdocxWwfq9+ewoN3Fpj3D69+rGtp176NOjCyXbKvhkwWJm3DyRs//Zp8F14VaWbadvSKejtis6pTqxA2RiJzWrxrzbbgT+AfyCb/mCqQGNqJH2lDrZ78ljuWcHEUV/TfX637EpfLAhhI83FDOxz9EfRFLrYDQaqydMcTgcjBw5skZr3CEZGRnMmzev3nONGzeu+vENN9zQtIG2UZUuL9llLkakh2KrNFJeXMGCO5eRWBjKuuTVpHfvWV1Wp4MfM21Nvth3x0gTU/pGkhhq5JVRSQ0fIEmtjKZpPPdrPncMjSXUaiXEasVojeLZPyo4J6QET5WHbsF/dde6bPRwvF6Nc0y9+F/RKvqndiUtLYUqh4P/vvAgl994L2s2bqPfKV2rz3/brndxaC5OC+tGRmhnXs/9jolxp3FupG8WyFfSrmNrXDbXv/EY60fuoHf3zmiaRn5hMZl796Fp8MmXS1i7SeVf/3clo84eCoDb4+HL75fz5gef8fGsJ9h3IJ+5n32Ly+3mvy88iDXY15Jhq6wiOMiMTqfD6/WyeXsmcxd+ywPTr8NWWYXT6SIqIqy6fG2vEW43zg1rKH3lKfTxCWg2G0pYOMZO6VjHTgBAsVjQnE6M3XsRN/crdEGWGuf4ddV6enXrjCuhPVk5+6msspM+7UHfWGKdDqfLhdlkrL4prIT/1VIYMeO+pvqVSy1k40E72aVOUsLMOF0ufv1lCd0HXwjAqX27ExUZRkJcNAlx0Tz/5V6WrVzH2UMH1HtOr+blrsz3+foU+f6QTiz1JndCiN6qqq4XQryDbzFzO75161rcmtwqCnQ2YiIiqNhjrx53oCgKHSjkjf/OY+LLD7Z0mJLUKukU+GhjCXFWA6nhOjQTjLhnABf//G+GL+xGF7uzuqzXC9sLHU2a3P1r8X5uGhhdPWYuIkiu1SO1PRrQPty3/lVibCwDe/ZA0ZvombuLkrIy9ChcHF7zC+bO5fsIyTLy6lX/YGHB7/w6dB+jIoyEWIN54p6bSYr/q3mp1FNJmCGYJztdgUHx1aFPe9yJQdGzbecevvhuGS6Xi369unLHDVfw+vsLeei2fzDp5vsxGgyktk9myuWjefSO69l3IJ8Hnn2d2KhIzGYTN939FB1SEnj9qXsIC7Uy6pyhjDx7CM+/MZeP//c911w+mi++W8rzb8xlyIBePHrnDUy98wkKi0qYcvlo9Ho96zdv57nXP8Dl9jDyrMHcNHksoWGhNa638rMP8drthEyYQuy7C4/qJZCzPw/X3lzyzZF888VSzh5aiaJAp/bJaJqX1+csZNnKtaQkxvHonTeSmZXD/K9+wGQ0snNPNi88PAOjwcDV0x/GZDSSnBDL1eMv4LwzfRNvOV0uVq3bQo8uqUSE14xNaj1CTDqCDDoUReGsjAz+2LCBPtEa7uwszlq9mLF3+5Ye+fbpVVx4xuksWPRDg8ldtqOQMEMw4Qa5OLh0YqkzuRNC3AZc7l9c/FmgA74umS9S+5i6ZnVZzwjm528hrjKcmIgI8ouKSElIAGBoz1T+7Tbxy+YsTushu3G1dmazmR9//LGlwzipBBl0zL+8AyuyK7n3x3zOw4O90sntuot5suRtrk76a7mJGKuBGwZE8aVaxoVdQv92F01N03h3XTHX9I1i9uoixnQL46aB0XSIaHjR17bg/9k77/Coqq0Pv2d6zUx6QnqdJKTQe+9dxAIoYu9iRa9e9SrqtXst3A/bpShgQxBFQCnSpPfOAAFCQkhv0zPlfH8MBkNoKkV03ufhYXLO3vvsM8k5e6+91/otj8eDOb+ApT9vQqmUc9eNVzNjzkIOFRxDq1GjVMgZd9tItu/ej0qlJMQYdFYxogBXDhJB4PoTeRdVSiVpiYk4XS5ctWuIDDNS4qzkeG0lieHRDXXSusWQ6PCrwfYy5lCWWcty13760Za8rDQqqmpYuGwNA3t2wijT8kby2EbXrKysRQB8J9wsZ32/hKF9u9LclMyGbXvQqlXM/vg1gg2NkzZnpGqZ/u4EhPx9SFLT+GnWpCZCLoIgMP6eMfi8Xr5fvIq33p/Oe52SCW2bhcVqp0vbPO68cXjDO6Jf9w70696B/BeeZsHib6n7cQp749KZVuxArVRwy/VDyL5qJPsPH6Vk/TY2bt/DsH7diI2OYNgtj5IcH8ORouM8+cAtdGvfgj37DzPzm4Uo5HIeumMUGpWK3PZ55iEAACAASURBVKxUxt8zBkOQDkEQSE2MpV/3Dk1+Fz9/8z/qrFZKy6sICzFy+Ggx9z/9GnVWO1mpifzjgZsDxt0VTFKwgnCtf8qbmZJMfmER/575M+PXfkS9xYl78VoiBnSlw5hMlCFyps75juraOrQaNQp507HIK/p4pXAOvYzZTc4FCHC5OdvO3UCgE/7FxRuAdLPZXG0ymdZckp6dhb3lTubvt/BYp464RS8bK3dQUlHRYNxJpRIyWrXn+9U7A8ZdgAC/g2KLm3Hzi3mjXxRzRyfywYz1fL3/IHkWPQnxUZStKSR2UFpD+UqHlwOVLlweHQoZvyntgNsHHi/4RJHMcCVOt0hS8IVR/7uc1Fr8E8Vvf1yBKSWBYf268cOytSz9eQM2h5OrB/Sgb7f23P/0G9TWWenQOptu7VsiCAIqpZKcjFTKq6rp0bE1AMvWbmbd5l1UVFWTmZ7MxBfHM/nzb7n5+iHIpIGdzSsRp9vHNV8UNIolVSmVrCWHgS1C2LphNh6nu+Hcuul7iWsZTky2f3fOKNNyTXhHbtk3sUG1ts5q479TvqJPt3aM3fIOk1reQ7DMv6suiiKvTpxGXvN0brl+CFnpSYy9dlCDsfXyk/edsa/1+3ajNGVRt2QBGo0WZVJqo/OiKILXiyCTYZ/zGb3DIug14T7E/H04Zk3FN+UdrmkWS03BRuSpGehGjqX2v2+gv/luEu66n/s1WnwSKZ++/SGdWkUjVleyuspJmsfLS+9NRSaV0b5VNsUl5aQlxfHpexMoPFZKc1MyuhNiNLdcP4Rbrh/SqF/XD+17Xr8LqVRCsCGowag1GvRMfvNZDEG68xKpCfDnZp65jgGVLtJC/d4gkaHB3F4yH6HTAKr2ViFfs5Zjjma0vtY/rn398Wv8sGwtazZt5+Un72/SnlSQ8H+pd54zt2KAAJeDsxl3PrPZ7DWZTK2AQ2azufrE8csuR5dgVDDUFMQGywFilWHEREayfZ+ZNr9aQHliUBZbdu7H6xORBnKHBAjwm4jSyshM0TFlazXDM4NwyOQsrKxktjeE5554AteKo3jqvcgUfqMiTCPj0U7hfLqtGplE4Ibc80usPH17Na2i1dzZxq9C1zn+ynFvWb91NykJMYQGG5i94Ceqay0kxTVDoZDTJjeTa+98EkOQju4dW9HclIzP52Pzzr10bJNLqNGAQuFfDX7+0TuJjmys1HDdkN5NrvfwHaPhDn+sU+Exfx6lkcP6Ig0IR12xKGUCU4bHNjk+e4yJjZaDyNRyrFZbw/Hm/RNQaBrvIoTLg5iX/VSDgZYcHwNqCQPmPot3cR0f585hy04z9469Bq/Xy7GScl598CbqPnwHQalCnpWDp+Awqg5dEH0+fLU1ePL3N8SyAfisFqpfeILw92cS9MATCFIptjmf4S0rRRIWgUSnxzJ1EoJGS+jb/0PVuQfS8CgEuRxatEY7fBS+ynLchw6A14MsyT+BVrbrBDIZsih/TK0EeOrZpqou0997ocmxZpHhNIs8vaz8hUAmlTZ5LgNcufRL0TVaNAwxGLDXVCMfdDX23mn8cNjJ010iGs4LQOtcE69P+oQ9+w+RlZ7ccE4URcaa3+PDtHvQSM+cbqfS7qHa4SU1NJCSJ8Cl5Vwxd+nArcB3J35ujj/J+GXlWJ2b1BAF9+cvY0xEN9pFpLJ49Ro8Xm/DCnZsdCTzCmV8sqGY2zrEXOYeBwhwZeHwiAyO11BU66a6yoWk3s2d7TQYxHCWrttJ91Q5UnlTo2KYKQiN4tzGRrXDi1oukBmmJFJ35aiIeb0+NmzbzcZte/hh+RruGD2cEYN6UnS8DEEQWPDTanp0bI1GrWLxF/9tUv906UB+6wRSJpWSFO+fDP+yYxHgysTjg0PV9bSMbiwmsvqojSXWAmKDI6ixWPB5fThqXPi8IkpdUxexQlcl31SsJ1sbRy9jDpH9m6H4/hA3+mrYIpfxwK3XkZOZym33PsUT1/ZBrtXhNgbjOZxP/b5dSAxGbF99iu7G25FnZqPq2R9vWQme4iKULdpg/XwqyradkBhOLtooO/XAuexHPAWH8NVWY3z6ZSRaHVJjMBgbuw0LUinSiCikEVGNjqvadb6A32aAAGemyuHF7RWRSQS8lRVov5jKwq5XEWc2kN0GbhL3UXuohvDs5gDMXfoTpRUV5GQn8/702Ux88fGGto64yqhwW85o2FU7vEzdWkWLKDUlVg8JRgVHauobdg1Px7bjDlJDlejOY/wMEOBcnG1W9QwwHTgCPGUymboDM4DrzlLnouMTRV5ZVc77Q5pRVl9LhNyAQi4nNNhIcWkp8b+Sze8S6uTx597ghpavo1Je+W5eAQJcKqqdXv5vfSUD0vTEhNuwypxk+aLITtOjqVTz5ReLsS5yM2xCx0b1jGopx+rcTN1azTPdI87QOry1ppyrMw20ibmwxkl+QREpCU13Qi4UEonAgUNHcdXX89HrTxMb7b/Hh+8YfdGuGeCvi8srMs9c18S4c3tFhod2RKOuYMOGnazYsIOgSA2eeh/tb8xo0k64PIjVdXv5rnIjFaVHmDz4AXZqVxC95BvaNo9BYUpEUMj58JGxGCPCkajU6Ebe3KiNX9w6G/q2cyu177yMNDoGX3Uloa+/36i8LKoZutGN0y4ECPBnZX+lizqXD7Vcgn3hXFi1hNbVNWSOe5FKLxyscuPy2rnqhAeYx+PXDmzfIouaWntDOz7Rx2bLIVrpkptcY+MxO6VWD4PT9WSFqxpExvaVO/l6T91px0SfKOL2inywqYqJf1AV+pu9tcTo5WRHqnhxeRn/7BaOXunf8PhuXx25USoSjYG58N+BMy4RmM3mjWazub3ZbB5pNputwDog2Ww2r7t03WuKRPC7sShlEsrdtYQr/P7xSTGxHCo61qhsTkoMcVkteOHr9ZejqwEuETNmzLjcXfjLERsk5/72oczYXoOjPphBbdqzw2xmyZq1tG/VnH3HjpA3Ngmv19ekbqhaSrcELdO2VuMTxUbn6r0im47ZebFXJK2bnV76/Pdy8HAhtz/2Ek6ni2Ml5ew7eIQlqzbgdNWfu/J5sGjFetZu3snY6wbz+L03NRh2AQL8XnQKCf/qEdnkeEyUhQ2+1QRrgnCITrrckU3r69JJ6BfGzHnf43S5GpXXSJV8lTmeWVnjyVy1E+e6leQO6I9uyDVYZ06m5s0J2OfPIax1W2Rxiafty6lCSMqclujG3IF2xGjC3pmCJMhwwe47QIBLTfdEXYOXiHPlUoQgA8ZjBSw77mCy2UpS354M7ZWNWO9/tkRE+nbqSK+YcKKjglm+ZjPVtRZuNk9kUfU2WqmTmLNgGR6v35lNFEUMKimHquuZu6+OAWknxXcywlU80z2CMpsHh/vkmLm33Mm4+cUoZRImDWnGkZp6nlpcAkC5zYNPFJm+vZrNxXZ8oki99+R46hNFKuwe6r0izy8rxeXxkR6qJEovQy0TGJltQKeQ8Obqcr8HjtOLUSUlv8pFUZ2bSrun4VruE+2KoojX13jMDnBlct77v2az2WU2m93nLnlxKba4WXTQAsC/k24kSOpf+U+Oi+VwURE+X+PJ5q0j+rJ69Toqq2sveV8DXBref//9cxcK8JsoqKmnoMbNC70iyT+wh0M/llFTZ6GkogKFXE6f7u34eMq3jHv8jSZ1VXIJ3RK1iIgsybeyvsi/6rm/0sXHm6r44aD1gic993p9vDv5C+66cTgSqYQHn3mDZ17/gNkLlvHxzG8Av8DJ+q27+WD6HDZu2wOAz+dj3uJV7Nh7EJ/Px+MvvceIO57goX+9xcefzQVgzsJlPPP6B7z98WeEhZxfLGGAAOdDncvLR5sqmxxfdrSSqT/EsLkafIIXUeqfcP28ZQtOl4uSioomdQRBIFSuJ+/OZ1D36AeAqnsf1L0HIE8xoRk8okmdc6HpMwhV204IqoCgSIArmzVHbdQ6/YaY7obb0PQfSkVUPMEOBy/3icIUpmTvSy+yZ9ZcFudbqLXaiYmIQDnjI/K/+Y7HX3qP2//9MtVuKx+k3U1qVRiv/HcaE6d8hU8UuWlOEc30cq7JMpAZdnr3y/9trmJnqRPwG1JpIUpe6+dXwpUIAsnBCu5u648/f2pJCcctHrLClSQFK/hsRw2L8y0cqq5n+vZqDlTW8+LyMhRSgT7JOkSgeYSKeIM/JViLaDWCINAjUUuwWsrNLYIxqqSYK+rZVepEp5AwOse/YHPf98cwV7jYXebi3nn+TZJSq8cvkhTgiuTKCXY5gUomEK6V4RG9NNfENSgVGfR6grRaCo+XkBBzcmu7U0YM7z9yLflWGQH18CsHt9vNc889R0FBAT6fj4cffpiXXnqJdu3aYTabEQSBSZMmMWPGDGpra3n++efJzc1l9uzZ+Hw+HnzwQcrLy/nkk09QKBQkJibywgsvMG/ePJYuXYrVaqW6upr777+f9PR0Hn/8cb7++msAHn74YW677TZyc3Mv87dw+dApJBTU1DNjezXBmTvpMyKHTpaWrNu+HVEUGX/3jWz95iDvzP2cg4cLSU2Ka9LGrS1D2HrcgULqN+REEbIilPRMunD58JyuepQKOYtWrsNV7+baIb1RyOXM/t/rDWV8Ph81tRaG3zaelMRYWmSb8IkiFquNlydO4+ixElo0Tyc3M5WrB/QgLMRIUXEpSfH+WN3qmjpa52bwyJ2jCQ0O7F4EuHAoJAKZ4U0NJ63WQkpsLbum1hDbTU9JRQXRYWFU1daSk57G8fJyEmOaxpK7tm+ifvcO9Df4YzsFQUA74oaLfh8BAvzZidTJUJ4Yi1RdeiI6O1Bm+AmrzMe6Qjsd4jQIg65F+8EEmnXvywGvF3lNFUE338OoNyagenQM33y/lpeEkbhcbkKDDUx47C6++WE5iCKv9olCp5CciJlrHBdbXVtHkE5HZ3kR6fo0RFHkP2sqaBGtpvev8sNKJUKD2+THw2IQBIGYIH9b12YZqHJ60cgkxBvkmMKUvHvCjbNLwpmFyE4NfRiUfnJHMTfK7z3zf0NikAogCDBpiP+98s8lJbzUO5Jo/YVNSVTr9GJQSfGJ4mlVtU91Dw/w+zjjzp3JZJp64v+7L113zk2wSkrLaDWFrgpuNTcWLMhITmZPfn6TOsmJCXy85igLVmy8VN0M8AeZNWsWwcHBzJw5k0mTJvHCCy9gs9kYPHgwM2bMICIigpUrV3LvvfdiMBh4/vnnAQgKCuLzzz8nIyODiRMn8sknn/D555+j1+v58ssvAbDb7UydOpUpU6bw6quvEhcXh0ql4uDBg9TU1FBUVPS3NuwAQjUybsoLxu0DV4UEhVVGi8wMlAoFNocDmUxG2+syaNszgxXrtpyxnZbRapKDFVTYPSQHKy6oYTf1y3kMHPMQRwqP07V9S9574bHT5iOSSCQYDXqWff0Bk996lnG3Xk/7ls3ZtvsAcpmMyW8+w+P33gRApza5pCfH06tL2wbRkttHX8XVA3oEDLsAFxyVXELX00zMyqsrCLMfI+nm5jiC09i6Zw8llZWEBRuJiYykuLSMWosFZ73f5VgURWzffU3tmy+gyAjk3QoQ4FRSQpSo5BJEr4fS6/sjaHU4bTIUTjtavYWnD39GeZaSHSE+WDUbt6Bg7bSv+HZvDQdTWtF/3go+HPcwP/20jqtufYy3PpzJgO7teadLGgUrVnEme+RYSTmPPPc2Pp+Pjz+by6gHn+eJD+dxe+sQOsSoOFZSDoDT2djV+lQDRyWX0Ewvx6iW0j3xwo2jAAqpgFQiIBEEZCcM4MnDY4nWy/loUyWbix2IoojT3TQM4xd+CcGosHkwV/jvpcTqxu31u3qWWN2Iosgd3xZRYffwybZqvtlbiyiK3PpNIbVOL6sKbLz2c/kFvbe/K2dzy2xhMpnewC+m8vKv/12qzp2OhQesbC52UO2xYZQ1HhTTkxIpKS+nqraxC6ZKLmF8Gw3/+XAm2/KPX8Le/nX4aFNlg/vQNV8UUFBTz95yJzfNLgTg7bUVzNjuz5YxcPphym0eNhfbufu7IgD+vaKMOXv8v5fuU/Kx1Z/5JQGwf/9+Vq5cyU033cSDDz6Ix+OhurqarKwsAKKjo3GdEncCkJSUBEBhYSGpqanodP6XYNu2bTlw4EDDZ4lEQlhYGEFBQVRVVXHdddcxZ84cvv/+e4YNG/aHvqu/AquP2vhsZw2PdQpDVenBc8Dvka3XarHY/NLsNcVWkm3x50yoPWdvLV/tquWWbwr/sJvH2s07qaiqYdmaTcz9YTlfvP9vkuKbodOoUanOLjd96mDZtX0LXnj87nPWCxDgYlFU6+ahBcVNjo/LHszjOcPRHKikf3Y8FquNjTt2EhMRSUxEBG6Phy8WLOSHlavw+XzY5nyGY8l8Qt74AGWrdpfhTgIE+HPz6bZqrC4vglRG5OwlCIJAcIgeU4jAPnc+31dtYnLBBr6KuRfZ3G9JlnvZ1edmYrp3JfyuO/Ha66ic8hELFq2nbesMosKDqV0wF9ucz6n47+u88b9ZTa5ptTv4YPpsMlITkMtlfPT6P4kJD6F76yxefnMSN497hpH3PMX0rxcw9qHn2bnv4HndS9Hx0iYhSBeLPil6EoxytpU4efYnfwqeg1Uuahxe/6LSibncNV8cpcrhYXe5i+0lDsA/7yu2uNl63MEHG6sQBIFZIxMI08i4IcdIlxOpj57rGUmQUkLHWA2Pd7546U3+TpzNLfNqoDMwBDBfmu6cm/6pOvIiVayy2JsYd3KZjByTia179tC7Y2MVP1NKAt2uHsX46T8z+7GBGPQXduXjr85dbUIbPv864e70a/zueI90PCnnvvAmv4EVrpXx4TC/S8DTv1KJWnFbyjmvl5ycTFRUFPfccw9Op5P333+fb7/99rTb9b82GCQncn7FxsaSn5+P3W5Ho9GwYcOGBsNv9+7dAFRUVGC1WgkNDWXAgAFMmTIFo9HIu+++e87+/dXpGKehY5yGG78uZMZN/Wmm9McB6LUarDYbhIejNihp3dvUkFD5TFzX3IhcAje3CP5D7hbb9xzgkeffZtFnE1m/ZRcvP3kfkWEhv7u9AAEuNzFBMu7rH93k+NTPFtO2ZRrJRgUSiYSQ+CwqCnbTIjMDqVTKgK5dsdisrN22naKSEtSL52N49JmGfHEBAgRozJhcI1qFBJ/djmvtCtS9B5KSFoe54AjFoTbuazaAOyJ7I+RKKHxhJblzpzNk2nAEqYyNdcVs6tqHvgvn8u29d2GtKMGzcwPu9ccwPPRPEn6cR/HWdew2t6e5KQVRFHlt0qekJ8ezdZeZf//jPgA0ahWT33waURQJlfRAoZCTm5FKndVGTHQE/3rjQ+4dew39unc46728+t9PSEmI5eE7R190F8bkE7kBwzQyTCdiCf+3uYrxncPZVuJk6tZq3hvUjJnXxqGUCnRPPDkvnzjY7+IZb5A3EVBTyiSEy/zztV9cUWXSi3orfyvOaNyZzeYjwBGTybQSCAKygANms3nbJerbaTla60YmFQiXB9HT2NT9JCc9jZnz5lFrsWDQ6xud+9d1HfhcXkdBtQvboWM4bRZ6dmpzqboe4DcwatQonnnmGcaMGYPVauWGG25oMNxOJSUlhfHjx9OpU6eGYyEhIYwbN46xY8cikUiIj49n/PjxzJ8/n4qKCm6++WYsFgvPPfccUqkUqVRK27ZtqaqqwmgMiGZUHTyEquo497cLpWptCaowGfo8NXqtlroTO3dKrZyIVCP3/uNVJjx+FxFnMLR+ibnTKn7/IPTyxKmsWLuF1/75AEF6LU8+cMvvbitAgD8LlhoL235aS5u+nfDV1eLasp6NLWL5Nn0zac1iyctKZvVRG1Z5GKOHDEYu8w/ZxiA9xiA9ZVVV7Ni6lRhDGGFpmZf5bv66OJwuai0WosIDSc2vVMq2bccniog1lVi//BRVrwGsfXU/3n5WeqhbYtTokUqkFDjL+aqDid69BpIg9T9v+53HsTQPIrjz2+jVGuSL5rHY1II+zZpRmdqCyZZErjJt4ZuFy3F7vDzw9OuIIiyc8S4jBvZs0hdBEOjYOqfh59BgA706t8Fqs7Ny3Va6dWjFklUbsNrsXDekD1Kpf+6zY+9Bjh4r4eUn7+euJ15m2lffc+vIoRw8UoQo+jAG6Rt50uw9cJjkhBiUij+e/sDucHC8ogKVQsH92bB5yzoqqmsYm5SIxxtJbU0VO8370WrURISEYnPYyT9aSEZKMkmxsWgCokyXlPMRVBkO3Ig/FcLjJpPpK7PZ/ObF7daZOVztjzHI1saTrY1vcl6pUNAiM5MVGzcxtGePJqsao4f3453VpcyftRBldQEHjxRx49UDqKqpJTa6qST1lYQoingKDiE6HcjTMvEWFyGoVEjCIq64AFWFQsHrr7/e6Nj111/f8Hn8+PENn6dPn37aNoYOHcrQoUObHG/btm2j+r/g8Xi47rrLmsbxT4Nq4wqYJuOWf7/JyB+n8KLBn8ctNjKStdu20yorC0EQWPruVjLCk3l38hcNq5MXEo/Xi0wqpX+Pjjx0+yi0mgubPiFAgMtJzYGDbKrwkVVfivvwARw/zuOhZ9vgkwoEy/3eJZ1PuC5ZXF7kp4zYiTExrN++g7KWnckVRaRX2Hv+SqDOamXesuVkp6UGjLsrGN2+LeB0IDocCCoVgiBwy8f9+X7ZcgSLl/hg/+9W65LhdgqYE320OlF3kyWfXok5hIam8cSi4wzqPgZXwUHmVx+hp7OOYWlaor6YQf4N95GZlsjkt56lqqaOIP2ZhU5Ox7B+3RjWrxtut4ctO/dx4PBRdpkPMf6eMTgcTh5/8V2eeuAWgvRaJr40noef+w9D+3Zly859zJi9ELfHw8QXx5OaFMecBct468OZtGuRxdsTHv1d35nH42Hjrl0cLirC7nASFRaGq74eh8tFq6wsWmRksGHHTjbt3IVBryczJRmvz8fBo0dRyGXkpKdz+FgR67fvIDYqkpT4eKSChBCjAb1We8XNS68kzse4uwHoYjabPSaTSQ6sAS6bcfdLUshPSpYRLNcxLLRtkzJ5GRkcLCjAfPgIGclJTc4/2DGcm7JuZe0xJysWL8bt8fDgs28xYlBPbhje/4w7RH9GRLcb0W5F0BuoeGAsuOoRfV5EpwNBrUF0OQl7bxq+uhqQSJAnJOPavhlFVg6CPJDM8hduu+02IiIi6HiKO+/fFomU4AwToihSFlxDRKxfTCQuOprVW7ZSeLyE+GbR9H+iDT3q8xhwwzhq6qwYg36bu/N7k7+kd5c2rFy3lT7d2nHgUCGd2+Wxa18+YSFGXp/0KdcM7sWgXp0vxl0GCHBZiQpSMPzwIuo+WISg9T87mRY5yopKDJknV7prnV7u+u4YM6+JaxA8ADA47Yzu2pmf9uzjWEkJ8c0CbplWu52DBUfxiT7Kq6oxJSaSGOt3DxNFEYvNhk6jaTLO17vdlFVW4vF6CTYY8Hq9rNu2naLSUjrk5ZFrSr8ctxPgAiKRyvA6nQgqFfV2N0U7KgjS6/jP/m/5MO4h7BYHsxb8iFFU8p/aBUjLlaSqongy7mp0Uv/z+ELPSBwekaK6BNokBLNkzVpuHT6UXbc/xK78w9g/20fLXn0wndiZszkcqJRKpL9hXimXy/jXI3dQZ7ExY85CFHIZb74/i+H9u9OjU2sAwkODmf7eBCQSCdcP7cP1Q/uwaMV6lq7eSHRkGJ9+PZ/PJ71EeWU1+UeP8tHMuWSmxZOWFE9aUiIRIaf3tBFFkUOFRVTW1LDDbCahWTP6dupEqNF42rnxkJ498Hi9DV4Fp5KelIjFZuNQYRG79h9AIhGoqqnF7fEQExlBWmIiNrudsqoq1EoVRr2e5mmpAcPvD3I+xp1gNps9AGaz2W0ymS5rrrufC2yMAw47yxoetlORSiT0bN+e75ctJyI0hBBDY5U7iURCaLABX6nA3WOvI0inYOJLj/Pcmx+yesN2Jow/s4vZnwV3/n5kiSk4li7EW1WB/obbCJnwFpKwCESHA9FhQxp6MjDVvW8XICKLTcD6+VTEulqCHnwSRUbzy3cTl4ERI06f62nKlCmXuCd/biyGCGoSMpDV1TN0SkvUbfwLAYIg0C4vlzVbtzZMlNy7Bcb2HYLN7vhNxt2m7Xv5btEKbh05hMiIUB54+g1EUaRr+xbMW7yKdVt2cfeYEeeMPwgQ4EqlUNCzrNMYeu+Zj7RZHJsGtqN5uMjt70zG0MEKCX5vEoNKyvRTDDuA+p1bUfq8pCaYOFBQ8Lc37urdbmb/uIjYqEiUCgUxkRGs3rqVFRs3EmwIwu3xUmupw+PxYgzSo1FrqHfXU+92U2e1ERZsRC6TUV1bh9vtpnV2c/p27nTGiWuAKwdREBCUSkSXA0Gpxu3yUnXUgpAtI7xch1IiZ+XOjZiSEhEECf8x5bGsZhffVW7ghYTRRCr84RoquQSVHMbkBVNYq6Wmfgd78vPZVlzCUEcVwpzPOLxtA/vG3o1KqWTL7j2oVSo6tWxBcpxfo8DpclFTZyEyLPSsRkyQXst9N1+LKIpIpRJuHDGw0flTja3eXdpQUpFIdV0trz3zAKHBQbi99azYuIlgQxAzZi1i1PA+7M3PJzk+jjbZ2ei1/t3FopISft68BZvDgV6rJcRg4PqBAwjSnX1MFwThnM+HXqslL8NEXoap4ZjT5WLN5u3MnLOQbbsOEhsdwTVDe1BrsQTSIVwAzueN9bPJZPoaWAV0AVZf3C6dnV8CL2s8NgyyM295h4eE0KFFCxauWMk1/fuhUjZVxBuWEQT4g0ND1Ao+fO2fzF6wFI1GzbGScgx6LTqtpkm9S43PasH58zIcyxdBfT3S2Hhcm9cR9u4U1P2HNjwE0nD/REDQaEDTuN+qzj0aPoe8MhHnz8uoefmfqLr0QjPsOqQRUQhnWFn6RbDk1w+b6K4HEQSFAm9tDYJMhiCXI7o9+Kx1CEoVghZUgAAAIABJREFUUmMgseCVis5SgfytZ1DNXY7knwoUwslXRXJsLDvN+/nx558JNhjo1rwdGb36oTacv+rkklUbeH3Sp7zy1APodVpGDOxJ367tKK+qQa/T8uo/H7gYtxUgwJ8Ko8RD4s7liIIP954ddHn0SXL/9Q8AhLJSSDgpPmWt9/LFzlrua+cXtxK9HjR9BwOQ6nCwaedO7A4HJRUVJMXG/mUmR/VuN4XHS9CoVUSFhSEIAtV1dew076eotBSZVIJSoSAiNBRRFImOCG8kqJaRnEyd1UppRSVyuYzk2Fi8Ph9VtbXY7HaUCgUymYwwoxGp9KSiQ2CC+ddCEEU8RUcRT+zcaYNVtLk+ncX5mwj1aLDZ7RwtLmbMVcMaYtTileEM2/0KJe4aktVNw3biDApG9mjPotWryUhKImpgf3zDr0cYdytly37gaOvOjBo8iDqrjR9/XoUxKIgQg4Gte/eyw7yf3PR0OrZsce6+CwITxp85K9mBIwVs2rULZ70LrVqDKIpo1CpWbd5EeEgIvTq0JzEmhv7dOvL2R58x5tqByCRyHpnwFkkJ0YSHGVAplfRs346QE8+B+jRz5j+CKIpYrHaC9FryC4qIjgijts5OvcvL4/fcRJ3FRve2bbA5nFeU99yflXMad2azebzJZBoMZALTzGbz/IvfrTMTrfd3WRAEYk4o+FkrHehCm8biZKYkU2Op47ufljG0Z0/UZ5A8H51jxOMTqXT60KS3R6dRM/eH5UyfvZBP3n6OqIjQ09a7FIiiSPUL/0ASEor26lFIdHo8BYfQjb4VaVjEuRs4DYIgoO7aC2VeayyffkjVU+OQGEMIe/tj3Af2IY2OQXS7cR89jCCTI9b7Uw4IMjmiux5ZYgo+h1/qViKXQ70Ln9XiN/gEAYkhGInm8hvFAX4/ssQUgp94gupCKyNtnRpNcgRBoGub1mzYsYPC4yWEJOs5erCUf0+YzJT//Aufz0ed1YYxSH/G9lvnZjDlP882inPV67Todb8tRiFAgCsZnVpOksaHIrkF+pvvwqwD+eDRyFdvRH/8WOOyCinxBn8eR1EUqXntedTde6Pq3BOtWk2zyEg+n78AiUTgeHkFnVu1vBy3dME4VFjI3vxDFJeVERUeRp3Fil6nJTw4hH2HDpGZmkKHvFwEQUAikXC4sIg6m5XubRuHasikUkIMhkYePBKJhMjQUAg989geMOz+WgjGEKTRzXDn70dQa9iz4yhV++oI62XEWK9i98F8UhMTGomPJKjCmZ/9NLHKM/+dxERGcMvVw08usiuUhL39MZInx5HedwBKrRa9VkuHFi34ZtFienbowP7DRxjSoztL166juq6OtIR4EmNikMtPPt8bd+5CqZCT0CwGm8O/CKFUKLA7nIQGG8k/epSq2lq0ag2bd++iV4cOGPX6JkKCv6ZltolP35sAQK3FSvsWR5n7w3LqLHbe+tdDxDdrxop1W3j29Q94618Po1Ermbd4FSXlVURFhDJiYE/Sk09qXXg8HmS/2rWzWG3odVpEUWTe4lX07daen1ZvJDGuGUq5nNH3P0NUeCiu+npef+ZBrh7Qg6sH9GjUR10grv6CcF6+BicMustq1P3CDwctPAy8nXIrAI46F4vf2sJVL3VCImn6Mu6QlwfA3KVLGNSt22n/8LUK/ypBQU09vhOq+tkdezDMYueND6bz3CN3/ubA2D+K6HJR8+YEjE9MIOTl9xB+9QApmuddkGtIggwYHnjCfz23X6jGsWop6m59EGQypBFR4HEjKJSIXh/4vNS5fdQVl2J3uJBJJaTog6jXG/F6vcjlMuQyWWDV5S/AXHUWt5RWUOkN5qODS3k78/ZG50ONRgZ268ZXC3/geHE5W6bkc6ykjEU/r8FT7+J4VRU3XTUMuUzWZJK098BhpFJpo0EiQIC/I9sIIyS7FVdbtiOLiWdy/lQGdW1FR58d7ynGnUIqMDhdz+ZiO1n56/AeO4qy3clY1H5dOnPk2DEiQ0P5cv4Cjhw7RmxUJG2aN0d7hSy2iaJIcVk5pRUV7Ny/n44tW9C3cycUcjkej4fDRceoqq1lUPfuRIY1nnAn/M1dUgOcnY0RufgsFtTd+6Du3ofp5gVIUwTuDR3Abvletu3bx7X9+jWpdzbD7hdOHeOk4ZHo7xyH9dMPUb75IQBZKSkE6/Ucem0CaakmYiKHM2rwIA4XFWE+UsCKjZuIi4oiKS6WyuoaikpLCQsOZuuevaiUSjxeLw6nE71WS01dHdER4USHR1B4/DgDunYlOvy35Ycz6HXcdePVjBkx8ITBaGDe4lW8PHEq770wnta5GdRZ7cTHRtOuRXPKKquRSiUcPlrMB9NnM7RvVyZO/YrP/+8lPpk1n29/XEFpRRWd2uTy4uP3sGbTDl6eOI2MlASefeR2UhJiWfrlJKpqakmIjQ4snlxkrjhH8sHpQdR5HMwsW8G9zQZwYFUx2QMTT2vYgf+h65CXh06tYc7ixXRv27bB7/lUEowKEowKvD6RGTtqeGL4QN6ZOpvjtXbqrFZKyqpolWO6aMaLz27HsWQBiD60V12PZth1IBEQpBf31+TxejlYUIzN4cTWvBMpuhCorqDW5cHnFXFYahFFkbhmkQgWK0aDnmZR4UgEAUEQcLnqqbXYcHs8eDweUhJikZ8q6xbgiuIaDuN7dxHadyZyIKPkjOXioqPYfnAfqbdGMXRzR44UFRFcUogqNILp335HnlpOXloa8vSTMu1TvviOLu1aBIy7AH97WkurGbxhJYqRNwP+cAPLXgel9hAiguxNyte5fMzaWc0Dsz6m4van0AkyymvrKapzo5JJaHlibGueloZapcLmsPPFgoWYkhJpn5eHXCaj3u0+7aLL5cbhdLJ+xw6OlZQSERpK386daBZx0jtFJpORlphwlhYCBDgzbfYvx5M/Cl+QAdFdz1qHmcOeUixFTlrkxKIshxCj4dwNnSeqDl1Rtu2Iz2bFc+QQiua5hNnqEMqPIakuxdmqFar2XTAlJWFKSsLpcnGosIgDRwqQSiUM7NYVnUbTyD3Y5/MhkUhwezwXLA5Uo1ahUfv1K8JCjLz29DjatsgCwBik44bh/RuVt9od5GWl8dHMuVw3pDcSiYSu7VrQpV0ecdGR7MsvQKNW8eo/H8BqdzTaiQvSay/5RsnflXP+dZhMpkiz2Vx6KTpzPhRb3Bx1lbO8Zjf3NhtASsdoBInA4re30Oa6NIJjm+7MCYJAjimdsJBglq1bj/nwEbq2aY3uDKuZUonAmycSy7YfcBVbq32kicd58fPl1L/1Eb07tebWkUMJDb4wLwKf3Yb926+wzZuNMrcVmhGjAFDmXFy3GofTRVVNHWEhBl5+byoSiYQgvZZsUwpdW2fg9XrxeL3oNOoGCfrTJX83GvQYDf7v/c8ep7By5UqOHz/OyJEjL3dX/tQUy40o8tpgnnuApPhwaJpSEoD2ubksXbuOtVu3YfSFYHSU0SF/J0ekORzyhrBfaqR5RRmyVBO+inIsKi0bt+/luUfvvLQ3dBrqzbuxf/c18jQTmgHD8TlsCDI5SKV4y0qQJ/rjnUSvl/ptm/BWVeA5ko8kONS/u61UUL9nJ6qO3fDVVoNUhufYUWQJyUhUJwc0b3UlrnU/oxl4Fd6yEgSNlvqdW5GnpPt3x88Tn9WCoFIjyGT+z1rdn/pZC3BuStBg/8cbROb5J1PVHhuJsRGEJuWgi246CZKv+oGn9u2gLKU5n9SEMabMxeJ8C10TtFQ7vA3lOrQ46d2RZzKxbMNGtuzeQ0p8HPOWLad5airtcnOatH+5qKypYd6y5cRHR3HtgP4XJC9XgACnIrqc+KwSPC4Hho0qknThbNAdoF9CC1omNVVW/6MIUhmuXdvwVZSjaJ6L9fNpaHv1R9mhKzUvP40ipyUSjf85VymVZKWmkJWa0riNX73jf9lYuFgCP7/OvXcmdBo1N1w9gBuuHtBwLDXp5IZJi+bpjcoGuDycz1/IbJPJVA5MBhaYzWbfRe7TWal1eilyVRKn9Ock8Xl8qI1K2o5MxxB19hWB6PBwrh80kE07d/HdTz8xevDgc06OrjohuuL0BNG8TzC3p3mZuWIP28q96I7sZs6Cn+jQOof2LZsTHRH2myZbPocd+/dzsH/7JYpW7Ql98wNkzWLPu/7vwefzsWLdFhav3MDazTu5dnAv7r/lugY/7F/Yu3cvYSG/PZn3n32y2a1bt8vdhSuCQkUoqo5ZRG7bSVBayhnLSSQSenfsQJwuGr1Uj2HvQnx5rWl9+wO0tNv4ctlyatKyCCs4RNXTD1EWGc9wU8xZ89XVm3cji4lHojtz7MAfxbF0IZZPP0I7fCSujWuxTP8fgkxGyKv/BYkE+7yvMYz7B9ZZM3D88B1CUBCymHhkcYn4amsQXU6QSnEf2IuqYzdq3nyB+j07kMXE+w09QYJEoyXo/vHI4pMQXf4YVcuUSTjXr0KenoWnsABVhy6IXi9Bd4xDdDjwlBajzGmJr7bG3755D67N63BtWY+vsgLtNTegG3ULFQ/dRti7U3Dt3o6yTYeLvrsf4OJQJ1Fhizi5G3VPdH8SFRHoVCqqX3kG42PPIiiU+Ow2nKuWIg2LxLJhNXGv/h+vREehkklo3cz/LDk9PhbnW+ib0vi50Wo0dG/bhlk//MCuAwdom5PNpl27qXe7aZ+XC1y8yeK5sNhsrN26jWNlZXRp1SqwMxfgomGLiEN0OVF16o7X66J3ipwPjv9IubOOTE3MRbuuqn0XwL/wLTdlou7aG2lEFKFvfoREo/3TL4gHuDI5H0GVLiaTKRO4DXjGZDItBSabzeZDF713pyEzXMWx+iqaKf1KjBu/3E/u0CTCEg1YKx2smbqHPo+2OqObpkwqpX1eLgXFxQ25us4HlUzCy338q+zD+4Xg9okkR8SRH9sbcedaJn2+gEfuu5UOphgW/LSanIxUTCkJqJRNVyCdPy9DEhyCxBiM58hBQl75L7K4Mw9qPp+Pleu2smPvQW4bNZSjx0pZuGwNEaHBREeG0To3o0G84nQvCVEU2W0+hNVup33LbFas20qb3EzG3zOGEGPQed3/pWbOnDksW7YMp9NJeXk5Y8eOZenSpRw4cIAnnniCkpISFi1ahMfjQa/XM3HiRL7//ntmz56Nz+fjwQcfpKioiJkzZ2IwGJDL5QwaNAiAQ4cOMWrUKB577DGioqIoLCwkJyeHCRMmnKNXfx9am5dhGdScqv2b+Nf4p85aViKRkJGXhMvmZs1buwjumUkLQUCq1ZGZksKeg/n07tiBVSPupaOthFErfqDygZtR9eqPukc/pKHhuLZuRJHdAsfyRdRNegtl244YHn76ogjziF4PipyWhLzyHrJmcWivHoXo8YDHg6Dyu6cYxp1QLFSpMD75AvK0jNO2pR/rVzALefHthkHaffgggkKBWF+PJMiARB+Edrh/N9745At4K8qQhkXgranG8eN3SIzBCCoV3uPH8BzOR5nTkroP3sa1cQ2yVBPKVu0wPv48sqTUBkXbiMmzAFC2ah8w7K5gUisPEL9wGtarbkOll9PTmM3aD/eQ2DaSqP5DAfDV1SLo9HiOHkHVsTuRM78/bVvVDi/5VfX0TBKRnTL+6bVabhg6FJfLhUGvJyUujgUrV/HpN3PxiSIDu3UlNioKt8eDTCo962TT6XKxdts2YqOiSEs4tzHm8/nIP3qUGosVi81GrimdsOBgrHY73y79ibSEBHJNpkCC8AAXFW1ZIdZZ06n9z0voHnuWXEkWQXI1Uq0EleTi7xQLgoDumhsbfpZGROJcuxL3gb3ox94dMPICXFDOd1ZQDBwCWuN30HrXZDJtNZvN/7poPTsDe8udvBHeGY/od0HpNe6kjKw2REXra9OQSATs1U40wafPgycIAq2zm7No9WrioqPJTEkmJjLyvJNM5kb5V0rtDgdPdAoiN34MO8o9rC9y0MrtZlGpim/XzqP44D5uuvVm7h7aic+++ZFucaHEtmqFEGRA0GiRxcRjfPz5s15r6y4zr0ychkqpoFuHVsjlMhRyGaHBBiqqati5L5+YqHBsdiej73ua6IgwoiPDGNSrE/17dOTzuYv4ev5SEEXGXuffqXz+T+ASdz7YbDamTJnC/PnzmTZtGl999RXr169n2rRpZGdnM23aNCQSCbfffjs7d+4EICgoiPfff5+qqiqef/555s6di0KhYOzYsU3aP3LkCJMnT0atVtOnTx/Ky8sJ/41ByX9VrC4PQdEJVDs78vKyr5kwYPQ56/i8Po5Fd2ZNSRG/PJWmpCSmz/2Oz2YvovBYGcM+fRv52Ntx79mJY9kP1E58neCn/419wTcoWrRBkEgIfftjrDP+R8W9NxL69sdIQy7cpM+5YTWutSsxPNTYYBVkMjjN7oV26LXn3fYvA7M8KfWs5X5RuZUag9GdiLUCkKdnNsQmGv9xfgsNwgl1tQBXJuvrDfQliONTdhPZMZj7tZNZfe/LAPjsOkSHnYqH70CQKwibNL2RsNapROvl3NM2lPVFdhKNCiJ1jcuqFApUJ9wdtRoN/Tp3orKmBoVczo8/rya+WTSFx0vISE6iU0t/SEBFdTVSiRSD3u8CXFFdzbpt21GplKzespVte/fRt1NHDHo9oihidzrRaTR4PB6Ky8rRqFXs2n+A0spKosLDCDYE8e2SpahVKpz1LlpmZdEyM7PJvQQIcDHwFhYAsKpoPUVykel9H0Ibcvp54qVAkdcaeaoJn91G+R0jCbr3UdRde122/gT463A+MXdf4TfoZgBjzGZz8Ynjm4BLbtw108spcJYTqTAiiiKbZx2g9bVpCBK/uEdoYhBOaz0/vr6JYS92Qio7vcGWGh9PXFQU+48cYcP2HdRarSQ0a0ZSbAwxkZEN6kQFxcWEGAwoFQq27N6DXqslIjQEiSDhx59/Rq1SsX1rHWqVinYJCUSG5fDQVR1ICe6GxVnPW2sqcdvsbFy3i08/2cM1g7pz862jkZ8lpkAURZav3Uy2KYWwECOP3X0j7Vo2b5g8pibFNfJx/oUfZr7H8bJKjpdWEBbijwe0Oxw8/9idZJtS/tCq0BfzF1BVW/u7659KiMHAqMGDzlom88Sgr9frSUnx999gMOB2u5HL5Tz66KNoNBpKSkrweDwAJJ3wmz969CgpKSmo1X5DvGXLpvGL8fHx6E4k6AwPD8flcl2w+7vSUUmhTiUhiyXMjh16XnUUXht9hoUz5u0f+McJiWSNSoVCpiAnK5mnx92O4oQxomiei6J5bkPd4Kf9E1p1b3+S1uBnXsH6xTQq7ruJkNcmIU/4/fEQosuF9esZKLLzULZogyL7ypaID/DXIUViI1zmIf3uXGb9bxXGvlr2rywisU0k9d9+Rf2ubQhKFfpb7jmrYfdr9le6CFVLmxh3p2L4lWz6Nf36UVxWSmp8PKu3bCVIp8NitWE+fBiZTIbd4fAbbV7vibxZHRCAXQcOMPvHRQgSgfDgEIrLytCo1dgdDsJCQqivrycsOJir+vRuMCxz0tKos9mQy2QNCZQDBLgUyFIz8BzcR5HcRXzfyMtq2AH+eLsTMXchL/6H6ufHI8hkKNt1CnhkBPhDnM9fz8fAWrPZbDWZTL/2Yexykfp0VrRyCROLF3BjRDc66UzIVVKEU1xQVDoFV/27MxKJwPJJ28kZkkRofFP3Q6VCQU56Ojnp6Vjtdo4UHWNv/iF+WrcevVaLTCrFJ/pw1bux2u1kJCdTU1fH/iNHqLNa6damDWmJCfh8PmrqLPy0bh2iKDYEsxvVal5PqaV2/D8IyxrN/QN7smT9DsZ9d5QnWiv5cNEOIoN1WI7uo2PP3rRJjuCLuT+wcfse3B4vzz96J+nJ8cQ1a5o887TfjUZNamIsqYkn4/ZuH33VH/i2T3IuQ+xicCZj1O12s2TJEmbNmoXD4WDEiBENidZ/CTiOj4/n0KFDOJ1OFAoFO3bsIDk5+bzaDwASYLuziM3X3st/487PuPOWl2DdcIgh4T3Zv6OQbQX7Ka+qpm+3duw7kk9ByTGSE84/tkE36hbU/Ych0emxzfsaVfsuuLZtwjrzf0j0BlQ9+yHRGxAUStQ9+jap7ykuxHPkEJYp/4c8NQNZvyEICiVCQKshwJ8ELW7UEh+H1h0n86YEDBU7qD1uRxRB2aYjtm8+RzPkGlQdup53mzflBePxiUxYVsrjXcLRyM/tkWIM0je49gfpdCxbtx6tRs3IQYNQq5TUu91U19URajQi+1Wi71yTiZz0dKpqa9mwYyfDevUCgYYF0dMhk8ka5ZwLEOCSoNYQ+spEvvrHOg6WHUH3WjXiq38eV0h5SjrGp16i9t1XsH75id/V32HHvmAu+lvuuSjXdG5Yjbf0OJoBwxDkJ59X0V2P8+dlqDr1wFNciDSyGfW7tuItL0U7eATuI/kICuVF14gI8Ps5H+OuEzAAeAx4z2QybTabza+azWbnxe3a6TlQ5aLG48Eo0yKRSsgbdnqxh19i7vKuSkEfrqZ4dyVHNpXS6eas05bXaTRkp6eRnZ6Gz+ejvLqa6tpa0hISkEql1LvdDbsOTa8lIcRoYHCPHsxdsgSlUkGLjAxscz7DPvcrgu55mGc69wRgQLdWlFk9CI4aDHotFZVVpMVEMbtQQfN4H+XSEAydr+Wda7IC+eLOgEwmQ61WM2LECBQKBeHh4ZSVlTUqExISwp133skNN9yA0WjE5XIhk8kadvgCnB1HvZdan5P4/Gq8VZVgOHcOKXlaJvFx6dxd7+X7N9ex0rydnD5JOBwuBnXrxmfff0+rrMyzJlk9FWlwCKIo+oVKLHWI1joMj/0LiUpNzdv/RmIwosjMBvpiX7IAVbvOuLZvxj73C7ylx5FGNSPovvEoW7X7A99GgAAXhyXuCEZJNHjdPgpXltMuJ5W2I0+ozaVlEPb+TAT1b9/dkkkEeibpkAq/XcE41Gjk2gGN5c8Vcrk/6fdpEAThRN7L8zdAAwS41PhcLioeuYPMvLGY26npGprzpzHsfkGRlUvYB59hnfY+Vf98kNB3pyA74ebvKS7EW1oCXg/KNh3xOR2IDgfS4JDfdS3nulXUffQusvgk7PPnEHTvYyjzWmOb9zWavkNw/ryM2ndeQWIMxldbjTw1A7kpC5/djufIIZBKkIZHYJ3xPzzFRcjTM1FkZCMJj0QWFcg5ebk5H+NumNlsbg1gNpuvM5lMq4FXL263zkxmuIr5nhqCZTqO7argwMpj9LjvzEm9g2P8bncRaUa0ISqsFQ4s5fb/Z+++w+SqygeOf+/caTvbe02yqSe9L2kQOiE0QQURRBBFUVABxYpoLAgK/ARRkY4CKr0ohKqUBAIJ6ZCTnmzK9l6m398fs1kSkt2dJDszm8n7eZ59dubOnTvv3Z2zO+eec96X4jE9F6a02WwU5ubu88+sp47d3lLcLs4+6USeffU1rPpahrz3Drm33YNZ8MnIm9M0KMt0QGY+N1x0Qvf2PSWif3rxydS0B7EwWF3tZUJhYqcNJMpnP/vZ7ttz587tznI5ZswYHnjggT6fHwwGqamp4emnnwbg4osvpri4mIqKiu59Hn/88QPeFuCxQ1O4k6GDa0mJIptxsGoXzX+4iZzf/hFnip2Sc7I4adF0Tj2jguyydMJhiwmjRvH+6tWcNHNm1OtbIfLhcU/nzDH8kzTLeXc+CHvV6wrX14Fl4Zo0DVtaOg41tjvNtBAD0VnmTorso0k7cyjpH3g4PmU8C29Zyuk/nA6AmXvoa4DnlqeysqqTf65u5renRl9yQ4hkZAuHcM8+nhGnz+Dy3X4yY5Csqz8YhkHaZd/EPnwUhs1GyvGnEO7ooOnXP8EKBbGCQQyHA6ujHfvQEeQsuI3WR+7DM/9crICf4JaN2NLS8a9bQ/tTj+GefTzpX70aw51CcOsm7MNG0vTbn+Ffs5zsH/8a54Qp+FYuxczJxQqHCVXtApeL7J/djBUKgc2G1d62T/bqPTNlQk2N4HDinnsy/uUf0LpsCaGd27FlZpN72z0Eq3ZhdbbjHN1DLSURM9F07sJKKafW2q+UchCZsZUwTd4Q3yyeR54jHftok+xB0Y0C2J0mmcWp1Gxson5ba6+du8PhMeA0e4hXG5qxLr2aaQXRTancw2kalGU4qGz28+TaZsYXuAbc1aUjgd1up7Ozk/POOw+Hw8HEiROZPn16osM6YrSoqcwvm8a6x8uwPvtJIeGwt5NwU+N+V+Y6X3kBx/BR3e/VORWToAJWPr+JluoO3v+HZv6NFby46E2eeGkh558+D3Ov6V2H4tPJRNK+8EnSHNeUik/vLsSAozOHYlbM4flfvIvnG6nstOo484Jp/Xb8cQVurpvtYGujn7e3t/OliVny/0QclYKzTsIYPYXHv7eY5iIfMyeNZsbFB86CnGiGYZAy95Tu+zaPh7w//x2IrCEP1ewGwCyLZKu1DxmKkZKCf8UHeBe/SbitFTOvgJzf3EHHS8/hW/oezvGTaXv0frJ+djNpX/gyxmVXdk+rdE365LNRxhXf+SSOrv/RRg9licysbNK/9DUAUo47ORKf30ewchs4HITrawi3t2GNHEPznZExIf/KZTjHTcQ1dQZWMEjKKfNlfWEMRPMTvRtYo5RaDYwGfhfNgZVSM4BbtNYnKKVGAA8BFrAGuKprt6eBYuAGrfWrSqlhwHe11t/t6bjBsMVZudMxDRsNO1pxZx7cApqCEVkUjMgiHAxj6yHZyuGwOtpJDfg497R5PP/Gf/EF/MyaPPmg/6EOynSy4KRCvIEw33t5F386q5RmbwibAekuk45AGI/DRtiysH3q2JXNfsIWDMlysr7ex+AMB4YB9Z0hStL7HoH0BsJYVuRn3eYPk+U2afOHMA2DFIeNJm+ITJeNUGSZ235ptweK6667juuuuy7RYRyRWrOLGZcziryhy/C/uRDHvLMAaLrppwR3VnYnS+vJAAAgAElEQVSn4gdoffhuOl5+gdxb/rzfcTIKPRSpHM66cQYpGS4umH86L775FivWrWPauHFxOx8hBqJ204Vz0GDmfr2Qu9e+TI3VzPkVc/rt+HabQX6qHV8wgMp10ewLs7nBz9QSKS4sji5Bw6Ttl9dy/P/9g8vb7+G7k85NdEiHxHC5sA8q32fbno5Vysnzu5OS7ZF59fXdt7NvvAXYdwZMv8fndHUf3zV1BhApP+SaNI3g9q3k3HQHvqVL8L77FmZ+AdhM2p9/ArOoBNe0GfjXrsI1cWrM4jta9Nm70VrfD8wh0qk7Xmv9cF/PUUr9ALgP2DOn8HYiHbjjAAP4DDAZ2EpkPd/VXfvdANzU27FdzgBnrvkNANs+rKaxsrWvcPZTta6B1+5YftDP60u4pRnD4ST13AtJ9Xg479RTqK6r59VFiwmGQod0TLfDxhXTInOqn9ctvLShleq2IFc8t4OwZfGDV6p4c2sb3kCYeX/bAsDHtT5WVUeWRD68vJG6zhC7W4Pcuqg2sm1FI4u3twPww1d2EwhZrK3x8tiqJgB+8GoVgXCk5xbs6sFZVuQLwBcMEwxDRyBMQ0dkDVukQ2gd0jmKgaf0vf/wnaW3UettJlxb1b3dVTGbcE0VVjgMRKZldLz0HJnf+v4BazUOnVGMK81BSoaLlc9vorWmg2OnTWXlunW0tLXF7XyEGIim1n2Ed+HzhAJhMlQq+bmZfPjkhn5/nbJMB8eUeajvCLK4sr3fjy/EQOde/CoAXqfFxbfMouEQPjuKQ2OYdlJOOp30y66M1JY95/Nk3/BbMr5xbWTZxfSZ2AcPJdzUSKimqu8Dij712blTSk0GFgDfAH6nlOp7wRNsAj671/1pwJtdt18CTgHagNSur3al1Bxgg9a6urcDV3f4yDQ9NGxvZcScUkrHH3wNrIJR2ZzwzYl973iQWv9+L+1P/6P7vtsVWYMH8OBTT/O/9z84pE7e5OLIVdZLJmVzwfgsCtPs3H9uGTbD4NZ5xcwdkorLbvDUFwYDcNqIdM5WkeygvzmliLIMB+XZTm4/PTKVbu6QVEbmugA4c1QGpg1yPSZj8iPbbptXjNM0sNsM8lIjg7vpLhOPM/J2KUxz4DANMlwmBWkOLMuiuj1I2IIOf5gm76F1ZMXAsWnG2VQBmzaWEdy0Hu8Hi/G+9zaeU8/CLCqh8/WXCGzegH/ZezgnTcN97Il9HjOzOA3TYSMjLY0pY8bwxntLCHd1EoU4Gr2eNhrvvNN45aEP8fr9uIMOjv1a7NanDM9xcfWMPJbt6uSlDfLhVhw9fJNmArD1jSpqxrXgcB3esgDRf+wlg7AXlWDm5uM5Jf6Z2ZNRNPMSHwI+BP6111evtNZPAYG9Nhla6z3DOq1AptZ6PbAD+APwS+Aa4F9Kqb8opW5SSh04tm0fkWH3ULWugcYdh/bPaU8mzSWPruu30abAlo14F/+P1M9dtM92u2ly6pzZXHT2Wfh8Pp54aSFvL12GPxA48IGi5N5rSqlhRGr8pUX5x2potpP8rk7b3PJUbIZBUZqDKV2dSId5cNMsDcNgSJYT02Zgs0XWDYojW0rVh7SYzRx/VWQ0rvPFZ+h46Tk6Fj6L58zz8C76Hw03XINj5GjSzr8kqmOWVxRi2AzqtjYzafRowGLlunUxPAshBjZn6CO++dqPeeGC5Xw961SO3azi8rol6XYyXDY6AmEWbZeRPJH8an3VvDg1n/mXVfCTn36B9IKBmVBFiP4QzZq7Kq31fYf5Ontfnk8HmgC01gsAlFIXAc8BVwD3AycAJwOvfvpARavXkb/xS+SOyKBwVPYhB+RIsZNdloYVtgj4gjg9fa9F60mwchuNv7iejG9cgy19/3p6hmGQmpLCacfOYWd1NRu2befxlxZSVlSEy+lgytix3QVej3TuGKxjFPFXoDeQFZpH5cY0pvx8/2W2qedeSMsDf8IKBHCMiP4Daf22Fhoq28grz+TkWbN4YuHLDCouJi/70NuyEEeqgqrtDN+dxpuZbSwt3sSwcw8uAdehKk53UJzu4IOdHexujUytP9iSCUIcSTJrG3jq7FOYtHQjbSN8HJ8la75F8ormk/hWpdSPlFLzlFKnKaVOO4TXWa6UOqHr9nzg7T0PKKXcwOeARwEPECKSeCWtp4PNK5iMGUVh1p6EmhoJrF1JWWAl2xdt4d2/fbzfPlbAT3D3Tnyrl9P5xkLan/kn3iXvANBy3x8J7tpBYMtGai45h7prLif9y1/fJ7vRgRiGQVlRESfOOIa506eTl52Fzx/gX/95kc2VlYd8PiI6V199dd87CQAMC3409PN4PK4e90k5+Qza/vHgQR23bGI+E88cSnujl/TUVI6dOpWX31lEdV2dTNEURx3LCnNB/lwubzuRJ2oXs81XG9fXryj18Plxmbxb2c7v3qkDYGO9L64xCBEPTtPBl0uPZ2uohpcbVyQ6HCFiKpqROxegur4g0vF65SBf53vAvUopJ/Ax8ORej10D3Km1tpRSDwJ/BVqAA6YyCrgcvDVoExcXzo3qhS3LIrR7J/aSMjoWPk/bvx7G6uzAPrgcTDvpvMycBbfj37CO4OYNeOadTeOCH+BbsRQzJw9bXj5mXgG2nFzM4lIA3DOPw5aRieEqIPeOB7ClZ2A4Dm7kbXBJcfftXTVD+N+S99mwdRuzp04hPVVqc8XCXXfdlegQjhiGZXFD47+4/YSv9biPY8hQsm/47UEfOxy2ePW2Dznt+mmMGlpOY0sLL7+ziJKCAtTQcgYVF/d1CCGSggOT1f5KRp04h3/vWE6qmZi6phWlHoZkOfEGwty2uI5b5xVjWVbUU/3702/fqmH24FQqSlO4a0k9183K469LG7h0SjZO0yAYtjCAB5c38q1jcnlzaxtuu40ZZR6eX9fC3CGp7GwNUN0W5KRhaayt8TIq10VtR5A11V5OG5G+z3KMkBXJKvrm1jaGZDopTLPz7Rd3cffZpVQ2BwhZFiNyer7IJY4MXitI+xA/KTYnad6js36wOHr02bnTWn9FKTUKGA6sBnZFc2Ct9VZgZtft9cDxPex38163VwAzejuuwxeg885qqi9tjGpaptXZSdPvfk7ubffgnDydnEnTMItKMAwDy++j6bc/w2pt5tVHaph9Yi4eIPP7N2KkeDB6KLTsHD+5+7aZc/AJXT6tpKCAC+afzocffcQTCxcyYZRi8pjROOxHb+2Pp59+mtdff522tjYaGxu56qqrsCyLRx99tHufO+64A4BrrrkGy7IIBAIsWLCA8vJyvvvd79LW1obX6+X6669nxowZzJkzhxdeeIGLL76YF198EcMwWLBgAbNnz2bw4MH8+te/BiArK4ubbrqJ9PToaigmI8OyqFzXwBsvr+Ckb0/u+wkHwWYzOGfBTGymDX9HgBmTJjJ5zGjeW7mSN95bwqwpkxlVXt6vrynEQDTMlc/ito3cqO9kZEoxabbEfOi024zuMjl/ObsUy7K44vld/Oz4AkrSHQe9Djtaz69rocUX4vNjM/nRa1X89pQivlmRi9thELZganEKpg0K0+x4HAYPr2jEZhicNyaDQZmReAtS7fiCkc7azpYAKQ4Dl93A7Fpb/681zVwxLRt/yMLflf35B69U8cUJWThNg9vfreOBc8sIhqHNH2aw3eBHx+ZjtxlsbvQTDFsMz3ayoyXAoIMsvSQGDqfp4L3H1zGipJjUWdJZF8mtz96DUupq4Dwgh0hylZF8UrogIVKnV5Fbvv/atk9re/JRXBWzyPvD/QD7FV42nC6yu9YTzb5iClmlkZmgttQeZ4TGjN1u55iJExk9bBjvrljBP/79H2ZNnsyIIYOP2nUQHR0dPPjggzQ0NHD++efzuc99jnvuuYeUlBRuvPFG3nnnHTIyMkhPT+e2225j48aNtLW1sX37durq6njooYeor69n69at3cfMyclBKcXSpUuZNGkS77//Pj/96U+56KKLuOmmmxgxYgRPPPEE9913H9dee23iTj7BLAPaynzMPH5MTI5vM21U6UZW/3szp35vGi6nk+MrKhg/ciTPv/Fflq5eQ8WE8RTl58tItkhaTYE2/EYmADcOuYAyV26CI4owDIM/nVmCy27j1kW1TCh0M7PMw8YGP9P6oUZeTXsQl2kwtzyVQMjC7bDx1ak5uO2RWqp7nDI88r/48+MiP6OvTMnpfmxPRugx+Z90iL95TOTnNyLH1T3a9suTPlnHOLxr20+PLyDDZcMAHjg3Usj55GGf/N8f0ZVNes+2sGVRHEWNWDFwdVoBVkzbxrnlM5mYV57ocISIqWgWrl1IpHRBk9b6DvoYWYsHV4NJX/0dKxSk/enHsKVFN/qSXZZO7aZmlj6+vh8iPHQZaWnMO/ZYTp41i5Xr1vGvl15ic2VlwmvILXtyA8u66i89ft2bNO9up25zM8/8ZBEA7z3yMav+E6mz99i33qC90cuuj+r596+WAPD2vWtY9/p2AB6+/BX8ncE+X7OiogKbzUZeXh4ZGRkYhsEPf/hDfvzjH6O1JhgMMnfuXCoqKvjWt77FnXfeic1mY+TIkVx88cVcd911LFiwYL+1XBdccAHPPPMMr732GieddBJ2u51NmzaxYMECLrnkEp566ilqamr67Wd3JAqaNjKCKfjbDi+ra2+KVDYnfnsyQX+IcDDyO8rNyuILZ8xn5uTJrNLreXLhy+yoqpL1eCIpNYU78doif9szzBTctoHTgXB1Jcf6ZkUuJw1No7o9yNKdHQB84/kd6Dofa6q9/H1lIwCLt7d3j4z15L9b2vAFw7xX2cHz61rIcpvdmZsnFLrjdiEzy21i68oyHQ2bESkNJI5crfjxtDtp83vJsssFQ5Hcopn3t6cDuOevdkJXWzcOKSZFDybgDWE6DrweINzWSss9d2AvG4KZmx/1sbNKUsGKfv9YKi0s4HPzTmPbrl28v2o1S9esZfr48QwtK03ISN60z4/svn3B7Z/MsD3vpjkAzPzSJyM8F/35JABSs92UjI1cST3uik9qN136QHQ5edauXQtAXV0dra2t/OMf/+DNNyPlEr/yla9gWRZLliyhoKCABx54gOXLl3P77bdzww030N7ezj333ENNTQ0XXnghJ574SR22WbNm8fvf/57q6mpuvPFGAIYOHcott9xCSUkJy5Yto7Y2vokNBpqXTxjB4ymXsend3fv87vub3Wmy7MkNpGQ6GXtqpOyCx+1m2KAyhg0qY2d1NQvffodQKMTxx1Sghg6NWSxCxNvqCaX84aQbaF77HJ9ZezNLp/4+qiuu8ZTaVd90VK6LUV0jWrfOKybdZVLTHmRiYWTk7M2t7YzIdVHdFmRHS4D5I9Np8obIcpvda+Heq+ygoiSFc0b3PfNGiP7UUjGFL++ey/Od75OS4eTs3IpEhyREzETTuXsMeAsYopR6EXg2tiH1zl1dx7hzKnGnH3jue7ByG40Lrsc1fRYZC249qGM7PQ4KRmazZuFWcgalUzIusVNkDMOgvLSUISUlbNmxg2Vr1/LeihVMGjOaEYMH0+n10tLWRkt7O61t7aSnpeJxu8nPyaGjs5NOr4+igvwjtsxCXV0dl156Ka2trfz85z/n6aef5rzzzsPj8ZCRkUFNTQ0nnXQS1157LQ8//DA2m42rrrqK8vJy/vSnP/Hss8/icDj4zne+s89xDcNg3rx5LF68mCFDIh2KX/ziF/zwhz8k1FVk/je/+U3cz3cgKdhWw/pBVZw4IXYFlfeYdM4wbHYbnc0+3OlOjL2ukJcWFnLZeefS1NrKwrfeprquntysLALBAONHjcJuSiFaceQqrm3nv7ve4PLpp3B/1Ws4jCPj/ZzelWilINVOQdfI24/nFgBQ0xakLMNBmz/Mt/69k799dhA17UFstk/2ESLevDu3ccw1o3lk4yImpA5JdDhCxFQ0CVXuUkq9DoyP3NWrYh9Wz2xhg+Ufuhh80f6PBXdV0vCT75B26TcOq8p9wfBMUvMOf11BfzEMg2GDBjG0rIxdNTWs+Hgdby9dRprHQ0ZaKhmpaaSlprK7phavz0ddUxNpHg8up4PX3n2X0sJC1NByhpSUYMbww3AoFCIYCuGw27H1kIzmYFRUVPD973+/+/7xxx8wJw8PPfTQftvuvPPO/bYtWrSo+/aVV17JlVde2X1//Pjx/P3vfz+MaJPLSCuDF99fyuD0XIbPim32Srsz8p5c/PBHjDllcPdo7x6maZKblcXnT5/Hog8/pKquDn/Az8ebNnPSrJkU5g6MdUpCHKwJZiFv167gb8bHXFl8KFWGBp7xXSN5wZDFg+eWYbcZfG1aTh/PEiK20oM2/vjPfxMeFKZ8klxkEMktmoQqN+51d4xS6lyt9S9jGFOvlk0upWLsgf9RmEWlZC+4Fceww5tGVjAykoXzrXtWM27eEHKHDIwpJIZhUFpYSGlhYdQFZ31+P5sqK1ml1/PfJe8zfPAgRg4Zgmkzu0f9MlJTKS0qxOvz0dbeQXbmwZ1vMBSio9OLz+/HbjcJBoM47HYy09OP2mQwR7pdo4dQlmriyYpfVrETr56MzWZQta6B3PIMHO59/zy5nE5OmjkTiJQ42bhtOy+++SZjhg9n2rhxR3V2WXGEGj6CLQXV/D74JU4smpjoaPqV3TSwI3//xcBQWZzOl8pHcv2Y8xIdihAxF82noequ7wYwleiSsMRM4fZq0hfs33lrvvNmUj970WF37PY29rTBZJemEfSHukcXBopoO00up5Oxw4czdvhwWtvb2bB1G4uXr8AwDDJSU0lPTWXDtm28vWwZnhQ3qSkp1Dc2MWFoOc2trYTCYRx2O6bNhs1mwzQ/6bwFgkH8gSCBYIAUl4vcrExsNluktmAofFgdu89+9rOH/Fxx+PLfXgJfrKB4WPyuuNu6pmNu/aAaZ6qDnEE9J0MyDIOR5UMoKSzgnWXL+Od/XmT2lCkMG1QmFxTEEaN+0auMHjqMUz8zRd63QsRQod5OyXfzGJEjdVRF8otmWuZf976vlHopduH0raTapP57K+D+cfts95z+Gcy8/h1qzyvPxLIsFv72A+Z8ZSzZZUd23bP01FSmjhvL1HFje90vHA7z0Ucf4XK5sBkGwWCQUDhMIBgkGAphN006vF7sponD4SA91bPPNEzDMLDbB1ZnWByctM4Qc5aOYeWaTUw6Z3hcX3vmJZHkPB8+vYGyCXnkD88C48AXNFJTUph37LHsrK7m7aXLWLNhA7OmTCY/O1s+LIsBz93pZ7o1iFdv/5DTvjct0eEIkbRSg9B5Zwf1l7eQO3hgzMYSIlaimZY5aq+7xcDg2IXTt9acMPPu2HdY3fL5MDypGO7+LwBrGAanfW8qTo+jx+liyWbPCJ3L4cAwDJyOgZOe+0iX6JIW0bIMGDV/EINdeQmLoXR8HhlFqWx4eyfNVR1UfGFUz/sWFnLB/NP5aNMmXnrzLXx+P7lZWYwePgyHaWf44EExXW8qxKGwWVCUlcuJV01KdChCJDXDsHHa9dNIcR+ZCeaEOBjRTLH8615fPwK+3/vuseUL21j/9Av7bAts2UjTbb+K2Ws6PZHOzbZlNbTUdMTsdQYSt9tNfX39EdMZORJYlkV9fT3uGFyE6HeGwZMfL6az2Z+wEApHZeNOdzJiTgnj5w/B1x7g9TuXY1kWK57bRGttZ/e+HY1eajc2M37kSKZnTOaLZ5yJbYuLDeu2s27LZv7+3PMsXb2GTq+3x9cLhy2ssLzfRfw4sPHv5hU072pPdChCJDUfQZZ/vJlwH7UYhUgG0UzLPLGvfeLJMjy0/nsl4fPPxpbiASC0ewf2ktKYv/aMi0cDsPzpjWQUeRg+uyTmr5koZWVl7Nix46iv99bf3G43ZWVliQ6jT3bTTvZ2D432VtLzE5s51ma3kZLhoqPRy+RzhmMYBukFHhxuk7b6TrCgrd5L9fpGCkdlE/ZZhNvhnK8ei2Ez2P1xA6vXb6K1tINHnn2BYYMHMbJ8CGErTFlREabNhmEYvHrrMsafUU5abgrr/lvJjIsi7b21vR2f30+6MxVXqhN/ZxBnip2G7a201nYwZFoh7Y1ePFkumQoqDooDGwX2bCpX1ZI3LDPR4QiRtFw2Jw2LWwiOGng5FITob9FMy1wJpANeYM+QgwFYWuthMYztgEKOFkbmbCFUU41tSKSgcahqF2ZR7Dt3e6iTB2EY0NHko2F7C2UTB0bh8/7kcDgYKgWjj1peK8jWafUMHjpwUkZ7st14siN/gvaUZ9j83m7a6r1MPHMoRSqS5XbCGfu+b0vG5pJVkkZqjhvHxhRsZoi331tGoD1IyB3CVuOgbHIeQz5bhN/txWfaKBmfy5KVq1i1ej2GE0xMAv4gzlQHwaYw6bkpFGTk0tnuI1TpZ+0jOzj2kvEEvCE2vr2T2V8ZR0dX3T6bTTp84sA6Qz520sSUc0ckOhQhklqnFWDKN0bgdsm0TJH8olk8thj4m9b6XaXUBOB64IrYhtWz2pRcdrimkV1bDV2du2DVLpzjJ8ctBk9mJD187aYmmna2UzYxn/b6TlJzB05tPCEOx+YiD6d8kEtNsLG7NMhANGxm35nPDJtBak6kU3jsxRMAmGVNwtceIGAEWLt2E2nZKWzeuYNAMEhTSwv+QICi/DzOOvEEcnMycbjtNLa0YNpstLV3EAqF2F1XR3qeh7UbNlI1vI43PmqlNL+Q3IosQuEwS/+1nvLphZSMz2X9/3Yw9rToC+fuXF1Hc1U7Y08dwjv3r2HMyYNxpTlY90Yl0y8YRc3GJjIKPIQtq/vvkTjytJTm05TqYN3r2xl9ckKXswuR1GqyXOgHKin5WjamQ0buRHKLpnM3Vmv9LoDWerVSarDW2hfjuHq0q9hFbp6TUG1V97ZwXU2/Z8qMRv7wLPKHZxH0h3j51mWc/fOZ2ExD/nCII17zyHKGDisgJY517uLJMAzcaU7cOJl5TKS+2PiRkTIqlmXh9flI+dTayJzMyLS5zPRI1tzBpZ9Myw6GQuyurWXH7ipWVn7EWx+9T3ppKjt2bSe/PZtAi0Xn2jaa1nXSaraQVZLGjm01ZOal4QmnEGqxKBmfy/aV1QwbX0pOdiYpNgfBUIjRJw8isySVcDBMbnkky1vzrnZaqjsYNqMo5j8rETthNZqKorFkm0d2JmYhBrpdeSmUTyzEMBNazUuIuIimc9eklPoV8D5wLLAttiH1rnx3LXkzsgju3tm9LZSgzt0edqfJeTfNwTAMFj+4loJR2YyYU4IVtjBkSpY4Ao1ZvpXwdyA9x5PoUOLOMIz9OnZ9sZsmg4qKGFRUxCwm4/X5aGlrw2azsbumlgZ3M16fn5a0Jopy8/CkpRDwQvmQQnZX1+IPe6mpbyDkDKErN1Pf0oxps+Fb4ycjLY3cXVnkZWeTNzibndU1pI9x4Q8G2F1fS152Ni6nTDU6EuW+u4y247IpnD1wR8eFSAYjd7RSPqtQpsmLo0I0nbuLgG8BpwOrgJ/GNKI+DE8/jrW7B6E6Xuvelv2zWzALE1uYck8ihZmXjCEctmja2cbb963h7J/PJBwKYzNtbFtWTUah54ivlyeS39CZ8wj9JcCuc+spGZeb6HCOOG6XC7crMuqZl93DB/dIvhZGDut5umYoFKKhuZm6xibqGhvZtnMnobBFOBzGYbdjWRZzK6ZL5+4IlXPMCXy+/jze+/vH3fUdhRD9b+6Y+Tx6xetcev9piQ5FiJiLpnPnBRqBNEADWUBdLIPqTeFQD8d8ey6G7QQgUuMu3NGGfYDUYrPZbdiArNI0Tr12KgCv37mCcV3rbXxtAYL+EEsfX8+Mi0dLdj0xIFmBAPNvqEh0GEc90zTJz8khPycn0aGIGLD8PsadNhjDkZzTn4UYKGxGmEvuPTXRYQgRF9HWuRsMnEYka+bfYhpRH1rWbqS5qpPOV14gVF9LuLmRjheeTGRIPXJnRK6mn/itSRSOymLItEKKRudE1s4MzsAwDFY+v4lNi3cB4GsPJDJcIbr5lr7LjtV1+DuCiQ5FiKTlffsNGtftonFHW6JDESKpBbZsonJ5TaLDECIuouncDdda3wh4tdYvAAktxuMLmLTtKV4cDmMWFJH1vRsTGVKf7C5znyQrTo+DkXMjpRtGHFdK8Zgcgv4Qz/z4HULBMA3bW6nZ2ET99hZev2M5AC3V7Xhb/fg7Aqz+zxYAqnQjNRubANi2rJqgP9RjDEFf5LFwV5HmgDdI7eZmANobvPg7Ix/iva2RotXhUFgKmB/NDINda+oJeKVzJ0TMWGFaajtp2iWdOyFiybIMdq6uT3QYQsRFNJ07u1IqD7CUUulAOMYx9So7K8ygyfl45p2DLSubzrdew/vOfxMZ0mFJ7ardZXeafOEPJ2DabXQ0eWmt7SRnUDpTPx/J4LdlSRW7P27A7jIJ+kNYYYtARwB/ewDLstj87m4MA7Z/WMOSx9YB8Obdq6jf2kLjzjb+e9cKAF789RJqNjbRXu9l3euVAKx7fTuVK2oJBcM885NFhIJh1v9vB+8+/DEA//7VEpp3t9OwvZXX/u9DAFY8t4mtH1R3Pz8UCFG1roFN7+4GoHp9Y/x+iKLfGYbBjItHk5Yn5T2EiBnLYvD0kqhKegghDp3NaTL7srGJDkOIuIhmzd1PgUVAMfAe8N2YRtSH2gYnO9fUUTzUTe3XL8Q5ejzuOSckMqR+syez5t5F0bNL0wCYdM7w7m1TzosUvB005ZMMoSdeHanzVzQmh+xBkYQtE88aSkahh6A/zNwrI+nez/jJMWCAzbRx3BXjAZh2/qju43zxjycCMPrkwYSCkX78qddNxWYa2MzIB36IFJE2nSaWZdG4ow2bacPuNEnNjqwdcbijeWuJAcuw8ebdq5hx8Wjc6ZKsQ4hYsMIWmxbvxpnvp7yiMNHhCJG0gr6wJC4SR41oPoEP0lorpVQ+UKe1TuhcvfR0i6ziVGypKTiGj7gfqVQAACAASURBVML3/iLSv/btRIY0oDhT7DhTIr/WPVk5954SarNHX+PF7NrXlfpJspr0As8+3wFmXRq5GpY37JMZuzmDJSPoEc1mMGxWMQ631GwUImYsi7wR2ZgZaYmORIikZjpMhkxPXMksIeIpms7d14FHtda1sQ4mGk63QUpmZHTIc9bnCG7dhFlU0sezhBAHx6BIZe9zYUAI0c+sMGn5adgzj756kkLEk2G3UTAiK9FhCBEX0QzjuJRSy5VS/1RKPaaUeizmUfViV2Mmuz5uiARWMZu8Pz4k5QSE6Gf2KTN54rq3Eh2GEEnNc/pneOtBzc5VCasuJMRRoc3MZ9GDHyU6DCHiIpqRux/GPIqDUD4lh7IJeUAk6YOR2UOBYCHEITMJcdGfT0p0GEIkNcvbyck/mSYXKIWIsawSD3O/PiHRYQgRFz2O3CmlbgDQWr8JrNdav7nnK27RHUDjxho6m32JDEGIpNe5eQtb3q9KdBhCJDXfqg/ZvHgn/g6pcSpELHVsr6JuS3OiwxAiLnqblrn3ZftHYx1ItALOdIL+hFZjECLp2UeOp6WqPdFhCJHUPKecQUNlO+GQ1BQVIpaCqfl0NsnAgDg69Na5M3q4nVD5eWHS86X2lhCxZNu9aZ/yG0KI/ud99y0qLlRSbkSIGEt3d+5TPkqIZNZb587q4XZCbV3VTEejN9FhCJHUmne2sOK5TYkOQ4ikFty+hf/8ekmiwxAi6dXranZ3JeMTItn1llBlmlJqMZFRu7F73ba01rPjEt0B5Jc4cO5Vd00I0f/crhDl06WoshAxZdiYdv6oREchRNJLy/eQVZqa6DCEiIveOncT+/OFlFI24M/AJMAHfA04HrgC+FBr/a2u/R4DrtRatxzoOA63rbu4thAiNgwbeHLciQ5DiORmGGQUSo07IWLN5jBxeWRgQBwdeuzcaa239fNrnQu4tdazlFIzgduALGA28IxSKrvr9ts9dewANq/1EfSHcLijqeIghDgUjW0pbHx7J2NPG5LoUIRIXobBm39ZxfwfVyQ6EiGSWvX6JlprO8ksltE7kfziOQR2LLAQQGv9HjAd6ADcgAMIA5cD9/Z2kNHHpEvHTogYy8/yScdOiBgzbDbp2AkRB6UT86VjJ44a8ezcZQB7FxkJAb8B/gE8DVwMPAD8QCn1F6WUOtBBdm3xxzpOIY56OxqzaKmWUghCxFLAncXWD6oTHYYQSa96SztWeMDkBhQipuI5BNYCpO9136a1fgd4RymVAfwVeB2YD/wMuINIh28fzy159v7n1LM74hCvEPFUlugA9vbSulfvf2nuq9LORLIZUO3skc2b7n/kS3OlnYlkM6DaGcAzy16+/5kxo6WtiWRzwLYWz87dIuBs4PGuNXer93rsx8AtgIfIiJ4FpB3oIFrrr8U4TiGOetLOhIg9aWdCxIe0NXE0iee0zGcAb1dJhf8DrgVQSpUDWVrrFcBKYDDwInBXHGMTQgghhBBCiCOaYVkyB1kIIYQQQgghjnRSME4IIYQQQgghkoB07oQQQgghhBAiCUjnTgghhBBCCCGSgHTuhBBCCCGEECIJSOdOCCGEEEIIIZJAPOvcHXGUUg8Ba7TWtyY6lt4opUzgNuB0Ir/TW7XWdx/MfkqpkcD9QB7QBnxZa72u67GngEld2wH+q7W+NqYnFaVYn3vX4y7g38BftdZPxvaMjj7SzqSdDeRzT1bS7gb+e68/zn2vfS4HztNanx3zwEU3aWdHRztTSlUAfwBSARO4RWv9SHzOYH8ycpccvgGMAsYDFcA1SqljDnK/R4G7tdZjgZ8DTyqljK7HZgFztdaTu74GRIPsEtNzV0rNAt4F5sT0LMSRQNrZ0XnuIrGO5vfeYZ+7UipHKXU3cAdgHOC5QoC0s0M+965zfAr4udZ6MjAfuL2rs5sQMnIHKKV+BHwVaAXeAs7VWpd/ap/LifxinUAOcLPW+i9KqSLgb0SuVAD8R2v9s5629xHHZcDntdZnffq+UupOYO6nnuLTWs8AzgPu0VoHgUal1D+BLwHvf2r/A+6nlNoJjAb+CaC1fkkp9RdgilKqEUgH7lVKDQaWAt/TWjf0cS5DgD8CQ4FyIlczVgHPAxuB6w7wtEuAW4HhQCmgge8D5yTi3IEPge8APwJ+0tv5ir5JO5N21p/nLqIj7e7obXdd+10A7Op6LRm1ixFpZ0d1O1sFLNBav9Z17juUUrVAGbCht3OMlaO+c6eUmgdcRqQX3gzcd4B90oArgDO01vVKqZnAq8BfurZv1lqfppRKBe5XSmX2tF1r3XwocWqtv9PLw4OAyr3u7wAmHsR+g4BdWuvwpx4rAxzAa0Q6ObuIDDs/AJzbR8iPAc9orc/p+nm8A/xea/1o1+N/6+F585RSJwB3dV0Boev1exLLc/9Qa/1FAKWUdO4Og7QzaWf0/7mLPki7O+rbHXtNG7usl9cRh0Ha2dHdzrTWXiLTUQFQSn2dSGf2vV5eM6aO+s4dcAbwhNa6CUAp9Sfg5L130Fq3KaXOAs7sGmadDKR1PbwQeLHrasRrwI+01s1KqQNuP8jYuqdQ9HHFxQZYn3pe6ADH62m/T2/vfkxrvYTI1Yo9cfwCqFJKObXW/gMF3fVHbCZwEkDXz+NBYB7wqFLqy/RwxUVrvfoAx0vIuR/o3MQhk3Ym7azfzl1ETdrd0d3uRHxIO5N2tud1fgR8Fzhda915oHOLB+ncQZB956Hv9wtVSpURWXd1D5ErB08CZwForT9QSg0FTiHyJnxfKTW/l+3L+ojHs9ftlD03+rjish0o2et+CZErCtHutx0oVkoZWmtr78eUUscB2Vrr57u2G0CY3v9xGEQawN6NwAICXefyN3q+4rKfRJ17tPGJqEg7k3bWn+cuoiPt7uhudyI+pJ0d5e1MRRLvPQSMBWZprbdGG1ssSEIV+A/wua4hX4jMmf701YfpQC3wa+AVuhqkUspUSt0M/Exr/SyR3vpaYHxP26OIZ4pSqkhFFmieGeU5PAdcrpSyK6WygAuBZ6PdT2u9g8j85S90ndc8Ig1vNZErS39USuV0HeN64EmtdY+NUmvdSmSu8pVdx8sAvkzk6lR/i+W5i/4j7UzaWb+du4iatLujuN3FIB5xYNLOpJ09AmQAsxPdsQPp3KG1fgO4F3hXKbUUyAQ6PrXbK0R65xr4GBhMpJGOIDJ3eLJSag2RRaJbiCwo7Wk7SqkVSqnpPYRUA7wIfET0V67/AmwCVgIfAPdrrd/seq1fKqV+2dd+wBeBK7vi/Q1wvtY6rLV+CbgTWKSU0kQWqV7ddezpSqkVPcR0EZF5z6uARcCjWusnojyfgxGzc49BrEctaWfSzjiEcxeHR9qdtDsRe9LOju52piJZ1T9P5He5qOt3s6Krg5sQhmV9+uLC0aWrcczWWt/Zdf86YIbW+gsxfM3fAH/Xe9VS69p+GXtlOToSKKWe0lp/LtFxiIFN2tnhkXYmDoW0u8Mj7U5EQ9rZ4ZF21v9kzR2sB36oItltLCJzar8eqxfrGibf+ukGeSRSSpUSyXgkRF+knR0iaWfiMEi7O0TS7sRBkHZ2iKSdxUZMRu5UpIr7vYAiMiT8FSKLIx8i8sZfA1zVtfvTQDFwg9b6VaXUMOC7Wuvv9ntgQgghhBBCCJGkYrXm7mwArfUc4Ebg9q6vG7TWxxHp6H2GSCrYrcDpfLLG4gbgphjFJYQQQgghhBBJKSadu67MPnuGpIcA1cA0YM+iy5eIpHZtI1JxPhVoV0rNATZoratjEZcQQgghhBBCJKuYZcvUWgeVUg8DfyRSz2Pv2hetQKbWej2R7EF/AH4JXAP8Syn1F6XUTUqpoz6bpxBCCCGEEEJEI+bZMpVSRcASIENrnd217TPAqVrrq/fa7yIinc1xwFPACcBKrfWrex/vsxdeaE2fNJn6phbKy4pjGrsQ8XLXXXfdr7X+WqLj2OOCiy6x0jLTGTN8GG6Xq9d9O0I+dOcupqQNjVN0QhyagdbOvvb1Ky0/FhNGjeyznQlxpBho7QzguFPmW8cfO4PC3NxEhyJEv+mprcUkW6ZS6hKgTGv9WyK1PsLAUqXUCVrr/wHzgf/utb8b+ByROhF/IJKExSJS+HAfmVnZXHfdtazVm2nv9DJ3xpQDxvBYzdvMy55MriO9f09OiBi46667diQ6hr0dN+sYSkYpZk+dQnF+fq/7LmvdxEc7/8O3R387TtEJcWgGWjubNGEc+cOGc+qc2eRmZSU6HCH6xUBrZwDTp07kS1/+MqOHDUt0KEL0m57aWqymPT4NTFFKvQW8TGS65VXAAqXUu4CTyFTNPa4B7uyatvkgcDeRJCuvfPrApmniDwTYvquaN9/9sMcA/rprv6cKIaK0Rm+io9OH1+frc9+aQDMFTvlgKsTBqm9spq6xmXA4nOhQhEhq6zZsY3d1XaLDECIuYjJyp7VuBy44wEPH97D/zXvdXgHM6OnYpmnD5/dTXJDLq28tOeA+LcEOAlaQHPt+A39CiCg0t7Th8wfojKJzV+1vosCRGYeohEguwWCIjo5OQtK5EyKm2to7aWxuTXQYQsTFEVfE3N41cldUkEdVTf0B99nmq2WwOx/DMOIcnRDJwTRNrLCF19t35+5z+bMIhENxiEqI5GKaNkKhMKGQdO6EiCWH3aS9ozPRYQgRF1F17rqyVhrAbGCJ1tof06h6YdpMfP4Ag0uK+fNNPzzgPqNSSrht2GXxDUyIJGK3m1gWUY3cbfXWMMiVF4eohEgupmliWRZhuTgiREw5HHa83oR9dBUirvpcc6eUugW4AvgV8FPg3lgH1RvTtOEP+LGbJjurag44slDpq8duSBUFIQ7V9IljOO6YKXh93j73/V3ls2zx1sQhKiGSS3ZmOhPHjpJpmULE2KRxI5g+eXSiwxAiLqLpAR2rtf4rMEtrfTpQFuOYemWakZE7gJv/9DC7avZfIHvf7ld5v3VjvEMTImnUNzazq6qWziimZe72N1IkCVWEOGjBYJCa2gZJqCJEjDW3tLNhy4BL4ilETETTuTOVUscAW5VSTqD3vOgxZtoia+4AMtPTaG5p22+fDZ27GeYujHdoQiSNqpo6Ply9vs9smR0hHy3BDgoloYoQBy0YCrNhS6WM3AkRYw1NLSxfsz7RYQgRF9Gsufsb8EfgcuB3wB0xjagP9q5pmQBZGek0teyb/ag95GWHv4FRKVLgXIhDZZomgUB02TJvHHIBNpkGLcRBM00bwUBIEqoIEWMOu53Ozr7/nwmRDPrs3Gmt/wz8uevuNbENp297T8s8/6yTKS7cP5HDr8ovxGE74hKBCjFgmDYbXn+ge5S8Jw7D5IycqXGKSojkYtpsBIN+QpJQRYiYcjjsdHT2vYZciGTQZw9IKfVl4EeAe882rfWwWAbVmz1FzAFmTB2/3xVPA4OTsiYkIjQhkkZJUT7nnTuPN95/D8uyeiwr8ljtO9T4m7l+0GfiHKEQRz6bzcasaRNlzZ0QMZaXm8XMuRWJDkOIuIhmLtUPgXOAMXt9JYxpixQxB3jk6Zf444P/2ufx23e8wFO17yUiNCGShsvpJDc7q2tkIdjjfjt8dZS6cuIYmRDJJTM9jWBQRu6EiKWwYbKipveZKEIki2g6d5u11hu11r49XzGPqhdOh4P2zg4AMtJSafpUQpWaQDP5zoxEhCZE0qiqrefGW/+K0+HA18vUzN3+Jkqc2XGMTIjk8uLri2jv6Eh0GEIktYA/wOIX/5PoMISIi2gWpnUopV4CVgAWgNb6JzGNqhdOh4O29g4syyIzY/9smTWBZgokc58Qh8W02Qj4gjidjl7X3Y1OKaXcXRDHyIRILmHLwuuXEQUhYik91UVHRw3BYBC7XXIyiOQWzTv8xZhHcRBM08Q0Tbw+H6VFBQwfUrrP48ekj6DEKdPEhDgcpmnD5w/gdDh77dxdXTo/jlEJkZz8XUsNhBCx0eIDu9vNI+9t59ixpYzIcSU6JCFiJpppmY8CacAxQBbwj5hGFIX0VA+t7e2MKC/j6q9csM9j15WdQ44jLUGRCZEcPJ4UTj62AqfDjr+HUYXOsJ9vrL87zpEJkVwmqBGJDkGIpJfrsXPKnMmYdjudASvR4QgRU9F07v4KDANeBcqB+2IZUDTSU1Npbe8gEAhy3YL/w7IiDbXG38x3Nt6f4OiEOPKleVK49Pwzex25q/Y3scvfEOfIhEguJx9bgc08cDZaIUT/CFpgFA3DSElnfb3UuxPJLZrO3Uit9fe01s9qra8FEn6ZMc2TSmt7Ow6HnQ9XraO9oxOAKn8jdYGWBEcnxJGvo9PLN398C06HA3/gwFPGqvxNsr5ViMP0z+deoam5NdFhCJHUvEGLLR99xGPPLOSY0pREhyNETEXTuXMrpTwASqkUwIxtSH3LSk+nqTXSicvLzaamvhGA2kAL+Q7JlClEf9i8bQeuXrJltoY6GeLOj3NUQiSXhsZW/IEA25v9MqIgRIxkuU3Kc1yM9XSwfLcUMxfJLZrO3R3ASqXUM0QyZv4htiH1LT8nh9qGSIeuMC+Hxq6rnn4rSJkrL5GhCZEU7KYZSajidPS45u7U7EncOOSCAz4mhIiOadoIhkIs3dnJxU9WJjocIZJSsy/M9laLzdUtpDhkGrRIbn1my9RaP9pVCmEYsEVrXR/7sHqXm51FY3MzoVCIu35zPYYRaajzc6YyP2dqgqMT4shnmjZyszNxOhy0tLUfcJ/XGlcxxJ3HyJSSOEcnRPLIycokbIU5Y2Q6gzMdiQ5HiKTkstsYXZLBO+0GpwyTpHsiufU4cqeUuqHr+z+Au4DrgD8qpR6LU2w9ctjtZKSnUd/UxFq9mRVr1wPwWuNKPmqXK59CHC7TNHnq3lu6EqoceM3dM3XvscvXGOfIhEgu37n8C9gMGz99vZqyAdS5syyL6vWN+DukBp848qU6TUpLCvjd9y/jsmd2JDocIWKqt2mZL3R9v5tIxsy9vxIuOz2D5rY2Vq/bxKtvLQHgpYblVPoSPrAoRFL4y9+ewrTZ8AeCB3y8OtBMoTMrzlEJkVwWf7AKn9/PBeMz+dJTlYTCsUnT3rijlTULt+63fU/nLegLYYUtWqo7WPzgWgA+emUboWCYxQ99ROMOSfoijlzbmwOs2dnMD/7wGL+fV5zocISIqd46d2uUUk7gu8Bi4F3gfeDn8QisLyluN51eH4X5OVTVRjp0DcE2cqXGnRD94vEXXgPoNVtmkXTuhDgsS1d9TMiyGJXj4LVLh2Ha+nc90M7VdWx4eydOjwOnZ9+VGJZl8dLNH9BS3cHaV7ax8vnNpOa6GTytAMMwOPHqyaRkuBhxbAmpOW52f9zA2/eu6df4hIiHwVlORuc6aVm/lN2tAYIhqXUnkldvnbvLAQ3M7/q+DlgNbI9DXH2KdO68lBbls6uqFoD6QCs59vQERyZEcnC7nIRC4R4Tqtw36ltkmp44RyVEcml3ZlMVSufqF3fx4vpWWn2hfjmuvyNAc1U7uUMycKU6SM1xM2J2Cev/t4Nw2GL9WzuwwhZn/WwGGYUeJp41lAlnlmPabZRN3DcLbsGILJweB/nDMpn8mWFYlkU4RiOMQsRCZ8CiOWDh7ezk4eUNtAXCiQ6pR3tqNwtxqHrs3Gmt79VaDwWu0loP01oPA8ZqrS+LW3S9SHG76PR5KR9UzA3XfBWAO0d8lUGu3ARHJkRyGD18CFsrdx+wFEKNvxm3zdGdzEgIcWhcBYMIhOCvZ+SzqzWAr59GFOq2tPDxq9txZzgZPLUAAMNm0LizjUBHkMYdbQS8IUxHpLqRYRjdt3tid5mkF3jQb1Ty3sMf9UucQsRDp+XAFvKCzc4v52aT5U54Va8DsiyLqzfey+8qn8EfjiyJCFthnqp9l9Xt21jYsJzTV/+Kb6y/m/pAKzdvf5rV7dsSHHUk7rpAC8/Xf4A3/Mlsn52+BhqDbfvs2xHysbmzep9OrGVZbPFW0xLsiFvMyazPbJmAXSn1f10FzP+tlPq71vrvsQ6sL3umZbqcTlI9KbR5O2kOdlDuLkh0aEIkhZ985ys4HCZPv/rafo89XrsInxXke2XnJCCyI1PYsqjrCFGQauf9HR2UZDgoy3CwoyVAWYaDZm8I04A0V98fOizLIhiGe5Y28I2KHOz9PJVPxM+4EUN4/sMQr29u42vTDu//lxW22PD2Tta/uYNTrptKybh9L3YaNoMZF48GYMZFow/5dUafPJhQMIyvLYAz1S4XecSAV57toiQvh7t++32eWd/JcUPtlGc5Ex3WfqoCTTgMO7pjF9/eeB+3Db+Mr6//C34riNOwYxo2biq/mOVtm2kItjHGU8bPt/6LB9VVZNpT4xKjN+zn1cZVvN64im+WzKMh2Ma1mx4kYIWYmT6SE7PG83bjSm7f8TzecICgFeKxMdcSssLctP0pVrdvx2NzcsuwS8i2p/HNDfeQbU+lOdTBLUMvYVJaeVzOI5lF07m7EpjddftM4C0g4Z07jysyLRPgxt//lUu/eja/N15k4YSfJTgyIZJDZkYa7yxZgT8QwLKs7g9wlmWxsHE5twz9coIjHHgsyyJsQas/zMINrVw4IYsXdAsu06Awzc4/Vjdx86nFbG8OkOay4Q2afP/l3TzyuUE8sbaZPI/JuWMy+fJTldx+ejFNvhDBkMXofDdPrm3mLJXOxno//93axtXH5FKSYZeO3RGuphNSbX4q61q5410bc8tTmVKcckjHqt/aQu2mZqacNwJ3Wmw/uNpMg//etYJp54+kfmsLhmmgThjU5/N87QFW/2cL0y8YFdP4hNjb5gY/lXUu3t+ylhPmzMHjiKbMc/wEwkEu03fxhxGX84cRlwN0/9/9YsFxzEgfSb4jA4iMsk9NHwbACHfR/7N33uFRVGsD/832kt0km94LCUvoNQRCB6lKExWwYC/Xcu16bdeKooiK+onl6lXsYsGGIiAdpLdANoGQ3nuyvcz3x0IwN5SgVJ3f8/Bk98w5Z95hd3bOe97GAUc5E/Y8w+Iu/2KXtYA3Sn/mhqhR5DkquD5yFEvrdlLtbsTmc5LvqGR24uX8WLuNYUFd0Ms1bWQ5fN5SZy0lrlq66OI46KgkSRNOk9fObfvfxqQwcGFIH2LUIUSpTHzf9SGMch0qmV+tGGA080bqTcSrQ6n32DDINdR4mpgZPoR+hg5oZCqcPjcamZLXU2+gwFHFiKCuyIRz63M5X2mPcue1WCwOAIvF4jabzeeEM7BWo25R7mIiQrGUFBKZLCV3kJA4Vchlcua9/TETxvbH6/WiUPh/LvIcFbh9XjrrYs+yhOce/9lWhyDA5d2DcHj8P5VdwzWoFQLRBiU9Iv2L9mldAlvGfHpJPADX9zG1tL0wJopQvQJLjZNGp49OYRqanF5sbh9BWjlT0gIRBIEpaYFInN/0idZSmFWHmF/ExElTMWn/uLtY9q9FdBmTQHDs6Y89FwSB0ff3RSYT0AapQQRbnYOiHVVEDQ7BqNDR6LHRmGUlumsIB38rpyKnnj6XpBIY6bcwFO2oIirNhKId1moJiT+DSSunf6dw3n5rCdP6dyJcH3G2RWrFyoYsbD4nob/LG3F4Q/WikL7HHCcIAnfHTmRWxHBMigB6ByQxK2IYi6o2kKKNRC7ISNKE823NJiJVQUwJ7Y8gCGxsymFJ3XZujBxFZFMQB/QVfPj1r6iClWyM289TidNJN6Ty7/xPqXA3kKQJ57bo8aQHpHBt2EjGhvb6H4t96w2pALmGgEOKo0kZQGlWDaYEA8OCurT00cnVAHRQR5KqjcLn9eGwutAYzz2L6vlGe5S7xWazeQ3+TJm9gcWnV6T2odVosDudAMTHRrG/qISoTsFnWSoJib8OcrmMSWOG4vLacLndLcpdmNLIs8lX/O1dsSqtHraW2hmXauDhZeVc38fErJ7BKGT+B+7Vvfy/R0nBJ/+gigjw/19nxh9xs7mmt1/5M/0xo47EOYogCKzzpBJQsYertDL+jCF20HVdT51g7UB2SNiAEP+XsjqvgTx9JS9mf88VlkE8nrKI2/eMYWSknsR+EcT2CEOtV5I6JAZRFCnaXokp3oBcJfvb/55InF4MahnJYQFoNUo+2lpKkSqGqZ3P/uZYrr0MnUzNf8qXc2PUBX/4PghR+pXCQIWeCSF9mBDSp+VYJ10Mb6Te1Kr/Y/GX8HThIh7O/ZgZnwxg2nOD0XZTYo9wca9+CuEGI0q5gm/N/wIlKAT/Bsyyl7fRbXgczaKd6vxGktIjaaqyozepkcnbWt2q8xsISTBSsrsabZAaa7UDp81NVJqJA+tK6TAwms2fWAhNMhKZZmLv0kL6XSZZ9f8sJ7R/WiyWp4Hb8St3d1osljmnXap2oFIq8Xi9eLxehmT0on+XLkwOST/bYklI/KXo0jEZt9vTKqlKo9dO2t/Qare/1r+ZtCS3if01TpqcXtyHkl/M6hVMfKASpVyQFqkSJ8Vne+q5KNZJiKOQD7dVsyyv+cSDjsJ3T2ykqfLsJSNw+tx8plnPc6pvmBE+mITEcG6LGcdrXX9ml6YAuVKOWn+kSLsgCAy8pgt6k4Z172ZRkVOH1+1l1w8HASjbV4O1xo7X7aW2UKqxJ/Hn2FZmZ22RC41aRXqoyMROxpOeQ/SJbFy4D3uDE59PxOf94xk38x2VbGzM4emCRSyr28m00AGMDe71h+c7WeQ+GTPzM/mu10Nc9swQVDIFPXsnkxFtZvv8HBpLbNSXNPPTc5vBDb+8tA2vx8fwW3sQ2yMMBAFrjR2AFa9sx9nspq64ieqDDTiaXZTsrkb0iWz5LBd7g4t+080ExwTg84lYaxwgQmVuPW6Hhx4Tk0lMj0QfrJEUu1PECZU7s9kcA9yLvzRCutls7n/apWoHgiCg02iw2u10Tk1i2oCh9DdKXwoJiVNJ97QUQkOCcf1Oubs/7wNy7WVnUaozT7XNw/yNNYii0A3+FAAAIABJREFUiFYhIAjQwaRuWSB0DFGf8vpkEn8PHh4SToVLxUVD+3Jjv9CjutqKokiz13HceUbc0RN9SNv4mdPJQUcFaxr2Hsp0V8lBeyWfd76H4eFdSegTwdTQDJ5NvoLPq9YDsNdadNR5ek3ugDFSh88r4nX5S0HU5DdhrXXgaHKz5fMcACy/FtFQZj0zFyfxl6JfjI4p3cLomBJLQkoqB+uOXr/1eIiiSGiSkeWvbGfxI+vJWVVyUuP328uZtvcFcu1l5DkqeKP0Z1yimxnhg5kWNuCMbAy67B6Wz9+Oy+ahocyK1+1tlSVXEAQufCyDkHgjAWFaxj7YD4DuE5IQZEcy6hrCtHQdlwTApKcHog1UU26pw+3wb8bU5DciyATGPtAXXZC6Zf6w5EBSB8cgyPybOyqdEo1BhVwhxdqdStrjlvkW8CLwKP5kKu8DGadTqPYSoNezMa+GrXVWvnvtIeYsuJshIV1OPFBCQqJd6LQa5DJ5K+Wu0Wv7y9e3c3tF3D6R8mYP/7ephhdGRzJvTBSCIDAsKeBsiyfxF+K3YhsbKuXoQrR0rGim3CFndErrmLmPq9bwfNE37Owz76hz2BucOBpd6IPPnHJX7Kzh6uzXEBGZEtqfu2IvYm6HWW36pRtSSTek4ha9PJz/MQONnbg79iLkv0ucoA854mvca0oKAF3HJba0XXB3bwA8Li/aIDWORhdylQylpj1LGAkJqGh2U24XMBp1OJSB7K5wkBqixuFwotGoTzje4/JSnddAyqAYYnuGISCg0rf/++cTfdyX9z4zwgeRqAknVRvFiKBu7R7vsrlZ/eZuRvyzV4s7dHvwuLw4rW70wRpcNjdKrYK0kfFojCrSZxw/Y65CdUTpi+h44rCntJHxLa+jO0tlyc4m7VGVNRaLZQUgWiwWC3D87cMzSJNPxbbCOq7tbUI0Kvn5841nWyQJib8cO7NysNrsLe/rPTaCFH8d5a7e4WVPhQOXV2ThzjpEUWTO2iq2lNhJCFRyR0YIgiCgkEuWOYlTT0mjm9mZKoy1e9m6cy9HK3N32F2ryt141Dkayqzkb644nWK2oc7TzD+ix/Jz98e4OXrMCfsrBTnvmW9jj7WQOUVfn9S5hEOL2S5jElFpFez8Lo/C7VWU7atlxzcHAFgxfwfOZjc1BY1U5tad/AVJ/KVpdPqodwoUFFfw9ftvM7VzIG63h8tueYjCkvITj6+wcfA3fz9NgAp1gJI9P+az+VNLu86/odGCRqbkktCBKIX2JxDyuLysf38vMoWMLmMTWyl2Xo/vkGxHrNk+j4/fPspuqSF3YF0pOSuLcdncLH50Az6v2KZEisRfj/ZsOzjNZvMYQG42mzM4gXJnNpuVwLtAIqAGngb2Av8FRGAPcOuh7l8BUcAjFovlF7PZnAz802Kx/LM9wneKDCJNJmDU+fCOmsjqNXupa2gkOPDkfaklJCTaIpPJkMlk1DU0tLRdFpZJgPz8zurh8Ym8uK6am/qZ+K3YhscnkmJS4fWB1wf3ZYaiPuQmEh8oZe6SOH1c09tEvzdrmaAJIri5hnGpra12btHLfns5o4N7srM5n0GBndDIjnwns1cUERilo8+01DMms9vnobMujm76hJMaF6TQ82KHWbxbvhyArU0HqPU0o5Ep6aZPIKiddboO1+qz1jpQ6RT4fCLm4bGodAocB104mlyYEoz8OHsT4x7sd0IL3+HU7y6bG5VOedy+EucnqSFq+sfpWWM0sDe7hHe21hJYtp2IUBN5BSXEx0Qec+yu7/MwxRsYMKtzq/bOo+OPmkTkMIe/V6Iokm5I5ZlEU7tdL30+kTVv7Wbg1Z0JTwlCoZITlWbi4KZymipspAyJ4adnNzPp6YGsWrCbEbf1YNWbu8m8tgumOAP2RhfZywvpNSWl5ZxT5wyS3B//JrTnU74RuAYIxR97d8sJ+l8B1FgslsHAOOA1YB5+BW4wIACTgJ5APjAWuO3Q2EeA2e0VvtyhoLqhmUavjckD7bxzzyXodef3olNC4lxDqVBQ13AkocHtMeNbuVSdT2RVOnh7ay0KmUBmvA61XGBMioEJHY1olDKu7hWMQi60KHYSEqebp1dVMrWjFpUcqmrrmf5FIVbXkUQN25vzeKXke2YnXc6o4O48VbCIlxd+yj1PvgxAcGwAetPpc8escNWzrmFfq7ava37j6cIv/tB8oUoj98dNAWBjUw4/1m7jv+W/cs+B/570XHqThpAEIzKZQEy3UIRDfzsMjEahkjP4+q4oNQosK4torrbjbHZTnl0LwJq392BvdLJnyUF2LPZb/xY/tgF7oxNrjZ3SvTUA5K4uwVbv/EPXKnHusLPczvpCK6ZgI/UNDXQLU/DKO58QHRVOVW19q74HN5XjbD4SihDXM4zgmLbu+HKlnKKdVVTlNbRqt3odLW7IufYyrsh+BS8+krXHL7/gsvlrylbk1IHot7ApNQpSMqNb+kR1NhHXOxxdoJoJj/ZHrpBx4WP90YdoybgijcBIPalDYlBpFai0SrzuI78lkmL39+GY21lms/nw1mAlcDLVir8AFv3uvQfoA6w69H4JMBp4BdAf+mc1m82ZQK7FYmm3b0mtR4nOaiVSFcxtEZP5KbsGRWkFHRL+fpn8JCROFx2TE5DLZTTb7FSJjTxdvIj/mG9tVdj8XKfa5sHm9pEaoqbJ6X/YDUpon5VAQuJ0cpHZQJPdyc5KN517pDGlQxAel4Nmj4BCLmdV/V6GBHZGgQxRFKlxN7L0kw0A1BQ2EpJobBUbcyp5sfhbvq/ZggjcETOeTtoY3ixbyk5rAa+lXP+n5781ehzgt3DUevxZQqfvm8eIoG6MN/UmVv3n3McO1/uTK+V4nF68bh/5myuIMAeT2DccpVpB6pBYFCr/onfqs5nIlXLyNpa1LIRt9Q5kkkv2eU9coJLUEDWVQYH07mpGb68hKiKUYRm9UamOWGtL9lSz/OXtXPLiENQB/na1Xumv5XgUvG4fv9/rLHPVcVX2fD5JuwsRken75vFQ3NRW1vZjsWNxHmq9gqYqO7ogNamDY9r00QSo0ASoWuSCI/XwTPFHrP4KlbxV3KrE34vj+SpY8LtRCof+8rvXycccZLE0A5jNZgN+Je8RYK7FYjk8RxMQaLFYcsxmczHw8qE+TwEPmM3mN4A6/Ja+4+aZndItgu9XZvNayRK6qFPYU+hm+Udf89YLDx//qiUkJE5Ig8OLQS2jS8dktmdZGHf5HSR1jMV7dSD7qhzc/kMpy64+5k/BWaOk0c2mEhtT0gIpbnATZVCwqdiGzS0yrYuKjLi/TrygxPlPQpCKq1dU0NenRK1RMGfBB+Rk5yFzWRk3IpMtwyt4NOFS7nr8JVRKJZ2uSkRzZT9uy5jEvqWFdBoZR2jSqavX5RG9/NaYQx9DB64IH8KUkP7o5Wq8og+ZIDA0qAv3x00hRm06ZecUBKGlTtft0eNZUb+bG3L+jwWpN5OgCfvT8//e8pFxZRoAcb3CAVoVUD+cCTA5I6qlrcfEDn/6/BJnnwCljECNHJ1Ww8xLL+LWN39mfPfODBt4pB6cx+tl8v338N1/5mGM9D8nfB4f3/57I5e+NAThKEp+UnokPq+PmvxGjAl6HsxbyIzwQYQqjTybdAWzEy9vtQl6eFO0Kq8BY4SO3DUlJGdEoQtS0296R0SfeFxXTwmJ9nDMb5DFYkmyWCzJh/8CfYHkQ6+Pi9lsjgN+BRZaLJaPgd8raQag/tA5nrBYLNM4Uhz9BuA/QC0w8njncLh9PLyqAavdzm/1FoI0Ch4a3QFLXiHNv0v+ICEh8ccY9f5BKps9qJQqEmIj+eGDl8nLLcYo02IOVfPpJfEnnuQ0UGf3suJ3tcC8PpG7lpRSa/eQVenApJVjd/v3kv5vcw25NU7GdzQyrcvZL1grIfG//OO7Et6eFEtcgEhtQz0hXTN45rmnWPnlm9x+7aWMq+1KqjaKIf17UVVTR4o8ktCMCBQKOZ2mxp5Sxc4n+nii4HPmFn/LxsZcIlRBJGsjiFAFEa02EakKZmpoxilV7P6XzMBOPJpwCXfHTiRYEUCOrZT/lq9gXcM+3i1fjk/00eCxtiSMAH8MYJGz+rTJJHH+s6HYxoYiGwE6HXgcrJh7Ew/cehVZOXk8+dI7AOzJ9rvnVhTV8fW/1uFociFTyJg+f9hxFa55O75l5Te7eOOrHwmtM3B1xPCWY79X7KryGlgxfwcA+9eUYK11oNYp8Xl8bP7UQs3BRkmxkzgltKfO3RCz2bwHWAc8YTabrztB/whgKfCAxWJ591DzdrPZPOzQ63HAmt/11wAXAx8BOsCL3zp43HzjCpnALelhGPV6ahobSNaEs8hiI6zfaLbs2Huiy5KQkDgB1/UORiYTUKmUuNxujAY9Ey8fQbIygm2ldtYV2Vj+Bwsu/xny6pxUWj3k17uY/kUhcpnA9X1MyAWBNzbXoFXKmNk9CIDHhobTKezM1v6SkDgZPr4knjC9AoNeR1lVFV3kJQw+5DJsdTp4/+VvWL16KwqFnNqGRnJ/3E/sZi0ff7qUl1/87JTKMr/kRwodVXzU6U6GBZ3dskIXBPfAqNBS62mmzFXPSyXf0+CxUetp5pH8Txi/5xluy32bz6vWIwgCd+5/l4UVq3D63G3m8oj+2nle0Uedp/Vv1ramPCpdrWOm8h2V/Ld8Bb/W7zl9FyhxRhmaGMDQRD0GvZ4mm41NJTZKm9yoVaoWpa6yuo7LR41D69Aw5r4+qAOUFO3wZ2U9Fo0eO18IG9g6qYBRYd25J3gi69/d6y/WXeugrriJyv311BU3EZJopN90MwADZnXGFGcgdUgMAaFaYnuEEhgthQpInBraky3zaWAI8CX+ZCfr8FvXjsVDQDDwqNlsfvRQ2z+B+Yfi+PbROibvTmC+xWIRzWbze8CbQCMw+XhCuXwi0UYFB4164r0mAhV6LuyoIU3Rg0iT4XhDJSQkToDPJxKqUxCuV+BoVLbUubtz/DTcHg+v7Wimb4yWuEAl1VYPoSdR7+fPsLvCQZ9oHX2idfhEkYVT4wDoEu5X4F6b0DpGQaOUdkElzm2+yGpgcicjBp2e8qpqip0avs6qY0SCktz6YsQkJd8vX8ek0UO45cqL+e6XNQQPDWPahEHc9dg86huaCAr8c888URRxiR6GB3Xl+qhR6OQnrvt1psgwdiTD2LFV2ysdriXPUck+WxFWrxOFIOe55CuZV/wtS+t28FzSlXhELzafk48qVrO91sLX4dfwtpDFh1VrGRXcnVClgdujx7O2cR/fVG/ivrjJ9DV0QCXIuSlnAcMCu/BD+QascQ6GBnWh3FVHqjb6GFJKnOvk17koaXRj0OspKCmlutGDSasgKiSYyupa9h8sol/PNIwGHZvydzMjcAy5q0vQh2iQK44dc7nPVsSAQDPPJM5ESBL85Qm84LZ5+Om5zaRf3glHk4vs5UWMvqcPxoijhwVEpUnlCSROHe1ZkfksFkut2WwWLRaLw2w2Nx2v86EyBkcrZTD0GP2f+93rHUD/dsjEznI7vx60MjLQxLXCMACCtXLcxii8ovW8SvYgIXGu4QPmrK2ic7iaEKUSl8uv3M2d/xmqagUPvDAdgGqrhzuWlPLRxXGn5X47fB/n17tIDFKxcGcddw8IJdKgRCYIyE5PHgkJiTNGaaPb76qi9y/6QnRKAuQu9heUkluaT/ebevLqoeQlVTV1zF2wENMwkXx7DaOG9OOz735hzNAMfli+jovHjyAy/PiLRLfoJd9RSaBch06uZk7R1yRqwllbv5f3Ot1+ui/3lCATZKRoI0nRHklfn6qN4vWUG1hUsRaVJYeGulIW5/7ExFwrtxU30BRdyazZLzLpJws5UfV4u0TT+P6bXKvVMaFGSd2uxyiIiSVFGcbiux7Et3IZ7iIDuu49sX22kJBBgyDuLF60xJ/C5vHh9IoYdTqabFZmHHLTF0UVyQkx/PvFt7jxiimUbK/h131buXzyWMr21TLo+q7HzTLZ39iRfoYj5QbkCllLHbmLnx/c0i91UNvkKBISp4v2KHf7zWbzs0CI2Wx+ECg4zTK1iwFxenrGKHli0/cMcCS2tK/Kt7Jq4WvMe/Am0pKlm0lC4o+gkAl8eHEckQEKfE4VdocTW72Tg0FVuCtFFu6o48qewYTqFadNscuvc/HgsnJeGhvFC2ureP3CGJ4fHXXigRIS5xH/HBAKgEGvR61UkhppJEbRTHF1PXabnWhVcEvfg0WlzHnkDn6S7+FH33bunzGRcE0Q9z09H2PA0S0C+Y5Kvq/ZyrbmA4w19eaHmi3kO6p4PvlK+hpSiFWH8NbXi0myGPHN8yGTtV3I7tybi93hIKN3tzbHfli+lkXfr8DhdDJ6aAbjhg88oYIJnPQGrM/ajGPlL3gb6pCHRyA2NeHcsgHR6SRk7gKaP3sfZaeuTJYbafzPfKJCwrhFH4f6wkyUqZ1QRPqtbtopVxJjMOKzNmNjH6LTSURYIrG3jMVbVYGg1qCWq2DkeLRy/+6RQqtDrzpupIjEOU7nMA3JwSqcLqhraGTZjhx2WoO5JzOMd+Y+wsjLbqV7WgpRqlDeXv4VNoeDoTd3P+G8/ylfztTQ/gQrpO+HxLlDe5S7m4HrgbVAM/6kJ2edtQVWaqikVN1AfXljS/uDg8PZmzWSBatyeUVS7iQk/hBNTi+vb6rh+Qsi8aqUWBsd/PDUb/SuSmWnLpt+4UfctkqaPCzJaeKGvqcuyYLN7SMxWMXCqXEo5QKvTZDcoST+evhEkWu/KebdybEY9HoC9HpkehP3rfVweWgdot3LQENaS//0nl1wNLvw2uRs8+QRHhzMsj3b2LY7mw3f/oeGJitbd2fTp5u/yLdb9HLngXcZYDRzQ9QF9DWksLXpAFdGDKP/IVfHSfI+fL7yGxJ7RFJb30hwoJGS8kpkMhmxUeG88/FiFv2wnFGD08no3Q2fz4fN4aSopJy46Aj6dEsjMiwEj8fLjr05hIcG09hkRaVSolG3Tv9eWV2LTqth6+5sHnjmNTp3TOKyi0YxKL3nMWvU+poaEe02vNWVOLdvQh4RhXtPOYLBiHbkONQD/U5B6ozByIyByINDCH3l3SPjfT5cLjf5+cU8OPs1Lhw1CIVCzsjMfugvvpINW3fRZLWRJOiJ7JpOTGTb7Jz6yZf9uQ9a4qyzpsDK6Gon5lD/sytn71amjPHXW9y8Yy/NVhvBgUYMXfVkpncnKyeP9J7Hjzt1+zy8VbqUGWGDTrv8EhInw3GVO7PZ3MNisew0m83v4i9m7sBft+6s4xOh1tOMyRCIx+PFZrej0/ofDg+OTODBZ17FN2vIUXchJSQkjo9WIcPh9vFNdiNquYioFJn4wgAGrXmQKxZlEtTsxJ//CII1cpKCT1zDpz0c3s2/c0kpDwwKo4PJ/yCWXKwl/ooIwN2HLHexERGMyOiPy+1hRvFWmq02VHIF6dqUVmPK9tYi7vdy88wxLKrawDzH9zz54q0oFAoqq2t56NnX+WD+E0SEmnD4XEwJzeCq8KEt99Cc5CvZsHU3czd/yK59+5k8dih3XHsZk8YM5WBhKTNvfQSNWs3MKWOYPmk0DqeTbmkp3HPT5VgOFHDdPU8hCAJxMZFcdtEoJo0Z2mKp69+7K06Xixm3PoLT5eLiccOZdemFzF3wIRFhJr5espIXHr2DAX26sfKLN1j923aWrFhPWlIsReXlFBWVMGrKRYhOB4JaDQg41q0ErxfdhCmoOvstKbX1jdjsdmKj/EWhP/v2F/bm5JF9oICHbruahNgofli+joOFJWzPyuEfs6YxbEAf7rphBis3bEWn0eD2eLDa7KzauB2dRs0Py9YxfdIFhIUEcfWdTzCgb3cumTASrVZNoCGAsopqoiJCz8j3QuLU0zVcQ4zRXxfukrFj+GXdeuzWJhx520h89Xk+mPMsAF8/tI777r2SwMgTW+K2Ww8SrTadUzGqEhJw/CLmdwOXHSou/gKQgN8l8yWOHlN3xvCJIpnxOpwNPpz2GMJMKiprakmM9Vvq0lISSJ84nZ1lNnrFSKZyCYk/wuxRkRysd/FVVgNxokhVVj1P75jBZyMjuTTqyH2lV8kYkaxn7roq7sgIRXWSBX/dXpGHlpUzZ3QkT/xayQ19TcwfF/2XSoYiiiKiKB51s6m8sgZLXgFDM3rzxLy3AbDZHThdbl5+4m5mv/oelgMFpCbF069HGmOGDUAURbxeLz5RRKVUtplT4vxABEw6v+ufXC4nzGTC7fGQVQuDEqLZU3eAsoZqOmhiAfA4vST2jSAp3R9rNi1sAHJBxrd12xlHOmmpSUwdP5w3PviSx+++AQGBWRHDWp2zpLyKJ196h5mTx3D7NZfSt0dai+L36nufcfWlFzJzytiW/rddc2nL69TEWD67eiyRo8cj1x3dDVStUvHd/z3JD+u2svqn5VRu/YnLuvVhYV4Bl44bRrdOKYg+H4JSyeihGYwemkHNA7dSUF7D63UqfvrvhziQ8Yi2gcU9L2B1aT3jhg+kb+5B0lKTeHze26zeuA2VUsmYYQO464YZlFZUo1QoeOAfVxETFY5KpeRgUSkxUeFMHjuMLuZkBEEgs18PMvv1aCXv7Af/0fozEUWeuPdGPl38C7PufJxZl0xg5pSxKJVnJmmUxOlBKafl2RRmMhESHMzPXy1l5qZPaLIq0G3LwZHQgUvmDkEUReYu+JDFP69iwXMP0sXcttahV/TxY802Lg8fcqYvRULihBzv12ocMBD/82cm0NFisdSZzeb1Z0Sy47Cz3MHCnXXMG9uDC4J7sKl6N2VVVS3KnSAIjOrXGXtzAyeoqCAhIXEUqu0ervmmmNmjIpl9QSRvfSzjucJsrugTR/Ta79hTkk7kLb0RZP6HZb3DS6dQNR6fiNPjw6BuX6YTURSRy2BymhEBuL5PMBq5cF4rds02Oz8uX0dUeCiBxgC6p6Vw2S0PUVVTT0bvrkSFh3LHdZfxr2dfZ0dWDnaHk+mTRjM0ozc3XTGVlRv8ae8z+/oXoVdNG09NXSNZlgPszj7AmGEDeOntj/nyh18JDQniy7fnoJBLmWXOR1xekQeXlvPBxUcydSgVCnwhZjp0N7J2bRY+u7fl2Jp39pCcEUlCn4iWtguCe7CifneL1XvquOHMuvNxiq3VXPbjc3w/5fFW8UAxkWF8vmA2hoC2adfn/fsu4OjxcLZffkA7YgyBHidifQ2iSoWgOLKE8JQUgkKJIiIK61efMCo+iXEP/gNvRRn2X77njpzNiNkilb8sRN1vAIG33U/NvTcT9K+nMT33GoMFgS71jXy3ZAU9u3cmLFhH1K793DUhnKWrN+LxeklLTSKzb3fuuPYyAg0BOJxOAO66YUaba3nkn9ee7McB+NcPqUnxPHpn66pPoaagPzSfxLnB2gIblzS4SDnkDRJuMpFUtQj1lFk0lTRRXlJKzbICek9N9a8hB6cjk8l4YcFC3pv377b3g8/J44mSu67EucnxlDufxWLxms3m3kCexWKpO9R+1v2jekVpSTWp+KJqPV318cRGRrBu2/ZWfToFiky9/3nemfcoyVFSilkJiZMhMkDJ+MFhLM5upN6hp0GUUypUc3tWFFEFFQwdIuLzicgPKXcmrYILzUbe2lKDSatod8HwZ1ZXcUGHADLj/QvNuMBT4955pvh1/RYO5JdQXF6JMUDHXTfM5LaHnifUFITD6WRAn+50T0vhg5cfp7HZyprfdtC9s9/N7qYrpqDVaggJMqI4tEiODA9h+qTRrc4RGxVBbFQEPTqntrTddcNMbrlqGh6PR1LszmM0Clkrxe4wcyYks7J+D2q9iobmI3XZht3SvVXxboAAuYZXU65vaQ8LCWb802O49JOncH9Vz0/K9VwwJJ3vl61FrVIRaNAzpkcqNY/fjaDWoOzcHU9BHpr+gxA0Gnw2G97SIgyzbm45h7eynKZ3X0czYAgB0y4HoOm9/8O1eweysHBkAQbsK5ciKFWEvvER+mlXIDtk2VNERKHu3ts/T1UFiCAL9ce1Bd3/BLKgoJaFsynIyKwZR6ogTYr2Wyz79ezc0nbBkCMJtY8Vpych8b+MTTW0KHYAwQYDJSgxduxESd8kVhdYeXRIeMvxlKRYgoP0FJQVY7XZW7LZgr9u4oW7Z7Ok2yPHdcncW+Wg1uZlUIJUv07izHKimLuOwDXAt4fed8FfZPys8p2lkQs6BPBT7Xbi1aF0DImivrERh8uFRuVfHAYZA4gdNpWXv9vK/BtHn2BGCQmJ31Nj8+DxwcWdjXibnKidLm7tYiZpQDyvNCSS7y3C2dQVXXDrAuHX9Ta1KHzHY2upneRgFTf3NRGgOn+sdM1WG599+wsOp4vrpk9k9cbthAQH0rNzaovr12vP3NdqIQCg0ajRaNRcPGFES1ti3B9PEiMIAlqNGpBiPc5nHB4fn++p59Kura1Ci7IasHitpIUm0NDUSFOVHVudA5fVTVyv8Dbz5NhKeaF4MV10cdwQNQqlUoHu2wZuVNWRGBWKWqVk5fqt7D+Qz3uzxiEL7k/AzGvx5OfhratF2bEztmU/EnDx5ai79ERmMOKyZOHJy0U7agIN//ciuglTkQUcqacXcPl1uHZtx1NajK+xnrD/LEKm1R2KlTs68rCI1u/DI47RU0Li1JJV6aDe4SVII8ddcBDNnMfYO+oyKtf4SB/qIKF0OXkrYugwyp+g58ufl2K120ntEMOmnbsZMfDIpkK2rYQwlfGYil2d3csjy8v5R3oIbp9Ind3LxmIb41KPXY/yq70NjO4QQEA7vV4kJI7H8VZVjwALgXDgFbPZPBT4Cbj3TAh2LHyiyL4qJ0qZQKW7kXBVIHK5nOjwcIpKS1v1fX5qF7KWfUN9w3FL80lISPwPIvDz/iY2FtnQhzmwh3oJsemI0cO1F2ZSmFvB8ld2tBknlwnk1bm46qui485XqAdSAAAgAElEQVS/s9xOebObUL3ilLpg7sjK4bNvfzll8/0er9fHw3PewHKgkKS4aHyiyL/vvoHbrrmUyWOHkZoUD9BGsZOQOCYieHxtm7tFaLgmqTd9NR0pzK1g13d51BU301zjwO5wtumfoo0kRmVipzWfL9a9x3VCFxa/OZsL+nena20RmppKXr5tJvMfvJkOEy5EkCtQ9+6PfuoMjNfdSsC0ywl59lXUfTOQBwUjyOXI9Aaa3n+TyisnIqjUBEy/utU5BZUadd8M9BOnYbjiev+44yh2EhJnE5+I/8EG2L7/EoryGfzd+1x0Q1f2a1VYIrpwMDixpb/+kOU5PiqKR59/G8eh+67CVc/GxhzSdLFtzrEkt4lXf6smWCvnzgGhdAnXMDwpgGaXlwZHW7uI1ydS3uTG6fGRW+NCo5BhdR3lB6GdLNhcw7IDzdjcPmZ8UUiT09sS7/3iuir2VTnanF/ir8kxV1UWi2WzxWLpb7FYLrNYLM3ARiDZYrFsPHPitUUmCNw/KAy5TKDG3UiIwghAUmwsecUlrfpGhoVwzT/vZuE+x9GmkpCQOAahOgX3Zoby84FmSiu1TOiQzpY9e1iyeg09Oqfy1BM3M/KBnlTV1LUZmxSk5PnRkTywtAyXt/XDo9rmYeGOOq7tbSItTNNm7J8lQK/ji++X8/bH37Av9yAATc3Wdo8XRZHGpqP3L6usRqdRM/vBWxg/MhOd9tTLL/H3QqOUMbN721iuZnUJ88u/JCI6GK/aQ+a1Xeg0Ig55Bx///fprmqytv6MyQcbjiZfxZurNDKrX4a0sRx8Vjf6iaTR/8i71zz6MKj+X3pn9kWnbt/mgiI3HNHs+oW98RPBDT7eKr5OQON/oFqEhSOu3ijk3r0cel0iTzsAbq8vIrnPTtW9nhgY58Tb4n2kut4tLxlxA//oKwk1Gvvl5FV7Rx5XZ8yl11TJO34v/e39Ry/wNDi/9Y7XolDLe2lJDasiRjY64QBXTuwXxW7GNKuuRhPPZ1U7mbahGrZDxwOAwDtS5uG9pGeAv3eD2isxeXcmq/GYanV6qbf6xoijS6PSytdSOyytyxZdFNDu9TOscSL8YvwxvXBiDQS3nxm9LyK11kRGno4NJzYYiK7vK7ZQ1ubnsi0IA8utduL0iHq9Ifr3r9H4QEmeEdm+ZWywWp8VicZ9OYdrDgVon7+/w33zfdX0Ig9y/wEqMjaGorAy3p3Wlhgv7JBHrKiEnr/CMyyohcb6yr8rBN/saeXlcFPvLt7JvVSkut5uGJr8VfNvubK6d/gwPP/pGmxggQRCIDFAyOc3IoqwGFmf761D+Vmzjna216E+DG2azzc7j894mNDiQl5+4i7z8YjZs3Y3P5+Pae57i3idfYeX6rWTl5AGw6IfluNzuFtktBwp488OvGHvFP5n96nsA/Hvum7z8zic8Pu9tHn1hAbFR4Tz70G0t8XESEn+WWpuHf/1S3qZ9XaGV5Sv68slvDSCjxVq3JSsLg15PeVXVUedTyRSkTJyFukcf//uuPQm8+1EMV96IduS4k5ZPmZyKPPjU1a+UkDhbfGdppOaQcmR68kV0F0zAFhzK6Bg5N/ULITZQydYPPmPXfz9i4Y46GpptGA1GlLu2cLHaym87s3jgwAckacJ5LOFShEI3G7fuBvwWsKu+KkIll3FVj+Bjxpzn1bmoOiRDg8NLWpia50dHtRw3h6p5aaz//W/FNmrtXq7vY2JAnJ4luU2sL7Sxq9zO06sqaXT6WJXfjEouMHdMJDqVjFC9gkCNX4E9rMi+NDaKVJOKzHg9KrmASi7g8opEGZR8eCje983NtRQ0uMipdfLeNv/6ekOR9U9ZESXOLufdKiXGoGR0hwBsXicWeykDjWYAdBoNUWFhHCgsolNyUkv/II0cd3MD93ycy5cPxKBSSv7MEhInIilIRbRByXXfFNMhvZCLZvRgkKcz3674Fa/XS2xUOL26mQmJN+Byu1Gr2iZCGRCnJyHIjUbhj8FLDlYRpAlsKSJ7KiivquGzb39h6cqNDMnoTaAxgKBAA88+dFtLn4WvPMF3y9byyeKljB8xkE4dElm/ZRerNmzD6XLz2F3X8+v6rVgOFPCfuQ8TE+mPaZo5ZSyrNmwjPiaSbp3apsKWkPizGDVyruvfNuFXSGAd/TruIWCrGU33YArLSomPisJmt9O7S2fKq6tJTUxsNUYURWzffIY7L5egex5taVf37Hu6L0NC4pxncIKeQLUcURTxWZvRTb6Map2JDcVN9FUbGNUhgMSrrsT18I1ccOk0vtwH3j070U+ezpDZD+OYMJgfP9rHlB4DEVNFduyxMKBPN268fzbPPHAL38xIOJIYSHtkaS2KIu9++i25B4tIiIlEHNCHn7Jr2SuPJy1Mw7hUQ8smoyAIqBX+zc97M8NayX9pl0AcHhGFTCBQIyfWqOTugf4+kQHHLofzvzF8faKPWO41h8717AWRLW1PjPAbTJbnWUkL0+ATRWSnsM7sjjI7PaO0NDi8aBT+67W6fOhV/r/NLh8RAeedanLOccwtdLPZ/N6hvzedOXFOjMsnEqSRU+Kq5YWib1od69Qhmaz9+9tYEqaOGYRCLuedT787k6JKSJy3eESRjFgdgxP0CAUCvmwv0eHh6DQamu12wkNNPPD0FVx/4yRkwrEtcdEGJfUOH9lVDqxu3ylT7Hw+Hza7A5fLjUqh4NVn7uOBW686arFzjUbNJReO5M05/2LSmKHI5TKee+g2+vXozIjMvsREhnHzlVN56fG7iI2KaJnD3CGBG6+YwtRxw1vi6SQkTiW/jwP6PZ5mL3FuJc3DE6nQp7Itay+lVVVEhoYSHR5OYVk5eUXF1DY0ACA6HDTMexr7ip8ImPnHSgBISPyV8flEBAHweWn67wIEQaC52EFvnQdjaCW9t97Lz9pCViSHcvCT9xAUaj5bvpcXd9jZlTGREYu+ISMwno8W/cTrH3yG5UABvVLiuarOwvePP8Xm4tau0i63m4rqWgDSUpNIS02ioKScRWuzePTZ+QwNqGF0Bz2iKPLY3Dd5bO6bx5VfEAS0ShlKuUBC0OnPKv3I0HCCNHKeXFnJ6ny/i2jBcVw2CxtciKJIVqWDVfn+DL8bi2xYXT7q7H4XUlEU+Xh3PdU2D9/nNPLpngZEUWT6F4U0OLxkVTr41tJ42q/t78Dx1OOeZrP5BeASs9mc8PsDFovlodMr1rHZXmYnt8aJU28lSNE6vWxSTAwbd+yktLKKmIgjGcUUcjlv3zSCx974nO0lzVJhcwmJE5Bb4+L7nEZSQ9QUlzpQ4d8ZNOj1NFutBAYEUJPfyC9vb2WdsIVXn77vOHM5aXB4WV1g5dXx0UdVwNrLlp37qK6tp6yymoOFpTx5303cMmvaSc+jUiq56pIJf1gOCYlTQZ3dw4e76nhwcOsMmLM6DSe/qYKcbaUMv7Yzi5dls27rNnqmpREREkKgwcDyDRvQajRcNm4s9vcXIDqdhLywAEEjxYJKSPwvawptOL0iOqWCkDmvAxCfFonB4CPPVYYXH5amaspSbmTokudICzQhu+QKxoeqqbUmUzZnN8P3rmNXYDyCDIb3SCUtfw/OABWm/D3MfX0B/Z69p+X5tuiHFbz+3heMGtyP+265koF9uwN+F87O8eE88Mx8Mvv14IFbZ1FZXUddfSM//bqBscMHnPBaVqzbQkbvrmck7vvBwWEg+uMDv9rbwL+HR7DsQDNdw9XoVTLKmz2khqh5cmUlz10QiUImtFj61hZaSQhS0uzysa3UTp9obYsb6uXdg1vqaS6emYBMEEiP1ZEeKyUkOxUcT7mbAmQCFwKWMyPOiRmaGED3SC3L6toqdzKZjN6dO7M1aw8xESNaHQs1BXHN1Vewr7IZk9hMQmwkEhISR6dXlJYhiXou/rSQ9Tdeh+xQecsAva4lmUNQjJ7BN3blnfu/OGrR48OMSfGnf/7fdO8ng8frpaC4jOSEGBYs/JL84jI+fPXJPzyfhMS5QESAktv/R7ETRZGHFyxk2sRMegbFkFPjQgzrjKnxAF1TUxAEgdGZA3G6XKzctIn84hIC1/6Kac5rkmInIXEMJnUyolPK8DXU0/zFQozX306I0kR28X4KghzcHXMRsyKHgxnybFtI3PgriVdfhyBXkONpZGvGQEYs+4VXR3WjOXsPzeVl2AsPEDL3TdTbt/BU30Etz8D9+cV0iI/hiXtv5Idla6mtb2zJoiyXCVwyKp3hvTuiUCjQaTW8Oedf5B4sRC6X8/PKDazZtJNbZ00jKiK0zXXszt7PQ8++zvCBfVqFH5wuDrtudovQ0C3C//uyv9ZJRpyOolobi3OsPDgkgncm+bOHhuoULR46dw8IQSbzj/99gpnDHP7/OpVunxJ+jqncWSyWfCDfbDavBoxAZyDXYrG0zX9+BtlUbAOgky62jXIH0DEpkS179lBaWUl0eOuHZr8YHU35e7nh+TWMTTeTFhPEuOEDz4TYEhLnFRWr1+Dq2ov543vxzUfr6ZwQT5fBCRj1AdQfSqoiV8pRupUEKHXkHiyiY/LpcV1ctWEbr773OUlxUbzw6D95+4WHsdkdUgFjifOeyqIyvnztYy6+bSaewoM0f/Y+78/ox/L+WUwISCc8JpiGGifRocFk9h7aMk6lVKJSKkmKjWXr1i1Ed+3LkOi2xdAlTg1VtbXU1De0iueXOL+o/epzPDffAo0NOLdsQLzuNgp/rsbWxcGMgEyMBr9H1wF7OW+lBHDp0IdJkvuXyBZ7Kd5uRjqO+z9qbCJ6UzgrSkq5IHMohcEJzFHpmCsXWbDwK0rKK1nz2w7mP3Uv/Xt3ZdTg9KPKE2pqvdl52PVfp9WwP7+Y2x5+ngmjBnHt9Imt+s1d8CGP3XU9XTt1wOfzkb2/gG17sjHodWT269Fm3lOB0+WirKqKsqoqNCoV/XQiXy9Zh9Vup0d0FGVVMipratmdk4NeqyXcZMJqt5NXXIw5KQlzUmKb9bjE6aU9UYuTgcvxl0K4z2w2f26xWOaeXrGOjUbp1/DDlYFEq4LbHJfLZAzo1ZNVmzZz6bixyOWtg0lHZPZlRZGbX9at4dKHZlFdW09NXQOlFVUMG9DnT7mMnW28dTU4t2xEtNtR907HnbMXQaNF3T8TQS4FqEq0H31xLrqlixgwfBDjU1axINUfepscF8uPq1bTr1s35DIZeRvLuGbiRGz2P1ZuJMtygGVrN3PNpRdhNOgpr6qhudlGSpJ/obo/v5inX3mXp++/mfReXQD/bp+k2En8FRDLS1DUNlH72BYQRVw7tvDRoDoUah3BhzYvU0PUpJhU7Klw0DWitWUuITqazTt3kZNoZoDbjVJ57MQKEieP1+ulsKyMlb9tYnhG/xMPkDhnUTmaEZx2fE4HglqDIAhc+EgGS1avYUfFfsYE+ZUwX6WTCLuBNbID9KEbABZ7CROi+qIxGHl/eyV9Y/pS6ijm5+Z9ZDaUMWdEFPYHrqchuT/BgcEs+fAVtJo/Fl8eGRbCP2ZNIyLMRO9unSguq2Tb7mx6dzMTFGjkrecfaklg9vHXP/HhV0vI7NcDgAmjBlFSXkVVTR0/rljHoPSeFJdV4nA4ufrSC1usaO1BFEVq6utZt20blTW1RISEEBkWRrPNjsPpZNyQwQQZjWzdk8WqTZsJMwUzIqM/Pp+PyppajAEBpHfvxsGSEpat34BMJqNDfBxymQxTYBAhQUEEGgJOSiaJ9tOeFf9MYJDFYvGYzWYlsB44a8pd9wj/om5+yQ+EKY1+M/r/0CEujtz8fLZmZZHevXub409PHwDTB/De9lqKd2/mh88/JjI8hOLSSq6cNv60X8OpxFNeimhtRpGUQu0Dt6Hs0BFfcyPWLz9CmdYVnA6UaV1xZ2f5Fb1e/bB+9QmaQSOQh0ecbfElzlGEAAOBg4Yg+kQ01QqC+voXmmEmE0FGA5a8g3RO6UD/yzship3wuN04Xa6jZs08Hg6Xm9LyKi69+V/cce1llFRU8eGXSxjYtzvXzZhEaHAgsx/8B/16dj4dlykhcVYxBekZ0FSKa3sl8ogofDodae4guq3LJrDjkXupyeXjP9tqeWF0FAr5kQ1IdWEeM7t05JeKWg6WlNDxfzJo/h0pqagkKzcXn+ijqrYOc1IiXVJTUSuVeH0+isrKCTQEEBwYiELuz57o8XqprKmloqYaj8dDsDEQr8/Hpl27UKtUjB6USUyE9Lw835GpNXgddgSNFluDk70/F2BI1fNe4a+MNadTUlHJL+vWo5PJede+nrp8BxlGM88nXYXvUOajh4aE4/aKBGsTCFeE8+PKldwwbTKbr7+bpLw8InZvoXRbAskDMgEoLCvDFBiIQd/W0+xYCILAtAkjAf8G5+KfV/H8Gwu5+8aZTB13ZM07c8pYZkwe02KUcLs93PX4PBQKBVHhIfTp1omo8BD+/eKbfPrtUvr3TaNXt46kJSfTKTm5jTHD4XLhcDjYl5dHTV09tQ0N9Ohk5sJhw9oYSg7Tv0d3+vdovc6OjTwS9tTLaKRnp06UV1eTX+KvRZ1bUMDGnTvx+XykJMRjtdmprK1Fq1ETZDAwLD1dUvr+JO1R7gSLxeIBsFgsbrPZfFZr3X2b3cjtQIPXRoo26qh9BEFgcN++LPp5KZGhocRHRx+1X79oHZPMw5k5qhcRwQY8bjflVTXs2rufC4akn3NWPNHtz1QkKFXYf/0ZTeYwPEX5+GqrUXboSOibHx9TZl9YBKLLiej14q2tpvqu6wiYeS26cZMRTsNNdLwYLIlznzpBQ6UDoprddNoUhXb8kYVmRs+eLFm9BqfLSbPNjrhNxdqibXQdnMTE0UPaNb8oivy6fitDMnrRp1sn9uYcJCsnjxtmTuaKqeP4dPFSvvlpJffefIWk2En8ZbF49HwcM5KZvqUI+gCybpjGII2bi1atwDS5HlL8CoVRLeelcW2fY6LNBgKkJsSTm1/wt1PurDYbcrkcjdpvJWm22fhpzRrSu3VDrVLRu0sXtmXt5ePvvkepVCL6fIQGB2O122lobkapUOB2uxEEgZCgICJCQ1EqFBwoKsLldjNywIBWydkkzn9EhwNBo0EulxEcZ6BEayXCa0AhyNmyZzcDe/VELpczNn4Eaxr28XLxdwQqdC1ltwCUcoF+MTqqrSrQGFm/bTv7CwuY6G7GU5hL3Qdv8o3VgUatorahEYfTSbeOHUnv7rcE1jc2UlBaRlqHZFQnsLanJMby+jP3s/CrJVw0anCb40cUOzdWu4N/3XEVMpmA3emksraG6vpaRg3rQ3RoJBVVtfRO68w3S1ewLWsfmX16ERwYiMfjIb+khO1796FQKIiLjCQuKopxQwYfU6k7GQRBICosjKiwI+Ud/ErrSnbuWsn0yReAT8623dmkJMZKa8dTQHuUu7Vms3kRsAYYBKw7vSIdn1HJfr/oOnfzUWPuDhOg0zFmUCY/rV7DpJEjMQW1LSp52MVlcbYMTY2dGd2C2H+wjv9+8T2LfljOXTfMIC317PvYu3L2Yv/5OxzrV4HbjSw0HLweVL3S0fQ7EjN4vBtCmXLkh8l4/e3oxlxEw6tzcKz6Bd1F01AmdkARl9jSR/R6wetBUKnx1tWC14Ms2IS3rBR5TBzurJ0giqi69aLh9bnIgoJRxMThrSjDsXYFql7pGK+99bT8f0icfoIc9ei+fBf1/7N33+FxVFcDh38z24tWq957Gcuy5CK5V1xwo5gWGwihhRAInQRIKEkISSAhjRCSfEBCqAmhd2zAuPde17Jlq1iyepe2znx/rC3bcZPB67XNfZ/HD6vd2dmzQrszZ+6951x6CefdPQiDfPCrIiEmhrTERJat34DT4WDW7Mk0LG5g9YZtfU7unnvtXeYtXMHA/nnEREXSPz+L/vnBz5rFbOL62ReG5H0Jwpkk2+Dhgq3vQqQdf3kZE/OfpOH6ywBQ6/fBId/b9V1+HltQz1MzgkleoKkR48ASJL2eLJ+PRavXsK8heHV8cP+Ckx5FPxOpqsq+xkYq9tZgtZgpzM1Fr9ezu7qaNZu30N7ViaZBQFVJiInBoNfTLzuLIiW/dx/Txo5B0zSa29ow6PU47MFziEAgQI/Hg8loRJakU3ISK5zZAg370Dw9SCYLJruBnJFJ7NxdTZTXQlNrKy3tHVwwIb/3byFCZ+Ef+z4n0XD0dWyxNj1XTRrJ4tVrGFtaSmxGBtrlV6O/5yZsX7xH/Zzvcv7o0Xh8Pt745FOinZHkpKWxbts2qvfVsbeujmljx5xwlMpsNnHTVbOO+fiy9etZv207ZpORrNQ0NE3FarawaUcZcdFRXHr+FJyOYGGzlrZ2Vqx2UV1bx3/fnU+//HSKCrJxRjqYPWN67wjjqb5A39HZhWtXJUOKFCpr6vjBT55g6oQRDBlQgJKVRXVtPa6dFcQ4nSK5OwVOmNy5XK4fKooyEygAXnC5XB+GPqxj6/GrAORbk8k2x+P3Bqjd2kzaoLgjtk2Ki2NMyRDemz+fCyaMJzbqyDV6ANcNjiKggavRw7x6Cy/96ee8++kCHnny7zz/5MM4Ivo+nH4qaYEAyDLd77+JPjOb2Kf/hWSxEaipQp+Zg6T/6uvo9GmZRD/+F3rmfYB70Xw8SxbgfOBRer6ch7GwGLWtlaYf3YIcEYHm9YIsQyCALi6B6Kf+iRYI0OP1YtA0jAUD8NdU41m5BAxGHLffjyEr7xT+JoTTzVMwBMeDP6F6cyMJzREw/vDHRw4aiNMRwZotW0Gvkm1J5bkN76JpGh6fj5q6erLTUo+67+4eN6+98ymvPfNLYqKOvOgiCN8UHlsk6uBRGC0SkXc/yNy2TcQ/9iTx732Ov7b6sG1jLDru2N/wXPP7afnF/dguuxrL2IkYDQaK8vN5a948kuLiaF3RwbjSEixm81l5oqRpGis2bmT7rnLMZhNZKanU1Dewdus2ohwO2js7GVtaQnRkJLIsY9DrKa+qpr2zk0H9C47Y34GRuUPpdDrsVlF2/ZvCn1eIPjmNwL4aDFk5LF64hcYNbRR/J4s93t1s2rGDwtycw5L8GEMEnxX/DN1xerlG2u3MnHDwACnp9cT8/lmkB26jf/UudAOLsep0TBo5kvkrVtDU0kp5VTVXTJvKlytW8tK775GTnk5eRjrxMTFIkoTP7+fTRYsxGY1kpCTT2d2N2WTCZDTS3dNDbFQUOysqaW5rw2axUN/czLWXzMJsNJ4wUYyKdPDaM4/R2d3DFlc5MU4Hqqbxs98/i5KejZQgU7OvgZ8++Xci7Dbcbg/Xz7mQ0UMH8vjT/+LiqeMpLghW7XXtqmDT9p1MO28UaBp2mxWP18v9v3wag17HHTfOwWwycuUPHsLt8RIfE8V137qAGZNG88ef33PY4En/iIMXeIWvr0/Zwf6ELqxJ3QGuRg8Ad6QEe1R5unyUL6slpTgWWT7yIJaXmYkky7w/fz6TRow46hRNSZLQS5AUoWdshg2dTsafPph/PDUW1etmxbotDB1YcNrmAGuaBl4vjbdfR+xT/8R578OHPS4fcjX365BkGevUi7BOPViNSfP0oHk9GHIVEt6Yi9rUiBwTx96KKlau38oq1252fO8BXv/747z+2rssfeVRcjNTSUqIY85tV6MBfkk64VQD4cy2xpxO59wPcI+fyS+73+I//PCwx60WCyWFhVTW1FLf3Ezz1k6mnzeSN+fOw9PZQaffzyVTpuCMiDjib2Hbzj2MHjqQhNjo0/mWBOGMU6NzsMmjMaGzAkmn46PmNcxKHY4yMA/f7rLDttXJEmmRBl5a38JFOz9CjozCPObg+pvhA4vpn5uD2WjkxXff46X33icmMpJhA4tJS0w8K5I8v9/Pph1l1DU10t7ZxazJk3A6HEDwuNje2UlzWxuJsbFY/qftQ//cnHCELJwl9mpW/DXVmIYMxzRkOEsq3iYu00GCLYp4ZzTby3dzzcUXHfG84yV2xyLp9ThuvZeud1/HOvMSAFIS4rlwwjj23nUTY/L6EXHpJVw0aSLNbW3srKjk82XLCagqWakptLZ3YDAYSE6IZ2dFJWaTEX8gQHdPDw67nS1lO8lITmZgP4XG5haGDyzGepJtUOxWC8P3FynTNI05F03hlh8/QUxUJK88/Qt++P1v4/Z4sdusZKYloQZU8rPTefCJZwB44sHbqW9sZsXazfzx2dcwGY18+OIf+L9X3iHCbiUvM40vl67hmstn8M7zv0VVNRwRNlRVRZblM2JW3LnsrCuhOCrdRqOvnV9Wvskfcq5n/bu7iE6PIOANIJuP/nZy09OxmS3MXbIEJSuL0gGF6I8y6uUw6RiYaCGgarS5Vcx6HZ9vbeTvL8+Fp19g1rQJXHz+OJyRESF5b75dO+h65z/IVhuOW+4h5snQN6TVNI0d5ZVs3r6L/Jx0iqZexPK1m2jevoSVG7aSFB/Dzd++lDfnL6exuZVRpcXc+d056HQyN8y5kIGF+ezdV09tXSM6nY41G7fx09/9H9+7ehbfunBKSGMXQmdc3WoC5Q0YRw2hO+3Yy2zzszJZsm4tSaVx6Ms1JDVAwrrlmNKyefOTTxlgNTE0Lgrz6IMnoSVF/Sgp6nca3oUgnNnyu6oZ72/HftMdALT6u2hY1sruxmiyBww6YntVA9XdQ/O7b7Hylt8xqTvAtkYPtR0+jDqJWQXBkfCxJSVE2G10dfeweM0aTEYTU8eMxm610tjSQoTNdsZN29zX0MiSdeuwmEykJMQzavDg3imUELwIGxkRQWREaI6/wrktY+cqAjVV+Ksr0DxuNvh2U6U1scVXxXkF/RnTk4HNcuqqMBuy83De/SC+3Tvx7dqBdfIMzLt3Emc1IzfV0/3Of7BdeiXRkZEMKy5iaNEAmlpb2VO9l+R4C8VKsA9eYW7ucV8nMyXla8cqSRIXnT+OkuICfD4fOp1MSfGRI+BzLj6f2RdNYcPWMixmE+eNKuW8UUVxJNUAACAASURBVKV4fT5kSUKv13Pb9VccMRByoMcfIAqlnCYnTO4URUlwuVx1pyOYvnA1eqj0NNLoawdg+FX90FSND3+5klHX9ic6/ehf/EnxcVwxbSqL1qzhPx99zHkjhh+z74ZOlripNDiqYI+O467bbsLZs48nv9jNyNYOOru7Wbh8HaOGDiQj5etfEfXt2kHnK8/jKy/DdtHlWM4PrjeSHaGdshYIqHz/gV9T39TCkAEKPr+fon65rFq/leraeoYO6k90ZPCq6Z03zjni+Xq9fv+Vn8Le+0YPHcjLTz2KP+APaexCaG2ILeSmYSY2/KcSJSsRBhx9u8LcXHw+H8vXbyTZnUzVF59yntFLU7SDys5OdhoNDItLQnO78axbSVNGPv9682N+cvv1p/cN/Q8tEKD743fofu+/GPL6YZ1xCZrbjS412GvIt2UDlonTAAg0NdAz70MCzY3495QjO6OxX3U9ktmCe8E87LOvxbt5PZrXE7wyPLAEyWZHttqRzGb8+2rofu+/OL53J+5VS5HNFjzrVmFQ+mMaNrpP3x9qRzu+su3oM7LQxcTh2bgWY8EAQAK9/qwYlRGOtNOSyL4bfkJsTrBwSou/i8Gjc8gwxGG0HHl49r/+T2auXkbXmMnU6+w0uwPUdPgYmWbFF9B6t8vPyuy9nZ2WytJ161m2bj2ZqSl8vmw5eRnpTBo5MtRvr892VVaxcPUqhhYVUZibK/6ehVPOa3eiedzoUjPweLvRL5WJtUSwKa6S7yZNpsB69GUEp4IuJg5N0+h44a9YJkzFMm4SzY/cg2XaRcjW4LIfSZKIjYo65vKh0yEl8cjlTf9LkiQGFeYfdt+hs3NE8nZm6MvI3ZuKojQAzwMfuVwuNcQxHZfNKLPX00SqKbj2oGp9A7FZDqbdX4reqCPgC6AzHH1htNViYeqYMZRXVfHposV8Z9bFJ1xEPSYj+MHz+LM53xdDXrqTl1dWs3Cvyitv/wZJZ+CxH36XAUo2LW0dxMX0/YMZaKij/dmn8Lm2YLv82zgfeBTJ+NV6o/TV7soaPvpiCV6vj7u/dxV33XQl/fOyDjuY3n7D7K/1GonxMV83TCHMYvR+jAWlFKxaSMXkqcfddlBBARnRKfg8Km+++0fUi6eQd8XV5Goa73z2OXstdtIb6+l642V6avfRP60/amcHsv3ICzGaptHz0TsYB5eiD2FT5u73/4t7yQIi73gAz+rldPzzr6CqRN7/cyS9Aa2nB4DW3/4Mz9qVmMdOQp+SjnnEONSuDmRHJJLZii4peNXUvWQ+vvKd6FPT6Xz1H6BpaF4PkXc9iHn4aEylIwDw79pB90dvYx43mc6Xn6Pz5efQAn6if/Vn1OZGfGXbsE69CO+2TWhuNz7XFjxrVuCvKEefk49l3GSs0y+m+/03MOQX4N2wJrhv0cfyrGSXVWyqt/fnp3JvxFyrxxflp+3Oa4j5w7PINjtqdzddb76CecRY/Lt3kjbnSm6PDK4hy48JHjNaegL8aVkjt4+IQT7k+1yWZYYVF/HuZ5+zZE0dF0+ayJcrV/LWvHmMKy0lEFBJiA3Pd3ZNfT1fLFuOLxDgggkTiIsWU7WF0NBpKmpXF8bYeIxGA/clO7h953M0+zrJO0bl9VPBkBUcedM0jYjrbsGQ1w/ZaiP26RdBp0N19yCbRd9W4dTqS0GVMYqiFAA3AA8pivI58LzL5SoPeXRHkeow0B7oIdMcHHVr2NWKI9GKJdJEZ1MP8363losfG3XU9XcHZKelsbmsjM1lZRQrSp+uEpr0MtcMDCZuUwoTGd8vgdRbp3HBy7uJiY9n1a46HvjTqzi6aikqyOWyGedRUlyA2+3B/D/NLLveeg05OgbT4GEY+xfhvOfh406/bG5tJzLCzpYd5bzzyZdYLWa2le3mnpuvJiM1iY+/WEpSfAxJCbEkJ8Qd0TzzwBznex/9I1t37GbqhBHMmBTswVKYn33C9y588yTvWEX7JaOpdNVzuXnECbePSnDQ1ewmKaKUbZqZsQSv8BXkZLN1107Sx45lx5W38epvfs8P9V4abrwC46BSLJNnYBoynK63XsU2azYdLz6Le+E85HkfEHn7/Rhy8k/42idDC/hRW1uwTr8Ey5QLkG12jEeZ/nZgnYR1+iwcP7gP+RiFFyzjJgPguPnu3vsc37sL9HqQJNA0JJ0O05BgA2T7nOuwz7kuGIuq4l2/GtkZhRzhQOvuQhcTvHLqWb0c79aNGHIV7FfdgLGw+LALP1EP/goA8/AxX/M3IoRTQuMeYhe/T3X3FURnR9AqdaGu85PcH+Ke+AuS1YZ32yb0GdnIEQ70GVlEPfTro+5LlmBI8tFPEo0GA5dPm4qmaciyzJwZM/h08RLemjsPk8lESWF/BuTl0djSQqTdftxm6E2trcxbupT0pCRKCgtPOL3T4/WyetNmWjs66OjqYlBBP7LT0mhobmbu4iVMGD6MpLi43nYGghAKuq522v/yW9r//AR892YaO4oZqeTji1HRS6GvlCpJEqaBJQd/1uvp+XIuvrLtOG66A/++GvSJR2/bJQgnq6+Xe2uAcqCE4AStPymKss7lcj0SssiOYfXebv4Vf7Dc+pDLDlZltMdYmPnwcCQJqjc0kFIce8zEbXhxMV+uXMWmHWX0y84mJz2NqP0Lt08k0R488NU1NfGLEg8EuohwOLj3ju8x0N7Dw182Uu02kN/jZfr9/0dcVyV5WWlcnGRj5FVXYhoxFtkRiWyPwDbryOmOBzQ2t/LC6x/w8RdL+cuv7iM+JoqiglxaWtv5/jWXkZoUj8fjZefuKhatWEdtfSNTxg7ne9++hHt+/gc0LZjYSZLEH39+D3feOIeUxHh0OjFsLhxfW4+P6MQs/N4kbl39LP+edu8Jn2OyGdCdN4GOzIOD+znp6Xy5chU33/dLfvfTe5h4w3WkTx2P1tUZHO3auhHT4GHBxUR6A5axE7FfeR3d779J8yP3EPXjxzAUDvza07Q0LThlzbNqGd71q3F8/26kPpxMHi3xO5G+7BeCBY1MQ4b1/qxPTkWfHJwaFHHNTSf9usLZZ647ljE4CaytZ1d5LQ9nvc7CSx8DwFdRjtrUQMvPfoTW3UXC2/OPWyE50qxjbIaN1ze3MiTJQm7M4X+HkiT1fo4kSWLiiOF097jR0Hhn3mds3O6io6uL1MREpo8L9tPaXr4bnU4mxukkwmajrKKCTa4dFORkU72vjpffe58xJUNIjIvDbDSyr7GR2KgoOru6qaqtxWoxs333bmwWKznpaTjsdpat38DCVasxm0xMGnn0ImeCEBJq8Ni0rmkbe3MT+GnBbEz28BV/M4+dhHnUeNTuLpp/dAuWKTOJ+M73whaPcO7oy5q71wkmdC8D33a5XDX7718NnPbkblCShY+b19HPmkymMZ5PnljNtPtLkfaP1BktetydXsoW15BcGENAVdEbj7wqkxAby7emT6O+qRnX7t289/kXGAwGslJSSElIIDEuFp/fz0aXi+jISCwmE6s3byHCZiM+JhpZllm9aTPZ6WlU1tTQ2NJCYmwsyePG8tRlcZgNEl1elW9/+0om66t57o2lbFu1iZKZM2gzR5JwyJS0Ayeeh57A7qmq4bs/fIzpE0fzn7/9itjo4BScS6ZNOOK9/Pj264647+ZvX8q++ia8Ph8jS4sBSE9J/Mq/d+GbxWGUqZK7MWdWMabgyMapR9Wwl0mZ7ehGTO7tkWPQ62lt6WLk8EJ0Oqn371eyRxxWpdU++zsAGPILen/Wp2XQ+rtfEPXw4xiyD17ECTQ1ItvsfS425K+ppv3//oRlwvmYx0/GJEa7hDPEREMDir4H++x+fPDOSpx6G8tf2sagi3MIrFlO92cfoU/Pwnnfz/rc+ibRbiDCpDthnyqjwdC7VubaS2bR3NaGyWjk82XLWbBqNR1dnQQCKjaLhbVbttLa0UFaUhL5WZkM7NePQQUF7K2rZ+2WLSxdtw6drMPt8aCTZSxmM2lJSbR1dpCTlkZRfn7vWpzLzp9y1GOeIISaZfIMvFWVbIq1UNovMayJHYCk04FOh2Q0Efv0v2h64DZAwzp9Frq4hLDGJpzd+nK0eBZY5nK5OhVFOXRicljOkLp9Km83Luf6xIlkGOMpvjCrN7E7wGw3ct4PBgLw6a9XUfqtPGIygqNysv7gqJUkSSTExpAQG8PY0hIampvZs7eGtVu3Ut/cjE6WyUxJob2jk6a2Ngb2U5AlifrmZto7O7lw4nm9i19VVeXz5cuZu3gJ08aNRSfLmHQS32lcRMfLzzP4grsYP/NG5m4r57EFZcRvfRtD7nCmjRrIpJwIrn3yLRL89cRGRTJtwgguOX8Mrz3zy5Naw3coJScDJSfjKz1XEAIBFZe7lu0Zs7nJMqnPz9tbo+fzhz8iul8EI88vJDktjvc+Wcwj997AgpWruOC8CX3el3nUeMyjxqNpGm1PPY5lykzcCz+j57OP0dQApqGjkO0OZHsEEdd9/7DnagE/7kXz8VfsovvTD7BffhXm0RPEyaRwRqkPGPDKOpa/uI3EmdFEtlmJz3OiN+kwjJlI97yPMI+deFIneuMybfhVjWvfquapGck4LSeecibLcu+xbOqYMazatInk+ASG9D/YAujA9P5DpSTEk5IQj8frZXNZWW9lvxNNsRSfQ+F00+wOIm66k3/c+CUNe+qoeKcG7fkBZ8zfohzpJPrR39Hx4t9p+uHNRD36eySTmY6//4Gon/72lL+eFvDT+doLBOpqibjmJnTxBy/+q91ddL70LOYx5+GvrkSfkoZn/WoCDXXBdeprVyBbbRgLB57yuIRToy/J3ShgGnAv8JSiKGtcLtfjLpfLHdrQjq65J0CLvxOn3obqV4nNOn5FySn3DkGnl9m7qRHX/Gom3TX4qNtJkkR8TAzxMcGF5f5AgPbOTqIcjiM+/AU5R/bTkWWZiSNG8MnCRXyxfDnnDSym489PENhXQ8wTf2F2WjDRumhEAdNLcmloLmBdRROpCTHkZsRx0RVXcFF6gBV72ljaZeNSSfrKiZ0gfF2+gEqb5iG/rQxd20CIP3FPGtkZRe7Vk6nOXsMnz6xk7aId3PGzy7nzxiuZMnokr7z/AfsaGkmMiz2pWCRJIuLam9E0MA0bg+2Ka5AjHLT98VdIFgv67OAJZftzf8Z2yRw8a1bQ/vRvMBQUYcjOJfapf6CLPXplXEEIp3LVhls2kjU8kWXvbefCCaVkDd9fgTk+kdi/vBhcu3mS9LLEE+cnYjZItLoDOM19X1NkMZsYN7T0iPuPVwXPZDRSUlh4zMcFIdykznbqZ09l2nd/iWWQnUt+MPyMSewO0MUl4Lz3EXoWfEbLI/cQ98Lb2K+7FU3TcC/6nMC+GggEsF78LdSmRgItTZiKhxxzlN5fU40uPvGIUX/N66H7w7fxbliDcfBQGu+6Ecct92AeM5G2P/ySyDsfQI500v7cn9HFxNH11p7e/oCazxus8KlpqO1ttD7+MP6aagz5BRj7DUCOS8A8fHTIiwMKx9eX5O4il8tVAuByua5QFGUJ8Hhowzq2TKeRDf4movV26ne2su2zSibdcfSEDeidkpk6MI6k/tE0lLfRVNFOv/OOX4lPr9MRHXlyrQh0ssz5Y0bz0YIFfLFsOcOiYoi556Ej/siDzSnjSE44WHb2/vHBQdGCrFQ8fpVOr8pHO9q5ojDyjPsCEs59Zh20aW6sw+IxxTlPuL2vbDutv3+M2GdeYsKoEmRZRm7UExFhZfZFU+hs6qF0wACWrV/PxJEjiDykf1VfyJHBCx26wUN773P+6GeHbWMsHoJktWGZOA3zmIlIFov47AhntNFyA9H6AKY8J5OiByHrJP59+5dc+XSwL6T0NcqKJ0UY2Fzn5sUNLfzm/NBVAxSEs4Xzp7+lJ7GQ/ivraG7vwFp0ZiYglvGTMWQHW4IYMrJQe7rxrF4OmorW2UnDtZeALAcLkhUPofXXDxNxzU0EWpvxrluJbHfg3b4Z7/pV6JLTcN73c2RHJJ6VSzCfN5XmB24n0NJE9C//iD45DeuMS4LHSk3DNGQ4kk5/WPGv/3VgxE5zu7HOmo0+PQvvulX4a6rwrFlO2+8eJeHfn+B1bUZtacEyYf9UbDWA2tqKHBX9tb7bhBPrS3KnKopidLlcXkVRDEBY/4/Ud/r5S+5NxBgi0PfXkdy/7yWcdQYdJpsBW1ToGoPLbS2M3bGBJcpgVhSWMlmn52TrMJn0Mh1dfgJacNTC41cx6cUHQTh9KovP47q86cz9yzr8Qw/+DQca6vBXVRxWCASg+5N3sUyc2ptMjR85BICVr20nJsPBxvfLmf7QMOqbm3hr7lzmzJh5RFXXr8s8bHTv7b6uTxKEcFpqz+OOKRfy5n2LUW+X2S3V8+M/XnrK9l8Yb+JXkxPZVOfmve3t/GRcnLjgIXwjdU66BPfOXbz1u0a2lzZwY2TflxuEgz4ts/e2bLHivOehwx7XVLU3QbJffQO61HQCzY1IJjOBliaM/YuDo4BfziVQvw85Khp/dQUAUb/8I5Jej2QIVrrVOQ/OErNMmNLnGCWzufe4q59+8cHY9q+plR1OJLMFzeel6c4bQScTaKhHdkQGRwH9fhzfvxskkEQ7n1OqLxnD34DNiqK8Cazf//MJKYoyXFGUL/ffzlUUZbGiKIsURfmroijy/n/vKIqyQlGUKfu3y1YU5U/H26/JACoaeklHxeo6Wqo7+hJOL0eClZTiWDoauk/qeX0lWW3YRoxlxvhxqKrKhwsW4vX5Tno/sTY9VxY5cftUvvV6JQBlTR7KW7xomoar0QNARauXdk8AVdOobAv2S1pb08OqvcH393FZB63uAO2eAIsqugBQNe0or3jQjiYPPT6Vmg4ff17RCMB729tZsCf4/D8sbcAb0NjT6mVdbc9JvzfhzBej89EV6GFmySb8X37Ye3/zg3fR+uvDDzLNj9yDe+HnWCbPPGI/6YPjyRqWyKxfjcZsMzJh2DBy0zNYvmFDyN+DIJzpUi0ass/DnKcmUF3bSMueTurKWk/Z/iVJQi9LpEcauLEkitpOP29tbTtl+xeEs4W1djfuV/7GzH+MYt2UCvpNSg93SF/LoSNfhozs3lYL9tnX4rjxNmyzZiOZTFinXohpUCmyxUrEd25GkiRki7U3sQtJbPsr8xqycjEqhUgGI84HHsU+53riX/uIyHseQo6KxlgwAEmvp/Ol5+j+7CNUdw/dn74fsri+SU6Y3LlcrueB0cBvgPEul+tfJ3qOoij3Ac8BB4bIfg885HK5xgIScDEwCNhDcD3fbfu3ewj41XF3rvNyz64XAPB7A6iB4ycqR1Nf1sqa/5ad9PNOxF9Vgb9iN6bBQ9HpdEwdM4bICDtvzZtHY0sLqnry/d/NBpmXLwtOId3d4mVPi5eG7gB/W9WEqmm8va2dsiYv3oDG/XP3AaARrCwPUNvhwx/Q6PKqbKoLLpP89cIGPi4LJsUXvbIHb0BjWVUXTyxqAOCNLW1UtvmINusoig/+LyyIM5EVFaws1T/eTLdXpak7QHV7MHF9b3s7Xd6w9rcXTqGIPVt5fOu/2dbiRG0/eDIYec9DaO4eNF/wQoK/di/+PbuI+dPz6KKObECc2C8aWS8jSRIL/28TDbtaGVZcREVNDbUNDaft/QjCmSi9swa/ayuu+VXYsi0kJUXRUH7qk69Is45EuwEZMOmDI3faCS7yCcK5RN6xBYDqzY3MeXw4+1wtYY7om0WfntVb1MzYbwD22ddimTQdAPuV12MeNR68XvSpZ3fSfaboSyuEQcD32J+oKYqCy+W64QRP2wVcCry0/+cSYMH+2x8D5wN/Amz7/3UpijIaKHO5XHXH23FNp4cofQQVa+pwJFh7q2CejEQlioT8E68jOlkd/3ga4+BhGPsFF5bLssy40lI2bN/Oh18uwGa1MnXMaCJstpPab4QpOCnu/NyD7RP+MD3YG+iukQeLU7x2RfBDUXJII9sbhhw84b51WHAK631jDq71e35WKgYZBiZayI0OTpP7ybiDxScmZAXXRuUd0jNp6v44SiwWSrAQUDXKW7xM10l8Ud6Jq9HDLcP6Pl1WOPOsyxxJl+yjxxOFZ92HoNNDwI957CT0Gdl0PP8XDEp/1I52TKUj0Scffw0rQNGMTCITbch6mXGlJXy+bDnfmj6ttxy7IHzTvO5OYuaQPLa9WUFEuhmpUqN45omLF31ViREGZkYY+KK8k5oOH98eKIp2Cd8MnQWDoXYr7kYfjdd240w5ufMwIXQkkwmJ4DmmqMB5avRlWuYLwFrgP4f8Oy6Xy/UmcOhcRMnlch24TNgBRLpcrh1ANfBH4FHgLuA/+6dt/kpRlKPGFrFpKRE6yxHtD06Wt9vPB48uR/WfmtEm99IF+KsqsB4y7xiCw9ODCgr4zqyLyU5L5d8ffsSbn86luS18U2MMOgmDLvj7i7PpkSQJq0EmzvbV5jzrZIm7RsZi0EkMTDQzu+jkCtEIZ56UPfPpooOSmwdhHjYaSZaRDEYC9fuIuO77SI5I2v/6eywTzsd+Td+arkalRtBc3cHuFbVkp6WRHB/HkrVrQ/xOBOHMlRZYxOMf/oxnBs5jduIYzo84PSc2I9KsDE2xUt3m47VNp24aqCCcqVq7Knn4qjzGTC/kgamXYbaHblqiIIRbX87m97lcrue+5uscmkFFAK0ALpfr5wCKolwFvAvcBDwPTAAmAfP+d0fOqjqGrlHQ8jTicr766JvJZmD0DYUE/Cptdd1EpZxc9T4Aze9HbW7Cu30THc8+RdQjvznmPGZJkhjSvz/9c3PZVVnJO599RoTVhtFoZNTgQcRFHzml7WwUYxWLYs8FztpGrtWNY9O77Yy/+fojHjeVjkQXn4Bn7QosE87v8351ern3wsyYkhJe//gTyquqyE478cifIJxrott6KO1x8lG9iX8nLKFkcPZpeV2rQUaJNbGr2UN2lBFV0+jwqESeRMsEQTibRLW72eEv4LNP11M3qJ2rE8aFOyRBCJm+nInvURTlAWAdweVcuFyuuSf5OusURZngcrm+BKYD8w88oCiKGbgMuJzgKF5g/+scM9uaPGYwButXq7QXaGnCt9NFoLoSuXYvNfnnU9dgZNiVysFtmhoI7Ksl0FhPoLEOtbEetbUVQ8EAbBddQfNP7yXi2mDT5JZfBPuBOB9+HENevxO+vtlopDA3l4zkZLrdbhpbWvhg/pcU5ORQWjQAvU4cXIXwkzSN2bnjaXN4j7mNsXAQPV98clL7jUqNwJlip3pDAylFsUweOZKPFy6ks7sHJSsTk1FcTRW+OTRNY1b8SIYnDeZv7fPINSee+EmnUE60iZxoWFLZxSdlHfx8YgIf7uhgRl4Euq85O+aravcEMMoSZkN4KkRrmoaqEbb3L4SGXtbzwNhL2dNTz/aeveEORxBCqi/fniZAAeYAV+7/78m6F/i5oijLACPwxiGP3QU8tX/a5j8JVuOcBhwzgXyheR7myL4ld5rHg3txMJfsfO2fNN56Dd3v/ZdAYz2STof1tV8w9PJsOleupOOFvwLQ8dzTdPzzGTzLFqK2tqBLTME0cizGomA/Pec9D6FPz8KQnUf8P98k9o/PY8zvf1K/ELvVSnx0NP1zcpg9YzqtHe28/vHHVNbUntR+BCEUJA0u2/EkjkTrMbfRJ6UQcfWNJ71vTYNdS2txd3hJjItl3NBSKvbu5b0v5rN2y1ZR6EH4xoiQzbzVsop19j10BdzYdaFr03M8o9NtPDoxAW9Ao7zZiy+gsb3BTUA9fZ9F3/7iaE8tb2Ll3h6auv18771qfAGNOz6qodMTLOC1t91HY7efuz+uAeCtrW184GoH4MklDTR0+VlS2cUrG4MFM953tdPSE8DV6OHZNc0ANHb5ewuN7WwKVp5+dWMr62t76PFrTH1pN35VY2V1N8uqglWiT+fvQjj13JqfP7d+TJutG5t8Zva3E4RT5YQjdy6X63pFUfKBHGATUNOXHbtcrj3AiP23dwDjj7Hd44fcXg8MP9G+I/6j0fCdVhL79WEqoyzjWbsC08hx2C69Ctuc63r7/GiahhzpJNDVxdw3eph8wzQAnPf//Pi7jDy1i9CtFgvTxo5lT/VeFq1ejdPhYPSQwTgdJ18sRhBOBUnTsNSaWbx4M5PvGnJK9y3LEuNvKSbgV2mu6iAnPZ2s1FRcu/ewpawMt9dDSWGhGMUTznnJBid7vI180vgB2eYEbGFK7iC4dMCsl7hzZCyapvGv9a3cOiwas14m0qzDqDv1I1mvbmylucfPd4dEc81bVbx4aRoPjQ8W9NI0jZ+Mjcegk7ipJBqbUWZBRRcBFWbkRXBzafD4PzbDhmd/Yjg4yUKMVYcimXrXkDd1BwCIsugYnhIsNva7pY3MKYrEbpT59+Y2Hhofz5AkM3ajDqtBZu53spAlCZNeQtUkVE1jb4eP9EjxnXS2MuoMGBdI1CY24xgjiqkI57a+VMu8DbgEiCZYXCWPg60LwsIxZm+fErvW3/0C6wWXEnnHA8E7/mfKoyRJ2OdcB8BFvxiD3qRDVTXkME3HyExNIS0pkY07dvDWvHkomVkMKeyPxRy+A77wzaRJEp2ZHiZfeGoTu0M1V7Sz/Ysqxt5UhCzLFORkk5WawvtfzGeTawcD8vJIS0okPTk5ZDEIQjjVe1vxEKw+/FrB3RikM2PNsiRJ/HpKcIron1c0osSYGJBgZlFFF7MHOKnt8BFr1fcW5jpZWxvc6GWJbxVGohEs8vWPWalYDpmKKUkSmVHBZKooIXgMnJl/8IJnv7jgfYcWApuUHVzNEWvVE7t//fd1gw9ejI3fv+2B9wb0JpMH9gcg778APDDxYOVpkdid3TpUN8vH7OK7+VMptmWEOxxBCKm+TMucA0wGWl0u15/ow8haqJm3xeDt8R93G9Xdg2fFYvSpffsQ6006arY0XJpXFgAAIABJREFUsfBvG09FiF+ZTqdjcEEBc2bMIKCqvPrBhyxfvwG3xxPWuIRvlnqniSHd2VSuqw/Za8TlOBl7UxFdTT2424Nr+8wmE1dMn8aVF8xE1TTmr1zFuq1baWwRPYmEc0+jGSzOYHIxv3UzGmfe1L/bhsUwJceOptGbMP12SQOVbV6WV3Xz2ILgd8RL61toPs5xWdU0/rKiiS6vSmtPgD0tXvSHVG4+0PJHEEKhxWYgozyWJWVbqfeFr1q5IJwOfUnuDmxz4KgT1iyjOTOZOM/xS+37du+k+b5bMY+egGzrexXMxIJohl3d74xY82O1WBg3tJRvTZ+G2+vl1Q8+YPmGDXT39IQ7NOEbYEVJGncnXICnw3fijb+mXUtrqdpweENzh93O2NISLp0ymYqaGj6Y/yWLVq8hoJ6a1iWCcCbYlRvPnBHf56L2Un68+xUkzrwiHpIkIUkSKQ5D78jY76clkxNtojjRzG37e5qa9BIWvczcnR38Y20zqqaxrjZ4vPrzikbqOv2kRhrQSTAq3XZY31ZBCDVPYQE3RExkdVM5m7sqwx2OIIRUX+aAvAosBDIURfkIeCe0IR1f9J4aoqbtwmg5eujulUto+9OvcdzwA8wTp53UvmVZwhppYsUr24nPc5I17PRWLjuaCJuNCcOGMqR/Aeu3bee1Dz8iOy2N3PQ0ut1u2ju76OjqpL2zC4fdhsVsJj46mu4eNz0eNykJiSTHx/WuMxSEvui/ZS/vmVdzw7hJIX+t4guD5d/rd7biTLEf9tmOsNmYNXkyHq+Xz5Yu493PPyfK4cDn8zNi0EAc9pNvYSIIZ4rcihZe3vQCpcVTaGrrRCeFp0LkV2U1yFgNwdvfGhBsTTQk2UJJsoVOr8qrG1spSjCjxJiIs+q5uJ9YRy6Eh3/HNiy3mNlTWc+wiLxwhyMIIdWXgipPK4ryOTAg+KMrrPMWW6MjWbcsBeXaIx/zV1fS/pcnif7pbzHkF3zl1xgwPROT3YDfG0BvPDOmijjsdsYNLWVoURGby8pYtXkzdqsVh81OYmwceRkZNLe14fZ4ce3eg81qwWQwsnjNGjxeL/mZmeRnZRIdGZoG45qmUVlTS11TE8nxcSTGxYm2DmexhLQCdi2rYaO+vDf5CrXdK/eRPSKJuOwj/0ZNRiMzxo9jS9lONDQ8Xh9vfDqX4QOL6Z+TIy5eCGelotTBfK7v4rOqt3kw/fJwh3NKxB7S6/S3U5MAxCidEHa2mCTmPr+GxMJIYgaJv0fh3NaXgiqPHPJjgaIos1wu16MhjOm4luTZmXRZ7BH3az4v+tR0Yv/6CrL12OXb+8IWHVxY/ckTqyi9Ip/Yo5xshovFbGJo0QCGFg044rG0pKQj7hsxaCCNLS3s2LOH97+Yj8VsJi8jHVnW0d7ZSUdXcMQvJSGBHo+Hzq5uop2RpCclYTQY0DTtuCfOqqpSXlXNmi1b9seQyIqNm+hxu7n6wgvESfdZal2Eh6JslazkI/+mQmX4VcE+kVvnVZA2KI6IuMM/x5IkMSD/4BXX7NRUPl++nF2VVYwrLRHVZYWzTm2Ejr02lffjfkx6XHy4wxGEc9YWUxf3zejHzzJCVyRMEM4UfZmWWbf/vxIwhL6t0wuZYVsbMV95ZAjND92N4/t3Y8jKPWWvdd5tgzDZDLTVduFItJ61iUpsVBSxUVGMGDiQmvp6yquqgeBoYHJ8PC1tbWwuK8NqtmCzWNi+q5z5y1dg0Ovp8XiIdTpxOhxYzWYiHRE0tbSSEBvDvsZG9u6rw2wyMby4mIyU5MPaTJytvy8Bhi7aSmDONAzm0z/6qjfo0Bt1aKqGdJzKtdHOSC49fwobXS7emjePgpwcSgoLMRoMpzFaQfjq5EULKcrJJyZbjCQIQigVueqxmUxYTaLqqXDu68u0zL8f+rOiKB+HLpwTs3Ub6X6l7oiandGP/QHkU3siarIFR65WvLqdoXMUolLO7vU9siyTmphIauL/riVMO2Jbn8+Hx+fDYjJR19REe2cnnd3d7K2rIz46moq9NcRERTF51CjiY6KPSOREYnd20ySJCytK2Fldw4Bpmaf1tfMnpAKw8O8byR6VTEKeE03jqOtsdbLM4IIC8jMzWb5+Pa998CGlAwaQFBeH3WYViZ5wRpOBaaYSVr9exvhbisMdjiCcs0w6E/I70D6nm6hUcTFFOLf1ZVpm/iE/JgHpoQvnxJqdPkb9/uLD7lM7O/BuWI159Hmn/PUkSWLKPUOQJIntn1eSPCAGR8K53wDTYDBg2H9inBwfT3K8mDL0TaIB0liZAY7MsMVQOlvBbDdQvryWpooOhl/dD2+3D4NFf8TFA5vFwqSRI6lramLFho2s3boVr9dHVmoKRqOBAXl5YtqmcMaRNI1yUwNXicROEEKqR/My4q4CnPpz//xNEPoyxfLvh/x7APhhSCM6AVU1s+3R5w9rV+DbvZOud/8bstc8cCIp62T0Jj0+t/+MaJcgCKEiyzpWrHBRv7M1bDFYnSZkvUzumBSGXqnQUd/N3CfXALD4uc20VHf0blu/s5WyxXtJiIkhtjKBS8ZMIasrC51bj07W89bcz/howUL21tUd87PbureT7jbRT1I4fUzo2dKzl13LasMdiiCc01QJVn3gwtMV+vY+ghBufZmWeeqHw74GVZYwuLajdXYgRQSvxAdq96JLSgn5ax+YLrbq3y4i4q30m3jkdEZBOBdIsoxR6suS3NNDliWsUSam3T8USZLIn5CKI8FKQ3kbkgTWKBNdzW4AMkriMZj1TLwuuHC+dlsTndV+kgdFMveLpVgiTGSnphEIqOTnZGCRLFgjTWz5tIJ+k9Nwt3lZ898dTLm3hNbGDhq7WujucBNtcuJMsdNZ1UNMdiT1rlZaqjoZMD2Tmi1NxGY5MOyfOiqmJQt9IQM2MZIgCCEnSzp0mg7x1Sx8E/RlWuYGIAJwA+b9d0uA5nK5Tk+N9EOoejfJcW0E6vch9yZ31eiTU09bDCVX5KGpGk2V7dRubT7ta5IEIdQ8mp+KnCbis5zhDqWXznBwTW18bjAuvydAZ0MPeeNSsMdYAEgpOryablJBDAlKNLIsod9rQk7UKNtdSce+LnZU7Mbd4sMebyG9OIldrRVE2GwUXJbK+1/MZ29NPbERURgMepa1rke/QYfX40O3XkecMwqf30/H+lbaN3gZEd+fpop2NrxXzrT7h1Jf1oI9zorVaQrZ70QULjq7uf0emukiZ+Tpq0orCN9EflTyZ6RiNIl12MK5ry+X5pcCL7pcrmWKohQBPwJuCm1Yx1ZjS2KHNJph9fsw5ASXAwbq92EqHXnaYpB1MujAaDHgTApeda1cW09qcSyy/uxqQisIR7M+x8lVc5OoGlZP2uAzd71lUkE09KGlpby/6qYyPjjanqeko2kamqZRs68Bk9lAxd4afAE/u6uraWptRcnKZMb4cciSjCRLvdM5A6qK3++ntqEBnayjtqGBjsRWXp//MUlxcaTMTKCxpYWqzY0k9neiMzhZ+aqLsTcNwOf2ozfqkGQJVVWRJOmoyVn58lqa9rQzdI7Ch4+tYOjsfEx2Iyte2c75PyyhfHkt0ekRGMz63tYtwtmnuSAHf2Qsq/7tYugcJdzhCMI5a0+CheWPbGXWL0adMf2LBSFU+pLc9Xe5XMsAXC7XJkVR0l0uV9gWptQk+eiX7iXQUNd7X6ChHl3s6T8BjYizEBFnwe8NUL68lpSiGDoaerA4jOhN4stDOIslJJAyNZaE+KhwRxIyBxKr1OQEAOKio0+4PYBep0Ov05GVGpwtkJ6cxPCBxbg9HvbW1VG1bx/bFu+i3dOFvEHGUWbDnGTm44WL8NT4aaUNa6SZpuZWTCYDdp0dtUsjPtdJzfYm0nITSEqKw+rQsa+xkYl3D8JsNaL6VYZ/O9gL0BJpoqOhh7SBcSH8DQmhJscnkB+fS2FRZrhDEYRzWqvVwOQ7B6MziAvwwrmvL8ldq6IovwBWAmOAitCGdHwle9wwJJFATXXvfWpHO3JcQthi0ht1TLh1IAA7FlTjTLaRPSKJ7hY3tv1TxQThbFKyphLDNWfOmruzgdlkIic9nZz0YEFhVVXRgMbmZprb2tHrdTQ6Whgcp2AwGGjb1kPyoGgaWpro7OhBb9bhKHAQMPnYUbkbnU7H1spdtLS1YbdZiXFGERvlRG6W0elkvDofjVsbyElPx2E/u9u0fFMlLlqN9bxc1Fg13KEIwjltyF4PHrdfTGMXvhH6cvZ2FXArMA3YCDwY0ohOIDVjPHVqOhnGht77Yp95KYwRHa7k8jwAWms6WfrPrcx4cBgdDd3YosxsmVtBTIaD5MKYMEcpCMeXOP0K5EUabRO6iMs5c9bdnU1kOXiFOCE2loTY4DrAvIyM3seT9082iIw8fmIWUFVa29tpbGmhsaUVTVMJqCpGvQGPpva+jnD2iZx5GdO1iyhfVkvxBad9CbsgfGOUlFzIype2c+FPR4Q7FEEIub4kd26gBbADLsAJNIYyqONxWrsp+vYIIPgBVTs7cC/5EuvUC8MV0lE5k+1M/8lQANa/s4vskUmkFMWi08u4O7x88ef1TP/xUFS/iqyXxdUk4YziqyhnzJO3hzsMgWCj9hinkxinEyUr3NEIp5Jvp4vsGyORrcnhDkUQzmlaWxMX/kwkdsI3Q1/73KUD5xOsmvliSCM6geayOmq2NNH+/NP4q/ag+Xyone3hDOmYDiRsY28qImVALNFpEUQm2TBa9Qy/qh+SJLHmjTK2fVYJQM3WJlT1YA8uTdNQA2K6jnD6+XZsY+MH5XS1uMMdiiCcs7yb11OzqpKqDQ0n3lgQhK/MV1PDurd2hjsMQTgt+pLc5bhcrkcAt8vleh+IDHFMx6XTg9GmxzxiHHJ0LLqoaOyXXR3OkE6arJOJyQy2cRg6R0E5Lw2/N8CmD3cDUL2xgfLltTRXdfDeI8sAqNnSREt1B+7O4KifpmnsWlbLnlX7AFjzRhk+t5+AX8XvDQDg9wZQ1WCC2FbbBUBbbReqX6WnzcOOBcF1i3U7Wnofr9nSBEB3m4fOph4Auprdvfvxe4L7PjQJFc49kixjdhiRdWJEWRBCRlPR24wYLWJ9qyCElCxjiQpdWxpBOJP0JbnTK4oSC2iKokQAYR1Ksts0YjMjMfQrJFC/j843XqHrvTfCGdLXIkkSOr2M3qhj6o9KkWUJi9OELdpMTLqDix4NtnjoafPg6fJjshnIH58CGjhTbDgSbWiahtGiR2/UUbm2niXPbwHgk8dX0bSnnebKDta9Hbxite7tnTRVtBPwqXh7/AC07u2kq8VNwK+y6aPdBPwqda4WyhbtBWDxc5tpqe6gpaqTj3+9CoBlL2xl+/wqABY9uwm/N8Dulft6E9Q9qw5WMxXOQpJE/rhULA5xMBSEUNFUjbjcaBLyz92qtIJwJpD1Mv3OSwt3GIJwWvTlcuGDwBIgCVgO3BnSiE6gps5E+fJaMgdG0nz/D9CnZ2I7y0buTiQm3dF7W9YF8++cUQfXZKQWxx2xXdHM4GKcrGGJZA1LBGDmQ8P399PSGH9LMUBvVU+gt/m6csgX3tQflfbup/e++0p7b1/w0+EAjLq+P1og2CcssV80OoNMfK6TmIxgTPH5ogjHWU2SeOuBxUz78VCskSLBE4SQ0DQ2fLgHS2IHBZPSwx2NIJyzfO4Anzy+imkPDA13KIIQcn1J7tJcLpeiKEoc0OhyucI6Hy8xMUD6kHhkow7T8DG4v5yLod+AcIZ0xpL2N24+0MD5lOxz/zpCSZKQ9MHbeWNTAA5rpiwSgrOcJDHtx0MxRxjDHYkgnLs0lf7TspCtopWFIISSwWJg/K3F4Q5DEE6LvkzL/B6Ay+VqCHdiB+AN6PG7g+u+bBddgaHfAHRRx28+LAjCSdLpad3beUovDAiCcDhJp6ezyYvqF4WzBCGUVE3C3e4NdxiCcFr0ZeTOpCjKOoJtEFQAl8t1VUijOo4OOZaOhm7MDiOGvH5E/+aZcIUiCOcs44gJbPpwN8n9RU9GQQgV26VXUr6mhcxSgxglF4QQ8jrTqVhdT1RqRLhDEYSQ60tyd3/IozgJiam6w5oqi/5wgnDqafXVTH289MQbCoLwlXm3b2bon3+AJBrRC0JI2c1eBs3KCXcYgnBaHPOIoijKQwAul2sBsMPlci048O+0RXcU+3Z10lHfHc4QBOGc5+nw9FZYFQQhNLTublb9ewc9bZ5whyII57T22nYq19WHOwxBOC2Od7lw4iG3Xwl1IH1lS4rCYBY9gQQhlIyZWUSni+krghBKpiHDiMl0oDPqwh2KIJzTDAnx2KLMJ95QEM4Bx0vupGPcDiu71IbZIdYmCEIoqdvXk1GSEO4wBOGc1v3h2+SMShZNzAUhxEzuJmIyHSfeUBDOAcdL7rRj3A6r7Wt76GjoCXcYgnBOa2kONq8XBCF01M4OXvzuvHCHIQjnvLqtDVSsqQt3GIJwWhzvcmGJoihLCY7a9T/ktuZyuUadluiOQhlkxh4jhtYFIZScNg8jry0IdxiCcG6TJa56ZuKJtxME4WuJz48mbVBcuMMQhNPieMndKe32qCiKDDwDDAQ8wHeB8cBNwFqXy3Xr/u1eBb7vcrnaj7afjjYVTdV6G3QLgnDqefwGOhvdRCbZwh2KIJy7JIk6VwspRbHhjkQQzmk9HT78ngBGq6hMK5z7jpncuVyuilP8WrMAs8vlGqkoygjgd4ATGAW8rShK1P7bi46V2AG0NgZQVa1P3dcFQfhq3H49LdUdIrkThJCSqNrQIJI7QQixrmYPvh4/Rqsh3KEIQsidzhxpDPAJgMvlWg6UAt2AGf6fvfsOj6pKHzj+vXd6S++FFuDQkSYgIAoIImLB3rvr2nftrro/u+vqrm11dcXurmvBXlkLiCCi0sulJkBCejJJJtPn/v6YEIikUSaBcD7Pk4dk5syd94Z5M3PuOec9mIhukH4p8K/WDtJ9gAOjrCwmSTGV4AzSY1RGZ4chSV2aoqqMOV9Of5akWEvtk4gj2dbZYUhSh+jIzl0c4N7t5zDwIPAfYA5wHvAScKsQ4jkhhGjuIJtX+2IdpyQd9jYWJ1O1vbazw5CkLs3jzGXN3AM9SUaSpN8qWFWLHjloagNKUkx1ZP3lGmD3jbNUTdMWAAuEEHHA88DXwHTgbuBJoh2+Jr5c+dFsIT7a3gHxSlJHyunsAHb37ZYvZo+Z/IXMM6mrOajy7O31y2a/fe1UmWdSV3NQ5RnA52u/mv15/34y16Suptlc68jO3Q/ATODthjV3K3e77w7gL4Cd6IieDjibO4imaZfHOE5JOuzJPJOk2JN5JkkdQ+aadDjpyGmZ7wO+hi0V/g78AUAI0QNI0DRtGbAc6AZ8BjzTgbFJkiRJkiRJkiQd0hRdl3OQJUmSJEmSJEmSDnVyRwFJkiRJkiRJkqQuQHbuJEmSJEmSJEmSugDZuZMkSZIkSZIkSeoCZOdOkiRJkiRJkiSpC5CdO0mSJEmSJEmSpC6gI/e5O6QJIV4BVmma9lhnx9IaIYQBeBw4nuj/72Oapv1zb9oJIWYCrwJbd3vIBE3TamMcfrsIIWYADwMWYAVwmaZpNe1t157fkRDiUuBUTdNmxvRkpD3IXDs8cu1gP/fDicy5g+d1t785t9v9ucCPwFBN08o7InapbTLXuk6uCSFswD+AIwEFWAxco2mat4NOoUVy5K7r+R3QFxgEjAJuFEIcuZftjiKaoEfs9nWwJGMq8DJwmqZpAtgMPLKX7Vo8dyFEkhDin8CTRJNVkloic63tdofkuUsHLZlz7WgnhLgQmA9kdUTcUpckc63tdn8i2qEd0vBlA+6IffRtkyN3vyGEuB24DKgl+sfxFE3TevymzaVEX9BmIAl4RNO054QQGcBrQEpD0081Tbu7pdvbiONi4HRN00787c9CiKeAo3/zEL+maaOBU4EXNE0LAVVCiLeA84GfftO+tXZHAUEhxFlADfAnTdPmtxGvAtwDnAHEAblEr9ZUAccA3zXzsHeAauAKYACwBfgS+Aho7qrWbUR/h0s0TdvQcNtzwHIhxDWapu2+aePUltq1ce5nAkXAzYActYshmWuHfa7t9blL+0fm3GGRc5nAKcA0QGvtvKTYkbl2WOTafCBf07RIQ+xLgYGtnV9HkZ273QghpgEXE7364AZebKaNk+gL6ARN0yqEEGOAuUT/w68ANmuaNlUI4QBmCyHiW7pd0zT3vsSpadr1rdydC2zb7eftRK8o7E27CuDfwHvAOOBDIcRQTdO2t/K8lwPTgTGaptUJIe4DxmqadlzD/Ue08th/CCHygfM0Tfu5tfYNfzB/G3cc4CL6x6O189vZrsVz3206wcWtxCvtJ5lrMtfYt3OX9pHMucMj5zRNKwJmNRyvlbCkWJG5dtjk2le7Ha87cCNwZSvxdRjZuWvqBOAdTdOqAYQQ/wAm796g4cV2IjBDCNGH6AvH2XD3F8BnQohuwP+A2zVNcwshmr19L2NrnCLYxtUWFdB/87hwM8drsZ2mabN2u32BEGIhcBzRoemWTANma5pW1/DzE8CdQggzYKeFqy2apj342xuFEFNo+WrLb+Pe6bfn2Fq79v6OpNiRucbhnWv7eO7SvpM5x2GRc1Lnk7nG4ZNrQogRwPvAM5qmfdJM2w4nO3dNhWi6zmqPF7IQIgdYBLwALADeBU4E0DRtiRCiJzAFmAT8JISY3srtv7QRj3237207v2njastWms6zzyJ6paFd7YQQCcDVwMO7DU0rQLCNWBWaJoBO9PcXbvgD19rVliY0TftfS+1FdP7z6N1uygaqNE3z/Kbp1pbaCSHa+zuSYkfm2mGca/tx7tK+kzl3GORce+OQYkrm2mGSa0KIs4FngWs1Tft3e2OLNVlQpalPgdMahr8hOl/6tz32kUAZ8ADwFQ3JKIQwCCEeAe7WNO0D4AZgNTCopdvbEc8wIUSGiM5BntHOc/gQuFQIYWxIrrOBD/aiXS1wDbumdQwjWgnoizae90vgYiHEzj8i1wL/0zTtQF9J/AoY03ClC+AqoueyN+3a+zuSYkfm2uGda/t67tK+kzl3eOSc1Plkrh0GuSai1UCfAqYeTB07kJ27JjRN+wb4F7BICPEzEA/U/6bZV0SvXmjAWqAb0QTtTXT4+AghxCrgZ6KLOt9q5XaEEMuEECNbCKkU+AxYQ/unWzwHbAKWA0uIDm/Pa3iu+0R0/nKL7RoS6GTg5oZ4XwbO0hpKKQshPhNCnNTM874IfEv0StIKonOuL2tnzO2maVopcAnwrhBiLTAYuKkhtpFCiGVttWvp3A90rFLLZK4d3rnW1rlLB57MucMm56ROJnPtsMm1x4iONL7Y8PtfJqJTcDudouvNTSU9PDUkxlGapj3V8PMfgdGapp0Vw+d8EHhd07R1v7n9YnarcHSwEEJcAWzTNE1eYZf2mcy1tslckw4kmXNtkzknHQgy19omcy225Jq7ptYDtwkhriQ6hL6VGFa+aRgiz/9tMh7kQsDXnR2EdMiTudY2mWvSgSRzrm0y56QDQeZa22SuxVBMRu5EdMf6fwGC6BDwJUSHLl8h+kJfRXQuLsAcovuy3KVp2lwhRC/gBk3TbjjggUmSJEmSJEmSJHVRsVpzNxNA07RxRDcj/FvD112apk0g2tE7mWgVm3zgeKKLJgHuAh6KUVySJEmSJEmSJEldUkw6dw2VfHYOQXcHSoARwM6iFZ8TLeVaBzgavjxCiHHABk3TSmIRlyRJkiRJkiRJUlcVs2qZmqaFhBCvAk8T3b9D2W2vi1ogXtO09USrBT0B3Ed0d/f/CiGeE0I8JISQ1TwlSZIkSZIkSZLaIebVMoUQGcBiIE7TtMSG204GjtM07drd2p1LtLM5EHgPOAZYrmna3N2PN+vss/Vxo47E6/MTH+eMaeyS1FGeeeaZ2ZqmXd7Zcex0yWVX6qrFhOjZA5vV2mpbd6ieX2o3MSlxcMcEJ0n76GDLs2uvv0H3BAL0752HzWLp7HAk6YA42PIM4PiTZumjhg8hLTm5s0ORpAOmpVyLSbVMIcQFQI6maQ8T3dsjAvwshDhG07TvgOlE97HY2d4KnAacTnQUL0y08MoevbeExER+f83VLFi8jOqaOs6cOaXZGP4v/79cmXkcWZakA3tykhQDzzzzzPbOjmF3w48YTE6//hw5ZAjZ6Wmttv2xZj1bdszlOnFNq+0kqbMdbHkm+vQmtVcex407iuSEhM4OR5IOiIMtzwAG9O/DuRdcSP+8Xp0diiQdMC3lWqymPc4Bhgkh5hPdbf5GotUx7xVCLALMRKdq7nQj8FTDtM2XgX8SLbLy1W8PbDAYCASCBIIhVq7b2OyT67rOl1XLcBpsB/KcJOmwseiXlVS5a/H5/W22LQ26STfLD6aStLeKyyooKikjHIl0diiS1KX9vGwdBdt3dHYYktQhYjJyp2maBzizmbsmttD+kd2+XwaMbunYBtWAPxggMy2Z4tKKZtuUh2qxqCbijLJzJ0n7Qtd1ImEdr9/XZtu6sI9sOUIuSXtNVRRCoTCRsOzcSVKs1Xm8nR2CJHWIdnXuGgqbKMBRwGJN0wIxjaoVBoOBQDBIZnoKXl/zowrFgSryrOkdHJkkdR0Gg4qu6+0auTs3bUIHRCRJXY+qqoTDEcKRcGeHIkldmslopK5edu6kw0ObnTshxF+AzUS3NBhOdFuDi2IcV4sMBpVAIEhetwzeePq+ZtsMdnTnxb5Xd3BkktR1xDkdOGy2Fi+g7O7jip8Z5uxJjkUuVJekvWExm7BYzHJapiTFWFycA7PR0NlhSFKHaM+au/Gapj0PjNU07XggJ8YxtcpoiE7LBHjrw6+oqfXs0eZ79xrWeQs7OjRJ6jL69+nJiCH92zVy92bpfKpCdR0QlSR1LS6nA9GrOxHZuZOkmOrTM5d+fXt0dhiS1CHa07nvpOg1AAAgAElEQVQzCCGOBPKFEGYgNcYxtR6MGi2oAvDBF/MoKd9z3d2c8sVs9ZV3dGiS1GVsKyph5bpNeNvRuSv0V5BllmvuJGlvBQJBNuVvlyN3khRjJeWVLFm2trPDkKQO0Z7O3WtENyJ/DHgUeDKmEbXBYFAJBKOdu/g4J+6aPUfutPpC+tozOzo0Seoyauo8bC4owtdGQZWaUD0hPUKSUe45KUl7S9d1dpSWEwnLNXeSFEv19T4258sZXdLhoc01d5qmPQs82/DjjbENp20GgwF/IDotMyHOSXVNbZP7K4N11IS9dLd06gCjJB3SDKpKwB/E52+9dpLNYOG1ftejKEoHRSZJXYfBoBIKhOXInSTFmNlkbNcacknqCtpTUOVC4HbAuvM2TdM6bRdIg2poHLm77tIzcTkdTe6PM9p4o98NqEqstvCTpK7PYDDg9wcbc60llcFabKq5g6KSpK5FVQ0EQ37CcisESYopk8lIvbftrX0kqStoTw/oNuAkoP9uX53GaFAJNBRUsVmteL1Nr8Rs9ZVjVU2dEZokdRm9umVx+3UXEwyF0HW9xXYfV/zMO2ULOzAySeo6VFXhxCnjZUEVSYqxzLQUzj9tWmeHIUkdoj2du82apm3UNM2/8yvmUbVi92mZX363iDff/6LJ/a+WfMsP7nWdEZokdRn+QJB1G/MxGgytjt5t85eTa0npwMgkqWspr6xuc4RckqT9U+sP8cp38rOhdHhoT+euXgjxuRDiYSHEQ0KIh2IeVSvMJhN1nnogWlDlt2vuSoNu0szxnRGaJHUZldU1vPjvDzGbTK1+8CwOVJNpTujAyCSpa1m4ZCUeOV1MkmLKrEL+z3KWiXR4aHPNHfBZzKPYC2aTiTqvl0gkQkKcC3dN0/21SgNu0kyycydJ+8NoMBCoD2A2t965OyXlSPraszswMknqWnRonI0iSVJsmMxmvP4APn8Aq0WuE5e6tvaM3L0JOIEjgQTgPzGNqA2qqmI1m6n3+ejVLZupE8c0uf/ijEnkWJI7KTpJ6hoMBpVgMITZZG6xc6frOscnDiPVFNfB0UlS16FA4zpySZJiwxvSMdvs/HXuRpbt8HZ2OJIUU+3p3D0P9ALmAj2AF2MZUHu4HA5qPR4y01M4ccr4xtt1XeeEpOE4DNZWHi1JUltcTgdXnHcqFpOJQKD5zl1t2MekFX/u4MgkqWuZPG4UqiqrO0tSLCXajJx78tEMyk0mGGm5SJgkdQXteUfpo2naTZqmfaBp2h+A3rEOqi1Oh4M6j4dgMMS0865vrDS2PVDByasf7uToJOnQZzGbGHXEAEwmU4ujCiXBKhKMjmbvkySpffr16dlqRVpJkvafN6Sz3uegwhum1i+r00pdW3s6d1YhhB1ACGEDDLENqW0uh50ajweTyUg4FMJdG113Vxpwk2yUU8QkaX956r2cdsVtWEwm/C1MyywOVJMui6lI0n7552vvUeWu6ewwJKmLU/AUaHz4yZf0TbZ0djCSFFPt6dw9CSwXQrwPLAOeiG1IbUuMi6PS7QYgJSmRsopqAMqDtaSYXJ0ZmiR1CQaDgUAg2GpBFbNiYrSrTwdHJkldi6qqhEJhlu3wMi/f09nhSFKX5DQbSE2wk+cI8cl6eTFF6tra7NxpmvYmMBp4EDhK07ROLagCkJqURFllFQAjh/YnHA4DEGe0MdKV15mhSVKXYDCoBILB6FYILay5Gx3Xh0syJnVwZJLUtRhUA6FwmPL6MDd/uaOzw5GkLqmsPsyOetB2uDm2p7Ozw5GkmGpxKwQhxF2apj0ghPgP0WrNO29H07RzOyS6FiTGx1Pn8RAIBrn5qvMbbx8bJxgbJzoxMknqGowGAxNGD8NsMlPvbf4q58vF3zDY0Y2Rrk5fhitJh6yBfXsRUvwc09PB/y7q2dnhSFKXlGQzMCAvhdVxGaQ6On11kSTFVGsjdx83/PtPohUzd//qVAZVJTkhgfKqKr5b+AtffrcIgFeLv2VhjdbJ0UnSoU9VVR649SrMJhP+FgqqLKrRCOjhDo5MkrqWM2ceh67DzDfz+aXo4CjRrkd0dF1n9Zf51Ff5CAdlnkuHOEVBtzq5cNZUrvu0qLOjkaSYaq1zt0oIYQZuABYCi4CfgIOi9nmc00mtx0NZZTW/rFwHwNK6LXjD/k6OTJK6hhvueRxVVVqcllkScJNuiu/gqCSpa3n3068JBIK8c2Y33l9bQzhGZdp3rK1kwexVe9xetqkaPaJTV+6l3u2nurCOzx9eAkAkpGO2m/ju2RWUb3YTCUWIhGSlQenQU+kNs728lpvufZw3T+/W2eFIUky11rm7FNCA6Q3/rgNWAls7IK422awWvD4/mWnJ7CitAKAiVEuSLKgiSQfEynUbQVcIhprv3FWGasmQ1TIlab8UFpcRQmFViZenZ2RhUJUDevyCn0tYOmcjqXnx9J2Y0+Q+XddZ8fEW6sq9FK4qJ39xMfFZDib+fgiKojB4Rk+MFgNjLxpAYjcXxVoVXz+59IDGJ0kdIdNlYkCaFbWqkLdXVeOTFymkLqzFNXeapv0L+JcQ4lJN014CEEKYNU1rfo5WB7NZrXj9PrIz0thRUg6AJ+wn2SgXykrSgeBy2PH5A/hbGLmbN/R+VEVuvixJ+6PKmkllKELlxlqWFPk4Z0gCKfYW35rbrbbMS125l+whKbjS7BjNBpK7u/jhpdWMOkfw81saI8/qy+QbhwEg0uyNj3UkWZscy54QLR2fNTCZdJFIKBDG6/bjSrUjSYeCKp+OoTaI1+unqj5EKKy38gm4c1WF6rCrFiyqqdn7y4M1xBvsmFQjvkgAq2ru4Ailg117PpkZhRB/b/j+EyHEBbEMqL1sFiten4/uORn8+x/3AzBn4K3kWlI6OTJJ6hqGDuhLSWlFs1shrK8vYkHNuk6ISpK6ll656Zgjfm4aHcfgdCsWw/6P3IUCYfyeAGUbqzGaDSR1i85oMZgMZA5IwmBUyOifhNG894UlDEaVLYuL2fi9XLckHTqsFgtOQwhnUgrnDbTjtBycRVV8kQAXrXuaaze+SL6vFAB3yMN9BW8zp/xH/lH0OSeveoRpK+9Hqy/k9s1v8FXVsk6JNaJHGr+2+Er43r2Gh7a+R3XIQyASojJYx5eVy1hSu5GIHh0pDelhltfl81HFEurDfoKREKUBN+6Qh48rlrDdX9Ep59LVtKdzdxVwR8P3M4CrYxdO++2clqmqKt8vXkapu4q3ShegKAd2SoskHa7uvflKxh85rNnO3TvlC1lfLz/ctVdE16nyhvmlqB6AOWvcrCnzoes632yuA2BjpZ8tVdGJEdW+MLre/NqriK7jDUYIhnX+8HkRoRit0ZI6RmJWd9Yr3Xl1uZtjejpxmvd9NFyP6Cx8ZQ1vXf8dzhQbQ2b22qNNrzGZGEwGeo3JRDXu23P1mZDNsFm9Kd1QRcgfJlAfRJevQ+kgluY0kZGSwsN/uponf65jdamvs0PaQyASwhP2c1LyKAbYc3it5Dvqwj5OWvUIVSEPS+u2UBpw8/GgO3mg57mkmxO4NGMSj2x9nxWeAiJ6BF3X8YRje27b/RU8snUO01bezzJPPt9Ur+TK9f/k6cLPSTA6cBiszK1azomrHuTjiiU8uPVdtvrLWekp4Njl93D/1nf4qnIZq+u3URio5PQ1f2XGqgf5pnoVQT0U09gPF+0ZlA5rmuYD0DQtKIQ4KP6C26zRkTuA/3zwJT5biDct8zk7bXwnRyZJXYPfH+Dl/36EYo52NHZeOAnrEeZWreCNfjd0coQHn3JPiFBER1UVnl9Swd3HpPPy0kpMqsL4bg5+3OZlRJad7DgTcRYD/rDOd/keju7hYFWJjxS7kZ6JZq78qJBnZ2SxodJPIAwTezh4eH4pVx+ZjFbuZ16+h5vHpXDJsESMB3iNltSxtrqDZKnVxIfM3PKlwokijok9HPt0rE0Li3Cm2jjt0fFYnbGdqqXrOht/2EE/q5EdayoJhyIMmdHyVg4hf5igP4QegQUvrmLqzSNiGp8k7W5liQ8twYT+y9dcctoMeiQcXFMZa8Nexi/7E3MH/5nLM6c0ue9ffX9PN2tKk+mXR5mi234lOB3cknsKt25+jTf73ciiGo37Ct7mnLQJFPjLeLzXxbxVuoCVngI8ET8lgWr+2/8mnin6nFNTRpNjSQbAGwlQH/aTaHCQ7y8jxRRHZaiW76pXMdjRnU2+Yo5NGIw/EuT8dU9yesoYnu9zFb1s6QBMSRzaJOYZySOYkdw0x+vCPuYMvI1UU1yT2+cO+TPlwVqyLUkH5pcptatz96EQ4nuilTKHAx/GNqT2ia65i1bGzM5IRdu+jYz+iZ0clSR1HRaLmR+WrGD8uIGEQiFMpuj8/9WeraSYXI1vChL4QhGsRpVvttRhM6mc2NfF5F7R9b+nDYjHblIxqgrXjI7+zkbn7FqrdN+k6JvjKf13VR797xm5KIpCpS9MnT86nWVCdwcGVaFvsoVB6VYURWFIhq2jTlGKkZP6xfHipiDbVv7Iw1ddgmE/lrH6agPkjc3EFmc5cAG2QFEUjrp4AABx6XbQoXyLm40Liog/zYWwZrGyfivKPJ1+k3LZvryM0g3VjDyrL8NPj+6NueyDTfSZmI0j0draU0nSfhMpFiaPzuKRJz+noqyEUG5aZ4fUxGcVvzIlYQhp5j0rUPe1Z7X62OlJw5ieFF07Oy1pGAMduXxS8QtTEoZgUFSmJh1BddhDhimBYxMGoygKdtXMdRtf5PTUsQwp6Ia3X5C3XphPUUYVVYPruaf7GQx35bGmfjtfVS2njy2TXtZ0+ngzebHb7+mdmLlX57f6ywK6DU8jNTVuj/v0Op0MewKhQJiqbbWk5slCbfurzc5dw0bmnwACeE3TtOWxD6ttdosFry86ral7Tia/bttExtCMzg5LkroMRVE4edrRlFSX4g8GGzt3gx3deb7PVZ0cXedbW+Zj0bZ6Lh2exPWfFXHr+FTOHLTrTemobtHRl7h9WNuxc5S0b/KuD+nju+/baI50cNtaHeTnYDcMdSFOrfBhMxnokbj3owreGj8Dj+/RKUsTdq7ds8abWTemmIUbv+OEb4fyzLFfcanjWHr7Muk1JvoFkNIjHl3XscWbsdhNBOqDmGxGuaxCihlfKAIGExazia/XlaGk1TO1995XV999FsuB8F31auKMNl4p+ZYHepyz38czKQZ6WtO5LvuExttSTXFckzW9SbtLMybjMtr5tXITrvkmZo4cw4CLc9DtOvWFPhLNcRgjKneYTyWxl4tIKIJqVFnw9iryjsqisraW/CXFDD+tDwW/lJA9KAVjM+91GxYU0ntcFhanCVWF7SvL8ZR76TMxh8VvrGXU2YLlH28muUcc2YOTqdgqO3cHQpvXCIUQ2cDNRLdGOFIIMTrmUbWD0RjtlwZDIaYfexSXzZjB5RlT2niUJEl7o0duFsFguMm6u+9r1nZiRJ1j97VxL/5SyeLt9WS5TAxJj444PDE9i95JsR8tkbqetWU+Lsnz0bt2GSsKa9jqbr46bVu+euwXakrqD3B07VccqOLWitf5MPATt/Y+hcnnDuPRnhcyO/Eb/uNbsEd7RVEQx+ZitBhY+MoaCleUE/SF+PaZaHGIdV9vpSK/hqAvxJafijv6dKQuprAmyJYaBZvVzBGJ4X3q2IVDET65bzFlm93UltXjq9n74vE1IS8hPcwC91pm7/gfj2//iEJ/BY/2uoARrry9Pt6+iIQjBL0hsj6K46E+53PSTWNRFAVnsg2n1Yb2TSG+mgDuYg9L39+Ery7Au7d+TygQZvxlg8jsn4Q90UK6iM6W2/prKZFwhO0ry9n6ayn1bj+rvshHj+hUFtTidQfoPS4LR7KN+AwH8VlOVFUhuUd0FG/UOYK8o6IzDvodm9shv4Ourj0TQF4AXgLMwHzgyZhG1E6KouCw26n1eHDEJxDQw6TIPe4k6YAaMbg/3bLSm2xk/o/CzykJVHdiVB3LG4xQ6Q2zpNCLrutM7OGgX4qFeKuBkdnR6ZV2k9wSQto35w5J4D8FdiYfP52zhqVzdDPr7XyRAFp9YavHOfm+o4jP6NjR3blVy/lH0edUhzwoKIxy9ebtATfT256JK9XGcFcv3ux/I95IkJAe5u2yhYT08B7HOfp3g8kckISiKvQeF52C5ki2YbQaCPrDlGhVAPz4+loqttZ06DlKXUP/VCuT+yQyZFAePQaP4PsCz14fw2BUmXDFIH59dwPznlvBjrV7V9nxu+rVTFt5H1t8JaSZ4qkM1THa1YcZSSMY7Oje6mN1XSdyAIoWBbwhPvjTQhRVIXtQMvzmmIqicPSVg4lLt5OY42LSdUdgdZqZ9cj4JtV1rS4z2YOi1eknXDEYs91EJBghPstBzQ4Pcel2FFVh9Hn9GrdSAXCl2sho6BT2PToHo9mAqipy1P4Aa88nEqumad8AuqZpGnDQlBiyWm18uqaMxxaWc90f/sKSyo2dHZIkdTnVNXVNRu5qwvXEGbv2/lZFtUGKa4NsrPRz3WdFJNkM3DYhFUVR6JMc7dhJ0oHw0boasp0KWkkt/1tdxIu/VO7R5vWSeZy59vEWK6i6d3jY/OOOWIfaxEpPAX/Z9j5L67bw79LvSTcncEnGJExK09zIMCdybfZ0VBR+cK/ld+v/uUc1P9WgYjAZMJoN5A6LroXKPSKV+AwH9ngLYy7oD0C34WkkZjupyK/BU9n0GEFfiEhYbkwtNW9TpZ+1lRFC4RA17hp2ForduGVbux5f7/az+ssCErKcTL1lBCfeM4bMgckEfe2r7uiLBLi/4G2e63MlfWxZ9LVncUvuKdzV/fR27Rfrrw0y57YFe93Bqympp2h1BXpEp3RjNWabkePvGIXJaqTn6PZXzG3PtindhqcRn+Ego18S3YYdXGsaDzft+V/1CyGmAQYhxBgOos5dULXi9dZz98R0DCKb2X//vMU3P0mS9p6qKmzcsp1az66rnO5QPQnGrrP+K786wDeb66j1h7n762J0Xee91W5KPCF6J1l49sRseVVRipkeiWbO6GvG7s5nR/5GxubueeHk8owpxBvsFPjLmj1GJBwhEurYjk2i0cn9Pc7hxb5Xc3XW8W22VxWVv+VdQpYliRs3vbxPz5k1MBnVoLJjbSVV22rZ/OMO5r+wEl3X+fjeHwn6wuQvKaHg5xKAdn/wlro+u0klyW6mvKKG999+m7G5DoLBEDf8+W+s3bClzcdHghFMDWvKdr4fbFlczKrP8tv1/L/UbmKAI5cjnC1XlG2Ory7AJ/f9iNlp4sS7R6OqSuO2IzUl0ffl/CXFhINhgr4Q/rogn9z3I5FwdFsEd7EHX22AoD/Mr+9tIByKYI+XSwi6uvZUy7wSeAxIIbr27vetNRZCmIhO4+wBWIAHgDXAK4AOrAKuaWg+B8gE7tI0ba4Qohdwg6Zp7aqxnpcWTzgSxqPXEx4/lB1f5VNWUUVaiiynKkkHgqIoGFSVSveuqVD39zgHh3povzmEIjrXflrEA5PSKa4N4TSrOM0q0/u4CEfgujEpjW3NB2BTaUlqybE9nYx6vpjjrMk4qivJjjM1KdpQH/bzTvkizkw9iuJANRbVRKoaRzAUwma1sPjNdaT1SaD3+OwOi9kd8mBUVMbGib16nEFRuavb6XxVFa3L9nbZQgr9FVhVE5MSBiPs7TuHQdN7ABCJ6OQMSQEdTrjzSCwOE4m5TjyVPkKBMHNuX8CsR8Zjsu75UWfn7zjgjY74WZ1mStZXkdo7AVVuL9LlZLpM9EmxkpTg4tcVW7jr62Ksqz4jOyOVwuIy+vdpudO1YPYqMvsn0feYnCa395/crdXnrA17cRls1ITqGRffn6F70bGLhCK8/6eFzHpkHOMuHYiqKljjzGjfbaNqex1DZvZi3nMrOeGuIylcWUG6SOSzB5cw/Y5RjL9sEN5qPz+8soYpNw5DbSjBe/xto9r9/NKhrcWROyGEWQhhBkqBC4FhwLlA6xP/4XygQtO0CcB04Bngb0Q7cBMABTgZOALIB44Hrm147F3AQ+0NfmUlFFfVEtF1bh2by6d/u1F27CTpADOZjLhragEI6WFGuPIO2ZGspTu83PV1MUZV4e6JaSRYDYzJtXNkjj1a2r2bA6PszEkd6NL3t3Hb2DhSTH7KK6u5aM42PIFdo3CLajR+cK/l2uwTGBPXl/sK3ub6Bx7n9CtuA2DIzJ5kDYjdtiS/1m7mhR1zCeu7Yppd/DUv7Ji7T8ezqCZmJo8EwGWwEWe044sEebLwUyBacKK9VFXBbDehqApWV7TCaHyGg6wByRjNBk57dAImq5Ef31hL1fZaakvrWTO3AF3X+eBPC6kr97Lum21s/D76sWbZh5sI1AepKKhhQ8Nti99c1zhCIh26FhR4+HxDLUmJ8dTW1HDVMBdffLeIXt2zKa2oamwX9IWY/8JK6sp3vQ7HXNCf7iPSmz3umrkFjSPFO62rLyQYCfH7Dc/zQflPnLn2cXyRAE5D61t+VBfVUV8dLUYSCkaYdN1QFEUhMWdXPYk+R+cw7NTe2OMtnPjn0RiMKuMuHYgtzsJJ947BnmAhIduJPcnKkBk9kZPZDk+tjdxpREfalIZ/2e37Xq087h3g3d1+DgEjgHkNP38OTCVamMXR8OURQowDNmia1jRLWpGZ6KJ2xw7ijXYm2kfx8LeFHBtfwcQxw9t7CEmS2jBq6EAUBRYvXU04Hv5S/zEfDLyNotoQ2XGmzg6vXVaV+KgPRhiVbSPTGf2zd6jELnVtT8/I5t1VlaiqwolTxjGxzs3qNdHORGpyAvMia5gYP5CaWg86OhbdyA8/rUZRFDb/uIP0PgnYkmMzkn775tdZULOO7pZUTIoBYc/m7i3/xmmw8c++v9vv4+/cmwsg0tB5nLnqIYY6e3Be2tGMjuuzX8ffuU6oz4RszHYTqkHBYFJRFIXjbx+J1WVusvH6tFuinc6K/BoSs6P7VGb0S8Qu9+E75I3JtTOll5PPtzo5feZk5v+8ih45mZx03NEYjbvWk61Zlk/BkhJGntm38baiVRXkHJHa7HHT8hJwJO96fayoy+f6TS/x7oCbmZp4BO+X/8hDPc5rsgF5S7avKMdoMWC0GAgHwk06dTupqoLFEX3v+u1F1t1HqBVFIaOfHOw4XLXYudM0rcn4sRAiCajSNK3V6wCaptU1tHcR7eTdBTy22+NqgXhN09YLIbYDTzS0uR+4TQjxHFBFdKSvxUUEuq4zrlcy72/ycOeWNznGNZQeNivPv/G+7NxJ0gGweHs9IzJt5GSmsWb9Zt79eB5un4eE67JYW+7n5i928NkFe7d+IFYCYb1x+uTGSj9z1tRw6/hUvi/wMDzTRjCiE4xEp2FluGSnTjp4rCnzsaY8SHIYqmvdvPNTPrVlZdiDNYwdMZhlYgsXpE/k97c8AsDRd04g7p4p/HnUeaz5ogC994GNpyZUz3/KFnBK8pE82PM8IDpiH9YjKIrCc31+R09b+h6FU/bXzqIS7wy4mQU1a7lty2s83+eqdk/VbE1y910bJ4tjoqXWW9vofWcVQKDFERvp0FLlDeP2hbHbbIwbM5xHPljKrCnj6d+nB/6GatBen5/L77uf92c/ys4aJ5FQhPXztpM7rPnOXUqveDzVXjb+UIRzlI3btrzB3d1OJ8UUx4Xpx3Bh+jGNbXVdJ+AJYXGa2DC/kKyBSSz9YBMDpnYnMcfJoON7xPi3IB0u2rPP3dFCiFXAD8C9QojL2vGYXOBb4HVN0/4N7N5JcwHVAJqm3atp2unAcOBD4ApgNlAJTG7tOTyBCKe/F91cOd9TQg9HEmeN6cWOihrKKw+fMu2SFCvXflpEcV0Is8lMWmoizz50GzsKy3DqVgakWvn0/B6dEld+dYA5a9z4QxHyqwKEIzoXvLeN8voQc9a4yUs0c2zPaMGXZcVeyjwhhmXaGNet6xSBkbqOj7Va7piQRpbLQIW7msycbG66+hKefeg2TpwynhPX9aenNY2rLzodp8NObl0CKc54Fv68AtsAI84U2wGLxRP2cc3Gf7HRW0xRoAqDomJQVCyqCbvBgk0109eedcA7drtLM8czK2UMr4jr6GXL4Hv3Gm7e9CrP7/iKP256GV8kwGrPNqpCdY1TRX2RAN9Ur4xZTNKhb3tNkO01QVwOB+Ggl7dvP4OzT57KryvXcf3djwHwy4roHq5l69x8ct9ifLUBVKPKlD8Mb3Upwh83v8Li/PV8PHsxMz0jmBQ/uNl25ZvdfPdcdL1pKBAmEtERx+QQl2bnm6eXUbxuz0q5krQv2lMt8wHgaKCY6Hq4q1trLIRIB74CbtM07aWGm5cKIY5p+H468P1u7a3AacCbgB0IE5366WzteZwWA5+c35MEl4vamjq6W1J5ZbmbrFGT+fHXVe04LUmSWvPQlHRcFhWz2UQgGMRqMXP7vZcxMXEgb6+q5tmfKvlY6/g9p0JhHadZpaw+zNOLKzCoCrNPycFqUCisCaIoCqMa9p+7bnQKPRLbng4jSZ3lvknpJNgMuBx2SioqyIzsYHB6dJpXxAivvvkJT77wFqu1zRRs38GKuWtJ22Tllx/W8dij/z6gsTyy7X3yrBk82vMChu1lVb8DrYc1DZNiIMeSzMSEART5Kzk+aRiBSIiPK5Zw/Ir7mbriXh7a+h4WxcTrJfO4ffPrFAeqmhzH27BHoK7rlASq+al2Q5P755T/yJLaXdso6brO55VLuXnTqzxd+Bk1oc7bGF46cIZl2jgi04bLEd0f+e1V1WjlflKSEilrWHNntVi4/eKLSU9L5vS/TsDqMrNmbkGr24yUBKpZpWyj5lgfs6YfxZnpR/HJfYsJBcJsXVpK4cpyNi3awaaFRSRkO5lyY3Qqcv8p3XCl2knNS8BoMTDmgv6Nm4JL0v5qT7XMiKZplUIIXdM0nxCito32dwKJwN1CiLsbbrsBeJNHwIIAACAASURBVKqhQMtamq7JuxF4StM0XQjxMvA8UAOc0tqTlNSFKK4L4nTZGWfsi91g4aqRZk7rOZ44p7xCL0n7IxTRWbzdy3F5LmpNpsZpK0c48vCVBViuKkzuFS0+snh7PaNzOmbfu3dWuzmlXxy9G9YYPX58JgBOc/Q61e5VLiXpUHD/dyXcMj4Vl8NBYUkpWp2VpxeVcmYflWXFm3BMTMJhtzF5wiimHTOW+594kU151dx8/ml8ct33bC0splt2xn7FENTDbPeXc2P2iSQZnQdVwaSe1nR6WtOZmbyr0t/t3WZxe7dZbPaWUBWqQ1EUnsy7jNdKvuP8dU/yXJ/fURf2sclbzHvli8gqruV+fTSfCZU5JQtx6kZcrmSe6X0FJsXAHVveYLprCNPSRpJrSeajip+YaRas3LaKJfYcRrjy2OIr7fQOr7Tvlhd70cr9OB0Oagu20j3HjMuikuBIoKyiim8X/kz3nEwq09289u3H3NL3An76t8aAqd1RjS3nQ0nQzTlp4/l9w3YgekTnuD8OR4/obFq4g6Eze2EwqWz+sZjuI9MxmJof9XbIdZ3SAdSezt1GIcTDQLIQ4nagoLXGDdsYNLeVwcQW2j+y2/fLgNHtiIkyT4iVJT76xCdwbCi66FRRFD7fBsnuFUyfMAKDoX2bM0qS1JQCfLiuhql5TnrYTAQbNjF/4b1PcRXauO2pcwCo9Yd5dWkVR2bbDvgHwoius70mSJbTxPwCD5N6Oan1h3H7w6TY2/OnS5IOfqNz7BhVBac9elGyX6KB4Zk6+YVFFBUXM2hmf67KmwXAryvXUbC9mFTFyk8bNnDmzCl88e0iBvfvzevvfcY1F5/BwL7N1zvzRQKoqLjD9SyuWY+wZ5NuSuAi7Sl6mdOpdLt5eWS7diHaQyQSwR8IYrN27BYpPZV4utX4wQUug5Vrso7nWD2LlM+/wV5TTlnhCh5duR2LyUpoQgKXTLqBk95fStXgvpgTelB3z82Mt9oYVV1BZMM3+FOTCegmnn32dXw/LWBiRSr20YOpeuAO+p1zMRzg9Y1Sx0mxG0myGTCaHdR5PIzOsROK6BhVEydPm8gzL7/D7ddcxI5FVazcsgmDyYAr3U5cuh2lla0xhji6M8TRvfFnpWHLAoBjrxnaeLssbiJ1pPZ8QroKuBxYANQRXRfX6QalW8lODvGHxXM5ybcrgVwWAy9+tAB7XAKThu9fpS1JOlypCnx/aS9MBoXaujAej49tS0uptnpZbyzl0QVl3Do+FZfFwP2T92/UoCXb3EH+vqicPx2dxtIdXib1cnLpcPkGKXUduq5zXF50pMzlcGCzWshOSaSk2k3QXY3P4yfTvOs1n5mWwjvPP8wb7u/5sm45fx12IenWBM76/Z1MmziW3j1y9niOH9xreb10Phu9O7gqcxrPFX2BVTXzeN7FuAxWbs45ifueng3rAvhmB7Ba9pzG/NnXP1BdW8e5p0zb474353zBM6+8jcFgYPyoodxw2dlkprc+gl7v9bGtqISczDQc9vatGQyXFlP75mwi7moMaenotbUEVi3D1H8wiXc+QM1Tj2AefiSiRx6eokLik1MZN3g6lsvHYUhNR1FV6jz1JP3+JpINRvRgEP/0U0BVsdvtmHr3I1xehmKxgMmEbcLkxv8j56xzMaZntStO6eCUHWci1WEkFLLjrqvjnXmLWVify9+nZ3HD5Wfz0ZlXM1D0Ii87h1ev/oiyiqp2FTi5dfNr3JxzMmnm+NifhCS1U6udOyHEUE3TlgshXiK6mbmP6NYGne7tVdXUG8sxxlmo2uFu3JB01oB45g0ZyZwf18vOnSTto2pfhAkvbebjc7sTZzbh9wfYsKCIzC3JmOvCXCJ2TX3eXBXguZ8q+Ou0zAP2/Bsr/PRMNPPE9OgHqpvGNV+pTJIOZboOR724iUVX9Cbe5STe6cLiSuKfywMc76gCf4QZSbuqP2emp+Au9jCuXpCXkYFqVHl0wTuUVVRz2TknsWHLNlat28SsE44FonvG3Z3/Fn/MmcnYOEGyyYU3EmCkK49+9mwCwSAp5XaMWoizTzkenz9AaXkla9ZvITUlkRGD+3H7Q8+wYMly/nD5Oei6zsb8bVS5a9lWWMKRwwZy2gnHMuuEY/H6/Kxcu5GMtGR+XbkOh92GyIuOaHh9fmxWCz8vX0tudjobt2zjqZf+S2VVDSdMOooLz5hBcmLzH46Dm9ajBwOodgfG7FwMw45E99ShuFw4z70EY24PACyXXEsABVuck/hrb218fEWVm3BlNdU1dVxw/T0cPWY4RoOBy84+iTgxhDfnfEGtp56eG0oZ1C+PYd17UlfvxdnQ6VQUBfPAIbH475c60MdaDeOKvAzPiv6/lhVu5pHTo+vfls1fiM8fwG6z4inycd6p0yktryQ1ufU1cPVhP/OqVzdWlZWkg0WLnTshxB+Bsxr2n/sr0J3olMy/0/y0yw41PNPGT/V1JAZdGA0GaurqiHdFp2fePr0/F1x/D/7Lj8NilsUUJGlvJdoMXD8mmXkFHtaVeklVIhx19QCeXvo5kz45gkBtEBr6W93iTFw3JrnxAsu+qA9GsJtU6vxhHGaV2b9W8buRSYddMZQ6Tz1Ohx1d1/H7A1itFvyBACajEVWV08y7GlVVWHBZHgCZqanMOGYiNXV1TNv4Pf5AdBQtl6aj1Z5KH7YSM9P7Deezyl+Yn7CBm566DEVRSIhz8vwbcxC9uzOwby/sBjOP9DqfI127LnSen3Y0r7z9CX9d/BKr12/m4duv4aHbr2bowL589vUPPPT0y0w48gimHjMGgIljh9OnZy6nzZiEtqmAW+5/isz0FLplp5OSnEBuVnSrAJvVwsSxw/EHArz6zqesWb8FkdeNP994GY/+8w0CwRCrtU08/+idjBs1lHGjhlKwvZiv5i3Cv2k9361Yw9JNW/ndhacTqnVjS02HpBTqSkqwKTrmfoNwduuJrussXrqKmloPo+KSSATu/du/0DYVsH1HKQ/fcQ0irzt/e+Hf5G8roqi4jEfvvp5RQwfw2etPMn/xUhw2K0mJ8SgKpKUk0T0nk00F2xnUL49AMMipl95C7565nDFjEt1yMundI4fNWwvp1W3/t2WQOseJfV0My4yua7vizDN4f+7XzF1bzDHad3R7YzZfPP0aACs+2cLZF07FnmChts6Dq4UaDrqu82HFT+TZMmJaPVaS9kVrI3fTgaOIVq48F+iraVqVEGJhh0TWCl8ogtOi0sfsxBUYgDHZR2lFRWPnLjM9hWlX3sIP2/1M6nV4fTiUpAOhPhjhqFQLTrMBXYeqHVD0azkXzpnA6ml9sGXsesMzGhQSrAau+KiQp0/Iwmbau05IIKxz6Qfb+ffpudz7XSlXjkzi4eNiM9Wzs+Rv34GCQvecDHw+P1arhVA4TGWVm9XrN7O1sISLzpjBfX+fTUHhDrxeP4kJLl594v+485Fn+X7xMnr3zGXMsEFcf9lZlFVUUVpeic1mlR84D2HhiM68/Oh6UkVRsJjNJCUksMidyFmD4pi39WeKaypwOaL5VlPiIS0vgawByQCckDQCl8HGc0VfMjNrFGkpSVx14Wm8+OYH/P3eP7LKs5UjHE2LgBSVlLPo5xWN6/Osu62TKywp44l7b2Lk0P6Nt00/9qjG7/v2zOXN4wZgn3oihtTm93+zmM08dtksVpdU8fOnnxG8bBY3jxzL3MRcTjrnBHo4rYRLizGkZZAbb+fyc06m5p9/I6u8ine3VXH+rQ9jNhj4u6mYp7uNYf7G7UwYNZRjA2amThzD+dfdQyQSISM1mY3527n6otM5cthABvfrzdSJozEYDBgNBkYdMYCTp01k6IA+jVNNkxPjOfX4Y5rEe96s4/c4h09f/zufzF3Ax3O/Z/qkcfTukUN6ipwSfijbXhOkxh8h3mrAZDSSkphA5dyPqVvzPeVqLo4vF6CcmMTkG6KjeTff9yTzfvyVv9x5LZPGj9rjeBGiWzf/IWdmh56HJLVHa527iKZpYSHEcGCzpmk76wt3ehmtTZUB/rOymgcmR1c3L0tdR1FpGX169Ghsc/KgVDZoGvQa0UlRStKhyxeM8JcF5Vw1MokzB8bxr1UGbti8iUtO7sna157hf59PYdb94zGao1csLQaFOyakUl4fnbWdG9++iyo1/jAus8rTJ2ShKgr3T04nHGn7cQezjVu28cb7X5CZlkxuVjonTBrH48+/yab8bZhNJo4YJPi/P17BpX+8n7KKKnIy0zjjxCkAPHLnNaxevxmjwUD/PtEP5X+963rC4Qhr1m+moLAYgM++WcgX3y7EZDLx0t/uxmiQV44PReGIzqJt9UzqtWvnH4OqMnZQXzL6qPiLw4S94cb71v5vG1kDk8k9Ytc05TGuvqyIL2gcOT/u6NF8/u1Cvij6lT+/9RJfXvMXzJboW30wGCIrPYXnH70TAgH0yK5jA1xx7ilE6j2EioswZjRdY1bz/BO4Lv49xuxuKFYb4YpyVJcLTGYIhfAt+AbFbMYy5mgCvyyif7eeDL3ndvTAH/HO/YwZS38i+MHXVLz3AvYTTsV5ziVU3nEdif/3V+Kvvpl44O/BEMvXbKB3jxwS4l2cs24jt2dn8vUPSxpHrh++4xqyM9KaFEzbvQO60287cXvDbDIxq2G66U7tXRsoHZyKakPUBaKdO4DUpERyytbiOPtifF4Dm7dXk7WhiqRu0UGCW66+gGPGjeBv/3qTiWP3LNC3un4bZ6WOQ1XkjArp4NPWmru+wCXARw0/DyS6D12nGphm5YHJGTy27UMmxA8gLyOdNRs3NmnTLcHM1W98SnxyKuMHduukSCXp0JRkN/Lg5HTeXFGN2xfCratkZ5fwyqZcQrqJ1ElODLuN0FmMKnlJFuascRNnMbS7c/e3heVM6eVkfPfoyITVeGi9UT732nts2LKV7TtK6Z6dwaN3Xc+TL/2X4YMEXp8fhy36gfDp+28mEomwfvPWxmlsrz7x5z2msaqqyuB+vfe4TVVVhg7sy9CBfQG46IwZXHTGjA44QymWzEaVmyam7XH7RSMyeL98MTaHjZq6XbsPjT6v3x5tTaqRa7KmUxOqJ85ox2m3EXdlNs+89gaGhR5edn7IaTMm8fTLbxPnsJOXm8lZR4+g/PpLUCxWjD17EyrYjHX0eNTUNFSrHVQV4/ST0cMhFIOR4Pq1+BbOw3nhldiOOQ6Aurdeoe6d11HjE1HjEyEYQLE7MA8ahmPWuY3xKTYjjpNOx3HS6XtM3U555tWm52IyNhk13JkLu3fU9nfbB+nwNDrHTnacqfHn+EiEFQnZhOO64+6by9IUL8eOTW68P6KH6Z6TxuhR/fF464lz7roA440EuHL9c3w79D5sSvPvdaGwzrwCD+X1Ic4alBC7E5OkZrTWubsLeB3IB+4QQkwE3gDO6IC4WqTrOn/5vozrxySz0rOVYxMGkZyQgC/gp9bjaZy+YrWYGTFhEq998aPs3EnSXiqsCbKk0Mu1o5Nxl9VQ6A5yWe5Q8kbk8Hz9AH7ZuJa+Wd3JHty0Kt6sAdGiCFXeMIm2lkeT3l3tZlS2jbsmptFKlemDztbCYp6c/RY+X4AHbruKpIQ4Zh43gZzMNLLSU1EUhafuu6nZtYeqqtKvd4/Gnw+mvcSkzuEJRHh4fil3HN20g/fM4nK85jjGZvSnqqyGbUtL8dUFCXpDDJjafY/jaPWFXL9xNrnWFO7rfjYzkkZQ+ut87jKVkHv0CDIyolUp589fxBWXzkBNTSf1+f8QLNhMxF2FGpdA4NfFWEdPwJgTfb/0LVlIYNnP2KbOpPqJB3FecDmqbdd+ls6zL8Y+/RRCOwqJuKuwjByDYmi9ALd8zUudZUGBh7M8IdIcRvzLf8H84J2UnX0NtnU6wuHjxJ/fZNm27gw7+1QAPvn2O4KhEAnxTt766CuuPHdW47FW1BUgbFnY1KYdu0BYJ6LreIM6F7+/jVdOzaWsPsSmSj+fbajlutHNV5ENRXSeXFTOlSOTcFn2fRaGLxTBYojmWGFNiFSHAUvDBdP9WRMvHXpavEyuadoSTdNGa5p2lqZpdcCPQC9N037suPD2pAMD0yxYjQqlQTdp5ngURSE3M5OCoqImbe85bQQFv3xPUVlV8weTJKlZ8RaVzVUB/u/bEvRkHWWgmf9n76zDo7ryBvzecc0kk0xciUwSYgR3KE4LpUZb6t6tbYXq1t1t+3UrtFvbdkvdKRQKheISnCFBQoS4Tcblfn8MBGgChBZr977Pw8PMnXPP/d3JzJzzc3+zh6DbzvkTBqNTatm+tKbLc7c3e7n+uypEUTzo/GatHLlMQCETkB3FBWf+4lXc/8zr+ANHP8DA4XQx/eGXyM1M4/a/XUh4mJFzJ49h5KA+ZKYld4RtSQuoRHdRyQVG7ReSuZfz841cmZtKhjyeXaW12Otd6M0ajNFaKnbv7vTdsuoSuDJuNOmaWOZ8808G18K7/32TnFGnYFqzGH/JCu4cUcTMGc8QO+lMBEFAZgpHXVCMdugo1IW9MV52XYdiB6DKysW9YA7ND92OduR4dKM7e4plpnBU2T3R9B9yWMVOQuJEkhahwqAKbXmd334GLicT/v0MA6ZZmR+QUzVyKsLQsR3jzeHhKBUKCrOzeG/m97S0tQOwrG0rPzavwarrnOv8ra2NJ36pR6cU+Oy8FCK0crIi1cQYlAxM1HUa3+IKsLzSCSIkmZSo5AKljZ4DxrR7ArR7Dr2etbpDr7+yrJFvbHbcfpGH59ciAjXtPrwBkSu/qmJdjavjHFEU2d7s7d6bJ/Gno9sxUDabzWOz2XzHUphuIcKpWcZQeWJBQaQiFB/dIzGJ7RWVBwzV67Rcf88DvLflxIstIfFnQquUcdcQCykmFU1VcvorsinZvIWfly0nNTGO664+i9RTLazdVNrp3B4RKt4/M4mz/rsLuyeAPxDaiIqiSFmjhwfm1XJKDwOJ+4XIHA1EUSQvO53m1jbueuwVZi9YBsC6zWX4/d3r4FJT38iaDTYglJ+0F7/fj8vtYdKYoVxx/umkJsVLSpzEH0Yug4JYTafjqxw7uXH9F+hjzIj6ADljkonvGUm1UMM3P8+nua2t0znnWAZxd/KZnNFjDIJWhyCToZ88Fef3X9Ly3MPIFHIiIrtfFERmCscy4xMsb32C4ZwL/9B9SkicaKL1CjSK0G+2f3sZqsLebO7ZhzdnVxBtUFCUGU2G7Vfadu7C4Q3idLk4Z9wYcpYvINli4tV3P8Hud3H79vfI1SVxmrqY6+55qmP+NbtdTLKG0TdBy6srmlDsF5JiUMnok6Bj5oYWtu6nvNm9AZZXOVHIBabmhVPR5uP9tS0AvLW6iXZPgGcXN7C+zk1Zo4ct9W4gVPCsxu7jk42teAMiV31Vid0T4LZBUUzMMqJVynjj9EQ0ChlPLqynqs3Hc+PiyI/R8PmmVuZub6e23c8Li+sBWLCznXZvEIc3yIKdjmP+t5A49vy5ElyALY0enlvcAMBXeXehk4cqfSXHx1HX2IjT5Tpg/IScSBIaSli0vOS4yyoh8WdlQ52bxxfWMTJNz4Lmn9lR3oggCLQ7Qj/8C5eV8Mj17/DcEx92qTjJZQJvnp7AzI2tzNzYCsAPpe38Z10LZ+aGHXV5d1ZUM+36e1HI5Tz1jxspzM0ERILBIDM+/JIzrriDV9/9lJ8WLkcURR5+YQYNTS20O0O/F/MWreCm+57l4pseYOW6zQBcd89TXH37Y0y95m6uuv1xoszhXHT2xKMuu8T/Lo3OADd+V93p+I4WF7ZV/fho9m6UCgUOlwtRFNmyfTsxkZHU1Nd3OZ8Y8GPM640ypQcAisRkIp97g8jn30RdUNzlOYdC0GgkI4bEX4LPN7XS6Ax5uCxvzUQ3YQqBcDNjs7WM6mHArFXw6zIbFe+/w8wNLdidLvR6A0qPm8vkzcgNci5c9yITw3pxtmUgDaUNqJRKHE4XgaDIeyXN+IIip1nDuHlAZJcyJIYpMe7xHpY1etAqZdywX6hmhlnNw6eEcrItOgX+INw7LJoBiToq2nxU2/2srnbx4M+1qBUyFLKQ9/+jc5IxquUIgnCAUgnw4oR40iJUhGtDrw9I0pFuVhFrVPLPU0Pexy31HppdfmrafWxrCimf75U00+g8KdpaS/wODtXn7t82m+0yq9V6jc1me/14CnUoci0arhsYRYvfwVcNy7kkNlTNSqlQkJaYyNadOynK2ZeQrZQLpCTGc/ecKj63tmMxdQ6BkZCQOJDCWC2je5q47ttqeg6wUzw+ncn6/rzz+Rf4/H6K863Unt1EdJIZDrL5i9QpOD8/HNWexWZchoHBybqOamVHg7KdlTz96ntsL6/khsumEm4KefL3V8JefmQ6G23b+GnRCowGPaIoYomM4Mrpj+L1+Xjj6X/g8ngoyMnk+Qdv6ag8+c9Hb2fVus1EmcNJT5HaDUgcfSx6BQ+fntjpuFJooH/yavJah2CPjaF0ZznpyUkERRFrjzRqGhrIzTiw8I7odtP6ytOIjnYiHni647g8uuuWBRIS/0ucmx+ORa9AFEWc33yKbtLZtNq9LLI1MVDUMriHisF/O4+6a8/ntGlT+RgZzh+/RRg6hvTlj9Akc7P6vw4UGQ48l3pZvcFGUc8sLrz+Xp574GZemJDUca39DSLBYJC7Hv8/tpTtJDkhhuFDBlBWUUN839FkRamJ0iloszsQETEZ9+1PJ2cfaAQdmRZ6TRRFciwxaJUyzsgJ5bjLjyBxPd7YOWLmmr77lNF0c8hholEI6FUyGp1+wtRylPKjY+SZuaGFc3qaKG/xoVIIxBuVrK1xkWPR0OD0U+/wUxgrVab9oxwqSL7IarU+A5xjtVoPyOC22Wz3HFuxDk5Fq5cmV4BGoYVvm1Z2KHcAuRnpzF2ylAKr9YCGv8P65tF32Vae/9d7PH7n3yRLpITEYahp96FWyLihfyRLtoJ7uwf1qSoMOh3tDicRpjDOOn8kjfVtVFbXkpoU3+U8OqWMZZVO7J4gvqDI+IyjY1xxuT3UNjQRGRHGBWeMZ2CffFTKg4d59rSm09Oa3vH82ovOpF9RLgq5nMS4aBLjOlcs1KhVDO5beFTklZDoinZvkF93ORicfGCj5Hi1GVWkgq2RkYQrwijZsgSVUkm8xUJCdAwr1m9g6dq1xEVZSEmIJ1BXQ/Ojd6NITsN050Mn6G4kJE5ebA0evAERJQECdTWhSJQKD+MLRNyWXQwueZsppqGEZefQb8YMtP1GcP8GEUWcgmGnXsagb2fQNOBUPpq1iOZWO60tDsYXZDDEU8qv99zF6tuf5execR3Xa2ppY0vZTgb1KeCO6y5ix65qNpfuoF1u4PMvv+PuJAs56X3x+/3cdN+zAMx47t7DtrURBAGt8tjvYafuqfD58tJG+iZo6RWrpaTGxYi0rtfwedvbGZaqZ/EuJ5VtPqYVhPPhuhbGZxpocQcp2e3ijJww2jxBmlwBNjd4CARF4rIU/HtNM4+PjsXuCVLR6pOUu6PAocIyzwBKABdg+82/E0ajK4DdG6TF78CkOHBBjLNY0Gu1bNu1q9N5j191Gpq4DL7a3HK8RJWQ+NNi94R+jHc0ezEkqojJjADAqNdj3xOa2bCjlbn/XMOjL719yLkUstBitL3J+4cNKytKNvHCmx/x2Etv8+EXs4gwhTF8YPEhFbuuEASB3gU5Ha0FJCROBN5AkPKWzjnhp0Tm09eZTsrc7ZxfHEtsVBSLVq0iIyWFCFMYWSkp7KioZN7SpThdLuzvvoa6zwBMt92HTCNtjCQkfkuD009QFBHkCsKuvBEAa79kRLWXKk8Tyeoo0rUxtAy4gqQdmyncsoIHbxjHsxcVkzNpABtyCxm+8ifG9S+kOD+T83ulkbTqFyJ79Waou44f33/rgEJHn3z7E7c9/CKnXz4dn89P36JcLj7nVC4bW8ybzz/E+59+zwtv/IdAMEhRXhZGg46Pv5pz2PsQRZFX/j2T6tquQ7OPNncMsTAyzUCjy09FW+i36vUVjexq9VJt9zFnW6hVy/KqkBE3x6Kmf2LoN2ivEmpUyTCoZAiCwJW9zUTqFEzINHKaNQxBEHhxQjw6pQxrlJrTrEc/beN/kYN67mw2205gp9Vq/QUIA3KBUpvNdkKT14pitfSIULG1yUHEb5Q7gN49c1m8poSMlJQDNpIatYrLp4xgTVk1i1dWMKhPwfEUW0LiT0VmpJqBSTqeWFjPkkvPQbYnJMOg12N3hpQ7S3o4E+/qy/9d/CGBQLBTk9e99I4P/dD/1jtxJNQ3NrNucxnF+VYWLF1NbUMTLz8y/XfPJyFxMmDWKphWcGAPLFEUue6j17l88mhGFOazqNzBDmUGfXLDSU8OhX4NKu7FQLGIuUuWsqO8nKhVy4h67UMpKkVC4iAMTtajUcgINNbT+uLjmB95AXYpKA2U0Ral5KKY4Uy1DMIT52Nj0xnoN28hXKtAEAR2iY1sKuxJlC/I33Oisf/wX2pUOlxb1xP55sd4hp/Kq4X5CIKA1+dj1s9LGNK3kPEjBjF/ySo0mgNbJhRlp/LFW890tCe4+crzaWppQ61SMvObOXz81RzuvfkKeuVZO93HD/MW89WPv7Cjoprn7r/5eL19pJvVHSGbPcwqTIoAO2sbaWgPre93DY3G5Xaj1qqw6BUEg0GGxASI0IRy/cZmGI+brBKHaWK+hynABYRaIdxutVpn2my2Z4+tWAfn+61t3AgMMeVQZEjt9HpSXBzydevZunMn1rS0A18zqWjVB/jbjBX0+GEVY3rGcuGZE46P4BISfyKaPv+Y1shofrz4LGa8M5u+aRn0HpuJyWigsXmf93v3qmZSouJZunr9MQthfOODL/j46zmcOXEko4b0Zfq1UuU+ib8Gu7bs4MV/vMrNj12HZ+1Kmu+fzi1PTmZTUSVXqWXowjRkq/ykhEeSZNoXpHqKXgAAIABJREFU8iUIAoIgkJIQz6Kly7CMOI1J4REn8E7+2mzduZNWu52++fknWhSJ30n7h2/jv/oacLQTaKgPednaZChMCqZq+xMdGco7q/W28FG4j3MvvAxhT3rPJmcFmuJIek9+kkXlTrJ69mXjuhJUY0/FqYjghVofDy9dxIM/raG2oRm9TsMDt15FckIsl5zTuYXIXvY3xpjDQx6ryWOGER1p5raHX2T8iIHccd3FB5zzy7I1PHvfTVgzUmmzO/hmzkI22LYRYTJy+XmTiTKHjEVujxe1StnpOkeKz++ntqGB6rp6aurrUatUBEWRT1bXYjIaCXi9rFVnUdfYyI6qKgQgKiICp9uN2+PBoNNhTUvDmpaKVtO5MrDEsaE7yt00YIjNZvNbrVYlsBg4YcpdUVzIStDid6AUOscmC4LAsD59+P6XX0iOi0erUR/wel52Ojee4WPV0iWcPm44G23b2Lqjguqaeq44/3Q0alWnOf8MiMEgnlVL8SxfjOh2oS7uh2/rJgS1FsP5lyGo1YefREJiDyq3g4i1CzFPO5dPBizlzOxBAGSlpjLzh1kMKCpEpVQiBkVuuuxcEpK7bs56OOYtWsHH3/zEzVeeR05mGms3lVJeuZuJowajkMtZs8HG9/N+5ePXHu9YtCQk/iqEORoZ2FRFzaSPkMclQDBAQ2MVZo2B8D2RKVF6BU5fkE83tnJWbtgBG7Xk+HiiY2KokctwulzotFJI5v643B40atXv3ty2trezZdt2bDt2cNqIEUdXOInjjsznIeB2d1SBHXBBDvZfm/ls12IujxiPSlBQubmSFFcEMz3L6S3mAhAQgwwz5yGTydjt8CMaLCwhD4V3K2kVNl6a2JP6Z2eQlZjCsEHFnDl+5O/+zGk0akYM6k2UOZzkhFg22rbxzU+LGNg7n4TYaJ6854aOsbMXLGXj1u3079UTfyBAhCmMeYtWUFPfyE+LVnDe5LGs3rCFuoYm7rrhEtQqJQa9Hrns8IXyfT4fpeXlLF+3HqPBQHy0hYJsKx6vF7fHw5hBA1EoFOyorGRHZSWxliiG9umNIAjUNzWhVCixmCPYXd/A+q02Vm7YQLTZjEwmwxxuIjI8nDiLhTBDKIfP7fUiFwSUR5hiIdE13VHuBJvN5gew2Ww+q9V6QpvGReyptPeful9IUJm5MGZ4pzExUZFkpaSwcNVKxgwa1OlLNnVwNlMHZ/PEL3WomhqY9/lX5Gam8uEXs7j8vMnH5T6OFt7N6wm2NKMeMBT3zz+izMgm2G7H/et8lDl5iB4PoteD87vPEMJMaEdNpPXpB9GOmYi6uP+JFl/iZMUSg2HyVAL+IGkrotDmhYweRr2e5Pg4SjZvoXdeT1IHxyC6Y6israVR1UpkhOmILpOVnsKgPgXc/OALTB4zlKT4GD77fh7vzvyWm6+axqA+BfzribskxU7iL4kq3ES8KRZWVyPojTgL8khTRnPT8z9ifvVa2NP3WCETaHT6CQRBsZ9NU1y2kNEqGYsSEinbVUGBVcohXWezsc62lWAwiMvtJiEmhp6ZGYQbw/AH/JSWl2MyGjGbTOi1Wrw+H16fj9qGRmobG/D7Q5vkQCBI2a5y0hKTOGPMaIz63x9WLnFyIOj0BD1uBLUGe52TpR9sQTdKS0ltGSpBQenOctbbtqKSySgRK7jI9hKjwgu4JXFSxxzn7ik00tOSit8Xx6c/fE9htpUNwyaQvbuKqG/epaStmp5nnosgCJRs3oIlIoKUhCPrjZqXHSoAFmOJRAyKTH/4JZ5/4BYyUvdV1508dhiTxw7reO7z+fl5ySo0ahVD+hYyemhfhg0o4s7HX+b0y6bTp8hKQV4PslJTGVBUiFq1z5khiiLVdXW0O51s2b6DtvZ2zCYTY4cMJj66c8GxvaQlJpKWeGDF38TY2I7HCTHRJMRE4/Z4qKqrQy7IaGxtoby6ml9Xr8FsMtHudOJyu5HL5Rh0Ws4eN+6AoogSR053lLtFVqv1U2AhMAT49diKdGhml9m5g5DnLleXdNBx/QoL+Hz2HNbZbBRmZ3c55uo+ZsLUFs4aXoROKSNSK2dz6Q5mzV/KVRdMwaA7uaygvvLt4PUiT0im/YM3MUy7HEGpQlCHrFDhdxy8Spp2/BTE9jYIBlEPGErr/z2LKq+IsCtuQBZ2ZBvywxF0uxBdLuQR3W+YK3Fy0e7w0FBSQkzhAAS3gFa2bxEYUFjIzB9m0Wq3ExRF1OsMLG9aT1JeNOdPGdut+b0+H69/8AWXnH0ql5xzKlPGD6d0ewV9CnOYNGYoy0s2UbajgqH9i4iL+X1eQQmJk53tXjUz26O5yByFf2cZtQ/fxXkNu4my+zDVNkN4qAqtSi5wTd9I3P4gCvZtEBXJodSDTIWGlRs3/k8pd8FgkOq6OuRyOWqVijC9nha7ndUbNzFh2FDUKjVGvW6PsmejobkFmUwgNz2D2oYGNpdto93lQq1UolAoiI2KokdSEiqFkua2Vrw+P+dNnCh5Q/9CiG4XotuFoNGiDVfTZ2om6xxlxPhDIZGrNm5k3NAhqJUqLoqawpI2G3dsf49opYlTI3sfMFeUXkGLSyA8Ko5ZCxfR3NrKWWFafEnJ2Gd9yUeCCoVGS1REBNsqdrGxrIyJw4chCAJVtXVs2lZGv/x8TMZD56JFmcO564ZLGDag10FrRfj9fmr39HkeMjAPmSDD5XGzfP16auobGNQ/j3tuuJydlTXkZqby0r8/ZPGqtYwY0JeoiHD8AT87q6ppaWvDZDTSIymRqIgI4iyWo/PGAxq1mvSk0J49NTGBeYtWsGp1GQ1NLdx/yxWUbq/gp4XL6VuUKyl2R4HDKnc2m2261Wo9FcgB3rHZbN8de7EOzul7+no0+doxKw5eVl2pUDBx+DA+mz0Ho95Aj6TOvYQidaHbL6lxo5ILTMg0Yoky0+5wcs7Vd3H1hWcwZdzwE5qkLooirllf4/zhS4L2NmR6A/7KctT9hoBMjjKjc8JtV8h0OtCFzMDa4aNR9xtE+wczaLjhYjTDx6DMykE7dFTH+GBrC/6qXcijY/HtKANAHh2Lf0cZmuFjcP8yF4IBNCPG0njrVchMESjiEwnU1eAtWYl+6kUYpl7cpSwSJz9GfBjKt6DWK3n46gtQ7BcCbdTryc/KZOWGjRh0Oi762yC8v7j4ccHSbit3j774Nm6PF5Uy9B00GQ30KQz1pxQEgf69etK/V8+jf2MSEicRVpWbaVu/REhMgfY2+qcNovaJCwhoTQR2V4N133egpt3HDd9V8+m5oc5EXttG5JYY5OYoEoNB5i5dyjqbjZ2VVQzp0xuz6ega7U4EdoeDbbsqKK+uQqfR0jc/nzCDnpUbNrB64yZ0Wi2iKCKXyfAFAmjVagpzsomJ2mcQ6pWbS6/cXAKBADKZrFvreSpSX8u/IqKjHXFPWGbQH0SlU9Lu9xLuVVNVWxvKY43f52Hrb8xkgDGLQaau91nhWjnnjhxAyeYtDOldTFhEBOLoCShefpJxsz/Fe+9TJKemEQwG+Xz2HJaWrKVvfh7rbLZQ4ZWFi5g4fFiXXuFgMNiRWysIQpc57aIoEhRFflz0Ky12O2EGPenJyYhBEb1WS01jA/lZWaQkxKOQy4mxRNJqb4eAjFXrtvLtj0sZNrCQkYOLUSlVnDNhPAq5HFEUaWt34Pfva2KuUHTHFxSi3elCpVSEUjdEkYrqWhqbW1m7qZQp44bT1NrGM699wM1Xnkd0lJmcjDQSYqIxhRlIiD24l1Ci+3Trr7VHoTuhSt1etjV5AJgU2QerLh5Pu49Ns8vpdWZGp7FGvZ6Jw4by3YIFBMUgGcnJXc45ZY/CuKrayeebXDx28xVs2rqDD7+YxeghfTEaTkw4RqCuFpklGtHjxnjVTah6FiLIZIiBAMJheqEcDplWR9hVN6EdOQ7PmhUEqisBsH8wA82QkaGwhRmvEKjbjSKlB0FHO6KjHVVeEY6CfoT3yKSioYkYt4fwW+/Dv7uSQHUF8vhEwm9/UMrx+5NTmzMA1W13smVRBWVVu5l03oEhvL1yc4mNsjBn8WJam+0Yqo2sXm/D7nDg8/vZtmvXQYsPNDS1sGhFCV+/8zwajfQ5kfjfpc4Qw67xF2H1NWD61wfM2P0T2c8/ifWzeQR2Vx4wNkav4L/nhNawYLudlifvx3TzPcjNUchlMob2LubX1WtIS0pk/vLl9M3LwxIZiUb158sj9wcCfL9gAQ3NLaQlJlBozaaxtYXPZs8mGAxiMUdw0emT0Wg0HflD9U1N2B1OUhO67rkp/4NrpsSfm+bCIcgtMShamtH0H8K8knW0bnQw9sJeVHnLKdm8hbyszAOUf71cwyuZVx1yXqVCQd/8vI7ngiBg+vvdiE/ej27Wl3DtLchkMsYOGcz85SuYO/9nqppbuej0yazetImZP4Ra+mQmp5CenIROq6Xd6eSTWbNQKpSkJiTQ7nSiUatQq1Q4XS6iIiIoK99FU2srRr0eg17PeadO7JRLl5rY2UhhMhq49+YrAGizOwgGg9Q3NXP9PU/juzpIalIcMpmMq25/DJlMhsvt4b6bL2fEwN5ccdujTB43jFFD+hITZWb2gmUsWbWOSWOGIgK987PZWVHNLQ++QDAY5KHp15AYF830R14iEAiSk5nGmo02Rg7qw8zXHj9gbx1uMjJyUJ/f86eV6ILuq+InCW6/iCiKnGoOJW62NjrwewMEgyIyWWeLXHRkJJNGjuS7+QtoaWujd8+eB7XcFcdpSTGpEEWRmVU6pt90NW2tTXz63TwmjRl6XPJ+xGAQsd2OoNbQ/MidRD7zGvop5x4w5o8qdvujzLAe4P1T9+qHPMKMzBRB5PNvdBxfsmo9cxetYMXaTbD2UT5/82lWlJTyf4/+nYRYC3ExUTxwy1U4XW4qK2uwpqd0dTmJPwkt2gjcLz2G9vqbeVM2j0kcqNwpFQqS4+OIjjRT29KIUi/jusvO4LMfZ+NxuwkKAnqtloToaExhB/atqW9s5pJzTjvpwp4lJI43PqUa+6bN+HVuANa0byfVGI0mNRn/1vUHjBUEAYcnwJML65le9hHqvoNQF4ZCxURRJCMlhYyUFERR5IOvv+GnJUsQRZECq5UCq/WIe0GeCOwOB4vXrKGhqZnIiHAuPWNKR4hWamICxbm5ON1utGp1p9Ati9mMxSylAkh0jbyxFl+ZDWVmNsrMbBbt+BBTkp4G7CQkxLK7uo5xQwYftesZr74J96L5Hc/DDAbG9etD/SVnkJeUimrSaQwsKqJffj4VNTWUle9i+bp1RJhMuDyekMctPp7y6mrCw4z4/QEcLhcxUVHUNTZSlJNNnMVCQ3MzcdHR3SqS8lvCjCHlKtxk5OVHpnPv06+h02p454X7+e79F/H5fOh1WlRKJf5AgHtuuowPPvuBt//7NY/cfi0ZaYlU1dTx3Ov/QafV8M9HprO8ZBNTJ40hMy2J6pp6inpmMfO1Jzq/PyfIafK/wmGVO6vVGmOz2WqPhzDdoWe0hipvE9eVvsHXeXdTubaeiEQDzZV2IhKNXSp4URERnD1uLLMW/Up1XR3D+/btMs5ZEASi9AqCosiYdAMmjYy5W3ysqAvy/rV307cwlxsum0pSfMxRvy/R58X10w84vpqJulcfwq65hciX/33MQ0Jb2tqZv3gl67dso09hDhNGDuLDL2ZRXdvA8pKNJMfH8uz9f6emvpH0lETOnzKWHskJCILA1EmjGT9iILvrGqiurUenVbOzcjd3P/4K408ZxI2XTT2mskscO3LKFhPwt+Eu3YZGd/CfiaKcHOb8upiIsDBamluJCwsjbt43VKdmMV8UyRCCDDao0U86GwhtQq3pKeRkph10zuNJoKUZmVbXbU/z3r5EEhJHg4TGnRRH6wj72wMAtPidtK93sMuRQOapXUSjqATOsLho/GApX136PJMbPCyrdOLwBvEFRW4aEIUgCIwfOgSdVovfH2DF+vV88PU3TBo5AovZzJbt24mzWA6b63O82VBayqoNG8nNSKfAaiUqIqKTAifsMRpJSBwpYZWlBJoa8M2yIbqd7ArWUadopTy3nilZfTk1M+eoVmqUm6PQTz4b99KF+LaXYpx2OcE1K9Bl90RmjqTtjRcx3XQXcrmc1IQEUhMSCAQCVNXWIpPJOoqSdGmwyMzseJh8lL4P2Rmp/OeVhwkEgshksj3G131zK+RyCnIyePreG2lqaSMYDBJlDqdHcgKXnbuv4MzUSaOPijwSf4zueO4+s1qt9cBbwPc2my14jGU6JCuqnPTzNGJRhrwBPcelAvDzKyUUTUknIrHrBUuv0zFl9CjWbbHx2ezZ9C8opGdm58UTQCYIHQ2XC5Mj6XneOGIvHc3fvtiOXKli/ZYy/vvVbAb3KaRfr55/yKMn+ny45v5A+8fvokjpgen66SjzioA/1pvkcAQCoT/j1bc/RnpqIsX52Rj25OSFGQ34/AEevPVqwoyhY2eMH9HlPGFGPWFGfYenriAng2/fewHffrHaEn8+ZsUO5Nq+EWz5pYqELDP063pcUmwsYwYN5OeFK4iqisU2+0MGp8aQOGwYqRWVLAsqGdp/CIHmRpzffEZVz7689MmP/OvJu47vDf2GYLudtjdfxrNkAXJLLNrRExE9btQDhgLgXjAH4yXXIgYCeFYtxTXrawJNDfjLdyALjyD8jgeRGYzY33+TiHseC+XEtrXir6pAXdQHQadH0BtQZmYTbKin7fUXMD/yAo7PPwJEPGtWoLT2DHnlFYqOokhdIfq8eDeuw2fbiLq4P8rMbBxf/BfthNMRXS5kxjCEI8iHkDh5WK1NZtDlNzIgOvQ76wp6KR6aQZI6imBjfSdjgv2Vp0lZOA/ZeVeQHW8kUifHGqUmP0aDSr5v3N6eXQBjBg9iY1kZC1etIiYyii3bt2M2mZg4fNgB1fJOFC63h3U2G9srK5gwbOgBsktIHC1ao5IQPR40g0fQ7m3HteAD1Aoly9u2Mj1xMnGao1c8ZH9UBcUoklMRRRH7W69guOAKNENG0vri4wSdDmS6fR4suVxOcnzXYcXHg+7+HuztySdx8tKdgipDrFZrDnA5cK/Vap0LvGWz2bYfc+m6wBqlptLTSKI6tACs+24HqX1iGHlDEaIo0rrbgSmua3evXCajV24OaYkJfPrjbNJTkg+bj5ARGbLo+4Mi00enE2/R8d0OL+6U/sxfspinPpzDI9ecSZ/8LJav2Uh+TsZhy8GLgQAEgwR2V9L88J3I4xIJv/NhVNndLyDxezwITpeb+YtX8d3cX1GrlTz/wC289dy9ndzjp40eckTz/hZBEP4UIUASB2e4rAZ5QiFJpd9w6rgbDjk2KS6OaVNOJSiIfDT1OTzDehPbbxAx/aDyl4XsaGvHag5HdDnQPjadyxIy8K5fg3JPDun+iG43ba89j2bkuI6Qs2OBa94sBLkcy9uf4V27Cu/GtRAIII+IBKUKVa+QNttw4yXIdHq04yejiE9CkZxGsN2OzBSOoFRhvOjqjjmD9jZUPQtw/vgNgkqF6PGgP2sa6j4DMd0UUmbl0bG4fv4R7ZhT8ZaspP6ysxADASxvfYJ/1w68a1ZgvPRanD99R7CxEZ9tI94NJShSeqDKzSfQWI8yMztU8U0ux19dgSy36wpqEic/WQoH6fU2SOoFwOc972D7smpaEtrxP34rkU//C8FgJOhop+WZhzBddxvyhCT0k6YwSRVam6L2eNZ3tXp5cmE9L0+MR/GbCJacHj1oaGqmpa2N8087lSVr1vDRd9/TryCfQCBAbnr6EeWkuT0elArFEZ0TCAYJBoMo9zNEbCgtZfGecuinjxqFTmpyLHGMMDVUEKjbja/Mhj45jQ+m3MKodQ/iCfhJUR8bxQ5AptN3KHBRr7wbMvzJZETc/ShBpxP/7ioUcVIBH4mjS3fNvdXAdqA3kAe8ZLVa19hstvuPmWQHQSkT0Ms19DGGeoAYIjUo1KEFxtHk5te3NzL+7r5dhmfuJTwsjJT4eJasKaF/QX63Sh0rZAK940PW1Sv6RuMqsqBTFnPj99UU9LSwbGcjry4sp/HFGejCI7lh2iTGj+jPus1lpCbGdcQ2A7S//ybyuAS0I8diuuVeVD0PvjkTRZGVazeTl53OirWbePWdT9Fp1Wwu28nLD08nPTWRl9/+mPjoKOJiosizppOWHE8gEEQulxEIhJJlYy2R3HjvM4QZDUwZP5yh/UObCSnuWaIrlNs2YzdOZtdONRmtMRBx6PEKlRx7nRNF2CRWeuSctud4bno6y9atQ9uzgO09h/DMd2uZUVxM22svEHS70I0/Hf2UqbQ+9yim2+6l9dVnCTbW0/r8oxgvvhrtqAlH9b4CTQ0Eanejm3Q2iCKCTIZm8Ag0g0ccME5dUAxA5LNvhCrN7ofMsC86QJEU8ljrJkzpOKYb17lXptwSCuXWDBmJZshIALTDRmO85BoEQxiCXI6gVCKPCm0yBJmcoMOOZuRYTDff06ldieH8ywBQ9excQU3iz4PYUIdQsoz1NeEk9bPwfXAN/fyhtc3y6gcAOGd9jTInD8NZ05BZYjCcdUGXcyUYlTw4MqaTYgcgk8kY3q9vx/NRAweypKSEVXsq3ja1tDKgqJDtFZUkxER3NBbuip1VVcz5dTExUVH0K8gnNurQrUpa7XbmLl2Kvd2B2+slu0caPZKSaGhqZt3WrZw7ccJJFyIq8dek/Z3XALBffilrt2RxxbiRKBOVxy3Ufv+1A8BbsgLfjjIM0y7HvWgemv5DEFRSkTGJP053cu5mElLoPgAutNls1XuOrwSOu3K3tdHDHeZeHc97DIjreGyI1DLhnr4EAyJrvtpG4aQeyBRdJ5kOKCpk5YaNfPTtd8RFR5OenERKfDyaQ+Te7PWWyQQBvUpg87ZtTArbzbadLZg1YdwxbTS9b5/CTV+XI7doaWz3cNfXNpzLn8Gg03JerIbz77wV/dSLEbTakIfrEIrdyrWbefW9T2mzO3j63hvpV5hLzPSraW5tIz87A7VKidvjpTjPSnVtAyvXbkIURdKS4znzqjtwe7xAqILR43ddx+tP3X1E5Wwl/ndxeP1ERqdg9Lq4pPRlFqQ+dthz9JEasi4fhNy8b6FMiovlh18W8vybH/D8/beS+MJDJKanIF56Nf7tpfgryhGUKrSjJ4BCifHy65GFmfCWrKDt/54j2NaKduxpyPQH32x2h6DbhSDICNRU49uyAVVOPnRjQf+tYne0kZn2ac0yg7Fj8deeMh4ps+ivzypfGOWyMNQCzP9kPW8OmsO0waHQYM/qZYguJ/a3XkH0uIn5fN4hN6FymUC0XsGTC+uYkGmkMPbQn6ABhYX0y8/H5/czZ/Fi3v3iSzRqNWu3KDlz7BiCwSBLS9Yil8uJjAjHqNdTurOc8uoqJg4fTlVtLT8uXESPpCRiLVFEmsLZVlFBtNlMW3s7FTU16DQayqurybdmkRqfgE6nZcW69azasBG1SsXpp5wiKXYSx53S2i24zk7n4tSRyJUnroqqZtBwNIOGE3Q6cX73Be6fZxN+z2NSmL3EH6Y7n6A3gSU2m63darXG7Xf8j8Xu/U4KY7W8vns2A41Z5GmT+c/f5nHha6MQ9lgrBUEg6A+g1CoQ5ALNVe2YYnSdlDyjXs/I/v0YXNyL7RUVbK+o5JcVK7GYzSTGxBAfE42AwPL16zGbTGjValZt3IhRr8diNiOTyahpaKA4N5em1lbqd2/F7nCQEzmaV6aEikW0ewJMP2c4A4abuO+H7Zh0jTQHFcxduYPR+YmYw8MQBIGWtnaaWlqJiTKj12kJBoPsqqrh8X/+m6svOIMxw/ojl4fk/20VSoNCweSxwzq9T5+/+TRNLaFGrAmxIW+ApNhJdBejUsZGfy01Iw08Unxet87xl24ibdsyPHGn43C60Ou0yGQyqnc3UZjfg8aWlo7PryAIKNOzUKaHmi6r+wwEQB4eUnbUxf0J/8djtH/8LuqBwxCUSlCEQn09KxYjMxhRpGch0xx6AyuKIp6lC2l782WMl/0N7dBRqKQwRomThOGKBrIUTvTjUqlbaidcoefbR5Yx8oZChN3VtP/336jyigi//6luexcuLIggXCunxRUgXHvwjasgCMjlcuRyOZNGjiQQDCITBBYsX8E3P8/H6XKRmpCAUa9nd10dm1rbSEmIZ9SAgSTERJMQE01Oeg82lW1jY2kpNfUNRJhMbK+owBxuIj05Ca/Ph7VH2gHNkIf2OXbh1hIShyLs+ttxNHv4NWwXp7RFHNT4f7yR6XSYH32RlsfupvmRu0Lh/HuiR6QiXhK/h+7s9gcB44HbgJetVusqm832pM1mcx9b0bqmtt3H7rZSehnSEGQC5744vEOx24tSoyBvfCoAa7/aRt6EVMJidLhavZ3y8VRKJdk9epDdowc+v5+q2lqq6+pYvHoNTW1tDCoqwuf309jSwlnjxiIIAnWNTbS1t3PW2DEHePrWbN7MN/N+Zsro0Wg1anRBD72/fhnHhhKmXf4P0ovzWL15B28urmDGq//Cntyf0wbnc82QFK56dwWedXOQKdRMOW0st14wnk9ef7JDqTtS5HIZlsjDxNJJSBwErz9Apa+Z8tpRTHN3r7KlIi4Rd7Kfz978GaNeT/KgKIaOKOKHn5by2jN3smDFCqZOGN/thUrZI5OIux8N5dI+9QCakWNxfvsZwaZGBLWGoNuJoFQhN0cS8cAzHecFnU4CVbtwzZ+Nf+c2gs1NmG75B+r8Xoe4moTE8Wej34hdpubnB5cSfmUYJqeOoVfmoTWqYOQ4vFs2oBk0/Ig2d4kmJf6gyAVfVvL6pIRDKnj7s7eU+tA+vSktL0en1ZIcF3fIc4x6Pf0LCxBFkcraWhJjYqSNqMRJScAQhjI9i+/v34R9uJG21e0IvU+ez6qgUBB+5yM4Z39D6zMPEnbddBRpGTTffyuWN/6t6UZLAAAgAElEQVR71K8XdDppeep+ArW7CbvyBlTF/Tty4INOB80P3YFm4FD8lRUoEpLwlKwkWF9L5AszcP74DXJzJJohIxEDAYItzcjCwxHkkgPhZKE7f4nJNputN4DNZjvHarX+Cjx5bMU6OAICTf52IhQG3G1e2mqdxGQdXIkZcV0oJ6VmSxM7ltUw8JLcg45VKhQdJWnh4BaTyPCuq2P2ysnB7fHw7fz5TOyZg+uJf6DIzCbyn+9g0YbCu07plcEpvTKA8bQ43IhBkQijllvPPYUxd09mTWUrCyt9AL9bsZOQ+KMIokhL0E1x4Be0remQcPDvDYSKBPkrdpIwqpAR8Xqeu/9DrOU9sMan8sbT91CUm8X2igq2VVSQkZx8ZLIIAuF3PQyAMj0rFMqoUOD86mNkkRYUyT0AaPrH3wm78U58WzbQ/tG/UfcbjHbUBDTDRkthLhInJQIiMkFgzG3FfP3KUq6cMgq/NxDyKCh0hN923++aVyET+M/ZSTi8QTbVu8m1dL9QiVwuJ7tHjyO6niAIJO0p3S4hcTIib2+j8darOOOR18lLUVNgOvl68QoaDfrJ56DKLaD5sXuwvP0pUf/6ANHjwf7BmwR2V0EggG7KVIJNjQSbm9CfeX6oyNdv8vlEnxf34l9Q5RchNx+YF+uvKMfxxUfIwsLQnXYmbTNeQT+pBu3EKTRcfxFR/3wH/dkX4FmxBLklBt+2rWiHj0aZnQdyOZrBIxDdTgINdTRcdyEoVAhaLaqsXGSWaDT9BqPMyT+qPZkljozu7HiCVqtVZbPZvFarVQmcUI0j2qDAHmglQqHHsdtN5bqGQyp3e4nNNhObbaZyfQOVJfUMuCjnsOf8HgvkgMJCFnlX8P26DYw+axrhhygIEa7ft+CemhUqLds/NYL+qVBt9/Hv1c3cM8wiWUIljjsqlQK74GPHyPH068ZGz7tmBfb/vEXUC29SmJvJ0/+6AXeTF6NRR6zZzK5VdfTNz2PRqtUo5QpSEn5fuWd5VHTHY/2UA8NFTX+/B5k5EsWIsWhHjP1d80tIHE9yFO0YNQpadzsYc2sxTpeH5a/bmHB338OffBgUMoF6h59ZpXZyotTSOiLxP42g02N+5T22boFds6rR9laQO+bkU/AAlBlWLK9/GPrOyhWIYqjSstwSg+hoxzHzfQC0oycC0PzwnZhuvBPvpnW4F85FZgjDV7YFWUQk9hkvE37vE8gtMTg+/Q/Gq27C/vYrAJhuvQ+ZMQxN30GIoghAxD8eR5Ar0PQdhKbvoC7lk0fuUxajXv8IeUQkvjIb/uoK/NvLsL/7OubHX8K1cB6BxjoMZ11AsLUZMRjEZ9uE0pobqkwtcczojnL3GrDBarWuB7KBp7szsdVq7Q88ZbPZRlit1gzgHUAENgDX7xn2ORAH3Guz2eZYrdYewN9tNtvfDzZvRauXOfmhhq9CD4GoHoduO/BbLGkmTLHHrkJkoKqC3O8/QX76NH7cvZvJLle3qnH+lkitnHEZBgRBoKzJQ4ZZqqAkcfxYkz+O/xZcyHcPLMBnbUCenAiAb9tWvJvWdTQlh1AIh/P7L9BNOL3jWKwlEiywaMYGEgqi2LW6jgEX51Ccm8vcpUs5Y8xoIsKObq8ceXTMUZ1PQuJY85WQxt8nn8v6l9awc3QD5bJ6Hr2762qYv4eMSDW3DrLw6y4Hn29q49lxsZKSJ/E/Sf2QSbT+33OUKs7is7wSno7PPtEiHZL9q2YKCsUBa+5vMT/2EoJSiaDRILdEE2y3o5t0NqqeBfjKbCAIyEzhHdWV909j6LjGnt8FRcKRRdbsVdKUGVaUGVYYtq+JuaqwGIJBRJ+XhpuvRHQ5UWbm4HvxcRQJSSCC+elXCTY1IjNHShE2R5HDeuFsNttbwGBCSt1wm8327uHOsVqtdwAzgL2uqecJKXBDAQE4HSgCdhLK59vbSOte4PFDzR2pE/iycTmCILDuux3UbGk6nDgHoDYokatkVK5vOKLzuos8IYnw66czsKiI9KRkPp/zE81tbUc8j1oho0+CDrcvyMPz6wD4dZeDZZVOfAGRzze1EhRFFpU7qGzz4Q+K/FhmB2B2mZ3vt4au+c9lDdS2+6lz+HlnTTMADU4/7Z4AAHUOP6Io4guIuHyhxuZzttlpdPrZ2eLl1lnVALyxspEvNrcCcO03VbR5AqyudvGt7cjvTeLkp8ixk7k1y+mduBr/igUdxxvvuA77Gy91PBdFkYbrL8ZXuhnNsFGd5ulzXhZp/WIZfm0BcoWMnPQe9O6Zy8KVKzsshRIS/6uMDnchbt/KmFuLsXtc+OsD2H6uOOrXGZSk46kxsWxr8vL84vqjPr+ExMmOZeNiApvXMuD+AlrSncT3/Ot4joQ9fYXllhjUxf3RDhvdUYldmWFFmZ4V8sYNHnFcjTvyiEjkkRYEpQrLax8S+eJbmB95Hstbn2C44ErCbrwDQaHAtfAnXHO+JdDchP2DGcdNvr8yh1XurFZrEfAQcA3wtNVqfbsb824DztzveW9g7w7xB2A00A7o9/xzWK3WwUCpzWarPdTEjqCHt2vmAhCXHYHRcuReMW+7j7qtzUd83uHwrF2Fe8FPyC2hpPK++Xn07pnLlz/9xKaybThcriOeU6OU8d6ZSQDolTK0CgG7N0CjK4BMEKhz+HH5ggSCIssrnQBkW9QdORYFMVp0SgGlTCDWELKKfLSuheVVIVmu+qoSXxCWVDh5YmFo0a9z+HH6giSFKbl5YMj9fn5+OGPTQzHddw2xoJQJmLVy4o1KRFHkyYV1tHkCiOI+JVHiz0vQ7eb98gWU7Iwl2NrScTzmw+9ApSLoDH3W/Nu2IqjVWN77qsvKlRqDquPx3JfWUFfaQn5WFk63m9Kd5cf+RiQkTmK8Xj943Sx6awPKWAXxCZFowlSHP/EIEQQBhVwg2qBgXIaRoCh2GPgkJP4ncDoA2Dq3mikvFVOxVjJyHE8EtRpFbCgdQ6Y3oC7uhzItAwDDWRegHX86Mr0e3djTDjWNRDfpjg/0HeAVoNvmRJvN9pnVak3d75Bgs9n2muntgMlms221Wq2VwIuEPHaPAHdardZ/Ac2EPH2dtIQ6l5sIRSQbfyzHGK3Fkt51cZNDEZ5goPisTHxuP0rN0XEDi8Eg9rdewXDepQccz0lPx6jXs35rKcvXreOUAf2JtVhQ7bG0HAlFcfs2z1f1NgNwZu6+sNT7RoTC0pJN+zYHw1P3haCOzwwpZzcO2Bcv/dW0VACGpeoZtmfsBQX7chj3zmVU70uMTY1QdfyfGgGBoEhxnBajSsYXm9tCHr9B+0pfS/z52ByTSaPCgzleg3vhPPy7doLfh27C6SjTMml+6HaUWbko4hNR9xvcLWvgKTcWdXzfRg0YwLc/zycu2oJRf+zCpCUkTmaW+gx8Ge3AleUmzR1N9eJGUi49duHFYWo5PaPl/FBqp6rNx5V71hEJib86zanpULGJyHgju57TkhD91/Hc/RUQBAFUauTRUmGmo0F3NJsam832R/2k+ytpRqAFwGazPQRgtVqnAV8BVwFvASOAUcCc304Us2gudedeSFyu+Q8pZh6Hj6/vX8JZTw05Kr1OHB+/i6DRoB7YuedcYmwsibGxVOzezcJVq3B7vAzv25e0xFBVTpnsz10VUy4TGJsRUhzPyDm6eVQSJ4bU1T+we8oQrBcPQ9M/jlC6LMhj4jHd+g/8VRW0PvMQlhkzEQzda0Ks1CioXFdP/bZWep2RQWF2NnOXLGXyKSP/9N8BCYnfQ0LjPBZ9t411vZKZl/YQjXL7cbnuuAwDTc4Aq6qdzN3u4I4hkjFO4q+Nr6aMS2/KZ1avVPJIPdHiSEgcU7qjHe20Wq13AWvYs8Oz2Wyzj/A6a6xW6wibzTYfmAD8vPcFq9WqAc4CzibkxQvsuY6hq4k0bQ4mzc2jztpC9ilJRyjGPtR6JWc8MRh7g4vWagfJxdGHP2kPgZZmZHoDoseNd+0qvJvX41m5FPMTLx/Sg5EUF8e0006jtrGRn35dzI+LFqFWqRjYq4icHj3+Eonuf4V7kAC1w8V9mqnMf8bG6Q93rpiliE9Cdt+TOL/7HMP5l3V7XnNKGPrIkAe6KCeb8upqSrZsoTj30K0WJCT+iqg8foao0qgq1fJY+GeMjirAwpEVCfs9yASBKL0CjVJGnFGJwxtke7OX/Jjut0yQkPgzofeLxJdm80XDYrYOqeXOpDNOtEgSEseM7ih3asC65x+EFK8jVe5uA960Wq0qYDPw6X6v3Qy8bLPZRKvV+m/gdaANmNLVREGFgv6XFRAuP/JQrkBLM76tm/CV2QhUluPfXY1/3CU4laHqQGLAjyBX4FmzAn/5dgINdQQa6gg21BFsaUbdbzBhV/+d1hcfx3jJNQhqDa4Fc5CZIjA/9lK3S7vGREYybVIorrixpYWfly6jrLycEf36EWboUqeVkDiuCKJIelo8MfdEHXSMPDEFxX75eN1BZ1KjMapY83kZ+aemMXrQQD79cTZNLa30ycsjPKx7XkAJib8Eosgwcx4Thk3ktsp3ETi+xjGDSoZBJWN1tYtVu11kRqp4cUkDNw2IQqc8Md70jXVuLHoF0XoFDm8QvUqGNyCikIWU0r34AiJKuYA3IKKSH733zR8UsTV4yI5SI5dJxsq/CoIg46nLLuGXlk04PZ4TLY6ExDHlsMqdzWa7zGq1ZgHpwHqgujsT22y2ncCAPY+3AsMPMu7J/R6XAP0PNa/M7+fdn7/husHnIFcevkFi0OnEMfM9jJdei3v+j3hWL0eZYUXdbzDqYJC2Vx8i6/2vqP76Z7RbFhBxx4P4tmwgaG9DHhWNMisHeVQ0MnMUMlMov8/84L4yshH3PHZYGbpir4crKiKCs8aNpWTL/7N332FuVFfjx7+jvlpt79VbbI97xRVsMBBMCz2EENIDIQnphbQ3IQlJ3pSXNAKk8wtJIIGQ0EPH2Bhww92+LnjX3t6rujS/P2bdsHdX2CvJFufzPPtYGo1GZ9a6K5259567kwf/+zQzdZ1Zkydhk5KwIok0Az606Tf8veiLw64jac3JxXrWsrd9bItFw+mxE40aZKSnc9m5y9hVV8fDzz5LaWEB5y1ahF3e/+IdoMCWwf/reJn2nQMMpPlJtyZnyZs5pWnMKU3DMAzOKHWTZtN4XPWxtCqdTGf8FiLu8Ufwh6MUe+wc6A1SkeVgfZOPGUUuLBp86vEm/nZ1Bdf9cz9/ubqCPZ1+AEoy7Nz8WCP/ft84/r65B6sFPjAzh5sebeD284rZ2Oxje3uAzy/K55evdvCeaVm09Id5VPVx27Ii1jV6mVTgwh+KsqHZxwXjM/j56nZml6SxsNzNXWs6+fUlpTy5q49QFC6flIk3FE1awitOXogo1778M95beiaebOmhFqlt1G9Quq7fAlwJ5GIWV5nA4aULkiJnEwxO9Y9aVSzq9aKlpWEtLMKIREi/4rpjFj62ZGYTjUTZvD2dJTfeCvC2hpmNBYvFwpwpUxhfWcnqNzZy/xNPsmj2LGorKmSYo0gKzTDI8qZzYGP7sMndyZhywTi83X4at3RQPb+YRbNmoVdVs2H7dp5euYpZkydRVlQk73+R0nKtbjojPp41NrPUPoUsqzup8Wiaxvm1HgzDoHUwTDhqsLXV7Ekr8oz9BZeX6wYZCEa5anIm//NCK3dfWsYHZx3+e/PXqyuwWzX+cnUFnqEevIhhUOSx8eB7zRE3H5iZjT9szgn+1tmFFKbbWFqVzvxy83c5qySNXJeVbJeVymxzdM36Jh8eh4Usl5W2wTAAH56di91iVqj+zaVlh54bNSBqGPQHJLk7ndksNkqashkw/JQWSiEhkdpi+Wt9HbAEeEEp9Utd19fGOaZRZU5rI69y9MId3bd9iYwP3IT74uHHVjvPWAjAhV+bjxE18PYGcGcl5+pppsfDhUvOorG1lVXrN7BF7WLBzBmUFsY+H1CIsRDVNPylYc5YPjFurxEORulvP7w8SG52FucuXMDL69azYu063C4XFSUlzJ06RZI8kZIa/B1ENPOz7NfjP57kaA7TNI2PzTG/AD+3d4CqkIO+QIRHdvbx5TMLWFU/iJ7vJCfNijcUfdu9ey/XDRKIRLls0uHP8T9fUX5MOz843NLjMJOqgwkbgG1oyKTVopHuMG8frO7sslk4WG/tyIrRB5OzT8w7PIXihplmMpmTduw5lGYcrmodj+RWJE5PxMu2GY18YeLlzMsYn+xwhIirWC5DHdzn4FIGSR+sbHu9Av9AcMR9wq3NhOv3YZ80NebjNm3rZM3fdp5seCetrKiI91y4nMm1Nbzw2us8+vwLNLfLmiwicXZVZnJl/3y2/rcubq+RWeRmxiXVtKhuuvabVQItFgvnzJ/HtRddyJTxtexvauKZV1azfc8eIlFZP1Gklpb8dMrLzcWGf3rgEULGqbf23LXTsplf7qbAbePyoWRsU4sfiwav7Pfyi1c7ALjtxVaa+0NEDQN/+Oi2GjUMoobB559qoscfoTjDdtSSPSDFuER8dRVlM2tDJf9e8yqbB2WNVZHaYknu/g68DIzXdf1J4D/xDWlkndWlTCq2YHMMf6XQ99IzdH3pJjzXfxTNHvuCsGXT81l68wyC3hDRcHK/SFosFibV1PC+Sy9hfNU4nlv9Ko++8AIHmpsxDGP0AwhxEnZNKOC6CUvIr45/5T5vtx9f79HXjOw2G3p1NZedu4yMdDe76up59PkX8Pp8wxxFiNNPS2kOy2dewZe1y/hn+yvYYvpITo7sNCsT8sxRLZ9ekEee28bZVel8e2h91UsnZlCYbuPBbb38bl0XkajBQ9t6MQyDbzzXQlNfmJvOyMXjsDAxz4men5wRMuKdKTJuHLcsuJj9rg7ag73JDkeIuIqloMqduq4/D0wz76rN8Q9reLn7mvAt24nNcdUxjxmGweAD9+J74b/kfO8O7DUT3vbxLRaNdQ/upljPoWZhyViEfFKsFgtTamvRq6rYXV/PKxveQNM0xo+rxOvz0zcwQP/gIH2Dg2Smp+N2uSjIzcXr9+HzBygrLmLCuHF43MmdyyFOL0te38/P+h/htonvjftrHWxnO188QMnkXLKKDw+jstlsLJ49G8MwWLtlKw/+92lcTiehUIgl885gXGlp3OMTIl7O2NnBXSt+SvHspZxjTDute6/OKDM/Y947zSw8NhCI0OENEzHgI7NzKcu0Ua7ZRzqEEHHj2rKV1R/axZbQARZnTUp2OELEVSwFVb59xN3Juq5foZT6XhxjGlFzZTEbV05j1o3HPhZpOkBg8wZyf3I31pwTnzC74Hodi81CX+sgGQVutFOgHLLVamVSTQ16dTX7m5tpaG4h05NOWVERmZ50MtLT6ertxR8I0NndQ2lhEU6HnfqmJv7x5FMU5OQwsbqKmooKHPax/4D1BwJs2bWL1o5OSgsLqSwtIT9n7AtxiARZdCbaqwavrtjO4o/EPrT5ZFhtFqzDFCzQNI35M6abyZwGgWCQFWvWUl5cxJlz5sTlPS1EvFXOPY+HSzrY3rSFn0z7YLLDGVMep5Wbh+a2SS+dSDb/hFq893i5ZMkc0iyxj+gS4nQUywzh1qF/NWAOsQ3ljJs3CiJc/t3KY7aHmxqwllaQ+8ORFxKPxcElFtbev4uZV9SQXxX/oWmx0jSNcaWlx+2xKCkoAKC6vPzQtpqKCpaccQb1jY2ofXWsWr+B8uJirFYLfQOD9A8OkpGeTnlREb5AgAGvl9ysTKrKyshIT8fr95OblTVsafpBn49NO3eyY++bVJeXM6m2hua2dl7duJFLzznntL4S/U62oX07sz80k0XViVtcfMISs0Ldqj9uZfJ5leRVHVs0qSj/cCGE9158Ea9seIMHnniSxXNmS3VZcdrZ072PSNjBS2f+CLtFCnYIES/1/Y28/9s1XJo7N9mhCBF3sQzL/O2R93Vdfyp+4YzuzO39OJ6qhxtmHbW9/9578Fz3oRMaijmccz83C03TqF/XSum0POyu0/PD12a1UltZSW1lJf5AgLrGRkA71OPX3dtLS0cH+dnZVJaU0NHdzar1G/D6fKS5XPQNDOB2uXCnpZGV4aGzu4ei/HxaOjroGxhAr67m2osuJCPdHE43vvLY5FucXhZvayerI4suW39MlWnHkr6sguyydHx9AVwex7A95w67nWUL5tPY2srK9evZtns3Z82dS152dkLjFeJEpW/ZTs0ZE2nb1kPZ9PxkhyNEypp8YBDbTo3AzBAuj/TcidQWy7DMI2uhlwBJ/eZui9px7j+2oEjON24f89fSNM1c72d3D3lVmadtcnckl9PJpJqao7ZlpKdTeURPYE1FOfNnTD90PxgKMeD1MjDopbOnB726mrbOTqrLZ1JcUCALTqcgQ9O4KDqHvhZvwpO7ghqzp3ztn7ZRvbCEnHIPIV+YnPKM4+5fVlTEtRdeyLY9e3j0+ReoKi+jOL+AgtwcGRosTmkW4Or0xbS/2SvJnRBxlGZzkn/AQ2hiWJI7kfJi+VZ+ZM+dH/hynGKJSY8nRPWXlx21LdLZju+Zx+Oy+Limacx/nw7A6j9vY+I55QmpIHgqcdjt5GZlkZuVRWWpWfyirKgoyVGJeIpqoMY3c2X+gqTFcNbHp6FZNBo2ttPTPEhWqYdW1U3xpBxCvjAO9+F5dhaLhekTJzKhqoqNO3awv7mJ1evfwO1y4XA5mD5xArWVlVgtp241QvEOZBg8HtrI7y//aLIjESKlDUQDlF1ZQIZTisuJ1DfqNx2l1LIjfi5SSiV1WKYRzWTHp3+HccSaV+H6Nwlu2xT31554djnZZR7627wEvaG4v54QyWK32Gh5sYv9G9qSFoPFakHTNCpmFzL94mr6W73sWdWIpmmsuHszHXWHy1nvf6ONdf/chcvhoOvxAPPGzeQMzyxm10xles1ENm1R/PXRx9iwfTv+wPGX6qxb20JP4wCGYRDyh0eNT5YkEScrXbNjwcob/9mT7FCESGl2i521v1P4+pK+VLMQcRfLsMxNQAZmr51raLMGGEqpmmGfGCdhq5+iwW1Ee7qw5prDWCLNTViLy+L+2vlDw8X2rWnBlelg4tLyUZ4hxOkpjEGoOkJOhSfZoRySVZLOkhvN4cJLPjEdZ7qdurUtWGwWSqfmkVGQBsA5n55JWpaD3ApzGGf73h6qBquouqCA//77VTZs205Rbj4hX5gpU2pwez0UTcxmsCtAfk0WnfV9vPaXHVzw9bns3FJHb6gP74CfLC2TwgnZdO7sZ9ysInp3+uio72PRDVPY9nQdVfOKsTosBAfDZBbJ1WExukg0SofmpWJGQbJDESKlBQhTtjg/JabXCDGaWN7lq4G/KKVe1XV9OvAV4DgLESRG1BIho8BOpL31UHIXbm7AWpK49a5mXGrmtA2b2zmwsZ1FH0xcRUEhEiFMlJaMHtJzXKPvnAQH50zkVWXS2+LF5rAempPnzj667HpBbTYFtWaRlUvetRTNbbB79376er3srT/A/sZmLFs18nNy2Ld5H1kZGdS+v4AHnngSzWelvLiQrAwPdQcaaN/TQVdXHztW7iYYCRGxRWh/vgWH30W2z42z38m2p+o5++YZ7F7ZSH515rBzBUdiGAbhQAS7y0Zf6yCuDAcOt52QP3zUl5NIKHKouq84/QQjQaKaRnb5qXMRRYhUZGhQUJs17HI7QqSSWJK7KUqpVwGUUlt0Xa9USiWtX7sup5Rd0QXktrWCbq6/FWlrxTFpWsJjKZmcS0aBGyNqsOFfu5l5WS02p3zREqe/dbMr+NjT5dQvbKN6fnGywxlWRoGbjILYe8lyK81Ea37+4b8XoXAYi8VCY0sroXCY7r5e9jc1s+SMuUctK7Jw/syjjjXo9WK12Whua6OhpYUVG9YQCAYpn17M9j178YaDRPvDaG0aL/9mC5d9dxG9zYMYligZ+W66+/qw2+2kWZ1omobdZSPoC+NIs7H75UY63uxl8Uemsv7B3cy6opbBLj8v37OFy29fzKbH3iS/OpPcygzSJLk7bXUunENWbjWv/3UnZ340MetJCvFOtL3Cw8qvbea6Xy3D5pC/mSK1xZLc9ei6/n1gDXAWUB/fkEY2kNXH3Ok+ou0th7ZFO9qw5Cd+WIvVbiWrJJ1wMIIr04nVYaFlZxdpWU6yStITHo8QY8UWDOG+3k11/qmb2I2Vg9VeDxYLgoqYnpfuNpPK6vLyQ0lg/+AgB5pbaGxrpTfQj3bAworN3aTNdvH//v0fHGEHvaE+sGhYvFYc2TZCwTCWsIX0HBd9HYPkF+RQUpKPMTPKZqXQry3Fne3EYbdz2fcXAVC7qISQP0xapiwOfTqz+gOE3GFJ7ISIM1vE4Pq7z8WiSc+dSH2xJHfXA58CLgQ2A9+Ma0SjWNCVR7d9HNkHth3aZi0sxlqYvC+hNoeVqcvHAdDbMohhQHqei6atnVTOKUxaXEKcqPkdFir25DFo8ZOee2oOzTwVZaSnM2V8LVPG1x7aFg6H6R0YwGaz0dHVTUlBPnabncE2P1kl6Qz6ffj8fgC0iEZ/YJD2rm4cVjvdfX3srquns7cXt9NJXk4OFouGxWIhFApj3Wdh9tQpFObmJuuUxUko3d/BxcFraFHdFOuybIcQ8XKmP5ddLzYw6VxZh1ekvliSOz/QDXgABWQDHfEMaiSFldOJltZiPyKXy/7a95IVzjH0c8yr/v1tXpq2m8ld49YOcso8bHu6nvzqTKoXlMhcGXFKK1h0PvYuS0xVI8XIbDbboYXVszyH51Zll5m3PW43HvfhoaX55Bw1HBQgGo3SOzBAR3c3RtQgEo3isNuIGga5mYldh1CMnYwzz2Wmey7ebn+yQxEipVXpC+huGEh2GEIkRCz907/FXLj8AsyqmX+Ja0SjSPO3UP2uqbiXXwZApKuDvt/9MpkhHVdGoZuFN0wGoOPNXnz9QaZfXE3J1Dx8vVK8lc8AACAASURBVAH+9dVVGIZBf5sXf38wydEKcbTA5g1Mv7ia7FIp9HAqsFgs5GRmMmHcOCZWVzG5tobaykomjBuHzSbV305XgbWvUlJho2Zhyeg7CyFOWLS5QYrfiXeMWJK7WqXUtwG/UuoxIKkreLfXe9m7uonu736VoNqG5nThnJO8hZZjMfOyWvIqM3FlOnB5HKRlObnqx2ehaRp161o58EY7hmGw+bE3iYQPr98X9IborOs75nhGVNbXEvEVaWthxT2b6W0ZTHYoQqSscEM9e1+uR710INmhCJHSQl3dPP+LN5IdhhAJEUtyZ9N1PR8wdF3PAKKjPSGeMjMiFE/OJfNzX8c+0bwK45g5N5khnZCD1ZqmX1zNhKVlREJRwqEIFqvGrpca2PLkPgKDITY/sQ8A9eIBmrZ14h8I8tBXV2JEDTY99ibbn63HMAye/sk6gt4Qvr4Avc3mF/Ku/f2EAxECg6FDi1HXr28l6AvT1+rl9b/tBGDPqkZad3WbCebjbwLQ3dBP845OABq2dBAORgj6wrTt6QGgv/34PY7RocRTFng+vWmaxtQLxh2zrIAQYgwZBoWT8ymdmp/sSIRIaRablemXVic7DCESIpbk7pvAK8AZwGvAd+Ma0SgsVg1Hmg3N5cL3/FMMPngfgw/9NZkhjQmbw8qcqyagaRqVZxRSvaCYjAI3yz5tll/PKfeQnuvC5XFw6bcXoFk0Jp9XQfUCc/LhjHfXYE+z0bW/n90rGwHY+tQ+elsH8fYEDvUAdh3oZ6DDhyPdRvkM8wtFWrYTh9tGNGIQ8oWJhKP4+0MMdpkrXjRu7qC/3Ye328+uFQ0A7H2lmaZtnRiGwcNfX0U4GGHniwdY+4ACQL0gV6JPa5pGWo4Tq5SMFiJuDMPA4Xbg9NiTHYoQqc2i4SlIS3YUQiRELJM1KpRSuq7rBUCHUiqpXTLtXQ4aNndQNSef/j/dhcXjIfNTX05mSGPO5XGY5WuOUDjhcCW1g+XPHe7DXwhKJpvV8sqm5VM2zUzaln5ixqHHc64aD8DsK8Yf2lY2Pf/Qcw6a+56JRx0PYMH7Jx26fdbHzPXBZl1hVgM0DINzb5mFzWGldlEJFqsGgL4stnLy4hSlaTzz0/Usv/UM3FnSeydEXBhRdq5owFXoY/J5UsVPiHgJB6K8fM8WLrz1jGSHIkTcxZLc3QT8TSnVHu9gYlFSEj20qHLaeRfifeSf2CdOTnJU71yaph2q+md3HX47aRYtWSGJsaBpXPnDM5MdhRCpzTCYeflELG5ZF1WIeHK47ZLYiXeMWJI7p67rb2AugxAFUEpdH9eoRtDnd9PbMkhWcTruS6+GUEg+GIUYY1p2Hlue2Mf0S2SOghDxYi0oom59B/kTIbNIPseEiJcQDurXtzJublGyQxEi7mJJ7m6NexRvg5adB0MDQ21FJWTe/IXkBiRECnJMm03QJ2vcCRFPaedcQChiIRqWAlRCxFV+KeFAJNlRCJEQwxZU0XX9WwBKqRXALqXUioM/CYvuOLLdXrJK5AqnEPEU3rODuddMSHYYQqQ0/ysvMXFp2aGh7UKI+HCGeqhdXJrsMIRIiJGqZZ57xO2/xTuQWNXvDtF1oD/ZYQiR0oLOHF68c2OywxAipVnLK3n6J+sY7PQlOxQhUlpXm1nRW4h3gpGGZWrD3E6q0umFZBa5kx2GECktrbSAOVdLz50Q8WQrKWfBdVNwSUVaIeIqq6aAyjmFyQ5DiIQYqefOGOZ2UoVbmqUSoxBx5l/3OjZHLMtgCiFO1ODD92NEDKw2aWtCxFOk6QA2u7Qz8c4w0jt9rq7rq3Vdf/XI27qur05UcMfTtD+Cvz+YzBCESHnekJPtz8kQFiHiyqKx4p7NyY5CiJTX2+ylfW9vssMQIiFGGpY5Y4THkqZmqpP0HFeywxAipWW4g8x778RkhyFEapP1JIVIiPyabMqm5yc7DCESYtjkTilVP5YvpOu6BbgLmAkEgI8DZwM3AhuUUp8a2u/vwM1Kqb7jHaflQJhIKILVbh3L8IQQRxjwO2na3knplLxkhyJE6tIsbH1qH9MukvUkhYinnqZBvL0B3DK/VbwDxLLO3Vi5AnAppRbpur4Q+D8gG1gM/FvX9Zyh2yuHS+wArDYLmiZz7oSIJ4sFmZ8gRLxpGva0RH4MC/HOpNksWKReg3iHSOS3t7OA/wIopV4DzgC8gAuwA1Hgo8DvRzpIXqkTi0w+FyKuHHaDwgk5yQ5DiNSmaUxYUpbsKIRIeZ4CN64MR7LDECIhNMNITCFMXdf/APxLKfXU0P39wPXAV4DHAAdQjzlsswL4hVJKDXOchoQELUTilCulPp7sIA6SdiZSlLQzIeLvlGpnIG1NpKzjtrVEJnd3AK8ppf45dL9BKVU+dDsT+C1wG/CZoX9/qZR6f0KCE0IIIYQQQojTXCLHN74CXAwwNOduyxGPfR34MeAGIpjr6nkSGJsQQgghhBBCnNYSmdz9G/APrZP3c+ALALquVwHZSqmNwCagEngSuDOBsQkhhBBCCCHEaS1hwzKFEEIIIYQQQsSPlJ0UQgghhBBCiBQgyZ0QQgghhBBCpABJ7oQQQgghhBAiBUhyJ4QQQgghhBApQJI7IYQQQgghhEgBtmQHcLrSdf1eYKtS6mfJjmUkuq5bgf8DLsT8//6ZUuqeYfbtABqO2PRTpdTf4h/l6HRdLwD+AowDosBNSqnVb2c/XdcvAX4EOIHNwMeUUn1HPLcCeA2YqZTqiO8ZiVhIO0useLezU/ncxWHS7hJrLNrd0OMacC+w5VT/vxPSzhJtjD7fbgC+grketxf4rFJqXWLOIHaS3KW+TwATgWlABvCqrusblFJrjtxJ13Ud6FJKzUpCjLH4DbBSKXWRruuzgCd0XZ+glPLGsh+QDvwZOFMptVvX9R8D/wt8CkDX9Q8C3wVKE3VCIqVIOxulnZ0G5y5OP9LuhvbTdX3y0OMLgC2JDV+kOGln5udbBfBTYI5SqlnX9YuBhzHX5z6lSHI3Cl3XvwZ8DOgHXgauUEpVvWWfj2K++R1ALvC/Sqm7dV0vxsz+84d2fUIp9T/DbR8ljg8D1yilLn3rfV3XfwUsfctTAkqpBcCVwO+UUmGgW9f1B4AbgDVv2X8xENF1fSWQBTwE/EApFRklrrMxv7xlAeOBALAX+DZw/vHiAq4BHgOKASvQCJyDuXi9+y37vwJ8DrgU+DSAUmqjruu7Ma8iPXxELLYR9ksD1iqldg/tfjewSdf1TwMlwBXAckCNdL4iPqSdvSPa2Qmdu4gfaXcp0+4eHtr+B2D/SOckEk/aWcq0sw3Ax5VSzUO7rwOKdV13KKWCI51joklyNwJd15cDHwbmAb2Yfzjfuo8HuBG4WCnVqev6QuBZzC81NwJvKqUu0HU9HfijrutZw21XSvWeSJxKqc+O8HAFcOCI+w3AjOPsZwOeA74G2IEngD7gF8MdeKjr+l/ApUqp13Rdn4LZiK5WSu0FHh0hrlm6rt8G5CulbhnatniY1ykGLEqp9recR/lbds0fYT83x/4eMoEMpVQTcNXQa40QsogHaWfvjHbGCZy7iB9pdynV7jj4OrquXzBCXCLBpJ2lTjtTSj0M1A0dTwPuAB491RI7kORuNBcDDyqlegB0Xf8NcN6ROyilBnRdvxS4ZKjbdhbgGXr4v8CTuq5XMvSGV0r16rp+3O1vMzbt4I1RrrhYMMcGH/m8Y66iKKV+f+R9XdfvAD7LyF+8FphPVa8NHWP70BWbs4G9o8R1DF3XV3P8Ky4/eMs5DHcebz3XI/c73mMc5xgi8aSdvQPa2Qmeu4gfaXep0+7EqUvaWYq1s6Fk+l7MpPfCYc8siSS5G1mYI978HOfNrOt6OfAq8DtgFWY39KUASqm1uq5XY3Yrnwus0XX9ohG2rx8lniPfsGkHb4xyxWU/R88jK+Xoya4Hz+MDwCal1OahTRoQGiUejWMbgXHweaPEdQyl1HBXXGyAput6rlKqa2jz8c6jbYT9ejH/iBxUBnQrpQbfTowiLqSdjSwl2tkJnruIH2l3Izud2p04dUk7G9lp1c6GkunHgB3AMqWU7+3ElyiyFMLIngCuHuoCB3PM9FvfhGcA7cDtwDMMNUhd1626rv8v8D9Kqf9gjvfdBkwbbnsM8czWdb14qDv4khjP4RHgo7qu23RdzwauA/5znP2mAd8bijsNuAX4xyjHfg1zDu1czBuTMLvEX44xtpgoc5z3E8BNQ68zA5gCvPQ29nsGWDh0VQzgZszfjUg+aWcjS5V2diLnLuJH2t3ITqd2J05d0s5Gdtq0M13XM4b2f1gpdd2pmtiBJHcjUkq9APweszLQOszJnm+tqvMMZkavMDP5SsxGOh6zK3qWrutbMSde7gMeGGE7uq5v1HX9jGFCasOcLLqd2Idi3I05MXUTsBb4o1JqxdBrfU/X9e8N7fddoAuzytZmYDVDY8N1Xb9Z1/VjxokPjUm+FrhL1/VNwH3AB5VS9THG9nZ8Cjhz6Hf2N+ADB4cg6Lr+pK7rl420n1KqDfgI8JCu6zuA6cCX4hCneJuknb1j2tmw5y4ST9pd6rS7OMQjxoi0s5RqZ7dgLo9w5dDv+OBPXhxiPSmaYRxveoQAGGoci5VSvxq6/0VggVLqvXF8zR8A9ymldr5l+4c5ospRIg1drfi1UurDiX5tkfqknR16bWlnImGk3R16bWl3Im6knR16bWlnCSRz7ka2C7hV1/WbMLvR9zPUVRsPQ93kdW9tkKeAWZhlaoWIB2lnJmlnIpGk3Zmk3Yl4knZmknaWQHHpudPN1ex/D+iY3b4fwZw0eS/mm3srQ2tIYK4vUQJ8Syn1rK7rNcDnlFKfG/PAhBBCCCGEECJFxWvO3bsBlFJnYi5CeMfQz7eUUkswE73LMTP5OsxSogfXqPgW8MM4xSWEEEIIIYQQKSkuyd1Q9Z6D3c7jgFZgLrBiaNtTmOVbB4D0oZ9BXdfPBHYrpVrjEZcQQgghhBBCpKq4zblTSoV1Xf9/wJXANZirzx8cA9oPZCmldum63oBZ9edbwPcxxybfDXRj9vRFjzzuNddfbxTkFbO3voF3v2vJsK/fHOwmYkQpd55yRWyEOMadd975R6XUx5Mdx0EXX3G1UVhUwHR9Ig67fcR9Dw7t1jRtxP2ESLZTrZ295/r3G440N7Mm67iczmSHI8SYONXaGcC0eUuMqy45n4K83GSHIsSYGa6txbWgilLqQ7qu3wq8zhGLJQIZQM/QPt8F0HX9esy1NG4E/gicA5wHPHvkMdMzMvj1z3+CzWbDMAz5QilSwp133nlKLUQ7f85sJsyayfIlZ5GfkzPivru8TdxW/w/+PvkLCYpOiBNzqrWzebNnU1g7nsvOW0ZuVtboTxDiNHCqtTOAM2ZP47ob3s+U8eOTHYoQY2a4thaXYZm6rn9A1/WvD931AlFgna7r5wxtuwhYecT+LuBqzPUk3JhFWAzA89ZjW61WgqEQ9//nGX79p38OG8P1O35Ovb99DM5GiHced5oLl8uFPxAYdd+2UC+ZNncCohIitdhtNmw2K9FodPSdhRAnLC3NSSgc67JuQpze4lVQ5WFgtq7rLwNPA5/HrI75XV3XXwUcwENH7P954FdDwzb/DNyDWWTlmbce2GaxEgiGyM3OoLn1+MlbxIiy29dMoUOuhApxItZv3kFPTz++GJK71mAPhXZpa0K8Xe2d3bS0dRKR5E6IuNq8bS8NTW3JDkOIhIjLsEyl1CDmivNvdfYw+//vEbc3AguGO7bZcxekpKiA5rbO4+7THOwmx+YhzeJ4e4ELIQCwWi0YBvj9oyd3HmsaszxV8Q9KiBRjtVqIRKLScydEnNlsVga83mSHIURCxJTc6bpuwVy+YDHwulIqGNeoRmC1WggEQ5QVF1BVUXLcfXzRIMuypyU4MiFSh8ViDhWLpeduee6sBEQkROqxWq1EIhFJ7oSIM7vNxqDXn+wwhEiIUZM7Xdd/DLyJuaTBHMxlDT4U57iGZbWYc+4qS7O47Us3HXefCWklfL3yqgRHJkTqKCnMo7ggP6Y5d79ufJLlObOY6C5NQGRCpI40l5Os7CwiEZkLJEQ8FRXmkpuTmewwhEiIWObcnaWU+i2wSCl1IVAe55hGZLVaCYTMjsPbf/FHOrp6jtnngbZVrO5TiQ5NiJRRWVbM5PHV+AKjX+l8qWcrUYxR9xNCHM2d5qKitEjm3AkRZ2UlBVSWFiU7DCESIpbkzqrr+nygTtd1B1AQ55hGDsZqIRgMAbBjT91xk7uVvdsJRkOJDk2IlLH7zf289OqGUXvuDMOgMdhFmVPWDhLi7fIHgmzctkuGZQoRZ/sbWnh2xZpkhyFEQsSS3P0F+DXwM+AnwC/jGtEobENLIQBkZXro7Rs46nHDMNjmbWCSuywZ4QmREkLhMF3dffhGKajSHR4k3eIkw5o24n5CiGNpGvQPeKXnTog4M6IGnd29yQ5DiIQYdc6dUuou4K6hu5+Pbzijs1qsBILmsMzszAz6BgaPerwj3E+axUGRPTsZ4QmREqxWc8mRg21tOLl2D8/M+E6CohIitVitVkL+sPTcCRFndruNQZ8UVBHvDLEUVPkg8DXAdXCbUqomnkGNxGq1HOq5u/2rN2OxHN35WGDP5PFp30DTtGSEJ0RKcDodeNxpBEIjD2/e4W1gIOJnXsb4BEUmROqwWiw4HVYpqCJEnLlcTlxOWR5LvDPEMizzVuAyYPIRP0lj9iiYvQm79x1g0/bdRz3+Su8OdnobkxGaECmjqryEL9x4/ahl2l/s2cra/j0JjEyI1KFpGhees4hIVAoSCRFPRfk5XLp8abLDECIhYknu3lRK7VFKBQ7+xD2qEdis1kNFHjZu28XTL7161ONPdG1gr78lGaEJkTJ6+vr5939fwm6zHeopP56GQCflzrwERiZEatmq3oxpyREhxInrGAjwq4deTnYYQiRELIuYe3VdfwrYCGa9c6XUN+Ia1QgcdjsDXi8AWRkeet5SUKUt1EuBXdYyEeJkDHr9PLPiNRYvnEIwFMLldB53v9Zgj8xvFeIk7NxTx5QJ1ckOQ4iUlp1mY/v27ckOQ4iEiCW5ezLuUbwNDrsdXyBAJBIhO+vYapntwT4K7VlJik6I1GC1Wgj6wzgc9hF77r5acQVl0nMnxIkzIBgauXCREOLkhLERikTpHxgkw5Oe7HCEiKtYhmX+DfAA84Fs4P64RjQKTdNwu1wM+HxMnlDNpz509VGP/7Tmg1Q485MUnRCpwWox15N02h0EgsdP7gzDwKJZ8Fhdx31cCBEDwxi2jQkhxoYBuD3pfOU/ilX1g6PuL8TpLJbk7rdADfAsUAX8IZ4BxSIjPZ2BwUEy0t1kZx0eghmKholi4LDE0iEphBhOTnYmP/7mZ3DY7YSG6bnrCg/wcXXXcR8TQsTm+isvwuGQzywh4inDaeXmD7ybaxfVIvWLRKqLJbmboJT6klLqP0qpLwBJr3mekZ5O/+AgUcPgmptuJTxURroh2MlX3/xLkqMTIjV09fTisNsJDDNkrDXYQ5FDhkALcTLC4Yj03AkRZ/0BgxV7u1m9s4E0uyyVJVJbLMmdS9d1N4Cu62mANb4hjS4jPZ2+gUFsVitZGR66e/oAaAtKMRUhxkIgEOTWH/wah334OXctoR6KHFJMRYiT8fCTL9DXPzD6jkKIE5Zmt+DsbUGtX0NVtqx3J1JbLMndL4FNuq7/G7Ni5i/iG9LocrOz6OzpAaAgL5v2TvN2R6iffEnuhDhpVouFYGjkgiqljlwuz5uX4MiESC0Wi5VwJMJ/d/dz5+sdyQ5HiJTksGlYHA7chp87Vks7E6lt1OROKfU3YAHwA2CxUiqpBVUACnNzae/qAuDy5WeT7k4DoCatiEvy5iYzNCFSgtVqJRgMmT13wwwZm+Qu4/ycmQmOTIjUYrVaiEQilGTYeGJXf7LDESIltQ1G6A5q7Grp5TvnFCY7HCHiatjkTtf1bw39ez9wJ/BF4Ne6rv89QbENK9PjIRQK4fX7ueaS8xhXXgzAZHc5S7OmJDk6IU5/VquVmz9wlVktc5ieu+/V/5MXerYkODIhUsv5Z81Hs2jkuW08cUNVssMRIiWVZNg5e0op11y8jE2t/mSHI0RcjdRz99jQv/dgVsw88iepNE0jPzeHjq5u/vXEC/zlwScAuL3+IZ7sWp/k6IQ4/VksGtdfeeHQnLvjF1TZ528jw5qW4MiESC1LFswmGony5aebuWdtF4YRv1J+RoxlAr29AQzDYOXvt9Df7qW/3Us0HI1bXELE22DIoDFgpVbXeWq39JCL1DZScrdV13UH8DlgNfAqsAb4TiICG43H7cbr92G1Wth3oAmAlmA3bousuSXEWFj+/s9iGMawwzJbgz0U2aVaphAn49d/+ifBcIj7r6lA04hLmXbDMGjY3M4TP1hzzPYdz+8n5A/TtqeHtt3d9DQO8MKvNgJQs7CE9Lw01v1jF137+wl6Q/S1yhph4vQTjBiEvQN85xvf5rZlRckOR4i4Gim5+yiggIuG/t0JbAH2JyCuUaW5XPj8fkqKCmhuMyfHdoUHyLN7khyZEKkhEg6jaZbhC6o4c6VaphAnyTAMAoaVv27q5pPz8rBaxrZM+95Xm1n5uy2UTc9n+VfmHuoZNAwDTdMIDoYI+SMEBkL4B0Jkl3m4+Jvz0TSNsun5WCwaZ39yBnnVmfQ0DrD5sX1jGp8QiZDvtlOV58YR9XP7ijYGgtITLVLXsMmdUur3Sqlq4NNKqRqlVA0wRSn14YRFN4I0pwuvP0BFaSFpTicAFc58CqUnQYgxkZudRd/A4LDJ3R8mfgqnxZ7gqIRILY3uaupCORjRKJ97sonGvrFZ8659bw+7XmqgZmExiz40BU3TsFg1HvvOa/j7gjz+3dfx9wWZeVkt7mwnFbMKqJxtFpqwvCXBtFgtaJpG4YQczvr4NAY6fDRulYqD4vTRPBiluc9HIBBicbkT2ym81N3Wwf00B7uP+1jYiLCiZxt7fS0A7PO3EjEkURVHs8Wyj67rPx9awPxxXdfvU0rdF+/ARpPmctLZ00NpUQE//+4XAfhxzQeSHJUQqePCZYsIBkLHTe5W9yn2+9u5rvCsJEQmAKKGQSQKdusp/C1FjOpMvYTNu3ZyxUQXy2ozKUiP5WN5ZJ11faRlORns8qNpGnaXeUyr3crZn5yBK9PB0k9Mx5V5Yut9dTcOEByUhdfF6aMkK40sl5VJM2cyr9h+yv7d7Aj1ccueP5BucXJr5ZUszZrCxoF9/HD/v5jpqaIj1E9rsIemYDf/V/MhHmh/hUlppXys5PyEx9oa7CHL5iZqGDzbvYmmYBcre3fwq/EfI2oY7PQ1sq5/D7k2D9cWnInTYmd/oJ3VfYp6fzufLF2OXbOxrn8POTYPT3Zt4Mr8BUxylyX8XFJNLOvc3Qx8fej2JcCn4hdO7NJcLnwBs+LR7/76b/a1NvPd+n8kOSohUsdHr7uMuTMmHze5+0/H61i1WP58CH8oSo8/wt6uAA9t6wXgF6928HqDl6hhcNeaTgBe2T/IukYvAGsavHhDx78a6w1FaegLEY3C+/91gHA8JmmJhHHmFLKXMn64qhtN0wicROESI2rw+Pdf58XfbMLhtlE1r/iYfbJK0o/690RUzCygZlEJ256up7/dR1+rl6A3tmTPMAyi8p4VCeawWcjMyuN911zEV1/oZmPLqVcxc4e3AYdm4wdV7+Pz5Zey19fCQMTPF/feyznZ06hxFTEvYzx/1m/hr5M+xwxPFZ8vu5T72l7m/raV+KJB+sI+Ng4cPXQ6ZESIGFFCRoTWYM9Jx/lK705u2PkL3rP9Z+z1tbJ+YC8rerfRHurj5tLl5Nky2OVr4r7WFbgtTnb7WuiP+Fg/sJebd/2W3d5mcm0eGgKd9Ed8/KX1Jb64988UOrLIs2ecdHwitp67iFLKD6CUCum6fkr8VTbn3AUAeGPbLvJqc9iYWZfcoIRIIX5/gK/84NdMmVx2aH4OgD8a5JXenXy98qokR3jq2dziw8Asu/3VZ5q598oK7tvUQ57bynk1Htx283d47bQsPA4L4SjkpFkJRw1sFg2Pw0yYH1V9TCl08fD2XiIGvGdqFjc+0sD3zy1iX0+Q9U0+blmQzx8uL8M2xnO0RGLt6ghQbHQw1xHkgS0ullV7mFt6YlVo1/1zF9MuHMe4uUVocX5faJqGI82G1WGh4Y12gr4Q0y6qJhqJYrEee+Gnt3mQwECQtGwXT/1oDdfecXZc4xPiSKojQGOXg64XH+WbN72XCSfYxuKlJdjNdTvuYOXM2zkza/JRjz05/Zs4Nfuhz2CAcmceAGXOXH474RP8vvk53p03j7X9u/nRgYepchVywN/Bk9O/xe+an+HJzg2EjAiFjkzu0z/HZ/f+kY8UncucjBrA7DHc2r2ffDLYZ21jZnoVISPC3U1PM90zjj2+Zj5YdA5OzcZ36h/gm5XXcFbWZOyaFYAlb1mG7KysyZz1lvMocmTz9IxvH3Puf9JvOflfoDhKLMndI7qur8SslDkHeCS+IcUmzenE5zevvJQVF7C7sZGS/JwkRyVE6nC5nBjRKGgawVAIp8McwrXP38YsTxU5NiledDDp3dcdZFy2nb5AFLtVozDdxo/fVQLAx+fmHPpQvnhiJgClGYfnKr5vulmUZkG5+9C2288ze1zeVeuh12/25Hzv3CIKPTby3TYWDu2b6bTG+QxFvF05JZOf7E3n1Q3buOPLC07qWKVT8yiozYp7YnfQhKXm8KkJZ5v/HtjYzo6X6ol+wMJ86wSeDm6k5OFM5l2r4+8P0lnXx5QLcrj6J0swDIPnf/EG86/XySw68V5EIWIxvcjF8oUl3LaqhzW7W7HnFFOVfWLDkuPh4Y7XubZgMZk29zGPuSwjx6m7y/hZ7YcAOC9nBosydV7r38U45jQa7gAAIABJREFUZwFWzcInS5YzI30c+fZMJrvLAbgmfxFffPNeFnt0zl0/ldJL8njtLzuoq+4gbZaLGlcxE9JKmO4ZR0Ogg+np4wAwtkV5sPLLZHncYGgQ45+a1fduZ/L5FeSUH+6ZO/j52VnfR1q2E5vdQsPmDmoWlsR2UDGsUcdVKaVuBz6Dmdx9Xin147hHFQNzWKa5Fk91RSlv7m+kWCr3CTGmLly2mFAoctTQzMnucu4cf2MSo0ouwzCIRA3WNXr5/oo2AO5e28m+7iBnjUs/lKQVecxrZ0debX27MpxWyrPMRLAkw45F07BZtZM6pji1bGjy0W/NZqcvgxf39rG+yXtCx2na3knhhGwc7sQXOdI08z2ZMzWD1Zfv4b9NG1l111b+1fEabyyux55mpWhiDlMuML8gWm1mgZbZV44no8BNd0M/QV844XGLd47m/hCtPgsOh526tl7aBk7s/TbW79P7WlfwaOdanuxaz/sLl47JMd1WJ+dmT6c2zbxIaNEsLMmaciixAzg7eyq/rP0ouquUdJuDye5yvvzZq7j7qpu5qe48qkOFEICl9ZP4ZuU1XBCcwYS0Ega7/ES9UTrq+lj52y0AbHh4N4GhObhHrtNpGAav/mU70ajBxKVluLNd7HmlifUP7SYaNXjyB2sIDIZo3NpJ574+AJzpUqRtLIya3Om6XgZ8GXNphPm6rp/cpcUxYrNasVmtBIJBrrp4GXfe8gW+ViHDxIQYS9UVJWhwVHL3/1pepCHYmbygksAbivLnDV0A/PDldl6qG2RWSRo3zc0F4CcXlFCb60xmiOI05bBqvKc6zLz0DrRICJftxOay7lnVRMgfGePoYrfb18Q1O39KgDDfnfxeLvvGIn49/uOste/l280PHPc5eVWZaBaNXS830vFmL4GBEP/66koA1j6gOLCpncBgiPUP7krkqYgUFIoYRDQ7aWlOajOizC8/todsNJFwlEe/vZrtz9ZTt7aVnsaBt32MDf1v0hf28p+ONXx+75/4Y8tzlDvzuG/S56hyFb7t452IwS4/gcEQdf/bxA2FS1l0lVlN1+YwR4JEw2aC5u8L0H2gH/9AkFV/2kY4GGHKu8aRNy6T/OpM5lwzAQCXx4HVZmH7M/XsfP4Avc2DvPKnbWBA4YQcAv1B8muycHrsVJ1RhL6sHItFY+ENk3G4bcy4pJqKWQU43HbKpucn5HeQ6mL5FPkd8CfAAbwM/DKuEb0NHreb9t4B6vsi3PPco3SG+5MdkhApZapeS2lx4VELmT/WtQ5vJJDEqBInFDHY0xXAMCDNbsEwDD6zMI9zq9OxWTSKM+Qqozg5iyrS+dFWN+NmL+aciXlMLXQds097qI//dr0x4nGW3jQdd3biLjBEjSg/b3iMG3fdzU5vI2WOPL5XdR0/qr6BLJs5zLLEkcOf9Vs4N2c6ISPCd+oeoCt07BfiBddPonRqHg63jQu/Pg8wh3wW6zlgQEaR+UX8yR+uoaOuN2HnKFJHZbaDMyqzmD93EiUzF/PPrW+/sIjVZuHKH56JrzdI8/ZOvD1v73Pw3pYX+Ebd3+iP+FmSNZnlObP4fNm7meOpGXWaQzRq0N92Yr36Rwr6wjzzs/VYbRbO/ewsrPajh/ZrmsbU5eNwZzvJLEpnztUTcHkcXPo/Cw4lf2Auj5JRYM5bnHLBOGxOK6XT8piwtIxIKMr0S6rQLBq1i0pIyzr8d8nmtOLJM5+XV5Upo1DiJJbkzqWUegEwlFIKOGVKDFkdaTyytZVn3/Tyt98/zraWumSHJERKiUSiNDS1EggGD23rDXvJOs68gFRwsFLhK/sHzQn4/SH+uL4bt13juunZaJpGptMqH0hizPx2XSfvm6DR2tLMA6v38D/Ptxyzz/Pdm7l1333DrmfVuqubjY/sjXeoR3m5dwerencwM72K7d4DuK1O5mdMOGY/p8XOBTmzsGtWShw5XLfjDhoDXcc9pmbRSM8xk9vsUg92lw2nx87EpeZwsmW3zCRvXCZ7X22ms74PI2ow2G1+Jemo6yUcjGAYxlFDw4QA2NjiY1VDkPbOHjr2bGVemfkZ9vhzqwhHRu/x7m7o55U/bcNqtzL3mgks+tAU3DnOmHvvusMD/LHlee7Vb6HMmUuePYOLcudwRf78UZ8b8ocJekO8cOfGmCrNhoOHz6d5Rxe7VjQQ8ofZ/mw9jjQbl9++GJvTSkbh2H6OZ5d6sDms5FZmyDzaJIsluQvour4csOq6vpBTKLmzO9PId4S5ZUEe2lkz+PHtjxCJyGKOQowVq9VCc1snvQOHP8D6wl4yrad/cheKmF8CN7X4+MfWHvoDEW56tBHDMOjyRXBaNaqyHfzoXcWSzIm4+eDMHPLSbbj8HUQ793PrkmOHZl1XeBa1rmK2ew8c81jQG8JTkEbp1LxEhHvIwswJ/GL8R7ml7CKuyl8Y03NuLl3ODUVLuWnX3Sf0mmmZTrNKp9tGNGKwb00LWx7fRzRqsObvCiNqsPOFA6gXGwDoeLNXEj0BwPgcB/MrPPh9AZ546lny3VZCoTB//sdjbNq+e8Tnerv9uDIcTFhSetT27oYB2nbH1gNY52/n0rwzKHbEXvgv5A/j7w/y72+8gsNt57LbFmFEDfz9QcLBCHVrWzCiBmsfUPj7gnTt78ffF+ShL68kGokSGAjhTLeTXebBarfg7Q4QCUexSIXllBdLtcybgJ8B+Zhz7z450s66rtsxh3FWAU7gdmA7cC9gAFuBTw/t/jBQAnxLKfWsrus1wOeUUp+LJfjK/Ez8gQBtoV5ss7KpHPTQ3dtHfq4UVhFirFgtVrp7+g7df3Ta10kbpXrXqSoSNfCFDVw2jese3M9vLi3FYdVYUOYmw2nlZ8tLiBjwbj0z2aGKdwiHTePOjSHOdhXi7Oxic4uPhRVuLEMXFDpD/fyi8XE+UrwMKxY2DdRRFsnBO+CjsqyY9Q/tJr8q61DlykQ4EOigIdDJokz9bT/3A4VnszhzEgDfr3+Q+kA7Ls3Ou/PmsTx3VkzHqJhZAEBBTdahynoXf8PsAaldXMpgl59wMMKaBxQXfGkumlXD+pa5jIPdftJzXPQ0DWAYkFPmYfuz9Yw/szQpRWlEfFk0DYdFIyc7g9VrFd9f0Ubjo3dRkJdNW8fxe5IP2vZ0PbnjMqlddHQVx+r5ZsGSI5cKOtJeXwu1acVsHqhjtqeaWelVMccbCUW4/5YX+eAf3sXVPz7rUEL25qomuhr6mX1FLQ2bO6icXUhWcTo2l5XX7tvBss/M5LpfncNgp49n/m8D775t4aHhlGdcOzHm1xent2F77nRdd+i67gDagA8Cs4HrgcZRjnkD0KmUWgJcBNwJ3IGZwC3BLJx6OTALqAMuBA4ucvEt4IexBv9cfZjGzn4yrGncueAc/vjtGyWxE2KMOex2evvNnjtvJECdv/207cna2ubnm8+1YLNoPPTeSoo9diYXuKjKMZPVgnSbrBsnEuqnq9r5zYV5VDt66ezp5VHVhzd0uLdpZe8OfJEg786bx5T0Cv6v4VE+/pXbufrGWwFYeMNkahfHp3R4c7Cb+1pX8Nk9f6Qv7MMwDFqDPdzR8BgbBt48oWNqmsb4oSp+NxQt5cbi87ksfx5vDB3vjYF9J9Xb5kizkVNmDg+7+BvzsTmtvHzPZtp2d9N1oJ+Vv9+KYRg8d8cGBjp8dNb10VVvXrzydgew2Cw0bG4/NMz1vz9eR/cJFM4Qp5atbX7WNPrIy8mmv7+fbyzMoLGlHb1mHB2dh3vfvN1+/vqJ5+isP3xBc951+jGJ3UFr/7GL3SuO/lr8SMca+sJevrD3z9xe/xD/U/cA/mhw1M/Nhi0dtOzs4qW7NuHrC/LBP7wL4Kh5cROWljH/fToOt52zPjYNi83CxHPKzff7N+eTlmnOb0vPS+OSb80/ap6ceOcYqedOYfa0aUP/csTtmhGe9yDw0BH3w8BcYMXQ/aeACzALs6QP/Qzqun4msFsp1Rpr8BdOLmTnjma6w4NkhAt4/wN7uMixmxuuuijWQwghRnH+WfPp7Onlz/94DE+5h/uz13K//mVeb/CxtOr0GFf/wpsDdPkiXDM1i19ebE7mPl0TVJFafn5RKTc+coDqqIsPXXsWz778Og/+2/xCNqG6ghV52zg3exr79jcRNaKU2nLY0bCe6ZPHs/6h3dQsLD5q7aixdH/bSnZ4Gyl35vKfztepcObz7br7WZw5iY8UnXvSx692FVHtKgLgghyz1+579f+kL+zl4yXn877CJSf9GgCLPzIVTTOLQEw+vwJN07j8+4sB8OQfXsz6YM+GO8fFxHFm7/2ZH5ly1D7i9DS/3M25NR4e3+/hq5/5ML98eBXjq8r5wDUXY7MeToCeX7WO8tn5ZJean22GYbDhX3uYfWUtFuux/SFTl4/DlXl4JMuTXev5Q8vznJ8zg69VXsUrvTu4e8JNw65VF41E0SzmUiJhfxiL1c70S6pHLI4U62eX9EC/cw2b3Cn1/9k77/CoyrQP32d6TWbSeyeTSkICgUAooShVKYq7YG9gL2tb1rboiqIua+8ouir2sopKFRAE6Z2BQEglvU4yfc73x2AgUkQFEb9zXxeXMPOe97xnnDPnfdrvsSYe+W+LxRIENFut1hO61KxWq+3QeCN+I+9e4IkjjmsHAq1W6x6LxVIJ/OfQmIeAuy0WywtAM/5I33EL6HyiiFylpb3DxuzyTxgd1JubcoKYNXsxUyeMlDZuEhK/kTc3NzM5K5AAg57ahkZqG5r4aNEyQu9MoqLVzfs7Ws6YcffTNJgDh5qIC4LAviYnT6xq4IVx0Xyyq5WCaB2pIWp8Uu2NxB+Qt7c2c35aIAc2uygpK2NLpwGj6CJSZsPlctPobqeXIYl7H3qW5tY2Lnj0PPTPjOG+5MlUbq1HH3S0uuZvodRRy5zKL7g6Yhi3x5wHdO9d9U32/ejkp0+V88OMO9nvqOGWkrn01CeQqY/9zXMe2TsrJDHwZ8cHxR42lk+16ITEmWFfk5N9TU70Wg2mQDOrK71ccdE41ColNfWNmAKNNLe2MfOFV3jn2Yeo3tlETM8QRJ+IQiU7pmEHoDOp2Vd+kLqVzSjHqJld8Skv9piOXq6hf4CF/kekLns9Ppor2glJDGTdfCspA6NZ+9/d5F+Qgj5ES3zvcGnvKnFKOJk+d4MsFst2YBXwT4vFctVJHBMLLAPeslqt7wBHGmlGoAXAarX+02q1XgDkAZ8B1wCvAU3AsBOdo9MtMucHf9i80tZAii6cyMgwHAo95VUnHfyTkJA4Ds+sbaSm3YNSqcRo1HHH9ItpaWrH4FWTEqxmzsgo2p2/f1+tDdWd/Of7BtqcXhbvs+H1iTyyoo6qNg+zVtSRHKTmoaH+aIDdLaJTyogJUBIXeHbWCUr8uRFFGJJoIFivob65mZQQJdddMIIbr5hMZloy8f9TEaMO5qYrJxMSZMJUriCq3si7H39DufPgKfXOlznqmbbnJfINSYQoD9ed/tikXBCE02rYAcgFGT20UXyceRcZuhjer1/NqG0PcXPJa5y7dSZNbhufNfzAytadVDr9/TYdPhcvHVyIWzxzff4kzg6MOj1Oh53HLhvMgD457CurYtaz8wBYu3EHBq2Wtl12SlZW4bS5QRDIOS/5uPOJosjtjW+wLmw/u14+wB3OccTag445tqXSxq7FflGkmJwQtAEqhtzQk9BkE6vn7qDW2nzqL1ji/yUno5b5MDAIqMFfD3f9iQZbLJZwYCFwt9VqnXvo5U0Wi2XIob+PAlYeMV4DTALeBnSAF3/q5wmbfhhUMl4dH4spwIjL5iROHcKmgw6SevZm595fVwsgISFxmPcnxxFpVKBWKXG53CjkcubNm8nFMYP51/I6HvuunhVlHb/7upLNakb1MOLyiuxpdCKXCbx0XjQRRgX5Uf70qRC9PylhSk8TZq1UcyDxx+XiHDMGlQyjXk9jSwsaex3hBv93VmaQs3TNeq6+42GWfLeO+sYWNqzcSVCLBmejh2eeef+UruW1miVcHTmMyyKKiVIfe4P6e6GRqRAEgXHBvXku5VqKAtN53XIjAQotHtHLGzXLuHj3U1xpfRa1oOSAo44Ldz7O+vaSbvNUOhv5uGENPtHH1o4yXq9Zit3nwuHzt3f5d+XnvFn7Lb5DbSbsPhdPVHzGqG0PMW3Pi5Q76vGI3q7xEmcnyUFqkoPUGPR62js7+PfqetZWdhIabKa+wW9UpaXEM/OW6QQlGym+MReNUcXGD/eyc1HZcefd56ilQ+4krXcMk28YxIDoDJa/uA2X3cOmj0soWVXNpk9LWPPWLgwhWgZekwVAZHowGqMKjcHvdBx+Wx4RaWf2npP483Ayapk+q9XaZLFYRKvV6rBYLD/XKXwGYAbus1gs9x167Rbg6UMCLbvoXpN3K/C01WoVLRbL68BLQBsw/kQnKWl0sqvBiTHAwARlb5QyBePTAznPMhaZ7GRsVgkJiePh9opc+3kln01JQKVU4nL7m5g3bm3B2eZkTG4s8SYVrQ4vb21p5pKck5d3/i08vLyOGwqCSQv1p6JdX+CXfxcEAYUA56ScntojCYnTxZQPy3nt/BgMen/6n9Vh4uFva7jc4mNz5V5CJ8dyacIY8rPTuPO6S5h60/1sSKtm9uQr+WT1Mrbs2ENOZupxFft+DrvPhVf0sbJ1FzMT/nKqL+83o5WpSNKGk6QN73ptUmghk0ILcfk82H1OBEHgkYSpLG/dwR375/FcyrU0uNv4tnU7u/ZvYGKdAVvbD1Sc348DJRuZsP1jNJExvNfjVoabc/j3gU/Yt+VbBmaeS399ChpByQvKc9hVuZ2GyHYCUVPmbaanPv4MfhISv4XlB2z0r7YToddh6+hkcl8TZo0cpaCkubWNufM/p1eWhca2Fr5evZqZf5vGkqc2M/Sm3BP2lpMLArdFj+uqGVXrlYya0Qefx4dcLT/UoiSYyq31qHQns+WWkPjtnMw3rcRiscwCgi0Wyz3A8V0YwKE2BsdqZTD4OOMfPeLvm4G+J7EmlHKBALUMr15FD+fhvkAPLqvDWP49N00Zg0YtpWFJSPwa5DKIN6nY2+gkSnXYuPvguxUYqjXcUpyNSSNHr/I7Un7txvJEtDq8bKi2kxel5YMdrVyTH0Rxoh6tUqpJkPjzMGt4BGqFgFGvRxAE+kdCQYZA+cE6mppasPRJYlBSLwC+XvY9AhCqMrNi006unnI+e0orsO4v54kX/8vLs2eQm3m03LkoipQ4agiU6zjoauarpo0MNWeTpo1hyJb7iPGaiW81M2pEr1+8fofTxYGKahxOFzkZPU5rzZAoiuDzIRwSwBAqKlCV74eBw/A1NTBYn8Jnxr/ife5VQm2tRNZVYOhwoU1JQzmwgDFhBQxetgtlxjBkOiNNl08iQqHg0c5O7OYA1C+tpF2t5Ya5H+Ju3UOEJgW1IZGGaVNIu+XvkHnaLk3iNNM7SkdGqBq3U097RwdBWjn1HR7iTSruuv5SXnv3M/r2yqRjp5vd1jLkSjlpQ2NRqE+c+RGvDu0SBfoRQRCQK+X0HHNYuiJ1UMxpuS4JiWNxMsbddOBq4DvAhr8u7owTb1Kh0nVw07qFTLbldb0+KTOQf61o5qPVu5la3PMMrlBC4uzFJ8KMQWEkmVW02rzYWu1s+rgEmVvBLqGa1zY08bcBoajkApfkmOl0+9CdYqPLK4rsrHfQN0ZHqM7/gB0Qd3aoc0pInAyiKNLp9iETBIw6PXqtloDAIL7Z20KYuwWn3UXkEU2PRwzqy7lD+vF67VI+a/iB1yw3EICW0ZfcyiuzZ5BzDMPuv7XLefngIkwKPTdHj2ZG6dtk6eO4ImIoAQot32Tdx+X3PMSW8jKaC9owB3bv8ejz+Xjq1fm0tLXzzzumHTX/h18u4fOFK/D5RFRKBU/efyuR4SEnvO6DtQ18v3EbmalJWJJPLhrmLi2h9YmZ+NpakYWEIra3ITqd6MZOBKDj0/dQZeZgSMvCmdMbeUgYJr0BZQ8LglyB0+Xih807yLvg4i51xJD/zAW5HEGrRabr/tuiyjy8fwie8wqCSnIWn824vSI+EfQ6HbbOTj5auIhaUx53DAhlaFEfHvrPa6QmxWO5PZ7/Xv4FJQcqScn7eYPs/B2P8Urq9F/UnFxC4nRzQuPOYrHkWK3WLRaLZS7+ZuYO/K0Nzjj/+b4BQduE2Wyipaodn8+HTCYjJ0JLXnYaK9dvl4w7CYlfSafbx0Xvl/PWxBgSApR48SBXy3CKHhJbzVyddFhBrqTRyexV9bx83qnzTC4saacgRseNff2bxPHpP69wJyFxtiECj66sZ97EWILNJiJDQwkyBfHl1jZ6yloQXCJTQoq6xsvlMmr3NJNXl0j/vDQ6vE7u/W4eCTGR5GSmsmHbbhYtX8M9N14OQIung1drFvNm2s0kaPwZLgEKHUmacALRsWPPfmrrGgl0aZj5wFUYdDoWr/yBrbv2YkmOZ8ywIi66bgZ1Dc08+687cbpcfLV0NS1tNiqqaxk5pJCpE0Zy8cRReL0+KqpriAgL5p1Pvsag1zF6aH8UCgX7y6uIiQzj4wXLKOiVid3uYNuuEl5951OiI8J48G/XEhUecsyon2PVMgCUaVkYr74JeXgEos2GEBCIPCgYQeUXeGkfcxE19Y30CghEd+44AGwdnWzcsosQswmFQs4jT7/uj/4JAg/e7u+L+8/ZL2LrsJMQG8mwoj6MGNSXzTv2kJGaiErpF6uR6U8oASBxFrC11kFFq4u0UA1ajYb21iZuGBaA6PFw4LNPCAkyoVQqsC6r4LYrp6BS/nzso9ljo8ndTphSej5J/LE47rfXYrHcDlx0qP/c40A8/pTMORw77fJ35dreQXzVVEpNZwAGrZam1lZCzH7PyfXn5nDB9PnY7JMwaE+vspeExJ+RALWcJ86NYEutgweWNDNI9JE6MgZdip6QJUnYfPDj4yw5SMWzY6JpsXsx/Urxkv3NLpLM/jTQuEAlZS1u8iL//7Uu2GHdR1x0BHaHk+raBnIzUyktr6bT4SAhJhK9Tuq39WdCJgjMm+iX+g82mRgxoD8tbW1kbd2AgAylRongBI7odqDSKQgzBxKlC2ZJ81ZcsQKD7xgAQEaPRB55ai5Lv1vH0KI+mBR6Psq4i2Dl4VrUXppE/j7rOX7YvIOwkCD+dfd1PP3QHYQEmXhm7nssWLqaKePPJSkuGoBZf7+RqoN1ZKelUFpezZqN24kMDyErLZkAo77LIJPLZSTERuF0uWht7+DTr5fz7Ovv88ZtlzL/++18t34LMpmMIf3zSYqLJtOSjNPlYv2WXSjWr+KDNRtZfKCe+ycMobWpmcTEOGxxKVR4FGSFByMPDkUeHEpLm42PV2yitb2DkUP6kd4jkak33kdDUwvmwACmXTKB9JREpt8zi9b2DmIjw/jHLVeSEhvLR6/MpuRABXqdhrCQIDweLzdcfiFajZr9ZVWkpSTgcrt57o0PKK+qYezwInrnZFCYn82OPfvJTD1Ri1+JPzKDEvRdtdpXTJzAx4sW89p3JUxdN5+Q1d/y/mOvA9DZ4mTQiF7YvQ5Wr99KQW4GCsXRW2Wv6OOZqgWk62OQCZLOg8QfixO5JkYB/fE7F6cAqVartdlisaz+XVZ2AlrsXrbXOcgNiyVBF0RzcB11jY1dxl2AUc+Qa2ewosLF6FTJuJOQ+KW0Orw0dHoZkmAgLVjFqsWw5/tq0t+MoHNKKGrt4YeZIAi0OT1c/0U18ybEoFX+sgedyyvy4NJa3pgYw4c7WhmfHsA1vf9cqmGLVqzF4/VSmJdNc2s7iXFRWPeVUV1bz849pdTWNzHzzmksW72BD79YgkIhZ2hRH3IzU/lowVI2bbdSebCOLEsyzz1yFzv27GeHdT8BBj0jiwvP9OVJ/ErcXpEX1jVyXZ/grtcCjUYWdyRye6GJ/+1aQqutndBAEwCV2xoIijF0NS0fZu5JuMrEjNK3uSyiGK1Gza3XTOGN9//H0KI+vFe3itHBed3O2dTaRnRkGAvuuBbDT5wFmalJTBhVTEzk4Tr2lIQYUhJiEH0+EmIiuEfTguGcsShiE455TWqViiszojgvYxKlGzaifONprnY6GZrTG2NUNKaSHbhqA1Bl5cLOrfTPzaHzmxLO7ZVGjaDmgrkLSA8y8Hi5lftbVtHg9OL2eBg5uB/XXXYBU2+8j4LcDKLCQ9i19wDpPRKZeec0HE4XmalJiKKIKIo89o+bCDYHEhJk6lqbXC7rlgaqUirpleXvQ5aWcvh6Xnn8H+zYs5/V67ag1fj3EOlHvC9x9rG1xk6tzUO4wb/tDTGbCVn4GY6GFqoUWZg+W0Rjh5FeE1IA+PsDz/Ldui3cdu0Upow/96j55IKM8SF9CVUGHPWehMSZ5kTGnc9qtXotFksesN9q7WrAccbVDBrtHvY2OekbF0qyEMHOMKiqrSUjJaVrzJU9dXzwyVeIPS6UmkJKSPxCZAJ8YW3zp2dmBrJaqeCWfTWMvTSdtW8+h+69ofz1qWIUKn+kLlgr5+1Jsayv7kQtl5EXdXIRpr2NTqKMSl6fEINMEPj7oLCfP+gPiN3hpLXdRnhIEAuWrmLe+18SERZMpiWJaRdPZHfJAcoqa/jXU3OZOLqY26+dyruffkN7RycxkeFcesFoAG68YjJX/uU8RFHsitLdMf1iADo67VQerAOgrPIg1n1lCILA8EEFXTVEEmcXAhD6EwU9QRC4+5x0VMZmvPsFnHZn13ttNR0o1XJ05sOhvAxdDLfFjOsSNeqXl8WKjZuYve0jPnpjIcX3ZmA0a3G6XLTbOjHqddx69V/w7NuDWy5HEZeIr6UZmTmI4sI8fC1NODf+gDqvoOscoijSdPcNmO+dhW70eOQR0bj37AJBQGYOArmCjg/eQlAq0V90GT67nbCoYKKnXQtci2v3drI2rcO9fyf2km3UEMWHAAAgAElEQVRoBgyBrFzsy75BEZ+EfuwkAG6eBFfbHWg1agRB4IlOO3qthj37y7tSJL+Y9++jnunJ8YdTwn/sx3eytXzHIzM1qVukTlLhPrvRKmUojvhfGGo2IW8oxzzpL9jVwfywu5GBR2SePH7fzVj3l3P7g3OYPHY4CkX339j361czPrgAlezE6Zs+UUQm7UElfmd+ruYuFbgC+PzQvzPx96E7o/zYr+TWfXMZF9Sb3hFJ/LB1azfFvrBAA19bmwlasZOpgyWJKwmJX4JRLWfOqChe3dDEe1ua6fSKFOfWYC2LoUVhIOqigC7DDvwbKqXcLwst/wV7oEX7bPSN0ZIfpfv5wX8gXG5/379b7n+SvaUV2Do6GT6oLw/efg37y6v527Sp2B1OYqP9Kmo3XXkRAB6PB/khQ+zBv117zLl1Ws0xX9frtF0b1tFDBzB66IBTfVkSvzMKucAFmUfX6/QI0TCvdhdGo47W9sPdhzJGHG2wyAQZRQFp7OqsJE0XjVKpYE9xO7ZnNxBeD6+89QlTJ47i9gfnEGLUM7h3Nn8Z0Z+W2Q8g2jsBEB0OFPGJKNOzUSamIAsKQfR6EG02ZIEmnGtWIrqcCAGBqA5FEd3l++l4dx4+eweIIuqCAciMAQhyBdqBQ7utUZWWhSot66i1m277x1GvHfn9/zGyeKShJjlrJX4NPYLVBB9ypIgeDwHl+1mc2Iu6zgj04ZGo0oOJTDic6WUtPUBVXS2jzy2gpb2tKzMM/LWsT1V+wcSQY4u7Ozw+ylrc7Kp3sL/Zxe39Q3/WyJOMQIlTyYmMu3uBt4ADwN8tFstg4L/Ahb/Duo6LKIpc/kkFz4+Nps7VSqgygAC9HrlcTnNrG0Em/4NSJpMxamAvvl68QjLuJCR+IfuanDz+Xb2/j5yjjfc3uBkf2IvMsdG87e3NylVbCOsIJXt0YrfjCmJ0eHwiK8s6GBh/fGXLOd83UJyg7+pTdyo5HW0ZfuSHzTt44ImX0WrUvPbkvVx78QTCQoIIDTJ1efZvumLycY8/Vu2GxP9f2pxervy0krnju4sRvbqhCXNAGn2joqneV8P2r0px2724nR4K/pp21Dzlzgam7X0RnygyJ+UKZiVezDuKxzhPsZuEcYNQxUQyedxwFnz8BefFm5GHRRDy0rv4mhoQ3W4EtQb3ji2o8gq6VCMdq5ZhX7wA9YBibPNeIvCuB7vdV7rhY9ANH4PP1o7odCIPPrFCpoTEmeSz3W1MaHUTE6jEsepb5E89gu6Cqwj0qokQRORL32XJbgsjpo4FYM3mzTjdbpQKBY+/8F8em3FT11ybbKX0NMSjELpH86rb3fhEMGvkPLu2kTkjI3H5RLbXOnh7awuzRkQcc21Oj4/rv6jmyZGRmDS/PgujpMlJiFaBTiXj21IbhbE6jIdaOTR0ejCp5SjkkgH5/4Hj+titVus6q9Xa12q1XmS1Wm3AGiDJarWu+f2Wd2zuGxyGTimjzt1KmCoQQRBIiI6itKqy27jpo/KRdzaxvaLxDK1UQuLsJN6kok+0lpnf1tKhVBCVG0ZtVR37K8oZPbiA3nnpyI7zkKhsc7OyrIP6jmML64qiyNhUI6khp74e9vOFK7hw2t9pa+845XPX1DVy3+Mv8cDtV/PRK49hDgwgOy2F8JAgKWVL4ldhUMl44tyjN3yX99GSn+AjTDRTU9WIKcZIysAokgZEsH77Drw+X7fxydoInk25hnviJtI8fy5BW3Zxx38eI3LUOOyff0DH5x9wvtHHm/OexlhUDBzqxRUciiIiCrk5CE1Rcbd2AOqCAYguF8713xN46wzU2cfugSczGCXDTuIPzznJBiKMfuda51efgtfDwPdeIrBfFM+XO2mfPI2c8SO7xpsCAoiLjKR/Xi/WbtxOdW09AG/XruCjhjWk6aKxdXR2O8fueidvbGqmxubmmTFRKOQCOqWMjDA1txQe7cisbHPz3vYW1AoZtxWGID9UDnEk+5qclLe6Tnht66s6EUWRdVV2Nh60IwN21DsxqGT8UNmJzellxuIarI1OvIcasnu8Iov2tZ9wXomzl5PekVitVqfVanWfzsWcDHa3iO6QYEO+IZkghb+wPDEmhv0V3Y07hVzOJdffzPJqEd9PHoYSEhLHx+720Ttax9AkA81VdqIaQthbVsaGHTsICTIxakR/KtUH+d+ilUcdm2BScXdRKNd9UcXBdjctDn8mt83lY1V5B1d8WkmPYHXXfXyqaGmzMbK4kP69s7ni9pm8+s5nAHz05dKuB/OPiOLRSpy2TjtLV63n46/80usHKg/i8XiwddrZX15FkDmAh+6cRr+8bCk1TOKU4PFBje1oJ8jSmjIeWLudNr0RUeMlKjMIY6iOTZU7+WHrVhqamo46JseQwLjg3gwbdxPKHv7onn7cBbhLdtPxyXyUqRm/aG2CUkXQv57CfM9DqPOPnX4mIXG20Or0dhk2MoMRzaDhbMvpz9fLK7k4x0SvKB2uD96gev0mKlpdtHV0Mqh3PtHznsUSrOef/36F/Z01vHDwa66NGM5IMYfLb5vZNf+721oojNUxJEHPhmp7t3PLBIEIg5I5q+tZU3HYINQpBYwq/3MwK1xDm9NHq9P/vJyxuIb6Dg8L9rRjd4ssP9DBmopO3F6R0mYXB5pdPLqyDpdX5KOdbdhcPv6abWJokgGFXOC2Qn9rkZVlHbQ4fbw0LprMMA2vbWzina0tNDu8bK/z1/O+vL6Rhk4P9R0e3tjUjMTZz1nnbq7p8LBonw2Ax5Iu6SpmjQoLw9bZSXNbd6/HsGQj3u1LeP+LJb/7WiUkzlZqbB7mb2tBKRP4zLGYGlU7QabALk/l8u83sOiZjXw0dxkO59FeRblM4IPJcSzcZ2NZqf9+XVfVyXdlnfxnZNQpXavL7WbB0lVcdN0MOjsd3HbNFGbcfAUD++bi8/mormvg0psf4IYZs5n3wZd4vT6m3ngfO/eUYt1Xhs/nY+78z5l643188tUyjHp//d+Lb37E8L/cyKipN/Pau5+hUiopyJVSvCVOHZ0uH5/tajvq9Va3nfrKOBZ/XoZWraG13YYoipRXHyQxJoaahoZjzueztSPa2pGb/VECWaCJ4P/MJfSV91DExJ3Wa5GQ+COzrdaB3e037sz3zkI3bhLeqGh6ZugxaeTolDL2uPU43p/HpoN27E4Hep0Oba8+TPccJGdIGpd9O4frTefS05DAnl0HSE2KY8vOvXi8PlodXlRygaJ4PZOzTN3O7XS5qGtoYkK6kV6R/prSb0tt1Nk8jE49rLYZHaBkak9/bd/UniaMahk39QvBEqImwqBAKRfY3eDkna0thBsUjEkNQCUXmDUioiv98qf8bUAoMQHKLofklXlBTEgPIFSv4LZCf8Q9NViNKILbJxJv8gsX/WNxDTW2Mx7PkfiVnKjP3etWq/UKi8UyzWq1vvR7LupEJJlVXJprptbVwjNVC3g4cQrgr7FLTUhg9/79FObmdjtmePEArvikkt4VtaTEhp+JZUtInFX0CFYzIdvEhe+VkTPAS0iKmSGBRbz6wYc4XS4GF+YTGhBERFQQGrXqmHMIgsClOaauh0pxooHixFPbDHjb7hJue+DfpCTGMnvGjZgC/ZH8/OzDdUk3XTGZKyaP5fsN28hITUIul3Ht1AnceO9slEolc5+8j769MrEkxzOgT07XcY/OuJGmljYCDDqpVk7itGDSyrnpGAqx9s46sgxl5MSNo1kRx46SEtKTk9CoVCTFxnCgsoqcn5TeeWqqaZl1L6r0bJQplq7XBUlJVUKCEclGTFo5os9H65yHCbz9Ptr3VLG1pAmvRo9ZbCFjynAaps+ll20L+0UFVc8/jTsjH6VPJGV3CenNgWzctIFh07LYtN1KbmYqz815geuu+ivT+/U55nl9Ph+3PTiHnXtKkctkDBkykA1bdnD97X9DIfMbUiUHKkEUSUmM7TouM6y7sJbliDKG7HBNt//+EhQyAYWse+bJkCOey1FG/5omZwUSqlOwo85BsE5OhEF50uc4Ud37g8tqmTEojE0H7eiVMrLCNby/vYUxqQFUtrnZ3+xiVA/jMY+VOHlOtGPJtVgsjwMXWiyWbhJdVqt1xuld1vHZVuugtNlFp6qdEkdNt/cyU5L5ZNFiemdmolQe/iJmJMVwTeIuHnryBV574l5poyYh8TPsbXRS0+7mlfNieHuxjLYN7QiXCBj1eto7Oggxm0lNimPvtgp27TvAoL7HrscRBIEFe9qp7XDT5vBxc7/g35TS6HZ7UCjk1DY0sWtvKYV52bz93MOEh5y4L55Br2PEoMOpZUP655ObmYpPFAkyBRAdEXrM44JMUg8jidNHk93DO1tbmNKzu6d/YEwGWco4mjx6jPJ4rNuWoZDLiQwLJSY8nFUbN/L50qXERUaSm56Oa+dWWmbdi/7CS9CNu+AMXY2ExB+X1eUdXOP2oZaJqPMLEQQBb5vIiAyBKp2V63e/Q291NpGWYsa89iahoyfyYOdgkhwB9JtyI2mvPMzg3sN5avlWfL438HkE+ppUDGjcwvePlbDsmn9x++jsrvPtK6tk6ar1XHrBaJ59+E6aW9s5UFGNVx/Mui072bT4f8SeMxBPYCx3P/w0LreHd557CKPh+EJkvyc5EX6l2g3VdnIiNNjdIktLbVyVd+xn7QvrGrmyl5kl+23srHdyx4BQZn5by1V5QTTZvSzY085dRSGMSDbg8PhQyATanF5EUaSx04taLmBUywjTS/vzU8GJ0jInAJsBO2D9yZ8zhlEtQ6+S0eKxESjvLp9uCgggKjycHSUlRx131YThFIy+gFc3tf5eS5WQOGsxqmTUd3p5YFktyUURZI5JAMCg13elZjraXRz4oZaX3vr4hHNlhKkZnmQgI0z9m2vVVq/fyrS7HuGuh5+hrLIGjUb9s4bd8TAFGiXjTeKMopLLutKgjsRcoSesMgDe2ckISxBJcfFs2rWLzJQU9Dodhbm5aDUaNu3aRUtbG7b5b2C4bBr686S+rhISxyJYp0AuE0AQ0PQbCEBqXhytHhu17lbGBOVzY9xQ+k26gkhHG/lLPuXVy7O5d0wK0X3j+L5wCP3XLeIffx3N+YN6c2W8DuPCTwm88GL6KRy0bFzarZZ7wZJVfP7NCorG+9vjBJsDye+ZTkFyGHNn/Y12WwfL12zC6/NxxV/Oo6ggl88WrqCj005z69Gp2j/icru5fsZjbNy2+7R/ZgCX5prJidBi0sjJCPVHD+/85iClzS5Kmpw8t9YvWBiuV+D0igxNNHBjX39a+IT0QIK0clKCVFyY5Rc/HBCnJ0AtJz9KS/84PYIgcF1BMAq5QJRRSf5J9siVODEnUss8YLVa3wbOAdYDncAWq9U67/da3LGID1QSplfQ4XMSrDw6dJufmcHm3VY83u7t+ARB4LLB6cS4qnjzwwU4HM6jlI4kJCT8hKt99I1U4fSKXKAoJFL0RxaMOh3tHX4lysBIPcOn51NWVYPTdXw1rwSTithAFSOSf12qhSiKrNm4jRfmfUhRQS4TRhfTK8vS1fhbQuJsRa+AwiC/2JcoivhaW7B57NxV/iZVuc2c/1AhX+1tZ609hpEDiwgP8dfIpCcnM6J/f5JiY9lXUkLHvr1oioae6FQSv5FjiTBJnD2kGkSUcgFvXQ0NN1wKQMPyDvaXl2MQNIww59ArIJ4BPYzsGj8WZ1oWMoXf8bLPXUdZRiytoy/knPhgLG+/QHNVJW7rDmSjJ1L24Dxm3nsLPp9ITV0jjz77BiOLC/ngpVm8MOseYqO6p16HBJl4+O7rmH7JRNQqFWOHF3H7tVOYOmEkXy1bzaRr7ubTr7+lpc121HW89NbHtLTZWLB09en/0I7ArJXTL8YfUJne2wQd9dSW7yE32P/7NSE9gJbGWmy2VlQy6LTbkbVVIseLVikjyXzs8g2J08PJxD/HA1Pxt0K402KxvG+1Wp84vcs6Pm9taeFm4BxzLueYc496P8RsJjw4mM07d9E7u3vTVINaToElhskL9/LGyhe5pjidv44/53dauYTE2UPHh29TK1PwxW23MvO/8zk/uYCcwiSCTSaq6+vItqQCsPXjUnonZLDku3W/qqm2z+fD5fYct27P6/Vxx8z/UFlTxzVTJiCXyxhV3J9Rxf1/0/VJSPwR2Lt1L39/9GMee/gyHMsX0/rkTCbNHIgt3IFJ6fdqFyfoGZZkQHWM1iMJUdEsWL4c87mT+ItG8nifDkRRZPXGTbi9XoYUHLuuSuKPT8fH7+C56SZEpwNBo0EURZJzomlrb6K/0IMok98A84kiK2R2xgwd3HXsto5yInIiyT13KP/d0ky/R+ZSv3E17kHD6eFTs6HFQdrrLzLtuxJaOxyMGlpIWEgQGo2a3jnpJ7U+pdK/Hb9gzDD65GRy2a0PUnKgkr9Nm9otGm/Q63j83puJjgjlQOVBXnn7E7Zb9xMZFsxDd04nNNgvyFJdW485MACvz4dKqUClPPmauSNpbGmhrKqa6ro6ahoaUKtUCIBepyPIFMi+LSvorI2kvrkJuUyO1+vFZverhUaEBLNq40YSYqJJS0wiNjJCyiz4nTgZ424KUGS1Wj0Wi0UJrAbOmHH312x/BGF9ewkamYos/dEKYEX5eXzw9dckxcUSFBjY7b2wkCCeuaKI7du2M3L4YL5cuppN23ZTXVvPP26+8ri1N390RJ8P2/w3cP6wCtHhQJ1XgHvPTgS1BtM9DyGo1SCXI8ilfGaJkyO8ow6AZdk7uSFjDAA9EuJZu3Ur7R0dGPV64vLCuL1wCuFR5l91jg++WMIrb3/C9ZdfyPhzB/P1t9+zdVcJV1w0jvCQILbu2ktHp535zz+CXH7WiftKSJyQONqZvG0eNeNe7XotuKkTtc6ISeGvvdEoZbQ5vTy6soEZA8O6NSGOjgind3YWO/aW0NLWhilASjMWRRGny4XP56O+qZmosNBuNfi/ZJ7Sykp2lJTg8XgZNXjQaVitxO+JzOvB47AjaDQgQmK/SMrWlvNCyQL+ETIFuU/GyuVribObeN62iExPMgICFl0UeYYkAFKC1LS7Bb7p7MEF2gp2bV/PzQOLqCpTU1yYQXp6BkMKC35T79P4mAi+eHMOeq2GVeu28PLbn1KYn01uZipXXDSua1xldS3pPRK57MKx2Do6MQcaefb196mpa2TTjj3cdd0lrN+6i+Xfb2TaJeNJSYwm2Gwm2GQ6oZHl8/los9nYvGs3pVVVpMTFkZ6SzLDCfjhcLhwOJ5Fh/r1ybloaFTU19LRYCAsOQhAEnC4XcrkchVyOy+1m+569rNmyhYWrViEIAsGmQIJNJqLDw0mMicHpdlPf1IRWrSbQYPhV96tEd05mpy9YrVYPgNVqdVssljOqjVp7qDHywuYtJGrCjmncGfV6+mRls2zNWsYPH4b8J2ph2QnhZCeE88iKOqIUIbS1d9AnJ4O1G7czYdQQgD+8d0H0ehDkChxrVuI9WIV+wl+QaXUEXH0TvvY2PBUHMFw2HZwOBJWajk/fA0A/aSoN1/4V7Tlj0E+aiiCJy0gcA09YNMLQ0bgdHnp/mYg2yx9ZU6tUZKQks27bNvrl5KAMF5A1qPlu5RaSU6NJiP1lbQ4mjiomKS6aF976iPVbdjFpzFBku/cx9YZ7ufyicVw8cRTPz7pbMuwk/pR0GIJoTMkkavX/UOX1pTLWTHxoIP/4x3toX7sWDgXjjCoZxYkGfnobOD6ZT4ZWizM+nr1lZfTJzj76JP+PEEWRpWvWUFJWjkIuxxQQQIfdjiUxEXNAAG6Ph71lBwg0GgkKNKHXanG53bjcbmobG6htaMTj9WAOCMTr8+H1eslK7YElIUESYjvLEQUBmd6A6LAjaLS01nSweM4mIi410FzdhkamYuPunbTZOtAqVIQazVy392Vy9QncFTsemeC/+frFHmqVc14c9bYI3v/yfxTabGyKTKR/Uy1Bz8xk7Zoielx5HUqFgu82bCQ0yEzPtDQ0qpNPTTTo/Dd/YX5PGptbeeSZ13ljzgPdxhQV5FJUcDiDzePxEGw2ER0Zxqih/RnQJ4eighxsjnaeef0DEmIjKCrMRq1S0b9XL6LCQrv2ui63m+17/c5Ua2kpPp+PXhnpTB55LnrdYX0LrUYDR/iQAo1GAo3dSy7UR1ynSqkkLzODXhnpdNjtyASBxpYWGlta2bBjJ1+vXIVSIcccEEB7ZycKuZyp48b+JuNY4uSMu+8sFsuHwEqgCFh1epd0Ykqb/bU9LZ6OLs/mschK7UFlbS3L162nuG/BMY21u4pCkQuh/JAUg0YhkBOhZc3Gbcyd/z/umH4xqUl/nL5Avg4bjtXLweVCHhOH7Z25mO64H2VyKoq4RAD0E/5y+IDC7l5Gw+RLEX0+BJkM80P/pu3lp3DcdjWBN9/T1fD2l/Kj3K2vsxPZoZtfdLtw79mFoNZ0k+OWOLvwtrVi//oz9L36UmNpQS077EnLz8zknS++pKm1DY1KRWRjNDsqSimtrebqKeef1Px1DU088OQrzLzjWvrkZpDfM426hmYiwoLJz07jyr+cx+6SAwDSpkriT0uzR8b2ihZiTEG4Nq5FPvUxblixAgBtXSOE+aXRBUGgb4wOa4OTtNDD8ue6sRPB6aCH28uSNWvonZWFKIp/uo3RT6XVRVGkvqmJytpa5DI5apWKsKAgbJ2d1DU1cdUFk5DL5QiCQGVNDRUHa9i5rwSlQklOWhoddjtNLa1U1daiVqlQKhQkREf7HVYKBc1tbbjcbuKjov50n+X/VwRRxNvSjOhw+DOaogxMml3EtyWbCPMY8Xq9bN1tZdzQYvRaLZer1axvL+GqPc+TrI3gwtDupQBymYBBrSA1KYVPFy1GpVSSlpKC96JLUH7wNkvNYbQHmMnLSKe5rY2Pv1nI5NGjUMjlWEtLWb99B0MK+hAdfuL2XHK5jPPPHUxRQS7B5sBjjrF1drL3wAE67A48goMOl5NdBxpobG+iobmJAX1ymHH9VdQ3tWAKMDLj0efYsGU3PZLiSE2MxecTqaqtJcRsJshkYtzQYoICAk5pBE0QBAyH9olajYYvFq7iowVLcbk9vPX0P1m1bgvzP1zK8IG/Leop4ednd01Wq/UOi8UyBkgH3rBarV+e/mUdn4HxfoOu3t12TEGVHxEEgeGF/fh40WLWbdtOn+ysowy8H3t9iCL4RPB4RXpY0hg5pIGb7n2cQf16cdd1l3blQp8JRFGk7bkncKxcgionH5nBiH3pV6j7DUIWHIrwC26CH8cqomIwPzAbx/JFNM+8G3l0LKqsXIwXX9011lNTjXPtd8jDInCXloAoIg+PwFO6D+PVN9H5+QeIDgf6yZfQMO2viF4viuhYvE0NyAIC0Y+dJBl3ZzEalQJ9aAhypZx559/S5bUEvyeub89sVq7fgFwuZ8ykwTjWd/LWRwtO2ri7d/aL9O6ZTkiQP81aJpMRERbc9X5IkKmbR1JC4s9IvMLJOft/QG7JRFNUTHhCPlWvfYYtKJWAmipUWYfvAZvLy0vrm5gzyh8dty/7BkV0HMrUdMJEEZ9PZMn3ayirrmZE/0Lion5ZFP2Phtvjobz6IPsqyimrqkan0dAnO5uQIDOrN22mqaWFELMZQRCQyWSs2bwZr9fL4L4F3RxCMRERxERE/KJz67RS/eKfEUEQuoy79vpObA12nHofAS41+ysqMQcGEGw63JYkR5/A9MhzGB9ccMz5DGo55xbkUH4wjPDgYDRqNeTkI1NpGPz2q2ifeImAqBgAvln5HV+vWMmQvgXsLNlHfFQki1d/z8De+cRFRaH4SYZZc1sbGpUarcavUPlTw87ldtPRaUejVvP50mWEmEyYAoyMHjwIn8+HXqulpqGR0CAzRr1/3xwVHkqn3cGgvnls2bmXT75YQW5WD8aPGoTZaGbEgP4IgoDH42Hb7n0EGPXY7U5iIsMwBRqxO5xd6zkRe0vL0Wm1REeEYuvo5LsfttDY3MqWnXu58YoL8Xp9bNi2m9fnPEBIkAmtRk1cdATDBhYQ+AdpBXG2c1JWyyGD7owadT+yodpfqHlX7Hji1CHY25ysfHk7w2/PQ/aTxoxKpZJxxUP4fOkyvD4v/XJyjhnB+zHMvumgnfe2t/Do6GKGDezDl0tW4fF6z4hxJ3o8ONetRlM4CM3g4RgvvRZZwLG9Nr8GQRDQDjkHdZ/+uEusiJ1+BcTmWfeiGzsJRWwC3upKXJvXo4hPxNfRgbeqnM6kdHZs3E7fYaPYVFKGdu8BEl95H5XdhreqAlQqVKkZp2ydEmeGbZZBiJfcwLZlpWzct48rp3cXHkpLSiI2MpKPFy6ivraZ9m9d7NxTSln1QTweN9bSA4w+To3K/vIqyqtqeP6Ru/7w6c8SEqeTUmMMWy8cTt+2PQRMu5X7D8xn4N1T6PvVBrzVVd3GBmkVzBkVhccnQk0Vba88Q/DjzwP+3/Nzigawftt2CnNzWbl+A2nJzcSERxAeEnysU/9hEUWRToeDD79ZiDkggOTYWIry8mhsbWXzrl18v3kzyXFxjBo0EPkRzk2ny4Xb4+mKDkhIHMnegrHIAk0oLRnIw8L55MAG7OVOLj5/CBVrStm4c+dRInxKmYLrokaecF5BEIj/iSNFN2YCntpqvK88DQ/MBmBoYT/WbtnK8vnvYNPqOG9oMZGhoWzbs5dla9YSHx1NSnwcsRERNLa08L+lyxCBoMBAbJ2daNRq1CoVnXY7IWYzFTUHUcgVdNrt9Eyz0L/X0b1mk2JjjnpNp9UwcXQxE0cXA37RsorqGh6Y/Ro7rQdIjIuiZ3oPZj37Bk6XG4NOy7UXT6BXpoXRl97KkMJ8zh3cj6KCHN755Bs+X7SCscOLALh44ii+37CNB598GZ/Px13XX8bAvrms2bQdr9dLblYq9U0t5Gen8crj/+j2/BcE4Ve3NZI4mrMu3ynCoE+0p7MAACAASURBVMAtelEJCvRyDTXVTcTkhHC8PaJOq+X8YcNYsGIFX69cydB+/brlAx9Jr0gtOREavD6R6V838cLY4ZSWl/P6e58zcfRQ+vbKPO3hYm9zE77mRhTxiTjXfoe6VwHq7GM3iD4VyPQG1Dn5Xf8OvOFOBL0BQS5Hdul0ZIKAQaflgy+W8MnX31L9w7f0yamlIPcm2judPPLcW9TUNWI06Jj//L+obWhix1fLGNS3V1dURuLsI0bpxnHVeEJeeJkPItZyJd2Nux9TLMKDg6lsrCG2OJg786ewaNUqXG43CpmMb9f+QEqAnuhUSzchH5VSyd9vulxKt5T4f49Z4cO84G28PfybsFpXC94mHy5tCIq9G44a3+rwcv0XVczZ+QK6SVNQRMchiiI2lw+dwcTowYMQRZEdJSUcqKxi+94SQs1m+mRnERr0x984VdfVsfT7NbR1dJCfmUnfnJ5d7+m0WmJPEIFTq1THfbZLSCRvXoRzy3hU6VnIg0P4ruxNBLNAvG038SmxHCyvJTE6+pSdz3jx1bh27/C30BBFlAoF/ZITqPvXncj0BjhnBMlxcSTHxdFht7OvvIKNO3eyePX3CILAkH59iY+Korq2Do1ahdfrw2bvJNBopK6xkYKcngQaDDiczt/0vZfLZSTERvHOcw/x6LPzaG2zMWHkEN5/cdZRYxe+8wwffLGEF9/6CIfTyXnnDCQlIYYlq9ahVatxutz4fD4euecGEmIjKS2vRqtR8+Dt1xw1l+TYPb387O7KYrGEW63W2t9jMSdDlFFBpbOBW/fN5X9ZM3C0uzBFGdi7vIqEgnBUuqNzhLUaNeOHDWXVxk28t+ArBvbOJzHmaI8GgEwQQIBnxkRh0sr5pN2A2lLIc298wKxn5/HEfTeTkhCDIAin9MvpOVhFxwdv4Vi9HP2FF6NM6kHgrTNO2fzHY/vufXy2cDnbdu1j6IDeXHvxBGbOeZXS8mr2l1eRnBDD3CfvIzE2iruvv5TM1MSuTfmQ/vkM6Z+Pz+ejoamFAKOelnYbq9dtpd3WyWUXjjnt65c4PYQ0lCHT6alfsZPQoOMr8PXN6clnS5chiKCQKYhUq8n46j32Jmey2+ejXaXAtHU9hosuA/xeQrVKyeB+eb/XpRwX0efDtXkd8qhYFBEnTmETvV4QRTwVB5CZgpCZ/OlgPocd2c9I0P/Yv0xu+nWKohJ/XgI7m0g7fwz6CRcB/lpyeRu0hWSTcE7xUeMNtkaeiD5I2+KDPNbnRiZXdrKs1EaQVo5PhOl9ghEEgbFDhqBSKhCBHXv38sW3yxlS0Ie4yEhWbdxEclwc0eFhR81/pvB6vSxft44DVVUMKywkJjz8KCE0CYnfgszlRHTY6VzwKT5bOw6XgwZdO+/mreTSpMFMzsg9pc57QaVG3TMP+7JvcO3YQuCNd+Fc+g26vkUoEpJoefyfBP3rPwhyBXqtlp6WVHpaUrE7HIii2JUaHBcVedTcYUc4ajTqn0+TPBkCjQZm/f2GE47RaTVcduEYLp44CrfH38Kob14WffMORzyPLKeQHPxnjpNxnX9ksVjqgdeABVar1Xea13RClpV20MvZSIzan2qS0Mfvydv6v/24Oj3HNO4A5HI5g/r0Jjkulm9/WEfFwRoG9el93POE6PwfzYXZwTjSzOgn5DPp3VJCw0JZsm4Hz742n4F56fTtlUXvnPSTykM+Ft66GmzvzcPx/Up0YyYQ+tK7yAJP7w3h8Xioa2wmPCSYp1+bz6DCPCaMLMag9/+YXDh2GLYOOz0zUro8Qifq1SKTyQg7FE5PiInkiftvOa3rlzj9/FdIZfqoWOy7KwnI1Rx3nCkggPHDhvLt0vXomoxsXvQMA/KyKZpyJT3LD/BlZS2MGo+7rJT2V5+mPLsf/1m1izeeevC4c/5UPOF04KmuoOXhGSCX4WtuQtUz71AN6aUIMhkdn8zHdPc/8VSUYV+xGPvCL/C1NCMPj8Rna8d05wPIzEG0Pf8kwbOfp+2Ff+NtqMNTXYk6tzeCTo9Mr0cz+BzEThutzz1B8KPP0vrs44i2dpyb16FMTcdw0eWIXg+qtEyQK0AmO0o4wrN/L86Na3Fbd6IdNgpN4SBanphJwHW3422oRxETK7U4OUv5zh1Mdp9JnHPIQRCvCaVnSiJhykCca1ai7luEIJMh+nyI7a10fPY+wqfvYb5jJlN6mMkJ1xIdoCQmwP/cs7t9aJWybs+jnLQ0zAEBrFi/gW2GPbhcbsoPHmRE//5nPGVTFEUqDh5k+94SBAEuPu+8X92PS0LiRDQEx/p/48dfRLvXjvz7ebh9Xta27+WhhL+iVR3/Ofdb0Bafi6b/EERRxPHdMgxTr0KV2xtkcnB7/L/7R47XnJ51nErkchlyuRQl/yNzMoIqRRaLJR24ErjXYrEsAV6zWq37T/vqjkFxop7KI4y7FS9vI2tkAj3HJeFxedm/5iCJfY/fKDE6PJzJI8/lzc8+Jzc9jQCD4YTnM6hkGFR+b878ixIJUMvpCIijaPJVhDTt5KnFVm5WKMlOieXdTxeSnZZCdloypsDji734WptBkOFtqKPp3lvRjTyf0JfeQWY8cY8ih9MFoohMLqO0vJq46IhfZFTu2LOfBUtWsXD5Wgb2zeX+267mpdkzjvqs0nsknvScEn9OLpHtg5BeBK+dz+3D5pxwbKDRyPnnFyOKIi1f2mgJiyEhKgZdVAzJP/yAtaKSvNRUXPkDULzzBveplXR8/A6awecgDw4BDht03roamv95F7rzLkB37nmn7frce63oxkxEO3o83oNVuK07wOdDmZgCCjnGK64DoO3Ff6OIS8A880kUUTEISlXXegGC/vUUAJpBw/E21qGIice+7BsQfXgbG/BWV6DMyiVo1jMA6EaPx7luNYbLpuHavJ7Wp2YhM5kw3z8b17a1OJYvxnTPTNrfeAFPZbm/V6VWhzq/H9oh5yAPDT90vmEIGg2CTJAMu7OYQaomispWQYq/rmd20qVs+XwfpImo16xElZOPoNMjOhw0PXgXQQ/ORl0wAFVWLsGHfrd/NOwOtLi4f2ktc8fHdImF/UhsZCT5mRnYOu3kZ2awY28JXy5fTlJsLF6v5//Yu+84qaq78eOfO71t772w7AGWDtIRsGPBqNEYNYnRaBI1UWOKUWPU/EyMyWOi8bH7mJhoEntH7GClClIPIGVp2/vOzu6U+/vjDstSdneAnZ1lOe/Xa19MuXPv9w5zZu5p38O0ceMO66Jyd1UVCXFxuCNMPBIKhWjxemnxeslMTcVkMqHrOguXLGV3dRW5GZlMnzB+vzl0itKXUmt34N+4jkD5VhxTT+Svo69g9sbbAciwRbdBXQv3rqXc91jnY54LLyOwq5xgTRX2Md13NCjKkYj0qmA3sAWYAIwE7hdCfCmlvD1qkXWj1htkhjOHYofRYzfqzEI8qcYPjN8XoPrrRgonZXY7Bw+MRCvDiop4//MvGF82grzMzIi64+PtxjCRbwxPgOEJ6HoRzUtqmTI2hXc3VLMkkMPaV9/hlsfauPKcGVz+jZP5z6vvUJCbxdCiPFKSEtA0jdbXX8RSUIRj+hzSHn6mx566Fm8bL775AWefMoO1G7dwyx/+15h4mpbCrT/9Phlpydzw2/vISk8lKyOVWVPGM3XCKDZtLSfO4yYUCrG7ooaJY4bz/OvvkZOVzlN/uZ3cLGNIjhr3rBxK286d+JIS2FY5BPP6CorH95yuGaCp0kur52I+bW2mMPzY8CFDeHvRxzTUtZJcMorr/Vm8dOPlBJZ+TM2138E+eQYJ1/2Cmuu+R+qD/6D5mSexnzCV5n88amRmvfr6w8oI2xv/lk0Etm7GefLczscs2blYsvcfpm1ON75f9lbeDtRZbsK9DLayfXODrMVDuz2+tXho5/OWrBxcc/dlF3VMPRH7JCPdtmPabIJVFcRdeR2WrIPngTgmTTf2kVfY7bGUgW9HXRvV5bvY/thqxJm5PBh4m5+MmIs7xYH7xlsBaHzoz1gyc0i+415MCUnYRx16eG9hoo1/nJdLe1A/qHKnaRrDhwzpvD9KlGKzWdmyYwdOh4N3Pv2U8WVlbN2xk5KCfLLTux+yuXL9er5cv95YVkAIRpYO7fF3ZGdFBW8tXITZbMbtcmI1WyjMzaGmvp6WVi/fPP101Vun9Avva88DsMnVwfp1w7n3/MtIKOi5gT+aQs1NBKur0IMBmv/+CO5vfAtzSlrM4lEGj0jm3D2HUaH7F3CZlHJ3+PFlQL9X7hrbg4yPK+68bzKbsIQrXc54O5MvHUZ7i59Fj63mpJ+OxWw59IXh5DGjkdu2seSr1Xy0ZCnFubkU5eaQlZ6+X+vh3h4FXdepbWggzu3GbrMRDIX4ZNlyEiv28P6nSaQkJvLrM4Yicqbyty9qmJhvZ1eDj39VpJD3+Zts+Ho7Z9nauOGeO/dbckDrpmLn87Xz3Ovv8a+X5jN5/Ej8gQAnTh7Hwhcfo9XbRnyckS7W7w/w+19dw56qWvZUVXcu9vzgU8+zaWs5fn+Q88+cw8Qxw7njpquP7s1XjhuBYIjElGwKfOu4vPkZTmFMr6+JS3dy2u/nYLLuu9BLT07G29bOB58v5e5fXsv7zz1sZJ+dOp34q28gsKsczWoj+Q9/Q7PaSPjpzWhmM65zvknDH39L4//8jrgrrjnqH7zAju1oHg+a04XmGriplvf2wllLh2Mt7X4otDI41IRstGo2iiZn8tYTS/n8fMntpRdisZvxvvsmodpqfIvexyrKcM27sNf9aZrG7R9UcMGIBCbn9pw1UhQVIYqKCIZCLF+zhqVfrcbpcLDgk0+54LRTae/o4MPFizGbzKQkJRLndrNp23a8vjYuPOMMahsaWL52HZu2bycjNYW0pGQ2bd9OekoyTS0t7NhTgcvpoMXr5azZszp77DZt205NQz2pSUnMmTxZVeyU/mM2QzBIec1WbD8ax4lpI3t/TRTZho3ENmwkus8HwSB1v7mRlHv+t08zoyvHp0h67h4HPpdStgghus7snBGlmHo0JNnOLVuf4azkCUz1lPLO/yznm3+aud82NpeFUWcVYbaY2L6skrSSRFyJ+w9ftFgslJWUUFZSQl1jI1t37uSLlatobGkhKy2N7PQ0NE1jyVerSU5IwOlwUFlbSyAQwOVwYLaYiXO5OWvWLGobG6iuq+OzLxaRcOKJ/GSKMdTMH9R58ILhpCxYyS/jhzFVWPlad/LE04s4R8ThTkymMDudtpZmvlwjyUhLJi05ieSkeNra2pFbtvPYvbdQmLcv2YPZbOqs2AFYrRZKivIoKcrb7/zuv+umvn7rleOI0wJfeLey7uIJ/GvM9yJ6Tceyzwm+8ylbR8+FqZCZZiR32LhpJ8NELhu3baO0sLBze83hwDqkFABzslFmtHASBXNKGom33E3be2+B2UKgcg/m5BRCra1433jRyPJ6wjRMyaloNhtaN5k3Q742Wv/7D7wL3iDxpt9gnzD5kD1hihILY8wN5FnaMefHUXZ5EUneVbxy62fMveUEzBYLLc//C8e0E0m8KfJ21N+fkgk6LN/tZXyWs9fRGWaTiUmjRzMp3Pn8ldzIS++8i67rTB8/Ho/bRW1DA/WNTUwdO5aUpEQ8Lhdxbje5GRnsqKhgd1U173/xBcOKi2jxtpGWnMyEsjLaO/wkxHn2S/pQWlRIaWffvqL0n+R7HqR8XQuLtGWc+rEN/bzoz++OhOZwEH/19TT//WFqf3YVrrMvwP2Nb6EHAwTKtxnTBaJA72hHb/NiSjh4NIDu96N1aXjRAwFj27h4dH8HWKwD4r1TDi2Syt004AzgJuABIcRyKeU9UkpfdEM7tG31HdS01+I02TCZTVz454PX0tJMGpnC+LDW7WgmuSCOlto2arc2UTDx4OFlyQkJJCckMKGsDG9bG7urqtldVUVdYyPzTj6JQCBAbUMjJ00xWhnrm5pobmklPzsLk8lEcmICQwsKyM3IYP7Hi5h30kmkJCaiVe7E88ffEkhJ464rbyIuPRW5o5K6Fh8vvrmU9VoOsycM58bThvHgJid5H86norGdSTNP5PaLZ3L3r66J+vupKIfi84eo1VsJfTWS5MkuiGBKgv2EabiTSlj130/5asEWGBHgkstO55PFX/HzH17Ge59/TnFubsRLIJgTk/B881J0Xaf5sfuxT5hCywv/wpyWju7yUHfzteh+P6akZNIeeZZQSzOay01w53Y6Vq+kbeE7BCt2Yxs9gdQH/95ZgVSUgeITfwrf1Bx8df9KtHPMeFxOLvwf4zdNn3UqoeZmrMMOb91Qi0kjENJ5fm0jQ5LtJDoOL+vkaFFKbmYGVotl3+LH3QzTtFgsFOXmUpSby4SyEX2WuU9R+lrAHU9wzy6+eK6e5rkaTrNtwFVOPN/7EfbJM2j6272EmhpxzT2Xln89QdJv7gEwKlW6jmY7+nIWrKqk9pc/Rve14Zp3Ia4z5mFOTiXU2gKaRvUV38Q+fhKBXTuw5OTRsWYl6JD66L/xvvpftLh43GdfgH/7VoJ7dmItLsWc3vv0DaV/RHKVNU9KOQFASnmhEOJT4J7ohtW9FJeZTYFWkqxuGitaqZT1lM469LIGAOPOM1o8asub8Da0AxDoCGKxHfoHz+V0UlKQT0lB/n6P52Ts+9CmJCaSknjw1W5+djYzxk/gjQ8/4kwxBP33t+D59hU4z/xG55fImOIsnrwmCw5YN+yhi0dRmDiBXc3+zoXagyEds2lgffkoxwerptMY8jFVX0CcfyjQcxp/vb0d7/xXSDr3Is65Yjo//+X9TFs1Hu/JHbz/34eIj3OTnpLM2s2bGTNs2GHFomka8T+8AQDbhMmdZck+bRbm5FTM4flydb/5GYm/uJ1QUyMd61fjOvsCrEVDseQVHP4boCj9YIjWhMcM59wxhYWPr+auWRexceFOSmfloplMuOd984j2azFp3HNqFhXNft4q93JmafcJvg4lOeHwh4Wpip0ykFlam2h88F6+/fAzXJCajF0beImoNE3DNnwUSb9/gMa//h5zWgZJv7mHkNdL/V2/JFRdha6HcM09l1BdLXqbl4QbbqFjwxqsQ4eBZuqcox6o2I339RdwTD0R20hjeYK904x8ny2k9bXncc09F+dJZ9D8z8fxb1iLacpMqq/6Fun/fI3Uh/5J+4rFOE89m8CObXi+/f3OOd7ui76L3u4jWF1J/R0/x5JfhH/TekxON6a0dByTZ+A6+wJjGGwfLxumRCaST3dICGGTUnYIIaxATNNZua0m8uwpJFuMSbBma2ThpOTHk5Ifz7allWxfXsmsH43u/UVHoKQgH399LW9u2Mzcm+/GNTKy4wxJNn4Y8xNs5CfY2N7QwT0fV/PQ2dmqYCj9zpKWjtMZx6YTfsCootJet/d98gHtK5fh/sa3yMvO4D//vJuAP0j1pkZCTTrL5m9k4imjeO2DDwgEg0woKzuiuLqWBfuYCfs9l/qXx40b2XmdP2aKMpCleGw4MzJYu2A7uRelUtnRRGh5sM/2H9TBFwjR2hGi1R8i3T3wLmgVpT+YC0tIfeIffPDYespXr2TkyYVMvvTwGhr7izkxieQ7/tR53+RyGRmcQyH01hY61qxEsztwnnImAN75r+KJT8C38D1aX/kvJk88obZWnLNPo+FPd5Lwk19iKSim4c93kfyHv9GxYQ32MRNwX3ApmsVC4s9u6zxWxrNvGjEkp+I6xVir2D7uhP3i08xmY+66y036Uy8CxhSIUEM9gS2b6Fi7CjQN7+svEKypJv7Ka2lftQxMZjpWLsM2YrSx/JDfj8nV89xg5chE8k3/CLBGCLEaGAbcG8mOhRCTgT9KKWcLIUqAvwM6sAbYu1LiS0AWcJuU8l0hRDFwvZSy24XSypv8/O/Qnxp3MiEh8/CSI+SNSyN/XPSyEQV37SDz1X8z/fvX8dbyFZyZlUNGyuGvJVSQaON3Jxm9hW9ubOKs0p6XSYiWRl+QBIeZlvYgZpOG02rCFwhhN6vWmMFscfZEflV6Np+/9SEdG9fjLDMmnnsXvI5/wxoSrv9157b+TRtofeNFPBdf3vmYpmlYbRZ2rKqmaFImriQ7Se545p00h/mLPiYzNXW/3nBFOR693JrGL048hfZXNvOhXMMucz1/+PZlfbb/nHgr549I4Ms9bby5sZlbT0xT39vKcamicBQ1F5+O85v389zli7lo+MFTegYym9jXIGqfOHW/5xLDmXXd3/oerrMvINTSjMkThykuHs/Fl6P7fJhS00m48VY0k4n4K3perPxImBxOTJlOLJnZOKbNMuI59yL0YADd30Hrc/8kUL4N56ln0fz0owTKt2FKSCTtiefwb1yHKSUNS0YWejCglvfpA712e0kpnwSmY1TqZkkp/9Hba4QQvwSeAPYunHMfRgVuJqAB5wJjgW0Y8/muC293G/D7nvadGQ9/3PEyAJ88uYbtyyt7C2c/ZouJxkova97edlivi5QlN5+k39zD0IIC5kyaxFsfLWTbzl1HtK9Ut4X2gM6qCmN648vrG3lzYxONviB/WFRFSNd59qsGNlT78Ad1/vp5DQDPftXAU1/WAXDD/N2UN3ZQ3misgQSwtsrHzkY/AAu3tRAM6dR6A8gaY9jqE8vr2NbQwdb6Dn7xzh5jn6sbeGtTMwDfeq6cam+QBZubeXJF3RG+U8pANrVqJU99/Tp5cV8SXLOi83Hv268ZSU7CdL+fut/ciDkxGfvEKQftZ/Ilw0gvSWTEqQW01vlIjktgxoTxLFq6jGCw73ooFOVYdHF8NcEvv2D8BUMJunRMLbD0P7LPjzMuy8lts9L5qtLH9W/t7vP9K8pAl778fQAKLixgyNYM7O7Bl6VVM5kwxcVjycrpXDfZFJ+AOT0DTdOwZGb3socoxGS2GBmx776ftKdfIe67V5Py1ydJ/+erpD3+HzSLhWB1FYHtWwhWVdL0cM/r6iqR6bVyJ4QYC9wJ/BC4VwjxfxHs92vg/C73JwALw7fnA6cALYA7/NcqhJgObJJS9lhbq2tv54umjQCccLEgu+zwe8UcHutB2TP7gvfdN2l54ZnOjH+FuTmcOWsWHy1dykdLlrCnqvqw9+mwmrjlRGMy+6QcF+OznNjNGjMK3Jg0jaEpts4J8znhxWzPGx7PRWXGnMCfT08j02MlzWXhO2OMx9ZW+ahqDQAwf1Mzug47Gv0s2tYKwLBUO4l2M4WJVh46y8gsePXEFC4YYczDePWSQtLdFmYWuJk7NI5gSOe7L+2g0Rekri1AeWPHEb1/ysDRnJzN+zUbWLyqiFBTQ+fjKfc9hub2EGqsB6Bj3SosOXkk/fbeXlvbVr7yNXU7WijKzSXe42HFunVRPQdFGeg2mVMwZ+Xwym2focfr5BWkUTIjehdgI9Md3H1KJi3tQbY1qO9p5fihJxkJtdY+v5XxCwqo3d4U44iOP3tHDWiahskTh2a1AeCcdQqOSdMxp2cQ/6MbYxnioBHJhLW/AyuA/3b565GU8kXA3+UhTUqph283AwlSyo3ATuCvwF3ADcB/hRAPCyF+L4Q4ZGx13kYSLW4WP7OB7UsrsToOv/vWmWCnaHImNdsaD/u13Qn52mh55klso8ft93hGagrnn3oKcW43b3/yMUtXr6GqtvaIjpETbyUrzorDamJmgTEc9YQcF5lxVqxmjQvLjMqX02rCbTPevtx4KzazMZxyaIpRob1oZCLjs42F3+85NQuLWWNslpOrJiYDMKPATaLTbLT0mLsfwuOymsiOs2I2afz+5EwSHGZW7vGxMFxJVI5dO91uKq2tjJptxTv/VSovPZvKi+fiffk/WMUIqn94CXW3Xo/e0rzfguA9mfXj0aQVJ6CHdGZPOoE1mzZRWVMT5TNRlIFre7CDO1s/YvMlVUztKMX0bIik3MNLfnI4zCYNj83E8j1tfLilBV3X6Qjqvb9QUY5xVWlGUrBRpxZS9kgxWcMPv2NAib7uljVSDk8k72KFlPKJozxOqMvtOKABQEp5J4AQ4hLgVeAq4ElgNnAy8O6BOypc8A6VZ19D2RkFOOJsRxyQvy3AkmclZ/xyIqZuFjqPlB4I0Hjf3djGTMBWenDa6niPhwllZQwtKGDJV6uZv2gzQwsLKM7Lw2a1HlFmsoEmN8HoNTyp2BPjSJS+UPzx2+w46xQ8U6eSfvIMjOmyoDlduM65gFBrK7U/uwrbyLGHXCOnO1sX72HPujqmfb+MEydO5L3PPueiuWdgVQsZK8ehnPKP8X9YzqsnFPJjcQajbijsl+POKvQwPV/n4+1e3tjYxL2nZfX+IkU5htm3buS8X05lgcvFGHf0GlAUZSCIpFazTQhxsxDidCHEaUKI03p/yUG+FELMDt+eC3y89wkhhAO4AHgGcAFBjCvJbmsJF2+aRMX6um6XM4iEzWVl7q/GU72lka9e39LjtnowSKixgVCjMTytfdVyQi3NBGuqaP77w9Tf/jPwd5Bw3S963E+8x8Mp06Zy0dy5tHrb+GT5Cl57/wMWLllKh9/f42sVpT9p6Pxf5vWsedaY9GxKSMKUkIRms6NZbZgTk0j67Z9oevxvh7XfvHHpTLxYADAkP5+s9DQ+Xr48GqegKAOeruucnTqZ8WsKuGXrMyzu2NRvx7aYNE4sdHPvaVnsaOxgwebmzpgUZbCxm23MKB/OgvdXcMf2XgegKcoxLZKeOzsgwn9gVLzeOczj3AQ8LoSwAeuBF7o8dwPwgJRSF0I8BTwKNAHfONSOgjYrnnlJlLhzDjMEI6ufb/En+DdLgju3E6yuxHLpT0gffRKhlmZCTY1YsnNpfe0F/HINweoqgjVVhOprjR6L084m7vIf0778C8xp6WgOJ5rbg2P2aThnn9o5frg3ToedU6dPA6C9o4PPv1zJf958ixNPmEhhzuGfl6L0uZCOTNjNaTdN6HYTS2Y27gsPL7OfxWYmcKgJrAAAIABJREFUFNJ57y8rmH5FGTMnTOCFBe/w7BtvMnPCeHIyMjCZYrraiqL0Gy2kk+SI49ZzzuPXW/+F2xybteJCurGuqs8f4po3d/O3M7M7h/b3twWbmxmSZGNIso111e2MSLPzdX0HueEpCWBUQDfWdiBS7exq8pPkNOOymmjzh3BYNPwhaA+EiLPvawAOhnT8Qb1zHwfqCOpoGBc4r6xv5PwRCVjUOrODRlDT+c55c9jYtgeP19H7CxTlGNZr5U5K+X0hRCkwBFgNRJRqS0q5DZgSvr0RmNXNdvd0ub0SmNzTfs0dfhY+/wn5c88lPqP3ZRBC3lYa7r6FpN/9hWBVBQCu08/BUlAEwRA1136HtKdPYd0LX5IbWkfiFT/CnJyCaeJUzKnpmNIyMKek7ldx65pG1nPhd3qNoSd2m43Zkyexs6KCj5YsZd3mr5k+fhwJcWrYgBI7GnDf6jfIN2VQOjv30NvY7VgLig973yaTxsgzi3DE2dBMGhecfho7KyqYv+hjnA4H3zjlZOLch7fEiaIci3JtybxUu5SVy6qwJlnwmGJz0VmQaKMg0fiNu3NOOi6rxp8+qeaS0YmdibqiYW2VjzZ/iLGZTj7c1sIpxR6sJg2TSaO2LciTK+r446lZ/PHjah4+O4d3v27GH9SZkO3ikaW1/GVuNiv2tNHQFuQ7Y5O47MUdPHxODit2t7GmysfPp6dx4/zdXD81lT3Nfl5ebwxBfWFtIzML3HQEQ7y1sZkfnpDCvZ9Uc0KOkzlFHqpbg1hMGk+vrKc9oHPVxGTq24IkOY98tJASaxq/mf8sJ2WPwl2gKnfK4NZr5U4IcR1wHpCMkVxlKPuWLogJT5sPLYIWNf+WTZgzsoj7/jUAOKbPxjF99n7bpD31IprLRbs7E8fpRip3x4w5fR5zb3IzM/n2WWfylZS8+M47DCsuZuLIkdjUXCQlBjRdx2q2RlTOjkSmSKKuvJmti/cw4cJSivPyuOqiXFZtkLz+4YeIoiJGlZaqz78yqNk0M3W0s9lewRXxJ5Fqjc16pl3lJRiVvOn5LtLdFt7a2ERuvJXRmc4+P5YvoNMW0NGB5bvbmJHv3m/e9n1nGJlDH5uXg6ZplCTbMWmQ4bHwl7nGc+eIfe/ZC9/KR9M0Ti/xcMZQo4H0Z9NSyY6zUphoY2qe0WgUZzfREQwRZzMzMsO40L9tVnrnfq6dbCTbuHhUIhoQ0nXU8oDHNt2kEdJ07CYrZW41x1QZ3CIZd3ExxtIFDVLK++mlZ60/xOc0EJfW+6r2jX++i8CucqwlAq2boV7mlDRMNhsTLizFZNGo2txwyO36g9lsZtyIEVx85pn42tt55vU3WLl+Pf5AIGYxKcenkAbBFJ2hM6M3TNiT6iBzWHLnfU3TGDt8GGOHD6eqto5/vfYa8xctUvNRlUFrt6+WNozv9+tzzibdNnCSa03Ld2M1ayQ7LSQ4zHxV0cZNbxvrnj69sp6t9R3UeAOsrzbWYY1krp7PHyKk67yyvpGnV9YzIdvJzALjODfPTMfZzZDJvSnUi5L29TD2tF3XhdrzEmyYD2ikOr0kjrwEG4lOM9Pzux8lYDNrWM0aJk3rXHJIOTY1BL2UF9UypqyIU5LGxDocRYmqSCp3e7fZ+83dHqVYItJht+J/t4S2xp7DaF+xmFBTA9aSYRHvu2F3K9uXVx1tiEfN5XRy0pQpzDtpDhU1tTzz+hus2iAJqEqe0k+WlmXw47rT+Pwf0VuLzuaykjMqlY2LdrJ9+b7lLUcMGcIZM2dw3qmn4nQ4eemdd/lo8RK8bW1Ri0VRYmFbfjLjhp8BwPc2PIA/NPC+46fkuShItCFS7dwxx+jdSnNbyI6zsKvJz2c7vABc/vJOdjX5aWoPHrTWaV1bgJCuc8UrO2nwBZlR4OYbw2PfS6kcP2qLczhz0Vj+/v77fNyo1lhVBrdIKnfPAouAEiHEW8Ar0Q2pZ80Zycyc3Iq9m2UQ9GCApscfoPFv95Jw0+3d9tgdSnpJIid8q5T6XS34mmO/wGtKYiJnzJzB2bNnsauykn+99jrL167F175/xdYfCFDX2Eh7R+xjVgaH6mQnsyaOZMy5Q6J+rOT8OJJy90+Oq2kaSfHxzDphojE82Wbl+QXvUFFdgz8QoFVV9JRBwBvvpKxwFI9n/ohtvmos2sDtHbJbTJ0JSuYOjcNuMTEm08mV443e97/OzSYn3srH21v5rNxLMKTz/xZWEQjp/PaDKnY1+XlsXg7JTgupLgvx9oF7rsrgE0pM5IbvnkNTYfuALmeK0hciSajyoBDifWCkcVd+Ff2wupdcXoWs2EnWIeYC6e3tNPzpDvT2dlL/9ndMniNLSrJ18R7ShiSSNybtaMPtE6lJSZw560RqGxpYuX4Dz7z2OtkZ6XjbfDS1ttDR4cfjcuH1GcNj0pKT8fp8tPl85GZmUFpYSH5WFmaz+kJTInPykl38av0/uSf/6BIGRSK10BiK9sU/11M8NYv0ksTO5zRNo6Qgn5KCfLLT03lr0SICgQBmk4nRw4YxvmwEZpVdUzlGTdlQz6Mf3MfO0nxmJZXtN5zwWLM32chZpUaPXEt7kBn5LiwmjbtPyVCVOSWmEldv4Lfr/8Nm0x4mxQ2NdTiKElWRJFS5vcvd4UKIb0gp74piTD3aPLKIIeWFh3wu1NiAOTuXuO9cjXYUiRjGn28U/B1fVpEhkrC5BkZSh5TERE6eOoUWr5c9VVV4XG7iPG7cTieapuH3++kIBKitb8DjcmGzWdm+ezcr12/gw8VLKMnPp7SwkIzUFDRNIxQK4fX5cNjtWI6y4renuprla9dRVVtDVlo6Rbk5iKKiY/pi5XjWeubpJO9ws3X3Hkafc/gZMY9EyYxsErM9BAMhzJaDK2yFOTlces7ZmM1mfO3tfLh4MS+98y4nT5lCcuLAmaukKJHKnH0275XsZOKeLL4/vP8TeUWTx25mdpHRI68qdkqs1Y4dzpQVGUyfMgyzphoElcEtknXu9k6G0YDxRDaUM2oqQvWk3zX8oMd9Sz7FNmrcfssUHK3KTQ3EZ7oHTOVuL4/LxdDCwoMet1qtWK1W3M59Wc3KSkooKymhqaWFjdu288HiLwgGQ5hMJlpaW7HZrASDIdJTUvC1t9Pi9ZKckEBhTjZxHg9tbT5Sk5NIio/HYjZjMpkIhUKYzWb8fj97qqtZsW49La2tjBsxglknTGR3VRX1jU39+I4ofW3L6o8ZesFQRhf3T8UOILXIqKAt+NMyxp1Xsl8P3l52mzEc2+Nycfbs2az/eguvvP8+w4uLmTCyTGXXVI4pa9d/QvyIVP429weY1AWnokRN486vGXFNAWeldL92q6IMFpEMy3y0630hxPzohdO7sZvb0R5aDbeM3O9x/4a1WHLzMTl7z6IZqYkXlaLrOsuf30jp7Dzi0vo+FXR/ifd4mDiyjAllI4yKlwbxbjcWiwWvz0dVTS1OpwO300lNfT3bdu1id1U1ToeDNZs20dTaiknT8LhcNDQ3k5KYSGNzMwlxcYwWpQwtKOhcfFoUFcX4bJWjNXJ7CyXluexoru734cmzfjwau9tK9ZZGEjJd3TauaJrGiJIhFOZk88WqVfz7jTeZPGYMoqhQ9Rgrx4Sk7XvIrI9jw7s7GHFaQazDUZRBK6/Gh2UhtMxqw5Ny7F7LKUokIhmWWdrlbhaQH71weqdbzWQXZh/0eNx3r47K8TRNIykvDkectdvhYscSTdMOGsLmcjgozN2X8t7jclGYc3AK/DZfO3WNjaQnJ1FVV0dSfDwup/qSHJQ0mJkxHLslks79vuXwGL1z25dVkjsmDZNZo7XOR9GkzENuvze7bGVNDZ8sX8GqDRvITEslLSmJoYWFaIAlBuehKL0x6XBh4nSc8fZYh6Iog5rTbGdIeuYxfw2nKJGI5FP+aJe/m4GfRzWiXjQ6/Djnle73WGBnOQ333N7NK45e8ZQsrA4LH9z/JZUb66N2nIHO6bCTk5GO1WolJyNDVewGsZAG7yeuIX1oUsximHhRKZkiCZNFw2I1EQrprH+vHF3Xqf664aB1tdKSkjn/tFMZL0YQ7/awasVGnnjuBf7x8it8snwFTS0tMToTRTk0DZ1/N3/WbcOFoih9ozXUDhPMOBNUQ4oy+EUyLHNgzfIOJrPmymfIffYGNLMRfmBXOXp79FOjz/zhKOxuK3vW12H3WEnOO7JsnIoy0NlMVvTXA2ws3Unp7NyYxpJamACF0LinlaA/iKZpbPhgJ8PNGin58Wgmja1LKti2tJI5145h3T92MfOqUaSKVBJynHjb2ln+5Tpe2LaArNR0xgwXZKWlHTR0c9VrX5M1IoWELDf1O1vIFD1XbDvaAmgaWB2qV1A5Mk7NRrIpgYWPfMWsH42OdTiKMmjZzTaW/HY9WTcn4k52xDocRYmqSIZlrgLiAB+wt0RogC6l7L9sC2EBWwOj7IsJ1dZgTjdaO4N7dmLOiv4F6N7hYt6GdvSQTiikEwqEsNhUJjBlcPERoP4kH8V5WbEOpVNClpuELGM+54wfGGnj1727HU3TGHZyHnljjbmB8+6cagw/Dje+6Hs0SpOLOW3yFJ578H0+qF+MPxAgEAgytCgfS6WdgkkZJBZ4SMx242v2s+Wz3VgzNJZ9to5mWmj3dRAXjCN9aCI165vIG52Oud5KxcY6Trp8PJ88sZYx84oxmTTqdjRTMCEjZu+TcuzwB/1UmJqZ+t2Dk4QpitJ32nQ/E38hcCaqnjtl8Iukyfkz4Gkp5edCiFHAL4CrohtW9/yaDV9yAcGqii6Vu92Yc/pvKuCQqcYF744vq9j82R7mXDum346tKP2hw2LCV91BR2oAywBMY763103MzqWpqg1N0zobWQ7skTMqhW4AvnXtqei6zp4dNbRUt9HiaGFdy9dUrqqkvqkJ02aNBE8cqSOTePX9D8iwpTG8cAieBCebN5ejaRpaos6uPZVUN9dhTjXz1Esvk5qXRHmDnRRXMo17WgFY9txGcsekkVGaSHurv7NxKBJtTe1469tJKYhn+/JKUgvjsbmtNOxuJa04gVAghMlioqMtgM2peg6PVe0mnUB4TulAy8qsKINJwKzh3+WHZB2jf0JRBq9IrgpGSCk/B5BSrhZC5Esp26McV7e2ZmdSby0hp6qi87FgdSW2cSf0eyx549LJKksh0BHkvb98ycnXj1VDtJRBYfVUwXXbCmiIb8E1gFs6zVYzSTmeiLffWwHML8mEEuOxSWNGAeD1+QgGg9Q3NlHb0MD5p55KYvy+odcF+eFETge05bR6veyoqGRHxR6WrVuDzWKlbkkN1lwb5a07af26lRX/t4Uzb5tIw1YvDe1NpOTHU1lRi8vjIM7shg4TKQXxVG2pJ7M4merNjTRVtpJSEE/djmZSCuPxNXWwbsF2Zv14NEv+LUnIdnc2NCnHpsZZ0xiaOZwdX1aTlKuG+StKtKwtSmTHgiqGjcyLdSiKEnWR1EQahBC/A5YAM4Dt0Q2pZ+ZgNcNGGhW6vYK11ZhT02MSj8VmRtd1xp8/BKvDwsaFO4nPcJE5LDkm8ShKX4jfUYn/Wp3stJRYh9JvXA5j1Hmc201+duSVJrfLxbDiIoYVF6HrOrUNDeyqrKKxuRldD7Ji81rqxzTy3/nzCfpD2Kw2nJV2mqSP7LHJVFTV0BHogKUaBMGy0kx6ajIha4iKzypIFYnU+xtJTUxi1o+NeVmTLxtGKBDCbB14vapK5Bxby9kddDP6nH6f4aAox5XkhjZOvXuCWsBcOS5EUrm7BLgGOAP4Crg1qhH1YkJAsCtQSOa2zzsfc0ybhTkjdi3YmqZ1ZhWMS3fiTLDja+lg/bvljDuvpHO7YCCEyaypNbiUAW+sPYeCz1Oon9ByWD1jxztN00hNSiI16eBkLLqu0+L14nY6je+AM4ztdV3vzPxpMplo9Xqprq/HbDbT0urtXHeypr4Bq8WCyWTCbDLR4fdjMpk4ddo0stL7dy1CpW9kam6+7Z/L15/tZsi0g5f4URSlb8x2D+Pz/1vHjCtH9r6xohzjIqnc+YB6wANIIBGoiWZQPUktKiJpYhku877U0Z5vfS9W4Rwka7jR0+FtbMeTaiwVsO7d7WQNT2bdO+WkFsdTOiuXuu3NJBfEqYqeMiAlDB9HRn4SNpcaZtxXNE0jzu0+5ONdvwfcLhdul+ug7XRdp7m11UjkFAphtVoI6Trxh9incmxwjhhDSf4w/L5grENRlEEtKXcoGaWxW9pHUfpTpOvc5QOnYWTNfDqqEfXCvEuSMSYPc24Bekc7gYrd1N16fSxDOiRXgp2hM42FwF2JdmxOC9O+P4KhJ+bia+pg8bMbAKjYUEft9qZYhqooB/F98iF5Y9NwJ6mU0QOFpmnEezwkxseRnJhAnNtNgsejGoiOYd533yTe6SO9JDHWoSjKoBb8WnZekynKYBdJ5W6IlPJ2wCelfB1IiHJMPdqzx8ya+Vtpuv8PBHaWY05NJ+GGW2IZUq8KT8jEnWIMxTKZNJwJds68ZRKapuFtaKe9xU8oGOL9B74kGAgR6Aiih3SaKlvZtGjXQftrrTXW9OtoCxAKhgDQQ/pB2ynKkQr52njl1k+pK2+OdSiKMmjpLc2seWsbX760OdahKMqgFvD6eP6mRbEOQ1H6RSSVO4sQIhXQhRBxQCjKMfUoM72DEacXknj7H7EWDyVYsRvd749lSEeleEoW2WUp6DqUzszBbDEhP9zJ0v9uxJlgx9toJCZd+h/J1sV78LV0sOBPy9FDOuvf3c5Xb2xF13X+fd2HdHj9NOxuYedX1QBsXbzHSKne2M6XrxgXD6te+xpvvY/a7U0s+NMyAL58aTPbl1cSCum8/8CX6LrO7rW1nRXLNfO30d7qx9vYjvxwBwB71tdRv9O48Pf7AgAEOoI0VXoB8DV39NM7qESDpmmcc8cUknLVfDtFiRpdZ8TcQsbMUwlVFCWazHYL5/1heqzDUJR+EUnl7lbgU2Ai8AVwZ1Qj6kVbu4XW2jb0Ni9Nj/wF7+sv0L7kk1iG1CfMFhN544yMn2WnFzDxwqFYHRbGhLOojTqziKyyFBweG+f9YTqaSWPMvCGMmVeMpmlceN+J2FxWAu1BWmt9ADRVteFvCxDwBUkOp9lOyHIT9IdIyvF0TiwumZlNWkki6DpDZ+QQCuq4Eu3EZxrzfmxOCx2tfkKBEIEOo27fWtuGr7kDXdf57/UfEegIsvmT3WxfbmQx3b22tv/ePKXvaRq719QSDMS0LUdRBjVd12ms8tFc3RbrUBRlUAuFoGpTQ6zDUJR+EUm2hDwppRBCpAE1UsqYjv9razfTUtOGJzWZ9mWfE/K2knT7vbEMKSpMlv3r3Y74fQsgd51js/f23vX1UosSSC0yRs6O6ZJeOz7DqKgVnrAvEY07OZz6PW1f8ob88UYFM7FLhsTS2bmdt8tOLwCgZMa+seuXPnIymqYh5uR2xlM8Ra2/dUzTNLYuqSB9aFLn2nCKovQxPUTdjlbsXhsJWSoxjqJESygIO1ZWk112/Czvoxy/Ium5uxpASlkd64odQEqqTtbwFDRNwzn3XPS2NqxDhsY6rOPa3gqdSuwwiGgmTvzhaOwea6wjUZTBS9cpmZlPwcSMWEeiKIOa1Wll8qXDYh2GovSLSHru7EKILzGWQQgBSCkviWpUPahujqNmSyOpxQm4Tp+HyROPZrX1/kJFUSJmyi3kwwdXMue6sbEORVEGLasoY9X8cvLGZZFaHNNcZYoyqPlwsWb+NkbOLYx1KIoSdZFU7n4V9SgOQ0JRGq4UYzihyROH6/RzYhyRogw+1uISiqeqobWKEk320ePJKkvHlWSPdSiKMqjZsjJJH6qWHFGOD90OyxRC3AYgpVwIbJRSLtz712/RHYKtpRJnnOqpU5Ro6ljxBXlj0mIdhqIMaq2vv0BKgQdnoqrcKUo0mWp3k6Z6x5XjRE9z7k7qcvuZaAcSqa1bTNRuU4t+K0o0BXJH8Ob/WxLrMBRlUHPOOZ1Xbv2c5iqVLVNRoqmm0cGq17fEOgxF6Rc9DcvUurkdU0Onpqm5CYoSZU5HiHN+MyXWYSjKoBZqaebC+2bFOgxFGfQyhsYz9twhsQ5DUfpFTz13eje3Y6puQwUd3mN30XJFORa0rllPxYa6WIehKIOab+G7bPxoZ6zDUJRBr2XzDpqrvbEOQ1H6RU89dxOEEJ9h9NqN6HJbl1JO65foDqHNqxMKDJi6pqIMSoGQmcY9rWQOS451KIoyeGkaVV837LeWqKIofc/vC9Le7CdOTSVXjgM9Ve5G91sUhyGryL7fgt6KovQ9lyOAmJMX6zAUZXDTNGZcOTLWUSjKoJeQ5VFTepTjRreVOynl9r48kBDCBDwEjAHagR8As4CrgBVSymvC2z0L/EhKecisKVvXtxNoD2Kxm/syPEVRuqhvdfH1Z7sZMi071qEoyiCm8fHja5h5largKUo0VW5upLm6jbg0Z6xDUZSoi2Sdu77yDcAhpZwqhJgC/A+QCEwDXhZCJIVvf9xdxQ4gPc+OydrTVEFFUY6W2+lXawIpSrSZTIg5akimokRbQpYbR5w11mEoSr/oz1rSDOBtACnlF8BEwAs4ACsQAq4AHu9pJyarCZNpwCTvVJRBqSNgwaXW3lKU6NIhPsMV6ygUZfDTTGrEl3Lc0HS9f5KTCCGeAF6UUs4P3y8HLgF+AbwO2IDtGMM284C/SillN/tR6cWUwSZXSvmDWAexlypnyiClypmiRN+AKmegypoyaB2yrPVn5e4+4Asp5XPh+zullLnh2/HAo8AdwE/C/94vpby0X4JTFEVRFEVRFEU5xvXnsMxPgTMBwnPuVnd57tfAHwEXEMRYV8/Tj7EpiqIoiqIoiqIc0/qzcvcy4Auvl/cX4EYAIUQhkCilXAmsAvKBt4AH+zE2RVEURVEURVGUY1q/DctUFEVRFEVRFEVRoketKaAoiqIoiqIoijIIqMqdoiiKoiiKoijKIKAqd4qiKIqiKIqiKIOAqtwpiqIoiqIoiqIMApZYB3C0hBATgZullN8cALEUAmuklMfUMg5CiLOAPwB24CvgSillU6TbCSHMwP8AZ2B8pv4spXzkgNdeAZwnpTwnqidzmMJx/RywAu8BP5VS+iPdTgjhAp4AxmE0lvxKSvnKAa+9C0iWUl4X1ZOJMlXWjp4qa9Epa0KInwC3AhXh3TRLKWdG+ZSiTpW5o6fK3JGXuS7PjwHellJm9UvgMaDK2tFTZe2oft/SMNb7LsE49zcxfuNCRxLPMd9zJ6VcNhAK47Eq/IF6CrhASimALcA9h7ndD4FSYCRwAnCDEGJS+HXJQohHgPsBLcqnc1iEECOBO4FZgAASCS/RcRjb3QG0SCmHA6cC/yuEyA2/LlcI8QJwU3TPpH+osnZ0VFmLXlkDpgE/k1KODf8d8xU7UGXuaKkyd3RlTghhEULcCCwA4von8thQZe3oqLJ21L9vfwHWSSlHA+OBycDlRxrTYOi5mw08KKUcKYRIAP4XGIuxEPp84BYpZUAI4cP4AJ0GZAH3SikfDu/jN8C3gQCwEbhOSlkhhDgfuA0IYSyu/gsp5SIhxEfACmAGkAr8U0r523BI5vAHcBKQAPxSSvli+Di3AhdgVKq3AddIKXeH91cHDAMeBp7G+ACPwqjdvx8+dqCX90IH0qSUNV3vh9+PPx/iJb8Kx79USrkp/NjDwCohxLVSyq7rZJzW3XbAecBj4fjqhRD/AS4DlgAXAbsxWioiamkRQjiAe4GTwvFlAJuBTeH9PHuIl92P0eJxFjAGWAM8Ez72zw6x/XeAecBrUsrq8HEfBR4IH7urc3vY7jzgEgApZbkQ4t3wOd8HXAl8BKwDkiM594FMlbX93gtV1gZWWZsGxAshbgb2AD+XUq6O5D0YyFSZ2++9UGWu/8vceIz/p/OAdyM5v2OVKmv7vReqrPV/WXsZ+BRASukTQqwBCiI5z0M55it3B3gAqMX4INuA1zD+A+/B6P6tkVJOE0JMAD4VQjyFURDnAidIKVuFEHcAf8foFv4TcKmU8gshxGnAbGBR+FgCmA64gS+EEEsxPgQO4F0p5Y+EEOdhFIQXhRDfDcc1KfwFcTXGEKMzw/url1KOABBC/B+wXEp5ebib+u8YH6oDPygRkVK+h1EoDxK+GNrR5aGdQDxGK13X7vS8HrY71HOjw8d+JHycyw8j5N+E9zkO40vyKcAvpbwq/PwhzyXs1vAX0Zy9X0wYX3AHCX+ZbDsg7txDbJrXw3aHOvdcACnlneHj3NFDvMcqVdYOQZW1/i9rQgg3sAH4Y/iC6SJgvhBimJSypYf4jzWqzB2CKnPRK3NSyiXAEmEMEzyeqLJ2CKqsRbWsvdhlf+MwGjJn9xBfjwZb5W4uMD3cStAebvW4gX1dvq+G/12BUUDd4dc8JaVsDT93P8Z/qg34D/CyEOJNjFarrgXiUWmMp20QQjwPnI5RIDu6/CetBNLDt8/GaIFZJoQAMAOuLvv7uMvts4FJQogrw/edh/tGCCG0LrdPofvWFhNGy9SBggfc72m7A5/TDvH6w3E68Ovw+4sQ4gFg79yaEXTT2iKlfOrAB8NfhN21tkQad0/b9fW5HytUWQtTZc0Qq7IW/jydvvdBKeVz4Rb0E4APD3GMY5Uqc2GqzBn6ocwdr1RZC1NlzdBfZU0IcTrwL+AnUsqVhz6t3g22yt2Bb5wJoyt6rzYAKaUeLhQaRsE48DUWQJNS3hpu+TgVY+zrTRiFCoxWgK6v2fsf1HUCpc6+scFmjJblvd33diCpy7ZdW5jNwIVSyvXhbRM5dGE4lL2FvLMQ99LakoYxtnevHIyWn9YDNi3vbjshRDmQ3eW5bIwWiSOlsf/56oTfVynlOnpubdmPlPJpum9tiTTunrbb+1xll+eOuEAeQ1RZU2VtP7Eqa0KIAmCelPJvXV6nsf9E0zl9AAAgAElEQVTnYzBQZU6Vuf30Q5k7XqmypsrafvqjrAkhfgbcDHw7/F4fsWM+ocoBFgDXCSG08Af+anofJ/42cEV4aA/ATzG6y4NCiG2AK9wdfA0wOrxfgMuEECYhRBLGWODXI4jtB0KI+PD9u4B/9rDtjV3O4zUg0kyLe7vmI80k9A4wRQgxNHz/R+xrlYp0u1cx3kNL+MvjYsKtI0doAXB1eH8acC3G/1Nfew2YJ4RIDx/nag4dd0/bvRq+jzCSO5wBvBGFWAcaVdZUWTsc0SxrrcD/E/sm3p+JcWGyJArnEUuqzKkydzj6oswdr1RZU2XtcBx1WRPG0M5rgSlHW7GDwVe5+ylG1/Xq8J8E7u7lNU9ipCNdIoRYjzGB+FJpTOi8AXhWCLECeB64QkrZHn6dE+Pi4QvgISnl+70c5wmMC5EvhBBrMcYRX97DebjD5/BV+N97AYQQdwkjtX53zhVCrAO+CzT0EhNSyirg+8AL4fMfRTi7oxBiohBiZW/bYUyI/RpYBSwFnpRSLuzt2EKIlcJIP3yg3wHVGD1ge5Mi3Nzb/g6XlPIrjC/GDzDm7ASBP4ZjmyeEeKu37YDfAp7w/+l7GJOVv+7rWAcgVdZUWYtYNMtaeD7ERcCj4ed+g5Equ6OvzyPGVJlTZS5ifVTmjleqrKmyFrGjLWvCGLp7D8Y8y5fC57NSGIlzjoim65H20Cp7CSMj0YNSyhdicOyhGGuCHPQBFQdkOBrohBB3Y2SH2hDrWJSBSZW1vqHKmhIpVeb6hipzSm9UWesbqqwdbLD13B0PBEYmp2NauEt6myqMygCmypqi9C9V5hSlf6iyNoipnjtFURRFURRFUZRBICrZMoWxnsbjGC0DQYzxtRrGGhs6RprXa8Obv4SxEORtUsp3hRDFwPVSyuujEZuiKIqiKIqiKMpgFK1hmecASCmnA7cD94X/bpNSzsSo6J2LkYZ0G0bWs70ZfG4Dfh+luBRFURRFURRFUQalqFTupJSvEE5ZDRRgrEs0Adib9WY+cArGehzu8F+rEGI6sElKWYmiKIqiKIqiKIoSsagtYi6lDAgh/gGcB3wTOFtKuXeCXzOQIKXcKITYCfwVo8fud8CvhBAPA/UYPX2hrvudc90F+vlidrTCVpSYePDBB5+UUv4g1nHsdcKPz9K/O+KMWIehKH1qoJWz8T+eq18o5uAxO3vfWFGOEQOtnAFceOP39ROLx8c6DEXpU92VtahV7gCklN8TQvwKWEyXVe6BOMLrZkgp7wQQQlyCsYDhVRjrhcwGTuaAhSNzs3P4yU9+gn/rZlqeeZKk2/4QzVNQlH7x4IMP7ox1DF3lZedyWeNu4q76KZaMrFiHoyh9YqCVsxH2BK50mkn/4U8O+fya1nIe2fMOD5YMqOtkRenRQCtnAKUNLVw1aRyOyTO63Wabr4pnqz7mlvwL+jEyRTly3ZW1qAzLFEJ8Rwjx6/BdLxAClgnR2eU2F/i4y/YO4ALgGcCFkYRFBzwH7ntSnLGovWaxENi1IxrhK8pxb07iSIKVe9C9rbEORVEGrQnOfCy793T7fLEjg2XNm2kLDbb12BWlfyX6TYQae16LO92awJu1y2kMqN895dgWrYQqLwHjhBCLgAXADRjZMe8UQnwO2ICuizbeADwQHrb5FPAIRpKVdw7c8cqWrQBoLre68FSUKPmkcT2a3YHua4t1KIoyaK3ylhMItHf7vMtsp9CRzqa27iuAiqL0rjnUDsFAj9u4zHYmxQ9lYeO6fopKUaIjKsMypZStwEWHeGpWN9vf0+X2SmByd/uu8jcBoDnd6G2qcqco0bC7ow7NnoHe3v2Fp6IoR6fC34geSOpxm8nh0SqKohy5DoLowWCv252WNEY1pijHvIgqd0IIE8byBdOAxVLKmI0RMaEBoDkc6O3t6MEgmtkcq3AUZVDS0NAcTvR2X6xDUZRBK2TSoJcLzhtzz+mnaBRlENM0CPTccwcwN3k8c/shHEWJpl4rd0KIPwJbMJY0GI+xrMH3ohxXt7S9lTuTybj4bPOieeJiFY6iDFqa3Y7uU5U7RYmWkEavF5wfNKzGG2zn7JSJ/ROUogxCuqZBqPeeO4C3677EG2rn/NQpUY5KUaIjkjl3M6SUjwJTpZRnALlRjqlHcxLLOm+7v3mp0RqjKEqfOj91CrbxkzGrTJmKEjVn5czANfnEHrep8TezomVLP0WkKINTRlYJloIhEW1r0UwsbFgb5YgUJXoiGZZpFkJMArYJIWxAWpRj6lGVv7HztufC78QwEkUZvHa01+A65fpYh6Eog9oWrRn7ty7rcZtUaxw1/uZ+ikhRBqeWrFTsE7pN57CfHHsKuzrqohyRokRPJD13TwN/A/4M3AvcH9WIeiG9uztvN/7197SvXBbDaBRlcFrRsoXW117Au+D1WIeiKIPWiqrVNN59S4/bpFrjqQknElMU5cg0bVqN982XI9o2x5ZCY8ALwLv1qwjpoWiG1mder13Gu/WrCB4j8SrR02vPnZTyIeCh8N0bohtO70xdhmF6vvcjTG53DKNRlMFJQ8MxfTaYorVaiqIobVYTtksu73GbMlce/xj20/4JSFEGqbaMNOxTZka0bbzFyTujbkfXdd6pX8k79Ss5K3kis7tMCxpotrRVct/O18ixJ/Nm7XL+WnJFrENSYiiShCrfBW4GHHsfk1IWRzOonuxNqAKge1sItrViyc6LVTiKMniZTCpbpqJEka6Bpus9bmPWTLxXt5JR7nyy7cn9FJmiDDKHkVAF4KE9b/Ni9Rc8UHIlf9rxCh81rBmQlbsFdStJtLgpcWZyT9F3mBRXQkOwFW+wnfXenUyIi2yeoTK4RNIs/ytgHjC8y1/MDHPldN72ffwBbe+/HcNoFGVwmhw3lPbPFtL60r9jHYqi/H/2zjs8jur63++07dqq3ovlce8NYxvTTe89hFCSkARCCHzzIyQEUkkghJAEQhohlU6A0IsDGGOwMQbjti6ybMlWl1bb68zvj5VlCbnIRrLAzPs8erRzd+bOmd2d8rnn3HMOW461jSF26//b73pro9v5V+ubh8AiA4PDk+LmIJGnHhn0+n9seoWUnmaCvZwH1Wv5QcV5/LPlDa7aeB/RTAJ/dMewhktrusZjbW/z1+bFRDMD681es+lPXLPpT9y78wUkQcCn5DDbWYsgCHhkBzuSndxQ9zfWRRqGzUaDTy+DSahS5/f7Nw+7JYPEJdl6X4tOF6mtW0bQGgODwxOfkgNms+G5MzAYRvLM7v3WuQO4OH8+5637JV8tOhGnbD0ElhkYHF6YJGVQ59oufjfqKrxytsyWIAj8fudLvBFYS6nZx8Ntb7Ez0UWR2cOVhccOi72vBlbz79YlzMwZRXsqSLmUh67r3N7wJN8oXsQ3S05hY2wnC1zjcMq2AdvXWou4vuRUbqh7kMsKjubC/HlAVjSKwudzusX2eBsexUGOdPhfQwcj7qKqqr4AfADoAH6/f98zwIeRlX1SQgs5LrRQ9z7WNjAwOBhe6vqAb5inGOLOwGAYebj9bS4bRGHlQpOHea6xvNz1AefmHXEILDv80DSNWCKBzWJBGIISSm2dnaTTGXK9HhR596NURtPo6u6mrqGBZCqF1+2moamJWDyB25lDRXExFcXFiH3mM4ciEWwWC5IkARBPJPBvraezuxubxUJTWxslBQWMrqpkZ0srG+u3ks5kk2a4HA5kWcJutVFdVorH6ezXdzKVQpHlITnmzzJt6RD6AYi7+a5x/ZYvLTiKrxadgACkdY3VkXp+uv0JLsmfj0U0DZmdrcluZEHineBGbiw9nSNdY9F1ne/U/R2XbGNFaDP/J53JaJud0bbiffZ1um8mLtmGaiuhJRngkbalPNW+nHtrv4xPziGciVNtLdjr9gktxbpoIzsSHeSbXHhkO7XWYnRdRxAE4lpyr8cezSRYFvQz2laMRTSRpzh5sHkxJ3mnUWByH/DnsjK0hbZUkAWucdgkM5CtAbo8uIkTvVPxyg7KzD4eblvKf9rfIZpJcnv1FxhtLeKuxmdoSHSwJrKdb5acwjm5cxARyKDhj+7s/QxeD6wlkA5TYcmn2pJPocnD8tAmZCSqrQW45X3n+EjrGVaHtxHMxFjoHs/rgTX8q3UJUxyV1FgKOdEz5ZCdh4MRd88PuxUHgNhnzp3odKEHDXFnYDDUCGSLmGOIOwODYUMTgcz+xR3AjyovQhGk4TXoMCWdyfDkyy8TikQRRYHaikrUqkrqGhqRJInJY1QUWUbvmf8oCAKJZBJZkhAEAVEUiScShCIR6hoa2bB1KwJgtVjoDoXIsdtJJJNAVkSaTArlRcWk0xlW+/1MGzsOq8VMVzDI+2vX8erbyygvLkKRZRLJFDtaWshoWjakzumkOxSisqSEfJ+XjkCAKWPGUL9jB4+98CIlBflMHD0aizn7gNsdCpHJaARCIV54403SmobDasVkUojFEwSCQew2G3keD5IkZY8TcNrtVJeV4XbmjMyXcqgRxEGfa3uir7fHJIhMc1Qz2lrMvTtf5IbS04fCQhoTHXyn7u+c6pvBt0tPxyH1prrgnNw5LA9t5u6aywd9HRAEgYXuCQCsDtfzbMdKLi04ipSWZm20gVvqH6LI5MYmWvjz6K+hiDJd6TB/aXqN4z2TKTZ7+e2O5/DIDtpSQc7LO4I8xcXpa25nkr2CznSYf465jiXd6+nORJlgKyehpxhrK+XSDffglG00JNqZ7xrLrRUXENdSnLPuTuyimek5Nfys6hKebH+H7fF2tiXasIgKt1d9gafblxPOxBltK6bM7KPQ5OGRtqUEMzF+vO0xTvPN4Kbys3mxcxWFJjd3NjzFab4ZXJQ/n85UmJvKziaiJWhNdjPRVs5YWykLXOOYYC/HIzt4s3sdN2/9J7oO+SYXN5aewRhbCS90vk+uksOrXatZ5J3KeXlzeaztbZqSXWyNtTLBXs4fRl/NI61LWRPdzlRHFdMc1VRa8vnp9sf5T/u7jLIWMclewVGucQTSUc7KncWWWAvrog0s8k7lD00vsznWxEV585EEkcmOSrbEmvkwUo+u673bvxL4kH+1vIkkiFhFE98qPY0Sk5dlQT9RLQnonOabudfvfjDi7l/AV4FxwEbg94P6VQ0TfVWv6HShdQdG0BoDg8MVAcFiRYsb4s7AYLjQANLp3pHwfaLr/L31db5YsPAQWPbZJp5IsHbzZuKJBLF4gtaODnweN+ctWkQoEmHVuvW8uOQtqktLCYSCPPzc85hNJgLBIHleL7IksaO1FQBJFKmtqKChuRlJFCnKz+O0oxficToRBIFQJEIgGMTpcIAgoGsabqdzj3aVFRUxSVWJxmJs29mEpmsICBx7xBxEUSSdTtPS0UGBz9cr3nZRWVrCwtmzBvRZnJ/f+3re9Gl0dQdJJJMkUymsFjMep5NAKERXd5B0JkM6nQYBAsEQT7z8Mg6bjVyPm5KCAnQdnA4HPreLWDyBzWrBbBo6r9SIIgoH5Lnbf3ciP6u6hLiWpCMVwiqaej1KB8OdDU/zTMdyLsyfx0V58/pdDwRBYLZzNLOdow+6/0mOSl6aeEu/fp8Z/112JjsJZmKk0fhJ/cMsDW7gaPcEgpkok5VKHlCvGdDXNSUnsypcx92jrkAURNZFG6iLt3L/zpcYby/njqpLeXTcjUgfC/+8uvhEvlJ0PI2JDtyyA13X6UpHsEtmTvJOpdpS2Hu89YlWHm17m0KTm9/XfoU7qr8IQFc6TFxLAfS29bftpAFtZ+fO6be8wDWO/07IBiB6ZEdv+29GXTlg2zurLwNA13USena/eSYnU8UqnutYybKgnzurL+O83LlcX3Jav9/AmbkDz9czfbN4PbCW79X/G9VazK9HXcHaaAMfhuuJayme6ljOL6ouZYq9Cm+JAw2dWCZJnuJkR7KDZzpWYJPMuCTbJxZ3fwACwCvAUcCfgYGf6CEiV9l90RQ9PjIBo9CkgcFQU2bO7fHcDZzIbWBgMDSMtpeAGMpm8ZP2fTuWBYm/NL3GiZ4pBxXWtIvm9nasFgsuh6NfeyqVYt2WOnI9Horyctne1ITdaiXPm83QuWrdegRBIJVOMbqqCpvFwpbt2ykrynqgPi2hf/6tW3lzxXtUlBTjc7vxulxMGqOS63YjCAJOh4OjZvV/KGppbwcE3M4c1mzahFkxcfJRCxAEgWg8zsat9Rw1ayblRUUD9pdjt5NzgCWZbFYrY2sGJh2XJYmK4n2H2u0Pj2ugsMz1eMj1eAa0z5k8iUAoRFNbOw3NzQgIrNm4ke5wGKvZTDQex2axkONwUFNehtfpwmG34bDZPhXf9YFgkS0wiBDoA0ESROyShd/seJ5is5fLPsHAyyX58/lK0XG49hP690n4+HfmVRx4ld3XgUsLFnKabwYzckbts5/z8+Zyft7c3uWvFS8asI7Enn8foiBSbsnrXd7TnMXTfTM5fS/Cpa8Y+yQcaD+CIGARsgMdx7gnAv1F4/5CZHdRYHJzQf6RXJB/ZG/b3o433+Tqt+yW7dyzBwG6JwYj7mr9fv+CntdPqar69qB6HiaqLLtHqUSni9zf/X0ErTEwODyZ4qhEqajBdeMtI22KgcFhy0L3BHx3fTsbMrYfBEFgek41/+14jysLjyWmJQ/KU7B42TvE4nHOOfHEfiF5yz9aQ3tXJx/5/aQyaRw2O+FohCOmTKErGGRrQyP5Ph8mReGJl17CpJgwKQpLV75PWtOwWyyo1dWoVZVEY3GC4TC1lRX7FAE7WlqwmM343AcvVnVdJxaPEwiFWLtpMztbWznnxBPwulz737iHgtzc3tfTx/dPd++w2Zg2ftzHNzksUBSFPK+3R8AP9Aql02mCkQid3d3UN+5gQ91WguEQDpsdp92OyaTQ1R3EYjaT63ETiydIf4LQx+Ekt0zF8YWLh6Xv030zubHuQS7Jn498EKHT7akggXRkxEudjLIWjuj+DYaOwYg7i6qqNr/fH1VV1QqMaND/xujO3teCKJLeVodgMiHah0bNGxgYwNvdG7hGktAjkZE2xcDgsOXJ9nf4KieArjGYykTfLj2dM9bcjke282LXB/y+9isH9DDZ1R0klU4zZdxY3vnwAxbN313UuaGpiaNnzybX4yYQCuF1uWhub+fZ/71OTXk5px1zdK+H6oipU2hsbqasqAhd00AQ6OruZkPdVh578aXepCVtnZ3MnTaVjKbR0NREXUMjdquVipJiUqk0ry1bBoLAuJoaRlWU43W5SKfT1O/ciTsnp9druItUOs32piZaOzowKQqKrLB28yYi0RiyLDNt3Fjmz5g+IKTR4OCQZRmvy4XX5WJUeTmQFdMNTU1E4wkSyQRqVRWRWIzOQDdetwtXzqdzDl9jsvOA6twdCOPtZeQrLl4PrOH1wFo602Fcso3vlZ/LT7Y9xlVFx+9ROO1IdHLPjmd5J7iRhe4J/Mh+4bDYZ/D5YzDi7h7gQ1VV15Cdd3fbsFq0H2Jast9yYuU7SAVFhrgzMBhCujNR9ESc0D//hPeHd420OQYGhyUtyW7Cf70P980/RRjEPazU7OO9aXeiofNc50qe61jJGXuY17E3Nm3bRlVpKZNVlX8/+xxNbW0osszaTZuJRKPkeT2IotjrSSvKy+Oq884d4H1TZJmq0tLsQk+Gx3yfj3yfj/kzpgOQSKX472uLefzFlwiGw/g8HqrLyojGY7yxYgWpVJpjj5iD2+lk1br1vPDmEqKxGLquY7fZSKaS5Hm8mE0mmtvbqSguZkdLCw6bjcK8POKJBA1NzVSVljJn8uTBzVs0+MQIgkD5nkJHKw69LQeCFugk+vxTOK+6dlj6v7HsDBJaivfCW7gkfwELXOMwCzIT7RVctfE+jnNP4pqSk3DLdlqSAdpSQdyyHdVWwoV58waE4BkYfBL2K+78fv+/ekohVANb/X5/x/CbtXeEj8XxOq8cONnTwMDgkyEgIDpyDGFnYDDMeH96zwGtLwgCEtkseOuijZzB4MRdIplk7aZNnHn8cciyzNxpU3n+jTcRBQG1uopp48f3S6Hfd38Hah+AxWTi7BOOp7m9HY/Tic26O9vgnMmT+22zaw5cPJFAURQEIJlO09TaSjyRYOq4sWzevp25U6dSWVqyz/0aGOyJZK532IQdwER7Vt0+M/67mMTdj9aXFCzgVN90HmheTEOinZZkgK9svJ8J9jJ+O+qqYauTZ/D5Zq/iTlXV7/v9/p+oqvoQPfXtetrx+/3DE7g8CBSxfwhK9MVnEOx2rPONE8TAYKjYdXMK3n83ji9+BdE2fJO8DQw+r1hFE6EH78d+9kWIzgMbuV/gGkdLsn+26NaODvK83n5CZ0dLK8FwmA83bECtrsLTk8lxVHk5BT4fiiwPWxijJEmUFOy9jtbH6WuHxWTa7R2EASGaBgYHgjkUJfKfh7GfNbyhj32F3S5csp3rS09D13VOXvMTrik5ifP6JCQxMBhq9hXk/9+e//eTzZjZ92/EGGsr7beshbpJb9k0QtYYGBye7MoGlXhvmVFuxMBgmPhS4dHE31qMFgkf8LaVlnwmOSp7a7PFEwkef+llWjp2B9ckkkmWvPceH23cyITRtcydOrVfHzl2uzE/zeBzQbFuI7586YjaoKHz/8rOHpCa38BgqNmXuFujqqoJuA54G1gGLAduPRSG7Y32VLDfsuTLI9PeOkLWGBgcntTFWgAQHDno4dAIW2NgcHjyfrgOZPmgU7SftuZnNCW7AGjryv7/cMMGNE3jww1+/vL4E+i6znmLTmRCbe2Q2W1g8FkjrCVgCOvcHQySILLQPX5A/TcDg6FmX3PurgBuBgoBf0+bBrw13Ebti/ZU/wdNqaCITEvTCFljYHB4siXeDIDoyEEzxJ2BwbDwXmgzguRBP8j08XNyRvO/wBouKVhAW0cnY6qrCIYjPPTsc2i6zvknLcKkKMZ8NIPPPUEtDp/SMg0GBkPNXsWd3+//E/AnVVWv8Pv9DwCoqmry+/3JvW1zKPh4QhWpsJhMy869rG1gYHAw7DrPBEcOWji4n7UNDAwOBgHhE3nuTvJO476dL3JJwQJaOjqoKS+jtqKC+h078LndOB1GFmkDAwBkCT184OHPBgafRQbjG5ZVVb275/WzqqpeOpwG7Q/xYwOQoseHFg6jx+MjY5CBwWHIrtNMNMIyDQyGFUGSD8hzt6OllUxPeNlsZy1jKeKh55+ntaOD8qIiBEGgqrTUEHYGBn3QFdNBzW01MPgsMpg6d1cDu9L6nAK8Cfxj2CzaD6Xm3H7LgihiO+Us9EQMwWIZIasMDA4vZuTUACBX1iDYjIdEA4Ph4HTfTGRZRlBMe3y/NZLGJAm4Ldks0alUimcWL8bjdHLCvCPJsdspWW+iekwpVQUlRnIUA4O9UGDPRS5OjbQZBgaHhMGIu4zf748D+P3+lKqq+v42GE5EBs4dMGrdGRgMLbsmfNtPPWeELTEwOHyRBQnXdd/d6/vr2xIsrgtjVQQ+aonzs3kWcj0eaivKeXPFe0wbN46IO8NvhMX8y/etQ2i5gcFnC1ExH3BNSQODzyqDCct8WlXVJaqq3qWq6v+Ap4fbqH2xsyczWF8i/32c6CvPjYA1BgaHJytCWwBIrvmA6PP/GWFrDAwOT55qf5fIM4+R2la3x/fnltn4wcJ8Lpro5rzxLlo7u8jzepkwejQdXV1s3r6Nyb4aNseaiGvZ6fDvhTbzatfqQ3kYBgafepqSXQT/eA9aNDrSphgYDDv7FXd+v/8nwLVkyyB8y+/3/2LYrdoHe8r5ZZk9H/NMoyCkgcFQ0TvnzpeHXD16RG0xMDhcEQQBubgM0Wbf4/vnP7qdtkiaCreJM8Y46ejqJN/rQZYkairK2VC3lerCYqqtBayLNtKVDnPlxvt4qHXJIT4SA4NPNwKgqOMRRKMMgcHhz37DMlVVLQFuBPKAx1VVtfj9/neH3bK9sKewTNHtJr1tK5LbMwIWGRgcfuzKlikVFiM6XSNsjYHB4Ytp+uy9vvfEheW9d7wXN4X492aJ300qAOComTMZW11Dvs/LWeIcRAQeaF7MpflHcWPZGei6bpRAMDDoRcAy7+iRNsLA4JAwmCGMPwIPACayyVRGNGjZKdsGtOmxGJ23XI+uj+h0QAODw4ZySzZxkdbWQvs1l42wNQYGhyeT7ZV0/+JW4m8tHvBeIJZhybZIr0BTbUHOLOrGlZMDZL1+Bbk+BEHg/Ly5THFUMSdnNNeWnMy7wU18d+s/D+mxGBh8mnHKVjq/dx2pdUbIssHhz2DEncXv9y8GdL/f7wdGtOaAWx4YviK63AiyTHLVCkPgGRgMAVWWrHdA9HjRAp3omjbCFhkYHH5Mz6nZa527cFJjc+fusrIbt25jp6kSbR/3uCNdYzCLCqNtRSzpXk9S+2RFm+vjrVyw7i7CGaPUkMFnG7dsR7TZjXIIBp8LBpMtM6Gq6omApKrqHPYj7lRVVch6+ioBM/ATYB3wIKADa4Bv9Kz+JFAEfN/v97+iqmo1cJ3f779ub/23JAN7bM+56psE//hrQMA8fTaWeUdjGjtxEId3aEmu/4jYS/9FsFqxnnAaStWo3vdSqRTN7e3Isky+z4f0GYwNj8bjBMNh2jo70TQdSRKRJYmivDzMJhOiKCKKIvFEgi3bG1i1bh26rlNZWkptRTnF+fmIn8HjPtxYHtoEgKCYEGx2tGC3EfZsYDDEPNb2Npd6Juyxzl2pS+HKaV50XWdjfT2NzU0ki0YRT+vYlH2HW3pkB1Nzqnmo7S0uK1h4ULa9H6rjZw1PYBYUmpKdjLIUDQjz1HWdtJ5BEQfzKGFgMHI0JbsQ7A70aGSkTTEwGHYGc0X+CvBLIJfs3Luv7Wf9LwAdfr//UlVVfcAq4AOyAu51VVXvB84AtgH1wOVkhd8rwPeBveeFBjT2PGppXXg8lqOOI71pA4lVK0ht8mMaO3UK/0kAACAASURBVJHO2/6PnC9djWhzEH35v0i+PERfLmKOE9GRg+DIQXS6ESRpEB/FwaNrGtGnHibyn4exn/sF9HicrttuJP9vTxF96b/EO9p5zu5jyhvPI8UidEgystVGvLSC6PipjBo/HqfXh5CII9kdaP61pLZsRJAVUGQE2YQydgJyQVF2f7oOug6ZNOmtW0CSQJYRRAkkCUGWEV0ekGX0WDRbrHrX6LEkDXquRncoRP2OnTS3t9PQ1ASAKycHn9uNIstkNI1kMsm7H64mnU6T0XU0TUORZcqLizjpqAVYzWa2NDSw7IMPCYZCmEwmnA4HlSUleF1OSgsLD5u5I7uKDwcjEeKJBNF4nLLCwhG2aiB9R/wlrw+ts90QdwYGQ0w0k0CQZUhnBrz31PpuTJLAglKZN5avYMrYsczqmW+3i1VNMSbmW4ilNVrCaWq8pt5r5Y2lp/NGYO0B26TrOm2pIBPt5VxecAzHeyYjCSKXb/wdp3ln8GFkG5cVLMSn5PCdur9TH2/j3tovU2stOqD9pPUMEuJhc203+HSj6Rqiw2V47gw+F+xV3KmququqaivwxQPo8zHg8T7LaWA68EbP8gvACWTn7tl7/iKqqh4JbPL7/S376nxPCVV2IQgCyuixKKPH9ra5vvn/EHOcaN0BBEkitcVP5t230MNBtHAIPRzC+/N70YIBIk8+hOf7txN68H6Sq98HkwnBbOn5M6NU1WA/+2Jii19ErqhGLq8k/Og/sjdnSUaQJZAVBEnGNGUGUl4BiXeXYDnyaEJ/+R0p/1p8d/0RKT/7MG8/+0LqG3fwQRJigoWxNdWMGXsdmXCIjtYWkuEwthVLyV3xFh9MOYKGshpOeOR+XrjgaqYkI7jqN5FjsWCTJUgmCd7/K0zjJ+O55eeE/vhrlHGTMU2YTPfvfwWZNGQy2RHiTAY9k8F1/feQC4vp+tF3yP3t3wg//CCRx/4JkoR56kzSDdvIdLRhmjgV7213EnnmcUyTphKz2mn98+8IFJawxuGjorKS0oIC5k+fhtls3qPHUYtFSTfUZ78nBESvDyk3n9S2rcjOEiZXVDDBohBPpshoGbqCIZo2+6kPdPE/aw6ySaHYZiO/shJXKolNEkEUsr0JAql0mmAiBS4Xrh3bMFXWYLHZSL74NHoiDoKIVFCE2CNQTGMmIFht6OkUonXgPM492R+MJ4gkk3SHwmiaBgI4rDaiiTi6rhOKREil0phNCqIgEkvECYYj6LqOjk5Xd5BoLAaAw27HbDKRY7fhcTr3u/9DjdDnPBO9uWid7VBdO4IWGRgcpkjyHj13s0qz16VguItcj4dZkyayrCHCG/URbpqfj6br3L6kjT+eXkJ7JM3j67q5dnYudlP23K205FNZmM/Ptj/BNcUn45StgzLn9oYnaUkGuGfUlZzim97b/vXiRdyy9SEuyD+yd+778Z7J6MCt9Q/z59FfZ0u8mYn2it5tHmldSlrPMC2nGouoUGUpoDnZxarwVu5ufJbz8+ZyVdFxB/WxRTMJNPRsFkRBxtTHe5jSMyS0FA7JwtLu9XwY2cbznSs5K3cOF+fP56/Ni+lIhTgzdxYe2YFXdrAqXAcIlJl95CpObJKZtJ7hX61LWNy1mnPyjuB030xWhbcyxV5piNLPHAKCzY5uiDuDzwH78tz5yYZRCj3/6fO6eq8b+f1hAFVVc8iKvO8Dv/T7/bv6CAEuv9+/UVXVRuDXPev8GPh/qqr+Hugi6+kbMNFH1A7sgip5s4khpNx8HBddvtf19FQRrm/dDIDt5DMxz11ANBBg/Yb1kEggZTKM8uVjB4QcJ4LJDLqOpuvsqK8nlUhgMynoqRTJeJyOYIS4r4DS155he0ZGKapCq53EeF3EE4uh6TrBcJj/vfsu82fOwGGzUeDLTo5XgJJdhp17EQClu5YvOJ9LolG2NDQSnjaLFZu3YLfZsJjNiNMXIKRTZN5cQv6k2fhKyyASR7/hVgAUWd5j2GPub/+GruvYL74CxyVXoge7iX+4EkteAVJpOTqQSCZpU0z4V6+hKRJlmtND/roPWdTUgHncJOSqUbAlD/HYRaRb2tC6OjCNm0Tor/cRe/0VtHAIubQcRAnQsR57EvZTzyH84H04r/kOmdZmgvf+Mutt1HUc6NTqUKvrKDf9hNTmDYSXvUad7XTSj/+NRFMDAIIO9Nzgo6PH03jsabhfeYYd1WOIIzCu3g82O6lkEmXlCuRkAkHXWTfvRHIzSYrfe4sVx5zO5HcX49m+u9bUrh9rpLSKbaecR+l//sm2qjEIpRVMevRP6CYzAJqm4RCyIjN97qXYC4owv/w0HSefTV79Zqq2b4FkAlwerGUVWIt8SB4P5skzSK5djZSXj+T69GWjtCZ2Xxokby6ZjvYRtMbA4PDErpuzg4Op1ID3TKKA2yKxuS2M0+EAYGKBFTXXzAubQpgkgUfPLyeR1qjymLhpfj7vNkZZ35bgS1N3e9lbk918c8uf8cgObi4/hzwlO5ik6RqiILI51syDzYsZbStmkr2CJd3reXzcjQPsmZVTy0uTftCv7by8uei6zlGucWxLtHHd5gcYby9DFiS+WXIyBSY3D7Ys5umO5RSbvNxdczm31j9CUk9za8V5THVU825wEw+2LGZWTi0X5s/DKppYGdrCWFtpr3iziCbeD9dhF834lBwKTG7uanyG5zvfR0fHIio8M/5mQOfWbY/wZmAtp/pm8MPKC3kvvIWEluKHFRciCiJmQUZHx6fkcO3mv/CLqktRBIl/tL6Jruv4Yzu5vPAYLs1fwOxVNzHDUcPXi0+i2lpANJPgR9sexSlZGWMr5czcWYy1lfLtLX+l1OyjzJzLEU6VUrNv6H4kBkOCmIG02UG6K4BjpI0xMBhmhMEmIFFV1Qt09RFp+1q3DPgPcJ/f739AVdVGv99f2vPeGcDxfr//mj7rX0w2uct44AlgIfCh3+9/5eN9/+znP9dvvummQdm8J3RdJ5FMEk8kSCSTff5SJFMpUuk06XSaVCZNU1sbZYVFFOXn0dHVxdrNW3DYbGiaRkbT0DSNZCpFcX4+tRUVfLTRjyIrjKnZs/aNxeOsWreedCaNIIgoksScqVOoKSs76ONJZzI0NjWT0fvoYB0am5sJRaO9/hdBEIjEYnQGAgiC0ONNyn4eXpeLrmAQTdPwuJyYTSZaOzoRhKz/ZtcIZa7Hw7hRNdSUlSHL2Yf/TGszqU3rSfrXoUcj5Fx1LZmGbSTXrcZ+xvkkPlqF5MtFys3PCuKDRNc0EIQDGi1NplI0t7WTyqSxms3k2Hcn49F1na5gEAFwOZ1072gkHdtDcVNZQXe5MckyJQUFCIKA1t66O4R1d4dIvjz0VJLUFj/myTOIL19KpnEbgtlKJtBJpnknaBnkskocF36JyH8fRxmlYho7EVVVf+j3+287uE9n6Lnxu9/Vf3n77QCk6jYh2OzIhcUjbJWBwSfj03aeXfG1r+n3X3dNT2TIqH7vXf/CTq6e6SOww48gCMyatHsO+ZbOBJIgsCOU4qn1QTZ1Jvjj6aUk0zoIUOpUetftSIVYFd4KwFynym92PMdYWykPtb3Fr2su5+y1d3CSdxoaGl2pCEe6xnBe3sHVjW1LBfkwXA/AjJyaPSZA+zjBdJTloc0817mSxYGPWDzph9y78wXWRLbTlgpyU9nZbI238FpgNeFMAtB5avxNmMXdxxjOxHFIFr65+S+UmX1cW3IyFtG0950OAl3XSegpzILS776T1NK8HfSzJd7MBFs5s521vNq1mk2xnexMdpGvuLi25GQebXubPMVJOBNnjK2EWmtRv/IUh3Opik/beQbw09tv188avYhIR4yZXzHqIhscHuztXBtMnbsFwH2ABDymquo2v9//l32sXwC8DFzj9/tf62leparqQr/f/zpwEvC/PutbgHOAc8l68TJkHSd7HFzZV6awvoSjUTq6AnR0B+gMdBOORghHY0SiUWRJwmw2YzaZ+v2ZFBlFlrHbrMiyTFlhEdVlpQiCQE1ZGdVlZWQyGpKUTQoi9SQHcdhsCIJAdVnWv7avC/aE2qENbZMlicrSkgHtNeV7FoypVCorksjaqQPNbW3ker2YFYXm9nZSqTQlBflIg5iHKOUXIuUXYjlyd/0YsXYMSu0YAMwTpx7UcX2cgyk8alIUyov3Pg9k12g4gEsdM+h+pbyCvb4nmM2YJ88AwDLrSJh15F7XtZ927qD3eajR+mTHlMur0ELdI2iNgcHhiabpKOo42EM22rtPyg6mvOIPD7iO1XizA2WlLoUjymxkNJDF7DU9ndG5Z1k7X53pxSKL+JQcjvNM6t12oXsCX9v0B24sPYNCk4dnJ3wPj2xHEATaUkF88sH7NfIUZ799DQanbOM4zySO80yiPRXEp+Tw/fJz+XfrEmY7a6m1FpPWM3y16AQ0siHustD/3uSQLADcXXM5kjA0CbkEQcAiDBSIJlFmoXs8Cxnf27bL/r7UxZp5petDnJKVzlSIMrOP89bdRa21EFmQyFOc/F/Zmdy+/UmqLPnMdaq94aAbojsIpCPkKU4C6QjTHNU817mSQDpCIB2h2lrAyd7pLO1ez9LgBqY6qpnmqMan5AzJsX+clJ5BEYY3L8Fwo+s6JUeMYuvyppE2xcBg2BlMQpWfAAvIetR+BiwF9irugJsBD3CLqqq39LRdB/ymZx7fevrPyfsW8Bu/36+rqvpX4A9AEDhzT51re0nJrus6rR0dbKzfRkNTE/FkglyPB5/LTUlBPk6HA7vVht1mRZEPLrNXrmffCSU+C6NwiqIMaCvtk9CjKC/vUJpj8Cml73mW3rqZ8CN/w/P9rCcvGouxvakJtarqM/GbNzD4tKLpGpH/PIwW6MR5xTd621MZnX+t7uLiCTl0h8P9BqL6IosCS7dHeL8pxrWzs1MQZEmgxmtib+Ogc5yjeXbC9yg2Ze9nXmV337tCNkeK3J79i4LIFwqO6m3fJeakfcy5B4ZM2A0FN5WfPaDtQfUa3g5uIJpJcpJ3GrquM9VRxdLgBv7c/CpTHVXcWX0Zz3WuZH20kR2JTlRrMdN7vKCrwltxy3beCW7iOPdkCkwechUnT7cv54fbHuFB9ZuICCwL+jk7bw7WfXgvw5k4D7e+xZrodvIVF0e7JzDNUc03Nv+JPMVJTEtSYc7j+tLTuGzDbwAYby/DJdm5puQklgX9NCQ68MoO8k0uJtkr6EiFhu3z/KTouk7rGyvpensLnDl6pM0xMBhWBqNyNL/f36mqqu73++Oqqu7z7O0pY7CnUgZH7aENv9//8z6vPwBm79OYPYi71o4Olry3klgiwZjqao4/ci65Ho/x4GlgcJBo2u4nQ6V2TK+wA3j17WXsbGvD7XRSmJs7EuYZGBwWiIKI6bRzMX1s0C2l6TS3d/LyW6vp7O7G53bvtQ8114ws9r/Xnao62dyRwG2RyLUPvM2XmL1DcwAGB4RPyeE038x+bYu8U1nkndqvRu8NpacP2HaeayzzXGP7tY2yFjLKWsgVhceS0TUEoDkZYEV4M7/e8Swu2cbvRn0Zt2zjyfZ3EYCknuYEzxQKFDc7kh2c6JlKZyqEW7ZjFhW+UnQ8rcluTKLMOFs2AuhvY77JytAWtsZbKDZlfzvb4m29HsaudJg/q99gaXADjYmOof3Qhghd17FPnsioceP3v7KBwWecwYi7zaqq3g74VFW9iWwJgxGjr7jTdZ33161jtX8jR0yZbHgSDAyGiI8PojQ/+AdWFVZw4gkn0NbZyejKCrY2NuJ1uQY8mBoYGAwOURRJNm5DD3Rhnpp96A8mMjhMIke4uli7uQmf273PcyzXJpNrG3grf3NbhIkFlj2KO4NPH5/02WWX17LY7OXXNVcQ05J0psI4ZSsdqRApPY0sSNhEMwkthVdxcGvFBQP6mZUzcOqIIkjMcY5mjnO3x+vC/HkD1ju9R7je84mOZHjQdSgoFvnX9cu5+P4T2Lmugw2LGzjhhun739jA4DPGYK76VwNXAW8BYeDLw2rRfujrUegKBvnIv5HzT1qE3Tq4NM8GBgb7p2+Cns5AN4E3XiU2fR6NkycjyTJqVRVPv7aYjkA3py7co1PewMBgP4iiQKpuE6nlS3vF3c2vNjOl0MqKzRLTRJGCg/SOXzHN8M59nrGKpl4PbY5k5bqSU0fYopFF13XWvVxPLSuIpReQV+3C6vxkSXcMDD6t7DNAXVXVyX6/Pw08QHauXJxs3boRo69HIRSJ4HW7DWFnYDDE9D3PVvv9KNW1lGeSrN+yhVy3m+L8fC44+SRa2tqIRPeQZdTAwGC/iKJIRlbQE4netkkFFhJpjTHiduZMmUxtRflB9//j11vY1JHY/4oGBoc5OjpVMwuxpjp5/OlXaN3RRTKaJpPKfKJ+tXCI2Osvo4WCQ2SpgcEnZ6/iTlXVbwN/VFVVBu4EjgMmAncfItv2iNbHoxCJxnDYDGFnYDDU9BV30XgcpboWb3cnWxt3kN9Tj9HndlNdXsbG+vqRM9TA4DOMKIpkJBk9EQcgltLwWGVOqJCxKAqTx4yhpGDv2Xn3x0UT3ZS5jLBpAwNd18FspiOVR6I7RVdzmBduX0Fnw8EVNdczGYIP3EvwT78h8ug/6PrpzYQf+TuBX9zKYEuMfV7RdR0tGgFAi4SJv/U/MoGuEbUp8eFK9FRyRG0YSvYVlnkSMJdsWYKLgdF+v79LVdW3D4lleyHT56EzEotit9pG0BoDg8OTvuHPsXgcU3UtpnfepGJhEVPH7Z7UP6aqmteXL2fK2LGfuvmu6Z0NZNrbME2c+qmzzcAAsglVMrKCFM+Ku2RGZ0tnglc2hikTP3nm4lE+M+/vjBGIZzim2ijdbPD5RdN16t4PYNXb0VzVeFQbV/37pEFtm/hoFakNa7Cf+4VsCalMGkGSkYtKsF58JUgigTtuy2aVvvmnaO2tRJ97kpwvfW2f/e7q53Ajta0O0ZGD5Nt9DUs31CMVl5F4/10ij/yd1JaNmKfOJLlhDUrNaHJKytATcWKvPk/OJVcSX76U1KYNaN0BREcO9jPOQzBb0bUMos1O4r1lmKbOIvrCU8TffBVECaWyBsFqxX7WRYhOFwB6KoWgKKQbtqEn4qR3NpDeupnUxvWIOS5MU2diPeFUos8+gWnMBBKr3yex4m1Em51MSxOC04Vos4Mg4LjgMhIfvIeUX4hgsxN64F4sRy6EVIr0jgZME6egjJ24z1qWyQ1rCT/0AKLThfuGH5DpbEd0ukEU0eMx0g31kE6jBbtRakYj2B2IdgeZ1ha0WASlopqkfy2CyYxcWbPX72BfvyrN7/dnVFWdBtT5/f5dsnpEn5L6ehQi0Ri53n2XJzAwMDhw+p5nsUQCy6ipJJoaWXTEHIQ+pUQK83LRdJ3mtnaK8oenjIaeTIBiIvnRKsyTpg1qm/iyN+n+3R2Yxk/BNH4ymWAAMcfVz3YDg5FGFAUyssyS9nkcuTlA/ig3F05w89CKLuZ5h6aumN0kkjE8CQafc3RdZ9Kp1bz3eApxkxl/tJFwWRJHrpWK6QO945qmsX5LHaMqypFLywncfguRJ/6N+8Zbib78LKbxk7CfcX7v+q4bbiG9rQ6TOh49kUCpHUt6ZyOpLX6s84/tXS/T2kJq6ybkimq6br0B1/XfQ5BktFh00Pe3kUZPpdDjMcQcZ++yoCjEl/6P5OpVxJYsRpBEHJdchZRXgGnaLLp/eweeH/wCPRTEftYFKGMmkFy3GufV1yPlZ8txZQJdmNRxAGjBbtB15PJK0vV1tH35AvRMBvtZF5HzhatIrFiGVFaJ7cTTEF3Z7Pip7VvRAp20XX0xrmu+gzJ6LIFf/gjv7b8l8d4yYq8+j1RQhFw1Cvs5l6AFA73ber73MyBbq1jyeLPiqnYsWjSCHg4ij1KzNra1IFgsyB4vStUoos8/hSDLSMVldN9zO4LFSu49DxC468dYjlyIlJtP53evRRk/GVJJUlu3kHPF1zFPmwVA929+Qc5lX0VPJui65XqkgmIEkwnBassK0niMvL8+QbpxG+ntdSgV1cRee5F03Ua8v7h3r9/RPp90VFUdDVwOPNOzPJ5skfERo+9DZzgWo8JaPILWGBgcnvQNf47G49jcbpgzn0xHO3Lh7nNOEAQmqaP5YMP6AxZ3qU0bkKtH7XPkUtd1Or51Fa7/u5Xg/b/CuuA49HiMTFsL1uNPxTxlBqF//hnbyWciur0gCCTeWULwvl/i/eFdKD0X5OBv78Bx8RVIpeWIlsGFcuuZDFpnO2Ju/l49f+ntW0k3bMt6CXc2oqdTWI44CvOsIw0habBfRFEkJcqMMb2PI+9Cnt8YpCOWIZNKkJCGpiC1mpsteP7wRwFOqs3BZflsF6M2MDgYdF1n28oWovZ89Lw0ObUWnG476USm9/3Vfj+VpaWYFYUXl7xFRyBAx85GJr7xAt4f3UVy3Ud0/fRmbItOx3psf6+faLFiUrNlFgSzGcuRC0nvbCT4+7tB14k++wSub92MnkigdXUiz55HzuVfJ/DT72U9eIpCzhXXYD3quF579FgM0bbn6LSMpoGuI0lDdz5nMhk0Xd9jLejUtjrib/2P2GsvQCaDHo1gP+dibKeeQ8d3vkbub/+GFgwilZThvf036PEYkUf/gWB3IJdX4rvjPgCsxyzq7bOv6AWQ3B6kGUcAYDvu5H7vOb9+AwhC773Y+bVv9+nnGAAsHA2A44LLQJKR3B7c3/4+giBgP+tC7GdduN/PwKSO7/0e94Tt+FN6X3+8T/3yr5FavwZd13FefT1CT2Rh3l+fIPHeMkRHDnJlDVJufu823tvu7H1d8OjLe92vedqsXkHo+voN+z2OfT19fB/4B1APfFdV1aOAfwLn7bfXYaRvuFgkGsW+lx++gYHBwaPr2ZuHpmnoPRd7940/QE8PzKc0prqaZas+IJVO7/GmANnRSsFmQ3RkH1jTDdvouPGrWI8/Bdc13wGyAzc7Xn2J6KvPklO/GX3aHPK+cSNd376Vzd0RZtz2Szq+finKmPGYJk8nsXwp5ikzME2cih6LEv/gPSJP/BstGMBz6529wg7A84NfABD4+Q/QQt0IFitadyB7oS0ownLkUcjF2ZpOuqYR+OnNJFe/DyYTot2B9diTsR5zIqLXh55IEl/6P2zHn0LsjVdJ1W9BLilHrB2LJEDkPw+T3laH/fwvgq4jyDKpLRtJN27HMncByMrexWLTDvR4DLm8CmEIb9oGn04kUSTl9tA640ryuxLMKbORzOic9o6NU8YO7XxykyQQT+u4+rSlMzqv10dYWGUfUCvPwOBwQtd1JEXEftH56Js/IGNKUzIxF7Hnd7+xvp6l768CBDKZDFaLhYtOOZmHn3+ecRddiTJqNMooFdsJpyJYLIPap1xciu3EU+m+84fYTj2bjm9/hbw/P4pSNQoAy5z5KLVjSDduR7TaiDz5EObps+m+68e4v/8z2r58Ppa5C3FcfAU740leX74cRZYxm0y0dXXhsFo5+4TjMZtMJFIpLKb+2T/T6TRbGhooLSzEZrEQikSIJ5M4HQ66gyG8bheZTIaMppHJZHj29TfQNI3506dTXlzUe59KfrSKwC9+gOXoRbi/cxuC2YJUUIQW6ESwO/D9/F4EScJ20hn99m+65ecMFYK4z/yP/egbDrrLK3goEEQR0/hJ2df23WHwgiMH68ITDpkdsA9x5/f7V9CnoLiqqu8A1X6/P3UoDNsb/TwKsRgOI1OmgcGQI4rZG1w8kcBqNmdDHrZupuObl2M97mRc1323d11FlvG4XLR3dVGU1997l3jvHaIvPo0ei+L5wR0ApLZuJvi7O7BfcBnW+ceQTCbZsHETTa8+j7r8DTj2FD6aPJucTet55ckn8ZWXE43FicRiCGd8Ae+48YyfPAWx58ZjnpytUxTOcRHK6BRPnYGSu2cvouuGW4i//QaCLCGYLaR37iDdUI/W0U5GNhG48zZ8d/4e59dvQLDasqERmzYQffU52q/9Es5v/B+WWUeSadxGMBxmcVE1rSZX9iE9mMBiNmM+6hTMQOVzT1P27ht4fvQrQg/cixYK0v2rHyPlFmCaPhvz9NmYZxxBZsd2UnWbsS48nuAffk2mqRFBMWE9ZhGJle+gZzK4vvF/6Ik4yfUfYT/tXIJ/vIdMazNaMICeSmGeNA1lzASU2jHo6TTpuk1Y5g4sUaF1BxBdey+IbXBoEUWRdDKF2LadV3/bScqucdJN8znDuYXq/COHdF9nj3PRFcvwTkOUyYUW7l7Wzgk1DprDKd7eHmVCgZlUBvLtkjFH1eCwQ9d1Sifl0fLcMpSlDrasaGd0XjfLHlzHrEtU1iyuJ6/GS2d3gHA0yvhRtdisViZZzby9vYGjiorJsdkQBynsdpFz2dVYTziNTrOV8JyjEYJB8uyO3nNM8uX1ihH3TT/KFls/9wsIkkze/f8m/Ojf8d98HctmHc3R48dhKq9E13XyfT5Wrl3Li0veorSwkA/Xr+fUo4+mINcHwIa6Ot5bsxar2cxb760EQJZlZFkmEovhdNiJRKIIooiWySCIItPHj8dht7Hsgw9YtX49ucEuytMJ4tPnkPr+ndhzcijMzcVizkYDtESi+JevwOt2sfPD1TgdDqpLyyjMO7jyLQZDx6Djhvx+/6cin/KusExd14knk70/MgMDg6FDFEVS6TSxeAJrz81MrqzB85O7CT34hwHrF/h8tHZ0DBB3Sf9a5NIK7OdegtYdIHDnbWSad2SF3aLT+cC/ifZ//4LCeJTJehrnl67GdsKpqGQ9h9PSaSwmEx2BAB/5N1J61NGs37yFdx5/IiukFIV8n4/Wzg7iiQRel5vX31zClDFjsJgt+Dxu2jo6sdusVBQXs3pLHR92Rcj1eKgsyEfJLUKaOpumtnZal6/AvPAUkq++Riwex2G3YbdaEQWRstMuoHHGfFo6unAtX0FnwTkZRAAAIABJREFUfgWh519g5oTxnDhvHjo6ZkUhEosRSyTIZDKs2biJlROPQH/iSfSJcynKzaWssICidILYR6tIPPUIOZU16OkMgiQSjcXw3JoVwIm3Xyex8l1sp56DYDZnPYbxWK930TR5OqRSoCiIDifxpf8j8p+H8HzvZ2ihIFo0mwEucOdtpLfXk2lrAUlCj0ZQ1PE4r7oWqbScxPKlWBccR/SFp9BCQazHnoweCSF6c9EUE9FolEgqTaKpkZyKKnI629Bamsi0taLUjCa5/iNMYyegjBpDMJlEj0VJSDKxeJxcjwfHxyIrdiUQ2DXRXM9kSKx8JxuaK4CgmBBsdvRYFDE3H7mwmFQ6TXtXV3aAIZ1mW+MOMju2Y+5sR4vHyOQXEPDmY1IUFEnCa7Vgr9uI2WpFkCQi8QShbfVYZg8sujzSiIJIWstQ69pK7pmn8rfX6gi+t50JeTIux9AnQGmNpNnYkWB2qZXpxVZmlNiYUWLjL+93Uu0xccfSNq6d7WOU12QIPIPDCl2HFQ/7ad2Zwn6KgtdnI6/axdHXTiaT0ohb4owtrmDzju10h0IcP3cu4Sf+RdlrL1J36sX865n/cvTsWYyt2XsSi48Tiyf40L+B7Tt3EkskEYDMljp0HSxmE/OmTSeWiBONxXE7c/C6XAiCgLPH+4PdQdeis3nXVcRRbjuO++7A96s/Inmz4mli4xZWWJx8sH4908eP44UlS3Dn5FBeXMQH6zcwr2Ej+SLIxyxCM1vJPPZ3RLcX+wVfRLTaCXd3Ed24Ae0PdxPy+CgKt6K1t7GwcRsdF3+FBknkrWAI784mJFEk0thIdzhCRXER3aEQgWCIUeXlNDa3MKqinEAwxMtLl1JeXMRkdQxNba0U5eXjcTn7fA/Z6Dvj+jK8fOYmhewSd4lkEpMsIx6Aq9bAwGBwSKJIOp0mlohj7RlAEQQBuaKGTMvOAevn+3xsbWxk8pgx/dpT/rXYTjkb0ZGDbrVhO+UszDPnItod7GhpYUNdHSd+9Vo8FjPJVSswTZzazwapJ8zE53azcHY23rymrIxkKkUoEiGZStHa0cGE2lG9JRo6AgHeX7eOdDrDyrVrKczNpau7m9eWvYPNYuHkoxbQ2tFBY0szyVQadJ3CvDxmzZxJPJFAkRXMJoVoPE4oEkHXddZs3Ijb6WT+9OkEI2GmucbhdbkGzHdwOhw4ex7Ki/Pz0XWdZCob7LCztZXtTU1sbGtH9xWTmJ1H/PUlvRmqhP8+i9vp7P2sxTHTcQsmily5FGY03N5cTD03dcvsef1ukrtCQQD4/+ydd2AUZd7HPzPb+242yaYnpC0BQgu9F+mIKHgc2A49Tz376dnL6b32dop6ljv72QsqKhZAkd47BAgBQnrZTbK9zLx/bAhEqoWTu9vPP4HdncmzM5nneX7t+zNZkNKziEajhIeMpsLdTMBgwq7TEdbpEXdsptbVjNKzHfX6dUidOuOTRKQN67G/8xpRpQoxFECUJHaVDKFhwAh6vfU8X0/6LcrqCrpsW0dQrUX/0Tu0JiRh+uR9/BYbG0aeSa9Fn1LXrYRIagb2hZ8TSnJgzneiEEX0Ozah2bYJ960P4Pvua8IeD7uLelL83ecYImE0SgViNIoQDBAQFXhGTaTObCPlg9cpnXYR2Uu+wrFzK7nhEBiMRDOyENRapOZGsi4YivjCE3i79sLtSMew+Bv80ShIUZQKBY4kB2bboQ3G6YIoioQlidXuvkjzq8hWuNDsb6b3qAGn5Pc5EzXtNXjj8g/V9F3S1vD8qYlpeIJRLvzwAM9OTsOk+XmpwZIsI/DTNnJH2wRGJDmePhrnJyHLMsUTO7FsgwupIUTtRx4YAlVbGsnolYi32U/l3GZcUT+JvWxEPRG8775O4pxXmWg0UVpezt7Kyh9l3C1eswZBgJKuXWnxeOjVpQuSJOFqaaHF4+H7tWuxmkyoVSr2VVVS1+RCIYp0ycsjEo2ya99ezAYjIwcOICc9HW/Ai9TSHBMBUSgQwyG6vTOH/udfir6oiO6dO1N+4AAHamoZN2QwjlAJgRXf43/6EWSvB/2U6QgqFYKoILhqKWJtFY7fXAhDR5JcW03ry88iBwPYZv8RR5diuqqObPJe29BIbWMjndIzSEtOQqXq2GqlR2cnyzdsYO6Cb0hJTGLlxk0U5eXRKSOdPRUH2LVvH5Isk2Axx/YUCmV7kEatUmLQ6SjMyUGlUtHodqPTaLCYfpn64/8ljmncOZ3Ol0tLS2c7nc7LSktLj3TV/0ocrLkLBENo4lG7OHFOCaIgEmlLyzw8Oi5arBAOI/m8MXngNjplpLNy40aq6+vbo3e+Lz8hUrEPdZe2HHSFoj3v3NXSwq59+8nPzsZujaUJagcOO6mxCYKARq1G02b4pSUnd3jfbrUyZtCgDq/Jskyr14vJYEAQBBJtNrrk55/09TjcaE3l5IVjDo4VoFNGBp0yMo4YlyzLRKIxI2R/dTU6jYZoW62jq7mFAzW1rN68BX8wiFqlIj05OeY1bW1Fp9GQmJCAVq3G4/fT3NpKq9eLKAhIsoxRrycvKxOdWs1+txuV7EfVyUk4GiHk9SP0GUKorAxTchrpV/wJfzSKQa/HoBAxmC2kHVy4x44hFwiHwzR7PO3fCUCtUhEMheiu1yOec3ash1FTI36VTOuuUgIrv0OWZFo7FVJ5yXWEfD5Sx07GKkXokplNePgw6hqbqGxqIhqN4PMHyE5PIxgMkaHVknXD7RQ7uyCfMQrJ1YSgNyLodEcYDPId9x2qZxx+ZErq6YgoCkQiUQb63mBhcCourQFNUCAj5ZetE5G8HgS94aSMLKNGwd8mpGLSKNjdFMSoElm018uMbhYkCZSKo5/DG5LwRySW7vfhDUn8ttjCzPcqeH5KOuuq/FS1hslLUOMPyydsyyDLMpd+UsktQ5MQgPe3tnDTkESu+LSSR8am8vomF067hjPyjMwrbWWy09Sepv2/ij8soVIISDKsqPAxLMfAd3s9JOiUFDu0RKLyMe/dhmo/NZ4I4wtMhKIy6mN87li0BKOYNQpag1GC0dNTmVWWZYLeMK27dqJXJBDxigiCgKcxQF21C0O+hokT+vHJM0tIDSZT/f12rEkOFMkOjEDX/HzWbd2Gx+cjHI4cEY2qa2oi2ZZAyBdBY1QRjkSoqK7mvDPPRKc9bA0VRexWK3ar9ajrQTAc5r0v5mM2Gjh3/HhMhkPrrGHyNACabrsG8+XXYzhnFqqiYlr+/jiRPbsw/uYC8tLS6GQy0vrikyhvugdjZjbGc88/4nroRoyJzQuCAIKAMjUd2233HVW+/3Acifb21M+joVGrGdGvHyP6xZyxPr+f5Rs28M2y5eRnZzNuyBB0Wg1Nzc3Iskw0KuFv6/MZjkRodLt5/eNPYq0H9HpC4TB6rRZfIIBRpyM3KxO1UkVORnqHa3PU+x0O0+hy0eh2E2q7ZxkOR4f168fS4HKxc+9eDDodjW43ZoORZHsCSqWSlMTEYwacQuEwSoXiqO9Ho1FEUfxFo5nHi9z1dDqdjwDnOp3O7MPfKC0tve0XG8GP5GDk7oebzjhx4vxyiG2Ru2AojEZ9yDMnCAKK5BSitdWInQ4ZRxq1mp5FRWwv20NqUhKyLON9/1/YbruvXS75IDvL97J4zRpC4TDTxo75t3wfQRDaI2qnE0Kb+pe6bcLPSU/v8H66w0G3wgIgtji4W1txNTdjNZmwmM243M14fD6CoRDZ6WlYTCbMRiMKUUSSZRS/cGaDSqUi0XZk+5nDF0tBEFDYEzFOmIrx5NpIxYxtju8RFxTKDipjR7x/FC/z6Y4oikSiEcqqrWSdr6DMo8aZnfKLZ6S0PP0IkqcV65/vRjCZCZduQ5VXiByNEFq/+gjHil2vxBeWeGBxPX8/M51AWEIUBG5bWM20LhbyEzQ0B6OoFQJ3L6zlxbMyeGhJPdO6mBndyYAnJCHL8PyZ6Vi1CoodWgoTNSgEKGsKIckyuxpD7VHEw9nrCpGgV/D4+FRMapEaT4TzulsRBIHbhydj1SmY2c2KUS3iC8vsagwiCmbe2uym0K6hJE2HJMv/NcaeLyyhVggoBNhUG6BHio6ypiDhqEy+XcPaKj/90nW8v7WZQERmSmczqypjxp1SFFCJ4AlJTH1rL99clMuSfV5kYGi2gRu/rOb6gYkkGZR4w7G91e/nHuCvox3YdApaAhIZFhVlTUESdEpMGpGdjUG6JGmZu70ZlUJgUqGZ339cybOT09jZEGRb/WlRwXMEsiyzZ3k1QbeSrGwPB8bF5os+vylk1759JNosKFQKzr4u5hjyfvgW0eKe7cdrNRqSEmx8umgRHq+PSSOGU7a/Al/AjyiI7F5/gO5JXQg3RskdnEpZoByH3d7BsKvZ0YQgCjgKbTRXexFEMDsOGSiCIKBRqThnzBmxNPMfRMUOYrnpnvbaaXVRMQl/fZzWN/5Jw1UXoR0yEuP5l2KYemJlSNFw5Jr4S6dL6nU6Rg8ceMTrx1uPRw0Y0G5kRqJRGlwuTAYDruZmyg9U0iJ5WLV5M2ajEZ1Gg0KhwGIyIkkyLR4PjW43GrUKV3MLFrOJtKRkVEol23eX8e2KlSRYrahVKlp9XvyBIGaDgXSHg4wUB6Io0tzqwdXSjEatJiUxkW27ywiFw0iyRH2Ti6K8XBpcblISE2lqdrNhxw4CwSCBYIiCnGxCoTDNnlZAAFmmtrEx5vSUZawmE3abFY/XR6PbjVajodXrxWIyYbdaSLIlkJOeji/gRxDE9uyppIQEGt1u9FotGo2G0j17SHcc2cLjIMcz7s4GBgOTgdKTvZGnmmiboEogFDfu4sQ5VYhirL4pFA6j/sGmWZndifC2ze2KXwfJTElhc2lsqojWVse8gYcpVkqShCAIrNmyhYnDh6FSKo9qKMQ5OmqViuSEBJITEtpfO177CcV/yeb2v5lYbWsUUaEl6vUyvlMeW5+vYGernvwhaYjKn2/kyZEIlj/dQetrL9Bw3SUoEpOIVlehzMrBfOWfCZftjDlrTOYO8u56lcgLU9JRiAKz29I27x7hQCUKLN7npdwd4oIeNh4cE4sy3jvq0EbD2JbOadXFfiYZDm01Uk0qalrDvLCmicfGp/K35Q0U2jX0y4iJoy0q95CboGF4jqH98wfJscbmosTDznfD4Ngz0N2hxa5TsKLCx4I9Hm4fnswtX1dzfncbnRM1CAIoTkFKZ1SST/q8kahMgz9CilHFonIPFo2C3mk6NlT76Zmq4/t9XvxhiVGdjFw+r5IXpqTz6gYXVq2CiQUmXlnv4okJOqpaI3hDEs5EDZ/tbMVp13BBT1v7hvjGtmsyOCt2DWVZ5qsLOwGxexGIxPZRV/azk6hXoFGKpJtj1/mFKelolALrqv0s3ufj+oGJzN/lYWCmnrwENc+tbuLJCan0SdejbPvab03PRCEKJGYpGZRl4BFOP2Rkep2TT/mbK9AHggSVAlFJQiGKuFta2lPiD7J7aRW+1O4cbpbkpKezbP0GBvfuzScLF5GVmkpNQz2CIJKbkE1dpJ5hk0tYOW8brmAzo87sgyTJRIJRVry2jd7TCyhddIDkAiuyJLNh7h6GX3EopV6KSMy9YxmT7+pPwBVGtIntc8DhRpfC2nHdFC02LFfeiPny60CMCSL98DP/SRx0ekJMsO1gNpBRryczNRWAISUlNLrcBIJBIlKU5tZWREHEbrUwoEcPmpqbyc3MOMJR5g8EaWp2EwyFMRsNaDUaWjweyg9UsnrzFsKRMDaLBYvRiNfnZ/mGjeSkpWG3xRxMFqOpQ9T2cOqbmijbX4HZZCQnIx2BWLZhSlIiGrWaqCRR39REc2srBp0Oq9lMMBQiwWKhwe2muaWFqrp65n37LUZDrF5dr9Xi9flpcLuxmkz4AwGC4TCdMtIpyM4+6jjg+GqZe4G9TqdzMWAGugC7SktLN5zU3TlFdIjc/YzQapw4cY5NLKIQbUuJ6OhEMZx7AXLwSO+szWImHI3Q4vFgTkkj8ZnX2idoWZZ5/8svUSiUqNVqUpOS4gXVcf7nUQixCHmxeQ9bowW8U6Vm5DmFLH5hMwa7lvTin68613THdZivuAHzJVei6dkHqdmFdvgYpMZ6FMkpmM7/PeHdpbju+TPBTeswX3pNe8uSHxotOlVsozSyk4FRQszzbtf/+NL9FJOKx8bHNmm/62VDrxLZ5w7x2gYXfx3901JSuybHhJ+SjUqyrTFD5cbBSZjUIqsqfXxV5uH2Ycm8tdnNrO5WNtUEMGtE8u0afGEJnTK2oTxRWpoky6yvDlCSpuOznS00+qKcU2Tmz1/V8Pcz01m4x4NWKTAo61BEpt4bwReWEAR4ZEk9T01MQ6MQsGhFPCGJVze46JqsxawRybGqEAS4YVAioiBwRV97+5iemBDrMTo0+9C5DzeqjzVuQRDaa3sPj5Z2sh25h9K23eOSND0labEN5pX9D6XhPTUxNoYM8yGj+1QYzb80sixT+m0FoVY1Ii50ybaYcJZej6ulhZy0jlkTmT0TMYzt1eG1vKwsWjxeenR2kpeViUGnY82WLSgVCroWFDD/++/5dvsKWlI89Enugdmm5+tH19LrnHxy+qZgtOsomV7A/nV16G0ahl/RHVelB2+jH0ehDaVGwdgbS1DrVWz4uAxHgY09K6pxjsokrUvsHkhRCQShvYXD4RyvZ+x/Gyql8riqnAlWy1Ff12k1pGs7RryMev0R5R0/haSEBJIOc77+EKVCQWpSUgfhuYOppQcdtwU5OSf8PSeaowBOxi04FfgnsSjeC06n88aTOOaUcbA+JRCMK2XGiXOqUCoUBILBo0buVJ3yUaamIwX8HV4XBIGivDy+Xb4Cz3uvdzAA6xobCYUjZKWmMnnE8LhhFycObTV30ShLPWcQqW7igm56ho5M55J/jW837NyVHlrrfdSXuVn9dml70+WTxXbHAyizcgDQlPRHN2o8gkLRof+TKt9J4vNvAdDy7KPIknS0U7XzSz6/Vq0CtUKgwK75yYbd4YiC0B7tS9Qr0ShFBmbquX5gIsFIzMBSCNDgi+AORHH5o1zwQQURCZ5a0cBH21uISDIrD/gAcPmjhNrqyKKSjDsQ5b2tbmRZpiRVx7QuFowaBVe1GUDJBiV2vZKIJDPm1T1EojJrq/ysqfKTZVEzZ1I6ghAz/vISNBjVIk9MSEOlEOiRoiPTokYhChQlHZLcj8+XPx9ZltFbtUhqEYW3Fb1Oh9cXu8fulhasbdEYORxClmUSLriIpiaRoOdQ9y+TwcCwvn2AmEEgCAJ9i4tRVxjY9tl+zhw5kqK8XMYMGkTxkDwMdh1DLu1GUp6FOoeR7fUBPtrezLIqP56GWJ1ZJBjF5w6yaV45OxZUYEyMRbD7zHCS3cdBr7PzcRTaWPv+Lmp3utgwt4xNn+z5d166OKcZJzMfnIyZPwsYUlpaGnE6nSpgGfDozxzbT0atUuHxeuM1d3HinELUKnW7GqX6KHn/rS8/i3bkuPYecwcpSUmm4uE78U6YgkGnQ5IkopLE8g0b6ZqfT68uRf+urxAnzmnPwdrWPn1D7K/eT0BWIsnQXOXF5w6S1tXO/vV1tNT6QIbULglEglGUJ6liGViyCEVmNirjidXmRL0ByxU30HTX9TTd9EcS7n8SQf3fscYKgoBFG7tmF/SIpauNPUwt9L0ZWYiCwMW9E9ApBapbIywq99A/Q8/zaxoZmKmna7KWW7+u4cWzMnhwTCzqmHJYyujByGE3R+ynLMt8NDMHpUJgfEFc7e/XRpZlMnsmIe8GoaIFg06H1+9HlmXcLa2oly2ieX85/i8+Rj95Gor0TCpau6ExqtAYD93naERC8YN06c6jMgj5IgiCcIRQl8GmjdWgh2NlCQMy9GhzjJg1sXMk5VpIyrXQXONtN+yA9sicNT0WIc/qnYwxUYujsABJkqnb7UahFrFnmQn5wqj1KpprvFhSji0yEud/h5Mx7oTS0tIIQGlpadjpdP6qTcxjRZA+AqEgiQb9iQ+IEyfOj0ajVtHq9REKh1CrjzTuLNcdXVMpvHkd0axO7OvenyRB5PVPPkWpUJBsT6BHZ+dRj4kT538VURQJRyOouvXE5allbXkYgyGEzRvG2xhAlmW6T87tcEzId/JLsOe91zFf8aeT/ryg1ZLw0LO0PPUg4fLdqLLzQKPp4Cn2f78Q/4IvMEyehixLBJd+i/naW3Hdfi3KvEI0/Qaj7lL8H5UidlB8xaiObbgzLCpuGRpL0zr40xOM8vDY1JM+p+z1oKmrQXKkIhqMyJJEtL4WhS3hCKNZ8nmJ1tcSra5EkZqOIjkFOeBHYTu2KuG/AzkSQVDG7qMcDhHeuQNBq0WZlYPkdiGHgijTs5DDYTj4Ob/v1xzyMZFlmUVPbyAsqtD17INBHzPuWl1NKCNhAs/PQdAb0E85F9PvLoeoRL8fNCz3NQd5/4bFXPDCGQhtxld9mRspKuMoPFTjFo7KhKIy2+sDzN/tYWdjkFfPzujwHN34ZTVX9bcTlWRSjKoTGmVJuYfSDEVRwNvoR6lWoLdomHfvSqbeN4hFT2/krHsHto/taAS9YTSGowu1xDk+kiQjigJSVEJUnN5t2E5m9l3idDrfB74HhgBLT+2Qjo9GHYsoxNMy48Q5dRyMkIcjkaNG7iI1VQRXLD5CkSu0fjXG3v1ZvmcPFpMRlVJB1/x8igsL4z0p48T5AbHIXZQVX/upys/h1mEOlAoFJGlJLrDy7TMb6TI2u33j2Frn4+sn1nHOAyduyC55WolWV6LMc/6oaJ8gCFiuvRWAvc+9SVKGBm2/IUgtbiLVB/C88Q90I8cBoOkzEFVBLBpvuuQqgquW0vrSM0Rrq9H07o9uzCQ0PUoIrFyCoFCi6RNTwSMYRPJ7OxgvsiwTXL0MTZ+BCKfhXCF+9wWmLt2JqjX45s9F0OpAEFCkpKFwpKJISkFhSyC4cQ1EowgGIy1PP0y0phpUahSOFKK1NSjTMrA/+hzB9atRde5KeNcOXPf8GdFoQpVbQKRiH9GmRlSFRdgffJrg2pUoM7MRDMb2XqCixYoU8CM11IMsoXCkIrW2EK2vRd25G+E9uwhtXg/RKGKCHSSJSGUFmt79UBUV47r3Zmx3PYQcDCBotAiiSKTqAJE9uwiVboVQCPMVf8L115sxnD0TQavD/X+3oEhORfJ5iFZXIVqtiBYb9idfwvfpe8jBIIZps/B+9PavfauOiiTLDL64K+WflWMaMwlDWRlev5+W6moMnhZ0YyZhOHsmYlJyTHlXBf6WIF8/vo7EbDMDLihCb9Ew6a7+cJjtFA5E2x0uG6r9pJpUrDzgwx2IMiTbwPndrfgj0hGpdFf3t5NpUfHUikaGZRvonabjx9Cp/yFHw7RHhiKKAmf+JdYfc/6ja8kZm03n7rHU7m/LPTQ2BZjstPDR7UuZ8bfh1Jc1k5BlQqmOzQtBb4h3N7vpnWsmW5Qx2LVs/2Y/Kc4ErOkGGve1kpRrYc+KauzZZkwOPe4DHhKyjh2V3usK4QlJdHNo2esOkWNVs67KT5JBgU2nZG2Vn2FZevY1h2ne7aKgm52ZHx3gg99ms6LChyTDsBwDO+oD5CdoUCoE/C1BRIVIUBRQRiQ0BhWeRj/mRB2eBj/N1V4yuiexd3UtyQVWwoEI37+4hUl39MPT4Edn0bR/5x/iaw6it2io2dGE1qxGpVPyxf2rmP7IMJa/tg1LioEuY7P51+ULmfHUCKq3NbJ3dS3DL+/Opnl7SO+eiDXVgLvKiz37kOiKFJHYt7aWTv1TcVd58LmCaHMtbN/rYWChmXW7WmB/Mz1GZ/HCe2WcPzKV/VGBA9VeJpQk8fL6JiYVmolKMk+taOSBMSl8WtrCmc5j9249oXFXWlp6o9PpnAQUAa+UlpZ+dqJjTiUHN51enw+D7sc9DHHixDk51G1OFFEholapkGSZtza7mVlsRRQEBFHE+9HbGKb+Ft+Xn6IbOZZI1QFCG9fi+MN19KqpY+GKlZR07XJEY/N/N1W19ei1Wmobmnhv3gJuvOw8tNq4YyjOr0+sn2SEM27tRfXs+3izcCgTuiWTZFDyzRPrcY7I6LBJMCbpOPu+wSc8b1NFK6ranShz8ihf00DtjiZ6TMll8+d7GXDB8VOjW+t9rH1vFwMuLGJbUz7jZjqp/GwZ2i+fZ4f+DAqyizHMuKh9s3pQlU+VV4gqrxDjzNlEG+oIrl5Gy5yHSHzmdQS1BkWyg8j+ctyP3ku0vhZkGdFswTB1BoLFhqZXPwKLF6DpO4jghjWgUKDu1vOk6kukZhfB9WtQpmWgyMxG1P34rB5ZliEcQvL5CK5ainduzEhRpmZgvvpmFPYk5HCoPeomeVoRRJHA0m+J1lQhNTZgf+rl2PvRKGpnVxLnvBqTQG+sJ7R1E9pBw9sjXOGynQgaDequ3XG8+TlCm7NalmVkrwehTaY+Wl+LaDIjqtT4F3xO89MPIxiMSG4XCnsiCAL2x14gUllBZH856s7d8Lz5EqI1AUGjIbRtE4JWhyItA0FvAFmOXXNRxDvvA7zvvo4ciSCaTKg6d0OZnYtu7JkA2P4Sq8CRgwFs9z2JKju3fYyH3xf92TMhFLs2pvMugXsf/tHX/1QjyzLNNT6kQAT3jZdh+NPdVNXV43E4SOjWA8vgQUccozWpMffRgCwjRWU2fLILe5aZivX15A9NIxqFl6qCXDkkJtCxoyGIDAzO0mPTKY7bjiO7Tfn1uoExA+zNTW7G5Rt/kkDRwRROhVIkKslkTi9gzuYWZvpqaUCgR5KG2k92o7i7PyvHFTBdgm8/24s3P4EuHj+fhhT8LlePfksTiek65r64BePMLqRolHyzrYUz/RHWzN/HtGt6sLXGT7cELWF/hK1f7UMzKZ/58/dx5+8KefzVnUxwmvHmWFFvracxy4qgEMg1KLjnyypuSlWxL8lI8+aSbfAVAAAgAElEQVRmuvd3sHZxJVKrn/2Ds7CsqCE7zcBjPU1s+mg3tqGZMYdPRGLOsnru6WtjvV+m/KUt/Paa7tz9eSVDq9wUX9adeXev5I8PD+HLzW5UtR4yuifx8bpGzjarcXQyM+LK7giCwPJPyykottOUoGf9k+v4/VMjWLWkCotCICnXzNd/28Bv7h9ES40PWZaxphk58+6YXmr/WZ0R2vo/nvfcKESFSGbPJFI6xwRUEjJN6Mwavtzkwv35Hmbd2Y+3n91MXqaBpMHprPquiuw+Dp75rpYzbEocaSa2vb6Nzpd1Y2etn0RfFKUIRn8EKSyhlAQa3ylF7p2IcX0N+6qaKRidRd9le4mOTKZAPn5d9En9FbUZdL+qUXeQg7VAzR4PltOwb1WcOP8NaFQqPD4fWo0GtUpFKCqzdL+PWcVW3t3iZkJBEnIwSLSxntCW9ehGT6Dl6UcwzroY0WKl2GIl2Z6A+SRqfX4sja5mqmrr6VqYe8JooCzL3HL/M8yeMZmBJd1pbmll4oXXccWF0zl38uhffGxx4vwYYi1Hoqx+txTXObNJshpQtW0gBl1UhD5B22ETLQgCNTtdSBGJtK7HTtnbv7aOyM4DhIX+DOifQlavJGQZ0rraj9iYB1pC7FhUQc+z8gh6wxjtOvIHp6E1qhl/cx88DX521joY/cpHtCyrJqHHiZVuFYnJ6CdMRdlnMCF/BE2vvgDIkoTxvEtQ5TsRbXZCWzaw5oVl6PLV9B6sZ2f6DHqHJCIVe/F/8TFSYjq70s+l7zgbu1Y0EtGa6THViafBj14dovW155H9PkJbNqDKLSDa1Ei0tprE595ENBjaex8G169GjoTR9O7Xni568Dp4576DaLOj6T+Y+vOngFKJuqgYyxU3ACAF/IgmM5qS/u3fz3ThZcf+7raOanmCIKBITEY3/IwOrxunn3fYQcoOnxcOmzf146e0/9t29yNIXg9SswuFI7VD6qume2803XvHPnfHA8e5O6Dp2SYKcu4F6MefFTNIlUoERceIxsH7LGh1iNm5R7ze4f+neSaVLEP5yhoiXrDfdj+SqMDj89K0ZSP6Y6QQRyWJJz96iz/94Tw+eG8RaQlJdMpO5ZM1O3jgY5nuezdT0qcnO9rEUX5bbP3J41OKoFacWK31eMzd3kxZU4gbBifxdLaJxR+WkZBmJCfXTPotfUCGWT1sKBUCA37XBYtGQXONl2n+KPlFNvIHx5RQnb8vpsihQ8gzoWsKkpaoYVev2Hv1WVY0qUYagxIbe6ZxdYaOoBa8TUFK+iRjNyhY3xTCfMDLpFGZ7FlezeY11Tx5RiblK2s4e1g6azbXoolIXPPbPERRiLV76BlzEqmNKiwaBUnJWubdu4LMS4u5p7uZLXPL6Du7G71uKcFsUvPE7ML2NMmr/jYMlVpBr5521IqYsVwwMpO0fCNfl3nYVhfgT4O0zEuxcbPTTpYI8lWxHobfVPiZ1M2KW61i+xl5CILA6gQDnS0aLGGJhZV+JhYqO7SlOZiSua8lQoMvQl+DiupEA8kmNZ1zzYSv6AFAU+9UJhRbaQhG0U0pQFSIXDgxE41SJNmg5NL7Yw6FWYc58C6aHXOG24Cih2MZGlNnFRINSWi1CgbPdCIA6fbjP2//OUnxbWjUKuqbXEQiEXQ/yIeOEyfOL4MoimjUarx+P2qVCo1S5OlJaXxd5mF3U4hab4SE3ALCe3ZhveEuQju2IrW40Y07tBFxJJ68jHt9o4vLb3mQ9194kP2Vtbz76dc487IZOagEkzFWi7Bo2RrWbyll9OC+3DfnZVo9PoYP6MUF0yeRnnJIWriiqpaUJDtLVm/g//72Eo6kBIYP6I0oijxy57XsrajiH29+zPRJo7j3iX9gMRspys+hS2EumWkO6htdJNkP1U/s2L2Xtz/+iqraBnRaDRfPOJPc7HSeeeW9I3730fB4fYiiiF534vkqGpUIhkIolTGvb1VtA/5ArDlqXnY6SoWC6rpGsjN+WqNrr8/PwqVraGn1kpGazPCBvWlu9XCgqpZOWeknNcY4vxyKNkGViBzBHg4wOEVCbBP+MNiPnpkiS1JMDv0HeF0BDLbY/es5NY/GJ+dyIDUPQRRQaWNLfVbvZHYsrMDT4KdkegEHNtaTkGVCZ1YjyzKf/mU5E27pS0aP2N+0qBAxOwyccX3MaDi4+Zv315UMvbTbEXVCa9/bSVKeFZVOgac+gCXNwLr3tzH2pj7sWnyAgqHpNGidpNsSWfj0BvrNLKLPQ8WodEokSUZv06BQi1SaBiDM6I9j33w0n71B08pKLCoT5suvo2npWha/vpez/n4OVdruaC0yKVNmETKnY0s1IAcCCFotLf98BmVaOtqR42h57vFYauH9dyAlOtjsCROQZOTzLmN0r/60SjKbVm7EePOjrN28gysunMaKdVvoXlSA2XR6CVSIBuNRm0//5POZjp3aBRCJRlm/uRSPz0erx8e4EQOoqKzlmyWrMei15GVn0L0oH5VKydpNO7CcZtfrILIs02dGAWveXofgbsSUnMaCGh1ZvhbGOI5ex1pd24AkSXw0fxHfr9yA3WLhBtvveWv+G/zhgt/x2vZvmHlJP7b5onRO+nnG7W+6WZFlmSs/q+L2YcntfQdlWWZddYCuyRq0PxByiUoyMrEs0V2NQaZ0NhNuU3YVBIHh0w6Ju6h1sTmgb3osqp1mEBFFAb8hzJpl3+NqTEelUpGZlkyfzFRq6hpJdSTSIyU2D902LFZ7enFbz8taIcKkQlPs97RlA4xyxM59AUCbcFHh8Iz2MfSYkgfEGscfC71Fg94Su5ZnXN8btS5mWI34Y4+2TxxyQByMWKra0izzEg7dg4mFMQfJ+HwjQ7NiyqYH23gEAkHEYA1bSxu59be5fLt8LeoGO9f1SSQQCNIlQcEN191IYW4W1T6oGVCEoXg4qxZ/S7Fd5JPyKNePzicxp4CXX32bxyrK8CoMDO/dmRsvPofFK9cz/+NSDlTV8s7uLC6cPpG8zrHnLMOs6mC8u5tbaXA1k5ORglKpJBAIsmT1RtIcSSiVCjRqNVnpDp5760MqKmvx+Hz06ubk0llTj3kN4SSMO6fT6SgtLa090ef+XRh0OlwtLdjM5rg8cJw4p5Bku529lZWoVSo21fiZv9uDQoBuyVoW7vGwOf0cLn/oAbq89QHqzl1JfP6tn/xMvv3J15R074wgCLz8zifodVqWrNrIui2lnD1uOM+88h4V1XU8eOuV9OhayLvPPcC+AzUsXrEOURDYvqucB595lfSUZFZt2Mqcv95In+5FvPK3uzEadB0MoZzMNP7v5isAGD9iINt2lbNgyRqWrN7IX/98OQ88/QoalYqigk5otWqmjB1OUUEnRg/phyRLJCXaEESRBKuFWVfegdGg54bLzqOrM5dvl60lJdlORmoy6SnJBAJBZlxxG15/AIvRwKevPs5bc7/iwy8WkZ6SxMjBfZg4ahCu5lbue+plVm/YhlKp4KN/PMzOPft56JnX0GhUGHQ6Lj1vKj26FnLdXx5Ho1IxffJohvbriSMpgfufehl7ghVHYgKD+nbHajLy6HNvoNdrKdtbSWqynTuuu4Q7Hn4OhSiS6kikweVm+MDevP3xVyxaupb6RhcDS4q558bLWL1xKwuXrsFmMZNgNTNiUAk2i4mKqlqyM1KJRGLtaKxmI4FAkHAk0m6EQ2xDqBBFdu7Zz74DNYwd3p/vVqwjKcFGgtWMxWxEp9UQCIZAlvH4/CRYY4vfQQPXoNchCAKSJHW4f9GohM/vx2Q0sH5LKaIoYjLqsZiM2G0Wlq/dTEVVLSajnvSUZLoW5qI4TYvfD/aTtPTWkPDK9/y1xsCVl08m8TipWalFseibLMkIYszTL0dlFjy5nlFX9SQaltixsIIirUjRsLwjji8cnk7QG0aSZHZ9X8Wwy4txjswEYPojw044ZlmWGfHH7hjtOnYuPkB2bweyJIMIBcPS0RjUSFEJT2OA5Hwr426ORYka97WSN1Bm1/eVWNON9PlNIUq12EHcIZDqRxAEkgqsqHVKDMMvpeSCQ5tVgGhjA5Pvy0EQBGxD+qExqlAkaFn8yFom39mf5sYIOmsY0+wrIBqhssFFy5/+jy7OPORQkPfe/YR5yzdgtZjQbipl2LABfPbFInaU7cPj8dGjayEKhYLN23fzl8deoGdXJ2eOHcrwAb1P/sb+h+DzB2hocpOSbEetUrG/sobSsn2U76/C6/Nz/R9m8dQ/32bD1l0k2a2YjAbGDh+AjIwgxJxyS1Zt5NJZU8nvlMm/PpzPhFFHpjeeDsiyzPq5ZSh8Sjz/fAbDpddgkppRyQHMiRlHPWZ/ZQ2ZaQ4uOncyg0aM4l+VFnYpjIRDIZYtWszEfoN49Lk3uPfGy0hM+PmRS0EQuG1oMmkmJW9ucpNiVNIrVce7W9w8OCaFF1dUMyxFJifdgSCK7KgP8PhnG5lcaGBTKIl7R6egUR5agyVJwuvzs2d/FTV1DXQvKiASjbJoaWy9m3zGEPr17EowGGLegiV4fQH+cN5UZBkuvek+xo8YSH5OBqOH9O0wv69ct4WS7p2xKGMOQ4P+1JRIaYwq7nniH9gsJpISrPQu7kzn/By+W76O9JQkopKExWQkJdnO+i2lLFy6BovJSFdnLgNLinnomddITrQxekhfGiSJrPQUnn31ff710XxSkxP50x9mEQqHeW/eAhpdzVRW13H+tIlcfsE5vPvc/ezYvZdwJELnvBzUJgPsMdDY5KJQdLNijYeb+xVz+dnD0KhHU1ldhy8Qa/8kSzJmo4Exwwawe28FarWKdz/9hn++9THNLR7SUpJ48t4b8Pr8XHHrQ9htFuobXdx53SUM6N2Ned8soba+kagkMWvqOLIzUjAZ9IwZ1g+DXte+Vh4PQZbl437A6XQuAeqJ9br7vLS09PiJnqeYOXPmyCkFhRh0OiYOP/FCFCfOfwJOp/Oe0tLSv/za4zjInDlz5P7Dh7Nmy1b+OGsmnpBEgy9CTludgCzLLP56Fd12fIv9mpt/1u/6dtlaHnjmVV55/C5SHR2jfbIs4/MHeO29z5hx1thjTmqhcJjNO8rYXV7BuOEDsFp+ejqox+dn3tffs2d/JWOG9qdvzy7H/KwkSVRU1ZFgNRGORHnu9Q+oa3BxoLqOcyaOpH+vrixfu5nzzh5Pk7sFu81CMBSisqaefQeq+eTLxdx9wx/YXV7Bzj37mDZpFKIgolIde3MvyzKrN27j84XLGNa/F8P69+TTb5bQ0OiisrYBu9XM1RfP4Lvl69hfWUN2Ziqd87JJTkzA5w8cMzpX19DEmo3bmTh6MOX7q1i3eQdNzS243C385swzUKtVXHX7I7R6Ymp41/9hJuNHDGTYtMtQiiJdnLmMHtyXaZNGMeOK26iorCUxwcqMKWM475zxvPPJ18yd/y0trTGhnq/eepo3537J0y+9i0ajJhQK89pTf8HrC3D1HY8QDIbQ6bRcet5UZk0dx1kX30inzDRq6hoZPqAXV1w0nb889gL7DtTQ6vVht1l4/qFbWbh0Das2bKXV42VvRTV/f+AW1mzcRqLdxrlTJ51Wz9lTTz0lq5KS0X1nY0Tat5j6DMI6fvIJm0J/+cgaepyZS0rnBJa/uo20bnaySxyxGhVvGFdFK6lFp15pcfPn5eT0cVCzw0VrvY/e0wp+0nnqGpr4avFKlq/ZzDP338S2neVs3rGbVo+P5EQbk88YwvK1m2l0NdPQ5GbSGUNwJCbEDFv5kPF3031zSKpJRJutRJOgpKtQyHPr36W12UdWloPpk0czpG8PRFE8KUdUTX0jG7bspL7JxQXTJnLr/U+T4kikR1EBg/v2QKVS4vX5WbBkNe9++g2OpAQeu+s6Xv/gc+YvWs6e/ZXk52Tw8hN34/H6MRn0REIRkAXkiMwXD6zirL8OomprIzKQ2SOJVW+VUjwpB29DgJY6H7kDUln+6jYKhqejNanZMLeMIZd0Y9Nn5STnW0nOs7DmvV30m+mkfFUNQW+YziMzmXvHMsbe0Bt3tZd1X+2kKb0Jz7owqnSB8y8czzN3fsi8yu9I0yWTmeTgocev5N6H/kFryEduRhrZqamMHzuQxr0tmJL1SFGZvStr6DI2m/3r69AYVDgKbayfu5viiZ2o3eli76paBl/c9bRbzwAuv/Za+c/n3cSinSuYtm8tuhFjeaY6xOpmE/8YkYgxr+Pf7j/WNtErScChCpGR6sDtj7K6ykfXZC2tdVUk2W3otRqefe19uhTkMmpIH7bv2svnC5Yy+7dnkpL0854/lz9Koz9CfpvRuG1nOVfc8ShGrZrk4oHMmjoOuWoHjz7/Bl0Lc3niL9cTCoeprK7n+5Xr6VXsRJbhspvvJy87A61GzR3XXsyDz7yK3WahpLiIKeOGxQScjkJlTT1ffbeC0rJ9nHfOeCwmIy+98ylSVGLjtp08+8AtLFq2hudf/xCNWkW/Xt24/ZrZfLdiHe/PW0ByYgJajZqrZv8GV3MLVTUNDBvQ64TPXUOTm+VrN7N77wGuvWQG8xctp6HJTX2TG5NBzyUzz+LhZ19jyeqNGA06enQp4LarZ3PP4y+SlZ5CIBBEkmX+eNF0tu0qZ+78b1myaiN9ehRx742X8dr7nzNl7FBsliP3Ej8nJfZEhMJhmls8WMxG9lZU40hKQK/VEo5E0Ou0tHq8yDI/OlPgWM/aCY27toOLgIuBocAC4J+lpaW/ShfFOXPmyL0HDwFkBvf+7/Okxfnf5HRbDOfMmSOfNX068xZ9yx9nzcTljyIKtPeK6vv8buadl0NFc4iSNN1PmhDdza1YLSZ27z0Askx+p8xf+mvEOQXU1DciS3K7IS7LMoFgiJXrtpDqSMSZlx0zMkJhtBr1SZ1TkiT8gWAHD3AkEsHrD2Ay6BFFkZr6RrbtLEejVjGoT/cf9TcntTXlLioqOu2eM40jBYtoYkj1Flp0CRRePPuExl3QG0atV8YicFEZpVqBWqdkyT+3kNkzCYd/G1JrM/oJx0/d+TXYvfcARoOuffPb6Grmwmv/QkZqMlfP/g0pyXZufeAZ0lOTMRn0TB0/nNr6Jv724lvkZqfjSLIz+zeTqayp465Hnqe6rpHEBCvvPnc/wXCYrxevpLnFQ6f0dPKNWWT1Tua7FzZRSwMF/dLZ8mwF5z8/mh0LKjAl63AU2vj+hS2MvKoHBzY3IAiQ0T2J3Uur6NTPwe4lVTTua2XQ77qw8pPtrKvfyo6t+9GENDz89JX838Mv0erzMnHcYJz52ZjVRlYs2ILdacahtdPgctOtTy5/f+RD3lnxJd2FIjDKPP7Pa1i+dDPvf72ALFMqoiRy2bVTKV9Vg8ohku5I4vO3VnDGeSWE6yVcUgsWoxG5GVKLEqjd6cKYqEOtV7J7SRVFZ2TRUuvF6wmwft8OWuq8ZOY56OUs5IY7nyK/SwYW2cSQET0oyM/k+w830WNcLr79IcKBCNl9HMy9bSljbyyhYW8LFevrGfL7bix9aStdx2ejM2vY9X0l3SbkULvTRSQUJb1bItu+3kdu/1SUWgX+5iCmJP1pt54BXHbNNfJjDz7IB19+xdm15SjSMrm0uZB8zy7unjEcRUJsPmtocvPW3C/JGzKBZMlNfloCVrORA81h0szKY4qkXHf34+zas5/ionz++ufLUShiDoSTmae27yrH3eJhYEkx4XAElUpJ2b4D5GSktWcd3P7Qs4wa3JfRQ/riDUYo27OXB+a8zK1Xz6Z7UT6RaJTzrroTd3MrIwf3oaS4M2OG9ScSiaBUHnIWuppbsJpNP3rNdjW38P3KDbiaWzlr3HCs5lhqsCzLNLlb+HzhUqZPGt2W5l9Hk7sFj8/H6CF92VtRzUPPvobX5yc9JYl7bvgDoXCERcvW0LtbZzrnZyOKInv2V/KHm+6nT/cizhwzlMF9e5xgVHF+rnFnIdbM/FygFRCB9aWlpXf9wuM8IXPmzJEvu/xyZFlGoz65jUOcOKc7p9tiOGfOHPnqq6/G4/Nh1Ot5d4sbST5UMF7rieAwKrn5q2puHZaMVXtyMusH2bO/kllX3skfL5zGhFGDOtS4xYlzqjgdnzNDajpSA5xlaWH5lmom3nsLSsWJN14ttT4WztnAmXf3R6GKPX/RiITPFUQvtCL7fSgzc07xNzgxW0vLCARDpKckk5xo4/nXP+SDLxZht1nIzUrn/lv+yOYdZXQvyj/ueX64SV27eQeV1XWMHdYffzB4VE/8QaSohCzJKFQKIqEoSrUC14FWAGwZJirW15HeI4n63W4AHIU2Vv5rB8WTO6E1qtoN6I2flJE3OI1oWKJmexPOkZl8+fgaSs4uQGNUs2NhBSXT81n+6nYGze7C5s/34iiwkpxvZeGcDfQ8LxetTk1NQyPZGans2rOf6toGKqrriEQizDhrLDvL9nH3oy/Q6vGSlpLES4/dyXcr1vHEi2/hDwRRqZS8+PDttHp9PPXPt9Fq1LhbPFwycwoDexdzxsyr6Nu9KFZrPLCEgSXFv8Bd/HGcbs8ZwB+uvlqeYJlBa76LcwxuZK+H56QEdHvKuPT2P6Fpy2h48c25vPDGR1x7yQxefO8r7rzmQkYM6MWFH1Tw0tkZR9S9Qcx59NEX33Lm2KGoVSre/fRr3v9sITaLmWfuv4m1m3bQr2eXIwyqYCjEqvXbSLCZue6ux+nmzMXrD/D8Q7dy/tV3kZWewsCSYgb37YFOqzlhTXSTuwWdVoPuNFSDliSJPfuraGxy06NrIbX1Tbz/2QIWr1iHq7mVx+++nkg0QkNTM5PPOHGrlzgxfrJx53Q63wW6AW8Qa4VQ1fb6mtLS0j6nYKzH5eCmM06c/yZOt8XwVD1nNfWNPPb8v3jw1qv45OvFPPbcGzxz30306HrsAus4cX4pTsfnzJKRTWCBwIwzBeTN67DeePI+00goikLVMcXQO/dttENHo7AfX+jn30Grx8tF191Dos3CgZo6zh4/gkvPO5uq2nqqahrIz8n4WSnUpxMBT4jaUhfZJY6ffS5ZlnG3tGIxGdvrMgUERFGgpq6RpEQbHq+fsn0HCIXCmI16stJTMOh1RKLRo/Ym/Xdyuj1nAJdedZV8/z338s3yFUzViwTXr+LaahUhbRo3zR5Hr7wUJFlm/ead3PHKlzTsWEtxr17ce91FZCT9OOdjfaOLZ199H1mWufzCaVxz56OcecZQdpVXcPu1s6msqad8fxWvvvcZWWkO7v3zZezZX8XmHbsZO6w/Br0Onz/AK+/Oo7RsHxNGDmL8yIGn6Mr8+jQ0uTHodaelUXq6c6xn7WTUMl8ElpeWlnqcTmfqYa/HTes4cf5H+GRHC71StWRaOkbLSxuCLCr3cHnfQ/UFDb4IFo2Cve4QBYfJ9UajEnc9/ByD+vZAoRA5e/wIxgzrj/EUFWPHifOfgEqlwDu4FdGWza7yGn5UsUFTLQ13/Qnt8DHoho4iUluN79MP2/uU/bsIhcOoVSo+X7CU9z9bwIRRg2lp9XDJzLN497n72yNuB53JaY4k0hy/vvH5S6I1qn8Rww5i9YOHRyIPr4s6mA5tNRspKT6yh6j6NGwAfzogyzK1O9woJBHRnkikvo6dZRH+8diFXPnnu/nijSfZ7Y7yXKnI549djdcfwKjX0eiL8OXuVlKMynblyBORZLdx958ubf//g7ddxYNPv0pedgYqpZL6BheffLWYccMHMOvscQiCQH5OBvk5h4Rd9Dotf7xo+i9+HU5HEhN+eguJOEfnZGaBQcA9bf9+yul03gJQWloaOGWjihMnzmmFUuSotQYOg5KBmR0bBv/zrd3M2+7mudVNHJ4Z8PI7n6JQKrlw+sT21+KGXZz/dRSCAvUOPZruvam/4p4TH9CG1OwiWl+L9aZ7kH1emv7yZ9z33Yb5ulsR9f8eOXp3cyurNmzl3MtuZcfuvQzq053ZM6Ywf9Ey1CoVkiR1SKWMK1zH+bWQZajZ4kKMKlB36Y7/0huw6VW0fP01lsRkHp2/lXXby/Etfh1BEDDqdTT4Ilz9eRU2rQKj+qcbzblZ6bzw8G3cfOWFiKJI/97dePLeGzjvnPHxZyLOKeFkIndTSktLSwBKS0vPdTqdS4EHT+2w4sSJczoxsfDo9SxWnQIhquTbf2yh7zn5lC6vZrhapluKlrO7dUxlyclMZcrYoT+pP1ucOP+tqJQKVFYFKo2aYU0bkeXRJ7XhizY1Et6yAePM2ajynZh+fzWy14NoPLVpjj5/gKdffpfpk0fzu+vuwWwycOtVv6Nzfg4AQ/v3ZGj/nqd0DHHi/FhkWaZwShobtjdT3dTMvY8+T9czziGxawJ57giJoTqUPj2dMtPaj0nUK3ntnJjQl/IEIkdx4pxOnMwuS3I6nWoAp9OpOslj4sSJ81/EBR9U0ByIHvW9VzY3U2bUEhLgvZX15J9TQNATZtPSKq7/oopoNMqCJasZNbgPyYkJ/+aRx4lzeqNSqzAXa6j2RJj//kIIhU7qOGVOHsaZswkEQ+w7UIMgCKfUsItEIjzxwpuMPPdy3C0eOmWm8e37z/HxS4/FVe3inPbIssy2D/YjBETczR5aXS4GFheS1yWXYYP6kehIwd1QR05GaofjttYFmPb2vl9p1HHi/DROJnL3HLDF6XRuBjoDD5/MiZ1OZ3/gof9n767D4yrWB45/1zfu2qSa5qTu3tKWtpRSoLhe7OLu0ovbRS5ycbg4P+AiF9fiLaVOXTjVNE3jnqzL/P442zQpMWg22yzzeZ59snt2ztl3NjvJzpk576iqOk1RlBzgNUAAG4HLA8U+BDKA21RV/VZRlL7A1aqqXv3HqiFJUjA9dER6i9NShBCM31nB54kxbFpbwY0XDiAxykhFqSDGpOeCIdF8/MHPfLxyJeasAUzpHR2C6CXp0KXz6Kn5yEvGDCNHP34/ug5kyvTbbVTNv5KkR57H4XQy/4GneQpKwUEAACAASURBVP3fd7W5PuKftW/trNiYKGrrG/jmv08TGx3V4TTvknQoEEKQkBtNlcmN3eEkrldvjqxZR92La+h56nX8Vu4gByfpqc3XWh2aZuWtk3qGKGpJ+nPaHYVTVfVlYBJap26qqqqvt7ePoig3AS8B+/K2PobWgZsC6IB5wHAgHzgSuCJQ7jbgn3+sCpIkBZPXJ6h2+Fpce8vvE8SmR3LnjHTi7B7qSmwAJPeNo8/YdPT51Xy/XHDKKcezs8bT1aFL0iHPHGUk+/QE6t1+dvznBVxrVra7j+OHrzFmZNHg8rBizSYS4mK5/p5/8/r7X3T4dR1OF05X26OEDTY7V9/+KP/9ZAFJCXHcdf1FxMVEy06d1O0IBDG9LZgsBnw+Hwa7g+vWm9BHxzAiw8q7/76fUUMHMGxg88XMdTrdQV1vJ0mh0O4nVlGU4WgJVS4GHlYU5ZUOHHcHcEKTx6OAhYH7XwEzgQYgKnCzKYoyCdimqmppx8OXJCnYGtx+Xvq16nfba4ttLH5xIwNm9MRoNjBkbh96j0lvVmaHqwB/aiFHj+7HOcMTqLR7uypsSeoWDDoj1Oqpc/opL6vBW7CrzfL++jrsn3+IYfaxvPG/L1n66wbuvuEiZk8bT2ZaMl6vl4qqmhb39fp8eDxe6htsnHzxLRx+8qU8+PRrCCEoKaukuLSiMQlSdW0dF9/8AH16ZnLzZed0er0lqSsJIfj1iV3g0BKaPHCYwo0//wt9tLagd73Nzpy/yUljUnjoyOmI14DVwLtNbm1SVfUDoOlpep2qqvvS5tUDcaqqbgUKgX8D9wDXAO8qivKcoij/VBRFnirpREIIvF4vLrcbp9PV7Ll6lw+n148Qgj01Lvx+Pw0uH+U27Yt4YZ0Hl9ffoddx+7Rf86YyJxV2L16/YGG+rfE4m8q0JKvLC+2Nz5cFXqfW6aMmcF1XYa32mk6vn/wa7ezywnwbKwrtANz2fQlOr5+F+TbuW1gGwF0/lvJLgQ2fX7Byr1Zue6WLwlrto1hp9+LzC1xef2Mno9rho8Gt1W17lfa+bCh1smyPtv+yPXbKbF5cXj9fbtUWvd1d426sxw87G6hx+LB7/Gws1bbZPX6cHn+z1+yOvD7BuhIH8REGHp+T+bvnIxMsDJrTq81jzD18IvecejRf/XMl5YX13PjOLmzujn2WJOmvIDEyFvsWD1lxJkYO64OvpKjVsn67jfKLTsOY1ZO7Pl3Mdz+v4NxTjyE5MZ65MyYz67BxfLNwOf944BkOXMNWCMGtDz7LCRfchMlo5LPXHuPbd55m09adrFi7mQefeZ2zr76LOx/5DyXllVRW1zFzylhuvPQsDAb571jq3nQ6HQMvy8Qab2bDb9v50RtFVZaCsXc/AM475RjOOvGodo4iSd1DRybol6iq+tJBvk7Tb3MxQA2Aqqp3AyiKcgbwCXAh8DIwDZgBfHuQr/uX4PP5cbhclJZXkpmZyUdf/URpaSkfbapmdk4Mx558Mq+/9T8W//A9QqfHoIOHH7iDLzeVEVu+GdUegWPvNu664kyu+SyfiI1f0H/IcMrsfv5x2lQeXljE3h/fxecTmNJ688RVJ/FZsZW+CWbm5cUy+41d/O/UnqwvdfLxljr+NTuDpXvsjMuKJNZiYMH2eqb2jqKozsOeOg+DUq1sLnOSHGnA6xPc9kMpL83L4psdDbi8fv42LIFHl5Rz7cRkhIDnVlTy0BEZGPWQFq19ZGf2jcao0zE+K4IRGdrs3+smJmMx6Khy+PhMrWdMj0i2Vbkx6CArzsSVXxTx1NxMCus8vLOhlgdmpfP2+hpykszMzonhvoVlPDO3B3FWPbsDHcptlS6SI7U0yJvLnRyVG8Ouajdun2BQqpWtlS6Gplmps/v4VK1jcJqV19dUkxJl5KRBcVzxRRHPHJ3J9ko372+q5V+zM7h/YRkjMiI4KjeGk9/dzfuntt1BCpUKh5cLPtnL+6f0ZFmhndOGNF+Lxu3wEpvWesr1T79ZRFxMNIeNH8GUi4ZQsauWK5P0rHxpA1MuGozBqK3d5PULmYlM+ssaPjyP4cNhRaGdWl8SowqXt1rWtXwx5gFDSLjtAU5YuY5RQwdgtTRfe3L2tAm89dHXzD37GuYcPokrzzsFgDUbVXYXljBsYH/Wbt7K+JFDiIqM4OVHb8dsMjFuxCDsDidPvfIei1es5aS5M5qtuyVJ3ZlBb6D412qSB8eyeoPK5pJ6Rlx0L2MC/9fOOkl27KTwoTvw7N6BFEV5Hu3auDVoCVFQVfWb9g6sKEpv4B1VVccrivIZ8Kiqqj8FjvejqqrvBspZgbeAk9BG8V4DDgd2qqr60YHHfeqpp8SVV17Z0fp1it01bhIiDJgNOj5X6zlpUBzLC+2UNng5Nq/lFPHVtXWUlFUyoH8fQDtr6vGD2aCjuN5DpElPjEXP2mInIzKsv7uGoaqmjuraOhwOF1mZaVgtZr5fvIKPFyxiV2Ep558yl54jJ3HHTbdQX1+P3mDkyOkTOGzeqXz03RLGJnlBpyfSamb0xMnsqnYxrU8Mi3fbGJ5uwY+Odbsr2LZ6OeWVNUwcPZQRg3OJioxgw287WL56A0LAEVPHkZWRSnFZJR4/LFy2hvHDFCKionno6VcZN3wgUyeNpXfm/gVp5fUYv+f1CTx+QYRJj9sn0Ou01Moen8Bk0KEoyt2qqt4V6jj3eeqpp8QxZ13MDzsbOCo3BrXCxdQDkqGsfEcluU8sfcZl/G5/j8fLceffyCO3X9XYBkBrB69+lE/KwESOyYsD4OnlFaRFm5jVLxqn1096tKlT6iCEoM7l55kVlVw/MZkNpU6GZ0R0u46k2+HFHNH5iTI6y77/Id2h3R+K7ez8sy9iwxe76HlMP+oK95Lw0HWkvvZhi+Xdv20Cn4+CyAQy0pKJamWdSIfTRXVtHUkJcTTYHCTGx6LT6XA4XURYLcGskiQdcu0M4MIrrhDT048l75gslq/djMlk4oIz5oU6LEk6KK21tY58Y7AASuAGWgev3c7dAa4HXgwsqbAF+F+T564BnlRVVSiK8irwAlAHHPcHX+NPK6r3YNbrSIo0cP+icuZPSWFxgZ0fdjZw9+FpfLiljmm9oxiUaqW4Xpvilx5tJNqsp7jew/sbqxng2UVpRRVV1XVMHjuMzPQUrr7jUSzWCKxmE6f//QJ+LtNz66R43lfdDIjXMbVfPC8uL+Lk9Fq+U6vokzeQc8dkcPlrv7D967eIiYvHpzdy95Vn8nlJJCW/rGHaYRPJ1mcyb3pPNlX5efTRBxmWGcl3OxuY3DOKCJOewy864nd17BWvnd2d3Gv/SMuU3DSm5B77u7JDB+QwdEBOs23ZmWkA9D3pSACcThdnHncEi1es5aLr7+GaC0/j8Elj+PGXVfTOziQtJRGPx0tyUjxLV23A4XSR0yeL1OTEv+TC1UaDDmMgC565STY8Uwcy44VK78Bnxuvjdx07gDGnKc0ee30+tu/aQ3JiPPc98Qq9stKbdexA6wCccnQvdq4q5aPvGrD0jOGCkYkYdLCu1MlP+TZumJSCw+MnwvTnp4KtLnLw5bZ6/nFYChOzIzEbdHz8Wx39Ei0s3aNNCT57eAJPLa/grKEJxFn1jZ2Tg33tjvD4BN5AZ3/vrlpsLj+5efvXBXTUufC5/VhjzXx488+c+sQ0di4txu8T9JuUyaLn1zP+rAE4al1U7KojZ3Im238tI7NvHJUVTkp+LWHs6Xms+3QHKf3iyRyUhN/nR2/Q8/6mWkZnRhBr0bMw38YJA+PYXukizmogJcrIV9vqmdM/hk9/q8Oo13FUbgxLCmyMyoxg5V4Hywrt3DAphfsWljG9TxTJkUYe/LmMV4/P5v1NtWREGxmdGcGln+/lxXlZLC+0U9Lg5cSBcby5rppJPaPIjjPh9IrfJSrYVumiT4KZepef0gYPeSlWNpU5SY824vELnl9ZxV3T0/hhZwMmg44pvaJ4a00Vc3Jjqap3s3JREaefksPiVeVEOj1kDk/huec3c+tlAzFHds5Jg85mNOlJzYkn3mogNTebioZ6/HZbiwuRm/rngV7P/Evmc8e1FzAkL6eFI0KE1UKEVTvhdu1djzMkLwerxcx5px4T1LpI0qHKYDCgG+EhItqKzeGk2mVm2R4747MjQx2aJHW6djt3qqqepyhKLtAP2AC0fkFA8/3ygfGB+1uBqa2Ue7DJ/bXAuI4cvzN9u6OB7FgTh/eNZmRGBD4B47MiGNdD64RcOyEZl9vN0pVruHL8SLbv2kNCfCy1leWUoKenxcSXX/9Cvieao4dmsGJ3DbuK43jv+Qd45pdCeujrOWp4FkmrN3Lc+ffj9fqIsFrIvP9GbpsQy8PPfoxfwOsfv0vmledwxvShDDxuEFhjqHb4UJItZPXxEjvt8mZfOsf02F+HWf2Cu3DtgaxWC5PHDmfy2OHccMnf8Hp9COCnpasp/OhrCgqLueGSv3H0zMl8smAhAsELbxYzeugA5l95Lt/9vILe2Zn069WjW5zx/ytavNvGyr0O3D6Bzw+Xj0tq9vyaj7cz8IieNLi0kYGzr7qTotIKzj/9WE4+egaDlH4tHjfarCcxxoQ/QpCeGYHFqOPdq3/iyFvGMHpSChtKnbyyuoojcmLw+ESLo+NCCHQ6HX6/QB8YiRN+wedPriVqajZThiaRGm1Er9MxrY/WMb1vhpbs5bBekdS6tJniSpIFY6DjV+P0cdLAOM79qJB3T+7Z2BlfusdGVqyJ7Djz716/o2pLbMSmRbJzaw0ep4/4fnG8eudyLrl2GB8vKaNPtIE+/eN587UtnHNeHqt/2ovVomfkEb049oHJ6HQ6dkZYyIsx4AeSR6RiiTZRUu7Ab9Szs9rNKz8Wc32Sla/LXMQkRTMWeLDQzaOjrCxVa1jz5BoufXoavtUleO3x+Ack0vDjbhg4lHV7bEQbdcwenMCWNeWMTTEztkcELp9ACME3OxoYlGpldGYEOUna+3DhwBgiLAZiYkzckmrAXutiYKQOX7kNa68oLoqD+qIGMoHST7bCwDHE7qzG7fVQ0DuOj+9ayhUPTeLlH0uIqXfyt9P6894TazntxL5UxVhZ++xalIcns/j9bQzoE8OYWT0Z+mshnglJWIvqKF9eAtePxPXZNipn98TaPwF9UQMAe/Y00DM9grQoIzN6R9LOBJWQMkea6DM2nfsWljE6M4LJJ56BcLmghc5d+d9Pour6+3C5PAxupX0d6ObLzuGsq+7grBPndHboktRtGPQGGr73Y8gz8LcT57Cr1kd6zKE7I0KSDooQos1bbm7uFbm5ud/n5uauyc3NvTo3N/fp9vYJ5u3JJ58U+7jsHvHVgyuE2+ERf0Z+tUv8tKte+P3+dss++sKb4q5H/yNcbrc45ZL5Yuapl4tzrr5LvPXhV41llhQ0CLfXLzyBW0tsdofweL0tPldX3yA8nj9Xl0NNa+/pvu2Pv/i2OPbc68Tcs68R/3nrIyGEED6fr8vic7pcoqyiShSXVgghhNhbUiZWrNkkFi5bLX5dv0X4fD5RXVsvVq7dLH5evkZ89/MKUbC3RPh8PvHWR1+LV975VLz32Xdi9YbfhBBClFVUCbf7z//ucnNz7xIhbFcH3p588klRUu8R2yudwuXxiV8/3SGcDe5mMa/9ZLt4+Jk3xG0PPyeEEMLhcIpN6k4x67TLhdfb/u/S5/OL5W9vEbYap/A4tTZRV2YTQghhc/tESb1H7KxyicW7G8Sb66qEEEKsKLQJj9cvLvqoQKibKsVTNy4We3fUiE8fWikKalxiyapS8e2marH9l70if1VJs9fzupu3O7/fL5wNbuHz+UX+2jJRUu8Rfp9fODxa7A8sKhWri+zi+x31Ytkem6i2e8WNC4qE3+8X9/xYIpYW2NqsX3mVU3z+0kbh9/nFp3cuFbYqh/jvN3vEW2+qQgghKvc2CJ/X19gmKuvc4pV/rxE+r088t6JCLC2wiT01bnHG+7uFEEJ8vKVWrNprEzuqXOLSzwqFEEL8a3GZ+Dm/QXs/A8fx+/fXweHWfta7fKK8zC78fr+o3lsvaooahMfpFes+2yGEEGLLDwVi1XtaXD89t07YqhyiRK0S3z2+WgghxLpPd4iCNWVCCCH+d+Mi4XF5xdZFhWLlu9o+y97cIjxOryjfUSPWfrJdCCHEtsV7RdWeOuH3+0XptmohhBBl26tFxe5arb4F2s/SgjqxdWmR9vsvtQmXzS18Hp+o3luv1aHWJWxVDiGEEAVryoTH6RVet1c4692N9e3I33AhDs12VllQJz685ec2416yar2Yddrlwu/1iKdffU88+cq7HarvPoXFZa3+35GkznaotTMhBFddf7149on3xO7CIrFrT5GoqWsI7psgSV2gtbbWkdMWpwFTgB9UVX1CUZT2F+HpIgaTntEn57L+812MOql/+zsE3PlDKReNTkSPYN2WnWxetI1fVq7jvpsvZeXazRSVllNRVUNFVS1P3HMdX3z/Cz/8soq3nr4Xs8nEO8/e3+JZ+wnZrSeX2CcywtrqczHR7e/fXbQ2qrFv+zUXnM7V55/G7sJiKqpqAbjytkdwulyMGT6ISWOGMiQvB79fG2HR6zs+Ta6u3obd4SQ9NYmvf1xKdo80zCYj9Q12Rg7J49EX3uKzbxYREWElOyOV//zrVpasWs/3P6/EYjFTU1vPfTdfQmV1HS/992MsZjNWi5noqEjt+sPSCsxmE0Wl5bjdHkYMVnjipXf4adlqeqSnkJQQxzP338SO3XtpsNlJSUogf08RI4fk4XJ7KK+sondWZlAWHO5M0WY9H2yuZXuJnaNKbI3XfZVWVPH6e59z1d9P5fZL1vLkvdcD2mjuwNw+PHbntY2jaW3yC9JyEzCa9BgtBlw2Dz88uZa5t4/D6PWTFm3CVuUkPSOCSJMeh8fP2+trGJlmYcaKAvrMGcfQcwcSnxUNU3qycq+DE0alAlC5W49Or6O+3M6aj3Yw5cLBfHjLL8y9fRz2Kid6o57aEht7N1Qw8bxBbPk6n9k3JrP+853oDXoGHdmLI3RelGQLdTtrSUiLxmVzM6O6HkE6V47XkvdsLnfy3sZa7pqexo4qF4kRRgx1LhoqHYj0KLZ5QKfXoc7qR1K9j1Nn7h+pTsxs3t4TY0ycd/VwAC4Zs3+U9OV5WlKLeU1GMJ89Whu2v2HS/mtd9YHj6nQ6rEbtvjUw0h9t1hOdos1EiM/cP8V26NF9Acibnt24beolQwEwR5mYeN5AALKGpRARGLmcee1IjGYD/afsnzow7sw8QFvfMLmvdi1lzqT9GVZTc7SkBSn99iflSczW6pOaHUNqtjbzICZ1/xSpfXFaY/ePmGYP319fg8nQWN/uLCErmmPvncgXW+uY3ica/YqFuH/bRMy5l2D74G3Mg4bTp2cPqmvr2fLxx8w6bBwxUX9sKlmP9JT2C0lSGDMYDIhYHxarmXsefZnKzDHMP2k8w9L/epeJSOGvI98u932r3jexxdVawa7mtnuIiDNjshgarydpzaYyJ5+pddwyJZXpaR4SLDoW/LiUxZ8sYMrY4Vx/8ZlkZaTiC1w3NHJIHiMG5WI2mVi/eRsPzL+cuBjty0Z3/zJxqNDpdPTOzqR3tvYl8NE7rmbd5m2sWLeZdz/5liF5OTz87Bt8/eNShgzsz6Dcvlxwxjy++WkZL7z5ITa7gwa7k3/ffS05vbO574lXqG+wsWP3Xm669CyOmjGJXzds4fX3P8fhdHHKMTMZOSSPmZPHcOEZxxEbs//L9UlzZ3DS3BnN4svKSOP5B+f/Lu7rLz7zd9vuu/lSnE4Xu/eWNNZt264C3v3kWyqqa8jOTEfp14vS8krufuwlikrLyUxP4ZoLTmfi6KGd+bZ2mv9bV02c10dWlJG+s3tTmV9Hct84/vvxAtYv3sF/d/+Iy+OhV1bzhCqD8zo2XUxv1NNrVFrjY0uUiWPvmYDf6+e9axcy+6bRrPivylG3jqWvzo/VqOOyGIFOr2fWZUMxWYwcNlC7Tu2YCanNjp3US+s4eN0+cg/TOlTH3T8Rk9VI8eZKzJEmeo9Jo+fIVPR6HUfePEaLfU5vPC6f1jHcWIFheAoVO2vwOL0kZkXTLysavU7Hmpc3MvjIXsQ4vBxe0wCkseDZ9Yye2xuzX/DhohLuvHoI11wyCICzhycQZdL/qb8d1iBf/9cao9mA0ax1oBJ77p/2HZcRPiehDgVum5eK/Fq21cP0PmAeNhrz4OHU/OtuPOpmPNu34jz7ck6YMIyH3/6C1048KdQhS1K3Y9AbMP0Ug/k4E3aHk/mHZzEkrfWT7ZLUnXWkc/c2sAjopSjKl8DHwQ2p44o3V1FbZGPECTmIVtYS+3JrPX0TTPRNMDM+toH5D7zPqnVbePXxO5h3xGEcf+S0ZuX79cpi/pXnNtt253UXBqkGUlNWq4VxIwczbuTgxm03X34OF591Ams3beW37bvx+XxMnTCSoQP7Ex0ZQUSEBaPBiN3h4Pg504iOiiSvX6/GLHK3XvX3373OsEG5QYtf6bd/WYM50ycyZ/rEZmWSE+N574UHcLnd7C4sITG+5Wyrh4JLxiSxbfFebPVOLPUmNq8qIs0Xz9Xnn8Y7id+wedMu/nnzpZ16skOn02EwGTjzuRno9Drm/GMswif44Yk1zLxuFA1lDjwOLwlZHbvG1Gg2kJ6XCIDJqv256zdx/4iSwdg8doPJ0DgitG8Ea/Cc/UlhciZpn6uRJ+YQkxyBo9aFJXDc08/NIzo5AqPFQIaSiKHJ6GVq1KE9SiuFjsvmpnBdBdcERj8xxVDz0J0Ih52kx/5D6cVncs6qO3nn4nmYDJWhDVaSuimDQY/niDqsVgs2u4P1lYKBftAbQh2ZJHW+jiRUeVpRlO+BwdpDdX3ww2rbrmo3Xr+g/3htxGDLdwXUldkZd0ZeY5l9Ge/irHrMRj3q1u088PDT/O34I7n9mvPbnB4pHTp0Oh0JcbFMnzia6RNHA2Ax87sU4DHRUUwdPzIUIf4pFrOZ3L49Qx1GmxbmN9B/WCr9Y7Qsg8u/28yPz6zkzn+fz+EjxnDU5EnEpf4+i2Zn0AU6Rnq9DvQ6jn9gMgAjTmg5O2BXi0vXRq+ikiKIStI+i007nPvWY5Sk9sSmRZFzfA43LijmX7O1/2kRs+ZiHjwMndlC8aTZpG0oItZRjy85tZ2jSZLUEoNej36XGbPZxIlzD2d9g4lDOM+SJB2Udr+BKIpyR5OHAxRFOU5V1XuCGFO7fspvwGzQ4d9QRkrfeHImZ2I4YOrS9QuKuWpcEpbqAvL32Jg+cRQfvvhQq+sCSZLUXFmpA8eCXWRePAQA40CIKLVSX2anYmsdkQnWoHXuJOmvonpvA9sW7eXvM/eP+ltGjm28v633IIYRh2fXDsx5g0IRoiR1ewaDAYPDiEGv59RjZ3FqqAOSpCDqyMUcpYFbGZAFhHy44bwRiZw6KB5LlBmDWY/JamTPmnKcDW4q7F68PsFjszPYuOIXbnv4OaIiI9DpdLJjJ0l/wLwh8Qwal974uL7BTkSEGb9fMGh2b/qMTW9jb0mSOiIixkz6oKRWR3tze2cxZd0PuFb8gnnQsC6OTpLCg9FgwDRKyyR4xBlXccf3HVrVS5K6pY5My3yh6WNFUb4KXjjts7v9nPJeAQjBe6fuP9NZtaee+B7RvL7TzoiMCBy71vJ/H3zFy4/eLjOFSdKfMH9hOTdNSm58PGKwgifPS0xKBMv+bwvD5/VrlslQkqQ/zhxtoizeyuvLKrlzupZgqLa+gc++/ZlRQ/LIzemN9e77qL7/Hxiye4c2WEnqpvTo8S8y45zjxm53MG9AfPs7SVI31ZFpmU2zT2QQ4pE7s1HHPdNT6Z9k4Z2rfuTEh6dgshoZcXwObp/g2vRIdEB14lBGDRlAempSu8eUJOn3Tks2suyxX5l31wQABvTvjd8nePeahYw8MQdThLwSXZIO1ocL9mBbspc7753QuO2Xlet44qV3sFrMzL/iXI6aMYnk596SmZol6U+KiLCQMycdm91BVKSVvGSZd0EKXx256r/pyJ0TuCFIsXSIEJAcaWRxgY3pt43HaNG+YO6pdvHBrUs4+tocnnnrfzx6x9V/aG00SZKay8hNYNTo8Y2P//Hgs8ycMpYTH57SuOadJEkHZ8CoVLb1jmPVXjuje2jr1xUWlTGgfx/ycnox53At467s2EnSn2cymRgxYgB2v4M+/fpx0zfFPHN0j/Z3lKRuqCPTMqd3RSAdVdrg5aXVVSSZdCSaIS1Vu44uO8HCyfNHc/eTTzLzsLGyYydJB2n+R7u5d0QcvYdp05pr6hqIjoyQHTtJ6kTfrqmkcq+N9FlZjdsKi8s45ZiZHD1zcggjk6Tw4XP7+e7x1Rz/z0k8f+81oQ5HkoKqI9My1wExaKN2+8axdYBQVbVvEGNrUVaciSunpNJQ4WD1h9thZApqhYtNZU6iS7cQXRPDCXMO7+qwJCns/HtaKkWbKiHQuaurbyAuVmbHlKTONCPDykcbKxifHdm4beqEkSj9Qp67TJLChtFi4Ph/TmJ7fiFf/bKGkVMOZ1LPqFCHJUlB0ZHhrSXAmaqqDgTmAYuBPGBAMANrTYXNy6J8G1vdsHaotiZQlElPRoyRiEgL0weNxu+Rq5dI0sHw+QWfVnsZMnf/At65fXuSlBAXwqgkKfwURZqpGZTC4t22xm0zJo8hKyMthFFJUnjxeXxsWpDP3uIyNmzeSmmDN9QhSVLQdKRzN1BV1aUAqqpuAHqqqupSVdUV3NBaJgCBILbKzsiyOpxeP7FWPb3NDiaMHsox10xEpwMhZAdPkv4sAfjza1n/2c7GbXdedyEpSQmhC0qSwpB9Vy3J60rol6hlnhVCcPgpl+LxyC+fktRZhACvy4fN4SAtPooTBsoTlVL46kjnrkZRlHsVRTlGUZSHgN3BDqotKVFG2o35fAAAIABJREFUpvaOJjUlgtge0azc6+CBReXc+tCzrFy7GYCvHlxJbbGtnSNJktQao17HaVMz6DFMWwrB7nAy/4FnQhyVJIWf2WNT6DM6jQij9u/Y4XTh9ngxmeS1rZLUWYxmA8OO7YfN7qTGY+DNddWhDkmSgqYjnbszgDrgSGAncH5QI2rH5nInb6+vISY1go/qBIkRBv45M42dBUUMzNWmkB01fyzxmfLaIEn6sxrcfm5cWE5cmnZNwuoNv1FdWxfiqCQp/Ny3pJKhSjzxgaVF6hpsxEZHtrOXJEl/hMvm4euHVjFv9mHceNFpTOstvyNK4asjnTsnUA1UACoQ0pUf+yaYObJ/NMvf+o0TnHa+29FATZ0NIQSx0doXUaPFwO5fS1n9wbZQhipJ3VaEUcffjF4taRGw9NcNTBg1NMRRSVL4OTHCT/lX+6c/C79g7IjBIYxIksKPyWpgwtkDyN9TTH1dHfFWmVFdCl8d+XS/gLZw+RFoWTPfCGpE7fD6tZ/jzsyj7/Rsal0+DHodl5x1YrN1gNL6J9D/MLmGidT1/IEP6ar3tlJT1EBDhYMfn14LQNHmSi0DJbDiv7/hdflCFmdb/AIyJ/dg7OkKAAVFpYwZNjDEUUlS+IkfmsKE8wY1Ps5IS+au6y4MYUSSFL4+/WYRr3y9ks+21oc6FEkKmo507vqpqnoH4FRV9TMgpFeh7qxysWyPg70bKjC4/dwxLY3YmChOP+6IZuWssWZiUiJZ8tpmqvbIRtydCSGoKWpgXSC5x+ZvdlO2rRohBLt/LQXAVuWkZm8DAFsXFWKvdtJQ4eD7J9cAsPyt39i6sBCA7x5fjcfppWBNGQufWw/AT8+uo3BDBV63j0UvaNuKNlWyc1kxAHs3VOCsd+Nxein5rQqAulIbdaV2ABa/tBGfx0fB6jJ+fGYdAJmDkrBEm4iMtzDwiF6/q1dynzhcdk/nv2GdoN7t4/++3kPZ9hoAnrr3hsZpz5IkdZ4Xv9nLtjXljY83/Lad//vgyxBGJEnhx233suIdlQa7g6k5iZw+JKST0CQpqDrSuTMqipIMCEVRYgB/kGNq09D0CI7KjaF4cxVuh5ZN7IMvfuDZ1//XYvm+EzKITYuU2TO7qUX/2UDV7nos0Sbi0rXrUBKyY7DGWvD7BNt/LsLv81NVUE/BmjJAy4jl8/qxxpgZdqy2FOOok/rTZ1w6AMr0bIwWA5mDkph4njYaNf7sAaQrWibI7OHaum6WGK1jBrB3YyUumwdXg4eti/YCULSxkopdtQBkDExECMgalszhVw4HtM5dRKwFvVFPWq527MyBSWQOSgKg7/gMohL2LR15aEmMMHKOEoOr3o0Qgv+8+RF+f0ibviSFpWtHJmB17c+Mmb+nmO279oQwIkkKP9YYM7OuHYnd7qTApkOtCEnCd0nqEh3p3N0K/AKMBpYBdwc1onZsr3KxpdzJmNMUYtO0L/uFJWVERUa0WD5dScBg1PPl/SuoK5UZNLsDt93DT8+uQ/gFg47oRUJ2NBGxFnqP0TpnGQMSiU2LxGDUM+OaEegNerKHpzD0aK0jN3BWL2JSIjFaDCT31gaajRYDJquWfS57eAo6nQ6jef82a7QZo9mA0Wygzzht/cSknrGk5yUCMPZ0hbj0KKKTIzjsoiEA5M3oSd/xWtl+EzMxmg3oDXp0+v3Tg7urGoeP1REWskek4nC6+L8PvkSvl9coSFJn+7DWT1bgbw5AfYONmGi5uLIkdSa33cOmBbs5+egZZPbsjdcvT/hL4asj39ayVVVVgH7AYFVVvwtyTG0yG3SIBjc/PbuucVtZRRWpya2vv6XT65h6yVBiUiMp3VqNy3ZoToX7q1v/2U62LirEZDXSZ2w6Or2OpN6x6A2yU9HV9Hrwryxm1/JiaurqiY+NCXVIkhSWYvJr2PJVfuPjugZ7Y3IwSZI6h06nw2Q1MGRADsePzGJQ6qE5a0aSOkNHFtK5CHhLVdXydkt2gZ5xZvpnRJJ4eHbjtuTEBLIz0trcLzpZG9krWF2G3qAjsVcser0uLEZZuiO3w4vwC8wRRtZ+soMhc/vQZ3w6ligTOr2OXqPb/n1KwRVrMTBzdk+MZj355cXExcq00ZIUDCfMymqWWOmiM4/D55NToCWpM5kijOROzeKUS+aTc8TpnDyhPyMyWp7xJUndXUeGRCyKoqxRFOUdRVHeVhTl7aBH1YYfdjawutBOcp/9eV2uvfB0Buf169D+Y05TSOkXz7af97LiHRVAXo/XirLtNQghcNS5GhNreN2+371fLpuHhgoHALXFNrzu5hkgvW4fbrsHv9fP1w+twu/zs2dNGWs/3oFOr8MSbcLn9hOTEok50tQ1lZPatKPKxYsbajBFGOnbM5P7b74s1CFJUtjx+wVnflyIOWr/edZf1/9GeZVcYFmSOlN1YT1L39iMze7k1BFp9E+yhDokSQqajnTubgauAZ5DWxbhhaBG1I6JPSOxqJVsbDKN5e7HXsTt+WNTLZVpWYw4PgeP08tH83/B5/EhAnOw60rtFG+p+kPH27fvJ7cvoaHCgb3a2ZhpcdOCfGyVDurLHag/aRfKV+yqxev24Wxwk7+ytNkx/ojynbX4vX4K15Wz+ZvdABSsKcNe48LvF7ibZGP0eZp3uoQQjWeMv35wJW67h+rCetZ8rK1ttv6zndiqnNgqnRSs1pKVLHltM9t/KUL4BZ/dtQy/z0/B6jJ2LCkCYNW7W6kvs1O9t4FPbl8CwOoPtrNzeQl6o55hx2oZF/tNzGTcmXmAdo2cJVp26g4lveLMjCqoZu+GShrsDoxGQ6hDkqSwo9PBP9INbPm2oHHbK+9+Rv6e4hBGJUnhJy49iuHH9cPucGK2WDDKqz2kMNbqx1tRlNsAVFVdCGxVVXXhvluXRdeCepef/rN6Mfw4baTO6XTx7aLlmIwdmWG6n06nwxxhxGQ1Muv6kRhMBn793zY2f7sbe42LmiItrf6v/9tGQ4UDv180dr5qi22NnSIhBPXlDj68ZTHCL5h13UiiEq04at14Atk8DUY9BpMB4ReNSSm2Ly6iMr8Oj91LVUEdAEvf2MJvP2idv2X/twWvy8eOJUVs/DofgF0rSrDXuKgvszd25LZ8V0BNsY24zOjGjIyVu+vwOLzYq5x8fu9yADZ+lc/mb7QvEB/OX4yjzsXuVaX8/OIGAEaemIPRakRv1NMjkM1x5rUjiU6KILlPHKNPyQXgsIuGkDMpE4AJZw8AoP+UHgw7Vvt9zLhmBAlZMcRnRDFn/hhAS0aSN12bRpsxIEleQ9cNOH1+ck7MpfeYNL5dtIK3P/o61CFJUtjxCzAPT2PE8TmA9v9k264Ccvv2DHFkkhRePC4fPo+fSaOH8toGG8X13vZ3kqRuqq0e0eHAfYH7bwUeh9yWchebfilCGZBAXHoUFdW1JCXENVvA/I+KSdGybo48IQev24c50tSYFj8+MwpzlImyrdWs/WQHs28czcIX1jPnljEUri9n57ISplw4mGPvmYBOryMykNo+qXcsSb1jAS2rImhr7+3L8Dn+rAGNrz/yxP6A1lny+wVCCOIyojCY9fQYkoyzzg1AXYmN5D5xmCKMGExaB2lf5kYAUrT54yOOy2ncdMIDkwHoOzEDa2B0bPaNo7FEm+k1Oq3x2rbU/lp949KjIL3ti/l1Oh3oILlv60se6vQ6OcWyGytt8LLw690cPTWDdz/9hhlTxoY6JEkKOx6f4INvC0kZlUjmwCTKKqsxGAwkJ8o1uCSpM9WX2ineVMUt117M0j02+iSYQx2SJAVNW507XSv3Q+qw3lHEIfB7tQvOK6trSeqkf4R6ox7zAWP1/SZqo1TpeYnM7BuHTq/jmDvHo9PpSOkXT0q/+EAWpj82ctgSnV6HIZDgZcDMQIcwxow1RvsjtG90DLS12v6IyLj988ujEmWWKKlt/RItjEmz8sPiVewtKSctOTHUIUlS2LGa9Jw5KhG/R/t/FhMVyUP/uCLEUUlS+EnuG0fysBjOuPkxrBNOYXqfaIwyoZ4UptqaHydauR9SKwrtpI3PJCFLS80+dEAOz95/U5e8ttGsXXe0b5TQEmXCEiVHp6Tws6vaTXnfBNSSXdx02dmccszMUIckSWGnwe3nC7uOrGEpANjsDnJ6Z4U4KkkKP+U7a9n9Wwne6lJeOS5LduyksNZW526UoihLFEVZ2vS+oihLuiq4liREGFj0yKrGqYqbt+6ksLgslCFJUtiJMukpf+836orsJCe2Pv1WkqQ/z6iH5K0VbP5Wu4b6uTc+4NufV4Q4KkkKP+ZIIx7hJSEmgmizvO5fCm9tzSUc2mVR/AH9kyxMnjewMXX0h1/9xKDcvuT0+WPTFCVJal1qtJHDT8ljwSPfkpKYEOpwJCksWY16Zs/t1XjdQ2FRKUcdPimkMUlSOIpLj8Jugny7nG0lhb9WO3eqqu7uzBdSFEUPPAsMA1zABcBU4EJgtaqqlwXKvQ1coqpqXUvHeXNtNScdKxozLu7aU8TcGfKfoSR1psW7bWRtr+fpf96IXuaMlqSgKKr3cMd3Jdw7Kx2APcVlZPdIC3FUkhR+tnxfwFkn9mTxS7eFOhRJCrqu/NZ2HGBVVXUCcAvwKHA2MBHooShKgqIoc4GfW+vYARyfE82at34DtLTR+XuK6NuzR/Cjl6S/kAnZkVgWFfDf974JdSiSFLbSo42cYfZRsLoMIQTzZk8lRWbKlKROlzs1iw3lpbz+za+hDkWSgu7gUzx23GTgawBVVZcpijIaWA9YARPgB/4OnNrWQZb/8huGuSlsek5l1mHjuP/my4iPiwly6JL017L4+51UHp7A8s+X87cz54Q6HEkKS8uX7KS6eglRqQK2wKXnnNS4FqokSZ3n829X8GtBA5GJKZw7e3Sow5GkoNIJ0TWJMBVFeQn4QFXVrwKPC4AzgBuBzwAzsBtt2mY28G9VVdVWjlPYJUFLUtfJUlX1glAHsY9sZ1KYku1MkoLvkGpnINuaFLZabGtd2bl7DFimqup7gceFqqpmBe7HAi8AdwFXBn4+oarqmV0SnCRJkiRJkiRJUjfXlfM/fgGOAlAUZTywoclz84GHgEjAh7auXnQXxiZJkiRJkiRJktStdWXn7iPAGVgn73HgWgBFUXoD8aqqrgXWAT2BL4GnuzA2SZIkSZIkSZKkbq3LpmVKkiRJkiRJkiRJwSPTckmSJEmSJEmSJIUB2bmTJEmSJEmSJEkKA7JzJ0mSJEmSJEmSFAZk506SJEmSJEmSJCkMGEMdwMFSFGU0cIuqqicdArH0BjaqqtqtlnFQFGUu8ABgAdYD56uqWtfRcoqiGIBHgSPRPlOPqKr6fGCfY4DXgYImh5qiqmp9EKvUYcGse5N9/w4cr6rqMUGtTJDJtnbwZFv7a9b9z5Jt7uD9lT93B1v3Js9nA8uAYaqqVnRF7F1NtrWDJ9vaQdU9AngGGAvogOXA5aqqOv5MPN1+5E5V1VWHQmPsrhRFSQFeBU5UVVUBdgIP/sFyFwO5wGBgDHCNoihjA89NRGugw5vcDpXGGNS6K4qSqCjK88ATaI21W5Nt7eDItvbXrPvBkG3u4PyVP3edVHcURTkbWARkdkXcoSLb2sGRbe2g634rWod2aOAWgbYG+J8SDiN304CnVVUdrChKHFrPdzjaQuhfAf9QVdWrKIoT7U08AsgAHlZV9bnAMW4HTge8wFbgClVVSxRFOQG4DfCjLa5+o6qqixRF+QlYDUwGkoH/U1X1zkBIhsAX+rFAHHCTqqofBF7nVuBEtE51PnCZqqpFgeNVAXnAc8AbaB2CIYAJ+D7w2t523gsBpOw7s7bvceD9eKSFXW4OxL9SVdVtgW3PAesURblcVdWm62Qc0Vo54HjgP4H4qhVFeQf4G7ACrUF6FEU5FagDblVVdVE79dABdwAnA7FANtrZmmpgGvBTC7u9D9QAFwIDgV3AAuDTENb9FKAIuAHo1qN2INvaAe+FbGuHVlv7w3XvDmSba/ZeyDbX9W0uAzgOmA2obdWru5Ntrdl7Idta17e1RUC+qqr+QOxrgEFt1a8t3b5zd4AngUq0D7IZ7ZdxA1pDtAAVqqpOVBRlFPCLoiivojXEOcAYVVVtiqLcBbyGNiz8L+BMVVWXKYpyBNqHYd+HSQEmAVHAMkVRVgIbASvwraqqlyiKcjzah+GDwNmvIcDYwB+Ii4CXgKMCx6tWVXUggKIorwC/qqp6bmCY+jXgOuDhP/OmqKr6HVqj/B1FUW4B9jTZVIjWCGLQGtA+2W2Ua+m5oYH7lcDbwAdo79cniqIMU1W1sI2QL0D7nYxXVbVBUZR7gAmqqs4KPN9iXQKeURQlH+33tqqt8sGuu7p/OsG5bcTbXcm21gLZ1rpV3bsb2eZaINtc8OquqmoRcELgeG2EFXZkW2uBbGtBbWvfNDleL+Aa4KI24mtTuHXu5gCTAj1lV+CsxzXsH/b8JPBzNVoDjQrs86qqqrbAc08AtyqKYgbeAT5SFOUL4FuaN4gXVFX1ADWKoryPdmZrI+Ded3YFWAukBu4fjXYGZlXgj6QBiGxyvJ+b3D8aGKsoyvmBxxF/9I0InLHYd38mrZ9x0KOdmTqQ74DHbZU78Dndvv1VVT2hyfbFiqIsAWahDU23ZjbwsqqqDYHH/wb+EfidRNLK2RZVVe8/cGMo6x7mZFsLkG1N0w3r3t3INhcg25ymC+r+VyXbWoBsa5quamuBEwYfoY0if95C2Q4Jt87dgW+cHm0oeh8HgKqqItAodGgN48B9jIBOVdVbA2c+ZgHnAtejNSrQht2b7rPvl+Npsl2w/1orA/BQk+F7C5DQpGxDk/sG4GRVVbcEysbT8geiJfsaeWMjbudsSwowrsmmHmhnfmwHFC1orZyiKAU0n4+fCRQG4r4MeKDJ0LSO5u9RS3Q0r69Ae399qqrWtFaXloSq7h2NrxuTbU22tWa6Yd27G9nmZJtrJth172gcYUi2NdnWmumKtqYoymnAs2jTed/uaGwt6fYJVQ6wALhCURRd4AN/EdpZkrZ8DfxdUZSowOOr0IbLfYEh2cjA9LrLgKGB4wL8TVEUvaIoCWjXVn3WgdguUBQlNvD4HuD/2ih7bZN6fApc0c7x99k3NN/Ra7y+AcYritI/8PgS9p+V6mi5T9DeQ2OgEZ4GfAzUA5ezf1rHCLQ/aF+3E9MC4FxFUfb9cbkC+E5V1c4+kxjMuoc72dZkW/sjDsW6dzeyzck290d0Rt3/qmRbk23tjzjouitaNtAngSMOtmMH4de5uwpt6HpD4KYCvxtePcDLwHfACkVRtgAj0ebYetGG4d9WFGU12kWWf1dV1RXYLwLtIs9lwLOqqn7fzuu8BHyONqd6E9o84nPbqEdUoA7rAz8fBlAU5R5FmzfcmnmKomwGzka7KLRNqqqWAecB/wvUfwjaWSUURRmtKMra9sqhXRS6A1gHrEQbBl8YaEDzgBsURdmINnx+qrr/It0vFUU5toWwXgJ+RPudrEd7r85vodxBCWbdOzvWQ5Bsa7KtdVgo6x5GZJuTba7DOqnuf1Wyrcm21mGdVPdH0EYaX1IUZW3g9syfjUknREdHaKV9FC0j0dOqqv4vBK/dH21djFtaeK5ZhqNDnaIoFwJ7VFUNtzPsUieRba1zyLYmdZRsc51DtjmpPbKtdQ7Z1n4v3Ebu/goUtKHbcOBFS80rSYci2dYkqWvJNidJXUO2tTAmR+4kSZIkSZIkSZLCQFCyZSraehovop0Z8KHNMdWhrbEh0NK8Xh4o/iHaQpC3qar6raIofYGrVVW9OhixSZIkSZIkSZIkhaNgTcs8BkBV1Uloq8M/FrjdpqrqFLSO3jy0tKL5aIs87svgcxvwzyDFJUmSJEmSJEmSFJaC0rlTVfVj9q+s3gsoBUYB+7IIfgXMRFuPIypwsymKMgnYpqpqaTDikiRJkiRJkiRJCldBW8RcVVWvoiivA8cDJwFHN1l8sB6IU1V1q6IohWirxt8G3AvcrCjKc0A12kifv+lxp19xojhBmYbw+8HrQWe2IEnd3dNPP/2yqqoXhDqOfcZcOleclXcE6HTodLr2d5CkbuBQa2cjL50jTs6dRrQxssXnhRA4/G4iDfL/nNR9HGrtDODka88TU/qMaPf/mRBC/s+Tuo3W2lrQOncAqqqeoyjKzcBymqxyD8QQWDdDVdW7ARRFOQNtMb8L0dYLmQbM4ICFIwdl53LllVfi2fYbtc8+QvLjLwWzCpLUJZ5++unCUMfQ1PCeeZy+ZTkJtz2IMatnqMORpE5xqLWzSeYULqgrJ+XOR1p8fm3DLh4o+JB3B17ZxZFJ0p93qLUzgCHFVVwwN4fImUe1WmarvYj7Cz7g9TzZ3qTuobW2FpRpmYqinKUoyvzAQzvgB1YpijItsG0O8HOT8lbgROAtIBItCYsAon937MhMAHSRkQi7LRjhS9Jf3tDo3ugsVoTTEepQJCls9Y3MwOBvPWP1oMhs9rgqqPHK/3WSdDAijFbw+dos0zcijd2uMopcVV0UlSQFR7ASqnwIjFAUZRGwALgGLTvm3YqiLAXMQNNFG68BngxM23wVeB4tyco3Bx54Uc1mAHSRUQi7PUjhS9Jf22eVK9FZLAiXK9ShSFLYWlS3BY+n9TZm0hvJichgp1Nehi5JB6PGZweft80yRp2BqXGD+KFmQxdFJUnBEZRpmaqq2oBTWnhqaivlH2xyfy0wrrVju4QHAH1EFH45cidJQeHwu9FZkhAuZ6hDkaSwZcfT7hfO45LHkmCM6qKIJCk8+XQg2hm5AzgmaTQ7nWVdEJEkBU+HOneKoujRli+YCCxXVdUd1KjaoCNwoavFAl4vwutFZwzqpYOS9JejQ6dNy5SdO0kKGqHTgbftL5wnJI/vomgkKYzpdO1OywQYHZPD6Jgc/MKPXhesyW2SFFzt9ooURXkI2Im2pMFItGUNzglyXK2KNWh5WXQ6HbqoKIStAV1cfKjCkaSwFG+Mkp07SQqyaGMkuna+cL5fvoQ6r53zM2Z2UVSSFH4MBiOinVHyff5b9jOVnnqu6NF68hVJOpR15LTEZFVVXwAmqKp6JJAV5JjaNDa2f+P9+OtuR2eNaKO0JEl/xqyEYUTOmYcpd2CoQ5GksDWtxzhiT2n7XKlBpyffVd5FEUlSeErpOxjLyFav+GmmhyWJTfY9QY5IkoKnI507g6IoY4F8RVHMQEqQY2rTNkdR431T3iCQy5FIUqdb07AT08ChGNLSQx2KJIWtpa58fGPGtlkm2RRLhaeuiyKSpPBUGq3H2LNPh8r2MCeyV2bMlLqxjnTu3gCeAh4BHgaeCGpE7Sh21TTer3vuMdybZVYjSepsu5xlNLz5EraP3w11KJIUtgor82m4/Nw2y2SYE4g1tLzIuSRJHePftJGGd17rUNkMcwI9LckAvFW2CKc/ZGkm/pB/7fmExws/kyeDpPavuVNV9Vng2cDDa4IbTvt0uv1DdfE33hnCSCQpfOnQEXP2RaEOQ5LCWn2EkahHXm+zTP+IDB7qe1YXRSRJ4alW6UvMmed3qGykwcITOedT6q5hfcNu3itbwqmpkzgjdUqQo/zzttgL+bl2MxNjFe4v+IDH+50X6pCkEOpIQpWzgVsA675tqqr2DWZQbWk61OhavxqdTod5yIhQhSNJYcuzazuioV62L0kKEr3Pj/f7BXDEvDbL/af4WybH5jEwKruLIpOk8GKurce9eT3mgUM7VP6K7S+ypE5lwZDbeaP0J2w+LbmYy+/BojcFM9QOqfHaiDJYeWrvF+REZDAzfijP9r+ILEsSQgj2uqpYVLuJ0w/hDqkUPB2ZlnkzcCwwoMktZEbH5DTe96ibcP26PITRSFJ4mp0wHM9vG3Es+j7UoUhS2Do5YSyu115st5xFb+KN0p+CH5AkhamsWi/OX37qcPkldSr9rOmkmxO4Kft4Tk2ZxPxdbzJz/V3sdpazoGotOx2lQYvX6XdzV/673LDjdQpdlY3bvULLrnvV9peZs+FedjnLGBndl0iDhSxLEqDNcLPqTbxVtkguyP4X1ZEF4naqqro96JF0kKPJ3Gd9bBye4r0hjEaSwlO9z6EtheB0hDoUSQpb1X57u4uYA5yQPI65xd9R7K4mw5zQBZFJhxIhRLNLUvx+f+Pjptul1vl0okPr3O3z4cCbiDXuv9b1rbJFWHQm/p4+g1X12ylyV7PJXsB1WccGI1zeLV9CsbuaoxJHEm+MauzU/V19hsf7ncfz/S+mzFNLL0tKi5+BJFMMd/Y6lSu2/T975x0nR1338feU7f32eq+ZSy6FJISEJBBK6AKiIB0VEPSRYkN4EMQKWFBAH1EQLFiwgIrSkdASQktvm3K53m/v9raXmXn+2MslR9olXHIQ5v165ZXdKb/5zu3O7Hx+3/YQnyk8kS8Un46ma7SngpQN5xMeqUTVBHEtRa7JPbJM13WeH1hNta2AOlvRBFp3eBiLuIspivIMsArQAQKBwK2H1Kp9EIjtFHOi24s2FJooUwwMjljeDG9BsCxETyYn2hQDgyOWF0Jr+Upm/+LOJdm4tOB4VkeaKMoxxN3BEI5G6RsYINfnw+VwvK+xovE4TW3tZNQMhXl5+NxuMsOfY0ZVaevqYltrK6l0Br/XS2tnJ/FkEq/LSXlxMbXl5fi9XgRBQNU0mtrbcdpsmEwm3E4n3X19bNi2jeBgCJvVSldvLyUFBUyqqqSjp4etzS2kMxnQddwuF7Ik4bDZqC4rpaSgAJfDgSAIZFSV7r4+cjwebFbrvk/qCGdAjaOP4VrbQY1tdKXoLxSfPur9lngnn9/8Sy7NP54C8/j1Wl4fbUUAwpk4Xy09h0n2YnRd58KN92ARTdhEMzmyE0EQqJTy9znWHFct/5n2DdySjbXRZn7e/gxvh7fys9qr8ch2ulIDLPRMRkLEJO4uB/rSQzwbXEm4tHUWAAAgAElEQVR7Kki+yUOdrYhjXHX0pofwynY2xdqZ6azaY7P3ddEWXhpcS52tiBJzDtOdlXyz6TEWeaYww1mJgIDf5Bq1T1pXMQnSHs/ljz2v0pUa5AzfTKptBVhFMw93vsjLofWc6juKWlshc1113NH8F14aWIsgwLcrLmKBp56rAr+gOdlLkdnL9SVnUWDyEsyEUXWNdyPbONE7DRGB33S9xGAmSoU1j7nuSUx3VIxETFRbC6iyFlBiydmjfSktw9Z4J+9EtpHQ0lxTdAp/6H6F/+t4lpnOKibZirmx5CwyuoosSGOalHnvpM6BMBZx9/RBjXyI2PVERZcbLWxUBTIwGG8EBASLzWhibmBwCFEFxuS5A7i26NRDa8wRTDQe5/Hnnsfv9dI3OIDH6UKpqqKxtRVZljj2qJm4HHYGw2HsVhuyLNHZ24tJljGbTOR4PPQNDNDd38/21jZ6gkGK8/Nx2Gys3byFWCKBJGYfcHVdp7KkBKWqikwmQ3d/P2efeCI2q4WBoSG2NDXx7KuvoQMmWSadyWC3WtE0jWQ6TTQWw+NyMaW2lml1k+gPDbL42Hk0tbezcds2ivML+NQZp2OzWEAQCIXDZFSVUDhMU1s7b6xaRTKVxiTLZFSVHI+HwXAYq9mMLMuYZBld13E7HdRWVFBZUoIs7fmB+ohCFMZ8rY2FOlsRVxScwN96l41Ls/OUluHl0Dp+1Povbi3/JNeVnDGyThAEflF3DWuizcx11R3QA3/esPcqz+TGIVn45aRrKTbnMJSJcV/709yy/Q9YBBPPT/8mAgJNiR5+1PYvLsk/joWeyXSnQ+SbPPSmQzQ4ykjrGT6+/m7yTG7muScx21XDb7peojM1wDRHOQBn++fwm66XKLbk8FxwFQ2OMqY7KznOM5m/9i7jjua/cLJ3Gt+quJDvtzxOU7KH5kQveSY3f5r8ZX7W/jQdqSCKrRjFXsKxbiWb6yiY+Pr23zPVXs7d1ZeT1lUuL1jEO+FtlFtyEQWRRZ4Gbio9lyE1TjAdxiqaua3ifApMXnxydtLj3fA2bm/6MwCznNXU20qotRXhkm1U2wpoTvQSTEeArODsTYVYGtpEjsnJXVWXcXfLE7wZ3sJMZxXHe6ZwgncqX2/8Pc3JXmY5q2lwlKHrOvX2Eh6b/BW2JbpIamkEQeCe1id5JbSBi/MXYhPNXJA3n2VDAV4cWA1AjbWQC/MX8ETfm/yg9R9ICNhEC/fVXkm5JY9He14mpqaQBIGvl5231899LOLuj8C1wBRgM/DAmL9VhwBxl8Z2otuDFhrcx9YGBgYHgwAIFosh7gwMDiGaAKjqmGZoU1qG25v+zF1Vl+5xptwgi67rtHV3s27zFhLJJPFEgnAsxuyGKRw9dSq6rrOpsZGWzi7qq6uJxGM8/txzZFQVp8Mx4oFz2u3oQDyRwGI2EYnFKSsqpLq8jDMXHY80LIhUTSOVSo14xt77WU6p3VknoDA3l8LcXBbO1gmGQiPhlTu8eADxRBKrxTzyvjAvd2ScXcfagd+b9RoV+P1MqqxE0zR0IJ1OYzFnx9E0jXA0iqpqpDPprCgcCrNq4yb+u+wNXE4npQUF6Oi4nU78Hg/xZBKHzUZeTg6yLI+I1w8tgoh+AGGZY+HThSei6zobY234ZRf5Zs9Bj3VH82M0J3q5tfyTnOidutv6XJObk7zTDnr8QrOPn+xaQdPi58mGW8igEVUTWEQTX2v8HSvD2/ly6dmUW3KxiWa+uoew0wfqrmFTrJ3LChYB2dYRoiDyWmgjtbYidF3nnprP7LbfKb4ZnOKbMfJe13VO9mXPqcKSN+IBPd4zhe2JHt4Kb6E7HWKuq44rC08GGCV6P198GgCn+o4aWbbYly2Y45btIzmIk+2lo+yY7arh6Wm37WbfnibQrho+7q5cmL+AT+TO4z/Bd9gQa+ME71S+V3UJTmm0d3xHjZAK68724DeXncfH/Efzu+4lVFkLAJAQqLeXktTStCb76E+HOdc/h7P9R6PrOnEthVu2050axCZayDW5cYr79sSPRdz9ChgEXgAWAb8GrhjDfoeEcsvOP5KYk4vwUZhxMjA4zEx1lCNYbQeUo2BgYHBgLPRMAfmd7HUm7/vn2CzKrI420ZLso9K673CsfbGpcTtWi5nKkpJRy0ORCG+tXkOuz0dNeRmbGrfjtNuYXFODruu8sGwZgiCQSmeYXF1Fvt/Pyg0bqSgpRpYkfG43dpvtoO0aDzRNY9nKlbR0djGlpga/z4vdasXrdo+IE0EQmFxTw+SampH9GmprkUQRSZJo6ezEYjZT4PePjNnR04PX7cZp373foCSKo0Iex+JV2SHo9oTNajmgc34v4vB5ShbLqGUe1+gQuAK/n0lVWTHYEwzS3dePIAgEQyFaOzuxWawMRSL0h0LIkkRVaQk+tweXw05pYSEWs/l92Xm4cZsdoGvjPq4gCDwdXIEA7yv/7s7KS9HQkQ7jxI0gCJiQ8MrZMOXvVl5MUkuPylXbE0e7akcVNzw95+AqaguCwLFuZbflM5yVzHBW8vHcYw5q3EPNDlH2FfvOz/u9wm5vCILAVEc5P6r+9Miyue5JzGXSXvexS9lrudTi55qiU8Z0nLGIu7pAIHD88Ot/KoqybEwjHyJ2nRkRnS5y/+/3E2iNgcGRSaU1H1NdPf4fTaij3sDgiKbBUUbhP3445u2P90zhJ23/5u6qywhmIiMz02NF13WWr1qFpuucftxCivN3isR3163HJMt09fXx9tq1KNVVbG9ro29gkFA4jA5UlpRgNplYHQjQ3dePUlXJ22vWouk64UiEwrw8lKoqovE4Q5EI846agWkvolXXdVZv2oTVYkWpqjzo3JJILEZfcIDB8BAbtm7DZrXyiVMWY7WMXSSZTTtL25cXjS62IIoipYWF793liEEUxRGP4t4YikRoam8nFI7Q1tXFkjffwmI2YzaZGAiFsFos5Pp8xJNJNG38BdR44Kmegvf66w/J2BfmLeDSjffy+aLT0NExiyYSWgqXZEPTtX162jO6yrpoC52pAc7ImXVI7BsrLsmGS5rYCRqD8WEs4s6qKIo9EAjEFEWxARPqKlsVaRr1Pvrk37AuPBEp58iu/mNgcDh5JriCLybiJF55Aftph6YamIHBR51fdjzHVc9XYTvhFATz/sXI9SVncnPjo/x3cC2/6nyOP9V/eVRFv/3R3d+PxWJm9pQGlq1YySdPOxVBELKhjF1dnH3iifg87pHQwqFIhCVvvklteQVKVSXysFCrr64iFo9js1pHRFk6nWZbaxsbt23DbrORzmR45tXXOOP44wiFw2xraaWxrRWH1UZFSTHpdIYtzc3IksTW5mZqysvI9/tJJJM0tXfg93pQqqoQBGHkWKFwmI2NjfT0BzGbZEyyidbOTvw+H7IkcdKx8yjw+40KkuOM2+lkurLTwxKLx0mkUiRTKfJycojG4wwMhrDZrLgcDr45gbbujca+bSTfeQPL0ceO+9ilFj+zXdX8J/gO/+p7m87UAFMd5Xy38iIu33Q/nys6hbNyZu0m8jbF2rmp8fd0pIJclLdgwsWdwZHDWMTdfcBqRVHWkc27+9YhtWg/vPeWLewhTMLAwGB8UDs7JtoEA4MjFh3ItDahq+puv217wiXZ+EXdNQC8HtrIv4PvcGn+8fvZayeBxu3UlJVTV1nB6k2bCGzfDsDawGZ0Xcfrzobu7RBHbqeTc0/ePecE2C0E02QyUV9dRX11FZANZ3z5rbd45PEnsFut1JaXc9LcuUTjCZra20ln0px23EJcDgdbm5tp7eri3fUbsFosuBx22ru7WLpiJRazmWg8To7HQzgaZXJNNTPqFdLpDL0DQRYePZu6ioox/w0M3j92m23U5+9xOvE4nRNo0f4RMxnUnq5DNv7NZeeR1lVeC23kq2Xn0GAvwyKa+HblRfyo9Z/8uec1vlVxIZPsxbwxFKAl0cfpOUdxW/n5NDjKRtWTMDB4v+xX3AUCgT8Ot0KoBrYHAoH+/e1zKHnvjJx98VkTZImBwZGLgIBoteH6zOcn2hQDgyMWAXBfdd1B7TvbVcOGaOuYtw+GQmxtaeGSj52FIAgcP2cOz772GnabjZkNU5ClsZXnHiuiKHLSvHksmDULs8k0auzqstEFDt6bA7eDaCxGIpXC63LR1N5BXo4P9y4ioq7SEHUGYyPjcWM/c+/VBd8vO4qB/Kz26lHLZzqr+EP9jbw4uAaPbOfV0Aa+2/w3zsudi0d2MNddd8hsMvjosldxpyjKbYFA4HuKovyZ4f52w8sJBAKXHBbr9oD7PfHA4T8+jOhw4vj4hRNkkYHBkceO8sn9N38R363fR/SMXx8fAwODLOXWPIJ3fA3PDbcg+Q8steAU33QWuutH3uu6zvqtW5lSUzNSVANg47Zt9ASDNLa0smDWrJHiHwW5fq74+LnAoW2E/X4KbzjsdhzD0Tk15WXjZZLBRxDXYIzQz3+I57qvH/Zji4LIqb6jUHWNj627k3trPssCz+TDbofBR4d9ee7+Pfz/Lw+HIWPlvVXCRLsDta9ngqwxMDgyme3KzqJrA0G0yJAh7gwMDgFn5sxCXfP6QbUc8clOmhLbKTB7kQSRaDzOq2+/g9Nmp7K0ZKTK41tr1lJRUszpxx9HUV7eqDGM3DSDjwp+0U6mrWVCbZAEkWem3bbfapQGBu+XfdVcXacoihm4EVgGvAG8BdxxOAzbG23J0VGhYm4eam/3BFljYHBksiqSzcURnS60cHiCrTEwODJ5LrgSJPmgmyvftv1PNCayv3+9wSCSJPHu+vVE43GWvPkmz732Ol63mxOOOWY3YWdg8FFiQI2BNvGtfQxhZ3A42Jfn7krgVqAQCAwv04DXD7VR+yKijp7hlAqKULs7J8gaA4Mjk970EACC04keMcSdgcGhoDnZhyD70DMHJ+5O8Dbw/MAq6mxF9AaDTJ80CR2dPz75b/L9OVxx3sdHhWgaGHxUSZCGzMSLOwODw8FexV0gEHgIeEhRlCsDgcAjAIqimAOBQOqwWbcHhPdUFJILilG7jIp+BgbjyY7rTHS60AxxZ2BwSBAAJCnbxPwgODNnNjdv/z1X555Me3cPR02up6q0lKl1ddis1r32mDMw+KihyzJaKDTRZhgYHBbGMqUnK4ry0+HX/1EU5fJDadD+EN+TIiC4PaCpxgOogcE4suMyE5xutKhxbRkYHCoEWT4gz92qjZsIR6MATLGX8nF1Fg8/8QSZTGakAbfb6TSEnYHBLugmM3o0MtFmGBgcFsZy9/88MH/49VnAq8Cjh8yi/VBrLRr1XhAE3J//ChiJ4QYG48aJ3mkAWI6eh2C17WdrAwODg+GqwsVYS/MQ3Z49rl/bncAkQpXPjEUWSSSTLF+9mhUbNnDSvLkU+HNRt0aYvrCBWf5aJEk6zGdgYPDhoMxVhPV4o3WGwUeDsXju1EAgkAAIBAJpdmmLMBGE1d2rillPPA3BYp0AawwMjky6UgMAWI9ZgGX6rAm2xsDgyGRLvAPHJy5GLi7d43qzJPDfxijf+G83c361le3d/RTm+jl53jzeXL2Gjp5uyDNzT+jp3ZqKGxgY7CQqZA66p6SBwYeNsYi7fymK8pqiKPcoirIE+NehNmpf9Gd2DxGLPPogsX//fQKsMTA4MgnEs3msiTdeZeiR/5tgawwMjkxeD21i6IGfkNqwZq/bXD3bx12LC/nzBWVEhwbJ8+VQVlRIPJlk/ZatzM6tpSs1yFAmDsAfu1/lvvb/HK5TMDD4UBBMR+i/5Tq08NBEm2JgcMjZr7gLBALfA64n2wbhS4FA4AeH3Kp9ILJ7+KXz0qtwnHfRBFhjYHBksuMqM0+bieMTl0yoLQYGRyqCAPaPX4hcWbPH9b96p59wSsMkCfhtMhu6QuTl5CCKIkfV19PR20tlUTFT7KWsjTbTGO/mh23/RNMnNMDGwOADhwB4vvg1BJt9ok0xMDjk7FfcKYpSAnyNbGuEYxRFmXvIrdoHe8qs0xMJYi88ddhtMTA4UtlRLVOQTajtrRNsjYHBEYyuwV4Kqvzk9GJy7dnU+HVdUd7oyFBaVAjAUZPrufqC88n1+fhc0SmUWHL4S+9SvlF+PjeWnEUwbRSPMDDYiYCuaejJ3VN7DAyONMYSlvkg8AhgJltM5b5DatF+2GMDSEEk/Kt70TXt8BtkYHAE0uAoA0CLRRi8+/YJtsbA4MjkZO90Io/+muSqt3db1xlO85sVwZH3eXofZ1fo2K0788vl4QIq89yTqLTm86m8+VyQeyxLhzZxU+PvxsXG6B7y3A0MPmzkmlyEf3Uv6a2B/W9sYPAhZyzizhoIBF4C9EAgEAAm9E5vEU27LRPtdgSPl+jf/2DEU3/ICEejI2W9DT44uKVs6Iro8aGFQwfdZNnAwGDv5Js8IEt79NyZJYEKr3nkfaCxiVeipaTVvYdc1tgKEQSB2c4aNsTaCGXe37313fA2Tlp9B53DBZYMDD6sWEQTgt1htEMw+EgwllYISUVRTgMkRVHmsR9xpyiKiaynrxKwAN8DNgC/JVtpcx3wxeHNnwCKgNsCgcALiqJUAzcGAoEb9zZ+d2pwj8t9t36f6D8fo/eqC5Ara7Cffg62k05HGwohOF0I4lh07KFF13XiT/+T2FNPINhs2M44F/vis9CTSQAGEgm2t7UhSzK15WU47B+e2HBd1wmFwwxFIgyGw/QGB9A0DUkSkSWZorxcLGYLoiggitmS3ttaW2nt7EQQBOxWG3UVFVSVlmK1mLGYzYgfgM/so8pb4S0ACJKE6PGiDQaRcvMn2CoDgyOLv/Qu5dq8o9H30MQ8xyZxUrUTTdNYumIlfYMDLJo+G1XXMe0xQWEndsnCmTmz+E3XEr5U+rGDsu3NoS18rfG3nOWfjQAMZqJ4ZceobSJqgr70EJVW495g8MGmMzWA4HCixQxxZ3DkMxZxdw3wYyCXbO7dF/az/WVAfyAQuFxRFD+wElhFVsC9rCjKL4FzgWagCfgsWeH3AnAb8L/7GlzcSz87U80kvF/9JnoySWr9anQ1OxM6+KNv4fr05xHMZoYe/jmSPw/Rn4fociM6XQhOF+apMxFkCS0aQfL5s14KSUIYx955WizK0P0/INPZhvu6m9CTSaJP/Bn74rOIvfgU4fY2nvaXcPJTjyEPDdIrinTLJsIFxQRrGyicMxdHSRnS5g3I02fh3rCK5NtvIMgyyCYEkwnztJmY6qcieX1oQyEwm0HTiS95FkGUQJYRJAlECUGWMTVMR7Q7SW/egHnaTDLdnWjBPgSrDbmyBn1oEHVwENFmR8ov2ON5NbW3s6W5me7ePjRdx+VwkOP1UJDrxyTLqJpGKpVia0srmUwGTddQNQ2TLFNeVMxxs2djMZvp6Olla3Mz/16yhIyaQdehvLiIHI+HydXVyLKM2bS71/bDRDQeZygSQdd1wtEoiWSKWCLO1Lq6iTZtN3b95os5uWjBfkPcGRiMMwKALO/Rc/fgO0HsJpFzak1saW7ihGPmUF3mJprSRn65/7x2kLMVN33RDGu6E5xa48Rqyk6KXVdyBu+Gtx2wTTE1yaZYO3NcNTxafyOV1nxUXeOCDT9mhrOCNZFm7qi8kAKTh8s33U9MS3Bn1WUc75lyQMdpS/bjlR04pcPTxmggE8Et2ZGEA5801IcL1IznM4HB4UUARIcTPWKIO4Mjn72KO0VRdsSD9ABXHMCYfwN27UuQAWYDrwy/fwY4lWzunmP4X1RRlAXAlkAg0L2vwYX9zFgKFguWWceMvM/57k8B0KIRHGdfgNrfg9rXi9rZRjoSQY+EMVXVkunvJfbPv+K95TuE7r+bxMvPg8mMYLEiWCwIFgvmKdPx3HAL4T89gnnKdMz1Uxm4+/aswJKk4f9lBFnGdvo5mCpriDz2W1xXXEv4kV8guN34v/IAgtkCgGXmHFZu2MjyoRSCu4BT5syh/Jyz0ZNJEpEwyXAY5+svkbt9K9sDLrYORZn0xKO8HU7gHejDI9vIc7so8ecgptNE//Yoos+P77a7iPzxYcwz52BumIHa0pQVu6qaFa6aiq6qSEUlgEDshacwT5tJauXbxP/7DNpgED2ZQE+lEHP8mGrr8X7lNkI/+wGWE0+jAxn9vu8RzCuitW4qynGLOKq+nlyfb48/fmpfD1O0NOntW0AQEBCQyyZhrleIv/wC+jHzKZTAN9DFHLsEgkwilSLYtJnBoTB/XbMGUily1RTOqTPI62rHnkpkS80JAoIgkFZVhsxWUmVVFKx5G2bMxu50Yv7zI5BKoiNAbj6CxwuCgPmk07G5PWTaW1Cr6jBn0gi7zJ6PnIZsQnQ4iW1cR0dGIyzKRNtbyUgyggAOu41YMoWuQyijktY0LCYToigSTySyYo6dDwdelwsEAZfdjtViwWm3f0A9lDs/R8nnRw328eGW1gYGHzwEQUCQ5JHJyF25enYOGU2np7cbv9dLTXk5LzVGeL0lyjdPKEDTdTb2JjmvHjzWbO6dLO28bn2yk8W+GVwZ+Dl3Vl1Kodm3X3s0XeP6rb+mwprHLFf1iEdOEkR+Vns1P2l7kpvLz6PWmi3qcl/tlSS0NPe3P4ViK+bV0AbOzz125HfgrpYn6E+HmeeehFu2cYp3BktC63g3vI2/9y3n2qJT+EzBiQCIByi61kabialJQMAt25hsz/YKTGsZNsTasEsW6mxF/L77ZZYPbeat8BY+kTuPL5V8jJsaf0d/JsK5/jk02MuosObxWO9SBATKLH6mOyopNvvYFG/nN10v8dLgWr5QfDpXFpzEn3pe47zcudglywHZazCxCAjDnjsjDcTgyGdfnrsA2TBKgZ2Ny3e8rt7rToFABEBRFBdZkXcb8ONAILBjjDDgCQQCmxVFaQPuHd7mu8DNiqI8AAyQ9fTtViFFVg/uQVh0OLEcPW+v66W8Asy3TAXA+5Xb0L90K92dHby8dCkkk5g0lbkzZ+LWdczzTwCHk4Sqop1wGms3biAZj+O22dAzaeLRKANvvEli9QZKW9tp+cc/EQqrQZaZtrURn9uNpusMRcKs2riRS8/+GA6bDWk4OV4wmbE7XdgLgToFgJIdhi4+mTpNozcYJJXJsG7zZl7v7sEk22Dhmdlt/vFP/JNnk2/3wvYm9PmL0dExyTIVxcWIgoiOTkjX0XUd7w230DcwQObo+XhPPA1JkujbEkCzO8FqJanrhDs76Zx+DIF1m3B5vDRc+BnKGgNUvPwfWPIkUlUdQ7l5uD53A2pLE+mW7dgXn8nAt28iFdiIlF+AqW4ygiii6xpijh+ATPM2LDPnoEWjpAPrQddB1xF1nVwgV9eZdfFlpFuaGFjxFr1eL/prL5Loas/Kj5EZVXDWKERqJtGXSDDU3kEokcBq9aD7bGTSaTxDQ8h9QUBnu3kZlqFBSpo2s/nohUx79TnyO5qyY+3yvQgWlrH6pLOpeud1tIYZWItKqP7tz2D4YUzX9ZHtM5/7Eqb8fLQ/PULiuluwv/Yi0orlkEwg+vyYiksQBs3IRaXYF55C/KVnkX1VmD6AzYe98Z0PLmJOLlp/3wRaY2BwZJKbdgx77tK7rVvZGafOb2EoEsXtdAGwqNLBiVUOfrdqAIsk8PUFuXRGMmi6zhl1Ll7YFmFDT4KvLsgbGafaWsinNtyDRTTxG+U68k0ezKJMMB3BK9t5KvguD3f9lyn2Mk70TiWupbit/Pzd7Cmx5HBPzWdGLdshqB6a9AW6UoM83recBzqeQxJE7q25kkvyj+Nf/W+xIdZKjuziVN9RrIk0IwoiT029Fb/sYkloHd9p/itHO2u5rOB4jnJW8dfepcx31xNRE3hkO2ZB5sn+t3FIFgpMXhZ5G1gTbWbJ4Dp0XSeiJnho0v/QlOjm5u1/wCVZOck7jTpbET7ZyVk5s/lh9RVE1QR2ycKn8hYA8LfeZcxx1aGhk9ZUNDSe6n+XtK5SlDObb2z/Eyd6p/LKjO9iFy3EtRRros080PksFZZ8rik6hUXeBk5d8x3KLH7KLLmc6J3KIm/DOH1DDMYLOSMS0VykeoO4JtoYA4NDjKCPsR+Ooig5wMAuIm1f25YB/wB+EQgEHlEUpS0QCJQOrzsXOCUQCFy3y/aXkC3u0gA8DpwArA4EAi+8d+w7775bv/WWW8Zk867ouk4ylSISixGLx0kkUyRTu/xLp0il06QzGTIZlXQmQzgS4bg5R1Ocn09fcIDnly4lo6qIoogoikjD/8+oV6irqGBNIIDZZGJyzZ57FsUTCVas3zA8hoAsycye2kCOx3PA57MrsXh8VF8jXddp6+oiHI1lnVsIIGTDAju6e2B4mSAIWZE3FMZpt2MxmxkMh7OCz+3GJEuAMOzFEsj1eZlcUzPKXl3NoPX3k968AS0axnbS6aj9fWRatmM9ZgHq4ACiy4UgjSUCePxJJJNkVBWzybRbWGc8kUQQwGqxkEilyOyjaIgsSVgt+5+p1VU1m5/mzyPd3Ig+FEKwWFEHg6hdnaBlkHILsC48kcTby5CLSpFLy1EU5duBQOBb7/d8x4uv3fK/+o/vvgsgm7dqtY54nA0MPqx80K6zz37hC/qvv/cdBFlG9Iz2rN2/vI/zJrtpbVyPxWxmdsNOwRBJqiAIbOlP8nJTlG3BJN8+sQCbKdsJ1mbaOQma0VX602EA/CYX/7v9D3hlB68MbuDR+hv4ny0PcmnB8aQ1lXciWznXfwwLPZMP6nx2PZZPdmIW93/f13WdnnSI/wTf5dHul3l8ytd5OriCBzufxyzIfK/qEpoSPQRiHUTUBEk9zY+rPz2quJqu6wiCwI9a/0WDo5Qzc2YflP1jpS89xLZ4NwVmD5XWfLpSA2yJd9KeDOKWbZyZM5tvNf0lW6FRTTDfrbDI20B3apB8kwcNnc7UAKUW/yG1c6L4oF1nAN+/6y790uMuIjGUpP7M+ok2x8BgXNjbtbbfO0IuzksAACAASURBVK+iKMcDvwAk4G+KojQHAoGH97F9AfA8cF0gEPjv8OKViqKcEAgEXgbOAJbssr0V+CRwPlkvnkrWO+jc0/jaGNodJFIpOrp76BsYIBgKEQyFiESjiKKI027HbrONFO2wmM3Y7TZ8Zg9mkwmTJGMyyciyjM1iweXIJpA77XauvuD8bBjNXuLuF8yatU+7nHY7px23cL/2Hyj2PXh+9iYw90QylcJsMiEIAql0Gk3TxiRkAARJRsovGJWTJxcWIxcWAyB59x8KdCjZ13nYrDvXWc3mbI7i+0SQJCR/dtbcVLHTwb2nkEbrnPnv+3iHCk0ffZ2lAhuwTJs5QdYYGByZaJoGgjBSVGtXbpiXC8D6SITa8opR65yWbJTHzCIbM4tG3/8zqs4X/9PO9xcX4rVKyIJEgdk7sv6LxWdw9eZf8O2Kiygwe3m84esj6z6ZNw9ZkA76fN57rLEgCAIFZi9XFZ7MVYUnA3B5wSIaHGXU20qwSxaOdSv7HQPgprJzD87wAyTX5B7VlqnQ7Nst7HWWq5rGYQGY0jMktBTXbvklCS2NSZD5mH821xSewmWb7qPKms98j0KdrZg6WxHPBVfRmuwj3+whlIlxWf7xPNT1Io3xLgbVGDXWQr5Weg5P9r/N433Lmems4mhXDUe7arGJ7/93bAdpLYNJlNkYa6PckovjMOVHHgp0XceRY6HxjQ7qz5xoawwMDi1jcad8DzierEftTmApsFdxB9wK+IDbFUXZ0SDrRuD+4Ty+jYzOyfsScH8gENAVRfkN8CtgCPj4ngbfm7hLplJs3t7E5qYmgqEQRXl55Ob4qCkrY860qbgcjvddkOODmRv1/rHsImo+7EVLDMYHdZfrTAuHSL27fETcdfX1sXl7E/NnzRzps2VgYHDg6DokV72DHgnjOOeCkeWJtMYdL3Vy81wng+Ewbtce5zoBeHZLmCXbI/zg1CIgm3d347G5eCx7/r2qtObzwrQ79jhJ+X6E3Xgzy7nX7I8PBef45+y27B9TbqYp2UNUTdJgL0MQBO6uvow3hgI8H1xNwNrBl0vPJqLGiagJAqF2am1FCILAYu90Vplc+GQnvekh0rrKIm8DhWYfKyLb+G3XEiqt+fSnw/yldykX5S2k0Owlz+TeYz5jd2qQu1qfYH20lXyTh4vzF3KKbwYnr7kDn+wkoaWZ46rlzqpLebT7FV4ZXE+FNY88k5v7aq/isZ7X2RBrw29yUmjycWH+ArbEO4irqcPx5z1gdF2nb00L1lALsGCizTEwOKSMRdxpgUAgqCiKHggEEoqihPe18XAbgz21Mli0l+3v3uX1KmDuPo3RRkeF6rrOhm3beGv1GooLCpgzfRol+fkj+WsGBgYHzq6TKHJJOa7PfGEk9On1d95lMBymsqSE8uKiCbTSwODDjSgKyMedvNukmiDANGeEF99Yy1A4gn8fofsnVTuZUTjaozLJb+GlxggVXhM1ObtHLxhVHycGQRCoso6uPF1myaUsL3ckDxDgk3nH7rZvta2Aatvofc2izFx3HXPdOysu+2QntdZCvtP8V/rSYX5a81lskpmftz+NgEBKz3B5wSLmuGo5zj2Zr5WeS386TJHZh0U08fTU2wlmwpgEmaJhL+ydVZcSykRpSfaNeCwrrHlIgshgJsr2RDdpXaUnFaI/88GsRqnpOkXHT8VcUTFS4AwdBNG4FgyOPMYi7rYqinIX4FcU5RayLQwmjF3DxTRN47nXlxKNxzn7pBPJ9U1sCKCBwZHCez3kLXd8jdfrpvOpiy4hGAoxTZnEpsZGnA77+84ZNTD4qCKKIomNa8m0t2A/IxussqYrTpnHTB5Btg0MUJibu8/JSrMkUOTaPeJC02Ef/c4NjlCckpWrihZzVdHikWWhTJRP5GZDbs2iTJklF6toHhGRu+b+uWUbbnn3VA+P7GDaLn0O9xQqu2A4V/OmcTub8UPXdRxihBd/+yYVs86m+d1uVjy+lQvuOX6iTTMwGHfGIu4+D1wNvA5EgM8dUov2w67hYn2DgwQHB7norDMNT52BwTiyq4e8paODcEcHnrxSGlvbsNtsTKqs5PHnnmcoEuH800+bQEsNDD68iIJIpq8XdeXbI+LuD2sGKXLKvLPNyskOMwW5uQc19uIaJxlVR9P1vfaHNfho4JEdnOybPtFmTCi6rrP62XYKE+tp7zuGsll5lM8yercaHJnsM4lMUZQZgUAgAzxCNlcuQbZv3YSxq0chGovhdbsNYWdgMM7sep1taW7BPmkypek4G7ZtI9fnxe/1cvUF5xONxwkOhibQUgODDy+iKKDKMnoyMbJsXqmdKq+JRZYtLJ4/nym1Yy+O9V5ueKaDjb27F2sxMPiooesw89xKrJlBXnhuOVvfaWfF41tJRnZvQ3IgpJu3M/TgfWRatqOn02jx2DhZbGBw8OxV3CmK8hXgQUVRZOBHwGJgGvDTw2TbHtnVoxCNxXHYP3g9wgwMPuzsGv4cTySQqmrwDPTT0dNDaUG2gbEoikyqqmTT9sYJstLA4MONJIqokmmkWuZQUmVzf5Jcc5qYyUd5URE+t3s/o+ydn5xWREP+h7fCoYHBeKHrOn1dGSIZGyk1jWCF/qYhYoOJ/e+rZtBV9T3LVIK3f5n48/9GCw0y8O2bGbznOwx886vowz2EDfaMFouSWrsSXddJb97I0CP/R6at5eDGCg2gxWPv++8dfeLPh02Y65qGntj/9+79sK+wzDOA+WTbElwCTAoEAgOKoiw7pBbth1Geu3gch80+gdYYGByZ7HqdxZNJzDWTEF58irkXTqehrnZknVJVxZP/fYl5M2Z8oKrJ6rpOcunLZLo7cHz8wgnrs2hgsC9EUUSVZVJRyNF0ZFGgxG3iwRVBjnYcvKjbgdUk8vzWMN3RDJfPMHLSDT666LpOZFDFQQ84JmMtlDnzG8eMad/UyreJL3kO58VXIpeWow4EEWw2nJddjVnJ9p8MP/oQ8eeeJOfHv0LtaGXooZ+R860f7dUWQRBQe7pHtZE6Uoi/9l9Eb86o9knxV1/EMmc+8ReeJvLYbxFtdvRUEkwmbCecimC2kN62mejjf8T79W8T/ddfSa58Gy00iOh04Tj3ArBYEd1eTBVVRP72B+xnnUfyneUMPfx/6Mk4psoaBKsN5+Wfw1w/FYBMVwdyYTHJd5aj9naR6Wgjs30r6c0bEF1eLPMX4bryf9CTCQSzhfhLzxJ77t+IdgdqdyeCx4todyBYLHi//m1izz6JXFmD6Mth4Fs3YV1wAnoqidrRhnnaTGyLz0R0ONFCAyCIYDaj9XYjFZeCKBF/8WnCv30Ac/1UfLffTXLtymzbLFkmvWUjmdZmyKTRQoOYahQElxvLjNmktwZQ+3uxzl1I7IWnQNOwLT5jr5/Bvp54tEAgoCqKMgtoDAQCA8PLJzR4f1ePQjQeozAvbwKtMTA4Mtk1tzWeSGCvriXe38vMqspRlfZyPB7cTifb29qoKS8fdzt0XSezfStyWSXRJ/+K4+zzd2umrqsqwi6h2bquE/nTIySWLsF20hkIkkxq/RrkqhpEu+O9hzAwmDBEQUSVZN4anIe4ro+ChlxOrXGRCnUx2TU+/cqOLrGRyBheBIOPNjo6NccW0/OgA2mlndXNTXQVD+HKtzH1jKrdto8nEixfvZpZkyfjqqtn6IGf0vc/l+G8+LMklr+G7YRTcZx30cj2zsuuxnHeRYhOF7qu477mRtKbN5J4aynOS69CTyYQrTbS2zaTWrsCy7zj6f/qNTgvuRLR4UTt78Xx8YtG/ZZ9EFGDfejp9IiYQRRRuzqQi0uJ/OV3JN9cihYaRM+kscyei5RfiOP8y0hvWIt13nGY6hT89/wKKb8AtbcHKS9/ZPJV9OXgvPwaAEy19UhFJYgeL5mmRqJP/Bld07AtOgVTRRWi0wXpFLaTz8DUMAMEAbW1CXUgyOAPv4Xzos9gmXUMofvuIufO+9FTSdKBDUiFxdjPPh9zwwy00ACC2YIgCDgv/iwA5umzEPMK0IcGkfKL0GJR9PAQUlm216hUUobo9iDlF+L54tdIrn4X0eHEtGgxyeWvE/nTI+Q/9gzh3/0K20mnI3q8DHz3FvREHD2RQHR7yLnzZ5gqst+55PLXkPILIZkg+tdHkQqKsjbZbMRffh49FsVUV58toTz8XKankqhtLSDu/buyz+lsRVEmAZ8Fnhx+30C2yfiEsatHIRKL49xDA28DA4P3h6btDCuJJxLYHE7Eiz6NHo/DewTSjMn1rNqw8YDFXezZJ7EuOAHRNdpDoWsaalc7UlEpgiAQefQhXFd+kfTGdQxu2YQeHkLt6cJ+9vk4zrmA0P1347zw04i+HNA0ov94jOQbr5Jz58+QvFlvRWLZy9jdbrT+XqT8omzD+kwGYS99HfV0mkxbM2pnO6bJU5F8/j1uF3/tv2S2bSbT0Yba3oqeSWM9dhH2cz+F5Ms5oL+HwUcPURRJWyzMr91KweSL+fvGEB3hDI09GYpdrnE5Ro4t+zN/56s9fPooHyVuo5epwUcPXddZ/1wzqeJaVCVG2YxaagsqyCSyj7SqqvLS8jeZrihYzCaefGkJDpuNpW8sY+4L/8R/38Nkmrcz9NB9OM79FNZFp4waXxAEBKdr5LVcXIo2FCL21BPoyQTxF57C89XbMVXVIlfVIRcW4//hLxj80bdRuzuRi0shlcJ58WfREnEEk5n0hjWYGmYgDEfFxOJxRFHEbDLRPziISTbhdY/PfQIgHI2SzmR2q4Ctp5IkXl9C/JUXSW9Yg65pyKXlmBtm4LzkSkL3fIecHz6Aecp0zNNnIVdUo6dSxJ/9F4LThTY0iPvzXwbAPHnayLhyYfHov6HJhFxUkt2uYWcBILPSgP20s0dtaz/j3N3GkQuyrZlsi88EVUWQZXK++1MEQcA6fxHW+aM7sonO3f92Um4+Uu7eC+3s6o00Tz0K89SjRt7bjjsZLREHwH39zSMT4XkPPkamvQXR6UL0jI6gcH/uhpHXOd+/b6/HFWsmYaqZBIDjrE/sdbsd7Evc3QY8CjQB/6soyiLgD8AF+9jnkDM6LDOGw26EZRoYjDfZSSINVdMQRRGTLCN/4hLUjla00MCoG1RVSQkvvbGcRDKJ1fIer1oigZ5Okdq0DvOkySP7JdeuZOih+0mtXYH3pm8BEB4coOO3D2J+6zXMyQRqTh6mm79D49kX079lG6ff+L8MXvdprMcej+NTl6N2dQDguvQqkCRS777J4D3fRa6owvf9+0aEHey8gQ49dD+JV18EQUCLRpFLy5EKinCcdxGm+qloA/2I3hyCt96AFgkjFRSRvv9uzFOPwnbS6dkbuSAQ+dPDuK/9MplMhrggIs9ZQPIUP06blcyrLxL779M4Tj0bdaAfubyK+AtPkWnZjnX+IqTislG27Upi2Sto4RCWOfORcnKzAlvT9jqbqycSIAq7eTMNPhyIokDa4eKtvkUc83Y3F84vRtV05j00yPXzxu+hDeCsSS58ttEe7mRG57F1IS6b4UU2+n0ZHMHouk7JND+Z/JMRXllNsDGCbZIF3ZWdxFy9KcDWlhb8Xi+pTJrainKOmT6dP//nKeJf+SZ+pwtzw3Ry7314zMcU3R7c136ZoYfuw33NjQzddxf+n/4ay4zZQLaHrP9Hv0QbGgRNI/bcv9EiYYK3fxn/PQ8S/s0D6JqK6zNfYGNK453mFgRBQNM0HHY7yWSScxefjMvhoH9wkML3VNYNR6Os2riJ8qIiPC4XfQMDJFJJ3E4noXCYwtxcNE0nnkwgSxLPL10KCEytq2PmlMnIooieSpLZsonY8//BftZ5eG/+dvb3RpLQ43FEux3/PQ8CZD15O7A7cF70mff1mR0sgiCAnJU3gnx4UzJE654dTnLJ+Ec27Yu9nnUgEHibXRqKK4qyHKgOBALvr7TQ+2TXgiqxWByH4bkzMBh3REEko6okkkls1mxBhkxzI/3XfwbL3IX4brtr57aiSF5ODj3BIOVFo5uaJ995g+h/HkcqKMIy8xj0dIr4Ky8QefQhPDfcgmXOfPo7O1m3ehWpd5dT09aI+cZbadJExC0baF63nqqqamRJ5l9L3yC2+BMUVlQwvbgCW9Uk5HQac34hyVSK1tJqem/6HnX19XsVT+7P3YCw+EysNjuixYra3UmmZTuiw0mqtYnIL+7Bd+f9OL5374hQ1WIxEkuXEHv6H+hmM9aZcxDrJtPY2sproQSStwg9lsGmDjIUjZLJLUdNq9Q++wyTmwNYrroefWsAs5ph6KGfoXa2IRWVYpk9F+eFV5DesonkO2/guuJa0ls2ovb1EP79g1imzyb57nJAJ+eun6PFoiSXv4b7czfQf/MXUbva0dNpSKUwTZqMqb4B2+Iz0SMRUutW4vjEJVlxmE6h9vWCJKG2NmOePtMQgx8QRFEkk0pylH8jq58x8dIf17Lwmwv5rG8Npf4zx/VYMwptbO1PsrwtxienePjiUx1cP9dPlc/E71cNcEKVk75ohplFNkySIfQMjix0XcfusxJ78Q3kdh99AxHCs2O8+JMVzDq/js2vdVAyt4D+wUHC0SjHTJ+GhM7MwW5ee3cFx2QylBQU7DaBuT9sJ5yCvOAEmjs66f/qd+kdHKLU5sAkSdhtNgSTCcmfTS9yXXY1AL7b70YQRXLu+RWJ119i9T//zobSGs6tKMF53ElZT5TFwqbG7Tz98ivk5/rZ1tLKcbNnU19dhSzLvP7uCjZs3Up9dTVvrV1LIpkk1+dDliW2NrfgdjpZtXETmXQalwj9qTRnLlqE3Wzh3fXr+MO/nsSbTlKhpYjXTyN93hXYbTaqEkny7A40TWNtSyuB7Y3keLx09PTgdjqpLitjal3tByoH/6PImCVtIBD4QNRT3pELpGkaqUwGi3l88hIMDAx2Iooi6UyGWCKBbfjHzFRZg//ehwnd+/3dti/w++np799N3KUC67HMOgbnp64g091J/3WfRq6uw3vr9xFrFJ5/4w30t5ZRI2rkRII4r/gc1rnzKQE4dh7HDo8Ti8dpam+ndOECNm5rZMnyN0mkUqRSKTxuF5FojKL8fPxeD0+9/AqFeXnYLBb8Pi+9wSAOmx2lqpK31q6lqa0ds9lMRVERsiwj55fRGdhG78AA8pyTUP/+ODrZSoYOmw1RFCgrKqFt4Rn0bWnG0dZDIpkkJ72Rk+fNpbSwcNQ5Z1QVdJ11W7bwTk4esWXL0MsUEskkpTMXUOTLwdy8FdvWTQw2bQfZjHXWPPra2ii++EpkScLW0UZm3UpcV31xOP7ejp5Oj4SHem+6I5trKIoILjfJ5a+R3rIJ0edHt1gxDSf5933hUtTuLiR/bjbnIycXtbsT7zfuxFRRTfSfj+G64lrCv32ATFsLtlPOQh0KkfH5iWcyxCQTYZcHYcVbmI9ZQE5HM5bGAOnuLqTqOtTAevQaBfP0WfQ43IhNjSS8PqKiTF6Oj+L8/FH36ExHK3JxGVosCpJEPKOSeeJPSLIMoohgMmfPNR7DpDRgmjKN7q5uukODyJEwqVSSrpYWzG3N2ENBSKWI5RXSVDkJVyqJZDbjdbkoXLEU2WIDUSSRTpPu6kA//ZzxuDTGFVEUSasatqoqGuaW8PtXA0ibmqkrKcRuG/8ql16rRJXPjM0kcuvxedTm7HhQjZJnl/jHhhB5Dhm/XcJt+WDn/hgYHAi6rrPi71sIhvPxnqDjdJnwl7u58N4TSMXSLG9cSZmrjvWdm4klEhTk5hK6/wcUD4Vorarn+aXLOPaoozhqcv2Yj9k3MMCylStp7+7BJMuUFhbQOzjI0hUrkSSJGYpCPJkkFo/jdbvJ8bgRRJGq0lIABsNhtnjy2ThjHmdNrkP77s3IVTWYarMN5IueeZxpFXU0JZKcc9KJrNywkbfWrqU4P4/e4ABnr3wV0+tPMfuUjyHY7UR+cy+iNwfv124HUUL1OlATSRJ/f5RUy3bMa5ahDgaZ1t7GzO/dR6sgERwKk2O14rSLRGJx/r1kCU67naFIlPycHGZOnkwoHGHOtKkMDoVZHQiweft2pimT6OzppaSggNqK8pEQxXA0ms1xM6LuDikfuhJyOwqqJFIpLGbzqOIOBgYG44MoCmQyajbfzrrzIVMqKELt6hyp9rWDgtxcVm3cyOyGhpHluqaRXr8G56evBbLx8P57f41UXIYgCDS2thGLJzj7xq9ikmUyXR1IOXvObbPbbEypzVbpnDtjOnNnTCedybbc7A0GycvJwTQcfnH01Kls3LaNTEals6eXglw/wdAQjz//AuVFRXz2k59gcGiItq5uUulsIMLsqQ0U5uaSSCYxm0xIkkQqlSIczZZYbmpvZ9qkOmrLywnHYniczr3OTMrDIZRHTZ7MUZMnjyyPxmK0dnXR2duHnlNA8igfiaZWIFuR1G7t48Vlb6BpWjb0RtfxvvkORXl5FOblkuvzYfLm4Abw+RmKRDCbTDitNmwnnIrthFNJplKEBImkvwBTXx/B67/B1q5OMqqO3+dFVVW0gQGk4BDmwTXYNJHU6tXE8svJdHRT8vADpCQJRzQMJjP9xxxHZvaxFAXW0VxYRqCzHe+mAJrDyf+zd96BUZR5H//MbO/pvbcNIUAg9F4EVJoFe7nzPPXOhuU929nOcrZTTzlPT89ynr2gh9hBUYp0pIYlgYRAek+275T3jyWBiGBUULzbzz9JNjM7z87OM/P82vdnXrOKzph4Yjasxbd1Cx1TZ5O2cim+woHorBbUR+5km9WBmpSCRhSJqihDDPipvPR6pDUr0TU3UDF8POm7KrFKIaxmExpFQRsM0CHLdISgedNWRr73Mh3X/YmoFUuIX72MRKsVbXoWYl4eotGIotEyeuYM3E//FXnISLrik9Ds3EwoJIEUwqbTYh81Fkfp0O8zBX4SNKKIpKjUBVMpf3An6ZleQsJ2Sk6ffEyOF2fREmcJz5MDhh1MyArX0V4/Jh53QOaShTU8PTsV2w808CRZRasRqGoPkmjRotcIaL5n2mezR8Jh1KDTCPhDCkadSH1XiCSbjqCsoigqRl0kOhChb6iqysgL+vHZyjbkqiA1L3tQx6kse3oLBVPS8LUEaV7hwbtXJWVEEp2V7UgrvyD+X+8yzWCgqraWLa6d38u4W7lxIykJCUwcPpz2rq4e56eqqnS63azZsoUomw271YLb62NbxS5kWWZ7xS5CkoTb4yErLZXZUyYTbbcTuPEuNPEJyE0NaOITMY4cR/Lj95M1bDT2sWNInTSRjq4uahobGV1cjG3mDIJbN+Jb/CGqx41j3s37I4UJBMu2IO3bg2XWXMwlpaiyFBYtCQSwnHYOotnCt8kVDhtQTGtHBw6rDZOxdxTTYbORkZJM2e7dVOypJjk+nq/LyijbvZus1FR2791La0c7IPQ857QaTU80VK/TYTaZcGZnodPqaGlvw2QwkJOejtfvx2gw9DznIxyZw54lp9P5vMvlusjpdF7mcrn+8VMO6kh019yF63siUbsIEY4FoigiyRL+YLDXPBOtNtBqUTvbEQ6qu8tKTWHd1i3sqt5LXmY4t7zrhSdBq0Xfr7hnO21qBqqqUl5VhauyityM9J6b9TeLq7+L7v1SEhIOeX2g03nI9hOHD+v5PS46mrjoQ1M39QcJrOi02p6a3uSEA4+5H9p3zGI2U5iTQ2FOzmG38fp8GPT68H1OEGjt6KC+qYmqfTWs37YNfyCIXqfD4/ViNZvxB4PotFoMej0enw9JlnFYrRj0OkKSjMNmpX+BE6NeT3NbOzqdFl1SEiFZJhgMIcTHow1JpBQWkTphIqqqYjGZ0Hyzxm9oKd0NMFT1/MM71SaGC9bVYAApN5f4ql20u8pQVRX/2RchZecTCyRfcjkOi4XxOh3KqafS2tFBY2srsiTT5feTmZJCfDDAYIuF6LPPJksUYdhQ4KbDnjvT1eH/JQE4+74A+zkJpz9L5H1+K/6pN7C4VUO0SUN8zNEV4wlVuNCkpPVJLdZq0PDy3HRkBd5zdTI4ycRzG1u5dUICdV0SiRYt2oPSNlu8ErFmLbtaA6jAmn0+WnwSvx8Wy71fNPK3GSm8u6OTms4Q/eIN1HVJXFhy5LYMqqpy++cN3DA2njafzNvbO7h7ciI3fFLP4yen8OTaFkpTTEzKtvLoV83835g4xP9xR+++jhAIYDeIvFPWya9Konl1SzupNh2j0824WgKH9DyUZBVRhOV7vFS0BvjNkBgq24KkO3R9qsHsdvKtr/UxJNlIbZdEi1c6Vh/xR6GqKvs2NdG+fiexMQ5a+4dVErOGJxHSB4kqNDP1pCEsWyCSmBCHd2c55qxcxP3PgMzkZD77ahV1jU34An5y0tNRFIWQJKECFVV7KEjPwtPiJzrNhs8foLGllZPGj0en1WK3WnvGIggCDpuNqaNHHzJORVH4z5LPiI+O5rSpJ/S61xoGDgGg9bZrsV9yNYbSEcQ9/hwdTz5C6+3XYTnlLKwFRRSYjXQ8cBvC/X/DMGgohkGHOrYMg4dhGHzgmShotFjPuOA7z6Nepzuktu9gBEGgKDeXotxcAEr6FbKtvIL65mYGFTpJSUhAr9PhDwRQVBVZlvH5w4mBkizR2tHBxu1lKIqC3WrF4/Ox+KtVmAwGQpJEWlISep2W3PQM0pOTDnlWKYqCPxhEFAQaW1tpaWunub0NSZKIstvJSUsjITYWQRDCNfOBwP4Mnb45inZWVbG9YhcWk4mW9nbsVisJMTFotBryMzMPiUiqqkp7Zxcenxf9/swSvU6Hoih0ut0YDQbaOjqJjnJgPIqZiEcygUucTudDwBlOpzPz4H+4XK5bjtoIvie9jLtI3UiECMcEUQinZQaDoV4GD4Sjd1JDHfqDjDtRFBnoLGRXdTV5mRmosoz/s4+IefjpQ+q71m3dxq7qajrdbsaWDvlJPs8vBfP+GuLuB1ZibCyJsbEMCoNClwAAIABJREFU2m+rBIJBvD4/dqsFjUaDPxAgEArhDwSwms2YjcbDGl6piUenn1JfsiUEvQHdfnUv25TD9+KB8LVzOGP7v53uCPkWzRTMCUH6pZnpt18t7mjiX/4Zvi8WE33rfWgSk/F99jHG0eNBEPF+vDAsfCDLPeqxoiAgo1LWFGBGgY0pOVZEQeCFjW1MzbWSYNVS3R4iM0rHPV808sycND4qdzOr0MZZxWGlPY0o8MyccHrZKYV2grKKpKi0+GQCksJHFW7mFNpp9kjEmjU919XKag+JVi1PzEhBEATizQq3TUhAEASeOzUNrShw07h4BCEc0StOMCIKAo+ubKI40cikbCvugEKUSUO7X8ZuEH9Rhp+iqoiCgLS/72FVWxCjVsBh1LBoZxdn9Hewep8Xv6QwJt3Cvze1cWFJNJsbfEhKOApr1YcXqiPTzJh14ejw/NUtPDUrlQXbO1BUmNvfwfkL9vLA1CRKko2kO8Lf/fzVzVw3Oh4BqGoPMibDwtJKNzkxehLMWj7Z5WZ2oZ1n1reiFeCiITG8ta2DzCgdbT6ZfZ3Hq3EHvq4gMjqi9X4qnOG1ZMbgBHbs3t2jEDnutLD6YceLHyEVHhAI0Wq1pCYm8uGyZQRDIUaVDGL33n2EJAlBEOjY7qXe1oFRMGKON1BprCQn/YDzUlVV6ne00rbPTb8TMqjf0UbAHSJrWO/7ctAjMWfK5CMaGzF3P9rzu+iIJur/7gj3Z1v0NjrnDixnXkD0zXcfnRP3IxFFkQHOAgY4C3q9fnBG0MGGb2piIgMKDmyrqiqKoqDRaPD6/VTX1iHLMuu2buXjFSvQaTVoRA0Omw1FUejyeAiGQqhAXFQU0Q476UlJ6LRaWto7WLzyKzw+HzqdjmAwiNFgwB8MkhwXR1pyEoIg0Nnlpq2zA4PeQFJcHNt37SIYCqIoKga9ntGDS/D4fJT0K6S1o4O2zk7cnV5e/+BD4qOjCYRCdHR1AeHecQaDAavZTCAYpNPtxmIy4fH50Gq1hEIhYqKiaO3owGQwEB8TTVZqKl6fH0EUwtlTBgPxMTG0tLdjNhoxGAy4dlf26jn8TY5k3J0KjAFmAq6+f5XHlm5BFX8g+L0LWyNEiNA3NGJYUCUYCqHX9fYm6QqKCK5fjb6gqNfrqYkJrNy4MXwzbmlCVzSwR5oY6PFSbS0v55QpU7BZLT0pjBH6hkGv71XDZjQYMBoMOA56OEb45dBd25piqKUllIMzKY3Nf95Ox9AWxl1ajNH64z25Un0t1gsvQ5uTT+vt1yGIIrq8QvwrlxJ9893hhsD/fhpVkrD9+vc9yqw6jcANY8MR6zEZ4YjfHyeEo+Tra30EZIXMKH2PAXfFiG9PqYawoWfaHwmyGTQ0eyQa3WEj4PWtHaQ7dAxMNNLokegMKDiMB9K+LfoDi9zuaFL3/4w6kZMLwqqiF5ZEYzNo2FDr4z1XJ3dPSeKeLxo5b2AUeTF6grJKrPnopnTJikqzVybRqsUdVHqMqsPR7JUobwkwKt3CK5vbSbJqmZRt4c1tHZxZHMWC7R34JZXTi+yc9O8qFv86m/V1PqKMGsZmmGn2hM+Z3SBi0AhoNQImnYg7qHBywYGMgtOLwoZKdvSB6+epWWGnweRsK34pbNi8Mje9x/DtrrF85MRwBoWrOUDD/u+oK6gQkFQUYE97EFVVuXBQVI/wzn1Tw3XHcWYtxYlGrv3hp/SYoaoq+WNT2VwNRp+bkN6OJElotVraOjuJ+kZGRvnqVqSC4Rx8VWelpbKntpY5Uyaz5KtV9MvNYceu3YgakeKB+VQ17eWEiaNZv3QHoW0aCmbl4Gnzo9GKfPTAOqZeP4Tg/u8wJsOGa+m+XsdUJIX37lzFnLtH0VrdjiPFgiiK4V7Y5sO3MBG0WszTZmKeNvPAi4dRbfylIQhCj7PTbDRSmBPuD9c/Py+8RgkGkWSZDrcbURAxGvRE2+095RMHk5sBwwcOIBgKEQqFwoI2+yN4lTU1NLa0EApJRDscZKak4A+Go6/jSocQGxUVvndqtb1SQw/OshhTOoS9dXUY9AZio8JzUFGUHqctQDAUwu3xYjGbwhE8VUUjisiyjNvrpbaxiaqaGqz7syzMRiNur489tVuJstmpaWgkEAySvT8CeTiOpJZZBVQ5nc4vATtQBJS7XK6v+/KFHCt6InfBSFpmhAjHClEUkSSJYCh0SJqB9exf9TQdPRibxYJep6OxpZXEhCSibzkgvKIoCgs+XYwsSWSkpBDt+GGpjREi/DfRnf4cZ2mn0e/hrTo9J98yEs/bLjrrvRjzfvwzrusfj2K76HJM409A5+yP6vejy8xGlSQErRbjmEnoCotpf/BOWq6+iKhb7vlO2e7SlB+3cIyzaLlkaHhRdPnw8M8Gt8T2pgC/HvzDIrjdhtvwNHPP+P4yPexc2ljnY5Grk3mj4rjvyybunpLIJxVuok0ipSlmNtf7KEky4QkpCAJHFJPxBBX+s6OTcwdG8frWDvQagRPzrFz07j7ePCuTf3/dRrRJw0ynnb0dQdIdeipaA7R4ZZxxBlZWexmZZmZ0uhm7UcQbUmn1yYRklZHpZmKMGgxakY8vzEYUhB5DDeD3w8OLuX7xB6IeZw+I+l7nKcqkAcKf70gRTWecAWdc2IE+y3ngfn3VyHBKnkH7y4mGQriJ+drXXARaDIi6JiyORDx+Pw6rlfbOTgqyejcy73/eCAxDhvd6LTc9HQiXApw/e1Y4vdJqBUkgKz0VymTe+2oJ6GHa2eNIyohl8SMbKD0znynzBmOJNmIqNbD5oz3Yog0MnJFN7bYW2mvdpA2Mw2DRc/pD4xBFgebKDhRFZesHVZTMySE+NwpBFOhs8CJqBKxx/x3G249Bq9Gg3W842b9hyB3J4anX6XplJGm1WvIzM8nPzDxk2yOVUXwTnVZLzv5r5EjHjok6MKc1++egRhOOPjpsNvrl9v2Yh6MvbqxTgPOAVcAfnE7nGy6X6y8/+sg/gmAoROBbempFiBDh6KDVhlMggqEgel3vhqaamDiC2zahkSU0Mb1z74f2L2Llh4uYUL2TqKtuQLSH962uq8NmNjN0QDHp31CXjBDhf5WwE0VmSfvJJNXu5brzraQkREPxyJ5t9m1qIuAJIQVkKlfXM/6yAZij+66kGXX7gz2RroMj6Qf3f9LExhNz/9/wvvMaHY/cQ9RtDxy2ncjRpntsSTbdDzbsvsk3xVsGJ5soSTLuT0e0oxUF0uxaHEYNQVnlnbJOSlNMvLSpnbxYPWMyLLzn6uScAVGUNfmJMWlJsGho8Eg4DBoCsoKqqpxWZMegERAEgRdODS/qZjnthBQVSVb5v4/refn0dBrd4bqsKKOG68eEo6FZB0XVfjcsbLSl2A6q+Y20oziqKKpKwYQ0KpZsRqh3YzaZ8Hp9OKzWcOTOakaVJKS9VWhi4jAMHoZreRPpg+OxxoYNCINe31NL1n3d5mdlsePzvXy9bhcjzx5EbkY6JqOxxyk69f9KEUWBhTs6sbll9rSHEE0GZqaGjQ9HsgVRI+D6fB9RKVbyx4cjrMUnhY3N+BwHWoOGpU9sIn9CGt4WPwAFE9N+upMX4RdHX4y7c4GxLpdLcjqdOmAl8LMZd3q9ji6PB1/EuIsQ4Zhh0Ol7ctf1+kPTQYJlWzHoDYcYd7laEeOHb+G97BqibXbaOjrRaESWr1vPyJISMlO+n2hKhAj/zYj7U4Jmz/Sw/esq1jbCSLOEuq+TjjoPzknpBH0SiqwiBRVGXtAPg63v0Tz36/9CX1yCvv+g79xWEATMp5yF3NJEy7yLiP3rc2iiDxV2UVW1Z3tVllE6O9BEx6D6/QjGo9++4WghCAIaAUpTwovugUkHIh/3nhB2OHWnljZ7JLptq1X7vAxJNuGTFO5f1sRTs1K5aHD4vBgPil51p49GHdQo/vUzwxHQ0RnfLWQT4diiqio6kxZZKyB2dWDdX/ckKwpdbg/i2y/T6tpGyLUdy5kXoomJQ2sbjnpQb2VVUWmv8xCd2jsqVDgpvWe7g9P0grKKKECnLxy1tepFRqaZ0WuiaPPLKKqKJcaIJcZIQl4UovbQtF6dMbxMH3NxMaJGQKsPX18Vy2sw2vWkDoijbnsryf1iqFxTT/aIpIiKfIQ+GXeCy+WSAFwuV8jpdP6sTcz1urBx5w8EibJFUrsiRDgWdDtRwjV3hxp31rnnfet+wU3rkAqL2WeNIkmWefPjj1EVhRGDBvWoaEaIECFMuM+dTI15MNVpXXiDAgFZweEIOy79XcFeizUpINNS2UFCft8iXL7FH2AYOb7P4xFEEfslV6NJTkV1dyGHQggGPeJB4kmeN/+Nd+GbWM/7LZr4RPwrPsd+9U00XXEBmrgEjCPGYBg+Fm3aL3e+x1m0nFkcTnfsNuTgQN1aX5AbGwhsWI0mKQVNUgqi3UFwwxo0CUnoCg60SFFDIYLbNyM31iPX1aBJTkWbkobq82EYOvIIRzh2qKEggk6PVF2JGBWDaHcgVVcS2LAawWhGm56F0tGGKkuYxk1BbmlG0GoQzBbk+tqfZczfhaqqrHtjJ5KiwzbzdMyCDo/PS2d9HQafl8DCN9Ekp2G7+Eosp5wVrtEThF7Gnd8dZOFtK7ngn1MR90eHK9fUo0gKuaMPOC6bPRIBWWV3W5CFOzrZ0Rxg4bmZvYyuu5Y2ct2oOMpbAxQnGEm2Hb6mDkBv6r1cj06zhVWIu4KULa4mPtfBvk1NZI9IQgrKaHTiIUaeqqrUbmshpX/scWkAqqpKoydcw3rU3lNRCXhCGL+HU6wbRVJwt/ixJ5rxtPgQtSImh4Hmyg5iMmzIIQUpIGNy/HSBJndAxtqHFjV9OYPLnU7nW8AyYCyw4keO7Udh0Otxe7yRVggRIhxD9Dodbo8XWZa/1bgL7dmN993Xccy7udfrgTUrcIybwpdVexAEkbjoKEaVlJAc/20dcyJE+N9Gs7+2tblRpUuj54JBMWGRIYceg0XH8me3UjQ1k5T+4YhS0Cex5cMqpvTBuJNbmsLN4hPT6WryYos3oyoqQh8k7i0zT0dVVbb9fRE5WRKa9Ezkmmqk2hr8Sz/Gce2tiHHx6DJz0JeOCKtaPvkSwc0b8a9ZjueP8xCMRswnzsF8yll4P3gH0WzBNGk6iteD3FCH3NSA3lnUYziqsoz7tRewnnMRQh9lyY8lirsrrCBqCzeW7nrhKfRDhiOaLXT96ykEgxEEocd40yalYigdge+zj1B8XgwDhxDauR3/siVI+/ager3oCvqhSUrBUdAPz6K3MY6djLS3iq4XnkITG4cut4DgxjV4FrwalqofOhLPwjfRF5cgRsfgffd19EOGo83MRd5bhdzcCKqKYeQ45Np9SPv2YJo4Dd/STwisWwWyjBgdA6qCVLMX8/RZGEaOo+lXpxL/4n+QG+tRAwFQZKQ9lUiV5QRd2xGtNqJvvQ/vh+9imnIyoT276XjoToyjJqB43fgWf4AYFY1os2McO5nAxtWobjfm2WcQ3Lb55/7qvhVVVRl/6QAq3i7HmJmNpbkFj89HJyo2nQbHtX/EOGYS7BesEgQBX0eA16/5gthMO9NvHIrBrOO8p6b0GHYA0elWJJ8MwDtlHeTHGKhoDSAIAmMyzJQkJaDbn7p7MA9MTcKkE9nc4McTUr7354nNOhDcmDIvrOo5/rKBqIrKwj+vJXpqJpPGhNOwF25vZ195BxdPSaHs02pSi+PYvaqOqNwooqINBFQItvp4akUTU4fHE9PiJaU4jg2v7iCtJJ74bAd7v24ib0wKX/9nFyn9Y4nLtlO5poGsEUm0+GQSLFpkWUGjOTB3v9rrwRdSmZxj5dNdXUzNtbFwRyfZ0XpS7VoWbGrjrFwr2/wqnrV1DJuSxuUf1vP6mRks2N6BVhQ4rcjBu2UdnJRvQ68RqN7eSlyKhR0dIWI8QeLyoti2toERo5Np2dNJY0U7RVMz2fzebtIHxyNqRL54ajOz7hxJXVkrtjgT1jgTvo5Ar/R2RVFpcLWS3C+W8uU12OJMOFIsLH1yE7PvHEX58lpsCWZyRyXz1YtlnHTzMBp2tlG5uoFxlxSz9O+bKJycTkymnT1rG8gfn4qnxYdGr0Fn1LD+rXKGne2kalsLwSYfliFJfLmyjjMmpfDBmiZMVe1MOq+Qex7dxBWnZbFDEqjY0MRFZ+Xyp4/rOH9oDIIo8JflTTw5O41nN7Ry8ZDDt8z5TuPO5XL9n9PpnAH0A15wuVzvf9+L8GjSHblzez3YLJFUhwgRjgUGfTgtU6vRoNfpkGSVS9+r4Z9zUhEFAdERjf+rL7FffRNd/5yP7YJLCW79GrmxgbhJ0zixvYOPli2jtH//n92wW7+5jPjYaDq63PzpkX/y55suJz87/bj0XEb430Lcr0o78Mxski69hXvTJ3Px6DTSHDqWPrmZ0rl5xGUfqHk1RxmYcvXgI7xjmD3rGzC3VqDNzqd2WwsNO9spGJ/KVy9u56Sbhx+y/cFGX1ejl48fWs/MO0bQbszGeFJ/tr+8itQ9q1nVXErJCedjKB3Rs2/3PBL0BgxDR2IYOhL199cj7dpJ59OPYZo6A8OgoYh2B6E9u2m57lJERxTa1HQ6Hi5DM3wiSmwacWefjmixIogi3g/eQfF5MZ84B9ESToGTQzKi9tBoBEBoZxm+Lz5Fk5KGLisPXdGAPs9vVVVBlkFVCO0sQ/F6CKxegf/LxaDRIBiMxD76DKZpMxEtFgSjGfPJp4YFaUQBqb4WqWo3gTUr0ebkoysauL9RdDyO/b0XVUVBrq9Fm3KgTkoTl4DS2oxhwGAMjz5z2PHpsvMQTGYEvQHBaKLrub8jN9ShzchCE58EgoBh9AQEvR5h/7mSqqvQDxiMYDAgN9YjGIwYSkeizSsEUUPcU68giCLBLRvxvPEigt6AJiERXdFALKedg2Fw+BqxXxbWvVRlmbinXu3p+fZNzCfMOPD7ibOBP/Tp3P+UqKpKxfIa5E6FjpuvxPzHB2hpa8djsxGdV4Bp5KFRUpPDQPxZFqx6A1qDhpXPbyMhN4o9GxspmJmDTla4e20rd5+eBYRrJqNMGk7p5zjkvQ55b13YCJpdGDbS7l7awMWlMb3qLr8vAUmhM6Aw/g+lPLu+jbY3K2g16zllSAxrq1rBmM6j8dFMlFWWb27F3ymTs6uV94Iif5iYyGhRJlsL//lkLxatnvSBCTxR7eMSk47PtraRPSqZtaKOiVoNYnUXXU0+1tf6+OxlF9ddPYA/Pb6F2f0c1OTFYPp8D2kzctD5QjTs7mDtji6sq2tInZhB0+Iq0k7MxFrvZvuuFtqHp2JERSMpPF5sYflTm5n2m2I6W/z4gjLVNV7qvH42mIzUv1fFr39XzBdftzKwxY0hzcaqj/YwZEg8b+3yEB0Iqz8+VxfkhsFgjjYy6oZwP78VG1oYPSyejS1Btv9tI797YhJvv1FBplVD+pAEPnu3knMLY7BEGzFYdZjsBmbfOQqAkjm5Ped51h3hayVtYDxpA8Prm2FnOzFYdLyxsRVpayv541P515u7GJZlxVCSyFavylBF5altXZyaYSRGKyDubMUzKIaoeBNWg4hOIzCpNB5LtJECBbztXgRBYFyHm47lPopnZjPy3e2EpiUx/dunYg9Cd/78L4U777pbHT15Mntqa7nwlDm9ZMEjRPil4nQ6/+Ryue78ucfRzWOPP64aEhLR67ScddJJ6Awm3nN1clo/O3ctbeSaUXEELptLzH2P41/xBZZZc2k4bybRt9zbs/DzBwLodDo0ffTCN7e2ExvtOOyirLthbF1jC6s3bGXCqCEkxR9eClhVVWrqG7n85ge5/drfMmSAk1ff/YSX3/mQiaNKueHyC3ua8PYFWVYQxUM9sBF+ORxv8+zhRx5R47JziNoajzzUS/Hw8WTYdRh1Ioqi9ooQdFO5uo5QQKZg/OEFFXatrMW9ciWNNTJTHz2rp07O0+JHb9YiakS0+1N7Ouo8bHyngomXD6JhZxuJBdH4O4MY7eFna2eDl/JlNQw5PY+Wyk5isuxIAfmQNDEAWVKQgzJ6sw5VUQn6QrTs6SS534E0MLmlCTE6FimoIHrbWf3XJcQOyKRg7gg+/PNapl4/BGXrOrxLP8FftoPVjl9xwokyOzbLKAkZlJ5bzN6v9pAxMIbW269D9XlR3G7MM09DaW1Gqqwg6rYHQAqFDSKzBc+CV1BDIYwjxqJJSkENBFBDQbRJKXQ+9wTaxBRM02fReus1CFot+v6DMJ98KggCqiyhiT78fSbCoRxv8wzgimuvVefNuZ7PKlZyyUWn09DSwpcbNhPf2oDZaGDUaXMP2ScQDDLzwut45M5reO3FT8nNSWf2KWN54q2VLBZzKd65lMvPnkOZ0YhRK/YYaj+EjXU++icY8UvKEdVaj8QiVyc1nSEu2y/QU7GuAcVhpCD/gLHZGZCxGzT4QwoGrYDfLyPJCraD2q7s6wiRYtciAC0+mViThnd3dHJqPwdfVnkYmGjE1RLgnbIO7p+aTMXKWpKc0bQLItGqwuoOGVNlGyMmplK7rYXOBi85I5NprGgnY3ACu1bWklIci8l+aDqjFJCRQjJGq57371nN6Iv6o9EK7FpZx8A5uYeIJR2ML6Qgq2DVi1S2BcmM0rF8j5dNDT6uGhHHk2tbOL3IgV0fbh8SZ9Hy4hd1jM+zobcbWLzLzYUl0dz/ZQN5Bg9TB6bz9Pp25o2IQiIsvhRl1OALKZh0IutrfeztCHJSrol3XR6m59lBCLdIiTVr+aSii0nZVjr8MjVdIQYlmZBkFVlVMXxLfeWR6HbAKZKCqBUJeEIYLLrDzrWj2/TlJ8Bg0FPf3IwoihHDLkKEY4QoCFhNJjo9HvQ6HRox3Ij48VUtxFu0bG30k5bbn8CqZVjPOJ/Qrp2YT5zdy6P/fQSPKir3ctmN97H49SdYsXYTz7z8Ls7cTGacMJaB/fIIhSSefW0hNfWNXHTWLMoqqnjmlXdJSYjj1msuJicztSeF9LMV6xg2qIjla7/m4adepqS4gKGDwjUu5512IqecOIHX/vMJqqpy3pW3YbWY6ZeXxZABhUwYNYRVG7ZQOqAfOp0WVVXZXFbBvY89R0NzKzqdljuuvYR++Vlcd+ejXDD3ZAYXO49olG5z7cLnD5KWnEBifAyt7Z0YDHqs5t5S1sFQiPLKvQQCQfKz0wkEQ6zbVIbX7ycQCDFuxCDsVitfrt7IqNIBxEZ/t3cYwkaxPxDEbDKyc3c173y4lI4uN9kZKVxy7imUV1azcetOBhTm0i8/+7vfMMJRo7vlSMJIO7qK9djy8tFGZ+7/37dfTzEZ9l51QN3s29xESnEcoiiQOzqFtq92YCsJN2TuvjatcSYqltfQsqeLYec42fp+JYUnZFA0NQNVDcuuW2ONWGIPXJv2RDOlc/MBiMtxoKoqi+5axZR5g3Ek9c6eWfXvMtJL4lEkhZY9XWSPSKJmcwvJhbGsermM0rn5uNa4KZgYzeJHNjD8PCej/nRmWJxFUSk9Ix+dUcv6qlj0/S4gf8xeShZ8THC9SJ7BgHl8fzqWfEnlp3VkjTuX6tIrsEZryRiTS82OTnLOTu6JQLrfehlNbBzGsZOQ9uxGMFnomP8ADTW1fKbYCOgNBIeP55LzzqC6rYtPn3sD28CpbNzq4rEzLuDVhYsZ0C+Pgf0O3yj4f4G9tQ0sWb4Wt8eL2+Nl3m/PYfvO3fzrzfexmE3kZqZx8pTR2CxmXlv4Kdnpx6dolqpC3vgUltQp+D9eiHXISD5ttJLl93LqYW6le2sbiXJYefSZV9lSVsE4rZ/UyjwWLXydC6bPYnFjOQFdG4VxWb3UT38Ig5NNqKrKZR/Ucs+UJGLNGgwaAb+ksqCsg9OLHBi/YRA0eyW8IYUYo4YVe73MdPY2LvOG9m6QDgfafHjcXagmI7WNTfzt+TfJyUxFr9MydFARxc4cVqzZxLgRJcTtbzFy6v5o5Pis8JwflGhkSHL4PpG3v96wW2ZmciyQE/6rO7IF4YbxQK/6xG+iNWh6HE8zbj2wnhh86nfPw+5oKIR7PEqyzPgsS8+Yfz8sFllW+Hr7TsrKK0mKj+W8sUN56t9vkxgfQ25CHDX1Er8d7ODXVz7A370+FFGDvGkYiaNn8cWCV4jVSaxpErlwTC4zZkzjsUce44GdO9HqDawcUMhjd8zj9YWf8tmKdeyrbeD97HTuu/kK8uxQ19BMp9tDYnwsBrsVSZLYsNVFfWML/fKzyExLxucP8K833iclKQ6tRkNaciKlAwu56raHqNpXh9vjY+KoIdx5/aVHPBffadw5nc5El8vV8J1n9SfCbDTiDwSIdvRtYRMhQoQfRkJsLJ0eD1qtljU1Pl7e3M6JeTbiLRoa3BJPxs7khlfvY8CsuehyC9DlFvyg46iqyvNvvMf5p5+EIAhsL69k7swpNLe2s2HLDrw+Hzf9+QlyM1O598bLSUqI5c7rLkGSZTZt20lifAyuXXu4/OYHiImy719wpDJtwkhOnDgK8RuRQ4vZxMXnzAHg7/fdhKuiirKKKir31jJh1BCWrf6ah558ifzsdBLjYrjmknO4/drfkpwYh6qqGA16jAY9l55/Ki++9QH3/+1fXH/ZeRQ5c3ju1YUkJ8SSmpzA4GInsqIw7/aHyclIpbGljQX/fJDla77mL0+9hMGgZ9LoUub99hxq65u49o5HcNit6HU6Hrv7ehqaW1n61XqMBj0Ws4n2DjcJcTGsWLuJR59+hXEjSjhj1gk4czO58Oo7iI2JIjE2mtnTJ1CQnc7ltzyAKIpUVtcyrKSI+2+5ktcXfkpacgIDi/J6DIRtrt3sqKjipbc/RKfV8MY/7uejpSt59d1PiHbYiHbY+fVZM0mIi+HzFesYXFxAMBRuxJuTkcqGLTuQZYXBA5w9Tel37q4GYOuOXTS2tPG7C07jpQUfYjToiXHYcdgann54AAAgAElEQVStlA7sR21DE3WNLXi8PpLiY0lNikeWFcoqqrBbzZhNJkxGPQlxMTS1tBEXE0VLWwftnW7ystJ4c9ESJEnGZjWTFB/L0EH9WPDB52zeUYHNYiYlMZ7TTp5IKCQdch0cD4iCQEiWUawS0bt38tKiDfzmsrQjNtt2JFvwtPmRgjJavYaAJ4TWoKFqbQPRqVa87QE2vrOLMc5CMkoGHLJ/3thUcseoqLKKHFLQ6sQegZYp13x3yqcgCJz65zEIgsDa11wUTEhDkRW0Bg3Dz3Gi0WtAUTFHG4lJtxFztrNn3Fq9Jqz+KSmceONQEA4YnrKqsKx8A3MLpjBoVk5YEELMJmtUb0GY6NR0Jk4Ln5+CGUVodCI6s47Gimqs+QY8lUHisx3Y5p6HLMl8snwNXUXjmDV1HA69nq+XrcG7uYwohw2jwYAtKoqvNu0gOspOl9vDGTOnoNVqiXbYuPWBJ5FkiXPmTOeCuSfj8fqwmL+9v5jb40VVVWzW8ELS7w9QXdtAUnwsdtu3l5B4Wv2YHHpEzZGvTVVRUQkb/H3NNvB1BHqEHoLeUDiaqqrIsoxWG3ZcbXXtYl9dI87cTHIyUlmxdhPrNpdRWV2L2+Pl6QdvYdmar6lvaiE+JorE+Fh0Wg1pKYmcMfMEvD4frl3VdLm92MwWgv4QUXbbd47t50BVVZb+fRMarRb/xk+xpmeRr2nAIrpxxOd+6/YNTS1kpCbxh99dwJp9Hu5eGyRDCgvtNLQ3cdb0E7hv/vPcc+PvsSeFHWOKovzge40gCPxjdhp6jcCzG1rRawTmFjkISipGrcjVr6xnTIwPpzOfrOQ4vEGZO5//kJJEPRbniEPez+cPsGVHBbuqaqhvbGb6xFG4vd7wPbKsnKt+cxZjhw1i1tRx7N5Tg8fnI8Zhp6mljceefY3nX19IXnY6554ynaz9Rrsky/z7rQ849aRJ+AMB9tY0kJwYR3JCXE+93ffJiDkc3T2tz778j0Q77MTHRDFh1BCmjh/BMy+/Q2J8LLKikJ6cyNBB/Xjno6Us+nQZdpuVoQP7cd5pJ3LN7Q9jMhqYPHYYBp2OCaNKufbOR6hvbKEgJ4ORg4tRlHBbk+07K/lk6SpOGD+CM2edwKJ/PUJreychSSIhNnx/HKKfRkt7J6PaO9DrdKTYdMy/40rsVgsNza10dLrD2w1wkpWeQmpSPLuq9mE2GVnw4ec8+8p/cNit1NQ38Y8HwnoFf3vuDbLSU3jxrQ/49ZkzmT5hJCajnrLyKmRFISEuBkEQuOD0k0lPTcRiNvUpG+o70zKdTudyoAl4FvjA5XJ9/8rPo8j8+fPVjKIijAYD08eO/TmHEiHCUeN4S2OZP3++OmbyFFZt2sTl554DhFMNulMimj0SwX3VRDdWYRo3+Ucd65V3P2bRp8t4+sFbsFoOTSSXZYXla75mzPBBPcbDt+H2eKnaW0dRQfaPWsgrisJW125279nH8MH9SUk8cs2gJIeL6UMhiY+XfkVjcxv76hqZMGoIg4ry2bm7mtFDB/baR1VVmlra+M8nX3LuKdOpqW/C7fEyZEBhn8bY3NrOZyvW4czJoLgwj4qqvTS3tlNT34SiKJw9Zxp7axtobG4lMy2ZuJjvbnQsywrVNfVkZ6TQ2eWhpr6Rto4u2jo6GV7SH0EQ+Os/X2Pdpu2owF3/dxnDBvXjshvvIxAMUdvQxKyp45h38dncfN8T7K6uITs9hdnTxjN66EBWrtvMZ8vX0tHlJhiSeOyu6/l46Ve8uWgJBr2expZWHrvrerxePw8//QqdXW48Xh+X/2ou0yaMZNavriMYCiHLMr+78HTmzpjCyws+oq6xmS63B5PJyE1X/IodFVWUV+6ly+2hcm8t115yLpvLyol22Jl98rTjap49/vjjqi4+AcPiKKbkriB+8FDMJ835zv2W/HUjA2dlE58bxYrnt5HcL4ackclIgfC1GPAEscQc+ybHezc2klgYQ8PONjpq3T29ub4vG7e6WPDh5wSDIe6/5cqexWeXx8ugogLmzpjMK+9+TEtrO82tHVzx6zPIzkihpa2D5tZ2bNawIX/rg0/RudWHEKNgj7YyUlPC1/ZtdDZ4Kduzm3NOn860IaOIybD1WnwebjEqywp7Kuuo3FPLlCnDuPKPDxIMSQwqyueEccNx5mZSUbmXt95fwqdfrqEgN5Mn77uRex57jo+XfkVyQhyBUIi3n3mAyuoaRFGkvdZNQnwMcQkOPrp/HafcM5ryZTXIkkK/KRl89MA6xlxUhLvFR2N5O4Nm5/LJX9YzaHYOerOWZc9sZfafRvHl01vIGppIUr8Ylvx1AyfdPJztn+7B3xlkyOn5LLhpOSfeOJTG3e18sWATZZZyYitiIUvmiqvP4PGr32adcTMDovOxGsxcf9c5PPu39xCiITM5mXhDNANG51CxrJbkohhUFTYt3MXYi4vZ8dleDBYt2SOSWfLXjYy5uD/NVZ3sWFzNCdcOOe6eZwC/u3qeevcf7mThys84vWozhpHjeLhJw+o2B29Oj8GYkdVr+5s/ref0IjuDk4xoNCKqqlLvloi3aPF4PFhMRjQaDR8vXUV8XDSZqUl8+uUa3l+ynPtvuYK05EOjZt8HVVUJyGpPtO7TL1dz/99fYkhxPnKCk1kTh7Lpy09YuXYzJ4wbzqXnn0p1TT1bXbv44qsNTB0/gvSURO59/HkK8zIxGQz85pzZ3PmXpxk6qIjSgYUU5mUd9viBYJBN28txVexhyrhhdHS6ueMvT4MACbHR3HPj5SxbvZFFi5dTU9+IXqfj+Udv54MlK3ns2ddIiI3GYNDx8O3XsK+ukU3bdnLOKdOJchzZ+F+9cRtLV66nobmFh2+/hj376mhu7aCptQ1VVZk+YRT/+fgLvtqwBavZRE5GKhfMPZkX3lhEv7ws/MEgXp+fEyeOwuvz88mXq1m+5muKnblcdNYsVm/cRunAwiOuJY41oVC4XvdojOFwc61PNXdOp7Mf8BtgHLAEeNblcu3+0aP6AcyfP18dO2UKsqIytLj/zzGECBGOOsfbw3D+/Pnq2eedx5sffczl557DzpYAApAfG/YED/tHBYvOy+KTii7OGRCF9gc03O2+yYZCEoFA8Dtv+hGObxqaW7GYTYekmx5N6pta0Go0fTJWv43jcZ4ZE5OwWsxM3FdBuVfP6Hm/Q9sHRUtFUWksbychzwGCgCgKrHh+G3FZdtI8a1A62rGdd/FP8Cm+H4sWL8dhszK4uABJkpFkmfOuup0Zk8dw5uwT0Gm1/OvNRaSnJGGzmBkxpD979tXz1qIlOHMzSYyPZeLocIT9joefJjUpHlVRefGxOzEaDWzfWUlHl5vkhFhi9FHYE82sf3MnIUuIrCFJrHlqJ7PvHMWGBeXYEy1kDU1kwU3LmfvQOHYuq0EUBQompLHmVRclc3LYt7mZ1uouhp5ZwMoXt+NL9bB9UxVyo8o195/J4w+9jiXWxEkzRpGcEIe3PcCG93ZSOreAxm3taE0iacUJvPjnj3mnYjE5pNPQ1cpzr/yR9xcv5+//epvM+GR0goYH7r2KusoWyuv34MzJYtGbKzj3kmnoFC3LNm4kJspOemIyWVlJBL0hRI2IqBWoL28lIT8aOaCwpayCpWvX43Z7GViUz5xp4/nrM6/RvzAHq8nM4OICzGYjjZXtJORE0bbPjSqrxGTaWPr3zYy8oJC2fW5aqjoZcHI22z/ZQ+bQRAxWHY3l7aT0j6WryYsUVIhOtdKws43YTDtag6anTvR4m2cAl119tXrHNbfzyfrlnNaxF01MHJd6iyltW8+8C+agiQpHZ3ZX1/DQky8x77p57HVtpyArhcy0JBbvcjM+y4L+W551qqpy5u9uJiUxnmElRZw1ayqdbg9An9LnV6zdROXeWs6aNZVde/bhzM3k/SUrmDhqSI/D8y9PvcTME8b2GGQ7d1fz+sJPuez8U0mIi0GSZX5z3d0YDXpmnjCW7IwUBhQemsr4QyOLwVCI6n31tHV0MWRAYS9VTID1W3YwsDAPWQ7372tt78Tj9ZGblUZTcxvPvb6QDz9biVar5ZUn7qbL7eWFNxZROrCQwcVOcjJS2by9nJvue4JzT5nOpDFDSUtO+N7j/F/jxxp3DsLNzM8AugAR2OhyuW4/yuP8TubPn69eddVVP/VhI0Q4phxvD8NvzrPPdofTDSbn9G7e+vzGVuYWObB9jwLwUEhib10D515+K5PHDuPcU6ZTXHhoWkyECEeb43GeWVLSkHbC6XkeXGV7GXHrTUcUDeimq8nHmpd3MPGKgWh0B+afqqqgKKg+L6L153WYqKrKOx8uxeP1kZoUT2F+Fjsq9vDUi2/T0NzKmGGD+PNNl9Pe6SbKbv3uNzyI2oYmutxenLmZP2hs/s4gKiomuwFvewBzlAF3sw9VBVu8ifLlNWQOSUBvPqBeWL2hkYT8KFRVpaPWQ1JhDOve2EnBhFRCfpkNCyo44ZrB7PxiH86J6VRvaMSRbMGeaGbL+5UUTctEa9D0RArbOjoJBELsq2skJEkMLnayt7aeZ17+DzsqqhhWUsTNV/2aZas3svCTZfgDQSqra/jbPX+gua2da+98FKPBQCgU4parLmLSmKFcdsOfGTdyMIlxMQzsl9eTTvdTcrzNM4BLr7pKPT3nQhpj6znN5kFubuRvAQu+fS3ccPs12Mzhmrm/PPUSry/8lAmjh7Fs7WbuvflKJg4fwD1fNHLbhIRvnZuKEk4lL8rPRhAE/v3WBzz98jvERNl5+Yl7ePv9zzj3lOloNGG11+4ocX1jC//55AtmTR3H72+6H0mSGVZSxJ3XX8qtDzzJrj37GFSUz4wTxlLszP3Fi3mpqorb48VsMtHe2cWar7fx5aqNbN2xi5uu/BVRDhtSSGJQ/x9W4vG/yA8WVHE6nW8AxcBLwPkul6t2/+vrgJ/cuIsQIcJPzzeNum4uGhxDXxxE3WzdsYs//+0F/v3Yn3j2kdu4b/4LPzgKEyHCfwM6UUPILaOPjaPIUN4nww7CBsjkeSUoHe2wP+qgKgodD9+N9dyL0Kb+9E3EVVVFUdQer77b4+Wr9ZtJSUpg0/Zy3F4fs6eNZ8ywgfgDQRy28H3l+xp2QDhd+kdkvnWrgUK4xQSEBWe6yR97aMPyjCEHIgndSn9DzwwvRFVFZfg5TgRBwDkx/ZDtB87K6fm9e5Ee7QgLYCQlHFDjzM/O4MFbezuwJ40eyqTRQ4GwISEIAhlpSXz+xpMEQiHMRiM6XXg59+Ljf+rbCfgfQ1VVSn+dx+KVTYi2OEKu7aytbiU3o4Av121hxvhSgrLK9BknsTNoY/u6pZx/5ixGDSpAKwrcOenwF5soivQvOPD9zp05BZPJgKIoSJLMus1llFfupay8kpfn38WWHbtYtWEL7y9ZwYVzTyYlMZ5X/34ve2sbKMgJz9u7/nAZX67aiGv3HoBfvGEH4c/QXZMaG+3gpEmjOWnSaGQ5XO31zWhghB9OX9QynwG+crlcbqfTmXzQ65GCtwgR/keYv7qZqTlWCuONvV7fWOfj1S3tPDjtwK1hU72PKKMGV3OAaXkHIgduj5c/Pvgk8y4+G40m/DB8af5dP9lniBDheESn16IO8KHGZLG8LsTM77Gv3FBHyzUXo3P2xzhuMkpLM3JjPZqkQw2TY8m+ukaSE+J4+4PPeOQfL3PS5NF0dLn5y23zeOi2eYdsb9Dr/+vUrgVROEQ99FjQnVKn1+lAB0Zj31WJ/5dRVZWdi2sQNSKanERkVaW+tpZHb7uSC6+4hZKih9jdLvHSF2U8ffUcYA7+kMKmBj9bG1vJi9EzIatvTgiT0cDcGVN6/r77/y7jhTcX8ZuzZ2E0Goh22NDrddx74+8pHRhWcjabjL2i0KIoMnF0KRNHlx7V83A8EjHqjj59Me5GAycC1wOPO53O9S6X636Xy+U/tkOLECHC8cLkbCtJ39JctV+8gdsm9M6LX/1mOcUnZVHeEmJq7gGhgof/8TIjBxczeczQn2TMESL8EtCoGnRfWbGcWULxHX2vI5fqawluXEP8CwsIrFqOf9WXSFW7ib79foRjLBbQHaFbvXErO3dX89KCD3ni3hs47aSJTB0/nOdee48ZU8YelwqlEf43UVUVUS+gFTXoCotpPdtBiuuvbH/2JRxJafz1o604E8xI2z+nO3YhKSrvuTq5fHjst9ba9ZUoh41rfntOz9952enkZaf/2I8UIcJh6YtxN9vlcpUCuFyuM5xO5wrg/mM7rAgRIhxPpNp12A2HLtSMWpFdTV5c/9jMlN8NYOlrO5kwKI7MBCOj8+wEpHCjVIAL587olX4UIUIE0Oo1GEvlsBrtq0+h/u5KhL4YRYKAYLUjGk2YJk7FNHHqMR+rJMts2l7OEy+8ye3XXMwLr7+H3Wblxcfu7FGVjXbYuf6y8475WCJE+D6oqkrKsGiadjRStbeO62/8M3Gzr2DE1FQ2vv0hSdpWLAF/rxpFq0HDreMTqHNLpHyLczNChOOVvrjVFKfTqQdwOp26Pu4TIUKE/yIufncfnYFv74LiagtRPSyFLp2GDUYDscOTaa7qYNXC3VywYB9en5/5z71OemoipkgKUYQIvdBqtZjsBtoDCp91WUGW+rSfaLFiGjcZr8/PirWbjvEoob3TzRW3PMh9819g4sghZKWn8PRDf+Qvt8/7znYhESL83KgqrP5bOXgFvD4fNpOB60us2Lua6D9sNNrUftTVNZCVltxrv93tQe7+ovFnGnWECD+MvkTungK2Op3OLUAh8GBf3tjpdI4AHnC5XBOdTmce8AKgAluBK/ZvtgBIBm51uVyfOp3OHGCey+U6NEk/QoQIPxtvn/3tinSKrKB/eweWCVnsWlLNZeMSibNocadaMdr1zBdFXnjyE3ZKAV7d0skFJdE/8cgjRDi+0SgaWj8PkHCKll/d+Js+CRTJzY20XHMx8c+/TUiSeOjJfzOwX16PWMHRpKmlje3llYwYXMyUscM4/eTJkRqZCL84VFWl39kp1LQ14Pb6sSYnY9mzA2/F15RceB2bK/YxZPzwHpGfborijTx3StrPNOoIEX4Y33mHdrlczwJjCBt1E1wu17++ax+n03kD8E+gW33hEcIG3DhAAOYAJUAV4Xq+K/dvdyv8P3v3HSZFkT5w/NszszuzOeclwzYZJEsGE4g5nqLemb3TU3+GM2fPdEbUU089PT0xnXrGw4ACAiI5Q5EXNuc0O3nq90fPLqwsuwvssMtQn+fZh5me6u63e+Ydprqqq3js4A5BUZRgcnr9fLqxer/l0i+REsZc2perJqTSq28C5jDjKyU6KYLk7nFUlFazuCqNy889hW7xoTWAgqK0B2tUGD0uSaLE7mX1/Q/g/Hluq+vUz/kC24SpFFXU8O9P/sewQX25+Pr7ePCZf7Rpn36/n5XrNrNi3WYcTtcBy+3KK+Sq2x4ld08hNms4F5x+oqrYKUclKSX1lS4sZjPRkTayvG5eWONAi46hW3w4Lz37HH6/pGtWekeHqiiHrS1TIQwFriFQUdN1HSHEFa2sth04B3g38Hw4MD/w+H/AycALQFTgz67r+jhgqxCi+GAPQjl2OT1+nF5JfISZSoePyDANq0X9+GhPXp+k2L5/V7GKvFoWv7WR0x8YA0D24P27ZpW4SxjeI48BacMIi7Lwa149o7Mjgx6zohwtNJ9G3SY3pv5AbDy+kpb/C3RvXIvj2y9x3fIQL7/5AVkZqdz95z+wp6AEn99PcVkF5RXV9M/psd+65ZXVeDxeoqMjef+/31JYUk6kzcqzD/4fS1dtQAJjhg0kKjKCguJSrr3jca7//XmccfLE4By8ohwhEsme+RXET7QyQO9F9wl9qX3jOUyXXIWmabjdHq667VGWfdNq+4WidHpt+RX8NrAS+HCfvxYJIT4BPPss0oQQDX1NaoE4IcQWIA94HngYuBn4UNf1V3Rdf0zXdfUL/TDtqnTj9RunPa+wmDUbtjR7tXZjqZPiOi9SSr7ZXEFZRRV5NR5WFToAWJhbR25xJYVllZTXG29rpcNHncsHwM5KNz6/pM7lY1eVG4A5W2vZWenG45O8tqwcgPXFTn7YbkyG/dH6KnKr3NR7/CzabQdge4WLLeVGXPN21lHj8lHl9DVOoP3R+iq+3VYLwPkf5uLw+FlW4OCVwPZfXlrO8gIHTq+f2WurArHbG4/jlz12XF4/lQ4fKwuMZRtKnORVG8f0lahBSsmi3fbGlqqP1lexs9KI87lfygBYUVDPTzuNmF5fXkFRrYcKh5fPNhnr7Kh0k1fjabJPh8dPUZ2xrLjOS80+566zcnn9fLCuimirmT+O3H8glKSusUy748AjX0opmXT8MK6YNo1vn15O3oZyvv9+D7VOXzDDVpSjSreMTGIjY0iOspDTrzu+ovwDlvVVlFH54O1EnnYO97/zBT6/n0vOmYbFYqFH10x6d89m28493PvUK5SWV1JTa29c1+3xcNP9z3D6H26hrq6ev913E++88CDZmWnk5hWybVceH37xPdfd+ThrNm7F75fcf/OVqmKnhARNM9H17ARsseHMW7yCt0o1Vk2+jPAhxlQDf7vvJp5uZtoORTkateWeuyIhxBuHuZ99R2KIAaoAhBAPAei6fjHwOXA18CYwGTgB+P4w9xtyXG43tXX12GxWoiMj2LZzDw6XC4fThb3eQd9BQ3j17Q8p3J2LKHXQI9HKVTfdwhdzllG2ZRU7Kj10jbNw1TVX8fo8gbZpHh5bPHu2buKFh2/hy5XVPP3122R16YLTp3HnFWcxZ3Mdiz7+JxINf3gkz/7lSpa7U+kWH870PjE8Mq+YWTOy2F7p5oftddw6LgWTZvS/1YDIQFc9q0UjMswYOTE50oLVrOHw+Pk51864rlHsrvbg8UlykqxsKHHRL8WGRLKtwsXUntEMy4wgNcr4yL54aiZWi8aEblFM6GbcZ3JvYEj+SocPf+C+lajwvdcIvt1WR/8UG+UOL0vz6xmWGYEoc5EdG0Z2XBgbSoz9DEixEWExKl1p0WFEWDTMGvRKMLoV2iwmLIGJhrsnhBEdbqLeK/H4jH2uLnSQEGEmOzaMOVvrGJBqY0eFmy9EDfdPTuM/G6oZkGplco9oZi0p47npe0fn6kyK6rw8v6SMQWk2vhK13DGhactc8ZZKIuLCCbM1/zXy0tsf0zUzjTNPmcRp942mbEc1M/vH8t2Dv3DKA2OIjjDWK6j1EG8zN35OFOVY0qtXF3r16sK322opq4ln2p7cA5Z1LpyH9fiJRF/4e546sZLE+Lj9ukmOGzmEOfN+4eLr7+P8007gmkvOBmDL9t10CeRjdW0d6alJmEwm7v+/qwAYoPfiKv+ZfPPjYmrr7Azp34fsjNT9YlCUo5HZbGLXt2V0nZzElj15lLm8DDr3YsK7G78fRg7t38ERKkr7aUvlbpeu63cCqzAGREEI8d1B7meVruuThRDzgOnATw0v6LpuA84FzsNoxfMF9tO22SKDxOHxU+f2kxJlYcEuO72Twom3mXl2cRn3TkplztZacqvcXDsyCSn3zuXVoKiknF9XrWfquBFICTLMRmm9l96JVpbm1ZMRYyEzJozZ66r43cB46j1+JBBvM+Px+Vm2aj1lFVU4nC7GjhhMVFQ0r7/3Cd8vWIrbDzdceibmXqP48NW3iTJLdtX4uOjkUcR29xLbazBnTB6BxWwmPMxCRpqNtPNOok/SaRTVeUgO/Kge2CWJLVuSKS2vYuxdV5AQF8Mrg0zUnTeINRu3AtCjaxaPDIqB840WmrWbtpGalECKx8Pvb76HH4f25+JJY7DINIakRzAkPQKgyeTVDYNo9Emy0ifJGC1xas+9b++dE4wfEFN67F12/ei9LUXXjDAe907cO9Jic3OuNUiIMHPJEGOfx2VENC5/cEoaAHE2c+O2zukf1/h6Q+UlMgyGRRjrTeq+d4CCM/rGAjAgde9E3if1Mo4z2goXDIzfb5sPTTX2OTQjgqGBWPY9ts5asQOjUv7YCel0iw/nwoFx+71esaeWeF80sWn7D+JQW2fn8znzeHeWMUm5yWwitY/xnvzg1Jizw855A+KQUjJ3Rx1xVjNDM2wU1HgZ0+Xwu21KKbF7JF6f5NZvC3n5tEw+3lDNmX1j0YCIsL0V9M5MSknp9mpSe8cj/RKtDTFLvwSN/b6TDpZfSky/2YbPL/H6JVaLiWqnj6gwExIoqvPQJS68yXeh2ycPa26o9iClxOcHSwfH0ZLaUgeL/rmeybcOxxE1APeXTzf7fwqAbdxkrKPG8uPCZfTt0/2A9789cvt1jY9Xb9hCvz7dGdi3F4/fdX2z5RuYTCZOO3H84R2QonRCFrMZf5QXqy0ce72DbsnRTf5/V5RQ0pbKnRXQA39gVLwOtnJ3K/B6YEqFTcB/9nntZmCWEELquv4W8BpQA5x1kPs4ZMvz6wM/+MO57NM83jwrmxUFDn7ZU8/t41MorfeSGWMhNcrCqCzjB/rYrpGMzo5kR6WbWT/n06P0V4pLK6ioqmbSuFEMGz6Mj7+ay9P/eB+TBrf/5VbWVcLkZCeryiHB5ObUUTol5VW8OfsnVu6uou/gIVw+qS+XvPkrsVvnEp+eTbnbxHEDdWatcZEQEc/zj9zOVm88Z/eLI7fKzdi/3klGTBi5VW6yYsKwmDWGZw7e7xjjbcaktunReytFidFWxgwbtF/Z6KhIxo0c0uy5GtK/D2DckP/pG0+xcOlq/jtnHk6Xm8nHD+OVdz6he5dM0lOSiI+Lpk+Prrz90Vc4XS56dcumZ7csBuT0POz3TAm+lCgL83bZibWamlSsG/Q7oWvjYykluflFrFy7maEDc7j5/meZdPxwMtKS91vvqolprP82l9dWFBE+KIU/DE2gtqSeQpePvBoPXr8kr9pDt/gwPH4OqYKwNN/B55treOzEdB6akobNYsKkgd65zWYAACAASURBVNWs8eNOO9vKXfx5TDI3fVPAPZNSiQ43YQJsYSZEmQs9ObhTNlQ6fOwudTKkaxSLFxbg9/gZP2XviGyl26uQEhK7xrB09mZm3Dea9XN24XP7GXxGTz6/dzHT7xpJVX4d+evK6HtmbxZ/tp0R49LZnmen5Od8Tr19ON+8vAZ9RBrdRqSSv6eObt1jeXphCaf3jSM63MTstVXcPj6Fhbl2EiPN9E+x8cyiUm46Ppl3Vldi0jT+cFwC/1pdyXn941iaX8/cHXU8ekI6s5aUMbF7FNmxYfxjeQVPnpzB80vK6ZMYzok9ozljdi7fXNqdOVtr2V3i4E8T0nh0fgnn9o+lR3w4GwrrGd41miqHD5MJYq1mvthYxUm9oimq97O1wM7JAxL4ZkMV/TIiMJs17vmhmHfP7cIHq8qx+CTnjUjmr5/lcvn4VErtXhb9Zxt/uu04Pv46lySvj/4ndOHvjyzjrnuGExHbOafhiIwP5/jL+pNf4yEuNgH8fvzVVZjj9x9Z1l9ThZbRhadeeYJXn7yrTdv/4PPvcHs8xERF8tBt17Z3+IpyVDCbzHiznNgirThdbrbUhZO+tZbpfWJaX1lRjjKtVu6EEJfrup4D9ALWAQVt2bAQYhcwJvB4CzDpAOWe2OfxamB0W7bfwOv2YQk3H8wq+6l0+jCbNDRN4+GpaZg1GN8tivGB7n7n9o+joqqG9z+byyXnTOeL7xaQEBdLflEJqUkJXDO8D4vm21jmy+b+8yZSbYrhy51e/v3iw/x3UzWppnqOz0knc73gyb+/T5jFQlxMNKNz0vnDcYl8UqAxsnsC3375MUMTzuXNqyeQaJuIHxNeP0SHm/hbTwDjx/TAQNz7jj54pEciNJlMJMbHcsbJExvvyXB7PKQkJbBlRy7fz1/CZefPwBoehjU8jPAwC0tXb6C2zs6AnJ688q//kJWRytgRg0lOjD+isStt88XmGmpdPnZVeVhd5OSq4YlNXv/+2ZUMndmDospyBuq9eOa19yivrCa/uJR/vfDAfkNKN9A0ja5DUuhuNROdYPzg/unFNUz58xD0AXFsLHXy0fpqcpKseP2SS4fE4/JKbIFumz6/McBLRrSF+konUYkReN0+zBYT79+7GPNJPTh/UjpD0o0W1uw444LGzMHGj+VpvaNxBq7Y3nx8MvE2M99uq2VHhZsrhiUwa0kZL0zPxOOXRISZ+Gh9FXqylSHpEXj9EotJo8rpI85qarV1rKEFZteyYjIHJrFseQmeWjcjTujCz88sJ/uW48hz+ukWbaHW4eXNx5Zz44OjWL6tlpRIM0k94+h7w3FomsbqjDhOyIrA7ZNkzOyPLSacPSYz8YNTqHX5WOGG0WFm8uIi4CRjMI3P0+J5vG8CP2+uZtPr6/jjsxPou7EET72D6PFZdJ27Hcan4K10Ul3hhxQb2TsqqOoTxSUD46i3e5BSEhZoMZzSI5pxacZ79qceESD9JCVaOb+qFnt5PBdlhFO8tQqbHstTCZJSUcHYpAhc72yDCWmMLagE3JQOTmX5w0sYNGsSn36fj62glov/OIj82ZvIO6sn/oxoCv6xFvncRGp+3kNecgRjz+nFGUt24TgplSE+H7sXF8CIZAaU1uItjKRb7wSqdeO7JCnBSkZqBOnRYVwzsw9h1rZcx+wYUhqfk/m77AxItTHkprvQwsJ+U0Zidzhxv/kS2067lOTE+P3m4zqQu/78By676UGuu/ScYISvKEcFs9lE/X9NmAeYuO26S6io93bqFn1FOSxSyhb/cnJybsjJyZmbk5OzKicn56acnJyXWlsnmH+zZs2SDRw1LvnhzfOk2+GRB8Pj80u/3y83lDjk338tk16fX9rrHdLr9Umfz/iTUkqPx9iu3++XN93/jHzxnx9Kl9stH3jmH/LG+56WT778L/nTouWN262o90qf339QsRyrvv5hobzzry/Kqef/Ud7/t1ellFLmF5VIt/vg3stD4fP55MYtO+RPi5bLBUtWSSmlXLlus3zrwy/lK+98It/95BvpdLnknoIi+fZHX8lX3/1EvvDGB3LNxq3S5/PJe574u7zt4eflA8/8Q37+7XwppZTL12yUG8QO6XC6pNfrO+iYcnJyHpQdmFe//Zs1a5Z0e/1yT5VbFlU65Q/Pr5TOWneTmPPWlso7Hn1RPv3qvxuXFRSVyqnn/7FN58Dr9sqvHlkiq4vsUkop/T6/3Lm0SPr9Rn66vH5Z6/LJudtr5UtLyqTH55f/XlMhvT6/vP697VJsKJdP3LxA5m6tlLPv+Fkuz7fLPaX1cn1RvVz9+Ta56cfdUkopfT4jJ0u3V0n/PvnpsrtlxZ4a6fP45OrPt0kppXTUuqTH5ZVSSnnnd4Xyl912uaXMKUWZU5baPfLs2buk1+eXD/9UJOftrJM+v186PM0f65Z8u3z7vl+k3+eX819fJ512t/xxdZmc88OeZsvb6z3y2292SZ/PL7/ZUiM3lDhkfo1b3vxNvpRSyuX5drm+2CF3V7nkY/OLpZRSzl5bKVcW1Ld6rpucd49Pelxe6fX4ZMHGMimllDuXFsmNP+RKKaVc+9UO6bK7ZeGmcvn9syuklFLOf22t3DI/T0op5bvX/iA9Lq/MX18mN/9knOMt8/Okz+uTNcV2uePXQimllMVbKqS9ytl4rqWU0lHtkvWBZQ3vi6PaJSv21DSJ0e/zS6/bu/d54H2zVzqbvIcHqzPmWVVBXeN5llJKv9crfbVNz8fn386XI6ZfJv1+v3z8pbflvz766qCO+1C+kxTlUHW2PJNScuNtt8mX335f7ikskguWrJJLRIGsc6m8UI5uB8q1tlzO/B0wAfhRCPGCruvLglzfbDNbTDjnPDmeH55bxSm3D2/1fpSGe0Cu/SKfeyalEu6pw71tGTd/uYG1G7fy9nMP8N2CJaxaLygtr8Ln8/Hhq4/x3D9mU1ldw3WXnoPFYuHBW65udvsJEYfXgngsOfWEcZx6wji8Ph9lFcbIln9/+z8sXLqaIQNymDpuBGeeMom8wmLcHi/dszMwmYyWG6/PR11dPXX1Duz1DrLSU4iOMu7T8vn8rN4gsFnDGaD34vk33icrPZUwixlN0zjzlEn834PPUVhcRtesdNJSEpkweih1dge19nps4WEUFJVSWVWL2+OluqYOqzWMuNhobNZwTCYT40YNwRoWRnWdnbhYo3Vq+dpNzP9lJTv3FJAQF8PX7zzPkpXr2bk7n5SkBHbtKeCCM06its7O8jWb6N29Cz27ZREZYWv+BHUCe2o8fL65htUF9VytJxIeGMxm5brNvPjGRzxxx/UsX7eJ2S890rhORloyX/7r2TbNhWUOMzPl+iHY4oyWII/Ty57VpWQPSaZocwUZ/ZOoFhVMGZhEvxQrfgnVTj/4/ZywsYie53XjikfGkBBhoc8Nx+HwSLIzI8kGXCd0xe/zU1VQx8I3NzDj3lEs/3gLE68bTOm2qsD+TRSLSoad1weP04f0S7YvKsDj8DH49B6cXVnDsIxUdiwuIGtgMq5yBzfV1WA2adw5JglTuIkNxU7+saKSF2dk8uOWanLSIrEU15K/vYZ+k7OxjstGM2l83yMZb5GLqUP2H3W0QWSEhZOnG5PF79tVqOG+zOGZe+9FvGuicZ/qRYMOvtXbvM9UIRn9jHi6j0xrXDZohtHql943kfS+RmvtuCsGYAp8v858ZSqappE5IAkw1u8zMQuAmNRIYlKNOBvusQQIjzRaomyxe3sYNGzPFhveZDmAZtIwm/Z+nza0kEbGd86ulYcjLiOKE/9vGM8uLuWq4YlYfp6Dd9cOoi+4jOoXn8Q2fiozTpjKM6/+m8//+jg33XYrPr+/9Q3vQ81NpxzrzCYzWqUZa1gYs//7LWF9x3FDUiI5SaH3naIobancNfyv0DCVwYFnPD3CKvNqcdV5GHx6DyTGyIwHsrLAwex1VTxxYirnxOWTHZ3GL5t3U1mUx1mnTOLxu64nOjKCC884mYzUZPr27k6vbtmYzSZ6dsvi8t+dgcXSebv2HK0sZjPpKcYPxEfv+CPVtXWsWLOJ3PwiAH5cuJxP/vcT9fVO+vbpzrP338xXcxfy8lsfERUZQWSEjTuv/z2JCXFceesj2OuddO+SwY1XXAhAl4w0Nm7dicPpYvqUsQA8dNu1xMc27TI4YfRQJoweul98N1554X7LGrazr2svOYdrLzG6PXm9XjRNIzzMQl5hCWs2bqVLZhoaGl6fn9UbtvCfr39kV14h9910BadMPv4wzmDwrCly0NfvpWu8ma5dItm1tIgeozPYnptPhNPGZ08uJCYqktTkpt01D6bCGpmwt2x4ZBgTrh6Iz+Njw5xcbLHh7FxaREa/ROrXl5E2Mo2hm4rxDYxlxt0jsYSbSQl0yR6R3fTGeGuUUZmIiLUy9cahaJrGtDtGNu7HFhtOQlZ049x8Iy7IAWDAKd0bBySJirPid/uwxYRTW1JPco84BpxodI3+/pmVDD+/D5HlTi4srQQy2f3uRpLP6U1YTDjv5Tn5m9XM704x7qN75ISje2LcfSuEhztQi9KUvdzBzmXF9OuVhM1iIuyEU5E11VTccyOm2Hgc337BQi2GP0wZycpff+Usm/oxqigHy2I2Yd5lJSwsDHu9gzvGZKiKnRKy2lJbmQ0sALrpuv4N8N/ghtR2jho39gon3Uek4axxN3tV940VFeQkWRmdZWWs3MoF1z5HUkIcwwf2ZtLxw5h0/LAm5eNjo/eb1+eC008K6nEoe8XFRDN1/MjG55edP4PLzp9BcVkFYnsumgZnT5vM2dMmN1nP6/Mx++VHiY6MwLbPj59zZ0zl3N/s47cVu/bWcBFg+OB+DB/cr8lrsTFRPHjrNYDRyujzd945387uF0feWjcuzU+UzcyajUVsrcjl/NNOICYqkp2783n99Hvafb/mMDOn/MUYnXX8lXFIvyR/XRnpegJdhqZgsZoxHURLRGRc0++FjH6JByhpaOgBMOAUoxWt63F7h4NP7mGMGjr97pFomkaK10/WAOPixCX3jcJkMWEyaTzbe//RRRWlOZpJIzzCwrTe0Y0V54pH78I6fAzRF1/B7kvP4uHn3uCLP56F5uzSwdEqytHJbDbjHWXHZrNir3fw6RYnvXr5sVlUq7YSetoyoMpLuq7PxRjHQwgh1gY/rJb9uKMOr19ycn/jR9Wmubtx2z0MOaNXY5mdlW66xoVxQs9oUiLNiG07mfPjQu658fL9fnArnV9aciJpyQf+UW4xm4+6gVnMZlOn7i71zupKRmfHNo4cWeutY9WPgpOnj2FgUm9GdOtPcmLwKzGaSWPiNcaorlGJnaMba8OPcJPF1Nil8HAHdlKOTZEJNmKHpXHOB7v57CLjgkL8XY9gSkhC0zQKTr+EXr9uJqy6En/i/qPPKorSOrPJhHl1JOG/s3D3jZez2ZN4VEyHoyiHotXKna7r9+/ztJ+u62cJIR4OYkytMpvAajGx7ANBl6EpTYZkb/DGigouGxrPsgU/4ff5uez8Gbz25F2qS5GitFFPzc+ml1ehPzAGANnFR/zmWMp2VuN1+fHZDu6+H0VR9le6vYp1X+zgoxuPa1xm3qcStzkslsE5PfBs3Yxt7MTmNqEoSivMJjOmND8mk4nM1GSGpSap34NKyGpLs0Fx4K8EyKZhPP4OIqVkZFYkqVEWeo/PJD7T6GK3+vPt1JY62FDipNLh4+EpqXz47r/5bt4STpw4ClD3iijKwRjTP56Tbtg732FNrR1zlkZ0cgTdR6aRlrP/PFyKohycxK4x9L1QZ32Js9nXT5kwkpPX/ohr2SLCBzQ//6iiKC0zW8yE9zLh9/s584rbuOST3R0dkqIETVu6Zb6273Nd1/8XvHBaV+f2c+q7OxmZFcn9w+KxBibljs+MwmzRWJnroC7ZT+6qxRQWl/HaU3cToW5AV5SDdsH7u3hsVCJ9kyIAmD51LB6PF2eNmx9nrWby9UNCcvRCRTmSvG4/RaUO1vtNHJdh5FpeYTEPPfsGZ02bhN6rO/2fe4Wa157DnJLWytYURWmOSWp4Pguj/mQXETYrfz8tu6NDUpSgaUu3zJx9nmbQwS13MVYzcy7rgc1i4uNbF3D2Y+OwWM10G5HGriIHlw5NwOl0MWzaZM44eSI265Gd3FtRQsXrk1PY+G0uffsaLXRuj5dom415T69l7BUD9hu+XlGUg/fegkJ868u4+va9g3stX7OJ1Ru2sHbTVl7+6x2YumcTf+v9LWxFUZSWREdHMuXhQdjrHUTYrOyp9hBnU/dJK6GpLaNl7tty5wRuC1IsbVLn9rGxxMWaIgfTHzwei9VIzrwqN9888AvHXZnKy+9+xLuzHiIsTE1doCiHaqVHY8qVAxufP//G+0ybfDxnPrr/VBCKohyaoUNSWJcRw9dbapiREwtAXmEJJ44fycjjBjBiiBoATFEOl4aGpTSc8HgLU6ZMZM62WgamdY4BuhSlvbWlW+aUIxFIW9V7JJvKnGSFa+xZkEd6YMLdLglWrpg1npk33Met185snPBaUZRDs2RVORnbKhgYmBKgptZOXExwp5FQlGPN6vXlFG6uZPwlezvJ5BWWMHXcCE6eNKYDI1OU0CH9ktyVJUwcMYg7rjqvo8NRlKBqS7fMNUAMRqtdw2UODZBCiJ5BjK1ZqVEWZg5OwFHtYuvuKgAW7bazstBBXO5SxoQNZcxxg450WIoScm6elErF7trG59U1dcTFRrWwhqIoB2tyTiz/LXHQPX5vN+erLjrzqJvaRVE6M3OYmYnXDGLNhi28+9V8Rs44nwsHqhxTQlNbmrcWAzOFEP2BM4GFQF+gQ/qK7Kl28+H6KlZUeZkdZtQ1h2VEcIYey6Sxw5hy+jCkX3ZEaIoSMrx+yaOrquk2fO8ADmecPIHUFuYaVBTl4M0t9/JruJW5O+oal5nNZmJj1IUURWkvXpePBf9YR3lVDa56O70S1D3jSuhqS+WuvxDiFwAhxDqgqxDCJYRwBTe05sXbzByXEUFmSR1nlVRT5fCxpdxFae52IiOsjDy1L9UFdvw+NQeXohwqEzDJ5WThm+sbl1189jTVLVNR2lnfKjvD1xYwLNMYKdPv9/O7P92NX6qLlIrSXkxmjR6j06l3OEmMiWBEVmRHh6QoQdOWyl2VruuP6Lp+uq7rTwK5wQ6qJRFhJrrHh5M9IJG6gamsK3GyNN/BU6+8Q1FpObB3zjtFUQ6NpsGEE7ow6uK+AFRW13D+tXd2cFSKEnqOG5vB5CsHEBVm/Hdc73Bis1qxmNVIforSXjSzRka/RMxmE5VaDE8vKu3okBQlaNpSubsYqAGmATuAK4MaUSs2lDh5e1UFboeXQruX7NgwrhqWQEFRKb27dwHghJuOIzYtEq/b15GhKspRq87t54YPd2GvMCZW/nXlBrplZXRwVIoSeu7+Ig+qXEQEKnc1dXZiolWrgqK0J5fdw+f3Lmb6lLE8f8tF3Hx8ckeHpChB05bKnROoBMoAAXToHahD0iO4ZkQSO5cUMcLj5qmFpZRW1GCzWZtMVr5tUQErPt7agZEqytErxmrmoYHRFG40WsN/WbGW44ergYoUpb3dOTAG657qxufW8HAuOvOUDoxIUUKPLTqcc5+awIJfV/HV4nXsqfZ0dEiKEjRtqdy9hjFx+ckYo2a+E9SIWlFU52FruYtBM3rQa3wWU3pEERtl44m7bmhSrufodIaf10fde3cMk1IiA/etOGpceJzeJq973b7GlilHjatdWnrd9R5qiusBmDtrFWU7q6kptvPJHT8DsHVhPtsW5gPw7VPLcda4D3ufweDw+NmRFE3/k4xpEGJjohl13IAOjkpRQs86azi9Tune+DwpIY6Z50zruIAUJQR53T52Li3il+XrWCV2s6Oyc/7fqyjtoS2Vu15CiPsBpxDiSyAuyDG1qN4tqXD4WPfNTlwVDi4YGI/H6yWnZ9cm5cxhZixWMz88t4rSHdUH2JrS2XmcXnxeP2U7qvn59XUA/PreZvasKkFKyYr/GK2z5bk17FlVAsDS9wXVhXZqiur59M5FAGz8NpedS4sA+ODGn/A4vRRsKGfNlzuMdWYLijYb3X0/vWshANsXF7D2K+P19XN2UVvqwFnrZsO3xm2nBRvKKRKVAHx+32I8Ti+lO6rZ+J3x+uiZfYnPjCYmNZIzHjoegNRe8aT0Mhq/R83si8miBfHsHTqPX7JrWRG7lhUDcOu1M+mSmdbKWoqiHKwda8vYMi+v8fnCpat54qW3Oy4gRQlBfq+fitwa7PUOhneN54SeanAwJXS1pXJn0XU9GZC6rscAHdoU1jMxnNHZkdhiwzGHGzecf/7dAl6f/d9my4+/aiDJPWJx1qqrNEeThhbXH2etpqqgjvjsaAZM6w7AgGndSM1JwO+TWMJN+H1+/F4/Xo+xTvaQZKzRYcRlRHHWX8cCMPz8HHImZgNw5qNjsVjNdD0ulbG/7w/ApOsGkz04BYvVzIk3DwMgc0ASPUanA2CNDMMcZgpMs2G0Bvo8/sZpNyZdNxhzmImsgcmMudSYJSQ6KQKL1YymaYTZjCkl4zKiiMswhjhPyIomPDIsaOfwcMRazZw5OpWY1AiklNz8wLP4/aoVXFHa28VjU8nqEdv4vKyiCrfH28IaiqIcrPDIMIafn0O9w8nqMh+/5tV3dEiKEjRtqdzdAywCRgBLgIeCGlErVhY6+GWPnT7js4iMM+6xKyopJyM1qdnykfFWkPDd0yuoKbYfyVCVg+T3S7xuH+56D18+tATpl0y5YQhJXWOxhJtJ7BIDGJUma1QYZouJIWf0wmQ2kdIrnh6jjIpYZv8kbDHGHDZmy/4f8YhYK5rWfIuZyaQRm2YMZhARZyUmxXjcZ2IWkfFWIuKsDAh0oeoyNIWMfsa8b/FZ0ZjMbUmno0NxnZf3itwkdYulzl7P6g1bMJlC5/gUpbO4f2U1MT32doiptdcTE6UGVFGU9uSsdbPonxu4/U+XMmPsELrEdc4Lq4rSHtrya62LEEIHegEDhRA/BDmmFvWMDyfT5+Ozuxc1ListryQlKeGA62gmjdPuH01sWhRb5udRVVB3wLJKx1n+4Ra2LSwgLMLC5D8OQTNpnbZlK9TF2Uz0WVPIxu9zqaqpIz5WdWFRlGCYUV/Pyn9vanweYbOSrbpAK0q7CrOZ6TMxi/KKanom2UiPtnR0SIoSNG35dF8DvCeE6BSTgtgsGmlZUZx696jGZaOGDqBPjy4trtfQqqJpGharGWeNG4kkItba4npK+5J+iWbSqC2px2X3kNQtlrmzVjHu8gEMO6c35nATmqY1dl1UOoZZ0zj+sn7EWs1s2raTxPjY1ldSFOWgSCkZcko3UqL2/ld83owTOjAiRQlNmqYRnxnFn25+gtQpFzFzXG/GdVW/M5TQ1JaWO6uu66t0Xf9A1/XZuq7PDnpULVi8p55l6ytx1Lgal507Yyrdu2S2af0+E7OIToqgYGM5G+YYA18469rvfrz6alfjCI1Hk4Z7x/a1+cc9+L1+aortbF1gjPBYmV+Ho9o49w33xVXm1TYOLJK7vBhnjRufx0dtqdGnvbrITk1xPX6vn/dv/Amv20d1cT0l26rQTBoDp/fAGh3WeH+a0vH21Hh465s91Fc4GZDTkzefua+jQ1KUkCMl3Dx7B5W7axuXffq/n9i8bVfHBaUoIai6yM6yD7Zgr3fw0MldVcVOCWltqdzdAdwMvIIxLcJrQY2oFVN7RpPp9DSOgCml5MI/3o3LfXAVtJ5jMhhxQQ4ep5cv7vsFn8dHfbUxHH7Jtqomo5e1Rvpl4/183/1tBXVlDqry6xpHcvz59fVUF9op21XN8o+2ALD9l0Ic1S7qyh2s+mwbAHVljoOuaIp5e3DWuNm6IJ9fZ28GYO3XO6kutON1+yjYYMxT5nF6Gytb9VVG5cxl91CZb3RRfe9PP+Kq81C0uYKfXlptlKt04qx1IyWNn5RdS4so21WD3y9595q5eN0+HNVu6socAJTtqsHr9lFX7mTRPzcAkLemlIrdtZgsJs57agKWcDPZg5Ibh9lP1xNC6n61UNAzIZyTIjXqyhxs3bmbFWs3d3RIihJyTCaNJ0bEU767pnHZVz8sxO5wdmBUihJ6ErJjGHfFAOodTlaU+KhyHP7UR4rSWR3wF7Wu6/cCCCHmA1uEEPMb/o5YdM3YWu4ibnAKfcZnAWB3OCkqKccaHn5I2wuzWTjvmYmYw8xs+zmfzT/uITzSQlSSDYDvnllBdaEdj9NLdZFRgdu5tAh3vQe/14+zxo29wsnPr69H+iVnPTqWmJRITBYTXYamAJAzKYvIBGNwjuzByQC46tw4atyYLCbis4z7mbYtKiB/bRlSSr58cAkep5fNc3fz63vGD+uVn26lushOxZ5aFr+9EQCPw4fX46PHmHSGnNETgNjUCMIiLLjsHrb+bLS4bZiT29i69r/Hl1Ff5aJ8Vw3bAq9f9NIUwqMsxGdFM+4KYz6zYef2ITLBRlx6VOP5Pu7s3nQZkoLJpHHZ6ydiCTeTOSCJ3uOMltPh5/UhOjmCuPQopt0xEoABp3Sn+0jjHhJ1D93RodTuxTk4lfS+ify6agM//7qqo0NSlJDj8UmW2myNI/n6fH6278ojp5XbDBRFOTh1ZQ5KtlVxzcyzya2VOL1q9GcldLV0z91U4NHA4/cCzzuc0yvZ/L+d9B2WSnLPOMoqqkhOOLyp90wmoyvg4NN6Ni6LzzQqXKMu0olOiaBidy2bvs9lwtWDyF9XRtdhqexeVUremlLGXzWQGfeObrLN2LTIxlEX03L2DvaS3tcYXbGh1QpoHOVx6Jm9AKM18vjL+mGxmsmZlI0v8CWU1C0Wa5TRfbHn8RkADJzevXE7lsDUEN1Hpjcum3TdYGPbZ/VqXHbuk+MBYyTRzAHGKKMNo0o2jDLZFppJdaEMVR6/ZM+cXXSbnI883QAAIABJREFUmsULb3zAhWec1NEhKUrIkVJSvaaErZV19BmfRV5hCfFxMcREqy5jitKefB4/HoeXU045kdnrqkhTA6ooIaylT7d2gMcdalCajd4Dk4hIMAZCcbnc9Mvp2cpah66hkpfSM46Ua42K0vgrBwLQfWRaY4tUe9I0jeSeRoVVs2iYAhWvbsP37itdP/DooIpyuDJjwjh+fAafz5uHxWJmwqihHR2SooSccIuJ8yZnNN7z3DUrjXdf6NDZhhQlJMVlRGHJMHHWVX9h/BW345Ng6TS/bBWlfbV0o5M8wOMONWdrLVpGNFEJRrdJvVc3Hv3LdR0claKElvXFTn71aBRVlfGXP13G6GEDOzokRQk5VU4fT66rbRwdeL3YTkV1TStrKYpysPLWlrJzbSGp0eE8MjUdi+p5pISwlip3w3VdX6zr+i/7PtZ1ffGRCq45o7Ij+OHuhdQHRmz8ceEylqxc15EhKUrI6ZEQju+jTdQVOklJjO/ocBQlJEWHmzixrJo1X+4A4MPPv2ej2NHBUSlK6EnpGYc53ow5PJwws6rYKaGtpW6Zg9tzR7qum4C/A0MAF3AVMAm4GlgphPhToNxs4DohRLOXL02axoWzJmMOXHX5cdFyjh8xqD1DVZRjngacdM8oxD8EmekpHR2OooQkKWHAGb1IjzEGmtpTWKwmMFeUoNDwW8LwxGV3dCCKEnQHrNwJIXLbeV9nATYhxPG6ro8BngHigbHAZ7quJwQe/3ygih3AT9tqOf+XQvoGRm/cuaeAi84+pZ1DVZRj24ZSJz/O2c2tV85UI5wqSpBUOHy8+c0erjs+maSuseQXltAlM7Wjw1KUkLNndSn9rkjnv49d09GhKErQHcnJxcYDcwCEEEuAEUA9YAPCAD9wBfB6Sxs5rU801bnGhK9+v5/d+UV0z84IYtiKcuwZmRVJWp2Tvz7/FlJ2mltuFSWkpEVbuKhbBPUVLqSU3H/L1cTHxnR0WIoScnqPz2TOxq3c+8bXHR2KogTdkRwLNhao3ue5D/gr8D7wKTAT+CfwF13XuwDPCyHEbzfy0zdrWTeklleXw+/Pn8GX/3qOqMiIIxC+ohw7Fny5jcUDXdSW56Np6v4ERQmGed9tYsPaD3FFG51VnnvoFpVvihIE//7oe5KX76T/4Ha940hROqUjWbmrAfa9JGkSQiwEFuq6Hgu8BswFpgP3AS9gVPia2JK/+M0t+YvzABZ/817Qg1aUI6RT3QiwNnfOm+SSB6Dr33R0OIrSXjpVnq3fMvdNtszNa3g+Wv2fpoSGTpVnAFUl4s2qEpG3bcnXfPGPxzs6HEVpL83m2pGs3C0CTgc+Ctxzt+8Ql3cBTwKRGC16EohubiNCiKuCHKeiHPNUnilK8Kk8U5QjQ+Waciw5kvfcfQY4A1MpPAf8H4Cu692BeCHEamAN0BX4BnjpCMamKIqiKIqiKIpyVNPUYAmKoiiKoiiKoihHvyPZcqcoiqIoiqIoiqIEiarcKYqiKIqiKIqihABVuVMURVEURVEURQkBqnKnKIqiKIqiKIoSAlTlTlEURVEURVEUJQQcyXnujghd10cAdwohzusEsXQH1gshmp2zr7PSdX0G8DhgBdYCVwohag6mnK7rZUDePsX/JoToFDP06rqeArwDdAP8wDVCiMUHU661c6TrehdgCTBECFEW3CM68lSeHT6VZ62XO1qPPVhU3h0+lXdtK6fruga8DawTQjx9BELvNFSeHT6VZ62X03X9EuB2jLm964EbhRDL2yO+kGu5E0Is7wwJebQKfBDfAs4VQujADuCJgymn67oOVAghhu7z1ykSMuBl4GchRH/gEuBjXdcj21qutXOk6/plwAIgM8jH0WFUnh0elWetlzvKjz0oVN4dHpV3bSun63o/YC5wTH7WVJ4dHpVnrZcLHN/fgGlCiKHAo8Cn7RVcKLbcTQZeEkIM1HU9DuPEDsWoGf8PuFsI4dV13YnxIToZyACeEkK8EtjGfcBFgBfYAtwghCjSdf0c4F6M2rcPuF0IsUDX9XnASmA8kAy8K4R4IBCSWdf1V4FRQBzwFyHEJ4H93AOci1HJ3gX8SQhRENheBdAXeAWj1v8CMAgIw/jSvV0I4W3lXEggpaHlqOF54Hw0dyXujkD8y4QQWwPLXgHW6Lp+vRBi30kRTz5QOWAs4NN1/efAMf8H+KsQwtdKvJMw3pM4oDfgArYD9wMnAhN/s4oL4z+fL4F0wAzkA5OBb4DfJtoi4CbgNOB6ACHEal3XtwLT2CexdF23tFAuooVjzwDOAk4BREvHezRTedbkXKg8a+pI5NkhHfvRTuVdk3Oh8q6p9sq7TwPL3wB2t3RMoUrlWZNzofKsqfbKs5XAVUKIwkDx5UC6ruvhQgh3S8fYFiFXufuNWUA5xoc5HPgCuA3jjbcCZUKIsbquDwcW6br+FkYyTgdGCiHsuq4/iNE1YRpGLXumEGKJrusnY7z5CwL70oFxQBSwRNf1ZcB6wAZ8L4S4Ttf1szGS4ZNA684gYFTgS+IajC/TUwPbqwzU9NF1/Z/ACiHEH3RdNwfiuQV46lBOihDiB4zE3I+u63cCe/ZZlAfEAjHAvk3qXVooZwF+AO7E+BL5OrDu8weKKXAF5xPgtMD57Y+RROcKIbZjvHcHMjTwPiULIW4ILBt7gP2kAyYhROlvYs/+TdHkFspFcoBjF0IUAOcE9tVCyCFF5VkzVJ4FL884hGMPQSrvmqHy7rDzjob9BD4HxzqVZ81QeXZ4eSaE+BSjMt7QBfpZ4Iv2qNhB6FfupgPjAlcKXIErHzezt3n488C/KzGSNCqwzltCCHvgtReAe3RdDwc+AD7Tdf1r4HuaJsVrQggPUKXr+scYLTfrAXfDFRZgNZAaeHwaxlWY5YFKgJmmVwh+3ufxacAoXdevDDyPONgTEfjwNDw+kQNfcTFhXJ36rd9eLTlgOSHE67/Z97PAjbT8w2s0IIQQSzAebAxcsZkEbNd1fRbNXHERQoxubmO6ri+m+Ssuf20mbo22HV9Dubaeo2OFyrMAlWfAEcizQzz2UKPyLkDlHdB+eac0pfIsQOUZ0M55put6FEZFuwtG5b9dhHrl7rcn1oRxBaCBA0AIIQOJoWEkx2/XsQCaEOKewNWPk4A/ALdiJBYYTe/7rtPw5nn2WS4D+yCwnyf3acK3Agn7lK3b57EZOF8IsSlQNp7mE6I5DR/MxkRu5YpLCkaCNMjCuPpj/03R3Qcqp+v6pcAaIcTawGsaTc9DczT2PybZsJ4Q4sZW1m9CCHGgKy4WQNN1PVEIURFYnEnTm3YBSlooV03bztGxQuWZyrMmgp1nh3jsoUblncq7Jtop75SmVJ6pPGuivfJM1/WuGN1BNwFThBCOg4mvJSE3oMpvfAvcoOu6FvjQX4NxpaQlc4ArArVpMK4ULMDo+7sLiBRCvAr8CRgc2C7AJbqum3RdTwAuwHjDWovtKl3XYwPPHwbebaHs/+1zHF8ANxyg7G81NM+f3sby3wFjdF3vE3h+HXuvTLW13EDgYV3XzbquRwRi/bCV/S7BuId2OMaDvhhN4gtaXOsgCaN/+dcYnwV0XR8M9AfmHUS5tp6jY4XKM5VnTRyBPDuUYw81Ku9U3jXRTnmnNKXyTOVZE+2RZ7quxwTKfyqE+F17Vuwg9Ct3N2I0X68L/AmM5tSWvInRz3epruubgGEY/aO9GE3xs3VdXwl8DFwhhHAF1osAlmJ8uP4uhJjbyn7eAL7C6Fe9ARiMcRXnQMcRFTiGtYF/nwLQdf1hXdcfbmE/Z+q6vhG4DKhqJSaEECXA5cB/Asc/COPKErquj9B1fXVr5YCHMG7kbYh3ceB40XX9Ol3X32hmv6UYX2Z/13V9DcYX1GVCiNzWYj4EfwLG6bq+HngPuFQIUR2I7xtd189oqVwrx34sUnmm8qw5wcyzAx77MUTlncq75hxW3gUhnqOdyjOVZ8053Dy7AWN6hLN1XV+9z19SewSnSdnWVlnlQHRjVKKXhBD/6YB998GYF+TOZl5rMspRZxC4WvGiEOIPHR2LcnRRedZ2Ks+U9qLyru1U3imHSuVZ26k8a12ot9wdC3SM0ZyOFkNpZr4TRenkVJ4pypGn8k5Rgk/lWYgJSsudbgyz+jrGB8aH0eyqYYwIIzFG/7k+UPxTjPlB7hVCfK/rek/gJiHETe0emKIoiqIoiqIoSogKVsvd6QBCiHEYEwc+G/i7VwgxAaOidyZG7XsXxvCfDTd23gs8FqS4FEVRFEVRFEVRQlJQKndCiP8SGB0G44bBYmA4MD+w7H8YM8XXYdzgGQXYdV0fB2wVQhQHIy5FURRFURRFUZRQFbR77oQQXl3X/wW8CPwHY36Phj6gtUCcEGILxnwPz2MM4Xoz8KGu66/ouv6YruvqnkBFURRFURRFUZQ2CPpombqupwO/ArFCiITAsjOBk4QQN+xT7mKMyuYA4BNgMsYEhk3mE5lyw7nyHH0y0u3CX12FOSUtqPErypHw0ksvvSmEuKqj42gw8o8z5Mz0kZhi49Aslo4OR1HaRWfLs1HXTJNnZ48gOqH5/8ecfjeVXjsZ4QnNvq4onVFnyzOAi/54sRzTcygmW8QBy0gpqfM7iTEfuIyidCYHyrWg/GrTjZnls4UQjwP1gB9Yruv6ZCHEPGA68NM+5W3AucB5GK14PoyBV6J/u+1xPYby5z//Gc/2LVTPeoLkhx8NxiEoyhH10ksv5XV0DPua2mskM7dvI+GKR7F06d7R4ShKu+hseXZSTDeulk6S//znZl/fUl/ALTve4rGBzb+uKJ1RZ8szgIFVTq4+bgCRJ844YBlRn88t29/mq4G3o2naEYxOUQ7NgXItWN0ePwWO03V9AfAtRnfL64GHdF3/BQjH6KrZ4GZgVqDb5lvAqxiDrHz32w3HW6IA0CKjkPX2IIWvKMe2JEsMmtWKdDo7OhRFCVkxlig0n/+Ar/eOSKfO56LQXXkEo1KU0GMxW8Dna7FMTkQmEskWR8ERikpRgiMoLXdCCDvGLPG/NekA5Z/Y5/FqYPSBtj2/agO3AabISPyqcqcoQfFF+TKutCYhXapypyjBMr92E5ebeh7wdZNmoqctlT2uMtU1U1EOQ6WvHrwtV+40TeOEhMHMrVqHHpl1hCJTlPZ31N1M09BUbrTc1XdwNIoSmjQNNJsN6XJ1dCiKErL8mtZqa8INmafSxZZ8hCJSlBClachWcg3g7KTRFHmqjkBAihI8barcBUat1ICxwK9CCHdQo2qBRqByFxYOgPS4Gx8ritI+NDQ0qw3pdHR0KIoSsvwa4PW2WGZgVFc8suUyiqK0wmQCf+t51DMijUxrAhWeOhLD9hv2QVGOCq1W7nRdfxLYgTFf3TCMOet+H+S4DijLmtj42BQVhb+uDnNCYgtrKIpysHrY0ox77lS3TEUJmuyIFDT3ge+5A/h3yXyqvHZuyT7jCEWlKKHHag5HttIts8H7JQspdFdyd9dzgxyVogRHWwZUGS+EeA04XggxDcgOckwt6he5d/dJL7yFKTauA6NRlNA0IqYXMb+/DuuocR0diqKErOMyh5J4+4MtlkkJi6XUU3NkAlKUEJU4YAQRJ57aprL9IrPZ7igKckSKEjxtqdyZdV0fBezSdT0cSAlyTC1aWbuj8bGvvAR/TXUHRqMooenHynXg9yMd6r5WRQmWrypWYHe0XHFLDoulzFN7hCJSlNCU6ygGT9vuKMoMT6BAjVCrHMXaUrl7B3gReBp4CnghqBG1os6/t5uYe8Wv+ErV1RVFaW9VPjuuFUtwr1vV0aEoSsiqq6/C/dF7LZbpak1hREyvIxSRooQmU1ExrhW/tqlsRngCMxKHA/Bw7kcUu6vwtOF+vY7klT5+v/lFrhQvs6RmS0eHo3SwVu+5E0L8Hfh74OnNwQ2ndQ0DqgBEX3R5B0aiKKFLQyNy+lkdHYaihLR6q5mIOx9osUyWNZFr0k9CSqkmVlaUQ1TXLYvIaW27bzXMZOHqjBOZV7WeSJOVU9f/lZmpEzrtfa9SSjbW52E1WTg9aSTLa7czJjano8NSOlBbBlS5DLgTsDUsE0IceGKeILNqe0N2zPsO0IiYfFJHhaMoIclmCsO1ahn+ynIipk7r6HAUJSTFSAvuN16Ba/+vxXIP5n7I1PhBTIofcIQiU5TQEl1aiWPBD0RMPLFN5U9e+zBVPju/DH2cfpHZmDQTLr+HXGcpOZGZQY62dctqt5EZnsj9u95ndGwOl6dPZVbvK7GZjNHjN9fn827xfB7qfiEWzdzB0SpHWlu6Zd4BnAH02+evw4yK7dP42F9Rhme7an5WlPY2PXEYvuJC3BvWdHQoihKyzk8cg2f+D62WGxXTh3eK5wU/IEUJURlOM57NG9pc3qKZmBjXn0izlRlJwxkQmc0FG59h5ubnWVO3izcLf2B57bagxVvhqeP8jX9j+rpH2FJf0Li8LDC40mdlv3LBxqcZGdObK9KnEqaZGyt2AD1taRS6K3i76KfGdZRjR1vmudshhAjeJ/gg5bnKGx9rsXH4c3d2YDSKEppEfT6aNUdNYq4oQbS6PpcLW5nnDuDkxKG8kP81m+rzmowYrXReUkr8fj8mkwmny4XD5SI2OhqLue2tKL7ApNvVdXXExcRgNhnX4yura/D6vGiaRmx0NGazGZOmqW67LbBLN7RhEvMG/x14J1YtrPH5oprN/CF9Cmnh8ZR5aggzWfiyfDkjYnoHI1zeLZnHoKhuzEydSE9bGnmucuIskfxu07N82O9WHusxs8Wu2uEmC/d3u4BrtryK1RTGpWmT+H/2zjM8jups2PeU7X3VuyzJHrl3G+zQMb2FDiEECHmBNwkpJISQQhLqF0IS0iAhpBES3hACIfRqwBhjbIyNsb22bKvL6tqirVO+HyvLEnIRjmwZM/d16dLOzJkzz8zO7JznPC2upVgW2cgS/8xxuVcyuopFHFV57VFhGAZpQ8Um7vqedEPnvf56ElqKRd5aBEEgravsSPfyz663mOuuPmgeEIZhUJ/qoMKWhyiMxpY2dozmKscVRXkWeA8wAEKh0M0HVKq90JLqGfwsev0YZrZME5MxJ5RozRYxN+vcmZgcMNbE6zG0fSt3FkHi66VnElHN7LX7S/OOHbS0d5Ab8FNaWIjNasUwDICPNNBVNY2mtjbqW1pQVY2ivDxyAn5SqfTg9uYdO9jW3ISm6fg9HiKxGHa7nf54nMK8PGrKy5lQWoIsy6TTadZt3ozb4cRikQl4fbR2tLNx6zbCsRh2mxVJlDAMg5qKClo7OognEtisWStNJBZD03XsNitVpWWUFhbgcbmwWiwkkil2dHWR4/eRGwxikSQkScKAQUXxk0JMT43qWduJR3IMW74k/6hhy+3pPi5o+wnnxLYz2z1hTGQEeLZnDbIgUm7LY3FeLflWH4ZhcNP2h9iWaOf0nLnkWDzAvu/bSns+L8z4PgBvhDfww4Z/ENWSaBUGFkHi/f4GjvFPxSXaUJwlg/uphoYsSLSkevhV6zM0p7rJt/hY6J3E6cE5rIzWUWbL5e3IZi7O/xTSbhSXJ7vf4anuVUx0FKM4izkrZz5Xb76PuZ4qZrgqcUs25nlqBp/BLjVKn9rPREfRbs/l+/WP0Jzq4pTgbGa7q5joKOI79X/jlb73OdY3lYXeSZydM5/PbPo5aV1FQ+fivG7OyV3AyetuxSnZWOiZSIkth7Z0L6ujW9ExeCdaxxUFx+GUbNy8/WF61RgVtjzOzJnHiYGZfGf738gYKlX2Aqa7Kljsq0UzdNKGimOIpbQ51c3ySIhV0TrShsrPq6/i7uYneKp7NQIC013l/LLmaprT3ThEK7kW716/u4iaoDXdgySIOEQreRbvMCV2X4xGuXtm1L0dBIbey6LHix41zc0mJmONgIBgt2MkTeXOxORAoQnAKCx3AKcEZx9YYQ4zdD1bHD6jqrR1dvLKireZWlPDxm3beX3VaipLitnW1IwsyyyeMxu300lvOEzQ58dqsbC1qQmLLGG1WKkuL2NbUzOtHR00tbXh93ooKyzC6bBT39LK2lAIr9sFgKEbVJaWMGvyZDJqho7uHqbUVCMIAqqmsbWxkfqWFpatXp2dLQemVFfRHQ6TzqRZ9f56igvyOf7II8gLBOgJh8kNBOju66OuoZGFM2dQWlCAOKCc7RwcR2Ixtjc3s35LHal0mnQmg8NmIyfgZ3tLM73hCJqmoek6AuBwOJhYUc6EklICfh92q/XDl/DwQhQ/kuVuXxRY/dxV9Vm2JnaMiXLXnOrmH51v8mT3O/xu4nXD4voEQeDPyvV0Z6LkW/evtvN8Tw2X5B/FOTkLsAgSGUPj5b51/KTp30S1JA/XfpVuNcKa2HZ+3PQEXyw+lc/kH8UxvqkELW460xFmuCuwCDK3NTxKyshwfckZSILId7b/je3Jdqa7KhAFgRtLz6Ex2cW5uUfQnOrGPqCU3FJxIU/3rObvHW+wyKsw113NFaFfsjnRhk20cKR3EndOuIxvbvsLm+It1DqLmews5cqC4zk5MJOMofFszxra02EmlhRxnH8aXyw+ldWxrSiOYkRB5MdVl1NqzSFlZOhV+7GLVl6ccQvWIRbDULyF18IbEIDZ7gmIgkCexcvVhSeSZ/HSkOqkxJYDwGnB2fSo/WxPtrM+3shiXy3X1/2eldE6quwFnBycxVWFJ/Cb1ueQBJEjvQq1zhIMw+CzBcdyQ+nZdGUiRLQ4giDwcu86HtzxMkd6FbySk+9WnM+/ulbwSMebgEG1o5DvlJ/P0vB6/rTjVXR0Elqau6s/h19yclvjP4nrKWyChQeVL+7x+x6NcvcwcA0wBdgM3Ldfd9YYIQ7Jlin6/OhhsxaJiclYI4BpuTMxOcDoAtl6krqOsA9LSlpXOW/D3fxr6o1YxiFBgq7rCB8D179MJsO6zZv5YEsdyVQKBIEcn49jF8ynqqwMgK7eXpp27OD8k08iFk+wfE225Ivf4+Gt994DBCZWlBM3oD+R4PVVq/A4nUybNJHpkyaSFwwOHq+2au/55Ya2lSUJZcIElAlZZWA013Tn/rmBALmBwIjtO/f1eTzMmjyZWZP3nhZhp9IbicVYF9rM8jVr6I1E8Hs8GIDX7SLH5yeRSuFyOMgNBvC63Pi9nr32e8gjChijnEgZLYu8CgDP97zHBHv+f5Vo5R+dbxLX0vxZuZ4K+8hy0pIg7rdiB2AXrXy+8IRh6+6ccNmw5R83Pc6mRAsP1V5PvsWHKIi7nVT6S+1XaE33MH/AJfXKwuMJq/2sjzdRZc8H4Eslp47Yr8pRwJdLhheS/2n1lVgECa/sHFz37bJz6VFjrIxuwS3ZMTBY7Mve18f6pw22WxKYCWQzCu+kzJabPV/BStHAhIX1Q66girOEu6suHyHfYl8twLDvcedxh/Krmi8A8GLfWrSByZXbKy8d8RwXWbPPa4HVTwF+AK4oPJ4L8hbxaOdySgdkneOuYrKzlISepjXVg2ZonJUzn7Ny5g/rr0/t54rC43GJtkGFeU+MRrn7LdAHvAgcA/weGHlVDhJD4w3EYC6WWjN7mInJWPMp32RErw+5tHy8RTExOWw5N/cILLUtoOtZy8JesIoysiCyOd7KVFfZfh/zrffeQxIl5k+fNmww0trRwatvryTX76e6vJxN27bhdjpZPHcOmq7zz+eeB7LKU21VFQW5uaz+4AMqS4qRJZmgz0dZUeGgRWk8SCRTvLj8TSyyzImLjiTH78ciyyNkGqoo+TweLjjl5MFtsXgcWZaHWbJS6TRWi2XMFdvxuFY7j+n3ejl6/jwA0pkM3X19iIJAdzhMJBrD63YRicVo/KCNvkgEh91OwOfF43Qxoax0MH4wEotht9lwO51ouo46xgrUWJHjyUMq2L3L339La7qH18MbuH3Cpfvdx6FQZuHWykswMPYZH1ZiCw5TqGochQDM3Y96nDtdTIcStLgJWtyD/R5q7PwdOCkwa8S60eCS7FxRePzgcuWAQgxZRW9P+GXX4ITCvhiNcjcxFAodPfD5CUVRlo+q5wPE0Dp3ot2B/4bvj6M0JiaHJwIgl1Xiu/6m8RbFxOSwJvjj34x6YHBGzjxu2v5Xfl1zNW3pPhYOyR49GjRN44MtdbicDvxeD5MqKwe3vR/azNSaajRdZ/2WzSgTqmhobeHppa8RicWoLi+jurwcm8XKhro63li9mrlTptATDqMbCbY2NbF05UomVlbQn0gQifVz0uJFeFyuwWOk0ulBZUvTNF56awUOm40jZ8/CIu9fooWmHTtobe8gHI1S39LC1Joajpw9a78VJ7fTOWKd7TB3WbRaLBTlZa1FBbm5I7bruk5Hdw+RWIy+aJSX3lxOMp1GNwy8bheJRBLdMMhkMng97oMt/qgQC4vxXHb1Aen73NyFnL7+DjozEfrUfuyihY50mLmearoz0d0qMDtpT/fxUu86YnqSa4pOOiDyjRZBEIaNsU0+vozm19SuKIozFArFFUVxAONaMGNzonXYcvhXP8Z17qXIxWYGMROTseLNSAi9P0b0z7/F9783jLc4JiaHJf/sWsEN9/8MzxXXIjpGKhUf5nMFx6IaGq3pXm7a/lceqr1+0A1pNDS3txP0+zhi5kxefmsFVWVlyJKEpmk0t7ezeO4c3E4nc6dmPWKqy8tYvf4DFs2ZTf4Q98LFc+eweO6cEf33hMNsqW+gMDeXoM/Hv19+hRMXHUlPX5i6xkZ2dHUhiSLlRUVoukZGVZElkb899TQVxcXk5wRJpdI0tLYQ9PmZWatgtVhp7+qiKD+P1o4O1oU209HdjdViwWKRSWdUaqsmEPT7OO6IhfutJJrsGVEUKczLpTAve68tmDEdXdfRdB2LLGMYBolkErvNhiiK/PCGQ++d0da+jf4nH8V11gVj3rdPdnFqcDYv9a7lL+1L6UiH+UzB0VQ7Cjl/w93rvZXpAAAgAElEQVScHJjFtcUn45ddw/ZbE9vO9XW/J9/i4/ScuWMul8knl9H8Ct4LrFUUZT3ZuLsfHFCJ9oH4oVkFx5LTEf0j/dBNTEz2HwEQrFas02fts62Jicn+Y508DWGU6fFlQRqc3T8lMIsXeteOiKPZGx9sqWNiRQXF+fnkBvyseG8tmq6xoW4rfo9nhNXKarFw5OzR/wYEfT4WzpwxuGyzWnl1xdsE/T6m1lRz6tFHkUqnqW9pIZNRmVxdhc1qpSccpnnHDpp37MButVFRUkI4EuWJl14mnckQ9PvpWd6H3WZj8ZzZFOYtIqOqdPb04HO7h8W1mRwcRFEctI4KgoDT4djHHuOLYbUil49dVssPc2PpOfTrKSJags/mH4NTsgHw2JQbub/tec754C7umvBZ5ntq+OOOV2hOdXNz+Xk8MfWmvVr2TEz2h30qd6FQ6OGBUghVwPZQKNS9r30OJB92X5ELSyCTGSdpTEwOTwQEBIsV2/xFo0r2YGJi8tERAPtRx4P40R1iprjKWBXdOur225qa6ejp4aTFiwD41Ny5LFu9GqfDwWfOPGMw6+JYMm3iRKZNHO46apHlEety/H5y/P4R+xuGgWEYiKJItL8fl8MxzN3S5z40XQBNDj10hx3rtAM3WWkRZfyiPMK1Mmhxc3P5eXy24BgKrQH+3P4qr/S9zxeKlmAVZXJEU7EzGXv2OGJTFOW7A///DvwK+DrwS0VR/naQZNst+Zbh2YL6H3+E+Av/GSdpTEwOT6oHApm7vnAxel/PPlqbmJjsD/M81XR+4WK0jh0fed/TgnP4YcVFg8uGYfDKihVkPjTZ+daa93j0ued5Y9UqTj3qU8gDbosel4tTjz6aY+bPx+t24/MceoNMQRAGlTmPyzWuyVpMPt7k9CbovWX83EXLbLmICPxfx5v8sPJijhuS9dHEZKzZm+Vup8Z0/8EQZLQUWIfP7onBXLTWpnGSxsTk8GRnIVHB7cGIRSE4+rgeExOT0THPU4Mgt8NHKK68E1mQ+FvHG5yfeyRWUSYcjbJp23YK8/KYUl1NMp1mQ10dG7Zu5chZs6goLsK1m2QhJiafBLyyE0Md37rIkiDy/AwzCaDJgWdv02DrFUWxAl8BlgNvASuBWw6GYHtiS6Jt2LKUl4/W2T5O0piYHJ681vcBAKLbgx6LjrM0JiaHJ39tfw0kedSFzD/ME11vsz7eCEBHTw9Oh4PV6z+goaWVp15dSn1LC8qESqbUVJuKncknmh1qZEyLmJuYHMrszXJ3FXAzUAiEBtbpwLIDLdTe0A192LJUWIy2o3UPrU1MTPaHtJEdbIput6ncmZgcIOJ6GkF2YeznoPPEwEye61nDHHcVnT09TJ80EZ/Hw2vvvENFSTFHz5t3yBcdNzE5GGgCGKqZn8Hkk8EelbtQKPQA8ICiKFeFQqE/ACiKYg2FQumDJt1u+HANDqmgCK29DcMwzJeYickYsfM5G3TLNDExGXME+K8sd6cF5/DZTfdyuf1TNLS2ccz8eZQUFFBdli1ybr4TTUwGsFgwesY1H6CJyUFjNNHJsqIoPxv4/JSiKJ89kALtC7toGbYsOl1gtaH39Y6TRCYmhx9eKZvW2nTLNDE5cORavAiyjDHKmDvDMHjhzTdpasuGJ5Tacrg+cRxPv7KUHJ+P4vx8YKAYsanYmZgMIlvtGPH+8RbDxOSgMJo6d9cCiwY+nw68Djx0wCTaB+X2vBHrgrf/HNFMiWxiMmYs8GZTlTtOPB3BahtnaUxMDk8+nbsQ1xlzkQqKd7t9WUM/XXGVWUUO/HYJSUvS2NpGW2cXs2prmVBaQnNbO5XHV7Mkd66p0JmY7IEidwGezx833mKYmBwURmO500KhUBIgFAplgLEvhvMR6MqMzHYkuj3okfA4SGNicngSircAIFdUIeaOnFAxMTH571kW3ohtzgJEr2+322tyrHhtEo+838eSP29n+44eCnNzOf2Yo1m7aRPN7e0EC/z8uv25wTIBhmGgfSg23cTkk06PHse+6JgDUs/RxORQYzTK3b8VRXlDUZR7FEV5Ffj3gRZqb8S05Ih1ybfeIL129ThIY2JyeNKWzro5J994mdifDqlqKCYmhw2heCt9t91Meu2q3W5f1ZJgfomDm47KZ8UXqknEeskLBsnx+7HIMms3bmJmXjUJLU1HOjvBeeP2v3Bl6FcH8zRMTA55YlqSzmsuRQ/3jbcoJiYHnH26ZYZCodsURXkKUIC/hEKhtQderD3z4YQqAK6zzh8HSUxMDl92PmeO407GcdzJ4yyNicnhiSBA4JYf73F7U2RXdr/GcIZlDTHOnVOOIAgcOXsW77y/noriYqa3VbCuvx5n0s7KSB23T7j0YIhvYvKxQQDy//jYeIthYnJQ2KflTlGUEuAbZEsjLFAUZeEBl2ov7E6508O9RH7783GQxsTk8EaPRYk//fh4i2FictiSWv02amvTbrddNz8Hj00CoK8/QUckQWlhIQCVJSVccMrJeFwubiw9mwWeSSztW89dVZcx2VnKuv6Gg3YOJiaHPgLJZa+idXeOtyAmJgec0bhl/g74A2Alm0zl3gMq0T4otgVGrBOcLuIv/AcjY9YwMTEZC47wTsp+0DSif31gfIUxMTlMuSjvUyRefobMltCIbXU9Kb7/SvvgshBt4+QaFxZ5pMNNuT0Pr+zgqyVncKRXoSHVye0N//yv5dMNnfdi2//rfkxMxptiW4D4c/9GbTDvZ5PDn9Eod/ZQKPQKYIRCoRAwMujtIKIaI4u9ChYrcmkFfff8iNR7qw55Jc9IJjF0M+DdMAy2NTVR19hI+hD/zj5p7IxtFby+7P2aTo2zRCYmhx/dmWi2zt1uSiGUeixcOXvXZOambdt5oCGfRGbP7w6nlM1sO8NVQVu6l8bk/lspNEPnP92r+Fzol2zo371l0cTk40JG1xBcbrMcgskngtGUQkgpinIyICmKcgT7UO4URbGQtfRVAjbgNmAD8CeymTbXA18caP4voAj4bigUelFRlCrgK6FQ6Ct76r83s/sHM3j7vSReeY7YX36H2tKI65yLcF9yJekN65DLJyC6PaM41QOLnkwQvf9nJF57EQwD5yln4732a6g7WkFVqdeg8d13kGSZqqoqcvILkL0+LFbrIZ/iOp5I0LyjnXAsRjgapbO3B103kEQRWZIoysvDZrUiiiKiKJBIpdje3ILVYsFus7H07ZWUFBRQVVaKzWrF5/EQ8HrH+7Q+sWyMNwPZelliIIjW041cuPt07SYmJvvHK33v8938+Ri7KWIey+gE7BKqpvHvl14mnUlzx0klWKR9vwtkQeJzBcfy147Xubn8vP2S7d3YNu5ufoJbKi6k2lHIe7HtzHBVkDJUbIKMKIjUJXZQn2znxMDM/TqGicnBoluNIrrc6P2x8RbFxOSAMxrl7n+AnwC5ZGPvrttH+8uA7lAo9FlFUXKANcB7ZBW4pYqi3A+cDTQA9cCVZBW/F4HvAt/eW+d7UnJEtwfXWRfgOusC9EgYPbwr25/zzAtQmxvp/eE3kXLzEIN5iB4votuD4PbgPOM8BEEg07gd2/TZqM2N2dkdmw3BZkcY+l8azSUbidq4nb67vo88sZb8vz8Dqkr/Yw+j6To9a1cTaW/nLV8+x654FbGjjUwiTncmQ8rmoLWsCssRR2GpVvC+8SLpJWdQvu4dUi89g2CRQbYgWCxYp83GOns+tumzyWzZhJiTiyBbiDz4KwRJAlnO/hclBFnGseQMxECQxMvP4jrrAtIb1pGpCyHYHdhmz0dtakDr6UIK5mKbswAjmQSrNZsFAFA1jTUbNrKloYFkMklJYQEel4uSggJm1CpYZRlN10ml07R1dqKqGhlVRdN1LLLM8QsXUpCbgyAIJNNptjc1U9/cQkZV6e7rwyLLBHxeJldVI8sSecEgNqt1v67/eKDrOv2JBIZhYAC9fWHCsSiGAdH+fpLpFPFEksVzZo+3qCMY+pRJwVz0ni4wlTsTkzFFgD1a7l7ZFkMQBE4ohf5EgtOPPQbJLlPfm6Ymx4ZhGHz+iWbuPrmIpnCG5+tifGlhDi5r1iHnswXHsinRQlRL4Bbto54gbEn18EZ4Axfnf4pls+4Asla8e1uepl9LsTW5g/snXkOh1c/nNv0Cv+yiNd3L5QXHjugrqaeRBAmLIA1bbxgGL/e9T42jkEp7/ke6ZvtDxtBYGdnCNFcZPtmFZuhoho5V3Pf7XDN0tiTaCMpu8q27L1lhcugjkA3hMUzlzuQTwB5/2RRF2TmK7gAu/wh9PgoMdfZXgbnAawPLzwInkY3dcw389SuKshjYEgqF2tkLo3k9iV7fYN0g7zVfA8AoKiHv/r+hdXeidXdiRCPosRhGfxRBEND6ekivXY1t+mwSr79E6p23IJVETyUxUkmMdBrrpMkEb/s5fffcim3BYmxzj6DzCxdmX85SVmESJBlDkrBddjVS7TQS99yK8zt3kHr5OeQzzsN36tmDL1nP567l5bfeojVpYM8r5ZSFC8g7+6xh55JatYKcLZtocntIaBp6LEpzewdvxzLYZx9FSW4OUydUYDEM0m+9TmblctSaWvR1q7FOmoKlRsE6fXZ28KBpGKoGuoqhaSDLYBigZV1d9f4Y2o5W9L4eYn/5LVJ+EVJZBXJpObY5C+j76a0YRy9hq2pQds/36c4rgnmLWHLSaQTKKrDYRha71vp6yWzdhK+9jcz2usH19gWLsOfNIPqn+3F9+iLkcB/Fzz5GsSAMfMlZha8/kWRDNIoYi6DXbyU2Yx5VW97HHY0M/FoLgIBm6PR4g3TXzkBZ/hL9C4/C7vGR+5v/B5k0CAJ6MA/D4wVBQL3gcjxeH9rGtfTPXYSrvRVrfOBHf3AQJCB4vUg1tURffJpGu5temwPL+++SkWQEwG6zk8qkMQyDdqeXuCTjEAx0u5NkOo3TYkHCQJct+L0e/B4vggAel4u8YAC304nfM/5W5ZHsetLEYC56b/c4ymJicrgiIMjybi13F07zA1Df0kLQ5yPH7+edljhr2hIUeSwYwFVzggQdEkGHhMsqIgmQzOjYLSJWUWaGq4Iz19/JDyoupNyeR0B2IX9I0QKIqHFsogUDg6s3/4bPfUhRkwSR30z8H/7e8Qbn5R6BT3ahGzqvzPwhXZko97c9z8Z4M490LOPa4pORBQm/5OR79Y+wNlbPYp9CnsXHdUUnc1/b87wd2UKPGuPqwhMosPrpykQotAZGKIF74+nu1ezI9CEAPsnJObkLkQSR5lQ3r4U/oMQa5Fj/NG6pf4Rl4Y34ZRfTXRXcWHYOZ31wJ2E1zsmBWZwcnIXiKOHWxkcRECiz5XBSYBYzXBU82vUWD7UvRRJEPl94AqcEZvP9hkf4XMFxVNrzsIvZYVJnJoJfcmIZhbJoMn6ILje66ZZp8glgb79EIbJulAK7Cpfv/Fy1x51CoRiAoigeskred4GfhEKhnX1EAV8oFNqsKEoz8POBNrcC31IU5T6gl6ylb0RwgVUd/Y//UARBQPD5EX1+LFUTR2wXfX4sFdnT8lx6Fa6Lr2BLQwOvvbMKqyyTVlXmT5tGprsb/ZKriRk6ek8PyW/dzjvvvYdkGFgEAV3NIOo6ifoWtJZOfCU19D37HPgKycQylL/5JgGvF90wiERjdIf7uPj003YbJA9gm3cEtnlHMBh5MXsWtYC+eBEZVeX9zVt4tq4OXdehuAYA49nnEEUXOV1h6FqJITkwRLA4ZCqKixBFMWtNisUxonGCi0+goa4O1ZtLztkXY7NaaWtvR9cNDAwwQF//AS0LT6C7vQelshLLH5+gassHJF96hswPv0EvWfe94F2/IrNpPel1a/BccS2Re+/AUDWkwmKsU2eCKAIGUn4RAHJVDVhsCA4nluqJWWXTAAMDt5H9qzzuWLTODlKFefRPnU0s3ofWOTAHMFCQVBYEqiomUDl1CmFDRQzm0NkfZ9OpF5IUJexWC3mpBFIqCYZBuDdCYuNG/O2tJD15BF5/Ac+O5l33y0C/kZwCtsxdTGnDNnJmzKY64Mfd3oSgaYCBqmlIooQAOC+7GqvLTeTXd2P/wU/QH/49ief/k+3PZkMqKEawWLBUT8J7zVeJPvwg1qkzkQey3x1K5PY7Bj/LxaVZq62JicmYUhr3gMUKu4nB/sf6Po6ucBGOxvC63QDML3Eyv8TJH9f0YJdFjq5w8UZDnISqs7jcxSvbY2zsTHHD4rzBfs7PO5Ibt/0FgD/XXo+AQL7Vx7pYPbXOEn7R8gxPdK+k3JbDOTkLUZzFXJz/qRHyOEQrVxWeMLgsCiI2QaTEFuTWykuIaylEQeCyjdnM1ffWfJ47Ki/l7egW2tN9uKSs9bDaXkiVvYAlgZlIgsiy8EZuqX8Eu2jl07kLuarweG5vfIwTAjNoS/WgOEtwS3Z+1fIsTslGodXPdUUn45edbEm0YWDQnOrmFH02r/St5ydN/+b4wDS8khOAi/M/xZWFx1Npzyejq1hEmYdrvwrA411vU+ssxSXaOC/3CHQMQvEW4noKA4P1/Y18rfRMjvdPByCjq0xxlnF93YP0a0muLzmdi/IXc8nGn5LUMxRa/Zyds4DPFhwzFreHyRhiy8h0JvykOsPUjrcwJiYHGMEwjH23AhRFCQK9Q5S0vbUtAx4HfhMKhf6gKEpzKBQqHdh2NrAkFAp9aUj7S8kmd5kKPAYcC6wNhUIvfrjvO+66y7j5pptGJfNO0pkMsf44sXicWCJOPJ4gmU6RSqdJpTMD/9OkMxkyqoo64DrosNs487jjyA0ECEejvPzWCjRdRxJFRFFEkkQkUWLapImUFxWxpb4ei2yhsrRkt3JkMhnWb6lD1TQEQcAiS0yursZqsXyk8xkNXb29xOJxBIQBQ5RAfyJBa0c7DKwThKySt6Ork7xA1uWxrbOTjKpSVlSILMkD7bJWnNxAgKrSUiRppIKt7mjFiPcjV0zASCTQI2Hk4tIxP6+PA4ZhIAgCeiIOgCBbBqyiLaBlg7otldWoTQ2IXi+iL4CiKD8MhUI/GF/Jd3HDTTcZ99x113iLYWIyphxqz9mV115n/PH++3a77T+hCEeWOtm4cS0el4tZk0cOSbf2pFjTlqQ5kuHquUHcAy6ZO613u+Nb2x6iLd1LWI3zUO31/KT5SS7MWwTAi71rOS04B8W5+3fYgWRFZDOPdi7n5vLzqEvs4Pq63zPNVc63yj5NKNFKWs8Q0RKk9AxXFZ6wW5fKf3S+yXRXBZOdB/7dE1b7sYoWHAPWu+5MlLZ0NhxkmqucCzfcQ0B20a8nOSUwm8sKjmFZeCO1zhJERLYk2ljonUhEjeOVnfslQ8bQEGC31tjx5FB7zgBuu/NO45qL/pdUNE3pzLx972Bi8jFgT8/aPn0IFEU5GvgNIAGPKorSEAqFHtxL+wLgBeBLoVDo5YHVaxRFOTYUCi0FTgVeHdLeDpwHnE/Wipc1iYB7d/3r+8gyqWkabZ2dNLe309XbR09fH8lUCrfLhdvpwO104nQ4cDtd5AYC2KzWwT+rxYIsyVhkCUmShsUo+Dwezj1pyV6PPbGycq/bLRYLs6dM3mubsSI3ECA3MLJsxOTqPRpd/yuGJtsQ3J5DIoHNeLHzvhEdu17Ykj+A5B/+fchlFQdVro/C0OdMbW8jvXoFztM+PY4SmZgcfuiGTmbrZoxkEuvUGcO2nalkE0pFYjFKCwt2u3910EZ10EZaM7BKO2OhDS7/VxO/O7sUv33koP875efzvfq/8cPKi/DKTn5UefHgtmmu8rE6tY/MEd5JgyVYciweXpzxAzwD1r5JztHF+16Yt/hAijgMn+watpxj8ZBj2fXeu33CpTQkO/FIDnyyk6Se5t/d73DT9odI6hluqbgIwzC4bNO9SILIIq/CXE81x/unc3/r82xKtJBv8RHXUtxaeQm3Nz7G29HN9Kr91DgKeXDSF/lH55v8suUZprnKmeeu5oK8RXhlJyICkrDvZOi6odOViZJn8SIIAqqh0ZDsJM/iI6mnSehpKux5/LtrJRMcBUx1lhHXU3gkxz77PtQwDINMezvvPrad0pknj7c4JiYHlNE4iN8GHE3WonYH8CawR+UOuBkIAN9TFOV7A+u+AvxiII5vI8Nj8r4K/CIUChmKovwR+C0QAc7ZXefaHpS7ju5u1oU2s725maDPR2lhIVOqq8nx+/C63Yd8tkkTk0MJXd9loBcsFrDuiqfctG0772/ezJnHH4f9Y5TgxsTkUMMwQI2EEQas/DuJpjQ+91gjPz3aSm8kgm8vcbn/CUV4KhTht2dlrVWyJPDnc8tw7MFy55Ud3Fvz+bE7iQOEV/74KRBDmegoYqKjaNi6u6suRzN0DIxBa9vjU7/FpngLyyOb6M1k477ne2qocRTSnOqm0p6PIAh8tfQM2tI9+CQXXZkIBgafyT+as3Lmsya2ndXRrQC8G93KnU3/4oK8RRRa/Czy1WIXst5BQ8dBzalurgr9mqSeJqol+HrpWVyc/ym+se3PdKTDWESZc3MXcn3J6fSqMR7c/jINqU7muat5UPkitzf+k3ej2whaPBRZA/yw4iLeim4mrA6/lw8VDMOgqz5KUeHI+FYTk8ON0Sh3eigU6lEUxQiFQklFUaJ7azxQxmB3pQx264QeCoXuGvL5PWDhXoX5kHKXSqdZvmYNja1tzKhVWDxnNg67fW9dmJiY7IOhz5kUzMV+/Cn0RSL4vV7e27iRjKrS3LaDmorxm+k3Mfm4I4ki4tSZI7IAu6wiX6yN8/qqdaQzmb2WhTlT8XJqzXDlz2EReei9Xibn2ZhXsn8ufyYHhg9b1CRBZKqrjKmussF1cz3VI/ZzS3YmOrIWzKFZOz2Sg6N9UzjaNwWAoOzmprJzeabnXZZnNlFg9RPXUwNxlwIZXeXrpWfy6dyF/KLm89Q6S1ANDcMwsAgSj0/91ohjX1F4PFcUHo9u6IgD8n+hcAnduVH61H66MlE0dHJkN9ZDzEV0J4ZhUHv2DDa/FsTQDTRVJxXL4Aqa40WTw4/RKHd1iqLcCeQoinIT2RIG48ZQi0Imk+FfL7xIUV4eF59+2scqTb6JyaGMbuxS7gzDoOkLF/PawuO54PIricRiLJgxg7WhELIsUVly8ONzTEwOB0RRILnqLVKb1uO9Klv+9clNEZRcG809UfrjccoKC/fpeSLvpvbdvBIHeU4ze+MnDUEQhrm4QvY3/P8m34AsSFgEGYdoQRREagdiK2VBGlUqcnGIYppv9Y0oDTEesZqjxTAM+rc2sP1fb1F7QjkNq9p5/5l6zrlt0XiLZmIy5uzbKRuuJavQLQNiwBcOqET7YKhFobsvjCxJHLtwganYmZiMIUPdnzfUbSVmd1KcSbK5fjtej5uainJEQWDZqtWMNimTiYnJcERRREsm0Vp3Zep9ty3Bs1uivN2ejfUuyM3dr74n59mJqzopde9x6iaHP4IgUGD1k2Px4JUdn8iSDYZh0LY1QVF6A2u3bKJ8Qb6p2JkctuxVuVMUZWYoFFKBP5CNlUuSrVs3bgy1KPQn4rhdrr20NjEx2R+GTqK0dnbgqp1CUSLGxq3byPVn6/Odc+IJyLJMW2fnOEpqYvLxRRRFVNmCkUoNrltS7WZukZVZciNnHHcsM5RJe+lh7/xuVQ8N4cxYiGpi8rHGMAxqjysllnHy7uubWPvMVv717WXEuhP732cmTfKd5XR/8zqS7yxH6+5CbaofO6FNTPaTPSp3iqJ8HfidoigycDdwIjAd+NlBkm23DB10xuIJXI6Pd9C1icmhyFD350QyhVRZg7enk55wmJrybJydIAgoVRPYtG3beIlpYvKxRhREdFnGSGUHmF39Kg++20tHuJ9uuQCf243dZttHL3vmthMKqQlaSWumdd3kk41hGDR8EENS42QcGYKTXVQfWbSrivNe0Pp60Xp7hvenaXRe+xm01mbsi48l/PM76L3tJsK/uhvDMNBje01P8YkmUxei/19/R+toJ/7043R/+8tktmzC0NRhE12Qvc6GumebUqZ+K5ltW8jUb8XIpDHSqT22HexTVVGb6tHDfYPt++7+AXp/DGOIjjG0vu/u5NLDfaM63w+j7mglvWn9fu07WvZmmz8VWET21r8UmBQKhXoVRVl+QCXaB0OVu/5EHJfTVO5MTMaaoRbyRDKJvUbBePxvnPGlYykv3pUBblJlJX9/6mmOmpvBcgDqNe4vRiZD7K8PoHW247vhewjSJ88NyeTQRxQFVNlCb9SLL6Xhd0hcNsPPfSt2cEb+2CR6eGJjhPZ+levm54xJfyYmH0cMAxy5LtxyB+iQSKaZclIFFvu+3w1qUz2xP9+P8/RzsR99AmpjPYIkkfvrvyDas2NQMSeX+H/+SfC2n6G1NhH+6W3k3PM7DF1HEHfZUQzDwEgkEGxW0mvewTbvyAN2zuOBoalE//RbpGAOzrMvHDz36J/uw3X+ZcQeeoDk28uwTJpM7J8PY50yHddZFyAVlaBu30r0D78meMcviPz25yRefwmjP4bo9uA47dOIdjtyeRW2eUfQ95Mf4bn8GvSebsI/vRUEAT3ej+h04brgchzHnIjgdpN6dyX2+YuIP/M4mboQamszamM9otuNHu7DdsRR+L56M47jTkZwuog//neif30Q0enMKuiSjOh0IRWVkPPj3xD5w6+xTp6OpVqh6/orsEyajJHJoLU2Y502C+dp52CdOpP0xvcR3V4Et4f0u29jmToTMhn6n3yU1Io3cJx0BtbaacSffhzb/CNBkkm88ixqUwOoGfRIGEv1JASPD9d5l5JesxK1uQnXWecTefBXGIkE3uu+tsfvYW93tR4KhTRFUeYA20KhUO/A+nGtKaANsSj0xxMEff5xlMbE5PBk6CRKIpnEUVZBfyJOieNDWf0cDkoKCti0fTvTJ+2/+9ieMAyDxAtPYZt3JJEH7sV79ZcRfZ4RNNgAACAASURBVH70WAwpEASys2BD6ywamQx9/+/7YBg4zzgPQZKJP/cktgWLkIL7F780GvRkAjIZRM+eMxuamAxFFEU0USYUq8W7uRdLlZ9yn4XPVicpHCOvlDMVL5puoBsGolkSyOQTimEYFE4KstWRg7TZzvboDrqXx5BtMouuyGYa1XUdQRAQBIH27m6Wvr2So2fPIsdiRXC6iP7pfpJvLiWzaT2ea76Go3zCYP+Oo07AcdQJAMgl5QR/8ltS768h/u9/4L/xB6iN9VhqFNLvriS9fg3O0z5N5Pe/xPLGK4huD1p3J74v3XhI1wg2NJXU6rchnUZtbcJx4mmIHi+plcuxLz6Wvh/fQvKt17HOmEsm9AGJpS8g5eTh/fK3kErKERxO3J/5PJ6rv5wtsfQhxBqFwK0/BcB1/mW4Lrwc0etFrd9G8vWX0BIJ5IEE3c7TzkEM5mDLLyDvoSfBMNAjYfTebmKP/AkpJwfL5Omk3l6Gbd6RWCZNAQPsi49DrqxGyhk+FtipZDvPuRjnaeeiR8KIOTkYqTRGLII4MN5wnX0hgsuNaHeQ97tHSG9ajyDLSMVlpFa8QeR395Lz8wdJr1+L/YijIJMh9e5KIr//JYLThXXSFHJ/+3dE50AWY1lGsNmyx+nvxzp5OoLNhmB3oDY3YETDGIk4clkloj8rg23+IvSujr1OWu91ykJRlEnAlcCTA8tTyRYZHzeGW+4SuE3LnYnJmKPr+mCilGQqhcPhwHLD9xEsIxMXzZpcyytvrfhIyp2haUR+8xOcZ12ApaJq2LZE+w6MDdkfRtHhROvuxBBF5OIyem+/Gb2rAyOTwb7oGHzX30T/ow/hPO3TiF4f2o5W+v/9DxAE/N/60a4XiACoGsmVbyIXlSDYHejhPuSKCSDJI2ZW1boQme112dm46bOxzpqHII1M8R37vz+TXvcuaksTejSMIMlYJtbivvx/sCpTR309TD6ZiKJI2ulk/lEiBZMCvNIUpzuh8bv1Fr4+e2ws4RZJwCIJXPefFm5YlEtNzv67eZqYfFwxMHj7b5tIT5yHVp6gaKaPGdUKO/OBxRMJHn/pZRbPmY3VYuH5N5YxoayMN95ZxZKt6wj+6Keo7W30P/YwwTt+gTxEsdsdgiBgVaYQ3rKJ3ltvIr1uDa7zLsV9yRVIpeVIeQXk/Oz3RB/4Bem1q5EnVBN54Bf4vnozauN25NJy4k/+E8cJpyJ6fei6TmNbG1aLBZvVyo6uLlx2B5WlY5ehtKG1FVVVqS4fXuJI6+mi/9GHSC5bihjMQbDZkMsnEH/yUdwXX0lq9QpsRxyF97ob8H3tuwgWC4amkn5vNYLbjehy4Vxyeva6eH27O/Su6zagsAxVvizVk7BUDx9fWKfM2LWPIIAgIPkDSP4AgW/fNrjN96Ubs33UKFhqlH1eA0EUwW5HGiipJjhlcO4qJyPl5A1+Fr0+7AsWDy7L51yE65yLAHBfcNngev83b8m6ew5MHAzFefKZg589V1y7Z8GcLqS8AgBsM+bs8zz2ptx9F3gIqAe+rSjKMcBfgQv22esBZFhClXgcl8Os4WNiMtYIgoCm66iqisViQZIkhGkz6f/nw9jmHzlMcSnMzSWVyRCLx3E7hz+PWm83WmsLme1bcBx1PKIvAEDytRdJv7caBAHfl27MxkNs2kDq/p/haK4nGcjF/fM76f7W7dTlldPz8qtcdO4laOvexfWZz+NYsBitsx0A77Vfw8ioZEIfEHngXqxTZ+H9n68Mmxl0nnxW9rgrlxG+987s7KzdgdbVgRjMxfuF67Et/BTpde9inTGH+PP/wUinkAqKiD38INqvfozj2CU4lpyB6HLRe9vN5Nx9H4lJU+l1+xGKSojZHPjsduzrVpHeWkfA40Ov24T9qBOI3PdT1IZt2Bcdg1xShnXabIQh9TgNTUWQZKJ/+wN6Txf2Y5ZgnTYLrbkRQ9eRyyoQRBFD0xAkKRsroGYG/1uqJiHIpuvpxw1JFFFtDt7aUsXEFxuYOctPXnWAP61sZXbZ2LpR3nFiIQGHhGEYCIJANKWR1gweWtvHlxbmIIumVc/k8MUwDOaeP5HQdpnQI+toifZQW5gh0hGnQAny7rub6I/H6ejuJpVOM2tyLbMmT+ZfL7xI+9mXEADkgiJ8//uNUR9TsNoIfO8uog89QM4v/kj0j79B7+tFLsiGNogOJ77rb8q6aiYTpN99G6M/RuS+nxK84xeobS10XvcZnGdfxFtWF70WO5qhYxhQkJNDc3s7xwqQGwhQ39zC1Ik1g8qDpmk0trXx7gcbKC8uwufx0NXbSzKVwut2E45GKczNQzd0EskUsiSxfssWREFg47btLJo9C1c6iZRJZ+PNZAvBu+9DKigaPIZhGGiahvfL3yKRTGJzuREHJkoFScY2d69lqz9RDJ1APhjscTQQCoXeYUhBcUVRVgBVoVBoXFNvDbXcxZNJnA6zAKWJyVgjiiKqqhJPJnHYszP9em8PyddfJrNlI8Ef3jPYVhAE8nNy6OjuHqHcpVavIPXmUuSJkxFcHjJbNxN//kmSby4l8N07ESdNYfOK5Wxdt5ZARyvFPh/5P3qMpp4eOru76EyrzJpcS2tHB39/5jnkI0/ClpGY2tGN0+7C2d5OXjBIZzRKvSHRee4VKBOq8EjSbv3HraecTWLmAgJ+P16XCzQNtaURwR+kc9MHCK+9hDRhIokLLsftdGK323FcfAVGUz2xV56nq7kJ77SZJC+7hhXvrWXjtkZKCgow+pM4NGjp7CLp8KNqGrz8CjPiYYziSmx5hfiLy1Bbmki+9Trqj2/BUjsN/023kdmwlsTSF/F/4/vIxWXobg/hn92+S5mzWPB94/sYkQjxF54icPNt9Pzgm2gdbQhW26BLj6V6Ep6rr0fv6ya14g2813wNta0Fvac7qwhLElpzA7ZFxyCXTxg2g2hk0iBb9llP7VBA0zQyAwH2GVXFIsuk0mmsA5MQ1t24+8QTCeRDUPkVBRE1lWRm7HG2hC5i+dObUb6+gAt9GynwTxzTYwUcEqtb47y4NcZXjsjlqieauf3EQo6b4OIHr7ZzyXQ/69qTnDnJg9t2aBaiNjHZXwzDILIjTtfTz2ORS8ikVXQd1j/XQLi1n8YVXUw4tpTuvjDhWJTjFizEiEaYsXIpy2IxesMRptRU4/N8NLdJYUINqS99i7q2HbQfcxqebQ2UxJM4bDZKCwuzbQQBweHEvvg4AIK3/xxBFPH97w04zziP155/jj4twhKnTPDyawb73tHZxTOvv47H5aIvEqG9u5tZk2sJ+nw8tXQpiWiMWbWT6Ij209MXJmi34vL66E8mKczNo3nHDpKRMJ6+brptDs44+hgcmRQbtmzhsedfwKJrlFokot4AmeJqnBs3U92fZFJlBeFYjNXrP6CusRGfx00imQLDoKKkhEWzZ+E0kx2OK6N+24VCoX2noDkI7FTuNE1D0/XdvshNTEz+O0RRJKOqJJIpHLbsBIpcVELgu3fQe8d3RrQvyMmhvbubqrKyYeszoY1YZy/Eddb5qO1t9HznKzhOPI28+x4mZbXzj6eeprivi+k5fpyNIdwXX4HV52OizwcTdrm9lBcVoUyYQI7fT31LC3WNjfTH4yTTaXrDEXL9fipKSpiuKKzfvJnVH3yAzWolx++jo6cHt8PJpAmVvLdxI7Ik0xuJ4HE5kWUZWZRo734Xt8tJsmwSltdex2a1Ek8kSKZSGEBRXh49vkKcXX1EnnqaoN9PfjDA+aecjGcP5Vha2ttpbG2jv6MTo3gCrZ2diCUTKZq1CCmVJNDSQP3GDRiyHccZFxBau46KabOzSumCoxDamvFPnobVbs+6yOo61plzAcj92QODbrOCIKA21ZPZuhm5pAwjrwCpMOuqE773TtBUxJx8SCURc/Lo/cE38X3xG8gVVUTu/xmB791F+Nf3kHz3bazHnUQ63EfaGySla0QDuXQVlpL7yjPElpxJUdNW3O8sx+juRC+vRNy6GbWyBmPWfJorJuJe+w6RohLCNhf5Hjcl5RUU5+chCAJ6LEpq1Qrsx5xIvLGBWLyfhNuL7f57dlmHrTYEhwMt3o/zmCWoM+bStHwZzU4PzvYWhJ5uIp0d+Lo7cEd6kTWV7rwith95PM6WBlIWG7LTyfQ3nkO0WDFEkYymYYv0Eb/gc//dQ3EAEEUB1TCILbqUyjlBVq5ux9W0jcnVVQckQdGMAgd5LhmHReQv55bhsGRnk3McMoUembqeNOGUTqg7zcwC+26Lo5uYfBwxDIO2jd0kCqcSzM1gtYp48hws+docDMNgRcsa3J1FbFQ3o+kaecEAvTdfT+HkaUydOJGN27YhSSILZszY98EG2NbUzGvvvIMoCKiaxuzJk1E1lfWbt9CfSBD0+Ugkk8STSfweD0GfD0EUmKkoSLJOQ0sLdQ2NxEsrOXP+HKLf+iKpmXOxzZwHgOWeH3BszWTaqxZz2jFH88GWOv7z6lJEQcDjcnHS1rVk/n4fhQsWITicJF5+DjSVnJ/9Hj0aocYhI1XPIPHi02Q2r0P9z8NEdZ1yt5fpN3yPdreP3nCEWp8XSZLojyd4b+NGlq5cidvpZGJFBZeecTrhaIyyokISySTrQpv5v2efo6a8jNaOTkoLC5g/fTpWiwXDMKhvacEwDII+P4IoIEsSdqsVwzCy74CPwQTjx4FDbypzH+xM0Z5MpbBbreaNYGJyAJAEEVXTSKR2We4ApPwitI72ERnAivPzWbpyJQumT0caiE3T+2Ok167CceKpQNalJf/vzww+sw3btpEbDHD82VmXSeOcC2EPz7MkSeQGsi6dE0pLmVBaOrhtp/VmJ9VlZezo7ELVVLr7+phSU0NvOMzm+gZqq6qZPmkiqqbR1dNLKpMGsq6lu0s5r2oahmHQ2NqG1+0iLxgc9TUsKSigpKBgcNkwDHrDEdq6OsGAVH4BiVQSdIPenj6cDjsvLHsTh92OpmePG928jYDXS2FeHjkBPzaLlZKCfPqiUfoiERw2O3nBALbiMtRgHs1dXYSjUVLpNJa+KD2fvpy6xkY0XSfo86FqKpnSSVh2dGP7/+ydZ2BUZfq3r3Om90kvk16YBAiEAAm9qhTBAipiL69rL+vqupYt9l5WrGvdRV1lrasiKoL03utASCOF9DK9nvfDJIEYkIgN/zvXp+TMKc+cOeW52+9uaEWdPZDAkiW0Z/RHo48had8eRK0WQ309Mo0GRaKF9ORktJlZyLQaamMSCWXlIwwuQdXWjDu/CGNjHf66OpS5/VEpFcSZo0gNBVE+dAc+jZYdRjMiAubGOlos6exrc6Iq20eUz01byXjUOQPwtLSgUshRAIqAnxZJQfv+ciitoGTTClJvugt5dRmqsj1oo6JRlYxAkZmDoFKRFQoyZvAwnB++gzBoGL6YOLxJcXi9XqRgELNSSXRaRrje4rY7+vz7/RKIoog/GEIt+Vn9oI2klBCujnIKLp/+sxxPIRNIM4XrZrsMO4AUU9iQPDPPiMMX4qUNzeTHqU7IuHN4gzj8IRL1Cr4td1CUrEGSQCYK6JV9T01aX+1iQLwamQj1jgBpJgWrD7oYlarlQKsPg1JGgv43N4X5RelKwY0AIUliwJQMar7eh6xCRt1SF9J4iU//tpbBszIJOILoYnUEP1eSOjaWg2urUJftJ/qBZyiWy0lNTGT5xo0/yLjbtHMnE4qHk5aUhMvj6eEIDAQC7Co9gNloQKlQ4HS5aWhpJhAI8vE3S/D7/STExpCdlkpWaipKhQLlw/MQY2LxblyLcvBQzLfeA4/9DeOOTaj/8ijFgwoYOnAAra2tGF12lKeeQqi9rVt1Mu7V9xC1egSVikB1Ff5SG6rkVAyXhmu9vJvXIbndqEdPACAVSO2MLnaRk55GKBTqTr8EuqOZWo2GEYWDyU5LpaKmltFFQ9hfWcW7ny8kOT6e8upqzEYjGpWK1o4OQp1pnV6fr1vsSafR0C8zA4VCQXNrGxqViqy0VFweDzqNhoSYmF/9mu66r/yBADJR7HEuThaO+WS0Wq1v2Gy2y61W69U2m+3lX3JQ30dXzZ3H5/tR/X8iRIhwbERRIBAI4PX5UCkP32eCWh2WEG5pQhYb3708OT6OKKORXaWlDLKGi5Y7nnsc5cBCFLn5h7cXBLw+Hxt27KT60CEG5+Ud/uwogiV9QXGUdLvEuHAxdlfaS2JsLPnZ2T22SYqP67Xdd5F3jik7LfU4ax4fQRCINpuINh+7oHzUkCE9/g8GgzS0tHCosYna+gba7HaWrF2L2WjEbDDQ0t6Ow+XC5/ejVasxGQyYDQYUCgUOl4sok5E506ehVippbmtHLpehkMvxBwL4/QEQwO/3o9fpiDIaj/3SzLyS7jMwafKxv2RBQfef0oivCByqI6psP6FQiEC2FYveQGIoRMzMmd1OAJhAIBCgzW4nGAzi8ngoSUrC5/ejUiiQXXxJeLWsTJhzUe9jdqKbdQEAWoBJU449xpMIURQJBAMkfv0weXP/yD+r9KQlKI4ZDT5R3EsWocixHlcEAkCvFLl/ciIuf4jHVjYyt8DE3YvreXNWChtq3GRFKYnThe+5kCSxpc7N0GQtq6qc6JUiFW1+HL4gcwaaWX3QxbgMHV+WOjjQ4iXdrGRfk5c/jP7+e0+SJJZXOkkxKajp8LOswslto+P4stTBgHg1aw66yI1WYVCJ3Ph5La+cacEbCDt+jzRa/5fYWucmJEGSQc7z65t5YHIiT65uIj9OxeQsPQv32ZnV34QvKCEXQRQE9jZ6yIhSsrTcydY6N3eOi+ebMgdDkzWY1d//PG5xB5AkiNHKeWd7G+cOMFHa4uVg+69auXNMJEliz+IqOnbUk5yqpW1aODgw4brBtAc7iM41MOjUTAzpKnRqLeKBbYh5A7prmRNiY3C63ew+cIAOh4OSQYNwezw43W4EQWDbXhtDMwfQUesmrSietg47DreL9ORkRFHsdU/L5XIG5/UU+MhJDwuZrNy0mRizqcc7C0AWH36febesR5ZsQZ6cSvTjL+L8z3yab70KzeRpqIrHYNZq6XjteaLvfQLRZEY385xe50OekoY8padwiqqobzVyxzNm4qKjux2hKYmJ1Dc1caipiZGFg9Fpe+tkdAm4hUIhWto7KK2qxOvzExcdhcvtYcXGjei1OtrsHfgDQRRyOdmpqWRYklGr1chFsTsN1OFy0dzWhkqhpKahnua2Nppb2/AHAkQZjWSmpJCcEI9SocDudOL2eDHodcSYTEe8k3o6RlxuN8FQiFAoxI59+9ldWopOo8HhcqFWqYiNikIul9MvI5305GSCoRBOlwsQkJBoaG7G6XajVCiINpmIMZvD42xtQ61S0tLeQWyUmRizGbVK9ZMYr9/n9iq0Wq2PA+dardb0Iz+w2Wx3/egjnyDBzrRMj9eLWtVbuS9ChAg/nnDNXRCfz49S0fMxIUtIJlhX08O4EwSBvKzMbuNO8njwblpL3KsLehUSr9m6FbfHi0IuJ/2InnkReiOTyUiKiyMp7vBk+Gje+O96Uo9Gl8H7SyHI5CgsqZgsxzeM5XJ5d2S2e9kJGvu/JUQhfJ+tCM0mLujivCHRJMvjf5aIS/Nt12C8+hbk6Vk4P1mA5pRpiOYYnO+9ifHqcL8k8YjWQhq5QP84FakmJY9PSQobA01eYjQyml0BNtW5GZuuY9F+B0OTtdQ7AuRn6hiceLjW5q5x4WfEtFwDkqQnGIJJmXraPUFe2dTCbaPj2FrnJsWkIFYbfs4s2NlGVpSS2zoNwGSDguGW8ITwvknhSPjFg8PXSjAkcee4OERB4KnVjYxK0zI8WUNZq49BiRo214aNUbPmt3EteQMhAiHQKUX2NnroF6tiQ40bnUIk1aRg3rpm7hkfz+f7OnD6QszKN3H3N4e4d2ICvqCEBMTp5Jw7IOxAumlELMGQhEwQqOw0ut7b2YbdG+K64hje2t7GdcUxTMnRMz4jbHzsbvQwNFnD3kYPWw95OL/AzPxtrRQlaUg3KXhhQwt/HBPHov0O1HKBWf1NeAMhnP4QOqXYbfifbEiSRHSaAaFMwBDy4FWF55LGBC1V+2uI6lRxzBwQbqtzaOEuAunD6MrVEEWR9OQklm/YiEqhwO5wUtvQcFhcZLcc39ZdRBvN7F9bQ2VcBUP653U/lz0dPhrL2qna3MCoy/tTt7uFhv1tFJ512ICTQhK1u5sZM/T71RCNV93U/bcgCOjPuwRV8Wg8335FoLwU7bQzib73iZ/kvP0UJMTGkhB77PdP1zmSyWQkxMaQENtTTGokhUBn9ktHB8FgkD0Hyli2YSMer7e7DjskSSgVcqJNZlra2+iXkUFKQiLDCwpQyOU0t7Wxv6KSnfv34/P7Meh0aNQq2h0O3B4PSXHxCIJAh8NBa0cHKoWCxLhYqg/VI+9MGY2NiuKCmTNwut3ERUXRbg+v6/F62LJ7D0vWriMUCqFRqaBTmC4pNha9TkuH3cHu0lLaOuzotFrMBgNev49YcxQHqqpos9vD8yJLMi63B1EUcHu8qFUq4qKjaGlrR6tWo1KpsJWVMW74sGOe0++7C88GRgMzAFsff8OfnVAP4y4SuYsQ4ecgnC4WwOf3o/xO+wNl4TC82zejLOgZZUqOj+ebNWsJBoPgcqCbfQHiEbLHlbW1aNVqDlRWMXfG6ZGC6xPkaJP+kzEtJMLx6YrclRhWUiZNxy/LYP0Le1gv7eGcJ8ZiiPvxatDejWtRFY8mOrsfbY/8BclhRzv9LDxLv8J44x3o5lyG85P3CJSXYrzpT8iiwhMrQRCYYQ33bOxKf7y0MGxU7WrwMCxZS5pJyd3jwwbcrP7HkTgXBOQykMsEHN4gwy3h+7+izYfdFyJGG2BnvYcB8Wqi+2iMyUSBnOjwPKBrHDvqPaw+6GJQooa11S6MahF3IESdPUBRsiZs7PxEyqBNzgD1zgD941TsavAyMEGNxx9CJe8teQ5Q1e5jWbmTiwujeHpNExlmBTP6GXlweQN/nRDP29vb0CpEzs438tiqJl4704I/KBGUSxhVIqNSw9dDiUWLNyghlwnMLTAjAcUph6+VLgNbKROgM7X29yPDk+uLB0d11+s+MPlwyp1WEV7vxpLwem5/iKyo8LO/IF5NrFaOUi5SmBiuAb5g0GFHwOVFYRPIrJaR9v2Xwa+GJEFCvyjYJKJy25G00Z3vNwVtHR1EGXsKpTTuqkUaPYSUI5ZlWFI4UHWQc6dNZdWmzUwoKWbD9h3IZDIKZvVj5ZbNDJ6Qxdb1+9BvMxNnjaN2VzOGeA1fPbGJGX8ZgSlJCxLEZpkI+oI9HDkSsGdxFXFZJio3NRCTHr7/ZEoRU+L3R/MVGdkoLrv2pzxlJx2CIBBtCl9g3y2R8Pn9YWEaOKZ4lkGnI8Ny9NYR7XY7Dc0t+AN+ok1WjHodHp+PhuYWRg0Z0ivy2iUeF2UyEmUK/079c3JwuFzhFNoTrJlubmujsraW5E6Hrkatxul209TahiUhHpfHg9frZeKIEpLj44+5n+9Ty6wAKqxW63LACPQH9ttstq0nNOKfiK7wrcfrQ62MGHcRIvwcdAmq+Px+jHp9j8/0514ER+l3p1apMBsMVNTWkmWxoJ9zWMDC7/fz9arV+Px+RgweHDHsIkQgnP7sDwRxy6MJOBzscSmZ/NBYWF9HwBfqtX7AF0Su/GFRKO+2jcgzs1GkZxH77BsQCiEc4RhVpGciO+8SHG+/RtP1l2C+4z5UncI9x2JA/I9TqdarZIzPCD9XzsoPT9baPUEaHIEfve+CBDUFCeF9XFccNlT3NXvZ0eAhK0rJdZ/V8K/Zqczf1kqCTsG4DB0f7W7n/AIz+5q9GFRid13i0Wh2BXhmTRP3T05k1UEXZrWI06fgpY3NPHe6hfnb29ApRC4YZGbxAQenZOvZVOumrNXH2XlGTOpwO4prhkWjkAkEQxKTMsNRzUsKo7pbUrx+VtisGJN+eFI5KSt8zmKPiI4VJv3wZ2lfosJJBgVJBkWvY5yWc/I22f4+JEnim2e34AmqkCkPoYuxdKfKtXZ0dKfwd5F/7/U9epoBZKRYOHPyZPRaLVPGjgFArVTiafVj1hsZO2YoX6xagV6rZcatE9DpNaydv4f8U9M4+6HRCKKATy6y/K29REWrGDQjiwNr6qjf10rG8ARUegWn3BKO2ik1ckSZwLZPyyialYOr1YPapKJmRxMKtZxEa89Mh/91fqy4oslg6KWEqtVouo3JvvJdxfAfSow5nJ75XazHz6jvQV/i52cBFwJrgdutVusCm832q8V7RUHE4/Xh8XlRRdIyI0T4WVDI5Thdbnx+X6+HpqBU4fr6c5R5A5CnZvT4bMyQQrb96zUMpTuIffyl7jSv/VVVJMXFMXFECVp1pH1JhAjQ2ecuEGB/qBBtbQ0XTtWTGKeFI1K1dn9VibPVS9AfpGZHE1NuH4Y+tm8TekmSMF55Q/f/wjEmQKJag/HKG1ANGU773x/GfNtfejQJ/iUwqWVMyNQff8UToF+Min6dzdufm5GMXBSYnmtELRdQygQEISw2U9riI1EvRykTeHVTK/eMj+erUjsZUUqyopRsqnVTbNEww2pEkiTOzDN2H+O508MRgSuLovAHJQLBcN3ghAwdarnAiBQNcpnAGZ3baDojZXJRYFzGT1tjGaE3kiQx6YZC/v1+KUKFHZ1Gg8vtJspopK3DjkmAYHMj3i3rkadmIKjUrH1nH9YJqUSnhSf9MlHslTKYEBtLRfkhaiqa6H9qOmmJiYgyGbLObIoRF4drzt/Y3IJBJcMdCKEtTGRkTvhazyxOwDIghtLVtUSnHjYu0oeFU5DHXxO+D1e8spP0YfEotYruiB/0zVCP8L9HX4y7C4AxNpstYLVaFcBq4Fcz7pRKBQ6XE4/Hi04b8f5HiPBzoFIqcbic4bQVZe8Joag3gKz34yOmrYncJbJXKAAAIABJREFUvVux//F+4k1mSiurkMlE1m7dxvRx4yKGXYQIR9CVlnnKVAV7VlTwwX4/0+U+hNJWGg+0Mfx8K/H9zCCBzxWgeK4VUdb3FNz2p+5HPXYy6uLRfVpfVVSC4ZLf0frgXUQ/+HcUGdm91gm5XIQ62pAnJhNqbyNwsALlwEL8pTbk6ZkIR4nqn0xEazoFMo5Q2ryos4avy1jzBEJMy+2a0AsoRYFD9gBLyx2UpGgpSTm2d14UBFTy8IS7q0bwx0YjI/x4JCQO2VoJemWI9vZuQYxAIBAWzPjHMzSX7SNkt2O87g/IomLIGZ2HLubwbxfwBjm4tYHMksO14pIkkTH8cNTvyBYmLe4AnoBEgyPAmflG9EoZCjFskG2uddNfIUOtEFEblQycmvG94x971cAeKZzbPy1DG60msySRXV9U0P+0dDZ9sJ/i860IP1HacYTfLn0x7gSbzRYAsNlsfqvV+qtKIakUSuxOJx6fl5io3qHLCBEi/Hi6lKS6FAu/i3rkOKRgoNdy39aNCP0HUdbSisXrZfGaNQjAtPHjfnFBjwgRTna6hIt2tPRj70AvAxM1GFUiuvwo4nJMNJa1Y0rSoew0SDwdPio21ZM38fgiNZIk4duyAcPFVx933SPRTDgNMToWWUwcvr27EI0m5MmHK4/sb76Ae/EX6GZfgGroCHx7dqIYMBj7Gy/gP7APZeEw1MWjUQ0b2V1zK0kSBIPdyoMnO2q5yNDksPN4cmcqpBQMcMeYuON+F0mSQJIIVJXj+u9/kCUmI0uyIItLxLP6W+RpGWhPOb17/WBzI+6lXxFsqCNYV4ssMRm5JQXJHwinwP9EdPXKFGQyAnU1yBKTjxr1kXxegk0NyJNT8axZjjw9C1lCIp5li/FuWoug1iBPzSDU1gpSCMPl1+EvtSGFgigysvHt+FUrd46JJEk0lLYR9MswX3sr2kONuNxuWivL0bS1ENi0FtXIcWgmT0NdEk65jAlJ2BtcyBQicqWMUEhi6ydlpA9PROw0oPYsriLoD1EwPbPb+NrX7MXhDdHmCbKswkmd3c/LZ1h6nO+F++0kGeR8vr2NYouGQYnHD1YcuX3ueAtBX4iAJ4gEiAoRQ6wGQRRorXGgMSlR68OOFm8gnOKtEGDH5+UMmpl19N/+O9HAUEiCkIQo/2Vquv1BiTWdCrveQKgzqv7jDFWfy0/D/jYsg2J/8L68Dj+1u5rILEmidlczGpOSqBQDe5ceJGd0Ml67D3ujm8S8aHzuAHKl+IOcb0cihaReRnnX9dSlcBsMwd4mLwUJatrcwe8ViurLk3al1Wp9H1gBjAFWndDIfyKUysPypRFBlQgRfh7CThQXUih01GbK/v176Xj5aWKeONwlRQoG8axaSsyF/48VdfV8vXo16clJTB458kfnw/8U+P0BnG4PZuPPk/oVIcIPRRQE/MEApjQzpg1VjEnTI+s0Gur3tXJgdR05Y5KJzwk7MkW5gLvN26d9B+uqQanCJRiw72omqX807nYfWvP3vzclScJvyUehUbF50SEGDa7Bt3sb/l3bcVY3IDu4l7hX3kNQKhENRpR5AwCIfvDvBNta8W5cjWfNcjpeehrV8JGYbrkLx7v/RBYTi2bKGfj37SZQVU6wsQFl/wKUhcPD/S39Ptoe+Qvmex7upbD7SyOFQvh2ho0UeWIysvhE2p96APWo8YjRsbQ+cCeCXAGCEDbeEpKQJyajm3Mpzo/+jeTxoJ16JorcfIL1tXiWLca3axvaKWcgudwAtD//BLqzzkMKBAk2HEIWE4+6eDSBg5X4y0q7Bavan3sM9ZhJyJNTaHv6QVRFxcgzsgkerCTYWA+ShP6CK/Af2Ifftgv9+Zdhf/NFPKuXQzCIGB0DUohAzUEMl1yN5rTTab3vDmJfmI9v13YCddUQDBKoKidQXoq/4gDq0RMxXX9b2MjLysW/ewfuxQvRTJ5GyOkgeKgW0RSFoNOFJezbWpH8PoR+/eEkDRpJksTgmVlsen8LVFehN0ZjdzpxKJWYEhKJefrVcC/MI/A5/Hz61zXIVTJmPTIGj93PtDuHEwqEEDtrX2OGJqDv/M7Pr29mUIIGCVDJBCZk6piQqevu4XYk93QKABVbNKSZlQQ6BXL6isZ4+D4ePDMLgP6npSOFJFa+v5+WggQuGJ+EAHyxvZXyDfVcf3Eu7Z4gAGs/2I/RGo0lQUO5WyLe4eG5VY2cMT4R9raQe0oaW/6xg6SRSaRmGNm3rJph5/Vj5as7yShJJCHXzI7Py8mdkcXuRi8lKVrqmz3EGBTddcGf2jqwe0PMLTDx7NpmbiiJ5vn1LQxOVJMfp+bpL2u4MU/PakkOyyo5bU4ui/Z1MMQk44MKN/6QxJVFUTyyopFbR8bi9IfYtbiKwpIEvqxwkd7sJH2shW/f2885l+VxYFsTDbuaGHNJf779xw5yRyVhTtZTtbWRlMFx7F16kOhUA8Y0A9XbGsk5IuLqdfgpXVXLgCnpbPmwlKhUPSmD4qjb00pmSRJeR7gnblQKtFbZEccKOFu9NJS2kZgXzbIXttF/SjoxaUa2fHKAkRfnU7+vFYVajilJy6LHNjL9zmJWLKlG3eTCfGoGn39wgKvnZPPOt4cwlzZz5i1DuOfONdx0aT+2BERKv6rk5tsKue3fZVw5PAZdnIYvVh+i4OwMFu7v4IJBx667PK5xZ7PZbrNaracD+cCbNpvt8z5ffT8DKoUCu9NFh9OJUReZpEWI8HPQ5URRyOWoFAq8gRBjXitj3e+yEQCZJY1ARRmSz0vz7dcS/chzeJZ+iaDVYywZw2y3m0UrVpKfl/2rG3YfLfqWzNRklAoFV952P3dcdwlTJ4xErY44hyL8unRF7pKGReH9bzmX/6ecv0xJIydaxYE1deRNOlzvA6DUKhhyds5xWyXsWFiO0VmOOi0PV6uX9kNOtGYVq97Yxen39O5lZW90dStzOhrdLH5mC6f/uQRtbhbykhTWvr6VIQkWNlUXUXzlechijh6Fl5mj0J5yOtpTTkfyebG/+RJSMIhu1lwEjZZgTRVtj/0NRW4+8pQ07G+8SLv/HYTkVDLvvBn1mXMQRJGOV54l2NyIfu7lkJCGQi3H0RzuJ6aLPpwmF3K7IBDAs3Ip7qWLkCWloMjMQTNlJqKmb8IGwZYmEEREjRbXoo8JOZ14N6yGYABCEpLXQ/TjL2K67a9AOKoR+/fXkYJBBEEkUF9L8FAtwYZDSPZ2dGecB6KIIJOhnXbmMY+rmzUXBAGFJQ3TdX/oXq4aNrLHeoaLfwcyGYJSiX72BXjWrsC3fQvytAxkCUkgCAgaLfKM7O72NPKsXMzjT0VQqQjWH0JQqZBbUhGMZgRBIO7FtwCQPG58m9chKFXIEpLQzbkEeVpmt2Jqd3+0hCSiBhYe85pTDRtx+O8+9kr7pZEkiU3v7yfQIuHb/V+0l1xLXWMTdpUKU1JSL8MOQG1U4hpnJzPVgkItZ9dXlZiT9Wz5tIyks3IwO308t9fJn+dmoQ5JnJVnIkYr+0G9Frsidtd+WsOfxsaRbj7xtObqdj/t3iCnXT+YRaUO3n9xBw2xWq4+PY09B9uQlDKeR80oCSoFGTIf1C+sYIVP4MZpKVw+0ERqspa3l9fSUu0m+Zx+/HN3BzdK8JlDYkhI4qtEExdEa1DUuyBBR2Wbn20vbWPQH4t4/NW9nJWpZYPFTMKn+xh3SxHOeiflG+pJ8oT45pktXHDNYDa+tA353DzOSFLRVu0gb3ACsmHxKFUyro2TsWXBPi67ehDVO5twOQMUqsG2qILmAfHUtvoo0Ssh4ECmEFHKBJwhCAVCrPCBPit87T4nqPhHmpGmEGhPC0dVv2rwMTtbxroyB1s+reCW4Ym89PwO8qOVZJ2SxsrSDvqfJpFQkohRK0eukjHqsv4APVJxR14aXhafY+52vJ36h7AI1SurGxDkCkYCzy6p49xBUXgEGduyY5kObJTJGV+YQLJBQWGsCikoMXFUAoqhMShkAn+4ZTBR0SomCwJZzeFo4zUWJYoGJzlWM9bNtfinpTBW6p05dSRCVxj2t8K9DzwgFY8bT1VdHVfMnnXUBsYRIvzWsFqt99pstr/92uPo4tlnn5WU8QnIRJELZ85ApVJTa/djMSqY+5+DvHyGBf9NF2G++yEklwO5JY2GC2cQM+/No9bpHA9Jkli1YRujhw9GEAT8/gCK7/TXc3u8tLR10NjcyocLlzJh1FBGDi1Acwwjrd3uYO2mHTz+0lv847G7yEqzsGWnjVff+QRRFJj3wO0camwmLjoK2fekUkiShNvjpba+CY1aRVJ8+OXh8wcivTZ/Y5xs99mTTz0lxWRkIn6pxzRLZMapkxCF7xdJ2P11Je52H0PPyT3mOo1l7TR//Dn7yjSc8dzZ3ctDwRDOZg9+T7DbaGyu7GDrJweYfNMQ9iyuIm9Sao/0IHeHl4b9baQVxeN3B1BqFTSUthGTYUT2nXQtR7MbKSihi1HjcwcI+kKUr6tjwJQM+M73aq7swByvYu+7qzEPyiVpiIV3rlvCnGfG462uxbt5PZ6P32aZ+gqmD91P5SEd4sBicidnseujXQw5rz/1N16J4LajyO6H/pwLCTY3EagoxXDptfgrSsNGT0IybU/eh+T3oy4ehSzRguT1gCShHjUex4J/IUuyoB4zCfvrz+NBwDR4KMqi4l89gvhb5WS7zwCu//3vpT9f/Wc+Wvs1V19yLo0trXyyZisJzXUkaARGzLmw1zYOl5sZF9/Cg3+6jkefmk9BbjZX/r8zePnDZZTGDie47N+8dPdNvFcXINEgZ87AEy8VanIGiNbK2NPoJT9OddRo3/FYV+2i1u7n7E4FWnuji6BMxBx97JrPo/ZNlaTu43e1DtlR76EgQU1Fm49EnZxt9R6WVTj545g42mocqAwKVAYlUlCi0h5A2eHBkmagpcqOq9VLyqBYOupdmJJ0tFbbMcRrj6v8u+r1XQw+IwuZUuTQ3lYyixO/d/3vfi+A7fUeDrT4mNXfxJIyB8MtGgwqWffnKw/YGZisRRQFttd7GJuu45Fv65DV7+X0Ybk8uraNBybGITNE4/IFyYlRs6Pew8B4Fcsrnawtree0xAAflYe4dGQGGdFquh6fW8sbGZgWQ0AS8IckjKoT77fp9wfocDhxON3odRqizUYEQTjmvfabs4zUKhW19fWoFIqIYRchws+EIAiYDQZa2ttRKhS4/eHmujcvrOPMPCNf7LeTlDuawW++iPnuhwjW12L6/d0nZNgBrN+6m8dfnM+Y4kLe/eQr5r2+gKz0ZOaccRrTJo6ivKqGl9/6EJ1Oy01XzGFgXjYffL6E+556hWcfuJ2cjBTKD9ZiSYjjX+8vZNb0iRyorOazxSs5b8YpZKWFleyGDLQy74HbWLlhG5IkcdcjL3Cgopp+WWmMGjaIy+fM5L3/fs3EUUOJjTbj9ngpLT/I/7v9QZLiYwmGQtx85RwG9c/lvKvvZPbpkygc0I+hg/LRH0Pg6cMvlmJ3uEhJimfiqKHsLa3E7nRhSYzDkhjX/WKtOFjL+q278fp8nD55DC1tHfz3q2W4XB68fj/nn3kaSfGxvPrOx0wYNZThg/t3b9vVr6mLUCiE1+dHrVJysLYep8tNfm4m36zcwD//8zkddgf5uZk8fOf1LF+3hSUrN1CQl8Op40owGnR4fT5konjMfkERfhrCgipBht2QivzTd9igjaFw5CDU8mNP7LJHJSP7TmRAkiS2fVJG/ynpyBQixngtct8eEk4f2/N4MpG2Wif2BhfmFD3LXtzO2KsGMvqyAeEWQx0+XG3eHtExjVFF+tCwMIhSq0CSJHYtqmDYHCsak7LHBG33V5WkDYmnqaKDg1sbGXJ2Ntqo8L6+fHQjk28ZwqrXdzF8Tj92LapgwJQMBlw+sXv785+dgFwpY/duP+r4ErKfKGH6os+RmxLISVOhHpWKY/dOZB0NiKZRVJ12P6YkPWnDEtj6WTnFc0d0G6bBQ7WIUdGQmoFywCAEjQ7//j1s/HIxHzT68Agizre/5vG/3MJO2wEW/PEhDHotVTX1vHvp1Tz47BvkZKYyafQw4mN79tT6X8Dl9qDVqFm3ZRcffL4Eh8uFw+HihUf+xIatu3jw76+j02nJSrNwzcWziI+N4r6nX2PIgH6/9tCPiiRJRGUYYLOE45Vn0U07m1XNOjJ9RuYkHz27pKr6EJakeN7/fAkdfgfRaXFsbwiw5KsvmVzQSo0YZHvlXi4tHo5B9eMcAbE6OZIk8da2Nm4cEXYgajvv8xfWN3PrqHC0XH2EQ6Ws1UebO0icTsYnezu4oaRnRP37+mTu3ldOlMlAY0sbtz/wLFlpFpQKOdMmjqKkaAALPl3MpefO6HZgdrUXyeiMLB4pLGS2HJFFJxfC/RE7eyTGpBuJSQ9/ZEoKq8JGpfStncboKwbgcLlRa5R9MuyONFQFQaC2vpGBcTHdfR8nZelp63Aw/7Pl7NlfQX5OBuefeRo3//VJEuNiSEqIRdeRx+1j+3HXI+9zx8JP8fj8LJafhrlgLK89+QghnxuXoGF0QRZ33nQlC177hHkuJ1W1jez5TMd/Xn6Yp/7xNp98uRyFQo5cJuPDVx/D3m5nQ2kF7R0OkhNjGVqQj9Pt4aMvllLf2ExeTgbjRxYRCoXnJZbEOGQykYK8HGaeOpazrrgNnz+ATqumeMgA7rrx8u89F8d9c1ut1gSbzVbflx/il6Cr6/t3e29FiBDhpyU+JpqW9nZkMhnlzV4W7bdz04gYNAqRVneQl3SDiF7/NWZRRG5JQ25JO6HjeH0+Xnn7I66cG05hijIZmD/vXppa2nC63Gzctpsb7nmc82aewo1XzEGtUnLezFM4b+YpOJwulEoFVdWHuPepV6iqqWfmqWPQqFWMH1HE+BFFvY4niiLjSsL1LK8/+WfsDid7SytxuT1AuPxn9lV3EGUyMnJoAXfeeBlrP30DUQwXd3e9QP7zj0f498dfsuC/iwkGgqRaErjz4RdIio/BkhTPqeNKkMtkvP7ufzllbDG2A5VMGj2MDoeDNxd8RlXNIaLNJp574Hb2lx/k7kdfYFzJEJRKBRq1CrVKSVx0FKokBTqtBqNeh9moJzkxjsdfnI8gCFx/6bmMGjaIiedci0atJD42msvOm8m4EUM4/9q7aGxuI8psZMYpY8jPzeRgbT3XX3oOifGxeLzh2i2lQs7AvGzWb93FvNff45sFL/L54lU89uJ8dBo1UWYjf7v1KpIT4/jH2x8xZEA/fP4AyQmxDB2UzzsfLcIfCDJ+ZBHRJiNGg46FS1bh8/nZufcAHQ4Xj91zI4889yY+f4Aos5Eok4GLZk1j+55Stu3ah9PtITEumnEjivD5/HyxdDVGvQ6tVk1GShL5uZmsWLeV7AwLdfVNeLw+Rg8fzEPz3sDl9mLUa0lPSWLOGafy4j/fZ+maTRh0WpIT47jzhstobbejOorq66+N2NkKoX5fG7kd7WzfXU7/4gLU3/NmVukUVG9rJDbLhNqgpLXajsakQm0Me81bD9nZ8O4+Jk2ZiTwjq9f2qYXh3l2hQIisEYnIlbJuA23IrJzjjlkQBCbeUAjAknlbGTwzC6/Tj1wlY/j51u7JlaUgFqVG3j3BLDw7B7lKRtaIJDRmFeOu7tlqweF08fq7n3LTlXMYfEbYSRQKhdBf8v96OBlMY2Mp7LRZh1+QjxSSEEWB6DQD67buJLhfIGeYBfOoCfjtQV58/T067E4unzORxEnTiCmv4tSqWsxGPSqlkoS4aGoONXLVhWdhd7jITrcgl8uZffok3lzwGa/++xMmjhrK3Tddge1AJRmpSaiUvSP2O/aWIiAwMC8bh9NFWVUtByqrSbMkUjTQ2is6EvAGqd7eRNqQuLBB2hnZ9HsCyJXhyILQ+cxxtXnDKWiacHqqIU6Lx+Hr/u2C/iAyhQy/J0AwEEKtV1K9oyncC02CthoHsVkmWuvs2P0uUlPjKa+sZcP23VTXNTBsUD7jRxbx1odfsGLdVsqratBpNXzwyqO43R7GFA8mPjYKg16HRqViRFEBC15+BKfLzd7SCox6HaJPxtShoygozuLB415FvzySBIuf3Iw8WkGguhxtcwNF6jrcoSDmuORe67d7gjg8XoqHDOCai2exudbNTV828nuliRRLEjkDUpiWPpL7nn+Vu2UyJo0ZDkBjcysmo/6EyhEEQeDhU8NGzEd72vEHJWb3NzEiRYtShFmPfMxQo5P8wqEUF+RgVEjc/fjrZJgVTD33gl77a23v4ONFyyirqqGuvolrLp5NZXUdby74DJfbwwN3XMuIooH86+9/o6yyBofTxchhgwj4A1QcrOPU868nzZLIHddfSv/cDPyBIB6vlwf//ga3XHU+be0ONu3YS1J8DIUD+hEfG43L7aGq5hDxsdGoOt9lgvDDhVHa2u0YDTrOuOxW/P4AcTFRTJ0wkqsuPItb//Y08bHRhKQQ+TmZzJo+kSdffpv/fPYNJqOeksIB3Hf71cx7fQE79pYycdQwTAY9l8+ZyXV3PkJWegoDrVmcOWU8oihy7ozJNLe2c7C2AbvDhUwm8ujdN/Ya0/QX76O13U5bu51AIIheJeP5+28Fws+qUGc08OqLZvG7C89Gr9NS39SCVqNm7eadLFq6GpNBz8Ilq4i61ohWo6a+qYWM1GQ277BhNhoYP7KIS86ZTn1jC8FQiEH5OQiCwPx59xFtNvYa0zGvpeOlZVqt1pVAI/AasNBms/XurPoLMm/ePCl70GCUCgWnjBp5/A0iRPgNcLKlscybN08af9pprNy0mesumNvr82/LHRQZvGjsrSgyjz8h/D7e+vALtu8u5aE/XXvMSFFDU0ufvOdHS+c8ETxeH1U1h0izJPY59TIQDFJWWUNjcyvVdQ3k5WQwwJpFXX0TqckJvdYPhUKs2bSD4YX9aW23IxNFYqP7ltYTCoXYU1qByaAnJSkeSZJo73BQU99IY1MrE0YNDUdivL5jpq325RgdDhetbR3Ex0YRDIb47JuVbNq+h1BI4rpLZ5OdnsIzr/4bvz/IsrWbmDZpNDdefh6vvvMJNYcayEqzcNqEESTERlNVc4iN2/bQbnfg9fm55uJZbNq+h+Vrt6BUKmhoauGmK8/H4/Xx4RdL6bA7cLo8zJ4+kaGD8rn9gWfZsacUlUrJbVdfxNiSQtZt3klzazt2p6v7Jd3a3kFLWwd2h4uKg7VMmzSK0vKDmIx6Jk8Yd9LdZ4q4eHRbzYyLXkts/4HoZsw+7nabP9xPZnEiUSkGtnxYSkymkbQh8bTWOFDpFKhFD8jliNqft39aR70LQ5yGlio7rTUOckb3niT3hf989g0fLfqW/JwMbr/2Yh574V9s31OKw+lixiljOHPKeJ56+W2aW9tpbGnjmXtvpV9WGtt376fsYC3RJiPjRxbxxnuf8uX762gP2clOT+U042gCJR5a9tv5ZOUyzjh9DOPShtFvQgqeDh9qozJsPLV60Eapj5qi1lLTQcX+QxRN6Mf9z7zGsrWbsWalM+PUMUybOIrl67bw6tsf0+5wMrJoIH+64TL+/PhLlFfVkpOZSnu7nSf+cgsrN2ylw+7EWe4hNtHM+NOGsGb+brJnJtKwth2fK0DJBfl8eOdKJt88BHuDi7I1dYy7ehCLn97M0HPDabjr37Ex5Y/DWPHKTtKK4kjMi+bju1Yx5+8T2P11JV6nnyFn5bD0+W2MvDSfhtJW3p//LUub1pHnySGjOIHLrjidf938FXUFdWRIKYgBkcvumsZHLywnsSiKRFMMvvog1gmpbHjXRs5YC6IosPzlHcz82wg2f7gfpUbBwGkZLLh1GdPvLsbT7qNsXR3Fc/NOuvcZwDU33Sw99egjvLvwC2ZV7EJVOIz7m40caBeZPz0FpeWwAq3T5ea2b1q4fEgUxUdpe3HkdXKgshqtWk1ru513Pv6STdv38OYzfyXhB0R7g8FQt/PwWLzyzscsWb2ZqeNLaFLGM7J/GvNffxOVUsHZUycwfmQRS1ZuYPNOG8vWbGbuWacxpriQf3/8FXk56ahVSiaPGc7fX3uPSaOHkZeTcdx3g8PpwnagEmt2OpU1h7j6joeRQhIXnD2Fay6ezYZtu1i3ZRe1hxrZuG0P77/yCF8tW8cHny+hpb0Dl8vDOy88wPbd+/lq2VrmnjWFlKQELImxR33XS5LEv95fyJJVG9BrtTz/0B+BcBS5qaUNl9tDbmYa+8urWLt5JzqthviYKMaPLGLFuq0UDsjF6/PT4XCSmRpWg91XVsXK9VvJTk9h/MgiGptbiYv5v9MA/lj3Wp9q7qxWaz5wBTAW+AZ4zWazlf3Ug+wL8+bNk6adcSbBUBBr5g9s2R4hwknKyfYynDdvnnTZFVewaMVKzp06hVVVTgBGp4Unixe+X8ULMyw8vqqRu8fF/6ACcoBAIMD8D77gvJmnoNNqCAZD31v3FiHCT8HJeJ9pEpNQq1Sc0lDF2lo/p999M4o+qOb5PQEqN9aTM8bSvWzbp2UY4zUkOreF1REvvurnHP4J8dwbC1Ao5Awb3B8BSLMkcuu9z3DJOdMZPXwwwWCQVRu2kZKcgFGvIyUpnoO19azcsA1rVhoJcTFYEuNY8OlinntjAaeOK8Hr8/Hnm69ErVbh8fo6nRpKFKIcUS5iW3oQfYKGRGs069/ey8hL+rPl41LUeiW5Yy18dNcqZj86hi0fH0CmECk8M5tFj25g7O8KaK914mz1kDvGwhcPb8B6loVdW8po3u3gsgemMv/pRaT2j2fUhAIkPwT8QdbO38PE6weza1ElhngN6cMSePfupewy2NB7dDR2tPLwY9fxn0+/4V/vf06HIyxe9fHrT1Df2MynX68gPzeTzz5dyd/uvgpBFHjj3U+JjjKSlWZh9PDBiKJIVc0h1ColzU0dpKbGE2Uy8u4nX/HNyvXYnS6z1mYxAAAgAElEQVTGjSjixsvPY+GSVQy0ZmM06DEb9Z3tGkAQBTwOH1JQQm1Usv2zcvInp2JvdONs8ZA2JJ7aXc3EZhqRq+V47T40JhVBf5BQUEKhlhPwBpF/p5boZLvPAK6+8Sbpljl/YH31VmZ5ahG0Ou6mP/nV6/ndVRchGsJRkU3b93D3oy/y7388zgefLWbE4DwGWLN4bl0zVw2L7pEW2UUoFGLKBTdxzoxJDBloJS05keq6BpRKBYPyj+/8XPjNKlZu2MaNl5/Hll02pk0cxTOvvsvZ0yaQkRIW8vjg8yWMGzGk2zA5WFvPhq27mXHqGJQKBYFgkIfnvYnRoGPGKWPQ67Q/yMDsC5Ik4XJ7jtpjuq3DgUGn7WGkhkIhRFHE7w/w2Tcr+WLJappb2nj+oTuormvg/r+/RtFAK0UFeUwcPYxtu/bxwj/f59bfXUBBfs6vLsb2W+DH1tzVAmXAUGAg8Her1brFZrP95acbYt/JST+x9K8IESL0HYNOx7lTpwBgVMl6KFy/fU74Hpyea/jBytfVdQ0EgyE+XLiURd+u4aYr5jB6+OCfZtARIvzGkIlyXCtDKEbGkNdUTl99HH5vkOYqO5mdKXkQlkSX/D4ExdSfccR9x+8P8Mjz/8TpcmNJjGNsyRBOnzyGV//9Md+s2MCp44opKsjjn8/8tUfUYsqEnlk5aZZELrD0rLk5e9oEZp46Fq2mp1iEWqXsFW23HtEXcOQlYaW7wjOzCQUlZHKRc54YiyAIFM3KIRQMJycVz81DY1Siizq8/7FXDURjUpKWnYDXEW75O2JoASmDY7HXu1n7rz1Mv7uYAVMyEGUilkGxaE0qkGD8RYOZkzexuz8awIWzpnLhrKk4XW78gSB6nQaf309CXAyrN2xjytQRJMbHsHHbHmJjzHi8PhZ+s4r83ExqDjXy5Mtv4fP5Mep1XHPJbIoK8jhYe4gr555JQmw0aSnhczZ9Us8m9oIgdLcs6OqFBocl9WPSFcSkh42d5AEx3Z9rTOFIj0whQ9Y57/6uYXeyIkkSrQcdyJAhRscRrKlC276WrU1BWlDTVa22dPUmmlvbuei6O2lobCb3/j8RlMJN71XHcLqIosjC+c90Z418+MVSHp73JiaDjreeu5+X/vUBt1x1AR6vl9goU3fUasPW3bz336954I5r+Wr5OmZfdQe/u+hsBEEgymTgylvvJzkxlkvOOZ3Zp0/qcczU5IQeGSFymYw/33LlT3/ijkAQhKMadsBRWwyJnYJECoWcs6dO4OypE7o/i44yMu/+21i2djMr1m3FkhhHSlICT/7lFhLjY3rtK8IPoy81dwsIG3RvARfZbLbazuUbgV/FuIsQIcIvS1cx9XcpSdHS5gmi7mPk7stv1/Dau//l388/yOtP/ZlX3vmYoQV5P+VQI0T4TSGXyfDGelHFxBEb2N5nlTytSUXxnBx82zYiDh6KIJMT8rhpvvUqou58AHlqxs878KPQbndQV99EbmYawVCQYDBESlI8lsQ4ag414vF6KRzQjwfvuK7HdifSqFipUPwoz74gCMjkh8UXIBzJkolhY+XIFhRd6GMPT2zl0eH1cseFI6cak4ppdxUjCEK41g2ITj28j+T+x56wHjlhToyL4dJzT+/x+fDC/gwv7N9jWUpSPAteerjXvv50w2XHPM7/MpIk0W9qMgfXVCEzxeLbvomNB2Da6FG888Gn3HjpLBy+EIWnnokqczB1W1dx9tTxFA20Iklw3nGUMI8sBzhrynjysjPwBwLERpsRRZEb73mMQw0tfPHWM3yxdDUfL1pGRXUd999+NWqVkmfuvbU70gVw2XkzmDZpFPvLqrBmp/+s5+bXQKlQkGZJ5OLZ03/tofyfpC+Ru1eANTabzWG1WpOOWD7mZxpThAgRTjJu+7KOuQVmhib39NptrnPzyd4OHph82Kv+3z3tJBgUrK92ceOIw+pdNYcaeeKlt5n3wG3IZCJxMVHHVXyKEOH/OkqlHHe6m1BSGu86E7jhB2wbamnG8c7rBJ9+CNXIcUiODhTZ/X5xw27Zms0ML+zPinVbeeXtj9BpNYRCEu88fz+Xz5n5i47l1+TIqFyEk4sQEpv+VYpgEZENSCeUmolzw3oumVrE+X94iNFjR9PsgS++XcvT158FUwpo9wR5Z2c7dfYA+XEqZlr7JmghiiL9+x0uG7rrxstYsmoTaZYE5HI5I4sKMOi05Gal9UidFL/TeiMhNvonT62M8L9BX4y7UcBU4A/As1ardZPNZnvEZrN5ft6hRYgQ4WThvkkJyI8ycRlu0TLcoiUYCHX3vLJ/uI+8i6zkxal6FJ4/98YCLjtvBnk5Gb/k0CNEOKmRhUTEhXqMc3K54eGb+7ydv9SG+9uviHniZQI1VXjWriDgcWP8Xd/3caI4XG4A3v34K/aVVVFWVcOTf7mFGaeMYerEkSxevp5hg/N7TVYjRPi1kCSJlJExuJrbCSZa2JBWQF5qFUvnf4oqLoWP1thIi9birbF1b6NRiEgS3DY6luCPkBKUy+WcNv5wc3ezycCY4sIf83UiRPhe+vLkPcNms/0BwGaznQv877jhIkSIAMDO+mP7cr62dfDOH5bTcrCDN36/jFPn5JKdrGNylp6yVl/3en++5UrmnnXaLzHcCBF+M8jVcoznCTh9QUp/fz1SINCn7WSJyWgmhWvr5JY09LMvxHzrPYj6vvWQOhFa2jqY//5C5lxzF06XG7vTRV5OOm8/dx/pnfVdcpmMqRNH9ln5NUKEXwJJklCa5MiVMqrrGnj5uVepHHo5Z99xNWeUWLEEGzH4WrsFTACUMoGZViOf2ex9EjmKEOFkoS/GXchqtSoBrFaroo/bRIgQ4f8QH+3pwBc8urKuQS2yf4aVZrUS19QcxGQ9ZWsOsfyfe3hydRONza3c+OcnUCoVEU9+hAjfQSHKEeoV+IOwfsxc+qpQFKg4gDw9C4fTxUeLvv1ZxwjhdiSX3PxXbGVV3HnDpSTERvP7q+ZyxflnHLXvW4QIJxOSJLHljTJwi7jcHoxxcXw20oN/1VLMA0cRyB1LZXUtGalJPbZr9QQpbfH+SqOOEOHE6Eta5kvATqvVugPIAx7ry46tVmsJ8KjNZptgtVpzgDcBCdgJXN+52odAEnCPzWb72mq1ZgE322y2nz+vJEKECH2mq7HqdwkGQpQ+tJbR1xdR9eF+pgyMIVYrJ3pUEqlD4sircfDSE99izs7jgeVN/G1i735vESL8LyOTZARr/Zg1MuaMzUVyuxGOE33zl5fS9vi9xL32HwCef2MBQwZaSbcknpA4yfexfU8pG7ft5vI5M3n4zuspyPtxfS0jRPg1kCQovDqDytpanC43Kr2BNRsPMLRtLzN/N56PF69i7gVn9RLpyYpScuuouF9p1BEinBjHdaPbbLbXgNGEjbrxNpvtn8fbxmq1/hF4FeiS2HuKsAE3lrBf8kygEKggXM/XVUN+D/DQD/sKESJE+DlxeIM8sqKh1/JgIISjyc2FL05myqBohp2RRXyOCQBRLqI2KDnkamO9Oo6rzxrPVUMjheERInwXpU5OyrQoKtp8rHniWbzrVx13G9fnH6GddiZ7yg5y/zOvcdHs6Vx689+4/Pf39emYbo+Xtz78gvkfLKS6rve93cXydVv4w73PYM1ORxCEiGEX4TeLJElUr2tGCIokJ8RRopOxdVclot6A9v+zd9/hUVXpA8e/d3omvUIKEFoO0pEiiAp2sfefbW1r77v21bWua1nbKuray65dUbGLCoogSO9caghJSCE9M5l+fn/cSQiaBiQZGM7nefJk5s6Ze987mXcy555mM/PW2x/x9cy5JMTHRjpURdlj7VbuhBAjgfuBK4HHhBCvdWC/G4HTm90fDfwUvv01cBRQD8SGf1xCiInAel3XSzsevrI/kVLiDRijmivcAUJSUlofYEV4PNiykgZK6vyRDDEqWUwa47Kdf9heW+Lit3d1tPBEK4mZsST0+N0/RnuAq8aDrSpAmtPM64srCYZa7t6pKPsjzWei7HsXaU4LWf2yCZaVtFne9flHeH75kVU5g3hk6huMGiq4+OwT+enjF3nlibtZsXYD3/00v8Xnrlm/mUXL12C1mPH7AxQUlXDt3x6lqKSc5974kGdfe5+txca/4I1bCvnnM6/z9P1/UetQKlFA4q3xY9ZM9MnpybnjB3Heyg/QYo1WcrvNyjOvvh/hGBWlc3RkAMwbwGLg/WY/bdJ1/WOg+bdsTdf1xm90dUCiruvrgELgaeAB4CbgfSHEC0KIfwoh1OCcPfTthjo8/iDBkGTe4hW88+m3vPz+l7z54ZcUlVZQsL0el7uBb9fXMmt5Pi53A4/+sIW5C5ezosTNF3otAP+ZU8gXPy/iq58X8pNeDsCqMg8FNcZkGV+tq8MflGyr8/PLFhcALy+sZHlJA95AiJu+LgZg9hYXby2tAuDxOeWsLvdQ4wnyxhJj2/xCd9PzX1tcSbkrQGl9gJcXVgLw7PwKvt1QD8ClnxbiCUhK6v0sLTFmblta4qHSE6TWG+S+mcYXlGmra5ixsQ6AN5dW4fKFKKzxM211DQDfb6xvqhw+PqecQEjy3YY6np23HYB//VLOilIPdd4gV39eBMB3G+p4f2U1AHfOKGFLtY/iOj9PzDFem7kFLpaHY3pjiXHMcleA3wrdACzZ1sDW8GvXGNveyOMPcd/MUmwWjcP7/vFqZnJOPEf/5cBWn19TV8/QQf057MDRrJ5RQP4vRZhWllFToSbaVZRGQwYNYOjkvpg0SM7tTaC4sNWyga35uD74L4l/vZs3vpjF5INHc9qUyU2PW8xmHHYbj//nv/z062KWrlrX9Fh1bT03P/A0V93xCGs25HPJ/53EXTdcyp/OPJ5QKMSwAwYQCIb4880PMv27n0lKiOflf93FENG/K09fUbqFyWQieXwMjjgbH3/5Iw+urmHGOffhONRYHHzqQ7fx3gsPRThKRekcHRlzV6Lr+it7eJzmk8jGA9UAuq7fDyCEOA/4DLgceBWYDBwJzNjD43aZQFDiD0liOrh4c2fZkF9IUUkZfXIyyc3J5PMZs41Zy9wekCEOPfo4HvrXs2xep+PzB7FZLdz9ryf5fnUtPevL+W6Tm0N7xVDqMfH8jMVs+u5dLPYYzEie+PsN2Hxunn/zIwqKS0HTiL/lChzBOL745gf8QcnadZv4x62XU5XQF4u3lpxRfVhZ5uHwvrG4/CGKwy1nh/RxkhFrwWLSOH+4MWvaAWl2eiUa/dlPPSCBHrHG2y/ebryGsVYTQYtxDSAr3to0O1W/FGOw/p8PTMFpNbZ9dl4uACN6xjCip7H22kUjjYVjXb4QUwYaV+PG5TiR0tinNdzCZDaBM/x3i7OZsIcXsh2YaicQkhzeN65pn2cNSSTNacZhNfGXg40124Y3W9D7stHJZMZb8QRCTMr9YwXIZtbQNKjxBFmz3cu4HCdbqn1o2OiVCOu2+zh6L/3utKXGz0/5LhYWNfDWsiqmnpC90+PrfykisWcsGQNanhXvif+8zeC8vpxzyjFMvmYE7hovyTkNzHh8IWNvG0u/VON1XFbSQGqMhZzE3V+QuDWeQIgGvyQ5xtzp+1aUzpCelkxKYiIzC1wU1SZx4tYtrZb1zJ+D47AjcYybyHNjJrQ4QdHAvr256KwT+eSbWZxw5MSm7dsrqznn5GMYdsAAMjN2rD955glHAtArqweHHTSKk485FLvNSmpyIqnJiZ14pooSOWaTmQ3vlDHwrJ5U19aRkhDD+OMOwZJqB4z3v6JEi45U7vKFEHcASzAmREHX9e928ThLhBCTdV2fBUwBZjY+IIRwAGcAZ2K04gXDx4nbxWN0Co8/hMNqoqTOT0l9gJGZMby9vIoxWU6yE6xcOb2Qt8/szXS9lq01fm6ckEa9N0icfceXx1AoxNbiMt7+5GuOmDgGm9VKRu/+rK/0cnjfOKatriEv1c4B6Xb+/mMp907OoKDGj6bBgBQ7m8rr+HHGj5RWVFHn9vCn047BY0viqaeeoay8EktKFreccyRfF9tYumADA1OsfLnBxe2njsNi1rjg0kuZlBuHxWrBbDIhgQm9xpPomMgtzc71lcsOo/LMkVTX1tG3VxaapjE4D+Rx91NTW49EEuNwMMlu4+LDbgOg3uUGTaOmtp6r75jKy1Jy9GEHUd53EgOyezIgxfigPCB9RwVobLhLX1qshcavFI3lAM4YbHyBGNqs0nTcwB0TChzZz3grxNo6VpGOtZk4KMc4Zk7CjgrDeeFKZqzNRGa8sX18rx3dDU8ZtGOB0h5xRmrkJu+YBS4v/E+gZ/yOffYPn4fNbGZM+DwP7r2jktd4zAGpdgaEn3/qATu+MF17UGqHzikSchKsvHRyNv2SbX9YvBwgJsGGNablj5D8rcXMXbiMW6++oGmbM9GOM9GO9cJhbKz0kxZjxmYzU1IfoN4XosYbZMm2Bi4YkbzHsXsDIQpr/WTGW7nhq2JePiWbR2aXc/1Bqbj8IZxWE2lOC4GgxGQCUydPQtFZgv4g+qxCBh/dB0+tD5NFw+a04qnzYY+ztjh5RsAbxFvvIzY1BleVh5gEGybzjtwJhiRSgqWdqb0r3AEsJo1Ex47PtjJXgFpvkAEpdlaVeciItRBjNbGo2M2k3DiqGoLE2kyYNKN1f2iGg4aAxKKBw2pqutCyO5N++IOSMleA7ASrcQ7Q4tqLjaSUuHwhNlX5GN7zj+/fvUVtmZvvn1rMGY8eSqjnSMo/fAAZCqG1UHGLPf1cpM/Lq+9+xqEHjSKvX+8W93n+6cdx/unGMgnvffYdx0waT99eWQzIzWk3nv592i+jKPsas9mEfSTExNpxuT0kxzvpm6RmeVWiU0e+LdsBAZwDnBv+vatuBu4XQvwK2ICPmj12E/BMuNvm6xizcx4H7GoFcrd9rtcyv9CNlJIT38nH4w9R7g6yutyY/jYv1U6iw4TTqvHo0cY0uacPTuTGCWlsrPRy/cdruer2hznx4ps59LTLeezl9ynw2tHQ+Ncb07nnyVdZt2kLq/QNvPruZ2xaPJfvvvqK8opKxiR5uPX+J7nz3kd4e9q3bC4s5Yb3l7O1uBQtIYO6hN6kJiUyb1uA0886k49eeoSH776RIw4ZyyUHJjP19ou55aoLmPn4lRx3yCgGpNg5dlAqDocdi9mMpmmYtJ2/oDWXkpRAv97ZO33Z0jSNpMR4khMTcNh3/vCLi3US54whu2c60994gqfu/ysWi5ltZRVU19ZzyV8e4P4nX+Y//53Gj3MW4vH6uPGeJ7ji1od49Lm3+HbWr130V1Q6m5SSO78v4ZsNdayr+ONU0DnD00nOjmsq+/XMuTz49KssWanz1/uf5tJzTiY+7o+tmacPSSJlWQkfvbeBj1fXcOyAeHLK6okPhRia4aDaE2TW5noa/CFK6ndtDKWrykONK8Caci/TVtfitJp49dQcHBYTxw2IJyXGTFGtn09WG12Oz/6ggJL6ACV1fvKrfYSkZNrqGgKtLPvQWUrq/Lz3XSFSSqZ9sIEvPtwA0FT52fxbCZvmbcNkNlFbYnw2bVlcyqpvthAKST6/bx7eej9bFpXy47NLKXMF+N+/FlOiVzHrt1JmvrUWgOlvr2PNnG1UuQLM/sHo7nfrJwXo29xsqvJx1XRj20erapq6RF/+WSH13iCz8l18uc7oNnznjBKqPUG21fn5fqPRNfq3Ijcl9QGqG4KsCX9WfrCymi/0WkISXl5UiYZkdn49L0w3WqOu+Hgry4rdVLkDvPN1AaFAiLXlHjaFpxp/cnoB1VUelha7ee1z4zn/mVHEkvU1lLoCPPGtEe/LP5Xw32+M2zdMXc3mgjrm6DU8/dfZxnPe0Pn8zTUA/PzQfFwVDZ3/R+wkiT1jOePRQ/k538VmrxXN6SS0vbzFsp6Z3+Lx+vjftG9ISUposczvVVTVcPmtD3H1nY90ZtiKsk8xm824A27sNispyQmsdjmZHh56oijRpt2WO13XLxFC5AH9gRVAcUd2rOt6PjA+fHsdMKmVco80u70UOKgj+29Us81FYubuz24kpaRvko14uwlN0/juwr6YNI1hPRwMC7ckjc12smHzVl5+5Ttuv/ZCHnnuTZIS4ikqKSOvb2+mnnEMSwcez+NLg0w7W7Dda2JZqZc7r7+YRcUN9HBq5CQ5GFhcymvvLcBmtZKYEAcSjhncg4zQUWiaxrez5lFZWcnn1x6Mpk3cKc5rMwAyABgRbtN0WCI/LHFAbg4Dcs8EIBgMcfVFZ1BUUs6Wwm1k90zHYbdx3qnHIpHkb91GTIzxmt54zxNkZqRy8NgRjB0xmBiHva3DKBEwd6ubybmxJDnMbKz07tQaC/DudTMZfnUftpRs4/gjJ5JfUIzb3cCn38xi2ittr5gy7PhcRtnN+BoChEKSLYtKGZuXRO8EOwU1PjZX+yhzGYs5nywSqGgIkh1uhQ0EJbMLXByS5WDDqkoGDk2luqie1D4JfPzkEkKT+3DewRkMn2C0itrCLVSNrbQTesUyoZfxmfHxOb3RNI2f8l1sq/OTFhPPtroAZhPkV/nIjLfw2uIqDki3M7lvHBsrvfRPsbO23EPPeCtJrVw0aeT3BLA6LCz6cB2Dj+3D97OKaShzc/olg3EvKqFyUAI9h6WSYTdRWuPjvVtnc90LhzO3LkifJBtZIUnFQcbFl8+tDs44vAc13hCWPw/HEW9jeYydgWfmEWPRkBOyScqOJdZpI6aP0TpcNCqT08alsCK/Hn1+CYccns3ZIR+W9ZVkjuvJwV+shZNz6O/1Iatd0CeWU7duJ1CTwgk5MdSUuJBScmovB2ZvgBE9Y0jb7iIUCHG0KUigoIacsT3oP3sLdblOTk00sWXRNmyDEzmnpIotC80cNiAJy5YqoC9XBBswLyvDf1gOvrlFhI7IZtXcbQRWbaff38aStbWaupJYMtKcJC4vRZ7Qm4H1HkwFIXIGJjJxYRGeY7M5M9fJlkXGLI8XDXAS6wty0IAE+l0zDICjJmWSlOwgzm7mz/+YgD1+771C76n3sW11JQ0psQRCkPLIc5hSdm7R9/p85G/dRmb+Jn4zJXDAwNwOLxJ+5QWnUVhcyjmnHtsV4SvKPsFiMuObY8ZyiIULTp/CBe0/RVH2XVLKNn/y8vKuy8vL+yEvL29JXl7ejXl5eVPbe05X/jzzzDOyUUOtV37x4Dzpa/DLXbG1xicDwZBcWOSWt3xTLL2+gFy2er1s8HhlbV29rHe5ZW1dvVy5doMMhUKyweOVZ191p/zs259kKBSS70+fIV/87zT5xfez5fpNBbt0bMWwcUuhfOujL+VVtz8sb7rnCSmllHMXLpf6xi0yGAx26bG3lW6XH37xvXzujQ/lq+9+JqWU8puZc+WtDz4jr7/7X/KOf06VtXX1cvW6TfK2fzwjb/j74/LK2/8pZ/26SAaDQXnan2+Vx19wozzryjvk1Nc/kFJK+fGXP8oPv/heLlq+Rq7dkC9DodAuxZSXl3efjGBe/f6nMc9WlzXIVYX18v2bZklPna8pXo/XKz31PnnNHY/KNz/4oml7bV29PP+6v8tAoP2/YcAXkNPv/VXWlrqklFL6Gvzyt/d0GfDveG4wFJK/bKmX//61XDb4gvKeH0pkKBSSj762VuprKuW9d82T+Zuq5XuPLpRf6DXSFwjJcpdfLv5kvVz+5SYZCoVkfUWDlFLKtT8WSK/b+KwIhUKyqrBO5i8okUF/UM54clHTturieimllP/6pUwuKHTJSndAlrv8sqzOJ6/+bKsMBEPypQUVclGRWzb4g3JjpfcP5xYKheSK/Dr5wg2zZCgYkrM+3Sg9noBcubVerl5X3eprsqWgVoaCIbm8pEGW1PllWb1fvrhgu5TS+NzaUu2V5fV++fayKimllD/n17d4/I5q/Ows0StlwZIyKaWUhSvKZcAXkNu31Mjf3l0rpZRy3ttrZP7CEimllN88ukAGA0FZWVgni1YasZVvrjY+K+u8smJLrZRSSneNR4aCoabXozXBYEgGg7uWL7trb8yz+u1uufDDdU0xBraXSd+m9VJKKUM+nwwFAvKrH+fIMVMulP5AQD7z2vvyk69ndsXLoyidYm/LMyklN95yi3zu7XdkcWmZ/O/HX8n//bxWltXv2ndHRdnbtJZrHRlzdw5wKPCjruv/FkIs6OL6Zoc54m0cd/sY3rvxJ859dvJO40p+LxCUbKv30yvRxuNzyrlpQhqWmmLsy7/nhNdXkJaSyL/+fgNffD+HT76eicfrY9zIwTx0xzXcdO8T9OudzUlHH4qmaZx90lHdeJbRqV/vbPr1zuZPZxgztQGsXLuBb2b+Sp3LzbGTJ3Dzlefz29JVlG2vYuSQPLJ7pqNpGsWl5WwuKMbl9uBqaGDcyCH0TE+ldHsFdfVufluyit7ZPZk04UCuvuMRemSkYrNaiHM6ueHP/8eTL71DjMNGn5xMksNdm3J7ZXHc5PHY7TaqauoIBIOkpSRx9GEHYbfZcNht9O2dhclk4qn7/ordbqW2zoXVYqRQrNPBgqWr+frHudjtNp7/5+18+s0sFi5bQ3paMvkFxdx146Vsr6xm2tczGZCbQ/8+OYj+fYiL/eMyA3uDX7e6WFDUwLcb6rnn4qHY44yWs4+//JHnX/6IJ2/5KyvXbeSRu65tek58XCz/e7Zja22ZrWZOum98s/sm4tNj0EwaSz/bSN6kHNb/XMjBJ/XjoBwnZg0Oy40lFAwx1tXAgIGJ3PcPo6FfXj6MQEhiNWukOS2kntyfUEhSs83FnNdWcfxd46gtdaNpsOaHApDQIy8Zr9uPyWJi4GHZIKGioI6QP0R8RgxDf8lnxOhRLPlkA/0nZiL9IU5evQ3zyTmcmmImJcOOXurho+VV3DMlm9e+3cr4EanEFNWxasl2jr/kAOqvGYlm0liXkwTbGpiU2/ZQ4vHUiTIAACAASURBVN69jLGmw5qNP71ijNGK09L40UP77NmaTFaH8f7tkbdjnGP2UGNkbGrvBFJ7G/lx0HmDmh4/9rYxACRnxzV1y03LNVoKHXE2HHFGK1lMwo7W+LbG2JnaGDe3P4hNjWH0mQO5cnohjxydiTN/E4FCo0tq9T/+RsyUU5hy5vm8/f50nrnmRv764lSkVMuJKMquMJvNmNbZsR5nYfb8pfQ5KBVvF3e/V5RI6UjlrrHG1JgFfxx8EyHFqytwVXo46/FDW63YBcJraq0s8/DFujquGGKhX/FsUswn4jJrDB/Un2svPIPMHsYXmsvOO4WjDhlLbq9MLOEv7ldecDqD8/ru1iQASvsaZ3y7/PzTuPz80ygpq0DfZHy5qamtZ86CZUx9/QMCwSBfvPkUmwuKeX/6DGKdMThjHIwcnEdRSRlX3/kosU4HI4fkMWHMcACuu+Qs9I0FuD0eJo03pu1/7O7r/xCD6N8H0b/PH7Yfdei4P2zrk9MTgJ7pO7pOHTt5AsdOnrBTuTEjBmOxWKioquGkYw5rqsQNyO3FxvxCvp01j/NPn8IRE8fs8mvWHaSEEQ0eDuxhpichlk3fyIiT+5PbK5Oxg4Yw87PFiP59WhxXtztMZhODjuhF0B8k6A8R9Iewx9kIBSUL39MZflI/XG+uwH/raCZfO2KnfGw+8Q2AZtIwmzSSsuI4/m/j0DSNsecIALKGpBKTYMPmtJLS26hM9RltzJTWf0Jm0z4OPGMgJsuOmFL7J3HEDSMBWPdTIXmTcnBWNHBUSTWQTbJegS3HCVnxLC3yMkXTGC+MSo9awF1pTXVxPSu/zueO0/NIsJswjz4IU0oqVffcjOOwI/H8NIM36qycc9BQKub9AuzehDSKsj8zm81oUsNmteJyN3DasPSdLpgpSjTpSOXuHeBnoI8Q4ivg064NqeNiEmyYTBruKi81JW7S+/1x2uYn5mxnVKaD0akhrCu/4dzn5nHiUYcQCoUYktePIXn9dipvMZsZ0LfXTttGDRVdeh7KznpmpNIzw6g4HX3YQRx92EFIKamoqsFmtTBx7IgWF9X98q2n/rBtiOgfsXWacjIzyMnM2Gmbw27bZ1p+D+4dS6UWIuAJEJsWw5r5m1n03GouvfYkAAqKS7nx2LM6/bhmq5nRZw4EYNARvZBSkpgZiy3GwjG3jG5qGeoo7XctQ4k9O1YZbVziIaHHjpZVm9P4MjDxkiEApPdLJHesUdk/LVzxA7j75JZnMVSU33Mm2RlwWA4uKTGH36u1LzxJ/GXX4zj0CDZddAYffv49n195EqFBvdrZm6IoLTGbTASFB4fDjrvBw2Pzank+N9Tty1kpSnfoyIQqU4UQPwBDjbv68q4Pq21T52/HYtK4ckwKmqZRsKQMn8u/U+Xu+431jM+J4ZqxydhMIUrKKnE4bHz40iMdnmVM2XtomtbhCQSUzvHgjyUc28vJuIFGlz1/vJ/Nq4uRUhK7PY4xPdO6pQVB0zQOONKoLFlsar06JbqYbWZCCTae/rWCZ0/IAiD1seebHt961uUMmbsMbXsZ5rSM1najKEobzCYz1lnxWM+18ObT97KpTmta41ZRok27lyyEEPcAZwEHAKeG70fUWUOSOGVQAj88vYSCJWX0HpXBgEN2XmC5oMbHtmo3Dz/9Ei+//Sl9cnpy45/PURU7Remg83OdFL21sum+L8FLj6p0ildVkN4vkYSee+dYQUXZl1Tk17LirdVNFbvfW1vlZmhKLP61K7HmHdDN0SlKdDCbTWjjvWiaxrzFq0iPNe+165sqyp7qSHt0afinDMgBItrfKCQlRbV+lmzzcMQNI8kZZoyV++mF5VRsqeXb8JpcZ+XZ+ecjT2GzWbnsvFMjGbKi7JOCiXZOuG/HOMLaehcMD5A1JJWEns4Od29UFKV1GQOSyLtsOJ+tbXnNrcvPP5XTQ9vxLV2IbfDwbo5OUaKDxWTGarISCAa5+9HnuffHskiHpChdpiPdMl9sfl8I8XXXhdM+tz/Es/O3Mz7HScFiF71GGd1Uhp/cj/j0GGxFDVhMGj/OWcgQ0Y+brzxfDT5XlN3w0qwSzk81M3JyDgAXn30ifl+ADXOKWfHFZo69bQyxKY529qIoSlsKC+vYvLKS+LwdEzStXreZi266j7NPOoqJY0cw/oY7aBg0BFO86nmiKLvDpJkILbHiPsOD0xnDy6eq8atK9Gq3chdewLxRJhFuuYuzmXn9NCMpZ05d2lS5i81w8u1PxUw5Iov8wm2ccuwkpJSqYqcou+meQ9PZsqi06f5vS1eT1SOVsnXVTLlzLI6EvXdhaEXZV/yyyUWwrIHzj9uxTMZKfSMAn8+Yzf+dfDQmp5PYU/8vUiEqyj4vPj6W066fiLvBmFRl+tpaTh6kLpYo0akjs2U2b7nzALd0USwdUuEOMH1tLQuLG7jovAMwW4yepZUNQQpmbeUT1zo++/Fn3njqnqYp9hVF2XWvb3Jz9TG5Tfffn/4dJx99GJMvHR25oBQlyiRnxTLbD68vqeSSUcaSGYXbSjnvtOM44uDR9M7uGeEIFWXfFwpKNn5Xijg+hysuPrdpmSxFiUYd6ZZ5eHcE0lEWk0aiw8y5fWMo/Hg9/S8fSiAoSYuzcO5twzj7yjt4/uE7VMVOUfZQSqWbhW+XMeHCwQDU1rlITGh7EW5FUXZN3cZqkhaUctato5q2FZWUc9JRhzJiSF4bz1QUpaM0jOWzHHYbJxx2IDarWuNOiV4d6Za5DIjHaLVrHGCjAVLX9X6tPrGLJDrMTMqNpbrGh294OgC/FLiYle8iI382pyUcQ++MHt0dlqJEnbMmZdJQ4226X1NXryp3itLJDhubzraQhjcoacyuR+68LqIxKUq0MVlMDD6mD78uWsHz73zJiNP/zC0T0yMdlqJ0iY40b80Fztd1fTBwCvALMAhjaYRut77Cy9O/bmdNlY8ffca2yX3juOOQdC499yROue1grDEd6W2qKEpr/EHJeR8VYI/dcXXz1qv/RFZGWgSjUpToM3V+BWtr/Wyo9DVtmzF7PppJjRdXlM7iawjw6V1zcLkbyEiK5YLhat1cJXp1pHI3WNf1XwF0XV8B9NZ13avrured53WJvsk2LjswmR4ldRy4qpTCGj+fra3ly+9mUbitjKz+aaz7qRC/JxCJ8BQlKlhMcF//GJZ9vgkAKSV9cjJxOOwRjkxRosv56RZSl5eRnWBcSAkGQ9z/5MtqDS5F6URWu5ljbhndNKGKmmxPiWYdqdxVCyEeFEKcJIR4FNjS1UG1pcEfotoTot/4TPLH9yK/2kes1cRbH3+FyWycjqfWh8/lj2SYirJPC0oIDUhm/AVGA31RSTkX3XhfZINSlCgU7JPIVXccSFa80eOkzuUm1hmjxo0rSieSUlJf4SEjLZnYnn34al3L60oqSjToyH+P84Ba4DhgE/DnLo2oHSX1ARYWuyldV0WOx0f/FBuH5zop315FTqaxLMKIk/tji7VSV+6OZKiKss/yBSSfzSymcFk5APMWr2D8gUMjHJWiRJ+v5pRSsLC0qaWurt5FfFxshKNSlOgS8AZZ9U0+4w8cxt/+dCyXHJgS6ZAUpct0pHLnAaqA7YAORLSj8sBUO6cekIir0kM/h4kbvyqmpLySxIS4nWY/KlhcxvqfiyIYqbK/C/iCyJAk4A1SU+L6w+OhQCgCUXWM02biytEphMLTRf+6aAUTRg+LcFSKEn0uGZVMRoy56X5yUgJ3XndRBCNSlOhjc1o5/LqRfPjFDzz3yc/8Vqgu/ivRqyOVuxcxFi4/BmPWzLe6NKJ2bKz0Mm+rm37jM+k1LI1rD0olMyOFd5/7x07l+k3IZNTpA3BVepBqPZOoEPAGAaNSJGX7f9OgP0hVYR0A2zfVUFtqVLDqytyEAiHqKxooXl0BQMnaStxVHqSUeMNdekMh2aHjNI+trsxN4YrtAHz3r0WUbajG5/Yz57VVACybvpHl4XFsH9/+S1NMe5tab5DP6yS9Rxmt4RPHjmDMiMERjkpRos+7JT4Sh++Ytc9iNjN00IAIRqQo0cfXEGDxtPVs2LwVX4Mbu0WNuVOiV0cqd/11Xb8H8Oi6/jmQ2MUxtSnOZibNaebnl1ZQt62eSblxFG4ro6S8YqdymqahaRpzX19FRb7qW72vqtxah8/tp3RdFT88swSAn19awaZftxEKSb59bCFSSgpXbGf1DGM46IwnF1O+sRp3lZeFH6wHYPuWWmpLjCt13z2+CCkl7iovVVuNyl/puipcVV78DQGm3fELAGtmFDDvv2sA+OWVlVQV1eOqaOCXV1cCsH52ERvnFgPw/l9+wuf243MHqCk2KmxT/jaWHnnJOJMdHP+3cQAMOS6XQUf1BuDMxw8lPsPZtS/gbrKaNNL18qbX9PQph5OaHNHUV5SolFNSh/7pxqb7s+cv4cGnXolgRIoSfUwmjaSsOFwNHkTPeEb0jIl0SIrSZTqyZoBFCJEGSCFEPBDRvmQ94iwMSLWTfEwf4tKM5PzhlwXUu9wMGpD7h/JH/uVATCaN0nVVpPdLxGRRg9T3Ba6KBmJTY1jxxWZGntqfjAFJHHmDscjvpKuHAyBDkqHH5yIlJPZ04ghP2z/xksE44m2YLCaO/uuBAAw6vFfTvs947FAAMgYkkTHA6GU84uT+TY+f++zhAAw5tk9Tt8RBR/QiNsVY5rHf+EwAUvskYHUY3anOe+5wNE0jNddKam4CQIuzcVlsO7pf7c2zdcVYTRxzYi5gzN53/J9u5Ov//VtN8qAonWzKUTkEfcGm+3X1bjXmTlE6mcVupt/4TIIzg/ywNYBlQx3HDIiPdFiK0iU68k3tLmAOMAaYB9zfpRG146f8er5eW4Mt1oLVYdRNS8sr6JGe2mJ5k0lDhiSrZxTgqorI6g1KB7mrvbhrvPjcfmY8uRgZkky6ejiJmbFoJg2L3agYNbbKmswmsoemYTJpxKc7SetntCw5kx2dVok3hdeaSuuXiC3Ggi3GQtYQ472W0ju+qeVtb66o7Y6tNT4e+L4EgNr6eoLBoKrYKUoXOPPdfOoadlTuautdJKjKnaJ0KneVhy8enM/Dd17LveccwiF9VI4p0asj39Z66bougP7AUF3Xv+/imNp0SO9YJiRb+fnFFU3byiuryUhNbvU5mknj8GtHEJ8ew7z/raFsfVV3hKp0UChoNAbrP26lZE0lNqeVE+8drxbxjaDsBCtnan6KV1ZQU1tPYoK6wqkoXeHxQbFs+21b0/3cnExGDB4YwYgUJfrEJNk59tbRfD5jNhu2VeEPqrkYlOjVkcrdFQC6rpfruh7xbCh3BwjG2zjh7oOatp17yrEMHdS/jWftMOCQLJJ7xVNdVE/pOlXJ605SyqZZI8s2VLP5txJkSPLp3XNxVXkYdfqApi6PzbsvKt3P7QvhPCSH/gdnEQyFGDVURDokRYk6Ukq2ZScw8tQdE6hMPng0kyYcGMGoFCX6+BsC1JS4eP39z/lqVTlbqn2RDklRukxHKnd2IcQSIcR7Qoh3hBDvdHlUbSiqDbB+aTlFK7c3bRP9e3d4soe03ESsDgvuam/TBBvFqyuaxlbtqeJVFfg9AYCmWTqbz7jY0dkXd5WvIYDPvfsLt1cW1BEKhAiFJN56Yz9zXl+FryHA9k01LHh/HQAFS8p2zEC5uQYwznnDL8ayE0s/3UhdeQM+t58ti0qNx1dXULquChmU/PDvJQR8QcwWExabGc2kcfxd44hNdux27Ernq/WGWPD9VrZvrqF/nxzuvvHSSIekKFFHSpg1e1vTZyXAv199j9+WropgVIoSfbz1fopXVeJq8HDVhEyGqwlVlCjWkcrd7cBNwAsYyyK82KURtWN0Vgy5KfamMU5SSo497wb8gcAu7SdrSCoDD8vG7wmw6ut8kJKK/Fqqi+vZsqiURR8Zsyw2TnHflmAgRMGSMsCYQTHgC1K6ropZzy8D4PN751G5tY6StZXMnGpsWzJtA1WFdVQV1TNz6lLAmI6/seLUuAaap9ZHQ03rYwXnv72W6qJ6ti4pY/G0DYAxm2RFfi0+t5+VX28GoKbE1dRSWbi8HCkldWVuChYbcc95fRUBf4jCZeX8+tZqAHKGpREKhohNc9BntDElvs8dIOgLEQqGmPvmaoL+IDGJtqaxZwk9nVjtZnwNgabj+VwBNA1MFhOnP3wIFpuZ1NwEeo00pv92xNnafY2V7pWVYGWySMRsNTF/ySqmf/dzpENSlKhjMmlcOzG9afw4wPwlq4h1qi+eitKZ4jOcDD+hLy53A9PWeSip2/2L4Yqyt2t1tkwhxN26rv9D1/WfhBCZuq5va61sd5pf6Oa0zHhyEo2ZEWtq63HG2HdawHxXWB0Wjr55NABVhXVY7BayhqSQlB0HwBcPzGPSNSOwx1opW19Nn9EZLHh/HcNP7IuUEleFB2eSna1Ly+k1Ip1JVxkzOZrMJiZcbKwLNuVvYzFbTSAhoYdRCeohkrHHWrE5rYw81ehSWr+9gVBQkpQt+d9VP3Dec0dQtHI71cUuRp85kB+eWcLIU/sjg5IF7+lMuXMc2cNSiUuPISk7jv4HZwEw/MS+xKY4CPp3TGxauraK2FSjdWz97GJS+iTgdflxhyuOJ907HoBeI9Kb1jbrM6ZH0/NjEuwADJiY1bTt5PsmAJCcs2M8VmO3SoBx5w4CIHfsjv0o+4Yt1T5WxDk4ISeetb/mU1NbH+mQFCXqeAMhXir0cdck4zPX7w9QUFRC/z45EY5MUaJL5dY68heU8swDN1MRE4tDzZyuRLG2lkI4AmhcGfzt8P2Iy06wsvbt1Zgm5ZA1JJXtVTWkdNL6WwMOyW66bXMalcWTHzwYTYOaYhd1ZW40k0ZiTye2WCslayopXV/FqFMHMPGSITvtyx67o7LZ/KqsM9z9sHHGRdhROWp+/AteOgqTSWuqsAGMO0cQk2THbDFx+LUjAchptvhto6SsuPBxYeiUvgDkTd7xZeHwa0cYsSTaSeu782unJjFRABIdZgKfb2DdYelMff0DzjrxyEiHpChRx2zSGFpUzbLpdYw4uT+l2ysZNmgADrvqzaAonckRbyM23U5+nZMv19dxVP+4SIekKF2mrcqd1srtiMqKs3DweaKpwmSzWphy+MFddrzGqfCTsuOaWvNEeM20rCGpO1XSuuK4zTVf8NqRoP75K10nwW7imIuH8MoHnzFm+AGcfdJRkQ5JUaKOWYNjT+lL48d9TmYGLzx8e2SDUpQoFJNgIxAT4G+3Pc4Ft9xLSBr5pyjRqK12adnK7Yj6YFUN+QX1mMJZ2Tu7J5f830kRjkpRosui4gbenVtGRUU1xx85kdxeWe0/SVGUXVLZEOSaj7bgdRljxr/7aR5LVuoRjkpRos/m30pYt2ArOSmxXDMuFbPqpaREsbYqd6OFEHOFEL82vy2EmNtdwbXknGFJFHydT8BrjCd7e9o3fPXDnEiGpChRZ2y2k4Fba6gtbyA9NSnS4ShKVEp1Wri9j51tqysA+H72AiqqaiIclaJEn37jM3H2duJB9XpSol9b3TKHd+aBhBAm4HlgBOAFLgMmAZcDi3VdvyZc7h3gKl3Xa1vaz4YKL1f9fTTWcMvd0tXrOOawg1oqqijKbiqp99P7T3mMnVVMbo5qtVOUruD2hajsn8KEXrEAFG4rpVeWmoBKUTpbdXE9MTYn/UeMjXQoitLlWq3c6bq+pZOPdSrg0HV9ghBiPPAEkAQcDHwihEgO357dWsUOoLjKx6IP1jH+XGNR5fytxfTtnd1acUVRdkO1J8Tyaes59YxJxCTaIx2OokQlTzDEmrnb6DUokezhaRRuKyM7MyPSYSlK1Kmv8NC3RzpPXX1ipENRlC7XnXPBHgJ8A6Dr+jxgDOAGHIAVCAGXAi+3tZOJfZyk9jImNgmFQmho9M5WVzoVpTMNSrMzoF88l976IFLuNUNuFSWqpMRYOPHAVByJRlexaa88Rpxa405ROl3OsDRe+3E+lzz0RqRDUZQu11a3zM6WADQfTBAEHgLeBaYB5wOvAbcJIXoBT+u6/oeR5T99sox3fjAW/b7juov44MWHuzpuRdnvLJi2gXd+eY7cVAuapgaeK0pXmDl9Jb/98Cy6fxMAX/336QhHpCjR6aW3P8GRspCzz1Atd0r0687KXS0Q3+y+Sdf1X4BfhBAJwIvAD8AU4O/AvzEqfDtZXTb3VcooBLjzpl+586Yuj1tRusNetWrx/OJvXqWYwkpAiI8iHY6idJa9Ks9WFcx6FYz/ZwATJ6jx40pU2KvyDMBXVfSqr6qo8JXH5vHKY3dHOhxF6Swt5lp3Vu7mACcBH4TH3K1o9tidwKOAE6NFTwItrjCp6/plXRynouz3VJ4pStdTeaYo3UPlmrI/6c4xd58AnvBSCk8BfwEQQuQCSbquLwWWAb2Br4Cp3RiboiiKoiiKoijKPk1TkyUoiqIoiqIoiqLs+7qz5U5RFEVRFEVRFEXpIqpypyiKoiiKoiiKEgVU5U5RFEVRFEVRFCUKqMqdoiiKoiiKoihKFFCVO0VRFEVRFEVRlCjQnevcdTshxBjgDl3Xz9wLYskFVuq63uL6fXsrIcQJwMOAHVgO/FnX9doWyj0BnAVUhjfpuq7/X7cF2g4hxJ3ARRjv+f8B9+u6/oepYlsrJ4RIB94C+gAh4Apd1+c2e54GvAGs0HX98S4+nb2KyrM9p/Ks/Tzb28+9u6m823Mq73YuJ4Q4BnhM1/WR3RL4PkDl2Z5Tedb0/20A8AKQDtiAV3Vdf6IrYo3qyp2u6wuBiCfkvir8Ret1YKKu6+uFEI8CjwDXtFD8YOCc5hWevYUQ4njgbGA0EAS+BVYDH+xCueeA2bquTxFCjAS+FEIM1HXdLYQ4IPz4QcCK7jmrvYfKsz2j8qxjecZefO6RoPJuz6i821FOCBED3AVcCxR1W/D7AJVne0bl2U7l3gDe0HX9FSFEIrBACLFE1/UfOzveqK7cCSEmA1N1XR8afiGfA0YCEvga+Juu6wEhhAfjzXYMkIlx5eqF8D7+DpwLBIB1wHW6rpcIIU4H7sa4uhwEbtV1/WchxCxgMXAIkAb8V9f1e8MhmYUQ/wHGAYnAbbqufxw+zl3AGRhdZfOBa3RdLw7vrxIYhFHjfwv4NzAMsAI/hI8daOe1kEC6ruvbm98Pvx4ttTTdHo5/ga7r68PbXgCWCSGu/d3VPjswCrhNCNE//Dr9Rdf1gnZiSgmfy0ggG0jGSIJfgffaiOscjIqUwKhMPQlkhbf/3pHAacA7uq67wsd9HbiA3yVla+WEENOAEzH+8aHr+lIhxHrgOGBaePsrQJvnG61Unu30Wqg864I8E0J8uTvnHs1U3u30Wqi82828C5c7FojFaG34Z1vntb9RebbTa6HybM/y7FXgfQBd12uEEBsweql0uqiu3P3OM0AFxpvZBkwHbsFIRjuwXdf1g4UQo4E54T/IucAUYKyu6y4hxH0YNe/jgH8B5+u6Pi/clWEy8HP4WAKYiPFhOU8IsQBYCTiAGbquXyWEOA3jTfexEOLCcFzjwh8SV2BUFo4P769K1/XBAEKI14BFuq5fLIQwh+P5K/DY7rwouq5/j5EUfyCEuAPY2mxTIZAAxAPNm9SzgB8xPqRWYbyunwkhDmypybqZZzE+cIZjfMB8CczSdf2h8OOtdQ35tln3hOZlHmnlPHphfHg1P4+cFoq2Vi4NMOm6Xt7SPnRdvy58nGNaiXd/ovKsBSrPdrI7eba7576/UHnXApV3O2m1nK7rnwKfhisySutUnrVA5dlO2sqz15vt7ziMlso/txLfHtmfKndTMJqFJeANX/m4iR1/yM/CvxdjJGls+DmvN9bAMa4O3CWEsGFcEfgkfEV5BjsnxYu6rvuBaiHEhxhXxVYCvsYrLMBSICN8+0SMqzALhRAAZsDZbH+zm90+ERgnhGh8Q8Ts6gshjPFhjbePovUrGyaMq1O/F2x+R9f1zez4AEEI8TjwdyAX2NxGKMcCE8J/E58Q4gWMv8lDbcWl6/q3LZzTHbR+xeX356H9/hzCWivX0uvQ2j72dyrPwlSedV6e7cG57y9U3oWpvNvlvFM6TuVZmMqz3c+zcEX8SeBMXde3tXZie2J/qtz9/gU3YdTyGzUA6MagRzD+IOYWnmMBNF3X7wpf/TgauBi4GSOxwGh6b/6cxj+sv9l2GT4G4eM82qwJ347RtNyovtltM3CWrutrwmWTaDlxWtKY6E2J3M4Vl3SMZutG2RhXf1y/KzccGKHr+n+bbdbY+XxbI393299eXC3Rdf0RWr/iUoBxVahRFsbVlN9rrVwZoAkhUnRdr/zdY8rOVJ6pPGvUaXm2h+e+P1B5p/Ku0a7mndJxKs9UnjXa5TwLV4gfxxjDeZSu60s7Gtuu2p+WQvgWuE4IoYXf9FdgXClpyzfApUKI2PD9GzCazINCiHzAqev6fzAGhg4P7xeM8SMmIUQyxsDKzzsQ22VCiITw/QeA/7ZR9i/NzmM6cF07+2/UeFXkpA6W/w4YL4QYGL5/FTuuTDUXAp4RQvQN378aWK7renv/OL4LlyV8FesKjNe8s30GnC+EiA2/ZhcDn3a0nG70Q/8yHF/jh9BgYFYXxLqvU3mm8qwr8mx3z31/ofJO5d1u5V0XxBPNVJ6pPNuTPHsMOAwY05UVO9i/Knc3YDRfrwj/6MBDbT7DGPz4PfCbEGINcCBG/+gARrPvO0KIxcCHwKW6rnvDz4sBfgPmAc/ruv7DH3e9k1eALzD6Va/C6Dt8cRvnERs+h+Xh348BCCEeEEI80MZxThFCrAYuBKrbiQld18uAS4CPwuc/DOPKEkKIMUKIpeFyK4Hrgc/D5U7D6GeOECJLCLFUCJHVwiGuBzKFECuAJeFzeaq9uHaVruufY0x88htGt4ZFGAOKEUJcJYR4pb1yGB+8E4UQK4G3CXiFogAAIABJREFUgT/pul7T2bFGAZVnKs86Pc/aOncFUHkHKu/2JO+UjlF5pvJst/JMCJGDMa4xDZgRPp+lQohLOjtWAE1KNRa9MwljVqKpuq5/FIFjD8RYP+SOFh7baZajbo7rDeB6XdfruvvYSnRSedZiXG+g8kzpQirvWozrDVTeKZ1I5VmLcb2ByrMO259a7vYHAmM2p72GEMIJfK8SUokiKs8UpfupvFOUrqfyLAp0ScudMKZWfRnjTRLEaJLVMKZblRhNldeGi0/DWBPkbl3XZwgh+gE36rp+Y6cHpiiKoiiKoiiKEqW6quXuJABd1ycC92BM+fkkRgXuUIyK3ikYs9jkY6z30TiY827UIpqKoiiKoiiKoii7pEsqd7qxIOYV4bt9gFJgNPBTeNvXwFEYU7PGhn9cQoiJwHpd10u7Ii5FURRFURRFUZRo1WVj7nRdDwgh3sRYPf4jjDU9GvuA1gGJuq6vw1j/4WmMaVtvAt4XQrwghPinEEKNCVQURVEURVEURemALp8tUwjRE5gPJOi6nhzedgpwtK7r1zUrdx5GZXMI8DEwGVim6/pOa4gcft2Z8nQxiZDbRbC4EOsA0aXxK0p3mDp16qu6rl8W6Tgajb36BHmuoz+WnN5oNnv7T1CUfcDelmcHX3qMPCltMHG9+rf4eG3ATZGvkgOcOd0cmaLsvr0tzwAuuOQMObb/SEyJSa2W8YR8lPtr6WVP68bIFGX3tZZrlq44mBDiT0COrusPA26MxQkXCiEm67o+C5gCzGxW3gGcgbFq+9MYk7BIIO73+z5+4ASuv/56/Js3UPPkg6Rdf31XnIKidKupU6fuVYsxnyWO4MJ1S0k88y9Y+/Rt/wmKsg/Y2/LslLQhXB5rIaWV/2NF3kouWPs0zw2/Dk3Tujk6Rdk9e1ueAQzzmrj8oANxHn1Cq2WKvJWcv/Yp/jn8GiyauRujU5Td01qudVW3x2nAKCHEz8C3GN0trwXuF0L8Ctgwumo2ugl4Jtxt83XgPxiTrHz3+x3XBhoA0JyxhNzuLgpfUfZvlYE6sDuQ3oZIh6IoUasm5CEU8Lf6eLY9BYfJRr63rBujUpToE9QkBINtlsm2p9DTlsyS+s3dFJWidI0uabnTdd0FnN3CQ5NaKf9Is9tLgYNa2/dSl5F0Jmcs0u3as0AVRWnRLzVrudWehvR6Ix2KokStJe58gta2W8Z72VMp9lbR19Gjm6JSlOhTG/Igg4F2yx2ZNIzvq5YzNn5AN0SlKF2jQ5W78MQmGnAwMF/XdV+XRtUGE0bXFC3GiXS7kVKq7iqK0sk0DTRHDHg9kQ5FUaJWSNMg0HZrwkN9zyfBHNNNESlKlNJM7bbcAZyeNp6GUMS+4ipKp2i3cieEeBTYhLGkwYEYyxpc1MVxtUprrNxZLGCxgNcLDkekwlGUqKShodntSI+q3ClKVwlptPuF0xvyUxxsUC13irIHpEnrUOUu1RpPvqeM9Q3FDIzJ6obIFKXzdWTM3SG6rr8ITNB1/TggotN2DWo2a5glK4dQgxp3pyidbXRcf0yxccg2xgMpirJnhsbnNvVGac2P1Sv4sHxuN0WkKNEpzuqEDs4O/1vdet4omdW1ASlKF+pIt0yzEGIckC+EsAHpXRxTm7JsyU230559I3KBKEoU6+vIIPGGGyIdhqJEtb6Zg9qd8TndmsAKV0E3RaQo0Slx1ARiTz+3Q2UHODL5omJRF0ekKF2nIy13b2EsRP448Bjw7y6NqB0/16xuut0w81sCReqfnqJ0tmnb5+NbvRzfutXtF1YUZbf8r/B7qn74vM0yadYEtvtruykiRYlOG4pW4Vu7qkNls+zJFPsqASjfh3Kvwl+nxgsqQAda7nRdfx54Pnz3pq4Np32m5pOnmNQ6JIrSFTQNQvV1aFZbpENRlKilSYksLWmzTH9HT87POLSbIlKU6GTy+wnVVneobLo1kTt7nQ7AY1s/4cSUMRzgzCHDltiVIe6R2kAD5615Cr8McmnPI7igR4uT0yv7iY5MqHIhcAfQNGuJruv9ujKotmjNxifETDoqUmEoSlTT0HCMmxjpMBQlqnmsJuznXNhmmRRrHOMTBL5QAJupS1YvUpSo58lI6/D/NLNmQjizebboK0bG9uWGja9ySuo4Hsg9p4uj3HWuoAdvKEC5v4Yjk4dxRtoE1jdsA1CfGfuxjnTLvB04GTig2U/EpFrjm27Xf/w2rmnvRDAaRYlOmbZkGmZ9R/17b0Q6FEWJWv1MyTTcdXO75e7Nf48vKhd2Q0SKEp3SCrdT//HbHS5/wsqHeLXkB87LOJTPhtzBuRmHUOyt5POKBU1lZAcnaNldtQE3pb6WWxvfLZvNd1VLOXnlw3xbtQThzOa2XqfRP6Ynx6WMYoVrC+evfYr6oJrxen/UkSr9Jl3XN3R5JB10QLPZMjWzhWB5aQSjUZToND4hD+n1Eixru8uYoii77/CkIYQWzWy33NnpB/PI1k84NXUcJq0j12QVRWkuGRuh7eUdLj8+Po9Bzmw0TSPXkcH82vVcok8lxmQjzhzDLzVrGBs/gONSRnVJvPmeMi5Y+zQWzcyLA69COLMBWFS3kQPj+tEQ8vFR+a/c0+dsJiUN+cPzhzp7M8iZw0MFH3FO+iGMiMvtkjiVvVNHKnduIcTXwFJAAui6/rcujaoNa91FTbdNCYn4N+qRCkVRotb82nVcZh+EVIuYK0qX+b5mJWcHAu2WGxc/EKtmZk7tWg5NHNwNkSl7qqauDp/fT3xsLKXbK2jwekiKTyAtJRmLuf35Aho8Hqrr6nA6HGwrLycrI4OEuDh8fj+bthYSDBlrtiXGxWExW4h1xhAfG9vVp7XPqg42IDuwzl2jF/Ou2um+O+TlTXE9dpOVqoCL4bF9+KZySadX7qSUaJrGR+W/ckmPI7iwx2SsJguzqleRao3jLxtf55Mht3NpzyO5tOeRre5H0zRuzjmZB7Z8QEWgDiklmzylTK9YwNVZx2LTLISQWLT234shGcKkmZpi2135njJyHRm7/fzfH7820ECRr2KnRp8Kfx1fVi6iIeTjwh6TsWsWVrm3stW7nY/Kf+XE1DGcnja+xf0HZQhvyI/TbN/tGAH8MohVM1MXbGBW9UpGxfUjy5bcrRfmOlK5+6rLo9gFNYEd69qZEpII1dZEMBpFiU5l/ho0hwPp9UY6FEWJWgW+Cgi2X7nTNI17+pxNjElNcLS7lq1dS0HxNtKSkxnQpzfpKSlU19ZhMmkkxMV1eD/VtbXom/PZXFRIIBAkMz2dtOQkvD5jlkJ/IEhhSQlujwdN0wgGg6SnpBDjsLNCX4eroYHc7Gxyc7KxWqz4/D6W6+v4f/bOO06Osn7875nZne39ei9J9nIpl14IqXQUEaQJIkVFVBRQAREQRRRFUPTHV0QUv2D7IogSSuiE0AKpmJBkL7nkWq7f7e1tLzPz+2MvR860I+S4EOb9et3rdmaeeeYzu/PMzOf5NJvFgmw04nE62d3VRWtHB067nWAoRHlxEW9u2IDH6aK3v5+ivDzMpuy1UL+rEUVRCEej2G02SvLzcdhsyEYj8WSCjp4evC43uYNKpdFgQAOcNhsWs/lDvax/nEiSGdFYOxBL3ZOHPlcA4ywF3Nu2gsd7Vh9QWTgc7m1bgc/oYJ5zAjPsVRhFA5qm8XL/Jt4c2MZVxacPC086GG6DjV9WXwbAi8H/cN3Oh6gw5zHOUkBGU3m2bwMLXDXYJTNn+eYOXQvb420UyB5CmRhf234/bakguUYnp3mnc1n+Mh7uXMl4SxHvhLdzU9nnSGkZBARMonFIAftl63L+2b2a8dZC6mwVXFtyBrc1/QOjYGCqvZwCo5vP5c6nNx1mQInRlOgmqWY4xTuNnvQAJtGIRZSHlM/Pb/0lDfEOTvXOYKl7Mkvdk7m64Y80J7qxSDJn+GbzlYIT+VL9/zDVVkFcTfJw50ouzV/KXS3LyZOdnDa4byC2m3/3voOiqawNN3BL+bnkGp2cu+UuVE3FYbBwWf4yLspfxLlbfkFMSVFpyWeeYzxfyF9MY6KL7vQA4y2FuA3ZCZWV/e/xSv8m1oYbkEUDj9dez50t/6YrFeLXu5/GLBp5ctL3eSe8g5SWZr7TP0yxTqsZ0poypFhuj7fzn0gjkiBhEWXmOMfhMWTvU6qmHlIxH4ly91fgq0AtUA/cN6KrapTYO1um4HSiDnx80tTq6Hx8EBBMZrREfKwF0dE5ZlEEAUZoTZhsK6M3HR5liY4dBiIR0pkM8USCtq5uArt2cfzMGfQEgzy18lWsZjPxRAJVU6mpqsJutdIXGiDP68VokNje1IzRYEA2GplQWcGOpiZ2d3YRSySYWFXF8TNmYDVbaGzbTVdvH067HUEASRRZPGc2uR4PqXSGcDRCns83JFdfKETT7jY2bt2KomoIAtRWj0PVVFKpNO3d3ZQU5LN07hxMsoyqqoiiSDqdpqWjk4LcHKxm8z7nq6gqnT09NLW10dMfJJVOYzGZKS0oJBgKsam+HkVRSGeUoe/H53ZTUVxMjsdNUV4egiAcu8qeKI54rI0Eh2ThjxO+Ts8RGpOrB+p5oP0FOtP9POy/Gq/x/QkHQRA+dDKXE9xT+N34K5ntqEZFQwNiSpKmZDcDmRgneepYF25gVWgLK/vf45L8JVxSsJT7xn8Vr8FOd3qAYpMXAYHV4Xoe61nNXVVfRBREvl7/e7bEWqk2F2AWjfxhwteZZC3jvNoFtCR7yDNms4zeO+4rrA7XsyXags/oQNM0vt3wJ/oyEcpNuUOuo3e1PMEr/ZvJMTqptZXw88qL+XX1lxAEgRV964eMPLeWn0epKYfGRBdeox1REHm09jqMg0qPoqlIgshDNcNricaVFAWDNbPP8M2iSPbiNdh5ZspNuCUbHel+TIIRgN+O/yoRJc7OeCcKKgB/63qN+ngbjYkuFjgn8pPKC9mZ6GCitYQv5C+iwpyf/c3KLxgaT2lNQRAEgpkID3eu5M6WJ3AZLPyl5hr+p20FD7a/hFE0kGt08cCEK+lLh9kYbUTTNOJqimpLAa3JXq6sv5+EmsJpsPJK3W0H/L1HotzdD/QDLwCLgT8AB0/vNYrsfdsRXR7U/r6xEkVH55hFAASTSbfc6eiMIpoAqCqaoiAcwlUvpWY4fdPtvFJ324dyGzqYa1U8kUCWZSRRHEoWsadtKBzGJMuk0ukhS5eiqkji0RUDOBCJ8Mb6DbR3d2OWZSxmMzkeN6ctWkiOx0NlSQl1NTX0BIPk+3wkUinWbtpMcGAAj9PJxq1bkWWZyePHAxrReJxX3n6bXI+XJXPm4HW7kI3GoeN53QdOj2+RJCzm4b+V1+XC63IxvXZkuenEwe/XaDRSVVpywHaSKFKUl0dR3sjc3lRVpb6xke6+INsbG+kNhVBVFYfNhs/tJp5IYLNayPF4cdpsVJYUY9zrvD92CALah7Dc7Y8ycy5l5lwe7HiJGmsJxzn9h91Xc7KHM3yz+ZR3BsZRyHApCAJzneOB9zMpXpS/aFibHfEOFE1l+eQbsUvZCYQSU3ZiolzKHWr3wISvM5CJkS+7AfjDhK8TU1MEYrspNeUgCAKneKcN2x/AKplY5p7CMveUoXUP1XxrH1l/VnUxmqaxKdqERTIhCuJQGYov5i8ZarfHxbPaUjC0zriXNUs6gBtkkcnLJXv1s4c9lrHCQcUPINfoJNfopNKcP7Tu+2WfA7Jupik1e03tz0V27/vsHrlO9U7nFM801kUahhTMrxScxNcKTyGupuhOD5BrdFEge5jrnDCsP03TWDHlZiyifEgXz5FcQeMDgcCeK+Dffr//zRHsM2rMtFcPfZa8PmxnnjuG0ujoHJt82jcLKTcf8wK9Vo6Ozmjx1aKTsVaqDIazHxRZNFBtKWBDZBcLXDWHfcxnXl2FBpx6/AIMhvdfAep3NfLqmjVYzWYqSkqob9yFzWLh9MWLSaZSPPHSS6RSaYxGIz63m4KcHDZu3UppYSEGScLrduGvrMRpt6NpGoqqHjK27MPG8OyNoijs7upi1Zq1+CsrWTxn9n4tXACy0TikBNksFhbPmT20bao/+0K1t1wzJ+2bsOLjjiiK1FRVUTNY2EpRBi0LAwOEwhEsZhOhcIS+UD/1TT2sfOcdrBYLDpuVypJSnHYbJqNM30AIs8lEjsdDIpEgPYIY0rGgKKcSuW7WqPSda3TycOfKw1buNE3jvNzjjrBUH5wvF46svJhFlLHI77uIi4KIXTIz01F9kL0+GIIgMPUoTwJzuPGDgiAwyzFuaHlPuQqbZMYm7f+etWc/p8E6omOMRLkz+/1+ayAQiPn9fgswppXDezPvm8AFo4zts0df3REdnY87zYlupNx8fXzp6Iwi70VbcFz18xErOJfkL+Xahgf5VfVltCR7uSDv+A90vGQqRVtXFwU5Ofynvp4Zte8nZwns2sUJ8+dhNBppbmvnM8uW0dDczD+fex5FVVgwYwbjysoA2NnSwo7mFs46+SSCoRCqptHTF+Sx557D63ITi8eJxuN8eskSCvOyM/6KqtLR3Y3NYsHtdJJIJnns2ecwm82cOH8+bqdjmJwGg2G/VkFVVQmFIxiN2e3bdu6ktbOTcCSKwWBg7tSpjK8o/0Dfy94cs26Jh0AaVMR9bjc+d9YiU5j7vrUmoyhEolH6w2HqdzWyq7WFZCpFjsdLLB6nLxTCajbjcowsHuyjJuyyYD3x9FHp+xTPdO5pfYod8Q6aEl2kNYWwEuecnPm8Fa5nvmPCfq8rTdN4c2AbT/WtQxYM/OgorKOn8/FkJMrdr4F3/X7/ZrJxdz8cVYkOQWtyuBtm7w3fwHnF1RirJxxgDx0dnQ/K5lgL6kCI4G3X47vr/rEWR0fnmGRVaAt9138dz613ItoP/VJ8incadslMhTmPHzT+HxOtJR8oxXnT7jaK8vJYMGM6j7/wAhOrqjDJMl19fXT09HDy8QswyTKlBVk3J4/Tic/tprSwcJgr4viKCsZXZI+bvyeerBoWzJhOc3s7VrOFVDrFs6+9xszJk+jrD7GztRWH1UoskcBgMCCKAuXFxbidDh577jlcdju5Ph/JZJK27i6cdgdTJ4zHJMt09PRQXlRE4+42NtfXIw+6h6qqitNuZ87UKUiSRFlh4Yi/C50PhkGScDuduJ1OKoqLx1qcD0yobRcDv/sVziuvPeJ9y6KBLxYsZVO0iXtanyJPdnJh3iIGlBi/bn2K+0QD15WcuY8lal2kgbtalzPVVs5xzsO3xuvo/DeHVO4CgcBfB0shVAG7AoFA7+iLdWD+ex7Pc+svECyWMZFFR+dYRURAsNlxffvmsRZFR+eYxvWtGz7QM2yPS+ZZOfN4fWDriJU7TdN4NxBgRm0tbqeT8eXlvPjWWyRTacLRCDkeDyZ5eDZOURSpHrTWjQRJkqgseT8u7NSFC9nSsAOvy805p5w85LLZHQySTqcpzM1FFEVqx42jq6eXzt5ezDk+Jk+YQCgcZkdzC4lkkhyPh2dWvUZhbg7nnX7aUMr//oEBZFk+oPuljs4e0m4XtrM/P2r9X5K/hLiawmuwD6s79/eJ1/Jk71q+s/Mhbi0/j+n2Sm5q/Bu96TAP+b/JP2uv02tX6hxxDqjc+f3+mwOBwO1+v//v7BUQ4Pf7CQQCF34k0u2H/zZtK20tCLKMoaxyjCTS0Tk2ESQJNRhEyytEMBz5AG8dnU86AmSTFqnqBw54mGovZ0u0Zdi6/cWwpdNpEATWv7cFRVWGknLMmzaN/wQC2CwW/JWVqKr6YU5lvxTm5Q65Ze5BEATyvN5h6yRR3KdtcX4etePej+FZOGvmPv27nc4jLLHOsYqgaajx2KjGFVlEeZ+C4qIgcmbOHE73zcQoSFy/82GMgsRPKy/SlTqdUeNgV9aTg/9/RzZj5t5/Y0a5aXgAY2L1ayTeWDk2wujoHKPMsGej7PvvvFXPSKujM0qc5p1B8I6bUXp7PvC+i1y1XFl0ytCyoig88swKYvHh5UteXv02f/rn47R1dfGZpUuHlD+jwcDMSZOoqapCEIShmCsdnWOR/KhK+Pe/HrPjGwUJRVMREfhe6dmUmnLGTBadY5+DKXeb/X6/DFwNvAm8BbwD3PpRCHYgLOLwVLySLwelt3uMpNHROTZxGrJuYqLdgRrWa0nq6IwGPqMDQTIcdnHlWxv/j6iSALL10/pCIbbt3DW0/Oxrr9Pa2cnZJ5/EZ5YtxaqHMOh8QpElGW2MM3lKgsjPqi4ecRFyHZ3D5WDK3eVAADht8P82YBPQ/BHIdUB2JDqGLUu5BShdHQdoraOjczi8FtoKgGB3oEX0wsk6OqPB37pWgUFCyxxeceWWZA9rwg0AdPcFyfG42bB1K2s2beLJl1/BbrUyf1odOR6PbpnT+UTTkRk4okXMdXSOZg4YSBMIBB4AHvD7/ZcHAoEHAfx+vxwIBFIfmXT7QWR4PIGUX4jS0T5G0ujoHJvsGWWiw4mqK3c6OqOE8KEsd6d6p7Oibz1L3JPo6uujpqqK0sJCVm98l9lTp1BbfeTqTunofKwRRbR0/NDtdHSOAUYSzWnw+/2/Gvz8lN/vv3g0BToUwj7KXQFKdyeaPiOjo3MEyY4zwWZHjerKnY7OaCAAGAxwmO5iJ3nq2BptYcPWLTTu3k1RXh4ep5PTFi3UFTsdnb0xGtFi0bGWQkfnI2Ekyt2VwI2Dnz8FfH30xDk0rv+qzi7IJkS3BzU4phUadHSOKUpM2dpVot2BFtaVOx2d0WCitQTBYEQboeVOVVX+svxJ1r33Hqqq4pZsXNY5h/pdTUweP44cj2eUJdbR+XhiM9tRo7pyp/PJYCT5zZVAIJAACAQCab/frx1qh9EkX3bvsy73D49kXVt0dHSOCDXWbJFa+3lfzFoWdHR0jjjHuybi+tLZiB7vfrev2B5me2+S6YUW4mmVWTkqiqLQ0t5BNBandlw1rYkexOkuzi+e/BFLr6Pz8cFr9eH7+b1jLYaOzkfCSCx3T/j9/tf8fv/dfr//FeCJ0RbqYOxO7muhSwe2kN4RGANpdHSOTdaEdwCgZTIo3Z1jLI2OzrHJv3reRkulDuiWubDcxmnjHTSHUty/to/Grj4KcnI48bj5bG9qoqW9nbLcQv7R/Saalp13jSgJetO6tV1HZ2860iHUaGTMM2bq6HwUHFK5CwQCtwPfJFsG4ZpAIPDzUZfqIKS1fWPr1FC/nq5dR+cIsie9eqapgdT6t8dYGh2dY5O+dIT4C0+RaW3a7/bfr+3FZ5W4aKqHf15QTjwcJNfrxW614nE62bB1G7X55VgkmaZkN5qmceHWX3H+1rs/4jPR0Tm6SWkZoo/9FS0eG2tRdHRGnUP6W/n9/mLgu0Au8Jjf7zcHAoExe9v774QqAOb5i8ZAEh2dY5c948w0Yy6mGXPHWBodnWMTQQDnl795wO11BRasxuwc7Mb2OE82JPnm4qzL9JK5c9hUX09pYSFT1XK2xXbz9sB2bJKJP/mv+kjk19H5uCAAnpvvGGsxdHQ+Ekbilvl74EFABlYBvx5ViQ6BQdi3Vo/S10Pv9WOa50VH55jCJBoBUIJ9hO69c4yl0dE5NrGKJqJPP04q8N4+2xRVY0mFDbMh+5g2aXFcDFCYmwuA1+Vi8ezZmE0mflJxIad6pxNSYvy08iIGlBhP9a79SM9FR+doRhIkIn//E5mW/VvJdXSOJUai3JkDgcDLgBYIBAJAYpRlOijFpn0Dz0WXh3RDPapubtfROSLMc04AQJBlEqteHGNpdHSOTS7IO570lv+gtLftsy3Qm+Qry3cPLYe6WphXmYco7vvYNopZJ5wrCk+i0pxPRlP4bduzQ3F4h0tCTfHXrlUfuh8dnbGm2OQltXUTSqdeF1nn2Gckyl3S7/efAkh+v38eY6zcDWT2LUIpSBKmmfPovfpLRB79M+ldO47ah5GWSpLatIH0jsCI019/HEml0yRTKTKZDKqq7rdNPJHgrY0beXPDBtq7u4/a3+yTSFOiGwDBagNVQ43pEyc6OkeaDZGdcIAi5rW5Zh74TNYFU1FVArsauWOLk0hq//fTvRlnLsz2H9112LJ1pvr5f7tXcGfLv3k32njY/ejoHA2EMjFEmwM1FhlrUXR0Rp2R5Di/ArgLyCEbe/e1gzX2+/1Gsm6cFYAJuB3YAvwvoAGbgW8MNn8cKARuDgQCL/j9/irg6kAgcPWB+o+q+9ct3Tf+mHTgPRIrX6D/jluQJ9fh+tb3iD23HNPMeYgOF0pfD5IvB0E2jeC0jzyZtlb6f/4D0DS0VBLR5cH38/8htW0zaizGesHIwCvPYwQKiotxejyIpZUY8/LJ9XoRhPfjDbVkEk1REIwGkAwI+5nN/SjQNI1UOk1HTw8t7e2EwhFC4TCRWAxRFFEUBVXTyM/xYZZNiKKAKIgkkkk6e3vxV1ZiMZt49Z01pNJpKkuKMckmXA47FcXFmGR5TM7rk05LsgcAQRAQvTmowR5Ea9kYS6Wjc2zxbqQJwZa/3wx+27oTiKJAhVPkT4//C4/TybOfrULc6zmgatqw5T0IgsBVxafxTO96ZtirDku2uJpiR7ydf0z8DhMsRfyt6zU+5Z3BjngH4y2FWESZ54Ib2R5v56qi04ashzo6RyNhJY5gs6FFdeVO59jngHdjv9+/5626C/jiB+jzC0BvIBC42O/3+4ANwEayCtxKv9//O+BMoAloBC4jq/i9ANzM+wXT98v+EqpA9mEm10xGrsnW+tESg0qgooAkkdndTP9Pb0Lp7UGwWBEdDkS7A8HuwPWN69FUhdT6t7GefhbJtauzpnuTjGAyD/6ZEN1ejOVVKF0dCFYbgtWW/WwYVLAMBjAY3l/eS+FKvLGSgfvuxnbBpVg/dTZoGsk3XyUWj9PS3MxAdzcdeUUstFtQezrpf6eRWDKOtbONHk8u706fR6pmMtVPPULbOZdQ8956xOd1M1kdAAAgAElEQVSeyKbQzqTBYECePB3z/IVYTz+L+GsvYygtR3S4CN5yLUhSVjZJAlFCMBiwX3olUl4hkYd+h+vqG4m/9hLJt99AMFswzZhDpqUJpbcbQ0k5ts+cQ6Z5F6I3B8FkYmDbFiJuHxt2NLC7q4sct5vK0hLyfTl43S7cDgeSlI2PTGcydHR3k1EUVFVFVVWMBiPF+XkYjdnYrtlTptDb309zWxvpjMLOlhZefWcNJlmmpqoKg0GiICeHwtzc/bol7Q9N01B7u9GSCRBEJF8ugunDK/Z7FFYBkCQJVdNA0whHY6QzaWRZRhQE4skk4UiE7GaN4ECIUDiChkY4EiWRShGLx/nMsqUfWqYjzd7jTPT6UPp6MRTryp2OzpFEgANa7trCGSRRwCMkcdptnHXiCTT1p2kOpVhcYUfVNBb8oYGnLqqgJZTm75v6+cGSfGxy9v54mmcGs+zjWB/eSZ29AkkY2X3z7YHtPB/cyC3l53L/hCsBUDWVHfF2Ttn0Y4pkLz+pvBCnZOWR7jewiibuaHmcH5SfR0ZThsXFNye6MYnGferThpU4D3a8xCz7OBa4ag7vy/sAtCR7eKZvPUtdk5lgLaIl2UMwHWGyrQzxEN9LZ6qf10JbqbWWUGsrRdO0YROtOh8PBAREm17IXOeTwcGm2gJkLW3C4H/2+nywqcBHgcf2Ws4AM4FXB5dXACeTTcxiG/yL+v3+BcD2QCBw0KJaI72lCmYzANbTzwJA8vjI/cM/0FQVdSCEFh5AjYbRImEEhwPCAwg2BwDqQD/ppp1oyQTRUD8kkwjpNLbq8bi+9m1iTz+OPGUGxtqp9N10NWoqhZbJIKgKKApaJk360m+gTKrD/INrSfzifqTOTrSvX4d37oLsg0EQMM5fxBMvv4xJtmEfn8spkydhO+kkAPIHz0Pp7iS9uwWnbCFtdyAsOhG3y8UbxdWkv/gtNE1jRu1EzKJIat1bROIJlNZWXKkUDkFAdLpw3XBb9uVBUbKuoIqCpigYCooRZBPmRScCYCgph0wGtT9I/IWnkPIKMZRXInl8AESfe5Ke8ZOoD0eofvR/scSiTJw+h2VLTkSunoDky80WvFZV9lwyqU0bSG5Yg6OzjfTOHSBkb7KWU87AeOZ59N36XVzfugGluwvtvrspHfxuqhCYLUAmk6E15zK03m5aHl/PE9WTmfXuatzB7myqOQBBQNU0On0FBKbNY9Ery9l93InITifV//cAWGxoqoIwEEIzGEGAri9fg91iwbjmTbpO+DTewCZMPXtdeoN9a7n5KHMXoj7+dxq8eXSbbZSueQ3FkFVKRVFEVbLlOXonTEbzeDHvbqKnoBSXpuBVFQyZNGm7E2txKWVFhQgI2G1WzLIJu82KPKjgHk0Ie1Uckbw+1N6esRNGR+cYRjBIaJl9S/wsq7ID0NDch9vhwGg0oqhJoimVXcEUogBPf6ECr8WA1yJR4splIKnQHkkzzmtCEATyZBdX7XiAM3yzKTX5mOMYj1UaPsGlaRprIjvIM7pwSBau3/kwd1dfMqyNKIjcUnYup3qnM9NePaQo/rnmasJKnH/3vMP6yE5+3vwvvlJ4IgZBYrq9kr90reKx7reotuRTYsrhl1WX8t2dD7EuspM5jnEscBroSQ+wJtzANHsFhbIHgLSaOaQl8IH2F9gSa0UAXAYr3yk5E7tk5pX+zTwf3IjfUsSlBcv4wrZ7aEn0ssQ9ieeDGykx+bii/ncYBBGDIHFF4clMsZXxxW2/QQBKTDlcmLeQkz11/LDpEVb2v8fxrokUm7zElCTnb/0ll+YvYaK1hApzHlbJxMr+zZSYcigxeTGLurfJUYmmIVjtaFG9BqTOsc8B756BQKBy72W/3+8FgoFA4KCBUYFAIDLY3kFWybsZuGuv/cKAKxAI1Pv9/lbgnsE2PwZu8Pv99wFBspa+fYILzOkP5/ohiCKS2wNuz9A6TdNQjDLanAWEwmHS0+eSmTKLHc3NNLe34XY4GYhE8LndFNTXo85fhqKqqA07iX/tBhqaW8j1euns7UUQIN+Xk+14dzvCl7+N1rATfIXEwwneWLECh82GpmlEojG8bjenHL/ggDOBUm4+Um4+JXtWlJVRAcyorQWgpb2d93bsQFU1yCvNns+OBjr7o7B2A5q2YegcjQYDZUVFiKKQtSZtDaBpGj63i76330FRFHz5ZZhKq9mdV4aqqWgaoGhoq1bR4S3BqwjUTplG5emPIfT1EH9pBckXnyH827vQYlHy/ryc1LtrSbz5Ku7rfkhizZsIBiPytNnYzvkCiBKgITpdADi/9A1EpxvBYsX1zRsYNHOh7ZlP0DTyq8ajhgdQJ09mbvUEeqfWkuoPZttpABqSKFJeXMK84jL65s/HLgmEwlE2f+P7ZBQVs0nGZZQR0inQVDIWG01dnZiKKzGh0RcKYejvz14je13hSVWgr2EnzqJy/JMmMcftQUyHERQFNI1UJoPRYEAALCedAppG7ImdOM8+i/DD95Nc/TqC2YLS34ca7AUE5CnT8N72S/rvug1l7vGwcNmHuKJHh/y4beizPGU6ot0+htLo6BybjB/wIRWVIjrd+2z72WtdnD/ZzUAkjHNw/I3zmRjnM/FM/QBGSaDMJfN0fZj+uMIX6jzUB5M0h7LK3R6+VnQq/+p5mzXhHdRaS9kYbcRvKeKpvnWc5pnOtQ1/oi8TxiBILHJN4hTvNGY5xu0jjyAIzHGM32e9Q7Jwcf5iNE3j3NzjWD6YpbPMlMv3yz7HlwtOpC8TwShICILAlUWnYBIMlJmzWT83RHaxom89dzT/kxmOKm6vuJDrdj7EDHsV7akgC5w1OA1Wbmn8O1bRRIHs5p7qyznRM5VKcx4aWUugRZT5bduzrOhbzyX5S/FbiwB4cMJVSII4zHL59OTvk9FU3g7XM9NejSwaeKT2O6iaSn28jWI5O6E5yVbKZQXLqDDnDe37o4rz+Uvnqzzc+SpfyF/EubnH8a+et6mPt9GbDnNu7gKuKz2TmJLEIsqkNWXo3HXGDmvGyO5EEbGeCNPHWhgdnVFGOFQSC7/fvwj4LSCRtco1BQKBPx5in1LgX8BvA4HAg36/vzUQCJQMbjsTOCkQCFy1V/sLySZ3mQT8E1gCvBsIBF74775/cscd2k03HtRzcwhN04gnEvSFQoSjMaKxGJF4jGgsTiKVIrnXnwDIRiMGgwHj4J/ZZGLp3DlYLRaSqRRrNm1CUVUkUUQURSRRQhJFxleU43I46AkGMUgSbqfzgPK0dXWRURQEQcBoMFCQkzMqN/2MopBOpwFhyMAViyfY3dmZtZ4JAoIgoGkaHd095Ho9mGSZju4eUpk0pQWFGAzZB5KQNbeR43bjcjiOuKyfFLRMBjQVEBCMxsGEOgKCJOH3+38UCAR+OMYiDvGd731Pu/tnPxtrMXR0jihH2zi79Mortf/93e/2u+29rgTlbpm1G9eS4/YwecK+ilV3NENjf4ruqMIJVTZMg2UTdvQlhyl4e3NXyxM81beWGmsJ9477Mo/3rGaJazJ2ycyL/f9hnmMCebLryJ3kCOlKhXiqby2fz1tIfybK1Tv+yHynn8sLlrE1thu3wUZYiZNS0xznrNnvc3P1QD1+axEew9hMRqXUDGEljs/oYPHGW1BRSWkKZ/pmc2Pp2fy+4wX8lmKMgkhvJsJnfLNZH9nJeEshDsly0L6DmQgSIv2ZKMUmH5Ig0pEK0p+JMd5SOGK324+Co22cAdz+059q3/jiN+lvi1I5p2CsxdHROSIcaKyNxAx2O7CIrNL1U+AN4IDKnd/vzweeB64KBAIvDa7e4Pf7lwQCgZXAacAre7U3A58DziFrxVPI+vTt9+58oMyLkFWeeoJBmtvb2d3ZSU+wH03T8LpcOO02bFYrOR4P5UVFmGUTJlnGJBsxyTIGw8G/CpMsc/zMmQdtk+PxHHS7IAgU5+cftM2RwiBJGKThNQHNJhNe974P7cnj339pqKk6vOB7nUMj/Nc1JkhHbwICZa9xlmneReyZf+O88tr3tyvKUEyljo7O4aGqGom1b6FFIliWnDS0XtM0cm0G7LLIQCRCVUnJfvfPtRlwyCI9MWVIscuoGne/0cMdJxbgtuw7Rq8p+TROg5Xzc4/DIEicl7tgaNtnfLOP8BmOnDzZxeUFJwBgkWX+UfvdoW17SrMcipG2Gy1k0YBPzE6Avlz3I7rTIWySGQmRlJbBLBj5a9cqokqCa0o+jaZpPNy5krcHtjPBUsRidy2XF5zAjbv+wtpwA7lGJyoaf6+5lt+3v8Bj3W/hMliJKAlW1d3Ou5Em7mt/lp70ANPsVdxYejZOg4WkmibHuP9J5r3pSAXZGttNntFFoezBJpl4tm8DebKLhJrGJBo5zunnV61PUiC7mWzLxl1PsZUTU5IA+7j5Hq1oGsTqd7L92UYq55w51uLo6IwqI3m7VAOBQJ/f79cCgUDC7/cfymH5+4AHuMXv998yuO5q4DeDSVq2Mjwm7xrgN4FAQPP7/X8C7gcGgM/uV5j9WBpVVWVLQwObAvWoqkpZURFTJ/jJ8XqwWSy6O4SOzgdk70kUMScPy7JTh5ZXb3yXjdu2cdEZn8Zhs+1vdx0dnREgCCD4cpFcwycGwymVK59o5pbpSfr6Q3hcB7akrWqKsrIxyk9PzFojDKLAfWdkSyjsL/lHNs7spH360TmySIJIgTz8d72kYCmXFAxPoHVP9eUk1BTrI7swCdlXsu+UfIa0prA72UuhnM2UfUPpWVxf8tlhv+cp3mmc4p1GbzrM+shO3AYrO+IdXLXjARa6aimQ3Vycvxi7aKYzHUJAIK1lyDE6CWYifGHrr6m1ldCbjnBh3kLO9M1mbaSBzlQ/smBgqTuboG6Wo5pn+tazvHcNE60lTLGV8+PmR3k5uGnoPB+t/S5P9q4lmDk6s1FqaPT1S/iqfWMtio7OqDMS5W6H3++/A/D5/f7vkc1yeUAGyxjsr5TB4gO0/9lenzcCcw/W/39b7vr6Q7z41luYZCOLZs+mKC9XV+Z0dD4kw5Q7q420xcqOnTsZV1XFjqYmfG43Le3t1I7bNzZHR0dnZIiCiFpYgiwNd6lzmiSunxRm3ebtGAwG7FbrAfs4eZyDk8ft6y7/i9e7qSsw73ebztGFWZQ5zukfWt5jdSv8L+XwQO82PqODkzx1ANTZK/jXpBt4uX8zvekB+tIRdmW6uG7nQxgECVk0cEXhSZzhm82KKTfvY3n7ccXn9+l/oauWha7aYevuqPwC6QqFuJKiLxNGABa7a4kqSY5Gh35N05h05kTeenAzqqKSCKcZ6IhSUOMda9F0dI44I1HurgS+DLwORICvjKpEh2Dvl85YIsETL73E3Lo6JlZX6Uqdjs4RQlXft5ArikLbTdewet4J5H7xUmLJJLOmTGbNps2k0hmmTRz9VOY6OscioiiSeO1lkhvexn39jwD41Vs9zCqy8FZzAns6TUlB/mE9274804vTdPTEYel8dOQYnZyXe9ywdS/V/Wifdh/WpdIoSBgNFpyGbLygx2Afs3jHQ6FpGu2vbyX2xiq0SyfT2zhA/apWXbnTOSY56J3f7/fXBQKBDNmi5FuBBNnSBmPG3rFAwdAALqeD2nHVumKno3ME2XsSZd17W0jkFlChpdna0IDP5aKypISq0hLWb9mCouybxl1HR+fQiKKAajCgJZND61RVY11bnGAkTlFeHoW5eQfp4cB4LBKvNkYJJfTxqaOjaRoZZAq17by4+i18NU6WfXPakelbfwbqHGUcULnz+/3fBn7v9/sNwC+AE4EpwK8+Itn2y94WhWg8hs1yYHcVHR2dw0PV9ppEGRjAOmEiOZEQ23buwudxY5JlFsyYgdflorGtbQwl1dH5+CKKIorBiJaIA9mY8iWVdk6qNFFt6OGMpUuYsp8smSOldSBNKKm/eOroaJpG6YwCmlPVNK7vZP0/6/njRc8SbD28uneaopDeESD670foPPdkoo//jXT9VhJvrTrCkh/7HCpr/ydFhiPJwSx3pwHHASpwIXDZYDzd2KXTYvhLZzQWx245ePpgHR2dD87elrt4IoGhshpHTxepdJpJe8XZ1VRVEti5ayxE1NH52DOk3A1a7npiCj9/vZtXdoRoFoqRJAlRPHzXyi9O8+CzGOiJjanDjY7OmKNpGu+t6saVaUHJSVFxQi6fuW0+Nq/5kPumm3aSrt86fKUoMvDHezGUlOH98a+ILn+M4O3fI/Hmq2iKQmrThlE6k4836kCI2Ion6P/ZD0i88Qr9v/oJneecRGrTBtREnPSOAABaIkGmo41U/RYyrc379JMtJQXJjWuJPbec2AtPk96+jUxL40GPrykKmdZm4i8/S3LtW2TaWrKZ9q+4ADUaQQn2kW5sQOnqILVpA+mmXSjdnaR3bs/Kn4ijpVNoqSTJd9ehaRqakkHp7R6Rgqglk8RXvUjs+ac+4Df3wThYzJ0aCAQUv98/A9gZCASCg+vH1P9x75fOaDx20EBzHR2dw2OYcpdMYJpYg/LQfVxw+mlDBZUBqktLeX3deqLxOLZRnmjRlMyIy0cowT7677wVbSCE7zcPHtVlJ3Q+uUiCSEaS2BmspK43Tp7Pwg3H53D7y21cWn5kHrUv7YzQE8tw+Qw9tkjnk4umaVTNK6L+b0GEoETH1iDj/KUomQOX10qmUphkGRAI/vwHmOcswH7xV0htXItgNuO74/8NtfXc9FPiK5/H8eVvorTvJv7Kcxgn1aGGgkie9zN0aukUSm8Pot1B7MnHsJ1zEXteqwWjcbRO/yNBDfWjdLYz8OD/IHl8OL/xXQSrDQSB4E1X477hNiJ/exClqx1jbR2x559Grp1K7u//juh0k2lrJfbck7jG+Qn99i5SmzciutyoPV0Yx/nBZMZUNwvraWfS+90rcd9wG6LLTeyJRwCI/N//QiqFPG0WtnMuQvLmEHvuSeznXET4f39Hcu1bKF3tCFY78qQ61IF+jBNqcVz8FXx3/Q7RZif25tPE/vV/qOEQUl4BajSKFhlAnj4H93duIfzAbzBNn4OxdgrhB36D0tcDigKSAdHhwHr6WdjOPI/Yc8sxlFcjuj3E/v0Ixkl1kE4Re3Y5gtGI5eQzAAjdeye2M88DUST80P1kWpogk0YN9WMc50ewO3Hf8EMSr71MuqEe55euou+H16GFQ3jv/O0Bf4uDvvH4/f4JwGXA8sHlSWTr0I0Zw5S7WJx8X84YSqOjc2yyt/tzPJHEUlhEVDZhHeiHvZQ7o9HI+IpyNtdvZ27d1CMuh6ZphO+/B+unziJ4+424vn0zgtmC2tuNPG02giiSXP82cm0dgjk7A6sE+wjedDWm4xZjPf2zCJKB0G/vxnbmuRiKy0ZFRjXYi9LWipbJIE+q+9g/pHU+GrKWOwOZDCTCKVpVAU2DS6ui5NqPTJbLM/zZfrqjGXxWCVGPT9f5BKJpGoJBJGbNRQgaCLZE2NbagiAKzL0omxRsIBLBZrEgSRJbGhp4bc1alkyfRumuAJYlJ5N67116r/kSWjKB+8bbh/VvHF+DcXy2H0NRCa5vfY/khjWE//c+PLfeSXLNm1hO+jSpTRtIrnsb++cvI71rB73fvRLBakPt7cHzw19gKNp/TcuxRtM0lM524i+tgFSSTFsrts9egKFqHNFH/4z94isY+P09ZFqbMc0+Di0WpfvSsxEcTnx3/hbHV76F4HTh/Nq33+/03C8MO4axvBLXN7L1Ld3fvnlovRqNkNq4Bk1VMZZnazF7b78H0ZZ9F/Hc+ov328aixFb8Gy0SgfxCBFlG0zRsZ38e84IlSHn5iK59a1LvWWc96VNYT/rUAb8H1zdvGPqcc+9DKP1BBElCsDtIb91E/KUVaJqG6PEh5RcgSAakgiLizz+JaHNgnr8I65nnIQx6ZFhPPgMxNx9SKSyLT0IqKEKQZQSLlUxLE1osAukM5vmLMc2an5Xh6htRQ8GDTlofTLm7Gfgz0Ajc6Pf7FwN/Ac49yD6jzt7KXSQex2bV3TJ1dI40qqZm3Q00jVQqhcVkwvzz/0H05e7Tts5fw+MvvMCcqVNGnNhITcTpu+5r2C+8HPP8RUPrlXSKjpUvYW3YhnXhCci1UzBMmU7Iasdx8RX0//QmtEwG0eVGtFjx3f170vVbEe1OEARSmzYQf/EZTMcvxXHh5UP9Wk88HdHlIfLPvyJ5fFkFMdSPoaIKKb8Q0ekeKjKvqSrx55aT3rkDpa0Fecp0LMtOQ8rLf1/+WBTRaiN0750kVr2IIJuQikpAVcm07cZ5xbewLDl5qL2mKJBODymgOjqQTaiStjkoPHE2dq+FxnCGcErljk1Wbj/uyEwQ7BmTt63s5Jr5OVR7h2dI3F8tPB2dYw1Ng8DLLSgzl6I6+/BMsTBrysSh7T3BIP964UUWzZ6FJEps2LKVhbNn8fa771IgpHBd/BXUWIzEG69gWXjCiO7lct1MAHq/eyWk0yTeWInn1juRp85EMBhw33g78WeXk67fgjRjDgP33Y3n5p+ReHMl5kUnEPrlT7Ce8TnkmskkUyne3RZANhowyTIdPb3YrRZmTZ580PGrquqIXLtVVWX9e1tIZzLMrZs6bJ9MSxOhX/+UTPtuzPMXI5hMmGbNI9PSiNFfi5SbD6qK+7ofDuvTfsGlCFYbgsGQbXOYiDY75gVL91m337ZWG/bPXTS0bPtMVmURnC5E54HrhR4ukvt9RVGunYpcm53kNs9Z8L4Mnz0f22fP3+/+xgmD16DZgnnBkuF95wxPpiWYsvduyeNF8hzcE+OAyl0gEFjDXjXn/H7/aqAqEAikD9rjKLO3RSEWj2PT3TJ1dI44oiCSURTS6TQmWc5aGESR3qsvx3bORcMUF7fTgdFgIBQO43Y6h/WTaW0mteU/KG2t2M46H9HlQVNVov94GMFmR2nfDWTH8rY3VuF94B5SdgfN3jzyX32R5ou/TkMsgdL6MqcvXoRSNxvnwmW4Z8yBdPZWZL/gUgDSu3aQbmzAetYF+8y87bmBGqv9RB//G4JkQDCZiT37BEpbK46vXoNl0QnEVjwx5CJhKK/ENOc4UmtX03PN5Rgrx2H/4hVI+UX0ffer5DzwCB2LT6Vjylwku4NoPI7LbscW7KFPUcnd9C6mTeuwf/4y+m66mkxjA6aZ8zCUlCHPnItxXA2CKKIlk1kXkJw8gnfcjNLRhmXxiVhOOJ3kutVoqoJ57kIEo4zS1Y6hrJLU5o2o4QHUUD9aJo1p6gyk4lLQAEmCTBrBKI/S1aFzpBBFkbRkYNcuC52P1eMcZ2LJwnH4rQNMKao8osf6zelFAMTSKhaDwKbOBBajyO/W9HHr0jycJumIHk9H52hC0zSmnzWOzVtSGP6QoGF7F9XeCLv/0834RSWsfzWAJEn09vcTTySpq/FTW11Ny+42Am43cwDRaj2oVee/EUQR7213E33yn5jPuoDIE/9A7Q8iDU6SCoKA9bQz4bQz0RQF83GLQYB0YAvmJScjT51B/89vhQm1rCwZj7u8AtFgRNM0CnJy2NLQgMFgoDg/n8319SyePRtJyo7jcDTKezt2sHHrNkoLCnA57PQEgySSKZx2O6FwmILcHFRVI55IYDQYiCUSGI1GHn5iOfPqpuLp78VtNCDlFSCfcS7ehcuGLE6QzV7f0duLe+lpNLV34HTY8TidQ8rmaChTOiNjxIEogUAgeehWo88ey52macQSCaz6TLiOzhFHFAUymQyxRALL4BgTTGbkSXXEn39qmHIHkOfz0dnbu49yl9y4hvTmd5Gnz0YwWYg9u5zYc08iCAKu639I3OFizSN/I7gjQAkq1mWnUvHVqwmFw8TiCXzBILNKS2hqa+OZV1dRNON4uprbKE29jdVsxmoxk+fz0dXbR+Pu3XQXT8DvymNmLIbFbEYSRdLpNKIoIkkSiWo/O865DJ/HTXlR9mVX1DSCkQiN69dhCQ2Q3LmTeOk47FYbNqsFoWI8uZd+jZ5VL1HfG8RucxG89oe89corxBNJ/JUVaJpGjsfNQCRK0O4ioyis3fweZWkN9Z01mI8/hZzT7eRGQ6htuxm45w6UUJDc+/5Kun4LybWrcV55LfYLLkVLxIn+48+E//wAppnzEEwm5IlTUHq6SK57G+fl3yD2wtNoA/1glBHtDqKP/hktncZ7x70onW0kXnke9w0/IvHWKpTOdpTuTpAklJYmTAsWY569AMHhBEVBMBpJN+1CtNn2mSncH5qqosWiiHYHSrAP0e35yCw/mqbR299PLJ7NLpnOKBgNBpKpFLLRiNFowON0IRsNQy85yVSK9tYWbA7nwboeE0RRJJNMUrvlHpqrbuSlJ9p5z25nmb2FHPeRSdO+B0EQeLs1xpOBAW5alMcD6/q4dUk+Ny/O49aXO7lsuofHtw7wtdk+8u16jKrOsYWGRtO6TtqffQljdTEWn4TFKZOOK7Rs7KZ35wDjF5XR299P/0CY6bUTSe/agf/Pv+XlU86hrauLedPqKMj5YKFAISSaZx1P27oNdFl9WN5ZS3F+PmaTibqaGjRNI5lKYbdah8IGnFdeC4D1lDMwHr+MJ596Eld/L7NWrcDz/Z8MKVhlTjv/Xv0OG7duw2Iy8c/nn2fKBD/lRYUsf+ll8tQMZ0+ootflJZ5IMEUCuTCXpNuLy+2htaOT2O5mvO+upS+vkJPmH4cx1M/u3gSbGhro7QtiNYgMbMkmEzH8619Ul5YxobKCUDjMhq1bSSSSpDMZCnNz6Q+HkSSRpXPnUpibi6IoQ/dhnY+Wj90dXBnMlpkZrCtiNHzsTkFH56hHFLOWu3gyicWcdQUQHU6snz2fvpuv2ad9vs9LZ08v/srh1oZ04D3kGXOwnvxplN5uEm+sxHbW+ZiPX8ZALMZjz6xgal4+82r8aK+9hPXUbJCxy+HA5XBQmJed4aytrqamqgpJFAlFIjTt3k04GijETmEAACAASURBVKM/HCawq5HC3Fxm1NbidbvYXL+dR55+hnQmg9vpZCAaRTYaGVdWxo6mJsaVl/Pu1m288PobQ/cPs8lEQW4uibLxyO0dyEYjHT29hKNRNE0jGo8hiRJlbiMNgXq8LhcTKioZX152wIdXZOoUOnt6iMYTaC4nO7q6eTWSwZpfgXB6Bc5UAmntOjSDEcuMBYRffoXy4mIE0YTw+a8gfv7LuN1u8rxeDAYDhuIyTHWzAHBfe9OwY6mJOGpfD4aiUgwlZZhmzgMgtXEtSUVB8/owA4Y5C4itfh0KSzG4PYTuuIncex8m8sYrxJc/ilZWhRoJk3J5SGsa3SWVNJWNY+oz/2DHp8+nsrmB/JXPQCaN4nAhhUNk7C6i0+ewdfIsylavpKOsmm6rg4p4BG/NJComTkQSRcSGAKm3VmG79Gt0rHmLWDhMqLCMol/eiqRkEEUpG2tgtZGJhJHPvpCBqbPoe+bfbCuuIq9pO7aO3RjTKTx93Zj7e7Gk04Qqx9N8ziU43l1DyOokiMDC5X9BkyQ0QUQTwJhO03/V9w5zNIweoiiSQaB58U24JphQlBBKR4C6mppReSmaW2KlLt+M2Sjy/z5VPLT+ZycVYDKIyJKApmn8eWOQs2td2GS9CLrOsYG6J5Nh3Wx8phhKJIPJbqTus9UIAqzcsprMu7m0iz3ILgNuh4Pe668k/6LLWFRZw+bt9WxvbPxAyt3GrVvZsGUreT4fA9EIJ8ybS0ZR2N3ZRVdvH39/6mlSqVS2sSDgcTkRBZF5dXUIgkB9YyO7WlspLC1j2Vln03/LtaT+sx7TtOxzIHP3jzjJbCV28ZVUjR9PS3sHm7ZvZ9WaNdSOG8f0pm3Ef/8rcgDBYoNUEkSBwht+TPo/66hRFUzzF5CyW0jv3E7stusBAe/02Xzq3PkkfLlEYzE8LheCIBBPJNi4dRuvr12H0+Fg7tSplBcXk0wmsVosaJrGrtbdPPfa6zhsNrr6+ijIyWHR7Fl4XS6CAwNsb2xC0zS8g30aDBLmQXdD2WjEYjbrRpsjwAE1I7/f/6dAIHCZ3+//aiAQuP+jFOpg7LHcJZLJoQtCR0fnyCKKIulMhngigdn0/o1Wys1H7etBy2SGYtQAygqLeOLll5k9ZcqQMphpaSK1YQ328y7J7uvLxfvjXw7t09i6m6qyUmbPHfT+HoxP2B+CICANWodcdjtT/f4Dtp0/fRozJtUC0BcKkePxEAqH2dnSyikLj6cwN6swZjIZUoOundZDZPoMDyqI2cxpI8NutWIvez+BS11NDYqq0tffD2QtSvFE1iEikUxSUlDAjqZmLCYTipqNedyycxd9oRA+txufx4NJNlKcl0d/OEL/wABmk4k8nxeTLBPNaITee49QOEwylcZoMNBXMZF4IolsNDIQiSAMJJFqZpHZtBUNYP4pGB59DM3goPRL38G+qx7Z7sARiyDbbVTWTmFKRTVSWSG5+UX0jhtH08SpyA4HYn8fSk4+lt4ulEScmbWTMNjMlBcVY4rHCN/3S3jy74QGX6oSZiutlX4aHn2UCi2DV0lDcTnNV3yX3u4u+oJBJCWDrCiY3R4Gkhru/7zLxESM0xYv4v+zd97hUdT5H3/NbO+76b0nGyAQepeOokhV7BV717OeZzn1sJzlBH5n73qe2CsqiggiRXoxsISShIT0tpvtuzO/PzYJRFo8RTlvX8/DQ3Z2Zuc7szsz3/enhrfZEHaUoLPFoMzIRpWdh6DRktjmokdsHD6FhKqgB2JMHOHp0/H5fMjhEGq1GrUt5rgMU42EP4fI1Ozm6yc1GOM1OPMaKZww/JjtU6s6WLBplJFlhfFaQmEZpUJAq4wIPUkGp1/Cpuue2HQ0+PGHJPok6Xh+bROziiw4/WF8IZkEgxKtUkCrPLJolGWZ1za1cGqBCYUg8GO9j+Hpet7Y1MKsIgurKz0km1QUxGrwhaSjft7/Gh3npMEdwqQRO7/fw9HsDSMjE6P74xrLZVkmc0AiJR+vgp16Gr8LwXR4+8ZvGXhhAaJSIH9AGruerCZ1eAKOT7cR625DO3oiee3Ca+HSZYwc0L0cVVmW2bKjlCnjxhJns3XJbc1KjRhWquvrsRiNiKJIIBikvqmJYCjEwmXLEAToXWDn1DFjsFkioY4xf3sSQaXC/fE7aEdNIObvT+N6fi6K+24mdM/DZBYWkZmaQtjjJvDDCnSnnYth5jkEHSXI7jbUxQM6n9tiTCzB7T8iGiNFPrTDRqEbfzKy39dZtMQIXSrSq4xGRg0aeNCxdjw/BUEgJz2N1MQEahoaSIqLY2dFBR8t/gatRkMoFCIzNQWdRsueykokWSbcbkQGCIZCeLxekuLiUKlUNLY0o9NoyUlPw+PzYdDpyM3IQKVUov4ZRcsCwSCBYBCDTveLo0xkWaa51Yler6OxuQWz0YDJYPhFn3ksONKV3Ndutz8KzLLb7ZkHvuFwOO48tsM6PF3E3c+YaEWJEqX7iILYKX4OFDSCUhmZPNfXokzeb/mPsVrISUtjs8PRWTXT+fTjGGadjzK9y+2DFqeTb1avpsXpYvTgY9M2s2PMHUIuzmYjzta1QpZSqUTZTc//r3XzVogi8TGHT4TOyzi4mmcwFKKusZHGlpb2UJjtWE0mrGYTTa1Otu4oxef3Y9DrsZiMJMXFoVapCYZC9MrLIzEuNpIzGQ53SZIPh8MIgkAwFEKjVrc/9CYcemB9B2ICkpNToLDw8AeYmtL5p3neSxFx0NIEsowiJo58WWaMLB82wT8cDncey36mRv4bOjLy7yco2o18BxbmUdpiOHS6/fGFKAoEQ2EUS/5Nn8uu5bkymcIE/a/+bHO9/jzKrFx0J4w76D3Z5+tSHEKpEDi7txVPUOLGz/dxz5hEbltUzRunpfPuj630T9aRG6NGkiEkyXy03ckZRVb+vaWFbKsahRgRCz0lGYtWxKIRKanzUdMWwqpVsG6fl1tHHlyYqXM87caAOL0CQYg0Yt/bGkTIEAi1i82wFMm/d/nDnPXOXj45N5PSxgBWreJ/IqQ0LMkoxK6T1EU7XchAr3gtd35dw2unpfP+tlaSjCom5Bp5cmUDd45KoMYVRBQFEgxKPt7uZGy2gW31PlZXerhpeDxPr2lkRg8zScYjT5631ftQiAIFsZrOYj0/1vkobQwcwyP/z5FlmdVvbse1t43s/ACtZ2oQBIEznhjN3roa4lotpPWJ54KnTyTkDSOtXUKwqG+nGIixWBCA5evW0+x0cvKoE2hsacHV5kYQBX7YuIUT8gbTUtZGzxMzqaqrQRQEYq1WICJ8Ap4gSrUCsV1sdzyfIBI9sr/NkIDFZOzyPuxvlSCoVAiCgCCKmK+4CXX/ITTffzuqnPxIrl5RX0KV5Z37VRf2Ouh8iHoDmv6DuyxTJqUctN5/gkat7kx76JWXR25GBs2trSTGxh61uIs/EKBiXzWSLNHHXoDH62VvdQ1Gg57ahkZWb9qMLMskJ8STlZKKVqtBISqwmkxIsoSzrY2G5hY0ahVVtXWRHEq/H5VSQTgskZWaSkpiAhqVGpfbjdfnw2Q0kJqQiM1i7iwk1+x0olGrMeh0VNbUEAiGkCSJraWlONva8Pr9xNtstLhciKKIUqGgICuLguwsAoEAra42ECK/u7rGRtxeL2qVihiLhVibjTa3m4bmFnQaDU3OiBE61mIlzmbtVi0Rj9eL6ggC90h3wRnACOBUwHHUPf1GdBRUiXruokQ5dnSEZQYCQdSqrrcJZVIKob1lXcQdQG5GOqs3b2EIkUalwV07upQo7mDlxo2kxCcQb4shIynpWB7GHwJVe8J8auJ/Xm0MOCjMr0PYHsucCEEQuvR4EgThiJZThULxP1UkSyFGjCir5ckkNLu5rE8q6kod/rYgmqNMrn8OmkHDaXnkHoI/bkKZlYv7wwXoJp6Cpv8QXC/Mx3jB5Yh6YxdDjF4l8qdhcaSZVbw+Mx1REEg0KrG0C7QVez3M6mWho8ZZXoyaXgla9Ad4Bs8oikxsh2fsN46MzTZQ4wpy37d1PD0llc9LXaSZVfROjAjMx1c0MCBFx+SCSI5kjE5JUft7F/eLGEbG5eyX7h+enYkoCPxQ6aFHvIZgWGZTrZfJBWbeL2llcKqeNMvx15qkyRtCQOjiEa1yRiIJUkxKPtjmZHoPMx9vd2LTKSlO0nLdZ/t4/bR0/r2lBadf4vIBMZz1bgUvTE2jIFaDBKRZVDw3NXJvvnxg5NoLSzL9kyPelfXVXtoCEmcUWWnyhvGHZYZnGDq/o2yrGqtWwepKD9+Vu7llRDwPLatjVJaBXglabvx8Hy9PT2OfK4SqXdyNyzaiUQj0jNeSZVUz5zc8j91FlmV6n5JN5QdrMQS8eIhER4hKkRanE1t78Q+1Rolao2TPDw6CcQPoMAkKgkBWWiqbHTtIiI3lvUWL8Hp9neGe2u0mNlXuICsnlU8eXEljRh2jRw5AECIe8JrtTYSDEtsW72XCjf2o3FjPnh9qGHXF/hZCUkhiy8I99D41hyM5mPQnT+/yWjtoOJqX38e/dgWixYYyKQXTeZf+qufvl6BVqw8SqodDo1aTn9XVIJz7E6NnOBymtLycmoZGfHU+QmEJp8uFIApo1RoS42Kpq26kICuLQb2LiLNF8sI9Xi+l5eVU19URCAYxGQxoNVrqGptYs2ULCjHSKsbl8WDQ6QiFQ6iUKhQKEZs5EkaanpzEgF69kGQZhSgiSRIenw+f38/23bsjXkq1OhLKSqTyeFJcHMnxCQSCgfb6APsw6HTYzGZ8gQAZySnUNzVSVVtLXWMTZqMRj8+H2B4Kq9VoiI+JoamlBZ1Oi06jYV9dPaeMHsXhOFK1zDKgzG63LwPMQE+g1OFwbOzWN3SM2O+5C0TFXZQox4iOgir+YAD1T8LZtKMmEK6rOWibpLg4GltaCAaDKEQx0pPugGt0Q0kJapWa6vp6JgwfHs2XjfI/T8SIEmKMdTElwlS2ebUY19ezbkEpMx4cQWzWLy8C0/bev9CdMJ64+a/gfPpxfN8txnzpdYRq96HKzsN698N4v16IZ+EHmK+4Cc0B4dGF8RFR1eElGp0VEVWeoMR5xVZidErO6h0RcINSuyfKBUEg3qDkzlGRyV6MToFeJbCh2svi3W1cMTCGnxM4pVJE1j6/b2QaXtEaQGj/BK1SRKmAHY1+ttT6OK2nhdq2EIlGJZIs/+KefyX1PurdYYak6fjU4eL0XhaqXUH0KhGLdr9ok2WZsAwVrUGeX9vEgxMSWbC1lYJYDUPS9Fz8wV7ePD2D1ZUedCqReIORPc0BREGgKEGLRilg1Sp4eGLEGHZuHytBKeJlfXl6Gka1iPUAkfjT0FuFKDApP9Lv8JSC/b+pi/od3O+rY72iBC3p7aL4sgExGDUiKlHgb+OTEASB8QcI7JGZEWGoUdLluI8nZFkm5A8RVihRuZtRmvX4A5F5ZLPTSZy167kI1VSjGtDV052dlsbO8gpmTBjPZscOcjLS+W7tOlRKBQPHF/Hh19+QmROPJHlJLE3EGDKy5bM9JPeIoeTLCsZeW0xSYQx+d5CkHjGYkwyE/GFCwTBao7rToxfyh9ny2R4S8q2E/GHMCfqj3gsEjeagdgF/VBQKBYU5ORTm5Pys7fQ6HcWHiTyRJAmXx0MwGMRmNiO2C7fWtjYsJhOKn3gcO9JERFGMpGDo9YwcMICRAw6fXgLQM+/QywtzIvUKwuEw1fX1ndFCOq0Wt9dLQ3Mztt5FeHw+/P4AY4cMOaIG6s7sajpwLrAKuNVut7/tcDge68Z2x4SO/lu+gB+tJhqWGSXKsUAhKggEQwSDIYy6rpM2/UlTDrmNUqkkOS6OrTt30ttmQXNAnxeP18u6H0vQqtWMHTIkKuyiRKFd3IXClEk9CTU7UaVp6H/jAHQ1begskedbR66OLMu07HNjS/15AaeixYZgNCPq9V36UHVMC0StDv3kmYhmC61zH8J45gXoT5p6xM/MtP6yZ69CFEhvP74haZH7iz8kYdaImH5hS4YMi5qM9s8+paC9EbwcJF6vpMUb5pYvq3l5RhpPrmwgy6pmYq6Rx1c0cPfoBJaWuYk3KChO0h0y9BGg0RPiqk+qWHBGBs3eMLG6iLW/2hXxui2v8CAKMLOHmXmrGrl+aCxL9rjZ1RTg0gE2ZvePiIirBkW8arIs8/SUVFQKgZk995eOv3lERPzmxe6fwKWa28PyBAF1+2n6pefrcBjUYmdBnTjD/vt1mvn484J2B1mW2f5NJSGnCkFuQ5+UgdvrRavR0OJ0HhQSnzf3oc4QvY5og9TERM4+dTIKhYJ+PSPtdcYOHkTjbheeiiAzT5rAklWrsdhMjLk20pbAWefBlKBnxLXFeCWodIao+GIP8UYVfafnsueHGqpLmojPtaA1qiiemgtAztAkDDFaShZVkFRoo3ZHM6YEPRXr69AYlGQPSf5tT+AfHFEUsRi73lsVCgUxlt+2nYNCoSDtJxFNapUKm/nnGfq6M8M6BxjpcDhCdrtdBawAfjdxp1Ao8Hi9Ec+dOuq5ixLlWKBWqWjzuPEHA4eM63a99iyagcM6G3Z2MLq4D1v/fj9N/jbi7n8CoT3UZWvpTnLS0xg3dOhvMv4oUf4bENtzHgOmZJQ1e5kwzUCiTY1stREORKJUVr+xHW+rn4A3hCzByEt6YYw7cgGgDsJNDehGT+zM1TkcgiCgGzUBVVYujXdcB+Ew+lNm/OLj+zlolOJBDdZ/LZJMKpJMkXPw6sw0REHgT8PjkGQQgMkFJlQKAYtWRKcSKW8JcO+SWl6Zkc4zaxoZlKqjT6KOt39s4dw+Np6bloYgCIw4INz0uqGRKoqzekXueaGwTKJRSViCwngNw9L1CIJAfmzXYxQEgTh91Nh1rJFlGHy2nZ3vbkHc4cSg0+H2eIm1Wml2ujC2NhFoqMH33TeoCnshmix8+42S4mm5JNn3e/V+WlTLoNfTpvYT9Iawmc3MPHFil/cLRqUB8NQPjaSYlJg0CtQj0+jb7gXPHpxE9uAkytbUYE7a/3uypUUME32nR8SeY0klgiiQ1ieOoC9EKBBGEEChOj49pVF+X7pzRxEcDkcIwOFwBO12++/axFyjUuFye/D5/Qckn0aJEuXXRK2OXGeBQBCN+uCJoXbUhM5GrAeiKd9JgrOJhlv+SoLZwsqNGxEFEceePUyfMP63GHqUKP81dOS29p0Yy+ZvNvCPtV7O7+dDta2Bup0tjLykiP6n5SFJMuGghMH280qEt859GP1JUyLNkbuBMiObmIfm0Xz/7Sgzc1H36nPQOqGafQRLt6MZOJRwbTXBnQ70E07B8/VnqPIKUWbm/GZ9D/8TOkIxRUGgwzHX4T08MLS0o+n7iXkmLBqRWneosyqntRuhh0qF0BmymmL67/R2/ZGQZRnHkr2EXCJKZAw6PW6vF38gQDAYxP/QXfhamxFtsWhHTUAwGBhzdQbqA7yW/rYg694tZfhFPTuXBbwh4nMtiIqDC4XsavKzqylAdVuIywfGoDzAE7xgawvjs43E6COe36xBR84/7zstt8vrDe/vRGfVkDMsme9f3MrIS4v4+h/rmXT7IIRDeJyj/G/RHXG33G63vwt8B4wEvj+2Qzoy6vYKNz6/n4TYw1edixIlyn+OWqXC5XYTDAUPWXJYmZRKuL4W0fTTpuVrUfbuT0l5BWmJiWzdUYrZaGTquHFRY0yUKD9BbC+osmRNHPt6DeeqQTEkmtRoRqVSMDqNnd/vIzHfiikhIjrcjV42fbyb4RcfXP3up8ihEMHtW1HffPfPGpMqM4fYh/4PMS4ez7LFKBOSUNl7giSBKOJ68f8IV1cR2lOKftI0RG2kv1WobDfuf7+KjIx28Ag0g0egLipGUKmR2lxI7jaUiUcOJTswBO73xtwe7phji3hqQlUVzMxJQA4GCNfVIGgj34loi+lsKg0Rb6mg1hAq24Xz2SdRJKWgSEpFkZCIb8VSVHmFmC+5BtnvR9BoCJbtou3NlwnXVROu2YciKQVlajqIItab70HyuBE0GgTF0adrcjiM0F4gSWpzQTiMoNOBQkG4tgZBb0BhteFd9jXakeO6jBva89LKdyM11KEZOIy2d95A028QYkws7ndex79uNYJGizIjO1IFF4iZMxffD99DKIRm4FC8Xy/85Sf/GCDLMhqjCkkpEnPjnRh2lOL2emnesR19Yx1yYz3GCy5H038IqtwCAIK+EKXfVWFLNRKfa0WpVSCIkcJ+YruA2rm8Cm9rgAGn5+MLSqiVAptqfOxtDZJqVtLiC+P0h1H85GctABqlwIPL6hmbbejiBe4OxdNyCAUkRFEgf1QqKq2SfjPzEUSBPT/UYEs1Ym0P4d7nCiJJkKQTWfbMFsZeVwwyB4nAOncIpUhnSwxnrRuNUY3GoOq8NgOeICqtslsCUpZlvCG5S5ElX1BCKQooFQIhSUYpRkLOQ2GZMPDaxmYuHxhLnTsEQIKh+15tKSSBKERqBgTCiEoRnzNA6bJK+kzpvtFJCkuIChFPi5+SReUMPKMAx7d7McbpSC2KY/mLWxlybiGuOi8Nu1spGJNGY4UTg1WL1nzokPVwSEKhFAmHJIT2MXbk/cqyjLvRhz5Wy646H7nxGjwhcAckEo1Kdjb5ybCoCYRlvtndxtRCMzsa/RTE/oKcO4fDcYvdbp8M9ABecTgcn3Xr7Bwj1GoVLk+kfKku2ugwSpRjgkYdMaIAneKuwRPqDB8KVVXQ+sTfiPvna4RbmhEtVmSfF99335B4+/2EKvbx0eJvyElPZ/yw3zcUs6O0ekNTC1U19fTtVfC7jidKlA46PHcDzylg2/MLCE6cDEIkVK/kq3IC7hAJedbO9bVmNWnFR686J0ky4b1liLFx1O0L07SqnMJx6dTtau0SYnYoQoEwleWQYgyz9BslY6e00fbem/iXfkVNtUC8tpGEF94GTaSUvCIh4nEwX3od8iXXEqrYg3/1ctr+9SJyMEDsY8/g/nABqjw7otGE9+uFhCr2EG6oR92zN/rJMyEcApWGhmvOJ/7FdyAc+tl9CX9NYSi5nHgWfQJEDFmaYaPwLv4cVWERCmsMzXP+HOn1KYpIHjeKxGSUSalYb7sP/5qVyG4X+lNmYrnhz4Rr9xGqrMC/ZgWGU09DMEQm3C2PP4BhxpkoklLQDB6GIjYBVU4eob3lBPfsjAhqwPnsk2iHjUKZkUXj9bNR9x+MMiuX8N5ywvW1IEtY736E4I8b8a//Act1t9N07y0Etm6MhONKEnIohGiLwTz7asShJ+BfsxLtCePxfvsVgZLNIEmEKnYT2r0TwWzBMOV0NAOHocqzo0hMRmpuRFBrsN31EJLHTbi6KpLLqTcgyzKK+EQIhRDUGtRH6Ff6eyLLMlmDkgiXhfB98i6GvkNpdjpxx8djy7eTcNEXiPquAksQBHauqkIOwMQ/9adhj5PCcek4a9xYkg2RCow94ilOiEyy565qZECKDptOpH+yjjSLigEphy401FFJ9oqBMcToFNS4giQald3+DYsKEbUuIprS+kTuCUl2G7Iks2uPk++bwlxn1hAUBHbsdrLjizJm39QXd0EsYRm+eHID5sHJ5MVpWO2S6O338+7mZqZMTGf5F3soPKcHNQvLEPJj6JGiZ8MHO5l48wBWvFpC1sBE4nOt/PDmdvLO78mqXW3M6h/Dso2NFCdpMSTokQNh3na4CEgyFxTbuOqTSh4bm8j/bWyhX7yGvik6/vz2Hv6cp+cTtZbEr3cz9coitP4QVSWN/CAokeVIyPQlH1Xx/NRUKlqDbFngYPy0bF5e30xRnZPC0/L5/In1zP7LINauqqFhbS1TbhnAR4+vp/9JmaQU2NCaIq1+Vr2xjbQ+cSgyzGz5ZDcnnt8DnytAKBAGYN07pYy+sg8rXikhLsdCweg0TPGREPjYDDOK9hzUxHwbSrUChUrsrGpcurSKvJEp1LYGWP/Kj8y4ewjblldhMqtJKoxhwU1LOXveGN7/YDex9W7iZhbw8bNbufayHjy7pIbMaien39SPdx5dz3nn5rNdoaRsgYPL/jaUtz6r4JRCM/EFNlrX1SDbTZTXen+ZuANoF3S/q6jroKM3hbOt7aDkxyhRovw6qFUq2tzuSANolQpvUOLk18tYfXkuzd4wMRlZhGqqkDweGm+8hLhn/oV7wauoe/dDV9iLqfmFrFi/gfzMzKPv7GfycydxTzz3Jv16FWDPy+KGex7npDFDufiMKSQnxv3qY4sS5eegEEWCoRC+sA/RZOKVdQ1cMTyJdIsahUokd0RK5+QCIvk1KUWx+FwBtKbDi5+Vr5ZgC+1FG9+HhHgdsgyuBi87llYeJO5kSWZfSSOpRZHrwe8OUrmpnowBCRTPKiKYoGfx+yEmXXQVDd9B8sTkLn3xDkQQBFSZOagyczCecQGerz5DDoc7y7KH62sJ/LgJVX4PNAOH4Vv1HZsvvBlNUW963Hcdijv+iYyA84k5BBw/YrjwSjzpg4jPsVC/q4WgL0xKr/ZCJJJE0PEjsteDb/kSvEsWoUxORZmdi/nyGxEt+0WxHAwC8iEFY6Bkc8SjlZSCc/4jSG43we1b0Q49AYBg6XZUPftguuCKzm0SXv2w82/J6yFcW024rgZZCncpOKXKs6PKsx/yXFlv+ytyMICo06OfMLlzudpiQ13Ud/96N/2l83jjX3wb36rlhOtq0AwZGRFVooBoMqMZOLyziJX5kmtQpKYjKJRILmfE83dAjQJruzdXlZOH1BIRbtqhI1H16I1o2D+v0vSL9CEVTWZMF1+9f/CFRV2ORZW9vwSgMvXgXp3HAzIyi+dtIKwQEG0hDHode2tqaK2uwqTRHCTsAJQaBculdQwdXITT5cZZ70ZUinz3wQ6Cg9KIqWxkbViDeVoWyUa4fmgsWuWR2738lPh2z9QDS+u4dWQ8Wb+gWNHqSg+th+Ue8wAAIABJREFUvjDjZuWTWO/nq6c305xq5tJZOeSRQVgUWKdRc6oAmlHpEKtj79Y6av0w+ZR0Lkk2EJtpYlt+LC3eMIlT8vhyp4uCeD2vxFgYJ8m8lhzLzZkW1L4wihFpCAh43tmGK70f36+tR5+q5R1RTY+PtjN9znCadrRQ8mUZV2SZ+G7+Bu78yxA++PNytNcU88jEZIKtfq7sEUs4T4/BqmVSYpDqkkZmTM3lxy/LCCVr+EsPAz+8sBXrjHyMhbGYEvSMKoI4n5EEo4qscRkolCINyWZMMyM5r8uLU5hUFEdJo5+aNCsFsszbGh3XxxtodAYp90Zymp94cTsn5JmIH5zMt1oto2QZ25RcbDolCJB5QqStSFzO/sIq+aMiy8xJejTt9+eB5xaiFAWWbW1GGJ8V+eySNv40Io5d+3zsmVaIqBBR9IonJT6NvFgN1hnZGGK0XH9GNmJ79ds/PzocZEgDGg09EASBi/vaEBQCKRY1Of4g4YBEH6V8xN+C0GHV/m/h/jlz5L7DhrOvro7LZp1+THs0RYnyW2G32+9zOBx//b3H0cG8efNkbVIy4XCYi2bMQKeNTAy8QYkL39/Lc9PSCN16CZbr70CRkobU3ETDVecS/9qHXfqadZdQKMRr7y7k4jOnIAgC20r3kJGahEG/f2Jb39iMY1c54bDE/JffZsyw/owZNoAe+dkoDpHvULqngk+/Ws5n33zPe88/gsVkpLahiXc/XczSVetZ8PSDrFi7mcT4WLLSk1Ee5l7i9njZVlrGvtp6tBoN/XvbsZqNbN9ZTi/78Z1fdCgCwSAerx+LyfBfN/ZfyvF2nT3xxBOyLSsL3XIbhlMEpkwYc9Rttny2B1mS6TPl8GXAA54g1c+/zrYKG5Me71oYpW5nC55mP1mDEgkHw7RUu9m2qIKRlxax7LktDL+wJ8oDKjBKIQlPqx9jrK7TsLJ9cQUZAxPRW7pajmu2N6HSKjHEaXE3+hAVAls/L2fkJb3wu4NoTWpkSUYQBXatrCazfwI1GyswpMVhTTby8b0rmXz3EFqr3YTLdyG98Tirw5MYZlpGg9eG7tTTiLEns+aNHxl363Aq//YQGsGPLj8X4+nnEG5qJLRnJ9oxJ+Jb9jWiLQZVQU8ar7sIORRC03cgiuQ0ZL8PJAnVmRchr1mOaLGh7tMf33eL2dPUhn3MaIJaPVpttGjbgYTDEgqFiD8QoL6xBaNeh8Vs7HIfCYclevbscVxdZwDX3HST/Nh9D/PyR+9z5bln0tTayovfbCSttZoCs4JBp5950DZNLU5Ou+x2rrpgJo8+/QZ5Ken86frzePathcSPnMnq1/7Bgn/cx5tlIbJsaqYV/uetSyRZRgDeK3FySoGpSyhjd6l0BqlyBjtzSGVJRpbkzhYLh8LnD9DS6iIxPgZJkgmFQwcVjYFI+xO9SiQkyYgCbK31sbXOzzl9rJ2eL2V7CVd/SEIpRoxX7mYfAXcQW5qJcDCMQqXotoG25KtycoYko1CLeJr9WJJ/XugqwN7WAFXOEEPT9ZQ1B8iwqhAFgXBYwuf30xJSYtUI6NQKqpxB0i1qXl1XS8nSLxjaK5en1juZc3IW2qRsKmsaGJRu5NsqiRlFMSza2caXKzYyyNzGZxUyl44pZHzfHFpaXTS2tFJRVYs9N4OkhDgE6Pbz9sDz06HRyvZWs7e6lja3h+SEOPoVRYxGh3umHdVzZ7fbEx0OR213T+SxRq/Vsq+uDoNOFxV2UaIcIwRBINZiobaxEbVKSU1bkO31fp5b28SsIivPrW0iKe8kJvzlBpL/9QmixUrsE8//R8IOYNGy1Xzz/VpmnzWVh+a/wtJV6/H5A5w17UTOmjqRZas38Mb7nzPxhMFccvY04mKtfLtiHfc/+QJ3XHMhmWnJfPP9WtKSE3j1nU+5bvaZiIKAVqPmjmsuxGKKWKMT42K45qJZnH5qpLjLtyvXsX7zduqbWhg3YiB/vflyHnvmDYYN6E2P/GxcbR4CwQBzX/w3KYnxSLKM1WIkJyOVex57loQ4G8U9Czhx9BByMlLxeH1dBKkkSTw4/xVcbW7SkhO45qJZLF+ziV1llaQmJTC4Xy+sZiOyLPPVstWsWLcFvz/AjZedTU1tA/NeWoDX58fvD3DLVedTkJPBzfc9yZjhAzh98jiMhshDfOOPO4iLsRAXY0OrUePzB9i6fRdmk4Fd5ZUICEwaO4yXF3zCyws+QaFQkBBnY8HTD/L2J1/x7qffUFSYy4mjhzC4by/qGpupa2gmxmrCZjGj12n/IyEoyzKuNg9mkwF/IHDIScOvjdfnB0CrUR/34rWjFUKPy5MxvDifD5plJk4dhfEI5e17T84+aJkUklg8byPDL+yBSqeksdyJuWU7Y04/9eB9KgQUahFJkvn0/tVMvnsIIy7phSzLZA9OIhQMdxF3olLEGBv5TXe0ZPC7gyjVClztZd47aNrrIskeQ+MeJ5WbGyielkOvSRHv/RePrOWUvwzmk7+u5MRbBtLW4MVV7yFtyP7jmXr/MARBoKnChcaYStq85zlp/Q8IpquwqtWocgrwO0rIL9Yimsy0jrocrUmNqU8ci57ezEm3Dez0IimSUhB0egStDstNf0HQGwju2MbiHzbz1MYyfGGJwAdrePvZh/liyQpeuudpTEY9FrORf516Krfd9yRWs4lxIwYyuF8v9Lr/jTQQSZKoqW+kucVJL3suHy9axrNvfIDb7cHj87N4wT9Zs7GEfzz/b9xeL2qVijm3X0VmWjLnXns3E0cN+b0P4ZDIsoyr3oOISMs9f0J30dXscikJhPQMijm0aNhbVUNWWjI1dU0MG9SPtD6DKZNsbNq4iR51TnompfHpsu+44cxDtwf6OXTkXnmCEr6QRGmjn1idEoNa5PZF1cybnEKlM0iubf997cc6H7uaAuTGqHlxfRNPTErp0qpCEIXO3LhQKNQ5Z25udfHtynVkpSWj12m58d4n8Pn8aDRqrr34DPr0yOfxZ9/g6gtPJzsjBY1a3Sk2laJAIBikT5KOPkmR+0KHqOtAc4CYNNi0nYWgOip7dve+3HNiJms2lhBjMxMfY+sUPaFw+CBjbCgcZldZJRazkRirGbVKxar1W8hKT2FoemRekmVT49hVzitvf4pjVzkzJo3hjKkTmHz+DcTGWElJiGP8CYO5YPwIXt8dx+p1GykKhmhqtmGwhnnr9TeYV1NNU6uLT/KyePmJe9izooba+gBxTbV8/nEp4/vewOvvf863K9aRmhTPrvIq3nn2IXaVV/LtinW0OttISYrnjCkTcLa5+cfz/6a2rpHC/CzOmX4SRoOemZfeRkpiPAqFyKih/bj83BncNmceqUkJGPRaCvOyOsXd4Tiq585uty8H6oEXgYUOh0Pq1rdyjJg3b55szcjEYjIybXy0+l6UPwbHm0dh/vz5cvHQYWwtLeXqc85md3OAtVUeptjNhCWZkATPvP4Np341n6J3Pv5F+6pvbOayW+dw29UXMHxgH3bsriA1KZ5gKEx9YxPBYJhbH5jLNRfN4uRxww/5YKisruPFf3/EzrK9zDxlHFMmnnBYT9yhaHN7aGhuJSstme/XbOLp195jT8U+zpp+ItddfMYhtwkGQ3y7ch07dlcwemh/VColF990PzqthtTkBM6beTIJcTaefP7fnDNjEnWNTZw382R27tnLwm9WUF5VzcatO1jwzINs2Orglbc/5bTJ41CrVJw0eihur49dZXvRatQY9DpsVjNmo4ENWx18+MVSFi9fww2XnsWMSWO48o6HaGhqoaGplcvPm8H5p53MXx55ml3llWSlpTBmWH9OGT+CJSvW0q+XHavFRDAYQqVSUlZZjdfrY83GElZt2Mo/59zGkhXreP3dhTS1OmlucfLYPTeQkZLEbXPm069XAYFgiAF9Chk/chB3/f1pgsEwY4b3Jz87nfzsDOa/tACX28PW7buIi7Ey74FbuPWBuVTsqyXGYiY2xsrfbruSb75fy1dLV9Hm9ZGcEMvFZ0zB4/PxzGvvYTYZ0eu0DO1fxIhBxTz3xgfkZKayr6YevV7L6ZPHc/FN9+N0tWE06snPzuCuG2bzxHP/4v3PvyUcDpOSEMcrT95LVU09ep2WieNGH1fX2bx582RVfAJJDcn0rf2Iih6jGXzGFHRHsdpvWbiHrIGJnX2vTAk6pLBMTLqJlmo3O76tZOCJNgSjCVF36JwfKSTR1ujFnPjzreEdLJ63geKpOTRXtiEAeSNTD7uup9WP3qLB0+xDf4iqn/tq65kz92X++eBtALja3Li9Pvz+IJlpkbw+r8+Pq81NfKyty31AlmWayl18vXk1whYVOcOSsGYb0Dg1PLTgZRqbW5l95hSKexUgCgJtHi9Wswm1SonRoKeuoQm9XkdbmweL2YhOq6HN7eG9hUtYu6kEjVrNY/fcwKKlq8jNSiM7PQXxgGIkPp+fD79cikGvY8rEE9i+s4wNW3ewu6KSjJQkzp5x0kH3I19bgB8/L6PfjDy8rgCCIKC3aqgrbSY2y4zfEwJJRm/TUrmlAWuKAa1JTc32JtL6xNNQ1orOrMEQo6Wx3ElsppmWfW2EAxKxWWY2f7qbgjFpBD0h6na1YOthZNeaKkLaEIOG9OTLT1ezetcWqqrrGTGomNlnT+G+J55n8fI1mAx6CvOzePSu6yndsxelUkGsxYoyrMAQo8XrDKBUiaj1KnZsriA5J5Zgc5iK7bUUT8ijsLDwuLrOAK664Qb5nD6X4pBLmVmxHMPUM3h2dx372hTcMjiNuKLiLus7Gvy01lVTVVHGjEljKKn38ffl9Zzbx8rGrz9hSJ9CkuLiuPXBuZwzYxJnTo20QFi7aRtZ6cnExVgPNYxus7zcTVtAYlK+iT3NARI0Yc645wViwy30GTGG8SMGkqoJctEtD2LTq3hkzt2kWdTIskxzqwubxURVTT1zX3yL3WWV7K2uY94DN7Ns1QY+XrSMgtxM7r7hErIzIlVhW11tuD1eUhLjCYXD/PuDL/lo0TJq6ht5/J4b6ZmfhWNXBZIsMWfey8x/4Ba27yzn6+9+IDkxjiH9ejG0fxEVVTWs3+IgIc6GRq2muFc+oVAYURS73Zva5w+wc89eeuRnc93dj9LQ2EJ9UwsD+xTy8J3XMeOSW7GYDYQlmQG9C7n5inO5+s5HqK1rxBcIkhQfwwuP3cUzr73Hewu/ITMtmeSEOO675Qpm33w/k8YMp2d+Fn165iMIAjX1jTQ1O6msriUnI5W87PQjjk+SJDxeX6dx9Uh0CNJNJaWs37Idi8lIWeU+TjtlPEaDju/XbCI7I4WSHXsoyMmgX5Gd5lYntfVNhCWJvKy0IxpFDzd37FZYpt1u7wHMBk4AFgMvOhyO3Ufd8Bgwf/58uXDAAJQKBWOHHJ8WoihRfi7Ho7gbf/IpfPvDD1x9ztkHvX/7ompuHxaDVQx2ydH4T/h40TIam1u5+Fewfv6aSJLUZQJ3NDrupS1OF5XVddgsZtKSE47osWpoasFmMRMKh1AqlIcMLz0coVAIWQaVan8ARiAYpGxvNQU5v27eiyzLBIJBtu8sZ+2mEiRZZsakMcRYzSxauppgKMTSlesZ3K8XZ0yZwLcr1tHQ3EJ2egr9iuyIokgwGGJ3RRUtzja8Xh9jhg+grLIax84y1GoVdQ3NTJ4wklAozPot23G62nB7fAwf2IfsjBReeutjtjp2odVomH3WVPKy0mhuddLqcuNqcxMOS12K5XSci5zMVKprG9DrtAwfOvi4u87U8QmY9to4QVxDTF4+hmmHNiYcyJ7V1cTnWTHG6ti5vApbmonYLDM7v99Hkt2G1leP7Pd1Vv07VnSEWXlb/bQ1eInP/fkT2lA4zJy5L3V67k+dMJKr73yE6toGFAoF180+g9zMNG772zz8/kjfzf+bcyupyQl8sHAJ5VU1xFrNXH7eTJauXMdbC76iye2kV3YO4xOHETtaT9nyOr7evIphY3qT2pzM8It7Ur+rFXOiHpVOyb4tDaT3S8DT7EMQBXQWDUFfCKVGQV1pCw0VrfSakMVrby5kwcJFBAJBZk4ay1WzT+etjxbx1KvvMqRfLwYW9+TMqRN59vX3aWxpJTczjdbmNi6/cAb/ev9zKqvrUVSqsMYbmH3jFNZ96qDZ0oK6Vovklxh7Xj++fWozA88owO8KsPnzPYy9upj17+2kYHQaUlhi21cVDD2/Bxs+3ElyYQy2dBNfPb6OU+8ZSvnaWgLeENnDEtn4yW6yTkhAdsk89ODrbGzdRrG2kKEn9mL69NG8cucXWMZrMNeb8TuDzPjTCXz2+GqGnGMn7JLZu7GegWcU8NUT6+k7PReNQcmyZ7dw6j1D2fp5GQhQNCmLhXN+YPSVvQmHJGq2NVMwJu24e54BXHn99fITjzzCgs8/Z+ZeB6rCIu5sTsTl8vDMlAJUSRGREwqHKd1dwWtlOs7va6Vn/JE9tk6XG51Ww8eLlvHmB1/g9ft57/m/d6YydIdWVxsKUTyiWPjb3JfweH3MmDSGCr+aTJuGex94nJPHDWfmKWNJjIvlqdfeZcNWB7vKKjl98jjOnn4SK9dtoTAvE6vZRFyMlY8XLWPciIGYjN0z6rja3Oh0Wsorq3nk/16l2eniqgtOZ9yIgZRX1lC6p4KqmnoWfvM9Tz90B5tKdrB05XqaWpy0ebzcf8sVbN5WyvyX3mbmyWNITU5g9ND+KJUKvL5Al/QAfyDA3X9/ltUbtjJuxEDu/dNlXcbS4bELhSPPCKNej16vJSstmdqGJhJ+YvSBiOFlY8kOkhPiyUxL6gwt/qPwS8WdhUgz81mACxCBDQ6H455feZxHZf78+fKss85GkiVSEhJ+691HiXJMON4ehvPnz5evuPJKtu3aRXFhIe+XtKIQhc6cgjc2NXNGkZUL3t/LM6emYtX9vBDpVlcbf5v7En+67JxoYZMovxnH43WmT05BqVRycmsNn/7YyvkP3Yrqp3XTD4G72UfJogoGnblfwFVuaSAcCJMk7ybc0tSlUMfxwr2PPUuLs42BxT0wGvSMHTaAZ15/jwtnnUpSQiyyLFNVU098jLUz563N7WFXWSW5WWnodVpEUeT9z5fw6Vffccr4Efj9QWZNGX/Iti0QyQXUmtVYkg3sXL6P/BNSKV1eBUDu0GSWPLWJsVcXs33JXiASDvburcuYdMcgAu4QQV+IxAIbH9+7klGX96astIbSpZXMuncM7z++jOJxuWT2SqKxzIkxTseXj65l+pwRrPm3g9hME7nDU3jj6q8Rx4dQBFXUNTVy5RUzWbR0NYuXr6GyupZQKMzzj/6FXWWVPP3ae/QsyObDL5by3vOP4HJ7eODJF4ixWsjOSOHcGZMISxKr1m1Bq9HQ4nQxoE8h2ekp3PPYcyz/YSOSJHHJ2dO4+Mwp7CqvJCcj9bDhcB3zwH0/NpJYYMPvDhLwhLClGnE3+9CZ1EfM2/opx9t1BnD5ddfJs0+4ih3BncyQ6xAUCv4u5JKxaxWzb7y600i5aOlqHnjyBV6ffz8PP/cOF04by5D+RVzz6T7+MSkZ7SG86pIkce1dj3LO9JPIy0qnqqaOrY7dWEwGpk8ac8Qcs2AwxNJV63nl7U+5+Mwp7NhdwZXnz+SGex5n1qkTGDGoD6IosnbTNooKczu9X00tTuoamijMywIi3+H7C5dgNhkYNyJSCOd4EjHbd5bxxbcraWhq5eYrzmF3xT5ufWAu8bE2+hXZOWf6Sewsq+SLJSu556ZLuuUVi/LLcu7eBoqAN4DzHA7Hvvbla4HfXNwBJMVHJ4NRohxr1CoVxYWFAIzIMCCz3xB0XnGk4t78U1KwaLv/AAmHJZauWk+fHnl4vT5Ov/wO7r/1CsaPHPTrDj5KlP8SFIKCwEIFqimxTE5qortzaI1BhSVJ3+k9A0jrHUeougpF0tDjIt/Q1ebm8tsexO3xkZoUz4yTx3LrVefz0ZfLKN1TwfCBxVgtJu649qLObQRBID0lscvnGA16in/SwmTmyWOZefLYbo0jqXB/T9z89up3+QeEkI6/vh8QEXWdn//wyEhj6gPSiKfeNwyAvql5FI+KNJUeOa0PMekm2uq9lH5XxchLipj8lyGIokDxtBw0ehUIcOY/xqLWdZ1ynTphJKdOGNn5WpZl9Dot11w0i+07y3ht7l9JTozDtbuCqy44HZ/Pz67ySlRKJU0NTWwqKSUQDGIyGijumY8kywztX8QtV55HjHV/cY/czLQjnp+O30pHxVSlWoGhvaiq4RAhtP+NyDIYErQoGkQUhjiCu3bgbmzhK08ck0JqOs5QTV0DIVlg1uV3gCBwyVmRiJJrh8QeUthBJHf2qQdv73xdVrmPF978EJ1Ww6C+vbjurkd54t4bqalvjEQyCCJKpYJ/ffAFXyxZyWtz/8ru8ire/OALzjvtZARB4LRTxvHQ/71MfWMLt151HmdMmdhlnzFWc5fvWBAETps87tc9ab8ihXlZnUIUYEBvM4sXPMWGrQ42b9uJJMv07VXA8AG9o4WMfgW60wrheWClw+Fos9vtB3YgHXm4DaJEifLHQhDAcogiDwoRNtf6KE7SHWKrg3nprY9Yv3UHo4f259G7b+DLb1cybsTAX3u4UaL816BSKfEMdKOKSaS1YVW3RZlSrSBvcAye915HO3oiyuRUwo0NNN12FTEPzkeZ/uu3ITka23eWUbJjD+NGDKTF1UZmahI3XnYOqUnxVFXXERtjxWjQc+7MSb/52H4u4lG8Hh2FKjr6EFpTjYy8JNIioKP3lda4Pxz7p8LukJ8pCOh1Wgb17cmgvj07lxfkZHSGWo9rN4RZLaaDwtYApp446qj7+V9ElmWS+ljZtlqBGGtD+uF7Vm2XuGn2SB5+dC7z7r+ZOncYf/Yw5s4dwd4fNzJpzBAMel17FcVDe4UPxdD+vfnqrX/i8/mxWkycN/NkLr3lb+i0Wj586TE++GIJ8196m8zUJB6963pEUeTy82Zw+Xn7K9uOHtaf0cP609TixGz6z/Nij2cEQaB/70L69y78vYfyh6M74m44MAm4GZhnt9vXORyOhx0Oh+/YDi1KlCjHC0/90Mj0QjN9k7uKuHp3mFV7PV3E3dzF1dgzjHzscPLPySmdk9UNWx28t3AJb8y/H4VCRKfQMH3SmN/yMKJEOe5QKhSoUCJmFbAweThXH30TINL3TPb7kVqbabr1SsT4JJAldCdP/02Fnc8f4OW3PmbGKWNxe7ys3rCVfzz/JnlZ6bz4+F0M6dcLgLTkaBpFlN8PWZZZ+uhW6KtANTAP55DRiBs/ZphR5qWKahZuKEcURTYvWsRld17G4MwxNHpCPPh1DXkxatIsKibmmrq9P61G3RlCOfOUsQzu1wuz0YBCIXL65PGMGzEIs1GPUnnkafiB3rkoUbpLd8TdVIfDMQDA4XDMstvt3wMPH9thRYkS5Xjir2MTD7ncHqfBHqehfncrMelGwkGJ1O/KKb60F0UnJHTxQny+ZAV/uX72L64iFiXKHwklCsStWvSXp3DVtTOOvkE7ga0b8Xz0Nra7H8Z0yXUESjYTKt+N/uTpx3C0EUp27CHGZubvT71G+d5qCnIzUatUDOjTgwF9elBeWUNqcvzPKkgUJcqxREZm0FV5bHCU4NEZeb8+wPABxWzZWoYpMY1N2/eQYNGh9Ls6t7HpFEzvYWZgiu4Xhzn/1LgRFW1RjiXdEXeS3W5XOxyOgN1uVxEpphIlSpT/IV7f1MxpPS2HbKz62rpGlJ/t5KTZPXn3vtWc+dgo9CoBd1Bm0U4XJ+aZ8PkD/Pnai46LPKAoUY4nVDollsnQ7A3humAK6W98iKA5es6Junc/VHmRcCZBqUTTpz+aPv2P6Vh/dOzijfc+Z6tjN289PYdJY4ZjNukZ2r93l/U6WhdEiXK8IMsyjXtciCipqW9k0TsfkjD7ASZNz6T+vS+QZR+y10tm+v7sI1EQiDcoue/busMaOKNEOR7pjlB7Bthqt9vfAza2vz4qdrt9iN1u/7b97zy73b7cbrd/Z7fbn7bb7WL7vw/tdvtqu90+sX29HLvdPvc/PJYoUaIcI0JhGfEwuqy/TmDlgHSqZJG8mwei0imp3NzA1g9K2VrnY/vOMs699m6cvtBvO+goUf4LUMgKQlsEVAqR7fe/0S1hJ8syng8XIKiUNDS18PD/vXJMxyjLMrUNTfzpvifp3TOf+XNuxaDXceLoIQcJuyhRjkdkWaZ6QzOKsIDb48OWm8vczDra3v0Xhh7DKIsbQENTC1lpyV22M6pFxmb/MXPeovxxOaq4czgcLwIjgL8Dox0Ox6tH28Zut98GvAB0lFl6ArjL4XCcAAjANKAvUEYkn+/a9vXuAh78eYcQJUqUY83F/WPQHqKMXzgksfstB9f3MVP3wQ5S97kwqEVyhiZzwkW9mKmWeH3uFgadOJW/LWv4HUYeJcrxjVKhRGNSYVSLjA6UEarZd9RtApvW4f3mc2SFEqVSyfI1m1j4zffsrqj61cf3wRffcv8/XiAxLoZ3nn2Yc6afdNAEOEqU4x1ZlsmblojarMLt8SJIMm+uqiJcV82MHmZMOxZzzUWzmHbS6C7bxemVjM76Zb1co0T5rTmquLPb7X2B+4ArgL/b7faXuvG5u4CZB7weACxt//tzYALQBhja/7ntdvsIoNThcNR2f/hRokQ51jR7w8z+sPKg5QFPkIp1tUy9fxj56UZGXVpE7vCuk75yfy1rYyRunDaYOROioVpRovwUtU5J4mAL2+t9fP3ahwQ2rT3qNp5P30M/+TSWr9nE7XPmc/MV5/LJou94ecEn3dqnq83NnQ8/xZ8f+ic/bPzxkOvIsszzb37Iq29/yuyzpgL8Yav2RfnjI8sypR9VIwQEeuRnc1ZWLIbNKxCNJgRB4Iulq/jr489FUwei/CHoTljmK8B6YMEB/46Iw+F4DwgesEhwOByX9Sb6AAAgAElEQVQdTbJcgMXhcOwAKoEngfuBG4EF7WGbD9rt9mhuX5QuNHhC1LRFflbfV7gJSTKljX4+L40kQH+2w4mjwf+L99PqC+MLSl2WybJMWNrf501qb/oaCsv80TFpRO4/RL6BzxWkuaqt87Var0Kl7ZrGm5uTxJzJBZR9X0M4GObWL/bhDkg//agoUf5nEfwiZa83kR+rYXj/bMK1NYddV5YkWh5/gEDJFhb6NTw472VmnTqescMH8vTDd/DArVeybPUGnnvjg0Nuv2jpKt7+5GtMRgPTThzF4H7/3959h0dVpQ8c/06f9A7pCUnghhp6ExEEUUEFC9Z17a59ratr2dVt6rrqKrqufdf6s6JixVUElaY0qZeWQCCN1EkyydTz++MmAaVFIEwS38/z5GHmzr133kzmHebcc857+nP3g0+xesNmfveXWVx1x/0sXr4agHWbCvlq4TKe+8fde607J0TXYyIy24HNYcVuszKwdwbjdyzCHBUDQGZqT35YvynEMQpxZLSnoEqZruvPHebz7PltLgqoBdB1/T4ATdPOB94HrgCeByYAk4DPD/N5f1GUUphMJsoqqmhodPPSiiqm50fSq1c2n3/5DavXbqCy0Uu03czF559JcUOQdHsz83fZUGWbOXviEG77pJhx5kIS8gawqdrHzZPzuGX2BtLrNqCUwtojixtPHc6XhW6SIiwUJIfx6MJdXD0igWKXjyU73PyqII57vijjpN5RDEsJY/rr2/js1734YIOLdbuauePYHlz30U4uGhxHr1g7jy6q5K+Tk/lQd+ENKM7oF8Nd/yvj6hEJBJTi399Vc/8Jyby7ro78RCfJkTbm6C6GpYZhM5twWo0rbWFWM5F2M+UNfm7+tIRXz8rk2WXVRNrNnDcwljv/V8bt45Ioa/Dz8UYXN41N4qWVNWTF2jkuO4LL3tvBk9NSWV3ezPyiRu46zojzgkGx9E10cvab25l7US/eXFNLRaOf60Ylcsl7O7h3olEV8tGFlcyalsrs9XXEOS1M6BXJ7z8v445jk6huCvB9SRMz+8fwoe4iM8bGoOQw/rW0imtGJhzw7xoqbl+Qi98t5oUZ6fjV3o3Y6J7hDD2j936P37p9J+FOJ4MG5LH+i2K2zCtmQnkzNbqNiIFJHRm6EF3GyJGD8PfzsavRjzsmhbg1C/e7r2/DGnybdRIeeYaNb33GfbdcyciWpQZa9evdiz89+hz+QIDUnolty43sKC3noadeodZVT1pyEseMKAAgOz2FnMw0rr14JvqWbdz5wL+YecpkLpo5jef+cTdhsqCw6AYsZjO2TBMWs533P1vAIr2KXlc+zU2TsgF49L6bCQbkwqPoHtrTuCvSNO0OYAWgAHRdn/szn2eFpmkTdF3/CjgZmNf6gKZpTuBM4CyMXrxAy/N0ukHOSikavEGiHBbKGnw0+RS94uz73DcQCGIycURLQQcCQT7+8lu2lZQzZkh/evXuzVMvvI7H7aaszk18hJ3TL7yEx596HX91CY1+EzsXhnHZDTexxp/E2OE23tvQwPCcCBxRMSz9YSNPz3mNisoahg8ZSOPovpxfEMf3Xy3j/bn/IhBUDI86nwuHZrLo6/V4/PDVR+/TN9pPeJrG8qVrST1mIDnxDsxmE9EOC70TjC8Ctx/bA4fFhNUM75xrrLk0tXcUJ/c21on558mpmE3gDSjOGWBcORueFkZr59jFQ+JIDLeACX4zPB6AK4fvbgQ9cIIx/C87zk52y9/g+JzItr/TrGmpAFw4KJbWZsmZ/aIJs5lJjrS2xXFcdgQRLRUgfzcuCavFxLisCI7JDAdg1tTd67TNvagXAKdp0dgsxrb/nJ6OyQT+IPxxglHqeFBPJ46W+WmtzxnmCxq/D9Ajwkp0y4LgefH7fv90BhsrPbj9im11PuboLu449selnJe/u4mkXjFkDNn3+lX/+PcrTD3+GE6ZPI6CU3MIBhXZ1c188sI68uLDGZJmDPH6fEs90Q4Lo9LDj/jv4PIE2FjpYXjakT+3EEeC3WSlobaJ7U4z5baejCnett99/cXbCD9hKtbkVO68/pJ97pMYH8ufb7uK1es3M0DLbdtus9m495YryM/LJjpy9/DKIQM0ACLCw8hKT+b4ccMxm0yyjIHoViwWM/oTFQy4Og13UxN9U2O5aIqGueX/4sjwsIOcQYiuoz2NOwegtfyA0fD6uY27W4BnNU2zA+uBt/d47EbgcV3XlaZpLwJPAy6g4xfr+QmlFCtKmxmS4mRjlZdNVR5O0aJ54OsKju8VSb8kB+e9XcyHF2SjV3oorfeTGWNjc7WX3vE2XA1uqmvrSIqPpbSiij//8zkmjh2OzWalYPSxrNjl56LBccxaUsnQlDBGp4dz7lvbeeXMDL7b6cZqNjM6I5x3lhbyzcezKauspcHdzJ3XXkiZJYEH77kbrXculRHpHDvCyhy9HhWTwuj8MN5c38Al04eSEG/nr7de2dbgaTUmox8mk4mpx+/eds/0wTB98I/2y06HKQN3f2lo7Q0cnjsTgFsvn0kwGKS61sUna37glVf+j945GWT+6gwK+vUmzmEDjApTrSLtJqPXz7J7LLu1pfSi02piUMsC2MmRtrbHWxuJwF6/y8GYTCbiw4y3tnOP0v3DUo0v+HaLhRin8YGeFbv73Fqi40fn2PPfPe15TkvL72G3QGKE8Zy58bvP09qoSI6ykRxl/H4j92jETMlr/6KoR9vglDD+MSWZ9Bj7Xg07gLxjUrE69/4IUUrx6VeL2LajjBOPG9223Ww2EZkYRs6v+uJ1+ygud+OMtJMQZiUuzMKSHW4WFDVy27jD79Ura/CxfpeH8VkRzF7vYkBPJ1e8v5NHT06hxOUjoGBIShhrK5rJi7e3NcZDIegPYrKY9nqvKaUI+IIsfHEt438zyBgCqxSxqZHsWF1JSn48waDC5/YRkRCGt8mPzWmhud5LTXEDqf0TKNtQTWRSGJEJu7+47HT5CLeZibKbqW0OtL1vW3kDCrvFxKYqDyYT5MU72j4HNlZ5KHH5mNArkk831ZMXbyc2zMKcDS4uGRqPXukhwm4mMdzC7PUuZvaPobTeR7Nf0TvBQWGNl8RwC1EtX6gOpPU5WzV4Aiwrbea47AhK6n2YMfKqotFPnNOCL6iocftJi7Hj9QTwNXhpCrPxv1XVnD0yEbOlczZWPA1e1n5axMTrBqOS+lP+9E5UwI/JsnduhZ94KgC/v/9Jzp0+hYJ+++45HzNsIGOGGVUs/zbrRc6cejwR4WFtvXUHYrUc/G8jRFdjsViIPsdMeIyTBncz5rBIGnxBItvxWSREV3PQxp2u65domtYHyAVWAwcv5WUcVwSMbrm9EThuP/s9sMftlcCo9pz/SHp2WTVpUUZvzosrqumXlILTamprAFw8OI4YpwVfcxMfXpCNUqqtelJRjZdnvt7Kslf+SRATPRJiGDl6NDmjJnLWtEm8810RjuZa+g4YgG/HDm75YCk2ZzibGxtJuexsbh0E0351PY3uZoYNG4Jz5kksKPYyduhAHEkZFDdCQb9sqrZ7ePmFJ+idFE69J0CUw0IBwODJAEydtPv3SdxHJ8WhThLe13Fms5nE+FgeuPM6PF4vi75fTVRkBK76RqZfeivJPRLomRjPuJEFnDblOGZceisNjW5ystKZMGYol557Gs3NHpwy3KdTK67z8uf5FUzPjyYx3NrWM7onZ5TROFZKMeuFN1m07AcuP38GCxav4PfXXYTNtvdHzLDUcJa9vYkFXkXU0GRO1aJY9tYmssamctGQOAprvHy+pZ6pfaJxeQL0S3LudY59UUqxdVkF5rQoTGFWKhr9WMymtkIuj56UQmK4lcpGP0W1xtzNl1fVcMvYJCqqvTR4gxQkO/nHt5Xc0dKLe6QppVBBRUljgLf+bxPXX6Tx8n83EGGGsy/vz84yN2nJ4az7fBu+Jj8Fp+WSOiARpRQ1JY34Gr3EpkWy+eudpPSNp2JzLdu/Lyf9jN589Mhyps3IYWmdn7DtdaT2T+Dt+aVMGdkDm4Kl7xVyzuX9eOaj7fxqVBK1cU6e+KSYh8/P5cklVWTF2jhFi+bUVwp585wsdrh8+IOK3Dg7M17fxvPT03FaTTS2zEe1mk1GD7aClDAzAX+QdbuaSQq3khoVTk11M0FPJMWVHrYsKqH3rzReX1DKjIFxxCU4eeNlnWsv78uCrQ2EqSBj+8fzuyfXctNpmWzxwcKPt/G76wZw7383MiE/hgEDEvhhYSnjM3P5dkUlpopGzjorj38+uZrLpmdTa7Xw/V+X8JsXTuDxp9eRk+jg1HN60/D2Bpq0kUTEt+99dLRFJIQx8brBvPZDLfmJDjKTUwlU7sLac++KlK5nHydwykwWL1/DPTde1q7zD+rbmytu+ysD8/N48m+/O9LhC9ElWMwW6jY0YettY2B+LitrzOiVnh9dVBaiuzho407TtOuA04F4jOIqvdm9dEFIKaXYuriMnNHJh9R48QcUnoDi9PxonDYzJpOJWdPSAKNHp7VXJznKxtdLVjL703k8cOd1XHX7/URGhLOzbBfDBuXz8PWXUD3xIR5b3tQ2v6qo1ssxJx7HqGPGEu2wEG4z0z+rJx+ZmrBZrcRERZIQF0NmqpO3nr4fEyY+m7+YqAgnj52ZB/T7UaynaLu/mLTnqvfR4rDbmTB2WNv9Of95hNKKSrbtKGN4QV+cDjuv/+svWMwWinaUEhFu/B6X3PwnbFYrY4YPYvzoIfTvkxOqX+FnUUrR0OjG4/UR5nQQ0Y2HcpTW+zmrfwx9Ex3UVjbv1ZPyv0dXEHOSgw1FRVx78Uz6azmEhTl44/25PPPQXQc899Az8xhmMrFrax0BX5DwWAdJyeFYrGYarAH693CyflczAPFhFnbU+RieFo4/qEAZjbIZmWF8O3c740/MpHxtNXnjUvluaQXN/YOc2TeG0zJ+/Ldp7aHKT3KS39JgbB3eW97gx+0LYgKOyQzHajHxzbZGcuPtvLKqltx4O9Pzo5lf1MjxOZF8vqWe3HgHOQfoVVZKUVPcQFxGJJ/9fRnjrxzA+59sp3m7i4tvH0ZepIXGmmYmzcwjygzldV5m37WQq5+eyFyznfzcGLKbA8wx27nJZOL5OsWZ/RLZWe9n2ZA0JtotfNhsYvDkXiRH2hh06QAyMiLw1Xrx9jeGMmdNyCCvTxSbStxY7GZUUHFBqgNbVRNpqeGMW10K5DJZ+Wjc7AItmms2lWGq68mwcDOl66sw5Ubx5NBoLHXNZKZGUjO/GH92BNmldTRs9pF1YjZNL62h+XfDGeH3sf6dLZhvHsrAjZWUx1oYlheLw2n0ms0IB0dZA1FpEeRFWLDYzFgqGqnZUA394zk1KxyrUgxKcmJJsBnx9ovGYTeTGGFlkMuNp9HHiVo0FWajkXndtEwiY+3k9oxgyHPGxa4bftMXs9WC1WLi8ofHt+ftHjKN1c1s+LKYgvEZJIRZSHjypb3+P6uqqeP7VesY3zufL5avZfTQAYSHta+xOm3SMdTUuph07IiOCF+ILsFiMePfqbBbrEw+diSTQx2QEB1JKXXAnz59+nzTp08fU58+fea13P/uYMd05M/jjz+uWjW5PGrRy+tUZVGd+jm+2FKvmrwBtXRHo7p9bokq2lGmXpv9mapzNajNRcVqc1GxWrx8jXrjg7nK4/WqXVU16sTzr1fLV29QSim1cu1G9fWSFWrrtp2qqdnzs55bGHw+n/r+h/Xq8RfeUP987nWllFKvzv5UvfvJPLWjtOKIPU8wGGy77fX6lFJKLfz+B3XfI8+qa37/oLrjb08opZR66a2P1LRf36gmnX21Ou2SW1T5riq1ZMUaY9s516hxMy5Xsz+ZpwKBgJpw1lVqyvnXq3EzLld3/HWWUkqph556Wd378DPqpbc/Uu9+Mk8Fg8EfPffB9OnT514Vwrz66U9rnn1VWK++0uvUa9d9qZrrvW2v6Vp9qwoGg+riG+9Tny9Y8qPX+P4n/qP8/sBBf2e/16/mPblSNVS6lVJKNTd41cd/W6p8Hv+P9ltT3qT+b3WNavIG1IXvbFd+f0C98PhKtWN7vbr/X2tUWYVbvfLsGvXcsiqllFK+QFCt+982tf7L7crvC6jiVcb7aeF/1ip3XbPy+wLK5/GrknVVasV7m1XAF1CvXfelCgaDascPu9r2f21VtdpS7VFNTX4VCAbVLpdH/ePjYuUPBNXHG12qsMajatx+9ekm14/iDQSCytvkUz8UutQjv52vgsGg+vjrUlXV6FMVLq9yNfn2+5q0vmd2NfiULxBUdc1+9cWWeuO19Rv33d6AWlzcqJRSamu1R9U2+fd7vvZyVTSqmp3G8zTVe1QwEFSuika1cf4OpZRSaz8rUmV6tVJKqe/f2qiCwaBy1zWr2pIG4zVv+Zv5fQHl9x5+PB2lM+ZZU71HbVlUopp8Rs54N21QTYu/Vkop5SvZoQL1LrVg8Qo1/ORfq4a6OvXGB3PVt9+t6rDXSIjD1dnyTCnFb2+7TT356muqtGKX+stjz6s/v7NUFdXI9zfRte0v19oz5651okJrXYrDrzV/hDij7Iw4V+P9uxcy469jDzinYlejn01VHsZmRrCh0kN+kgNvySa2vv1/XNnQyLgRBXi8Xpav1nns+f8jPzeLY0YUYLNaufzWv3DmtOPbJp7vb56DaD+r1cqwgfkMG5jfti0xLpYFS1bw75ffJSczjafuv50P5i5g/aYihgzoQ78+OaQlJ7F6wxaWLF9Ng7uZRncTM0+ZRHZGCl8vWYmroZGlK9YyoqAfZ047nknnXENifCx2m43sjFT+8rurWLFGR8vNYspxo4iKMMawnnrCsZwwfhQOh43augaiIyMYmJ/Hs3+/C4fDjtNhx2G3YzabmffWU4BR4Ea1pMVpU8azRt9CUXEJtXX1mEwmnnrpHebOX0xSQhxFxSW8+OgfKauo5JFnXiMvO5287AyOGzOUzLTOuf7b7PV1lLp8zN7g4sHbRmBv6fma9cKbvDd7Pnecewlbtu1g3Mjd8zZtNit3XHtRu85vsVmYcM3uOUCOCBsjzuuDyQSfP7qcoafnsWrOViZeV0D/Hk6UUtw9Ig6TCYbnRJOaHsEdVxuVAk+9MB9zS2+H1Wyi76RMAFzlbrYvqyBtYCI9esfiiLTzw5yt2JwWckanYLWbMVvNTP/TGCMmuwWUIhgIEnxqOWkPjWfJS+voNSqZsBg7BXoFlpPTyStzkZySRPEuN7u21kFeFH97dj0TxiUTV+VmxbJdnHdDAWl/G4vJZMLUM4LCWm/bvM/9ae2xae1ljHZY2obD2iwmbC3zoVqLz+yvoNPPFZW0Oy5npL1tW+v2flOy2h4fdpbx+RcW7SAs2hhabbUbcVlCOHexq3JG2skZncJprxbx4unpRAMEgzR/O4/aR/5C+NTTOfay6xiZ34v7Lr+ev7/531CHLESXYzVbsH4XgfUkK0XFpRzbfyhRDvm8Et1Texp3rwELgCxN0z4G3uvYkNpvy8ISXBVuznhg3H73qXT7cVrNNHqD6JUeVPlmfnj3Y06/6Qpys9L54y1X0Dcvu60y2MxTJnHm1Ik/qhT28uP3ERUpi7d2tCnHjWLKcaNQSlFSXglATmYadfUNfDZ/MY89/wbvPPMADruNQFCRGB9DZmpPEuJiqKqp47P5i4kMD2P00AFMGDscgLmvzWJz0Q6amj0M7JsHwDUXnbXXc8fG7C5sEhcT3Xb7QGXALXtcTOiTk0mfnMwfPX7FBTM4ccJoqqrryEjrSc/EeBLiYrjz+ovZXLSDLdt2UF3r6rSNu4LkMHpuqWZ0gomYnfV8/elWxlzSj5mnTMJT62P52g1MP/E4nI4jV/EzMTsGpRTacenEpkcycGo2wYDi0weWMv43g9CfX0PmrUMZOK3Xj46L3s9Q5eie4Yy9xGgA5o41Kqj2PzELq8OCyWQiLMb4+4bHGUPckrW4tmPPmzURs9XMMZf2J+ANYA+3MfnGoQD4mvw013kJd3kY5DPm753Q00FujB1/WgQem4Oggvhw4yP2pN6dt3COCK3KwjoWvbSe9/9gTDc35WkEyktw/fufxFx/O/X/fZrfb2nkwtEDcC+uCXG0QnRNFouZQKYHp8NGo7uJkVmxbYXXhOhu2lNQ5QlN074ABhh39R86PqwDa513kzYwkZ6eACVrq/C6fWSP2PtL8ksraxiWGk66qYavXnqOTz1eLj3nVBLjY7HZrCT32HuNsZ+WgJaG3dFlMplISzYqJg7Iz2VAfi4Xnjm17XEtNwstN2uv4x66+4a9tlmtVvLzsjss1gOxWizkZKaRk5nWts3psNNfy6X/HiXKO6vkSCspE9IJePw4o+ysW17IY395k1vvO5+zZh5PRVUNo36yxtaRYDKZyBxqVOdMyo0FYOT5+UQlhXHqvaMPdGi7/HSh9f0xt/RCWazmvXqkWnuyYtMiSRuQCMCI03bPG71kVOJhxyl+GeKzoplwyzA+3dzQtkSL57tFxP3x79jyNIpef4WNm4sYOE7D3ys1xNEK0TVZzBZUvB+Hw0FMVCS/+bSaT36TSbhNeu9E99Oegip/2ONuX03TZui6/qcOjOmg7l9QgdNq5qo+ETii7NgjrJjMP56APmtJJafnx3DVkCga3U3YbLH86oyTmTRuhKzfI0Q7/GluCZMjTEyeYDROzVmK+l2N+D0BKhfWE58ae9RiScqJOWrPJcTR5HX7KN/qYksTgNG4i7nxzrbHt8+8goKFywmU7cTSo3P28gvR2VnMZmxfxmD9tYWnHrgDf0DRSVdHEeKwteetXd7yUwGkA5kH3r3j3X1cD64fncCaT4rYtaWWxOwYevaOJdi6AjYwNCWMxppKLr35z3wwdwGx0ZGcMH6UNOyEaKd7Rifg2LR7GFijaqJHdSKVRS4KTssha/i+Fy8XQrRfU62HqjWVXLef3t6aBjcFARfe1Sux9R14lKMTonuwWC1YTmvCZDLx6HNv8M22hkNeIkqIzq49wzKf3vO+pmmfdFw4B+cPKv67spZ6T4DrL9q9XMAnD3zHyPM03q0OMDgljFTquO6ev3PR2acw85RJBzijEGJf5lf5Gf/rvm33nQ47iZMjSdbiKN9YQ0J29AGOFkK0R1x6FM7js5i1pJLr92jg+Xx+TGYT5844kQbfLhpemou9T78DnEkIsT8WkwVzsQOvz8cb73+GV5vIhByZCy26p/YMy+yzx90UQtxz5w0oqpv8FCSH8f2bGxk8Ixer3cJJtw/HYrNwYryX2DALa36o4OqLzuKUyfsvtiKE2L+iQhcxy0s45lyjSuxpU8YT8AdZ9N91NFQ2ceyVA9uqJAohDs2yVZXsXLmLk0/ZPWdz4fc/8Ns/PMygvnmcOe14Tp5xNvb8/picnXMhdiE6O6vFgrXehtvdTESYk9uPlZEnovtqT2WBPXvumoFbOyiWdgm3mbl+bBKBoGLNOmtb0YMGb5DXn13PxZf2Zt6iJZw0YYx0uQtxGC4Zk0hV4e5KmO9+PA8tL4uErGhGnKdJw06II6DWbCHQM4KUKFvbtsLtJQBUVNYwbsRgTDY79oFDQhWiEF1eZGQ45117AmUVVTicTh5fXMkNo6Xwleie2jMsc+LRCKS9Sup9PLGkkuUlTdw2LhVzSyEVZTYRZzfz7Evvsb28jJMmjAlxpEJ0bTd9tYvHpu6uzjd/8XKSEuI4dsLgAxwlhPg5irxBfghYqF1TyyVD4wEoLi3nhsvO4eSJY4mOkmrNQhwuvzfAF4+sYOJvC3j4T7fRZJe8Et1Xe4ZlrsIo4dUMtI4JMQFK1/Wc/R7YQeLDLEzrE835GU4WPrWSvn8YTb0nQLNfMebkeK685VteferP0msnxGE6L0Lx1aPLOfE2Y81AV30jMdGRIY5KiO4lrbKRunnbueQfu6cQmDCh5WaRGH/0KtIK0Z2ZrWYGnZSDu7kZ/F4GZMgQZ9F9tWdY5kLgJV3XF2maNhC4DbiiY8PaP7PJRHyYhXqTjdiZ+QCs2+Xh2+2NRBcu4ayok4hyhIcqPCG6BaUU2shkek5Mb9tWV98gvQhCHGGDxqawKSaMrTVecuKMYdC3X/vrEEclRPdiAqJ7hrNiwwaeeuNTMk+6iL9NlqVFRPfUnnUB+um6vghA1/XVQKau6x5d1z0dG9q+ba/18t+VNdRUeygucwMwKj2cm8cmcfkF07noiSnYw20HOYsQ4kD8QbjtLWOpkVYvPPIH0lNkEroQR9KL88vYvq6GZn+wbdvDT7+K3+8PYVRCdC8+T4C5D31Po7uZjIRIadiJbq09jbtaTdP+rGnaqZqmPQhs6+igDiQvwcENoxOJb/SSX+tmTXkzsxZX8tBTL7Ny7UZsDiuLX15PY3VzKMMUokuzWUz8fUwClUUuAILBICvX6FgtUkRFiCPpivwonFVu4pxGbvl8ft6a8z8skmtCHDH2MCsz/noMjU1NBMx2VpY2hTokITpMexp35wMu4CRgK3BZh0Z0EOUNPpaVNJE2MJEP46Ko8wSYkhfFvIXfk5yUAEDqgARsYe0ZcSqE2BevP8g3QQv9TsgCYFNhMbNeeCPEUQnR/XwXNHP9NQPaqmXWN7qJjAyXeeNCHEF+b4AN84rp17sXA4cOpbRBesZF99Wexl0zUANUAjoQ0hnegSB4A0G2LCxhmiNIXJiF7GgztXUNJCXEAZA5pAdNtR5K11eHMlQhuqwg0Liukg1fFgOwaNlqxgwfFNqghOiG6jbVsOnTorb7roZGoiNlbqsQR5JSCk+9Fy03iwtPGMbJvWUBc9F9tadx9zTGwuVTMKpmvtShER1EarSNMRkRRPUIp2fPcO75opyq2nrye2djsez+ddy1HurL3SGMVPzSVWyuxdPoo7nBS+HSMgCaG7x43T4Adm2pRSkVyhD3y2k1c8b4FHr0Nq7lLFm+htFDB4Q4KiG6n+mje7ralVIAABQWSURBVKANTWq7n56cxNMP/j6EEQnR/dgcVgpOy2XWC29w97Nz+HijK9QhCdFh2tO4y9V1/Q9As67rc4CYDo7pgFaVNfHRRhdJOTGk9ormnyenkNojnhcevudH+6X0jafPhHRK1lbha5bu964u6A9SWVQHQM2OeprqjHo++2oceRp8BHwBvG4fWxYaiwEXLi2jfGNN222/N0DVNhcb5hk9U/pXxdTsqEcFFTtXVwLGBYKGyqb9Ps+e20vXV6OUorKwjnVzjWmpWxeV0ljdjAkTFZuMwiSbvylhw7wdAPzwUSH1FZ3zAkR1k58HV9YSk2L0IFz16zMZMkALcVRCdD93L6rCE+Nou1/raqCyuvYARwghfq7mBi9fPLaCuvpGchLCGJwSFuqQhOgw7WncWTVNSwSUpmlRGCO2QiYv3sHYjAjm3LeYqkIXGTF2Vq7dyLyF3+9z/+0rKtq+oIuuZ+viUtw1zVQWudC/NBpFhUvLqN5eT8Af5NWrvkAFFZu/2cnS1zcAMO/JldQUN+DzBKgtaQTAFmbFYjfe7kXflWO2mLDaLUTEGWvd2MNtmK1mfJ4A6/63HTAabFsXlQLw4X1LqNxaR11pI3PuWwzAqjlbWf1xEQA/fLgVX5MfR6Sd6GRjKY7RF/YlPiMKR6SNURcYy3YMOCmbQdN6ATDphiFE9+ycw6+i7RZOqK5n9YeFACTGxxIeJusCCXGkzbQG2DJ7c9v971et5+V3PglhREJ0P/YwK0PPzMPd1Ex8dBjxTilYJLqv9lQduQv4FkgBFgO/7dCIDkKhsJrhlHtGgdmYcP7dyrX4A0Emjh2+1/6jf9UXpRTr5m6j1+hkwqIde+0jOpegP0jJuirSByVRX+GmZ+9YeuQZPwBDz+jdtu/Zjx4HQPbIZNIHGUObTrp9RNvjw84y9k0fmNi2beK1BQDEpES09Uz1Grm7LPIJNw0FIHdMStu2qXeNwGQ2gcnE5BuHAKBNSMfeUrindaFve7iNqKSuf0UwCPQ5JYfUKBs+n58zr7idRR88H+qwhOh2UkalkBmT2Xa/vtFNdKSs1SrEkWZ1WMhI6cHSajuOokaZdye6rfb03GXouq4BucAAXdf/18ExHdC6Cg/fb2ukeOUuzC2Nu7Jd1ST3SNj/QQr8viAWq5mgP6Qdj+IAyjZUU11s9MjpX+5ABRUFp+USkbD/xpI93IbJbPTCOaPtHRabxWbBbDFjNpsIaxlC5YyyY7a2J4W6niq3n1c/Lqa6uJ66+gZioiKkep8QHeCh97dRvG538a/6hkYiI6RxJ8SR5GnwseTVDVx90Vn87ewR0rAT3Vp7vpleCaDr+i5d10Ne/WFkejgjk+w/qoRZWV1LUvz+i3iazCYGTeuFPdzGvCdXUbK26miEKtqpfpcx76yxuplmlxeb08qkG4cYPWUiJFKibJye7qTZ5aXW1UBMdGSoQxKiW7p7RDz+ljnEAKOHDuT4Y0Yc4AghxM8VFuNg8o1D+edzr/PO99vZ4fKFOiQhOkx7hmU6NE1bgbEMQhBA1/XzOzSqA9ArPVSazIy+sG/btntuvIyI8PYNhRt3+QBsYVZK1lbRWNNM73FpHRWq+ImgP0jhd+Xkjklh+4oK6koaGXByNvOeWMWkG4eQOzY11CGKFpWNfoqSo5mcG0lJ+S5mnHhcqEMSottRSvF+I1x9zO7PPi0360eVn4UQh89d66FwSSnzFi5jRv5IfIGQ91UI0WHa8z/I7cCNwFMYyyI83aERHYTFDOVLy9i0YGfbtsLtJdhs7Vu03BFhM4bWxTqISgxDKcWqOVsJ+AJt+wT8QVTw0BJ/zSeFNLk8BIOqraJjfYXbOKdSbb1UrVUWg0FF8BCfq/X41udorSZ5KENPNy3Yid8bwNPgo2qbUSL4g3sX4a71sHNNJV88tgKAtZ8VtfWatlaa3LKolOXvbgJg3hMrqd3ZgLvOw4qWIgGbFuykcGkZJouJnasr8XsDJGRFk16QiMls4tR7R7cVNhGdg8kE9fO3s3N1Jak9kzj/9JNCHZIQ3Y4CnFuq2yrsAtz+11l8+e2+C4QJIQ6RyRjF5XY3M2NgEr3iOm4ahxChtt/GnaZpdwPouj4f2Kjr+vzWn6MW3T7kxTvIL0hoW38rEAhywx/+8bPPE5cWSXJ+PAFvkGAgiNlqZtuyckrWVlH0XTnfPL8GgKoiF35P4IDn8jb52xoyKghWu4WS1ZUsedWo3rjopfW4yhopXVfNitlbAPjqyVWUrq+mZns9c+5dBMDmb3a2DRndtbWOYMAo/1+x2SiL7Wv2o5TRGKzfZVQA/eKxFVQVuagrd1Ox0djvkwe+o0yvoanOw4JnVgNG5cei74y11lbN2WosLVBYx+qPCtueD6C6uJ7CJcZ+E68djCPCSkrfeMZdZqxxlpQTQ2Sik2AgSPW2evzeAKn94smfZBQEGHx6HpGJYZhNJiJbCovEpUeS2CsGk8nE+CsHGlUq453EpRtj3mUuV+eTEG5lwpQM4jKj+GTeQp59dXaoQxKi2zGbTMyckk56we6CT4Xbd5KVnnyAo4QQP1d4jIPsEck0upt4aLGLohpvqEMSosMcqOfu+D1uv9rRgbTXp5vq2elVbRUJa+pcREdFYrUcWllbq8PCkBl5mEwmHBE2bE4LuWNSGHNRP8BoCDXVeagrbWTVB1tQQcWnD35Pk8tDXVkjWxaWEPQHCYt1oIKKgdN6YXNaSS9IYsI1RlXGKbcOIy49ipR+8Yy7rD8Aoy7Ip2efWBKyozn1j6MBiEwMIyzGjlKKxS+vRwUVAW+Q2p0NAHz20DJ2bamjeruLxS+vB2D0BfnEZ0WRPjCRflOyAJh610h69o7F6rCQO9ao+Oj3BohONipDmkzgdftxRNhIzDGWLRx7cT+sdgspfeMZfnYfAKKSwtoKiTgibQD06B1HVFI4Zou57ZiwGAfhLUVG4tIisTqM4iatQ14Tc2K6RQXJX5L1u5p5t8iNI9zK5qIdeH2yVqQQR5rbF+SG/5XjjDJ6ERrdTVRU1ZCVnnKQI4UQP0e5XsO6z7fx5VtPcf7gRJIj2zfaS4iu6EDvbtN+bofUsVkRlLy/mchxaWQMTqKyupaEuCOzrnpyfnzbbavdaCwef/1gABprmonPiMJkNjFsZm+cUXZjgWqzCWeUnfyJGQc9v8lkwmQxXsrwPYYhmlvmV+z5/K0Nvp594ujZJw4wln9QSmEymTjhZqNcf1SPvauqmUwmMIHNaSVtgHFFOKMgqe3xQafkAOCMtu/zeCFy4+ykLythmaeal976iHOnTwl1SEJ0O06riSucAdZ9vp3B03NpcDdxxtTjD/lipRBi3xJ6RZM6NJ5Z7y9iUSCbN8/JPPhBQnRRB+q5U/u5HVJuX5BxVw0iY7DRWEmMj+WK86d3+PNGxDnJGNIDMIYmmkwmErNjyBl9dK+wyhBGcTR4Aoohlw/k2y0rOGvaJC4/CjkmxC9NIAjxx6QzeHouAD0T47npivNCHJUQ3Y/fE6C61sWHs2dz/egELPJVSnRjB2rcDdM0baGmaYv2vK1p2sKjFdy+rChtYvXn2/E1G8PEYqMjmTBmWChDEqLbKa7zMf/jImoq6ino15uYKFkKQYgjze0L8tbcHZRvrAHgxTfmSDEVITpAVZGL0q1VpMZFMLFXpFwoF93agYZlDjpqUfwMU/KiUHusCTTrhTfpkRjPBWdINT8hjpR+PZz0jbQwv9pFUsL+15AUQhy6GKeFGf2j2y5Wrlq3iZwsWZ5HiCMtbWAi5kQ71T6Zaye6v/2+y3Vd37a/xw6Fpmlm4F9AAeABLgeOA64Aluu6fk3Lfq8BV+m67trXeRYXu7ny6t7YrEanY2FxCcMK+u5rVyHEIdpc5WH8zGQu7Hkyeb0OPp9UCPHz1TYH+NZs49yBxgWU4pJyMlJ7hjgqIbqf0g3V9JwaxzkzZ4Q6FCE63NFcKXUG4NR1fQxwB/Aw8GtgLJCmaVqcpmnTgK/317ADiDIp5j2yvO1+UXEpvTJk8WshjqQwm5mtL64hIz6F6MiIUIcjRLdkNYPjh3I2LtiBUgqlFKk9kw5+oBDiZ7GHWYmNiWDmMfmhDkWIDnc0G3fjgE8BdF1fDAwH3IATsAFB4FLg2QOdJNLvJnFsBOs3FVJd62JQv97yn6EQR1jzrjp6jnJy/s13ydwEITpIXWUDqRlBmiKb2LC5iDf//TecDllcWYgjrcbk4YFX5/Dr+54PdShCdDiTUkenEKamac8B7+i6/knL/e3A+cBtwBzADmzDGLaZAfxT13V9P+fZcVSCFuLoSdd1/fJQB9FK8kx0U5JnQnS8TpVnILkmuq195trRbNw9AizWdf3Nlvs7dF1Pb7kdDTwN3Atc3/LvY7quX3BUghNCCCGEEEKILu5oDsv8FpgKoGnaaGD1Ho/9HngQCAcCGOvqSe11IYQQQgghhGino9m4mw00t6yT9yhwE4CmadlArK7rK4FVQCbwMfDEUYxNCCGEEEIIIbq0ozYssyvSNG0FUNdytxBj6OhjgB+Yq+v6fUc5nlHAg7quT9A0LQ/4D0Yv5xrgWl3Xg5qm/RGY1hLjjbquLw1BbEMx5lFuann4KV3X3zjasWmaZgNeALIBB/AXYB2d6HUTnS/PWmLqlLkmeSYOleTZYcUW8lyTPOs6OluuSZ797Ji6fK7Jao77oWmaE0DX9Ql7bFsJnAlsBT7SNG2oruvL932GIx7P74ALgcaWTY8Ad+u6/pWmaf8Gpmuatg1j7cBRGEVp3gFGhCC2ocAjuq4/vMc+Q0MQ26+AKl3XL9Q0LQFYAaykk7xuovPlWcvzd8pckzwTh0ry7LBj6wy5JnnWBXS2XJM8OyRdPteO5rDMrqYACNc0ba6maV9qmjYecOi6vkXXdQV8Bkw6ivFsAc7Y4/4wYH7L7U+AyRjLTczVdV3pur4dsGqadjTWidhXbNM0TVugadrzmqZFhSi2t4B79rjvp3O9bqLz5Rl03lyTPBOHSvLs8GMLda5JnnUNnS3XJM9+vi6fa9K42z838A/gROAq4MWWba3qgZijFYyu6+8Avj02mVo+KPaMJZrdQwH23H60Y1sK3Kbr+niMK1V/DEVsuq436Lpe3/IB8TZwN53odRNAJ8sz6Ly5JnkmDoPk2eHFFvJckzzrMjpVrkmeHVJcXT7XpHG3fxuBV1pa5Bsx/oDxezweBdSGJDJDcI/brbG4Wm7/dPvRNlvX9WWtt4EhhCg2TdMygHnAy7quv0bnft1+iTp7nkHnfc9Inon2kjw7PJ0i1yTPuoTOnmud+T3TKfIMun6uSeNu/y4FHgbQNC0VY5mGRk3TcjVNM2Fclfk6hPGt0DRtQsvtk1ti+RY4UdM0s6ZpmYBZ1/XKEMT2maZpI1tuTwKWhSI2TdN6AnOB23Vdf6Flc2d+3X6JOnueQed9z0ieifaSPDs8Ic81ybMuo7PnWmd+z4Q8z6B75JoUVNm/54H/aJr2DUZ1nEsxWu6vAhaMcbZLQhjfLcCzmqbZgfXA27quBzRN+xpYhNFwvzZEsV0NPKFpmhcoA67Udd0VgtjuBOKAezRNax0//Vvg8U76uv0SdfY8g86ba5Jnor0kzw5PZ8g1ybOuobPnmuTZwXX5XJOlEIQQQgghhBCiG5BhmUIIIYQQQgjRDUjjTgghhBBCCCG6AWncCSGEEEIIIUQ3II07IYQQQgghhOgGpHEnhBBCCCGEEN2ALIXQBWia9jAwDEjGWDNlK7BL1/WZ7Th2MHCarut/2s/jJwGZuq4/cxjxnQzcilHu1wI8r+v6q5qmXQxU67r+waGeW4ijSXJNiI4neSZEx5M8++WSpRC6kJY3fL6u63eEOpY9aZq2DSjQdb1W07QoYBUwWtf1ihCHJsQhkVwTouNJngnR8STPfnmk564L0zRtAvAg4AWeAZowFlE0texyFjAAuErX9XM1TdsEfAtoQDlwJnAhkA/8G3gdKAZygaW6rl+taVoi8BrgAHTgeF3X834SSjnwW03T3gbWAX11XfdomnYvxkKU5RgLQAKkA8W6rk/UNO1+YDzG8OBHdF1/60i9NkIcSZJrQnQ8yTMhOp7kWfcnc+66Pqeu68fquv4y0AeYpuv6BIxkOvEn++YA9+i6PgZIAkb85PE+wGXASGCqpmnJwF3Ae7quHwe8xb4vCJyG0eX/OlAK/F7TtNYPCXRdn90S08VANXBxS3d8L13XjwEmAndpmhZ7aC+BEEeF5JoQHU/yTIiOJ3nWjUnjruvT97hdAfxX07QXgUGA7Sf7Vuq6Xtxyuxhw/uTxzbqu1+u6HsBINCfQF1jY8vjXP31yTdPigCxd12/XdX0Qxvjuk4BTfrJfMvA2cJmu69uAgcAwTdO+Aj5tiTWr3b+1EEef5JoQHU/yTIiOJ3nWjUnjrusLAmiaFgPcB5wLXI7RzW76yb4Hm2C5r8fXAGNabo/ex+MO4E1N0zJa7pdidKd7WndouaryHnCzruurWzZvAOa1XJU5HngTY7KvEJ2V5JoQHU/yTIiOJ3nWjUnjrvtwYYyJXo5xlaQJSD0C530AOE3TtHnAFYBvzwd1XS8Drgfe1TRtEbAYWK7r+tw9dvtrSyx/1DTtK03T5gJzgAZN074GlgFK1/X6IxCvEB1Nck2Ijid5JkTHkzzrhqRapjggTdOmYpTO/U7TtMnAnbquHx/quITobiTXhOh4kmdCdDzJs9CSapniYAqBFzRN82OsQ3JDiOMRoruSXBOi40meCdHxJM9CSHruhBBCCCGEEKIbkDl3QgghhBBCCNENSONOCCGEEEIIIboBadwJIYQQQgghRDcgjTshhBBCCCGE6AakcSeEEEIIIYQQ3YA07oQQQgghhBCiG/h/taSJUnFrHFYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119d35be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_selections(['glass', 'ionosphere'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Information Density" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "def plot_learning_curves(measure):\n", " format_as_percent_plot = lambda x, pos: \"{:.0f}%\".format(x * 100)\n", " fig = plt.figure(figsize=(15, 20))\n", " \n", " selected_methods = []\n", " for policy in ['w-confidence', 'w-margin']:\n", " for gamma in [50, 60, 70, 90, 95, 99]:\n", " selected_methods.append('{}-gamma-{}'.format(policy, gamma))\n", " \n", " for (i, dataset) in enumerate(uci_sets):\n", " initial_n = 10\n", " \n", " learning_curves = {}\n", " for method in selected_methods:\n", " learning_curves[method] = load_results(dataset, method, measure, True)\n", " \n", " maximum = load_results(dataset, 'asymptote', 'asymptote_{}'.format(measure), True)\n", " sample_size = learning_curves['w-margin-gamma-50'].shape[0] + 9\n", "\n", " ax = fig.add_subplot(4, 3, i + 1)\n", " for method in selected_methods:\n", " xticks = np.arange(initial_n, initial_n + len(learning_curves[method]))\n", " method_label = 'exp3++' if method == 'exp++' else method\n", " ax.plot(xticks, learning_curves[method], label=method_label, linewidth=1)\n", "\n", " ax.legend(loc='lower right', frameon=True)\n", " ax.get_yaxis().set_major_formatter(FuncFormatter(format_as_percent_plot))\n", " ax.set_title(dataset)\n", " ax.tick_params(top='off')\n", " ax.set_ylabel(titles[measure])\n", "# ax.set_xscale(\"log\")\n", "\n", " ax.plot([initial_n, sample_size], [maximum, maximum], ls='--', color='#377eb8')\n", " ax.set_xlim(initial_n, sample_size)\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", "# fig.savefig('figures/learning_curves-thompson-{}-{}.pdf'.format(measure, data), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a0db78c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_learning_curves('mpba')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def plot_strength(measure='mpba', data='small'):\n", " fig = plt.figure(figsize=(10, 8))\n", " fig.subplots_adjust(hspace=.6)\n", " methods = []\n", " method_names = []\n", " letters = 'AB'\n", " \n", " for policy in ['w-confidence', 'w-margin']:\n", " for gamma in [50, 60, 70, 90, 95, 99]:\n", " methods.append('{}-gamma-{}'.format(policy, gamma))\n", " method_names.append('{}, {}th'.format(policy, gamma))\n", " \n", " for (i, dataset) in enumerate(['glass', 'ionosphere']):\n", " results = {}\n", " for method in methods + ['passive']:\n", " results[method] = load_results(dataset, method, measure, mean=False)\n", " results['max'] = load_results(dataset, 'max', 'max_' + measure, False)\n", " strength_dict = {}\n", " for method in methods:\n", " s = calculate_strength(results['max'], results['passive'], results[method])\n", " strength_dict[method] = s\n", " strength_df = pd.DataFrame(strength_dict)\n", " strength_df.columns = ['{}-{}, {}th'.format(x.split('-')[0], x.split('-')[1], x.split('-')[3])\n", " for x in strength_df.columns]\n", " sorted_cols = (-strength_df.median()).sort_values().index\n", " strength_df = strength_df[sorted_cols]\n", "\n", " ax = fig.add_subplot(2, 1, i + 1)\n", " strength_df.index.name = 'trial'\n", " strength_df = strength_df.reset_index()\n", " strength_df = strength_df.melt(id_vars=['trial'], value_vars=method_names)\n", "# strength_df.loc[strength_df['variable'].isin(methods_al), 'type'] = 'single'\n", "# strength_df.loc[strength_df['variable'].isin(methods_bandits), 'type'] = 'bandit'\n", "# strength_df.loc[strength_df['variable'].isin(methods_rank), 'type'] = 'rank'\n", "# strength_df.loc[strength_df['variable'] == 'baseline', 'variable'] = 'explore'\n", "# strength_df.loc[strength_df['variable'] == 'exp++', 'variable'] = 'exp3++'\n", " sorted_cols = list(sorted_cols)\n", "# sorted_cols[sorted_cols.index('baseline')] = 'explore'\n", "# sorted_cols[sorted_cols.index('exp++')] = 'exp3++'\n", " # We could use hue here, but I think there is a bug in seaborn that squishes\n", " # the boxplot\n", " \n", " \n", " sns.boxplot(data=strength_df, x='variable', y='value', order=sorted_cols,\n", " width=0.4, linewidth=1, fliersize=3,\n", " color='#3498db')\n", " ax.set_title('({}) {}'.format(letters[i], dataset))\n", " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, rotation_mode='anchor', ha='right')\n", " ax.xaxis.set_visible(True)\n", " ax.set_ylabel(titles[measure] + ' Strength')\n", " ax.set_xlabel('')\n", " ax.axhline(linewidth=1)\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", " \n", " # set bar width\n", " new_width = 0.5\n", " for bar in ax.patches:\n", " x = bar.get_x()\n", " width = bar.get_width()\n", " centre = x + new_width / 2.\n", "\n", " bar.set_x(centre - new_width / 2.)\n", " bar.set_width(new_width)\n", " \n", " fig.savefig('figures/strengths-info_density-params.pdf'.format(measure, data), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1160e7908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_strength()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smaller Pool" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "methods_al = ['baseline', 'margin', 'w-margin', 'confidence',\n", " 'w-confidence', 'entropy', 'w-entropy',\n", " 'qbb-margin', 'qbb-kl']\n", "methods_bandits = ['thompson', 'ocucb', 'klucb', 'exp++',]\n", "methods_rank = ['borda', 'geometric', 'schulze']\n", "methods_no_passive = methods_al + methods_bandits + methods_rank\n", "methods = ['passive'] + methods_no_passive\n", "measures = ['f1', 'accuracy', 'mpba']" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "for (i, dataset) in enumerate(['sdss-small-train', 'pageblocks-small-train', 'sdss-small-pool', 'pageblocks-small-pool']):\n", " maximum = {}\n", " for measure in measures:\n", " asymptote_measure = 'asymptote_' + measure\n", " max_measure = 'max_' + measure\n", " results = {}\n", " for method in methods:\n", " results[method] = load_results(dataset, method, measure, False)\n", " results[method] = np.max(results[method], axis=1)\n", " results['asymptote'] = load_results(dataset, 'asymptote', asymptote_measure, False)\n", " maximum[max_measure] = results['asymptote']\n", " for method in methods:\n", " maximum[max_measure] = np.maximum(maximum[max_measure], max(results[method]))\n", " save_results(dataset, 'max', maximum)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def calculate_strength(asymptote, passive, policy):\n", " n_trials, n_samples = passive.shape\n", " asymptote = np.repeat(asymptote, n_samples).reshape((n_trials, n_samples))\n", " deficiency = np.sum(asymptote - policy, axis=1) / np.sum(asymptote - passive, axis=1)\n", " strength = 1 - deficiency\n", " return strength" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "titles = {\n", " 'f1': 'F1',\n", " 'accuracy': 'Accuracy',\n", " 'mpba': 'MPBA'\n", "}" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def plot_mpba_strength(measure, datasets):\n", " fig = plt.figure(figsize=(15, 10))\n", " fig.subplots_adjust(hspace=.6)\n", " \n", " for (i, dataset) in enumerate(datasets):\n", " results = {}\n", " for method in methods:\n", " results[method] = load_results(dataset, method, measure, mean=False)\n", " results['max'] = load_results(dataset, 'max', 'max_' + measure, False)\n", " strength_dict = {}\n", " for method in methods_no_passive:\n", " s = calculate_strength(results['max'], results['passive'], results[method])\n", " strength_dict[method] = s\n", " strength_df = pd.DataFrame(strength_dict)\n", " sorted_cols = (-strength_df.median()).sort_values().index\n", " strength_df = strength_df[sorted_cols]\n", "\n", " ax = fig.add_subplot(3, 2, i + 1)\n", " strength_df.index.name = 'trial'\n", " strength_df = strength_df.reset_index()\n", " strength_df = strength_df.melt(id_vars=['trial'], value_vars=methods_no_passive)\n", " strength_df.loc[strength_df['variable'].isin(methods_al), 'type'] = 'single'\n", " strength_df.loc[strength_df['variable'].isin(methods_bandits), 'type'] = 'bandit'\n", " strength_df.loc[strength_df['variable'].isin(methods_rank), 'type'] = 'rank'\n", " strength_df.loc[strength_df['variable'] == 'baseline', 'variable'] = 'explore'\n", " strength_df.loc[strength_df['variable'] == 'exp++', 'variable'] = 'exp3++'\n", " sorted_cols = list(sorted_cols)\n", " sorted_cols[sorted_cols.index('baseline')] = 'explore'\n", " sorted_cols[sorted_cols.index('exp++')] = 'exp3++'\n", " # We could use hue here, but I think there is a bug in seaborn that squishes\n", " # the boxplot\n", " \n", " palette_map = {\n", " {m: sns.color_palette()[0] for m in methods_al},\n", " {m: sns.color_palette()[2] for m in ['thompson', 'ocucb', 'klucb', 'exp3++', 'explore']},\n", " *{m: sns.color_palette()[1] for m in methods_rank},\n", " }\n", " sns.boxplot(data=strength_df, x='variable', y='value', order=sorted_cols,\n", " width=0.4, linewidth=1, palette=palette_map, fliersize=3)\n", " \n", " if '-' in dataset:\n", " dataset, size, kind = dataset.split('-')\n", " if size == 'small' and kind == 'train':\n", " title = '{} (small training set)'.format(dataset)\n", " elif size == 'small' and kind == 'pool':\n", " title = '{} (small training and test sets)'.format(dataset)\n", " else:\n", " title = '{} (full dataset)'.format(dataset)\n", " ax.set_title(title)\n", " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, rotation_mode='anchor', ha='right')\n", " ax.xaxis.set_visible(True)\n", " ax.set_ylabel(titles[measure] + ' Strength')\n", " ax.set_xlabel('')\n", " ax.axhline(linewidth=1)\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", " \n", " # set bar width\n", " new_width = 0.5\n", " for bar in ax.patches:\n", " x = bar.get_x()\n", " width = bar.get_width()\n", " centre = x + new_width / 2.\n", "\n", " bar.set_x(centre - new_width / 2.)\n", " bar.set_width(new_width)\n", " \n", " fig.savefig('figures/strengths-pool-{}.pdf'.format(measure), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d693b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_mpba_strength('mpba', ['sdss', 'pageblocks', 'sdss-small-train' ,'pageblocks-small-train', 'sdss-small-pool' ,'pageblocks-small-pool'])" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "lc_colors = {'passive': '#9b59b6',\n", " 'borda': '#3498db',\n", " 'exp++': '#95a5a6',\n", " 'confidence': '#e74c3c'}\n", "lc_line = {'passive': ':',\n", " 'borda': '-',\n", " 'exp++': '-.',\n", " 'confidence': '--'}" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "def plot_learning_curves(measure, datasets):\n", " letters = 'ABCDEF'\n", " selected_methods = ['passive', 'confidence', 'borda', 'exp++']\n", " format_as_percent_plot = lambda x, pos: \"{:.0f}%\".format(x 100)\n", " fig = plt.figure(figsize=(15, 10))\n", " for (i, dataset) in enumerate(datasets):\n", " initial_n = 10\n", " \n", " learning_curves = {}\n", " for method in selected_methods:\n", " learning_curves[method] = load_results(dataset, method, measure, True)\n", " \n", " maximum = load_results(dataset, 'asymptote', 'asymptote_{}'.format(measure), True)\n", "# maximum = np.max(maximum)\n", " sample_size = learning_curves['passive'].shape[0] + 9\n", "\n", " ax = fig.add_subplot(2, 3, i + 1)\n", " for method in selected_methods:\n", " xticks = np.arange(initial_n, initial_n + len(learning_curves[method]))\n", " method_label = 'exp3++' if method == 'exp++' else method\n", " ax.plot(xticks, learning_curves[method], label=method_label, linewidth=1, color=lc_colors[method], ls=lc_line[method])\n", "\n", " ax.legend(loc='lower right', frameon=False)\n", " ax.get_yaxis().set_major_formatter(FuncFormatter(format_as_percent_plot))\n", " if '-' in dataset:\n", " dataset, size, kind = dataset.split('-')\n", " if size == 'small' and kind == 'train':\n", " title = '({}) {} (small training set)'.format(letters[i], dataset)\n", " elif size == 'small' and kind == 'pool':\n", " title = '({}) {} (small training and test sets)'.format(letters[i], dataset)\n", " else:\n", " title = '({}) {} (full dataset)'.format(letters[i], dataset)\n", " ax.set_title(title)\n", " ax.tick_params(top='off')\n", " ax.set_ylabel(titles[measure])\n", "# ax.set_xscale(\"log\")\n", "\n", " ax.plot([initial_n, sample_size], [maximum, maximum], ls=':', linewidth=1, color='black')\n", "# ax.set_xlim(initial_n, sample_size)\n", " ax.set_xlim(initial_n, 201)\n", " if 'sdss' in dataset:\n", " ax.set_ylim(0.63, 0.91)\n", " else:\n", " ax.set_ylim(0.45, 0.78)\n", " \n", " if i in (3, 4, 5):\n", " ax.set_xlabel('Training Size')\n", " [i.set_linewidth(0.5) for i in ax.spines.values()]\n", " fig.savefig('figures/learning_curves-pool-{}.pdf'.format(measure), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119d7b208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_learning_curves('mpba', ['pageblocks', 'pageblocks-small-train', 'pageblocks-small-pool', 'sdss', 'sdss-small-train', 'sdss-small-pool', ])" ] }, { "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
hparik11/Deep-Learning-Nanodegree-Foundation-Repository	intro-to-tensorflow/intro_to_tensorflow.ipynb	2	55738	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<h1 align=\"center\">TensorFlow Neural Network Lab</h1>" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<img src=\"image/notmnist.png\">\n", "In this lab, you'll use all the tools you learned from Introduction to TensorFlow to label images of English letters! The data you are using, <a href=\"http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html\">notMNIST</a>, consists of images of a letter from A to J in different fonts.\n", "\n", "The above images are a few examples of the data you'll be training on. After training the network, you will compare your prediction model against test data. Your goal, by the end of this lab, is to make predictions against that test set with at least an 80% accuracy. Let's jump in!" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "To start this lab, you first need to import all the necessary modules. Run the code below. If it runs successfully, it will print \"`All modules imported`\"." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All modules imported.\n" ] } ], "source": [ "import hashlib\n", "import os\n", "import pickle\n", "from urllib.request import urlretrieve\n", "\n", "import numpy as np\n", "from PIL import Image\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelBinarizer\n", "from sklearn.utils import resample\n", "from tqdm import tqdm\n", "from zipfile import ZipFile\n", "\n", "print('All modules imported.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this data, 15,000 images for each label (A-J)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All files downloaded.\n" ] } ], "source": [ "def download(url, file):\n", " \"\"\"\n", " Download file from <url>\n", " :param url: URL to file\n", " :param file: Local file path\n", " \"\"\"\n", " if not os.path.isfile(file):\n", " print('Downloading ' + file + '...')\n", " urlretrieve(url, file)\n", " print('Download Finished')\n", "\n", "# Download the training and test dataset.\n", "download('https://s3.amazonaws.com/udacity-sdc/notMNIST_train.zip', 'notMNIST_train.zip')\n", "download('https://s3.amazonaws.com/udacity-sdc/notMNIST_test.zip', 'notMNIST_test.zip')\n", "\n", "# Make sure the files aren't corrupted\n", "assert hashlib.md5(open('notMNIST_train.zip', 'rb').read()).hexdigest() == 'c8673b3f28f489e9cdf3a3d74e2ac8fa',\\\n", " 'notMNIST_train.zip file is corrupted. Remove the file and try again.'\n", "assert hashlib.md5(open('notMNIST_test.zip', 'rb').read()).hexdigest() == '5d3c7e653e63471c88df796156a9dfa9',\\\n", " 'notMNIST_test.zip file is corrupted. Remove the file and try again.'\n", "\n", "# Wait until you see that all files have been downloaded.\n", "print('All files downloaded.')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 210001/210001 [00:44<00:00, 4766.39files/s]\n", "100%\|██████████\| 10001/10001 [00:02<00:00, 4807.84files/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "All features and labels uncompressed.\n" ] } ], "source": [ "def uncompress_features_labels(file):\n", " \"\"\"\n", " Uncompress features and labels from a zip file\n", " :param file: The zip file to extract the data from\n", " \"\"\"\n", " features = []\n", " labels = []\n", "\n", " with ZipFile(file) as zipf:\n", " # Progress Bar\n", " filenames_pbar = tqdm(zipf.namelist(), unit='files')\n", " \n", " # Get features and labels from all files\n", " for filename in filenames_pbar:\n", " # Check if the file is a directory\n", " if not filename.endswith('/'):\n", " with zipf.open(filename) as image_file:\n", " image = Image.open(image_file)\n", " image.load()\n", " # Load image data as 1 dimensional array\n", " # We're using float32 to save on memory space\n", " feature = np.array(image, dtype=np.float32).flatten()\n", "\n", " # Get the the letter from the filename. This is the letter of the image.\n", " label = os.path.split(filename)[1][0]\n", "\n", " features.append(feature)\n", " labels.append(label)\n", " return np.array(features), np.array(labels)\n", "\n", "# Get the features and labels from the zip files\n", "train_features, train_labels = uncompress_features_labels('notMNIST_train.zip')\n", "test_features, test_labels = uncompress_features_labels('notMNIST_test.zip')\n", "\n", "# Limit the amount of data to work with a docker container\n", "docker_size_limit = 150000\n", "train_features, train_labels = resample(train_features, train_labels, n_samples=docker_size_limit)\n", "\n", "# Set flags for feature engineering. This will prevent you from skipping an important step.\n", "is_features_normal = False\n", "is_labels_encod = False\n", "\n", "# Wait until you see that all features and labels have been uncompressed.\n", "print('All features and labels uncompressed.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<img src=\"image/Mean_Variance_Image.png\" style=\"height: 75%;width: 75%; position: relative; right: 5%\">\n", "## Problem 1\n", "The first problem involves normalizing the features for your training and test data.\n", "\n", "Implement Min-Max scaling in the `normalize_grayscale()` function to a range of `a=0.1` and `b=0.9`. After scaling, the values of the pixels in the input data should range from 0.1 to 0.9.\n", "\n", "Since the raw notMNIST image data is in [grayscale](https://en.wikipedia.org/wiki/Grayscale), the current values range from a min of 0 to a max of 255.\n", "\n", "Min-Max Scaling:\n", "$\n", "X'=a+{\\frac {\\left(X-X_{\\min }\\right)\\left(b-a\\right)}{X_{\\max }-X_{\\min }}}\n", "$\n", "\n", "If you're having trouble solving problem 1, you can view the solution [here](https://github.com/udacity/deep-learning/blob/master/intro-to-tensorflow/intro_to_tensorflow_solution.ipynb)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed!\n" ] } ], "source": [ "# Problem 1 - Implement Min-Max scaling for grayscale image data\n", "def normalize_grayscale(image_data):\n", " \"\"\"\n", " Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]\n", " :param image_data: The image data to be normalized\n", " :return: Normalized image data\n", " \"\"\"\n", " # TODO: Implement Min-Max scaling for grayscale image data\n", " \n", " x_prime = map(lambda x: 0.1 + ((x0.8)/(255)), image_data)\n", " return np.array(list(x_prime))\n", "\n", "### DON'T MODIFY ANYTHING BELOW ###\n", "# Test Cases\n", "np.testing.assert_array_almost_equal(\n", " normalize_grayscale(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 255])),\n", " [0.1, 0.103137254902, 0.106274509804, 0.109411764706, 0.112549019608, 0.11568627451, 0.118823529412, 0.121960784314,\n", " 0.125098039216, 0.128235294118, 0.13137254902, 0.9],\n", " decimal=3)\n", "np.testing.assert_array_almost_equal(\n", " normalize_grayscale(np.array([0, 1, 10, 20, 30, 40, 233, 244, 254,255])),\n", " [0.1, 0.103137254902, 0.13137254902, 0.162745098039, 0.194117647059, 0.225490196078, 0.830980392157, 0.865490196078,\n", " 0.896862745098, 0.9])\n", "\n", "if not is_features_normal:\n", " train_features = normalize_grayscale(train_features)\n", " test_features = normalize_grayscale(test_features)\n", " is_features_normal = True\n", "\n", "print('Tests Passed!')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Labels One-Hot Encoded\n" ] } ], "source": [ "if not is_labels_encod:\n", " # Turn labels into numbers and apply One-Hot Encoding\n", " encoder = LabelBinarizer()\n", " encoder.fit(train_labels)\n", " train_labels = encoder.transform(train_labels)\n", " test_labels = encoder.transform(test_labels)\n", "\n", " # Change to float32, so it can be multiplied against the features in TensorFlow, which are float32\n", " train_labels = train_labels.astype(np.float32)\n", " test_labels = test_labels.astype(np.float32)\n", " is_labels_encod = True\n", "\n", "print('Labels One-Hot Encoded')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training features and labels randomized and split.\n" ] } ], "source": [ "assert is_features_normal, 'You skipped the step to normalize the features'\n", "assert is_labels_encod, 'You skipped the step to One-Hot Encode the labels'\n", "\n", "# Get randomized datasets for training and validation\n", "train_features, valid_features, train_labels, valid_labels = train_test_split(\n", " train_features,\n", " train_labels,\n", " test_size=0.05,\n", " random_state=832289)\n", "\n", "print('Training features and labels randomized and split.')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving data to pickle file...\n", "Data cached in pickle file.\n" ] } ], "source": [ "# Save the data for easy access\n", "pickle_file = 'notMNIST.pickle'\n", "if not os.path.isfile(pickle_file):\n", " print('Saving data to pickle file...')\n", " try:\n", " with open('notMNIST.pickle', 'wb') as pfile:\n", " pickle.dump(\n", " {\n", " 'train_dataset': train_features,\n", " 'train_labels': train_labels,\n", " 'valid_dataset': valid_features,\n", " 'valid_labels': valid_labels,\n", " 'test_dataset': test_features,\n", " 'test_labels': test_labels,\n", " },\n", " pfile, pickle.HIGHEST_PROTOCOL)\n", " except Exception as e:\n", " print('Unable to save data to', pickle_file, ':', e)\n", " raise\n", "\n", "print('Data cached in pickle file.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint\n", "All your progress is now saved to the pickle file. If you need to leave and comeback to this lab, you no longer have to start from the beginning. Just run the code block below and it will load all the data and modules required to proceed." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data and modules loaded.\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "# Load the modules\n", "import pickle\n", "import math\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "\n", "# Reload the data\n", "pickle_file = 'notMNIST.pickle'\n", "with open(pickle_file, 'rb') as f:\n", " pickle_data = pickle.load(f)\n", " train_features = pickle_data['train_dataset']\n", " train_labels = pickle_data['train_labels']\n", " valid_features = pickle_data['valid_dataset']\n", " valid_labels = pickle_data['valid_labels']\n", " test_features = pickle_data['test_dataset']\n", " test_labels = pickle_data['test_labels']\n", " del pickle_data # Free up memory\n", "\n", "print('Data and modules loaded.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "## Problem 2\n", "\n", "Now it's time to build a simple neural network using TensorFlow. Here, your network will be just an input layer and an output layer.\n", "\n", "<img src=\"image/network_diagram.png\" style=\"height: 40%;width: 40%; position: relative; right: 10%\">\n", "\n", "For the input here the images have been flattened into a vector of $28 \\times 28 = 784$ features. Then, we're trying to predict the image digit so there are 10 output units, one for each label. Of course, feel free to add hidden layers if you want, but this notebook is built to guide you through a single layer network. \n", "\n", "For the neural network to train on your data, you need the following <a href=\"https://www.tensorflow.org/resources/dims_types.html#data-types\">float32</a> tensors:\n", " - `features`\n", " - Placeholder tensor for feature data (`train_features`/`valid_features`/`test_features`)\n", " - `labels`\n", " - Placeholder tensor for label data (`train_labels`/`valid_labels`/`test_labels`)\n", " - `weights`\n", " - Variable Tensor with random numbers from a truncated normal distribution.\n", " - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#truncated_normal\">`tf.truncated_normal()` documentation</a> for help.\n", " - `biases`\n", " - Variable Tensor with all zeros.\n", " - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#zeros\"> `tf.zeros()` documentation</a> for help.\n", "\n", "If you're having trouble solving problem 2, review \"TensorFlow Linear Function\" section of the class. If that doesn't help, the solution for this problem is available [here](intro_to_tensorflow_solution.ipynb)." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed!\n" ] } ], "source": [ "# All the pixels in the image (28 28 = 784)\n", "features_count = 784\n", "# All the labels\n", "labels_count = 10\n", "\n", "# TODO: Set the features and labels tensors\n", "features = tf.placeholder(tf.float32, [None, features_count])\n", "labels = tf.placeholder(tf.float32, [None, labels_count])\n", "\n", "\n", "# TODO: Set the weights and biases tensors\n", "weights = tf.Variable(tf.truncated_normal((features_count, labels_count)))\n", "biases = tf.Variable(tf.zeros(labels_count))\n", "\n", "\n", "### DON'T MODIFY ANYTHING BELOW ###\n", "\n", "#Test Cases\n", "from tensorflow.python.ops.variables import Variable\n", "\n", "assert features._op.name.startswith('Placeholder'), 'features must be a placeholder'\n", "assert labels._op.name.startswith('Placeholder'), 'labels must be a placeholder'\n", "assert isinstance(weights, Variable), 'weights must be a TensorFlow variable'\n", "assert isinstance(biases, Variable), 'biases must be a TensorFlow variable'\n", "\n", "assert features._shape == None or (\\\n", " features._shape.dims[0].value is None and\\\n", " features._shape.dims[1].value in [None, 784]), 'The shape of features is incorrect'\n", "assert labels._shape == None or (\\\n", " labels._shape.dims[0].value is None and\\\n", " labels._shape.dims[1].value in [None, 10]), 'The shape of labels is incorrect'\n", "assert weights._variable._shape == (784, 10), 'The shape of weights is incorrect'\n", "assert biases._variable._shape == (10), 'The shape of biases is incorrect'\n", "\n", "assert features._dtype == tf.float32, 'features must be type float32'\n", "assert labels._dtype == tf.float32, 'labels must be type float32'\n", "\n", "# Feed dicts for training, validation, and test session\n", "train_feed_dict = {features: train_features, labels: train_labels}\n", "valid_feed_dict = {features: valid_features, labels: valid_labels}\n", "test_feed_dict = {features: test_features, labels: test_labels}\n", "\n", "# Linear Function WX + b\n", "logits = tf.matmul(features, weights) + biases\n", "\n", "prediction = tf.nn.softmax(logits)\n", "\n", "# Cross entropy\n", "cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)\n", "\n", "# Training loss\n", "loss = tf.reduce_mean(cross_entropy)\n", "\n", "# Create an operation that initializes all variables\n", "init = tf.global_variables_initializer()\n", "\n", "# Test Cases\n", "with tf.Session() as session:\n", " session.run(init)\n", " session.run(loss, feed_dict=train_feed_dict)\n", " session.run(loss, feed_dict=valid_feed_dict)\n", " session.run(loss, feed_dict=test_feed_dict)\n", " biases_data = session.run(biases)\n", "\n", "assert not np.count_nonzero(biases_data), 'biases must be zeros'\n", "\n", "print('Tests Passed!')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy function created.\n" ] } ], "source": [ "# Determine if the predictions are correct\n", "is_correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))\n", "# Calculate the accuracy of the predictions\n", "accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))\n", "\n", "print('Accuracy function created.')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<img src=\"image/Learn_Rate_Tune_Image.png\" style=\"height: 70%;width: 70%\">\n", "## Problem 3\n", "Below are 2 parameter configurations for training the neural network. In each configuration, one of the parameters has multiple options. For each configuration, choose the option that gives the best acccuracy.\n", "\n", "Parameter configurations:\n", "\n", "Configuration 1\n", "* Epochs: 1\n", "* Learning Rate:\n", " * 0.8\n", " * 0.5\n", " * 0.1\n", " * 0.05\n", " * 0.01\n", "\n", "Configuration 2\n", "* Epochs:\n", " * 1\n", " * 2\n", " * 3\n", " * 4\n", " * 5\n", "* Learning Rate: 0.2\n", "\n", "The code will print out a Loss and Accuracy graph, so you can see how well the neural network performed.\n", "\n", "If you're having trouble solving problem 3, you can view the solution [here](intro_to_tensorflow_solution.ipynb)." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Epoch 1/5: 0%\| \| 0/1114 [00:00<?, ?batches/s]\u001b[A\n", "Epoch 1/5: 0%\| \| 1/1114 [00:00<10:08, 1.83batches/s]\u001b[A\n", "Epoch 1/5: 5%\|▍ \| 51/1114 [00:01<06:50, 2.59batches/s]\u001b[A\n", "Epoch 1/5: 9%\|▉ \| 101/1114 [00:01<04:37, 3.65batches/s]\u001b[A\n", "Epoch 1/5: 14%\|█▎ \| 151/1114 [00:02<03:08, 5.12batches/s]\u001b[A\n", "Epoch 1/5: 100%\|██████████\| 1114/1114 [00:12<00:00, 86.66batches/s]\n", "Epoch 2/5: 100%\|██████████\| 1114/1114 [00:12<00:00, 86.96batches/s]\n", "Epoch 3/5: 100%\|██████████\| 1114/1114 [00:12<00:00, 91.04batches/s]\n", "Epoch 4/5: 100%\|██████████\| 1114/1114 [00:12<00:00, 90.54batches/s]\n", "Epoch 5/5: 100%\|██████████\| 1114/1114 [00:13<00:00, 83.65batches/s]\n" ] }, { "data": { "image/png": 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vMCNPstPVQyG0HtAhvpLiZ4w5al8+BvjZlwu6LVQt+yu2gPRSSUTqA22xeg9O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\n27aNdu3aFViumJgYEhISaN++PQ899BD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"text/plain": [ "<matplotlib.figure.Figure at 0x117dbbeb8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Validation accuracy at 0.7718666791915894\n" ] } ], "source": [ "# Change if you have memory restrictions\n", "batch_size = 128\n", "\n", "# TODO: Find the best parameters for each configuration\n", "epochs = 5\n", "learning_rate = 0.05\n", "\n", "\n", "\n", "### DON'T MODIFY ANYTHING BELOW ###\n", "# Gradient Descent\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) \n", "\n", "# The accuracy measured against the validation set\n", "validation_accuracy = 0.0\n", "\n", "# Measurements use for graphing loss and accuracy\n", "log_batch_step = 50\n", "batches = []\n", "loss_batch = []\n", "train_acc_batch = []\n", "valid_acc_batch = []\n", "\n", "with tf.Session() as session:\n", " session.run(init)\n", " batch_count = int(math.ceil(len(train_features)/batch_size))\n", "\n", " for epoch_i in range(epochs):\n", " \n", " # Progress bar\n", " batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n", " \n", " # The training cycle\n", " for batch_i in batches_pbar:\n", " # Get a batch of training features and labels\n", " batch_start = batch_ibatch_size\n", " batch_features = train_features[batch_start:batch_start + batch_size]\n", " batch_labels = train_labels[batch_start:batch_start + batch_size]\n", "\n", " # Run optimizer and get loss\n", " _, l = session.run(\n", " [optimizer, loss],\n", " feed_dict={features: batch_features, labels: batch_labels})\n", "\n", " # Log every 50 batches\n", " if not batch_i % log_batch_step:\n", " # Calculate Training and Validation accuracy\n", " training_accuracy = session.run(accuracy, feed_dict=train_feed_dict)\n", " validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n", "\n", " # Log batches\n", " previous_batch = batches[-1] if batches else 0\n", " batches.append(log_batch_step + previous_batch)\n", " loss_batch.append(l)\n", " train_acc_batch.append(training_accuracy)\n", " valid_acc_batch.append(validation_accuracy)\n", "\n", " # Check accuracy against Validation data\n", " validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n", "\n", "loss_plot = plt.subplot(211)\n", "loss_plot.set_title('Loss')\n", "loss_plot.plot(batches, loss_batch, 'g')\n", "loss_plot.set_xlim([batches[0], batches[-1]])\n", "acc_plot = plt.subplot(212)\n", "acc_plot.set_title('Accuracy')\n", "acc_plot.plot(batches, train_acc_batch, 'r', label='Training Accuracy')\n", "acc_plot.plot(batches, valid_acc_batch, 'x', label='Validation Accuracy')\n", "acc_plot.set_ylim([0, 1.0])\n", "acc_plot.set_xlim([batches[0], batches[-1]])\n", "acc_plot.legend(loc=4)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print('Validation accuracy at {}'.format(validation_accuracy))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Test\n", "You're going to test your model against your hold out dataset/testing data. This will give you a good indicator of how well the model will do in the real world. You should have a test accuracy of at least 80%." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Epoch 1/5: 100%\|██████████\| 1114/1114 [00:00<00:00, 1291.46batches/s]\n", "Epoch 2/5: 100%\|██████████\| 1114/1114 [00:00<00:00, 1314.22batches/s]\n", "Epoch 3/5: 100%\|██████████\| 1114/1114 [00:00<00:00, 1299.73batches/s]\n", "Epoch 4/5: 100%\|██████████\| 1114/1114 [00:00<00:00, 1302.03batches/s]\n", "Epoch 5/5: 100%\|██████████\| 1114/1114 [00:00<00:00, 1306.21batches/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Nice Job! Test Accuracy is 0.8379999995231628\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "### DON'T MODIFY ANYTHING BELOW ###\n", "# The accuracy measured against the test set\n", "test_accuracy = 0.0\n", "\n", "with tf.Session() as session:\n", " \n", " session.run(init)\n", " batch_count = int(math.ceil(len(train_features)/batch_size))\n", "\n", " for epoch_i in range(epochs):\n", " \n", " # Progress bar\n", " batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n", " \n", " # The training cycle\n", " for batch_i in batches_pbar:\n", " # Get a batch of training features and labels\n", " batch_start = batch_ibatch_size\n", " batch_features = train_features[batch_start:batch_start + batch_size]\n", " batch_labels = train_labels[batch_start:batch_start + batch_size]\n", "\n", " # Run optimizer\n", " _ = session.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})\n", "\n", " # Check accuracy against Test data\n", " test_accuracy = session.run(accuracy, feed_dict=test_feed_dict)\n", "\n", "\n", "assert test_accuracy >= 0.80, 'Test accuracy at {}, should be equal to or greater than 0.80'.format(test_accuracy)\n", "print('Nice Job! Test Accuracy is {}'.format(test_accuracy))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Multiple layers\n", "Good job! You built a one layer TensorFlow network! However, you might want to build more than one layer. This is deep learning after all! In the next section, you will start to satisfy your need for more layers." ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
landmanbester/fundamentals_of_interferometry	2_Mathematical_Groundwork/2_6_cross_correlation_and_auto_correlation.ipynb	1	6891	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", " [Outline](../0_Introduction/0_introduction.ipynb)\n", "* [Glossary](../0_Introduction/1_glossary.ipynb)\n", "* [2. Mathematical Groundwork](2_0_introduction.ipynb)\n", " * Previous: [2.5 Convolution](2_5_convolution.ipynb)\n", " * Next: [2.7 Fourier Theorems](2_7_fourier_theorems.ipynb)\n", "\n", "**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import standard modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import section specific modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6. [Cross-correlation and Auto-correlation](2_6_cross_correlation_and_auto_correlation.ipynb)\n", " 1. [Cross-correlation](#math:sec:cross_correlation)\n", " 2. [Auto-correlation](#math:sec:auto_correlation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.6. Cross-correlation and Auto-correlation<a id='math:sec:cross_correlation_and_auto_correlation'></a>\n", "Auto- and cross-correlation are less frequently used than the Fourier transform and convolution are. Nevertheless, they are important in interferometry. We therefore give a brief introduction here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6.1. Cross-correlation<a id='math:sec:cross_correlation'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cross-correlation $\\star$ is an operation acting on two complex-valued functions (remember the definition of the convolution $\\circ$ in [$\\S$ 2.5](2_5_convolution.ipynb#math:sec:convolution) <!--\\ref{math:sec:convolution}-->)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='math:eq:7_001'></a><!--\\label{math:eq:7_001}-->$$\n", "\\star: \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\}\\,\\times\\, \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\} \\rightarrow \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\}\\\\\n", "\\begin{split}\n", "(f\\star g)(x) \\,&=\\, ({f_-}^\\circ g)(x)\\\\\n", "&=\\, \\int_{-\\infty}^{+\\infty} f^(t-x)\\,g(t)\\,dt\\\\\n", "&\\underset{t^\\prime = t-x}{=}\\, \\int_{-\\infty}^{+\\infty} f^(t^\\prime)\\,g(t^\\prime+x)\\,dt^\\prime\\\\\n", "\\end{split}\\qquad \\text{,}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $f_-(x) = f(-x)$. In more than one dimension this reads" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <a id='math:eq:7_002'></a><!--\\label{math:eq:7_002}-->$$\n", "\\star: \\left\\{f\\,\|\\, f:\\mathbb{R}^n\\rightarrow \\mathbb{C}\\right\\}\\,\\times\\, \\left\\{f\\,\|\\, f:\\mathbb{R}^n\\rightarrow \\mathbb{C}\\right\\} \\rightarrow \\left\\{f\\,\|\\, f:\\mathbb{R}^n\\rightarrow \\mathbb{C}\\right\\}\\, \\quad n \\in \\mathbb{N} \\\\\n", "\\begin{align}\n", "(f\\star g)(x_1,\\ldots,x_n ) \\,&=\\, (f\\star g)({\\bf x})\\\\\n", "&=\\, ({f_-}^\\circ g)(x)\\\\\n", "&=\\, \\int_{-\\infty}^{+\\infty} \\ldots \\int_{-\\infty}^{+\\infty} f^(t_1-x_1, \\ldots , t_n-x_n)\\,g(t_1, \\ldots, t_n) \\,d^nt\\\\\n", "\\,&=\\, \\int_{-\\infty}^{+\\infty} f^({\\bf t}-{\\bf x})\\,g({\\bf t}) \\,d^nt\\\\\n", "\\,&=\\, \\int_{-\\infty}^{+\\infty} f^({\\bf t})\\,g({\\bf t}+{\\bf x}) \\,d^nt\\\\\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is clear that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <a id='math:eq:7_003'></a><!--\\label{math:eq:7_003}-->$$\n", "\\begin{align}\n", "(f\\star g)(x) \\,&=\\, \\int_{-\\infty}^{+\\infty} f^(t-x)\\,g(t) \\,dt\\\\\n", "\\,&=\\, \\int_{-\\infty}^{+\\infty} [f(t-x)\\,g^(t)]^* \\,dt\\\\\n", "\\,&=\\, \\left(\\int_{-\\infty}^{+\\infty} f(t-x)\\,g^(t) \\,dt\\right)^\\\\\n", "\\,&\\underset{t^\\prime = t-x}{=}\\, \\left(\\int_{-\\infty}^{+\\infty} f(t^\\prime)\\,g^(t^\\prime+x) \\,dt^\\prime\\right)^\\\\\n", "\\,&=\\, \\left(g\\star f\\right)_-^\\\\\n", "\\end{align}\n", "\\qquad \\text{,}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and that the cross-correlation is identical to the convolution if one of the functions $f$ or $g$ is even and real-valued." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6.2. Auto-correlation<a id='math:sec:auto_correlation'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The auto-correlation of a complex-valued function is defined as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='math:eq:7_004'></a><!--\\label{math:eq:7_004}-->$$\n", "R: \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\}\\,\\times\\, \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\} \\rightarrow \\left\\{f\\,\|\\, f:\\mathbb{R}\\rightarrow \\mathbb{C}\\right\\}\\\\\n", "\\begin{split}\n", "R\\{f\\}(x) \\,&=\\, (f\\star f)(x)\\\\\n", "&=\\, (f_-^\\circ f)(x)\\\\\n", "&=\\, \\int_{-\\infty}^{+\\infty} f^(t-x)\\,f(t)\\,dt\\\\\n", "&\\underset{t^\\prime = t-x}{=}\\, \\int_{-\\infty}^{+\\infty} f^(t^\\prime)\\,f(t^\\prime+x)\\,dt^\\prime\\\\\n", "\\end{split}\\qquad \\text{.}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is often easier to measure the auto-correlation of a function than to measure the function itself." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", " Next: [2.7 Fourier Theorems](2_7_fourier_theorems.ipynb)" ] } ], "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
raghakot/keras-vis	examples/resnet/attention.ipynb	1	2078936	null	mit
mohanprasath/Course-Work	numpy/numpy_exercises_from_kyubyong/String_operations.ipynb	2	12361	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## String operations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "author = \"kyubyong. https://github.com/Kyubyong/numpy_exercises\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1.11.3'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q1. Concatenate x1 and x2." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Hello world' 'Say something']\n" ] } ], "source": [ "x1 = np.array(['Hello', 'Say'], dtype=np.str)\n", "x2 = np.array([' world', ' something'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q2. Repeat x three time element-wise." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Hello Hello Hello ' 'Say Say Say ']\n" ] } ], "source": [ "x = np.array(['Hello ', 'Say '], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q3-1. Capitalize the first letter of x element-wise.<br/>\n", "Q3-2. Lowercase x element-wise.<br/>\n", "Q3-3. Uppercase x element-wise.<br/>\n", "Q3-4. Swapcase x element-wise.<br/>\n", "Q3-5. Title-case x element-wise.<br/>" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "capitalized = ['Hello world' 'Say something']\n", "lowered = ['hello world' 'say something']\n", "uppered = ['HELLO WORLD' 'SAY SOMETHING']\n", "swapcased = ['HEllO WOrlD' 'sAY SoMETHING']\n", "titlecased = ['Hello World' 'Say Something']\n" ] } ], "source": [ "x = np.array(['heLLo woRLd', 'Say sOmething'], dtype=np.str)\n", "capitalized = ...\n", "lowered = ...\n", "uppered = ...\n", "swapcased = ...\n", "titlecased = ...\n", "print(\"capitalized =\", capitalized)\n", "print(\"lowered =\", lowered)\n", "print(\"uppered =\", uppered)\n", "print(\"swapcased =\", swapcased)\n", "print(\"titlecased =\", titlecased)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q4. Make the length of each element 20 and the string centered / left-justified / right-justified with paddings of `_`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "centered = ['____hello world_____' '___say something____']\n", "left = ['hello world_________' 'say something_______']\n", "right = ['_________hello world' '_______say something']\n" ] } ], "source": [ "x = np.array(['hello world', 'say something'], dtype=np.str)\n", "centered = ...\n", "left = ...\n", "right = ...\n", "\n", "print(\"centered =\", centered)\n", "print(\"left =\", left)\n", "print(\"right =\", right)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q5. Encode x in cp500 and decode it again." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "encoded = [b'\\x88\\x85\\x93\\x93\\x96@\\xa6\\x96\\x99\\x93\\x84'\n", " b'\\xa2\\x81\\xa8@\\xa2\\x96\\x94\\x85\\xa3\\x88\\x89\\x95\\x87']\n", "decoded = ['hello world' 'say something']\n" ] } ], "source": [ "x = np.array(['hello world', 'say something'], dtype=np.str)\n", "encoded = ...\n", "decoded = ...\n", "print(\"encoded =\", encoded)\n", "print(\"decoded =\", decoded)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q6. Insert a space between characters of x." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['h e l l o w o r l d' 's a y s o m e t h i n g']\n" ] } ], "source": [ "x = np.array(['hello world', 'say something'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q7-1. Remove the leading and trailing whitespaces of x element-wise.<br/>\n", "Q7-2. Remove the leading whitespaces of x element-wise.<br/>\n", "Q7-3. Remove the trailing whitespaces of x element-wise." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stripped = ['hello world' 'say something']\n", "lstripped = ['hello world ' 'say something\\n']\n", "rstripped = [' hello world' '\\tsay something']\n" ] } ], "source": [ "x = np.array([' hello world ', '\\tsay something\\n'], dtype=np.str)\n", "stripped = ...\n", "lstripped = ...\n", "rstripped = ...\n", "print(\"stripped =\", stripped)\n", "print(\"lstripped =\", lstripped)\n", "print(\"rstripped =\", rstripped)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q8. Split the element of x with spaces." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['Hello', 'my', 'name', 'is', 'John']]\n" ] } ], "source": [ "x = np.array(['Hello my name is John'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q9. Split the element of x to multiple lines." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['Hello', 'my name is John']]\n" ] } ], "source": [ "x = np.array(['Hello\\nmy name is John'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q10. Make x a numeric string of 4 digits with zeros on its left." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['0034']\n" ] } ], "source": [ "x = np.array(['34'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q11. Replace \"John\" with \"Jim\" in x." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Hello nmy name is Jim']\n" ] } ], "source": [ "x = np.array(['Hello nmy name is John'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q12. Return x1 == x2, element-wise." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ True True True True False]\n" ] } ], "source": [ "x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\n", "x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q13. Return x1 != x2, element-wise." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False False False False True]\n" ] } ], "source": [ "x1 = np.array(['Hello', 'my', 'name', 'is', 'John'], dtype=np.str)\n", "x2 = np.array(['Hello', 'my', 'name', 'is', 'Jim'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## String information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q14. Count the number of \"l\" in x, element-wise." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2 0 0 0 1]\n" ] } ], "source": [ "x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q15. Count the lowest index of \"l\" in x, element-wise." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2 -1 -1 -1 2]\n" ] } ], "source": [ "x = np.array(['Hello', 'my', 'name', 'is', 'Lily'], dtype=np.str)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q16-1. Check if each element of x is composed of digits only.<br/>\n", "Q16-2. Check if each element of x is composed of lower case letters only.<br/>\n", "Q16-3. Check if each element of x is composed of upper case letters only." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Digits only = [False False False True False False]\n", "Lower cases only = [False False True False True True]\n", "Upper cases only = [False True False False False False]\n" ] } ], "source": [ "x = np.array(['Hello', 'I', 'am', '20', 'years', 'old'], dtype=np.str)\n", "out1 = ...\n", "out2 = ...\n", "out3 = ...\n", "print(\"Digits only =\", out1)\n", "print(\"Lower cases only =\", out2)\n", "print(\"Upper cases only =\", out3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q17. Check if each element of x starts with \"hi\"." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False True True True]\n" ] } ], "source": [ "x = np.array(['he', 'his', 'him', 'his'], dtype=np.str)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "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
wangyu16/General_Chemistry	Chapter 17 Acid-Base Equilibria and Solubility Equilibria .ipynb	1	17104	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\require{mhchem}$$\n", "\n", "### [HOME](https://wangyu16.github.io/GeneralChemistry/)\n", "\n", "# <center>General Chemistry Study Guide</center>\n", "\n", "### <center>Chapter 17. Acid-Base Equilibria and Solubility Equilibria</center>\n", "\n", "---\n", "\n", "<center>Yu Wang</center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Buffer Solutions\n", "\n", "A buffer solution is a solution of (1) a weak acid or a weak base and (2) its salt. The solution has the ability to resist changes in $\\ce{pH}$ upon the addition of small amounts of either acid or base. \n", "\n", "Why a buffer solution can resist changes in $\\ce{pH}$? \n", "Because both acid and base coexist in a buffer solution under equilibrium. The acid can react with any added $\\ce{OH-}$ and the base can react with any added $\\ce{H+}$. \n", "An example is a solution with comparable amounts of $\\ce{CH3COOH}$ and $\\ce{CH3COONa}$. $\\ce{CH3COONa}$ ionize completely in water to give: \n", "$$\\ce{CH3COONa ->[\\ce{H2O}] CH3COO- (aq) + Na+ (aq)}$$ \n", "If an acid is added, $\\ce{H+}$ will be consumed by $\\ce{CH3COO-}$ \n", "$$\\ce{CH3COO- (aq) + H+ (aq) -> CH3COOH (aq)}$$ \n", "If a base is added, $\\ce{OH-}$ will be consumed by $\\ce{CH3COOH}$ \n", "$$\\ce{CH3COOH(aq) + OH- (aq) -> CH3COO- (aq) + H2O (l)}$$ \n", "This buffer system can be written as either $\\ce{CH3COONa/CH3COOH}$ or $\\ce{CH3COO- /CH3COOH}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the $\\ce{pH}$ of a Buffer System \n", "Example (a) Calculate the pH of a buffer system containing 1.0 M $\\ce{CH3COOH}$ and 1.0 M $\\ce{CH3COONa}$ (Textbook Example 17.2). \n", "Strategy: Note the initial concentrations of $\\ce{CH3COOH}$ and $\\ce{CH3COO-}$ are both 1.0 M. Then use ICE table and write the equation of $K_a$ under equilibrium. \n", "$$K_a=\\frac{[\\ce{H+}][\\ce{CH3COO-}]}{[\\ce{CH3COOH}]}=\\frac{x(1.0+x)}{1.0-x}=1.8\\times 10^{-5}$$ \n", "Solve the equation and get $x=[\\ce{H+}]=1.8\\times 10^{-5}\\,\\text{M}$ \n", "Thus $\\ce{pH}=-\\log (1.8\\times 10^{-5})=4.74$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-1.00003599935203, 1.79993520349897e-5]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sympy import \n", "x= symbols('x')\n", "solve(x(1.0+x)/(1.0-x)-1.8e-5)\n", "# Ignore the nagtive value. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(b) Calculate the $\\ce{pH}$ of this system after adding of 0.10 mole of $\\ce{HCl}$ to 1.0 L of this buffer solution. \n", "Stragegy: (1) $\\ce{HCl}$ ionize completely to release equal amount of $\\ce{H+}$; (2) Assume $\\ce{CH3COO-}$ react with all $\\ce{H+}$, calculate $[\\ce{CH3COO-}]$ and $[\\ce{CH3COOH}]$; (3) Calculate the equilibrium concentration of $\\ce{H+}$ after partial ionization of $\\ce{CH3COOH}$. \n", "$$K_a=\\frac{[\\ce{H+}][\\ce{CH3COO-}]}{[\\ce{CH3COOH}]}=\\frac{x(0.90+x)}{1.1-x}=1.8\\times 10^{-5}$$ \n", "Solve the equation and get $x=[\\ce{H+}]=2.2\\times 10^{-5}\\,\\text{M}$ \n", "Thus $\\ce{pH}=- \\log (2.2\\times 10^{-5}) = 4.66$\n", "\n", "Note If you add same amount of $\\ce{HCl}$ to 1.0 L pure water, the $\\ce{pH}$ would decrease from $7$ to $1$!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-0.900039999022290, 2.19990222895745e-5]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve(x(0.9+x)/(1.1-x)-1.8e-5)\n", "# Ignore the nagtive value. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Preparing a Buffer Solution with a Specific $\\ce{pH}$* \n", "\n", "$$\\ce{p}K_a=- \\log K_a$$ \n", "Because \n", "$$K_a=\\frac{[\\text{conjugate base}][\\ce{H+}]}{[\\text{acid}]}$$ \n", "We have \n", "$$\\begin{align}\\ce{pH}=\\ce{p}K_a + \\log\\frac{[\\text{conjugate base}]}{[\\text{acid}]}\\end{align}$$ \n", "Use this equation to calculate the ratio between an acid and its conjugate base in order to prepare a buffer solution with a specific $\\ce{pH}$. \n", "\n", "To prepare a buffer solution, we choose a weak acid whose $\\ce{p}K_a$ is close to the desired $\\ce{pH}$. Thus the acid and its conjugate base would have comparable concentration in the buffer solution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Requirements \n", "> 1. Understand what is a buffer solution and why a buffer solution can resist the change of $\\ce{pH}$. \n", "> 2. Know how to calculate the $\\ce{pH}$ of a buffer solution and the $\\ce{pH}$ change after adding a little amount of acid or base (Textbook Example 17.2). \n", "> 3. Know how to prepare a buffer solution with a specific $\\ce{pH}$ (Textbook Example 17.3). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Titrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strong Acid-Strong Base Titrations \n", "The $\\ce{pH}$ value changes sharply around the equivalence point. \n", "At the equivalence point, $\\ce{pH}=7$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Weak Acid-Strong Base Titrations \n", "At the equivalence point, $\\ce{pH}>7$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strong Acid-Weak Base Titrations \n", "At the equivalence point, $\\ce{pH}<7$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the $\\ce{pH}$ during a titration process \n", "1. Reaction between a strong acid and a strong base results in a neutral salt which does not affect the $\\ce{pH}$ value. Use the remaining amount of acid or base and the total volumn to calculate the $\\ce{pH}$. \n", "2. Weak acid and strong base react to give a salt which can hydrolyze to release $\\ce{OH-}$. The hydrolysis of the salt should be considered when performing calculation on $\\ce{pH}$. \n", " 1. Before reaching equivalence point, $\\ce{pH}$ can be calculated via equation 1 (see above), knowing the ratio between [conjugate base] and [acid]. \n", " 2. At equivalence point, only the hydrolysis of the conjugate base affects the $\\ce{pH}$. \n", " 3. After equivalence point, only the excess amount of added strong base affects the $\\ce{pH}$. \n", "3. Strong acid-weak base titrations are similar as weak acid-strong base titrations. \n", "\n", "Example Calculate the $\\ce{pH}$ in the titration of 25.0 mL of 0.100 M acetic acid by sodium hydroxide after the addition to the acid solution of (a) 10.0 mL of 0.100 M NaOH, (b) 25.0 mL of 0.100 M NaOH, (c) 35.0 mL of 0.100 M NaOH. (Textbook Example 17.4)\n", "\n", "(a) At this moment, $[\\ce{CH3COO-}]/[\\ce{CH3COOH}]=1.00/1.50$, thus \n", "$$\\ce{pH}=\\ce{p}K_a + \\log\\frac{[\\ce{CH3COO-}]}{[\\ce{CH3COOH}]}=-\\log \\left( 1.8\\times 10^{-5}+\\log \\frac{1.00}{1.50} \\right) = 4.57$$\n", "\n", "(b) At this moment, it is the same as to calculate the $\\ce{pH}$ of a 0.0500 M $\\ce{CH3COONa}$ solution. \n", "\n", "(c) There is 1.00 mol excessive $\\ce{NaOH}$. The $\\ce{pH}$ only depends on this amount of $\\ce{NaOH}$. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acid Base Indicators \n", "\n", "Indicators are substances that have distinctly different colors in acidic and basic media. \n", "A given indicator changes from the acid color to the base color in a certain $\\ce{pH}$ range (Textbook Table 17.1). \n", "The titration curve usually has a steep part around the equivalence point. Only when the indicator would change its color within this steep portion of the curve, the indicator can be used in such titration experiment. \n", "\n", "> Requirements\n", "> 1. Calculate the pH in a titration process (Textbook Section \"Strong Acid-Strong Base Titrations\" and Example 17.4). \n", "> 2. Tell which indicator or indicators listed in Table 17.1 could be used in given acid-base titration curves (Textbook Example 17.5). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Solubility Equilibria\n", "\n", "Solubility Product,$K_{sp}$ , is the product of the molar concentrations of the constituent ions, each raised to the power of its stoichiometric coefficient in the equilibrium equation. \n", "Values of $K_{sp}$ of some slightly soluble ionic compounds are summerized in Table 17.2. \n", "\n", "In the reaction \n", "$$\\ce{AgCl(s) <=> Ag+ (aq) + Cl- (aq)}$$ \n", "$$K_{sp}=[\\ce{Ag+}][\\ce{Cl-}]$$ \n", "\n", "Knowing the concentrations of $\\ce{Ag+}$ and $\\ce{Cl-}$, the ion product, $Q$, can be calculated as \n", "\n", "$$Q=[\\ce{Ag+}][\\ce{Cl-}]$$ \n", "Compare $Q$ with $K_{sp}$. \n", "\n", "* $Q < K_{sp}$ results in unsaturated solution; \n", "* $Q = K_{sp}$ saturated solution; \n", "* $Q > K_{sp}$ supersaturated solution. Precipitation will happen. \n", "\n", "Molar solubility: the number of moles of solute in 1 L of a saturated solution; \n", "Solubility: the number of grams of solute in 1 L of a saturated solution. \n", "\n", "Knowing the solubility, calculate the value of $K_{sp}$ (Textbook Example 17.6). \n", "\n", "Knowing the value of $K_{sp}$, calculate the solubility (Textbook Example 17.7). \n", "\n", "Predict precipitation reactions (Textbook Example 17.8). Calculate the value of $Q$ and compare to $K_{sp}$ to determine whether precipitation would happen. \n", "\n", "The common ion effect and solubility. One example is to calculate the molar solubility of $\\ce{AgCl}$ in a $\\ce{AgNO3}$ solution (Textbook Example 17.9). \n", "\n", "Application of the solubility product principle to qualitative analysis \n", "Qualitative analysis: the determination of the types of ions present in a solution (Read Section 17.8). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Requirements \n", "> 1. Understand the concepts: solubility product, molar solubility, solubility. \n", "> 2. Predict whether precipitation would happen by comparing $Q$ with $K_{sp}$ (Textbook Example 17.8). \n", "> 3. Knowing the solubility, calculate the value of $K_{sp}$ (Textbook Example 17.6); or knowing the value of $K_{sp}$, calculate the solubility (Textbook Example 17.7)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Complex Equilibria\n", "\n", "Complex ions are formed when metal cations (Lewis acids which are electron-pair acceptors) combine with Lewis bases (electron-pair donors). An example is: \n", "$$\\ce{Ag+ (aq) + 2NH3 (aq) <=> Ag(NH3)2^+ (aq)}\\qquad K_f=\\frac{[\\ce{Ag(NH3)2^+}]}{[\\ce{Ag+}][\\ce{NH3}]^2}$$ \n", "More examples see Table 17.4. \n", "\n", "Formation constant, $K_f$, is the equilibrium constant for complex ion formation. The larger $K_f$ is, the more stable the complex ion is. \n", "\n", "Calculate the concentration of naked metal ions in an complex equilibrium (Textbook Example 17.10). \n", "\n", "Phenomenon of $\\ce{Al(OH)3}$ reacts with acids and bases. \n", "$$\\ce{Al(OH)3 (s) + 3H+ (aq) -> Al^3+ (aq) + 3H2O(l)}$$ \n", "$$\\ce{Al(OH)3 (s) + OH- (aq) <=> Al(OH)4^- (aq)}$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "> Requirements\n", "> 1. Understant what are complex ions and what is the formation constant. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "---" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Please ignore the cell below. It just loads our style for the notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<link href=\"https://fonts.googleapis.com/css?family=Lora:400,400i,700,700i\|Open+Sans:400,400i,700,700i\|Raleway:400,400i,700,700i\" rel=\"stylesheet\" type='text/css'>\n", "<style type=\"text/css\">\n", "div.text_cell_render {\n", "\tfont-family: Lora, serif;\n", "\tcolor: #222222;\n", "}\n", ".text_cell_render p {\n", "\tpadding-left: 1.5em;\n", "\ttext-align: justify;\n", "}\n", ".text_cell_render h3, h4, h5, h6 {\n", "\tfont-family: Raleway, sans-serif;\n", "\tcolor: #005c99;\n", "}\n", ".text_cell_render h1 {\n", "\tfont-family: \"Open Sans\", sans-serif;\n", "\tfont-weight: 700;\n", "\tmargin: auto;\n", "\tdisplay: block;\n", "}\n", ".text_cell_render h2 {\n", "\tfont-family: Raleway, sans-serif;\n", "\tfont-weight: 700; \n", "\tmargin-top: 50px;\n", "\tdisplay:inline-block;\n", "\tpadding: 10px 15px;\n", "\tbackground-color: #007acc;\n", "\tcolor: #f8f8f8;\n", "\tborder-radius: 10px 40px 10px 40px;\n", "\tletter-spacing: 3px;\n", "\tfont-variant: small-caps;\n", "}\n", "div #notebook {\n", "\tbackground-color: #f8f8f8;\n", "\twidth: 1110px;\t\n", "\tmargin: auto;\n", "\tpadding-left: 0em;\n", "\tborder: 3px solid #cbd3e5; \n", "\tbox-shadow: 0px 0px 7px 1px #cccccc;\n", "}\n", "div.info {\n", "\tfont-family: \"Open Sans\", sans-serif;\n", "\tfloat: right; \n", "\tbackground-color: #BBDEFB; \n", "\tcolor: #005c99;\n", "\twidth: 20em; \n", "\tpadding: 1.4em; \n", "\tborder: 1px solid #42A5F5; \n", "\tborder-radius: 10px; \n", "\tmargin-bottom: 14px; \n", "\tmargin-top: 14px; \n", "\tmargin-left: 14px; \n", "\tmargin-right: 14px; \n", "\tbox-shadow: 7px 7px 5px #cccccc; \n", "\toverflow: hidden;\n", "}\n", "div #notebook-container {\n", "\tbackground-color: #f8f8f8;\n", "\tfont-size: 1.3em; \n", "\tline-height: 150%;\n", "\twidth: 1100px;\n", "\tmargin: auto;\n", "\tpadding-right: 150px;\n", "}\n", ".CodeMirror-code {\n", "\tfont-size: 1.15em;\n", "\tline-height: 125%;\n", "\topacity: 0.9;\n", "}\n", "\n", ".text_cell_render td {\n", " border-style: hidden;\n", "}\n", ".text_cell_render table {\n", " border-style: hidden;\n", "\tborder-collapse: collapse;\n", "\twidth: 100%;\n", " font-size: 90%;\n", "}\n", "\n", ".text_cell_render th, td {\n", "\tpadding: 8px;\n", "}\n", "\n", ".text_cell_render tr:nth-child(even){background-color: #e8e8e8}\n", "\n", ".text_cell_render th {\n", " border-style: hidden;\n", "\tbackground-color: #007acc;\n", "\tcolor: #f8f8f8;\n", "}\n", "table td, table th, table tr {\n", " text-align:left !important;\n", "}\n", "</style>\n", "\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] }, { "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": "3.7.1" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
liangjg/openmc	examples/jupyter/hexagonal-lattice.ipynb	1	21329	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we will create a hexagonal lattice and show how the orientation can be changed via the cell rotation property. Let's first just set up some materials and universes that we will use to fill the lattice." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import openmc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fuel = openmc.Material(name='fuel')\n", "fuel.add_nuclide('U235', 1.0)\n", "fuel.set_density('g/cm3', 10.0)\n", "\n", "fuel2 = openmc.Material(name='fuel2')\n", "fuel2.add_nuclide('U238', 1.0)\n", "fuel2.set_density('g/cm3', 10.0)\n", "\n", "water = openmc.Material(name='water')\n", "water.add_nuclide('H1', 2.0)\n", "water.add_nuclide('O16', 1.0)\n", "water.set_density('g/cm3', 1.0)\n", "\n", "materials = openmc.Materials((fuel, fuel2, water))\n", "materials.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our three materials, we will set up two universes that represent pin-cells: one with a small pin and one with a big pin. Since we will be using these universes in a lattice, it's always a good idea to have an \"outer\" universe as well that is applied outside the defined lattice." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "r_pin = openmc.ZCylinder(r=0.25)\n", "fuel_cell = openmc.Cell(fill=fuel, region=-r_pin)\n", "water_cell = openmc.Cell(fill=water, region=+r_pin)\n", "pin_universe = openmc.Universe(cells=(fuel_cell, water_cell))\n", "\n", "r_big_pin = openmc.ZCylinder(r=0.5)\n", "fuel2_cell = openmc.Cell(fill=fuel2, region=-r_big_pin)\n", "water2_cell = openmc.Cell(fill=water, region=+r_big_pin)\n", "big_pin_universe = openmc.Universe(cells=(fuel2_cell, water2_cell))\n", "\n", "all_water_cell = openmc.Cell(fill=water)\n", "outer_universe = openmc.Universe(cells=(all_water_cell,))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a hexagonal lattice using the `HexLattice` class:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "lattice = openmc.HexLattice()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to set the `center` of the lattice, the `pitch`, an `outer` universe (which is applied to all lattice elements outside of those that are defined), and a list of `universes`. Let's start with the easy ones first. Note that for a 2D lattice, we only need to specify a single number for the pitch." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lattice.center = (0., 0.)\n", "lattice.pitch = (1.25,)\n", "lattice.outer = outer_universe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to set the `universes` property on our lattice. It needs to be set to a list of lists of Universes, where each list of Universes corresponds to a ring of the lattice. The rings are ordered from outermost to innermost, and within each ring the indexing starts at the \"top\". To help visualize the proper indices, we can use the `show_indices()` helper method." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (0, 0)\n", " (0,17) (0, 1)\n", " (0,16) (1, 0) (0, 2)\n", "(0,15) (1,11) (1, 1) (0, 3)\n", " (1,10) (2, 0) (1, 2)\n", "(0,14) (2, 5) (2, 1) (0, 4)\n", " (1, 9) (3, 0) (1, 3)\n", "(0,13) (2, 4) (2, 2) (0, 5)\n", " (1, 8) (2, 3) (1, 4)\n", "(0,12) (1, 7) (1, 5) (0, 6)\n", " (0,11) (1, 6) (0, 7)\n", " (0,10) (0, 8)\n", " (0, 9)\n" ] } ], "source": [ "print(lattice.show_indices(num_rings=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n", "\n", "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", "\n", "We build these rings through 'list concatenation' as follows: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "outer_ring = [big_pin_universe] + [pin_universe]17 # Adds up to 18\n", "\n", "ring_1 = [big_pin_universe] + [pin_universe]11 # Adds up to 12\n", "\n", "ring_2 = [big_pin_universe] + [pin_universe]5 # Adds up to 6\n", "\n", "inner_ring = [big_pin_universe]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now assign the rings (and the universes they contain) to our lattice. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HexLattice\n", "\tID =\t4\n", "\tName =\t\n", "\tOrientation =\ty\n", "\t# Rings =\t4\n", "\t# Axial =\tNone\n", "\tCenter =\t(0.0, 0.0)\n", "\tPitch =\t(1.25,)\n", "\tOuter =\t3\n", "\tUniverses \n", " 2\n", " 1 1\n", " 1 2 1\n", "1 1 1 1\n", " 1 2 1\n", "1 1 1 1\n", " 1 2 1\n", "1 1 1 1\n", " 1 1 1\n", "1 1 1 1\n", " 1 1 1\n", " 1 1\n", " 1\n" ] } ], "source": [ "lattice.universes = [outer_ring, \n", " ring_1, \n", " ring_2,\n", " inner_ring]\n", "print(lattice)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's put our lattice inside a circular cell that will serve as the top-level cell for our geometry." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n", "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", "geometry = openmc.Geometry([main_cell])\n", "geometry.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a plot to see what our geometry looks like." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot = openmc.Plot.from_geometry(geometry)\n", "plot.color_by = 'material'\n", "plot.colors = colors = {\n", " water: 'blue',\n", " fuel: 'olive',\n", " fuel2: 'yellow'\n", "}\n", "plot.to_ipython_image()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, if we wanted to simulate the model, we would need to create an instance of `openmc.Settings`, export it to XML, and run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lattice orientation\n", "\n", "Now let's say we want our hexagonal lattice orientated such that two sides of the lattice are parallel to the x-axis. This can be achieved by two means: either we can rotate the cell that contains the lattice, or we can can change the `HexLattice.orientation` attribute. By default, the `orientation` is set to \"y\", indicating that two sides of the lattice are parallel to the y-axis, but we can also change it to \"x\" to make them parallel to the x-axis." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Change the orientation of the lattice and re-export the geometry\n", "lattice.orientation = 'x'\n", "geometry.export_to_xml()\n", "\n", "# Run OpenMC in plotting mode\n", "plot.to_ipython_image()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we change the orientation to 'x', you can see that the first universe in each ring starts to the right along the x-axis. As before, the universes are defined in a clockwise fashion around each ring. To see the proper indices for a hexagonal lattice in this orientation, we can again call `show_indices` but pass an extra orientation argument:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (0,12) (0,13) (0,14) (0,15)\n", "\n", " (0,11) (1, 8) (1, 9) (1,10) (0,16)\n", "\n", " (0,10) (1, 7) (2, 4) (2, 5) (1,11) (0,17)\n", "\n", "(0, 9) (1, 6) (2, 3) (3, 0) (2, 0) (1, 0) (0, 0)\n", "\n", " (0, 8) (1, 5) (2, 2) (2, 1) (1, 1) (0, 1)\n", "\n", " (0, 7) (1, 4) (1, 3) (1, 2) (0, 2)\n", "\n", " (0, 6) (0, 5) (0, 4) (0, 3)\n" ] } ], "source": [ "print(lattice.show_indices(4, orientation='x'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hexagonal prisms\n", "\n", "OpenMC also contains a convenience function that can create a hexagonal prism representing the interior region of six surfaces defining a hexagon. This can be useful as a bounding surface of a hexagonal lattice. For example, if we wanted the outer boundary of our geometry to be hexagonal, we could change the `region` of the main cell:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "main_cell.region = openmc.model.hexagonal_prism(\n", " edge_length=4lattice.pitch[0],\n", " orientation='x',\n", " boundary_type='vacuum'\n", ")\n", "geometry.export_to_xml()\n", "\n", "# Run OpenMC in plotting mode\n", "plot.color_by = 'cell'\n", "plot.to_ipython_image()" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
weleen/mxnet	example/notebooks/basic/optimizer.ipynb	1	4000	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Optimizer\n", "\n", "In gradient-base optimization algorithms, we update the parameters (or weights) using the gradients in each iteration. We call this updating function as `Optimizer`. \n", "\n", "The main method of an optimizer is `update(weight, grad)`, which updates a NDArray weight using a NDArray gradient. But given that a multi-layer neural network often has more than one weights, we assign each weight a unique integer index. Furthermore, an optimizer may need space to store auxiliary state, such as momentum, we also allow a user-defined state for updating. In summary, an optimizer has two major methods\n", "\n", "- `create_state(index, weight)`: create auxiliary state for the `index`-th weight. \n", "- `update(index, weight, grad, state)`: update the `index`-th weight given the gradient and auxiliary state. The state can be also updated.\n", "\n", "\n", "## Basic Usage\n", "\n", "### Create and Update\n", "MXNet has already implemented several popular optimizers in [python/mxnet/optimizer.py](https://github.com/dmlc/mxnet/blob/master/python/mxnet/optimizer.py). An convenient way to create one is by using `optimizer.create(name, args...)`. The following codes create a standard SGD updater which does\n", "\n", "```\n", "weight = weight - learning_rate * grad\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mxnet as mx\n", "opt = mx.optimizer.create('sgd', learning_rate=.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can use the `update` function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.89999998 0.89999998 0.89999998]\n", " [ 0.89999998 0.89999998 0.89999998]]\n" ] } ], "source": [ "grad = mx.nd.ones((2,3))\n", "weight = mx.nd.ones((2,3))\n", "index = 0\n", "opt.update(index, weight, grad, state=None)\n", "print(weight.asnumpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When momentum is non-zero, the sgd optimizer needs extra state. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.1 -0.1 -0.1]\n", " [-0.1 -0.1 -0.1]]\n" ] } ], "source": [ "mom_opt = mx.optimizer.create('sgd', learning_rate=.1, momentum=.01)\n", "state = mom_opt.create_state(index, weight)\n", "opt.update(index, weight, grad, state)\n", "print(state.asnumpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flexible Learning Rate\n", "\n", "- [lr scheduler](https://github.com/dmlc/mxnet/blob/master/python/mxnet/lr_scheduler.py)\n", "- layer-wise lr: set_lr_mult, set_wd_mult\n", "\n", "\n", "### More optimizers\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customized Optimizer" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
fabianrost84/Rost-Rodrigo-Albors-et-al-2016	calculations/spatial_analysis/step_model_fixed_density_visualize_fit_results_per_timepoint_constant_density.ipynb	1	3571693	null	bsd-3-clause
syedjafri/ThinkStats2	code/chap04soln.ipynb	2	49270	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Read the pregnancy file." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import nsfg\n", "preg = nsfg.ReadFemPreg()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select live births, then make a CDF of <tt>totalwgt_lb</tt>. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import thinkstats2\n", "live = preg[preg.outcome == 1]\n", "firsts = live[live.birthord == 1]\n", "others = live[live.birthord != 1]\n", "cdf = thinkstats2.Cdf(live.totalwgt_lb)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the CDF." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import thinkplot\n", "thinkplot.Cdf(cdf, label='totalwgt_lb')\n", "thinkplot.Show(loc='lower right')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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amDx5svjggw9cnoeLj4iIZCR0p1AQEZHHGOpERDLCUCcikhGGOhGRjDDUiYhk\nhKFORCQjDHUiIhlhqBMRycj/AfQDtuhyDNjJAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x3f52bd0>" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find out how much you weighed at birth, if you can, and compute CDF(x). " ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.Prob(8.4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "0.81422881168400085" ] } ], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are a first child, look up your birthweight in the CDF of first children; otherwise use the CDF of other children." ] }, { "cell_type": "code", "collapsed": false, "input": [ "other_cdf = thinkstats2.Cdf(others.totalwgt_lb)\n", "other_cdf.Prob(8.4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "0.79657754010695192" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the percentile rank of your birthweight" ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.PercentileRank(8.4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "81.422881168400082" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the median birth weight by looking up the value associated with p=0.5." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.Value(0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "7.375" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the interquartile range (IQR) by computing percentiles corresponding to 25 and 75. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.Percentile(25), cdf.Percentile(75)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "(6.5, 8.125)" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a random selection from <tt>cdf</tt>." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.Random()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "7.0" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf.Sample(10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "[6.25, 5.1875, 8.1875, 6.5, 7.9375, 6.6875, 5.75, 6.5625, 7.8125, 5.25]" ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>, then compute the percentile rank for each value, and plot the distribution of the percentile ranks." ] }, { "cell_type": "code", "collapsed": false, "input": [ "t = [cdf.PercentileRank(x) for x in cdf.Sample(1000)]\n", "cdf2 = thinkstats2.Cdf(t)\n", "thinkplot.Cdf(cdf2)\n", "thinkplot.Show(legend=False)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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fAwcOLPlccVs0dXV1Gh8f18TEhK5fv67+/n41NzdHHPPee+9px44dev3111VRUbHkwVgZ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\nAFgUAQ8AFkXAA4BF/T8RiQJEMgtbWgAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x62826d0>" ] } ], "prompt_number": 50 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate 1000 random values using <tt>random.random()</tt> and plot their PMF." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import random\n", "t = [random.random() for _ in range(1000)]\n", "pmf = thinkstats2.Pmf(t)\n", "thinkplot.Pmf(pmf, linewidth=0.1)\n", "thinkplot.Show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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2Pz+f8vJyAMrLy1m1ahUAy5Yt49133+XChQsMDg7y+uuvM2/evCv29tgPn+Cv/voHrHrg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\n", "text": [ "<matplotlib.figure.Figure at 0x3518bd0>" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming that the PMF doesn't work very well, try plotting the CDF instead." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cdf = thinkstats2.Cdf(t)\n", "thinkplot.Cdf(cdf)\n", "thinkplot.Show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x5158910>" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.stats" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "scipy.stats.norm.cdf(0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "0.5" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
seth2000/chinesepoem	.ipynb_checkpoints/PrepareData-checkpoint.ipynb	1	17442	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is the test file to the idea prove.\n", "\n", "Try to do the Json formatted corpus, but it is so hard, then I find the word2vec can avoid this hard work.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -- coding: utf-8 --\n", "\n", "import os\n", "import re\n", "import time\n", "import codecs\n", "import argparse\n", "\n", "TIME_FORMAT = '%Y-%m-%d %H:%M:%S'\n", "BASE_FOLDER = \"C:/Users/sethf/source/repos/chinesepoem/\" # os.path.abspath(os.path.dirname(__file__))\n", "DATA_FOLDER = os.path.join(BASE_FOLDER, 'data')\n", "DEFAULT_FIN = os.path.join(DATA_FOLDER, '唐诗语料库.txt')\n", "DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n", "reg_noisy = re.compile('[^\\u3000-\\uffee]')\n", "reg_note = re.compile('(（.）)') # Cannot deal with （） in seperate lines\n", "# 中文及全角标点符号(字符)是\\u3000-\\u301e\\ufe10-\\ufe19\\ufe30-\\ufe44\\ufe50-\\ufe6b\\uff01-\\uffee\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 读取数据，去掉不用的数据" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-15 14:34:54 START\n", "2017-10-15 14:34:56 STOP\n" ] } ], "source": [ "if __name__ == '__main__':\n", " # parser = set_arguments()\n", " # cmd_args = parser.parse_args()\n", "\n", " print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "\n", " fd = codecs.open(DEFAULT_FIN, 'r', 'utf-8')\n", " fw = codecs.open( DEFAULT_FOUT, 'w', 'utf-8')\n", " reg = re.compile('〖(.)〗')\n", " start_flag = False\n", " for line in fd:\n", " line = line.strip()\n", " if not line or '《全唐诗》' in line or '<http' in line or '□' in line:\n", " continue\n", " elif '〖' in line and '〗' in line:\n", " if start_flag:\n", " fw.write('\\n')\n", " start_flag = True\n", " g = reg.search(line)\n", " if g:\n", " fw.write(g.group(1))\n", " fw.write('\\n')\n", " else:a\n", " # noisy data\n", " print(line)\n", " else:\n", " line = reg_noisy.sub('', line)\n", " line = reg_note.sub('', line)\n", " line = line.replace(' .', '')\n", " fw.write(line)\n", "\n", " fd.close()\n", " fw.close()\n", "\n", " print('{} STOP'.format(time.strftime(TIME_FORMAT)))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 分词实验\n", "#DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n", " \n", "#thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n", "#text = thu1.cut(\"我爱北京天安门\", text=True) #进行一句话分词\n", "#print(text)\n", "\n", "thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n", "thu1.cut_f(DEFAULT_FOUT, outp) #对input.txt文件内容进行分词，输出到output.txt" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-15 16:26:15 START\n", "Model loaded succeed\n", "2017-10-15 16:27:58 STOP\n" ] } ], "source": [ "print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "\n", "import thulac \n", "DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "\n", "fd = codecs.open(DEFAULT_FOUT, 'r', 'utf-8')\n", "fw = codecs.open(DEFAULT_Segment, 'w', 'utf-8')\n", "\n", "thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n", "\n", "\n", "for line in fd:\n", " #print(line)\n", " fw.write(thu1.cut(line, text=True))\n", " fw.write('\\n')\n", " \n", "fd.close()\n", "fw.close()\n", "\n", "print('{} STOP'.format(time.strftime(TIME_FORMAT)))\n", " " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-15 16:30:20 START\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:862: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n", " warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2017-10-15 16:30:31 STOP\n" ] } ], "source": [ "print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "from gensim.models import word2vec\n", "\n", "\n", "#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "DEFAULT_Word2Vec = os.path.join(DATA_FOLDER, 'Word2Vec150.bin')\n", "\n", "sentences = word2vec.Text8Corpus(DEFAULT_Segment)\n", "\n", "model = word2vec.Word2Vec(sentences, size=150)\n", "\n", "#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "model.save(DEFAULT_Word2Vec)\n", "\n", "print('{} STOP'.format(time.strftime(TIME_FORMAT)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.30962595, 0.16889741, -0.01463027, -0.15809815, 0.09206317,\n", " -0.1456935 , 0.16657346, -0.16048834, 0.03577007, -0.13513733,\n", " -0.09294472, -0.11723404, -0.12365381, -0.02067957, 0.1038581 ,\n", " 0.00641506, -0.0062934 , 0.23415405, 0.37439978, -0.0564473 ,\n", " -0.23397736, -0.19426669, 0.06946895, -0.3208392 , 0.19368722,\n", " 0.02603251, -0.00743247, -0.22094592, 0.01184341, -0.12694272,\n", " -0.32603887, -0.20273098, -0.07396571, 0.01315944, -0.10838111,\n", " -0.0909251 , 0.00180263, -0.03625318, -0.2046182 , -0.09922028,\n", " 0.34920788, 0.08904874, -0.25203493, -0.09772593, -0.03779411,\n", " -0.17694817, 0.07821831, 0.08035509, 0.25622529, -0.08985876,\n", " 0.03270766, -0.19293341, -0.30891556, 0.05773695, -0.03148178,\n", " 0.33995509, -0.22352351, 0.09742409, 0.14914362, -0.07318434,\n", " 0.03735919, -0.08370081, -0.16495866, 0.14458466, -0.04542416,\n", " -0.24301586, 0.08908165, 0.06313832, 0.0586113 , -0.15221816,\n", " 0.06224625, 0.08598434, -0.0115755 , -0.09099659, 0.06226088,\n", " -0.07644724, 0.02220215, 0.07566795, 0.04833851, 0.00838657,\n", " -0.05597517, -0.06397859, 0.03784521, 0.02023427, -0.12724152,\n", " -0.01048566, 0.1487288 , 0.08827937, -0.17855296, 0.31425136,\n", " 0.06090816, -0.16096003, -0.07982934, 0.10440107, -0.04465724,\n", " 0.06235282, -0.1461063 , 0.22972585, -0.02483237, 0.1252525 ,\n", " -0.17958631, 0.04755906, 0.26136953, 0.16259584, 0.11282863,\n", " 0.10273369, -0.1521662 , -0.11136056, 0.44112033, -0.1723136 ,\n", " 0.08373854, 0.16581547, -0.06470159, -0.14097695, 0.07161622,\n", " 0.22370109, 0.26647383, 0.24355215, -0.11299301, 0.14951281,\n", " -0.05022607, 0.196927 , -0.06548793, 0.50461113, 0.18641786,\n", " -0.2149298 , -0.05788758, 0.28251058, 0.14605965, 0.4527784 ,\n", " 0.00892602, 0.08880702, 0.16401401, -0.03404955, -0.3267473 ,\n", " 0.14250852, 0.20599096, 0.13325472, -0.12572202, 0.02558975,\n", " -0.06050026, -0.09717743, -0.20002677, 0.14861256, 0.22908178,\n", " -0.05484885, 0.08654279, 0.07304503, 0.17076297, 0.38086078], dtype=float32)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model[u'男']\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "饮马长城窟行\n", "\n", "塞外悲风切，交河冰已结。瀚海百重波，阴山千里雪。迥戍危烽火，层峦引高节。悠悠卷旆旌，饮马出长城。寒沙连骑迹，朔吹断边声。胡尘清玉塞，羌笛韵金钲。绝漠干戈戢，车徒振原隰。都尉反龙堆，将军旋马邑。扬麾氛雾静，纪石功名立。荒裔一戎衣，灵台凯歌入。\n", "\n", "饮马长城窟行\n", "\n", "塞外悲风切，交河冰已结。瀚海百重波，阴山千里雪。迥戍危烽火，层峦引高节。悠悠卷旆旌，饮马出长城。寒沙连骑迹，朔吹断边声。胡尘清玉塞，羌笛韵金钲。绝漠干戈戢，车徒振原隰。都尉反龙堆，将军旋马邑。扬麾氛雾静，纪石功名立。荒裔一戎衣，灵台凯歌入。\n", "\n" ] } ], "source": [ "DEFAULT_FIN = os.path.join(DATA_FOLDER, '唐诗语料库.txt')\n", "DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n", "DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "def GetFirstNline(filePath, linesNumber):\n", " fd = codecs.open(filePath, 'r', 'utf-8')\n", " for i in range(1,linesNumber):\n", " print(fd.readline())\n", " fd.close()\n", "\n", "GetFirstNline(DEFAULT_Segment, 3)\n", "GetFirstNline(DEFAULT_FOUT, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 分词不是很成功，我们转向直接用汉字字符来代替分段，我们保留标点符号\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-15 17:22:55 START\n", "2017-10-15 17:23:02 STOP\n" ] } ], "source": [ "print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "\n", "DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n", "DEFAULT_charSegment = os.path.join(DATA_FOLDER, 'Charactersegment.txt')\n", "\n", "fd = codecs.open(DEFAULT_FOUT, 'r', 'utf-8')\n", "fw = codecs.open(DEFAULT_charSegment, 'w', 'utf-8')\n", "\n", "start_flag = False\n", "for line in fd:\n", " if len(line) > 0:\n", " for c in line:\n", " if c != '\\n':\n", " fw.write(c)\n", " fw.write(' ')\n", " fw.write('\\n')\n", "\n", "fd.close()\n", "fw.close()\n", "\n", "print('{} STOP'.format(time.strftime(TIME_FORMAT)))\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "饮马长城窟行 \n", "\n", "塞外悲风切，交河冰已结。瀚海百重波，阴山千里雪。迥戍危烽火，层峦引高节。悠悠卷旆旌，饮马出长城。寒沙连骑迹，朔吹断边声。胡尘清玉塞，羌笛韵金钲。绝漠干戈戢，车徒振原隰。都尉反龙堆，将军旋马邑。扬麾氛雾静，纪石功名立。荒裔一戎衣，灵台凯歌入。 \n", "\n" ] } ], "source": [ "GetFirstNline(DEFAULT_charSegment, 3)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-16 22:17:17 START\n", "2017-10-16 22:17:32 STOP\n" ] } ], "source": [ "print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "from gensim.models import word2vec\n", "\n", "\n", "#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "DEFAULT_Char2Vec = os.path.join(DATA_FOLDER, 'Char2Vec100.bin')\n", "\n", "fd = codecs.open(DEFAULT_charSegment, 'r', 'utf-8')\n", "\n", "sentences = fd.readlines()\n", "\n", "fd.close\n", "\n", "\n", "model = word2vec.Word2Vec(sentences, size=100)\n", "\n", "#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "model.save(DEFAULT_Char2Vec)\n", "\n", "print('{} STOP'.format(time.strftime(TIME_FORMAT)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.2900829 , -0.04809159, -0.46607766, -0.60195959, -0.79692709,\n", " 1.45317233, -0.73875636, -0.23516993, 0.52468306, -0.4141095 ,\n", " 0.31254441, 0.06157973, 0.52587473, 0.98117661, 0.76936024,\n", " 0.17090531, 0.54503411, 0.89224559, 0.63628626, -0.65704244,\n", " 0.19324228, -2.19337821, -0.0736718 , -1.12545574, 0.36714867,\n", " -0.23592179, 0.65851527, 1.97759676, 0.0664974 , 0.34336987,\n", " 0.16321452, -0.45230347, -1.16129088, -1.37885571, -0.70058161,\n", " -2.71629333, -0.47714323, -1.35716736, -0.5040586 , 0.84255946,\n", " 0.29387042, 0.96084136, 0.5980038 , 1.53590572, 0.78642726,\n", " -0.70572197, 2.15199852, -0.09091973, 0.70999056, -1.26367903,\n", " -0.23834354, 0.40385616, 0.76464611, -0.65731245, 0.3340157 ,\n", " 0.97213268, 1.46448743, 1.32762229, 0.21536438, -0.69748122,\n", " -1.24047554, 0.52763128, 0.48480916, -0.98241204, -0.71260804,\n", " -0.54136884, -1.04192448, 1.04139686, 0.46493888, 0.94138777,\n", " 0.21847701, -0.44784865, -1.06913686, -1.06480539, -0.28641865,\n", " -0.57710785, -0.42219958, 0.06467494, 0.29220659, 0.56308562,\n", " -0.69409251, -1.28817475, 0.24338399, -0.0228632 , 0.33695638,\n", " 0.73314172, 0.78557426, 0.78446829, 0.42267925, -0.7360608 ,\n", " -0.18527743, 0.4405438 , 1.22639728, 1.25485229, 1.98212445,\n", " 0.5071575 , -0.30095363, -0.10453363, -0.94564468, 0.3795009 ], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model[u'男']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-16 20:25:31 START\n", "2017-10-16 20:25:41 STOP\n" ] } ], "source": [ "print('{} START'.format(time.strftime(TIME_FORMAT)))\n", "from gensim.models import word2vec\n", "\n", "DEFAULT_charSegment = os.path.join(DATA_FOLDER, 'Charactersegment.txt')\n", "DEFAULT_Char2Vec50 = os.path.join(DATA_FOLDER, 'Char2Vec50.bin')\n", "\n", "fd = codecs.open(DEFAULT_charSegment, 'r', 'utf-8')\n", "\n", "sentences = fd.readlines()\n", "\n", "fd.close\n", "\n", "model = word2vec.Word2Vec(sentences, size=50)\n", "\n", "#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n", "model.save(DEFAULT_Char2Vec50)\n", "\n", "print('{} STOP'.format(time.strftime(TIME_FORMAT)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('最', 0.7617394924163818),\n", " ('爱', 0.7001036405563354),\n", " ('共', 0.6234053373336792),\n", " ('赏', 0.5743197202682495),\n", " (' ', 0.5637354850769043),\n", " ('似', 0.560402512550354),\n", " ('近', 0.5548217296600342),\n", " ('谢', 0.5457607507705688),\n", " ('伴', 0.5440549850463867),\n", " ('待', 0.5435962677001953)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.wv.most_similar([u'好'])" ] }, { "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
scotchka/noodle-in-a-haystack	get_menus.ipynb	1	3264153	null	mit
lionell/university-labs	ml/lab1.ipynb	3	243918	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2018-06-05 08:17:39-- http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\n", "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... failed: Temporary failure in name resolution.\n", "wget: unable to resolve host address ‘archive.ics.uci.edu’\n" ] } ], "source": [ "! wget -N http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Abalone" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='https://cdn.shopify.com/s/files/1/2086/1263/products/1d89434927bffb6fd1786c19c2d921fb_2000x.jpg?v=1522240385' width='500px'/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Abalone vary in size from 20 mm (0.79 in) (Haliotis pulcherrima) to 200 mm (7.9 in) while Haliotis rufescens is the largest of the genus at 12 in (30 cm).\n", "\n", "The shell of abalones is convex, rounded to oval in shape, and may be highly arched or very flattened. The shell of the majority of species has a small, flat spire and two to three whorls. The last whorl, known as the body whorl, is auriform, meaning that the shell resembles an ear, giving rise to the common name \"ear shell\". Haliotis asinina has a somewhat different shape, as it is more elongated and distended. The shell of Haliotis cracherodii cracherodii is also unusual as it has an ovate form, is imperforate, shows an exserted spire, and has prickly ribs.\n", "\n", "A mantle cleft in the shell impresses a groove in the shell, in which are the row of holes characteristic of the genus. These holes are respiratory apertures for venting water from the gills and for releasing sperm and eggs into the water column. They make up what is known as the selenizone which forms as the shell grows. This series of eight to 38 holes is near the anterior margin. Only a small number is generally open. The older holes are gradually sealed up as the shell grows and new holes form. Each species has a typical number of open holes, between four and 10, in the selenizone. An abalone has no operculum. The aperture of the shell is very wide and nacreous." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 3, "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>Sex</th>\n", " <th>Length</th>\n", " <th>Diameter</th>\n", " <th>Height</th>\n", " <th>Whole weight</th>\n", " <th>Shucked weight</th>\n", " <th>Viscera weight</th>\n", " <th>Shell weight</th>\n", " <th>Rings</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>M</td>\n", " <td>0.455</td>\n", " <td>0.365</td>\n", " <td>0.095</td>\n", " <td>0.5140</td>\n", " <td>0.2245</td>\n", " <td>0.1010</td>\n", " <td>0.150</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>M</td>\n", " <td>0.350</td>\n", " <td>0.265</td>\n", " <td>0.090</td>\n", " <td>0.2255</td>\n", " <td>0.0995</td>\n", " <td>0.0485</td>\n", " <td>0.070</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>F</td>\n", " <td>0.530</td>\n", " <td>0.420</td>\n", " <td>0.135</td>\n", " <td>0.6770</td>\n", " <td>0.2565</td>\n", " <td>0.1415</td>\n", " <td>0.210</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>M</td>\n", " <td>0.440</td>\n", " <td>0.365</td>\n", " <td>0.125</td>\n", " <td>0.5160</td>\n", " <td>0.2155</td>\n", " <td>0.1140</td>\n", " <td>0.155</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>I</td>\n", " <td>0.330</td>\n", " <td>0.255</td>\n", " <td>0.080</td>\n", " <td>0.2050</td>\n", " <td>0.0895</td>\n", " <td>0.0395</td>\n", " <td>0.055</td>\n", " <td>7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Sex Length Diameter Height Whole weight Shucked weight Viscera weight \\\n", "0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n", "1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n", "2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n", "3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n", "4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n", "\n", " Shell weight Rings \n", "0 0.150 15 \n", "1 0.070 7 \n", "2 0.210 9 \n", "3 0.155 10 \n", "4 0.055 7 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('abalone.data', names=['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'])\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's convert categorical feature 'Sex' to numerical via one-hot encoding" ] }, { "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>Length</th>\n", " <th>Diameter</th>\n", " <th>Height</th>\n", " <th>Whole weight</th>\n", " <th>Shucked weight</th>\n", " <th>Viscera weight</th>\n", " <th>Shell weight</th>\n", " <th>Rings</th>\n", " <th>Sex_F</th>\n", " <th>Sex_I</th>\n", " <th>Sex_M</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.455</td>\n", " <td>0.365</td>\n", " <td>0.095</td>\n", " <td>0.5140</td>\n", " <td>0.2245</td>\n", " <td>0.1010</td>\n", " <td>0.150</td>\n", " <td>15</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.350</td>\n", " <td>0.265</td>\n", " <td>0.090</td>\n", " <td>0.2255</td>\n", " <td>0.0995</td>\n", " <td>0.0485</td>\n", " <td>0.070</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.530</td>\n", " <td>0.420</td>\n", " <td>0.135</td>\n", " <td>0.6770</td>\n", " <td>0.2565</td>\n", " <td>0.1415</td>\n", " <td>0.210</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.440</td>\n", " <td>0.365</td>\n", " <td>0.125</td>\n", " <td>0.5160</td>\n", " <td>0.2155</td>\n", " <td>0.1140</td>\n", " <td>0.155</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.330</td>\n", " <td>0.255</td>\n", " <td>0.080</td>\n", " <td>0.2050</td>\n", " <td>0.0895</td>\n", " <td>0.0395</td>\n", " <td>0.055</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Length Diameter Height Whole weight Shucked weight Viscera weight \\\n", "0 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n", "1 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n", "2 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n", "3 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n", "4 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n", "\n", " Shell weight Rings Sex_F Sex_I Sex_M \n", "0 0.150 15 0 0 1 \n", "1 0.070 7 0 0 1 \n", "2 0.210 9 1 0 0 \n", "3 0.155 10 0 0 1 \n", "4 0.055 7 0 1 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.get_dummies(data)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis" ] }, { "cell_type": "code", "execution_count": 5, "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>Length</th>\n", " <th>Diameter</th>\n", " <th>Height</th>\n", " <th>Whole weight</th>\n", " <th>Shucked weight</th>\n", " <th>Viscera weight</th>\n", " <th>Shell weight</th>\n", " <th>Rings</th>\n", " <th>Sex_F</th>\n", " <th>Sex_I</th>\n", " <th>Sex_M</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " <td>4177.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.523992</td>\n", " <td>0.407881</td>\n", " <td>0.139516</td>\n", " <td>0.828742</td>\n", " <td>0.359367</td>\n", " <td>0.180594</td>\n", " <td>0.238831</td>\n", " <td>9.933684</td>\n", " <td>0.312904</td>\n", " <td>0.321283</td>\n", " <td>0.365813</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.120093</td>\n", " <td>0.099240</td>\n", " <td>0.041827</td>\n", " <td>0.490389</td>\n", " <td>0.221963</td>\n", " <td>0.109614</td>\n", " <td>0.139203</td>\n", " <td>3.224169</td>\n", " <td>0.463731</td>\n", " <td>0.467025</td>\n", " <td>0.481715</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.075000</td>\n", " <td>0.055000</td>\n", " <td>0.000000</td>\n", " <td>0.002000</td>\n", " <td>0.001000</td>\n", " <td>0.000500</td>\n", " <td>0.001500</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.450000</td>\n", " <td>0.350000</td>\n", " <td>0.115000</td>\n", " <td>0.441500</td>\n", " <td>0.186000</td>\n", " <td>0.093500</td>\n", " <td>0.130000</td>\n", " <td>8.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.545000</td>\n", " <td>0.425000</td>\n", " <td>0.140000</td>\n", " <td>0.799500</td>\n", " <td>0.336000</td>\n", " <td>0.171000</td>\n", " <td>0.234000</td>\n", " <td>9.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>0.615000</td>\n", " <td>0.480000</td>\n", " <td>0.165000</td>\n", " <td>1.153000</td>\n", " <td>0.502000</td>\n", " <td>0.253000</td>\n", " <td>0.329000</td>\n", " <td>11.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>0.815000</td>\n", " <td>0.650000</td>\n", " <td>1.130000</td>\n", " <td>2.825500</td>\n", " <td>1.488000</td>\n", " <td>0.760000</td>\n", " <td>1.005000</td>\n", " <td>29.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Length Diameter Height Whole weight Shucked weight \\\n", "count 4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 \n", "mean 0.523992 0.407881 0.139516 0.828742 0.359367 \n", "std 0.120093 0.099240 0.041827 0.490389 0.221963 \n", "min 0.075000 0.055000 0.000000 0.002000 0.001000 \n", "25% 0.450000 0.350000 0.115000 0.441500 0.186000 \n", "50% 0.545000 0.425000 0.140000 0.799500 0.336000 \n", "75% 0.615000 0.480000 0.165000 1.153000 0.502000 \n", "max 0.815000 0.650000 1.130000 2.825500 1.488000 \n", "\n", " Viscera weight Shell weight Rings Sex_F Sex_I \\\n", "count 4177.000000 4177.000000 4177.000000 4177.000000 4177.000000 \n", "mean 0.180594 0.238831 9.933684 0.312904 0.321283 \n", "std 0.109614 0.139203 3.224169 0.463731 0.467025 \n", "min 0.000500 0.001500 1.000000 0.000000 0.000000 \n", "25% 0.093500 0.130000 8.000000 0.000000 0.000000 \n", "50% 0.171000 0.234000 9.000000 0.000000 0.000000 \n", "75% 0.253000 0.329000 11.000000 1.000000 1.000000 \n", "max 0.760000 1.005000 29.000000 1.000000 1.000000 \n", "\n", " Sex_M \n", "count 4177.000000 \n", "mean 0.365813 \n", "std 0.481715 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.000000 \n", "75% 1.000000 \n", "max 1.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "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>Length</th>\n", " <th>Diameter</th>\n", " <th>Height</th>\n", " <th>Whole weight</th>\n", " <th>Shucked weight</th>\n", " <th>Viscera weight</th>\n", " <th>Shell weight</th>\n", " <th>Rings</th>\n", " <th>Sex_F</th>\n", " <th>Sex_I</th>\n", " <th>Sex_M</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Length</th>\n", " <td>1.000000</td>\n", " <td>0.986812</td>\n", " <td>0.827554</td>\n", " <td>0.925261</td>\n", " <td>0.897914</td>\n", " <td>0.903018</td>\n", " <td>0.897706</td>\n", " <td>0.556720</td>\n", " <td>0.309666</td>\n", " <td>-0.551465</td>\n", " <td>0.236543</td>\n", " </tr>\n", " <tr>\n", " <th>Diameter</th>\n", " <td>0.986812</td>\n", " <td>1.000000</td>\n", " <td>0.833684</td>\n", " <td>0.925452</td>\n", " <td>0.893162</td>\n", " <td>0.899724</td>\n", " <td>0.905330</td>\n", " <td>0.574660</td>\n", " <td>0.318626</td>\n", " <td>-0.564315</td>\n", " <td>0.240376</td>\n", " </tr>\n", " <tr>\n", " <th>Height</th>\n", " <td>0.827554</td>\n", " <td>0.833684</td>\n", " <td>1.000000</td>\n", " <td>0.819221</td>\n", " <td>0.774972</td>\n", " <td>0.798319</td>\n", " <td>0.817338</td>\n", " <td>0.557467</td>\n", " <td>0.298421</td>\n", " <td>-0.518552</td>\n", " <td>0.215459</td>\n", " </tr>\n", " <tr>\n", " <th>Whole weight</th>\n", " <td>0.925261</td>\n", " <td>0.925452</td>\n", " <td>0.819221</td>\n", " <td>1.000000</td>\n", " <td>0.969405</td>\n", " <td>0.966375</td>\n", " <td>0.955355</td>\n", " <td>0.540390</td>\n", " <td>0.299741</td>\n", " <td>-0.557592</td>\n", " <td>0.252038</td>\n", " </tr>\n", " <tr>\n", " <th>Shucked weight</th>\n", " <td>0.897914</td>\n", " <td>0.893162</td>\n", " <td>0.774972</td>\n", " <td>0.969405</td>\n", " <td>1.000000</td>\n", " <td>0.931961</td>\n", " <td>0.882617</td>\n", " <td>0.420884</td>\n", " <td>0.263991</td>\n", " <td>-0.521842</td>\n", " <td>0.251793</td>\n", " </tr>\n", " <tr>\n", " <th>Viscera weight</th>\n", " <td>0.903018</td>\n", " <td>0.899724</td>\n", " <td>0.798319</td>\n", " <td>0.966375</td>\n", " <td>0.931961</td>\n", " <td>1.000000</td>\n", " <td>0.907656</td>\n", " <td>0.503819</td>\n", " <td>0.308444</td>\n", " <td>-0.556081</td>\n", " <td>0.242194</td>\n", " </tr>\n", " <tr>\n", " <th>Shell weight</th>\n", " <td>0.897706</td>\n", " <td>0.905330</td>\n", " <td>0.817338</td>\n", " <td>0.955355</td>\n", " <td>0.882617</td>\n", " <td>0.907656</td>\n", " <td>1.000000</td>\n", " <td>0.627574</td>\n", " <td>0.306319</td>\n", " <td>-0.546953</td>\n", " <td>0.235391</td>\n", " </tr>\n", " <tr>\n", " <th>Rings</th>\n", " <td>0.556720</td>\n", " <td>0.574660</td>\n", " <td>0.557467</td>\n", " <td>0.540390</td>\n", " <td>0.420884</td>\n", " <td>0.503819</td>\n", " <td>0.627574</td>\n", " <td>1.000000</td>\n", " <td>0.250279</td>\n", " <td>-0.436063</td>\n", " <td>0.181831</td>\n", " </tr>\n", " <tr>\n", " <th>Sex_F</th>\n", " <td>0.309666</td>\n", " <td>0.318626</td>\n", " <td>0.298421</td>\n", " <td>0.299741</td>\n", " <td>0.263991</td>\n", " <td>0.308444</td>\n", " <td>0.306319</td>\n", " <td>0.250279</td>\n", " <td>1.000000</td>\n", " <td>-0.464298</td>\n", " <td>-0.512528</td>\n", " </tr>\n", " <tr>\n", " <th>Sex_I</th>\n", " <td>-0.551465</td>\n", " <td>-0.564315</td>\n", " <td>-0.518552</td>\n", " <td>-0.557592</td>\n", " <td>-0.521842</td>\n", " <td>-0.556081</td>\n", " <td>-0.546953</td>\n", " <td>-0.436063</td>\n", " <td>-0.464298</td>\n", " <td>1.000000</td>\n", " <td>-0.522541</td>\n", " </tr>\n", " <tr>\n", " <th>Sex_M</th>\n", " <td>0.236543</td>\n", " <td>0.240376</td>\n", " <td>0.215459</td>\n", " <td>0.252038</td>\n", " <td>0.251793</td>\n", " <td>0.242194</td>\n", " <td>0.235391</td>\n", " <td>0.181831</td>\n", " <td>-0.512528</td>\n", " <td>-0.522541</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Length Diameter Height Whole weight Shucked weight \\\n", "Length 1.000000 0.986812 0.827554 0.925261 0.897914 \n", "Diameter 0.986812 1.000000 0.833684 0.925452 0.893162 \n", "Height 0.827554 0.833684 1.000000 0.819221 0.774972 \n", "Whole weight 0.925261 0.925452 0.819221 1.000000 0.969405 \n", "Shucked weight 0.897914 0.893162 0.774972 0.969405 1.000000 \n", "Viscera weight 0.903018 0.899724 0.798319 0.966375 0.931961 \n", "Shell weight 0.897706 0.905330 0.817338 0.955355 0.882617 \n", "Rings 0.556720 0.574660 0.557467 0.540390 0.420884 \n", "Sex_F 0.309666 0.318626 0.298421 0.299741 0.263991 \n", "Sex_I -0.551465 -0.564315 -0.518552 -0.557592 -0.521842 \n", "Sex_M 0.236543 0.240376 0.215459 0.252038 0.251793 \n", "\n", " Viscera weight Shell weight Rings Sex_F Sex_I \\\n", "Length 0.903018 0.897706 0.556720 0.309666 -0.551465 \n", "Diameter 0.899724 0.905330 0.574660 0.318626 -0.564315 \n", "Height 0.798319 0.817338 0.557467 0.298421 -0.518552 \n", "Whole weight 0.966375 0.955355 0.540390 0.299741 -0.557592 \n", "Shucked weight 0.931961 0.882617 0.420884 0.263991 -0.521842 \n", "Viscera weight 1.000000 0.907656 0.503819 0.308444 -0.556081 \n", "Shell weight 0.907656 1.000000 0.627574 0.306319 -0.546953 \n", "Rings 0.503819 0.627574 1.000000 0.250279 -0.436063 \n", "Sex_F 0.308444 0.306319 0.250279 1.000000 -0.464298 \n", "Sex_I -0.556081 -0.546953 -0.436063 -0.464298 1.000000 \n", "Sex_M 0.242194 0.235391 0.181831 -0.512528 -0.522541 \n", "\n", " Sex_M \n", "Length 0.236543 \n", "Diameter 0.240376 \n", "Height 0.215459 \n", "Whole weight 0.252038 \n", "Shucked weight 0.251793 \n", "Viscera weight 0.242194 \n", "Shell weight 0.235391 \n", "Rings 0.181831 \n", "Sex_F -0.512528 \n", "Sex_I -0.522541 \n", "Sex_M 1.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dleTEzM+XP6a7z6+q1yTZ0N3rh2NPqaoLkmxJ8ofd/b3l3lunHgAAAJapu09IcsKCfa+a2O4kLxleK0anHgAAgHGakefUT5M59QAAADBSOvUAAAAwUobfT8kud7nftEtg5Dafd/K0S5h5uz3ksGmXMAp97dXTLoGbgi2bpl3BzNuy6UPTLoGbgI2br5t2CTNvzz02T7sEVtOMLJQ3TZJ6AAAAGClJPQAAAOPUFsqT1AMAAMBISeoBAAAYJ3PqJfUAAAAwVpJ6AAAARqnnzKmX1AMAAMBISeoBAAAYJ3PqJfUAAAAwVpJ6AAAAxklSL6kHAACAsZLUAwAAME5t9XtJPQAAAIyUpB4AAIBxMqdeUg8AAABjpVMPAAAAI2X4PQAAAKPUht9L6gEAAGCsJPUAAACMk6ReUg8AAABjJakHAABgnObmpl3B1K1oUl9Vb6yqF0+8P7Gq3jnx/m+q6iVVdVhVfeQGXvvkqjp4Jevdxn2eXlUvX6LNNuuvqhdX1S12TnUAAADwYys9/P4zSR6dJFW1S5K9kzx44vijk5y6wvdcUd29vrtft4xLvDiJTj0AAMDONter95pRK92pPzVDpz7znfnzklxZVberqpsleVCSzw/H96qq46vqS1X17qqqJKmqJ1bV56vqC1V1zHDeT6iqp1TVaVV1VlUdV1V7LTh+p6o6c9h+eFV1Vd1zeP+/q+oWVXXHqvpAVZ0xvA4djh9RVUcP2/etqtOH46+pqqsmbnO9+qvqRUnumuQTVfWJlflKAQAAYHEr2qnv7kuTbB460I9OclqSzyY5JMnBSc7t7uuG5o/IfKq9f5L7JDm0qvZMcmyS53T3QzM/5/93Ju9RVXsn+eMkT+ruA5NsSPKSBXV8J8meVXXrJI8d2jy2qu6V5Dvd/X+TvCnJG7v7p5I8M8k7c31vSvKmoc2lC45dr/7ufvPQ7vHd/fgd+9YAAAC4UST1O2X1+61p/dZO/WkT7z8z0e5z3X1Jd88lOTvJvkkekOSr3X3R0OZdSX5mwfV/OvMd6VOr6uwkz0tyr0Xq+EySQ4fz/3z4+9gkpwzHn5Tk6OEa65PcuqputeAahyQ5bth+z4Jji9W/XVV1ZFVtqKoN73z/+qWaAwAAwHbtjNXvt86rf2jmh99/I8l/S/LDJMdMtNs4sb1lqKV24PqV5KPd/ctLtDsl8534eyX5UJKXJekkWxe42yXJId19zU9cvHakhCSL179d3b0uybok2XjRp2f3px4AAIAR6Nat2llJ/dOSXN7dW7r78iS3zXzqfdoS534pyb5Vdb/h/a8n+eSCNqdnfqj+/ZJkmB9//0Wu9akkv5bky0OafnmS/5IfL9R3UpKjtjauqgMWucbpmR+anySHL1H7VlcmWZj4AwAAwIrbGZ36L2R+1fvTF+y7orsv296J3X1tkt9IclxVfSHJXJJ3LGjz3SRHJHlvVZ073OeBi1zra8Pmp4a/n07yg+7+/vD+RUkOrqpzq+qCJC9cpKQXJ3lJVX0uyT5Jrthe/YN1Sf6XhfIAAAB2MnPqU4YrbNvwvPlrurur6vAkv9zdz1iJaxt+z3LNXXrR0o3WuN0ecti0SxiFvvbqaZfATcGWTdOuYOZd95Y/nXYJM+/b/3bttEuYeQ+86LxplzDzzrrbgdMuYeY99Ksf3uE5x7Puh7/1lFXrV936706aye9tZ8ypvyk5KPOL6VWSHyT5zSnXAwAAwFYznKCvFp367ejuU5I8fNp1AAAAwGJ2xpx6AAAAYBVI6gEAABilNvxeUg8AAABjJakHAABgnCT1knoAAAAYK0k9AAAA4zQ37QKmT1IPAAAAIyWpBwAAYJSsfi+pBwAAgNGS1AMAADBOknpJPQAAAIyVpB4AAIBxsvq9pB4AAADGSlIPAADAKFn9XlIPAAAAoyWpBwAAYJzMqZfUAwAAwFhJ6qflumumXcFsm/OT25Ku/uG0K5h5fe3V0y5hFGrPW067hJk39/3/mHYJM6+vvnzaJcy86778g2mXMPM2XrPXtEuYeXvstvu0SwBmjE49AAAAo2ShPMPvAQAAYLQk9QAAAIyTWbuSegAAABgrST0AAACj1JJ6ST0AAACMlaQeAACAcZLUS+oBAABgrCT1AAAAjJI59ZJ6AAAAGC1JPQAAAOMkqZfUAwAAwFhJ6gEAABglc+ol9QAAADBaknoAAABGSVIvqQcAAIDR0qkHAACAkTL8HgAAgFEy/F5SDwAAAKMlqQcAAGCcuqZdwdQtmdRX1Suq6vyqOreqzq6qRw37v1ZVey+3gKo6tqqedSPP3beqzltuDQuueUJV3XaJNidX1cGL7D+gqv7LStYDAAAA27LdpL6qDknytCQHdvfGoRO/x6pUNiXdvZxO+QFJDk5ywgqVAwAAwDaYU790Ur9Pksu6e2OSdPdl3X3pxPHfq6qzquoLVfXAJKmqV1fVS7c2qKrzqmrfYfu5Q+J/TlX948KbVdWfDcn9LlV1UFV9sqrOrKoTq2qfoc1Bw/mnJfndxYquqrdV1dOH7X+pqmOG7edX1f8Ytn+tqj43jD74n1W167D/RyMQquqVVfWlqvpoVb138nMlefZw/kVV9diq2iPJa5I8Z7jmc5b4bgEAAGBZlurUn5TkHkPH9W1V9bgFxy/r7gOTvD3JS69/+o9V1YOTvCLJE7r74Ul+f8Hxv0pypyS/kWTXJG9J8qzuPijJMUleOzT9f5O8qLsP2c7tPpXkscP23ZLsP2w/JskpVfWgJM9Jcmh3H5BkS5JfXVDPwUmemeQRSX4x8wn8pN26+5FJXpzkT7r7uiSvSvL+7j6gu9+/ve8DAACA5em5WrXXrNpup767r0pyUJIjk3w3yfur6oiJJv88/D0zyb5L3OsJSY7v7suGa18+ceyVSW7b3b/d3Z3kAUkekuSjVXV2kj9Ocvequs3Q7pPDeddL+wenJHlsVe2f5IIk3x6S/kOSfCbJE4fPdcZw/Scmuc+CazwmyYe6+5ruvjLJhxccvyGfPUlSVUdW1Yaq2vDO4/51R04BAACAbVpy9fvu3pLk5CQnV9UXkjwvybHD4Y3D3y0T19qcn/yxYM/hbyXpbdzmjCQHVdXth85+JTl/YRo/LGC3rWtM1vzNqrpdkqdmPrW/fZJfSnJVd19ZVZXkXd39R9u5zFI/xSz22Zeqa12SdUmy8byPLvk5AAAA2DZz6pdI6qvqAVW138SuA5J8fYlrfi3JgcP5Bya597D/Y0l+qaruMBy7/cQ5/5bkdUn+tapuleTCJHccFupLVe1eVQ/u7h8kuaKqHjOc9xND5hc4LfND4z+V+eT+pcPfrbU8q6rutLWWqrrXgvM/neTnqmrPqtoryc8u8bmT5Mokt9qBdgAAALBsS82p3yvJu6rqgqo6N/Nz01+9xDkfSHL7YVj77yS5KEm6+/zMz4v/ZFWdk+QNkyd193FJ/i7J+szPqX9Wkr8c2p6d5NFD099I8tZhobxrtlPHKZmf9/6VJGdlPq0/ZbjXBZkf0n/S8Lk+mvlFASfrOWOo5ZzMD7XfkOSKJT77J5Lsb6E8AACAna+7Vu01q2p+CjuLqaq9uvuqqrpF5hP/I7v7rJW4tuH3S5gzjmYpc9/44rRLmHm7PvyJ0y5hFGrPW067hJk39/3/mHYJM6+vvnzpRmvc//2T10+7hJn3H+ftNe0SZt5Pfev8aZcw806/80OnXcLMe+hXPzy7PdQb6JuHPGHV+lV3O+3jM/m97dBc8DVs3bDY3p6Zn4O/Ih16AAAAls+cep367eruX5l2DQAAALAtOvUAAACM0iw/P361LLVQHgAAADCjdOoBAABgpAy/BwAAYJQ8zE1SDwAAAKMlqQcAAGCULJQnqQcAAIDRktQDAAAwSpJ6ST0AAACMlqQeAACAUbL6vaQeAAAAlq2qnlpVF1bVV6rq5Yscf2FVfaGqzq6qT1fV/itxX0k9AAAAozQrc+qratckb03y5CSXJDmjqtZ39wUTzd7T3e8Y2j89yRuSPHW595bUAwAAwPI8MslXuvvi7r4uyfuSPGOyQXf/cOLtLZOsyOQBST0AAACj1D0bSX2SuyX5xsT7S5I8amGjqvrdJC9JskeSJ6zEjSX1AAAAsISqOrKqNky8jpw8vMgp10viu/ut3X3fJC9L8scrUZekHgAAgFHquVW8V/e6JOu2cfiSJPeYeH/3JJdu53LvS/L2lahLUg8AAADLc0aS/arq3lW1R5LDk6yfbFBV+028/dkkX16JG0vqp6X8nrJdvp6l7b7HtCvgJmLu+/8x7RJm3i63u8u0S5h5c9ddM+0SZt7u+9x82iXMvD0v3jTtEmbedZt9R0vZMiOrobM65mZkTn13b66qo5KcmGTXJMd09/lV9ZokG7p7fZKjqupJSTYl+X6S563EvXXqAQAAYJm6+4QkJyzY96qJ7d/fGfeVhwIAAMBISeoBAAAYpRl6pN3USOoBAABgpCT1AAAAjFJbGFFSDwAAAGMlqQcAAGCUuqddwfRJ6gEAAGCkJPUAAACMkjn1knoAAAAYLUk9AAAAozTnOfWSegAAABgrST0AAACj1JJ6ST0AAACMlaQeAACAUfKcekk9AAAAjJakHgAAgFGy+r2kHgAAAEZLpx4AAABGyvB7AAAARskj7W5kUl9VJ1fVf1qw78VV9baqumtVHb8y5a2+qnpnVe2/RJtjq+pZi+zft6p+ZedVBwAAAD92Y4ffvzfJ4Qv2HZ7kvd19aXdfr8O7Eqpqp48s6O4XdPcFN/L0fZPo1AMAAKyC7tV7zaob26k/PsnTqupmyXxCneSuST49pNXnDfsfXFWfq6qzq+rcqtpv2P/c4f05VfWPw747VtUHquqM4XXosP/VVbWuqk5K8g/D9U+pqrOG16MXFldV/09VvWjYfmNVfXzYfmJV/X/D9lOq6rThGsdV1V7D/pOr6uBh+/lVddGw7++q6uiJ2/xMVX2mqi6eSO1fl+Sxw+f9gxv53QIAAMAOuVHJd3d/r6o+l+SpST6U+ZT+/d3dVT8xp+GFSd7U3e+uqj2S7FpVD07yiiSHdvdlVXX7oe2bkryxuz9dVfdMcmKSBw3HDkrymO6+pqpukeTJ3X3t8CPBe5McvKDETyX5b0nePBy7WVXtnuQxSU6pqr2T/HGSJ3X31VX1siQvSfKarReoqrsmeWWSA5NcmeTjSc6ZuMc+w/UemGR95n/oeHmSl3b3027YNwoAAMAN5ZF2y1v9fnII/uHD+4VOS/Lfh07zvbr7miRPSHJ8d1+WJN19+dD2SUmOrqqzM99JvnVV3Wo4tn44N0l2T/J3VfWFJMclWWz++5lJDhrO3zjUcXCSxyY5JclPD+edOtzveUnuteAaj0zyye6+vLs3Dfea9MHunhuG6t958a/oJ1XVkVW1oao2vPO4j+zIKQAAALBNy5mj/sEkb6iqA5PcvLvPWtigu99TVZ9N8rNJTqyqFySpJIvNSNglySETnfckyZD8Xz2x6w+SfDvJw4dzrl3kvpuq6mtJfiPJZ5Kcm+TxSe6b5IvD34929y9v5/Mt9ZPPxhvQdmtd65KsS5KN539shmdlAAAAzD6r3y8jqe/uq5KcnOSYLJ7Sp6ruk+Ti7n5z5tP3hyX5WJJfqqo7DG22Dr8/KclRE+cesI1b3ybJt7p7LsmvJ9l1G+0+leSlw99TMj8V4Ozu7iSnJzm0qu433OsWVXX/Bed/Lsnjqup2wwJ9z9zGfSZdmeRWS7YCAACAFbCc4ffJfGf+4Unet43jz0ly3jDE/YFJ/qG7z0/y2iSfrKpzkrxhaPuiJAcPC+hdkPlO+GLeluR5VXV6kvvnJ1P8Sadkft77ad397cwn+qckSXd/N8kRSd5bVedmvpP/wMmTu/ubSf48yWeT/HuSC5JcsY17bXVuks3DAoAWygMAANiJ5rpW7TWrqmd5bf4pq6q9uvuqIan/lyTHdPe/rMS1Db9fQs9Nu4KZN3fpl6ddwszbdf/HTruEUehrrpx2CTNvl9vdZdolzLy5b3912iXMvOve8vpplzDzvnuq//4v5QEXnj/tEmbeGfscNO0SZt4BX18/uz3UG+izd/3FVetXPerSf57J722nP/d95F5dVU9Ksmfmpwd8cMr1AAAAMJCvFNPcAAAgAElEQVSU6tRvV3e/dNo1AAAAwLbo1AMAADBKszzXfbUsd6E8AAAAYEok9QAAAIyS59RL6gEAAGC0JPUAAACMkgdhSuoBAABgtHTqAQAAYKQMvwcAAGCUOhbKk9QDAADASEnqAQAAGKW5nnYF0yepBwAAgJGS1AMAADBKc+bUS+oBAABgrCT1AAAAjJLV7yX1AAAAMFqSegAAAEZpbtoFzABJPQAAAIyUpB4AAIBRMqdep3562kCR7SqDSJa0x57TrmD2bdk07QpGoa++fNolzLy5666Zdgkzb5c733vaJcy8Xe9/j2mXMPtO/fq0K5h5VTowS+n2HbG26NQDAAAwSqJSc+oBAABgtCT1AAAAjJKkXlIPAAAAo6VTDwAAACNl+D0AAACj5JF2knoAAAAYLUk9AAAAozQnqJfUAwAAwFhJ6gEAABilOXPqJfUAAAAwVpJ6AAAARqmnXcAMkNQDAADASEnqAQAAGKW5aRcwAyT1AAAAMFKSegAAAEZprqx+L6kHAACAkZLUAwAAMEpWv5fUAwAAwGhJ6gEAABglq9+vYlJfVa+oqvOr6tyqOruqHjXs/1pV7X0DrnNYVX1k2D6iqo5ewRrvWlXH70C7q7ax/+erav+VqgcAAAC2Z1WS+qo6JMnTkhzY3RuHTvweq3HvG6K7L03yrGVc4ueTfCTJBStTEQAAAGzbaiX1+yS5rLs3Jkl3XzZ0oLf6vao6q6q+UFUPTJKqumVVHVNVZ1TV56vqGTt6s+E6t61536uq5w77/7GqnlRVu1bV64drn1tVvz0c37eqzhu2b1FV/zQcf39VfbaqDp64x2ur6pyqOr2q7lxVj07y9CSvH0Yi3He5XxoAAADbNler95pVq9WpPynJParqoqp6W1U9bsHxy7r7wCRvT/LSYd8rkny8u38qyeMz31m+5Q7e79QkhyZ5cJKLkzx22P/TSU5P8vwkVwzX/qkkv1VV915wjf+a5Pvd/bAkf5bkoIljt0xyenc/PMmnkvxWd38myfokf9jdB3T3/97BWgEAAOBGWZVOfXdflflO8ZFJvpvk/VV1xESTfx7+nplk32H7KUleXlVnJzk5yZ5J7rmDtzwlyc8Mr7cneWhV3S3J5UMtT0ny3OHan01yhyT7LbjGY5K8b6j/vCTnThy7LvPD7BfWvF1VdWRVbaiqDe887l938KMAAACwmLnUqr1m1aqtft/dWzLfOT+5qr6Q5HlJjh0Obxz+bpmoqZI8s7svnLxOVd15B273qSS/m/kfAV6R5BcyP1f+lIlr/153n7jg2vtOvt3O9Td199ZHIk7WvF3dvS7JuiTZeN5HPVIRAACAZVmVpL6qHlBVk0n4AUm+vsRpJ2Z+rn0N13jEjt6vu7+RZO8k+3X3xUk+nflh/Vs79Scm+Z2q2n249v0XGdr/6SS/NBzfP8lDd+DWVya51Y7WCQAAwI3Xq/iaVas1p36vJO+qqguq6twk+yd59RLn/FmS3ZOcOyxe92c38J6fTXLRsH1KkrtlvqOeJO/M/Ar1Zw3X/p+5ftr+tiR3HOp9WeaH31+xxD3fl+QPh4X9LJQHAADATrUqw++7+8wkj97GsX0ntjckOWzYvibJby/S/uTMD+NPdx+bHw/hX9ju1ye2P5OJHzC6ey7Jfx9ek65I8pBh+9okv9bd1w4d9I9lGF3Q3XtNXOv4JMcP26dm/gcLAAAAdrJZXpV+tazanPoRukWSTwxD9CvJ73T3dVOuCQAAAH5Ep34buvvKJAcv2RAAAICpmJt2ATNgtebUAwAAACtMUg8AAMAozfKq9KtFUg8AAAAjJakHAABglKx+L6kHAACA0ZLUAwAAMEpWv5fUAwAAwGjp1AMAAMBIGX4PAADAKBl+L6kHAACA0ZLUAwAAMErtkXaSegAAABgrST0AAACjZE69pB4AAABGS1IPAADAKEnqJfUAAACwbFX11Kq6sKq+UlUvX+T4zarq/cPxz1bVvitxX516AAAARqlX8bU9VbVrkrcm+c9J9k/yy1W1/4Jmz0/y/e6+X5I3JvnLG/mxf4Lh91Oy6d3vmHYJM612333aJcy83rRp2iXMvC2bPjTtEkbhui//YNolzLzd97n5tEuYebve/x7TLmHm7f7cP5p2CTPv6rf8/rRLmHn77HX7aZcw8666zr8jmYpHJvlKd1+cJFX1viTPSHLBRJtnJHn1sH18kqOrqrp7qd8MtkunHgAAgFGam53n1N8tyTcm3l+S5FHbatPdm6vqiiR3SHLZcm5s+D0AAAAsoaqOrKoNE68jJw8vcsrCBH5H2txgknoAAABGaTVXv+/udUnWbePwJUkm56LdPcml22hzSVXtluQ2SS5fbl2SegAAAFieM5LsV1X3rqo9khyeZP2CNuuTPG/YflaSjy93Pn0iqQcAAGCkZuU59cMc+aOSnJhk1yTHdPf5VfWaJBu6e32Sv0/yj1X1lcwn9IevxL116gEAAGCZuvuEJCcs2Peqie1rkzx7pe9r+D0AAACMlKQeAACAUVr2hPSbAEk9AAAAjJSkHgAAgFGaW+zJ72uMpB4AAABGSlIPAADAKM3KI+2mSVIPAAAAIyWpBwAAYJSsfi+pBwAAgNGS1AMAADBKc7J6ST0AAACMlaQeAACAUbL6vaQeAAAARktSDwAAwCiZUb8Gk/qq2lJVZ1fVeVX14aq67bD/rlV1/LTrAwAAgB215jr1Sa7p7gO6+yFJLk/yu0nS3Zd297OmWxoAAAA7am4VX7NqLXbqJ52W5G5JUlX7VtV5w/YRVfXPVfVvVfXlqvqrrSdU1fOr6qKqOrmq/q6qjh72P3tI/8+pqk9N5dMAAACwpqzZOfVVtWuSJyb5+200OSDJI5JsTHJhVb0lyZYkr0xyYJIrk3w8yTlD+1cl+U/d/c2tQ/oBAABgZ1qLSf3Nq+rsJN9LcvskH91Gu4919xXdfW2SC5LcK8kjk3yyuy/v7k1Jjptof2qSY6vqt5LsutgFq+rIqtpQVRuOOfurK/V5AAAA1qS5Wr3XrFqLnfpruvuAzHfS98gwp34RGye2t2R+VMM2/6fs7hcm+eMk90hydlXdYZE267r74O4++DcPuPeNrR8AAACSrM1OfZKku69I8qIkL62q3XfwtM8leVxV3a6qdkvyzK0Hquq+3f3Z7n5Vkssy37kHAABgJ5lLr9prVq3ZOfVJ0t2fr6pzkhye5JQdaP/NqvrzJJ9Ncmnmh+VfMRx+fVXtl/k0/2P58Vx7AAAA2CnWXKe+u/da8P7nJt4+ZNh3bJJjJ9o8baLNe7p73ZDU/0uSk4Y2v7iTSgYAAGARs5ufr541O/x+GV49LLR3XpKvJvnglOsBAABgjVpzSf1ydfdLp10DAAAAydy0C5gBknoAAAAYKUk9AAAAozTLq9KvFkk9AAAAjJSkHgAAgFGS00vqAQAAYLQk9QAAAIyS1e8l9QAAADBaknoAAABGyer3knoAAAAYLZ16AAAAGCnD7wEAABglg+8l9QAAADBaknoAAABGySPtJPUAAAAwWpJ6AAAARqnNqpfUAwAAwFhJ6gEAABglc+p16qfmuyf8cNolzLSqaVcw+7ZsNtCGlbHxmr2mXcLM2/PiTdMuYfad+vVpVzDzrn7L70+7hJn3wDPeNO0SZt5193vatEuYeT977VnTLmHm6YnctOjUAwAAMEpz5tSbUw8AAABjJakHAABglOT0knoAAAAYLUk9AAAAo2ROvaQeAAAARktSDwAAwCh5Tr2kHgAAAEZLpx4AAABGyvB7AAAARqktlCepBwAAgLGS1AMAADBKFsqT1AMAAMBoSeoBAAAYJXPqJfUAAAAwWpJ6AAAARsmcekk9AAAAjJakHgAAgFGaa3PqJfUAAAAwUpJ6AAAARklOL6kHAACA0ZLUAwAAMEpzsvq1ldRX1Suq6vyqOreqzq6qR63QdU+uqguHa55dVc9aiesCAADA9qyZpL6qDknytCQHdvfGqto7yR4reItf7e4NK3g9AAAAtqMl9Wsqqd8nyWXdvTFJuvuy7r60qg6qqk9W1ZlVdWJV7VNVu1XVGVV1WJJU1V9U1WunWTwAAAAstJY69ScluUdVXVRVb6uqx1XV7knekuRZ3X1QkmOSvLa7Nyc5Isnbq+rJSZ6a5E+XuP67J4bf32GxBlV1ZFVtqKoN7/3eJSv2wQAAAFib1szw++6+qqoOSvLYJI9P8v4k/yPJQ5J8tKqSZNck3xran19V/5jkw0kO6e7rlrjFksPvu3tdknVJ8tWHP9k4EQAAgGWYm3YBM2DNdOqTpLu3JDk5yclV9YUkv5vk/O4+ZBunPDTJD5LceXUqBAAAgB23ZobfV9UDqmq/iV0HJPlikjsOi+ilqnavqgcP27+Y5A5JfibJm6vqtqtdMwAAANs2l16116xaS0n9XkneMnTONyf5SpIjMz8c/s1VdZvMfx9/W1XfTvK6JE/s7m9U1dFJ3pTkedMpHQAAAK5vzXTqu/vMJI9e5NBlmU/jF7r/xLlvXuLahy2rOAAAAG4wj7RbQ8PvAQAA4KZmzST1K6Gq/iXJvRfsfll3nziNegAAANYyq9/r1N8g3f0L064BAAAAttKpBwAAYJS6zak3px4AAABGSlIPAADAKM3y8+NXi6QeAAAARkpSDwAAwChZ/V5SDwAAAKMlqQcAAGCU2px6ST0AAACMlU49AAAAjJTh9wAAAIySR9pJ6gEAAGC0JPUAAACMUrekXlIPAAAAIyWpBwAAYJTmpl3ADJDUAwAAwEhJ6qfkAV/64rRLmGlz5sYsac/d9ph2CTNv4+brpl3CKOyx2+7TLmHmXbd507RLmHlVNe0SZt4+e91+2iXMvOvu97RplzDz/s9XPjLtEmbebe/5hGmXwCpqq99L6gEAAGCsJPUAAACMkufUS+oBAABgp6qq21fVR6vqy8Pf2y3S5l5VdWZVnV1V51fVC3fk2jr1AAAAjFJ3r9prmV6e5GPdvV+Sjw3vF/pWkkd39wFJHpXk5VV116UurFMPAAAAO9czkrxr2H5Xkp9f2KC7r+vujcPbm2UH++vm1AMAADBKI5pTf+fu/laSdPe3qupOizWqqnsk+dck90vyh9196VIX1qkHAACAJVTVkUmOnNi1rrvXTRz/9yR3WeTUV+zoPbr7G0keNgy7/2BVHd/d397eOTr1AAAAjNJqPqd+6MCv287xJ23rWFV9u6r2GVL6fZJ8Z4l7XVpV5yd5bJLjt9fWnHoAAADYudYned6w/bwkH1rYoKruXlU3H7Zvl+TQJBcudWFJPQAAAKM0t/xV6VfL65L8U1U9P8n/SfLsJKmqg5O8sLtfkORBSf6mqjpJJfnr7v7CUhfWqQcAAICdqLu/l+SJi+zfkOQFw/ZHkzzshl7b8HsAAAAYKUk9AAAAozSawfc7kaQeAAAARkpSDwAAwCjNyeol9QAAADBWknoAAABGSVIvqQcAAIDRktQDAAAwSt2Sekk9AAAAjJSkHgAAgFEyp34NJ/VV9YqqOr+qzq2qs6vqUSt03ZOr6uCVuBYAAABsz5pM6qvqkCRPS3Jgd2+sqr2T7DHlsgAAALgBWlK/ZpP6fZJc1t0bk6S7L+vuS6vqoKr6ZFWdWVUnVtU+VbVbVZ1RVYclSVX9RVW9dprFAwAAQLJ2O/UnJblHVV1UVW+rqsdV1e5J3pLkWd19UJJjkry2uzcnOSLJ26vqycn/396dR1lWlmcbv+5uQAYFFFBwYBaIogytUUCJ4mzUL0EUpzglonHAIeZL1ORzxClqZHCAJBA0UVFxAJMoBoWAiEgDyuQUBVGIERUEHBj6+f7Yu+hTTXVVqV317lPn+q11VtXZp7r77r1OVe1nP+/Ao4HX/zb/aJJDkpyb5NxVt9ywbv4nkiRJkjShqmrRHkM1kcPvq+r6JCuABwMPBU4A3gTsDnw+CcBy4Kr+6y9O8kHgZGCfqrrxt/x3jwGOAdjgdncf7rtCkiRJkjQWJrKoB6iqW4DTgNOSXAi8CLi4qvZZyx+5D3ANcJfFSShJkiRJmo2r30/o8Pskuya558ihPYFLga36RfRIsn6Se/efHwhsAewPHJFk88XOLEmSJEnSmia1U3974Mi+OL8Z+A5wCN3Q+COSbEZ3bt6d5EfAW4GHVdUVSY4CDgee1Sa6JEmSJEmdiSzqq2olsO8ML11N141f0y4jf/aIOf7uh/xO4SRJkiRJ8zLkBewWy0QOv5ckSZIkaSmYyE79upDkk8AOaxz+q6r6XIs8kiRJkjRpXCjPov63VlV/3DqDJEmSJGmyWdRLkiRJksZS2al3Tr0kSZIkSePKTr0kSZIkaSytcvV7O/WSJEmSJI0rO/WSJEmSpLHknHo79ZIkSZIkjS079ZIkSZKkseScejv1kiRJkiSNLTv1kiRJkqSx5Jx6O/WSJEmSJI0tO/WSJEmSpLHknHo79ZIkSZIkjS2LekmSJEmSxpTD7yVJkiRJY8mF8uzUS5IkSZI0tuzUN3LO1nu3jqAxd9PNy1tHGLwNN7i5dQQtEbesSusIg1flOZrL9Teu3zrC4P3hr85rHWHwNt/2gNYRBu+a73+hdQQtIhfKs1MvSZIkSdLYslMvSZIkSRpLzqm3Uy9JkiRJ0tiyUy9JkiRJGktVq1pHaM5OvSRJkiRJY8pOvSRJkiRpLK1yTr2dekmSJEmSxpWdekmSJEnSWCr3qbdTL0mSJEnSuLJTL0mSJEkaS86pt1MvSZIkSdLYslMvSZIkSRpLzqm3Uy9JkiRJ0tiyqJckSZIkaUw5/F6SJEmSNJZWOfzeTr0kSZIkSePKTr0kSZIkaSyVW9rZqZckSZIkaVzZqZckSZIkjSW3tLNTL0mSJEnS2LJTL0mSJEkaS6ucUz8Znfokr0lycZKvJ7kgyQPW0d97WpLvJ8nIsU8luX5d/P2SJEmSJM1myXfqk+wDPA7Yu6p+nWRLYIN1+E9cA+wHnJlkc2Cbdfh3S5IkSZLWwjn1k9Gp3wa4uqp+DVBVV1fVlUlWJDk9ycokn0uyTZL1knw1yUMAkrwlyWFz/P0fAZ7Sf34g8ImF+o9IkiRJkjRqEor6U4B7JPlWkvcm+YMk6wNHAgdV1QrgWOCwqroZeDbwviSPAB4NvH6Ov/9UYP8ky+mK+xPW9oVJDklybpJzT7z+8t/9fyZJkiRJE2xV1aI9hmrJD7+vquuTrAAeDDyUruh+E7A78Pl+Ovxy4Kr+6y9O8kHgZGCfqrpxjn/iFuBM4GBgo6q6bGSK/ZpZjgGOAbhguycM910hSZIkSRoLS76oB6iqW4DTgNOSXAi8CLi4qvZZyx+5D91c+bvM85/4CPBJ4HW/W1JJkiRJ0nw5p34Cht8n2TXJPUcO7QlcCmzVL6JHkvWT3Lv//EBgC2B/4Ih+8bu5nAG8BfjwOg0vSZIkSdIsJqFTf3vgyL44vxn4DnAI3TD4I5JsRnce3p3kR8BbgYdV1RVJjgIOB5412z9Q3e2hdyzg/0GSJEmStAb3qZ+Aor6qVgL7zvDS1XTd+DXtMvJnj5jj737IWo7f/jeIKEmSJEnSb2XJD7+XJEmSJGmpWvKd+nUhySeBHdY4/FdV9bkWeSRJkiRJLpQHFvXzUlV/3DqDJEmSJElrsqiXJEmSJI2lVXbqnVMvSZIkSdK4slMvSZIkSRpL5ZZ2duolSZIkSRpXduolSZIkSWPJOfV26iVJkiRJGlt26iVJkiRJY8l96u3US5IkSZI0tuzUS5IkSZLGkqvf26mXJEmSJGls2amXJEmSJI0l59TbqZckSZIkaWzZqZckSZIkjSU79XbqJUmSJEkaWxb1kiRJkiSNKYffS5IkSZLGkoPvIc5BEECSQ6rqmNY5hsxzNDfP0dw8R/PjeZqb52hunqO5eY7m5jmam+dobp4jLSSH32vKIa0DjAHP0dw8R3PzHM2P52lunqO5eY7m5jmam+dobp6juXmOtGAs6iVJkiRJGlMW9ZIkSZIkjSmLek1xjs/cPEdz8xzNzXM0P56nuXmO5uY5mpvnaG6eo7l5jubmOdKCcaE8SZIkSZLGlJ16SZIkSZLGlEW9JEmSJEljyqJekhZQklPnc2zSJXnSfI5NsiQvnc+xSZZkh/kckyRpKbGol9YiyfIkf9c6x9BZtM4syYZJ7gRsmeSOSe7UP7YH7to23SC9ap7HJtmzZjj27MUOMXAnznDs44ueYoz0P5/u2zrHUCQ5cOTzO7bMMlRJ9p7t0TqfNInWax1A7STZBfhLYDtG3gtVdUCzUANSVbckWZEk5YqSt5FkQ2Bj+qIVSP/Spli0AjwfeBnduVjJ6vPzc+A9rUINTZLHAI8F7pbkiJGXNgVubpNqWJI8FXgasEOSk0ZeugPwkzaphiXJbsC9gc1GizK699GGbVINV5LTgCfQ/e6/APhxktOr6hVNgw3D3wCf6D8/FbBIva13zvJaARN/HZnkOrpzceuh/nmAqqpNmwTTkmVRP9k+Brwf+AfglsZZhup84NNJPgbcMHWwqj6x9j8yMSxaZ1FVhwOHJ3lJVR3ZOs+AXQmcS1dgrBw5fh3w8iaJhucs4CpgS6ZfTF8HfL1JouHZFXgcsDnw+JHj1wHPa5Jo2Darqp8n+TPguKp6bRLfS52s5XP1quqh8/m6JI+oqs8vdJ6BOhXYmu4G0Ueq6vuN82iJc0u7CZZkZVWtaJ1jyJIcN8PhqqrnLnqYgbJonVuSfYHtmT4i5gPNAg1QkvWr6qbWOTTekuxTVV9unWPoklwIPBI4HnhNVX01yderauKH4Sf5BvBUuimq/0I3SubW4r6qzmsUbewkOa+qJnakQ5LNgAOBp9CNGDqBrsD/adNgWpIs6idQP88X4FDgf4FPAr+eet0fNvpNWbSuXZIPAjvRDXGdGhFTVXVou1TDk2Q/4HWsng40NURxx5a5hqQfVv424M5058dhnGtIshVdZ357pv888kbsiH4Ryr8FzqyqFybZEfi7qnpi42jNJfniLC+XUxTnL8n5VbVX6xytJVkGHAwcCby5qt7VOJKWIIv6CZTke6ye17MmL6JH9OsOvA+4S1Xt3i8m9ISqelPjaINh0Tq7JJcC93Jdhtn13bGX0w3Bv3U6UFU5Z7yX5DvA46vq0tZZhirJWcAZ3PZ9NNMCetJvbcKHls+LnfrsSzfq48HAmcAJVXVG21RaqizqJ1iSDavqV3Mdm2RJTqdbTPDoqbvNSS6qqt3bJhsOi9bZ9esxHFpVV7XOMmRJvlJVD2idY8iSfKmq9mudY8iSXFBVe7bOMXRrLEo55Vrg3Kr69GLnGUeTXrDOxySfoySXAdcAHwG+wBoLvzqNQ+uaC+VNtrO47aquMx2bZBtX1TnJtEENrsg93UV0i8FYtI5IcjLdiJg7AJckOYfp01ye0CrbkIxsf/TFfgvJTzD9PE38hc/Iau7nJjkB+BTTz5ELd672mSSPrap/bx1k4DYEdqNbMBfgicDFwJ8meWhVvaxZsvHhInpzu6x1gIYuo7sGeBTd+hWj7xd3CNA6Z1E/gZJsDdwN2CjJXkzfimzjZsGG6eokO9FvS5LkICxeAYvWeXhH6wBjYs2tke438rkXPp3R1dx/QXeBOKVYvf3WxBrZPirAq5P8GrgJ1x1Ym52BA6rqZoAk7wNOAR4BXNgy2BiZ+NFpSd4IvH7kfbQpcHhVPQegqg6c7c8vZVX1kPl8ndM4tK5Y1E+mRwHPBu4OjC7WcR3w6haBBuxFwDHAbkl+CHwPeHrbSINh0TqLqjq9dYZxMN+tkSbZ1AWy1q6q7tA6w5i5G7AJ3ZB7+s/vWlW39DdEpPlYD/hKkufQjdg7sn9o/t4GWNTrd2ZRP4Gq6njg+CRPdPGgOVVVPTzJJsCyqrouyQ6tQw2BRev8jHQQR11Ltzf7X1TVdxc/1fAkecUMh68FVlbVBYudZ4icBz23kekco64FLp/qJgqAtwMXJDmNbjTD/sCb+991/9ky2Bi5rHWA1qrqVUlOBb4C/AzYv6q+0zjWuHEah9YJF8qbYF5Ez22mRV6SrKyqFa0yDY1F6+ySvB64EvgQ3S/vp9B1NL4J/Pl8h+gtdUk+RDf0/uT+0B8CX6Wf91tVb2+VbSiSHMPM86DvAXzXedCQ5Gy6dWGmhpDfB/gasAXwgqo6pVW2oUmyDfD7dD+XzqmqKxtHGpS5hpYLkuxPt0PQv9B9r90JeK7vpfmb5MUEtW7ZqZ9s92Pmi+gXJJnoi+gkuwH3BjYbWaQKunUHNmyTarDexdqL1mOBhzRLNgyPXmNV92OSnF1Vb0jidJfVtgD2rqrrAZK8Fvg4XQdxJV1ncdI5D3pulwF/WlUXAyS5F90OJm+kW3vAon61ZcCP6a4Fd06yc1X9V+NMQ+LQ8rm9A3hSVV0Cty7q+QW6m4+SFpFF/WTzInrtdgUeB2zO9EWqrgOe1yTRcFm0zm5VkifTfW8BHDTymkOlVtsWuHHk+U3AdlX1S+f43sp50HPbbaqgB6iqS5LsVVXfXWMXk4mW5G3AwXQjPVb1hwuwqO85tHxe9qmqW6aeVNUn+q2ANX+XtQ6gpcGifrJ5Eb0W/fzUTyfZp6q+3DrPwFm0zu7pwOHAe+nOx9nAM5JsBLy4ZbCB+RBwdpKpueGPBz7cz/G9pF2sQXEe9Ny+2Y9g+Ej//GDgW0luR/c7Tp0/Anatqon+XT+bfmj54cAb6IaWH5XEoeXTbZnkzcDdqurR/ciYfYB/apxrMNwhQIvFOfUTLMnfAn8MjF5En0S3xdQxVTXxq7wn2YVuvthdqmr3JPcFnlBVb2ocbTCS7Eh34bMPq4vWlwM/BFZU1ZkN42mMJFkBPIiuYD2zqs5tHGlwnAc9u/5m2QsZeR/R3VD7FbDx1Mi0SZfkP+iGTXs+1qLfpvXZawwtf3NVObS817+PjgNeU1V7JFkPOL+q7tM42mAkeQvdNqTTpnFU1VFNg2nJsaifcISL3p4AABItSURBVEnuB+yHF9Ez6oeR/SVwdFXt1R+7qKp2b5tMQ5fk/1bV25McyQwjFqrq0AaxBifJplX18yR3mun1qvrpYmcamiS7VdU31rKyO1V13mJn0nhLciKwB3AqcGu33p9LqyVZPjq0vD+2RVX9pFWmoUny1aq6f5LzR66RLqiqPVtnG5IkD6dbv8ppHFowDr/X+XSLnK0HkGTbqvp+20iDsnFVnbPGXEy3RcKidR4u7T96o2x2H6Jbv2Il3fsoa3zcsV20wXgFcAjdKKo1FXDA4sYZniQfraonJ7mQmX8e3bdBrCE7qX9o7RxaPrcbkmxB/z2X5IGsXvNDOI1Di8dO/QRL8hLgtcCPgFvoL6K9+FmtH1r2YrottfZOchDdysqPaRytuSSPr6qTkzxrpter6vjFzjRkSTapqhta55CWoiTbVNVVSbab6fWqunyxM2m8ObR8bv3ooSOB3YGLgK2Ag6rq602DDYjTOLRYLOonWJLvAA9wKNna9fPFjwH2pRs29T3gGVV1WctcQ2TROrMkU52d21fVtkn2AJ5fVS9sHG1Q0g2HeTqwQ1W9Mcm2wNZVdU7jaIORZGO6rv22VXVIknvSLXb2mcbRBqUv7O9ZVf/Zz7Ffr6qua51rCBzRMH8OLV+7JPcHrqiq/+lvdjwfeCLdoqb/z2lTqzmNQ4tlWesAauoKHCY1q6r6blU9nO7u825V9SAL+umS7JPkEvrh5kn2SPLexrGG5N3Ao4CfAFTV1+hWLdd076Ub2vq0/vl1wHvaxRmk4+h2LNm3f/4DwEU7RyR5Ht1OHEf3h+4OfKpdosF5af/xcXSL46750GoOLV+7o1m9e9K+wGvofl7/jK4RotW2TPJPST4L0E/j+KPGmbQEOad+sn0XOC3JvzF9oZx3tYs0LEk2B54JbA+sNzW33vni00wVrSdBV7T2c8jUq6or1liX4Za1fe0Ee0A/xeV8gKr6WZINWocamJ2q6uAkTwXotx918/XpXkS3O8BXAKrq20nu3DbScFTVVf3HadMRkiwHngI4TWG1V9D9XtspyZfoh5a3jTQYy0e68QfT7Zh0InBikgsa5hqif6afxtE//xZwAq7NoHXMTv1k+z7weWAD4A4jD63273QF/YV0C3lNPTSiqq5Y45BF62pXJNkXqCQbJHklqxfR02o39YXFVFdsK2BV20iDc2M/nHzqHO3EyA1ZAfDrqprqINIPDXaeYS/JpkleleSoJI9M5yV0N/mf3DrfECS5f5Kt+10l/gB4Nd332Sl0o2MEy/vvLYCHAV8Yec2G4XRbVtVH6X+f9fvVe42kdc5vvAlWVa8H50LPYcOqekXrEAM3rWgFDsWiddQL6Fa+vRvdBeEpdN1ETXcE8EngzkkOo+uI/U3bSIPzOuCzwD2S/CvddqTPbhlogE5P8mpgoySPoNuz/uTGmYbkg3RDpL8M/Bndlq0bAP+nquywdo4GHt5/PjW0/CXAnnRDy+3Ww4fpvteuBn4JnAGQZGecorAmp3FoUbhQ3gRzAa+5JXk5cD3wGaZPUXARmF6SLemK1ofT7aBwCvBSF4HpJNmwqn7VOsc4SLIbXdcnwKlV5c2hNfQXhw+kO0dnV9XVjSMNSpJlwJ8Cj6Q7R58D/rG82AEgyYVTq7f3I2Ouplt40YUEe0m+VlV79J+/B/hxVb2uf+5Ceb2+ON0GOGWqMZRkF7pryvOahhsQdwjQYrGon2BJvkJ3x/mkkZVdL6qq3dsmG44kLwIOA65h9RDOqir3zta89LtM/Iiuk/FfwJeqyrv0a0jyBrpzdJYjh2aW5IN076EzquobrfMMUZID6G52/KJ1liFKcl5V7b225+qug4A9q+rmJN8ADqmq/5p6zWskzYc7BGixOfx+wrmA15xeAexsN+y2khzJLHNVXUywU1U799uzPZhuxen3JrnGbs9tXAY8FTgiyXX0N0Gq6tNNUw3LccCDgCP77TYvoDtHh7eNNSjPBt6f5Cd076EzgDOr6mdNUw3HHkl+3n8eumkKP+8/r6ratF20wXBoudYFp3FoUVnUTzbnQs/tYsCOz8zOHfn89cBrWwUZsiR3p5v7/GBgD7r31JlNQw1QVR0LHJtka7oFu14JHIKLd96qqr6Q5HTg/sBD6dZruDfd9BcBVfVMgCR3pbtofg9wV7zeAaCqlrfOMHRVdViSU1k9tHzq5vUyuqJMmg93CNCicvj9BFvLXOhDHRK0WpJP0l00f5Hpc+rtQo9Icv7UFA5Nl2QV8FXgzXad1y7JPwL3YvVUhTOB8/qVggX0hcYmdIucTXWg/7dtqmFJ8gy6G2j3oZsvfibddIUvNw0maaI4jUOLzTvXE6wfUv700WNJXka377g6n+ofmp13B9duL7oh009L8tfAt4HTq8o9aqfbAlhOt37FT4GrLehv4+vACroFl64Frkny5ar6ZdtYg/Ju4L+B9wNfrKrL2saRNKGcxqFFZade0yT5flVt2zqHxouLLc0uye3pCvsHA8+gm7u6fdNQA5Xk94BHAS+nG75498aRBqd/Pz2HborC1lV1u8aRBiXJvYH96b7n7gl8s6r+pG0qSZPGHQK0mOzUa02Z+0smR5J7Am+hGxa84dRxV7+HfjGzqbuCG6+x+JILLvWSnAvcDjiLbijw/lV1edtUw5PkcXQ3PfYH7gh8gb6zoU6SF9OdoxXA5cCxeI6mSbIpsC2wHbA9sBmwqmUmSZOpqs6e4di3WmTR0mdRrzU5dGO64+gWgPt7uoWpnoM3PgCoKhcwm5/HVNWPW4cYA4+h267t8Kq6snWYgdoIeBew0qkJa3XmyOOoqvpB4zySJC04h99PoDU6rNNeAjaqKm/29JKsrKoVSS6sqvv0x86oqge3ziZJkiRJFm8TyA7rb+RXSZYB3+6Hvv4QuHPjTJIkSZIE2KmXZpXk/sClwObAG+nmZ759pnlSkiRJkrTYLOolaQEl2Rj4C2Dbqnpev/jirlX1mcbRJEmStAQ4/F6aQZJ3V9XLkpzMDOsPVNUTGsTSeDoOWAns0z//AfAxwKIeSHIhsyzQWVX3XcQ4g9Zvj3Qk8HvABsBy4AZ3moAkRzL7++jQRYwjSdKisqiXZvbB/uM7mqbQUrBTVR2c5KkAVfXLJO6gsNrj+o8v6j9Ofe89HfjF4scZtKOAp9DdFLof8Exg56aJhuPc1gEkSWrFol6aQVWt7D+enmSr/nO3JdNv48YkG9F3EZPsBPy6baThqKrLAZLsV1X7jbz010m+BLyhTbJhqqrvJFleVbcAxyU5q3WmIaiq41tnkCSpFYt6aQZ9J/W1wIvptvpbluRm4MiqssjQb+K1wGeBeyT5V2A/4NlNEw3TJkkeVFVnAiTZF9ikcaah+UWSDYALkrwduArPEQBrmyo1xSlTkqSlzIXypBkkeTnwWOCQqvpef2xH4H3AZ6vq71vm03hJsgXwQLobRGdX1dWNIw1OkhXAsXQ7TBRwLfDcqjqvabABSbId8CO6+fQvpztX762q7zQNNgBJ/mC216vq9MXKIknSYrOol2aQ5HzgEWsWX/1Q/FOqaq82yTQukuw92+sWqzNLsind76ZrW2cZkiTLgeOr6hmtswxdP91l26r6ZusskiQtBoffSzNbf6ZualX9OMn6LQJp7LxzltcKOGCxgoyDJHcB3gzctaoek+RewD5V9U+Now1CVd2SZKskG1TVja3zDFWSx9MtcLoBsEOSPYE3OPxekrSUWdRLM5vtotkLas2pqh7aOsOY+We67f9e0z//FnACYFG/2mXAl5KcBNwwdbCq3tUs0fC8Dvh94DSAqrogyfbt4kiStPAs6qWZ7ZHk5zMcD7DhYofR+OpHdvw5sH9/6DTg6Kq6qVmoYdqyqj6a5FUAVXVzkltahxqYK/vHMuAOjbMM1c1Vda27RkqSJolFvTSDqlreOoOWjPcB6wPv7Z//SX/sz5olGqYb+gUFp7b+eyDdYnnqVdXrAZJsUlU3zPX1E+qiJE8Dlie5J3Ao4LZ/kqQlzYXyJGkBJflaVe0x17FJ1y8seCSwO3ARsBVwUFV9vWmwAUmyD910hNtX1bZJ9gCeX1UvbBxtMJJsTDeF45F0I6s+B7yxqn7VNJgkSQvIol6SFlCS84AnVdV/9893BD5eVbOujj+JkqwH7EpXjH0TWFZVv26bajiSfAU4CDhpageOJBdV1e5tk0mSpJYcfi9JC+svgS8m+S5dsbod8Jy2kYYnybFV9Vzg4v75JsBJwMOaBhuYqrpijfnirjswIskuwCuB7Rm5xqkqd5uQJC1ZFvWStICq6tR+bu9UB/obdp9n9MMk76uqP09yR+DfgH9oHWpgrkiyL1BJNqCbL35p40xD8zHg/cA/4g0PSdKEcPi9JC2wvhDbnumdww80CzRQSd4GbAasAN5aVSc2jjQoSbYEDgceTneD6BTgpVX1k6bBBiTJyqpa0TqHJEmLyaJekhZQkg8COwEXsLpzWFV1aLtUw5HkwNGnwN8C5wCfBaiqT7TIpfGS5E79p4cC/wt8Erh1RExV/bRFLkmSFoNFvSQtoCSXAvcqf9jOKMlxs7xc/Tx7AUmOp+vMX9M/vyPwTs8RJPke3XaIM21QX1W14yJHkiRp0TinXpIW1kXA1sBVrYMMUVW5aOD83XeqoAeoqp8l2atloKGoqh1aZ5AkqZVlrQNI0lKU5OQkJwFbApck+VySk6YerfMNTZLjk2w+8vyOSY5tmWmAlvXdeeDWIefenAeS3D/J1iPPn5nk00mOGBmaL0nSkuTFgCQtjHe0DjBm7ELP7Z3AWUk+3j9/EnBYwzxDcjTdAoIk2R94K/ASYE/gGOCgdtEkSVpYFvWStDD2Ar4EnF9VN7cOMwaWJbljVf0M7ELPpKo+kORc4AC6ueMHVtUljWMNxfKRxfAOBo7pd084MckFDXNJkrTgvGCSpIVxd7rtx3ZL8nXgLLoi/8uuxD0ju9BzSLIT8N9VdUmShwAPT3Ll6AiHCbY8yXr9DbSHAYeMvOa1jiRpSXP1e0laQEk2AO4H7Avs0z+uqap7NQ02QEnuxeou9Kl2oafrO873A7an2/LvZGDXqnpsy1xDkOQ1wGOBq4Ftgb2rqpLsDBxfVfs1DShJ0gLy7rUkLayNgE2BzfrHlcCFTRMNUJJtgeuBk0aPVdX326UanFVVdXOSA4HDq+rIJOe3DjUEVXVYklOBbYBTRraQXEY3t16SpCXLol6SFkCSY4B7A9cBX6Ebfv+uqTnjuo1/o9tnHLobITsA36Q7h+rclOSpwDOBx/fH1m+YZ1Cq6uwZjn2rRRZJkhaTRb0kLYxtgdsB3wZ+CPwAcO7zWlTVfUafJ9kbeH6jOEP1HOAFwGFV9b0kOwD/0jiTJElqzDn1krRAkoSu07xv/9gd+CndYnmvbZltHCQ5r6r2bp1DkiRpyCzqJWmBJbk7sB9dYf84YIuq2rxtqmFJ8oqRp8uAvenO06MaRRqMJB+tqicnuZDVUxRuVVX3bRBLkiQNhEW9JC2AJIfSFfH7ATfRb2fXf7ywqlY1jDc4SUZHLtwMXAacWFW/apNoOJJsU1VXJdlupter6vLFziRJkobDol6SFkCSd9HvTV9VV7XOo/GV5CjgQ1V1VusskiRpeCzqJUnNJdkFeCXdHuy3LuJaVQe0yjQUSV4KPIVuu7YTgA9X1QVtU0mSpKGwqJckNZfka8D7gZXALVPHq2pls1AD0w+/f0r/2BD4MPARt22TJGmyWdRLkppLsrKqVrTOMS6S7AUcC9y3qpa3ziNJktpZ1jqAJGlyJblTkjsBJyd5YZJtpo71x9VLsn6Sxyf5V+A/gG8BT2wcS5IkNWanXpLUTJLv0W3TlhlerqracZEjDU6SRwBPBf4QOAf4CPCpqrqhaTBJkjQIFvWSJA1Yki8CH6Lb4u+nrfNIkqRhsaiXJDWT5P7AFVX1P/3zZ9INKb8ceJ1FrCRJ0uycUy9Jaulo4EaAJPsDbwU+AFwLHNMwlyRJ0lhYb+4vkSRpwSwf6cYfDBxTVScCJyZxL3ZJkqQ52KmXJLW0PMnUDeaHAV8Yec0bz5IkSXPwgkmS1NKHgdOTXA38EjgDIMnOdEPwJUmSNAsXypMkNZXkgcA2wClT27Ql2QW4fVWd1zScJEnSwFnUS5IkSZI0ppxTL0mSJEnSmLKolyRJkiRpTFnUS5IkSZI0pizqJUmSJEkaUxb1kiRJkiSNqf8PJjTqhAYXtXAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e6f0bf908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr = data.corr()\n", "fig, ax = plt.subplots(figsize=(18,10)) \n", "sns.heatmap(corr)\n", "corr" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e6a48aa20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ((ax1, ax2), (ax3, ax4),(ax5, ax6),(ax7,ax8)) = plt.subplots(4, 2, figsize = (15,10), sharex=False)\n", "axs = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8]\n", "plt.tight_layout()\n", "for n in range(0, 8):\n", " axs[n].hist(data[data.columns[n]], bins=30)\n", " axs[n].set_title(data.columns[n], fontsize=10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e6a5ed198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(18, 10))\n", "plt.hist(data['Rings'], bins=30)\n", "plt.title(\"Rings\", fontsize=16)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(data.drop(columns=['Rings']), data['Rings'], test_size=.2, random_state=17)\n", "sc = StandardScaler().fit(X_train)\n", "X_train, X_test = sc.transform(X_train), sc.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def approx(y_pred, y_true):\n", " predictions = list(zip(y_pred, y_true))\n", " return [len(list(filter(lambda a: abs(a[0] - a[1]) <= d, predictions))) / len(predictions) for d in [0.5, 1, 2]]\n", "\n", "def score(model):\n", " model.fit(X_train, y_train)\n", " print('Train score: {}'.format(approx(model.predict(X_train), y_train)))\n", " print('Test score: {}'.format(approx(model.predict(X_test), y_test)))\n", " \n", "def grid_search(model, params):\n", " gs = GridSearchCV(model, params)\n", " return gs.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-Neighbors" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.3214606405267884, 0.6596827297216402, 0.7955701885662975]\n", "Test score: [0.2619617224880383, 0.6363636363636364, 0.7990430622009569]\n" ] } ], "source": [ "score(KNeighborsClassifier(29))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SVM + linear kernel" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.27357078718946426, 0.6381322957198443, 0.7898832684824902]\n", "Test score: [0.25478468899521534, 0.6411483253588517, 0.7858851674641149]\n" ] } ], "source": [ "score(SVC(kernel='linear'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision tree" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.31188266985932356, 0.6390302304699191, 0.8054474708171206]\n", "Test score: [0.24162679425837322, 0.6267942583732058, 0.7966507177033493]\n" ] }, { "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", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: Tree Pages: 1 -->\n", "<svg width=\"11739pt\" height=\"821pt\"\n", " viewBox=\"0.00 0.00 11739.00 821.00\" xmlns=\"http://www.w3.org/2000/svg\" 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ 0.115</text>\n", "<text text-anchor=\"start\" x=\"7225\" y=\"-633.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.872</text>\n", "<text text-anchor=\"start\" x=\"7212.5\" y=\"-618.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2397</text>\n", "<text text-anchor=\"start\" x=\"7070\" y=\"-603.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 13, 73, 293, 459, 461, 368, 195, 152</text>\n", "<text text-anchor=\"start\" x=\"7119.5\" y=\"-588.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">102, 86, 52, 44, 30, 24, 18, 9, 5, 8, 1, 1, 2</text>\n", "<text text-anchor=\"start\" x=\"7261\" y=\"-573.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1]</text>\n", "<text text-anchor=\"start\" x=\"7237\" y=\"-558.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 0->32 -->\n", "<g id=\"edge32\" class=\"edge\"><title>0->32</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6216.54,-730.937C6448.14,-703.982 6818,-660.934 7051.81,-633.72\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7052.27,-637.19 7061.8,-632.558 7051.46,-630.237 7052.27,-637.19\"/>\n", "<text text-anchor=\"middle\" x=\"7046.29\" y=\"-648.46\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n", "</g>\n", "<!-- 2 -->\n", "<g id=\"node3\" class=\"node\"><title>2</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.121569\" stroke=\"black\" d=\"M2319.5,-507.5C2319.5,-507.5 1975.5,-507.5 1975.5,-507.5 1969.5,-507.5 1963.5,-501.5 1963.5,-495.5 1963.5,-495.5 1963.5,-421.5 1963.5,-421.5 1963.5,-415.5 1969.5,-409.5 1975.5,-409.5 1975.5,-409.5 2319.5,-409.5 2319.5,-409.5 2325.5,-409.5 2331.5,-415.5 2331.5,-421.5 2331.5,-421.5 2331.5,-495.5 2331.5,-495.5 2331.5,-501.5 2325.5,-507.5 2319.5,-507.5\"/>\n", "<text text-anchor=\"start\" x=\"2063\" y=\"-492.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Viscera weight ≤ -1.512</text>\n", "<text text-anchor=\"start\" x=\"2108.5\" y=\"-477.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.75</text>\n", "<text text-anchor=\"start\" x=\"2096\" y=\"-462.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 180</text>\n", "<text text-anchor=\"start\" x=\"1971.5\" y=\"-447.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 13, 48, 64, 37, 11, 5, 1, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2041\" y=\"-432.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"2116\" y=\"-417.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 1->2 -->\n", "<g id=\"edge2\" class=\"edge\"><title>1->2</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4184.91,-592.967C3753.5,-564.706 2762.15,-499.764 2341.7,-472.222\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2341.77,-468.719 2331.57,-471.558 2341.32,-475.704 2341.77,-468.719\"/>\n", "</g>\n", "<!-- 17 -->\n", "<g id=\"node18\" class=\"node\"><title>17</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.113725\" stroke=\"black\" d=\"M4577,-507.5C4577,-507.5 4206,-507.5 4206,-507.5 4200,-507.5 4194,-501.5 4194,-495.5 4194,-495.5 4194,-421.5 4194,-421.5 4194,-415.5 4200,-409.5 4206,-409.5 4206,-409.5 4577,-409.5 4577,-409.5 4583,-409.5 4589,-415.5 4589,-421.5 4589,-421.5 4589,-495.5 4589,-495.5 4589,-501.5 4583,-507.5 4577,-507.5\"/>\n", "<text text-anchor=\"start\" x=\"4346\" y=\"-492.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sex_I ≤ 0.39</text>\n", "<text text-anchor=\"start\" x=\"4348\" y=\"-477.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.814</text>\n", "<text text-anchor=\"start\" x=\"4340\" y=\"-462.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 764</text>\n", "<text text-anchor=\"start\" x=\"4202\" y=\"-447.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 2, 34, 150, 223, 153, 89, 55, 24, 17, 10</text>\n", "<text text-anchor=\"start\" x=\"4267\" y=\"-432.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4360\" y=\"-417.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 1->17 -->\n", "<g id=\"edge17\" class=\"edge\"><title>1->17</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4391.5,-558.367C4391.5,-545.422 4391.5,-531.281 4391.5,-517.846\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4395,-517.741 4391.5,-507.741 4388,-517.741 4395,-517.741\"/>\n", "</g>\n", "<!-- 3 -->\n", "<g id=\"node4\" class=\"node\"><title>3</title>\n", "<path fill=\"#e5ce39\" fill-opacity=\"0.380392\" stroke=\"black\" d=\"M1241.5,-358.5C1241.5,-358.5 915.5,-358.5 915.5,-358.5 909.5,-358.5 903.5,-352.5 903.5,-346.5 903.5,-346.5 903.5,-272.5 903.5,-272.5 903.5,-266.5 909.5,-260.5 915.5,-260.5 915.5,-260.5 1241.5,-260.5 1241.5,-260.5 1247.5,-260.5 1253.5,-266.5 1253.5,-272.5 1253.5,-272.5 1253.5,-346.5 1253.5,-346.5 1253.5,-352.5 1247.5,-358.5 1241.5,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"1020.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Length ≤ -2.249</text>\n", "<text text-anchor=\"start\" x=\"1035\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.665</text>\n", "<text text-anchor=\"start\" x=\"1031.5\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 73</text>\n", "<text text-anchor=\"start\" x=\"911.5\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 13, 37, 15, 4, 2, 1, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"972\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1047\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 3</text>\n", "</g>\n", "<!-- 2->3 -->\n", "<g id=\"edge3\" class=\"edge\"><title>2->3</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1963.43,-432.188C1767.69,-405.272 1459.86,-362.941 1263.7,-335.967\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1263.95,-332.469 1253.57,-334.574 1263,-339.403 1263.95,-332.469\"/>\n", "</g>\n", "<!-- 10 -->\n", "<g id=\"node11\" class=\"node\"><title>10</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.215686\" stroke=\"black\" d=\"M2310.5,-358.5C2310.5,-358.5 1984.5,-358.5 1984.5,-358.5 1978.5,-358.5 1972.5,-352.5 1972.5,-346.5 1972.5,-346.5 1972.5,-272.5 1972.5,-272.5 1972.5,-266.5 1978.5,-260.5 1984.5,-260.5 1984.5,-260.5 2310.5,-260.5 2310.5,-260.5 2316.5,-260.5 2322.5,-266.5 2322.5,-272.5 2322.5,-272.5 2322.5,-346.5 2322.5,-346.5 2322.5,-352.5 2316.5,-358.5 2310.5,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"2065.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Whole weight ≤ -1.463</text>\n", "<text text-anchor=\"start\" x=\"2104\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.676</text>\n", "<text text-anchor=\"start\" x=\"2096\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 107</text>\n", "<text text-anchor=\"start\" x=\"1980.5\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 11, 49, 33, 9, 4, 1, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2041\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"2116\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 2->10 -->\n", "<g id=\"edge10\" class=\"edge\"><title>2->10</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M2147.5,-409.367C2147.5,-396.422 2147.5,-382.281 2147.5,-368.846\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2151,-368.741 2147.5,-358.741 2144,-368.741 2151,-368.741\"/>\n", "</g>\n", "<!-- 4 -->\n", "<g id=\"node5\" class=\"node\"><title>4</title>\n", "<path fill=\"#e5ce39\" fill-opacity=\"0.435294\" stroke=\"black\" d=\"M697.5,-217C697.5,-217 371.5,-217 371.5,-217 365.5,-217 359.5,-211 359.5,-205 359.5,-205 359.5,-131 359.5,-131 359.5,-125 365.5,-119 371.5,-119 371.5,-119 697.5,-119 697.5,-119 703.5,-119 709.5,-125 709.5,-131 709.5,-131 709.5,-205 709.5,-205 709.5,-211 703.5,-217 697.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"445.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ -1.596</text>\n", "<text text-anchor=\"start\" x=\"491\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.627</text>\n", "<text text-anchor=\"start\" x=\"487.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 68</text>\n", "<text text-anchor=\"start\" x=\"367.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 13, 37, 13, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"428\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"503\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 3</text>\n", "</g>\n", "<!-- 3->4 -->\n", "<g id=\"edge4\" class=\"edge\"><title>3->4</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M903.381,-263.593C844.683,-248.541 779.062,-231.714 719.624,-216.472\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"720.313,-213.036 709.757,-213.942 718.574,-219.816 720.313,-213.036\"/>\n", "</g>\n", "<!-- 7 -->\n", "<g id=\"node8\" class=\"node\"><title>7</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1237,-217C1237,-217 920,-217 920,-217 914,-217 908,-211 908,-205 908,-205 908,-131 908,-131 908,-125 914,-119 920,-119 920,-119 1237,-119 1237,-119 1243,-119 1249,-125 1249,-131 1249,-131 1249,-205 1249,-205 1249,-211 1243,-217 1237,-217\"/>\n", "<text text-anchor=\"start\" x=\"1017\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Diameter ≤ -2.11</text>\n", "<text text-anchor=\"start\" x=\"1039.5\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.64</text>\n", "<text text-anchor=\"start\" x=\"1036\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n", "<text text-anchor=\"start\" x=\"916\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 2, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"981\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1047\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 3->7 -->\n", "<g id=\"edge7\" class=\"edge\"><title>3->7</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1078.5,-260.492C1078.5,-249.751 1078.5,-238.246 1078.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1082,-227.096 1078.5,-217.096 1075,-227.097 1082,-227.096\"/>\n", "</g>\n", "<!-- 5 -->\n", "<g id=\"node6\" class=\"node\"><title>5</title>\n", "<path fill=\"#e5a639\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M329,-83C329,-83 12,-83 12,-83 6,-83 0,-77 0,-71 0,-71 0,-12 0,-12 0,-6 6,-0 12,-0 12,-0 329,-0 329,-0 335,-0 341,-6 341,-12 341,-12 341,-71 341,-71 341,-77 335,-83 329,-83\"/>\n", "<text text-anchor=\"start\" x=\"127\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n", "<text text-anchor=\"start\" x=\"128\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n", "<text text-anchor=\"start\" x=\"8\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"73\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"139\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 2</text>\n", "</g>\n", "<!-- 4->5 -->\n", "<g id=\"edge5\" class=\"edge\"><title>4->5</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M394.028,-118.954C362.646,-108.22 329.524,-96.8914 298.669,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"299.766,-83.0142 289.171,-83.0895 297.5,-89.6375 299.766,-83.0142\"/>\n", "</g>\n", "<!-- 6 -->\n", "<g id=\"node7\" class=\"node\"><title>6</title>\n", "<path fill=\"#e5ce39\" fill-opacity=\"0.462745\" stroke=\"black\" d=\"M697.5,-83C697.5,-83 371.5,-83 371.5,-83 365.5,-83 359.5,-77 359.5,-71 359.5,-71 359.5,-12 359.5,-12 359.5,-6 365.5,-0 371.5,-0 371.5,-0 697.5,-0 697.5,-0 703.5,-0 709.5,-6 709.5,-12 709.5,-12 709.5,-71 709.5,-71 709.5,-77 703.5,-83 697.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"491\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.604</text>\n", "<text text-anchor=\"start\" x=\"487.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 65</text>\n", "<text text-anchor=\"start\" x=\"367.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 11, 37, 13, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"428\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"503\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 3</text>\n", "</g>\n", "<!-- 4->6 -->\n", "<g id=\"edge6\" class=\"edge\"><title>4->6</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M534.5,-118.865C534.5,-110.498 534.5,-101.771 534.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"538,-93.2309 534.5,-83.231 531,-93.231 538,-93.2309\"/>\n", "</g>\n", "<!-- 8 -->\n", "<g id=\"node9\" class=\"node\"><title>8</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1057,-83C1057,-83 740,-83 740,-83 734,-83 728,-77 728,-71 728,-71 728,-12 728,-12 728,-6 734,-0 740,-0 740,-0 1057,-0 1057,-0 1063,-0 1069,-6 1069,-12 1069,-12 1069,-71 1069,-71 1069,-77 1063,-83 1057,-83\"/>\n", "<text text-anchor=\"start\" x=\"864\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n", "<text text-anchor=\"start\" x=\"856\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n", "<text text-anchor=\"start\" x=\"736\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"801\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"867\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 7->8 -->\n", "<g id=\"edge8\" class=\"edge\"><title>7->8</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1008.91,-118.865C994.6,-108.969 979.567,-98.5715 965.359,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"967.317,-85.8425 957.101,-83.0325 963.335,-91.5996 967.317,-85.8425\"/>\n", "</g>\n", "<!-- 9 -->\n", "<g id=\"node10\" class=\"node\"><title>9</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M1416,-83C1416,-83 1099,-83 1099,-83 1093,-83 1087,-77 1087,-71 1087,-71 1087,-12 1087,-12 1087,-6 1093,-0 1099,-0 1099,-0 1416,-0 1416,-0 1422,-0 1428,-6 1428,-12 1428,-12 1428,-71 1428,-71 1428,-77 1422,-83 1416,-83\"/>\n", "<text text-anchor=\"start\" x=\"1214\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n", "<text text-anchor=\"start\" x=\"1215\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n", "<text text-anchor=\"start\" x=\"1095\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"1160\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1226\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 7->9 -->\n", "<g id=\"edge9\" class=\"edge\"><title>7->9</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1147.71,-118.865C1161.93,-108.969 1176.88,-98.5715 1191.01,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1193.01,-91.6158 1199.22,-83.0325 1189.02,-85.8692 1193.01,-91.6158\"/>\n", "</g>\n", "<!-- 11 -->\n", "<g id=\"node12\" class=\"node\"><title>11</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.380392\" stroke=\"black\" d=\"M2126,-217C2126,-217 1809,-217 1809,-217 1803,-217 1797,-211 1797,-205 1797,-205 1797,-131 1797,-131 1797,-125 1803,-119 1809,-119 1809,-119 2126,-119 2126,-119 2132,-119 2138,-125 2138,-131 2138,-131 2138,-205 2138,-205 2138,-211 2132,-217 2126,-217\"/>\n", "<text text-anchor=\"start\" x=\"1890.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ -1.489</text>\n", "<text text-anchor=\"start\" x=\"1924\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.653</text>\n", "<text text-anchor=\"start\" x=\"1920.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 71</text>\n", "<text text-anchor=\"start\" x=\"1805\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 9, 37, 16, 6, 3, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"1861\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1936\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 10->11 -->\n", "<g id=\"edge11\" class=\"edge\"><title>10->11</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M2085.55,-260.492C2070.14,-248.546 2053.5,-235.654 2037.72,-223.423\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2039.61,-220.456 2029.56,-217.096 2035.32,-225.989 2039.61,-220.456\"/>\n", "</g>\n", "<!-- 14 -->\n", "<g id=\"node15\" class=\"node\"><title>14</title>\n", "<path fill=\"#ace539\" fill-opacity=\"0.207843\" stroke=\"black\" d=\"M2485,-217C2485,-217 2168,-217 2168,-217 2162,-217 2156,-211 2156,-205 2156,-205 2156,-131 2156,-131 2156,-125 2162,-119 2168,-119 2168,-119 2485,-119 2485,-119 2491,-119 2497,-125 2497,-131 2497,-131 2497,-205 2497,-205 2497,-211 2491,-217 2485,-217\"/>\n", "<text text-anchor=\"start\" x=\"2237.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ -1.356</text>\n", "<text text-anchor=\"start\" x=\"2283\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.654</text>\n", "<text text-anchor=\"start\" x=\"2279.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 36</text>\n", "<text text-anchor=\"start\" x=\"2164\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 2, 12, 17, 3, 1, 1, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2220\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"2295\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 5</text>\n", "</g>\n", "<!-- 10->14 -->\n", "<g id=\"edge14\" class=\"edge\"><title>10->14</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M2209.1,-260.492C2224.43,-248.546 2240.97,-235.654 2256.67,-223.423\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2259.05,-226.004 2264.78,-217.096 2254.75,-220.483 2259.05,-226.004\"/>\n", "</g>\n", "<!-- 12 -->\n", "<g id=\"node13\" class=\"node\"><title>12</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.188235\" stroke=\"black\" d=\"M1775,-83C1775,-83 1458,-83 1458,-83 1452,-83 1446,-77 1446,-71 1446,-71 1446,-12 1446,-12 1446,-6 1452,-0 1458,-0 1458,-0 1775,-0 1775,-0 1781,-0 1787,-6 1787,-12 1787,-12 1787,-71 1787,-71 1787,-77 1781,-83 1775,-83\"/>\n", "<text text-anchor=\"start\" x=\"1573\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.686</text>\n", "<text text-anchor=\"start\" x=\"1569.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 53</text>\n", "<text text-anchor=\"start\" x=\"1454\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 9, 23, 16, 4, 1, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"1510\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1585\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 11->12 -->\n", "<g id=\"edge12\" class=\"edge\"><title>11->12</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1832.05,-118.954C1801.91,-108.267 1770.12,-96.989 1740.48,-86.4747\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1741.53,-83.1338 1730.93,-83.0895 1739.19,-89.7311 1741.53,-83.1338\"/>\n", "</g>\n", "<!-- 13 -->\n", "<g id=\"node14\" class=\"node\"><title>13</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.749020\" stroke=\"black\" d=\"M2125.5,-83C2125.5,-83 1817.5,-83 1817.5,-83 1811.5,-83 1805.5,-77 1805.5,-71 1805.5,-71 1805.5,-12 1805.5,-12 1805.5,-6 1811.5,-0 1817.5,-0 1817.5,-0 2125.5,-0 2125.5,-0 2131.5,-0 2137.5,-6 2137.5,-12 2137.5,-12 2137.5,-71 2137.5,-71 2137.5,-77 2131.5,-83 2125.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"1932.5\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.37</text>\n", "<text text-anchor=\"start\" x=\"1924.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n", "<text text-anchor=\"start\" x=\"1813.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 14, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"1865\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"1940\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 11->13 -->\n", "<g id=\"edge13\" class=\"edge\"><title>11->13</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1969.05,-118.865C1969.32,-110.498 1969.6,-101.771 1969.87,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1973.37,-93.3382 1970.19,-83.231 1966.37,-93.1134 1973.37,-93.3382\"/>\n", "</g>\n", "<!-- 15 -->\n", "<g id=\"node16\" class=\"node\"><title>15</title>\n", "<path fill=\"#ace539\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M2475.5,-83C2475.5,-83 2167.5,-83 2167.5,-83 2161.5,-83 2155.5,-77 2155.5,-71 2155.5,-71 2155.5,-12 2155.5,-12 2155.5,-6 2161.5,-0 2167.5,-0 2167.5,-0 2475.5,-0 2475.5,-0 2481.5,-0 2487.5,-6 2487.5,-12 2487.5,-12 2487.5,-71 2487.5,-71 2487.5,-77 2481.5,-83 2475.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"2278\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.626</text>\n", "<text text-anchor=\"start\" x=\"2274.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21</text>\n", "<text text-anchor=\"start\" x=\"2163.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 3, 12, 3, 1, 1, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2215\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"2290\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 5</text>\n", "</g>\n", "<!-- 14->15 -->\n", "<g id=\"edge15\" class=\"edge\"><title>14->15</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M2324.57,-118.865C2324.23,-110.498 2323.88,-101.771 2323.54,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2327.03,-93.0824 2323.14,-83.231 2320.04,-93.3634 2327.03,-93.0824\"/>\n", "</g>\n", "<!-- 16 -->\n", "<g id=\"node17\" class=\"node\"><title>16</title>\n", "<path fill=\"#d4e539\" fill-opacity=\"0.400000\" stroke=\"black\" d=\"M2835,-83C2835,-83 2518,-83 2518,-83 2512,-83 2506,-77 2506,-71 2506,-71 2506,-12 2506,-12 2506,-6 2512,-0 2518,-0 2518,-0 2835,-0 2835,-0 2841,-0 2847,-6 2847,-12 2847,-12 2847,-71 2847,-71 2847,-77 2841,-83 2835,-83\"/>\n", "<text text-anchor=\"start\" x=\"2633\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.524</text>\n", "<text text-anchor=\"start\" x=\"2629.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 15</text>\n", "<text text-anchor=\"start\" x=\"2514\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 9, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2579\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"2645\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 4</text>\n", "</g>\n", "<!-- 14->16 -->\n", "<g id=\"edge16\" class=\"edge\"><title>14->16</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M2461.57,-118.954C2491.61,-108.267 2523.32,-96.989 2552.88,-86.4747\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"2554.14,-89.7386 2562.39,-83.0895 2551.8,-83.1434 2554.14,-89.7386\"/>\n", "</g>\n", "<!-- 18 -->\n", "<g id=\"node19\" class=\"node\"><title>18</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4123.5,-358.5C4123.5,-358.5 3779.5,-358.5 3779.5,-358.5 3773.5,-358.5 3767.5,-352.5 3767.5,-346.5 3767.5,-346.5 3767.5,-272.5 3767.5,-272.5 3767.5,-266.5 3773.5,-260.5 3779.5,-260.5 3779.5,-260.5 4123.5,-260.5 4123.5,-260.5 4129.5,-260.5 4135.5,-266.5 4135.5,-272.5 4135.5,-272.5 4135.5,-346.5 4135.5,-346.5 4135.5,-352.5 4129.5,-358.5 4123.5,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"3862.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ -0.613</text>\n", "<text text-anchor=\"start\" x=\"3908\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.843</text>\n", "<text text-anchor=\"start\" x=\"3900\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 250</text>\n", "<text text-anchor=\"start\" x=\"3775.5\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 4, 18, 44, 51, 51, 43, 16, 12, 7, 1</text>\n", "<text text-anchor=\"start\" x=\"3836\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3920\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 17->18 -->\n", "<g id=\"edge18\" class=\"edge\"><title>17->18</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4247.73,-409.468C4202,-394.188 4151.24,-377.232 4104.85,-361.734\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4105.93,-358.402 4095.33,-358.553 4103.71,-365.042 4105.93,-358.402\"/>\n", "</g>\n", "<!-- 25 -->\n", "<g id=\"node26\" class=\"node\"><title>25</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.121569\" stroke=\"black\" d=\"M4837.5,-358.5C4837.5,-358.5 4475.5,-358.5 4475.5,-358.5 4469.5,-358.5 4463.5,-352.5 4463.5,-346.5 4463.5,-346.5 4463.5,-272.5 4463.5,-272.5 4463.5,-266.5 4469.5,-260.5 4475.5,-260.5 4475.5,-260.5 4837.5,-260.5 4837.5,-260.5 4843.5,-260.5 4849.5,-266.5 4849.5,-272.5 4849.5,-272.5 4849.5,-346.5 4849.5,-346.5 4849.5,-352.5 4843.5,-358.5 4837.5,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"4579.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ -1.002</text>\n", "<text text-anchor=\"start\" x=\"4613\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.764</text>\n", "<text text-anchor=\"start\" x=\"4605\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 514</text>\n", "<text text-anchor=\"start\" x=\"4471.5\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 30, 132, 179, 102, 38, 12, 8, 5, 3, 2</text>\n", "<text text-anchor=\"start\" x=\"4541\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4625\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 17->25 -->\n", "<g id=\"edge25\" class=\"edge\"><title>17->25</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4478.27,-409.367C4504.58,-394.772 4533.63,-378.657 4560.51,-363.748\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4562.49,-366.653 4569.53,-358.741 4559.09,-360.532 4562.49,-366.653\"/>\n", "</g>\n", "<!-- 19 -->\n", "<g id=\"node20\" class=\"node\"><title>19</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.039216\" stroke=\"black\" d=\"M3575.5,-217C3575.5,-217 3231.5,-217 3231.5,-217 3225.5,-217 3219.5,-211 3219.5,-205 3219.5,-205 3219.5,-131 3219.5,-131 3219.5,-125 3225.5,-119 3231.5,-119 3231.5,-119 3575.5,-119 3575.5,-119 3581.5,-119 3587.5,-125 3587.5,-131 3587.5,-131 3587.5,-205 3587.5,-205 3587.5,-211 3581.5,-217 3575.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"3319\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Viscera weight ≤ -1.178</text>\n", "<text text-anchor=\"start\" x=\"3360\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.845</text>\n", "<text text-anchor=\"start\" x=\"3352\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 218</text>\n", "<text text-anchor=\"start\" x=\"3227.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 4, 13, 34, 38, 49, 42, 15, 12, 7, 1</text>\n", "<text text-anchor=\"start\" x=\"3288\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3372\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 18->19 -->\n", "<g id=\"edge19\" class=\"edge\"><title>18->19</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M3767.5,-261.66C3712.94,-247.772 3653.03,-232.52 3597.84,-218.47\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"3598.5,-215.029 3587.95,-215.953 3596.78,-221.812 3598.5,-215.029\"/>\n", "</g>\n", "<!-- 22 -->\n", "<g id=\"node23\" class=\"node\"><title>22</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.137255\" stroke=\"black\" d=\"M4110,-217C4110,-217 3793,-217 3793,-217 3787,-217 3781,-211 3781,-205 3781,-205 3781,-131 3781,-131 3781,-125 3787,-119 3793,-119 3793,-119 4110,-119 4110,-119 4116,-119 4122,-125 4122,-131 4122,-131 4122,-205 4122,-205 4122,-211 4116,-217 4110,-217\"/>\n", "<text text-anchor=\"start\" x=\"3893.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Length ≤ -0.544</text>\n", "<text text-anchor=\"start\" x=\"3908\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.707</text>\n", "<text text-anchor=\"start\" x=\"3904.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n", "<text text-anchor=\"start\" x=\"3789\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 5, 10, 13, 2, 1, 1, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"3845\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3920\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 18->22 -->\n", "<g id=\"edge22\" class=\"edge\"><title>18->22</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M3951.5,-260.492C3951.5,-249.751 3951.5,-238.246 3951.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"3955,-227.096 3951.5,-217.096 3948,-227.097 3955,-227.096\"/>\n", "</g>\n", "<!-- 20 -->\n", "<g id=\"node21\" class=\"node\"><title>20</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.117647\" stroke=\"black\" d=\"M3194,-83C3194,-83 2877,-83 2877,-83 2871,-83 2865,-77 2865,-71 2865,-71 2865,-12 2865,-12 2865,-6 2871,-0 2877,-0 2877,-0 3194,-0 3194,-0 3200,-0 3206,-6 3206,-12 3206,-12 3206,-71 3206,-71 3206,-77 3200,-83 3194,-83\"/>\n", "<text text-anchor=\"start\" x=\"2992\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.829</text>\n", "<text text-anchor=\"start\" x=\"2988.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 53</text>\n", "<text text-anchor=\"start\" x=\"2873\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 2, 7, 10, 3, 8, 15, 5, 0, 2, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"2929\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3004\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 19->20 -->\n", "<g id=\"edge20\" class=\"edge\"><title>19->20</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M3261.48,-118.954C3229.76,-108.22 3196.27,-96.8914 3165.08,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"3166.07,-82.9789 3155.48,-83.0895 3163.83,-89.6097 3166.07,-82.9789\"/>\n", "</g>\n", "<!-- 21 -->\n", "<g id=\"node22\" class=\"node\"><title>21</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.047059\" stroke=\"black\" d=\"M3571,-83C3571,-83 3236,-83 3236,-83 3230,-83 3224,-77 3224,-71 3224,-71 3224,-12 3224,-12 3224,-6 3230,-0 3236,-0 3236,-0 3571,-0 3571,-0 3577,-0 3583,-6 3583,-12 3583,-12 3583,-71 3583,-71 3583,-77 3577,-83 3571,-83\"/>\n", "<text text-anchor=\"start\" x=\"3360\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.834</text>\n", "<text text-anchor=\"start\" x=\"3352\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 165</text>\n", "<text text-anchor=\"start\" x=\"3232\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 2, 6, 24, 35, 41, 27, 10, 12, 5, 1</text>\n", "<text text-anchor=\"start\" x=\"3288\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3372\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 19->21 -->\n", "<g id=\"edge21\" class=\"edge\"><title>19->21</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M3403.5,-118.865C3403.5,-110.498 3403.5,-101.771 3403.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"3407,-93.2309 3403.5,-83.231 3400,-93.231 3407,-93.2309\"/>\n", "</g>\n", "<!-- 23 -->\n", "<g id=\"node24\" class=\"node\"><title>23</title>\n", "<path fill=\"#5be539\" stroke=\"black\" d=\"M3930,-83C3930,-83 3613,-83 3613,-83 3607,-83 3601,-77 3601,-71 3601,-71 3601,-12 3601,-12 3601,-6 3607,-0 3613,-0 3613,-0 3930,-0 3930,-0 3936,-0 3942,-6 3942,-12 3942,-12 3942,-71 3942,-71 3942,-77 3936,-83 3930,-83\"/>\n", "<text text-anchor=\"start\" x=\"3737\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n", "<text text-anchor=\"start\" x=\"3729\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n", "<text text-anchor=\"start\" x=\"3609\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"3674\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"3740\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 22->23 -->\n", "<g id=\"edge23\" class=\"edge\"><title>22->23</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M3881.91,-118.865C3867.6,-108.969 3852.57,-98.5715 3838.36,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"3840.32,-85.8425 3830.1,-83.0325 3836.33,-91.5996 3840.32,-85.8425\"/>\n", "</g>\n", "<!-- 24 -->\n", "<g id=\"node25\" class=\"node\"><title>24</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.105882\" stroke=\"black\" d=\"M4280.5,-83C4280.5,-83 3972.5,-83 3972.5,-83 3966.5,-83 3960.5,-77 3960.5,-71 3960.5,-71 3960.5,-12 3960.5,-12 3960.5,-6 3966.5,-0 3972.5,-0 3972.5,-0 4280.5,-0 4280.5,-0 4286.5,-0 4292.5,-6 4292.5,-12 4292.5,-12 4292.5,-71 4292.5,-71 4292.5,-77 4286.5,-83 4280.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"4083\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.733</text>\n", "<text text-anchor=\"start\" x=\"4079.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 27</text>\n", "<text text-anchor=\"start\" x=\"3968.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 5, 10, 8, 2, 1, 1, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"4020\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4095\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 22->24 -->\n", "<g id=\"edge24\" class=\"edge\"><title>22->24</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4019.16,-118.865C4032.94,-109.062 4047.41,-98.7658 4061.11,-89.0201\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4063.41,-91.6814 4069.53,-83.0325 4059.35,-85.9776 4063.41,-91.6814\"/>\n", "</g>\n", "<!-- 26 -->\n", "<g id=\"node27\" class=\"node\"><title>26</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.003922\" stroke=\"black\" d=\"M4828.5,-217C4828.5,-217 4484.5,-217 4484.5,-217 4478.5,-217 4472.5,-211 4472.5,-205 4472.5,-205 4472.5,-131 4472.5,-131 4472.5,-125 4478.5,-119 4484.5,-119 4484.5,-119 4828.5,-119 4828.5,-119 4834.5,-119 4840.5,-125 4840.5,-131 4840.5,-131 4840.5,-205 4840.5,-205 4840.5,-211 4834.5,-217 4828.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"4572\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Viscera weight ≤ -1.333</text>\n", "<text text-anchor=\"start\" x=\"4613\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.733</text>\n", "<text text-anchor=\"start\" x=\"4605\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 308</text>\n", "<text text-anchor=\"start\" x=\"4480.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 28, 106, 107, 41, 11, 7, 3, 2, 1, 1</text>\n", "<text text-anchor=\"start\" x=\"4541\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4625\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 25->26 -->\n", "<g id=\"edge26\" class=\"edge\"><title>25->26</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4656.5,-260.492C4656.5,-249.751 4656.5,-238.246 4656.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4660,-227.096 4656.5,-217.096 4653,-227.097 4660,-227.096\"/>\n", "</g>\n", "<!-- 29 -->\n", "<g id=\"node30\" class=\"node\"><title>29</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.074510\" stroke=\"black\" d=\"M5385,-217C5385,-217 5050,-217 5050,-217 5044,-217 5038,-211 5038,-205 5038,-205 5038,-131 5038,-131 5038,-125 5044,-119 5050,-119 5050,-119 5385,-119 5385,-119 5391,-119 5397,-125 5397,-131 5397,-131 5397,-205 5397,-205 5397,-211 5391,-217 5385,-217\"/>\n", "<text text-anchor=\"start\" x=\"5140.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ -0.847</text>\n", "<text text-anchor=\"start\" x=\"5174\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.755</text>\n", "<text text-anchor=\"start\" x=\"5166\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 206</text>\n", "<text text-anchor=\"start\" x=\"5046\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 2, 26, 72, 61, 27, 5, 5, 3, 2, 1, 1</text>\n", "<text text-anchor=\"start\" x=\"5111\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"5186\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 25->29 -->\n", "<g id=\"edge29\" class=\"edge\"><title>25->29</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4849.56,-260.492C4907.24,-246.15 4970.4,-230.446 5027.85,-216.159\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"5028.9,-219.504 5037.76,-213.694 5027.21,-212.711 5028.9,-219.504\"/>\n", "</g>\n", "<!-- 27 -->\n", "<g id=\"node28\" class=\"node\"><title>27</title>\n", "<path fill=\"#ace539\" fill-opacity=\"0.403922\" stroke=\"black\" d=\"M4630.5,-83C4630.5,-83 4322.5,-83 4322.5,-83 4316.5,-83 4310.5,-77 4310.5,-71 4310.5,-71 4310.5,-12 4310.5,-12 4310.5,-6 4316.5,-0 4322.5,-0 4322.5,-0 4630.5,-0 4630.5,-0 4636.5,-0 4642.5,-6 4642.5,-12 4642.5,-12 4642.5,-71 4642.5,-71 4642.5,-77 4636.5,-83 4630.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"4433\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.689</text>\n", "<text text-anchor=\"start\" x=\"4429.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\n", "<text text-anchor=\"start\" x=\"4318.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 8, 25, 7, 6, 1, 1, 1, 0, 1, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"4370\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4445\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 5</text>\n", "</g>\n", "<!-- 26->27 -->\n", "<g id=\"edge27\" class=\"edge\"><title>26->27</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4586.91,-118.865C4572.6,-108.969 4557.57,-98.5715 4543.36,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4545.32,-85.8425 4535.1,-83.0325 4541.33,-91.5996 4545.32,-85.8425\"/>\n", "</g>\n", "<!-- 28 -->\n", "<g id=\"node29\" class=\"node\"><title>28</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.105882\" stroke=\"black\" d=\"M5008,-83C5008,-83 4673,-83 4673,-83 4667,-83 4661,-77 4661,-71 4661,-71 4661,-12 4661,-12 4661,-6 4667,-0 4673,-0 4673,-0 5008,-0 5008,-0 5014,-0 5020,-6 5020,-12 5020,-12 5020,-71 5020,-71 5020,-77 5014,-83 5008,-83\"/>\n", "<text text-anchor=\"start\" x=\"4797\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.725</text>\n", "<text text-anchor=\"start\" x=\"4789\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 258</text>\n", "<text text-anchor=\"start\" x=\"4669\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1, 20, 81, 100, 35, 10, 6, 2, 2, 0, 1</text>\n", "<text text-anchor=\"start\" x=\"4725\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"4809\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 26->28 -->\n", "<g id=\"edge28\" class=\"edge\"><title>26->28</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M4727.64,-118.865C4742.26,-108.969 4757.63,-98.5715 4772.15,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"4774.28,-91.5353 4780.6,-83.0325 4770.35,-85.7378 4774.28,-91.5353\"/>\n", "</g>\n", "<!-- 30 -->\n", "<g id=\"node31\" class=\"node\"><title>30</title>\n", "<path fill=\"#83e539\" fill-opacity=\"0.227451\" stroke=\"black\" d=\"M5385,-83C5385,-83 5050,-83 5050,-83 5044,-83 5038,-77 5038,-71 5038,-71 5038,-12 5038,-12 5038,-6 5044,-0 5050,-0 5050,-0 5385,-0 5385,-0 5391,-0 5397,-6 5397,-12 5397,-12 5397,-71 5397,-71 5397,-77 5391,-83 5385,-83\"/>\n", "<text text-anchor=\"start\" x=\"5174\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.743</text>\n", "<text text-anchor=\"start\" x=\"5166\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 109</text>\n", "<text text-anchor=\"start\" x=\"5046\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 2, 19, 44, 25, 10, 2, 4, 1, 1, 1, 0</text>\n", "<text text-anchor=\"start\" x=\"5111\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"5186\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 6</text>\n", "</g>\n", "<!-- 29->30 -->\n", "<g id=\"edge30\" class=\"edge\"><title>29->30</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M5217.5,-118.865C5217.5,-110.498 5217.5,-101.771 5217.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"5221,-93.2309 5217.5,-83.231 5214,-93.231 5221,-93.2309\"/>\n", "</g>\n", "<!-- 31 -->\n", "<g id=\"node32\" class=\"node\"><title>31</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.117647\" stroke=\"black\" d=\"M5753.5,-83C5753.5,-83 5427.5,-83 5427.5,-83 5421.5,-83 5415.5,-77 5415.5,-71 5415.5,-71 5415.5,-12 5415.5,-12 5415.5,-6 5421.5,-0 5427.5,-0 5427.5,-0 5753.5,-0 5753.5,-0 5759.5,-0 5765.5,-6 5765.5,-12 5765.5,-12 5765.5,-71 5765.5,-71 5765.5,-77 5759.5,-83 5753.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"5547\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.741</text>\n", "<text text-anchor=\"start\" x=\"5543.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 97</text>\n", "<text text-anchor=\"start\" x=\"5423.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 7, 28, 36, 17, 3, 1, 2, 1, 0, 1</text>\n", "<text text-anchor=\"start\" x=\"5484\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"5559\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 29->31 -->\n", "<g id=\"edge31\" class=\"edge\"><title>29->31</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M5361.44,-118.954C5393.6,-108.22 5427.54,-96.8914 5459.16,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"5460.52,-89.5756 5468.89,-83.0895 5458.3,-82.9357 5460.52,-89.5756\"/>\n", "</g>\n", "<!-- 33 -->\n", "<g id=\"node34\" class=\"node\"><title>33</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.023529\" stroke=\"black\" d=\"M7449.5,-507.5C7449.5,-507.5 7087.5,-507.5 7087.5,-507.5 7081.5,-507.5 7075.5,-501.5 7075.5,-495.5 7075.5,-495.5 7075.5,-421.5 7075.5,-421.5 7075.5,-415.5 7081.5,-409.5 7087.5,-409.5 7087.5,-409.5 7449.5,-409.5 7449.5,-409.5 7455.5,-409.5 7461.5,-415.5 7461.5,-421.5 7461.5,-421.5 7461.5,-495.5 7461.5,-495.5 7461.5,-501.5 7455.5,-507.5 7449.5,-507.5\"/>\n", "<text text-anchor=\"start\" x=\"7223\" y=\"-492.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sex_I ≤ 0.39</text>\n", "<text text-anchor=\"start\" x=\"7225\" y=\"-477.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.836</text>\n", "<text text-anchor=\"start\" x=\"7217\" y=\"-462.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 945</text>\n", "<text text-anchor=\"start\" x=\"7083.5\" y=\"-447.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 10, 62, 224, 242, 142, 86, 59, 37</text>\n", "<text text-anchor=\"start\" x=\"7135\" y=\"-432.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">30, 25, 9, 6, 4, 3, 2, 2, 1, 1, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7237\" y=\"-417.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 32->33 -->\n", "<g id=\"edge33\" class=\"edge\"><title>32->33</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7268.5,-550.926C7268.5,-540.205 7268.5,-528.942 7268.5,-518.128\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7272,-517.929 7268.5,-507.929 7265,-517.929 7272,-517.929\"/>\n", "</g>\n", "<!-- 48 -->\n", "<g id=\"node49\" class=\"node\"><title>48</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.031373\" stroke=\"black\" d=\"M9682,-515C9682,-515 9311,-515 9311,-515 9305,-515 9299,-509 9299,-503 9299,-503 9299,-414 9299,-414 9299,-408 9305,-402 9311,-402 9311,-402 9682,-402 9682,-402 9688,-402 9694,-408 9694,-414 9694,-414 9694,-503 9694,-503 9694,-509 9688,-515 9682,-515\"/>\n", "<text text-anchor=\"start\" x=\"9422.5\" y=\"-499.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ 1.131</text>\n", "<text text-anchor=\"start\" x=\"9453\" y=\"-484.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.868</text>\n", "<text text-anchor=\"start\" x=\"9440.5\" y=\"-469.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1452</text>\n", "<text text-anchor=\"start\" x=\"9307\" y=\"-454.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 3, 11, 69, 217, 319, 282, 136, 115</text>\n", "<text text-anchor=\"start\" x=\"9352\" y=\"-439.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">72, 61, 43, 38, 26, 21, 16, 7, 4, 7, 1, 1, 2</text>\n", "<text text-anchor=\"start\" x=\"9489\" y=\"-424.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1]</text>\n", "<text text-anchor=\"start\" x=\"9465\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 32->48 -->\n", "<g id=\"edge48\" class=\"edge\"><title>32->48</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7475.11,-592.868C7899.13,-564.892 8863.42,-501.27 9288.57,-473.219\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9289.07,-476.694 9298.82,-472.543 9288.61,-469.709 9289.07,-476.694\"/>\n", "</g>\n", "<!-- 34 -->\n", "<g id=\"node35\" class=\"node\"><title>34</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.050980\" stroke=\"black\" d=\"M7091,-358.5C7091,-358.5 6738,-358.5 6738,-358.5 6732,-358.5 6726,-352.5 6726,-346.5 6726,-346.5 6726,-272.5 6726,-272.5 6726,-266.5 6732,-260.5 6738,-260.5 6738,-260.5 7091,-260.5 7091,-260.5 7097,-260.5 7103,-266.5 7103,-272.5 7103,-272.5 7103,-346.5 7103,-346.5 7103,-352.5 7097,-358.5 7091,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"6833\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ 0.04</text>\n", "<text text-anchor=\"start\" x=\"6871\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.857</text>\n", "<text text-anchor=\"start\" x=\"6863\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 628</text>\n", "<text text-anchor=\"start\" x=\"6734\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 6, 43, 119, 145, 104, 62, 52, 30</text>\n", "<text text-anchor=\"start\" x=\"6781\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">24, 22, 8, 5, 1, 2, 1, 2, 1, 1, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"6883\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 33->34 -->\n", "<g id=\"edge34\" class=\"edge\"><title>33->34</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7152.59,-409.367C7116.43,-394.353 7076.4,-377.73 7039.64,-362.465\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7040.95,-359.219 7030.37,-358.616 7038.26,-365.683 7040.95,-359.219\"/>\n", "</g>\n", "<!-- 41 -->\n", "<g id=\"node42\" class=\"node\"><title>41</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.035294\" stroke=\"black\" d=\"M7789,-358.5C7789,-358.5 7454,-358.5 7454,-358.5 7448,-358.5 7442,-352.5 7442,-346.5 7442,-346.5 7442,-272.5 7442,-272.5 7442,-266.5 7448,-260.5 7454,-260.5 7454,-260.5 7789,-260.5 7789,-260.5 7795,-260.5 7801,-266.5 7801,-272.5 7801,-272.5 7801,-346.5 7801,-346.5 7801,-352.5 7795,-358.5 7789,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"7544.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ -0.174</text>\n", "<text text-anchor=\"start\" x=\"7578\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.771</text>\n", "<text text-anchor=\"start\" x=\"7570\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 317</text>\n", "<text text-anchor=\"start\" x=\"7450\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 4, 19, 105, 97, 38, 24, 7, 7, 6</text>\n", "<text text-anchor=\"start\" x=\"7506\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">3, 1, 1, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7590\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 33->41 -->\n", "<g id=\"edge41\" class=\"edge\"><title>33->41</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7384.09,-409.367C7420.14,-394.353 7460.06,-377.73 7496.71,-362.465\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7498.07,-365.691 7505.96,-358.616 7495.38,-359.229 7498.07,-365.691\"/>\n", "</g>\n", "<!-- 35 -->\n", "<g id=\"node36\" class=\"node\"><title>35</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.007843\" stroke=\"black\" d=\"M6522,-217C6522,-217 6169,-217 6169,-217 6163,-217 6157,-211 6157,-205 6157,-205 6157,-131 6157,-131 6157,-125 6163,-119 6169,-119 6169,-119 6522,-119 6522,-119 6528,-119 6534,-125 6534,-131 6534,-131 6534,-205 6534,-205 6534,-211 6528,-217 6522,-217\"/>\n", "<text text-anchor=\"start\" x=\"6273\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ -0.31</text>\n", "<text text-anchor=\"start\" x=\"6306.5\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.88</text>\n", "<text text-anchor=\"start\" x=\"6294\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 438</text>\n", "<text text-anchor=\"start\" x=\"6165\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 5, 28, 57, 79, 76, 52, 49, 29, 23</text>\n", "<text text-anchor=\"start\" x=\"6225.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">21, 7, 4, 1, 2, 1, 2, 1, 1, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"6314\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 34->35 -->\n", "<g id=\"edge35\" class=\"edge\"><title>34->35</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6725.82,-262.242C6667.45,-247.93 6602.97,-232.124 6543.96,-217.655\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"6544.7,-214.233 6534.15,-215.251 6543.03,-221.032 6544.7,-214.233\"/>\n", "</g>\n", "<!-- 38 -->\n", "<g id=\"node39\" class=\"node\"><title>38</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.031373\" stroke=\"black\" d=\"M7086.5,-217C7086.5,-217 6742.5,-217 6742.5,-217 6736.5,-217 6730.5,-211 6730.5,-205 6730.5,-205 6730.5,-131 6730.5,-131 6730.5,-125 6736.5,-119 6742.5,-119 6742.5,-119 7086.5,-119 7086.5,-119 7092.5,-119 7098.5,-125 7098.5,-131 7098.5,-131 7098.5,-205 7098.5,-205 7098.5,-211 7092.5,-217 7086.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"6832.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Viscera weight ≤ 0.479</text>\n", "<text text-anchor=\"start\" x=\"6871\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.742</text>\n", "<text text-anchor=\"start\" x=\"6863\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 190</text>\n", "<text text-anchor=\"start\" x=\"6738.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 1, 15, 62, 66, 28, 10, 3, 1, 1, 1</text>\n", "<text text-anchor=\"start\" x=\"6808\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"6883\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 34->38 -->\n", "<g id=\"edge38\" class=\"edge\"><title>34->38</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6914.5,-260.492C6914.5,-249.751 6914.5,-238.246 6914.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"6918,-227.096 6914.5,-217.096 6911,-227.097 6918,-227.096\"/>\n", "</g>\n", "<!-- 36 -->\n", "<g id=\"node37\" class=\"node\"><title>36</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.019608\" stroke=\"black\" d=\"M6131,-83C6131,-83 5796,-83 5796,-83 5790,-83 5784,-77 5784,-71 5784,-71 5784,-12 5784,-12 5784,-6 5790,-0 5796,-0 5796,-0 6131,-0 6131,-0 6137,-0 6143,-6 6143,-12 6143,-12 6143,-71 6143,-71 6143,-77 6137,-83 6131,-83\"/>\n", "<text text-anchor=\"start\" x=\"5920\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.864</text>\n", "<text text-anchor=\"start\" x=\"5912\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 230</text>\n", "<text text-anchor=\"start\" x=\"5792\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 5, 27, 39, 45, 41, 26, 18, 8, 7</text>\n", "<text text-anchor=\"start\" x=\"5848\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">8, 3, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"5932\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 35->36 -->\n", "<g id=\"edge36\" class=\"edge\"><title>35->36</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6198.08,-118.954C6165.01,-108.174 6130.09,-96.7937 6097.59,-86.2018\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"6098.63,-82.8606 6088.04,-83.0895 6096.46,-89.516 6098.63,-82.8606\"/>\n", "</g>\n", "<!-- 37 -->\n", "<g id=\"node38\" class=\"node\"><title>37</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.003922\" stroke=\"black\" d=\"M6517.5,-83C6517.5,-83 6173.5,-83 6173.5,-83 6167.5,-83 6161.5,-77 6161.5,-71 6161.5,-71 6161.5,-12 6161.5,-12 6161.5,-6 6167.5,-0 6173.5,-0 6173.5,-0 6517.5,-0 6517.5,-0 6523.5,-0 6529.5,-6 6529.5,-12 6529.5,-12 6529.5,-71 6529.5,-71 6529.5,-77 6523.5,-83 6517.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"6302\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.879</text>\n", "<text text-anchor=\"start\" x=\"6294\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 208</text>\n", "<text text-anchor=\"start\" x=\"6169.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 1, 18, 34, 35, 26, 31, 21, 16</text>\n", "<text text-anchor=\"start\" x=\"6225.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">13, 4, 3, 0, 2, 1, 1, 1, 1, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"6314\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 35->37 -->\n", "<g id=\"edge37\" class=\"edge\"><title>35->37</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6345.5,-118.865C6345.5,-110.498 6345.5,-101.771 6345.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"6349,-93.2309 6345.5,-83.231 6342,-93.231 6349,-93.2309\"/>\n", "</g>\n", "<!-- 39 -->\n", "<g id=\"node40\" class=\"node\"><title>39</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.043137\" stroke=\"black\" d=\"M6895,-83C6895,-83 6560,-83 6560,-83 6554,-83 6548,-77 6548,-71 6548,-71 6548,-12 6548,-12 6548,-6 6554,-0 6560,-0 6560,-0 6895,-0 6895,-0 6901,-0 6907,-6 6907,-12 6907,-12 6907,-71 6907,-71 6907,-77 6901,-83 6895,-83\"/>\n", "<text text-anchor=\"start\" x=\"6684\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.745</text>\n", "<text text-anchor=\"start\" x=\"6676\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 165</text>\n", "<text text-anchor=\"start\" x=\"6556\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 1, 15, 57, 52, 27, 6, 2, 1, 1, 1</text>\n", "<text text-anchor=\"start\" x=\"6621\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"6696\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 38->39 -->\n", "<g id=\"edge39\" class=\"edge\"><title>38->39</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6842.2,-118.865C6827.34,-108.969 6811.72,-98.5715 6796.96,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"6798.64,-85.661 6788.38,-83.0325 6794.76,-91.4878 6798.64,-85.661\"/>\n", "</g>\n", "<!-- 40 -->\n", "<g id=\"node41\" class=\"node\"><title>40</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.450980\" stroke=\"black\" d=\"M7245.5,-83C7245.5,-83 6937.5,-83 6937.5,-83 6931.5,-83 6925.5,-77 6925.5,-71 6925.5,-71 6925.5,-12 6925.5,-12 6925.5,-6 6931.5,-0 6937.5,-0 6937.5,-0 7245.5,-0 7245.5,-0 7251.5,-0 7257.5,-6 7257.5,-12 7257.5,-12 7257.5,-71 7257.5,-71 7257.5,-77 7251.5,-83 7245.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"7048\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.618</text>\n", "<text text-anchor=\"start\" x=\"7044.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 25</text>\n", "<text text-anchor=\"start\" x=\"6933.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 5, 14, 1, 4, 1, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"6985\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7060\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 38->40 -->\n", "<g id=\"edge40\" class=\"edge\"><title>38->40</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M6982.93,-118.865C6996.87,-109.062 7011.51,-98.7658 7025.36,-89.0201\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7027.71,-91.6485 7033.88,-83.0325 7023.68,-85.923 7027.71,-91.6485\"/>\n", "</g>\n", "<!-- 42 -->\n", "<g id=\"node43\" class=\"node\"><title>42</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.168627\" stroke=\"black\" d=\"M7793.5,-217C7793.5,-217 7449.5,-217 7449.5,-217 7443.5,-217 7437.5,-211 7437.5,-205 7437.5,-205 7437.5,-131 7437.5,-131 7437.5,-125 7443.5,-119 7449.5,-119 7449.5,-119 7793.5,-119 7793.5,-119 7799.5,-119 7805.5,-125 7805.5,-131 7805.5,-131 7805.5,-205 7805.5,-205 7805.5,-211 7799.5,-217 7793.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"7532.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ -0.708</text>\n", "<text text-anchor=\"start\" x=\"7578\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.756</text>\n", "<text text-anchor=\"start\" x=\"7570\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 232</text>\n", "<text text-anchor=\"start\" x=\"7445.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 19, 91, 62, 19, 14, 5, 6, 5, 3</text>\n", "<text text-anchor=\"start\" x=\"7515\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7590\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 41->42 -->\n", "<g id=\"edge42\" class=\"edge\"><title>41->42</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7621.5,-260.492C7621.5,-249.751 7621.5,-238.246 7621.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7625,-227.096 7621.5,-217.096 7618,-227.097 7625,-227.096\"/>\n", "</g>\n", "<!-- 45 -->\n", "<g id=\"node46\" class=\"node\"><title>45</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.243137\" stroke=\"black\" d=\"M8350,-217C8350,-217 8015,-217 8015,-217 8009,-217 8003,-211 8003,-205 8003,-205 8003,-131 8003,-131 8003,-125 8009,-119 8015,-119 8015,-119 8350,-119 8350,-119 8356,-119 8362,-125 8362,-131 8362,-131 8362,-205 8362,-205 8362,-211 8356,-217 8350,-217\"/>\n", "<text text-anchor=\"start\" x=\"8128.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Height ≤ 0.429</text>\n", "<text text-anchor=\"start\" x=\"8139\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.738</text>\n", "<text text-anchor=\"start\" x=\"8135.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 85</text>\n", "<text text-anchor=\"start\" x=\"8011\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 0, 14, 35, 19, 10, 2, 1, 1, 0</text>\n", "<text text-anchor=\"start\" x=\"8076\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"8151\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 41->45 -->\n", "<g id=\"edge45\" class=\"edge\"><title>41->45</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7801.32,-263.786C7862.47,-248.581 7930.97,-231.546 7992.86,-216.156\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7993.88,-219.51 8002.74,-213.7 7992.19,-212.717 7993.88,-219.51\"/>\n", "</g>\n", "<!-- 43 -->\n", "<g id=\"node44\" class=\"node\"><title>43</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.125490\" stroke=\"black\" d=\"M7595.5,-83C7595.5,-83 7287.5,-83 7287.5,-83 7281.5,-83 7275.5,-77 7275.5,-71 7275.5,-71 7275.5,-12 7275.5,-12 7275.5,-6 7281.5,-0 7287.5,-0 7287.5,-0 7595.5,-0 7595.5,-0 7601.5,-0 7607.5,-6 7607.5,-12 7607.5,-12 7607.5,-71 7607.5,-71 7607.5,-77 7601.5,-83 7595.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"7398\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.844</text>\n", "<text text-anchor=\"start\" x=\"7394.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 39</text>\n", "<text text-anchor=\"start\" x=\"7283.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 1, 7, 11, 2, 6, 1, 3, 3, 0</text>\n", "<text text-anchor=\"start\" x=\"7335\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7410\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 42->43 -->\n", "<g id=\"edge43\" class=\"edge\"><title>42->43</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7551.91,-118.865C7537.6,-108.969 7522.57,-98.5715 7508.36,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7510.32,-85.8425 7500.1,-83.0325 7506.33,-91.5996 7510.32,-85.8425\"/>\n", "</g>\n", "<!-- 44 -->\n", "<g id=\"node45\" class=\"node\"><title>44</title>\n", "<path fill=\"#5be539\" fill-opacity=\"0.231373\" stroke=\"black\" d=\"M7973,-83C7973,-83 7638,-83 7638,-83 7632,-83 7626,-77 7626,-71 7626,-71 7626,-12 7626,-12 7626,-6 7632,-0 7638,-0 7638,-0 7973,-0 7973,-0 7979,-0 7985,-6 7985,-12 7985,-12 7985,-71 7985,-71 7985,-77 7979,-83 7973,-83\"/>\n", "<text text-anchor=\"start\" x=\"7762\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.721</text>\n", "<text text-anchor=\"start\" x=\"7754\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 193</text>\n", "<text text-anchor=\"start\" x=\"7634\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 18, 84, 51, 17, 8, 4, 3, 2, 3</text>\n", "<text text-anchor=\"start\" x=\"7699\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"7774\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 7</text>\n", "</g>\n", "<!-- 42->44 -->\n", "<g id=\"edge44\" class=\"edge\"><title>42->44</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M7692.64,-118.865C7707.26,-108.969 7722.63,-98.5715 7737.15,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"7739.28,-91.5353 7745.6,-83.0325 7735.35,-85.7378 7739.28,-91.5353\"/>\n", "</g>\n", "<!-- 46 -->\n", "<g id=\"node47\" class=\"node\"><title>46</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.168627\" stroke=\"black\" d=\"M8350,-83C8350,-83 8015,-83 8015,-83 8009,-83 8003,-77 8003,-71 8003,-71 8003,-12 8003,-12 8003,-6 8009,-0 8015,-0 8015,-0 8350,-0 8350,-0 8356,-0 8362,-6 8362,-12 8362,-12 8362,-71 8362,-71 8362,-77 8356,-83 8350,-83\"/>\n", "<text text-anchor=\"start\" x=\"8139\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.756</text>\n", "<text text-anchor=\"start\" x=\"8135.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 78</text>\n", "<text text-anchor=\"start\" x=\"8011\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 0, 13, 29, 19, 10, 2, 1, 1, 0</text>\n", "<text text-anchor=\"start\" x=\"8076\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"8151\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 45->46 -->\n", "<g id=\"edge46\" class=\"edge\"><title>45->46</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M8182.5,-118.865C8182.5,-110.498 8182.5,-101.771 8182.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"8186,-93.2309 8182.5,-83.231 8179,-93.231 8186,-93.2309\"/>\n", "</g>\n", "<!-- 47 -->\n", "<g id=\"node48\" class=\"node\"><title>47</title>\n", "<path fill=\"#39e53c\" fill-opacity=\"0.831373\" stroke=\"black\" d=\"M8709,-83C8709,-83 8392,-83 8392,-83 8386,-83 8380,-77 8380,-71 8380,-71 8380,-12 8380,-12 8380,-6 8386,-0 8392,-0 8392,-0 8709,-0 8709,-0 8715,-0 8721,-6 8721,-12 8721,-12 8721,-71 8721,-71 8721,-77 8715,-83 8709,-83\"/>\n", "<text text-anchor=\"start\" x=\"8507\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.245</text>\n", "<text text-anchor=\"start\" x=\"8508\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n", "<text text-anchor=\"start\" x=\"8388\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 1, 6, 0, 0, 0, 0, 0, 0, 0</text>\n", "<text text-anchor=\"start\" x=\"8453\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"8519\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 8</text>\n", "</g>\n", "<!-- 45->47 -->\n", "<g id=\"edge47\" class=\"edge\"><title>45->47</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M8324.52,-118.954C8356.24,-108.22 8389.73,-96.8914 8420.92,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"8422.17,-89.6097 8430.52,-83.0895 8419.93,-82.9789 8422.17,-89.6097\"/>\n", "</g>\n", "<!-- 49 -->\n", "<g id=\"node50\" class=\"node\"><title>49</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.078431\" stroke=\"black\" d=\"M9673,-366C9673,-366 9320,-366 9320,-366 9314,-366 9308,-360 9308,-354 9308,-354 9308,-265 9308,-265 9308,-259 9314,-253 9320,-253 9320,-253 9673,-253 9673,-253 9679,-253 9685,-259 9685,-265 9685,-265 9685,-354 9685,-354 9685,-360 9679,-366 9673,-366\"/>\n", "<text text-anchor=\"start\" x=\"9410.5\" y=\"-350.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ 0.393</text>\n", "<text text-anchor=\"start\" x=\"9453\" y=\"-335.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.852</text>\n", "<text text-anchor=\"start\" x=\"9440.5\" y=\"-320.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1024</text>\n", "<text text-anchor=\"start\" x=\"9316\" y=\"-305.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 3, 10, 64, 194, 258, 173, 80, 72</text>\n", "<text text-anchor=\"start\" x=\"9356.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">47, 39, 21, 21, 14, 12, 6, 3, 2, 4, 0, 1, 0</text>\n", "<text text-anchor=\"start\" x=\"9489\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0]</text>\n", "<text text-anchor=\"start\" x=\"9465\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 48->49 -->\n", "<g id=\"edge49\" class=\"edge\"><title>48->49</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9496.5,-401.926C9496.5,-393.511 9496.5,-384.762 9496.5,-376.158\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9500,-376.153 9496.5,-366.153 9493,-376.153 9500,-376.153\"/>\n", "</g>\n", "<!-- 56 -->\n", "<g id=\"node57\" class=\"node\"><title>56</title>\n", "<path fill=\"#39e58c\" fill-opacity=\"0.129412\" stroke=\"black\" d=\"M10804.5,-358.5C10804.5,-358.5 10460.5,-358.5 10460.5,-358.5 10454.5,-358.5 10448.5,-352.5 10448.5,-346.5 10448.5,-346.5 10448.5,-272.5 10448.5,-272.5 10448.5,-266.5 10454.5,-260.5 10460.5,-260.5 10460.5,-260.5 10804.5,-260.5 10804.5,-260.5 10810.5,-260.5 10816.5,-266.5 10816.5,-272.5 10816.5,-272.5 10816.5,-346.5 10816.5,-346.5 10816.5,-352.5 10810.5,-358.5 10804.5,-358.5\"/>\n", "<text text-anchor=\"start\" x=\"10546.5\" y=\"-343.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shucked weight ≤ 1.178</text>\n", "<text text-anchor=\"start\" x=\"10589\" y=\"-328.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.872</text>\n", "<text text-anchor=\"start\" x=\"10581\" y=\"-313.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 428</text>\n", "<text text-anchor=\"start\" x=\"10456.5\" y=\"-298.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 1, 5, 23, 61, 109, 56, 43, 25</text>\n", "<text text-anchor=\"start\" x=\"10494.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">22, 22, 17, 12, 9, 10, 4, 2, 3, 1, 0, 2, 1]</text>\n", "<text text-anchor=\"start\" x=\"10596.5\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 10</text>\n", "</g>\n", "<!-- 48->56 -->\n", "<g id=\"edge56\" class=\"edge\"><title>48->56</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9694.05,-431.937C9902.9,-404.912 10230.2,-362.552 10437.9,-335.675\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"10438.6,-339.122 10448.1,-334.367 10437.7,-332.18 10438.6,-339.122\"/>\n", "</g>\n", "<!-- 50 -->\n", "<g id=\"node51\" class=\"node\"><title>50</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.082353\" stroke=\"black\" d=\"M9468.5,-217C9468.5,-217 9124.5,-217 9124.5,-217 9118.5,-217 9112.5,-211 9112.5,-205 9112.5,-205 9112.5,-131 9112.5,-131 9112.5,-125 9118.5,-119 9124.5,-119 9124.5,-119 9468.5,-119 9468.5,-119 9474.5,-119 9480.5,-125 9480.5,-131 9480.5,-131 9480.5,-205 9480.5,-205 9480.5,-211 9474.5,-217 9468.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"9222.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ 0.376</text>\n", "<text text-anchor=\"start\" x=\"9253\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.897</text>\n", "<text text-anchor=\"start\" x=\"9245\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 398</text>\n", "<text text-anchor=\"start\" x=\"9120.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 3, 19, 43, 79, 50, 31, 43, 30</text>\n", "<text text-anchor=\"start\" x=\"9163\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">27, 20, 17, 8, 11, 6, 2, 2, 4, 0, 1, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"9265\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 49->50 -->\n", "<g id=\"edge50\" class=\"edge\"><title>49->50</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9416.88,-252.965C9402.67,-243.057 9387.9,-232.749 9373.76,-222.887\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9375.72,-219.989 9365.52,-217.139 9371.72,-225.731 9375.72,-219.989\"/>\n", "</g>\n", "<!-- 53 -->\n", "<g id=\"node54\" class=\"node\"><title>53</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.058824\" stroke=\"black\" d=\"M9882,-217C9882,-217 9511,-217 9511,-217 9505,-217 9499,-211 9499,-205 9499,-205 9499,-131 9499,-131 9499,-125 9505,-119 9511,-119 9511,-119 9882,-119 9882,-119 9888,-119 9894,-125 9894,-131 9894,-131 9894,-205 9894,-205 9894,-211 9888,-217 9882,-217\"/>\n", "<text text-anchor=\"start\" x=\"9633\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Diameter ≤ 0.759</text>\n", "<text text-anchor=\"start\" x=\"9653\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.807</text>\n", "<text text-anchor=\"start\" x=\"9645\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 626</text>\n", "<text text-anchor=\"start\" x=\"9507\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 1, 7, 45, 151, 179, 123, 49, 29, 17</text>\n", "<text text-anchor=\"start\" x=\"9576.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">12, 1, 4, 6, 1, 0, 1, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"9665\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 49->53 -->\n", "<g id=\"edge53\" class=\"edge\"><title>49->53</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9576.12,-252.965C9590.33,-243.057 9605.1,-232.749 9619.24,-222.887\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9621.28,-225.731 9627.48,-217.139 9617.28,-219.989 9621.28,-225.731\"/>\n", "</g>\n", "<!-- 51 -->\n", "<g id=\"node52\" class=\"node\"><title>51</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.094118\" stroke=\"black\" d=\"M9095.5,-83C9095.5,-83 8751.5,-83 8751.5,-83 8745.5,-83 8739.5,-77 8739.5,-71 8739.5,-71 8739.5,-12 8739.5,-12 8739.5,-6 8745.5,-0 8751.5,-0 8751.5,-0 9095.5,-0 9095.5,-0 9101.5,-0 9107.5,-6 9107.5,-12 9107.5,-12 9107.5,-71 9107.5,-71 9107.5,-77 9101.5,-83 9095.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"8880\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.872</text>\n", "<text text-anchor=\"start\" x=\"8872\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 198</text>\n", "<text text-anchor=\"start\" x=\"8747.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 3, 16, 31, 47, 27, 16, 17, 12</text>\n", "<text text-anchor=\"start\" x=\"8808\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">8, 5, 7, 0, 4, 0, 1, 2, 2, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"8892\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 50->51 -->\n", "<g id=\"edge51\" class=\"edge\"><title>50->51</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9152.56,-118.954C9120.4,-108.22 9086.46,-96.8914 9054.84,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9055.7,-82.9357 9045.11,-83.0895 9053.48,-89.5756 9055.7,-82.9357\"/>\n", "</g>\n", "<!-- 52 -->\n", "<g id=\"node53\" class=\"node\"><title>52</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.035294\" stroke=\"black\" d=\"M9473,-83C9473,-83 9138,-83 9138,-83 9132,-83 9126,-77 9126,-71 9126,-71 9126,-12 9126,-12 9126,-6 9132,-0 9138,-0 9138,-0 9473,-0 9473,-0 9479,-0 9485,-6 9485,-12 9485,-12 9485,-71 9485,-71 9485,-77 9479,-83 9473,-83\"/>\n", "<text text-anchor=\"start\" x=\"9262\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.906</text>\n", "<text text-anchor=\"start\" x=\"9254\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 200</text>\n", "<text text-anchor=\"start\" x=\"9134\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 2, 0, 3, 12, 32, 23, 15, 26, 18</text>\n", "<text text-anchor=\"start\" x=\"9176.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">19, 15, 10, 8, 7, 6, 1, 0, 2, 0, 1, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"9274\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 50->52 -->\n", "<g id=\"edge52\" class=\"edge\"><title>50->52</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9299.98,-118.865C9300.58,-110.498 9301.22,-101.771 9301.83,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9305.33,-93.4573 9302.56,-83.231 9298.34,-92.9525 9305.33,-93.4573\"/>\n", "</g>\n", "<!-- 54 -->\n", "<g id=\"node55\" class=\"node\"><title>54</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.133333\" stroke=\"black\" d=\"M9859.5,-83C9859.5,-83 9515.5,-83 9515.5,-83 9509.5,-83 9503.5,-77 9503.5,-71 9503.5,-71 9503.5,-12 9503.5,-12 9503.5,-6 9509.5,-0 9515.5,-0 9515.5,-0 9859.5,-0 9859.5,-0 9865.5,-0 9871.5,-6 9871.5,-12 9871.5,-12 9871.5,-71 9871.5,-71 9871.5,-77 9865.5,-83 9859.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"9644\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.788</text>\n", "<text text-anchor=\"start\" x=\"9636\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 272</text>\n", "<text text-anchor=\"start\" x=\"9511.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 1, 5, 32, 65, 93, 35, 20, 9, 5, 3</text>\n", "<text text-anchor=\"start\" x=\"9581\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"9656\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 53->54 -->\n", "<g id=\"edge54\" class=\"edge\"><title>53->54</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9693.02,-118.865C9692.42,-110.498 9691.78,-101.771 9691.17,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9694.66,-92.9525 9690.44,-83.231 9687.67,-93.4573 9694.66,-92.9525\"/>\n", "</g>\n", "<!-- 55 -->\n", "<g id=\"node56\" class=\"node\"><title>55</title>\n", "<path fill=\"#39e58c\" fill-opacity=\"0.007843\" stroke=\"black\" d=\"M10245.5,-83C10245.5,-83 9901.5,-83 9901.5,-83 9895.5,-83 9889.5,-77 9889.5,-71 9889.5,-71 9889.5,-12 9889.5,-12 9889.5,-6 9895.5,-0 9901.5,-0 9901.5,-0 10245.5,-0 10245.5,-0 10251.5,-0 10257.5,-6 10257.5,-12 10257.5,-12 10257.5,-71 10257.5,-71 10257.5,-77 10251.5,-83 10245.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"10030\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.807</text>\n", "<text text-anchor=\"start\" x=\"10022\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 354</text>\n", "<text text-anchor=\"start\" x=\"9897.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 2, 13, 86, 86, 88, 29, 20, 12</text>\n", "<text text-anchor=\"start\" x=\"9958\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">9, 0, 4, 4, 0, 0, 1, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"10037.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 10</text>\n", "</g>\n", "<!-- 53->55 -->\n", "<g id=\"edge55\" class=\"edge\"><title>53->55</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M9841.99,-118.954C9874.49,-108.22 9908.8,-96.8914 9940.75,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"9942.19,-89.5488 9950.59,-83.0895 9940,-82.9019 9942.19,-89.5488\"/>\n", "</g>\n", "<!-- 57 -->\n", "<g id=\"node58\" class=\"node\"><title>57</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.015686\" stroke=\"black\" d=\"M10795.5,-217C10795.5,-217 10469.5,-217 10469.5,-217 10463.5,-217 10457.5,-211 10457.5,-205 10457.5,-205 10457.5,-131 10457.5,-131 10457.5,-125 10463.5,-119 10469.5,-119 10469.5,-119 10795.5,-119 10795.5,-119 10801.5,-119 10807.5,-125 10807.5,-131 10807.5,-131 10807.5,-205 10807.5,-205 10807.5,-211 10801.5,-217 10795.5,-217\"/>\n", "<text text-anchor=\"start\" x=\"10558.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ 2.811</text>\n", "<text text-anchor=\"start\" x=\"10589\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.911</text>\n", "<text text-anchor=\"start\" x=\"10581\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 153</text>\n", "<text text-anchor=\"start\" x=\"10465.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 3, 2, 21, 14, 15, 19, 12</text>\n", "<text text-anchor=\"start\" x=\"10508\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">16, 16, 8, 7, 5, 7, 3, 1, 2, 1, 0, 1, 0]</text>\n", "<text text-anchor=\"start\" x=\"10601\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 56->57 -->\n", "<g id=\"edge57\" class=\"edge\"><title>56->57</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M10632.5,-260.492C10632.5,-249.751 10632.5,-238.246 10632.5,-227.145\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"10636,-227.096 10632.5,-217.096 10629,-227.097 10636,-227.096\"/>\n", "</g>\n", "<!-- 60 -->\n", "<g id=\"node61\" class=\"node\"><title>60</title>\n", "<path fill=\"#39e58c\" fill-opacity=\"0.231373\" stroke=\"black\" d=\"M11355,-217C11355,-217 11020,-217 11020,-217 11014,-217 11008,-211 11008,-205 11008,-205 11008,-131 11008,-131 11008,-125 11014,-119 11020,-119 11020,-119 11355,-119 11355,-119 11361,-119 11367,-125 11367,-131 11367,-131 11367,-205 11367,-205 11367,-211 11361,-217 11355,-217\"/>\n", "<text text-anchor=\"start\" x=\"11113.5\" y=\"-201.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Shell weight ≤ 2.599</text>\n", "<text text-anchor=\"start\" x=\"11144\" y=\"-186.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.819</text>\n", "<text text-anchor=\"start\" x=\"11136\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 275</text>\n", "<text text-anchor=\"start\" x=\"11016\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 1, 2, 21, 40, 95, 41, 24, 13</text>\n", "<text text-anchor=\"start\" x=\"11072\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">6, 6, 9, 5, 4, 3, 1, 1, 1, 0, 0, 1, 1]</text>\n", "<text text-anchor=\"start\" x=\"11151.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 10</text>\n", "</g>\n", "<!-- 56->60 -->\n", "<g id=\"edge60\" class=\"edge\"><title>56->60</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M10816.5,-262.242C10874.8,-247.592 10939.3,-231.377 10998,-216.632\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"10998.9,-220.016 11007.7,-214.184 10997.2,-213.227 10998.9,-220.016\"/>\n", "</g>\n", "<!-- 58 -->\n", "<g id=\"node59\" class=\"node\"><title>58</title>\n", "<path fill=\"#39e564\" fill-opacity=\"0.015686\" stroke=\"black\" d=\"M10613.5,-83C10613.5,-83 10287.5,-83 10287.5,-83 10281.5,-83 10275.5,-77 10275.5,-71 10275.5,-71 10275.5,-12 10275.5,-12 10275.5,-6 10281.5,-0 10287.5,-0 10287.5,-0 10613.5,-0 10613.5,-0 10619.5,-0 10625.5,-6 10625.5,-12 10625.5,-12 10625.5,-71 10625.5,-71 10625.5,-77 10619.5,-83 10613.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"10407\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.909</text>\n", "<text text-anchor=\"start\" x=\"10399\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 147</text>\n", "<text text-anchor=\"start\" x=\"10283.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 3, 2, 21, 14, 15, 19, 12</text>\n", "<text text-anchor=\"start\" x=\"10326\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">16, 12, 8, 6, 5, 7, 2, 1, 2, 1, 0, 1, 0]</text>\n", "<text text-anchor=\"start\" x=\"10419\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 9</text>\n", "</g>\n", "<!-- 57->58 -->\n", "<g id=\"edge58\" class=\"edge\"><title>57->58</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M10562.1,-118.865C10547.7,-108.969 10532.5,-98.5715 10518.1,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"10520,-85.7898 10509.8,-83.0325 10516,-91.5673 10520,-85.7898\"/>\n", "</g>\n", "<!-- 59 -->\n", "<g id=\"node60\" class=\"node\"><title>59</title>\n", "<path fill=\"#3978e5\" fill-opacity=\"0.600000\" stroke=\"black\" d=\"M10973,-83C10973,-83 10656,-83 10656,-83 10650,-83 10644,-77 10644,-71 10644,-71 10644,-12 10644,-12 10644,-6 10650,-0 10656,-0 10656,-0 10973,-0 10973,-0 10979,-0 10985,-6 10985,-12 10985,-12 10985,-71 10985,-71 10985,-77 10979,-83 10973,-83\"/>\n", "<text text-anchor=\"start\" x=\"10780\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n", "<text text-anchor=\"start\" x=\"10772\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n", "<text text-anchor=\"start\" x=\"10652\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4</text>\n", "<text text-anchor=\"start\" x=\"10717\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]</text>\n", "<text text-anchor=\"start\" x=\"10778.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 15</text>\n", "</g>\n", "<!-- 57->59 -->\n", "<g id=\"edge59\" class=\"edge\"><title>57->59</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M10702.9,-118.865C10717.3,-108.969 10732.5,-98.5715 10746.9,-88.7444\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"10749,-91.5673 10755.2,-83.0325 10745,-85.7898 10749,-91.5673\"/>\n", "</g>\n", "<!-- 61 -->\n", "<g id=\"node62\" class=\"node\"><title>61</title>\n", "<path fill=\"#39e58c\" fill-opacity=\"0.258824\" stroke=\"black\" d=\"M11359.5,-83C11359.5,-83 11015.5,-83 11015.5,-83 11009.5,-83 11003.5,-77 11003.5,-71 11003.5,-71 11003.5,-12 11003.5,-12 11003.5,-6 11009.5,-0 11015.5,-0 11015.5,-0 11359.5,-0 11359.5,-0 11365.5,-0 11371.5,-6 11371.5,-12 11371.5,-12 11371.5,-71 11371.5,-71 11371.5,-77 11365.5,-83 11359.5,-83\"/>\n", "<text text-anchor=\"start\" x=\"11144\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.793</text>\n", "<text text-anchor=\"start\" x=\"11136\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 243</text>\n", "<text text-anchor=\"start\" x=\"11011.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 1, 2, 21, 39, 92, 33, 24, 8, 5</text>\n", "<text text-anchor=\"start\" x=\"11081\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">4, 3, 3, 4, 1, 1, 1, 0, 0, 0, 0, 1]</text>\n", "<text text-anchor=\"start\" x=\"11151.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 10</text>\n", "</g>\n", "<!-- 60->61 -->\n", "<g id=\"edge61\" class=\"edge\"><title>60->61</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M11187.5,-118.865C11187.5,-110.498 11187.5,-101.771 11187.5,-93.3346\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"11191,-93.2309 11187.5,-83.231 11184,-93.231 11191,-93.2309\"/>\n", "</g>\n", "<!-- 62 -->\n", "<g id=\"node63\" class=\"node\"><title>62</title>\n", "<path fill=\"#39e5b4\" fill-opacity=\"0.078431\" stroke=\"black\" d=\"M11719,-83C11719,-83 11402,-83 11402,-83 11396,-83 11390,-77 11390,-71 11390,-71 11390,-12 11390,-12 11390,-6 11396,-0 11402,-0 11402,-0 11719,-0 11719,-0 11725,-0 11731,-6 11731,-12 11731,-12 11731,-71 11731,-71 11731,-77 11725,-83 11719,-83\"/>\n", "<text text-anchor=\"start\" x=\"11517\" y=\"-67.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.854</text>\n", "<text text-anchor=\"start\" x=\"11513.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n", "<text text-anchor=\"start\" x=\"11398\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 8, 0, 5, 1, 2</text>\n", "<text text-anchor=\"start\" x=\"11463\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">6, 2, 0, 2, 0, 0, 1, 0, 0, 1, 0]</text>\n", "<text text-anchor=\"start\" x=\"11524.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 11</text>\n", "</g>\n", "<!-- 60->62 -->\n", "<g id=\"edge62\" class=\"edge\"><title>60->62</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M11331.4,-118.954C11363.6,-108.22 11397.5,-96.8914 11429.2,-86.3382\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"11430.5,-89.5756 11438.9,-83.0895 11428.3,-82.9357 11430.5,-89.5756\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.files.Source at 0x7f4e6a049908>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import graphviz\n", "from sklearn.tree import export_graphviz\n", "dt = DecisionTreeClassifier(max_depth=5)\n", "score(dt)\n", "dot_data = export_graphviz(dt, out_file=None, \n", " feature_names=data.drop(columns=['Rings']).columns, \n", " class_names=[str(i + 1) for i in range(29)],\n", " filled=True, rounded=True, \n", " special_characters=True)\n", "graph = graphviz.Source(dot_data)\n", "graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random forest" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.29841364860820113, 0.6438192158036516, 0.7832984136486082]\n", "Test score: [0.27751196172248804, 0.6435406698564593, 0.7834928229665071]\n" ] } ], "source": [ "score(RandomForestClassifier(max_depth=4, n_estimators=83, max_features=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-layer perceptron" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.2837473810236456, 0.6569889254714157, 0.8021550434001796]\n", "Test score: [0.26674641148325356, 0.6686602870813397, 0.8086124401913876]\n" ] } ], "source": [ "score(MLPClassifier(alpha=2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AdaBoost" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.21430709368452558, 0.5501346902125113, 0.7306195749775516]\n", "Test score: [0.23205741626794257, 0.569377990430622, 0.7296650717703349]\n" ] } ], "source": [ "score(AdaBoostClassifier())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVR\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.neural_network import MLPRegressor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear regression" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.23585752768632146, 0.43998802753666566, 0.7357078718946424]\n", "Test score: [0.23205741626794257, 0.4258373205741627, 0.7165071770334929]\n" ] } ], "source": [ "score(LinearRegression())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SVM + RBF kernel" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.2927267285243939, 0.5175097276264592, 0.7803052978150254]\n", "Test score: [0.27392344497607657, 0.49401913875598086, 0.7763157894736842]\n" ] } ], "source": [ "score(SVR(C=250, gamma=0.01))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SVM + polynomial kernel" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.3163723436096977, 0.5474408859622868, 0.7880873989823406]\n", "Test score: [0.25239234449760767, 0.4880382775119617, 0.757177033492823]\n" ] } ], "source": [ "score(SVR(kernel='poly', C=100, degree=4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision tree" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.26578868602214906, 0.4890751272074229, 0.7692307692307693]\n", "Test score: [0.23205741626794257, 0.45454545454545453, 0.7332535885167464]\n" ] } ], "source": [ "score(DecisionTreeRegressor(max_depth=6, criterion=\"mse\", min_samples_leaf=20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-layer perceptron" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train score: [0.2529182879377432, 0.4681233163723436, 0.7482789583956899]\n", "Test score: [0.2583732057416268, 0.465311004784689, 0.7332535885167464]\n" ] } ], "source": [ "score(MLPRegressor(alpha=1e-2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import urllib\n", "import tempfile\n", "\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "FLAGS = None\n", "LEARNING_RATE = 0.001\n", "\n", "tf.logging.set_verbosity(tf.logging.INFO)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def maybe_download(train_data=None, test_data=None, predict_data=None):\n", " \"\"\"Maybe downloads training data and returns train and test file names.\"\"\"\n", " if train_data:\n", " train_file_name = train_data\n", " else:\n", " train_file = tempfile.NamedTemporaryFile(delete=False)\n", " urllib.request.urlretrieve(\n", " \"http://download.tensorflow.org/data/abalone_train.csv\",\n", " train_file.name)\n", " train_file_name = train_file.name\n", " train_file.close()\n", " print(\"Training data is downloaded to %s\" % train_file_name)\n", " \n", " if test_data:\n", " test_file_name = test_data\n", " else:\n", " test_file = tempfile.NamedTemporaryFile(delete=False)\n", " urllib.request.urlretrieve(\n", " \"http://download.tensorflow.org/data/abalone_test.csv\", test_file.name)\n", " test_file_name = test_file.name\n", " test_file.close()\n", " print(\"Test data is downloaded to %s\" % test_file_name)\n", " \n", " if predict_data:\n", " predict_file_name = predict_data\n", " else:\n", " predict_file = tempfile.NamedTemporaryFile(delete=False)\n", " urllib.request.urlretrieve(\n", " \"http://download.tensorflow.org/data/abalone_predict.csv\",\n", " predict_file.name)\n", " predict_file_name = predict_file.name\n", " predict_file.close()\n", " print(\"Prediction data is downloaded to %s\" % predict_file_name)\n", "\n", " return train_file_name, test_file_name, predict_file_name" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def model_fn(features, labels, mode, params):\n", "\n", " first_hidden_layer = tf.layers.dense(features[\"x\"], 10, activation=tf.nn.relu)\n", "\n", " second_hidden_layer = tf.layers.dense(\n", " first_hidden_layer, 10, activation=tf.nn.relu)\n", "\n", " output_layer = tf.layers.dense(second_hidden_layer, 1)\n", "\n", " predictions = tf.reshape(output_layer, [-1])\n", "\n", " if mode == tf.estimator.ModeKeys.PREDICT:\n", " return tf.estimator.EstimatorSpec(\n", " mode=mode,\n", " predictions={\"ages\": predictions})\n", "\n", " loss = tf.losses.mean_squared_error(labels, predictions)\n", "\n", " optimizer = tf.train.GradientDescentOptimizer(\n", " learning_rate=params[\"learning_rate\"])\n", " train_op = optimizer.minimize(\n", " loss=loss, global_step=tf.train.get_global_step())\n", "\n", " eval_metric_ops = {\n", " \"rmse\": tf.metrics.root_mean_squared_error(\n", " tf.cast(labels, tf.float64), predictions)\n", " }\n", "\n", " return tf.estimator.EstimatorSpec(\n", " mode=mode,\n", " loss=loss,\n", " train_op=train_op,\n", " eval_metric_ops=eval_metric_ops)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "ename": "URLError", "evalue": "<urlopen error [Errno -3] Temporary failure in name resolution>", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, *http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1318\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1234\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\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;32m-> 1026\u001b[0;31m 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self._create_connection(\n\u001b[0;32m--> 936\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 937\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\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;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m 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"\u001b[0;32m<ipython-input-27-5fb2968259b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabalone_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabalone_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabalone_predict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_download\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m training_set = tf.contrib.learn.datasets.base.load_csv_without_header(\n\u001b[1;32m 4\u001b[0m filename=abalone_train, target_dtype=np.int, features_dtype=np.float64)\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-25-c525ea9a9b4f>\u001b[0m in \u001b[0;36mmaybe_download\u001b[0;34m(train_data, test_data, predict_data)\u001b[0m\n\u001b[1;32m 7\u001b[0m urllib.request.urlretrieve(\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\"http://download.tensorflow.org/data/abalone_train.csv\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m train_file.name)\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mtrain_file_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mtrain_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0murl_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplittype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m 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543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 544\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, args)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1346\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1348\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, *http_conn_args)\u001b[0m\n\u001b[1;32m 1318\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1321\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno -3] Temporary failure in name resolution>" ] } ], "source": [ "abalone_train, abalone_test, abalone_predict = maybe_download()\n", "\n", "training_set = tf.contrib.learn.datasets.base.load_csv_without_header(\n", " filename=abalone_train, target_dtype=np.int, features_dtype=np.float64)\n", "\n", "test_set = tf.contrib.learn.datasets.base.load_csv_without_header(\n", " filename=abalone_test, target_dtype=np.int, features_dtype=np.float64)\n", "\n", "prediction_set = tf.contrib.learn.datasets.base.load_csv_without_header(\n", " filename=abalone_predict, target_dtype=np.int, features_dtype=np.float64)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_params = {\"learning_rate\": LEARNING_RATE}\n", "\n", "nn = tf.estimator.Estimator(model_fn=model_fn, params=model_params)\n", "\n", "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={\"x\": np.array(training_set.data)},\n", " y=np.array(training_set.target),\n", " num_epochs=None,\n", " shuffle=True)\n", "\n", "nn.train(input_fn=train_input_fn, steps=5000)\n", "\n", "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={\"x\": np.array(test_set.data)},\n", " y=np.array(test_set.target),\n", " num_epochs=1,\n", " shuffle=False)\n", "\n", "ev = nn.evaluate(input_fn=test_input_fn)\n", "print(\"Loss: %s\" % ev[\"loss\"])\n", "print(\"Root Mean Squared Error: %s\" % ev[\"rmse\"])\n", "\n", "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={\"x\": prediction_set.data},\n", " num_epochs=1,\n", " shuffle=False)\n", "predictions = nn.predict(input_fn=predict_input_fn)\n", "for i, p in enumerate(predictions):\n", " print(\"Prediction %s: %s\" % (i + 1, p[\"ages\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t_fn = tf.estimator.inputs.numpy_input_fn(\n", " x={\"x\": test_set.data},\n", " num_epochs=1,\n", " shuffle=False)\n", "t_pred = nn.predict(input_fn=t_fn)\n", "t_pred = list(map(lambda x: x['ages'], t_pred))\n", "\n", "approx(t_pred, test_set.target)" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Radcliffe/project-euler	Euler 062 - Cubic permutations.ipynb	1	1550	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Euler Problem 62\n", "================\n", "\n", "The cube, 41063625 (345^3), can be permuted to produce two other cubes: 56623104 (384^3) and 66430125 (405^3). In fact, 41063625 is the smallest cube which has exactly three permutations of its digits which are also cube.\n", "\n", "Find the smallest cube for which exactly five permutations of its digits are cube." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "127035954683\n" ] } ], "source": [ "from collections import defaultdict\n", "cubes = defaultdict(list)\n", "for n in range(1, 10000):\n", " cube = nnn\n", " key = ''.join(sorted(str(cube)))\n", " cubes[key].append(cube)\n", "print (min(cubes[key][0] for key in cubes if len(cubes[key]) == 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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
snotskie/teaching	4176-157-10Z2/Lab2.ipynb	1	17935	{ "cells": [ { "cell_type": "markdown", "source": [ "# Lab 2: Can You Picture It?\n", "\n", "In this lab we will focus on:\n", "\n", "- Business Planning (and how that might lead into a good website design)\n", "- Text, Images, and Tables (three fundamental ways to present and format information on the web, as well as pros and cons for each)\n", "- Bootstrap (a tool for easily starting complex and visually appealing layouts)\n", "\nThis lab will be due Sunday, Feb. 18th, midnight eastern time." ], "metadata": { "deletable": true, "editable": true } }, { "cell_type": "markdown", "source": [ "# Business Planning\n", "\n", "Last week we watched a video on the \"Business Model Canvas\" (BMC) being applied to the Pokemon Go app.\n", "\n", "What does this have to do with web design?\n", "\n", "This is not a class on business planning, but some business planning, even if for a fake business, will help you answer questions like:\n", "\n", "- Does my website have features the business doesn't need?\n", "- Is my website missing any features that we need?\n", "- Does the website meet the needs and expectations of audience of the business, not just those who stumble upon our site from elsewhere?\n", "- Does the layout and features of the website make it seem like we are a business with clear goals?\n", "\n", "The BMC is the simplest and most visual way I've found to explore business plans--it's even what we used to help children design their own businesses a few years ago at the Governor's School of Entrepreneurship!\n", "\n", "I bring this up because many new web programmers make the mistake of adding in too many features that have no purpose for the job at hand. For example, comment sections. Comment sections might be cool and sound like a good idea at the time, but you should stop and ask yourself, \"Do I or my users really need a comment section?\" Comment sections are hard to program, require a database, and allow for users to post spam to your site, so if there is no need to code one, save yourself the time and move on to the next idea.\n", "\n", "If you just want to program a small piece of a website to get a good idea of how it would work in general, go right ahead. We learn best when experimenting. If you are designing a website that you plan to \"take live,\" then I recommend going about it with clear attention to what the goals of the website are.\n", "\n", "By the end of the semester when I ask you to make a website for a business of your own, I want you to be able to demonstrate and explain why you have a big red button in the top right corner of your home page. Maybe you are a natural gas company, and you want to prioritize helping people if they smell a leak, so your button is:\n", "\n", "- big so it is noticable and quicker to click\n", "- in the top right because users typically expect a main \"call to action\" at that location\n", "- is red to both stand out and represent a sense of alertness\n", "\nDeveloping this ability to select and use the most appropriate features for your website is, for our class, more important than a mastery of HTML." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Text, Images, and Tables\n", "\n", "Now, so far we've only seen text for our websites. But there's two other, fundamental, important, and common \"content types\" that we'll talk about today: images and tables.\n", "\n", "## Text\n", "\n", "For text, I will just say this: You have control over the size, color, decoration (underline, etc.), style (italics, bold), and font of your text. Use these powers responsibly. By \"responsibly,\" I might mean to choose colors that are easy to read, don't have too many different fonts on one page because that's annoying, and be cognizant of how your HTML might be used by a screen reader.\n", "\n", "For example, header tags (`<h1>`, etc.) can be used by a screen reader to detect the hierarchy and help the user \"jump\" between sections. Further, tags like `<b>` and `<i>`, which make font bold and italics, are usually better replaced with `<strong>` and `<em>` nowadays. Simply put, the first two change the way the font looks. The second two change the way the font looks and sounds when read by a screen reader. This makes it clear that the text is receiving semantic emphasis, not just some visual difference.\n", "\n", "## Images\n", "\n", "Images in HTMl use the `<img />` tag. Unlike paragraphs and headers and so on, the image tag is a \"self-closing tag.\" This means (in strict versions of HTML), there is no `</img>` to be found, and the tag closes itself with a space and a slash at the end like so:\n", "\n", "```html\n", "<img src=\"http://google.com/favicon.ico\" alt=\"Google Logo\" />\n", "```\n", "\n", "So, to specify where the image file is stored, we use the `src` attribute. In the above example, I've used the URL to Google's browser tab logo. To explain that to screen readers, I've briefly labled the image with the `alt` attribute.\n", "\n", "You can also control the size of the image like so:\n", "\n", "```html\n", "<img src=\"http://google.com/favicon.ico\"\n", " alt=\"Google Logo\"\n", " height=\"64\"\n", " width=\"128\" />\n", "```\n", "\n", "This will make the image 64 pixels tall and 128 pixels wide. It will look pretty distorted, however, since the original image is very small and square, not rectangular.\n", "\n", "Finally, in my above example I wrote the attributes on one line each. You do not have to do this, but you have the option it if it would make your code easier to read by other programmers. All that matters is that I've correctly named, labeled, spaced, and closed off everything\n", "\n", "## Tables\n", "\n", "Tables used to be used for layouts, but we don't do that anymore because it causes a lot of problems.\n", "\n", "Instead, tables are useful for displaying \"tabular\" data, like a spreadsheet or [the Kentucky Mesonet's yearly weather data](http://kymesonet.org/summaries.html).\n", "\n", "Tables start out like this:\n", "\n", "```html\n", "<table>\n", "</table>\n", "```\n", "\n", "Then we add in the rows that we want:\n", "\n", "```html\n", "<table>\n", " <tr></tr>\n", " <tr></tr>\n", " <tr></tr>\n", " <tr></tr>\n", "</table>\n", "```\n", "\n", "Next, it's a good idea to have a \"header row\" at the top:\n", "\n", "```html\n", "<table>\n", " <tr>\n", " <th>Pet Name</th>\n", " <th>Species</th>\n", " <th>Color</th>\n", " </tr>\n", " <tr></tr>\n", " <tr></tr>\n", " <tr></tr>\n", "</table>\n", "```\n", "\n", "This is where it's a little tricky to imagine. Although our code is written top to bottom, the labels (Pet Name, etc.) will appear left to right along the top of our table. Also, because we used the `<th>` tags, the browser (and screen readers) will know that these are headers. The `<th>` tag is to tables as the `<h1>` tag is to paragraphs.\n", "\n", "Next, we fill in our data, one row at a time:\n", "\n", "```html\n", "<table>\n", " <tr>\n", " <th>Pet Name</th>\n", " <th>Species</th>\n", " <th>Color</th>\n", " </tr>\n", " <tr>\n", " <td>Spencer</td>\n", " <td>Cat</td>\n", " <td>White with Blue</td>\n", " </tr>\n", " <tr>\n", " <td>Clarence</td>\n", " <td>Cat</td>\n", " <td>Blue with White</td>\n", " </tr>\n", " <tr>\n", " <td>Molly</td>\n", " <td>Dog</td>\n", " <td>Gold</td>\n", " </tr>\n", "</table>\n", "```\n", "\n", "Here we fill in the data in a way that corresponds with our header row: The first line of each \"chuck\" is a Pet Name, the second is a Species, and so on. Also, we use `<td>` tags here instead of `<th>` tags.\n", "\nIf you have not guessed or read so already, these tag names are abbreviations for Table Row (`<tr>`), Table Header (`<th>`), and Table Data (`<td>`)." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Bootstrap\n", "\n", "Bootstrap is a set of code that helps you get started with a webpage quicker. It was written by developers working at Twitter, and has since taken on a popularity of its own.\n", "\n", "We won't go too much into detail into Bootstrap here, but I need to mention it because the tool I am about to introduce is intended to help you write websites with Bootstrap.\n", "\n", "In other words, we'll be using a tool to write code for us using code that was meant to help write websites for us.\n", "\n", "The most important take away from Bootstrap itself is that it comes with a lot of components. These are common UI features that appear on websites, like navbars, buttons, lists, progress bars, ... In addition, Bootstrap is \"responsive\" from the get go, meaning that your layouts should automatically adjust to work ideally on phones, tablets, laptops, desktops, and so on, with you only needing to code one version of the website!\n", "\n", "[You can see the full list of components with example codes here](https://getbootstrap.com/docs/3.3/components/) it you are interested.\n", "\n", "What we will be using is a program called [Pingendo](https://pingendo.com/download.html). If for some reason Pingendo does not work, you can use a similar tool that runs in the browser named [LayoutIt](http://www.layoutit.com/build).\n", "\n", "Before we get started, here are some useful links about Pingendo:\n", "\n", "- [Learn Pingendo](https://pingendo.com/learn.html)\n", "- [Pingedo Video Tutorial](https://www.youtube.com/watch?v=Z2RdS0eJhPY) (~9 minutes, 2 years old)\n", "- [Student Discount](https://pingendo.com/educational.html) if you ever want to buy the \"enterprise\" version, though we can make do with the free version just fine. All you'll have to do is ignore the ads that pop up occasionally asking you if you want to buy the full version.\n", "\n", "To get started with Pingendo:\n", "\n", "1. Install the program on your computer. If you are using a Mac, you may need to grant it permission to run. Shoot me an email if you have issues with that\n", "1. Create a new blank project.\n", "1. Turn on the source code view by going to View > View Source Code. This will let you edit the HTML directly, similar to our Live Preview setup we have with Visual Studio Code.\n", "1. Drag and drop components from the toolbar on the left and modify the text/images/etc. by editing the HTML code below.\n", "1. Finally, once you've saved your project, test it in your browser with File > Preview in Browser.\n", "\n", "For example, here is a test webpage I made in Pingendo:\n", "\n", "![Lab2-Screen1.png](https://raw.githubusercontent.com/snotskie/teaching/303795e5ca6de871fae591590bf198632a1ef824/4176-157-10Z2/Lab2-Screen1.png)\n", "\n", "And here is the same webpage as it appears in my browser:\n", "\n![Lab2-Screen2.png](https://raw.githubusercontent.com/snotskie/teaching/303795e5ca6de871fae591590bf198632a1ef824/4176-157-10Z2/Lab2-Screen2.png)" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Grading and Submission Instructions\n", "To submit, write your responses to the following questions in a Word document, then upload it to Blackboard under this week's folder in Current Assignments.\n", "\n", "Labs are due two weeks from when they are assigned, on Sunday at 11:59pm eastern time.\n", "\n", "Labs are each worth 5% of your final grade. Scoring on your submission will be based on the following rubric:\n", "\n", "0% - Student does not submit on time or submits plagiarized or unacceptable work. Double check that you have attached the right file, as usually students get zeros because they upload a previous week's Lab by accident.\n", "\n", "1% - Student answers less than half of the questions with sufficient or accurate responses. Make sure that you are leaving yourself enough time to complete the Lab each week, as usually students submit incomplete work because they were rushed at the last minute.\n", "\n", "3% - Student answers almost all questions with sufficient and accurate responses. If you encounter a problem or have a question about one of the questions, be sure to post in Ask the Instructor well before 24 hours before the due date, then continue to attempt to resolve the issue on your own while you wait for a reply. \n", "\n", "5% - Great job, maximum points! The student answers all questions accurately and sufficiently, demonstrating the best of their ability.\n", "\nNote, because Labs span two weeks' worth of reading, it is recommended to go through the Lab twice, once after the first reading where you answer everything that you can, then again after the second reading where you answer everything else." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Part 1\n", "\n", "1. According to Garrett, what makes up the Product Objectives?\n", "1. What makes up the User Needs?\n", "1. What is \"task analysis\"?\n", "1. Image tags (`<img />`) at a minimum must have what two attributes?\n", "1. As you were using Pingendo, what features (like autocomplete, etc.) did it have in common with our Visual Studio Code? What does it have that Visual Studio Code does not?\n", "1. Go into your kitchen again. (Or imagine a kitchen you are familiar with.) How is it \"organized\" currently.\n", "1. If you could rearrange your kitchen with zero effort, what are three different ways you might choose to organize it?" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Part 2\n", "\n", "1. Garrett divides the strategy plane into two halves, twice: once into User Needs vs Business Needs, and again into Functionality and Information. With that in mind, imagine a day care service. To the best of your knowledge about day cares, what are some \"functions\" of a day care that are important to its \"users\"?\n", "1. What are some functions of a day care that are important to the business itself?\n", "1. What information does a day care business provide that is important to the users?\n", "1. And what information might the day care collect that is important to the business itself?\n", "1. In my notes I gave the code for a table for showing information about my pets (current and past). In your own day-to-day life, what are some examples of things that you could \"display\" in a tabular format like this?\n", "1. Using Pingendo, create a website similar to my example in the notes above. (I used a Navbar, a Title, two Articles, and a Footer.) Come up with your own text for the actual content, and feel free to experiment with other components, colors, etc. When you are done, embed a screenshot of your Pingendo setup and your website running in your browser." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Part 3\n", "\n", "1. Imagine all of the users of a day care service. How might these users be \"segmented\"? What functions/information might be most important to each segment?\n", "1. In Part 1 you listed different ways of organizing your kitchen. For each way, list only the benefits of organizing it that way.\n", "1. In Part 2 you created a webpage with Pingendo. Describe in detail the process you took in using the tool, making decisions on what components to use, how easy the tool was to use or any frustrations you had using it, and so on. Would you like to continue using this tool in the future?\n", "1. Finally, in your opinion, is it better to program using a \"visuals first\" tool like Pingendo, or a \"code first\" tool like our Visual Studio Code? Explain." ], "metadata": {} } ], "metadata": { "kernelspec": { "name": "node_nteract", "language": "javascript", "display_name": "Node.js (nteract)" }, "language_info": { "name": "javascript", "version": "7.9.0", "mimetype": "application/javascript", "file_extension": ".js" }, "kernel_info": { "name": "node_nteract" }, "nteract": { "version": "0.6.2" }, "gist_id": "5262272c40c19be30ac5042db00ef74b" }, "nbformat": 4, "nbformat_minor": 2 }	mit
joosthoeks/jhTAlib	example/example-4-plot-quandl.ipynb	1	2561	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "example-4-plot-quandl.ipynb", "version": "0.3.2", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "metadata": { "id": "Mj7YX4fsIYm-", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "!pip install --upgrade quandl\n", "!pip install --upgrade jhtalib" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "2rC37p9wIjUj", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "import quandl\n", "import jhtalib as jhta\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "#quandl_data = quandl.get('BCHARTS/BITSTAMPUSD', start_date='2011-01-01', end_date='2018-11-01', order='asc', collapse='daily', returns='numpy', authtoken='YOUR_AUTH_TOKEN')\n", "quandl_data = quandl.get('BCHARTS/BITSTAMPUSD', start_date='2011-01-01', end_date='2018-11-01', order='asc', collapse='daily', returns='numpy')\n", "\n", "df = {'datetime': [], 'Open': [], 'High': [], 'Low': [], 'Close': [], 'Volume': []}\n", "i = 0\n", "while i < len(quandl_data['Close']):\n", " df['datetime'].append(str(quandl_data['Date'][i]))\n", " df['Open'].append(float(quandl_data['Open'][i]))\n", " df['High'].append(float(quandl_data['High'][i]))\n", " df['Low'].append(float(quandl_data['Low'][i]))\n", " df['Close'].append(float(quandl_data['Close'][i]))\n", " df['Volume'].append(int(quandl_data['Volume (BTC)'][i]))\n", " i += 1\n", "\n", "x = df['datetime']\n", "\n", "plt.figure(1, figsize=(20, 10))\n", "\n", "plt.subplot(211)\n", "plt.title('Time / Price')\n", "plt.xlabel('Time')\n", "plt.ylabel('Price')\n", "plt.grid(True)\n", "plt.plot(x, df['Close'], color='blue')\n", "plt.plot(x, df['High'], color='grey')\n", "plt.plot(x, df['Low'], color='grey')\n", "plt.plot(x, jhta.ATH(df)['ath'], color='red')\n", "plt.plot(x, jhta.LMC(df)['lmc'], color='green')\n", "plt.legend(['Close', 'High', 'Low', 'ATH', 'LMC'], loc='upper left')\n", "\n", "plt.show()\n" ], "execution_count": 0, "outputs": [] } ] }	gpl-3.0
mas-dse-greina/neon	Basic Neural Network.ipynb	1	20338	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Basic Neural Network\n", "\n", "Here's a neural network with one input layer and one output layer (just a single neuron).\n", "This shows the idea behind forward and backward propagation.\n", "\n", "This is taken from:\n", "\n", "https://iamtrask.github.io/2015/07/12/basic-python-network/\n", "\n", "Here are some great references for this:\n", "\n", "http://neuralnetworksanddeeplearning.com/chap3.html\n", "\n", "https://youtu.be/QWfmCyLEQ8U\n", "\n", "https://www.ics.uci.edu/~pjsadows/notes.pdf\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!/usr/bin/env python\n", "'''\n", "Simple two layer neural network (input/output) with backprogation\n", "Sigmoid activation function\n", "Cross entropy loss function\n", "'''\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def activation_function(x,backprop=False):\n", "\n", " '''\n", " We are using the sigmoid activation function.\n", " In forward propagation, we just calculate the sigmoid.\n", " In backward propagation, we calculate the derivative of the sigmoid\n", " '''\n", " if(backprop==True):\n", " return x(1-x) # Derviative of the sigmoid function\n", "\n", " return 1/(1+np.exp(-x))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# input dataset\n", "\n", "X = np.array([ [0,0,1],\n", " [0,1,1],\n", " [1,0,1],\n", " [1,1,1] ])\n", "\n", "# output dataset \n", "\n", "y = np.array([[0,0,1,1]]).T\n", "\n", "assert X.shape[0] == y.shape[0], 'input and output must have same number of samples'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# seed random numbers to make calculation\n", "# deterministic (just a good practice)\n", "np.random.seed(1)\n", "\n", "# initialize weights randomly with mean 0\n", "syn0 = 2np.random.random((X.shape[1],1)).clip(0,1) - 1" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output Before Training:\n", "[[ 0.2689864 ]\n", " [ 0.36375058]\n", " [ 0.23762817]\n", " [ 0.3262757 ]]\n", "Output After Training:\n", "[[ 0.07553274]\n", " [ 0.06168483]\n", " [ 0.95008079]\n", " [ 0.93870164]]\n", "Weights After Training:\n", "[[ 5.45668723]\n", " [-0.21743969]\n", " [-2.50760127]]\n" ] }, { "data": { "image/png": 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RsaPYtZhlxdNHZmbWzyMFMzPr55GCmZn1cyiYmVk/h4KZmfVzKJiZ\nWT+HgpmZ9fv/AVjZS2a93KUuAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10933e1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loss = []\n", "\n", "num_epochs = 200\n", "\n", "for iter in xrange(num_epochs):\n", "\n", " # forward propagation\n", " layer0 = X\n", " layer1 = activation_function(np.dot(layer0,syn0)) #Dot product is another way to multiply weights and then sum\n", " \n", " if (iter == 0):\n", " print \"Output Before Training:\"\n", " print layer1\n", "\n", " # how much did we miss?\n", " layer1_error = y - layer1\n", "\n", " # Backward propagation\n", " # multiply how much we missed by the\n", " # slope of the sigmoid at the values in layer1\n", " layer1_delta = layer1_error * activation_function(layer1, True)\n", " \n", " # Keep track of the cross entropy (binary) loss\n", " loss.append(np.mean(np.log(1-layer1)(y - 1) - np.log(layer1)y))\n", "\n", " # update weights\n", " syn0 += np.dot(layer0.T, layer1_delta)\n", "\n", "plt.plot(loss);\n", "plt.title('Cross entropy (binary) loss');\n", "plt.xlabel('Epoch');\n", "plt.ylabel('Loss');\n", "\n", "print \"Output After Training:\"\n", "print layer1\n", "\n", "print \"Weights After Training:\"\n", "print syn0" ] }, { "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 }	apache-2.0
jakevdp/PracticalLombScargle	figures/FloatingMean.ipynb	1	83382	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plt.style.use('seaborn-white')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rng = np.random.RandomState(1324)\n", "f0 = 0.3\n", "t = rng.randint(0, 200, 100) + 0.15 * rng.randn(100)\n", "dy = 0.3\n", "y = 16 + 2 * np.sin(2 * np.pi * f0 * t) + dy * rng.randn(len(t))\n", "\n", "mask = (y < 16.7)\n", "t_out, y_out = t[~mask], y[~mask]\n", "t, y = t[mask], y[mask]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3\n", "0.5999233595\n", "0.300213114519\n" ] } ], "source": [ "from astropy.timeseries import LombScargle\n", "\n", "ls_standard = LombScargle(t, y, dy, fit_mean=False)\n", "ls_generalized = LombScargle(t, y, dy, fit_mean=True)\n", "\n", "freq, power_standard = ls_standard.autopower(maximum_frequency=1)\n", "freq, power_generalized = ls_generalized.autopower(maximum_frequency=1)\n", "\n", "fmax_standard = freq[np.argmax(power_standard)]\n", "fmax_generalized = freq[np.argmax(power_generalized)]\n", "\n", "print(f0)\n", "print(fmax_standard)\n", "print(fmax_generalized)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "phase = (t * f0) % 1\n", "phase_fit = np.linspace(-1, 2, 1000)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bxrZt2/D19WX06NFotVrWr18PQGBgIP369SM4OJjPP/+csWPHsn37dmbMmMH6\n9evZs2ePer5ffvmF2rVrs3nzZlq1asW0adN49OgRANOmTSMyMpLVq1fz6aefsnLlSvW4R48eMXjw\nYBo1asTmzZtZsWIF165d4/vvv1f3CQkJoWrVqvz+++/4+fkRGBjIP//8w9KlS1mwYAGrVq166v1+\n//33LFmyhJkzZ/Lzzz8zfPhwPvroI1asWMHJkyfVe96wYQMzZ85k8ODBbNq0iYYNGzJo0CBu3779\n1HtRFIXhw4fj4ODAxo0bmT17Nn/99Rfffvttrn8vQgghCicJmIQQOTHpeTRLlSpFrVq1qFWrFhER\nEaxevZrWrVu/0DkVRWHlypVERUXlUSpzRzcWS6PRPHXf69evo9FoqFixIk5OTowePZpmzZqh1Wqx\nt7cHwNbWlhIlSmBjY8OsWbNo1aoVAE5OTqxcuZKwsDB1W40aNRg4cCAAo0aNYtWqVYSFhVGtWjW2\nbdvGqlWrqF27NgBDhw7ls88+AyApKYmhQ4eq6a5cuTKtWrXSG0um0WgYMmQI1tbWKIrCunXrGD9+\nPPXq1QNg0qRJDBo0KMf7HTp0KG5ubri5ufHFF1/Qvn17GjVqBECDBg2IjIwE4Oeff6Z379507twZ\ngHHjxnH06FFWr17NoEGDcryXI0eOcOPGDdatW4eZmRkuLi6MHz+eiRMnMmzYsKf+ToQQQhReMq24\nECInJvkJERYWxsOHD/H19VW3ubq6EhwcbMRU5R8/Pz+qV69Ox44dqVWrFs2bN6dbt24GP9Dd3d2x\ntrZm4cKFhIeHc/HiRa5evYqfn5+6z2uvvab+XLJkSQBSU1O5fPkyaWlpuLm5qe97eHioPzs6OtK5\nc2d+/PFHzp8/r56/bt266j4ODg5YW1sDcP/+fWJjY6lZs6bB82WncuXK6s/W1tZUqlRJ73VKSgoA\nERERWYKbOnXqEBER8dR7iYiI4MGDB/j4+KjbtFotSUlJ3L9//6lpFEIIUbhk7PkhY5hM25dffknx\n4sWlglMYjUkGTP/88w8bNmxg27ZtaovM2bNncXFxeeFzazQaPvjgA548efLC53oWlpaWuWpdAihe\nvDjr1q0jODhYfRZr1qxhw4YNlC9fXm/fAwcOMGzYMDp37kzjxo0ZNmwYM2bMyHLtzLLrIlisWDH1\n59u3b9O1a1dq165Nw4YN6d69O3v37iU0NFTdx8rKKsdzG7p2Zpm7QmT3xWXoWmlpaWi1WoP7Z7yX\n1NRUXFyT1pjbAAAgAElEQVRcWLJkSZb9SpUq9dQ0CiGEKFwyfnfk9vtZ5L/79+/z8ccfA/Dee+9h\na2tr5BSJosgkq1Q6depETEwMX375JVeuXOGXX37hzz//ZPDgwXlyfo1GQ7FixfL137N8GIeEhPDd\nd9/xxhtvMHHiRLZv305ycjLHjx/Psu+6devo2rUrn332Gd26dcPV1ZVr167lasyUi4sLlpaWepMj\nnDt3Tv15165d2Nra8t133/H+++/j6+tLVFRUtucuU6YMZcuWzfZ8L8rZ2VkvWAMIDQ3F2dn5qffi\n7OzMjRs3sLe3p0qVKlSpUoXo6GgWLlwoX5RCCFEEyaQ/BYOulwnAjRs3jJgSUZSZZMD0yiuvsGLF\nCo4ePcpbb73FL7/8woIFC9SxKYWdtbU1ixcvZt26dURHR7NlyxYSExPVRVhtbGwICwvj0aNH2NnZ\nERISwsWLFwkLC2PChAnExMTofcBkp2TJkrz11lvMnDmT0NBQgoKCWLRokfq+nZ0dN27c4PDhw0RF\nRfH999+zc+fObM+t0Wjo1asXCxcu5NChQ5w+fZrZs2fnzUMhff2p1atXs2nTJi5fvsyXX37JhQsX\nePvtt596L35+flSqVImPP/6YixcvcuzYMaZMmULx4sVlsK8QQhRBEjAVDBl7BEVHRxsxJaIoM5ku\neRcvXtR7XadOHX7//Xcjpca4atasyaxZs1iyZAmfffYZFStWZN68ebi6ugLQu3dv5s6dy7Vr1xg+\nfDgTJ07knXfeoWTJkrz55pv06NGD8+fP5+paU6ZMYebMmXzwwQfY2trSu3dv5syZA0Dbtm05evQo\nI0eORKPR4OHhwfjx4wkMDMw2aPrwww95/PgxY8aMwdzcnGHDhqkTL7yodu3acffuXRYuXEhMTAw1\na9bkhx9+UJ9LTvdibm7O0qVLmTlzJt27d8fGxoY2bdowfvz4PEmbEEKIgkUCpoIhY8B08+ZNI6ZE\nFGUa5VnmuzZRzZs3B9CbSlsIIcTLJ5+/2ZNnY9oePnyInZ0dAG3atGHbtm1GTpEw5OLFi+qETt9/\n/706668QOcnrz1+T7JInhBBCCPEySQtTwZCxhSk3ww2EeBkkYBJCCCFEkSMBU8EgAZMwBRIwCSGE\nEKLIyRgwZbc8hTA+CZiEKZCASQghhBBFTsaASVqbTFfGICm/19AUQkcCpnyi1Wo5c+YMZ86ckZos\nUeBI/hWmaujQoURERBg7GaIAkoCpYJAWJmEKJGASQghRYJ04cQILC5NZIUMUIBmDpNTUVCOmRORE\nAiZhCuRbRgghRIHVs2dPxowZw7vvvkvFihWxsrLSe79evXpGSpkwddLCVDBIwCRMgQRMQgghCqwl\nS5YAMHXq1CzvaTSaXC/iLYoeCZgKBhnDJEyBBEzZ0Gq1nDt3DoBatWoB6L02M8u5N6Oh44XIL5J/\nRVFx4cIFYydBFFASMBUM0sIkTIGMYRJCCFGgpaWlsXfvXn788Ufi4uIIDQ3l0aNHuT4+OTmZSZMm\n4evri5+fHz/88EO2+w4ZMoQaNWro/fvnn3/y4jZEPpOAqWCQgEmYAmlhEkIIUWDdvHmTfv368fDh\nQx4+fEjz5s1Zvnw5ISEhLF++HDc3t6eeY+7cuZw5c4affvqJGzduMH78eCpWrEibNm2y7BsREcG8\nefNo0KCBus3W1jZP70nkDwmYCgYJmIQpkBYmIYQQBdZnn32Gr68vBw4coFixYgDMnz+fhg0bMmvW\nrKcen5iYyLp165g8eTK1a9emZcuWDBgwgF9++SXLvikpKURHR+Ph4YGjo6P6T3ddUbDILHkFg4xh\nEqZAAiYhhBAF1rFjx+jXrx/m5ubqNktLS4YOHcqZM2eeevyFCxdITU3F29tb3ebj40NoaGiWNcci\nIyPRaDRUrlw5725AGI20MBUM0sIkTIEETPkoMTGRO3fuEBcXZ+ykCPHMJP8KU2Rtbc29e/eybL98\n+TIlS5Z86vExMTGUKVNGr5WobNmyJCcn8+DBA719IyMjKVmyJJ988gl+fn68/fbb7Nu378VvQhiF\nBEwFgwRMwhRIwJRPQkJC2LZtGwcOHGDhwoWcOHHC2EkSItck/wpT9e677zJ16lT27t0LpAdKf/zx\nB1OmTOHtt99+6vGPHz/O0qVO9zpz4SwyMpKkpCT8/PxYvnw5b775JkOGDOH06dN5czMiX0nAVDBI\nwCRMgUz68JwyT7uc0zTNcXFxbNmyRX2tKAqbN2/GxcUFOzu7l55WITKT/CsKi2HDhlG6dGmmT5/O\n48ePGTRoEA4ODvTt25f+/fs/9XgrK6sshTDda2tra73tQ4cOpXfv3uokD25ubpw9e5bff/8dDw+P\nPLojkV8kYCoYMgZMMoZJGIsETJlkLEgmJiYSHx+Pk5MTpUuX1nudsauHVqs1WODU7a8oCoqi6L2n\nKAqxsbFS4BR5SvKvKGqioqLo3bs3vXv3JjExkbS0NEqVKpXr48uXL8/9+/dJTU3FwiL9KzEmJgZr\na2tKly6tt6+ZmVmWGfFcXFwIDw9/8RsR+U4mfSgYMlZoSAuTMBYJmLJx+fJltdvRwYMH8fDw4NSp\nU+rrdu3aZal9zEjXhQnSV5vPTKPRYG9v/xJSLoTkX1F0tG3blkqVKtG4cWMaN25M/fr1n+n4mjVr\nYmFhwcmTJ/H19QXg+PHjeHh4ZKlImDBhAhqNhtmzZ6vbLly4QPXq1V/8RkS+yziph7QwmS7pkidM\ngYxhMiAxMVFvjIaiKGphU/d669atJCYmGjzeUBemjIVOjUaDt7d3rgYkC/GsJP+KoiQ4OJiJEydi\nbm7Ol19+Sf369enXrx8rV67MVctP8eLF6dy5M9OnT+fUqVPs3r2bH374gT59+gDprU1JSUkA+Pv7\n89dff7Fp0yauXr3KokWLOH78OO+9995LvUfxcphal7wbN24we/ZsYmJijJ0UkyIBkzAF0sJkQHx8\n/FP3URSFhIQEbGxssrx37949g12YXn/9daytrSlRooTB44TIC5J/RVFiY2ND06ZNadq0KZA+McPi\nxYuZO3cuc+fO5fz58089x8SJE5k+fTrvv/8+JUuWZMSIEbRq1QoAPz8/Zs+eTUBAAK1atWLatGks\nXbqUGzduUK1aNZYvX46Tk9PLvEXxkphawNSjRw/279/P33//za5du4ydHJMhY5iEKZCAyYDc1Jxr\nNBpKlChh8D0HBwc0Go1eoVOj0eDg4CAFTfHSSf4VRcn169c5ceIEJ06c4Pjx40RGRuLs7Mw777yj\ndrF7muLFizNnzhzmzJmT5b2LFy/qve7WrRvdunXLk7QL4zK1gGn//v0A7N6928gpMS0yhkmYAgmY\nDLCxsaFu3bqEhISo3ZEqV65MVFSU+rpt27YUL14cSO/DDv/NNla6dGnat2/Pli1b1P3bt2+PlZWV\nMW9LFBGSf0VR0rx5c8zMzGjSpAmjRo3C19c3y8QMQhgikz4UDNIlT5gCCZiy4ezsTPny5YmLi8PO\nzo6rV69So0YNLCwscHJywsvLi0uXLmV7vLe3N2lpaSQkJODt7U3p0qXV2cvEf1JTU7ly5Qp37txB\nURQcHR1xcXFRZ6sSz0eXfx89eoSjoyPh4eEUL14cMzMzKleuLPk3jyiKwrVr17hx4wZpaWnY29vj\n7OysBqPi5Zs7dy7Hjh3j6NGjTJw4kTp16uDr60vdunXx9PTMssaSEDqm1sJkbW2tjpcT/5GASZiC\nIlEqfZY1ZzIeExUVxcWLF7P0mdUNJLaxsTE4g5iOjY0NNjY2Waam1U3XXLFixSI705iiKBw9epT9\n+/eTkJCg9561tTVNmjShfv36ufpdFXbPk38VRSEmJoazZ8/y+PFjvffCwsJ4/PgxDg4OmJubZ3sO\nyb85O3/+PLt27eL+/ft62y0tLalXrx5NmzbF0tLSSKkrOjp16kSnTp2A9PF3x44dY9++fSxatAiN\nRkNoaKiRUyhMlakFTBIMGCZjmIQpMImAKSUlhYCAAKZMmZJlSthHjx7Rrl07xowZQ0BAQL6kJykp\niSNHjnDv3j0AbG1tKV26tFoITUpKYvv27ZQrV46qVatia2uLjY1NrmrgM0/33KFDB+rWrftS78fU\nJCcns27dOiIiIgAoVaoUVapUQaPRcPXqVeLi4ti5cyeXLl2iW7duMm7mGaWlpXHs2DGio6OB9MCn\nTJkymJubc+/ePRISEjhw4AC2tra4ublhb28v+fcZpKWlsW3bNo4fPw6kL3zq4uJCsWLFuH79Onfv\n3uXQoUNcunSJnj17UqZMGSOnuPCLj4/n+PHjBAUFERQUxMWLF6lZsyZ+fn7GTpowYaYWMGWc5lz8\nR8YwCVNg9IApOTmZjz76iLCwMIPvz5s3jzt37uRbeuLj49m7dy8JCQmYm5vj7e1Nq1at1IG/Wq2W\n8PBwzpw5w507d9S01a1bF2dn5xzPbWi6582bN1O1atUstfiFVXJyMqtWreLGjRtYWFjQsmVLfHx8\n1JYOrVZLSEgIO3bs4MqVK6xatYo+ffpI0JRLaWlpHDhwQA32a9euTYcOHdRWUUVRiI6OJiQkhIcP\nHxIUFARI/s0trVbLpk2bOHPmDJA+g1rjxo3Vbl+KohAWFsbmzZu5e/cuK1asoF+/fkW6Je5l69q1\nKxcvXqRs2bI0atSI/v3707BhQ1lUWTxV5taK7BbxNobMyzkUZdIlT5gCo34yhIeH0717d65du2bw\n/WPHjnHkyBEcHR3z7Jrnzp3LthYnNTWVlStXql3E0tLS0Gq1aDQaEhMTuXPnDklJSTg5OWWZdvnE\niRPZrmujY2i6Z0VRiI2Nfc67KVi0Wi1//PEHN27cwMbGhg8++IDXX39dr1uYmZkZPj4+DBgwgBIl\nSnD79m1+//13k6j9MwU55V9FUfjxxx/VYAnSWz8sLCzU/Kvripe5oCD5N3e2b9/OmTNnMDMz4513\n3qF58+Z6Y2Q0Gg3Vq1dn4MCBlC9fnoSEBH755ZenPlvx/Dp27Mj//vc/9u7dy6xZs2jXrp0ESyJX\nMn8OmtLED8nJycZOgsnI+HtKTU3NUv4SIj8YNWAKDg6mfv36rF27Nst7KSkpTJkyhalTp+b5oF2t\nVsuZM2c4c+aMXuFz48aNPHjwQG/fkJAQDh8+zLZt2zhw4ADbtm3Ldl2PnAqOZmZmBqe41Wg0Rab2\nec+ePYSFhWFhYUHPnj2pWLFitvuWK1eOPn36UKxYMa5evcrOnTvzMaWmLbv8+88//3Djxg29fZ8l\n/2YMtDKT/AuhoaEcPXoUgICAANzc3LLdt1SpUvTq1QtbW1tiY2NZv369fMm/JH379lV7KnTp0oVO\nnToxatQogoODjZ00YeIyt1YYs2Iu8+dD5rGnRVnmwNbY45j27dtH+/btiYyMNGo6RP4yasDUs2dP\nJk2aZHBGqW+//ZZatWq9lD7ohmroz507Z3AMh6Io/P3333rbrly5YvC8cXFxOV5TN31z5vMXBVeu\nXOHQoUMAdOnShUqVKj31mHLlyqnj1oKDg3Oc1a0oMZR/b926xcGDB7Nsf5b8m9Pq8kU9/8bGxrJ1\n61YAmjZtSu3atZ96TKlSpejZsyeWlpZcvnxZ7f4o8tauXbvo3r07iqIQEBBAQEAAGo2Gfv36yXo2\nIkeZC97GDJgytyhJq/R/Mge2xu6W17RpU7Zu3UqvXr2Mmg6Rv0yjs24m4eHh/Pbbb0ycODFfrpeY\nmKgWhl7E5cuXc/zANdSlCXJumSoMUlJS+N///gekT1ddq1atXB9bo0YNGjRoAMDmzZtlylUD0tLS\n2Lhx4wsHL9euXcvx+RbV/KsoCn/++ScpKSlUqVKFxo0b5/rYcuXK0apVKyB9McqcglLxfBYsWMC4\nceOYP38+vXv3pm/fvnzzzTeMGzeOwMBAYydPmDBTCpgyf/ZKwPSfzL8nYwdMOtKKXbSYXMCkKAqf\nfvopI0eOpGzZsvlyzV27dpGQkICjoyPt2rVTB1pqNBrc3d2zHXipK6Dq/k9KSmLz5s1qC4Cbmxvu\n7u7qINKSJUtmOUdR6NK0f/9+Hjx4gJ2dHa1bt37m45s1a4a9vT2PHj1iz549LyGFBVtwcDB37tzB\nxsaGli1bPnf+TUtLY/v27ZJ/Mzl16hRXr17F0tKSzp07P/OgcB8fH6pWrao+36LSKpdfoqKiaNas\nWZbtzZo14/Lly0ZIkSgoTClgytwFT7rk/cdUAyaZ1bBoMbmA6caNG4SEhDBnzhy8vb3x9vbmxo0b\nTJs2jQEDBjz3eXWD3jPX2ty8eZOTJ08C6et5+Pj40KZNG5o0aYK/vz9///23XiEyI41Gk2UmmydP\nnjBo0CC1+1lGNjY21K1bV69A2759+0I9w1hsbCxHjhwBoE2bNlhZWT3zOSwtLenYsSMAx48fz9dZ\nE01FdvlXN6sjQPPmzXnjjTfU/NusWTP27dvHtm3bcp1/09LSGDlyJP/++2+W/Yti/k1KSmLXrl0A\nNGnS5LkmE9BoNLRt2xZzc3MiIyPVGTdF3nB1dWX//v1Ztu/bty9XXX9F0ZW54G3MSR+khSl7pjaG\nSRRNRp9WPLPy5ctnGeDfu3dvevfurS5OmFu6BT8zrh0D6QWY4sWLoyiKWhjy8PDAyckJrVaLjY0N\n4eHhfPrpp2oNZaVKlXB3d89yjcy19xqNBh8fH3799VeOHDnCN998o1egdHZ2VmfPKlGiBN7e3s90\nTwXNrl27SEtLw9XVlerVqz/3eV577TXc3Ny4cOECu3btKhJ9h5+WfyG9UJiSkkLFihXx9vZGURRs\nbGy4fPkyEyZMUMcrOTk55Sr/QnqLyJo1azhy5AgLFiwo0vn38OHDJCQk4ODgoHYNfR729vY0aNCA\ngwcPsnPnTqpVq5bjosEi90aMGMGIESMIDQ3Fy8sLgJMnT7Jjxw7mzp1r5NQJU2ZKLUyZAyZpYfqP\nKbUwZewhIJ/hRYvJtTBZWFhQpUoVvX8WFhY4ODhQvnz5Zz5f5rVjALZt20ZiYiK3b9/m6tWrWFhY\n0Lx5cyD9j+H333/n/fff5/Llyzg6OvLjjz8aLGzWqFHD4DXLlStHs2bNePXVVxk+fHiWySRsbGxw\ndHQs9GsL3bx5kwsXLqDRaGjVqtULrynRokULzMzMCA8PLzJdbXLKv/Hx8WrraOvWrdXnu3HjRt57\n7z2uXLmCo6MjK1eufKb8W7p0aZo1a0aVKlWKdP5NSEjg8OHDQHrr3Yt+OTZu3JgSJUpw//599fcm\nXlyzZs1YtmwZycnJrFmzhg0bNqAoCr/++ivt2rUzdvKECTOlgClzgCQtTP8xpUkfZLr3osvkAqa8\nlt3aMfHx8eqsX76+vtja2qIoChMmTGDmzJmkpqbSsmVLNm7cSKNGjQyeu3z58gYLojoajQYXFxcC\nAgLUbmmQffeqwkbXTcbd3Z1y5cq98PkcHByoW7cuAAcOHHjh8xUE2eXfhIQEzp8/j1arxdXVlVdf\nfRWtVsvkyZOZOnUqqamptGjRgo0bN2Y702Ru82+XLl3Ubn9QdPLvgQMHePLkCRUqVMhxCvHcKlas\nmPq72L9/v0mt+VLQNWjQgMDAQLZu3crGjRuZP38+np6exk6WMHGmFDBJl7zsmVILU8bfi4xhKlpM\npkteTv36M0+L/CwMDVSH9Bnt7t27h5mZGQ0bNkSr1TJy5EgWL14MwMiRIxkwYEC2g9o1Gg0lSpR4\n6gBujUZDmTJlaNmyJV9//TUVKlTQazEwNzfHx8fnue/PVN26dUsNSJ9lVrGn8fPz48SJE1y+fJno\n6GicnJzy7NymKLv8GxUVpS743KxZMxRFYdSoUSxatAiAQYMGMWzYMMzMzF44/9rb29O+fXsWL16M\no6Njkci/iYmJHD9+HAB/f/8Xbh3V8fHx4dChQ8TFxRESEkK9evXy5LxFTUJCAl988QW7du3C0tKS\n5s2b8/HHH1OqVCljJ00UIKYcMEmXvP+Y0himjL8XRVFITU3FwsJkitLiJSr0LUw2NjYGa9Gjo6OB\n9BqCsLAwRo8ezeLFi9FoNEydOpWBAweqhaTSpUtnGezetm1bbGxssi3QZvTKK6+QmJjIlClTsnSv\n2rJlS47rNxVUutal2rVr4+jomGfntbW1VWuOi0IrU3b5N2OXxNu3bzN+/HgWLVqERqNhxowZjBgx\nQp3N7UXz76uvvkpSUhITJ04sMvk3ODiY1NRUKlSogKura56d19LSUm2xPnLkiNRQPqevv/6aAwcO\nMGDAAPr168e///7LpEmTjJ0sUcCY0qQP0iUve6bawgTplTeiaCgSYXGZMmVyfP+vv/7ip59+AuCH\nH37A19c3yz6ZB7t7eXlx6dIldeawkJAQg7X1tWrVYsKECbz11ltERUVleV9RFGJjYwvVTGP37t3j\n/PnzQPrMYnnNz8+P0NBQLl26xK1bt3jllVfy/BqmJDf597vvvgNg6dKlBruQPm/+dXV1Zfz48Tx4\n8ECtZMioMObfJ0+eqOtrNGzYMM9al3S8vb3Zu3cvsbGxXLx4kZo1a+bp+YuC7du3880336if1Q0a\nNKB79+6kpKRQrFgxI6dOFBSm1HIhLUzZ0wVIupldTSlgio+Px9bW1kipEfmp0LcwQfbdmjKyt7dn\n3rx59OnTR92mG6uhq0HPONg941os5cuXp169erz++us0bdqUOnXqYG1trZ7XysqKqVOn0q1bN4PX\nvnPnDmfOnCk0tc26wma1atXyZOxSZg4ODtSuXRuAoKCgPD+/qclt/p0/fz4DBw5Ut71I/tVNn63R\naLC2tmbGjBnZ5l87OzvOnDlTaPJwSEgIjx8/xs7O7pkWWc6tYsWKqQV93aQS4tnExsZSpUoV9bUu\n6Lx3756xkiQKoMwBkjEXRpcxTNnT/Z5KlCgBGLeFKXMgKy1MRUehDphy+0el1Wpp0qQJY8aMwczM\nDHd3d5KTk9m2bRsHDhxg0aJFWWZl0+2XlJTEtm3bCA4OJjg4mLi4OFxdXdXplo8dO8bdu3fZvXt3\ntjXVt2/ffrEbNSFJSUnqDGD169d/adfRnfv06dOF9gPrWfJvw4YNGTVqVJ7lX93zDQ8P59q1a+zc\nuTPb/BsREfFiN2pCFEVRJ2hp0KDBMy9Sm1uvv/465ubmREVFGWy5EznTarV6vxuNRoOlpaVMpCGe\nSeaAyZgzoMnCtdnLHDAZsyVQuuQVXYU6YIL0zJ1TgUSr1XL27FnGjh2rFgjj4uLYsmWLuo+iKISE\nhGT5Q4mLi2Pr1q1623T7VahQgTJlypCUlMSxY8dyHFxv6NwZ01eQau9PnjxJSkoKZcuWxcXF5aVd\np1KlSlSsWJG0tLQs42oKk9zk3zNnzjBu3Dh1W17k35IlS6pjd44cOZJj/t2yZUuOtaEFKQ+Hh4dz\n//59rK2tqVOnzku7TqlSpfDw8ADQm0FT5I5Go8nzrpKi6MlcKSUtTKbJlFqYDHXJE0VDoR7DFBoa\nyvbt2w2+pygK69atIyUlhWXLlumtsXLv3r0sBUTdVM4Z15552n716tVj586dhIWFqX1vs0tLYail\n0Gq1ane8+vXrv9QCjUaj4fXXX2fTpk0cO3aMRo0avbTWAGPJbf5dsWIFlpaW6nt5lX99fX2JiIgg\nMjIyV/m3MKzLdOzYMQC8vLxe+liY+vXrc/LkSc6fP6+OLRO5oygKn3/+OVZWVuq2J0+eMG/evCzP\ncfbs2fmdPFFASJc805eWlqZWtJlCwCRd8oquwlXCzCAuLo4dO3Zk+/5ff/3FhQsXWLZsGWXLltV7\nz8HBIUthXzcNM6RP5GBmZvbU/Tw8PLCwsCA2Nvapg8cLQ2EpMjJSrZ3PjzVQateujY2NDXFxceoU\n5oXF0/Lvjh07OH/+PIsXL84ybXhe5V9XV1fKlClDcnIy7u7u2ebfjMcUZA8ePCAsLAzA4MQvee2V\nV16hUqVKaLVaQkJCXvr1CpMuXbroBUsAHTt2LBT5UOSfzF3wTClgki556TIGtaYQMEmXvKKr0LYw\nGao91wkJCeHEiRP069cPPz+/LIXt0qVL065dO7Zu3YqiKGg0Gtq3b5/lC9rQft7e3mpNu7W1Ne7u\n7pw8eZL4+HjatGlDQkICsbGxnDlzBkhvlbl27VqhqJ3XFfo8PT3zZaYqCwsL6taty8GDBzlx4sRL\nGaBvLDnl38uXL3PkyBH69++Pv7//S8u/Go1GbSW9c+cOrVu3JjExMUv+jYmJKRT59/jx4yiKgrOz\nc5ZKlJfFx8eH69evc+LECRo1aiTdzHJJWo1EXshc+DWFMUxmZmZotVppYfr/DAVMpjSGSbrkFR2F\nNmDS1Z4b6nL0999/U7VqVYYNG5bt8XXq1EGr1ZKQkICXlxd2dnacO3cuy35169ZV9ytRokSWgmPd\nunU5efIkZ8+epUqVKjg6OuLo6EjlypUJDQ3l448/5uHDh9SrV69AF/gTEhLUgnvdunXz7bq6gCki\nIoKHDx8Wmuk9s8u/kL7+VLVq1fIl/9apU4c9e/Zw+/ZtUlJS9PLvqVOnGDduHA8fPqR27dq8+eab\nL37jRpKWlqYG/PnRuqTj7u7Ojh07uH//PpGRkXm65pMQIme6wq/us9YUWpjs7e25e/eutDD9f9LC\nJExFoe2SV7p0aVq3bp2lxjYsLIzExEQ+//zzp7aC6KZhLl26tDqrmLu7e5axMhmna87MyckJR0dH\nUlNTuXbtmt4xDRo0oH///gDMmjWrQP/hhYaGotVqqVixIuXLl8+365YpU4bXXnsNQJ2drzDILv/G\nxsZy5coVZsyYoTduyZC8yL/FixfHzc0NgKtXr+od88YbbzBgwAAAPvvsMx49evRc92oKIiMj1aCx\nRo0a+XZdS0tLvLy8gPQWLiEKstu3bxeomQp1hV/dWnemEjCBjGHSyRgcFS9eHDCNlkCdhIQEbt68\nyYoVK7Kd8TguLo7169cbtWVMvLhCGzBptVpKlChBmzZt8PPzU4OjkJAQ+vXrp67jA1nXq3kRusIl\npFSUhToAACAASURBVDetazQatcXl8uXLWVoMpk6dStWqVSlWrBiff/75C1/fGHSzsEH+ti7p6KZw\nP3nyZI6zuRUkGfNvkyZNKFWqFJBeqO7Vq5c6wxq83PwL/z3fa9eukZaWprf/jBkzqFatGklJSaxc\nuTJP0mAMp06dAtLHxWWcACY/6P5mLl68KIUkYTIuXryoV0nyNKtWraJChQpqJYqxHD9+nC1btuTq\nu0D396YLUkyhIK5Li7QwpdMFGcWKFVMDJmMGtoa65L3//vsMGDCA4cOHGzxm+vTpdOvWTa0gLyh+\n/PFH2rRpQ2ho6HOfY926dcyYMaNQlM0KbcCk8/jxY6KiokhJSSEhIYFHjx7pLe558uRJdb2ahQsX\nvtAU1bVq1cLCwiJLTb6Xlxfm5uY8fPiQ+/fv6x0TFhbGe++9R9++fbG2tmb58uUmP/VyZtHR0dy9\nexdLS0vc3d3z/fo1a9bEysqKBw8eZFlvqKB7/Pgxt27d4tGjR2i1Wm7fvq33oZwf+dfZ2ZnSpUvz\n5MkTbty4oXdM8eLFmTJlCqNHj6ZSpUosWLCgwLWUJCcnq91J82OykszKly9PhQoV0Gq1nD59Ot+v\nL0Rm9+/fx8fHh+rVq3Pnzp1cHTN69GgUReGnn37i4cOHLzmFhiUmJtKkSRM6dOjAd999x+LFi3nw\n4AGAWtlz6NAhfv75Z3V/+C9IMYWCuIODg97rombJkiXUrFlTrYTVtTAVK1ZM7ZL3PL1xUlJSaNmy\nJR07dnyhMpahLnm7du0CYP369QaP+frrrwHUfFdQfPDBB+zYsYOZM2c+1/GXLl2ie/fuTJ8+nY0b\nN+q1wBmzW+XzKtQB09GjR9m7d69aS5aYmMi4cePUWorExES9dWgURWHz5s15XkuesVvTtWvX1J8z\nX9/MzIyoqKgsXzZ52YLwMugK6bVr184ysUB+sLS0VFtcCtNsY7r8e+nSJSC9JmvkyJFq17n8yr9m\nZmZqIHHlyhW9Vqi4uDguX76s181vy5YtWdJgynn4woULpKamYm9vT8WKFY2SBl23vBepySvKoqKi\nmDNnDkOHDuXOnTusX79enSK+sEpNTc1SAZdXQkNDSUhIICUlJctabYbExMTopcVY3aOvXLmiFmiH\nDBnC8OHD+eqrrwgKCqJkyZJMmTKFRo0a0adPH44cOZIlYDJmq44uCHB0dARyFzA9efKELVu2GLVl\nLK88ePCA+/fvM3r0aC5cuEC7du2A/55LxjG2zxMw/f777+zevZvNmzdz9OjR506nLo9YWKRPARAV\nFaX3/ujRo9mwYYPeNmtra/XnU6dOER4eTnJyssGgYf369VSpUiXX6/NptdqXUlGcMW3h4eHPdY6M\nladdu3bFxcWFCxcu8MMPP2BlZZVtgGmqCm3AdP36db0xQwBly5ZVx9fUqlULR0dHg5NCxMbG5nl6\ndAXOqKgotaYrPj4+y/XNzMz4888/1dchISF51oLwMjx58kSdTOBlLvT5NLpuY+fPny8UXRkM5d9S\npUqpX6b5nX91Bfo7d+7oBfSxsbFPTYOp52Fdq46np6fRZqnz8PDAzMyMmzdvZtsPXhh29OhROnXq\nxPXr1zlw4ADJyclERkbSt29fdu7caezkvTTvvPMOTk5OuS78hYWFsX79+izdag05e/as+nNuCktX\nrlzRe22sgMlQF8K1a9fywQcfkJSUpNft/dSpU2pQoqso0bVGvYijR4/SoUMH9u7dy8aNGw0uPH7j\nxg2mT59OUFCQuk0325ruM97Q91hwcDCzZ89Wx4lNmzaNDh06MHbsWCC923/G393JkyeZOnVqngRU\nWq02162Nz+r27dtUrlyZihUrql3wbt26Bfz3XEqUKPFCLUx79+5Vfz548GCuj4uNjdUbe6TLM7rf\n0/nz5/X2X7BgAV27dtVrrcwYfHh5eeHp6YmHhwcuLi56lYiKotCtWzeuXbvGmDFjcpW+pUuX4uLi\nwowZM3J9T7lx8+ZN9Wfd38Wvv/5KYGBgrrvXZf7sSExMZN26dWrXxJwmroL03/POnTtNptdVoQ2Y\ndOupZKTRaPTGCWS3Dk3mdW3ygouLC1ZWVqSkpBAREQFAyZIls1xfq9Uyf/58UlJSiIuLY8uWLep7\nL6sF4UVcunSJlJQUbG1tefXVV42WjgoVKlCuXDnS0tIMzgZX0Jha/rW3t1e/IDJ2G7O3tzeYh3U1\ngaaeh+Pj44mMjATQGxeW32xsbKhevTogrUzPat68eXz00UcsXLhQrfX95JNPGDduHAsXLjRy6tLz\n/IoVK7h3716ennPDhg0kJiYycuTIp+5/79496tWrR7du3XI1JXvGz1BDn0WZZQ5UjJWHMwduAA8f\nPjQ42D7j55iTkxNArn9H/4+98w5r6nz7+PcECHvJFFSGULYKoihunKi4Wrcobmstota2aq3iqFqt\n1bpHrVXrwP1z7yqOghVUkCFbhgIieyc57x+852lCQliRBDyf6+K6yBnPuXNyJ+e5n3slJyfj8ePH\nEvf17t0bV65cQb9+/TBmzBg4OjqKGSzLly9HYGAghgwZQmRjjABjY2MA4uWqCwoK4OHhgeXLl5OV\neeaz3L17NwQCAXr06AFnZ2c8evQIQNVC4tq1a0lIWGPYvn07TExMcPjw4UaPVZ2wsDAUFRWJGBnM\nc0XYw1Qfg6msrAwLFizAtWvX8P79e5w/f57si42NrZNchw4dgoGBAWbMmEG2VTeYauoByUS7MOH0\nwpSWliIuLg7p6enQ1dUlbTqEv3fVw99rYu/evQCq8qSAKh2+ePEi0Ssej4fDhw+LGECSyM/Px7p1\n64juCBv66enpyM3NxeTJk+Hv71/nhShJiy0//vgj+b+2xW0/Pz8MHjwYf/zxR52u97FpsQaTlpZW\njfuYL5uOjg6GDRtGvpgURWH48OHQ0dGRuTzKysrEA8N8OTQ0NDB06FCRCee9e/fw8uVL7NmzR2Iv\nno/lQWgozEPHxcVFrj1kKIoiXryWkAeiaPrL4XDg6ekJoEp/Gb2sLoNAIMClS5dw5MgRAJL7SSmS\nDjPvpU2bNh/F0KwPjBfv5cuXCrOi1hx4/fq1xJL2/fv3F/PSyoOCggLMmjUL/fr1E9menJyM8ePH\n48CBAxLPq6iowNKlS3Ht2jWxfdnZ2eT/unyXrl27RjzDhw4dqnWFuKEeJib06MGDByLXaKrKeZI8\nTFlZWWJhU8B/k2aKotC2bVsA/91LmqZx7949/Pnnn+jcubNI7glN0xg8eDB69OiBU6dOiYwpqTR5\nYWGhyESYpmmyiJSXl4fLly8D+M9AsrW1BVA1gRU2mph5AwDcv39f7P0kJCSQSfGZM2dEjD9ZhKcy\nXqzp06c3eiyg6lnBVFaV5IVjjpFkMAmHKx4+fFjihPr48ePYtWsXhg4dihkzZoh8T+ra6J7x2hw7\ndozcT2axj4lWqqnyHRMmJ0n3qvPdd9+hoKBA5HPNzs4W+Q6Vl5fj8uXLSE9PFzlXuNpzVlYWvvvu\nO4waNQrz5s0DAKxbtw7Tp0/H1KlTpcqwadMmrFy5Er169UJlZaXIdXg8Hs6ePUteM966DRs2YOvW\nrTWOWdtvR2FhIVasWCHRI83n88nCwJo1a6SO01S0WIOJWa2VRFlZGVF6V1dXUonM399fpMqbrPMu\nmAkR45UBqsKAmOt7e3vDy8sLQJWCKCkpNZkHoSEwqySAfFfnGZiCEykpKXJLOpYViqi/Dg4OUFZW\nRk5ODnHRCwQCqKqqEhmUlZURHh6On376CXl5eU3qBWsITHU8RdBfW1tbaGhooLi4uMEx458i5ubm\nEhdJ/v77b5ibm9dpjPLycixfvhzu7u7o2bMnDh06VOOxUVFRGDt2LDp27IjPP/9cZCIrCWZSGBER\ngYyMDBw4cAC3b9/GvHnzEBQUhDlz5oiECzEcPXoUW7ZswdChQ8VK9gvrR2JiYq0hV8LekKSkpFpD\n5oQNpri4uFoNLMZQmTVrFrS0tJCQkEBCb+/evQtjY2PMnj2bGAvXr1+XOl51bt26JfWcq1evwtTU\nFJs2bZK4X9L9YXTGwMCAeAuYSfFvv/0GLy8v+Pn5ISwsDFOnTiWT9NjYWJJXumPHDgAg96cmr4Dw\n5xUXF4f379+T18xElDEMzMzMSJlzYY+Z8BiPHj0Sy1+7ffs2+T8mJoZEsgCiBnZt0DQt9nlL8nbV\nRkVFBXbv3o3g4GCJ+/38/GBoaIjLly9LNJhomkZOTo5UD1NKSgqmT5+OGTNmkEgBBuFwx0uXLgEA\ntm3bBqBuBtOHDx9EjB0mH4cxvCwsLKSen5SUBIFAUKe86qtXr0JXV1ckRK20tFTkc1u7di18fHxI\nbheDsC5FRETg999/BwDyG/bLL78AqNIPaZ65e/fuAai670+fPhUzzG7cuEH+j42NRXx8PJYvX44l\nS5aQc4WhaVrMO/3zzz9j/vz5JIUCAH766Se4urqKLaoI64SkBUSBQPDRcjhrosUaTKqqqhLd80BV\nHLBwLoVwvxqGFy9ekLyLnTt31ph3Ia2/TXVMTU1hZGREYsiZc4T74IwePRrOzs748OEDdu/e3WQe\nhIYQFRUFgUAAExMTEkYgT3R1dcmPWHP3Mqmrq9f4HuSlv6qqqsSQKykpETmHkWHEiBFwcnJCbm4u\nfv755yb1gtWX9+/f4+3bt6AoSqTNgLxQUlIiRn9tk3CW/wgICMDKlSuxadMm8Pl8XLhwAd999x02\nbdqEr7/+uk5j/Pzzz4iMjMSff/6JVatWYefOnRIn6CUlJZgzZw7c3d1x7tw5uLq6Yu7cuVIT9IUn\nAoMHD8acOXMwcOBAkQnIwoULxXKLhI2of/75Bx8+fMDcuXNx5swZkYkIj8fD5MmT4eDggK+//ppM\ndvl8PvmfWRhgCAoKqlHe7OxskUlYYWGhxAktj8dDeHg4ysrKyLPW2dmZePvu3LmD+/fvY+HChcjN\nzcXBgwcxYsQIDB8+HN7e3jVWDEtKSiITxYqKCsTHx2PQoEHw9vYWmxQzzJo1q965f0yOjKGhIQwN\nDQFU5dIIBAKJIWyMp084HCk0NBQLFiyAvr4+VqxYQUL7qsMYOzweT8yAuHfvHmiaFsnVsbGxASCa\nNC9sMEVGRiI0NFRkHOHc5+vXr4sYjykpKSgsLIS7uzucnJzIe4+Pjxcxfq5fvw5TU1M4ODjg9evX\nWLNmDWxtbclCLsP9+/cRHx8v8nm8ffsW27Ztw9u3b1FaWoohQ4bgq6++wtChQ8Vyw86ePYujR4+i\noqICy5cvr/Fzffv2bY1FHyIiIjB//nwR2Z8/f47u3bsjMDBQ7P4YGxuT3Jns7GxkZGRg3bp1uHDh\ngsRrBwcHixiOTJgpYzAxXsmaSEpKgp+fH6ZMmVLjMfv375c6hnB+FGP0vXz5ErGxsYiKisIPP/wg\n4kWvbpzl5OSIeDwfP36MqVOnwsPDgxgqv/32G2bPni1SZCI0NFTMYBI2imJjY0VyJ4ODgxEfH4+e\nPXuSapnJycnIzs6GsrIyLly4gOXLl2Px4sXYtWsXwsLCSP9MBka/mXsurBNpaWno06cPli9fTo4Z\nMGAAWrVqhX379km9hzKFbgF4eXnRXl5eItu2bt1KL1y4kF69ejV94MAB+vz58/Tq1atF/gIDA+nc\n3Fw6IiKCjoiIoPl8Pk3TNJ2fn08HBgaKHZufn99oWYODg+nVq1fTf/zxB03TNF1ZWUmuz/xduHCB\nBkBraGjQGRkZdEhICH337l06Nze30deXJX/88Qe9evVq+uHDh/IWhfDs2TN69erV9O7du+UtCk3T\nNM3n88X0qy7s3r2bHjduHL169Wp627ZtCqO/0dHR9OrVq+ktW7bQfD5f5P0xf+fPn6cB0Orq6nR6\nejrN5/MVUofv3LlDr169mv7rr7/kLQohNTWVXr16Nb1+/Xq6vLxc3uLUSX8l/f42NdHR0fTSpUvp\nMWPG0CNHjqQDAgLo58+f1+nc4uJi2sXFhf7nn3/Itl27dtFTpkwRO/b06dO0l5cXLRAIaJqmaYFA\nQA8cOJA+e/asxLG9vLxoS0tLGoDEPyMjI1pPT48GQO/fv5+maZoODQ2l7e3tRY7z9/enJ02aRF77\n+fnVOKanpyc9fvx4Wl9fn3Z0dKQ/fPhAa2tr0wDoVatW0QBoLS0tOi0tTaLMV65coQHQtra2tIuL\nCw2Anjx5Mn337l3azc2NHjx4MF1QUEAvW7aMBkD37t2btrW1pQHQN2/epDdv3lyjbMJ/BgYGdHZ2\nNk3TNH379m36m2++obdu3UoDoPX19elhw4aJnbNgwQI6ISFBRN60tDSx44YOHUp36dKF7tKlS61y\n9OzZk87Oziav//jjD/L/nDlzaEdHRxoArampSfv7+9OdO3eu0/sT/psxYwYdEBBAczgcsm3x4sW0\nqqoqDYDu27cv2Z6cnEwHBgaKvOeKigp64sSJImOqqanV+fpKSkr0n3/+SV5PmjSJvnjxIk1RFG1q\nakpfu3aNDg4Optu3b0+O+eyzz0TkFf7T0NCgAdAqKir0o0ePaB6PR3SlX79+9Ndffy1y/PLly+mN\nGzfSsbGx9MOHD2mKoup9DydNmkTfvXtX6ufYv3//GvcPHjyYpmmabteuHQ2A7tatG7k3sbGxIjpV\nVlZGBwQEiF3//fv35J4I64mkPxMTk1rf04sXL2hNTU2x7V5eXjQAev369fSrV6/oFStW1Oke6ejo\niLzeuXNnve8zANrX15ceNWpUjfu5XC69cOFCqWMoKyuT+yyJnj17ihx/8uRJetGiRbXKFhERQd+4\ncYO8NjQ0rPFZKetnE0XTzb+bVP/+/VFRUYE9e/bA0dERZWVlGD9+PNzd3ckxtra2EpNXfX19yeqg\no6MjOBwOkpKSSA6GMNOmTROziutLfn4+cQsvXLgQWlpaxD1cUlKCoqIiEmYVGhqKgIAAsirCyKcI\nCL+PgIAA6OrqylmiKsrKyrBlyxbw+XzMmzePxBnLC4FAQOLXa/r8qh9TXl4OHx8f9OzZk3hmFEV/\n+Xw+tmzZgrKyMvj6+sLS0pLILqy/Pj4+ePToEb788kvs3Lmz1nvQ1NA0jd9++w15eXn4/PPP5dI/\nTBI0TWPHjh3Izc1VCLnqor/9+/cHUOVRaI6EhYVhypQpeP78OckHCAkJwezZs/H8+XOR97xy5UqU\nl5fj559/Jtu+//57cLlciXH2/fv3R2JiItq2bSsxNGnZsmUwMTFBQEAAjIyMcOnSJYwePbrWBG2G\nVq1a1TkfUEVFBYWFhejXrx+ePHkCLS0tTJw4EXl5ecjJyYGenh7y8/MRERGBrKws+Pn5gcvlSlwF\nX7RoEQ4cOCAWqpWRkYHMzEyRkBsAGDRoENTV1XHx4kV4enri/fv3eP36NcaOHYvFixfDy8urztVN\nKYrCoEGDkJubCzU1NTx48EDsmOzsbBgaGuKnn37CihUrpI43evRonDt3Dubm5iIhdTNnzsTBgwfx\n9OlTdO3aVew8Ly8v3L17t04yS+LGjRvYsGGDWDhmaWkpysvL4ebmJtHzYmdnV+eiBbImMDAQv/76\nq4jHiCkm8u2334odb2FhIZJXpqqqCpqmUVFRgSFDhiArK6vOVVOnT5+OzZs3E29gfVmyZAm2bNmC\nwYMHSyxaYGFhgc2bN+PVq1ciFef8/PwkFrqIiIgQCeVevHgxtm7dCoqi6lRFTktLC5mZmXB3dxfx\nJNnZ2WH+/PlYuHAhXFxcUFZWVqfCK6qqqh+lvLyrq2uD27Woqqri+vXr6Nu3r9i+oUOHSszPbAg/\n//wzevfujYSEBHC5XNy7d494z4yMjGT2bGqQwVRTnC5FUVBRUYG+vj6UlJQaLVxdqW4wbdu2DQUF\nBbUWIaAoCv7+/iTcgJkQFBQUYNu2bSJKT1EUAgICZBJKdOTIESQlJaFfv37w9PRETEwMkpKSyA8H\nRVGwsrLC1KlToaamhitXrsDY2FhhJptAVQz17du3YWFhAT8/P3mLI8KpU6cQExMDT09PDBw4UK6y\n8Hg8YhDb29uTKl7CVJ+U/vLLLyguLlZY/b106RLCwsLQqVMn+Pj4ICoqSkx/P/vsM0ycOBFcLhdx\ncXEk7ENRdDg1NRWHDh0Cl8vFN998AxUVFXmLRLh79y6Cg4PJPZQnwroJSP785GEwLVu2rM7H1lYV\n7saNG1izZg2pDgVUJdAPHToUT548Ecm3mzdvHmxsbPDNN9+QbZs3b0ZcXJxEw8LFxQU8Hg+tW7dG\namoqeDweyesrLy+HgYEBOBwO0tLS6t3IUVlZGYaGhsjMzASHw4GZmRnevn1bY4EFNTU1UrY5PT29\n1sIirVu3BkVRda7WpaKiQsKUsrOzUVJSAn19fVAUBQ0NDSgpKYHP50NJSQnl5eXIyMioc3ni+mJt\nbQ2gKqSP+X3U1NQkv6scDoeEQLZq1Qp6enrIy8sjxieXy4WpqSn5vc7JyRHJi+VyuTAyMhILWwKq\nwqn5fH6tn6eVlRXy8/PFDF5Gdj6fj3fv3olNgtu1a4f8/Hwij5GRkUiuS+vWrfH+/XtSjKD6BF5N\nTU1qg14DAwOUl5cTY1hXVxc8Hg80TcPExAQCgQDl5eWgaRrZ2dk16pGuri7U1NQkhkoqKSmhTZs2\nqKioQGZmJpSVlcn3rKSkBHw+XyzfxszMDGpqaiTkj0FdXR0VFRUSy+ULGxEmJibQ1NQU+Zxrg8Ph\noG3btnjz5o2YrlpbWyMlJYVc18rKCuXl5VBWVkZqaipomgaHwyFGooGBATIzM8HlckmOmqqqKt69\ne0cWPXV0dKClpUV+E6qjpqYGPT09ElIp/D719fXFtku6DwYGBigqKhLTK0au6p+XoaEhCdHlcDhQ\nUVGp0TAzMjJCWVkZeDweuFwutLW1RYpSCJOdnS2Wm1mb7MJQFAUtLS2pY6ioqEBVVVVmKRris7c6\nMHDgQPIlYZRIeHKnrKyMAQMGYO3atVKrfX0MSktLcfz4cfj4+Ijt69atG0JCQkDTtNRcCibv4sqV\nK7Ue2xBcXFyQlJSEiIgIdO/eHSUlJSKrLDRNIykpiaxgHThwoNZVsqZGuDqeotGhQwfExMQgMjIS\nAwYMkGv1vvpSWlqKEydOYOTIkWL7FEV/O3TogLCwMERHR8Pb21ui/r5+/RqDBg3CzZs3sXnzZsyd\nO1cm15YVTE6Hg4ODQhlLQNV3iokJLykpIXH7LB+H0tJSsYc687r6pLemY2uaHKuqqoKiKFAUJbXt\nQk25L3XBysqK/F+X1g4qKir18jQzE/j6wBRRqA6zkKqqqioi98eCy+XWWX49PT3o6elJ3GdgYAAD\nAwOx7Q25N3W9ppKSUo1FS6rLo62tLbK/tvyaulBTXrKSkhL5TWKKMEhD2j1SV1cX08Xafu9at25d\n6zWlIe2e10RNulq98ANTJbKm4yV9P01NTSUeK+2+1bSvrvooLSJI0hj1mTtU18WaMDIyqvF3oj5I\nGyM7O7tGg60hNMhgCgwMxMGDB/HDDz+QUtkRERH46aef4OPjg27dumHz5s3YuHGjSKO4puD333+X\n+PCiKApdunSBvr4+iouL4erqCj09vRpXR1xdXckqB3OsrHBwcMCVK1fw/v17ZGZmioU1AFWTzgUL\nFuDu3bs4e/YsZs2aBUdHR5nJ0BiysrLIqqaiyCSMra0tVFVVUVBQgJSUlEaHockKxtMkzcty6NAh\npKenE0OHQZH0t127dtDR0UFBQQFev35do/7OmTMHN2/exMGDBzFmzBiZ/DjKAj6fT6qAKaLBb2Rk\nBBMTE2RmZiI6OhqdO3eWt0iEqKgohfAS1qWXUF1h+uMJw7xmJkC1HVv9OAZZlHNmYWFhYZE/DTKY\nduzYgV9//VWkhHH37t2xdu1aLFy4EHPmzMGyZcswY8aMJjWYysvLsWnTJnh4eIhspygKrq6u0NHR\ngYaGBjQ0NOpkMdfn2PqgpqYGOzs7REVFITIyUmKJZYqi0L9/f/Tu3RsPHjzAkSNHxPp4yAvGu2Rr\nawt1dXU5SyOOsrIyHB0dER4ejpcvXyqMwcQgEAgkTjgrKyuxefNmWFhYiBlLiqS/FEXBxcUFjx49\nQmRkpMQVKYqiMGDAAHh6euLx48c4fPgwli5dKlM5Gkp8fDxKS0uhpaXVJKvcDcHFxQWZmZmIiIhQ\nKIMJqFl/5UVN4XlMiLiRkREGDRpUY6l+ExMT5ObmgsfjkRCs7OxsqKmpiX13TExMRCrIAVXVFhWh\nSigLCwsLy8ejQU+94uJiibkYHA6HxBNqaWnV2NDrY3H+/HlkZ2cTr0e3bt3Qu3dvDBkypMaJUX3K\nKssSZmX71atXUFdXh5ubm8TSy9999x0A4PTp0zLtFN9QaJpW6HA8BqaJbVRUVJM1TWwsly5dQmpq\nKpkg29vbK7z+xsfHQ1lZWaL+6urqYuXKlQAUR3+B/wz+pr5n9UG4p5is+mi1VDQ1NXHhwgUkJSVB\nV1cXOjo6SE1Nxblz55CTk4OIiAiMHTtWYq8Q4L/+YsK9iZ49ewYXFxcx/ejYsSPCw8NJKDpN0wgL\nCyM99lhYWFhYWiYNmi0MHjwYy5cvR2hoKEpKSlBcXIzQ0FD88MMPGDBgAEpLS7F//34yaW0qfv/9\nd9jZ2UFFRQV6enowMzMj/Y0UDRsbG6ipqaGoqAhpaWnQ1NREnz59xBqQDh48GA4ODigtLcWuXbsg\nEAgQGRmJyMjIWpN2PwapqanIz88Hl8uV2lxV3lhYWEBbWxvl5eXNogkoj8fD77//Dj09PRLTbG1t\nrbD6y/Te4vP5SEpKkqq/Xbp0QWlpKY4cOSJ3/S0vLycVphTZ4NfV1SXx7mxPJumkpKTgyy+/xMmT\nJ/H9999j2bJlOHbsGAICAsDj8bBv3z788MMP2L59u8Tz1dXVMWrUKKxevRovX77E7du3cejQIUyd\nOhVAlbeJSZIfMmQICgoKsH79esTHx2P9+vUoLS2Ft7d3k71fFhYWFpamp0EG048//ohOnTphUS+e\nPwAAIABJREFU5syZ6Ny5M9zd3TFz5ky4urpi9erVePToEV69eoUffvihTuNVVFRg+PDhIp2Z161b\nBzs7O5G/Y8eO1ThGSUkJ3r17R0qJOzs7K3Syv7KyMhwcHABUNQkLDg7G33//jaKiIpEwEIqiMGPG\nDABVoZCS8kWaEmZ1XhGT5YWhKIqs0su7iW1JSQmysrKkNre8ceMG3rx5Q8JJLS0tFTLcURjG4IiM\njJSqv0zBklOnTolUmpIH0dHR4PF4MDQ0bHTy8MdGUZrY1kV/5cnTp08xYsQIse1DhgzB48ePAQA9\nevRAUlJSjWMsW7YMTk5OmDZtGgIDA/H1119j0KBBAICePXvi6tWrAKoiJ/bt24dnz55hzJgxePHi\nBfbv36+QixosLCwsLLKjQTlMqqqqWLduHenQrKysjHbt2pGHxoABAzBgwIA6jVVeXo4lS5aI1ZlP\nSEjAkiVLMHr0aLJNWsW9oqIikYorzs7OpNQmk6QsjxVtabRv316svn14eDh69epFkvQ5HA4WLVqE\n/fv3Iy4uDgcPHiQP8qZG0ZPlq9OhQwc8efIEsbGxKCsrqzEx+2Py/PlzkV4Dbm5usLe3FzmGpmn8\n/vvvAKpyAQHR+6uo+lu9QhAgrr8A4OPjA0dHR0RFReHQoUNyXY0XDidV5AUVAHBycsL169fx9u1b\nvH//vsH9RxpDeHh4rforb9q2bYsbN26IVWK8desWMYqTk5Ml5ooyqKurY9OmTdi0aZPYvuo9bzp0\n6IDz58/LQHIWFhYWluZCgwP4P3z4gOjoaJSWlqKwsBCRkZF4+PChxF4UNREfH49x48bhzZs3YvsS\nEhLg6OhISg8aGRlJXXHn8XikKIKJiYnCVOSShqT3Q9O0WI8AJSUl0hRu69at9e7XISsSEhJQWloK\nTU1NhU2WF8bExASGhobg8/mkQl1TUlBQQFamGcLDw8VyUm7fvo3MzEx069YNQNXnrWiTUklIyg2T\npL8cDgdLliwBAGzfvr3JcxsZCgsLSSPI5mDwa2hooH379gDk4yUtKCjAlStXRLaFhYWJ6W9paalc\njfnvvvsOu3btgq+vLzF6fH19sWPHDixbtgzR0dFYtGgRJkyYIDcZWVhYWFiaNw0ymIKCgtC7d29M\nnjwZU6dOha+vL6ZOnYrZs2fXq3lhaGgoPDw8cOrUKZHtRUVFyMzMrFd1M11dXdKNW9pKoiIhacWY\noiiJ8vv6+sLMzAzp6eky645cX5pDsrwwTDU3QD4TzpycHLFmdzRNIzc3V2Qbk28xZMgQAFX9L+Th\nDasvkvqS1KS/kydPhqmpKdLT03H9+vWmEE8MJrStbdu2pHGgoiMclvexmnzWhCT9BapC4IQpLCyU\na2GKnj174sqVK3Bzc0NSUhLevHkDNzc30mFeWVkZP/30k8L1AmNhYWFhaT40aNa7d+9ezJs3Dy9f\nvoSBgQHu3buHy5cvw8HBAQMHDqzzOJMmTcLy5cvFPC0JCQmgKAp79+5F7969MWLEiHqFQMTExDT6\nAd4U1cd0dHTQq1cv8lq4fHR1VFVV4e/vDwA4evRok0+eKioqmkWyfHUYWZOSkurUVVqW1NQwTdhD\nGBoaCmtraxEde/fuXbPR3+rhoXXR3z/++KPJ9Rf4r1ltc9Jfe3t7KCsr48OHD8jIyGjSa0syiAEg\nJCSE6GdISAhUVFTk8nkyzJ8/HxUVFVi0aBH27t2LXbt2YdGiRTAzMwNQ1f6gf//+cpOPhYWFhaX5\n06CZVFZWFkaNGgUulwsnJyc8f/4cNjY2WL58OU6fPt1ooRITE0FRFKytrbF//36MHTsWK1euxK1b\nt+p0vqSwIEB+JZil0adPH5Kb5eTkJDXUbfbs2dDQ0EBsbGyTN0SMiYlBZWUlWrVqRSYizQF9fX20\nadMGNE2T/KumoqbQyaCgIISFhQEA/vzzTzFdbE766+HhQcJf27dvL1V/582bB01NTcTFxZFk/KYi\nOzsb7969A4fDgZOTU5NeuzFwuVwSntnUXlIdHR0SJioMTdP4559/AABHjhxpUpkkERYWJrHNBQsL\nCwsLi6xo0KyrVatWZEJnbW2N6OhoACDd6RvLqFGj8OTJE8yYMQP29vbw9fXF+PHjceLEiTqdT1EU\nDA0NFW5yKQmKokjyfG33rlWrVqTUbVNPVJpTsnx15BWWV9MKPVDVcykhIQFBQUFi+R/NSX8BkNDZ\nd+/eSfU06OvrY+bMmQCqvExNCeNdsrGxaXYVzYSrETZ1rhDT0646T548QVJSEoKCgppUHklMmjQJ\nixYtQlBQEB4+fIinT5+K/LGwsLCwsDSWBs3EvL298d133yEsLAw9e/bE2bNncePGDezcuZP0DmkM\nFEWJVNkCqgyzuhhjwk1fmwtt27YFULUK3q5dO6kTZCas6f79+03WX6i4uBgJCQkAmlc4E4OTkxMo\nikJGRkaTNk/V0dEhZcIlceTIEbx//54sOADNU3/NzMygpKSE4uJi6OvrS9XfgIAAcDgchISENFm5\nbJqmybWaujecLGjfvj00NDRQXFxMilY0FdIKdBw7dgzv379HRUWFXBdRdu/ejaioKPz444+YNWsW\nfH19yR+zwMTCwsLCwtIYGmQwffPNNxg2bBhyc3PRo0cPjB07FqtWrcLLly8RGBjYaKG2b98OPz8/\nkW0xMTGwtraWep6+vr5I08zmgqamJvFG1DaJtLOzw4ABA2BhYYFdu3Y1hXgk4dzMzEyq10RR0dTU\nlFu1sZpW6AGQ8FUm74/L5eLrr79udvqrrKxMwjRru78WFhYYMmQILC0tsWfPnqYQD6mpqcjLy1P4\nZss1oaSkRPSoqfVXWq81Rn81NDTkauDHxMTU+Ce8GMHCwsLCwtJQGmQwVVZWQltbGw8ePMCyZcuQ\nmZmJfv36oVOnTjIJ0ejXrx+ePn2K33//HW/evMHx48dx4cIF0sC1JmxsbJrVyrwwjGeutglReHg4\nevbsCT8/P+jp6eHhw4cfXTbhcLzminBYXlMlqAsEAqlewNLSUlhaWkJTUxNAVWibrq5uk8gmaxgv\n6atXr6SGjYWHh6Nr167w8/ODsbFxk+gvE47n6Oio0M2WpcF4xqKjo5usrYBAIBDrjydMYWEhzMzM\noKysLPewUR6Ph8zMTGRkZCAjIwPp6elISkoSK+vPwsLCwsLSEBr0lFu8eDF279790UrJdujQAdu3\nb8fFixcxfPhwHD16FL/88gtcXV1rPIeiqGZVjKA65ubm4HA4ePfuHWm4W53qfVE4HA7u3LnzUUv6\nfvjwAenp6aAoipQ4bo40RbUxgUCAyMhIkVyTmpotCwQCfPjwAQsWLCDhjrIIZ5UXJiYmUFdXlxo2\nJg/95fP5iIqKAtC8Df42bdpAX18flZWVYo1UZUVD9Perr76SW184htu3b6NXr17o27cv+vfvj/79\n+2PAgAEYOnQoNm7cKFfZWFhYWFhaBg0qLRQSEoJDhw5JNWDqS/VJwIABAzBgwIA6n6+kpNSsKyWp\nqqqiffv2iIuLw8uXLyWWwa2pL0p2dvZH86wx3iVra+saJ0/NAabaWGRkJCIiImBubt4k19XQ0ICb\nmxupisdw6dIlVFZWwt3dHX///Td0dXWbrXcJqDJ+HB0d8ezZM0RGRsLGxkbsGHno7+vXr1FaWgpt\nbe169XVTNJgFi+DgYERERDSZ8SdNfysqKjBs2DCcOXOmSWSpiV9++QUDBw6En58fJk6ciP379yMv\nLw9r167F/Pnz5SobCwsLC0vLoEEeJmtra5SVlclalkbRnI0lBuFqWHw+X2y118DAQCy5WiAQIDw8\n/KPIQ9N0iwjHY2DeQ21hY7JAIBAQz4aVlRW8vb3RtWtXeHh4IDY2FuHh4Zg8eTIJ2WvO3iUG5v5G\nR0ejvLy8zvr7/PnzjybTixcviGzyDhtrLExYXnx8PIqLiz/qtaTpb1xcHMLDw+Hr64vk5OSPKkdd\nSE1NxaxZs2BtbQ1nZ2dkZ2ejT58+WLVqVZNXY2RhYWFhaZk0yMrYuHEjFixYAB8fH5iZmYlNREaN\nGiUT4eqDkpJSk19TFjC9dYCq3DAul4u8vDykpaWJHaujo4Nhw4bhypUroGkaNE3j0qVLCAkJwRdf\nfCFz2ZiqcsrKyqQXTHOmffv2UFdXR1FREZKTk2stIiJLNDQ0oKGhgezsbJIsP23aNNy5cwcURZEc\noOaGsP7SNA1dXV3k5+fj9evXYsZRTfr7+PFjfP755zKXraSkhOTgdOzYUebjNzWGhoZo3bo13r59\ni1evXqFr165Ndm1Gf3Nzc4n+zp49G3fu3GkyGWpCR0cHpaWlAKqMu5iYGAwYMADW1tYSf0dZWFhY\nWFjqS4MMpqCgIKSkpODEiRNQVVUV2UdRlFwMppaAiooKHBwc8OLFC0REREgMIXJ1dQWfz0dxcTFa\ntWqFNWvWgKZpxMfHSwyDagyMd8ne3l7sc26OMNXGnj17hoiIiCY1mBjOnDkDHo8HT09P8Hg8AFUe\nW3V19SaXRdZQFAUXFxc8fPgQkZGREr2SwvpraGiItWvXQiAQIDo6Gg4ODjKVJyIiAgKBAK1bt4ax\nsbFMx5YXLi4uePv2LSIiIprUYGL43//+h4qKCnTu3Bnq6uooLy+Xu+euT58+CAwMxJo1a+Dh4YGf\nf/4Z/fr1w40bN1rM587CwsLCIl8aZDCdOXMGW7duxdChQ2UtzyePi4sLXrx4gaioqBp7MjGrvY6O\njhgyZAiuXbuGffv2YfPmzTKTgwkJZGRqKbi4uODZs2eIiorC0KFDZVo1TTjMT1LIX2VlJcn3mD9/\nPqne1pyLaVSHMZji4+Px2WefSTS0hfXXx8cHFy9exM6dO2VeJp8Jx2sJ3iUGZ2dn3Lp1C2lpafjw\n4QNatWols7Fr01+apon+zp07l+gvl8uVmQwNYcWKFVi/fj0iIyMxcuRI3LhxA1988QXU1dWxZcsW\nucrm7u6OiooKGBkZyVUOFhYWlk+N7OxscLlc/PvvvzIZr0FLg/r6+jL3ZjQWLpcLZ2dnua92NhYr\nKytoamqitLS0To16maTmQ4cOkbAUWRAXF4fi4mJoaWkp3GfdGNq1awddXV1UVFQgJibmo13n9evX\nYtvu3buHrKwsGBsbo1u3bsjNzYWKigocHR3h7OzcIvTX2NgYpqamEAgEePPmTa3HL1iwAABw9OhR\nFBUVyUyOrKwsvH37ViRksCWgra0NKysrAP8ZhB8DSfr777//Ijk5GVpaWhgxYgTJv5O391lLSwsb\nNmzA0KFDQVEUtmzZggsXLiAkJAReXl5yla28vJx4kllYWFhYmg4ej4fy8nKZjdcgD9OqVauwZs0a\nfPXVV2jTpo1Y/lBzLu8tbzgcDpycnBAaGoo3b96gdevWUo/39vaGhYUFUlJSEBQUhGnTpslEDqaQ\nRIcOHZr9JF4YiqLQsWNHPHjwAOHh4R/de1ZSUoKioiJoaWnh5MmTAICZM2eShHoHBwe5r9DLGldX\nV1y7dg3Jycm1GtteXl6wsbFBfHw8Tpw4gdmzZ8tEBsaYsLW1JX2uWgqurq5ITEzE8+fP0adPn4/6\n/RTWX8a7NH78eCQlJZFm1vL+fUhLS0NAQAA8PDywdOlSAICfnx/atWuH7du3w9TUVG6yMSGBipDr\nxcLCwvIpIanadGNo0JNu7ty5+PfffzF9+nQMGjSI9L7w8vKSuYCfIkw1rLdv39ba40RJSQnz5s0D\nAOzevVsm1y8sLCTJ8rIsHa8oMO8pKSkJubm5H+06SUlJuHbtGoKDg3Ht2jUoKyvD2toa48ePJ+GO\nLfH+uri4QElJCQUFBcjLy5N6LIfDwdy5cwEAe/fulcn1BQIByb9rSeF4DPb29lBTU0NBQUGNPa9k\nQXX9LSgogKWlJaZMmULKjHfq1OmjXb+urF69Gubm5iKNza9evQoTExMEBgbKUTIWFhYWlpZCgzxM\n7GrZx8XMzAxGRkbIzs5GampqrZOSGTNmYNWqVQgNDcW///4Ld3f3Rl3/5cuXoGkabdu2haGhYaPG\nUkT09PRgbW2NxMREhIeHyzRsh1mRV1JSEutdM2jQIFAUhQsXLgAAWrVqBQsLC5ldW1FQV1eHvb09\nXr16heTkZPTo0UPq8X5+flixYgXCwsJkor+JiYkoLCyEuro6bG1tGzWWIqKsrIwOHTogNDQU4eHh\nMg2Zlaa//fv3B0VRePDgAWiahoqKikLkNz579gwXL16EgYEB2aavr49FixZ9lOqLLCwsLCyfHg3y\nMJmbm0v9Y2kcFEXBzc0NQFUuQX5+vtTjjY2NMXbsWADAnj17GnVtmqZJOJ4irB5/LBjPzosXL2TW\nk+n58+dkRf7vv/8W21+9zLajo6PYtpYCozspKSnIycmReqyhoSHRX1l4mZ49ewagytPVEvqzSYL5\nfYiJiZFZT6a66i/TfNjW1hZqamoyuXZj0NfXJyGuwiQmJjbrZtssLCwsLIpDy0lOaaGUlJRgx44d\nYqu91WGKPxw/fhwfPnxo8PVSU1ORk5MDFRUVODk5NXgcRcfe3h7q6uooKChAQkJCo8crKCjA1atX\n63VOS871Y0Lx+Hw+du/eXav+MmGlJ06cqDWMTxqFhYWIjY0FgEZ7qhQZExMTmJmZQSAQkGp1jaEh\n+qso3lFfX1+sXLkSu3btwt9//42///4be/fuxYoVKzBlyhR5i8fCwsLC0gJgDSYFpKCgADdv3iSv\naZrG5cuXUVBQQKp+Va+o1r17d3Ts2BFlZWU4duxYg6/NrM47OTnJvfrVx4QJawJQ62S+LuTk5JCV\nd0lI2tdSvbGSJt+16W+PHj3g6OiIkpKSRulvWFgYaJpGu3btWnwpZ8ZLyrznxtAQ/bWzs2vUNWXF\n9OnTsWTJEty5cweLFi3C0qVLcfv2bSxbtgxz5syRt3gsLCwsLC0A1mBSQCRNXmialuo5oiiKVBg7\ncOBAgyZQRUVFePXqFQCgS5cu9T6/ucGENcXGxqKgoKBRYxkYGEgNr6u+z8PDAzo6Oo26pqLSUP1l\nvEx79+5tkP4KBAJi/Hbu3Lne5zc3nJ2doaKigvfv3yMlJaVRY9VXf52cnKCrq9uoa8qSCRMm4Ny5\ncwgPD8fTp09x5swZjBw5Ut5isbCwsLC0EFiDSQGpafJSW5PKyZMnQ11dHZGRkQgJCan3dcPCwsDn\n82Fubt6iw8UYjI2NYWlpCZqm8fTp00aNpaOjI7XfD03TZEVeW1sbgwYNatT1FJmG6q+vry/U1dXx\n6tUrPH78uN7XjYuLQ0FBAdTV1eHo6Fjv85sbampqpApgQ77vwtSmvwKBgHi0OByOQjQtLykpwdWr\nV0VyuP7880/MmzcPy5YtQ3R0tBylY2FhYWFpSbAGkwKio6ODYcOGiUw6NTU1a01g1tPTw7hx4wAA\n+/fvr9c1BQIB6YbctWvXekrcfGHe67Nnz1BZWdngcQoKCkip8OoIBAJYWlri7du3AKq8S/LuXfMx\nkaS/HA4HKioqUs/T09PDxIkTATSs+MM///wDoKrgREst9lAdRn9jY2MblftVm/7q6OggKysLQFXb\nAw0NjQZfSxa8efMGQ4YMwcqVK4nncu3atdi4cSM0NDTA5XJFyp+zsLCwsLA0hpY7a2vmuLq6YsiQ\nIejRowdUVVVRXFyM169f13oeE5Z36tSpWqvrCRMTE4PCwkJoaGh8EqvzDHZ2dtDV1UVpaanECaNA\nIEBkZCQiIyOlVtOrKQckIiICx48fR7du3VBQUAAlJaUW2XupOoz+9urVCwYGBhAIBKT6ojSYsLzT\np0/j/fv3db7e27dvkZycDIqi4OHh0WC5mxtGRkawtrYGTdMIDQ0V299Y/aVpGvfv38eYMWOQnp4O\nAApxf7du3YqOHTviyZMnaNu2LbKysnDy5EkMGzYMW7duRWBgIL7++mts375d3qKysLD8P5WVldix\nYwf69+8PZ2dn9O3bFxs2bEBRURE5JicnB9euXftoMnh5eeHcuXMyG8/X1xc7duyQuG/Hjh2ws7Mj\nfw4ODvDw8MCyZcvIAlRDsLOza3BUwY4dO+Dr69vga3/KsAaTAqOhoQFTU1NS7evJkye1nuPp6QkH\nBweUlJTg+PHjdboOTdMkBKpz586fzOo8UOX5YPK1QkJCGpw8L9wDRhgnJyd4e3sT752lpaVClGJu\nCjQ0NGBsbIzu3bsDAEJDQ8Hn86We4+7uDldXV5SXl+PPP/+s87UY75Ki5dY0BYwBEx4eXmuj65qo\nKYySoij07duXVOIzNjaGsbFxw4WVEU+ePMH8+fPB5XIBAPfv34dAIMDo0aPJMT169CANjFlYWOTP\nli1bcPPmTaxbtw7Xr1/Hhg0b8OjRI3zzzTcix9y/f1+OUsoWV1dXPHz4EA8fPsT9+/dx8OBBRERE\niLzn+vLw4cNPYuFV0WANpmZAly5dwOFw8ObNG6SlpUk9lqIoUhnqwIEDdRo/KSkJ6enpUFZW/qTC\n8Rjc3NygoqKCzMxMxMXFNWgMHR0diSvvHA4HHh4epHS5LJuMNhdcXFygpaWF/Pz8WktgUxSFuXPn\nAqh78RLhcDLGOPuUsLW1RatWrVBWVkYM8/qio6NTY14SRVHk/ipKI+DS0lJoa2uT10+ePIGamppI\nsZqGLPxUVFRg+PDhUldvo6KiMHbsWHTs2BGff/55jaGMLCwsopw/fx4LFy5E9+7d0aZNG3Tv3h2B\ngYG4d+8e8bg0tuKnoqGiogIjIyMYGRnB2NgYLi4u+PLLLxESElKvKCBhjIyMyGIRS9PBGkzNAG1t\nbbi4uABAnZLhfX19weVyER4eTsqES+Phw4cAqgyHT7HRo7q6OvHiPXjwoME/2JKMTZqmSVK6mZnZ\nJ3l/lZWViSHz8OHDWhsFT5o0CZqamoiNjcWDBw9qHf/JkycQCASwsLD4JIqVVIeiKPTs2RNA1e9D\nQ3PxOnXqhL59+0rcx+PxoK2tDRMTk4aKKVNsbGyI8V1SUoIHDx6gZ8+eIpOI27dvo3379nUes7y8\nHIsXL5a6aFJSUoI5c+bA3d0d586dg6urK+bOnYuSkpKGvxkWlk8EiqLwzz//iDwDOnXqhCtXrkBf\nXx87duzA+fPncf78eXh5eQEA4uPjMXPmTLi6usLFxQWTJk0iC5AhISHw8vLC8ePH0atXL3Tq1AlL\nly4V8bSfPHkSffv2hZubG3bv3i0iT1FREZYtW4bu3bvD2dkZQ4YMwe3bt8l+Ozs7bN++HR4eHiRc\n/NatWxg8eDA6deqENWvW1Bo1IQklJSVQFEXyev/991+MGTMGHTp0gI+PD27cuEGO/f777/H9999j\nxIgR6N69O5KTk0VC8srLy7F582b06dMHnTp1wrx580i+NHP/Jk6ciI4dO2Lq1KnIzc0VkSU8PBwT\nJ05Ep06d4OXlhRMnTojsP3z4MHr16gU3NzesW7cOvr6+JKTRy8sLmzdvRs+ePTFq1CjQNI07d+5g\n1KhRcHFxgbu7OxYvXkzmQDt27MC3336LtWvXwtXVFV5eXnj48CGOHTsGT09PdOvWDUeOHKn3/Wwq\nWIOpmeDp6QkAiI6Oxrt376Qea2BggC+++AJA7cUf0tLSkJSUBA6HQ67xKeLp6QllZWWkp6cjMTGx\nQWPo6OjAzc2NhDYJBAJoaGiQHy97e3uZydvccHd3h7q6Oj58+ICoqCipx2pra2PSpEkAatffwsJC\n4lXp0aOHbIRthnTo0AG6urooLi5uVKEDAwMDuLm5kQkNTdNQUlICUKW/0kqPNyUzZszAjz/+iA0b\nNmDGjBkoLS3FrFmzAACZmZk4cuQIdu3aRYqI1EZ8fDzGjRuHN2/eSD3u6tWrUFVVxbfffov27dtj\nxYoV0NTUxPXr1xv9nlhYWjpTp07F0aNH4eXlhVWrVuHGjRsoKyuDjY0NVFRUMGPGDHh7e8Pb2xtn\nzpyBQCDAvHnzYG5ujosXL+LkyZPg8/nYvHkzGTMrKws3btzAwYMHsWPHDty8eRMXLlwAAAQHB2P9\n+vUICAjAqVOnEBERQXIxAWD9+vVISkrCoUOHcPnyZbi7u2PFihUiBte9e/dw4sQJfPPNN4iPj0dA\nQAAmTpyIs2fPgsfj1WlRWpjk5GTs378f3bt3h4aGBrKzszF37lyMGTMGly5dwqxZs/D999+LRAtc\nvHgRAQEB2LdvHywtLUXGW7VqFW7duoVNmzbh5MmT4PF4mD9/PgQCASoqKjBnzhy0bdsW586dw+DB\ng3Hq1ClybkJCAqZNm4YuXbrg3Llz+Prrr7Fp0ybcunULAPC///0Pv/32G5YvX45Tp04hLS1NrKLw\npUuX8Pvvv2Pjxo1ITU3FwoULMWnSJFy7dg3btm3D48ePERQURI6/evUqtLW1cfHiRXTo0AEBAQF4\n+PAhjh49Cl9fX2zatElqCxJ5whpMzQRjY2NS9vfvv/+u9Xim+MPx48dFEiqrw8QKMxOuTxUtLS3S\nu6cxXiYrKyuoqanh8OHDOHr0KMltsrW1hb6+vszkbW5wuVwSsvjgwYNavUxMWOmZM2eQk5NT43GP\nHj0Cj8dDmzZtPslwRwYlJSXiZWLuSUPJy8vDtm3bcPz4cVhZWYHP58PAwABt27aVlbiNZvjw4di4\ncSMyMjJgbGyMQ4cOkRLr+/fvx86dO+Hv748xY8bUabzQ0FB4eHiITCYk8eLFC3Tu3JkYjhRFwc3N\nDc+fP2/cG2Jh+QT46quvsHnzZpiamiIoKAj+/v7o1asXzp49C6CqGrCamhrU1NRImPGECRPw/fff\no127dnBycsLo0aMRHx9PxqysrMQPP/wAOzs79OrVC7169SK5i6dPn4aPjw9GjRoFW1tb/PTTT1BV\nVSXndunSBWvWrIGDgwMsLS0xY8YM5OXliTxzxo8fD2tra9jY2ODs2bNwd3eHn58f2rdvj5UrV9aa\n0/nvv//C1dUVrq6uxIuloaGBdevWAQD++usveHp6YsqUKbCwsMDIkSMxfvx4kRxeFxfwb6BAAAAg\nAElEQVQXeHl5oUOHDiJj5+fn4+LFi/jxxx/RrVs32NvbY8uWLUhKSsKjR4/w+PFj5OXlYfXq1Wjf\nvj0mT56MAQMGkPODgoLg6OiIxYsXw9raGqNHj8aUKVNw8OBBAFXzx2nTpsHb2xu2trbYtGmTWA72\niBEjYGdnB3t7ewgEAvzwww8YN24c2rRpg549e8LT01PEa6+vr4+FCxeiXbt2GD16NAoLC7FixQq0\nb98eM2fOBI/Ha3RfwY/Fp5Pd3wLo06cPXr16hdjYWKSlpaFNmzZSj7W1tUVcXBxOnjxJVl+FSUxM\nRHx8PDgcDnr16vUxRW8W9OjRA//++y/evHmDuLg4fPbZZw0a5/z580hOTsb8+fNJzlmfPn1gbm4u\nS3GbHV27dsU///yD7OxsvHjxQmrSqru7O9zc3BAWFoY///wTixcvFjsmLy+PrML17dtXYbwf8qJT\np04IDg5GQUEBQkNDG+wxPn36NAoKCjB48GCiv7179yZhwYrCgAEDRB7+DEuWLMGKFSvqVbqf8WjW\nRnZ2tphhbmBg0ODcRxaWT40RI0ZgxIgRyM3NJeFYK1asgJ2dnVgvOA0NDUycOBEXLlxAZGQkEhMT\nERUVBUNDQ5HjLCwsyP9aWlpkwSghIQETJkwg+/T19UUWfkaNGoXbt28jKCgIiYmJePXqFQCIhNkJ\nP7cTEhLg4OBAXquoqIi8loSzszO2bNkCoCqnuVWrVtDU1CT7ExMTce/ePZHnYWVlJaysrCTKIExy\ncjIEAgFZLAKq2nNYWVkhISEBPB4PlpaWIm0gXFxcyEJ5QkKCmBHm6uqKkydPAqhqV8EsXgKArq6u\niFzVZbO0tASXy8WePXsQFxeHuLg4xMfHizQRb9OmDXlWM8YXMwbzuqHFiz42rIepGWFoaEiU+/r1\n61K9IBRFES+TpOIPNE0Tt6u7u3utTUU/BbS1tYkX5ObNm/WOTeZwOEhNTcU///wDiqLIxMrR0fGT\nN5aAqlyx3r17AwDu3r1b648i80O9f/9+ibp++/Zt8Pl8WFpawtraWvYCNzOUlZXRr18/AFVePOGG\nrnWBw+EgPz+fxM97enqCx+PB1NRU4YylmvDx8UF+fv5H63NWWloqlmzN5XIV9gHPwqIoxMTEYOPG\njeS1vr4+fHx8cPToUZiampJKp8IUFxfjiy++wOXLl2FtbQ1/f398++23YsdV/04KPy+qPzuE+wF+\n++232LRpE3R0dDBx4kTs27dPbGxhj1Rt40lCTU0NFhYWsLCwQNu2bUWMJaAqP9THxwcXLlwgf1eu\nXBHpRVhdhtq28/l8kbDqmuSVdL5AICBzHyUlJbHzq78WHiMmJgbDhg1DfHw83N3dsX79erFiQpKK\n8TSXvpTNQ0oWQv/+/cHlcpGeno4XL16I7Kvec2XatGlQUVFBaGio2LHPnj3Du3fvoKqqij59+jTl\nW1BoevfuDU1NTeTk5IjF6tYFJhly5MiRKCgoAIfDQf/+/WUtZrOlS5cu0NPTQ1FREYKDg0X2Vdff\niRMnkuIP1Y9NSUnBq1evQFEUBg8e/Ml7lxg6duyI1q1bo7y8HHfv3q33+ZcvX0ZFRQW6du2KgoIC\nAMDAgQObzf1NS0trVDhibaiqqooZRxUVFZ9MqwAWlobC5/Pxxx9/iOWwcrlcEoIHQOS3JjQ0FFlZ\nWThy5AhmzZoFT09PZGRk1Dlk3tbWVqS1QFFREQn3KioqwuXLl/Hrr7/C398fAwcOJFXrahq/+ngC\ngQAxMTF1kqUmrKyskJKSQowqCwsL3LlzB5cuXar13LZt20JZWVkkJDg3NxcpKSmwsrKCra0tkpOT\nUVhYSPZHR0eLXLv63DA8PJx4kWxsbIjXDRC9f5K4ePEiunTpgl9++QWTJk1Chw4dkJKS0mIqH7IG\nk4LC4XDg7OwMZ2dnEetbW1ubrNLfunVL6iqysbExcYUKe5kKCgpIJZi+ffuKuGs/dVRVVckq/d27\nd5Gbm4uSkhJkZWWRCWRNVFZW4sKFC1BXVyfudU9Pz0/Se1eT/iorK2Pw4MEAqnJthKv5VIdZ9QNE\niz9UVlaSh4mbmxtMTU0/xltoljAGJACEhYUhOTm5zvpL0zTOnDkDDoeDIUOGgKZp2NnZsd47IUxM\nTMQaKr9//14helOxsCgyTk5O6Nu3L+bPn49Lly4hLS0Nz58/x6pVq1BRUYFBgwYBqIpESE9PR2Zm\nJvT09FBSUoLbt28jLS0Np0+fxl9//VVnj+6UKVNw7do1BAUFISEhAT/++CPKysoAVBlq6urquHnz\nJtLS0hAcHIw1a9YAqDkkbNy4cYiMjMSePXuQmJiITZs2ISMjo1H3ZdKkSYiMjMSvv/6K5ORkXLp0\nCVu3bq1TxVdNTU2MHTsWa9euRUhICGJiYrB06VKYmpqiR48e8PT0ROvWrbFixQokJCTg3LlzuHr1\nqsi1o6OjsXXrViQlJeH8+fM4fvw4Jk+eDKCq4vKRI0dw8+ZNJCQkYPny5SgpKalxAU1PTw+xsbF4\n+fIlkpKSsHHjRkRERLQYDzxrMDVDPDw8YGxsjJKSEly6dEmq9c6ENR07dgwlJSWgaRqXL19GeXk5\nzM3NP8m+S7Xh5uYGCwsLVFZW4vjx47h27RqCg4Px22+/Sa1AduXKFbx//x4jRowATdMwMDBgvXcS\nsLe3h6OjI2iaxv/+9z+pHgGmJ5Nw8Yc7d+4gJycHWlparPdOAhYWFqSAyZkzZ+qsv0+ePEF8fDz6\n9OkDDocDFRUVeHt7N5XYMsHc3PyjNt7u2LEjwsPDyW8uTdMICwsTySFgYWGRzLZt2zBy5Ejs3LkT\n3t7emDt3LoqKinDs2DHScmPkyJFISkrCiBEj0KlTJ3z11VcIDAzEiBEjcO7cOfz444/IyclBZmZm\nrddzd3fHhg0bsG/fPnzxxRdo1aoVyTnicrnYvHkzbty4gWHDhmHjxo348ssvYWRkJOKFEcbCwgJ7\n9uzBlStXMGrUKGRnZzf6GW9ubo69e/ciODgYw4cPx7Zt20gZ8brw3XffwdPTE/7+/pg4cSJUVVVx\n+PBhcLlcqKioYN++fcjPz8fo0aNx4sQJYgwBVa1O9u3bh+DgYPj4+GDPnj34/vvv8fnnnwMAhg0b\nhhkzZmDVqlUYO3YszM3NYW5uXmMYoq+vLzp16gQ/Pz9MmjQJGRkZ+Oqrr2qtjNtcoOgW4CtjJk13\n7tyRsyRNR2ZmJg4cOAA+n48hQ4bAw8MDAoGAKKajoyM4HA4EAgFsbGyQlJSEw4cPw8rKCvfu3YOS\nkhLmzJnDrozWQG5uLnbv3i02macoCgEBAdDR0RE7Z9iwYcjMzISPjw8AYPr06WjXrl2TyNvcKCoq\nwu7du1FaWgpXV1f4+PiApmkx/aVpGp07d0Z4eDi2bt0Kb29vUsls8uTJn3RlPGmUl5dj165dIqEY\ngHT99fPzw4MHDzB16lRQFAV3d3d4e3vXGl/e0n5/7ezscOTIEZLPmJ2dDW1tbaipqaGoqAgDBw7E\nsGHDMGHCBJw8eRLXr1/HzZs3JXrqW9q9YWFh+XQIDQ1F27Zt0bp1awBV+VbdunXDrl27yO+jIiPr\n31+F8DBJ6q6ekZGB2bNno2PHjhg4cKCIG5GlKjSEUYYbN24gNjZW4nEcDgczZ84EAFy4cAH37t0D\nAAwdOpQ1lqSgr6+Pbt26iW2naVpij4A3b94gJiaGJDg6OTlJrWL4qaOlpUVKPoeHhyM0NFTicRRF\nES/p6dOnSfnZrl27ssaSFFRVVUmZcWFq0t/c3FzcvHkTY8eOBUVRaNeuXbMy9i9cuIAJEyagS5cu\npESvcAPKxtCzZ0/y/NHS0sK+ffvw7NkzjBkzBi9evMD+/fvZsGYWFpYWx+3bt+Hv74+oqCikpKRg\nw4YN0NLSQqdOneQtmlyQe1nx8vJyLFmyRKQsK4/Hw9y5c9GmTRucP38eoaGh+Pbbb2FjY9PgUs8t\nkW7duiE7Oxvh4eE4c+aMSM8RYW/TtGnTcOrUKVLpysrK6pNV+PrQpUsXPHz4UGQbRVESc5IOHTqE\nCRMmQElJCU5OTvj888+bTaK8vLCxsUH//v1x584dXL9+HXw+H9ra2qAoSkR/J0yYgF9++QW9e/cG\nj8eDiYkJBg4cKGfpFR97e3tcu3ZNZFtN+nv48GFMmjQJGhoaMDMzg6+v70cNbZMlTM+oqVOnYu7c\nuRAIBHj58iW+/fZb+Pv7w8/Pr17jVV98qv66Q4cOOH/+fGPFZmFhYVFo/P39sWbNGkyfPh3l5eVw\ndXXFwYMHa6zO19KR6xMxPj4eS5YsEcvBuX//Pt6+fYsTJ05AS0sL1tbWePDgAcLDw1mDSQiKojBs\n2DCUlJQgNjYWQUFBaN++Pezt7ckxpaWlSEpKIjGphYWFrLFUR3R0dDBs2DBcuXKFbDM3NxfR15KS\nElLBjcvlQkNDAyNHjmSNpTrSo0cPlJSU4MmTJ7h16xbMzMxEenFUVlYiLCwMkydPBkVRKCoqwogR\nI5pNGVJ5oqOjg+HDh+Py5ctkm6GhIUpLS0lIXkVFBZ4+fYoPHz5AW1sbSkpKmDhxYrMxlgDg1KlT\n2LRpEynWAlSFYtjb22P9+vX1NphYWFhYWKo86j///LO8xVAY5PpUZLqrL1q0SGQSHxoaiu7du5Mk\nQADYvXu3PERUeJSUlDBu3Dhcv34dT58+RUJCAhISEhAaGory8nKRyliPHz9GaGgoqTzGUjtubm7g\n8/l49eoVUlNTkZaWhm3btsHQ0BBKSkrIysoCTdOgKAqRkZH4448/au3LwPIfFEVh4MCB0NbWxq1b\nt5CRkYGMjAw8e/YMPB4PBQUF5P6+evUKV69eFWmCxyIdV1dX8Pl8xMfHIzExEdnZ2di7dy/09fWh\nqqqK9+/fg8fjQVlZGYmJidi4caPI725zgKZpEmMvjJWVFcrLy+UgEQsLCwtLS0Ouy7STJk3C8uXL\noa6uLrI9NTUVpqam2LJlC3r16oURI0bILB69JcLhcDB06FBMnjwZenp6AKoSlRljqW3btvD19UV0\ndDTy8vLYe1lPNDU10bVrV0yfPh2WlpYAqkoJZ2ZmgqZplJSU4K+//kKbNm2a3WRTEaAoCt27d8fs\n2bNhZGQEAMjJyUF+fj5omoaRkRE+//xzxMXFobi4uE79KVj+Q0NDAx06dMCcOXPg4OAADoeD3Nxc\nvHv3Djwej5TD53A4Eg0PRWfBggVYtWoVEhISyLa3b99i/fr1mDdvnhwlY2FhYWFpKShk3EVJSQnO\nnz+PoUOHYu/evQgJCYG/v79IHg6LOPn5+cjLyyOvbWxsYGtrC3d3d3A4HMyYMQOBgYE4e/Ysli5d\nKkdJmydt2rTBtGnTUFhYiKysLAgEAggEAjg5OYHP52P27NnyFrFZk5GRgezsbPLaxsYG1tbW6Nq1\nK5SUlDBr1iwsWLAAZ86cwU8//SRHSZsnhoaGGDduHEpLS/Hu3TtUVlZCSUkJLi4uKC0txZ49e+Qt\nYp2xt7cXCXulaRrDhw+Huro6OBwOiouLQVEU4uPjSdEbFhYWFhaWhqKQBpOSkhL09PSw+v/Yu++w\npq7/D+DvhD3cIgpqRa2KyNSKAweKBbcgUOuorbMOrFpH3aLW3daB1brqQK0bv4pVtC4ciCJVQUUQ\nVHAAIoiySc7vD365TUjAICE3hM/reXiecHOTfHI4Cedzz1q8GEKhEDY2Nrh9+zYOHTpECVMJMjMz\nZebaAMCTJ0/w+eefc79/9913WLp0KW7duoXHjx/LzHUiyqtWrRqqVasGAFi+fDlEIhE6deqE1q1b\n8xxZ5VVa/ZU0jIcOHYqZM2ciPj4eV69epT2uPpGRkRG3k3tAQABycnJga2tbKZaJldizZw/fIRBC\nCKlCNDJhqlevHgQCgczEbisrqxKXziZFQ5iKL57BGENWVhb3e6NGjdC5c2eEhoZix44dWLNmjcK9\nm8jHicViREVFcXPrJEtfk0+jTP2tUaMGevfujWPHjmHbtm3o1q0b1d9PJBaLER0djQ0bNgAoqr+V\naaES2nCbEEKIOmlk68Le3h6xsbEQiUTcsSdPnsDS0pLHqDRbnTp15Bo8AoEAJiYmMse8vb0BALt3\n70Z+fr7a4tNGYWFhePHiBWrUqAEfHx++w6nUylp/jxw5onA/IaK8e/fuITY2FoaGhhg+fDjf4Xyy\n27dvY9CgQbCzs4O1tbXcDyGEEFJeGpkw9evXD2KxGP7+/nj27Bn27duH0NBQ+Pr68h2axpIsgS1p\ndAoEAjg6OsptqPjFF1/AwcEBeXl5OHHiBB+hao0jR44AAIYNG0YbV5aTsvW3adOm6NKlCwwMDLB3\n714+QtUakvrr6+vLLRZTGc2bNw8NGzZEQEAAdu/eLfdDCCGElJdGDskzNTXFn3/+icWLF6Nfv36w\nsLDAb7/9BhsbG75D02iSJYSzsrK4K/MpKSlo2LAhatasicjISISEhGDQoEEQi8U4ceIEtz8TKZvk\n5GRcvHgRADBmzBieo9EO0vXX1tYW8fHxSElJgYWFBWrXro3IyEicOXMGPXv2hKurK0JCQjB58mS+\nw66U3r17hzNnzgCo/MNJU1JSsGXLFm5eFiGEEKJqGpMwFZ+f1Lx5cwQGBvIUTeVlbGwMY2NjJCQk\n4M6dOwCAq1evomfPnvjnn3+484RCIZo1a4aoqCia96HAx+bG7NmzB4WFhbCzs4O9vT0fIWol6fr7\n999/Ayi5/rZr1w7//PMPLCws+ApXY32s/u7fvx+5ublo3rw5OnTowEeIKtO/f38EBwdT8kwIIaTC\naEzCRFQnOzubS5aAosnzivZeEgqFOHjwIG1kW0aMMWzfvh0AqIeuAmRnZ3O9H0Dp9ffIkSOYMmWK\nOsOr9Bhj2Lp1K4CiOWGVabEHRcaMGQNvb28cO3YMlpaWcu+HVtQjhBBSXpQwaaEPHz4odZ5YLMah\nQ4fg7e0NPT29Co5Ke1y6dAlxcXEwMTGBh4cH3+FonQ8fPsitmKeIWCzG8ePHMXLkSNSoUUMNkWmH\n8PBw3Lt3DwYGBujXrx/f4ZTbjBkzULt2bbi5ucHQ0JDvcAghhGghSpi0kKmpqdwxgUCAHj164MKF\nC2CMQSAQcA3/K1euoGfPnjxEWjlJrs736dOHFnuoAKamphAIBDJJk6L6GxkZiTdv3uDUqVMYNmwY\njxFXLpL66+7urhWJZkxMDI4dO4ZmzZrxHQohhBAtRZNXtJCxsTGcnJxkVhzr168fOnXqBA8PD3Tt\n2hVTpkzh5i6cOnUKKSkpyMzM5DPsSuHNmzc4duwYgP+WuCaqZWxsjN69e3+0/kp694KDg6n+Kikj\nIwN//fUXAO2pv23btsWTJ0/4DoMQQogWox4mLWVlZQVzc3NkZWXB0dERNWvWhFgs5ibVm5qaomvX\nrjh37hw6deqE0NBQXL16Ff369YOTkxPf4WusPXv2ID8/H05OTmjSpInMKoREdezt7bmNa+3t7VG7\ndm25+uvo6Ij27dvDw8OD6q+SAgMDkZ2djTZt2qBFixZaUX9dXFwwd+5chISEoFGjRtDR0ZG5nxaD\nIIQQUl6UMGkxSeOyevXqcvc9evQIZmZmGDBgAHclnzGGU6dOoWnTppW6AaWsj60kVpz0ZPlhw4bJ\nrOJGDXXV+1j91dHRkemJovr78fq7ZcsWAMDIkSO5hTUqe/29ePEirK2tkZycjOTkZJn7KvuCFoSQ\nIiKRCAKBgFb1JbyhhKkK+/Dhg1yDgjGGt2/fVokGZ1ldvXoVMTExqFevnszCGpKGevPmzRU27knF\noPpbNteuXUN0dDTq1auH7Oxs7nhlr7+0gTEh2k0kEsHR0REGBga4efMmJU2EF5QwaRGhUIg2bdpA\nLBbj9u3b+PDhg8IFICRKWhyidu3aFRmmRip+tR4oWt76w4cP3JAl6aWYi6/iJmmoV8YGp6aQ1F8A\nKCws5MpfsnFtcVR//6NM/ZX0Lvn4+Ghd/X3w4AF27NiB+Ph4iEQiWFlZYdiwYWjfvj3foRFCyunN\nmze4f/8+AOD169e09x7hBaXpWigyMhJ///03QkND8ffffyMyMlLheZLFISSNJ8YYHB0dK22jSVXE\nYjGCg4O5MtywYQNCQ0Nx+PBhAICvr69cz0ZVbahXlH///Zcr/4CAAJl9xSSK11+xWEz1F4rr75Ur\nV7j6O2TIEK2qv+fOnYOvry8YY/Dy8oKXlxcEAgFGjRqlcP8uQkjlIhaLudsvXrzgMRJSlVEPk5bJ\nzMxEcHCwzLHg4GB8/vnnCq/IW1lZIS8vDzNnzkRGRgaOHj2qrlA1VmZmptzGvxcuXICBgQFatWqF\nrl27wtTUFMHBwdwS1/369avyDXVVyczMxOnTp7nfpecmFWdlZQUTExNMmDABr1+/xqZNm9QZqkZS\nVH8vXrwIQ0ND2NnZwcXFBUZGRlpTf9evX48ZM2bg22+/lTm+a9cubNy4EW5ubvwERghRiYKCAu52\nYmIivvjiCx6jIVUVJUxaJi0trcThNiUNz2vVqhVq166Np0+f4vjx4+jevbsaItVcb9++VXi8du3a\nmDRpEgQCARwdHSESiWRWISSqUVodVqRevXqwtrbG48ePcfjwYQwdOlQdYWqs0urv999/DwBaVX8T\nExPh6uoqd9zV1RW//vorDxGRyqSgoACvX79Go0aN+A6FlEA6YUpPT+cxElKV0ZA8LVOnTp0Sh9sI\nhUJufkNxkj1ZDh8+jMLCwgqPU5MpGpokFouRn58v0xg3NjaGmZlZpb0yr6lKqsN169Ytsf76+PgA\nAM6ePVvl/6GWVn+HDBnCHdOW+tusWTNcuXJF7vjly5dhaWnJQ0SkMomJicGuXbtkhn0RzZKfn8/d\nlk6eCFEnSpi0TPXq1dG3b1+5TT8VNYqys7ORkpKC7OxsuLu7o3bt2khOTsb//vc/dYetUapXry6z\n8S9jDCdPnoS3tzdMTEx4jk77KVuHpeuvnZ0dPv/8c+Tl5WHfvn18hK0xSqq/Xl5eWll//fz8sGrV\nKsyYMQN79+7F3r178eOPP2L16tXw8/PjOzyi4UQiEcRisVyvNtEc0kmSdPJEiDrRkDwtpMxwm4SE\nBJl5Dk5OTvDy8sL27dvx+++/cz1OVZVk49+nT59i7NixyMzMxIEDB/gOq8r4WB1WVH+9vb2xYsUK\nbNu2DX5+flV6Dx5J/U1KSsLo0aORkZGBPXv28B1WhXB1dcW2bduwf/9+HDhwAAYGBrCyssL+/fth\nZ2fHd3iEkHKSTpioh4nwhRImLVXapp9paWlyq45FRkaid+/euHDhAiIiIvDgwYMShz9pE+mlq4sz\nNjbGuXPnkJmZiZ49e6Jly5Y8RFh1lVSHS6q/Xbp0wdGjR/H8+XOEhYWhY8eO6gyXFx+rvxcuXEBG\nRgZcXFy4Jdu1UceOHeX+3nl5eUhMTKS5KYRUctTDRDQBJUxVTGRkJC5duiR3nDGG+/fv45tvvoFY\nLMbOnTuxdu1a9QeoRpLl14GiTWltbGxQq1YtrvGZn5+PY8eOAQAmTpzIW5zkP6XV3/DwcAwdOhRi\nsRj79u3T+oTpY/W3oKCAW0pcsthDVRIeHo5x48bh4cOHfIdCCCkH6SSJEibCF0qYqhBFS44rIhQK\nYWJighcvXmjtpOniZcEYQ1RUFID/Gp8xMTEoLCyEpaUlBgwYwFeo5P+Vpf7WqVMHz58/R+PGjdUQ\nmfopU3/j4uKQm5uL+vXrc4tiEEJIZUND8ogmoEUfqhBFyzWXRCgU4tChQxUcEX9KKwtJ47OgoABT\np07F2LFjoatL1xb4Vtb6q817iilTf3NzczF16lSMHz8e+vr6ao6QEEJUg4bkEU1ACVMVomi5ZgD4\n6quv5I6LxWIEBgZq7cpBJZVFcUKhEAKBAJmZmXLH27RpgzZt2kAopI+ROpS1/u7bt4/qr1AIoVBI\n9ZcQUmlRwkQ0gVZdNqeu2iLS+0kUFBRwDSIjIyN4eHjgzJkzYIxxG7BaWVnJHQ8JCcGdO3dw7tw5\nhZtCVnbFy6I0jDGkpKTAyMhITdERRXW4rPU3IiICZ86cgZubG4/vpGJU9fp769atj54TExOjhkhI\nZSQWi3Hjxg20bdtW5nhSUhLev38Pa2trniIjitAcJqIJtCZhKiwsxOvXr/kOQyNIN6CSk5NlrkTX\nr18fHh4eyMrKgomJCYyNjZGcnCxzvH79+oiNjUVYWBh+/fVXrf3nIf2es7OzERERobDxKRAIqH6p\nWUl1WNn6m5CQgLCwMKxZs0ZrV4fTlPrLx0bXI0aMUOq8qry0PCmZWCzG5cuXkZOTAzMzMwBF+zEd\nO3YMjRo10tr/eZUVzWEimkBrEiaBQAAdHR2+w9AYtWvXVnicMcYt1ywhKTfpZZxHjRqFwMBAhISE\nIDExEU2aNFFH2GolXRY1a9aEmZkZsrKycPbsWRgYGHDD8VxcXBQuz04qlqI6rGz9HTNmDHbv3o2L\nFy8iISEBzZs3V1vc6lJS/T137hz09PTUVn/5SEoePXqk0ufLy8uDv78/QkJCYGhoiFGjRmHUqFEK\nz50wYQIuXLggc2zLli1a2ROvrXR1ddG+fXuEh4dzPdD3799Heno6fH19eY6OFEdD8ogm0JqECQAl\nTEpQdAW6eLnp6OigdevWcHV1xcWLF7Fjxw4sX75cXSGqjXRZ6OjowNjYGDo6Oti+fTsKCgowd+5c\nDB48GNWqVeMxSiJN2frbvHlzfPnllzh79ix27NiB1atXqytEtVFUf/X09PDnn38iOzsbs2fPhq+v\nL9VfJaxevRpRUVHYvXs3Xr58idmzZ8PCwgIeHh5y5z558gRr1qyRWba+Ro0a6gyXqECnTp0QHh6O\nuLg4AEWrS1pbW6N+/fo8R0aKo4SJaAKa7UtKNGHCBADAgQMHkJGRwXM06hEcHIy0tDSYmJjA3d0d\npqamfIdEPtG4ceMAAIcPH8bbt295jkY9zp07h9evX0NfXx+9e/em+quE7OxsHOOnvyYAACAASURB\nVD58GPPmzYONjQ169eqFMWPGYN++fXLn5ufnIykpCba2tjAzM+N+aBXCysfY2Bjt27dHfHw8ACAj\nIwPdunXjOSqiCA3JI5qAEiZSoi5duqB169bIycnB3r17+Q6nwjHGuPc5dOhQWkq8kuvUqRPatGmD\nnJwchY1fbcMYw65duwAUrRyop6fHb0CVxKNHj1BYWAhHR0fuWNu2bXH37l2ZxUcAID4+HgKBAI0a\nNVJ3mKQCdOrUibvdqlUrmJub8xgNKQkt+kA0ASVMpEQCgQDjx48HAOzYsUPrv6hu3LiBuLg4GBsb\nw8vLi+9wSDkJBAKMHTsWALBz506tvzJ58+ZNPHz4EEZGRhgyZAjf4VQaqampqFWrlkwvUd26dZGX\nlyfXsx4fHw9TU1PMmjULLi4u8Pb2xuXLl9UdMlERY2Njbn5j165deY6GlISG5BFNoBEJU35+Pvr1\n64ebN28CAH766Se0bNlS7uebb77hOdKqx9PTE+bm5nj9+jVOnDjBdzgVStK7NGTIEFrkQUsMGjQI\nZmZmePXqFU6ePMl3OBVK0rs0ZMgQ1KxZk99gKpGcnBy5IXWS34s3zuLj45GbmwsXFxds374d3bp1\nw4QJE3D//n21xUtUy8vLCz4+PmjQoAHfoZAS0JA8ogl4T5jy8vIwffp0xMbGcsfmzZuHq1evcj8H\nDx6Evr4+JUwqVrduXZiZmZW6ypW+vj63WtSWLVu0diPQR48e4erVqxAIBBg9ejTf4RAlKFN/DQwM\nuPq7adMmra2/Dx48wLVr1yAUCrleYaIcAwMDucRI8ruhoaHM8YkTJ+LKlSvw8vJCq1at4Ofnh65d\nu+LQoUNqi5eolr6+Plq3bs13GKQU1MNENAGvCVNcXBx8fX3x/PlzmePVqlWTmVC7ceNGeHh4aOUG\nlOomEAi4cpVuaJZ0HAC++eYbGBkZITo6GlevXlV3yBVG+j0HBAQAANzc3GBlZcVzZKQkn1J/v/32\nW5iYmCA6Ohr//POPukOuMNLvecuWLQCAXr16oXHjxjxHVrmYm5sjPT1dZj+p1NRUGBoayvU0C4VC\nuRXxmjZtiuTkZLXESkhVRHOYiCbgNWEKDw+Hs7MzDh48WOI5N27cwK1btzB9+nQ1Rkak1apVC19/\n/TUAYMOGDTxHo3rPnj1DUFAQAGDmzJm02aWWqVWrFtc7vXHjRp6jUb0XL15w9Xfq1KlUf8vI2toa\nurq6+Pfff7ljERERsLW1hVAo+y/yp59+wpw5c2SOPXr0CE2bNlVLrIRURTQkj2gCXhOmoUOHYu7c\nuTAyMirxnK1bt8LT05PGF/NswoQJ0NXVRWhoKG7fvs13OCq1adMmiMViuLq6ws7Oju9wSAUYP348\n9PX1cfPmTYSFhfEdjkr98ccfKCwsRMeOHWVWeiPKMTIywqBBg7B48WLcu3cP58+fx86dO7kkOzU1\nFbm5uQCAHj164OTJkwgKCsKzZ88QEBCAiIgIDB8+nM+3QIhWoyF5RBPwPoepNImJiQgLC8OIESP4\nDqXKa9SoEXx8fAAA69at4zka1UlOTsZff/0FAJgyZQqA0od3kcqpfv36+OqrrwBoVy9pSkoK9uzZ\nA4Dqb3nMmTMHNjY2GDlyJPz9/eHn54cvv/wSAODi4oLTp08DAL788kssWrQImzdvRr9+/XDhwgVs\n374dDRs25DN8oiVCQkLQqlUrhIaG8h2KRqGEiWgCjd5o5uzZs7C2tuaW/ST8mjJlCg4ePIjz58/j\n/v37sLW15TukctuyZQvy8/PRvn17dOjQge9wSAWaNGkS9u3bhwsXLiAqKgpt2rThO6Ry27x5M3Jz\nc+Hk5ITu3bvzHU6lZWRkhFWrVmHVqlVy98XExMj87uPjw108IkSVPDw8wBiDm5sb8vLy+A5HY0gn\nSTQkj/BFo3uYQkND0bNnT77DIP/PysoKnp6eALSjlyk9PV3m6jxdjdduTZo0wcCBAwEA69ev5zma\n8nvz5g23lPj06dOp/hJSyUlW8aReFFnUw0Q0gcYmTIwx3L9/H05OTnyHQqRIhv0EBwfj0aNHPEdT\nPps3b0ZWVhZsbGwoMa8iJInxyZMnER0dzXc45bJlyxbk5OTA3t6e6i8hnyglJQWbNm1CTk4O36GQ\nElDCRDSBxiZML168QFZWFg3H0zAtW7ZE3759AQC//fYbz9F8utTUVGzbtg0AMGvWLLo6X0VYW1tz\nvUyKhl9VFmlpadi5cycA4Mcff6T6S8gnaty4MSZPnsz9P+ATfY4Vo4SJaAKNTZjS0tIAQG7PC8I/\nyRLvJ06cqLQ73AcEBCAnJwcODg7c5G5SNcycORNCoRAhISG4c+cO3+F8kj/++APZ2dlo06YNevXq\nxXc4hFRKYrGYmyt069YtnqOB1m6sXV60rDjRBBqTMMXExMDZ2Zn73d7eHjExMdDX1+cxKqKIjY0N\nN5dpxYoVPEdTdq9eveLmfsyePZuu6lUxzZo1g6+vL4DK2cuUnJzMXQ2n3iVCPp30MLzatWvzGAkp\nDW1cSzSBxiRMpHKZNWsWdHV1ceHChUq3r826deuQl5cHZ2dnWlmsipo+fTr09PRw+fJlXL9+ne9w\nyuSXX35BTk4O2rVrBw8PD77DIaTSkk6YxGIxj5HIKyws5DsEjSHdqyQSiTTub0WqBkqYyCexsrLC\n0KFDAQDLly+vNEMJYmNjERgYCIDmLlVljRs35urvihUrKk39jYuLw759+wAA8+fPp/pLSDlkZ2dz\nt/le9KH4dxDf8WiS4sPwaFge4QMlTOSTTZs2DYaGhggPD8eZM2f4DkcpS5cuhUgkwpdffonOnTvz\nHQ7h0dSpU2FkZIRbt27h5MmTfIejlBUrVnD1l/YNI6R8pJMS6eSJD8WHmvEdjyYpniDRsDzCB0qY\nyCdr0KABxo8fDwBYvHixxm+0FxoaipCQEOjq6mLhwoV8h0N41qBBA0yaNAkAsGTJEuTm5vIcUelu\n376N4OBgCIVCzJkzh+9wCKn0pJOSrKwsHiOR71GihOk/xRMkSpgIHyhhIuUyZcoUmJub49mzZ9i6\ndSvf4ZRIJBJh8eLFAICRI0fScvUEADBx4kRYWFggKSlJ4+vv3LlzAQBfffUVrK2teY6IkMpPOkmh\nhElzUQ8T0QSUMJFyMTExwfz58wEU7cv0+vVrniNS7K+//kJ0dDRq1KiBH3/8ke9wiIYwNjbGvHnz\nAADr169HcnIyzxEpFhgYiHv37qFGjRpc4kQIKR9NGpJXvIeb73g0Cc1hIpqAEiZSboMHD4aTkxOy\ns7OxbNkyvsORk5qaiqVLlwIoWh2Nlo8l0jw9PeHk5ISsrCz4+/vzHY6ct2/fYuXKlQCKFioxMzPj\nOSJCtAMNyascqIeJaAJKmEi5CYVCLlE6cuQIQkNDeY5Ilr+/PzIyMtCmTRuMHj2a73CIhhEKhfj5\n558hFApx7Ngx/PPPP3yHJGPFihVIT09H69atMXLkSL7DIURraNKQPOphKhnNYSKagBImohJOTk74\n7rvvABRtpsn3Px+JK1eu4MiRIxAIBFizZg10dXX5DoloIEdHR4wdOxZA0WbGmlJ/r1+/jr179wIo\nSpyo/hKiOtJJCd8JCvUwlYyG5BFNQAkTUZl58+bB0tISz58/x+rVq/kOB9nZ2Zg9ezYAYNSoUXB0\ndOQ5IqLJZs2ahYYNGyIpKUkj6m9WVhamTZsGABgxYgScnZ15jogQ7UI9TJUDDckjmoASJqIypqam\nWLNmDQBg69atuH37Nq/xLFmyBAkJCWjQoAF++uknXmMhms/ExIRLlLZt24abN2/yGs/KlSvx7Nkz\nWFpa0jL4hFSA4gkTnxtYUw9TyTQtYXr8+DHmzJmDjIwMXuMg6kUJE1GpHj16wMfHB4wxTJo0Ce/f\nv+cljvPnz2PXrl0AilY/q1atGi9xkMqlR48e8PX1hVgsxqRJk/Du3Tte4rh48SK2bdsGAFi7di3V\nX0IqgHRSIhKJeB3qVbyHqXgCVZVJ/i46Ojoyv/Pl22+/xcqVK+Ht7c1rHES9KGEiKrds2TI0bNgQ\nz549w8yZM9V+1S41NRVTp04FAIwbNw5du3ZV6+uTym358uX47LPPkJSUhNmzZ6u9/iYnJ2Py5MkA\nivYMc3V1VevrE1JVFE9K+ByWRz1MJZP0KJmYmMj8zpcbN24AgMYtEEQqFiVMROVq1KiBP/74Azo6\nOggKCsK+ffvU9toFBQUYO3Ys3rx5g1atWtGeNaTMTE1NsXnzZujq6iIoKIjrqVQHkUiESZMmIS0t\nDa1bt9bIZc4J0RaalKTQHKaSSXqUNCFhEovFvL024RclTKRCtG3blps3NGfOHISFhanldRcvXoyw\nsDBUq1YN27dvh6GhoVpel2gXJycnLtlesGABrl+/rpbXXbx4Ma5evQpjY2Ns3bqV6i8hFah4UkI9\nTJqHMaZRCdOHDx9kfqcEquqghIlUmEmTJqF///4oKCjAqFGj8OzZswp9vd27d2PHjh0AgICAADRv\n3rxCX49otwkTJsDLywuFhYUYM2ZMhdffXbt2cfOW1q9fT/WXkAqmSUPyqIdJMZFIxN2WJEx8zmEq\nPq+VFn6oOihhIhVGKBRi/fr1sLOzw9u3b/H111/j9evXFfJaJ06c4Hq0Zs6cCXd39wp5HVJ1CAQC\n/PLLL1z99fHxwatXryrktU6fPo158+YBAObOnYv+/ftXyOsQQv5TPCnhM0mhHibFpHuTNKGHqXiC\nlJqaylMkRN0oYSIVytjYGLt370bDhg0RHx8PHx8flX/BhISEYPLkyWCMYeTIkZg+fbpKn59UXUZG\nRti7dy+srKzw/Plz+Pr6qrz+njlzBuPGjYNIJMKQIUPg5+en0ucnhCimST1Mklj09fUBUMIkId2b\npAkJU/EeJr5WUiXqRwkTqXANGjTA0aNHYWlpidjYWHh6eiIhIUElz33gwAF89913KCgowMCBA7F8\n+XIIBAKVPDchAGBubo5Dhw7BwsICsbGx6NevH548eaKS5w4KCsLYsWNRWFgILy8v/PLLL1R/CVGT\n4vNRNGFIXp06dQBQwiShaQlT8R4mGpJXdVDCRNTis88+w5EjR2BpaYm4uDj06dMH165d++TnKyws\nxMqVKzFt2jSIRCL4+voiICCA26eBEFVq1KgRjhw5gsaNG+PZs2fo379/ueqvSCTCmjVr8P3336Og\noACDBg3Chg0bqP6SSis3Nxdr1qzB/fv3+Q5FacUTJk0Ykle7dm3eY9EkkoRJKBRyi+BoWg9TYGAg\nbG1tcfToUYWPuXnzJtzd3fHw4UN1hEgqCCVMRG2srKxw+vRpODg4ID09HT4+Pli+fDny8vLK9DxP\nnjyBt7c31q1bB6BocYn169dDT0+vIsImBADQtGlTBAcHw8HBAW/fvoW3tzeWL19e5g0mnz59isGD\nB+OXX34BULS4xO+//w5dXd2KCJuQMmOMYf78+VizZo3Sj1m8eDFmzZqFgQMHqn3vMgnGGCZPnoxe\nvXrhxYsXHz1fkjDVqlULgGYMyavqPUzJycn4+++/udXnJAmTnp4ejIyMAHz6pr4RERGIjIwsV3yK\nephGjx6NqKgoTJs2TeFjvLy8EBISgj59+pTrtdVt9+7daNeuHa5cufJJj2eMYd68efD09CxzO08T\nUcJE1Mrc3BzHjx+Hj48PxGIxNmzYgM6dO+PgwYMf/UC9evUKixYtgqurK8LCwmBiYoLNmzdjwYIF\nNIyJqIWZmRmOHj2Kr7/+GowxbNiwAS4uLjhw4IDcKlfFvXz5EosXL0bXrl25+rthwwYsWrQIQiF9\nFRPNERsbi59//hmzZs3C3bt3lXrMgQMHAAAJCQmIi4uryPBKlJycjE2bNuH8+fOYPHkyvL298fDh\nQ4jFYgQGBiIpKQm///47vv32WxQWFuL9+/cAiv4vAfwmTJLXNjMzA1B1EyY/Pz/06dOH29ZBkhwZ\nGhpyQ/KK9wwq49mzZ2jXrh1cXFzKNYyueA9TWloa1+OVmJio8DEvX74EUHSx7GMKCwsRGRnJ20UH\nad9++y0iIiKwfv36T3p8eHg4li9fjqCgIHh5eWHz5s1gjCEjIwMbN27kPn+VBV3SJGpnZGSEjRs3\nwt3dHfPnz0dSUhJ++OEH+Pv7w93dHc7OzmjcuDEMDAzw7t07PHr0CJcvX8a1a9dQWFgIAHB1dcWK\nFSvQpEkTft8MqXJMTEzw22+/wc3NDQsWLMCLFy8wbdo0LF26FL169UKHDh3QpEkTGBgYID09HY8f\nP8aVK1dw5coVboncbt26YdWqVVR/ySeLjo5GdHQ0fHx8VH7BKDo6mrt99epV2Nvbl3r++/fv8fz5\nc+73+/fv4/PPP1dpTMqQXvo/KCgIAFC3bl00atQI8+fPR5cuXRAaGgoA6Nu3L9fwNjc3x6NHj1SW\npOTl5cHAwKBMj5EkTPXq1QOgXMIUGxuL2bNnY8mSJWjTpk3ZA9Ugp0+fRoMGDXD48GEAwKpVq7By\n5UquXExNTbmE6VMSW8nzZmdn49y5c/Dx8fmkOIsnW1FRUTK/W1paYsCAAdi8eTMA+SXQp0+fDktL\nS1hbWyMrK0sujgULFmDlypXYvn07Ro8e/dF4nj17ho0bN+KHH35Ao0aNPuUtKSR9AftTh9lKl83p\n06dx+vRpNG7cGOvXr8e5c+fw4MEDrpwqA7qsSXjTr18/XL9+HfPmzUODBg3w9u1bHDhwAFOnToWX\nlxf69u2LoUOHYsmSJbh8+TIKCwvRoUMH7Nu3D/v376fGJuFV3759cfXqVcyfPx8WFhZ4+/YtDh48\niGnTpsHT0xN9+vTBsGHD4O/vj4sXL0IkEqFjx44IDAzEX3/9RfWXlMvXX3+Nr776Cps2bVLq/EWL\nFqFHjx6Ij4//6LkPHjzgbsfGxn70/OLPydc8JkVX8G/evIkNGzYAAJcsAUBcXByXMFlaWgIA0tPT\nyx3Djh07UK1aNcybNw+dO3fGr7/+KndOUFAQ6tatiylTpnDHJLGU1sM0ZcoUNGzYkHufXl5eOH78\nOL788ksAwMaNGzFz5kyusbt27Vo0btxYJYvUJCYmYvHixSopo+Kio6PRt29fODk5yd0nKRcTE5My\nJ0zv37/nhvbdvHmTO67sXKL3799j+fLlCA8P544V72EqnjC9fPkSW7Zs4VZTTUlJkbn/t99+w4wZ\nM9C3b1/4+vrKxCISibBy5UoAkKkbpVm6dCl++eUXlSfMSUlJ3O23b9+CMQZ3d3fY29sjMzNTqed4\n/Pix3LHly5fj3LlzAIAtW7aU+vjo6GhMmTKlQurcp6CEifDK2NgYfn5+uHXrFv766y9MnDgRLi4u\naNasGRo3boxWrVqhX79+mD9/Pq5fv46goCD07NmThuARjWBsbIzJkyfj5s2bOHz4ML7//nt069YN\nVlZWXP3t06cPFi5ciCtXruD48eNwc3Oj+luFFBQUwMnJCSdOnJC778OHD1yveVkUFhZyScmff/75\n0fPv3LmDJUuW4OLFi9x+daUpb8J07969jz6mIihKmF68eAFTU1O547GxsVxjulmzZgDkG7cikQhX\nr16VS17WrVuHIUOGKBzaNWbMGBQUFGD58uW4fv06fvzxR7n9B9euXYu0tDRs3LiRm2slSQKkkzdJ\nfEBRGUvO/+uvvwD811h/9eoVcnJyMGXKFKxduxZbtmyBSCTCzJkzkZiYiLVr15ZQYsqbOHEi/P39\nMXjw4HI/V3HSPZoSkgVwJOVSUsIkFosVDl978OAB6tSpAx8fH9y+fRtHjhzh7lN2yOi0adMwb948\nDB06lHsNyd+8YcOGJcYO/PcZ+Njefa1bt8aFCxcAFPXmSiiqs4pcunQJAJCZmYmCggIEBweje/fu\nuHPnDoCi5LBdu3ZcnSnJo0eP4ObmxiX40j3GaWlpuHv3LkJCQnDv3j3s2bMHQFEvVGnzyRR9d1y/\nfp27bWxsXOrQw/bt22Pjxo2YPXt2qbGrCyVMRCPo6uqie/fuWLhwIY4cOYJr164hPDwcly5dwvbt\n2zF58mQ0bdqU7zAJUUhPTw9dunTB4sWLcfDgQdy4cYOrvzt37sTEiRPRokULvsMkJcjLy8PcuXO5\nOQ47d+4s8dwHDx7Ax8cH9vb2GDx4sNwV5uLevn2LyMhIDBo0SKZxcOjQIZibm8PGxkbhXi7Jyclo\n27YtlixZInef9NCz4o18RUJCQrjbwcHBH500/6kJk4WFBQDg/PnzXM9AYWEhAgICSmxYqoJYLEZY\nWJjC3rPU1FSFxyXvUVdXF1ZWVgD+K8uUlBR4e3tDV1cXXbp0gaenJ/e4nJwcTJs2DQcPHsT8+fNl\nnrP48CsJ6V6E3Nxc3L59m/s9MDAQwH89KS1atICOjg7y8/NlEq2YmBjutmTEhTTpJPXff/+VSR6V\n7REoCWMMp06dAgBcvHhR6fk10glfcbdv38bPP/+MrKwshX8fkUiE3NzcUofk5ebmwsHBAfb29nJl\nf/ToURQUFODYsWNwc3OTuU+ZHjfGGI4dO8adL6kvkt4OyQiBki54SN5T8WRZkUWLFuGPP/7AwYMH\nuWOpqakyqwHeuXMH7u7u2LZtm8xjjY2NuduxsbGYN28eLl++jG7dugEARo4ciYiICG7ebUnWrFmD\nf/75Bz/++CPS09Pl5mNJJ5xRUVHIyclB8+bNYWdnV+KcMEU9TNKys7NhbW2tcFhebm4ud6FC0iPF\nN41ImPLz89GvXz+ZLtPbt2/Dy8sLDg4OGDhwoExWSgghhKjK6tWrERUVhd27d2PRokUICAjAmTNn\n5M7Lzs7GuHHj0K5dOxw7dgyOjo4YP358qfNNpBtyJ06cQNeuXfHDDz9gzpw5yM7OxuPHj7F8+XK5\nx23ZsgV37tzBokWLuKvUkl4H6YZIUlISUlJScPr0aZlhNNIkV5sl7+Hs2bMlxisSifDo0SPu9/j4\n+I8u4yxpHH7zzTf47LPPkJmZya2stXXrVvj5+aFnz56IjY2Fq6sr3N3dkZaWVupzSojFYvj6+sLT\n05ObA1icv78/OnbsiK1btyr1nAC4xSzMzMxQv359AP8lTIsWLZJZIjokJITrlZBeMSwkJATJycnc\nokXSDV5p0g308PBwmfkhkr+FJAmoWbMmNxdFOpGQ7hUJCwuTSZoByNTX6OhomYRJsu9haGgoLl++\nrDBGoOjCwebNm7mYXrx4gYMHD8oMZSweS0lCQ0PRsGFDeHt7y92XnZ2NgQMHYv78+Zg9e3aJw0ST\nk5NL7WE6fvw47t+/j/v373OJXGRkJNLS0hAREcE9z7t37yAQCLikT/L3YIyVmETExsbKDAX7999/\nAQBv3rwBADRv3rzU9x8fH487d+5gzJgxpZ4HFPUsff/99zKJA2NMJmnx9/dHSEgIxo0bJ1N/pD/z\nUVFRXL3+8OEDGGMyn+VXr15BJBIhKysLHz58wIULF5CVlQXGmMzf+NKlS3IJ0z///MPdjomJQURE\nBJKSkhAXF8cllmFhYVyPaV5eHnexpXHjxtDV1cXDhw9x9+5drFu3jlvVOCYmBhMnTpRbyEN6r870\n9HQ8ffpU5jvj9u3bWLlyJd6+fVt64aoQ7wlTXl4epk+fLnMVKy0tDd9//z369OmDkydPonfv3pg4\ncaJSmTohhBCirOzsbBw+fBjz5s2DjY0NevXqhTFjxmDfvn1y554+fRoGBgaYNWsWmjVrhnnz5sHE\nxERhciUhnTB5enoiNDQUGzZskGkkrl+/nmugiMViBAcHyyQ1165dw/nz51GvXj24urrKNIKAokUL\n+vbtC0dHR7x69QoFBQXYv38/17Mk6X1o3bo1gKJ5NiWJj49Hbm4u9PX1Ua1aNYhEIu7q+qVLl7iG\no2TVxxs3bnDvpVmzZujVqxcAYNmyZfD09MSkSZMAFDV+W7RogUuXLiEkJARz5syReV3GGN6/f48f\nf/wRGzZswL1797BixQqsW7cOhw8fRlBQkExPmYRIJFLYC/cxklUtzczMuOFVT548QU5ODvbv3y93\n/uHDh5GbmysTQ2xsLCwsLDBkyBAYGhpixIgRCl8rLi4Ob968walTp3D69GkAgIODA4CiIUpxcXFc\nUmxqasotsrF582augS7dRsrIyOAaqRL/+9//uNu3bt2S6VVJSEhAVFQUunXrhu7du+Py5cvIzMzE\npEmTsGXLFi5pmDp1KiZOnAgPDw/8/vvvcHd3x5AhQ7jeConVq1fDz88P06dP5xrvW7duRaNGjbB7\n927cunULPXr0wKtXr3D06FGZOW1ZWVnw9vbmVo7bs2dPib2Ys2fP5no2pBOm9+/fY8aMGRg6dCh3\n7rVr17B161Y4OTnB2dlZZngbAFhbW8PFxQVAUV0MCQlBnTp14ObmprCXSDJMTkJS7yVzkz6WMD18\n+BA9evRAcnJyiecoGqYrTbr3Wvr20KFDsWrVKtSrV08mqbt48aLM42/duiWzEt3ChQthZWWF+vXr\n4/PPP0fPnj1hZWUFPT09mb/B7du35RLysLAw7nZMTIxML2lkZCQ2bdqEjh07om3btoiMjERgYCDy\n8/NhZmaGx48fIzExEa1atYKdnR1++OEHtGrVSub579+/j8TERGzevBmhoaEy85vevXsHKysrdOrU\nCQUFBXj//j2+/PJLzJkzB+PGjSu1DFVJwHhcuzAuLg4//vgjGGOIiYnBnj174OzsjHPnzmH+/Pky\nPU7Ozs7w9/eHh4eH3PP07NkTIpGoxKs7hBBCKsZXX30F4L+x9JXNnTt3MHz4cPz777/Q19cHUDRB\nfOzYsfj3339llnxfsGAB8vLysHr1au7YTz/9BH19fYWN9p49eyI+Pr7E5YR79eqFvLw8XLlyBcOH\nD8fevXsxZ84cbuJ3SczNzUttiEmbPHkyAgICABRdJXZ3d0dhYSGOHDmCXr16QSAQICEhAS1atMCj\nR48QFhaGCRMmoFOnTjAyMuKuLH/++eeIjY2Fjo4Orl27hpUrVyIoKAh1O5t17QAAIABJREFU6tRB\nYWEh3r17h9DQUCQmJso0ZIGixm5BQYFMT5VAIMCtW7fg5OSEESNG4MiRI0rt1TJ16lTMmTMHCQkJ\nqFatGu7evSv3eqdPn8b79+9x8uRJbshbSdzc3BAcHAxTU1MUFBSgadOmiI+PR6NGjXDlyhX873//\nww8//AAAMDAw+KT9ZAYPHozk5GSZRvymTZuwfPlyuf2ikpOTcfPmTQwYMAAAUK1aNSxcuBAzZ84s\n8+tKmz17NlatWgWg6H20adOG64XR1dVFz549S+15BIpWHJQkcBJLly7FqFGjuLlXBgYGsLS0lLkg\noKOjAysrKwwdOhQZGRncIhxl8c0338DPzw9ffPFFmR8LFC2Ssn//fjRq1AhJSUnQ0dHheiz9/f3h\n6OiI3r17Iz09Hbt27cKWLVu4epCYmIgOHTqgd+/eWLRoEYCiIWrSvWeGhoYf3VqiuOfPn6Ndu3Zy\nw2qHDx+OwMBAzJ07FyYmJpg3b94nvee+ffsiODi4zI+ztLTEu3fv8OHDBxgbGyvsQXd2dpZpo5dk\nypQpCpcl7927t8yFpg0bNmD9+vUfHS45ffp05Obm4vfff+eOJSQkyCyiVFBQAD09PfTs2ROAbO9Y\nefDawxQeHg5nZ2e5RKdmzZrIyMhASEgIGGM4f/48srKyaA4AIYQQlUpNTUWtWrW4ZAkoahjm5eXJ\njc1PTU3lln2WqFOnzkdHP0g38pydndGyZUsARY2JNWvWQCAQIDAwEF5eXh9NlgBwyVL//v25Y9On\nT1d4riRZMjMzg6urK2bNmgUA8Pb2Ro0aNVC9enXY29vDyMgIjo6OmDBhAoCiZE/SWwT818MhEong\n7e3NLdudlpbGDXlycHCAh4cHqlWrJhPDkiVLuP33jhw5gmHDhoExhoEDB8LHxwf79u1TOhFZt24d\nzM3N0aFDB9jY2MglS0DRthO+vr7o2rUrd6ykhVYaN24MfX19tG/fHsB/w+AmTJiAJk2aYPjw4ahR\nowaA/5Za1tPTw9KlS5WKFyiaT1O8x6Nbt27o0aOH3LlmZmbo378/10h+//69TLI0Z84cmbratm1b\nmcfb2dkpjEGSLEneh/SQtcLCQi5Z+uKLL9C4cWPuvoYNG3IXDYKDgzFp0iRuzhcArFixgis7yXPH\nx8fDwsKCSzRFIhHi4uKwZMkSLlk6deoUxo4dyz3OysoKrq6uAKBwXzorKyvY2dl98oI5kh69jh07\ncjFJLFq0CAMGDECHDh3wxRdfYNasWVw9kCRIYWFh3G0AMnULKOqR6tChA/z8/BS+vqR3Cyj62/fu\n3RuWlpbcpskS3333HdfQX758uVyyVNL779Kli9wxRclS8e8vaZLvqRcvXnBD5CQrMBanTLLk7e1d\n4oIjxedRTpkyRam5Zb/++qtMsgQAAwcOhK+vL5ycnODk5ARDQ0M4ODgofVFJWbz2MElr2bIl18PE\nGMPSpUuxf/9+CIVCiEQirFixAl5eXgofa2trC5FIVGpFIIQQonopKSnQ0dHhbSnp8goKCsL69etl\nhrMkJibCzc0Nly9f5ua3AEUTqNu2bSuz5O/69esRGRmJXbt2yT23ra0t8vLy0KBBA2RkZCArKwvm\n5ubQ09ODSCTiGr7p6ekKl841MDBAfn4+N2RK+mpvjRo1UKtWLaSmpkJXVxd16tRBRkYGd5VbR0cH\nhYWFXMOkevXqqFu3LhhjePnyZakJio6ODho2bAiBQICXL1+WOodJIBCAMQZTU1Puf3Bubi5yc3NR\nvXp1hY1fkUiEly9fygxX1NHRgVgs5q7UM8a420KhEAYGBqUuVqGvr4/8/Hzo6upyDX6xWIznz59D\nLBajbt26yM7Ohkgkgo6ODleO9erVg6mpKfLz87myNDIyQvXq1bnnzs/Px4cPH5CZmQmxWMyVfWJi\nIkQiEQwNDZGfn4/q1atzMWdkZEAgEHBtmOKxNmzYENnZ2TLJtp6ensxeOnl5eXj58iX399fV1UWj\nRo1kFoSwtLTE69evkZ+fDx0dHTRu3Bi5ubl4/fo1BAIBDA0Nufeqo6MDMzMzpKamQiQSQVdXFyYm\nJsjPz+fqZO3atZGTk8P1JJmbm0MoFIIxJrPAAFA0LFO6V0W6B65BgwbQ1dVFUlIS97fMy8vjnqd+\n/foQi8XIyMiArq4uqlWrBsYYV2fT0tIgEAhQs2ZN5OTkoH79+hAKhcjKykJqaioMDQ1hYGAAAwMD\nZGdncwtb6OrqckPsatWqxX2uLC0tuTokGf5Y/PNVnJGRERo0aICkpCSZz4ChoSEsLCy41Qklf08J\nyfnVqlVD9erVIRKJYGxsjIyMDOjr68uUo+Szr6ury5UZY4yrt9Jq1aqFWrVqyZV7nTp1YGpqyg2j\nK97bVadOHW7+j4WFBXJzc7m6LBaLYWBggBo1asDY2BgvXrzgPpe6urqoX78+Xrx4wf0N9fT0uGF+\nQqEQAoEAIpEIJiYmMDMzQ2FhIVfvJSsdKvLu3bsS5zFK/oYGBgaoXr06UlNToaenx9UPyXusWbNm\nqRer9PT0YGBgoLL/TRq5cW1WVhYSExMxefJkuLq6IiQkBMuWLYO9vT23/Kc0yYdUQ3I/QgipMnR0\ndLgJvJWRpNEkTfK7oaGhUucWP0/6fIFAAF1dXdStWxd169bl7pNuTEgaQp/C3Nycu12zZs2Pni8Q\nCLjhU8qQbggqy9DQsMQyAYreuyo32SyJUCiUGaojnQQVp6+vX2K56Ovro3bt2qhdu7bM8c8++0zh\n+cbGxnLnlnReaau/GhgYyPTmSB+Xfu3ifyMjIyOFj5MoKW4JPT29UstKQrIqYmlKi0MoFMqUk0Ag\ngL6+PvT19blePQAynw3puUwSxsbGMp8tacU/V0ZGRmVecbekz0CDBg3KdL6iz6eiz75AICh1n76S\nyr209yVdnpJkQxFFn8vif0PJXmGKSPd+lqZGjRoyMZWmeI+1tNLec2pqqtLxKEMjE6bt27eDMYbJ\nkycDAGxsbLi13/39/eXOl558RgghhCjL3Nwc6enpKCwshK5u0b9EyRXs4o1Gc3NzuTkcb968KXF0\nA/1vIoQQ7cD7KnmKREdHy62gYW1tza2qQgghhKiCtbU1dHV1uVWwACAiIgK2trZyw8ns7e0RGRnJ\njWZgjOHOnTvcqmaEEEK0k0YmTPXq1ZNb5z8+Pv6ThgYQQgghJTEyMsKgQYOwePFi3Lt3D+fPn8fO\nnTvxzTffACjqbZLMB/Dw8EBmZiZ+/vlnxMXF4eeff0ZOTg569+7N51sghBBSwTQyYfLx8cGVK1ew\na9cuJCYmYteuXbh69arC1XAIIYSQ8pgzZw5sbGwwcuRI+Pv7w8/Pj1sdysXFhds7x9TUFH/88Qci\nIiLg5eWFu3fvYuvWrXKT4QkhhGgXjUyYHBwcsHHjRhw/fhwDBgzA//73P2zduhWff/55hb4uYwxr\n165Fhw4d0L59e6xevVpulRJp//77L4YMGQJHR0e4u7vj8OHDMvcPGDAALVu2lPmR3qH9U+Tl5WHu\n3Llo164dXFxcsHPnzhLPffDgAXx8fGBvb4/BgwfLbHwGFC3r6ebmBnt7e0yaNKlCdkwuS7yXLl3C\nwIED4ejoiP79+8utnd+uXTu58pTs+M1XzBMmTJCLSXq1LU0q4xEjRsjF2rJlS5kNJNVVxkDRZPl+\n/fqVujypJtThssSrKXVY2Xg1of5qAiMjI6xatQqRkZEIDQ3Ft99+y90XExMjs0KrnZ0djh8/jnv3\n7uHw4cNo1qyZyr6TtYkqv/u1SVnKRSIpKQmOjo5KLeVcmZWlbGJiYvD111/Dzs4O/fv3l9lYVduU\npVzOnTuH3r17w9HREV9//TWio6PVGCl/VNGe+ChGODt27GDdunVjt27dYjdu3GAuLi5s+/btCs9N\nSUlh7dq1Y7/88gtLSEhgp06dYra2tuzixYuMMcYKCwuZra0tCw8PZykpKdxPQUFBuWJcsmQJ69+/\nP4uKimIhISHM0dGR/f3333LnZWVlsc6dO7OVK1eyuLg4tnTpUtapUyeWlZXFGGPs7t27zM7Ojh0/\nfpw9fPiQDR8+nI0bN65csZUn3ocPHzIbGxu2e/du9vTpUxYYGMhsbGzYw4cPGWOMvX79mrVo0YI9\nf/5cpjzFYjFvMTPGWK9evdiJEydkYsrLy2OMaV4Zp6eny8R57tw5ZmNjw+7du8cYU28Z5+bmskmT\nJrEWLVqwsLAwhedoSh1WNl5NqsPKxMuYZtTfyk5V38naRlXf/dqmLP9fJEaPHv3Rz7I2ULZsMjMz\nWadOndj8+fPZ06dP2fr161nbtm3ZmzdveIi64ilbLo8fP2a2trbs+PHj7NmzZ8zf35917tyZZWdn\n8xC1+qiiPaEMSpikdOvWjR09epT7PSgoiLm6uio8d//+/czDw0Pm2IIFC9j06dMZY4w9ffqUtWrV\niuXm5qosvqysLGZraytTITZt2sSGDx8ud+7hw4dZjx49uMaYWCxmvXr14t7fzJkz2ezZs7nzX758\nyVq2bMmeP3/OS7xr1qxho0ePljk2atQo9uuvvzLGGLt27Rrr3LmzymIrSVlizsvLY9bW1iw+Pl7h\nc2laGUsrLCxkffr0Yb/99ht3TF1lHBsbywYMGMD69+9f6hecJtThssSrKXVY2Xg1of5Wdqr8TtYm\nqvzu1yaf8n194sQJNmTIEK1PmMpSNrt372Zubm6ssLCQO+bl5cUuXbqklljVqSzl8ueffzJPT0/u\n9/fv37MWLVpwF0W1karaE8rQyCF5fEhOTsarV69kdmRv27YtXrx4gZSUFLnzu3TpghUrVsgdl+yO\nHBcXhwYNGsDAwEBlMT569AiFhYVwdHSUifHu3btyQwfv3r2Ltm3bcrtCCwQCODk5cStB3b17F+3a\ntePOb9CgASwsLHD37l1e4vX09MSMGTPknkOyQVpcXFyp+zmoSllijo+Ph0AgKHE/EU0rY2nHjh3D\nu3fvZHZaV1cZh4eHw9nZGQcPHiz1PE2ow2WJV1PqsLLxakL9rexU+Z2sTVT53a9Nyvp9nZ6ejjVr\n1mDJkiXqDJMXZSmb8PBw9OzZU2Yvs6NHj6Jbt25qi1ddylIuNWvWRFxcHCIiIiAWi3Hs2DGYmppy\nGzlrI1W1J5Shkfsw8SE1NRUAZPbTkGyE9vr1a7l9Nho2bCizal9aWhqCg4Ph5+cHAHjy5An09PQw\nfvx4REVFwcrKCrNmzYKdnV25YqxVq5bMRlx169ZFXl4eMjIyZDaAS01NRfPmzWUeX6dOHcTGxgIA\nUlJS5N5TnTp1St01uSLjLb4hcWxsLG7cuIEhQ4YAKCrPnJwcjBgxAgkJCbC2tsbcuXNV3gAtS8zx\n8fEwNTXFrFmzEB4ejvr168PPz4/70ta0MpZgjGH79u345ptvZDYAVFcZK7t4iybUYUD5eDWlDisb\nrybU38pOld/J2kSV3/3apKzf1ytXroSnp2eFz9/WBGUpm8TERNjZ2WHBggW4cOECLC0tMXv2bLRt\n25aP0CtUWcqlT58+uHDhAoYOHQodHR0IhUL88ccfSm8QWxmpqj2hjCrVw5Sbm4tnz54p/MnOzgYg\nu0ux5Hbxnd0VPa+fnx/q1q2Lr776CgCQkJCAd+/ewcfHB1u3bkWzZs0wcuRIvHr16pPjz8nJkdu1\nuKQYSzpXcl5ubm6p96tCWeKV9vbtW/j5+cHJyQk9e/YEUNS4e/fuHSZMmIDff/8dhoaG+Pbbb7ke\nPT5ijo+PR25uLlxcXLB9+3Z069YNEyZMwP379wFobhnfvHkTr1+/hq+vr8xxdZWxsjShDn8qPuuw\nsjSh/lZ2qvxO1iaq/O7XJmUpl+vXryMiIgITJ05UW3x8KkvZZGdnY+vWrTAzM8O2bdvwxRdfYPTo\n0eVqX2mqspRLeno6UlNTsXDhQhw6dAgDBw7EnDlzkJaWprZ4NZUqvn+rVA/T3bt3ub01ips5cyaA\nogooGUYnKUgjI6MSnzMrKwsTJ07E06dPsX//fu7cpUuXIjc3F6ampgCAxYsX486dOzhx4gS+//77\nT4rfwMBA7o8r+d3Q0FCpcyXnlXR/ae+1IuOVePPmDb777jswxrBhwwZu48gdO3agoKCA6xFZu3Yt\nunXrhosXL6J///68xDxx4kSMGDGCu3rTqlUrREdH49ChQ7C1tdXYMj579iy6du2KmjVryhxXVxkr\nSxPq8Kfguw4rSxPqb2Wnyu9kbaLK735tomy55ObmYuHChVi0aJFW1g9FylJndHR0YG1tjSlTpgAA\nWrdujWvXrpWrfaWpylIua9euRYsWLTBs2DAARe3Q3r174+jRoxg3bpx6AtZQqvj+1b5vpFI4Ozsj\nJiZG4Y+kwSIZmid928zMTOHzffjwAaNHj0ZsbCx2796NJk2acPfp6upyyRJQNF6yadOmSE5O/uT4\nzc3NkZ6ejsLCQpkYDQ0NUb16dblz37x5I3PszZs33BCbku4v6b1WdLxA0TyyYcOGIT8/H3v27JHp\natbX15cZPmZgYICGDRuWqzzLG7NQKJTr6pb+G2tiGQNAaGiowqu36ipjZWlCHS4rTajDytKE+lvZ\nqfI7WZuo8rtfmyhbLvfu3UNiYiKmTJkCR0dHbv7K2LFjsXDhQrXHrQ5lqTNmZmZo2rSpzLEmTZpo\nZQ9TWcolOjoarVq14n4XCoVo1aoVXr58qbZ4NZUqvn+rVMJUGnNzc1hYWCAiIoI7FhERAQsLC4UF\nKhaLMXnyZCQlJWHv3r1yY4xHjBiBgIAAmfNjYmLkPuRlYW1tDV1dXZlJahEREbC1tZW7Gmdvb4/I\nyEgwxgAUzVu5c+cO7O3tuful3+urV6/w6tUr7n5VKEu82dnZGDNmDIRCIQIDA2Fubs7dxxiDm5sb\njh07JnP+s2fPylWe5Y35p59+ktnDCCiaoCmJSdPKGCga8pKYmCg31ludZawsTajDZaEpdVhZmlB/\nKztVfidrE1V992sbZcvFzs4OISEhCAoK4n4AYNmyZfjhhx/UHrc6lKXOODg4ICYmRuZYfHw8LC0t\n1RKrOpWlXOrVq4cnT57IHEtISJCZb19VqeT799MW8tNOf/zxB3NxcWFhYWEsLCyMubi4sJ07d3L3\np6WlsQ8fPjDGGDt48CBr1aoVu3jxosweJunp6Ywxxnbu3Mnatm3Lzp8/z548ecIWLVrEOnXqxN6/\nf1+uGBcsWMD69u3L7t69y86dO8ecnJzY2bNnGWNFe0Pl5OQwxoqWk+zQoQNbunQpi42NZUuXLmWd\nO3fm1py/c+cOs7GxYYcOHeL2WBk/fny5YitPvL/++iuzs7Njd+/elSnPzMxMxhhjS5cuZd27d2dh\nYWHs8ePHbNKkSaxfv34yy4qqO+azZ88yGxsbdvz4c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"text/plain": [ "<matplotlib.figure.Figure at 0x1102ed588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 3))\n", "gs = plt.GridSpec(2, 2)\n", "\n", "# Phased data\n", "ax = fig.add_subplot(gs[:, 0])\n", "\n", "ax.fill_between([-1, 2], 18, 16.7, color='lightgray', alpha=0.5)\n", "for offset in (-1, 0, 1):\n", " ax.errorbar(phase + offset, y, dy, fmt='.',\n", " color='gray', ecolor='lightgray', capsize=0)\n", "ax.plot(phase_fit, ls_generalized.model(phase_fit / f0, fmax_generalized),\n", " color='black', label='floating mean model')\n", "ax.plot(phase_fit, ls_standard.model(phase_fit / f0, fmax_standard),\n", " color='gray', label='standard model')\n", "ax.legend(loc='upper left')\n", "ax.set(xlim=(-0.4, 1.6),\n", " ylim=(18, 12),\n", " xlabel='phase',\n", " ylabel='mag');\n", "\n", "#periodograms\n", "ax1 = fig.add_subplot(gs[0, 1])\n", "ax1.plot(freq, power_generalized, '-', color='black')\n", "ax1.xaxis.set_major_formatter(plt.NullFormatter())\n", "ax1.set(xlim=(0, 1),\n", " ylim=(0, 1.2))\n", "ax1.text(0.95, 0.9, 'Floating Mean Periodogram',\n", " ha='right', va='top', transform=ax1.transAxes)\n", "ax1.annotate('', (0.3, 0.95), (0.3, 1.2),\n", " arrowprops=dict(arrowstyle=\"->\", color='gray'))\n", " \n", "ax2 = fig.add_subplot(gs[1, 1])\n", "ax2.plot(freq, power_standard, '-', color='black')\n", "ax2.set(xlim=(0, 1),\n", " ylim=(0, 1.2),\n", " xlabel='Frequency')\n", "ax2.text(0.95, 0.9, 'Standard Periodogram',\n", " ha='right', va='top', transform=ax2.transAxes)\n", "ax2.annotate('', (0.3, 0.45), (0.3, 0.7),\n", " arrowprops=dict(arrowstyle=\"->\", color='gray'))\n", " \n", "fig.text(0.52, 0.5, 'Lomb-Scargle Power', ha='right', va='center', rotation=90)\n", "\n", "fig.savefig('fig20_standard_vs_floatingmean.pdf')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3.5", "language": "python", "name": "python3.5" }, "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 }	bsd-3-clause
wcmckee/wcmckee.com	posts/pyguessgame.ipynb	2	21759	{ "cells": [ { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "import urwid\n", "import os\n", "from pyfiglet import Figlet\n", "import json\n", "import random\n", "import clint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Started as a simple python guessing game to be ran in terminal. \n", "Asks for a number between 0-10 and works up to 100 - asking for the next 10 each time. \n", "On win a urwid screen shows with 'You Win'. It would be nice if the whole game was inside of urwid.\n", "Figlet intergration. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = Figlet()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opusr = os.listdir('/home/wcmckee/signinlca/usernames/')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['tnow',\n", " 'signinlca.py',\n", " 'charle',\n", " 'wcm',\n", " 'wmck',\n", " 'wmen',\n", " 'webmck',\n", " 'pjohns',\n", " 'red',\n", " 'wez',\n", " 'checkthis',\n", " 'blah',\n", " 'jchick',\n", " 'poiu',\n", " 'poi',\n", " 'qwe',\n", " 'point',\n", " 'cvb',\n", " 'blag',\n", " 'gerty',\n", " 'jblog',\n", " 'ssung',\n", " 'clittle',\n", " 'yellow']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opusr" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ranumz = len(opusr)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ranin = random.randint(0, ranumz)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ranin" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "opza = opusr[ranin]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'qwe'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opza" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lidte = open('/home/wcmckee/signinlca/usernames/' + opza + '/.signups.json', 'r')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plid = lidte.read()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tlid = json.loads(plid)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "firna = tlid['firstname']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tlicha = tlid['lastname']" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fullna = firna + ' ' + tlicha" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "qwe qwe\n" ] } ], "source": [ "from clint.textui import colored, puts\n", "\n", "perf = puts(colored.yellow(fullna))\n", "\n", "perf" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", " __ ___ _____ \n", " / _` \\ \\ /\\ / / _ \\\n", "\| (_\| \|\\ V V / __/\n", " \\__, \| \\_/\\_/ \\___\|\n", " \|_\| \n", "\n", "qwe qwe\n" ] } ], "source": [ "print f.renderText(opza)\n", "print fullna" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def exitq(key):\n", " if key in ('enter', 'return'):\n", " raise urwid.ExitMainLoop()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pallette = [\n", " ('banner', 'dark red', 'white'),\n", " ('streak', 'dark red', 'white'),\n", " ('bg', 'dark red', 'white'),]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "numchez = 0" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number between 70 and 80: \n" ] }, { "ename": "ValueError", "evalue": "invalid literal for int() with base 10: ''", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-48-ff8888390e77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mguessnum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraw_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minnumz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mguesintz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mguessnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'Guess was: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mguesintz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: ''" ] } ], "source": [ "for guesz in range(10):\n", " farchez = (numchez)\n", " numchez = (numchez + 10)\n", " \n", " def GetNum():\n", " return random.randint(farchez,numchez)\n", " \n", " randnum = GetNum()\n", " \n", " lownumz = (numchez)\n", " \n", " innumz = ('Enter a number between ' + str(farchez) + ' and ' + str(lownumz) + ': ')\n", " \n", " guessnum = raw_input(innumz)\n", " \n", " guesintz = int(guessnum)\n", " \n", " print ('Guess was: ' + str(guesintz))\n", " #colomod\n", " print ('Correct was: ' + str(randnum))\n", " \n", " if guesintz == randnum:\n", " txt = urwid.Text(f.renderText( guessnum + ' ' + str(randnum) + ' You Win!'))\n", " numpor = urwid.Text(f.renderText(guessnum))\n", " map1 = urwid.AttrMap(txt, 'streak')\n", " mep = urwid.AttrMap(numpor, 'streak')\n", " fil = urwid.Filler(map1)\n", " fel = urwid.Filler(mep)\n", " map2 = urwid.AttrMap(fil, 'bg')\n", " loopa = urwid.AttrMap(fel, 'bg')\n", " looena = urwid.MainLoop(loopa, pallette, unhandled_input=exitq)\n", " looena.run()\n", " loop = urwid.MainLoop(fil, pallette, unhandled_input=exitq)\n", " loop.run()\n", " print f.renderText(guessnum + ' ' + str(randnum) + ' You Win!')\n", " else:\n", " txt = urwid.Text(f.renderText(guessnum + ' ' + str(randnum) + ' You Lose!'))\n", " map1 = urwid.AttrMap(txt, 'streak')\n", " fil = urwid.Filler(map1)\n", " map2 = urwid.AttrMap(fil, 'bg')\n", " loop = urwid.MainLoop(fil, pallette, unhandled_input=exitq)\n", " loop.run()\n", " print f.renderText(guessnum + ' ' + str(randnum) + ' You lose!')\n", " " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b7\u001b[?47h\u001b[?1002l\u001b[?1000l\u001b[?1000h\u001b[?1002h" ] }, { "ename": "TypeError", "evalue": "ord() expected a character, but string of length 0 found", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-35-0a8c9e47ba8d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;31m#looena.run()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mloop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murwid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMainLoop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfil\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpallette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munhandled_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexitq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mloop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\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 13\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderText\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'You Win!'\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/urwid/main_loop.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 276\u001b[0m \"\"\"\n\u001b[0;32m 277\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 278\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run\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 279\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mExitMainLoop\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 280\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/urwid/main_loop.pyc\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscreen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 374\u001b[0m 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urwid.AttrMap(fil, 'bg')\n", "#loopa = urwid.AttrMap(fel, 'bg')\n", "#looena = urwid.MainLoop(loopa, pallette, unhandled_input=exitq)\n", "#looena.run()\n", "loop = urwid.MainLoop(fil, pallette, unhandled_input=exitq)\n", "loop.run()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print 'The End'" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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
hemagso/neuralmon	pre-processing.ipynb	1	540962	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Data Preparation" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Let's get a look on our Pokémons. The function below will plot all the sprites of a specific Pokémon on screen. That way we can have an idea of what kind of problem we can find in out sprite dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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kOr6NioXkeR9kFa/wP3eKVMyhFa5qWeXoUfrXrw/eXrSo99d5mWu55sgYYy4B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\nkiyK2QvnY5ENDg5yuHKRDfF9f2hZvWz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jhm8+yZFrbmGhV/7adU6Amqp0Lu6+pbt3w8hI\nzQT/pFLS5z6WlGuux58fqc5L80a37+WfM85HN3H/ycmoFO/AzRtqjnH/A/H5eHwyyb40Oh/TQ87U\niWk/v/Phfo57sYZwZ8c9LyuGIr2gYUfGGPMnwGvW2hfjVYtrWGutMSa4im0vrHQrAPwbFEfpAe4/\nyKW7dyfruThuyBiTUVneNdeuqXp89bLoCrCfuck7ybSqGIi3TZrnZ15u+OaTVXOXYGbOSvqqrptk\nXE+6ktINcdlY38jnPha8WqxYtubH8fm4bPfuZD0Xxw0ZYzIajbxmTfX5eM3SKEPqZ27SWZh6dD52\nTqO/a6izo3lKUhR5MjLXAH9qjBkhWgT2t4wxTwKvGmP6rLVTxpg+4LW5bGheyXCv09VDwZLFKOus\n7dKK5PjepP8edhkFiqPjT/wfYleSKamMnUuGh/VzcaaIw8Q0BAo6VCoVXr/lI8n90HoxlbFzVZkY\nJ11YoHI0qlB39cP38vxdn23p9xIREekV6rRIUTXsyFhr7wXuBYiv5G+31t5ijPkccCuwJ/75lTls\nZy7J/BhSQ7pSHY2WSyGfvFhdtayOUPnlevt00Ctuoa9ej2OaP1/G6R9ezi6izsiufYeqSmfXs2vr\nxpptlbFz0XwYd987vs+fd7PoQ9HwNn3wt85djXVDyJbu3s3k6Cg3p3esTAPewm7eQ81kY/zXlda5\nDFq9hUyX7t5dNVk/T/U4PwsTmk9R7/VU7rV93PnohpAti8/HW9I7TkajkAdGRqICAN5DzWRj/NcV\nEZmN2awjswd4xhizBXgJuLE9TZIOUxylK9JjtKF2Ffd0FSw39yI9z0I6IzRBOz3My3VmoHaoWEjo\ny6wb1uJKwsrc8+NwVb3zMVW5zJ2P6QUvRUQ6pamOjLX2JFFVK6y1PyWaV9lTqiqLBUyPHa36wF56\n021Vj2U5TVQ4INFgSFm9YWynT59OXqsbV6SKEMcQf5iZm5QPsGvX1uBaMzXirIqfhan39+9bu7Yq\nU+NMT08nJd6kfVx5T9ehcR0Vv054VeUxbxE26YzVe/dyKl65ffXevXX3q1d4oVluHtX+ZeuS1zu1\nbVuSqdHV/Lnz4/h8dB0a11E57+3z9sD5eF7no4h02GwyMr0t1ck4uitaHiX9n5+/GOXChVHnYtHw\n+uAhp8eOJoXK6g0bWzS8PumkrD/5UFItbdWOoarbRxt0miSbP5elMnauqmPTSJ4vQFkVyTQvZu64\nDo3jSoVK943v3JlUJwOSjInfuXDbZ8uvnuW/5kINJ+uoH6fOx6t0PopIjylvR0ZECs9f90C6K92B\nSKobzWHnQp2W3uLWndH5KCK9ohQdGTfJf9/WzSzfHF2pP3dgX7LmCOT7D7FeDX3/cffYvgP7ql7L\nv/0bvxFlefxha0eH7p65nRreJvn5E//dhPz+4eVVi1c6E7v2lfbvbIy5HHgM+ABggY8C3weeBvqJ\n6hbcaK0t5x+gJBTHclAcRUS6oxQdmS/d9edA1KFxQ8Vue/iLLX+JzXqee2zpTbclr1Xvtj9sDb6E\ntFdoXoxfXWxBby1G2m4PA2PW2o8YY94G/CbwKeA5a+0eY8wOYAfwyW42crZCC7UBHPvMZ8qymGVh\n41iCv307FTaOzbhq3bokK+N+AhzbvVuLWYpIV7yl2w0QkaZdAvwhsB/AWvumtfYN4HrgiXifJ4Dw\nZK8C8Tsv/uKZ6+67rwxfmuZNHEtu3sTR77z4i2eu09wlEemSUmRkHJeZSd+eC362pd5taT9Xtaxy\n9Cj969cHb7shhSX+j/VtwE+ALxljfh94EbgLuNJaOxXvMw1c2aX2tVW6AECJzKs4lti8imO6AEDJ\n/IYx5px3/33A/wlcDvwnojgDfMpaO9rpxolIrVJ1ZKT8/DkyrjyyPxdq0aJFZe7AOAYYBD5urf2W\nMeZhomErCWutNcbY4JONuQO4A2Dx4sVz3VapT3EsB8WxPP7FWzj6EuAV4MvAbcDnrbUPdLNxIlJL\nQ8tEiudN4GVr7bfi+39L9EXqVWNMH0D887XQk621j1prV1prV15xxRUdabAEKY7loDiW03XA/2et\nfanbDRGR+tSRkUKa+trXuHDhQvDfPPAr4EfGmN+N719HtFbds8Ct8bZbga90oW2Sn+JYDopjOW0E\n/sa7/3FjzHeMMY8bY4KlTY0xdxhjXjDGvPCTn/wktIuItJmGlokU08eBg3GFpB8SDX14C/CMMWYL\n8BJwYxfbJ/kojuWgOJZIHMc/Be6NN/0V8Bmi0tqfAR4kKrFdxVr7KPAowMqVK4NDCUWkvdSRESkg\na+05YGXgoes63RZpneJYDopj6awFzlhrXwVwPwGMMX8NfLVbDRORahpaJiIiIjLjJrxhZW6uU+zD\nwN93vEUiEqSMjIiIiAhgjHkH8EeAv4bDfzPGLCcaWlZJPSYiXaSOjIiIiAhgrf0F8NupbX/WpeaI\nSAMaWiYiIiIiIoWjjoyIiIiIiBSOOjIiIiIiIlI46siIiIiIiEjhqCMjIiIiIiKFo46MiIiIiIgU\nTq6OjDHmcmPM3xpjJowx48aYq40x7zLGfN0Y8w/xz4Vz3ViZHcVRRERERMoib0bmYWDMWrsE+H1g\nHNgBPGetfT/wXHxfepviKCIiIiKl0LAjY4z5N8AfAvsBrLVvWmvfAK4Hnoh3ewJYP1eNlLa4BMVR\nREREREoiT0ZmAPgJ8CVjzFljzGPGmHcAV1prp+J9poEr56qR0hZvQ3EUERERkZLI05F5KzAI/JW1\ndgXwC1LDj6y1FrChJxtj7jDGvGCMeeEnP/nJbNsrrTMojiIiIiJSEnk6Mi8DL1trvxXf/1uiL8Sv\nGmP6AOKfr4WebK191Fq70lq78oorrmhHm6U1b6I4ioiIiEhJNOzIWGungR8ZY3433nQdcB54Frg1\n3nYr8JU5aaG0y69QHEVERESkJN6ac7+PAweNMW8DfgjcRtQJesYYswV4CbhxbpoobaQ4ioiIiEgp\n5OrIWGvPASsDD13X3ubIXFIcRURERKQsTDS/u0MvZsxPiCaZv96xF83ndyh/m/6dtbYtk1sUx6bM\nRZvaGcufAd9vx7EKopfeI/PhnJwrimM59FIcQZ+tIb0WozzaFkfpfR3tyAAYY16w1oayAl2jNjWv\nF9unNjWv19vXbmX+fcv8u6WV+Xct8++WVubftSy/W1l+DymvPFXLREREREREeoo6MiIiIiIiUjjd\n6Mg82oXXbERtal4vtk9tal6vt6/dyvz7lvl3Syvz71rm3y2tzL9rWX63svweUlIdnyMjIiIiIiIy\nWxpaJiIiIiIihaOOjIiIiIiIFE7HOjLGmGFjzPeNMT8wxuzo1Oum2vBeY8wJY8x5Y8z3jDF3xdt3\nGWNeMcaci/+NdKFtFWPMd+PXfyHe9i5jzNeNMf8Q/1zY6XYF2tn1OMbt6MlYFiWO0DuxnCtFisVs\nKI6KY1HMh1gWOY7zIT5SPh2ZI2OMuQT4X8AfAS8D3wZustaen/MXr25HH9BnrT1jjHkn8CKwHrgR\n+Lm19oFOtifVtgqw0lr7urftvwH/aK3dE38gLrTWfrKLbeyJOMZt6clYFiGOcZt6JpZzpSixmA3F\nUXEskrLHsuhxLHt8pJw6lZH5A+AH1tofWmvfBA4B13fotRPW2ilr7Zn49s+AceDdnW5HE64Hnohv\nP0H0Rb2beiKOULhY9locoYdi2WG9GIvZUBwVx6IrUyzLGMcyxUdKqFMdmXcDP/Luv0yXv3QaY/qB\nFcC34k0fN8Z8xxjzeJdSpxb4O2PMi8aYO+JtV1prp+Lb08CVXWiXr+fiCD0XyyLEEXo0lm1WlFjM\nhuKoOBZJ2WNZ9DiWPT5SQm/tdgO6wRhzGXAYuNta+0/GmL8CPkN0En8GeBD4aIeb9UFr7SvGmH8L\nfN0YM+E/aK21xhjVyk7pwVgqjr1DsSgHxbE8FMvepvhI4XQqI/MK8F7v/nvibR1njLmU6IvvQWvt\nEQBr7avW2l9ba/8V+Gui9HBHWWtfiX++Bnw5bsOr8VwQNyfktU63K6Vn4gi9GcuCxBF6LJZzoUCx\nmA3FUXEsjHkQy0LHcR7ER0qoUx2ZbwPvN8YMGGPeBmwEnu3QayeMMQbYD4xba//S297n7fZh4O87\n3K53xBPWMca8A/jjuA3PArfGu90KfKWT7QroiThCb8ayQHGEHorlXChYLGZDcVQcC2GexLKwcZwn\n8ZES6sjQMmvtr4wxdwL/A7gEeNxa+71OvHbKNcCfAd81xpyLt30KuMkYs5xoOFIF+PMOt+tK4MvR\nd3PeCjxlrR0zxnwbeMYYswV4iagiV9f0UByhN2NZiDhCz8VyLhQmFrOhOCqOBVL6WBY8jqWPj5RT\nR8ovi4iIiIiItFPHFsQUERERERFpF3VkRERERESkcNSRERERERGRwlFHRkRERERECkcdGRERERER\nKRx1ZEREREREpHDUkRERERERkcL5/wFeD3rjpfOhywAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e09f54bf98>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SNHbCX7m3G8BppdQHAJx275Ock+mIjNfx3NgWFUWJwlxpDx7DXJn3zmkpyvbO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\ndOut1iaY6FwT6sw0k0c2dGN4b6U2xmzYSeKx7dA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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a3f378d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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VgTFLQMcNNsQkhBBCCCGEJI7YRGQIIYQQQgghsFYJM6M0YZgVz0yq6usAZCIw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\nF+K55cUsJ0RzaPolAEADgOUcX4K7ro4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se3vHGOabcsdMt0XKT/CfLADrP1oAef/ZApl/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\nIv9BKfUtOP9w77g/TwIYcn9/tXJTJBGgHdMB7ZhOjgH4svH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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a3fc0240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from utility.plot import plot_all\n", "\n", "#Plotting Bulbassaur ID = 1\n", "plot_all(1)\n", "\n", "#Plotting Charmander ID = 4\n", "plot_all(4)\n", "\n", "#Plotting Squirtle ID = 7\n", "plot_all(7)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now let's take a look at the sprites of an Evolutionary Chain.\n", "\n", "Plotting the evolutionary chain of Bulbasaur (Bulbasaur -> Ivysaur -> Venusaur) from the fifth generation games let's us see a problem with out dataset: Centering and cropping of images." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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h69mzuXYXjTOOY1W5OXjIKi9nAW28ZuOikxO2iXejEQxLq9C3iW37mXt7vvMqYT9ztWcV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\ntlO9Pw6h6mNnd9p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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a43c8978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from utility.plot import plot_chain\n", "\n", "plot_chain(\"gen05_black-white\",[1,2,3])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "On this step, we will build and test our pre-processing pipeline. Its goal is to identify the main object in the image (A simple task in out sprite dataset), find out its bounding box and redimensionate the image to an adequate size (We will use a 64 x 64 pixels image on this article)\n", "\n", "This routine will be tested on Bulbasaur, Charmander, Squirtle and Venusaur sprites from the fifth generation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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GrpQs45rbWtvB4SOmH6dPnx5S/f0hiugIYg8md3/F+IWnp6cDZvIpXF7YCuhnP/tZAP70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\nmOyrWBdAZCGKSW75PB+TXg4GY72sra0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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a5b3f588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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yoiKYb1U7a46bSmRdZQstTebnaHfRTY0Ia/MIUNNkVS5r3Y7H1iwrI6PCmWJV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\n9lQt3jYx1rTxPydn8eyh0wAcPGvOK74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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0aacd74e0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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f7+0Rzc/P54orrqCyspJjx44RFRWFYRhUVen1x6zhtdnZ2bhcLoqKilizZg09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\ne/fuve19lpeX2xjOTe3atQuAI0eO8LnPHAfgxo0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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a3be4128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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E9/hVHcIK2tXRQUVOPgBhk8V1NpZRR8itX6z86p//5L8PismO2yKEi+t1YV3G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\nvR6TGWdgN5AYr1W/39hXmc/6TBaupnh9d4wdO5b/+Z//4amnnuKJJ55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"text/plain": [ "<matplotlib.figure.Figure at 0x1e0a3aec748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from utility.preprocessing import center_and_resize\n", "import matplotlib.image as mpimg\n", "import os\n", "\n", "main_folder = \"./sprites/pokemon/main-sprites/\"\n", "game_folder = \"gen05_black-white\"\n", "pkm_list = [1, 4, 7, 3]\n", "\n", "for pkm in pkm_list:\n", " img_file = \"{id}.png\".format(id=pkm)\n", " img_path = os.path.join(main_folder,game_folder,img_file)\n", " img = mpimg.imread(img_path)\n", " center_and_resize(img,plot=True,id=img_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Finnaly, let's call our centering pipeine on all sprites of a generation. To ensure the process is going smoothly, one in each thirty sprites will be ploted for visual inspection." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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vZFh6jhrjYjAYxjJTduzFV96m/EUZIaRHfBzY8SYA+/acAGDp3WI3/mnTd/nM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uv+elAkH34uyqmzpojiWFSYLebdLJPgYHEQcvhQFY1NspU1Pt0zJzI4JNDjFWCHLa5d0dBq35Pj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\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9s3x37Ft0pPdrTnZXBydm0hdOrGiIPdBze2DXPolzzc0RJdiJvZw5QTzeJs/7cvcd9xOd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\nKDmM27ZJpdWhQ6UkdFBQsGsV2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as8Cve45166k5WV5aF4zp07u9Y+jVU0z0dWVpbfPp6K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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e09f545f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from utility.preprocessing import center_and_resize\n", "import matplotlib.image as mpimg\n", "from math import ceil\n", "import matplotlib.pyplot as plt\n", "\n", "main_folder = \"./sprites/pokemon/main-sprites/\"\n", "game_folder = \"gen05_black-white\"\n", "pkm_list = range(1,650)\n", "\n", "image_list = []\n", "for pkm in pkm_list:\n", " try: \n", " image_file = \"{id}.png\".format(id=pkm)\n", " image_path = os.path.join(main_folder,game_folder,image_file)\n", " image = mpimg.imread(image_path)\n", " image_resize = center_and_resize(image,plot=False,id=image_path)\n", " plot = (pkm % 30 == 0)\n", " if plot:\n", " image_list.append((image,image_resize))\n", " except ValueError as e:\n", " print(\"Out of Bounds Error:\", e)\n", " \n", "n_cols = 6\n", "n_rows = ceil(2len(image_list)/n_cols)\n", "plt.figure(figsize=(16,256))\n", "for idx, image_pair in enumerate(image_list):\n", " image, image_resize = image_pair\n", " plt.subplot(100,6,2idx+1)\n", " plt.imshow(image)\n", " plt.subplot(100,6,2*idx+2)\n", " plt.imshow(image_resize) " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "At least, let's process all the images and save them to disk for further use." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting gen03_emerald\n", "Starting gen01_yellow\n", "Starting gen05_black-white\n", "Starting gen04_platinum\n", "Starting gen02_crystal\n", "Starting gen04_heartgold-soulsilver\n", "Starting gen01_red-green\n", "Starting gen01_red-blue\n", "Starting gen02_silver\n", "Starting gen03_firered-leafgreen\n", "Starting gen03_ruby-sapphire\n", "Starting gen04_diamond-pearl\n", "Starting gen02_gold\n", "Finished\n" ] } ], "source": [ "import warnings\n", "import os\n", "import matplotlib.image as img\n", "from skimage import io\n", "from utility.preprocessing import center_and_resize\n", "\n", "main_folder = \"./sprites/pokemon/main-sprites/\"\n", "dest_folder = \"./sprites/pokemon/centered-sprites/\"\n", "\n", "if not os.path.exists(dest_folder):\n", " os.makedirs(dest_folder)\n", "\n", "gen_folders = { \n", " \"gen01_red-blue\" : 151,\n", " \"gen01_red-green\" : 151,\n", " \"gen01_yellow\" : 151,\n", " \"gen02_crystal\" : 251,\n", " \"gen02_gold\" : 251,\n", " \"gen02_silver\" : 251,\n", " \"gen03_emerald\" : 386,\n", " \"gen03_firered-leafgreen\" : 151,\n", " \"gen03_ruby-sapphire\" : 386,\n", " \"gen04_diamond-pearl\" : 493,\n", " \"gen04_heartgold-soulsilver\" : 386,\n", " \"gen04_platinum\" : 386,\n", " \"gen05_black-white\" : 649\n", "}\n", "\n", "for gen, max_pkm in gen_folders.items():\n", " print(\"Starting\",gen)\n", " main_gen_folder = os.path.join(main_folder,gen)\n", " dest_gen_folder = os.path.join(dest_folder,gen)\n", " if not os.path.exists(dest_gen_folder):\n", " os.makedirs(dest_gen_folder)\n", " for pkm_id in range(1,max_pkm+1):\n", " image_file = \"{id}.png\".format(id=pkm_id)\n", " image_path = os.path.join(main_gen_folder,image_file) \n", " try:\n", " image = mpimg.imread(image_path)\n", " new_image = center_and_resize(image,plot=False,id=image_path)\n", " new_image_path = os.path.join(dest_gen_folder,image_file)\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " io.imsave(new_image_path,new_image)\n", " except FileNotFoundError:\n", " print(\" - {file} not found\".format(file=image_path))\n", "print(\"Finished\")\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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
skkandrach/foundations-homework	descriptive_statistics_classwork.ipynb	2	1610	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')\n", "import dateutil.parser\n", "import math" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's build a data set\n", "reviews_df = pd.DataFrame([\n", " { 'restaurant': 'Burger King', 'reviewer': 'Jeff', 'yelp_stars': 2 } ,\n", " { 'restaurant': 'Burger King', 'reviewer': 'Jen', 'yelp_stars': 2 },\n", " { 'restaurant': 'Burger King', 'reviewer': 'James', 'yelp_stars': 5 },\n", " { 'restaurant': 'Burger King', 'reviewer': 'John', 'yelp_stars': 4 },\n", " { 'restaurant': 'Burger King', 'reviewer': 'Josephine', 'yelp_stars': 4 },\n", " { 'restaurant': 'Burger King', 'reviewer': 'Jacques', 'yelp_stars': 3 },\n", " { 'restaurant': 'Burger King', 'reviewer': 'Jill', 'yelp_stars': 2 } \n", "])\n", "reviews_df" ] } ], "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
esa-as/2016-ml-contest	MSS_Xmas_Trees/ml_seg_sub4a.ipynb	2	16451	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### Contest entry by Wouter Kimman \n", "\n", "\n", "Strategy: \n", "----------------------------------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy.fft import rfft\n", "from scipy import signal\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import plotly.plotly as py\n", "\n", "\n", "import pandas as pd\n", "import timeit\n", "from sqlalchemy.sql import text\n", "from sklearn import tree\n", "from sklearn.model_selection import LeavePGroupsOut\n", "\n", "#from sklearn import cross_validation\n", "#from sklearn.cross_validation import train_test_split\n", "from sklearn import metrics\n", "\n", "from sklearn.tree import export_graphviz\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import linear_model\n", "#import sherlock.filesystem as sfs\n", "#import sherlock.database as sdb\n", "\n", "from sklearn import preprocessing\n", "from sklearn.model_selection import cross_val_score\n", "\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#filename = 'training_data.csv'\n", "filename = 'facies_vectors.csv'\n", "training_data0 = pd.read_csv(filename)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "def magic(df):\n", " df1=df.copy()\n", " b, a = signal.butter(2, 0.2, btype='high', analog=False)\n", " feats0=['GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n", " #feats01=['GR','ILD_log10','DeltaPHI','PHIND']\n", " #feats01=['DeltaPHI']\n", " #feats01=['GR','DeltaPHI','PHIND']\n", " feats01=['GR',]\n", " feats02=['PHIND']\n", " #feats02=[]\n", " for ii in feats0:\n", " df1[ii]=df[ii]\n", " name1=ii + '_1'\n", " name2=ii + '_2'\n", " name3=ii + '_3'\n", " name4=ii + '_4'\n", " name5=ii + '_5'\n", " name6=ii + '_6'\n", " name7=ii + '_7'\n", " name8=ii + '_8'\n", " name9=ii + '_9'\n", " xx1 = list(df[ii])\n", " xx_mf= signal.medfilt(xx1,9)\n", " x_min1=np.roll(xx_mf, 1)\n", " x_min2=np.roll(xx_mf, -1)\n", " x_min3=np.roll(xx_mf, 3)\n", " x_min4=np.roll(xx_mf, 4)\n", " xx1a=xx1-np.mean(xx1)\n", " xx_fil = signal.filtfilt(b, a, xx1) \n", " xx_grad=np.gradient(xx1a) \n", " x_min5=np.roll(xx_grad, 3)\n", " #df1[name4]=xx_mf\n", " if ii in feats01: \n", " df1[name1]=x_min3\n", " df1[name2]=xx_fil\n", " df1[name3]=xx_grad\n", " df1[name4]=xx_mf \n", " df1[name5]=x_min1\n", " df1[name6]=x_min2\n", " df1[name7]=x_min4\n", " #df1[name8]=x_min5\n", " #df1[name9]=x_min2\n", " if ii in feats02:\n", " df1[name1]=x_min3\n", " df1[name2]=xx_fil\n", " df1[name3]=xx_grad\n", " #df1[name4]=xx_mf \n", " df1[name5]=x_min1\n", " #df1[name6]=x_min2 \n", " #df1[name7]=x_min4\n", " return df1\n", "\n", " \n", "\n", "\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['SHRIMPLIN' 'ALEXANDER D' 'SHANKLE' 'LUKE G U' 'KIMZEY A' 'CROSS H CATTLE'\n", " 'NOLAN' 'Recruit F9' 'NEWBY' 'CHURCHMAN BIBLE']\n" ] } ], "source": [ "all_wells=training_data0['Well Name'].unique()\n", "print all_wells" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.5515\n", "SHRIMPLIN\n", "471\n", "using median of local\n", "ALEXANDER D\n", "466\n", "using median of total\n", "SHANKLE\n", "449\n", "using median of local\n", "LUKE G U\n", "461\n", "using median of local\n", "KIMZEY A\n", "439\n", "using median of total\n", "CROSS H CATTLE\n", "501\n", "using median of local\n", "NOLAN\n", "415\n", "using median of local\n", "Recruit F9\n", "80\n", "using median of local\n", "NEWBY\n", "463\n", "using median of local\n", "CHURCHMAN BIBLE\n", "404\n", "using median of local\n", "4149\n", "4149\n" ] } ], "source": [ "# what to do with the naans\n", "training_data1=training_data0.copy()\n", "me_tot=training_data1['PE'].median()\n", "print me_tot\n", "for well in all_wells:\n", " df=training_data0[training_data0['Well Name'] == well] \n", " print well\n", " print len(df)\n", " df0=df.dropna()\n", " #print len(df0)\n", " if len(df0) > 0:\n", " print \"using median of local\"\n", " me=df['PE'].median()\n", " df=df.fillna(value=me)\n", " else:\n", " print \"using median of total\"\n", " df=df.fillna(value=me_tot)\n", " training_data1[training_data0['Well Name'] == well] =df\n", " \n", "\n", "print len(training_data1)\n", "df0=training_data1.dropna()\n", "print len(df0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4149\n", "4149\n", "4149\n", "4143\n" ] } ], "source": [ "#remove outliers\n", "df=training_data1.copy()\n", "print len(df)\n", "df0=df.dropna()\n", "print len(df0)\n", "df1 = df0.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)\n", "#df=pd.DataFrame(np.random.randn(20,3))\n", "#df.iloc[3,2]=5\n", "print len(df1)\n", "df2=df0[(np.abs(stats.zscore(df1))<8).all(axis=1)]\n", "print len(df2)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_test(remove_well, df_train):\n", " \n", " df_test=training_data2\n", " blind = df_test[df_test['Well Name'] == remove_well] \n", " training_data = df_train[df_train['Well Name'] != remove_well] \n", " \n", " correct_facies_labels_train = training_data['Facies'].values\n", " feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)\n", " #rf = RandomForestClassifier(max_depth = 15, n_estimators=600) \n", " #rf = RandomForestClassifier(max_depth = 7, n_estimators=600) \n", " rf = RandomForestClassifier(max_depth = 5, n_estimators=300,min_samples_leaf=15)\n", " rf.fit(feature_vectors, correct_facies_labels_train)\n", "\n", " correct_facies_labels = blind['Facies'].values\n", " features_blind = blind.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)\n", " scaler = preprocessing.StandardScaler().fit(feature_vectors)\n", " scaled_features =feature_vectors\n", " predicted_random_forest = rf.predict(features_blind)\n", "\n", " out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')\n", " return out_f1\n", "\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "training_data2=magic(training_data1)\n", "df_train=training_data2" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "well : CHURCHMAN BIBLE, f1 for different runs:\n", "average f1 is 0.540594, 2std is 0.006715\n", "well : SHANKLE, f1 for different runs:\n", "average f1 is 0.546993, 2std is 0.013393\n", "well : NOLAN, f1 for different runs:\n", "average f1 is 0.578795, 2std is 0.014426\n", "well : NEWBY, f1 for different runs:\n", "average f1 is 0.560259, 2std is 0.010439\n", "well : Recruit F9, f1 for different runs:\n", "average f1 is 0.487500, 2std is 0.075829\n", "well : CROSS H CATTLE, f1 for different runs:\n", "average f1 is 0.328543, 2std is 0.033836\n", "well : LUKE G U, f1 for different runs:\n", "average f1 is 0.637310, 2std is 0.012393\n", "well : SHRIMPLIN, f1 for different runs:\n", "average f1 is 0.595329, 2std is 0.005760\n", "overall average f1 is 0.534415\n" ] } ], "source": [ "wells=['CHURCHMAN BIBLE','SHANKLE','NOLAN','NEWBY','Recruit F9' ,'CROSS H CATTLE','LUKE G U','SHRIMPLIN']\n", "av_all=[]\n", "for remove_well in wells:\n", " all=[]\n", " print(\"well : %s, f1 for different runs:\" % (remove_well))\n", " for ii in range(5):\n", " out_f1=run_test(remove_well,df_train) \n", " if remove_well is not 'Recruit F9':\n", " all.append(out_f1) \n", " av1=np.mean(all) \n", " av_all.append(av1)\n", " print(\"average f1 is %f, 2std is %f\" % (av1, 2np.std(all)) )\n", "print(\"overall average f1 is %f\" % (np.mean(av_all)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train for the test data\n", "---------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = 'validation_data_nofacies.csv'\n", "test_data = pd.read_csv(filename)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_data1=magic(test_data)\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#test_well='STUART'\n", "test_well='CRAWFORD'\n" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [], "source": [ "blind = test_data1[test_data1['Well Name'] == test_well] \n", "training_data = training_data2\n", "\n", "correct_facies_labels_train = training_data['Facies'].values\n", "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)\n", "rf = RandomForestClassifier(max_depth = 14, n_estimators=2500,min_samples_leaf=15) \n", "rf.fit(feature_vectors, correct_facies_labels_train)\n", "\n", "features_blind = blind.drop(['Formation', 'Well Name', 'Depth'], axis=1)\n", "predicted_random_forest = rf.predict(features_blind)\n", "\n" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 8, 8, 8,\n", " 8, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6,\n", " 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 7, 9, 6, 6, 6,\n", " 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n", " 6, 6, 6, 6, 6, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 7, 7, 7, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 2, 2,\n", " 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2, 2, 2,\n", " 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n", " 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 3, 3, 3, 2, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 6, 6, 6, 8, 8, 8, 8, 9, 9,\n", " 9, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6,\n", " 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4,\n", " 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 8, 8, 4, 4, 8, 4, 4, 4, 4, 4, 4, 4, 4,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predicted_stu=predicted_random_forest\n", "predicted_stu" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 8, 7, 7, 7, 4, 4, 4, 4, 4, 4,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 8, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 8,\n", " 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 6, 6, 6, 6,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 6, 6,\n", " 6, 6, 6, 6, 6, 6, 6, 6, 8, 3, 2, 6, 8, 8, 8, 6, 6, 6, 5, 6, 5, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n", " 8, 8, 8, 8, 8, 6, 6, 8, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7,\n", " 7, 7, 7, 7, 8, 8, 8, 8, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n", " 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 6, 7, 7, 7, 8, 7, 7, 7, 7, 7, 7, 1,\n", " 1, 1, 1, 7, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 1, 1, 7, 7, 7, 7, 7,\n", " 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 6, 6, 8, 8, 3, 3, 3, 3, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3])" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predicted_craw=predicted_random_forest\n", "predicted_craw" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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 }	apache-2.0
joefutrelle/scipy-talk	talk5/decorators1.ipynb	1	16348	{ "metadata": { "name": "", "signature": "sha256:c2c5a9a7b82245e2217ae3cb3b1614575a29e5728fefe56536955b4bf182adbb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# A function can accept a function as an argument, and can return a function\n", "# This is useful if you want to reuse code that calls a function,\n", "# and don't want to specify in advance which function it calls" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# first, a little syntax\n", "# if you want a function to take any number of arguments,\n", "# use \"\" before the parameter name and the parameter's value\n", "# will be a tuple of arguments passed to the function\n", "\n", "def multiple(args):\n", " return args\n", "\n", "multiple('blah', 1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "('blah', 1)" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "multiple('one', 2, 'tres', 4.0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "('one', 2, 'tres', 4.0)" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# you can also use \"\" before a parameter name you're passing to a function,\n", "# and it will convert the value of the parameter (which must be iterable)\n", "# to the arguments of the function being called, in order\n", "\n", "def print_msg(text, number):\n", " print '%s %g' % (text, number)\n", " \n", "args = ('blah', 1)\n", "\n", "print_msg(args)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "blah 1\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "args = ('one', 2, 'tres', 4.0)\n", "\n", "print_msg(args)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "print_msg() takes exactly 2 arguments (4 given)", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-5-fd04e664eaf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'one'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tres'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.0\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[0mprint_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: print_msg() takes exactly 2 arguments (4 given)" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# more syntax: you can define a function inside another function. its name is only usable\n", "# in the function that it is defined in. it can reference variables in the enclosing function\n", "\n", "def hello(who='world'):\n", " def make_greeting():\n", " return 'Hello, %s.' % who\n", " print make_greeting()\n", "\n", "hello()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hello, world.\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# but be very very careful in the inner function not to try to assign a value to a variable\n", "# from the enclosing function, as the assignment will create a new variable with the same name\n", "# only visible within the inner function.\n", "\n", "# this example does not work:\n", "\n", "def avg(numbers):\n", " sm = 0\n", " ct = 0\n", " def add(n):\n", " sm = sm + n\n", " ct = ct + 1\n", " for n in numbers:\n", " add(n)\n", " return sm / ct\n", "\n", "avg(range(10))" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "UnboundLocalError", "evalue": "local variable 'sm' referenced before assignment", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-5-c60ae4e18382>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msm\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mavg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-5-c60ae4e18382>\u001b[0m in \u001b[0;36mavg\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mct\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msm\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-5-c60ae4e18382>\u001b[0m in \u001b[0;36madd\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mct\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'sm' referenced before assignment" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# because local variables are usually desirable in a function,\n", "# this behavior is correct and by design, but not useful in this case.\n", "# there is no clean way to solve this problem in Python 2.7.\n", "# here is a workaround:\n", "\n", "def avg(numbers):\n", " state = {\n", " 'sum': 0.,\n", " 'count': 0.\n", " }\n", " def add(n):\n", " state['sum'] += n\n", " state['count'] += 1\n", " for n in numbers:\n", " add(n)\n", " return state['sum'] / state['count']\n", "\n", "# this works because \"state\" is never assigned in the inner function. modifying its\n", "# contents doesn't assign a new value to the variable called \"state\"; its value is\n", "# still the same mutable dict object that was assigned to it in the outer function.\n", "\n", "avg(range(10))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "4.5" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# back to decorators.\n", "\n", "# lets define a function that does something. this converts its argument\n", "# to a string and returns a list of the characters in the string\n", "\n", "str(3.1415926535)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "'3.1415926535'" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "list(str(3.1415926353))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "['3', '.', '1', '4', '1', '5', '9', '2', '6', '3', '5', '3']" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "def charlist(value):\n", " return list(str(value))\n", "\n", "charlist(78)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "['7', '8']" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "d = {\n", " 'x': 3\n", "}\n", "\n", "d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "{'x': 3}" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "charlist(d)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "['{', \"'\", 'x', \"'\", ':', ' ', '3', '}']" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "# here's another simple function that just computes the sum of all\n", "# integers up to n non-inclusive\n", "\n", "def sum_to(n):\n", " return sum(range(n))\n", "\n", "sum_to(10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "45" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "# as_charlist, given a function fn, returns a funtion inner\n", "# that calls fn with its arguments and passes the result to\n", "# charlist before returning it\n", "\n", "def as_charlist(fn):\n", " def inner(args):\n", " return charlist(fn(args))\n", " return inner\n", "\n", "s = as_charlist(sum_to)\n", "s" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "<function __main__.inner>" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "s(10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "['4', '5']" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "# what if we made this the new definition of the function\n", "# we want to treat this way?\n", "\n", "def sum_to(n):\n", " return sum(range(n))\n", "\n", "sum_to = as_charlist(sum_to)\n", "\n", "sum_to(13)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "['7', '8']" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "# that's precisely what the decorator notation does\n", "\n", "@as_charlist\n", "def sum_to(n):\n", " return sum(range(n))\n", "\n", "sum_to(20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "['1', '9', '0']" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "# decorators can be stacked\n", "\n", "def backwards(fn):\n", " def inner(args):\n", " return list(reversed(fn(args)))\n", " return inner\n", "\n", "@backwards\n", "@as_charlist\n", "def sum_to(n):\n", " return sum(range(n))\n", "\n", "sum_to(20)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 68, "text": [ "['0', '9', '1']" ] } ], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "# decorators can take arguments, but that requires an additional level of \"wrapping\"\n", "\n", "def as_charlist(upcase=False):\n", " def outer(fn):\n", " def inner(args):\n", " s = str(fn(args))\n", " if upcase:\n", " s = s.upper()\n", " return list(s)\n", " return inner\n", " return outer" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "@as_charlist(True)\n", "def greeting(who='world'):\n", " return 'Hi, %s.' % who\n", "\n", "greeting('everyone')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 90, "text": [ "['H', 'I', ',', ' ', 'E', 'V', 'E', 'R', 'Y', 'O', 'N', 'E', '.']" ] } ], "prompt_number": 90 } ], "metadata": {} } ] }	mit
radhikapc/foundation-homework	homework06/Homework06-Dark Sky Forecast API-Radhika.ipynb	1	13140	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Make a request from the Forecast.io API for where you were born (or lived, or want to visit!) ##\n", "\n", "Tip: Once you've imported the JSON into a variable, check the timezone's name to make sure it seems like it got the right part of the world!\n", "Tip 2: How is north vs. south and east vs. west latitude/longitude represented? Is it the normal North/South/East/West?\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bangalore is in Asia/Kolkata timezone\n" ] } ], "source": [ "#https://api.forecast.io/forecast/APIKEY/LATITUDE,LONGITUDE,TIME\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/12.971599,77.594563')\n", "data = response.json()\n", "#print(data)\n", "#print(data.keys())\n", "print(\"Bangalore is in\", data['timezone'], \"timezone\")\n", "timezone_find = data.keys()\n", "#find representation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The longitude is 77.594563 The latitude is 12.971599\n" ] } ], "source": [ "print(\"The longitude is\", data['longitude'], \"The latitude is\", data['latitude'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. What's the current wind speed? How much warmer does it feel than it actually is?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The current windspeed at New York is 6.31\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "#print(data.keys())\n", "print(\"The current windspeed at New York is\", data['currently']['windSpeed'])\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It is 64.26 warmer it feels than it actually is\n" ] } ], "source": [ "#print(data['currently']) - find how much warmer\n", "print(\"It is\",data['currently']['apparentTemperature'], \"warmer it feels than it actually is\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Moon Visible in New York \n", "\n", "The first daily forecast is the forecast for today. For the place you decided on up above, how much of the moon is currently visible?" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The visibility of moon today in New York is 0.13 and is in the middle of new moon phase and the first quarter moon\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "#print(data.keys())\n", "#print(data['daily']['data'])\n", "\n", "now_moon = data['daily']['data']\n", "for i in now_moon:\n", " print(\"The visibility of moon today in New York is\", i['moonPhase'], \"and is in the middle of new moon phase and the first quarter moon\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. What's the difference between the high and low temperatures for today?" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temparature difference for today approximately is 9\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941, 2016-06-08T09:00:46-0400')\n", "data = response.json()\n", "TemMax = data['daily']['data']\n", "for i in TemMax:\n", " tem_diff = i['temperatureMax'] - i['temperatureMin']\n", " print(\"The temparature difference for today approximately is\", round(tem_diff))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Next Week's Prediction\n", "\n", "Loop through the daily forecast, printing out the next week's worth of predictions. I'd like to know the high temperature for each day, and whether it's hot, warm, or cold, based on what temperatures you think are hot, warm or cold." ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The high temperature for the day 1 is 66.3 and the low temperature is 56.93\n", "It's a warm day!\n", "The high temperature for the day 2 is 74.47 and the low temperature is 53.1\n", "It's a warm day!\n", "The high temperature for the day 3 is 75.89 and the low temperature is 56.6\n", "It's a warm day!\n", "The high temperature for the day 4 is 75.17 and the low temperature is 59.3\n", "It's a warm day!\n", "The high temperature for the day 5 is 79.85 and the low temperature is 65.16\n", "It's very hot weather\n", "The high temperature for the day 6 is 68.26 and the low temperature is 60.65\n", "It's very hot weather\n", "The high temperature for the day 7 is 74.71 and the low temperature is 62.07\n", "It's very hot weather\n", "The high temperature for the day 8 is 76.22 and the low temperature is 61.69\n", "It's very hot weather\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.712784,-74.005941')\n", "data = response.json()\n", "temp = data['daily']['data']\n", "#print(temp)\n", "count = 0\n", "for i in temp:\n", " count = count+1\n", " print(\"The high temperature for the day\", count, \"is\", i['temperatureMax'], \"and the low temperature is\", i['temperatureMin'])\n", " if float(i['temperatureMin']) < 40:\n", " print(\"it's a cold weather\")\n", " elif (float(i['temperatureMin']) > 40) & (float(i['temperatureMin']) < 60):\n", " print(\"It's a warm day!\")\n", " else:\n", " print(\"It's very hot weather\")\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.Weather in Florida\n", "What's the weather looking like for the rest of today in Miami, Florida? I'd like to know the temperature for every hour, and if it's going to have cloud cover of more than 0.5 say \"{temperature} and cloudy\" instead of just the temperature." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temperature in Miami, Florida on 9th June in the 1 hour is 77.52\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 2 hour is 77.49\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 3 hour is 77.69\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 4 hour is 77.69\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 5 hour is 77.83\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 6 hour is 78.02\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 7 hour is 77.77\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 8 hour is 77.99\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 9 hour is 78.94\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 10 hour is 80.37\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 11 hour is 82.11\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 12 hour is 83.76\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 13 hour is 84.98\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 14 hour is 85.84\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 15 hour is 86.27\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 16 hour is 85.79\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 17 hour is 85.37\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 18 hour is 84.97\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 19 hour is 84.2\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 20 hour is 83.44\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 21 hour is 82.71\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 22 hour is 82.04\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 23 hour is 81.28\n", "and is cloudy\n", "The temperature in Miami, Florida on 9th June in the 24 hour is 80.47\n", "and is cloudy\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/25.761680,-80.191790, 2016-06-09T12:01:00-0400')\n", "data = response.json()\n", "#print(data['hourly']['data'])\n", "Tem = data['hourly']['data']\n", "count = 0\n", "for i in Tem:\n", " count = count +1\n", " print(\"The temperature in Miami, Florida on 9th June in the\", count, \"hour is\", i['temperature'])\n", " if float(i['cloudCover']) > 0.5:\n", " print(\"and is cloudy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Temperature in Central Park \n", "\n", "What was the temperature in Central Park on Christmas Day, 1980? How about 1990? 2000?" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The temperature in Central Park, NY on the Christmas Day of 1980 was 3.57\n", "The temperature in Central Park, NY on the Christmas Day of 1990 was 30.28\n", "The temperature in Central Park, NY on the Christmas Day of 2000 was 20.68\n" ] } ], "source": [ "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 1980-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 1980 was\", Temp)\n", "\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 1990-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 1990 was\", Temp)\n", "\n", "response = requests.get('https://api.forecast.io/forecast/4da699cf85f9706ce50848a7e59591b7/40.771133,-73.974187, 2000-12-25T12:01:00-0400')\n", "data = response.json()\n", "Temp = data['currently']['temperature']\n", "print(\"The temperature in Central Park, NY on the Christmas Day of 2000 was\", Temp)" ] }, { "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
diging/methods	1.2 Change and difference/1.2.1 Linear model with OLS.ipynb	1	94993	{ "cells": [ { "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": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nltk\n", "from tethne.readers import zotero\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "# I moved our normalization and filtering functions into\n", "# an external module. If you want to change these functions,\n", "# you an either redefine them in this notebook or modify\n", "# them in the file helpers.py.\n", "from helpers import normalize_token, filter_token" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.2. Change over time\n", "\n", "In computational humanities, we are often interested in whether and how phenomena change over time. In this notebook we will perform a simple time-series analysis of tokens in our corpus. This will get us moving toward analyizing more complex temporal trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load corpus and metadata\n", "\n", "Just as we did in the last notebook, we'll load our texts and metadata." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "text_root = '../data/EmbryoProjectTexts/files'\n", "zotero_export_path = '../data/EmbryoProjectTexts'\n", "\n", "documents = nltk.corpus.PlaintextCorpusReader(text_root, 'https.+')\n", "metadata = zotero.read(zotero_export_path, index_by='link', follow_links=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Has the prevalence of a token increased or decreased over time?\n", "\n", "In the last notebook, we looked at the distribution (frequency) of tokens over time. It is one thing to make a pretty figure; it is another to say with some degree of confidence that a token is becoming more or less prevalent over time.\n", "\n", "The simplest approach to this problem is a linear regression model:\n", "\n", "$Y_i = \\beta_0 + \\beta X_i + \\epsilon_i$\n", "\n", "where $Y$ is the response variable (frequency of a token), $X$ is the predictor variable (publication date), $\\beta$ is the regression coefficient, and $\\epsilon_i$ is the error for observation $i$.\n", "\n", "Up to now, we have discussed token frequency in terms of raw token counts. Since the number of texts per year may not be fixed, however, what we really want to model is the probability of a token. We don't have direct access to the probability of a token, but for most practical purposes the Maximum Likelihood Estimator for that probability is just the frequency $f(t) = \\frac{N_{token}}{N_{total}}$ of the token.\n", "\n", "To get the Probability Distribution of a token, we first calculate the frequency distribution:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "word_counts = nltk.FreqDist([normalize_token(token) \n", " for token in documents.words() \n", " if filter_token(token)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$N_{embryo}$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N_e 5302\n", "N 544480\n" ] } ], "source": [ "print 'N_e', word_counts['embryo']\n", "print 'N', word_counts.N()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$f(\"embryo\") = \\frac{N_{embryo}}{N}$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.009737731413458713" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word_counts.freq('embryo')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...and then we use NLTK's [``MLEProbDist``](http://www.nltk.org/api/nltk.html#nltk.probability.MLEProbDist) (Maximum Likelihood Estimator) to obtain the probability distribution." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "word_probs = nltk.MLEProbDist(word_counts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$p(\"embryo\") ~= \\hat{p}(\"embryo\") = f(\"embryo\") $" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.00973773141346\n" ] } ], "source": [ "print word_probs.prob('embryo') # Probability of an observed token to be 'embryo'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are interested in change over time, we need to generate a conditional probability distribution. \n", "\n", "Here is our conditional frequency distribution (as before):\n", "\n", "$N(\"embryo\" \\Bigm\| year)$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "word_counts_over_time = nltk.ConditionalFreqDist([\n", " (metadata[fileid].date, normalize_token(token))\n", " for fileid in documents.fileids()\n", " for token in documents.words(fileids=[fileid])\n", " if filter_token(token)\n", " ])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "embryo_counts = pd.DataFrame(columns=['Year', 'Count'])\n", "for i, (year, counts) in enumerate(word_counts_over_time.items()):\n", " embryo_counts.loc[i] = [year, counts['embryo']]" ] }, { "cell_type": "code", "execution_count": 27, "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>Year</th>\n", " <th>Count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2016</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2007</td>\n", " <td>434</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2008</td>\n", " <td>433</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2009</td>\n", " <td>350</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2010</td>\n", " <td>1140</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2011</td>\n", " <td>575</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2012</td>\n", " <td>366</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2013</td>\n", " <td>482</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2014</td>\n", " <td>1242</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2015</td>\n", " <td>254</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Count\n", "0 2016 26\n", "1 2007 434\n", "2 2008 433\n", "3 2009 350\n", "4 2010 1140\n", "5 2011 575\n", "6 2012 366\n", "7 2013 482\n", "8 2014 1242\n", "9 2015 254" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embryo_counts" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x117fb3e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(embryo_counts.Year, embryo_counts.Count)\n", "plt.ylabel('Word count')\n", "plt.xlabel('Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$f(\"embryo\" \\Bigm\| year)$" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11524c6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "embryo_freq = pd.DataFrame(columns=['Year', 'Frequency'])\n", "for i, (year, counts) in enumerate(word_counts_over_time.items()):\n", " embryo_freq.loc[i] = [year, counts.freq('embryo')]\n", " \n", "plt.scatter(embryo_freq.Year, embryo_freq.Frequency)\n", "plt.ylabel('Word frequency')\n", "plt.xlabel('Year')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\hat{p}(\"embryo\" \\Bigm\| year)$" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "word_probs_over_time = nltk.ConditionalProbDist(word_counts_over_time, nltk.MLEProbDist)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e471250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "embryo_prob = pd.DataFrame(columns=['Year', 'Probability'])\n", "for i, (year, probs) in enumerate(word_probs_over_time.items()):\n", " embryo_prob.loc[i] = [year, probs.prob('embryo')]\n", " \n", "plt.scatter(embryo_prob.Year, embryo_prob.Probability)\n", "plt.ylabel('Conditional word probability')\n", "plt.xlabel('Year')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N(w\|c=2016) = 26\n", "f(w\|c=2016) = 0.00832\n", "^p(w\|c=2016) = 0.00832\n" ] } ], "source": [ "print 'N(w\|c=2016) =', word_counts_over_time[2016]['embryo']\n", "print 'f(w\|c=2016) =', word_counts_over_time[2016].freq('embryo')\n", "print '^p(w\|c=2016) =', word_probs_over_time[2016].prob('embryo')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll take a look at the token that we'd like to analyze. Let's try ``chicken``.\n", "\n", "Here we get the probability for each year for the token ``chicken``:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "chicken_data = pd.DataFrame(columns=['Year', 'Probability'])\n", "for i, (year, probs) in enumerate(word_probs_over_time.items()):\n", " chicken_data.loc[i] = [year, probs.prob('chicken')]" ] }, { "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>Year</th>\n", " <th>Probability</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2016</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2007</td>\n", " <td>0.000223</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2008</td>\n", " <td>0.000286</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2009</td>\n", " <td>0.000115</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2010</td>\n", " <td>0.000170</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2011</td>\n", " <td>0.000094</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2012</td>\n", " <td>0.000052</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2013</td>\n", " <td>0.000165</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2014</td>\n", " <td>0.000071</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2015</td>\n", " <td>0.000049</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Probability\n", "0 2016 0.000000\n", "1 2007 0.000223\n", "2 2008 0.000286\n", "3 2009 0.000115\n", "4 2010 0.000170\n", "5 2011 0.000094\n", "6 2012 0.000052\n", "7 2013 0.000165\n", "8 2014 0.000071\n", "9 2015 0.000049" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chicken_data" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11810abd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a scatterplot.\n", "plt.scatter(chicken_data.Year, chicken_data.Probability) \n", "\n", "# Scale the Y axis.\n", "plt.ylim(chicken_data.Probability.min(), chicken_data.Probability.max()) \n", "\n", "# Scale the X axis.\n", "plt.xlim(chicken_data.Year.min(), chicken_data.Year.max())\n", "\n", "plt.ylabel('$\\\\hat{p}(\\'chicken\\'\|year)$')\n", "plt.show() # Render the figure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SciPy package provides a Ordinary Least Squares linear regression function called [``linregress()``](http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html). We can use that to estimate the model parameters from our data." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^Beta: -2.38956045956e-05\n", "^Beta_0: 0.0481885497774\n", "r-squared: 0.676091199243\n", "p: 0.00350319816755\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x118104cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import linregress\n", "Beta, Beta0, r, p, stde = linregress(chicken_data.Year, chicken_data.Probability)\n", "\n", "print '^Beta:', Beta\n", "print '^Beta_0:', Beta0\n", "print 'r-squared:', rr\n", "print 'p:', p\n", "\n", "plt.scatter(chicken_data.Year, chicken_data.Probability) \n", "plt.plot(chicken_data.Year, Beta0 + Betachicken_data.Year) # Array math!\n", "plt.ylim(chicken_data.Probability.min(), chicken_data.Probability.max()) \n", "plt.xlim(chicken_data.Year.min(), chicken_data.Year.max())\n", "\n", "plt.ylabel('$\\\\hat{p}(\\'chicken\\'\|year)$')\n", "plt.show() # Render the figure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first pass, our linear model looks like a remarkably good fit. Our r-squared value is not too bad, and we have a very low p value. The problem with interpreting that p-value, however, is that data derived from texts rarely satisfy the assumptions of the t-test used to assess significance. Aside from the fact that we have very few \"observations\", does the distribution of Y values (token probabilities) shown below look normally distributed to you?" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11566add0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(chicken_data.Probability)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to use an hypothesis test that does not assume normality, and can handle the small sample size. One such approach is a [permutation test](http://avesbiodiv.mncn.csic.es/estadistica/permut2.pdf). \n", "\n", "Our null hypothesis is that:\n", "\n", "$H_0: \\beta = 0$\n", "\n", "That is, that there is no change in the probability of our token (``chicken``, in this case) over time. \n", "\n", "We will shuffle our response variable, $Y$, a whole bunch of times. Each time we shuffle the data (a permutation), we will re-calculate the regression parameter $\\beta_{\\pi}$. We can reject $H_0$ with a confidence of $p = 0.05$ iff our observed $\\hat{\\beta}$ falls outside the inner 95% of the resampled distribution." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We can use underscores `_` for values that we don't want to keep.\n", "samples = pd.DataFrame(columns=['Beta_pi', 'Beta0_pi'])\n", "for i in xrange(1000):\n", " shuffled_probability = np.random.permutation(chicken_data.Probability)\n", " # linregress() returns five parameters; we only care about the first two.\n", " Beta_pi, Beta0_pi, _, _, _ = linregress(chicken_data.Year, shuffled_probability)\n", " samples.loc[i] = [Beta_pi, Beta0_pi]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11789a890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "\n", "plt.subplot(121)\n", "plt.hist(samples.Beta_pi) # Histogram of Beta values from permutations.\n", "plt.plot([Beta, Beta], [0, 200], # Beta from the observed data.\n", " lw=5, label='$\\\\hat{\\\\beta}$')\n", "\n", "# Plot the upper and lower bounds of the inner 95% probability.\n", "Beta_upper = np.percentile(samples.Beta_pi, 97.5)\n", "Beta_lower = np.percentile(samples.Beta_pi, 2.5)\n", "plt.plot([Beta_upper, Beta_upper], [0, 200], color='k', lw=2, label='$p = 0.05$')\n", "plt.plot([Beta_lower, Beta_lower], [0, 200], color='k', lw=2)\n", "\n", "plt.legend()\n", "plt.xlabel('$\\\\beta_{\\\\pi}$', fontsize=24)\n", "\n", "# Same procedure for Beta0.\n", "plt.subplot(122)\n", "plt.hist(samples.Beta0_pi)\n", "plt.plot([Beta0, Beta0], [0, 200], lw=5, label='$\\\\hat{\\\\beta_0}$')\n", "\n", "Beta0_upper = np.percentile(samples.Beta0_pi, 97.5)\n", "Beta0_lower = np.percentile(samples.Beta0_pi, 2.5)\n", "plt.plot([Beta0_upper, Beta0_upper], [0, 200], color='k', lw=2, label='$p = 0.05$')\n", "plt.plot([Beta0_lower, Beta0_lower], [0, 200], color='k', lw=2)\n", "\n", "plt.legend()\n", "plt.xlabel('$\\\\beta_{0\\\\pi}$', fontsize=24)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the plots above, our $\\hat{\\beta}$ calculated from the observed data falls outside the inner 95% of the samples, and so we can reject $H_0$: the term ``chicken`` is decreasing in prevalance over time." ] } ], "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
jaduimstra/nilmtk	notebooks/experimental/mle.ipynb	7	187635	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This script is a example of the Maximum Likelihood Estimation algorithm for energy disaggregation. \n", "\n", "It is liable when a single disaggregated appliance is desired instead of disaggregating many. Beside, the appliance should be mostly resistive to achieve accuracy. \n", "\n", "It is based on paired Events: \n", " - OnPower: delta value when the appliance is turned on.\n", " - OffPOwer: delta value when the appliance is turned off.\n", " - Duration: duration between onpower and offpower. \n", " \n", "Also, to discard many unlikely events, three constrains are set: \n", " - PowerNoise: mininum delta value below which the delta is considered noise.\n", " - PowerPair: maximum difference between OnPower and OffPower (considering appliances with constans energy consumption). \n", " - timeWindow: maximum time frame between Onpower and Offpower. \n", " \n", "Features aforementioned are modeled with Gaussian, Gaussian Mixtures or Poisson. For each incoming paired event, the algorithm will extract these three features and will evaluate the maximum likelihood probability for that paired event of being a certain appliance. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### IMPORTS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from os.path import join\n", "from pylab import rcParams\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "rcParams['figure.figsize'] = (13, 6)\n", "#plt.style.use('ggplot')\n", "from datetime import datetime as datetime2\n", "from datetime import timedelta\n", "\n", "import nilmtk\n", "from nilmtk.disaggregate.maximum_likelihood_estimation import MLE\n", "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n", "from scipy.stats import poisson, norm\n", "from sklearn import mixture\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Functions " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_all_appliances(appliance):\n", "\n", " # Filtering by appliances: \n", " print \"Fetching \" + appliance + \" over data loaded to nilmtk.\"\n", " metergroup = nilmtk.global_meter_group.select_using_appliances(type=appliance)\n", " \n", " if len(metergroup.appliances) == 0: \n", " print \"None \" + appliance + \" found on memory.\"\n", " pass\n", "\n", " # Selecting only single meters: \n", " print \"Filtering to get one meter for each \" + appliance\n", "\n", " meters = [meter for meter in metergroup.meters if (len(meter.appliances) == 1)]\n", " metergroup = MeterGroup(meters)\n", " print metergroup\n", " print \"Found \" + str(len(metergroup.meters)) + \" \" + appliance\n", "\n", " return metergroup\n", "\n", "\n", "def get_all_trainings(appliance, train):\n", "\n", " # Filtering by appliances: \n", " print \"Fetching \" + appliance + \" over data train data.\"\n", " elecs = []\n", " for building in train.buildings: \n", " print \"Building \" + str(building) + \"...\"\n", " elec = train.buildings[building].elec[appliance]\n", " if len(elec.appliances) == 1: \n", " print elec\n", " print \"Fetched elec.\"\n", " elecs.append(elec)\n", "\n", " else: \n", " print elec\n", " print \"Groundtruth does not exist. Many appliances or None\"\n", "\n", " metergroup = MeterGroup(elecs)\n", "\n", " return metergroup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "path = '../../../nilmtk/data/ukdale'\n", "ukdale = train = DataSet(join(path, 'ukdale.h5'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And spliting into train and test data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded 5 buildings\n" ] } ], "source": [ "train = DataSet(join(path, 'ukdale.h5'))\n", "test = DataSet(join(path, 'ukdale.h5'))\n", "train.set_window(end=\"17-5-2013\")\n", "test.set_window(start=\"17-5-2013\")\n", "#zoom.set_window(start=\"17-5-2013\")\n", "print('loaded ' + str(len(ukdale.buildings)) + ' buildings')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting the training data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The selected appliance might not be trained from ElecMeters where other appliances are presented as we can extract the groundtruth" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching kettle over data train data.\n", "Building 1...\n", "ElecMeter(instance=10, building=1, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1), Appliance(type='food processor', instance=1), Appliance(type='toasted sandwich maker', instance=1)])\n", "Groundtruth does not exist. Many appliances or None\n", "Building 2...\n", "ElecMeter(instance=8, building=2, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n", "Fetched elec.\n", "Building 3...\n", "ElecMeter(instance=2, building=3, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n", "Fetched elec.\n", "Building 4...\n", "ElecMeter(instance=3, building=4, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1), Appliance(type='radio', instance=1)])\n", "Groundtruth does not exist. Many appliances or None\n", "Building 5...\n", "ElecMeter(instance=18, building=5, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n", "Fetched elec.\n" ] } ], "source": [ "# Appliance to disaggregate: \n", "applianceName = 'kettle'\n", "# Groundtruth from the training data: \n", "metergroup = get_all_trainings(applianceName,train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MLE algorithm " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we create the model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ " mle = MLE()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we update the model parameter with some guessing values. \n", "\n", "First guess for features: onpower and offpower gaussian mixtures and duration poisson. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Updating model\n", "{'resistive': True, 'appliance': 'kettle', 'sampling_method': 'first', 'sample_period': '10S', 'thLikelihood': 1e-10, 'timeWindow': 400, 'units': ('power', 'active'), 'thDelta': 1500, 'powerNoise': 50, 'powerPair': 100}\n" ] } ], "source": [ "# setting parameters in the model: \n", "mle.update(appliance=applianceName, resistive=True, units=('power','active'), thDelta= 1500, thLikelihood= 1e-10, powerNoise= 50, powerPair= 100, timeWindow= 400, sample_period= '10S', sampling_method='first')\n", "\n", "# Settings the features parameters by guessing: \n", "mle.onpower = {'name':'gmm', 'model': mixture.GMM(n_components=2)}\n", "mle.offpower = {'name':'gmm', 'model': mixture.GMM(n_components=2)}\n", "mle.duration = {'name':'poisson', 'model': poisson(0)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Training the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We train the model with all ocurrences of that model of appliance found on the training data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('kettle', 1)\n", "Training on chunk\n", "Samples of onpower: 214\n", "Samples of offpower: 214\n", "Samples of duration: 214\n", "Training onpower\n", "Training offpower\n", "Training duration\n", "('kettle', 2)\n", "Training on chunk\n", "Samples of onpower: 92\n", "Samples of offpower: 92\n", "Samples of duration: 92\n", "Training onpower\n", "Training offpower\n", "Training duration\n", "('kettle', 3)\n", "Chunk empty\n" ] } ], "source": [ "mle.train(metergroup)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we visualize features with featureHist_colors() to see the distribution and how many samples we have for each appliance (same model from different houses). " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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joBGBxMuIjtaXZtKWAYcC9wAHp21JkiRJbaIRTZueBratSnuUCC4kSZIktaFG\njtokSZIkaZwwkJAkSZJUmIGEJEmSpMIaEUjMAr4P3AXcCbwJmA2sIjpbX5U+I0mSJKlNNCKQ+DLw\nH8CuwO7A3cASIpDYBbg6bUuSJElqE2UHEtsAbwa+lbZfAB4HFgErUtoK4IiS8yFJkiRpDJUdSOxE\nzGZ9LnAT8E1iXom5xOzWpNe5JedDkiRJ0hgqO5CYAuwF/Ft6fZrNmzH1p0WSJElSmyh7Qro1afl1\n2v4+cDLQB3Sm13nAujr7d2fWe9IiSZIkqbaFaSld2YFEH/Ag0an6HmI26zvSshg4Nb2urLN/d8n5\nkyRJksaTHgY/fF9a1onKDiQAjge+A0wF7gM+CnQAFwHHAL3AkQ3IhyRJkqQx0ohA4lbgjTXSD2nA\nuSVJkiSVwJmtJUmSJBVmICFJkiSpMAMJSZIkSYU1oo9EL/AE8CKwCdgHmA1cCOxIpbP1hgbkRZIk\nSdIYKFojMRvYveA+/cRYtnsSQQTEpHSriGFhr2bzSeokSZIktbA8gcQ1wNZEEHEjcDZwesHzTKra\nXgSsSOsrgCMKHk+SJElSE+UJJLYhmib9OXAeUatQZOjWfuDHwGrgYyltLrA2ra9N25IkSZLaRJ4+\nEh3APKIfw+dSWn+BcxwAPAxsRzRnurvq/f6Cx5MkSZLUZHkCic8DPwKuBW4AdgZ+W+AcD6fXR4DL\niBqNtUAn0EcEKevq7NudWe9h8HTfktTinlwAC5YX3299H/Tad0ySNBIL01K6PIHEwwzuYH0f+ftI\nzCBqNJ4EXga8DTgFuBxYDJyaXlfW2b8753kkKYeuZTCns/h+mxYQI8wVNHM6rB7Bfnt3jeh0kiRt\n/vB9aVknyhNIfJUYcSnrK8BeOfadS9RCDJzrO8BVRH+Ji4BjqAz/Kkklm9M5shv7BQeOeVYkSWpz\nQwUS+wH7E30bTqQy8tJWRC1DHg8Ae9RIf5RiHbYlSZIktZChAompVIKGrTLpTwDvKzNTkiRJklrb\nUIHENWlZjo11JUmSJGXk6SMxDfgm0JX5fD9wcEl5kiRJktTi8gQSFwNnEjNav5jSnPdBkiRJmsDy\nBBKbiEBipDqIUZrWAO8CZgMXAjtSGbFpwyiOL0mSJKnBJuf4zBXAccTEcbMzS14nAHdSqcVYQsxw\nvQtwddqWJEmS1EbyBBJHA58GrgNuzCx5bA+8nWgWNTB87CJgRVpfARyR81iSJEmSWkSepk1dozj+\n6cBngK0ttwJlAAAgAElEQVQzaXOBtWl9bdqWJEmS1EbyBBKLqd25+rxh9nsnsA64GVhY5zP9dY49\noDuz3sPg6b4lSZIkDbaQ+vfeYypPIPFGKjf704lhX29i+EBif6IZ09uBLYlaifOJWohOoI/od7Fu\niGN058ifJEmSpNDD4IfvS8s6UZ5A4m+rtmcRoy4N57NpATiI6GfxYeA0opbj1PS6MldOJUmSJLWM\nPJ2tq20EdhrBfgO1GsuAQ4F7iNqNZSM4liRJkqQmylMjcUVmfTKwG3BRwfNckxaAR4FDCu4vSZIk\nqYXkCSS+mF77gReA3wEPlpYjSZIkSS0vT9OmHuBuorP0y4HnysyQJEmSpNaXJ5A4ErgeeH9avyGt\nS5IkSZqg8jRt+hwxBOzAMK3bAVcDFw+z35ZEv4hpwFTgB8DJwGxi1KcdgV4iONlQMN+SJEmSmihP\njcQk4JHM9vqUNpxngT8F9gB2T+sHAkuAVcAuRECypEB+JUmSJLWAPDUSVwI/Ar5LBBAfAP4z5/E3\nptepQAfwGDFJ3UEpfQXRB8NgQpIkSWojQwUSrwXmAp8B3gsckNKvI4KKPCYTs2DvDJwJ3JGOuTa9\nvzZtS5IkSWojQzVtOgN4Iq1fApyYlpXA6TmP/xLRtGl74C1E86asfioT1UmSJElqE0PVSMwFbquR\nfhvFZ7Z+HPghsICohegE+oB5VDpx19KdWe9JiyRJkqTaFqaldEMFErOGeG/LHMfelpjAbgMwHTgU\nOAW4HFgMnJpeVw5xjO4c55EkSZIUehj88H1pWScaKpBYDXwcOKsq/WPAjTmOPY/oTD05LecTozTd\nDFwEHENl+FdJkiRJbWSoQOKTwGXAX1AJHBYQ80K8J8exbwf2qpH+KHBIgTxKkiRJajFDBRJ9wP5E\nB+k/ITpF/zvwkwbkS5IkSVILG24eiX4icDB4kCRJkvQ/8sxsLUmSJEmD5JnZejR2AM4DXkHUbpwF\nfAWYDVwI7Eilw/WGkvMiaaKbcf8Cph+wR+H9nrtrPtMOOKLwfhsfnM0zhfeSJKktlB1IbAI+BdwC\nzCQ6ba8CPppeTwNOApakRZLKs+UL0zl4/prC+13z6w4Oml/8YceqO189skDiyQWwYHnx/db3Qa/X\nUklSQ5QdSPSlBeAp4C5gPrAIOCilryDGuvXHT5IAmDkdVvcW32/vrqjklSSpfI3sI9EF7AlcT8ya\nvTalr03bkiRJktpEowKJmcAlwAnAk1Xv9adFkiRJUpsou2kTwBZEEHE+sDKlrQU6iWZP84B1dfbt\nzqz3MHi6b0mSJEmDLUxL6coOJCYB5wB3Amdk0i8HFgOnpteVm+8KDA4kJEmSJA2th8EP35eWdaKy\nA4kDgA8BtwE3p7STgWXARcAxVIZ/lSRJktQmyg4kfkH9fhiHlHxuSZIkSSVxZmtJkiRJhTWis7Uk\nTUz9G6cxxxmxJUnjk4GEJJVlWv+kxs6ILUlS49i0SZIkSVJhZQcS3yLmjLg9kzYbWAXcA1wFzCo5\nD5IkSZLGWNmBxLnAYVVpS4hAYhfg6rQtSZIkqY2U3Ufi50BXVdoi4KC0voKYMMNgQlJ+s1jGdDoL\n7/f8s3OANWOfIUmSJp5mdLaeSzR3Ir3ObUIeJLWz6XRyLL2F9/u3/o6xz0wJRjra0zP3d7CxhPxI\nklRDs0dt6k9LPd2Z9R4GT/ctSePTSEd7uvr27Q0kJGnCW5iW0jUjkFgLdAJ9wDxg3RCf7W5EhiRJ\nkqRxoofBD9+XlnWiZgz/ejmwOK0vBlY2IQ+SJEmSRqHsQOIC4DrgdcCDwEeBZcChxPCvB6dtSZIk\nSW2k7KZNR9VJP6Tk80qSJEkqUbM7W0uSxspL/XNgwfLiO67vg16H4ZYkFWIgIan9PD9tATfN3KPw\nfv2PTyshNy1kSges7i2+395djGA0XUnSxGYgIan99E+dzrSDi08sN2nlpBJyI0nShNSMUZsGHAbc\nDfwWOKmJ+ZAkSZJUULNqJDqArxGdrh8Cfk0MC3tXk/Kj8WkhTmKo0XjooVnMH8HEcM3ijNitaCFe\nhzQ6C7EMqUU1K5DYB7iXSqPc7wHvZuSBxBbA7BHuu46hZ9dW+1qIF1+Nxrp17RVIOCN2K1qI1yGN\nzkIsQ2pRzQok5hPzSgxYA7xpxEfr6Pgkc7Y6lsmTXyy034svdvDI4/8MLB/xuRumaxnM6Sy+n6Ox\nTEyWF41Hluva/LuoESxn2lyzAomxrgHooGPSJCYV7PLR/9IkmttPpIA5nY7GovwsLxqPLNe1+XdR\nI1jOtLlmjWCyL9BNdLgGOBl4CTg185l7gZ0bmy1JkiRpXLkPeE2zMzGWphBfqguYCtwC7NrMDEmS\nJElqD4cD/0XUPJzc5LxIkiRJkiRJkqTx7O+IvhHZoVtPJiapuxt4WyZ9AXB7eu/LmfRpwIUp/VfA\njiXmV63jn4BbiWZxVwM7pPQu4Bng5rT8W2Yfy5Cy6pUh8DqkfP6VGLb8VuBSYJuU3oXXIeVTrwyB\n1yHl837gDuBFYK9Mehfj/Dq0A3Al8ACVQGI34kd9C+IPcC+VDuE3EPNPAPwHlY7af0Plj/MBYk4K\njX9bZdaPB85O613Ef45aLEPKqleGvA61r78AftTA8x1KZeS/ZWkBr0PKr14Z8jqkvP4I2AX4KZsH\nEuP6OnQxsDuDA4mTgZMyn7mSGOFpHoMnq/sg8PXMZwbmoJgCPFJSftW6Tmb4H3DLkIaSLUNlX4d6\ngY3Ak2l5AhjB2OybHfPgUR5jNLqBTcR3eYLo//ZVRv+9htJF1Gi3yhDe7wG+nda78Dqk4rJlyPsh\nFZU3kBjTMtSsC/C7iUnobqtKf2VKH7CGmLyuOv2hlA6DJ7d7AXickc9yrfbyL8DvgMVUbgIBdiKq\n8XqAA1PafCxD2txAGToa+EJKK/s61A+8k6gR2QrYGugbxXcYOOZohvPuGIPzX0B8l5cTN0SdwI2M\nPJjI+/vUrGHMq/0l8WRvgNchFZUtQ94PaSyUfh0qM5BYRURC1csiItJemvlsq/wQqLXUK0PvSu//\nA/AqYmby01Pa74lmc3sCJwLfZXATFk0secvQucAZzchgxjbAOUQZXkP04Ri4Ru8M/AT4A/GE6NtU\n2lKfT3yHK4gajk8DC6n8GAzopVJr0Q18P+37OBGMD3X+4Uyich1/EbiTqBZ/hOgLBxGs/bxqv5eA\nV6f15cCZxI3UU+k7vIP4EXycCPiyvxs/S68biFqQfWucY3/g1+kzNwD7Zd7rAT4P/CLt/yNgTo3v\nNlwZgihHzxPXG/A6pMFGUoakrDxlqFpDrkNlzmx9aJ30PyEipFvT9vbEU6s3EVFRtsPj9sQP2kNp\nvTqd9N6riD/YFOLH8NHRZ18toF4ZqvZdKk9xnk8LwE3EfCWvxTI0UY2kDDXiOlTr4clyomZiZ2Am\n8O9EMHBWev9fiJvnbYBLiGDgU8CHiSdNxxDBBsRNeLX+qu1FwPvS/lsSNQr1zv8q4pr9egY/yRrK\nS8APgD/L+XmAo4ihwX9JdPrbF/gQ0ZHw9cSP6S3puG8mmsZuk84F0VZ4wGzgh8Dfpu92ZNreGXis\n6nxrgP8kgrDq4ciHK0NHA28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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5b7fdd0d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mle.featuresHist_colors()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, we have more events from some houses than others, as we see on the figure above. Therefore, we need to crop information to keep the same number of samples for everyhouse. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Retraining onpower\n", "Retraining offpower\n", "Retraining duration\n" ] } ], "source": [ "mle.no_overfitting()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iIUmSJA1ebvfVebeRWJ/+TgDGEY33jiQGogM4j+jWzHqzkoaxjgUwfWb29Vav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\nEWhFeZ9DGnpZ67znXd/9scemMnu2FSe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5b7f56ce50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mle.featuresHist_colors()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is another visualization tool to see how model distributions fit over the data: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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d1vPyyzlkHDKf26a/CVwCPIRjFDAK+LL0hwySTHwlEp8C6Z6uLSIihRwZwJ5A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\nPO+wJKmrMJHotqqkP3K3Vqm/cSWuY3npsEQv4BzgdOAIEv9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5b7ecae090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mle.featuresHist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Onpower and Offpower seem to fit well with the data but we need to change the model for duration" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mle.duration = {'name':'gmm', 'model': mixture.GMM(n_components=10)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we retrain the model and use no_overfitting" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('kettle', 1)\n", "Training on chunk\n", "Samples of onpower: 214\n", "Samples of offpower: 214\n", "Samples of duration: 214\n", "Training onpower\n", "Training offpower\n", "Training duration\n", "('kettle', 2)\n", "Training on chunk\n", "Samples of onpower: 92\n", "Samples of offpower: 92\n", "Samples of duration: 92\n", "Training onpower\n", "Training offpower\n", "Training duration\n", "('kettle', 3)\n", "Chunk empty\n", "Retraining onpower\n", "Retraining offpower\n", "Retraining duration\n" ] }, { "data": { "image/png": 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d1vPyyzlkHDKf26a/CVwCPIRjFDAK+LL0hwySTHwlEp8C6Z6uLSIihRwZwJ5A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\nkVbL25LFyNcGM+STfPotzaf7mnxs2NM52AhKvwbWuP0BpC0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gMy3/0E3XtUji1VgZcuiE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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5b7f6c18d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mle.train(metergroup)\n", "mle.no_overfitting()\n", "mle.featuresHist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the final model distribution for each feature. We need to the integrity of each distribution. Each CDF has to be bounded by one. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Onpower cdf: 0.986177684776\n", "Offpower cdf: 1.0\n", "Duration cdf: 0.987375070732\n" ] } ], "source": [ "mle.check_cdfIntegrity(step=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disaggregation" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Building to disaggregate: \n", "building = 2\n", "mains = test.buildings[building].elec.mains()\n", "\n", "# File to store the disaggregation\n", "filename= '/home/energos/Escritorio/ukdale-disag-ml.h5'\n", "output = HDFDataStore(filename, 'w')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step will take a few minutes" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25656 events found.\n", "12419 onEvents found\n", "4244 onEvents no paired.\n", "1 chunks disaggregated\n" ] } ], "source": [ "mle.disaggregate(mains, output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also receive some information such as total number of events, number of onpower events, number of onevents that have not been paired and chunks disaggregated" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing disaggregation with the groundtruth" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PYABTGh/XROjEr2CnrXxSLcU7YPRB5BsGaqi8LihqJyJKkzYjBDvKtQAhF+Va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\nURRFURRFURRFURRFURRFUZoq/w9hqtb27P1nfQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5b7efde790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Groundtruth\n", "kettle = test.buildings[building].elec.select_using_appliances(type=applianceName)\n", "output.load(mains.key).next().plot()\n", "kettle.plot()\n", "output.close()" ] }, { "cell_type": "markdown", "metadata": {}, "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 }	apache-2.0
elliotk/twitter_eda	develop/20171010_fastforwardlabs_tweet_counts.ipynb	1	524278	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.style.use('fivethirtyeight')\n", "\n", "import tweepy\n", "import numpy as np\n", "import pandas as pd\n", "from collections import Counter\n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Turn on retina mode for high-quality inline plot resolution\n", "from IPython.display import set_matplotlib_formats\n", "set_matplotlib_formats('retina')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.7.13'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Version of Python\n", "import platform\n", "platform.python_version()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import Twitter API keys\n", "from credentials import " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Helper function to connect to Twitter API\n", "def twitter_setup():\n", " auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n", " auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n", " \n", " api = tweepy.API(auth)\n", " return api" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of tweets extracted: 200.\n", "\n" ] } ], "source": [ "# Extract Twitter data\n", "extractor = twitter_setup()\n", "\n", "# Twitter user\n", "twitter_handle = 'fastforwardlabs'\n", "\n", "# Get most recent two hundred tweets\n", "tweets = extractor.user_timeline(screen_name=twitter_handle, count=200)\n", "print('Number of tweets extracted: {}.\\n'.format(len(tweets)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tweet activity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's explore counts by hour, day of the week, and weekday versus weekend hourly trends." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['__class__', '__delattr__', '__dict__', '__doc__', '__eq__', '__format__', '__getattribute__', '__getstate__', '__hash__', '__init__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_api', '_json', 'author', 'contributors', 'coordinates', 'created_at', 'destroy', 'entities', 'favorite', 'favorite_count', 'favorited', 'geo', 'id', 'id_str', 'in_reply_to_screen_name', 'in_reply_to_status_id', 'in_reply_to_status_id_str', 'in_reply_to_user_id', 'in_reply_to_user_id_str', 'is_quote_status', 'lang', 'parse', 'parse_list', 'place', 'possibly_sensitive', 'retweet', 'retweet_count', 'retweeted', 'retweets', 'source', 'source_url', 'text', 'truncated', 'user']\n" ] } ], "source": [ "# Inspect attributes of tweepy object\n", "print(dir(tweets[0])) # look at the first element/record" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmmm, what's this `created_at`* attribute?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017-10-02 17:30:43\n" ] } ], "source": [ "# What format is it in? answer: GMT, according to Twitter API\n", "print(tweets[0].created_at)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create datetime index: convert to GMT then to Eastern daylight time EDT\n", "tweet_dates = pd.DatetimeIndex([tweet.created_at for tweet in tweets], tz='GMT').tz_convert('US/Eastern')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hourly counts:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Count the number of tweets per hour\n", "num_per_hour = pd.DataFrame( { 'counts': Counter(tweet_dates.hour) })" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create hours data frame\n", "hours = pd.DataFrame({'hours': np.arange(24)})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there are hours of the day where there are no tweets, one must explicitly add any zero-count hours to the index." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Merge data frame objects on common index, peform left outer join and fill NaN with zero-values\n", "hour_counts = pd.merge(hours, num_per_hour, left_index=True, right_index=True, how='left').fillna(0)" ] }, { "cell_type": "code", "execution_count": 37, "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>hours</th>\n", " <th>counts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>6</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>7</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>8</td>\n", " <td>7.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>9</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>10</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>11</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>12</td>\n", " <td>34.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>13</td>\n", " <td>23.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>14</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>15</td>\n", " <td>23.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>16</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>17</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>18</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>19</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>20</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>21</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>22</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hours counts\n", "0 0 0.0\n", "1 1 0.0\n", "2 2 0.0\n", "3 3 1.0\n", "4 4 0.0\n", "5 5 0.0\n", "6 6 1.0\n", "7 7 2.0\n", "8 8 7.0\n", "9 9 11.0\n", "10 10 38.0\n", "11 11 15.0\n", "12 12 34.0\n", "13 13 23.0\n", "14 14 25.0\n", "15 15 23.0\n", "16 16 9.0\n", "17 17 4.0\n", "18 18 2.0\n", "19 19 1.0\n", "20 20 4.0\n", "21 21 0.0\n", "22 22 0.0\n", "23 23 0.0" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hour_counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Day of the week counts:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Count the number of tweets by day of the week\n", "num_per_day = pd.DataFrame( { 'counts': Counter(tweet_dates.weekday) })" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create days data frame\n", "days = pd.DataFrame({'day': np.arange(7)})" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Merge data frame objects on common index, perform left outer join and fill NaN with zero-values\n", "daily_counts = pd.merge(days, num_per_day, left_index=True, right_index=True, how='left').fillna(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weekday vs weekend hourly counts:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Flag the weekend from weekday tweets\n", "weekend = np.where(tweet_dates.weekday < 5, 'weekday', 'weekend')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Construct multiply-indexed DataFrame obj indexed by weekday/weekend and by hour\n", "by_time = pd.DataFrame([tweet.created_at for tweet in tweets], \n", " columns=['counts'],\n", " index=tweet_dates).groupby([weekend, tweet_dates.hour]).count() " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Optionally, set the names attribute of the index\n", "by_time.index.names=['daytype', 'hour']" ] }, { "cell_type": "code", "execution_count": 19, "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>\n", " <th></th>\n", " <th colspan=\"16\" halign=\"left\">counts</th>\n", " </tr>\n", " <tr>\n", " <th>hour</th>\n", " <th>3</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", " <th>17</th>\n", " <th>18</th>\n", " <th>19</th>\n", " <th>20</th>\n", " </tr>\n", " <tr>\n", " <th>daytype</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>weekday</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>11.0</td>\n", " <td>37.0</td>\n", " <td>15.0</td>\n", " <td>30.0</td>\n", " <td>22.0</td>\n", " <td>23.0</td>\n", " <td>23.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>weekend</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>4.0</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": [ " counts \\\n", "hour 3 6 7 8 9 10 11 12 13 14 15 16 \n", "daytype \n", "weekday 1.0 1.0 2.0 5.0 11.0 37.0 15.0 30.0 22.0 23.0 23.0 5.0 \n", "weekend NaN NaN NaN 2.0 NaN 1.0 NaN 4.0 1.0 2.0 NaN 4.0 \n", "\n", " \n", "hour 17 18 19 20 \n", "daytype \n", "weekday 4.0 2.0 1.0 4.0 \n", "weekend NaN NaN NaN NaN " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show two-dimensional view of multiply-indexed DataFrame\n", "by_time.unstack()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ellkle/anaconda2/lib/python2.7/site-packages/pandas/core/reshape/merge.py:551: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n", " warnings.warn(msg, UserWarning)\n" ] } ], "source": [ "# Merge DataFrame on common index, perform left outer join and fill NaN with zero-values\n", "by_time = pd.merge(hours, by_time.unstack(level=0), left_index=True, right_index=True, how='left').fillna(0)" ] }, { "cell_type": "code", "execution_count": 21, "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>hours</th>\n", " <th>(counts, weekday)</th>\n", " <th>(counts, weekend)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>19</th>\n", " <td>19</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>20</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>21</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>22</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hours (counts, weekday) (counts, weekend)\n", "19 19 1.0 0.0\n", "20 20 4.0 0.0\n", "21 21 0.0 0.0\n", "22 22 0.0 0.0\n", "23 23 0.0 0.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show last five records\n", "by_time.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize tweet counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### By hour:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Optional: Create xtick labels in Standard am/pm time format\n", "xticks = pd.date_range('00:00', '23:00', freq='H', tz='US/Eastern').map(lambda x: pd.datetime.strftime(x, '%I %p'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if we can \"fancy-it-up\" a bit by making it 538 blog-like. Note: The following cell disables notebook autoscrolling for long outputs. Otherwise, the notebook will embed the plot inside a scrollable cell, which is more difficult to read the plot." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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pFNu2bSNSzh88eFClyph/G2lpaVi3bp3aIk6KioqwatUqlXmNFxYWIjQ0VKFz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\nX3/9dY0v3GnTpjlt5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yELAAAAqkz8KVg/5juelfKnTiW6pnnN2S4V\nFunKraGyyuTwtZv7FcvBRBgAAAAAaLQIWbxg8+bNGjFihEpLS/X4449r1qxZdV7G4cOH1bdvX5WX\nl+uSSy7RDz/84IGRNn5VIZI3PujewPo2baxv03ahra904a0z69u0NZX17fOPTB3Ir3D4+Je3xal7\nbFCt69vpr+k66aTc2LdD43VZdGD9DbYBNZXfr6tY36bvQltn1rdpY32bNta36bvQ1pn1RX25IBvf\nV9e/f39NnDhRkrRgwQLt37+/zsto27atrrvuOknSwYMHlZGRUZ9DBAAAwAXIqPG9o54skpQc7rx8\nLX1ZAAAAAKB+XPAhiyTdcsstkiSLxaJ169a5tYwOHTrYbp88ebJexgUAAIALU2G5RWfKHU849zNJ\ncSGOd+WTI5zXAksrJGQBAAAAgPpAyCIpNjbWdruqb0tdmUyOa14DAAAAdZHppNSXJMWH+Mnfz/H+\nZ3K485AllZksAAAAAFAvCFlkP/MkPDzcrWXs3bvXdjsxMfG8xwQAAIALV7rZeQiSYNC13ihkSSt0\n3OsFAAAAAOA6QhZJa9eutd3u1KlTnV9/5MgRbdq0SZJ08cUXq2XLlvU2NgAAAFx4Ms+jH4vkQrkw\nZrIAAAAAQL1o0iHLmjVrVFhY6PQ5H374oZYvXy5Jatasma0/S5V169bJanVcDzsjI0NjxoxReXm5\nJOm+++47z1EDAADgQpduFLKEOt+NN5zJQsgCAAAAAPUiwNsD8KSXX35ZM2fO1KBBg3TllVeqXbt2\nioyMVHFxsfbv36+PP/5YGzZskHS2p8r8+fMVExNjt4wxY8aoTZs2uu2229SrVy+1atVKwcHBys7O\n1pYtW7RixQqdPn1aknTllVdq/PjxDb6eAAAAaFoyzc57shjNZGkd4Xw3/0RRpSxWq/zoKwgAAAAA\n56VJhyySVFBQoNWrV2v16tUOnxMTE6P/+7//05133lnr40ePHtXSpUudvs8dd9yhF198UUFBQec1\nXgAAACDDYCZLkkHIEh1kUniASUUVtc/ILrdIp8wWw7AGAAAAAOBckw5Z/vKXv2jz5s3asmWLfvnl\nF2VlZSknJ0dBQUFq3ry5OnfurBtvvFHDhw9XdHR0rctYs2aNvv/+e/3www9KTU1VTk6OioqKFBER\nodatW6tv374aNWqUevbs2cBrBwAAgKbKKGRJMCgXZjKZlBzur335jhvcpxVVErIAAAAAwHlq0iFL\nSkqKxowZozFjxri9jJtvvlk333xzPY4KAAAAcC7DoFyY0UwWSUqOMAhZCivVO67OQwMAAAAAVNOk\nG98DAAAAjVGm0UwWV0KWcOfPSS1yHMAAAAAAAFxDyAIAAAD4kKJyiwrKa++lIkl+JikuxHg33ihk\nSSt0HuQAAAAAAIwRsgAAAAA+JNOgVFhciJ8C/EyGy0mOcF4ZOK2IkAUAAAAAzhchCwAAAOBD0g1K\nhbnarN5wJgshCwAAAACcN0IWAAAAwIcY9WNJDHVtF751BOXCAAAAAMDTCFkAAAAAH5JuUC7M1Zks\nSWH+clZULKfUouIK5+8F7yuplLJKTbJaHffpAQAAAOA9hCwAAACAD8kwmMmS4GLIEuxvUoLBrJcT\nlAzzWfllFo3dlKsbvw3VLd+Hqt3qDL35SyFhCwAAAOBjCFkAAAAAH2JULiwp1LWQRZKSKRnWKJVW\nWjXo39n66KhZpZaz85FySy169L/5+r+dZ7w8OgAAAADVEbIAAAAAPsSo8X1CmOu78MnhAU4fT2Um\ni0/613GzdueW1/rY0p8KlVPC7w0AAADwFYQsAAAAgA/JNOjJkuRiuTBJSg43mMlCyOKTPj5a4vCx\nwgqrPksrbcDRAAAAAHCGkAUAAADwIUY9WVxtfC9RLqyxOlBQ4fTxrzMIWQAAAABfQcgCAAAA+Iii\ncosKyh03NjdJigupS7kwZrI0NlarVUcIWQAAAIBGg5AFAAAA8BFGpcLiQ/0U4GdyeXmGIUuh85P5\naHinzBYVVTgO2iTpyJlKnSQgAwAAAHwCIQsAAADgI4xKhSWEul4qTJJaG5QLO1FcKYvV+Ql9NKzD\nZ1wLvrZmMpsFAAAA8AWELAAAAICPMApZksLqtvvePNhPof6OZ76UVkrZJc5nz6BhHTIoFVaFkmEA\nAACAbyBkAQAAAHxEhkG5sIQ6NL2XJJPJpGSD2SxphZSd8iVG/ViqbM0o8/BIAAAAALiCkAUAAADw\nEUYzWRLrGLJIxn1ZUunt4VMOF7j2+9iXX6EsM787AAAAwNsIWQAAAAAfkWFw0jyxjj1ZJKmVQciS\nRsjiU1wtFyZJWzOZzQIAAAB4GyELAAAA4CMyip2XC0usY08WyXgmS1qh6yf14VlWq1VHXGx8L9GX\nBQAAAPAFhCwAAACAjzAsF+bGTBbDnizMZPEZ2SUWnSm3uvx8QhYAAADA+whZAAAAAB9hWC7MjZ4s\nrSkX1mjUpVSYJO05XaHTpc5nPwEAAADwLEIWAAAAwAcUV1hUUOZ4FoNJUnyoO+XCApw+nlZIyOIr\nDtcxZLFK2spsFgAAAMCrCFkAAAAAH5Bp0I8lLtRPAX6mOi+3pcFMlqwSi8wVrpeoguccPlP3wGtr\nZpkHRgIAAADAVYQsAAAAgA9I90A/FkkKDTApLsT5bv9JSob5hCN1nMki0ZcFAAAA8DZCFgAAAMAH\nZBr2Y3F/1z05wqgvS91P7qP+1bUniyTtyi1Xfhl9WQAAAABvIWQBAAAAfEC6Qbkwd5reV0k2KBmW\nykwWr7NarTp8pu4hi8UqfXeKkmEAAACAtxCyAAAAAD4g06BcWIKb5cIk45AlrZCQxdtySy0qKHOv\nNw4lwwAAAADvIWQBAAAAfEC6QbmwpPOZyRIR4PTxNGayeJ07pcKqELIAAAAA3kPIAgAAAPiATINy\nYQmh59GTxWgmCyGL1x0ucP93sD27XEXl9GUBAAAAvIGQBQAAAPABGQblws5nJktryoX5PHf6sVSp\nsErfZ9GXBQAAAPAGQhYAAADAB2QYlAtLOK9yYUYzWSpktbrXDwT14/B5lAuTpP9kELIAAAAA3kDI\nAgAAAHhZcYVF+U6anpskxZ9HubDYED8FO8lZSiqlnFLKTXmTUcjSJy7Q6eNb6csCAAAAeAUhCwAA\nAOBlRv1YYkP8FOhncnv5fiaTWhnMhKFkmHcZhSxj2oc7ffzH7DKVVDAbCQAAAGhohCwAAACAlxmV\nCks8j1JhVZIjApw+nlpEyOItp0stynMyk8lPVg29ONTpbKTSSumHbEqGAQAAAA2NkAUAAADwMuOm\n9+e/254czkwWX3XIYBZLYrBVkYF+6h0X5PR5lAwDAAAAGh4hCwAAAOBlGQblwhJC62Mmi0HIwkwW\nrzEqFdY69Owsl6sSg50+7+sMZrIAAAAADY2QBQAAAPAyo5ks9VIuzGgmS5HzE/3wHKOQJTnkbAh3\nVYLzkOW7U2Uqq6QvCwAAANCQCFkAAAAALzPuyXL+u+2tKRfms1ydydInPlCBTj4K5kqrttOXBQAA\nAGhQhCwAAACAlxmVC0tsgHJhJygX5jWHzxjNZDkbsoQF+KlnrEFHiQeZAAAgAElEQVRflkxCFgAA\nAKAhEbIAAAAAXpZpOJPl/EOWVgYzWTLMFpVSasorDhc4//2nhP4vhLsq0XnI8nVGab2MCQAAAIBr\nCFkAAAAAL0tvgJ4sYQF+ahHsfPffaByof3mlFuWWOp7JZJJVLUP+F35dlei8L8t/M8tUYSEsAwAA\nABoKIQsAAADgReYKq/LLHJ8UN0mKD62f3XajkmGp9GVpcEb9WBKCraqejV0RHyR/k+PnF1ZY9VNu\neT2NDgAAAIARQhYAAADAi4xKhcWG+CnQz8lZ9TpINigZlkZflgZn1I+ldYh9ABcZ6KduLQKdvuY/\nlAwDAAAAGgwhCwAAAOBFGQYluhLqoVRYFcOQpdD5CX/UP6OZLK1Da5YSMyoZ9nVG2XmNCQAAAIDr\nCFkAAAAAL8oodtyPQ5KS6qlUmGRcLoyZLA3vkEHIkhxSs5TclQlBTl/zTWapLFb6sgAAAAANgZAF\nAAAA8KKGaHpfpXV4gNPHCVka3pEC5//nrUNrhiX9EoLlrIBcfplVP59mVhIAAADQEAhZAAAAAC8y\n6slSr+XCjGay0Pi+wRn3ZKk50yk62E9dmjvvy/I1fVkAAACABkHIAgAAAHiR0UyWpLB6LBfmQuN7\nK2WmGkx+mUXZJc7LxbWqpVyYZFwybCshCwAAANAgCFkAAAAAL8o0Oz/JnhBafzNZ4kP9FOjkCKCo\nwqq8MkKWhnLEoB9LqzB/hTj49V+VGOz0tVszywjMAAAAgAZAyALg/7F359Fx3WW6759dVSqNJcuy\nrCrZUmJb2A5OZyATiZSBDpCkM9ik0xACHbgsZrgNLHID5zb0vZyz0pxOp+HQ3WlyQy9ON5yGtGka\nyAAkZCAhtiADGQxJsI2lJJatkmTJGkpTqar2/cNHxoldv19Jqmnv+n7WYi2TrWGXtqRVux69zwsA\nAEoobp1kyV/IEnAcrbVMs+xPsMujWHotIcuGxuzXqjtmnmQ5NJvR7nGuJQAAAFBohCwAAABACdlC\nlmhtfp+y51IZhuLonTR/rTc0hrIeW1UT1Bubsh+X2MsCAAAAFAMhCwAAAFAiMznUc+Vz8b2UQ8iS\nIGQpln3WSRZziNJlqwyLJxd9TgAAAAAWh5AFAAAAKJHBGXOg0VITUFXAyevnbG8wv3DPJEvx2Hay\n2EKW7qi5MmxnfI69LAAAAECBEbIAAAAAJWKrCovleYpFkjqoCysbvZOWkCWyvEmW+ExGvRNcTwAA\nAKCQCFkAAACAEhmcyRiPx/K8j0WS2huoCysHE8mMhizXf13EfK1idUF1NprfZucge1kAAACAQiJk\nAQAAAEpkoASTLPbF9+bpCuRHn2WKpa0uoPoq++1at2WaZWeckAUAAAAoJEIWAAAAoESsdWG1+Q9Z\n1lpCloHpjOYz7PEotD5LjZdtH8sCe8iSzPmcAAAAACweIQsAAABQIvadLPl/ut5QFdDKaifrcVfS\nQfayFNxy97Es6IqGjcf7p9J6NcF0EgAAAFAohCwAAABAicRtO1kKUBcmSe315hfw+wlZCm7fhCVk\nyXGSpaMhpJMse3aYZgEAAAAKh5AFAAAAKJHBEuxkkXLZy0LIUmi9eQpZJPayAAAAAKVEyAIAAACU\niHXxfW1hnq63WyYf+hOELIXWl8eQxVYZ1kPIAgAAABQMIQsAAABQArMpV2NJ84L51gIsvpekDusk\nCzs8Cikxn7FWxa2P5H7tL7RMsvROpq2BHgAAAIClIWQBAAAASiA+Y37Ru6UmoHAw+4L65bDWhTHJ\nUlB9k+avb7Q2oIaq3G/V1kWCWlNnfnsqwwAAAIDCIGQBAAAASsC2jyVaoKowKYe6MHayFFQ+97FI\nkuM46rJMs/TEk4v6mAAAAAByQ8gCAAAAlICtLqqtQEvvJam93vwi/v5EWq5rrjLD0uU7ZJGk7qg5\nZGGSBQAAACgMQhYAAACgBOK2SZYChizR2oBChiayRMrVuGVfDJbOGrJElhCyxMLG47vHUxq2VNQB\nAAAAWDxCFgAAAKAEbCFLW4GW3ktSMOBojW0vC5VhBdM7aQ5ZOpcwybJxRUira8y3dz2DVIYBAAAA\n+UbIAgAAAJSArS4sallkvlzt1pDFHARg6foskyzrGxcfsB3Zy2KeZqEyDAAAAMg/QhYAAACgBGyT\nLLEC1oVJUnuDJWRJMMlSCNOpjA5OmwO2pexkkdjLAgAAAJQCIQsAAABQAtaQpYB1YZLUQV1YSfRN\nmL+urbUBRaqWdpvWHTOHLC8eTunwnDngAQAAALA4hCwAAABACcQtS8hjBa8LM09LELIUhm0fy1KW\n3i9448qQVlY7WY+7kn45yDQLAAAAkE+ELAAAAECRzaZcHZ5zjW8TLfAkC3VhpdFr3cey9JAl4Di6\nwFoZllzyxwcAAABwPEIWAAAAoMgGLVMsq6oDCgezTyTkg33xPSFLIdhClg2R5YVrtsow9rIAAAAA\n+UXIAgAAABSZfel94Z+mr7WELAen00plzNM2WDxbyNK5jEkWSeqOho3Hd43OayLJXhYAAAAgXwhZ\nAAAAgCKLz5hf5I7VFbYqTJIawwGtCGeflsm40oAlDMLi9VoW329YZshyWnOVGqvM1/WJISrDAAAA\ngHwhZAEAAACKzD7JUviQRaIyrNhmUq4OWK79cnaySFIw4Oh8yzQLlWEAAABA/hCyAAAAAEVm28kS\nqy3O0/T2BvML+v0JQpZ8ennSXBXWUhPQivDyr31X1LyXpSfOJAsAAACQL4QsAAAAQJENTJe+Lkxi\nkqXY7EvvlzfFsqA7Zg5ZnjmU1NQ8e1kAAACAfCBkAQAAAIps0FIZFa0lZPEjW8iyvjE/1/3MlirV\nh7LvZUm50lPDTLMAAAAA+UDIAgAAABSZbSdLW7lMsiTMoQAWp9dSF7bcpfcLqgKOzms172XZQWUY\nAAAAkBeELAAAAECRxWfMVU3RumLtZDGHLPuZZMmr3gnz17MzTyGLJHVFzSFLT3wub58LAAAAqGSE\nLAAAAEARzaVdjc5ZQhbqwnxpX5F2skj2vSy/PpTUbMrN2+cDAAAAKhUhCwAAAFBEtqqwVdUBVQez\n79PIp7a6oAKGTzWRdDWeZEF6PsymXB2whFb5qguTpLNXh1VtyNDm0tLTh6gMAwAAAJaLkAUAAAAo\nosEZy9L7IlWFSVIo4GiNZf+LLRhAbl5JpGSaG2muDqipOn/Xvjro6JzVVIYBAAAAhUbIAgAAABTR\nwLR5MqRYS+8XWCvDEoQs+dBrqwprzP91t1WG7YwzyQIAAAAsFyELAAAAUESDlrqwYu1jWdDewF6W\nYijmPpYF3VFzyPLkUFLJNHtZAAAAgOUgZAEAAACKKG6pC2srYl2YlMMky5Q5HEBu+iaLt49lwbmt\nVaoyfDvNpF09N8I0CwAAALAchCwAAABAEcUtdWFFn2ShLqwo7HVh+Q9Z6kIBndVi3stCZRgAAACw\nPIQsAAAAQBHFLXVhsWLvZLHUhe2nLiwvrHVhBQhZJKk7ZgtZ5gryeQEAAIBKQcgCAAAAFJGtLixW\n9Low84v77GRZvrm0a/06bogUJlzrsuxleWIoqVSGvSwAAADAUhXmz6XKxPT0tB588EE988wzevbZ\nZ9Xf36+RkRFNTU2psbFRGzdu1Fve8ha9//3v15o1a6wfb8+ePfrGN76hRx55RAMDA6qpqVFnZ6eu\nvfZaffCDH1RNTU0RHhUAAAC8zFYXVvRJFktd2MGptNIZV8GAU6Qz8p9XEymZcoymsKPmmsJc9zdH\nwwo6Urb99pPzrn4zOq83WWrFAAAAAJyYr0OW3bt36/3vf/8Jj42OjuqJJ57QE088odtvv1233Xab\n3vOe92T9WN/5znd00003aXZ29uh/m5mZ0dNPP62nn35a3/72t7V9+3atW7cu3w8DAAAAPjGXdjU6\nV147WVaEHUWqHE3On/hV+LQrxWcyWmsJY5BdqarCJClSFdAZq6r0zKH5rG+zIz5HyAIAAAAska9D\nFkmKxWK66KKLdMYZZ6ijo0OxWEzBYFAHDx7Uz372M33/+9/X1NSUPvnJT6qlpUWXXXbZcR/jkUce\n0ac+9Sml02mtWrVKn/3sZ3XeeedpampK27dv11133aXdu3fr+uuv18MPP6yGhoYSPFIAAACUu0FL\nVVhzdUDVweJOjDiOo/b6oF4ayx4E9CdShCzL0DthqQorYMgiHakMM4UsPfGk/uKPCnoKAAAAgG/5\nOmQ5/fTT9bvf/S7r8a1bt+oDH/iArrjiCs3Pz+uWW245LmRJpVK6+eablU6n1dDQoPvvv18bN248\nevwtb3mLNmzYoL/+67/W7t279U//9E/6/Oc/X7DHBAAAAO8atFWF1ZZmZaI1ZJlK681FPB+/6Svh\nJIskdcfCuv2F7Md/OTinjOsq4FAJBwAAACyWrxffB4P2v7Y7++yzdfHFF0uSdu3apUQi8ZrjP/7x\nj7Vv3z5J0qc//enXBCwLbrrpJnV2dkqS7rjjDqVS5psoAAAAVKaBadvS+9JMi7Q3mD+vbWk7zHon\nLSFLpLAhywXRapnik7GkqxcOcw8DAAAALIWvQ5ZcHVvvlUwmX3PsvvvuO/rvP//zPz/h+wcCAd1w\nww2SpLGxMe3YsaMAZwkAAACvi5dryFJvfpG/P0HIshz2nSyFve5N1QH9UXOV8W12xucKeg4AAACA\nX1V8yHLo0CE99thjkqRVq1apubn5Ncd/+ctfSpI6OzvV1taW9eNcdNFFx70PAAAAcCzbTpZYXYnq\nwphkKZhk2tWrlpCqs8B1YZLUFTUvtu8hZAEAAACWpCJDltnZWb388sv613/9V7397W/X2NiYJOnj\nH//4a94ukUjowIEDkqTNmzcbP+amTZuO/nv37t15PmMAAAD4wYB1J0upJlkIWQplfyKtjJv9eGPY\nUXN14W/LumPVxuM9g0m5ruFEAQAAAJyQMzY2VhHPpO+//369+93vznr8Pe95j772ta8pHP7DX3jt\n3btX5557riTpwx/+sG677Tbj51izZo2mp6d17rnn6sEHH1z0Oe7du3fR7wMAAADv+IvfVutXY9kD\njVtPmdOlLcUPNA7OOtr2dG3W4ytCrh46f6aIZ+QfO0cD+syLNVmPv7EhrW+fWfgpksPz0mVP1Bnf\nZvtZM9pQVxG3hwAAAPCpE+1UL7SKnGQ51oYNG3T33Xfr61//+msCFunIJMuC+vp668daeJupqan8\nniQAAAB84VDStH5cagmX5gXu1rArR9k/93jKkWWdDLLYP2u+5WqvKc41X1klra8zT1I9O16aSSoA\nAADAywpf/lsmuru71dPTI+nIcvtXX31VP/3pT/W9731PH/vYx/TFL35R733ve1/zPjMzf/hrvaoq\n86JISaqurj7u/RajFClbMS1M6vj9cS7g8fobj9ffKu3xSpX3mHm8/lbOj/fwUwOSsr/Qfc6mk3Vy\nZHFP0fP1eNueG9BBQ51ZTWydNjbZnxMXWjlf3xOZHBmTlP2PsM5Yu1IbNzZmPZ7Px3vpoTF983fZ\nz2VvZoU2bmzOerwYvHZ986HSHjOP1994vP7G4/W/SnvMPF7kS8VMskQiEW3ZskVbtmzRmWeeqa1b\nt+qOO+7QD37wA42OjuqTn/ykbr311te8T23tHyoT5ufnrZ9jbm7uuPcDAAAApCML0EfmLDtZ6ko3\nSdBebw532MuyNH0TKePxDZHiXfPuaNh4fGd8jr0sAAAAwCJVTMiSzSWXXKKPfexjkqRbb71Ve/bs\nOXqsoaHh6L9zqQBbeJtcqsUAAABQWQZnzCHFympH1UFznVghtTeYX+zvTxCyLMU+W8jSWLxyga5Y\ntfF4fCajvkmuMwAAALAYFR+ySNKVV14pScpkMrr33nuP/ve2tjY5zpEb3QMHDhg/xuHDhzU9PS1J\nWrt2bYHOFAAAAF4VN1RxSVJbbWn3YbTXmz//fiZZFm0+4+pVSzjVWcSQJVYXVGej+TrviM8V6WwA\nAAAAfyBkkdTS0nL03/v37z/674aGhqOBye7du40f49gJmM2bN+f5DAEAAOB1ccskS7SEVWGSPWTp\nT5gnMnC8/kRaKUP7VqTKUUtNcW/Jui3TLDsJWQAAAIBFIWSRdPDgwaP/fn3V1wUXXCBJ2rdvnwYG\nBrJ+jB07dhz3PgAAAMCC+LQ5ZCnlPhYph7owJlkWzVYVtj4SOjo5XyxdUXPI0jOYLNKZAAAAAP5A\nyCLp7rvvPvrvLVu2vObY1VdfffTf//Zv/3bC989kMrrrrrskSU1NTeru7i7AWQIAAMDLBm11YXWl\nfWpunWQhZFm03jLax7KgOxY2Ht+fSOtVppYAAACAnPk6ZPn3f/93JRIJ49v88Ic/1L/8y79Ikhob\nG4/uZ1lw1VVXqbOzU5L093//99q7d+9xH+OrX/2qfv/730uSPv7xj6uqqiofpw8AAAAfGbDVhZV4\nJ0tHg/kF/wNTaWVcQ/cVjtM7aQ4rbPtRCqGjIaSTLFNLO+NMswAAAAC5Kv6fThXR7bffrs997nO6\n6qqr1NXVpc7OTkUiEU1PT2vPnj2655579OCDD0qSHMfR3/zN32jlypWv+RihUEi33Xab3vnOdyqR\nSOiKK67QTTfdpPPOO09TU1Pavn27vvvd70o6sovlk5/8ZNEfJwAAAMpfudeFNYUd1YccTWVZIjKf\nkYZmMiU/Ty+xTbKsL8Eki3RkL8urv5/OerwnPqcb3lBXxDMCAAAAvMvXIYskTUxM6K677jpa53Ui\nK1eu1N/+7d/qne985wmPX3rppfqHf/gH3XTTTRoZGdFf/uVfHvc2mzdv1vbt29XQ0JC3cwcAAIB/\nWEOW2tIOmTuOo/b6oHaPZw8G+qfShCyL0DthvuYbIqW5HeuKhnWXIWTZGZ8r4tkAAAAA3ubrkOW7\n3/2uHnvsMT3++ON66aWXNDw8rJGREYXDYTU3N+vUU0/V2972Nv3Zn/2ZmpqajB/rve99r84991zd\neeedeuSRRzQwMKCamhq94Q1v0Dve8Q598IMfVG1tbZEeGQAAALwmbtnJUg7hRXuDJWRJpHXO6iKe\nkIelMq5esew26SzRJMuFsWrj8d7JtAam02org+9JAAAAoNz5OmQ56aSTdOONN+rGG2/My8fbtGmT\nvvKVr+TlYwEAAKByJNOuRubMIUupd7JIUnu9+Rz2T7EQPVf9U2nNGy55fchRa4mml9ZFglpTF9BB\nQ/C3Mz6nP9tAZRgAAABg4+vF9wAAAEA5GLQsvV9Z7agm5BTpbLKzhSz9CfPjwB/kso/FcUpzzR3H\nUZdlmqUnnizS2QAAAADeRsgCAAAAFNjgjKUqrAymWCSpvcE86N4/RciSK1vI0tlY2mveHTWHLOxl\nAQAAAHJDyAIAAAAU2IBt6X2Z7L6wTrIQsuSsd9IcspRq6f2C7ljYeHz3eErDlgksAAAAAIQsAAAA\nQMENWkKWaIl2c7xeRwN1Yfmyb8L8tVpfoqX3CzauCGl1jfn7rmeQyjAAAADApjzu5gAAAAAfixsW\njEtSW5lMsrTVBWXaEjIyl9F0yvxYcESfpS5sQ4lDliN7WczTLFSGAQAAAHaELAAAAECBxS21S9Ey\nCVmqg451quYAlWFW6Yyrly11YZ0lDlkk9rIAAAAA+UDIAgAAABRY3FIXVi6TLJLUTmXYsvVPpZU0\nDPzUBh3FyqAiritmDllePJzS4TkmlwAAAACT0j+zBwAAAHzOuvi+DF5wX9Beb56w2M8ki1WfZYpl\nfWNQjmMqZiuOLStDWlmd/TxcSb8cZJoFAAAAMCmfuzkAAADApwZnzNMA5VIXJknt9ZZJFkIWq17L\n0vtyqAqTpIDj6AJrZViySGcDAAAAeBMhCwAAAFBAybSrQ7PmkCVWW0YhC3Vhy9ZrW3ofKY+QRZK6\nLZVhPUyyAAAAAEaELAAAAEABDVmW3jeFHdWESl8dtYBJluXbZwtZymSSRZK6o2Hj8edH5jVhWjAD\nAAAAVDhCFgAAAKCA4paqsHJaei/lELIkzAECctnJUj4hy2nNVWqsyh7yZVzpiSEqwwAAAIBsCFkA\nAACAAopblt6X0z4WSeqw1IUdmE4r47pFOhvvybiuNWQpl50skhQMODrfMs2yM05lGAAAAJANIQsA\nAABQQLaQJVZbXk/Jm6sDqg1mn2yYS8u6Y6aSHZhKa85wyWuCUltdeV3zrqhlL0ucSRYAAAAgm/J6\ndg8AAAD4jK0uLFZmkyyO46jdMs3Sn2AvSza9E+avzfpISAGnfHbwSFJ3zByyPHMoqal5gjUAAADg\nRAhZAAAAgAKyTrKUWcgi2fey7J8iZMnGVhVWTkvvF5zZUqW6UPbgJ+VKTw0zzQIAAACcCCELAAAA\nUECD1row74Us/YQsWfVOeC9kqQo4Oq/VtpeFkAUAAAA4EUIWAAAAoIAGrHVh5feU3F4XZg4SKtk+\nW8gSKb+QRZK6o7aQZa5IZwIAAAB4S/nd0QEAAAA+Yp1k8WBdGJMs2fV5cJJFsu9l+fWhpGZTbpHO\nBgAAAPAOQhYAAACgQOYzroZnLZMsZVkXZg4CCFlOLOO66ps0f202NJbf9Zaks1rCqjac2lz6SNAC\nAAAA4LUIWQAAAIACsU2xNIUd1RgWjpdKh7UujJDlRAamM5pJZ5/2qA5Kay1TQqVSE3J0zmoqwwAA\nAIDFImQBAAAACmTQuo+lPF9wX2M5r+HZjGaojjqOben9uoaQAk75hWoLbJVhO+NMsgAAAACvR8gC\nAAAAFMiAB/exSEemGlprzbcKB6kMO07fpDf3sSzojponWZ4cSippmNQBAAAAKhEhCwAAAFAggzPm\nICJqCTJKqd1Sa9U/ZQ4UKtG+cW+HLOe2hlVl+JacSbt6boRpFgAAAOBY5XtXBwAAAHjcwLS5Lqyt\nTCdZJPvukP1Mshyn1zrJUr7XW5LqQgGd1WLby0LIAgAAAByLkAUAAAAoENvi+2ht+b7obp1kSRCy\nvJ5tJ0tnmU+ySFJ3zByy9MTninQmAAAAgDcQsgAAAAAFEreELOU8ydLeYA4E+plkeQ3XddU3af6a\nrI+Uf8jSFa02Hv/VUFKpDHtZAAAAgAWELAAAAECBxGfMdWHRuvJ9Om7fyULIcqz4TEbTqezhQ1XA\n/jUtB2+OhhV0sh+fnHf1m9H54p0QAAAAUObK964OAAAA8DgvT7J0UBe2KLaqsHWRkIIBQ3pRJiJV\nAZ2xqsr4NjuoDAMAAACOImQBAAAACmA+4+rQrGWSpZx3sjTYJllScl1qoxbYQpYNHtjHssBWGdYT\nTxbpTAAAAIDyR8gCAAAAFMDQTEamCGJF2FFtqHwnG1pqAqo25CyzaWlkzhwiVRJryBIp30Dt9bpj\nYePxXw7OKUPABgAAAEgiZAEAAAAKYtDDVWGSFHAcrbWcI5Vhf9A76Z9Jlgui1TLFf2NJVy8cNj9e\nAAAAoFIQsgAAAAAFMGAJWcq5KmxBe4M5GNg/RciyoHfC/LXo9FDI0lQd0KnN5r0sPexlAQAAACQR\nsgAAAAAFEZ8xv+geqyv/p+Lt9Uyy5MJ1XfX5aCeLJHVHzZVhOwlZAAAAAEmELAAAAEBBxKfN+0pi\nnphksYQsTLJIOrJ/J5HKvqMk5NgDq3LTHas2Hu8ZTMplLwsAAABAyAIAAAAUQtxSFxYr850sUg6T\nLFPs5ZDs+1jWRUIKBUxbTspPV8w8yXJoNqM941x/AAAAgJAFAAAAKIBBH9SFdVAXlpNea1VY+Qdq\nr9dSE9QpTeaKs53xZJHOBgAAAChf5X9nBwAAAHjQgK0uzAuTLNSF5cQWsqyPeGsfywJbZRh7WQAA\nAABCFgAAAKAgrJMsHtjJstYyyTI4k9Fcmr0cvRPma+21pfcLuqPmyrCewTn2sgAAAKDiEbIAAAAA\neZbKuBqeMU+yRD1QF1YXCmhVtfk8DzLNYt3J0unRkKXLMskyMJ1R3yTXHwAAAJWt/O/sAAAAAI8Z\nmsnI9Pf9jWFHdSFvPBW3VYbtr/CQxXXdHHayeDNkidUF1WnZJ7ODyjAAAABUOG/c2QEAAAAeEp82\nBw9tHqgKW9BuqQzrT5gDBr87NJvR5Hz2SC3oSB2WoKqc2fay9BCyAAAAoMIRsgAAAAB5FrfsY4l6\nYOn9AmvIUuGTLLYplpMbgqoKOEU6m/zrippDlp2DySKdCQAAAFCeCFkAAACAPItPm/exxDywj2WB\nrS7sQKWHLJadJF6tClvQHQsbj+9PpPVqhU8zAQAAoLJ55+4OAAAA8AjbJIuX6sI66s0hQaVPsuyz\nTLKs93jI0tEQ0kmWoG1nnGkWAAAAVC5CFgAAACDPbDtZPFUXZnmBvT9R2SFLnyVk6fR4yCJJXVHz\nNAt7WQAAAFDJCFkAAACAPLMuvvdSXVgOO1lcN/vid7/rnTSHLBsi3g9ZumOWvSyELAAAAKhg3rm7\nAwAAADzCtpMl6qG6sNbagKoMdw1TKVdjycoMWVzXtdaFbWj0zrXO5kJLyNI7mdaAJVgEAAAA/IqQ\nBQAAAMgz604WD9WFBRxHay3TLPsrdPH56FxGE4aAKeBIJzV4f5JlXSRonb6iMgwAAACVipAFAAAA\nyKNUxtXwjGWSxUN1YVJulWGVqHfC/LhPaggqHHSKdDaF4zhODpVhySKdDQAAAFBevHV3BwAAAJS5\noZmMTOVZjWFHdSFvPQ23hiyJygxZrFVhPtjHsqA7yl4WAAAA4ES8dXcHAAAAlLlBS1VYzEP7WBa0\nWyqvKnaSxbb0vtE/IUtXLGw8vns8pWHL9z4AAADgR4QsAAAAQB7ZFoDHPLSPZUEHdWEn1Gddeu+f\nkGXTipBW11j2sgxSGQYAAIDKQ8gCAAAA5NHgtHkfS6zWe0/B2xuoCzuRXmvI4r1ALRvHcazTLFSG\nAQAAoBJ57w4PAAAAKGMDtrowD06y2Bffm8MGv6qknSySfS8LkywAAACoRIQsAAAAQB4N+rAubK0l\nZBmYzmg+4xbpbMrD4bmMxpLZH3PAkU72WcjSFTOHLC+Mzhr4Cu4AACAASURBVGtszjzJBQAAAPgN\nIQsAAACQR3FbyOLBurCGqoBWVjtZj7uSDlbYXhZbVVh7fVDVwexfMy/asjJk/T7oGaQyDAAAAJXF\ne3d4AAAAQBmLz1h2snhwkkWS2uvNUxn9FRayWKvCfLT0fkHAcXSBpTJsZ5zKMAAAAFQWQhYAAAAg\nj6yTLJ4NWWx7WSorZLEuvfdZVdiCrmjYeJxJFgAAAFQaQhYAAAAgT1IZV0OWSZaoB+vCJKm9wRKy\nJCosZJm0TbJ4M0yzudCyl+X5kXlNJNnLAgAAgMrhzTs8AAAAoAwNz2ZkWv/eWOWovsqbT8E7rJMs\n5tDBb/oqsC5Mkk5rrlKkKvtelowrPTFEZRgAAAAqhzfv8AAAAIAy5NeqMCmHurAKm2TZN2F+vH4N\nWYIBR+e3WirD4lSGAQAAoHIQsgAAAAB5YgtZvFoVJuVQF1ZBO1nG5jIancteieVIWtfgz5BFkrot\nlWE740yyAAAAoHJ49y4PAAAAKDPxafMuijZPT7KYQ4P9ibRc11SW5h99ln0sa+uDqgllr9TyOlvI\n8syhpKbm2csCAACAykDIAgAAAORJfMYyyeLhkCVaG5ApN0ikXI0nKyNk2Veh+1gWnNlSpTrDN0PK\nlZ4eZpoFAAAAlYGQBQAAAMgTP+9kCQYcrbHtZamQyrBeW8gS8e51zkVVwNF5lr0sO6gMAwAAQIUg\nZAEAAADyJD5jrkiKeXgniyS1W0MWc/jgF7aQpdPnkyyS1B01hyw743NFOhMAAACgtLx9lwcAAACU\nET9PskhSe4MlZElUyiSL+XGur4SQxbKX5deHkppNVUZ9HAAAACobIQsAAACQJ4OWkMXLi+8lqYO6\nMElSr2Xxvd93skjSWS1hVRu+HebSR4IWAAAAwO8IWQAAAIA8SGVcDc2a68Kinq8LM4cHlRCyjCcz\nOmS5zusj/g9ZakKOzllNZRgAAADg7bs8AAAAoEwMz2aUMbQjNVY5qq/y9tNv6sKkPss+lrV1QdWG\nnCKdTWnZKsN6BplkAQAAgP95+y4PAAAAKBO2qrCox6vCpFwW3/s/ZLEtvV/f6P3rnKvuqHmS5cmh\npOZNySMAAADgA4QsAAAAQB4M2Jbee7wqTJLWWkKWg9NppXz+onrvpPk6V8I+lgXntoZlGs6aTrl6\nlr0sAAAA8Dnv3+kBAAAAZWBwxrynI+aDSZbGcEArwtmrsDKuPWzyOtskS2cFhSx1oYDOarHtZSFk\nAQAAgL8RsgAAAAB5YJ1k8UHIIlEZZq0Lq4Cl98fqslSG9cTninQmAAAAQGkQsgAAAAB5YN3J4oO6\nMElqbzCHCP0Jn4csk+aQpZLqwiSpO1ZtPP6roaTvK+QAAABQ2fxxpwcAAACU2IClLqzNJ5MsHRU8\nyTI5n9GQ5Tqvj/jjOufqzdGwAtkb5DQ57+o3o/PFOyEAAACgyAhZAAAAgDywTrL4JGSp5LqwPktV\nWFtdQPWmTfA+FKkK6IxVVca32UllGAAAAHyssu4AAAAAgAKJW0KWtlqfhCwNlpAlYQ4ivKx3wnyN\nK20fy4LuqLkybGc8WaQzAQAAAIqPkAUAAABYpnTG1dCsuUYqWuePp962SZb9Pp5kse1j6aywfSwL\numNh4/FfDs4p47KXBQAAAP7kjzs9AAAAoISGZzMy7faOVDlq8EmNVCXXhfVa6sIqben9ggui1TKs\nZdFY0tWLh/074QQAAIDK5o87PQAAAKCEbFVhMZ/sY5GOPJag4RX1iaSr8aR5qser9hGynFBTdUCn\nNrOXBQAAAJWJkAUAAABYpviMZel9rX+edocCjtosodEBn06z2Bbfr4/4J0xbrO6ouTKMkAUAAAB+\n5Z+7PQAAAKBE4tPmyQ1bKOE1HQ2WyrCE/0KWqfmM4jPm61ypkyyS1B2rNh7vGUzKZS8LAAAAfIiQ\nBQAAAFgmW11YtNZfIcvaCtzL0jtpn1byy96dpeiKmSdZDs1mtGecvSwAAADwn8q9CwAAAADyxL6T\nxV9Pu9utIYv/Xkxn6b1ZS01QpzSZvwY748kinQ0AAABQPP662wMAAABKwFYj5afF91IOIYsP68Js\n+1gqPWSRcqkMYy8LAAAA/IeQBQAAAFgm+ySLz0IWy06W/b6sC7OELBFClq6ouTJsZ3yOvSwAAADw\nHUIWAAAAYJkGZywhS62/nna315sDBT/uZNlnnWTxV5C2FLZJloHpjPosu20AAAAAr/HX3R4AAABQ\nZOmMq0FLXVjUb5Mslrqwg1NppTP+mliw1YWtZ5JFsbqgOi1h0444lWEAAADwF9+HLM8995xuu+02\nXXfddTr11FPV2tqqNWvW6Mwzz9SHPvQhPfTQQ9aP8fjjj6upqSmn/3384x8vwqMCAABAuTg0m5Ep\nT2gIOYpU+etp94qwo0iVk/V42rXvqfGS6VRGB6fNj4edLEd0RS17WQhZAAAA4DO+vhO48sor1dPT\nc9x/TyaTevnll/Xyyy/r+9//vi6//HJ94xvf0IoVK0pwlgAAAPCygQrbxyJJjuOovT6ol8ayT3f0\nJ1Jaa5l48Yq+CfM1Xl0TUGPYX0HaUnXHqvW/9k5nPb5zMFnEswEAAAAKz9chy8DAgCSptbVV27Zt\nU1dXlzo6OuQ4jp599lndcccd2rdvnx544AHdcMMNuu+++xQImG+Obr/9dp111llZjzc1NeX1MQAA\nAKC82arCYnX+fPHdGrJMpfXmIp5PIVmX3jPFclR3LGw8vj+R1quJlE5q4GsGAAAAf/D1M9tNmzbp\ni1/8orZt26ZQ6LUP9eyzz9YNN9ygP/3TP9UTTzyhnp4e/cd//Ieuv/5648c8+eSTtWXLlkKeNgAA\nADwkXoGTLJLU3mB+XP1T/llwbtvHQsjyBx0NIXU0BLU/kf3698STOukNfM0AAADgD/78s7r/bfv2\n7bruuuuOC1gW1NfX66tf/erR//+jH/2oWKcGAAAAn4jPWEKWWp+GLPXmF8n7DS+ye80+W8gS8ec1\nXqruqHmaZSd7WY4amU3r91OO4nPZdxwBAACgvPk6ZMnFqaeequbmZklSX19fic8GAAAAXmObZIn6\ntS7MMsmy30eTLL1MsixKd6zaeJyQRTo8l9Gndx7WG7fHdcOztbrmqVpd/9CI9o2bv9cAAABQfvx5\nx7dIqdSRJ7K2fSwAAADA6w1Mm3eytPm1Lsyy1N5XdWGT5sfSScjyGhdaQpbeybQGLOGknz0xOKeL\n7h7St/ZMK3nMr48H9s/qknuG9B/7pkt3cgAAAFi0ik8Vnn/+eU1MTEiSNm/ebH37W265Raeddppa\nW1t18sknq6urSzfffLNeeOGFQp8qAAAAytCgpS4s6tu6MEvIkvDHX+TPpFxrYLQuQshyrHWRoNos\nE1w9FTjNknFdfW3XpK786aGs31OJlKsP/+Kw/mLHYU2nzAEuAAAAyoMzNjbmlvokSunGG2/Uvffe\nK0n69re/ra1btx73No8//riuueYa68f66Ec/qltuuUVVVVVLOpe9e/cu6f0AAABQOlc+WaPhZPYX\nlL9/9oxOrvXfU+5URurqqZWr7LskHj1/WpbVLWVv35Sjdz9bm/V4U8jVg+fPFPGMvOGLu8N6YDj7\nxb8uNq//8ob5Ip5RaY0mpf93T7V+NZZ76LqhLqP/fsqcNtT57/cHAABAoWzcuLHon9PjtzzL84Mf\n/OBowPKmN73JGKREo1FdffXVuuCCC7Ru3TqFQiHF43E9/PDD+s53vqPp6WndeeedmpiY0B133FGs\nhwAAAIASSrvSaNK8sLqlyp8vkIYC0uqwqyHD4x9MOtoQ8vbj7581T2R01DJtcCJnNWb0wHD2489M\nBCVVRsjy67GAvrgnrEOGMPZEeqcDet9zNfpcZ1LXtKblmH/VAAAAoEQqdpLlt7/9rS6//HJNTU2p\nrq5Ojz76qDZt2nTCt52amlI4HM46obJ371694x3v0IEDByRJ27dv1+WXX16wc/eqhUmdUqSJpcDj\n9Tcer79V2uOVKu8x83j9rZiPd3A6rc3b41mPN4Qc9d+4pqDnUMrre9l9w3pyOJn1+Pffvkpva6/J\n6+cs9uP9x99O6q+emsh6/PrOWt15cXPBPr9Xf353j83rzT8cMr7N72+IqaXmtZMdXn28J5LOuLrt\n+Un97fOTyizzrvtdnbX6ygVNilR5v/HbT9c4Fzxef+Px+lulPV6p8h4zjxf54v1naEvwyiuv6F3v\nepempqYUCAR0xx13ZA1YJKm+vt5YAbZx40bdeeedR///sf8GAACAf8Vt+1gseym8rr3BXH10wLLL\nxAt6J8y7ZTaw9P6ENq0IaXWN+ft/Zzx7QOd1A9NpveOBQ/qb55YfsEjS9/bN6I/vGdauEf9+zQAA\nALzK33d9JxCPx3Xttdfq4MGDkqSvfe1r2rZt27I/7oUXXqjNmzdLknp6epTJUBsAAADgd/Fp83O+\nWJ0/l94vaK83P779vghZzI9hA0vvT8hxHHXFwsa32RmfK9LZFNfDB2Z10d1DejzPIdLvJ1J6+4+H\n9c3fJeS6FVlIAQAAUJYqKmQZGRnRtddeq97eXknSl7/8Zb3vfe/L28c/5ZRTJEmzs7MaHR3N28cF\nAABAeRq0TLLEais7ZOlPmKdAvKB30vwYOplkyaorWm083jPor6mMVMbVf316XNf9bESHZnP/o7vV\n4dzfdi4t3fTLcX3g0cMaT/KHfQAAAOWgYkKWsbExXXvttXrppZckSV/4whf0iU98Iq+fw2ETIQAA\nQEUZmLaELH6fZLHUhfV7fJJlLu2qP2GZZCFkyao7Zg5ZXhid19icP4KC/kRKV//0kP7HbxKLer/P\nnNage86Z1RfeMKeaRfy6+NHLM7r47iE9Y9iJBAAAgOKoiJAlkUjone98p3bt2iVJ+sxnPqObb745\n75/nd7/7nSSpurpazc2FW34JAACA8jBorQvz99Nt6ySLx0OWlydTMpUyrax21FTt72u8HFtWh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uOleE9ODVq/WRN9bn/D4ZV/rrZyf1pz8bsYbTAAAAXkbIAgAAACwCO1mya2+wTLIkyvuF1l72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\nIAsREREREREREREREdEq/A+YCvpjU7EwhQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91e84c9090>" ] }, "metadata": { "image/png": { "height": 589, "width": 812 } }, "output_type": "display_data" } ], "source": [ "# Plot\n", "ax = hour_counts.plot(x='hours', y='counts', kind='line', figsize=(12, 8))\n", "ax.set_xticks(np.arange(24))\n", "\n", "#ax.set_xticklabels(xticks, rotation=50)\n", "#ax.set_title('Number of Tweets per hour')\n", "#ax.set_xlabel('Hour')\n", "#ax.set_ylabel('No. of Tweets')\n", "\n", "#ax.set_yticklabels(labels=['0 ', '5 ', '10 ', '15 ', '20 ', '25 ', '30 ', '35 ', '40 '])\n", "ax.tick_params(axis='both', which='major', labelsize=14)\n", "ax.axhline(y=0, color='black', linewidth=1.3, alpha=0.7)\n", "ax.set_xlim(left=-1, right=24)\n", "ax.xaxis.label.set_visible(False)\n", "\n", "now = datetime.strftime(datetime.now(), '%a, %Y-%b-%d at %I:%M %p EDT')\n", "ax.text(x=-2.25, y=-5.5,\n", " s = u\"\\u00A9\" + 'THE_KLEI {} Source: Twitter, Inc. '.format(now),\n", " fontsize=14, color='#f0f0f0', backgroundcolor='grey')\n", "\n", "ax.text(x=-2.35, y=44, s=\"When does @{} tweet? - time of the day\".format(twitter_handle),\n", " fontsize=26, weight='bold', alpha=0.75)\n", "\n", "ax.text(x=-2.35, y=42, \n", " s='Number of Tweets per hour based-on 200 most-recent tweets as of {}'.format(datetime.strftime(datetime.now(), '%b %d, %Y')),\n", " fontsize=19, alpha=0.85)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### By day of the week:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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aOs9vWTemzKWbxhSloSOgfV013KM/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\nyCWXXNIeqQMAkNEMw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AAAAAAYBT45tS7GV2f/OW/R8vYYTNsGAXiX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\n4q8f3xD3v9kb18xuiok1uT9aCyhcI7YJ8t6GtLm5OU488cRhf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91e8350090>" ] }, "metadata": { "image/png": { "height": 519, "width": 800 } }, "output_type": "display_data" } ], "source": [ "# Plot\n", "daily_counts.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n", "daily_counts['counts'].plot(title='Daily tweet counts', figsize=(12, 8), legend=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### By weekday and weekend:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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biRn2rwgFAAAASjvzLVQzMjKs1jNfFZj/DMeiJCQkGJ/Lly9vs66b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vpg4vH2tsaYUfDxEyhfa9saY/Sm4BG9g6V4cPfEtQUHAODY+MQJAAA1pn+4FI/1JM/wPH22wLMS\nrZ5bSLxg3z1QjCd7R8b1e/YMFOO27SntbJdqZwuUt9lNdam/+9Z3amsLAFCuBJ4AAFBjHu0ZiuGE\nTX4zGnJxfHNddgUxbmYW8nFKS33imnvGeY7nD57tj6Hi6MfrcxGvPl7gCZS/tLa2Gzonpi04AADH\nTuAJAAA1Jq2d7amtDZHPJe0TpJylz/Ec38AzrZ3tBQsbY1ajj55A+WtvS541fM/OwegdTLjDAwCA\nSeNTJwAA1BjzO6vbWSlzPMdzh+e+oWJqi8fLl9jdCVSGc+cXYkrd6Df8jJQi7thhlycAQDkSeAIA\nQI0ReFa3s1MCz4f3DMW+pB60Y/CjZ/tjIGEkaD4X8ZolyTumAMpFY10u1ixI/hnaoa0tAEBZEngC\nAEANKZZKsSkl8Dx9tsCzkp08sz5mFEbfoVQsRTy4O/k1cKS+uzW5ne158wsxb4p5sEDlWJsyx7Nj\nm8ATAKAcCTwBAKCGPNM3EvuGSqMer89FnNIi8Kxk+Vwuzpwz8XM8DwwX40e/TL7wr50tUGnS5ng+\n3jscz/QNZ1QNAABHSuAJAAA15KGu5J19K1rqozFhfhmVIYs5nrduG4gDw6OH5xERrxN4AhVm5az6\nmD8l+XLZBm1tAQDKjsATAABqyEPmd9aEtDme9+0cjFIpOaxMc/PTye1sz55biLZp2tkClSWXy2lr\nCwBQgQSeAABQQzamBZ6zk4MyKsPqlJa2XQPFeGrfyFE//8BIKf7l2f7ENa9bmtwWEqBcrVuU/PNr\nw/b+GCke200jAACML4EnAADUkE0pLW3t8KwOLY35WDGzPnHNPccwx3ND50D0JsyCjdDOFqhcaxcm\n7/DcM1BK7ZgAAEC2BJ4AAFAjuvpHYtuB5F19As/qkTbH895jmON509bkdravmN0QS6cnB64A5Wr+\n1LpYOSv5Z1iHOZ4AAGVF4AkAADViU8pulOOm1cWsRh8RqkXaHM+j3eE5VCzF959JDjxfv9TuTqCy\nrWtLbmvbsS25rTcAANlyNQMAAGpEWvs9uzury1lzkwPPX+wZiv1DxTE/7x3bB2LPQHI728uXmN8J\nVLb2RcltbX+yc/CofoYCADAxBJ4AAFAjNqYFnrMFntVkRUt9zGjIjXq8WIp4YPfYZ9CltbNd2VIf\nJ830WgIq23nzC1FIuGo2VIy467mjbw0OAMD4EngCAECN2NRlh2ctyedysTpll+dY53iOFEvxva3J\nbRxfp50tUAWm1ufjvPnJuzzXa2sLAFA2BJ4AAFAD+odLsXnvcOIagWf1OWuc53j+ZOdg7OpPbuFo\nfidQLdrbkgPPDZ0DGVUCAEAagScAANSAR3uGYiRh7OKMhlwsaa7LriAycXbKDs/7dg1GqZQ8j/PF\nvvt0cjvbk2bUx8ta6o/4+QDKWdocz0d6hmP7gZGMqgEAIInAEwAAasBDKfM7X97aELnc6PMeqUxn\npgSeu/uL8fS+I7tYXyyV4nsp8zsvX9rkdQRUjdNaG2JOU/Klsw5tbQEAyoLAEwAAasDGlMBTO9vq\n1NKYj5NnJu+4vOcI53jev2soOg8kt7O9fIl2tkD1yOdycdFCbW0BACqBwBMAAGrAprTAc7bAs1ql\nzfG89wjneN6Usrvz+Oa6eIXXEVBl0trabtg+EMUxtAYHAGBiCDwBAKDKFUul9MDTDs+qlTbH854j\nCDxLpVLq/M7Ll0zRzhaoOu1tTYnHdx4sxi/2DGdUDQAAoxF4AgBAldu6byT2DY2++6Q+F3FKi8Cz\nWqXt8PzFnqHYP5TcqvbnXUPxTF/yrM/LlyaHAgCVaNG0utTW4BvM8QQAmHQCTwAAqHIPpezuXNFS\nH411duZVqxUz62N6w+j/viOliAe7kl8jN6e0s104NR9npuwkBahU7W3JbW3Xm+MJADDpBJ4AAFDl\nNmpnW9Pq8rlYnRJGJs3xfL6dbfLupdctmRJ57WyBKpU2x/Pu5waif9gcTwCAySTwBACAKpcaeM62\nM6/anXUMczwf6RmOx3uT59NdvnTKUdUFUAnOX9AYDQlX0PpHIn6y0y5PAIDJJPAEAIAqtymlXakd\nntXv7JQ5nvftGoxS6fC7k256Ormd7dymfJyX8vwAlay5IZ9648j6bQJPAIDJJPAEAIAq1tU/EtsO\njCSuEXhWv7T5mrv6i7G17/Cvk5tS5ne+dklT1OW1swWqW9oczw5zPAEAJpXAEwAAqtimlHa2x02r\ni1mNPhZUu1mN+Vg+sz5xzeHa2j6+dyge3pPSznaJdrZA9Vu3qCnx+Mbuodh5MPkGIwAAJo4rGwAA\nUMUeSpvfaXdnzUhrx3jvYQLPm7b2Jz6mpZCL8xcm73oCqAavnN0QLYXk3ey32eUJADBpBJ4AAFDF\nNqYFnrMFnrUibY7nvbsOE3imzO989fFTokE7W6AG1OVzcZG2tgAAZUvgCQAAVWxTlx2ePC9th+em\n7qHof1E3xq37huNnKa+f1y/VzhaoHe1tyW1tOzr7o1QqZVQNAAAvJvAEAIAq1T9cis17k+cvCjxr\nxykt9TG9YfTdmMOliIf7fvUR8eatybs7ZzTkYm3KbieAapL2M2/7gWLq710AACaGwBMAAKrUoz1D\nMZKw0WRGQy6WNNdlVxCTqi6fi1Vzknd5btz3q4+INz2dPL/z0sVN0VinnS1QO5ZOr48Tpyf/3uzY\npq0tAMBkEHgCAECVeihlfufLWxsilxNY1ZKzUuZ4bup9/iNi5/6RuOcwMz1f7HVLtLMFak/7ovS2\ntgAAZE/gCQAAVWpjSuCpnW3tOTtljudD++qiVIr4Xko726n1ubjkOO1sgdqT1tb2jh2DMZDUXgEA\ngAkh8AQAgCq1KSXwPH22wLPWnDk3+d+8eygXnQO5uCkl8PwPxzXG1HofJ4Hac8GCxkjq5n1guBT3\n7EzeIQ8AwPjzCRUAAKpQsVRKDTzt8Kw9rU11cdKM+sQ1t3XVxV3PJV+sv1w7W6BGtTTmY3XKPOQN\n2toCAGRO4AkAAFXo6X0jsW9o9JZ6DfmIU1oEnrUobY7n9c82RDGhG2NjXcT/szh5hh1ANWtflNzW\ntqNzIKNKAAB4gcATAACqUNr8zhUtDVFI6slH1Uqb49kznPy6WNfWFNMbfJQEald7yhzPB3cPRXf/\nSEbVAAAQIfAEAICqtLFLO1sO78yUHZ5pLl+qnS1Q21bPLcT0htFvDilFxO3bzfEEAMiSwBMAAKrQ\nxu7kC60Cz9q1sqU+muuPbndvfS7iN7SzBWpcQz4XFyxMa2trjicAQJYEngAAUIXSWtoKPGtXXT4X\nq1La2o7morbGaGn0MRIgra3t+s6BKJUSBiIDADCufFIFAIAqs7t/JDoPFBPXCDxrW9ocz9G8Xjtb\ngIh4fp5xkmf7RuLJXnM8AQCyUp/VN+rp6YkHH3ww7r///njggQfigQceiB07dkRExJo1a+KWW25J\nfPzWrVvjFa94xRF9ryN5PgAAqFabUnZ3Lm6us0uvxp05b+yBdz4X8erjtbMFiIg4cUZdLG6ui2f7\nRg8113f2x7KZzRlWBQBQuzILPC+88MJ45plnsvp2AABQszZ2aWdLsrOOYofnmvmFmNNUNwHVAFSe\nXC4X7W2N8bXHDoy6pqNzIN7xMoEnAEAWMgs8Xzy3YN68eXHGGWfED3/4w6N6rr/4i7+IV7/61aMe\nnzp16lE9LwAAVAPzO0kzu6kuls2oiyfG0G7xcu1sAX7NuramxMDzju0DMVQsRUM+l2FVAAC1KbPA\n853vfGccf/zxsWrVqli8eHFERLS0tBzVcy1cuDBWrlw5nuUBAEDVEHhyJM6aW4gneg8e0dpcRLx2\nicAT4MUuXFiIXESURjneO1SK+3cNxrnzG7MsCwCgJmUWeF5zzTVZfSsAAKhZB4dL8dje4cQ1Ak8i\nIs6e1xjfeuLIAs9z5hVi4VTtbAFerLWpLl45pyEe3D36jUYdnQMCTwCADOQnuwAAAGD8PNozFCOj\nbTWJiBmFXBzfLLgi4sy5Rx58v047W4DDWteWHGZ2bBvIqBIAgNom8AQAgCpyJO1sczmzxIhYOash\nptUf2WvhdUuaJrgagMq0ti355+P9uwdj72Axo2oAAGpXRQaeX/rSl2LVqlUxf/78WLx4cZx11lnx\nJ3/yJ3H33XdPdmkAADCpNnaZ38mRqc/nYtWc9NfDqjkNcXxzZtNQACrK2fMKiTePjJQifrzdLk8A\ngIlWkYHnz3/+83jyySdjYGAg9u3bF1u2bIlvfOMb8Ru/8Rvxh3/4h7Fv377JLhEAACbFkezwhBec\nPa+QuubyJdrZAoymsS4XaxYk/yzt6BR4AgBMtFxPT0/ChJ+J1dLSEhERa9asiVtuuSVx7datW+PC\nCy+M17zmNXH++efHsmXLYsqUKbFr166444474qtf/Wrs2bMnIiLWrl0b//zP/xz19Ud3F/KWLVuO\n6nEAADCZiqWI9p9MiQMjo+80+cYrD8aK5kn7CECZub2rLq59JHn+3LdXH4zFU7xmAEbzzW318T+e\nGj30XNxUjG+f2Z9hRQAA2Vi+fPlkl3BIxfQlWrhwYTzyyCMxderUlxy7+OKL413vele84Q1viF/8\n4hexYcOGuP766+Md73jHJFQKAACTY1t/LjHsrM+V4sSpgit+5fQZI1EXpRiJw79ulk8tCjsBUpw7\nayTiqdGPP9ufj239uVjU5OcpAMBEqZjAs1AoRKEw+t1yCxYsiK9//etxzjnnxNDQUHzxi1886sCz\nnBLpcvTCDljn6cg5Z2PjfI2N8zU2ztfYOWdj43yNjfM1Nmnn6+GnD0ZE96iPP2VWIVauOG4iSitb\nXmPp3rC7O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fjaY/vj7x/Zn7ruhOl18ZW1rVGfz2VQFQBQbtrT5nh2DmRUCQBAZcj19PRM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7926NHDlSd911l9599119+eWX+uyzz/Tmm2/q/vvv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\nWocBAABCJSSB54IFC7Ry5UqtXr1aX3/9tQ4cOKBDhw4pOTlZXbp0UUFBgS6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cl9JXAv49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b/t/69vySjsZD/3k2l3Vp0UUFi5rGHT1bs4MSo8AQDATBF4AgAAoGi2GIHnOa1pnd3qXeH5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5GUDJrrzOnTuL/C50S4zfycrmTGXFxsbi7bffBgD0798fmzZtEiFy/aiJuXXp34kePXrA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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91e83a6790>" ] }, "metadata": { "image/png": { "height": 753, "width": 926 } }, "output_type": "display_data" } ], "source": [ "# Plot\n", "fig, ax = plt.subplots(2, 1, figsize=(14, 12))\n", "# weekdays\n", "by_time.loc[:, [('counts', 'weekday')]].plot(ax=ax[0], title='Weekdays', kind='line')\n", "# weekends\n", "by_time.loc[:, [('counts', 'weekend')]].plot(ax=ax[1], title='Weekend', kind='line')\n", "\n", "ax[0].set_xticks(np.arange(24))\n", "#ax[0].set_xticklabels(xticks, rotation=50)\n", "\n", "ax[1].set_xticks(np.arange(24))\n", "#ax[1].set_xticklabels(xticks, rotation=50)\n", "\n", "plt.show()" ] } ], "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
nens/python-subgrid	notebooks/changeinpoly.ipynb	1	345953	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os.path\n", "import logging\n", "\n", "import osgeo.gdal\n", "import netCDF4\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas\n", "\n", "# for points in poly (faster than shapely)\n", "import matplotlib.path\n", "\n", "\n", "import python_subgrid.wrapper\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "mdu = '../../models/delfland-model-voor-3di/hhdlipad.mdu'\n", "molen = {'x': 87519, 'y': 450328}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "subgrid = python_subgrid.wrapper.SubgridWrapper(mdu=mdu)\n", "python_subgrid.wrapper.logger.setLevel(logging.WARNING)\n", "subgrid.start()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:python_subgrid.wrapper:Ignored 'Teta' in MDU file, please use 'IntegrationMethod' instead.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "No handlers could be found for logger \"progress\"\n", "ERROR:python_subgrid.wrapper:No network found, 1d boundaries are ignored.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:python_subgrid.wrapper:File \n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "ERROR:python_subgrid.wrapper:No 1d network found. Structures are disabled.\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "subgrid.get_var_shape('dps')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "(5032, 5322)" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "pixels = {}\n", "pixels['dps'] = subgrid.get_nd('dps')[1:-1,1:-1] # contains ghost cells? # why transposed\n", "pixels['dsnop'] = subgrid.get_nd('dsnop')\n", "pixels['dps'] = np.ma.masked_array(-pixels['dps'], mask=pixels['dps']==pixels['dsnop'])\n", "pixels['x0p'] = subgrid.get_nd('x0p')\n", "pixels['dxp'] = subgrid.get_nd('dxp')\n", "pixels['y0p'] = subgrid.get_nd('y0p')\n", "pixels['dyp'] = subgrid.get_nd('dyp')\n", "pixels['imax'] = subgrid.get_nd('imax')\n", "pixels['jmax'] = subgrid.get_nd('jmax')\n", "pixels['x'] = np.arange(pixels['x0p'], pixels['x0p'] + pixels['imax']pixels['dxp'], pixels['dxp'])\n", "pixels['y'] = np.arange(pixels['y0p'], pixels['y0p'] + pixels['jmax']pixels['dyp'], pixels['dyp'])\n", "pixels['Y'], pixels['X'] = np.meshgrid(pixels['y'], pixels['x'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "thin = 10\n", "# Add the grid\n", "ax.pcolorfast(pixels['x'][::thin], \n", " pixels['y'][::thin], \n", " pixels['dps'][::thin,::thin], \n", " vmin=-20, vmax=20, cmap='gist_earth')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "<matplotlib.image.AxesImage at 0x65efb50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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hXaXoas1o0mau6JeY+rWsctVEqeMV7u+mLK8VSGD35v2lAyoGIgTfPb2yVpBq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TaT8FhpfoRTmRiggVKlw8zDOIKOJtKiaVawf20vwEaVaghdFhRQyVu68AQxIqsJapVFSqz0zx6R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Pid26aKXChesmTqDL9HIiPDbLUuRSovH0Y5JAsUE070iWYTFJMVDEZpOCHPwkgOU0RSbIXc34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\nzm18/+kek7EOvH9vOJN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"text": [ "<matplotlib.figure.Figure at 0x65f9b90>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# parallellogram\n", "coords = [[77500,445000], [80000,447500], [80000,450000], [77500,447500]]\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "poly = matplotlib.path.Path(vertices =coords, closed=False)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "ax.add_patch(matplotlib.patches.PathPatch(poly))\n", "fig" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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hXaXoas1o0mau6JeY+rWsctVEqeMV7u+mLK8VSGD35v2lAyoGIgTfPb2yVpBq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\nji9jbURxcJzB8AZRNKdqVoIOkYjSaebFOtlgx+305wG9BDDIN0MrCXA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oI7T5KsD/8bIR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1jLxBQaKFU6zDyMk0jzXSTSomnmsHV+M8BGlWoIXSZ6IT6tSxSeIEZkNUGtskiJlf7YI5KMlwFh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\nSwiLzA+phdSBddw1c36wvQ+jkykSXngdz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wq8u+dkODImVBv83Ow0xtUAP551AlU1jzQb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\nbESbUvDBYxoUTyf+6q4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"prompt_number": 17, "text": [ "<matplotlib.figure.Figure at 0x65f9b90>" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "pts = np.c_[pixels['X'].ravel(), pixels['Y'].ravel()]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "# keep this below a second\n", "inpoly = poly.contains_points(pts)\n", "pixels['inpoly'] = inpoly.reshape(pixels['X'].shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 561 ms, sys: 58.3 ms, total: 619 ms\n", "Wall time: 619 ms\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "subgrid.get_nd('dps')[1:-1,1:-1][pixels['inpoly'].T] += 10" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "subgrid.update(-1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:python_subgrid.wrapper:Progress stopped but no current progress message\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:python_subgrid.wrapper:Progress stopped but no current progress message\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "0" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "pixels['dps'] = subgrid.get_nd('dps')[1:-1,1:-1] # contains ghost cells? # why transposed\n", "pixels['dsnop'] = subgrid.get_nd('dsnop')\n", "pixels['dps'] = np.ma.masked_array(-pixels['dps'], mask=pixels['dps']==pixels['dsnop'])\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "thin = 10\n", "# Add the grid\n", "ax.pcolorfast(pixels['x'][::thin], \n", " pixels['y'][::thin], \n", " pixels['dps'][::thin,::thin], \n", " vmin=-20, vmax=20, cmap='gist_earth')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "<matplotlib.image.AxesImage at 0x69dcd10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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hXaXoas1o0mau6JeY+rWsctVEqeMV7u+mLK8VSGD35v2lAyoGIgTfPb2yVpBq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RF6s0+PcCCGBsb8pZUDvn7/b3IUFyLoQNaA+EOwyUxE3DOdTWYWppsTLo5Bqm1iLzZkSJNyRq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\ngFCjRo0aNQD4f0ItyBqTdSaKAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x69f0110>" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
birdsarah/bokeh-miscellany	rows to columns - py2_3 - py2.ipynb	1	5619	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import networkx as nx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define as currently and check working" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def test__rows_to_columns(test_func):\n", " G = nx.Graph()\n", " G.add_nodes_from([(0, {\"attr_1\": \"a\", \"attr_2\": 10}),\n", " (1, {\"attr_1\": \"b\"}),\n", " (2, {\"attr_1\": \"c\", \"attr_2\": 30})])\n", " G.add_edges_from([(0, 1, {\"attr_1\": \"A\"}),\n", " (0, 2, {\"attr_1\": \"B\", \"attr_2\": 10})])\n", " node_attr_keys = [\"attr_1\", \"attr_2\"]\n", " node_dict = test_func(G.nodes(data=True), node_attr_keys)\n", " assert node_dict[\"attr_1\"] == [attr['attr_1'] for _, attr in G.nodes(data=True)], \"returned: %s\" % node_dict['attr_1']\n", " assert node_dict[\"attr_2\"] == [attr['attr_2'] if key != 1 else None for key, attr in G.nodes(data=True)]\n", " edge_attr_keys = [\"attr_1\", \"attr_2\"]\n", " edge_dict = test_func(G.edges(data=True), edge_attr_keys)\n", " assert edge_dict[\"attr_1\"] == [\"A\", \"B\"]\n", " assert edge_dict[\"attr_2\"] == [None, 10]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-4-548cf34272e6>, line 4)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-4-548cf34272e6>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m attr_dict[attr_key] = [attr[attr_key] if attr_key in attr.keys() else None for _, attr in source]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "def _rows_to_columns_orig(source, attr_keys):\n", " attr_dict = {}\n", " for attr_key in attr_keys:\n", " attr_dict[attr_key] = [attr[attr_key] if attr_key in attr.keys() else None for _, attr in source]\n", " return attr_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test__rows_to_columns(_rows_to_columns_orig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Redefine in py2/3 syntax" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def _rows_to_columns_23(nx_data, attr_keys):\n", " # nx_data may be coming from edges or nodes\n", " # So extract the data depending on the type\n", " if isinstance(nx_data, nx.classes.reportviews.EdgeDataView):\n", " source = [i[2] for i in nx_data]\n", " elif isinstance(nx_data, nx.classes.reportviews.NodeDataView):\n", " source = [data for node, data in nx_data]\n", " \n", " attr_dict = {}\n", " for attr_key in attr_keys:\n", " compiled_data = []\n", " for data in source:\n", " compiled_data.append(data.get(attr_key))\n", " attr_dict[attr_key] = compiled_data\n", " return attr_dict" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "test__rows_to_columns(_rows_to_columns_23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A quick look at the inputs to understand what's happening here" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "G = nx.Graph()\n", "G.add_nodes_from([(0, {\"attr_1\": \"a\", \"attr_2\": 10}),\n", " (1, {\"attr_1\": \"b\"}),\n", " (2, {\"attr_1\": \"c\", \"attr_2\": 30})])\n", "G.add_edges_from([(0, 1, {\"attr_1\": \"A\"}),\n", " (0, 2, {\"attr_1\": \"B\", \"attr_2\": 10})])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "EdgeDataView([(0, 1, {'attr_1': 'A'}), (0, 2, {'attr_2': 10, 'attr_1': 'B'})])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.edges(data=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NodeDataView({0: {'attr_2': 10, 'attr_1': 'a'}, 1: {'attr_1': 'b'}, 2: {'attr_2': 30, 'attr_1': 'c'}})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G.nodes(data=True)" ] }, { "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": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
AlbertoAlfredo/exercicios-cursos	ExerciciosGerais/slides/37.Prática em Python/scripts/8.comunidades.ipynb	1	4872	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Formação Cientista de Dados - Fernando Amaral e Jones Granatyr\n", "# Igraph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Importação das bibliotecas\n", "from igraph import Graph\n", "from igraph import plot\n", "import igraph\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Carregamento de grafo no formato graphml\n", "grafo = igraph.load('Grafo.graphml')\n", "print(grafo)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização do grafo\n", "plot(grafo, bbox = (0,0,600,600))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização das comunidades\n", "comunidades = grafo.clusters()\n", "print(comunidades)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização em qual comunidade qual registro foi associado\n", "comunidades.membership" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização do grafo\n", "cores = comunidades.membership\n", "# Array de cores para defirmos cores diferentes para cada grupo\n", "cores = np.array(cores)\n", "cores = cores * 20\n", "cores = cores.tolist()\n", "plot(grafo, vertex_color = cores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "exemplo 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Criação de grafo direcionado com pesos nas arestas\n", "grafo2 = Graph(edges = [(0,2),(0,1),(1,4),(1,5),(2,3),(6,7),(3,7),(4,7),(5,6)],\n", " directed = True)\n", "grafo2.vs['label'] = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n", "grafo2.es['weight'] = [2,1,2,1,2,1,3,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização do grafo\n", "plot(grafo2, bbox = (0,0,300,300))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Visualização de comunidades e em qual comunidade cada registro foi associado\n", "comunidades2 = grafo2.clusters()\n", "print(comunidades2)\n", "comunidades2.membership" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Função mais otimizada para visualização das comunidades\n", "c = grafo2.community_edge_betweenness()\n", "print(c)\n", "# Obtenção do número de clusters\n", "c.optimal_count\n", "# Visualização da nova comunidade\n", "comunidades3 = c.as_clustering()\n", "print(comunidades3)\n", "comunidades3.membership" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Geração do grafo das comunidades colocando cores entre os grupos identificados\n", "plot(grafo2, vertex_color = comunidades3.membership)\n", "cores = comunidades3.membership\n", "# Array de cores para defirmos cores diferentes para cada grupo\n", "cores = np.array(cores)\n", "cores = cores * 100\n", "cores = cores.tolist()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot(grafo2, bbox = (0,0,300,300), vertex_color = cores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# Visualização dos cliques\n", "cli = grafo.as_undirected().cliques(min = 4)\n", "print(cli)\n", "len(cli)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "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
radajin/whoscored	predict_rating/defenser_ols.ipynb	1	34210	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Defencer OLS\n", "- Import Package\n", "- Connect DB & get Defence Player Data\n", "- Scaling\n", "- Summary OLS \n", "- Remove Feature\n", "- Anova & Remove Feature" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Package" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_formats = {'png', 'retina'}\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import seaborn as sns\n", "import MySQLdb\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import cross_val_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connect DB & get Forword Player Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "817" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = MySQLdb.connect(\n", " \"db.fastcamp.us\",\n", " \"root\",\n", " \"dkstncks\",\n", " \"football\",\n", " charset='utf8',\n", ")\n", "\n", "def make_query(position):\n", " \"\"\"\n", " \n", " parameter------------\n", " position : M, D, F, G\n", " \n", " return---------------\n", " SQL_QUERY String\n", " \n", " \"\"\"\n", " \n", " SQL_QUERY = \"\"\"\n", " SELECT \n", " age, tall, weight, apps_start, apps_sub, mins, goals, assists, yel, red\n", " , spg, ps_x, motm, aw, tackles, inter, fouls, offsides, clear, drb, blocks\n", " , owng, keyp_x, fouled, off, disp, unstch, avgp, ps_y, rating \n", " FROM player\n", " \"\"\"\n", " \n", " if position == \"F\":\n", " SQL_QUERY += \"\"\"\n", " WHERE position not like \"%,%\" and position like \"%FW%\" and mins > 270\n", " \"\"\"\n", " \n", " if position == \"M\":\n", " SQL_QUERY += \"\"\"\n", " WHERE position not like \"%,%\" and position like \"%M%\" and mins > 270\n", " \"\"\"\n", " \n", " if position == \"D\":\n", " SQL_QUERY += \"\"\"\n", " WHERE position not like \"%,%\" and position like \"%D%\" and position not like \" DMC\" and mins > 270\n", " \"\"\"\n", " \n", " if position == \"G\":\n", " SQL_QUERY += \"\"\"\n", " WHERE position not like \"%,%\" and position like \"%G%\" and mins > 270\n", " \"\"\"\n", " \n", " return SQL_QUERY\n", "\n", "# defencer\n", "SQL_QUERY = make_query(\"D\")\n", "defenser_df = pd.read_sql(SQL_QUERY, db)\n", "\n", "len(defenser_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Scaling" ] }, { "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>age</th>\n", " <th>tall</th>\n", " <th>weight</th>\n", " <th>apps_start</th>\n", " <th>apps_sub</th>\n", " <th>mins</th>\n", " <th>goals</th>\n", " <th>assists</th>\n", " <th>yel</th>\n", " <th>...</th>\n", " <th>blocks</th>\n", " <th>owng</th>\n", " <th>keyp_x</th>\n", " <th>fouled</th>\n", " <th>off</th>\n", " <th>disp</th>\n", " <th>unstch</th>\n", " <th>avgp</th>\n", " <th>ps_y</th>\n", " <th>rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>5.342523</td>\n", " <td>15.105863</td>\n", " <td>10.954348</td>\n", " <td>2.615560</td>\n", " <td>0.466727</td>\n", " <td>2.632199</td>\n", " <td>2.827191</td>\n", " <td>0.900141</td>\n", " <td>2.688382</td>\n", " <td>...</td>\n", " <td>1.967895</td>\n", " <td>2.427616</td>\n", " <td>0.654447</td>\n", " <td>2.379510</td>\n", " <td>0.000000</td>\n", " <td>0.672855</td>\n", " <td>1.001993</td>\n", " <td>4.325854</td>\n", " <td>11.328504</td>\n", " <td>6.93</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>6.799575</td>\n", " <td>14.146761</td>\n", " <td>9.665601</td>\n", " <td>3.452539</td>\n", " <td>0.466727</td>\n", " <td>3.454909</td>\n", " <td>0.000000</td>\n", " <td>2.700424</td>\n", " <td>2.304327</td>\n", " <td>...</td>\n", " <td>0.327983</td>\n", " <td>0.000000</td>\n", " <td>2.290563</td>\n", " <td>1.665657</td>\n", " <td>0.000000</td>\n", " <td>2.018565</td>\n", " <td>2.337983</td>\n", " <td>2.718860</td>\n", " <td>10.961246</td>\n", " <td>6.90</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>6.556733</td>\n", " <td>14.466461</td>\n", " <td>9.923350</td>\n", " <td>1.569336</td>\n", " <td>3.733816</td>\n", " <td>1.738155</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.384055</td>\n", " <td>...</td>\n", " <td>0.327983</td>\n", " <td>0.000000</td>\n", " <td>1.308893</td>\n", " <td>0.951804</td>\n", " <td>1.435429</td>\n", " <td>1.009282</td>\n", " <td>1.669988</td>\n", " <td>3.083691</td>\n", " <td>10.890619</td>\n", " <td>6.89</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>6.799575</td>\n", " <td>14.786162</td>\n", " <td>10.181100</td>\n", " <td>2.929427</td>\n", " <td>0.933454</td>\n", " <td>2.954387</td>\n", " <td>0.000000</td>\n", " <td>0.900141</td>\n", " <td>1.920273</td>\n", " <td>...</td>\n", " <td>2.295878</td>\n", " <td>2.427616</td>\n", " <td>0.654447</td>\n", " <td>0.237951</td>\n", " <td>1.435429</td>\n", " <td>0.672855</td>\n", " <td>1.001993</td>\n", " <td>3.274793</td>\n", " <td>11.342629</td>\n", " <td>6.86</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>7.285259</td>\n", " <td>14.546387</td>\n", " <td>9.407851</td>\n", " <td>3.347916</td>\n", " <td>0.000000</td>\n", " <td>3.369309</td>\n", " <td>0.942397</td>\n", " <td>3.600565</td>\n", " <td>4.224600</td>\n", " <td>...</td>\n", " <td>0.655965</td>\n", " <td>0.000000</td>\n", " <td>2.945009</td>\n", " <td>2.141559</td>\n", " <td>0.000000</td>\n", " <td>2.691419</td>\n", " <td>3.339975</td>\n", " <td>4.508270</td>\n", " <td>11.653386</td>\n", " <td>7.60</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 31 columns</p>\n", "</div>" ], "text/plain": [ " const age tall weight apps_start apps_sub mins \\\n", "0 1 5.342523 15.105863 10.954348 2.615560 0.466727 2.632199 \n", "1 1 6.799575 14.146761 9.665601 3.452539 0.466727 3.454909 \n", "2 1 6.556733 14.466461 9.923350 1.569336 3.733816 1.738155 \n", "3 1 6.799575 14.786162 10.181100 2.929427 0.933454 2.954387 \n", "4 1 7.285259 14.546387 9.407851 3.347916 0.000000 3.369309 \n", "\n", " goals assists yel ... blocks owng keyp_x \\\n", "0 2.827191 0.900141 2.688382 ... 1.967895 2.427616 0.654447 \n", "1 0.000000 2.700424 2.304327 ... 0.327983 0.000000 2.290563 \n", "2 0.000000 0.000000 0.384055 ... 0.327983 0.000000 1.308893 \n", "3 0.000000 0.900141 1.920273 ... 2.295878 2.427616 0.654447 \n", "4 0.942397 3.600565 4.224600 ... 0.655965 0.000000 2.945009 \n", "\n", " fouled off disp unstch avgp ps_y rating \n", "0 2.379510 0.000000 0.672855 1.001993 4.325854 11.328504 6.93 \n", "1 1.665657 0.000000 2.018565 2.337983 2.718860 10.961246 6.90 \n", "2 0.951804 1.435429 1.009282 1.669988 3.083691 10.890619 6.89 \n", "3 0.237951 1.435429 0.672855 1.001993 3.274793 11.342629 6.86 \n", "4 2.141559 0.000000 2.691419 3.339975 4.508270 11.653386 7.60 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = defenser_df.ix[:,:-1]\n", "scaler = StandardScaler(with_mean=False)\n", "X_scaled = scaler.fit_transform(X)\n", "\n", "dfX0 = pd.DataFrame(X_scaled, columns=X.columns)\n", "dfX = sm.add_constant(dfX0)\n", "dfy = pd.DataFrame(defenser_df.ix[:,-1], columns=[\"rating\"])\n", "d_df = pd.concat([dfX, dfy], axis=1)\n", "d_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Summary OLS" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: rating R-squared: 0.767\n", "Model: OLS Adj. R-squared: 0.759\n", "Method: Least Squares F-statistic: 92.66\n", "Date: Mon, 27 Jun 2016 Prob (F-statistic): 1.84e-227\n", "Time: 11:06:23 Log-Likelihood: 525.03\n", "No. Observations: 817 AIC: -992.1\n", "Df Residuals: 788 BIC: -855.6\n", "Df Model: 28 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 5.4647 0.108 50.706 0.000 5.253 5.676\n", "age -0.0031 0.005 -0.617 0.538 -0.013 0.007\n", "tall -0.0080 0.008 -1.015 0.310 -0.023 0.007\n", "weight 0.0149 0.008 1.758 0.079 -0.002 0.032\n", "apps_start 0.1055 0.069 1.537 0.125 -0.029 0.240\n", "apps_sub 0.0161 0.007 2.295 0.022 0.002 0.030\n", "mins -0.0982 0.068 -1.451 0.147 -0.231 0.035\n", "goals 0.0297 0.006 5.268 0.000 0.019 0.041\n", "assists 0.0319 0.006 5.303 0.000 0.020 0.044\n", "yel -0.0056 0.006 -0.908 0.364 -0.018 0.007\n", "red -0.0275 0.005 -5.747 0.000 -0.037 -0.018\n", "spg 0.0102 0.006 1.820 0.069 -0.001 0.021\n", "ps_x 0.0198 0.003 5.674 0.000 0.013 0.027\n", "motm 0.0265 0.006 4.537 0.000 0.015 0.038\n", "aw 0.0636 0.007 9.345 0.000 0.050 0.077\n", "tackles 0.0985 0.006 15.168 0.000 0.086 0.111\n", "inter 0.0741 0.006 13.223 0.000 0.063 0.085\n", "fouls -0.0288 0.006 -5.060 0.000 -0.040 -0.018\n", "offsides -0.0173 0.006 -2.901 0.004 -0.029 -0.006\n", "clear 0.0528 0.008 6.648 0.000 0.037 0.068\n", "drb -0.0172 0.006 -3.072 0.002 -0.028 -0.006\n", "blocks 0.0246 0.006 4.055 0.000 0.013 0.037\n", "owng -0.0200 0.005 -4.238 0.000 -0.029 -0.011\n", "keyp_x 0.0413 0.007 5.662 0.000 0.027 0.056\n", "fouled 0.0158 0.005 3.040 0.002 0.006 0.026\n", "off 0.0119 0.005 2.505 0.012 0.003 0.021\n", "disp 0.0201 0.007 3.001 0.003 0.007 0.033\n", "unstch 0.0003 0.007 0.046 0.963 -0.014 0.015\n", "avgp 0.0507 0.008 6.381 0.000 0.035 0.066\n", "ps_y 0.0198 0.003 5.674 0.000 0.013 0.027\n", "==============================================================================\n", "Omnibus: 15.069 Durbin-Watson: 1.400\n", "Prob(Omnibus): 0.001 Jarque-Bera (JB): 27.400\n", "Skew: -0.012 Prob(JB): 1.12e-06\n", "Kurtosis: 3.897 Cond. No. 9.03e+15\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The smallest eigenvalue is 6.75e-27. This might indicate that there are\n", "strong multicollinearity problems or that the design matrix is singular.\n" ] } ], "source": [ "model = sm.OLS(d_df.ix[:, -1], d_df.ix[:, :-1])\n", "result = model.fit()\n", "print(result.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Remove Some Feature" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: rating R-squared: 0.764\n", "Model: OLS Adj. R-squared: 0.758\n", "Method: Least Squares F-statistic: 129.1\n", "Date: Mon, 27 Jun 2016 Prob (F-statistic): 1.06e-233\n", "Time: 11:06:24 Log-Likelihood: 520.29\n", "No. Observations: 817 AIC: -998.6\n", "Df Residuals: 796 BIC: -899.8\n", "Df Model: 20 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 5.5091 0.073 75.010 0.000 5.365 5.653\n", "goals 0.0299 0.006 5.442 0.000 0.019 0.041\n", "assists 0.0338 0.006 5.881 0.000 0.023 0.045\n", "red -0.0269 0.005 -5.724 0.000 -0.036 -0.018\n", "spg 0.0115 0.006 2.088 0.037 0.001 0.022\n", "ps_x 0.0209 0.003 6.153 0.000 0.014 0.028\n", "motm 0.0285 0.006 4.948 0.000 0.017 0.040\n", "aw 0.0672 0.007 10.156 0.000 0.054 0.080\n", "tackles 0.0965 0.006 15.117 0.000 0.084 0.109\n", "inter 0.0712 0.005 13.063 0.000 0.061 0.082\n", "fouls -0.0308 0.005 -6.002 0.000 -0.041 -0.021\n", "offsides -0.0184 0.006 -3.114 0.002 -0.030 -0.007\n", "clear 0.0478 0.007 6.551 0.000 0.033 0.062\n", "drb -0.0171 0.006 -3.051 0.002 -0.028 -0.006\n", "blocks 0.0235 0.006 3.934 0.000 0.012 0.035\n", "owng -0.0203 0.005 -4.338 0.000 -0.029 -0.011\n", "keyp_x 0.0376 0.007 5.299 0.000 0.024 0.052\n", "fouled 0.0143 0.005 2.826 0.005 0.004 0.024\n", "off 0.0111 0.005 2.359 0.019 0.002 0.020\n", "disp 0.0193 0.006 3.252 0.001 0.008 0.031\n", "avgp 0.0470 0.007 6.387 0.000 0.033 0.061\n", "ps_y 0.0209 0.003 6.153 0.000 0.014 0.028\n", "==============================================================================\n", "Omnibus: 14.080 Durbin-Watson: 1.393\n", "Prob(Omnibus): 0.001 Jarque-Bera (JB): 23.488\n", "Skew: -0.084 Prob(JB): 7.94e-06\n", "Kurtosis: 3.814 Cond. No. 2.92e+17\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The smallest eigenvalue is 2.91e-30. This might indicate that there are\n", "strong multicollinearity problems or that the design matrix is singular.\n" ] } ], "source": [ "# remove features\n", "remove_column_list = [\n", " \"age\", \"tall\", \"weight\", \"apps_start\", \"apps_sub\", \"mins\", \"yel\", \"unstch\"\n", "]\n", "removed_d_df = d_df.drop(remove_column_list, axis=1) \n", "\n", "model = sm.OLS(removed_d_df.ix[:, -1], removed_d_df.ix[:, :-1])\n", "result = model.fit()\n", "print(result.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Anova & Remove Feature" ] }, { "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>df</th>\n", " <th>sum_sq</th>\n", " <th>mean_sq</th>\n", " <th>F</th>\n", " <th>PR(>F)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>goals</th>\n", " <td>1.0</td>\n", " <td>7.351482</td>\n", " <td>7.351482</td>\n", " <td>437.207013</td>\n", " <td>1.015348e-77</td>\n", " </tr>\n", " <tr>\n", " <th>assists</th>\n", " <td>1.0</td>\n", " <td>4.562316</td>\n", " <td>4.562316</td>\n", " <td>271.329858</td>\n", " <td>1.116092e-52</td>\n", " </tr>\n", " <tr>\n", " <th>red</th>\n", " <td>1.0</td>\n", " <td>0.018432</td>\n", " <td>0.018432</td>\n", " <td>1.096175</td>\n", " <td>2.954250e-01</td>\n", " </tr>\n", " <tr>\n", " <th>spg</th>\n", " <td>1.0</td>\n", " <td>1.880096</td>\n", " <td>1.880096</td>\n", " <td>111.812964</td>\n", " <td>1.524576e-24</td>\n", " </tr>\n", " <tr>\n", " <th>ps_x</th>\n", " <td>1.0</td>\n", " <td>2.231002</td>\n", " <td>2.231002</td>\n", " <td>132.682065</td>\n", " <td>1.671015e-28</td>\n", " </tr>\n", " <tr>\n", " <th>motm</th>\n", " <td>1.0</td>\n", " <td>7.489474</td>\n", " <td>7.489474</td>\n", " <td>445.413638</td>\n", " <td>7.204196e-79</td>\n", " </tr>\n", " <tr>\n", " <th>aw</th>\n", " <td>1.0</td>\n", " <td>4.249837</td>\n", " <td>4.249837</td>\n", " <td>252.746118</td>\n", " <td>1.245196e-49</td>\n", " </tr>\n", " <tr>\n", " <th>tackles</th>\n", " <td>1.0</td>\n", " <td>8.439651</td>\n", " <td>8.439651</td>\n", " <td>501.922530</td>\n", " <td>1.403409e-86</td>\n", " </tr>\n", " <tr>\n", " <th>inter</th>\n", " <td>1.0</td>\n", " <td>3.188942</td>\n", " <td>3.188942</td>\n", " <td>189.652623</td>\n", " <td>7.383641e-39</td>\n", " </tr>\n", " <tr>\n", " <th>fouls</th>\n", " <td>1.0</td>\n", " <td>0.364824</td>\n", " <td>0.364824</td>\n", " <td>21.696824</td>\n", " <td>3.741386e-06</td>\n", " </tr>\n", " <tr>\n", " <th>offsides</th>\n", " <td>1.0</td>\n", " <td>0.108970</td>\n", " <td>0.108970</td>\n", " <td>6.480675</td>\n", " <td>1.109283e-02</td>\n", " </tr>\n", " <tr>\n", " <th>clear</th>\n", " <td>1.0</td>\n", " <td>0.577817</td>\n", " <td>0.577817</td>\n", " <td>34.363883</td>\n", " <td>6.693984e-09</td>\n", " </tr>\n", " <tr>\n", " <th>drb</th>\n", " <td>1.0</td>\n", " <td>0.071945</td>\n", " <td>0.071945</td>\n", " <td>4.278703</td>\n", " <td>3.891519e-02</td>\n", " </tr>\n", " <tr>\n", " <th>blocks</th>\n", " <td>1.0</td>\n", " <td>0.122185</td>\n", " <td>0.122185</td>\n", " <td>7.266577</td>\n", " <td>7.173233e-03</td>\n", " </tr>\n", " <tr>\n", " <th>owng</th>\n", " <td>1.0</td>\n", " <td>0.329726</td>\n", " <td>0.329726</td>\n", " <td>19.609450</td>\n", " <td>1.082698e-05</td>\n", " </tr>\n", " <tr>\n", " <th>keyp_x</th>\n", " <td>1.0</td>\n", " <td>1.273226</td>\n", " <td>1.273226</td>\n", " <td>75.721244</td>\n", " <td>1.859889e-17</td>\n", " </tr>\n", " <tr>\n", " <th>fouled</th>\n", " <td>1.0</td>\n", " <td>0.185722</td>\n", " <td>0.185722</td>\n", " <td>11.045265</td>\n", " <td>9.297657e-04</td>\n", " </tr>\n", " <tr>\n", " <th>off</th>\n", " <td>1.0</td>\n", " <td>0.090795</td>\n", " <td>0.090795</td>\n", " <td>5.399748</td>\n", " <td>2.039063e-02</td>\n", " </tr>\n", " <tr>\n", " <th>disp</th>\n", " <td>1.0</td>\n", " <td>0.183902</td>\n", " <td>0.183902</td>\n", " <td>10.936982</td>\n", " <td>9.849140e-04</td>\n", " </tr>\n", " <tr>\n", " <th>avgp</th>\n", " <td>1.0</td>\n", " <td>0.685988</td>\n", " <td>0.685988</td>\n", " <td>40.797051</td>\n", " <td>2.870051e-10</td>\n", " </tr>\n", " <tr>\n", " <th>ps_y</th>\n", " <td>1.0</td>\n", " <td>0.016720</td>\n", " <td>0.016720</td>\n", " <td>0.994387</td>\n", " <td>3.189756e-01</td>\n", " </tr>\n", " <tr>\n", " <th>Residual</th>\n", " <td>796.0</td>\n", " <td>13.384460</td>\n", " <td>0.016815</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " df sum_sq mean_sq F PR(>F)\n", "goals 1.0 7.351482 7.351482 437.207013 1.015348e-77\n", "assists 1.0 4.562316 4.562316 271.329858 1.116092e-52\n", "red 1.0 0.018432 0.018432 1.096175 2.954250e-01\n", "spg 1.0 1.880096 1.880096 111.812964 1.524576e-24\n", "ps_x 1.0 2.231002 2.231002 132.682065 1.671015e-28\n", "motm 1.0 7.489474 7.489474 445.413638 7.204196e-79\n", "aw 1.0 4.249837 4.249837 252.746118 1.245196e-49\n", "tackles 1.0 8.439651 8.439651 501.922530 1.403409e-86\n", "inter 1.0 3.188942 3.188942 189.652623 7.383641e-39\n", "fouls 1.0 0.364824 0.364824 21.696824 3.741386e-06\n", "offsides 1.0 0.108970 0.108970 6.480675 1.109283e-02\n", "clear 1.0 0.577817 0.577817 34.363883 6.693984e-09\n", "drb 1.0 0.071945 0.071945 4.278703 3.891519e-02\n", "blocks 1.0 0.122185 0.122185 7.266577 7.173233e-03\n", "owng 1.0 0.329726 0.329726 19.609450 1.082698e-05\n", "keyp_x 1.0 1.273226 1.273226 75.721244 1.859889e-17\n", "fouled 1.0 0.185722 0.185722 11.045265 9.297657e-04\n", "off 1.0 0.090795 0.090795 5.399748 2.039063e-02\n", "disp 1.0 0.183902 0.183902 10.936982 9.849140e-04\n", "avgp 1.0 0.685988 0.685988 40.797051 2.870051e-10\n", "ps_y 1.0 0.016720 0.016720 0.994387 3.189756e-01\n", "Residual 796.0 13.384460 0.016815 NaN NaN" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "formula_str = \"\"\"\n", "rating ~ goals + assists + red + spg + ps_x + motm + aw\n", "+ tackles + inter + fouls + offsides + clear + drb + blocks\n", "+ owng + keyp_x + fouled + off + disp + avgp + ps_y\n", "\"\"\"\n", "\n", "model = sm.OLS.from_formula(formula_str, data=removed_d_df)\n", "result = model.fit()\n", "table_anova = sm.stats.anova_lm(result)\n", "table_anova" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: rating R-squared: 0.740\n", "Model: OLS Adj. R-squared: 0.736\n", "Method: Least Squares F-statistic: 163.4\n", "Date: Mon, 27 Jun 2016 Prob (F-statistic): 1.28e-223\n", "Time: 11:06:26 Log-Likelihood: 480.88\n", "No. Observations: 817 AIC: -931.8\n", "Df Residuals: 802 BIC: -861.2\n", "Df Model: 14 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const 5.4991 0.076 72.405 0.000 5.350 5.648\n", "goals 0.0330 0.006 5.826 0.000 0.022 0.044\n", "assists 0.0320 0.006 5.331 0.000 0.020 0.044\n", "spg 0.0124 0.006 2.158 0.031 0.001 0.024\n", "ps_x 0.0447 0.007 6.347 0.000 0.031 0.059\n", "motm 0.0264 0.006 4.422 0.000 0.015 0.038\n", "aw 0.0684 0.007 9.977 0.000 0.055 0.082\n", "tackles 0.0944 0.006 16.304 0.000 0.083 0.106\n", "inter 0.0667 0.006 11.839 0.000 0.056 0.078\n", "fouls -0.0341 0.005 -6.418 0.000 -0.045 -0.024\n", "clear 0.0480 0.007 7.289 0.000 0.035 0.061\n", "owng -0.0200 0.005 -4.123 0.000 -0.030 -0.010\n", "keyp_x 0.0475 0.007 6.701 0.000 0.034 0.061\n", "fouled 0.0171 0.005 3.274 0.001 0.007 0.027\n", "avgp 0.0400 0.008 5.304 0.000 0.025 0.055\n", "==============================================================================\n", "Omnibus: 9.852 Durbin-Watson: 1.362\n", "Prob(Omnibus): 0.007 Jarque-Bera (JB): 15.202\n", "Skew: -0.008 Prob(JB): 0.000500\n", "Kurtosis: 3.668 Cond. No. 211.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "# remove feature 2\n", "remove_column_list = [\n", " \"red\", \"offsides\", \"drb\", \"blocks\", \"off\", \"disp\", \"ps_y\"\n", "]\n", "removed2_d_df = removed_d_df.drop(remove_column_list, axis=1) \n", "\n", "model = sm.OLS(removed2_d_df.ix[:, -1], removed2_d_df.ix[:, :-1])\n", "result = model.fit()\n", "print(result.summary())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# tackles\n", "# aw : 공중볼 경합\n", "# inter\n", "# clear" ] }, { "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
wikp/peaches	2017-09-13-MeetIT-Torun/sejm.ipynb	1	369578	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import datetime\n", "\n", "plt.style.use('seaborn-whitegrid')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sejm = pd.read_csv('./data/sejm/wystapienia.csv')\n", "slowa = pd.read_csv('./data/sejm/slowka.csv')\n", "\n", "slowa_l = slowa.to_dict('records')\n", "slowa_d = {}\n", "\n", "# for performance reasons, I've put words in dictionary - selecting them from DataFrame is much slower than using dict\n", "for sl in slowa_l:\n", " slowa_d[sl['word']] = sl\n", " \n", "# @TODO: refactor with index\n" ] }, { "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>word</th>\n", " <td>mamrotać</td>\n", " <td>uparty</td>\n", " <td>żądać</td>\n", " <td>rozrzutny</td>\n", " <td>moralista</td>\n", " </tr>\n", " <tr>\n", " <th>category</th>\n", " <td>A</td>\n", " <td>A</td>\n", " <td>A</td>\n", " <td>A</td>\n", " <td>A</td>\n", " </tr>\n", " <tr>\n", " <th>nl</th>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>syn</th>\n", " <td>verb</td>\n", " <td>adjective</td>\n", " <td>verb</td>\n", " <td>adjective</td>\n", " <td>noun</td>\n", " </tr>\n", " <tr>\n", " <th>f</th>\n", " <td>3.5</td>\n", " <td>7.11</td>\n", " <td>45.95</td>\n", " <td>0.9</td>\n", " <td>1.46</td>\n", " </tr>\n", " <tr>\n", " <th>VA</th>\n", " <td>-1</td>\n", " <td>-0.77</td>\n", " <td>-1.08</td>\n", " <td>-1.12</td>\n", " <td>-0.85</td>\n", " </tr>\n", " <tr>\n", " <th>AR</th>\n", " <td>2.46</td>\n", " <td>2.54</td>\n", " <td>2.31</td>\n", " <td>2.42</td>\n", " <td>2.04</td>\n", " </tr>\n", " <tr>\n", " <th>IM</th>\n", " <td>5.58</td>\n", " <td>5.96</td>\n", " <td>5.88</td>\n", " <td>5.62</td>\n", " <td>4.58</td>\n", " </tr>\n", " <tr>\n", " <th>mean_H</th>\n", " <td>1.42</td>\n", " <td>2.65</td>\n", " <td>1.65</td>\n", " <td>1.96</td>\n", " <td>1.81</td>\n", " </tr>\n", " <tr>\n", " <th>mean_A</th>\n", " <td>2.96</td>\n", " <td>3.62</td>\n", " <td>3.81</td>\n", " <td>3.85</td>\n", " <td>3.62</td>\n", " </tr>\n", " <tr>\n", " <th>mean_S</th>\n", " <td>2.31</td>\n", " <td>2.38</td>\n", " <td>2.81</td>\n", " <td>2.5</td>\n", " <td>2.12</td>\n", " </tr>\n", " <tr>\n", " <th>mean_F</th>\n", " <td>2.31</td>\n", " <td>1.88</td>\n", " <td>3.31</td>\n", " <td>2.73</td>\n", " <td>2.23</td>\n", " </tr>\n", " <tr>\n", " <th>mean_D</th>\n", " <td>1.92</td>\n", " <td>2.04</td>\n", " <td>2.38</td>\n", " <td>2.69</td>\n", " <td>2.58</td>\n", " </tr>\n", " <tr>\n", " <th>dist_H</th>\n", " <td>6.8</td>\n", " <td>6.21</td>\n", " <td>7.44</td>\n", " <td>7.06</td>\n", " <td>6.91</td>\n", " </tr>\n", " <tr>\n", " <th>dist_A</th>\n", " <td>5.38</td>\n", " <td>5.46</td>\n", " <td>5.44</td>\n", " <td>5.29</td>\n", " <td>5.21</td>\n", " </tr>\n", " <tr>\n", " <th>dist_S</th>\n", " <td>6.11</td>\n", " <td>6.76</td>\n", " <td>6.54</td>\n", " <td>6.82</td>\n", " <td>6.83</td>\n", " </tr>\n", " <tr>\n", " <th>dist_F</th>\n", " <td>6.15</td>\n", " <td>7.22</td>\n", " <td>6.06</td>\n", " <td>6.57</td>\n", " <td>6.74</td>\n", " </tr>\n", " <tr>\n", " <th>dist_D</th>\n", " <td>6.51</td>\n", " <td>7.05</td>\n", " <td>6.88</td>\n", " <td>6.57</td>\n", " <td>6.38</td>\n", " </tr>\n", " <tr>\n", " <th>dist_N</th>\n", " <td>3.45</td>\n", " <td>4.54</td>\n", " <td>5.12</td>\n", " <td>4.94</td>\n", " <td>4.15</td>\n", " </tr>\n", " <tr>\n", " <th>badness</th>\n", " <td>4.98</td>\n", " <td>5.08</td>\n", " <td>5.07</td>\n", " <td>4.95</td>\n", " <td>4.87</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4\n", "word mamrotać uparty żądać rozrzutny moralista\n", "category A A A A A\n", "nl 8 6 5 9 9\n", "syn verb adjective verb adjective noun\n", "f 3.5 7.11 45.95 0.9 1.46\n", "VA -1 -0.77 -1.08 -1.12 -0.85\n", "AR 2.46 2.54 2.31 2.42 2.04\n", "IM 5.58 5.96 5.88 5.62 4.58\n", "mean_H 1.42 2.65 1.65 1.96 1.81\n", "mean_A 2.96 3.62 3.81 3.85 3.62\n", "mean_S 2.31 2.38 2.81 2.5 2.12\n", "mean_F 2.31 1.88 3.31 2.73 2.23\n", "mean_D 1.92 2.04 2.38 2.69 2.58\n", "dist_H 6.8 6.21 7.44 7.06 6.91\n", "dist_A 5.38 5.46 5.44 5.29 5.21\n", "dist_S 6.11 6.76 6.54 6.82 6.83\n", "dist_F 6.15 7.22 6.06 6.57 6.74\n", "dist_D 6.51 7.05 6.88 6.57 6.38\n", "dist_N 3.45 4.54 5.12 4.94 4.15\n", "badness 4.98 5.08 5.07 4.95 4.87" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "slowa.head().T" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "labs = {\n", " \"1\":\"Platforma Obywatelska\",\n", " \"10\":\"Koło poselskie Bezpieczeństwo i Gospodarka\",\n", " \"11\":\"Zjednoczona Prawica\",\n", " \"12\":\"Bialo-Czerwoni\",\n", " \"13\":\"Kukiz'15\",\n", " \"14\":\"Nowoczesna\",\n", " \"2\":\"Prawo i Sprawiedliwosc\",\n", " \"3\":\"Polskie Stronnictwo Ludowe\",\n", " \"4\":\"Sojusz Lewicy Demokratycznej\",\n", " \"5\":\"Ruch Palikota\",\n", " \"6\":\"Solidarna Polska\",\n", " \"7\":\"Niezrzeszeni\",\n", " \"8\":\"Koło Poselskie Inicjatywa Dialogu\",\n", " \"9\":\"Klub Parlamentarny Sprawiedliwa Polska\",\n", " \"0\":\"None\"\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sc = sejm.copy()\n", "sc.set_index('id')\n", "sc['slowa'] = sc.stem.str.split(' ')\n", "sc['klub_nazwa'] = sc.klub_id.apply(lambda x: labs[str(x)])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_param_calc_func(param):\n", " def func(x):\n", " sre = []\n", " try:\n", " for slowo in x.loc['slowa']:\n", " ary = slowa_d[slowo] if slowo in slowa_d else dict()\n", " if param in ary:\n", " sre.append(ary[param])\n", " elif param.startswith('impact_') and slowo in slowa_d and param.endswith(ary['category']):\n", " sre.append(calc_kolaps_factor(ary, param))\n", " except Exception as e:\n", " pass\n", " return (np.mean(sre)len(sre))/len(x.loc['slowa']) if len(sre) > 0 else 1\n", "\n", " return func\n", "\n", "def calc_kolaps_factor(ar, typ):\n", " pre = ar['AR'] + ar['IM']\n", " if typ.endswith('H'):\n", " return pre 2 * ar['mean_H']+ 1.5 * np.mean([ar['dist_A'], ar['dist_S'], ar['dist_F'], ar['dist_D'], ar['dist_N']])\n", " elif typ.endswith('A'):\n", " return pre * 2 * ar['mean_A'] + 1.5 * np.mean([ar['dist_H'], ar['dist_S'], ar['dist_F'], ar['dist_D'], ar['dist_N']])\n", " elif typ.endswith('S'):\n", " return pre * 2 * ar['mean_S'] + 1.5 * np.mean([ar['dist_H'], ar['dist_A'], ar['dist_F'], ar['dist_D'], ar['dist_N']])\n", " elif typ.endswith('F'):\n", " return pre * 2 * ar['mean_F'] + 1.5 * np.mean([ar['dist_H'], ar['dist_A'], ar['dist_S'], ar['dist_D'], ar['dist_N']])\n", " elif typ.endswith('D'):\n", " return pre * 2 * ar['mean_D'] + 1.5 * np.mean([ar['dist_H'], ar['dist_A'], ar['dist_S'], ar['dist_F'], ar['dist_N']])\n", " return 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating impact_H...\n", "Calculating impact_A...\n", "Calculating impact_S...\n", "Calculating impact_F...\n", "Calculating impact_D...\n", "Calculating f...\n", "Calculating VA...\n", "Calculating AR...\n", "Calculating IM...\n", "Calculating mean_H...\n", "Calculating mean_A...\n", "Calculating mean_S...\n", "Calculating mean_F...\n", "Calculating mean_D...\n", "Calculating dist_H...\n", "Calculating dist_A...\n", "Calculating dist_S...\n", "Calculating dist_F...\n", "Calculating dist_D...\n", "Calculating dist_N...\n", "Calculating badness...\n", "CPU times: user 1min 48s, sys: 587 ms, total: 1min 48s\n", "Wall time: 1min 49s\n" ] } ], "source": [ "%%time\n", "par = [\n", " 'impact_H',\n", " 'impact_A',\n", " 'impact_S',\n", " 'impact_F',\n", " 'impact_D',\n", " 'f',\n", " 'VA',\n", " 'AR',\n", " 'IM',\n", " 'mean_H',\n", " 'mean_A',\n", " 'mean_S',\n", " 'mean_F',\n", " 'mean_D',\n", " 'dist_H',\n", " 'dist_A',\n", " 'dist_S',\n", " 'dist_F',\n", " 'dist_D',\n", " 'dist_N',\n", " 'badness'\n", "]\n", "for a in par:\n", " print(\"Calculating {}...\".format(a))\n", " sc[a] = sc.apply(get_param_calc_func(a), axis=1)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sc['Happiness'] = sc['impact_H']\n", "sc['Anger'] = sc['impact_A']\n", "sc['Sadness'] = sc['impact_S']\n", "sc['Fear'] = sc['impact_F']\n", "sc['Disgust'] = sc['impact_D']\n", "sc['Neutrality Distance'] = sc['dist_N']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sc.set_index(sc.id, inplace=True)" ] }, { "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>id</th>\n", " <th>133746</th>\n", " <th>133843</th>\n", " <th>133863</th>\n", " <th>133784</th>\n", " <th>133838</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>id</th>\n", " <td>133746</td>\n", " <td>133843</td>\n", " <td>133863</td>\n", " <td>133784</td>\n", " <td>133838</td>\n", " </tr>\n", " <tr>\n", " <th>stem</th>\n", " <td>bardzo być czyli do dziękować formalny głos gł...</td>\n", " <td>dziękować głos ludowy marek mój pan państwo po...</td>\n", " <td>1 167 256 424 6 być do dziękować głosować i kt...</td>\n", " <td>0 15 5 50 a ale by być chcieć czy dać dla do d...</td>\n", " <td>a ale być dziękować dzienny ja jeżeli na nie N...</td>\n", " </tr>\n", " <tr>\n", " <th>ludzie_nazwa</th>\n", " <td>Marek Kuchciński</td>\n", " <td>Marek Kuchciński</td>\n", " <td>Marek Kuchciński</td>\n", " <td>Tomasz Jaskóła</td>\n", " <td>Marek Kuchciński</td>\n", " </tr>\n", " <tr>\n", " <th>posel_id</th>\n", " <td>205</td>\n", " <td>205</td>\n", " <td>205</td>\n", " <td>1244</td>\n", " <td>205</td>\n", " </tr>\n", " <tr>\n", " <th>ludzie_id</th>\n", " <td>195</td>\n", " <td>195</td>\n", " <td>195</td>\n", " <td>1666</td>\n", " <td>195</td>\n", " </tr>\n", " <tr>\n", " <th>data</th>\n", " <td>2017-06-22</td>\n", " <td>2017-06-22</td>\n", " <td>2017-06-22</td>\n", " <td>2017-06-22</td>\n", " <td>2017-06-22</td>\n", " </tr>\n", " <tr>\n", " <th>klub_id</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>slowa</th>\n", " <td>[bardzo, być, czyli, do, dziękować, formalny, ...</td>\n", " <td>[dziękować, głos, ludowy, marek, mój, pan, pań...</td>\n", " <td>[1, 167, 256, 424, 6, być, do, dziękować, głos...</td>\n", " <td>[0, 15, 5, 50, a, ale, by, być, chcieć, czy, d...</td>\n", " <td>[a, ale, być, dziękować, dzienny, ja, jeżeli, ...</td>\n", " </tr>\n", " <tr>\n", " <th>klub_nazwa</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>impact_H</th>\n", " <td>3.06518</td>\n", " <td>5.6195</td>\n", " <td>3.3717</td>\n", " <td>1.36691</td>\n", " <td>2.80975</td>\n", " </tr>\n", " <tr>\n", " <th>impact_A</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>impact_S</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>impact_F</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.877914</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>impact_D</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>f</th>\n", " <td>110.724</td>\n", " <td>200.354</td>\n", " <td>102.716</td>\n", " <td>102.249</td>\n", " <td>135.469</td>\n", " </tr>\n", " <tr>\n", " <th>VA</th>\n", " <td>0.0863636</td>\n", " <td>0.176667</td>\n", " <td>0.0813333</td>\n", " <td>0.0952703</td>\n", " <td>0.0483333</td>\n", " </tr>\n", " <tr>\n", " <th>AR</th>\n", " <td>0.340909</td>\n", " <td>0.6</td>\n", " <td>0.201333</td>\n", " <td>0.375811</td>\n", " <td>0.490833</td>\n", " </tr>\n", " <tr>\n", " <th>IM</th>\n", " <td>0.875455</td>\n", " <td>1.59889</td>\n", " <td>0.631</td>\n", " <td>0.919054</td>\n", " <td>1.24389</td>\n", " </tr>\n", " <tr>\n", " <th>mean_H</th>\n", " <td>0.497273</td>\n", " <td>0.868889</td>\n", " <td>0.355667</td>\n", " <td>0.531486</td>\n", " <td>0.609444</td>\n", " </tr>\n", " <tr>\n", " <th>mean_A</th>\n", " <td>0.302727</td>\n", " <td>0.542222</td>\n", " <td>0.135667</td>\n", " <td>0.314324</td>\n", " <td>0.451667</td>\n", " </tr>\n", " <tr>\n", " <th>mean_S</th>\n", " <td>0.290909</td>\n", " <td>0.495556</td>\n", " <td>0.139667</td>\n", " <td>0.311486</td>\n", " <td>0.470833</td>\n", " </tr>\n", " <tr>\n", " <th>mean_F</th>\n", " <td>0.342121</td>\n", " <td>0.544444</td>\n", " <td>0.153333</td>\n", " <td>0.378784</td>\n", " <td>0.486944</td>\n", " </tr>\n", " <tr>\n", " <th>mean_D</th>\n", " <td>0.281818</td>\n", " <td>0.437222</td>\n", " <td>0.137</td>\n", " <td>0.287297</td>\n", " <td>0.386667</td>\n", " </tr>\n", " <tr>\n", " <th>dist_H</th>\n", " <td>0.738182</td>\n", " <td>1.33444</td>\n", " <td>0.391</td>\n", " <td>0.806892</td>\n", " <td>1.20083</td>\n", " </tr>\n", " <tr>\n", " <th>dist_A</th>\n", " <td>1.01242</td>\n", " <td>1.77278</td>\n", " <td>0.679667</td>\n", " <td>1.09216</td>\n", " <td>1.45917</td>\n", " </tr>\n", " <tr>\n", " <th>dist_S</th>\n", " <td>1.02424</td>\n", " <td>1.82722</td>\n", " <td>0.677333</td>\n", " <td>1.09595</td>\n", " <td>1.44333</td>\n", " </tr>\n", " <tr>\n", " <th>dist_F</th>\n", " <td>0.973636</td>\n", " <td>1.77944</td>\n", " <td>0.662333</td>\n", " <td>1.02608</td>\n", " <td>1.43</td>\n", " </tr>\n", " <tr>\n", " <th>dist_D</th>\n", " <td>1.03242</td>\n", " <td>1.875</td>\n", " <td>0.675333</td>\n", " <td>1.11365</td>\n", " <td>1.51917</td>\n", " </tr>\n", " <tr>\n", " <th>dist_N</th>\n", " <td>0.61303</td>\n", " <td>0.964444</td>\n", " <td>0.323</td>\n", " <td>0.637973</td>\n", " <td>0.813611</td>\n", " </tr>\n", " <tr>\n", " <th>badness</th>\n", " <td>121.274</td>\n", " <td>222.336</td>\n", " <td>33.4673</td>\n", " <td>135.227</td>\n", " <td>139.025</td>\n", " </tr>\n", " <tr>\n", " <th>Happiness</th>\n", " <td>3.06518</td>\n", " <td>5.6195</td>\n", " <td>3.3717</td>\n", " <td>1.36691</td>\n", " <td>2.80975</td>\n", " </tr>\n", " <tr>\n", " <th>Anger</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Sadness</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Fear</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.877914</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Disgust</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>Neutrality Distance</th>\n", " <td>0.61303</td>\n", " <td>0.964444</td>\n", " <td>0.323</td>\n", " <td>0.637973</td>\n", " <td>0.813611</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "id 133746 \\\n", "id 133746 \n", "stem bardzo być czyli do dziękować formalny głos gł... \n", "ludzie_nazwa Marek Kuchciński \n", "posel_id 205 \n", "ludzie_id 195 \n", "data 2017-06-22 \n", "klub_id 0 \n", "slowa [bardzo, być, czyli, do, dziękować, formalny, ... \n", "klub_nazwa None \n", "impact_H 3.06518 \n", "impact_A 1 \n", "impact_S 1 \n", "impact_F 1 \n", "impact_D 1 \n", "f 110.724 \n", "VA 0.0863636 \n", "AR 0.340909 \n", "IM 0.875455 \n", "mean_H 0.497273 \n", "mean_A 0.302727 \n", "mean_S 0.290909 \n", "mean_F 0.342121 \n", "mean_D 0.281818 \n", "dist_H 0.738182 \n", "dist_A 1.01242 \n", "dist_S 1.02424 \n", "dist_F 0.973636 \n", "dist_D 1.03242 \n", "dist_N 0.61303 \n", "badness 121.274 \n", "Happiness 3.06518 \n", "Anger 1 \n", "Sadness 1 \n", "Fear 1 \n", "Disgust 1 \n", "Neutrality Distance 0.61303 \n", "\n", "id 133843 \\\n", "id 133843 \n", "stem dziękować głos ludowy marek mój pan państwo po... \n", "ludzie_nazwa Marek Kuchciński \n", "posel_id 205 \n", "ludzie_id 195 \n", "data 2017-06-22 \n", "klub_id 0 \n", "slowa [dziękować, głos, ludowy, marek, mój, pan, pań... \n", "klub_nazwa None \n", "impact_H 5.6195 \n", "impact_A 1 \n", "impact_S 1 \n", "impact_F 1 \n", "impact_D 1 \n", "f 200.354 \n", "VA 0.176667 \n", "AR 0.6 \n", "IM 1.59889 \n", "mean_H 0.868889 \n", "mean_A 0.542222 \n", "mean_S 0.495556 \n", "mean_F 0.544444 \n", "mean_D 0.437222 \n", "dist_H 1.33444 \n", "dist_A 1.77278 \n", "dist_S 1.82722 \n", "dist_F 1.77944 \n", "dist_D 1.875 \n", "dist_N 0.964444 \n", "badness 222.336 \n", "Happiness 5.6195 \n", "Anger 1 \n", "Sadness 1 \n", "Fear 1 \n", "Disgust 1 \n", "Neutrality Distance 0.964444 \n", "\n", "id 133863 \\\n", "id 133863 \n", "stem 1 167 256 424 6 być do dziękować głosować i kt... \n", "ludzie_nazwa Marek Kuchciński \n", "posel_id 205 \n", "ludzie_id 195 \n", "data 2017-06-22 \n", "klub_id 0 \n", "slowa [1, 167, 256, 424, 6, być, do, dziękować, głos... \n", "klub_nazwa None \n", "impact_H 3.3717 \n", "impact_A 1 \n", "impact_S 1 \n", "impact_F 1 \n", "impact_D 1 \n", "f 102.716 \n", "VA 0.0813333 \n", "AR 0.201333 \n", "IM 0.631 \n", "mean_H 0.355667 \n", "mean_A 0.135667 \n", "mean_S 0.139667 \n", "mean_F 0.153333 \n", "mean_D 0.137 \n", "dist_H 0.391 \n", "dist_A 0.679667 \n", "dist_S 0.677333 \n", "dist_F 0.662333 \n", "dist_D 0.675333 \n", "dist_N 0.323 \n", "badness 33.4673 \n", "Happiness 3.3717 \n", "Anger 1 \n", "Sadness 1 \n", "Fear 1 \n", "Disgust 1 \n", "Neutrality Distance 0.323 \n", "\n", "id 133784 \\\n", "id 133784 \n", "stem 0 15 5 50 a ale by być chcieć czy dać dla do d... \n", "ludzie_nazwa Tomasz Jaskóła \n", "posel_id 1244 \n", "ludzie_id 1666 \n", "data 2017-06-22 \n", "klub_id 0 \n", "slowa [0, 15, 5, 50, a, ale, by, być, chcieć, czy, d... \n", "klub_nazwa None \n", "impact_H 1.36691 \n", "impact_A 1 \n", "impact_S 1 \n", "impact_F 0.877914 \n", "impact_D 1 \n", "f 102.249 \n", "VA 0.0952703 \n", "AR 0.375811 \n", "IM 0.919054 \n", "mean_H 0.531486 \n", "mean_A 0.314324 \n", "mean_S 0.311486 \n", "mean_F 0.378784 \n", "mean_D 0.287297 \n", "dist_H 0.806892 \n", "dist_A 1.09216 \n", "dist_S 1.09595 \n", "dist_F 1.02608 \n", "dist_D 1.11365 \n", "dist_N 0.637973 \n", "badness 135.227 \n", "Happiness 1.36691 \n", "Anger 1 \n", "Sadness 1 \n", "Fear 0.877914 \n", "Disgust 1 \n", "Neutrality Distance 0.637973 \n", "\n", "id 133838 \n", "id 133838 \n", "stem a ale być dziękować dzienny ja jeżeli na nie N... \n", "ludzie_nazwa Marek Kuchciński \n", "posel_id 205 \n", "ludzie_id 195 \n", "data 2017-06-22 \n", "klub_id 0 \n", "slowa [a, ale, być, dziękować, dzienny, ja, jeżeli, ... \n", "klub_nazwa None \n", "impact_H 2.80975 \n", "impact_A 1 \n", "impact_S 1 \n", "impact_F 1 \n", "impact_D 1 \n", "f 135.469 \n", "VA 0.0483333 \n", "AR 0.490833 \n", "IM 1.24389 \n", "mean_H 0.609444 \n", "mean_A 0.451667 \n", "mean_S 0.470833 \n", "mean_F 0.486944 \n", "mean_D 0.386667 \n", "dist_H 1.20083 \n", "dist_A 1.45917 \n", "dist_S 1.44333 \n", "dist_F 1.43 \n", "dist_D 1.51917 \n", "dist_N 0.813611 \n", "badness 139.025 \n", "Happiness 2.80975 \n", "Anger 1 \n", "Sadness 1 \n", "Fear 1 \n", "Disgust 1 \n", "Neutrality Distance 0.813611 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sc.head().T" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def plot_emotion(emotion):\n", " sc \\\n", " .groupby('klub_nazwa') \\\n", " .agg(np.mean) \\\n", " .sort_values(by=emotion)[emotion] \\\n", " .T \\\n", " .plot(kind='bar', title=emotion)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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EJAhLloiISBCWLBERkSAsWSIiIkFYskRERIKwZImIiARhyRIREQnCkiUiIhKEJUtERCQI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\nan4jGzp0KIYNG4a33noLiYm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"text/plain": [ "<matplotlib.figure.Figure at 0x11c3a6390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Happiness')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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JiIgEYQkTEREJwhImIiIShCVMREQkCEuYiIhIEJYwERGRICxhIiIiQVjCREREgrCEiYiI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\nMeLi4gCU/T8dNWqU4GREZdzc3DBw4EC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"text/plain": [ "<matplotlib.figure.Figure at 0x11c3a6f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Anger')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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rK+Vr06ZNU3783XffwcTEpMICJiIiolIVlnCvXr2QmJgId3d3SJKEoKAghIaGwszMDM7O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\nhKamJgYMGAB7e3u1uZwguzPh9957D12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"text/plain": [ "<matplotlib.figure.Figure at 0x11d261e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Sadness')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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fPo2HDx9i/PjxkNsN/EZGRvD29oa7uzvCw8NlsVVc8bBsjx490KNHDwDA+PHj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\nK7p16yY6FktYU6xevRoTJkzA9OnTX5j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"text/plain": [ "<matplotlib.figure.Figure at 0x11ce54668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Fear')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WVlYvvN+ECRNUl4qIiOgdwMU6iIiIBGEJExERCcISJiIiEoQlTEREJAhLmIiISBCWMBER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\nWAAAR9kqmdGjR+PYsWM4cuQIHjx4gE6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"text/plain": [ "<matplotlib.figure.Figure at 0x109c9fb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Disgust')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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mvUePHiE8PFx0jBJkd04YALKzs6Gjo4NHjx7ByMhIdBzJFBYW4rPPPoOfnx8a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\nATp37iw6Fr2mzz//HEFBQaJjSO7GjRs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"text/plain": [ "<matplotlib.figure.Figure at 0x11c8e2860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_emotion('Neutrality Distance')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11d929588>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Sw3ImIiKSGJYzERGRxLCciYiIJIblTEREJDEsZyIiIonRFzsA6a6hXgfKvnD4nbde5IfA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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d273358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.groupby('klub_nazwa') \\\n", " .agg(np.std) \\\n", " .sort_values(by='dist_N')['dist_N'] \\\n", " .plot(kind='bar', title='Neutrality distance (stddev)')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sc['lk'] = sc['ludzie_nazwa'] + ' (' + sc['klub_nazwa'] + ')'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11d91f320>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EVBAu2k70P0iuDwUAPxgQ5cWSJSJVUNvZgpfOCyiemV4/TqtIREQkEx7JEhERAPWdLVAD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6MjDAwMSEdi/l87d1ADAAwCQRAp+MINyqvi0qSdUcBvcx/4niULD+numpna3dunAOWtIgDEWLIA\nECKyABAisgAQIrIAECKyABAisgAQIrIAECKyABByAKoPdWV/FcJpAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ce897b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.groupby('lk') \\\n", " .agg(np.mean) \\\n", " .sort_values(by='Happiness', ascending=False) \\\n", " .head(10)['Happiness'] \\\n", " .plot(kind='bar', title='Neutrality distance (stddev)')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11cf9dda0>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B+g2hbtr5W3zn+R9jKD/SzNMKFaKsecyJaCLQHHDJ1I6TQtmNU2Yec7m5hrlV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"text/plain": [ "<matplotlib.figure.Figure at 0x109c66748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.groupby(['data']) \\\n", " .agg(np.mean)[['Anger', 'Fear', 'Sadness', 'Disgust']] \\\n", " .plot(figsize=(8,8))" ] } ], "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 }	mit
andrie/jupyter-notebook-samples	Fitting a LASSO regression model and publishing to Azure ML using R.ipynb	1	11817	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting a LASSO regression model and publishing to Azure ML using R\n", "\n", "## Introduction\n", "\n", "### About the method\n", "\n", "LASSO, which stands for Least Absolute Shrinkage and Selection Operator, is one of the model complexity control techniques like variable selection and ridge regression. In this notebook we'll demonstrate how to use the glmnet package for LASSO regression. For more information about LASSO you can refer to the [LASSO Page][lasso link].\n", "\n", "[lasso link]: http://statweb.stanford.edu/~tibs/lasso.html\n", "\n", "### Target audience\n", "\n", "This notebook is targeted toward data scientists who understand linear regression and want to find out how to fit LASSO regression in R. An operationalization step is also included to show how you can deploy in Azure a web service based on the selected model. \n", "\n", "## Data\n", "\n", "In this example, we'll use the housing data from the R package `MASS`. There are 506 rows and 14 columns in the dataset. Available information includes median home price, average number of rooms per dwelling, crime rate by town, etc. More information about this dataset can be found at [UCI][uci link] or by typing `help(Boston)` in an R terminal.\n", "\n", "[uci link]: https://archive.ics.uci.edu/ml/datasets/Housing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(MASS) # to use the Boston dataset\n", "?Boston" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis\n", "\n", "For illustration purposes, we'll use \"medv\" - median home price - as the response variable and the remaining variables as predictors.\n", "\n", "The first step in fitting LASSO regression is to determine the value of tuning parameter λ which controls the overall strength of the penalty. Here we'll use cross-validation to choose the λ that gives the least validation error." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# to make results replicable\n", "set.seed(123)\n", "\n", "# load libraries\n", "if(!require(\"glmnet\", quietly = TRUE)) install.packages(\"glmnet\")\n", "library(glmnet) # to fit a LASSO model\n", "library(MASS) # to use the Boston dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define response variable and predictor variables\n", "response_column <- which(colnames(Boston) %in% c(\"medv\"))\n", "train_X <- data.matrix(Boston[, -response_column])\n", "train_y <- Boston[,response_column]\n", "\n", "# use cv.glmnet with 10-fold cross-validation to pick lambda\n", "model1 <- cv.glmnet(x = train_X, \n", " y = train_y, \n", " alpha = 1, \n", " nfolds = 10, \n", " family = \"gaussian\", \n", " type.measure = \"mse\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "summary(model1)\n", "options(repr.plot.width = 5, repr.plot.height = 5)\n", "plot(model1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the plot above:\n", "\n", "* The red dotted line shows the cross-validation error and the error bars show the uppper and lower standard deviation.\n", "* The dotted vertical line to the left is for the optimal λ that gives minimum mean cross-validation error.\n", "* The vertical line to the right is for the λ where cross-validation error falls within one standard error of the minimum error.\n", "* The number of nonzero coefficients for different λ is shown along the axis at the top. The values for λ and associated coefficients are printed below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lambda that gives minimum mean cross-validated error:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "round(model1$lambda.min, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Largest lambda with mean cross-validated error within 1 standard error of the minimum error:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "round(model1$lambda.1se, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Coefficients based on lambda that gives minimum mean cross-validated error:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "coef(model1, s = \"lambda.min\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the model selected by cv.glmnet() can be used for making predictions, we also want to better understand how the values of λ impact the estimated coefficients. Such information can be produced by the glmnet() function. In the plot that's generated below, it can be observed that the coefficients shrink toward zero as the value of λ increases." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model2 <- glmnet(x = train_X, \n", " y = train_y, \n", " alpha = 1, \n", " family = \"gaussian\")\n", "\n", "# identify variable names\n", "vn = colnames(train_X)\n", "vid <- as.character(seq(1, length(vn)))\n", "\n", "# check and exclude the variables with coefficient value 0 \n", "vnat = coef(model2)\n", "vnat_f <- vnat[-1, ncol(vnat)] \n", "vid <- vid[vnat_f != 0]\n", "vn <- vn[vnat_f != 0]\n", "\n", "#define the legend description, line type, and line color\n", "nvars <- length(vn)\n", "legend_desc <- paste(vid, vn, sep=\": \")\n", "legend_desc\n", "mylty <- rep(1,nvars)\n", "mycl <- seq(1,nvars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Varying lambda\n", "\n", "By increasing the value of lambda, the regression parameters are increasingly penalised, and thus move closer to zero.\n", "\n", "In the lambda plot below, you can see how the coefficients gradually decrease in value as lambda increaes. This has a particularly high impact on variable 5 (nox, nitrogen oxides concentration (parts per 10 million)).\n", "\n", "This shows the value of LASSO regression: the algorithm deals very well with problematic predictors, for example situations where the predictors are higly correlated with one another (multi-collinearity)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# plot\n", "options(repr.plot.width = 6, repr.plot.height = 6)\n", "plot(model2, xvar = \"lambda\", label = TRUE, col = mycl, xlim = c(-5.5, 2))\n", "legend(-0.5,-2, legend_desc, lty = mylty, col = mycl, cex = 0.8) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coefficients from this model with the optimal λ are also printed below. As we would expect, they are the same as those from using cv.glmnet()." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "coef(model2, s = model1$lambda.min)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make predictions, you can use either of the two models:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_new <- data.matrix(train_X[, -response_column])\n", "pred1 <- predict(model1, newx = x_new, s = \"lambda.min\")\n", "pred2 <- predict(model2, newx = x_new, s = model1$lambda.min)\n", "\n", "head(\n", " data.frame(actual = Boston$medv, model1 = as.vector(pred1), model2 = as.vector(pred2))\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Web service\n", "\n", "### Deploy a web service\n", "\n", "With the developed model, we can deploy a web service on Azure so that others can use it to make predictions. The \"AzureML\" package will be used for this purpose. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define predict function\n", "mypredict <- function(newdata){\n", " if(\"medv\" %in% names(newdata)) {\n", " w <- match(\"medv\", names(newdata))\n", " newdata <- newdata[, -w]\n", " }\n", " require(glmnet)\n", " newdata <- data.matrix(newdata) # the prediction data need to be a matrix for glmnet\n", " predict(model2, newx = newdata, s = model1$lambda.min)\n", "}\n", "\n", "# test the prediction function\n", "newdata <- Boston[1:10, ]\n", "mypredict(newdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load the library\n", "library(AzureML)\n", "\n", "# workspace information\n", "ws <- workspace()\n", "\n", "# deploy the service\n", "ep <- publishWebService(ws = ws, fun = mypredict, \n", " name = \"LASSOPrediction\", \n", " inputSchema = newdata, \n", " outputSchema = list(ans = \"numeric\"))\n", "str(ep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consume a web service\n", "\n", "With information about the workspace and and service ID, we can consume the web service with the following code." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "newdata <- Boston[1:10, ]\n", "pred <- consume(ep, newdata)$ans\n", "data.frame(actual = newdata$medv, prediction = pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Using the `Boston` housing dataset, we demonstrated how to carry out LASSO regression analysis. We started the analysis by determining the optimal value of the tuning parameter λ using cross-validation. Then we examined the impact of λ on the coefficient estimates. A web service was deployed based on the selected model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "Created by a Microsoft Employee. \n", "Copyright (C) Microsoft. All Rights Reserved." ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
CalPolyPat/phys202-2015-work	assignments/assignment02/ProjectEuler6.ipynb	1	2080	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 6" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=6\n", "\n", "The sum of the squares of the first ten natural numbers is,\n", "\n", "$$1^2 + 2^2 + ... + 10^2 = 385$$\n", "\n", "The square of the sum of the first ten natural numbers is,\n", "\n", "$$(1 + 2 + ... + 10)^2 = 552 = 3025$$\n", "\n", "Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25164150\n" ] } ], "source": [ "sumofsquare = sum([x2 for x in range(1,101)])\n", "squareofsum = sum([x for x in range(1,101)])2\n", "diff = squareofsum-sumofsquare\n", "print(diff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "4a8ce9efca8c824de365eec816018842", "grade": true, "grade_id": "projecteuler6", "points": 10 } }, "outputs": [], "source": [ "# This cell will be used for grading, leave it at the end of the notebook." ] } ], "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
maxrose61/GA_DS	FInal_Project/Quantifying_Influence_Analysis_maxrose_DSFinal.ipynb	1	450064	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Quantifying Influence of The Beatle and The Rolling Stones<br><br>\n", "### With the data exported from the MusicBrainz database, which is further cleaned and aggregated in [this](Quantifying_Influence_Acquire_Clean_Merge.ipynb) notebook, I have refined the set of observations around the songs recorded and released by two artists. The response by which I will measure their influence is measured by other artists recorded use of their songs, \"cover versions\" or \"covers\" in the vernacular. This response is the \"times_covered\"<br><br>\n", "\n", "### I also acquired the lyrics for as many of the original songs as I could using Beautiful Soup to scrape the lyrics from (lyrics.wikia.com)[http://lyrics.wikia.com). I was able to acquire lyrical content for 96.5% of the original songs to apply sentiment analysis using TextBlob. The sentiment polarity was applied separately to both the song title and the lyrics themselves to create features to augment the song/release data. As I am able to show below, the lyric sentiment is one of the more predictive measurements, second only to the year a song was released.<br><br>\n", "\n", "### With each row in my dataset - songname, artist, etc., I then merged the number of times the song was covered (\"times_covered\") and the number of artists covering the song (\"artist_cnt\"). I also created a binary response (\"is_covered\") as a simpler indicator that the song was used over the number of times used. This proved to be more predictable than the number of times covered. Also included is the average rating of the cover versions per song though I suspect the data is not so useful." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### Import as many items as possible to have available.\n", "### Import data from CSV\n", "\n", "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn import metrics\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.cross_validation import cross_val_score\n", "from sklearn.naive_bayes import MultinomialNB\n", "\n", "data = pd.read_csv('data/Influence_clean.csv', header=0,encoding= 'utf-8', delimiter='\|')\n", "data['minreleasedate'] = pd.to_datetime(pd.Series(data.minreleasedate))\n", "data['times_covered'].fillna(0, inplace=True)\n", "data['artistid'] = data.artist.map({'The Beatles':0, 'The Rolling Stones':1})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['artist'] = data.artist.astype('category')\n", "data['songname'] = data.songname.astype('category')\n", "# Add column for year of release for simpler grouping.\n", "data['year'] = data['minreleasedate'].apply(lambda x: x.year)\n", "\n", "# Make binary response - song has been covered or not. Far better accuracy over \"times covered\".\n", "data['is_covered'] = data.times_covered.map(lambda x: 1 if x > 0 else 0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check coverage of lyrics for the original songs.\n", "### 96.6% have lyrics (and lyric sentiment polarity score) in the dataset. Lyric sentiment is a valuable predictor." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.033457249070631967" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[(data.is_cover == 0) & (data.lyrics.isnull())].workid.count().astype(float)/data[(data.is_cover == 0)].workid.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create base set of features for fitting to models" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(269, 6)\n", "(269,)\n", "(269,)\n" ] } ], "source": [ "feature_cols = [ 'year','num_releases','lyric_sent','title_sent', 'countries', 'avg_rating']\n", "X= data[data.is_cover == 0][feature_cols]\n", "y = data[data.is_cover == 0].is_covered\n", "y_regress = data[data.is_cover == 0].times_covered\n", "print X.shape\n", "print y.shape\n", "print y_regress.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build model with Random Forest Classifier (RFC)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_range = range(1, len(feature_cols)+1)\n", "\n", "# list to store the accuracy score for each value of max_features\n", "Acc_scores = []\n", "\n", "# use 10-fold cross-validation with each value of max_features (WARNING: SLOW!)\n", "for feature in feature_range:\n", " rfclass = RandomForestClassifier(n_estimators=500, max_features=feature, random_state=50)\n", " acc_val_scores = cross_val_score(rfclass, X, y, cv=10, scoring='accuracy')\n", " Acc_scores.append(acc_val_scores.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I have chosen 6 features for the RFC model after running a looped evaluation of the maximum features for the model using cross validation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x118a1a8d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DJ/L58ruDpGw9QvH5crxM8JvYBvS/NYpO0fXw8qqZV/c30hi7K42xc2icXc/u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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x106ec6950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot max_features (x-axis) versus Accuracy score (y-axis)\n", "plt.plot(feature_range, Acc_scores)\n", "plt.xlabel('max_features')\n", "plt.ylabel('Accuracy (higher is better)')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.921933085502\n", "0.903347578348\n" ] } ], "source": [ "rfclass = RandomForestClassifier(n_estimators=175, max_features=6,oob_score=True, random_state=50)\n", "rfclass.fit(X, y)\n", "print rfclass.oob_score_\n", "print cross_val_score(rfclass, X, y, cv=10, scoring='accuracy').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### At 92% and 90% respectively, both the out of bag and cross-validation scores are quite positive for the Random Forest Classifier. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing Random Forest Regressor to predict \"times_covered\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(269, 4)\n" ] } ], "source": [ "from sklearn.feature_selection import SelectFromModel\n", "rfreg = RandomForestRegressor(n_estimators=100, max_features=6, random_state=111)\n", "rfreg.fit(X,y_regress)\n", "sfm = SelectFromModel(rfreg, threshold='mean', prefit=True)\n", "X_important = sfm.transform(X)\n", "print(X_important.shape[0],X_important.shape[1])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "28.102616089590231" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfreg = RandomForestRegressor(n_estimators=100, max_features=3, random_state=111)\n", "scores = cross_val_score(rfreg, X_important, y_regress, cv=10, scoring='mean_squared_error')\n", "np.mean(np.sqrt(-scores))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I conclude that I can predict whether a song will be covered far more accurately than how many times it will be covered.<br>\n", "90% vs. 28%\n", "<br><br>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test with DecisionTreeClassifier\n", "### While seeking a less opaque model than the Random Forest, I tried the DecisionTreeClassifier, to take advantage of the very nice method for ranking the importance of the features." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.94857603796242917" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "treeclf = DecisionTreeClassifier(max_depth = 15, random_state=123)\n", "treeclf.fit(X, y)\n", "scores = cross_val_score(treeclf, X, y, cv=10).mean()\n", "np.mean(np.sqrt(scores))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### As shown in the bar chart below, year and lyric sentiment are better predictors of whether or not a song is covered." ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x13ab7af10>" ] }, "execution_count": 243, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dO6YJEyYYkAiubtSoUaqoqGgwXlZWpptvvtmARHBlo0aN0vbt242O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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13acf9490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.DataFrame({'feature':feature_cols, 'importance':treeclf.feature_importances_}).sort_values('importance').plot(kind='bar',x='feature',figsize=(16,5),fontsize='14',title=\"Feature Importance\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## While year may be important, I feel the weight of it is skewed by the results indicating the vast majority of the Beatles catalog has been covered, and well distributed across all years represented." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's review some simples metrics comparing the two bands." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A quick measure of songs covered by release year for both artists.\n", "<br> While The Beatle disbanded by 1970, The Stones continue to this day. However, their early work appears far more influential, with the greatest body of influence more or less paralelling that of The more covered Beatles. Let's put a number on that, shall we?" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x13aef1b50>" ] }, "execution_count": 244, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bMjIyXJa7UmG30axZM2t9bk8ccztm+bVu3Tqny22fdcMwVK9ePacj5tq6Nm/a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\nkyf1j3/8Q/n5+Zo/f77q1avn9WgAAAAAADzh0XN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Wjm/CuVVyP7/W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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13adaa5d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yticks = np.arange(100, 1000, 100)\n", "data[data.times_covered > 0].groupby('year').times_covered.sum().plot(kind='bar',x='year',y='times_covered',figsize=(16,9))\n", "plt.yticks(yticks)\n", "plt.title('Total songs covered by year of original',size =28)\n", "plt.ylabel('Times Covered (Sum)', size = 24)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11cb3f410>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CAswPhiWtoyRVrVpV2dnZ5vz5LnWUoGCIzz/1LH+bFaWo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\nk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"text/plain": [ "<matplotlib.figure.Figure at 0x11c2b4110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### First plot is The Beatles, second The Rolling Stones. Sum of times_covered by year of oriiginal relase of the song.\n", "bar = data.sort_values(by='year').groupby(['year', 'artist'])['times_covered'].sum().unstack('artist')\n", "yticks = np.arange(25, 1000, 50)\n", "bar.plot(kind='bar', stacked=True,figsize=(16,12),subplots='True')\n", "plt.yticks(yticks)\n", "plt.title('Total songs covered per Artist, by year of original',size =24)\n", "plt.ylabel('Times Covered', size = 24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## It appears that a pretty large percentage of the artist's catalogs have been covered at least once.<br>\n", "### 90% of Beatles songs have been covered, 66% of Rolling Stones. By this measure, The Liverpuddlians may be deemed \"more influential\"." ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x139d98190>" ] }, "execution_count": 246, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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//ny5urrq7rvv1uLFi2/0cAAAuOEqKir06KOP6ujRo5o4caIiIyMV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13acf91d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Throw out covers recorded by each band and see what percentage of their catalogs have been covered.\n", "inf = data[(data.is_cover == 0) & (data.is_covered == 1)].groupby('artist').workid.count()/data[(data.is_cover == 0)].groupby('artist').workid.count()\n", "inf.plot(kind='bar', figsize=(16,5),fontsize=18,rot=40)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create new DataFrame with Top 10 most covered songs for each band.\n", "top10 = data[(data.artistid == 1) & (data.is_cover ==0)][['artist','songname','minreleasedate', 'is_cover','times_covered']].sort_values(by='times_covered',ascending=False)[:10]\n", "top10 = top10.append(data[(data.artistid == 0) & (data.is_cover ==0)][['artist','songname','minreleasedate', 'is_cover','times_covered']].sort_values(by='times_covered',ascending=False)[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## \"Top of the Pops\"" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1308c32d0>" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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68803NXz4cEVERMjPz0/jxo1Tenq65s2bp5SUFA0cOLBaCgcAAAAAoLarUuj2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9m6dSvdunVj7dq1dOnSBYBt27axY8cO4LeHjN+uxZg8+BS6RURERETkoWGxWCgqKmL+/PmcOHGC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\np3v37lgsF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"text/plain": [ "<matplotlib.figure.Figure at 0x12e92db50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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1795d6enpNvubzWabU5YL4udXWh4e7sVeb1qaT7HPaW/+/j4KCChj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\n5s2bR2BgIABr1qxh3rx5bNq06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eHDh8nPz2fSpEmEh4cTGxvrOEf7MIqIiIiIiNy7GiQN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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x130e75b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top10['dateint'] = top10['minreleasedate'].apply(lambda x: x.year)\n", "colors = np.where(top10.artist == 'The Beatles', 'b', 'g')\n", "top10.groupby('artist').plot(kind='bar', x='songname', y='times_covered',color=colors,rot=30,fontsize=14,legend=True,figsize=(16,5))\n", "#top10.plot(kind='scatter', x='dateint', y='times_covered', s=220,c=colors, legend='artist',figsize=(12,8))\n", "plt.title('Top 10 Covered Per Artist',size=20)\n", "plt.xlabel('Times Covered')\n", "plt.ylabel('Song')" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "artist\n", "The Beatles 137.0\n", "The Rolling Stones 59.4\n", "Name: times_covered, dtype: float64\n" ] } ], "source": [ "print top10.groupby('artist').times_covered.mean()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11dcaee10>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XOCaHKJ2S5OrhLXfPMvc6BgAAAIB8jF7heLinEwAAAABgGkonAAAAAMA0lE4A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\nqbi4uKADAQAAAADRKei3TLn33nvd7AAAAAAARKH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"text/plain": [ "<matplotlib.figure.Figure at 0x11da91210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data[data.is_covered > 0].groupby(['artist', 'year']).songname.count().unstack('artist').plot(kind='bar',subplots='True',figsize=(16,9))\n", "plt.title(\"Original Songs Released Per Year\", size=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot times covered by year." ] }, { "cell_type": "code", "execution_count": 204, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x134fb6210>" ] }, "execution_count": 204, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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aRY0aNQgICCAwMBCdTsdDDz3Eiy++yAsvvJBjWbIQQggh7KvI1Y/vpJTKs7iA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\n1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"text/plain": [ "<matplotlib.figure.Figure at 0x1348b9f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bar2 = data[data.year < 2000].sort_values(by='year',ascending='True').groupby(['year', 'artist'])['times_covered'].sum().unstack('artist')\n", "bar2.plot(kind='area',figsize=(16,5),fontsize=16)\n", "plt.title(\"Original Songs Released Per Year\", size=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Beatles catalog essentialy ends in 1970 when they disbanded (the outliers are most likely bad release dates in my data). The Rolling Stones continue into this century. However, their years of greatest influence are similar, spanning the first 8 years of releases.<br><br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Logistic Regression Model" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=101)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.36823482428085047" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute Null accuracy\n", "y_null = np.zeros_like(y_test, dtype=float)\n", "# fill the array with the mean value of y_test\n", "y_null.fill(y_test.mean())\n", "y_null\n", "np.sqrt(metrics.mean_squared_error(y_test, y_null))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Null Accuracy result is 37%. \n", "#### As we will see below, when we fit the Logistic Regression estimator with our data and compute a cross validation score we improve significantly over the null test result." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('year', -6.00176939197862e-05),\n", " ('num_releases', -0.12490383180456067),\n", " ('lyric_sent', 2.6363935941680432),\n", " ('title_sent', -1.0990470630763087),\n", " ('countries', 0.7737027649489393),\n", " ('avg_rating', -0.021820203303004219)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logreg = LogisticRegression(C=1e9)\n", "#solver='newton-cg',multi_class='multinomial',max_iter=100\n", "logreg.fit(X_train, y_train)\n", "zip(feature_cols, logreg.coef_[0])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.866157916158\n" ] } ], "source": [ "print cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.805913171628\n" ] } ], "source": [ "y_pred_prob = logreg.predict_proba(X_test)[:,1 ]\n", "print(y_pred_prob).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I am able to predict with 80% probability that a song _will be_ covered.<br><br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute ROC curve and AUC score." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11dd62d50>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AcBNwZhwAADZDuQMAYDPlKvfCwkIdPHhQhmGosLDQ6kwAAOAGuC33jIwM9evXT8OHD1dW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\nAGzm/wHMkeYspzuHBgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11e303890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot ROC curve\n", "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)\n", "plt.plot(fpr, tpr)\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.0])\n", "plt.xlabel('False Positive Rate (1 - Specificity)')\n", "plt.ylabel('True Positive Rate (Sensitivity)')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.881977671451\n", "0.797994071146\n" ] } ], "source": [ "# calculate AUC\n", "print metrics.roc_auc_score(y_test, y_pred_prob)\n", "print cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lyric Sentiment by Artist" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([(u'The Beatles',\n", " <matplotlib.axes._subplots.AxesSubplot at 0x13a0a0850>),\n", " (u'The Rolling Stones',\n", " <matplotlib.axes._subplots.AxesSubplot at 0x139d5bc50>)])" ] }, "execution_count": 248, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VlWlqakq3bq/PtP7sZz+bQw89NH369MmIESNSVVXVPtUCAABAG5UUhKuqqtLQ0NDy+s0h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\nAFAo/x8aWFqyyNqphgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12ce62f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data[['artist','lyric_sent']].groupby('artist').boxplot(return_type='axes',figsize=(16,6),fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where to go?\n", "## Clean up the queries for the raw data, and try to measure impact based on the trajectory of the artist's career.\n", "## With the models trained on these two artists, could additional artist's data be pulled in to predict influence?\n", "## Would data from an artist representing a shorter timespan acheive the same predictive value? 3 years or 5 years?" ] }, { "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": 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 }	gpl-3.0
jcbozonier/research	notebooks/Doing Bayesian Data Analysis Week 1.ipynb	1	1124005	null	mit
Kaggle/learntools	notebooks/time_series/raw/ex6.ipynb	1	13431	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction #\n", "\n", "Run this cell to set everything up!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Setup feedback system\n", "from learntools.core import binder\n", "binder.bind(globals())\n", "from learntools.time_series.ex6 import \n", "\n", "# Setup notebook\n", "from pathlib import Path\n", "import ipywidgets as widgets\n", "from learntools.time_series.style import # plot style settings\n", "from learntools.time_series.utils import (create_multistep_example,\n", " load_multistep_data,\n", " make_lags,\n", " make_multistep_target,\n", " plot_multistep)\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.multioutput import RegressorChain\n", "from sklearn.preprocessing import LabelEncoder\n", "from xgboost import XGBRegressor\n", "\n", "\n", "comp_dir = Path('../input/store-sales-time-series-forecasting')\n", "\n", "store_sales = pd.read_csv(\n", " comp_dir / 'train.csv',\n", " usecols=['store_nbr', 'family', 'date', 'sales', 'onpromotion'],\n", " dtype={\n", " 'store_nbr': 'category',\n", " 'family': 'category',\n", " 'sales': 'float32',\n", " 'onpromotion': 'uint32',\n", " },\n", " parse_dates=['date'],\n", " infer_datetime_format=True,\n", ")\n", "store_sales['date'] = store_sales.date.dt.to_period('D')\n", "store_sales = store_sales.set_index(['store_nbr', 'family', 'date']).sort_index()\n", "\n", "family_sales = (\n", " store_sales\n", " .groupby(['family', 'date'])\n", " .mean()\n", " .unstack('family')\n", " .loc['2017']\n", ")\n", "\n", "test = pd.read_csv(\n", " comp_dir / 'test.csv',\n", " dtype={\n", " 'store_nbr': 'category',\n", " 'family': 'category',\n", " 'onpromotion': 'uint32',\n", " },\n", " parse_dates=['date'],\n", " infer_datetime_format=True,\n", ")\n", "test['date'] = test.date.dt.to_period('D')\n", "test = test.set_index(['store_nbr', 'family', 'date']).sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Consider the following three forecasting tasks:\n", "\n", "a. 3-step forecast using 4 lag features with a 2-step lead time<br>\n", "b. 1-step forecast using 3 lag features with a 1-step lead time<br>\n", "c. 3-step forecast using 4 lag features with a 1-step lead time<br>\n", "\n", "Run the next cell to see three datasets, each representing one of the tasks above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datasets = load_multistep_data()\n", "\n", "data_tabs = widgets.Tab([widgets.Output() for _ in enumerate(datasets)])\n", "for i, df in enumerate(datasets):\n", " data_tabs.set_title(i, f'Dataset {i+1}')\n", " with data_tabs.children[i]:\n", " display(df)\n", "\n", "display(data_tabs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1) Match description to dataset\n", "\n", "Can you match each task to the appropriate dataset?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# YOUR CODE HERE: Match the task to the dataset. Answer 1, 2, or 3.\n", "task_a = ____\n", "task_b = ____\n", "task_c = ____\n", "\n", "# Check your answer\n", "q_1.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_1.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_1.solution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "task_a = 2\n", "task_b = 1\n", "task_c = 3\n", "\n", "q_1.assert_check_passed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Look at the time indexes of the training and test sets. From this information, can you identify the forecasting task for Store Sales?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"Training Data\", \"\\n\" + \"-\" * 13 + \"\\n\", store_sales)\n", "print(\"\\n\")\n", "print(\"Test Data\", \"\\n\" + \"-\" * 9 + \"\\n\", test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2) Identify the forecasting task for Store Sales competition\n", "\n", "Try to identify the forecast origin and the forecast horizon. How many steps are within the forecast horizon? What is the lead time for the forecast?\n", "\n", "Run this cell after you've thought about your answer." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# View the solution (Run this cell to receive credit!)\n", "q_2.check()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "In the tutorial we saw how to create a multistep dataset for a single time series. Fortunately, we can use exactly the same procedure for datasets of multiple series.\n", "\n", "# 3) Create multistep dataset for Store Sales\n", "\n", "Create targets suitable for the Store Sales forecasting task. Use 4 days of lag features. Drop any missing values from both targets and features." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# YOUR CODE HERE\n", "y = family_sales.loc[:, 'sales']\n", "\n", "# YOUR CODE HERE: Make 4 lag features\n", "X = ____\n", "\n", "# YOUR CODE HERE: Make multistep target\n", "y = ____\n", "\n", "#_UNCOMMENT_IF(PROD)_\n", "#y, X = y.align(X, join='inner', axis=0)\n", "\n", "# Check your answer\n", "q_3.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_3.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_3.solution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "y = family_sales.loc[:, 'sales']\n", "X = make_lags(y, lags=4).dropna()\n", "y = make_multistep_target(y, steps=16).dropna()\n", "y, X = y.align(X, join='inner', axis=0)\n", "\n", "q_3.assert_check_passed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "In the tutorial, we saw how to forecast with the MultiOutput and Direct strategies on the Flu Trends series. Now, you'll apply the DirRec strategy to the multiple time series of Store Sales.\n", "\n", "Make sure you've successfully completed the previous exercise and then run this cell to prepare the data for XGBoost." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "le = LabelEncoder()\n", "X = (X\n", " .stack('family') # wide to long\n", " .reset_index('family') # convert index to column\n", " .assign(family=lambda x: le.fit_transform(x.family)) # label encode\n", ")\n", "y = y.stack('family') # wide to long\n", "\n", "display(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4) Forecast with the DirRec strategy\n", "\n", "Instatiate a model that applies the DirRec strategy to XGBoost." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.multioutput import RegressorChain\n", "\n", "# YOUR CODE HERE\n", "model = ____\n", "\n", "# Check your answer\n", "q_4.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_4.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_4.solution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "from sklearn.multioutput import RegressorChain\n", "\n", "model = RegressorChain(XGBRegressor())\n", "q_4.assert_check_passed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run this cell if you'd like to train this model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.fit(X, y)\n", "\n", "y_pred = pd.DataFrame(\n", " model.predict(X),\n", " index=y.index,\n", " columns=y.columns,\n", ").clip(0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And use this code to see a sample of the 16-step predictions this model makes on the training data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "FAMILY = 'BEAUTY'\n", "START = '2017-04-01'\n", "EVERY = 16\n", "\n", "y_pred_ = y_pred.xs(FAMILY, level='family', axis=0).loc[START:]\n", "y_ = family_sales.loc[START:, 'sales'].loc[:, FAMILY]\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n", "ax = y_.plot(*plot_params, ax=ax, alpha=0.5)\n", "ax = plot_multistep(y_pred_, ax=ax, every=EVERY)\n", "_ = ax.legend([FAMILY, FAMILY + ' Forecast'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Next Steps #\n", "\n", "Congratulations! You've completed Kaggle's Time Series* course. If you haven't already, join our companion competition: [Store Sales - Time Series Forecasting](https://www.kaggle.com/c/29781) and apply the skills you've learned.\n", "\n", "For inspiration, check out Kaggle's previous forecasting competitions. Studying winning competition solutions is a great way to upgrade your skills.\n", "\n", "- [Corporación Favorita](https://www.kaggle.com/c/favorita-grocery-sales-forecasting): the competition Store Sales is derived from.\n", "- [Rossmann Store Sales](https://www.kaggle.com/c/rossmann-store-sales)\n", "- [Wikipedia Web Traffic](https://www.kaggle.com/c/web-traffic-time-series-forecasting/)\n", "- [Walmart Store Sales](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting)\n", "- [Walmart Sales in Stormy Weather](https://www.kaggle.com/c/walmart-recruiting-sales-in-stormy-weather)\n", "- [M5 Forecasting - Accuracy](https://www.kaggle.com/c/m5-forecasting-accuracy)\n", "\n", "# References #\n", "\n", "Here are some great resources you might like to consult for more on time series and forecasting. They all played a part in shaping this course:\n", "\n", "- Learnings from Kaggle's forecasting competitions, an article by Casper Solheim Bojer and Jens Peder Meldgaard.\n", "- Forecasting: Principles and Practice, a book by Rob J Hyndmann and George Athanasopoulos.\n", "- Practical Time Series Forecasting with R, a book by Galit Shmueli and Kenneth C. Lichtendahl Jr.\n", "- Time Series Analysis and Its Applications, a book by Robert H. Shumway and David S. Stoffer.\n", "- Machine learning strategies for time series forecasting, an article by Gianluca Bontempi, Souhaib Ben Taieb, and Yann-Aël Le Borgne.\n", "- On the use of cross-validation for time series predictor evaluation, an article by Christoph Bergmeir and José M. Benítez.\n" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "ipynb,md" }, "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": "3.6.12" } }, "nbformat": 4, "nbformat_minor": 5 }	apache-2.0
roebius/deeplearning_keras2	nbs/lesson3.ipynb	2	572551	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training a better model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:12.804550Z", "start_time": "2018-01-09T13:57:09.972965Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using cuDNN version 6021 on context None\n", "Mapped name None to device cuda0: GeForce GTX TITAN X (0000:04:00.0)\n", "Using Theano backend.\n" ] } ], "source": [ "from __future__ import division, print_function\n", "%matplotlib inline\n", "from importlib import reload # Python 3\n", "import utils; reload(utils)\n", "from utils import " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:12.809534Z", "start_time": "2018-01-09T13:57:12.806094Z" } }, "outputs": [], "source": [ "#path = \"data/dogscats/sample/\"\n", "path = \"data/dogscats/\"\n", "model_path = path + 'models/'\n", "if not os.path.exists(model_path): os.mkdir(model_path)\n", "\n", "#batch_size=1\n", "batch_size=64" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Are we underfitting?" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Our validation accuracy so far has generally been higher than our training accuracy. That leads to two obvious questions:\n", "\n", "1. How is this possible?\n", "2. Is this desirable?\n", "\n", "The answer to (1) is that this is happening because of dropout. Dropout refers to a layer that randomly deletes (i.e. sets to zero) each activation in the previous layer with probability p* (generally 0.5). This only happens during training, not when calculating the accuracy on the validation set, which is why the validation set can show higher accuracy than the training set.\n", "\n", "The purpose of dropout is to avoid overfitting. By deleting parts of the neural network at random during training, it ensures that no one part of the network can overfit to one part of the training set. The creation of dropout was one of the key developments in deep learning, and has allowed us to create rich models without overfitting. However, it can also result in underfitting if overused, and this is something we should be careful of with our model.\n", "\n", "So the answer to (2) is: this is probably not desirable. It is likely that we can get better validation set results with less (or no) dropout, if we're seeing that validation accuracy is higher than training accuracy - a strong sign of underfitting. So let's try removing dropout entirely, and see what happens!\n", "\n", "(We had dropout in this model already because the VGG authors found it necessary for the imagenet competition. But that doesn't mean it's necessary for dogs v cats, so we will do our own analysis of regularization approaches from scratch.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing dropout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our high level approach here will be to start with our fine-tuned cats vs dogs model (with dropout), then fine-tune all the dense layers, after removing dropout from them. The steps we will take are:\n", "- Re-create and load our modified VGG model with binary dependent (i.e. dogs v cats)\n", "- Split the model between the convolutional (conv) layers and the dense layers\n", "- Pre-calculate the output of the conv layers, so that we don't have to redundently re-calculate them on every epoch\n", "- Create a new model with just the dense layers, and dropout p set to zero\n", "- Train this new model using the output of the conv layers as training data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before we need to start with a working model, so let's bring in our working VGG 16 model and change it to predict our binary dependent..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.410534Z", "start_time": "2018-01-09T13:57:12.810670Z" } }, "outputs": [], "source": [ "model = vgg_ft(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...and load our fine-tuned weights." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.592995Z", "start_time": "2018-01-09T13:57:17.412312Z" }, "scrolled": true }, "outputs": [], "source": [ "model.load_weights(model_path+'finetune3.h5')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We're going to be training a number of iterations without dropout, so it would be best for us to pre-calculate the input to the fully connected layers - i.e. the Flatten() layer. We'll start by finding this layer in our model, and creating a new model that contains just the layers up to and including this layer:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.597054Z", "start_time": "2018-01-09T13:57:17.594681Z" } }, "outputs": [], "source": [ "layers = model.layers" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.623463Z", "start_time": "2018-01-09T13:57:17.598792Z" } }, "outputs": [], "source": [ "last_conv_idx = [index for index,layer in enumerate(layers) \n", " if type(layer) is Convolution2D][-1]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.647142Z", "start_time": "2018-01-09T13:57:17.625127Z" } }, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "last_conv_idx" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.670781Z", "start_time": "2018-01-09T13:57:17.648968Z" } }, "outputs": [ { "data": { "text/plain": [ "<keras.layers.convolutional.Conv2D at 0x7f3b43384d68>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layers[last_conv_idx]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:17.837400Z", "start_time": "2018-01-09T13:57:17.673355Z" } }, "outputs": [], "source": [ "conv_layers = layers[:last_conv_idx+1]\n", "conv_model = Sequential(conv_layers)\n", "# Dense layers - also known as fully connected or 'FC' layers\n", "fc_layers = layers[last_conv_idx+1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use the exact same approach to creating features as we used when we created the linear model from the imagenet predictions in the last lesson - it's only the model that has changed. As you're seeing, there's a fairly small number of \"recipes\" that can get us a long way!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:18.745451Z", "start_time": "2018-01-09T13:57:17.838857Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 23000 images belonging to 2 classes.\n", "Found 2000 images belonging to 2 classes.\n" ] } ], "source": [ "batches = get_batches(path+'train', shuffle=False, batch_size=batch_size)\n", "val_batches = get_batches(path+'valid', shuffle=False, batch_size=batch_size)\n", "steps_per_epoch = int(np.ceil(batches.samples/batch_size))\n", "validation_steps = int(np.ceil(val_batches.samples/batch_size))\n", "\n", "val_classes = val_batches.classes\n", "trn_classes = batches.classes\n", "val_labels = onehot(val_classes)\n", "trn_labels = onehot(trn_classes)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T13:57:40.670673Z", "start_time": "2018-01-09T13:57:18.747689Z" } }, "outputs": [], "source": [ "val_features = conv_model.predict_generator(val_batches, validation_steps)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:37.879083Z", "start_time": "2018-01-09T13:57:40.672616Z" } }, "outputs": [], "source": [ "trn_features = conv_model.predict_generator(batches, steps_per_epoch)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:40.797807Z", "start_time": "2018-01-09T14:00:37.880594Z" } }, "outputs": [], "source": [ "save_array(model_path + 'train_convlayer_features.bc', trn_features)\n", "save_array(model_path + 'valid_convlayer_features.bc', val_features)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:43.430008Z", "start_time": "2018-01-09T14:00:40.799009Z" } }, "outputs": [], "source": [ "trn_features = load_array(model_path+'train_convlayer_features.bc')\n", "val_features = load_array(model_path+'valid_convlayer_features.bc')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:43.433652Z", "start_time": "2018-01-09T14:00:43.431195Z" } }, "outputs": [ { "data": { "text/plain": [ "(23000, 512, 14, 14)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trn_features.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our new fully connected model, we'll create it using the exact same architecture as the last layers of VGG 16, so that we can conveniently copy pre-trained weights over from that model. However, we'll set the dropout layer's p values to zero, so as to effectively remove dropout." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:43.491775Z", "start_time": "2018-01-09T14:00:43.434825Z" } }, "outputs": [], "source": [ "# SINCE KERAS MAKES USE OF INVERTED DROPOUT WE \"NEUTRALIZE\" proc_wgts(layer):\n", "def proc_wgts(layer): return [o for o in layer.get_weights()]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:43.529622Z", "start_time": "2018-01-09T14:00:43.493298Z" } }, "outputs": [], "source": [ "# Such a finely tuned model needs to be updated very slowly!\n", "opt = RMSprop(lr=0.00001, rho=0.7)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:43.557641Z", "start_time": "2018-01-09T14:00:43.530914Z" } }, "outputs": [], "source": [ "def get_fc_model():\n", " model = Sequential([\n", " MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n", " Flatten(),\n", " Dense(4096, activation='relu'),\n", " Dropout(0.),\n", " Dense(4096, activation='relu'),\n", " Dropout(0.),\n", " Dense(2, activation='softmax')\n", " ])\n", "\n", " for l1,l2 in zip(model.layers, fc_layers): l1.set_weights(proc_wgts(l2))\n", "\n", " model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:00:44.318255Z", "start_time": "2018-01-09T14:00:43.558910Z" } }, "outputs": [], "source": [ "fc_model = get_fc_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And fit the model in the usual way:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:40.761665Z", "start_time": "2018-01-09T14:00:44.319815Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 23000 samples, validate on 2000 samples\n", "Epoch 1/8\n", "23000/23000 [==============================] - 12s 522us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 2/8\n", "23000/23000 [==============================] - 12s 525us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 3/8\n", "23000/23000 [==============================] - 12s 526us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 4/8\n", "23000/23000 [==============================] - 12s 528us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 5/8\n", "23000/23000 [==============================] - 12s 528us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 6/8\n", "23000/23000 [==============================] - 12s 528us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 7/8\n", "23000/23000 [==============================] - 12s 530us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 8/8\n", "23000/23000 [==============================] - 12s 529us/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3b08061908>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fc_model.fit(trn_features, trn_labels, epochs=8, \n", " batch_size=batch_size, validation_data=(val_features, val_labels))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:43.941613Z", "start_time": "2018-01-09T14:02:40.763202Z" } }, "outputs": [], "source": [ "fc_model.save_weights(model_path+'no_dropout.h5')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:44.140280Z", "start_time": "2018-01-09T14:02:43.944442Z" } }, "outputs": [], "source": [ "fc_model.load_weights(model_path+'no_dropout.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reducing overfitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've gotten the model to overfit, we can take a number of steps to reduce this." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Approaches to reducing overfitting" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "We do not necessarily need to rely on dropout or other regularization approaches to reduce overfitting. There are other techniques we should try first, since regularlization, by definition, biases our model towards simplicity - which we only want to do if we know that's necessary. This is the order that we recommend using for reducing overfitting (more details about each in a moment):\n", "\n", "1. Add more data\n", "2. Use data augmentation\n", "3. Use architectures that generalize well\n", "4. Add regularization\n", "5. Reduce architecture complexity.\n", "\n", "We'll assume that you've already collected as much data as you can, so step (1) isn't relevant (this is true for most Kaggle competitions, for instance). So the next step (2) is data augmentation. This refers to creating additional synthetic data, based on reasonable modifications of your input data. For images, this is likely to involve one or more of: flipping, rotation, zooming, cropping, panning, minor color changes.\n", "\n", "Which types of augmentation are appropriate depends on your data. For regular photos, for instance, you'll want to use horizontal flipping, but not vertical flipping (since an upside down car is much less common than a car the right way up, for instance!)\n", "\n", "We recommend always using at least some light data augmentation, unless you have so much data that your model will never see the same input twice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About data augmentation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keras comes with very convenient features for automating data augmentation. You simply define what types and maximum amounts of augmentation you want, and keras ensures that every item of every batch randomly is changed according to these settings. Here's how to define a generator that includes data augmentation:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:45.123159Z", "start_time": "2018-01-09T14:02:44.141968Z" } }, "outputs": [], "source": [ "# dim_ordering='tf' uses tensorflow dimension ordering,\n", "# which is the same order as matplotlib uses for display.\n", "# Therefore when just using for display purposes, this is more convenient\n", "gen = image.ImageDataGenerator(rotation_range=10, width_shift_range=0.1, \n", " height_shift_range=0.1, shear_range=0.15, zoom_range=0.1, \n", " channel_shift_range=10., horizontal_flip=True, data_format='channels_last')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at how this generator changes a single image (the details of this code don't matter much, but feel free to read the comments and keras docs to understand the details if you're interested)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:45.186462Z", "start_time": "2018-01-09T14:02:45.125664Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/roebius/anaconda/envs/f1/lib/python3.5/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " \n" ] } ], "source": [ "# Create a 'batch' of a single image\n", "img = np.expand_dims(ndimage.imread(path+'cat.jpg'),0)\n", "# Request the generator to create batches from this image\n", "aug_iter = gen.flow(img)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:45.423127Z", "start_time": "2018-01-09T14:02:45.189581Z" } }, "outputs": [], "source": [ "# Get eight examples of these augmented images\n", "aug_imgs = [next(aug_iter)[0].astype(np.uint8) for i in range(8)]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:45.700887Z", "start_time": "2018-01-09T14:02:45.424699Z" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f3b4373f390>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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w3aiEOstpL5/SX10xM1NSpshsDiKioicpQW4snbYkP5DEaFmtzUhoMrEFP2IgBkdEYKQgzwyZ0YSh59nVBXlM5Hk+ghwnJ9y9e5urZw9pmoZCSfJJydQ7jo+P6YYemzLatuaNN95ks1nRdC3eRw4PD0EqpMrIyoKuH7i4uODrX/86hYhEnfH+B+9y+7W3ePLkEV1Vcf/+fdSb93h2ueajBw9YryvOzm4znc9JSRDCaCEOjhccHB1jy5I+eELYJWIjfd/v0bHr6+v9WIokEXJ012ISL7hqNx8vTy/ygveyszwff+/jz18WQ/0w+YIoT0IQURKUZIxj2AV8IBl91wioG8pDHJEVqzVKjPwDKSXGmDEXlBJlnjObzTj+8pvYvACh0CbDR3ApIoNHJY/oPEEI+i4w9C1hGMizjLIsSUKwqdZkNieEQNu2Yx7G7BRaUG5hcte1SK0wW4QnDD2r1QplDZdXV7QYlmevofSoICLFcfUTgiQMSXjm8yXZwRFCSYTUuKHF5BnzMuP5ZSQvDJvrK66eP2M5nfDeu+/wpTffRIRIEpKHj57xldMz1qsrVqur8V7YjKrpufvGG2RFzvX6mvPzc+7fv8dms+Hw8JiPnqyYzWZ0gyPLDZPJhKv1irppOTg44vLyEjXN2VQbbp2e4oaOZrViWk747ne/i3c9VTMCIK/dvYdShtXlBikVi8WCxfwA4rjgdes1ccvg2OVkwmBxES4vL2jbmjzPRxddCpIc2Qsjw+Dj3Ey8gaz9oHn/SQDhJoQdv0+Z/pwpz02cXQhBCB/TbHaf33xkW2rKDr+/eTOA/Q01W2iyKAoeP31GUU6x5YR8KrDlBC0UQiikEvRVhbWWZlPRts0+frJbBA8kPjiUksAYo9g8AxIxekLosHbKfD6n6zp6N0BMSJNRzBYIWkppcNHhhgZlDVIJ8szSe8sQa6JQOC+xRYbOStAGbQvq1TnZpETJRGEkfdcR+o7vfvs7nJ0csVkc0NQ1eTYZ4fToaDZr6tUlwTmU0cwXByxv32V6uKTralJKnJwccX19zcnJCUkYTD6hnC1Hi54ij58+I6XEdDql6xoOD5ek6Fgul6zqmqruWFcN4uiU+196i2EYOD65B8gxibmpubpcjXhOEFTreqQDOU9SCpVZILFDh733FGXG4289ZDabEZNHCUMSEIgfL5q8qDw3LY+4Qbn55PwSn4h5xn28qDR/7pRHCkGZ2495ZSkgUmKcpoLgA8gx+FdCkkJEbM2t0XqEj/UIV48WB3KboZUmOM9mtabODNoUpOiJrqevA9bmKCEZ2gbLwGqzGpVOmzFfEwKhHUGBGNw2ByBJKRJiYBhG+FYgybRhaDuSGRGgzBb4NGatg5BEYVgcz6j70U2Z5GNc1ncOYwtU5hAyx8cWnRQhSablnK7v0UWGuxzzS8ZolM5o25bBBQ6+/BXeHr7LfL5kVTdoITk7OYXQU1cb1lv3xwvB7PgE5wdQY0y2Xl2NgIDStJ3j+NatMec0DAihqJoarTX3ju5xdXVF17QMfqBzA7PZHJFyjg4OeP+DB1yursmzkn/x+99DCMG91+9RZNmYUDY5MSbK+YSQPGVZEoTAFDl9340MgO3Y1W5D29bEFPZcxEiCJEiJfX5nBxrtQButNYPfcdnGeXUTWLopu+9JKYnRj7S4m4jdp5QvhPJAQqSISBHilkKzfY8EWu5cHEbFkRKxwzlj2isUgNWG4DwpRILz+xttT2/RNR1GK2SW4dww0kCEQiXouzXBBZTWGCmwVpOQL7AYIIKIkATWGsQWCk2E8bH1xwOJvusRUiOEQeeGyaykbVumpSAlQdV2ZLagLArW6zWtk3gMyhborETabKRAWoP1BUlpprMF7vEzsiJnU3X8Kz/zs6yrntde/xJN0xCiZDKbkClIrqderyAFinLKfHFIkAZbGrSXxM6QWY1EEENC2wnoDJNlRDpCcJycnLFcznn8+DHeew6PDli1NbM8QwvBo+ePiChu37nL0ekdfILXyfGDYzlbQkpEH+ianiLLcH2LyuwLMPJ8Pqdtr4khMZ/PWV0/347/OMG3DN1xrNPHsfBNT2RnfcZx+n7L89IZ94KyfLaynC+I8ojvC/ZvumN6+9mezwaILd1iF+eo7WdGKVIIqO0A6W0MVK8aFnlObixmy6mSMeGahr53pDTmXaaTGZPpnHXdIOSYuEtyZOCmlEjRI4QihITaZtGllEQfSAJS0iAkxlqQCqEkKQmu2g1FuUD4QG4t3gVcBN86dLFAB0VCgTQonWPzgpAECYncJhLXTcvi4IQ2KGYHc05vv847b7+LzuYYL0HUzOZzCi3YXD4jugGtR9pLVkywszlTK/HriNKCMsvRZpeTKSkOT/DDgLE51WbF8fExjx8+xDnH2a1Tmk1FlheIGPFdzd07t3A+8fzqkoDm+NYdstyiZjm+7bg8v6LMM2SMdE2NlIogwDpHUgoZAkIwusgaVl3F+fkzur7Z0nUsIaYRbUvjorOTl8U8n3q2fSKR+lkr2r4QyiMEGK2IcaTX74ACISBub9LLkl7wsY+q9XgpMcY9YrejZ6SUmM+WqCQZOkfUDdJocqmwSSCUxnlI3tO1NUIo8rwgppHzlQCpdnkFOcKgW4rIEAZCiiTv8N4T4orOeaQyoDVSGYRQOC2YREmuFTLAdH6wZQ1HopTYYk6RT+ijR2c5k3JGpSRd16JSJAJJK47PbvF81fLVn/4ZfJIUswWrq2tMPuXuGzl3z04I3YrN5ROsUeTeorVmvlyMbG093pc8z4ldS24sXpltiYNHKYPrWzJb8PTpc4wx5MslV1dX5GZEHDebFZqESIHMlrx573XWfeLBo4dYazg5OhlhXwPr9TXV1QolNJOiIMZ8tCjbBXEYBpTyiODQWlPXNSE4lMz24zsqD6T04rh/MubZlUf86Pkm9t/blX98FvliKA+CqZzg3IAIIz8pSYgSXIIo21FxlEVLhe89RmcYZSBGWlPg08hbU0ru2QB5ZrBaYaQi+g2uGsjtATNV4lMkuIGLeoUWEunHgVwIzcFxRuc6ohREmej7npkHvGM5ndDXFUrnpGxKS0YXoHu04eHDD7i6fkaIHZ3v6APYyYLJbEkxL7BKc+f0jOPlMarrkcqg8pxBRTwRsTzDmRl1NuMgP6IcerRPaCTL7Bj91k/T+cTBpOPk9dc4Pz+nXOYUs2MkkeQ1ojjgshZMbn2Nsu45PfQ8u14htWWSH6B8i5GKrq0ojMfHDVErWtmTz2ZIApkZGKoV88wRXKTvIIszfCPArylthtaWoXf0Q6Sur9nUDV3X4a3lnQePuH//q+RqwsZ3lIcnTKYlWWFHEq9mtNqpQ0mBNYbaVQxEzlcVppxtF6Ydez6gRAIZ6MJ2IRXiBeXZLbgAIqbRE4mj25gQiDTWfokEIXZkxpJwSGmJIt6YjZ++PvQLoTwpgROJZAUSixSJITpc8qAlMMLFnhHnl9bioiBGgZYZSn5smYRIlLPpeNPj1twrswU4BT6O7AMfPEIkcpXRtTVaSMoyRxvJk0cPx/KC4PEpMgzDyBzebHjf9+Q2Y7o4IJtDtImq81xfPqd3A9frFd3QUC5mCCPY1C3vfPgIR4eIksJYDqYLvv71r3N0dML9r32FGEZWebQZy6zA2Jy6bVAkhhjI8oyoBEJqDg7nHN2e0juPygoOj08IbqCu1oTGMZlM4PIKZSw2z7EqMFtIlLYMQ4c1I/fPaUt0DuQIpBTFDK22rmwIhMHRNRV+8AxNRIqChEXZRKbUHrJv+4HpZE7uDNOyYL48gGRpmg5rJMfHx2RFgbEaIRLKSJQykCTeByDhfMvyYM7bbz/aF/jJLcv6JsL28XxJe2vxafI8L59zPz40/Un5QigPEmImSU6S2DIHtsiJ1ApJvg3EIUZBZnKCc/gISWsy2TNCDJGEYvABrSza5ngfGELEKcFiNkUYTVVVNE2FIrGczlhkU1bNeky4SoVzjvX5JWxrPOq6pncOq8fstvfXTBvPMTmTowlCCObLGb//rd9HasGt+/fxJFoXEe2AmXjaLrGuVtxaFjQh8Fvf/F201vz00yf83M//VUTwYwJXqj3TOrOaJCJCS6JWLE7uorMCFyWmmEDmEFojkkLlEVdfYfOMw+Nj2uvnlNM5rrlEZyVRG4wBk1vaDbQhIjBoPaEoDsjzJSQ1cvHaQN8ONFUNIaK0ZTEvsPmE2o8WJoRx0h0eHtJ13ZbV3lGtKqwpUWKE+AUKvXWhvR9Z0FqPVBxBwvsBcLz93bf5xm/9+gux7467tnv9SUh557p9vN2nU57vT65+NvliKA9im29JaDTJewxyW9oLSfhtma1EKZD4LfCyK2EOJBIBQUwSYQu8kLgoCVvmQn48oyxzYnTI6JhPSnRIDFXDEMCUY9Fbvd5w8ew5Wki6pkVs8wB18jib89prrzE7OMTmM+aHJwhb4tY1D64fcLVZ81Nf+wodAi8Uz6sV5xfXrNYVvexZVw2bquN0ueStN15HJPjOt79FV2341/7GXyc/PsXanNZ5psWEJABrqYcOmU8opxM8ijQkdDFHCjmyGFQiKy05Ah9hOp9TXZ9TzOZ4FZlPl5hyBkRC8rTeM6CJPjKfLcjKAxI5CgUOXO9xnUOIkRNorSLKgX4ItM7jfdwyrA2u7whuGCtHjaRuazbrliKfU0w0wQw4p9BJIbd8wBBGoCXGyKa6outX/Ms//CZttyY3dh/TjlDy6FK9jE1wE2nbxUafJub5SSnPF6JvW0qjnypRI7cogE4SnRQiCGT0qBTQIiJFhORQBKQICPyYRBMQkyAgGGJkiJE2BKS1TA+OODg+RdqRruOcgxjRShFdpG87hmGgzMbKQ9f1DF0/+vZCUq83TCajhZHKMJsfcnhyymSxRGfjCqu1HEscBIQkuapbruuWVd2xrjour9ZkxRRpM1rn6foem48J3AcPP+L82XNSGitPxwVjiyKaDKE1Ji9AGBAaneVEFFJphDZIkyOM3VOVFgeHNH2HVAaTF5SzOUIbfIpbZgUgLT5pktAgDSmNeRFrLZNyynJxRGYLTJ4jjSYS6YaWiMRszzvPc+aLGdOywPUdbTfW8UwnBTbT2EwTgic4RwhuS7UJED3B9fihYTmfoCVEP2C3tVE3AaIfPGdeTqn5SRI/f5R8QSwPGF1SasvQNGgScZvsNFLh44aUEjbLEGK0JF3XEZLA+4HLLpJlxTiokzlHhydjkGhGcmNWljQyIKTAp0QmEm1d0/pEs6oJPlFqzXvvvM3q8oq+7TBK4YSkyHJOT4/prOXO3dscnd0hmy5R+ZSkMqSA6XTK9eocIRLLwyOebjqqrqUeoPOK66pjeXxI17YsZlM29ZrLuiZJwb07t3j20Yrf/q3f5Bdv3cNmJZmdYHRBSgGEQUhNAAafsEVBRIIwhAh5McWrHkkiTEoiiaquOb1zl+b5B7SuwUzntMpiVE6Siqw8oFo1mGnB0Z03MNmcYrqg6YftPmcUmR0repOj9z0heWQp6Dc9Lnikl9tJHxiGjiLXuKGhKAyRhMDTNWvabTXqdD5BKnChxdqcGEdlvbi44KOPPtzyCB0Cs1egkWWd9girlBIjzcgnvMF13Ddf2ebZPqk+TdMwn88Zun5f2r8rzQ4hjKmPG4nTTytfCOURShPLOc+uV2zOVxzMZyM0S0AIiTQThuDxQRODwCpBNjlkMV9QFAWtLrAmHzllecHgAlIq8mKCzixSaFrf0a7r0eqEnr6tCZ1DSMP86BDCmutNhR9GyFRIibaWrCyYTCYcHhxQlHOEyUBbki4YAntYfGIsaut+9kPAlnPcZYuxE4p8zr/1b/wiv/qr/zvdEKjaAZEZuuiRVvLGl+7xR7//+1w8veTunTfpY6SpWrLCopQmLxdsNps9bSnXFtSYRzJKIoKnqSrKPGPTexo3lnlfVw2TfIIXBpmXCHK0yZDaMT24zawsMNkUlyIMLdIYCjsFPzBUiWJxglSCqYismms2VcXyYEJZFnR1w/mzJxjAmrE6tTCSTXWOkjmzxQlNvRkTpQhCAO8DWSno+yuyrKDadDx69IiHDz9EJIHE7IvddgjazrLsJvXN2pxPwtV7es6P8MSEEC8U1f1AUtyPkD+iEKHhAAAgAElEQVSR8ggh3gc2jGw9n1L6OSHEIfA/AG8C7wN/K6V09UP3Yw0HX/0KB4NHOEdhNKQtPqYEgxtvkLY5QmqG4LF5DkIRSGTC7DloUkomW7JUSommH3C+IUhD3/S4rqfvVqiUxqrOaHDAsLrGdy02GxXH5GM3GpVZssUEm+fobDx+EBqPZDaf07Ut5WxKpkClSFd3fPVrX+fb7z+GdIWSCiUL/tLX/hL/7P/9Z7Su4eTsFklr8vkEPSkQriOb5rz39jvcv/9lysWCybQEEj6ASB6rM+bGIpUGY8iKCU3bQgoYNbLKjS7IRKRnoAsepS35tEDajKgzrJiNtTj5EpPlzCYl0TtInrzIuKg2aKWQQWCLGVKME7ePPdnkmGx2Qrt6ytXVFSl6ZrMRto/eMS0zqsohhMNkljxLOAdh8PihxmUJYxSDr6mahvVG8uEHj/now7F0XMhEDALki7HIiy29xnZgOwX5PuX5MTy2vVIqSXoJovdp5Cdhef5mSun8xutfBv7vlNLf3bbZ/WXgv/hhO4hC4qclwjlyplv6TRgpMUoiugyZEirPkcoQvUPmBYMfO6AoLUDJsdjN9ZS7mowUkVqR6RKbldCvSK2lqjxSQlbMqBvP5eqaaUzMZjPK2ZQEFPMpkUTb93gS+Y6KkxJN22NETzkZSapuiGTGYPVY/v3k8VMmkxkgCcHRdwO/8l/9CtJIiGksB8iOR6UrLG1Xsalrnj56SrOpyCcTXOfQmUYxJmoXiwO078iKjD5Kuq4hxcR0MiVaTVtVZFlG5VqabmAYBkYYRZDlBWYyQ7mctq7IbYbRhiQSvXdU1ZoPH3zAxgWiC/jWsZguOD4+HvtIFJYhBdq+QTtHlmWje9V3zGYzfN9BCBwsl/iVo2sbKrVGm5xcKAbvxlJqrehdi1aRiEBJWMynXF2t9oWBnv77aDe7GOhlxM7PGvT/SYCCnXwebtsvAf/m9vk/AP4pP0J5RErM7YRkxl5rXdeBEPRbzD8VEaU0QkFMA0IE2vbi45ZCSdP0IwKUF4awtVpsG+illNhsno/NJ1TBbH4LmyJWaYJu8TgGUTA7fY3ZrVvovGDoa9hcIevn9FfX1J3i4EhSykQmEyoONHWgmBziRcbJ5Iy7h094+uDb3P/Zv8hH5x9wdrji8knFwjQ8jx0TXTLLJYup5vZMclQq7NDz/PwCeofICp5uNpizMzI1dsnJtIYwIHykJ8MFiS2muK5nNi9JUjD0DeVsymX1mOAdBT0X64qDozfR0zlidkbVd9j1GMuJANcXl3z7O3/Iw0cf8L333qNuKoxn31JLpMRbX3qDxWLG6dkBr71+m7PZBJmfsdpcsulW9L5GpQSDQ4XE1JZomdNVT8lJqEwj0RipcW2ibxq0GatC16sNbVvR9GvQHi/GCtvEtlnkjjYjxL7bjRAC4cZuRxIBIY5cx23vPbkd87TlIUrJCOK4NMZTu/cjI3fOB5LSYw85IUjqx8PP/qTKk4B/Isaz+q/T2IvtLKX0GCCl9FgIcfqyL95senh2dkY/jKtM261fwPfHDjiKFAR+R/60OYUeSwZijEiR9r3atNYMw8D2+LtjoYSjvV7RrFekriaXiauuoa06Bu+4ffcOh8fHZJORU+ZTwrmIcwETE6G+xllofIfxLbpcMHjwXlGUC07v30N/8C7D4wtE45kLgzo9Azf2YDCuxGjBwaLgztkxJyeHLOdT6usKTDHy1WYzLp6fc+/LX966oZGUHCkmfAwIk+2D6SzLQCqs1fheUff9vqdd03SYLGc6W5DNFvjoESEhTM2mqfjww/f5p//P/8Vms6auxzhQKcWkmDGblMyPDvHDwLsfvI/SgqMnC549f8KX3ryPtlMms5zMGPJshu97goehb6hcvUfsLi4umM5KTDYhyIjrPQiFjZI8z/d5HOdGWhNKjsTPT7heN2Of3VjelBdZBt83xz4VAPAyq/Zp5E+qPH8jpfRoqyC/JoT49qf9YrrR9PAv/IWfTmiL3qIgYnuj1DaTfTO4U0rht8VxSikUkKtE141wc9M09NuJtKPXd12Hry5IQ0u9ukS4lnboMEqOrWyTHwvj9Fg92vcDm6sr2utLYt2isgydWlyVcE4zkYfjzRMa8pYyO2JYzrn95pvUrufZOx9y994bzEzEnc7I55rZ1RhPnZ4ecXS4JM9z6qrnweNzrq42PL1qGZqG6AeUgDA4tJb0g0NFyLMSL0AIs1WaHKPlyBGTGhdGwunGV9RNSzlZkBXl2Myv9yQ38N6DP+S3f/ubvP3295jOlmSLJVftgJNQZgUXfU2dPHqWc7SYc/veKY8+esD1akNwERk1B8cnhDQH4fCuRkuBSttarDiOz3w+p6s2dF1HVszQWYZiBHDyYpxyLqQXJu0OAPjk/H2BwPmSROgPIobe5EJ+GvlTV56U0qPt/2dCiH/M2OD9qRDi9tbq3Aae/aj9CCFwPoJQ44NRGawV5Hmx7+K4W9XKssQ5ty+Iu7i+2G+zQ2Z2sON8Puf4+Jj+HJ49eoDrOnQc8K4nuITQBpsZooDHjx/TbbvfhG6gzHOEMmghmRYa5z1VvUYbw+AjWVKkAFYqDk5f5y/+zF/mw3e/x/HyhHf+4A/56s9+naNFjl4oXp8fEKNntpgxLQrOL9dsNjXOJR49fM7y8DbJjbHD0PegDSlJjNQoGPu9GYWx+b4/s1Sji5qQRCRD71HGYGyJMDlCWVIMROd4/OAD/ud/9I+Yzg+5deceTQ+Pn13ThclYkBcig+pJBCo/kPuOJVPeeOs+3/q9P+Ds5DYubFs/xcRsWtLEESLXRiFjIgVo+3pfTl83G/K+5+TohCEKEoKmaWjblpDG52MTyLGMI74EZt6N6W6evOyzHxT3/DiWZ/f/T6UYTggxAWRKabN9/u8C/yXwvwH/KfB3t///1x+1r93Fn5+f77vn7PoT9H2/7wC6K7HevbdrkZrLuG1nq/d4/diEI9/XxitpODk6xsSezcUTiGOPMR/GxuFWGGQMMHT0Yew9duvOaxBg6B1FvESHgJ1MWDdbXx9JtaqIQ8+AYmoyfuEXfoF/+c1vcnZ6yv/327/F8Vt3OLp1yLJc0vc9IQWefPgRSVhi63jv2+8xzWa88fp9prlFkzBAkWUkIVBiLAwUSqC215cXE5IcC8XY9miIMUJKVJuGkBRW5VSdwxiFVZrf/a1v8OW3vkYQhvPrnsXBER8+GTg4OeLxhw+YZCWmrNFGstqsmRaKIYztZ2+//gbSlBwe3+boqKTINdEPlCYjxYCRmqg0Qii0M7RVNbpmBPq+5/z8HFNMyfKxY9B0OuXh46cMw0DbD2MRvoCxpVR8YW58cvLvXMy4tXY3P1dKEbY9/W66ezdZCvIG8PMyus+fivIAZ8A/3mqtBv5hSun/EEL8DvA/CiH+M+BD4D/8UTuKweP7hkk+dgvdlVDvej4vpsXen08poQhkemw2KJPHKLO/UbtcSAiBpmn2jIKsd1w8v6Rd12TZBOLAZGmJ25Lq+mrF1XmLMhJbFpRljswtJ6d3Ob+8RjWSrqlIyaG3Td1V0wOaiVWEaok8OGB5POev/PWf49d+7Z9wenrKxaOnPPnoXQ6mr9EO/Z5Q2dSOq+s1y8UZt05uY71gXpbkRiNh7FuHJLOWPtT7ylkhFUmOFtp7j4hj2/iEpGlaQILQSJtjs5zN+opq9Yz3vvPHfO2rb+FJLKYzemnQJuNv/Ud/m//uv/1vKK3h4voRJmnQnqauWK1WLJdLEAaXJJP5McoMOOeQQ4eRHiVHVzcJqJqO6azcV3B67wkpIfqeIMbiwhB7FovFC50+f5j8qMn8svZTu6/cbD21e/2DKktvbvNp5TMrT0rpXeAvv+T9C+Df/nH2pZRiOSsZhgEjx/yL954wtFtqeUAgIThICSE0hJGKYstsf8E7kuCuEfyug05RFMha8dq9N7l8rGmqS6wumMwn1E3HdX3BNElkksQQqasN076h8Z6LtiEWBSIsCV7QNdeU0yXT2QzXdqyvN1w/fUDvEqX+Eq222MWUX/yl/4B3v/3HvP+db1FdPscFiUGRK83B4Yy+8Nw7uUtmpxhZUhQTitxS5gXBOepNxWSxZIS7A0qN+a1se527YkApxnokZSyBMZseyMb4Ii8xSvEH3/znY5dVv+b46Iwn64aLqyuQLf/T//IP8PGKi8s1OoJFMs0KlrM5RTneu2ePHnHn1hHZZMF83nP9/AnPHj/GNysyM9YC2XJkPqzXa6IbW4g1TTMCBiEgtuOSiFRVxdXV1UsZ0y+ZTz/0808T89yMrX6Y8vxpAwY/GUmRMPREN/7cRNN3HzfkE6OrJqVE36hNr92A78dfJDB5wa6OfVcMt3Pp+n6kZJRSs15dsq5rjNTofOwPNzssyaZT4vtP6IJA5Qqvx34Jxlou1xvO7r4+JikPjzh//hG5jCgcKcsorCX2nrqreP973yU7OCJbHrNYLLn/lZ/i/tkpcbPiexctVVWxvr6ClFieHY7U/KDRZEwmM/LDgmgNzjnEFpodF4NIbiSSsbRAKoNUW4a38/tfDEjOMQyecnGAUmaPZn3jG9/g1skpUvYIOopCoSvP6dmMjz78gL66RoeeRXZCIS2ZEkzzDCMVIiWGwXF0dIzSFmsTZ2dnDJtrnq8vGLoOMwz4zRptc2LyZFrT19XoBWz7SSfnEFLjQ0ff9/uWYPwYfdJeJi9TntFCf7/S/Cjl+POpPEgiBmU0JstwvgcivXdk2ag4u4rQYduMe/cAWFX9CFNvs9NaSqQYKf1in/PpmZARhozNxXOKbE65OGMQOZvrhnB4hR9AZRl4T321QoW3yTKLoKEqDigzy9HBIe3mks2mJ4RIJMfrRJElQozIrqJ91lGIQD5dEE5PsW/c42d8g9aWtnFoVSCDwDuH3zRcPH7IJDO02ZIueprmiuXZElJLlAYvwWuLVDO0yhFekNwAw0DyA6GLDG1kWUXKKEfKjvSEbuD6wUckF/jo+TmmXFJEy0xr7s0TT59f8sZkynUQmCynL2vKRUYRI68fHWKj4MF7H3BW5hzNNMdHhnlp2awDp3fvkeUTPnz3HZ49fYY1ijJ3TKaW+WzKaujHvE0KCN8RQwepZZqVKKt56+4Rz5++h9YlgxOkZEbi642OoPD9EzpFDUkRQ0IgEeJmzCMgiW1Hso+L5Hbpi8zYPRPlZkdZ+Lhz06dxJXfyhVCeRNq22B1pJkqPDTiapiGEj1E1rTXWWtq23XYOHdvyuhSIfkBuAYIXSnV3xHGpyfIJ5WRKaGuuLp5isgyVzdGhIaKYLw84OjrCWsuTJ0+4vrzi+cWKtvfMb2sGo/D1mtzKsevkNt+ik8LKhM4sCcngEs8fP2Z+6NAmIz9SNKlkOT1kPh+rMJMLJOdZq2eYrsHFgE49oa8JvRhreRDUbQtpTCwqsV1N2f6KWUr4wW1jwUCfEjK3OMYf1TIpcfH0CYWStJ2j8Z6NG7BWYKY5pZsQjMAuLTGCVoKT+ZyJkMzzkg+/9wHXF9f87F/711kuj8jz/5+6N4u1bUvvu35jjDnGbFa327NPd7u6VXWrcbk64ybCBGEsE8sioTExIQ4JyFiCIF54AAFJHhDiBSEIRXABVimKFCMkpERWQhS5wbHcle0qu9zVrdvfc0+/u9XNZnQ8jDnXXnufc6tu2WXnekhTe++1Vz/nN77u//3/FVHbRKhvDWSKo5tH2GbO4uyM9eqMdV0xXy5S2N17nizLEitrT0wZYyL7+I5Pf4YvfeV38T6iTco/Q/zmdv938zxD6XswoO1rYjsPAt7192+03hfGIxA0TcNq5ZJ6mJZJi6ZP/LXUm2JAUikwmw+ZxgvAdh3Bpce7/nmzLMP0IZ/QivbsIasmsK5bcgnz4wfMdiz7ZUFr9tE6R0hDiIr9g+uEqOgeP+Zs0XDWvc7zt28znUxolmfkJkuepGkxuUFISVmNCQHKSmI6i4weVy958OY5o+c+jaUkCI0LAtvVEBxLuyaYiGsawvkZLgZ2zB7RWmRW4X2HzvNURYoRoicEAT6xhzrXIQjkxuD6apPJNUYLFg8e0ZydcX1nRr0+59HDE6TQTGZTZrMp+/sTprNy813uKkGRaarMcO+tu9jW8dGPfBv7e0dcO7qBygy17whKIY0mMxm2n6tyrsG2NcYY9kYjrLWs63pTuCl6gG1rA+OypLUN+/v73L55kzv3H2KdSzKY3+S18zTjSWPa8glDGSpvV2+7yovxXtf7w3hkImnv+mpU29VUVZHK0EW+qabBhaLXsGKM5HnJqJykseCezVNKSWYKTJGkFVfWsnN4EyMicyNYPn5AvTondOskhDXdhyAS3F0oWmcpR1P2ZEZTdzhbc+fNN5hUOYezCUTBo8f3kNowEgI9nvXsng3GFIzLcVJUq0qWyyXL42NKkVGOR4zKHGuXSDyhOyOLC7rujPW6RVYTdqazRAclFVJkmJ6pNApHCBIZIXhL29Z0bZvEtQS4oqBbNBgCvlvRLk/JfMPtg33yQnL3/JiwaDmb36f0MJuN0IXBe0dRGGRjMUh+78u/TZVPeOnDH2O2u8doMqWqCnKTYV3k7OyM2DVY34GMifh+b0awFQHN/fv3AXp64oviDfQj4DadP+c6PvHtH8daxzt3HyZNVH35kvxGBYOnlZiVUsh+WO6ql9n2RFc9zp9Yn+dbuYYPP+jqhNZtchxrLYqU3wy7Rp4nZhVrbfrQMZBJwWQyYbGqCbCJdTOdk2lDFjUuNqhijBntUrkEIm2aBZ1dI21MfSXnyXSOkIrZ3j47sz3iNGKbBcvzM9bzUx42SzKZnj8Ctu3wuiHLDcrk6KLEO5ju7iGEQuoxRdRIt6KIGbHpkHZF6BqUW9KcPyZ2a7yqUCrHRcm0mBLIKMsRgqRjI4IgOo/1lq5raJsGvAMR8cEhpKYwBZkAX69YnDwE17C/N6WYFezOZjx48ID1eol9fMpbb73NweEugsDcW5wLlMWIDzz/ItpU7B9eY7K7x7WbtygLTdetWJ4d49o1vl4R2o71aoXWmtOmYTVfgDTkZbHhXBvK1oNamw2gVCDiUFFQLxd88AMvUC9rTs7mhOxyAWEbFBpCUq5Lqm/qqROhKYdJxB/DPNCm16MuWJaG9UcZnnufGA+9oSRMWmqGZr1LdiyXS6bT6aZCM2DYQgiJOdO1eDciLyrKXOOQCKXRpkCojC5AWY6QwZAmgD1ZlsYZTu47FoszOH2Hzrokdyg1yIyHDx9zcO06L7zwApPZlFxEFlhsk+Z+zHiUxgFiZLk4RwjBeLILymBdoA0GmeXIImOSy57UwhCjpyqndFEQvEJmJRNTsNYF04PbVHtHyGKK8xFwKBFRIuAiBBs3HkcETySxpyohKUzFql6mXOr4EYuTh5S5xIxLdnevMzIjlsslZyfHvPraKwTnEU4QfEBnhtn+LqYo2dk54IMf/hjFZJRK/SND8Ctss0J1K+L6nPpsTr1e4pqaxekcKUt0IWjqFefn5wkipRSj6WSjYJHy1pwQHK7XAyoLw6jM+OhHPsSv/epvcLV4/UQP5opjuNrjQSSla2R8ook6rKve6A+73hfGo6RkNpslLU0pWa0XG6/jvSUT2QZRMODVIH1ZxhjGkyrtTERcjKzrNUKl8eR8pKjKEh/SKLHMx+TTgMokSIF1Dq806vg+GQKR5zgkylS0PrBenPLGK45xmaFkAOcI1iG8Z71cIdYt49kO2XiEyWRSiBMFh9efw4x2EdmIECV5HolBII2GEFmdn+G0Jht1RDFBi8C1G/vsHd4mn1xj2Q4dcgAPkqTlI1JvR8aAzCTCRaIERaRdtVSZ4fHje/j1HOE7ytEIMyqIRUZ5sMONDz9PYQyfXf4ZHj96yPnJKZJEgKgKw3g0I69GSJU43kJw+NBwfHKf48f3Ues1i7PzHkuYNjYRFJ21rOs0cm29S0DT8XiDihh2f6M0EFI0kSWGo8XqjHFV8uwzN3n5wZOjX5fDrsv/226SprmgxKA0GM/TjOSS4NUfYTThfWE8A4RmoMYdjUbAQNqeRm83PNbiomoDKYaWPS7KB08UA6GdS8DEpkFlhizTRCRKKGJPoG6qimq6k07s+hTjDTIzOCQehYoSpMK6lqbp0oCe65DeomUfQvVNP2JEygxlxrjg8TEmBlAUQWZ4KUjUhamKJswYaEGPyEqR+jiTCUIXRCkRqBT2kHIGJSOppxi4Gq/jU3M4WIfUSW+mabutUCbxOnglkIUhKEU5m3C9yJnt7eGtQ4m0gdWNw8fEyOpCwLYtTVOzWsxpmiUj51ACbNtwfnZODAKt88QLN5mxPFsTXXrdAU41nGOAzqZGtw8ea9nkJt5ajg4OnzCe94I7+3oG8I0QBX+U9f4wHiGIxThVkKRE9bX7WZkl3BZ1qqp1LTEk8KNSYjPp2DQOkESh8ARGZZXg76YgVwqcJZoJmVFE3yGlwKMQomBaXsOfnqInU+bzeRpz7g14AJiu12uEdZydHtPUK3IpcNbhiWS6RARQxSGx3EWPxoz3r1PtXaPzIKWnKEuE0Ji+NyURtCID2aBGHrNrkjfJCpxUOASolkwlrFeWlzRtS9WzlFrv+/95Ao4oI61ryTJHtC0ToZif11TjCdYIxpMCbEe9jOijETpLCg1KdxTViLpOeYvrOkJsCa5hvaxxzZq2WTM/eYSIgUkmOVuc0LYWbQRFoXn06JjpdMp0MmaxWHDthRfp1iua5QpCoOx7K6Y0oARNXfdlbAuxIlOSvKhYruaM9ypuH+1z5+27VOMRq3qd1K9F4qwTWiNs3fePYl91zBLZo1ujQkP0EZNJIhHXBXIiRoLspWjatsUHS4gO61omZtRPlJKEr+Kfsj6P4IJqaOg+EyJlXmCM2YgeCaFQWtG2NVmRI4RC4CmKgrpLatNCZmQyNVyVUljrKMoK533S/1EKpEkJvxK4TmHHluh22M2rBC6NIeUZKsnW7+3u0CxTGbabjGlW5z041RKV4fDoWca3nmM2m5HlI/JiRESiTZEUEEyONomZJxU4ItIqiqpkMpmgtWa9XvN4se6bv4JMmcSgKhL/NloTerEt+k1jkEsfel6+bWm6llFRMtvd4/jxPYzSCA+ZkCiV8GaxJ3xEhORBzk+p6xVlVjBfnOFtR5VntKs5rksoDde1dF0DwrFcrui6juUiYQfn8wUxJiKU48WCZrlARRhXFZA8g3MOvEeIAiFSSFUUxQZ6VJVjisLwoQ+8yKP7D4jBkUlBFKlnI/CIAJmSSC3ROvXLqqpiVI3IZM/MExPJfoyRGCCGjkyl3pjtamxrCdYiY0SEQBiUsWNEfpMh3PvCeCKRztkNCjrPc7qmxccUzmUq9XmCCASfiNZdhNBZrG2RUW5GFdouEa4rkzOdzbDOJ4K+LCCEApVKvT66hE4uEqk605x6tWK1nLM8P0H0ynGpWw8xaqrpDtZ5dsYzJtMZURmiNoymB+STMXk5QmlDiJpl6ykqiSkqtCmR2UVfwXnPeG8Hb90G2d24rtcpBe8cMvY7qA8EZ/Fdi7AW5ywSkDHgvO3zQMi1oi0NoVVgMqrZLuu6ZjItUCpjrAqyIid0HatmhevWeO+IwUG0GBFZzE/wziGi5/xkgZKBGFLvzAqwtqVpbbrgpU5Cv/EUax2np6esVw01nna9RgtJ0QuAaZ0Av0JKFvOu15v1NG3dq/cFQvCsljU7431efO5Z3rzzNrNRMj6pM3Ru0qTwQFzfb6y5KdFaY0SgWZwSo0g9J+t7VL7deKomJhmTruvIhMCoiIypzB+Fx6hIEH/KStVCCNZNi1IZPqYyo5AK08fLWid4e3Cetq2p8hwRoQ01Rihypanbhs61mLKknEzJlAEhKccJBt95iDEkkglC3wsSRB/QKqOVCq1GTPIJRTmlXZ0T2hUyeKxrKcf7KAll7POt8ZTp3nXQFTEz6MoQhSTqHKVy8pg415CaKDO8TF5CxsSp7Ih4CWgFmSQrctrGIUWWVB6UAucRIdE80RuOtWnMYIjz1+tlL/ko8RJUmWOFRE92yeZLpDa0zZLZbEqmMpzvCLYjeo/vatp6iSCQCZhWUxbLeeJPkIKmbpCkNoAymqKoCAJcAEieY7mu6VyNUJK6a8knI4wUuCbh7YSM6LZF1WkWSOsybYQxoJTpZSBrYuyhMcHz4Q++CMFTjsteGV0hdTIaLbexaoqUSgXqxRl1fz113vagU4FzASX7RrmQGC2ZqSRmnCmJdBbvLME5YtsQ/9TBcyJonZOXI7LO4qxFF5o8SyDJxWoOgFaSTJd0tk0nVeUpQSZSyBEhRDwS7yIIj4mC4CHTGi2TukFSxtZorTbiRiEEQjYmlh2+XpOZkulsN7G+1GuC7Qh9bDwpZ4nyqawI+RRVTZFZSVQOqVTi0s4MpS4QUiMyjaePpYcZFCVTiNmXXjtrCQKkGAzcslwtGeUG1zUYILYWIQOZEtiuIXiPyRRGZzjb9WVwySp4MpNkSm6/+BHOju+TZQqhNMhE2miMQeYZC1uzM5tw/PABNnQ8evCY2WyGzhIdmGLQdO2IPmKDB5nGH0IIhJjGITKjmYxnaWJXeJZ1jW/TMCGkYkHXdRRlSZYJrE3tBu99Er/K801ZWalkLM888wwh9tAeelbYGAFP8D3axKWZqsGQBqNSCrTJkDJDikHgKo15x9irKXmPtQ22C5uxla7rnugDfb31vjAehKAaj2htt7H8ruvSxKH3dJ0jzxT0WLaqLPsYOpVEZXDEriOIiDYlre1QMSJ0S6VTBU30+UGSLAEcINPuFIIgy0cQLZkqoCzx9ZqiGFNOPTJCHWWCwkTIC5NUqclST0hpVDZU+aA0iRYry7JeOgXcMJzlex3MfiIzpiZX/zUIord468BZOm/RQkJnyUJgbTukTGJfSoL3rieysLgQqNs6kWMIg0UhtaHcuYZtzolZROokmhVjREXHrZ5v8fIAACAASURBVGee47Wv/d6m1PvsrSPW6zWnp6e9zHvczOXMz87TyEieQrDz8zlvvXUnfacezs7mHB4e0oaGjIitGzIpKcsEyxFm4Kvu8N5tSswbo5Gpb9cFwWwyYVdmHJ88omstYiMelkrQQwPc6Cwh0yEZipQIJF55gicZWIwEn17XdgneZfvq5DBTNODwhjmy97reH8YDLFc1ZV6kcrJOlEu0Xa+eliohzWrNer2mKktMlpFpQ3CewuREKTEyo5pM6VwqGwttQCam/UEga7hQXUjy4kIIhNLEvnstVZqPESon2J7CKVhiltP0qAAXHaNRnuT+QkTJiNIZhc4hJHFirTQ66/WGgk/q0v0IvogQhUSIvnEoJCLTtNbTtpa2XUPbpiavjLSrNQSPj23PJOMIIe3KSkQIIZE4ti1GVwihaK1kvDPj8OZ1uvqUxw/v0EaJjpqoAllmuPfwHXRZUhSG40f3uH8vGYPOBLZzG48QQsAFj/WO9TK1FKTIqMZjQLJa1UghqLsW61t838iejseoLCE/0AIfkqgWcaDVTZrMMUDsvYPUFa0TKF2i8xFOrFGCxGOuBFEOuePFzxjAOo9zKQdsou29ie9HU5IxXfSEBFf7Q2klPoj3ur7hPYUQPwn8EPAwxvht/W1PJTYU6V38T8APAmvgr8YYf/O9vBGtU4XMtl0aZpOKpmuJbcNoNKLIcmIpqKqK3KRdKnoPMqOxbf93ZDFfMZ7NUCYNhPmYOs4xXFSmohBJe4eIUhqZZQg/9CJSgUCoDK0rdJWS/C56VFFA16CiJ4iAlhlRRWLXsbaOokigVa0MRqt0kftE2hdc2CB9iTHh0/r8bhihFkLgukTMiLN0IRKFxNY10/G4P+G+bx47fLB9/yMlx6Hp0LJEIdEmJ4gMJzKK2Q5ycUyez8hyjW0DPrTocoQUkfX8hHI0oV4vqdcp/2hsx4OHa5TMqMYj9vb2sNZztjjbjLWnt528RwyC5WJNlifhrIw0i7Szu5vK4MJvvFiqgtreAGQ/c+XIMsN4NMV1Fp1rJrv71IsMES3BdzjvaKztQy6P7QsuMYh+ELIv5Qvfl7LZ/BwkGYWQPcFMQiNIeVHISXf41tLtfgH4X4C/u3XbuxEb/jngQ/3xXcDf6X9+3RVjxLpkAIVJUI7OOkwxQUqJKQxKZ5R5Qa4V87NzFst5GlmWMrHYkHYhIzJiTATuVrpecBdiYTavJQDTM/NEbyGkExdlz0DpAkrKpBoWkxKDDA4ZwaKwUaCkIkjVG4dAyxKipm0izrbkDpomDetVVYWwieDcNksyPN52m1n8LkSq8ZTO1mi3Itan6BCIQjHvAlRj5uuGCS1ZlgYEnQDhPcFZQr3Cr5fEKEF4vF9SzTJM4bA4gi+YHn4EFSMxeqTUOLdCKp1I33VHvWiZr2uOHz/GNx1FngOBoBz1OtDYGpkpjhtPjIpre7tkznF6ekw+TYNz8/kc51tiMWK8u5cqWyojdKnCVVVjondIqYneEoXA9LmPzDKESaPkxhi0SfRUZ8t7dHVNxGPbhsZdxrNtg4SHJfqm+nAfJS9TWsltRAJsmu7OOYrsWxi2xRh/QQjx/JWb343Y8M8DfzcmM/4VIcSO6Jl0vt5rSCkoy5JMKnSmOTubo4RKEBroJ0NTcnna1kSfElAZU+yaCcloNKKsKsqyTHqgXEDQVaY2PZFtWMaAjxtGt4fp1e2kcRgVHvB0xphL0Pbh56isNgTiF58rhSfz+RwVA6XOkApcl9AKtrO4GPEB5g+WrM7m2MU5FQGTG+rlGlOMiSRj9k4SXZI/9wGkVPgY8VETRU7brhnNNCYvEEr2hYMM1YciRWaIIuK8xHbgO4h5BVVHcB4jjmjbjpP6EXXTEmMK1zwRWWRY70DvUGpD1zSICEYoTs/P2JnOuHn9Bqdnj2nbRPc18FEMVGDOdXgXEFhcSGFW3bTkVcm4lyyRIkvXQpYhyBmPx3z1nTsUJktVOaE35xaejiAYxvCHc3C1CKDFhXTJNjfGnxQByLsRG94C3t66353+tq9rPDGkeRZpDKiM6WSEIFVJrHXUdZ3Kyv1uYfIclSV8V2qGRYRKiacLHhUCQimKMlV7bGeTuO6VkdzBeNJ7uAhBsl7Eahu+HuIFZHF7Pn54/EB9NTxvnvdc172Ha1dzOt/SrM6RzjKuigQnimkkY7VasDx9jF3M2T884PzRI+68eYfDGzeZ7B2SVxVNF/B9mDFQTdngaTpB5zMa5xOC3KRKX+z7GkEmNs5l0yIluBBoG0+zWuMajwiSmJVkZszB0TNIYVguFuAT31u9XhC8xwWP0Q2xkqzrOiGlO8tI53TrmofzBTY6xuMxVTnaVNQGtHzTNOQmw8c+PFbJoKXSdNZTVposT+8/BI8PgYPDI1577VV8DEiZEbgwindbQl4QfzwNihNiJAg2JXMXAwqRJFj+OSIMnkq79dQ7bjGGXr9+k1wIXNvSujSz7/yFNPt4uouUvUcIjsViQcRjVGrAHezvJ/7kvuLSWp/4ZLo0SJZXJUFeHpbalq/I87yvyLiNh9qQKvaeKHLhoS4ztcQLqYr+hA0MOUPDV0pJXuUEB75TWNfg8cS+coSUjCYVzA33H66589bbjFXGbDzCr5csraVzltyUoCRKpynOzvqkRdS1BOuRyiB06jcJmXI7Qb8pIPDI1CQWEKQkq6YAeCHAC5YdnC5aHp2tMUozmc7IvaWNkdPzE0xZsDg9x9YtSkRMphN9rgsYrdmdTcimE0Jwfcn5wkt0XZc2k0zSrte44MlNIs/PdJ7C5R6V4b2nKAxZplivFhwcXuP+vbvpPPgL3Nz2z8sX3EUesxlHuHKRXmXvucrC817WH9Z43o3Y8A7wzNb9bgN3n/YEcYsx9GMf/Xj0XZ1cutZobTAmx3pH8PDw4UPG41G6kKVgNBqhMoEiJac+BE7PzgCZhs3G02Qgw3wQ8Qmvsz1JmL7guJkZGcKMIVSDixMyGNCgEbN98gYDGgCs1trEIKM1JgcVI0U5YjwqEH3fwnae1geKqkRNp6xmU175rd/lrZdf5tnr1/nUJ7+dQmesbUN9/6sU1ZjzuqEYTYi6QAoJXcvYlKwpWNQd1/YPab1DuFRg6DpHaXKUmaCUorENUSicD0RlUCbQrltUOQPdMDu8xb233+T+g2NGo5Ldg0OkyWm6lv3rJU3TEJxNoNG1xbuOwuQYlbE6WzGZjFBKk9Sre/VsrVBBQtD4ENGZQWSaEAXrtmFntgcy8Q9oUxAFtF2DdY7JbId79+6lIs6maHoRGWz/LmViWR3W9nne/glsGIiGWbFMa+xTcqh3W39Y43k3YsN/CPx1IcRPkQoF598o3wEgRpRzxBhQUSFiQJLI/qSC0Wi0+dBDZUprQ2lSw280GuFiSNUbkdyvQCAFfTinNhp474amvTQz0i9r7WYobxtPFmPcQIkGhp6s94zb5IxDyOaco6lT8zMTjlIJcp0hhcAUEhFiYsg5OEDIDCULDg9vEFZLvvbKq3zy4x9NWK9uTtfOkSJDaMl6vWLlPHXrqSYT2vERs/1DxpMZ3WrRq8tpSqPQKoOQVBAIntjrg3prCXXHYrXm3p2HnJ+eY0SgnOzy3HPPJwiPdBztzFgs5tiTOePxmOAsy+US5QVdTCFd07XoYkxRjFLBQTggcXBHBp5xgzIa7yPzxQqUTQOHQiJlBkrS+YB0IYkSUzHynnJUYduOQfj96iDc1Whg20gSHGjrNik2uW6MkdZ274kG6+p6L6Xqv08qDhwIIe4Af5NkNE8jNvxHpDL1K6RS9V97L29CClCk+NzGmFhwyAiRBBqsqs2sjxKCpq0xPiOEdHE2tqMokgqclBIbEphQ9V31GECqrz+7sS1nMdxvOxRTPYP+wLbSNM1mQtIYk1j6+/sOYcq2MVnnoe9peCGYL9PnKasxmTIIFbHRome77PqMIh8j6jX3X38ZT2S1WhCdo+4CQUtefeNtljZQ7lzDi4xl7Xnp0x/h6PptVu0a50LC5AlPrjKEjGTC40MkEwEnQ6LZdQ2Lrub8/Jys2uHmZI9CCqRrCbbFupr54pR2vkwNSp1xNp+n6qh1SBHT6ESWkWmd3jeKrrM41yKkx5gMRJJbOV+uNlTB0+mUohrTdDblIRHazqZcikizrhP6WknyvGS1WqHlheDvUPQZQuXh9iFnHcKygVF1OHy83CS9TBb/LSQAiTH+u+/yryeIDfsq23/ynl+9XyFEXNdAjBhj+g/rCVFsOt0DZVBV5uzOdsi0RBE3O5nS6QvrvEvNRSE2nGBR0IM7372iMhQPruYx8HRvte15nHObsd9h7Hg4ecPJLctJv/M3+GaBQEMUtK2DvlQaTAHBYWYSERXNyWPGeweczBccn57RrC26KDk5rxnt3+SlZ19k5+g2Mq8oqjGyyPEh0FpHXlQIBa6zoALGFNhVP3VLxLUNy+UcoifXhsP9PZZtpDQau5wDgvv33sHaDmdXKK2wnUV5x2gyBiq6tmVxdorONUVRUo5HeB/xPjIqc8bjEet6jtYSHxwhCMqySLNXPiKUxnrXb0D5Jmz2IZIplfRYRSQ3JaPJhJOTkyfOxXZeM5zXtrOXDGKIGAZP40R8ao6jlEK8B27rzTXznu/5x7kECC3BN1jbobVhNa8xeoxy0JoVVVVRFAV5nlNV1aaCk2UZvicpTEz9YdM8FLGH7yiBD0+O7G4fXZfKm5lWmxMk1UA6aDe9pOGxcDnpFDLlEM4n0kWtDdZamnYNQKZWvRGV6OlOotOKkc77HnwpiZ0lkhGLEXEsWJ0+ZtmtGdkV3eqYPK5pu8DZ2rH/0k3MtWcwh0eMd3bRWuFEYFHXBCnITdIUdSZdnBGQo4wyjrHdOg3QZYp6scL7jsooRpMjHjx4QN0KmqUl373JVCcYzLgqiN4xrSJn58esFqcsV+esbJsQzsUYr3JEOUKWBW3wLBcLpuMCoiXXJilndyuUMSBzLAZEzmxnjxgiIXqqqgAtiZkmOgEStFFMy4KxhrUbCP3TOVivGuixdt73vZ9wkfM8rRckhWHDlCAueA1ijOhvZZ/nT2qlEChH9RfsdLLDet1tdHeKoqAsS8qy3FTAhqrZcF0Pyd+wtpN5ecXdD0nisGsNmjHD2qbsHcKAYW33eQZPab27NFsz9I3yPFWSnO3QOsP7yHK5JOuVDowpNq+nRT/41b/ueDxmbQoW88csm5aR0dROsn90g4PrN9g52Ge8MyPTirprieJiEwkh0DTN5n0P1cXQq1LnhaFepwvl+PiYs+PHyOo+ITjq1ZqbRwd84LlnIUTuvvMmr7/+Ve689RbjPKNulpS5wBjNzZs3uXX9FlU5IgR453iO9w7nE07RufTZpUjlaZ0rMl0AGidg1G+KwXliTAgNGzyhJ7fMtGa5XDAaj8nzkvP6pCd+UWkD8nFTlR0QBeIKCenVPo8U35rL/v1hPEIQe5xXINI2lsmkpBqNCV5SjUYURXGpvDzkF1JK4pYbfho3cTrkEwaQXvriYr1a1hxuBy6FBUMYMLyPqzxgA1FJlmWbMG54/m3hru3XGxDW26+tlELrnDoAQuOlJi+nTK7fZrq7RzmqSDjjiI+BuBWqbG8cQ2gpRcR3iTVI4tPw2sO76CxnZ2ePk3rO8fExZWF49PgdvvbV32Y6nnDr5nU+85lP8ulPfYLf+/KXOT5J6g1ZJjg7myO8pCxHgKSY7lLmhrZespifsG4bSmOQwidlA5EQ3kpokKYfastwISZ8oRBoqQhDf80Pm6LuaatsohfLDF3XJQ5z5MZ44GK4crtRur3kFQjOpQrctzLn+ZNaSpdoFdBakRnLYlVjshFHRzewRpOZ9OVpnbrmMutnWqQgxstz/d8IVr6NsRo8gPeXifO26Ve3+0LbF/v231mWbSprbdsCKawbPGS2wXPFTfPQe78pi2ut6ayls0nY1nctJi+Z7O/x+N4bOFMwufUhbj73Ajs3n0FOp8i85KxeocnxMiJc3HBBbLr0QmwYV6Xw+M5iu4boHcv5OVVVQQg0qzV5Gdg/rKhXa0xesX8wQ0R4443XuHvnLSQC13R89CMf4+b1Q6QKvPrK1+jWLSEkroTF/JSVlGitGE9nSBFo2xoZFOPJmNZZhEzCxMV4Sl5WZJlJuRlA9JgsR6jkTW3XMa7GNMszRuMZZXnM2dlZqswxGMZQzOl7P9nFhjicw0vnn8vGsy1B8qfOeISQKK2JMeADWBc4OLzOetWxWtfs7B9cEEmIi6Pfvy9xV2/v7MPavsivVmm2kbbbXms74Ryg6sNzhBAuvVYypAsoyHa5NA2vaZxL8vZSpkpUQk3IPsTztG2Hb+seARwJXYfoGkbVhNHedVyUHLzwCfafvY3KDa0QWGeJAlAS13UYkUYAtj3m8L0JIejaBUVpKPIMby17OzNe/eofcPfuPZ5/5lnk5Ijf//3fpyymZEpTlBOUlGg5IhOS4D1RLbl//z7z80cYowje89xzz1EUFW1r+YPXXyVRgjUslx5C4JlnntkAWMtS0nYOFyS57CdBsxyrkk6PEhLbdWmEA0WeF7jgkFlOMZ5x+/btJO+idA9IhcF4RB+vxS2SlMHjX74g3u06/FPIGOpDYFlbVBbpupYs06w7y8H1m1TllJVNam1VVV3KVTa4JXl5pv9qA1SIRL86VM8GLwLbYduFZxmea/j/4FW2YTlDTjQY2fZ9N1WjrXkRSSLjU1LjXaQskvRG8AHvA11nU7jiXbp42pbQtAQFrdRU+9dhvMOjZY2xltq1VNMRmckTFdbW97n9PrdDTOsS4UfXJAKO195+C2MKPvXJz/D44UPO7jXs7zzPzRs30MBrr7xCJgX7e7sEa6nXaxAtCEuWwWicc3p8wu//we8Qo2BUjdmdjbn93LMs5isePnzMYrXkjbfvsb+/j9aGg90D4mpNvWrQpqRuHV1n6doanE30XzLD+ZggWDIjRIlSOSFKJpMJ169f5969B70SeSJ/SWvgJb+MYXyyQncBxdrGwX2z631hPEJIytE4TXfWK0II7B9cpyzHKGWYFGXq5/QhTtFPKAopU5eYixr/9trOLayzFwWGp2Cetg1hmyx+WNsVGeDJHe1Kc26QRbE9hD704Vrbthu09baBtW1L2MLUSSHoYmDReGJeUZYVp11D5gITWaJkoF0toW2h79a3bv1EDjaU3K21VJXi/v276CyjXde0bcsffO0VRmXFhz7wIs8cPEvXNbzx+huUWlGWJTeOrlEWmt/89V/jYH+X0BmujafceesVTs+WaKPwOXSdxbo13bnj0a8/xBQjRpMpL3zgQxhdsFjXrFcNjQtM9w7x2ZLFqsWYyM5kTHSOEAN5pglCEkVSwW5biyBQViVFNcXbjps3b3J+vkg5jxBcoML6TfPdXMu3eL1PjEewXNc4n+L13d3dhN+SGbG/SIfddEA1Dyt1/y9+H3aUbcMZHr/N97ZdfRt+bhchrsbJw3Nu9xW2PY2z/lLpenjNjdF5yXK53ni08/PTzXMMRrb2NUpEQtcSQxqMq+saHwQyCKpJjlEgsNSrFaNqnKAsUuMcm+9nKOMPzcGhw75en1FWOWcnp/gufc+LgwOCizx48AhTd0ymIz77Hd/OO2++yltvvMzZ2dt03YLpuOS5F3Y5fVjz1tuvcePmAccnj9BZZL1sEyeB1kgtaZoAIn221159g9ZGnn3+eT7y0W+j9U0iZYl1Qh30CnfDeWjqmiAVWV6RKUPXrRMBiQ9oU9A1qfp6eHjI22+//UT+8ie53hfGE2JEZorpeBelBHmep1heql6s1qOyvjzdu1ogdT+jQF0xnqthG7CpfAGbMvO24QCXPNOQ9A/rquEMucS2wW6Hb4N25kZisL9PWZa0bbspYV+t4kklEUoRBYwmYyICH0EaQ9PVSK0ocsWoLHC2o7ULdBHJ8hGL9Xrz2YaNYDssGT7XeDzmzdfe5OX7X+W7v+O7eOO11ym0wUbLG2+8ysOHb/LSB29zdPRtnBw/5NVXjwk4fu+rv4Zykk99+hPkWnDnnddRoxHXjg6Yny9ZLtd0q46yTDjEk+MzysmMW/tHnM1XvPrGm4ynJdPpDrdu3eLe/YccHR3hmhpCIjgxJiPEdK5tIIl+FYb56clGmxbg1q1bnJycUK8bnvQ8fzLrfWE8gsS1bNskmDsu98jEAHlJU38SgYikMec+OVciHYJeLXsDGnSb5x2iLymBfqxAChLlEmw4E2J0RJKyPNCTCl54vKZLxiSV6PkK/OaidM7hiAidYDzWWoRO9FYuDMZWkbDZEZ0b5BahubUW3XWItp+MNBdFhxK9FUJKVvM1jAwhJA0jqaB1LbrQoCcEZahdROAQ0dE0C6SKdF3D6YNzJpMJ8/PHXL9+kw996CV+9Zd/ic98+pO88sorlN7ygdsz3n7zTf7ff/hFvvO7PktVGD7z7Z/l3t23efDgAUZZfucrv0tpSj74wifY39nl1a+9QrPwZORkI4WPDqUFO9WIfFTRuCV7hzOmOzNkNqVxLWG1wmjB6vyE60eHvHn+GIQklOO+/SDAOYoio2kWCA0iaNwiJk4E6Tm8doN33nmH1vpNLpya40+G5ZfQBMKROgMelbGp1oYQ/vQhDHxIJV5jisQZ4D1ZdlEO3s4PhpBk27sIcRnYeTXketq6mh9dzXGGsG6DoO4v9uF+V0vXV4fktnFTkHoL2/2GIScaXssYQ5Dt5vMPr2GMSfle0/TfT9N7k8h8sexDQ5Wop6JEqIDONOvVkno9R6rIejEnLzTBNtx/55Rnb9/i1VdfYW93xp/5rs/y8z//8xTG8Jnv+Qw/+3M/w+H+jJ3piNde/n20Elw72uf2zRvcvnHIy199jfn5KbvTfe7fv8/rr7/KbDzhB37w+/nZn/1ZWjJ2ZruoLKdxEe8gCEVejCBouq7D5BpUhnMd61UiHNnf36frCS+H71JKyXK53LQVjDHUUtF0SfN0f3+fx48fE2ODCxd4RPVNcK/9UdY/v4Bxa0kpOTk5wXvP/v4+A9BvuBDhoiq2fRtwyVC2a/tXj+2wa7jIt8Ospx3bYdV2CLZtuMATzdBBsW5AVqdwcaBP8nifyBoTk0wSqFJKUI4KylFBNS4ZTSqUlkQRyIyiGpcI4dndnWFMhg+WqioxJj33arWga845fnSXxclDRGhw7ZLm/JTV/ITzRw8ptGR++pjf+92v8PGPfJDf+fJv8ku/8PP8e3/xh1ktzvl7P/l/8EPf/318+de/yPX9PQ53pkxHJcf33uF3fus3OH30gA88/0FuXH+GO3fvJ03XyYwo4Su/+9vMdqd8/GOfYm//Op0VVOWMotphf+86VbkDstj0nwaRskHlbz6f91VBlcSLO8t6XW/GU7ROkKfMFAQkMks0vteuXWM2myGi72eMnpSJ/2O7bv9Yn/09ruAv1A+6rmNnZ+cSXGYwmEuuWTyJkr7qqrfXVeP5Rl9s0zRYa8myjPF4fMkoh/e0DX3Zfp3tUvFwZFk6lBI97W0yHO8tISRPEkTSkBGZQGqJMgoXHVJLJjsThIyYPKMoNbPZOOWHhSbTYF2NipZuPWd++pDH9+8yKQtOHj2kynPuvX2H/d0Z46rg9PgRv/HFL/IX/vUfYn52yuf+9v/M9/8rfxZnW/7Pz/8EP/qX/hL33n6L+dkJ9WKOziTRWt589RXefONtbt18lk9+8tOcni8IMVKORgSpcERee+0dmjbw6c9+NyYfM5sdkudTfJRU5WSjcDEgMY6Ojjg+Pma9XqeiyarB2UCmEhlIWYwwuki6PC5iXcC6fgOVGbdv3+bw8JBMCrztCO4yru2P04DeF8Yj+gG3YRc5PT3FWrsZZd4OcYbm5FDJ2r5wt0u/g9sfdv5tb7VdGIDLFzywUWvY7tlsc3td6h/1BYyhYrRdJr6A2KTiwvB8w+/De1ZKbZAB29U618tEDiiB8bhEyogQEec7hPTE6HAuJdtNuyZEx3q9ZL1a8OabbzIajfCdoyhKfv4XfpFr164lQhIh+Omf/ml++C/+CN57vvTl3+av/OUfZWdnh5/6qZ/ie7/3e/nA8y9S1zXReV544QUO9w9YnJ/y8tf+gLZt+cQnPoE0OZ6sL0FfwxQFZ6dzXvnaaxzduMXu7j6d9zgvaG36jpbL5RO4u6ZpNnjAoSDknLs0kZtwjBkoBUKlPo+1zCZjdmZTpACjeGKju7q2z9H231/vMU99nvd8zz/GNeQVTdMwn883MzCJ8LB7Knx8u99ydT0tWdz+/SrM5mpp+ur9t+d6rn65T/MyTwsdpRJ9sUGiMkleGEyuUZkkRI/zFtsfTdewbtY0XUNrWzrX0bkO61qsa2m7FU2zYrGYs1ies1zNWa7OeHzyiLpe0XlHZy0PHj5mNN7hbL5mMtvngx96icW64/kXXyLIjBvPPMfP/X//jP/8v/ivuHP3Hr/5pa/wL/3Z70ObnF/8pV/h7HzOv/Vv/whn8zWvvfE2i2WNygKrxRl3796h6Swf+egnmEz3qSYHBFGwt7fHsy88z9GNa6ybmrv373F0dMTOzpSzxXyzaQwb2rZnT5CleGkKNcsMeV7ifeLiy3SO85F1T/4/bKDPPPMMWiX22G92/WE91PuiYKC15vnnn6eqxgmlbMNmyMw5vwEMbkMuNp17KVFXULRPQxlcDde2jW64bRitHvoil5qo8nLetD23k44n38P2T8TFa17NmYb3E33YKEIM8ufb71e4um+oppCyblY9Ct2R+LcLrHPszXYIAcrRjLrtEMowme7SuKSNc/vZZ2k6i20b9me7fP4nv8C/+e/8CH/vJ/42Dx4+4i/8Gz/M53/iczx48IBf+pVf4z/7T/9j/tkv/hx1XbO7V5Jlmtb22qQoxtNdxjPNw4cP0Sbx1a3bhhAlxajk1ddfJTM5h4dHdE0SLivLkjzX2KYlU4LxeIzry/tD0WD4joa5qBACaOYzTAAAIABJREFUeVltBhGFyBiNNPUqEp3i2du3uPPWG98UceH2efhmjeh94XmEEJcu2CGsGKosVz/UEMq9m5u9euE+Lc/ZBg0OgM5tVMCwM171JMMaAKVwoch8FaGw/RghUrglZQJQJvGuxPlmTEZRGJSWlKMCnWf46Ah4rO9obUPdJrbUpklaRc6nIkNSIMgZjUaMpxMOj66T5QXFqGI8nYJUHBxcYzLdYXfvgPFkxr0HD3n+Ay8ymkyZznZ56SMf4x//k3/Kj/3Yj/PSxz7O3/+//m/+1e//AXZ29/nq117h//kH/4DP/gvfgwv0BQ7LaFSys7ND1suoPDo+59u+/TvIc40pDBAoipwskxwe7jMajehcy2QySWQtfSg6nJ/RaNRvRoLVKhEveh+p6/bS36vVis55EBfh+GSSuBn29/eZTqff9PX3zeTC2+sbGo8Q4ieFEA+FEL+zddvfEkK8I4T4cn/84Nb//kshxCtCiK8KIX7gvbyJxDdgyHSOVDopBxhJ1AKXpW41IiBk4jSwru1r9CL9b/gwfY5ztaAgZSID3D6cb4k4ED6Jy/bFCLhASF86bJoWjT5ASEyfrrME58mkIleSXEm0ABUDKgZk8MjgybjwVEMOVhRFr26gNka7PbdUVVUyiPE4cdKVJTrXCCXRuaEaTSirKVLlhKiJGAph0EEwzktKk5QA8lFFMZuw6BpiuyCXaXZpsVwznu1BZjg4OOCjL32IX/m1X+bZ55/h9u3bfOlLv8Xt28/xj3/6n/DFX/4SP/m/fYHv/7N/jrfvNMx2PsDZeSRQ4p3k5DjxGrzxxmscHDyH0VNG1Q5CZOAhQ6BlxNUr7h8vyKtdJjuHOC9Ytx0+BkyeYQqFjB06Czhbs17PE8m8FJyv1izqBhkkwguM0BDAekFnI1k5Iaico+deRGc1gpboA1IYfFRpq1KJxJEoN0cMaexBiozgSYp873G9F8/zBeBfe8rt/2OM8VP98Y8AhBAfA34E+Hj/mP9ViKujSU8uIVKoNOzm255j+4LbTvKftrZL0E87tgfFnpb3DIn/8FxfbzcaejHbHuzqPPw2cuHqGnK57XL2AK0xxmymZreNaTKZMJvNEi9ab1yz2YydnR2qqrr0mcqyYGdnh1FZkknIJIQo6JznfL6ksY5qMuX09JRHjx4lVTzgK1/6DT7+0ZeIwfHFX/0VfvRHf5QvfOELzOdzPv/5z/NX/9pf4Z27b3Pjxg1EH75GPPiES7t//x57e7ssl8sNikPKFC2UZZnwcT2WbzvCqJsOKbMNXm1AEwxeaogGqqraqKHHnrzQkwo1psgp8pKja9cZpksvzqXoufnEE+fp6t/vdX1D44kx/gJw8h6f788DPxVjbGOMr5OIQL7zGz1IiCTMu208V3Ocp+UJV97n5ot4wmv4C1nx7Vn27arcNor6acfVdWmI7SmP2z4R7xZabn+uIQyFCy3Psiw3HkprTVGNKKoReVmh8wKdF8hMb/4eTycbGfflfMFyPocQKIyBEPBCkZmc0XRG27a8+eab7O/vszObcOPoCNssOX54jzdefZmPfPhD5Drj6PCAH/sP/wM+97nPAfA3/5u/wXPPPMt4VOK9QyvBuBqBCNTNislkwv3797l1+yanp8c4NxR8PEJEFudnRO9YLeas12v29vYoxylkK6vxpkk8NIeHKGDUD0QWZYXJCzyDXIxCygyZGbLMkBnNiy++lKRStMLalkGeZDAgFy8fHnHp7/e6/ig5z18XQvx2H9bt9re9G2PoE0sI8R8JIX5dCPHrp6fHl3KM/v9P5CzvtjM87X5POwac19BDGp5vu7mZZlHqJ4zv6rpqPFerbdvv7WnGM3jawaCHqtFQIh/+n+cpn5lMkmEUVUleFug8XSgyU+RlQTUeIaVC64vdOriO5fyE9eKU0iRK4sePH3N+cozWmuuHB5w+esA7b77O/TtvcrQ7JVeC3//Kb1GZDK0k0TueuXWDv/U3/mv++//uv0Urwc/8zD9lfnbOZFzRtg2ua3BdixKSrmsQIvLgwQM+/OEPJX49NRyK2STxVZdl8qpN05ApQzEakeU5zz77LI8fP8YYQ9M0nJ+fp9Fsa5nP5zx6dJyMK8+JIg0YWpfOT2YMRTWGmHHz5u2+yrn1pccMounlFt/9eK/rD2s8fwd4EfgUiUr3f+hvf8+MoTHGz8cYvyPG+B17ewdXkuvLowLDz20k9GAQQ2iwnec8LdHfDseG34fHDj2c7f7RVU9ylWFyG2UNlxknt4sG2zM124+92vMZPsOQe20DPDekfCZHaYPShszkZCYnL6tEJayyxI6a54lJCE9uMqrc0K2XLE6PeXz/Lgd7u4wKTWiWNItTbh7ucvtoj7MHb/PaV3+X1dljJpXh977yJb7vX/4X+ff/8o+wXC5Zr9d87nOf48d//Md4eP8uv/RLv8jXvvoy9+/e4/HjR1w7OEBET9vVaKMwJuPu3bvs7OzQtKkBKiUIArnJyJQieAsyhZKZzrEuhXOz2Yz1es10OmU2m22ikgEYjEgc3ZnOKUcTTJn0kBIVryR4wWy6x61bt2iaNUmSBZzzOJdG1pNieRr7R4pEASxF4uN+j+sPZTwxxgcxRh9jDMD/zkVo9p4ZQ7/J17v086pHubqe5rWeFoJtV9q+UaXlai529b1t//+qIQ2vP/y9Xcx4t/U0b7VtTE8LcTcCTyLxACil0CZD6ySG1bU19WqJJCJioKsX1IszcuHRMnKwt8N0XLE7GRG95+WXX2Zvb4/zsxNGoxFvvPEG3jpGoxF379zhi1/8Vb77e74TJSR3775zaQxdSkndrJCyb0jLNFWbCdAyeaJh0+i6jtZ6imr8xCbI/0/dm8ba1qT3Xb+qWuOeznDPHd+h3x7jofEMGZAQSiSHBCQrYlACJDFCspGMBBIfiPnEF0v5AEGRkkgYgZSEoNBxAjGWJUiMgzCO3W4PaXqQ7Xa/b7/Tfe9wztlnD2uuKj7UqrVrr7PPvafbDrpdV1tn373XrlVrreepZ/o/zwND1zalFFGa7UGhjAFrdtJfxRFRlGCM4fT0dC/x0TGW++vqHjjv3vj9bce3xDzCldj1488A3hP3s8CfFUKkQoiP41qNfP7lE95saxwiojBn5RARjiPIoeu4X/8eHg2uFwQZj7G95Oc9VNvNM0tofx3KHwoRCf7YUHKOR121rk2k2qUvSxEhRUSk+nuidpVLy+0G3dTEArJIcDrL0NUG0VUktsaUK568+zW+9uXf4sFRzjtf+222VxcU2zVprChWS/7O3/7b/ORP/iT3753x6NEj/t7PfI7/6Md/jCiSfPqTn+Rv/LW/zvf/wPdSlxW6bfu4k8PrHR8fc3l5yWIxp21bsjyhrgq6tkaYnYTelq6baNPpARPoOwOuVqu9Og9x7ECl1gi0tT4rBY3rlWq0K3bZts5O+sxnPsN8PifLMib5jDSduLyjKHJtTZSCHsWtraW7BajYj2+1Yui/KoT4PpxK9g7w4z1BfFkI8TngK7jGhT9hrb1uMIzPwYhZwh1eCKSvySb2i24MLdS7/dybUEXyqlA4Z+iI6NeNYF/VG9s5oZfukFPAq3x+XaEHzs8XqnHh+mAfKxeuLRzpJN/7vLPO0+TWr4j6HVjYbnA4oBvKqnBdw1FEWOhqymLD++98nbN5ypP332H6+n1mk5Q0y7lcruhMR9cpptMpv/xLv8SdO3fQBrJVzM/93M/xo3/hL/L3/v7PcO/eXf7GX/vr/Lv//r/H4w+fsF4uHSTICjIBKlIURUGeZ6zXa0zZISNFYlxxxqqpybIJ2gJdx535nFZ3nJ9fslwumc+P9u6lK/hvQTpkSqQE4NvC9GEK3fbtTRru379HozuWFyVCZg7lLdshUdA/f28Th3Cvl41vtWLof/+C438K+KlbrwCXNmsTQWs6IhUBfS0C6VoPKpkMxwqxS1WwxtK1LhtxoEchMWaXlu2Kbsi+rYXLX8eG0kghpCs84Qk5lBShLQQM5Y/8TQ5VsZtqJCiliESIRjCuLYpXPawBqxHS21LQ6QapGCLucaLo8f079a3v+WOMwUgXdBRKokSGtZCahu3zS6ivENsVH15VfOzNRzz78F0yaXj9SLPdPuPkdM6z9RYjBFVVMp8m2E6TTTOyPOX86pzPfu8f51d+7QskqmWaTVk+fc6/9a//m/zC//mP+NjdO/zM3/xvOT6Zc/bwY6TmjNIKNlHOvUcfo2k7lE1oKkkauSKG69WSyWyKlRKtO5I0RqBYblakeUo2jdAI0kyipEV3kjjOaNqtq6gzm2GEC2hHsaKqXSvHummI0ym0NVkiqbZLZlnGKqrRUtHJCEODFQprBEoIpHXPMu7MwU3rpvFKIAzgMAoADqtz42PHnrGbHA/hKwSMHrphL4rT+JiQV7M8XCeUOGPnQ2ivhKiJFwFZPerCM6f/3P8udHj4Kj1CiAEj+MEHj6nqmrpqWa23pEnEb/76F/jow/e5Wl7w/rvvcnn+zHWsayoevvYGVgimswUyTphMpxgsGsGXvvQVfvhP/qnBTvnKV75CnCi+53u+h/Pzcx49esR3f/dnef+dt1ldXDLPJ8ynE8rNmjRPaJqKs3t3aNuW58+fu0zRvqeRv+Y4jnswcMbZ2RnWWsqydN7GxXQIKUwmk2uOIr8ufw99FaEocfGu+WyGbjvXJQ6J9Y/UuvfWukTFbzt4DrzY5rmJeYA9wh3n3LzopQJV8NAY20DhcSGRh+cM33v9XAixq2cdBOJ8cHSXshDtqY1jtER4Tti1OgEGwhMWYuWAlqenp3zHd3wHs9mCqu24c+8+Z6dHfOdnPo3RHRfPn2FNi65rVstL6nLN0dl9GmJaK8gXCzokcTbj3v2HnN494//+5f+HkxMXlUiSmC984fM8e/aEH/oXfwAhBL/6q7/Kv/HD/xpf+o3f4Dc//3nOnz5hu1lSlRus6FiXly6uk+fM533LzD4Q6gOnZVFzcXGB0a4eeNM0rNZLlssly+VFf25XpbUoimHzUEoNWEBhQUax61OkIuIoYTabESlJqlxjMMdAEhM8fmM0+uVWxo4Obn3kP8/hVRvvjnyBs8CPMQIgfB1ilkNxmNAjdmh+t7R9poWdrRUGXr108J/7ndGDGv2chzYCYO/4cF3hPQljUoekmY+l+M/LquHozhkf//RnaDVIYanLLW++9oij+ZRZnvHg/l2OF3MePXzApmq4/+g1kumC2fyEoqwpm5psknOxvOTevXs8vPfQNRw2DavlOd/z2e+kqyuKzYb7Z/f53//Xf8i/8kf+CF25RZqWJBK0zZY0FZge2Hp8fDwAYP2987lc6/WW9WpL23YcHR1RVlvHTKYbVHaPifNpClLKIXbnN6FWa+I0R6qIJEtZLGbMspimWPVEb/pWNgZEh6V1L9PcmmxfDeYZSZJxjMaXbwJHuL7bWgiR8Qwx7twWEugh97F/HVKfQmbx8JnQFgoDpd7gbNt2eB9eyxiys18w0e4xT3hNnjhcx+h9KeSxcf7YuGcgKSUGiVAxTWd58vSc2fEdTo7vsDg+5t69e8znR0gZUfXz1lXDxWpNnOfUbcOde3f5Q9/1h3j06AFluSVWkq6p+LVf/Tzf+z2fJVKCJBb8L//gc7z5xmt8//d+H5fnF6RJxLtf/13K9YoPvvE2j9/9OnWxpN5cIHXNdusK3q/X674YZDfk9jgIz5TT0zN3z2VMlmW4nrXpgGP090lKV1AlSRLquh56oDqQbwpRQpzPaDqD7TSnixlSN9A5D6TQDUpq2mqDMCXSVkyz2+fzvBIpCeMxTo9WKt7bcT1SwNsEYbHDEOAZSi8pD3tRhu8DB0BIxH5OT+ShnRNKunANoWfQD69uHZI83kYap3l7Nc/nvJhu34t4XT+3CGux3nupHBzn9N5Dnn/0Hk83S06OZjz56D2sinjzE5+gKArm8yNXRGN2gkSjbMuz549JfOaubSi3W9ZXF0ync9555x3ee/8d8jwlnyT84i/+AlJGnJ6eoddXtF3DX/jzf47/8XOfo+1qtsUVb771CabzGTJ7MBS6d01/p9SdptUdjW04ObpDWW2oyoIoFrz11lv81m9+kTt3ztC63kNldJ1T02zvYEoSB7DVdee6YmCxVoJUTGc5mZI0d4744OoS3dRI3ZIYjYoNkywliVOOjua3ptNXknnGKQBeJfLvQ0LzO32oyoxbShxS/0LJAyBHtlQoATyo1P/On89/P4bj+M9DBhs7JQ6plELtQ5OstXtG9VjNG6uboq+EafuoeWcM0gqaVnNyeg91dodnjz/k9O5rKFMjdMtRtnCEi6V49hGnJ0fEyrK6uqLQLcJ1BiNRMWkacf/0Ef/si7/OD/+JP84//oX/wxX0OLnHH/1jf5TlcsO773yVYr3icz/zOT7z6U/w9tffYXt1gWgqvus7P0sXdcRScnx8TFlXg80qlETJuE8/cIlvWjdImfLGm6/xzjtv8+DBw0HiAnSdGq43vD9KKbTVWOFayCfZBL1dEym4e3LEcrukw5DlKZNpQqRyJmmCVLBYTG5Jpa8o88DhoGX4WahS2cDDFhLqi+ymcazFmuv1CLyqFbqux44GL5nCbmRjNWzs4AgZ18+XJAl12+xdR7gpwK7658uuy3qUcZIhlGJ+dEyxXmKM5f5rr1Otl8zSBbouaLsGoQ3pVDFrtlTFikmasuwqYildsSxj0V2LFZJ3332fo6Mjfvt3vuokT+5yq375l3+F4+NTslnGuthydjbnOz79KZ5/9JhIJbTrLe9/7XcxDyJee/31oR2lFLpv6hu5wpFNRxRJpJJ0GtbrNYvFgvffc0AVHyAPU999gUVwm40wrsmZxuVQTWJJkuduU0kqHt49xXQNSaqIpEVJi5AW01XQFrcjUF4h5jHa9xt1sR3wMR0Qqid0a6Gv2Wax+OCOiqNgHjOUW5VCIPsW86EL24+QgYYSrZ5BlQQsqo8TYfZtoVC1M8Y4dyd9xBuGfj2urblCiV3Kg2caP7wEVX0bFNt7npRSJAGSwnvsQunjr8NaS2Wla45lOqRuoSuxbU1brZnlCVk6dZIijjFNyXQ6R1rNk8cfOHT0nQVPqg1F1ZIlJ1TlliRxqRBd78atyisePvgk/9c/+SW+7/t+gC996YuUxZKTkwWf+sxDfu3zv0kSpVwtl/z8//Zz/OEf+kF+4ze/wGa15vLpByQXS07k95Defcid4zO2laFtNE0LyITT0y1l3bCtNHVrWMxPaY3haHFKXRScnL5G23YYk1A3tauhpzVX6xV3zhaugEockxIjMQgLxWaNkJDkCza1YTqLqKs1eWYwpkDXBRiBbjSmRzvcZrwizLOvvnwzvnZgb4cObR7YxYBC+8irfXsrOLCTh1IgUmoPajNWycIYwVhNCyVlqKoduo6b4kuhFPPfHSpI4mp3SyQKYRSGiMlkxuZqyXq1JE0Tl04gDavlBVhNnKYU2zXr3vU7m0/QZUEczem6lidPPnKt7Cc5qYL3332PNEl4+tETNus1SRLx/NkzfvWf/goqyrh79y4Xv/McKV2C35MnT3h4/wHWWoqy4PLiOVpbkmxCU7TIbMHpyRFF1XK12hJnOZN8QtuVLJdLFpMpx0dzNsslVVU5LFzlgqLatIPjYGdrukKG1liwLi5UliWbzcYVVEHT6cgl27WOedqmwTQMKuFtxqvBPOImA/ibnGakzoSEFZa/PWgvjJgnRGyHXjHftMof4/8KdThmNKwpIPqb4kvhmsZOBa/Lhxi4UHWUUjqAl2sBjhSWaX5MW20RmfNIVcUl6/Waj56uwLbM8xzdllyeX9DUNSpqaKoSdEuSp2yvliRRzMNHdymqkqoq0J1lubzkzTdf5+JiyaNHj1ivl1R1w+XlOT/4Q3+ML/7WP+Pe/TPOnz3n137t1/iu7/ounj15OhDxh9/4Pd63b/P4g/e5/8ZbxNmKCNPjzaZYHaGF64xwsriDrguSJGWrO5ZXF3St4eTklDhKMdYFW03bUJYVUaR2UlsplJRURUlZluRphrUaGUUoGbOuGzbLFXEkaOvOZZ5+EwiDV4N57H436hfFdw6NEOAZ/j50Nx+K5YTjRZLHGNO38hN7gNSQeEPwafj98H/2medlsaXwr2eekGnGtqBX9wTGJYgpQaNb4jTD6JZUzEG2qCTGdI5JVhfPHZPNFyRJRatXDq5iLHVZICUY01FsK1rdkSaKbV0Sq4iT4yPef/c97t27x9MnHyKEJUtTzs/PqfvKnwCXl5dkuUM5L5dLsklGtTqHKOEbv/Oc8ycfMT26w/HJe5zde0By701kDFk+ZTbNaZqa7fKK06Mps8mUJ8WGs7N7CAGbzcY9H9sxnc2cNy6WdK3LXrV2Zw5MJjNiJXokRorOWmbzEy6fn1PXFREWATTfRErCq8E8sKcO+Z19IA52xB/CMHZG+S4/56adY1wwcew69jEjn7k4Ju7QCQE7lPXQyryPcodeOD+MMejAAxi6u8du9RD06lVNf1yYIxTOPcSthqZOFoEAGWEQxEmMSAUqctmeTVkwSZx6dbW8oCkKrq4uacsSJCwvzzmZTKl7o77crlCRoNCayWRGlk6GdTx7/pTT02ParqRpHDrgrbfe4ktf/iI/+P0/wDe+/ntUVYUxfUUkaVFpRJRkSKOhLXn+/tuIruLR3SNs15LnE4fjs86DeP/hA5rtislsitkWVFVBEkNVlw5NnriYUBS5ZDylYsC4e4DrBG6tJktS6uoKGStSmxNLxWR2weP3vs50ktI1BWd379yaZl8Z5gl1+lCS3LQLjwlvPJcfY09deMxN34XjprnHaQ5jVexlKmhog/m5Quh9qKKNHQXhGsL7Fg+bTn9tPndIShCCOJsRxTlNUlKXW6q6wtqE+fHUVcB51tJVFVFSs1pvqa62JEqSxilFsSXOImzX15irShazKQBZFvHh4xV5GqPSjEePHtFphxh48OABRblhMpnQdR3bzRXWWnJAWEtXFyxmM66ePeHXf+WSu5/e8slPfQenDx7SaItNEq42K5QwpHnOdOYYIU4UqnaFVIwxrNcbVARRJCnLou8A7pxKSrhcp6pyOUF1U9E2DZMs58HDN7l8fs56c0mWKcpvN4eBZRfo9CrNIelw0wh34kMG9yF76qb/j13J4zlCFSrMBg1REWOkwKH1jwnfWlfLLJSqISYO2JNEh5g/orfNRJ/gJftrFAIjQJoUi3YR+0lK15Rk6YLV1TlSTfnUp76b1cUlFyKlWl6y/OgZle5cJ2thWZ6v6HRNns0wnaVrXFRfSYikIlYRKo6dkb9YUG4L11KlKZkscpqmIT+bUWwrjIGi2iCFoFiviKKEWAgu3vtdppGmaTbkR3dolGJ65OA8m65hOs15990PhjYmnlaSJOm9tAYpI6wVaGMwuiVKE6SQVG2FlIokTfGFFXUnOD27z2bt+iUVVfViYg3Gq8E8dgfu88wzRs16QvGespBQnX677+2Cw6DO8Jx+hMw1Zh7/ffg7LxHcA/NB1sMJeOO5wuE3CL9phNCekAk9cDQ8v5fQe/MF+roQDolhcS53AGUVAoWKFEY3KGkxtiZJ5lTllt97+12ySIGImc+OeeP1jzvEdbmmKDdEIsLIjEglQ1EOgLIs+fgnPsZms+Lo7mt0TctkmrGUl0wzx7Dl1tWdq0sHYUryCXmeo7VhnuZYJFVVEUdbnj9+h9VmzfHrb3H88E3Wmw1F3xkhFoL5fEZVVZzdeUDX7p5129U0TTdI6zRSZOkEt7caoih2pYmFIY4ThJUkWc7du/fZrM65vHxMFMUvoNT98UowjzCWVBtiJRCmQ1pXiMEgXZafDgoQKpfrEsvYQW6sxbu6b3LxwvVOcCF4MyTEUGLsqYZyH2Hgq7sMkgWwgRSJVW87dRqhIIp3rmWl1NA8y6toTVNh7T4Ozp/Hn8N3096LTwWSCNM7JHpGDiUVQNfHPbAGpSR1VSFtRzJx6OPX5COqYsO6qtiUBTpN0K1l3dZoYH5yyuW2QE3nHB8tiJKM9XZDW1Too2OuSsUxHfPjCXk2ZXF6wvnFkpP5Av38OW+89nFstaIqtzz/6ENiCYoOqwuIUrJpxsZoNusLzNUFSQr3jyakyT3SNKexMaZ4xr2TGU8+eganC7S1IBRtB1WtsULRaUuWZUSxQmBRsUJ3HVZJ2tpgZYKxYNuKfJ6BaHn9rU/y7OKSjumt6faVYB6EKwxhjCPCWKk+vVaAsdfgqyHR+3EbN/chSXLTGEuyQ96xm2ycQyqfT9Dzn3ldPSyDZfSuOImXRockYghNCs8b2l3hmoffqp3L3BjDbDajKTZsNxuWF89JraBtXUMuoVyKcqYysnsPWF9dsN5sXCpzX3zeM+iDB8dk04mLg3VrjMbVUkOyWCxoGgfs3WxXVMtzZpmrCNSWrmWisYIOibYOHOoBtlZ3nJ+fczc/xkQKFSeuI0O/uVRVBTLBWkPbatpWI6Md4t0lIULbNpigX5K1so8F2eFvkiRMp9Nr0K4XjdukYb8B/C3gAa5G7E9ba/+qEOIU+J+Bt3Cp2P+OtfZSuKf3V4E/DRTAj1prf+OFJzGGzeoSlcREcUrT1sRpSpKlriKKuA7V8eqNLxf1shFKntDOGMdxxs6IYdfW3TWk8/4l7DxhnsDDudteLVV9sHUcDPZqlv9teC4/vI7v7aAx+lyyU/XG1whgtfNMGt1B17Ktt5i2RqiI2eKYclWQTFK0dTt3m2W0dUW5umB6cp/juw/ZFgUqSYmiiLv37hElMU+eP8NYwZsf+zjtZknTVmzrBoqa7XbTe9lc9Zq7Z6egNdI0RJMMbVo6belQCBmz1h3zfEbXdTz98EPWmwqhEhZnr5PImChSlGXLZJK5unRRhpARBuWgRFqQhmhz4TyoRgjX4kVIGl0BrpWnNQZtDXGW8ebHPs47b//eS2nJj9tIng74z6y1vyGEmANAdCkmAAAgAElEQVS/LoT4R8CPAr9grf3LQoi/BPwl4D8H/hSu8MengT+MK1P1h190Ams1zfaKpEuJZgapMqyWtLU3fq/nwvgxrldwyDkAu1YenrD8nGMJM5ZoOyY57AX0x98U+PTE7NcVShohxIAuXi6XJLGr/6yUGnoDhUztjx9f+7Ch6P0KmKFdaK1Fmx0iASCJMzqc1JciIs2OXDwrSmmrLVE6Je0aFqdnpHFEU5Us+rSCsqkpmo7N8orJZMJkPnObQr4gSadMsMQRdO99gziS3L97h65p6foCIDaO6aotSjqJgzFYYXj44B7L8wswHbM8ZnX5jLe/qvnUZ2MmqcJGEoFlkmV8+OSc2fwYlaRk+Zwo6rNCdesklxHYvlKPu399IqONEdISixjd1cRxitGujvaj19649gxvGrepYfAYV5sNa+1aCPFVXCHDH8EVBgH4m8A/wTHPjwB/y7on9CtCiGMhxMN+nsPDaDaXj5EqQa0yVDpBpTnTxQmT2XyvzloI0PTxmduoY2MCDgOoY+bxc4UvqXbvw0Ds4OkKgKGhw2OQSEoOjBMm0Xm8m1Ku2IbfMUOJGjK8P94YMySADcw1wPN2HsHw/yoSKBFBpJA2Io4imjKm0w1adkiV06xXzE6nmK6hWC3RbYMUlmySIauKan3BnaM7aK0pyy3TPnV6ebV05XOTKdJ0RLEgV5LTu3cpVkuaqqbYrjmezdl2jolbo1HCYRPn0yPmJ6fUQnI0mxNJxTtvv00T1di24Ok3vobqGk5ee931DEKQJhGLoxnWCMq6xODK8QrpbD0lQUmBlNBZVzvCGFerTWgwEXTaECcJSiYUwL1791/EDnvjm7J5hBBvAd8P/Cpw3zOEtfaxEOJef9hNVUP3mEcI8WPAjwE8uHeXentB21my+TE5rvqjsNCUFWoxG4gA2FN5PCEe8miFbt9wBz4UaB2rbWNktmRXmwD2oT+eoULmAfaY1DeN9Z+HG4EQgpOTEwTRgBQO7aHQJTvOUg1VtEjug02vbxAGEfWSWUPbdYg4IY5i4tTFJecnd8BorE5dC8OuoSoKLouCcltxdnSHuipQccI0TdmuV2y2W6I4J04z6gaSJKOut5wvLzie5dT6Eiti4nxG1VkaAxdXW6Z5wmQ6A6HokDQGam2IJhllWSAt0LU0bcNlo2mrmuTkiCxO0QgmmcPXnZyeEakYi6TcbkkSX43VYHq0vENiO9S6QZBETs1rmg6jG/IkxljBZPbPwWEghJgBfx/4T621qxfs8oe+uKZHWWt/GvhpgM9+5pM2FS1pnlBXW5LFKV1ZsZUr7t1/RBcQdhgY9YQ7Wuc1lSpkutD2OZS0Fv42lDxGB5H8IB41JuAxQw3udLXLPvVSJWRUrTVG72dKelUzZLIx3i6sJadGCX9h7MyV3wqOjXeub3++OHbr6Kx1+f8ywkog6shmEXE+BRryJEMIS9c2zI4jHjx6jbqp2GxWSBreffcdklgxn03okHQioTNQNQ1tseX0+JjPfPYOm/WlKxCfT0nyGVYpsiTn3Xfepi7WnMxPODm+w3vvfcCmaqg2V6yWl0yncxZHd+g0tMsNVbnl7O5DWmPRJrBvh/tv+voGrqOFsjGd1dhOk0+nWJ0Q9YQ7nf0B5/MIIWIc4/wda+0/6D9+4tUx4YogPu0//xaqhlrK1Tm1USzOHtE2JdPjI+I0o2tayBzS9RBw8iYGgut1Doaz9QQbqkVj+E3o9RJCYPV+A+DQpTxe06H3Xddek1Z+HaEtdkgCegni8XX+uzA2ppTqy3bthmdS72SIFShchqkzppWrjqM1AoVp2z6v36Jh8OalmetMIDCslue0XUMSR+RxTNcqLq9WdF0NFnRT8vrDB8hIDZ0g5qf3hnrTttxQFhtXCiyekOYxZd2wWhVYqchmEW+89WkmaUS9vuS3v/JlojTjJJ1QNS1d27LZbJjOFmRJzHSaU1clZbnFiIiqbEjzzCURWs0kTdC6pdhuSdOYfJZRtQ1GGyKlEBJM19BhEdJVIL3tuI23TeDqtH3VWvtXgq9+FviLwF/u//7D4PP/WAjxd3GOgqsX2juA7jqyJEYYRVMW5NNTV12/rEjz2Z4oGzNCv8ZrRn7IVKHq4wl4HIgMS0T534ylkJ/XS4pDEm3svvafV/UO1e3XETo7XAG/dPgdsBc09XUcQsnjazkMWbQjoe/bdXgVTuvGuXEjhdGWVrsazlprOqOZCgFBJSBh9xP7qrphtjhyTXOtpqsLVJTQak0SJ0ynE0wiiLOMrutYXq3JJzOMgSR3lXDmCt5/9xtU5ZrUGKJIcrI4Zro4oeks67Kh1jXn7z+FtuCNNz6GlJLl5RXbsuL58+eunFbTkU8XzKZHPD2/YLvdMp+fIPuepEdHR+iuwXauVG+SJCRJvNvwrCRJnaYjcN0FhQD9TXDPbSTPvwz8eeD/FUL8Vv/Zf4Fjms8JIf5D4F3g3+6/+3mcm/prOFf1f/CyE0hhuDp/z6XDHt2nvviILEk5vXeHKBds6qaHgbgd2HS7bErhXCjDXHtGekBoIbOERntoE3hVC3ZI6d3O3yHwOqlxyH+xcxP7AKkJ7ColHCrZz1NVFZPJZKixFqqhURS5pweuIKJy/WakctJBxREq2peqDrJmEX0mpQ42BwDUTl/290NbjW13RVKkw48SI0E7NVD1NmGeLIY1WmtRaYYSQU5U7FqDnE7uub6yWhPFp/h+1Eczx5hRr14aWtbbZ5h4hjUxVbd1+TVxTGliZCQ5PbnD5mpJnRlEOmNdrkgiTXaUozI4yyd89NEKuXkCeouxDYmuqTcR+fSEKJ2Rph1S1qhEUTYGawVSJuguouzACueQaWqDkhnWdiAVZbX9g43zWGt/icN2DMCfOHC8BX7i1ivAXQwqpWgtm/MrZqcTzHqLvVwzISae5wCDhEiSZE9XD71foTNhbP/47w/ZJ+ExocoUeujC4VHCUroMUTP6fqwu+vN48OeLhv/eOzZ8gcVI7adJgDOEjTFDvtJYLQw3Gd3t9yAaw3vsyIXvy0P5zUYphTW7eJVfa2h3RSbe6+3qGzV7LKBNpuQLyVGS7EGPjDEURcEkyWmNJccQR4IT7rC+es7m6gIZ564Xa1lSFAWzRcOxSMjzKYXu0G2JIaJVKV0HxrjU8Ww6c4lzWlPXNbGKXSsUY8kzl5EqhGs94yFHtxmvBMLAIti0AkvC6ekD1GRB0Rq61Zp5lHNnoof4Rhjb8LD9pnN7nSdyTzRhXCasuhLaFuMROgXGhBoOb4wP3rURw+4hB3rVy7c+HxNtOMZud61dR/C2bUmTXZFFz2D+esLYVcgwe/ckUD8P5RR5NTD0HvpjtdauWHuyX6vBM+6gogpFmk0wxtC0HcYKh68TCiEgmR0jkslAzK5crisQmS8cfChZHDMv71AXa7brS6Yn90kmC6pii9Ab3njrLZqyZLVaY3WHaSo26yu2RcXHPv4ppEh7G7EkzzM0lrrH/UVZTISiLLegDZAM2aOme/GzGY9XgnkQijc+/Z2cHN+j0xKiKWp6TDw7xkQJq9WKo6Mj8jzfQzL7hzcuPhhKk9D+CdEB3h4IRyjNxlCX8bGh2qWUw04Nbu1+/jGRJcFue4hxh9sRMKBSaugQJ/oSS+EavWs7VEn9CAOxQggiuWOmQ0XNx5LX22c+YDudTtFtvatQKlxpX3+f3XPQTn20BisFCEk6yUn6ORMVoWpXQqquayZ5EIaQlsvlsrdJFCKZ8OCNOxSbK1YX57RE1JcrjDHUdUPXdTz58F2yyYyHb3ycpxdrqNdsmhhtO05PT6naCm06ZNTfU9PRVK6lo2l7kGrkaMLXnbjteCWYR0iFFQmtgcXxGUQZyewUG6c0SNLcYan8DheqbWGcxxODL9c0zB9IAtgZ8odUtjA+sg/R2W+gFRKrR1gfqpcdlt/1Hbf9zu6lyOC9s/vpC2GcRgjhQI49vCeUNp5By7J0rQd77Fkcx8P6nfqnrzFYeO2I3Tm9ZAoZzN/7MMfIl4IaNigbIYVFJeoaAqKqKqyQLrvVGNcWUu5qbxutmR0f9yczbK+WfOP9x5SbNcJqIEIbaDu3EcWRoikrmqbk8vH7bDcV7xQbZq//C5ycnlJv18hIIWNFUZXO7qxrFqkzA7TWZGmMFDunkr9ntxmvBPOAa5dRliVN+xyijJkWzE7OmM0XWLUfkxi7kbXd4bx8uVVjnG/f49/C7mtj4gwJ0Y8x3EbrHdo6JKhQPRvbUYdUzbEL288ppevOHH4Xzmeti+OM1VF/X5qmYbFYDKDI0Js3rCnoGRiuc3ixP+ehXdgYvcd8oVR3jpO+qpHceSvazuECVZwg2Tl0Dt3zVneuUpEQJGnKo0ePKLcFAkO52dKqDV1dUa4uqbdrjhYzttstui04mWWoSBCxRXYJtdau5txkijIQxQmd9Ckfuy7knnmSJL92vS8arwTzaKMpioJ4lpBnrvh2vd1Sa8OxNUSzo6FLNDDUKg4fekiUIYRnz5jtGQt2OUShDRCOcD7//zH0JnQqhOWAx6iFkCHGjBP+xlcd98ddj/t0e7/xROx3zNAOuo1a6McALrW3UFnsfhGTQ1IsfIVICWNcjQWsq7fgPZTgskqxljiSvYYBkRJYqzg9OaEpC2KpEPldPnrvvd75kLBZXbjPhaDarplOZzz/6PdYX+RYmZBNj5jqO0iVUtZb4iRDCMfMiXJSMpJicCZ8M+OVYJ44ilAIiqLg5PgunQEVO3GruwYVqC5An0TlVIemaYjTZI8pDu38YUcBf5yvspKm6R7gEq437A2lVOh589JOsI9B82raTQjq8P1Obdv/bLzzG3NdWvj5fdHz8B6MVTQvWUKnwN6a7M1pFuH1ju2qvTrfXu1TCqfsgvKJfMbggPk9VEg4sKY7l1uhFfSBWkBKolhxtbxAGEtZFNTrJXEcUxhNXRXO6JcKYSy6a/jo8YfMXnudk6MZcTohzY9pDMRJymbbMJ8uiNO+O7exdG1NlDgHw9Vmc9A5dNN4JZjHGsssn5FNpxgpmCyO6VCg1EDU/iGFOrknUNcsdtctICT8EN7vQZX+t6F+K+x+XQIlpCM267JEjY8NBFLPtz2fTCZIIoRQCCRGG1pjADk05rK4NXpv25hB3K7cbxL0CW2yB7F69/oBVW4fg+eZxb2UcnkrWhusdQTjHR1gHQrZOkK21qGuB+YREMXy2vmM3m+VAiO3vO2dEr0jw99v20sWI5QLfEiLCUtoid5205DEU4QEtEujzhYLTNuhlaCTr7NdXmBVjUpais0WaDGdptyssUKiiyWbC0uULNikFTKZM5EZi5OF6yIuKtrOoI1ByIii7lwek4iJJ4tb0+0rwTxCSFdls9GgWjo25PNjJtMpKo7x+/zYE+aZQ8lorxrnoV1+3M3NSyPPSMbue+kOjbGBr5RyrQKNYT492lNnrs1jdtV5xmpc6Ih48X3aTxcPg7huzn01b6zGeiMkVDdDFdTPPUZMhIxySCqGv9eB7edHqDr7etyhFA+9oENGr9VuCwjOK6VE2B5Zri1tq0mynEgKjOxI04ZGG6w2jvFMh9Kuq16Wxi5Y21UIuXN4hKiRPM8H7+FtxivBPMYaQCDjyKn9BrZFBXHNcT67xjyw34LDP5BDJaNCAvJqlA9wetUuiiKHoWPfEzeeJ8SZedfzbDYbHBNh8HA8/JwhKNSfLySQF42xZzDcSNy6dvcptOlUL8F1UAvb3z+/Lr+RjKWhH55Bu3q/VnZoNwoh6EaeznCeMLZ0k5Nl2ABxNpGzWyUax3xZnrJYLJilgpU0rJ9XNG1HsS2o69oxZBODsSRKEqcx0yxFCU3XlgjTsqnLYTNNkoT1ej20a/m2qxgqhcRYQdN0xNKiMoW2UNU1m7IgnR3vEdl4F6R3y3q1yI/xDu/dkWmaDu+hx4C9PIt7Z5v0jNN13RB76toRkHTEfFLtgKBjF7C/pttInkPH74j5cCxqMNYD5htLPeCazTRWmQ85QcI6d8YY5GgDCJ0azimi9+YYg12HDUG6Rs6CHlWiIqJIUq01F1VFtdmyqSpMlCJlxASQ1rDdrFAIrO5oqxrDllo/RSQbpEqI0xSNGiRMkiRMJpNB3c/z23vcXgnm8RmOaZI4g1EosjRDRglG4yAV3q0YeHl2BLHvOeqCgGV4nHfj+l3WR86bpiGNe9skkG5+PiHE0L/HS54wBbxtW4c6HjFpaJT7fB6vpowdEqGHLVyDn8dnl4aBUdg5KUJvoVdLB5V0UI/ENbvR/z9JEuh2UKDQHR7ej5DIx25qpRRG7zpKeDjVnnQJ1hwy6NiG88f6Z6Wblu12S1sVnJ6e0k1S5nnGxZMPKNdL1yxLOPU81650ru4ayvWKeZyhhIa+hFZVVkMdOY/+SJJk0EJuO14J5hFIV9x7MiPOpxStIUpSksmEOMkgioeHOZZAh1zKnkiLohh2Fo8o8DugZxop+5Z8+sVelnC3HRNMHMdgrkNiQgY9kNJ0qzGWtv784fc7L99L0svZv19jxut6FHd4jmvq5QF1NhyeWUPnTHisB/f6jeJl3i3X+NepY/P5nJqOrmuoC1w5qsnMEb7QLJ8+Jkti2qYhKgvSKCJNM3RXEZkEhMA0miSZ7zmNHOI6cQXkvwl39SvBPFI5h8Fqs+UkmzGfH2GkpKk7ohiiAMt1kCgClSTU6eM4RmvNer0exHJoKPvdvCgK8jR74RrHxBu+4jhGtzuVBnaqjtehQ5Xnmx1jl7K3U0KEgZO29hpj7b2CetmewUMJ4iv6hLbIeB7Gc44YtRthD69fyz7m7zbXHscxnbGsVktUU1PWFeuiZLOt2K5XtOWGennBLEmYpBKjFUIqV4gx6gs+0neLsx3byt2/PM8Hm7VpGsqy/INFVf//May1ZNkEZA/pMJo4zpFRjLYWMYJ5hL+z1mK02dv1wyCpr7oZBkxDD4sn/tusMWTQUD+XMqjO2Q+lXOlWz6Q+HXhsM9zmvGPvWXit+yrUzRAiP1cY1A2Dl97OCGtDhMZ9qHaF//fENpZMnjnG9za8S7fZTNwzdOjuo6Mj4i4hqUpMH9BNkwhdT9mImuL5R2zLNdZEpNMZKnNaSxxnVGVBay1RnGJlNqzZOw686nZ0dHSr5wKvCPNgDfX2OdPFHXTX0G4LBBHzNHN1DDyc3V6vkmmtRXl3a09EPi7Sti0CyJIUFUEcuZpgXdsMD9YRjcUaB0s3WriqK/25pIgQQiKtxXa7BsLg4jGiZ4pI7daD7Y1iU1MWFdPplEjudPv+Df0kgPM4ChEAZLwzoD8PQmBMB3bXK0hYF603xmD8vekdKP0ktHXQ3VlI2tbbM14NNmhte6dH6P0CLxhc/Moda62LX/n5rTXDcq0Fo3dxMCmh65rejujTOkTPQLKH8ngGx30u2AWgjQArBEYqGjS61Sgh6KKE5GhB3DXU5QpjHWL96OFDLp4Jik5SmhZbrVC2IpMdVqZYIpq6YzFVRE1HOkld/eo4Q5CC7bDdtxmq2hpDsVkj44xMpsjI0tYlm7ViNp8DO1xayEDj3S+MnI9VglA98cZhaHjvrSeQXn4HDt3XL9otQ7UtSRK22+3QuPYPeoxV1fD8h3b1sR3jr81L50TE1+Y4dM6xChse771vXqpJ6ZIAvbs8lGaha9qred5r6Ye3QbxTw3T+eQsmkwnZ2RndNOPdi6c8e/qY08WEpnQdEeZHM2QSUzQaFUMcZ6AURjscpY02yHSGtm6zzeKENE1vff9f2slHCPGGEOIXhRBfFUJ8WQjxn/Sf/5dCiA+EEL/Vv/508JufFEJ8TQjx20KIP/myc/gbW202nD/9AJqCVAmU0NR9m/GbjGDv9g2dAGFx9LF6Ez5gf8wY8za2W3w69E2vUHUKz+lL5Y4hLH9QI7TfxsHOQ+cJmSfcYLwXMlz/TWrl2N4bv/yG5J+pEGKAP4UIDj+/j68Ae4zk15nn+eByb5oG3XXE/fNaXq744IMP+eDxEybzBZ/45KepG00UJxRlzWrjYj9CCCIhURJiKZnO8kEaRlFEHPsES0Pd/MEGSW8qegjw31hr/6vRzf0u4M8C3w08Av6xEOIz1tr9VMvwBF1LubkizXIsEVfPHrv8kTgjjhT1aMd/EUrZ6+vjhyDYxTHC5K3QnRy+D9MKYF9yhQw8tjFCh4W1ltls1q/pdoHQb2aELmH/d7y+8Qg3Bn+8R1vERNeYYTxe9F1ol4XnDmNM42c1fkVqv/5diARQSoHuKMsSo+Hs7C55pKCref7+27z7wQfEMmG9KRDRLvZXNRXaWFTdYYQkSWtENCEWgixLsCJis9kgLYjbBPz68fspenjT+BHg71pra+BtIcTXgH8J+Kc3/UAA6A5TlVgruLxy4L/J9Iimlugo3YtPjHcwf3O9RBkTvTHGdXYeEdOeRLLXkQnheUIPVOh+DVEH4Zq8MepjHUrt0pU9uiFcw3hev4axwyBkcP+Zl27jFiTj6w3hS16ihupp2zVkWbYn0fw9HLxpvWQZF3D0x4RJgv77sFCjR2F4STSu+FrX1TUG9e7vpmmILUQqgbij6TRJ2qdtT484u/uQxx++ixGSs5NTOtPQalfPLooUeZbRNB1ZnqPSnCiJaJoKlEGbltls/k0hDG7fgNFd3Fvsih6Cq5LzRSHE/yCEOOk/u6no4YsmJk0iluePMfWKSWyhLXn6+D3kiChv2g1vWO/BdAO4Hj8Zn+M2u7d/P/5/qBId2mlvUt1edJ3jz8Y7+W3GIdV3rFKGDDlmxLGtc+jaxufxUt7Hw0IVzKNBDj2LMFs1/L+SMQqBMdC2mqt1QdVoRJxjREI6OeK1199yxeOtQwwkaURT1Tx7+hGma7m8vHDMbDvqpqRtXandKJJsNqtb389bM48YFT3E1aD+JPB9OMn0X/tDD/z8GrUIIX5MCPEFIcQXLq9WrC6eYduaZnvFPI0ori6IhcWib3yAwVx7Esl/FuLfri3oJXMeIpyxFPDHhYTnidHvqH4X9+8P4eZuGiED3ub1smsY22Veit0UbxnHfML17ANSr7vPQ2nqzxOWFx7H3Pzvsywb3Nv+OP87L/GMcR7AKEo4Oj5lcXTCZDpnfnKHN978BGVdsy625PlkAO4mkeRksUBiWBzN6LqGzWbDZrNxZbKWF5yfn1PVxa2eDdySecSBoofW2ifWWm2dr/K/w6lmcMuih9ban7bW/pC19odOjxbOg6I7mmLL1cVzTo7nPPnoQ5qqHiAj/sZfu4jA2O/Xu7dj3QTWDB9ymELtVZJwRzzEaGOiCqXOIclzkxT0Y7wGP+dNjPKtME/IQN7REjJDWA9i/LtxsPbQuQ/do/D6vRQKQw+hKhf+zqu8IVNGalcAxQrnctcIhIqI4pRtUbtWJdmEunYSRTftoNYeHS2GNWvd0jQ16/VVTyu3i/n58S0XPRT7xdv/DPCl/v3PAv+TEOKv4BwGnwY+/6JzWGMQrUYJSZQo6mLLZnlOms45f/J17s7uEXcWpUApCZ2/qX2ei3ZVV4RlVwKqD3dI0b8R+0QrZS858JH3vmwTHuZiEAIsHdZCZJSLuQjh7KPOoDzxALZrXZxCCCLh8lNk/wJLa3fM4P6G99i9rN0nvlBqAUgzZuJdYLK3Toad2iOS966579Hp9QBjNPQVQkW/kNAOGv91176PmnbM7WvISYyWexWKfHA6PN7P6W0Zf5yUknLb9flHoFRM12r3MK27/41QdFiMabG6Jk5cyrbpJKu2wuYZi0YRRXO22y3LZyvm8zlZlrG6WnF1uebo0Rt0bY0UiqlKkaIj1g1KJkO+1m3G76fo4Z8TQnwf7lG8A/x4f3O+LIT4HPAVnKfuJ+wLPG1+iP7Bda0hy6csl0smd6c0TcPV1RXGGCaTCWg5ZCti9yXDofEyVW/3/uZ1Aa4QYaCqjV3bh2yUcA1em32RjRLaGf5Yv86xrTSe55A94hHLL5NOQwq53PWAHc81SLuRVh5KstBe8mNc42G8Bn/NYUUfD6Ua16oAhrSDAX7VVTSNC8QeHx/z9PFH3Dk6wlrLarUaUNJ1XQ/OgGfPnpHkOdPZgjTL6IygaSpkp1/4fMbj91P08Odf8JufAn7q1qsQkKYJdBG6c1CMPM+JVEKWTYY4jL/JQu4yE8e6/GgdO+IeaUvXiG9klvkdcZBQI+Icn+8mA39YA/uEeBND3/R/YwwyYKpDw9rdOULbKrRvwvGitO2xejhIQXO9+OP+OncMErqY/fnH1+0Zyj9jn1cVritcjwPhalrdDsUmoygC0/L06VOePn1Keix58uQJ0+mUkzunzpapKubzOfP5nEk6decygqYuXWsbK4ljyXxy+yDpq4EwsK6LQBTLoV6z1pZpkpGlrvGrD4ImSRAFv8HgDyUE9I2tXsA8LzPghRBOuQuO8y7xIU/lwG9C5mFEiOMKpH6d4fWM4fzhzh1KpN3v95krBJCGtlm4Rv+ZlBKjr0u20FaTUg6YMj98eMAfqzG7xLkAwXHThhG6tr2kCbN8fZAzdLZ4ZkvTFKyk2GxZXl6SJAmf+MQnWL77VV579AbGGJ4+ec7x8TGvv/bm4OGr6oLF/JhZlmNwxd27ztA2FZvltxkwFCxluSXt0dVHxyfUIiKOY6bTGW0fGwkJ1TNPSACHjFf/ucdghYw19iA5JrleIXRMtMCepyn83v/2kOHpjwlru4X5SeF7b0yHsZBQfQudFDsI0fXr9mv189/k5ABQcj9F3EumUJoxUi1h32HjDPGdpzEsvhLed/+9vz6vrrXdDhsXeuRCdbD161WK7WY9xI/Ozs64fH5OOpmyXG8QQvDw9Tdo25bnl0uSJOH8/IJsMUPMDU0fUzLacnF+SVmWHPu6cbcYrwjzgLUaYyFJYsqyREcxHz1+gokXnMxfv1bSafe727l9/YMa6947Btg/fic5zDWmDM/rPw+LkPhzhecI5xh70G4aocfLWgd4DVAfSYMAACAASURBVHfwMP/fMdpupz90X0IJNMbrhdcyXtd4Y3jRGMOVPOMO3rKg7YlfQ3hP/b3za/OtSUL8orN1LI2v4R1FLmerT69uuw4VRZyenhInCVmeM5lOuby8REiXbfzs2TPSyDksqqJ0G0XTsLk4f+k1+vFqMI+1GKvRGoji4Saf3ntAHKVcXFwwn8+ZTqfESqJGsJRvxsgbxzp2D+/F9oYS0d7vx4Q3FK4IGDR0BV/zfAU76k0bwNg1LMW+1AxLazmC2//+JgfBOH7j5z/kXAjfG2NeGtsIMWz+XHvqKzupFnoTw01tvLH579vWtQuJlUR37VBrTSKoii26R3BPjk9JkoS6rinLmqIoaJqGyWTC8fEptWmYZilZllGVJYmS2E5jm5I4uj1u4NVgHixpGiP7/PbT0zs0Yu7wYD2E3oMXxzu4J9KXjTAHP3yYQ0FEta9m7Rn71iLkPpHfZPj634b4OiklZmS0h4RzyG7z8+z9vSEbdUfwIyfJiHnGWLjQVhnf2/C3oXolX3KrD0nZUBqGzCuE2EsWdDAp9iRTKHF2FZBcG/muZwirDaarkWlMU1Y8O38+wLXyPCfNM/LpZLj+PM4QxlJst5iupS5L1lcrNqvlt18NA7y3pU9l3m63zM/u0kURZVXRrVaDapJIMRTRg9urbTd5y24i3OtLvA4IDcc4XjH2LI2Pv420HKtOvtlUeE5vSLv1vdjVeoiwv9W13TQOee7Cz8Na14fu+/g34TUCQ04Q9M4GYzGmrxmBGQKjHpC7Wq0Gp4aUknv37nG1uaTaFmy3WyS272DnHBTRy3aHYLwSzCMsNJuCSIBWIGJJoQ35fM4sO6VCohKJUa7deGcNCIEkwsp9AxU4KImkDtUTiIRCG93bOgIVwIAkAonsS2J5eInfAa8HL4VwAUKfZ7Kzd1yhQWtd92UAhevQ7BnBqYSuwWzV1dck005SunX64oVad72naudMGNtIY+L0NpFncmPclUvhkv4QBiEFCJdcJ6Vz4Jg+TKUiMThpxnbnTpLbPrHN/XNJfhKlDrcuEUIM6fLGGKTqrwOHPXP/bwapJ2WE7a+9rXYpDHEcUxYb1zysKHjy/BlSulpsVgqiKEEbw/uPP+Ti4sJ5bpUkixM26wvodK+af7upbQLiJEMi0NqgrXUXrBJEHBNnvbs6gI4IJAhD6DkNDenxkIEBf8hmGe/C1z1K13fjsc3i5/Q73SFvnn8/duGGUXmv6oXGtbUWEwRaD20Q4fzjOI/bWLxENDv0+QuEbujdO3RPwuNukt5xHA/o8nHOEFy368LWLP7cHpYVRdGAGNkhKVqs3tV4S5IEXcmh15Av8BFqBr70WCwFaRTzxhtvUG83FNvtrUwAP14J5rFW0BhQ0hGIaTV2U2Kna9JoQhKng9GulMJYu4PKjHbxm09y3Wu2bwPtfz9Wuw4FGP1O6hvXWmuHlol5nu95naQ8jNoWASFE8fXeQ2PmCZlrPMbZrv792MsWfk4v0Q7Fwsaeu2+FecLfGuMKvYdgUX+9Y1zh2Cvq77OghxP1qr7riyRRsaSyu8xYf5+MMWy32yGe1HWd6zOkNevlJc+unrCYThB6l8R32/FKMI9QChtlA65ommQYBG2rSdlv2d51HSpOdwQkRd/hy42XPcTw/7fR+f0xakRZXnp5j5e3c4Brtcjcg3wxOsAjCLyzIcxf8nP5OgUhYRxat/8brslJNt89IZBMdude96Wrxtd+k+cuPGb8PhxhPyV7YBPyjBIWE/HPO0xctNb29cP3UdtKSJJIUJXboQJOiEiRUg5FDqMoYrFYuKZWSnI0m7NeXvLkyRPqqvr2KwCiophsdkRTbN0Oow1pFBMFJXF3O5VBGFcQ3JoWGSnCvWKchzJ8ZnYB1X2J4O2XfZiPV792v9ntmF7lCQOdfhedTqf7Ozs7T5GfbxwgDXfg8W/89UdRhNC7vjZhABGuR/DHkiuMnXRdvwmpeCh24gnbAznHaPSbNpqx2hWu3+/koVt8V2h+l6IxZhp/bJhO768BC0nUo7K7FqNdxdfnT8+5Wl7QlBVJf798YcOLiwtgvx6FX1+apmR37zLLUqqyvFUsy49XgnnatqM2knx6RF0VWBGh4gQhXDWYsdrgbB7rrHcjhuZEL9ohx2OsoiH26yaHwTq/Kx/a6T0TjGMW41RwT9yhsRwGbv333l7yIMkwlhOxzyDj9YzVrPHwDXqFCLpJWIkQ/jf2pXOMPx/f7xBS5NftbQ2lFAQODq+Khe7opu6Gz4ZnHah21his7sBqdNuSJhHC7rJNq6rCSnedZVlireXs7Gyvxcxms3H2kHSe3e3qinK9AruLnd1mvBLMI5Uizad89Ph9RGe4c3/hqnjK/ZTjQVwHAUFvr4ylzctGKFVuYoqQ2MOWhKHUCKEv4W+AkWRguA5/XMg4URShxf4OHwZerd2BS/0YX+tYCoy/99LMG+VpmrtWJsOxv3/mGQdf0zQdDH6tNZHabTDGmMHT5lvFeGnjXz6N2xiXhpLmEwcMbSo0sF6v6ZqW1dUVUkqHKqAgz/Nh8/NM5M8bxzF1XVPVFW3fn/RkPnPB+m83yaOUIslyjk9O2V5dEcdOZWvZh6j4xzTe8dHXYfEvG55oB68N+y0IvZ7uzxfaDx6zFUqWEIQZethClW5sxPvz7zxrO4PZ2zY+VTlU4/w5xrtkSNgvs1EGyRrUm7uN0D7EkOEYSx5/Lwa7yu73hh1LHg8EtdY5X3xg1DNRU68R1tWhtn336jiPSOO7bDcrys2WclsOktVXMAoBqEXhquropmY+mVJuN3SCa0XqXzZeCeaxQpGePeDDp084Oj52uwmXzM/m6EpjZgKtQRuDlh1xpsAItHFNjUwTFgSBsf/V7WARnXExByEFRrqH3BlDFKfQV6Wkf0kRIfoYkhHOIyjlDqIjZdRLFtdAKrRbQobaEbGzmxzhe6ZziWSOiCqEtS6Rrr8EiUWKvh9N7Vy11riW8EqAbvfrKluxr+KGUtsYHTDqfnqAJ1CJwui+FJdQ6O4AILa/Bzc5LVACY5xzw0tkhMQagzGWtvPPSoE0WK2RAkzbQNcwjSSr1catWRuElBgj0H0sLZ/NMFrTVQUGC21J25aszp+xWj6nrRumqqAVmk6lyDRHJAqrWyJapLLcnU0x8ylF3TsRkiNM3dIVFbr9NkNVW2uZz+fcv/+QSSR57533mEcRVsBsNtmpNyNngNYuE1LY/dJPh3ZdY67bM6F6NiaS0Gi11mLsDvgZqmtDIpnav5WhpDlkyIeASU/ISuyrqf78bn61R6jjdQJ7iGcvUd21mz0nR9eNS23dvOOGKqkQAinsntt3D+XOvtrpR2jfDciCkQQL0de+TLK1lrZpiGNBlLjNqq6qoSlw01TUmzVtU3G1WoFxhRDTJKdoNE3XYlWFUoJJLCiLks3yAmEFdduwrUqnSgqJbTtiocjjb7f+PH0A78HD1ymvlkTZBBEnGNNxcfGc/OTM7ZhBDx5pfYMq50mDEXR+NDzzhCN0KQuu483COX2/zpDJwsDmoXETsYeer1C/F8Fn/q+3h5RSDgQZOCfkgePDexraaJ7YnSqk9ySQGGcKjsaeU8Tqvc9C+y1k5LG7POxMbozBGreGpmkwfbHKqqqwbUXbtq4zujYkfesWr8bVdY2xmrYsqLf/H3VvEmNblqVpfbs57T33XmuevefveRNNRkR2oSRRoWoQqKSkVKIZFAMQiAmgkkogGCEQMKETDJCYIIGEagIFkwSlhCghGFGUEiQoShRR2VRmhkfj4eH+GnvW3O50++yGwT7n2DXz5x4vyQjkcSSTmd3+nrPXXmv961//2iF9BA+qakmiFZlWNNawrBJUmpAqibcdZr/DjdJa/b5GOEc5UoWCdbTGYI1l+Ann4vh4G8XQXAjxfwkh/q6IiqH//nj714QQf0sI8aEQ4r8VQqTj7dn4//fG+7/6k94jhAAuYCyobEFSVBhr+f73v0+a3JEW5zrPA4XQ4x3+ixLdz3vcQ6913PowIV7HxFS4z55+k7Eev+cX3XZsQA+NKU3v5F+NMbMUk7X2bgrakTBK3/cMwzB7nOPP/bBp72EO9pOO4/NzrMH2WRrRm/OuY3h4qmM9/PzHgMF02+SpptcvshRNiJoR4+0+BMzgsM4TlIa0JFvEMSL1YcfN5SVts8OZYVQdjcXWqshZLUqqRUGZpeAtXX14q/MBb+d5euA3QggHEVV0/nchxP8M/GtExdDfFEL8F8BfJspR/WXgNoTwDSHEPwv8x8A/8xMuDQRJmuV4lVCtz/jw+z/g3fc+YLAd0lry/E7ZPu6w444qmOeJThfpeJzgcc4xLayp+n/cIzR5lun1j430OLmf3mMqvk2f6TMs6iPQAO6Pd5+Kf8de7CHyN32uaWjw9JnugRzhwZiQuY5zN+R4euzx+z/kAB57xYcgyLGH8T5W96fvMIEW9zYc/9kp29PrTsiaEJHc68NdfjEl9V3f0LYx4Rf+btiWHWWXFbG4abQkqUqEc6TpOrYjiFiQ1cGBM0jnsN6wLFL6zrBpGw6HA9oYPIHtbseuPpDphMPNhjBY3n367IuX6tHxNhoGAZjMMRl/AvAbwD833v7XgH+PaDx/afwb4LeA/0wIIcIXbHGC2HYtpSIrSx49SRm84PLyEoDq0Vdo25Zk3InjYhkvuhQMbpiNZV5ID/If7+084Oo4fJhRJ+7Puzl+vhDiDmuGeSGM52e89c0s6uPcZfIAD3Xd5vDqQdHzWHJrMp6j63IvbDw26MnoJwOd/p+Ged3LGaVEyvvQ+ueFocff6/hzPAzXjg1m+lzHod30OCnumNKT15y+dwiBgEBPG+B0HY3BhIDpe6zpWa6qqC5b5jPU7Hc31NtbpDPU2xukjMTaarHi5PQcs9uBlCRZVMu5fn3F6fqczetrdvu31217q5xHCKGA/xv4BvCfA98HNiHMW8exKuisGBpCsEKILXAOXD14zb8C/BWAZ0+fxt4dJfEyIShHbx1N3/Gti3MOh8iWnRaEEozjF+Mf0svZcKbOwqP3mRfvNJ7xeFceP+c84gM+q/18XEQ9Dk3ux/qfD10f/34YLh3nDROBcvrcU9L8MMy7K9zez8MmT/Km1me4a1QLIS5WpcRIz7nTULuPEL5xLdz7//OM5/jcHz93liYOMQ/VWs9yYW3bIn3Md7z3pEVJmqbxXIwGVWiNEIGT1RJrC25ur9n5zbw5vXz5HNW+wg0tKRZ8FEnsvUCmJTovOMszHPD80ygn6AZL2/YUqxXB/JTRthB5778uhDgB/nvgl9/0sOkcfcF9x6/5V4G/CvDtX/6VcTVOu6cmzTKKIuP5i09YPqnmnXOqJDOxnAP3Fv0xKXC6DZi7CSdFyuOLfhyOjJ/t3i4Z4db7taTjx8X3eKCFcLS7Twv+OFc49o7zwrt/fuahwZMHOt7Rj73Y9L7mKFx9SIuZNpDJG0z3S/Hmhf95x8P7j0O0EML8JabXOv7+wN1ndneh5zT6sus6Lp+/whgzjzk8HA4sl2uEEGw2G/b9bmZGA1xcXMTRi13sGL24eMK5TBm6PbdXl0g8QShqA52XWBfzIyfhyTvP0Fqz3+3Y32zYH2qqvPzC7398/LHQthDCRgjxN4E/C5wIIfTofY5VQSfF0E+EEBpYAzdf+MJC4KWIuD8WPdI4lBPkIUM0l6gyEHQOQTAMccdSEsLgCGmcbnzMPYuzLAfSNOVwONC30XslgnHwq8GOHDoFGDkSKImjEHtjSRNN6waKtED0wwyhJjJhc3tLlmUopaJu2HCIozSynOAD+11DYKThJCmJj+3j0+AtqRSeQO/t7EXFcD+fuOdxkJRa4L3EjZtE3w2gNEprpJCk+o7a6QIMPnKQnYeAIJET6DIwrXmpAOGQSiNkQMgwF06VmjxIABFZHf6BbYlxqlfwfhwu9mb0cfbmTiKljmib0Bg3YE2PtZ4QBFUesF3HYXvg5PQJi7TEdD3G9ghpWWXLuPl4gxCeYXtD6waa/Y7BNIQQ+Hi3GfOxk/n6H5oDzhlOTnKMAuEDqdL4voemJgkDRZmx32++cKkeH2+jGHoBDKPhFMBfIIIA/yvwTwG/CfzzwP8wPuWvj///H+P9f+OL8h04clnjbtW2NQpBphWma5CFpqsPeG0p1knc3Dx4BCFY7JhUT9pfkweC6HHatmXwsEgyRJJjfPzq1gUCjjIvSJTDeUtwARUsqbCYw4G+bxFdzkJ6rHOYvqcZPcir53v6vqeqKqpqgek7nLuh6w1ZGbXBpEoIpsNY5t4SoRXeD3PT2AR8OOPuAQcTQVNrjVSS3kT1Uk/AhTi2xA0Gmfi5Q3NSGZrCGJxjbk97gOZNl2V6j4ch15v+fnhMeeMxaHAcxh17R+89BB/1x4+8Dt7hBjlHF1prqirHe8thv2ewlt61WD+MnXmAt2RZEhkL3uGFJC/ilIN3n75D27bzYLEgJCdn57Rty8vL12gNRZ6jBfzww+9yUi2xg0ELyWpV/SSTmI+38TxPgb825j0S+O9CCP+jEOLvAb8phPgPgf+HKMnL+Pu/EXG0yA1xVs9PPOQoe+TtgMTj3YB3A4P3pEZze9Vz8vgZ2CFy3gSIIMF7AkO8GAQYQyI7GlPbtnRNQ7o6JagUG2KM68cRiYsioe0NgY40UShnCM7T7m4Y2pplldNvbrjebbm+vkYIMQvqZVkWe+h9y6dXz/Hek6Q5J2fn2GaHHXd8rTWJkPhB0jfguBNvnBbc4N3syaYQp8wznDN422G9p+9HXWedgoycQB/8HW8sy7BBxHmEMnrERCqkEgy9wQdxz3imEG/adB6CJG9jPMfF3mgk93O8Y6OM7PY7dValFP0RPC2EoCgKNrev6YxjuTydhf61gDzPELaO8roepAqR6UHclG5vb3n+6hLhW9q2RYiokbBYLGjbFp2lvP/Vr+C7hv1+y+//7u9yvl7Rtg1KgnEea36K07BDCL9DHCvy8PYfcCfufnx7B/zTb/0JpucdKb9E9KWlbxsUAV0LdL7A9h1D2qEKhQjqXpJwnL8A86495QxlmhA8DC5SQpy1SDzWKvq2Y7VMkEHQ9z3G1HT7W8LQcVNfs9/e0m031HU9D+kNicBj6VzP7tqws7G6nZwm2L6JNBQVF2U3SJioPeForAd3NP00TbGhIahpVmhATwheGLWoQ1w0wg3gFUKDVgLhA/Fhd6wLNRI+nR9GlU9PCJ+VmnqYd023f97fD4OIY2QyGtJ9qtLDfMoHcZSnxo1lOPJYRbFAiKuZwCqFxuNIUkGqNUJJJAJjPM4NmC7mkFmaUuQlrvJU5Zqmae7VooZh4Pr6OuZfpkFKyfvvPuP2+holBErKsRzyc0YMhYkkOODcgDMtpmkYTEPfGqQa8PUelSboLCfNC/CWYRhrBolG4NFKYMfiGcFjh56m3lOWJd5Z3CBo9y2nywqDYWhbrm+vqKoK29R0fcfli0+4vvyUR+sFly8+pt7dYvoW39SzZKuUkk+efzRfmCzL0BfvQgLmIDiYBqESehMNt6gq5Fgpn4qbbduSSEVRFNgO2mFApSKqwozIUlmW95q3ZH4eARWdslyuMa1DpQlhpNs4CxKBDRahdMxZRk6b8JZhuKsDCRHHeUzgwrGXOb4mE0AxHQ+BiCnMvPv/uO37PrF3es1o1J5h6EmT5K5fSQjavidflPSD5/b2msViiQyeYMG0O7RoR88ZyJKU5WIVPxegBHg78Pu/8wcMw0BZllxcXFBVFV/74H2+/pUP+L3f+z2Ci/lxvd+hpcT0LcFLFIGLi4u3XrNfGuORQhGEQwRHsJbrq5d8/Effpcoygl+TFBUZnkQG+rYBpXEukCSKIPQ9qHe6cF3X3XVl9gf8oChVoL7+lO3VNcI7EiXY7V5wWd+w29zStweCN/zudy/pmx1KBsCTBEGeaA7bzRz2TLM2EylodzeRpNjVFEWBsZ5yUbEsCoa+Zru/vqu9+OhtpBcMdaSj2N7gXBynMsnCXo6EzbIs43coTqPBqoQ0z6hWa9I0R6jIyjYiI8kiN8sJhZzGc4xeyJhoBMc1p8lbvW378TG693me6DjveRPMPoXX3quZIXLHgtBcX9/S1C2nq1PKsqRt9mxur+iaPVrGpsJluQAhefX8xwTrsEPs9RmGgYuTZcx1QqDd3dLtN+xvXpMkCV9//xndZkHdHFCAcwO+iMVbM3Tc7L4Y2zo+viTGc+fih2Fg6A3BDey2N3itefSoRPoBZ3vaw57yNCdISfAWZy1pGtukvXdIoGtqQojaXlqOu299QzP0vN5uwPR09ZZgDGEYaOo9mQ40zYH9fkvX1kgJbjAIERdXnqT3KvzTBZ8o+GWqKFKFDAOmsSzXp/Ttnm17GPtYwmw8OE9WLWNu1XXUdc3QG9rD7b0i6rRL2118DydekCYZSIFQCa+CIM0z8qykrBYYmVBVK9JyQZIWOJmATBBJitQJg9P3IONp8X8RP+/hcRymvek4Bg2mazr9DiHg3X2u3HBEcdJa44lzXG9vNhwOB7JUc3665mxd0LR7QtPGtgXvMYeGJOr7IIWjMzXNbkciHUWeUxTlWCvzuO6AqR3d/pZC57SHGhEcwjuapiHLE7IsY7H4GUHVP6sjhIAde1n6vsfZnlQnRPFGz+3rl6SLFYuzR6zTCtP35IsMnef3aicTy9cYc+//3W4H9Qv2m1tMfcD1LfXtNTJYvOnRUnDbReQsyMge6JqeKMUUF3zfDrOhzEVKodFJjk5y2sMWZ1rSvOTk5IR6e4MLkBULrLO0XQ1A30baez92LgYb5xJlSpGmSfSWfU+Yq+zQjQs2LRNsv8MMDqU0WbGgawNWp4R2gZGKYVdEL12doIslTuaEJEeqLBYJ9Z3IyDE69xMA0fk4fi58tp/noad5SBj1HhB+ZlUnSUI3mPla5XmOThJWqxWmNdzc3OBNifcdIQzQOLY3tzg3IIXn8vVzcBYpYm/SMPScr5e8vn3NarWK/T5JAiGQJwlJImkPLalWeCSbzQ1Ki1E/I5DnP2dTEhCC3lgGYxAqgSDZ7GJn4Hq1okwl7X5P7i20OygKfBaNZwiChJa+8wxB0g9RjsqYGukahv2Gq5efUDWvGJqGpq7nJiutdTRW59AKrHB45zk0+3sFPoiiJG4cM67SFC0Ewg3sbq4YioL1xQWus+wPN9iuJxmVMPebm4jC+dgiPHHVXtT1rGhpraWu66hbZyL7IdGaIo0XUoSoFbq5iQiSTiRBDUgatFZRuK82kGRo7dGDxx8Mig4fJEImZHlJMiwJKkUXS0IfsHJsjXYeqRxCpQhUlPUCCHeM8hgdCKT8LJn02BOFcN+wZqrNBF2PbHnno/5eojUq0bjgcH1HlS1IpWCotygcC5VC19LvNyzKnPLsEZfXDaar6bsDFydlnCjYN3jTU2hJby0qTdkeDnjvyfN8DFvN3IINUBQFy/VyLkDneT5zKN/m+FIYzzFS5kdqSVEUZFkWE+jecHr2aFQSfUJRZLjgsF1LlqQEE+NdEQLCDTS7Dc4ONDcv2V89p95tOLTXNE1zL/SaWLxpmmIHN98HzFDnVI0//r/rukhAHD1R3/e8+ORTirKcWwdQcReVI6gw7aqnp6d0XcfJyQne+9l4syyjPezjwjOe1nt6HVG4LEnv6Y0h/PwZsyzeJ4PEDgNd02I9FCKhrRssCudbNpsNg9MUizX58pSTd967z5QIsZ8IPqvC8zBMO0bSjhkG49W8d12Poezj50/nciLrTuc1DJEFsl6v6Zo9l5eXeNMh/cBHP7ylXD/i6TuP+dHmllVVsTtsEcGjgidJUrQUvD4c5pA6Gb1Y0zQkSUJVVTNg0/f9vWtZ1zVPnjx563X7pTAeuC/0XRQF+/0+LgrvOewPnD96zHa75axvYyJfCqSSmLqlrw94RgpMbzDb13jTs33xEdvXn8Yx9T4WTYdhmNkIE11fSonpDMNg57wr5gQSJTVKKnQR2QXWe9IkIUhFb0dafdfjzEBXxwuUFjnL1QodBBJBojTGGHa7Hbvd4Y7JPR4hCEIQnJycxdczY+3DOpwL1DYmv2FccEmq5ur/VAxWg8CKkQExWHxQLFaKNEkg1ajBoaRme3ONQ1GdGhbV0dAq7td54M3GI8T9Vvc3Gc8xPP0QOLB2AOIm6ZwjTRKaeo+1Q1zYuw4/eumJUiWcYzA9WZbR1HtevBhYVAVtHY1EBEUiPHrUxlsul/daHl69eoUdmfnHzJNpMzou8t7e3r71mv1SGM9xNVoKsH3DO++8QzL0vPr448g/2u9ZSMXVq5egEwpAe0vf9oDADYama9jfXrG/ucS0B/avXyBsh7Mdg8jvjaqYLtDUN+OGgbquyfM8QtsPCJ3+SC5qGAYOh8O9RHeVFjA4mt7EguRgWZ6sUUpx07ZkVclitbzHHvDez7Ujay11GHXKnEeOtRAlJHqiHU0XWgZCmHbvsfXCC1SakqYxZwjexqRYGeQ4ecJ4jQiBzc01Z++8h9aaVGmEvt+XA59lGNzddnfNjiHp6TgOdY+fOx2RtOtH4fvAYbsjyzKcs/fO+TGfb3pe23Q469mZBhEgTzV60mBAMozntLPmnndzzt0znInZ4H0c1ZkkyYzM/twJvU/5hR0G0kTMO86nn37KSVUhfMvt7S1pkdPWO25fPgdv8fKW3b4mK0qGvmN3e8Xh5pLD7SUEj28PuKEjUZpmhIGniz2FC4tFnHvqbexG9N5zONR3IRIxds/LgsPhQNM0szxUWZbkeTTKuq5J03RGsbTWmLZDJhqtFLv9gbSfVGsixO2DxTrPYB3eBwYTw67I25MwQshDiGyJNI0eLJJUo9FOiyzgWJ7kEakMAm8DpCC0xfUdSZJgB8PN7ZbzJx+wrhaRU5ZkJCpBZOozIMCbQISHooUPj4fduMeUIIAkzbA2ep1hGMiyjL5r5s0kG0Oo58+f8+u/wRJVCwAAIABJREFU9qv86PvfpR96NpsNWarJUklVVjNzRBUFQoFQGiE00nlWZT4LiTjnWK/XM9m0qiquriLBP8sy6rrm5OQk1vpG5v3bHl8K45loHEpmeNfPO8RiscB0HVp6VqtYDGv2O4QImL6hrBb0TcP+Bkzf0W5v6XY3mPZAwMXKu5T03mHMHV1nWtxpmt5VooVmGKYahsK5+3Dr8xevZrpHOtJohBB0ffRmGSHu4FqDFDR9F/uPxhqMlDp6hbEtYLPZjQsLsqyIIU5mqapYhLUmQvYyxLpMQMwtFcb0OBfztiSJ30NpxW63I01TimJkZJuBJIuUfmcE5fKcdx6dcfLoDGc7VKjm3dk7eJtSz9uictPxsL8q1l/cKB8mscMdyvbJ69dkQ83NzQ0ffPAB3/nOdzhsrlkWWQRWhp5MKer9nqIoSJZL2raFAYw0c61IO8/Z2RnOOW5vb+dhvpPU7jvvvMP1dcyBp81z8nI/d55n2umstehRTX+73dJ1HcIYDt2ex48fkyoZm55sT5Yn3L78BOstnQmR/9XWMDRgTeR8IXFC03Y9uU7mPvrlcjn3w2dZFgt1NjZcCSFQUuFsNDZjDG1bU1YLiqIgz/M55Ivs5JECkgp6bzGDo0wXkQ2RJugiI01TVosTjDFj05cjHanviY/9/cYYsiRhu92O6jiSsloQnEcYg0LQ1C3GGIoyi0wM5wghnRNeVHrX+jAYFosF6qg/qN3v6B0U6xo3WIo0wzlH13WU1YK3K5P+yY6YB02iJLFYa/q7tvFPP/o0Fkb3Ox4/fsw7j06pt7eYZk9VVegwUOQp/WDwjjnEHoYB52NflncDP/rRj6iqitVqda9mttlsOD095fHjx9R1PXunKZSeWh3e5vhSGM9xUjnpEkx9N0oIsiTi95uNxTqD1go/dGw2G7SWHOoe7wbC0KKCR8iAM47OBshzrFYMQzOzAia0zR8tXBmSedDSxNCekLjlcglJRG+sc9RNfa9PRSaaMLYySK0QShKkwI699sY7XlzeEkKICNyILFlrOYzIUFEUeKFYLtd0Xcd+s6XrDMmoypMkyYzQ7fbxe0+Gf3MTaxXV+jFZllGkGXmeIkWc2WOtJU9SuqGn7yxD16PEOF5d538slcw/ruc5ZjHEutWdtjcogo1IW99H1CtNUz788EMen53y6tUrVmU2X7fBdGh9p81g7IDpI0KaFTly7KXwtubp06dYa7m6umK1Ws25aZ7nbDab+XtMoeOk0/emWbKfd3w5jIeATCTSSoJpEc5y8+pTGGpEsBSJwDlDmZeEviFLKnYjoc/UPX3n44mTFf2Ipnkfq+naOnzfY50f9cg0dvCYUQet7waU0uRVMdcCpvZcmUoWI0U9T9I551FKsViURwYkSLMMORJBk6wkzTO0SsnK6LG+8Y2zKLbXDnSd4WwVW4kv/adsb19zs72eYVRvoyKmlxLGLs/u4KizaODFMnqxxnhC0HNYaE2Nty1maDiRJ+gkw3nonee2byi15jRbsDAduWspFp4+65Eo7M6iThaIENuf4XiMSZh/S3G/3+ghIPAQsZtbIxjrP8HgfWxJIASCj0ViFeJ0jCwpeHLxlN//3d/hyaMnvHrxCc8en8bSQ2+olCJJ4qjNsixJEzE31yXjjFGVSNo25s1FUSBQKKnROqXrOlS+YFUW9Ps9HknTtMgkpVisad+eF/rlMJ6pltIHS7Pf8tEPvoe3I0XfS5S8Lyi42+1muDnysrL574kXNr3usbj3BAJMJ3ViIkS0504x1FpLVVUsFgv2+32cpny4nOsGE4VmCuNCCAy2Q6n79Pupmm+M4dWrVxhj8T5gjaNvWpQSnJ0+oigSmsOeuh67XdOM5XJFc6gx/V1n7KQ1HdnHxbxLTt9xGKJGQT8WJperE4qyIisjJd+NVCCdNmxvbxnWJ4S1RKc5afrZ4uCbvcz9275IOWi6/5gC9PAlp2tzrDXtzMB7z95lt70mz1Nev37NyboiTTWb7YakaVitVnMuo0Z0zo7wd5JIqqqYJZIFcZBXCIHz83OuDls22z3LLENIhcoyOuvQqeerH3ztrdftl8J4Qgh0TU3fNnRNgzcdSky9FR4nFUJpeuvQWmAGS5AKLwR+HN1xbDjHai8TfWexWNzVRMaO0Kn6XBQFKk3iwhqBhKKI6Nq0w06GMoEZxhj2+z37/X6svUjyvLiHuPV9T28jBcYLwAu0jhSRZJ2As/SmQamE9dkp+bjITdfR1M2s8yzDqC6j0znUm2oVc3irFErEkMYGj3dwODSYwVFUy5gIJ5pXP35FVqzZ7/fk1pJIFYeJCfEF0of3rxV81sO86TFv81ilFF3TIsZzvK4WfPT976EVpKnm0clT+nbPzdUr1usleZlxe3vLy5cv53Eg08Y4t5eHbiwmK5JE0/cDaZpTVRVN07BanmDzAdcZ0qJAOUe1ULSD4Xa7f+t1+6Uxnrquse0e6ToSBoLtSWSkpkxFywltiuIVak4UpS5mw5kSwMl4hBCs1+vPKLhMBjHRY7Y3e5bLJev1mhACL168GFt59ejBIoVmOqaLNQt4cFc/MqO0UZ6VJCM1JLZnA0hMF2s5LsAwOOpmhzFdHP03WJCaLC9j27ZK5mRWy0ghmYYzTejVpIojibkiUhNEQCXQ9wOeBoFkUaQ8efaU7tDh+oFUJxRZjg8KqT8LF7wRqj4yiOn7Hh/HjIJ7DIYjuPr4mPKtqaCqtSbPUja31xSp4g9+/3d58vicd999ysvnn+BTzfr0hCSLIZjUI8KmIsqZZCl2nNcUjAOhOTldMQyWthtYrk452Ja0qPDax74uLciKFIaOvjdvvW7fpg07B34byMbH/1YI4d8VQvxXwJ8HtuND/4UQwndEPKv/KfCPA814+9/5ovcIIcSeir6l3d3QHzYsMw1+UriJSJnzYDuDcx6pEnpj0Dp6jGki29SUNRnLhMY8nAFTVXeiIlOxbBgGXrx4ce9iH482nCDVSYFn1i/QmqK88wA6SUhH1Kbvowp/EJKm7xi6AYEi+JjoJlnGWp/ifKwVTcwCoSRuHBeYpimLrKJt9rMnPZa+mtkSCOzgQXnyPCXRGVJrXAi8fn3NlXYs04qL9TvkaUqiNEpIBuuwypHK9F6bwUNqzazNduRJJm8+HQ/7d6ZNbHr+9JjJ+M1gIogznvPrq9dcPDrjsL/F9B2rVUV92LG/veLsdEma3JcMm0LkiUHQ9z0iSWI+VMS+n7rtWVax1rNrWpyMG5zSGSfnF1hr6E3Hyek5+qc8Sv7zRA8B/o0Qwm89ePw/Bnxz/PkzRC23P/OT3qTrGmTbYNoDKRbjDcMQdZVlkmC9nyvISimMtVjvsSNaNqnhTxdOSjnfNl28KdSZxk8cJ7Jd182acFJKFovFTBM6DvsmLtqjR49mcb6I4iRIqe7F94tyQZAxRGzrwzjSIyVPMobBELzFDB4fFEJAWa04Pc9i1X/8vJlOCC562NOT5dwZO/GzptqTc44YwU7ihwJjBmxrRqaDpx06pGvQ/pZkecbN5WuW5RKZVKAjCz0ZJY2PmdaTwTz0Jm8Kw477fB5y2uLf96k7MJYoxo1ptVzw27/9tzlZVmwPO85OTwl+oNndcnt7S1Hm8/PSIo/hsJJkeYbSmq7ryNM0zrwQGp0mLHTOoem4ePSE29tbslxiHARZsOkcq2pFliTIVNH3NW97/ElEDz/v+EvAfz0+7/8UQpwIIZ6GEF58wXtgzYD2FpyJsLOLfS/eE9um3Z1E6wQjH3uEaYc7Fv+bDOcYVj6WZTrOgVSazF2eVRWF9JqmmbH/rmnnCz6FfpOIIkDXudl4pPa44CPKk0aD7HqLIMGa+DjvIqug73t2u1vaJvb9VFXF6WpNnufsdlEiVojYJiGCmYGSPM9no4+CGRVu1GXASaR0ICRJcle3cKPewSBjQ54QAi0lXkahwTxL7rEDHv7+vPuOj2OjeJMRHQuPwAjqcB846JuWbgzH6vqACuN8HnlXYpiAnQn6n/KeoijGqaUSpWMEELzgUPdIrcjLAq8cSgiEzjBDQOgEgUVKODs7+4Klff/4/yR6GEL4W0KIfxn4j4QQ/w7wvwD/Vgih50j0cDwmQcTPNR6Iu3+uJH4wuL4hDANx+FtC00dqjR09SBCCputiz45z5Gk+txgcky6nEzxdvEk4caqxWGvnE97byGiYwrT9fj/XeZqmoSrKu4LqmKNNHsZaixkGtI6hlA4CoWTMkbpYiC0WFTJIXIjFzTTP0UJwelLx6NE5XX+AINluNhzajr4fKPIF5DCM7F8R+hklvLq64vz8nLOzMzabTUTpkhh2oSRSKIRQrFYrWhND2kSmiCF+ru12y7KNLRZZlqFGHYXj0OpNjW0/yfs87N85fs608Rw/Vo7T7vxYC2r2O7797W+z295gOklX70ctgxBHVqq7iOL4mvfGoEY0tFquR+FHHwGTYsGiWtK0HeWiorMtQieofE2ucqQIXDw+5/LTH7Dd/pSJoeGB6KEQ4tvAvw28BFKieOG/CfwHvKXooThSDH385EmMXb2n3m9JtcZYg7MBaw3WBpy7GwcohGIYHEmSIcSdgSyXSw6Hw5x7TLHw1DoA3BMVn0iBU84z0VumUG1iGJRlSd/FBTwMA+fn52y3WxaLxR1nK4lh26TW2e8N1UJQVBlJkpBVFUolnKw1XWfYbm7ww0DdbBlMixBh9pqEwBDi1LPgPKnWkQPWDjOF/vT0lP1+z83NDU+ePIlh1lhwjIJ2sf26aSK/Ls9KXl4+pxQ5echQQlLvD5iuRxaOgQE3ggbHEl7j9f+MFzkOjx+slc+Eaw/vG68/IQSatgUfYtgkJc+ePWO/3/Ly5UuEM6SJxFlLwLJrapxgLiovFos51Jx6cUIIc3iutUTrdA7rQxCx8LxIwScgFMvVCVevX+GuWrKyRPAzms8T7kQP/9EQwn8y3twLIf5L4F8f/59ED6fjWBDx+LVmxdBf/OY3Qt7t2e+vGZzAWIF1k6xSj3N37b8hBA6Hw8xBEkLgZE5RFdT9QDdYqiLWLAbT4e0AMpAkEZ1xQTEESbl6giyX9IFI9TgM+FDRHHpwCQKHsBB8j9IJvcjxOPKqokci8pKWgNTxM8kujv9QSYrWCiFShiBxbYeTmtA68jIHnZCfVFSPHsfP8+mPsJsbVLAMXew3ckPMqxZljsCOhFaPlgUiOKzx1LYjTTOyLJvbN5KsJFGKVKf3JKy8d+y315y98wGFl4jGc7O95sniV8eOzEAiBcFLgojyUIhJh80hJXg/4H1ACz3WamKDHv6+ODzCR4pMOBoi7Cx4jwSUSmM+FwacH9AqznvtuxolAz/e7ni53fD+V97j1Sc/wDRb8BYXQuyenVBB6+gOcUS8dT1OCIQbG9/sEOW9vMRqT7ZY0hmLkBqpFU4E0jKn6w2HzStK5TD7PdmyRPNT5LaJzxE9nPKYEV37J4HfG5/y14F/VQjxm0SgYPtF+U68DKPrHmP2fmxDds4xOEsId+ouU+4z7Yzee04WGue62PNelAidYK2lswEpUwY0ndPIrECqhCLNQWdUj55wcnaGSjLsYc/f/c7fQScFbWcoiwKdRGXLbd/jQktVVSglaA813dCh9SiqriAfaf8iBPCONEvQqUQqjQqBZmx0U2mBSuyM2n3rW9+i3t5ye31FV0c+X3M4IIeewcZ25Sk3S1SOTjO8jzSlpmvnmF/Iu9mbU5/S1GY9navdzS3p8ozHj86pO4fQCVInY85wp902e40H3uNNYMBDBrb3dwIhX9TeLcSRSOJRPe7s7IzXr19jD1t0MNhhQEvmAvVso+JuCsZE9J0ijcPmJgqjJClJKKFvWa5OuLy6ZrU8wQwGlWYUWc7hcGBVFmzbGuk6zk9PfpJJzMefRPTwb4yGJYDvAP/S+Pj/iQhTf48IVf+LP+kNQvB0bc3hsKNvWobBooTEOhuF+sLdzua9R0iNkBpnLVleIl1P1xvyLIk1gkNLZywOSbU6oR0GnFry3gfv886zd3nn6fusTk5ZrE+QUtEZQ8FA0CnXV5c8/+RHWGcJeU7XNoQkYZFCXpZ0fTOGCClKTxcwoNzUg+IJ0sWeHuvwISb7SaXwSRKp8yqZSaKH3YAfOpIsxw39LNFrrcV0Hd4apIemN2SSmZtHkGRZgfcxtk+SyAafQtKHHL4sy6hSxdXVFafFKV6k2CAJIkGqZO5WnfKSO5j6/qiUKZQ7hqsfwtnTMeWD96/1nae6q5Hdac7tdjuePn3K937vEus7MqVQ40vGOp+eEc6ptWN6zQk4KPMCpBjFRX3sjwLOT8+4vHodKTs+kBYpy3LB5uaap08u+NEPvkd7+CnmPOHzRQ9/43MeH4B/5a0/AdH6vLP4wd5D1IIQBKFx+HnYqg0eBAw+eqV8UdLs9qxOz2i6gXbw9F6Srx9RnZxhg+Lx6Sln736NX/zlX2G5XFIsKpI0R6joLVLnYHfDP/Qbf4FXL57zR3/4h9zeXrPb3LBen8cakrnlZrtltV5izYAUsTkNG8ZhwJLBBobBoj2AvWu/1gqJxw2GwXlcZ1mu1zG5dp6qXFCVBeH8nN3mNopeNAeEGgghYZGX7Pd7QNL1A35EnxKrWK1ivSrg8I4Z0Jg827ToY/2jRMmEprNYqdFZhdAZQqjxnD9Exz7b1PZwjMlEaxqvfeSrHf28Cc6eXm/yHri73CrP87vW6BBIiPJgwNi4mMzPP26em8CDNE0J1uIReMzYYazZbG5Yn56zKEpA4q3HdA1KSK5vLgl9QaICq+rnrCUBILie3XaDc+ZOu8DHDsF4kvUIOQsWiwVN01CWFd5DUq7onIB0wWChevSIJK9YXTzlnfe+wjd/8Rc5GRXxh2FgUa1ml+9CbAuQytG2LRdP32N1fs5+v+f5jz9BKcHLly/xh5LFueXy1QskglSlpEpGccXe0PY9cXSjYBg8WZIilSTJMkDStTULpdBpjj7SklZac6ijrrIk0LYtm13sV1mkOWcnK+q6xliLOTRjgqxBgnGWzf5AkkQgpczyGYY/HA5cXFyw30cqv3OOfR+5fPtDzerxBUFmlNUaKZIIWR95Fe/9qJUwoWN3wMibEDVgNuLj46GnCv4+bWp6/gTOOOdYrVZcK0WZlriunkPBY684vXZd1/e8WV1H2TCQBKUYXMB5WJ2ecdhtUELSmYFFWaFV7FD+4L1nfPTdP+DiZEHT/JzRcwiBw24bx91Ncq1KxR0+SYji4JLBDqRZwW5fx7BinABAfobUCUhFKqM6zPnFY37tH/izPH7ylKwo6W0cnlRVVdSkHi+AsBYb7CxRpLUmtRlFWVEt1xFVW58h/ft8/8PvcaFz9rsNfX3g0BqqsqAxBqkKvLd4bBTTEwkCGSn4OlbQtRIURU5SruLc1amXxBYopXjx6Y8JPvD+136BT3/8Mc0hCmCokUn85PGarm/GkK9DyahlEA3W0dPPLRR1XfPDH/6Qb33rW3z44Ye8//77bPY9WVpS1wPPTh5x9vgZQhe4seX7YXYSvcaD1uyj/x+iZ28ifh7npiEElLwzvrh55LTWMZgwn48siwjl9fVLqkyTjrnlFJUczxGaCt7T601Ai/OeINVYKNUE60iLjLJa4K+j3G7fdtjBUJYJWgl2m2vWP2Wh95/54Zzl8sVL+ramTMccQiUMQ0ORL/CmnYccTXNbyrKcL14rS7TKWK5P+Ma3fokPvv4NkjSnWK4JOsWhOF0v7oiDtkOrWDy0YxNdlqUkiwI3Vu6991TrFWmRj8orT2iGQHCeH33/e/zBq79HIgSrpCQtFZvtFiUVQkrMYOidQRPo+oFy8CwKzW6zoe4sqwsF48gSoRIkEUl88vRdvDW8fPkSnWZ87Ree8OLTT2LNyQfMbkee5/FcqMhr89aP4WFcqFNn7OnpKSEEPvroI9577z2eP39OcXYKHpIkQ6oEmRRYF5BjTvmm1P4uP4meWob7AMHxog7hbjzJdBxz2qb8aGZ2DA4huKMXjQz3m5sbzs7OaHdXESAS96eVTy30Ex3nGJRwzrHd3hKCAKHwUiKbjrOLBDEo3NahVUZ7qDl9dM7rq0tSGXPYdrela3+6M0n/fzmyPKXIVrg+JslaSwbvKIVgcJZCScT4c/bofKb7v/feeyy//qd59OgRZbVitT4lzQuCkCAUqHGord+jpEQAi6ochQxrtJScn64ZejP3vU+8MaGjMERVVeybPR98/ZvcXF3zwdcFSiU02z3OGlKhWYuowaaVpG0OLMuCrq3pm4a2MwjfotISnKALl3HgrI4doNZEIZJibO9+9OgRiZZcXV2h04zT04g0HS5fjcaRzjJKTTMJkSRzW3EIgc1mwzvvvMMnn3zC9fU15+fnfPTqJb/0zW+zuQ386q/9faRZEYuFIsSo7AGtKy7YKSQah/KGu/AO7rhsU37n/Ju5bpMRehfmXMx0scYzMQuMMZycnPD06VP+9v/2N1lVFe3ulmGIrzFHBmPTnBBiDvWstTNJNpEBxukQYTTY7XZL2vYU1QJJHAL84sUL8kSzvb0hSRVysQB3//N/0fGlMJ7gLcLWmFG43aFJZEqqUsJgqaQgCYKD19z0il/9lT/F1775Szx68jhCsek6tuhqjdKCuBICYJDSIqVAy7s+nP2hBUConCAEvRUIEpSWc+IMkfRZpLHt+nSR0tQdVZ4g1mvkV79F23a8evk6Lpq6RklBIqDQBYlwyK6nzHK8UzRljk5TCp0hnMDe7DHO4UXC4uSELMkJmz37vmUnA9VqgVaCD776Adv9hk8++YRUZ6yrNfV+x+tXl3zw7nusVxHaXa0W2DxjIAonLquMy+srFlWBlornn37M4/UTnl/u+eaf+4u01ZpCB0rXEpTmEFJSJk8T0LG4A0iED7Ho6gN2nKR5DFEfMy0mcURGCDo2oo2SWInGi4HeGfAekaionmoGJALnBde7mqRcUZ1esLl6jvCCItPIka+mdEpPwBoTp0EohZSxfSF4S6JTMJIgFVImoBKETvFegExAaA71FVVVUtc1p6slh82eRGmsiNLEb3t8KYwHQOsUISJiJJMwIi55bFRLM1RWcrrM+Yf/4j/Bk/e+RlCKzvScnF+QF6cxiQb8mL+EMIUS4yQFedfDcwyTwp3E0zEyNAEKU8w+jK3hWqdImVIUC65e3/De+8+QQnPz/Dl91+KdZXAOpRXl6pS+O+DaNlLfk5QyyUjyFEWM/fvB0jc1tbOcLRdUi3Pq9sD+doNKFfvtBikl62qJbD3b7RalNF/9yte5vnnNalHx7Nkzbm5uGLzl/PwclcUi8ZixkJcFZblA6ZRqtearX/8GHhEhfxELom867s7HZ7lsk/eZwrIJZDiGtKdwbjqGYUCMiB7hbsJCnDMbkbd1teT5xz+cGeuFHmf6eDGz4JWKQitSqhkKFyOoIIUizRNctHe8sCAkZbGgbvas1gvyPGW327EsC3788ccslwuuXr+Kbe7Dz4hh8LM8XAh4JEILBBItAkEMDM4jF2ekyxV/7s//IxRnTzjYQJkVXFw8ja5cxhMZWdEOH2JhdRh6vBexFuLvtJEn6Sg4iukRiAlEGONq6T1yLHxqtRj1jM1cWzg5OYmt1b2hqqqoauMlQSa0boDg8F5hSOgOB5KQYLUjywLS27hgtCTXGp1WtPvdLDOcS027bymqAiUU3hiscayrNQ5H1xnyrGJwgevNlvOLx1gfWd8SyNMU6+wsOFIultRtz3lZcbvd8rVnX8c5Sxg126TS8ICaMm8uTIXTMG8mx1y1Y+OZCp9TMXt6nbn9Y5pFOrUuuPG1iAbX1Hu0VKyWS15LCULghcAG0EKSJClSxQFxwTm0TqK2XaJwZhjb0xuEjIxqpRKM6+gNpJnC24Ysy0chTI9SgsF0lFlONwqCvO3xpTAeISV6VHJJdBZ5bNJhrGVRLTiIgmdf+2Xe/fovcrXvKNcnrE/OIr0jKyh0xm63w5i4iBOVxbrHjPLIe9Xo4xh8OllS3R+ya33sqRFeErwnURnWepQKcwNdUWbsD1sWi5JCJchE0R5qPrq9RUpJXfckqcIFTWMCaeIxQyCVjr5tSZKEIkvobYd3BtO1eGtRSYJ3jkKnZEFT5RXnixPqLtJ2dvs93WAQUkRCpyq4fH1LnqesVlEZSE19L6mm61rysmLwBplkrE8eMThPVmRIHTcH6wa0+qweATCXe449zzG7/E2E0en+44ZBuGt+8+Nj+5E1PhWGRQAtBcbEWUhDW0d4O0g8irbtxyKpRElFMJ4BB51BKYFKUlKd0A+RgW5DQ1GWbDfXXFxccPX6JUoVVIsF2+0tq+WCV89fkKYJaXJHaXqb40thPARG2NlFnTIXm5iUTkEoTi6e8ff/6X8QK1J0rskWK7yQpCPc+/LFJdZaHj95NDephbElQOuoiiIe1B8eGpBQ0UgmTfMw/ug0zm4JxpLoSG8PWlBVYa6hWGtIEk1RlDgfqE7OuLq55unXvoExFqkVX8m+yQ8//C77wSOEJ5eKwXlsUxPC2OUaLFKNhUgh8NaRFikpGuEF7z55j6vbGxblksVhQTP0dKbFeUtaljhrGew4/a53LKs1N68vybKC/b5GJhWrsws66zjPS6KnGaHmh5ckxNGUAFreGcQxsnZMsTk2lHt1nyMjc87FwVvTBnWkDT4peXpr2F7fEJzHtAYpJOVigQ8ianAXcXqblJIsTTB9OwqABITUBAv9WCuUOoEhTslWUvLy+Sd8/RvfIJCw2WxYViW72w1VtaBrawYXZcne9vhyGA9xl8vzctYgAIlSkTl9/s4zyuWK15sdJxfvkGcFaZYgRMCYKGpxdnaGs4HB9iyXCyKbCPAaHzxKfbY35aH3me6DO1GSECbWNsQxgGO3qo67o3PDWKEXswfNqyXffPoe5XJFEIrFckUYGgaZ8+MPPyTNUoT0MU63YF3MxZw3lFk+8tkciUr2AURCAAAgAElEQVRj4h5kbDP2gqqoMM5QlksWyQlNV7NvavrBYEPL4D15tsCYHh8EF0/epTnsCCqhunjK7a7m/XxBP1imVp+4wC1STlSXERqew7G7Xpy5gMod9HwMR08FT+Bez9X0+MGOPVRTPjmd//EaFFlO33b0TfTMu82BsqhI8gW5joTd+LqWwQaStCCZjHPoqUcx/8FCVVWcVMsortIcODs5Z3N1S7k8oVoUHLY7FosS07dURcn+sP0MWvhFx5fDeMaYtkiTmECbUYg7XZCOkwXE2BmqtSZPE6SWUXJX+HGOZZxkEKvpgWl30noU8Q79Zxqxjo3HhdhTEr3UiNeJcfyfc8hxUrUY5/eokekgRKCuPU3dEwRkRcHJowv2TccyK1Fpwbbp0EXG+7/0bbxQHF6+oqu3SG+RfsA7S8CRJwE7Cj+54Mm0QuoUECidsLnZsj49YbfbUVQLWtuR5AWlkAjTEwbN4XCgPrRUZc6PfvRjPvjgA4pFLJoWiyW9kFgXImAw6047vD9mGNw3HiHujCc+/n6LwjEAM4U9x2DLvcf4u+dNz5mMRwiB7Q11vefq6oplmXF29ghjHViHRZKIQJJkKJWP4pDQu5bt7QYpRWxTQVMfOkJoUSrh4uIpy64ZI4SEarVgt9vx7NlTfvzxR5i24/zRKVqdkOc/Z2jbw90suuRi7Bj1s+CHENOCFZEPFRxZkuJyZmZwmqYIOV1sSwhTZftO9Ht6n+PjYQ/KMW/KOUc6NslFQXKH89FDlmWJ8wPKp/gAbT/QmZ7ziyd01oLrWJ6eERKBaRt+5df/FIeXn5L5gTAYdlevuHr9grY+4Nwh5jI+wADZOrYcJDKK9Vmv2Ww2FPmC7W7H4qRCEuhHJdTD/kCZFyglKLKE0/UJry5f8sF778bdmkgs7UyUNA54GKdNi3vfPybwE10gED5zjo5ZBhNH7Vjg8LjD95jjNoMJ0/3csRsmek2iNXYYaOrAYDoeP3mKShKCSiGYWRg/UQJr4to4Pz8nhMButyUvloQg6Nqel90l3/3DP0IqWC1KykVOM/Qs8oLr62veffddPv7oB7GlXoBzb6+b+qUwHh/g9PE7eNMR7C0XJ2sO9YHOSGSSYn3NdneFFSmtc6TZAuc9eZZh+p6QeFSeoaXCSdBigqpBhpik9kgQIJSPIYoIaKli/4n3CMdcaR/MEPUAbCAMnkRorAKhFSqoODx2sJhReTMV0Gd7kmVB7y3nyyXGtOiQkFQ5tqu5kCVG5XQ9JOdfwXmDcIazi0dc/MIH/L/tnV2sZNl1139r730+6lTVvd093TM9M7bHtmIk8oDAWMEBFCEEEjIRvIBiQMCDn+AlCCGwhYRAgofwQBACKUQyEiBEQgCJvKAoguQNOSQQOwbL8Ywznpmentvd97s+zjn7i4e9z6lz7/R47gwTujvcJZWq7qlzq84+tT/WXuu//v9vff3X6E/WnB6dYaJhb7ZPaQpqIxQ6gFvRiqOXHmUiz7/0HMenJ0mW8fyc6Bwv3rlNVVWpKEwEZTRmuWDb9xQ3b1GoksODt/lMd4xuT/CzGRttAE8hFijzhAFBUq5LicIjOf9Vg2uz+2awPhDCQL5RQlQ43xNCRCRz5iFEpem9JyIUbkuwjqhKEJP3Mj2FpFWlmRccnhzSi6CMoVncoQ9Qo5iXJVLXeNcnaUqtiJnyOPTp2HPzJfPKIIubuY7IIc/fxPY9XbdFIqyODtnmle786JDFYknTNMznc+bz+ZX77VMxeJBUj9Fu0yxYliWsEn+AqRR923F+dka1fG5E7SoRvIvEsAs/p5lu+rEp/Kxywk9kUDkb5ExSkCKEkHBokyjRUJ0KXHBFhpyQj+C12/n4FoJNhWsxpEy8jS6TMu7olbTSIKnGXqExQVHoBK8nu6WhCyO+a0w+xkAIHlMkiRFrO6ztWK/OiAQWy4b5rEnZ97rKEcPAspmzQYhK6PrtSFPlnMMwIKGFyA5/NnWppu2euldTGzL8QxnEUCI9RjIvcEkEAgolw7427el8tAS/YxgtjEEiI5ynKsqMRNA4KUAFCq3wMSTWoPx7Jlc9138pgZBkWApjqOsS2/Wgwli64b2n27YQ4kiuf1V7KgZPamcquVZK0XVbrO0wxQwV4fDBIa8X3+XlTxsWyw1alWhT0juHVopZVVCWRXb9Ap6IIoWfU0WyIPHij59yC5PQtHVjsGJKjTQK3wI6R5O0KHrJNK8hEuqaOpb025a6rJCy4J2DR0RdUs4SmUjbdahcqh1iYDGfEfpIe7yFsGHZVGwfJmKKKMJisRhdnJhdRTGaGBLUZNO2o8TJfD5nsbfErpM8fSUaXRToIiGVTTNP9FzBcmMx5/DBQ+5+6nelGdunSs4gMLpw+f5M9ySDW0bc5cKGxzBopuUJA9/35YK53mtsyAQtCIUuiKLobaDrO4LtUTGgBZbNjJs3UnFaoSRp1PpAU1SoItBtW4woooFS1RQmwXzE58RpDGgp6boWUxQYU9EpTaEuBo/6vsdZx2p9yvbsWcO2xchqdQ4+UJcFhECwDoul6y1d94BoI2XRsL+4SaEMqlnStz31PPOyhYyxIq1KQUAj+OiJ01LhcebMK4xPEiHtphsrL1Pkr77g46up/kbuTGiFSEQXhsoVaAUqRjabc2zfoWuNKPCux0dB5ZXFe4/tNqhgkehYnR1z9OiAkIsAi7IYB471Dmd7nLOcnm5xMa1KN/eXlLM6V7JqvLPcmu8lpWxJ2IJERGJSArhuWG37pP/pemzXoVRJ1Gnl8SqimQJBL278gXHwTPcww37VZy2hYb9TFMU4AQ34Nq01QRm6ziN4ZqaiyyHqGCPtesPh/Xv0mxWGQF0oZjn3YjLiVAE6BrRSiXrKu7xny5zXOnkkIaQBKCoPblJgZLFYIFpdoPCaV/W4Emn9jO15Yox0my3RtjSF4vjwEdoUhOiZNwv6Tce8UJwevM29wtBv1shsCbrmOf0iRWnQQmJiIaalTFIloWJIeuaaenaRo+ADPnju379PaapRPXlYzodZ0xhD1GlFKgZutiHbnutcjErw+NXpiugd+/tLTlYtru+SsltV55XKYYymXa+ptdDUiuN7x9jtGV3bYqRk78aN1AmNobU96/UKiYF501DmWd6GpD9kqgR8lbKEPiGkU2cXJEQWzZzO9qPe6NZZcElEa1bNcU4ozK6wbcjNDADMaSQthKz7Okl4bjab8X1rLZJzjFOV8gu5npBWJSEdb+qa0/MzfNfStRuOHhzQlAW+VZQ6FRJWRdqLlcagK8N2s6GpK7rNJhXKBfA+T5wIKhPA2K6lriv6vks5vJjySTpoYgg0dSpn6LqOWVVjlP7t0eeRlDj5VeBejPFHReRTwM8At4D/AfzFGGMvIhXwr4DfDxwCPxZjfP37fniMCRxKIEZJvmzweOc5aS2qusXZ4X027fdYnz1isz6nWj7H7Y9/mrI0CQmQZ0cV07ZGyNimOF7/+F2Q/HDbW46Oj1gul1TFTpZ+oJ+6gIHTaWiiFCokYkFlNLgUSCicoIlIDHR9y2rVU8+XKFJHklpAUiK0NIZ5U9OtTjh+5x6uPcf1a5SPVHWZ/H0ky0meUlYFTTMb9xJRyOdEVEyPKFBW9a6jEwmSK0h1ylmVSjhvt5yfnhG8zdIqSVENSEDOSyvP7ifa7TUv9YuxTCSEkGqvJhG44X8hTVizuqTrOpyzdNsNod2wXZ2wPn7Eo/tv0K7PcN2GG4uGpjRUWqhMGiBagQpQoChEiIXGqBQcikbjg8X5nlJfHPRlWWYNVEYU9zBZDHIl0z3eVe3qWAT4ceBbk79/AvjJGONngGPgS/n4l4DjGOMPAD+Zz/u+lm9tYsbsk6zd+vyczfqcttvQrU/x7QrlNmxO3qE7f0i/PcF1G7p2Nf7gEYgqh0SHsSKT15OE3qCEPJvNxtqg6QwbJh0Jdu4MKm2+VcZdiVaQdUGNFpaLBYU23Nhf0tuOGD1aIj46GLLvIVAazXxWUZWKfpOI/QptRkIPrTVt2zKfz9nb3ycQcbZLGXXbE6OnKDRloSlKTVUkvumpPpAyGhsDUQSMRsfIjf0l3XrN+ckpvrckbVPNUIo9zYVdht5Mk8tTNPXQEcc92iSvM9iYLA0OFS2h37I9P+Hk6IDNySGP7n2PB/d+i2h7uvWKQmA5q6kLQ601pVZJZQ8olUKHQKVM4n3wHqUzeaWWdw2IwWUc9GZFK0xZpH6hJGHgCjPeu6vaVUkPPwb8SeAfAH9dUo/6o8Cfz6f8S+Dvkqh1/3R+DfDvgX8qIhKnSZTLn0/an/TB4qzFtlv6vsOUM2xvMbOevjunbVva7Tmt87z8A1Dt3+aO/QQuLlExpPxITvAp0qDJ+b1dtMc7YvS02y0a2fFOq4u3Ygo1cc6BUfghYhcinksdK/vQMXradkPXR3oPtutHRHdZN/gY6Pot9Uwn149AVRcsmhk2aoxOqEddJleqmqWZ2rqefpvqdZr5fIyqFXUiwUAJ+NxhcifrnaMuE5NQFBIxhoemmXFw/x1mt16AsiTGIoX1J51tcOHgEjJjmJwmP6dzLinbVdUYzXwvFIKJiVo3+J7DR484enBA3J5zcvAWod1gIiyaOU09oy4rCqWTPmuMo1pEGBlXXZaNySLCKpFkarXDMU4nQO89IZImFxL0qh+IE7VOqYXqo0+S/mPgbwID8Oc54CTGOMBwB1ZQmDCGxhidiJzm8x9NP1AmpId3bu3j2xVh2+J6h7dgVEkI0PseXTt626aseAiE1Zr1o1P8jXP8wQn9rKTe25u4CslxC9nPDSEx2uA6xLWsj48oyhmm3iPGCikM2q9GrNXQcbzdrT7S2VRebT2eFD52UeF8wEXFum7olwHbW3SlKZRPedyZIjaGeTSULmLKEms7VkdnRLvBWQXU6PomwSu2zlPXmrJUid9gu0muR9Xw8ku3x4pLkR3dUl4E6WKf3ElJXNWVUThnEevQIXAchbqpKboeWR/AydsouYvUC5xqqDhLCABJJQBdxuUVSqcAjDF0wROAoEiIaJGErBgFkAOSyzs2m01muFE4B31vCVoRTMGs2Wdzdo+3XnsT3Z9ThRbttvQhMJ8vmS/3KOsZqiiwIaDKkqAU4juQHpCMGNBEkrjYkExHJQqsRH5ZoJSh3WwpiiqpmueJrygKAqAxRBdR0VCoq8sqvu8aJSI/CjyIMf7a9PBjTo1XeG93IMafjjF+Lsb4ueV8lmeONENbn7Q9e5t0VXSE0CXQ6O2bt7l9+zaiFN/5rdd4496bxCh4H/PsaVKSLz+G4922ZbPZ8ODBQ773xlu5vCCZ9x7nfQaFDsGGOD58XrXGIEKeAY1OOR+lFIsoVA4qB7cX+1RRQeeJW8tMzaCsCCLYGJPrl+UOh/C48/04aIcBopTi7t27fOITn+Du3bsj+fw0B3Tpno4z/OBSDWyaRVGwqGaUmUfObTvufe91Qt/hu5bt2dmFMPVlVpz3Iv0Y3hvqbEDhfWJ4NabEWo+1KWENanR5Qwg8d/MGL73wAhIim81mvN7Ej7fjKZgSfly+puEeDnvVwVsYKHmHvdgUsDp81jRAMkVAXNWusvL8IeBPicgXgBrYI61EN0TE5NVnygo6MIa+JWknug8cvd+XbLoOt+3wbviRoG0t9aKALlAojVJJCOls7VBtZO/OKxyuTnnrrbd4+eWXR/rby4VaMUZc13JyfEi7OgdV4IICSfuV6CJOkksShYSlItWxDJJPyu7CtkpFjCiCkFa0AHQW8bDZbFk/OiZ0npKSuVlShJIwq7EiKFIuJ0hAG5jNCtx52tgvymYUoR3886EdaaZUF9ygqUsCjKXpADHrGg3HRIQCkBAotSF4z+bklDdefZWPfaam2b89ckOEPFGUdTXq3wzl2FNXlRgvuLfWWnzc7ZmUUrhM2OgzYt12jiLvDxWBVz52l8KesT6K4FqqLPc+3OthhRiCOVN40OBWDr/zcL7O7ETee4ocPh9YhUSEkLnoBirm6f9+pIMnxvgVEi81IvJHgL8RY/wLIvJzwJ8hRdz+MvCf8r/8fP77v+X3/+v32+8AxBCxziUXyFqs9YnFMwZENG7doYxBFypJ6e0tiIWh9R2H54dU999BKc3NmzdZLpePHTxhu8G2Fu8iVb1AFzUeQWLM0b2syO3Tijey6+SOZ10qTTYmuUmEVMhF9BA90vfgLMF5VGEINmB9pPce7RM6rCqSehvtFt9t0SEFAKy13Llzh5v1czRNM1E5y5tdMnGJ7y4EMKZ7krQf2UWMQtix0RAld570eaWSpB4XPScP3sEHw/7tu9z51AtJHAsuDJQQwjgo1OT7iBHvHCJpgD58+JC9G/tjZwfQusi5n0QbJtok+UciwXasTw6ZVQWuqggG5rPZuAIN4e8pciG6OL4/rNLTCWVQ9t7dh5DzTmUu9VD4rNY3qKNPgyAf9crzXva3gJ8Rkb8P/E/gq/n4V4F/LSKvklacL77fB6XCqASyPO1cjpCpBNfwgQqNEYO1DouniBFjFHdeuM0LH3uJ0hjeev11cI6YSb4Ht6MoCrq2ZabhxmKPtqxxPiK6IkShzDMS+cZ55yBGQnbRBmlDY6rRnZHMROlsl5Dd1hJUYO22tGLpi4jMK1QQnIqIESod0KFFR4uKLWUROX54zOH9t3npzi0IHoMQvMX2aUa37KAtCeYfLvy9K99IHdo6f4GkPcah5DmvUJ4Uqs/k8cGlAXR2+A6bzYYXP/MyXdfBpFOObqBkVYMYxnCvc7sEp7WW8/NzlrdujJo5CZUe6b1L6QRJpQjt1hHbLcum5P7RI84OD9isTrhz+9aIWBhcsUEXacg9Rb9jBh0muLIsRzjVdM+ayPG342BOzEl+5J4DRqqr4f22ba88AD4o0fsvA7+cX38X+KHHnNMCf/aDfC4C1oWUm4hCVc0S8rcsE9TGVGxtRwjQ7O2n0LLSFAL7yyU6CsoL9994jQf3NPv7Sd9mb2+PbpVu1ur4nLJZ4INiefMmupol1TTv08YTwfuA8hEdQGuF0grX9igRVu2WqiypTHIDjJa00lhLdA7rA1vbs7YtnW/pfeT8rGUJqGXFIswJvqc9ecTpw/s8fPN1Cq2oC+H08Ii6rrFuM/LHKZU0eIYOHIPBFOWYGU/k7zm3Naw4cacYAQllPqANgCQ9Ejx520WpDN5FFJ6yiCN7kJmwgQ5h/jB6hzFNHpNZepio5vM5Iabcmg8RkfQckTxm0xdrVRAVbLdrDu69AbbDu7TPjVUcB9DlEpLpSjh0/st7mWGFSq6YHmuLpjYkbge0x4Vo4gfI9TwVCAMQPBEtKc9hrcf6QNMsEKNpbSCKop7XKT7vPIsbM1649Rz7zQzpe9bbVX7esj5MRIE3btxguUxqav2mp17uc/vlVzg5WzMPwnwxQ7xFJCUanXVE68AnSE9rk9Sh1hpfzilNlQISvsd1Pb63ONvTbTesT09ZnZ3Rn63R3sF2zTwE5t053Vvf5TvfOcJ3G7R37NeGV24ucsSsTJTCAZrldHMe6HOkLcZI0DohJiab6MsuxlQCUmQHr4FMrkiu1gwWH8GUdQrxR0+h4fQ0KWTOM/VWCOHdgycmTR01yZ05lyA5ScIyBWmcG1znyLDPdy5gcZgQEO84OnyIBItWUGVtpHSeG922aZAgAXh33zs8T/dBUxcM3j244KJYwLDiDPd9oB27ij0VgyfdAEEbjfOwmDcUVU1Rz+ijp4sWLYbZco+yLGmamm6z5c3Xv0e79fTteSq11fqCorUEx703Xufg4IDbN++w//xLLJ67y3ffeptPfPIVlIYYLJVW+PVq1N8Zokdt2yZtnapi61PHtJ2B6HDtFh0d0fWcHD1CnbdsHx5x/ugBMbQYrVgUJerkkGADs8bRzEpqpcB2lCpJdQglumrwURHD+QU/fniMblrc7UWm+4JpwdkAxhw67zB4Qgh4rSGCV3m1CB4UaBQuOk5OTpLCdk4aP24O9j4pzona7TMgjht6RYSYsIaQB2AODVvvaG2bRDyc4/jRQ+azmtXJMfu39qlnM2a5GG26yR/aEWNMeTAu7skuDx7IGkOiRhT542xaJj7cx2eOw6Aqa/7g536EQmB7ek5TVwQJRAmZTL0YGzgNR4ZwBvf/N01ZUYeA7z0NgUpXBLvBHjykDoFXGli39ym3gftfP8S0Dokn9M0eRhdQN3i3RYfAdnXGZrPh5OQIUZG9vT1sjMRa0SyXbLqevu2oi5JN19FtW85PTgndITEE7jRCVaV0mLUWVWu0Lom2JVVkKlQ1B2PGzun9efrh6wUSw1B6RpGz3YmYI7LI4VdRJu0JAxRlqqPpnUVlWtq0WU9K2sAYwdS9ReuCSEGPRkVJcP7QE04f0orHVUKYl2xLg9INogyoVO4uIswcUKhU72oUoHFthxZhdXKGDklhLgSh6zrqMuVWnLfgoTx/gBA4evN1VkfvQL9GQsdcKaJt6UUSCkAEJbtSBmNMko1USWXCQyon8QFjEnmMUYagNIRclCeC6IreWsj8cU4JRpISeXCe2XwxSlWKTvf1qvZUDB6tNMvlEu09jSlp6gok4kjVj2J3cuqwq0aEtFmUnKCb+sRwka2ykcisSADSqBSHB+9wrB4hFGlDabc432dQqE21M8Zwjk+Ax0pz+vAh2+2Wuixp6hntKom/NrMZUu6ifMPKMbgESikM9diGAW0MjHIZSimcLpBB0oNUYpw60QB3yZlzlUSoiqJAGZ0UsNkpGMC7lauLogAf8IMUSszh4yhJucF7+m3Lg3fept7bZ1kv8bHNkB+TRax2bqXtHCG6FHUMAds7NpsNZdHgbJuCEmVJuz5N6HBr031cn/Pw3tsc338z6aIqRRTNut1SN/OxHQNCe2qPrdeaHLichxqudXDPpue91+upm/h+9lQMHlGKRTMn2p5ipiD4VCIcIz4GElY+ZVqnWqJlmaEUWhDZURxNO+cYxtUZxFiUeBOIQWeug7Thr43HCzSFQesE+TCZEN5aTcz/v1/n0Kj3SJH43jSOcja74D4AI8YKAJ8I4E0+Zwp9HzqBNtWFweMzzm8KL0mly2kvEqPLKPHk6jnfIzmcTN6kk5O6hdIkIulMlk5OBgdGcd/N8QlNMeP47QOgYu/O8ylMLpFoPSH6VAoNmQI3JHJ+FzL5fA+rY8qyTJWbQooeth2np8fJtT5/xMODt8FuWZ0eszevuXH7FrqeEcxOb3QqhTl13YZ80/S+TbF1lx9DX7gwqCafMZ2Qp991FXs6Bg8kdecYUCH9xiGQ4DgkmtdCG0pTEIqQxW0TPKQoChwXycKnNsz0pc4Zdx3ovQMJmLpEJCmqGVFQDODQtGFPs3+gaUzicRNBVWUSS1IFfZFg/yGkArwdXGa3ER028cOPPrz/uHyNUxdXnhE/F1xO/pkLK49SOrPOpFTuANuBgekmgz39riMx5IxCxBTFeJ8BbpUNdr3m/voNfNS43mHKgmreEGIq/IvzOSUJCgPJ9cFZQkisr2fHx3TbNYVWSZhLwHYtZydHrFYrni8KTu+/Q1lq5k2DC4HFrMFFKEw1bt6HDj/UBQ2rgtHvrnCd2uWI3Iipm6jlDUnScWK9lBO6qj0VgwdAYmSxWLA9OUcylF4VCtdbJCTlhGHZrusEOe+6jqpKxIGQip0KvWuSUopoFDEa6LcplNu2VFk1OgYHEZpSoSShGpSKiEoBDAiJOASbGDidI/ih2rTH4BOLD4qgijHvMggKW2t3kaMcFjV5tZzC9XdoblIHzbPg6M5ZRpjJ6MZl+XcfA1WZSBiVxLQ6hEBwEe9dUs/2djIbT+Q9tE4JyyFvEiMmLdEcvPkmjw6PqecN+zdvsFymfEthPbfv3MHnHJFEcH3SHI0x0m/WPLh/j6au+O5rv8nHX7yLt1vOTk9wvaVCMS8KysowW1Qs9hYJKxcihakyp3VKYE5XlgGKU5gUwrbWJlJEay+UlsNFd30Q9+26jqZJ1GZ1qUYg65SsZKg/+gB99uoZ1d8uE5Fz4NtP+jo+QrvNJSDsM2z/P7bllRjjnfc76WlZeb4dY/zck76Ij8pE5Fd/p7Tnui3vbR+kGO7aru3aJnY9eK7t2j6kPS2D56ef9AV8xPY7qT3XbXkPeyoCBtd2bc+iPS0rz7Vd2zNnT3zwiMifEJFvi8irIvLlJ30972ci8i9E5IGIfHNy7JaI/KKIfCc/38zHRUT+SW7bN0Tks0/uyt9tIvJxEfklEfmWiPwvEfnxfPyZa4+I1CLyKyLy9dyWv5ePf0pEvpbb8rMiUubjVf771fz+Jz/wlz4O0vD/6kEC3rwGfBooga8DP/gkr+kK1/wjwGeBb06O/UPgy/n1l4GfyK+/APxnEoji88DXnvT1X2rLi8Bn8+sl8JvADz6L7cnXtMivC+Br+Rr/HfDFfPyngL+SX/9V4Kfy6y8CP/uBv/MJN/iHgV+Y/P0V4CtP+oe4wnV/8tLg+TbwYn79IilvBfDPgT/3uPOexgeplP6PP+vtARoSEecfICVFzeX+BvwC8MP5tcnnyQf5niftto00VdmmFFbPkr0QY7wPkJ+fz8efmfZlt+X3kWbsZ7I9IqJF5NeBB8AvkryaK1GkAQNF2pXtSQ+eK9FUPcP2TLRPRBbAfwD+Wozx7Pud+phjT017Yow+xvh7SWxOPwT87sedlp//r9vypAfPQFM12JTC6lmyAxF5ESA/P8jHn/r2iUhBGjj/Jsb4H/PhZ7Y9ADHGExLXxufJFGn5rcdRpCEfgCJtak968Px34DM5IlKSNm4//4Sv6cPYQLcF76bh+ks5SvV54HRwh54Gk4TF/yrwrRjjP5q89cy1R0TuiMiN/HoG/DESt/ovkSjQ4PEUaXBFirR32VOwufsCKcrzGvC3n/T1XOF6/y1wH7Ck2etLJF/5vwDfyc+38rkC/LPctt8APvekr/9SW/4wyVX5BvDr+fGFZ7GVkvMAAABxSURBVLE9wO8hUaB9A/gm8Hfy8U8DvwK8CvwcUOXjdf771fz+pz/od14jDK7t2j6kPWm37dqu7Zm168Fzbdf2Ie168FzbtX1Iux4813ZtH9KuB8+1XduHtOvBc23X9iHtevBc27V9SLsePNd2bR/S/g+HuH5kqPIb5AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3afaac9470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The original\n", "plt.imshow(img[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see below, there's no magic to data augmentation - it's a very intuitive approach to generating richer input data. Generally speaking, your intuition should be a good guide to appropriate data augmentation, although it's a good idea to test your intuition by checking the results of different augmentation approaches." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:46.196185Z", "start_time": "2018-01-09T14:02:45.702228Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3b2899f978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Augmented data\n", "plots(aug_imgs, (20,7), 2)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:46.199475Z", "start_time": "2018-01-09T14:02:46.197598Z" } }, "outputs": [], "source": [ "# If we cheanged it then ensure that we return to theano dimension ordering\n", "# K.set_image_dim_ordering('th')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding data augmentation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try adding a small amount of data augmentation, and see if we reduce overfitting as a result. The approach will be identical to the method we used to finetune the dense layers in lesson 2, except that we will use a generator with augmentation configured. Here's how we set up the generator, and create batches from it:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:46.242433Z", "start_time": "2018-01-09T14:02:46.200651Z" } }, "outputs": [], "source": [ "gen = image.ImageDataGenerator(rotation_range=15, width_shift_range=0.1, \n", " height_shift_range=0.1, zoom_range=0.1, horizontal_flip=True)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:47.149096Z", "start_time": "2018-01-09T14:02:46.243731Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 23000 images belonging to 2 classes.\n", "Found 2000 images belonging to 2 classes.\n" ] } ], "source": [ "batches = get_batches(path+'train', gen, batch_size=batch_size)\n", "# NB: We don't want to augment or shuffle the validation set\n", "val_batches = get_batches(path+'valid', shuffle=False, batch_size=batch_size)\n", "\n", "steps_per_epoch = int(np.ceil(batches.samples/batch_size))\n", "validation_steps = int(np.ceil(val_batches.samples/batch_size))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using data augmentation, we can't pre-compute our convolutional layer features, since randomized changes are being made to every input image. That is, even if the training process sees the same image multiple times, each time it will have undergone different data augmentation, so the results of the convolutional layers will be different.\n", "\n", "Therefore, in order to allow data to flow through all the conv layers and our new dense layers, we attach our fully connected model to the convolutional model--after ensuring that the convolutional layers are not trainable:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:47.819815Z", "start_time": "2018-01-09T14:02:47.151923Z" } }, "outputs": [], "source": [ "fc_model = get_fc_model()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:47.830772Z", "start_time": "2018-01-09T14:02:47.821436Z" } }, "outputs": [], "source": [ "for layer in conv_model.layers: layer.trainable = False\n", "# Look how easy it is to connect two models together!\n", "conv_model.add(fc_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can compile, train, and save our model as usual - note that we use fit_generator() since we want to pull random images from the directories on every batch." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:02:47.887626Z", "start_time": "2018-01-09T14:02:47.832099Z" } }, "outputs": [], "source": [ "conv_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:29:37.681460Z", "start_time": "2018-01-09T14:02:47.889194Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/8\n", "360/360 [==============================] - 206s 571ms/step - loss: 8.0625 - acc: 0.4998 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 2/8\n", "360/360 [==============================] - 200s 556ms/step - loss: 8.0556 - acc: 0.5002 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 3/8\n", "360/360 [==============================] - 200s 555ms/step - loss: 8.0614 - acc: 0.4999 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 4/8\n", "360/360 [==============================] - 199s 554ms/step - loss: 8.0556 - acc: 0.5002 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 5/8\n", "360/360 [==============================] - 199s 553ms/step - loss: 8.0532 - acc: 0.5004 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 6/8\n", "360/360 [==============================] - 198s 549ms/step - loss: 8.0614 - acc: 0.4999 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 7/8\n", "360/360 [==============================] - 199s 551ms/step - loss: 8.0614 - acc: 0.4999 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 8/8\n", "360/360 [==============================] - 199s 553ms/step - loss: 8.0602 - acc: 0.4999 - val_loss: 8.0590 - val_acc: 0.5000\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3aeb74c0b8>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conv_model.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=8, \n", " validation_data=val_batches, validation_steps=validation_steps)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:34.529254Z", "start_time": "2018-01-09T14:29:37.684063Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/3\n", "360/360 [==============================] - 199s 554ms/step - loss: 8.0590 - acc: 0.5000 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 2/3\n", "360/360 [==============================] - 199s 552ms/step - loss: 8.0509 - acc: 0.5005 - val_loss: 8.0590 - val_acc: 0.5000\n", "Epoch 3/3\n", "360/360 [==============================] - 199s 552ms/step - loss: 8.0602 - acc: 0.4999 - val_loss: 8.0590 - val_acc: 0.5000\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3afa80bc88>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conv_model.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=3, \n", " validation_data=val_batches, validation_steps=validation_steps)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:38.625983Z", "start_time": "2018-01-09T14:39:34.533132Z" } }, "outputs": [], "source": [ "conv_model.save_weights(model_path + 'aug1.h5')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:38.859650Z", "start_time": "2018-01-09T14:39:38.628971Z" } }, "outputs": [], "source": [ "conv_model.load_weights(model_path + 'aug1.h5')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Batch normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### About batch normalization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch normalization (batchnorm) is a way to ensure that activations don't become too high or too low at any point in the model. Adjusting activations so they are of similar scales is called normalization. Normalization is very helpful for fast training - if some activations are very high, they will saturate the model and create very large gradients, causing training to fail; if very low, they will cause training to proceed very slowly. Furthermore, large or small activations in one layer will tend to result in even larger or smaller activations in later layers, since the activations get multiplied repeatedly across the layers.\n", "\n", "Prior to the development of batchnorm in 2015, only the inputs to a model could be effectively normalized - by simply subtracting their mean and dividing by their standard deviation. However, weights in intermediate layers could easily become poorly scaled, due to problems in weight initialization, or a high learning rate combined with random fluctuations in weights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batchnorm resolves this problem by normalizing each intermediate layer as well. The details of how it works are not terribly important (although I will outline them in a moment) - the important takeaway is that all modern networks should use batchnorm, or something equivalent. There are two reasons for this:\n", "1. Adding batchnorm to a model can result in 10x or more improvements in training speed\n", "2. Because normalization greatly reduces the ability of a small number of outlying inputs to over-influence the training, it also tends to reduce overfitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As promised, here's a brief outline of how batchnorm works. As a first step, it normalizes intermediate layers in the same way as input layers can be normalized. But this on its own would not be enough, since the model would then just push the weights up or down indefinitely to try to undo this normalization. Therefore, batchnorm takes two additional steps:\n", "1. Add two more trainable parameters to each layer - one to multiply all activations to set an arbitrary standard deviation, and one to add to all activations to set an arbitary mean\n", "2. Incorporate both the normalization, and the learnt multiply/add parameters, into the gradient calculations during backprop.\n", "\n", "This ensures that the weights don't tend to push very high or very low (since the normalization is included in the gradient calculations, so the updates are aware of the normalization). But it also ensures that if a layer does need to change the overall mean or standard deviation in order to match the output scale, it can do so." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding batchnorm to the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use nearly the same approach as before - but this time we'll add batchnorm layers (and dropout layers):" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.111228Z", "start_time": "2018-01-09T14:39:38.861277Z" } }, "outputs": [ { "data": { "text/plain": [ "(512, 14, 14)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conv_layers[-1].output_shape[1:]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.134716Z", "start_time": "2018-01-09T14:39:39.113536Z" } }, "outputs": [], "source": [ "def get_bn_layers(p):\n", " return [\n", " MaxPooling2D(input_shape=conv_layers[-1].output_shape[1:]),\n", " Flatten(),\n", " Dense(4096, activation='relu'),\n", " Dropout(p),\n", " BatchNormalization(),\n", " Dense(4096, activation='relu'),\n", " Dropout(p),\n", " BatchNormalization(),\n", " Dense(1000, activation='softmax')\n", " ]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.165088Z", "start_time": "2018-01-09T14:39:39.136829Z" } }, "outputs": [], "source": [ "p=0.6" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.777584Z", "start_time": "2018-01-09T14:39:39.167135Z" } }, "outputs": [], "source": [ "bn_model = Sequential(get_bn_layers(0.6))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.780950Z", "start_time": "2018-01-09T14:39:39.779107Z" } }, "outputs": [], "source": [ "# where is this file?\n", "# bn_model.load_weights('/data/jhoward/ILSVRC2012_img/bn_do3_1.h5')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:39.822188Z", "start_time": "2018-01-09T14:39:39.782158Z" } }, "outputs": [], "source": [ "# SINCE KERAS MAKES USE OF INVERTED DROPOUT WE \"NEUTRALIZE\" proc_wgts(layer):\n", "def proc_wgts(layer, prev_p, new_p):\n", " scal = 1\n", " return [o*scal for o in layer.get_weights()]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:40.159583Z", "start_time": "2018-01-09T14:39:39.823609Z" } }, "outputs": [], "source": [ "for l in bn_model.layers: \n", " if type(l)==Dense: l.set_weights(proc_wgts(l, 0.3, 0.6))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:40.163350Z", "start_time": "2018-01-09T14:39:40.161130Z" } }, "outputs": [], "source": [ "bn_model.pop()\n", "for layer in bn_model.layers: layer.trainable=False" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:40.247517Z", "start_time": "2018-01-09T14:39:40.164750Z" } }, "outputs": [], "source": [ "bn_model.add(Dense(2,activation='softmax'))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:39:40.258197Z", "start_time": "2018-01-09T14:39:40.248897Z" } }, "outputs": [], "source": [ "bn_model.compile(Adam(), 'categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:28.109562Z", "start_time": "2018-01-09T14:39:40.259454Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 23000 samples, validate on 2000 samples\n", "Epoch 1/8\n", "23000/23000 [==============================] - 5s 214us/step - loss: 0.3113 - acc: 0.8721 - val_loss: 0.3675 - val_acc: 0.8500\n", "Epoch 2/8\n", "23000/23000 [==============================] - 5s 215us/step - loss: 0.2746 - acc: 0.8995 - val_loss: 0.3533 - val_acc: 0.8670\n", "Epoch 3/8\n", "23000/23000 [==============================] - 5s 215us/step - loss: 0.3031 - acc: 0.8931 - val_loss: 0.3329 - val_acc: 0.8760\n", "Epoch 4/8\n", "23000/23000 [==============================] - 5s 218us/step - loss: 0.3037 - acc: 0.8962 - val_loss: 0.3745 - val_acc: 0.8725\n", "Epoch 5/8\n", "23000/23000 [==============================] - 5s 214us/step - loss: 0.3138 - acc: 0.8946 - val_loss: 0.3632 - val_acc: 0.8760\n", "Epoch 6/8\n", "23000/23000 [==============================] - 5s 215us/step - loss: 0.3136 - acc: 0.8960 - val_loss: 0.3830 - val_acc: 0.8695\n", "Epoch 7/8\n", "23000/23000 [==============================] - 5s 215us/step - loss: 0.3009 - acc: 0.9013 - val_loss: 0.3471 - val_acc: 0.8790\n", "Epoch 8/8\n", "23000/23000 [==============================] - 5s 215us/step - loss: 0.3118 - acc: 0.8976 - val_loss: 0.4080 - val_acc: 0.8675\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3ae94ff320>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bn_model.fit(trn_features, trn_labels, epochs=8, validation_data=(val_features, val_labels))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:30.094583Z", "start_time": "2018-01-09T14:40:28.111092Z" } }, "outputs": [], "source": [ "bn_model.save_weights(model_path+'bn.h5')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:30.300636Z", "start_time": "2018-01-09T14:40:30.097422Z" } }, "outputs": [], "source": [ "bn_model.load_weights(model_path+'bn.h5')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:31.367450Z", "start_time": "2018-01-09T14:40:30.302280Z" } }, "outputs": [], "source": [ "bn_layers = get_bn_layers(0.6)\n", "bn_layers.pop()\n", "bn_layers.append(Dense(2,activation='softmax'))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:32.042562Z", "start_time": "2018-01-09T14:40:31.369851Z" } }, "outputs": [], "source": [ "final_model = Sequential(conv_layers)\n", "for layer in final_model.layers: layer.trainable = False\n", "for layer in bn_layers: final_model.add(layer)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:32.262109Z", "start_time": "2018-01-09T14:40:32.044131Z" } }, "outputs": [], "source": [ "for l1,l2 in zip(bn_model.layers, bn_layers):\n", " l2.set_weights(l1.get_weights())" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:40:32.275674Z", "start_time": "2018-01-09T14:40:32.264249Z" } }, "outputs": [], "source": [ "final_model.compile(optimizer=Adam(), \n", " loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:44:23.452678Z", "start_time": "2018-01-09T14:40:32.276963Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "360/360 [==============================] - 201s 559ms/step - loss: 0.2925 - acc: 0.9541 - val_loss: 0.0678 - val_acc: 0.9790\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3ae8fbe588>" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_model.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=1, \n", " validation_data=val_batches, validation_steps=validation_steps)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:44:27.290191Z", "start_time": "2018-01-09T14:44:23.453964Z" } }, "outputs": [], "source": [ "final_model.save_weights(model_path + 'final1.h5')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:57:50.941290Z", "start_time": "2018-01-09T14:44:27.292982Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/4\n", "360/360 [==============================] - 202s 560ms/step - loss: 0.0950 - acc: 0.9674 - val_loss: 0.0595 - val_acc: 0.9790\n", "Epoch 2/4\n", "360/360 [==============================] - 202s 560ms/step - loss: 0.0760 - acc: 0.9731 - val_loss: 0.0750 - val_acc: 0.9770\n", "Epoch 3/4\n", "360/360 [==============================] - 200s 556ms/step - loss: 0.0672 - acc: 0.9769 - val_loss: 0.1177 - val_acc: 0.9750\n", "Epoch 4/4\n", "360/360 [==============================] - 200s 556ms/step - loss: 0.0672 - acc: 0.9770 - val_loss: 0.0566 - val_acc: 0.9840\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3b43642d30>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_model.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=4, \n", " validation_data=val_batches, validation_steps=validation_steps)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:57:53.296978Z", "start_time": "2018-01-09T14:57:50.943741Z" } }, "outputs": [], "source": [ "final_model.save_weights(model_path + 'final2.h5')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T14:57:53.304079Z", "start_time": "2018-01-09T14:57:53.300155Z" } }, "outputs": [], "source": [ "final_model.optimizer.lr=0.001" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T15:11:16.161753Z", "start_time": "2018-01-09T14:57:53.307635Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/4\n", "360/360 [==============================] - 200s 556ms/step - loss: 0.0599 - acc: 0.9787 - val_loss: 0.0456 - val_acc: 0.9875\n", "Epoch 2/4\n", "360/360 [==============================] - 201s 558ms/step - loss: 0.0573 - acc: 0.9787 - val_loss: 0.0601 - val_acc: 0.9815\n", "Epoch 3/4\n", "360/360 [==============================] - 200s 557ms/step - loss: 0.0477 - acc: 0.9824 - val_loss: 0.0786 - val_acc: 0.9835\n", "Epoch 4/4\n", "360/360 [==============================] - 200s 556ms/step - loss: 0.0508 - acc: 0.9819 - val_loss: 0.0814 - val_acc: 0.9830\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7f3ae90f7b00>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_model.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=4, \n", " validation_data=val_batches, validation_steps=validation_steps)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2018-01-09T15:11:19.523199Z", "start_time": "2018-01-09T15:11:16.164362Z" } }, "outputs": [], "source": [ "bn_model.save_weights(model_path + 'final3.h5')" ] }, { "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": "3.5.4" }, "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": 1 }	apache-2.0
ivannz/crossing_paper2017	experiments/full_experiment.ipynb	1	48777	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# the Monte Carlo experiment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A handy routines to store and recover python objects, in particular, the experiment resutls dictionaires." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import time, gzip\n", "import os, pickle\n", "\n", "def save(obj, path, prefix=None):\n", " prefix_ = \"\" if prefix is None else \"%s_\"%(prefix,)\n", " filename_ = os.path.join(path, \"%s%s.gz\"%(prefix_, time.strftime(\"%Y%m%d-%H%M%S\"),))\n", " with gzip.open(filename_, \"wb+\", 9) as fout_:\n", " pickle.dump(obj, fout_)\n", " return filename_\n", "\n", "def load(filename):\n", " with gzip.open(filename, \"rb\") as f:\n", " return pickle.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The path analyzer" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from crossing_tree import structural_statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect a list of results returned by path_analyze into aligned data tensors." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from crossing_tree import collect_structural_statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A function implementing various delta choices." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "def get_delta_method(delta=1.0):\n", " if isinstance(delta, str):\n", " if delta == \"std\":\n", " # the standard deviation of increments\n", " delta_ = lambda X: np.diff(X).std()\n", " elif delta == \"med\":\n", " # Use the median absolute difference [Jones, Rolls; 2009] p. 11 (arxiv:0911.5204v2)\n", " delta_ = lambda X: np.median(np.abs(np.diff(X)))\n", " elif delta == \"mean\":\n", " # Use the mean absolute difference\n", " delta_ = lambda X: np.mean(np.abs(np.diff(X)))\n", " elif delta == \"iqr\":\n", " # Interquartile range\n", " delta_ = lambda X: np.subtract(np.percentile(np.diff(X), [75, 25]))\n", " elif delta == \"rng\":\n", " # Use the range estimate as suggested by Geoffrey on 2015-05-28\n", " warnings.warn(\"\"\"Use of `range`-based grid resolution \"\"\"\n", " \"\"\"is discouraged since it may cause misaligned \"\"\"\n", " \"\"\"crossing trees.\"\"\", RuntimeWarning)\n", " delta_ = lambda X: (X.max() - X.min()) / (2*12)\n", " else:\n", " raise ValueError(\"\"\"Invalid `delta` setting. Accepted values \"\"\"\n", " \"\"\"are: [`iqr`, `std`, `med`, `rng`, `mean`].\"\"\")\n", " elif isinstance(delta, float) and delta > 0:\n", " delta_ = lambda X: delta\n", " else:\n", " raise TypeError(\"\"\"`delta` must be either a float, or a method \"\"\"\n", " \"\"\"identifier.\"\"\")\n", " return delta_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An MC experiment kernel." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from sklearn.base import clone\n", "\n", "def experiment(experiment_id, n_replications, methods, generator):\n", " generator = clone(generator)\n", " generator.start()\n", "\n", " deltas = [get_delta_method(method_) for method_ in methods]\n", "\n", " results = {method_: list() for method_ in methods}\n", " for j in xrange(n_replications):\n", " T, X = generator.draw()\n", "\n", " # Apply all methods to the same sample path.\n", " for delta, method in zip(deltas, methods):\n", " result_ = structural_statistics(X, T, scale=delta(X), origin=X[0])\n", " results[method].append(result_)\n", "\n", " generator.finish()\n", "\n", " return experiment_id, results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from joblib import Parallel, delayed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A couple of random seeds from [here](https://www.random.org/bytes/)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Extra random seeds should be prepended to the array.\n", "master_seeds = [0xD5F60A17, 0x26F9935C, 0x0E4C1E75, 0xDA7C4291, 0x7ABE722E,\n", " 0x126F3E10, 0x045300B1, 0xB0A9AD11, 0xEED05353, 0x824736C7,\n", " 0x7AA17C9C, 0xB695D6B1, 0x7E214411, 0x538CDEEF, 0xFD55FF46,\n", " 0xE14E1801, 0x872F687C, 0xA58440D9, 0xB8A273FD, 0x0BD1DD28,\n", " 0xAB6A6AE6, 0x7180E905, 0x870E7BAB, 0x846D0C7A, 0xAEF0422D,\n", " 0x16C53C83, 0xE32EA61D, 0xE0AD0A26, 0xCC90CA9A, 0x7D4020D2,]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the Monte Carlo experiemnt is run in parallel batches, with each\n", "initialized to a randomly picked seed." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "MAX_RAND_SEED = np.iinfo(np.int32).max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The folder to store the results in" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "OUTPUT_PATH = \"../results/\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## fBM experiment" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed 7D4020D2\n" ] } ], "source": [ "from crossing_tree.processes import FractionalBrownianMotion\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, methods = 1 << 23, [\"med\", \"std\", \"iqr\", \"mean\",]\n", "hurst_exponents = [0.500, 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experiment for the Fractional Brownian Motion." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FBM-8388608-0.500-125x8 1056.620sec. ../results/FBM-8388608-0.500-125x8_20161108-162012.gz\n", "FBM-8388608-0.550-125x8 1133.918sec. ../results/FBM-8388608-0.550-125x8_20161108-164004.gz\n", "FBM-8388608-0.600-125x8 1186.027sec. ../results/FBM-8388608-0.600-125x8_20161108-170050.gz\n", "FBM-8388608-0.650-125x8 1231.505sec. ../results/FBM-8388608-0.650-125x8_20161108-172209.gz\n", "FBM-8388608-0.700-125x8 1288.416sec. ../results/FBM-8388608-0.700-125x8_20161108-174427.gz\n", "FBM-8388608-0.750-125x8 1351.370sec. ../results/FBM-8388608-0.750-125x8_20161108-180744.gz\n", "FBM-8388608-0.800-125x8 1420.173sec. ../results/FBM-8388608-0.800-125x8_20161108-183207.gz\n", "FBM-8388608-0.850-125x8 1500.556sec. ../results/FBM-8388608-0.850-125x8_20161108-185753.gz\n", "FBM-8388608-0.900-125x8 1604.555sec. ../results/FBM-8388608-0.900-125x8_20161108-192519.gz\n", "FBM-8388608-0.910-125x8 1635.019sec. ../results/FBM-8388608-0.910-125x8_20161108-195316.gz\n", "FBM-8388608-0.915-125x8 1634.690sec. ../results/FBM-8388608-0.915-125x8_20161108-202113.gz\n", "FBM-8388608-0.920-125x8 1645.911sec. ../results/FBM-8388608-0.920-125x8_20161108-204921.gz\n", "FBM-8388608-0.925-125x8 1658.832sec. ../results/FBM-8388608-0.925-125x8_20161108-211742.gz\n", "FBM-8388608-0.930-125x8 1667.693sec. ../results/FBM-8388608-0.930-125x8_20161108-214610.gz\n", "FBM-8388608-0.935-125x8 1683.263sec. ../results/FBM-8388608-0.935-125x8_20161108-221451.gz\n", "FBM-8388608-0.940-125x8 1697.326sec. ../results/FBM-8388608-0.940-125x8_20161108-224346.gz\n", "FBM-8388608-0.945-125x8 1714.388sec. ../results/FBM-8388608-0.945-125x8_20161108-231258.gz\n", "FBM-8388608-0.950-125x8 1726.202sec. ../results/FBM-8388608-0.950-125x8_20161108-234226.gz\n", "FBM-8388608-0.990-125x8 2272.542sec. ../results/FBM-8388608-0.990-125x8_20161109-002058.gz\n" ] } ], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for hurst_ in hurst_exponents:\n", " name_ = \"FBM-%d-%0.3f-%dx%d\"%(n_samples, hurst_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " FractionalBrownianMotion(N=n_samples,\n", " hurst=hurst_,\n", " random_state=seed_))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Hermite process experiment" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed CC90CA9A\n" ] } ], "source": [ "from crossing_tree.processes import HermiteProcess\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup: use no downsampling." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, n_downsample = 1 << 23, 1\n", "degrees, methods = [2, 3, 4], [\"med\", \"std\", \"iqr\", \"mean\",]\n", "hurst_exponents = [ 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experiment for the Hermite process." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HRP2_1-8388608-0.550-125x8 1313.578sec. ../results/HRP2_1-8388608-0.550-125x8_20161109-004331.gz\n", "HRP2_1-8388608-0.600-125x8 1335.730sec. ../results/HRP2_1-8388608-0.600-125x8_20161109-010637.gz\n", "HRP2_1-8388608-0.650-125x8 1373.954sec. ../results/HRP2_1-8388608-0.650-125x8_20161109-013022.gz\n", "HRP2_1-8388608-0.700-125x8 1412.096sec. ../results/HRP2_1-8388608-0.700-125x8_20161109-015439.gz\n", "HRP2_1-8388608-0.750-125x8 1457.555sec. ../results/HRP2_1-8388608-0.750-125x8_20161109-021942.gz\n", "HRP2_1-8388608-0.800-125x8 1519.344sec. ../results/HRP2_1-8388608-0.800-125x8_20161109-024547.gz\n", "HRP2_1-8388608-0.850-125x8 1585.849sec. ../results/HRP2_1-8388608-0.850-125x8_20161109-031254.gz\n", "HRP2_1-8388608-0.900-125x8 1681.800sec. ../results/HRP2_1-8388608-0.900-125x8_20161109-034137.gz\n", "HRP2_1-8388608-0.910-125x8 1697.750sec. 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"HRP4_1-8388608-0.600-125x8 1342.353sec. ../results/HRP4_1-8388608-0.600-125x8_20161109-181446.gz\n", "HRP4_1-8388608-0.650-125x8 1375.779sec. ../results/HRP4_1-8388608-0.650-125x8_20161109-183835.gz\n", "HRP4_1-8388608-0.700-125x8 1417.405sec. ../results/HRP4_1-8388608-0.700-125x8_20161109-190300.gz\n", "HRP4_1-8388608-0.750-125x8 1471.192sec. ../results/HRP4_1-8388608-0.750-125x8_20161109-192813.gz\n", "HRP4_1-8388608-0.800-125x8 1529.610sec. ../results/HRP4_1-8388608-0.800-125x8_20161109-195425.gz\n", "HRP4_1-8388608-0.850-125x8 1609.494sec. ../results/HRP4_1-8388608-0.850-125x8_20161109-202153.gz\n", "HRP4_1-8388608-0.900-125x8 1697.865sec. ../results/HRP4_1-8388608-0.900-125x8_20161109-205053.gz\n", "HRP4_1-8388608-0.910-125x8 1707.985sec. ../results/HRP4_1-8388608-0.910-125x8_20161109-212000.gz\n", "HRP4_1-8388608-0.915-125x8 1725.050sec. ../results/HRP4_1-8388608-0.915-125x8_20161109-214924.gz\n", "HRP4_1-8388608-0.920-125x8 1737.762sec. ../results/HRP4_1-8388608-0.920-125x8_20161109-221901.gz\n", "HRP4_1-8388608-0.925-125x8 1751.786sec. ../results/HRP4_1-8388608-0.925-125x8_20161109-224851.gz\n", "HRP4_1-8388608-0.930-125x8 1761.887sec. ../results/HRP4_1-8388608-0.930-125x8_20161109-231853.gz\n", "HRP4_1-8388608-0.935-125x8 1780.897sec. ../results/HRP4_1-8388608-0.935-125x8_20161109-234912.gz\n", "HRP4_1-8388608-0.940-125x8 1798.063sec. ../results/HRP4_1-8388608-0.940-125x8_20161110-001948.gz\n", "HRP4_1-8388608-0.945-125x8 1811.657sec. ../results/HRP4_1-8388608-0.945-125x8_20161110-005038.gz\n", "HRP4_1-8388608-0.950-125x8 1831.093sec. ../results/HRP4_1-8388608-0.950-125x8_20161110-012148.gz\n", "HRP4_1-8388608-0.990-125x8 2651.580sec. ../results/HRP4_1-8388608-0.990-125x8_20161110-020638.gz\n" ] } ], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for degree_ in degrees:\n", " for hurst_ in hurst_exponents:\n", " name_ = \"HRP%d_%d-%d-%0.3f-%dx%d\"%(degree_, n_downsample, n_samples, hurst_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " HermiteProcess(N=n_samples,\n", " degree=degree_,\n", " n_downsample=n_downsample,\n", " hurst=hurst_,\n", " random_state=seed_))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weierstrass experiment -- $\\lambda_0 = 1.2$" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed E0AD0A26\n" ] } ], "source": [ "from crossing_tree.processes import WeierstrassFunction\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, lambda_0 = 1 << 23, 1.2\n", "methods = [\"med\", \"std\", \"iqr\", \"mean\",]\n", "holder_exponents = [0.500, 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experimnet for the random Weierstrass function $[0, 1]\\mapsto \\mathbb{R}$:\n", "$$ W_H(t) = \\sum_{k\\geq 0} \\lambda_0^{-k H} \\bigl(\\cos(2 \\pi \\lambda_0^k t + \\phi_k) - \\cos \\phi_k\\bigr)\\,, $$\n", "with $(\\phi_k)_{k\\geq0} \\sim \\mathbb{U}[0, 2\\pi]$, and $\\lambda_0 > 1$ -- the fundamental harmonic." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WEI_1.2-8388608-0.500-125x8 13288.305sec. ../results/WEI_1.2-8388608-0.500-125x8_20161110-054850.gz\n", "WEI_1.2-8388608-0.550-125x8 13345.497sec. ../results/WEI_1.2-8388608-0.550-125x8_20161110-093202.gz\n", "WEI_1.2-8388608-0.600-125x8 13390.412sec. ../results/WEI_1.2-8388608-0.600-125x8_20161110-131601.gz\n", "WEI_1.2-8388608-0.650-125x8 13429.911sec. ../results/WEI_1.2-8388608-0.650-125x8_20161110-170034.gz\n", "WEI_1.2-8388608-0.700-125x8 13551.845sec. ../results/WEI_1.2-8388608-0.700-125x8_20161110-204707.gz\n", "WEI_1.2-8388608-0.750-125x8 13604.540sec. ../results/WEI_1.2-8388608-0.750-125x8_20161111-003431.gz\n", "WEI_1.2-8388608-0.800-125x8 13661.736sec. ../results/WEI_1.2-8388608-0.800-125x8_20161111-042252.gz\n", "WEI_1.2-8388608-0.850-125x8 13766.987sec. ../results/WEI_1.2-8388608-0.850-125x8_20161111-081257.gz\n", "WEI_1.2-8388608-0.900-125x8 13917.185sec. ../results/WEI_1.2-8388608-0.900-125x8_20161111-120532.gz\n", "WEI_1.2-8388608-0.910-125x8 13925.786sec. ../results/WEI_1.2-8388608-0.910-125x8_20161111-155816.gz\n", "WEI_1.2-8388608-0.915-125x8 13833.949sec. ../results/WEI_1.2-8388608-0.915-125x8_20161111-194928.gz\n", "WEI_1.2-8388608-0.920-125x8 13777.711sec. ../results/WEI_1.2-8388608-0.920-125x8_20161111-233944.gz\n", "WEI_1.2-8388608-0.925-125x8 13778.000sec. ../results/WEI_1.2-8388608-0.925-125x8_20161112-032958.gz\n", "WEI_1.2-8388608-0.930-125x8 13784.810sec. ../results/WEI_1.2-8388608-0.930-125x8_20161112-072020.gz\n", "WEI_1.2-8388608-0.935-125x8 13807.603sec. ../results/WEI_1.2-8388608-0.935-125x8_20161112-111107.gz\n", "WEI_1.2-8388608-0.940-125x8 13819.266sec. ../results/WEI_1.2-8388608-0.940-125x8_20161112-150202.gz\n", "WEI_1.2-8388608-0.945-125x8 13829.962sec. ../results/WEI_1.2-8388608-0.945-125x8_20161112-185307.gz\n", "WEI_1.2-8388608-0.950-125x8 13843.953sec. ../results/WEI_1.2-8388608-0.950-125x8_20161112-224428.gz\n", "WEI_1.2-8388608-0.990-125x8 14000.682sec. ../results/WEI_1.2-8388608-0.990-125x8_20161113-023826.gz\n" ] } ], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for holder_ in holder_exponents:\n", " name_ = \"WEI_%g-%d-%0.3f-%dx%d\"%(lambda_0, n_samples, holder_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " WeierstrassFunction(N=n_samples,\n", " lambda_0=lambda_0,\n", " holder=holder_,\n", " random_state=seed_,\n", " one_sided=False))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional experiments" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Hermite process experiment: with downsampling" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed E32EA61D\n" ] } ], "source": [ "from crossing_tree.processes import HermiteProcess\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, n_downsample = 1 << 19, 1 << 4\n", "degrees, methods = [2, 3, 4], [\"med\", \"std\", \"iqr\", \"mean\",]\n", "hurst_exponents = [ 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experiment for the Hermite process." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HRP2_16-524288-0.550-125x8 408.249sec. ../results/HRP2_16-524288-0.550-125x8_20161113-024553.gz\n", "HRP2_16-524288-0.600-125x8 417.433sec. ../results/HRP2_16-524288-0.600-125x8_20161113-025327.gz\n", "HRP2_16-524288-0.650-125x8 419.657sec. ../results/HRP2_16-524288-0.650-125x8_20161113-030100.gz\n", "HRP2_16-524288-0.700-125x8 415.866sec. ../results/HRP2_16-524288-0.700-125x8_20161113-030827.gz\n", "HRP2_16-524288-0.750-125x8 418.563sec. ../results/HRP2_16-524288-0.750-125x8_20161113-031559.gz\n", "HRP2_16-524288-0.800-125x8 426.054sec. ../results/HRP2_16-524288-0.800-125x8_20161113-032334.gz\n", "HRP2_16-524288-0.850-125x8 430.168sec. ../results/HRP2_16-524288-0.850-125x8_20161113-033113.gz\n", "HRP2_16-524288-0.900-125x8 431.081sec. ../results/HRP2_16-524288-0.900-125x8_20161113-033851.gz\n", "HRP2_16-524288-0.910-125x8 434.201sec. 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"HRP3_16-524288-0.920-125x8 483.183sec. ../results/HRP3_16-524288-0.920-125x8_20161113-062851.gz\n", "HRP3_16-524288-0.925-125x8 479.245sec. ../results/HRP3_16-524288-0.925-125x8_20161113-063717.gz\n", "HRP3_16-524288-0.930-125x8 483.988sec. ../results/HRP3_16-524288-0.930-125x8_20161113-064548.gz\n", "HRP3_16-524288-0.935-125x8 485.709sec. ../results/HRP3_16-524288-0.935-125x8_20161113-065421.gz\n", "HRP3_16-524288-0.940-125x8 482.878sec. ../results/HRP3_16-524288-0.940-125x8_20161113-070249.gz\n", "HRP3_16-524288-0.945-125x8 486.342sec. ../results/HRP3_16-524288-0.945-125x8_20161113-071123.gz\n", "HRP3_16-524288-0.950-125x8 488.548sec. ../results/HRP3_16-524288-0.950-125x8_20161113-071957.gz\n", "HRP3_16-524288-0.990-125x8 533.592sec. ../results/HRP3_16-524288-0.990-125x8_20161113-072917.gz\n", "HRP4_16-524288-0.550-125x8 502.737sec. ../results/HRP4_16-524288-0.550-125x8_20161113-073806.gz\n", "HRP4_16-524288-0.600-125x8 508.714sec. 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529.015sec. ../results/HRP4_16-524288-0.925-125x8_20161113-091854.gz\n", "HRP4_16-524288-0.930-125x8 528.211sec. ../results/HRP4_16-524288-0.930-125x8_20161113-092810.gz\n", "HRP4_16-524288-0.935-125x8 532.982sec. ../results/HRP4_16-524288-0.935-125x8_20161113-093729.gz\n", "HRP4_16-524288-0.940-125x8 533.696sec. ../results/HRP4_16-524288-0.940-125x8_20161113-094650.gz\n", "HRP4_16-524288-0.945-125x8 531.881sec. ../results/HRP4_16-524288-0.945-125x8_20161113-095609.gz\n", "HRP4_16-524288-0.950-125x8 539.908sec. ../results/HRP4_16-524288-0.950-125x8_20161113-100535.gz\n", "HRP4_16-524288-0.990-125x8 593.903sec. ../results/HRP4_16-524288-0.990-125x8_20161113-101556.gz\n" ] } ], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for degree_ in degrees:\n", " for hurst_ in hurst_exponents:\n", " name_ = \"HRP%d_%d-%d-%0.3f-%dx%d\"%(degree_, n_downsample, n_samples, hurst_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " HermiteProcess(N=n_samples,\n", " degree=degree_,\n", " n_downsample=n_downsample,\n", " hurst=hurst_,\n", " random_state=seed_))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weierstrass experiment -- $\\lambda_0 = 3$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed 16C53C83\n" ] } ], "source": [ "from crossing_tree.processes import WeierstrassFunction\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, lambda_0 = 1 << 23, 3.0\n", "methods = [\"med\", \"std\", \"iqr\", \"mean\",]\n", "holder_exponents = [0.500, 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experimnet for the random Weierstrass function $[0, 1]\\mapsto \\mathbb{R}$:\n", "$$ W_H(t) = \\sum_{k\\geq 0} \\lambda_0^{-k H} \\bigl(\\cos(2 \\pi \\lambda_0^k t + \\phi_k) - \\cos \\phi_k\\bigr)\\,, $$\n", "with $(\\phi_k)_{k\\geq0} \\sim \\mathbb{U}[0, 2\\pi]$, and $\\lambda_0 > 1$ -- the fundamental harmonic." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for holder_ in holder_exponents:\n", " name_ = \"WEI_%g-%d-%0.3f-%dx%d\"%(lambda_0, n_samples, holder_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " WeierstrassFunction(N=n_samples,\n", " lambda_0=lambda_0,\n", " holder=holder_,\n", " random_state=seed_,\n", " one_sided=False))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Weierstrass experiment -- $\\lambda_0 = 1.7$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed AEF0422D\n" ] } ], "source": [ "from crossing_tree.processes import WeierstrassFunction\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples, lambda_0 = 1 << 23, 1.7\n", "methods = [\"med\", \"std\", \"iqr\", \"mean\",]\n", "holder_exponents = [0.500, 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches = 125, 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experimnet for the random Weierstrass function $[0, 1]\\mapsto \\mathbb{R}$:\n", "$$ W_H(t) = \\sum_{k\\geq 0} \\lambda_0^{-k H} \\bigl(\\cos(2 \\pi \\lambda_0^k t + \\phi_k) - \\cos \\phi_k\\bigr)\\,, $$\n", "with $(\\phi_k)_{k\\geq0} \\sim \\mathbb{U}[0, 2\\pi]$, and $\\lambda_0 > 1$ -- the fundamental harmonic." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for holder_ in holder_exponents:\n", " name_ = \"WEI_%g-%d-%0.3f-%dx%d\"%(lambda_0, n_samples, holder_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " WeierstrassFunction(N=n_samples,\n", " lambda_0=lambda_0,\n", " holder=holder_,\n", " random_state=seed_,\n", " one_sided=False))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### fBM experiment: super long" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using seed 846D0C7A\n" ] } ], "source": [ "from crossing_tree.processes import FractionalBrownianMotion\n", "\n", "seed = master_seeds.pop()\n", "print(\"Using seed %X\"%(seed,))\n", "random_state = np.random.RandomState(seed)\n", "\n", "skip = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "n_samples, methods = 1 << 25, [\"med\", \"std\", \"iqr\", \"mean\",]\n", "hurst_exponents = [0.500, 0.550, 0.600, 0.650, 0.700, 0.750, 0.800, 0.850, 0.900,\n", " 0.910, 0.915, 0.920, 0.925, 0.930, 0.935, 0.940, 0.945, 0.950,\n", " 0.990,]\n", "n_per_batch, n_batches, n_threads = 334, 3, 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the experiment for the Fractional Brownian Motion." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FBM-33554432-0.500-334x3 7922.633sec. ../results/FBM-33554432-0.500-334x3_20161108-184220.gz\n", "FBM-33554432-0.550-334x3 8994.173sec. ../results/FBM-33554432-0.550-334x3_20161108-211311.gz\n", "FBM-33554432-0.600-334x3 9671.584sec. ../results/FBM-33554432-0.600-334x3_20161108-235516.gz\n", "FBM-33554432-0.650-334x3 9985.724sec. ../results/FBM-33554432-0.650-334x3_20161109-024230.gz\n", "FBM-33554432-0.700-334x3 10591.847sec. ../results/FBM-33554432-0.700-334x3_20161109-053945.gz\n", "FBM-33554432-0.750-334x3 11290.630sec. ../results/FBM-33554432-0.750-334x3_20161109-084839.gz\n", "FBM-33554432-0.800-334x3 11924.460sec. ../results/FBM-33554432-0.800-334x3_20161109-120805.gz\n", "FBM-33554432-0.850-334x3 12146.259sec. ../results/FBM-33554432-0.850-334x3_20161109-153112.gz\n", "FBM-33554432-0.900-334x3 13212.555sec. ../results/FBM-33554432-0.900-334x3_20161109-191203.gz\n", "FBM-33554432-0.910-334x3 13205.301sec. ../results/FBM-33554432-0.910-334x3_20161109-225250.gz\n", "FBM-33554432-0.915-334x3 13467.640sec. ../results/FBM-33554432-0.915-334x3_20161110-023756.gz\n", "FBM-33554432-0.920-334x3 13369.743sec. ../results/FBM-33554432-0.920-334x3_20161110-062125.gz\n", "FBM-33554432-0.925-334x3 13347.232sec. ../results/FBM-33554432-0.925-334x3_20161110-100430.gz\n", "FBM-33554432-0.930-334x3 13490.713sec. ../results/FBM-33554432-0.930-334x3_20161110-135002.gz\n", "FBM-33554432-0.935-334x3 13567.554sec. ../results/FBM-33554432-0.935-334x3_20161110-173648.gz\n", "FBM-33554432-0.940-334x3 13844.194sec. ../results/FBM-33554432-0.940-334x3_20161110-212810.gz\n", "FBM-33554432-0.945-334x3 13908.407sec. ../results/FBM-33554432-0.945-334x3_20161111-012036.gz\n", "FBM-33554432-0.950-334x3 13572.029sec. ../results/FBM-33554432-0.950-334x3_20161111-050726.gz\n", "FBM-33554432-0.990-334x3 8535.372sec. ../results/FBM-33554432-0.990-334x3_20161111-173449.gz\n" ] } ], "source": [ "if not skip:\n", " par_ = Parallel(n_jobs=-1, verbose=0)\n", " for hurst_ in hurst_exponents:\n", " name_ = \"FBM-%d-%0.3f-%dx%d\"%(n_samples, hurst_, n_per_batch, n_batches)\n", " print(name_,)\n", "\n", " # Schedule the experiments\n", " seeds = random_state.randint(MAX_RAND_SEED, size=(n_batches,))\n", " schedule_ = (delayed(experiment)(seed_, n_per_batch, methods,\n", " FractionalBrownianMotion(N=n_samples,\n", " hurst=hurst_,\n", " random_state=seed_,\n", " n_threads=n_threads))\n", " for seed_ in seeds)\n", "\n", " # Run the experiment and collect the results\n", " tick_ = time.time()\n", " experiment_ids = list()\n", " results_ = {method: list() for method in methods}\n", " for id_, dict_ in par_(schedule_):\n", " experiment_ids.append(id_)\n", " for method in methods:\n", " results_[method].extend(dict_[method])\n", " results = {key_: collect_structural_statistics(list_)\n", " for key_, list_ in results_.iteritems()}\n", " tock_ = time.time()\n", "\n", " # Save the results and log\n", " filename_ = save((tick_, tock_, experiment_ids, results), OUTPUT_PATH, name_)\n", " print(\"%0.3fsec.\"%(tock_ - tick_,), filename_)" ] }, { "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
Zeng-hans/machine-learning-zhzhou	unit3/ch3.3 logistic regression assignment .ipynb	2	76872	{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt \n", "%matplotlib inline\n" ] }, { "cell_type": "code", "execution_count": 20, "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>编号</th>\n", " <th>色泽</th>\n", " <th>根蒂</th>\n", " <th>敲声</th>\n", " <th>纹理</th>\n", " <th>脐部</th>\n", " <th>触感</th>\n", " <th>密度</th>\n", " <th>含糖率</th>\n", " <th>好瓜</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>青绿</td>\n", " <td>蜷缩</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.697</td>\n", " <td>0.46</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>乌黑</td>\n", " <td>蜷缩</td>\n", " <td>沉闷</td>\n", " <td>清晰</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.774</td>\n", " <td>0.376</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>乌黑</td>\n", " <td>蜷缩</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.634</td>\n", " <td>0.264</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>青绿</td>\n", " <td>蜷缩</td>\n", " <td>沉闷</td>\n", " <td>清晰</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.608</td>\n", " <td>0.318</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>浅白</td>\n", " <td>蜷缩</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.556</td>\n", " <td>0.215</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>青绿</td>\n", " <td>稍蜷</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>稍凹</td>\n", " <td>软粘</td>\n", " <td>0.403</td>\n", " <td>0.237</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>乌黑</td>\n", " <td>稍蜷</td>\n", " <td>浊响</td>\n", " <td>稍糊</td>\n", " <td>稍凹</td>\n", " <td>软粘</td>\n", " <td>0.481</td>\n", " <td>0.149</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>乌黑</td>\n", " <td>稍蜷</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>稍凹</td>\n", " <td>硬滑</td>\n", " <td>0.437</td>\n", " <td>0.211</td>\n", " <td>是</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>乌黑</td>\n", " <td>稍蜷</td>\n", " <td>沉闷</td>\n", " <td>稍糊</td>\n", " <td>稍凹</td>\n", " <td>硬滑</td>\n", " <td>0.666</td>\n", " <td>0.091</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10</td>\n", " <td>青绿</td>\n", " <td>硬挺</td>\n", " <td>清脆</td>\n", " <td>清晰</td>\n", " <td>平坦</td>\n", " <td>软粘</td>\n", " <td>0.243</td>\n", " <td>0.267</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>11</td>\n", " <td>浅白</td>\n", " <td>硬挺</td>\n", " <td>清脆</td>\n", " <td>模糊</td>\n", " <td>平坦</td>\n", " <td>硬滑</td>\n", " <td>0.245</td>\n", " <td>0.057</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>12</td>\n", " <td>浅白</td>\n", " <td>蜷缩</td>\n", " <td>浊响</td>\n", " <td>模糊</td>\n", " <td>平坦</td>\n", " <td>软粘</td>\n", " <td>0.343</td>\n", " <td>0.099</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>13</td>\n", " <td>青绿</td>\n", " <td>稍蜷</td>\n", " <td>浊响</td>\n", " <td>稍糊</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.639</td>\n", " <td>0.161</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>14</td>\n", " <td>浅白</td>\n", " <td>稍蜷</td>\n", " <td>沉闷</td>\n", " <td>稍糊</td>\n", " <td>凹陷</td>\n", " <td>硬滑</td>\n", " <td>0.657</td>\n", " <td>0.198</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>15</td>\n", " <td>乌黑</td>\n", " <td>稍蜷</td>\n", " <td>浊响</td>\n", " <td>清晰</td>\n", " <td>稍凹</td>\n", " <td>软粘</td>\n", " <td>0.36</td>\n", " <td>0.37</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>16</td>\n", " <td>浅白</td>\n", " <td>蜷缩</td>\n", " <td>浊响</td>\n", " <td>模糊</td>\n", " <td>平坦</td>\n", " <td>硬滑</td>\n", " <td>0.593</td>\n", " <td>0.042</td>\n", " <td>否</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>17</td>\n", " <td>青绿</td>\n", " <td>蜷缩</td>\n", " <td>沉闷</td>\n", " <td>稍糊</td>\n", " <td>稍凹</td>\n", " <td>硬滑</td>\n", " <td>0.719</td>\n", " <td>0.103</td>\n", " <td>否</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 编号色泽根蒂敲声纹理脐部触感密度含糖率好瓜\n", "0 1 青绿蜷缩浊响清晰凹陷硬滑 0.697 0.46 是\n", "1 2 乌黑蜷缩沉闷清晰凹陷硬滑 0.774 0.376 是\n", "2 3 乌黑蜷缩浊响清晰凹陷硬滑 0.634 0.264 是\n", "3 4 青绿蜷缩沉闷清晰凹陷硬滑 0.608 0.318 是\n", "4 5 浅白蜷缩浊响清晰凹陷硬滑 0.556 0.215 是\n", "5 6 青绿稍蜷浊响清晰稍凹软粘 0.403 0.237 是\n", "6 7 乌黑稍蜷浊响稍糊稍凹软粘 0.481 0.149 是\n", "7 8 乌黑稍蜷浊响清晰稍凹硬滑 0.437 0.211 是\n", "8 9 乌黑稍蜷沉闷稍糊稍凹硬滑 0.666 0.091 否\n", "9 10 青绿硬挺清脆清晰平坦软粘 0.243 0.267 否\n", "10 11 浅白硬挺清脆模糊平坦硬滑 0.245 0.057 否\n", "11 12 浅白蜷缩浊响模糊平坦软粘 0.343 0.099 否\n", "12 13 青绿稍蜷浊响稍糊凹陷硬滑 0.639 0.161 否\n", "13 14 浅白稍蜷沉闷稍糊凹陷硬滑 0.657 0.198 否\n", "14 15 乌黑稍蜷浊响清晰稍凹软粘 0.36 0.37 否\n", "15 16 浅白蜷缩浊响模糊平坦硬滑 0.593 0.042 否\n", "16 17 青绿蜷缩沉闷稍糊稍凹硬滑 0.719 0.103 否" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#load the watermelon dataset\n", "\n", "def get_watermelon_dataset():\n", " file = open('/Users/HansZeng/Desktop/watermelon-dataset.txt')\n", " lines = file.readlines()\n", " m = []\n", " i = 0\n", " for line in lines:\n", " m.append(line.split(\",\"))\n", " i = i + 1\n", " df = pd.DataFrame(m[1:])\n", " l1 = ['是']8\n", " l2 = ['否']9\n", " l1.extend(l2)\n", " df[9] = l1\n", " df.columns = ['编号','色泽','根蒂','敲声','纹理','脐部','触感','密度','含糖率','好瓜']\n", " return df\n", "\n", "df = get_watermelon_dataset()\n", "df" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x116879390>" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1147a3650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"preprocess the dataset \"\"\"\n", "\n", "#从原始数据集中抽取新的数据集，新的数据集只包含“密度”和“含糖量”的数据\n", "s1 = df['密度']\n", "s2 = df['含糖率']\n", "s3 = [1]8\n", "s3.extend([0]9)\n", "df = pd.DataFrame({'label':s3,'密度':s1,'含糖率':s2})\n", "\n", "#convert the dataframe to matrix\n", "m = df.values\n", "\n", "#convert the string in matrix to float\n", "for i in range(m.shape[0]):\n", " for j in range(m.shape[1]):\n", " m[i,j] = float(m[i,j])\n", "\n", "#draw scatter diagram to plot raw data\n", "xdim = m[:,1]\n", "ydim = m[:,2]\n", "plt.scatter(xdim, ydim, c=m[:,0])\n", "plt.xlabel(\"ratio sugar\")\n", "plt.ylabel(\"density\")" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 0.20 0.33 5\n", " 1 0.50 1.00 0.67 4\n", "\n", "avg / total 0.78 0.56 0.48 9\n", "\n" ] } ], "source": [ "\"\"\"use the sklearn libaray to complete logistic regression homework\"\"\"\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression \n", "from sklearn import preprocessing\n", "from sklearn import utils\n", "from sklearn.metrics import classification_report\n", "from sklearn import model_selection\n", "\n", "\n", "#make the y_label encoder\n", "lab_enc = preprocessing.LabelEncoder()\n", "y_label = lab_enc.fit_transform(m[:,0])\n", "#select the train and test set\n", "X_train, X_test, y_train, y_test = model_selection.train_test_split(m[:,1:3], y_label,test_size=0.5, random_state=42)\n", "#train the X_train\n", "classifier = LogisticRegression()\n", "classifier.fit(X_train, y_train)\n", "y_pred = classifier.predict(X_test)\n", "\n", "#get the summarize of fitting report\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以看出上面的分类情况不是很好，如何改进？" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面自己编程得到梯度下降算法" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "import numpy as np\n", "import math\n", "def likehood_func(X, y, beta): #the goal function to get the minimal value as the (3.27) equation in zzh's book\n", " \"\"\"\n", " @para X: X is the sample matrix, the dim is (3,17)\n", " @para y: y is the label array\n", " @return beta: the parameter of (3.27) in zzh's book\n", " \"\"\"\n", " r, c = X.shape\n", " sum = 0\n", " for i in range(r):\n", " exp = -y[i]np.dot(beta, X[i,]) + math.log(1+math.e(np.dot(X[i,],beta)))\n", " sum = sum + exp\n", " return sum\n" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gradient_descent(X, y,h):\n", " \"\"\"\n", " @para X: X is the sample matrix, the dim is (3,17)\n", " @para y: y is the label array\n", " @para h: h is the step length of iteration\n", " @return beta: the best parameter of (3.27) in zzh's book\n", " \"\"\"\n", " maxtime = 500 #give the iterative time limit\n", " r, c = X.shape\n", " delta_beta = np.array([h]c)\n", " beta = np.zeros(c)\n", " cur_lh = 0 #the initial function value and set it 0\n", " lhs = [] #记录似然函数在不同beta下的值\n", " \n", " for i in range(maxtime):\n", " temp_beta = beta\n", " #patrial part\n", " for j in range(c):\n", " beta[j] += delta_beta[j]\n", " new_lh = likehood_func(X, y, beta)\n", " delta_beta[j] = -h * (new_lh - cur_lh) / delta_beta[j]\n", " beta = temp_beta\n", " beta += delta_beta\n", " cur_lh = likehood_func(X, y, beta)\n", " lhs.append(cur_lh)\n", " return (beta,lhs)" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = m[:,0:3]\n", "y = m[:,0]\n" ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T8ULf0KzkhAVVmG3j9IZhGLuR8ULf1Bxl7KAS8nNCNnxjGIaRgIwX+sbmKIW5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\n3Hj+UFXtDdybzCYRKcANRQT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"text/plain": [ "<matplotlib.figure.Figure at 0x117265450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"draw line diagram to view the converage condition\"\"\"\n", "x = range(500)\n", "plt.subplot(3,1,1)\n", "plt.plot(range(500),gradient_descent(X,y,0.1)[1])\n", "plt.title(\"converage diagram with 0.1 step\")\n", "\n", "plt.subplot(3,1,2)\n", "plt.plot(range(500),gradient_descent(X,y,0.05)[1])\n", "plt.title(\"converage diagram with 0.05 step\")\n", "\n", "plt.subplot(3,1,3)\n", "plt.plot(range(500),gradient_descent(X,y,0.01)[1])\n", "plt.title(\"converage diagram with 0.01 step\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 270, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gradient_descent_version2(X, y, h):\n", " \"\"\"\n", " @para X: X is the sample matrix, the dim is (3,17)\n", " @para y: y is the label array\n", " @para h: h is the step length of iteration\n", " @return beta: the best parameter of (3.27) in zzh's book\n", " \"\"\"\n", " maxtime = 500 #give the iterative time limit\n", " r, c = X.shape\n", " delta_beta = np.array([h]c)\n", " beta = np.zeros(c)\n", " cur_lh = 0 #the initial function value and set it 0\n", " lhs = [] #记录似然函数在不同beta下的值\n", " \n", " for i in range(maxtime):\n", " temp_beta = beta\n", " #patrial part\n", " for j in range(c):\n", " beta[j] += delta_beta[j]\n", " new_lh = likehood_func(X, y, beta)\n", " delta_beta[j] = -h partial_likehood(X,y,beta)[j]\n", " beta = temp_beta\n", " beta += delta_beta\n", " cur_lh = likehood_func(X, y, beta)\n", " lhs.append(cur_lh)\n", " return (beta,lhs)\n", "\n", "def partial_likehood(X, y, beta):\n", " r, c = X.shape\n", " partial = np.ones(3)\n", " \n", " for i in range(r):\n", " for j in range(c):\n", " partial[j] += X[i,j] (1-1/(1+math.enp.dot(beta,X[i,]))-y[i])\n", " return partial" ] }, { "cell_type": "code", "execution_count": 277, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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FwJIlSxq0Hx/nymoPva6ujuH0F3SGYRj5gIj06yu1+TCGzl2L1nLZvXYhMAzD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\niOwGdvp6JwXq2hg0RES+LiJ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"text/plain": [ "<matplotlib.figure.Figure at 0x117207350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"draw line diagram to view the converage condition for gradient_descent_version2\"\"\"\n", "x = range(500)\n", "plt.subplot(3,1,1)\n", "plt.plot(range(500),gradient_descent_version2(X,y,0.1)[1])\n", "plt.title(\"converage diagram with 0.1 step\")\n", "\n", "plt.subplot(3,1,2)\n", "plt.plot(range(500),gradient_descent_version2(X,y,0.05)[1])\n", "plt.title(\"converage diagram with 0.05 step\")\n", "\n", "plt.subplot(3,1,3)\n", "plt.plot(range(500),gradient_descent_version2(X,y,0.01)[1])\n", "plt.title(\"converage diagram with 0.01 step\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.76953419512\n", "1.79955262728\n", "1.60347272539\n", "0.581473002551\n", "0.987732994081\n", "2.88670747429\n", "0.649089572923\n", "1.01016379169\n", "1.35509081156\n", "y_test: [1 1 1 0 0 0 0 0 1] y_pred: [0.7347152331729239, 0.6428000708917684, 0.6158976469209585, 0.36767810870828854, 0.4969143225081142, 0.7427128214264584, 0.39360480084320604, 0.5025281003798534, 0.5753879234324673]\n" ] } ], "source": [ "'''\n", "use the own logistic regression function to complete the homework\n", "'''\n", "X_train, X_test, y_train, y_test = model_selection.train_test_split(m[:,1:3], y_label,test_size=0.5, random_state=42)\n", "\n", "def prob_select_true(beta,x):\n", " print np.e(np.dot(beta,x))\n", " return 1 - 1 / (1 + np.e*(np.dot(beta,x)))\n", "\n", "\n", "beta = gradient_descent(X_train, y_train, 0.1)[0] #get the optimized sequence\n", "\n", "y_pred = []\n", "\n", "for i in range(len(y_test)):\n", " y_pred.append(prob_select_true(beta,X_test[i,]))\n", " \n", "print \"y_test: \", y_test, \" y_pred:\" ,y_pred #print the pro_y by the X_test to compare with y_test" ] }, { "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 }	bsd-2-clause
nist-ionstorage/electrode	examples/tutorial.ipynb	1	172786	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tutorial\n", "========\n", "\n", "All parts of the tutorial are presented to be run in IPython. The\n", "required packages are only imported ones but used in all sections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup\n", "-----\n", "\n", "First import the needed packages and modules. From the `electrode`\n", "package, a set of electrodes form a `System`. Here, the electrodes are\n", "either `PointPixelElectrodes` for point approximations or\n", "`PolygonPixelElectrodes` for polygonal structures." ] }, { "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": [ "import matplotlib.pyplot as plt, numpy as np, scipy.constants as ct\n", "from electrode import (System, PolygonPixelElectrode, euler_matrix,\n", " PointPixelElectrode, PotentialObjective,\n", " PatternRangeConstraint, shaped)\n", "\n", "np.set_printoptions(precision=2) # have numpy print fewer digits\n", "\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linear surface trap\n", "-------------------\n", "\n", "Let's start with a very simple five-wire linear surface electrode trap.\n", "\n", "We start with a function that returns a parametrized system of surface\n", "electrodes. This way different designs can be compared quickly and the\n", "design parameter space can be explored.\n", "\n", "There ware two rf wires running along `x` with width in the `y`\n", "direction of `top` and `bottom`. Between them, there is a long `dc`\n", "electrode `c` of width `mid`. Above and below the rf electrodes there\n", "are three dc electrodes `tl, tm, tr` and `bl, bm, br` to provide stray\n", "field compensation, axial confinement and knobs to change the curvature\n", "tensor." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def five_wire(edge, width, top, mid, bot):\n", " e, r, t, m, b = edge, width/2, top + mid/2, mid/2, -bot - mid/2\n", " electrodes = [\n", " (\"tl\", [[(-e, e), (-e, t), (-r, t), (-r, e)]]),\n", " (\"tm\", [[(-r, e), (-r, t), (r, t), (r, e)]]),\n", " (\"tr\", [[(r, e), (r, t), (e, t), (e, e)]]),\n", " (\"bl\", [[(-e, -e), (-r, -e), (-r, b), (-e, b)]]),\n", " (\"bm\", [[(-r, -e), (r, -e), (r, b), (-r, b)]]),\n", " (\"br\", [[(r, -e), (e, -e), (e, b), (r, b)]]),\n", " (\"r\", [[(-e, t), (-e, m), (e, m), (e, t)],\n", " [(-e, b), (e, b), (e, -m), (-e, -m)]]),\n", " (\"c\", [[(-e, m), (-e, -m), (e, -m), (e, m)]]),\n", " ]\n", " s = System([PolygonPixelElectrode(name=n, paths=map(np.array, p))\n", " for n, p in electrodes])\n", " s[\"r\"].rf = 1.\n", " return s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can retrieve such a system and plot the electrodes' shapes, and\n", "the rf voltages on them." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2a8d43e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s = five_wire(5, 2., 1., 1., 1.)\n", "\n", "fig, ax = plt.subplots(1, 2)\n", "s.plot(ax[0])\n", "s.plot_voltages(ax[1], u=s.rfs)\n", "\n", "r = 5\n", "for axi in ax.flat:\n", " axi.set_aspect(\"equal\")\n", " axi.set_xlim(-r, r)\n", " axi.set_ylim(-r, r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To trap an ion in this trap, we find the potential minimum in `x0` the `yz`\n", "plane (`axis=(1, 2)`) and perform a analysis of the potential landscape\n", "at and around this minimum assuming some typical operating parameters.\n", "Again, we constrain the search for the minimum and the saddle point to\n", "the `yz` plane since there is no adequate axial confinement yet." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parameters:\n", " f=100 MHz, m=25 amu, q=1 qe, l=30 µm, scale=0.0521 V'/V_SI\n", "corrdinates:\n", " analyze point: [ 0.00e+00 2.28e-17 8.36e-01]\n", " ([ 0.00e+00 6.85e-16 2.51e+01] µm)\n", " minimum is at offset: [ 0. 0. 0.]\n", " ([ 0. 0. 0.] µm)\n", "potential:\n", " dc electrical: 0 eV\n", " rf pseudo: 1e-16 eV\n", " saddle offset: [ 1.17e-04 2.76e-05 7.21e-01]\n", " ([ 3.51e-03 8.29e-04 2.16e+01] µm)\n", " saddle height: 0.0099 eV\n", "force:\n", " dc electrical: [ 0. 0. 0.] eV/l\n", " ([ 0. 0. 0.] eV/m)\n", " rf pseudo: [ 0.00e+00 0.00e+00 -8.48e-09] eV/l\n", " ([ 0. 0. -0.] eV/m)\n", "modes:\n", " pp+dc normal curvatures: [ 1.01e-05 3.58e-01 3.62e-01]\n", " motion is bounded: True\n", " pseudopotential modes:\n", " a: 0.0331 MHz, [ 1. 0. 0.]\n", " b: 6.233 MHz, [ 0. 0. 1.]\n", " c: 6.267 MHz, [ 0. 1. 0.]\n", " euler angles (rzxz): [ 180. 90. 0.] deg\n", " mathieu modes:\n", " a: 0.03311 MHz, [ 1.00e+00 6.19e-19 3.65e-19]\n", " b: 6.272 MHz, [ -6.89e-16 -9.47e-13 9.83e-01]\n", " c: 6.306 MHz, [ 4.19e-16 9.83e-01 -6.78e-13]\n", " euler angles (rzxz): [ 1.80e+02 9.00e+01 2.13e-17] deg\n", " heating for 1 nV²/Hz white uncorrelated on each electrode:\n", " field-noise psd: [ 9.35e-13 5.52e-12 1.35e-10] V²/(m² Hz)\n", " a: ndot=6587 /s, S_Ef=3.094e-08 (V² Hz)/(m² Hz)\n", " b: ndot=4839 /s, S_Ef=0.0008159 (V² Hz)/(m² Hz)\n", " c: ndot=197.2 /s, S_Ef=3.362e-05 (V² Hz)/(m² Hz)\n" ] } ], "source": [ "l = 30e-6 # µm length scale\n", "u = 20. # V rf peak voltage\n", "m = 25ct.atomic_mass # ion mass\n", "q = 1ct.elementary_charge # ion charge\n", "o = 2np.pi100e6 # rf frequency in rad/s\n", "s[\"r\"].rf = unp.sqrt(q/m)/(2lo)\n", "\n", "x0 = s.minimum((0, 0, 1.), axis=(1, 2))\n", "\n", "for line in s.analyze_static(x0, axis=(1, 2), m=m, q=q, l=l, o=o):\n", " print(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The seven dc electrodes (three top, three bottom and the center wire)\n", "can be used to apply electrical fields and electrical curvatures to\n", "compensate stray fields and confine the ion axially.\n", "\n", "The `shim()` method can be used to calculate voltage vectors for these\n", "dc electrodes that are result in orthogonal effects with regards to some\n", "cartesian partial derivatives at certain points. To use it we build a\n", "temporary new system `s1` holding only the dc electrodes. Since these dc\n", "electrode instances also appear in our primary system `s`, changes in\n", "voltages are automatically synchronized between the two systems.\n", "\n", "We then can calculate the shim voltage vectors that result in unit\n", "changes of each of the partial derivatives `y, z, xx, xy, xz, yy, yzz` at\n", "`x0` and plot the voltage distributions. We disturb the `x0` position slightly to \n", "avoid numerical problems due to the high symmetry of the trap." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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e8lxeeP5lvPRpu3LlXffREW188PBn89FfX8uy1es47Vl789pn7ll22SOvl+2i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\n2MBk3jkiTgc2ZeYtETHetiuAFQBMmz2Zp5UkSZKk3zFusYmI7wIHjbD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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff27963a668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s1 = System([e for e in s if not e.rf])\n", "derivs = \"y z xx xy xz yy yzz\".split()\n", "u = s1.shims([(x0 + 1e-3, None, deriv) for deriv in derivs])\n", "fig, ax = plt.subplots(3, len(derivs)//2, figsize=(14, 14))\n", "for d, ui, axi in zip(derivs, u, ax.flat):\n", " with s1.with_voltages(dcs=ui):\n", " s.plot_voltages(axi)\n", " axi.set_aspect(\"equal\")\n", " axi.set_xlim(-r, r)\n", " axi.set_ylim(-r, r)\n", " um = ui[np.argmax(np.fabs(ui))]\n", " axi.set_title(\"%s, max=%g\" % (d, um))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rf/dc pattern optimization\n", "--------------------------\n", "\n", "Define a function that generates the pixels and electrode. Here \n", "we return pixel electrodes with `n` pixels per unit\n", "length in a hexagonal pattern.\n", "\n", "If `points` is True, each pixel is approximated as a point\n", "else each pixel is a hexagon." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hextess(n, points):\n", " x= np.vstack([[i + j.5, j3*.5.5]\n", " for j in range(-n - min(0, i), n - max(0, i) + 1)]\n", " for i in range(-n, n + 1))/(n + .5) # centers\n", " if points:\n", " a = np.ones(len(x))3.5/(n + .5)2/2 # areas\n", " return [PointPixelElectrode(points=[xi], areas=[ai]) for\n", " xi, ai in zip(x, a)]\n", " else:\n", " a = 1/(3.5(n + .5)) # edge length\n", " p = x[:, None] + [[anp.cos(phi), anp.sin(phi)] for phi in\n", " np.arange(np.pi/6, 2np.pi, np.pi/3)]\n", " return [PolygonPixelElectrode(paths=[i]) for i in p]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now define a function that returns a System instance with a single hexagonal\n", "rf pixel electrode.\n", "\n", "The pixel factors (whether a pixel is grounded or at rf) are optimized\n", "to yield three trapping sites forming an equilateral triangle with\n", "\n", " `n` pixels per unit length,\n", " * trap separation `d`,\n", " * trap height `h` above the surface electrodes, and\n", " * trapping frequencies with a ratio `2:1:1` (radial being the strongest)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " pcost dcost gap pres dres k/t\n", " 0: 1.8926e-01 -4.9783e+02 5e+02 9e-14 3e-15 1e+00\n", " 1: 1.5552e-01 -5.6524e+01 6e+01 9e-15 4e-15 1e-01\n", " 2: -2.9777e-01 -2.0601e+01 2e+01 4e-14 2e-15 2e-02\n", " 3: -4.4200e-01 -3.2442e+00 3e+00 6e-14 6e-16 3e-03\n", " 4: -5.0508e-01 -3.0367e+00 3e+00 7e-14 6e-16 3e-03\n", " 5: -7.9738e-01 -1.1889e+00 4e-01 2e-14 2e-16 4e-04\n", " 6: -9.2171e-01 -1.0940e+00 2e-01 2e-14 1e-16 2e-04\n", " 7: -9.1804e-01 -1.0767e+00 2e-01 2e-13 1e-16 2e-04\n", " 8: -9.2821e-01 -1.0773e+00 1e-01 1e-13 2e-16 2e-04\n", " 9: -9.4948e-01 -1.0497e+00 1e-01 1e-13 2e-16 1e-04\n", "10: -9.8984e-01 -1.0090e+00 2e-02 3e-14 8e-17 2e-05\n", "11: -9.9464e-01 -1.0015e+00 7e-03 5e-14 1e-16 7e-06\n", "12: -9.9554e-01 -9.9939e-01 4e-03 5e-13 1e-16 4e-06\n", "13: -9.9692e-01 -9.9755e-01 6e-04 1e-13 1e-16 7e-07\n", "14: -9.9721e-01 -9.9722e-01 1e-05 3e-13 1e-16 1e-08\n", "15: -9.9721e-01 -9.9721e-01 1e-07 2e-13 2e-16 1e-10\n", "Optimal solution found.\n" ] } ], "source": [ "def threefold(n, h, d, points=True):\n", " s = System(hextess(n, points))\n", " ct = []\n", " ct.append(PatternRangeConstraint(min=0, max=1.))\n", " for p in 0, 4np.pi/3, 2np.pi/3:\n", " x = np.array([d/3*.5np.cos(p), d/3*.5np.sin(p), h])\n", " r = euler_matrix(p, np.pi/2, np.pi/4, \"rzyz\")[:3, :3]\n", " for i in \"x y z xy xz yz\".split():\n", " ct.append(PotentialObjective(derivative=i, x=x,\n", " rotation=r, value=0))\n", " for i, v in (\"xx\", 1), (\"yy\", 1):\n", " ct.append(PotentialObjective(derivative=i, x=x,\n", " rotation=r, value=v))\n", " s.rfs, c = s.optimize(ct)\n", " return s, c\n", "\n", "points, n, h, d = True, 12, 1/8., 1/4.\n", "s, c = threefold(n, h, d, points)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analysis of the result. `c` is the obtained strength of the constraints,\n", "the rf field should vanish and the rf curvature should be `(2, 1, 1)`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c: 8.35200136634\n", "rf'/c: [ -4.72e-16 2.93e-16 -6.26e-16]\n" ] }, { "data": { "text/plain": [ "(\"rf''/c:\",\n", " array([ -2.00e+00, 1.48e-15, -8.33e-15, 1.00e+00, 9.95e-17]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x0 = np.array([d/3*.5, 0, h])\n", "print(\"c:\", c)\n", "print(\"rf'/c:\", s.electrical_potential(x0, \"rf\", 1)[0]/c)\n", "\"rf''/c:\", s.electrical_potential(x0, \"rf\", 2)[0]/c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the electrode pattern, white is ground, black/red is rf." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OWt799aNs+c977V3obFHh1FfVnKp6FQRPOX5rZa+HosBObekpOkG92YTRamnP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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2a8d05f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.set_aspect(\"equal\")\n", "ax.set_xlim((-1,1))\n", "ax.set_ylim((-1,1))\n", "s.plot_voltages(ax, u=s.rfs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some textual analysis of one of the trapping sites." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parameters:\n", " f=50 MHz, m=24 amu, q=1 qe, l=320 µm, scale=0.00997 V'/V_SI\n", "corrdinates:\n", " analyze point: [ 0.14 0. 0.12]\n", " ([ 46.19 0. 40. ] µm)\n", " minimum is at offset: [ 0. 0. 0.]\n", " ([ 0. 0. 0.] µm)\n", "potential:\n", " dc electrical: 0 eV\n", " rf pseudo: 2.4e-30 eV\n", " saddle offset: [-0.14 -0. 0.42]\n", " ([ -46.15 -1.4 135.72] µm)\n", " saddle height: 0.0064 eV\n", "force:\n", " dc electrical: [ 0. 0. 0.] eV/l\n", " ([ 0. 0. 0.] eV/m)\n", " rf pseudo: [ 5.55e-15 2.01e-15 -3.85e-15] eV/l\n", " ([ 1.73e-11 6.27e-12 -1.20e-11] eV/m)\n", "modes:\n", " pp+dc normal curvatures: [ 5.55 5.55 22.2 ]\n", " motion is bounded: True\n", " pseudopotential modes:\n", " a: 2.349 MHz, [ -1.03e-15 -1.00e+00 3.14e-02]\n", " b: 2.349 MHz, [ -8.77e-15 -3.14e-02 -1.00e+00]\n", " c: 4.698 MHz, [ 1.00e+00 -1.31e-15 -8.73e-15]\n", " euler angles (rzxz): [ 90. 90. 178.2] deg\n", " mathieu modes:\n", " a: 2.357 MHz, [ 5.48e-16 9.25e-01 3.12e-01]\n", " b: 2.357 MHz, [ -3.37e-15 -2.32e-03 9.88e-01]\n", " c: 4.766 MHz, [ 9.61e-01 -4.21e-16 2.69e-15]\n", " euler angles (rzxz): [ 90. 90. 17.53] deg\n", " heating for 1 nV²/Hz white uncorrelated on each electrode:\n", " field-noise psd: [ 6.57e-12 6.48e-12 1.35e-11] V²/(m² Hz)\n", " a: ndot=706.1 /s, S_Ef=1.615e-05 (V² Hz)/(m² Hz)\n", " b: ndot=1359 /s, S_Ef=3.108e-05 (V² Hz)/(m² Hz)\n", " c: ndot=309.5 /s, S_Ef=2.893e-05 (V² Hz)/(m² Hz)\n" ] } ], "source": [ "l = 320e-6 # length scale, hexagon radius\n", "u = 20. # peak rf voltage\n", "o = 2np.pi50e6 # rf frequency\n", "m = 24ct.atomic_mass # ion mass\n", "q = 1ct.elementary_charge # ion charge\n", "s.rfs = unp.sqrt(q/m)/(2lo)\n", "\n", "for line in s.analyze_static(x0, l=l, o=o, m=m, q=q):\n", " print(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the horizontal logarithmic pseudopotential at the ion height\n", "and the logarithmic pseudopotential and the separatrix in the xz plane." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "main saddle: [ 1.14e-08 -1.11e-08 5.49e-01] 0.00642773798929\n" ] }, { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7ff2780ca048>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+x3fqS+8+AR76IDuJfH5EbIXNT0BWEnT/Feo9Wfg93i2khOAFcOhTSA4FxwrQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff272aab908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 50\n", "xyz = np.mgrid[-d:d:1jn, -d:d:1jn, h:h+1]\n", "fig, ax = plt.subplots(1, 2, subplot_kw=dict(aspect=\"equal\"))\n", "pot = shaped(s.potential)(xyz)\n", "v = np.arange(-15, 3)\n", "x, y, p = (_.reshape(n, n) for _ in (xyz[0], xyz[1], pot))\n", "ax[0].contour(x, y, np.log2(p), v, cmap=plt.cm.hot)\n", "\n", "(xs1, ps1), (xs0, ps0) = s.saddle(x0+1e-2), s.saddle([0, 0, .8])\n", "print(\"main saddle:\", xs0, ps0)\n", "xyz = np.mgrid[-d:d:1jn, 0:1, .7h:3h:1jn]\n", "pot = shaped(s.potential)(xyz)\n", "x, z, p = (_.reshape(n, n) for _ in (xyz[0], xyz[2], pot))\n", "ax[1].contour(x, z, np.log2(p), v, cmap=plt.cm.hot)\n", "ax[1].contour(x, z, np.log2(p), np.log2((ps1, ps0)), color=\"black\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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 }	gpl-3.0
jGaboardi/AAG_16	AAG_16.ipynb	2	3913694	null	lgpl-3.0
hglanz/phys202-2015-work	assignments/assignment09/IntegrationEx01.ipynb	1	4812	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import integrate" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Trapezoidal rule" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The [trapezoidal](http://en.wikipedia.org/wiki/Trapezoidal_rule) rule generates a numerical approximation to the 1d integral:\n", "\n", "$$ I(a,b) = \\int_a^b f(x) dx $$\n", "\n", "by dividing the interval $[a,b]$ into $N$ subdivisions of length $h$:\n", "\n", "$$ h = (b-a)/N $$\n", "\n", "Note that this means the function will be evaluated at $N+1$ points on $[a,b]$. The main idea of the trapezoidal rule is that the function is approximated by a straight line between each of these points.\n", "\n", "Write a function `trapz(f, a, b, N)` that performs trapezoidal rule on the function `f` over the interval $[a,b]$ with `N` subdivisions (`N+1` points)." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "nbgrader": { "checksum": "0502d257f547b022ec1fbe354a75bbc2", "solution": true } }, "outputs": [], "source": [ "def trapz(f, a, b, N):\n", " \"\"\"Integrate the function f(x) over the range [a,b] with N points.\"\"\"\n", " pts = np.linspace(a, b, N + 1)\n", " vals = f(pts)\n", " h = (b - a) / (1.0 * N)\n", " \n", " area = .5 * h * sum(vals[0:N] + vals[1:(N+1)])\n", " return area\n", " #raise NotImplementedError()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "f = lambda x: x**2\n", "g = lambda x: np.sin(x)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3ee11e4e20322adf86beac9605ef3b1a", "grade": true, "grade_id": "integrationex01a", "points": 5 } }, "outputs": [], "source": [ "I = trapz(f, 0, 1, 1000)\n", "assert np.allclose(I, 0.33333349999999995)\n", "J = trapz(g, 0, np.pi, 1000)\n", "assert np.allclose(J, 1.9999983550656628)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now use `scipy.integrate.quad` to integrate the `f` and `g` functions and see how the result compares with your `trapz` function. Print the results and errors." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trapezoid Rule: 0.333334\n", "Scipy: 0.333333\n", "Error: -0.000000\n", "Trapezoid Rule: 0.459698\n", "Scipy: 0.459698\n", "Error: 0.000000\n" ] } ], "source": [ "def compare(f, a, b, N):\n", " trapint = trapz(f, a, b, N)\n", " quadint = integrate.quad(f, a, b)[0]\n", " \n", " print(\"Trapezoid Rule: %f\" % trapint)\n", " print(\"Scipy: %f\" % quadint)\n", " print(\"Error: %f\" % (quadint - trapint))\n", "\n", "compare(f, 0, 1, 1000)\n", "compare(g, 0, 1, 1000)\n", "#raise NotImplementedError()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "071dda1b7a2edcac2945239a2f53139d", "grade": true, "grade_id": "integrationex01b", "points": 5 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the previous one" ] } ], "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
miykael/nipype_tutorial	notebooks/basic_model_specification_fmri.ipynb	1	7689	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Specification for 1st-Level fMRI Analysis\n", "\n", "Nipype provides also an interfaces to create a first level Model for an fMRI analysis. Such a model is needed to specify the study-specific information, such as condition, their onsets, and durations. For more information, make sure to check out [nipype.algorithms.modelgen](http://nipype.readthedocs.io/en/latest/interfaces/generated/nipype.algorithms.modelgen.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## General purpose model specification\n", "\n", "The `SpecifyModel` provides a generic mechanism for model specification. A mandatory input called `subject_info` provides paradigm specification for each run corresponding to a subject. This has to be in the form of a `Bunch` or a list of `Bunch` objects (one for each run). Each `Bunch` object contains the following attributes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Required for most designs\n", "\n", "- `conditions` : list of names\n", "\n", "\n", "- `onsets` : lists of onsets corresponding to each condition\n", "\n", "\n", "- `durations` : lists of durations corresponding to each condition. Should be left to a single 0 if all events are being modeled as impulses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional\n", "\n", "- `regressor_names`: list of names corresponding to each column. Should be None if automatically assigned.\n", "\n", "\n", "- `regressors`: list of lists. values for each regressor - must correspond to the number of volumes in the functional run\n", "\n", "\n", "- `amplitudes`: lists of amplitudes for each event. This will be ignored by SPM's Level1Design.\n", "\n", "\n", "The following two (`tmod`, `pmod`) will be ignored by any `Level1Design` class other than `SPM`:\n", "\n", "- `tmod`: lists of conditions that should be temporally modulated. Should default to None if not being used.\n", "\n", "- `pmod`: list of Bunch corresponding to conditions\n", " - `name`: name of parametric modulator\n", " - `param`: values of the modulator\n", " - `poly`: degree of modulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Together with this information, one needs to specify:\n", "\n", "- whether the durations and event onsets are specified in terms of scan volumes or secs.\n", "\n", "- the high-pass filter cutoff,\n", "\n", "- the repetition time per scan\n", "\n", "- functional data files corresponding to each run.\n", "\n", "Optionally you can specify realignment parameters, outlier indices. Outlier files should contain a list of numbers, one per row indicating which scans should not be included in the analysis. The numbers are 0-based" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example\n", "\n", "An example Bunch definition:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from nipype.interfaces.base import Bunch\n", "condnames = ['Tapping', 'Speaking', 'Yawning']\n", "event_onsets = [[0, 10, 50],\n", " [20, 60, 80],\n", " [30, 40, 70]]\n", "durations = [[0],[0],[0]]\n", "\n", "subject_info = Bunch(conditions=condnames,\n", " onsets = event_onsets,\n", " durations = durations)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "subject_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input via textfile\n", "\n", "Alternatively, you can provide condition, onset, duration and amplitude\n", "information through event files. The event files have to be in 1, 2 or 3\n", "column format with the columns corresponding to Onsets, Durations and\n", "Amplitudes and they have to have the name event_name.run<anything else>\n", "e.g.: `Words.run001.txt`.\n", " \n", "The event_name part will be used to create the condition names. `Words.run001.txt` may look like:\n", "\n", " # Word Onsets Durations\n", " 0 10\n", " 20 10\n", " ...\n", "\n", "or with amplitudes:\n", "\n", " # Word Onsets Durations Amplitudes\n", " 0 10 1\n", " 20 10 1\n", " ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example based on dataset\n", "\n", "Now let's look at a TSV file from our tutorial dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat /data/ds000114/task-fingerfootlips_events.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use [pandas](http://pandas.pydata.org/) to create a data frame from our dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "trialinfo = pd.read_table('/data/ds000114/task-fingerfootlips_events.tsv')\n", "trialinfo.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we can use the onsets, we first need to split them into the three conditions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for group in trialinfo.groupby('trial_type'):\n", " print(group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last thing we now need to to is to put this into a ``Bunch`` object and we're done:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from nipype.interfaces.base import Bunch\n", "\n", "conditions = []\n", "onsets = []\n", "durations = []\n", "\n", "for group in trialinfo.groupby('trial_type'):\n", " conditions.append(group[0])\n", " onsets.append(group[1].onset.tolist())\n", " durations.append(group[1].duration.tolist())\n", "\n", "subject_info = Bunch(conditions=conditions,\n", " onsets=onsets,\n", " durations=durations)\n", "subject_info.items()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sparse model specification\n", "\n", "In addition to standard models, `SpecifySparseModel` allows model generation for sparse and sparse-clustered acquisition experiments. Details of the model generation and utility are provided in [Ghosh et al. (2009) OHBM 2009](https://www.researchgate.net/publication/242810827_Incorporating_hemodynamic_response_functions_to_improve_analysis_models_for_sparse-acquisition_experiments)" ] } ], "metadata": { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
cdawei/digbeta	dchen/music/pldata_results.ipynb	2	40783	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import os, sys, time, gzip\n", "import pickle as pkl\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "datasets = ['aotm2011', '30music']\n", "TOPs = [5, 10, 20, 30, 50, 100, 200, 300, 500]#, 1000]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dix = 0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dataset_name = datasets[dix]\n", "data_mlr = 'data/%s/setting1' % dataset_name\n", "data_pla = 'data/%s/setting2' % dataset_name" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'aotm2011': {'Test': {'Hit-Rate': {5: 0.002037258128094966,\n", " 10: 0.004267092231542343,\n", " 20: 0.007304099443219223,\n", " 30: 0.009986434935105334,\n", " 50: 0.014788249763578438,\n", " 100: 0.024276317744211903,\n", " 200: 0.03980738494100749,\n", " 300: 0.053413120346921715,\n", " 500: 0.07632332219998214,\n", " 1000: 0.12413879252547974},\n", " 'R-Precision': 0.0009217638965627175}}}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perf_br1 = pkl.load(open(os.path.join(data_mlr, 'perf-br1.pkl'), 'rb'))\n", "perf_br1" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3468abe160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hitrates_br1 = perf_br1[dataset_name]['Test']['Hit-Rate']\n", "#assert np.all(TOPs == sorted(hitrates_br1.keys()))\n", "ax = plt.subplot(111)\n", "ax.plot(TOPs, [hitrates_br1[t] for t in TOPs], ls='--', c='g', marker='o', label='BR')\n", "ax.legend(loc='upper left')\n", "ax.set_xlabel('#Recommendation')\n", "ax.set_ylabel('Hit Rate')\n", "ax.set_xscale('log')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'aotm2011': {'Test': {'Hit-Rate': {5: 0.0025689113872080305,\n", " 10: 0.0040436085026485605,\n", " 20: 0.0068752311617703175,\n", " 30: 0.009470601467714423,\n", " 50: 0.01451484007258096,\n", " 100: 0.02320954500276045,\n", " 200: 0.037637848617184694,\n", " 300: 0.049367276085280146,\n", " 500: 0.0690716063042735,\n", " 1000: 0.10602769423371815},\n", " 'R-Precision': 0.002047527589028852}}}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perf_br2 = pkl.load(open(os.path.join(data_pla, 'perf-br2.pkl'), 'rb'))\n", "perf_br2" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'aotm2011': {'Test': {'Hit-Rate': {5: 0.0037007335109578267,\n", " 10: 0.007267207856212396,\n", " 20: 0.012838721554086673,\n", " 30: 0.01655845730553857,\n", " 50: 0.023467438981302585,\n", " 100: 0.03985192702296024,\n", " 200: 0.06703559651297722,\n", " 300: 0.08651699692306024,\n", " 500: 0.1194141649978842,\n", " 1000: 0.18300560205378802},\n", " 'R-Precision': 0.003671795096342878}}}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perf_pop = pkl.load(open(os.path.join(data_pla, 'perf-pop.pkl'), 'rb'))\n", "perf_pop" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3468c21128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hitrates_br2 = perf_br2[dataset_name]['Test']['Hit-Rate']\n", "hitrates_pop = perf_pop[dataset_name]['Test']['Hit-Rate']\n", "#assert np.all(TOPs == sorted(hitrates_br2.keys()))\n", "#assert np.all(TOPs == sorted(hitrates_pop.keys()))\n", "ax = plt.subplot(111)\n", "ax.plot(TOPs, [hitrates_br2[t] for t in TOPs], ls='--', c='g', marker='o', label='BR')\n", "ax.plot(TOPs, [hitrates_pop[t] for t in TOPs], ls=':', c='r', marker='D', label='PopRank')\n", "ax.legend(loc='upper left')\n", "ax.set_xlabel('#Recommendation')\n", "ax.set_ylabel('Hit Rate')\n", "ax.set_xscale('log')\n", "plt.savefig('pla_hitrate_%s.svg' % dataset_name)" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
rsignell-usgs/notebook	NEXRAD/.ipynb_checkpoints/THREDDS_Radar_Server-checkpoint.ipynb	1	27610	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Python to Access NCEI Archived NEXRAD Level 2 Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to access the THREDDS Data Server (TDS) instance that is serving up archived NEXRAD Level 2 data hosted on Amazon S3. The TDS provides a mechanism to query for available data files, as well as provides access to the data as native volume files, through OPeNDAP, and using its own CDMRemote protocol. Since we're using Python, we can take advantage of Unidata's Siphon package, which provides an easy API for talking to THREDDS servers.\n", "\n", "NOTE: Due to data charges, the TDS instance in AWS only allows access to .edu domains. For other users interested in using Siphon to access radar data, you can access recent (2 weeks') data by changing the server URL below to: http://thredds.ucar.edu/thredds/radarServer/nexrad/level2/IDD/\n", "\n", "But first!\n", "Bookmark these resources for when you want to use Siphon later!\n", "+ [latest Siphon documentation](http://siphon.readthedocs.org/en/latest/)\n", "+ [Siphon github repo](https://github.com/Unidata/siphon)\n", "+ [TDS documentation](http://www.unidata.ucar.edu/software/thredds/current/tds/TDS.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading the single latest volume\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just a bit of initial set-up to use inline figures and quiet some warnings." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", category=matplotlib.cbook.MatplotlibDeprecationWarning)\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll create an instance of RadarServer to point to the appropriate radar server access URL." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The S3 URL did not work for me, despite .edu domain\n", "#url = 'http://thredds-aws.unidata.ucar.edu/thredds/radarServer/nexrad/level2/S3/'\n", "\n", "#Trying motherlode URL\n", "url = 'http://thredds.ucar.edu/thredds/radarServer/nexrad/level2/IDD/'\n", "from siphon.radarserver import RadarServer\n", "rs = RadarServer(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll create a new query object to help request the data. Using the chaining methods, let's ask for the latest data at the radar KLVX (Louisville, KY). We see that when the query is represented as a string, it shows the encoded URL." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "time=2016-06-20T16%3A50%3A48.016854&stn=KLVX" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datetime import datetime, timedelta\n", "query = rs.query()\n", "query.stations('KLVX').time(datetime.utcnow())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the RadarServer instance to check our query, to make sure we have required parameters and that we have chosen valid station(s) and variable(s)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rs.validate_query(query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the request, which returns an instance of TDSCatalog; this handles parsing the returned XML information." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "catalog = rs.get_catalog(query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at the datasets on the catalog to see what data we found by the query. We find one volume in the return, since we asked for the volume nearest to a single time." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('Level2_KLVX_20160620_1633.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bf07bb590>)])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catalog.datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pull that dataset out of the dictionary and look at the available access URLs. We see URLs for OPeNDAP, CDMRemote, and HTTPServer (direct download)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'CdmRemote': 'http://thredds.ucar.edu/thredds/cdmremote/nexrad/level2/IDD/KLVX/20160620/Level2_KLVX_20160620_1633.ar2v',\n", " 'HTTPServer': 'http://thredds.ucar.edu/thredds/fileServer/nexrad/level2/IDD/KLVX/20160620/Level2_KLVX_20160620_1633.ar2v',\n", " 'OPENDAP': 'http://thredds.ucar.edu/thredds/dodsC/nexrad/level2/IDD/KLVX/20160620/Level2_KLVX_20160620_1633.ar2v'}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = list(catalog.datasets.values())[0]\n", "ds.access_urls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the CDMRemote reader in Siphon and pass it the appropriate access URL." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from siphon.cdmr import Dataset\n", "data = Dataset(ds.access_urls['CdmRemote'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define some helper functions to make working with the data easier. One takes the raw data and converts it to floating point values with the missing data points appropriately marked. The other helps with converting the polar coordinates (azimuth and range) to Cartesian (x and y)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "run_control": { "marked": false } }, "outputs": [], "source": [ "import numpy as np\n", "def raw_to_masked_float(var, data):\n", " # Values come back signed. If the _Unsigned attribute is set, we need to convert\n", " # from the range [-127, 128] to [0, 255].\n", " if var._Unsigned:\n", " data = data & 255\n", "\n", " # Mask missing points\n", " data = np.ma.array(data, mask=data==0)\n", "\n", " # Convert to float using the scale and offset\n", " return data * var.scale_factor + var.add_offset\n", "\n", "def polar_to_cartesian(az, rng):\n", " az_rad = np.deg2rad(az)[:, None]\n", " x = rng * np.sin(az_rad)\n", " y = rng * np.cos(az_rad)\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CDMRemote reader provides an interface that is almost identical to the usual python NetCDF interface. We pull out the variables we need for azimuth and range, as well as the data itself." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sweep = 0\n", "ref_var = data.variables['Reflectivity_HI']\n", "ref_data = ref_var[sweep]\n", "rng = data.variables['distanceR_HI'][:]\n", "az = data.variables['azimuthR_HI'][sweep]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then convert the raw data to floating point values and the polar coordinates to Cartesian." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ref = raw_to_masked_float(ref_var, ref_data)\n", "x, y = polar_to_cartesian(az, rng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MetPy is a Python package for meteorology (Documentation: http://metpy.readthedocs.org and GitHub: http://github.com/MetPy/MetPy). We import MetPy and use it to get the colortable and value mapping information for the NWS Reflectivity data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from metpy.plots import ctables # For NWS colortable\n", "ref_norm, ref_cmap = ctables.registry.get_with_steps('NWSReflectivity', 5, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we plot them up using matplotlib and cartopy. We create a helper function for making a map to keep things simpler later." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy\n", "\n", "def new_map(fig, lon, lat):\n", " # Create projection centered on the radar. This allows us to use x\n", " # and y relative to the radar.\n", " proj = cartopy.crs.LambertConformal(central_longitude=lon, central_latitude=lat)\n", "\n", " # New axes with the specified projection\n", " ax = fig.add_subplot(1, 1, 1, projection=proj)\n", "\n", " # Add coastlines\n", " ax.coastlines('50m', 'black', linewidth=2, zorder=2)\n", "\n", " # Grab state borders\n", " state_borders = cartopy.feature.NaturalEarthFeature(\n", " category='cultural', name='admin_1_states_provinces_lines',\n", " scale='50m', facecolor='none')\n", " ax.add_feature(state_borders, edgecolor='black', linewidth=1, zorder=3)\n", " \n", " return ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download a collection of historical data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time we'll make a query based on a longitude, latitude point and using a time range." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "time_start=2016-06-08T18%3A00%3A00&time_end=2016-06-08T19%3A00%3A00&latitude=41.175&longitude=-73.687" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = rs.query()\n", "#dt = datetime(2012, 10, 29, 15) # Our specified time\n", "dt = datetime(2016, 6, 8, 18) # Our specified time\n", "query.lonlat_point(-73.687, 41.175).time_range(dt, dt + timedelta(hours=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The specified longitude, latitude are in NY and the TDS helpfully finds the closest station to that point. We can see that for this time range we obtained multiple datasets." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('Level2_KOKX_20160608_1828.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c7d0>),\n", " ('Level2_KOKX_20160608_1801.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c850>),\n", " ('Level2_KOKX_20160608_1821.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c810>),\n", " ('Level2_KOKX_20160608_1814.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c890>),\n", " ('Level2_KOKX_20160608_1835.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c8d0>),\n", " ('Level2_KOKX_20160608_1841.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c910>),\n", " ('Level2_KOKX_20160608_1848.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c950>),\n", " ('Level2_KOKX_20160608_1854.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c990>),\n", " ('Level2_KOKX_20160608_1807.ar2v',\n", " <siphon.catalog.Dataset at 0x7f8bec11c9d0>)])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat = rs.get_catalog(query)\n", "cat.datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grab the first dataset so that we can get the longitude and latitude of the station and make a map for plotting. We'll go ahead and specify some longitude and latitude bounds for the map." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = list(cat.datasets.values())[0]\n", "data = Dataset(ds.access_urls['CdmRemote'])\n", "# Pull out the data of interest\n", "sweep = 0\n", "rng = data.variables['distanceR_HI'][:]\n", "az = data.variables['azimuthR_HI'][sweep]\n", "ref_var = data.variables['Reflectivity_HI']\n", "\n", "# Convert data to float and coordinates to Cartesian\n", "ref = raw_to_masked_float(ref_var, ref_var[sweep])\n", "x, y = polar_to_cartesian(az, rng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the function to make a new map and plot a colormapped view of the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(10, 10))\n", "ax = new_map(fig, data.StationLongitude, data.StationLatitude)\n", "\n", "# Set limits in lat/lon space\n", "ax.set_extent([-77, -70, 38, 42])\n", "\n", "# Add ocean and land background\n", "ocean = cartopy.feature.NaturalEarthFeature('physical', 'ocean', scale='50m',\n", " edgecolor='face',\n", " facecolor=cartopy.feature.COLORS['water'])\n", "land = cartopy.feature.NaturalEarthFeature('physical', 'land', scale='50m',\n", " edgecolor='face',\n", " facecolor=cartopy.feature.COLORS['land'])\n", "\n", "ax.add_feature(ocean, zorder=-1)\n", "ax.add_feature(land, zorder=-1)\n", "#ax = new_map(fig, data.StationLongitude, data.StationLatitude)\n", "ax.pcolormesh(x, y, ref, cmap=ref_cmap, norm=ref_norm, zorder=0);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can loop over the collection of returned datasets and plot them. As we plot, we collect the returned plot objects so that we can use them to make an animated plot. We also add a timestamp for each plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "meshes = []\n", "for item in sorted(cat.datasets.items()):\n", " # After looping over the list of sorted datasets, pull the actual Dataset object out\n", " # of our list of items and access over CDMRemote\n", " ds = item[1]\n", " data = Dataset(ds.access_urls['CdmRemote'])\n", "\n", " # Pull out the data of interest\n", " sweep = 0\n", " rng = data.variables['distanceR_HI'][:]\n", " az = data.variables['azimuthR_HI'][sweep]\n", " ref_var = data.variables['Reflectivity_HI']\n", "\n", " # Convert data to float and coordinates to Cartesian\n", " ref = raw_to_masked_float(ref_var, ref_var[sweep])\n", " x, y = polar_to_cartesian(az, rng)\n", "\n", " # Plot the data and the timestamp\n", " mesh = ax.pcolormesh(x, y, ref, cmap=ref_cmap, norm=ref_norm, zorder=0)\n", " text = ax.text(0.65, 0.03, data.time_coverage_start, transform=ax.transAxes,\n", " fontdict={'size':16})\n", " \n", " # Collect the things we've plotted so we can animate\n", " meshes.append((mesh, text))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using matplotlib, we can take a collection of ``Artists`` that have been plotted and turn them into an animation. With matplotlib 1.5 (1.5-rc2 is available now!), this animation can be converted to HTML5 video viewable in the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IOError", "evalue": "[Errno 32] Broken pipe", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mIOError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m/home/usgs/miniconda/envs/ioos/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 341\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_safe_get_formatter_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 342\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwriter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 957\u001b[0m \u001b[1;31m# Now open and base64 encode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/miniconda/envs/ioos/lib/python2.7/site-packages/matplotlib/animation.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs)\u001b[0m\n\u001b[0;32m 808\u001b[0m \u001b[1;31m# TODO: Need to see if turning off blit is really necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 809\u001b[0m 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kwargs)\u001b[0m\n\u001b[0;32m 2230\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2231\u001b[0m \u001b[0mbbox_inches_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2232\u001b[1;33m *kwargs)\n\u001b[0m\u001b[0;32m 2233\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2234\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/usgs/miniconda/envs/ioos/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_raw\u001b[1;34m(self, filename_or_obj, args, **kwargs)\u001b[0m\n\u001b[0;32m 517\u001b[0m \u001b[0mclose\u001b[0m \u001b[1;33m=\u001b[0m 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up matplotlib to do the conversion to HTML5 video\n", "import matplotlib\n", "matplotlib.rcParams['animation.html'] = 'html5'\n", "\n", "# Create an animation\n", "from matplotlib.animation import ArtistAnimation\n", "ArtistAnimation(fig, meshes)" ] }, { "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tensorflow/docs-l10n	site/ja/probability/examples/HLM_TFP_R_Stan.ipynb	1	926085	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "UIp998OHnZSN" }, "source": [ "##### Copyright 2018 The TensorFlow Probability Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "li5wNGR6naj0" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "ykgJW69K4TNL" }, "source": [ "# {TF Probability、R、Stan} における線形混合効果回帰\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a target=\"_blank\" href=\"https://www.tensorflow.org/probability/examples/HLM_TFP_R_Stan\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org で表示</a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/probability/examples/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab で実行</a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/probability/examples/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHubでソースを表示</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/probability/examples/HLM_TFP_R_Stan.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "epdzLgKQRfAI" }, "source": [ "## 1 はじめに" ] }, { "cell_type": "markdown", "metadata": { "id": "bMstt69hR44D" }, "source": [ "このコラボでは、線形混合効果回帰モデルを人気のあるトイデータセットに適合させます。R の `lme4`、Stan の混合効果パッケージ、および TensorFlow Probability (TFP) プリミティブを使用して、これを 3 回適合させます。そして、これらからほぼ同じ適合パラメータと事後分布を得られることを示します。\n", "\n", "主な結論として、TFP には HLM のようなモデルを適合させるために必要な一般的な要素があり、`lme4` や `rstanarm` などの他のソフトウェアパッケージと一致する結果を生成します。このコラボは、比較したパッケージの計算効率を正確に反映したものではありません。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0axKjgZvRtL9" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import os\n", "from six.moves import urllib\n", "import numpy as np\n", "import pandas as pd\n", "import warnings\n", "\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "\n", "from IPython.core.pylabtools import figsize\n", "figsize(11, 9)\n", "\n", "import tensorflow.compat.v1 as tf\n", "import tensorflow_datasets as tfds\n", "import tensorflow_probability as tfp" ] }, { "cell_type": "markdown", "metadata": { "id": "IFC9r-h0XlQ3" }, "source": [ "## 2 階層線形モデル\n" ] }, { "cell_type": "markdown", "metadata": { "id": "wqS-HhvhDlno" }, "source": [ "R、Stan、TFP を比較するために、[階層線形モデル](https://en.wikipedia.org/wiki/Multilevel_model) (HLM) を[ラドンデータセット](http://www.stat.columbia.edu/~gelman/arm/examples/radon/)に適合させます。このデータセットはゲルマンら (559 ページ、第 2 版、250 ページ、第 3 版) による[ベイジアンデータ分析](http://www.stat.columbia.edu/~gelman/book/)で有名になったものです。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fAD8am2a4TaY" }, "source": [ "次の生成モデルを前提としています。\n", "\n", "$$\\begin{align} \\text{for } & c=1\\ldots \\text{NumCounties}:\\ & \\beta_c \\sim \\text{Normal}\\left(\\text{loc}=0, \\text{scale}=\\sigma_C \\right) \\ \\text{for } & i=1\\ldots \\text{NumSamples}:\\ &\\eta_i = \\underbrace{\\omega_0 + \\omega_1 \\text{Floor}i}\\text{fixed effects} + \\underbrace{\\beta_{ \\text{County}i} \\log( \\text{UraniumPPM}{\\text{County}i}))}\\text{random effects} \\ &\\log(\\text{Radon}_i) \\sim \\text{Normal}(\\text{loc}=\\eta_i , \\text{scale}=\\sigma_N) \\end{align}$$\n" ] }, { "cell_type": "markdown", "metadata": { "id": "5styKLl_MyWu" }, "source": [ "R の `lme4` 「チルダ表記」では、このモデルは次と同等です。\n", "\n", "> `log_radon ~ 1 + floor + (0 + log_uranium_ppm \| county)`\n" ] }, { "cell_type": "markdown", "metadata": { "id": "SpurhP2gP_T8" }, "source": [ "${\\beta_c}_{c=1}^\\text{NumCounties}$ の事後分布 (証拠を条件とする) を使用して、$\\omega, \\sigma_C, \\sigma_N$ の MLE を見つけます。" ] }, { "cell_type": "markdown", "metadata": { "id": "Nj6adNwgPTUP" }, "source": [ "本質的に同じモデルですが、ランダム切片があります。[付録 A](#scrollTo=tsXhZ4rtNUXL) を参照してください。\n", "\n", "HLM のより一般的な仕様については、[付録 B](#scrollTo=H0w7ofFvNsxi) を参照してください。" ] }, { "cell_type": "markdown", "metadata": { "id": "LR0ZC0dE4MWb" }, "source": [ "## 3 データマンジング" ] }, { "cell_type": "markdown", "metadata": { "id": "OdboSs9G3JlE" }, "source": [ "このセクションでは、[<code>radon</code> データセット](http://www.stat.columbia.edu/~gelman/arm/examples/radon/)を取得し、想定されるモデルに準拠するように最小限の前処理を行います。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4LjOBqLDV0IQ" }, "outputs": [], "source": [ "def load_and_preprocess_radon_dataset(state='MN'):\n", " \"\"\"Preprocess Radon dataset as done in \"Bayesian Data Analysis\" book.\n", " \n", " We filter to Minnesota data (919 examples) and preprocess to obtain the\n", " following features:\n", " - `log_uranium_ppm`: Log of soil uranium measurements.\n", " - `county`: Name of county in which the measurement was taken.\n", " - `floor`: Floor of house (0 for basement, 1 for first floor) on which the\n", " measurement was taken.\n", "\n", " The target variable is `log_radon`, the log of the Radon measurement in the\n", " house.\n", " \"\"\"\n", " ds = tfds.load('radon', split='train')\n", " radon_data = tfds.as_dataframe(ds)\n", " radon_data.rename(lambda s: s[9:] if s.startswith('feat') else s, axis=1, inplace=True)\n", " df = radon_data[radon_data.state==state.encode()].copy()\n", "\n", " # For any missing or invalid activity readings, we'll use a value of `0.1`.\n", " df['radon'] = df.activity.apply(lambda x: x if x > 0. else 0.1)\n", " # Make county names look nice. \n", " df['county'] = df.county.apply(lambda s: s.decode()).str.strip().str.title()\n", " # Remap categories to start from 0 and end at max(category).\n", " county_name = sorted(df.county.unique())\n", " df['county'] = df.county.astype(\n", " pd.api.types.CategoricalDtype(categories=county_name)).cat.codes\n", " county_name = list(map(str.strip, county_name))\n", "\n", " df['log_radon'] = df['radon'].apply(np.log)\n", " df['log_uranium_ppm'] = df['Uppm'].apply(np.log)\n", " df = df[['idnum', 'log_radon', 'floor', 'county', 'log_uranium_ppm']]\n", "\n", " return df, county_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hJE3-eC0I-Lm" }, "outputs": [], "source": [ "radon, county_name = load_and_preprocess_radon_dataset()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nV-IAEW2FIqX" }, "outputs": [], "source": [ "# We'll use the following directory to store our preprocessed dataset.\n", "CACHE_DIR = os.path.join(os.sep, 'tmp', 'radon')\n", "\n", "# Save processed data. (So we can later read it in R.)\n", "if not tf.gfile.Exists(CACHE_DIR):\n", " tf.gfile.MakeDirs(CACHE_DIR)\n", "with tf.gfile.Open(os.path.join(CACHE_DIR, 'radon.csv'), 'w') as f:\n", " radon.to_csv(f, index=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "ubvf1vHenyCx" }, "source": [ "### 3.1 データを調査する" ] }, { "cell_type": "markdown", "metadata": { "id": "r39MiQV2zUz0" }, "source": [ "このセクションでは、提案されたモデルが合理的である理由を理解するために、`radon` データセットを調べます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GRCyjhSknu9z" }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>log_radon</th>\n", " <th>floor</th>\n", " <th>county</th>\n", " <th>log_uranium_ppm</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.788457</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>-0.689048</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.788457</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>-0.689048</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.064711</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>-0.689048</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.000000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>-0.689048</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.131402</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>-0.847313</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " log_radon floor county log_uranium_ppm\n", "0 0.788457 1 0 -0.689048\n", "1 0.788457 0 0 -0.689048\n", "2 1.064711 0 0 -0.689048\n", "3 0.000000 0 0 -0.689048\n", "4 1.131402 0 1 -0.847313" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "radon.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gdASxsWHjvw4" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc96630d10>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(22, 5));\n", "county_freq = radon['county'].value_counts()\n", "county_freq.plot(kind='bar', color='#436bad');\n", "plt.xlabel('County index')\n", "plt.ylabel('Number of radon readings')\n", "plt.title('Number of radon readings per county', fontsize=16)\n", "county_freq = np.array(zip(county_freq.index, county_freq.values)) # We'll use this later." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k9yN_rGLlqHE" }, "outputs": [ { "data": { "image/png": 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O1kO82bk2gYFeUCpvfYLKrSovv70vPc4sKMiHOzmIgO95WwxiJBnXiqthtlhve6C+jTrA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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbcf6b92b10>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=[10, 4]);\n", "\n", "radon['log_radon'].plot(kind='density', ax=ax[0]);\n", "ax[0].set_xlabel('log(radon)')\n", "\n", "radon['floor'].value_counts().plot(kind='bar', ax=ax[1]);\n", "ax[1].set_xlabel('Floor');\n", "ax[1].set_ylabel('Count');\n", "\n", "fig.subplots_adjust(wspace=0.25)" ] }, { "cell_type": "markdown", "metadata": { "id": "vuy-t8oNoDTH" }, "source": [ "結論:\n", "\n", "- 85 の郡のロングテールがあります (GLMM でよく発生します)。\n", "- $\\log(\\text{Radon})$ には制約がありません (したがって、線形回帰は理にかなっているかもしれません)。\n", "- 測定はほとんど $0$ 階で行われます。$1$ より上の階では測定は行われませんでした (したがって、固定効果には 2 つの重みしかありません)。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "zSCWOamv3nXU" }, "source": [ "## 4 R を使用して HLM を適合させる" ] }, { "cell_type": "markdown", "metadata": { "id": "ZPtvJXUdn6an" }, "source": [ "このセクションでは、R の [`lme4`](https://cran.r-project.org/web/packages/lme4/index.html) パッケージを使用して、上記の確率モデルを適合させます。" ] }, { "cell_type": "markdown", "metadata": { "id": "3iX5L0srRGIQ" }, "source": [ "注: このセクションを実行するには、R colab ランタイムに切り替える必要があります。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZBqZjyHdsPIB" }, "outputs": [], "source": [ "suppressMessages({\n", " library('bayesplot')\n", " library('data.table')\n", " library('dplyr')\n", " library('gfile')\n", " library('ggplot2')\n", " library('lattice')\n", " library('lme4')\n", " library('plyr')\n", " library('rstanarm')\n", " library('tidyverse')\n", " RequireInitGoogle()\n", "})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Lq3_yATCshI-" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Parsed with column specification:\n", "cols(\n", " log_radon = col_double(),\n", " floor = col_integer(),\n", " county = col_integer(),\n", " log_uranium_ppm = col_double()\n", ")\n" ] } ], "source": [ "data = read_csv(gfile::GFile('/tmp/radon/radon.csv'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_3gj9hfxshE8" }, "outputs": [ { "data": { "text/plain": [ "# A tibble: 6 x 4\n", " log_radon floor county log_uranium_ppm\n", " <dbl> <int> <int> <dbl>\n", "1 0.788 1 0 -0.689\n", "2 0.788 0 0 -0.689\n", "3 1.06 0 0 -0.689\n", "4 0 0 0 -0.689\n", "5 1.13 0 1 -0.847\n", "6 0.916 0 1 -0.847" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "head(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uRqAdn3WsoN-" }, "outputs": [], "source": [ "# https://github.com/stan-dev/example-models/wiki/ARM-Models-Sorted-by-Chapter\n", "radon.model <- lmer(log_radon ~ 1 + floor + (0 + log_uranium_ppm \| county), data = data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MuMBVnkAsoMS" }, "outputs": [ { "data": { "text/plain": [ "Linear mixed model fit by REML ['lmerMod']\n", "Formula: log_radon ~ 1 + floor + (0 + log_uranium_ppm \| county)\n", " Data: data\n", "\n", "REML criterion at convergence: 2166.3\n", "\n", "Scaled residuals: \n", " Min 1Q Median 3Q Max \n", "-4.5202 -0.6064 0.0107 0.6334 3.4111 \n", "\n", "Random effects:\n", " Groups Name Variance Std.Dev.\n", " county log_uranium_ppm 0.7545 0.8686 \n", " Residual 0.5776 0.7600 \n", "Number of obs: 919, groups: county, 85\n", "\n", "Fixed effects:\n", " Estimate Std. Error t value\n", "(Intercept) 1.47585 0.03899 37.85\n", "floor -0.67974 0.06963 -9.76\n", "\n", "Correlation of Fixed Effects:\n", " (Intr)\n", "floor -0.330" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "summary(radon.model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0qZXx27dp7aZ" }, "outputs": [ { "data": { "text/plain": [ "$county\n" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "qqmath(ranef(radon.model, condVar=TRUE))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nCsGcLnP40Lg" }, "outputs": [], "source": [ "write.csv(as.data.frame(ranef(radon.model, condVar = TRUE)), '/tmp/radon/lme4_fit.csv')" ] }, { "cell_type": "markdown", "metadata": { "id": "2XrrSLW43pHL" }, "source": [ "## 5 Stan を使用して HLM を適合させる\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-ddXXuiWnv2-" }, "source": [ "このセクションでは、[rstanarm](http://mc-stan.org/users/interfaces/rstanarm) を使用して、上記の `lme4` モデルと同じ式/構文を使用して Stan モデルを適合させます。\n", "\n", "`lme4` や以下の TF モデルとは異なり、`rstanarm` は完全なベイズモデルです。つまり、すべてのパラメータは正規分布から引き出され、パラメータ自体は分布から引き出されていると推定されます。" ] }, { "cell_type": "markdown", "metadata": { "id": "c6IpkPOOnsmQ" }, "source": [ "注：このセクションを実行するには、`R` colab ランタイムに切り替える必要があります。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s-p-rAMZuaGh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).\n", "\n", "Chain 1, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 1, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 1, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 1, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 1, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 1, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 1, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 1, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 1, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 1, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 1, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 1, Iteration: 2000 / 2000 [100%] (Sampling)\n", " Elapsed Time: 7.73495 seconds (Warm-up)\n", " 2.98852 seconds (Sampling)\n", " 10.7235 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).\n", "\n", "Chain 2, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 2, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 2, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 2, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 2, Iteration: 800 / 2000 [ 40%] (Warmup)\n", "Chain 2, Iteration: 1000 / 2000 [ 50%] (Warmup)\n", "Chain 2, Iteration: 1001 / 2000 [ 50%] (Sampling)\n", "Chain 2, Iteration: 1200 / 2000 [ 60%] (Sampling)\n", "Chain 2, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 2, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 2, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 2, Iteration: 2000 / 2000 [100%] (Sampling)\n", " Elapsed Time: 7.51252 seconds (Warm-up)\n", " 3.08653 seconds (Sampling)\n", " 10.5991 seconds (Total)\n", "\n", "\n", "SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).\n", "\n", "Chain 3, Iteration: 1 / 2000 [ 0%] (Warmup)\n", "Chain 3, Iteration: 200 / 2000 [ 10%] (Warmup)\n", "Chain 3, Iteration: 400 / 2000 [ 20%] (Warmup)\n", "Chain 3, Iteration: 600 / 2000 [ 30%] (Warmup)\n", "Chain 3, Iteration: 800 / 2000 [ 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"Chain 4, Iteration: 1400 / 2000 [ 70%] (Sampling)\n", "Chain 4, Iteration: 1600 / 2000 [ 80%] (Sampling)\n", "Chain 4, Iteration: 1800 / 2000 [ 90%] (Sampling)\n", "Chain 4, Iteration: 2000 / 2000 [100%] (Sampling)\n", " Elapsed Time: 7.6801 seconds (Warm-up)\n", " 3.23663 seconds (Sampling)\n", " 10.9167 seconds (Total)\n", "\n" ] } ], "source": [ "fit <- stan_lmer(log_radon ~ 1 + floor + (0 + log_uranium_ppm \| county), data = data)" ] }, { "cell_type": "markdown", "metadata": { "id": "KZNNvBB8TWTW" }, "source": [ "注: ランタイムは単一の CPU コアからのものです。(このコラボは、Stan または TFP ランタイムを忠実に表現することを目的としたものではありません。)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yNKzX9rnuz9H" }, "outputs": [ { "data": { "text/plain": [ "stan_lmer(formula = log_radon ~ 1 + floor + (0 + log_uranium_ppm \| \n", " county), data = data)\n", "\n", "Estimates:\n", " Median MAD_SD\n", "(Intercept) 1.5 0.0 \n", "floor -0.7 0.1 \n", "sigma 0.8 0.0 \n", "\n", "Error terms:\n", " Groups Name Std.Dev.\n", " county log_uranium_ppm 0.87 \n", " Residual 0.76 \n", "Num. levels: county 85 \n", "\n", "Sample avg. posterior predictive \n", "distribution of y (X = xbar):\n", " Median MAD_SD\n", "mean_PPD 1.2 0.0 \n", "\n", "Observations: 919 Number of unconstrained parameters: 90 " ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iO6In1K3uz7B" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "color_scheme_set(\"red\")\n", "ppc_dens_overlay(y = fit$y,\n", " yrep = posterior_predict(fit, draws = 50))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZAMwe8rWvId4" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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" }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "color_scheme_set(\"brightblue\")\n", "ppc_intervals(\n", " y = data$log_radon,\n", " yrep = posterior_predict(fit),\n", " x = data$county,\n", " prob = 0.8\n", ") +\n", " labs(\n", " x = \"County\",\n", " y = \"log radon\",\n", " title = \"80% posterior predictive intervals \\nvs observed log radon\",\n", " subtitle = \"by county\"\n", " ) +\n", " panel_bg(fill = \"gray95\", color = NA) +\n", " grid_lines(color = \"white\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "h9HtqG65x1a6" }, "outputs": [], "source": [ "# Write the posterior samples (4000 for each variable) to a CSV.\n", "write.csv(tidy(as.matrix(fit)), \"/tmp/radon/stan_fit.csv\")" ] }, { "cell_type": "markdown", "metadata": { "id": "FedP5SMQ3u4z" }, "source": [ "注: Python TF カーネルランタイムに切り替えます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wwhJD-t86Dnq" }, "outputs": [], "source": [ "with tf.gfile.Open('/tmp/radon/lme4_fit.csv', 'r') as f:\n", " lme4_fit = pd.read_csv(f, index_col=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Qs9VpUOz6LZR" }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>grpvar</th>\n", " <th>term</th>\n", " <th>grp</th>\n", " <th>condval</th>\n", " <th>condsd</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>county</td>\n", " <td>log_uranium_ppm</td>\n", " <td>0</td>\n", " <td>0.667653</td>\n", " <td>0.465584</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>county</td>\n", " <td>log_uranium_ppm</td>\n", " <td>1</td>\n", " <td>0.697805</td>\n", " <td>0.123133</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>county</td>\n", " <td>log_uranium_ppm</td>\n", " <td>2</td>\n", " <td>-0.010856</td>\n", " <td>0.847489</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>county</td>\n", " <td>log_uranium_ppm</td>\n", " <td>3</td>\n", " <td>-0.068872</td>\n", " <td>0.422883</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>county</td>\n", " <td>log_uranium_ppm</td>\n", " <td>4</td>\n", " <td>0.036075</td>\n", " <td>0.825677</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " grpvar term grp condval condsd\n", "1 county log_uranium_ppm 0 0.667653 0.465584\n", "2 county log_uranium_ppm 1 0.697805 0.123133\n", "3 county log_uranium_ppm 2 -0.010856 0.847489\n", "4 county log_uranium_ppm 3 -0.068872 0.422883\n", "5 county log_uranium_ppm 4 0.036075 0.825677" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "lme4_fit.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "EJktXVyR6zK6" }, "source": [ "後で視覚化するために、lme4 からグループ変量効果の点推定と条件付き標準偏差を取得します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "le7XkSvL6a2Z" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(85,) (85,)\n" ] } ], "source": [ "posterior_random_weights_lme4 = np.array(lme4_fit.condval, dtype=np.float32)\n", "lme4_prior_scale = np.array(lme4_fit.condsd, dtype=np.float32)\n", "print(posterior_random_weights_lme4.shape, lme4_prior_scale.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "42fI-VbduCXy" }, "source": [ "lme4 の推定平均と標準偏差を使用して、郡の重みのサンプルを抽出します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "S8TQNRaKFecg" }, "outputs": [], "source": [ "with tf.Session() as sess:\n", " lme4_dist = tfp.distributions.Independent(\n", " tfp.distributions.Normal(\n", " loc=posterior_random_weights_lme4,\n", " scale=lme4_prior_scale),\n", " reinterpreted_batch_ndims=1)\n", " posterior_random_weights_lme4_final_ = sess.run(lme4_dist.sample(4000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KA2OZUp3FltF" }, "outputs": [ { "data": { "text/plain": [ "(4000, 85)" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "posterior_random_weights_lme4_final_.shape" ] }, { "cell_type": "markdown", "metadata": { "id": "y8GpeGOauTar" }, "source": [ "また、Stan の適合から郡の重みの事後サンプルを取得します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YxXhcMfG3uoX" }, "outputs": [], "source": [ "with tf.gfile.Open('/tmp/radon/stan_fit.csv', 'r') as f:\n", " samples = pd.read_csv(f, index_col=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Arc-QdJ33ukk" }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-width:1500px;overflow:auto;\">\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>X.Intercept.</th>\n", " <th>floor</th>\n", " <th>b.log_uranium_ppm.county.0.</th>\n", " <th>b.log_uranium_ppm.county.1.</th>\n", " <th>b.log_uranium_ppm.county.2.</th>\n", " <th>b.log_uranium_ppm.county.3.</th>\n", " <th>b.log_uranium_ppm.county.4.</th>\n", " <th>b.log_uranium_ppm.county.5.</th>\n", " <th>b.log_uranium_ppm.county.6.</th>\n", " <th>b.log_uranium_ppm.county.7.</th>\n", " <th>...</th>\n", " <th>b.log_uranium_ppm.county.76.</th>\n", " <th>b.log_uranium_ppm.county.77.</th>\n", " <th>b.log_uranium_ppm.county.78.</th>\n", " <th>b.log_uranium_ppm.county.79.</th>\n", " <th>b.log_uranium_ppm.county.80.</th>\n", " <th>b.log_uranium_ppm.county.81.</th>\n", " <th>b.log_uranium_ppm.county.82.</th>\n", " <th>b.log_uranium_ppm.county.83.</th>\n", " <th>b.log_uranium_ppm.county.84.</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1.450331</td>\n", " <td>-0.757375</td>\n", " <td>1.093099</td>\n", " <td>0.603985</td>\n", " <td>-0.661680</td>\n", " <td>-0.667052</td>\n", " <td>0.549178</td>\n", " <td>-0.150145</td>\n", " <td>1.486173</td>\n", " <td>0.053578</td>\n", " <td>...</td>\n", " <td>0.323107</td>\n", " <td>0.282121</td>\n", " <td>-0.188251</td>\n", " <td>0.889385</td>\n", " <td>1.008348</td>\n", " <td>0.429430</td>\n", " <td>0.404690</td>\n", " <td>0.112424</td>\n", " <td>-0.071612</td>\n", " <td>0.778706</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.453383</td>\n", " <td>-0.659085</td>\n", " <td>0.560266</td>\n", " <td>0.580569</td>\n", " <td>0.419441</td>\n", " <td>-0.154925</td>\n", " <td>-1.263534</td>\n", " <td>0.166291</td>\n", " <td>1.534917</td>\n", " <td>1.141392</td>\n", " <td>...</td>\n", " <td>0.306941</td>\n", " <td>0.331696</td>\n", " <td>0.614852</td>\n", " <td>0.153913</td>\n", " <td>1.253688</td>\n", " <td>0.246746</td>\n", " <td>0.849947</td>\n", " <td>-1.399027</td>\n", " <td>-1.697360</td>\n", " <td>0.754960</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.460351</td>\n", " <td>-0.804513</td>\n", " <td>1.145694</td>\n", " <td>0.705337</td>\n", " <td>2.216364</td>\n", " <td>-1.044031</td>\n", " <td>1.510397</td>\n", " <td>-0.472675</td>\n", " <td>0.427448</td>\n", " <td>0.940869</td>\n", " <td>...</td>\n", " <td>1.941637</td>\n", " <td>0.188311</td>\n", " <td>-0.690044</td>\n", " <td>0.946370</td>\n", " <td>1.626093</td>\n", " <td>0.930756</td>\n", " <td>0.141125</td>\n", " <td>0.041392</td>\n", " <td>-0.135538</td>\n", " <td>0.763605</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.438725</td>\n", " <td>-0.565721</td>\n", " <td>0.204148</td>\n", " <td>0.810580</td>\n", " <td>-1.982466</td>\n", " <td>1.044825</td>\n", " <td>-1.339406</td>\n", " <td>0.643384</td>\n", " <td>2.379191</td>\n", " <td>0.295981</td>\n", " <td>...</td>\n", " <td>-0.832814</td>\n", " <td>0.246694</td>\n", " <td>-0.801078</td>\n", " <td>0.072430</td>\n", " <td>0.108882</td>\n", " <td>-0.203805</td>\n", " <td>0.443365</td>\n", " <td>-0.901578</td>\n", " <td>-0.063022</td>\n", " <td>0.750404</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1.457569</td>\n", " <td>-0.658910</td>\n", " <td>0.140554</td>\n", " <td>0.593056</td>\n", " <td>-2.618940</td>\n", " <td>0.484812</td>\n", " <td>-0.417834</td>\n", " <td>0.735558</td>\n", " <td>1.693997</td>\n", " <td>0.230496</td>\n", " <td>...</td>\n", " <td>-0.608215</td>\n", " <td>-0.586245</td>\n", " <td>0.204547</td>\n", " <td>0.350195</td>\n", " <td>0.164781</td>\n", " <td>-0.009945</td>\n", " <td>0.023533</td>\n", " <td>-0.177017</td>\n", " <td>-0.425871</td>\n", " <td>0.755898</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 88 columns</p>\n", "</div>" ], "text/plain": [ " X.Intercept. floor b.log_uranium_ppm.county.0. \\\n", "1 1.450331 -0.757375 1.093099 \n", "2 1.453383 -0.659085 0.560266 \n", "3 1.460351 -0.804513 1.145694 \n", "4 1.438725 -0.565721 0.204148 \n", "5 1.457569 -0.658910 0.140554 \n", "\n", " b.log_uranium_ppm.county.1. b.log_uranium_ppm.county.2. \\\n", "1 0.603985 -0.661680 \n", "2 0.580569 0.419441 \n", "3 0.705337 2.216364 \n", "4 0.810580 -1.982466 \n", "5 0.593056 -2.618940 \n", "\n", " b.log_uranium_ppm.county.3. b.log_uranium_ppm.county.4. \\\n", "1 -0.667052 0.549178 \n", "2 -0.154925 -1.263534 \n", "3 -1.044031 1.510397 \n", "4 1.044825 -1.339406 \n", "5 0.484812 -0.417834 \n", "\n", " b.log_uranium_ppm.county.5. b.log_uranium_ppm.county.6. \\\n", "1 -0.150145 1.486173 \n", "2 0.166291 1.534917 \n", "3 -0.472675 0.427448 \n", "4 0.643384 2.379191 \n", "5 0.735558 1.693997 \n", "\n", " b.log_uranium_ppm.county.7. ... b.log_uranium_ppm.county.76. \\\n", "1 0.053578 ... 0.323107 \n", "2 1.141392 ... 0.306941 \n", "3 0.940869 ... 1.941637 \n", "4 0.295981 ... -0.832814 \n", "5 0.230496 ... -0.608215 \n", "\n", " b.log_uranium_ppm.county.77. b.log_uranium_ppm.county.78. \\\n", "1 0.282121 -0.188251 \n", "2 0.331696 0.614852 \n", "3 0.188311 -0.690044 \n", "4 0.246694 -0.801078 \n", "5 -0.586245 0.204547 \n", "\n", " b.log_uranium_ppm.county.79. b.log_uranium_ppm.county.80. \\\n", "1 0.889385 1.008348 \n", "2 0.153913 1.253688 \n", "3 0.946370 1.626093 \n", "4 0.072430 0.108882 \n", "5 0.350195 0.164781 \n", "\n", " b.log_uranium_ppm.county.81. b.log_uranium_ppm.county.82. \\\n", "1 0.429430 0.404690 \n", "2 0.246746 0.849947 \n", "3 0.930756 0.141125 \n", "4 -0.203805 0.443365 \n", "5 -0.009945 0.023533 \n", "\n", " b.log_uranium_ppm.county.83. b.log_uranium_ppm.county.84. sigma \n", "1 0.112424 -0.071612 0.778706 \n", "2 -1.399027 -1.697360 0.754960 \n", "3 0.041392 -0.135538 0.763605 \n", "4 -0.901578 -0.063022 0.750404 \n", "5 -0.177017 -0.425871 0.755898 \n", "\n", "[5 rows x 88 columns]" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "samples.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-TdxDTcJ40QY" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4000, 85)\n" ] } ], "source": [ "posterior_random_weights_cols = [\n", " col for col in samples.columns if 'b.log_uranium_ppm.county' in col\n", "]\n", "posterior_random_weights_final_stan = samples[\n", " posterior_random_weights_cols].values\n", "print(posterior_random_weights_final_stan.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "Qv_caP1t4FVF" }, "source": [ "[この Stan の例](https://github.com/stan-dev/example-models/blob/master/ARM/Ch.16/radon.3.stan)は、確率モデルを直接指定することにより、TFP に似たスタイルで LMER を実装する方法を示しています。" ] }, { "cell_type": "markdown", "metadata": { "id": "QkchUh3V382r" }, "source": [ "## 6 TF Probability を使用して HLM を適合させる" ] }, { "cell_type": "markdown", "metadata": { "id": "Ywj6S9iQ0aZe" }, "source": [ "このセクションでは、低レベルの TensorFlow Probability プリミティブ (`Distributions`) を使用して、階層線形モデルを指定し、不明なパラメータを適合させます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TOh_69los9gK" }, "outputs": [], "source": [ "# Handy snippet to reset the global graph and global session.\n", "with warnings.catch_warnings():\n", " warnings.simplefilter('ignore')\n", " tf.reset_default_graph()\n", " try:\n", " sess.close()\n", " except:\n", " pass\n", " sess = tf.InteractiveSession()" ] }, { "cell_type": "markdown", "metadata": { "id": "g6xl7I6XTTg5" }, "source": [ "### 6.1 モデルの指定" ] }, { "cell_type": "markdown", "metadata": { "id": "GjzA6-vAXXLS" }, "source": [ "このセクションでは、TFP プリミティブを使用して[ラドン線形混合効果モデル](#scrollTo=IFC9r-h0XlQ3)を指定します。これを行うには、2 つの TFP 分布を生成する 2 つの関数を指定します。\n", "\n", "- `make_weights_prior`: ランダムな重みの多変量正規分布 (線形予測子を計算するために $\\log(\\text{UraniumPPM}_{c_i})$ を掛けます)。\n", "- `make_log_radon_likelihood`: 観測された各 $\\log(\\text{Radon}_i)$ 従属変数にわたる `Normal` 分布のバッチ。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "_QtzWC-ZZ9U-" }, "source": [ "これらの各分布のパラメータを適合するため、TF 変数 ([`tf.get_variable`](https://www.tensorflow.org/api_docs/python/tf/get_variable)) を使用する必要があります。ただし、制約なしの最適化を使用したいので、必要なセマンティクスを実現するために実数値を制約する方法を見つける必要があります (例: 標準偏差を表す正の値)。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NzpFXkvOXMav" }, "outputs": [], "source": [ "inv_scale_transform = lambda y: np.log(y) # Not using TF here.\n", "fwd_scale_transform = tf.exp" ] }, { "cell_type": "markdown", "metadata": { "id": "817V1h2_aCFp" }, "source": [ "次の関数は、事前の$p(\\beta\|\\sigma_C)$ を作成します。ここで、$\\beta$ は変量効果の重みを示し、$\\sigma_C$ は標準偏差を示します。\n", "\n", "`tf.make_template` を使用して、この関数の最初の呼び出しで使用する TF 変数がインスタンス化され、その後のすべての呼び出しで reuse 変数の最新の値がインスタンス化されるようにします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JnPFL-pKXMRl" }, "outputs": [], "source": [ "def _make_weights_prior(num_counties, dtype):\n", " \"\"\"Returns a `len(log_uranium_ppm)` batch of univariate Normal.\"\"\"\n", " raw_prior_scale = tf.get_variable(\n", " name='raw_prior_scale',\n", " initializer=np.array(inv_scale_transform(1.), dtype=dtype))\n", " return tfp.distributions.Independent(\n", " tfp.distributions.Normal(\n", " loc=tf.zeros(num_counties, dtype=dtype),\n", " scale=fwd_scale_transform(raw_prior_scale)),\n", " reinterpreted_batch_ndims=1)\n", "\n", "\n", "make_weights_prior = tf.make_template(\n", " name_='make_weights_prior', func_=_make_weights_prior)" ] }, { "cell_type": "markdown", "metadata": { "id": "3-aeEIbmaJQ1" }, "source": [ "次の関数は、尤度 $p(y\|x,\\omega,\\beta,\\sigma_N)$ を作成します。ここで、$y,x$ は応答と証拠を示し、$\\omega,\\beta$ は固定効果と変量効果の重みを示し、$\\sigma_N$ は標準偏差を示します。\n", "\n", "ここでも、`tf.make_template` を使用して、TF 変数が呼び出し間で再利用されるようにします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wNQTcHcQXMIp" }, "outputs": [], "source": [ "def _make_log_radon_likelihood(random_effect_weights, floor, county,\n", " log_county_uranium_ppm, init_log_radon_stddev):\n", " raw_likelihood_scale = tf.get_variable(\n", " name='raw_likelihood_scale',\n", " initializer=np.array(\n", " inv_scale_transform(init_log_radon_stddev), dtype=dtype))\n", " fixed_effect_weights = tf.get_variable(\n", " name='fixed_effect_weights', initializer=np.array([0., 1.], dtype=dtype))\n", " fixed_effects = fixed_effect_weights[0] + fixed_effect_weights[1] * floor\n", " random_effects = tf.gather(\n", " random_effect_weights * log_county_uranium_ppm,\n", " indices=tf.to_int32(county),\n", " axis=-1)\n", " linear_predictor = fixed_effects + random_effects\n", " return tfp.distributions.Normal(\n", " loc=linear_predictor, scale=fwd_scale_transform(raw_likelihood_scale))\n", "\n", "\n", "make_log_radon_likelihood = tf.make_template(\n", " name_='make_log_radon_likelihood', func_=_make_log_radon_likelihood)" ] }, { "cell_type": "markdown", "metadata": { "id": "_dGxuoLPbRMs" }, "source": [ "最後に、事前と尤度生成器を使用して、同時対数密度を構築します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_UBayNK538JD" }, "outputs": [], "source": [ "def joint_log_prob(random_effect_weights, log_radon, floor, county,\n", " log_county_uranium_ppm, dtype):\n", " num_counties = len(log_county_uranium_ppm)\n", " rv_weights = make_weights_prior(num_counties, dtype)\n", " rv_radon = make_log_radon_likelihood(\n", " random_effect_weights,\n", " floor,\n", " county,\n", " log_county_uranium_ppm,\n", " init_log_radon_stddev=radon.log_radon.values.std())\n", " return (rv_weights.log_prob(random_effect_weights)\n", " + tf.reduce_sum(rv_radon.log_prob(log_radon), axis=-1))" ] }, { "cell_type": "markdown", "metadata": { "id": "xkeKH0rTTWDo" }, "source": [ "### 6.2 トレーニング (期待値最大化の確率的近似)" ] }, { "cell_type": "markdown", "metadata": { "id": "h7Xr0X4Qbe9C" }, "source": [ "線形混合効果回帰モデルを適合させるために、期待値最大化アルゴリズム (SAEM) の確率的近似バージョンを使用します。基本的な考え方は、事後のサンプルを使用して、予想される同時対数密度 (E ステップ) を概算することです。次に、この計算を最大化するパラメータを見つけます (M ステップ)。具体的には、不動点イテレーションは次の式で取得できます。\n", "\n", "$$\\begin{align} \\text{E}[ \\log p(x, Z \| \\theta) \| \\theta_0] &\\approx \\frac{1}{M} \\sum_{m=1}^M \\log p(x, z_m \| \\theta), \\quad Z_m\\sim p(Z \| x, \\theta_0) && \\text{E-step}\\ &=: Q_M(\\theta, \\theta_0) \\ \\theta_0 &= \\theta_0 - \\eta \\left.\\nabla_\\theta Q_M(\\theta, \\theta_0)\\right\|_{\\theta=\\theta_0} && \\text{M-step} \\end{align}$$\n", "\n", "ここで、$x$ は証拠、$Z$ は無視する必要のある潜在変数、および $\\theta,\\theta_0$ の可能なパラメータ化を示します。\n", "\n", "詳細については、次を参照してください。[Convergence of a stochastic approximation version of the EM algorithms by Bernard Delyon, Marc Lavielle, Eric, Moulines (Ann. Statist., 1999)](https://projecteuclid.org/euclid.aos/1018031103)。" ] }, { "cell_type": "markdown", "metadata": { "id": "hwEoELEueaeZ" }, "source": [ "E ステップを計算するには、事後からサンプリングする必要があります。事後分布からサンプリングをすのは簡単ではないため、ハミルトニアンモンテカルロ (HMC) を使用します。HMC は、非正規化された事後対数密度の勾配 (パラメータではなく、wrt 状態) を使用して新しいサンプルを提供するマルコフ連鎖モンテカルロ法の手順です。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JOTzK4qne9Qr" }, "source": [ "正規化されていない事後対数密度を指定するのは簡単です。これは、条件付けする「固定」された同時対数密度にすぎません。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FSwVJAkNEx6Y" }, "outputs": [], "source": [ "# Specify unnormalized posterior.\n", "\n", "dtype = np.float32\n", "\n", "log_county_uranium_ppm = radon[\n", " ['county', 'log_uranium_ppm']].drop_duplicates().values[:, 1]\n", "log_county_uranium_ppm = log_county_uranium_ppm.astype(dtype)\n", "\n", "def unnormalized_posterior_log_prob(random_effect_weights):\n", " return joint_log_prob(\n", " random_effect_weights=random_effect_weights,\n", " log_radon=dtype(radon.log_radon.values),\n", " floor=dtype(radon.floor.values),\n", " county=np.int32(radon.county.values),\n", " log_county_uranium_ppm=log_county_uranium_ppm,\n", " dtype=dtype)" ] }, { "cell_type": "markdown", "metadata": { "id": "khZHTgVYfASP" }, "source": [ "これで、HMC 遷移カーネルを作成して E ステップのセットアップを完了しました。\n", "\n", "注意:\n", "\n", "- `state_stop_gradient=True`を使用して、M ステップが MCMC からの抽出を介してバックプロパゲーションするのを防ぎます。(E ステップでは前の最もよく知られている推定量で意図的にパラメータ化されているため、バックプロパゲーションを行う必要はありません。)\n", "\n", "- [`tf.placeholder`](https://www.tensorflow.org/api_docs/python/tf/placeholder) を使用して、最終的に TF グラフを実行するときに、前のイテレーションのランダム MCMC サンプルを次のイテレーションのチェーンの値としてフィードできるようにします。\n", "\n", "- TFP の適応型 `step_size` ヒューリスティック、`tfp.mcmc.hmc_step_size_update_fn` を使用します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WnZ_KMP0E0ot" }, "outputs": [], "source": [ "# Set-up E-step.\n", "\n", "step_size = tf.get_variable(\n", " 'step_size',\n", " initializer=np.array(0.2, dtype=dtype),\n", " trainable=False)\n", "\n", "hmc = tfp.mcmc.HamiltonianMonteCarlo(\n", " target_log_prob_fn=unnormalized_posterior_log_prob,\n", " num_leapfrog_steps=2,\n", " step_size=step_size,\n", " step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(\n", " num_adaptation_steps=None),\n", " state_gradients_are_stopped=True)\n", "\n", "init_random_weights = tf.placeholder(dtype, shape=[len(log_county_uranium_ppm)])\n", "\n", "posterior_random_weights, kernel_results = tfp.mcmc.sample_chain(\n", " num_results=3,\n", " num_burnin_steps=0,\n", " num_steps_between_results=0,\n", " current_state=init_random_weights,\n", " kernel=hmc)" ] }, { "cell_type": "markdown", "metadata": { "id": "-GmtUwoLff1y" }, "source": [ "ここで、M ステップを設定します。これは基本的に、TF で行うことのある最適化と同じです。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wceMwnGwvUfF" }, "outputs": [], "source": [ "# Set-up M-step.\n", "\n", "loss = -tf.reduce_mean(kernel_results.accepted_results.target_log_prob)\n", "\n", "global_step = tf.train.get_or_create_global_step()\n", "\n", "learning_rate = tf.train.exponential_decay(\n", " learning_rate=0.1,\n", " global_step=global_step,\n", " decay_steps=2,\n", " decay_rate=0.99)\n", "\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", "train_op = optimizer.minimize(loss, global_step=global_step)" ] }, { "cell_type": "markdown", "metadata": { "id": "s_ykFN0Cfoel" }, "source": [ "最後に、いくつかのハウスキーピングタスクを実行します。すべての変数が初期化されていることを TF に通知する必要があります。また、TF 変数へのハンドルを作成して、プロシージャの各イテレーションでそれらの値を `print` できるようにします。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PakV59O8E3m5" }, "outputs": [], "source": [ "# Initialize all variables.\n", "\n", "init_op = tf.initialize_all_variables()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FziBCkW_NXFF" }, "outputs": [], "source": [ "# Grab variable handles for diagnostic purposes.\n", "\n", "with tf.variable_scope('make_weights_prior', reuse=True):\n", " prior_scale = fwd_scale_transform(tf.get_variable(\n", " name='raw_prior_scale', dtype=dtype))\n", " \n", "with tf.variable_scope('make_log_radon_likelihood', reuse=True):\n", " likelihood_scale = fwd_scale_transform(tf.get_variable(\n", " name='raw_likelihood_scale', dtype=dtype))\n", " fixed_effect_weights = tf.get_variable(\n", " name='fixed_effect_weights', dtype=dtype)" ] }, { "cell_type": "markdown", "metadata": { "id": "pwjiJLywlZgF" }, "source": [ "### 6.3 実行" ] }, { "cell_type": "markdown", "metadata": { "id": "ouO-E4Ncf0KE" }, "source": [ "このセクションでは、SAEM TF グラフを実行します。ここで重要な点は、HMC カーネルからの最後のものを次のイテレーションにフィードすることです。これは、`feed_dict` 呼び出しで `sess.run` を使用することで実現されます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Cy36-LMMNbTc" }, "outputs": [], "source": [ "init_op.run()\n", "w_ = np.zeros([len(log_county_uranium_ppm)], dtype=dtype)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OrzwMVaoE0i2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "global_step: 0 loss: 1966.948 acceptance:1.0000 step_size:0.2000 prior_scale:1.0000 likelihood_scale:0.8529 fixed_effect_weights:[ 0. 1.]\n", "global_step: 100 loss: 1165.385 acceptance:0.6205 step_size:0.2040 prior_scale:0.9568 likelihood_scale:0.7611 fixed_effect_weights:[ 1.47523439 -0.66043079]\n", "global_step: 200 loss: 1149.851 acceptance:0.6766 step_size:0.2081 prior_scale:0.7465 likelihood_scale:0.7665 fixed_effect_weights:[ 1.48918796 -0.67058587]\n", "global_step: 300 loss: 1163.464 acceptance:0.6811 step_size:0.2040 prior_scale:0.8445 likelihood_scale:0.7594 fixed_effect_weights:[ 1.46291411 -0.67586178]\n", "global_step: 400 loss: 1158.846 acceptance:0.6808 step_size:0.2081 prior_scale:0.8377 likelihood_scale:0.7574 fixed_effect_weights:[ 1.47349834 -0.68823022]\n", "global_step: 500 loss: 1154.193 acceptance:0.6766 step_size:0.1961 prior_scale:0.8546 likelihood_scale:0.7564 fixed_effect_weights:[ 1.47703862 -0.67521363]\n", "global_step: 600 loss: 1163.903 acceptance:0.6783 step_size:0.2040 prior_scale:0.9491 likelihood_scale:0.7587 fixed_effect_weights:[ 1.48268366 -0.69667786]\n", "global_step: 700 loss: 1163.894 acceptance:0.6767 step_size:0.1961 prior_scale:0.8644 likelihood_scale:0.7617 fixed_effect_weights:[ 1.4719094 -0.66897118]\n", "global_step: 800 loss: 1153.689 acceptance:0.6742 step_size:0.2123 prior_scale:0.8366 likelihood_scale:0.7609 fixed_effect_weights:[ 1.47345769 -0.68343043]\n", "global_step: 900 loss: 1155.312 acceptance:0.6718 step_size:0.2040 prior_scale:0.8633 likelihood_scale:0.7581 fixed_effect_weights:[ 1.47426116 -0.6748783 ]\n", "global_step:1000 loss: 1151.278 acceptance:0.6690 step_size:0.2081 prior_scale:0.8737 likelihood_scale:0.7581 fixed_effect_weights:[ 1.46990883 -0.68891817]\n", "global_step:1100 loss: 1156.858 acceptance:0.6676 step_size:0.2040 prior_scale:0.8716 likelihood_scale:0.7584 fixed_effect_weights:[ 1.47386014 -0.6796245 ]\n", "global_step:1200 loss: 1166.247 acceptance:0.6653 step_size:0.2000 prior_scale:0.8748 likelihood_scale:0.7588 fixed_effect_weights:[ 1.47389269 -0.67626756]\n", "global_step:1300 loss: 1165.263 acceptance:0.6636 step_size:0.2040 prior_scale:0.8771 likelihood_scale:0.7581 fixed_effect_weights:[ 1.47612262 -0.67752427]\n", "global_step:1400 loss: 1158.108 acceptance:0.6640 step_size:0.2040 prior_scale:0.8748 likelihood_scale:0.7587 fixed_effect_weights:[ 1.47534692 -0.6789524 ]\n", "global_step:1499 loss: 1161.030 acceptance:0.6638 step_size:0.1941 prior_scale:0.8738 likelihood_scale:0.7589 fixed_effect_weights:[ 1.47624075 -0.67875224]\n", "CPU times: user 1min 16s, sys: 17.6 s, total: 1min 33s\n", "Wall time: 27.9 s\n" ] } ], "source": [ "%%time\n", "maxiter = int(1500)\n", "num_accepted = 0\n", "num_drawn = 0\n", "for i in range(maxiter):\n", " [\n", " _,\n", " global_step_,\n", " loss_,\n", " posterior_random_weights_,\n", " kernel_results_,\n", " step_size_,\n", " prior_scale_,\n", " likelihood_scale_,\n", " fixed_effect_weights_,\n", " ] = sess.run([\n", " train_op,\n", " global_step,\n", " loss,\n", " posterior_random_weights,\n", " kernel_results,\n", " step_size,\n", " prior_scale,\n", " likelihood_scale,\n", " fixed_effect_weights,\n", " ], feed_dict={init_random_weights: w_})\n", " w_ = posterior_random_weights_[-1, :]\n", " num_accepted += kernel_results_.is_accepted.sum()\n", " num_drawn += kernel_results_.is_accepted.size\n", " acceptance_rate = num_accepted / num_drawn\n", " if i % 100 == 0 or i == maxiter - 1:\n", " print('global_step:{:>4} loss:{: 9.3f} acceptance:{:.4f} '\n", " 'step_size:{:.4f} prior_scale:{:.4f} likelihood_scale:{:.4f} '\n", " 'fixed_effect_weights:{}'.format(\n", " global_step_, loss_.mean(), acceptance_rate, step_size_,\n", " prior_scale_, likelihood_scale_, fixed_effect_weights_)) " ] }, { "cell_type": "markdown", "metadata": { "id": "QR50yLYygWdg" }, "source": [ "約 1500 ステップ後、パラメータの推定値は安定しました。" ] }, { "cell_type": "markdown", "metadata": { "id": "x2BtkWEIVsB9" }, "source": [ "### 6.4 結果" ] }, { "cell_type": "markdown", "metadata": { "id": "HYs17VUto_te" }, "source": [ "パラメータを適合させたので、多数の事後サンプルを生成して結果を調べてみます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "v-X0DhqHjdue" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 42s, sys: 26.6 s, total: 2min 8s\n", "Wall time: 35.1 s\n" ] } ], "source": [ "%%time\n", "posterior_random_weights_final, kernel_results_final = tfp.mcmc.sample_chain(\n", " num_results=int(15e3),\n", " num_burnin_steps=int(1e3),\n", " current_state=init_random_weights,\n", " kernel=tfp.mcmc.HamiltonianMonteCarlo(\n", " target_log_prob_fn=unnormalized_posterior_log_prob,\n", " num_leapfrog_steps=2,\n", " step_size=step_size))\n", "\n", "[\n", " posterior_random_weights_final_,\n", " kernel_results_final_,\n", "] = sess.run([\n", " posterior_random_weights_final,\n", " kernel_results_final,\n", "], feed_dict={init_random_weights: w_})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LCECfwexk1DN" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prior_scale: 0.873799\n", "likelihood_scale: 0.758913\n", "fixed_effect_weights: [ 1.47624075 -0.67875224]\n", "acceptance rate final: 0.7448\n" ] } ], "source": [ "print('prior_scale: ', prior_scale_)\n", "print('likelihood_scale: ', likelihood_scale_)\n", "print('fixed_effect_weights: ', fixed_effect_weights_)\n", "print('acceptance rate final: ', kernel_results_final_.is_accepted.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "qbzMVKPykLYq" }, "source": [ "次に、$\\beta_c \\log(\\text{UraniumPPM}_c)$ 変量効果の箱ひげ図を作成します。郡の頻度を減らして、変量効果を並べ替えます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vjA7TVT4wuQa" }, "outputs": [ { "data": { "image/png": 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Upaen65BDDtHpp5/epuMRbiwkJSVpx44dOuWU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MWL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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc95c5a0d0>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "x = posterior_random_weights_final_ * log_county_uranium_ppm\n", "I = county_freq[:, 0]\n", "x = x[:, I]\n", "cols = np.array(county_name)[I]\n", "pw = pd.DataFrame(x)\n", "pw.columns = cols\n", "\n", "fig, ax = plt.subplots(figsize=(25, 4))\n", "ax = pw.boxplot(rot=80, vert=True);" ] }, { "cell_type": "markdown", "metadata": { "id": "DXIqPxN1j3wI" }, "source": [ "この箱ひげ図から、郡レベルの $\\log(\\text{UraniumPPM})$ 変量効果の分散は、データセットにある郡のデータが少ない場合に増加することがわかります。直感的には、これは理にかなっています。証拠が少ない場合は、特定の郡の影響について確信が持てません。" ] }, { "cell_type": "markdown", "metadata": { "id": "DtlOjtAkyE76" }, "source": [ "## 7 並べて比較する" ] }, { "cell_type": "markdown", "metadata": { "id": "QF6OwHJSk7xM" }, "source": [ "次に、3 つの手順すべての結果を比較します。これを行うために、Stan と TFP によって生成された事後サンプルの非パラメータ推定値を計算します。また、R の `lme4` パッケージによって生成されたパラメトリック (概算) 推定値と比較します。" ] }, { "cell_type": "markdown", "metadata": { "id": "Qg8pYIRQy9Ea" }, "source": [ "次のプロットは、ミネソタ州の各郡の各重みの事後分布を示しています。Stan (赤)、TFP (青)、および R の`lme4` (オレンジ) の結果を示します。Stan と TFP の結果をシェーディングするため、2つが一致すると紫色になると予想されます。簡単にするために、R からの結果はシェーディングしません。各サブプロットは単一の郡を表し、ラスタースキャンの順序で頻度の降順で並べられます (つまり、左から右、次に上から下)。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9vhm-sNWrhmB" }, "outputs": [ { "data": { "image/png": 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lypWcOHGCZcuWERAQUGAMP/74I0lJSTz33HNe3SV2797N7Nmzi3ZQPqxZs4Z9+/YxcOBA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\n0el09OrVl6wso3vmB5D6IHy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CTMWYl8NVBZ2mYU/ef50iFEJcD3abiwBdBlZr4QdGhL9a\nHNjLoqkKzoyE6xSdECWLggtV9X1boJEzU5FOEgfiCucSMqniXIxqCeJCQiXCoyr53dYVGEpqSj+C\nVCt3WT4nM811AyMVQlwte+YxNLsdk7Hw4xvk0uyV0XQGMKSiZWRcpwjFjVAqEgdmM0Q4c+b3sZgL\nN5tCXnatBjpFQZ8hUzIKUZLYs9JQNBV7ERMHOgXsrrJomg5X+unrE5wQJYmm5YxxoPlOvCsE5vyv\nSVcFcZmmgXphCUp2GqlnGhIc6j9pkCtLV5fsS/WI0CeSse2XGxClEOJa2dKOo3PZMaaEExQaVaR9\nNVcEdkdZQstcwng66TpFKG6EUpE4yM5WCNNOYLcaCFAKP0BaLpurMih6wtQENFVGWBeipNCyEtHQ\nsFkjiryvw1UWVVPQZV+4DpEJUcJoTtA0VNXgc7Wa2+IASRyIy9IvplPJ8QsOawjpF+9BHxRSqP0y\nbR0JUBRIWIDmkJk6hLjVubIOo1mt2I0VIdh7LLmCZFtqYgi0k3ZEuoWXZKUicWDJthConcOWFQVF\nzIIBaFoAVmt5wkMvYr4oAyQKUVKoxvOgadhtRb+IqUTlzMRokzEOhECzowEul+/EAYoOTdWhIA95\n4jJb0v/QWdO5cLIBweEFtzbIZXdUw5R9J5Ghp8javfk6RiiEuGaqnQDLAbKNZdEbQgve3ge7rQYq\neqzn9xRzcOJG8nOHULK4Mk+Bw4HdGg063wN27Nn0Vb5lWKxVKFPmPJlH9xFQN+Z6hCmEKGaK+TxO\nl4pe9T0tUH71XnNFoKJDs6Vfr/CEKDk0W06LgzzTGefWn7Aw0BQdmkuPziAtDkQOh9VImHEFNlMI\nWen3EFrWe4Da/L6D001tCAs5SdbeudCr5/UMVQhxDVTTYbCZyUyrhb/hDQp6zsod58BpOZUzDXZQ\n4Qe0FreOUtHiQJ9xEFXVsFkLN6eoLxZnNXQoZByUUX6FKCl09guoLhc6nfcI3gXuq4bhQofiMoJL\nBugStzmXHTTQ/HRV0BQFTdVLVwXhZj6zAoPDyLnTdQkKKHp3MdV2Fw4tgiDdAZznpcuYELcq26V9\nKE47aRfKExxc9LoOoNrL49KCMQRfhExjMUcobpRSkTgIMB1F0zRUW5WrLsOpVgbFgHrxYDFGJoS4\nnnT2i2guFUVfscj7GnRBuNRAUEwyPZC47bmcdkDD5fIzzZZOj6rq0SnSVUGQMzXbpTgcRg3j+bvQ\nhxd9YGowkJHVgECDmaQNS4o9RCFE8VCN+9BsDmxpFYo85f1lerLN1QkKzSA78WyxxidunBKfOHA4\nINh1As3pRFXuvIaCyuHSBaCo58FkKr4AhRDXjV5NxeUyoCpFH+MgyKBisUah6UwokjgQtzlNzWlx\n4G9wRCCnq4IkDgRgO/8rOksayWcboQ8IvupyrLYYNF0gWSdWgV2m+hTiluMyorOcJDO1IoGhIUWe\n8j4vi7UmigZZp7YVY4DiRirxiQNzlotQw1nM5kg0feFG8/VFc5XBoYUSEJSJLSmxGCMUQlwXmoaB\nNGzWMKDoF7KgABdWe1kUnRXVJPMKi9ubas9Jnqku/7cFqmaQxIEA1Y7rwko0s5MLp2oRelWtDXJo\ntqrYXGUJCj6BIyGhGIMUQhQHJfsAqtWGMaMGKOo1leV01EBDjyt9XzFFJ260Ep84cCSfQKfYMGWV\nv8aSFLItVQgMNWE+fKBYYhNCXD/OrEx0OgtWy9X1twvQa1jtOTMrODIlWShub6ojJ3Hgcubf4kCR\nxMFtz5X2O6ophQsJ9xEYFIoSePUvbUBHpqkBBoOdlC3Liy1GIUTxcKTtQXE6SL9YieBrquuAvRKa\nPgCdKyGnybgocUp84oDUQ7hcKnZL/tMANWozlkZtxua7jcVRBQ0Fkg8XZ4RCiOvAcekMABar/8RB\nQfXe5ohEU3WomfKmS9zmnH8lDrTLiYMr64+qGdDrHOTMYypuS5qGPWkpisXG+TP3YQjMf2T0wtx7\nOW33oeqCcKb8KePNCHEr0TRU4z5Uq57s1GAMYf4HoS9MXde0YGzWigSHXsSVmlLc0YoboMQnDnRZ\nR3E6HBj0d1xzWQ5rBZyKAcxJkJ197cEJIa4bV8Y5NDTs1rCrLsOplkVDQTWeK8bIhCh5NEduVwU/\ngyMCmqoHFTRVWh3ctrJ2oBrPkHqmBnpXEMFhV99NIZfLWhWHVpawyDNYjp0shiCFEMXCcQHNdpHM\nlIoEhwaDcu2Pjdmmu9EpTrLPyDgHJVHJThxoGnrbKVQVVLXaNRen2svj0gWgUy7hOC8PEkLcylTT\nOVSXC5xX303J6SqDqilgTi3GyIQoeVSHDdBQXf67KqhqAKDlDKQobkv2c0vRmU2cP10PQ7HNw64j\n29KQgAAnF/5cWkxlCiGulc68B6fFSUZqdQICr35QxLystjoomoLt3O/FUp64sUp04kDJzMCgS8bu\nCLjFzxQAACAASURBVMHlLPqo6lfSnOVQ9YEoQUasx44UQ4RCiOvGfAGXw4nOUPmqi3Day6KiQ3FI\nkzlxe9McVjTAlc+sCmh60DRc0jf1tqRYjqGl7sZ4oTJmcwQh4dc6tlQezjqo+iDI2ikzWwlxi3Bl\n7geHnfTUygTo/LdGKwqd7g6czhAC7Lul21sJVKITB85zSegN6WRnR3M1o6pfKUAJxGovjxJmQp+U\ndO0BCiGuG8V2AU3VUHQVrroMzRmZkzjgEjil+bW4fWlOK2iAmk9XBU0PGjgd0uLgdqRe+B+aKZuE\no/cRVeZq53L3zWWrjkOLJCLqDKaDJ4q1bCHEVdBUVON+XJZQLOkagcXQLQkgQK8j49Jd6LRMlPSD\nxVKmuHFKduIg+RCqy4HFXrVYylMUsForoQU50WUlg9lcLOUKIYqZqqJTU3FqehT16sc4CNTrsFoj\n0ZQMFKulGAMUomRRnTY0TQMtwO82mmYANPfUjeI2Yr+AlrwBW1Z5UlIrEhhydbPZ+Kcjy1SfgEAX\nF/78n7yJFOImU6wncFlNGC9UIiAwCAz+rw1FlZ5ZH82p4khYW2xlihsjnzaJtzinEzXzBC4FrOYq\nBBaw+Z5NXxWqWIe9Ao4wAwHOVNSUi+hq3XHNoQohipdizkany8RmDSe//GdB9T4kwEm2uQJRERfA\nYoTw4r4ZFqKEcOUkDnTK5ZvD3PoT9lduTlMNoIHLZrsZEYqbSJ/xKw6jmRMHGlClUv6zWOVV2Hsv\nAKflHlxl/kRn34+SloZWvhi7QgghikRn3ofLYictpRpBIQW/Zy5KXc+yNwCXHseFDRh47lrCFDdY\niW1xoFy6hKqm4HSB8v/s3Xd4VFXewPHvvVOTSUIqhNB7VYoNO4oKVmzruuhaVn3tva9tbaxrbwsq\nimIFXVdd6aKCivQWSoBAGqRnZjK93fL+ERkISSCBSeV8nsfHJzP3nnsuM2fOOb97inboc5z3p4Uz\nUTBhsLjx79wRs3QFQYgdxWFHNngIBA6vo282avgD6WgqqC6xmrdw5NIjQTRdx8CBFryrGXGgihEH\nRxYtgl6ygEjQTElxT8zWWC2KWFs40J2InkKntELsazc1yzUEQWgczZ2NHorgKE8n3hwX07RtNhOO\nij4QKILA7pimLTSvdhs4kO1VqFoJmm7EqMUwKh3piiYZUa0u5N1FsUtXEISYCVfkoWsa4cDhl/1w\nJB1dkwlXbo9BzgShnVKC6JqO0XCATqFmAF1CCYlpfEcS2bsSxVVBRVEfEhKSm/NKuN0jMZh1PJtm\ni3VnBKG1aEFUz3bCnlQUXwSDLbblPs6kUuk4ikhQQ971Q0zTFppXuw0cSFWVSHI5gXBnJGI378Yi\nxxOOJKLEu5HtDgiKJyuC0NZoznw0RSUSg9FGYbVzTWfIkR+DnAlC+yRpAXRdQzI0/GRJl2pmNypi\nPZAjiqFyLqo/ws5t/UjNSGnWayneoWhGKybTJvRisS22ILQGKZBDxBeguqILJqulZhG4GHOFR4EK\nwaJFMU9baD7tM3Dg9RJ2lSEZIgT9sZ0DZzSAx9MdzRrGELEjOewxTV8QhMOne3eh6jqq0vi5tg1R\ntXQ0JHSfaKQKRzDFDxrIcnzDx+g1gYNIQGyXd6SQwsWolWtxO7IIKRnIse8/1KJEUnD7B5CQXEnl\nb6JDIQitQfJmo/lD2CsysdkOtorcoYmLj8dZ2RP8ueAvb5ZrCLHXLgMHhooygqESVAz4vYe+FVtD\nAr5uRHQTJoudQGFBzNMXBOEw+HxIehmKJmHk8Mu/rCWgYoJwGWhaDDIoCO3PnhEHsnyAEQdaTQMy\n5BOBgyOFbF+A4gmye2c/klJjsx3bwfg9I1BlC0rlYvHwRhBageJcB0ENR4kNa2J6s1wjKS5MmX0U\nYb+KvGths1xDiL12GTiQKyoI+vPQZSNauFujzhlx0m2MOOm2Rh0b8vdEwYBmc6HkiQXTBKEtkT1u\nZGMpoXAcqAdeHLEx5T7OBH5/OpLsAJ8vllkVhHZD0kJoqgHdsPfp0v7lR8eCBEQCnlbIodDitDCG\n8vkEA/HkF/cnOaHpG3E1pe0VvWygL2FSiO+0A9+aVU2+piAIhyFcjlZdgNeZhSabQGpcV7GpZV2W\nwBUaia5AIF8EDtqL9hc4CASQXC40gxNVA0MMd1TYQ1bSiChxRBIcGCsrQGw9JQhtRri8GEly4vdm\nAoc/btZs1PD6uoAUBpdYEFU4Mkm6H00xgmxo+Jg/AgdqUIw4OBLInqUoLjsVRf2xxHdqsevquhFn\n9TFg1vBtXYDkqm6xawvCkU72rUPxBCnbnUlaavNuUR1vTaC6uhtSYDu4xXSF9qDdBQ7kinIURUGy\nOAgFk5B0a8yvkWDRcDr7ETH7MUQqoEJ8mQWhrYhUbEZTVQLB7jFL0x/qiq5KBEuyY5amILQnshZE\nUQ+y0LBsRgL0iFgc8UhgKP0f4YDG9rxhpCc3zzznhiieo4gYE5DNGwit39Ci1xaEI1mobA1SJERV\neQZWW/MuhtopLkypczThgIK+c16zXkuIjXYXODCUFGMvz8NkVfF5Yj/aAGqGz7hd/QlqFuJsZfhz\nc5vlOoIgNJGmgT8bVdMJ+vrGLNmw1g2QCBZvjlmagtCeSARR1QN3DnXdWhM4CIsRBx2dFMxHt2/C\nWdGTsJJGnLmFm4taIi73URgT/TjX/iBGHQhCS9DCULUavzcZRWn+UUaSBN7wCDRNxr9zgVhnqh1o\nV4EDyeNGcrsJ6uXoQMAbuyeO+wt6+6PoBtQUO1JervgyC0IbIDkdyIYdBEJWjHoMy7+WjoYB3ZUH\nuh67dAWhHdDCChIhVPXAc9gV3VqzK1dErAXS0RlKviHsi5CXP4TkTg0vmNmcFM9IwoZ4MKwjuHpt\nq+RBEI4kmmsDms9NeVEGmV06t8g1k80JVHu7g7oTuUxsi93WtavAgVxcjBKJoJrsqIqGSe3dbNeK\nM1hwVPckbHMguyuQqp3Ndi1BEBpHK9+CLrlwOnpikBqei91UZmwoxKGp5WKBROGIo3h9SJKGohx4\nxIFGPJIEsiamKnRoSjWU/YDXnUhxZX+S42P3W9ukbIQyCYZ7Y0m3U7nqV6RyMW1UEJpTcPtCUFXK\nKnsjGQ4ydS1GzEYNu2c0mqJSveY/LXJN4dA1fYnc1qJpyCXFlBVXEZ9ZSSQUjxpp/NZAG36f0qTL\nJVrDVNmH0S1tB8mWAsIFBZhS05qYaUEQYilcvgpNUfB4BtKY1U0aW+5NBgmvtzspiTmEdxdgHjz8\n8DIqCO2I6nUhA4pWu4O4p/zYbH8cp8chSWDCj6KAsf20IIQmMBbOIuQJkJ93ChnJhzfaoKltr/0F\nHWOwZhVhsi3Hu3QEtosvArldPfMShPZBDWFwLsXjshBnHNTk0w+nrKvhYSjMB/sSJOdN6Ckts/Wr\n0HTt5tdXqqxEikSoDLgwGLz4XL2JxYrqDZEl8LsHENRMyCkVhDdliyHMgtCaNA3Jn42q6oT9Ta/U\nDsYf6guahHPzkpinLQhtmeLzAjqacuAnTJpkBl3GSIBwuGXyJrSwiAet+H/4fRYKSkeSYmvd6FDI\n3wu/bxCmdDeu7UuQC8QW2YLQHPxbF4EaoLS0N7aEhBa9tlW2YXcPxWgqx736pxa9ttA07SZwYNhV\nSFW5G2NiOZqioAeHNfs1bYZEHNXdCSU4MNoLkaqqmv2agiDUTyovQDbsosqRSYI19pWaqvREl4yo\nFatAVWOeviC0VVrAia7raJH4Ax8oyaiROKwmN4rSMnkTWpZx6zRCfh87do6gc2LrrG2wv0DVKUQM\nSdi6rsT501zwisU5BSGmdB0Kv0KJhPE4R7RKFgL+Y1AxEcj/L5LX0yp5EA6uXQQOJI8b2W6ncHeY\n5LQcUK0EvD2a/brJcSHKyo/Fo5iJj9tBaMvGZr+mIAgN2P0LSkShuro/cnMMNopkEdYTsVjzkMrK\nmuECgtA26T47mqqi60kHPVZRbFiNXsJBMQKvo5EcO1Er5uJ1x1FWMYbEuLYxF0WJpGIvG0fYYkKS\n5qAs/1UEdwUhhoIbf8KgF1Ja2p0kW79WyYMU7oUv3ANr/Hbcq39vlTwIB9cuAgdyYSE+bxg1vgRJ\n9xJwjQaaf7EeSQLVN5CgYiGcWomyKRvJ42726wqCsB+/H9W+DFXVcLmbaf0B3YDTMxyTyYd91Zzm\nuYYgtEGa14GqqsjGg+/ZrURsyAaFkFOMwOtQwiHIfoGAP8zmHafRpY2MNthD8w2nsvoYNJuHYNEH\nyNnrWztLgtAxeJxIRe8QiURwVp3SihmRqa4+DR0Dvu0fipFFbVTbDxwEAhhKi8nb4SSrx1qMgMs+\nqsUun2GD8srh+ExBLKEdKNnZLXZtQRAAVcW0YTkaO3B6MjCT0WyXCnpHoUlm1Mr5SG5Xs11HENoS\nLeBAV3UMhsSDHquqCUg6RFzFLZAzoUVEIkR+e5WQP5fi8l5o/uGYTW2teSgRqhpHmXcwkm0Xrg1T\nMORsae1MCUL7pqqoq55Dp4L8wqOIMw5s1ezowQF4Qv2Ij8+lavG3rZoXoX5trWaow7Ajl4A3jGrb\nidFQRcB1DKrS9PnNI066jREn3db068s6HvvxBFQzSucSQmtWIZeWNDkdQRAOjXFTNqp7JYFQhArn\nSJLiGj9PoanlXlY74w72wRJXiGOpWCRROALoOlLIjoaETO01DuorP+FIKhLgL9vWgpkUmovu8eJd\n+DJaYAHO6jh2F1xIqu3A23I2xaG2vepjMhhwlVxKeaAXhvhNuFa9hiF7A2haTNIXhCOKrmPcMBkt\ntIbKqgwC1eccVnKxKesSLud5aJIVyTEN79bth5meEGttOnAgOewYSorZtTuHbr1XoUesOMpPbvF8\nJBmScTr7442vhmA+gV8WiykLgtACDDtzkUpL8PiWohnjcFU1dzRcwuc9EU0y490xk3CZo5mvJwit\nzOdDlirRkQiHDr4FlqplIiOhVW1ugcwJzUnfuRb/j/di1OfjcFrYlXcFabZOrZ2tA7KZTFTtvpJy\nf08kazbV2c8hr/gBgsHWzpogtCumzW+gOH6k2pnMlpw/EW8+yOK4LUSPZGB3jMMo+fCtegJ3sWiH\ntSVtN3AQDGLcmE21ax3JPX9CiWjYiy9B0xqze3tsGWQI2k8jrJjx98rFvWULypKfxVBmQWhGclkp\nhh07cDh/QjOHKCkdSrLp4HOwD5fqH0BY705S8jYK/zcbXROLwAkdV7DMgdVWRDBkQ1UO3nAMKH2R\nMJAsrRMPetspqXoHypJHiWx7EEnayu6yLlTsugGrIau1s9Yo8QYbVbuuoaj6aHTTbjy7nsM//zWk\ngnyxbbYgHITkdmFe8zJqxbc47Ims3DCJLoltK2AY8J6EL3A0Fmkn/sX3ULShTNQ3bUTbWDJ3P5LD\njrxxNT73d6jWzWh6AsU7xyNHmn8nhYYY9VS8lWeQ1HkR5mHL8BTmkbi4GPOAsWg9eqAnHHxuqCAI\njSNVVmLcuIFK++9IiesJRZJw7BpDJ2tzbKdQ5+p4HOOI61JCQmAGZYtH0PXM1tmeSBCalc8HOYsA\nP27HUODg5UvTbHiq+2BLy8W5bQNpQ0TZaBd0nUjJJtS8LzD6lqOoGtUOG0W7xhHHKAxS232OVJ84\no5lQxeVsrR5G726zidf/i3vlMgybL8Y2+HT0zEyw2Vo7m4LQZkgOO9LOVaj2bwlIW3G54li28TrS\nrFaMhpZoWzWFhNNxGUYpjDVuC8Yt15K3/VriRl5C135xyO3r56pDab3AgapCJIIUCUM4jBSuRneW\nEtpdRKh6KwbzChQphMvfm12540gydWm1rO4Rco/EgwlLym8kZJagKjPwbfkeefsozLZRGLoORk9L\nR0tMgvj4mm0ZBEGon65DJLJ3Wy1JQgoGkHfvRi3IocL9C1JCDsGwjYKci+hkTW6xrAV8ffD6TsBm\nW0qo+H48c08jLms4ps5HoSd1rSnfouYS2rNAAFauIehdj2xR8PpHYGnkqX7vKOKTt1O15isMKYNI\nzmz5kYBCXboOEX8E1RdE9VYjuctQqwvQPDswaZswGktA16isSmJnwXEY9WNIsLbJ50eNYjSAUR1C\nSVEvTIm/kpW2HHzvUL36a2AYBmsfpMQekNwNUjKQEjuB1YbBJGM0gqH5N+cShJajaaAooChIqoKu\nhFEDdnR7LpSvQQ9vRdOKiag6Va7ubM65gjSzuc3+Bui6kYqqq0jrtIiE5N9IVd5AXzOT0jUno6cM\nw5TSHWtGMqbkVMw2K5JsQjZIouvVzJr/26KqGFevQgr4QdOQdA3JsAjJsAUkFb9PRdUi6LqOpulo\nioJukvCE4ikvH0vQeQxJ5sY2Z5pf0D0Mn2sIeUoJKenr6dF5E8bIYhTXz0heGXZasViMxNtkkAwg\nGdAxo3S5E7Xfaa2dfUFoPZqGcdVKJJ8XSVNB1QgGIT9fJqKoZPWehclcBVIQTQ0jJZjx+lKpKLyQ\neEPLBw7t5WejpBqx2ZaiuWcTDi9A2WUEyYquWZHUM0hKHIIuyyDJIElomV1RBw9p8bwKQqOFw4SX\nrKRwow9rwhpS0tfgCaVh0ns1OokgQ7BiJd3wI9XfGJGyzqLTeSeAydSMGRfqU/LpqaiKgoSOjo6E\nhoyODOi6hqSqqIpCRJMpL+1GeclwjPKxJFvaZmfhUBiJR/eMZ3dgNGbbKtJSNmCQfkEKLcGgykge\nA3KJAUmSQQJdNxDGgI4R1WIjEp8K1AQTUnoMQ+16e2vfkiAckGHbVuTSEhxVOiW7dWwJm0jt/DOS\npCChgaQiyxF0HXRq+lYRVabC3gencwSKZxC9ktrDb4CM3XUObv8xpHVahDE+B5v8NZL7WwwBA8aK\nmnKtQrRs67oRqPl/INCLspJLUeMTcQw4DiQJgwGGDVNJPfiSPkI9JF1vmQlhubm5+H0+JEBXFFBV\nJFVFj0SoLCvDW+3CoukYsKBE4jC1kUU6DkbXdSLhALIhSJwVwloYU0oyqd27I5nNNZ0KWaZnz56k\npDT//GxBaOt27NiBz+NBUlUIh1HDYQqKilB1CTWsEKdESDBYCAYlDMZOyG3gyb6maUQiLjQpSNAk\nIcWZ6darJzabDemPMq7LMl0yM8nMzGzt7ArCQW1ct45ty1djjZiQpQRMpqYH6KtVlYDmZOhxQzh2\nzJhmyKVwMLNmfomu6WiqBhpoiooeVpDCEUxhDashDrM5AaPxyAvqqKqKEgmg6WECSoiALKNZzGA2\nYjAbMJkkumRmkJ6ehiRBYmIiffr0ae1sC0KjlJeXU1ZWht/rZVdhISgqsqpApObBjKqDrkkYFA0r\nZmQpHrOlffStDkbXdbxBP+5IAN2sY4g3I5sMf4z01jAYJWSD/MdKfjIJCTa6deuGJIEkSQwePBiz\nOXa7xxxJWixwIAiCIAiCIAiCIAhC+9P6j/IEQRAEQRAEQRAEQWizROBAEARBEARBEARBEIQGicCB\nIAiCIAiCIAiCIAgNEoEDQRAEQRAEQRAEQRAaFNO9OBRFxen0xzLJFpeSEh/ze7CuHw5AcOSmmKbb\nkOa4h5bWEe4hIyOx3tdFOWkb4rOPQtP0FiuXzaUjfBb1lRVRTppPU+uktnofTdER7qGjlhPoGJ9P\nrO6hpduM++oIn4Noe7VtDd1Da37vm6ojfA4NlZODiWngwGg0xDK5ViHuoW3oCPfQkI5wb+35HiSH\nHcOOHci7Qmi92v8WZe35sziQjnBfHeEeoGPcR0e4h/p0lPvqCPch7qFt6wj3Ju6hbegI93CoYho4\nEARBaNN8PkxrV1O8s5pQXk9SqrzYjm3tTAmCIAiCIAhC2yYCBy2gPQy7EYQjgXH7Vlz2CjzGZST0\nyiB7y1iG7PKQ3OPQhmwJQnsk6iRBaLtE+RSOROJ73z6IxRGFer377r/56quZh52O0+ng6qv/hKIo\nMciVIBwGnw+5ooyw/DVpXXZg67qbQaO+p2jJ1+j64Sf/7bdf89Zbrx52OpFIhKuuupzq6urDz5Qg\ntEG33noDubnbDzud3377haee+nsMciQIbdfKlcv5+98fjElaN910LQUF+TFJSxBaQ35+HjfeeE1M\n0nrssQdZuXJ5TNI6UogRB4fp8ssvxOl0YDAYiYuL44QTTuS++x7GarXWOu6DD97lo4/eZ9q0GQwe\nPDT6+rx5s5k8+Wn+/OeruOOOe6Kv//LLYh577EHOPfcC/v73pwCYPftbvvjiU6qqKrFarQwaNJSn\nn55MXFwckyc/zQ8/zMdkMiNJoGk63bt35513PuTaa//C9dffyPjx50XTnz79PVavXsmUKe/Xuafq\n6moWLJjLzJnfRF8LhYK89dbrLF68CEVR6d9/AG+//V70/SlT3mTOnO+QJInzzruI2267C4CUlFRG\njz6W7777mssu+/Nh/msL7cmGDet55503yc/Pw2Aw0KtXH+66635WrVrOxx9/iCRJKIqCqipYLFZ0\nXadr1658/PEsTj31OKzWOCRJQtd1JEniuutuZNKkv0bTnzv3e/75z2d45pl/csYZZ0VfX7duDXff\nfesf50N6egZXXXUtFw4eQljbiGSu5vwXvPRMSeW9KxS6maZh39mPb37eRGVlRaPK2/4UReHjj6cz\nbdqM6L0/8MBdSJIEgK7rBIMBnnvuRU4//QwAZs36jM8//5hQKMzYsWfywAOPYjQaMZlMnH/+RD79\n9KNavwlCxzN37vfMmvUZxcW7sdkSOPXUsdxyyx0kJCQANb/TxcW7eOKJZ+uce/nlF+Jw2Pn223kk\nJXWKvn7ddZPYuTOXr776nszMTCorK3jjjZdZv34tiqLSpUsmV155FVlZ3aPfUV3XCAaDxMXFR8vb\np59+yTPPPMGWLZsxmYzR4Nro0cfwwguv1ipnAAkJCQwffjSTJv21Vh23v6VLf8VmszFgwECgpg58\n4YVno78BkiTx4ouvMXLkaADuuusW8vJ2oigRunbN4oYbbuaUU04H4JRTTmPatCnk5e2gb9/+h/+B\nCG3KokUL+PLLL8jP30lcXDxdu2YxYcL5XHLJ5dFjNm7cwPvvv0NOzhZkWWbkyFHccsud9O7dJ3qM\n1+vlnXfe4tdfF+P3+8nK6s6f/zyJ8867MHrMnrac0WhElg307t2H8ePPY+LES6O/45MnP03nzl24\n8cZboueVlZXypz9dxJIlKwC4886bGT/+PC64YGL0mHXr1vDss0/y3//OAWhU/bav996bwv33PwyA\n0+mMludgMEjfvv244457GDp0eJ3zJk9+mnnzZjNz5jd069YdgEmT/sr770/luedebNqHIbQ7jemf\nNKb8fPzxdL7//jtcrmoSEhI46qgRPP30ZADuuOP/2LJlM0bj3u7knjpij9LSEv7854u5+OLLuO++\nh6Ov71t2ZHnvc+z6ytm+PvjgHSZNqh04WLRoAR999D7l5WWkpaXz978/xdFHj4xeY9+67aqrruHa\na28A4Oqrr+Pll1/g+OPHHMo/8RFJBA4OkyRJvPTSG4wefSxOp4N7772DTz75kJtuurXWcQsXzqNT\np07Mmze7TqOqW7fu/PTTD9x2213RwrNgwRx69uwVPWbdujW8995UXn31bfr3H4DH42Hp0l9qpXPV\nVddy4423kJGRSGWlJ/r6o48+wWOPPcjxx59ISkoKBQX5fPnlF9FOzv7mzv2eMWNOwmw2R1/717+e\nR9M0Pv/8axITk8jN3RZ979tvv2bp0l+YMWMWAPfccxvdunVn4sRLATj77Am89NJkETg4gvj9Ph5+\n+F4efPDvnHnmWUQiETZsWIfZbOKvf72ev/71eqCm0zB79nf8+9/Tap0vSRIzZnxBVla3Bq8xf/6c\nP8rUnFqBA6gJFuxppC1btpRHHrmPUfc/Qoq6HEwWwIvDH+L7xUcz7pTNyIWvogb3LnbQmPK2r19/\nXUzv3n1IS0sHYMSIkfzww97j161bwyOP3MeYMScCsGLFMj7//GPefPNd0tLSefTR+/ngg3e5+ebb\nATj77PFcf/0kbrnljloVstBxfPHFp8yc+QmPP/40o0cfR2VlJa+88k/uvfc2pk6dvs/nLtV7viRJ\ndO2axQ8/LOCyy64AIC9vB+FwKNrRAXj22ScZMGAQX389B5PJxM6dO3A47LW+o2VlpVxxxUQWLFhc\n61xJkrj//oe57rqratUpe+xbzqqqKvnuu/9y22038fLLNXVifb777utaQWyA4cOPrvMbsMc99zxA\n7959kWWZLVs2cc89tzNz5n9JTU0DYNy4c/juu/9y770P1Xu+0D7tKR/33fcIxx8/hri4OHJztzNz\n5idceOHFGI1GNm3K5r777uSWW27nhRdeRVEUZs78lFtvvYHp0z+la9csFEXh7rtvJS0tjXffnUFG\nRgarV6/k+ef/gdfr4YorJgG123J+v49169by+usvs2XLpmgwuSH7lpnGaEz9tsfWrVvw+bwMGTIM\ngEDAz9Chw7j77vtJTk7h+++/5aGH7uE//5ldq0OYnb2ekpLiOnk7+eTTeOmlf+Jw2KNlSOiYDtY/\naUz5mTdvNgsXzufNN6fStWsWTqeD3377pdY17r//Yc4//6IG8zF//hySkpL48ceF3HXX/XXy2BR2\nexXr1q3hqaeej762atVy3n333zzzzD8ZMmQYVVVVda6xf922x5Ahw/D7fWzbtpVBgwY3KS9HKjFV\nIQb0Px7FpKSkcvzxY+oMwVy/fi12exV33fUAixYtqDNsPzU1jb59+7FixTIA3G43mzZlc/LJp0WP\n2bo1h+HDj6Z//wEAJCYmMmHC+fU+/dzfiBGjGDfuHF57rSbC/OKLz3PNNdfTo0fPeo9fseJ3Ro48\nJvp3UVEhv//+Kw899BhJSZ2QJImBA/cWsAUL5nDllVeTnp5Oeno6V155FfPmzY6+P3TocEpKiikv\nLztoXoWOoaioCEmSGDfubCRJwmw2c9xxJzT6qaCu69FyVZ+yslI2bFjHgw8+xsqVy3A6nQ0eYZMZ\n/AAAIABJREFUe+KJJ5OUlMTO/I1grKLS0QuQOH/QWL4q2knFmmOJeAIE7Ruj5zS1vC1f/nv0CWl9\n5s2bzdix47BYahp28+fP4fzzJ9KrV28SEhK47robmTv3f9HjMzI6k5iYxObNGxtKUmjH/H4f06e/\nx733PsRxx43BYDCQmZnJM8+8QFlZGQsXzmtUOuPHn8f8+Xt/a+fNm8O5515Q65icnC2ce+4FWCwW\nZFlmwICBnHDCifWmV1+ZO1A53Fd6egY33HAzF144kalT36z3GEVRWLNmFaNGHVPv+/Xp27d/radR\nqqpQUVEe/XvUqGP4/feljU5PaPt8Pi/Tp7/L/fc/yumnnxH93R0wYCBPPPFsNKg2depbnHfeBVx2\n2Z+Ji4sjMTGRm266lWHDhjN9es2IyPnzZ1NZWcGzz/6LzMxMDAYDJ5xwInff/QDTpr2D3793S7U9\n3/X4eBsnn3wqzzwzmfnz55CfnxfT+ztY/bavmrplb3nJyurGFVdMIiUlFUmSuOiiS4hEIhQVFUSP\nUVWV119/ifvue6jOdcxmM4MGDRbDs48QB+qfNKb8bN26hRNOGEPXrlnRdC688OJ6r9GQ+fPncOON\nt2I0Gg/4AKYxVq1awcCBgzGZ9u6INX36e1x33Y3R4Nqevsi++dM0rcE0R448hmXLfjusfB1JROAg\nhioqylmx4nd69OhR6/X58+dw8smncuaZNU9Ff/+99hdUkiQmTDg/2gD88ceFnHrq2FoFY+jQ4axc\nuYwPPniXjRs3EIlEmpS3W265k5yczTz22INEImH+8pf6h8QB7Ny5o9Zohy1bNtGlS1c++OAdLrjg\nLK699i8sWfJT9P38/LxoBwugf/+B5OfvjP5tMBjo1q0HO3bkNinPQvvVs2dPDAaZ55//B8uX/47H\nU/dp5eGYP38OgwYN4fTTz6BXr9788EP9HS1d1/nttyW4XS6yUlyEVZmqypoh0sd2PwqrKZ51ZT48\nZV3QghWoYR/Q9PKWl1e7zOwrFAqyePFPtYbF1pSZgdG/+/cfgNPpxO12R1/r1as3O3Yc/jxwoe3Z\nuDGbSCTMaaedUev1uLg4xow5iVWrVjQqnWHDjsLv91NUVICmafz00w+cc865tRpyw4cfxSuvvMCP\nPy5skeDt6aefyfbt2wiFgnXe27WrCFk2kJ6eUev17du3ccEFZzNp0mV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vvvoW7703hXfe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pq7gRi/cT8nLXYuMN2qreIb5zFk7vHFwTJ8Kxk8FUh8RV30HH1KDlI/Sq1zGD2zFiSeKxDCp2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SVhexELE8S2q924ZhsFCKoqJsNg66rLCfgzqX1XPaKBZYDdoXJ635oXjXXBOCCR1m7d0QYO\nang7qVkLBGD1apXq6moMw0hYJ4RA1SMMrnoBd/ZSxAHlJaypaHpsexHbOFY8a7YRAkTNQxBC1GxT\n87/YfwgUdN1ENGohEjUTjZiJRl0I3YJuWNB1G+GoB91IxUQq0VAahlH3G5XKo8r/z9X3Y+14nANP\nRB5MKgjRjkCgHYEAUEbsW0s1hKpUYqIEk1aKyVKBzV6NzR7AYi3CbMlFVUT8dCEAlJpTWc1CAXzz\nv36cc+F5xzUPR6K4WGHtWoWqqqr4MtHIedNlymVYyiNY/RXoul6zLYRVBS2qx+uJEDX7EIKIoaLX\nlP9Q0EnA78LrT8HrTSMQaI/d3gqHNXa82CfcNNfO2jLWvK7hdmAwVA1G7AtiEvko6j5MtgrsLj8O\nhxebYw8m83YUIOBXMIRAqMoB11QlXg6VOhdepYF/m4C1S35Ljx5O3B6BffAQlM5djntOf05JiY9Q\nwEw44iASthKNWIlGXOh6GoqSji5SELoDpc6jkVkBRCXeQNOk+0BHex5rDmXzmJ2L/RrQtubnQAaK\nqEShHJRiNK0SkyWAxRLCYgthsQQxm32oqo6qGrHfWs11PV6gD7jHjJf9n9b5SxSEccA5rrau/PTu\nhP0pJNYVVVEIpWRRHhrKhsqbG81i3frWqZOVIUOatsWEIho7u0stQiAQwOv1NnqRrqXrOi6XC7db\ndsBqakVFRU2dhKQhhIg/yNX+rl1mGAZCCHRdjy8TQiT8HLgfqH+Sbmw5gN1ux+1216t7jdXFlJQU\nbLLFQYtWWlpK9MCvVQ+gKArl5eVEIpH461qqqsaXqaqa8KNpWvynoXImndzqns/at2+egxwbhkFx\ncXHCssbKc+05sqSkBCChDmiahslkwmQyyfrQRAzDIBqNYhgGuq4n/G7s+tbQdTUzMxNFUcjMzETT\ntBOV/EbJey/pcB1431i3Dhx4z9nQT+26A7epZTabSa0z1eShPI57PB7sTdzkQAYOJEmSJEmSJEmS\nJElqlBx6UpIkSZIkSZIkSZKkRsnAgSRJkiRJkiRJkiRJjZKBA0mSJEmSJEmSJEmSGiUDB5IkSZIk\nSZIkSZIkNeqYTscYjeqUl/uP5S5PuLQ0R7POg21tbP63YP8NjW7T3PNwKJIhD61aNTyDhawnzUND\neTiU+tXcJMNn0VBdac715FDLSTJ8NpAc+UiGPLS0enI4kuHzOTAPLfFaAsnxOch7r+btWOShqetX\nMnwOjdWTn3NMWxyYTE0/3crRknloHpIhD41JhrzJPDQfyZKPupIhX8mQB0iOfCRDHhqSLPlKhnzI\nPDRvyZA3mYfmIRnycKRkVwVJkiRJkiRJkiRJkhp1TLsqSMdfS2v2Jkktiaxf0qGQ5USSpIOR5whJ\nOn5k/Wo6ssWBlGDXrp1cf/01x2Rff/7z3SxfvvSY7EuSWpLly5fypz/dfUz2JeuRJMUcy3p1ww2/\nYffuXcdkX5J0ojzwwH0sWbL4kLa97767WLly+XFOkSQ1vX/840U++uiDo95PJBLhV7+6lIqKimOQ\nquQkAwfHwKWXTubcc4dRVVWZsPzaa69ixIjBFBQUJCx//fV/MGLEYLZs2ZSwfO7czxk1agjjx49i\n4sRzmDr1Kn74IXaBWLduLePGjWT8+FGMGzeCESMG1/w7tqyoqJDbbruRzz//LwBr1qxixIjBTJ/+\nZMIxpk27nrlzP280L6+//jJXXVU/cJCbu5dzzx3Oo4/+Jb5s9eqV/OY3VzJx4mgmTRrLn/98NyUl\nxfH1v/71tfzjHy8e7E8nJZF33pnF3Xf/X8KyK6/8Bffcc0edZRfz1VdfJCy77LIpXH315fX2eeut\nv+Pcc4fX1InR3Hrr79i5c3u97ebM+YwRIwazcOGX9da9/fYbXHbZFMaPH8XFF1/Agw/+Kb7uwDpT\na82aVVx88QX19vPYYw8xatQQSktLGsh9oldemcnVV18LQGFhQbye1tbZESMG8+GH/4xvX1FRwcMP\n38/EiaM5//wxPProA/F1sh41b5dddiGrVq2Iv/7yy/mcd965rFu3BojdiLz88gtccskkxo49m1/+\n8mLee++devv5/vvvuOGG3zBu3AgmTRrLo48+QHFxUcI2paUlPPHEo0yZMpEJE0bx619fxhtvvEIo\nFARgxIjB5OXtS3jPG2+8klCe6m6zd+8eHnjgPiZNGsvEiaO59tqr+PDDfyKEoKBgPyNGDMYwjIR9\n/vWvD/Paay8D1Nvmscceqnd9y8vbx4gRgxP2sWLFUm6//SbGjx/FpEljue66X/Hee28TiUQa/Vsf\nWK8Abr/9JiZNGhe/Xi5evKjB9/31rw/Tq1evhHxfddXVvPbaS40eS2p+Lr10MmPGxK4H558/hnvu\nuZOiosL4+r/+9WFGjz4r4Vw7depVQP1yWutvf/t/8fPz6NFncc45Q+Pvv+++u+Jld/z4UUyYMIrL\nL5/SYP0F+OUvf8mkSWOJRqMJyx955AFGjBjM0qU/JCx/9tm/MWLEYBYsmAfA559/ym233dho/nNy\ntrJ37x7OOutsAFauXM4111xRcw82jvvvvzfh+vTrX1/LK6/M/Lk/q9TC1b0GQex5Ytq06xOWzZnz\nGb/5zZWMHXs2U6ZM5Omnn8Dr9cbXH3her1W33qxbt5abb76OiRPP4YILxjBt2vVs2bKZd955M16P\nzj13ePxZZty4kVxzzRXx/b333ttMmDCBsWPP5tJLJ/Pyyy8knPMP9fpxoIqKCubPn8OUKRcDsHHj\nBu688xbOP38MkyeP5y9/+WO9+7atW7dw662/Y9y4kUyZMoGPP44FHcxmMxdcMIV3353V6PFOdjJw\ncAwoikKbNm354ov58WU7d24nHA6hKEq97RcsmEtKSkqDD/B9+57OggWLmDfvGy666FIefPBP+Hxe\n+vXrzxdffMuCBYt4551/oSgK8+d/E1/WunVWvX3ZbHbmzZtdL3DRmNLSkpqAw6h666ZPf5JTT+2T\nsKxz5648++wLzJu3kE8/nUe7dh14+unH4+t79+6D3+9j69Yth3R8qWXr338A69f/iBACgLKyUnRd\nZ+vWLQnL8vP30b//gPj71q5dTUVFOfn5eWzZsjlhn4qi8Pvf38uCBYuYM+crBgw4IyF4VWvevNk1\ndWp2wvK5cz9nwYJ5PP/8SyxYsIjXX3+HQYPOPOy8BYNBFi1aiNvtjt/kNWbLlk34fF56947Vl6ys\n7Hg9XbBgEW+//QGqqnLOOWPi7/nzn+8mM7MV//73bD77bAFXXnl1fJ2sRy3H3LmfM2PGUzz99PP0\n6xcr4/fffw+rV6/kmWf+zoIF3/LAA4/wv//9hxkzno6/b+HCL3nkkfu54oqrmD37K95551+YTGam\nTbs+fmNXVVXFTTddRyQS5pVXZjF//iKmT38Rr7c6/kDc0PUm5qflB26Tl7ePG2+cSnZ2G95++0Pm\nzVvIo48+wdatW/D7fT+zzwP2riTuPyUlhVdeeanRbb7++kseeOA+xo8/j08++ZzPP/+Shx9+nKKi\nooQHwQPVrVcAd9zxB/73v/nMm/cNd9/9Jx555C+UlZUmvO/HH9eSn59XLx/Dh49k9epV9baXmi9F\nUXjqqedYsGAR//3vPNLS0pg+/amEbX71q9/Ez7VffPEtb775XsL767r33vvj5+errrqG8ePPi7//\niSeejb9vwYJFzJ+/iIceeow33vgHa9asSthPXt4+1q1bh2GIei0CFAU6duyUcM8XjUZZtGgh7dq1\nr5fHxnz66SdMmHBe/HXXrt2YMWNmzT3YXLKz2/Dss3+Lr+/b93QqKyvIydnW6D6l5HVgWXr//Xf5\nxz9e4NZb72D+/EX84x+zKCzcz513TqsX6GpsP36/j3vvvZNLL/0lc+cu5D//mcvUqTdgsZi5+uqp\n8Xp0991/jD/LfPHFt7z99odA7Dnis88+5amnnmLBgm95+unnWbVqBX/5y30Jx/q560ddc+Z8xtCh\nw7BYLABUV1cxZcrFfPzxZ3z88WfY7Xb++tdH4ttXVlbwhz/czkUXXcLcuV/zwQefcuaZQ+Prx42b\nwLx5n//s3+VkJQMHx8iECeczb95PF4W5c2dz3nmT6m23du1qSktLuP32P/Dll/MPWjAnTjyfYDBA\nbm5ug+trH8Ya43a7Oe+8ybzxxj8OKQ8rViyjR49emM3mhOVffjkft9vNGWckRvzS0tLIyMgEwDAM\nVFWt921X//5nHHKzOqll6927D9FohJycrQCsXbuGAQPOoGPHTgnL2rZtHy83EHvYGjlyFGedNTyh\nDtWqLeeqqjJmzHj27NmdsL6gYD/r1q3h7rv/zPLlSygvL4+v27JlE0OGDKVNm7YApKWlM3nyRYed\nt4ULv8Tt/v/s3Xd4HcW98PHvltOberMld1xxxRVMc6W3BHJJbgLkJrk3yZtGEiAhEEhI4d6E3kIN\noScBQrAtU4xtim3cjdybLKvXc3T62fb+IVtYlmzLRrJkeT7Powe8Z8vsOTs7s7Mzv/Fxww3/xaJF\n/z7quitXfsL48ZOO+PmiRW8zfvxEcnPzgJY3r7W1tXz3uz/A7XajKArDhp3RZhuRj3q/f/3rdR55\n5AH+/OeHGT26ZaqoFStWsGbNp/zud//LwIGDkGWZUaPGcMcdd/PGG39vvV8+8sgD3HDDt5g9ex52\nu5309AxuvfVXuFyu1p4pr7zyAm63h1/96jet1052dg4/+MHNDB48FDh2mXD4Ok8//QRnnjmO733v\nh2RkZAJQWFjEHXf8Bo/He8Lfxfz5l7J7987WXheHe/jh+7jppm9z6aVX4PP5Wo/7ox/9tN2D1EEd\n5avBg4ciy59XYwxDb9PwYBgG99//v/zkJz9v993Y7XaGDx8hhgGdYg7+jjabjfPPn8W+fSd3uMmo\nUWMoKhrY7mG8uHgBkyZNYt68i1m4sH05ds4557Fhw9rWBrkVKz5i5MhRpKWld/rYLXlgYuu/09Mz\nWvOtYRjIskRFRUWbbcaNm8DKlR93+hhC3xOLRXnmmb/w4x//nMmTp6EoCnl5edx99x+orq7mnXcW\ndWo/ZWVlSJLErFlzkCQJu93O5MlTW8ufoykv38+bb/6TO++8h7FjxyLLMgMHDuKee+5l1aoVrFu3\npnXdY5Ufh1u1qm3ZMG3aDM4/fxZutxuHw8E111xLScnG1s9feeVFpk6dzuzZ81BVFZfLRVHRwNbP\ns7Nz8Pn8bN78WaeOf7oRDQddZPToM4nFYpSVlWKaJkuWvMvcuRe1q6wUFy/g7LNncuGFswFahyIc\nzjAM3n77LWw2G3l5+Secrm984yaWLVvC/v1lx1x3z55dFBUNaLMsGo3w9NNP8P3v/7jDSmlNTTXz\n51/A7Nnn8OqrL/LVr36jzecDBw5k1y7R2n06UFWVUaPGsGFDy81+48Z1jB8/kbFjxx+27PPeBslk\ngqVL32fOnIuYM2f+URvTNE1j8eKFjBo1ps3y4uIFDB8+kvPOu4ABAwby7rufF4KjR59JcfECXnrp\nb2zbtrVdN9WOdHSdFxcvZM6c+a0NFwcbQjqye3f7fHSoxYsXtmlU3Ly5hMLCIn772zu45JJZfOtb\n32DDhnVtthH5qHd7442/88wzT/Dgg49xxhkjWpd/8sknjBo1hqys7Dbrjxo1huzsHNauXU1ZWSk1\nNdVccMGsNutIksR5513ImjWrAFi7djXnnXdBl6Z77dpP2x23I51pkDiU0+nk61+/scMhNvv2lVJf\nX8e55154XPs8Ur76+c9/zIUXns13vnMjEyeexYgRo1o/e/XVF5kwYdIRK7YDBgxi166dx5UOoXdI\nJBIsWfIuY8aMPanH3bRpA2VlpfTvX9hmeXHxQi6//HLmzJnHypUftxu66nQ6mT79HJYseffA+guY\nP/+STuetaDRCbW1NmwccgKqqSubPv4A5c2byj3+8yle/2nao6cCBg0TZcRo69LratGkjmpbi3HPb\nlh8ul4tp02awevWqTu2zqKgIRZG5555fs3LlJ4TD4U6nZ82aT8nJyWXEiJFtlufk5DJq1Jg2aTha\n+dGRY9W5NmxYx6BBQ1r/vWVLCT6fn//5n5u47LK53HrrT6ipadsze8AAUec6EjGrQheaN+9iFi1a\nwPjxExkwYGC7ymIymeCDD97jV7/6Daqqcv75sw68bT2/dZ2Skk1cdNGFxOMxVFXlV7+6m7S0tNbP\nnRvG4GgEcHcqTenpGVxxxTU89dTj3HXX7466bjgcaXMsgKeeeoLLLruK7OycDrfJzc2juPgDwuEw\n//73GxQWts28breHcDjS4bZC3zN+/EQ2blzHtdf+Bxs3buDaa68nMzOLt956vXXZV77y1db1ly5d\ngt3uYOrU6ei6jmGYrFjxETNnnt+6zgMP/B+PPPIAiUQch8PJ737XtmtqcfFCvvSllvgIs2fPZ9Gi\nt7n22pZxrXPnXoQkSSxc+G+effZJHA47X/nK1/ja125o3f7++/+XRx55AADJCKEb4A3ktX5eXV3N\n+vVr+MEPfkJ6egZnnTWVRYveZtiw4R1+B5FIGLe74/y5ceN6mpqa2gxTqK2tYc2aVdx666/4xS9+\nzdKl73PrrTfz2mtv4vcHAJGPehvnhpbGq4ORndes+ZQJE85q94Da1NTUpnfNoTIzswiFggSDQSRJ\n6nC9zMys1iBNoVDoiPs61De/+TUkqeWdgGVZaFqqzfV2qM7s07Ispk+fjmlarf9OpZIdxsI51OWX\nt8RyWLVqRZuHrFAoeODcMluX3XnnL1i1agW6rvHzn/+SuXMvare/I+Wre++9D8MwWLPm0za9kWpq\nqnnrrTd55pkXjphGt9sthiqcYm677acoikIsFiUjI5M//emhNp+/9NLf+Oc/X8OyLCRJYubM8/jF\nL+78Qse0LIuLLrqQVCqJpmlcf/3XmTHjnNbP161bQ0NDHfPnzyeRgNzcfJY+ey7Xnme0if4+f/4l\nPPnkY5x77oWUlHzGXXf9npdfPvL1eahwOIIkSe3yQH5+AcXFH9Dc3HygDlbU5nO32y3KjtPAwXxx\nkKalGD685SG9uT21jVUAACAASURBVDlEIJDWpnfWQZmZWezY0blhkG63h0cffYoXXvgr9957D42N\nDUybNoNbbvkV6elH7zkTCgWPWRYe6kjlx0GHlsFHq3Pt2rWT5557mj/+8c+ty2pra9ixYzv33/8o\ngwcP4ZFHHuDXv/4ljz32dJtzPZ6GkdOJ6HHQhebOvZh33y1m4cJ/M39+++Bqy5Z9gKqqTJs2A4A5\nc+azcuXHbTLMmDFjWbRoCcXFSzn77HM73VXnaL72tW/w6acrj/lmxefztXajg5ZAPGvWrOLaa//j\nmMfw+XzMn38Jt912c5u3urFYFJ/vxLu8CqeW8eMnsmnTRsLhMKFQkH79+nPmmWMpKdlEOBxm797d\nbbpaFhcv4MILZyNJEjabjXPPPb9dnIIf/vCnLFq0hA8+WMEf/3gfv/zlz1sDJG7atIGqqgpmzZoL\ntIxN2717V5trfc6c+dx33yMUF3/AT396G08//QSrV3/ePflHP/oZixYtYdGiJSy5N8l9/51qc/zF\nixcwcOAghgxpeSicPXsu7767GMMwOvwOfD4/sVisw8+Kixdw/vkX4nQ6W5c5HE7y8vK5+OLLUBSF\nWbPmkpuby6ZNn3etE/mod/vpT29j//4yfv/7u9ssT09PP2IwzYaGegKBNNLS0rAsq8P1GhrqWxtz\nA4FApwJzPvPMi63Xc3HxB+16gR2qM/uUJIlVq1a12efs2fOOmQ6bzcYNN/wXTz31WJu3X4FAWuu5\nHXTXXb+juPgDzjhjxAnlK0VRmDp1OqtWreDjjz8E4KGH/syNN/7XESuUALFYDK/Xd8xzEXqPP/zh\nTyxatISlS1fyox/9jO9//9s0NTW2fn799f/Zep0uWrTkCzcaQEseWLRoCe+99xH//d//j/Xr17a5\nTouLFzB16ozWYTdz5szj7U+VdvsZP34itbW1/O1vzzJz5nmoauff3R28/x8pD/j9fubOvYhbbvlJ\nm+WxWEyUHaeBg/ni4N/NN38eNyAQSCMUCnbY4/JgOQQt99HDe3zquo4kSa2NDkVFA/nFL+7k9dcX\n8Pzzr1JfX8+DD/7pmOkLBNKOWRYe6kjlR0eOVDaUl+/nZz/7IT/60c8488xxrcsdDifnnns+w4e3\nDM2+6aZvUVKyqc3zT0udS5QNHRENB10oLy+P/PwCVq36pMMupcXFC4jH41xzzaVcccU87rjjNgzD\n4L33Frdb1+l0cvPNt1BcvPALB7bx+wNce+1/8NRTjx01wMjQocPaDGlYv34d1dXVrel9+eUXWLr0\nfb75zf/scHtd1wkGm4hGP898paWlDB16RofrC33P6NFnEomEeeut11tv1G63h8zMbN5663WysrJb\nh97U1dWybt0aFi9exBVXzOOKK+axbNmSDrt5HjRu3Hj69+/fOi75YCPDDTdczxVXzOM737kBSZIo\nLl7QbltFUTj//FkMGTKMPXt2d/qcFi9eSGVlRWsaH3nkfkKhYLsI2QcNGTKU/fv3tVueTCb54IP3\n2sU+GTJk6DED0Il81Lulp2fwwAOPsnHjBv7v//7QunzGjBls2VLSbnaEg8smTZpMUdFAsrNzWLKk\n7YwglmWxbNkSzjprKgBnnTWF5cuXHjMtxzOs4KyzprB06ftdus9DXXzxZUQiEZYv/6B12cHeeMuW\nLTmufR0pXx3KMPTWuBFr1qzm0UcfaM23AP/93ze1KW/37dvL0KHDjisdQs86eC22DOW5AFmW2bRp\nw0k5tiRJXH99S/3nX/96HWgZMrF06RLWrl3NOeecwxVXzOOf/3yNbWUSe6vb39fnzp3Pa6+9xPz5\n7WNgHY3H4yU3N/+oeUDXdZqaGts8RJWW7hVlx2ngaPfoMWPOxGazt7vnxuNxVq78pDVgdG5uHlVV\nlW3WqaysICcnj44UFQ3goosu7VR9atKkydTW1rSbTa6mppotW0qYPHlqu206Kj860lHZUF1dxY9/\n/D1uvPFbzJ07v936h9e5JElq8x2KOteRiYaDLnbbbXfwwAOP43A42yyvq6tl7drV3Hvv/Tz33Es8\n99zL/PWvL3P99V/vMJAOtDzwX375lTz77JPtPjveitx1111PScmmowYSmjx5Kjt2bGudGuWKK67m\ntdfebE3vlVdew4wZM7nvvoeBlh4UZWX7sCyLpqYmHnroPs44Y0SbVroNG9a29rAQ+j6Hw8GIESN5\n9dWXGDdufOvysWPH8eqrL7WJb1BcvIDCwgG8/PLrPPfcyzz33Mu8/HJL48KhM5QcqqRkE6WlpQwe\nPJRUKsUHH7zHLbfc3nqNPvfcy/zwhz/l3XcXYZomixa9zYoVHxGLxbAsixUrPqa0dA+jR5/ZqfMp\nKdlEZWUFTz75fOv+//a315g9e94RgyROn352u4jbwIFZGfxMmNA2wNu5515AOBymuHgBpmnywQfv\nUV9fx9ixn7eQi3zU+2VmZvHgg4/x6acreOihlm6R06dPZ9KkKfzylz9n7949mKZJScln3H33HVx1\n1ZdaAwF+73s/4vnnn+a99xaTTCZpaKjn97+/m1gsxpe/3NLj67rrvko0GuW3v72zdaacurpaHnro\nvg6nKO2Mm276DiUlm3j00Qdbu+yXl+/nN7/5FdFoS/fm4w24eChFUbjxxm/z4ot/bbP8e9/7Ec8+\n+yRvv/1ma3fQ/fvLaGxs7Gg3QPt8VVZWysqVn5BMJtF1ncWLF7Jp0wYmTGjp0fTKK2+05tmDkfXv\nvfe+1nG+mqaxffu2Diuswqnhww+XEomEGThwcKfWbxlmk2rzdyLX99e+9g1eeOE5dF1n6dL3sdvt\nvPTSP/nXv/7Fc8+9zIsv/p0xgywWrGrf6+C6667nvvseYcyYzpVBh5o+fQbr138e/2bZsiWUl7cE\nz25qauSRR+5n5MjRbXrZbNy4XpQdpzmPx8uNN/4X99//vweGhOlUVVVyxx23kpubx7x5FwNw3nkX\nsmLFx6xevQrTNKmvr+P5559h9uyWHp1lZaW88soLrQ3hNTXVvPfe4k5dy4WFRVx++dXcddftB2Yf\nMdmzZze3334LkydPZeLEs9ptc6Ty43CHlw11dbX88If/w9VXX8vll1/Vbv1LLrmc5cuXsmvXTnRd\n57nnnmLs2PGtAYHr6+uIRJo7XU883YgYB13i85argoJ+bT850Kq1ePFCzjhjeLup4L70pa/w6qsv\nsnfvng73/OUv/wfXXXcVe/bsajN+tqM3lEd7a+l2e7j++q/z+OMPH3Gd9PQMJk6czPLlS5k1aw4O\nhwOHw9H6ucvlwm63t467rq+v5eGH7ycYbMLtdjNhwiTuuefe1vW3bt2My+VuE6xK6PvGj5/E5s0l\njB17aMPBBF5//e9tIt8uXryQq6++tt3YuCuvvIbi4re55pqWuAX33XcvDz7Y8iCWkZHJt7/9XaZM\nmcb777+D0+lk3ryL24ztu/TSK3jmmb+watUnuN0enn/+WfbtuxPTNMjNzeenP72tNaDWsd70Fxcv\nYObM8xk0qG3F9Mtf/grf+963CYfD7bqznXHGCLxeH1u3bm4zddzBYFiH8/v9/OEPf+JPf/oDf/7z\nvQwYMIA//OHPrflM5KPe7vNrKCcnlwceeIzvf//b2O0Obr/9Vu65516efvoJbr75/9HcHCIrK4fL\nL7+yTYyAg/fbv/71Kf74x3uw221MmTKdxx57Gr/fD7RcJ48//jRPPvkY3/nON0gkEmRn5zB79jz6\n9WsZA3q8Uyf269efxx9/lr/85VH+8z+vxTBM8vPzufjiy1vHeB7vPg83Z848Xnjh2Tbzhc+aNQef\nz8fzzz/Dgw/eh91uIycnjyuvvLo1cPDhDs9XlgXPPPMX9u3biywr9O9fyN13/7419sjh8XokScLv\nD7RO2fXhh8uYOHFSp+JGCL3HLbf8GFlWkCTIy8vn9tvvYsCAga2fv/TS87z22stAywO/w+Hg7bdb\nAhJKksTcuee2fiZJEvfd90i7GaMOd/j1fc455/HEE4/w9tv/YtmyJVx22ZVkZWWRmenDNFuur2vP\n1XnoTRvfPKzRwe8PtHlI6kz+Ouiyy67it7+9s7XXQ01NDQ8//ADBYBMej+fAdMWf93gqKdmE3x84\nYjweoa849jV0/fVfJxBI45FH7qeysgKPx8PMmRdw5533tA6ZGTRoML/+9T08/vjDVFSU4/P5uPDC\nOdx447eAlueILVs28+qrLxGJRPD5fMyYMZPvfvcHnUrlzTffwksvPc/PfvYzampqCATSmDNnPt/8\n5neOuE1H5cfh5s+/hBtv/CqpVAq73c7bb/+LqqpKnn32SZ599snWvP7OO8sAmDjxLL797e/ys5/9\nkGQyydix47jzzt+27u+ddxYxf/6lxzWU6HQiWSfaB/EI6upO7WAS2dm+Xn0Ohwfl6sgXOYfS0r3c\nc8+vefLJo7fwdcbtt/+cyy67iqlTpx/3tr39d+iM7Owjj4/qC+fWF8+hM/mrM1avXskbb/yzXSDH\nE3GsfNRXfouO9Nbz6ux10hd+G+g95/FF8tXh5/Cd79zIrbf+ql2jYG92quWT49FbrrEv4tBz6Kqy\n5HB33nkb8+dfwvTp5xxz3dtuu5mrr772uHrV9JXf4Uj6wrmJc2ifv/7yl0dJT8/gy1/+yhfar6Zp\n3Hjj9Tz88JPtGp8P1Vd+hxMhGg4O01cuBnEOPU8UXr1bXzgH6Bvn0VcfiPrCbwN94zz6yjl05FQ/\nL+g7v484h54n6l69mziH3uFEGw5EjANBEARBEARBEARBEI5INBwIgiAIgiAIgiAIgnBEouFAEATh\nUIkEHGGubEEQBEEQBEE4HYmQkYIgCACmibJ1C8qB6a3MvDz0M8eBLNpXBUEQBEEQhNObaDgQBEEA\nlC1rie1cTVN9KaoexFtj4mE2xtgvgSRulYIgCIIgCMLpS9SGTwGWBfv2SVRWyqg7biI7rZ5+F7/B\ngemoBUE4UZaFHF2DvPdlkpUbsKRSPIV2tEQ/0KPES0twKB9hnXE72PN6OrVCL9FdU60JgtA3iHuE\nIHQfkb96jmg46OUsC0pKZCoqJOrqQvSL2QhF+rP3I5g6FTyenk6hIJyaLC2Erfw30LSBeGWQxvpM\nNDVGuivEju1fI9gUZbBvKdlKCW7198jDfgeKyHCCIAiCIAjC6UcM3u3lKislKislamrq8fvdnDfu\nQwbm7uPTTyN89JGGpvV0CgXhFGQmSO24Aym4nsbd2ZSs/g8+WXENhXl1+JwpHGYBuYFhhPbNZ/eq\nQTRX7MWofKWnUy0IgiAIgiAIPUL0OOjFNA22b5eJNX7G6NyduKUYjmCYkbHPmNH4MQ1vpigN9uOM\neYOw0tJ7OrmCcMpQ657HatxM486BbN9wHvvqYgwvykCS2q7nKMjE3DWMpsFBZNdi/DkXgyO/ZxIt\nCIIgCIIgCD1ENBz0Yrt2JCnUH2J82vu40ZF0HVUJQ67FBOcfqNpXSNO6HBpiE8mefCbGyFGgKD2d\nbEHo1aT4TpSqt0g1+9iybgalQYsRhZkdrms43PgyA9RvHoozZxvS3oX4RnzzJKdYEARBEARBEHqW\naDjopZqbEmSV/hSnthEj1Z/9pUPQUjbGTdiO5DNRkm4KR5WTHi3HZn1McvNg7KGvY065sKeTLgi9\nmrr/GaRIhG0b51OeyKB/Rgr58K4Gh4in5ZNWVkeq0U7M9S6OAddid/lOYooFQRAEQRAEoWeJhoNe\nyNINjI9vx2Nuoq6qkNrSC3B685BUO+s3TTu4FnZnLZpajiuwjqK0vVgNv0Vap0H+V3o0/YLQW0mx\n3SgNq2iqK2B/eBJ2KYjf9XkvnY2fPNpuG93pwe3LJLo7DzVtP7VbF9N/4pdOZrKFXkZEchYE4WjE\nPUIQuo/IXz1HBEfshaKfPI9bWkNdTTrNDV/GlVaEpB4+96JEKpGLGZ7EmjU3s3b3PCQlBTX/h7Vn\nS4+kWxB6O3XvS5iayebtU4jpMv38R+5pcKhYWj6OinSUhIlWs4hgk9HNKRUEQRAEQRCE3kM0HPQy\nesVe1NBrRKMW9dXXIinuo64vSTAkO8KmLZewoXQuqtRMeOU9kEqdpBQLwiki1YQSWk5TvYfG5DR8\nhLCpnWs40F0+VGcOqfJ07FodezasxLK6Ob2CIAiCIAiC0EuIhoPexLJIrH8My4qwr3QaDjWrU5s5\nVJOReWFWrr+IqtAALGMN0vbF3ZxYQTi1qPv+jq5p7C6dQjIeI9dvO67to5mFSHtzcRthpMYF7N/f\nuUYHQRAEQRAEQTjViYaDXiS29WNs0mrqGwIo+nnHtW2aO8XgbIMP1l9HLK5ilj0meh0IwkGmhtKw\nkHCTTEXjdDJsMWT5+B78DYcbXT0Ds9ZJvrqJDSu3k0x2U3oFQRAEQRAEoRcRDQe9hGUamKVPoKVS\n1JTNRpaOf1rFPH8Ml1TE7rJxpOK1sPPf3ZBSQTj1KBXFaLEg5RVjMVMWAffx9TY4KJZRgFk2GI/W\niC+6iC1bzC5OqSAIgiAIgiD0PqLhoJeIbP4nNqucitLBOO0jj7jeuBnfZdyM7x7x8wEZYSqbriCV\nhPjOv4EhgrgJpznLQql6nXjMZMe+qeR6jpwnjpW/LMVGE2dji1uM9i5m9YpqQqHuSLTQmzk3jMG5\nYUxPJ0MQhF5K3CMEofuI/NVzRMNBL2CmGlGrXiAelQjVXfCF9iVJ0C/NS1X1WLREOakd73VRKgXh\n1CQ3rkOL7KW2ZjCSlY7Tfvy9eQ6V8mYSq56Al1oGG2+xbl2ii1IqCIIgCIIgCL2TaDjoBeJbn0bW\nwuzZOg63r98X3p9NsQhHzgcNmj/76xdPoCCcwpTSV0jELbbtmUS+v2v2WZO6ELsmMzbzdTZvDNHY\n2DX7FQRBEARBEITeSDQc9DAztBalfglNDZkkI+O6bL8Oey7h4FBUfSeRHWu6bL+CcEpp3ofRvIbm\npgz01OBOT794LIbsI1Q/CZ/awBBrIevWxbtkv4IgCIIgCILQG4mGg55kRNF2PwEJjR3rJ+FLz+vS\n3YcjZyMZFsH1z3TpfgXhVGHf9TeSCZNteyeT7f1iQxQOVxc5H7shMT77H2zbEiMS6dLdC4IgCIIg\nCEKvIRoOelL1CxjBCsp2j0dRCloCFHQhnWGkYnl4zLU07dnbpfsWhN5OaqzCbP6QSMRFMDQep72L\n85eURrh+LAF7DYXGe2zdKqY/FQRBEARBEPomtacTcLqSoptIVr2H1uSlbNsgcguyO7Xdxk8ePZ6j\n0BycRqb7TZrXPkX64HtOLLGCcApSd7xCIhFnT8V5pDk710Z6fPkLapsvYEjOBsZn/YPF6+czfrwd\n24nN9CicQhLjS3o6CYIg9GLiHiEI3Ufkr54jehz0BCOKVPEYWjDJrpKZuHxeJKVru1EfFDcnQCqA\nR1tKuLquW44hCL2NXF2FlFhONK5QWTOVdE/X9jY4KCXnEKsdTqajjEDiE8rLrW45jiAIgiAIgiD0\nJNFw0APUuueIBetp3DeeUL0drzezO49GsGkKspEkuOLZbjyOIPQSpom6820SyTqqmybhNLv3NlfX\ndC52K8X49H+yfn24W48lCIIg9E7JJIRC0NQEwSDE42CJtmRBEPoQMVThJJMjqzEblhGtS2PvthG4\n3SkUtXv7Nkf1aaSZH+MMLyBa+208OWndejxB6Eny/jIk40NiSZk9ZdPI93dvw0FMHYheV0RuzlbW\n120kGJxJmshifZtpgmG0Xy5JoIpiVRD6NE1DioRJNDUQqd1DsrkOLdGIyxkjlUoAEpYk0YSXpJSP\n6svFnjEQd85gAmmI4WyCcCzxOFKwBqu5llhjiljQJBZTiCWdpCw3KdWN5vIhZaRjdys4neB2WwQC\nFoEAyOK1eLcRNZyTyWhGrfkLwaBF1Wcz0BJR0vP6dfthLctJsGkyaZnLCH70PJ6rf9DtxxSEHmEY\nKKUfkkjuoikyDiPqRcnonmEKrSSZhrqzycp5lZGu19m+fQpTpzq695hCt9F1CIch1mygVzegNYSx\nmiNI0QhSIgGmiYyJqhg4nEmcDgu7XcHplLHbFZBlLLsDy+OBQf2QLAdWRoaoyQjCKUxqakSu2oFe\n/QladDsm1SCHcJsmNk3HMi0UQ8JhmAc2kAEJkJASEnIDsNtD0BhKSh2OmTEWW9FkAlluXK4ePDFB\n6A0MA7l6NXL9xxjBzZipCnQjia5bYBi4TBOHZZKmgmnJGIYdM+LEbHaiKy50mwfD4SHkCdCseFAc\nadj9+XgyinBnD0dSRCbrKqLh4CRSa54mEQ5St3syddUKvoALSe6e2AaHiyWnky6tRA3+i9j+63EX\nZp2U4wrCySTvL0OyPiCWVNhdNpNsj0VL5a17hZURZNZn0i9nA+9v3cb48eNwiLaDU0YkAjU1EnUV\nOmZZBY5gFa7ETiyrFsuqR7YasDvCqM4Ydk8cRU2g2jQkycKUJVKyhK5LSDpYhhtZd6LghupsLLkA\nm2sQZs5IzIJCrPSMnj5dQRA6Q9OQykrQ9y7ESn2GaVWhazqGJRGLOYmE8kiFM7FLmUhmAIeSTjRi\ngGlgYiLLYVRbEGzNqK4QTk81Ds8qbKk1yKlXUBrsxMwh1NonYhWcjadgFJlZNtHGKJw2pFANatnr\nWE3voSUbiCVNTM0gHE0nFM7C1F2oqoTNBk67hV1NocgJVDmOZMVAjYBkYlkylm7DCllosoqp2KFZ\nJVVnx9hjx3CORMo4F2/BVFS7s6dP+5QmGg5OEjn8CYRWECzPpGLrICSpGbcn97j3M27Gd4Hjj/5u\nmj6aIxPwe1ZS8+6LDLrxB10+/aMg9CjDQCldQSq1leb4aCJNmWQd55CBE85fNgfNtWfhzXqPAbzB\nrl1jGD365DQKCidG06CqSqKqIolSv4608Mf0T2zG4azHnluLhAWyjKrIKKp64H4pY+ge0rPXYxgK\n5aVXkDJkNN3AlHQkWcfhjOB2NaMqcSy5Hklbh6GnUBIqanUhsmcURt40jH7TwRbo6a9BEITDRYJo\nWxZi1CxBVnajaxopXaa+sYhQeCTx0EB89kwUuaUOFT+wmeRwkNKTwGFliQlEoTkKqhzGpe5Glvag\nuvfjDmzEn9qMsvcVqPBQZZuCnn0haQPOIpDh6YGTF4RuZprI5TtQy/+BEVtOLBUnGYPq2jOoqBxB\nJD4Mt2onwyuhyKADCeDQCFKH5i/J0FHjYVLRBkw9BQ4J/Cqq20CxV+BKr8Cb/iFKwyoiZQES7nOQ\nc+aTUTBIjCw8AeIrOxn0RpSapwjXGlRtnklzOERBfv5JT0YsNoNA2nrskQU0b7oc/7hBJz0NgtBd\n5LJ9SNZS4imVPRXnE1AN4OQ9vAetSfjDHzMk8BHvri5l0KAhuN0n7fBCJzU26DSW78RoXEtG6hNG\nprYhGxqWU0dxmZimE0MfSDKVi5ZKQ0sF0DQ/esqPrnkBhXEztgBQX35Vu/2HDYlIUiGmhbB7Iij2\nOtID1WT7y/E7yrCFS1FD7+CocENgHHrBfMzANFB8J/mbEAThUEbdZpKb/4kaW4FpxtG1FHU1eTQ0\nTSIWGkWa241DAscXeGGpmz7CqfHAeEhCqCGMhxIs227s6bW4A+8ix5ZBnZ8qzzko+ReRWTQGRRUv\neoRTXCKBUraFZPBtzOaPaI7HSCTd7C07m/Lq6XhkhWy/QuZxjiqwFBXNm47kTUcBZC2BHA2RaGgm\nRREJq5B6l4Yzu5zMvB04fa8i171B3c5JJLOuJn3AJAJpoptPZ4mGg+5mWag1TxKvb6Ru+xT2l6XI\nyc5Alk/+V68lM4nEJuH1rqBm6RN4zrgbxWU/6ekQhC6n6yilK0lpJYTiZ1BfnceQ9A6C13VnEpxe\notVn4fZ9SL/km6xZ80NmzlRFx55eIJU0adi3CaN+OR5tDbmJOkhEsQyZZCiNRCQLLTmYpHUGhu7l\niwxvsSkW6W6ddDx4PBk0h/NoKp/IzjAkTYPMtFKKvJ+RlbULb/YKnDXrUHwepJyzMXKvwnIOF73B\nBOFkMRNYVe+j7XwD4ruRDZNQSKWyegzx6BQ8ai5OCZzd9PJfl32EmA7GdNTyKLZd25F8u3HkVeFJ\nexsp9B7B/UMwsi8lbfCF2J1irLZwapGaGpFLN6JXLyAlbyCWShJJeNi7fx51teNJc6gMSu+6B3fT\n5sRMc6KmHejVbZk4kzGsmgC1+wqx+6rwFO7Gm7Ucd3gFWmV/dvmuwzd4Fjn5blH8HoNoOOhmcvgj\ntNpPCe9PZ0dJIf40O3ZHz3U/C9Wfj7v/NjICS6ko/oiiqy7ssbQIQldRykqR5XeIJRX2ls8mw54A\nTn7o6npjBkMSnzAmo5hX111LQUERQ4eK+bh6SrSphlDpEuzRZXi1asxoGD3ioCE4kGR9FpFQAQ5f\nAdi6b8yjIltkeFIc7HUcTw2ktGEoG0tTBIx99MtZQ96wCgL1i1HLliFnjULufzWmbxpIoogWhO4g\nJfdjVvwbY/87mNEQhmFStT+fqqrRuGyTsNsUvCe5CNGdHnTnRCRjLPr2akLWLpz9ygkUbMUR2k6s\n6m80ZlyBf8h83P70k5s4QTgehoFcWYG282O08MeglGDoOuF4GlV1F1FXOYxsj53CwEl4SpdkdKcX\nnF5kQLdGEqueTLJ0K86cLbj7lZIVuxcr9BfKdlyGfdAV5BbmilgjRyBqJd1Jb4LyZ0hUx9i+cR6S\nDfzenp2nzTA8BIOzyUh7A6n6fpq3jcA/oqBH0yQIX4imIZe9Tzy5g8bIeOqqchic3jMP64bNS6hu\nKr6i5YyTX2fp0ptIT/eTmSkaD06maEMp0dK3cMY+xJeIoocNGmqHEm4YRrzJi6rasPtzcGSc/JqB\ny27Q327QPx0McwBNsSFULa/HoX5G0ajt5A5ag62qBCW9P+qAqzHTZ4MsgjkJwhdmWUjxbZj7X8Oo\nXoERTZKMO9m7YyQ1tWPIyRxEwN3zTwuWopLK6A9WP1L1Qar37cOWvYe0gbtwNT+OXvcyNWkX4Rp0\nBf5sUX8TlUQxHQAAIABJREFUepF4HPZuJ75zMRZrgSp00yIUyqa69mySoeEUZPmwBZI9l0ZJQnP5\n0VxTSSYmkSgpx+5ehatfKf7435Cjf6di9zykAdeTP7AARYSrakM0HHQXy0Kuepp4ZSWVu6fR1KhQ\nWJjT06kCIBIai8O1E793PfUf/Bl30e9Q3WLIgnBqUvZuAxYRSdjZsXcWuW6Nnry11abOJ11fxZm5\n/2DTlotYtkzlkktcYpaFkyBWt5V46Rs4wivxJGNEG300VJ9NrC4X3ZRxOry4M3tPQEJFtsjwajA0\ngGmdQ82+s9i9fjsFQ0voP3I3zrr7UfzPoxZdjpV7CajiLaMgnAgp9hlW6fPotZswYinCwVx2bB5G\nU2gw/fPzyc/phf2TJQndmw7edHRtBHVry1D9O/AN2Ik7/BLUv0GdZybK4K+Q3n+Y6GIt9Bgp3Aw7\n3iNVtQSTbZhWnISm0NA4klDDBBwMwiVJuNy9aySepagkvANJWANI7KpHlT/FXbgTb9qbyNFFVO2+\nEGPADeQN7CfqcAdIlmV16auwurrwsVfqxbKzfV1yDnJ4Bal1dxOuzuKjJTMZWFSIdJL6vXg8DqLR\no7fmyUqcvIK/IFFLxHULhdd8+aSkrbO66nfoSdnZRw521hfOrVecQzyOfeXtNEdXsbd6DpW7J5Hr\n71z/0s7kkxOV4f+YnJx/s69uBm803MU5czKYNq178n+v+S2+gCPllU6dl2URqVyLvudlHInNSMkE\nwcZcqveNJREtRLLA7U1HUk9+4+iJXmNaJEmwqoz0gg0Ujd6NJwCq14OSex5S0ZVYzqEntfbVV66x\nvnAOHTnVzwu67/eR4jsxdj2DWbcWI64RrC9iW8lw4vH+9CvI7dJs1J1lSivTQG2uxVI/w1e4HU9G\nEzanjZRjDFb/60gfcQ6ycuIn1ZfzCZz6eaW3/T5SsAZ55ytojUvR9Ea0lEEwnE59w3iIjUOV2/8W\nJyWffAG2eAjVWoWnaAuOQDOyw0HINotEv5soGJKP19v7focTcbR8cjSix0F30ENon92PHtZZu3IC\nRf3yTlqjQWeZhov6+qvJy3kGZ+Qh6lefQdbkcT2dLEE4Lrbtb5DQ1hCM5LFn5yQGpJ38uAYdaWye\nRlrGOgqzVzCx9h1Wr7qIfv0yKCwUQxa6SjQcJ75jEfaGf+Mw9qHoBvW1/ajcMwrDHIDT7sYb8PZ0\nMk+Izesge9gw9NRgPlvWiMu3ioGjtuIL/RtHeTFKYDBS/8sws2eBIqZsE4TDSfFSUlueQmpaiZE0\naKrPZ+tn40ml+tEvP6unk3fiZAU9LR/IJ1I1jWh5Ca7sjXhzV+OIbSBWWoiVdTneMy9Bcvt7OrVC\nHyU1VCLteAkj8j7xZJRUUqK8ajiNjZPwSEXYVBl612NPp2muABpzSVWcjaNiJb7+G/D5FhDY8y6N\nu85jX86NnHnemT2dzB4jGg66mmVhrvsNRrCWnZsn4HUXodh6Z/+WVLyQhoaLyEx/i8TWXxLt9wye\ngt4xnEIQjkWuXI/e8CzRiETJtivp5+tNtzOFqrorGdj/SSYPfZTGrbksXz6NK6/04BHPeScsmbBo\n2rMFyhbgN5biI0YqZVJRUURlxXjsan883r7TnV+1K2QOyMa0LmXHZ3MxtTX0H1hCzsAtOOt3YnM/\njpx1Dmbh1VheMRuDIFjB3aS2PIscXoGpmwQb0tm+ZSJ6cjA5OZk9nbwupTv9wAy06DQS20tR3R+T\nlr8TW/wBknXPIXtm4Bh6BVa/sYhIb0JXkGr2wc5XMKJLSaWiJJI29uybTmPDFDJdHtJsfacMMhwe\nYswiUXU2/tqVuPJW43MX469ZSsUrM2l0X0PG+HHk9ZNPq6K3N9W0T326jrXuEfT6tdRU5VNXP5Wc\njN49N3c0MhlFrsXvX0F02a3YLnkQu//UfEsnnEbC+7C2/ZpYNMmGrVcQkNJRv0D3zO4QjxZSVXcp\n+ZlvMnv03byz/TZWrpzNeeepqOLO22m6ZtG0bwdG6Tu4Ex+RLtWg6QaxiEpVxSgSofE43YWk+ftu\nxViWIC3bDsygqnEmO3dWkJ29igFDt+EOLsRZ+Q6qdxDkXYTR72KwiXu4cHrR969H2/kqavJTME0a\n6n3s2nUWaKPICPTxN++STMo+mJQ+mOi+JmzqKjKy1+HyLcCKvoe6dRhq/nysIReBu3fXSYVeyDSh\nYjepba8h6R+ia1ESSQd79p1DqGkSGS4Peb7eVf/qSqbqJMj5BKvPId21Gk/GcrzOd3HGlpBaNohS\nx1wcQ2aTe2YBitp3v4eDRPW1i0hNjVjrXkFPvUk4bGPXrnnkZPTsDAqdI9EcvgibWo/Ts4nYBz9B\nmftnFJeoeAq9kxQrw1r/ExLxEJu3n4OaHIHL1TtvZcHGKWBCfvYbzB15D6s3VrAx7b+YONE6rVqo\nT0TNrs00bPgXjsjH+KlBNwxScYvyuv7EmoeDNQFJdnK63aoCXoOAN4+kfhWfbrBwWesoGrCW3KLt\n2Bp2Yd/zNPhmIBddipkzUfRCEPosKxYhvnkx1C5AlfaCYVDXEGDPnkmoTCTN3Tt7e3YnSU5HN+ez\nv3IeKWM7uRkfkp1TghLbjr3iryj+mchDrsbKGSbuDcLRJZMkdnyGvvctbPIqTDNGJOagrHwmzY1n\nkel1k+M9ja4hSaUpMZ2myinkZG/DJn+My70Du74LZc/TNO8ejpQ2Ge+ImagFw/tsL5/eWds+lZgm\nys4d6LvfJWm+TjSpsGXbtWT783o6ZZ1nKTQ0fpX01Eu45XUkl34Tx9S7UDJG9HTKBKGt5hLMdb8g\nFW1m8+ZJJMJnk+bu3bexYHAKetJLQe4rTO73NGXrdlIi/ZoxE5yi3nYU5sob8SQ1kgmJ8tp+hIND\nUM1xYPO1fG+n+XfnUE0Kc8CyJlDeMJUte2opyFxB4YCNeNIWo9YtQZL7o3svwD5oLkpuAaKri3Cq\nszSd6I51GGULseufosgxtFSK8pr+1FSNx2WbQJqrb1bYj4dDkXAoIwgGR1FW00S6bzn9CzbgjL6J\nLViMJY9C7ncJtuFzwC5m1RIOMAySZRXEtn+EElmKw7UTyUwRjLioqDwXPTIFt8PJCcbV6yMUorGJ\nRKOjsdka8XvWItm24XBvQG76DH3Di7A5HVv6aJScSei5M8B1Cj0THoOYVeEwxxMpU2psQN26mXDt\ncgz5HRKGg13brsIjDem29I2b8V0ANn7y6BHXOdGIpaal45LeJmvAajwZaagDv4R8xg09Mod4X49Y\n2hfO7aSeg2Vhlr6Kuetp9KTBxvXTMFPT8H2BN0od5ZPO5K8TZZPr6J/zHClnhKg+gGjR3Qw9Z/gX\nniO4r+aVNXddRX3dYGzWSGRH7+q91dnr5GRHj9YNicaIjF3aRG7OanLy96DaJCTVg5Yajm6bhJI7\nGc+AAmy56XT24usr11hfOIeOnOrnBUf/fcxYguj2NRhVy7Fpa1HVRnRNIxZzUFU9hHh4Gh57v5Oc\n4vYOze/dWZacCMuCUELCpqwlL/cT0tNrUJ02kLJJKudCwUV4hw2mYFDWKX89ibrX8Uk1NJPYtxW9\ncj1KfD0O514sK04yZRAK5dDUMBnFGIdE1wSf7opysafzV0fnIMkhJLajqlvIzNmPy2Ngc6o4nAqq\nOw/LPxYjawpmxqReMbWymFXhZIrHUXbtJLXnM0LJRWAvI64FKNtxOR6pqKdTd8JkSSVuXcnujUPp\nP+Qt/KkXsdcuRR7+feTcc3o6ecJpKhWuQ1v/R2yRtSTjDlavm4VPHYPH3TtmUOgszcymtPqH5Kf9\nHV9gK86K/6b8798i67xL8eT38TG4J8DUb8Jh771TNvVGqmKREzCA0cTjo9m+I47XtZL0zPU43Wux\nmWug/DlCe4ZiGoMxnSOx543EW5SBPS8dnCe/kVgQ2jEMUpXb0crXYDZuRDW3YZdj6FqKZFKmsq6Q\nSHAMqjIeGQWPeGF+TJIEaS4LmEiwYRK1dVX4XcvIytuKzfYqcunrRHYMYbt9Ks22Sdj7DcZTmIYv\nw9ZXe1yfdiwLos0GycpdmLVbkZq3YkvtxG6vwCEnUbQUmgSNjQFCwXGgT8RI9RMPip1kmQEsppBM\nTqFkm51YvIxM72Zyc/aSmVOJ3bMfR9U7qA4F2VOE6RuFmT4Oyzscy14I0qmR0cT1cDwiEZS9JVhV\nHxGObsOUdmA4FIJNwwhVzsEl9XwL0hclS+BMG8Pu3UPxl71F4ZgtuMK3k3RMRhv0PdIGDvzCb0gF\noTNScY3mda/gCb2EYsWprshh24455GcUnbJd/C3sVAavJ01fhT9zMbJ5P41vf0pzzpfJmjYSW25G\nTydR6CNkCdw2F6Z+AQ0152Nz1GJ378DjLsFm34oib0G23oYqB+Hy/mAVIDv6o2YOwZk/AHt+EZY/\nIMZBC93HTEKsEq1+Hw076ohU7kRO7EUxyoAUimViJlOEEx6aGouIN49ClUcDNmyiHnLCFNnCJeeh\naddRXRHH4dqEx7Uep3cramob6frzGNszSG0ewH7zDAzvYJT0QTjyivDme/EEVHFb6OV0HSL1TaRq\n92A27ECJ7kTVS3Gp5dgkDdM00VMapmXS1JhOOFiEqQ8ChmOk+tbsIyebJEGmN0WmN4+E1o8t+1Sa\nN8fJcO4hJ30buXlV+LL2YHfuRrUtRLIrSHYvlmsIln8kcsYocA/CsuX1yvJXNBwcja4jhZuR6zdD\n7UqM6CZSVjmJhI4uKYQi/QnWnIWcGoXaC3/cLyKQ5sQwrmXn2jJy+r9JIG85zugq6jdOIeKbhVo0\nFX9OBoFAn43/IfQETSNRvpv4nmXYY4vxyY1EwxK7d0xGtc6hILMvvBGVCEamETcGkJX7T7zuFZjh\ntdS8NQ3LNwvHsDNxDcrBkybe9AhdRUJL5qIlc4k2zUS1NeP07Ed17Mdu34vNVopl7UU2TKRGCT1s\nw9ztwLLSsGxZ1KUXkpD8yJ4sZG8Wqi8LyZWFZQuA7OuVlRuhh+k6xBqRE7VY8TqMSA1GtBorWQta\nHZJRB2YzlmFhWZBSJKRkCl0zCIbTCUcKiTbnYJljcKh5gIQq7oddzjJdJKJTSUSnooaayczajWRt\nQXWUIatrSWMNlqYgN8rIISfWliwajUwsNRvJmY3szEJ2ZaP6crH5srH5HEgOe0vcBFUV94ZuYpkm\nergePVSNEanFiNZhxuqQE5VIWi2qVYNLjuHEwtANDN3AMiyaggGi4TySsRxsymB0YyCm2RfqVb2T\n02ZQmGFAhkxKP4PG+Cj2bNKQtBgetZT0tP1kZlbjz27E5V+Noq5FkiRQZFBc6FI/dNtALGd/JE8B\nsr8/trT+2L2eHstap3bDgWG0/FlWyx+0/e8h/6/rYOoJ0GOYpgmmBpoGRgpLS0EijJWI0GhPkmiq\nR0o1IZtVSEoZFiG0lIFhyTSFCqhtGkMiVETAlo18inQtORGKAq6MIsLR76HtWYs34yOcgeU4Qp+g\nblOwdqbRRD6y3Yfi8CA7/Mh2P9gCWDYvks0PDj+SzYclO5FkBUlRsewBJPnzFutD/2s7tXqfC4cw\nzZa6oqUlINGEZOlYhgamhmVoSHoMKRVB0pqRtAhmKoyVimEkmrG0JiSjDpkgkpTAaRok4yZllcNJ\nhC7EZc/q6dPrcsl4PpX7voM/cw2+wEo88nJklpHckkds82CapP5IngxUjw+nz45ql1FsMopNQlZV\nLNkBipN4JA0tbIKsIql2FJuCJCsgKSB7QD7kNn8ws9lsokJ3GtM1P5HgaGA0AJKcwu6ox1Iasaxq\nFKqwOYK4XHU4HWWYsU0t14uqYkkSBlLL5aPIICuY+LAkL5bswZJcWIobFDeW5EJSXSA7kRQnlmQD\nxQ6SveW6VGxIih0kG8g2JLnlQUOSpJbjSfLnLdOSjCRLWByYM1uSD1nvwDYcXFUG9eAr6ZblyaiB\nFmsZG2x1cO23lD8HCyMnyKdG//dEMEG8MY5khpGwsCwTsMAyaQlhZbasaJoH6kQH/z7/98H1JDOF\nZRlYpt5y39Z1MFNg6FimBgfu55gtdSfJiIERQzGjSGas5c+IIlkRZCkMGGgmmGbLMSzTxDJNDN0i\nHvUSi2WQiPuIJ7yYegZaKhPVPhCnrSV2jV0G+m4Vq9fRNT+R5mlEoxMAC5ujEaerArtcg0QtltKA\n3VWBrOxDTgE6SDEJRVGQ6xUMVDTDh6H7wXICTpBdWIoHFBeoDiTVhqTawWZHttlAtiOpKsgOUJSW\n+4GiIsk2kBUkRWm5D0hKS96XZSxJaflMlkFWsQ5cJC33BYmjRe7TtEMeDw75R0tWMJGMg/EDWq5Z\n6UD+scwD+arlH3weHc5qyVtYB7KW+fny1vxGSx4zDUzDBNNqaTAzjZZeN6aGZaSQrJb/YmkH6k5x\nJD0MejOyEUYyIySVJEayCUVqBkwOZhHFNDEMA1M3MA2J5riPeCyTRNyPkczBJheiWwOwDolTkDIR\nTiK7apLtSx24PF1Y1kgS+hjK6iVSeyNYyTqc7v14/Q14A414/Y24/Z8hq5+hqgqKIiMdeOaMG94D\n5a4PS/Ziyn4s2QuqE8nmQFIcyKodFAfIDixZbakXyjZQZCRZJTt79gmdxynbcCBFwthWfAzmsWM7\nBoNQVhZl4MgnkCT9sGDchzQ0mCYxCUzdoOV2IZGIOKirH0YoOpxo80Bcshe/S8Z5atQpuoakkGAK\nicbJuEOluGwbMWy12F1N2NyfISm09ItVZVBk5NZKXAcPJhJEHMNYqz/V4edDhpgMHdql8TqFk8A0\nYdkyBS2W4Gz9OlS1mUN/xYO/tIXVkmWtlgruwcqkaZhomo1E0kcslkckWIgqTQF82PpwXrMsG6H6\n6TQ3nIUvfQsu9xZcjl0YZhVOJCxNRgpLKDEJWVGQJKnlwU2SDnynEiFVxtQPqQEo4PUe+PbNXMz4\nDe2PGwigTZtxEs5QOBVYpp1kvAAoAMagAYkoBE1IagYue5BYuBKsJmR7FIcjgd0ex26PY7PHsTub\nsTnqUG2pAw/yIMsS0oGH+5Y/4GCDA7Q87Lf8z3Gl9UhrH3q/sSRIuQOY6ufBUzWbgqYZR92332/h\ndIIlOUkNuh/U3j90aNOf3iWQ/RIe/06g7ffTNU2D1uffrXWwkaHlQcg6UG8yrAPrmRaGoZLSnCQS\nHpJJL4mkGz3pQk/50FJp6Ho2SNl4PXbsast9yyaBJ+3kBhIVjkXi/7N333FWVAfDx38zt9ftBZbe\ny9I7WFBBwBqNLfZYYsQnMRpN9HkfNbEmMTHFGI0Fxd5FEVlQEQTpwtL7FnaX7e32MuX9Y+GyC7vU\n7Zzv57PK3pk5c87eOXPOnDklGk4iGj6y27qOwRDAYKoFvRZJrkXXKzEYqjFaPVgsPszWMiR0QELS\ndSQVJEmn7rleAvlw42DsHiE3fJNUrxnwqH8deV0fdZ0PXN9oinbtgnXr6hoUbVUHSMzLbrC9W9+3\nsTkKGw33uOc8QQ1H2BzMW4f/U5ev6v6BrulouoZ+qOFNh6BmJhwyE4k4CYfshEI2ImEHetSJkQSM\npnR0OQmk+i8MICqq1e2OJIHNpGIzAS4r0P3gD0QiUFmqUl4QwiCVoOolSKZKzDYPVrsXq82HyVyN\nwRhFlkA+WO5CXV46lKekeo3qscb1g2WvNnkd8il0a232VRVak67rbN++HUWpawzQDzUjNtIDIaoo\n5OTk1O13KBOqOpKmY9QljEiYTSYMsQH8MmDGbLZiMHTY9pVWo2kawVCQYDhARFdRUNENdQ85kkFG\nlkGSJWRZom/fvhgMhqN6HAD07t0bt1tMFNdR5eTk4PV6ycvLJxQKoal1hZ+uaKCqGCWwSGBAwmAw\nYZBNyLIVs9l6SjewM4EOhMMhQqEAmhZGQ0WTdFSp7gEJo6GuIibXVcTiXC7S09IOB1DvreyhzJac\nnExGRtvPQn4syz9d2tZREE6Rjk40Go39aJqCho6qq2i6ji7p6GhoOujSwQozEpKhrvJTV+mp+wyo\nVwk6eIJDnRAONkQcen8gSVLdW0JJok+fPhiPWH7yeJ1sBg8ejLmDLU334QcfcOgxprHq3KE06/Xf\nLkqHf9e1ww8qh9686uqhetTB3XUZgyQjAQZkDJKEjAGj0YjRUPd/Wa57G2yQxeQDQtNUVUFRFDRN\nQ9cVokoEXVfQNBVF19CoK9c0OHgPqKMfbHw8VM4Bdf+v/zuH/3nNtdc2en6Px0Nubi4AkUgk9u8G\nWafeL7F/6kffP/QjXlzq9XeOde6p19imEctjkl53/5KluoJckuS63gOSjIyELBkwGU2YzWbMZjPS\nKTdVCGcaRTlY9ioKqhpF11VUXUej7keX6l7cqTrccOeNJx1+h244EARBEARBEARBEAShZYlXfIIg\nCIIgCIIgCIIgNEk0HAiCIAiCIAiCIAiC0CTRcCAIgiAIgiAIgiAIQpNEw4EgCIIgCIIgCIIgCE1q\n1uUCFEWlujrQnEG2uoQEe7tKgzU7E4DQyK0nfEx7S8Op6AxpSGliLeEzOZ+cyvXcUjrDNQadIx2N\n5ZUzOZ+AyCvNrTOkobPmE+i434/k9WBa+QOG4l8i9TIQ/uEW/CNGYb3ksraO2inpqN9DfaLu1TpO\ntYxqT2k4VZ0hDU3lk+Np1h4HRmPHX4ZHpKF96AxpaEpnSJtIQ/vRWdJxpM6Qrs6QBugc6egMaWhM\nZ0lXR02HXFJCJBJGT5GQbDLmxES03TuRqqvaOmqnpKN+DyeiM6RNpKF96AxpOFViqIIgCIIgCIIg\nnCS5tIRARSVSnASAlpKGM+AntGVzG8dMEASh+TXrUAWh+bWHbqqC0FzE9SwIJ0bkFUFo50IhpGAu\n1uQsdG08Jr0b0bggFqcL37YtMHEymM1tHUtBaBGijDoziR4HZ5A//OH/sWLFstMOZ9++vdx9923N\nECNB6DzmzfuE559/rlnCuvPOW8jLy22WsAShvbvzzpvJz8877XB27NjGr3511+lHSBBOgFyxD8n6\nHiZbAcgVqFI2su1N9C4BTFVVcKDotMK/++7b2bNn9wntK8oM4Ux3MvnlWMQzzrGJHget6KqrLqW6\nugqDwYjNZmXChMncf//vsVqt/OpXdzFjxkVccsnlbNz4I7/+9S+58sqrue++38WOnz37Di699CfM\nmnUJCxd+yZ/+9AQWixUAXdeRJIn33vuk0Qkv9u3by759e/jDH54CYOPGH7n33ruxWm2xY++//3fM\nnHlx7JhvvlnEG2+8SmlpCUlJyfzv/z7G8OEj6du3Hy6Xm5UrVzB58lkt/FcTOrLFi7P48MN3yc/P\nw+Fw0L//AG6++TaGDRvBnDkvU1RUwCOPPNHgmLPPHsf7739GRka3o/ZZvnwpc+a8THHxAYxGE/36\n9eehhx4lPT09dvxXX83nmWce5/HHn+G886Y1CDsQCPDaay/x/fdLqa2twe2OY/DgoVx//U0MHjw0\ntt+7777JF1/Mo6KijPj4BKZNm8Htt9+FyWRqNJ2KovDmm3N45ZW5sc/+8penyM7eQGFhAQ8//Ciz\nZl0S27Zw4Zd8/PEHFBbux+FwMm3aDH75y/9Bluvacq+//iZeffVFnnzyL6f4lxfam/r3//r366Sk\nZKDu2rz00umMHz+RZ575W4Njr7jiIjye2oNlh41Jk6Zw330Pxu7/TzzxCN269eDnP7+ToqJCrrvu\nCmw2OwA2m42pU8/n3nsfiF1fh9x99+0UFOQzb14WRuPh6kD9suaQ9evX8uc/P8lHH32BqqpMnTqR\njz6a3yDvvfbaf3n//XeQJAlFUVBVBYvFiq7r9OzZi1dfffOov8t3331DSkoaPXv2AmD+/Hl89tlH\nFBUV4nS6mDHjIn7xi9mx/T/88F2ysr4iN3cfF198OQ888FBs2+DBQ5FlA+vXr2Xs2PEn9f0IHVtj\nZc1NN/2c4cNHxvZpqmyoXx+SJEhOTuGGG27hoosuBeCGG65q8PshH330ZxauquSB867i6UXZFFbn\nYDRqSMwHVWbY7p38de57xw2/MT/8sDyWDoBoNMqLL/6LJUu+IRKJMG3ahdx77wMYDHVjrUWZcWa7\n+urLeOihRxgzZlzss4ULv2T+/Hn85z+vAg3rVofU1bEKeeSRxxtcpwBOp5OLL76M228/3Bh79tnj\nDl7HEmazmXHjJvDAAw/hcDgbxOepp/7A4sUL+fTTBbEy7tD53nrrdUwmMwaDgV69enPPPb8hM3MY\nQKNxyMwczvXX38SgQUOaTP+R+eV4dazp089BkuqGF+m6TiQS5oorruY3v3lAPOMch2g4aEWSJPHs\ns/9k9OixVFRUcP/99zB37mvcddc9R+1rtdrIylrAz352c4OKWX2ZmcN54YVXTujcn3/+CRdeOKvB\nZ8nJKXz66YJG91+3bjX//e8LPP74MwwePJSKiooG26dNm8m8eZ+ITCU06f333+bdd9/iwQcfZvz4\niRiNJtasWcXy5csYNmzEwb2ko447dDOv9wkAhYUFPPXUH3j66b8yevRYgsEga9euRpYb7p+VtYC4\nuDgWLlzQoHIYjUb59a9/idvt5tln/8nYscMoKqpg9eqVrF69MtZw8Pe//4W1a1fz6KOPM2jQEPbv\nz+epp/5Afn7uUQ90hyxfvpRevXo3KCD79x/ItGkzePHFfx21fzgc5t57f8uQIZnU1NTw+9/fx3vv\nvcUNN9wCwJQp5/Dss89QVVVJYmLSMf/OQsdQ//7fmCVLvsZqtbJ69UpqamqIj49vcOxzz/2bESNG\nUVlZwX333cM777zJbbf9oslzLV5c17usurqK3/zmHubN+4Qrr7w6tk9RUSHbt2/F4XCyatUKzj57\n6omkosE5jnT77XfFKpnz5n3CsmVL+PvfXzhmiPPmfdogXtFolPvvf4jBg4dQVVXJAw/cS0JCIldf\nfR0Aqalp3H77XSxfvrTR8KZPn8G8eZ+IhoMzSFNlzYoV3zdoOGiqbICG9aFVq37goYfuZ9iwEXTv\n3oOExMLzAAAgAElEQVSZMy8hK2tBwwf9aDlZq3cxtZ+TnXkTCIU2cl6f67loTJAh/ZcQX9GT2opB\nEAweN/zGfP75J8yYcVHs97feep3du3fx9tsfoaoKv/vdfcyd+1rsHiDKDKEx9e/Tjd2zj1T/Oi0p\nKWb27DsYOHAQZ511biyMuXPfo2vXDAKBAI888hBz5rzMr351fyyMUCjEsmXf4XK5WLw4i5/97MYG\n57jooot48MFH0DSNV199iUcffajBs0j9OFRUlPP5558ye/ad/PWvTZefR+aX49Wxvv76+wbxveyy\nGZx//uF7gnjGaZoYqtDKdF0HIDk5mYkTJ5OTs7fR/VwuF7NmXcqcOf9tlvOuXr2SkSNHn/D+c+a8\nzK233hF7mEpOTiY5+fBD0ejRY/jxx7UoitIs8RM6F7/fx2uvvcxvf/t7zj57KhaLFYPBwOTJZzF7\n9q+PeeyhPHKkvXt307VrRqzgsNlsnHvueaSmpsX2KSkpZtOmjTz44P9j7dpVVFdXx7ZlZS2goqKc\nZ575G7169UaSJCwWK+eeez4///mdQF3jxLx5n/DYY08xZEgmsizTq1dvnnrqL6xZs4oNG9Y3GrfG\n8tcVV1zF6NFjMZmOHuP6k5/8lOHDR2I0GklOTubCC2eyZcum2Haz2czAgYNYu3b1Mf9WQsfS1LUN\nddfnT396LT179ubrr7OaPDYpKZlx4yaecJfMhIRExo4dT15ezlHnGz58JDNmXMRXX315EqloGJ/T\nEQ6H2LRpA6NGHc47V155NZmZwzAYDKSkpDJt2oUN8sbUqRcwZcrZuFzuRsMcNWoMa9eubpb4Ce3f\niZY1xyobjjRp0hTc7jj27dsDwMyZF7F5czalpSWxfQo2vM3eEpXuhgmkWwM4zSqpjij5+yZQXWsg\nGFeIweuBesc0Ff6RFEXhxx/XMWrUmNhnK1eu4KqrrsXpdBIXF89VV13LggVfxLaLMkM4npO9J6an\ndyEzczi5uYeHwOi6HgvHbrdz1lnnNNgOdb3IXC4Xt956BwsXzm8yfFmWufDCWVRUlFNbW9PoPsnJ\nKdx++11ceunljb6Egcbzy/HqWEfGNyEhoUEjo3jGaZpoOGgjpaUlrFr1AwMGDGpyn1tuuY1ly5ZQ\nULD/tM4VCoUoLj5Ajx49G3xeU1PN5ZfP4JprLuf5558jFAoBoGkaO3fuoLq6iuuuu4Irr7yYv//9\nL0QikdixyckpGI1G9u/PO624CZ3T1q1biEYjJ/gW88QMGDCI/Pw8nn/+OTZsWE/w4Juc+rKyFjBw\n4GDOPfc8evbsxddfL4xtW79+LePHT8RisTR5jvXr15KamsagQYMbfJ6amsaQIZmsW7em0eNycvYe\nlb9ORnb2Rnr37tvgs549e7N3b+MVS6FzKSoqZMuWTUyfPpPp02eQldV4TzCoKzvWrFlF9+7dTyjs\n8vIy1q1bTWbm8AafZ2V9xYwZs5g+fQarV/+Ax1N7Wmk4FXl5eTidTtzuuCb3qcsbfU44zIyMbkSj\nEYqKCpsjikI7d6JlzbHKhvp0XWfFimV4PLVkZNTlsZSUVEaNGsOiRV8d3EllwcIsxvcx4ynvi9Na\nN1zAYtTolahQWDQanxrC6S7Ht2P7ccM/UkHBfmTZQHJySoPj6j/46bpOeXkZgYA/9pkoM4T6Trfx\ntKBgP1u2bIoNIziSx+Nh+fKlR23PyvqK6dNncsEFF5Kfn8eePbsaPT4ajbJw4Ze43XFNNgQfcu65\n57N79y7C4VCj8TwyvxypsTrW4fguaDBMG8QzzrGIoQqt7OGH68akOZ1OJk8+i5tu+nmT+yYkJPLT\nSbW8/tfLefSfG4/avnXrZmbNOh+ou0HEx8fz/vufHbWfz+dFkiTsdkfss169evP66+/Ss2cvSkqK\nefLJx/j3v//OAw88TFVVFYqisGzZEl588TUMBgO///39zJ37GnfeeXcsDLvdgdfrO50/h9BJ1dbW\nEhcXf9SYamt2JnB4Nt4lS75m5coVse2Hxn43pmvXDJ5//r988ME7PPbY/xII+Lngggtj84RAXYF1\n1VXXAHVdzRYu/JJrrrn+YJxqGsxjsHPnTm644UZ0XSM5OYV33vmY2tqaBsMN6ktKSm6yVdzr9TXI\nXydjwYIv2LVrBw8//EiDz+12O1VVlacUptA+Hbr/Q92b8aeffhaoq7gMGDCQ7t17MG3aDF5++T8U\nLRxG3y56LK/87nf3ARAMBhg3biK33npHk+fRdZ1Zs85H13UCAT/Dh4/knHPOi23fsGE9lZXlTJ06\nDafTSVpaF77+ehE//ek1LZX0Rvl8Xmy2pvPNZ599zP79+Tz++NMnFa7dbsfn855u9IQOoKmy5kjH\nKhugrkv0rFnnEw6HUFWV//mf+2LjpQFmzbqEN954lZtvvg3Jt5Gv11Vz/fBeJCl1DzzOuN28vSkH\nmflouobREOGaobu5zjYc0tOPG359Pp8Xu93e4LOJEyfz0UfvM2rUWFRV4eOPPwDqXgwdKntEmXFm\nq1++AESjEQYOHHyMI4526DrVNJVgMMg550ytN7S0zu233whIBAMeeqTqXPa/f4htKykpYePG9fz6\n1/cf7O02gYULv6R//4Gxfb766iuWLPmOQMCPy+XiySf/ctz8m5ycjK7reL2+2Nw+hzSWX+prqo51\nKL7Z2Rt4+OFHj9omnnEaJ3octLI//elvLFy4hI8++oL77vsd5uMs1XPLdIXVO+RGW5EzM4ezcOES\nFi5cQlbWd402GgA4nXWTJdZvmU5ISIxNRpWe3oW77/41S5d+CxB7I3vVVdeRkJCI2x3HddfdwKpV\nPzQIty7TN5wQRRAA4uLiqK2tQdO0Y+53/vnTY9fwoev4WK3kQ4Zk8sc/PsP8+Yt54YVXyc7ewNy5\nrwGweXM2xcVFXHDBhUDdWOd9+/bG8k5cXByVlYfn6hg0aBBZWd/x1FPPEolED+4T32Cf+iorK4iL\ni290m8vlapC/TtT33y/l5Zdf4G9/e/6ot66BQCCWd4XO4dD9f+HCJbFGA4BFi75i+vS6OWjS0tIZ\nNmwEX64xNDj22Wf/weLFy/jnP18kLy8Hj8fT5HkkSYrlp6+/Xs7AgYP57W9/FduelbWACRMm43TW\n3b/rejkcHq5gMBhQ1YZdNBVFwWhsGKfT5XK5CAYbzzfffvs1b7zxKs899/xRE28dj8g7Z44TKWuO\nVzZA3RvGhQuXsHjx91x11XVs2LCuQRjnnnseVVWVbN++lXXLPyAc1UkOD8edcPi+/dvLgjx34f/x\nx3Oe4KWfd+fGcxUMtdUQCh03/PpcLjeBQKDBZzfffBsDBgzk5z+/ntmz7+Ccc6ZiNBpJSEiM7SOu\n+zNb/fJl4cIl/Pa3DzXYLstyE/f1w++QD12nixYtIyvrO8xmC08++ViDY+bMeYesrO9Y+Y8wPz1L\nZfbs24lG6+pQixYtoFev3vTt2w+AadMu5OuvF6Gqauz4iy66iIULlzB//mJ69+7Lzp0Ne+U0pry8\nHEmSGn3maCy/HHKsOhZAVtaXDB8+kvT0LkdtE884jRMNB63sZLsOxTngZ+cpvPrqiyc0sUljrFYr\nXbt2O+6Qh0Nxc7lcpKSkHnPfiooKFEWhR49epxQnoXPLzByG2WxpcgKz5jBo0GDOPfd8cnP3AbBw\nYV337ltvvZ7LL5/BXXfdiiRJsW7fY8aMZ+3a1Y12dTtkzJhxlJWVHlWQlZaWsH37VsaNm9Docf36\n9T/pIUWrV6/k2Wef5s9//kejXbHz83Pp16//SYUptG+N3f83bdpIcfEB5s59lcsvn8Hll89g166d\nZK0zUH/3Q8eOHj2W6dNn8u9//+OEzmmxWJg16xI2b87G7/cRCoVYunQJP/64Lna+Tz75kF27dsaW\nc0tLS6e4uLhBOMXFBxqtXJ2Onj17EwgEjhomsWLFMv75z7/yt7893+TkcU0pKirEbDbTtWtGc0ZV\naKdOpKw5XtlQn9Fo5O67f8XevXsbLF9tsViZOvUCFn41j0XfruecAS4C/lRopF7mtkapqBxKUFcw\nUoRWr6daU+HX161bd0BvMCm1xWLhN795kM8++4oPPpiHy+Vm4MBBDeqFosw4sx3v+eJk7+t2u4Pp\n02c06BVa/zwGGX4yWaW4+AA5OXX1sEWLvuLAgaJY2fLCC/+gtraG1atXHhW+2x3Hgw8+zJw5rxy3\np8yyZUsYMGDgUb0NoPH8AsevYx2Kb2Orm4hnnKaJhoMO4PrzVLZu3Ux+fsMJSE6mEWLSpCls3Lgh\n9vvGjT/GJvopLS3hv//9d4MxghdffBkff/wB1dXVeDwePvzwPaZMObve8esZM2Zcg5ZKQTjE4XBy\n++2/4Lnn/szy5UsJh0MoisIP22Se//zUrpnNm7OZP39ebFKr/Pw8VqxYxtChw4lEInz33Tf8/vf/\nxxtvvMsbb7zHG2+8x733PsDixQvRNI2ZMy8mKSmZhx9+kJycfWiaRiQSYUe9Majdu/fgssuu5I9/\n/D+2bduKpmnk5Ozj//7v94wbN6HJGX0nTpzCxo0/NvhMURTC4TC6rqMoCpFIJJZnf/xxHU888QhP\nPvmXo+ZTgLqxf7t27WyyoULoPL76aj4TJ07m7bc/jl23c+e+hy8Iq3c0XkRfe+31rF79A7m5OY1u\nr182RCIRsrIWkJKSisPhZOnSbzGbzbz77iex873zzkcMHTos9iB1wQXT+fLLz9m5cwcAOTk5fPTR\ne0ybNqPBeSKRMJFIJPZzvB5GR7JYLIwcOYbs7MND8dasWcUzzzzOM8/8rdGHIFVVCYfDaJqKqqpH\nnTc7ewPjxk04btdXoXNoqqxZteoHXnzx+RMqG45kNBq57robmDOn4apVM2dezJIli/l+k4+RyQNx\nOhrvMSpJEPb1IaKZkJ2VhIsPnFD49bePHTue7OzDZUpFRXnswWjr1i3Mnfsat9/+y9h2UWYIx3PB\nBRcyd+5rlJeXoes669atYeXK5UydekGj+wcCAb75ZlGTD92aBp+vMhx8OZnB1q2bOXCgiFdeeTOW\nz95660OmTZvBwoWNT8Dbo0cvJkyYxDvvzG10e0VFOXPmvMyCBV9w113/0+g+jeWX49WxALZs2URF\nRUWj6RfPOE0Tf5FW1XSPgWP1JnBY4frrb+all/7d4PNt27Zw4YV1S6QcGhv+r3+9RErK0ctQXXrp\nT3jssYe56aZbAdi9eyePP/4IPp8XtzuOc845r8Fa2bfccjs1NTX87GdXYrFYuOCC6dx8822x7V9/\nncXll//0hFItnJmuvfYGEhOTmDt3Do8//ih2u50hXQzcNuPYs9Q2lRecThcrVizjlVdeJBQKERcX\nz7RpF3L99TexdOm3WK1WZsy4qMEYv0suuZw5c15mzZqVTJp0Fs8//xKvvfZffve73+Dx1I2NHThw\nME888UzsmN/+9ve8++6bPPHEI1RUlBMXF8/06TMbrGV8pClTzub555+jsrIiNkfCfffdQ3b2BiRJ\nYtu2LTz77NP8618vMXLkaObOfQ2/38+DD94by7sjRozk2Wf/CcDy5csYPXpMk/MtCB3R0dd1OBxi\n2bIlPP74n0hISIh9npAAs8apfLnGwKjrjzqMxMQkLrxwFm+88Sp//OPR4/8lSYqVDUajkf79B/Dn\nPz8H1A1TuPTSnzRYJQfqVjN48cXnueuue5g06SzuvPOXPPnkY1RUlJGcnMyll17BxRdf1uAcN95Y\nt4zioWv44YcfZdasS07qr3L55VeQlbWAc86ZCsDrr7+C3+/nvvvuiYU7btx4nnqqbmjHK6+8yDvv\nzI3dJxYs+Jy77rontszW4sVZ3HjjLScVB6Fja6ysGThwMDfffBvLly89btlwaM34+i655DJef/2V\nBmu5jxw5GodVwmyRsfkG43Y0HFf91y9sSPrjQF2e6PajxkuXViBVlJ9Q+PVddtkVfPLJh7HGuqKi\nQp588jFqaqpJTU1j9uxfN1hyVJQZZ7rj90i+9dY7eO21/zJ79h14vV4yMrrx2GNPNWgYqKysiJUd\nZrOJIUOG8eijTxw+iyRx663XI0kSsm6hZ6rO00//FZfLRVbWAs4+e+pRDQ1XX30d99zzC7zexued\n+dnPbuTee2dz0023NYiDrus4nU4yM4fz73+/3GCOqiMdmV+OV8eCurJw6tTzsdmOzv/iGadpkt7M\naxaVl3fsCYlSUlztKg1HTiZ3IppKw+OPP8L550+Lrcd6qnJy9vLss0/z4otzTiucY2lv38OpSElp\neqxhZ0jbqaThVK7nltLc19j8+fPIy8tpsJ7xqbrrrp/z0EOPnNBs8p05r3SGdJ1qGjpzXjnSL35x\nK//v//0hNu/Oqdq5cwcvvPAPnn/+6GWMRT5p3zrE96OF0LfdhmcvrF1wFl369IkNVRgxeTaSLJG9\n4gUAdB1MqZ/SzZmNXHYF9v/5HZxkL5h77rmT3/zmwSYnUazvZMqMY+kQ38NxiLpX6zjVMqql0nAy\n+eVYTuQZpz19D6fqWPnkWETDwRE6y8Ug0tD2ROHVvnWGNEDnSEdnfSDqDN8NdI50dJY0NKajpws6\nxvcj+TcT2f5Hijf2Ym/2YFKPWBLV4bDg94djv6v2H+mR+iVy8Vji73wS/ThLzrUHHeF7OB5R92rf\nRBrah1NtOBCDAAVBEARBEAThGOTgNtRglIrSdJz243cN1yMZqJIZRSpCqapqhRgKgiC0LNFwIAiC\nIAiCIAjH4tuOFopSXeLC5kw47u6ykoKi2TA4Kwme5Ko7giAI7ZFoOBAEQRAEQRCEpmgRFO8+It4k\n1JCGZHEc9xAZA15fH4x2H5HCxldAEQRB6EhEw4EgCIIgCIIgNEEK56OGQnirkzBYGl+GsTGRUHdU\nXUat2tWCsRMEQWgdouFAEARBEARBEJoghfNRAlGqK+NxO05iJfNIOppsIhrOg3D4uLsLgiC0Z6Lh\noD3TIlg39MW6oQ9EK9o6NoJw2qzZmbElfARBaJrIK4LQfujB/ejhKN4KGzbX0fMbjJg8m/6j7jj6\nQCUFDQsGaxl4PK0QU0FoHaKMOjOJhoP2KlqJOf8BJNWLpPow5/0G2bumrWMlCIIgCIJwRol48pFU\nBX+NDUzWEz5O140E/F2xuWqozRMTJAqC0LGJhoP2SItiOvBnpGgJumxDN7hAMmAs/juSf3Nbx04Q\nBEEQBOHMoOtovlxCXhdGo+2kDw+GeoIu4ctf1wKREwRBaD2i4aAdMlR9ihzOQ3WeC34bUq0ZxX0P\n6GAq+RdEK9s6ioIgCIIgCJ2fUoEe9FBbk4jTKp304arSHSQDkfItLRA5QRCE1iMaDtoZKZyPoeoz\nCJhga3cC68xUrHJR/lU5/t2ZSGV5mHf9EdRoW0dVEARBEAShU9MDeahhBW+1C4fNfvLHR7qgSwbM\nFICmtUAMBUEQWsdJTA0rtDhdxVjyIlJNFdX7L2dbdgjIRJMlqlbXYJYyGDo8hbQ+67F4/0RkxG/B\nfvKFmCAIgiAIgnB8wer9yGqUYJUVuzPupI/XNCuRUBJWeznBci+2tJMPQxAEoT0QDQftiKH6C6Ty\nzZTnD+DHtWlUk4yZm7FIYEkw4g3KfLP5ZmY6/0586AuMy9NgyvXgdLZ11AXhhIRGbm3rKAhChyDy\niiC0DxFPPuZohEggGVu8qdF9Nq38Dw6HBWh8ycVQoCvO+GJq87ZjS5vUgrEVhNYhyqgzkxiq0E5I\nob1Q8DaeYplN68dRq9jp4QzSK1GiS4JEqlulb1qUQV1MrNp2G76IjWjNO6grvoZIpK2jLwiCIAiC\n0Pn4cogETJgMp/6SRlEyMCBRuWd1M0ZMEAShdYmGg/ZAC0H+PwmW1LJr07l4Ahb6pRqwmo/+eqwm\nlS72ZLblziAoq0QOvI2+epUYNycIgiAIgtCM1GgQY6SQgC8Jg+HUq8xRpQeyJCF5dzZj7ARBEFqX\naDhoB/Si1wgW7aMkfwRFhfH07JpwzP2tJhVjcBzFnn5o7mJ8mz9B3iZm6xUEQRAEQWgu3vL9SGoU\nb60Ls+XUexxElDR03YTLuJ9AoBkjKAiC0IpEw0Eb0yq/J5j/LcEKFzs29KNbt64ndJzDrFN14DJq\nlDgMyRvx/LAQufhAC8dWEARBEAThzBCq2Q/RCJFaF7LdfRohyYR9aThslZQXljdb/ARBEFqTaDho\nQ2qgmMCeV9Frgmz/8SwSkhOQDSc+X6XL6KC4cCY+2YTBspzA90sh3PjEPIIgCIIgCMKJU7z5aOEI\neiQF3dD4xIgnHFYoHQmo2LGueSInCILQykTDQRvRNYXarf9C8laQs20SuhSP3XH0EIURk2czYvLs\nJsMxRwZSXjWMkNODWrYEfbuY5VRov6zZmVizM9s6GoLQ7om8IghtS1XBENwLGkTCx15CccTk2fQf\ndccx94lGu2FAR63c0ZzRFIQ2IcqoM5NoOGgj5TvmYwrspCK3K76avrgaaTQ4EbIkESi/AL8SB0l7\n8K/8Dmprmzm2giAIgiAIZ47qKg2Hvo+gPxGd0+ttABCI9sIo69ij2wkGmyGCgiAIrUw0HLSBygNF\nmKs+IlIRoeLA+cgmEwbjqRdKNqOFqvJJBIwSWnQzwfXrmzG2giAIgiAIZxZf5QEkxUfAm4jFbDvt\n8KJSIlrQRZItl7LSaDPEUBAEoXWJhoNWpioa0dyXkP2VlOWfTygUxWE7dhe4EyH5hxNWEtFTcqhc\n+QNSbU0zxFYQBEEQBOHME67JRQtHUHxxGOzxpx+gJBEJpGE1BCjavev0wxMEQWhlJz4Tn9AsKvcs\nwh7dQk1BBp7yZFyu5GYJV8KMv2oC5pQsooFNSJs2YT7n3GYJWxAEQWhhuoYU2oPs30C0dg+emigR\nxUh4xZ1EtTgUYwYG91ASe43F4kxs69gKQqemKEAwB4MaJRxIQTc3T3VZiWRg1nNQS7cCYny4IAgd\ni2g4aEUhbxXmyvdRKkNUFJ6DwWxFlpvvK1B8Q9FTV2NK3ceBpRvoPWoUuut0lg8SBEEQWoquQ8RT\niFb5DUbfcvSoj3AEdFVH81jQwxCRPJjkQizyBqTKBUSLLYRs47EOvBlLSu+2ToIgdEqVlRIubSfo\nOoFAIjZz84Trj/TCLi3D6t9KKARWa/OEKwiC0BpEw0Er8ux+H1uwnOL8iUSCOs6E43d927TyPycc\nvq6bCNeMwZz4HVF1A9VrJxF/wcTTibIgNKvQSLHqh3Bmq6mBinJQqjfiCn+BU9oOgE91UFkxjJrC\nNNQyJ7pixmbRIViLrEQwmT3IjhKcabtxJy1Cr1mK3zAJ68CbMPQaCLIYeSgIzaW8VCFD3UE4lIqu\nH7/VYNPK/+BwWIBjL4kdUHtgRCbBsIOqKujatZkiLAitTNTnzkyi4aCVeEp2YfUvIVBmxVucgSMu\npUXO460aSVL6OhJ757P3qzWMHpeJ7Ha2yLkEQRCE49N1KC2VyM3RsASXkyp/hknNB12jvLYHVYWD\nCBenYjZZMSrVGBQ/mhTEqFgxutPQDUaQJHRVwVc0hdqinSRkfI87/huCG9eib7wI5+CLkfr3AdPp\nz/4uCGcyXYdA5T6kqJ+Qpz9Wi6P5wjZYifpSSHAdICe3iK5dM5otbEFoDZpW939JqvsRziyi4aAV\n6JpGJG8OFl8txbkXIxssSIaWqdzpuolg7QTMrsVYTBvY//UUev10bIucSxAEQTg2nw+2bZMxeFfR\njTlYKMUQ0QjXDsF3IJNwIAmDtwKLVgthDxZHPJIlNXa8Xi8s3WBEt7mQGEdV5Wiqfdmkpy3CYnyP\nyrWrMW65ioTx49B79hQ9EAThFFVVSdj17Ri0KP7KBGRb8w75DPn6YHRWEM7/AaZc06xhC0JzCwbr\nGr4rKiS8XolIpO5zSQKLpe7HZtNxOnWcTnA6dRzN19YmtDOi4aAVVO1bgjW0i8rCnkRqnNiT0lr0\nfL6asaS6l9NtcB7rFq4icfJg3F1ELhYEQWhNRUWweU0BXaWXsLMNi6YRLBtMVckY/NUKWjiALhVj\ncyQgue0nFbYsGSAyhrID/XEkf4Pbvg1JeY78L6eQ0vsi7OOHoaemHj8gQRAaKC2VcKgbMegaPk8X\nzO7mfa3qCwwmhVXERdcQCl0j5jkQ2qXqKp0D+yvxlpdiC+7CXLuP9GglFmowSQF0FTTNiIqFsB5P\ntTGDEkd/onEjwNaV5GRISdFJStIxiqfNTkN8lS0sEvQil72HWhOgIn8cFkczLOlzHLpuprpiAomJ\n35Iav4ktH29m0j2TxAsoQRCEVpKzT4PyD+knvY456kGp7sOBgin4y8Kokh+b1YWclHTa59FVN77S\nK1Bcg3GnfIszeRXe4q1UfnQJ3SdOQR86RMzAJggnSNehoizCoOgmlGgKUcVOM82LGBOQekDITpIt\nm+oqhS5dRVVcaAd0HT2Uh+fAFsIHNmEJb6NntBI9HEZCxxAvgRYFTUPicG84SdfQdQkNA6pkRA9K\nhANOasv7UJI3hAP2IVgSBpDUpSspoi27wxN3qxZWs/MDHMESCvNGo0RNWJyuVjlvbcVoElNW03fE\nHpZ+tZa8CQPoM/70K6mCcCI0DZSojhrxoIdLkCLFyFotku5D1hWQwGA0YjA7wOhGN6Wjm9PBkCAG\nzQkdmq7D3h1V2Cuew6FsAS9UFVxGZaEDTY3gcKeCsbmHqkmEvIOJBHrhTlmKNX4zLuVtClduIjH3\nYhwTx6J1697M5xSEzqe2FmzKRsyal9ryTKzW5l+ZSjeYCFX1xtJ1F+V5K+jSdWqzn0MQTli0DK1i\nOdHCb9E8eVgiYYyRKMGAk9qaVIi6CIXcaEoCBmMaiuRGU60g6UiSiiwpmIw1WPRijBQhW8qxuj04\nnNlo0a1EPAbUoBW1MoX95uHUZkxGd2eSmCLmX+uIRMNBC/KU7sXqXUSwwoSnuD9298k3tY2YPBs4\nudUVADTNQnXFeJJSltKnx1Z2vbeU1IE/wRlnOOk4CEJTIhGoLlepLYsQ9VdjCu/ArOzEKu/DatiS\nhYYAACAASURBVCzBIAXrdjzYFiAHdgCg2YfEwpBkCckgIxlkdMmCauiCYu6Nbu2H7OyP2dUNu8Mg\n2hOEdk/TYO/mvSR6/oLBl4dWO5CC3eMJesPY3QnIJssJh3Uq935NtVFTMgurYzDOlG+xpu4m5NuH\nb+E0uoydiTJ0GJib+/2pIHQeZWUybn05Rk2ltjQDg/3EXvaMmDwbSZbIXvHCCe0f8A3Fre+A4sXA\n1FOPsCCcIim0F63gY9SSFai+ILoCZcUZVBakEQ51xyS7sDoSwXSwx5oEinr4+BGTDpdRiuIiyMHG\n6TDIlSFMvmJ0aT/meC/GZD82dzkmy1fowe/QLXb2m89BTr2E1B49RbHUgYiGgxaiqTrhnDnY/DUU\n7ZuJJptbbELEptSUjyE+eT29x+xi/ye92fn5NsbcNFw8gAmnTFM0avNqqFp1gOLthVgiW3HZt5Nq\n24vRXIl2qO+aJhEKxhMKxh38fzyRkJM+5v1omsye4PmAitEUwWQOYrPXYrFVY3V4sDg2Ips2gMGI\nbDISNTopkgajWIdijMvEnd4Xd5y4dQnti6JA3sZlpAX/AzVlVBedh7e0L4oi40xq3f6ZIX8vwoGb\nsSaswZmwCrv+BaU/biW+9GqM485Fb4YhEoLQGZWXhuin/ABqPH5fMo7EljmPX+qLy+8g3roenyeA\n8yTnOBGEU6LrSL6N6HnvoVRsQg1G8XmSydsxmPLyniS54rHGJWG2n97YZs1kJZzQG+iNGgki76qg\nMlCLwRXG1dOPxb0Du3MeeL6itHACSvLVdOk3WIyq6wBE7buFVOxZgj20jeqiHgRrE3EmtP7AHk2z\nUn7gAtK7z2fUpPWsyUogY1QGXYaJSqNw4jQNKnM8eLcWEsnLxWbZjd25gy72XRjcIQB0zYy/IpWQ\nJ5WgL52QJwVdM6JLdS0Jkg4GdFLHlgNQsM4AugFNMqPqdgJ6EgFA13RUTcPoDGBy12B1V+GML8Xl\n+AZNXopUYUQrjKPYOhJjymTie4zFZG2d4T+C0JRQUKN007ukBT9CrfJTknsZNSUukrumIIX14wfQ\nAnTdRLDqLELeAdiSviHBtY9g1XPIX+/DNf4K1N59xbAgQajH5wNHZBkWtZbqstGYzS33FKNanITK\n+mBybacmbxXO4Re02LkEAV1BLv8OuegDIlW5REMa1WVp7NkxlJAvg/Su6WR0b5keyarZhprUHUNS\ndwyRIPqBGsq2JmBJqcLZPQdn/DfonuVUFI4m1OVOMgb0w2ZrkagIzUA0HLSAoLcaU/nbqLUByvLG\nYbG13YONt3oo7oStJPTZR7d9O9jx1hISH7kci0v0CxKaputQVa5RtbWE6J5NuOUN2ExbSeyajy5p\nmE1GdC0Rv3ccPs8Agv5uoB8sdExgbKJtSs+o63Vjyu9zzJPLShg5GCJS7aV0TxDZGMDqLscYX4Ul\nrhS7Kwu58hvC+U78rpFYepyHLW0iGMRbG6F1ear9eLf9i+Tg9wQrjBTtupxIMA5nUhoGoxnC4TaN\nnx5NxV98LV77NtLTs3DKH1G1ooCk6ptQho8EU+v2hBOE9qq0BFLl+RgUlfL9vTA7W/YlSzg4GKu2\nlcj+RSAaDoSWoAQwFMzDWD6PqK8MfxDKi3qyY8dwjFI3UlLiIaH1oqOabegJ8ciOLmjhAL7tvfAZ\nc3D32YU9YTnu4CpqcsZRlnEb6SOGYjnx0X1CKxENB81M13Rqt7+Gy19EQe44IlErLkdbvhGVKC2c\nSc8Bcxh49mZWz0sg/4ueDLhhfBvGSWivwmEo3u3Ds2kXcaElJNg3YE7MRUfDaDSiKj3xefrjV4ZS\nUxVHbPKC5iRJaCYrmskKjngOtYGHNRW1OkC4oJpKrQRT/AHsqQXEpX6HXvkDIbsbUs7B2udisA0Q\nb1OFFldWUIy07ykSAjuoKUunaOcUTOYUbPHNP6Ha6ZAkA4bgcIryupCU8SmJlrVUbS0h3nMb+phJ\n6O64to6iILQ5f9lWEsJ7CXoGE2mB1RSOFDL2wlETh9OxgYCnBru75VfdEs4QvlJMee9j8CxGjfqp\n9UgUF2ayddcozIY0uqSYkNu4iqRa7KgpPYGe1JSMx166CVv6WqzuFdgL1lCdN5Fot1tJHzsUk1nU\n59oL0XDQzMp2f4/LvwxPWSJVJf1xudPaOkookXhKCy6mS89PGTF1LT8uiiN5SBcSR4lZtoU61ZUa\n5ZsKkfd/S7x5JQnWnUiWMJIko0b64fVl4q/th6LUNYI5HBagld+kygYUqwusLiR6oGgqoSIPFTv3\nY3TtIaVPHm7PZ0SKs5DiemHudQl64rlgFA9FQvPSdSjauQXXgacxBg5QWpBJSe5IHHFdkQ3tt1g1\n6ylU7r+FYOqXpMdtp7bwOeyeGzGNnorWvUdbR08Q2ozPB+7IQoyRAMUFfbDZWv4hXrE6iZT3xZqY\nja9gOfahl7b4OYXOTarKxZT3FnLgezQtSk21gbz8CeQVTyaqWOmSoGNthw/hqsWBl8l4qybh9G7B\nGb8Ei30ptqJVVO0/C7XXzaSOGYLR1P7ifqZpvzWcDqim7ADWkhdRawMU7J6JzZ6AJJ/eBCMnu5pC\nU3y1A6mpGI87fS2DM5ez9e1kxmZciT1VLIdyplIUKN3nJ7htGa7gN3S1b0FzetF1HT2Sis8/Bm/t\nUJRo8709ba7rGQDZQMSRgNmRAFomZTt95Ae3ktR9B2l9tqNX7sXoegVD+hTochG6fZjohSCctmhE\nozT7QxJr56L5g+zfNQVv7VBcicnNep5mzSv1mGUrobIryQ+l0C39B+TAq4SXl+EeNh1lSCYYxMo7\nwpmnoriCJG0FQW8yntokXAknN8h608r/nHyDuiQRDGViUzeiFswH0XAgnCKpbCemvDeRI2tQlAi1\nNTb25k+jzHseXk+EZEuIuo5wzVcHapkySsIXHY6vfBhO2yZc8d9hNn2LXPgDVQWTUfvcStqoIZzm\no5VwGkTDQTPxeyNEt/8Fh6+EnF3nIJGIydy+ZvcoPzAVs6WC5P578Xuz2PlOFzLvvgCzVeTAM4mv\nRqFq8yYMxVnEW9YQJ1ejmiOEgk6i/kl4faOJhFp/Ms/TIhswJMThTJiC3zeeTV8XY3dvJGNwDvE1\nCzEXfIsxPgMpYxZqwjQwiQlChZPnqaoitOlZkoNr8XvM5O24EIkB2F0da4JOoyyje88hL5JM9x4L\ncPIplatLSKy+BG3YcPSEFppKXhDaIV0HrSwL3e+hpuRcrLbWG2oUtWcQLknHZN9FxJOP2d2z1c4t\ndHxS5U5MOa8jh9cRjUSoqnGzd/8sKoPn4K0J4pRq6RtvRJI6WoOwhC84El9wGC5nNk7HMkzW77Dl\nr6Rq/wT0freQNHS4aEBoA6LhoBmEQzrVG54nyb+ZkoL+eL2DcDnbY8XLQHH+5XTv/xY9h+Wxfe1n\nbP2oK0OuHiqWQOnkolGo3rkNLW8xdmUVqaZSIsYooZCMv6Y/0cgEgv6eQMe/CxttJhJ69UBVu7Fn\nYxAlvJWMvjvp0j8Pe/lLmJxvYEgajSpdA/pQkMRtUDg2TdWp2J6F/cDLuCJVlJd0oTDnfJzObkhy\nR6uQ1ZEkCUtkKPn7EujS8xPiE1ZRsbUMV9ksLJkjUfsPEL0PhDNCVbmPpMh81JCZqvJuOONaryem\narbhrxpCYtdv8e3JInHMXa12bqHjkqp3Ysp5DSmwnnBIoboqgdzi6ZR5xhP0hXDJZfR2GzDIHb1+\nY8DrG4PXNwqHdTN2xzLMzuWY9q2lKncsSp9bSBk8UhRVraijX1FtLhqFkjVvkOZfQE1FHKX5k3DF\ntd+3mZpmpSjnGnr0e4OhY35kw7o32WD9FSMv7oZdTEjfqYQCGr5929GLvsUaWkWcqZSoGiUa0TlQ\n0g09NJJQeBi63jlnVTcYZBK6OtD1CZRVT2D358Wkpm6kx6AcEtK+h9LVmMxucJ+DmnI+WqLopi0c\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/SGnHkg4kOkgdhEDK7eee3nA+aOEdyB15BCAqSuJy27HkiUACxLuwVXkayqFGB0JoxCh1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\ntFHIz19CTs5YRo3K+dWyXzcSX3HSSady0kmn8OWXc/b4nE16egZDhgw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FF19v85lwm83G\nqaeewZw5n0VnpJ8z53Puv/9v0TKdeOIxnHLK6dx5590A3HPP/Tz88N857bSpJCUlce21N0aH2gaD\nQX788QdefPHGNr9/5VDbeaympaXz+ONPc9NN1wKyzXP2Kafses5uXgeuuOJq5s79PHq85OZ+xnnn\nXbTH9ufJl1/iGcJY1lrinGWcPfgkhqXuvCvz0/Y8FhdGni2VUqJpIZ78rUYwCH/60228995bPPLI\nPykpKSY2No6RI0e3+e7tSZMm88QTj1BZWUFycgrhcIiZM/9FcXEhNpuNfv0G8NBDj0XrSUlJMRdc\ncBZCCIQQTJ06iYyMntF31y9YMJ+xY8d1eK4FpXPbUzvUlo62N7us3W26jz76II8/HumjSSnp3bsP\nL7zwKgBZWdkkJSWTkpKM1xsT/ayhQ4ezePEvjOobuWhfuXIFJSXFnHPO+c0eazvmmGPp1SuLL7+c\nw7nnXtDis88++1xycz+Ltq95eUv44x+vb9Yu5OSM5fHHnwHgwQf/zuzZn0bXv/baf/jLX/4avZj6\n8ss5/PWv/7vb8ipd1+2334Km6QgBGRk9+H//72/07t2nze0vvngGTz45k3POOb/d/TOIjLr55psv\noyOzm5YtXPh9s5s20LxuaprGlVdexz333NlmnW2tvwTqGuRgEHJvx2a1obz88J50IjU19pCX4deT\ngHTU/izDvffexZQpJ7b67F1HVFdX84c/XMd//vPfVocn/Vpn+B32VWpq289RdYWydeYytKcOdcYy\n1NTUcNNN1/DSS//F4XC0a5+2yvH++29TVlbGDTf8YX9nc79rq650VuqJVAAAIABJREFUtt+nozrj\nMdaauvyvcRTcy5r84djEziDZ6ImRoNOG/BdpbNz5tpSe8U9gJVQipi1kb67XP/nkI7Zs2cQf/tBy\nfp2Ouu66K7jjjrvafF1jk8Plt9idrlpPoOv8Pq2VYV/7dOFwmCuvvITHHnuapKTkfcrj998vYO7c\nz/nb3/7R6vqu8ju0pSuUrTuWobU61JH+UkevQfakq/wOe0MFDn6lqxwMqgyHnmq8OreuUAboGuXo\nqhdEh8tvU/PzRzhLH2F9/ng0fVqL9V6vs1ngICPmebSk7TROmkNWn/a/NvFQOlx+i93pqvUEus7v\no8pw6Km+V+emytA57G3gQM1xoCiKoijdWbAu8gy0bN8bOKTlAinxVZfveWNFURRFUboEFThQFEVR\nlG5MhuqxTAtNb98dCMtyIwBfB1/FqCiKoijK4UsFDhRFURSlOzMakZZE0+LatbmUkde4GfVVBzJX\niqIoiqJ0IipwoCiKoijdmDDrQEoQ7nZtb0kPQkosnwocKIqiKEp3oQIHiqIoitKNCbMBKQHhbdf2\nloxBA2Sw+oDmS1EURVGUzsO2502Ug21fX92jKN2dqkOK0n7CakQCluVqtnzX1zHuyjDj0ISEoBpx\noCh7otojRdk3qg51HmrEgaIoiqJ0Y5pswDRtIPV2bR+WsWiawG5WY1kHOHOKoiiKonQKKnCgKIqi\nKN2VZSHwEzac7d7FsGIRSJxaLYHAAcyboiiKoiidhgocKIqiKEo3ZfhCaHqAcLj9gQMTD0gdp1ZP\nKHQAM6coiqIoSqehAgeKoiiK0k2F6n1oWhAj1P7AAULDDLtw2RtobDQOXOYURVEURek0VOBAURRF\nUbopsyEywWE41L5XMTaxTBdOm4/6+vCByJaiKIqiKJ2MeqtCJ6RmDVWUfaPqkKK0j9FYhV1KjHDL\nwEH+D08B4G3lLY2W4cHmqqWmsg7oWNBBUboT1R4pyr5RdajzUCMOFEVRFKWbshorsKSFNGI6tp/h\nRiAJVVccoJwpiqIoitKZqMCBoiiKonRXoWosw0QQ36HdpOUFCfirD0y+FEVRFEXpVFTgQFEURVG6\nKRmqwTItdL1jgQNLehBSIlXgQFEURVG6BRU4UBRFUZRuSoSrQUro4IgDw4xBExYEKg9MxhRFURRF\n6VRU4EBRFEVRuinNqkJKMK2EDu1nWLFogAipEQeKoiiK0h2otyp0Qq68EYCaRVRR9paqQ4rSPppV\niZQC02z56oTRE28EYEP+iy3Wha0YNA1sZhWGATbVm1CUVqn2SFH2japDnYcacaAoiqIo3ZBhgE2r\nIRT00tHugGHFowFOqggGD0j2FEVRFEXpRFTgQFEURVG6IX9DGJteRzDg6fC+YSsBBHhs1YRCByBz\niqIoiqJ0KmpwoaIoiqJ0Q8GqYhxAaC8CBxIHluHCY69RgQNF+ZW6OigrE9TXC/Q1Y3DYwrgTBWlp\nErf7UOdOURRl76jAgaIoiqJ0Q1btViSSgL9jb1RoYoRj8DjrqK0NkJ7u2s+5U5TDS6Cokorvf8Bf\nuhHLqEazgoigHXu4Ep/mYfl32/AkZNGjh8bAgRaxsYc6x4rSuYTDUFoqqKoII30b8bCWWMd2Ehos\nYt0N2Lffi7SnYrkGYsWMA1vioc5yt6MCB4qiKIrSHfkKkZaFEUxEt3d8dyMci+6oIlBVDmTt9+wp\nyuGgdt0a5Or/ErLn4zVrcOghEBaakGTE6CS61yOlxqDw76huTKV01QiWFE4lKXsC/QfqeDo+4EdR\nugzDH6Z6RSGBrYux+fOIcW8iwV6EhcRCIIWOJExtvRs9tAinW+BwfQEOO1bMUZhJv0G6+h7qYnQb\nKnDQCalZQxVl36g6pCh7JgIFGIaFbiW3uj7/h6cA8LZ84QIAhhGHXUKwfDsqcKB0NyUFjbD8MRKt\nLzFkiPp6J9XlfbFpA5BWEpZhwyYbqbHKkZTjiikjKamIpJgiQqGvCW6Oo7h0Mp4+U8gYMAqhqS65\n0n3U1VhU//QzrooPiY1dhkurxXKZYAoCdUmEGlIJ1KXRUJdCdW0itT4NzQHpmUF6ZFWSlLEaV8Jc\n9NIFaMlHYmVcDo6MQ12sLk+dpRRFOaxJGZkd3jBAWiZaeBtauAjNrEWIMDaHB2FPxnL1AVvSoc6u\nonQaWrgYaVnoZGDuxf6mFY8TiVlXtN/zpiidVWMjbMtfRGbNg+jhEqqq3ZQVH0+cdxKBRqPtHSsl\ntsJ6nHIlroT1ONNLcYZnoa39nMaSDFzZx2JPm4R0Dweh5i5XuqaGBli1YCNpJQ+T5lpJyBmivt5N\nyDcWIzgUX0NvpNwxBE4HPd4kzRsiwwwT8JuUFzjZvt6JQyaTllVMn1HriEv7ClGwGHqdj7PXOaA5\nDm0huzAVOFAU5bBhmlBRISgvj0w6FWgwsDdsJVEuJlFfTJxrNUL4sKSFJQEkYUAI0DSQJGHIoQjn\nKMKDj0ATTqzEJHCoRkbpfmxWCeGgE6kn7NX+poxDALKucP9mTFE6qYLtJsF1z9E7+B5GIEThpmEE\n/Keg2zyADuwmcCAEhjsOg6NpDByJY2UV0lwN8UXEZ21Hq38HUfQZrtQMrMSpmAnTVLBb6VI2bzJx\nbHySPv5XMXQflWUphHyT8TUOB6m3uo/UdEyHGxM3uhsykiCDSH8wWJPJqjk9cSevo+/4lXhqnidY\nsAB98P/gTe59cAvXTajAgaIonV4wCJs3axQVCaitwl39LXHhn+jnXY3HW4ZEIsMmvlo3deU9aKxL\nIBTwYhkamsPA6W4kJqGChOQibPat6La5FP/oxdcwCF0bhbNHDp5BWcj0dLCp06LS9QUDBk6tnIaG\nGKy9meAACMskNE1g8xfv59wpSucSCsHaFdWkVv4vCf5FNNbFUbz+FDTnSDSb6HiCmk4oLhVIRQ/5\nKfyhnHXmZjKGFpM9tAhP2ps4qj/FTDwNM+k80Jz7vUyKcrAYBmzKW01q6QO45RZ8jRoVhScTCk8A\n9qL+ALoOnmQ3nuT+6MEMir/vjbPXEjIGLUXUXk9x8tUkjz4Xh3Pv0ldap3rIiqJ0WpYFmzcLtq1v\nJKnuC/r65pAUuxZbcghNhpFSp6EmnUBtNsHGIYStnliaDd2l4/7VJO+NDVBVC7pjO073GlJS1mCz\n5wOLCRW7qd44DE/sEcQPPxoxoC+41CzxStcVqCvFZRn4GtMiQ3L2QthKRAhwWZX7OXeK0nlUVAi2\nrFjJgOC92PwF1Fb0p3jz8bhieu6X9E2HG2fvbOzhHhQvr2H998X0m1DFgHHr8YTeR6//kXDGH5Du\ngfvl8xTlYGqsb6R6yX/I8n2IEQpQUT6Y6rJpCH3/vRHBdHrRs4dBIIuSRb+QNHQhHt+j1JbMwxj8\nFzIG9dzbZk75FRU4OFRCQYSvBBGowArWEA42EMJBSLoxNTeWLQFTS8bUkrA7dBwOiccDThV0VrqJ\nqkrJ1mVrSK17kyOs7xDOMJrDIByMobZiMMHASHyBfkhrx2MG2o4/bRACXDbAysJqzKKR0wlbG3DE\nrMcTsxxXTD6a/IW6FW9iW59DbP9pWIMmqwCC0iVZddtAWvj9cWh72aEywrFITcetV2KGLXS7ei5b\n6TrCYVi/TkDhWwwJPw/hECWbJ1BTfSSumL17vGd3NLud5EGpeBqS2PJLEVvzUhl1UhGZI9ZjN+7B\nSLsaK/6E/f65inJASEldwUJY/xTJvgLqa7wUbZxKXPwEhL6bR3r2geGKBdcUajcPISZ5Fs6Un7Hl\nX87mFdeTMvk04tJUf25fqcDBwVJXjF72I2b5CqRvE1iFWDKEaUbuqkoiP4YuQQ9vQwJhRx+kbiMk\nkwmSTp3Wg5C9F8T0wu5Nw5OQRnxyEk6XCqMpXUcoaFG8fB7esncYbK7GsgzCfi+1VUMI+nMIG9ns\naWjb6Ik3AjtnhW+dIOTPJuTPpqHieFyereBaR6xnGW45j8aN32EvHISzzwWYA04G+94N51aUzkjW\nbsGyJKFAEi5369s01aMN+S+2ut4yXZiWC6ezjlBVA+70uAOVXUU5qIqLBZtXltIv8AAx5i8YIS9b\n1pyEkENxuTt2B6d97dFO7hidzDFZ1BY2svh9QdHGeEZNWYwn/BRacDNG6u9AtP48uKJ0BjJUTsPq\n57GXfYvlC7Jlw0jqq47EE5OG0PYwF0grOlqHgq6eBBuuI8H6ltjUb0g0H6DqwwVU97mWrOMHoDlV\nf25vqcDBgSIlom4l1tavkJWLEMZ2/GEwwgbSktTXJ1JTk0Ew4MEMOTGCdnRpYNMNBvUtRJcWlbWp\n2N0+bJ4yXK5tuDWBEAJqBOgamt2GYXcT1NLA3QM9rifO1IFY3rEgE9WsvMphRVqSqvUL0Lf8h3Rj\nC+FQmKqyHtRVjMYSYw9sR0nqBBr7QWM/GstPxC8KSI77lozUVZjr78dV8gZa78sw+0yLzLKoKIe7\n+m2YpoVmpexDIoJgMAm3ZzvB8joVOFAOe34/rFpuElvwGiO11xEiQGN1Jts2TMXtPXiTrWkCEnt5\n8SQNonpNAfMKj+TIM/OIz/4YR6iEcM9bQGsj4qcoh4q0kBWzCax/DXtdOTWl6WxcNQ63uy+emDbe\n63ugCI0a//H4ywaTlv4eCTHf0bB1HZteuIzMEyfiHtRrrx/T685U4GB/kpJA+WqsrXOx1X2PCFdg\nmZJw0KSyMoOS8r4EAn0JBnvitrmJdYNNl+g6ODw7k4npOxuAuh+uRKsPoRlBrEA9iEp0RzU2VwO6\nvQHsDdjcDTi8m3C51yAqdMxtNspWObF0F5Z3CFrG0ThSj0Dae6gKonRK0pLUbvkRfdNLxIQ2YIRN\nCjZnUV9xJHbPoIN+oa5rNmLoQ311PzYXVdAnNZfMzNX/n737js+quh84/rnj2dmDJOwNysbNEARZ\nLupALa1ttWqr1Vbb2tb+Wqu1VavWba21WlfVWlcVZSmgCCIzYe+RhOz9jDzj3nt+fzzJAw9JIEAg\nJJ7368V47nrOTc73nnPPPfcc7IE/4ip6F6v/rVjZo05qmiSpremhfEzDxKYd33vaYSMVh3MfNQU7\nSRnavY1SJ0knX/4+qFy9kj7Wk9ht+ZiGjf37LiASOBuXp31m3nG4dbJH9cJXkMTy13WGX7yOrMAy\nHMEqrF53gy29XdIlSYdSQvuI7PobRvF6hA+2bTyH2sp+pKXnNPQyaB+hYA6FBTeTmTOfpNT1OP1P\nsPeD9XQddgnJ5w+DhIR2S1tHJBsOjkM4DDXVEKzYhr10AZ7Ql9hFGZoQBAIKpUU9KS3tT31wEClJ\nqXgcFh4X4ILoywlHZul2LN0OzkSgKxYQATBACZuoNSGUcBBffR1hswK7u4TUjGo86RUkpCxFL1lB\nyGFDOLtiZYzH0eN8VHd/2YggtTsjIqjcvQ77vhdwG1sxIhaF+d2pKD4Lt2cgNk/7dsW06xZdE9Mo\nr/4BWwtKGNH3fTKz83D6f4q9YCzGoJ9AUo92TaMkHSubWUid34PiOL5eAhEzHQWFYOEmYGKbpE2S\nTibDgB2fF5JU/E8GJCxGKGFqKgZQXTYDRUlDa++asqKQ0DMNV7qLrXOdeCtW02fEOux1v0A7/Q8I\nZ592TqD0TWdVfo655Uksbw1VJQPYvPY0kpJySM9s+7FAjoWwHJTtv4zE1L6kdllAtn0Rvr0bCOy/\nkm6Tx2H16Svvi1qpvS+HHUrjHPJlpRCp2U6ydwEZfEmiKMI0LCIRG8XFPSkt7YNqDiEpKYm0RCAR\nwGrz9AhVw3S4weHGmZiGk94AqKpG0VYvQV8Rdtc2UnOKSe26G2fVbsL73kK4sxGZE3B1H4/iGSiD\nRTqp6uuhZOduEgqfJkXkYUQiFO3vRkn+mSQkno4n8dR6dzPZFSbJmcaOwtvYvG8zowZ8QLJ/Cc7a\nVdi6XIQx6EZwJLd3MiWp1Yy6clRRh9937DMqNLKsdFBURE1+G6VOkk6eQHEtxQu/oJv7VXAVEK5P\npKpkJsHgkFOuaqR5XGSP7E/NrhQ2VS5l8Jgt2Ot+hnbaz1GzJrZ38qRvImEQ2vg8StH7mCGNTevO\np7aqB1nZ7dvLoCXe6qHU+3qQlrMQZ8YO9MjfKPp0FdmnXYUy6gxEQmJ7J/GUJxsOWsHng317FepK\ndpIZWkCOuRiHVQJCYBo2isr74vUOIuzviduVTmp793rRdewpbuwp/YH+BPwRalbWYImNJGfvIb1H\nPs6qVwjlvw3uLJScCThzxiBcg+W4CNIJY5qwd1sl9l1/I0ddjBGJUFaSSXnhOdicw0hMPvUKmUaK\nAtnJ9ZhWX9buuJuEPcs4feB8Evzv4qxcjNbjWsy+14AqB9yRTn1m0SYAfP60w01E0irCTIpOgWqU\nR4NcO3XjWJJiQiHqVm7Hv+MDstM+JWyEqK85g+qK6Qdm6jkVaRqJfbKI+C5l46I0BpyzlIT6e1Ey\np2EbeSfoctR46eQw6soJr/wDWmgL3toEctdOICWhP1nZp/bYG0YkmbL8K3EnbcOVugRX4mq8hRtx\nVE7DOWwWVt9+8oHqYciGg8OoKIfSPbuxVywkx1pMT7MIRQiEZcdb1ZeqmoH46nrhcaajKwp6G8VK\na0cNbS3FZsPRJRO4gIgxjvw11RjWFhKz9pDRqxB39WsE976DmtgFvet41LTzEK7T5Ki9Upsp2x8g\nmPsyOeIDTKOemhoPFfsnAGdi97T9ZaitY6iRpgp6pgWJmGeycuM5ZHrm0n/gctz+53Hs/wht4A8x\ns6fIQkc6pVkV28A0CAezcB6mrasxjjyHGdPKCKWBqmKz1aD4vIjkU6NrqiQ1yzBQ9+yhclUeQv0I\nT/IO6kMuvOVX4/f2PyFfeSLKI1uCA819AdvWDKBnv/+QHPgQq2IDtoG3o/Y9r82/T5JihMCbuwx9\n/6OooprC/K7sz59GVnrOCfvKto8hhUDdYALeviieXDIylqCE38fIW0liyTVYIy6WvQ9aIBsODmFZ\nUFJQRf32eaSEFtDH2otlmJimhq+qF9VVA/D7+uB0ZaIoCgmndsNaE0KzYcvsgo0uWJFz2fdVBRF2\nkNStkOw++/FUvIHueRc9KR01ezJW2mSE4+SNJCx1LoG6CNVfv0NK8E08VjUBr05l0RjCoTEoHfjJ\niE0T9MwwCBvT+HrtRLqlvk+vgRtxBf6MPeV1lAHfw0q/QDa+SackxbsLwzAxGl5vOx5GJAlTuHC6\nq4lU1aLLhgPpVGRZqAX5mFt2UFKaiytxPhERxO8fTE3JJZjmSR7xvQ2oqkJiZg/2l9xBnfd/dO21\nFmvjr1B2TcY5+kdwAm/kpG8ms8ZLzeev49HeJRIKsGfnmYjINNJSOujtpLAjfGdTUDcUe9ISenZZ\nia/sCexLP8c24AasfmfKB0GH6KC/6bYXDITwbl1MXdVnpETWkRQJRwdvK++Gt3oAYf8AdHf0fVCX\n+8jH6wgsmwNH12446IblD7BpYSWWvoesPoXk9CvCU/Yadveb2JL6YaZPwcyaAjY53ZZ0ZFYwTNXK\nT3BUvUW6XkwgYFK+fwThwAUoWiJKJ7ny2HWL7pk26iPf5usVZfTo8hHdBuzA7f0T9rRXEL1nY6VN\nBvUU7voqfbMEgyjGPsKGhk3NaoMDKtTXZ5GUuJmK7fvI7iMbmqVTi1pSjLp9OzVFhQQiC7An7yVk\n2qgrvZhA7SigY98YuN06hnUl27afRfeu/yUx+RPEspVoaVdiH3Xt4bsMSVJrmCbVK7dh7XkRd8JK\nvHUqJQVXolpDoRM8H3Gqbsy6i8itPJeeXd8nM2UN9q1bcBRdiDLyR4ikzPZO4imjk1Tfj004aFG3\nbSUUz8dtriCJekzTpKo8iZqK4UT8Q9Cc3UBR0Dv7ddftJrWfG+hBbbXBrv+Vk5i+nb4Dt5PebSPO\n0s3Ydz+H4h6FmTIJM2ssJMpGBCmeqKjEv2E+Su2HJNpKCJph8gsGEPFORSgZnfYBvMtm4uqSTk39\njexbVECvrgvpPngP7uqHsae+gugxCzNjBmidpNVR6rDUyiIUrZya2iwcWtuMaROKdAPbFsKFa4Dz\n2+SYknS8lKpK9O3b8BcVUOv9HHvKFnSngq+uJ76ySzEinWdQW0UBt70npaV3UFHzJd2yF+MI/wPj\n0/loXa/GMXIm2OQYPNLRC+yroGrJYpI876I58qmqTqe2bDaKldbeSWtTmgoZjjQqi29if+lmBvb4\nkITgR9i/XIGecw3KabPA2XF7yraVb1TDgRBQVxIgsHsDauUSPGIFiXo1kYhBwOegpHgQWmQ0Jj1B\nUdE62GsIbcWTrONJziEY6c667ZOx8krp1XUVPXpvwZO0FL10GfqeFHT7KETKeETmMKz0DHA42jvp\nUnsIBDB3b6B+z0IUay26VknIjLC/sC8h72QUunb0BzqtluAySOiRQ5nvh+yYW0D/Xovocfoe3FVP\nY09+GbImY2bPlNNnSe3GLF6LaUTw+3u0WVgaoR6IBBtWzeZoQSu7dkrtSKmrRd22jUBhLoHgSjTX\nTvQUlUB9Gt7ScYQDp9FZCyWbpoExgT37ziQxYT5ZXdaiF/wVq/RtrKwrcY+ciWKTNz/SkdWVBCj/\nfDNO7yekpH9GxAzjrxlFbcWMU3sA0ePkslu4GMyO3afhdH5G/56fYw89h148D73H91EHX9DeSWxX\nnb7hIFAVxLu7BKNoDXpgLR77VlLtFYSNCOGwRlFZb+prh6Bpw0HRcCY48PtD7Z3sU4LTZtInC0wr\nndr6y1iy5hJcrnz65qwmO2sTdttnqNWLsHZnoikj0ZPPxNZtMKJLZnSALFl57LREOAw7lxLcuwzh\n34Cq70cxIwTDGhXl/Qn5LkC1unTSqtmRpSVESO2XTbH3B+z4pJDePb6kx8DdJFS9h33fHLSkfpAz\nCSttPMLetb2TK32DRCrWYFkWPt8A2mrmUzPYDaHq2F0FiNo6lJTO8yRX6kD8ftRtG6jPX0zEWg1a\nGbh06gJZ1JWPxgoMo1P0q24Fp+YhUn8Fu/ZMJMn5KRnZG9DDTxAqex0jdQruYVehJsqyR4onBFSW\nWVR8tQVP+cdkpCxDpJQSCidQVzELf92A9k7iSZPiElhiEht2nEtmykfkZG/AFroXdd+r1A64CnpN\nAWdn747eVKdrOPBX+gns3YZZuhHVuxWHuo9kVwmGGcTULcIRjbKS7piB4YSN4QhhQzvFfgojxtwK\nnLiR4Y+WpkKaJ0yaB4ToSmXVVewqmIk7aQfZGRvIytiMrizArFqAtzQdyzoNVT8NpcsIXL27Yc9J\nk70ROoNwFRR8gbF/ORWRLYSCXizDIGIIKku7UVc7AjUyFFVxHvcUb8frVIghRYEuSWEyE7tQHfg2\nOxfXkelZS6/+G0jvvgV76TY0x4sIdy/ImoieMw5h7ykb3KQTx1uLYm0kGLKjGj2PeA/VGEc78148\n7HaW6cLn601i4mYqN+SRMV6+riCdRPX1iC2fEy5cgFA2YlBPSNioqRyKr3wEmtWb9uxh0J7lkUNN\nIxS+mn17puLSvyAtKw9H/ZtEat7HdJ6Bo8+laD3HyWlUv+EiEdi/L0L9xiWk+hfS3bMOM82HQCXs\nH0Nlybh2HUC0vWJIVSDV6SZcfw3bdk0gLXEhGZnbEVsfQdv1PFbSWOyDLkXNGP6NqbudYrfMR0GY\nKKEigsXbiZTuQNTtRgnlY1PLScIkEo5gqhaWUCgvS8OsH4ppnk4w0B0hOu5ptzdFgWRXmGQXQH+M\n2v7srroEzb0dT8JW0pJ3oqrLUMUXqBU2vEU9sMzeCHtPlNSB2LMHkNAtGS0lURZUpzphIOq2EClY\njlK1EjW4B9MQmIaJL+hif0l/At7+YA3EpXuiF5NvxnXzqCgKpHlCpPVzEDbGsj1/HJG8AlKTN5Hd\ncw9p3bdgr9iBue1fWI5sjMRz0LqOx5k1DEWVMSK1ob3zsYSXouLBeBxt+75zIHAGSc5t1O94BcaO\nA7W9mw+lzs4q20t461yUus9BKSFiGXjrE6momEDEOwK7kvQN6V9wZLqWQkRcxv786ajWKlK7rCQ5\ncymqfyXW1i6oSePRu44H95ntnVTpJBECqsrr8O9dhV66lC6sRii1mM4IwXo3Id9EvDVnYBhyWkJV\nAbeWTTBwHTt21ZLkXkZq6jpcgTkotQvB0Q0lYzz2vhMhcXCnbkQ49e+grQhKsAirZg9m7R6suj2I\n+gJUoxgRCYEFOgIjHCYc0qj2pRLwpaCa3VC0/oSCWQghB4Q5URQFnLoNwkMIVw2htDqC3Z2P5ijA\n6diF25WPqu5FRaDWKagBJ75t2QirC9izwd0VLaErtrQe2NMyURNc4PqGDi7RziJhg2D5dkR5Hkrd\neuzhTRD2gqChV0EGZRW98db2ITVlMDoGLhXavXtBB2LXLbLTgLSuCNGVsgrBvh1lOJzrycjZR1rX\nfThq96KWvEe9mkhIG4GZfDb2nOEkdOuJqnXewkg6wUJerNI3McIWVRXnkt7GD4/CgUEEPVkkutdT\n9eWnpJ0/tW2/QPrGE+F6QoXrMQpXo/lWobEPLItgyKS0vA/VVWdiNwdg0zXs8lLZLN1uB8ZSVTOG\nstJ83Ilfkt1tO/bAm2hV71C2vQcGvdFSB6JnDcHKHARuORB2pyAEIlJOfcUOgvs3QnUuHrELdziE\nYVjUBzS8tQOJBEcQCvcHIZvdmuPSk7HbLie/YAaKsZnExJVkdt+D3bcPpfhtVGcaJIzG1mU0Vvoo\n8GR1qoaEdmk4sExBxB/GCNRiBaqw/JUQqkYEK1HCNShGOZooRxMVaNQgLAshAARCCMJB8NUm4fN2\nwedPo96fikPrhdOdjXzk2b6EsBHy9wN/PwJMpFoN4XSXoNrKUJUi7HoBDncBirIXNSxQDVADGqJS\nIyg8WFYqWCmE3RmErESwp6I601HcaWieNPSEZDSnB8XpQOg2sNvlk63WsCwIhxH1QUI1VUTqSjD9\npViBYggXollF2JUi7EQAgWGY1Na6qa7oS0VlD4K+3qRlZOOH0NLUAAAgAElEQVTUFZwp4PFo+P1G\ne59Vh6Yo4HYruN1ZwBSCQZP8DX6EuQVX6nbSs/dj9yzEFliMrUInmJdEUPTDsPdAuHqgJPVATcrB\npvQnGIy+DdSJyiapLfm8KOt+jxGuZvvuUSQ5sk/Al6hUVl9KV+eLWPv+QsmXKWSPO/sEfI/UaQmB\niPgI1VYQqSvDrC1B+IpR6wvQjEJsyn4UYaJZJpGwQWlVDlVV/QgHzybBnohHoSM8DjslaKqC5uqF\nEenF1h0GmraetJSNZGTuQ9d2oQaWoJZq6DYdoaQg7FngygFXDoq7K1pSV1RPDjgy5GwNpxIhMCNe\njEApIrAfxbsX4StACRaiRIqxwkE0I4LbEoTDgsrKVOqqBxCq74uqDULV5O+ytZx2BexDCJtD2Lm5\nHiuynsTUbWR3z8fh+RijbAE2mwIiFfQcFHtXcHVH8/QAdxeEIxMcdoTDGY0hTesQ9zNteokt+PB5\nAl4vlmkgrAiKFQSrHhUfqgigCj8KATS1Hk0PomM17CmwLIGwLCzLxDIFhhD4AwnU1ydTX59EwJ9E\nwJ9CKNAFhz2HhKQEdB3sKthlL5pTlrAc1Pt6AQfN7a2Y2GzVWJoXRalEpQzdVoHdUYXTVYCm7sHy\nKyAs1HoFfCqoKpaqElEUDEVDWA4sywnCiYUTVCdodlAcCMUBqgOhOUGL/h/NjqJpoOigagj0hs8a\nqDaEqgMaqNE/iqrF7sIUhQP/V1UURWlcGD2fWKArCBRQExBaGpmZzWfM2m1fEaj2AQIE0cqSEAc+\nE10WbSijYZkAYaEIgbBMhCkQpokwI2BGEFYYjAiKGQQzjGIGUSwfWD5Uy4ci/KAEULU6FMXAhkAz\nLUzTwDIshKlS7U+lrrYrdTVZROp7kpDQA83uIDkBkhPaKkdILVI19MQk4BwMcQ6l+w3sogChbUN3\nFZCYUo7DuQw1qKL6VbQaDU1Tqd+hIww7XpGERRKW6gbNiVCcCNUJmqvh/w5Q7aDqoOgoqoZQdBRF\nQyg2FE1DUXRQFBRNRVVVUJVoLldVVEVBVQU0xgAAykGtFQroDYUgoCgKpt4FVHfD5wOnqijRqcVl\nQ8cJ4PejhIKovl2I2v1EKrdiBdZhWqWUV2RRUzmRzMQT84M3jV5U104nLeUTbCV34J0/DNU5AFwD\nUBIH4UpLR3XZEZ4E+apaO7Is8PsPfBYifn3jZ8WsQjXrEEKgRIIokRDCsqJlEdF/EVa0/BIWIFBp\nqP8JA8wQqhVGxP4NI4wwmEEwo2WTMP0olo9SPYQR8UZfQxVgBwTRV+NMw8QwFGp8adTWZBHwdUVR\nR+DUE7ATfX4gHRtFgUS7Doymvm40+fV26gL5OG278Tj243BUkpBQi9NThKavR9U0FE3DQmm4fqsI\n4cESiQiSEQ3lj6K5QHeD5kboLhRNR6g6qqaDpqOoOui2WDkkUEFRY4mKljGNnzmwDhWUhrpW4/er\nOvYUFzabgtCSgVPgpsD0oxjlBwVXND5CITAiIvYwFMuEgD8alA0PRxEWwhIgGu6NTIFpWVimoN7j\npLYmgAiHEIYXxahFMb1oohqbKMGmlqPhRxEWihWrUBKJqHhrPAS96fi8Gfi8megiB6cnG0W3o8sY\nOi4ujws4h4h5Dps2WQhzJ27XblIyCklJK8fpLEZV1kXzsaqgqkq0kcZKRFjJCJEAOEG4sBQPKK5o\nnGg2hGYD1R79V9MRmh007cBn3Qaa2nDfEo0P1Gg8KWo0jpSGexZLTUIkdAVdJzPz2M61TRsOzJJn\n0c1o5o/dA8X+UrCEQsRwYETsRMIewiEnRsRJOOzCMjxgJqFqyShkYFipaLoNtaF+Y9fAngTIHlMd\nn9CIhDOADCA6LV0YCERXomg+nB6LQKgaqEUVNWhKLZrmQ9WC6LYQmh5Ct9Vh0ytQNQNVid5hK9BQ\nyDTc8DQWQAqxG36Fgz4T3SHuBqaxkeDAgqM+xb1bb6bPiJubXVe/6mdohnXQEtHsdjS3RUMhdKAH\nDg0ND1bDvzQUPCJWXkVQEEIhHHYRCnoIBj0YIQ9WOAmVLFQlB8NKjzaqAG4XIN8WaX+qTpg+QB8i\n9VBfL9DVGmyUglKGpZSD5sPlrkfVAtgcJdgde9GUhkhoyPvRupVyICaI5v3GPN/4f+XQO3uay/lH\njoVwQipWw1OLeqs7efVPNLtdnz6CgQOtZtdJx0YpK8O2bg2KnofimIu3ziIStjCFSmHxICpLppGZ\n6D6hafDVjcEKJeC0L8Zl5KLb1qNqKoaRREHprfTvr2Olp2OcKXsjtJeNG1WKiw8fy3algtHuWzER\nKMLC7q1oTVHVQBz0NyhCYIn4myIhBIYVLaciESem6SYU9BAOO4iEXYSDLiIRD4qZgk3rAVpm9GYT\ncMmbnBNCUSDBoaAa2UA2Zhh8Iais0ggHQ4hwMbpagmavxOHx4XT5sDsDOBwBHM5KNNWg8bmKoijR\nm5YDCw7UwRofvCiNdweHlixHLmcO3SKckIQnx4FAg5w5bfQTOXb2/LtRIiVxy0wLgpVKXEOdPVCD\nGgm3cJSD4qihTqeqComGGavrHVwHFKaKN5BAMJBKvT+J+vok/IFkQoEUVCuFlJRk7B4niqKSKO+l\nTghNhdQkFRgIDKSuRqW4TKPe8GK3l5DgLMHlqMZmq8HmqMPh9OJ0lKMoInq/3xg7CIg05vMDUxwr\nh8SRoh7U2BZLxeHiR2Xr3t9S0n8q/fod2zkqQhza1nzsls/7Ek0+RZBOAUIILMuK/hEWlmlhmAaW\naWFaJkI0rCP6tMRSok83Gi/TlhBYInpTYzXErOBAGXjgDgwODtL4ey+FWbNmNUnb2//5Dw1X/Ybr\nwUEB33h9UJRoizMN06IDiqKiQPTJLyoKKqqioqo6um5D1zQ0XUNVVDRNO3CBkb6RREMeNs2GnlyW\nhSWizz2shvgwLDO6XpjRSggWpmU15Pdo5T7aYi0a1h+IASFEQ+F14Ds1TaNfv35x+a65LKgoCn36\n9CEx8RR4MtTJmKbJpo0bUU2TJLudOq+XstJq3O6UdrkeGIZBMOjHNAMkZ6ThTE4mp1s3unTpctLT\nIh1QVVVFYWEhB1cBj1Qb3LdvH6HQgemqYz3lINZ7DkugKApa9LKBrihggU3X0TQbmmZHVXRUVUXT\nNXQt+n+p8xBCEDEimIbZUBczsISJaUawLIOIYYIisJRomSIOuixZ4kDZEj0WBwqRWAcEJe7eSFEU\nRo8eTb9jvRM6ybZv3059fX3sc1VVFWVl5bHPjWGlKCqI6IOfxvqermnomopN09FQ0FUFXdPRdA1N\nDqbcoQkhMAwDwzRjPZEPXGPj/5iWiWFEov8KK9oIq0Tjx2qsmzXGS+z6Gr1Wp6WlkdnQ3WDkyBFH\nnc42bTiQJEmSJEmSJEmSJKlzkc28kiRJkiRJkiRJkiS1SDYcSJIkSZIkSZIkSZLUItlwIEmSJEmS\nJEmSJElSi2TDgSRJkiRJkiRJkiRJLWrT6RgNw6S6OtCWhzzpUlPdHfocnLlDUVWFwPAN7Z2U49LR\nfw8AmZnNjxgv4+TkqvzqvzhKnyR/43gsbSIAI8bciqJA7rK/xbbr4nkOLaME56Vf4EnsOPN9daTf\nRUuai5XOFifO3KEABEdubM8kHZPOkMc6wzl01jiBzvH7aTwHGevtS9a9Toy2ytedIY91hnNoKU6O\npE17HOh6x58KpDOcQ2eYga8z/B5a0hnOrUOdQ6gay7JQlZT45YcEihnxoAgLI1h7EhN3/DrU7+Io\ndIbz6gznAJ3jPDrDOTSns5xXZzgPeQ6nts5wbvIcTg2d4RyOlXxVQZKkTk0xahrmFk867Ham4QYh\nCNWVH3Y7SZIkSZIkSfqmadNXFaT2Fxy5kczMRPzl3vZOiiSdEhSjGiEEwkqAhkbivOV/w+NxAKHY\ndqbpRhEQrisGTm+XtEqdV0fstixJ0tGTsS51RjJfSyB7HEit9MEH7/L004+1atunn36cDz549wSn\nSJJaR7XqQIClHP59LsvyABDxVpyMZAFHF1eHE4lE+M53rqKmpqYNUiVJbUeWHZLUdm655Yfs2LH9\nuI+za9dObrnlhjZIkSSdfJFIhO9+92qqq6uO+1gyFo6ObDhoQ1dddSmTJ49l6tQJXHTRZH71qzsp\nKyuNrX/ggfu44ILzmDp1AlOnTmDKlPO5/vrZAJSUFDN+/Fn86ld3xh3z/vt/z7/+9QILFsxjypTz\nmTp1ApMnj+X888+OHWPq1Amx7T/55CMuvfRSLrxwHDNnTufRRx/C5/PF1r/44vOMH38WS5Z8Fltm\nmibjx59FSUlJs+dlGAavvvoSs2d/r8m6uXPnMH78WcyZ87/Ystmzr+PVV1/CMIyj/AlKp6oFC+Zx\n443fY8qU8/nWt2Zw8803s2FDHgAvvfQP7r//983ud3BMNObVJ554JG6btWtXM378Wbzxxmtxyxtj\nojFeZs2ayeuvvxxb/9prL3PXXT+L2+faay/nV7+6I27Zzc+tZclmBUtxxpb9btGj/GZefDp+s3QF\nV78kuOneJ5gw4RwmTRobS/Nrr73M3LlzmDDhnLj4nTp1ApWVFU3OdebM6TzwwH0Eg8EWf6bNxdXD\nD/+Z2bOv5Pzzz2bu3Dkt7vvTn/6Y8ePPwrIsAGw2GxdfHP/zkTq32267mRkzJjW5zj7wwH38859/\nJy8vN5ZHp0wZH4ulxmVlZaXcfvuP4q7da9euZsaMSXz22UIAxo8/i/37C4G2Kzt27NjGD394HRde\nOI4bb/xe3E2QLDukWbMuY82aVbHPn346nxkzJpGXty62LBgMMmXK+CbXeohehy+7bBqh0IFr75w5\nH3D77T+KfR4//qxYHFxyyYXcccetsTzfqDE2qquruOSSC8nNXRu3/oEH7uO++34X+3ykuldL5eTB\nMXa4shRg2bKleDweBgwYCMBnny1g9uwrmT59IpddNo0HHriPQCA6YFskEuGhh+7nqqsuZdq0Cdxw\nw3dZsWJ57Fj9+vUnMTGJ5cu/bPH7pI6ptXWja6+9okm+nzVrJtddd3WTY+7Zs5uf//w2ZsyYxIwZ\nk7jxxu/F8tO6dWti9yQHlzGbNh3oofD1119x2203M3XqBMaMGcPtt/+IL7/8Iu47WqoLNufDD99j\n5MjRpKamAfDLX/409r1Tp07gggvO4/vf/3Zs+x07tvOTn9zE9OkTueKKi3n55X/G1slYODryVYU2\npCgKjzzyJKNHn0kkEuHRRx/k8ccf4cEHH41t853vfJ8bb/xxi8fYvHkDGzeuZ+jQ4XHLp06dztSp\n04FokN5//z28997Hcdu8+ebrvPXWazzyyCP06zeE8vJy/vrXB7nzzlt57rmX0HUdRVFITk7mn/98\nngkTJqE0DBCnHGZExaVLl9C7dx/S0zPilnu9Xl5//WX69u0Xtzw9PYPevfuwbNkXTJgw6TA/Makj\neOut13njjde46667Ofvsc9F1G1u35vL5558zbNiIhq2azz8Hx0RL5s37mOTkZObNm8Ps2dc12X/+\n/CUoisLWrVu4/fabGTz4dM4882xGjhzFv//9CkIIFEWhqqoS0zTZtm1r3LLimggD0lOwfNE07qjc\ngzfspy7sY1/tfnoldwPgz2Nmktn3dUo8F/HM4kKmT7+Yiy++LJaWuXPnMHTocJ599oUjnmt1dRV3\n3nkbr732L2666ZZmt28urgYMGMSFF07jueeeavHntWDBPCzLahKzU6ZM4/rrZ/PjH9+GrstLe2dW\nUlLMhg15JCQk8OWXnzNx4uQm24wYMZKFC7+IbX/11TNjsdSclStXcM89d/O7393LuHHRxuiDt22L\nssMwDO6++5dcc813uPzyq/jgg3e4++5f8NZb76Pruiw7pDhz587h2Wef4NFHn2LIkKGx5YsXf4rd\nbmflyhVUVVWSlpYeW6coCpZl8vbbb3LdddfHLT/4/6+88iZdu3ajrq6Wr75axuOPP0xBwT5+8IMb\n49KQmprG7bf/nIce+hOvvvoWdrud1atXsmLFcl5//W2gdXWvhm9uco5N46flePrf/95l2rSLYp+H\nDx/J3//+EklJyQSDQR5++M+88MJz/Oxnv8A0TbKysnn22RfIyspm+fIvueeeu3n11f+QnZ0NwIUX\nTueDD95lzJhxLX6n1PG0tm5UVFTIyJGjYvvl5q6lpqYayzLZunULgwefFlv361/fyRVXzOLhh58A\nYOvWzQghYuszMjKb3JM0Wrz4Ux566H5++tNf8PDDj9OrVzaffvoF8+d/wrhx58e2O1xd8FD/+997\n/OpX/xf7/Oij8XWm22//EWeeeXbs8333/Y6JEyfx7LMvsH9/IbfeeiMDBgxi7NjxgIyFoyF7HLSx\nxkCy2WxMnDiZffv2HNX+s2d/j3/8429H3vAQgYCfl176B3fe+SvGjh2LpmlkZ2fzxz8+RElJCQsW\nzI1te/bZ52Gz6cybdyDID74AHGrFiuWMHDm6yfLnn3+GWbOuJSkpucm6kSNHy9a7TsDv9/Hii//g\nF7/4NePHT8ThcKJpGhMnTuTWW3/aqmMcLm+FQkGWLFnEnXf+msLCArZt29ri/oMHn0bv3n1jTyhP\nO20IhhFhx45tAOTmrmPUqDPo2bNXbNnaNavJSVVJ0D2x431VuI6R2aczImcwKwrXHfQ9CSiAEqk6\nYrqPdK6pqWmcffa5h+1S2lxcXX75VYwefSY2W/PTQfr9Pl5++YVmf/aZmV1ITExi06aOPRWrdGTz\n5n3MkCHDmDHjUj75pOWeKYdqKU8vW7aUe+65m/vueyDWaNDc9sdbdqxbtxrLspg161p0Xeeqq65F\nCMHatatj28iyQ4LojcGzzz7JY489E9doANH8/61vXUW/fgPi6jaNvv3t63jrrdfx+31N1kE0zzbm\n26SkZKZNu4hf/vI3vPrqv6irq2uy/bRpF9GrVy/++c+/EwqFePTRB7njjrtISkqOr3sNGoRjQx7d\nS4v50y0/bVL3aiktrWEYBmvWrGLUqDNiyzIzu8TqX5Zloaoq+/cXAOB0Orn++pvIyoo2EowZM46c\nnK5s27Yltv/o0WewZs1K2cOnk2lN3Sg3dx1du3aPe3Axd+4czj9/AuedN5Z58w6UK7W1NZSUFHPp\npd9C13V0XWfo0OEHPTg6vGeeeYLrr7+Jiy++DLc7WhcbMWJU3I1/a+qCjUpLSygq2s/ppw9tdn1x\ncRHr1+fGNbKVlhYzZUr04Wu3bt0ZPnwke/bsiq2XsdB6suHgBAkGgyxatLBJz4HDURSFK664moKC\n/Liueq2xYcN6IpEw559/Qdxyl8vFueeOYdWqr+O+58Ybb+Ff/3oB0zSPeOzdu3fSs2evuGWbN29k\n27YtfOtbVzW7T69efdi5c8dRnYN06tm4cQORSJjx4yeekOMvXvwZbrebSZMu5Kyzzom7IWnUWLHa\nuHEDe/fupnv37gDous7ppw8lNzd685+Xt5aRI0czfPjI2LJ1a1cyvIdGOBR9TSFsRlhbspGzu47g\nvJ6jWVWUh2lFY8AUiSiKgmYe/zgBZWWlfP31cnr06NHiNs3F1ZE8//yzXH75rLgnbAfr1as3O3ce\n//uv0qlt3ryPmTp1BlOmTGflyq+orq4+5mMtW/YF999/Dw888AjnnHPeYbc93rJjz57d9OvXP26b\nfv0GxFXgZNkhvf/+f3npped56qnnGDhwcNy6kpIS1q1b05D/pzF3btMyY/Dg0xk16oxWdXluNG7c\nBEzTYMuWTc2u/+Uv7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bs9vPLKizz++CO4XE569erDt799HZs2bTxiXfCKK2Y1Obdo2j5m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i+9HbatHDNVixvZCqRrNqEKRxIjEMk0RMpSM8knB7EclIKX73WFSPH6cT8uxAwSlDEQ4UWY6Z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K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\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ZQFJqtR9Koh1JPs6zVI+QUzFr/mIB4utn/uj2Fvv/KCow0XMLkbRYovmpeWMaGjZzIxLB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\nLulA73w+15aWFoaHh2lubiYIAgqFAul0+lI3dUHnMw9ntvcDH/gAf/m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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbc8bd79ad0>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "nrows = 17\n", "ncols = 5\n", "fig, ax = plt.subplots(nrows, ncols, figsize=(18, 21), sharey=True, sharex=True)\n", "with warnings.catch_warnings():\n", " warnings.simplefilter('ignore')\n", " ii = -1\n", " for r in range(nrows):\n", " for c in range(ncols):\n", " ii += 1\n", " idx = county_freq[ii, 0]\n", " sns.kdeplot(\n", " posterior_random_weights_final_[:, idx] * log_county_uranium_ppm[idx],\n", " color='blue',\n", " alpha=.3,\n", " shade=True,\n", " label='TFP',\n", " ax=ax[r][c])\n", " sns.kdeplot(\n", " posterior_random_weights_final_stan[:, idx] \n", " log_county_uranium_ppm[idx],\n", " color='red',\n", " alpha=.3,\n", " shade=True,\n", " label='Stan/rstanarm',\n", " ax=ax[r][c])\n", " sns.kdeplot(\n", " posterior_random_weights_lme4_final_[:, idx] \n", " log_county_uranium_ppm[idx],\n", " color='#F4B400',\n", " alpha=.7,\n", " shade=False,\n", " label='R/lme4',\n", " ax=ax[r][c])\n", " ax[r][c].vlines(\n", " posterior_random_weights_lme4[idx] * log_county_uranium_ppm[idx],\n", " 0,\n", " 5,\n", " color='#F4B400',\n", " linestyle='--')\n", " ax[r][c].set_title(county_name[idx] + ' ({})'.format(idx), y=.7)\n", " ax[r][c].set_ylim(0, 5)\n", " ax[r][c].set_xlim(-1., 1.)\n", " ax[r][c].get_yaxis().set_visible(False)\n", " if ii == 2:\n", " ax[r][c].legend(bbox_to_anchor=(1.4, 1.7), fontsize=20, ncol=3)\n", " else:\n", " ax[r][c].legend_.remove()\n", " fig.subplots_adjust(wspace=0.03, hspace=0.1)" ] }, { "cell_type": "markdown", "metadata": { "id": "bv4rVc4Mye7J" }, "source": [ "## 8 結論" ] }, { "cell_type": "markdown", "metadata": { "id": "jNtzJUQXksvZ" }, "source": [ "このコラボでは、線形混合効果回帰モデルをラドンデータセットに適合させました。R、Stan、TensorFlow Probability の 3 つの異なるソフトウェアパッケージを試し、3 つの異なるソフトウェアパッケージによって計算された 85 の事後分布をプロットしました。 " ] }, { "cell_type": "markdown", "metadata": { "id": "tsXhZ4rtNUXL" }, "source": [ "## 付録 A：代替のラドン HLM (ランダム切片の追加)" ] }, { "cell_type": "markdown", "metadata": { "id": "F9PqNJQK002P" }, "source": [ "このセクションでは、各郡に関連付けられたランダム切片も持つ代替 HLM について説明します。" ] }, { "cell_type": "markdown", "metadata": { "id": "qt8a50GYSqbe" }, "source": [ "$$\\begin{align} \\text{for } & c=1\\ldots \\text{NumCounties}:\\ & \\beta_c \\sim \\text{MultivariateNormal}\\left(\\text{loc}=\\left[ \\begin{array}{c} 0 \\ 0 \\end{array}\\right] , \\text{scale}=\\left[\\begin{array}{cc} \\sigma_{11} & 0 \\ \\sigma_{12} & \\sigma_{22} \\end{array}\\right] \\right) \\ \\text{for } & i=1\\ldots \\text{NumSamples}:\\ & c_i := \\text{County}i \\ &\\eta_i = \\underbrace{\\omega_0 + \\omega_1\\text{Floor}i \\vphantom{\\log( \\text{CountyUraniumPPM}{c_i}))}}{\\text{fixed effects}} + \\underbrace{\\beta_{c_i,0} + \\beta_{c_i,1}\\log( \\text{CountyUraniumPPM}{c_i}))}{\\text{random effects}} \\ &\\log(\\text{Radon}_i) \\sim \\text{Normal}(\\text{loc}=\\eta_i , \\text{scale}=\\sigma) \\end{align}$$\n" ] }, { "cell_type": "markdown", "metadata": { "id": "oI-DkfJWxK5K" }, "source": [ "R の `lme4` 「チルダ表記」では、このモデルは次と同等です。\n", "\n", "> `log_radon ~ 1 + floor + (1 + log_county_uranium_ppm \| county)`" ] }, { "cell_type": "markdown", "metadata": { "id": "H0w7ofFvNsxi" }, "source": [ "## 付録 B: 一般化線形混合効果モデル" ] }, { "cell_type": "markdown", "metadata": { "id": "5g4VAJP5xOPZ" }, "source": [ "このセクションでは、本文で使用されているものよりも、階層線形モデルのより一般的な特性を示します。このより一般的なモデルは、[一般化線形混合効果モデル](https://en.wikipedia.org/wiki/Generalized_linear_mixed_model) (GLMM) として知られています。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lA1xwCdENyTx" }, "source": [ "GLMM は、[一般化線形モデル](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) を一般化したものです。GLMM は、サンプル固有のランダムノイズを予測線形応答に組み込むことにより、GLM を拡張します。これは、まれな特徴がより一般的な特徴と情報を共有できるため、便利なこともあります。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9RJ6ryvUvJ5O" }, "source": [ "生成プロセスとして、一般化線形混合効果モデル (GLMM) には次の特徴があります。\n", "\n", "\\begin{align} \\text{for } & r = 1\\ldots R: \\hspace{2.45cm}\\text{# for each random-effect group}\\ &\\begin{aligned} \\text{for } &c = 1\\ldots \|C_r\|: \\hspace{1.3cm}\\text{# for each category (\"level\") of group $r$}\\ &\\begin{aligned} \\beta_{rc} &\\sim \\text{MultivariateNormal}(\\text{loc}=0_{D_r}, \\text{scale}=\\Sigma_r^{1/2}) \\end{aligned} \\end{aligned}\\\\ \\text{for } & i = 1 \\ldots N: \\hspace{2.45cm}\\text{# for each sample}\\ &\\begin{aligned} &\\eta_i = \\underbrace{\\vphantom{\\sum_{r=1}^R}x_i^\\top\\omega}\\text{fixed effects} + \\underbrace{\\sum{r=1}^R z_{r,i}^\\top \\beta_{r,C_r(i) }}\\text{random effects} \\ &Y_i\|x_i,\\omega,{z{r,i} , \\beta_r}_{r=1}^R \\sim \\text{Distribution}(\\text{mean}= g^{-1}(\\eta_i)) \\end{aligned} \\end{align}\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ycA3XujsN5FJ" }, "source": [ "ここでは、\n", "\n", "\\begin{align} R &= \\text{number of random-effect groups}\\ \|C_r\| &= \\text{number of categories for group $r$}\\ N &= \\text{number of training samples}\\ x_i,\\omega &\\in \\mathbb{R}^{D_0}\\ D_0 &= \\text{number of fixed-effects}\\ C_r(i) &= \\text{category (under group $r$) of the $i$th sample}\\ z_{r,i} &\\in \\mathbb{R}^{D_r}\\ D_r &= \\text{number of random-effects associated with group $r$}\\ \\Sigma_{r} &\\in {S\\in\\mathbb{R}^{D_r \\times D_r} : S \\succ 0 }\\ \\eta_i\\mapsto g^{-1}(\\eta_i) &= \\mu_i, \\text{inverse link function}\\ \\text{Distribution} &=\\text{some distribution parameterizable solely by its mean} \\end{align}\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-JAvS_UfN7gl" }, "source": [ "つまり、これは、各グループのすべてのカテゴリが iid MVN、$\\beta_{rc}$ に関連付けられていることを意味します。$\\beta_{rc}$ の抽出は常に独立していますが、グループ $r$ に対してのみ同じように分散されます。$r\\in{1,\\ldots,R}$ ごとに 1 つの $\\Sigma_r$ があることに注意してください。\n", "\n", "サンプルのグループの特徴である $z_{r,i}$ と密接に組み合わせると、結果は $i$ 番目の予測線形応答 (それ以外の場合は $x_i^\\top\\omega$) のサンプル固有のノイズになります。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "G4W5ijp1OFPW" }, "source": [ "${\\Sigma_r:r\\in{1,\\ldots,R}}$ を推定する場合、基本的に、変量効果グループがもつノイズの量を推定します。そうしないと、 $x_i^\\top\\omega$ に存在する信号が失われます。" ] }, { "cell_type": "markdown", "metadata": { "id": "7Dl2iZkAODfG" }, "source": [ "$\\text{Distribution}$ および[逆リンク関数](https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function) $g^{-1}$ にはさまざまなオプションがあります。一般的なオプションは次のとおりです。\n", "\n", "- $Y_i\\sim\\text{Normal}(\\text{mean}=\\eta_i, \\text{scale}=\\sigma)$,\n", "- $Y_i\\sim\\text{Binomial}(\\text{mean}=n_i \\cdot \\text{sigmoid}(\\eta_i), \\text{total_count}=n_i)$\n", "- $Y_i\\sim\\text{Poisson}(\\text{mean}=\\exp(\\eta_i))$\n", "\n", "その他のオプションについては、[`tfp.glm`](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/python/glm) モジュールを参照してください。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "HLM_TFP_R_Stan.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
ecalio07/enron-paper	dev/environment_setup2.ipynb	2	9191	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Modules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nltk\n", "import sklearn\n", "import matplotlib\n", "#print('The nltk version is {}.'.format(nltk.__version__))\n", "#print('The scikit-learn version is {}.'.format(sklearn.__version__))\n", "#print('The matplotlib version is {}.'.format(matplotlib.__version__))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import cPickle\n", "from time import time\n", "\n", "from sklearn import model_selection\n", "# from sklearn import cross_validation\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_selection import SelectPercentile, f_classif\n", "\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import accuracy_score\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from collections import Counter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def printTXT(txt1,txt2):\n", " msg = str(txt1) + str(txt2)\n", " print\n", " print(msg)\n", " print\n", "\n", "def train_predict(desc):\n", " printTXT(\"Processing \", desc)\n", " \n", " ##Reduce DataSet (1%)\n", " features_train_small = features_train[:len(features_train)/100]\n", " labels_train_small = labels_train[:len(labels_train)/100]\n", " \n", " t0 = time()\n", " clf.fit(features_train_small, labels_train_small)\n", " traintime = \"Training time: \" + str(round(time()-t0, 3))\n", "\n", " t1 = time()\n", " pred = clf.predict(features_test)\n", " predtime = \"Predicting time: \" + str(round(time()-t1, 3))\n", " \n", " print traintime, \"s\"\n", " print predtime, \"s\"\n", " print\n", " plotgraph (pred,str(desc))\n", " accuracy(labels_test, pred)\n", "\n", "def train_predict_fulldataset(desc):\n", " printTXT(\"Processing \", desc)\n", " \n", " t0 = time()\n", " clf.fit(features_train, labels_train)\n", " traintime = \"Training time: \" + str(round(time()-t0, 3))\n", "\n", " t1 = time()\n", " pred = clf.predict(features_test)\n", " predtime = \"Predicting time: \" + str(round(time()-t1, 3))\n", "\n", "# print \"******** Results for experiment: \\\"\",desc,\"\\\" ********\"\n", "\n", " print traintime, \"s\"\n", " print predtime, \"s\"\n", " print\n", " plotgraph (pred,str(desc))\n", " accuracy(labels_test, pred)\n", " \n", " \n", "def grid_train_predict(desc):\n", " printTXT(\"Processing \", desc)\n", " \n", " ##Reduce DataSet (1%)\n", " features_train_small = features_train[:len(features_train)/100]\n", " labels_train_small = labels_train[:len(labels_train)/100]\n", " \n", " t0 = time()\n", " clf.fit(features_train_small, labels_train_small)\n", " traintime = \"Training time: \" + str(round(time()-t0, 3))\n", "\n", " t1 = time()\n", " pred = clf.predict(features_test)\n", " predtime = \"Predicting time: \" + str(round(time()-t1, 3))\n", "\n", "#print \"****** Results for experiment: \\\"\",desc,\"\\\" ********\"\n", "\n", " print traintime, \"s\"\n", " print predtime, \"s\"\n", " print\n", " plotgraph (pred,str(desc))\n", "# accuracy(labels_test, pred)\n", "\n", "def grid_train_predict_fulldataset(desc):\n", " printTXT(\"Processing \", desc)\n", " \n", " t0 = time()\n", " clf.fit(features_train, labels_train)\n", " traintime = \"Training time: \" + str(round(time()-t0, 3))\n", "\n", " t1 = time()\n", " pred = clf.predict(features_test)\n", " predtime = \"Predicting time: \" + str(round(time()-t1, 3))\n", " \n", "# print \"****** Results for experiment: \\\"\",desc,\"\\\" *********\"\n", "\n", " print traintime, \"s\"\n", " print predtime, \"s\"\n", " print\n", " plotgraph (pred,str(desc))\n", "# accuracy(labels_test, pred)\n", "\n", "def plotgraph (pred, desc):\n", "\n", " c = Counter(pred)\n", "\n", " men = c[1]\n", " \n", " print \"Number of Predicted emails for Chris\", men\n", " women = c[0]\n", " print \"Number of Predicted emails for Sara\", women\n", "\n", " bar_heights = (men, women)\n", " x = (1, 2)\n", "\n", " fig, ax = plt.subplots()\n", " width = 0.4\n", "\n", " ax.bar(x, bar_heights, width)\n", "\n", " ax.set_xlim((0, 3))\n", " ax.set_ylim((0, max(men, women)1.1))\n", "\n", " ax.set_xticks([i+width/2 for i in x])\n", " ax.set_xticklabels(['Cris', 'Sarah'])\n", "\n", " plt.show()\n", "\n", "def accuracy(labels_test, pred):\n", " accuracy = accuracy_score(labels_test, pred)\n", " print \"Total Accuracy:\", accuracy\n", " \n", "def preprocess(words_file = \"../data/word_data.pkl\", authors_file=\"../data/email_authors.pkl\"):\n", " \"\"\"\n", " this function takes a pre-made list of email texts (by default word_data.pkl)\n", " and the corresponding authors (by default email_authors.pkl) and performs\n", " a number of preprocessing steps:\n", " -- splits into training/testing sets (10% testing)\n", " -- vectorizes into tfidf matrix\n", " -- selects/keeps most helpful features\n", "\n", " after this, the feaures and labels are put into numpy arrays, which play nice with sklearn functions\n", "\n", " 4 objects are returned:\n", " -- training/testing features\n", " -- training/testing labels\n", "\n", " \"\"\"\n", " ### the words (features) and authors (labels), already largely preprocessed\n", " ### this preprocessing will be repeated in the text learning mini-project\n", " word_data = cPickle.load( open(\"../data/word_data.pkl\", \"r\"))\n", " authors = cPickle.load( open(\"../data/email_authors.pkl\", \"r\") )\n", "\n", " ### test_size is the percentage of events assigned to the test set (remainder go into training)\n", " features_train, features_test, labels_train, labels_test = model_selection.train_test_split(word_data, authors, test_size=0.1, random_state=42)\n", "\n", "\n", "\n", " ### text vectorization--go from strings to lists of numbers\n", " vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,\n", " stop_words='english')\n", " features_train_transformed = vectorizer.fit_transform(features_train)\n", " features_test_transformed = vectorizer.transform(features_test)\n", "\n", "\n", "\n", " ### feature selection, because text is super high dimensional and\n", " ### can be really computationally chewy as a result\n", " selector = SelectPercentile(f_classif, percentile=1)\n", " selector.fit(features_train_transformed, labels_train)\n", " features_train_transformed = selector.transform(features_train_transformed).toarray()\n", " features_test_transformed = selector.transform(features_test_transformed).toarray()\n", "\n", " ### info on the data\n", " print\n", " print \"Number of trained emails for Chris:\", sum(labels_train)\n", " print \"Number of trained emails for Sara:\", len(labels_train)-sum(labels_train)\n", " \n", " \n", " return features_train_transformed, features_test_transformed, labels_train, labels_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data e Preprocess" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "features_train, features_test, labels_train, labels_test = preprocess()" ] } ], "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
wcmckee/wcmckee-notebook	karen.ipynb	2	25794	{ "metadata": { "name": "karen" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Product Inventory Project - Create an application which manages\n", "an inventory of products. Create a product class which has a\n", "price, id, and quantity on hand. Then create an inventory class\n", "which keeps track of various products and can sum up the inventory\n", "value.\n", "\"\"\"\n", "\n", "class Product:\n", "\n", " def __init__(self, price, pid, qty):\n", " \"\"\"\n", "Class constructor that needs a price, a product id,\n", "and quantity.\n", "\"\"\"\n", " self.price = price\n", " self.pid = pid\n", " self.qty = qty\n", "\n", " def update_qty(self, qty, method='add'):\n", " \"\"\"\n", "Updates the quantity of produts. By default, adds the\n", "passed quantity. Pass method as 'subtract' to subtract\n", "the quantity.\n", "\"\"\"\n", " if method == 'add':\n", " self.qty += qty\n", " elif method == 'subtract':\n", " self.qty = max(0, self.qty - qty)\n", "\n", " def print_product(self):\n", " \"\"\"\n", "Prints a single product.\n", "\"\"\"\n", " print '%d\\t%s\\t%.02f each' % (self.pid, self.qty, self.price)\n", "\n", "class Inventory:\n", "\n", " def __init__(self):\n", " \"\"\"\n", "Initializes the class instance.\n", "\"\"\"\n", " self.products = [] # list to hold all products\n", "\n", " def add(self, product):\n", " \"\"\"\n", "Adds a passed Product to the list of products.\n", "\"\"\"\n", " self.products.append(product)\n", "\n", " def print_inventory(self):\n", " \"\"\"\n", "Prints the current inventory, and the total value\n", "of products.\n", "\"\"\"\n", " value = 0\n", " for product in self.products:\n", " print '%d\\t%s\\t%.02f each' % (product.pid,\n", " product.qty,\n", " product.price)\n", " value += (product.price * product.qty)\n", " print '\\nTotal value: %.02f' % value\n", "\n", "if __name__ == '__main__':\n", " p1 = Product(1.4, 123, 5)\n", " p2 = Product(1, 3432, 100)\n", " p3 = Product(100.4, 2342, 99)\n", "\n", "\n", " i = Inventory()\n", " i.add(p1)\n", " i.add(p2)\n", " i.add(p3)\n", " i.print_inventory()\n", "\n", " p1.update_qty(10)\n", " i.print_inventory()\n", " \n", " p1.update_qty(10, method='subtract')\n", " i.print_inventory()\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123\t5\t1.40 each\n", "3432\t100\t1.00 each\n", "2342\t99\t100.40 each\n", "\n", "Total value: 10046.60\n", "123\t15\t1.40 each\n", "3432\t100\t1.00 each\n", "2342\t99\t100.40 each\n", "\n", "Total value: 10060.60\n", "123\t5\t1.40 each\n", "3432\t100\t1.00 each\n", "2342\t99\t100.40 each\n", "\n", "Total value: 10046.60\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "i.print_inventory" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 2, "text": [ "<bound method Inventory.print_inventory of <__main__.Inventory instance at 0x37ff560>>" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "i.add('food')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "for data in i:\n", " print data" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "iteration over non-sequence", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-5-cc773922fb80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: iteration over non-sequence" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# -- coding: cp1252 --\n", "\"\"\"\n", "Page Scraper - Create an application which connects to a\n", "site and pulls out all links, or images, and saves them to\n", "a list. Optional: Organize the indexed content and don\u2019t\n", "allow duplicates. Have it put the results into an easily\n", "searchable index file.\n", "\"\"\"\n", "\n", "import urllib2\n", "from bs4 import BeautifulSoup\n", "\n", "\n", "def print_list(stuff):\n", " print '\\n'.join(stuff)\n", " print '\\n====================\\n'\n", "\n", "if __name__ == '__main__':\n", " \n", " url = raw_input('Enter a URL: ')\n", "\n", " choice = input('What to scrape?\\n1. Links\\n2. Images\\n3. Both\\n')\n", " \n", " soup = BeautifulSoup(urllib2.urlopen(url).read())\n", "\n", " if choice == 1 or choice == 3:\n", " urls = [link.get('href') for link in soup.findAll('a')]\n", " print 'URLs:'\n", " print_list(urls)\n", " if choice == 2 or choice ==3:\n", " images = [image['src'] for image in soup.findAll(\"img\")]\n", " print 'Images:'\n", " print_list(images)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "encoding declaration in Unicode string (<ipython-input-6-3d5607a81a64>, line 0)", "output_type": "pyerr", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-6-3d5607a81a64>\"\u001b[0;36m, line \u001b[0;32munknown\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m encoding declaration in Unicode string\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "import requests" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "from bs4 import BeautifulSoup" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def printInfo(alarm):\n", " print 'data: '.join(stuff)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "if __name__ == '__main__':" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected EOF while parsing (<ipython-input-13-0104a1ff0b9f>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-13-0104a1ff0b9f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m if __name__ == '__main__':\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "print 'hello world'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hello world\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "webGet = requests.get('" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "EOL while scanning string literal (<ipython-input-15-754769277393>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-15-754769277393>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m webGet = requests.get('\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "import praw" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/dist-packages/update_checker.py:11: UserWarning: Module dap was already imported from None, but /usr/lib/python2.7/dist-packages is being added to sys.path\n", " from pkg_resources import parse_version as V\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "r = praw.Reddit(user_agent='RedditGetsDrawn by u/artcontrol')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "subz = r.get_subreddit('redditgetsdrawn').get_hot(limit=5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "[str(x) for x in subz]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 21, "text": [ "[\"25 :: It's time for State of the Union AND it's time to start the process for...\",\n", " '127 :: My corgi and I for our 2013 Christmas Card',\n", " \"143 :: My girlfriend's favorite picture of us. Would love to surprise her wit...\",\n", " '10 :: Me, deep in thought on the loveseat...',\n", " \"3 :: I got down on one knee in Millennium Park, Chicago. I'd be very grateful...\"]" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "usern = '__artcontrol'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "user = r.get_redditor(usern)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "HTTPError", "evalue": "404 Client Error: Not Found", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-24-72d6d458fc24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_redditor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musern\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/praw/__init__.pyc\u001b[0m in \u001b[0;36mget_redditor\u001b[0;34m(self, user_name, args, kwargs)\u001b[0m\n\u001b[1;32m 841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 842\u001b[0m \"\"\"\n\u001b[0;32m--> 843\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRedditor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muser_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 845\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdecorators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestrict_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'read'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/praw/objects.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, reddit_session, user_name, json_dict, fetch)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0minfo_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreddit_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_about'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0muser_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m super(Redditor, self).__init__(reddit_session, json_dict,\n\u001b[0;32m--> 646\u001b[0;31m fetch, info_url)\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m 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"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/praw/objects.pyc\u001b[0m in \u001b[0;36m_get_json_dict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m response = self.reddit_session.request_json(self._info_url,\n\u001b[0;32m--> 116\u001b[0;31m as_objects=False)\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreddit_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_oauth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprev_use_oauth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/praw/decorators.pyc\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(reddit_session, args, *kwargs)\u001b[0m\n\u001b[1;32m 156\u001b[0m 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"prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "done = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "while True:\n", " sibredd = r.get_subreddit('redditgetsdrawn')\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [ "for submission in subreddit.get_hot(limit=10):\n", " #test if it contans a PRAW-related question" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "" }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0

Subsets and Splits